[med-svn] [python-biopython] 04/06: Imported Upstream version 1.67+dfsg
Andreas Tille
tille at debian.org
Wed Jun 8 20:01:29 UTC 2016
This is an automated email from the git hooks/post-receive script.
tille pushed a commit to branch master
in repository python-biopython.
commit f356f2ab2fe0599015334a1afc5faef3723dd7da
Author: Andreas Tille <tille at debian.org>
Date: Wed Jun 8 21:25:42 2016 +0200
Imported Upstream version 1.67+dfsg
---
.gitattributes | 20 +
.gitignore | 89 +
.travis.yml | 76 +
Bio/Affy/CelFile.py | 2 -
Bio/Affy/__init__.py | 2 -
Bio/Align/AlignInfo.py | 67 +-
Bio/Align/Applications/_ClustalOmega.py | 12 +-
Bio/Align/Applications/_Clustalw.py | 12 +-
Bio/Align/Applications/_Dialign.py | 19 +-
Bio/Align/Applications/_MSAProbs.py | 12 +-
Bio/Align/Applications/_Mafft.py | 1 -
Bio/Align/Applications/_Muscle.py | 14 +-
Bio/Align/Applications/_Prank.py | 15 +-
Bio/Align/Applications/_Probcons.py | 11 +-
Bio/Align/Applications/_TCoffee.py | 12 +-
Bio/Align/Applications/__init__.py | 2 -
Bio/Align/Generic.py | 26 +-
Bio/Align/__init__.py | 11 +-
Bio/AlignIO/ClustalIO.py | 4 +-
Bio/AlignIO/EmbossIO.py | 2 -
Bio/AlignIO/FastaIO.py | 8 +-
Bio/AlignIO/Interfaces.py | 2 -
Bio/AlignIO/NexusIO.py | 1 -
Bio/AlignIO/PhylipIO.py | 1 -
Bio/AlignIO/StockholmIO.py | 14 +-
Bio/AlignIO/__init__.py | 13 +-
Bio/Alphabet/IUPAC.py | 2 -
Bio/Alphabet/Reduced.py | 1 -
Bio/Alphabet/__init__.py | 2 -
Bio/Application/__init__.py | 3 +-
Bio/Blast/Applications.py | 64 +-
Bio/Blast/NCBIStandalone.py | 33 +-
Bio/Blast/NCBIWWW.py | 23 +-
Bio/Blast/NCBIXML.py | 12 +-
Bio/Blast/Record.py | 10 +-
Bio/Blast/__init__.py | 2 -
Bio/CAPS/__init__.py | 2 -
Bio/Cluster/__init__.py | 8 +-
Bio/Compass/__init__.py | 2 -
Bio/Crystal/__init__.py | 2 -
Bio/Data/CodonTable.py | 25 +-
Bio/Data/IUPACData.py | 2 -
Bio/Data/SCOPData.py | 2 -
Bio/Data/__init__.py | 2 -
Bio/DocSQL.py | 1 -
Bio/Emboss/Applications.py | 13 +-
Bio/Emboss/Primer3.py | 3 +-
Bio/Emboss/PrimerSearch.py | 2 -
Bio/Emboss/__init__.py | 3 +-
Bio/Entrez/Parser.py | 20 +-
Bio/Entrez/XSDs/IPGReportSet.xsd | 94 +
Bio/Entrez/__init__.py | 68 +-
Bio/ExPASy/Enzyme.py | 2 -
Bio/ExPASy/Prodoc.py | 2 -
Bio/ExPASy/Prosite.py | 2 -
Bio/ExPASy/ScanProsite.py | 2 -
Bio/ExPASy/__init__.py | 2 -
Bio/FSSP/FSSPTools.py | 20 +-
Bio/FSSP/__init__.py | 2 +
Bio/FSSP/fssp_rec.py | 6 +-
Bio/File.py | 25 +-
Bio/GA/Crossover/GeneralPoint.py | 6 +-
Bio/GA/Crossover/Uniform.py | 2 -
Bio/GenBank/Record.py | 2 -
Bio/GenBank/Scanner.py | 92 +-
Bio/GenBank/__init__.py | 15 +-
Bio/GenBank/utils.py | 2 -
Bio/Geo/Record.py | 2 +-
Bio/Graphics/BasicChromosome.py | 19 +-
Bio/Graphics/ColorSpiral.py | 2 -
Bio/Graphics/Comparative.py | 10 +-
Bio/Graphics/DisplayRepresentation.py | 3 +-
Bio/Graphics/Distribution.py | 4 +-
Bio/Graphics/GenomeDiagram/_AbstractDrawer.py | 7 +-
Bio/Graphics/GenomeDiagram/_CircularDrawer.py | 5 +-
Bio/Graphics/GenomeDiagram/_CrossLink.py | 2 +-
Bio/Graphics/GenomeDiagram/_Diagram.py | 374 +-
Bio/Graphics/GenomeDiagram/_FeatureSet.py | 17 +-
Bio/Graphics/GenomeDiagram/_LinearDrawer.py | 4 +-
Bio/Graphics/GenomeDiagram/_Track.py | 6 +-
Bio/Graphics/GenomeDiagram/__init__.py | 8 +-
Bio/Graphics/KGML_vis.py | 3 -
Bio/Graphics/__init__.py | 1 -
Bio/HMM/DynamicProgramming.py | 2 +-
Bio/HMM/Trainer.py | 2 -
Bio/Index.py | 6 +-
Bio/KDTree/KDTree.py | 2 -
Bio/KDTree/__init__.py | 2 -
Bio/KEGG/Enzyme/__init__.py | 29 +
Bio/KEGG/KGML/KGML_parser.py | 2 -
Bio/KEGG/KGML/KGML_pathway.py | 2 -
Bio/KEGG/REST.py | 8 +-
Bio/KEGG/__init__.py | 5 +-
Bio/MarkovModel.py | 2 +-
Bio/Medline/__init__.py | 4 +-
Bio/Motif/Applications/__init__.py | 9 -
Bio/Motif/Parsers/AlignAce.py | 53 -
Bio/Motif/Parsers/MAST.py | 150 -
Bio/Motif/Parsers/MEME.py | 358 -
Bio/Motif/Parsers/__init__.py | 0
Bio/Motif/Thresholds.py | 94 -
Bio/Motif/_Motif.py | 800 -
Bio/Motif/__init__.py | 172 -
Bio/Motif/_pwm.c | 166 -
Bio/NMR/NOEtools.py | 2 -
Bio/NMR/xpktools.py | 1 -
Bio/NaiveBayes.py | 102 +-
Bio/NeuralNetwork/Gene/Motif.py | 6 +-
Bio/NeuralNetwork/Gene/Pattern.py | 6 +-
Bio/NeuralNetwork/Gene/Schema.py | 8 +-
Bio/NeuralNetwork/Gene/Signature.py | 4 +-
Bio/NeuralNetwork/Training.py | 8 +-
Bio/Nexus/.project | 17 +
Bio/Nexus/Nexus.py | 351 +-
Bio/Nexus/Nodes.py | 21 +-
Bio/Nexus/StandardData.py | 107 +
Bio/Nexus/Trees.py | 3 +-
Bio/PDB/Chain.py | 6 +-
Bio/PDB/DSSP.py | 8 +-
Bio/PDB/Entity.py | 5 +-
Bio/PDB/FragmentMapper.py | 6 +-
Bio/PDB/HSExposure.py | 2 -
Bio/PDB/MMCIFParser.py | 241 +-
Bio/PDB/NACCESS.py | 1 +
Bio/PDB/NeighborSearch.py | 2 +-
Bio/PDB/PDBIO.py | 7 +-
Bio/PDB/PDBList.py | 2 -
Bio/PDB/PDBParser.py | 6 +-
Bio/PDB/QCPSuperimposer/__init__.py | 1 +
Bio/PDB/Residue.py | 1 +
Bio/PDB/Selection.py | 2 +-
Bio/PDB/StructureAlignment.py | 4 +-
Bio/PDB/StructureBuilder.py | 4 +-
Bio/PDB/__init__.py | 8 +-
Bio/PDB/mmCIF/Makefile | 31 +
Bio/PDB/mmCIF/__init__.py | 1 +
Bio/PDB/mmCIF/mmcif.lex | 62 +
Bio/ParserSupport.py | 161 +-
Bio/Pathway/Rep/Graph.py | 6 +-
Bio/Pathway/Rep/MultiGraph.py | 6 +-
Bio/Pathway/__init__.py | 25 +-
Bio/Phylo/Applications/_Fasttree.py | 1 -
Bio/Phylo/Applications/_Phyml.py | 9 +-
Bio/Phylo/Applications/_Raxml.py | 7 +-
Bio/Phylo/Applications/__init__.py | 1 -
Bio/Phylo/BaseTree.py | 23 +-
Bio/Phylo/CDAO.py | 1 -
Bio/Phylo/CDAOIO.py | 5 +-
Bio/Phylo/Consensus.py | 6 -
Bio/Phylo/NeXML.py | 1 -
Bio/Phylo/NeXMLIO.py | 5 +-
Bio/Phylo/Newick.py | 2 -
Bio/Phylo/NewickIO.py | 23 +-
Bio/Phylo/NexusIO.py | 3 +-
Bio/Phylo/PAML/_paml.py | 17 +-
Bio/Phylo/PAML/_parse_baseml.py | 7 +-
Bio/Phylo/PAML/_parse_codeml.py | 44 +-
Bio/Phylo/PAML/_parse_yn00.py | 2 +-
Bio/Phylo/PAML/baseml.py | 53 +-
Bio/Phylo/PAML/chi2.py | 10 +-
Bio/Phylo/PAML/codeml.py | 68 +-
Bio/Phylo/PAML/yn00.py | 23 +-
Bio/Phylo/PhyloXML.py | 8 +-
Bio/Phylo/PhyloXMLIO.py | 5 +-
Bio/Phylo/TreeConstruction.py | 39 +-
Bio/Phylo/__init__.py | 1 -
Bio/Phylo/_cdao_owl.py | 1 +
Bio/Phylo/_io.py | 8 +-
Bio/Phylo/_utils.py | 36 +-
Bio/PopGen/Async/Local.py | 42 +-
Bio/PopGen/Async/__init__.py | 77 +-
Bio/PopGen/FDist/Async.py | 6 +-
Bio/PopGen/FDist/Controller.py | 4 +-
Bio/PopGen/FDist/Utils.py | 1 +
Bio/PopGen/FDist/__init__.py | 2 -
Bio/PopGen/GenePop/Controller.py | 6 +-
Bio/PopGen/GenePop/EasyController.py | 2 -
Bio/PopGen/GenePop/FileParser.py | 189 +-
Bio/PopGen/GenePop/__init__.py | 2 -
Bio/PopGen/SimCoal/Cache.py | 22 +-
Bio/PopGen/SimCoal/Controller.py | 6 +-
Bio/PopGen/SimCoal/Template.py | 45 +-
Bio/PopGen/SimCoal/__init__.py | 5 +-
Bio/PopGen/__init__.py | 4 +-
Bio/Restriction/PrintFormat.py | 31 +-
Bio/Restriction/RanaConfig.py | 95 +-
Bio/Restriction/Restriction.py | 436 +-
Bio/Restriction/Restriction_Dictionary.py | 3781 +-
Bio/Restriction/__init__.py | 3 +-
Bio/SCOP/Raf.py | 8 +-
Bio/SCOP/Residues.py | 2 +-
Bio/SCOP/__init__.py | 15 +-
Bio/SVDSuperimposer/__init__.py | 26 +-
Bio/SearchIO/BlastIO/__init__.py | 2 -
Bio/SearchIO/BlastIO/blast_tab.py | 8 +-
Bio/SearchIO/BlastIO/blast_text.py | 2 -
Bio/SearchIO/BlastIO/blast_xml.py | 26 +-
Bio/SearchIO/BlatIO.py | 4 +-
Bio/SearchIO/ExonerateIO/exonerate_vulgar.py | 2 +-
Bio/SearchIO/FastaIO.py | 9 +-
Bio/SearchIO/HmmerIO/__init__.py | 5 +-
Bio/SearchIO/HmmerIO/_base.py | 4 +-
Bio/SearchIO/HmmerIO/hmmer2_text.py | 1 -
Bio/SearchIO/HmmerIO/hmmer3_domtab.py | 3 -
Bio/SearchIO/HmmerIO/hmmer3_tab.py | 4 +-
Bio/SearchIO/HmmerIO/hmmer3_text.py | 6 +-
Bio/SearchIO/__init__.py | 2 -
Bio/SearchIO/_index.py | 3 -
Bio/SearchIO/_model/__init__.py | 2 -
Bio/SearchIO/_model/_base.py | 3 -
Bio/SearchIO/_model/hit.py | 7 +-
Bio/SearchIO/_model/hsp.py | 7 +-
Bio/SearchIO/_model/query.py | 7 +-
Bio/Seq.py | 83 +-
Bio/SeqFeature.py | 38 +-
Bio/SeqIO/AbiIO.py | 11 +-
Bio/SeqIO/AceIO.py | 8 +-
Bio/SeqIO/FastaIO.py | 76 +-
Bio/SeqIO/IgIO.py | 49 +-
Bio/SeqIO/InsdcIO.py | 259 +-
Bio/SeqIO/Interfaces.py | 2 -
Bio/SeqIO/PdbIO.py | 62 +-
Bio/SeqIO/PhdIO.py | 12 +-
Bio/SeqIO/PirIO.py | 37 +-
Bio/SeqIO/QualityIO.py | 14 +-
Bio/SeqIO/SeqXmlIO.py | 24 +-
Bio/SeqIO/SffIO.py | 88 +-
Bio/SeqIO/SwissIO.py | 26 +-
Bio/SeqIO/TabIO.py | 44 +-
Bio/SeqIO/UniprotIO.py | 12 +-
Bio/SeqIO/__init__.py | 87 +-
Bio/SeqIO/_convert.py | 74 +-
Bio/SeqIO/_index.py | 13 +-
Bio/SeqRecord.py | 33 +-
Bio/SeqUtils/CheckSum.py | 56 +-
Bio/SeqUtils/CodonUsage.py | 2 -
Bio/SeqUtils/CodonUsageIndices.py | 2 -
Bio/SeqUtils/IsoelectricPoint.py | 1 -
Bio/SeqUtils/MeltingTemp.py | 361 +-
Bio/SeqUtils/ProtParam.py | 2 -
Bio/SeqUtils/ProtParamData.py | 4 +-
Bio/SeqUtils/__init__.py | 85 +-
Bio/SeqUtils/lcc.py | 18 +-
Bio/Sequencing/Ace.py | 2 -
Bio/Sequencing/Applications/_Novoalign.py | 10 +-
Bio/Sequencing/Applications/__init__.py | 1 +
Bio/Sequencing/Applications/_bwa.py | 13 +-
Bio/Sequencing/Applications/_samtools.py | 12 +-
Bio/Sequencing/Phd.py | 1 -
Bio/Statistics/lowess.py | 1 +
Bio/SubsMat/FreqTable.py | 89 +-
Bio/SubsMat/MatrixInfo.py | 4981 ++-
Bio/SubsMat/__init__.py | 1 -
Bio/SwissProt/KeyWList.py | 2 -
Bio/SwissProt/__init__.py | 10 +-
Bio/TogoWS/__init__.py | 3 +-
Bio/UniGene/__init__.py | 4 +-
Bio/UniProt/GOA.py | 1 +
Bio/Wise/__init__.py | 16 +-
Bio/Wise/dnal.py | 17 +-
Bio/Wise/psw.py | 17 +-
Bio/__init__.py | 10 +-
Bio/_py3k/__init__.py | 124 +-
Bio/_py3k/_ordereddict.py | 1 +
Bio/_utils.py | 2 +-
Bio/bgzf.py | 8 +-
Bio/codonalign/__init__.py | 65 +-
Bio/codonalign/chisq.py | 255 +-
Bio/codonalign/codonalignment.py | 24 +-
Bio/codonalign/codonalphabet.py | 12 +-
Bio/codonalign/codonseq.py | 74 +-
Bio/kNN.py | 4 +-
Bio/motifs/__init__.py | 14 +-
Bio/motifs/applications/__init__.py | 2 -
Bio/motifs/applications/_alignace.py | 146 -
Bio/motifs/applications/_xxmotif.py | 11 +-
Bio/motifs/jaspar/__init__.py | 2 +-
Bio/motifs/jaspar/db.py | 5 +-
Bio/motifs/matrix.py | 100 +-
Bio/motifs/meme.py | 10 +-
Bio/motifs/thresholds.py | 4 +-
Bio/motifs/transfac.py | 23 +-
Bio/pairwise2.py | 3 +-
Bio/phenotype/__init__.py | 244 +
Bio/phenotype/phen_micro.py | 1131 +
Bio/phenotype/pm_fitting.py | 133 +
Bio/triefind.py | 1 +
BioSQL/BioSeq.py | 19 +-
BioSQL/BioSeqDatabase.py | 54 +-
BioSQL/DBUtils.py | 19 +-
BioSQL/Loader.py | 129 +-
CONTRIB | 26 +-
DEPRECATED | 36 +-
Doc/Tutorial.html | 10952 ------
Doc/Tutorial.pdf | 34830 -------------------
Doc/Tutorial.tex | 8 +-
Doc/Tutorial/chapter_align.tex | 5 +-
Doc/Tutorial/chapter_appendix.tex | 91 +-
Doc/Tutorial/chapter_blast.tex | 4 +
Doc/Tutorial/chapter_cluster.tex | 39 +-
Doc/Tutorial/chapter_cookbook.tex | 37 +-
Doc/Tutorial/chapter_entrez.tex | 38 +-
Doc/Tutorial/chapter_graphics.tex | 1 -
Doc/Tutorial/chapter_kegg.tex | 6 +-
Doc/Tutorial/chapter_learning.tex | 3 +-
Doc/Tutorial/chapter_motifs.tex | 61 +-
Doc/Tutorial/chapter_phenotype.tex | 271 +
Doc/Tutorial/chapter_quick_start.tex | 6 +-
Doc/Tutorial/chapter_searchio.tex | 7 +-
Doc/Tutorial/chapter_seq_annot.tex | 60 +-
Doc/Tutorial/chapter_seqio.tex | 101 +-
Doc/biopdb_faq.lyx | 2724 ++
Doc/biopdb_faq.pdf | Bin 174565 -> 0 bytes
Doc/cookbook/README | 13 +
Doc/cookbook/Restriction/Restriction.html | 1942 +-
Doc/cookbook/Restriction/Restriction.md | 1625 +
Doc/cookbook/motif/Makefile | 30 -
Doc/cookbook/motif/motif.tex | 590 -
Doc/doc.rst | 508 +
Doc/examples/ACT_example.py | 95 +-
Doc/examples/Plates.csv | 1202 +
Doc/examples/Proux_et_al_2002_Figure_6.py | 5 +-
Doc/examples/clustal_run.py | 1 -
Doc/examples/getgene.py | 63 +-
Doc/examples/nmr/simplepredict.py | 76 +-
Doc/examples/www_blast.py | 7 +-
Doc/images/BlastRecord.dia | Bin 0 -> 3498 bytes
Doc/images/BlastRecord.png | Bin 64168 -> 83852 bytes
Doc/images/PSIBlastRecord.dia | Bin 0 -> 2513 bytes
Doc/images/smcra.dia | Bin 0 -> 2251 bytes
Doc/images/smcra.eps | 2453 ++
Doc/install/Installation.html | 385 -
Doc/install/Installation.pdf | Bin 127585 -> 0 bytes
MANIFEST.in | 8 +-
NEWS | 109 +
PKG-INFO | 11 -
README | 21 +-
README.rst | 1 +
Scripts/GenBank/check_output.py | 4 +-
Scripts/GenBank/check_output_simple.py | 57 +-
Scripts/Performance/biosql_performance_load.py | 5 +-
Scripts/Performance/biosql_performance_read.py | 7 +-
Scripts/Restriction/ranacompiler.py | 255 +-
Scripts/Restriction/rebase_update.py | 40 +-
Scripts/SeqGui/SeqGui.py | 1 +
Scripts/Structure/hsexpo | 9 +-
Scripts/debug/debug_blast_parser.py | 3 +-
Scripts/xbbtools/nextorf.py | 22 +-
Scripts/xbbtools/xbb_blast.py | 10 +-
Scripts/xbbtools/xbb_blastbg.py | 49 +-
Scripts/xbbtools/xbb_help.py | 8 +-
Scripts/xbbtools/xbb_search.py | 13 +-
Scripts/xbbtools/xbb_translations.py | 38 +-
Scripts/xbbtools/xbb_utils.py | 14 +-
Scripts/xbbtools/xbb_widget.py | 26 +-
Scripts/xbbtools/xbbtools.py | 7 +-
Tests/.cvsignore | 2 +
Tests/BioSQL/biosqldb-pg.sql | 9 +
Tests/BioSQL/biosqldb-sqlite.sql | 116 +-
Tests/EMBL/DS830848.embl | 86 +
Tests/GenBank/.cvsignore | 1 +
Tests/GenBank/DS830848.gb | 73 +
Tests/GenBank/EU851978.gbk | 87 +
Tests/GenBank/HM138502.gbk | 79 +
Tests/GenBank/KF527485.gbk | 82 +
Tests/Graphics/.cvsignore | 6 +
Tests/NeuralNetwork/.cvsignore | 1 +
Tests/Nexus/vSysLab_Ganaspidium_multistate.nex | 48 +
.../vSysLab_Heptascelio_no-states_10+chars.nex | 105 +
.../vSysLab_Oreiscelio_discrete+continuous.nex | 162 +
Tests/PAML/Control_files/codeml/m2a_rel.ctl | 25 +
Tests/PAML/Results/codeml/m2a_rel/m2a_rel-4_6.out | 145 +
Tests/PAML/Results/codeml/m2a_rel/m2a_rel-4_7.out | 145 +
Tests/PAML/Results/codeml/m2a_rel/m2a_rel-4_8.out | 145 +
Tests/PAML/Results/codeml/m2a_rel/m2a_rel-4_9a.out | 144 +
Tests/PAML/gen_results.py | 10 +-
Tests/PDB/1LCD.pdb | 3884 +++
Tests/PDB/1MOT.pdb | 8220 -----
Tests/PDB/4zhl.cif | 3693 ++
Tests/PDB/a_structure.pdb | 1646 +-
Tests/biosql.ini.sample | 25 +
Tests/common_BioSQL.py | 281 +-
Tests/output/test_KEGG | 94 +
Tests/output/test_SeqIO | 16 +
Tests/phenotype/BadPlate.json | 1 +
Tests/phenotype/Plate.json | 1 +
Tests/phenotype/Plate_2.json | 1 +
Tests/phenotype/Plates.csv | 602 +
Tests/phenotype/SmallPlate.json | 1 +
Tests/phenotype/SmallPlate_2.json | 1 +
Tests/phenotype/SmallPlates.csv | 600 +
Tests/run_tests.py | 43 +-
Tests/search_tests_common.py | 12 +-
Tests/seq_tests_common.py | 7 +-
Tests/test_AlignIO.py | 12 +-
Tests/test_AlignInfo.py | 119 +
Tests/test_BioSQL_MySQLdb.py | 16 +-
Tests/test_BioSQL_mysql_connector.py | 16 +-
Tests/test_BioSQL_psycopg2.py | 15 +-
Tests/test_BioSQL_sqlite3.py | 9 +-
Tests/test_Chi2.py | 6 +-
Tests/test_Cluster.py | 4 +-
Tests/test_Crystal.py | 4 -
Tests/test_Emboss.py | 42 +-
Tests/test_Entrez.py | 323 +-
Tests/test_Entrez_online.py | 9 +
Tests/test_GAOrganism.py | 6 +-
Tests/test_GenBank_unittest.py | 60 +
Tests/test_GenomeDiagram.py | 211 +-
Tests/test_KEGG.py | 13 +-
Tests/test_KEGG_online.py | 60 +-
Tests/test_KGML_graphics.py | 8 +-
Tests/test_MMCIF.py | 137 +-
Tests/test_Mafft_tool.py | 10 +-
Tests/test_Motif.py | 1536 -
Tests/test_Muscle_tool.py | 4 +-
Tests/test_NCBI_BLAST_tools.py | 41 +-
Tests/test_NCBI_qblast.py | 17 +-
Tests/test_NNGene.py | 49 +-
Tests/test_NaiveBayes.py | 116 +
Tests/test_Nexus.py | 305 +-
Tests/test_PAML_baseml.py | 102 +-
Tests/test_PAML_codeml.py | 50 +-
Tests/test_PDB.py | 117 +-
Tests/test_PDB_KDTree.py | 2 +-
Tests/test_Pathway.py | 20 +-
Tests/test_Phylo.py | 28 +
Tests/test_PhyloXML.py | 12 +-
Tests/test_Phylo_NeXML.py | 6 +-
Tests/test_Phylo_depend.py | 30 +
Tests/test_PopGen_GenePop_nodepend.py | 30 +-
Tests/test_PopGen_SimCoal_nodepend.py | 11 +-
Tests/test_Prank_tool.py | 10 +-
Tests/test_QCPSuperimposer.py | 8 +-
Tests/test_RCSBFormats.py | 69 +
Tests/test_SCOP_Raf.py | 9 +
Tests/test_SCOP_Scop.py | 2 +-
Tests/test_SVDSuperimposer.py | 15 +-
Tests/test_SearchIO_model.py | 12 +-
Tests/test_SeqFeature.py | 28 +
Tests/test_SeqIO.py | 260 +-
Tests/test_SeqIO_FastaIO.py | 32 +-
Tests/test_SeqIO_PdbIO.py | 12 +-
Tests/test_SeqIO_QualityIO.py | 8 +-
Tests/test_SeqIO_convert.py | 9 +-
Tests/test_SeqIO_features.py | 40 +-
Tests/test_SeqIO_index.py | 235 +-
Tests/test_SeqIO_online.py | 4 +-
Tests/test_SeqIO_write.py | 8 +-
Tests/test_SeqRecord.py | 30 +
Tests/test_Seq_objs.py | 1 +
Tests/test_SffIO.py | 225 +-
Tests/test_SwissProt.py | 295 +-
Tests/test_TogoWS.py | 4 +-
Tests/test_Tutorial.py | 92 +-
Tests/test_XXmotif_tool.py | 2 +-
Tests/test_codonalign.py | 8 +-
Tests/test_motifs.py | 19 +-
Tests/test_motifs_online.py | 42 +-
Tests/test_phenotype.py | 304 +
Tests/test_phenotype_fit.py | 63 +
Tests/test_phyml_tool.py | 19 +-
Tests/test_prodoc.py | 192 +-
Tests/test_prosite1.py | 24 +-
Tests/test_prosite2.py | 21 +-
Tests/test_raxml_tool.py | 9 +-
Tests/test_samtools_tool.py | 15 +-
Tests/test_seq.py | 112 +-
setup.py | 11 +-
469 files changed, 35382 insertions(+), 69360 deletions(-)
diff --git a/.gitattributes b/.gitattributes
new file mode 100644
index 0000000..12f3515
--- /dev/null
+++ b/.gitattributes
@@ -0,0 +1,20 @@
+# On Windows, git can be setup to convert text files to use
+# DOS/Windows newlines (\r\n) rather than Unix style (\n).
+# This should be harmless for most of our unit tests...
+#
+# However, where testing indexing and get_raw (including the
+# BGZF tests comparing the uncompressed file to the compressed
+# file) it can be important to preserve the line endings in git.
+
+Tests/Fasta/f002 binary
+Tests/GenBank/cor6_6.gb binary
+Tests/GenBank/NC_000932.gb binary
+Tests/Quality/example.fastq binary
+Tests/Quality/example_dos.fastq binary
+Tests/Blast/wnts.xml binary
+
+# This pickle file has to be using Unix new lines otherwise at
+# least Python 3.4's C pickle parser fails with exception:
+# _pickle.UnpicklingError: the STRING opcode argument must be quoted
+
+Tests/SubsMat/acc_rep_mat.pik binary
diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..1dd13b8
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,89 @@
+#Ignore the build directory (and its sub-directories):
+build
+
+#Ignore the distribution directory
+dist
+
+#Ignore backup files from some Unix editors,
+*~
+*.swp
+*.bak
+
+#Ignore patches and any original files created by patch command
+*.diff
+*.patch
+*.orig
+*.rej
+
+#Ignore these hidden files from Mac OS X
+.DS_Store
+
+#Ignore hidden files from Dolphin window manager
+.directory
+
+#Ignore all compiled python files (e.g. from running the unit tests):
+*.pyc
+*.pyo
+*.py{}
+*.py-e
+
+#Ignore all Jython class files (present if using Jython)
+*.class
+
+#Ignore the MANIFEST file created automatically from MANIFEST.in
+MANIFEST
+
+#Ignore potential directory created during install:
+biopython.egg-info
+
+#The graphics unit tests produce output files for human inspection
+#(at the time of writing, only PDF and PNG files are created)
+Tests/Graphics/*.pdf
+Tests/Graphics/*.eps
+Tests/Graphics/*.svg
+Tests/Graphics/*.png
+
+#Ignore the local BioSQL test settings:
+Tests/biosql.ini
+
+#TODO - The unit tests shouldn't leave temp files after running:
+Tests/BioSQL/temp_sqlite.db
+Tests/BioSQL/temp_sqlite.db-journal
+
+#Ignore LaTeX temp files, and compiled output
+Doc/*.aux
+Doc/*.log
+Doc/*.out
+Doc/*.toc
+Doc/*.haux
+Doc/*.htoc
+Doc/*/*.aux
+Doc/*/*.log
+Doc/*/*.out
+Doc/*/*.toc
+Doc/*/*.haux
+Doc/*/*.htoc
+Doc/*/*/*.aux
+Doc/*/*/*.log
+Doc/*/*/*.out
+Doc/*/*/*.toc
+Doc/*/*/*.haux
+Doc/*/*/*.htoc
+Doc/Tutorial.txt
+Doc/Tutorial.pdf
+Doc/Tutorial.html
+Doc/biopdb_faq.pdf
+Doc/install/Installation.txt
+Doc/install/Installation.pdf
+Doc/install/Installation.html
+Doc/cookbook/motif/motif.pdf
+Doc/cookbook/motif/motif.html
+
+#Ignore the hevea.sty file if present (used for HTML docs)
+Doc/hevea.sty
+Doc/*/hevea.sty
+Doc/*/*/hevea.sty
+
+#Ignore IntelliJ IDEA directory and project files
+.idea
+*.iml
diff --git a/.travis.yml b/.travis.yml
new file mode 100644
index 0000000..cfee4f5
--- /dev/null
+++ b/.travis.yml
@@ -0,0 +1,76 @@
+# Special configuration file to run tests on Travis-CI via GitHub notifications
+# See https://travis-ci.org/biopython/biopython/builds for results
+#
+# The tests are run via the coverage script, and if the tests pass the coverage
+# information is pushed to https://codecov.io/github/biopython/biopython
+#
+# Note when testing Python 3, the 'python' command will invoke Python 3
+# and similarly for PyPy too.
+
+# Environment variables setup via the matrix
+# - DEP = list of python packages to install via default pip install
+# - EXT = list of python packages to install via pip install --allow-all-external
+# - BIN = list of python packages to install via pip install --only-binary
+# - COV = yes/no; should tests be run with coverage metric collection
+language: python
+matrix:
+ include:
+ - python: "2.6"
+ env: DEP="reportlab psycopg2 mysql-python unittest2" EXT=mysql-connector-python-rf BIN=scipy COV=yes
+ - python: "2.7"
+ env: DEP="reportlab psycopg2 mysql-python" EXT=mysql-connector-python-rf COV=yes
+ - python: "3.3"
+ env: DEP="reportlab psycopg2" EXT=mysql-connector-python-rf COV=yes
+ - python: "3.4"
+ env: DEP="reportlab psycopg2" EXT=mysql-connector-python-rf COV=yes
+ - python: "3.5"
+ env: DEP=reportlab psycopg2" EXT=mysql-connector-python-rf BIN=scipy COV=yes
+ - python: "pypy"
+ env: EXT="mysql-connector-python-rf" COV=yes
+ - python: "pypy3"
+ env: COV=no
+
+sudo: false
+addons:
+ apt:
+ packages:
+ - bwa
+ - clustalo
+ - clustalw
+ - emboss
+ - mafft
+ - muscle
+ - probcons
+ - samtools
+ - wise
+
+before_install:
+ - "pip install --upgrade pip"
+ - "pip install rdflib"
+ - "pip install $DEP"
+ - "pip install --only-binary=:all: $BIN"
+ - "pip install --allow-all-external $EXT"
+ - "if [[ $COV == 'yes' ]]; then pip install coverage; fi"
+
+
+install:
+#The yes is in case we get our prompt about missing NumPy
+ - "/usr/bin/yes | python setup.py install"
+
+before_script:
+ - cd Tests
+ - cp biosql.ini.sample biosql.ini
+
+script:
+#Using just coverage should match up to the current Python version:
+ - "if [[ $COV == 'yes' ]]; then coverage run --source=Bio,BioSQL run_tests.py --offline; fi"
+ - "if [[ $COV != 'yes' ]]; then python run_tests.py --offline; fi"
+
+after_success:
+#See https://codecov.io/ and https://github.com/codecov/example-python
+ - "if [[ $COV == 'yes' ]]; then pip install codecov; fi"
+ - "if [[ $COV == 'yes' ]]; then codecov; fi"
+
+#The email defaults are too talkative
+notifications:
+ email: false
diff --git a/Bio/Affy/CelFile.py b/Bio/Affy/CelFile.py
index f683bf0..db82f4e 100644
--- a/Bio/Affy/CelFile.py
+++ b/Bio/Affy/CelFile.py
@@ -23,8 +23,6 @@ except ImportError:
raise MissingPythonDependencyError(
"Install NumPy if you want to use Bio.Affy.CelFile")
-__docformat__ = "restructuredtext en"
-
class Record(object):
"""Stores the information in a cel file
diff --git a/Bio/Affy/__init__.py b/Bio/Affy/__init__.py
index e0ea1c4..925dd85 100644
--- a/Bio/Affy/__init__.py
+++ b/Bio/Affy/__init__.py
@@ -5,5 +5,3 @@
"""Deal with Affymetrix related data such as cel files.
"""
-
-__docformat__ = "restructuredtext en"
diff --git a/Bio/Align/AlignInfo.py b/Bio/Align/AlignInfo.py
index 2d8a386..b2fc980 100644
--- a/Bio/Align/AlignInfo.py
+++ b/Bio/Align/AlignInfo.py
@@ -20,7 +20,6 @@ from Bio.Alphabet import IUPAC
from Bio.Seq import Seq
from Bio.SubsMat import FreqTable
-__docformat__ = "restructuredtext en"
# Expected random distributions for 20-letter protein, and
# for 4-letter nucleotide alphabets
@@ -104,8 +103,8 @@ class SummaryInfo(object):
if require_multiple and num_atoms == 1:
consensus += ambiguous
- elif (len(max_atoms) == 1) and ((float(max_size) / float(num_atoms))
- >= threshold):
+ elif (len(max_atoms) == 1) and ((float(max_size) /
+ float(num_atoms)) >= threshold):
consensus += max_atoms[0]
else:
consensus += ambiguous
@@ -161,8 +160,8 @@ class SummaryInfo(object):
if require_multiple and num_atoms == 1:
consensus += ambiguous
- elif (len(max_atoms) == 1) and ((float(max_size) / float(num_atoms))
- >= threshold):
+ elif (len(max_atoms) == 1) and ((float(max_size) /
+ float(num_atoms)) >= threshold):
consensus += max_atoms[0]
else:
consensus += ambiguous
@@ -315,9 +314,9 @@ class SummaryInfo(object):
def _get_all_letters(self):
"""Returns a string containing the expected letters in the alignment."""
all_letters = self.alignment._alphabet.letters
- if all_letters is None \
- or (isinstance(self.alignment._alphabet, Alphabet.Gapped)
- and all_letters == self.alignment._alphabet.gap_char):
+ if all_letters is None or \
+ (isinstance(self.alignment._alphabet, Alphabet.Gapped) and
+ all_letters == self.alignment._alphabet.gap_char):
# We are dealing with a generic alphabet class where the
# letters are not defined! We must build a list of the
# letters used...
@@ -697,55 +696,3 @@ def print_info_content(summary_info, fout=None, rep_record=0):
for pos in sorted(summary_info.ic_vector):
fout.write("%d %s %.3f\n" % (pos, rep_sequence[pos],
summary_info.ic_vector[pos]))
-
-if __name__ == "__main__":
- print("Quick test")
- from Bio import AlignIO
- from Bio.Align.Generic import Alignment
-
- filename = "../../Tests/GFF/multi.fna"
- format = "fasta"
- expected = FreqTable.FreqTable({"A": 0.25, "G": 0.25, "T": 0.25, "C": 0.25},
- FreqTable.FREQ,
- IUPAC.unambiguous_dna)
-
- alignment = AlignIO.read(open(filename), format)
- for record in alignment:
- print(record.seq)
- print("=" * alignment.get_alignment_length())
-
- summary = SummaryInfo(alignment)
- consensus = summary.dumb_consensus(ambiguous="N")
- print(consensus)
- consensus = summary.gap_consensus(ambiguous="N")
- print(consensus)
- print("")
- print(summary.pos_specific_score_matrix(chars_to_ignore=['-'],
- axis_seq=consensus))
- print("")
- # Have a generic alphabet, without a declared gap char, so must tell
- # provide the frequencies and chars to ignore explicitly.
- print(summary.information_content(e_freq_table=expected,
- chars_to_ignore=['-']))
- print("")
- print("Trying a protein sequence with gaps and stops")
-
- alpha = Alphabet.HasStopCodon(Alphabet.Gapped(Alphabet.generic_protein, "-"), "*")
- a = Alignment(alpha)
- a.add_sequence("ID001", "MHQAIFIYQIGYP*LKSGYIQSIRSPEYDNW-")
- a.add_sequence("ID002", "MH--IFIYQIGYAYLKSGYIQSIRSPEY-NW*")
- a.add_sequence("ID003", "MHQAIFIYQIGYPYLKSGYIQSIRSPEYDNW*")
- print(a)
- print("=" * a.get_alignment_length())
-
- s = SummaryInfo(a)
- c = s.dumb_consensus(ambiguous="X")
- print(c)
- c = s.gap_consensus(ambiguous="X")
- print(c)
- print("")
- print(s.pos_specific_score_matrix(chars_to_ignore=['-', '*'], axis_seq=c))
-
- print(s.information_content(chars_to_ignore=['-', '*']))
-
- print("Done")
diff --git a/Bio/Align/Applications/_ClustalOmega.py b/Bio/Align/Applications/_ClustalOmega.py
index 2e97e11..fbce5b8 100644
--- a/Bio/Align/Applications/_ClustalOmega.py
+++ b/Bio/Align/Applications/_ClustalOmega.py
@@ -13,8 +13,6 @@
from __future__ import print_function
-__docformat__ = "restructuredtext en" # Don't just use plain text in epydoc API pages!
-
from Bio.Application import _Option, _Switch, AbstractCommandline
@@ -201,12 +199,6 @@ class ClustalOmegaCommandline(AbstractCommandline):
AbstractCommandline.__init__(self, cmd, **kwargs)
-def _test():
- """Run the module's doctests (PRIVATE)."""
- print("Running ClustalOmega doctests...")
- import doctest
- doctest.testmod()
- print("Done")
-
if __name__ == "__main__":
- _test()
+ from Bio._utils import run_doctest
+ run_doctest()
diff --git a/Bio/Align/Applications/_Clustalw.py b/Bio/Align/Applications/_Clustalw.py
index bb6e2f5..c328189 100644
--- a/Bio/Align/Applications/_Clustalw.py
+++ b/Bio/Align/Applications/_Clustalw.py
@@ -7,8 +7,6 @@
from __future__ import print_function
-__docformat__ = "restructuredtext en" # Don't just use plain text in epydoc API pages!
-
import os
from Bio.Application import _Option, _Switch, AbstractCommandline
@@ -330,12 +328,6 @@ class ClustalwCommandline(AbstractCommandline):
AbstractCommandline.__init__(self, cmd, **kwargs)
-def _test():
- """Run the module's doctests (PRIVATE)."""
- print("Running ClustalW doctests...")
- import doctest
- doctest.testmod()
- print("Done")
-
if __name__ == "__main__":
- _test()
+ from Bio._utils import run_doctest
+ run_doctest()
diff --git a/Bio/Align/Applications/_Dialign.py b/Bio/Align/Applications/_Dialign.py
index 409b67e..2277c37 100644
--- a/Bio/Align/Applications/_Dialign.py
+++ b/Bio/Align/Applications/_Dialign.py
@@ -7,7 +7,6 @@
from __future__ import print_function
-__docformat__ = "restructuredtext en" # Don't just use plain text in epydoc API pages!
from Bio.Application import _Option, _Argument, _Switch, AbstractCommandline
@@ -103,7 +102,7 @@ class DialignCommandline(AbstractCommandline):
_Switch(["-lo", "lo"],
"(Long Output) Additional file *.log with information "
"about fragments selected for pairwise alignment and "
- "about consistency in multi-alignment proceedure."),
+ "about consistency in multi-alignment procedure."),
_Switch(["-ma", "ma"],
"`mixed alignments' consisting of P-fragments and "
"N-fragments if nucleic acid sequences are aligned."),
@@ -137,7 +136,7 @@ class DialignCommandline(AbstractCommandline):
_Switch(["-nta", "nta"],
"`no textual alignment' - textual alignment suppressed. "
"This option makes sense if other output files are of "
- "intrest -- e.g. the fragment files created with -ff, "
+ "interest -- e.g. the fragment files created with -ff, "
"-fop, -fsm or -lo."),
_Switch(["-o", "o"],
"Fast version, resulting alignments may be slightly "
@@ -161,7 +160,7 @@ class DialignCommandline(AbstractCommandline):
"Maximum number of `*' characters indicating degree "
"of local similarity among sequences. By default, no "
"stars are used but numbers between 0 and 9, instead.",
- checker_function = lambda x: x in range(0, 10),
+ checker_function=lambda x: x in range(0, 10),
equate=False),
_Switch(["-stdo", "stdo"],
"Results written to standard output."),
@@ -171,7 +170,7 @@ class DialignCommandline(AbstractCommandline):
"options, e.g. -lgs)"),
_Option(["-thr", "thr"],
"Threshold T = x.",
- checker_function = lambda x: isinstance(x, int),
+ checker_function=lambda x: isinstance(x, int),
equate=False),
_Switch(["-xfr", "xfr"],
"'exclude fragments' - list of fragments can be "
@@ -184,12 +183,6 @@ class DialignCommandline(AbstractCommandline):
AbstractCommandline.__init__(self, cmd, **kwargs)
-def _test():
- """Run the module's doctests (PRIVATE)."""
- print("Running modules doctests...")
- import doctest
- doctest.testmod()
- print("Done")
-
if __name__ == "__main__":
- _test()
+ from Bio._utils import run_doctest
+ run_doctest()
diff --git a/Bio/Align/Applications/_MSAProbs.py b/Bio/Align/Applications/_MSAProbs.py
index 5838339..8cda414 100644
--- a/Bio/Align/Applications/_MSAProbs.py
+++ b/Bio/Align/Applications/_MSAProbs.py
@@ -8,7 +8,6 @@
from __future__ import print_function
-__docformat__ = "restructuredtext en" # Don't just use plain text in epydoc API pages!
from Bio.Application import _Argument, _Option, _Switch, AbstractCommandline
@@ -76,13 +75,6 @@ class MSAProbsCommandline(AbstractCommandline):
AbstractCommandline.__init__(self, cmd, **kwargs)
-def _test():
- """Run the module's doctests (PRIVATE)."""
- print("Running MSAProbs doctests...")
- import doctest
- doctest.testmod()
- print("Done")
-
-
if __name__ == "__main__":
- _test()
+ from Bio._utils import run_doctest
+ run_doctest()
diff --git a/Bio/Align/Applications/_Mafft.py b/Bio/Align/Applications/_Mafft.py
index 45bdaf2..a47f631 100644
--- a/Bio/Align/Applications/_Mafft.py
+++ b/Bio/Align/Applications/_Mafft.py
@@ -7,7 +7,6 @@
from __future__ import print_function
-__docformat__ = "restructuredtext en"
import os
from Bio.Application import _Option, _Switch, _Argument, AbstractCommandline
diff --git a/Bio/Align/Applications/_Muscle.py b/Bio/Align/Applications/_Muscle.py
index a4e71da..8a7ab88 100644
--- a/Bio/Align/Applications/_Muscle.py
+++ b/Bio/Align/Applications/_Muscle.py
@@ -7,8 +7,6 @@
from __future__ import print_function
-__docformat__ = "restructuredtext en" # Don't just use plain text in epydoc API pages!
-
from Bio.Application import _Option, _Switch, AbstractCommandline
@@ -406,7 +404,7 @@ class MuscleCommandline(AbstractCommandline):
# One of either -le, -sp, or -sv
#
# According to the doc, spn is default and the only option for
- # nucleotides: this doesnt appear to be true. -le, -sp, and -sv can
+ # nucleotides: this doesn't appear to be true. -le, -sp, and -sv can
# be used and produce numerically different logs (what is going on?)
#
# spn fails on proteins
@@ -468,12 +466,6 @@ class MuscleCommandline(AbstractCommandline):
AbstractCommandline.__init__(self, cmd, **kwargs)
-def _test():
- """Run the module's doctests (PRIVATE)."""
- print("Running MUSCLE doctests...")
- import doctest
- doctest.testmod()
- print("Done")
-
if __name__ == "__main__":
- _test()
+ from Bio._utils import run_doctest
+ run_doctest()
diff --git a/Bio/Align/Applications/_Prank.py b/Bio/Align/Applications/_Prank.py
index a60cb21..c0d362d 100644
--- a/Bio/Align/Applications/_Prank.py
+++ b/Bio/Align/Applications/_Prank.py
@@ -7,7 +7,6 @@
from __future__ import print_function
-__docformat__ = "restructuredtext en" # Don't just use plain text in epydoc API pages!
from Bio.Application import _Option, _Switch, AbstractCommandline
@@ -159,13 +158,13 @@ class PrankCommandline(AbstractCommandline):
_Switch(["-printnodes", "printnodes"],
"Output each node; mostly for debugging"),
# -matresize=# [matrix resizing multiplier]
- # Doesnt specify type but Float and Int work
+ # Doesn't specify type but Float and Int work
_Option(["-matresize", "matresize"],
"Matrix resizing multiplier",
checker_function=lambda x: isinstance(x, float) or
isinstance(x, int)),
# -matinitsize=# [matrix initial size multiplier]
- # Doesnt specify type but Float and Int work
+ # Doesn't specify type but Float and Int work
_Option(["-matinitsize", "matinitsize"],
"Matrix initial size multiplier",
checker_function=lambda x: isinstance(x, float) or
@@ -207,12 +206,6 @@ class PrankCommandline(AbstractCommandline):
AbstractCommandline.__init__(self, cmd, **kwargs)
-def _test():
- """Run the module's doctests (PRIVATE)."""
- print("Running modules doctests...")
- import doctest
- doctest.testmod()
- print("Done")
-
if __name__ == "__main__":
- _test()
+ from Bio._utils import run_doctest
+ run_doctest()
diff --git a/Bio/Align/Applications/_Probcons.py b/Bio/Align/Applications/_Probcons.py
index 7231648..ab3f452 100644
--- a/Bio/Align/Applications/_Probcons.py
+++ b/Bio/Align/Applications/_Probcons.py
@@ -7,7 +7,6 @@
from __future__ import print_function
-__docformat__ = "restructuredtext en" # Don't just use plain text in epydoc API pages!
from Bio.Application import _Option, _Switch, _Argument, AbstractCommandline
@@ -112,12 +111,6 @@ class ProbconsCommandline(AbstractCommandline):
AbstractCommandline.__init__(self, cmd, **kwargs)
-def _test():
- """Run the module's doctests (PRIVATE)."""
- print("Running modules doctests...")
- import doctest
- doctest.testmod()
- print("Done")
-
if __name__ == "__main__":
- _test()
+ from Bio._utils import run_doctest
+ run_doctest()
diff --git a/Bio/Align/Applications/_TCoffee.py b/Bio/Align/Applications/_TCoffee.py
index 537a62b..18f3bc2 100644
--- a/Bio/Align/Applications/_TCoffee.py
+++ b/Bio/Align/Applications/_TCoffee.py
@@ -7,8 +7,6 @@
from __future__ import print_function
-__docformat__ = "restructuredtext en" # Don't just use plain text in epydoc API pages!
-
from Bio.Application import _Option, _Switch, AbstractCommandline
@@ -104,12 +102,6 @@ class TCoffeeCommandline(AbstractCommandline):
AbstractCommandline.__init__(self, cmd, **kwargs)
-def _test():
- """Run the module's doctests (PRIVATE)."""
- print("Running modules doctests...")
- import doctest
- doctest.testmod()
- print("Done")
-
if __name__ == "__main__":
- _test()
+ from Bio._utils import run_doctest
+ run_doctest()
diff --git a/Bio/Align/Applications/__init__.py b/Bio/Align/Applications/__init__.py
index 635f810..795c507 100644
--- a/Bio/Align/Applications/__init__.py
+++ b/Bio/Align/Applications/__init__.py
@@ -4,8 +4,6 @@
# as part of this package.
"""Alignment command line tool wrappers."""
-__docformat__ = "restructuredtext en" # Don't just use plain text in epydoc API pages!
-
from ._Muscle import MuscleCommandline
from ._Clustalw import ClustalwCommandline
from ._ClustalOmega import ClustalOmegaCommandline
diff --git a/Bio/Align/Generic.py b/Bio/Align/Generic.py
index c732855..0f1411e 100644
--- a/Bio/Align/Generic.py
+++ b/Bio/Align/Generic.py
@@ -1,6 +1,6 @@
# Copyright 2000-2004 Brad Chapman.
# Copyright 2001 Iddo Friedberg.
-# Copyright 2007-2010 by Peter Cock.
+# Copyright 2007-2016 by Peter Cock.
# All rights reserved.
# This code is part of the Biopython distribution and governed by its
# license. Please see the LICENSE file that should have been included
@@ -12,9 +12,6 @@ specific to a particular program or format.
"""
from __future__ import print_function
-__docformat__ = "restructuredtext en" # Don't just use plain text in epydoc API pages!
-
-# biopython
from Bio.Seq import Seq
from Bio.SeqRecord import SeqRecord
from Bio import Alphabet
@@ -40,6 +37,7 @@ class Alignment(object):
e.g.
>>> from Bio.Alphabet import IUPAC, Gapped
+ >>> from Bio.Align.Generic import Alignment
>>> align = Alignment(Gapped(IUPAC.unambiguous_dna, "-"))
>>> align.add_sequence("Alpha", "ACTGCTAGCTAG")
>>> align.add_sequence("Beta", "ACT-CTAGCTAG")
@@ -53,8 +51,8 @@ class Alignment(object):
import warnings
import Bio
warnings.warn("With the introduction of the MultipleSeqAlignment class in Bio.Align, this base class is deprecated and is likely to be removed in a future release of Biopython.", Bio.BiopythonDeprecationWarning)
- if not (isinstance(alphabet, Alphabet.Alphabet)
- or isinstance(alphabet, Alphabet.AlphabetEncoder)):
+ if not (isinstance(alphabet, Alphabet.Alphabet) or
+ isinstance(alphabet, Alphabet.AlphabetEncoder)):
raise ValueError("Invalid alphabet argument")
self._alphabet = alphabet
# hold everything at a list of SeqRecord objects
@@ -87,6 +85,7 @@ class Alignment(object):
single screen. e.g.
>>> from Bio.Alphabet import IUPAC, Gapped
+ >>> from Bio.Align.Generic import Alignment
>>> align = Alignment(Gapped(IUPAC.unambiguous_dna, "-"))
>>> align.add_sequence("Alpha", "ACTGCTAGCTAG")
>>> align.add_sequence("Beta", "ACT-CTAGCTAG")
@@ -144,6 +143,7 @@ class Alignment(object):
e.g.
>>> from Bio.Alphabet import IUPAC, Gapped
+ >>> from Bio.Align.Generic import Alignment
>>> align = Alignment(Gapped(IUPAC.unambiguous_dna, "-"))
>>> align.add_sequence("Alpha", "ACTGCTAGCTAG")
>>> align.add_sequence("Beta", "ACT-CTAGCTAG")
@@ -210,6 +210,7 @@ class Alignment(object):
e.g.
>>> from Bio.Alphabet import IUPAC, Gapped
+ >>> from Bio.Align.Generic import Alignment
>>> align = Alignment(Gapped(IUPAC.unambiguous_dna, "-"))
>>> align.add_sequence("Alpha", "ACTGCTAGCTAG")
>>> align.add_sequence("Beta", "ACT-CTAGCTAG")
@@ -264,6 +265,7 @@ class Alignment(object):
by finding the maximum length of sequences in the alignment.
>>> from Bio.Alphabet import IUPAC, Gapped
+ >>> from Bio.Align.Generic import Alignment
>>> align = Alignment(Gapped(IUPAC.unambiguous_dna, "-"))
>>> align.add_sequence("Alpha", "ACTGCTAGCTAG")
>>> align.add_sequence("Beta", "ACT-CTAGCTAG")
@@ -343,6 +345,7 @@ class Alignment(object):
e.g.
>>> from Bio.Alphabet import IUPAC, Gapped
+ >>> from Bio.Align.Generic import Alignment
>>> align = Alignment(Gapped(IUPAC.unambiguous_dna, "-"))
>>> align.add_sequence("Alpha", "ACTGCTAGCTAG")
>>> align.add_sequence("Beta", "ACT-CTAGCTAG")
@@ -365,6 +368,7 @@ class Alignment(object):
We'll use the following example alignment here for illustration:
>>> from Bio.Alphabet import IUPAC, Gapped
+ >>> from Bio.Align.Generic import Alignment
>>> align = Alignment(Gapped(IUPAC.unambiguous_dna, "-"))
>>> align.add_sequence("Alpha", "ACTGCTAGCTAG")
>>> align.add_sequence("Beta", "ACT-CTAGCTAG")
@@ -435,12 +439,6 @@ class Alignment(object):
raise TypeError("Invalid index type.")
-def _test():
- """Run the Bio.Align.Generic module's doctests."""
- print("Running doctests...")
- import doctest
- doctest.testmod()
- print("Done")
-
if __name__ == "__main__":
- _test()
+ from Bio._utils import run_doctest
+ run_doctest()
diff --git a/Bio/Align/__init__.py b/Bio/Align/__init__.py
index b74775d..2af9ab1 100644
--- a/Bio/Align/__init__.py
+++ b/Bio/Align/__init__.py
@@ -11,8 +11,6 @@ class, used in the Bio.AlignIO module.
"""
from __future__ import print_function
-__docformat__ = "restructuredtext en" # Don't just use plain text in epydoc API pages!
-
from Bio.Seq import Seq
from Bio.SeqRecord import SeqRecord
from Bio import Alphabet
@@ -128,6 +126,7 @@ class MultipleSeqAlignment(_Alignment):
>>> from Bio.Alphabet import generic_dna
>>> from Bio.Seq import Seq
>>> from Bio.SeqRecord import SeqRecord
+ >>> from Bio.Align import MultipleSeqAlignment
>>> a = SeqRecord(Seq("AAAACGT", generic_dna), id="Alpha")
>>> b = SeqRecord(Seq("AAA-CGT", generic_dna), id="Beta")
>>> c = SeqRecord(Seq("AAAAGGT", generic_dna), id="Gamma")
@@ -145,8 +144,7 @@ class MultipleSeqAlignment(_Alignment):
compatible "hack" so as not to disrupt existing scripts and users, but
is deprecated and will be removed in a future release.
"""
- if isinstance(records, Alphabet.Alphabet) \
- or isinstance(records, Alphabet.AlphabetEncoder):
+ if isinstance(records, (Alphabet.Alphabet, Alphabet.AlphabetEncoder)):
if alphabet is None:
# TODO - Remove this backwards compatible mode!
alphabet = records
@@ -163,8 +161,7 @@ class MultipleSeqAlignment(_Alignment):
else:
raise ValueError("Invalid records argument")
if alphabet is not None:
- if not (isinstance(alphabet, Alphabet.Alphabet)
- or isinstance(alphabet, Alphabet.AlphabetEncoder)):
+ if not isinstance(alphabet, (Alphabet.Alphabet, Alphabet.AlphabetEncoder)):
raise ValueError("Invalid alphabet argument")
self._alphabet = alphabet
else:
@@ -315,7 +312,7 @@ class MultipleSeqAlignment(_Alignment):
self._records.append(record)
def __add__(self, other):
- """Combines to alignments with the same number of rows by adding them.
+ """Combines two alignments with the same number of rows by adding them.
If you have two multiple sequence alignments (MSAs), there are two ways to think
about adding them - by row or by column. Using the extend method adds by row.
diff --git a/Bio/AlignIO/ClustalIO.py b/Bio/AlignIO/ClustalIO.py
index 525d9c3..aed9489 100644
--- a/Bio/AlignIO/ClustalIO.py
+++ b/Bio/AlignIO/ClustalIO.py
@@ -16,8 +16,6 @@ from Bio.SeqRecord import SeqRecord
from Bio.Align import MultipleSeqAlignment
from .Interfaces import AlignmentIterator, SequentialAlignmentWriter
-__docformat__ = "restructuredtext en"
-
class ClustalWriter(SequentialAlignmentWriter):
"""Clustalw alignment writer."""
@@ -104,7 +102,7 @@ class ClustalIterator(AlignmentIterator):
raise StopIteration
# Whitelisted headers we know about
- known_headers = ['CLUSTAL', 'PROBCONS', 'MUSCLE', 'MSAPROBS','Kalign']
+ known_headers = ['CLUSTAL', 'PROBCONS', 'MUSCLE', 'MSAPROBS', 'Kalign']
if line.strip().split()[0] not in known_headers:
raise ValueError("%s is not a known CLUSTAL header: %s" %
(line.strip().split()[0],
diff --git a/Bio/AlignIO/EmbossIO.py b/Bio/AlignIO/EmbossIO.py
index ba7c473..d395fe3 100644
--- a/Bio/AlignIO/EmbossIO.py
+++ b/Bio/AlignIO/EmbossIO.py
@@ -19,8 +19,6 @@ from Bio.SeqRecord import SeqRecord
from Bio.Align import MultipleSeqAlignment
from .Interfaces import AlignmentIterator, SequentialAlignmentWriter
-__docformat__ = "restructuredtext en"
-
class EmbossWriter(SequentialAlignmentWriter):
"""Emboss alignment writer (WORK IN PROGRESS).
diff --git a/Bio/AlignIO/FastaIO.py b/Bio/AlignIO/FastaIO.py
index 7ff9326..eb153c8 100644
--- a/Bio/AlignIO/FastaIO.py
+++ b/Bio/AlignIO/FastaIO.py
@@ -1,4 +1,4 @@
-# Copyright 2008-2011 by Peter Cock. All rights reserved.
+# Copyright 2008-2016 by Peter Cock. All rights reserved.
#
# This code is part of the Biopython distribution and governed by its
# license. Please see the LICENSE file that should have been included
@@ -10,7 +10,7 @@ Bio.SeqIO functions if you want to work directly with the gapped sequences).
This module contains a parser for the pairwise alignments produced by Bill
Pearson's FASTA tools, for use from the Bio.AlignIO interface where it is
-refered to as the "fasta-m10" file format (as we only support the machine
+referred to as the "fasta-m10" file format (as we only support the machine
readable output format selected with the -m 10 command line option).
This module does NOT cover the generic "fasta" file format originally
@@ -27,8 +27,6 @@ from Bio.Align import MultipleSeqAlignment
from Bio.Alphabet import single_letter_alphabet, generic_dna, generic_protein
from Bio.Alphabet import Gapped
-__docformat__ = "restructuredtext en"
-
def _extract_alignment_region(alignment_seq_with_flanking, annotation):
"""Helper function for the main parsing code (PRIVATE).
@@ -111,7 +109,7 @@ def FastaM10Iterator(handle, alphabet=single_letter_alphabet):
% (query_id, match_id))
assert query_tags, query_tags
assert match_tags, match_tags
- evalue = align_tags.get("fa_expect", None)
+ evalue = align_tags.get("fa_expect")
q = "?" # Just for printing len(q) in debug below
m = "?" # Just for printing len(m) in debug below
tool = global_tags.get("tool", "").upper()
diff --git a/Bio/AlignIO/Interfaces.py b/Bio/AlignIO/Interfaces.py
index 768d4c1..2493155 100644
--- a/Bio/AlignIO/Interfaces.py
+++ b/Bio/AlignIO/Interfaces.py
@@ -14,8 +14,6 @@ import sys # for checking if Python 2
from Bio.Alphabet import single_letter_alphabet
-__docformat__ = "restructuredtext en"
-
class AlignmentIterator(object):
"""Base class for building MultipleSeqAlignment iterators.
diff --git a/Bio/AlignIO/NexusIO.py b/Bio/AlignIO/NexusIO.py
index ab8dddb..544734b 100644
--- a/Bio/AlignIO/NexusIO.py
+++ b/Bio/AlignIO/NexusIO.py
@@ -21,7 +21,6 @@ from Bio.Align import MultipleSeqAlignment
from .Interfaces import AlignmentWriter
from Bio import Alphabet
-__docformat__ = "restructuredtext en"
# You can get a couple of example files here:
# http://www.molecularevolution.org/resources/fileformats/
diff --git a/Bio/AlignIO/PhylipIO.py b/Bio/AlignIO/PhylipIO.py
index ebd686c..95c9703 100644
--- a/Bio/AlignIO/PhylipIO.py
+++ b/Bio/AlignIO/PhylipIO.py
@@ -43,7 +43,6 @@ from Bio.SeqRecord import SeqRecord
from Bio.Align import MultipleSeqAlignment
from .Interfaces import AlignmentIterator, SequentialAlignmentWriter
-__docformat__ = "restructuredtext en"
_PHYLIP_ID_WIDTH = 10
diff --git a/Bio/AlignIO/StockholmIO.py b/Bio/AlignIO/StockholmIO.py
index 2904427..9358f93 100644
--- a/Bio/AlignIO/StockholmIO.py
+++ b/Bio/AlignIO/StockholmIO.py
@@ -1,8 +1,10 @@
-# Copyright 2006-2015 by Peter Cock. All rights reserved.
+# Copyright 2006-2016 by Peter Cock. All rights reserved.
+# Revisions copyright 2015 by Ben Woodcroft. All rights reserved.
#
# This code is part of the Biopython distribution and governed by its
# license. Please see the LICENSE file that should have been included
# as part of this package.
+
"""Bio.AlignIO support for "stockholm" format (used in the PFAM database).
You are expected to use this module via the Bio.AlignIO functions (or the
@@ -131,11 +133,11 @@ secondary structure string here, are also sliced:
"""
from __future__ import print_function
-__docformat__ = "restructuredtext en" # not just plaintext
from Bio.Seq import Seq
from Bio.SeqRecord import SeqRecord
from Bio.Align import MultipleSeqAlignment
from .Interfaces import AlignmentIterator, SequentialAlignmentWriter
+from Bio._py3k import OrderedDict
class StockholmWriter(SequentialAlignmentWriter):
@@ -337,7 +339,7 @@ class StockholmIterator(AlignmentIterator):
# if present it agrees with our parsing.
seqs = {}
- ids = []
+ ids = OrderedDict() # Really only need an OrderedSet, but python lacks this
gs = {}
gr = {}
gf = {}
@@ -365,10 +367,10 @@ class StockholmIterator(AlignmentIterator):
if len(parts) != 2:
# This might be someone attempting to store a zero length sequence?
raise ValueError("Could not split line into identifier "
- + "and sequence:\n" + line)
+ "and sequence:\n" + line)
id, seq = parts
if id not in ids:
- ids.append(id)
+ ids[id] = True
seqs.setdefault(id, '')
seqs[id] += seq.replace(".", "-")
elif len(line) >= 5:
@@ -419,7 +421,7 @@ class StockholmIterator(AlignmentIterator):
# assert len(gs) <= len(ids)
# assert len(gr) <= len(ids)
- self.ids = ids
+ self.ids = ids.keys()
self.sequences = seqs
self.seq_annotation = gs
self.seq_col_annotation = gr
diff --git a/Bio/AlignIO/__init__.py b/Bio/AlignIO/__init__.py
index 62026cb..57e9e26 100644
--- a/Bio/AlignIO/__init__.py
+++ b/Bio/AlignIO/__init__.py
@@ -1,4 +1,4 @@
-# Copyright 2008-2010 by Peter Cock. All rights reserved.
+# Copyright 2008-2016 by Peter Cock. All rights reserved.
# This code is part of the Biopython distribution and governed by its
# license. Please see the LICENSE file that should have been included
# as part of this package.
@@ -84,7 +84,7 @@ overwrite the existing file each time.
Conversion
----------
The Bio.AlignIO.convert(...) function allows an easy interface for simple
-alignnment file format conversions. Additionally, it may use file format
+alignment file format conversions. Additionally, it may use file format
specific optimisations so this should be the fastest way too.
In general however, you can combine the Bio.AlignIO.parse(...) function with
@@ -127,8 +127,6 @@ same length.
from __future__ import print_function
from Bio._py3k import basestring
-__docformat__ = "restructuredtext en" # not just plaintext
-
# TODO
# - define policy on reading aligned sequences with gaps in
# (e.g. - and . characters) including how the alphabet interacts
@@ -220,8 +218,8 @@ def write(alignments, handle, format):
count = 0
for alignment in alignments:
if not isinstance(alignment, Alignment):
- raise TypeError(
- "Expect a list or iterator of Alignment objects.")
+ raise TypeError("Expect a list or iterator of Alignment "
+ "objects, got: %r" % alignment)
SeqIO.write(alignment, fp, format)
count += 1
elif format in _FormatToIterator or format in SeqIO._FormatToIterator:
@@ -266,7 +264,7 @@ def _SeqIO_to_alignment_iterator(handle, format, alphabet=None, seq_count=None):
if len(records) == seq_count:
yield MultipleSeqAlignment(records, alphabet)
records = []
- if len(records) > 0:
+ if records:
raise ValueError("Check seq_count argument, not enough sequences?")
else:
# Must assume that there is a single alignment using all
@@ -274,7 +272,6 @@ def _SeqIO_to_alignment_iterator(handle, format, alphabet=None, seq_count=None):
records = list(SeqIO.parse(handle, format, alphabet))
if records:
yield MultipleSeqAlignment(records, alphabet)
- raise StopIteration
def _force_alphabet(alignment_iterator, alphabet):
diff --git a/Bio/Alphabet/IUPAC.py b/Bio/Alphabet/IUPAC.py
index 19cb4f4..a1433b5 100644
--- a/Bio/Alphabet/IUPAC.py
+++ b/Bio/Alphabet/IUPAC.py
@@ -10,8 +10,6 @@
from Bio import Alphabet
from Bio.Data import IUPACData
-__docformat__ = "restructuredtext en"
-
# #################### Protein
diff --git a/Bio/Alphabet/Reduced.py b/Bio/Alphabet/Reduced.py
index 7ca4c08..a375ae5 100644
--- a/Bio/Alphabet/Reduced.py
+++ b/Bio/Alphabet/Reduced.py
@@ -27,7 +27,6 @@ the tables here, or a user-defined table.
from Bio import Alphabet
-__docformat__ = "restructuredtext en"
murphy_15_tab = {"L": "L",
"V": "L",
diff --git a/Bio/Alphabet/__init__.py b/Bio/Alphabet/__init__.py
index bf62cb9..022a2e3 100644
--- a/Bio/Alphabet/__init__.py
+++ b/Bio/Alphabet/__init__.py
@@ -10,8 +10,6 @@
This is used by sequences which contain a finite number of similar words.
"""
-__docformat__ = "restructuredtext en"
-
class Alphabet(object):
"""Generic alphabet base class.
diff --git a/Bio/Application/__init__.py b/Bio/Application/__init__.py
index 661cd48..1cb7e36 100644
--- a/Bio/Application/__init__.py
+++ b/Bio/Application/__init__.py
@@ -32,7 +32,6 @@ from subprocess import CalledProcessError as _ProcessCalledError
from Bio import File
-__docformat__ = "restructuredtext en"
# Use this regular expression to test the property names are going to
# be valid as Python properties or arguments
@@ -81,7 +80,7 @@ class ApplicationError(_ProcessCalledError):
# get first line of any stderr message
try:
msg = self.stderr.lstrip().split("\n", 1)[0].rstrip()
- except:
+ except Exception: # TODO, ValueError? AttributeError?
msg = ""
if msg:
return "Non-zero return code %d from %r, message %r" \
diff --git a/Bio/Blast/Applications.py b/Bio/Blast/Applications.py
index 982f02a..2d2f388 100644
--- a/Bio/Blast/Applications.py
+++ b/Bio/Blast/Applications.py
@@ -30,8 +30,6 @@ from __future__ import print_function
from Bio.Application import _Option, AbstractCommandline, _Switch
-__docformat__ = "restructuredtext en"
-
class _NcbibaseblastCommandline(AbstractCommandline):
"""Base Commandline object for (new) NCBI BLAST+ wrappers (PRIVATE).
@@ -89,7 +87,7 @@ class _NcbibaseblastCommandline(AbstractCommandline):
# Miscellaneous options
_Switch(["-parse_deflines", "parse_deflines"],
"Should the query and subject defline(s) be parsed?"),
- ]
+ ]
try:
# Insert extra parameters - at the start just in case there
# are any arguments which must come last:
@@ -238,7 +236,7 @@ class _NcbiblastCommandline(_NcbibaseblastCommandline):
"""Execute search remotely?
Incompatible with: gilist, negative_gilist, subject_loc, num_threads, ..."""),
- ]
+ ]
try:
# Insert extra parameters - at the start just in case there
# are any arguments which must come last:
@@ -316,7 +314,7 @@ class _Ncbiblast2SeqCommandline(_NcbiblastCommandline):
Incompatible with: culling_limit.""",
equate=False),
- ]
+ ]
try:
# Insert extra parameters - at the start just in case there
# are any arguments which must come last:
@@ -359,7 +357,7 @@ class _NcbiblastMain2SeqCommandline(_Ncbiblast2SeqCommandline):
Incompatible with: db_soft_mask, subject, subject_loc""",
equate=False),
- ]
+ ]
try:
# Insert extra parameters - at the start just in case there
# are any arguments which must come last:
@@ -431,7 +429,7 @@ class NcbiblastpCommandline(_NcbiblastMain2SeqCommandline):
# Miscellaneous options:
_Switch(["-use_sw_tback", "use_sw_tback"],
"Compute locally optimal Smith-Waterman alignments?"),
- ]
+ ]
_NcbiblastMain2SeqCommandline.__init__(self, cmd, **kwargs)
@@ -465,8 +463,8 @@ class NcbiblastnCommandline(_NcbiblastMain2SeqCommandline):
Values allowed are "both" (default), "minus", "plus".""",
checker_function=lambda value: value in ["both",
- "minus",
- "plus"],
+ "minus",
+ "plus"],
equate=False),
# General search options:
_Option(["-task", "task"],
@@ -475,10 +473,10 @@ class NcbiblastnCommandline(_NcbiblastMain2SeqCommandline):
Allowed values 'blastn', 'blastn-short', 'dc-megablast', 'megablast'
(the default), or 'vecscreen'.""",
checker_function=lambda value: value in ['blastn',
- 'blastn-short',
- 'dc-megablast',
- 'megablast',
- 'vecscreen'],
+ 'blastn-short',
+ 'dc-megablast',
+ 'megablast',
+ 'vecscreen'],
equate=False),
_Option(["-penalty", "penalty"],
"Penalty for a nucleotide mismatch (integer, at most zero).",
@@ -546,12 +544,12 @@ class NcbiblastnCommandline(_NcbiblastMain2SeqCommandline):
Added in BLAST 2.2.23+
""",
equate=False),
- ]
+ ]
_NcbiblastMain2SeqCommandline.__init__(self, cmd, **kwargs)
def _validate(self):
if (self.template_type and not self.template_length) \
- or (self.template_length and not self.template_type):
+ or (self.template_length and not self.template_type):
raise ValueError("Options template_type and template_type require each other.")
_NcbiblastMain2SeqCommandline._validate(self)
@@ -635,7 +633,7 @@ class NcbiblastxCommandline(_NcbiblastMain2SeqCommandline):
"Perform ungapped alignment only?"),
_Switch(["-use_sw_tback", "use_sw_tback"],
"Compute locally optimal Smith-Waterman alignments?"),
- ]
+ ]
_NcbiblastMain2SeqCommandline.__init__(self, cmd, **kwargs)
@@ -718,7 +716,7 @@ class NcbitblastnCommandline(_NcbiblastMain2SeqCommandline):
Incompatible with: remote, query""",
filename=True,
equate=False),
- ]
+ ]
_NcbiblastMain2SeqCommandline.__init__(self, cmd, **kwargs)
@@ -776,7 +774,7 @@ class NcbitblastxCommandline(_NcbiblastMain2SeqCommandline):
Format: "yes", "window locut hicut", or "no" to disable.
Default is "12 2.2 2.5""",
equate=False),
- ]
+ ]
_NcbiblastMain2SeqCommandline.__init__(self, cmd, **kwargs)
@@ -846,6 +844,13 @@ class NcbipsiblastCommandline(_Ncbiblast2SeqCommandline):
"File name to store ASCII version of PSSM.",
filename=True,
equate=False),
+ _Switch(["-save_pssm_after_last_round", "save_pssm_after_last_round"],
+ "Save PSSM after the last database search."),
+ _Switch(["-save_each_pssm", "save_each_pssm"],
+ """Save PSSM after each iteration
+
+ File name is given in -save_pssm or -save_ascii_pssm options.
+ """),
_Option(["-in_msa", "in_msa"],
"""File name of multiple sequence alignment to restart PSI-BLAST.
@@ -887,7 +892,7 @@ class NcbipsiblastCommandline(_Ncbiblast2SeqCommandline):
Incompatible with: in_pssm""",
filename=True,
equate=False),
- ]
+ ]
_Ncbiblast2SeqCommandline.__init__(self, cmd, **kwargs)
def _validate(self):
@@ -895,7 +900,7 @@ class NcbipsiblastCommandline(_Ncbiblast2SeqCommandline):
"in_msa": ["in_pssm", "query"],
"in_pssm": ["in_msa", "query", "phi_pattern"],
"ignore_msa_master": ["msa_master_idx", "in_pssm",
- "query", "query_loc", "phi_pattern"],
+ "query", "query_loc", "phi_pattern"],
}
self._validate_incompatibilities(incompatibles)
_Ncbiblast2SeqCommandline._validate(self)
@@ -971,7 +976,7 @@ class NcbirpsblastCommandline(_NcbiblastCommandline):
# Misc options:
_Switch(["-use_sw_tback", "use_sw_tback"],
"Compute locally optimal Smith-Waterman alignments?"),
- ]
+ ]
_NcbiblastCommandline.__init__(self, cmd, **kwargs)
def _validate(self):
@@ -1009,8 +1014,8 @@ class NcbirpstblastnCommandline(_NcbiblastCommandline):
Values allowed are "both" (default), "minus", "plus".""",
checker_function=lambda value: value in ["both",
- "minus",
- "plus"],
+ "minus",
+ "plus"],
equate=False),
# Input query options:
_Option(["-query_gencode", "query_gencode"],
@@ -1026,7 +1031,7 @@ class NcbirpstblastnCommandline(_NcbiblastCommandline):
# Extension options:
_Switch(["-ungapped", "ungapped"],
"Perform ungapped alignment only?"),
- ]
+ ]
_NcbiblastCommandline.__init__(self, cmd, **kwargs)
@@ -1072,7 +1077,7 @@ class NcbiblastformatterCommandline(_NcbibaseblastCommandline):
"Maximum number of aligned sequences to keep",
checker_function=lambda value: value >= 1,
equate=False),
- ]
+ ]
_NcbibaseblastCommandline.__init__(self, cmd, **kwargs)
def _validate(self):
@@ -1144,6 +1149,13 @@ class NcbideltablastCommandline(_Ncbiblast2SeqCommandline):
"File name to store ASCII version of PSSM.",
filename=True,
equate=False),
+ _Switch(["-save_pssm_after_last_round", "save_pssm_after_last_round"],
+ "Save PSSM after the last database search."),
+ _Switch(["-save_each_pssm", "save_each_pssm"],
+ """Save PSSM after each iteration
+
+ File name is given in -save_pssm or -save_ascii_pssm options.
+ """),
# PSSM engine options
_Option(["-pseudocount", "pseudocount"],
"Pseudo-count value used when constructing PSSM (integer, default 0).",
@@ -1164,7 +1176,7 @@ class NcbideltablastCommandline(_Ncbiblast2SeqCommandline):
"""Show domain hits?
Incompatible with: remote, subject""")
- ]
+ ]
_Ncbiblast2SeqCommandline.__init__(self, cmd, **kwargs)
diff --git a/Bio/Blast/NCBIStandalone.py b/Bio/Blast/NCBIStandalone.py
index 8fe0c12..d2f1f70 100644
--- a/Bio/Blast/NCBIStandalone.py
+++ b/Bio/Blast/NCBIStandalone.py
@@ -24,21 +24,22 @@ wrappers for these under Bio.Blast.Applications (see the tutorial).
from __future__ import print_function
-from Bio import BiopythonDeprecationWarning
-import warnings
-warnings.warn("This module has been deprecated. Consider Bio.SearchIO for "
- "parsing BLAST output instead.", BiopythonDeprecationWarning)
-
-import os
+import sys
import re
-from Bio._py3k import StringIO
+from Bio._py3k import StringIO
+from Bio.ParserSupport import AbstractParser, AbstractConsumer
+from Bio.ParserSupport import read_and_call, read_and_call_until
+from Bio.ParserSupport import read_and_call_while, attempt_read_and_call
+from Bio.ParserSupport import is_blank_line, safe_peekline, safe_readline
from Bio import File
-from Bio.ParserSupport import *
from Bio.Blast import Record
-from Bio.Application import _escape_filename
-__docformat__ = "restructuredtext en"
+from Bio import BiopythonDeprecationWarning
+import warnings
+warnings.warn("This module has been deprecated. Consider Bio.SearchIO for "
+ "parsing BLAST output instead.", BiopythonDeprecationWarning)
+
_score_e_re = re.compile(r'Score +E')
@@ -605,7 +606,7 @@ class _Scanner(object):
try:
read_and_call(uhandle, consumer.noevent, start='Lambda')
read_and_call(uhandle, consumer.ka_params)
- except:
+ except Exception: # TODO: ValueError, AttributeError?
pass
# This blank line is optional:
@@ -1427,7 +1428,7 @@ class _ParametersConsumer(object):
def threshold(self, line):
if line[:2] == "T:":
- # Assume its an old stlye line like "T: 123"
+ # Assume its an old style line like "T: 123"
self._params.threshold, = _get_cols(
line, (1,), ncols=2, expected={0: "T:"})
elif line[:28] == "Neighboring words threshold:":
@@ -1636,9 +1637,9 @@ class Iterator(object):
if not line:
break
# If I've reached the next one, then put the line back and stop.
- if lines and (line.startswith('BLAST')
- or line.startswith('BLAST', 1)
- or line.startswith('<?xml ')):
+ if lines and (line.startswith('BLAST') or
+ line.startswith('BLAST', 1) or
+ line.startswith('<?xml ')):
self._uhandle.saveline(line)
break
# New style files omit the BLAST line to mark a new query:
@@ -1769,7 +1770,7 @@ class BlastErrorParser(AbstractParser):
Current BLAST problems this detects are:
o LowQualityBlastError - When BLASTing really low quality sequences
- (ie. some GenBank entries which are just short streches of a single
+ (ie. some GenBank entries which are just short stretches of a single
nucleotide), BLAST will report an error with the sequence and be
unable to search with this. This will lead to a badly formatted
BLAST report that the parsers choke on. The parser will convert the
diff --git a/Bio/Blast/NCBIWWW.py b/Bio/Blast/NCBIWWW.py
index 4081afe..2145027 100644
--- a/Bio/Blast/NCBIWWW.py
+++ b/Bio/Blast/NCBIWWW.py
@@ -21,10 +21,11 @@ from Bio._py3k import urlopen as _urlopen
from Bio._py3k import urlencode as _urlencode
from Bio._py3k import Request as _Request
-__docformat__ = "restructuredtext en"
+NCBI_BLAST_URL = "http://blast.ncbi.nlm.nih.gov/Blast.cgi"
-def qblast(program, database, sequence,
+
+def qblast(program, database, sequence, url_base=NCBI_BLAST_URL,
auto_format=None, composition_based_statistics=None,
db_genetic_code=None, endpoints=None, entrez_query='(none)',
expect=10.0, filter=None, gapcosts=None, genetic_code=None,
@@ -39,9 +40,18 @@ def qblast(program, database, sequence,
format_entrez_query=None, format_object=None, format_type='XML',
ncbi_gi=None, results_file=None, show_overview=None, megablast=None,
):
- """Do a BLAST search using the QBLAST server at NCBI.
+ """Do a BLAST search using the QBLAST server at NCBI or a cloud service
+ provider.
Supports all parameters of the qblast API for Put and Get.
+
+ Please note that BLAST on the cloud supports the NCBI-BLAST Common
+ URL API (http://ncbi.github.io/blast-cloud/dev/api.html). To
+ use this feature, please set url_base to
+ 'http://host.my.cloud.service.provider.com/cgi-bin/blast.cgi' and
+ format_object='Alignment'. For more details, please see
+ https://blast.ncbi.nlm.nih.gov/Blast.cgi?PAGE_TYPE=BlastDocs&DOC_TYPE=CloudBlast
+
Some useful parameters:
- program blastn, blastp, blastx, tblastn, or tblastx (lower case)
@@ -62,6 +72,7 @@ def qblast(program, database, sequence,
This function does no checking of the validity of the parameters
and passes the values to the server as is. More help is available at:
http://www.ncbi.nlm.nih.gov/BLAST/Doc/urlapi.html
+
"""
import time
@@ -116,7 +127,7 @@ def qblast(program, database, sequence,
# Note the NCBI do not currently impose a rate limit here, other
# than the request not to make say 50 queries at once using multiple
# threads.
- request = _Request("http://blast.ncbi.nlm.nih.gov/Blast.cgi",
+ request = _Request(url_base,
message,
{"User-Agent": "BiopythonClient"})
handle = _urlopen(request)
@@ -160,7 +171,7 @@ def qblast(program, database, sequence,
else:
delay = 120
- request = _Request("http://blast.ncbi.nlm.nih.gov/Blast.cgi",
+ request = _Request(url_base,
message,
{"User-Agent": "BiopythonClient"})
handle = _urlopen(request)
@@ -185,7 +196,7 @@ def qblast(program, database, sequence,
def _parse_qblast_ref_page(handle):
"""Extract a tuple of RID, RTOE from the 'please wait' page (PRIVATE).
- The NCBI FAQ pages use TOE for 'Time of Execution', so RTOE is proably
+ The NCBI FAQ pages use TOE for 'Time of Execution', so RTOE is probably
'Request Time of Execution' and RID would be 'Request Identifier'.
"""
s = _as_string(handle.read())
diff --git a/Bio/Blast/NCBIXML.py b/Bio/Blast/NCBIXML.py
index e0d889d..8b5d32f 100644
--- a/Bio/Blast/NCBIXML.py
+++ b/Bio/Blast/NCBIXML.py
@@ -16,8 +16,6 @@ import xml.sax
from xml.sax.handler import ContentHandler
from functools import reduce
-__docformat__ = "restructuredtext en"
-
class _XMLparser(ContentHandler):
"""Generic SAX Parser (PRIVATE).
@@ -61,7 +59,7 @@ class _XMLparser(ContentHandler):
# Note could use try / except AttributeError
# BUT I found often triggered by nested errors...
if hasattr(self, method):
- eval("self.%s()" % method)
+ getattr(self, method)()
if self._debug > 4:
print("NCBIXML: Parsed: " + method)
elif self._debug > 3:
@@ -96,7 +94,7 @@ class _XMLparser(ContentHandler):
# Note could use try / except AttributeError
# BUT I found often triggered by nested errors...
if hasattr(self, method):
- eval("self.%s()" % method)
+ getattr(self, method)()
if self._debug > 2:
print("NCBIXML: Parsed: %s %s" % (method, self._value))
elif self._debug > 1:
@@ -164,7 +162,7 @@ class BlastParser(_XMLparser):
# These are required for "old" pre 2.2.14 files
# where only <BlastOutput_query-ID>, <BlastOutput_query-def>
# and <BlastOutput_query-len> were used. Now they
- # are suplemented/replaced by <Iteration_query-ID>,
+ # are supplemented/replaced by <Iteration_query-ID>,
# <Iteration_query-def> and <Iteration_query-len>
if not hasattr(self._blast, "query") \
or not self._blast.query:
@@ -327,11 +325,11 @@ class BlastParser(_XMLparser):
# pass # XXX TODO PSI
def _end_Parameters_sc_match(self):
- """match score for nucleotide-nucleotide comparaison (-r) (PRIVATE)."""
+ """match score for nucleotide-nucleotide comparison (-r) (PRIVATE)."""
self._parameters.sc_match = int(self._value)
def _end_Parameters_sc_mismatch(self):
- """mismatch penalty for nucleotide-nucleotide comparaison (-r) (PRIVATE)."""
+ """mismatch penalty for nucleotide-nucleotide comparison (-r) (PRIVATE)."""
self._parameters.sc_mismatch = int(self._value)
def _end_Parameters_gap_open(self):
diff --git a/Bio/Blast/Record.py b/Bio/Blast/Record.py
index be11d77..e48e28c 100644
--- a/Bio/Blast/Record.py
+++ b/Bio/Blast/Record.py
@@ -24,8 +24,6 @@ from Bio.Seq import Seq
from Bio.SeqRecord import SeqRecord
from Bio.Align import MultipleSeqAlignment
-__docformat__ = "restructuredtext en"
-
class Header(object):
"""Saves information from a blast header.
@@ -112,13 +110,13 @@ class HSP(object):
- expect Expect value. (float)
- num_alignments Number of alignments for same subject. (int)
- identities Number of identities (int) if using the XML parser.
- Tuple of numer of identities/total aligned (int, int)
+ Tuple of number of identities/total aligned (int, int)
if using the (obsolete) plain text parser.
- positives Number of positives (int) if using the XML parser.
- Tuple of numer of positives/total aligned (int, int)
+ Tuple of number of positives/total aligned (int, int)
if using the (obsolete) plain text parser.
- gaps Number of gaps (int) if using the XML parser.
- Tuple of numer of gaps/total aligned (int, int) if
+ Tuple of number of gaps/total aligned (int, int) if
using the (obsolete) plain text parser.
- align_length Length of the alignment. (int)
- strand Tuple of (query, target) strand.
@@ -133,7 +131,7 @@ class HSP(object):
- sbjct_end The end residue for the sbjct sequence. (1-based)
Not all flavors of BLAST return values for every attribute::
-
+
score expect identities positives strand frame
BLASTP X X X X
BLASTN X X X X X
diff --git a/Bio/Blast/__init__.py b/Bio/Blast/__init__.py
index bd8e089..ea0f6db 100644
--- a/Bio/Blast/__init__.py
+++ b/Bio/Blast/__init__.py
@@ -3,5 +3,3 @@
# license. Please see the LICENSE file that should have been included
# as part of this package.
"""Code for dealing with BLAST programs and output."""
-
-__docformat__ = "restructuredtext en"
diff --git a/Bio/CAPS/__init__.py b/Bio/CAPS/__init__.py
index 48d77d3..1820490 100644
--- a/Bio/CAPS/__init__.py
+++ b/Bio/CAPS/__init__.py
@@ -12,8 +12,6 @@ be found in the paper `Konieczny and Ausubel (1993)`_ (PMID 8106085).
.. _`Konieczny and Ausubel (1993)`: http://dx.doi.org/10.1046/j.1365-313X.1993.04020403.x
"""
-__docformat__ = "restructuredtext en"
-
class DifferentialCutsite(object):
"""Differential enzyme cutsite in an alignment.
diff --git a/Bio/Cluster/__init__.py b/Bio/Cluster/__init__.py
index a46b9b7..e3dea5c 100644
--- a/Bio/Cluster/__init__.py
+++ b/Bio/Cluster/__init__.py
@@ -7,8 +7,6 @@ import numpy
from Bio.Cluster.cluster import *
-__docformat__ = "restructuredtext en"
-
def _treesort(order, nodeorder, nodecounts, tree):
# Find the order of the nodes consistent with the hierarchical clustering
@@ -397,7 +395,7 @@ Return values:
return clustercentroids(self.data, self.mask, clusterid, method,
transpose)
- def clusterdistance(self, index1=[0], index2=[0], method='a', dist='e',
+ def clusterdistance(self, index1=0, index2=0, method='a', dist='e',
transpose=0):
"""Calculate the distance between two clusters.
@@ -526,8 +524,8 @@ Arguments:
if geneclusters is not None and expclusters is not None and \
type(geneclusters) != type(expclusters):
raise ValueError("found one k-means and one hierarchical "
- + "clustering solution in geneclusters and "
- + "expclusters")
+ "clustering solution in geneclusters and "
+ "expclusters")
gid = 0
aid = 0
filename = jobname
diff --git a/Bio/Compass/__init__.py b/Bio/Compass/__init__.py
index 577c0b1..75afe99 100644
--- a/Bio/Compass/__init__.py
+++ b/Bio/Compass/__init__.py
@@ -16,8 +16,6 @@ Tested with COMPASS 1.24.
import re
-__docformat__ = "restructuredtext en"
-
def read(handle):
"""Reads a COMPASS file containing one COMPASS record."""
diff --git a/Bio/Crystal/__init__.py b/Bio/Crystal/__init__.py
index e50736f..c381df4 100644
--- a/Bio/Crystal/__init__.py
+++ b/Bio/Crystal/__init__.py
@@ -17,8 +17,6 @@ from functools import reduce
from Bio._py3k import map
from Bio._py3k import basestring
-__docformat__ = "restructuredtext en"
-
class CrystalError(Exception):
pass
diff --git a/Bio/Data/CodonTable.py b/Bio/Data/CodonTable.py
index edebc26..4d96cb2 100644
--- a/Bio/Data/CodonTable.py
+++ b/Bio/Data/CodonTable.py
@@ -15,7 +15,6 @@ from Bio import Alphabet
from Bio.Alphabet import IUPAC
from Bio.Data import IUPACData
-__docformat__ = "restructuredtext en"
unambiguous_dna_by_name = {}
unambiguous_dna_by_id = {}
@@ -206,8 +205,8 @@ def list_possible_proteins(codon, forward_table, ambiguous_nucleotide_values):
stops.append(y1 + y2 + y3)
if stops:
if possible:
- raise TranslationError("ambiguous codon '%s' codes " % codon
- + "for both proteins and stop codons")
+ raise TranslationError("ambiguous codon %r codes for both"
+ " proteins and stop codons" % codon)
# This is a true stop codon - tell the caller about it
raise KeyError(codon)
return list(possible)
@@ -895,6 +894,26 @@ register_ncbi_table(name='Pterobranchia Mitochondrial',
start_codons=['TTG', 'CTG', 'ATG', 'GTG', ],
)
+register_ncbi_table(name='Candidate Division SR1 and Gracilibacteria',
+ alt_name=None, id=25,
+ table={
+ 'TTT': 'F', 'TTC': 'F', 'TTA': 'L', 'TTG': 'L', 'TCT': 'S',
+ 'TCC': 'S', 'TCA': 'S', 'TCG': 'S', 'TAT': 'Y', 'TAC': 'Y',
+ 'TGT': 'C', 'TGC': 'C', 'TGG': 'W', 'CTT': 'L', 'CTC': 'L',
+ 'CTA': 'L', 'CTG': 'L', 'CCT': 'P', 'CCC': 'P', 'CCA': 'P',
+ 'CCG': 'P', 'CAT': 'H', 'CAC': 'H', 'CAA': 'Q', 'CAG': 'Q',
+ 'CGT': 'R', 'CGC': 'R', 'CGA': 'R', 'CGG': 'R', 'ATT': 'I',
+ 'ATC': 'I', 'ATA': 'I', 'ATG': 'M', 'ACT': 'T', 'ACC': 'T',
+ 'ACA': 'T', 'ACG': 'T', 'AAT': 'N', 'AAC': 'N', 'AAA': 'K',
+ 'AAG': 'K', 'AGT': 'S', 'AGC': 'S', 'AGA': 'R', 'AGG': 'R',
+ 'GTT': 'V', 'GTC': 'V', 'GTA': 'V', 'GTG': 'V', 'GCT': 'A',
+ 'GCC': 'A', 'GCA': 'A', 'GCG': 'A', 'GAT': 'D', 'GAC': 'D',
+ 'GAA': 'E', 'GAG': 'E', 'GGT': 'G', 'GGC': 'G', 'GGA': 'G',
+ 'GGG': 'G', 'TGA': 'G', },
+ stop_codons=['TAA', 'TAG', ],
+ start_codons=['TTG', 'CTG', 'ATG', ]
+ )
+
# Basic sanity test,
for key, val in generic_by_name.items():
assert key in ambiguous_generic_by_name[key].names
diff --git a/Bio/Data/IUPACData.py b/Bio/Data/IUPACData.py
index 5998e02..0b39a4a 100644
--- a/Bio/Data/IUPACData.py
+++ b/Bio/Data/IUPACData.py
@@ -4,8 +4,6 @@
"""Information about the IUPAC alphabets."""
-__docformat__ = "restructuredtext en"
-
protein_letters = "ACDEFGHIKLMNPQRSTVWY"
extended_protein_letters = "ACDEFGHIKLMNPQRSTVWYBXZJUO"
diff --git a/Bio/Data/SCOPData.py b/Bio/Data/SCOPData.py
index de8ac90..25834fa 100644
--- a/Bio/Data/SCOPData.py
+++ b/Bio/Data/SCOPData.py
@@ -7,8 +7,6 @@
See Bio.SCOP for more information about SCOP and Biopython"s SCOP module.
"""
-__docformat__ = "restructuredtext en"
-
protein_letters_3to1 = {
"00C": "C", "01W": "X", "02K": "A", "03Y": "C", "07O": "C",
diff --git a/Bio/Data/__init__.py b/Bio/Data/__init__.py
index 93f5557..df21e03 100644
--- a/Bio/Data/__init__.py
+++ b/Bio/Data/__init__.py
@@ -3,5 +3,3 @@
# as part of this package.
"""Collections of various bits of useful biological data."""
-
-__docformat__ = "restructuredtext en"
diff --git a/Bio/DocSQL.py b/Bio/DocSQL.py
index 16bf73a..9bace71 100755
--- a/Bio/DocSQL.py
+++ b/Bio/DocSQL.py
@@ -35,7 +35,6 @@ except:
raise MissingPythonDependencyError("Install MySQLdb if you want to use "
"Bio.DocSQL.")
-__docformat__ = "restructuredtext en"
connection = None
diff --git a/Bio/Emboss/Applications.py b/Bio/Emboss/Applications.py
index 22f1772..cc1ed47 100644
--- a/Bio/Emboss/Applications.py
+++ b/Bio/Emboss/Applications.py
@@ -16,8 +16,6 @@ from __future__ import print_function
from Bio.Application import _Option, _Switch, AbstractCommandline
-__docformat__ = "restructuredtext en"
-
class _EmbossMinimalCommandLine(AbstractCommandline):
"""Base Commandline object for EMBOSS wrappers (PRIVATE).
@@ -646,7 +644,7 @@ class FDNAParsCommandline(_EmbossCommandLine):
_Option(["-thorough", "thorough"],
"more thorough search (Y/n)"),
_Option(["-rearrange", "rearrange"],
- "Rearrange on jsut 1 best tree (Y/n)"),
+ "Rearrange on just 1 best tree (Y/n)"),
_Option(["-transversion", "transversion"],
"Use tranversion parsimony (y/N)"),
_Option(["-njumble", "njumble"],
@@ -1256,11 +1254,6 @@ class SeqmatchallCommandline(_EmbossCommandLine):
_EmbossCommandLine.__init__(self, cmd, **kwargs)
-def _test():
- """Run the Bio.Emboss.Applications module doctests."""
- import doctest
- doctest.testmod()
-
if __name__ == "__main__":
- # Run the doctests
- _test()
+ from Bio._utils import run_doctest
+ run_doctest()
diff --git a/Bio/Emboss/Primer3.py b/Bio/Emboss/Primer3.py
index 86f7eb1..45e0591 100644
--- a/Bio/Emboss/Primer3.py
+++ b/Bio/Emboss/Primer3.py
@@ -17,7 +17,6 @@ function. If you ran eprimer3 with multiple targets, use the parse
function to iterate over the retsults.
"""
-__docformat__ = "restructuredtext en"
# --- primer3
@@ -62,7 +61,7 @@ class Primers(object):
- internal_length
- internal_tm
- internal_gc
-
+
"""
def __init__(self):
self.size = 0
diff --git a/Bio/Emboss/PrimerSearch.py b/Bio/Emboss/PrimerSearch.py
index 3fbb92c..530ce17 100644
--- a/Bio/Emboss/PrimerSearch.py
+++ b/Bio/Emboss/PrimerSearch.py
@@ -6,8 +6,6 @@
"""Code to interact with the primersearch program from EMBOSS.
"""
-__docformat__ = "restructuredtext en"
-
class InputRecord(object):
"""Represent the input file into the primersearch program.
diff --git a/Bio/Emboss/__init__.py b/Bio/Emboss/__init__.py
index faf1253..36032f6 100644
--- a/Bio/Emboss/__init__.py
+++ b/Bio/Emboss/__init__.py
@@ -3,5 +3,4 @@
# as part of this package.
#
-"""Code to interact with the ever-so-useful EMBOSS programs.
-"""
+"""Code to interact with the ever-so-useful EMBOSS programs."""
diff --git a/Bio/Entrez/Parser.py b/Bio/Entrez/Parser.py
index 016d974..73e8666 100644
--- a/Bio/Entrez/Parser.py
+++ b/Bio/Entrez/Parser.py
@@ -35,6 +35,7 @@ written solution, since the number of DTDs is rather large and their
contents may change over time. About half the code in this parser deals
wih parsing the DTD, and the other half with the XML itself.
"""
+import sys
import re
import os
import warnings
@@ -47,7 +48,6 @@ from Bio._py3k import urlopen as _urlopen
from Bio._py3k import urlparse as _urlparse
from Bio._py3k import unicode
-__docformat__ = "restructuredtext en"
# The following four classes are used to add a member .attributes to integers,
# strings, lists, and dictionaries, respectively.
@@ -207,10 +207,20 @@ class DataHandler(object):
# expects binary data
if handle.__class__.__name__ == 'EvilHandleHack':
handle = handle._handle
+ if handle.__class__.__name__ == 'TextIOWrapper':
+ handle = handle.buffer
if hasattr(handle, "closed") and handle.closed:
# Should avoid a possible Segmentation Fault, see:
# http://bugs.python.org/issue4877
raise IOError("Can't parse a closed handle")
+ if sys.version_info[0] >= 3:
+ # Another nasty hack to cope with a unicode StringIO handle
+ # since the Entrez XML parser expects binary data (bytes)
+ from io import StringIO
+ if isinstance(handle, StringIO):
+ from io import BytesIO
+ from Bio._py3k import _as_bytes
+ handle = BytesIO(_as_bytes(handle.read()))
try:
self.parser.ParseFile(handle)
except expat.ExpatError as e:
@@ -451,10 +461,10 @@ class DataHandler(object):
return
# First, remove ignorable parentheses around declarations
while (model[0] in (expat.model.XML_CTYPE_SEQ,
- expat.model.XML_CTYPE_CHOICE)
- and model[1] in (expat.model.XML_CQUANT_NONE,
- expat.model.XML_CQUANT_OPT)
- and len(model[3]) == 1):
+ expat.model.XML_CTYPE_CHOICE) and
+ model[1] in (expat.model.XML_CQUANT_NONE,
+ expat.model.XML_CQUANT_OPT) and
+ len(model[3]) == 1):
model = model[3][0]
# PCDATA declarations correspond to strings
if model[0] in (expat.model.XML_CTYPE_MIXED,
diff --git a/Bio/Entrez/XSDs/IPGReportSet.xsd b/Bio/Entrez/XSDs/IPGReportSet.xsd
new file mode 100644
index 0000000..da50ae2
--- /dev/null
+++ b/Bio/Entrez/XSDs/IPGReportSet.xsd
@@ -0,0 +1,94 @@
+<?xml version="1.0" ?>
+<xs:schema
+ xmlns:xs="http://www.w3.org/2001/XMLSchema"
+ xmlns:ncbi="http://www.ncbi.nlm.nih.gov"
+ elementFormDefault="unqualified"
+ attributeFormDefault="unqualified"
+>
+ <xs:element name="CDS">
+ <xs:complexType>
+ <xs:attribute name="accver" type="xs:string" use="required"/>
+ <xs:attribute name="gi" type="xs:integer" use="required"/>
+ <xs:attribute name="kingdom" type="xs:string" use="required"/>
+ <xs:attribute name="org" type="xs:string" use="required"/>
+ <xs:attribute name="start" type="xs:integer" use="required"/>
+ <xs:attribute name="stop" type="xs:integer" use="required"/>
+ <xs:attribute name="strand" type="xs:string" use="required"/>
+ <xs:attribute name="taxid" type="xs:integer" use="required"/>
+ </xs:complexType>
+ </xs:element>
+
+ <xs:element name="CDSList">
+ <xs:complexType>
+ <xs:sequence maxOccurs="unbounded">
+ <xs:element ref="CDS"/>
+ </xs:sequence>
+ </xs:complexType>
+ </xs:element>
+
+ <xs:element name="IPGReport">
+ <xs:complexType>
+ <xs:sequence>
+ <xs:element ref="Product"/>
+ <xs:element ref="RedundantGiList"/>
+ <xs:element ref="Statistics"/>
+ </xs:sequence>
+ <xs:attribute name="accver" type="xs:string" use="required"/>
+ <xs:attribute name="gi" type="xs:integer" use="required"/>
+ <xs:attribute name="kingdom" type="xs:string" use="required"/>
+ <xs:attribute name="org" type="xs:string" use="required"/>
+ <xs:attribute name="taxid" type="xs:integer" use="required"/>
+ </xs:complexType>
+ </xs:element>
+
+ <xs:element name="IPGReportSet">
+ <xs:complexType>
+ <xs:sequence>
+ <xs:element ref="IPGReport"/>
+ </xs:sequence>
+ </xs:complexType>
+ </xs:element>
+
+ <xs:element name="Product">
+ <xs:complexType>
+ <xs:simpleContent>
+ <xs:extension base="xs:string">
+ <xs:attribute name="accver" type="xs:string" use="required"/>
+ <xs:attribute name="gi" type="xs:integer" use="required"/>
+ <xs:attribute name="slen" type="xs:integer" use="required"/>
+ </xs:extension>
+ </xs:simpleContent>
+ </xs:complexType>
+ </xs:element>
+
+ <xs:element name="RedundantGi">
+ <xs:complexType>
+ <xs:sequence minOccurs="0">
+ <xs:element ref="CDSList"/>
+ </xs:sequence>
+ <xs:attribute name="accver" type="xs:string" use="required"/>
+ <xs:attribute name="gi" type="xs:integer" use="required"/>
+ <xs:attribute name="kingdom" type="xs:string" use="required"/>
+ <xs:attribute name="org" type="xs:string" use="required"/>
+ <xs:attribute name="priority" type="xs:string" use="required"/>
+ <xs:attribute name="source" type="xs:string" use="required"/>
+ <xs:attribute name="taxid" type="xs:integer" use="required"/>
+ </xs:complexType>
+ </xs:element>
+
+ <xs:element name="RedundantGiList">
+ <xs:complexType>
+ <xs:sequence maxOccurs="unbounded">
+ <xs:element ref="RedundantGi"/>
+ </xs:sequence>
+ </xs:complexType>
+ </xs:element>
+
+ <xs:element name="Statistics">
+ <xs:complexType>
+ <xs:attribute name="nuc_count" type="xs:integer" use="required"/>
+ <xs:attribute name="prot_count" type="xs:integer" use="required"/>
+ </xs:complexType>
+ </xs:element>
+
+</xs:schema>
\ No newline at end of file
diff --git a/Bio/Entrez/__init__.py b/Bio/Entrez/__init__.py
index be65ed6..d2d1f19 100644
--- a/Bio/Entrez/__init__.py
+++ b/Bio/Entrez/__init__.py
@@ -1,5 +1,7 @@
# Copyright 1999-2000 by Jeffrey Chang. All rights reserved.
-# Copyright 2008 by Michiel de Hoon. All rights reserved.
+# Copyright 2008-2013 by Michiel de Hoon. All rights reserved.
+# Revisions copyright 2011-2015 by Peter Cock. All rights reserved.
+# Revisions copyright 2015 by Eric Rasche. All rights reserved.
# This code is part of the Biopython distribution and governed by its
# license. Please see the LICENSE file that should have been included
# as part of this package.
@@ -9,13 +11,30 @@
The main Entrez web page is available at:
http://www.ncbi.nlm.nih.gov/Entrez/
-A list of the Entrez utilities is available at:
-http://www.ncbi.nlm.nih.gov/entrez/utils/utils_index.html
+Entrez Programming Utilities web page is available at:
+http://www.ncbi.nlm.nih.gov/books/NBK25501/
+
+This module provides a number of functions like ``efetch`` (short for
+Entrez Fetch) which will return the data as a handle object. This is
+a standard interface used in Python for reading data from a file, or
+in this case a remote network connection, and provides methods like
+``.read()`` or offers iteration over the contents line by line. See
+also "What the heck is a handle?" in the Biopython Tutorial and
+Cookbook: http://biopython.org/DIST/docs/tutorial/Tutorial.html
+http://biopython.org/DIST/docs/tutorial/Tutorial.pdf
+
+Unlike a handle to a file on disk from the ``open(filename)`` function,
+which has a ``.name`` attribute giving the filename, the handles from
+``Bio.Entrez`` all have a ``.url`` attribute instead giving the URL
+used to connect to the NCBI Entrez API.
+
+The Entrez module also provides an XML parser which takes a handle
+as input.
Variables:
- email Set the Entrez email parameter (default is not set).
- - tool Set the Entrez tool parameter (default is biopython).
+ - tool Set the Entrez tool parameter (default is ``biopython``).
Functions:
@@ -85,7 +104,6 @@ from Bio._py3k import HTTPError as _HTTPError
from Bio._py3k import _binary_to_string_handle, _as_bytes
-__docformat__ = "restructuredtext en"
email = None
tool = "biopython"
@@ -99,7 +117,7 @@ def epost(db, **keywds):
environment to use with subsequent search strategies.
See the online documentation for an explanation of the parameters:
- http://www.ncbi.nlm.nih.gov/entrez/query/static/epost_help.html
+ http://www.ncbi.nlm.nih.gov/books/NBK25499/#chapter4.EPost
Return a handle to the results.
@@ -118,7 +136,7 @@ def efetch(db, **keywords):
more UIs or from user's environment.
See the online documentation for an explanation of the parameters:
- http://www.ncbi.nlm.nih.gov/entrez/query/static/efetch_help.html
+ http://www.ncbi.nlm.nih.gov/books/NBK25499/#chapter4.EFetch
Return a handle to the results.
@@ -133,6 +151,9 @@ def efetch(db, **keywords):
LOCUS AY851612 892 bp DNA linear PLN 10-APR-2007
>>> handle.close()
+ This will automatically use an HTTP POST rather than HTTP GET if there
+ are over 200 identifiers as recommended by the NCBI.
+
**Warning:** The NCBI changed the default retmode in Feb 2012, so many
databases which previously returned text output now give XML.
"""
@@ -148,11 +169,15 @@ def efetch(db, **keywords):
if isinstance(ids, list):
ids = ",".join(ids)
variables["id"] = ids
+ elif isinstance(ids, int):
+ ids = str(ids)
+ variables["id"] = ids
+
if ids.count(",") >= 200:
# NCBI prefers an HTTP POST instead of an HTTP GET if there are
# more than about 200 IDs
post = True
- return _open(cgi, variables, post)
+ return _open(cgi, variables, post=post)
def esearch(db, term, **keywds):
@@ -163,7 +188,7 @@ def esearch(db, term, **keywds):
for future use in the user's environment.
See the online documentation for an explanation of the parameters:
- http://www.ncbi.nlm.nih.gov/entrez/query/static/esearch_help.html
+ http://www.ncbi.nlm.nih.gov/books/NBK25499/#chapter4.ESearch
Return a handle to the results which are always in XML format.
@@ -201,7 +226,7 @@ def elink(**keywds):
database, or lists LinkOut URLs and attributes for multiple IDs.
See the online documentation for an explanation of the parameters:
- http://www.ncbi.nlm.nih.gov/entrez/query/static/elink_help.html
+ http://www.ncbi.nlm.nih.gov/books/NBK25499/#chapter4.ELink
Return a handle to the results, by default in XML format.
@@ -237,7 +262,7 @@ def einfo(**keywds):
available links for each Entrez database.
See the online documentation for an explanation of the parameters:
- http://www.ncbi.nlm.nih.gov/entrez/query/static/einfo_help.html
+ http://www.ncbi.nlm.nih.gov/books/NBK25499/#chapter4.EInfo
Return a handle to the results, by default in XML format.
@@ -265,7 +290,7 @@ def esummary(**keywds):
from the user's environment.
See the online documentation for an explanation of the parameters:
- http://www.ncbi.nlm.nih.gov/entrez/query/static/esummary_help.html
+ http://www.ncbi.nlm.nih.gov/books/NBK25499/#chapter4.ESummary
Return a handle to the results, by default in XML format.
@@ -297,7 +322,7 @@ def egquery(**keywds):
using Global Query.
See the online documentation for an explanation of the parameters:
- http://www.ncbi.nlm.nih.gov/entrez/query/static/egquery_help.html
+ http://www.ncbi.nlm.nih.gov/books/NBK25499/#chapter4.EGQuery
Return a handle to the results in XML format.
@@ -330,7 +355,7 @@ def espell(**keywds):
ESpell retrieves spelling suggestions, if available.
See the online documentation for an explanation of the parameters:
- http://www.ncbi.nlm.nih.gov/entrez/query/static/espell_help.html
+ http://www.ncbi.nlm.nih.gov/books/NBK25499/#chapter4.ESpell
Return a handle to the results, by default in XML format.
@@ -454,13 +479,21 @@ def parse(handle, validate=True):
return records
-def _open(cgi, params=None, post=False, ecitmatch=False):
+def _open(cgi, params=None, post=None, ecitmatch=False):
"""Helper function to build the URL and open a handle to it (PRIVATE).
Open a handle to Entrez. cgi is the URL for the cgi script to access.
params is a dictionary with the options to pass to it. Does some
simple error checking, and will raise an IOError if it encounters one.
+ The arugment post should be a boolean to explicitly control if an HTTP
+ POST should be used rather an HTTP GET based on the query length.
+ By default (post=None), POST is used if the query URL would be over
+ 1000 characters long.
+
+ The arugment post should be a boolean to explicitly control if an HTTP
+ POST should be used rather an HTTP GET based on the query length.
+
This function also enforces the "up to three queries per second rule"
to avoid abusing the NCBI servers.
"""
@@ -500,12 +533,17 @@ is A.N.Other at example.com, you can specify it as follows:
In case of excessive usage of the E-utilities, NCBI will attempt to contact
a user at the email address provided before blocking access to the
E-utilities.""", UserWarning)
+
# Open a handle to Entrez.
options = _urlencode(params, doseq=True)
# _urlencode encodes pipes, which NCBI expects in ECitMatch
if ecitmatch:
options = options.replace('%7C', '|')
# print cgi + "?" + options
+
+ # By default, post is None. Set to a boolean to over-ride length choice:
+ if post is None and len(options) > 1000:
+ post = True
try:
if post:
# HTTP POST
diff --git a/Bio/ExPASy/Enzyme.py b/Bio/ExPASy/Enzyme.py
index 2c81885..a10ce64 100644
--- a/Bio/ExPASy/Enzyme.py
+++ b/Bio/ExPASy/Enzyme.py
@@ -22,8 +22,6 @@ Classes:
"""
-__docformat__ = "restructuredtext en"
-
def parse(handle):
"""Parse ENZYME records.
diff --git a/Bio/ExPASy/Prodoc.py b/Bio/ExPASy/Prodoc.py
index 41650fc..65817df 100644
--- a/Bio/ExPASy/Prodoc.py
+++ b/Bio/ExPASy/Prodoc.py
@@ -23,8 +23,6 @@ Classes:
- Reference Holds data from a Prodoc reference.
"""
-__docformat__ = "restructuredtext en"
-
def read(handle):
"""Read in a record from a file with exactly one Prodoc record."""
diff --git a/Bio/ExPASy/Prosite.py b/Bio/ExPASy/Prosite.py
index f0322bb..c195f50 100644
--- a/Bio/ExPASy/Prosite.py
+++ b/Bio/ExPASy/Prosite.py
@@ -24,8 +24,6 @@ Classes:
- Record Holds Prosite data.
"""
-__docformat__ = "restructuredtext en"
-
def parse(handle):
"""Parse Prosite records.
diff --git a/Bio/ExPASy/ScanProsite.py b/Bio/ExPASy/ScanProsite.py
index a6caf9e..881d040 100644
--- a/Bio/ExPASy/ScanProsite.py
+++ b/Bio/ExPASy/ScanProsite.py
@@ -10,8 +10,6 @@ from Bio._py3k import urlencode as _urlencode
from xml.sax import handler
from xml.sax.expatreader import ExpatParser
-__docformat__ = "restructuredtext en"
-
class Record(list):
"""Represents search results returned by ScanProsite.
diff --git a/Bio/ExPASy/__init__.py b/Bio/ExPASy/__init__.py
index a8a59dd..72cc6d9 100644
--- a/Bio/ExPASy/__init__.py
+++ b/Bio/ExPASy/__init__.py
@@ -21,8 +21,6 @@ Functions:
from Bio._py3k import urlopen as _urlopen
from Bio._py3k import urlencode as _urlencode
-__docformat__ = "restructuredtext en"
-
def get_prodoc_entry(id, cgi='http://www.expasy.ch/cgi-bin/get-prodoc-entry'):
"""get_prodoc_entry(id,
diff --git a/Bio/FSSP/FSSPTools.py b/Bio/FSSP/FSSPTools.py
index 7cb31cf..09f6e67 100644
--- a/Bio/FSSP/FSSPTools.py
+++ b/Bio/FSSP/FSSPTools.py
@@ -1,7 +1,16 @@
# This code is part of the Biopython distribution and governed by its
# license. Please see the LICENSE file that should have been included
# as part of this package.
-#
+"""Several routines used to extract information from FSSP sections.
+
+filter: filters a passed summary section and alignment section according to a numeric
+ attribute in the summary section. Returns new summary and alignment sections
+
+For example, to filter in only those records which have a zscore greater than
+4.0 and lesser than 7.5:
+
+new_sum, new_align = filter(sum, align, 'zscore', 4, 7.5)
+"""
from Bio import FSSP
import copy
@@ -42,14 +51,6 @@ def mult_align(sum_dict, align_dict):
sum_dict[i].pdb2 + sum_dict[i].chain2))
return fssp_align
-
-# Several routines used to extract information from FSSP sections
-# filter:
-# filters a passed summary section and alignment section according to a numeric
-# attribute in the summary section. Returns new summary and alignment sections
-# For example, to filter in only those records which have a zscore greater than
-# 4.0 and lesser than 7.5:
-# new_sum, new_align = filter(sum, align, 'zscore', 4, 7.5)
#
# Warning: this function really slows down when filtering large FSSP files.
# The reason is the use of copy.deepcopy() to copy align_dict into
@@ -57,6 +58,7 @@ def mult_align(sum_dict, align_dict):
# Took me ~160 seconds for the largest FSSP file (1reqA.fssp)
#
+
def filter(sum_dict, align_dict, filter_attribute, low_bound, high_bound):
"""Filters a passed summary section and alignment section according to a numeric
attribute in the summary section. Returns new summary and alignment sections"""
diff --git a/Bio/FSSP/__init__.py b/Bio/FSSP/__init__.py
index 6c61d27..189bb03 100644
--- a/Bio/FSSP/__init__.py
+++ b/Bio/FSSP/__init__.py
@@ -21,6 +21,8 @@ import re
from . import fssp_rec
from Bio.Align import Generic
from Bio import Alphabet
+
+
fff_rec = fssp_rec.fff_rec
header_records = {
'database': re.compile('^DATABASE'),
diff --git a/Bio/FSSP/fssp_rec.py b/Bio/FSSP/fssp_rec.py
index 044d9c8..6b79d88 100644
--- a/Bio/FSSP/fssp_rec.py
+++ b/Bio/FSSP/fssp_rec.py
@@ -1,4 +1,6 @@
-# A superclass for reading [f]ixed-column type [f]lat-[f]ile records. (e.g.
+"""A superclass for reading [f]ixed-column type [f]lat-[f]ile records."""
+
+
class fff_rec(object):
def __init__(self, inrec=''):
self.data = inrec
@@ -21,8 +23,8 @@ class fff_rec(object):
return self.data[index]
-# Definition of the align section in a FSSP file
class align(object):
+ """Definition of the align section in a FSSP file."""
abs_res_num = (0, 4)
pdb_res_num = (4, 9)
chain_id = 10
diff --git a/Bio/File.py b/Bio/File.py
index 2f54edd..e5c2014 100644
--- a/Bio/File.py
+++ b/Bio/File.py
@@ -43,8 +43,6 @@ except ImportError:
_sqlite = None
pass
-__docformat__ = "restructuredtext en"
-
@contextlib.contextmanager
def as_handle(handleish, mode='r', **kwargs):
@@ -229,7 +227,12 @@ class _IndexedSeqFileProxy(object):
raise NotImplementedError("Subclass should implement this")
def get_raw(self, offset):
- """Returns bytes string (if implemented for this file format)."""
+ """Return the raw record from the file as a bytes string (if implemented).
+
+ If the key is not found, a KeyError exception is raised.
+
+ This may not have been implemented for all file formats.
+ """
# Should be done by each sub-class (if possible)
raise NotImplementedError("Not available for this file format.")
@@ -371,14 +374,9 @@ class _IndexedSeqFileDict(_dict_base):
return d
def get_raw(self, key):
- """Similar to the get method, but returns the record as a raw string.
+ """Return the raw record from the file as a bytes string.
If the key is not found, a KeyError exception is raised.
-
- Note that on Python 3 a bytes string is returned, not a typical
- unicode string.
-
- NOTE - This functionality is not supported for every file format.
"""
# Pass the offset to the proxy
return self._proxy.get_raw(self._offsets[key])
@@ -602,7 +600,7 @@ class _SQLiteManySeqFilesDict(_IndexedSeqFileDict):
# Since user gave BOTH filename & index as relative paths,
# we will store this relative to the index file even though
# if it may now start ../ (meaning up a level)
- # Note for cross platfrom use (e.g. shared data drive over SAMBA),
+ # Note for cross platform use (e.g. shared data drive over SAMBA),
# convert any Windows slash into Unix style / for relative paths.
f = os.path.relpath(filename, relative_path).replace(os.path.sep, "/")
elif (os.path.dirname(os.path.abspath(filename)) + os.path.sep).startswith(relative_path + os.path.sep):
@@ -714,14 +712,9 @@ class _SQLiteManySeqFilesDict(_IndexedSeqFileDict):
return d
def get_raw(self, key):
- """Similar to the get method, but returns the record as a raw string.
+ """Return the raw record from the file as a bytes string.
If the key is not found, a KeyError exception is raised.
-
- Note that on Python 3 a bytes string is returned, not a typical
- unicode string.
-
- **NOTE** - This functionality is not supported for every file format.
"""
# Pass the offset to the proxy
row = self._con.execute(
diff --git a/Bio/GA/Crossover/GeneralPoint.py b/Bio/GA/Crossover/GeneralPoint.py
index f299d8c..c98340d 100644
--- a/Bio/GA/Crossover/GeneralPoint.py
+++ b/Bio/GA/Crossover/GeneralPoint.py
@@ -40,8 +40,6 @@ import random
from Bio._py3k import range
-__docformat__ = "restructuredtext en"
-
class GeneralPointCrossover(object):
"""Perform n-point crossover between genomes at some defined rates.
@@ -132,8 +130,8 @@ class GeneralPointCrossover(object):
- new organisms
- - locs (int list, int list)
-
+ - locs (int list, int list)
+
- lists of locations,
[0, +n points+, bound]
for each genome (sync'd with x)
diff --git a/Bio/GA/Crossover/Uniform.py b/Bio/GA/Crossover/Uniform.py
index 8eed37e..623a688 100644
--- a/Bio/GA/Crossover/Uniform.py
+++ b/Bio/GA/Crossover/Uniform.py
@@ -21,8 +21,6 @@ rapid mutation-behavior.
# standard modules
import random
-__docformat__ = "restructuredtext en"
-
class UniformCrossover(object):
"""Perform single point crossover between genomes at some defined rates.
diff --git a/Bio/GenBank/Record.py b/Bio/GenBank/Record.py
index 951d0a4..b148be9 100644
--- a/Bio/GenBank/Record.py
+++ b/Bio/GenBank/Record.py
@@ -17,8 +17,6 @@ classes:
# local stuff
import Bio.GenBank
-__docformat__ = "restructuredtext en"
-
def _wrapped_genbank(information, indent, wrap_space=1, split_char=" "):
"""Write a line of GenBank info that can wrap over multiple lines.
diff --git a/Bio/GenBank/Scanner.py b/Bio/GenBank/Scanner.py
index e53da35..391e43d 100644
--- a/Bio/GenBank/Scanner.py
+++ b/Bio/GenBank/Scanner.py
@@ -30,13 +30,12 @@ from __future__ import print_function
import warnings
import re
+from collections import defaultdict
from Bio.Seq import Seq
from Bio.SeqRecord import SeqRecord
from Bio.Alphabet import generic_protein
from Bio import BiopythonParserWarning
-__docformat__ = "restructuredtext en"
-
class InsdcScanner(object):
"""Basic functions for breaking up a GenBank/EMBL file into sub sections.
@@ -809,10 +808,24 @@ class EmblScanner(InsdcScanner):
# Remove trailing ; at end of authors list
consumer.authors(data.rstrip(";"))
elif line_type == 'PR':
- # Remove trailing ; at end of the project reference
- # In GenBank files this corresponds to the old PROJECT
- # line which is being replaced with the DBLINK line.
- consumer.project(data.rstrip(";"))
+ # In the EMBL patent files, this is a PR (PRiority) line which
+ # provides the earliest active priority within the family.
+ # The priority number comes first, followed by the priority date.
+ #
+ # e.g.
+ # PR JP19990377484 16-DEC-1999
+ #
+ # However, in most EMBL files this is a PR (PRoject) line which
+ # gives the BioProject reference number.
+ #
+ # e.g.
+ # PR Project:PRJNA60715;
+ #
+ # In GenBank files this corresponds to the old PROJECT line
+ # which was later replaced with the DBLINK line.
+ if data.startswith("Project:"):
+ # Remove trailing ; at end of the project reference
+ consumer.project(data.rstrip(";"))
elif line_type == 'KW':
consumer.keywords(data.rstrip(";"))
elif line_type in consumer_dict:
@@ -1246,6 +1259,9 @@ class GenBankScanner(InsdcScanner):
# handled individually.
GENBANK_INDENT = self.HEADER_WIDTH
GENBANK_SPACER = " " * GENBANK_INDENT
+ STRUCTURED_COMMENT_START = "-START##"
+ STRUCTURED_COMMENT_END = "-END##"
+ STRUCTURED_COMMENT_DELIM = " :: "
consumer_dict = {
'DEFINITION': 'definition',
'ACCESSION': 'accession',
@@ -1258,7 +1274,6 @@ class GenBankScanner(InsdcScanner):
'AUTHORS': 'authors',
'CONSRTM': 'consrtm',
'PROJECT': 'project',
- 'DBLINK': 'dblink',
'TITLE': 'title',
'JOURNAL': 'journal',
'MEDLINE': 'medline_id',
@@ -1268,6 +1283,7 @@ class GenBankScanner(InsdcScanner):
# ORIGIN (locus, size, residue_type, data_file_division and date)
# COMMENT (comment)
# VERSION (version and gi)
+ # DBLINK (database links like projects, newlines important)
# REFERENCE (eference_num and reference_bases)
# ORGANISM (organism and taxonomy)
lines = [_f for _f in lines if _f]
@@ -1296,6 +1312,20 @@ class GenBankScanner(InsdcScanner):
consumer.gi(data.split(' GI:')[1])
# Read in the next line!
line = next(line_iter)
+ elif line_type == 'DBLINK':
+ # Need to call consumer.dblink() for each line, e.g.
+ # DBLINK Project: 57779
+ # BioProject: PRJNA57779
+ consumer.dblink(data.strip())
+ # Read in the next line, and see if its more of the DBLINK section:
+ while True:
+ line = next(line_iter)
+ if line[:GENBANK_INDENT] == GENBANK_SPACER:
+ # Add this continuation to the data string
+ consumer.dblink(line[GENBANK_INDENT:].strip())
+ else:
+ # End of the DBLINK, leave this text in the variable "line"
+ break
elif line_type == 'REFERENCE':
if self.debug > 1:
print("Found reference [" + data + "]")
@@ -1366,26 +1396,54 @@ class GenBankScanner(InsdcScanner):
consumer.taxonomy(lineage_data.strip())
del organism_data, lineage_data
elif line_type == 'COMMENT':
+ # A COMMENT can either be plain text or tabular (Structured Comment),
+ # or contain both. Multiline comments are common. The code calls
+ # consumer.comment() once with a list where each entry
+ # is a line. If there's a structured comment consumer.structured_comment()
+ # is called with a dict of dicts where the secondary key/value pairs are
+ # the same as those in the structured comment table. The primary key is
+ # the title or header of the table (e.g. Assembly-Data, FluData). See
+ # http://www.ncbi.nlm.nih.gov/genbank/structuredcomment
+ # for more information on Structured Comments.
+ data = line[GENBANK_INDENT:]
if self.debug > 1:
print("Found comment")
- # This can be multiline, and should call consumer.comment() once
- # with a list where each entry is a line.
comment_list = []
- comment_list.append(data)
+ structured_comment_dict = defaultdict(dict)
+ structured_comment_key = ''
+
+ if STRUCTURED_COMMENT_START in data:
+ structured_comment_key = re.search(r"([^#]+){0}$".format(STRUCTURED_COMMENT_START), data).group(1)
+ if self.debug > 1:
+ print("Found Structured Comment")
+ else:
+ comment_list.append(data)
+
while True:
line = next(line_iter)
+ data = line[GENBANK_INDENT:]
if line[0:GENBANK_INDENT] == GENBANK_SPACER:
- data = line[GENBANK_INDENT:]
- comment_list.append(data)
- if self.debug > 2:
- print("Comment continuation [" + data + "]")
+ if STRUCTURED_COMMENT_START in data:
+ structured_comment_key = re.search(r"([^#]+){0}$".format(STRUCTURED_COMMENT_START), data).group(1)
+ elif structured_comment_key is not None and STRUCTURED_COMMENT_DELIM in data:
+ match = re.search(r"(.+?)\s*{0}\s*(.+)".format(STRUCTURED_COMMENT_DELIM), data)
+ structured_comment_dict[structured_comment_key][match.group(1)] = match.group(2)
+ if self.debug > 2:
+ print("Structured Comment continuation [" + data + "]")
+ elif STRUCTURED_COMMENT_END not in data:
+ comment_list.append(data)
+ if self.debug > 2:
+ print("Comment continuation [" + data + "]")
else:
# End of the comment
break
- consumer.comment(comment_list)
- del comment_list
+ if comment_list:
+ consumer.comment(comment_list)
+ if structured_comment_dict:
+ consumer.structured_comment(structured_comment_dict)
+ del comment_list, structured_comment_key, structured_comment_dict
elif line_type in consumer_dict:
- # Its a semi-automatic entry!
+ # It's a semi-automatic entry!
# Now, this may be a multi line entry...
while True:
line = next(line_iter)
diff --git a/Bio/GenBank/__init__.py b/Bio/GenBank/__init__.py
index 0b7b117..776d79a 100644
--- a/Bio/GenBank/__init__.py
+++ b/Bio/GenBank/__init__.py
@@ -1,5 +1,5 @@
# Copyright 2000 by Jeffrey Chang, Brad Chapman. All rights reserved.
-# Copyright 2006-2013 by Peter Cock. All rights reserved.
+# Copyright 2006-2016 by Peter Cock. All rights reserved.
#
# This code is part of the Biopython distribution and governed by its
# license. Please see the LICENSE file that should have been included
@@ -13,7 +13,7 @@ SeqRecord and SeqFeature objects (see the Biopython tutorial for details).
Using Bio.GenBank directly to parse GenBank files is only useful if you want
to obtain GenBank-specific Record objects, which is a much closer
-representation to the raw file contents that the SeqRecord alternative from
+representation to the raw file contents than the SeqRecord alternative from
the FeatureParser (used in Bio.SeqIO).
To use the Bio.GenBank parser, there are two helper functions:
@@ -53,7 +53,6 @@ from Bio import SeqFeature
from .utils import FeatureValueCleaner
from .Scanner import GenBankScanner
-__docformat__ = "restructuredtext en"
# Constants used to parse GenBank header lines
GENBANK_INDENT = 12
@@ -97,7 +96,7 @@ _simple_location = r"\d+\.\.\d+"
_re_simple_location = re.compile(r"^%s$" % _simple_location)
_re_simple_compound = re.compile(r"^(join|order|bond)\(%s(,%s)*\)$"
% (_simple_location, _simple_location))
-_complex_location = r"([a-zA-z][a-zA-Z0-9_]*(\.[a-zA-Z0-9]+)?\:)?(%s|%s|%s|%s|%s)" \
+_complex_location = r"([a-zA-Z][a-zA-Z0-9_\.]*[a-zA-Z0-9]?\:)?(%s|%s|%s|%s|%s)" \
% (_pair_location, _solo_location, _between_location,
_within_location, _oneof_location)
_re_complex_location = re.compile(r"^%s$" % _complex_location)
@@ -132,10 +131,12 @@ assert _re_complex_location.match("41^42") # between
assert _re_complex_location.match("AL121804:41^42")
assert _re_complex_location.match("AL121804:41..610")
assert _re_complex_location.match("AL121804.2:41..610")
+assert _re_complex_location.match("AL358792.24.1.166931:3274..3461") # lots of dots in external reference
assert _re_complex_location.match("one-of(3,6)..101")
assert _re_complex_compound.match("join(153490..154269,AL121804.2:41..610,AL121804.2:672..1487)")
assert not _re_simple_compound.match("join(153490..154269,AL121804.2:41..610,AL121804.2:672..1487)")
assert _re_complex_compound.match("join(complement(69611..69724),139856..140650)")
+assert _re_complex_compound.match("join(complement(AL354868.10.1.164018:80837..81016),complement(AL354868.10.1.164018:80539..80835))")
# Trans-spliced example from NC_016406, note underscore in reference name:
assert _re_complex_location.match("NC_016402.1:6618..6676")
@@ -948,6 +949,9 @@ class _FeatureConsumer(_BaseGenBankConsumer):
except KeyError:
self.data.annotations['comment'] = "\n".join(content)
+ def structured_comment(self, content):
+ self.data.annotations['structured_comment'] = content
+
def features_line(self, content):
"""Get ready for the feature table when we reach the FEATURE line.
"""
@@ -1383,6 +1387,9 @@ class _RecordConsumer(_BaseGenBankConsumer):
def comment(self, content):
self.data.comment += "\n".join(content)
+ def structured_comment(self, content):
+ self.data.structured_comment = content
+
def primary_ref_line(self, content):
"""Data for the PRIMARY line"""
self.data.primary.append(content)
diff --git a/Bio/GenBank/utils.py b/Bio/GenBank/utils.py
index 4220b80..a4c4304 100644
--- a/Bio/GenBank/utils.py
+++ b/Bio/GenBank/utils.py
@@ -6,8 +6,6 @@
"""Useful utilities for helping in parsing GenBank files.
"""
-__docformat__ = "restructuredtext en"
-
class FeatureValueCleaner(object):
r"""Provide specialized capabilities for cleaning up values in features.
diff --git a/Bio/Geo/Record.py b/Bio/Geo/Record.py
index f7176e9..026f3fe 100644
--- a/Bio/Geo/Record.py
+++ b/Bio/Geo/Record.py
@@ -48,7 +48,7 @@ class Record(object):
try:
output += '%s: %s\n' % (key, item[:40])
output += out_block(item[40:])
- except:
+ except Exception: # TODO: IndexError?
pass
elif isinstance(contents, str):
output += '%s: %s\n' % (key, contents[:40])
diff --git a/Bio/Graphics/BasicChromosome.py b/Bio/Graphics/BasicChromosome.py
index 8bdde68..72ebd92 100644
--- a/Bio/Graphics/BasicChromosome.py
+++ b/Bio/Graphics/BasicChromosome.py
@@ -30,23 +30,20 @@ http://www.reportlab.com
This provides nice output in PDF, SVG and postscript. If you have
reportlab's renderPM module installed you can also use PNG etc.
"""
-# standard library
-import os
# reportlab
-from reportlab.pdfgen import canvas
from reportlab.lib.pagesizes import letter
from reportlab.lib.units import inch
from reportlab.lib import colors
from reportlab.pdfbase.pdfmetrics import stringWidth
from reportlab.graphics.shapes import Drawing, String, Line, Rect, Wedge, ArcPath
-from reportlab.graphics import renderPDF, renderPS
from reportlab.graphics.widgetbase import Widget
from Bio.Graphics import _write
from Bio.Graphics.GenomeDiagram._Colors import ColorTranslator as _ColorTranslator
+
_color_trans = _ColorTranslator()
@@ -255,11 +252,11 @@ class Chromosome(_ChromosomeComponent):
# will actually be the picture of the chromosome
cur_y_pos = self.start_y_position
if self.scale_num:
- y_pos_change = ((self.start_y_position * .95 - self.end_y_position)
- / self.scale_num)
+ y_pos_change = ((self.start_y_position * .95 - self.end_y_position) /
+ self.scale_num)
elif len(self._sub_components) > 0:
- y_pos_change = ((self.start_y_position * .95 - self.end_y_position)
- / self.subcomponent_size())
+ y_pos_change = ((self.start_y_position * .95 - self.end_y_position) /
+ self.subcomponent_size())
# no sub_components to draw
else:
pass
@@ -598,7 +595,7 @@ def _place_labels(desired_etc, minimum, maximum, gap=0):
class AnnotatedChromosomeSegment(ChromosomeSegment):
def __init__(self, bp_length, features,
default_feature_color=colors.blue,
- name_qualifiers=['gene', 'label', 'name', 'locus_tag', 'product']):
+ name_qualifiers=('gene', 'label', 'name', 'locus_tag', 'product')):
"""Like the ChromosomeSegment, but accepts a list of features.
The features can either be SeqFeature objects, or tuples of values:
@@ -619,7 +616,7 @@ class AnnotatedChromosomeSegment(ChromosomeSegment):
is used, unless the feature's qualifiers include an Artemis colour
string (functionality also in GenomeDiagram). The caption also follows
the GenomeDiagram approach and takes the first qualifier from the list
- specified in name_qualifiers.
+ or tuple specified in name_qualifiers.
Note additional attribute label_sep_percent controls the percentage of
area that the chromosome segment takes up, by default half of the
@@ -661,7 +658,7 @@ class AnnotatedChromosomeSegment(ChromosomeSegment):
try:
# Handles Artemis colour integers, HTML colors, etc
color = _color_trans.translate(f.qualifiers['color'][0])
- except:
+ except Exception: # TODO: ValueError?
color = self.default_feature_color
fill_color = color
name = ""
diff --git a/Bio/Graphics/ColorSpiral.py b/Bio/Graphics/ColorSpiral.py
index 4163a3c..696b74b 100644
--- a/Bio/Graphics/ColorSpiral.py
+++ b/Bio/Graphics/ColorSpiral.py
@@ -23,8 +23,6 @@ import colorsys # colour format conversions
from math import log, exp, floor, pi
import random # for jitter values
-__docformat__ = "restructuredtext en"
-
class ColorSpiral(object):
"""Implement a spiral path through HSV colour space.
diff --git a/Bio/Graphics/Comparative.py b/Bio/Graphics/Comparative.py
index c1c46fc..e44d786 100644
--- a/Bio/Graphics/Comparative.py
+++ b/Bio/Graphics/Comparative.py
@@ -10,20 +10,18 @@ compare different types of information. The most basic example is comparing
two variables in a traditional scatter plot.
"""
# reportlab
-from reportlab.pdfgen import canvas
from reportlab.lib import colors
from reportlab.graphics.charts.lineplots import LinePlot
from reportlab.lib.pagesizes import letter
from reportlab.lib.units import inch
-from reportlab.graphics.shapes import Drawing, String, Group
-from reportlab.graphics import renderPDF, renderPS
-from reportlab.graphics.charts.markers import *
+from reportlab.graphics.shapes import Drawing, String
+from reportlab.graphics.charts.markers import makeEmptySquare, makeFilledSquare
+from reportlab.graphics.charts.markers import makeFilledDiamond, makeEmptyCircle
+from reportlab.graphics.charts.markers import makeFilledCircle, makeSmiley
from Bio.Graphics import _write
-__docformat__ = "restructuredtext en"
-
class ComparativeScatterPlot(object):
"""Display a scatter-type plot comparing two different kinds of info.
diff --git a/Bio/Graphics/DisplayRepresentation.py b/Bio/Graphics/DisplayRepresentation.py
index 502b25a..a72f55c 100644
--- a/Bio/Graphics/DisplayRepresentation.py
+++ b/Bio/Graphics/DisplayRepresentation.py
@@ -15,6 +15,7 @@ from reportlab.lib import colors
from Bio.Graphics.BasicChromosome import ChromosomeSegment
from Bio.Graphics.BasicChromosome import TelomereSegment
+
# --- constants
# This is a default color scheme based on the light spectrum.
# Based on my vague recollections from biology, this is our friend ROY G. BIV
@@ -91,7 +92,7 @@ class ChromosomeCounts(object):
raise KeyError("Segment name %s not found." % segment_name)
def add_label(self, segment_name, label):
- """Add a label to a specfic segment.
+ """Add a label to a specific segment.
Raises a KeyError is the specified segment name is not found.
"""
diff --git a/Bio/Graphics/Distribution.py b/Bio/Graphics/Distribution.py
index 022b78e..4422903 100644
--- a/Bio/Graphics/Distribution.py
+++ b/Bio/Graphics/Distribution.py
@@ -65,8 +65,8 @@ class DistributionPage(object):
end_y_pos = .5 * inch
x_pos_change = ((end_x_pos - cur_x_pos) /
float(self.number_of_columns))
- num_y_rows = math.ceil(float(len(self.distributions))
- / float(self.number_of_columns))
+ num_y_rows = math.ceil(float(len(self.distributions)) /
+ float(self.number_of_columns))
y_pos_change = (cur_y_pos - end_y_pos) / num_y_rows
self._draw_distributions(cur_drawing, cur_x_pos, x_pos_change,
diff --git a/Bio/Graphics/GenomeDiagram/_AbstractDrawer.py b/Bio/Graphics/GenomeDiagram/_AbstractDrawer.py
index 51d9380..090c2cd 100644
--- a/Bio/Graphics/GenomeDiagram/_AbstractDrawer.py
+++ b/Bio/Graphics/GenomeDiagram/_AbstractDrawer.py
@@ -46,7 +46,6 @@ from reportlab.graphics.shapes import Polygon
from math import pi, sin, cos
-__docformat__ = "restructuredtext en"
################################################################################
# METHODS
@@ -273,7 +272,7 @@ def draw_arrow(point1, point2, color=colors.lightgreen, border=None,
def angle2trig(theta):
- """Convert anngle to a reportlab ready tuple.
+ """Convert angle to a reportlab ready tuple.
Arguments:
@@ -346,7 +345,7 @@ class AbstractDrawer(object):
Attributes:
- - tracklines Boolean for whether to draw lines dilineating tracks
+ - tracklines Boolean for whether to draw lines delineating tracks
- pagesize Tuple describing the size of the page in pixels
@@ -512,7 +511,7 @@ class AbstractDrawer(object):
self.length = self.end - self.start + 1
def is_in_bounds(self, value):
- """Check if given value is withing the region selected for drawing,
+ """Check if given value is within the region selected for drawing,
Arguments:
diff --git a/Bio/Graphics/GenomeDiagram/_CircularDrawer.py b/Bio/Graphics/GenomeDiagram/_CircularDrawer.py
index 5b441a2..29519e6 100644
--- a/Bio/Graphics/GenomeDiagram/_CircularDrawer.py
+++ b/Bio/Graphics/GenomeDiagram/_CircularDrawer.py
@@ -16,7 +16,6 @@ from __future__ import print_function
from reportlab.graphics.shapes import Drawing, String, Group, Line, Circle, Polygon
from reportlab.lib import colors
-from reportlab.pdfbase import _fontdata
from reportlab.graphics.shapes import ArcPath
from Bio._py3k import range
@@ -27,7 +26,7 @@ from ._AbstractDrawer import _stroke_and_fill_colors
from ._FeatureSet import FeatureSet
from ._GraphSet import GraphSet
-from math import ceil, pi, cos, sin, asin
+from math import pi, cos, sin
class CircularDrawer(AbstractDrawer):
@@ -231,7 +230,7 @@ class CircularDrawer(AbstractDrawer):
for track in range(bot_track, top_track + 1): # track numbers to 'draw'
try:
trackheight = self._parent[track].height # Get track height
- except:
+ except Exception: # TODO: ValueError? IndexError?
trackheight = 1
trackunit_sum += trackheight # increment total track unit height
trackunits[track] = (heightholder, heightholder + trackheight)
diff --git a/Bio/Graphics/GenomeDiagram/_CrossLink.py b/Bio/Graphics/GenomeDiagram/_CrossLink.py
index 380163f..c42008f 100644
--- a/Bio/Graphics/GenomeDiagram/_CrossLink.py
+++ b/Bio/Graphics/GenomeDiagram/_CrossLink.py
@@ -58,7 +58,7 @@ class CrossLink(object):
track, start, end = self.featureA
assert track in tracks
return track
- except:
+ except Exception: # TODO: ValueError?
for track in tracks:
for feature_set in track.get_sets():
if hasattr(feature_set, "features"):
diff --git a/Bio/Graphics/GenomeDiagram/_Diagram.py b/Bio/Graphics/GenomeDiagram/_Diagram.py
index 02dbdd2..3a39f6e 100644
--- a/Bio/Graphics/GenomeDiagram/_Diagram.py
+++ b/Bio/Graphics/GenomeDiagram/_Diagram.py
@@ -6,204 +6,79 @@
# Contact: Leighton Pritchard, Scottish Crop Research Institute,
# Invergowrie, Dundee, Scotland, DD2 5DA, UK
# L.Pritchard at scri.ac.uk
-################################################################################
+"""Provides a container for information concerning the tracks to be drawn in a diagram.
-""" Diagram module
+It also provides the interface for defining the diagram (possibly split these
+functions in later version?).
- Provides:
+For drawing capabilities, this module uses reportlab to draw and write the
+diagram:
- o Diagram - Container for information concerning the tracks to be
- drawn in a diagram, and the interface for defining the
- diagram (possibly split these functions in later version?)
+http://www.reportlab.com
- For drawing capabilities, this module uses reportlab to draw and write
- the diagram:
-
- http://www.reportlab.com
-
- For dealing with biological information, the package expects BioPython
- objects - namely SeqRecord ojbects containing SeqFeature objects.
+For dealing with biological information, the package expects BioPython
+objects - namely SeqRecord objects containing SeqFeature objects.
"""
-# -----------------------------------------------------------------------------
-# IMPORTS
-
-# ReportLab
try:
from reportlab.graphics import renderPM
except ImportError:
# This is an optional part of ReportLab, so may not be installed.
renderPM = None
-# GenomeDiagram
from ._LinearDrawer import LinearDrawer
from ._CircularDrawer import CircularDrawer
from ._Track import Track
from Bio.Graphics import _write
-# -----------------------------------------------------------------------------
-# CLASSES
-
-# -----------------------------------------------------------
-# Diagram
-
class Diagram(object):
- """ Diagram
-
- Provides:
-
- Attributes:
-
- o name String, identifier for the diagram
-
- o tracks List of Track objects comprising the diagram
-
- o format String, format of the diagram (circular/linear)
-
- o pagesize String, the pagesize of output
-
- o orientation String, the page orientation (landscape/portrait)
-
- o x Float, the proportion of the page to take up with even
- X margins
-
- o y Float, the proportion of the page to take up with even
- Y margins
-
- o xl Float, the proportion of the page to take up with the
- left X margin
-
- o xr Float, the proportion of the page to take up with the
- right X margin
-
- o yt Float, the proportion of the page to take up with the
- top Y margin
-
- o yb Float, the proportion of the page to take up with the
- bottom Y margin
-
- o circle_core Float, the proportion of the available radius to leave
- empty at the center of a circular diagram (0 to 1).
-
- o start Int, the base/aa position to start the diagram at
-
- o end Int, the base/aa position to end the diagram at
-
- o tracklines Boolean, True if track guidelines are to be drawn
-
- o fragments Int, for a linear diagram, the number of equal divisions
- into which the sequence is divided
-
- o fragment_size Float, the proportion of the space available to each
- fragment that should be used in drawing
-
- o track_size Float, the proportion of the space available to each
- track that should be used in drawing
-
- o circular Boolean, True if the genome/sequence to be drawn is, in
- reality, circular.
-
- Methods:
-
- o __init__(self, name=None) Called on instantiation
-
- o draw(self, format='circular', ...) Instructs the package to draw
- the diagram
-
- o write(self, filename='test1.ps', output='PS') Writes the drawn
- diagram to a specified file, in a specified format.
-
- o add_track(self, track, track_level) Adds a Track object to the
- diagram, with instructions to place it at a particular level on
- the diagram
-
- o del_track(self, track_level) Removes the track that is to be drawn
- at a particular level on the diagram
-
- o get_tracks(self) Returns the list of Track objects to be drawn
- contained in the diagram
-
- o renumber_tracks(self, low=1) Renumbers all tracks consecutively,
- optionally from a passed lowest number
-
- o get_levels(self) Returns a list of levels currently occupied by
- Track objects
-
- o get_drawn_levels(self) Returns a list of levels currently occupied
- by Track objects that will be shown in the drawn diagram (i.e.
- are not hidden)
-
- o range(self) Returns the lowest- and highest-numbered positions
- contained within features in all tracks on the diagram as a tuple.
-
- o __getitem__(self, key) Returns the track contained at the level of
- the passed key
-
- o __str__(self) Returns a formatted string describing the diagram
-
+ """Diagram container.
+
+ Arguments:
+ - name - a string, identifier for the diagram.
+ - tracks - a list of Track objects comprising the diagram.
+ - format - a string, format of the diagram 'circular' or
+ 'linear', depending on the sort of diagram required.
+ - pagesize - a string, the pagesize of output describing the ISO
+ size of the image, or a tuple of pixels.
+ - orientation - a string describing the required orientation of the
+ final drawing ('landscape' or 'portrait').
+ - x - a float (0->1), the proportion of the page to take
+ up with even X margins t the page.
+ - y - a float (0->1), the proportion of the page to take
+ up with even Y margins to the page.
+ - xl - a float (0->1), the proportion of the page to take
+ up with the left X margin to the page (overrides x).
+ - xr - a float (0->1), the proportion of the page to take
+ up with the right X margin to the page (overrides x).
+ - yt - a float (0->1), the proportion of the page to take
+ up with the top Y margin to the page (overrides y).
+ - yb - a float (0->1), the proportion of the page to take
+ up with the bottom Y margin to the page (overrides y).
+ - circle_core - a float, the proportion of the available radius to
+ leave empty at the center of a circular diagram (0 to 1).
+ - start - an integer, the base/aa position to start the diagram at.
+ - end - an integer, the base/aa position to end the diagram at.
+ - tracklines - a boolean, True if track guidelines are to be drawn.
+ - fragments - and integer, for a linear diagram, the number of equal
+ divisions into which the sequence is divided.
+ - fragment_size - a float (0->1), the proportion of the space
+ available to each fragment that should be used in drawing.
+ - track_size - a float (0->1), the proportion of the space
+ available to each track that should be used in drawing with sigils.
+ - circular - a boolean, True if the genome/sequence to be drawn
+ is, in reality, circular.
"""
def __init__(self, name=None, format='circular', pagesize='A3',
- orientation='landscape', x=0.05, y=0.05, xl=None,
- xr=None, yt=None, yb=None, start=None, end=None,
- tracklines=False, fragments=10, fragment_size=0.9,
- track_size=0.75, circular=True, circle_core=0.0):
- """ __init__(self, name=None)
-
- o name String describing the diagram
-
- o format String: 'circular' or 'linear', depending on the sort of
- diagram required
-
- o pagesize String describing the ISO size of the image, or a tuple
- of pixels
-
- o orientation String describing the required orientation of the
- final drawing ('landscape' or 'portrait')
-
- o x Float (0->1) describing the relative size of the X
- margins to the page
-
- o y Float (0->1) describing the relative size of the Y
- margins to the page
-
- o xl Float (0->1) describing the relative size of the left X
- margin to the page (overrides x)
-
- o xl Float (0->1) describing the relative size of the left X
- margin to the page (overrides x)
-
- o xr Float (0->1) describing the relative size of the right X
- margin to the page (overrides x)
-
- o yt Float (0->1) describing the relative size of the top Y
- margin to the page (overrides y)
-
- o yb Float (0->1) describing the relative size of the lower Y
- margin to the page (overrides y)
-
- o start Int, the position to begin drawing the diagram at
-
-
- o end Int, the position to stop drawing the diagram at
-
- o tracklines Boolean flag to show (or not) lines delineating
- tracks on the diagram
-
- o fragments Int, for linear diagrams, the number of sections into
- which to break the sequence being drawn
-
- o fragment_size Float (0->1), for linear diagrams, describing
- the proportion of space in a fragment to take
- up with tracks
-
- o track_size Float (0->1) describing the proportion of space
- in a track to take up with sigils
-
- o circular Boolean flag to indicate whether the sequence being
- drawn is circular
+ orientation='landscape', x=0.05, y=0.05, xl=None,
+ xr=None, yt=None, yb=None, start=None, end=None,
+ tracklines=False, fragments=10, fragment_size=0.9,
+ track_size=0.75, circular=True, circle_core=0.0):
+ """Called on instantiation.
+ gdd = Diagram(name=None)
"""
self.tracks = {} # Holds all Track objects, keyed by level
self.name = name # Description of the diagram
@@ -226,16 +101,16 @@ class Diagram(object):
self.circular = circular
self.circle_core = circle_core
self.cross_track_links = []
+ self.drawing = None
def set_all_tracks(self, attr, value):
- """ set_all_tracks(self, attr, value)
+ """Set the passed attribute of all tracks in the set to the passed value.
- o attr An attribute of the Track class
+ Arguments:
+ - attr - An attribute of the Track class.
+ - value - The value to set that attribute.
- o value The value to set that attribute
-
- Set the passed attribute of all tracks in the set to the
- passed value
+ set_all_tracks(self, attr, value)
"""
for track in self.tracks.values():
if hasattr(track, attr): # If the feature has the attribute
@@ -248,6 +123,8 @@ class Diagram(object):
fragment_size=None, track_size=None, circular=None,
circle_core=None, cross_track_links=None):
"""Draw the diagram, with passed parameters overriding existing attributes.
+
+ gdd.draw(format='circular')
"""
# Pass the parameters to the drawer objects that will build the
# diagrams. At the moment, we detect overrides with an or in the
@@ -281,35 +158,36 @@ class Diagram(object):
self.drawing = drawer.drawing # Get the completed drawing
def write(self, filename='test1.ps', output='PS', dpi=72):
- """ write(self, filename='test1.ps', output='PS', dpi=72)
-
- o filename String indicating the name of the output file,
- or a handle to write to.
-
- o output String indicating output format, one of PS, PDF,
- SVG, or provided the ReportLab renderPM module is
- installed, one of the bitmap formats JPG, BMP,
- GIF, PNG, TIFF or TIFF. The format can be given
- in upper or lower case.
-
- o dpi Resolution (dots per inch) for bitmap formats.
-
- Write the completed drawing out to a file in a prescribed format
-
+ """Writes the drawn diagram to a specified file, in a specified format.
+
+ Arguments:
+ - filename - a string indicating the name of the output file,
+ or a handle to write to.
+ - output - a string indicating output format, one of PS, PDF,
+ SVG, or provided the ReportLab renderPM module is installed, one
+ of the bitmap formats JPG, BMP, GIF, PNG, TIFF or TIFF. The
+ format can be given in upper or lower case.
+ - dpi - an integer. Resolution (dots per inch) for bitmap formats.
+
+ Returns:
No return value.
+
+ write(self, filename='test1.ps', output='PS', dpi=72)
"""
return _write(self.drawing, filename, output, dpi=dpi)
def write_to_string(self, output='PS', dpi=72):
"""Returns a byte string containing the diagram in the requested format.
- o output String indicating output format, one of PS, PDF,
- SVG, JPG, BMP, GIF, PNG, TIFF or TIFF (as
- specified for the write method).
-
- o dpi Resolution (dots per inch) for bitmap formats.
+ Arguments:
+ - output - a string indicating output format, one of PS, PDF,
+ SVG, JPG, BMP, GIF, PNG, TIFF or TIFF (as specified for the write
+ method).
+ - dpi - Resolution (dots per inch) for bitmap formats.
- Return the completed drawing as a bytes string in a prescribed format
+ Returns:
+ Return the completed drawing as a bytes string in a prescribed
+ format.
"""
# The ReportLab drawToString method, which this function used to call,
# just used a cStringIO or StringIO handle with the drawToFile method.
@@ -324,14 +202,17 @@ class Diagram(object):
return handle.getvalue()
def add_track(self, track, track_level):
- """ add_track(self, track, track_level)
+ """Adds a Track object to the diagram.
- o track Track object to draw
+ It also accepts instructions to place it at a particular level on the
+ diagram.
- o track_level Int, the level at which the track will be drawn
- (above an arbitrary baseline)
+ Arguments:
+ - track - Track object to draw.
+ - track_level - an integer. The level at which the track will be
+ drawn (above an arbitrary baseline).
- Add a pre-existing Track to the diagram at a given level
+ add_track(self, track, track_level)
"""
if track is None:
raise ValueError("Must specify track")
@@ -348,13 +229,15 @@ class Diagram(object):
self.tracks[track_level].track_level = track_level
def new_track(self, track_level, **args):
- """ new_track(self, track_level) -> Track
+ """Add a new Track to the diagram at a given level.
- o track_level Int, the level at which the track will be drawn
- (above an arbitrary baseline)
+ The track is returned for further user manipulation.
- Add a new Track to the diagram at a given level and returns it for
- further user manipulation.
+ Arguments:
+ - track_level - an integer. The level at which the track will be
+ drawn (above an arbitrary baseline).
+
+ new_track(self, track_level)
"""
newtrack = Track()
for key in args:
@@ -372,44 +255,48 @@ class Diagram(object):
return newtrack
def del_track(self, track_level):
- """ del_track(self, track_level)
+ """Removes the track to be drawn at a particular level on the diagram.
- o track_level Int, the level of the track on the diagram to delete
+ Arguments:
+ - track_level - an integer. The level of the track on the diagram
+ to delete.
- Remove the track at the passed level from the diagram
+ del_track(self, track_level)
"""
del self.tracks[track_level]
def get_tracks(self):
- """ get_tracks(self) -> list
+ """Returns a list of the tracks contained in the diagram.
- Returns a list of the tracks contained in the diagram
+ get_tracks(self)
"""
return list(self.tracks.values())
def move_track(self, from_level, to_level):
- """ move_track(self, from_level, to_level)
-
- o from_level Int, the level at which the track to be moved is
- found
+ """Moves a track from one level on the diagram to another.
- o to_level Int, the level to move the track to
+ Arguments:
+ - from_level - an integer. The level at which the track to be
+ moved is found.
+ - to_level - an integer. The level to move the track to.
- Moves a track from one level on the diagram to another
+ move_track(self, from_level, to_level)
"""
aux = self.tracks[from_level]
del self.tracks[from_level]
self.add_track(aux, to_level)
def renumber_tracks(self, low=1, step=1):
- """ renumber_tracks(self, low=1, step=1)
+ """Renumbers all tracks consecutively.
- o low Int, the track number to start from
+ Optionally from a passed lowest number.
- o step Int, the track interval for separation of tracks
+ Arguments:
+ - low - an integer. The track number to start from.
+ - step - an integer. The track interval for separation of
+ tracks.
- Reassigns all the tracks to run consecutively from the lowest
- value (low)
+ renumber_tracks(self, low=1, step=1)
"""
track = low # Start numbering from here
levels = self.get_levels()
@@ -422,46 +309,49 @@ class Diagram(object):
self.tracks = conversion # Replace old set of levels with new set
def get_levels(self):
- """ get_levels(self) -> [int, int, ...]
+ """Return a sorted list of levels occupied by tracks in the diagram.
- Return a sorted list of levels occupied by tracks in the diagram
+ get_levels(self)
"""
return sorted(self.tracks)
def get_drawn_levels(self):
- """ get_drawn_levels(self) -> [int, int, ...]
+ """Return a sorted list of levels occupied by tracks.
- Return a sorted list of levels occupied by tracks that are not
- explicitly hidden
+ These tracks are not explicitly hidden.
+
+ get_drawn_levels(self)
"""
return sorted(key for key in self.tracks if not self.tracks[key].hide)
def range(self):
- """ range(self) -> (int, int)
+ """Returns lowest and highest base numbers from track features.
+
+ Returned type is a tuple.
- Returns the lowest and highest base (or mark) numbers containd in
- track features as a tuple
+ range(self)
"""
lows, highs = [], []
for track in self.tracks.values(): # Get ranges for each track
low, high = track.range()
lows.append(low)
highs.append(high)
- return (min(lows), max(highs)) # Return extremes from all tracks
+ return min(lows), max(highs) # Return extremes from all tracks
def __getitem__(self, key):
- """ __getitem__(self, key) -> Track
+ """Returns the track contained at the level of the passed key.
- o key The id of a track in the diagram
+ Arguments:
+ - key - The id of a track in the diagram.
- Return the Track object with the passed id
+ __getitem__(self, key)
"""
return self.tracks[key]
def __str__(self):
- """ __str__(self) -> ""
+ """Returns a formatted string describing the diagram.
- Returns a formatted string with information about the diagram
+ __str__(self)
"""
outstr = ["\n<%s: %s>" % (self.__class__, self.name)]
outstr.append("%d tracks" % len(self.tracks))
diff --git a/Bio/Graphics/GenomeDiagram/_FeatureSet.py b/Bio/Graphics/GenomeDiagram/_FeatureSet.py
index 38a2aea..1add356 100644
--- a/Bio/Graphics/GenomeDiagram/_FeatureSet.py
+++ b/Bio/Graphics/GenomeDiagram/_FeatureSet.py
@@ -22,29 +22,14 @@ For drawing capabilities, this module uses reportlab to draw and write
the diagram: http://www.reportlab.com
"""
-# -----------------------------------------------------------------------------
-# IMPORTS
-
-# ReportLab
from __future__ import print_function
-from reportlab.pdfbase import _fontdata
-from reportlab.lib import colors
-
# GenomeDiagram
from ._Feature import Feature
# Builtins
import re
-__docformat__ = "restructuredtext en"
-
-# ------------------------------------------------------------------------------
-# CLASSES
-
-# ------------------------------------------------------------
-# FeatureSet
-
class FeatureSet(object):
"""FeatureSet object."""
@@ -122,7 +107,7 @@ class FeatureSet(object):
# self.set_all_feature("color",value)
def get_features(self, attribute=None, value=None, comparator=None):
- """Retreive features.
+ """Retrieve features.
Arguments:
- attribute: String, attribute of a Feature object
diff --git a/Bio/Graphics/GenomeDiagram/_LinearDrawer.py b/Bio/Graphics/GenomeDiagram/_LinearDrawer.py
index 4fbc741..6a4cfec 100644
--- a/Bio/Graphics/GenomeDiagram/_LinearDrawer.py
+++ b/Bio/Graphics/GenomeDiagram/_LinearDrawer.py
@@ -121,7 +121,7 @@ class LinearDrawer(AbstractDrawer):
Attributes:
- o tracklines Boolean for whether to draw lines dilineating tracks
+ o tracklines Boolean for whether to draw lines delineating tracks
o pagesize Tuple describing the size of the page in pixels
@@ -356,7 +356,7 @@ class LinearDrawer(AbstractDrawer):
for track in range(bot_track, top_track + 1): # for all track numbers to 'draw'
try:
trackheight = self._parent[track].height # Get track height
- except:
+ except Exception: # TODO: IndexError?
trackheight = 1 # ...or default to 1
trackunit_sum += trackheight # increment total track unit height
trackunits[track] = (heightholder, heightholder + trackheight)
diff --git a/Bio/Graphics/GenomeDiagram/_Track.py b/Bio/Graphics/GenomeDiagram/_Track.py
index d89807b..7245fdc 100644
--- a/Bio/Graphics/GenomeDiagram/_Track.py
+++ b/Bio/Graphics/GenomeDiagram/_Track.py
@@ -102,7 +102,7 @@ class Track(object):
as numerals. Setting this to 'SInt' invokes SI
unit-like multiples, such as Mbp, Kbp and so on.
- o scale_color colors.Color to draw the elemnts of the scale
+ o scale_color colors.Color to draw the elements of the scale
o scale_font String describing the font to use for the scale labels
@@ -178,7 +178,7 @@ class Track(object):
o scale Boolean, 1 if a scale is to be drawn on the track
- o scale_color colors.Color to draw the elemnts of the scale
+ o scale_color colors.Color to draw the elements of the scale
(overridden by backwards compatible argument with UK
spelling, colour).
@@ -347,7 +347,7 @@ class Track(object):
"""
if not verbose: # Return the short description
return "%s" % self # Use __str__ method instead
- else: # Return the long desription
+ else: # Return the long description
outstr = ["\n<%s: %s>" % (self.__class__, self.name)]
outstr.append("%d sets" % len(self._sets))
for key in self._sets:
diff --git a/Bio/Graphics/GenomeDiagram/__init__.py b/Bio/Graphics/GenomeDiagram/__init__.py
index 5d11602..9014ea7 100644
--- a/Bio/Graphics/GenomeDiagram/__init__.py
+++ b/Bio/Graphics/GenomeDiagram/__init__.py
@@ -9,6 +9,8 @@
# L.Pritchard at scri.ac.uk
# #############################################################################
+"""GenomeDiagram module integrated into Biopython."""
+
# Local imports, to make these classes available directly under the
# Bio.Graphics.GenomeDiagram namespace:
@@ -19,6 +21,6 @@ from ._GraphSet import GraphSet
from ._CrossLink import CrossLink
# Not (currently) made public,
-# from _Colors import ColorTranslator
-# from _Feature import Feature
-# from _Graph import GraphData
+# from ._Colors import ColorTranslator
+# from ._Feature import Feature
+# from ._Graph import GraphData
diff --git a/Bio/Graphics/KGML_vis.py b/Bio/Graphics/KGML_vis.py
index e214d65..0219a7a 100644
--- a/Bio/Graphics/KGML_vis.py
+++ b/Bio/Graphics/KGML_vis.py
@@ -10,12 +10,9 @@ from __future__ import print_function
import os
import tempfile
-from itertools import chain
from io import BytesIO
-from reportlab.lib import pagesizes
from reportlab.lib import colors
-from reportlab.lib.utils import ImageReader
from reportlab.graphics.shapes import *
from reportlab.pdfgen import canvas
diff --git a/Bio/Graphics/__init__.py b/Bio/Graphics/__init__.py
index a104069..19818b9 100644
--- a/Bio/Graphics/__init__.py
+++ b/Bio/Graphics/__init__.py
@@ -17,7 +17,6 @@ except ImportError:
"to use Bio.Graphics. You can find ReportLab at "
"http://www.reportlab.com/software/opensource/")
-__docformat__ = "restructuredtext en"
# The following code is to allow all the Bio.Graphics
# code to deal with the different ReportLab renderers
diff --git a/Bio/HMM/DynamicProgramming.py b/Bio/HMM/DynamicProgramming.py
index f49bb0c..d48c4a3 100644
--- a/Bio/HMM/DynamicProgramming.py
+++ b/Bio/HMM/DynamicProgramming.py
@@ -269,7 +269,7 @@ class ScaledDPAlgorithms(AbstractDPAlgorithms):
cur_state)]
state_pos_sum += prev_forward * cur_trans_prob
- # if we have the possiblity of having a transition
+ # if we have the possibility of having a transition
# return the recursion value
if have_transition:
return (scale_emission_prob * state_pos_sum)
diff --git a/Bio/HMM/Trainer.py b/Bio/HMM/Trainer.py
index 8b432c4..696571d 100644
--- a/Bio/HMM/Trainer.py
+++ b/Bio/HMM/Trainer.py
@@ -23,8 +23,6 @@ import math
# local stuff
from .DynamicProgramming import ScaledDPAlgorithms
-__docformat__ = "restructuredtext en"
-
class TrainingSequence(object):
"""Hold a training sequence with emissions and optionally, a state path.
diff --git a/Bio/Index.py b/Bio/Index.py
index 22108d8..3d2b492 100644
--- a/Bio/Index.py
+++ b/Bio/Index.py
@@ -14,10 +14,12 @@ _ShelveIndex An Index class based on the shelve module.
_InMemoryIndex An in-memory Index class.
"""
+
import os
import array
import shelve
+
try:
import cPickle as pickle # Only available under Python 2
except ImportError:
@@ -51,13 +53,13 @@ class _ShelveIndex(dict):
os.unlink(file)
raise Exception("open a new shelf")
self.data = shelve.open(indexname, flag='r')
- except:
+ except Exception: # TODO: Which exception?
# No database exists.
self.data = shelve.open(indexname, flag='n')
self.data[self.__version_key] = self.__version
else:
# Check to make sure the database is the correct version.
- version = self.data.get(self.__version_key, None)
+ version = self.data.get(self.__version_key)
if version is None:
raise IOError("Unrecognized index format")
elif version != self.__version:
diff --git a/Bio/KDTree/KDTree.py b/Bio/KDTree/KDTree.py
index 5762a53..cb83a45 100644
--- a/Bio/KDTree/KDTree.py
+++ b/Bio/KDTree/KDTree.py
@@ -20,8 +20,6 @@ from numpy import random
from Bio.KDTree import _CKDTree
-__docformat__ = "restructuredtext en"
-
def _dist(p, q):
diff = p - q
diff --git a/Bio/KDTree/__init__.py b/Bio/KDTree/__init__.py
index 0ffd69e..56646bb 100644
--- a/Bio/KDTree/__init__.py
+++ b/Bio/KDTree/__init__.py
@@ -14,5 +14,3 @@ Otfried Schwarzkopf).
"""
from .KDTree import KDTree
-
-__docformat__ = "restructuredtext en"
diff --git a/Bio/KEGG/Enzyme/__init__.py b/Bio/KEGG/Enzyme/__init__.py
index 61d6672..ff6d3ad 100644
--- a/Bio/KEGG/Enzyme/__init__.py
+++ b/Bio/KEGG/Enzyme/__init__.py
@@ -308,6 +308,35 @@ def parse(handle):
record.sysname.append(data.strip(";"))
+def read(handle):
+ """Parse a KEGG Enzyme file with exactly one entry.
+
+ If the handle contains no records, or more than one record,
+ an exception is raised. For example:
+
+ >>> with open("KEGG/enzyme.new") as handle:
+ ... record = read(handle)
+ ... print("%s %s" % (record.entry, record.name[0]))
+ ...
+ 6.2.1.25 benzoate---CoA ligase
+ """
+
+ iterator = parse(handle)
+ try:
+ first = next(iterator)
+ except StopIteration:
+ first = None
+ if first is None:
+ raise ValueError("No records found in handle")
+ try:
+ second = next(iterator)
+ except StopIteration:
+ second = None
+ if second is not None:
+ raise ValueError("More than one record found in handle")
+ return first
+
+
if __name__ == "__main__":
from Bio._utils import run_doctest
run_doctest()
diff --git a/Bio/KEGG/KGML/KGML_parser.py b/Bio/KEGG/KGML/KGML_parser.py
index 8c38115..c15596c 100644
--- a/Bio/KEGG/KGML/KGML_parser.py
+++ b/Bio/KEGG/KGML/KGML_parser.py
@@ -30,8 +30,6 @@ from Bio._py3k import StringIO
from Bio.KEGG.KGML.KGML_pathway import *
-__docformat__ = "restructuredtext en"
-
def read(handle, debug=0):
"""Parses a single KEGG Pathway from given file handle.
diff --git a/Bio/KEGG/KGML/KGML_pathway.py b/Bio/KEGG/KGML/KGML_pathway.py
index a07b769..ccdc439 100644
--- a/Bio/KEGG/KGML/KGML_pathway.py
+++ b/Bio/KEGG/KGML/KGML_pathway.py
@@ -28,8 +28,6 @@ import xml.etree.ElementTree as ET
from Bio._py3k import _is_int_or_long, _as_string
-__docformat__ = "restructuredtext en"
-
# Pathway
class Pathway(object):
diff --git a/Bio/KEGG/REST.py b/Bio/KEGG/REST.py
index b5129bf..df5ce2b 100644
--- a/Bio/KEGG/REST.py
+++ b/Bio/KEGG/REST.py
@@ -28,6 +28,7 @@ Nucleic Acids Res. 28, 29-34 (2000).
"""
from Bio._py3k import urlopen as _urlopen
+from Bio._py3k import _binary_to_string_handle
def _q(op, arg1, arg2=None, arg3=None):
@@ -38,7 +39,12 @@ def _q(op, arg1, arg2=None, arg3=None):
args = "%s/%s/%s" % (op, arg1, arg2)
else:
args = "%s/%s" % (op, arg1)
- return _urlopen(URL % (args))
+ resp = _urlopen(URL % (args))
+
+ if "image" == arg2:
+ return resp
+
+ return _binary_to_string_handle(resp)
# http://www.kegg.jp/kegg/rest/keggapi.html
diff --git a/Bio/KEGG/__init__.py b/Bio/KEGG/__init__.py
index 880e3c3..d413c3a 100644
--- a/Bio/KEGG/__init__.py
+++ b/Bio/KEGG/__init__.py
@@ -3,8 +3,7 @@
# license. Please see the LICENSE file that should have been included
# as part of this package.
-"""
-This module provides code to work with data from the KEGG database.
+"""Code to work with data from the KEGG database.
References:
@@ -13,6 +12,8 @@ Nucleic Acids Res. 28, 29-34 (2000).
URL: http://www.genome.ad.jp/kegg/
"""
+
+
KEGG_ITEM_LENGTH = 12
KEGG_LINE_LENGTH = 80
KEGG_DATA_LENGTH = KEGG_LINE_LENGTH - KEGG_ITEM_LENGTH
diff --git a/Bio/MarkovModel.py b/Bio/MarkovModel.py
index 3e6d6b8..632f88b 100644
--- a/Bio/MarkovModel.py
+++ b/Bio/MarkovModel.py
@@ -8,7 +8,6 @@ This is an implementation of a state-emitting MarkovModel. I am using
terminology similar to Manning and Schutze.
-
Functions:
train_bw Train a markov model using the Baum-Welch algorithm.
train_visible Train a visible markov model using MLE.
@@ -23,6 +22,7 @@ MarkovModel Holds the description of a markov model
import numpy
+
try:
logaddexp = numpy.logaddexp
except AttributeError:
diff --git a/Bio/Medline/__init__.py b/Bio/Medline/__init__.py
index 76cb0fd..25b5ae0 100644
--- a/Bio/Medline/__init__.py
+++ b/Bio/Medline/__init__.py
@@ -13,8 +13,6 @@ Functions:
- parse Allows you to iterate over a bunch of Medline records
"""
-__docformat__ = "restructuredtext en"
-
class Record(dict):
"""A dictionary holding information from a Medline record.
@@ -112,7 +110,7 @@ def parse(handle):
from Bio import Medline
with open("mymedlinefile") as handle:
records = Medline.parse(handle)
- for record in record:
+ for record in records:
print(record['TI'])
"""
diff --git a/Bio/Motif/Applications/__init__.py b/Bio/Motif/Applications/__init__.py
deleted file mode 100644
index 8f08069..0000000
--- a/Bio/Motif/Applications/__init__.py
+++ /dev/null
@@ -1,9 +0,0 @@
-# Copyright 2009 by Bartek Wilczynski. All rights reserved.
-# Revisions copyright 2009-2013 by Peter Cock.
-# This code is part of the Biopython distribution and governed by its
-# license. Please see the LICENSE file that should have been included
-# as part of this package.
-"""Motif command line tool wrappers (DEPRECATED, see Bio.motifs instead)."""
-from Bio.motifs.applications import AlignAceCommandline
-from Bio.motifs.applications import CompareAceCommandline
-from Bio.motifs.applications import XXmotifCommandline
diff --git a/Bio/Motif/Parsers/AlignAce.py b/Bio/Motif/Parsers/AlignAce.py
deleted file mode 100644
index 40f6c6a..0000000
--- a/Bio/Motif/Parsers/AlignAce.py
+++ /dev/null
@@ -1,53 +0,0 @@
-# Copyright 2003 by Bartek Wilczynski. All rights reserved.
-# This code is part of the Biopython distribution and governed by its
-# license. Please see the LICENSE file that should have been included
-# as part of this package.
-
-"""Parsing AlignACE files: AlignAceParser
-"""
-
-from Bio.Motif import Motif
-from Bio.Alphabet import IUPAC
-from Bio.Seq import Seq
-
-
-class Record(object):
- def __init__(self):
- self.motifs = []
- self.current_motif = None
- self.param_dict = None
-
-
-def read(handle):
- """read(handle)"""
- record = Record()
- record.ver = next(handle)
- record.cmd_line = next(handle)
- for line in handle:
- if line.strip() == "":
- pass
- elif line[:4] == "Para":
- record.param_dict = {}
- elif line[0] == "#":
- seq_name = line.split("\t")[1]
- record.seq_dict.append(seq_name)
- elif "=" in line:
- par_name = line.split("=")[0].strip()
- par_value = line.split("=")[1].strip()
- record.param_dict[par_name] = par_value
- elif line[:5] == "Input":
- record.seq_dict = []
- elif line[:5] == "Motif":
- record.current_motif = Motif()
- record.motifs.append(record.current_motif)
- record.current_motif.alphabet = IUPAC.unambiguous_dna
- elif line[:3] == "MAP":
- record.current_motif.score = float(line.split()[-1])
- elif len(line.split("\t")) == 4:
- seq = Seq(line.split("\t")[0], IUPAC.unambiguous_dna)
- record.current_motif.add_instance(seq)
- elif "*" in line:
- record.current_motif.set_mask(line.strip("\n\c"))
- else:
- raise ValueError(line)
- return record
diff --git a/Bio/Motif/Parsers/MAST.py b/Bio/Motif/Parsers/MAST.py
deleted file mode 100644
index d38def9..0000000
--- a/Bio/Motif/Parsers/MAST.py
+++ /dev/null
@@ -1,150 +0,0 @@
-# Copyright 2008 by Bartek Wilczynski.
-# Adapted from Bio.MEME.Parser by Jason A. Hackney. All rights reserved.
-# This code is part of the Biopython distribution and governed by its
-# license. Please see the LICENSE file that should have been included
-# as part of this package.
-
-from Bio.Alphabet import IUPAC
-from Bio.Motif.Parsers.MEME import MEMEMotif
-
-
-class Record(object):
- """The class for holding the results from a MAST run.
-
- A MAST.Record holds data about matches between motifs and sequences.
- The motifs held by the Record are objects of the class MEMEMotif.
-
- Methods:
- get_motif_by_name (motif_name): returns a MEMEMotif with the given
- name.
- """
-
- def __init__(self):
- self.sequences = []
- self.version = ""
- self.database = ""
- self.diagrams = {}
- self.alphabet = None
- self.motifs = []
-
- def get_motif_by_name(self, name):
- for m in self.motifs:
- if m.name == name:
- return m
-
-
-def read(handle):
- """read(handle)"""
- record = Record()
- __read_version(record, handle)
- __read_database_and_motifs(record, handle)
- __read_section_i(record, handle)
- __read_section_ii(record, handle)
- __read_section_iii(record, handle)
- return record
-
-
-# Everything below is private
-
-
-def __read_version(record, handle):
- for line in handle:
- if "MAST version" in line:
- break
- else:
- raise ValueError("Improper input file. Does not begin with a line with 'MAST version'")
- record.version = line.strip().split()[2]
-
-
-def __read_database_and_motifs(record, handle):
- for line in handle:
- if line.startswith('DATABASE AND MOTIFS'):
- break
- line = next(handle)
- if not line.startswith('****'):
- raise ValueError("Line does not start with '****':\n%s" % line)
- line = next(handle)
- if 'DATABASE' not in line:
- raise ValueError("Line does not contain 'DATABASE':\n%s" % line)
- words = line.strip().split()
- record.database = words[1]
- if words[2] == '(nucleotide)':
- record.alphabet = IUPAC.unambiguous_dna
- elif words[2] == '(peptide)':
- record.alphabet = IUPAC.protein
- for line in handle:
- if 'MOTIF WIDTH' in line:
- break
- line = next(handle)
- if '----' not in line:
- raise ValueError("Line does not contain '----':\n%s" % line)
- for line in handle:
- if not line.strip():
- break
- words = line.strip().split()
- motif = MEMEMotif()
- motif.alphabet = record.alphabet
- motif.name = words[0]
- motif.length = int(words[1])
- # motif.add_instance(words[2])
- record.motifs.append(motif)
-
-
-def __read_section_i(record, handle):
- for line in handle:
- if line.startswith('SECTION I:'):
- break
- for line in handle:
- if line.startswith('SEQUENCE NAME'):
- break
- line = next(handle)
- if not line.startswith('---'):
- raise ValueError("Line does not start with '---':\n%s" % line)
- for line in handle:
- if not line.strip():
- break
- else:
- sequence, description_evalue_length = line.split(None, 1)
- record.sequences.append(sequence)
- line = next(handle)
- if not line.startswith('****'):
- raise ValueError("Line does not start with '****':\n%s" % line)
-
-
-def __read_section_ii(record, handle):
- for line in handle:
- if line.startswith('SECTION II:'):
- break
- for line in handle:
- if line.startswith('SEQUENCE NAME'):
- break
- line = next(handle)
- if not line.startswith('---'):
- raise ValueError("Line does not start with '---':\n%s" % line)
- for line in handle:
- if not line.strip():
- break
- elif line.startswith(" "):
- diagram = line.strip()
- record.diagrams[sequence] += diagram
- else:
- sequence, pvalue, diagram = line.split()
- record.diagrams[sequence] = diagram
- line = next(handle)
- if not line.startswith('****'):
- raise ValueError("Line does not start with '****':\n%s" % line)
-
-
-def __read_section_iii(record, handle):
- for line in handle:
- if line.startswith('SECTION III:'):
- break
- for line in handle:
- if line.startswith('****'):
- break
- for line in handle:
- if line.startswith('*****'):
- break
- for line in handle:
- if line.strip():
- break
diff --git a/Bio/Motif/Parsers/MEME.py b/Bio/Motif/Parsers/MEME.py
deleted file mode 100644
index 36f8092..0000000
--- a/Bio/Motif/Parsers/MEME.py
+++ /dev/null
@@ -1,358 +0,0 @@
-# Copyright 2008 by Bartek Wilczynski
-# Adapted from Bio.MEME.Parser by Jason A. Hackney. All rights reserved.
-# This code is part of the Biopython distribution and governed by its
-# license. Please see the LICENSE file that should have been included
-# as part of this package.
-
-from __future__ import print_function
-
-from Bio.Alphabet import IUPAC
-from Bio import Seq
-import re
-from math import sqrt
-import sys
-from Bio.Motif import Motif
-
-
-def read(handle):
- """Parses the text output of the MEME program into MEME.Record object.
-
- Example:
-
- >>> from Bio.Motif.Parsers import MEME
- >>> with open("meme.output.txt") as f:
- ... record = MEME.read(f)
- >>> for motif in record.motifs:
- ... for instance in motif.instances:
- ... print(instance.motif_name, instance.sequence_name, instance.strand, instance.pvalue)
-
- """
- record = MEMERecord()
- __read_version(record, handle)
- __read_datafile(record, handle)
- __read_alphabet(record, handle)
- __read_sequence_names(record, handle)
- __read_command(record, handle)
- for line in handle:
- if line.startswith('MOTIF 1'):
- break
- else:
- raise ValueError('Unexpected end of stream')
- while True:
- motif = __create_motif(line)
- motif.alphabet = record.alphabet
- record.motifs.append(motif)
- __read_motif_name(motif, handle)
- __read_motif_sequences(motif, handle, 'revcomp' in record.command)
- __skip_unused_lines(handle)
- try:
- line = next(handle)
- except StopIteration:
- raise ValueError('Unexpected end of stream: Expected to find new motif, or the summary of motifs')
- if line.startswith("SUMMARY OF MOTIFS"):
- break
- if not line.startswith('MOTIF'):
- raise ValueError("Line does not start with 'MOTIF':\n%s" % line)
- return record
-
-
-class MEMEMotif (Motif):
- """A subclass of Motif used in parsing MEME (and MAST) output.
-
- This sublcass defines functions and data specific to MEME motifs.
- This includes the evalue for a motif and the PSSM of the motif.
-
- Methods:
- add_instance_from_values (name = 'default', pvalue = 1, sequence = 'ATA', start = 0, strand = +): create a new instance of the motif with the specified values.
- add_to_pssm (position): add a new position to the pssm. The position should be a list of nucleotide/amino acid frequencies
- add_to_logodds (position): add a new position to the log odds matrix. The position should be a tuple of log odds values for the nucleotide/amino acid at that position.
- compare_motifs (other_motif): returns the maximum correlation between this motif and other_motif
- """
- def __init__(self):
- Motif.__init__(self)
- self.evalue = 0.0
-
- def _numoccurrences(self, number):
- if isinstance(number, int):
- self.num_occurrences = number
- else:
- number = int(number)
- self.num_occurrences = number
-
- def get_instance_by_name(self, name):
- for i in self.instances:
- if i.sequence_name == name:
- return i
- return None
-
- def add_instance_from_values(self, name='default', pvalue=1, sequence='ATA', start=0, strand='+'):
- inst = MEMEInstance(sequence, self.alphabet)
- inst._pvalue(pvalue)
- inst._seqname(name)
- inst._start(start)
- inst._strand(strand)
- if self.length:
- inst._length(self.length)
- else:
- inst._length(len(sequence))
- if self.name:
- inst._motifname(self.name)
- self.add_instance(inst)
-
- def _evalue(self, evalue):
- if isinstance(evalue, float):
- self.evalue = evalue
- else:
- evalue = float(evalue)
- self.evalue = evalue
-
-
-class MEMEInstance(Seq.Seq):
- """A class describing the instances of a MEME motif, and the data thereof.
- """
- def __init__(self, *args, **kwds):
- Seq.Seq.__init__(self, *args, **kwds)
- self.sequence_name = ""
- self.start = 0
- self.pvalue = 1.0
- self.strand = 0
- self.length = 0
- self.motif_name = ""
-
- def _seqname(self, name):
- self.sequence_name = name
-
- def _motifname(self, name):
- self.motif_name = name
-
- def _start(self, start):
- start = int(start)
- self.start = start
-
- def _pvalue(self, pval):
- pval = float(pval)
- self.pvalue = pval
-
- def _score(self, score):
- score = float(score)
- self.score = score
-
- def _strand(self, strand):
- self.strand = strand
-
- def _length(self, length):
- self.length = length
-
-
-class MEMERecord(object):
- """A class for holding the results of a MEME run.
-
- A MEMERecord is an object that holds the results from running
- MEME. It implements no methods of its own.
-
- """
- def __init__(self):
- """__init__(self)"""
- self.motifs = []
- self.version = ""
- self.datafile = ""
- self.command = ""
- self.alphabet = None
- self.sequence_names = []
-
- def get_motif_by_name(self, name):
- for m in self.motifs:
- if m.name == name:
- return m
-
-
-# Everything below is private
-
-
-def __read_version(record, handle):
- for line in handle:
- if line.startswith('MEME version'):
- break
- else:
- raise ValueError("Improper input file. File should contain a line starting MEME version.")
- line = line.strip()
- ls = line.split()
- record.version = ls[2]
-
-
-def __read_datafile(record, handle):
- for line in handle:
- if line.startswith('TRAINING SET'):
- break
- else:
- raise ValueError("Unexpected end of stream: 'TRAINING SET' not found.")
- try:
- line = next(handle)
- except StopIteration:
- raise ValueError("Unexpected end of stream: Expected to find line starting with '****'")
- if not line.startswith('****'):
- raise ValueError("Line does not start with '****':\n%s" % line)
- try:
- line = next(handle)
- except StopIteration:
- raise ValueError("Unexpected end of stream: Expected to find line starting with 'DATAFILE'")
- if not line.startswith('DATAFILE'):
- raise ValueError("Line does not start with 'DATAFILE':\n%s" % line)
- line = line.strip()
- line = line.replace('DATAFILE= ', '')
- record.datafile = line
-
-
-def __read_alphabet(record, handle):
- try:
- line = next(handle)
- except StopIteration:
- raise ValueError("Unexpected end of stream: Expected to find line starting with 'ALPHABET'")
- if not line.startswith('ALPHABET'):
- raise ValueError("Line does not start with 'ALPHABET':\n%s" % line)
- line = line.strip()
- line = line.replace('ALPHABET= ', '')
- if line == 'ACGT':
- al = IUPAC.unambiguous_dna
- else:
- al = IUPAC.protein
- record.alphabet = al
-
-
-def __read_sequence_names(record, handle):
- try:
- line = next(handle)
- except StopIteration:
- raise ValueError("Unexpected end of stream: Expected to find line starting with 'Sequence name'")
- if not line.startswith('Sequence name'):
- raise ValueError("Line does not start with 'Sequence name':\n%s" % line)
- try:
- line = next(handle)
- except StopIteration:
- raise ValueError("Unexpected end of stream: Expected to find line starting with '----'")
- if not line.startswith('----'):
- raise ValueError("Line does not start with '----':\n%s" % line)
- for line in handle:
- if line.startswith('***'):
- break
- line = line.strip()
- ls = line.split()
- record.sequence_names.append(ls[0])
- if len(ls) == 6:
- record.sequence_names.append(ls[3])
- else:
- raise ValueError("Unexpected end of stream: Expected to find line starting with '***'")
-
-
-def __read_command(record, handle):
- for line in handle:
- if line.startswith('command:'):
- break
- else:
- raise ValueError("Unexpected end of stream: Expected to find line starting with 'command'")
- line = line.strip()
- line = line.replace('command: ', '')
- record.command = line
-
-
-def __create_motif(line):
- line = line[5:].strip()
- ls = line.split()
- motif = MEMEMotif()
- motif.length = int(ls[3])
- motif._numoccurrences(ls[6])
- motif._evalue(ls[12])
- return motif
-
-
-def __read_motif_name(motif, handle):
- for line in handle:
- if 'sorted by position p-value' in line:
- break
- else:
- raise ValueError('Unexpected end of stream: Failed to find motif name')
- line = line.strip()
- ls = line.split()
- name = " ".join(ls[0:2])
- motif.name = name
-
-
-def __read_motif_sequences(motif, handle, rv):
- try:
- line = next(handle)
- except StopIteration:
- raise ValueError('Unexpected end of stream: Failed to find motif sequences')
- if not line.startswith('---'):
- raise ValueError("Line does not start with '---':\n%s" % line)
- try:
- line = next(handle)
- except StopIteration:
- raise ValueError("Unexpected end of stream: Expected to find line starting with 'Sequence name'")
- if not line.startswith('Sequence name'):
- raise ValueError("Line does not start with 'Sequence name':\n%s" % line)
- try:
- line = next(handle)
- except StopIteration:
- raise ValueError('Unexpected end of stream: Failed to find motif sequences')
- if not line.startswith('---'):
- raise ValueError("Line does not start with '---':\n%s" % line)
- for line in handle:
- if line.startswith('---'):
- break
- line = line.strip()
- ls = line.split()
- if rv:
- # seq = Seq.Seq(ls[5], record.alphabet)
- motif.add_instance_from_values(name=ls[0], sequence=ls[5], start=ls[2], pvalue=ls[3], strand=ls[1])
- else:
- # seq = Seq.Seq(ls[4], record.alphabet)
- motif.add_instance_from_values(name=ls[0], sequence=ls[4], start=ls[1], pvalue=ls[2])
- else:
- raise ValueError('Unexpected end of stream')
-
-
-def __skip_unused_lines(handle):
- for line in handle:
- if line.startswith('log-odds matrix'):
- break
- else:
- raise ValueError("Unexpected end of stream: Expected to find line starting with 'log-odds matrix'")
- for line in handle:
- if line.startswith('---'):
- break
- else:
- raise ValueError("Unexpected end of stream: Expected to find line starting with '---'")
- for line in handle:
- if line.startswith('letter-probability matrix'):
- break
- else:
- raise ValueError("Unexpected end of stream: Expected to find line starting with 'letter-probability matrix'")
- for line in handle:
- if line.startswith('---'):
- break
- else:
- raise ValueError("Unexpected end of stream: Expected to find line starting with '---'")
- for line in handle:
- if line.startswith('Time'):
- break
- else:
- raise ValueError("Unexpected end of stream: Expected to find line starting with 'Time'")
- try:
- line = next(handle)
- except StopIteration:
- raise ValueError('Unexpected end of stream: Expected to find blank line')
- if line.strip():
- raise ValueError("Expected blank line, but got:\n%s" % line)
- try:
- line = next(handle)
- except StopIteration:
- raise ValueError("Unexpected end of stream: Expected to find line starting with '***'")
- if not line.startswith('***'):
- raise ValueError("Line does not start with '***':\n%s" % line)
- for line in handle:
- if line.strip():
- break
- else:
- raise ValueError("Unexpected end of stream: Expected to find line starting with '***'")
- if not line.startswith('***'):
- raise ValueError("Line does not start with '***':\n%s" % line)
diff --git a/Bio/Motif/Parsers/__init__.py b/Bio/Motif/Parsers/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/Bio/Motif/Thresholds.py b/Bio/Motif/Thresholds.py
deleted file mode 100644
index 103f089..0000000
--- a/Bio/Motif/Thresholds.py
+++ /dev/null
@@ -1,94 +0,0 @@
-# Copyright 2008 by Norbert Dojer. All rights reserved.
-# Adapted by Bartek Wilczynski.
-# This code is part of the Biopython distribution and governed by its
-# license. Please see the LICENSE file that should have been included
-# as part of this package.
-"""Approximate calculation of appropriate thresholds for motif finding
-"""
-import math
-import random
-
-
-class ScoreDistribution(object):
- """ Class representing approximate score distribution for a given motif.
-
- Utilizes a dynamic programming approch to calculate the distribution of
- scores with a predefined precision. Provides a number of methods for calculating
- thresholds for motif occurences.
- """
- def __init__(self, motif, precision=10 ** 3):
- self.min_score = min(0.0, motif.min_score())
- self.interval = max(0.0, motif.max_score()) - self.min_score
- self.n_points = precision * motif.length
- self.step = self.interval / (self.n_points - 1)
- self.mo_density = [0.0] * self.n_points
- self.mo_density[-self._index_diff(self.min_score)] = 1.0
- self.bg_density = [0.0] * self.n_points
- self.bg_density[-self._index_diff(self.min_score)] = 1.0
- self.ic = motif.ic()
- for lo, mo in zip(motif.log_odds(), motif.pwm()):
- self.modify(lo, mo, motif.background)
-
- def _index_diff(self, x, y=0.0):
- return int((x - y + 0.5 * self.step) // self.step)
-
- def _add(self, i, j):
- return max(0, min(self.n_points - 1, i + j))
-
- def modify(self, scores, mo_probs, bg_probs):
- mo_new = [0.0] * self.n_points
- bg_new = [0.0] * self.n_points
- for k, v in scores.items():
- d = self._index_diff(v)
- for i in range(self.n_points):
- mo_new[self._add(i, d)] += self.mo_density[i] * mo_probs[k]
- bg_new[self._add(i, d)] += self.bg_density[i] * bg_probs[k]
- self.mo_density = mo_new
- self.bg_density = bg_new
-
- def threshold_fpr(self, fpr):
- """
- Approximate the log-odds threshold which makes the type I error (false positive rate).
- """
- i = self.n_points
- prob = 0.0
- while prob < fpr:
- i -= 1
- prob += self.bg_density[i]
- return self.min_score + i * self.step
-
- def threshold_fnr(self, fnr):
- """
- Approximate the log-odds threshold which makes the type II error (false negative rate).
- """
- i = -1
- prob = 0.0
- while prob < fnr:
- i += 1
- prob += self.mo_density[i]
- return self.min_score + i * self.step
-
- def threshold_balanced(self, rate_proportion=1.0, return_rate=False):
- """
- Approximate the log-odds threshold which makes FNR equal to FPR times rate_proportion
- """
- i = self.n_points
- fpr = 0.0
- fnr = 1.0
- while fpr * rate_proportion < fnr:
- i -= 1
- fpr += self.bg_density[i]
- fnr -= self.mo_density[i]
- if return_rate:
- return self.min_score + i * self.step, fpr
- else:
- return self.min_score + i * self.step
-
- def threshold_patser(self):
- """Threshold selection mimicking the behaviour of patser (Hertz, Stormo 1999) software.
-
- It selects such a threshold that the log(fpr)=-ic(M)
- note: the actual patser software uses natural logarithms instead of log_2, so the numbers
- are not directly comparable.
- """
- return self.threshold_fpr(fpr=2 ** -self.ic)
diff --git a/Bio/Motif/_Motif.py b/Bio/Motif/_Motif.py
deleted file mode 100644
index 2bd2990..0000000
--- a/Bio/Motif/_Motif.py
+++ /dev/null
@@ -1,800 +0,0 @@
-# Copyright 2003-2009 by Bartek Wilczynski. All rights reserved.
-# This code is part of the Biopython distribution and governed by its
-# license. Please see the LICENSE file that should have been included
-# as part of this package.
-"""Implementation of sequence motifs (PRIVATE).
-"""
-
-from __future__ import print_function
-
-from Bio._py3k import range
-
-from Bio.Seq import Seq
-from Bio.SubsMat import FreqTable
-from Bio.Alphabet import IUPAC
-import math
-import random
-
-
-__docformat__ = "restructuredtext en"
-
-
-class Motif(object):
- """
- A class representing sequence motifs.
- """
- def __init__(self, alphabet=IUPAC.unambiguous_dna):
- self.instances = []
- self.has_instances = False
- self.counts = {}
- self.has_counts = False
- self.mask = []
- self._pwm_is_current = False
- self._pwm = []
- self._log_odds_is_current = False
- self._log_odds = []
- self.alphabet = alphabet
- self.length = None
- self.background = dict((n, 1.0 / len(self.alphabet.letters))
- for n in self.alphabet.letters)
- self.beta = 1.0
- self.info = None
- self.name = ""
-
- def _check_length(self, len):
- # TODO - Change parameter name (len clashes with built in function)?
- if self.length is None:
- self.length = len
- elif self.length != len:
- raise ValueError("You can't change the length of the motif "
- "%r %r %r" % (self.length, self.instances, len))
-
- def _check_alphabet(self, alphabet):
- if self.alphabet is None:
- self.alphabet = alphabet
- elif self.alphabet != alphabet:
- raise ValueError("Wrong Alphabet")
-
- def add_instance(self, instance):
- """
- adds new instance to the motif
- """
- self._check_alphabet(instance.alphabet)
- self._check_length(len(instance))
- if self.has_counts:
- for i in range(self.length):
- let = instance[i]
- self.counts[let][i] += 1
-
- if self.has_instances or not self.has_counts:
- self.instances.append(instance)
- self.has_instances = True
-
- self._pwm_is_current = False
- self._log_odds_is_current = False
-
- def set_mask(self, mask):
- """
- sets the mask for the motif
-
- The mask should be a string containing asterisks in the position of significant columns and spaces in other columns
- """
- self._check_length(len(mask))
- self.mask = []
- for char in mask:
- if char == "*":
- self.mask.append(1)
- elif char == " ":
- self.mask.append(0)
- else:
- raise ValueError("Mask should contain only '*' or ' ' and not a '%s'" % char)
-
- def pwm(self, laplace=True):
- """
- returns the PWM computed for the set of instances
-
- if laplace=True (default), pseudocounts equal to self.background multiplied by self.beta are added to all positions.
- """
- if self._pwm_is_current:
- return self._pwm
- # we need to compute new pwm
- self._pwm = []
- for i in range(self.length):
- dict = {}
- # filling the dict with 0's
- for letter in self.alphabet.letters:
- if laplace:
- dict[letter] = self.beta * self.background[letter]
- else:
- dict[letter] = 0.0
- if self.has_counts:
- # taking the raw counts
- for letter in self.alphabet.letters:
- dict[letter] += self.counts[letter][i]
- elif self.has_instances:
- # counting the occurences of letters in instances
- for seq in self.instances:
- # dict[seq[i]]=dict[seq[i]]+1
- try:
- dict[seq[i]] += 1
- except KeyError: # we need to ignore non-alphabet letters
- pass
- self._pwm.append(FreqTable.FreqTable(dict, FreqTable.COUNT, self.alphabet))
- self._pwm_is_current = 1
- return self._pwm
-
- def log_odds(self, laplace=True):
- """
- returns the logg odds matrix computed for the set of instances
- """
-
- if self._log_odds_is_current:
- return self._log_odds
- # we need to compute new pwm
- self._log_odds = []
- pwm = self.pwm(laplace)
- for i in range(self.length):
- d = {}
- for a in self.alphabet.letters:
- d[a] = math.log(pwm[i][a] / self.background[a], 2)
- self._log_odds.append(d)
- self._log_odds_is_current = 1
- return self._log_odds
-
- def ic(self):
- """Method returning the information content of a motif.
- """
- res = 0
- pwm = self.pwm()
- for i in range(self.length):
- res += 2
- for a in self.alphabet.letters:
- if pwm[i][a] != 0:
- res += pwm[i][a] * math.log(pwm[i][a], 2)
- return res
-
- def exp_score(self, st_dev=False):
- """
- Computes expected score of motif's instance and its standard deviation
- """
- exs = 0.0
- var = 0.0
- pwm = self.pwm()
- for i in range(self.length):
- ex1 = 0.0
- ex2 = 0.0
- for a in self.alphabet.letters:
- if pwm[i][a] != 0:
- ex1 += pwm[i][a] * (math.log(pwm[i][a], 2) - math.log(self.background[a], 2))
- ex2 += pwm[i][a] * (math.log(pwm[i][a], 2) - math.log(self.background[a], 2)) ** 2
- exs += ex1
- var += ex2 - ex1 ** 2
- if st_dev:
- return exs, math.sqrt(var)
- else:
- return exs
-
- def search_instances(self, sequence):
- """
- a generator function, returning found positions of instances of the motif in a given sequence
- """
- if not self.has_instances:
- raise ValueError("This motif has no instances")
- for pos in range(0, len(sequence) - self.length + 1):
- for instance in self.instances:
- if str(instance) == str(sequence[pos:pos + self.length]):
- yield (pos, instance)
- break # no other instance will fit (we don't want to return multiple hits)
-
- def score_hit(self, sequence, position, normalized=0, masked=0):
- """
- give the pwm score for a given position
- """
- lo = self.log_odds()
- score = 0.0
- for pos in range(self.length):
- a = sequence[position + pos]
- if not masked or self.mask[pos]:
- try:
- score += lo[pos][a]
- except:
- pass
- if normalized:
- if not masked:
- score /= self.length
- else:
- score /= len([x for x in self.mask if x])
- return score
-
- def search_pwm(self, sequence, normalized=0, masked=0, threshold=0.0, both=True):
- """
- a generator function, returning found hits in a given sequence with the pwm score higher than the threshold
- """
- if both:
- rc = self.reverse_complement()
-
- sequence = str(sequence).upper()
- for pos in range(0, len(sequence) - self.length + 1):
- score = self.score_hit(sequence, pos, normalized, masked)
- if score > threshold:
- yield (pos, score)
- if both:
- rev_score = rc.score_hit(sequence, pos, normalized, masked)
- if rev_score > threshold:
- yield (-pos, rev_score)
-
- def dist_pearson(self, motif, masked=0):
- """
- return the similarity score based on pearson correlation for the given motif against self.
-
- We use the Pearson's correlation of the respective probabilities.
- """
-
- if self.alphabet != motif.alphabet:
- raise ValueError("Cannot compare motifs with different alphabets")
-
- max_p = -2
- for offset in range(-self.length + 1, motif.length):
- if offset < 0:
- p = self.dist_pearson_at(motif, -offset)
- else: # offset>=0
- p = motif.dist_pearson_at(self, offset)
-
- if max_p < p:
- max_p = p
- max_o = -offset
- return 1 - max_p, max_o
-
- def dist_pearson_at(self, motif, offset):
- sxx = 0 # \sum x^2
- sxy = 0 # \sum x \cdot y
- sx = 0 # \sum x
- sy = 0 # \sum y
- syy = 0 # \sum x^2
- norm = max(self.length, offset + motif.length)
-
- for pos in range(max(self.length, offset + motif.length)):
- for l in self.alphabet.letters:
- xi = self[pos][l]
- yi = motif[pos - offset][l]
- sx = sx + xi
- sy = sy + yi
- sxx = sxx + xi * xi
- syy = syy + yi * yi
- sxy = sxy + xi * yi
-
- norm *= len(self.alphabet.letters)
- s1 = (sxy - sx * sy * 1.0 / norm)
- s2 = (norm * sxx - sx * sx * 1.0) * (norm * syy - sy * sy * 1.0)
- return s1 / math.sqrt(s2)
-
- def dist_product(self, other):
- """
- A similarity measure taking into account a product probability of generating overlaping instances of two motifs
- """
- max_p = 0.0
- for offset in range(-self.length + 1, other.length):
- if offset < 0:
- p = self.dist_product_at(other, -offset)
- else: # offset>=0
- p = other.dist_product_at(self, offset)
- if max_p < p:
- max_p = p
- max_o = -offset
- return 1 - max_p / self.dist_product_at(self, 0), max_o
-
- def dist_product_at(self, other, offset):
- s = 0
- for i in range(max(self.length, offset + other.length)):
- f1 = self[i]
- f2 = other[i - offset]
- for n, b in self.background.items():
- s += b * f1[n] * f2[n]
- return s / i
-
- def dist_dpq(self, other):
- r"""Calculates the DPQ distance measure between motifs.
-
- It is calculated as a maximal value of DPQ formula (shown using LaTeX
- markup, familiar to mathematicians)::
-
- \sqrt{\sum_{i=1}^{alignment.len()} \sum_{k=1}^alphabet.len() \
- \{ m1[i].freq(alphabet[k])*log_2(m1[i].freq(alphabet[k])/m2[i].freq(alphabet[k])) +
- m2[i].freq(alphabet[k])*log_2(m2[i].freq(alphabet[k])/m1[i].freq(alphabet[k]))
- }
-
- over possible non-spaced alignemts of two motifs. See this reference:
-
- D. M Endres and J. E Schindelin, "A new metric for probability
- distributions", IEEE transactions on Information Theory 49, no. 7
- (July 2003): 1858-1860.
- """
-
- min_d = float("inf")
- min_o = -1
- d_s = []
- for offset in range(-self.length + 1, other.length):
- # print("%2.3d"%offset)
- if offset < 0:
- d = self.dist_dpq_at(other, -offset)
- overlap = self.length + offset
- else: # offset>=0
- d = other.dist_dpq_at(self, offset)
- overlap = other.length - offset
- overlap = min(self.length, other.length, overlap)
- out = self.length + other.length - 2 * overlap
- # print("%f %f %f" % (d,1.0*(overlap+out)/overlap,d*(overlap+out)/overlap))
- # d = d/(2*overlap)
- d = (d / (out + overlap)) * (2 * overlap + out) / (2 * overlap)
- # print(d)
- d_s.append((offset, d))
- if min_d > d:
- min_d = d
- min_o = -offset
- return min_d, min_o # ,d_s
-
- def dist_dpq_at(self, other, offset):
- """
- calculates the dist_dpq measure with a given offset.
-
- offset should satisfy 0<=offset<=len(self)
- """
- def dpq(f1, f2, alpha):
- s = 0
- for n in alpha.letters:
- avg = (f1[n] + f2[n]) / 2
- s += f1[n] * math.log(f1[n] / avg, 2) + f2[n] * math.log(f2[n] / avg, 2)
- return math.sqrt(s)
-
- s = 0
- for i in range(max(self.length, offset + other.length)):
- f1 = self[i]
- f2 = other[i - offset]
- s += dpq(f1, f2, self.alphabet)
- return s
-
- def _read(self, stream):
- """Reads the motif from the stream (in AlignAce format).
-
- the self.alphabet variable must be set beforehand.
- If the last line contains asterisks it is used for setting mask
- """
-
- while True:
- ln = stream.readline()
- if "*" in ln:
- self.set_mask(ln.strip("\n\c"))
- break
- self.add_instance(Seq(ln.strip(), self.alphabet))
-
- def __str__(self, masked=False):
- """ string representation of a motif.
- """
- str = "".join(str(inst) + "\n" for inst in self.instances)
-
- if masked:
- for i in range(self.length):
- if self.mask[i]:
- str += "*"
- else:
- str += " "
- str += "\n"
- return str
-
- def __len__(self):
- """return the length of a motif
-
- Please use this method (i.e. invoke len(m)) instead of refering to the m.length directly.
- """
- if self.length is None:
- return 0
- else:
- return self.length
-
- def _write(self, stream):
- """
- writes the motif to the stream
- """
-
- stream.write(self.__str__())
-
- def _to_fasta(self):
- """
- FASTA representation of motif
- """
- if not self.has_instances:
- self.make_instances_from_counts()
- return "".join(">instance%d\n%s\n" % (i, inst) for i, inst in enumerate(self.instances))
-
- def reverse_complement(self):
- """
- Gives the reverse complement of the motif
- """
- res = Motif()
- if self.has_instances:
- for i in self.instances:
- res.add_instance(i.reverse_complement())
- else: # has counts
- res.has_counts = True
- res.counts["A"] = self.counts["T"][:]
- res.counts["T"] = self.counts["A"][:]
- res.counts["G"] = self.counts["C"][:]
- res.counts["C"] = self.counts["G"][:]
- res.counts["A"].reverse()
- res.counts["C"].reverse()
- res.counts["G"].reverse()
- res.counts["T"].reverse()
- res.length = self.length
- res.mask = self.mask
- return res
-
- def _from_jaspar_pfm(self, stream, make_instances=False):
- """
- reads the motif from Jaspar .pfm file
-
- The instances are fake, but the pwm is accurate.
- """
- return self._from_horiz_matrix(stream, letters="ACGT", make_instances=make_instances)
-
- def _from_vert_matrix(self, stream, letters=None, make_instances=False):
- """reads a vertical count matrix from stream and fill in the counts.
- """
-
- self.counts = {}
- self.has_counts = True
- if letters is None:
- letters = self.alphabet.letters
- self.length = 0
- for i in letters:
- self.counts[i] = []
- for ln in stream.readlines():
- rec = [float(x) for x in ln.strip().split()]
- for k, v in zip(letters, rec):
- self.counts[k].append(v)
- self.length += 1
- self.set_mask("*" * self.length)
- if make_instances is True:
- self.make_instances_from_counts()
- return self
-
- def _from_horiz_matrix(self, stream, letters=None, make_instances=False):
- """reads a horizontal count matrix from stream and fill in the counts.
- """
- if letters is None:
- letters = self.alphabet.letters
- self.counts = {}
- self.has_counts = True
-
- for i in letters:
- ln = stream.readline().strip().split()
- # if there is a letter in the beginning, ignore it
- if ln[0] == i:
- ln = ln[1:]
- # print(ln)
- try:
- self.counts[i] = [int(x) for x in ln]
- except ValueError: # not integers
- self.counts[i] = [float(x) for x in ln]
- # print(counts[i])
-
- s = sum(self.counts[nuc][0] for nuc in letters)
- l = len(self.counts[letters[0]])
- self.length = l
- self.set_mask("*" * l)
- if make_instances is True:
- self.make_instances_from_counts()
- return self
-
- def make_instances_from_counts(self):
- """Creates "fake" instances for a motif created from a count matrix.
-
- In case the sums of counts are different for different columnes, the
- shorter columns are padded with background.
- """
- alpha = "".join(self.alphabet.letters)
- # col[i] is a column taken from aligned motif instances
- col = []
- self.has_instances = True
- self.instances = []
- s = sum(self.counts[nuc][0] for nuc in self.alphabet.letters)
- for i in range(self.length):
- col.append("")
- for n in self.alphabet.letters:
- col[i] = col[i] + n * (self.counts[n][i])
- if len(col[i]) < s:
- print("WARNING, column too short %i %i" % (len(col[i]), s))
- col[i] += (alpha * s)[:(s - len(col[i]))]
- # print("column %i, %s" % (i, col[i]))
- # iterate over instances
- for i in range(s):
- inst = "" # start with empty seq
- for j in range(self.length): # iterate over positions
- inst += col[j][i]
- # print("%i %s" % (i,inst)
- inst = Seq(inst, self.alphabet)
- self.add_instance(inst)
- return self.instances
-
- def make_counts_from_instances(self):
- """Creates the count matrix for a motif with instances.
-
- """
- # make strings for "columns" of motifs
- # col[i] is a column taken from aligned motif instances
- counts = {}
- for a in self.alphabet.letters:
- counts[a] = []
- self.has_counts = True
- s = len(self.instances)
- for i in range(self.length):
- ci = dict((a, 0) for a in self.alphabet.letters)
- for inst in self.instances:
- ci[inst[i]] += 1
- for a in self.alphabet.letters:
- counts[a].append(ci[a])
- self.counts = counts
- return counts
-
- def _from_jaspar_sites(self, stream):
- """
- reads the motif from Jaspar .sites file
-
- The instances and pwm are OK.
- """
- while True:
- ln = stream.readline() # read the header "$>...."
- if ln == "" or ln[0] != ">":
- break
-
- ln = stream.readline().strip() # read the actual sequence
- i = 0
- while ln[i] == ln[i].lower():
- i += 1
- inst = ""
- while i < len(ln) and ln[i] == ln[i].upper():
- inst += ln[i]
- i += 1
- inst = Seq(inst, self.alphabet)
- self.add_instance(inst)
-
- self.set_mask("*" * len(inst))
- return self
-
- def __getitem__(self, index):
- """Returns the probability distribution over symbols at a given position, padding with background.
-
- If the requested index is out of bounds, the returned distribution comes from background.
- """
- if index in range(self.length):
- return self.pwm()[index]
- else:
- return self.background
-
- def consensus(self):
- """Returns the consensus sequence of a motif.
- """
- res = ""
- for i in range(self.length):
- max_f = 0
- max_n = "X"
- for n in sorted(self[i]):
- if self[i][n] > max_f:
- max_f = self[i][n]
- max_n = n
- res += max_n
- return Seq(res, self.alphabet)
-
- def anticonsensus(self):
- """returns the least probable pattern to be generated from this motif.
- """
- res = ""
- for i in range(self.length):
- min_f = 10.0
- min_n = "X"
- for n in sorted(self[i]):
- if self[i][n] < min_f:
- min_f = self[i][n]
- min_n = n
- res += min_n
- return Seq(res, self.alphabet)
-
- def max_score(self):
- """Maximal possible score for this motif.
-
- returns the score computed for the consensus sequence.
- """
- return self.score_hit(self.consensus(), 0)
-
- def min_score(self):
- """Minimal possible score for this motif.
-
- returns the score computed for the anticonsensus sequence.
- """
- return self.score_hit(self.anticonsensus(), 0)
-
- def weblogo(self, fname, format="PNG", **kwds):
- """
- uses the Berkeley weblogo service to download and save a weblogo of itself
-
- requires an internet connection.
- The parameters from ``**kwds`` are passed directly to the weblogo server.
- """
- from Bio._py3k import urlopen, urlencode, Request
-
- al = self._to_fasta()
- url = 'http://weblogo.berkeley.edu/logo.cgi'
- values = {'sequence': al,
- 'format': format,
- 'logowidth': '18',
- 'logoheight': '5',
- 'logounits': 'cm',
- 'kind': 'AUTO',
- 'firstnum': "1",
- 'command': 'Create Logo',
- 'smallsamplecorrection': "on",
- 'symbolsperline': 32,
- 'res': '96',
- 'res_units': 'ppi',
- 'antialias': 'on',
- 'title': '',
- 'barbits': '',
- 'xaxis': 'on',
- 'xaxis_label': '',
- 'yaxis': 'on',
- 'yaxis_label': '',
- 'showends': 'on',
- 'shrink': '0.5',
- 'fineprint': 'on',
- 'ticbits': '1',
- 'colorscheme': 'DEFAULT',
- 'color1': 'green',
- 'color2': 'blue',
- 'color3': 'red',
- 'color4': 'black',
- 'color5': 'purple',
- 'color6': 'orange',
- 'color1': 'black',
- }
- for k, v in kwds.items():
- values[k] = str(v)
-
- data = urlencode(values)
- req = Request(url, data)
- response = urlopen(req)
- with open(fname, "w") as f:
- im = response.read()
- f.write(im)
-
- def _to_transfac(self):
- """Write the representation of a motif in TRANSFAC format
- """
- res = "XX\nTY Motif\n" # header
- try:
- res += "ID %s\n" % self.name
- except:
- pass
- res += "BF undef\nP0"
- for a in self.alphabet.letters:
- res += " %s" % a
- res += "\n"
- if not self.has_counts:
- self.make_counts_from_instances()
- for i in range(self.length):
- if i < 9:
- res += "0%d" % (i + 1)
- else:
- res += "%d" % (i + 1)
- for a in self.alphabet.letters:
- res += " %d" % self.counts[a][i]
- res += "\n"
- res += "XX\n"
- return res
-
- def _to_vertical_matrix(self, letters=None):
- """Return string representation of the motif as a matrix.
- """
- if letters is None:
- letters = self.alphabet.letters
- self._pwm_is_current = False
- pwm = self.pwm(laplace=False)
- res = ""
- for i in range(self.length):
- res += "\t".join(str(pwm[i][a]) for a in letters)
- res += "\n"
- return res
-
- def _to_horizontal_matrix(self, letters=None, normalized=True):
- """Return string representation of the motif as a matrix.
- """
- if letters is None:
- letters = self.alphabet.letters
- res = ""
- if normalized: # output PWM
- self._pwm_is_current = False
- mat = self.pwm(laplace=False)
- for a in letters:
- res += "\t".join(str(mat[i][a]) for i in range(self.length))
- res += "\n"
- else: # output counts
- if not self.has_counts:
- self.make_counts_from_instances()
- mat = self.counts
- for a in letters:
- res += "\t".join(str(mat[a][i]) for i in range(self.length))
- res += "\n"
- return res
-
- def _to_jaspar_pfm(self):
- """Returns the pfm representation of the motif
- """
- return self._to_horizontal_matrix(normalized=False, letters="ACGT")
-
- def format(self, format):
- """Returns a string representation of the Motif in a given format
-
- Currently supported fromats:
- - jaspar-pfm : JASPAR Position Frequency Matrix
- - transfac : TRANSFAC like files
- - fasta : FASTA file with instances
- """
-
- formatters = {
- "jaspar-pfm": self._to_jaspar_pfm,
- "transfac": self._to_transfac,
- "fasta": self._to_fasta,
- }
-
- try:
- return formatters[format]()
- except KeyError:
- raise ValueError("Wrong format type")
-
- def scanPWM(self, seq):
- """Matrix of log-odds scores for a nucleotide sequence.
-
- scans a nucleotide sequence and returns the matrix of log-odds
- scores for all positions.
-
- - the result is a one-dimensional list or numpy array
- - the sequence can only be a DNA sequence
- - the search is performed only on one strand
- """
- # TODO - Code itself tolerates ambiguous bases (as NaN).
- if not isinstance(self.alphabet, IUPAC.IUPACUnambiguousDNA):
- raise ValueError("PSSM has wrong alphabet: %s - Use only with DNA motifs"
- % self.alphabet)
- if not isinstance(seq.alphabet, IUPAC.IUPACUnambiguousDNA):
- raise ValueError("Sequence has wrong alphabet: %r - Use only with DNA sequences"
- % sequence.alphabet)
-
- seq = str(seq)
-
- # check if the fast C code can be used
- try:
- import _pwm
- except ImportError:
- # use the slower Python code otherwise
- return self._pwm_calculate(seq)
-
- # get the log-odds matrix into a proper shape
- # (each row contains sorted (ACGT) log-odds values)
- logodds = [[y[1] for y in sorted(x.items())] for x in self.log_odds()]
- return _pwm.calculate(seq, logodds)
-
- def _pwm_calculate(self, sequence):
- logodds = self.log_odds()
- m = len(logodds)
- s = len(sequence)
- n = s - m + 1
- result = [None] * n
- for i in range(n):
- score = 0.0
- for j in range(m):
- c = sequence[i + j]
- temp = logodds[j].get(c)
- if temp is None:
- break
- score += temp
- else:
- result[i] = score
- return result
diff --git a/Bio/Motif/__init__.py b/Bio/Motif/__init__.py
deleted file mode 100644
index d146ced..0000000
--- a/Bio/Motif/__init__.py
+++ /dev/null
@@ -1,172 +0,0 @@
-# Copyright 2003-2009 by Bartek Wilczynski. All rights reserved.
-# This code is part of the Biopython distribution and governed by its
-# license. Please see the LICENSE file that should have been included
-# as part of this package.
-"""Tools for sequence motif analysis (DEPRECATED, see Bio.motifs instead).
-
-This module (Bio.Motif) has been deprecated and will be removed in a
-future release of release of Biopython. Please use the new module
-Bio.motifs instead.
-
-This contains the core Motif class containing various I/O methods as
-well as methods for motif comparisons and motif searching in sequences.
-It also inlcudes functionality for parsing AlignACE and MEME programs.
-"""
-
-from __future__ import print_function
-
-import warnings
-from Bio import BiopythonDeprecationWarning
-warnings.warn("The module Bio.Motif has been deprecated and will be "
- "removed in a future release of Biopython. Instead "
- "please use the new module Bio.motifs instead. Please "
- "be aware that though the functionality of Bio.Motif "
- "is retained (and extended) in Bio.motifs, usage may "
- "be different.",
- BiopythonDeprecationWarning)
-
-
-from Bio.Motif._Motif import Motif
-from Bio.Motif.Parsers.AlignAce import read as _AlignAce_read
-from Bio.Motif.Parsers.MEME import read as _MEME_read
-from Bio.Motif.Thresholds import ScoreDistribution
-
-__docformat__ = "restructuredtext en"
-
-_parsers = {"AlignAce": _AlignAce_read,
- "MEME": _MEME_read,
- }
-
-
-def _from_pfm(handle):
- return Motif()._from_jaspar_pfm(handle)
-
-
-def _from_sites(handle):
- return Motif()._from_jaspar_sites(handle)
-
-_readers = {"jaspar-pfm": _from_pfm,
- "jaspar-sites": _from_sites
- }
-
-
-def parse(handle, format):
- """Parses an output file of motif finding programs.
-
- Currently supported formats:
-
- - AlignAce
- - MEME
-
- You can also use single-motif formats, although the Bio.Motif.read()
- function is simpler to use in this situation.
-
- - jaspar-pfm
- - jaspar-sites
-
- For example:
-
- >>> from Bio import Motif
- >>> with open("Motif/alignace.out") as handle:
- ... for motif in Motif.parse(handle, "AlignAce"):
- ... print(motif.consensus())
- ...
- TCTACGATTGAG
- CTGCACCTAGCTACGAGTGAG
- GTGCCCTAAGCATACTAGGCG
- GCCACTAGCAGAGCAGGGGGC
- CGACTCAGAGGTT
- CCACGCTAAGAGAAGTGCCGGAG
- GCACGTCCCTGAGCA
- GTCCATCGCAAAGCGTGGGGC
- GAGATCAGAGGGCCG
- TGGACGCGGGG
- GACCAGAGCCTCGCATGGGGG
- AGCGCGCGTG
- GCCGGTTGCTGTTCATTAGG
- ACCGACGGCAGCTAAAAGGG
- GACGCCGGGGAT
- CGACTCGCGCTTACAAGG
- """
- try:
- parser = _parsers[format]
-
- except KeyError:
- try: # not a true parser, try reader formats
- reader = _readers[format]
- except:
- raise ValueError("Wrong parser format")
- else: # we have a proper reader
- yield reader(handle)
- else: # we have a proper reader
- for m in parser(handle).motifs:
- yield m
-
-
-def read(handle, format):
- """Reads a motif from a handle using a specified file-format.
-
- This supports the same formats as Bio.Motif.parse(), but
- only for files containing exactly one record. For example,
- reading a pfm file:
-
- >>> from Bio import Motif
- >>> with open("Motif/SRF.pfm") as handle:
- ... motif = Motif.read(handle, "jaspar-pfm")
- ...
- >>> motif.consensus()
- Seq('GCCCATATATGG', IUPACUnambiguousDNA())
-
- Or a single-motif MEME file,
-
- >>> from Bio import Motif
- >>> with open("Motif/meme.out") as handle:
- ... motif = Motif.read(handle, "MEME")
- ...
- >>> motif.consensus()
- Seq('CTCAATCGTA', IUPACUnambiguousDNA())
-
- If the handle contains no records, or more than one record,
- an exception is raised:
-
- >>> from Bio import Motif
- >>> with open("Motif/alignace.out") as handle:
- ... motif = Motif.read(handle, "AlignAce")
- ...
- Traceback (most recent call last):
- ...
- ValueError: More than one motif found in handle
-
- If however you want the first record from a file containing
- multiple records this function would raise an exception (as
- shown in the example above). Instead use:
-
- >>> from Bio import Motif
- >>> with open("Motif/alignace.out") as handle:
- ... motif = next(Motif.parse(handle, "AlignAce"))
- ...
- >>> motif.consensus()
- Seq('TCTACGATTGAG', IUPACUnambiguousDNA())
-
- Use the Bio.Motif.parse(handle, format) function if you want
- to read multiple records from the handle.
- """
- iterator = parse(handle, format)
- try:
- first = next(iterator)
- except StopIteration:
- first = None
- if first is None:
- raise ValueError("No motifs found in handle")
- try:
- second = next(iterator)
- except StopIteration:
- second = None
- if second is not None:
- raise ValueError("More than one motif found in handle")
- return first
-
-
-if __name__ == "__main__":
- from Bio._utils import run_doctest
- run_doctest(verbose=0)
diff --git a/Bio/Motif/_pwm.c b/Bio/Motif/_pwm.c
deleted file mode 100644
index 8e36f22..0000000
--- a/Bio/Motif/_pwm.c
+++ /dev/null
@@ -1,166 +0,0 @@
-#include <Python.h>
-#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION
-#include "numpy/arrayobject.h"
-
-
-
-static PyObject*
-calculate(const char sequence[], int s, PyObject* matrix, npy_intp m)
-{
- npy_intp n = s - m + 1;
- npy_intp i, j;
- char c;
- double score;
- int ok;
- PyObject* result;
- PyArrayObject* array;
- float* p;
- npy_intp shape = (npy_intp)n;
- float nan = 0.0;
- nan /= nan;
- if ((int)shape!=n)
- {
- PyErr_SetString(PyExc_ValueError, "integer overflow");
- return NULL;
- }
- result = PyArray_SimpleNew(1, &shape, NPY_FLOAT32);
- if (!result)
- {
- PyErr_SetString(PyExc_MemoryError, "failed to create output data");
- return NULL;
- }
- p = PyArray_DATA((PyArrayObject*)result);
- array = (PyArrayObject*)matrix;
- for (i = 0; i < n; i++)
- {
- score = 0.0;
- ok = 1;
- for (j = 0; j < m; j++)
- {
- c = sequence[i+j];
- switch (c)
- {
- case 'A':
- case 'a':
- score += *((double*)PyArray_GETPTR2(array, j, 0)); break;
- case 'C':
- case 'c':
- score += *((double*)PyArray_GETPTR2(array, j, 1)); break;
- case 'G':
- case 'g':
- score += *((double*)PyArray_GETPTR2(array, j, 2)); break;
- case 'T':
- case 't':
- score += *((double*)PyArray_GETPTR2(array, j, 3)); break;
- default:
- ok = 0;
- }
- }
- if (ok) *p = (float)score;
- else *p = nan;
- p++;
- }
- return result;
-}
-
-static char calculate__doc__[] =
-" calculate(sequence, pwm) -> array of score values\n"
-"\n"
-"This function calculates the position-weight matrix scores for all\n"
-"positions along the sequence, and returns them as a Numerical Python\n"
-"array.\n";
-
-static PyObject*
-py_calculate(PyObject* self, PyObject* args, PyObject* keywords)
-{
- const char* sequence;
- PyObject* matrix = NULL;
- static char* kwlist[] = {"sequence", "matrix", NULL};
- npy_intp m;
- int s;
- PyObject* result;
- PyArrayObject* array;
- if(!PyArg_ParseTupleAndKeywords(args, keywords, "s#O&", kwlist,
- &sequence,
- &s,
- PyArray_Converter,
- &matrix)) return NULL;
-
- array = (PyArrayObject*) matrix;
- if (PyArray_TYPE(array) != NPY_DOUBLE)
- {
- PyErr_SetString(PyExc_ValueError,
- "position-weight matrix should contain floating-point values");
- result = NULL;
- }
- else if (PyArray_NDIM(array) != 2) /* Checking number of dimensions */
- {
- result = PyErr_Format(PyExc_ValueError,
- "position-weight matrix has incorrect rank (%d expected 2)",
- PyArray_NDIM(array));
- }
- else if(PyArray_DIM(array, 1) != 4)
- {
- result = PyErr_Format(PyExc_ValueError,
- "position-weight matrix should have four columns (%" NPY_INTP_FMT
- " columns found)", PyArray_DIM(array, 1));
- }
- else
- {
- m = PyArray_DIM(array, 0);
- result = calculate(sequence, s, matrix, m);
- }
- Py_DECREF(matrix);
- return result;
-}
-
-
-static struct PyMethodDef methods[] = {
- {"calculate", (PyCFunction)py_calculate, METH_VARARGS | METH_KEYWORDS, calculate__doc__},
- {NULL, NULL, 0, NULL} /* sentinel */
-};
-
-
-#if PY_MAJOR_VERSION >= 3
-
-static struct PyModuleDef moduledef = {
- PyModuleDef_HEAD_INIT,
- "_pwm",
- "Fast calculations involving position-weight matrices",
- -1,
- methods,
- NULL,
- NULL,
- NULL,
- NULL
-};
-
-PyObject*
-PyInit__pwm(void)
-
-#else
-
-void init_pwm(void)
-#endif
-{
- PyObject *m;
-
- import_array();
-
-#if PY_MAJOR_VERSION >= 3
- m = PyModule_Create(&moduledef);
- if (m==NULL) return NULL;
-#else
- m = Py_InitModule4("_pwm",
- methods,
- "Fast calculations involving position-weight matrices",
- NULL,
- PYTHON_API_VERSION);
- if (m==NULL) return;
-#endif
-
- if (PyErr_Occurred()) Py_FatalError("can't initialize module _pwm");
-#if PY_MAJOR_VERSION >= 3
- return m;
-#endif
-}
diff --git a/Bio/NMR/NOEtools.py b/Bio/NMR/NOEtools.py
index bab5b1e..7e87454 100644
--- a/Bio/NMR/NOEtools.py
+++ b/Bio/NMR/NOEtools.py
@@ -12,8 +12,6 @@ input assignment peaklist.
from . import xpktools
-__docformat__ = "restructuredtext en"
-
def predictNOE(peaklist, originNuc, detectedNuc, originResNum, toResNum):
"""Predict the i->j NOE position based on self peak (diagonal) assignments
diff --git a/Bio/NMR/xpktools.py b/Bio/NMR/xpktools.py
index 7485f05..9f71e7a 100644
--- a/Bio/NMR/xpktools.py
+++ b/Bio/NMR/xpktools.py
@@ -9,7 +9,6 @@ from __future__ import print_function
import sys
-__docformat__ = "restructuredtext en"
HEADERLEN = 6
diff --git a/Bio/NaiveBayes.py b/Bio/NaiveBayes.py
index cf35338..3956722 100644
--- a/Bio/NaiveBayes.py
+++ b/Bio/NaiveBayes.py
@@ -12,17 +12,17 @@ the Bayes assumption that the features are independent. Although this
is hardly ever true, the classifier works well enough in practice.
Glossary:
-observation A feature vector of discrete data.
-class A possible classification for an observation.
+ - observation - A feature vector of discrete data.
+ - class - A possible classification for an observation.
Classes:
-NaiveBayes Holds information for a naive Bayes classifier.
+ - NaiveBayes - Holds information for a naive Bayes classifier.
Functions:
-train Train a new naive Bayes classifier.
-calculate Calculate the probabilities of each class, given an observation.
-classify Classify an observation into a class.
+ - train - Train a new naive Bayes classifier.
+ - calculate - Calculate the probabilities of each class, given an observation.
+ - classify - Classify an observation into a class.
"""
@@ -42,11 +42,11 @@ def _contents(items):
class NaiveBayes(object):
"""Holds information for a NaiveBayes classifier.
- Members:
- classes List of the possible classes of data.
- p_conditional CLASS x DIM array of dicts of value -> P(value|class,dim)
- p_prior List of the prior probabilities for every class.
- dimensionality Dimensionality of the data.
+ Attributes:
+ - classes - List of the possible classes of data.
+ - p_conditional - CLASS x DIM array of dicts of value -> ``P(value|class,dim)``
+ - p_prior - List of the prior probabilities for every class.
+ - dimensionality - Dimensionality of the data.
"""
def __init__(self):
@@ -56,16 +56,17 @@ class NaiveBayes(object):
self.dimensionality = None
-def calculate(nb, observation, scale=0):
- """calculate(nb, observation[, scale]) -> probability dict
+def calculate(nb, observation, scale=False):
+ """Calculate ``log P(class|observation)`` for each class.
- Calculate log P(class|observation) for each class. nb is a NaiveBayes
- classifier that has been trained. observation is a list representing
- the observed data. scale is whether the probability should be
- scaled by P(observation). By default, no scaling is done. The return
- value is a dictionary where the keys is the class and the value is the
- log probability of the class.
+ - nb - A NaiveBayes classifier that has been trained.
+ - observation - A list representing the observed data.
+ - scale - Boolean to indicate whether the probability should be
+ scaled by ``P(observation)``. By default, no scaling is done.
+ Returns:
+ A dictionary where the keys is the class and the value is the log
+ probability of the class.
"""
# P(class|observation) = P(observation|class)*P(class)/P(observation)
# Taking the log:
@@ -73,8 +74,8 @@ def calculate(nb, observation, scale=0):
# Make sure the observation has the right dimensionality.
if len(observation) != nb.dimensionality:
- raise ValueError("observation in %d dimension, but classifier in %d"
- % (len(observation), nb.dimensionality))
+ raise ValueError("observation in {0} dimension, but classifier in {1}".format(len(observation),
+ nb.dimensionality))
# Calculate log P(observation|class) for every class.
n = len(nb.classes)
@@ -107,13 +108,13 @@ def calculate(nb, observation, scale=0):
def classify(nb, observation):
- """classify(nb, observation) -> class
+ """ Classify an observation into a class.
- Classify an observation into a class.
+ ``classify(nb, observation) -> class``
"""
# The class is the one with the highest probability.
- probs = calculate(nb, observation, scale=0)
+ probs = calculate(nb, observation, scale=False)
max_prob = max_class = None
for klass in nb.classes:
if max_prob is None or probs[klass] > max_prob:
@@ -122,14 +123,16 @@ def classify(nb, observation):
def train(training_set, results, priors=None, typecode=None):
- """train(training_set, results[, priors]) -> NaiveBayes
+ """ Train a naive bayes classifier on a training set.
- Train a naive bayes classifier on a training set. training_set is a
- list of observations. results is a list of the class assignments
- for each observation. Thus, training_set and results must be the same
- length. priors is an optional dictionary specifying the prior
- probabilities for each type of result. If not specified, the priors
- will be estimated from the training results.
+ ``train(training_set, results[, priors]) -> NaiveBayes``
+
+ - training_set - List of observations.
+ - results - List of the class assignments for each observation.
+ Thus, training_set and results must be the same length.
+ - priors - Optional dictionary specifying the prior probabilities
+ for each type of result. If not specified, the priors will be
+ estimated from the training results.
"""
if not len(training_set):
@@ -179,7 +182,7 @@ def train(training_set, results, priors=None, typecode=None):
for i in range(len(results)):
klass, obs = results[i], training_set[i]
observations[c2i[klass]].append(obs)
- # Now make the observations Numeric matrics.
+ # Now make the observations Numeric matrix.
for i in range(len(observations)):
# XXX typecode must be specified!
observations[i] = numpy.asarray(observations[i], typecode)
@@ -200,38 +203,3 @@ def train(training_set, results, priors=None, typecode=None):
# Estimate P(value|class,dim)
nb.p_conditional[i][j] = _contents(values)
return nb
-
-if __name__ == "__main__":
- # Car data from example 'Naive Bayes Classifier example' by Eric Meisner November 22, 2003
- # http://www.inf.u-szeged.hu/~ormandi/teaching/mi2/02-naiveBayes-example.pdf
- xcar = [
- ['Red', 'Sports', 'Domestic'],
- ['Red', 'Sports', 'Domestic'],
- ['Red', 'Sports', 'Domestic'],
- ['Yellow', 'Sports', 'Domestic'],
- ['Yellow', 'Sports', 'Imported'],
- ['Yellow', 'SUV', 'Imported'],
- ['Yellow', 'SUV', 'Imported'],
- ['Yellow', 'SUV', 'Domestic'],
- ['Red', 'SUV', 'Imported'],
- ['Red', 'Sports', 'Imported']
- ]
-
- ycar = [
- 'Yes',
- 'No',
- 'Yes',
- 'No',
- 'Yes',
- 'No',
- 'Yes',
- 'No',
- 'No',
- 'Yes'
- ]
-
- carmodel = train(xcar, ycar)
- carresult = classify(carmodel, ['Red', 'Sports', 'Domestic'])
- print('Is Yes? %s' % carresult)
- carresult = classify(carmodel, ['Red', 'SUV', 'Domestic'])
- print('Is No? %s' % carresult)
diff --git a/Bio/NeuralNetwork/Gene/Motif.py b/Bio/NeuralNetwork/Gene/Motif.py
index 8a13866..647cd4d 100644
--- a/Bio/NeuralNetwork/Gene/Motif.py
+++ b/Bio/NeuralNetwork/Gene/Motif.py
@@ -9,7 +9,7 @@ Representing DNA (or RNA or proteins) in a neural network can be difficult
since input sequences can have different lengths. One way to get around
this problem is to deal with sequences by finding common motifs, and counting
the number of times those motifs occur in a sequence. This information can
-then be used for creating the neural networks, with occurances of motifs
+then be used for creating the neural networks, with occurrences of motifs
going into the network instead of raw sequence data.
"""
# biopython
@@ -207,8 +207,8 @@ class MotifCoder(object):
# otherwise we'll just return 0 for everything
if max_count > 0:
for motif in seq_motifs:
- seq_motifs[motif] = (float(seq_motifs[motif] - min_count)
- / float(max_count))
+ seq_motifs[motif] = (float(seq_motifs[motif] - min_count) /
+ float(max_count))
# return the relative motif counts in the specified order
motif_amounts = []
diff --git a/Bio/NeuralNetwork/Gene/Pattern.py b/Bio/NeuralNetwork/Gene/Pattern.py
index 5ade29d..2909f63 100644
--- a/Bio/NeuralNetwork/Gene/Pattern.py
+++ b/Bio/NeuralNetwork/Gene/Pattern.py
@@ -17,13 +17,11 @@ import random
from Bio.Alphabet import _verify_alphabet
from Bio.Seq import Seq, MutableSeq
-__docformat__ = "restructuredtext en"
-
class PatternIO(object):
"""Allow reading and writing of patterns to files.
- This just defines a simple persistance class for patterns, making
+ This just defines a simple persistence class for patterns, making
it easy to write them to a file and read 'em back.
"""
def __init__(self, alphabet=None):
@@ -50,7 +48,7 @@ class PatternIO(object):
"""
for pattern in pattern_list:
# deal with signatures, concatentate them with the separator
- if isinstance(pattern, list) or isinstance(pattern, tuple):
+ if isinstance(pattern, (list, tuple)):
string_pattern = self.separator.join(pattern)
# deal with the normal cases
else:
diff --git a/Bio/NeuralNetwork/Gene/Schema.py b/Bio/NeuralNetwork/Gene/Schema.py
index 8d10335..f8853ac 100644
--- a/Bio/NeuralNetwork/Gene/Schema.py
+++ b/Bio/NeuralNetwork/Gene/Schema.py
@@ -38,8 +38,6 @@ from Bio.GA.Repair.Stabilizing import AmbiguousRepair
from Bio.GA.Selection.Tournament import TournamentSelection
from Bio.GA.Selection.Diversity import DiversitySelection
-__docformat__ = "restructuredtext en"
-
class Schema(object):
"""Deal with motifs that have ambiguity characters in it.
@@ -129,7 +127,7 @@ class Schema(object):
"""Return all non-overlapping motif matches in the query string.
This utilizes the regular expression findall function, and will
- return a list of all non-overlapping occurances in query that
+ return a list of all non-overlapping occurrences in query that
match the ambiguous motif.
"""
try:
@@ -292,8 +290,8 @@ class DifferentialSchemaFitness(object):
def calculate_fitness(self, genome):
"""Calculate the fitness for a given schema.
- Fitness is specified by the number of occurances of the schema in
- the positive sequences minus the number of occurances in the
+ Fitness is specified by the number of occurrences of the schema in
+ the positive sequences minus the number of occurrences in the
negative examples.
This fitness is then modified by multiplying by the length of the
diff --git a/Bio/NeuralNetwork/Gene/Signature.py b/Bio/NeuralNetwork/Gene/Signature.py
index 418aa2d..97730e8 100644
--- a/Bio/NeuralNetwork/Gene/Signature.py
+++ b/Bio/NeuralNetwork/Gene/Signature.py
@@ -213,8 +213,8 @@ class SignatureCoder(object):
# otherwise we'll just return 0 for everything
if max_count > 0:
for sig in sequence_sigs:
- sequence_sigs[sig] = (float(sequence_sigs[sig] - min_count)
- / float(max_count))
+ sequence_sigs[sig] = (float(sequence_sigs[sig] - min_count) /
+ float(max_count))
# return the relative signature info in the specified order
sig_amounts = []
diff --git a/Bio/NeuralNetwork/Training.py b/Bio/NeuralNetwork/Training.py
index fe7fa9e..6c26410 100644
--- a/Bio/NeuralNetwork/Training.py
+++ b/Bio/NeuralNetwork/Training.py
@@ -3,17 +3,15 @@
# as part of this package.
#
-"""Provide classes for dealing with Training Neural Networks.
-"""
+"""Provide classes for dealing with Training Neural Networks."""
+
# standard modules
import random
class TrainingExample(object):
- """Hold inputs and outputs of a training example.
+ """Hold inputs and outputs of a training example."""
- XXX Do I really need this?
- """
def __init__(self, inputs, outputs, name=""):
self.name = name
self.inputs = inputs
diff --git a/Bio/Nexus/.project b/Bio/Nexus/.project
new file mode 100644
index 0000000..8bfeb52
--- /dev/null
+++ b/Bio/Nexus/.project
@@ -0,0 +1,17 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<projectDescription>
+ <name>Nexus</name>
+ <comment></comment>
+ <projects>
+ </projects>
+ <buildSpec>
+ <buildCommand>
+ <name>org.python.pydev.PyDevBuilder</name>
+ <arguments>
+ </arguments>
+ </buildCommand>
+ </buildSpec>
+ <natures>
+ <nature>org.python.pydev.pythonNature</nature>
+ </natures>
+</projectDescription>
diff --git a/Bio/Nexus/Nexus.py b/Bio/Nexus/Nexus.py
index d05fcb1..f4231f7 100644
--- a/Bio/Nexus/Nexus.py
+++ b/Bio/Nexus/Nexus.py
@@ -1,4 +1,5 @@
# Copyright 2005-2008 by Frank Kauff & Cymon J. Cox. All rights reserved.
+# 2014-2015 by Joe Cora (standard data)
# This code is part of the Biopython distribution and governed by its
# license. Please see the LICENSE file that should have been included
# as part of this package.
@@ -26,9 +27,9 @@ from Bio.Alphabet import IUPAC
from Bio.Data import IUPACData
from Bio.Seq import Seq
-from .Trees import Tree
+from Bio.Nexus.StandardData import StandardData
+from Bio.Nexus.Trees import Tree
-__docformat__ = "restructuredtext en"
INTERLEAVE = 70
SPECIAL_COMMANDS = ['charstatelabels', 'charlabels', 'taxlabels', 'taxset',
@@ -119,18 +120,20 @@ class CharBuffer(object):
This deals with single and double quotes, whitespace and punctuation.
"""
-
word = []
quoted = False
- first = self.next_nonwhitespace() # get first character
- if not first: # return empty if only whitespace left
+ # get first character
+ first = self.next_nonwhitespace()
+ if not first:
+ # return empty if only whitespace left
return None
word.append(first)
- if first == "'": # word starts with a quote
+ if first == "'":
quoted = "'"
elif first == '"':
quoted = '"'
- elif first in PUNCTUATION: # if it's punctuation, return immediately
+ elif first in PUNCTUATION:
+ # if it's non-quote punctuation, return immediately
return first
while True:
c = self.peek()
@@ -141,7 +144,8 @@ class CharBuffer(object):
elif quoted: # second single quote ends word
break
elif quoted:
- word.append(next(self)) # if quoted, then add anything
+ # if quoted, then add anything
+ word.append(next(self))
elif not c or c in PUNCTUATION or c in WHITESPACE:
# if not quoted and special character, stop
break
@@ -159,7 +163,6 @@ class StepMatrix(object):
See Wheeler (1990), Cladistics 6:269-275.
"""
-
def __init__(self, symbols, gap):
self.data = {}
self.symbols = sorted(symbols)
@@ -245,14 +248,11 @@ def quotestrip(word):
return word
-def get_start_end(sequence, skiplist=None):
+def get_start_end(sequence, skiplist=('-', '?')):
"""Return position of first and last character which is not in skiplist.
Skiplist defaults to ['-','?'].
"""
- if skiplist is None:
- skiplist = ["-", "?"]
-
length = len(sequence)
if length == 0:
return None, None
@@ -281,9 +281,13 @@ def _make_unique(l):
def _unique_label(previous_labels, label):
"""Returns a unique name if label is already in previous_labels."""
while label in previous_labels:
- if label.split('.')[-1].startswith('copy'):
- label = '.'.join(label.split('.')[:-1]) \
- + '.copy' + str(eval('0' + label.split('.')[-1][4:]) + 1)
+ label_split = label.split('.')
+ if label_split[-1].startswith('copy'):
+ copy_num = 1
+ if label_split[-1] != "copy":
+ copy_num = int(label_split[-1][4:]) + 1
+ new_label = "%s.copy%s" % ('.'.join(label_split[:-1]), copy_num)
+ label = new_label
else:
label += '.copy'
return label
@@ -367,13 +371,21 @@ def combine(matrices):
m_only = [t for t in m.taxlabels if t not in both]
for t in both:
# concatenate sequences and unify gap and missing character symbols
- combined.matrix[t] += Seq(str(m.matrix[t]).replace(m.gap, combined.gap).replace(m.missing, combined.missing), combined.alphabet)
+ combined.matrix[t] += Seq(str(m.matrix[t])
+ .replace(m.gap, combined.gap)
+ .replace(m.missing, combined.missing),
+ combined.alphabet)
# replace date of missing taxa with symbol for missing data
for t in combined_only:
- combined.matrix[t] += Seq(combined.missing * m.nchar, combined.alphabet)
+ combined.matrix[t] += Seq(combined.missing * m.nchar,
+ combined.alphabet)
for t in m_only:
- combined.matrix[t] = Seq(combined.missing * combined.nchar, combined.alphabet) + \
- Seq(str(m.matrix[t]).replace(m.gap, combined.gap).replace(m.missing, combined.missing), combined.alphabet)
+ combined.matrix[t] = Seq(combined.missing * combined.nchar,
+ combined.alphabet) + \
+ Seq(str(m.matrix[t])
+ .replace(m.gap, combined.gap)
+ .replace(m.missing, combined.missing),
+ combined.alphabet)
combined.taxlabels.extend(m_only) # new taxon list
for cn, cs in m.charsets.items(): # adjust character sets for new matrix
combined.charsets['%s.%s' % (n, cn)] = [x + combined.nchar for x in cs]
@@ -614,7 +626,7 @@ class Nexus(object):
file_contents = fp.read()
self.filename = getattr(fp, 'name', 'Unknown_nexus_file')
except (TypeError, IOError, AttributeError):
- # 2 Assume we have a string from a fh.read()
+ # 2. Assume we have a string from a fh.read()
if isinstance(input, basestring):
file_contents = input
self.filename = 'input_string'
@@ -630,7 +642,7 @@ class Nexus(object):
try:
if cl[:6].upper() == '#NEXUS':
commandlines[i] = cl[6:].strip()
- except:
+ except IndexError:
pass
# now loop through blocks (we parse only data in known blocks, thus ignoring non-block commands
nexus_block_gen = self._get_nexus_block(commandlines)
@@ -674,10 +686,10 @@ class Nexus(object):
def _parse_nexus_block(self, title, contents):
"""Parse a known Nexus Block (PRIVATE)."""
- # attached the structered block representation
+ # attached the structured block representation
self._apply_block_structure(title, contents)
# now check for taxa,characters,data blocks. If this stuff is defined more than once
- # the later occurences will override the previous ones.
+ # the later occurrences will override the previous ones.
block = self.structured[-1]
for line in block.commandlines:
try:
@@ -706,15 +718,17 @@ class Nexus(object):
self.respectcase = True
# adjust symbols to for respectcase
if 'symbols' in options:
- self.symbols = options['symbols']
+ self.symbols = ''.join(options['symbols'].split())
if (self.symbols.startswith('"') and self.symbols.endswith('"')) or\
- (self.symbols.startswith("'") and self.symbols.endswith("'")):
- self.symbols = self.symbols[1:-1].replace(' ', '')
+ (self.symbols.startswith("'") and self.symbols.endswith("'")):
+ self.symbols = self.symbols[1:-1]
if not self.respectcase:
- self.symbols = self.symbols.lower() + self.symbols.upper()
- self.symbols = list(set(self.symbols))
+ self.symbols = list(self.symbols.upper())
+ # self.symbols = self.symbols.lower() + self.symbols.upper()
+ # self.symbols = list(set(self.symbols))
if 'datatype' in options:
self.datatype = options['datatype'].lower()
+
if self.datatype == 'dna' or self.datatype == 'nucleotide':
self.alphabet = IUPAC.IUPACAmbiguousDNA() # fresh instance!
self.ambiguous_values = IUPACData.ambiguous_dna_values.copy()
@@ -726,16 +740,29 @@ class Nexus(object):
elif self.datatype == 'protein':
# TODO - Should this not be ExtendedIUPACProtein?
self.alphabet = IUPAC.IUPACProtein() # fresh instance
- self.ambiguous_values = {'B': 'DN', 'Z': 'EQ', 'X': IUPACData.protein_letters}
+ self.ambiguous_values = {'B': 'DN',
+ 'Z': 'EQ',
+ 'X': IUPACData.protein_letters}
# that's how PAUP handles it
self.unambiguous_letters = IUPACData.protein_letters + '*' # stop-codon
elif self.datatype == 'standard':
- raise NexusError('Datatype standard is not yet supported.')
- # self.alphabet = None
- # self.ambiguous_values = {}
- # if not self.symbols:
- # self.symbols = '01' # if nothing else defined, then 0 and 1 are the default states
- # self.unambiguous_letters = self.symbols
+ self.alphabet = None
+ self.ambiguous_values = {}
+ if not self.symbols:
+ # PARSER BUG ##
+ # This error arises when symbols are absent or when
+ # whitespace is located within the SYMBOLS command values.
+ # The Nexus parser quits reading the SYMBOLS line upon
+ # finding a whitespace character.
+ raise NexusError(
+ "Symbols must be defined when using standard datatype. "
+ "Please remove any whitespace (spaces, tabs, etc.) "
+ "between values for symbols as this confuses the Nexus "
+ "parser.")
+
+ self.unambiguous_letters = ''.join(self.symbols)
+ if not self.respectcase:
+ self.unambiguous_letters += self.unambiguous_letters.lower()
else:
raise NexusError('Unsupported datatype: ' + self.datatype)
self.valid_characters = ''.join(self.ambiguous_values) + self.unambiguous_letters
@@ -754,9 +781,9 @@ class Nexus(object):
if self.missing not in self.ambiguous_values:
self.ambiguous_values[self.missing] = self.unambiguous_letters + self.gap
self.ambiguous_values[self.gap] = self.gap
- elif self.datatype == 'standard':
- if not self.symbols:
- self.symbols = ['1', '0']
+ # elif self.datatype == 'standard':
+ # if not self.symbols:
+ # self.symbols = ['0', '1']
if 'missing' in options:
self.missing = options['missing'][0]
if 'gap' in options:
@@ -831,8 +858,65 @@ class Nexus(object):
raise NexusError('Missing \',\' in line %s.' % options)
def _charstatelabels(self, options):
- # warning: charstatelabels supports only charlabels-syntax!
- self._charlabels(options)
+ self.charlabels = {}
+ self.statelabels = {}
+ opts = CharBuffer(options)
+
+ # Make sure symbols are defined
+ if not self.symbols:
+ raise NexusError(
+ 'Symbols must be defined when using character states')
+
+ while True:
+ # get id and character name
+ w = opts.next_word()
+
+ # McClade saves and reads charlabel-lists with terminal comma?!
+ if w is None:
+ break
+
+ identifier = self._resolve(w, set_type=CHARSET)
+ character = quotestrip(opts.next_word())
+
+ self.charlabels[identifier] = character
+ self.statelabels[identifier] = []
+
+ # check for comma, slash or end of command
+ c = opts.next_nonwhitespace()
+
+ if c is None:
+ break
+ elif c != ',':
+ # Check if states are defined, otherwise report error
+ if c != '/':
+ raise NexusError('Missing \',\' in line %s.' % options)
+
+ # Get the first state
+ state = quotestrip(opts.next_word())
+
+ if state is None:
+ raise NexusError(
+ 'Missing character state in line %s.' % options)
+
+ while True:
+ # Make sure current state does not exceed number of
+ # available symbols
+ if len(self.statelabels[identifier]) > len(self.symbols):
+ raise NexusError(
+ 'Character states exceed number of available symbols in line %s.' % options)
+
+ # Add the character state to the statelabels
+ self.statelabels[identifier].append(state)
+
+ # Check for another state or comma to end states (last
+ # character should not have comma at end of states - but
+ # we'll ignore)
+ state = quotestrip(opts.next_word())
+
+ if state is None:
+ return
+ elif state is ',':
+ break
def _statelabels(self, options):
# self.charlabels = options
@@ -861,7 +945,6 @@ class Nexus(object):
break
# count the taxa and check for interleaved matrix
taxcount += 1
- # print taxcount
if taxcount > self.ntax:
if not self.interleave:
raise NexusError('Too many taxa in matrix - should matrix be interleaved?')
@@ -875,7 +958,6 @@ class Nexus(object):
chars = ''
if self.interleave:
# interleaved matrix
- # print 'In interleave'
if l:
chars = ''.join(l.split())
else:
@@ -886,22 +968,42 @@ class Nexus(object):
while len(chars) < self.nchar:
l = next(lineiter)
chars += ''.join(l.split())
- iupac_seq = Seq(_replace_parenthesized_ambigs(chars, self.rev_ambiguous_values), self.alphabet)
- # first taxon has the reference sequence if matchhar is used
- if taxcount == 1:
- refseq = iupac_seq
+
+ # Reformat sequence for non-standard datatypes
+ if self.datatype != 'standard':
+ iupac_seq = Seq(_replace_parenthesized_ambigs(
+ chars, self.rev_ambiguous_values), self.alphabet)
+ # first taxon has the reference sequence if matchhar is used
+ if taxcount == 1:
+ refseq = iupac_seq
+ else:
+ if self.matchchar:
+ while True:
+ p = str(iupac_seq).find(self.matchchar)
+ if p == -1:
+ break
+ iupac_seq = Seq(str(iupac_seq)[:p] + refseq[
+ p] + str(iupac_seq)[p + 1:], self.alphabet)
+
+ # Check for invalid characters
+ for i, c in enumerate(str(iupac_seq)):
+ if c not in self.valid_characters and c != self.gap and c != self.missing:
+ raise NexusError("Taxon %s: Illegal character %s in sequence %s "
+ "(check dimensions/interleaving)" % (id, c, iupac_seq))
else:
- if self.matchchar:
- while True:
- p = str(iupac_seq).find(self.matchchar)
- if p == -1:
- break
- iupac_seq = Seq(str(iupac_seq)[:p] + refseq[p] + str(iupac_seq)[p + 1:], self.alphabet)
- # check for invalid characters
- for c in str(iupac_seq):
- if c not in self.valid_characters and c != self.gap and c != self.missing:
- raise NexusError("Taxon %s: Illegal character %s in sequence %s "
- "(check dimensions/interleaving)" % (id, c, iupac_seq))
+ iupac_seq = StandardData(chars)
+
+ # Check for invalid characters
+ for i, c in enumerate(iupac_seq):
+ # Go through each coding for each character
+ for coding in c['d']:
+ if (coding not in self.valid_characters and
+ coding != self.gap and coding != self.missing):
+ raise NexusError("Taxon %s: Illegal character %s "
+ "in sequence %s "
+ "(check dimensions/interleaving)"
+ % (id, coding, iupac_seq))
+
# add sequence to matrix
if first_matrix_block:
self.unaltered_taxlabels.append(id)
@@ -946,7 +1048,7 @@ class Nexus(object):
raise NexusError('Missing \',\' in line %s.' % options)
except NexusError:
raise
- except:
+ except Exception: # TODO: ValueError?
raise NexusError('Format error in line %s.' % options)
def _utree(self, options):
@@ -979,7 +1081,8 @@ class Nexus(object):
rooted = False
elif special == 'W':
weight = float(value)
- tree = Tree(name=name, weight=weight, rooted=rooted, tree=opts.rest().strip())
+ tree = Tree(name=name, weight=weight, rooted=rooted,
+ tree=opts.rest().strip())
# if there's an active translation table, translate
if self.translate:
for n in tree.get_terminals():
@@ -1056,8 +1159,9 @@ class Nexus(object):
name = self._name_n_vector(opts)
if not name:
raise NexusError('Formatting error in charpartition: %s ' % options)
- # now collect thesubbpartitions and parse them
- # subpartitons separated by commas - which unfortunately could be part of a quoted identifier...
+ # now collect the subpartitions and parse them
+ # subpartitions separated by commas - which unfortunately could be part
+ # of a quoted identifier...
sub = ''
while True:
w = next(opts)
@@ -1146,9 +1250,11 @@ class Nexus(object):
taxrange = self.taxlabels[start:end + 1]
plain_list.extend(taxrange)
else:
- if isinstance(start, list): # start was the name of charset or taxset
+ if isinstance(start, list):
+ # start was the name of charset or taxset
plain_list.extend(start)
- else: # start was an ordinary identifier
+ else:
+ # start was an ordinary identifier
plain_list.append(start)
except NexusError:
raise
@@ -1229,7 +1335,7 @@ class Nexus(object):
pass
def write_nexus_data_partitions(self, matrix=None, filename=None, blocksize=None,
- interleave=False, exclude=[], delete=[],
+ interleave=False, exclude=(), delete=(),
charpartition=None, comment='', mrbayes=False):
"""Writes a nexus file for each partition in charpartition.
@@ -1246,7 +1352,7 @@ class Nexus(object):
if charpartition:
pfilenames = {}
for p in charpartition:
- total_exclude = [] + exclude
+ total_exclude = list(exclude)
total_exclude.extend(c for c in range(self.nchar) if c not in charpartition[p])
total_exclude = _make_unique(total_exclude)
pcomment = comment + '\nPartition: ' + p + '\n'
@@ -1267,7 +1373,7 @@ class Nexus(object):
comment=comment, append_sets=False, mrbayes=mrbayes)
return fn
- def write_nexus_data(self, filename=None, matrix=None, exclude=[], delete=[],
+ def write_nexus_data(self, filename=None, matrix=None, exclude=(), delete=(),
blocksize=None, interleave=False, interleave_by_partition=False,
comment=None, omit_NEXUS=False, append_sets=True, mrbayes=False,
codons_block=True):
@@ -1301,7 +1407,8 @@ class Nexus(object):
raise NexusError('Unknown partition: %r' % interleave_by_partition)
else:
partition = self.charpartitions[interleave_by_partition]
- # we need to sort the partition names by starting position before we exclude characters
+ # we need to sort the partition names by starting position
+ # before we exclude characters
names = _sort_keys_by_values(partition)
newpartition = {}
for p in partition:
@@ -1342,9 +1449,9 @@ class Nexus(object):
if self.charlabels:
newcharlabels = self._adjust_charlabels(exclude=exclude)
clkeys = sorted(newcharlabels)
- fh.write('charlabels '
- + ', '.join("%s %s" % (k + 1, safename(newcharlabels[k])) for k in clkeys)
- + ';\n')
+ fh.write('charlabels ' +
+ ', '.join("%s %s" % (k + 1, safename(newcharlabels[k])) for k in clkeys) +
+ ';\n')
fh.write('matrix\n')
if not blocksize:
if interleave:
@@ -1354,7 +1461,8 @@ class Nexus(object):
# delete deleted taxa and ecxclude excluded characters...
namelength = max(len(safename(t, mrbayes=mrbayes)) for t in undelete)
if interleave_by_partition:
- # interleave by partitions, but adjust partitions with regard to excluded characters
+ # interleave by partitions, but adjust partitions with regard
+ # to excluded characters
seek = 0
for p in names:
fh.write('[%s: %s]\n' % (interleave_by_partition, p))
@@ -1391,7 +1499,7 @@ class Nexus(object):
fh.write(self.append_sets(exclude=exclude, delete=delete, mrbayes=mrbayes))
return filename
- def append_sets(self, exclude=[], delete=[], mrbayes=False, include_codons=True, codons_only=False):
+ def append_sets(self, exclude=(), delete=(), mrbayes=False, include_codons=True, codons_only=False):
"""Returns a sets block."""
if not self.charsets and not self.taxsets and not self.charpartitions:
return ''
@@ -1493,7 +1601,7 @@ class Nexus(object):
fh.write('%s %s\n' % (safename(taxon), str(self.matrix[taxon])))
return filename
- def constant(self, matrix=None, delete=[], exclude=[]):
+ def constant(self, matrix=None, delete=(), exclude=()):
"""Return a list with all constant characters."""
if not matrix:
matrix = self.matrix
@@ -1512,14 +1620,16 @@ class Nexus(object):
# print '%d (paup=%d)' % (site[0],site[0]+1),
seqsite = matrix[taxon][site[0]].upper()
# print seqsite,'checked against',site[1],'\t',
- if seqsite == self.missing \
- or (seqsite == self.gap and self.options['gapmode'].lower() == 'missing') \
- or seqsite == site[1]:
+ if (seqsite == self.missing or
+ (seqsite == self.gap and
+ self.options['gapmode'].lower() == 'missing') or
+ seqsite == site[1]):
# missing or same as before -> ok
newconstant.append(site)
- elif seqsite in site[1] \
- or site[1] == self.missing \
- or (self.options['gapmode'].lower() == 'missing' and site[1] == self.gap):
+ elif (seqsite in site[1] or
+ site[1] == self.missing or
+ (self.options['gapmode'].lower() == 'missing' and
+ site[1] == self.gap)):
# subset of an ambig or only missing in previous -> take subset
newconstant.append((site[0], self.ambiguous_values.get(seqsite, seqsite)))
elif seqsite in self.ambiguous_values:
@@ -1536,7 +1646,7 @@ class Nexus(object):
cpos = [s[0] for s in constant]
return cpos
- def cstatus(self, site, delete=[], narrow=True):
+ def cstatus(self, site, delete=(), narrow=True):
"""Summarize character.
narrow=True: paup-mode (a c ? --> ac; ? ? ? --> ?)
@@ -1560,7 +1670,7 @@ class Nexus(object):
cstatus.sort()
return cstatus
- def weighted_stepmatrix(self, name='your_name_here', exclude=[], delete=[]):
+ def weighted_stepmatrix(self, name='your_name_here', exclude=(), delete=()):
"""Calculates a stepmatrix for weighted parsimony.
See Wheeler (1990), Cladistics 6:269-275 and
@@ -1574,7 +1684,7 @@ class Nexus(object):
m.add(b1.upper(), b2.upper(), 1)
return m.transformation().weighting().smprint(name=name)
- def crop_matrix(self, matrix=None, delete=[], exclude=[]):
+ def crop_matrix(self, matrix=None, delete=(), exclude=()):
"""Return a matrix without deleted taxa and excluded characters."""
if not matrix:
matrix = self.matrix
@@ -1595,7 +1705,7 @@ class Nexus(object):
else:
return dict((t, matrix[t]) for t in self.taxlabels if t in matrix and t not in delete)
- def bootstrap(self, matrix=None, delete=[], exclude=[]):
+ def bootstrap(self, matrix=None, delete=(), exclude=()):
"""Return a bootstrapped matrix."""
if not matrix:
matrix = self.matrix
@@ -1611,7 +1721,8 @@ class Nexus(object):
alphabet = matrix[list(matrix.keys())[0]].alphabet
else:
sitesm = list(zip(*[cm[t] for t in undelete]))
- bootstrapsitesm = [sitesm[random.randint(0, len(sitesm) - 1)] for i in range(len(sitesm))]
+ bootstrapsitesm = [sitesm[random.randint(0, len(sitesm) - 1)]
+ for i in range(len(sitesm))]
bootstrapseqs = [''.join(x) for x in zip(*bootstrapsitesm)]
if seqobjects:
bootstrapseqs = [Seq(s, alphabet) for s in bootstrapseqs]
@@ -1675,7 +1786,8 @@ class Nexus(object):
sitesm = list(zip(*[str(self.matrix[t]) for t in self.taxlabels]))
sitesm[pos:pos] = [['-'] * len(self.taxlabels)] * n
mapped = [''.join(x) for x in zip(*sitesm)]
- listed = [(taxon, Seq(mapped[i], self.alphabet)) for i, taxon in enumerate(self.taxlabels)]
+ listed = [(taxon, Seq(mapped[i], self.alphabet))
+ for i, taxon in enumerate(self.taxlabels)]
self.matrix = dict(listed)
self.nchar += n
# now adjust character sets
@@ -1770,3 +1882,70 @@ else:
# nexus file under normal circumstances)
commandlines = _adjust_lines(decommented.split(chr(7)))
return commandlines
+
+
+# Test code from command line (python Nexus.py)
+if __name__ == '__main__':
+ print()
+ print()
+ print('=== NEXUS PARSING TEST ===')
+ print()
+
+ # 1. Check basic tree file with TREES and TAXA block
+ print('Testing file "bats.nex": TREES and TAXA')
+ nexus1 = Nexus()
+ try:
+ nexus1.read('../../Tests/Nexus/bats.nex')
+ except Exception as nexus_error:
+ raise Exception(nexus_error.message)
+
+ # 2. Check simple sequence data file with DATA and CODONS block
+ print('Testing file "codonposset.nex": DATA and CODONS')
+ nexus2 = Nexus()
+ try:
+ nexus2.read('../../Tests/Nexus/codonposset.nex')
+ except Exception as nexus_error:
+ raise Exception(nexus_error.message)
+
+ # 3. Check sequence data file with DATA, SETS, TREES and an unknown block
+ print('Testing file "test_Nexus_input.nex": DATA, SETS, TREES and unknown')
+ nexus3 = Nexus()
+ try:
+ nexus3.read('../../Tests/Nexus/test_Nexus_input.nex')
+ except Exception as nexus_error:
+ raise Exception(nexus_error.message)
+
+ # 4. Check simple multi-state character data file with TAXA and CHARACTERS
+ # block
+ print('Testing file "vSysLab_Ganaspidium_multistate.nex": TAXA, CHARACTERS (multi-state)')
+ nexus4 = Nexus()
+ try:
+ nexus4.read('../../Tests/Nexus/vSysLab_Ganaspidium_multistate.nex')
+ except Exception as nexus_error:
+ raise Exception(nexus_error.message)
+
+ # 5. Check character data file with TAXA and CHARACTERS block with more
+ # than 9 codings and a character without states
+ print('Testing file "vSysLab_Heptascelio_no-states_10+chars.nex": TAXA, CHARACTERS (stateless and 10+ codings)')
+ nexus5 = Nexus()
+ try:
+ nexus5.read('../../Tests/Nexus/vSysLab_Heptascelio_no-states_10+chars.nex')
+ except Exception as nexus_error:
+ raise Exception(nexus_error.message)
+
+ # TODO: Not supported
+ # 6. Check character data file with TAXA and two CHARACTERS blocks (one with continuous characters)
+ # print('Testing file "vSysLab_Oreiscelio_discrete+continuous.nex": TAXA, 2 CHARACTERS (discrete and continuous)')
+ #
+ # nexus6 = Nexus()
+ #
+ # try:
+ # nexus6.read('../../Tests/Nexus/vSysLab_Oreiscelio_discrete+continuous.nex')
+ # except Exception as nexus_error:
+ # raise Exception(nexus_error.message)
+
+ # Made it here, so success
+ print()
+ print('Successfully completed test suite.')
+ print()
+ print()
diff --git a/Bio/Nexus/Nodes.py b/Bio/Nexus/Nodes.py
index a1b3b3f..70c46a9 100644
--- a/Bio/Nexus/Nodes.py
+++ b/Bio/Nexus/Nodes.py
@@ -2,17 +2,16 @@
# This code is part of the Biopython distribution and governed by its
# license. Please see the LICENSE file that should have been included
# as part of this package.
-#
-# Nodes.py
-#
-# Provides functionality of a linked list.
-# Each node has one (or none) predecessor, and an arbitrary number of successors.
-# Nodes can store arbitrary data in a NodeData class.
-#
-# Subclassed by Nexus.Trees to store phylogenetic trees.
-#
-# Bug reports to Frank Kauff (fkauff at biologie.uni-kl.de)
-#
+"""Linked list functionality for use in Bio.Nexus.
+
+Provides functionality of a linked list.
+Each node has one (or none) predecessor, and an arbitrary number of successors.
+Nodes can store arbitrary data in a NodeData class.
+
+Subclassed by Nexus.Trees to store phylogenetic trees.
+
+Bug reports to Frank Kauff (fkauff at biologie.uni-kl.de)
+"""
class ChainException(Exception):
diff --git a/Bio/Nexus/StandardData.py b/Bio/Nexus/StandardData.py
new file mode 100644
index 0000000..3cf7709
--- /dev/null
+++ b/Bio/Nexus/StandardData.py
@@ -0,0 +1,107 @@
+# Copyright 2014 Joe Cora.
+# All rights reserved.
+# This code is part of the Biopython distribution and governed by its
+# license. Please see the LICENSE file that should have been included
+# as part of this package.
+"""Provides objects to represent NEXUS standard data type matrix coding.
+"""
+from __future__ import print_function
+
+
+class NexusError(Exception):
+ pass
+
+
+class StandardData(object):
+ """Create a StandardData iterable object.
+
+ Each coding specifies t [type] => (std [standard], multi [multistate] or
+ uncer [uncertain]) and d [data]
+ """
+ def __init__(self, data):
+ self._data = []
+ self._current_pos = 0
+
+ # Enforce string data requirement
+ if not isinstance(data, basestring):
+ raise NexusError("The coding data given to a StandardData object should be a string")
+
+ # Transfer each coding to a position within a sequence
+ multi_coding = False
+ uncertain_coding = False
+ coding_list = {'t': 'std', 'd': []}
+
+ for pos, coding in enumerate(data):
+ # Check if in a multiple coded or uncertain character
+ if multi_coding:
+ # End multicoding if close parenthesis
+ if coding == ')':
+ multi_coding = False
+ else:
+ # Add current coding to list and advance to next coding
+ coding_list['d'].append(coding)
+ continue
+ elif uncertain_coding:
+ # End multicoding if close parenthesis
+ if coding == '}':
+ uncertain_coding = False
+ else:
+ # Add current coding to list and advance to next coding
+ coding_list['d'].append(coding)
+ continue
+ else:
+ # Check if a multiple coded or uncertain character is starting
+ if coding == '(':
+ multi_coding = True
+ coding_list['t'] = 'multi'
+ continue
+ elif coding == '{':
+ uncertain_coding = True
+ coding_list['t'] = 'uncer'
+ continue
+ elif coding in [')', '}']:
+ raise NexusError('Improper character "' + coding +
+ '" at position ' + pos +
+ ' of a coding sequence.')
+ else:
+ coding_list['d'].append(coding)
+
+ # Add character coding to data
+ self._data.append(coding_list.copy())
+ coding_list = {'t': 'std', 'd': []}
+
+ def __len__(self):
+ """Returns the length of the coding, use len(my_coding)."""
+ return len(self._data)
+
+ def __getitem__(self, arg):
+ return self._data[arg]
+
+ def __iter__(self):
+ return self
+
+ def next(self):
+ try:
+ return_coding = self._data[self._current_pos]
+ except:
+ self._current_pos = 0
+ raise StopIteration
+ else:
+ self._current_pos += 1
+ return return_coding
+
+ def raw(self):
+ """Returns the full coding as a python list."""
+ return self._data
+
+ def __str__(self):
+ """Returns the full coding as a python string, use str(my_coding)."""
+ str_return = ''
+ for coding in self._data:
+ if coding['t'] == 'multi':
+ str_return += '(' + ''.join(coding['d']) + ')'
+ elif coding['t'] == 'uncer':
+ str_return += '{' + ''.join(coding['d']) + '}'
+ else:
+ str_return += coding['d'][0]
+ return str_return
diff --git a/Bio/Nexus/Trees.py b/Bio/Nexus/Trees.py
index c1e7dc5..9d00ff8 100755
--- a/Bio/Nexus/Trees.py
+++ b/Bio/Nexus/Trees.py
@@ -18,6 +18,7 @@ import random
import sys
from . import Nodes
+
PRECISION_BRANCHLENGTH = 6
PRECISION_SUPPORT = 6
NODECOMMENT_START = '[&'
@@ -318,7 +319,7 @@ class Tree(Nodes.Chain):
t = t.replace(' ', '_')
try:
genus = t.split('_', 1)[0]
- except:
+ except IndexError:
genus = 'None'
if genus not in genera:
genera.append(genus)
diff --git a/Bio/PDB/Chain.py b/Bio/PDB/Chain.py
index 4ed7e3d..74ab570 100644
--- a/Bio/PDB/Chain.py
+++ b/Bio/PDB/Chain.py
@@ -7,8 +7,6 @@
from Bio.PDB.Entity import Entity
-__docformat__ = "restructuredtext en"
-
class Chain(Entity):
def __init__(self, id):
@@ -111,12 +109,12 @@ class Chain(Entity):
"""Return 1 if a residue with given id is present.
The id of a residue is (hetero flag, sequence identifier, insertion code).
-
+
If id is an int, it is translated to (" ", id, " ") by the _translate_id
method.
Arguments:
-
+
- id - (string, int, string) or int
"""
id = self._translate_id(id)
diff --git a/Bio/PDB/DSSP.py b/Bio/PDB/DSSP.py
index 38d2852..152f461 100644
--- a/Bio/PDB/DSSP.py
+++ b/Bio/PDB/DSSP.py
@@ -22,8 +22,6 @@ The DSSP codes for secondary structure used here are:
from __future__ import print_function
-__docformat__ = "restructuredtext en"
-
import re
from Bio._py3k import StringIO
import subprocess
@@ -108,13 +106,13 @@ def dssp_dict_from_pdb_file(in_file, DSSP="dssp"):
p = subprocess.Popen([DSSP, in_file], universal_newlines=True,
stdout=subprocess.PIPE, stderr=subprocess.PIPE)
out, err = p.communicate()
-
+
# Alert user for errors
if err.strip():
warnings.warn(err)
if not out.strip():
raise Exception('DSSP failed to produce an output')
-
+
out_dict, keys = _make_dssp_dict(StringIO(out))
return out_dict, keys
@@ -225,7 +223,7 @@ class DSSP(AbstractResiduePropertyMap):
>>> model = structure[0]
>>> dssp = DSSP(model, "1MOT.pdb")
>>> # DSSP data is accessed by a tuple (chain_id, res_id)
- >>> a_key = list(dssp)[2]
+ >>> a_key = list(dssp.keys())[2]
>>> # residue object, secondary structure, solvent accessibility,
>>> # relative accessiblity, phi, psi
>>> dssp[a_key]
diff --git a/Bio/PDB/Entity.py b/Bio/PDB/Entity.py
index 6f9e94b..de591ad 100644
--- a/Bio/PDB/Entity.py
+++ b/Bio/PDB/Entity.py
@@ -13,6 +13,7 @@ from copy import copy
from Bio.PDB.PDBExceptions import PDBConstructionException
+
class Entity(object):
"""
Basic container object. Structure, Model, Chain and Residue
@@ -136,14 +137,14 @@ class Entity(object):
Residue with id (" ", 10, "A")
The Residue id indicates that the residue is not a hetero-residue
- (or a water) beacuse it has a blank hetero field, that its sequence
+ (or a water) because it has a blank hetero field, that its sequence
identifier is 10 and its insertion code "A".
"""
if self.full_id is None:
entity_id = self.get_id()
l = [entity_id]
parent = self.get_parent()
- while not (parent is None):
+ while parent is not None:
entity_id = parent.get_id()
l.append(entity_id)
parent = parent.get_parent()
diff --git a/Bio/PDB/FragmentMapper.py b/Bio/PDB/FragmentMapper.py
index edad0b0..0bb36b2 100644
--- a/Bio/PDB/FragmentMapper.py
+++ b/Bio/PDB/FragmentMapper.py
@@ -41,7 +41,6 @@ from Bio.PDB.PDBExceptions import PDBException
from Bio.PDB.PDBParser import PDBParser
from Bio.PDB.Polypeptide import PPBuilder
-__docformat__ = "restructuredtext en"
# fragment file (lib_SIZE_z_LENGTH.txt)
# SIZE=number of fragments
@@ -233,9 +232,8 @@ class FragmentMapper(object):
Map polypeptides in a model to lists of representative fragments.
"""
def __init__(self, model, lsize=20, flength=5, fdir="."):
- """
- ::
-
+ """Create instance of FragmentMapper
+
@param model: the model that will be mapped
@type model: L{Model}
diff --git a/Bio/PDB/HSExposure.py b/Bio/PDB/HSExposure.py
index 433e966..bbcab07 100644
--- a/Bio/PDB/HSExposure.py
+++ b/Bio/PDB/HSExposure.py
@@ -15,8 +15,6 @@ from Bio.PDB.PDBParser import PDBParser
from Bio.PDB.Polypeptide import CaPPBuilder, is_aa
from Bio.PDB.Vector import rotaxis
-__docformat__ = "restructuredtext en"
-
class _AbstractHSExposure(AbstractPropertyMap):
"""
diff --git a/Bio/PDB/MMCIFParser.py b/Bio/PDB/MMCIFParser.py
index b4dc778..d673dc9 100644
--- a/Bio/PDB/MMCIFParser.py
+++ b/Bio/PDB/MMCIFParser.py
@@ -3,7 +3,7 @@
# license. Please see the LICENSE file that should have been included
# as part of this package.
-"""mmCIF parser"""
+"""mmCIF parsers"""
from __future__ import print_function
@@ -12,6 +12,7 @@ from string import ascii_letters
import numpy
import warnings
+from Bio.File import as_handle
from Bio._py3k import range
from Bio.PDB.MMCIF2Dict import MMCIF2Dict
@@ -21,19 +22,21 @@ from Bio.PDB.PDBExceptions import PDBConstructionWarning
class MMCIFParser(object):
- """Parse a PDB file and return a Structure object."""
+ """Parse a mmCIF file and return a Structure object."""
def __init__(self, structure_builder=None, QUIET=False):
"""Create a PDBParser object.
- The PDB parser call a number of standard methods in an aggregated
+
+ The mmCIF parser calls a number of standard methods in an aggregated
StructureBuilder object. Normally this object is instanciated by the
MMCIParser object itself, but if the user provides his/her own
StructureBuilder object, the latter is used instead.
+
Arguments:
- structure_builder - an optional user implemented StructureBuilder class.
- QUIET - Evaluated as a Boolean. If true, warnings issued in constructing
the SMCRA data will be suppressed. If false (DEFAULT), they will be shown.
- These warnings might be indicative of problems in the PDB file!
+ These warnings might be indicative of problems in the mmCIF file!
"""
if structure_builder is not None:
self._structure_builder = structure_builder
@@ -57,8 +60,9 @@ class MMCIFParser(object):
with warnings.catch_warnings():
if self.QUIET:
warnings.filterwarnings("ignore", category=PDBConstructionWarning)
- self._mmcif_dict = MMCIF2Dict(filename)
- self._build_structure(structure_id)
+ self._mmcif_dict = MMCIF2Dict(filename)
+ self._build_structure(structure_id)
+
return self._structure_builder.get_structure()
# Private methods
@@ -72,7 +76,7 @@ class MMCIFParser(object):
except KeyError:
element_list = None
seq_id_list = mmcif_dict["_atom_site.label_seq_id"]
- chain_id_list = mmcif_dict["_atom_site.label_asym_id"]
+ chain_id_list = mmcif_dict["_atom_site.auth_asym_id"]
x_list = [float(x) for x in mmcif_dict["_atom_site.Cartn_x"]]
y_list = [float(x) for x in mmcif_dict["_atom_site.Cartn_y"]]
z_list = [float(x) for x in mmcif_dict["_atom_site.Cartn_z"]]
@@ -115,7 +119,7 @@ class MMCIFParser(object):
# Historically, Biopython PDB parser uses model_id to mean array index
# so serial_id means the Model ID specified in the file
current_model_id = -1
- current_serial_id = 0
+ current_serial_id = -1
for i in range(0, len(atom_id_list)):
# set the line_counter for 'ATOM' lines only and not
@@ -131,7 +135,7 @@ class MMCIFParser(object):
altloc = alt_list[i]
if altloc == ".":
altloc = " "
- resseq = seq_id_list[i]
+ int_resseq = int(seq_id_list[i])
icode = icode_list[i]
if icode == "?":
icode = " "
@@ -150,6 +154,9 @@ class MMCIFParser(object):
hetatm_flag = "H"
else:
hetatm_flag = " "
+
+ resseq = (hetatm_flag, int_resseq, icode)
+
if serial_list is not None:
# model column exists; use it
serial_id = serial_list[i]
@@ -167,10 +174,10 @@ class MMCIFParser(object):
if current_chain_id != chainid:
current_chain_id = chainid
structure_builder.init_chain(current_chain_id)
+ current_residue_id = None
if current_residue_id != resseq:
current_residue_id = resseq
- int_resseq = int(resseq)
structure_builder.init_residue(resname, hetatm_flag, int_resseq, icode)
coord = numpy.array((x, y, z), 'f')
@@ -201,6 +208,220 @@ class MMCIFParser(object):
pass # no cell found, so just ignore
+class FastMMCIFParser(object):
+ """Parse an MMCIF file and return a Structure object."""
+
+ def __init__(self, structure_builder=None, QUIET=False):
+ """Create a FastMMCIFParser object.
+
+ The mmCIF parser calls a number of standard methods in an aggregated
+ StructureBuilder object. Normally this object is instanciated by the
+ parser object itself, but if the user provides his/her own
+ StructureBuilder object, the latter is used instead.
+
+ The main difference between this class and the regular MMCIFParser is
+ that only 'ATOM' and 'HETATM' lines are parsed here. Use if you are
+ interested only in coordinate information.
+
+ Arguments:
+ - structure_builder - an optional user implemented StructureBuilder class.
+ - QUIET - Evaluated as a Boolean. If true, warnings issued in constructing
+ the SMCRA data will be suppressed. If false (DEFAULT), they will be shown.
+ These warnings might be indicative of problems in the mmCIF file!
+ """
+ if structure_builder is not None:
+ self._structure_builder = structure_builder
+ else:
+ self._structure_builder = StructureBuilder()
+
+ self.line_counter = 0
+ self.build_structure = None
+ self.QUIET = bool(QUIET)
+
+ # Public methods
+
+ def get_structure(self, structure_id, filename):
+ """Return the structure.
+
+ Arguments:
+ - structure_id - string, the id that will be used for the structure
+ - filename - name of the mmCIF file OR an open filehandle
+ """
+ with warnings.catch_warnings():
+ if self.QUIET:
+ warnings.filterwarnings("ignore", category=PDBConstructionWarning)
+ with as_handle(filename) as handle:
+ self._build_structure(structure_id, handle)
+
+ return self._structure_builder.get_structure()
+
+ # Private methods
+
+ def _build_structure(self, structure_id, filehandle):
+
+ # Read only _atom_site. and atom_site_anisotrop entries
+ read_atom, read_aniso = False, False
+ _fields, _records = [], []
+ _anisof, _anisors = [], []
+ for line in filehandle:
+ if line.startswith('_atom_site.'):
+ read_atom = True
+ _fields.append(line.strip())
+ elif line.startswith('_atom_site_anisotrop.'):
+ read_aniso = True
+ _anisof.append(line.strip())
+ elif read_atom and line.startswith('#'):
+ read_atom = False
+ elif read_aniso and line.startswith('#'):
+ read_aniso = False
+ elif read_atom:
+ _records.append(line.strip())
+ elif read_aniso:
+ _anisors.append(line.strip())
+
+ # Dumping the shlex module here since this particular
+ # category should be rather straightforward.
+ # Quite a performance boost..
+ _record_tbl = zip(*map(str.split, _records))
+ _anisob_tbl = zip(*map(str.split, _anisors))
+
+ mmcif_dict = dict(zip(_fields, _record_tbl))
+ mmcif_dict.update(dict(zip(_anisof, _anisob_tbl)))
+
+ # Build structure object
+ atom_id_list = mmcif_dict["_atom_site.label_atom_id"]
+ residue_id_list = mmcif_dict["_atom_site.label_comp_id"]
+
+ try:
+ element_list = mmcif_dict["_atom_site.type_symbol"]
+ except KeyError:
+ element_list = None
+
+ seq_id_list = mmcif_dict["_atom_site.label_seq_id"]
+ chain_id_list = mmcif_dict["_atom_site.auth_asym_id"]
+
+ x_list = [float(x) for x in mmcif_dict["_atom_site.Cartn_x"]]
+ y_list = [float(x) for x in mmcif_dict["_atom_site.Cartn_y"]]
+ z_list = [float(x) for x in mmcif_dict["_atom_site.Cartn_z"]]
+ alt_list = mmcif_dict["_atom_site.label_alt_id"]
+ icode_list = mmcif_dict["_atom_site.pdbx_PDB_ins_code"]
+ b_factor_list = mmcif_dict["_atom_site.B_iso_or_equiv"]
+ occupancy_list = mmcif_dict["_atom_site.occupancy"]
+ fieldname_list = mmcif_dict["_atom_site.group_PDB"]
+
+ try:
+ serial_list = [int(n) for n in mmcif_dict["_atom_site.pdbx_PDB_model_num"]]
+ except KeyError:
+ # No model number column
+ serial_list = None
+ except ValueError:
+ # Invalid model number (malformed file)
+ raise PDBConstructionException("Invalid model number")
+
+ try:
+ aniso_u11 = mmcif_dict["_atom_site.aniso_U[1][1]"]
+ aniso_u12 = mmcif_dict["_atom_site.aniso_U[1][2]"]
+ aniso_u13 = mmcif_dict["_atom_site.aniso_U[1][3]"]
+ aniso_u22 = mmcif_dict["_atom_site.aniso_U[2][2]"]
+ aniso_u23 = mmcif_dict["_atom_site.aniso_U[2][3]"]
+ aniso_u33 = mmcif_dict["_atom_site.aniso_U[3][3]"]
+ aniso_flag = 1
+ except KeyError:
+ # no anisotropic B factors
+ aniso_flag = 0
+
+ # if auth_seq_id is present, we use this.
+ # Otherwise label_seq_id is used.
+ if "_atom_site.auth_seq_id" in mmcif_dict:
+ seq_id_list = mmcif_dict["_atom_site.auth_seq_id"]
+ else:
+ seq_id_list = mmcif_dict["_atom_site.label_seq_id"]
+
+ # Now loop over atoms and build the structure
+ current_chain_id = None
+ current_residue_id = None
+ structure_builder = self._structure_builder
+ structure_builder.init_structure(structure_id)
+ structure_builder.init_seg(" ")
+
+ # Historically, Biopython PDB parser uses model_id to mean array index
+ # so serial_id means the Model ID specified in the file
+ current_model_id = -1
+ current_serial_id = -1
+ for i in range(0, len(atom_id_list)):
+
+ # set the line_counter for 'ATOM' lines only and not
+ # as a global line counter found in the PDBParser()
+ # this number should match the '_atom_site.id' index in the MMCIF
+ structure_builder.set_line_counter(i)
+
+ x = x_list[i]
+ y = y_list[i]
+ z = z_list[i]
+ resname = residue_id_list[i]
+ chainid = chain_id_list[i]
+ altloc = alt_list[i]
+ if altloc == ".":
+ altloc = " "
+ int_resseq = int(seq_id_list[i])
+ icode = icode_list[i]
+ if icode == "?":
+ icode = " "
+ name = atom_id_list[i].strip('"') # Remove occasional " from quoted atom names (e.g. xNA)
+
+ # occupancy & B factor
+ try:
+ tempfactor = float(b_factor_list[i])
+ except ValueError:
+ raise PDBConstructionException("Invalid or missing B factor")
+
+ try:
+ occupancy = float(occupancy_list[i])
+ except ValueError:
+ raise PDBConstructionException("Invalid or missing occupancy")
+
+ fieldname = fieldname_list[i]
+ if fieldname == "HETATM":
+ hetatm_flag = "H"
+ else:
+ hetatm_flag = " "
+
+ resseq = (hetatm_flag, int_resseq, icode)
+
+ if serial_list is not None:
+ # model column exists; use it
+ serial_id = serial_list[i]
+ if current_serial_id != serial_id:
+ # if serial changes, update it and start new model
+ current_serial_id = serial_id
+ current_model_id += 1
+ structure_builder.init_model(current_model_id, current_serial_id)
+ current_chain_id = None
+ current_residue_id = None
+ else:
+ # no explicit model column; initialize single model
+ structure_builder.init_model(current_model_id)
+
+ if current_chain_id != chainid:
+ current_chain_id = chainid
+ structure_builder.init_chain(current_chain_id)
+ current_residue_id = None
+
+ if current_residue_id != resseq:
+ current_residue_id = resseq
+ structure_builder.init_residue(resname, hetatm_flag, int_resseq, icode)
+
+ coord = numpy.array((x, y, z), 'f')
+ element = element_list[i] if element_list else None
+ structure_builder.init_atom(name, coord, tempfactor, occupancy, altloc,
+ name, element=element)
+ if aniso_flag == 1:
+ u = (aniso_u11[i], aniso_u12[i], aniso_u13[i],
+ aniso_u22[i], aniso_u23[i], aniso_u33[i])
+ mapped_anisou = [float(x) for x in u]
+ anisou_array = numpy.array(mapped_anisou, 'f')
+ structure_builder.set_anisou(anisou_array)
+
if __name__ == "__main__":
import sys
diff --git a/Bio/PDB/NACCESS.py b/Bio/PDB/NACCESS.py
index c50d18c..7205bd9 100644
--- a/Bio/PDB/NACCESS.py
+++ b/Bio/PDB/NACCESS.py
@@ -26,6 +26,7 @@ import warnings
from Bio.PDB.PDBIO import PDBIO
from Bio.PDB.AbstractPropertyMap import AbstractResiduePropertyMap, AbstractAtomPropertyMap
+
def run_naccess(model, pdb_file, probe_size=None, z_slice=None,
naccess='naccess', temp_path='/tmp/'):
diff --git a/Bio/PDB/NeighborSearch.py b/Bio/PDB/NeighborSearch.py
index 2d09b62..c6ce599 100644
--- a/Bio/PDB/NeighborSearch.py
+++ b/Bio/PDB/NeighborSearch.py
@@ -74,7 +74,7 @@ class NeighborSearch(object):
Return all atoms/residues/chains/models/structures
that have at least one atom within radius of center.
- What entitity level is returned (e.g. atoms or residues)
+ What entity level is returned (e.g. atoms or residues)
is determined by level (A=atoms, R=residues, C=chains,
M=models, S=structures).
diff --git a/Bio/PDB/PDBIO.py b/Bio/PDB/PDBIO.py
index a4febf9..67a088c 100644
--- a/Bio/PDB/PDBIO.py
+++ b/Bio/PDB/PDBIO.py
@@ -10,7 +10,6 @@ from Bio._py3k import basestring
from Bio.PDB.StructureBuilder import StructureBuilder # To allow saving of chains, residues, etc..
from Bio.Data.IUPACData import atom_weights # Allowed Elements
-__docformat__ = "restructuredtext en"
_ATOM_FORMAT_STRING = "%s%5i %-4s%c%3s %c%4i%c %8.3f%8.3f%8.3f%s%6.2f %4s%2s%2s\n"
@@ -156,7 +155,7 @@ class PDBIO(object):
- accept_chain(chain)
- accept_residue(residue)
- accept_atom(atom)
-
+
These methods should return 1 if the entity is to be
written out, 0 otherwise.
@@ -211,8 +210,8 @@ class PDBIO(object):
fp.write("TER\n")
if model_flag and model_residues_written:
fp.write("ENDMDL\n")
- if write_end:
- fp.write('END\n')
+ if write_end:
+ fp.write('END\n')
if close_file:
fp.close()
diff --git a/Bio/PDB/PDBList.py b/Bio/PDB/PDBList.py
index 14e8f7c..20f72f0 100644
--- a/Bio/PDB/PDBList.py
+++ b/Bio/PDB/PDBList.py
@@ -31,8 +31,6 @@ import shutil
from Bio._py3k import urlopen as _urlopen
from Bio._py3k import urlretrieve as _urlretrieve
-__docformat__ = "restructuredtext en"
-
class PDBList(object):
"""
diff --git a/Bio/PDB/PDBParser.py b/Bio/PDB/PDBParser.py
index 23c9df8..a8bf608 100644
--- a/Bio/PDB/PDBParser.py
+++ b/Bio/PDB/PDBParser.py
@@ -78,7 +78,7 @@ class PDBParser(object):
# Make a StructureBuilder instance (pass id of structure as parameter)
self.structure_builder.init_structure(id)
- with as_handle(file) as handle:
+ with as_handle(file, mode='rU') as handle:
self._parse(handle.readlines())
self.structure_builder.set_header(self.header)
@@ -133,7 +133,7 @@ class PDBParser(object):
current_residue_id = None
current_resname = None
for i in range(0, len(coords_trailer)):
- line = coords_trailer[i]
+ line = coords_trailer[i].rstrip('\n')
record_type = line[0:6]
global_line_counter = self.line_counter + local_line_counter + 1
structure_builder.set_line_counter(global_line_counter)
@@ -201,7 +201,7 @@ class PDBParser(object):
global_line_counter)
bfactor = 0.0 # The PDB use a default of zero if the data is missing
segid = line[72:76]
- element = line[76:78].strip()
+ element = line[76:78].strip().upper()
if current_segid != segid:
current_segid = segid
structure_builder.init_seg(current_segid)
diff --git a/Bio/PDB/QCPSuperimposer/__init__.py b/Bio/PDB/QCPSuperimposer/__init__.py
index 2bc88a5..220a332 100644
--- a/Bio/PDB/QCPSuperimposer/__init__.py
+++ b/Bio/PDB/QCPSuperimposer/__init__.py
@@ -17,6 +17,7 @@ from Bio import BiopythonExperimentalWarning
from numpy import dot, sqrt, array, matrix, inner, zeros
from .qcprotmodule import FastCalcRMSDAndRotation
+
warnings.warn('Bio.PDB.QCPSuperimposer is an experimental submodule which may undergo '
'significant changes prior to its future official release.',
BiopythonExperimentalWarning)
diff --git a/Bio/PDB/Residue.py b/Bio/PDB/Residue.py
index 6def595..076286e 100644
--- a/Bio/PDB/Residue.py
+++ b/Bio/PDB/Residue.py
@@ -9,6 +9,7 @@
from Bio.PDB.PDBExceptions import PDBConstructionException
from Bio.PDB.Entity import Entity, DisorderedEntityWrapper
+
_atom_name_dict = {}
_atom_name_dict["N"] = 1
_atom_name_dict["CA"] = 2
diff --git a/Bio/PDB/Selection.py b/Bio/PDB/Selection.py
index 63c484a..5d07ef6 100644
--- a/Bio/PDB/Selection.py
+++ b/Bio/PDB/Selection.py
@@ -54,7 +54,7 @@ def unfold_entities(entity_list, target_level):
raise PDBException("%s: Not an entity level." % target_level)
if entity_list == []:
return []
- if isinstance(entity_list, Entity) or isinstance(entity_list, Atom):
+ if isinstance(entity_list, (Entity, Atom)):
entity_list = [entity_list]
level = entity_list[0].get_level()
diff --git a/Bio/PDB/StructureAlignment.py b/Bio/PDB/StructureAlignment.py
index 335d91f..ccf3d51 100644
--- a/Bio/PDB/StructureAlignment.py
+++ b/Bio/PDB/StructureAlignment.py
@@ -14,8 +14,6 @@ from Bio.Data import SCOPData
from Bio.PDB import Selection
from Bio.PDB.Polypeptide import is_aa
-__docformat__ = "restructuredtext en"
-
class StructureAlignment(object):
"""
@@ -25,7 +23,7 @@ class StructureAlignment(object):
def __init__(self, fasta_align, m1, m2, si=0, sj=1):
"""
Attributes:
-
+
- fasta_align --- Alignment object
- m1, m2 --- two models
- si, sj --- the sequences in the Alignment object that
diff --git a/Bio/PDB/StructureBuilder.py b/Bio/PDB/StructureBuilder.py
index 9132cdd..da38469 100644
--- a/Bio/PDB/StructureBuilder.py
+++ b/Bio/PDB/StructureBuilder.py
@@ -20,8 +20,6 @@ from Bio.PDB.Atom import Atom, DisorderedAtom
from Bio.PDB.PDBExceptions import PDBConstructionException
from Bio.PDB.PDBExceptions import PDBConstructionWarning
-__docformat__ = "restructuredtext en"
-
class StructureBuilder(object):
"""
@@ -102,7 +100,7 @@ class StructureBuilder(object):
Initiate a new Residue object.
Arguments:
-
+
- resname - string, e.g. "ASN"
- field - hetero flag, "W" for waters, "H" for
hetero residues, otherwise blank.
diff --git a/Bio/PDB/__init__.py b/Bio/PDB/__init__.py
index c02bbd5..10f476b 100644
--- a/Bio/PDB/__init__.py
+++ b/Bio/PDB/__init__.py
@@ -14,12 +14,8 @@ Author: Thomas Hamelryck. Additional code by Kristian Rother.
# Get a Structure object from a PDB file
from .PDBParser import PDBParser
-try:
- # Get a Structure object from an mmCIF file
- from .MMCIFParser import MMCIFParser
-except:
- # Not compiled I guess
- pass
+from .MMCIFParser import MMCIFParser
+from .MMCIFParser import FastMMCIFParser
# Download from the PDB
from .PDBList import PDBList
diff --git a/Bio/PDB/mmCIF/Makefile b/Bio/PDB/mmCIF/Makefile
new file mode 100644
index 0000000..79bd95a
--- /dev/null
+++ b/Bio/PDB/mmCIF/Makefile
@@ -0,0 +1,31 @@
+PYTHON_INC_DIR=-I/usr/include/python2.3/ -I/usr/include/python2.3/Numeric/
+CC=gcc
+LEX=flex
+
+all: MMCIFlexmodule.so mmcif_test
+
+MMCIFlexmodule.so: lex.yy.o MMCIFlexmodule.o
+ $(CC) -fPIC -shared -o $@ $^ -lfl
+
+MMCIFlexmodule.o: MMCIFlexmodule.c
+ $(CC) -c -fPIC $(PYTHON_INC_DIR) -o $@ $<
+
+#mmcifparser.o: mmcifparser.c
+# $(CC) -c -fPIC $(PYTHON_INC_DIR) $<
+
+lex.yy.c: mmcif.lex
+ $(LEX) mmcif.lex
+
+lex.yy.o: lex.yy.c
+ $(CC) -fPIC -c $<
+
+mmcif_test: mmcif_test.o lex.yy.o
+ $(CC) $^ -lfl -o $@
+
+.PHONY: links clean veryclean
+
+clean:
+ rm *.o
+
+veryclean: clean
+ - rm *.so lex.yy.c mmcif_test *~
diff --git a/Bio/PDB/mmCIF/__init__.py b/Bio/PDB/mmCIF/__init__.py
new file mode 100644
index 0000000..4adc984
--- /dev/null
+++ b/Bio/PDB/mmCIF/__init__.py
@@ -0,0 +1 @@
+# Python module
diff --git a/Bio/PDB/mmCIF/mmcif.lex b/Bio/PDB/mmCIF/mmcif.lex
new file mode 100644
index 0000000..b7fba51
--- /dev/null
+++ b/Bio/PDB/mmCIF/mmcif.lex
@@ -0,0 +1,62 @@
+%option noyywrap
+COMMENT #.*\n
+NAME _[^ \t\n]+
+LOOP [Ll][Oo][Oo][Pp]_
+DATA [Dd][Aa][Tt][Aa]_[^ \t\n]+
+FREE_VALUE [^ \t\n]+
+SINGLE_QUOTE_VALUE '[^'\n]*'
+DOUBLE_QUOTE_VALUE \"[^"\n]*\"
+SEMICOLON_VALUE ^;(.*\n[^;])*.*\n;
+
+/*
+Number code of the tokens is:
+
+NAME (eg. _entity.id) 1
+LOOP (loop_) 2
+DATA (eg. data_something) 3
+SEMICOLON (eg. ;value\n;) 4
+DOUBLE QUOTE (eg. "value") 5
+SINGLE QUOTE (eg. 'value') 6
+FREE (eg. value ) 7
+
+*/
+
+%%
+
+{COMMENT} /* ignore */
+
+{NAME} { return 1; }
+
+{LOOP} { return 2; }
+
+{DATA} { return 3; }
+
+{SEMICOLON_VALUE} { return 4; }
+
+{DOUBLE_QUOTE_VALUE} { return 5; }
+
+{SINGLE_QUOTE_VALUE} { return 6; }
+
+{FREE_VALUE} { return 7; }
+
+[ \t\n]+ /* ignore */
+
+%%
+
+
+void mmcif_set_file(FILE *fp)
+{
+ yyin=fp;
+}
+
+int mmcif_get_token(void)
+{
+ extern int yylex(void);
+ return yylex();
+}
+
+char *mmcif_get_string(void)
+{
+ return yytext;
+}
+
diff --git a/Bio/ParserSupport.py b/Bio/ParserSupport.py
index 225094b..62437f9 100644
--- a/Bio/ParserSupport.py
+++ b/Bio/ParserSupport.py
@@ -10,8 +10,6 @@ Classes:
- AbstractParser Base class for parsers.
- AbstractConsumer Base class of all Consumers.
- TaggingConsumer Consumer that tags output with its event. For debugging
- - EventGenerator Generate Biopython Events from Martel XML output
- (note that Martel has been removed)
Functions:
@@ -25,34 +23,14 @@ Functions:
"""
+import sys
+from Bio._py3k import StringIO
+
from Bio import BiopythonDeprecationWarning
import warnings
warnings.warn("Bio.ParserSupport is now deprecated will be removed in a "
"future release of Biopython.", BiopythonDeprecationWarning)
-import sys
-try:
- from types import InstanceType
-except ImportError:
- # Python 3, see http://bugs.python.org/issue8206
- InstanceType = object
-from types import MethodType
-
-from Bio._py3k import StringIO
-
-from Bio import File
-
-# XML from python 2.0
-try:
- from xml.sax import handler
- xml_support = 1
-except ImportError:
- sys.stderr.write("Warning: Could not import SAX for dealing with XML.\n" +
- "This causes problems with some ParserSupport modules\n")
- xml_support = 0
-
-__docformat__ = "restructuredtext en"
-
class AbstractParser(object):
"""Base class for other parsers.
@@ -133,139 +111,6 @@ class TaggingConsumer(AbstractConsumer):
return method
-# onle use the Event Generator if XML handling is okay
-if xml_support:
- class EventGenerator(handler.ContentHandler):
- """Handler to generate events associated with a Martel parsed file.
-
- This acts like a normal SAX handler, and accepts XML generated by
- Martel during parsing. These events are then converted into
- 'Biopython events', which can then be caught by a standard
- biopython consumer.
-
- Note that Martel is now DEPRECATED.
- """
- def __init__(self, consumer, interest_tags, callback_finalizer=None,
- exempt_tags=[]):
- """Initialize to begin catching and firing off events.
-
- Arguments:
- o consumer - The consumer that we'll send Biopython events to.
-
- o interest_tags - A listing of all the tags we are interested in.
-
- o callback_finalizer - A function to deal with the collected
- information before passing it on to the consumer. By default
- the collected information is a list of all of the lines read
- for a particular tag -- if there are multiple tags in a row
- like:
-
- <some_info>Spam<some_info>
- <some_info>More Spam<some_info>
-
- In this case the list of information would be:
-
- ['Spam', 'More Spam']
-
- This list of lines will be passed to the callback finalizer if
- it is present. Otherwise the consumer will be called with the
- list of content information.
-
- o exempt_tags - A listing of particular tags that are exempt from
- being processed by the callback_finalizer. This allows you to
- use a finalizer to deal with most tags, but leave those you don't
- want touched.
- """
- self._consumer = consumer
- self.interest_tags = interest_tags
- self._finalizer = callback_finalizer
- self._exempt_tags = exempt_tags
-
- # a dictionary of content for each tag of interest
- # the information for each tag is held as a list of the lines.
- # This allows us to collect information from multiple tags
- # in a row, and return it all at once.
- self.info = {}
- for tag in self.interest_tags:
- self.info[tag] = []
-
- # the previous tag we were collecting information for.
- # We set a delay in sending info to the consumer so that we can
- # collect a bunch of tags in a row and append all of the info
- # together.
- self._previous_tag = ''
-
- # the current character information for a tag
- self._cur_content = []
- # whether we should be collecting information
- self._collect_characters = 0
-
- def startElement(self, name, attrs):
- """Determine if we should collect characters from this tag.
- """
- if name in self.interest_tags:
- self._collect_characters = 1
-
- def characters(self, content):
- """Extract the information if we are interested in it.
- """
- if self._collect_characters:
- self._cur_content.append(content)
-
- def endElement(self, name):
- """Send the information to the consumer.
-
- Once we've got the end element we've collected up all of the
- character information we need, and we need to send this on to
- the consumer to do something with it.
-
- We have a delay of one tag on doing this, so that we can collect
- all of the info from multiple calls to the same element at once.
- """
- # only deal with the tag if it is something we are
- # interested in and potentially have information for
- if self._collect_characters:
- # add all of the information collected inside this tag
- self.info[name].append("".join(self._cur_content))
- # reset our information and flags
- self._cur_content = []
- self._collect_characters = 0
-
- # if we are at a new tag, pass on the info from the last tag
- if self._previous_tag and self._previous_tag != name:
- self._make_callback(self._previous_tag)
-
- # set this tag as the next to be passed
- self._previous_tag = name
-
- def _make_callback(self, name):
- """Call the callback function with the info with the given name.
- """
- # strip off whitespace and call the consumer
- callback_function = getattr(self._consumer, name)
-
- # --- pass back the information
- # if there is a finalizer, use that
- if self._finalizer is not None and name not in self._exempt_tags:
- info_to_pass = self._finalizer(self.info[name])
- # otherwise pass back the entire list of information
- else:
- info_to_pass = self.info[name]
-
- callback_function(info_to_pass)
-
- # reset the information for the tag
- self.info[name] = []
-
- def endDocument(self):
- """Make sure all of our information has been passed.
-
- This just flushes out any stored tags that need to be passed.
- """
- if self._previous_tag:
- self._make_callback(self._previous_tag)
-
-
def read_and_call(uhandle, method, **keywds):
"""read_and_call(uhandle, method[, start][, end][, contains][, blank][, has_re])
diff --git a/Bio/Pathway/Rep/Graph.py b/Bio/Pathway/Rep/Graph.py
index f325897..9669f6c 100644
--- a/Bio/Pathway/Rep/Graph.py
+++ b/Bio/Pathway/Rep/Graph.py
@@ -11,7 +11,7 @@ from functools import reduce
class Graph(object):
"""A directed graph abstraction with labeled edges."""
- def __init__(self, nodes=[]):
+ def __init__(self, nodes=()):
"""Initializes a new Graph object."""
self._adjacency_list = {} # maps parent -> set of child objects
for n in nodes:
@@ -119,7 +119,7 @@ class Graph(object):
for n in self._adjacency_list.keys():
self._adjacency_list[n] = set(x for x in self._adjacency_list[n]
if x != node)
- # remove all refering pairs in label map
+ # remove all referring pairs in label map
for label in list(self._label_map.keys()): # we're editing this!
lm = set(x for x in self._label_map[label]
if (x[0] != node) and (x[1] != node))
@@ -128,7 +128,7 @@ class Graph(object):
self._label_map[label] = lm
else:
del self._label_map[label]
- # remove all refering entries in edge map
+ # remove all referring entries in edge map
for edge in list(self._edge_map.keys()): # we're editing this!
if edge[0] == node or edge[1] == node:
del self._edge_map[edge]
diff --git a/Bio/Pathway/Rep/MultiGraph.py b/Bio/Pathway/Rep/MultiGraph.py
index be3e9c7..37ad546 100644
--- a/Bio/Pathway/Rep/MultiGraph.py
+++ b/Bio/Pathway/Rep/MultiGraph.py
@@ -12,7 +12,7 @@ from functools import reduce
class MultiGraph(object):
"""A directed multigraph abstraction with labeled edges."""
- def __init__(self, nodes=[]):
+ def __init__(self, nodes=()):
"""Initializes a new MultiGraph object."""
self._adjacency_list = {} # maps parent -> set of (child, label) pairs
for n in nodes:
@@ -114,7 +114,7 @@ class MultiGraph(object):
for n in self._adjacency_list:
self._adjacency_list[n] = set(x for x in self._adjacency_list[n]
if x[0] != node)
- # remove all refering pairs in label map
+ # remove all referring pairs in label map
for label in list(self._label_map.keys()): # we're editing this!
lm = set(x for x in self._label_map[label]
if (x[0] != node) and (x[1] != node))
@@ -129,7 +129,7 @@ class MultiGraph(object):
# hm , this is a multigraph - how should this be implemented?
raise NotImplementedError("remove_edge is not yet implemented")
-# auxilliary graph functions
+# auxiliary graph functions
def df_search(graph, root=None):
diff --git a/Bio/Pathway/__init__.py b/Bio/Pathway/__init__.py
index 3eb2d15..369f6de 100644
--- a/Bio/Pathway/__init__.py
+++ b/Bio/Pathway/__init__.py
@@ -34,8 +34,6 @@ from functools import reduce
from Bio.Pathway.Rep.MultiGraph import *
-__docformat__ = "restructuredtext en"
-
class Reaction(object):
"""Abstraction for a biochemical transformation.
@@ -57,9 +55,9 @@ class Reaction(object):
Attributes:
- - reactants -- map of involved species to their stochiometric coefficients:
+ - reactants -- dict of involved species to their stochiometric coefficients:
reactants[S] = stochiometric constant for S
- - catalysts -- list of tuples of catalysts required for this reaction
+ - catalysts -- list/tuple of tuples of catalysts required for this reaction
- reversible -- true iff reaction is reversible
- data -- reference to arbitrary additional data
@@ -70,15 +68,18 @@ class Reaction(object):
"""
- def __init__(self, reactants={}, catalysts=[],
+ def __init__(self, reactants=None, catalysts=(),
reversible=0, data=None):
"""Initializes a new Reaction object."""
# enforce invariants on reactants:
- self.reactants = reactants.copy()
- # loop over original, edit the copy
- for r, value in reactants.items():
- if value == 0:
- del self.reactants[r]
+ if reactants is None:
+ self.reactants = {}
+ else:
+ self.reactants = reactants.copy()
+ # loop over original, edit the copy
+ for r, value in reactants.items():
+ if value == 0:
+ del self.reactants[r]
self.catalysts = sorted(set(catalysts))
self.data = data
self.reversible = reversible
@@ -160,7 +161,7 @@ class System(object):
None
"""
- def __init__(self, reactions=[]):
+ def __init__(self, reactions=()):
"""Initializes a new System object."""
self.__reactions = set(reactions)
@@ -262,7 +263,7 @@ class Network(object):
None
"""
- def __init__(self, species=[]):
+ def __init__(self, species=()):
"""Initializes a new Network object."""
self.__graph = MultiGraph(species)
diff --git a/Bio/Phylo/Applications/_Fasttree.py b/Bio/Phylo/Applications/_Fasttree.py
index 9834e9a..5796f1e 100644
--- a/Bio/Phylo/Applications/_Fasttree.py
+++ b/Bio/Phylo/Applications/_Fasttree.py
@@ -5,7 +5,6 @@
"""Command-line wrapper for tree inference program Fasttree."""
from __future__ import print_function
-__docformat__ = "restructuredtext en"
from Bio.Application import _Option, _Switch, _Argument, AbstractCommandline
diff --git a/Bio/Phylo/Applications/_Phyml.py b/Bio/Phylo/Applications/_Phyml.py
index 9bff1ea..54e1823 100644
--- a/Bio/Phylo/Applications/_Phyml.py
+++ b/Bio/Phylo/Applications/_Phyml.py
@@ -3,7 +3,6 @@
# Please see the LICENSE file that should have been included as part of this
# package.
"""Command-line wrapper for the tree inference program PhyML."""
-__docformat__ = "restructuredtext en"
from Bio._py3k import basestring
@@ -105,8 +104,8 @@ class PhymlCommandline(AbstractCommandline):
# Amino acid models:
'LG', 'WAG', 'JTT', 'MtREV', 'Dayhoff', 'DCMut',
'RtREV', 'CpREV', 'VT', 'Blosum62', 'MtMam', 'MtArt',
- 'HIVw', 'HIVb')
- or isinstance(x, int)),
+ 'HIVw', 'HIVb') or
+ isinstance(x, int)),
equate=False,
),
@@ -118,11 +117,11 @@ class PhymlCommandline(AbstractCommandline):
e : Empirical frequencies, determined as follows :
- Nucleotide sequences: (Empirical) the equilibrium base
- frequencies are estimated by counting the occurence of the
+ frequencies are estimated by counting the occurrence of the
different bases in the alignment.
- Amino-acid sequences: (Empirical) the equilibrium
amino-acid frequencies are estimated by counting the
- occurence of the different amino-acids in the alignment.
+ occurrence of the different amino-acids in the alignment.
m : ML/model-based frequencies, determined as follows :
diff --git a/Bio/Phylo/Applications/_Raxml.py b/Bio/Phylo/Applications/_Raxml.py
index 048b28c..19d40b4 100644
--- a/Bio/Phylo/Applications/_Raxml.py
+++ b/Bio/Phylo/Applications/_Raxml.py
@@ -10,8 +10,6 @@ should work for any version 7.X (and probably earlier for most options).
from __future__ import print_function
from Bio._py3k import basestring
-__docformat__ = "restructuredtext en"
-
from Bio.Application import _Option, _Switch, AbstractCommandline
@@ -370,3 +368,8 @@ class RaxmlCommandline(AbstractCommandline):
# ENH: enforce -s, -n and -m
if not self.parsimony_seed:
self.parsimony_seed = 10000
+
+
+if __name__ == "__main__":
+ from Bio._utils import run_doctest
+ run_doctest()
diff --git a/Bio/Phylo/Applications/__init__.py b/Bio/Phylo/Applications/__init__.py
index 3d2b3ff..dd5b82f 100644
--- a/Bio/Phylo/Applications/__init__.py
+++ b/Bio/Phylo/Applications/__init__.py
@@ -3,7 +3,6 @@
# license. Please see the LICENSE file that should have been included
# as part of this package.
"""Phylogenetics command line tool wrappers."""
-__docformat__ = "restructuredtext en"
from ._Phyml import PhymlCommandline
from ._Raxml import RaxmlCommandline
diff --git a/Bio/Phylo/BaseTree.py b/Bio/Phylo/BaseTree.py
index 04c6956..362f3a2 100644
--- a/Bio/Phylo/BaseTree.py
+++ b/Bio/Phylo/BaseTree.py
@@ -8,7 +8,6 @@
All object representations for phylogenetic trees should derive from these base
classes in order to use the common methods defined on them.
"""
-__docformat__ = "restructuredtext en"
from Bio._py3k import basestring, filter, unicode, zip
@@ -20,7 +19,6 @@ import re
from Bio import _utils
-
# NB: On Python 2, repr() and str() are specified to return byte strings, not
# unicode. On Python 3, it's the opposite. Horrible.
import sys
@@ -335,8 +333,8 @@ class TreeMixin(object):
Example
-------
- >>> from Bio.Phylo.IO import PhyloXMIO
- >>> phx = PhyloXMLIO.read('phyloxml_examples.xml')
+ >>> from Bio import Phylo
+ >>> phx = Phylo.PhyloXMLIO.read('PhyloXML/phyloxml_examples.xml')
>>> matches = phx.phylogenies[5].find_elements(code='OCTVU')
>>> next(matches)
Taxonomy(code='OCTVU', scientific_name='Octopus vulgaris')
@@ -591,8 +589,13 @@ class TreeMixin(object):
For example, this will safely collapse nodes with poor bootstrap
support:
- >>> tree.collapse_all(lambda c: c.confidence is not None and
- ... c.confidence < 70)
+ >>> from Bio import Phylo
+ >>> tree = Phylo.read('PhyloXML/apaf.xml', 'phyloxml')
+ >>> print("Total branch length %0.2f" % tree.total_branch_length())
+ Total branch length 20.44
+ >>> tree.collapse_all(lambda c: c.confidence is not None and c.confidence < 70)
+ >>> print("Total branch length %0.2f" % tree.total_branch_length())
+ Total branch length 21.37
This implementation avoids strange side-effects by using level-order
traversal and testing all clade properties (versus the target
@@ -600,7 +603,13 @@ class TreeMixin(object):
specification in the original tree, it will be collapsed. For example,
if the condition is:
+ >>> from Bio import Phylo
+ >>> tree = Phylo.read('PhyloXML/apaf.xml', 'phyloxml')
+ >>> print("Total branch length %0.2f" % tree.total_branch_length())
+ Total branch length 20.44
>>> tree.collapse_all(lambda c: c.branch_length < 0.1)
+ >>> print("Total branch length %0.2f" % tree.total_branch_length())
+ Total branch length 21.13
Collapsing a clade's parent node adds the parent's branch length to the
child, so during the execution of collapse_all, a clade's branch_length
@@ -1023,7 +1032,7 @@ class Clade(TreeElement, TreeMixin):
def __getitem__(self, index):
"""Get clades by index (integer or slice)."""
- if isinstance(index, int) or isinstance(index, slice):
+ if isinstance(index, (int, slice)):
return self.clades[index]
ref = self
for idx in index:
diff --git a/Bio/Phylo/CDAO.py b/Bio/Phylo/CDAO.py
index 38b45e6..91a79e9 100644
--- a/Bio/Phylo/CDAO.py
+++ b/Bio/Phylo/CDAO.py
@@ -8,7 +8,6 @@
See classes in `Bio.Nexus`: Trees.Tree, Trees.NodeData, and Nodes.Chain.
"""
-__docformat__ = "restructuredtext en"
from Bio.Phylo import BaseTree
diff --git a/Bio/Phylo/CDAOIO.py b/Bio/Phylo/CDAOIO.py
index ab3aca1..b9ef6be 100644
--- a/Bio/Phylo/CDAOIO.py
+++ b/Bio/Phylo/CDAOIO.py
@@ -19,8 +19,6 @@ the CDAOIO.Writer can store triples in a triple store instead of serializing
them to a file.
"""
-__docformat__ = "restructuredtext en"
-
from Bio._py3k import StringIO
from Bio.Phylo import CDAO
@@ -117,7 +115,8 @@ class Parser(object):
if 'base_uri' in kwargs:
base_uri = kwargs['base_uri']
else:
- base_uri = "file://" + os.path.abspath(self.handle.name)
+ # Windows style slashes cannot be used in an RDF URI
+ base_uri = "file://" + os.path.abspath(self.handle.name).replace("\\", "/")
graph.parse(file=self.handle, publicID=base_uri, format=parse_format)
diff --git a/Bio/Phylo/Consensus.py b/Bio/Phylo/Consensus.py
index 9e6f1c9..3d7805b 100644
--- a/Bio/Phylo/Consensus.py
+++ b/Bio/Phylo/Consensus.py
@@ -17,8 +17,6 @@ import itertools
from ast import literal_eval
from Bio.Phylo import BaseTree
-__docformat__ = "restructuredtext en"
-
class _BitString(str):
"""Helper class for binary string data (PRIVATE).
@@ -114,14 +112,10 @@ class _BitString(str):
False
>>> bitstr2.independent(bitstr3)
False
- >>> bitstr2.independent(bitstr4)
- True
>>> bitstr1.iscompatible(bitstr2)
True
>>> bitstr2.iscompatible(bitstr3)
False
- >>> bitstr2.iscompatible(bitstr4)
- True
"""
def __new__(cls, strdata):
diff --git a/Bio/Phylo/NeXML.py b/Bio/Phylo/NeXML.py
index 3b0ca05..60fb873 100644
--- a/Bio/Phylo/NeXML.py
+++ b/Bio/Phylo/NeXML.py
@@ -8,7 +8,6 @@
See classes in `Bio.Nexus`: Trees.Tree, Trees.NodeData, and Nodes.Chain.
"""
-__docformat__ = "restructuredtext en"
from Bio.Phylo import BaseTree
diff --git a/Bio/Phylo/NeXMLIO.py b/Bio/Phylo/NeXMLIO.py
index eba2411..d009b04 100644
--- a/Bio/Phylo/NeXMLIO.py
+++ b/Bio/Phylo/NeXMLIO.py
@@ -11,7 +11,6 @@
See: http://www.nexml.org
"""
-__docformat__ = "restructuredtext en"
from Bio._py3k import StringIO
@@ -185,8 +184,8 @@ class Parser(object):
# with the first node that's not a child of any other nodes
rooted = False
possible_roots = (node.attrib['id'] for node in nodes
- if node.attrib['id'] in srcs
- and not node.attrib['id'] in tars)
+ if node.attrib['id'] in srcs and
+ node.attrib['id'] not in tars)
root = next(possible_roots)
else:
rooted = True
diff --git a/Bio/Phylo/Newick.py b/Bio/Phylo/Newick.py
index e5fd8c1..5442a3a 100644
--- a/Bio/Phylo/Newick.py
+++ b/Bio/Phylo/Newick.py
@@ -8,8 +8,6 @@
See classes in `Bio.Nexus`: Trees.Tree, Trees.NodeData, and Nodes.Chain.
"""
-__docformat__ = "restructuredtext en"
-
from Bio.Phylo import BaseTree
diff --git a/Bio/Phylo/NewickIO.py b/Bio/Phylo/NewickIO.py
index 1d98ce7..8f2f5b5 100644
--- a/Bio/Phylo/NewickIO.py
+++ b/Bio/Phylo/NewickIO.py
@@ -10,8 +10,6 @@
See: http://evolution.genetics.washington.edu/phylip/newick_doc.html
"""
-__docformat__ = "restructuredtext en"
-
import re
from Bio._py3k import StringIO
@@ -24,15 +22,15 @@ class NewickError(Exception):
tokens = [
- (r"\(", 'open parens'),
- (r"\)", 'close parens'),
- (r"[^\s\(\)\[\]\'\:\;\,]+", 'unquoted node label'),
- (r"\:[0-9]*\.?[0-9]+([eE][+-]?[0-9]+)?", 'edge length'),
- (r"\,", 'comma'),
- (r"\[(\\.|[^\]])*\]", 'comment'),
- (r"\'(\\.|[^\'])*\'", 'quoted node label'),
- (r"\;", 'semicolon'),
- (r"\n", 'newline'),
+ (r"\(", 'open parens'),
+ (r"\)", 'close parens'),
+ (r"[^\s\(\)\[\]\'\:\;\,]+", 'unquoted node label'),
+ (r"\:[+-]?[0-9]*\.?[0-9]+([eE][+-]?[0-9]+)?", 'edge length'),
+ (r"\,", 'comma'),
+ (r"\[(\\.|[^\]])*\]", 'comment'),
+ (r"\'(\\.|[^\'])*\'", 'quoted node label'),
+ (r"\;", 'semicolon'),
+ (r"\n", 'newline'),
]
tokenizer = re.compile('(%s)' % '|'.join(token[0] for token in tokens))
token_dict = dict((name, re.compile(token)) for (token, name) in tokens)
@@ -273,8 +271,7 @@ class Writer(object):
'\\', '\\\\').replace("'", "\\'")
if clade.is_terminal(): # terminal
- return (label
- + make_info_string(clade, terminal=True))
+ return (label + make_info_string(clade, terminal=True))
else:
subtrees = (newickize(sub) for sub in clade)
return '(%s)%s' % (','.join(subtrees),
diff --git a/Bio/Phylo/NexusIO.py b/Bio/Phylo/NexusIO.py
index e9ad09e..cf6b5af 100644
--- a/Bio/Phylo/NexusIO.py
+++ b/Bio/Phylo/NexusIO.py
@@ -4,7 +4,6 @@
# as part of this package.
"""I/O function wrappers for `Bio.Nexus` trees."""
-__docformat__ = "restructuredtext en"
from itertools import chain
@@ -25,7 +24,7 @@ End;
"""
# 'index' starts from 1; 'tree' is the Newick tree string
-TREE_TEMPLATE = "Tree tree%(index)d=[&U]%(tree)s"
+TREE_TEMPLATE = "Tree tree%(index)d=%(tree)s"
def parse(handle):
diff --git a/Bio/Phylo/PAML/_paml.py b/Bio/Phylo/PAML/_paml.py
index 9304bea..9625171 100644
--- a/Bio/Phylo/PAML/_paml.py
+++ b/Bio/Phylo/PAML/_paml.py
@@ -8,6 +8,7 @@ from __future__ import print_function
import os
import subprocess
+
try:
from os.path import relpath as _relpath
except ImportError:
@@ -42,14 +43,14 @@ except ImportError:
class PamlError(EnvironmentError):
"""paml has failed. Run with verbose = True to view the error
-message"""
+ message"""
class Paml(object):
"""Base class for wrapping PAML commands."""
def __init__(self, alignment=None, working_dir=None,
- out_file=None):
+ out_file=None):
if working_dir is None:
self.working_dir = os.getcwd()
else:
@@ -76,7 +77,7 @@ class Paml(object):
"""Set the value of an option.
This function abstracts the options dict to prevent the user from
- adding options that do not exist or mispelling options.
+ adding options that do not exist or misspelling options.
"""
for option, value in kwargs.items():
if option not in self._options:
@@ -142,7 +143,7 @@ class Paml(object):
else:
# To suppress output, redirect it to a pipe to nowhere
result_code = subprocess.call([command, self.ctl_file],
- stdout=subprocess.PIPE)
+ stdout=subprocess.PIPE)
else:
if not os.path.exists(ctl_file):
raise IOError("The specified control file does not exist.")
@@ -150,14 +151,14 @@ class Paml(object):
result_code = subprocess.call([command, ctl_file])
else:
result_code = subprocess.call([command, ctl_file],
- stdout=subprocess.PIPE)
+ stdout=subprocess.PIPE)
os.chdir(cwd)
if result_code > 0:
# If the program fails for any reason
raise PamlError(
- "%s has failed (return code %i). Run with verbose = True to view error message"
- % (command, result_code))
+ "%s has failed (return code %i). Run with verbose = True to view error message"
+ % (command, result_code))
if result_code < 0:
# If the paml process is killed by a signal somehow
raise EnvironmentError("The %s process was killed (return code %i)."
- % (command, result_code))
+ % (command, result_code))
diff --git a/Bio/Phylo/PAML/_parse_baseml.py b/Bio/Phylo/PAML/_parse_baseml.py
index 22405d3..edd6973 100644
--- a/Bio/Phylo/PAML/_parse_baseml.py
+++ b/Bio/Phylo/PAML/_parse_baseml.py
@@ -5,6 +5,7 @@
import re
+
line_floats_re = re.compile("-*\d+\.\d+")
@@ -62,7 +63,7 @@ def parse_parameters(lines, results, num_params):
def parse_parameter_list(lines, parameters, num_params):
- """ Parse the parameters list, which is just an unlabeled list of numeric values.
+ """Parse the parameters list, which is just an unlabeled list of numeric values.
"""
for line_num in range(len(lines)):
line = lines[line_num]
@@ -78,7 +79,7 @@ def parse_parameter_list(lines, parameters, num_params):
if len(line_floats) == num_params:
parameters["parameter list"] = line.strip()
# Find SEs. The same format as parameters above is maintained
- # since there is a correspondance between the SE format and
+ # since there is a correspondence between the SE format and
# the parameter format.
# Example match:
# "SEs for parameters:
@@ -238,7 +239,7 @@ def parse_freqs(lines, parameters):
elif "(frequency parameters for branches)" in line:
parameters["nodes"] = {}
branch_freqs_found = True
- elif branch_freqs_found is True:
+ elif branch_freqs_found:
if len(line_floats) > 0:
node_res = re.match("Node \#(\d+)", line)
node_num = int(node_res.group(1))
diff --git a/Bio/Phylo/PAML/_parse_codeml.py b/Bio/Phylo/PAML/_parse_codeml.py
index 8760987..7b8bca1 100644
--- a/Bio/Phylo/PAML/_parse_codeml.py
+++ b/Bio/Phylo/PAML/_parse_codeml.py
@@ -1,10 +1,11 @@
-# Copyright (C) 2011 by Brandon Invergo (b.invergo at gmail.com)
+# Copyright (C) 2011, 2016 by Brandon Invergo (b.invergo at gmail.com)
# This code is part of the Biopython distribution and governed by its
# license. Please see the LICENSE file that should have been included
# as part of this package.
import re
+
line_floats_re = re.compile("-*\d+\.\d+")
try:
@@ -99,11 +100,12 @@ def parse_nssites(lines, results, multi_models, multi_genes):
if siteclass_model is None:
siteclass_model = "one-ratio"
current_model = {"one-ratio": 0,
- "NearlyNeutral": 1,
- "PositiveSelection": 2,
- "discrete": 3,
- "beta": 7,
- "beta&w>1": 8}[siteclass_model]
+ "NearlyNeutral": 1,
+ "PositiveSelection": 2,
+ "discrete": 3,
+ "beta": 7,
+ "beta&w>1": 8,
+ "M2a_rel": 22}[siteclass_model]
if multi_genes:
genes = results["genes"]
current_gene = None
@@ -199,7 +201,7 @@ def parse_model(lines, results):
elif len(line_floats) == num_params and not SEs_flag:
parameters["parameter list"] = line.strip()
# Find SEs. The same format as parameters above is maintained
- # since there is a correspondance between the SE format and
+ # since there is a correspondence between the SE format and
# the parameter format.
# Example match:
# "SEs for parameters:
@@ -256,7 +258,7 @@ def parse_model(lines, results):
if parameters.get("genes") is None:
parameters["genes"] = {}
parameters["genes"][gene_num] = {"kappa": line_floats[0],
- "omega": line_floats[1]}
+ "omega": line_floats[1]}
# Find dN values.
# Example match: "tree length for dN: 0.2990"
elif "tree length for dN" in line and len(line_floats) > 0:
@@ -289,7 +291,7 @@ def parse_model(lines, results):
site_classes = parameters.get("site classes")
branch_type_no = int(branch_type.group(1))
site_classes = parse_clademodelc(branch_type_no, line_floats,
- site_classes)
+ site_classes)
parameters["site classes"] = site_classes
# Find the omega values of the foreground branch for each site
# class in the branch site A model
@@ -344,7 +346,8 @@ def parse_model(lines, results):
def parse_siteclass_proportions(line_floats):
- """For models which have multiple site classes, find the proportion of the alignment assigned to each class.
+ """For models which have multiple site classes, find the proportion of the
+ alignment assigned to each class.
"""
site_classes = {}
if len(line_floats) > 0:
@@ -354,7 +357,8 @@ def parse_siteclass_proportions(line_floats):
def parse_siteclass_omegas(line, site_classes):
- """For models which have multiple site classes, find the omega estimated for each class.
+ """For models which have multiple site classes, find the omega estimated
+ for each class.
"""
# The omega results are tabular with strictly 9 characters per column
# (1 to 3 digits before the decimal point and 5 after). This causes
@@ -390,11 +394,9 @@ def parse_branch_site_a(foreground, line_floats, site_classes):
if site_classes[n].get("branch types") is None:
site_classes[n]["branch types"] = {}
if foreground:
- site_classes[n]["branch types"]["foreground"] =\
- line_floats[n]
+ site_classes[n]["branch types"]["foreground"] = line_floats[n]
else:
- site_classes[n]["branch types"]["background"] =\
- line_floats[n]
+ site_classes[n]["branch types"]["background"] = line_floats[n]
return site_classes
@@ -427,11 +429,11 @@ def parse_pairwise(lines, results):
pairwise[seq2][seq1] = pairwise[seq1][seq2]
elif len(line_floats) == 6:
pairwise[seq1][seq2] = {"t": line_floats[0],
- "S": line_floats[1],
- "N": line_floats[2],
- "omega": line_floats[3],
- "dN": line_floats[4],
- "dS": line_floats[5]}
+ "S": line_floats[1],
+ "N": line_floats[2],
+ "omega": line_floats[3],
+ "dN": line_floats[4],
+ "dS": line_floats[5]}
pairwise[seq2][seq1] = pairwise[seq1][seq2]
if len(pairwise) > 0:
results["pairwise"] = pairwise
@@ -461,7 +463,7 @@ def parse_distances(lines, results):
# Parse AA distances (raw or ML), in a lower diagonal matrix
matrix_row_res = matrix_row_re.match(line)
if matrix_row_res and (raw_aa_distances_flag or
- ml_aa_distances_flag):
+ ml_aa_distances_flag):
seq_name = matrix_row_res.group(1).strip()
if seq_name not in sequences:
sequences.append(seq_name)
diff --git a/Bio/Phylo/PAML/_parse_yn00.py b/Bio/Phylo/PAML/_parse_yn00.py
index 3d41376..d9f5bc5 100644
--- a/Bio/Phylo/PAML/_parse_yn00.py
+++ b/Bio/Phylo/PAML/_parse_yn00.py
@@ -38,7 +38,7 @@ def parse_yn00(lines, results, sequences):
""" Parse the Yang & Nielsen (2000) part of the results.
Yang & Nielsen results are organized in a table with
each row comprising one pairwise species comparison.
- Rows are labeled by spequence number rather than by
+ Rows are labeled by sequence number rather than by
sequence name."""
# Example (header row and first table row):
diff --git a/Bio/Phylo/PAML/baseml.py b/Bio/Phylo/PAML/baseml.py
index ff37c21..d85138e 100644
--- a/Bio/Phylo/PAML/baseml.py
+++ b/Bio/Phylo/PAML/baseml.py
@@ -11,7 +11,7 @@ from . import _parse_baseml
class BasemlError(EnvironmentError):
"""BASEML has failed. Run with verbose = True to view BASEML's error
-message"""
+ message"""
class Baseml(Paml):
@@ -32,30 +32,30 @@ class Baseml(Paml):
self.tree = tree
self.ctl_file = "baseml.ctl"
self._options = {"noisy": None,
- "verbose": None,
- "runmode": None,
- "model": None,
- "model_options": None,
- "Mgene": None,
- "ndata": None,
- "clock": None,
- "fix_kappa": None,
- "kappa": None,
- "fix_alpha": None,
- "alpha": None,
- "Malpha": None,
- "ncatG": None,
- "fix_rho": None,
- "rho": None,
- "nparK": None,
- "nhomo": None,
- "getSE": None,
- "RateAncestor": None,
- "Small_Diff": None,
- "cleandata": None,
- "icode": None,
- "fix_blength": None,
- "method": None}
+ "verbose": None,
+ "runmode": None,
+ "model": None,
+ "model_options": None,
+ "Mgene": None,
+ "ndata": None,
+ "clock": None,
+ "fix_kappa": None,
+ "kappa": None,
+ "fix_alpha": None,
+ "alpha": None,
+ "Malpha": None,
+ "ncatG": None,
+ "fix_rho": None,
+ "rho": None,
+ "nparK": None,
+ "nhomo": None,
+ "getSE": None,
+ "RateAncestor": None,
+ "Small_Diff": None,
+ "cleandata": None,
+ "icode": None,
+ "fix_blength": None,
+ "method": None}
def write_ctl_file(self):
"""Dynamically build a BASEML control file from the options.
@@ -145,7 +145,8 @@ class Baseml(Paml):
self._options[option] = None
def _set_rel_paths(self):
- """Convert all file/directory locations to paths relative to the current working directory.
+ """Convert all file/directory locations to paths relative to the current
+ working directory.
BASEML requires that all paths specified in the control file be
relative to the directory from which it is called rather than
diff --git a/Bio/Phylo/PAML/chi2.py b/Bio/Phylo/PAML/chi2.py
index ffb398f..fcf2ec5 100644
--- a/Bio/Phylo/PAML/chi2.py
+++ b/Bio/Phylo/PAML/chi2.py
@@ -9,8 +9,6 @@
from math import log, exp
-__docformat__ = "restructuredtext en"
-
def cdf_chi2(df, stat):
if df < 1:
@@ -45,9 +43,9 @@ def _ln_gamma_function(alpha):
x = z
f = -log(f)
z = 1 / (x * x)
- return f + (x - 0.5) * log(x) - x + .918938533204673 \
- + (((-.000595238095238 * z + .000793650793651) * z - .002777777777778) * z
- + .083333333333333) / x
+ return f + (x - 0.5) * log(x) - x + .918938533204673 + \
+ (((-.000595238095238 * z + .000793650793651) * z - .002777777777778) * z +
+ .083333333333333) / x
def _incomplete_gamma(x, alpha):
@@ -64,7 +62,7 @@ def _incomplete_gamma(x, alpha):
RATNEST FORTRAN by
Bhattacharjee GP (1970) The incomplete gamma integral. Applied Statistics,
19: 285-287 (AS32)
-
+
"""
p = alpha
g = _ln_gamma_function(alpha)
diff --git a/Bio/Phylo/PAML/codeml.py b/Bio/Phylo/PAML/codeml.py
index cb0ddcf..666478d 100644
--- a/Bio/Phylo/PAML/codeml.py
+++ b/Bio/Phylo/PAML/codeml.py
@@ -13,14 +13,14 @@ from . import _parse_codeml
class CodemlError(EnvironmentError):
"""CODEML has failed. Run with verbose = True to view CODEML's error
-message"""
+ message"""
class Codeml(Paml):
"""This class implements an interface to CODEML, part of the PAML package."""
def __init__(self, alignment=None, tree=None, working_dir=None,
- out_file=None):
+ out_file=None):
"""Initialize the codeml instance.
The user may optionally pass in strings specifying the locations
@@ -36,34 +36,34 @@ class Codeml(Paml):
self.tree = tree
self.ctl_file = "codeml.ctl"
self._options = {"noisy": None,
- "verbose": None,
- "runmode": None,
- "seqtype": None,
- "CodonFreq": None,
- "ndata": None,
- "clock": None,
- "aaDist": None,
- "aaRatefile": None,
- "model": None,
- "NSsites": None,
- "icode": None,
- "Mgene": None,
- "fix_kappa": None,
- "kappa": None,
- "fix_omega": None,
- "omega": None,
- "fix_alpha": None,
- "alpha": None,
- "Malpha": None,
- "ncatG": None,
- "getSE": None,
- "RateAncestor": None,
- "Small_Diff": None,
- "cleandata": None,
- "fix_blength": None,
- "method": None,
- "rho": None,
- "fix_rho": None}
+ "verbose": None,
+ "runmode": None,
+ "seqtype": None,
+ "CodonFreq": None,
+ "ndata": None,
+ "clock": None,
+ "aaDist": None,
+ "aaRatefile": None,
+ "model": None,
+ "NSsites": None,
+ "icode": None,
+ "Mgene": None,
+ "fix_kappa": None,
+ "kappa": None,
+ "fix_omega": None,
+ "omega": None,
+ "fix_alpha": None,
+ "alpha": None,
+ "Malpha": None,
+ "ncatG": None,
+ "getSE": None,
+ "RateAncestor": None,
+ "Small_Diff": None,
+ "cleandata": None,
+ "fix_blength": None,
+ "method": None,
+ "rho": None,
+ "fix_rho": None}
def write_ctl_file(self):
"""Dynamically build a CODEML control file from the options.
@@ -108,7 +108,7 @@ class Codeml(Paml):
if "=" not in uncommented:
raise AttributeError(
"Malformed line in control file:\n%r" % line)
- (option, value) = uncommented.split("=")
+ (option, value) = uncommented.split("=", 1)
option = option.strip()
value = value.strip()
if option == "seqfile":
@@ -159,7 +159,8 @@ class Codeml(Paml):
print("%s = %s" % (option[0], option[1]))
def _set_rel_paths(self):
- """Convert all file/directory locations to paths relative to the current working directory.
+ """Convert all file/directory locations to paths relative to the current
+ working directory.
CODEML requires that all paths specified in the control file be
relative to the directory from which it is called rather than
@@ -169,8 +170,7 @@ class Codeml(Paml):
if self.tree is not None:
self._rel_tree = _relpath(self.tree, self.working_dir)
- def run(self, ctl_file=None, verbose=False, command="codeml",
- parse=True):
+ def run(self, ctl_file=None, verbose=False, command="codeml", parse=True):
"""Run codeml using the current configuration and then parse the results.
Return a process signal so the user can determine if
diff --git a/Bio/Phylo/PAML/yn00.py b/Bio/Phylo/PAML/yn00.py
index 6155ac5..2b8df97 100644
--- a/Bio/Phylo/PAML/yn00.py
+++ b/Bio/Phylo/PAML/yn00.py
@@ -7,20 +7,20 @@ import os.path
from ._paml import Paml
from . import _parse_yn00
+
# TODO - Restore use of with statement for closing handles automatically
# after dropping Python 2.4
class Yn00Error(EnvironmentError):
"""yn00 has failed. Run with verbose = True to view yn00's error
-message"""
+ message"""
class Yn00(Paml):
"""This class implements an interface to yn00, part of the PAML package."""
- def __init__(self, alignment=None, working_dir=None,
- out_file=None):
+ def __init__(self, alignment=None, working_dir=None, out_file=None):
"""Initialize the Yn00 instance.
The user may optionally pass in strings specifying the locations
@@ -30,10 +30,10 @@ class Yn00(Paml):
Paml.__init__(self, alignment, working_dir, out_file)
self.ctl_file = "yn00.ctl"
self._options = {"verbose": None,
- "icode": None,
- "weighting": None,
- "commonf3x4": None,
- "ndata": None}
+ "icode": None,
+ "weighting": None,
+ "commonf3x4": None,
+ "ndata": None}
def write_ctl_file(self):
"""Dynamically build a yn00 control file from the options.
@@ -96,8 +96,7 @@ class Yn00(Paml):
else:
self._options[option] = None
- def run(self, ctl_file=None, verbose=False, command="yn00",
- parse=True):
+ def run(self, ctl_file=None, verbose=False, command="yn00", parse=True):
Paml.run(self, ctl_file, verbose, command)
if parse:
results = read(self.out_file)
@@ -119,13 +118,13 @@ def read(results_file):
ng86_start = line_num + 1
elif "(B) Yang & Nielsen (2000) method" in line:
(results, sequences) = _parse_yn00.parse_ng86(lines[ng86_start:line_num],
- results)
+ results)
yn00_start = line_num + 1
elif "(C) LWL85, LPB93 & LWLm methods" in line:
results = _parse_yn00.parse_yn00(lines[yn00_start:line_num], results,
- sequences)
+ sequences)
results = _parse_yn00.parse_others(lines[line_num + 1:], results,
- sequences)
+ sequences)
if len(results) == 0:
raise ValueError("Invalid results file.")
return results
diff --git a/Bio/Phylo/PhyloXML.py b/Bio/Phylo/PhyloXML.py
index 7a17260..273ffbd 100644
--- a/Bio/Phylo/PhyloXML.py
+++ b/Bio/Phylo/PhyloXML.py
@@ -13,8 +13,6 @@ Journal article:
Han and Zmasek (2009), doi:10.1186/1471-2105-10-356
"""
-__docformat__ = "restructuredtext en"
-
import re
import warnings
@@ -77,7 +75,7 @@ class Phyloxml(PhyloElement):
def __getitem__(self, index):
"""Get a phylogeny by index or name."""
- if isinstance(index, int) or isinstance(index, slice):
+ if isinstance(index, (int, slice)):
return self.phylogenies[index]
if not isinstance(index, basestring):
raise KeyError("can't use %s as an index" % type(index))
@@ -257,7 +255,7 @@ class Phylogeny(PhyloElement, BaseTree.Tree):
# Special case: mirror the behavior of _get_confidence
self.confidences = []
return
- if isinstance(value, float) or isinstance(value, int):
+ if isinstance(value, (float, int)):
value = Confidence(value)
elif not isinstance(value, Confidence):
raise ValueError("value must be a number or Confidence instance")
@@ -390,7 +388,7 @@ class Clade(PhyloElement, BaseTree.Clade):
# Special case: mirror the behavior of _get_confidence
self.confidences = []
return
- if isinstance(value, float) or isinstance(value, int):
+ if isinstance(value, (float, int)):
value = Confidence(value)
elif not isinstance(value, Confidence):
raise ValueError("value must be a number or Confidence instance")
diff --git a/Bio/Phylo/PhyloXMLIO.py b/Bio/Phylo/PhyloXMLIO.py
index b4fc0dd..95e79d3 100644
--- a/Bio/Phylo/PhyloXMLIO.py
+++ b/Bio/Phylo/PhyloXMLIO.py
@@ -16,7 +16,6 @@ About capitalization:
`Bio.Phylo.read`!), containing a list of Phylogenies (objects derived from
`BaseTree.Tree`)
"""
-__docformat__ = "restructuredtext en"
import sys
@@ -36,6 +35,7 @@ try:
except ImportError:
from xml.etree import ElementTree as ElementTree
+
# Recognize the phyloXML namespace when parsing
# See http://effbot.org/zone/element-namespaces.htm
NAMESPACES = {
@@ -120,8 +120,7 @@ def write(obj, file, encoding=DEFAULT_ENCODING, indent=True):
if isinstance(obj, PX.Phyloxml):
pass
- elif (isinstance(obj, PX.BaseTree.Tree) or
- isinstance(obj, PX.BaseTree.Clade)):
+ elif isinstance(obj, (PX.BaseTree.Tree, PX.BaseTree.Clade)):
obj = fix_single(obj).to_phyloxml()
elif hasattr(obj, '__iter__'):
obj = PX.Phyloxml({}, phylogenies=(fix_single(t) for t in obj))
diff --git a/Bio/Phylo/TreeConstruction.py b/Bio/Phylo/TreeConstruction.py
index 6f3b657..c975a46 100644
--- a/Bio/Phylo/TreeConstruction.py
+++ b/Bio/Phylo/TreeConstruction.py
@@ -4,7 +4,6 @@
# as part of this package.
"""Classes and methods for tree construction"""
-__docformat__ = "restructuredtext en"
import itertools
import copy
@@ -103,10 +102,10 @@ class _Matrix(object):
self.matrix = matrix
else:
# check if all elements are numbers
- if (isinstance(matrix, list)
- and all(isinstance(l, list) for l in matrix)
- and all(_is_numeric(n) for n in [item for sublist in matrix
- for item in sublist])):
+ if (isinstance(matrix, list) and
+ all(isinstance(l, list) for l in matrix) and
+ all(_is_numeric(n) for n in [item for sublist in matrix
+ for item in sublist])):
# check if the same length with names
if len(matrix) == len(names):
# check if is lower triangle format
@@ -375,15 +374,15 @@ class DistanceCalculator(object):
# BLAST nucleic acid scoring matrix
blastn = [[5],
- [-4, 5],
- [-4, -4, 5],
- [-4, -4, -4, 5]]
+ [-4, 5],
+ [-4, -4, 5],
+ [-4, -4, -4, 5]]
# transition/transversion scoring matrix
trans = [[6],
- [-5, 6],
- [-5, -1, 6],
- [-1, -5, -5, 6]]
+ [-5, 6],
+ [-5, -1, 6],
+ [-1, -5, -5, 6]]
protein_alphabet = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'K', 'L',
'M', 'N', 'P', 'Q', 'R', 'S', 'T', 'V', 'W', 'X', 'Y',
@@ -411,8 +410,8 @@ class DistanceCalculator(object):
self.scoring_matrix = self._build_protein_matrix(
self.protein_matrices[model])
else:
- raise ValueError("Model not supported. Available models: "
- + ", ".join(self.models))
+ raise ValueError("Model not supported. Available models: " +
+ ", ".join(self.models))
def _pairwise(self, seq1, seq2):
"""Calculate pairwise distance from two sequences.
@@ -543,8 +542,8 @@ class DistanceTreeConstructor(TreeConstructor):
methods = ['nj', 'upgma']
def __init__(self, distance_calculator=None, method="nj"):
- if (distance_calculator is None
- or isinstance(distance_calculator, DistanceCalculator)):
+ if (distance_calculator is None or
+ isinstance(distance_calculator, DistanceCalculator)):
self.distance_calculator = distance_calculator
else:
raise TypeError("Must provide a DistanceCalculator object.")
@@ -681,8 +680,8 @@ class DistanceTreeConstructor(TreeConstructor):
inner_clade.clades.append(clade1)
inner_clade.clades.append(clade2)
# assign branch length
- clade1.branch_length = (dm[min_i, min_j] + node_dist[min_i]
- - node_dist[min_j]) / 2.0
+ clade1.branch_length = (dm[min_i, min_j] + node_dist[min_i] -
+ node_dist[min_j]) / 2.0
clade2.branch_length = dm[min_i, min_j] - clade1.branch_length
# update node list
@@ -693,8 +692,8 @@ class DistanceTreeConstructor(TreeConstructor):
# set the distances of new node at the index of min_j
for k in range(0, len(dm)):
if k != min_i and k != min_j:
- dm[min_j, k] = (dm[min_i, k] + dm[min_j, k]
- - dm[min_i, min_j]) / 2.0
+ dm[min_j, k] = (dm[min_i, k] + dm[min_j, k] -
+ dm[min_i, min_j]) / 2.0
dm.names[min_j] = "Inner" + str(inner_count)
del dm[min_i]
@@ -924,7 +923,7 @@ class ParsimonyScorer(Scorer):
terms = tree.get_terminals()
terms.sort(key=lambda term: term.name)
alignment.sort()
- if not all([t.name == a.id for t, a in zip(terms, alignment)]):
+ if not all(t.name == a.id for t, a in zip(terms, alignment)):
raise ValueError(
"Taxon names of the input tree should be the same with the alignment.")
# term_align = dict(zip(terms, alignment))
diff --git a/Bio/Phylo/__init__.py b/Bio/Phylo/__init__.py
index 86a7027..8bf9708 100644
--- a/Bio/Phylo/__init__.py
+++ b/Bio/Phylo/__init__.py
@@ -7,7 +7,6 @@
See also: http://biopython.org/wiki/Phylo
"""
-__docformat__ = "restructuredtext en"
from Bio.Phylo._io import parse, read, write, convert
from Bio.Phylo._utils import (draw, draw_ascii, draw_graphviz, to_networkx)
diff --git a/Bio/Phylo/_cdao_owl.py b/Bio/Phylo/_cdao_owl.py
index f9b476a..ca386d0 100644
--- a/Bio/Phylo/_cdao_owl.py
+++ b/Bio/Phylo/_cdao_owl.py
@@ -5,6 +5,7 @@
import xml.etree.ElementTree as ET
+
cdao_namespaces = {
'cdao': 'http://purl.obolibrary.org/obo/cdao.owl#',
'obo': 'http://purl.obolibrary.org/obo/',
diff --git a/Bio/Phylo/_io.py b/Bio/Phylo/_io.py
index 9cde2f1..e51b1df 100644
--- a/Bio/Phylo/_io.py
+++ b/Bio/Phylo/_io.py
@@ -10,8 +10,6 @@ This API follows the same semantics as Biopython's `SeqIO` and `AlignIO`.
from __future__ import print_function
-__docformat__ = "restructuredtext en"
-
from Bio import File
from Bio.Phylo import (
BaseTree,
@@ -76,7 +74,7 @@ def read(file, format, **kwargs):
def write(trees, file, format, **kwargs):
"""Write a sequence of trees to file in the given format."""
- if isinstance(trees, BaseTree.Tree) or isinstance(trees, BaseTree.Clade):
+ if isinstance(trees, (BaseTree.Tree, BaseTree.Clade)):
# Passed a single tree instead of an iterable -- that's OK
trees = [trees]
with File.as_handle(file, 'w+') as fp:
@@ -84,7 +82,9 @@ def write(trees, file, format, **kwargs):
return n
-def convert(in_file, in_format, out_file, out_format, parse_args={}, **kwargs):
+def convert(in_file, in_format, out_file, out_format, parse_args=None, **kwargs):
"""Convert between two tree file formats."""
+ if parse_args is None:
+ parse_args = {}
trees = parse(in_file, in_format, **parse_args)
return write(trees, out_file, out_format, **kwargs)
diff --git a/Bio/Phylo/_utils.py b/Bio/Phylo/_utils.py
index 5c06606..6c55a1a 100644
--- a/Bio/Phylo/_utils.py
+++ b/Bio/Phylo/_utils.py
@@ -7,7 +7,6 @@
Third-party libraries are loaded when the corresponding function is called.
"""
-__docformat__ = "restructuredtext en"
import math
import sys
@@ -225,8 +224,8 @@ def draw_ascii(tree, file=None, column_width=80):
depths = tree.depths(unit_branch_lengths=True)
# Potential drawing overflow due to rounding -- 1 char per tree layer
fudge_margin = int(math.ceil(math.log(len(taxa), 2)))
- cols_per_branch_unit = ((drawing_width - fudge_margin)
- / float(max(depths.values())))
+ cols_per_branch_unit = ((drawing_width - fudge_margin) /
+ float(max(depths.values())))
return dict((clade, int(blen * cols_per_branch_unit + 1.0))
for clade, blen in depths.items())
@@ -280,7 +279,7 @@ def draw_ascii(tree, file=None, column_width=80):
def draw(tree, label_func=str, do_show=True, show_confidence=True,
# For power users
- axes=None, branch_labels=None, *args, **kwargs):
+ axes=None, branch_labels=None, label_colors=None, *args, **kwargs):
"""Plot the given tree using matplotlib (or pylab).
The graphic is a rooted tree, drawn with roughly the same algorithm as
@@ -322,6 +321,10 @@ def draw(tree, label_func=str, do_show=True, show_confidence=True,
But if you would like to alter the formatting of confidence values,
or label the branches with something other than confidence, then use
this option.
+ label_colors : dict or callable
+ A function or a dictionary specifying the color of the tip label.
+ If the tip label can't be found in the dict or label_colors is
+ None, the label will be shown in black.
"""
try:
@@ -365,6 +368,20 @@ def draw(tree, label_func=str, do_show=True, show_confidence=True,
"branch_labels must be either a dict or a callable (function)"
format_branch_label = branch_labels
+ # options for displaying label colors.
+ if label_colors:
+ if callable(label_colors):
+ def get_label_color(label):
+ return label_colors(label)
+ else:
+ # label_colors is presumed to be a dict
+ def get_label_color(label):
+ return label_colors.get(label, 'black')
+ else:
+ def get_label_color(label):
+ # if label_colors is not specified, use black
+ return 'black'
+
# Layout
def get_x_positions(tree):
@@ -419,14 +436,14 @@ def draw(tree, label_func=str, do_show=True, show_confidence=True,
Graphical formatting of the lines representing clades in the plot can be
customized by altering this function.
"""
- if (use_linecollection is False and orientation == 'horizontal'):
+ if not use_linecollection and orientation == 'horizontal':
axes.hlines(y_here, x_start, x_here, color=color, lw=lw)
- elif (use_linecollection is True and orientation == 'horizontal'):
+ elif use_linecollection and orientation == 'horizontal':
horizontal_linecollections.append(mpcollections.LineCollection(
[[(x_start, y_here), (x_here, y_here)]], color=color, lw=lw),)
- elif (use_linecollection is False and orientation == 'vertical'):
+ elif not use_linecollection and orientation == 'vertical':
axes.vlines(x_here, y_bot, y_top, color=color)
- elif (use_linecollection is True and orientation == 'vertical'):
+ elif use_linecollection and orientation == 'vertical':
vertical_linecollections.append(mpcollections.LineCollection(
[[(x_here, y_bot), (x_here, y_top)]], color=color, lw=lw),)
@@ -446,7 +463,8 @@ def draw(tree, label_func=str, do_show=True, show_confidence=True,
label = label_func(clade)
if label not in (None, clade.__class__.__name__):
axes.text(x_here, y_here, ' %s' %
- label, verticalalignment='center')
+ label, verticalalignment='center',
+ color=get_label_color(label))
# Add label above the branch (optional)
conf_label = format_branch_label(clade)
if conf_label:
diff --git a/Bio/PopGen/Async/Local.py b/Bio/PopGen/Async/Local.py
index 893e42d..52d1468 100644
--- a/Bio/PopGen/Async/Local.py
+++ b/Bio/PopGen/Async/Local.py
@@ -3,43 +3,39 @@
# license. Please see the LICENSE file that should have been included
# as part of this package.
-'''
-Asynchronous local execution.
+"""Asynchronous local execution.
Supports multicore architectures.
-'''
+"""
from Bio.PopGen.Async import Async
import threading
-__docformat__ = "restructuredtext en"
-
class Local(Async):
- '''Execution on Local machine.
- '''
+ """Execution on Local machine."""
def __init__(self, num_cores=1):
- '''Constructor.
+ """Constructor.
- parameters:
+ parameters:
- - num_cores - Number of cores (for multiprocessor machines,
- multiply accordingly)
- '''
+ - num_cores - Number of cores (for multiprocessor machines,
+ multiply accordingly)
+ """
Async.__init__(self)
self.num_cores = num_cores
self.cores_used = 0
def _run_program(self, id, hook, parameters, input_files):
- '''Run program.
+ """Run program.
- For parameters, please check Async.run_program.
+ For parameters, please check Async.run_program.
- Either runs a program if a core is available or
- schedules it.
- '''
+ Either runs a program if a core is available or
+ schedules it.
+ """
self.access_ds.acquire()
self.waiting.append((id, hook, parameters, input_files))
if self.cores_used < self.num_cores:
@@ -48,13 +44,13 @@ class Local(Async):
self.access_ds.release()
def start_work(self):
- '''Starts work.
+ """Starts work.
- Thread initial point.
- While there are tasks to be done, runs them.
- The thread dies as soon as there is nothing waiting to be
- executed.
- '''
+ Thread initial point.
+ While there are tasks to be done, runs them.
+ The thread dies as soon as there is nothing waiting to be
+ executed.
+ """
self.access_ds.acquire()
while (len(self.waiting) > 0):
id, hook, parameters, input_files = self.waiting[0]
diff --git a/Bio/PopGen/Async/__init__.py b/Bio/PopGen/Async/__init__.py
index 7f8d096..2b9336b 100644
--- a/Bio/PopGen/Async/__init__.py
+++ b/Bio/PopGen/Async/__init__.py
@@ -3,32 +3,26 @@
# license. Please see the LICENSE file that should have been included
# as part of this package.
-
-'''
-Support for asynchronous execution.
-
-'''
+"""Support for asynchronous execution."""
import os
import threading
-__docformat__ = "restructuredtext en"
-
class Async(object):
- '''Abstract Asynchronous execution class.
+ """Abstract Asynchronous execution class.
- This is the top abstract class.
- Concrete classes must implement the _run_program method.
- '''
+ This is the top abstract class.
+ Concrete classes must implement the _run_program method.
+ """
def __init__(self):
- '''Async constructor.
+ """Async constructor.
- Initializes the queues, among other things.
- Of notice, is the access_ds lock for controlling exclusive
- access to this object.
- '''
+ Initializes the queues, among other things.
+ Of notice, is the access_ds lock for controlling exclusive
+ access to this object.
+ """
self.running = {}
self.waiting = []
self.done = {}
@@ -37,22 +31,22 @@ class Async(object):
self.access_ds = threading.Lock()
def run_program(self, program, parameters, input_files):
- '''Runs a program.
+ """Runs a program.
- Real _run_program to be implemented by concrete classes.
+ Real _run_program to be implemented by concrete classes.
- parameters:
- program String identifying program.
- parameters List of String parameters.
- input_files Hash of Input file descriptors.
+ parameters:
+ program String identifying program.
+ parameters List of String parameters.
+ input_files Hash of Input file descriptors.
- returns:
- Task Id.
+ returns:
+ Task Id.
- The input_files hash key is the path that is passed
- to the program. It should always be relative.
- Value is a stream.
- '''
+ The input_files hash key is the path that is passed
+ to the program. It should always be relative.
+ Value is a stream.
+ """
if program in self.hooks:
self.access_ds.acquire()
self.id += 1
@@ -70,19 +64,17 @@ class Async(object):
raise NotImplementedError("This object should be subclassed")
def get_result(self, id):
- ''' Returns the results for a certain Id, the info for that Id is
- forgotten.
+ """ Returns results for a certain Id, the info for that Id is forgotten.
- parameters:
- id Id of the task.
+ parameters:
+ id Id of the task.
- returns:
- (return_code, output_files) return code and file access
- object.
+ returns:
+ (return_code, output_files) return code and file access object.
- The output_files hash key is a relative file name, and the value a
- output stream.
- '''
+ The output_files hash key is a relative file name, and the value a
+ output stream.
+ """
self.access_ds.acquire()
if id in self.done:
returnCode, fileObject = self.done[id]
@@ -94,15 +86,13 @@ class Async(object):
class FileRetriever(object):
- '''An Abstract Support class to retrieve files.
- '''
+ """An Abstract Support class to retrieve files."""
def __init__(self):
self.file_list = []
def get_File_list(self):
- '''Returns the list of available files.
- '''
+ """Returns the list of available files."""
return self.file_list
def get_file(self, name):
@@ -110,8 +100,7 @@ class FileRetriever(object):
class DirectoryRetriever(FileRetriever):
- '''Retrieves a directory content.
- '''
+ """Retrieves a directory content."""
def __init__(self, directory):
FileRetriever.__init__(self)
diff --git a/Bio/PopGen/FDist/Async.py b/Bio/PopGen/FDist/Async.py
index 416d16c..4436eb0 100644
--- a/Bio/PopGen/FDist/Async.py
+++ b/Bio/PopGen/FDist/Async.py
@@ -18,8 +18,6 @@ from time import sleep
from Bio.PopGen.Async import Local
from Bio.PopGen.FDist.Controller import FDistController
-__docformat__ = "restructuredtext en"
-
class FDistAsync(FDistController):
"""Asynchronous FDist execution.
@@ -29,7 +27,7 @@ class FDistAsync(FDistController):
"""Constructor.
Parameters:
-
+
- fdist_dir - Where fdist can be found, if = "", then it
should be on the path.
- ext - Extension of binary names (e.g. nothing on Unix,
@@ -80,7 +78,7 @@ class SplitFDist(object):
"""Constructor.
Parameters:
-
+
- report_fun - Function that is called when a single packet is
run, it should have a single parameter: Fst.
- num_thr - Number of desired threads, typically the number
diff --git a/Bio/PopGen/FDist/Controller.py b/Bio/PopGen/FDist/Controller.py
index db444b8..11ef391 100644
--- a/Bio/PopGen/FDist/Controller.py
+++ b/Bio/PopGen/FDist/Controller.py
@@ -19,8 +19,6 @@ from random import randint
from time import strftime, clock
# from logging import debug
-__docformat__ = "restructuredtext en"
-
def my_float(f):
# Because of Jython, mostly
@@ -202,7 +200,7 @@ class FDistController(object):
- try_runs - Number of simulations on the part trying to get
Fst correct
- limit - Interval limit
-
+
Other parameters can be seen on run_fdist.
"""
max_run_fst = 1
diff --git a/Bio/PopGen/FDist/Utils.py b/Bio/PopGen/FDist/Utils.py
index da19ee8..8d76cbd 100644
--- a/Bio/PopGen/FDist/Utils.py
+++ b/Bio/PopGen/FDist/Utils.py
@@ -7,6 +7,7 @@
from Bio.PopGen.GenePop import FileParser
import Bio.PopGen.FDist
+
# Quite a few utility functions could be done (like remove pop,
# add locus, etc...). The recommended strategy is convert back
# and forth from/to GenePop and use GenePop Utils
diff --git a/Bio/PopGen/FDist/__init__.py b/Bio/PopGen/FDist/__init__.py
index b2add1a..d55c9f5 100644
--- a/Bio/PopGen/FDist/__init__.py
+++ b/Bio/PopGen/FDist/__init__.py
@@ -20,8 +20,6 @@ read Parses a FDist record (file) into a Record object.
"""
-__docformat__ = "restructuredtext en"
-
def read(handle):
"""Parses FDist data into a Record object.
diff --git a/Bio/PopGen/GenePop/Controller.py b/Bio/PopGen/GenePop/Controller.py
index 02556f4..a2ee560 100644
--- a/Bio/PopGen/GenePop/Controller.py
+++ b/Bio/PopGen/GenePop/Controller.py
@@ -16,8 +16,6 @@ import tempfile
from Bio.Application import AbstractCommandline, _Argument
-__docformat__ = "restructuredtext en"
-
def _gp_float(tok):
"""Gets a float from a token, if it fails, returns the string (PRIVATE)."""
@@ -232,7 +230,9 @@ class GenePopController(object):
opts["HWtests"] = "MCMC"
return opts
- def _run_genepop(self, extensions, option, fname, opts={}):
+ def _run_genepop(self, extensions, option, fname, opts=None):
+ if opts is None:
+ opts = {}
cwd = os.getcwd()
temp_dir = tempfile.mkdtemp()
os.chdir(temp_dir)
diff --git a/Bio/PopGen/GenePop/EasyController.py b/Bio/PopGen/GenePop/EasyController.py
index c352198..ac80da6 100644
--- a/Bio/PopGen/GenePop/EasyController.py
+++ b/Bio/PopGen/GenePop/EasyController.py
@@ -13,8 +13,6 @@ This interface is less efficient than the standard GenePopControler
from .Controller import GenePopController
from Bio.PopGen import GenePop
-__docformat__ = "restructuredtext en"
-
class EasyController(object):
def __init__(self, fname, genepop_dir=None):
diff --git a/Bio/PopGen/GenePop/FileParser.py b/Bio/PopGen/GenePop/FileParser.py
index f2cfba8..ebdd948 100644
--- a/Bio/PopGen/GenePop/FileParser.py
+++ b/Bio/PopGen/GenePop/FileParser.py
@@ -23,8 +23,6 @@ Functions:
"""
from Bio.PopGen.GenePop import get_indiv
-__docformat__ = "restructuredtext en"
-
def read(fname):
"""Parses a file containing a GenePop file.
@@ -186,7 +184,7 @@ class FileRecord(object):
else:
self.current_ind += 1
indiv_name, allele_list, ignore = get_indiv(line)
- return (indiv_name, allele_list)
+ return indiv_name, allele_list
return False
def remove_population(self, pos, fname):
@@ -196,28 +194,67 @@ class FileRecord(object):
fname - file to be created with population removed
"""
old_rec = read(self.fname)
- f = open(fname, "w")
- f.write(self.comment_line + "\n")
- for locus in old_rec.loci_list:
- f.write(locus + "\n")
- curr_pop = 0
- l_parser = old_rec.get_individual()
- start_pop = True
- while l_parser:
- if curr_pop == pos:
- old_rec.skip_population()
- curr_pop += 1
- else:
- if l_parser is True:
+ with open(fname, "w") as f:
+ f.write(self.comment_line + "\n")
+ for locus in old_rec.loci_list:
+ f.write(locus + "\n")
+ curr_pop = 0
+ l_parser = old_rec.get_individual()
+ start_pop = True
+ while l_parser:
+ if curr_pop == pos:
+ old_rec.skip_population()
curr_pop += 1
- start_pop = True
else:
- if start_pop:
- f.write("POP\n")
- start_pop = False
+ if l_parser is True:
+ curr_pop += 1
+ start_pop = True
+ else:
+ if start_pop:
+ f.write("POP\n")
+ start_pop = False
+ name, markers = l_parser
+ f.write(name + ",")
+ for marker in markers:
+ f.write(' ')
+ for al in marker:
+ if al is None:
+ al = '0'
+ aStr = str(al)
+ while len(aStr) < 3:
+ aStr = "".join(['0', aStr])
+ f.write(aStr)
+ f.write('\n')
+
+ l_parser = old_rec.get_individual()
+
+ def remove_locus_by_position(self, pos, fname):
+ """Removes a locus by position.
+
+ pos - position
+ fname - file to be created with locus removed
+ """
+ old_rec = read(self.fname)
+ with open(fname, "w") as f:
+ f.write(self.comment_line + "\n")
+ loci_list = old_rec.loci_list
+ del loci_list[pos]
+ for locus in loci_list:
+ f.write(locus + "\n")
+ l_parser = old_rec.get_individual()
+ f.write("POP\n")
+ while l_parser:
+ if l_parser is True:
+ f.write("POP\n")
+ else:
name, markers = l_parser
f.write(name + ",")
+ marker_pos = 0
for marker in markers:
+ if marker_pos == pos:
+ marker_pos += 1
+ continue
+ marker_pos += 1
f.write(' ')
for al in marker:
if al is None:
@@ -228,48 +265,7 @@ class FileRecord(object):
f.write(aStr)
f.write('\n')
- l_parser = old_rec.get_individual()
- f.close()
-
- def remove_locus_by_position(self, pos, fname):
- """Removes a locus by position.
-
- pos - position
- fname - file to be created with locus removed
- """
- old_rec = read(self.fname)
- f = open(fname, "w")
- f.write(self.comment_line + "\n")
- loci_list = old_rec.loci_list
- del loci_list[pos]
- for locus in loci_list:
- f.write(locus + "\n")
- l_parser = old_rec.get_individual()
- f.write("POP\n")
- while l_parser:
- if l_parser is True:
- f.write("POP\n")
- else:
- name, markers = l_parser
- f.write(name + ",")
- marker_pos = 0
- for marker in markers:
- if marker_pos == pos:
- marker_pos += 1
- continue
- marker_pos += 1
- f.write(' ')
- for al in marker:
- if al is None:
- al = '0'
- aStr = str(al)
- while len(aStr) < 3:
- aStr = "".join(['0', aStr])
- f.write(aStr)
- f.write('\n')
-
- l_parser = old_rec.get_individual()
- f.close()
+ l_parser = old_rec.get_individual()
def remove_loci_by_position(self, positions, fname):
"""Removes a set of loci by position.
@@ -278,43 +274,42 @@ class FileRecord(object):
fname - file to be created with locus removed
"""
old_rec = read(self.fname)
- f = open(fname, "w")
- f.write(self.comment_line + "\n")
- loci_list = old_rec.loci_list
- positions.sort()
- positions.reverse()
- posSet = set()
- for pos in positions:
- del loci_list[pos]
- posSet.add(pos)
- for locus in loci_list:
- f.write(locus + "\n")
- l_parser = old_rec.get_individual()
- f.write("POP\n")
- while l_parser:
- if l_parser is True:
- f.write("POP\n")
- else:
- name, markers = l_parser
- f.write(name + ",")
- marker_pos = 0
- for marker in markers:
- if marker_pos in posSet:
+ with open(fname, "w") as f:
+ f.write(self.comment_line + "\n")
+ loci_list = old_rec.loci_list
+ positions.sort()
+ positions.reverse()
+ posSet = set()
+ for pos in positions:
+ del loci_list[pos]
+ posSet.add(pos)
+ for locus in loci_list:
+ f.write(locus + "\n")
+ l_parser = old_rec.get_individual()
+ f.write("POP\n")
+ while l_parser:
+ if l_parser is True:
+ f.write("POP\n")
+ else:
+ name, markers = l_parser
+ f.write(name + ",")
+ marker_pos = 0
+ for marker in markers:
+ if marker_pos in posSet:
+ marker_pos += 1
+ continue
marker_pos += 1
- continue
- marker_pos += 1
- f.write(' ')
- for al in marker:
- if al is None:
- al = '0'
- aStr = str(al)
- while len(aStr) < 3:
- aStr = "".join(['0', aStr])
- f.write(aStr)
- f.write('\n')
+ f.write(' ')
+ for al in marker:
+ if al is None:
+ al = '0'
+ aStr = str(al)
+ while len(aStr) < 3:
+ aStr = "".join(['0', aStr])
+ f.write(aStr)
+ f.write('\n')
- l_parser = old_rec.get_individual()
- f.close()
+ l_parser = old_rec.get_individual()
def remove_locus_by_name(self, name, fname):
"""Removes a locus by name.
diff --git a/Bio/PopGen/GenePop/__init__.py b/Bio/PopGen/GenePop/__init__.py
index 717075e..8c6feab 100644
--- a/Bio/PopGen/GenePop/__init__.py
+++ b/Bio/PopGen/GenePop/__init__.py
@@ -21,8 +21,6 @@ Partially inspired on MedLine Code.
"""
from copy import deepcopy
-__docformat__ = "restructuredtext en"
-
def get_indiv(line):
def int_no_zero(val):
diff --git a/Bio/PopGen/SimCoal/Cache.py b/Bio/PopGen/SimCoal/Cache.py
index 23e7b8b..b975b4e 100644
--- a/Bio/PopGen/SimCoal/Cache.py
+++ b/Bio/PopGen/SimCoal/Cache.py
@@ -1,18 +1,15 @@
# Copyright 2007 by Tiago Antao <tiagoantao at gmail.com>. All rights reserved.
-"""
-(DEPRECATED)
-This module allows to cache Simcoal2 results, and return on the fly
-in case the calculation was done.
+"""Cache for Simacoal2 results ((DEPRECATED).
+This module allows you to cache Simcoal2 results, and return on the fly
+in case the calculation was done.
"""
import os
import tarfile
from .Controller import SimCoalController
-__docformat__ = "restructuredtext en"
-
class SimCoalCache(object):
def __init__(self, data_dir, simcoal_dir):
@@ -57,9 +54,9 @@ class SimCoalCache(object):
tf.close()
def listSimulations(self, ploidy='1'):
- '''
+ """
Lists available simulations.
- '''
+ """
files = os.listdir(self.cacheDir + os.sep + ploidy)
sims = []
for file in files:
@@ -68,13 +65,12 @@ class SimCoalCache(object):
return sims
def getSimulation(self, sim_name, ploidy='1', parDir=None):
- '''
- Makes available a cached simulation.
+ """Makes available a cached simulation.
- @param sim_name simulation name.
+ @param sim_name simulation name.
- This mainly means untaring a file.
- '''
+ This mainly means untaring a file.
+ """
if parDir is None:
parDir = os.sep.join([self.dataDir, 'SimCoal', 'runs'])
tar_name = os.sep.join([self.cacheDir, ploidy, sim_name +
diff --git a/Bio/PopGen/SimCoal/Controller.py b/Bio/PopGen/SimCoal/Controller.py
index 4f7b52e..a238542 100644
--- a/Bio/PopGen/SimCoal/Controller.py
+++ b/Bio/PopGen/SimCoal/Controller.py
@@ -21,7 +21,7 @@ class SimCoalController(object):
simcoal_dir is the directory where simcoal is.
- The initializer checks for existance and executability of binaries.
+ The initializer checks for existence and executability of binaries.
"""
self.simcoal_dir = simcoal_dir
self.os_name = os.name # remove this?
@@ -199,7 +199,7 @@ class FastSimCoalController(object):
if not os.access(os.path.join(self.fastsimcoal_dir, self.bin_name), os.X_OK):
raise IOError("Fastsimcoal not executable")
- def run_fastsimcoal(self, par_file, num_sims, par_dir='.', opts={}):
+ def run_fastsimcoal(self, par_file, num_sims, par_dir='.', opts=None):
"""Executes Fastsimcoal.
par_file is the input parameter file (--ifile) for fastsimcoal.
@@ -207,6 +207,8 @@ class FastSimCoalController(object):
par_dir is the directory where par_file is and where output will be written.
opts is a dictionary of additional options to fastsimcoal.
"""
+ if opts is None:
+ opts = {}
if par_dir is None:
par_dir = os.sep.join([".", "Fastsimcoal", "runs"])
if not os.path.exists(par_dir):
diff --git a/Bio/PopGen/SimCoal/Template.py b/Bio/PopGen/SimCoal/Template.py
index b1af549..189afee 100644
--- a/Bio/PopGen/SimCoal/Template.py
+++ b/Bio/PopGen/SimCoal/Template.py
@@ -11,8 +11,6 @@ from functools import reduce
from Bio.PopGen.SimCoal import builtin_tpl_dir
-__docformat__ = "restructuredtext en"
-
def exec_template(template):
executed_template = template
@@ -148,16 +146,15 @@ def generate_model(par_stream, out_prefix, params,
def get_demography_template(stream, model, tp_dir=None):
- '''
- Gets a demograpy template.
+ """Gets a demograpy template.
- Most probably this model needs to be sent to GenCases.
+ Most probably this model needs to be sent to GenCases.
- - stream - Writable stream.
- - param - Template file.
- - tp_dir - Directory where to find the template, if None
- use an internal template
- '''
+ - stream - Writable stream.
+ - param - Template file.
+ - tp_dir - Directory where to find the template, if None
+ use an internal template
+ """
if tp_dir is None:
# Internal Template
filename = sep.join([builtin_tpl_dir, model + '.par'])
@@ -181,13 +178,12 @@ def _gen_loci(stream, loci):
def get_chr_template(stream, chrs):
- '''
- Writes a Simcoal2 loci template part.
+ """Writes a Simcoal2 loci template part.
- stream - Writable stream.
- chr - Chromosome list.
+ stream - Writable stream.
+ chr - Chromosome list.
- Current loci list:
+ Current loci list:
- [(chr_repeats,[(marker, (params))])]
@@ -195,7 +191,7 @@ def get_chr_template(stream, chrs):
- marker --> 'SNP', 'DNA', 'RFLP', 'MICROSAT'
- params --> Simcoal2 parameters for markers (list of floats
or ints - if to be processed by generate_model)
- '''
+ """
num_chrs = reduce(lambda x, y: x + y[0], chrs, 0)
stream.write('//Number of independent (unlinked) chromosomes, and "chromosome structure" flag: 0 for identical structure across chromosomes, and 1 for different structures on different chromosomes.\n')
if len(chrs) > 1 or num_chrs == 1:
@@ -213,15 +209,14 @@ def get_chr_template(stream, chrs):
def generate_simcoal_from_template(model, chrs, params, out_dir='.', tp_dir=None):
- '''
- Writes a complete SimCoal2 template file.
-
- This joins together get_demography_template and get_chr_template,
- which are feed into generate_model
- Please check the three functions for parameters (model from
- get_demography_template, chrs from get_chr_template and
- params from generate_model).
- '''
+ """Writes a complete SimCoal2 template file.
+
+ This joins together get_demography_template and get_chr_template,
+ which are feed into generate_model
+ Please check the three functions for parameters (model from
+ get_demography_template, chrs from get_chr_template and
+ params from generate_model).
+ """
with open(out_dir + sep + 'tmp.par', 'w') as stream:
get_demography_template(stream, model, tp_dir)
get_chr_template(stream, chrs)
diff --git a/Bio/PopGen/SimCoal/__init__.py b/Bio/PopGen/SimCoal/__init__.py
index 7c2709a..7f5bb6b 100644
--- a/Bio/PopGen/SimCoal/__init__.py
+++ b/Bio/PopGen/SimCoal/__init__.py
@@ -3,13 +3,12 @@
# license. Please see the LICENSE file that should have been included
# as part of this package.
-'''
-SimCoal2 execution module and support functions.
-'''
+"""SimCoal2 execution module and support functions."""
import os
import sys
+
# This is a workaround to work with the test system
# In any case the problem is with the test system
for instance in sys.path:
diff --git a/Bio/PopGen/__init__.py b/Bio/PopGen/__init__.py
index 7c1a735..e763de1 100644
--- a/Bio/PopGen/__init__.py
+++ b/Bio/PopGen/__init__.py
@@ -3,6 +3,4 @@
# license. Please see the LICENSE file that should have been included
# as part of this package.
-"""
- PopGen: Population Genetics and Genomics library in Python
-"""
+"""PopGen: Population Genetics and Genomics library in Python."""
diff --git a/Bio/Restriction/PrintFormat.py b/Bio/Restriction/PrintFormat.py
index d2ca85b..1cd8928 100644
--- a/Bio/Restriction/PrintFormat.py
+++ b/Bio/Restriction/PrintFormat.py
@@ -52,8 +52,6 @@ from Bio._py3k import range
from Bio.Restriction import RanaConfig as RanaConf
-__docformat__ = "restructuredtext en"
-
class PrintFormat(object):
"""PrintFormat allow the printing of results of restriction analysis."""
@@ -112,7 +110,7 @@ class PrintFormat(object):
print(self.make_format(ls, title, nc, s1))
return
- def make_format(self, cut=[], title='', nc=[], s1=''):
+ def make_format(self, cut=(), title='', nc=(), s1=''):
"""PF.make_format(cut, nc, title, s) -> string
Virtual method.
@@ -134,9 +132,9 @@ class PrintFormat(object):
enzyme2 : position1, position2, position3.
Arguments:
- - ls is a list of cutting enzymes.
+ - ls is a tuple or list of cutting enzymes.
- title is the title.
- - nc is a list of non cutting enzymes.
+ - nc is a tuple or list of non cutting enzymes.
- s1 is the sentence before the non cutting enzymes.
"""
return self._make_list_only(ls, title) + self._make_nocut_only(nc, s1)
@@ -201,22 +199,21 @@ class PrintFormat(object):
"""
return title + self._make_nocut_only(nc, s1)
- def _make_nocut_only(self, nc, s1, ls=[], title=''):
+ def _make_nocut_only(self, nc, s1, ls=(), title=''):
"""PF._make_nocut_only(nc, s1) -> string.
return a formatted string of the non cutting enzymes.
Arguments:
- - nc is a list of non cutting enzymes.
+ - nc is a tuple or list of non cutting enzymes.
- s1 is the sentence before the non cutting enzymes.
"""
if not nc:
return s1
- nc.sort()
st = ''
stringsite = s1 or '\n Enzymes which do not cut the sequence.\n\n'
Join = ''.join
- for key in nc:
+ for key in sorted(nc):
st = Join((st, str.ljust(str(key), self.NameWidth)))
if len(st) > self.linesize:
stringsite = Join((stringsite, st, '\n'))
@@ -224,7 +221,7 @@ class PrintFormat(object):
stringsite = Join((stringsite, st, '\n'))
return stringsite
- def _make_list_only(self, ls, title, nc=[], s1=''):
+ def _make_list_only(self, ls, title, nc=(), s1=''):
"""PF._make_list_only(ls, title) -> string.
return a string of form::
@@ -236,7 +233,7 @@ class PrintFormat(object):
...
Arguments:
- - ls is a list of results.
+ - ls is a tuple or list of results.
- title is a string.
- Non cutting enzymes are not included.
"""
@@ -244,7 +241,7 @@ class PrintFormat(object):
return title
return self.__next_section(ls, title)
- def _make_number_only(self, ls, title, nc=[], s1=''):
+ def _make_number_only(self, ls, title, nc=(), s1=''):
"""PF._make_number_only(ls, title) -> string.
return a string of form::
@@ -267,6 +264,7 @@ class PrintFormat(object):
"""
if not ls:
return title
+ # TODO: Use key to sort!
ls.sort(lambda x, y: cmp(len(x[1]), len(y[1])))
iterator = iter(ls)
cur_len = 1
@@ -282,7 +280,7 @@ class PrintFormat(object):
title += "\n\nenzymes which cut %i times :\n\n" % cur_len
return self.__next_section(new_sect, title)
- def _make_map_only(self, ls, title, nc=[], s1=''):
+ def _make_map_only(self, ls, title, nc=(), s1=''):
"""PF._make_map_only(ls, title) -> string.
return a string of form::
@@ -324,7 +322,7 @@ class PrintFormat(object):
l.append(key)
else:
remaining.append(key)
- mapping = remaining
+ mapping = remaining
cutloc[x] = mapping
sequence = str(self.sequence)
revsequence = str(self.sequence.complement())
@@ -396,7 +394,7 @@ class PrintFormat(object):
"""FP.__next_section(ls, into) -> string.
Arguments:
- - ls is a list of tuple (string, [int, int]).
+ - ls is a tuple/list of tuple (string, [int, int]).
- into is a string to which the formatted ls will be added.
Format ls as a string of lines:
@@ -408,12 +406,11 @@ class PrintFormat(object):
then add the formatted ls to tot
return tot.
"""
- ls.sort()
indentation = '\n' + (self.NameWidth + self.Indent) * ' '
linesize = self.linesize - self.MaxSize
pat = re.compile("([\w,\s()]){1,%i}[,\.]" % linesize)
several, Join = '', ''.join
- for name, sites in ls:
+ for name, sites in sorted(ls):
stringsite = ''
l = Join((', '.join(str(site) for site in sites), '.'))
if len(l) > linesize:
diff --git a/Bio/Restriction/RanaConfig.py b/Bio/Restriction/RanaConfig.py
index 700c88c..d3043c5 100644
--- a/Bio/Restriction/RanaConfig.py
+++ b/Bio/Restriction/RanaConfig.py
@@ -1,56 +1,58 @@
-#
-# Restriction Analysis Libraries.
-# Copyright (C) 2004. Frederic Sohm.
-#
+# Copyright (C) 2004. Frederic Sohm.
# This code is part of the Biopython distribution and governed by its
# license. Please see the LICENSE file that should have been included
# as part of this package.
-#
-###############################################################################
-# Configuration of the console.
-#
-# Mainly used by PrintFormat.PrintFormat
-#
-# ConsoleWidth : width of the console used default to 80.
-# should never be less than 60.
-# NameWidth : space attributed to the name in PrintList method.
-# Indent : Indent of the second line.
-# MaxSize : Maximal size of the sequence (default=6:
-# -> 99 999 bp + 1 trailing ','
-# people are unlikely to ask for restriction map of sequences
-# bigger than 100.000 bp. This is needed to determine the
-# space to be reserved for sites location.
-#
-# MaxSize = 5 => 9.999 bp
-# MaxSize = 6 => 99.999 bp
-# MaxSize = 7 => 999.999 bp
-# example:
-#
-# <------------ ConsoleWidth --------------->
-# <- NameWidth ->
-# EcoRI : 1, 45, 50, 300, 400, 650,
-# 700, 1200, 2500.
-# <-->
-# Indent
-#
+
+"""Configuration of the console.
+
+Mainly used by PrintFormat.PrintFormat
+
+ - ConsoleWidth : width of the console used default to 80.
+ should never be less than 60.
+ - NameWidth : space attributed to the name in PrintList method.
+ - Indent : Indent of the second line.
+ - MaxSize : Maximal size of the sequence (default=6:
+ -> 99 999 bp + 1 trailing ','
+ people are unlikely to ask for restriction map of sequences
+ bigger than 100.000 bp. This is needed to determine the
+ space to be reserved for sites location.
+
+ - MaxSize = 5 => 9.999 bp
+ - MaxSize = 6 => 99.999 bp
+ - MaxSize = 7 => 999.999 bp
+
+Example::
+
+ <------------ ConsoleWidth --------------->
+ <- NameWidth ->
+ EcoRI : 1, 45, 50, 300, 400, 650,
+ 700, 1200, 2500.
+ <-->
+ Indent
+
+"""
+
+
ConsoleWidth = 80
NameWidth = 10
Indent = 4
MaxSize = 6
-###############################################################################
-# Proxies
+
+# Proxies
+#
+# Enter here the address of your proxy if any.
+# If you don't use proxy use an empty string
#
-# Enter here the address of your proxy if any.
-# If you don't use proxy use an empty string
-# i.e.
-# ftp_proxy = ''
-# -> no proxy
+# Example:
#
-# ftp_proxy = 'http://www.somewhere.something:one_number'
-# -> www.somewhere.something is the address of the proxy.
-# one_number is the port number.
+# ftp_proxy = ''
+# -> no proxy
#
+# ftp_proxy = 'http://www.somewhere.something:one_number'
+# -> www.somewhere.something is the address of the proxy.
+# one_number is the port number.
ftp_proxy = ''
+
###############################################################################
# Rebase ftp location
#
@@ -70,12 +72,7 @@ ftp_emb_r = ftp_Rebase + 'pub/rebase/emboss_r.###'
# connect as anonymous user (rebase_name) and providing your e-mail address
# as password.
#
-# Therefore, you need to enter your e-mail address in rebase_password.
-# The address will not be send to anyone but is necessary to login the
-# ftp server of rebase when connecting as anonymous user.
-#
-# Do not forget to enclose the address between "'".
+# However, a password is not required (any more?) for connecting tho REBASE,
+# so we don't send our e-mail
#
Rebase_name = 'anonymous'
-Rebase_password = ''
-# Rebase_password = 'your_address at somewhere.something'
diff --git a/Bio/Restriction/Restriction.py b/Bio/Restriction/Restriction.py
index 0ced980..14ff5a8 100644
--- a/Bio/Restriction/Restriction.py
+++ b/Bio/Restriction/Restriction.py
@@ -18,8 +18,8 @@ Notes about the diverses class of the restriction enzyme implementation::
----------------------------------------------------------------------------
NoCut, OneCut,TwoCuts represent the number of double strand cuts
produced by the enzyme.
- they correspond to the 4th field of the rebase
- record emboss_e.NNN.
+ they correspond to the 4th field of the
+ rebase record emboss_e.NNN.
0->NoCut : the enzyme is not characterised.
2->OneCut : the enzyme produce one double strand cut.
4->TwoCuts : two double strand cuts.
@@ -34,17 +34,17 @@ Notes about the diverses class of the restriction enzyme implementation::
with palindromic sites.
----------------------------------------------------------------------------
Unknown, Blunt, represent the overhang.
- Ov5, Ov3 Unknown is here for symetry reasons and
- correspond to enzymes that are not characterised
- in rebase.
+ Ov5, Ov3 Unknown is here for symmetry reasons and
+ correspond to enzymes that are not
+ characterised in rebase.
----------------------------------------------------------------------------
Defined, Ambiguous, represent the sequence of the overhang.
NotDefined
- NotDefined is for enzymes not characterised in
- rebase.
+ NotDefined is for enzymes not characterised
+ in rebase.
- Defined correspond to enzymes that display a
- constant overhang whatever the sequence.
+ Defined correspond to enzymes that display
+ a constant overhang whatever the sequence.
ex : EcoRI. G^AATTC -> overhang :AATT
CTTAA^G
@@ -59,8 +59,8 @@ Notes about the diverses class of the restriction enzyme implementation::
CTGN^NNNNNCAG
note : these 3 classes refers to the overhang not the site.
- So the enzyme ApoI (RAATTY) is defined even if its restriction
- site is ambiguous.
+ So the enzyme ApoI (RAATTY) is defined even if its
+ restriction site is ambiguous.
ApoI R^AATTY -> overhang : AATT -> Defined
YTTAA^R
@@ -69,11 +69,12 @@ Notes about the diverses class of the restriction enzyme implementation::
----------------------------------------------------------------------------
Not_available, as found in rebase file emboss_r.NNN files.
Commercially_available
- allow the selection of the enzymes according to
- their suppliers to reduce the quantity
- of results.
- Also will allow the implementation of buffer
- compatibility tables. Not implemented yet.
+ allow the selection of the enzymes
+ according to their suppliers to reduce the
+ quantity of results.
+ Also will allow the implementation of
+ buffer compatibility tables. Not
+ implemented yet.
the list of suppliers is extracted from
emboss_s.NNN
@@ -98,7 +99,6 @@ from Bio.Restriction.Restriction_Dictionary import suppliers as suppliers_dict
from Bio.Restriction.RanaConfig import *
from Bio.Restriction.PrintFormat import PrintFormat
-__docformat__ = "restructuredtext en"
# Used to use Bio.Restriction.DNAUtils.check_bases (and expose it under this
# namespace), but have deprecated that module.
@@ -149,15 +149,17 @@ class FormattedSeq(object):
Retains information about the shape of the molecule linear (default)
or circular. Restriction sites are search over the edges of circular
- sequence."""
+ sequence.
+ """
def __init__(self, seq, linear=True):
"""FormattedSeq(seq, [linear=True])-> new FormattedSeq.
seq is either a Bio.Seq, Bio.MutableSeq or a FormattedSeq.
if seq is a FormattedSeq, linear will have no effect on the
- shape of the sequence."""
- if isinstance(seq, Seq) or isinstance(seq, MutableSeq):
+ shape of the sequence.
+ """
+ if isinstance(seq, (Seq, MutableSeq)):
stringy = str(seq)
self.lower = stringy.islower()
# Note this adds a leading space to the sequence (!)
@@ -178,7 +180,8 @@ class FormattedSeq(object):
return len(self.data) - 1
def __repr__(self):
- return 'FormattedSeq(%s, linear=%s)' % (repr(self[1:]), repr(self.linear))
+ return 'FormattedSeq(%s, linear=%s)' % (repr(self[1:]),
+ repr(self.linear))
def __eq__(self, other):
if isinstance(other, FormattedSeq):
@@ -224,7 +227,8 @@ class FormattedSeq(object):
the latter is used with non palindromic sites.
pattern is the regular expression pattern corresponding to the
enzyme restriction site.
- size is the size of the restriction enzyme recognition-site size."""
+ size is the size of the restriction enzyme recognition-site size.
+ """
if self.is_linear():
data = self.data
else:
@@ -240,9 +244,10 @@ class FormattedSeq(object):
class RestrictionType(type):
"""RestrictionType. Type from which derives all enzyme classes.
- Implement the operator methods."""
+ Implement the operator methods.
+ """
- def __init__(cls, name='', bases=(), dct={}):
+ def __init__(cls, name='', bases=(), dct=None):
"""RE(name, bases, dct) -> RestrictionType instance.
Not intended to be used in normal operation. The enzymes are
@@ -265,7 +270,8 @@ class RestrictionType(type):
"""RE.__add__(other) -> RestrictionBatch().
if other is an enzyme returns a batch of the two enzymes.
- if other is already a RestrictionBatch add enzyme to it."""
+ if other is already a RestrictionBatch add enzyme to it.
+ """
if isinstance(other, RestrictionType):
return RestrictionBatch([cls, other])
elif isinstance(other, RestrictionBatch):
@@ -366,7 +372,8 @@ class RestrictionType(type):
"""RE >> other -> bool.
neoschizomer : same recognition site, different restriction. -> True
- all the others : -> False"""
+ all the others : -> False
+ """
if not isinstance(other, RestrictionType):
return False
elif cls.site == other.site and cls.charac != other.charac:
@@ -378,7 +385,8 @@ class RestrictionType(type):
"""a % b -> bool.
Test compatibility of the overhang of a and b.
- True if a and b have compatible overhang."""
+ True if a and b have compatible overhang.
+ """
if not isinstance(other, RestrictionType):
raise TypeError(
'expected RestrictionType, got %s instead' % type(other))
@@ -419,7 +427,8 @@ class RestrictionType(type):
sorting order:
1. size of the recognition site.
- 2. if equal size, alphabetical order of the names."""
+ 2. if equal size, alphabetical order of the names.
+ """
if not isinstance(other, RestrictionType):
raise NotImplementedError
elif len(cls) < len(other):
@@ -434,7 +443,8 @@ class RestrictionType(type):
sorting order:
1. size of the recognition site.
- 2. if equal size, alphabetical order of the names."""
+ 2. if equal size, alphabetical order of the names.
+ """
if not isinstance(other, RestrictionType):
raise NotImplementedError
elif len(cls) < len(other):
@@ -450,7 +460,8 @@ class AbstractCut(RestrictionType):
All the methods are classmethod.
- For internal use only. Not meant to be instantiate."""
+ For internal use only. Not meant to be instantiate.
+ """
@classmethod
def search(cls, dna, linear=True):
@@ -465,7 +476,8 @@ class AbstractCut(RestrictionType):
will be included.
The positions are the first base of the 3' fragment,
- i.e. the first base after the position the enzyme will cut. """
+ i.e. the first base after the position the enzyme will cut.
+ """
#
# Separating search from _search allow a (very limited) optimisation
# of the search when using a batch of restriction enzymes.
@@ -494,7 +506,8 @@ class AbstractCut(RestrictionType):
True if other is an isoschizomer of RE.
False else.
- equischizomer <=> same site, same position of restriction."""
+ equischizomer <=> same site, same position of restriction.
+ """
return not self != other
@classmethod
@@ -504,7 +517,8 @@ class AbstractCut(RestrictionType):
True if other is an isoschizomer of RE.
False else.
- neoschizomer <=> same site, different position of restriction."""
+ neoschizomer <=> same site, different position of restriction.
+ """
return self >> other
@classmethod
@@ -524,7 +538,8 @@ class AbstractCut(RestrictionType):
return a tuple of all the isoschizomers of RE.
if batch is supplied it is used instead of the default AllEnzymes.
- equischizomer <=> same site, same position of restriction."""
+ equischizomer <=> same site, same position of restriction.
+ """
if not batch:
batch = AllEnzymes
r = [x for x in batch if not self != x]
@@ -551,7 +566,8 @@ class AbstractCut(RestrictionType):
"""RE.isoschizomers([batch]) -> list.
return a tuple of all the equischizomers and neoschizomers of RE.
- if batch is supplied it is used instead of the default AllEnzymes."""
+ if batch is supplied it is used instead of the default AllEnzymes.
+ """
if not batch:
batch = AllEnzymes
r = [x for x in batch if (self >> x) or (not self != x)]
@@ -576,14 +592,15 @@ class NoCut(AbstractCut):
which the pattern of cut is to complex to be recorded in Rebase
(ncuts values of 0 in emboss_e.###).
- When using search() with these enzymes the values returned are at the start of
- the restriction site.
+ When using search() with these enzymes the values returned are at the start
+ of the restriction site.
Their catalyse() method returns a TypeError.
Unknown and NotDefined are also part of the base classes of these enzymes.
- Internal use only. Not meant to be instantiated."""
+ Internal use only. Not meant to be instantiated.
+ """
@classmethod
def cut_once(self):
@@ -647,7 +664,8 @@ class NoCut(AbstractCut):
fst3 -> first 3' cut (complementary strand) or None
scd5 -> second 5' cut (current strand) or None
scd5 -> second 3' cut (complementary strand) or None
- site -> recognition site."""
+ site -> recognition site.
+ """
return None, None, None, None, self.site
@@ -656,20 +674,23 @@ class OneCut(AbstractCut):
Correspond to ncuts values of 2 in emboss_e.###
- Internal use only. Not meant to be instantiated."""
+ Internal use only. Not meant to be instantiated.
+ """
@classmethod
def cut_once(self):
"""RE.cut_once() -> bool.
- True if the enzyme cut the sequence one time on each strand."""
+ True if the enzyme cut the sequence one time on each strand.
+ """
return True
@classmethod
def cut_twice(self):
"""RE.cut_twice() -> bool.
- True if the enzyme cut the sequence twice on each strand."""
+ True if the enzyme cut the sequence twice on each strand.
+ """
return False
@classmethod
@@ -720,7 +741,8 @@ class OneCut(AbstractCut):
fst3 -> first 3' cut (complementary strand) or None
scd5 -> second 5' cut (current strand) or None
scd5 -> second 3' cut (complementary strand) or None
- site -> recognition site."""
+ site -> recognition site.
+ """
return self.fst5, self.fst3, None, None, self.site
@@ -742,7 +764,8 @@ class TwoCuts(AbstractCut):
def cut_twice(self):
"""RE.cut_twice() -> bool.
- True if the enzyme cut the sequence twice on each strand."""
+ True if the enzyme cut the sequence twice on each strand.
+ """
return True
@classmethod
@@ -756,7 +779,7 @@ class TwoCuts(AbstractCut):
_modify returns the real place where the enzyme will cut.
example::
-
+
EcoRI pattern : GAATTC
EcoRI will cut after the G.
so in the sequence:
@@ -795,33 +818,38 @@ class TwoCuts(AbstractCut):
fst3 -> first 3' cut (complementary strand) or None
scd5 -> second 5' cut (current strand) or None
scd5 -> second 3' cut (complementary strand) or None
- site -> recognition site."""
+ site -> recognition site.
+ """
return self.fst5, self.fst3, self.scd5, self.scd3, self.site
class Meth_Dep(AbstractCut):
"""Implement the information about methylation.
- Enzymes of this class possess a site which is methylable."""
+ Enzymes of this class possess a site which is methylable.
+ """
@classmethod
def is_methylable(self):
"""RE.is_methylable() -> bool.
- True if the recognition site is a methylable."""
+ True if the recognition site is a methylable.
+ """
return True
class Meth_Undep(AbstractCut):
"""Implement information about methylation sensitibility.
- Enzymes of this class are not sensible to methylation."""
+ Enzymes of this class are not sensible to methylation.
+ """
@classmethod
def is_methylable(self):
"""RE.is_methylable() -> bool.
- True if the recognition site is a methylable."""
+ True if the recognition site is a methylable.
+ """
return False
@@ -854,7 +882,8 @@ class Palindromic(AbstractCut):
def is_palindromic(self):
"""RE.is_palindromic() -> bool.
- True if the recognition site is a palindrom."""
+ True if the recognition site is a palindrom.
+ """
return True
@@ -895,7 +924,8 @@ class NonPalindromic(AbstractCut):
def is_palindromic(self):
"""RE.is_palindromic() -> bool.
- True if the recognition site is a palindrom."""
+ True if the recognition site is a palindrom.
+ """
return False
@@ -905,7 +935,8 @@ class Unknown(AbstractCut):
These enzymes are also NotDefined and NoCut.
- Internal use only. Not meant to be instantiated."""
+ Internal use only. Not meant to be instantiated.
+ """
@classmethod
def catalyse(self, dna, linear=True):
@@ -918,7 +949,8 @@ class Unknown(AbstractCut):
dna must be a Bio.Seq.Seq instance or a Bio.Seq.MutableSeq instance.
if linear is False, the sequence is considered to be circular and the
- output will be modified accordingly."""
+ output will be modified accordingly.
+ """
raise NotImplementedError('%s restriction is unknown.'
% self.__name__)
catalyze = catalyse
@@ -932,7 +964,8 @@ class Unknown(AbstractCut):
see also:
RE.is_3overhang()
RE.is_5overhang()
- RE.is_unknown()"""
+ RE.is_unknown()
+ """
return False
@classmethod
@@ -944,7 +977,8 @@ class Unknown(AbstractCut):
see also:
RE.is_3overhang()
RE.is_blunt()
- RE.is_unknown()"""
+ RE.is_unknown()
+ """
return False
@classmethod
@@ -956,21 +990,24 @@ class Unknown(AbstractCut):
see also:
RE.is_5overhang()
RE.is_blunt()
- RE.is_unknown()"""
+ RE.is_unknown()
+ """
return False
@classmethod
def overhang(self):
"""RE.overhang() -> str. type of overhang of the enzyme.,
- can be "3' overhang", "5' overhang", "blunt", "unknown" """
+ can be "3' overhang", "5' overhang", "blunt", "unknown"
+ """
return 'unknown'
@classmethod
def compatible_end(self):
"""RE.compatible_end() -> list.
- list of all the enzymes that share compatible end with RE."""
+ list of all the enzymes that share compatible end with RE.
+ """
return []
@classmethod
@@ -979,7 +1016,8 @@ class Unknown(AbstractCut):
for internal use only
- test for the compatibility of restriction ending of RE and other."""
+ test for the compatibility of restriction ending of RE and other.
+ """
return False
@@ -990,7 +1028,8 @@ class Blunt(AbstractCut):
The enzyme cuts the + strand and the - strand of the DNA at the same
place.
- Internal use only. Not meant to be instantiated."""
+ Internal use only. Not meant to be instantiated.
+ """
@classmethod
def catalyse(self, dna, linear=True):
@@ -1003,7 +1042,8 @@ class Blunt(AbstractCut):
dna must be a Bio.Seq.Seq instance or a Bio.Seq.MutableSeq instance.
if linear is False, the sequence is considered to be circular and the
- output will be modified accordingly."""
+ output will be modified accordingly.
+ """
r = self.search(dna, linear)
d = self.dna
if not r:
@@ -1050,7 +1090,8 @@ class Blunt(AbstractCut):
see also:
RE.is_3overhang()
RE.is_5overhang()
- RE.is_unknown()"""
+ RE.is_unknown()
+ """
return True
@classmethod
@@ -1062,7 +1103,8 @@ class Blunt(AbstractCut):
see also:
RE.is_3overhang()
RE.is_blunt()
- RE.is_unknown()"""
+ RE.is_unknown()
+ """
return False
@classmethod
@@ -1074,21 +1116,24 @@ class Blunt(AbstractCut):
see also:
RE.is_5overhang()
RE.is_blunt()
- RE.is_unknown()"""
+ RE.is_unknown()
+ """
return False
@classmethod
def overhang(self):
"""RE.overhang() -> str. type of overhang of the enzyme.,
- can be "3' overhang", "5' overhang", "blunt", "unknown" """
+ can be "3' overhang", "5' overhang", "blunt", "unknown"
+ """
return 'blunt'
@classmethod
def compatible_end(self, batch=None):
"""RE.compatible_end() -> list.
- list of all the enzymes that share compatible end with RE."""
+ list of all the enzymes that share compatible end with RE.
+ """
if not batch:
batch = AllEnzymes
r = sorted(x for x in iter(AllEnzymes) if x.is_blunt())
@@ -1100,7 +1145,8 @@ class Blunt(AbstractCut):
for internal use only
- test for the compatibility of restriction ending of RE and other."""
+ test for the compatibility of restriction ending of RE and other.
+ """
return issubclass(other, Blunt)
@@ -1110,7 +1156,8 @@ class Ov5(AbstractCut):
The enzyme cuts the + strand after the - strand of the DNA.
- Internal use only. Not meant to be instantiated."""
+ Internal use only. Not meant to be instantiated.
+ """
@classmethod
def catalyse(self, dna, linear=True):
@@ -1123,7 +1170,8 @@ class Ov5(AbstractCut):
dna must be a Bio.Seq.Seq instance or a Bio.Seq.MutableSeq instance.
if linear is False, the sequence is considered to be circular and the
- output will be modified accordingly."""
+ output will be modified accordingly.
+ """
r = self.search(dna, linear)
d = self.dna
if not r:
@@ -1170,7 +1218,8 @@ class Ov5(AbstractCut):
see also:
RE.is_3overhang()
RE.is_5overhang()
- RE.is_unknown()"""
+ RE.is_unknown()
+ """
return False
@classmethod
@@ -1182,7 +1231,8 @@ class Ov5(AbstractCut):
see also:
RE.is_3overhang()
RE.is_blunt()
- RE.is_unknown()"""
+ RE.is_unknown()
+ """
return True
@classmethod
@@ -1194,14 +1244,16 @@ class Ov5(AbstractCut):
see also:
RE.is_5overhang()
RE.is_blunt()
- RE.is_unknown()"""
+ RE.is_unknown()
+ """
return False
@classmethod
def overhang(self):
"""RE.overhang() -> str. type of overhang of the enzyme.,
- can be "3' overhang", "5' overhang", "blunt", "unknown" """
+ can be "3' overhang", "5' overhang", "blunt", "unknown"
+ """
return "5' overhang"
@classmethod
@@ -1211,7 +1263,8 @@ class Ov5(AbstractCut):
list of all the enzymes that share compatible end with RE."""
if not batch:
batch = AllEnzymes
- r = sorted(x for x in iter(AllEnzymes) if x.is_5overhang() and x % self)
+ r = sorted(x for x in iter(AllEnzymes) if x.is_5overhang() and
+ x % self)
return r
@classmethod
@@ -1220,7 +1273,8 @@ class Ov5(AbstractCut):
for internal use only
- test for the compatibility of restriction ending of RE and other."""
+ test for the compatibility of restriction ending of RE and other.
+ """
if issubclass(other, Ov5):
return self._mod2(other)
else:
@@ -1233,7 +1287,8 @@ class Ov3(AbstractCut):
The enzyme cuts the - strand after the + strand of the DNA.
- Internal use only. Not meant to be instantiated."""
+ Internal use only. Not meant to be instantiated.
+ """
@classmethod
def catalyse(self, dna, linear=True):
@@ -1246,7 +1301,8 @@ class Ov3(AbstractCut):
dna must be a Bio.Seq.Seq instance or a Bio.Seq.MutableSeq instance.
if linear is False, the sequence is considered to be circular and the
- output will be modified accordingly."""
+ output will be modified accordingly.
+ """
r = self.search(dna, linear)
d = self.dna
if not r:
@@ -1293,7 +1349,8 @@ class Ov3(AbstractCut):
see also:
RE.is_3overhang()
RE.is_5overhang()
- RE.is_unknown()"""
+ RE.is_unknown()
+ """
return False
@classmethod
@@ -1305,7 +1362,8 @@ class Ov3(AbstractCut):
see also:
RE.is_3overhang()
RE.is_blunt()
- RE.is_unknown()"""
+ RE.is_unknown()
+ """
return False
@classmethod
@@ -1317,24 +1375,28 @@ class Ov3(AbstractCut):
see also:
RE.is_5overhang()
RE.is_blunt()
- RE.is_unknown()"""
+ RE.is_unknown()
+ """
return True
@classmethod
def overhang(self):
"""RE.overhang() -> str. type of overhang of the enzyme.,
- can be "3' overhang", "5' overhang", "blunt", "unknown" """
+ can be "3' overhang", "5' overhang", "blunt", "unknown"
+ """
return "3' overhang"
@classmethod
def compatible_end(self, batch=None):
"""RE.compatible_end() -> list.
- list of all the enzymes that share compatible end with RE."""
+ list of all the enzymes that share compatible end with RE.
+ """
if not batch:
batch = AllEnzymes
- r = sorted(x for x in iter(AllEnzymes) if x.is_3overhang() and x % self)
+ r = sorted(x for x in iter(AllEnzymes) if x.is_3overhang() and
+ x % self)
return r
@classmethod
@@ -1343,7 +1405,8 @@ class Ov3(AbstractCut):
for internal use only
- test for the compatibility of restriction ending of RE and other."""
+ test for the compatibility of restriction ending of RE and other.
+ """
#
# called by RE._mod1(other) when the one of the enzyme is ambiguous
#
@@ -1371,8 +1434,9 @@ class Defined(AbstractCut):
for internal use only.
- drop the site that are situated outside the sequence in linear sequence.
- modify the index for site in circular sequences."""
+ drop the site that are situated outside the sequence in linear
+ sequence. modify the index for site in circular sequences.
+ """
#
# remove or modify the results that are outside the sequence.
# This is necessary since after finding the site we add the distance
@@ -1410,7 +1474,8 @@ class Defined(AbstractCut):
see also:
RE.is_ambiguous()
- RE.is_unknown()"""
+ RE.is_unknown()
+ """
return True
@classmethod
@@ -1423,7 +1488,8 @@ class Defined(AbstractCut):
see also:
RE.is_defined()
- RE.is_unknown()"""
+ RE.is_unknown()
+ """
return False
@classmethod
@@ -1435,7 +1501,8 @@ class Defined(AbstractCut):
see also:
RE.is_defined()
- RE.is_ambiguous()"""
+ RE.is_ambiguous()
+ """
return False
@classmethod
@@ -1451,7 +1518,7 @@ class Defined(AbstractCut):
'G_GTAC^C'
>>> EcoRV.elucidate() # blunt
'GAT^_ATC'
- >>> SnaI.elucidate() # NotDefined, cut profile unknown.
+ >>> SnaI.elucidate() # NotDefined, cut profile unknown.
'? GTATAC ?'
>>>
"""
@@ -1482,7 +1549,8 @@ class Defined(AbstractCut):
for internal use only
- test for the compatibility of restriction ending of RE and other."""
+ test for the compatibility of restriction ending of RE and other.
+ """
#
# called by RE._mod1(other) when the one of the enzyme is ambiguous
#
@@ -1504,7 +1572,8 @@ class Ambiguous(AbstractCut):
Blunt enzymes are always defined. even if there site is GGATCCNNN^_N
There overhang is always the same : blunt!
- Internal use only. Not meant to be instantiated."""
+ Internal use only. Not meant to be instantiated.
+ """
@classmethod
def _drop(self):
@@ -1512,14 +1581,16 @@ class Ambiguous(AbstractCut):
for internal use only.
- drop the site that are situated outside the sequence in linear sequence.
- modify the index for site in circular sequences."""
+ drop the site that are situated outside the sequence in linear
+ sequence. modify the index for site in circular sequences.
+ """
length = len(self.dna)
drop = itertools.dropwhile
take = itertools.takewhile
if self.dna.is_linear():
self.results = [x for x in drop(lambda x: x < 1, self.results)]
- self.results = [x for x in take(lambda x: x < length, self.results)]
+ self.results = [x for x in take(lambda x: x <
+ length, self.results)]
else:
for index, location in enumerate(self.results):
if location < 1:
@@ -1543,7 +1614,8 @@ class Ambiguous(AbstractCut):
see also:
RE.is_ambiguous()
- RE.is_unknown()"""
+ RE.is_unknown()
+ """
return False
@classmethod
@@ -1556,7 +1628,8 @@ class Ambiguous(AbstractCut):
see also:
RE.is_defined()
- RE.is_unknown()"""
+ RE.is_unknown()
+ """
return True
@classmethod
@@ -1568,7 +1641,8 @@ class Ambiguous(AbstractCut):
see also:
RE.is_defined()
- RE.is_ambiguous()"""
+ RE.is_ambiguous()
+ """
return False
@classmethod
@@ -1577,7 +1651,8 @@ class Ambiguous(AbstractCut):
for internal use only
- test for the compatibility of restriction ending of RE and other."""
+ test for the compatibility of restriction ending of RE and other.
+ """
#
# called by RE._mod1(other) when the one of the enzyme is ambiguous
#
@@ -1633,7 +1708,8 @@ class Ambiguous(AbstractCut):
elif f3 + length < 0:
re = 'N^' * abs(f5) * 'N' + '_' + abs(length + f3) * 'N' + site
elif f5 > length:
- re = site + (f5 - length) * 'N' + '^' + (length + f3 - f5) * 'N' + '_N'
+ re = site + (f5 - length) * 'N' + '^' + (length +
+ f3 - f5) * 'N' + '_N'
else:
re = 'N^' + abs(f5) * 'N' + site + f3 * 'N' + '_N'
elif self.is_blunt():
@@ -1659,9 +1735,11 @@ class Ambiguous(AbstractCut):
elif f3 > 0:
re = site + f3 * 'N' + '_' + (f5 - f3 - length) * 'N' + '^N'
elif f5 < 0:
- re = 'N_' + abs(f3 - f5 + length) * 'N' + '^' + abs(f5) * 'N' + site
+ re = 'N_' + abs(f3 - f5 + length) * 'N' + '^' + abs(f5) * 'N' \
+ + site
else:
- re = 'N_' + abs(f3 + length) * 'N' + site + (f5 - length) * 'N' + '^N'
+ re = 'N_' + abs(f3 + length) * 'N' + site + (f5 - length) * \
+ 'N' + '^N'
return re
@@ -1671,7 +1749,8 @@ class NotDefined(AbstractCut):
Correspond to NoCut and Unknown.
- Internal use only. Not meant to be instantiated."""
+ Internal use only. Not meant to be instantiated.
+ """
@classmethod
def _drop(self):
@@ -1679,8 +1758,9 @@ class NotDefined(AbstractCut):
for internal use only.
- drop the site that are situated outside the sequence in linear sequence.
- modify the index for site in circular sequences."""
+ drop the site that are situated outside the sequence in linear
+ sequence. modify the index for site in circular sequences.
+ """
if self.dna.is_linear():
return
else:
@@ -1707,7 +1787,8 @@ class NotDefined(AbstractCut):
see also:
RE.is_ambiguous()
- RE.is_unknown()"""
+ RE.is_unknown()
+ """
return False
@classmethod
@@ -1720,7 +1801,8 @@ class NotDefined(AbstractCut):
see also:
RE.is_defined()
- RE.is_unknown()"""
+ RE.is_unknown()
+ """
return False
@classmethod
@@ -1741,9 +1823,11 @@ class NotDefined(AbstractCut):
for internal use only
- test for the compatibility of restriction ending of RE and other."""
+ test for the compatibility of restriction ending of RE and other.
+ """
#
- # Normally we should not arrive here. But well better safe than sorry.
+ # Normally we should not arrive here. But well better safe than
+ # sorry.
# the overhang is not defined we are compatible with nobody.
# could raise an Error may be rather than return quietly.
#
@@ -1779,34 +1863,38 @@ class Commercially_available(AbstractCut):
"""Implement the methods specific to the enzymes which are commercially
available.
- Internal use only. Not meant to be instantiated."""
+ Internal use only. Not meant to be instantiated.
+ """
@classmethod
def suppliers(self):
"""RE.suppliers() -> print the suppliers of RE."""
- for s in self.suppliers_dict():
- print(s + ',')
+ for s in self.suppl:
+ print(suppliers_dict[s][0] + ',')
return
@classmethod
def supplier_list(self):
"""RE.supplier_list() -> list.
- list of the supplier names for RE."""
+ list of the supplier names for RE.
+ """
return [v[0] for k, v in suppliers_dict.items() if k in self.suppl]
@classmethod
def buffers(self, supplier):
"""RE.buffers(supplier) -> string.
- not implemented yet."""
+ not implemented yet.
+ """
return
@classmethod
def is_comm(self):
"""RE.iscomm() -> bool.
- True if RE has suppliers."""
+ True if RE has suppliers.
+ """
return True
@@ -1814,7 +1902,8 @@ class Not_available(AbstractCut):
"""Implement the methods specific to the enzymes which are not commercially
available.
- Internal use only. Not meant to be instantiated."""
+ Internal use only. Not meant to be instantiated.
+ """
@staticmethod
def suppliers():
@@ -1825,21 +1914,24 @@ class Not_available(AbstractCut):
def supplier_list(self):
"""RE.supplier_list() -> list.
- list of the supplier names for RE."""
+ list of the supplier names for RE.
+ """
return []
@classmethod
def buffers(self, supplier):
"""RE.buffers(supplier) -> string.
- not implemented yet."""
+ not implemented yet.
+ """
raise TypeError("Enzyme not commercially available.")
@classmethod
def is_comm(self):
"""RE.iscomm() -> bool.
- True if RE has suppliers."""
+ True if RE has suppliers.
+ """
return False
@@ -1852,7 +1944,7 @@ class Not_available(AbstractCut):
class RestrictionBatch(set):
- def __init__(self, first=[], suppliers=[]):
+ def __init__(self, first=(), suppliers=()):
"""RestrictionBatch([sequence]) -> new RestrictionBatch."""
first = [self.format(x) for x in first]
first += [eval(x) for n in suppliers for x in suppliers_dict[n][1]]
@@ -1889,7 +1981,8 @@ class RestrictionBatch(set):
if add is True and enzyme is not in B add enzyme to B.
if add is False (which is the default) only return enzyme.
if enzyme is not a RestrictionType or can not be evaluated to
- a RestrictionType, raise a ValueError."""
+ a RestrictionType, raise a ValueError.
+ """
e = self.format(enzyme)
if e in self:
return e
@@ -1904,7 +1997,8 @@ class RestrictionBatch(set):
"""B.lambdasplit(func) -> RestrictionBatch .
the new batch will contains only the enzymes for which
- func return True."""
+ func return True.
+ """
d = [x for x in filter(func, self)]
new = RestrictionBatch()
new._data = dict(zip(d, [True] * len(d)))
@@ -1916,7 +2010,8 @@ class RestrictionBatch(set):
letter represents the suppliers as defined in the dictionary
RestrictionDictionary.suppliers
return None.
- raise a KeyError if letter is not a supplier code."""
+ raise a KeyError if letter is not a supplier code.
+ """
supplier = suppliers_dict[letter]
self.suppliers.append(letter)
for x in supplier[1]:
@@ -1927,7 +2022,8 @@ class RestrictionBatch(set):
"""B.current_suppliers() -> add a new set of enzyme to B.
return a sorted list of the suppliers which have been used to
- create the batch."""
+ create the batch.
+ """
suppl_list = sorted(suppliers_dict[x][0] for x in self.suppliers)
return suppl_list
@@ -1943,12 +2039,14 @@ class RestrictionBatch(set):
return new
def remove(self, other):
- """B.remove(other) -> remove other from B if other is a RestrictionType.
+ """B.remove(other) -> remove other from B if other is a
+ RestrictionType.
- Safe set.remove method. Verify that other is a RestrictionType or can be
- evaluated to a RestrictionType.
+ Safe set.remove method. Verify that other is a RestrictionType or can
+ be evaluated to a RestrictionType.
raise a ValueError if other can not be evaluated to a RestrictionType.
- raise a KeyError if other is not in B."""
+ raise a KeyError if other is not in B.
+ """
return set.remove(self, self.format(other))
def add(self, other):
@@ -1970,7 +2068,8 @@ class RestrictionBatch(set):
if y is a RestrictionType return y
if y can be evaluated to a RestrictionType return eval(y)
- raise a Value Error in all other case."""
+ raise a Value Error in all other case.
+ """
try:
if isinstance(y, RestrictionType):
return y
@@ -1985,15 +2084,17 @@ class RestrictionBatch(set):
def is_restriction(self, y):
"""B.is_restriction(y) -> bool.
- True is y or eval(y) is a RestrictionType."""
- return isinstance(y, RestrictionType) or \
- isinstance(eval(str(y)), RestrictionType)
+ True is y or eval(y) is a RestrictionType.
+ """
+ return (isinstance(y, RestrictionType) or
+ isinstance(eval(str(y)), RestrictionType))
def split(self, *classes, **bool):
"""B.split(class, [class.__name__ = True]) -> new RestrictionBatch.
it works but it is slow, so it has really an interest when splitting
- over multiple conditions."""
+ over multiple conditions.
+ """
def splittest(element):
for klass in classes:
b = bool.get(klass.__name__, True)
@@ -2015,21 +2116,24 @@ class RestrictionBatch(set):
def elements(self):
"""B.elements() -> tuple.
- give all the names of the enzymes in B sorted alphabetically."""
+ give all the names of the enzymes in B sorted alphabetically.
+ """
l = sorted(str(e) for e in self)
return l
def as_string(self):
"""B.as_string() -> list.
- return a list of the name of the elements of B."""
+ return a list of the name of the elements of B.
+ """
return [str(e) for e in self]
@classmethod
def suppl_codes(self):
"""B.suppl_codes() -> dict
- letter code for the suppliers"""
+ letter code for the suppliers
+ """
supply = dict((k, v[0]) for k, v in suppliers_dict.items())
return supply
@@ -2171,7 +2275,8 @@ class Analysis(RestrictionBatch, PrintFormat):
Changing one of these parameters here might not give the results
you expect. In which case, you can settle back to a 80 columns shell
or try to change self.Cmodulo and self.PrefWidth in PrintFormat until
- you get it right."""
+ you get it right.
+ """
for k, v in what.items():
if k in ('NameWidth', 'ConsoleWidth'):
setattr(self, k, v)
@@ -2199,13 +2304,15 @@ class Analysis(RestrictionBatch, PrintFormat):
def full(self, linear=True):
"""A.full() -> dict.
- Full Restriction Map of the sequence."""
+ Full Restriction Map of the sequence.
+ """
return self.mapping
def blunt(self, dct=None):
"""A.blunt([dct]) -> dict.
- Only the enzymes which have a 3'overhang restriction site."""
+ Only the enzymes which have a 3'overhang restriction site.
+ """
if not dct:
dct = self.mapping
return dict((k, v) for k, v in dct.items() if k.is_blunt())
@@ -2213,7 +2320,8 @@ class Analysis(RestrictionBatch, PrintFormat):
def overhang5(self, dct=None):
"""A.overhang5([dct]) -> dict.
- Only the enzymes which have a 5' overhang restriction site."""
+ Only the enzymes which have a 5' overhang restriction site.
+ """
if not dct:
dct = self.mapping
return dict((k, v) for k, v in dct.items() if k.is_5overhang())
@@ -2221,7 +2329,8 @@ class Analysis(RestrictionBatch, PrintFormat):
def overhang3(self, dct=None):
"""A.Overhang3([dct]) -> dict.
- Only the enzymes which have a 3'overhang restriction site."""
+ Only the enzymes which have a 3'overhang restriction site.
+ """
if not dct:
dct = self.mapping
return dict((k, v) for k, v in dct.items() if k.is_3overhang())
@@ -2229,7 +2338,8 @@ class Analysis(RestrictionBatch, PrintFormat):
def defined(self, dct=None):
"""A.defined([dct]) -> dict.
- Only the enzymes that have a defined restriction site in Rebase."""
+ Only the enzymes that have a defined restriction site in Rebase.
+ """
if not dct:
dct = self.mapping
return dict((k, v) for k, v in dct.items() if k.is_defined())
@@ -2237,7 +2347,8 @@ class Analysis(RestrictionBatch, PrintFormat):
def with_sites(self, dct=None):
"""A.with_sites([dct]) -> dict.
- Enzymes which have at least one site in the sequence."""
+ Enzymes which have at least one site in the sequence.
+ """
if not dct:
dct = self.mapping
return dict((k, v) for k, v in dct.items() if v)
@@ -2245,7 +2356,8 @@ class Analysis(RestrictionBatch, PrintFormat):
def without_site(self, dct=None):
"""A.without_site([dct]) -> dict.
- Enzymes which have no site in the sequence."""
+ Enzymes which have no site in the sequence.
+ """
if not dct:
dct = self.mapping
return dict((k, v) for k, v in dct.items() if not v)
@@ -2253,7 +2365,8 @@ class Analysis(RestrictionBatch, PrintFormat):
def with_N_sites(self, N, dct=None):
"""A.With_N_Sites(N [, dct]) -> dict.
- Enzymes which cut N times the sequence."""
+ Enzymes which cut N times the sequence.
+ """
if not dct:
dct = self.mapping
return dict((k, v) for k, v in dct.items()if len(v) == N)
@@ -2266,7 +2379,8 @@ class Analysis(RestrictionBatch, PrintFormat):
def with_name(self, names, dct=None):
"""A.with_name(list_of_names [, dct]) ->
- Limit the search to the enzymes named in list_of_names."""
+ Limit the search to the enzymes named in list_of_names.
+ """
for i, enzyme in enumerate(names):
if enzyme not in AllEnzymes:
print("no data for the enzyme: %s" % name)
@@ -2278,7 +2392,8 @@ class Analysis(RestrictionBatch, PrintFormat):
def with_site_size(self, site_size, dct=None):
"""A.with_site_size(site_size [, dct]) ->
- Limit the search to the enzymes whose site is of size <site_size>."""
+ Limit the search to the enzymes whose site is of size <site_size>.
+ """
sites = [name for name in self if name.size == site_size]
if not dct:
return RestrictionBatch(sites).search(self.sequence)
@@ -2287,7 +2402,8 @@ class Analysis(RestrictionBatch, PrintFormat):
def only_between(self, start, end, dct=None):
"""A.only_between(start, end[, dct]) -> dict.
- Enzymes that cut the sequence only in between start and end."""
+ Enzymes that cut the sequence only in between start and end.
+ """
start, end, test = self._boundaries(start, end)
if not dct:
dct = self.mapping
@@ -2308,7 +2424,8 @@ class Analysis(RestrictionBatch, PrintFormat):
"""A.between(start, end [, dct]) -> dict.
Enzymes that cut the sequence at least in between start and end.
- They may cut outside as well."""
+ They may cut outside as well.
+ """
start, end, test = self._boundaries(start, end)
d = {}
if not dct:
@@ -2325,7 +2442,8 @@ class Analysis(RestrictionBatch, PrintFormat):
"""A.show_only_between(start, end [, dct]) -> dict.
Enzymes that cut the sequence outside of the region
- in between start and end but do not cut inside."""
+ in between start and end but do not cut inside.
+ """
d = []
if start <= end:
d = [(k, [vv for vv in v if start <= vv <= end])
@@ -2339,7 +2457,8 @@ class Analysis(RestrictionBatch, PrintFormat):
"""A.only_outside(start, end [, dct]) -> dict.
Enzymes that cut the sequence outside of the region
- in between start and end but do not cut inside."""
+ in between start and end but do not cut inside.
+ """
start, end, test = self._boundaries(start, end)
if not dct:
dct = self.mapping
@@ -2360,7 +2479,8 @@ class Analysis(RestrictionBatch, PrintFormat):
"""A.outside((start, end [, dct]) -> dict.
Enzymes that cut outside the region in between start and end.
- No test is made to know if they cut or not inside this region."""
+ No test is made to know if they cut or not inside this region.
+ """
start, end, test = self._boundaries(start, end)
if not dct:
dct = self.mapping
@@ -2377,7 +2497,8 @@ class Analysis(RestrictionBatch, PrintFormat):
def do_not_cut(self, start, end, dct=None):
"""A.do_not_cut(start, end [, dct]) -> dict.
- Enzymes that do not cut the region in between start and end."""
+ Enzymes that do not cut the region in between start and end.
+ """
if not dct:
dct = self.mapping
d = self.without_site()
@@ -2403,8 +2524,8 @@ class Analysis(RestrictionBatch, PrintFormat):
# which provide a very efficient layout for the class themselves mostly
# alleviating the need of if/else loops in the class methods.
#
-# It is essential to run Restriction with doc string optimisation (-OO switch)
-# as the doc string of 660 classes take a lot of processing.
+# It is essential to run Restriction with doc string optimisation (-OO
+# switch) as the doc string of 660 classes take a lot of processing.
#
CommOnly = RestrictionBatch() # commercial enzymes
NonComm = RestrictionBatch() # not available commercially
@@ -2462,5 +2583,6 @@ except NameError:
# Scoping changed in Python 3, the variable isn't leaked
pass
locals().update(dict(zip(names, AllEnzymes)))
-__all__ = ['FormattedSeq', 'Analysis', 'RestrictionBatch', 'AllEnzymes', 'CommOnly', 'NonComm'] + names
+__all__ = ['FormattedSeq', 'Analysis', 'RestrictionBatch', 'AllEnzymes',
+ 'CommOnly', 'NonComm'] + names
del k, enzymes, TYPE, bases, names
diff --git a/Bio/Restriction/Restriction_Dictionary.py b/Bio/Restriction/Restriction_Dictionary.py
index 99c1098..8f06e1c 100644
--- a/Bio/Restriction/Restriction_Dictionary.py
+++ b/Bio/Restriction/Restriction_Dictionary.py
@@ -1,4 +1,3 @@
-
#!/usr/bin/env python
#
# Restriction Analysis Libraries.
@@ -15,11 +14,10 @@
# The following dictionaries used to be defined in one go, but that does
# not work on Jython due to JVM limitations. Therefore we break this up
# into steps, using temporary functions to avoid the JVM limits.
-
+#
+# Used REBASE emboss files version 605 (2016).
rest_dict = {}
-
-
def _temp():
return {
'compsite': '(?P<AanI>TTATAA)',
@@ -35,14 +33,13 @@ def _temp():
'inact_temp': 65,
'ovhg': 0,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (3, -3, None, None, 'TTATAA'),
'ovhgseq': '',
}
rest_dict['AanI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<AarI>CACCTGC)|(?P<AarI_as>GCAGGTG)',
@@ -58,14 +55,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -4,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (11, 8, None, None, 'CACCTGC'),
'ovhgseq': 'NNNN',
}
rest_dict['AarI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<AasI>GAC......GTC)',
@@ -81,14 +77,13 @@ def _temp():
'inact_temp': 65,
'ovhg': 2,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (7, -7, None, None, 'GACNNNNNNGTC'),
'ovhgseq': 'NN',
}
rest_dict['AasI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<AatII>GACGTC)',
@@ -104,13 +99,56 @@ def _temp():
'inact_temp': 65,
'ovhg': 4,
'scd3': None,
- 'suppl': ('F', 'I', 'K', 'M', 'N', 'R'),
+ 'suppl': ('B', 'I', 'K', 'M', 'N', 'V'),
'scd5': None,
'charac': (5, -5, None, None, 'GACGTC'),
'ovhgseq': 'ACGT',
}
rest_dict['AatII'] = _temp()
+def _temp():
+ return {
+ 'compsite': '(?P<Aba6411II>C[AG][AG]TAAG)|(?P<Aba6411II_as>CTTA[CT][CT]G)',
+ 'results': None,
+ 'site': 'CRRTAAG',
+ 'substrat': 'DNA',
+ 'fst3': None,
+ 'fst5': None,
+ 'freq': 4096,
+ 'size': 7,
+ 'opt_temp': 37,
+ 'dna': None,
+ 'inact_temp': 65,
+ 'ovhg': None,
+ 'scd3': None,
+ 'suppl': (),
+ 'scd5': None,
+ 'charac': (None, None, None, None, 'CRRTAAG'),
+ 'ovhgseq': None,
+ }
+rest_dict['Aba6411II'] = _temp()
+
+def _temp():
+ return {
+ 'compsite': '(?P<AbaCIII>CTATCA[ACG])|(?P<AbaCIII_as>[CGT]TGATAG)',
+ 'results': None,
+ 'site': 'CTATCAV',
+ 'substrat': 'DNA',
+ 'fst3': None,
+ 'fst5': None,
+ 'freq': 4096,
+ 'size': 7,
+ 'opt_temp': 37,
+ 'dna': None,
+ 'inact_temp': 65,
+ 'ovhg': None,
+ 'scd3': None,
+ 'suppl': (),
+ 'scd5': None,
+ 'charac': (None, None, None, None, 'CTATCAV'),
+ 'ovhgseq': None,
+ }
+rest_dict['AbaCIII'] = _temp()
def _temp():
return {
@@ -134,6 +172,27 @@ def _temp():
}
rest_dict['AbaSI'] = _temp()
+def _temp():
+ return {
+ 'compsite': '(?P<AbaUMB2I>[CT]CCG[CG][CG])|(?P<AbaUMB2I_as>[CG][CG]CGG[AG])',
+ 'results': None,
+ 'site': 'YCCGSS',
+ 'substrat': 'DNA',
+ 'fst3': None,
+ 'fst5': None,
+ 'freq': 512,
+ 'size': 6,
+ 'opt_temp': 37,
+ 'dna': None,
+ 'inact_temp': 65,
+ 'ovhg': None,
+ 'scd3': None,
+ 'suppl': (),
+ 'scd5': None,
+ 'charac': (None, None, None, None, 'YCCGSS'),
+ 'ovhgseq': None,
+ }
+rest_dict['AbaUMB2I'] = _temp()
def _temp():
return {
@@ -157,7 +216,6 @@ def _temp():
}
rest_dict['AbsI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Acc16I>TGCGCA)',
@@ -173,14 +231,13 @@ def _temp():
'inact_temp': 65,
'ovhg': 0,
'scd3': None,
- 'suppl': ('I',),
+ 'suppl': ('I', 'V'),
'scd5': None,
'charac': (3, -3, None, None, 'TGCGCA'),
'ovhgseq': '',
}
rest_dict['Acc16I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Acc36I>ACCTGC)|(?P<Acc36I_as>GCAGGT)',
@@ -203,7 +260,6 @@ def _temp():
}
rest_dict['Acc36I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Acc65I>GGTACC)',
@@ -219,14 +275,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -4,
'scd3': None,
- 'suppl': ('F', 'I', 'N', 'R'),
+ 'suppl': ('B', 'I', 'N', 'V'),
'scd5': None,
'charac': (1, -1, None, None, 'GGTACC'),
'ovhgseq': 'GTAC',
}
rest_dict['Acc65I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<AccB1I>GG[CT][AG]CC)',
@@ -242,14 +297,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -4,
'scd3': None,
- 'suppl': ('I',),
+ 'suppl': ('I', 'V'),
'scd5': None,
'charac': (1, -1, None, None, 'GGYRCC'),
'ovhgseq': 'GYRC',
}
rest_dict['AccB1I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<AccB7I>CCA.....TGG)',
@@ -265,14 +319,13 @@ def _temp():
'inact_temp': 65,
'ovhg': 3,
'scd3': None,
- 'suppl': ('I',),
+ 'suppl': ('I', 'V'),
'scd5': None,
'charac': (7, -7, None, None, 'CCANNNNNTGG'),
'ovhgseq': 'NNN',
}
rest_dict['AccB7I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<AccBSI>CCGCTC)|(?P<AccBSI_as>GAGCGG)',
@@ -288,14 +341,13 @@ def _temp():
'inact_temp': 65,
'ovhg': 0,
'scd3': None,
- 'suppl': ('I',),
+ 'suppl': ('I', 'V'),
'scd5': None,
'charac': (3, -3, None, None, 'CCGCTC'),
'ovhgseq': '',
}
rest_dict['AccBSI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<AccI>GT[AC][GT]AC)',
@@ -311,14 +363,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -2,
'scd3': None,
- 'suppl': ('B', 'J', 'K', 'M', 'N', 'Q', 'R', 'S', 'U', 'X'),
+ 'suppl': ('B', 'J', 'K', 'M', 'N', 'Q', 'R', 'S', 'X'),
'scd5': None,
'charac': (2, -2, None, None, 'GTMKAC'),
'ovhgseq': 'MK',
}
rest_dict['AccI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<AccII>CGCG)',
@@ -341,7 +392,6 @@ def _temp():
}
rest_dict['AccII'] = _temp()
-
def _temp():
return {
'compsite': '(?P<AccIII>TCCGGA)',
@@ -364,7 +414,6 @@ def _temp():
}
rest_dict['AccIII'] = _temp()
-
def _temp():
return {
'compsite': '(?P<AceIII>CAGCTC)|(?P<AceIII_as>GAGCTG)',
@@ -387,6 +436,27 @@ def _temp():
}
rest_dict['AceIII'] = _temp()
+def _temp():
+ return {
+ 'compsite': '(?P<AchA6III>AGCCAG)|(?P<AchA6III_as>CTGGCT)',
+ 'results': None,
+ 'site': 'AGCCAG',
+ 'substrat': 'DNA',
+ 'fst3': None,
+ 'fst5': None,
+ 'freq': 4096,
+ 'size': 6,
+ 'opt_temp': 37,
+ 'dna': None,
+ 'inact_temp': 65,
+ 'ovhg': None,
+ 'scd3': None,
+ 'suppl': (),
+ 'scd5': None,
+ 'charac': (None, None, None, None, 'AGCCAG'),
+ 'ovhgseq': None,
+ }
+rest_dict['AchA6III'] = _temp()
def _temp():
return {
@@ -410,7 +480,6 @@ def _temp():
}
rest_dict['AciI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<AclI>AACGTT)',
@@ -426,14 +495,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -2,
'scd3': None,
- 'suppl': ('I', 'N'),
+ 'suppl': ('I', 'N', 'V'),
'scd5': None,
'charac': (2, -2, None, None, 'AACGTT'),
'ovhgseq': 'CG',
}
rest_dict['AclI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<AclWI>GGATC)|(?P<AclWI_as>GATCC)',
@@ -456,6 +524,27 @@ def _temp():
}
rest_dict['AclWI'] = _temp()
+def _temp():
+ return {
+ 'compsite': '(?P<Aco12261II>CC[AG]GAG)|(?P<Aco12261II_as>CTC[CT]GG)',
+ 'results': None,
+ 'site': 'CCRGAG',
+ 'substrat': 'DNA',
+ 'fst3': None,
+ 'fst5': None,
+ 'freq': 2048,
+ 'size': 6,
+ 'opt_temp': 37,
+ 'dna': None,
+ 'inact_temp': 65,
+ 'ovhg': None,
+ 'scd3': None,
+ 'suppl': (),
+ 'scd5': None,
+ 'charac': (None, None, None, None, 'CCRGAG'),
+ 'ovhgseq': None,
+ }
+rest_dict['Aco12261II'] = _temp()
def _temp():
return {
@@ -479,7 +568,6 @@ def _temp():
}
rest_dict['AcoI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<AcsI>[AG]AATT[CT])',
@@ -495,14 +583,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -4,
'scd3': None,
- 'suppl': ('I',),
+ 'suppl': ('I', 'V'),
'scd5': None,
'charac': (1, -1, None, None, 'RAATTY'),
'ovhgseq': 'AATT',
}
rest_dict['AcsI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<AcuI>CTGAAG)|(?P<AcuI_as>CTTCAG)',
@@ -525,7 +612,6 @@ def _temp():
}
rest_dict['AcuI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<AcvI>CACGTG)',
@@ -548,7 +634,6 @@ def _temp():
}
rest_dict['AcvI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<AcyI>G[AG]CG[CT]C)',
@@ -571,7 +656,6 @@ def _temp():
}
rest_dict['AcyI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<AdeI>CAC...GTG)',
@@ -587,14 +671,13 @@ def _temp():
'inact_temp': 65,
'ovhg': 3,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (6, -6, None, None, 'CACNNNGTG'),
'ovhgseq': 'NNN',
}
rest_dict['AdeI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<AfaI>GTAC)',
@@ -617,7 +700,6 @@ def _temp():
}
rest_dict['AfaI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<AfeI>AGCGCT)',
@@ -640,7 +722,6 @@ def _temp():
}
rest_dict['AfeI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<AfiI>CC.......GG)',
@@ -663,7 +744,6 @@ def _temp():
}
rest_dict['AfiI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<AflII>CTTAAG)',
@@ -686,7 +766,6 @@ def _temp():
}
rest_dict['AflII'] = _temp()
-
def _temp():
return {
'compsite': '(?P<AflIII>AC[AG][CT]GT)',
@@ -709,7 +788,6 @@ def _temp():
}
rest_dict['AflIII'] = _temp()
-
def _temp():
return {
'compsite': '(?P<AgeI>ACCGGT)',
@@ -732,7 +810,6 @@ def _temp():
}
rest_dict['AgeI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<AgsI>TT[CG]AA)',
@@ -755,7 +832,6 @@ def _temp():
}
rest_dict['AgsI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<AhaIII>TTTAAA)',
@@ -778,7 +854,6 @@ def _temp():
}
rest_dict['AhaIII'] = _temp()
-
def _temp():
return {
'compsite': '(?P<AhdI>GAC.....GTC)',
@@ -801,7 +876,6 @@ def _temp():
}
rest_dict['AhdI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<AhlI>ACTAGT)',
@@ -824,6 +898,27 @@ def _temp():
}
rest_dict['AhlI'] = _temp()
+def _temp():
+ return {
+ 'compsite': '(?P<AhyYL17I>[CT]AA[AC]GAG)|(?P<AhyYL17I_as>CTC[GT]TT[AG])',
+ 'results': None,
+ 'site': 'YAAMGAG',
+ 'substrat': 'DNA',
+ 'fst3': None,
+ 'fst5': None,
+ 'freq': 4096,
+ 'size': 7,
+ 'opt_temp': 37,
+ 'dna': None,
+ 'inact_temp': 65,
+ 'ovhg': None,
+ 'scd3': None,
+ 'suppl': (),
+ 'scd5': None,
+ 'charac': (None, None, None, None, 'YAAMGAG'),
+ 'ovhgseq': None,
+ }
+rest_dict['AhyYL17I'] = _temp()
def _temp():
return {
@@ -840,14 +935,13 @@ def _temp():
'inact_temp': 65,
'ovhg': 0,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (3, -3, None, None, 'CACGTC'),
'ovhgseq': '',
}
rest_dict['AjiI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<AjnI>CC[AT]GG)',
@@ -870,7 +964,6 @@ def _temp():
}
rest_dict['AjnI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<AjuI>GAA.......TTGG)|(?P<AjuI_as>CCAA.......TTC)',
@@ -886,14 +979,13 @@ def _temp():
'inact_temp': 65,
'ovhg': 5,
'scd3': 6,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': 25,
'charac': (-7, -26, 25, 6, 'GAANNNNNNNTTGG'),
'ovhgseq': 'NNNNN',
}
rest_dict['AjuI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<AleI>CAC....GTG)',
@@ -916,7 +1008,6 @@ def _temp():
}
rest_dict['AleI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<AlfI>GCA......TGC)',
@@ -932,14 +1023,13 @@ def _temp():
'inact_temp': 65,
'ovhg': 2,
'scd3': 10,
- 'suppl': ('F',),
+ 'suppl': (),
'scd5': 24,
'charac': (-10, -24, 24, 10, 'GCANNNNNNTGC'),
'ovhgseq': 'NN',
}
rest_dict['AlfI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<AloI>GAAC......TCC)|(?P<AloI_as>GGA......GTTC)',
@@ -955,14 +1045,13 @@ def _temp():
'inact_temp': 65,
'ovhg': 5,
'scd3': 7,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': 25,
'charac': (-7, -25, 25, 7, 'GAACNNNNNNTCC'),
'ovhgseq': 'NNNNN',
}
rest_dict['AloI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<AluBI>AGCT)',
@@ -985,7 +1074,6 @@ def _temp():
}
rest_dict['AluBI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<AluI>AGCT)',
@@ -1001,14 +1089,13 @@ def _temp():
'inact_temp': 65,
'ovhg': 0,
'scd3': None,
- 'suppl': ('B', 'C', 'F', 'I', 'J', 'K', 'M', 'N', 'O', 'Q', 'R', 'S', 'U', 'V', 'X', 'Y'),
+ 'suppl': ('B', 'C', 'I', 'J', 'K', 'M', 'N', 'O', 'Q', 'R', 'S', 'V', 'X', 'Y'),
'scd5': None,
'charac': (2, -2, None, None, 'AGCT'),
'ovhgseq': '',
}
rest_dict['AluI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Alw21I>G[AT]GC[AT]C)',
@@ -1024,14 +1111,13 @@ def _temp():
'inact_temp': 65,
'ovhg': 4,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (5, -5, None, None, 'GWGCWC'),
'ovhgseq': 'WGCW',
}
rest_dict['Alw21I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Alw26I>GTCTC)|(?P<Alw26I_as>GAGAC)',
@@ -1047,14 +1133,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -4,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (6, 5, None, None, 'GTCTC'),
'ovhgseq': 'NNNN',
}
rest_dict['Alw26I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Alw44I>GTGCAC)',
@@ -1070,14 +1155,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -4,
'scd3': None,
- 'suppl': ('F', 'J'),
+ 'suppl': ('B', 'J'),
'scd5': None,
'charac': (1, -1, None, None, 'GTGCAC'),
'ovhgseq': 'TGCA',
}
rest_dict['Alw44I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<AlwFI>GAAA[CT].....[AG]TG)|(?P<AlwFI_as>CA[CT].....[AG]TTTC)',
@@ -1100,7 +1184,6 @@ def _temp():
}
rest_dict['AlwFI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<AlwI>GGATC)|(?P<AlwI_as>GATCC)',
@@ -1123,7 +1206,6 @@ def _temp():
}
rest_dict['AlwI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<AlwNI>CAG...CTG)',
@@ -1146,7 +1228,6 @@ def _temp():
}
rest_dict['AlwNI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Ama87I>C[CT]CG[AG]G)',
@@ -1169,7 +1250,6 @@ def _temp():
}
rest_dict['Ama87I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Aor13HI>TCCGGA)',
@@ -1192,7 +1272,6 @@ def _temp():
}
rest_dict['Aor13HI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Aor51HI>AGCGCT)',
@@ -1215,7 +1294,6 @@ def _temp():
}
rest_dict['Aor51HI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<AoxI>GGCC)',
@@ -1231,14 +1309,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -4,
'scd3': None,
- 'suppl': (),
+ 'suppl': ('I',),
'scd5': None,
'charac': (0, 0, None, None, 'GGCC'),
'ovhgseq': 'GGCC',
}
rest_dict['AoxI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<ApaBI>GCA.....TGC)',
@@ -1261,7 +1338,6 @@ def _temp():
}
rest_dict['ApaBI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<ApaI>GGGCCC)',
@@ -1277,14 +1353,13 @@ def _temp():
'inact_temp': 65,
'ovhg': 4,
'scd3': None,
- 'suppl': ('B', 'F', 'I', 'J', 'K', 'M', 'N', 'Q', 'R', 'S', 'U', 'V', 'X'),
+ 'suppl': ('B', 'I', 'J', 'K', 'M', 'N', 'Q', 'R', 'S', 'V', 'X'),
'scd5': None,
'charac': (5, -5, None, None, 'GGGCCC'),
'ovhgseq': 'GGCC',
}
rest_dict['ApaI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<ApaLI>GTGCAC)',
@@ -1300,14 +1375,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -4,
'scd3': None,
- 'suppl': ('C', 'K', 'N', 'U'),
+ 'suppl': ('C', 'K', 'N'),
'scd5': None,
'charac': (1, -1, None, None, 'GTGCAC'),
'ovhgseq': 'TGCA',
}
rest_dict['ApaLI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<ApeKI>GC[AT]GC)',
@@ -1330,7 +1404,6 @@ def _temp():
}
rest_dict['ApeKI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<ApoI>[AG]AATT[CT])',
@@ -1353,7 +1426,6 @@ def _temp():
}
rest_dict['ApoI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<ApyPI>ATCGAC)|(?P<ApyPI_as>GTCGAT)',
@@ -1376,7 +1448,6 @@ def _temp():
}
rest_dict['ApyPI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<AquII>GCCG.AC)|(?P<AquII_as>GT.CGGC)',
@@ -1399,7 +1470,6 @@ def _temp():
}
rest_dict['AquII'] = _temp()
-
def _temp():
return {
'compsite': '(?P<AquIII>GAGGAG)|(?P<AquIII_as>CTCCTC)',
@@ -1422,7 +1492,6 @@ def _temp():
}
rest_dict['AquIII'] = _temp()
-
def _temp():
return {
'compsite': '(?P<AquIV>G[AG]GGAAG)|(?P<AquIV_as>CTTCC[CT]C)',
@@ -1445,7 +1514,6 @@ def _temp():
}
rest_dict['AquIV'] = _temp()
-
def _temp():
return {
'compsite': '(?P<ArsI>GAC......TT[CT]G)|(?P<ArsI_as>C[AG]AA......GTC)',
@@ -1468,7 +1536,6 @@ def _temp():
}
rest_dict['ArsI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<AscI>GGCGCGCC)',
@@ -1491,7 +1558,6 @@ def _temp():
}
rest_dict['AscI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<AseI>ATTAAT)',
@@ -1507,14 +1573,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -2,
'scd3': None,
- 'suppl': ('J', 'N', 'O'),
+ 'suppl': ('J', 'N'),
'scd5': None,
'charac': (2, -2, None, None, 'ATTAAT'),
'ovhgseq': 'TA',
}
rest_dict['AseI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Asi256I>GATC)',
@@ -1537,7 +1602,6 @@ def _temp():
}
rest_dict['Asi256I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<AsiGI>ACCGGT)',
@@ -1560,7 +1624,6 @@ def _temp():
}
rest_dict['AsiGI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<AsiSI>GCGATCGC)',
@@ -1583,7 +1646,6 @@ def _temp():
}
rest_dict['AsiSI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Asp700I>GAA....TTC)',
@@ -1606,7 +1668,6 @@ def _temp():
}
rest_dict['Asp700I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Asp718I>GGTACC)',
@@ -1629,7 +1690,6 @@ def _temp():
}
rest_dict['Asp718I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<AspA2I>CCTAGG)',
@@ -1652,7 +1712,6 @@ def _temp():
}
rest_dict['AspA2I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<AspBHI>[CT][CG]C.[CG])|(?P<AspBHI_as>[CG].G[CG][AG])',
@@ -1675,6 +1734,49 @@ def _temp():
}
rest_dict['AspBHI'] = _temp()
+def _temp():
+ return {
+ 'compsite': '(?P<AspDUT2V>G.GCAAC)|(?P<AspDUT2V_as>GTTGC.C)',
+ 'results': None,
+ 'site': 'GNGCAAC',
+ 'substrat': 'DNA',
+ 'fst3': None,
+ 'fst5': None,
+ 'freq': 4096,
+ 'size': 7,
+ 'opt_temp': 37,
+ 'dna': None,
+ 'inact_temp': 65,
+ 'ovhg': None,
+ 'scd3': None,
+ 'suppl': (),
+ 'scd5': None,
+ 'charac': (None, None, None, None, 'GNGCAAC'),
+ 'ovhgseq': None,
+ }
+rest_dict['AspDUT2V'] = _temp()
+
+def _temp():
+ return {
+ 'compsite': '(?P<AspJHL3II>CGCCCAG)|(?P<AspJHL3II_as>CTGGGCG)',
+ 'results': None,
+ 'site': 'CGCCCAG',
+ 'substrat': 'DNA',
+ 'fst3': None,
+ 'fst5': None,
+ 'freq': 16384,
+ 'size': 7,
+ 'opt_temp': 37,
+ 'dna': None,
+ 'inact_temp': 65,
+ 'ovhg': None,
+ 'scd3': None,
+ 'suppl': (),
+ 'scd5': None,
+ 'charac': (None, None, None, None, 'CGCCCAG'),
+ 'ovhgseq': None,
+ }
+rest_dict['AspJHL3II'] = _temp()
def _temp():
return {
@@ -1698,7 +1800,6 @@ def _temp():
}
rest_dict['AspLEI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<AspS9I>GG.CC)',
@@ -1721,29 +1822,27 @@ def _temp():
}
rest_dict['AspS9I'] = _temp()
-
def _temp():
return {
- 'compsite': '(?P<AssI>AGTACT)',
+ 'compsite': '(?P<Asu14238IV>CGT[AG]AC)|(?P<Asu14238IV_as>GT[CT]ACG)',
'results': None,
- 'site': 'AGTACT',
+ 'site': 'CGTRAC',
'substrat': 'DNA',
- 'fst3': -3,
- 'fst5': 3,
- 'freq': 4096,
+ 'fst3': None,
+ 'fst5': None,
+ 'freq': 2048,
'size': 6,
'opt_temp': 37,
'dna': None,
'inact_temp': 65,
- 'ovhg': 0,
+ 'ovhg': None,
'scd3': None,
- 'suppl': ('U',),
+ 'suppl': (),
'scd5': None,
- 'charac': (3, -3, None, None, 'AGTACT'),
- 'ovhgseq': '',
+ 'charac': (None, None, None, None, 'CGTRAC'),
+ 'ovhgseq': None,
}
-rest_dict['AssI'] = _temp()
-
+rest_dict['Asu14238IV'] = _temp()
def _temp():
return {
@@ -1767,7 +1866,6 @@ def _temp():
}
rest_dict['AsuC2I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<AsuHPI>GGTGA)|(?P<AsuHPI_as>TCACC)',
@@ -1790,7 +1888,6 @@ def _temp():
}
rest_dict['AsuHPI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<AsuI>GG.CC)',
@@ -1813,7 +1910,6 @@ def _temp():
}
rest_dict['AsuI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<AsuII>TTCGAA)',
@@ -1836,7 +1932,6 @@ def _temp():
}
rest_dict['AsuII'] = _temp()
-
def _temp():
return {
'compsite': '(?P<AsuNHI>GCTAGC)',
@@ -1859,7 +1954,6 @@ def _temp():
}
rest_dict['AsuNHI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<AvaI>C[CT]CG[AG]G)',
@@ -1875,14 +1969,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -4,
'scd3': None,
- 'suppl': ('J', 'N', 'Q', 'R', 'U', 'X'),
+ 'suppl': ('J', 'N', 'Q', 'X'),
'scd5': None,
'charac': (1, -1, None, None, 'CYCGRG'),
'ovhgseq': 'YCGR',
}
rest_dict['AvaI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<AvaII>GG[AT]CC)',
@@ -1898,14 +1991,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -3,
'scd3': None,
- 'suppl': ('J', 'N', 'R', 'X', 'Y'),
+ 'suppl': ('J', 'N', 'R', 'X'),
'scd5': None,
'charac': (1, -1, None, None, 'GGWCC'),
'ovhgseq': 'GWC',
}
rest_dict['AvaII'] = _temp()
-
def _temp():
return {
'compsite': '(?P<AvaIII>ATGCAT)',
@@ -1928,7 +2020,6 @@ def _temp():
}
rest_dict['AvaIII'] = _temp()
-
def _temp():
return {
'compsite': '(?P<AvrII>CCTAGG)',
@@ -1951,17 +2042,38 @@ def _temp():
}
rest_dict['AvrII'] = _temp()
-
def _temp():
return {
- 'compsite': '(?P<AxyI>CCT.AGG)',
+ 'compsite': '(?P<Awo1030IV>GCC[AG]AG)|(?P<Awo1030IV_as>CT[CT]GGC)',
'results': None,
- 'site': 'CCTNAGG',
+ 'site': 'GCCRAG',
'substrat': 'DNA',
- 'fst3': -2,
- 'fst5': 2,
- 'freq': 4096,
- 'size': 7,
+ 'fst3': None,
+ 'fst5': None,
+ 'freq': 2048,
+ 'size': 6,
+ 'opt_temp': 37,
+ 'dna': None,
+ 'inact_temp': 65,
+ 'ovhg': None,
+ 'scd3': None,
+ 'suppl': (),
+ 'scd5': None,
+ 'charac': (None, None, None, None, 'GCCRAG'),
+ 'ovhgseq': None,
+ }
+rest_dict['Awo1030IV'] = _temp()
+
+def _temp():
+ return {
+ 'compsite': '(?P<AxyI>CCT.AGG)',
+ 'results': None,
+ 'site': 'CCTNAGG',
+ 'substrat': 'DNA',
+ 'fst3': -2,
+ 'fst5': 2,
+ 'freq': 4096,
+ 'size': 7,
'opt_temp': 37,
'dna': None,
'inact_temp': 65,
@@ -1974,7 +2086,6 @@ def _temp():
}
rest_dict['AxyI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BaeGI>G[GT]GC[AC]C)',
@@ -1997,7 +2108,6 @@ def _temp():
}
rest_dict['BaeGI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BaeI>AC....GTA[CT]C)|(?P<BaeI_as>G[AG]TAC....GT)',
@@ -2020,6 +2130,27 @@ def _temp():
}
rest_dict['BaeI'] = _temp()
+def _temp():
+ return {
+ 'compsite': '(?P<Bag18758I>CCCGAG)|(?P<Bag18758I_as>CTCGGG)',
+ 'results': None,
+ 'site': 'CCCGAG',
+ 'substrat': 'DNA',
+ 'fst3': None,
+ 'fst5': None,
+ 'freq': 4096,
+ 'size': 6,
+ 'opt_temp': 37,
+ 'dna': None,
+ 'inact_temp': 65,
+ 'ovhg': None,
+ 'scd3': None,
+ 'suppl': (),
+ 'scd5': None,
+ 'charac': (None, None, None, None, 'CCCGAG'),
+ 'ovhgseq': None,
+ }
+rest_dict['Bag18758I'] = _temp()
def _temp():
return {
@@ -2043,7 +2174,6 @@ def _temp():
}
rest_dict['BalI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BamHI>GGATCC)',
@@ -2059,14 +2189,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -4,
'scd3': None,
- 'suppl': ('B', 'C', 'F', 'I', 'J', 'K', 'M', 'N', 'O', 'Q', 'R', 'S', 'U', 'V', 'X', 'Y'),
+ 'suppl': ('B', 'C', 'I', 'J', 'K', 'M', 'N', 'O', 'Q', 'R', 'S', 'V', 'X', 'Y'),
'scd5': None,
'charac': (1, -1, None, None, 'GGATCC'),
'ovhgseq': 'GATC',
}
rest_dict['BamHI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BanI>GG[CT][AG]CC)',
@@ -2082,14 +2211,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -4,
'scd3': None,
- 'suppl': ('N', 'R', 'U'),
+ 'suppl': ('N', 'R'),
'scd5': None,
'charac': (1, -1, None, None, 'GGYRCC'),
'ovhgseq': 'GYRC',
}
rest_dict['BanI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BanII>G[AG]GC[CT]C)',
@@ -2112,6 +2240,27 @@ def _temp():
}
rest_dict['BanII'] = _temp()
+def _temp():
+ return {
+ 'compsite': '(?P<BanLI>[AG]TCAGG)|(?P<BanLI_as>CCTGA[CT])',
+ 'results': None,
+ 'site': 'RTCAGG',
+ 'substrat': 'DNA',
+ 'fst3': None,
+ 'fst5': None,
+ 'freq': 2048,
+ 'size': 6,
+ 'opt_temp': 37,
+ 'dna': None,
+ 'inact_temp': 65,
+ 'ovhg': None,
+ 'scd3': None,
+ 'suppl': (),
+ 'scd5': None,
+ 'charac': (None, None, None, None, 'RTCAGG'),
+ 'ovhgseq': None,
+ }
+rest_dict['BanLI'] = _temp()
def _temp():
return {
@@ -2135,30 +2284,6 @@ def _temp():
}
rest_dict['BarI'] = _temp()
-
-def _temp():
- return {
- 'compsite': '(?P<BasI>CCA.....TGG)',
- 'results': None,
- 'site': 'CCANNNNNTGG',
- 'substrat': 'DNA',
- 'fst3': -7,
- 'fst5': 7,
- 'freq': 4096,
- 'size': 11,
- 'opt_temp': 37,
- 'dna': None,
- 'inact_temp': 65,
- 'ovhg': 3,
- 'scd3': None,
- 'suppl': ('U',),
- 'scd5': None,
- 'charac': (7, -7, None, None, 'CCANNNNNTGG'),
- 'ovhgseq': 'NNN',
- }
-rest_dict['BasI'] = _temp()
-
-
def _temp():
return {
'compsite': '(?P<BauI>CACGAG)|(?P<BauI_as>CTCGTG)',
@@ -2174,14 +2299,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -4,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (1, -1, None, None, 'CACGAG'),
'ovhgseq': 'ACGA',
}
rest_dict['BauI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Bbr7I>GAAGAC)|(?P<Bbr7I_as>GTCTTC)',
@@ -2204,7 +2328,6 @@ def _temp():
}
rest_dict['Bbr7I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BbrPI>CACGTG)',
@@ -2227,7 +2350,6 @@ def _temp():
}
rest_dict['BbrPI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BbsI>GAAGAC)|(?P<BbsI_as>GTCTTC)',
@@ -2250,7 +2372,6 @@ def _temp():
}
rest_dict['BbsI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Bbv12I>G[AT]GC[AT]C)',
@@ -2273,7 +2394,6 @@ def _temp():
}
rest_dict['Bbv12I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BbvCI>CCTCAGC)|(?P<BbvCI_as>GCTGAGG)',
@@ -2296,7 +2416,6 @@ def _temp():
}
rest_dict['BbvCI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BbvI>GCAGC)|(?P<BbvI_as>GCTGC)',
@@ -2319,7 +2438,6 @@ def _temp():
}
rest_dict['BbvI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BbvII>GAAGAC)|(?P<BbvII_as>GTCTTC)',
@@ -2342,7 +2460,6 @@ def _temp():
}
rest_dict['BbvII'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BccI>CCATC)|(?P<BccI_as>GATGG)',
@@ -2365,6 +2482,27 @@ def _temp():
}
rest_dict['BccI'] = _temp()
+def _temp():
+ return {
+ 'compsite': '(?P<Bce3081I>TAGGAG)|(?P<Bce3081I_as>CTCCTA)',
+ 'results': None,
+ 'site': 'TAGGAG',
+ 'substrat': 'DNA',
+ 'fst3': None,
+ 'fst5': None,
+ 'freq': 4096,
+ 'size': 6,
+ 'opt_temp': 37,
+ 'dna': None,
+ 'inact_temp': 65,
+ 'ovhg': None,
+ 'scd3': None,
+ 'suppl': (),
+ 'scd5': None,
+ 'charac': (None, None, None, None, 'TAGGAG'),
+ 'ovhgseq': None,
+ }
+rest_dict['Bce3081I'] = _temp()
def _temp():
return {
@@ -2388,7 +2526,6 @@ def _temp():
}
rest_dict['Bce83I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BceAI>ACGGC)|(?P<BceAI_as>GCCGT)',
@@ -2411,6 +2548,27 @@ def _temp():
}
rest_dict['BceAI'] = _temp()
+def _temp():
+ return {
+ 'compsite': '(?P<BceSIV>GCAGC)|(?P<BceSIV_as>GCTGC)',
+ 'results': None,
+ 'site': 'GCAGC',
+ 'substrat': 'DNA',
+ 'fst3': -10,
+ 'fst5': -7,
+ 'freq': 1024,
+ 'size': 5,
+ 'opt_temp': 37,
+ 'dna': None,
+ 'inact_temp': 65,
+ 'ovhg': -2,
+ 'scd3': 11,
+ 'suppl': (),
+ 'scd5': 14,
+ 'charac': (-7, -10, 14, 11, 'GCAGC'),
+ 'ovhgseq': 'NN',
+ }
+rest_dict['BceSIV'] = _temp()
def _temp():
return {
@@ -2434,7 +2592,6 @@ def _temp():
}
rest_dict['BcefI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BcgI>CGA......TGC)|(?P<BcgI_as>GCA......TCG)',
@@ -2457,7 +2614,6 @@ def _temp():
}
rest_dict['BcgI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BciT130I>CC[AT]GG)',
@@ -2480,7 +2636,6 @@ def _temp():
}
rest_dict['BciT130I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BciVI>GTATCC)|(?P<BciVI_as>GGATAC)',
@@ -2503,7 +2658,6 @@ def _temp():
}
rest_dict['BciVI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BclI>TGATCA)',
@@ -2519,14 +2673,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -4,
'scd3': None,
- 'suppl': ('C', 'F', 'J', 'M', 'N', 'O', 'R', 'S', 'U', 'Y'),
+ 'suppl': ('B', 'C', 'J', 'M', 'N', 'O', 'R', 'S'),
'scd5': None,
'charac': (1, -1, None, None, 'TGATCA'),
'ovhgseq': 'GATC',
}
rest_dict['BclI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BcnI>CC[CG]GG)',
@@ -2542,14 +2695,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -1,
'scd3': None,
- 'suppl': ('F', 'K'),
+ 'suppl': ('B', 'K'),
'scd5': None,
'charac': (2, -2, None, None, 'CCSGG'),
'ovhgseq': 'S',
}
rest_dict['BcnI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BcoDI>GTCTC)|(?P<BcoDI_as>GAGAC)',
@@ -2572,7 +2724,6 @@ def _temp():
}
rest_dict['BcoDI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BcuI>ACTAGT)',
@@ -2588,14 +2739,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -4,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (1, -1, None, None, 'ACTAGT'),
'ovhgseq': 'CTAG',
}
rest_dict['BcuI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BdaI>TGA......TCA)',
@@ -2618,7 +2768,6 @@ def _temp():
}
rest_dict['BdaI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BetI>[AT]CCGG[AT])',
@@ -2641,7 +2790,6 @@ def _temp():
}
rest_dict['BetI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BfaI>CTAG)',
@@ -2664,6 +2812,27 @@ def _temp():
}
rest_dict['BfaI'] = _temp()
+def _temp():
+ return {
+ 'compsite': '(?P<BfaSII>GA.GGAG)|(?P<BfaSII_as>CTCC.TC)',
+ 'results': None,
+ 'site': 'GANGGAG',
+ 'substrat': 'DNA',
+ 'fst3': None,
+ 'fst5': None,
+ 'freq': 4096,
+ 'size': 7,
+ 'opt_temp': 37,
+ 'dna': None,
+ 'inact_temp': 65,
+ 'ovhg': None,
+ 'scd3': None,
+ 'suppl': (),
+ 'scd5': None,
+ 'charac': (None, None, None, None, 'GANGGAG'),
+ 'ovhgseq': None,
+ }
+rest_dict['BfaSII'] = _temp()
def _temp():
return {
@@ -2687,7 +2856,6 @@ def _temp():
}
rest_dict['BfiI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BfmI>CT[AG][CT]AG)',
@@ -2703,14 +2871,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -4,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (1, -1, None, None, 'CTRYAG'),
'ovhgseq': 'TRYA',
}
rest_dict['BfmI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BfoI>[AG]GCGC[CT])',
@@ -2726,14 +2893,13 @@ def _temp():
'inact_temp': 65,
'ovhg': 4,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (5, -5, None, None, 'RGCGCY'),
'ovhgseq': 'GCGC',
}
rest_dict['BfoI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BfrI>CTTAAG)',
@@ -2756,7 +2922,6 @@ def _temp():
}
rest_dict['BfrI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BfuAI>ACCTGC)|(?P<BfuAI_as>GCAGGT)',
@@ -2779,7 +2944,6 @@ def _temp():
}
rest_dict['BfuAI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BfuCI>GATC)',
@@ -2802,7 +2966,6 @@ def _temp():
}
rest_dict['BfuCI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BfuI>GTATCC)|(?P<BfuI_as>GGATAC)',
@@ -2818,14 +2981,13 @@ def _temp():
'inact_temp': 65,
'ovhg': 1,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (12, 5, None, None, 'GTATCC'),
'ovhgseq': 'N',
}
rest_dict['BfuI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BglI>GCC.....GGC)',
@@ -2841,14 +3003,13 @@ def _temp():
'inact_temp': 65,
'ovhg': 3,
'scd3': None,
- 'suppl': ('C', 'F', 'I', 'J', 'K', 'N', 'O', 'Q', 'R', 'U', 'V', 'X', 'Y'),
+ 'suppl': ('B', 'C', 'I', 'J', 'K', 'N', 'O', 'Q', 'R', 'V', 'X'),
'scd5': None,
'charac': (7, -7, None, None, 'GCCNNNNNGGC'),
'ovhgseq': 'NNN',
}
rest_dict['BglI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BglII>AGATCT)',
@@ -2864,14 +3025,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -4,
'scd3': None,
- 'suppl': ('B', 'C', 'F', 'I', 'J', 'K', 'M', 'N', 'O', 'Q', 'R', 'S', 'U', 'X', 'Y'),
+ 'suppl': ('B', 'C', 'I', 'J', 'K', 'M', 'N', 'O', 'Q', 'R', 'S', 'V', 'X'),
'scd5': None,
'charac': (1, -1, None, None, 'AGATCT'),
'ovhgseq': 'GATC',
}
rest_dict['BglII'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BinI>GGATC)|(?P<BinI_as>GATCC)',
@@ -2894,7 +3054,6 @@ def _temp():
}
rest_dict['BinI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BisI>GC.GC)',
@@ -2917,7 +3076,6 @@ def _temp():
}
rest_dict['BisI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BlnI>CCTAGG)',
@@ -2940,6 +3098,27 @@ def _temp():
}
rest_dict['BlnI'] = _temp()
+def _temp():
+ return {
+ 'compsite': '(?P<BloAII>GAGGAC)|(?P<BloAII_as>GTCCTC)',
+ 'results': None,
+ 'site': 'GAGGAC',
+ 'substrat': 'DNA',
+ 'fst3': None,
+ 'fst5': None,
+ 'freq': 4096,
+ 'size': 6,
+ 'opt_temp': 37,
+ 'dna': None,
+ 'inact_temp': 65,
+ 'ovhg': None,
+ 'scd3': None,
+ 'suppl': (),
+ 'scd5': None,
+ 'charac': (None, None, None, None, 'GAGGAC'),
+ 'ovhgseq': None,
+ }
+rest_dict['BloAII'] = _temp()
def _temp():
return {
@@ -2963,7 +3142,6 @@ def _temp():
}
rest_dict['BlpI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BlsI>GC.GC)',
@@ -2986,7 +3164,6 @@ def _temp():
}
rest_dict['BlsI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BmcAI>AGTACT)',
@@ -3009,7 +3186,6 @@ def _temp():
}
rest_dict['BmcAI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Bme1390I>CC.GG)',
@@ -3025,14 +3201,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -1,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (2, -2, None, None, 'CCNGG'),
'ovhgseq': 'N',
}
rest_dict['Bme1390I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Bme18I>GG[AT]CC)',
@@ -3055,7 +3230,6 @@ def _temp():
}
rest_dict['Bme18I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BmeDI>C)|(?P<BmeDI_as>G)',
@@ -3078,7 +3252,6 @@ def _temp():
}
rest_dict['BmeDI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BmeRI>GAC.....GTC)',
@@ -3101,7 +3274,6 @@ def _temp():
}
rest_dict['BmeRI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BmeT110I>C[CT]CG[AG]G)',
@@ -3124,7 +3296,6 @@ def _temp():
}
rest_dict['BmeT110I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BmgBI>CACGTC)|(?P<BmgBI_as>GACGTG)',
@@ -3147,7 +3318,6 @@ def _temp():
}
rest_dict['BmgBI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BmgI>G[GT]GCCC)|(?P<BmgI_as>GGGC[AC]C)',
@@ -3170,7 +3340,6 @@ def _temp():
}
rest_dict['BmgI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BmgT120I>GG.CC)',
@@ -3193,7 +3362,6 @@ def _temp():
}
rest_dict['BmgT120I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BmiI>GG..CC)',
@@ -3216,7 +3384,6 @@ def _temp():
}
rest_dict['BmiI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BmrFI>CC.GG)',
@@ -3239,7 +3406,6 @@ def _temp():
}
rest_dict['BmrFI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BmrI>ACTGGG)|(?P<BmrI_as>CCCAGT)',
@@ -3262,7 +3428,6 @@ def _temp():
}
rest_dict['BmrI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BmsI>GCATC)|(?P<BmsI_as>GATGC)',
@@ -3278,14 +3443,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -4,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (10, 9, None, None, 'GCATC'),
'ovhgseq': 'NNNN',
}
rest_dict['BmsI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BmtI>GCTAGC)',
@@ -3308,7 +3472,6 @@ def _temp():
}
rest_dict['BmtI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BmuI>ACTGGG)|(?P<BmuI_as>CCCAGT)',
@@ -3331,7 +3494,6 @@ def _temp():
}
rest_dict['BmuI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BoxI>GAC....GTC)',
@@ -3347,14 +3509,13 @@ def _temp():
'inact_temp': 65,
'ovhg': 0,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (5, -5, None, None, 'GACNNNNGTC'),
'ovhgseq': '',
}
rest_dict['BoxI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BpiI>GAAGAC)|(?P<BpiI_as>GTCTTC)',
@@ -3370,14 +3531,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -4,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (8, 6, None, None, 'GAAGAC'),
'ovhgseq': 'NNNN',
}
rest_dict['BpiI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BplI>GAG.....CTC)',
@@ -3393,14 +3553,13 @@ def _temp():
'inact_temp': 65,
'ovhg': 5,
'scd3': 8,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': 24,
'charac': (-8, -24, 24, 8, 'GAGNNNNNCTC'),
'ovhgseq': 'NNNNN',
}
rest_dict['BplI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BpmI>CTGGAG)|(?P<BpmI_as>CTCCAG)',
@@ -3423,7 +3582,6 @@ def _temp():
}
rest_dict['BpmI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Bpu10I>CCT.AGC)|(?P<Bpu10I_as>GCT.AGG)',
@@ -3439,14 +3597,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -3,
'scd3': None,
- 'suppl': ('F', 'I', 'N', 'V'),
+ 'suppl': ('B', 'I', 'N', 'V'),
'scd5': None,
'charac': (2, -2, None, None, 'CCTNAGC'),
'ovhgseq': 'TNA',
}
rest_dict['Bpu10I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Bpu1102I>GCT.AGC)',
@@ -3462,14 +3619,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -3,
'scd3': None,
- 'suppl': ('F', 'K'),
+ 'suppl': ('B', 'K'),
'scd5': None,
'charac': (2, -2, None, None, 'GCTNAGC'),
'ovhgseq': 'TNA',
}
rest_dict['Bpu1102I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Bpu14I>TTCGAA)',
@@ -3492,7 +3648,6 @@ def _temp():
}
rest_dict['Bpu14I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BpuEI>CTTGAG)|(?P<BpuEI_as>CTCAAG)',
@@ -3515,7 +3670,6 @@ def _temp():
}
rest_dict['BpuEI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BpuMI>CC[CG]GG)',
@@ -3538,30 +3692,6 @@ def _temp():
}
rest_dict['BpuMI'] = _temp()
-
-def _temp():
- return {
- 'compsite': '(?P<BpvUI>CGATCG)',
- 'results': None,
- 'site': 'CGATCG',
- 'substrat': 'DNA',
- 'fst3': -4,
- 'fst5': 4,
- 'freq': 4096,
- 'size': 6,
- 'opt_temp': 37,
- 'dna': None,
- 'inact_temp': 65,
- 'ovhg': 2,
- 'scd3': None,
- 'suppl': ('V',),
- 'scd5': None,
- 'charac': (4, -4, None, None, 'CGATCG'),
- 'ovhgseq': 'AT',
- }
-rest_dict['BpvUI'] = _temp()
-
-
def _temp():
return {
'compsite': '(?P<Bsa29I>ATCGAT)',
@@ -3584,7 +3714,6 @@ def _temp():
}
rest_dict['Bsa29I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BsaAI>[CT]ACGT[AG])',
@@ -3607,7 +3736,6 @@ def _temp():
}
rest_dict['BsaAI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BsaBI>GAT....ATC)',
@@ -3630,7 +3758,6 @@ def _temp():
}
rest_dict['BsaBI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BsaHI>G[AG]CG[CT]C)',
@@ -3653,7 +3780,6 @@ def _temp():
}
rest_dict['BsaHI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BsaI>GGTCTC)|(?P<BsaI_as>GAGACC)',
@@ -3676,7 +3802,6 @@ def _temp():
}
rest_dict['BsaI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BsaJI>CC..GG)',
@@ -3699,7 +3824,6 @@ def _temp():
}
rest_dict['BsaJI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BsaWI>[AT]CCGG[AT])',
@@ -3722,7 +3846,6 @@ def _temp():
}
rest_dict['BsaWI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BsaXI>AC.....CTCC)|(?P<BsaXI_as>GGAG.....GT)',
@@ -3745,7 +3868,6 @@ def _temp():
}
rest_dict['BsaXI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BsbI>CAACAC)|(?P<BsbI_as>GTGTTG)',
@@ -3768,7 +3890,6 @@ def _temp():
}
rest_dict['BsbI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Bsc4I>CC.......GG)',
@@ -3791,7 +3912,6 @@ def _temp():
}
rest_dict['Bsc4I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BscAI>GCATC)|(?P<BscAI_as>GATGC)',
@@ -3814,7 +3934,6 @@ def _temp():
}
rest_dict['BscAI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BscGI>CCCGT)|(?P<BscGI_as>ACGGG)',
@@ -3837,7 +3956,6 @@ def _temp():
}
rest_dict['BscGI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Bse118I>[AG]CCGG[CT])',
@@ -3860,7 +3978,6 @@ def _temp():
}
rest_dict['Bse118I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Bse1I>ACTGG)|(?P<Bse1I_as>CCAGT)',
@@ -3883,7 +4000,6 @@ def _temp():
}
rest_dict['Bse1I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Bse21I>CCT.AGG)',
@@ -3906,7 +4022,6 @@ def _temp():
}
rest_dict['Bse21I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Bse3DI>GCAATG)|(?P<Bse3DI_as>CATTGC)',
@@ -3929,7 +4044,6 @@ def _temp():
}
rest_dict['Bse3DI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Bse8I>GAT....ATC)',
@@ -3952,7 +4066,6 @@ def _temp():
}
rest_dict['Bse8I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BseAI>TCCGGA)',
@@ -3968,14 +4081,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -4,
'scd3': None,
- 'suppl': ('C', 'M'),
+ 'suppl': ('C',),
'scd5': None,
'charac': (1, -1, None, None, 'TCCGGA'),
'ovhgseq': 'CCGG',
}
rest_dict['BseAI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BseBI>CC[AT]GG)',
@@ -3998,7 +4110,6 @@ def _temp():
}
rest_dict['BseBI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BseCI>ATCGAT)',
@@ -4021,7 +4132,6 @@ def _temp():
}
rest_dict['BseCI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BseDI>CC..GG)',
@@ -4037,14 +4147,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -4,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (1, -1, None, None, 'CCNNGG'),
'ovhgseq': 'CNNG',
}
rest_dict['BseDI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BseGI>GGATG)|(?P<BseGI_as>CATCC)',
@@ -4060,14 +4169,13 @@ def _temp():
'inact_temp': 65,
'ovhg': 2,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (7, 0, None, None, 'GGATG'),
'ovhgseq': 'NN',
}
rest_dict['BseGI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BseJI>GAT....ATC)',
@@ -4083,14 +4191,13 @@ def _temp():
'inact_temp': 65,
'ovhg': 0,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (5, -5, None, None, 'GATNNNNATC'),
'ovhgseq': '',
}
rest_dict['BseJI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BseLI>CC.......GG)',
@@ -4106,14 +4213,13 @@ def _temp():
'inact_temp': 65,
'ovhg': 3,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (7, -7, None, None, 'CCNNNNNNNGG'),
'ovhgseq': 'NNN',
}
rest_dict['BseLI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BseMI>GCAATG)|(?P<BseMI_as>CATTGC)',
@@ -4129,14 +4235,13 @@ def _temp():
'inact_temp': 65,
'ovhg': 2,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (8, 0, None, None, 'GCAATG'),
'ovhgseq': 'NN',
}
rest_dict['BseMI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BseMII>CTCAG)|(?P<BseMII_as>CTGAG)',
@@ -4152,14 +4257,13 @@ def _temp():
'inact_temp': 65,
'ovhg': 2,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (15, 8, None, None, 'CTCAG'),
'ovhgseq': 'NN',
}
rest_dict['BseMII'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BseNI>ACTGG)|(?P<BseNI_as>CCAGT)',
@@ -4175,14 +4279,13 @@ def _temp():
'inact_temp': 65,
'ovhg': 2,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (6, -1, None, None, 'ACTGG'),
'ovhgseq': 'GN',
}
rest_dict['BseNI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BsePI>GCGCGC)',
@@ -4205,7 +4308,6 @@ def _temp():
}
rest_dict['BsePI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BseRI>GAGGAG)|(?P<BseRI_as>CTCCTC)',
@@ -4228,7 +4330,6 @@ def _temp():
}
rest_dict['BseRI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BseSI>G[GT]GC[AC]C)',
@@ -4244,14 +4345,13 @@ def _temp():
'inact_temp': 65,
'ovhg': 4,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (5, -5, None, None, 'GKGCMC'),
'ovhgseq': 'KGCM',
}
rest_dict['BseSI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BseX3I>CGGCCG)',
@@ -4274,7 +4374,6 @@ def _temp():
}
rest_dict['BseX3I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BseXI>GCAGC)|(?P<BseXI_as>GCTGC)',
@@ -4290,14 +4389,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -4,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (13, 12, None, None, 'GCAGC'),
'ovhgseq': 'NNNN',
}
rest_dict['BseXI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BseYI>CCCAGC)|(?P<BseYI_as>GCTGGG)',
@@ -4320,7 +4418,6 @@ def _temp():
}
rest_dict['BseYI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BsgI>GTGCAG)|(?P<BsgI_as>CTGCAC)',
@@ -4343,7 +4440,6 @@ def _temp():
}
rest_dict['BsgI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Bsh1236I>CGCG)',
@@ -4359,14 +4455,13 @@ def _temp():
'inact_temp': 65,
'ovhg': 0,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (2, -2, None, None, 'CGCG'),
'ovhgseq': '',
}
rest_dict['Bsh1236I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Bsh1285I>CG[AG][CT]CG)',
@@ -4382,14 +4477,13 @@ def _temp():
'inact_temp': 65,
'ovhg': 2,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (4, -4, None, None, 'CGRYCG'),
'ovhgseq': 'RY',
}
rest_dict['Bsh1285I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BshFI>GGCC)',
@@ -4412,7 +4506,6 @@ def _temp():
}
rest_dict['BshFI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BshNI>GG[CT][AG]CC)',
@@ -4428,14 +4521,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -4,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (1, -1, None, None, 'GGYRCC'),
'ovhgseq': 'GYRC',
}
rest_dict['BshNI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BshTI>ACCGGT)',
@@ -4451,14 +4543,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -4,
'scd3': None,
- 'suppl': ('B', 'F'),
+ 'suppl': ('B',),
'scd5': None,
'charac': (1, -1, None, None, 'ACCGGT'),
'ovhgseq': 'CCGG',
}
rest_dict['BshTI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BshVI>ATCGAT)',
@@ -4481,7 +4572,6 @@ def _temp():
}
rest_dict['BshVI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BsiEI>CG[AG][CT]CG)',
@@ -4504,7 +4594,6 @@ def _temp():
}
rest_dict['BsiEI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BsiHKAI>G[AT]GC[AT]C)',
@@ -4527,7 +4616,6 @@ def _temp():
}
rest_dict['BsiHKAI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BsiHKCI>C[CT]CG[AG]G)',
@@ -4550,7 +4638,6 @@ def _temp():
}
rest_dict['BsiHKCI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BsiI>CACGAG)|(?P<BsiI_as>CTCGTG)',
@@ -4573,7 +4660,6 @@ def _temp():
}
rest_dict['BsiI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BsiSI>CCGG)',
@@ -4589,14 +4675,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -2,
'scd3': None,
- 'suppl': ('C',),
+ 'suppl': ('C', 'Y'),
'scd5': None,
'charac': (1, -1, None, None, 'CCGG'),
'ovhgseq': 'CG',
}
rest_dict['BsiSI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BsiWI>CGTACG)',
@@ -4619,7 +4704,6 @@ def _temp():
}
rest_dict['BsiWI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BsiYI>CC.......GG)',
@@ -4642,7 +4726,6 @@ def _temp():
}
rest_dict['BsiYI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BslFI>GGGAC)|(?P<BslFI_as>GTCCC)',
@@ -4665,7 +4748,6 @@ def _temp():
}
rest_dict['BslFI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BslI>CC.......GG)',
@@ -4688,7 +4770,6 @@ def _temp():
}
rest_dict['BslI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BsmAI>GTCTC)|(?P<BsmAI_as>GAGAC)',
@@ -4711,7 +4792,6 @@ def _temp():
}
rest_dict['BsmAI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BsmBI>CGTCTC)|(?P<BsmBI_as>GAGACG)',
@@ -4734,7 +4814,6 @@ def _temp():
}
rest_dict['BsmBI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BsmFI>GGGAC)|(?P<BsmFI_as>GTCCC)',
@@ -4757,7 +4836,6 @@ def _temp():
}
rest_dict['BsmFI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BsmI>GAATGC)|(?P<BsmI_as>GCATTC)',
@@ -4780,7 +4858,6 @@ def _temp():
}
rest_dict['BsmI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BsnI>GGCC)',
@@ -4803,7 +4880,6 @@ def _temp():
}
rest_dict['BsnI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Bso31I>GGTCTC)|(?P<Bso31I_as>GAGACC)',
@@ -4826,7 +4902,6 @@ def _temp():
}
rest_dict['Bso31I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BsoBI>C[CT]CG[AG]G)',
@@ -4849,7 +4924,6 @@ def _temp():
}
rest_dict['BsoBI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Bsp119I>TTCGAA)',
@@ -4865,14 +4939,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -2,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (2, -2, None, None, 'TTCGAA'),
'ovhgseq': 'CG',
}
rest_dict['Bsp119I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Bsp120I>GGGCCC)',
@@ -4888,14 +4961,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -4,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (1, -1, None, None, 'GGGCCC'),
'ovhgseq': 'GGCC',
}
rest_dict['Bsp120I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Bsp1286I>G[AGT]GC[ACT]C)',
@@ -4918,7 +4990,6 @@ def _temp():
}
rest_dict['Bsp1286I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Bsp13I>TCCGGA)',
@@ -4941,7 +5012,6 @@ def _temp():
}
rest_dict['Bsp13I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Bsp1407I>TGTACA)',
@@ -4957,14 +5027,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -4,
'scd3': None,
- 'suppl': ('F', 'K'),
+ 'suppl': ('B', 'K'),
'scd5': None,
'charac': (1, -1, None, None, 'TGTACA'),
'ovhgseq': 'GTAC',
}
rest_dict['Bsp1407I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Bsp143I>GATC)',
@@ -4980,14 +5049,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -4,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (0, 0, None, None, 'GATC'),
'ovhgseq': 'GATC',
}
rest_dict['Bsp143I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Bsp1720I>GCT.AGC)',
@@ -5010,7 +5078,6 @@ def _temp():
}
rest_dict['Bsp1720I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Bsp19I>CCATGG)',
@@ -5033,7 +5100,6 @@ def _temp():
}
rest_dict['Bsp19I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Bsp24I>GAC......TGG)|(?P<Bsp24I_as>CCA......GTC)',
@@ -5056,6 +5122,27 @@ def _temp():
}
rest_dict['Bsp24I'] = _temp()
+def _temp():
+ return {
+ 'compsite': '(?P<Bsp3004IV>CCGCAT)|(?P<Bsp3004IV_as>ATGCGG)',
+ 'results': None,
+ 'site': 'CCGCAT',
+ 'substrat': 'DNA',
+ 'fst3': None,
+ 'fst5': None,
+ 'freq': 4096,
+ 'size': 6,
+ 'opt_temp': 37,
+ 'dna': None,
+ 'inact_temp': 65,
+ 'ovhg': None,
+ 'scd3': None,
+ 'suppl': (),
+ 'scd5': None,
+ 'charac': (None, None, None, None, 'CCGCAT'),
+ 'ovhgseq': None,
+ }
+rest_dict['Bsp3004IV'] = _temp()
def _temp():
return {
@@ -5072,14 +5159,13 @@ def _temp():
'inact_temp': 65,
'ovhg': 0,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (3, -3, None, None, 'TCGCGA'),
'ovhgseq': '',
}
rest_dict['Bsp68I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BspACI>CCGC)|(?P<BspACI_as>GCGG)',
@@ -5102,6 +5188,27 @@ def _temp():
}
rest_dict['BspACI'] = _temp()
+def _temp():
+ return {
+ 'compsite': '(?P<BspANI>GGCC)',
+ 'results': None,
+ 'site': 'GGCC',
+ 'substrat': 'DNA',
+ 'fst3': -2,
+ 'fst5': 2,
+ 'freq': 256,
+ 'size': 4,
+ 'opt_temp': 37,
+ 'dna': None,
+ 'inact_temp': 65,
+ 'ovhg': 0,
+ 'scd3': None,
+ 'suppl': ('X',),
+ 'scd5': None,
+ 'charac': (2, -2, None, None, 'GGCC'),
+ 'ovhgseq': '',
+ }
+rest_dict['BspANI'] = _temp()
def _temp():
return {
@@ -5125,7 +5232,6 @@ def _temp():
}
rest_dict['BspCNI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BspD6I>GACTC)|(?P<BspD6I_as>GAGTC)',
@@ -5148,7 +5254,6 @@ def _temp():
}
rest_dict['BspD6I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BspDI>ATCGAT)',
@@ -5171,7 +5276,6 @@ def _temp():
}
rest_dict['BspDI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BspEI>TCCGGA)',
@@ -5194,7 +5298,6 @@ def _temp():
}
rest_dict['BspEI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BspFNI>CGCG)',
@@ -5217,7 +5320,6 @@ def _temp():
}
rest_dict['BspFNI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BspGI>CTGGAC)|(?P<BspGI_as>GTCCAG)',
@@ -5240,7 +5342,6 @@ def _temp():
}
rest_dict['BspGI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BspHI>TCATGA)',
@@ -5263,7 +5364,6 @@ def _temp():
}
rest_dict['BspHI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BspLI>GG..CC)',
@@ -5279,14 +5379,13 @@ def _temp():
'inact_temp': 65,
'ovhg': 0,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (3, -3, None, None, 'GGNNCC'),
'ovhgseq': '',
}
rest_dict['BspLI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BspLU11I>ACATGT)',
@@ -5309,6 +5408,27 @@ def _temp():
}
rest_dict['BspLU11I'] = _temp()
+def _temp():
+ return {
+ 'compsite': '(?P<BspMAI>CTGCAG)',
+ 'results': None,
+ 'site': 'CTGCAG',
+ 'substrat': 'DNA',
+ 'fst3': -5,
+ 'fst5': 5,
+ 'freq': 4096,
+ 'size': 6,
+ 'opt_temp': 37,
+ 'dna': None,
+ 'inact_temp': 65,
+ 'ovhg': 4,
+ 'scd3': None,
+ 'suppl': ('X',),
+ 'scd5': None,
+ 'charac': (5, -5, None, None, 'CTGCAG'),
+ 'ovhgseq': 'TGCA',
+ }
+rest_dict['BspMAI'] = _temp()
def _temp():
return {
@@ -5332,7 +5452,6 @@ def _temp():
}
rest_dict['BspMI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BspMII>TCCGGA)',
@@ -5355,7 +5474,6 @@ def _temp():
}
rest_dict['BspMII'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BspNCI>CCAGA)|(?P<BspNCI_as>TCTGG)',
@@ -5378,7 +5496,6 @@ def _temp():
}
rest_dict['BspNCI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BspOI>GCTAGC)',
@@ -5394,14 +5511,13 @@ def _temp():
'inact_temp': 65,
'ovhg': 4,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (5, -5, None, None, 'GCTAGC'),
'ovhgseq': 'CTAG',
}
rest_dict['BspOI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BspPI>GGATC)|(?P<BspPI_as>GATCC)',
@@ -5417,14 +5533,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -1,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (9, 5, None, None, 'GGATC'),
'ovhgseq': 'N',
}
rest_dict['BspPI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BspQI>GCTCTTC)|(?P<BspQI_as>GAAGAGC)',
@@ -5447,7 +5562,6 @@ def _temp():
}
rest_dict['BspQI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BspT104I>TTCGAA)',
@@ -5470,7 +5584,6 @@ def _temp():
}
rest_dict['BspT104I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BspT107I>GG[CT][AG]CC)',
@@ -5493,7 +5606,6 @@ def _temp():
}
rest_dict['BspT107I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BspTI>CTTAAG)',
@@ -5509,13 +5621,34 @@ def _temp():
'inact_temp': 65,
'ovhg': -4,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (1, -1, None, None, 'CTTAAG'),
'ovhgseq': 'TTAA',
}
rest_dict['BspTI'] = _temp()
+def _temp():
+ return {
+ 'compsite': '(?P<BspTNI>GGTCTC)|(?P<BspTNI_as>GAGACC)',
+ 'results': None,
+ 'site': 'GGTCTC',
+ 'substrat': 'DNA',
+ 'fst3': 5,
+ 'fst5': 7,
+ 'freq': 4096,
+ 'size': 6,
+ 'opt_temp': 37,
+ 'dna': None,
+ 'inact_temp': 65,
+ 'ovhg': -4,
+ 'scd3': None,
+ 'suppl': ('X',),
+ 'scd5': None,
+ 'charac': (7, 5, None, None, 'GGTCTC'),
+ 'ovhgseq': 'NNNN',
+ }
+rest_dict['BspTNI'] = _temp()
def _temp():
return {
@@ -5539,7 +5672,6 @@ def _temp():
}
rest_dict['BsrBI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BsrDI>GCAATG)|(?P<BsrDI_as>CATTGC)',
@@ -5562,7 +5694,6 @@ def _temp():
}
rest_dict['BsrDI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BsrFI>[AG]CCGG[CT])',
@@ -5585,7 +5716,6 @@ def _temp():
}
rest_dict['BsrFI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BsrGI>TGTACA)',
@@ -5608,7 +5738,6 @@ def _temp():
}
rest_dict['BsrGI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BsrI>ACTGG)|(?P<BsrI_as>CCAGT)',
@@ -5631,30 +5760,6 @@ def _temp():
}
rest_dict['BsrI'] = _temp()
-
-def _temp():
- return {
- 'compsite': '(?P<BsrSI>ACTGG)|(?P<BsrSI_as>CCAGT)',
- 'results': None,
- 'site': 'ACTGG',
- 'substrat': 'DNA',
- 'fst3': -1,
- 'fst5': 6,
- 'freq': 1024,
- 'size': 5,
- 'opt_temp': 37,
- 'dna': None,
- 'inact_temp': 65,
- 'ovhg': 2,
- 'scd3': None,
- 'suppl': ('R',),
- 'scd5': None,
- 'charac': (6, -1, None, None, 'ACTGG'),
- 'ovhgseq': 'GN',
- }
-rest_dict['BsrSI'] = _temp()
-
-
def _temp():
return {
'compsite': '(?P<BssAI>[AG]CCGG[CT])',
@@ -5677,7 +5782,6 @@ def _temp():
}
rest_dict['BssAI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BssECI>CC..GG)',
@@ -5700,7 +5804,6 @@ def _temp():
}
rest_dict['BssECI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BssHII>GCGCGC)',
@@ -5716,37 +5819,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -4,
'scd3': None,
- 'suppl': ('J', 'K', 'M', 'N', 'Q', 'R', 'S', 'X'),
+ 'suppl': ('J', 'K', 'M', 'N', 'Q', 'R', 'X'),
'scd5': None,
'charac': (1, -1, None, None, 'GCGCGC'),
'ovhgseq': 'CGCG',
}
rest_dict['BssHII'] = _temp()
-
-def _temp():
- return {
- 'compsite': '(?P<BssKI>CC.GG)',
- 'results': None,
- 'site': 'CCNGG',
- 'substrat': 'DNA',
- 'fst3': 0,
- 'fst5': 0,
- 'freq': 256,
- 'size': 5,
- 'opt_temp': 37,
- 'dna': None,
- 'inact_temp': 65,
- 'ovhg': -5,
- 'scd3': None,
- 'suppl': ('N',),
- 'scd5': None,
- 'charac': (0, 0, None, None, 'CCNGG'),
- 'ovhgseq': 'CCNGG',
- }
-rest_dict['BssKI'] = _temp()
-
-
def _temp():
return {
'compsite': '(?P<BssMI>GATC)',
@@ -5769,7 +5848,6 @@ def _temp():
}
rest_dict['BssMI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BssNAI>GTATAC)',
@@ -5792,7 +5870,6 @@ def _temp():
}
rest_dict['BssNAI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BssNI>G[AG]CG[CT]C)',
@@ -5815,7 +5892,6 @@ def _temp():
}
rest_dict['BssNI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BssSI>CACGAG)|(?P<BssSI_as>CTCGTG)',
@@ -5838,7 +5914,6 @@ def _temp():
}
rest_dict['BssSI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BssT1I>CC[AT][AT]GG)',
@@ -5861,7 +5936,6 @@ def _temp():
}
rest_dict['BssT1I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Bst1107I>GTATAC)',
@@ -5877,14 +5951,13 @@ def _temp():
'inact_temp': 65,
'ovhg': 0,
'scd3': None,
- 'suppl': ('F', 'K'),
+ 'suppl': ('B', 'K'),
'scd5': None,
'charac': (3, -3, None, None, 'GTATAC'),
'ovhgseq': '',
}
rest_dict['Bst1107I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Bst2BI>CACGAG)|(?P<Bst2BI_as>CTCGTG)',
@@ -5900,14 +5973,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -4,
'scd3': None,
- 'suppl': ('I', 'V'),
+ 'suppl': ('I',),
'scd5': None,
'charac': (1, -1, None, None, 'CACGAG'),
'ovhgseq': 'ACGA',
}
rest_dict['Bst2BI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Bst2UI>CC[AT]GG)',
@@ -5930,7 +6002,6 @@ def _temp():
}
rest_dict['Bst2UI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Bst4CI>AC.GT)',
@@ -5953,7 +6024,6 @@ def _temp():
}
rest_dict['Bst4CI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Bst6I>CTCTTC)|(?P<Bst6I_as>GAAGAG)',
@@ -5976,7 +6046,6 @@ def _temp():
}
rest_dict['Bst6I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BstACI>G[AG]CG[CT]C)',
@@ -5999,7 +6068,6 @@ def _temp():
}
rest_dict['BstACI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BstAFI>CTTAAG)',
@@ -6022,7 +6090,6 @@ def _temp():
}
rest_dict['BstAFI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BstAPI>GCA.....TGC)',
@@ -6045,7 +6112,6 @@ def _temp():
}
rest_dict['BstAPI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BstAUI>TGTACA)',
@@ -6068,7 +6134,6 @@ def _temp():
}
rest_dict['BstAUI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BstBAI>[CT]ACGT[AG])',
@@ -6091,7 +6156,6 @@ def _temp():
}
rest_dict['BstBAI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BstBI>TTCGAA)',
@@ -6114,7 +6178,6 @@ def _temp():
}
rest_dict['BstBI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BstC8I>GC..GC)',
@@ -6137,7 +6200,6 @@ def _temp():
}
rest_dict['BstC8I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BstDEI>CT.AG)',
@@ -6160,7 +6222,6 @@ def _temp():
}
rest_dict['BstDEI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BstDSI>CC[AG][CT]GG)',
@@ -6183,7 +6244,6 @@ def _temp():
}
rest_dict['BstDSI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BstEII>GGT.ACC)',
@@ -6199,14 +6259,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -5,
'scd3': None,
- 'suppl': ('C', 'J', 'N', 'R', 'S', 'U'),
+ 'suppl': ('C', 'J', 'N', 'R', 'S'),
'scd5': None,
'charac': (1, -1, None, None, 'GGTNACC'),
'ovhgseq': 'GTNAC',
}
rest_dict['BstEII'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BstENI>CCT.....AGG)',
@@ -6229,7 +6288,6 @@ def _temp():
}
rest_dict['BstENI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BstF5I>GGATG)|(?P<BstF5I_as>CATCC)',
@@ -6252,7 +6310,6 @@ def _temp():
}
rest_dict['BstF5I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BstFNI>CGCG)',
@@ -6275,7 +6332,6 @@ def _temp():
}
rest_dict['BstFNI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BstH2I>[AG]GCGC[CT])',
@@ -6298,7 +6354,6 @@ def _temp():
}
rest_dict['BstH2I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BstHHI>GCGC)',
@@ -6321,7 +6376,6 @@ def _temp():
}
rest_dict['BstHHI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BstKTI>GATC)',
@@ -6344,7 +6398,6 @@ def _temp():
}
rest_dict['BstKTI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BstMAI>GTCTC)|(?P<BstMAI_as>GAGAC)',
@@ -6367,7 +6420,6 @@ def _temp():
}
rest_dict['BstMAI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BstMBI>GATC)',
@@ -6390,7 +6442,6 @@ def _temp():
}
rest_dict['BstMBI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BstMCI>CG[AG][CT]CG)',
@@ -6413,7 +6464,6 @@ def _temp():
}
rest_dict['BstMCI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BstMWI>GC.......GC)',
@@ -6436,7 +6486,6 @@ def _temp():
}
rest_dict['BstMWI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BstNI>CC[AT]GG)',
@@ -6459,7 +6508,6 @@ def _temp():
}
rest_dict['BstNI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BstNSI>[AG]CATG[CT])',
@@ -6475,37 +6523,13 @@ def _temp():
'inact_temp': 65,
'ovhg': 4,
'scd3': None,
- 'suppl': ('I',),
+ 'suppl': ('I', 'V'),
'scd5': None,
'charac': (5, -5, None, None, 'RCATGY'),
'ovhgseq': 'CATG',
}
rest_dict['BstNSI'] = _temp()
-
-def _temp():
- return {
- 'compsite': '(?P<BstOI>CC[AT]GG)',
- 'results': None,
- 'site': 'CCWGG',
- 'substrat': 'DNA',
- 'fst3': -2,
- 'fst5': 2,
- 'freq': 512,
- 'size': 5,
- 'opt_temp': 37,
- 'dna': None,
- 'inact_temp': 65,
- 'ovhg': -1,
- 'scd3': None,
- 'suppl': ('R',),
- 'scd5': None,
- 'charac': (2, -2, None, None, 'CCWGG'),
- 'ovhgseq': 'W',
- }
-rest_dict['BstOI'] = _temp()
-
-
def _temp():
return {
'compsite': '(?P<BstPAI>GAC....GTC)',
@@ -6528,7 +6552,6 @@ def _temp():
}
rest_dict['BstPAI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BstPI>GGT.ACC)',
@@ -6551,7 +6574,6 @@ def _temp():
}
rest_dict['BstPI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BstSCI>CC.GG)',
@@ -6574,7 +6596,6 @@ def _temp():
}
rest_dict['BstSCI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BstSFI>CT[AG][CT]AG)',
@@ -6597,7 +6618,6 @@ def _temp():
}
rest_dict['BstSFI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BstSLI>G[GT]GC[AC]C)',
@@ -6620,7 +6640,6 @@ def _temp():
}
rest_dict['BstSLI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BstSNI>TACGTA)',
@@ -6643,7 +6662,6 @@ def _temp():
}
rest_dict['BstSNI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BstUI>CGCG)',
@@ -6666,7 +6684,6 @@ def _temp():
}
rest_dict['BstUI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BstV1I>GCAGC)|(?P<BstV1I_as>GCTGC)',
@@ -6689,7 +6706,6 @@ def _temp():
}
rest_dict['BstV1I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BstV2I>GAAGAC)|(?P<BstV2I_as>GTCTTC)',
@@ -6712,7 +6728,6 @@ def _temp():
}
rest_dict['BstV2I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BstX2I>[AG]GATC[CT])',
@@ -6735,7 +6750,6 @@ def _temp():
}
rest_dict['BstX2I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BstXI>CCA......TGG)',
@@ -6751,14 +6765,13 @@ def _temp():
'inact_temp': 65,
'ovhg': 4,
'scd3': None,
- 'suppl': ('F', 'I', 'J', 'K', 'M', 'N', 'Q', 'R', 'V', 'X'),
+ 'suppl': ('B', 'I', 'J', 'K', 'M', 'N', 'Q', 'R', 'V', 'X', 'Y'),
'scd5': None,
'charac': (8, -8, None, None, 'CCANNNNNNTGG'),
'ovhgseq': 'NNNN',
}
rest_dict['BstXI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BstYI>[AG]GATC[CT])',
@@ -6781,7 +6794,6 @@ def _temp():
}
rest_dict['BstYI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BstZ17I>GTATAC)',
@@ -6804,7 +6816,6 @@ def _temp():
}
rest_dict['BstZ17I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BstZI>CGGCCG)',
@@ -6827,7 +6838,6 @@ def _temp():
}
rest_dict['BstZI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Bsu15I>ATCGAT)',
@@ -6843,14 +6853,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -2,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (2, -2, None, None, 'ATCGAT'),
'ovhgseq': 'CG',
}
rest_dict['Bsu15I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Bsu36I>CCT.AGG)',
@@ -6866,13 +6875,34 @@ def _temp():
'inact_temp': 65,
'ovhg': -3,
'scd3': None,
- 'suppl': ('N', 'R'),
+ 'suppl': ('N',),
'scd5': None,
'charac': (2, -2, None, None, 'CCTNAGG'),
'ovhgseq': 'TNA',
}
rest_dict['Bsu36I'] = _temp()
+def _temp():
+ return {
+ 'compsite': '(?P<Bsu7003I>GACGAGC)|(?P<Bsu7003I_as>GCTCGTC)',
+ 'results': None,
+ 'site': 'GACGAGC',
+ 'substrat': 'DNA',
+ 'fst3': None,
+ 'fst5': None,
+ 'freq': 16384,
+ 'size': 7,
+ 'opt_temp': 37,
+ 'dna': None,
+ 'inact_temp': 65,
+ 'ovhg': None,
+ 'scd3': None,
+ 'suppl': (),
+ 'scd5': None,
+ 'charac': (None, None, None, None, 'GACGAGC'),
+ 'ovhgseq': None,
+ }
+rest_dict['Bsu7003I'] = _temp()
def _temp():
return {
@@ -6896,7 +6926,6 @@ def _temp():
}
rest_dict['BsuI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BsuRI>GGCC)',
@@ -6912,13 +6941,34 @@ def _temp():
'inact_temp': 65,
'ovhg': 0,
'scd3': None,
- 'suppl': ('F', 'I'),
+ 'suppl': ('B', 'I'),
'scd5': None,
'charac': (2, -2, None, None, 'GGCC'),
'ovhgseq': '',
}
rest_dict['BsuRI'] = _temp()
+def _temp():
+ return {
+ 'compsite': '(?P<BsuTUI>ATCGAT)',
+ 'results': None,
+ 'site': 'ATCGAT',
+ 'substrat': 'DNA',
+ 'fst3': -2,
+ 'fst5': 2,
+ 'freq': 4096,
+ 'size': 6,
+ 'opt_temp': 37,
+ 'dna': None,
+ 'inact_temp': 65,
+ 'ovhg': -2,
+ 'scd3': None,
+ 'suppl': ('X',),
+ 'scd5': None,
+ 'charac': (2, -2, None, None, 'ATCGAT'),
+ 'ovhgseq': 'CG',
+ }
+rest_dict['BsuTUI'] = _temp()
def _temp():
return {
@@ -6942,7 +6992,6 @@ def _temp():
}
rest_dict['BtgI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BtgZI>GCGATG)|(?P<BtgZI_as>CATCGC)',
@@ -6965,7 +7014,6 @@ def _temp():
}
rest_dict['BtgZI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BthCI>GC.GC)',
@@ -6988,7 +7036,6 @@ def _temp():
}
rest_dict['BthCI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BtrI>CACGTC)|(?P<BtrI_as>GACGTG)',
@@ -7011,7 +7058,6 @@ def _temp():
}
rest_dict['BtrI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BtsCI>GGATG)|(?P<BtsCI_as>CATCC)',
@@ -7034,7 +7080,6 @@ def _temp():
}
rest_dict['BtsCI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BtsI>GCAGTG)|(?P<BtsI_as>CACTGC)',
@@ -7057,7 +7102,6 @@ def _temp():
}
rest_dict['BtsI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BtsIMutI>CAGTG)|(?P<BtsIMutI_as>CACTG)',
@@ -7080,7 +7124,6 @@ def _temp():
}
rest_dict['BtsIMutI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BtuMI>TCGCGA)',
@@ -7103,7 +7146,6 @@ def _temp():
}
rest_dict['BtuMI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<BveI>ACCTGC)|(?P<BveI_as>GCAGGT)',
@@ -7119,14 +7161,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -4,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (10, 8, None, None, 'ACCTGC'),
'ovhgseq': 'NNNN',
}
rest_dict['BveI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Cac8I>GC..GC)',
@@ -7149,7 +7190,6 @@ def _temp():
}
rest_dict['Cac8I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<CaiI>CAG...CTG)',
@@ -7165,13 +7205,78 @@ def _temp():
'inact_temp': 65,
'ovhg': 3,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (6, -6, None, None, 'CAGNNNCTG'),
'ovhgseq': 'NNN',
}
rest_dict['CaiI'] = _temp()
+def _temp():
+ return {
+ 'compsite': '(?P<Cal14237I>GGTTAG)|(?P<Cal14237I_as>CTAACC)',
+ 'results': None,
+ 'site': 'GGTTAG',
+ 'substrat': 'DNA',
+ 'fst3': None,
+ 'fst5': None,
+ 'freq': 4096,
+ 'size': 6,
+ 'opt_temp': 37,
+ 'dna': None,
+ 'inact_temp': 65,
+ 'ovhg': None,
+ 'scd3': None,
+ 'suppl': (),
+ 'scd5': None,
+ 'charac': (None, None, None, None, 'GGTTAG'),
+ 'ovhgseq': None,
+ }
+rest_dict['Cal14237I'] = _temp()
+
+def _temp():
+ return {
+ 'compsite': '(?P<CalB3II>G[AG]TT[AG]AG)|(?P<CalB3II_as>CT[CT]AA[CT]C)',
+ 'results': None,
+ 'site': 'GRTTRAG',
+ 'substrat': 'DNA',
+ 'fst3': None,
+ 'fst5': None,
+ 'freq': 4096,
+ 'size': 7,
+ 'opt_temp': 37,
+ 'dna': None,
+ 'inact_temp': 65,
+ 'ovhg': None,
+ 'scd3': None,
+ 'suppl': (),
+ 'scd5': None,
+ 'charac': (None, None, None, None, 'GRTTRAG'),
+ 'ovhgseq': None,
+ }
+rest_dict['CalB3II'] = _temp()
+
+def _temp():
+ return {
+ 'compsite': '(?P<Cau10061II>GTTAAT)|(?P<Cau10061II_as>ATTAAC)',
+ 'results': None,
+ 'site': 'GTTAAT',
+ 'substrat': 'DNA',
+ 'fst3': None,
+ 'fst5': None,
+ 'freq': 4096,
+ 'size': 6,
+ 'opt_temp': 37,
+ 'dna': None,
+ 'inact_temp': 65,
+ 'ovhg': None,
+ 'scd3': None,
+ 'suppl': (),
+ 'scd5': None,
+ 'charac': (None, None, None, None, 'GTTAAT'),
+ 'ovhgseq': None,
+ }
+rest_dict['Cau10061II'] = _temp()
def _temp():
return {
@@ -7195,6 +7300,49 @@ def _temp():
}
rest_dict['CauII'] = _temp()
+def _temp():
+ return {
+ 'compsite': '(?P<Cba13II>AGGAAT)|(?P<Cba13II_as>ATTCCT)',
+ 'results': None,
+ 'site': 'AGGAAT',
+ 'substrat': 'DNA',
+ 'fst3': None,
+ 'fst5': None,
+ 'freq': 4096,
+ 'size': 6,
+ 'opt_temp': 37,
+ 'dna': None,
+ 'inact_temp': 65,
+ 'ovhg': None,
+ 'scd3': None,
+ 'suppl': (),
+ 'scd5': None,
+ 'charac': (None, None, None, None, 'AGGAAT'),
+ 'ovhgseq': None,
+ }
+rest_dict['Cba13II'] = _temp()
+
+def _temp():
+ return {
+ 'compsite': '(?P<Cba16038I>CCT.A[CT].C)|(?P<Cba16038I_as>G.[AG]T.AGG)',
+ 'results': None,
+ 'site': 'CCTNAYNC',
+ 'substrat': 'DNA',
+ 'fst3': None,
+ 'fst5': None,
+ 'freq': 2048,
+ 'size': 8,
+ 'opt_temp': 37,
+ 'dna': None,
+ 'inact_temp': 65,
+ 'ovhg': None,
+ 'scd3': None,
+ 'suppl': (),
+ 'scd5': None,
+ 'charac': (None, None, None, None, 'CCTNAYNC'),
+ 'ovhgseq': None,
+ }
+rest_dict['Cba16038I'] = _temp()
def _temp():
return {
@@ -7218,7 +7366,6 @@ def _temp():
}
rest_dict['CchII'] = _temp()
-
def _temp():
return {
'compsite': '(?P<CchIII>CCCAAG)|(?P<CchIII_as>CTTGGG)',
@@ -7241,7 +7388,6 @@ def _temp():
}
rest_dict['CchIII'] = _temp()
-
def _temp():
return {
'compsite': '(?P<CciI>TCATGA)',
@@ -7264,7 +7410,6 @@ def _temp():
}
rest_dict['CciI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<CciNI>GCGGCCGC)',
@@ -7287,12 +7432,33 @@ def _temp():
}
rest_dict['CciNI'] = _temp()
+def _temp():
+ return {
+ 'compsite': '(?P<CcrNAIII>CGACCAG)|(?P<CcrNAIII_as>CTGGTCG)',
+ 'results': None,
+ 'site': 'CGACCAG',
+ 'substrat': 'DNA',
+ 'fst3': None,
+ 'fst5': None,
+ 'freq': 16384,
+ 'size': 7,
+ 'opt_temp': 37,
+ 'dna': None,
+ 'inact_temp': 65,
+ 'ovhg': None,
+ 'scd3': None,
+ 'suppl': (),
+ 'scd5': None,
+ 'charac': (None, None, None, None, 'CGACCAG'),
+ 'ovhgseq': None,
+ }
+rest_dict['CcrNAIII'] = _temp()
def _temp():
return {
- 'compsite': '(?P<Cdi630V>CAAAAA)|(?P<Cdi630V_as>TTTTTG)',
+ 'compsite': '(?P<Cdi11397I>GCGCAG)|(?P<Cdi11397I_as>CTGCGC)',
'results': None,
- 'site': 'CAAAAA',
+ 'site': 'GCGCAG',
'substrat': 'DNA',
'fst3': None,
'fst5': None,
@@ -7305,11 +7471,10 @@ def _temp():
'scd3': None,
'suppl': (),
'scd5': None,
- 'charac': (None, None, None, None, 'CAAAAA'),
+ 'charac': (None, None, None, None, 'GCGCAG'),
'ovhgseq': None,
}
-rest_dict['Cdi630V'] = _temp()
-
+rest_dict['Cdi11397I'] = _temp()
def _temp():
return {
@@ -7333,7 +7498,6 @@ def _temp():
}
rest_dict['CdiI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<CdpI>GCGGAG)|(?P<CdpI_as>CTCCGC)',
@@ -7356,6 +7520,27 @@ def _temp():
}
rest_dict['CdpI'] = _temp()
+def _temp():
+ return {
+ 'compsite': '(?P<Cdu23823II>GTGAAG)|(?P<Cdu23823II_as>CTTCAC)',
+ 'results': None,
+ 'site': 'GTGAAG',
+ 'substrat': 'DNA',
+ 'fst3': None,
+ 'fst5': None,
+ 'freq': 4096,
+ 'size': 6,
+ 'opt_temp': 37,
+ 'dna': None,
+ 'inact_temp': 65,
+ 'ovhg': None,
+ 'scd3': None,
+ 'suppl': (),
+ 'scd5': None,
+ 'charac': (None, None, None, None, 'GTGAAG'),
+ 'ovhgseq': None,
+ }
+rest_dict['Cdu23823II'] = _temp()
def _temp():
return {
@@ -7379,7 +7564,6 @@ def _temp():
}
rest_dict['CfoI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Cfr10I>[AG]CCGG[CT])',
@@ -7395,14 +7579,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -4,
'scd3': None,
- 'suppl': ('F', 'K'),
+ 'suppl': ('B', 'K'),
'scd5': None,
'charac': (1, -1, None, None, 'RCCGGY'),
'ovhgseq': 'CCGG',
}
rest_dict['Cfr10I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Cfr13I>GG.CC)',
@@ -7418,14 +7601,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -3,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (1, -1, None, None, 'GGNCC'),
'ovhgseq': 'GNC',
}
rest_dict['Cfr13I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Cfr42I>CCGCGG)',
@@ -7441,14 +7623,13 @@ def _temp():
'inact_temp': 65,
'ovhg': 2,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (4, -4, None, None, 'CCGCGG'),
'ovhgseq': 'GC',
}
rest_dict['Cfr42I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Cfr9I>CCCGGG)',
@@ -7464,14 +7645,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -4,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (1, -1, None, None, 'CCCGGG'),
'ovhgseq': 'CCGG',
}
rest_dict['Cfr9I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<CfrI>[CT]GGCC[AG])',
@@ -7494,7 +7674,6 @@ def _temp():
}
rest_dict['CfrI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Cgl13032I>GGCGCA)|(?P<Cgl13032I_as>TGCGCC)',
@@ -7517,7 +7696,6 @@ def _temp():
}
rest_dict['Cgl13032I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Cgl13032II>ACGA[CGT]GG)|(?P<Cgl13032II_as>CC[ACG]TCGT)',
@@ -7540,7 +7718,6 @@ def _temp():
}
rest_dict['Cgl13032II'] = _temp()
-
def _temp():
return {
'compsite': '(?P<ChaI>GATC)',
@@ -7563,7 +7740,6 @@ def _temp():
}
rest_dict['ChaI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<CjeFIII>GCAAGG)|(?P<CjeFIII_as>CCTTGC)',
@@ -7586,7 +7762,6 @@ def _temp():
}
rest_dict['CjeFIII'] = _temp()
-
def _temp():
return {
'compsite': '(?P<CjeFV>GG[AG]CA)|(?P<CjeFV_as>TG[CT]CC)',
@@ -7609,7 +7784,6 @@ def _temp():
}
rest_dict['CjeFV'] = _temp()
-
def _temp():
return {
'compsite': '(?P<CjeI>CCA......GT)|(?P<CjeI_as>AC......TGG)',
@@ -7632,7 +7806,6 @@ def _temp():
}
rest_dict['CjeI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<CjeNII>GAG.....GT)|(?P<CjeNII_as>AC.....CTC)',
@@ -7655,7 +7828,6 @@ def _temp():
}
rest_dict['CjeNII'] = _temp()
-
def _temp():
return {
'compsite': '(?P<CjeNIII>G[GT]AA[CT]G)|(?P<CjeNIII_as>C[AG]TT[AC]C)',
@@ -7678,7 +7850,6 @@ def _temp():
}
rest_dict['CjeNIII'] = _temp()
-
def _temp():
return {
'compsite': '(?P<CjeP659IV>CAC.......GAA)|(?P<CjeP659IV_as>TTC.......GTG)',
@@ -7701,7 +7872,6 @@ def _temp():
}
rest_dict['CjeP659IV'] = _temp()
-
def _temp():
return {
'compsite': '(?P<CjePI>CCA.......TC)|(?P<CjePI_as>GA.......TGG)',
@@ -7724,7 +7894,6 @@ def _temp():
}
rest_dict['CjePI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<CjuI>CA[CT].....[AG]TG)',
@@ -7747,7 +7916,6 @@ def _temp():
}
rest_dict['CjuI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<CjuII>CA[CT].....CTC)|(?P<CjuII_as>GAG.....[AG]TG)',
@@ -7770,6 +7938,27 @@ def _temp():
}
rest_dict['CjuII'] = _temp()
+def _temp():
+ return {
+ 'compsite': '(?P<Cla11845III>GCGAA)|(?P<Cla11845III_as>TTCGC)',
+ 'results': None,
+ 'site': 'GCGAA',
+ 'substrat': 'DNA',
+ 'fst3': None,
+ 'fst5': None,
+ 'freq': 1024,
+ 'size': 5,
+ 'opt_temp': 37,
+ 'dna': None,
+ 'inact_temp': 65,
+ 'ovhg': None,
+ 'scd3': None,
+ 'suppl': (),
+ 'scd5': None,
+ 'charac': (None, None, None, None, 'GCGAA'),
+ 'ovhgseq': None,
+ }
+rest_dict['Cla11845III'] = _temp()
def _temp():
return {
@@ -7786,13 +7975,56 @@ def _temp():
'inact_temp': 65,
'ovhg': -2,
'scd3': None,
- 'suppl': ('B', 'K', 'M', 'N', 'Q', 'R', 'S', 'U', 'X'),
+ 'suppl': ('B', 'K', 'M', 'N', 'Q', 'R', 'S', 'X'),
'scd5': None,
'charac': (2, -2, None, None, 'ATCGAT'),
'ovhgseq': 'CG',
}
rest_dict['ClaI'] = _temp()
+def _temp():
+ return {
+ 'compsite': '(?P<Cly7489II>AAAAG[AG]G)|(?P<Cly7489II_as>C[CT]CTTTT)',
+ 'results': None,
+ 'site': 'AAAAGRG',
+ 'substrat': 'DNA',
+ 'fst3': None,
+ 'fst5': None,
+ 'freq': 8192,
+ 'size': 7,
+ 'opt_temp': 37,
+ 'dna': None,
+ 'inact_temp': 65,
+ 'ovhg': None,
+ 'scd3': None,
+ 'suppl': (),
+ 'scd5': None,
+ 'charac': (None, None, None, None, 'AAAAGRG'),
+ 'ovhgseq': None,
+ }
+rest_dict['Cly7489II'] = _temp()
+
+def _temp():
+ return {
+ 'compsite': '(?P<Cma23826I>CGGAAG)|(?P<Cma23826I_as>CTTCCG)',
+ 'results': None,
+ 'site': 'CGGAAG',
+ 'substrat': 'DNA',
+ 'fst3': None,
+ 'fst5': None,
+ 'freq': 4096,
+ 'size': 6,
+ 'opt_temp': 37,
+ 'dna': None,
+ 'inact_temp': 65,
+ 'ovhg': None,
+ 'scd3': None,
+ 'suppl': (),
+ 'scd5': None,
+ 'charac': (None, None, None, None, 'CGGAAG'),
+ 'ovhgseq': None,
+ }
+rest_dict['Cma23826I'] = _temp()
def _temp():
return {
@@ -7809,14 +8041,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -3,
'scd3': None,
- 'suppl': ('F', 'K'),
+ 'suppl': ('B', 'K'),
'scd5': None,
'charac': (2, -2, None, None, 'CGGWCCG'),
'ovhgseq': 'GWC',
}
rest_dict['CpoI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<CseI>GACGC)|(?P<CseI_as>GCGTC)',
@@ -7832,14 +8063,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -5,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (10, 10, None, None, 'GACGC'),
'ovhgseq': 'NNNNN',
}
rest_dict['CseI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<CsiI>ACC[AT]GGT)',
@@ -7855,37 +8085,57 @@ def _temp():
'inact_temp': 65,
'ovhg': -5,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (1, -1, None, None, 'ACCWGGT'),
'ovhgseq': 'CCWGG',
}
rest_dict['CsiI'] = _temp()
-
def _temp():
return {
- 'compsite': '(?P<Csp6I>GTAC)',
+ 'compsite': '(?P<Csp2014I>GGAGGC)|(?P<Csp2014I_as>GCCTCC)',
'results': None,
- 'site': 'GTAC',
+ 'site': 'GGAGGC',
'substrat': 'DNA',
- 'fst3': -1,
- 'fst5': 1,
- 'freq': 256,
- 'size': 4,
+ 'fst3': None,
+ 'fst5': None,
+ 'freq': 4096,
+ 'size': 6,
+ 'opt_temp': 37,
+ 'dna': None,
+ 'inact_temp': 65,
+ 'ovhg': None,
+ 'scd3': None,
+ 'suppl': (),
+ 'scd5': None,
+ 'charac': (None, None, None, None, 'GGAGGC'),
+ 'ovhgseq': None,
+ }
+rest_dict['Csp2014I'] = _temp()
+
+def _temp():
+ return {
+ 'compsite': '(?P<Csp6I>GTAC)',
+ 'results': None,
+ 'site': 'GTAC',
+ 'substrat': 'DNA',
+ 'fst3': -1,
+ 'fst5': 1,
+ 'freq': 256,
+ 'size': 4,
'opt_temp': 37,
'dna': None,
'inact_temp': 65,
'ovhg': -2,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (1, -1, None, None, 'GTAC'),
'ovhgseq': 'TA',
}
rest_dict['Csp6I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<CspAI>ACCGGT)',
@@ -7908,7 +8158,6 @@ def _temp():
}
rest_dict['CspAI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<CspCI>CAA.....GTGG)|(?P<CspCI_as>CCAC.....TTG)',
@@ -7931,7 +8180,6 @@ def _temp():
}
rest_dict['CspCI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<CspI>CGG[AT]CCG)',
@@ -7954,7 +8202,6 @@ def _temp():
}
rest_dict['CspI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<CstMI>AAGGAG)|(?P<CstMI_as>CTCCTT)',
@@ -7977,7 +8224,6 @@ def _temp():
}
rest_dict['CstMI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<CviAII>CATG)',
@@ -8000,7 +8246,6 @@ def _temp():
}
rest_dict['CviAII'] = _temp()
-
def _temp():
return {
'compsite': '(?P<CviJI>[AG]GC[CT])',
@@ -8023,7 +8268,6 @@ def _temp():
}
rest_dict['CviJI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<CviKI_1>[AG]GC[CT])',
@@ -8046,7 +8290,6 @@ def _temp():
}
rest_dict['CviKI_1'] = _temp()
-
def _temp():
return {
'compsite': '(?P<CviQI>GTAC)',
@@ -8069,7 +8312,6 @@ def _temp():
}
rest_dict['CviQI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<CviRI>TGCA)',
@@ -8092,7 +8334,6 @@ def _temp():
}
rest_dict['CviRI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<DdeI>CT.AG)',
@@ -8115,7 +8356,6 @@ def _temp():
}
rest_dict['DdeI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<DinI>GGCGCC)',
@@ -8138,7 +8378,6 @@ def _temp():
}
rest_dict['DinI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<DpnI>GATC)',
@@ -8154,14 +8393,13 @@ def _temp():
'inact_temp': 65,
'ovhg': 0,
'scd3': None,
- 'suppl': ('B', 'E', 'F', 'K', 'M', 'N', 'O', 'Q', 'R', 'S', 'X'),
+ 'suppl': ('B', 'E', 'K', 'M', 'N', 'O', 'Q', 'R', 'S', 'X'),
'scd5': None,
'charac': (2, -2, None, None, 'GATC'),
'ovhgseq': '',
}
rest_dict['DpnI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<DpnII>GATC)',
@@ -8184,7 +8422,6 @@ def _temp():
}
rest_dict['DpnII'] = _temp()
-
def _temp():
return {
'compsite': '(?P<DraI>TTTAAA)',
@@ -8200,14 +8437,13 @@ def _temp():
'inact_temp': 65,
'ovhg': 0,
'scd3': None,
- 'suppl': ('B', 'F', 'I', 'J', 'K', 'M', 'N', 'Q', 'R', 'S', 'U', 'V', 'X', 'Y'),
+ 'suppl': ('B', 'I', 'J', 'K', 'M', 'N', 'Q', 'R', 'S', 'V', 'X'),
'scd5': None,
'charac': (3, -3, None, None, 'TTTAAA'),
'ovhgseq': '',
}
rest_dict['DraI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<DraII>[AG]GG.CC[CT])',
@@ -8230,7 +8466,6 @@ def _temp():
}
rest_dict['DraII'] = _temp()
-
def _temp():
return {
'compsite': '(?P<DraIII>CAC...GTG)',
@@ -8253,7 +8488,6 @@ def _temp():
}
rest_dict['DraIII'] = _temp()
-
def _temp():
return {
'compsite': '(?P<DraRI>CAAG.AC)|(?P<DraRI_as>GT.CTTG)',
@@ -8276,7 +8510,6 @@ def _temp():
}
rest_dict['DraRI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<DrdI>GAC......GTC)',
@@ -8299,7 +8532,6 @@ def _temp():
}
rest_dict['DrdI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<DrdII>GAACCA)|(?P<DrdII_as>TGGTTC)',
@@ -8322,7 +8554,6 @@ def _temp():
}
rest_dict['DrdII'] = _temp()
-
def _temp():
return {
'compsite': '(?P<DriI>GAC.....GTC)',
@@ -8345,7 +8576,6 @@ def _temp():
}
rest_dict['DriI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<DsaI>CC[AG][CT]GG)',
@@ -8368,7 +8598,6 @@ def _temp():
}
rest_dict['DsaI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<DseDI>GAC......GTC)',
@@ -8391,7 +8620,6 @@ def _temp():
}
rest_dict['DseDI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<EaeI>[CT]GGCC[AG])',
@@ -8414,7 +8642,6 @@ def _temp():
}
rest_dict['EaeI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<EagI>CGGCCG)',
@@ -8437,7 +8664,6 @@ def _temp():
}
rest_dict['EagI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Eam1104I>CTCTTC)|(?P<Eam1104I_as>GAAGAG)',
@@ -8453,14 +8679,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -3,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (7, 4, None, None, 'CTCTTC'),
'ovhgseq': 'NNN',
}
rest_dict['Eam1104I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Eam1105I>GAC.....GTC)',
@@ -8476,14 +8701,13 @@ def _temp():
'inact_temp': 65,
'ovhg': 1,
'scd3': None,
- 'suppl': ('F', 'K'),
+ 'suppl': ('B', 'K'),
'scd5': None,
'charac': (6, -6, None, None, 'GACNNNNNGTC'),
'ovhgseq': 'N',
}
rest_dict['Eam1105I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<EarI>CTCTTC)|(?P<EarI_as>GAAGAG)',
@@ -8506,7 +8730,6 @@ def _temp():
}
rest_dict['EarI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<EciI>GGCGGA)|(?P<EciI_as>TCCGCC)',
@@ -8529,7 +8752,6 @@ def _temp():
}
rest_dict['EciI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Ecl136II>GAGCTC)',
@@ -8545,13 +8767,34 @@ def _temp():
'inact_temp': 65,
'ovhg': 0,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (3, -3, None, None, 'GAGCTC'),
'ovhgseq': '',
}
rest_dict['Ecl136II'] = _temp()
+def _temp():
+ return {
+ 'compsite': '(?P<Ecl35734I>GAAA[CT]TC)|(?P<Ecl35734I_as>GA[AG]TTTC)',
+ 'results': None,
+ 'site': 'GAAAYTC',
+ 'substrat': 'DNA',
+ 'fst3': None,
+ 'fst5': None,
+ 'freq': 8192,
+ 'size': 7,
+ 'opt_temp': 37,
+ 'dna': None,
+ 'inact_temp': 65,
+ 'ovhg': None,
+ 'scd3': None,
+ 'suppl': (),
+ 'scd5': None,
+ 'charac': (None, None, None, None, 'GAAAYTC'),
+ 'ovhgseq': None,
+ }
+rest_dict['Ecl35734I'] = _temp()
def _temp():
return {
@@ -8568,14 +8811,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -4,
'scd3': None,
- 'suppl': ('M', 'S'),
+ 'suppl': ('S',),
'scd5': None,
'charac': (1, -1, None, None, 'CGGCCG'),
'ovhgseq': 'GGCC',
}
rest_dict['EclXI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Eco105I>TACGTA)',
@@ -8591,14 +8833,13 @@ def _temp():
'inact_temp': 65,
'ovhg': 0,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (3, -3, None, None, 'TACGTA'),
'ovhgseq': '',
}
rest_dict['Eco105I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Eco130I>CC[AT][AT]GG)',
@@ -8614,14 +8855,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -4,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (1, -1, None, None, 'CCWWGG'),
'ovhgseq': 'CWWG',
}
rest_dict['Eco130I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Eco147I>AGGCCT)',
@@ -8637,14 +8877,13 @@ def _temp():
'inact_temp': 65,
'ovhg': 0,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (3, -3, None, None, 'AGGCCT'),
'ovhgseq': '',
}
rest_dict['Eco147I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Eco24I>G[AG]GC[CT]C)',
@@ -8660,14 +8899,13 @@ def _temp():
'inact_temp': 65,
'ovhg': 4,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (5, -5, None, None, 'GRGCYC'),
'ovhgseq': 'RGCY',
}
rest_dict['Eco24I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Eco31I>GGTCTC)|(?P<Eco31I_as>GAGACC)',
@@ -8683,14 +8921,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -4,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (7, 5, None, None, 'GGTCTC'),
'ovhgseq': 'NNNN',
}
rest_dict['Eco31I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Eco32I>GATATC)',
@@ -8706,14 +8943,13 @@ def _temp():
'inact_temp': 65,
'ovhg': 0,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (3, -3, None, None, 'GATATC'),
'ovhgseq': '',
}
rest_dict['Eco32I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Eco47I>GG[AT]CC)',
@@ -8729,14 +8965,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -3,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (1, -1, None, None, 'GGWCC'),
'ovhgseq': 'GWC',
}
rest_dict['Eco47I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Eco47III>AGCGCT)',
@@ -8752,14 +8987,13 @@ def _temp():
'inact_temp': 65,
'ovhg': 0,
'scd3': None,
- 'suppl': ('F', 'M', 'R'),
+ 'suppl': ('B', 'M', 'R'),
'scd5': None,
'charac': (3, -3, None, None, 'AGCGCT'),
'ovhgseq': '',
}
rest_dict['Eco47III'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Eco52I>CGGCCG)',
@@ -8775,14 +9009,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -4,
'scd3': None,
- 'suppl': ('F', 'K'),
+ 'suppl': ('B', 'K'),
'scd5': None,
'charac': (1, -1, None, None, 'CGGCCG'),
'ovhgseq': 'GGCC',
}
rest_dict['Eco52I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Eco53kI>GAGCTC)',
@@ -8805,7 +9038,6 @@ def _temp():
}
rest_dict['Eco53kI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Eco57I>CTGAAG)|(?P<Eco57I_as>CTTCAG)',
@@ -8821,14 +9053,13 @@ def _temp():
'inact_temp': 65,
'ovhg': 2,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (22, 14, None, None, 'CTGAAG'),
'ovhgseq': 'NN',
}
rest_dict['Eco57I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Eco57MI>CTG[AG]AG)|(?P<Eco57MI_as>CT[CT]CAG)',
@@ -8851,7 +9082,6 @@ def _temp():
}
rest_dict['Eco57MI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Eco72I>CACGTG)',
@@ -8867,14 +9097,13 @@ def _temp():
'inact_temp': 65,
'ovhg': 0,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (3, -3, None, None, 'CACGTG'),
'ovhgseq': '',
}
rest_dict['Eco72I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Eco81I>CCT.AGG)',
@@ -8890,14 +9119,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -3,
'scd3': None,
- 'suppl': ('F', 'K'),
+ 'suppl': ('B', 'K'),
'scd5': None,
'charac': (2, -2, None, None, 'CCTNAGG'),
'ovhgseq': 'TNA',
}
rest_dict['Eco81I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Eco88I>C[CT]CG[AG]G)',
@@ -8913,14 +9141,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -4,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (1, -1, None, None, 'CYCGRG'),
'ovhgseq': 'YCGR',
}
rest_dict['Eco88I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Eco91I>GGT.ACC)',
@@ -8936,13 +9163,34 @@ def _temp():
'inact_temp': 65,
'ovhg': -5,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (1, -1, None, None, 'GGTNACC'),
'ovhgseq': 'GTNAC',
}
rest_dict['Eco91I'] = _temp()
+def _temp():
+ return {
+ 'compsite': '(?P<EcoE1140I>ACC[CT]AC)|(?P<EcoE1140I_as>GT[AG]GGT)',
+ 'results': None,
+ 'site': 'ACCYAC',
+ 'substrat': 'DNA',
+ 'fst3': None,
+ 'fst5': None,
+ 'freq': 2048,
+ 'size': 6,
+ 'opt_temp': 37,
+ 'dna': None,
+ 'inact_temp': 65,
+ 'ovhg': None,
+ 'scd3': None,
+ 'suppl': (),
+ 'scd5': None,
+ 'charac': (None, None, None, None, 'ACCYAC'),
+ 'ovhgseq': None,
+ }
+rest_dict['EcoE1140I'] = _temp()
def _temp():
return {
@@ -8966,7 +9214,6 @@ def _temp():
}
rest_dict['EcoHI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<EcoICRI>GAGCTC)',
@@ -8989,6 +9236,27 @@ def _temp():
}
rest_dict['EcoICRI'] = _temp()
+def _temp():
+ return {
+ 'compsite': '(?P<EcoMII>CA.CATC)|(?P<EcoMII_as>GATG.TG)',
+ 'results': None,
+ 'site': 'CANCATC',
+ 'substrat': 'DNA',
+ 'fst3': None,
+ 'fst5': None,
+ 'freq': 4096,
+ 'size': 7,
+ 'opt_temp': 37,
+ 'dna': None,
+ 'inact_temp': 65,
+ 'ovhg': None,
+ 'scd3': None,
+ 'suppl': (),
+ 'scd5': None,
+ 'charac': (None, None, None, None, 'CANCATC'),
+ 'ovhgseq': None,
+ }
+rest_dict['EcoMII'] = _temp()
def _temp():
return {
@@ -9012,7 +9280,6 @@ def _temp():
}
rest_dict['EcoNI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<EcoO109I>[AG]GG.CC[CT])',
@@ -9028,14 +9295,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -3,
'scd3': None,
- 'suppl': ('F', 'J', 'K', 'N'),
+ 'suppl': ('B', 'J', 'K', 'N'),
'scd5': None,
'charac': (2, -2, None, None, 'RGGNCCY'),
'ovhgseq': 'GNC',
}
rest_dict['EcoO109I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<EcoO65I>GGT.ACC)',
@@ -9058,7 +9324,6 @@ def _temp():
}
rest_dict['EcoO65I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<EcoRI>GAATTC)',
@@ -9074,14 +9339,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -4,
'scd3': None,
- 'suppl': ('B', 'C', 'F', 'I', 'J', 'K', 'M', 'N', 'O', 'Q', 'R', 'S', 'U', 'V', 'X', 'Y'),
+ 'suppl': ('B', 'C', 'I', 'J', 'K', 'M', 'N', 'O', 'Q', 'R', 'S', 'V', 'X', 'Y'),
'scd5': None,
'charac': (1, -1, None, None, 'GAATTC'),
'ovhgseq': 'AATT',
}
rest_dict['EcoRI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<EcoRII>CC[AT]GG)',
@@ -9097,14 +9361,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -5,
'scd3': None,
- 'suppl': ('F', 'J'),
+ 'suppl': ('B', 'J'),
'scd5': None,
'charac': (0, 0, None, None, 'CCWGG'),
'ovhgseq': 'CCWGG',
}
rest_dict['EcoRII'] = _temp()
-
def _temp():
return {
'compsite': '(?P<EcoRV>GATATC)',
@@ -9120,14 +9383,13 @@ def _temp():
'inact_temp': 65,
'ovhg': 0,
'scd3': None,
- 'suppl': ('B', 'C', 'I', 'J', 'K', 'M', 'N', 'O', 'Q', 'R', 'S', 'U', 'V', 'X', 'Y'),
+ 'suppl': ('B', 'C', 'I', 'J', 'K', 'M', 'N', 'O', 'Q', 'R', 'S', 'V', 'X'),
'scd5': None,
'charac': (3, -3, None, None, 'GATATC'),
'ovhgseq': '',
}
rest_dict['EcoRV'] = _temp()
-
def _temp():
return {
'compsite': '(?P<EcoT14I>CC[AT][AT]GG)',
@@ -9150,7 +9412,6 @@ def _temp():
}
rest_dict['EcoT14I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<EcoT22I>ATGCAT)',
@@ -9173,7 +9434,6 @@ def _temp():
}
rest_dict['EcoT22I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<EcoT38I>G[AG]GC[CT]C)',
@@ -9196,7 +9456,6 @@ def _temp():
}
rest_dict['EcoT38I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<EgeI>GGCGCC)',
@@ -9219,7 +9478,6 @@ def _temp():
}
rest_dict['EgeI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<EheI>GGCGCC)',
@@ -9235,14 +9493,13 @@ def _temp():
'inact_temp': 65,
'ovhg': 0,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (3, -3, None, None, 'GGCGCC'),
'ovhgseq': '',
}
rest_dict['EheI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<ErhI>CC[AT][AT]GG)',
@@ -9258,14 +9515,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -4,
'scd3': None,
- 'suppl': ('I', 'V'),
+ 'suppl': ('I',),
'scd5': None,
'charac': (1, -1, None, None, 'CCWWGG'),
'ovhgseq': 'CWWG',
}
rest_dict['ErhI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<EsaBC3I>TCGA)',
@@ -9288,7 +9544,6 @@ def _temp():
}
rest_dict['EsaBC3I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<EsaSSI>GACCAC)|(?P<EsaSSI_as>GTGGTC)',
@@ -9311,6 +9566,27 @@ def _temp():
}
rest_dict['EsaSSI'] = _temp()
+def _temp():
+ return {
+ 'compsite': '(?P<Esp3007I>CAGAAG)|(?P<Esp3007I_as>CTTCTG)',
+ 'results': None,
+ 'site': 'CAGAAG',
+ 'substrat': 'DNA',
+ 'fst3': None,
+ 'fst5': None,
+ 'freq': 4096,
+ 'size': 6,
+ 'opt_temp': 37,
+ 'dna': None,
+ 'inact_temp': 65,
+ 'ovhg': None,
+ 'scd3': None,
+ 'suppl': (),
+ 'scd5': None,
+ 'charac': (None, None, None, None, 'CAGAAG'),
+ 'ovhgseq': None,
+ }
+rest_dict['Esp3007I'] = _temp()
def _temp():
return {
@@ -9327,14 +9603,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -4,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (7, 5, None, None, 'CGTCTC'),
'ovhgseq': 'NNNN',
}
rest_dict['Esp3I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<EspI>GCT.AGC)',
@@ -9357,7 +9632,6 @@ def _temp():
}
rest_dict['EspI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<FaeI>CATG)',
@@ -9380,7 +9654,6 @@ def _temp():
}
rest_dict['FaeI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<FaiI>[CT]AT[AG])',
@@ -9403,7 +9676,6 @@ def _temp():
}
rest_dict['FaiI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<FalI>AAG.....CTT)',
@@ -9426,7 +9698,6 @@ def _temp():
}
rest_dict['FalI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<FaqI>GGGAC)|(?P<FaqI_as>GTCCC)',
@@ -9442,14 +9713,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -4,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (15, 14, None, None, 'GGGAC'),
'ovhgseq': 'NNNN',
}
rest_dict['FaqI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<FatI>CATG)',
@@ -9472,7 +9742,6 @@ def _temp():
}
rest_dict['FatI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<FauI>CCCGC)|(?P<FauI_as>GCGGG)',
@@ -9495,7 +9764,6 @@ def _temp():
}
rest_dict['FauI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<FauNDI>CATATG)',
@@ -9518,7 +9786,6 @@ def _temp():
}
rest_dict['FauNDI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<FbaI>TGATCA)',
@@ -9541,7 +9808,6 @@ def _temp():
}
rest_dict['FbaI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<FblI>GT[AC][GT]AC)',
@@ -9564,7 +9830,6 @@ def _temp():
}
rest_dict['FblI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<FinI>GGGAC)|(?P<FinI_as>GTCCC)',
@@ -9587,7 +9852,6 @@ def _temp():
}
rest_dict['FinI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<FmuI>GG.CC)',
@@ -9610,7 +9874,6 @@ def _temp():
}
rest_dict['FmuI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Fnu4HI>GC.GC)',
@@ -9633,7 +9896,6 @@ def _temp():
}
rest_dict['Fnu4HI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<FnuDII>CGCG)',
@@ -9656,7 +9918,6 @@ def _temp():
}
rest_dict['FnuDII'] = _temp()
-
def _temp():
return {
'compsite': '(?P<FokI>GGATG)|(?P<FokI_as>CATCC)',
@@ -9672,14 +9933,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -4,
'scd3': None,
- 'suppl': ('I', 'J', 'K', 'M', 'N', 'V', 'X'),
+ 'suppl': ('B', 'I', 'J', 'K', 'M', 'N', 'V', 'X', 'Y'),
'scd5': None,
'charac': (14, 13, None, None, 'GGATG'),
'ovhgseq': 'NNNN',
}
rest_dict['FokI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<FriOI>G[AG]GC[CT]C)',
@@ -9702,7 +9962,6 @@ def _temp():
}
rest_dict['FriOI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<FseI>GGCCGGCC)',
@@ -9725,7 +9984,6 @@ def _temp():
}
rest_dict['FseI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Fsp4HI>GC.GC)',
@@ -9748,7 +10006,6 @@ def _temp():
}
rest_dict['Fsp4HI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<FspAI>[AG]TGCGCA[CT])',
@@ -9764,14 +10021,13 @@ def _temp():
'inact_temp': 65,
'ovhg': 0,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (4, -4, None, None, 'RTGCGCAY'),
'ovhgseq': '',
}
rest_dict['FspAI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<FspBI>CTAG)',
@@ -9787,14 +10043,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -2,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (1, -1, None, None, 'CTAG'),
'ovhgseq': 'TA',
}
rest_dict['FspBI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<FspEI>CC)|(?P<FspEI_as>GG)',
@@ -9817,7 +10072,6 @@ def _temp():
}
rest_dict['FspEI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<FspI>TGCGCA)',
@@ -9840,6 +10094,27 @@ def _temp():
}
rest_dict['FspI'] = _temp()
+def _temp():
+ return {
+ 'compsite': '(?P<FtnUV>GAAACA)|(?P<FtnUV_as>TGTTTC)',
+ 'results': None,
+ 'site': 'GAAACA',
+ 'substrat': 'DNA',
+ 'fst3': None,
+ 'fst5': None,
+ 'freq': 4096,
+ 'size': 6,
+ 'opt_temp': 37,
+ 'dna': None,
+ 'inact_temp': 65,
+ 'ovhg': None,
+ 'scd3': None,
+ 'suppl': (),
+ 'scd5': None,
+ 'charac': (None, None, None, None, 'GAAACA'),
+ 'ovhgseq': None,
+ }
+rest_dict['FtnUV'] = _temp()
def _temp():
return {
@@ -9863,6 +10138,27 @@ def _temp():
}
rest_dict['GauT27I'] = _temp()
+def _temp():
+ return {
+ 'compsite': '(?P<Gba708II>ATGCAC)|(?P<Gba708II_as>GTGCAT)',
+ 'results': None,
+ 'site': 'ATGCAC',
+ 'substrat': 'DNA',
+ 'fst3': None,
+ 'fst5': None,
+ 'freq': 4096,
+ 'size': 6,
+ 'opt_temp': 37,
+ 'dna': None,
+ 'inact_temp': 65,
+ 'ovhg': None,
+ 'scd3': None,
+ 'suppl': (),
+ 'scd5': None,
+ 'charac': (None, None, None, None, 'ATGCAC'),
+ 'ovhgseq': None,
+ }
+rest_dict['Gba708II'] = _temp()
def _temp():
return {
@@ -9886,7 +10182,6 @@ def _temp():
}
rest_dict['GdiII'] = _temp()
-
def _temp():
return {
'compsite': '(?P<GlaI>GCGC)',
@@ -9909,7 +10204,6 @@ def _temp():
}
rest_dict['GlaI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<GluI>GC.GC)',
@@ -9932,7 +10226,6 @@ def _temp():
}
rest_dict['GluI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<GsaI>CCCAGC)|(?P<GsaI_as>GCTGGG)',
@@ -9955,7 +10248,6 @@ def _temp():
}
rest_dict['GsaI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<GsuI>CTGGAG)|(?P<GsuI_as>CTCCAG)',
@@ -9971,14 +10263,13 @@ def _temp():
'inact_temp': 65,
'ovhg': 2,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (22, 14, None, None, 'CTGGAG'),
'ovhgseq': 'NN',
}
rest_dict['GsuI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<HaeI>[AT]GGCC[AT])',
@@ -10001,7 +10292,6 @@ def _temp():
}
rest_dict['HaeI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<HaeII>[AG]GCGC[CT])',
@@ -10024,7 +10314,6 @@ def _temp():
}
rest_dict['HaeII'] = _temp()
-
def _temp():
return {
'compsite': '(?P<HaeIII>GGCC)',
@@ -10040,14 +10329,13 @@ def _temp():
'inact_temp': 65,
'ovhg': 0,
'scd3': None,
- 'suppl': ('B', 'I', 'J', 'K', 'M', 'N', 'O', 'Q', 'R', 'S', 'U', 'X', 'Y'),
+ 'suppl': ('B', 'I', 'J', 'K', 'M', 'N', 'O', 'Q', 'R', 'S', 'X'),
'scd5': None,
'charac': (2, -2, None, None, 'GGCC'),
'ovhgseq': '',
}
rest_dict['HaeIII'] = _temp()
-
def _temp():
return {
'compsite': '(?P<HapII>CCGG)',
@@ -10070,7 +10358,6 @@ def _temp():
}
rest_dict['HapII'] = _temp()
-
def _temp():
return {
'compsite': '(?P<HauII>TGGCCA)',
@@ -10093,6 +10380,27 @@ def _temp():
}
rest_dict['HauII'] = _temp()
+def _temp():
+ return {
+ 'compsite': '(?P<HbaII>GCCCAG)|(?P<HbaII_as>CTGGGC)',
+ 'results': None,
+ 'site': 'GCCCAG',
+ 'substrat': 'DNA',
+ 'fst3': None,
+ 'fst5': None,
+ 'freq': 4096,
+ 'size': 6,
+ 'opt_temp': 37,
+ 'dna': None,
+ 'inact_temp': 65,
+ 'ovhg': None,
+ 'scd3': None,
+ 'suppl': (),
+ 'scd5': None,
+ 'charac': (None, None, None, None, 'GCCCAG'),
+ 'ovhgseq': None,
+ }
+rest_dict['HbaII'] = _temp()
def _temp():
return {
@@ -10116,7 +10424,6 @@ def _temp():
}
rest_dict['HgaI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<HgiAI>G[AT]GC[AT]C)',
@@ -10139,7 +10446,6 @@ def _temp():
}
rest_dict['HgiAI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<HgiCI>GG[CT][AG]CC)',
@@ -10162,7 +10468,6 @@ def _temp():
}
rest_dict['HgiCI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<HgiEII>ACC......GGT)',
@@ -10185,7 +10490,6 @@ def _temp():
}
rest_dict['HgiEII'] = _temp()
-
def _temp():
return {
'compsite': '(?P<HgiJII>G[AG]GC[CT]C)',
@@ -10208,7 +10512,6 @@ def _temp():
}
rest_dict['HgiJII'] = _temp()
-
def _temp():
return {
'compsite': '(?P<HhaI>GCGC)',
@@ -10224,14 +10527,13 @@ def _temp():
'inact_temp': 65,
'ovhg': 2,
'scd3': None,
- 'suppl': ('B', 'F', 'J', 'K', 'N', 'Q', 'R', 'U', 'X', 'Y'),
+ 'suppl': ('B', 'J', 'K', 'N', 'Q', 'R', 'X'),
'scd5': None,
'charac': (3, -3, None, None, 'GCGC'),
'ovhgseq': 'CG',
}
rest_dict['HhaI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Hin1I>G[AG]CG[CT]C)',
@@ -10247,14 +10549,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -2,
'scd3': None,
- 'suppl': ('F', 'K'),
+ 'suppl': ('B', 'K'),
'scd5': None,
'charac': (2, -2, None, None, 'GRCGYC'),
'ovhgseq': 'CG',
}
rest_dict['Hin1I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Hin1II>CATG)',
@@ -10270,14 +10571,13 @@ def _temp():
'inact_temp': 65,
'ovhg': 4,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (4, -4, None, None, 'CATG'),
'ovhgseq': 'CATG',
}
rest_dict['Hin1II'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Hin4I>GA[CT].....[ACG]TC)|(?P<Hin4I_as>GA[CGT].....[AG]TC)',
@@ -10300,7 +10600,6 @@ def _temp():
}
rest_dict['Hin4I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Hin4II>CCTTC)|(?P<Hin4II_as>GAAGG)',
@@ -10323,7 +10622,6 @@ def _temp():
}
rest_dict['Hin4II'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Hin6I>GCGC)',
@@ -10339,14 +10637,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -2,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (1, -1, None, None, 'GCGC'),
'ovhgseq': 'CG',
}
rest_dict['Hin6I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<HinP1I>GCGC)',
@@ -10369,7 +10666,6 @@ def _temp():
}
rest_dict['HinP1I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<HincII>GT[CT][AG]AC)',
@@ -10385,14 +10681,13 @@ def _temp():
'inact_temp': 65,
'ovhg': 0,
'scd3': None,
- 'suppl': ('B', 'F', 'J', 'K', 'N', 'O', 'Q', 'R', 'U', 'X', 'Y'),
+ 'suppl': ('B', 'J', 'K', 'N', 'O', 'Q', 'R', 'X'),
'scd5': None,
'charac': (3, -3, None, None, 'GTYRAC'),
'ovhgseq': '',
}
rest_dict['HincII'] = _temp()
-
def _temp():
return {
'compsite': '(?P<HindII>GT[CT][AG]AC)',
@@ -10415,7 +10710,6 @@ def _temp():
}
rest_dict['HindII'] = _temp()
-
def _temp():
return {
'compsite': '(?P<HindIII>AAGCTT)',
@@ -10431,14 +10725,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -4,
'scd3': None,
- 'suppl': ('B', 'C', 'F', 'I', 'J', 'K', 'M', 'N', 'O', 'Q', 'R', 'S', 'U', 'V', 'X', 'Y'),
+ 'suppl': ('B', 'C', 'I', 'J', 'K', 'M', 'N', 'O', 'Q', 'R', 'S', 'V', 'X', 'Y'),
'scd5': None,
'charac': (1, -1, None, None, 'AAGCTT'),
'ovhgseq': 'AGCT',
}
rest_dict['HindIII'] = _temp()
-
def _temp():
return {
'compsite': '(?P<HinfI>GA.TC)',
@@ -10454,14 +10747,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -3,
'scd3': None,
- 'suppl': ('B', 'C', 'F', 'I', 'J', 'K', 'M', 'N', 'O', 'Q', 'R', 'U', 'V', 'X', 'Y'),
+ 'suppl': ('B', 'C', 'I', 'J', 'K', 'M', 'N', 'O', 'Q', 'R', 'V', 'X', 'Y'),
'scd5': None,
'charac': (1, -1, None, None, 'GANTC'),
'ovhgseq': 'ANT',
}
rest_dict['HinfI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<HpaI>GTTAAC)',
@@ -10477,14 +10769,13 @@ def _temp():
'inact_temp': 65,
'ovhg': 0,
'scd3': None,
- 'suppl': ('B', 'C', 'I', 'J', 'K', 'M', 'N', 'Q', 'R', 'S', 'U', 'V', 'X'),
+ 'suppl': ('B', 'C', 'I', 'J', 'K', 'M', 'N', 'Q', 'R', 'S', 'V', 'X'),
'scd5': None,
'charac': (3, -3, None, None, 'GTTAAC'),
'ovhgseq': '',
}
rest_dict['HpaI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<HpaII>CCGG)',
@@ -10500,14 +10791,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -2,
'scd3': None,
- 'suppl': ('F', 'I', 'N', 'Q', 'R', 'S', 'U', 'V', 'X'),
+ 'suppl': ('B', 'I', 'N', 'Q', 'R', 'S', 'V', 'X'),
'scd5': None,
'charac': (1, -1, None, None, 'CCGG'),
'ovhgseq': 'CG',
}
rest_dict['HpaII'] = _temp()
-
def _temp():
return {
'compsite': '(?P<HphI>GGTGA)|(?P<HphI_as>TCACC)',
@@ -10523,14 +10813,13 @@ def _temp():
'inact_temp': 65,
'ovhg': 1,
'scd3': None,
- 'suppl': ('F', 'N'),
+ 'suppl': ('B', 'N'),
'scd5': None,
'charac': (13, 7, None, None, 'GGTGA'),
'ovhgseq': 'N',
}
rest_dict['HphI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Hpy166II>GT..AC)',
@@ -10553,7 +10842,6 @@ def _temp():
}
rest_dict['Hpy166II'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Hpy178III>TC..GA)',
@@ -10576,7 +10864,6 @@ def _temp():
}
rest_dict['Hpy178III'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Hpy188I>TC.GA)',
@@ -10599,7 +10886,6 @@ def _temp():
}
rest_dict['Hpy188I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Hpy188III>TC..GA)',
@@ -10622,7 +10908,6 @@ def _temp():
}
rest_dict['Hpy188III'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Hpy8I>GT..AC)',
@@ -10638,14 +10923,13 @@ def _temp():
'inact_temp': 65,
'ovhg': 0,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (3, -3, None, None, 'GTNNAC'),
'ovhgseq': '',
}
rest_dict['Hpy8I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Hpy99I>CG[AT]CG)',
@@ -10668,7 +10952,6 @@ def _temp():
}
rest_dict['Hpy99I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Hpy99XIII>GCCTA)|(?P<Hpy99XIII_as>TAGGC)',
@@ -10691,7 +10974,6 @@ def _temp():
}
rest_dict['Hpy99XIII'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Hpy99XIV>GG[AT]TAA)|(?P<Hpy99XIV_as>TTA[AT]CC)',
@@ -10714,6 +10996,49 @@ def _temp():
}
rest_dict['Hpy99XIV'] = _temp()
+def _temp():
+ return {
+ 'compsite': '(?P<Hpy99XIV_mut1>GG[AT]C.A)|(?P<Hpy99XIV_mut1_as>T.G[AT]CC)',
+ 'results': None,
+ 'site': 'GGWCNA',
+ 'substrat': 'DNA',
+ 'fst3': None,
+ 'fst5': None,
+ 'freq': 512,
+ 'size': 6,
+ 'opt_temp': 37,
+ 'dna': None,
+ 'inact_temp': 65,
+ 'ovhg': None,
+ 'scd3': None,
+ 'suppl': (),
+ 'scd5': None,
+ 'charac': (None, None, None, None, 'GGWCNA'),
+ 'ovhgseq': None,
+ }
+rest_dict['Hpy99XIV_mut1'] = _temp()
+
+def _temp():
+ return {
+ 'compsite': '(?P<Hpy99XXII>TCA......T[AG]G)|(?P<Hpy99XXII_as>C[CT]A......TGA)',
+ 'results': None,
+ 'site': 'TCANNNNNNTRG',
+ 'substrat': 'DNA',
+ 'fst3': None,
+ 'fst5': None,
+ 'freq': 2048,
+ 'size': 12,
+ 'opt_temp': 37,
+ 'dna': None,
+ 'inact_temp': 65,
+ 'ovhg': None,
+ 'scd3': None,
+ 'suppl': (),
+ 'scd5': None,
+ 'charac': (None, None, None, None, 'TCANNNNNNTRG'),
+ 'ovhgseq': None,
+ }
+rest_dict['Hpy99XXII'] = _temp()
def _temp():
return {
@@ -10737,6 +11062,71 @@ def _temp():
}
rest_dict['HpyAV'] = _temp()
+def _temp():
+ return {
+ 'compsite': '(?P<HpyAXIV>GCGTA)|(?P<HpyAXIV_as>TACGC)',
+ 'results': None,
+ 'site': 'GCGTA',
+ 'substrat': 'DNA',
+ 'fst3': None,
+ 'fst5': None,
+ 'freq': 1024,
+ 'size': 5,
+ 'opt_temp': 37,
+ 'dna': None,
+ 'inact_temp': 65,
+ 'ovhg': None,
+ 'scd3': None,
+ 'suppl': (),
+ 'scd5': None,
+ 'charac': (None, None, None, None, 'GCGTA'),
+ 'ovhgseq': None,
+ }
+rest_dict['HpyAXIV'] = _temp()
+
+def _temp():
+ return {
+ 'compsite': '(?P<HpyAXVI_mut1>C[AG]TTAA)|(?P<HpyAXVI_mut1_as>TTAA[CT]G)',
+ 'results': None,
+ 'site': 'CRTTAA',
+ 'substrat': 'DNA',
+ 'fst3': None,
+ 'fst5': None,
+ 'freq': 2048,
+ 'size': 6,
+ 'opt_temp': 37,
+ 'dna': None,
+ 'inact_temp': 65,
+ 'ovhg': None,
+ 'scd3': None,
+ 'suppl': (),
+ 'scd5': None,
+ 'charac': (None, None, None, None, 'CRTTAA'),
+ 'ovhgseq': None,
+ }
+rest_dict['HpyAXVI_mut1'] = _temp()
+
+def _temp():
+ return {
+ 'compsite': '(?P<HpyAXVI_mut2>C[AG]TC.A)|(?P<HpyAXVI_mut2_as>T.GA[CT]G)',
+ 'results': None,
+ 'site': 'CRTCNA',
+ 'substrat': 'DNA',
+ 'fst3': None,
+ 'fst5': None,
+ 'freq': 512,
+ 'size': 6,
+ 'opt_temp': 37,
+ 'dna': None,
+ 'inact_temp': 65,
+ 'ovhg': None,
+ 'scd3': None,
+ 'suppl': (),
+ 'scd5': None,
+ 'charac': (None, None, None, None, 'CRTCNA'),
+ 'ovhgseq': None,
+ }
+rest_dict['HpyAXVI_mut2'] = _temp()
def _temp():
return {
@@ -10760,7 +11150,6 @@ def _temp():
}
rest_dict['HpyCH4III'] = _temp()
-
def _temp():
return {
'compsite': '(?P<HpyCH4IV>ACGT)',
@@ -10783,7 +11172,6 @@ def _temp():
}
rest_dict['HpyCH4IV'] = _temp()
-
def _temp():
return {
'compsite': '(?P<HpyCH4V>TGCA)',
@@ -10806,7 +11194,6 @@ def _temp():
}
rest_dict['HpyCH4V'] = _temp()
-
def _temp():
return {
'compsite': '(?P<HpyF10VI>GC.......GC)',
@@ -10822,14 +11209,13 @@ def _temp():
'inact_temp': 65,
'ovhg': 3,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (7, -7, None, None, 'GCNNNNNNNGC'),
'ovhgseq': 'NNN',
}
rest_dict['HpyF10VI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<HpyF3I>CT.AG)',
@@ -10845,14 +11231,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -3,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (1, -1, None, None, 'CTNAG'),
'ovhgseq': 'TNA',
}
rest_dict['HpyF3I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<HpySE526I>ACGT)',
@@ -10875,6 +11260,93 @@ def _temp():
}
rest_dict['HpySE526I'] = _temp()
+def _temp():
+ return {
+ 'compsite': '(?P<HpyUM032XIII>C[CT]A.......T[AG]G)',
+ 'results': None,
+ 'site': 'CYANNNNNNNTRG',
+ 'substrat': 'DNA',
+ 'fst3': None,
+ 'fst5': None,
+ 'freq': 1024,
+ 'size': 13,
+ 'opt_temp': 37,
+ 'dna': None,
+ 'inact_temp': 65,
+ 'ovhg': None,
+ 'scd3': None,
+ 'suppl': (),
+ 'scd5': None,
+ 'charac': (None, None, None, None, 'CYANNNNNNNTRG'),
+ 'ovhgseq': None,
+ }
+rest_dict['HpyUM032XIII'] = _temp()
+
+def _temp():
+ return {
+ 'compsite': '(?P<HpyUM032XIII_mut1>C[CT]A.......TTC)|(?P<HpyUM032XIII_mut1_as>GAA.......T[AG]G)',
+ 'results': None,
+ 'site': 'CYANNNNNNNTTC',
+ 'substrat': 'DNA',
+ 'fst3': None,
+ 'fst5': None,
+ 'freq': 2048,
+ 'size': 13,
+ 'opt_temp': 37,
+ 'dna': None,
+ 'inact_temp': 65,
+ 'ovhg': None,
+ 'scd3': None,
+ 'suppl': (),
+ 'scd5': None,
+ 'charac': (None, None, None, None, 'CYANNNNNNNTTC'),
+ 'ovhgseq': None,
+ }
+rest_dict['HpyUM032XIII_mut1'] = _temp()
+
+def _temp():
+ return {
+ 'compsite': '(?P<HpyUM032XIV>GAAAG)|(?P<HpyUM032XIV_as>CTTTC)',
+ 'results': None,
+ 'site': 'GAAAG',
+ 'substrat': 'DNA',
+ 'fst3': None,
+ 'fst5': None,
+ 'freq': 1024,
+ 'size': 5,
+ 'opt_temp': 37,
+ 'dna': None,
+ 'inact_temp': 65,
+ 'ovhg': None,
+ 'scd3': None,
+ 'suppl': (),
+ 'scd5': None,
+ 'charac': (None, None, None, None, 'GAAAG'),
+ 'ovhgseq': None,
+ }
+rest_dict['HpyUM032XIV'] = _temp()
+
+def _temp():
+ return {
+ 'compsite': '(?P<HpyUM037X>T.GG.AG)|(?P<HpyUM037X_as>CT.CC.A)',
+ 'results': None,
+ 'site': 'TNGGNAG|GTGGNAG',
+ 'substrat': 'DNA',
+ 'fst3': None,
+ 'fst5': None,
+ 'freq': 1024,
+ 'size': 7,
+ 'opt_temp': 37,
+ 'dna': None,
+ 'inact_temp': 65,
+ 'ovhg': None,
+ 'scd3': None,
+ 'suppl': (),
+ 'scd5': None,
+ 'charac': (None, None, None, None, 'TNGGNAG|GTGGNAG'),
+ 'ovhgseq': None,
+ }
+rest_dict['HpyUM037X'] = _temp()
def _temp():
return {
@@ -10898,7 +11370,6 @@ def _temp():
}
rest_dict['Hsp92I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Hsp92II>CATG)',
@@ -10921,7 +11392,6 @@ def _temp():
}
rest_dict['Hsp92II'] = _temp()
-
def _temp():
return {
'compsite': '(?P<HspAI>GCGC)',
@@ -10944,7 +11414,6 @@ def _temp():
}
rest_dict['HspAI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Jma19592I>GTAT.AC)|(?P<Jma19592I_as>GT.ATAC)',
@@ -10967,6 +11436,27 @@ def _temp():
}
rest_dict['Jma19592I'] = _temp()
+def _temp():
+ return {
+ 'compsite': '(?P<Jma19592II>G[AG]GC[AG]AC)|(?P<Jma19592II_as>GT[CT]GC[CT]C)',
+ 'results': None,
+ 'site': 'GRGCRAC',
+ 'substrat': 'DNA',
+ 'fst3': None,
+ 'fst5': None,
+ 'freq': 4096,
+ 'size': 7,
+ 'opt_temp': 37,
+ 'dna': None,
+ 'inact_temp': 65,
+ 'ovhg': None,
+ 'scd3': None,
+ 'suppl': (),
+ 'scd5': None,
+ 'charac': (None, None, None, None, 'GRGCRAC'),
+ 'ovhgseq': None,
+ }
+rest_dict['Jma19592II'] = _temp()
def _temp():
return {
@@ -10990,7 +11480,6 @@ def _temp():
}
rest_dict['KasI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<KflI>GGG[AT]CCC)',
@@ -11006,14 +11495,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -3,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (2, -2, None, None, 'GGGWCCC'),
'ovhgseq': 'GWC',
}
rest_dict['KflI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Kpn2I>TCCGGA)',
@@ -11029,14 +11517,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -4,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (1, -1, None, None, 'TCCGGA'),
'ovhgseq': 'CCGG',
}
rest_dict['Kpn2I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<KpnI>GGTACC)',
@@ -11052,13 +11539,34 @@ def _temp():
'inact_temp': 65,
'ovhg': 4,
'scd3': None,
- 'suppl': ('B', 'C', 'F', 'I', 'J', 'K', 'M', 'N', 'O', 'Q', 'R', 'S', 'U', 'V', 'X', 'Y'),
+ 'suppl': ('B', 'C', 'I', 'J', 'K', 'M', 'N', 'O', 'Q', 'R', 'S', 'V', 'X', 'Y'),
'scd5': None,
'charac': (5, -5, None, None, 'GGTACC'),
'ovhgseq': 'GTAC',
}
rest_dict['KpnI'] = _temp()
+def _temp():
+ return {
+ 'compsite': '(?P<KpnNIH30III>GTTC.AC)|(?P<KpnNIH30III_as>GT.GAAC)',
+ 'results': None,
+ 'site': 'GTTCNAC',
+ 'substrat': 'DNA',
+ 'fst3': None,
+ 'fst5': None,
+ 'freq': 4096,
+ 'size': 7,
+ 'opt_temp': 37,
+ 'dna': None,
+ 'inact_temp': 65,
+ 'ovhg': None,
+ 'scd3': None,
+ 'suppl': (),
+ 'scd5': None,
+ 'charac': (None, None, None, None, 'GTTCNAC'),
+ 'ovhgseq': None,
+ }
+rest_dict['KpnNIH30III'] = _temp()
def _temp():
return {
@@ -11082,7 +11590,6 @@ def _temp():
}
rest_dict['KroI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Ksp22I>TGATCA)',
@@ -11105,7 +11612,6 @@ def _temp():
}
rest_dict['Ksp22I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Ksp632I>CTCTTC)|(?P<Ksp632I_as>GAAGAG)',
@@ -11128,7 +11634,6 @@ def _temp():
}
rest_dict['Ksp632I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<KspAI>GTTAAC)',
@@ -11142,84 +11647,168 @@ def _temp():
'opt_temp': 37,
'dna': None,
'inact_temp': 65,
- 'ovhg': 0,
+ 'ovhg': 0,
+ 'scd3': None,
+ 'suppl': ('B',),
+ 'scd5': None,
+ 'charac': (3, -3, None, None, 'GTTAAC'),
+ 'ovhgseq': '',
+ }
+rest_dict['KspAI'] = _temp()
+
+def _temp():
+ return {
+ 'compsite': '(?P<KspI>CCGCGG)',
+ 'results': None,
+ 'site': 'CCGCGG',
+ 'substrat': 'DNA',
+ 'fst3': -4,
+ 'fst5': 4,
+ 'freq': 4096,
+ 'size': 6,
+ 'opt_temp': 37,
+ 'dna': None,
+ 'inact_temp': 65,
+ 'ovhg': 2,
+ 'scd3': None,
+ 'suppl': ('M', 'S'),
+ 'scd5': None,
+ 'charac': (4, -4, None, None, 'CCGCGG'),
+ 'ovhgseq': 'GC',
+ }
+rest_dict['KspI'] = _temp()
+
+def _temp():
+ return {
+ 'compsite': '(?P<Kzo9I>GATC)',
+ 'results': None,
+ 'site': 'GATC',
+ 'substrat': 'DNA',
+ 'fst3': 0,
+ 'fst5': 0,
+ 'freq': 256,
+ 'size': 4,
+ 'opt_temp': 37,
+ 'dna': None,
+ 'inact_temp': 65,
+ 'ovhg': -4,
+ 'scd3': None,
+ 'suppl': ('I',),
+ 'scd5': None,
+ 'charac': (0, 0, None, None, 'GATC'),
+ 'ovhgseq': 'GATC',
+ }
+rest_dict['Kzo9I'] = _temp()
+
+def _temp():
+ return {
+ 'compsite': '(?P<Lba2029III>C[CT]AAA.G)|(?P<Lba2029III_as>C.TTT[AG]G)',
+ 'results': None,
+ 'site': 'CYAAANG',
+ 'substrat': 'DNA',
+ 'fst3': None,
+ 'fst5': None,
+ 'freq': 2048,
+ 'size': 7,
+ 'opt_temp': 37,
+ 'dna': None,
+ 'inact_temp': 65,
+ 'ovhg': None,
+ 'scd3': None,
+ 'suppl': (),
+ 'scd5': None,
+ 'charac': (None, None, None, None, 'CYAAANG'),
+ 'ovhgseq': None,
+ }
+rest_dict['Lba2029III'] = _temp()
+
+def _temp():
+ return {
+ 'compsite': '(?P<LguI>GCTCTTC)|(?P<LguI_as>GAAGAGC)',
+ 'results': None,
+ 'site': 'GCTCTTC',
+ 'substrat': 'DNA',
+ 'fst3': 4,
+ 'fst5': 8,
+ 'freq': 16384,
+ 'size': 7,
+ 'opt_temp': 37,
+ 'dna': None,
+ 'inact_temp': 65,
+ 'ovhg': -3,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
- 'charac': (3, -3, None, None, 'GTTAAC'),
- 'ovhgseq': '',
+ 'charac': (8, 4, None, None, 'GCTCTTC'),
+ 'ovhgseq': 'NNN',
}
-rest_dict['KspAI'] = _temp()
-
+rest_dict['LguI'] = _temp()
def _temp():
return {
- 'compsite': '(?P<KspI>CCGCGG)',
+ 'compsite': '(?P<LmnI>GCTCC)|(?P<LmnI_as>GGAGC)',
'results': None,
- 'site': 'CCGCGG',
+ 'site': 'GCTCC',
'substrat': 'DNA',
- 'fst3': -4,
- 'fst5': 4,
- 'freq': 4096,
- 'size': 6,
+ 'fst3': -1,
+ 'fst5': 6,
+ 'freq': 1024,
+ 'size': 5,
'opt_temp': 37,
'dna': None,
'inact_temp': 65,
'ovhg': 2,
'scd3': None,
- 'suppl': ('M', 'S'),
+ 'suppl': ('I',),
'scd5': None,
- 'charac': (4, -4, None, None, 'CCGCGG'),
- 'ovhgseq': 'GC',
+ 'charac': (6, -1, None, None, 'GCTCC'),
+ 'ovhgseq': 'CN',
}
-rest_dict['KspI'] = _temp()
-
+rest_dict['LmnI'] = _temp()
def _temp():
return {
- 'compsite': '(?P<Kzo9I>GATC)',
+ 'compsite': '(?P<Lmo370I>AGCGCCG)|(?P<Lmo370I_as>CGGCGCT)',
'results': None,
- 'site': 'GATC',
+ 'site': 'AGCGCCG',
'substrat': 'DNA',
- 'fst3': 0,
- 'fst5': 0,
- 'freq': 256,
- 'size': 4,
+ 'fst3': None,
+ 'fst5': None,
+ 'freq': 16384,
+ 'size': 7,
'opt_temp': 37,
'dna': None,
'inact_temp': 65,
- 'ovhg': -4,
+ 'ovhg': None,
'scd3': None,
- 'suppl': ('I',),
+ 'suppl': (),
'scd5': None,
- 'charac': (0, 0, None, None, 'GATC'),
- 'ovhgseq': 'GATC',
+ 'charac': (None, None, None, None, 'AGCGCCG'),
+ 'ovhgseq': None,
}
-rest_dict['Kzo9I'] = _temp()
-
+rest_dict['Lmo370I'] = _temp()
def _temp():
return {
- 'compsite': '(?P<LguI>GCTCTTC)|(?P<LguI_as>GAAGAGC)',
+ 'compsite': '(?P<Lmo911II>TAG[AG]AG)|(?P<Lmo911II_as>CT[CT]CTA)',
'results': None,
- 'site': 'GCTCTTC',
+ 'site': 'TAGRAG',
'substrat': 'DNA',
- 'fst3': 4,
- 'fst5': 8,
- 'freq': 16384,
- 'size': 7,
+ 'fst3': None,
+ 'fst5': None,
+ 'freq': 2048,
+ 'size': 6,
'opt_temp': 37,
'dna': None,
'inact_temp': 65,
- 'ovhg': -3,
+ 'ovhg': None,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': (),
'scd5': None,
- 'charac': (8, 4, None, None, 'GCTCTTC'),
- 'ovhgseq': 'NNN',
+ 'charac': (None, None, None, None, 'TAGRAG'),
+ 'ovhgseq': None,
}
-rest_dict['LguI'] = _temp()
-
+rest_dict['Lmo911II'] = _temp()
def _temp():
return {
@@ -11243,7 +11832,6 @@ def _temp():
}
rest_dict['LpnI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<LpnPI>CC[AGT]G)|(?P<LpnPI_as>C[ACT]GG)',
@@ -11266,7 +11854,6 @@ def _temp():
}
rest_dict['LpnPI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Lsp1109I>GCAGC)|(?P<Lsp1109I_as>GCTGC)',
@@ -11282,13 +11869,56 @@ def _temp():
'inact_temp': 65,
'ovhg': -4,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (13, 12, None, None, 'GCAGC'),
'ovhgseq': 'NNNN',
}
rest_dict['Lsp1109I'] = _temp()
+def _temp():
+ return {
+ 'compsite': '(?P<Lsp48III>AGCACC)|(?P<Lsp48III_as>GGTGCT)',
+ 'results': None,
+ 'site': 'AGCACC',
+ 'substrat': 'DNA',
+ 'fst3': None,
+ 'fst5': None,
+ 'freq': 4096,
+ 'size': 6,
+ 'opt_temp': 37,
+ 'dna': None,
+ 'inact_temp': 65,
+ 'ovhg': None,
+ 'scd3': None,
+ 'suppl': (),
+ 'scd5': None,
+ 'charac': (None, None, None, None, 'AGCACC'),
+ 'ovhgseq': None,
+ }
+rest_dict['Lsp48III'] = _temp()
+
+def _temp():
+ return {
+ 'compsite': '(?P<Lsp6406VI>C[AG]AGCAC)|(?P<Lsp6406VI_as>GTGCT[CT]G)',
+ 'results': None,
+ 'site': 'CRAGCAC',
+ 'substrat': 'DNA',
+ 'fst3': None,
+ 'fst5': None,
+ 'freq': 8192,
+ 'size': 7,
+ 'opt_temp': 37,
+ 'dna': None,
+ 'inact_temp': 65,
+ 'ovhg': None,
+ 'scd3': None,
+ 'suppl': (),
+ 'scd5': None,
+ 'charac': (None, None, None, None, 'CRAGCAC'),
+ 'ovhgseq': None,
+ }
+rest_dict['Lsp6406VI'] = _temp()
def _temp():
return {
@@ -11305,14 +11935,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -4,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (10, 9, None, None, 'GCATC'),
'ovhgseq': 'NNNN',
}
rest_dict['LweI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<MabI>ACC[AT]GGT)',
@@ -11335,7 +11964,6 @@ def _temp():
}
rest_dict['MabI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<MaeI>CTAG)',
@@ -11358,7 +11986,6 @@ def _temp():
}
rest_dict['MaeI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<MaeII>ACGT)',
@@ -11381,7 +12008,6 @@ def _temp():
}
rest_dict['MaeII'] = _temp()
-
def _temp():
return {
'compsite': '(?P<MaeIII>GT.AC)',
@@ -11404,7 +12030,6 @@ def _temp():
}
rest_dict['MaeIII'] = _temp()
-
def _temp():
return {
'compsite': '(?P<MalI>GATC)',
@@ -11427,7 +12052,6 @@ def _temp():
}
rest_dict['MalI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<MaqI>C[AG]TTGAC)|(?P<MaqI_as>GTCAA[CT]G)',
@@ -11450,7 +12074,6 @@ def _temp():
}
rest_dict['MaqI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<MauBI>CGCGCGCG)',
@@ -11466,13 +12089,34 @@ def _temp():
'inact_temp': 65,
'ovhg': -4,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (2, -2, None, None, 'CGCGCGCG'),
'ovhgseq': 'CGCG',
}
rest_dict['MauBI'] = _temp()
+def _temp():
+ return {
+ 'compsite': '(?P<Mba11I>AGGCGA)|(?P<Mba11I_as>TCGCCT)',
+ 'results': None,
+ 'site': 'AGGCGA',
+ 'substrat': 'DNA',
+ 'fst3': None,
+ 'fst5': None,
+ 'freq': 4096,
+ 'size': 6,
+ 'opt_temp': 37,
+ 'dna': None,
+ 'inact_temp': 65,
+ 'ovhg': None,
+ 'scd3': None,
+ 'suppl': (),
+ 'scd5': None,
+ 'charac': (None, None, None, None, 'AGGCGA'),
+ 'ovhgseq': None,
+ }
+rest_dict['Mba11I'] = _temp()
def _temp():
return {
@@ -11489,14 +12133,13 @@ def _temp():
'inact_temp': 65,
'ovhg': 0,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (3, -3, None, None, 'CCGCTC'),
'ovhgseq': '',
}
rest_dict['MbiI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<MboI>GATC)',
@@ -11512,14 +12155,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -4,
'scd3': None,
- 'suppl': ('B', 'C', 'F', 'K', 'N', 'Q', 'R', 'U', 'X', 'Y'),
+ 'suppl': ('B', 'C', 'K', 'N', 'Q', 'R', 'X', 'Y'),
'scd5': None,
'charac': (0, 0, None, None, 'GATC'),
'ovhgseq': 'GATC',
}
rest_dict['MboI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<MboII>GAAGA)|(?P<MboII_as>TCTTC)',
@@ -11535,14 +12177,13 @@ def _temp():
'inact_temp': 65,
'ovhg': 1,
'scd3': None,
- 'suppl': ('F', 'I', 'J', 'K', 'N', 'Q', 'R', 'V', 'X'),
+ 'suppl': ('B', 'I', 'J', 'K', 'N', 'Q', 'R', 'V', 'X'),
'scd5': None,
'charac': (13, 7, None, None, 'GAAGA'),
'ovhgseq': 'N',
}
rest_dict['MboII'] = _temp()
-
def _temp():
return {
'compsite': '(?P<McaTI>GCGCGC)',
@@ -11565,6 +12206,27 @@ def _temp():
}
rest_dict['McaTI'] = _temp()
+def _temp():
+ return {
+ 'compsite': '(?P<Mcr10I>GAAG.....CTC)|(?P<Mcr10I_as>GAG.....CTTC)',
+ 'results': None,
+ 'site': 'GAAGNNNNNCTC',
+ 'substrat': 'DNA',
+ 'fst3': None,
+ 'fst5': None,
+ 'freq': 16384,
+ 'size': 12,
+ 'opt_temp': 37,
+ 'dna': None,
+ 'inact_temp': 65,
+ 'ovhg': None,
+ 'scd3': None,
+ 'suppl': (),
+ 'scd5': None,
+ 'charac': (None, None, None, None, 'GAAGNNNNNCTC'),
+ 'ovhgseq': None,
+ }
+rest_dict['Mcr10I'] = _temp()
def _temp():
return {
@@ -11588,7 +12250,6 @@ def _temp():
}
rest_dict['McrI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<MfeI>CAATTG)',
@@ -11611,7 +12272,6 @@ def _temp():
}
rest_dict['MfeI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<MflI>[AG]GATC[CT])',
@@ -11634,7 +12294,6 @@ def _temp():
}
rest_dict['MflI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<MhlI>G[AGT]GC[ACT]C)',
@@ -11657,7 +12316,6 @@ def _temp():
}
rest_dict['MhlI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<MjaIV>GT..AC)',
@@ -11680,7 +12338,6 @@ def _temp():
}
rest_dict['MjaIV'] = _temp()
-
def _temp():
return {
'compsite': '(?P<MkaDII>GAGA[CT]GT)|(?P<MkaDII_as>AC[AG]TCTC)',
@@ -11703,7 +12360,6 @@ def _temp():
}
rest_dict['MkaDII'] = _temp()
-
def _temp():
return {
'compsite': '(?P<MlsI>TGGCCA)',
@@ -11719,14 +12375,13 @@ def _temp():
'inact_temp': 65,
'ovhg': 0,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (3, -3, None, None, 'TGGCCA'),
'ovhgseq': '',
}
rest_dict['MlsI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<MluCI>AATT)',
@@ -11749,7 +12404,6 @@ def _temp():
}
rest_dict['MluCI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<MluI>ACGCGT)',
@@ -11765,14 +12419,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -4,
'scd3': None,
- 'suppl': ('B', 'F', 'I', 'J', 'K', 'M', 'N', 'O', 'Q', 'R', 'U', 'V', 'X'),
+ 'suppl': ('B', 'I', 'J', 'K', 'M', 'N', 'O', 'Q', 'R', 'V', 'X'),
'scd5': None,
'charac': (1, -1, None, None, 'ACGCGT'),
'ovhgseq': 'CGCG',
}
rest_dict['MluI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<MluNI>TGGCCA)',
@@ -11795,7 +12448,6 @@ def _temp():
}
rest_dict['MluNI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Mly113I>GGCGCC)',
@@ -11818,7 +12470,6 @@ def _temp():
}
rest_dict['Mly113I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<MlyI>GAGTC)|(?P<MlyI_as>GACTC)',
@@ -11841,7 +12492,6 @@ def _temp():
}
rest_dict['MlyI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<MmeI>TCC[AG]AC)|(?P<MmeI_as>GT[CT]GGA)',
@@ -11857,14 +12507,13 @@ def _temp():
'inact_temp': 65,
'ovhg': 2,
'scd3': None,
- 'suppl': ('N',),
+ 'suppl': ('N', 'X'),
'scd5': None,
'charac': (26, 18, None, None, 'TCCRAC'),
'ovhgseq': 'NN',
}
rest_dict['MmeI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<MnlI>CCTC)|(?P<MnlI_as>GAGG)',
@@ -11880,13 +12529,34 @@ def _temp():
'inact_temp': 65,
'ovhg': 1,
'scd3': None,
- 'suppl': ('F', 'I', 'N', 'Q', 'V', 'X'),
+ 'suppl': ('B', 'I', 'N', 'Q', 'V', 'X'),
'scd5': None,
'charac': (11, 6, None, None, 'CCTC'),
'ovhgseq': 'N',
}
rest_dict['MnlI'] = _temp()
+def _temp():
+ return {
+ 'compsite': '(?P<Mox20I>TGGCCA)',
+ 'results': None,
+ 'site': 'TGGCCA',
+ 'substrat': 'DNA',
+ 'fst3': -3,
+ 'fst5': 3,
+ 'freq': 4096,
+ 'size': 6,
+ 'opt_temp': 37,
+ 'dna': None,
+ 'inact_temp': 65,
+ 'ovhg': 0,
+ 'scd3': None,
+ 'suppl': ('I',),
+ 'scd5': None,
+ 'charac': (3, -3, None, None, 'TGGCCA'),
+ 'ovhgseq': '',
+ }
+rest_dict['Mox20I'] = _temp()
def _temp():
return {
@@ -11903,14 +12573,13 @@ def _temp():
'inact_temp': 65,
'ovhg': 4,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (5, -5, None, None, 'ATGCAT'),
'ovhgseq': 'TGCA',
}
rest_dict['Mph1103I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<MreI>CGCCGGCG)',
@@ -11926,14 +12595,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -4,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (2, -2, None, None, 'CGCCGGCG'),
'ovhgseq': 'CCGG',
}
rest_dict['MreI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<MroI>TCCGGA)',
@@ -11956,7 +12624,6 @@ def _temp():
}
rest_dict['MroI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<MroNI>GCCGGC)',
@@ -11979,7 +12646,6 @@ def _temp():
}
rest_dict['MroNI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<MroXI>GAA....TTC)',
@@ -12002,7 +12668,6 @@ def _temp():
}
rest_dict['MroXI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<MscI>TGGCCA)',
@@ -12025,7 +12690,6 @@ def _temp():
}
rest_dict['MscI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<MseI>TTAA)',
@@ -12041,14 +12705,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -2,
'scd3': None,
- 'suppl': ('B', 'N'),
+ 'suppl': ('N',),
'scd5': None,
'charac': (1, -1, None, None, 'TTAA'),
'ovhgseq': 'TA',
}
rest_dict['MseI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<MslI>CA[CT]....[AG]TG)',
@@ -12071,7 +12734,6 @@ def _temp():
}
rest_dict['MslI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Msp20I>TGGCCA)',
@@ -12087,14 +12749,13 @@ def _temp():
'inact_temp': 65,
'ovhg': 0,
'scd3': None,
- 'suppl': ('I', 'V'),
+ 'suppl': ('V',),
'scd5': None,
'charac': (3, -3, None, None, 'TGGCCA'),
'ovhgseq': '',
}
rest_dict['Msp20I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<MspA1I>C[AC]GC[GT]G)',
@@ -12117,7 +12778,6 @@ def _temp():
}
rest_dict['MspA1I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<MspCI>CTTAAG)',
@@ -12140,7 +12800,6 @@ def _temp():
}
rest_dict['MspCI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<MspI>CCGG)',
@@ -12156,13 +12815,34 @@ def _temp():
'inact_temp': 65,
'ovhg': -2,
'scd3': None,
- 'suppl': ('F', 'I', 'J', 'K', 'N', 'Q', 'R', 'S', 'U', 'V', 'X', 'Y'),
+ 'suppl': ('B', 'I', 'J', 'K', 'N', 'Q', 'R', 'S', 'V', 'X'),
'scd5': None,
'charac': (1, -1, None, None, 'CCGG'),
'ovhgseq': 'CG',
}
rest_dict['MspI'] = _temp()
+def _temp():
+ return {
+ 'compsite': '(?P<MspI7II>ACG[AG]AG)|(?P<MspI7II_as>CT[CT]CGT)',
+ 'results': None,
+ 'site': 'ACGRAG',
+ 'substrat': 'DNA',
+ 'fst3': None,
+ 'fst5': None,
+ 'freq': 2048,
+ 'size': 6,
+ 'opt_temp': 37,
+ 'dna': None,
+ 'inact_temp': 65,
+ 'ovhg': None,
+ 'scd3': None,
+ 'suppl': (),
+ 'scd5': None,
+ 'charac': (None, None, None, None, 'ACGRAG'),
+ 'ovhgseq': None,
+ }
+rest_dict['MspI7II'] = _temp()
def _temp():
return {
@@ -12186,7 +12866,6 @@ def _temp():
}
rest_dict['MspJI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<MspR9I>CC.GG)',
@@ -12207,54 +12886,95 @@ def _temp():
'charac': (2, -2, None, None, 'CCNGG'),
'ovhgseq': 'N',
}
-rest_dict['MspR9I'] = _temp()
-
+rest_dict['MspR9I'] = _temp()
+
+def _temp():
+ return {
+ 'compsite': '(?P<MssI>GTTTAAAC)',
+ 'results': None,
+ 'site': 'GTTTAAAC',
+ 'substrat': 'DNA',
+ 'fst3': -4,
+ 'fst5': 4,
+ 'freq': 65536,
+ 'size': 8,
+ 'opt_temp': 37,
+ 'dna': None,
+ 'inact_temp': 65,
+ 'ovhg': 0,
+ 'scd3': None,
+ 'suppl': ('B',),
+ 'scd5': None,
+ 'charac': (4, -4, None, None, 'GTTTAAAC'),
+ 'ovhgseq': '',
+ }
+rest_dict['MssI'] = _temp()
+
+def _temp():
+ return {
+ 'compsite': '(?P<MstI>TGCGCA)',
+ 'results': None,
+ 'site': 'TGCGCA',
+ 'substrat': 'DNA',
+ 'fst3': -3,
+ 'fst5': 3,
+ 'freq': 4096,
+ 'size': 6,
+ 'opt_temp': 37,
+ 'dna': None,
+ 'inact_temp': 65,
+ 'ovhg': 0,
+ 'scd3': None,
+ 'suppl': (),
+ 'scd5': None,
+ 'charac': (3, -3, None, None, 'TGCGCA'),
+ 'ovhgseq': '',
+ }
+rest_dict['MstI'] = _temp()
def _temp():
return {
- 'compsite': '(?P<MssI>GTTTAAAC)',
+ 'compsite': '(?P<MteI>GCGC.GCGC)',
'results': None,
- 'site': 'GTTTAAAC',
+ 'site': 'GCGCNGCGC',
'substrat': 'DNA',
'fst3': -4,
'fst5': 4,
'freq': 65536,
- 'size': 8,
+ 'size': 9,
'opt_temp': 37,
'dna': None,
'inact_temp': 65,
- 'ovhg': 0,
+ 'ovhg': -1,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('I',),
'scd5': None,
- 'charac': (4, -4, None, None, 'GTTTAAAC'),
- 'ovhgseq': '',
+ 'charac': (4, -4, None, None, 'GCGCNGCGC'),
+ 'ovhgseq': 'N',
}
-rest_dict['MssI'] = _temp()
-
+rest_dict['MteI'] = _temp()
def _temp():
return {
- 'compsite': '(?P<MstI>TGCGCA)',
+ 'compsite': '(?P<MtuHN878II>CACGCAG)|(?P<MtuHN878II_as>CTGCGTG)',
'results': None,
- 'site': 'TGCGCA',
+ 'site': 'CACGCAG',
'substrat': 'DNA',
- 'fst3': -3,
- 'fst5': 3,
- 'freq': 4096,
- 'size': 6,
+ 'fst3': None,
+ 'fst5': None,
+ 'freq': 16384,
+ 'size': 7,
'opt_temp': 37,
'dna': None,
'inact_temp': 65,
- 'ovhg': 0,
+ 'ovhg': None,
'scd3': None,
'suppl': (),
'scd5': None,
- 'charac': (3, -3, None, None, 'TGCGCA'),
- 'ovhgseq': '',
+ 'charac': (None, None, None, None, 'CACGCAG'),
+ 'ovhgseq': None,
}
-rest_dict['MstI'] = _temp()
-
+rest_dict['MtuHN878II'] = _temp()
def _temp():
return {
@@ -12271,14 +12991,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -4,
'scd3': None,
- 'suppl': ('F', 'K', 'M'),
+ 'suppl': ('B', 'K', 'M'),
'scd5': None,
'charac': (1, -1, None, None, 'CAATTG'),
'ovhgseq': 'AATT',
}
rest_dict['MunI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Mva1269I>GAATGC)|(?P<Mva1269I_as>GCATTC)',
@@ -12294,14 +13013,13 @@ def _temp():
'inact_temp': 65,
'ovhg': 2,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (7, -1, None, None, 'GAATGC'),
'ovhgseq': 'CN',
}
rest_dict['Mva1269I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<MvaI>CC[AT]GG)',
@@ -12317,14 +13035,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -1,
'scd3': None,
- 'suppl': ('F', 'M', 'S'),
+ 'suppl': ('B', 'M', 'S'),
'scd5': None,
'charac': (2, -2, None, None, 'CCWGG'),
'ovhgseq': 'W',
}
rest_dict['MvaI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<MvnI>CGCG)',
@@ -12347,30 +13064,6 @@ def _temp():
}
rest_dict['MvnI'] = _temp()
-
-def _temp():
- return {
- 'compsite': '(?P<MvrI>CGATCG)',
- 'results': None,
- 'site': 'CGATCG',
- 'substrat': 'DNA',
- 'fst3': -4,
- 'fst5': 4,
- 'freq': 4096,
- 'size': 6,
- 'opt_temp': 37,
- 'dna': None,
- 'inact_temp': 65,
- 'ovhg': 2,
- 'scd3': None,
- 'suppl': ('U',),
- 'scd5': None,
- 'charac': (4, -4, None, None, 'CGATCG'),
- 'ovhgseq': 'AT',
- }
-rest_dict['MvrI'] = _temp()
-
-
def _temp():
return {
'compsite': '(?P<MwoI>GC.......GC)',
@@ -12393,7 +13086,6 @@ def _temp():
}
rest_dict['MwoI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<NaeI>GCCGGC)',
@@ -12409,13 +13101,34 @@ def _temp():
'inact_temp': 65,
'ovhg': 0,
'scd3': None,
- 'suppl': ('C', 'K', 'N', 'U'),
+ 'suppl': ('C', 'K', 'N'),
'scd5': None,
'charac': (3, -3, None, None, 'GCCGGC'),
'ovhgseq': '',
}
rest_dict['NaeI'] = _temp()
+def _temp():
+ return {
+ 'compsite': '(?P<Nal45188II>ACCAGC)|(?P<Nal45188II_as>GCTGGT)',
+ 'results': None,
+ 'site': 'ACCAGC',
+ 'substrat': 'DNA',
+ 'fst3': None,
+ 'fst5': None,
+ 'freq': 4096,
+ 'size': 6,
+ 'opt_temp': 37,
+ 'dna': None,
+ 'inact_temp': 65,
+ 'ovhg': None,
+ 'scd3': None,
+ 'suppl': (),
+ 'scd5': None,
+ 'charac': (None, None, None, None, 'ACCAGC'),
+ 'ovhgseq': None,
+ }
+rest_dict['Nal45188II'] = _temp()
def _temp():
return {
@@ -12432,14 +13145,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -2,
'scd3': None,
- 'suppl': ('J', 'M', 'N', 'Q', 'R', 'U', 'X'),
+ 'suppl': ('J', 'M', 'N', 'Q', 'R', 'X'),
'scd5': None,
'charac': (2, -2, None, None, 'GGCGCC'),
'ovhgseq': 'CG',
}
rest_dict['NarI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<NciI>CC[CG]GG)',
@@ -12462,7 +13174,6 @@ def _temp():
}
rest_dict['NciI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<NcoI>CCATGG)',
@@ -12478,14 +13189,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -4,
'scd3': None,
- 'suppl': ('B', 'C', 'F', 'J', 'K', 'M', 'N', 'O', 'Q', 'R', 'S', 'U', 'X', 'Y'),
+ 'suppl': ('B', 'C', 'J', 'K', 'M', 'N', 'O', 'Q', 'R', 'S', 'X', 'Y'),
'scd5': None,
'charac': (1, -1, None, None, 'CCATGG'),
'ovhgseq': 'CATG',
}
rest_dict['NcoI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<NdeI>CATATG)',
@@ -12501,14 +13211,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -2,
'scd3': None,
- 'suppl': ('B', 'F', 'J', 'K', 'M', 'N', 'Q', 'R', 'S', 'U', 'X', 'Y'),
+ 'suppl': ('B', 'J', 'K', 'M', 'N', 'Q', 'R', 'S', 'X'),
'scd5': None,
'charac': (2, -2, None, None, 'CATATG'),
'ovhgseq': 'TA',
}
rest_dict['NdeI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<NdeII>GATC)',
@@ -12531,6 +13240,27 @@ def _temp():
}
rest_dict['NdeII'] = _temp()
+def _temp():
+ return {
+ 'compsite': '(?P<NgoAVII>GCCGC)|(?P<NgoAVII_as>GCGGC)',
+ 'results': None,
+ 'site': 'GCCGC',
+ 'substrat': 'DNA',
+ 'fst3': 7,
+ 'fst5': 12,
+ 'freq': 1024,
+ 'size': 5,
+ 'opt_temp': 37,
+ 'dna': None,
+ 'inact_temp': 65,
+ 'ovhg': 0,
+ 'scd3': None,
+ 'suppl': (),
+ 'scd5': None,
+ 'charac': (12, 7, None, None, 'GCCGC'),
+ 'ovhgseq': '',
+ }
+rest_dict['NgoAVII'] = _temp()
def _temp():
return {
@@ -12554,7 +13284,6 @@ def _temp():
}
rest_dict['NgoAVIII'] = _temp()
-
def _temp():
return {
'compsite': '(?P<NgoMIV>GCCGGC)',
@@ -12577,7 +13306,6 @@ def _temp():
}
rest_dict['NgoMIV'] = _temp()
-
def _temp():
return {
'compsite': '(?P<NhaXI>CAAG[AG]AG)|(?P<NhaXI_as>CT[CT]CTTG)',
@@ -12600,7 +13328,6 @@ def _temp():
}
rest_dict['NhaXI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<NheI>GCTAGC)',
@@ -12616,13 +13343,34 @@ def _temp():
'inact_temp': 65,
'ovhg': -4,
'scd3': None,
- 'suppl': ('B', 'C', 'F', 'J', 'K', 'M', 'N', 'O', 'Q', 'R', 'S', 'U', 'X'),
+ 'suppl': ('B', 'C', 'J', 'K', 'M', 'N', 'O', 'Q', 'R', 'S', 'X'),
'scd5': None,
'charac': (1, -1, None, None, 'GCTAGC'),
'ovhgseq': 'CTAG',
}
rest_dict['NheI'] = _temp()
+def _temp():
+ return {
+ 'compsite': '(?P<NhoI>GC[AT]GC)',
+ 'results': None,
+ 'site': 'GCWGC',
+ 'substrat': 'DNA',
+ 'fst3': None,
+ 'fst5': None,
+ 'freq': 512,
+ 'size': 5,
+ 'opt_temp': 37,
+ 'dna': None,
+ 'inact_temp': 65,
+ 'ovhg': None,
+ 'scd3': None,
+ 'suppl': (),
+ 'scd5': None,
+ 'charac': (None, None, None, None, 'GCWGC'),
+ 'ovhgseq': None,
+ }
+rest_dict['NhoI'] = _temp()
def _temp():
return {
@@ -12646,7 +13394,6 @@ def _temp():
}
rest_dict['NlaCI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<NlaIII>CATG)',
@@ -12669,7 +13416,6 @@ def _temp():
}
rest_dict['NlaIII'] = _temp()
-
def _temp():
return {
'compsite': '(?P<NlaIV>GG..CC)',
@@ -12692,7 +13438,6 @@ def _temp():
}
rest_dict['NlaIV'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Nli3877I>C[CT]CG[AG]G)',
@@ -12715,7 +13460,6 @@ def _temp():
}
rest_dict['Nli3877I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<NmeAIII>GCCGAG)|(?P<NmeAIII_as>CTCGGC)',
@@ -12738,7 +13482,6 @@ def _temp():
}
rest_dict['NmeAIII'] = _temp()
-
def _temp():
return {
'compsite': '(?P<NmeDI>[AG]CCGG[CT])',
@@ -12761,7 +13504,6 @@ def _temp():
}
rest_dict['NmeDI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<NmuCI>GT[CG]AC)',
@@ -12777,14 +13519,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -5,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (0, 0, None, None, 'GTSAC'),
'ovhgseq': 'GTSAC',
}
rest_dict['NmuCI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<NotI>GCGGCCGC)',
@@ -12800,14 +13541,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -4,
'scd3': None,
- 'suppl': ('B', 'C', 'F', 'J', 'K', 'M', 'N', 'O', 'Q', 'R', 'S', 'U', 'X', 'Y'),
+ 'suppl': ('B', 'C', 'J', 'K', 'M', 'N', 'O', 'Q', 'R', 'S', 'X'),
'scd5': None,
'charac': (2, -2, None, None, 'GCGGCCGC'),
'ovhgseq': 'GGCC',
}
rest_dict['NotI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<NruI>TCGCGA)',
@@ -12823,14 +13563,13 @@ def _temp():
'inact_temp': 65,
'ovhg': 0,
'scd3': None,
- 'suppl': ('B', 'C', 'I', 'J', 'K', 'M', 'N', 'Q', 'R', 'U', 'X'),
+ 'suppl': ('B', 'C', 'I', 'J', 'K', 'M', 'N', 'Q', 'R', 'X'),
'scd5': None,
'charac': (3, -3, None, None, 'TCGCGA'),
'ovhgseq': '',
}
rest_dict['NruI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<NsbI>TGCGCA)',
@@ -12846,14 +13585,13 @@ def _temp():
'inact_temp': 65,
'ovhg': 0,
'scd3': None,
- 'suppl': ('F', 'K'),
+ 'suppl': ('B', 'K'),
'scd5': None,
'charac': (3, -3, None, None, 'TGCGCA'),
'ovhgseq': '',
}
rest_dict['NsbI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<NsiI>ATGCAT)',
@@ -12869,14 +13607,13 @@ def _temp():
'inact_temp': 65,
'ovhg': 4,
'scd3': None,
- 'suppl': ('J', 'M', 'N', 'Q', 'R', 'S', 'U', 'X'),
+ 'suppl': ('J', 'M', 'N', 'Q', 'R', 'S', 'X'),
'scd5': None,
'charac': (5, -5, None, None, 'ATGCAT'),
'ovhgseq': 'TGCA',
}
rest_dict['NsiI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<NspBII>C[AC]GC[GT]G)',
@@ -12899,7 +13636,6 @@ def _temp():
}
rest_dict['NspBII'] = _temp()
-
def _temp():
return {
'compsite': '(?P<NspI>[AG]CATG[CT])',
@@ -12922,7 +13658,6 @@ def _temp():
}
rest_dict['NspI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<NspV>TTCGAA)',
@@ -12945,7 +13680,6 @@ def _temp():
}
rest_dict['NspV'] = _temp()
-
def _temp():
return {
'compsite': '(?P<OliI>CAC....GTG)',
@@ -12961,13 +13695,34 @@ def _temp():
'inact_temp': 65,
'ovhg': 0,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (5, -5, None, None, 'CACNNNNGTG'),
'ovhgseq': '',
}
rest_dict['OliI'] = _temp()
+def _temp():
+ return {
+ 'compsite': '(?P<OspHL35III>[CT]AGGAG)|(?P<OspHL35III_as>CTCCT[AG])',
+ 'results': None,
+ 'site': 'YAGGAG',
+ 'substrat': 'DNA',
+ 'fst3': None,
+ 'fst5': None,
+ 'freq': 2048,
+ 'size': 6,
+ 'opt_temp': 37,
+ 'dna': None,
+ 'inact_temp': 65,
+ 'ovhg': None,
+ 'scd3': None,
+ 'suppl': (),
+ 'scd5': None,
+ 'charac': (None, None, None, None, 'YAGGAG'),
+ 'ovhgseq': None,
+ }
+rest_dict['OspHL35III'] = _temp()
def _temp():
return {
@@ -12991,6 +13746,27 @@ def _temp():
}
rest_dict['PabI'] = _temp()
+def _temp():
+ return {
+ 'compsite': '(?P<Pac19842II>CCTTGA)|(?P<Pac19842II_as>TCAAGG)',
+ 'results': None,
+ 'site': 'CCTTGA',
+ 'substrat': 'DNA',
+ 'fst3': None,
+ 'fst5': None,
+ 'freq': 4096,
+ 'size': 6,
+ 'opt_temp': 37,
+ 'dna': None,
+ 'inact_temp': 65,
+ 'ovhg': None,
+ 'scd3': None,
+ 'suppl': (),
+ 'scd5': None,
+ 'charac': (None, None, None, None, 'CCTTGA'),
+ 'ovhgseq': None,
+ }
+rest_dict['Pac19842II'] = _temp()
def _temp():
return {
@@ -13007,14 +13783,13 @@ def _temp():
'inact_temp': 65,
'ovhg': 2,
'scd3': None,
- 'suppl': ('F', 'N', 'O'),
+ 'suppl': ('B', 'N', 'O'),
'scd5': None,
'charac': (5, -5, None, None, 'TTAATTAA'),
'ovhgseq': 'AT',
}
rest_dict['PacI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<PaeI>GCATGC)',
@@ -13030,14 +13805,13 @@ def _temp():
'inact_temp': 65,
'ovhg': 4,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (5, -5, None, None, 'GCATGC'),
'ovhgseq': 'CATG',
}
rest_dict['PaeI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<PaeR7I>CTCGAG)',
@@ -13060,7 +13834,6 @@ def _temp():
}
rest_dict['PaeR7I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<PagI>TCATGA)',
@@ -13076,13 +13849,34 @@ def _temp():
'inact_temp': 65,
'ovhg': -4,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (1, -1, None, None, 'TCATGA'),
'ovhgseq': 'CATG',
}
rest_dict['PagI'] = _temp()
+def _temp():
+ return {
+ 'compsite': '(?P<Pal408I>CC[AG]TGAG)|(?P<Pal408I_as>CTCA[CT]GG)',
+ 'results': None,
+ 'site': 'CCRTGAG',
+ 'substrat': 'DNA',
+ 'fst3': None,
+ 'fst5': None,
+ 'freq': 8192,
+ 'size': 7,
+ 'opt_temp': 37,
+ 'dna': None,
+ 'inact_temp': 65,
+ 'ovhg': None,
+ 'scd3': None,
+ 'suppl': (),
+ 'scd5': None,
+ 'charac': (None, None, None, None, 'CCRTGAG'),
+ 'ovhgseq': None,
+ }
+rest_dict['Pal408I'] = _temp()
def _temp():
return {
@@ -13106,7 +13900,6 @@ def _temp():
}
rest_dict['PalAI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<PasI>CCC[AT]GGG)',
@@ -13122,14 +13915,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -3,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (2, -2, None, None, 'CCCWGGG'),
'ovhgseq': 'CWG',
}
rest_dict['PasI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<PauI>GCGCGC)',
@@ -13145,14 +13937,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -4,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (1, -1, None, None, 'GCGCGC'),
'ovhgseq': 'CGCG',
}
rest_dict['PauI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<PceI>AGGCCT)',
@@ -13175,7 +13966,6 @@ def _temp():
}
rest_dict['PceI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<PciI>ACATGT)',
@@ -13198,7 +13988,6 @@ def _temp():
}
rest_dict['PciI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<PciSI>GCTCTTC)|(?P<PciSI_as>GAAGAGC)',
@@ -13221,7 +14010,6 @@ def _temp():
}
rest_dict['PciSI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<PcsI>[AT]CG.......CG[AT])',
@@ -13244,7 +14032,6 @@ def _temp():
}
rest_dict['PcsI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<PctI>GAATGC)|(?P<PctI_as>GCATTC)',
@@ -13267,6 +14054,27 @@ def _temp():
}
rest_dict['PctI'] = _temp()
+def _temp():
+ return {
+ 'compsite': '(?P<Pdi8503III>CCGG.AG)|(?P<Pdi8503III_as>CT.CCGG)',
+ 'results': None,
+ 'site': 'CCGGNAG',
+ 'substrat': 'DNA',
+ 'fst3': None,
+ 'fst5': None,
+ 'freq': 4096,
+ 'size': 7,
+ 'opt_temp': 37,
+ 'dna': None,
+ 'inact_temp': 65,
+ 'ovhg': None,
+ 'scd3': None,
+ 'suppl': (),
+ 'scd5': None,
+ 'charac': (None, None, None, None, 'CCGGNAG'),
+ 'ovhgseq': None,
+ }
+rest_dict['Pdi8503III'] = _temp()
def _temp():
return {
@@ -13283,14 +14091,13 @@ def _temp():
'inact_temp': 65,
'ovhg': 0,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (3, -3, None, None, 'GCCGGC'),
'ovhgseq': '',
}
rest_dict['PdiI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<PdmI>GAA....TTC)',
@@ -13306,13 +14113,34 @@ def _temp():
'inact_temp': 65,
'ovhg': 0,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (5, -5, None, None, 'GAANNNNTTC'),
'ovhgseq': '',
}
rest_dict['PdmI'] = _temp()
+def _temp():
+ return {
+ 'compsite': '(?P<Pdu1735I>CACCAC)|(?P<Pdu1735I_as>GTGGTG)',
+ 'results': None,
+ 'site': 'CACCAC',
+ 'substrat': 'DNA',
+ 'fst3': None,
+ 'fst5': None,
+ 'freq': 4096,
+ 'size': 6,
+ 'opt_temp': 37,
+ 'dna': None,
+ 'inact_temp': 65,
+ 'ovhg': None,
+ 'scd3': None,
+ 'suppl': (),
+ 'scd5': None,
+ 'charac': (None, None, None, None, 'CACCAC'),
+ 'ovhgseq': None,
+ }
+rest_dict['Pdu1735I'] = _temp()
def _temp():
return {
@@ -13336,7 +14164,6 @@ def _temp():
}
rest_dict['PenI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<PfeI>GA[AT]TC)',
@@ -13352,14 +14179,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -3,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (1, -1, None, None, 'GAWTC'),
'ovhgseq': 'AWT',
}
rest_dict['PfeI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Pfl1108I>TCGTAG)|(?P<Pfl1108I_as>CTACGA)',
@@ -13382,7 +14208,6 @@ def _temp():
}
rest_dict['Pfl1108I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Pfl23II>CGTACG)',
@@ -13398,14 +14223,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -4,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (1, -1, None, None, 'CGTACG'),
'ovhgseq': 'GTAC',
}
rest_dict['Pfl23II'] = _temp()
-
def _temp():
return {
'compsite': '(?P<PflFI>GAC...GTC)',
@@ -13428,7 +14252,6 @@ def _temp():
}
rest_dict['PflFI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<PflMI>CCA.....TGG)',
@@ -13451,7 +14274,6 @@ def _temp():
}
rest_dict['PflMI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<PfoI>TCC.GGA)',
@@ -13467,14 +14289,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -5,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (1, -1, None, None, 'TCCNGGA'),
'ovhgseq': 'CCNGG',
}
rest_dict['PfoI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<PinAI>ACCGGT)',
@@ -13490,13 +14311,34 @@ def _temp():
'inact_temp': 65,
'ovhg': -4,
'scd3': None,
- 'suppl': ('M', 'Q', 'X'),
+ 'suppl': ('Q', 'X'),
+ 'scd5': None,
+ 'charac': (1, -1, None, None, 'ACCGGT'),
+ 'ovhgseq': 'CCGG',
+ }
+rest_dict['PinAI'] = _temp()
+
+def _temp():
+ return {
+ 'compsite': '(?P<PkrI>GC.GC)',
+ 'results': None,
+ 'site': 'GCNGC',
+ 'substrat': 'DNA',
+ 'fst3': -3,
+ 'fst5': 3,
+ 'freq': 256,
+ 'size': 5,
+ 'opt_temp': 37,
+ 'dna': None,
+ 'inact_temp': 65,
+ 'ovhg': 1,
+ 'scd3': None,
+ 'suppl': ('I',),
'scd5': None,
- 'charac': (1, -1, None, None, 'ACCGGT'),
- 'ovhgseq': 'CCGG',
+ 'charac': (3, -3, None, None, 'GCNGC'),
+ 'ovhgseq': 'N',
}
-rest_dict['PinAI'] = _temp()
-
+rest_dict['PkrI'] = _temp()
def _temp():
return {
@@ -13520,7 +14362,6 @@ def _temp():
}
rest_dict['PlaDI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Ple19I>CGATCG)',
@@ -13543,7 +14384,6 @@ def _temp():
}
rest_dict['Ple19I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<PleI>GAGTC)|(?P<PleI_as>GACTC)',
@@ -13566,6 +14406,27 @@ def _temp():
}
rest_dict['PleI'] = _temp()
+def _temp():
+ return {
+ 'compsite': '(?P<PliMI>CGCCGAC)|(?P<PliMI_as>GTCGGCG)',
+ 'results': None,
+ 'site': 'CGCCGAC',
+ 'substrat': 'DNA',
+ 'fst3': None,
+ 'fst5': None,
+ 'freq': 16384,
+ 'size': 7,
+ 'opt_temp': 37,
+ 'dna': None,
+ 'inact_temp': 65,
+ 'ovhg': None,
+ 'scd3': None,
+ 'suppl': (),
+ 'scd5': None,
+ 'charac': (None, None, None, None, 'CGCCGAC'),
+ 'ovhgseq': None,
+ }
+rest_dict['PliMI'] = _temp()
def _temp():
return {
@@ -13589,7 +14450,6 @@ def _temp():
}
rest_dict['PluTI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<PmaCI>CACGTG)',
@@ -13612,6 +14472,27 @@ def _temp():
}
rest_dict['PmaCI'] = _temp()
+def _temp():
+ return {
+ 'compsite': '(?P<Pme5II>GACGAG)|(?P<Pme5II_as>CTCGTC)',
+ 'results': None,
+ 'site': 'GACGAG',
+ 'substrat': 'DNA',
+ 'fst3': None,
+ 'fst5': None,
+ 'freq': 4096,
+ 'size': 6,
+ 'opt_temp': 37,
+ 'dna': None,
+ 'inact_temp': 65,
+ 'ovhg': None,
+ 'scd3': None,
+ 'suppl': (),
+ 'scd5': None,
+ 'charac': (None, None, None, None, 'GACGAG'),
+ 'ovhgseq': None,
+ }
+rest_dict['Pme5II'] = _temp()
def _temp():
return {
@@ -13635,6 +14516,27 @@ def _temp():
}
rest_dict['PmeI'] = _temp()
+def _temp():
+ return {
+ 'compsite': '(?P<PmeS132I>GACGAG)|(?P<PmeS132I_as>CTCGTC)',
+ 'results': None,
+ 'site': 'GACGAG',
+ 'substrat': 'DNA',
+ 'fst3': None,
+ 'fst5': None,
+ 'freq': 4096,
+ 'size': 6,
+ 'opt_temp': 37,
+ 'dna': None,
+ 'inact_temp': 65,
+ 'ovhg': None,
+ 'scd3': None,
+ 'suppl': (),
+ 'scd5': None,
+ 'charac': (None, None, None, None, 'GACGAG'),
+ 'ovhgseq': None,
+ }
+rest_dict['PmeS132I'] = _temp()
def _temp():
return {
@@ -13658,7 +14560,6 @@ def _temp():
}
rest_dict['PmlI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<PpiI>GAAC.....CTC)|(?P<PpiI_as>GAG.....GTTC)',
@@ -13681,7 +14582,6 @@ def _temp():
}
rest_dict['PpiI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<PpsI>GAGTC)|(?P<PpsI_as>GACTC)',
@@ -13704,7 +14604,6 @@ def _temp():
}
rest_dict['PpsI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Ppu10I>ATGCAT)',
@@ -13727,7 +14626,6 @@ def _temp():
}
rest_dict['Ppu10I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Ppu21I>[CT]ACGT[AG])',
@@ -13743,14 +14641,13 @@ def _temp():
'inact_temp': 65,
'ovhg': 0,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (3, -3, None, None, 'YACGTR'),
'ovhgseq': '',
}
rest_dict['Ppu21I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<PpuMI>[AG]GG[AT]CC[CT])',
@@ -13773,7 +14670,6 @@ def _temp():
}
rest_dict['PpuMI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<PscI>ACATGT)',
@@ -13789,13 +14685,34 @@ def _temp():
'inact_temp': 65,
'ovhg': -4,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (1, -1, None, None, 'ACATGT'),
'ovhgseq': 'CATG',
}
rest_dict['PscI'] = _temp()
+def _temp():
+ return {
+ 'compsite': '(?P<Pse18267I>[AG]CCGAAG)|(?P<Pse18267I_as>CTTCGG[CT])',
+ 'results': None,
+ 'site': 'RCCGAAG',
+ 'substrat': 'DNA',
+ 'fst3': None,
+ 'fst5': None,
+ 'freq': 8192,
+ 'size': 7,
+ 'opt_temp': 37,
+ 'dna': None,
+ 'inact_temp': 65,
+ 'ovhg': None,
+ 'scd3': None,
+ 'suppl': (),
+ 'scd5': None,
+ 'charac': (None, None, None, None, 'RCCGAAG'),
+ 'ovhgseq': None,
+ }
+rest_dict['Pse18267I'] = _temp()
def _temp():
return {
@@ -13819,7 +14736,6 @@ def _temp():
}
rest_dict['PshAI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<PshBI>ATTAAT)',
@@ -13842,7 +14758,6 @@ def _temp():
}
rest_dict['PshBI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<PsiI>TTATAA)',
@@ -13865,6 +14780,27 @@ def _temp():
}
rest_dict['PsiI'] = _temp()
+def _temp():
+ return {
+ 'compsite': '(?P<Psp0357II>GCGAAG)|(?P<Psp0357II_as>CTTCGC)',
+ 'results': None,
+ 'site': 'GCGAAG',
+ 'substrat': 'DNA',
+ 'fst3': None,
+ 'fst5': None,
+ 'freq': 4096,
+ 'size': 6,
+ 'opt_temp': 37,
+ 'dna': None,
+ 'inact_temp': 65,
+ 'ovhg': None,
+ 'scd3': None,
+ 'suppl': (),
+ 'scd5': None,
+ 'charac': (None, None, None, None, 'GCGAAG'),
+ 'ovhgseq': None,
+ }
+rest_dict['Psp0357II'] = _temp()
def _temp():
return {
@@ -13888,6 +14824,27 @@ def _temp():
}
rest_dict['Psp03I'] = _temp()
+def _temp():
+ return {
+ 'compsite': '(?P<Psp10HII>G[AG]AGCAG)|(?P<Psp10HII_as>CTGCT[CT]C)',
+ 'results': None,
+ 'site': 'GRAGCAG',
+ 'substrat': 'DNA',
+ 'fst3': None,
+ 'fst5': None,
+ 'freq': 8192,
+ 'size': 7,
+ 'opt_temp': 37,
+ 'dna': None,
+ 'inact_temp': 65,
+ 'ovhg': None,
+ 'scd3': None,
+ 'suppl': (),
+ 'scd5': None,
+ 'charac': (None, None, None, None, 'GRAGCAG'),
+ 'ovhgseq': None,
+ }
+rest_dict['Psp10HII'] = _temp()
def _temp():
return {
@@ -13911,7 +14868,6 @@ def _temp():
}
rest_dict['Psp124BI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Psp1406I>AACGTT)',
@@ -13927,14 +14883,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -2,
'scd3': None,
- 'suppl': ('F', 'K'),
+ 'suppl': ('B', 'K'),
'scd5': None,
'charac': (2, -2, None, None, 'AACGTT'),
'ovhgseq': 'CG',
}
rest_dict['Psp1406I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Psp5II>[AG]GG[AT]CC[CT])',
@@ -13950,14 +14905,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -3,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (2, -2, None, None, 'RGGWCCY'),
'ovhgseq': 'GWC',
}
rest_dict['Psp5II'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Psp6I>CC[AT]GG)',
@@ -13980,7 +14934,6 @@ def _temp():
}
rest_dict['Psp6I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<PspCI>CACGTG)',
@@ -14003,7 +14956,6 @@ def _temp():
}
rest_dict['PspCI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<PspEI>GGT.ACC)',
@@ -14026,6 +14978,27 @@ def _temp():
}
rest_dict['PspEI'] = _temp()
+def _temp():
+ return {
+ 'compsite': '(?P<PspFI>CCCAGC)|(?P<PspFI_as>GCTGGG)',
+ 'results': None,
+ 'site': 'CCCAGC',
+ 'substrat': 'DNA',
+ 'fst3': -1,
+ 'fst5': 1,
+ 'freq': 4096,
+ 'size': 6,
+ 'opt_temp': 37,
+ 'dna': None,
+ 'inact_temp': 65,
+ 'ovhg': -4,
+ 'scd3': None,
+ 'suppl': ('B',),
+ 'scd5': None,
+ 'charac': (1, -1, None, None, 'CCCAGC'),
+ 'ovhgseq': 'CCAG',
+ }
+rest_dict['PspFI'] = _temp()
def _temp():
return {
@@ -14049,7 +15022,6 @@ def _temp():
}
rest_dict['PspGI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<PspLI>CGTACG)',
@@ -14072,7 +15044,6 @@ def _temp():
}
rest_dict['PspLI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<PspN4I>GG..CC)',
@@ -14095,7 +15066,6 @@ def _temp():
}
rest_dict['PspN4I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<PspOMI>GGGCCC)',
@@ -14118,7 +15088,6 @@ def _temp():
}
rest_dict['PspOMI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<PspOMII>CGCCCA[AG])|(?P<PspOMII_as>[CT]TGGGCG)',
@@ -14141,7 +15110,6 @@ def _temp():
}
rest_dict['PspOMII'] = _temp()
-
def _temp():
return {
'compsite': '(?P<PspPI>GG.CC)',
@@ -14164,7 +15132,6 @@ def _temp():
}
rest_dict['PspPI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<PspPPI>[AG]GG[AT]CC[CT])',
@@ -14187,7 +15154,6 @@ def _temp():
}
rest_dict['PspPPI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<PspPRI>CC[CT]CAG)|(?P<PspPRI_as>CTG[AG]GG)',
@@ -14210,7 +15176,6 @@ def _temp():
}
rest_dict['PspPRI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<PspXI>[ACG]CTCGAG[CGT])',
@@ -14233,7 +15198,6 @@ def _temp():
}
rest_dict['PspXI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<PsrI>GAAC......TAC)|(?P<PsrI_as>GTA......GTTC)',
@@ -14256,7 +15220,6 @@ def _temp():
}
rest_dict['PsrI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<PssI>[AG]GG.CC[CT])',
@@ -14279,6 +15242,27 @@ def _temp():
}
rest_dict['PssI'] = _temp()
+def _temp():
+ return {
+ 'compsite': '(?P<Pst14472I>C.[CT]ACAC)|(?P<Pst14472I_as>GTGT[AG].G)',
+ 'results': None,
+ 'site': 'CNYACAC',
+ 'substrat': 'DNA',
+ 'fst3': None,
+ 'fst5': None,
+ 'freq': 2048,
+ 'size': 7,
+ 'opt_temp': 37,
+ 'dna': None,
+ 'inact_temp': 65,
+ 'ovhg': None,
+ 'scd3': None,
+ 'suppl': (),
+ 'scd5': None,
+ 'charac': (None, None, None, None, 'CNYACAC'),
+ 'ovhgseq': None,
+ }
+rest_dict['Pst14472I'] = _temp()
def _temp():
return {
@@ -14295,14 +15279,13 @@ def _temp():
'inact_temp': 65,
'ovhg': 4,
'scd3': None,
- 'suppl': ('B', 'C', 'F', 'I', 'J', 'K', 'M', 'N', 'O', 'Q', 'R', 'S', 'U', 'V', 'X', 'Y'),
+ 'suppl': ('B', 'C', 'I', 'J', 'K', 'M', 'N', 'O', 'Q', 'R', 'S', 'V', 'X'),
'scd5': None,
'charac': (5, -5, None, None, 'CTGCAG'),
'ovhgseq': 'TGCA',
}
rest_dict['PstI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<PstNI>CAG...CTG)',
@@ -14325,6 +15308,27 @@ def _temp():
}
rest_dict['PstNI'] = _temp()
+def _temp():
+ return {
+ 'compsite': '(?P<PsuGI>[CGT][CGT]CG[AGT])|(?P<PsuGI_as>[ACT]CG[ACG][ACG])',
+ 'results': None,
+ 'site': 'BBCGD',
+ 'substrat': 'DNA',
+ 'fst3': None,
+ 'fst5': None,
+ 'freq': 16,
+ 'size': 5,
+ 'opt_temp': 37,
+ 'dna': None,
+ 'inact_temp': 65,
+ 'ovhg': None,
+ 'scd3': None,
+ 'suppl': (),
+ 'scd5': None,
+ 'charac': (None, None, None, None, 'BBCGD'),
+ 'ovhgseq': None,
+ }
+rest_dict['PsuGI'] = _temp()
def _temp():
return {
@@ -14341,14 +15345,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -4,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (1, -1, None, None, 'RGATCY'),
'ovhgseq': 'GATC',
}
rest_dict['PsuI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<PsyI>GAC...GTC)',
@@ -14364,14 +15367,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -1,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (4, -4, None, None, 'GACNNNGTC'),
'ovhgseq': 'N',
}
rest_dict['PsyI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<PteI>GCGCGC)',
@@ -14387,14 +15389,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -4,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (1, -1, None, None, 'GCGCGC'),
'ovhgseq': 'CGCG',
}
rest_dict['PteI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<PvuI>CGATCG)',
@@ -14410,14 +15411,13 @@ def _temp():
'inact_temp': 65,
'ovhg': 2,
'scd3': None,
- 'suppl': ('B', 'F', 'K', 'M', 'N', 'O', 'Q', 'R', 'S', 'U', 'X', 'Y'),
+ 'suppl': ('B', 'K', 'M', 'N', 'O', 'Q', 'R', 'S', 'X'),
'scd5': None,
'charac': (4, -4, None, None, 'CGATCG'),
'ovhgseq': 'AT',
}
rest_dict['PvuI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<PvuII>CAGCTG)',
@@ -14433,36 +15433,34 @@ def _temp():
'inact_temp': 65,
'ovhg': 0,
'scd3': None,
- 'suppl': ('B', 'C', 'F', 'I', 'J', 'K', 'M', 'N', 'O', 'Q', 'R', 'S', 'U', 'X', 'Y'),
+ 'suppl': ('B', 'C', 'I', 'J', 'K', 'M', 'N', 'O', 'Q', 'R', 'S', 'V', 'X'),
'scd5': None,
'charac': (3, -3, None, None, 'CAGCTG'),
'ovhgseq': '',
}
rest_dict['PvuII'] = _temp()
-
def _temp():
return {
- 'compsite': '(?P<R2_BceSIV>GCAGC)|(?P<R2_BceSIV_as>GCTGC)',
+ 'compsite': '(?P<Rba2021I>CACGAG[ACT])|(?P<Rba2021I_as>[AGT]CTCGTG)',
'results': None,
- 'site': 'GCAGC',
+ 'site': 'CACGAGH',
'substrat': 'DNA',
- 'fst3': -10,
- 'fst5': -7,
- 'freq': 1024,
- 'size': 5,
+ 'fst3': None,
+ 'fst5': None,
+ 'freq': 4096,
+ 'size': 7,
'opt_temp': 37,
'dna': None,
'inact_temp': 65,
- 'ovhg': -2,
- 'scd3': 11,
+ 'ovhg': None,
+ 'scd3': None,
'suppl': (),
- 'scd5': 14,
- 'charac': (-7, -10, 14, 11, 'GCAGC'),
- 'ovhgseq': 'NN',
+ 'scd5': None,
+ 'charac': (None, None, None, None, 'CACGAGH'),
+ 'ovhgseq': None,
}
-rest_dict['R2_BceSIV'] = _temp()
-
+rest_dict['Rba2021I'] = _temp()
def _temp():
return {
@@ -14486,7 +15484,6 @@ def _temp():
}
rest_dict['RceI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<RdeGBI>CCGCAG)|(?P<RdeGBI_as>CTGCGG)',
@@ -14509,7 +15506,6 @@ def _temp():
}
rest_dict['RdeGBI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<RdeGBII>ACCCAG)|(?P<RdeGBII_as>CTGGGT)',
@@ -14532,7 +15528,6 @@ def _temp():
}
rest_dict['RdeGBII'] = _temp()
-
def _temp():
return {
'compsite': '(?P<RdeGBIII>TG[AG][CT]CA)',
@@ -14555,7 +15550,6 @@ def _temp():
}
rest_dict['RdeGBIII'] = _temp()
-
def _temp():
return {
'compsite': '(?P<RflFIII>CGCCAG)|(?P<RflFIII_as>CTGGCG)',
@@ -14578,7 +15572,6 @@ def _temp():
}
rest_dict['RflFIII'] = _temp()
-
def _temp():
return {
'compsite': '(?P<RgaI>GCGATCGC)',
@@ -14601,7 +15594,6 @@ def _temp():
}
rest_dict['RgaI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<RigI>GGCCGGCC)',
@@ -14624,7 +15616,6 @@ def _temp():
}
rest_dict['RigI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<RlaI>[ACG]C[AT])|(?P<RlaI_as>[AT]G[CGT])',
@@ -14647,7 +15638,6 @@ def _temp():
}
rest_dict['RlaI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<RleAI>CCCACA)|(?P<RleAI_as>TGTGGG)',
@@ -14670,7 +15660,6 @@ def _temp():
}
rest_dict['RleAI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<RpaB5I>CG[AG]GGAC)|(?P<RpaB5I_as>GTCC[CT]CG)',
@@ -14693,7 +15682,6 @@ def _temp():
}
rest_dict['RpaB5I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<RpaBI>CCCGCAG)|(?P<RpaBI_as>CTGCGGG)',
@@ -14716,7 +15704,6 @@ def _temp():
}
rest_dict['RpaBI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<RpaI>GT[CT]GGAG)|(?P<RpaI_as>CTCC[AG]AC)',
@@ -14739,7 +15726,6 @@ def _temp():
}
rest_dict['RpaI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<RpaTI>G[AG]TGGAG)|(?P<RpaTI_as>CTCCA[CT]C)',
@@ -14762,7 +15748,6 @@ def _temp():
}
rest_dict['RpaTI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<RruI>TCGCGA)',
@@ -14778,14 +15763,13 @@ def _temp():
'inact_temp': 65,
'ovhg': 0,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (3, -3, None, None, 'TCGCGA'),
'ovhgseq': '',
}
rest_dict['RruI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<RsaI>GTAC)',
@@ -14801,14 +15785,13 @@ def _temp():
'inact_temp': 65,
'ovhg': 0,
'scd3': None,
- 'suppl': ('C', 'F', 'I', 'J', 'M', 'N', 'Q', 'R', 'S', 'V', 'X', 'Y'),
+ 'suppl': ('B', 'C', 'I', 'J', 'M', 'N', 'Q', 'R', 'S', 'V', 'X', 'Y'),
'scd5': None,
'charac': (2, -2, None, None, 'GTAC'),
'ovhgseq': '',
}
rest_dict['RsaI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<RsaNI>GTAC)',
@@ -14831,7 +15814,6 @@ def _temp():
}
rest_dict['RsaNI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<RseI>CA[CT]....[AG]TG)',
@@ -14847,14 +15829,13 @@ def _temp():
'inact_temp': 65,
'ovhg': 0,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (5, -5, None, None, 'CAYNNNNRTG'),
'ovhgseq': '',
}
rest_dict['RseI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Rsr2I>CGG[AT]CCG)',
@@ -14877,7 +15858,6 @@ def _temp():
}
rest_dict['Rsr2I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<RsrII>CGG[AT]CCG)',
@@ -14900,7 +15880,6 @@ def _temp():
}
rest_dict['RsrII'] = _temp()
-
def _temp():
return {
'compsite': '(?P<SacI>GAGCTC)',
@@ -14916,36 +15895,56 @@ def _temp():
'inact_temp': 65,
'ovhg': 4,
'scd3': None,
- 'suppl': ('B', 'F', 'J', 'K', 'M', 'N', 'O', 'Q', 'R', 'S', 'U', 'X'),
+ 'suppl': ('B', 'J', 'K', 'M', 'N', 'O', 'Q', 'R', 'S', 'X'),
'scd5': None,
'charac': (5, -5, None, None, 'GAGCTC'),
'ovhgseq': 'AGCT',
}
rest_dict['SacI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<SacII>CCGCGG)',
'results': None,
- 'site': 'CCGCGG',
+ 'site': 'CCGCGG',
+ 'substrat': 'DNA',
+ 'fst3': -4,
+ 'fst5': 4,
+ 'freq': 4096,
+ 'size': 6,
+ 'opt_temp': 37,
+ 'dna': None,
+ 'inact_temp': 65,
+ 'ovhg': 2,
+ 'scd3': None,
+ 'suppl': ('B', 'J', 'K', 'N', 'O', 'Q', 'R', 'X'),
+ 'scd5': None,
+ 'charac': (4, -4, None, None, 'CCGCGG'),
+ 'ovhgseq': 'GC',
+ }
+rest_dict['SacII'] = _temp()
+
+def _temp():
+ return {
+ 'compsite': '(?P<Saf8902III>CAAT.AG)|(?P<Saf8902III_as>CT.ATTG)',
+ 'results': None,
+ 'site': 'CAATNAG',
'substrat': 'DNA',
- 'fst3': -4,
- 'fst5': 4,
+ 'fst3': None,
+ 'fst5': None,
'freq': 4096,
- 'size': 6,
+ 'size': 7,
'opt_temp': 37,
'dna': None,
'inact_temp': 65,
- 'ovhg': 2,
+ 'ovhg': None,
'scd3': None,
- 'suppl': ('B', 'J', 'K', 'N', 'O', 'Q', 'R', 'X'),
+ 'suppl': (),
'scd5': None,
- 'charac': (4, -4, None, None, 'CCGCGG'),
- 'ovhgseq': 'GC',
+ 'charac': (None, None, None, None, 'CAATNAG'),
+ 'ovhgseq': None,
}
-rest_dict['SacII'] = _temp()
-
+rest_dict['Saf8902III'] = _temp()
def _temp():
return {
@@ -14962,14 +15961,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -4,
'scd3': None,
- 'suppl': ('B', 'C', 'F', 'I', 'J', 'K', 'M', 'N', 'O', 'Q', 'R', 'S', 'U', 'V', 'X', 'Y'),
+ 'suppl': ('B', 'C', 'I', 'J', 'K', 'M', 'N', 'O', 'Q', 'R', 'S', 'V', 'X'),
'scd5': None,
'charac': (1, -1, None, None, 'GTCGAC'),
'ovhgseq': 'TCGA',
}
rest_dict['SalI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<SanDI>GGG[AT]CCC)',
@@ -14992,7 +15990,6 @@ def _temp():
}
rest_dict['SanDI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<SapI>GCTCTTC)|(?P<SapI_as>GAAGAGC)',
@@ -15015,7 +16012,6 @@ def _temp():
}
rest_dict['SapI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<SaqAI>TTAA)',
@@ -15031,14 +16027,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -2,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (1, -1, None, None, 'TTAA'),
'ovhgseq': 'TA',
}
rest_dict['SaqAI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<SatI>GC.GC)',
@@ -15054,14 +16049,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -1,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (2, -2, None, None, 'GCNGC'),
'ovhgseq': 'N',
}
rest_dict['SatI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Sau3AI>GATC)',
@@ -15077,14 +16071,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -4,
'scd3': None,
- 'suppl': ('C', 'J', 'K', 'M', 'N', 'R', 'S', 'U'),
+ 'suppl': ('C', 'J', 'K', 'M', 'N', 'R', 'S'),
'scd5': None,
'charac': (0, 0, None, None, 'GATC'),
'ovhgseq': 'GATC',
}
rest_dict['Sau3AI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Sau96I>GG.CC)',
@@ -15100,14 +16093,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -3,
'scd3': None,
- 'suppl': ('J', 'N', 'U'),
+ 'suppl': ('J', 'N'),
'scd5': None,
'charac': (1, -1, None, None, 'GGNCC'),
'ovhgseq': 'GNC',
}
rest_dict['Sau96I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<SauI>CCT.AGG)',
@@ -15130,7 +16122,6 @@ def _temp():
}
rest_dict['SauI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<SbfI>CCTGCAGG)',
@@ -15153,6 +16144,27 @@ def _temp():
}
rest_dict['SbfI'] = _temp()
+def _temp():
+ return {
+ 'compsite': '(?P<Sbo46I>TGAAC)|(?P<Sbo46I_as>GTTCA)',
+ 'results': None,
+ 'site': 'TGAAC',
+ 'substrat': 'DNA',
+ 'fst3': None,
+ 'fst5': None,
+ 'freq': 1024,
+ 'size': 5,
+ 'opt_temp': 37,
+ 'dna': None,
+ 'inact_temp': 65,
+ 'ovhg': None,
+ 'scd3': None,
+ 'suppl': (),
+ 'scd5': None,
+ 'charac': (None, None, None, None, 'TGAAC'),
+ 'ovhgseq': None,
+ }
+rest_dict['Sbo46I'] = _temp()
def _temp():
return {
@@ -15169,14 +16181,13 @@ def _temp():
'inact_temp': 65,
'ovhg': 0,
'scd3': None,
- 'suppl': ('B', 'C', 'F', 'J', 'K', 'M', 'N', 'O', 'Q', 'R', 'S', 'X'),
+ 'suppl': ('B', 'C', 'J', 'K', 'M', 'N', 'O', 'Q', 'R', 'S', 'X'),
'scd5': None,
'charac': (3, -3, None, None, 'AGTACT'),
'ovhgseq': '',
}
rest_dict['ScaI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<SchI>GAGTC)|(?P<SchI_as>GACTC)',
@@ -15192,14 +16203,13 @@ def _temp():
'inact_temp': 65,
'ovhg': 0,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (10, 5, None, None, 'GAGTC'),
'ovhgseq': '',
}
rest_dict['SchI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<SciI>CTCGAG)',
@@ -15222,7 +16232,6 @@ def _temp():
}
rest_dict['SciI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<ScrFI>CC.GG)',
@@ -15245,7 +16254,6 @@ def _temp():
}
rest_dict['ScrFI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<SdaI>CCTGCAGG)',
@@ -15261,14 +16269,13 @@ def _temp():
'inact_temp': 65,
'ovhg': 4,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (6, -6, None, None, 'CCTGCAGG'),
'ovhgseq': 'TGCA',
}
rest_dict['SdaI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<SdeAI>CAG[AG]AG)|(?P<SdeAI_as>CT[CT]CTG)',
@@ -15291,7 +16298,6 @@ def _temp():
}
rest_dict['SdeAI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<SdeOSI>GAC....[AG]TGA)|(?P<SdeOSI_as>TCA[CT]....GTC)',
@@ -15314,7 +16320,6 @@ def _temp():
}
rest_dict['SdeOSI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<SduI>G[AGT]GC[ACT]C)',
@@ -15330,14 +16335,13 @@ def _temp():
'inact_temp': 65,
'ovhg': 4,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (5, -5, None, None, 'GDGCHC'),
'ovhgseq': 'DGCH',
}
rest_dict['SduI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<SecI>CC..GG)',
@@ -15360,7 +16364,6 @@ def _temp():
}
rest_dict['SecI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<SelI>CGCG)',
@@ -15383,6 +16386,71 @@ def _temp():
}
rest_dict['SelI'] = _temp()
+def _temp():
+ return {
+ 'compsite': '(?P<Sen13311III>GATCAG)|(?P<Sen13311III_as>CTGATC)',
+ 'results': None,
+ 'site': 'GATCAG',
+ 'substrat': 'DNA',
+ 'fst3': None,
+ 'fst5': None,
+ 'freq': 4096,
+ 'size': 6,
+ 'opt_temp': 37,
+ 'dna': None,
+ 'inact_temp': 65,
+ 'ovhg': None,
+ 'scd3': None,
+ 'suppl': (),
+ 'scd5': None,
+ 'charac': (None, None, None, None, 'GATCAG'),
+ 'ovhgseq': None,
+ }
+rest_dict['Sen13311III'] = _temp()
+
+def _temp():
+ return {
+ 'compsite': '(?P<Sen1736II>GATCAG)|(?P<Sen1736II_as>CTGATC)',
+ 'results': None,
+ 'site': 'GATCAG',
+ 'substrat': 'DNA',
+ 'fst3': None,
+ 'fst5': None,
+ 'freq': 4096,
+ 'size': 6,
+ 'opt_temp': 37,
+ 'dna': None,
+ 'inact_temp': 65,
+ 'ovhg': None,
+ 'scd3': None,
+ 'suppl': (),
+ 'scd5': None,
+ 'charac': (None, None, None, None, 'GATCAG'),
+ 'ovhgseq': None,
+ }
+rest_dict['Sen1736II'] = _temp()
+
+def _temp():
+ return {
+ 'compsite': '(?P<SenTFIV>GATCAG)|(?P<SenTFIV_as>CTGATC)',
+ 'results': None,
+ 'site': 'GATCAG',
+ 'substrat': 'DNA',
+ 'fst3': None,
+ 'fst5': None,
+ 'freq': 4096,
+ 'size': 6,
+ 'opt_temp': 37,
+ 'dna': None,
+ 'inact_temp': 65,
+ 'ovhg': None,
+ 'scd3': None,
+ 'suppl': (),
+ 'scd5': None,
+ 'charac': (None, None, None, None, 'GATCAG'),
+ 'ovhgseq': None,
+ }
+rest_dict['SenTFIV'] = _temp()
def _temp():
return {
@@ -15406,7 +16474,6 @@ def _temp():
}
rest_dict['SetI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<SexAI>ACC[AT]GGT)',
@@ -15429,7 +16496,6 @@ def _temp():
}
rest_dict['SexAI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<SfaAI>GCGATCGC)',
@@ -15445,14 +16511,13 @@ def _temp():
'inact_temp': 65,
'ovhg': 2,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (5, -5, None, None, 'GCGATCGC'),
'ovhgseq': 'AT',
}
rest_dict['SfaAI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<SfaNI>GCATC)|(?P<SfaNI_as>GATGC)',
@@ -15475,7 +16540,6 @@ def _temp():
}
rest_dict['SfaNI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<SfcI>CT[AG][CT]AG)',
@@ -15498,7 +16562,6 @@ def _temp():
}
rest_dict['SfcI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<SfeI>CT[AG][CT]AG)',
@@ -15521,7 +16584,6 @@ def _temp():
}
rest_dict['SfeI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<SfiI>GGCC.....GGCC)',
@@ -15537,14 +16599,13 @@ def _temp():
'inact_temp': 65,
'ovhg': 3,
'scd3': None,
- 'suppl': ('C', 'F', 'I', 'J', 'K', 'M', 'N', 'O', 'Q', 'R', 'S', 'U', 'V', 'X'),
+ 'suppl': ('B', 'C', 'I', 'J', 'K', 'M', 'N', 'O', 'Q', 'R', 'S', 'V', 'X'),
'scd5': None,
'charac': (8, -8, None, None, 'GGCCNNNNNGGCC'),
'ovhgseq': 'NNN',
}
rest_dict['SfiI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<SfoI>GGCGCC)',
@@ -15567,7 +16628,6 @@ def _temp():
}
rest_dict['SfoI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Sfr274I>CTCGAG)',
@@ -15590,7 +16650,6 @@ def _temp():
}
rest_dict['Sfr274I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Sfr303I>CCGCGG)',
@@ -15613,7 +16672,6 @@ def _temp():
}
rest_dict['Sfr303I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<SfuI>TTCGAA)',
@@ -15636,7 +16694,6 @@ def _temp():
}
rest_dict['SfuI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<SgeI>C..G)',
@@ -15652,14 +16709,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -4,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (13, 13, None, None, 'CNNG'),
'ovhgseq': 'NNNN',
}
rest_dict['SgeI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<SgfI>GCGATCGC)',
@@ -15682,7 +16738,6 @@ def _temp():
}
rest_dict['SgfI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<SgrAI>C[AG]CCGG[CT]G)',
@@ -15705,7 +16760,6 @@ def _temp():
}
rest_dict['SgrAI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<SgrBI>CCGCGG)',
@@ -15728,7 +16782,6 @@ def _temp():
}
rest_dict['SgrBI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<SgrDI>CGTCGACG)',
@@ -15744,14 +16797,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -4,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (2, -2, None, None, 'CGTCGACG'),
'ovhgseq': 'TCGA',
}
rest_dict['SgrDI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<SgrTI>CC[AGT][CG])|(?P<SgrTI_as>[CG][ACT]GG)',
@@ -15774,7 +16826,6 @@ def _temp():
}
rest_dict['SgrTI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<SgsI>GGCGCGCC)',
@@ -15790,14 +16841,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -4,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (2, -2, None, None, 'GGCGCGCC'),
'ovhgseq': 'CGCG',
}
rest_dict['SgsI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<SimI>GGGTC)|(?P<SimI_as>GACCC)',
@@ -15820,6 +16870,27 @@ def _temp():
}
rest_dict['SimI'] = _temp()
+def _temp():
+ return {
+ 'compsite': '(?P<SinI>GG[AT]CC)',
+ 'results': None,
+ 'site': 'GGWCC',
+ 'substrat': 'DNA',
+ 'fst3': -1,
+ 'fst5': 1,
+ 'freq': 512,
+ 'size': 5,
+ 'opt_temp': 37,
+ 'dna': None,
+ 'inact_temp': 65,
+ 'ovhg': -3,
+ 'scd3': None,
+ 'suppl': ('X',),
+ 'scd5': None,
+ 'charac': (1, -1, None, None, 'GGWCC'),
+ 'ovhgseq': 'GWC',
+ }
+rest_dict['SinI'] = _temp()
def _temp():
return {
@@ -15836,14 +16907,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -4,
'scd3': None,
- 'suppl': ('C',),
+ 'suppl': ('C', 'Y'),
'scd5': None,
'charac': (1, -1, None, None, 'CTCGAG'),
'ovhgseq': 'TCGA',
}
rest_dict['SlaI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<SmaI>CCCGGG)',
@@ -15859,14 +16929,13 @@ def _temp():
'inact_temp': 65,
'ovhg': 0,
'scd3': None,
- 'suppl': ('B', 'C', 'F', 'I', 'J', 'K', 'M', 'N', 'O', 'Q', 'R', 'S', 'U', 'V', 'X', 'Y'),
+ 'suppl': ('B', 'C', 'I', 'J', 'K', 'M', 'N', 'O', 'Q', 'R', 'S', 'V', 'X', 'Y'),
'scd5': None,
'charac': (3, -3, None, None, 'CCCGGG'),
'ovhgseq': '',
}
rest_dict['SmaI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<SmiI>ATTTAAAT)',
@@ -15882,14 +16951,13 @@ def _temp():
'inact_temp': 65,
'ovhg': 0,
'scd3': None,
- 'suppl': ('F', 'I', 'K', 'V'),
+ 'suppl': ('B', 'I', 'K', 'V'),
'scd5': None,
'charac': (4, -4, None, None, 'ATTTAAAT'),
'ovhgseq': '',
}
rest_dict['SmiI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<SmiMI>CA[CT]....[AG]TG)',
@@ -15912,7 +16980,6 @@ def _temp():
}
rest_dict['SmiMI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<SmlI>CT[CT][AG]AG)',
@@ -15935,7 +17002,6 @@ def _temp():
}
rest_dict['SmlI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<SmoI>CT[CT][AG]AG)',
@@ -15951,14 +17017,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -4,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (1, -1, None, None, 'CTYRAG'),
'ovhgseq': 'TYRA',
}
rest_dict['SmoI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<SnaBI>TACGTA)',
@@ -15974,14 +17039,13 @@ def _temp():
'inact_temp': 65,
'ovhg': 0,
'scd3': None,
- 'suppl': ('C', 'K', 'M', 'N', 'R', 'U'),
+ 'suppl': ('C', 'K', 'M', 'N', 'R'),
'scd5': None,
'charac': (3, -3, None, None, 'TACGTA'),
'ovhgseq': '',
}
rest_dict['SnaBI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<SnaI>GTATAC)',
@@ -16004,7 +17068,6 @@ def _temp():
}
rest_dict['SnaI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Sno506I>GGCCGAG)|(?P<Sno506I_as>CTCGGCC)',
@@ -16027,7 +17090,6 @@ def _temp():
}
rest_dict['Sno506I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<SpeI>ACTAGT)',
@@ -16043,14 +17105,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -4,
'scd3': None,
- 'suppl': ('B', 'J', 'K', 'M', 'N', 'O', 'Q', 'R', 'S', 'U', 'X'),
+ 'suppl': ('B', 'J', 'K', 'M', 'N', 'O', 'Q', 'R', 'S', 'X'),
'scd5': None,
'charac': (1, -1, None, None, 'ACTAGT'),
'ovhgseq': 'CTAG',
}
rest_dict['SpeI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<SphI>GCATGC)',
@@ -16073,7 +17134,6 @@ def _temp():
}
rest_dict['SphI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<SplI>CGTACG)',
@@ -16096,6 +17156,27 @@ def _temp():
}
rest_dict['SplI'] = _temp()
+def _temp():
+ return {
+ 'compsite': '(?P<SpnRII>TCGAG)|(?P<SpnRII_as>CTCGA)',
+ 'results': None,
+ 'site': 'TCGAG',
+ 'substrat': 'DNA',
+ 'fst3': None,
+ 'fst5': None,
+ 'freq': 1024,
+ 'size': 5,
+ 'opt_temp': 37,
+ 'dna': None,
+ 'inact_temp': 65,
+ 'ovhg': None,
+ 'scd3': None,
+ 'suppl': (),
+ 'scd5': None,
+ 'charac': (None, None, None, None, 'TCGAG'),
+ 'ovhgseq': None,
+ }
+rest_dict['SpnRII'] = _temp()
def _temp():
return {
@@ -16119,7 +17200,6 @@ def _temp():
}
rest_dict['SpoDI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<SrfI>GCCCGGGC)',
@@ -16135,14 +17215,13 @@ def _temp():
'inact_temp': 65,
'ovhg': 0,
'scd3': None,
- 'suppl': (),
+ 'suppl': ('N',),
'scd5': None,
'charac': (4, -4, None, None, 'GCCCGGGC'),
'ovhgseq': '',
}
rest_dict['SrfI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Sse232I>CGCCGGCG)',
@@ -16165,7 +17244,6 @@ def _temp():
}
rest_dict['Sse232I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Sse8387I>CCTGCAGG)',
@@ -16188,7 +17266,6 @@ def _temp():
}
rest_dict['Sse8387I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Sse8647I>AGG[AT]CCT)',
@@ -16211,7 +17288,6 @@ def _temp():
}
rest_dict['Sse8647I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Sse9I>AATT)',
@@ -16234,7 +17310,6 @@ def _temp():
}
rest_dict['Sse9I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<SseBI>AGGCCT)',
@@ -16257,7 +17332,6 @@ def _temp():
}
rest_dict['SseBI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<SsiI>CCGC)|(?P<SsiI_as>GCGG)',
@@ -16273,13 +17347,56 @@ def _temp():
'inact_temp': 65,
'ovhg': -2,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (1, -1, None, None, 'CCGC'),
'ovhgseq': 'CG',
}
rest_dict['SsiI'] = _temp()
+def _temp():
+ return {
+ 'compsite': '(?P<Ssp6803IV>GAAGGC)|(?P<Ssp6803IV_as>GCCTTC)',
+ 'results': None,
+ 'site': 'GAAGGC',
+ 'substrat': 'DNA',
+ 'fst3': None,
+ 'fst5': None,
+ 'freq': 4096,
+ 'size': 6,
+ 'opt_temp': 37,
+ 'dna': None,
+ 'inact_temp': 65,
+ 'ovhg': None,
+ 'scd3': None,
+ 'suppl': (),
+ 'scd5': None,
+ 'charac': (None, None, None, None, 'GAAGGC'),
+ 'ovhgseq': None,
+ }
+rest_dict['Ssp6803IV'] = _temp()
+
+def _temp():
+ return {
+ 'compsite': '(?P<Ssp714II>CGCAGCG)|(?P<Ssp714II_as>CGCTGCG)',
+ 'results': None,
+ 'site': 'CGCAGCG',
+ 'substrat': 'DNA',
+ 'fst3': None,
+ 'fst5': None,
+ 'freq': 16384,
+ 'size': 7,
+ 'opt_temp': 37,
+ 'dna': None,
+ 'inact_temp': 65,
+ 'ovhg': None,
+ 'scd3': None,
+ 'suppl': (),
+ 'scd5': None,
+ 'charac': (None, None, None, None, 'CGCAGCG'),
+ 'ovhgseq': None,
+ }
+rest_dict['Ssp714II'] = _temp()
def _temp():
return {
@@ -16303,7 +17420,6 @@ def _temp():
}
rest_dict['SspD5I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<SspDI>GGCGCC)',
@@ -16319,14 +17435,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -4,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (1, -1, None, None, 'GGCGCC'),
'ovhgseq': 'GCGC',
}
rest_dict['SspDI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<SspI>AATATT)',
@@ -16342,13 +17457,34 @@ def _temp():
'inact_temp': 65,
'ovhg': 0,
'scd3': None,
- 'suppl': ('B', 'C', 'F', 'I', 'J', 'K', 'M', 'N', 'Q', 'R', 'S', 'U', 'V', 'X'),
+ 'suppl': ('B', 'C', 'I', 'J', 'K', 'N', 'Q', 'R', 'V', 'X'),
'scd5': None,
'charac': (3, -3, None, None, 'AATATT'),
'ovhgseq': '',
}
rest_dict['SspI'] = _temp()
+def _temp():
+ return {
+ 'compsite': '(?P<SspMI>CTAG)',
+ 'results': None,
+ 'site': 'CTAG',
+ 'substrat': 'DNA',
+ 'fst3': -1,
+ 'fst5': 1,
+ 'freq': 256,
+ 'size': 4,
+ 'opt_temp': 37,
+ 'dna': None,
+ 'inact_temp': 65,
+ 'ovhg': -2,
+ 'scd3': None,
+ 'suppl': ('I',),
+ 'scd5': None,
+ 'charac': (1, -1, None, None, 'CTAG'),
+ 'ovhgseq': 'TA',
+ }
+rest_dict['SspMI'] = _temp()
def _temp():
return {
@@ -16372,7 +17508,6 @@ def _temp():
}
rest_dict['SstE37I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<SstI>GAGCTC)',
@@ -16395,7 +17530,6 @@ def _temp():
}
rest_dict['SstI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Sth132I>CCCG)|(?P<Sth132I_as>CGGG)',
@@ -16409,15 +17543,36 @@ def _temp():
'opt_temp': 37,
'dna': None,
'inact_temp': 65,
- 'ovhg': -4,
+ 'ovhg': -4,
+ 'scd3': None,
+ 'suppl': (),
+ 'scd5': None,
+ 'charac': (8, 8, None, None, 'CCCG'),
+ 'ovhgseq': 'NNNN',
+ }
+rest_dict['Sth132I'] = _temp()
+
+def _temp():
+ return {
+ 'compsite': '(?P<Sth20745III>GGACGAC)|(?P<Sth20745III_as>GTCGTCC)',
+ 'results': None,
+ 'site': 'GGACGAC',
+ 'substrat': 'DNA',
+ 'fst3': None,
+ 'fst5': None,
+ 'freq': 16384,
+ 'size': 7,
+ 'opt_temp': 37,
+ 'dna': None,
+ 'inact_temp': 65,
+ 'ovhg': None,
'scd3': None,
'suppl': (),
'scd5': None,
- 'charac': (8, 8, None, None, 'CCCG'),
- 'ovhgseq': 'NNNN',
+ 'charac': (None, None, None, None, 'GGACGAC'),
+ 'ovhgseq': None,
}
-rest_dict['Sth132I'] = _temp()
-
+rest_dict['Sth20745III'] = _temp()
def _temp():
return {
@@ -16441,30 +17596,6 @@ def _temp():
}
rest_dict['Sth302II'] = _temp()
-
-def _temp():
- return {
- 'compsite': '(?P<StrI>CTCGAG)',
- 'results': None,
- 'site': 'CTCGAG',
- 'substrat': 'DNA',
- 'fst3': -1,
- 'fst5': 1,
- 'freq': 4096,
- 'size': 6,
- 'opt_temp': 37,
- 'dna': None,
- 'inact_temp': 65,
- 'ovhg': -4,
- 'scd3': None,
- 'suppl': ('U',),
- 'scd5': None,
- 'charac': (1, -1, None, None, 'CTCGAG'),
- 'ovhgseq': 'TCGA',
- }
-rest_dict['StrI'] = _temp()
-
-
def _temp():
return {
'compsite': '(?P<StsI>GGATG)|(?P<StsI_as>CATCC)',
@@ -16487,7 +17618,6 @@ def _temp():
}
rest_dict['StsI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<StuI>AGGCCT)',
@@ -16503,13 +17633,34 @@ def _temp():
'inact_temp': 65,
'ovhg': 0,
'scd3': None,
- 'suppl': ('B', 'J', 'K', 'M', 'N', 'Q', 'R', 'U', 'X'),
+ 'suppl': ('B', 'J', 'K', 'M', 'N', 'Q', 'R', 'X'),
'scd5': None,
'charac': (3, -3, None, None, 'AGGCCT'),
'ovhgseq': '',
}
rest_dict['StuI'] = _temp()
+def _temp():
+ return {
+ 'compsite': '(?P<Sty13348III>GATCAG)|(?P<Sty13348III_as>CTGATC)',
+ 'results': None,
+ 'site': 'GATCAG',
+ 'substrat': 'DNA',
+ 'fst3': None,
+ 'fst5': None,
+ 'freq': 4096,
+ 'size': 6,
+ 'opt_temp': 37,
+ 'dna': None,
+ 'inact_temp': 65,
+ 'ovhg': None,
+ 'scd3': None,
+ 'suppl': (),
+ 'scd5': None,
+ 'charac': (None, None, None, None, 'GATCAG'),
+ 'ovhgseq': None,
+ }
+rest_dict['Sty13348III'] = _temp()
def _temp():
return {
@@ -16533,7 +17684,6 @@ def _temp():
}
rest_dict['StyD4I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<StyI>CC[AT][AT]GG)',
@@ -16556,7 +17706,6 @@ def _temp():
}
rest_dict['StyI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<SwaI>ATTTAAAT)',
@@ -16579,7 +17728,6 @@ def _temp():
}
rest_dict['SwaI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<TaaI>AC.GT)',
@@ -16595,14 +17743,13 @@ def _temp():
'inact_temp': 65,
'ovhg': 1,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (3, -3, None, None, 'ACNGT'),
'ovhgseq': 'N',
}
rest_dict['TaaI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<TaiI>ACGT)',
@@ -16618,14 +17765,13 @@ def _temp():
'inact_temp': 65,
'ovhg': 4,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (4, -4, None, None, 'ACGT'),
'ovhgseq': 'ACGT',
}
rest_dict['TaiI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<TaqI>TCGA)',
@@ -16641,14 +17787,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -2,
'scd3': None,
- 'suppl': ('B', 'C', 'F', 'I', 'J', 'K', 'M', 'N', 'Q', 'R', 'S', 'U', 'V', 'X', 'Y'),
+ 'suppl': ('B', 'C', 'I', 'J', 'K', 'M', 'N', 'Q', 'R', 'S', 'V', 'X'),
'scd5': None,
'charac': (1, -1, None, None, 'TCGA'),
'ovhgseq': 'CG',
}
rest_dict['TaqI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<TaqII>GACCGA)|(?P<TaqII_as>TCGGTC)',
@@ -16671,6 +17816,27 @@ def _temp():
}
rest_dict['TaqII'] = _temp()
+def _temp():
+ return {
+ 'compsite': '(?P<TaqIII>CACCCA)|(?P<TaqIII_as>TGGGTG)',
+ 'results': None,
+ 'site': 'CACCCA',
+ 'substrat': 'DNA',
+ 'fst3': 9,
+ 'fst5': 17,
+ 'freq': 4096,
+ 'size': 6,
+ 'opt_temp': 37,
+ 'dna': None,
+ 'inact_temp': 65,
+ 'ovhg': 2,
+ 'scd3': None,
+ 'suppl': (),
+ 'scd5': None,
+ 'charac': (17, 9, None, None, 'CACCCA'),
+ 'ovhgseq': 'NN',
+ }
+rest_dict['TaqIII'] = _temp()
def _temp():
return {
@@ -16687,14 +17853,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -4,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (0, 0, None, None, 'AATT'),
'ovhgseq': 'AATT',
}
rest_dict['TasI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<TatI>[AT]GTAC[AT])',
@@ -16710,14 +17875,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -4,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (1, -1, None, None, 'WGTACW'),
'ovhgseq': 'GTAC',
}
rest_dict['TatI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<TauI>GC[CG]GC)',
@@ -16733,14 +17897,13 @@ def _temp():
'inact_temp': 65,
'ovhg': 3,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (4, -4, None, None, 'GCSGC'),
'ovhgseq': 'CSG',
}
rest_dict['TauI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<TfiI>GA[AT]TC)',
@@ -16763,7 +17926,6 @@ def _temp():
}
rest_dict['TfiI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Tru1I>TTAA)',
@@ -16779,14 +17941,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -2,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (1, -1, None, None, 'TTAA'),
'ovhgseq': 'TA',
}
rest_dict['Tru1I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Tru9I>TTAA)',
@@ -16809,7 +17970,6 @@ def _temp():
}
rest_dict['Tru9I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<TscAI>CA[CG]TG)',
@@ -16825,14 +17985,13 @@ def _temp():
'inact_temp': 65,
'ovhg': 10,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (7, -7, None, None, 'CASTG'),
'ovhgseq': 'NNCASTGNN',
}
rest_dict['TscAI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<TseFI>GT[CG]AC)',
@@ -16855,7 +18014,6 @@ def _temp():
}
rest_dict['TseFI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<TseI>GC[AT]GC)',
@@ -16878,7 +18036,6 @@ def _temp():
}
rest_dict['TseI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<TsoI>TA[AG]CCA)|(?P<TsoI_as>TGG[CT]TA)',
@@ -16901,7 +18058,6 @@ def _temp():
}
rest_dict['TsoI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Tsp45I>GT[CG]AC)',
@@ -16924,7 +18080,6 @@ def _temp():
}
rest_dict['Tsp45I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Tsp4CI>AC.GT)',
@@ -16947,6 +18102,27 @@ def _temp():
}
rest_dict['Tsp4CI'] = _temp()
+def _temp():
+ return {
+ 'compsite': '(?P<TspARh3I>G[AG]ACGAC)|(?P<TspARh3I_as>GTCGT[CT]C)',
+ 'results': None,
+ 'site': 'GRACGAC',
+ 'substrat': 'DNA',
+ 'fst3': None,
+ 'fst5': None,
+ 'freq': 8192,
+ 'size': 7,
+ 'opt_temp': 37,
+ 'dna': None,
+ 'inact_temp': 65,
+ 'ovhg': None,
+ 'scd3': None,
+ 'suppl': (),
+ 'scd5': None,
+ 'charac': (None, None, None, None, 'GRACGAC'),
+ 'ovhgseq': None,
+ }
+rest_dict['TspARh3I'] = _temp()
def _temp():
return {
@@ -16970,7 +18146,6 @@ def _temp():
}
rest_dict['TspDTI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<TspEI>AATT)',
@@ -16993,7 +18168,6 @@ def _temp():
}
rest_dict['TspEI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<TspGWI>ACGGA)|(?P<TspGWI_as>TCCGT)',
@@ -17016,7 +18190,6 @@ def _temp():
}
rest_dict['TspGWI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<TspMI>CCCGGG)',
@@ -17039,7 +18212,6 @@ def _temp():
}
rest_dict['TspMI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<TspRI>CA[CG]TG)',
@@ -17062,7 +18234,6 @@ def _temp():
}
rest_dict['TspRI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<TssI>GAG...CTC)',
@@ -17085,7 +18256,6 @@ def _temp():
}
rest_dict['TssI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<TstI>CAC......TCC)|(?P<TstI_as>GGA......GTG)',
@@ -17108,7 +18278,6 @@ def _temp():
}
rest_dict['TstI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<TsuI>GCGAC)|(?P<TsuI_as>GTCGC)',
@@ -17131,7 +18300,6 @@ def _temp():
}
rest_dict['TsuI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Tth111I>GAC...GTC)',
@@ -17154,7 +18322,6 @@ def _temp():
}
rest_dict['Tth111I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Tth111II>CAA[AG]CA)|(?P<Tth111II_as>TG[CT]TTG)',
@@ -17177,7 +18344,6 @@ def _temp():
}
rest_dict['Tth111II'] = _temp()
-
def _temp():
return {
'compsite': '(?P<UbaF11I>TCGTA)|(?P<UbaF11I_as>TACGA)',
@@ -17200,7 +18366,6 @@ def _temp():
}
rest_dict['UbaF11I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<UbaF12I>CTAC...GTC)|(?P<UbaF12I_as>GAC...GTAG)',
@@ -17223,7 +18388,6 @@ def _temp():
}
rest_dict['UbaF12I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<UbaF13I>GAG......CTGG)|(?P<UbaF13I_as>CCAG......CTC)',
@@ -17246,7 +18410,6 @@ def _temp():
}
rest_dict['UbaF13I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<UbaF14I>CCA.....TCG)|(?P<UbaF14I_as>CGA.....TGG)',
@@ -17269,7 +18432,6 @@ def _temp():
}
rest_dict['UbaF14I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<UbaF9I>TAC.....[AG]TGT)|(?P<UbaF9I_as>ACA[CT].....GTA)',
@@ -17292,7 +18454,6 @@ def _temp():
}
rest_dict['UbaF9I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<UbaPI>CGAACG)|(?P<UbaPI_as>CGTTCG)',
@@ -17315,7 +18476,6 @@ def _temp():
}
rest_dict['UbaPI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<UcoMSI>GAGCTC)',
@@ -17338,7 +18498,6 @@ def _temp():
}
rest_dict['UcoMSI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<UnbI>GG.CC)',
@@ -17361,7 +18520,6 @@ def _temp():
}
rest_dict['UnbI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Van91I>CCA.....TGG)',
@@ -17377,14 +18535,13 @@ def _temp():
'inact_temp': 65,
'ovhg': 3,
'scd3': None,
- 'suppl': ('F', 'K'),
+ 'suppl': ('B', 'K'),
'scd5': None,
'charac': (7, -7, None, None, 'CCANNNNNTGG'),
'ovhgseq': 'NNN',
}
rest_dict['Van91I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Vha464I>CTTAAG)',
@@ -17407,7 +18564,6 @@ def _temp():
}
rest_dict['Vha464I'] = _temp()
-
def _temp():
return {
'compsite': '(?P<VneI>GTGCAC)',
@@ -17430,7 +18586,6 @@ def _temp():
}
rest_dict['VneI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<VpaK11AI>GG[AT]CC)',
@@ -17453,7 +18608,6 @@ def _temp():
}
rest_dict['VpaK11AI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<VpaK11BI>GG[AT]CC)',
@@ -17476,7 +18630,6 @@ def _temp():
}
rest_dict['VpaK11BI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<VspI>ATTAAT)',
@@ -17492,13 +18645,34 @@ def _temp():
'inact_temp': 65,
'ovhg': -2,
'scd3': None,
- 'suppl': ('F', 'I', 'R', 'V'),
+ 'suppl': ('B', 'I', 'R', 'V'),
'scd5': None,
'charac': (2, -2, None, None, 'ATTAAT'),
'ovhgseq': 'TA',
}
rest_dict['VspI'] = _temp()
+def _temp():
+ return {
+ 'compsite': '(?P<Vtu19109I>CAC[AG]A[CT]C)|(?P<Vtu19109I_as>G[AG]T[CT]GTG)',
+ 'results': None,
+ 'site': 'CACRAYC',
+ 'substrat': 'DNA',
+ 'fst3': None,
+ 'fst5': None,
+ 'freq': 4096,
+ 'size': 7,
+ 'opt_temp': 37,
+ 'dna': None,
+ 'inact_temp': 65,
+ 'ovhg': None,
+ 'scd3': None,
+ 'suppl': (),
+ 'scd5': None,
+ 'charac': (None, None, None, None, 'CACRAYC'),
+ 'ovhgseq': None,
+ }
+rest_dict['Vtu19109I'] = _temp()
def _temp():
return {
@@ -17522,7 +18696,6 @@ def _temp():
}
rest_dict['WviI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<XagI>CCT.....AGG)',
@@ -17538,14 +18711,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -1,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (5, -5, None, None, 'CCTNNNNNAGG'),
'ovhgseq': 'N',
}
rest_dict['XagI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<XapI>[AG]AATT[CT])',
@@ -17561,14 +18733,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -4,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (1, -1, None, None, 'RAATTY'),
'ovhgseq': 'AATT',
}
rest_dict['XapI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<XbaI>TCTAGA)',
@@ -17584,14 +18755,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -4,
'scd3': None,
- 'suppl': ('B', 'C', 'F', 'I', 'J', 'K', 'M', 'N', 'O', 'Q', 'R', 'S', 'U', 'V', 'X', 'Y'),
+ 'suppl': ('B', 'C', 'I', 'J', 'K', 'M', 'N', 'Q', 'R', 'S', 'V', 'X'),
'scd5': None,
'charac': (1, -1, None, None, 'TCTAGA'),
'ovhgseq': 'CTAG',
}
rest_dict['XbaI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<XceI>[AG]CATG[CT])',
@@ -17607,14 +18777,13 @@ def _temp():
'inact_temp': 65,
'ovhg': 4,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (5, -5, None, None, 'RCATGY'),
'ovhgseq': 'CATG',
}
rest_dict['XceI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<XcmI>CCA.........TGG)',
@@ -17637,7 +18806,6 @@ def _temp():
}
rest_dict['XcmI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<XhoI>CTCGAG)',
@@ -17653,14 +18821,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -4,
'scd3': None,
- 'suppl': ('B', 'F', 'J', 'K', 'M', 'N', 'O', 'Q', 'R', 'S', 'U', 'X', 'Y'),
+ 'suppl': ('B', 'J', 'K', 'M', 'N', 'O', 'Q', 'R', 'S', 'X'),
'scd5': None,
'charac': (1, -1, None, None, 'CTCGAG'),
'ovhgseq': 'TCGA',
}
rest_dict['XhoI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<XhoII>[AG]GATC[CT])',
@@ -17676,14 +18843,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -4,
'scd3': None,
- 'suppl': ('R',),
+ 'suppl': (),
'scd5': None,
'charac': (1, -1, None, None, 'RGATCY'),
'ovhgseq': 'GATC',
}
rest_dict['XhoII'] = _temp()
-
def _temp():
return {
'compsite': '(?P<XmaI>CCCGGG)',
@@ -17699,14 +18865,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -4,
'scd3': None,
- 'suppl': ('I', 'N', 'R', 'U', 'V'),
+ 'suppl': ('I', 'N', 'R', 'V'),
'scd5': None,
'charac': (1, -1, None, None, 'CCCGGG'),
'ovhgseq': 'CCGG',
}
rest_dict['XmaI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<XmaIII>CGGCCG)',
@@ -17729,7 +18894,6 @@ def _temp():
}
rest_dict['XmaIII'] = _temp()
-
def _temp():
return {
'compsite': '(?P<XmaJI>CCTAGG)',
@@ -17745,14 +18909,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -4,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (1, -1, None, None, 'CCTAGG'),
'ovhgseq': 'CTAG',
}
rest_dict['XmaJI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<XmiI>GT[AC][GT]AC)',
@@ -17768,14 +18931,13 @@ def _temp():
'inact_temp': 65,
'ovhg': -2,
'scd3': None,
- 'suppl': ('F',),
+ 'suppl': ('B',),
'scd5': None,
'charac': (2, -2, None, None, 'GTMKAC'),
'ovhgseq': 'MK',
}
rest_dict['XmiI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<XmnI>GAA....TTC)',
@@ -17791,14 +18953,13 @@ def _temp():
'inact_temp': 65,
'ovhg': 0,
'scd3': None,
- 'suppl': ('N', 'R', 'U'),
+ 'suppl': ('N', 'R'),
'scd5': None,
'charac': (5, -5, None, None, 'GAANNNNTTC'),
'ovhgseq': '',
}
rest_dict['XmnI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<XspI>CTAG)',
@@ -17821,7 +18982,6 @@ def _temp():
}
rest_dict['XspI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<YkrI>C)|(?P<YkrI_as>G)',
@@ -17844,6 +19004,27 @@ def _temp():
}
rest_dict['YkrI'] = _temp()
+def _temp():
+ return {
+ 'compsite': '(?P<Yps3606I>CGGAAG)|(?P<Yps3606I_as>CTTCCG)',
+ 'results': None,
+ 'site': 'CGGAAG',
+ 'substrat': 'DNA',
+ 'fst3': None,
+ 'fst5': None,
+ 'freq': 4096,
+ 'size': 6,
+ 'opt_temp': 37,
+ 'dna': None,
+ 'inact_temp': 65,
+ 'ovhg': None,
+ 'scd3': None,
+ 'suppl': (),
+ 'scd5': None,
+ 'charac': (None, None, None, None, 'CGGAAG'),
+ 'ovhgseq': None,
+ }
+rest_dict['Yps3606I'] = _temp()
def _temp():
return {
@@ -17867,7 +19048,6 @@ def _temp():
}
rest_dict['ZraI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<ZrmI>AGTACT)',
@@ -17890,7 +19070,6 @@ def _temp():
}
rest_dict['ZrmI'] = _temp()
-
def _temp():
return {
'compsite': '(?P<Zsp2I>ATGCAT)',
@@ -17914,24 +19093,20 @@ def _temp():
rest_dict['Zsp2I'] = _temp()
suppliers = {}
-
-
def _temp():
return (
'Life Technologies',
- ['MluI', 'SacI', 'SalI', 'BshTI', 'NcoI', 'ClaI', 'DraI', 'PvuI', 'DpnI', 'TaqI', 'KpnI', 'NdeI', 'HapII', 'BglII', 'SacII', 'BmeT110I', 'MseI', 'NruI', 'HincII', 'XbaI', 'MboI', 'AluI', 'SmaI', 'NheI', 'StuI', 'SphI', 'PvuII', 'SpeI', 'HinfI', 'EcoRV', 'EcoRI', 'XhoI', 'PstI', 'HaeIII', 'AccI', 'SspI', 'ApaI', 'EcoT22I', 'ScaI', 'BalI', 'AfaI', 'NotI', 'HindIII', 'BamHI', 'HpaI', 'HhaI'],
+ ['BshTI', 'MluI', 'HpaII', 'MreI', 'BclI', 'SacI', 'PauI', 'BglI', 'SalI', 'MspI', 'ScaI', 'Bsu15I', 'Mva1269I', 'Bsp68I', 'LweI', 'SmiI', 'PteI', 'TscAI', 'NcoI', 'PsyI', 'BseJI', 'ClaI', 'MauBI', 'CaiI', 'Eco24I', 'CseI', 'Eco47III', 'Eco91I', 'DraI', 'BseXI', 'BstXI', 'RruI', 'Esp3I', 'BseSI', 'Cfr9I', 'AarI', 'OliI', 'PvuI', 'BspOI', 'DpnI', 'Hin6I', 'Van91I', 'Bst1107I', 'Bme1390I', 'BveI', 'Psp5II', 'TaqI', 'Eco52I', 'GsuI', 'KpnI', 'SspDI', 'SsiI', 'RseI', 'MlsI', 'NdeI', [...]
)
suppliers['B'] = _temp()
-
def _temp():
return (
'Minotech Biotechnology',
- ['SgrBI', 'BclI', 'BglI', 'SalI', 'PspPI', 'SnaBI', 'BstEII', 'NcoI', 'BshFI', 'AsuII', 'BssAI', 'BseAI', 'TaqI', 'KpnI', 'BglII', 'NaeI', 'BseBI', 'NruI', 'SlaI', 'RsaI', 'BsiSI', 'XbaI', 'Sau3AI', 'MboI', 'AluI', 'SseBI', 'SmaI', 'NheI', 'SstI', 'SphI', 'PvuII', 'ApaLI', 'HinfI', 'MspCI', 'EcoRV', 'EcoRI', 'BseCI', 'PstI', 'SfiI', 'SspI', 'CspAI', 'ScaI', 'NotI', 'HindIII', 'BamHI', 'HpaI', 'StyI'],
+ ['SgrBI', 'BclI', 'BglI', 'SalI', 'PspPI', 'ScaI', 'SnaBI', 'BstEII', 'NcoI', 'BshFI', 'AsuII', 'BssAI', 'BseAI', 'TaqI', 'KpnI', 'BglII', 'NaeI', 'BseBI', 'NruI', 'RsaI', 'BsiSI', 'XbaI', 'MboI', 'AluI', 'SlaI', 'SseBI', 'SmaI', 'NheI', 'SstI', 'Sau3AI', 'SphI', 'PvuII', 'ApaLI', 'HinfI', 'MspCI', 'HindIII', 'EcoRV', 'EcoRI', 'BseCI', 'PstI', 'SfiI', 'SspI', 'CspAI', 'NotI', 'BamHI', 'HpaI', 'StyI'],
)
suppliers['C'] = _temp()
-
def _temp():
return (
'Agilent Technologies',
@@ -17939,121 +19114,91 @@ def _temp():
)
suppliers['E'] = _temp()
-
-def _temp():
- return (
- 'Thermo Scientific Fermentas',
- ['MluI', 'CseI', 'HpaII', 'Cfr9I', 'MreI', 'BclI', 'SacI', 'PauI', 'BglI', 'SalI', 'MspI', 'Bsu15I', 'Mva1269I', 'Bsp68I', 'LweI', 'SmiI', 'PteI', 'BshTI', 'TscAI', 'NcoI', 'PsyI', 'BseJI', 'MauBI', 'Eco24I', 'Eco47III', 'Eco91I', 'DraI', 'BseXI', 'BstXI', 'RruI', 'Esp3I', 'BseSI', 'AdeI', 'AarI', 'RseI', 'PvuI', 'BspOI', 'DpnI', 'Hin6I', 'Van91I', 'Bst1107I', 'Bme1390I', 'BveI', 'Psp5II', 'TaqI', 'Eco52I', 'KpnI', 'SspDI', 'SsiI', 'MlsI', 'NdeI', 'Cfr13I', 'MboII', 'SdaI', 'BmsI [...]
- )
-suppliers['F'] = _temp()
-
-
def _temp():
return (
'SibEnzyme Ltd.',
- ['AsuNHI', 'AgsI', 'BstSFI', 'MluI', 'CciI', 'BstHHI', 'HpaII', 'AhlI', 'KroI', 'PspN4I', 'BglI', 'SalI', 'PspEI', 'MspI', 'VneI', 'BstH2I', 'BisI', 'BmtI', 'PspXI', 'AsiGI', 'CciNI', 'Sfr274I', 'SmiI', 'Ksp22I', 'BssT1I', 'MspA1I', 'Bsp19I', 'Bse1I', 'AspS9I', 'AbsI', 'FauNDI', 'BstMWI', 'AclWI', 'DraI', 'Bst2UI', 'AluBI', 'PsrI', 'BstACI', 'BstXI', 'BstDEI', 'GluI', 'AcoI', 'XmaI', 'BstF5I', 'BstENI', 'BssECI', 'FalI', 'EgeI', 'Ama87I', 'BstDSI', 'BstV2I', 'AjnI', 'AspLEI', 'Pa [...]
+ ['AsuNHI', 'AgsI', 'MluI', 'CciI', 'BstHHI', 'HpaII', 'AhlI', 'KroI', 'PspN4I', 'BglI', 'SalI', 'PspEI', 'MspI', 'VneI', 'BstH2I', 'BisI', 'BmtI', 'PspXI', 'AsiGI', 'CciNI', 'Sfr274I', 'SmiI', 'Ksp22I', 'BstSFI', 'BssT1I', 'MspA1I', 'Bsp19I', 'Bse1I', 'AspS9I', 'AbsI', 'FauNDI', 'BstMWI', 'LmnI', 'AclWI', 'DraI', 'Bst2UI', 'AluBI', 'PsrI', 'BstACI', 'BstXI', 'BstDEI', 'GluI', 'AcoI', 'XmaI', 'BstF5I', 'BstMBI', 'BstENI', 'BssECI', 'FalI', 'EgeI', 'Ama87I', 'BstDSI', 'BstV2I', 'Aj [...]
)
suppliers['I'] = _temp()
-
def _temp():
return (
'Nippon Gene Co., Ltd.',
- ['BssHII', 'AxyI', 'MluI', 'BclI', 'SacI', 'EcoT38I', 'BglI', 'SalI', 'MspI', 'BstEII', 'NcoI', 'DraI', 'BstXI', 'SwaI', 'AvaI', 'TaqI', 'AseI', 'KpnI', 'Sau96I', 'HaeII', 'NdeI', 'MboII', 'AflII', 'BglII', 'AccII', 'SacII', 'NruI', 'NarI', 'RsaI', 'HincII', 'XbaI', 'Sau3AI', 'AluI', 'ScrFI', 'EcoRII', 'SmaI', 'NheI', 'StuI', 'SphI', 'NciI', 'FspI', 'PvuII', 'FokI', 'SpeI', 'HinfI', 'EcoO109I', 'EcoRV', 'EcoRI', 'XhoI', 'Bsp1286I', 'AccIII', 'PstI', 'BsmI', 'Alw44I', 'SfiI', 'Hae [...]
+ ['BssHII', 'AxyI', 'MluI', 'BclI', 'SacI', 'EcoT38I', 'BglI', 'SalI', 'MspI', 'ScaI', 'BstEII', 'NcoI', 'DraI', 'BstXI', 'SwaI', 'AvaI', 'TaqI', 'AseI', 'KpnI', 'Sau96I', 'HaeII', 'NdeI', 'MboII', 'AflII', 'BglII', 'AccII', 'SacII', 'NruI', 'NarI', 'RsaI', 'HincII', 'XbaI', 'AluI', 'ScrFI', 'EcoRII', 'SmaI', 'NheI', 'StuI', 'Sau3AI', 'SphI', 'FspI', 'PvuII', 'FokI', 'SpeI', 'HinfI', 'NciI', 'EcoO109I', 'HindIII', 'EcoRV', 'EcoRI', 'XhoI', 'Bsp1286I', 'AccIII', 'AvaII', 'PstI', 'B [...]
)
suppliers['J'] = _temp()
-
def _temp():
return (
'Takara Bio Inc.',
- ['BssHII', 'MluI', 'BspT107I', 'SacI', 'XspI', 'BglI', 'SalI', 'MspI', 'BstPI', 'BanII', 'PmaCI', 'SnaBI', 'SmiI', 'BmgT120I', 'NcoI', 'ClaI', 'DraI', 'BstXI', 'PshAI', 'PvuI', 'DpnI', 'Van91I', 'Bst1107I', 'TaqI', 'EaeI', 'Eco52I', 'BspT104I', 'KpnI', 'HaeII', 'EcoO65I', 'NdeI', 'HapII', 'MboII', 'AflII', 'EcoT14I', 'BglII', 'NaeI', 'AccII', 'SacII', 'BmeT110I', 'Aor51HI', 'Bsp1407I', 'NruI', 'Sse8387I', 'CpoI', 'HincII', 'XbaI', 'Sau3AI', 'Cfr10I', 'MboI', 'AluI', 'BcnI', 'SmaI [...]
+ ['BssHII', 'PshBI', 'MluI', 'BspT107I', 'SacI', 'XspI', 'BglI', 'SalI', 'MspI', 'BstPI', 'ScaI', 'BanII', 'PmaCI', 'SnaBI', 'SmiI', 'BmgT120I', 'NcoI', 'ClaI', 'DraI', 'BstXI', 'PshAI', 'PvuI', 'DpnI', 'Van91I', 'Bst1107I', 'TaqI', 'EaeI', 'Eco52I', 'BspT104I', 'KpnI', 'HaeII', 'EcoO65I', 'NdeI', 'HapII', 'MboII', 'AflII', 'EcoT14I', 'BglII', 'NaeI', 'AccII', 'SacII', 'BmeT110I', 'Aor51HI', 'Bsp1407I', 'NruI', 'Sse8387I', 'CpoI', 'HincII', 'XbaI', 'MboI', 'AluI', 'BcnI', 'SmaI', [...]
)
suppliers['K'] = _temp()
-
def _temp():
return (
'Roche Applied Science',
- ['BssHII', 'MluI', 'BclI', 'SacI', 'SalI', 'Asp718I', 'SnaBI', 'NcoI', 'ClaI', 'Eco47III', 'DraI', 'BstXI', 'SwaI', 'PvuI', 'BseAI', 'DpnI', 'TaqI', 'SexAI', 'KpnI', 'NdeI', 'PinAI', 'BglII', 'MaeI', 'NruI', 'MvaI', 'NarI', 'RsaI', 'MaeII', 'AflIII', 'XbaI', 'Sau3AI', 'MvnI', 'AluI', 'CfoI', 'SmaI', 'NheI', 'StuI', 'BbrPI', 'SphI', 'MaeIII', 'PvuII', 'FokI', 'SpeI', 'HinfI', 'DraIII', 'MluNI', 'EcoRV', 'AatII', 'EcoRI', 'XhoI', 'MunI', 'EclXI', 'PstI', 'BsmI', 'SfiI', 'BlnI', 'Ha [...]
+ ['BssHII', 'MluI', 'BclI', 'SacI', 'SalI', 'Asp718I', 'ScaI', 'SnaBI', 'NcoI', 'ClaI', 'Eco47III', 'DraI', 'BstXI', 'SwaI', 'PvuI', 'DpnI', 'TaqI', 'SexAI', 'KpnI', 'NdeI', 'BglII', 'MaeI', 'NruI', 'MvaI', 'NarI', 'RsaI', 'MaeII', 'AflIII', 'XbaI', 'MvnI', 'AluI', 'CfoI', 'SmaI', 'NheI', 'StuI', 'Sau3AI', 'BbrPI', 'SphI', 'MaeIII', 'PvuII', 'FokI', 'SpeI', 'HinfI', 'DraIII', 'MluNI', 'HindIII', 'EcoRV', 'AatII', 'EcoRI', 'XhoI', 'MunI', 'PstI', 'BsmI', 'SfiI', 'BlnI', 'HaeIII', ' [...]
)
suppliers['M'] = _temp()
-
def _temp():
return (
'New England Biolabs',
- ['BssHII', 'EciI', 'BsrFI', 'DpnII', 'AlwI', 'MluI', 'NgoMIV', 'HpaII', 'TspMI', 'BclI', 'MlyI', 'BsaWI', 'SacI', 'MwoI', 'BfaI', 'DrdI', 'BmgBI', 'BglI', 'SalI', 'MspI', 'BanII', 'MslI', 'BmtI', 'PspXI', 'BsaBI', 'SnaBI', 'BstEII', 'TspRI', 'NcoI', 'MspA1I', 'BtgI', 'ClaI', 'BsaI', 'BsrBI', 'AlwNI', 'XmnI', 'DraI', 'Hpy166II', 'Hpy99I', 'StyD4I', 'BstXI', 'PspGI', 'BsiHKAI', 'BsoBI', 'BlpI', 'PshAI', 'XmaI', 'BtsIMutI', 'SwaI', 'AvaI', 'PvuI', 'DpnI', 'CspCI', 'PflFI', 'BpuEI', [...]
+ ['BssHII', 'EciI', 'BsrFI', 'DpnII', 'AlwI', 'MluI', 'AlwNI', 'NgoMIV', 'HpaII', 'TspMI', 'BclI', 'MlyI', 'BsaWI', 'SacI', 'MwoI', 'BfaI', 'DrdI', 'BmgBI', 'BglI', 'SalI', 'MspI', 'ScaI', 'BanII', 'MslI', 'BmtI', 'PspXI', 'BsaBI', 'SnaBI', 'BstEII', 'TspRI', 'NcoI', 'MspA1I', 'BtgI', 'ClaI', 'BsaI', 'BsrBI', 'XmnI', 'DraI', 'Hpy166II', 'Hpy99I', 'StyD4I', 'BstXI', 'PspGI', 'BsiHKAI', 'BsoBI', 'BlpI', 'PshAI', 'XmaI', 'BtsIMutI', 'SwaI', 'AvaI', 'PvuI', 'DpnI', 'CspCI', 'PflFI', ' [...]
)
suppliers['N'] = _temp()
-
def _temp():
return (
'Toyobo Biochemicals',
- ['MluI', 'BclI', 'SacI', 'BglI', 'SalI', 'NcoI', 'PvuI', 'DpnI', 'AseI', 'KpnI', 'BglII', 'SacII', 'HincII', 'XbaI', 'AluI', 'MscI', 'SmaI', 'NheI', 'SphI', 'PvuII', 'SpeI', 'HinfI', 'EcoRV', 'EcoRI', 'XhoI', 'PstI', 'SfiI', 'HaeIII', 'ScaI', 'PacI', 'DdeI', 'NotI', 'MroI', 'HindIII', 'BamHI'],
+ ['MluI', 'BclI', 'SacI', 'BglI', 'SalI', 'ScaI', 'NcoI', 'PvuI', 'DpnI', 'KpnI', 'BglII', 'SacII', 'HincII', 'AluI', 'MscI', 'SmaI', 'NheI', 'SphI', 'PvuII', 'SpeI', 'HinfI', 'HindIII', 'EcoRV', 'EcoRI', 'XhoI', 'PstI', 'SfiI', 'HaeIII', 'PacI', 'DdeI', 'NotI', 'MroI', 'BamHI'],
)
suppliers['O'] = _temp()
-
def _temp():
return (
'Molecular Biology Resources - CHIMERx',
- ['BssHII', 'MluI', 'HpaII', 'SacI', 'BglI', 'SalI', 'MspI', 'NcoI', 'ClaI', 'CviJI', 'DraI', 'BstXI', 'AcvI', 'AvaI', 'PvuI', 'DpnI', 'TaqI', 'KpnI', 'NdeI', 'PinAI', 'MboII', 'BglII', 'SacII', 'NruI', 'NarI', 'TaqII', 'RsaI', 'HincII', 'XbaI', 'TspGWI', 'MboI', 'AluI', 'RsrII', 'SmaI', 'NheI', 'StuI', 'SphI', 'PvuII', 'SpeI', 'HinfI', 'BsiHKCI', 'EcoRV', 'EcoRI', 'XhoI', 'MnlI', 'PstI', 'SfiI', 'HaeIII', 'AccI', 'SspI', 'Tth111I', 'NsiI', 'ApaI', 'ScaI', 'TspDTI', 'BalI', 'DdeI' [...]
+ ['BssHII', 'MluI', 'HpaII', 'SacI', 'BglI', 'SalI', 'MspI', 'ScaI', 'NcoI', 'ClaI', 'CviJI', 'DraI', 'BstXI', 'AcvI', 'AvaI', 'PvuI', 'DpnI', 'TaqI', 'KpnI', 'NdeI', 'MboII', 'BglII', 'SacII', 'NruI', 'NarI', 'TaqII', 'RsaI', 'HincII', 'XbaI', 'TspGWI', 'MboI', 'AluI', 'RsrII', 'SmaI', 'NheI', 'StuI', 'SphI', 'PvuII', 'SpeI', 'HinfI', 'BsiHKCI', 'HindIII', 'EcoRV', 'EcoRI', 'XhoI', 'MnlI', 'PinAI', 'PstI', 'SfiI', 'Tth111I', 'HaeIII', 'AccI', 'SspI', 'NsiI', 'ApaI', 'TspDTI', 'Ba [...]
)
suppliers['Q'] = _temp()
-
def _temp():
return (
'Promega Corporation',
- ['BssHII', 'MluI', 'HpaII', 'BclI', 'SacI', 'BglI', 'SalI', 'BstOI', 'MspI', 'SnaBI', 'BstEII', 'NcoI', 'MspA1I', 'ClaI', 'XmnI', 'Eco47III', 'DraI', 'BstXI', 'XmaI', 'AvaI', 'PvuI', 'DpnI', 'TaqI', 'KpnI', 'HaeII', 'NdeI', 'MboII', 'BglII', 'SacII', 'Acc65I', 'NruI', 'CspI', 'NarI', 'Bsu36I', 'RsaI', 'HincII', 'XbaI', 'BsrSI', 'Sau3AI', 'MboI', 'BanI', 'AluI', 'CfoI', 'SmaI', 'NheI', 'StuI', 'SphI', 'NciI', 'PvuII', 'SpeI', 'HinfI', 'SgfI', 'EcoRV', 'AatII', 'EcoRI', 'XhoI', 'Ac [...]
+ ['BssHII', 'MluI', 'HpaII', 'BclI', 'SacI', 'BglI', 'SalI', 'MspI', 'ScaI', 'SnaBI', 'BstEII', 'NcoI', 'MspA1I', 'ClaI', 'XmnI', 'Eco47III', 'DraI', 'BstXI', 'XmaI', 'PvuI', 'DpnI', 'TaqI', 'KpnI', 'HaeII', 'NdeI', 'MboII', 'BglII', 'SacII', 'NruI', 'CspI', 'NarI', 'RsaI', 'Hsp92II', 'HincII', 'XbaI', 'MboI', 'BanI', 'AluI', 'CfoI', 'SmaI', 'NheI', 'StuI', 'Sau3AI', 'SphI', 'PvuII', 'SpeI', 'HinfI', 'NciI', 'HindIII', 'SgfI', 'EcoRV', 'EcoRI', 'XhoI', 'AccIII', 'AvaII', 'PstI', ' [...]
)
suppliers['R'] = _temp()
-
def _temp():
return (
'Sigma Chemical Corporation',
- ['BssHII', 'HpaII', 'BclI', 'SacI', 'SalI', 'MspI', 'BstEII', 'NcoI', 'ClaI', 'DraI', 'PvuI', 'DpnI', 'TaqI', 'KpnI', 'NdeI', 'BglII', 'MvaI', 'RsaI', 'XbaI', 'Sau3AI', 'AluI', 'CfoI', 'SmaI', 'NheI', 'SphI', 'PvuII', 'SpeI', 'EcoRV', 'EcoRI', 'XhoI', 'EclXI', 'PstI', 'BsmI', 'SfiI', 'BlnI', 'HaeIII', 'AccI', 'SspI', 'NsiI', 'ApaI', 'ScaI', 'KspI', 'DdeI', 'NotI', 'HindIII', 'BamHI', 'HpaI'],
+ ['HpaII', 'BclI', 'SacI', 'SalI', 'MspI', 'ScaI', 'BstEII', 'NcoI', 'ClaI', 'DraI', 'PvuI', 'DpnI', 'TaqI', 'KpnI', 'NdeI', 'BglII', 'MvaI', 'RsaI', 'XbaI', 'AluI', 'CfoI', 'SmaI', 'NheI', 'Sau3AI', 'SphI', 'PvuII', 'SpeI', 'HindIII', 'EcoRV', 'EcoRI', 'XhoI', 'EclXI', 'PstI', 'BsmI', 'SfiI', 'BlnI', 'HaeIII', 'AccI', 'NsiI', 'ApaI', 'KspI', 'DdeI', 'NotI', 'BamHI', 'HpaI'],
)
suppliers['S'] = _temp()
-
-def _temp():
- return (
- 'Bangalore Genei',
- ['MluI', 'HpaII', 'BclI', 'SacI', 'BglI', 'SalI', 'MspI', 'SnaBI', 'BstEII', 'NcoI', 'ClaI', 'XmnI', 'DraI', 'XmaI', 'AvaI', 'PvuI', 'AssI', 'TaqI', 'KpnI', 'Sau96I', 'NdeI', 'BglII', 'NaeI', 'NruI', 'NarI', 'HincII', 'XbaI', 'Sau3AI', 'StrI', 'MboI', 'BanI', 'AluI', 'SmaI', 'BasI', 'NheI', 'StuI', 'PvuII', 'ApaLI', 'SpeI', 'HinfI', 'MvrI', 'EcoRV', 'EcoRI', 'XhoI', 'PstI', 'SfiI', 'HaeIII', 'AccI', 'SspI', 'NsiI', 'ApaI', 'NotI', 'HindIII', 'BamHI', 'HpaI', 'HhaI'],
- )
-suppliers['U'] = _temp()
-
-
def _temp():
return (
'Vivantis Technologies',
- ['BssMI', 'AsuNHI', 'MluI', 'BstHHI', 'HpaII', 'AhlI', 'BglI', 'SalI', 'PspEI', 'MspI', 'VneI', 'BstH2I', 'BmtI', 'AsiGI', 'CciNI', 'Sfr274I', 'SmiI', 'Ksp22I', 'BssT1I', 'MspA1I', 'Bsp19I', 'Bse1I', 'AspS9I', 'BmcAI', 'FauNDI', 'DraI', 'Bst2UI', 'Vha464I', 'BstXI', 'BstDEI', 'XmaI', 'BstF5I', 'BpvUI', 'BstENI', 'Ama87I', 'BstDSI', 'BstV2I', 'AspLEI', 'Zsp2I', 'DseDI', 'BstAUI', 'Bpu14I', 'TaqI', 'KpnI', 'BstSNI', 'MboII', 'BmrFI', 'BmeRI', 'BseX3I', 'Bpu10I', 'Rsr2I', 'BtuMI', ' [...]
+ ['BssMI', 'AsuNHI', 'MluI', 'BstHHI', 'HpaII', 'AhlI', 'BglI', 'SalI', 'PspEI', 'MspI', 'VneI', 'BstH2I', 'BmtI', 'AsiGI', 'CciNI', 'Sfr274I', 'SmiI', 'Ksp22I', 'BssT1I', 'MspA1I', 'Bsp19I', 'Bse1I', 'AspS9I', 'BmcAI', 'FauNDI', 'DraI', 'Bst2UI', 'Vha464I', 'BstXI', 'BstDEI', 'XmaI', 'BstF5I', 'BstMBI', 'BstENI', 'Ama87I', 'BstDSI', 'BstV2I', 'Zsp2I', 'DseDI', 'BstAUI', 'Bpu14I', 'TaqI', 'KpnI', 'BstSNI', 'AclI', 'MboII', 'BmrFI', 'BglII', 'AcsI', 'BstNSI', 'BmeRI', 'BseX3I', 'Bp [...]
)
suppliers['V'] = _temp()
-
def _temp():
return (
'EURx Ltd.',
- ['BssHII', 'MluI', 'HpaII', 'SacI', 'BglI', 'SalI', 'MspI', 'BanII', 'NcoI', 'ClaI', 'CviJI', 'DraI', 'BstXI', 'AcvI', 'AvaI', 'PvuI', 'DpnI', 'TaqI', 'KpnI', 'NdeI', 'PinAI', 'MboII', 'BglII', 'SacII', 'NruI', 'NarI', 'TaqII', 'RsaI', 'HincII', 'XbaI', 'TspGWI', 'MboI', 'AluI', 'RsrII', 'SmaI', 'NheI', 'StuI', 'SphI', 'PvuII', 'FokI', 'SpeI', 'HinfI', 'BsiHKCI', 'EcoRV', 'EcoRI', 'XhoI', 'MnlI', 'PstI', 'SfiI', 'HaeIII', 'AccI', 'SspI', 'Tth111I', 'NsiI', 'ApaI', 'ScaI', 'TspDTI [...]
+ ['BssHII', 'MluI', 'HpaII', 'BspTNI', 'SacI', 'BglI', 'SalI', 'MspI', 'ScaI', 'BanII', 'NcoI', 'ClaI', 'CviJI', 'DraI', 'BstXI', 'AcvI', 'AvaI', 'PvuI', 'DpnI', 'TaqI', 'SinI', 'KpnI', 'NdeI', 'MboII', 'BglII', 'SacII', 'NruI', 'NarI', 'TaqII', 'RsaI', 'HincII', 'XbaI', 'BspANI', 'TspGWI', 'MboI', 'AluI', 'RsrII', 'SmaI', 'NheI', 'StuI', 'SphI', 'PvuII', 'FokI', 'SpeI', 'HinfI', 'BsiHKCI', 'HindIII', 'EcoRV', 'EcoRI', 'XhoI', 'MnlI', 'AvaII', 'PinAI', 'PstI', 'BspMAI', 'SfiI', 'T [...]
)
suppliers['X'] = _temp()
-
def _temp():
return (
'SinaClon BioScience Co.',
- ['BclI', 'BglI', 'SalI', 'MspI', 'NcoI', 'DraI', 'PvuI', 'TaqI', 'KpnI', 'NdeI', 'BglII', 'RsaI', 'HincII', 'XbaI', 'MboI', 'AluI', 'SmaI', 'PvuII', 'HinfI', 'EcoRV', 'EcoRI', 'XhoI', 'PstI', 'HaeIII', 'NotI', 'HindIII', 'BamHI', 'AvaII', 'HhaI'],
+ ['NcoI', 'BstXI', 'KpnI', 'RsaI', 'BsiSI', 'MboI', 'AluI', 'SlaI', 'SmaI', 'FokI', 'HinfI', 'HindIII', 'EcoRI', 'BamHI'],
)
suppliers['Y'] = _temp()
typedict = {}
-
-
def _temp():
return (
('Palindromic', 'TwoCuts', 'Ov5', 'Ambiguous', 'Meth_Dep', 'Not_available', 'AbstractCut', 'RestrictionType'),
@@ -18061,7 +19206,6 @@ def _temp():
)
typedict['type130'] = _temp()
-
def _temp():
return (
('Palindromic', 'TwoCuts', 'Ov5', 'Ambiguous', 'Meth_Undep', 'Not_available', 'AbstractCut', 'RestrictionType'),
@@ -18069,7 +19213,6 @@ def _temp():
)
typedict['type132'] = _temp()
-
def _temp():
return (
('Palindromic', 'TwoCuts', 'Ov3', 'Ambiguous', 'Meth_Dep', 'Not_available', 'AbstractCut', 'RestrictionType'),
@@ -18077,63 +19220,62 @@ def _temp():
)
typedict['type142'] = _temp()
-
def _temp():
return (
('Palindromic', 'TwoCuts', 'Ov3', 'Ambiguous', 'Meth_Undep', 'Commercially_available', 'AbstractCut', 'RestrictionType'),
- ['FalI', 'BplI', 'AlfI'],
+ ['FalI', 'BplI'],
)
typedict['type143'] = _temp()
-
def _temp():
return (
('Palindromic', 'TwoCuts', 'Ov3', 'Ambiguous', 'Meth_Undep', 'Not_available', 'AbstractCut', 'RestrictionType'),
- ['BdaI'],
+ ['BdaI', 'AlfI'],
)
typedict['type144'] = _temp()
-
def _temp():
return (
('NonPalindromic', 'NoCut', 'Unknown', 'NotDefined', 'Meth_Dep', 'Not_available', 'AbstractCut', 'RestrictionType'),
- ['SpoDI', 'Cgl13032I', 'Cdi630V', 'EsaSSI', 'RflFIII', 'Cgl13032II', 'CjeFV', 'BscGI', 'Sno506I', 'Hpy99XIV', 'RdeGBI', 'Hpy99XIII', 'CjeFIII', 'GauT27I', 'DrdII', 'NhaXI', 'RpaTI', 'MkaDII', 'Jma19592I', 'CjeNII'],
+ ['Sen13311III', 'SpoDI', 'OspHL35III', 'Cgl13032I', 'Pal408I', 'EsaSSI', 'Pse18267I', 'HpyUM032XIV', 'Hpy99XIV_mut1', 'Cdu23823II', 'RflFIII', 'Pdi8503III', 'Pme5II', 'Cgl13032II', 'Yps3606I', 'CjeFV', 'Bsu7003I', 'Psp10HII', 'Lsp48III', 'HpyAXVI_mut1', 'BscGI', 'Bsp3004IV', 'SpnRII', 'EcoE1140I', 'PliMI', 'Ecl35734I', 'Pac19842II', 'Sno506I', 'Hpy99XIV', 'Aco12261II', 'AspJHL3II', 'Sth20745III', 'Cdi11397I', 'Mba11I', 'TspARh3I', 'RdeGBI', 'Jma19592II', 'Vtu19109I', 'CjeFIII', ' [...]
)
typedict['type146'] = _temp()
-
def _temp():
return (
('NonPalindromic', 'NoCut', 'Unknown', 'NotDefined', 'Meth_Undep', 'Not_available', 'AbstractCut', 'RestrictionType'),
- ['UbaF14I', 'CjeP659IV', 'UbaPI', 'CjuII', 'AlwFI', 'BspGI', 'Pfl1108I', 'UbaF13I', 'RlaI', 'PenI', 'UbaF12I', 'BspNCI', 'TsuI', 'UbaF9I', 'FinI', 'UbaF11I', 'BmgI'],
+ ['UbaF14I', 'CjeP659IV', 'PsuGI', 'UbaPI', 'CjuII', 'AlwFI', 'BspGI', 'Pfl1108I', 'UbaF13I', 'AbaUMB2I', 'RlaI', 'PenI', 'UbaF12I', 'BspNCI', 'TsuI', 'UbaF9I', 'FinI', 'UbaF11I', 'BmgI'],
)
typedict['type148'] = _temp()
-
def _temp():
return (
('Palindromic', 'NoCut', 'Unknown', 'NotDefined', 'Meth_Dep', 'Not_available', 'AbstractCut', 'RestrictionType'),
- ['MjaIV'],
+ ['MjaIV', 'HpyUM032XIII'],
)
typedict['type2'] = _temp()
-
def _temp():
return (
('NonPalindromic', 'OneCut', 'Blunt', 'Defined', 'Meth_Dep', 'Commercially_available', 'AbstractCut', 'RestrictionType'),
- ['MlyI', 'BsrBI'],
+ ['MlyI', 'BmgBI', 'BsrBI'],
)
typedict['type209'] = _temp()
+def _temp():
+ return (
+ ('NonPalindromic', 'OneCut', 'Blunt', 'Defined', 'Meth_Dep', 'Not_available', 'AbstractCut', 'RestrictionType'),
+ ['NgoAVII'],
+ )
+typedict['type210'] = _temp()
def _temp():
return (
('NonPalindromic', 'OneCut', 'Blunt', 'Defined', 'Meth_Undep', 'Commercially_available', 'AbstractCut', 'RestrictionType'),
- ['BmgBI', 'SchI', 'BtrI', 'AjiI', 'AccBSI', 'MbiI'],
+ ['SchI', 'BtrI', 'AjiI', 'AccBSI', 'MbiI'],
)
typedict['type211'] = _temp()
-
def _temp():
return (
('NonPalindromic', 'OneCut', 'Blunt', 'Defined', 'Meth_Undep', 'Not_available', 'AbstractCut', 'RestrictionType'),
@@ -18141,7 +19283,6 @@ def _temp():
)
typedict['type212'] = _temp()
-
def _temp():
return (
('NonPalindromic', 'OneCut', 'Ov5', 'Defined', 'Meth_Dep', 'Commercially_available', 'AbstractCut', 'RestrictionType'),
@@ -18149,15 +19290,13 @@ def _temp():
)
typedict['type221'] = _temp()
-
def _temp():
return (
('NonPalindromic', 'OneCut', 'Ov5', 'Defined', 'Meth_Undep', 'Commercially_available', 'AbstractCut', 'RestrictionType'),
- ['SsiI', 'Bst2BI', 'BseYI', 'BauI'],
+ ['SsiI', 'Bst2BI', 'PspFI', 'BseYI', 'BauI'],
)
typedict['type223'] = _temp()
-
def _temp():
return (
('NonPalindromic', 'OneCut', 'Ov5', 'Defined', 'Meth_Undep', 'Not_available', 'AbstractCut', 'RestrictionType'),
@@ -18165,39 +19304,34 @@ def _temp():
)
typedict['type224'] = _temp()
-
def _temp():
return (
('NonPalindromic', 'OneCut', 'Ov5', 'Ambiguous', 'Meth_Dep', 'Commercially_available', 'AbstractCut', 'RestrictionType'),
- ['AlwI', 'Esp3I', 'Bpu10I', 'Lsp1109I', 'BfuAI', 'BbvI', 'HgaI', 'BspMI', 'BccI', 'FokI', 'Alw26I', 'Eco31I', 'BceAI', 'BsmAI', 'BsmFI', 'FauI', 'BsmBI', 'BtgZI'],
+ ['AlwI', 'Esp3I', 'Bpu10I', 'Lsp1109I', 'BfuAI', 'BbvI', 'HgaI', 'BspMI', 'BccI', 'BcoDI', 'FokI', 'Alw26I', 'Eco31I', 'BceAI', 'BsmAI', 'BsmFI', 'FauI', 'BsmBI', 'BtgZI'],
)
typedict['type225'] = _temp()
-
def _temp():
return (
('NonPalindromic', 'OneCut', 'Ov5', 'Ambiguous', 'Meth_Dep', 'Not_available', 'AbstractCut', 'RestrictionType'),
- ['StsI'],
+ ['StsI', 'BscAI'],
)
typedict['type226'] = _temp()
-
def _temp():
return (
('NonPalindromic', 'OneCut', 'Ov5', 'Ambiguous', 'Meth_Undep', 'Commercially_available', 'AbstractCut', 'RestrictionType'),
- ['CseI', 'LweI', 'BsaI', 'AclWI', 'BseXI', 'BstV2I', 'AarI', 'BveI', 'BmsI', 'BspQI', 'MspJI', 'BstV1I', 'Bso31I', 'PleI', 'FaqI', 'FspEI', 'BcoDI', 'PpsI', 'Eam1104I', 'Acc36I', 'SapI', 'BspPI', 'BslFI', 'LguI', 'PciSI', 'Bst6I', 'BpiI', 'LpnPI', 'BstMAI', 'EarI', 'BbsI', 'SfaNI'],
+ ['BspTNI', 'LweI', 'BsaI', 'CseI', 'AclWI', 'BseXI', 'BstV2I', 'AarI', 'BveI', 'BmsI', 'BspQI', 'MspJI', 'BstV1I', 'Bso31I', 'PleI', 'FaqI', 'FspEI', 'PpsI', 'Eam1104I', 'Acc36I', 'SapI', 'BspPI', 'BslFI', 'LguI', 'PciSI', 'Bst6I', 'BpiI', 'LpnPI', 'BstMAI', 'EarI', 'BbsI', 'SfaNI'],
)
typedict['type227'] = _temp()
-
def _temp():
return (
('NonPalindromic', 'OneCut', 'Ov5', 'Ambiguous', 'Meth_Undep', 'Not_available', 'AbstractCut', 'RestrictionType'),
- ['SgrTI', 'Sth132I', 'BbvII', 'BinI', 'AspBHI', 'Bbr7I', 'BscAI', 'BspD6I', 'BcefI', 'AceIII', 'Ksp632I'],
+ ['SgrTI', 'Sth132I', 'BbvII', 'BinI', 'AspBHI', 'Bbr7I', 'BspD6I', 'BcefI', 'AceIII', 'Ksp632I'],
)
typedict['type228'] = _temp()
-
def _temp():
return (
('NonPalindromic', 'OneCut', 'Ov3', 'Defined', 'Meth_Undep', 'Commercially_available', 'AbstractCut', 'RestrictionType'),
@@ -18205,7 +19339,6 @@ def _temp():
)
typedict['type235'] = _temp()
-
def _temp():
return (
('NonPalindromic', 'OneCut', 'Ov3', 'Ambiguous', 'Meth_Dep', 'Commercially_available', 'AbstractCut', 'RestrictionType'),
@@ -18213,39 +19346,34 @@ def _temp():
)
typedict['type237'] = _temp()
-
def _temp():
return (
('NonPalindromic', 'OneCut', 'Ov3', 'Ambiguous', 'Meth_Dep', 'Not_available', 'AbstractCut', 'RestrictionType'),
- ['RpaBI', 'DraRI', 'SdeAI', 'RceI', 'WviI', 'BfiI', 'CstMI', 'PspOMII', 'CchII', 'PlaDI', 'SstE37I', 'RpaB5I', 'MaqI', 'CdpI', 'CchIII', 'Tth111II', 'CjeNIII', 'NlaCI', 'AquII', 'AquIV', 'ApyPI', 'RpaI', 'PspPRI', 'AquIII', 'RdeGBII'],
+ ['MaqI', 'RpaBI', 'DraRI', 'SdeAI', 'RceI', 'WviI', 'BfiI', 'CstMI', 'PspOMII', 'TaqIII', 'CchII', 'PlaDI', 'SstE37I', 'RpaB5I', 'CdpI', 'CchIII', 'Tth111II', 'CjeNIII', 'BsbI', 'NlaCI', 'AquII', 'AquIV', 'ApyPI', 'RpaI', 'PspPRI', 'AquIII', 'RdeGBII'],
)
typedict['type238'] = _temp()
-
def _temp():
return (
('NonPalindromic', 'OneCut', 'Ov3', 'Ambiguous', 'Meth_Undep', 'Commercially_available', 'AbstractCut', 'RestrictionType'),
- ['EciI', 'Mva1269I', 'Bse1I', 'BtsIMutI', 'BtsCI', 'BsuI', 'AsuHPI', 'BsrSI', 'BseGI', 'Bse3DI', 'GsuI', 'BsrDI', 'AbaSI', 'BciVI', 'BtsI', 'BfuI', 'BsmI', 'BseMI', 'TspDTI', 'BmuI', 'PctI', 'BseNI'],
+ ['EciI', 'Mva1269I', 'Bse1I', 'LmnI', 'BtsIMutI', 'GsuI', 'BtsCI', 'BsuI', 'AsuHPI', 'AbaSI', 'BseGI', 'Bse3DI', 'BsrDI', 'BciVI', 'BtsI', 'BfuI', 'BsmI', 'BseMI', 'TspDTI', 'BmuI', 'PctI', 'BseNI'],
)
typedict['type239'] = _temp()
-
def _temp():
return (
('NonPalindromic', 'OneCut', 'Ov3', 'Ambiguous', 'Meth_Undep', 'Not_available', 'AbstractCut', 'RestrictionType'),
- ['BmeDI', 'Bce83I', 'Hin4II', 'TsoI', 'Eco57MI', 'BsbI', 'YkrI', 'RleAI'],
+ ['BmeDI', 'Bce83I', 'Hin4II', 'TsoI', 'Eco57MI', 'YkrI', 'RleAI'],
)
typedict['type240'] = _temp()
-
def _temp():
return (
('NonPalindromic', 'TwoCuts', 'Ov5', 'Ambiguous', 'Meth_Dep', 'Not_available', 'AbstractCut', 'RestrictionType'),
- ['R2_BceSIV'],
+ ['BceSIV'],
)
typedict['type274'] = _temp()
-
def _temp():
return (
('NonPalindromic', 'TwoCuts', 'Ov3', 'Ambiguous', 'Meth_Dep', 'Commercially_available', 'AbstractCut', 'RestrictionType'),
@@ -18253,7 +19381,6 @@ def _temp():
)
typedict['type285'] = _temp()
-
def _temp():
return (
('NonPalindromic', 'TwoCuts', 'Ov3', 'Ambiguous', 'Meth_Dep', 'Not_available', 'AbstractCut', 'RestrictionType'),
@@ -18261,7 +19388,6 @@ def _temp():
)
typedict['type286'] = _temp()
-
def _temp():
return (
('NonPalindromic', 'TwoCuts', 'Ov3', 'Ambiguous', 'Meth_Undep', 'Commercially_available', 'AbstractCut', 'RestrictionType'),
@@ -18269,7 +19395,6 @@ def _temp():
)
typedict['type287'] = _temp()
-
def _temp():
return (
('NonPalindromic', 'TwoCuts', 'Ov3', 'Ambiguous', 'Meth_Undep', 'Not_available', 'AbstractCut', 'RestrictionType'),
@@ -18277,23 +19402,20 @@ def _temp():
)
typedict['type288'] = _temp()
-
def _temp():
return (
('Palindromic', 'NoCut', 'Unknown', 'NotDefined', 'Meth_Undep', 'Not_available', 'AbstractCut', 'RestrictionType'),
- ['CjuI', 'AvaIII', 'TssI', 'SnaI', 'HgiEII'],
+ ['NhoI', 'CjuI', 'AvaIII', 'TssI', 'SnaI', 'HgiEII'],
)
typedict['type4'] = _temp()
-
def _temp():
return (
('Palindromic', 'OneCut', 'Blunt', 'Defined', 'Meth_Dep', 'Commercially_available', 'AbstractCut', 'RestrictionType'),
- ['SnaBI', 'MspA1I', 'CviJI', 'DraI', 'PshAI', 'SwaI', 'NaeI', 'RsaI', 'HincII', 'BstC8I', 'PmlI', 'AluI', 'Hpy8I', 'SmaI', 'FspI', 'PvuII', 'BsuRI', 'EcoRV', 'BstUI', 'HaeIII', 'SspI', 'ScaI', 'BalI', 'NlaIV', 'HpaI', 'Cac8I', 'HindII', 'BsaAI'],
+ ['ScaI', 'SnaBI', 'MspA1I', 'CviJI', 'DraI', 'AluBI', 'PshAI', 'SwaI', 'NaeI', 'SfoI', 'RsaI', 'HincII', 'BstC8I', 'PmlI', 'AluI', 'Hpy8I', 'SmaI', 'FspI', 'PvuII', 'BsuRI', 'EcoRV', 'BstUI', 'HaeIII', 'SspI', 'BalI', 'NlaIV', 'HpaI', 'Cac8I', 'HindII', 'BsaAI'],
)
typedict['type65'] = _temp()
-
def _temp():
return (
('Palindromic', 'OneCut', 'Blunt', 'Defined', 'Meth_Dep', 'Not_available', 'AbstractCut', 'RestrictionType'),
@@ -18301,63 +19423,55 @@ def _temp():
)
typedict['type66'] = _temp()
-
def _temp():
return (
('Palindromic', 'OneCut', 'Blunt', 'Defined', 'Meth_Undep', 'Commercially_available', 'AbstractCut', 'RestrictionType'),
- ['PspN4I', 'MslI', 'Bsp68I', 'PmaCI', 'BsaBI', 'SmiI', 'BseJI', 'BshFI', 'BmcAI', 'XmnI', 'Eco47III', 'Hpy166II', 'AluBI', 'AcvI', 'RruI', 'EgeI', 'RseI', 'DpnI', 'AssI', 'Bst1107I', 'BstSNI', 'MlsI', 'AccII', 'BtuMI', 'Aor51HI', 'NruI', 'SmiMI', 'Bsh1236I', 'MalI', 'MssI', 'BsnI', 'SfoI', 'FspAI', 'BmiI', 'BoxI', 'BssNAI', 'GlaI', 'MvnI', 'ZraI', 'Eco72I', 'MscI', 'SseBI', 'Ppu21I', 'StuI', 'BbrPI', 'Eco53kI', 'EheI', 'BstPAI', 'Ecl136II', 'Acc16I', 'MluNI', 'PsiI', 'MroXI', 'Zr [...]
+ ['PspN4I', 'MslI', 'Bsp68I', 'PmaCI', 'BsaBI', 'SmiI', 'BseJI', 'BshFI', 'BmcAI', 'XmnI', 'Eco47III', 'Hpy166II', 'AcvI', 'RruI', 'EgeI', 'OliI', 'DpnI', 'Bst1107I', 'BstSNI', 'RseI', 'MlsI', 'AccII', 'BtuMI', 'Aor51HI', 'NruI', 'Bsh1236I', 'MalI', 'MssI', 'BsnI', 'FspAI', 'BmiI', 'BoxI', 'BssNAI', 'BspANI', 'GlaI', 'MvnI', 'ZraI', 'Eco72I', 'MscI', 'SseBI', 'SmiMI', 'Ppu21I', 'StuI', 'BbrPI', 'Eco53kI', 'EheI', 'BstPAI', 'Ecl136II', 'Acc16I', 'MluNI', 'PsiI', 'MroXI', 'ZrmI', 'D [...]
)
typedict['type67'] = _temp()
-
def _temp():
return (
('Palindromic', 'OneCut', 'Blunt', 'Defined', 'Meth_Undep', 'Not_available', 'AbstractCut', 'RestrictionType'),
- ['NspBII', 'HaeI', 'SciI', 'LpnI', 'SrfI', 'AhaIII', 'Sth302II', 'MstI'],
+ ['NspBII', 'HaeI', 'SciI', 'LpnI', 'AhaIII', 'Sth302II', 'MstI'],
)
typedict['type68'] = _temp()
-
def _temp():
return (
('Palindromic', 'OneCut', 'Ov5', 'Defined', 'Meth_Dep', 'Commercially_available', 'AbstractCut', 'RestrictionType'),
- ['BssHII', 'BsrFI', 'DpnII', 'MluI', 'NgoMIV', 'HpaII', 'Cfr9I', 'BclI', 'BsaWI', 'SalI', 'MspI', 'Bsu15I', 'NcoI', 'ClaI', 'XmaI', 'TaqI', 'EaeI', 'AseI', 'AclI', 'ApoI', 'HpyCH4IV', 'NdeI', 'HapII', 'BglII', 'MseI', 'BsaHI', 'XbaI', 'Sau3AI', 'Cfr10I', 'MboI', 'CviQI', 'CviAII', 'MfeI', 'ApaLI', 'HinP1I', 'HspAI', 'EcoRI', 'XhoI', 'BseCI', 'MunI', 'EagI', 'AscI', 'VspI', 'KasI', 'Kpn2I', 'BspHI', 'BstYI', 'AgeI', 'BfuCI', 'XhoII', 'Sse9I', 'NotI', 'HindIII', 'FatI', 'BamHI', 'P [...]
+ ['BssHII', 'BsrFI', 'DpnII', 'MluI', 'NgoMIV', 'HpaII', 'TspMI', 'BclI', 'BsaWI', 'SalI', 'MspI', 'Bsu15I', 'NcoI', 'ClaI', 'XmaI', 'Cfr9I', 'TaqI', 'EaeI', 'AseI', 'AclI', 'ApoI', 'HpyCH4IV', 'NdeI', 'HapII', 'MseI', 'BsaHI', 'XbaI', 'MboI', 'CviQI', 'NheI', 'Sau3AI', 'CviAII', 'MfeI', 'ApaLI', 'Cfr10I', 'HinP1I', 'HspAI', 'HindIII', 'EcoRI', 'XhoI', 'BseCI', 'MunI', 'EagI', 'AscI', 'VspI', 'KasI', 'Kpn2I', 'SgrAI', 'BspHI', 'BstYI', 'AgeI', 'BfuCI', 'Sse9I', 'NotI', 'FatI', 'Ba [...]
)
typedict['type77'] = _temp()
-
def _temp():
return (
('Palindromic', 'OneCut', 'Ov5', 'Defined', 'Meth_Dep', 'Not_available', 'AbstractCut', 'RestrictionType'),
- ['XmaIII', 'CfrI'],
+ ['XmaIII', 'CfrI', 'XhoII'],
)
typedict['type78'] = _temp()
-
def _temp():
return (
('Palindromic', 'OneCut', 'Ov5', 'Defined', 'Meth_Undep', 'Commercially_available', 'AbstractCut', 'RestrictionType'),
- ['BssMI', 'AsuNHI', 'CciI', 'TspMI', 'MreI', 'AhlI', 'XspI', 'KroI', 'BfaI', 'PauI', 'Asp718I', 'VneI', 'PspXI', 'AsiGI', 'CciNI', 'Sfr274I', 'PteI', 'Ksp22I', 'BshTI', 'Bsp19I', 'MauBI', 'AbsI', 'AsuII', 'FauNDI', 'Vha464I', 'BstACI', 'AcoI', 'BssAI', 'BseAI', 'Hin6I', 'PalAI', 'BstAUI', 'Bpu14I', 'Eco52I', 'BspT104I', 'SspDI', 'PinAI', 'AflII', 'TasI', 'AcsI', 'BseX3I', 'FspBI', 'RsaNI', 'Acc65I', 'AvrII', 'MaeI', 'XapI', 'PscI', 'Bsp1407I', 'PciI', 'Bse118I', 'SlaI', 'NarI', ' [...]
+ ['BssMI', 'AsuNHI', 'PshBI', 'BshTI', 'CciI', 'MreI', 'AhlI', 'XspI', 'KroI', 'BfaI', 'PauI', 'Asp718I', 'VneI', 'PspXI', 'AsiGI', 'CciNI', 'Sfr274I', 'PteI', 'Ksp22I', 'Bsp19I', 'MauBI', 'AbsI', 'AsuII', 'FauNDI', 'Vha464I', 'BstACI', 'AcoI', 'BstMBI', 'BssAI', 'BseAI', 'Hin6I', 'PalAI', 'BstAUI', 'Bpu14I', 'Eco52I', 'BspT104I', 'SspDI', 'AflII', 'BglII', 'TasI', 'AcsI', 'BseX3I', 'FspBI', 'RsaNI', 'Acc65I', 'AvrII', 'MaeI', 'XapI', 'PscI', 'Bsp1407I', 'SspMI', 'PciI', 'Bse118I' [...]
)
typedict['type79'] = _temp()
-
def _temp():
return (
('Palindromic', 'OneCut', 'Ov5', 'Defined', 'Meth_Undep', 'Not_available', 'AbstractCut', 'RestrictionType'),
- ['SelI', 'BspLU11I', 'TspEI', 'Asi256I', 'Ppu10I', 'Sse232I', 'BetI', 'SplI', 'AoxI', 'BspMII'],
+ ['SelI', 'BspLU11I', 'SplI', 'TspEI', 'Asi256I', 'Ppu10I', 'Sse232I', 'BetI', 'BspMII'],
)
typedict['type80'] = _temp()
-
def _temp():
return (
('Palindromic', 'OneCut', 'Ov5', 'Ambiguous', 'Meth_Dep', 'Commercially_available', 'AbstractCut', 'RestrictionType'),
- ['PspPI', 'AspS9I', 'StyD4I', 'PspGI', 'BsoBI', 'BlpI', 'BssECI', 'AjnI', 'AvaI', 'SexAI', 'Sau96I', 'BstNI', 'Cfr13I', 'TseI', 'MvaI', 'BssKI', 'AflIII', 'TfiI', 'BanI', 'RsrII', 'BcnI', 'ScrFI', 'EcoRII', 'EcoNI', 'NciI', 'ApeKI', 'HinfI', 'Fsp4HI', 'EcoO109I', 'PpuMI', 'AccI', 'Tth111I', 'DdeI', 'AvaII', 'BsaJI', 'Fnu4HI', 'Tsp45I'],
+ ['PspPI', 'AspS9I', 'StyD4I', 'PspGI', 'BsoBI', 'BlpI', 'BssECI', 'AjnI', 'AvaI', 'SexAI', 'SinI', 'Sau96I', 'BstNI', 'Cfr13I', 'TseI', 'MvaI', 'AflIII', 'TfiI', 'BanI', 'RsrII', 'BcnI', 'ScrFI', 'EcoRII', 'EcoNI', 'ApeKI', 'HinfI', 'NciI', 'Fsp4HI', 'EcoO109I', 'PpuMI', 'AvaII', 'Tth111I', 'AccI', 'DdeI', 'BsaJI', 'Fnu4HI', 'Tsp45I'],
)
typedict['type81'] = _temp()
-
def _temp():
return (
('Palindromic', 'OneCut', 'Ov5', 'Ambiguous', 'Meth_Dep', 'Not_available', 'AbstractCut', 'RestrictionType'),
@@ -18365,23 +19479,20 @@ def _temp():
)
typedict['type82'] = _temp()
-
def _temp():
return (
('Palindromic', 'OneCut', 'Ov5', 'Ambiguous', 'Meth_Undep', 'Commercially_available', 'AbstractCut', 'RestrictionType'),
- ['AxyI', 'BstSFI', 'BspT107I', 'BstOI', 'PspEI', 'BisI', 'BstPI', 'BstEII', 'BmgT120I', 'BssT1I', 'PsyI', 'BtgI', 'Eco91I', 'Bst2UI', 'BstDEI', 'GluI', 'BstENI', 'Ama87I', 'BstDSI', 'PflFI', 'Bme1390I', 'Psp5II', 'EcoO65I', 'BmrFI', 'EcoT14I', 'PspPPI', 'SmlI', 'BseBI', 'PfoI', 'BshNI', 'Rsr2I', 'BmeT110I', 'PasI', 'TseFI', 'CpoI', 'Eco130I', 'CspI', 'BfmI', 'Eco47I', 'Bsu36I', 'Bme18I', 'MabI', 'Hpy188III', 'Bse21I', 'BstSCI', 'BpuMI', 'SgeI', 'SmoI', 'Bsp1720I', 'MaeIII', 'ErhI [...]
+ ['AxyI', 'BspT107I', 'PspEI', 'BisI', 'BstPI', 'BstSFI', 'BstEII', 'BmgT120I', 'BssT1I', 'PsyI', 'BtgI', 'Eco91I', 'Bst2UI', 'BstDEI', 'GluI', 'BstENI', 'Ama87I', 'BstDSI', 'PflFI', 'Bme1390I', 'Psp5II', 'EcoO65I', 'BmrFI', 'EcoT14I', 'PspPPI', 'SmlI', 'BseBI', 'PfoI', 'BshNI', 'Rsr2I', 'BmeT110I', 'PasI', 'TseFI', 'CpoI', 'Eco130I', 'CspI', 'BfmI', 'Eco47I', 'Bsu36I', 'Bme18I', 'MabI', 'Hpy188III', 'Bse21I', 'BstSCI', 'BpuMI', 'SgeI', 'XagI', 'SmoI', 'Bsp1720I', 'MaeIII', 'ErhI' [...]
)
typedict['type83'] = _temp()
-
def _temp():
return (
('Palindromic', 'OneCut', 'Ov5', 'Ambiguous', 'Meth_Undep', 'Not_available', 'AbstractCut', 'RestrictionType'),
- ['SanDI', 'DsaI', 'DraII', 'SauI', 'UnbI', 'VpaK11AI', 'SfeI', 'CauII', 'Hpy178III', 'AsuI', 'SecI', 'EspI', 'Sse8647I'],
+ ['SanDI', 'DsaI', 'SauI', 'DraII', 'UnbI', 'VpaK11AI', 'SfeI', 'CauII', 'Hpy178III', 'AsuI', 'SecI', 'EspI', 'Sse8647I'],
)
typedict['type84'] = _temp()
-
def _temp():
return (
('Palindromic', 'OneCut', 'Ov3', 'Defined', 'Meth_Dep', 'Commercially_available', 'AbstractCut', 'RestrictionType'),
@@ -18389,7 +19500,6 @@ def _temp():
)
typedict['type89'] = _temp()
-
def _temp():
return (
('Palindromic', 'OneCut', 'Ov3', 'Defined', 'Meth_Dep', 'Not_available', 'AbstractCut', 'RestrictionType'),
@@ -18397,15 +19507,13 @@ def _temp():
)
typedict['type90'] = _temp()
-
def _temp():
return (
('Palindromic', 'OneCut', 'Ov3', 'Defined', 'Meth_Undep', 'Commercially_available', 'AbstractCut', 'RestrictionType'),
- ['SgrBI', 'BstHHI', 'BstH2I', 'BmtI', 'BpvUI', 'PvuI', 'BspOI', 'AspLEI', 'Zsp2I', 'FaeI', 'SdaI', 'BstNSI', 'Ple19I', 'Hin1II', 'Sse8387I', 'TaiI', 'Psp124BI', 'Sfr303I', 'Mph1103I', 'CfoI', 'PaeI', 'BstKTI', 'SstI', 'SphI', 'MvrI', 'SfaAI', 'RgaI', 'SgfI', 'SbfI', 'XceI', 'NsiI', 'EcoT22I', 'PacI', 'KspI', 'Hsp92II', 'BfoI', 'RigI'],
+ ['SgrBI', 'BstHHI', 'BstH2I', 'BmtI', 'PvuI', 'BspOI', 'Zsp2I', 'FaeI', 'SdaI', 'BstNSI', 'Ple19I', 'Hin1II', 'Sse8387I', 'TaiI', 'Hsp92II', 'Psp124BI', 'Sfr303I', 'Mph1103I', 'CfoI', 'PaeI', 'BstKTI', 'SstI', 'AspLEI', 'SphI', 'SfaAI', 'RgaI', 'SgfI', 'SbfI', 'BspMAI', 'XceI', 'NsiI', 'EcoT22I', 'PacI', 'KspI', 'BfoI', 'RigI'],
)
typedict['type91'] = _temp()
-
def _temp():
return (
('Palindromic', 'OneCut', 'Ov3', 'Defined', 'Meth_Undep', 'Not_available', 'AbstractCut', 'RestrictionType'),
@@ -18413,15 +19521,13 @@ def _temp():
)
typedict['type92'] = _temp()
-
def _temp():
return (
('Palindromic', 'OneCut', 'Ov3', 'Ambiguous', 'Meth_Dep', 'Commercially_available', 'AbstractCut', 'RestrictionType'),
- ['MwoI', 'EcoT38I', 'BglI', 'BanII', 'TspRI', 'Hpy99I', 'BstXI', 'BsiHKAI', 'BaeGI', 'Hpy188I', 'Bsc4I', 'DraIII', 'Bsp1286I', 'AhdI', 'SfiI', 'XcmI', 'BslI'],
+ ['AgsI', 'MwoI', 'EcoT38I', 'BanII', 'TspRI', 'Hpy99I', 'BstXI', 'Bsc4I', 'BaeGI', 'Hpy188I', 'DraIII', 'Bsp1286I', 'AhdI', 'SfiI', 'XcmI', 'BslI'],
)
typedict['type93'] = _temp()
-
def _temp():
return (
('Palindromic', 'OneCut', 'Ov3', 'Ambiguous', 'Meth_Dep', 'Not_available', 'AbstractCut', 'RestrictionType'),
@@ -18429,15 +19535,13 @@ def _temp():
)
typedict['type94'] = _temp()
-
def _temp():
return (
('Palindromic', 'OneCut', 'Ov3', 'Ambiguous', 'Meth_Undep', 'Commercially_available', 'AbstractCut', 'RestrictionType'),
- ['AgsI', 'DrdI', 'TscAI', 'BstMWI', 'Eco24I', 'AlwNI', 'BseSI', 'AdeI', 'Van91I', 'DseDI', 'SetI', 'BmeRI', 'TaaI', 'BstMCI', 'HpyF10VI', 'Bst4CI', 'SduI', 'Alw21I', 'AccB7I', 'BasI', 'BsiEI', 'BseLI', 'FriOI', 'AasI', 'Eam1105I', 'TauI', 'AfiI', 'CaiI', 'Bbv12I', 'BstAPI', 'HpyCH4III', 'BstSLI', 'BlsI', 'PcsI', 'PstNI', 'MhlI', 'DriI', 'Bsh1285I', 'PflMI'],
+ ['AlwNI', 'DrdI', 'BglI', 'TscAI', 'CaiI', 'BstMWI', 'Eco24I', 'BsiHKAI', 'BseSI', 'Van91I', 'DseDI', 'SetI', 'BmeRI', 'TaaI', 'BstMCI', 'HpyF10VI', 'Bst4CI', 'SduI', 'Alw21I', 'AccB7I', 'AdeI', 'BsiEI', 'BseLI', 'FriOI', 'AasI', 'Eam1105I', 'TauI', 'AfiI', 'Bbv12I', 'BstAPI', 'HpyCH4III', 'BstSLI', 'BlsI', 'PcsI', 'PstNI', 'PkrI', 'MhlI', 'DriI', 'Bsh1285I', 'PflMI'],
)
typedict['type95'] = _temp()
-
def _temp():
return (
('Palindromic', 'OneCut', 'Ov3', 'Ambiguous', 'Meth_Undep', 'Not_available', 'AbstractCut', 'RestrictionType'),
@@ -18446,3 +19550,4 @@ def _temp():
typedict['type96'] = _temp()
del _temp
+
diff --git a/Bio/Restriction/__init__.py b/Bio/Restriction/__init__.py
index 26f5ae0..02dd01b 100644
--- a/Bio/Restriction/__init__.py
+++ b/Bio/Restriction/__init__.py
@@ -13,7 +13,7 @@ Example:
>>> from Rana.fts import fts #
>>> from Rana.Vector import * # Just a way to get a sequence.
- >>> from Bio.Seq import Seq # Use your prefered method here.
+ >>> from Bio.Seq import Seq # Use your preferred method here.
>>> pbr = fts(pBR322) #
>>> seq = Seq(str(pbr)) #
>>>
@@ -56,7 +56,6 @@ Example:
from Bio.Restriction.Restriction import *
-__docformat__ = "restructuredtext en"
#
# OK can't put the following code in Bio.Restriction.__init__ unless
diff --git a/Bio/SCOP/Raf.py b/Bio/SCOP/Raf.py
index 5e6d9de..eac25aa 100644
--- a/Bio/SCOP/Raf.py
+++ b/Bio/SCOP/Raf.py
@@ -36,8 +36,6 @@ from Bio.Data.SCOPData import protein_letters_3to1
from Bio.SCOP.Residues import Residues
-__docformat__ = "restructuredtext en"
-
def normalize_letters(one_letter_code):
"""Convert RAF one-letter amino acid codes into IUPAC standard codes.
@@ -285,9 +283,9 @@ class SeqMap(object):
# for k in resFound:
# del resSet[k]
# print(resSet)
-
- raise RuntimeError('I could not find at least one ATOM or HETATM'
- + ' record for each and every residue in this sequence map.')
+ raise RuntimeError("Could not find at least one ATOM or HETATM"
+ " record for each and every residue in this"
+ " sequence map.")
class Res(object):
diff --git a/Bio/SCOP/Residues.py b/Bio/SCOP/Residues.py
index d3a17c2..9805a00 100644
--- a/Bio/SCOP/Residues.py
+++ b/Bio/SCOP/Residues.py
@@ -5,10 +5,10 @@
# Gavin E. Crooks 2001-11-03
# Minor extensions, some bug fixes, and major changes to the interface
+"""A collection of residues from a PDB structure."""
import re
-"""A collection of residues from a PDB structure."""
_pdbid_re = re.compile(r"^(\w\w\w\w)(?:$|\s+|_)(.*)")
_fragment_re = re.compile(r"\(?(\w:)?(-?\w*)-?(-?\w*)\)?(.*)")
diff --git a/Bio/SCOP/__init__.py b/Bio/SCOP/__init__.py
index 3b3313d..d2849c4 100644
--- a/Bio/SCOP/__init__.py
+++ b/Bio/SCOP/__init__.py
@@ -60,7 +60,6 @@ from . import Residues
from Bio import SeqIO
from Bio.Seq import Seq
-__docformat__ = "restructuredtext en"
nodeCodeDict = {'cl': 'class', 'cf': 'fold', 'sf': 'superfamily',
'fa': 'family', 'dm': 'protein', 'sp': 'species', 'px': 'domain'}
@@ -319,7 +318,7 @@ class Scop(object):
def write_des(self, handle):
"""Build a DES SCOP parsable file from this object"""
- # Origional SCOP file is not ordered?
+ # Original SCOP file is not ordered?
for n in sorted(self._sunidDict.values(), key=lambda n: n.sunid):
if n != self.root:
handle.write(str(n.toDesRecord()))
@@ -574,7 +573,7 @@ class Node(object):
return self.scop.getNodeBySunid(self.parent)
def getDescendents(self, node_type):
- """ Return a list of all decendent nodes of the given type. Node type can a
+ """ Return a list of all descendant nodes of the given type. Node type can a
two letter code or longer description. e.g. 'fa' or 'family'
"""
if node_type in _nodetype_to_code:
@@ -698,8 +697,8 @@ class Astral(object):
"""
if astral_file is None and dir_path is None and db_handle is None:
- raise RuntimeError("Need either file handle, or (dir_path + "
- + "version) or database handle to construct Astral")
+ raise RuntimeError("Need either file handle, or (dir_path + version),"
+ " or database handle to construct Astral")
if not scop:
raise RuntimeError("Must provide a Scop instance to construct")
@@ -872,8 +871,8 @@ def search(pdb=None, key=None, sid=None, disp=None, dir=None, loc=None,
return _open(cgi, variables)
-def _open(cgi, params={}, get=1):
- """_open(cgi, params={}, get=1) -> UndoHandle
+def _open(cgi, params=None, get=1):
+ """Open a hnadle to SCOP, returns an UndoHandle
Open a handle to SCOP. cgi is the URL for the cgi script to access.
params is a dictionary with the options to pass to it. get is a boolean
@@ -884,6 +883,8 @@ def _open(cgi, params={}, get=1):
from Bio._py3k import urlopen, urlencode
# Open a handle to SCOP.
+ if params is None:
+ params = {}
options = urlencode(params)
if get: # do a GET
if options:
diff --git a/Bio/SVDSuperimposer/__init__.py b/Bio/SVDSuperimposer/__init__.py
index f14c112..a6af2ff 100644
--- a/Bio/SVDSuperimposer/__init__.py
+++ b/Bio/SVDSuperimposer/__init__.py
@@ -2,7 +2,8 @@
# This code is part of the Biopython distribution and governed by its
# license. Please see the LICENSE file that should have been included
# as part of this package.
-"""
+"""Align on protein structure onto another using SVD alignment.
+
SVDSuperimposer finds the best rotation and translation to put
two point sets on top of each other (minimizing the RMSD). This is
eg. useful to superimpose crystal structures. SVD stands for singular
@@ -16,7 +17,8 @@ from numpy.linalg import svd, det
class SVDSuperimposer(object):
- """
+ """Class to run SVD alignment,
+
SVDSuperimposer finds the best rotation and translation to put
two point sets on top of each other (minimizing the RMSD). This is
eg. useful to superimpose crystal structures.
@@ -44,7 +46,7 @@ class SVDSuperimposer(object):
self.init_rms = None
def _rms(self, coords1, coords2):
- "Return rms deviations between coords1 and coords2."
+ """Return rms deviations between coords1 and coords2."""
diff = coords1 - coords2
l = coords1.shape[0]
return sqrt(sum(sum(diff * diff)) / l)
@@ -52,12 +54,12 @@ class SVDSuperimposer(object):
# Public methods
def set(self, reference_coords, coords):
- """
- Set the coordinates to be superimposed.
+ """Set the coordinates to be superimposed.
+
coords will be put on top of reference_coords.
- o reference_coords: an NxDIM array
- o coords: an NxDIM array
+ - reference_coords: an NxDIM array
+ - coords: an NxDIM array
DIM is the dimension of the points, N is the number
of points to be superimposed.
@@ -74,7 +76,7 @@ class SVDSuperimposer(object):
self.n = n[0]
def run(self):
- "Superimpose the coordinate sets."
+ """Superimpose the coordinate sets."""
if self.coords is None or self.reference_coords is None:
raise Exception("No coordinates set.")
coords = self.coords
@@ -95,7 +97,7 @@ class SVDSuperimposer(object):
self.tran = av2 - dot(av1, self.rot)
def get_transformed(self):
- "Get the transformed coordinate set."
+ """Get the transformed coordinate set."""
if self.coords is None or self.reference_coords is None:
raise Exception("No coordinates set.")
if self.rot is None:
@@ -105,13 +107,13 @@ class SVDSuperimposer(object):
return self.transformed_coords
def get_rotran(self):
- "Right multiplying rotation matrix and translation."
+ """Right multiplying rotation matrix and translation."""
if self.rot is None:
raise Exception("Nothing superimposed yet.")
return self.rot, self.tran
def get_init_rms(self):
- "Root mean square deviation of untransformed coordinates."
+ """Root mean square deviation of untransformed coordinates."""
if self.coords is None:
raise Exception("No coordinates set yet.")
if self.init_rms is None:
@@ -119,7 +121,7 @@ class SVDSuperimposer(object):
return self.init_rms
def get_rms(self):
- "Root mean square deviation of superimposed coordinates."
+ """Root mean square deviation of superimposed coordinates."""
if self.rms is None:
transformed_coords = self.get_transformed()
self.rms = self._rms(transformed_coords, self.reference_coords)
diff --git a/Bio/SearchIO/BlastIO/__init__.py b/Bio/SearchIO/BlastIO/__init__.py
index d616d6f..6fd05a3 100644
--- a/Bio/SearchIO/BlastIO/__init__.py
+++ b/Bio/SearchIO/BlastIO/__init__.py
@@ -402,8 +402,6 @@ from .blast_tab import BlastTabParser, BlastTabIndexer, BlastTabWriter
from .blast_xml import BlastXmlParser, BlastXmlIndexer, BlastXmlWriter
from .blast_text import BlastTextParser
-__docformat__ = "restructuredtext en"
-
# if not used as a module, run the doctest
if __name__ == "__main__":
diff --git a/Bio/SearchIO/BlastIO/blast_tab.py b/Bio/SearchIO/BlastIO/blast_tab.py
index cd37f4b..be45202 100644
--- a/Bio/SearchIO/BlastIO/blast_tab.py
+++ b/Bio/SearchIO/BlastIO/blast_tab.py
@@ -16,8 +16,6 @@ from Bio.SearchIO._model import QueryResult, Hit, HSP, HSPFragment
__all__ = ['BlastTabIndexer', 'BlastTabParser', 'BlastTabWriter']
-__docformat__ = "restructuredtext en"
-
# longname-shortname map
# maps the column names shown in a commented output to its short name
@@ -602,7 +600,7 @@ class BlastTabIndexer(SearchIndexer):
break
def get_raw(self, offset):
- """Returns the raw string of a QueryResult object from the given offset."""
+ """Returns the raw bytes string of a QueryResult object from the given offset."""
if self._kwargs['comments']:
getfunc = self._get_raw_qresult_commented
else:
@@ -611,7 +609,7 @@ class BlastTabIndexer(SearchIndexer):
return getfunc(offset)
def _get_raw_qresult(self, offset):
- """Returns the raw string of a single QueryResult from a noncommented file."""
+ """Returns the raw bytes string of a single QueryResult from a noncommented file."""
handle = self._handle
handle.seek(offset)
qresult_raw = _as_bytes('')
@@ -638,7 +636,7 @@ class BlastTabIndexer(SearchIndexer):
return qresult_raw
def _get_raw_qresult_commented(self, offset):
- """Returns the raw string of a single QueryResult from a commented file."""
+ """Returns the bytes raw string of a single QueryResult from a commented file."""
handle = self._handle
handle.seek(offset)
qresult_raw = _as_bytes('')
diff --git a/Bio/SearchIO/BlastIO/blast_text.py b/Bio/SearchIO/BlastIO/blast_text.py
index cbcdec9..832c440 100644
--- a/Bio/SearchIO/BlastIO/blast_text.py
+++ b/Bio/SearchIO/BlastIO/blast_text.py
@@ -22,8 +22,6 @@ with warnings.catch_warnings():
__all__ = ['BlastTextParser']
-__docformat__ = "restructuredtext en"
-
class BlastTextParser(object):
diff --git a/Bio/SearchIO/BlastIO/blast_xml.py b/Bio/SearchIO/BlastIO/blast_xml.py
index 0ee785d..f4555f6 100644
--- a/Bio/SearchIO/BlastIO/blast_xml.py
+++ b/Bio/SearchIO/BlastIO/blast_xml.py
@@ -14,7 +14,6 @@ from xml.sax.saxutils import XMLGenerator, escape
from Bio import BiopythonParserWarning
-
# For speed try to use cElementTree rather than ElementTree
try:
if (3, 0) <= sys.version_info[:2] <= (3, 1):
@@ -27,18 +26,15 @@ except ImportError:
from xml.etree import ElementTree as ElementTree
-from Bio._py3k import _as_bytes, _bytes_to_string, unicode
-_empty_bytes_string = _as_bytes("")
-
from Bio.Alphabet import generic_dna, generic_protein
from Bio.SearchIO._index import SearchIndexer
from Bio.SearchIO._model import QueryResult, Hit, HSP, HSPFragment
+from Bio._py3k import _as_bytes, _bytes_to_string, unicode
+_empty_bytes_string = _as_bytes("")
__all__ = ['BlastXmlParser', 'BlastXmlIndexer', 'BlastXmlWriter']
-__docformat__ = "restructuredtext en"
-
# element - optional qresult attribute name mapping
_ELEM_QRESULT_OPT = {
@@ -199,8 +195,8 @@ def _extract_ids_and_descs(concat_str):
# create a list of lists, each list containing an ID and description
# or just an ID, if description is not present
- id_desc_pairs = [re.split(_RE_ID_DESC_PATTERN, x, 1) \
- for x in re.split(_RE_ID_DESC_PAIRS_PATTERN, concat_str)]
+ id_desc_pairs = [re.split(_RE_ID_DESC_PATTERN, x, 1)
+ for x in re.split(_RE_ID_DESC_PAIRS_PATTERN, concat_str)]
# make sure empty descriptions are added as empty strings
# also, we return lists for compatibility reasons between Py2 and Py3
add_descs = lambda x: x if len(x) == 2 else x + [""]
@@ -409,7 +405,6 @@ class BlastXmlParser(object):
else:
blast_hit_id = ''
-
# combine primary ID and defline first before splitting
full_id_desc = hit_id + ' ' + hit_desc
id_descs = _extract_ids_and_descs(full_id_desc)
@@ -612,6 +607,7 @@ class BlastXmlIndexer(SearchIndexer):
return next(iter(generator))
def get_raw(self, offset):
+ """Return the raw record from the file as a bytes string."""
qend_mark = self.qend_mark
handle = self._handle
handle.seek(offset)
@@ -654,7 +650,7 @@ class _BlastXmlGenerator(XMLGenerator):
'<!DOCTYPE BlastOutput PUBLIC "-//NCBI//NCBI BlastOutput/EN" '
'"http://www.ncbi.nlm.nih.gov/dtd/NCBI_BlastOutput.dtd">\n')
- def startElement(self, name, attrs={}, children=False):
+ def startElement(self, name, attrs=None, children=False):
"""Starts an XML element.
:param name: element name
@@ -665,6 +661,8 @@ class _BlastXmlGenerator(XMLGenerator):
:type children: bool
"""
+ if attrs is None:
+ attrs = {}
self.ignorableWhitespace(self._indent * self._level)
XMLGenerator.startElement(self, name, attrs)
@@ -673,7 +671,7 @@ class _BlastXmlGenerator(XMLGenerator):
XMLGenerator.endElement(self, name)
self.write(u'\n')
- def startParent(self, name, attrs={}):
+ def startParent(self, name, attrs=None):
"""Starts an XML element which has children.
:param name: element name
@@ -682,6 +680,8 @@ class _BlastXmlGenerator(XMLGenerator):
:type attrs: dictionary {string: object}
"""
+ if attrs is None:
+ attrs = {}
self.startElement(name, attrs, children=True)
self._level += self._increment
self.write(u'\n')
@@ -747,7 +747,7 @@ class BlastXmlWriter(object):
return self.qresult_counter, self.hit_counter, self.hsp_counter, \
self.frag_counter
- def _write_elem_block(self, block_name, map_name, obj, opt_dict={}):
+ def _write_elem_block(self, block_name, map_name, obj, opt_dict=None):
"""Writes sibling XML elements.
:param block_name: common element name prefix
@@ -760,6 +760,8 @@ class BlastXmlWriter(object):
:type opt_dict: dictionary {string: string}
"""
+ if opt_dict is None:
+ opt_dict = {}
for elem, attr in _WRITE_MAPS[map_name]:
elem = block_name + elem
try:
diff --git a/Bio/SearchIO/BlatIO.py b/Bio/SearchIO/BlatIO.py
index ac2fac9..e2314a7 100644
--- a/Bio/SearchIO/BlatIO.py
+++ b/Bio/SearchIO/BlatIO.py
@@ -192,8 +192,6 @@ from Bio.SearchIO._model import QueryResult, Hit, HSP, HSPFragment
__all__ = ['BlatPslParser', 'BlatPslIndexer', 'BlatPslWriter']
-__docformat__ = "restructuredtext en"
-
# precompile regex patterns
_PTR_ROW_CHECK = r'^\d+\s+\d+\s+\d+\s+\d+'
@@ -567,7 +565,7 @@ class BlatPslIndexer(SearchIndexer):
break
def get_raw(self, offset):
- """Returns the raw string of a QueryResult object from the given offset."""
+ """Returns raw bytes string of a QueryResult object from the given offset."""
handle = self._handle
handle.seek(offset)
query_id_idx = 9
diff --git a/Bio/SearchIO/ExonerateIO/exonerate_vulgar.py b/Bio/SearchIO/ExonerateIO/exonerate_vulgar.py
index 956936c..da8edf8 100644
--- a/Bio/SearchIO/ExonerateIO/exonerate_vulgar.py
+++ b/Bio/SearchIO/ExonerateIO/exonerate_vulgar.py
@@ -179,7 +179,7 @@ class ExonerateVulgarIndexer(_BaseExonerateIndexer):
return id.group(1)
def get_raw(self, offset):
- """Returns the raw string of a QueryResult object from the given offset."""
+ """Returns the raw bytes string of a QueryResult object from the given offset."""
handle = self._handle
handle.seek(offset)
qresult_key = None
diff --git a/Bio/SearchIO/FastaIO.py b/Bio/SearchIO/FastaIO.py
index 6e49976..e0d88dd 100644
--- a/Bio/SearchIO/FastaIO.py
+++ b/Bio/SearchIO/FastaIO.py
@@ -116,8 +116,6 @@ from Bio.SearchIO._model import QueryResult, Hit, HSP, HSPFragment
__all__ = ['FastaM10Parser', 'FastaM10Indexer']
-__docformat__ = "restructuredtext en"
-
# precompile regex patterns
# regex for program name
@@ -153,7 +151,7 @@ _STATE_HIT_BLOCK = 2
_STATE_CONS_BLOCK = 3
-def _set_qresult_hits(qresult, hit_rows=[]):
+def _set_qresult_hits(qresult, hit_rows=()):
"""Helper function for appending Hits without alignments into QueryResults."""
for hit_row in hit_rows:
hit_id, remainder = hit_row.split(' ', 1)
@@ -536,14 +534,15 @@ class FastaM10Indexer(SearchIndexer):
start_offset = end_offset - len(line)
# yield whenever we encounter a new query or at the end of the file
if qresult_key is not None:
- if (not peekline.startswith(query_mark)
- and query_mark in peekline) or not line:
+ if (not peekline.startswith(query_mark) and
+ query_mark in peekline) or not line:
yield qresult_key, start_offset, end_offset - start_offset
if not line:
break
start_offset = end_offset
def get_raw(self, offset):
+ """Return the raw record from the file as a bytes string."""
handle = self._handle
qresult_raw = _as_bytes('')
query_mark = _as_bytes('>>>')
diff --git a/Bio/SearchIO/HmmerIO/__init__.py b/Bio/SearchIO/HmmerIO/__init__.py
index 3bb9b3d..bf9c6ea 100644
--- a/Bio/SearchIO/HmmerIO/__init__.py
+++ b/Bio/SearchIO/HmmerIO/__init__.py
@@ -242,6 +242,8 @@ The following SearchIO objects attributes are provided:
| | evalue | hit-level e-value |
| +-------------------------+----------------------------------+
| | id | hit sequence ID |
+| +-------------------------+----------------------------------+
+| | seq_len | length of hit sequence or HMM |
+-----------------+-------------------------+----------------------------------+
| HSP | acc_avg | expected accuracy per alignment |
| | | residue (acc column) |
@@ -291,9 +293,6 @@ from .hmmer3_text import Hmmer3TextParser, Hmmer3TextIndexer
from .hmmer3_tab import Hmmer3TabParser, Hmmer3TabIndexer, Hmmer3TabWriter
-__docformat__ = "restructuredtext en"
-
-
# if not used as a module, run the doctest
if __name__ == "__main__":
from Bio._utils import run_doctest
diff --git a/Bio/SearchIO/HmmerIO/_base.py b/Bio/SearchIO/HmmerIO/_base.py
index c8e279b..bd396d0 100644
--- a/Bio/SearchIO/HmmerIO/_base.py
+++ b/Bio/SearchIO/HmmerIO/_base.py
@@ -9,9 +9,6 @@ from Bio._py3k import _as_bytes
from Bio.SearchIO._index import SearchIndexer
-__docformat__ = "restructuredtext en"
-
-
class _BaseHmmerTextIndexer(SearchIndexer):
"""Base indexer class for HMMER plain text output."""
@@ -21,6 +18,7 @@ class _BaseHmmerTextIndexer(SearchIndexer):
self._preamble = _as_bytes('')
def get_raw(self, offset):
+ """Return the raw record from the file as a bytes string."""
handle = self._handle
qresult_raw = _as_bytes('')
diff --git a/Bio/SearchIO/HmmerIO/hmmer2_text.py b/Bio/SearchIO/HmmerIO/hmmer2_text.py
index 32e8af2..54e20e2 100644
--- a/Bio/SearchIO/HmmerIO/hmmer2_text.py
+++ b/Bio/SearchIO/HmmerIO/hmmer2_text.py
@@ -16,7 +16,6 @@ from ._base import _BaseHmmerTextIndexer
__all__ = ['Hmmer2TextParser', 'Hmmer2TextIndexer']
-__docformat__ = "restructuredtext en"
_HSP_ALIGN_LINE = re.compile(r'(\S+):\s+domain (\d+) of (\d+)')
diff --git a/Bio/SearchIO/HmmerIO/hmmer3_domtab.py b/Bio/SearchIO/HmmerIO/hmmer3_domtab.py
index 03265ad..de3ce51 100644
--- a/Bio/SearchIO/HmmerIO/hmmer3_domtab.py
+++ b/Bio/SearchIO/HmmerIO/hmmer3_domtab.py
@@ -13,9 +13,6 @@ from Bio.SearchIO._model import QueryResult, Hit, HSP, HSPFragment
from .hmmer3_tab import Hmmer3TabParser, Hmmer3TabIndexer
-__docformat__ = "restructuredtext en"
-
-
class Hmmer3DomtabParser(Hmmer3TabParser):
"""Base hmmer3-domtab iterator."""
diff --git a/Bio/SearchIO/HmmerIO/hmmer3_tab.py b/Bio/SearchIO/HmmerIO/hmmer3_tab.py
index 720b50a..44e0062 100644
--- a/Bio/SearchIO/HmmerIO/hmmer3_tab.py
+++ b/Bio/SearchIO/HmmerIO/hmmer3_tab.py
@@ -15,8 +15,6 @@ from Bio.SearchIO._model import QueryResult, Hit, HSP, HSPFragment
__all__ = ['Hmmer3TabParser', 'Hmmer3TabIndexer', 'Hmmer3TabWriter']
-__docformat__ = "restructuredtext en"
-
class Hmmer3TabParser(object):
@@ -199,7 +197,7 @@ class Hmmer3TabIndexer(SearchIndexer):
break
def get_raw(self, offset):
- """Returns the raw string of a QueryResult object from the given offset."""
+ """Returns the raw bytes string of a QueryResult object from the given offset."""
handle = self._handle
handle.seek(offset)
query_id_idx = self._query_id_idx
diff --git a/Bio/SearchIO/HmmerIO/hmmer3_text.py b/Bio/SearchIO/HmmerIO/hmmer3_text.py
index 1652d45..a49478e 100644
--- a/Bio/SearchIO/HmmerIO/hmmer3_text.py
+++ b/Bio/SearchIO/HmmerIO/hmmer3_text.py
@@ -16,8 +16,6 @@ from ._base import _BaseHmmerTextIndexer
__all__ = ['Hmmer3TextParser', 'Hmmer3TextIndexer']
-__docformat__ = "restructuredtext en"
-
# precompile regex patterns for faster processing
# regex for program name capture
@@ -219,8 +217,8 @@ class Hmmer3TextParser(object):
def _create_hits(self, hit_attrs, qid, qdesc):
"""Parses a HMMER3 hsp block, beginning with the hsp table."""
# read through until the beginning of the hsp block
- self._read_until(lambda line: line.startswith('Internal pipeline')
- or line.startswith('>>'))
+ self._read_until(lambda line: line.startswith('Internal pipeline') or
+ line.startswith('>>'))
# start parsing the hsp block
hit_list = []
diff --git a/Bio/SearchIO/__init__.py b/Bio/SearchIO/__init__.py
index bb73f9b..78cbd01 100755
--- a/Bio/SearchIO/__init__.py
+++ b/Bio/SearchIO/__init__.py
@@ -213,8 +213,6 @@ warnings.warn('Bio.SearchIO is an experimental submodule which may undergo '
__all__ = ['read', 'parse', 'to_dict', 'index', 'index_db', 'write', 'convert']
-__docformat__ = "restructuredtext en"
-
# dictionary of supported formats for parse() and read()
_ITERATOR_MAP = {
diff --git a/Bio/SearchIO/_index.py b/Bio/SearchIO/_index.py
index b6d272b..613cdde 100644
--- a/Bio/SearchIO/_index.py
+++ b/Bio/SearchIO/_index.py
@@ -12,9 +12,6 @@ from Bio import bgzf
from Bio.File import _IndexedSeqFileProxy, _open_for_random_access
-__docformat__ = "restructuredtext en"
-
-
class SearchIndexer(_IndexedSeqFileProxy):
"""Base class for file format specific random access.
diff --git a/Bio/SearchIO/_model/__init__.py b/Bio/SearchIO/_model/__init__.py
index ebfa545..ecd90cc 100644
--- a/Bio/SearchIO/_model/__init__.py
+++ b/Bio/SearchIO/_model/__init__.py
@@ -51,8 +51,6 @@ from .hsp import HSP, HSPFragment
__all__ = ['QueryResult', 'Hit', 'HSP', 'HSPFragment']
-__docformat__ = "restructuredtext en"
-
# if not used as a module, run the doctest
if __name__ == "__main__":
diff --git a/Bio/SearchIO/_model/_base.py b/Bio/SearchIO/_model/_base.py
index 8c08a35..260ecdb 100644
--- a/Bio/SearchIO/_model/_base.py
+++ b/Bio/SearchIO/_model/_base.py
@@ -9,9 +9,6 @@
from Bio._utils import getattr_str, trim_str
-__docformat__ = "restructuredtext en"
-
-
class _BaseSearchObject(object):
"""Abstract class for SearchIO objects."""
diff --git a/Bio/SearchIO/_model/hit.py b/Bio/SearchIO/_model/hit.py
index c27b7a3..006322a 100644
--- a/Bio/SearchIO/_model/hit.py
+++ b/Bio/SearchIO/_model/hit.py
@@ -18,9 +18,6 @@ from ._base import _BaseSearchObject
from .hsp import HSP
-__docformat__ = "restructuredtext en"
-
-
class Hit(_BaseSearchObject):
"""Class representing a single database hit of a search result.
@@ -107,7 +104,7 @@ class Hit(_BaseSearchObject):
# from this one
_NON_STICKY_ATTRS = ('_items', )
- def __init__(self, hsps=[], id=None, query_id=None):
+ def __init__(self, hsps=(), id=None, query_id=None):
"""Initializes a Hit object.
:param hsps: HSP objects contained in the Hit object
@@ -129,6 +126,8 @@ class Hit(_BaseSearchObject):
self._description_alt = []
self._query_description = None
+ # TODO - Move this into the for look below in case
+ # hsps is a single use iterator?
for attr in ('query_id', 'query_description', 'hit_id',
'hit_description'):
# HACK: setting the if clause to '> 1' allows for empty hit objects.
diff --git a/Bio/SearchIO/_model/hsp.py b/Bio/SearchIO/_model/hsp.py
index 8f282b4..e87e617 100644
--- a/Bio/SearchIO/_model/hsp.py
+++ b/Bio/SearchIO/_model/hsp.py
@@ -24,9 +24,6 @@ from Bio.SearchIO._utils import singleitem, allitems, fullcascade, \
from ._base import _BaseHSP
-__docformat__ = "restructuredtext en"
-
-
class HSP(_BaseHSP):
"""Class representing high-scoring region(s) between query and hit.
@@ -253,7 +250,7 @@ class HSP(_BaseHSP):
# from this one
_NON_STICKY_ATTRS = ('_items', )
- def __init__(self, fragments=[]):
+ def __init__(self, fragments=()):
"""Initializes an HSP object.
:param fragments: fragments contained in the HSP object
@@ -269,6 +266,8 @@ class HSP(_BaseHSP):
if not fragments:
raise ValueError("HSP objects must have at least one HSPFragment "
"object.")
+ # TODO - Move this into the for look in case hsps is a single use
+ # iterable?
# check that all fragments contain the same IDs, descriptions, alphabet
for attr in ('query_id', 'query_description', 'hit_id',
'hit_description', 'alphabet'):
diff --git a/Bio/SearchIO/_model/query.py b/Bio/SearchIO/_model/query.py
index 4101b31..0839998 100644
--- a/Bio/SearchIO/_model/query.py
+++ b/Bio/SearchIO/_model/query.py
@@ -21,9 +21,6 @@ from ._base import _BaseSearchObject
from .hit import Hit
-__docformat__ = "restructuredtext en"
-
-
class QueryResult(_BaseSearchObject):
"""Class representing search results from a single query.
@@ -188,7 +185,7 @@ class QueryResult(_BaseSearchObject):
# from this one
_NON_STICKY_ATTRS = ('_items', '__alt_hit_ids', )
- def __init__(self, hits=[], id=None,
+ def __init__(self, hits=(), id=None,
hit_key_function=lambda hit: hit.id):
"""Initializes a QueryResult object.
@@ -497,7 +494,7 @@ class QueryResult(_BaseSearchObject):
else:
hit_key = hit.id
- if hit_key not in self and all([pid not in self for pid in hit.id_all[1:]]):
+ if hit_key not in self and all(pid not in self for pid in hit.id_all[1:]):
self[hit_key] = hit
else:
raise ValueError("The ID or alternative IDs of Hit %r exists in "
diff --git a/Bio/Seq.py b/Bio/Seq.py
index ae3e42f..1474c74 100644
--- a/Bio/Seq.py
+++ b/Bio/Seq.py
@@ -1,6 +1,6 @@
# Copyright 2000-2002 Brad Chapman.
# Copyright 2004-2005 by M de Hoon.
-# Copyright 2007-2014 by Peter Cock.
+# Copyright 2007-2015 by Peter Cock.
# All rights reserved.
# This code is part of the Biopython distribution and governed by its
# license. Please see the LICENSE file that should have been included
@@ -17,8 +17,6 @@ See also the Seq_ wiki and the chapter in our tutorial:
"""
from __future__ import print_function
-__docformat__ = "restructuredtext en" # Don't just use plain text in epydoc API pages!
-
import string # for maketrans only
import array
import sys
@@ -881,7 +879,7 @@ class Seq(object):
return Seq(str(self).replace("U", "T").replace("u", "t"), alphabet)
def translate(self, table="Standard", stop_symbol="*", to_stop=False,
- cds=False):
+ cds=False, gap=None):
"""Turns a nucleotide sequence into a protein sequence. New Seq object.
This method will translate DNA or RNA sequences, and those with a
@@ -908,6 +906,8 @@ class Seq(object):
single in frame stop codon at the end (this will be excluded
from the protein sequence, regardless of the to_stop option).
If these tests fail, an exception is raised.
+ - gap - Single character string to denote symbol used for gaps.
+ It will try to guess the gap character from the alphabet.
e.g. Using the standard table:
@@ -949,12 +949,24 @@ class Seq(object):
>>> coding_dna2.translate(to_stop=True)
Seq('LAIVMGR', ExtendedIUPACProtein())
+ When translating gapped sequences, the gap character is inferred from
+ the alphabet:
+
+ >>> from Bio.Alphabet import Gapped
+ >>> coding_dna3 = Seq("GTG---GCCATT", Gapped(IUPAC.unambiguous_dna))
+ >>> coding_dna3.translate()
+ Seq('V-AI', Gapped(ExtendedIUPACProtein(), '-'))
+
+ It is possible to pass the gap character when the alphabet is missing:
+
+ >>> coding_dna4 = Seq("GTG---GCCATT")
+ >>> coding_dna4.translate(gap='-')
+ Seq('V-AI', Gapped(ExtendedIUPACProtein(), '-'))
+
NOTE - Ambiguous codons like "TAN" or "NNN" could be an amino acid
or a stop codon. These are translated as "X". Any invalid codon
(e.g. "TA?" or "T-A") will throw a TranslationError.
- NOTE - Does NOT support gapped sequences.
-
NOTE - This does NOT behave like the python string's translate
method. For that use str(my_seq).translate(...) instead.
"""
@@ -1000,13 +1012,26 @@ class Seq(object):
# The same table can be used for RNA or DNA (we use this for
# translating strings).
codon_table = CodonTable.ambiguous_generic_by_id[table_id]
- protein = _translate_str(str(self), codon_table,
- stop_symbol, to_stop, cds)
- if stop_symbol in protein:
- alphabet = Alphabet.HasStopCodon(codon_table.protein_alphabet,
- stop_symbol=stop_symbol)
+
+ # Deal with gaps for translation
+ if hasattr(self.alphabet, "gap_char"):
+ if not gap:
+ gap = self.alphabet.gap_char
+ elif gap != self.alphabet.gap_char:
+ raise ValueError("Gap {0!r} does not match {1!r} from alphabet".format(
+ gap, self.alphabet.gap_char))
+
+ protein = _translate_str(str(self), codon_table, stop_symbol, to_stop,
+ cds, gap=gap)
+
+ if gap and gap in protein:
+ alphabet = Alphabet.Gapped(codon_table.protein_alphabet, gap)
else:
alphabet = codon_table.protein_alphabet
+
+ if stop_symbol in protein:
+ alphabet = Alphabet.HasStopCodon(alphabet, stop_symbol)
+
return Seq(protein, alphabet)
def ungap(self, gap=None):
@@ -1108,7 +1133,7 @@ class UnknownSeq(Seq):
NNNNN
However, this is rather wasteful of memory (especially for large
- sequences), which is where this class is most usefull:
+ sequences), which is where this class is most useful:
>>> unk_five = UnknownSeq(5)
>>> unk_five
@@ -1871,11 +1896,15 @@ class MutableSeq(object):
Because str(my_seq) will give you the full sequence as a python string,
there is often no need to make an explicit conversion. For example,
- print("ID={%s}, sequence={%s}" % (my_name, my_seq))
+ >>> my_seq = Seq("ATCGTG")
+ >>> my_name = "seq_1"
+ >>> print("ID={%s}, sequence={%s}" % (my_name, my_seq))
+ ID={seq_1}, sequence={ATCGTG}
On Biopython 1.44 or older you would have to have done this:
- print("ID={%s}, sequence={%s}" % (my_name, my_seq.tostring()))
+ >>> print("ID={%s}, sequence={%s}" % (my_name, my_seq.tostring()))
+ ID={seq_1}, sequence={ATCGTG}
"""
from Bio import BiopythonDeprecationWarning
warnings.warn("This method is obsolete; please use str(my_seq) "
@@ -1949,7 +1978,7 @@ def back_transcribe(rna):
def _translate_str(sequence, table, stop_symbol="*", to_stop=False,
- cds=False, pos_stop="X"):
+ cds=False, pos_stop="X", gap=None):
"""Helper function to translate a nucleotide string (PRIVATE).
Arguments:
@@ -1968,6 +1997,8 @@ def _translate_str(sequence, table, stop_symbol="*", to_stop=False,
single in frame stop codon at the end (this will be excluded
from the protein sequence, regardless of the to_stop option).
If these tests fail, an exception is raised.
+ - gap - Single character string to denote symbol used for gaps.
+ Defaults to None.
Returns a string.
@@ -1988,7 +2019,7 @@ def _translate_str(sequence, table, stop_symbol="*", to_stop=False,
...
TranslationError: Codon 'TA?' is invalid
- In a change to older verions of Biopython, partial codons are now
+ In a change to older versions of Biopython, partial codons are now
always regarded as an error (previously only checked if cds=True)
and will trigger a warning (likely to become an exception in a
future release).
@@ -2034,17 +2065,21 @@ def _translate_str(sequence, table, stop_symbol="*", to_stop=False,
n -= 6
amino_acids = ["M"]
elif n % 3 != 0:
- from Bio import BiopythonWarning
warnings.warn("Partial codon, len(sequence) not a multiple of three. "
"Explicitly trim the sequence or add trailing N before "
"translation. This may become an error in future.",
BiopythonWarning)
+ if gap is not None:
+ if not isinstance(gap, basestring):
+ raise TypeError("Gap character should be a single character string.")
+ elif len(gap) > 1:
+ raise ValueError("Gap character should be a single character string.")
+
for i in range(0, n - n % 3, 3):
codon = sequence[i:i + 3]
try:
amino_acids.append(forward_table[codon])
except (KeyError, CodonTable.TranslationError):
- # TODO? Treat "---" as a special case (gapped translation)
if codon in table.stop_codons:
if cds:
raise CodonTable.TranslationError(
@@ -2055,6 +2090,9 @@ def _translate_str(sequence, table, stop_symbol="*", to_stop=False,
elif valid_letters.issuperset(set(codon)):
# Possible stop codon (e.g. NNN or TAN)
amino_acids.append(pos_stop)
+ elif gap is not None and codon == gap * 3:
+ # Gapped translation
+ amino_acids.append(gap)
else:
raise CodonTable.TranslationError(
"Codon '{0}' is invalid".format(codon))
@@ -2062,7 +2100,7 @@ def _translate_str(sequence, table, stop_symbol="*", to_stop=False,
def translate(sequence, table="Standard", stop_symbol="*", to_stop=False,
- cds=False):
+ cds=False, gap=None):
"""Translate a nucleotide sequence into amino acids.
If given a string, returns a new string object. Given a Seq or
@@ -2088,6 +2126,8 @@ def translate(sequence, table="Standard", stop_symbol="*", to_stop=False,
single in frame stop codon at the end (this will be excluded
from the protein sequence, regardless of the to_stop option).
If these tests fail, an exception is raised.
+ - gap - Single character string to denote symbol used for gaps.
+ Defaults to None.
A simple string example using the default (standard) genetic code:
@@ -2126,8 +2166,6 @@ def translate(sequence, table="Standard", stop_symbol="*", to_stop=False,
or a stop codon. These are translated as "X". Any invalid codon
(e.g. "TA?" or "T-A") will throw a TranslationError.
- NOTE - Does NOT support gapped sequences.
-
It will however translate either DNA or RNA.
"""
if isinstance(sequence, Seq):
@@ -2146,7 +2184,8 @@ def translate(sequence, table="Standard", stop_symbol="*", to_stop=False,
codon_table = table
else:
raise ValueError('Bad table argument')
- return _translate_str(sequence, codon_table, stop_symbol, to_stop, cds)
+ return _translate_str(sequence, codon_table, stop_symbol, to_stop, cds,
+ gap=gap)
def reverse_complement(sequence):
diff --git a/Bio/SeqFeature.py b/Bio/SeqFeature.py
index f990c26..3c6c751 100644
--- a/Bio/SeqFeature.py
+++ b/Bio/SeqFeature.py
@@ -44,8 +44,8 @@ classes:
- CompoundLocation - Collection of FeatureLocation objects (for joins etc).
- ExactPosition - Specify the position as being exact.
- - WithinPosition - Specify a position occuring within some range.
- - BetweenPosition - Specify a position occuring between a range (OBSOLETE?).
+ - WithinPosition - Specify a position occurring within some range.
+ - BetweenPosition - Specify a position occurring between a range (OBSOLETE?).
- BeforePosition - Specify the position as being found before some base.
- AfterPosition - Specify the position as being found after some base.
- OneOfPosition - Specify a position where the location can be multiple positions.
@@ -54,9 +54,9 @@ classes:
from __future__ import print_function
-from Bio.Seq import MutableSeq, reverse_complement
+from Bio._py3k import _is_int_or_long
-__docformat__ = "restructuredtext en"
+from Bio.Seq import MutableSeq, reverse_complement
class SeqFeature(object):
@@ -88,8 +88,8 @@ class SeqFeature(object):
values.
- sub_features - Obsolete list of additional SeqFeatures which was
used for holding compound locations (e.g. joins in GenBank/EMBL).
- This is now superceded by a CompoundFeatureLocation as the location,
- and should not be used (DEPRECATED).
+ This is now superceded by a CompoundLocation as the location, and
+ should not be used (DEPRECATED).
"""
def __init__(self, location=None, type='', location_operator='',
@@ -545,13 +545,27 @@ class Reference(object):
return "%s(title=%s, ...)" % (self.__class__.__name__,
repr(self.title))
+ def __eq__(self, other):
+ """Check if two Reference objects should be considered equal
+
+ Note that the location is not compared, as __eq__ for the
+ FeatureLocation class is not defined.
+ """
+ return self.authors == other.authors and \
+ self.consrtm == other.consrtm and \
+ self.title == other.title and \
+ self.journal == other.journal and \
+ self.medline_id == other.medline_id and \
+ self.pubmed_id == other.pubmed_id and \
+ self.comment == other.comment
+
# --- Handling feature locations
class FeatureLocation(object):
"""Specify the location of a feature along a sequence.
- The FeatureLocation is used for simple continous features, which can
+ The FeatureLocation is used for simple continuous features, which can
be described as running from a start position to and end position
(optionally with a strand and reference information). More complex
locations made up from several non-continuous parts (e.g. a coding
@@ -670,13 +684,13 @@ class FeatureLocation(object):
# TODO - Check 0 <= start <= end (<= length of reference)
if isinstance(start, AbstractPosition):
self._start = start
- elif isinstance(start, int) or isinstance(start, long):
+ elif _is_int_or_long(start):
self._start = ExactPosition(start)
else:
raise TypeError("start=%r %s" % (start, type(start)))
if isinstance(end, AbstractPosition):
self._end = end
- elif isinstance(end, int) or isinstance(end, long):
+ elif _is_int_or_long(end):
self._end = ExactPosition(end)
else:
raise TypeError("end=%r %s" % (end, type(end)))
@@ -896,9 +910,9 @@ class FeatureLocation(object):
def parts(self):
"""Read only list of parts (always one, the Feature Location).
- This is a convience property allowing you to write code handling
+ This is a convenience property allowing you to write code handling
both simple FeatureLocation objects (with one part) and more complex
- CompoundLocation objects (with multiple parts) interchangably.
+ CompoundLocation objects (with multiple parts) interchangeably.
"""
return [self]
@@ -1513,7 +1527,7 @@ class WithinPosition(int, AbstractPosition):
def __new__(cls, position, left, right):
assert position == left or position == right, \
"WithinPosition: %r should match left %r or right %r" \
- (position, left, right)
+ % (position, left, right)
obj = int.__new__(cls, position)
obj._left = left
obj._right = right
diff --git a/Bio/SeqIO/AbiIO.py b/Bio/SeqIO/AbiIO.py
index cd4ec24..efeac1d 100644
--- a/Bio/SeqIO/AbiIO.py
+++ b/Bio/SeqIO/AbiIO.py
@@ -1,5 +1,5 @@
# Copyright 2011 by Wibowo Arindrarto (w.arindrarto at gmail.com)
-# Revisions copyright 2011, 2014 by Peter Cock.
+# Revisions copyright 2011-2016 by Peter Cock.
# This code is part of the Biopython distribution and governed by its
# license. Please see the LICENSE file that should have been included
# as part of this package.
@@ -14,7 +14,6 @@ http://www.appliedbiosystem.com/support/software_community/ABIF_File_Format.pdf
"""
-__docformat__ = "restructuredtext en"
import datetime
import struct
@@ -170,9 +169,9 @@ _INSTRUMENT_SPECIFIC_TAGS['general'] = {
'User1': 'Name of user who created the plate (optional)',
}
-# No instrument specific tags
-#_INSTRUMENT_SPECIFIC_TAGS['abi_prism_3100/3100-Avant'] = {
-#}
+# No instrument specific tags
+# _INSTRUMENT_SPECIFIC_TAGS['abi_prism_3100/3100-Avant'] = {
+# }
_INSTRUMENT_SPECIFIC_TAGS['abi_3130/3130xl'] = {
'CTOw1': 'Container owner',
@@ -405,7 +404,7 @@ def AbiIterator(handle, alphabet=None, trim=False):
# use the file name as SeqRecord.name if available
try:
file_name = basename(handle.name).replace('.ab1', '')
- except:
+ except AttributeError:
file_name = ""
record = SeqRecord(Seq(seq, alphabet),
id=sample_id, name=file_name,
diff --git a/Bio/SeqIO/AceIO.py b/Bio/SeqIO/AceIO.py
index c63cbfb..389606c 100644
--- a/Bio/SeqIO/AceIO.py
+++ b/Bio/SeqIO/AceIO.py
@@ -1,4 +1,4 @@
-# Copyright 2008-2010 by Peter Cock. All rights reserved.
+# Copyright 2008-2015 by Peter Cock. All rights reserved.
#
# This code is part of the Biopython distribution and governed by its
# license. Please see the LICENSE file that should have been included
@@ -18,8 +18,6 @@ from Bio.SeqRecord import SeqRecord
from Bio.Alphabet import generic_nucleotide, generic_dna, generic_rna, Gapped
from Bio.Sequencing import Ace
-__docformat__ = "restructuredtext en"
-
def AceIterator(handle):
"""Returns SeqRecord objects from an ACE file.
@@ -97,8 +95,8 @@ def AceIterator(handle):
# Consensus base quality (BQ lines). Note that any gaps (originally
# as * characters) in the consensus do not get a quality entry, so
- # we assign a quality of None (zero would be missleading as there may
- # be excelent support for having a gap here).
+ # we assign a quality of None (zero would be misleading as there may
+ # be excellent support for having a gap here).
quals = []
i = 0
for base in consensus_seq:
diff --git a/Bio/SeqIO/FastaIO.py b/Bio/SeqIO/FastaIO.py
index 4e2589e..d279f18 100644
--- a/Bio/SeqIO/FastaIO.py
+++ b/Bio/SeqIO/FastaIO.py
@@ -1,4 +1,4 @@
-# Copyright 2006-2014 by Peter Cock. All rights reserved.
+# Copyright 2006-2015 by Peter Cock. All rights reserved.
# This code is part of the Biopython distribution and governed by its
# license. Please see the LICENSE file that should have been included
# as part of this package.
@@ -18,8 +18,6 @@ from Bio.Seq import Seq
from Bio.SeqRecord import SeqRecord
from Bio.SeqIO.Interfaces import SequentialSequenceWriter
-__docformat__ = "restructuredtext en"
-
def SimpleFastaParser(handle):
"""Generator function to iterate over Fasta records (as string tuples).
@@ -137,7 +135,7 @@ class FastaWriter(SequentialSequenceWriter):
def __init__(self, handle, wrap=60, record2title=None):
"""Create a Fasta writer.
- Arguements:
+ Arguments:
- handle - Handle to an output file, e.g. as returned
by open(filename, "w")
@@ -213,71 +211,5 @@ class FastaWriter(SequentialSequenceWriter):
self.handle.write(data + "\n")
if __name__ == "__main__":
- print("Running quick self test")
-
- import os
- from Bio.Alphabet import generic_protein, generic_nucleotide
-
- # Download the files from here:
- # ftp://ftp.ncbi.nlm.nih.gov/genomes/Bacteria/Nanoarchaeum_equitans
- fna_filename = "NC_005213.fna"
- faa_filename = "NC_005213.faa"
-
- def genbank_name_function(text):
- text, descr = text.split(None, 1)
- id = text.split("|")[3]
- name = id.split(".", 1)[0]
- return id, name, descr
-
- def print_record(record):
- # See also bug 2057
- # http://bugzilla.open-bio.org/show_bug.cgi?id=2057
- print("ID:" + record.id)
- print("Name:" + record.name)
- print("Descr:" + record.description)
- print(record.seq)
- for feature in record.annotations:
- print('/%s=%s' % (feature, record.annotations[feature]))
- if record.dbxrefs:
- print("Database cross references:")
- for x in record.dbxrefs:
- print(" - %s" % x)
-
- if os.path.isfile(fna_filename):
- print("--------")
- print("FastaIterator (single sequence)")
- with open(fna_filename, "r") as h:
- iterator = FastaIterator(h, alphabet=generic_nucleotide,
- title2ids=genbank_name_function)
- count = 0
- for record in iterator:
- count += 1
- print_record(record)
- assert count == 1
- print(str(record.__class__))
-
- if os.path.isfile(faa_filename):
- print("--------")
- print("FastaIterator (multiple sequences)")
- with open(faa_filename, "r") as h:
- iterator = FastaIterator(h, alphabet=generic_protein,
- title2ids=genbank_name_function)
- count = 0
- for record in iterator:
- count += 1
- print_record(record)
- break
- assert count > 0
- print(str(record.__class__))
-
- from Bio._py3k import StringIO
- print("--------")
- print("FastaIterator (empty input file)")
- # Just to make sure no errors happen
- iterator = FastaIterator(StringIO(""))
- count = 0
- for record in iterator:
- count += 1
- assert count == 0
-
- print("Done")
+ from Bio._utils import run_doctest
+ run_doctest(verbose=0)
diff --git a/Bio/SeqIO/IgIO.py b/Bio/SeqIO/IgIO.py
index 5cdda57..7a1380f 100644
--- a/Bio/SeqIO/IgIO.py
+++ b/Bio/SeqIO/IgIO.py
@@ -1,4 +1,4 @@
-# Copyright 2008-2010 by Peter Cock. All rights reserved.
+# Copyright 2008-2015 by Peter Cock. All rights reserved.
# This code is part of the Biopython distribution and governed by its
# license. Please see the LICENSE file that should have been included
# as part of this package.
@@ -18,8 +18,6 @@ from Bio.Alphabet import single_letter_alphabet
from Bio.Seq import Seq
from Bio.SeqRecord import SeqRecord
-__docformat__ = "restructuredtext en"
-
def IgIterator(handle, alphabet=single_letter_alphabet):
"""Iterate over IntelliGenetics records (as SeqRecord objects).
@@ -34,6 +32,31 @@ def IgIterator(handle, alphabet=single_letter_alphabet):
start with a semi-colon) are recorded as a single string with embedded
new line characters in the SeqRecord's annotations dictionary under the
key 'comment'.
+
+ Example:
+
+ >>> with open("IntelliGenetics/TAT_mase_nuc.txt") as handle:
+ ... for record in IgIterator(handle):
+ ... print("%s length %i" % (record.id, len(record)))
+ ...
+ A_U455 length 303
+ B_HXB2R length 306
+ C_UG268A length 267
+ D_ELI length 309
+ F_BZ163A length 309
+ O_ANT70 length 342
+ O_MVP5180 length 348
+ CPZGAB length 309
+ CPZANT length 309
+ A_ROD length 390
+ B_EHOA length 420
+ D_MM251 length 390
+ STM_STM length 387
+ VER_AGM3 length 354
+ GRI_AGM677 length 264
+ SAB_SAB1C length 219
+ SYK_SYK length 330
+
"""
# Skip any file header text before the first record (;; lines)
while True:
@@ -47,7 +70,7 @@ def IgIterator(handle, alphabet=single_letter_alphabet):
# Now iterate over the records
if line[0] != ";":
raise ValueError(
- "Records should start with ';' and not:\n%s" % repr(line))
+ "Records should start with ';' and not:\n%r" % line)
# Try and agree with SeqRecord convention from the GenBank parser,
# (and followed in the SwissProt parser) which stores the comments
@@ -88,19 +111,5 @@ def IgIterator(handle, alphabet=single_letter_alphabet):
assert not line
if __name__ == "__main__":
- print("Running quick self test")
-
- import os
- path = "../../Tests/IntelliGenetics/"
- if os.path.isdir(path):
- for filename in os.listdir(path):
- if os.path.splitext(filename)[-1] == ".txt":
- print("")
- print(filename)
- print("-" * len(filename))
- with open(os.path.join(path, filename)) as handle:
- for record in IgIterator(handle):
- print("%s %i" % (record.id, len(record)))
- print("Done")
- else:
- print("Could not find input files")
+ from Bio._utils import run_doctest
+ run_doctest(verbose=0)
diff --git a/Bio/SeqIO/InsdcIO.py b/Bio/SeqIO/InsdcIO.py
index 33fc993..43af8dd 100644
--- a/Bio/SeqIO/InsdcIO.py
+++ b/Bio/SeqIO/InsdcIO.py
@@ -1,4 +1,4 @@
-# Copyright 2007-2011 by Peter Cock. All rights reserved.
+# Copyright 2007-2016 by Peter Cock. All rights reserved.
#
# This code is part of the Biopython distribution and governed by its
# license. Please see the LICENSE file that should have been included
@@ -39,13 +39,12 @@ from Bio import BiopythonWarning
from Bio.Seq import UnknownSeq
from Bio.GenBank.Scanner import GenBankScanner, EmblScanner, _ImgtScanner
from Bio import Alphabet
-from .Interfaces import SequentialSequenceWriter
+from Bio.SeqIO.Interfaces import SequentialSequenceWriter
from Bio import SeqFeature
from Bio._py3k import _is_int_or_long
from Bio._py3k import basestring
-__docformat__ = "restructuredtext en"
# NOTE
# ====
@@ -62,7 +61,35 @@ def GenBankIterator(handle):
a single SeqRecord with associated annotation and features.
Note that for genomes or chromosomes, there is typically only
- one record."""
+ one record.
+
+ This gets called internally by Bio.SeqIO for the GenBank file format:
+
+ >>> from Bio import SeqIO
+ >>> for record in SeqIO.parse("GenBank/cor6_6.gb", "gb"):
+ ... print(record.id)
+ ...
+ X55053.1
+ X62281.1
+ M81224.1
+ AJ237582.1
+ L31939.1
+ AF297471.1
+
+ Equivalently,
+
+ >>> with open("GenBank/cor6_6.gb") as handle:
+ ... for record in GenBankIterator(handle):
+ ... print(record.id)
+ ...
+ X55053.1
+ X62281.1
+ M81224.1
+ AJ237582.1
+ L31939.1
+ AF297471.1
+
+ """
# This calls a generator function:
return GenBankScanner(debug=0).parse_records(handle)
@@ -74,7 +101,41 @@ def EmblIterator(handle):
a single SeqRecord with associated annotation and features.
Note that for genomes or chromosomes, there is typically only
- one record."""
+ one record.
+
+ This gets called internally by Bio.SeqIO for the EMBL file format:
+
+ >>> from Bio import SeqIO
+ >>> for record in SeqIO.parse("EMBL/epo_prt_selection.embl", "embl"):
+ ... print(record.id)
+ ...
+ A00022.1
+ A00028.1
+ A00031.1
+ A00034.1
+ A00060.1
+ A00071.1
+ A00072.1
+ A00078.1
+ CQ797900.1
+
+ Equivalently,
+
+ >>> with open("EMBL/epo_prt_selection.embl") as handle:
+ ... for record in EmblIterator(handle):
+ ... print(record.id)
+ ...
+ A00022.1
+ A00028.1
+ A00031.1
+ A00034.1
+ A00060.1
+ A00071.1
+ A00072.1
+ A00078.1
+ CQ797900.1
+
+ """
# This calls a generator function:
return EmblScanner(debug=0).parse_records(handle)
@@ -183,8 +244,8 @@ def _insdc_location_string_ignoring_strand_and_subfeatures(location, rec_length)
# Treat the unknown end position as an AfterPosition
return "%s%s..>%i" \
% (ref,
- _insdc_feature_position_string(location.start),
- location.nofuzzy_start)
+ _insdc_feature_position_string(location.start, +1),
+ location.nofuzzy_start + 1)
else:
# Typical case, e.g. 12..15 gets mapped to 11:15
return ref \
@@ -350,7 +411,7 @@ class _InsdcWriter(SequentialSequenceWriter):
# Now the qualifiers...
for key in sorted(feature.qualifiers.keys()):
values = feature.qualifiers[key]
- if isinstance(values, list) or isinstance(values, tuple):
+ if isinstance(values, (list, tuple)):
for value in values:
self._write_feature_qualifier(key, value)
else:
@@ -408,7 +469,7 @@ class _InsdcWriter(SequentialSequenceWriter):
# It would need the addition of the comma splitting logic...
# are there any other cases where that would be sensible?
contig = record.annotations.get("contig", "")
- if isinstance(contig, list) or isinstance(contig, tuple):
+ if isinstance(contig, (list, tuple)):
contig = "".join(contig)
contig = self.clean(contig)
answer = []
@@ -433,8 +494,12 @@ class GenBankWriter(_InsdcWriter):
"""Used in the 'header' of each GenBank record."""
assert len(tag) < self.HEADER_WIDTH
if len(text) > self.MAX_WIDTH - self.HEADER_WIDTH:
- warnings.warn("Annotation %r too long for %r line" % (text, tag),
- BiopythonWarning)
+ if tag:
+ warnings.warn("Annotation %r too long for %r line" % (text, tag),
+ BiopythonWarning)
+ else:
+ # Can't give such a precise warning
+ warnings.warn("Annotation %r too long" % text, BiopythonWarning)
self.handle.write("%s%s\n" % (tag.ljust(self.HEADER_WIDTH),
text.replace("\n", " ")))
@@ -683,7 +748,7 @@ class GenBankWriter(_InsdcWriter):
comment = record.annotations["comment"]
if isinstance(comment, basestring):
lines = comment.split("\n")
- elif isinstance(comment, list) or isinstance(comment, tuple):
+ elif isinstance(comment, (list, tuple)):
lines = comment
else:
raise ValueError("Could not understand comment annotation")
@@ -866,8 +931,8 @@ class EmblWriter(_InsdcWriter):
index = LETTERS_PER_LINE * line_number + \
LETTERS_PER_BLOCK * block
handle.write((" %s" % data[index:index + LETTERS_PER_BLOCK]))
- handle.write(str((line_number + 1)
- * LETTERS_PER_LINE).rjust(POSITION_PADDING))
+ handle.write(str((line_number + 1) *
+ LETTERS_PER_LINE).rjust(POSITION_PADDING))
handle.write("\n")
if seq_len % LETTERS_PER_LINE:
# Final (partial) line
@@ -1049,7 +1114,7 @@ class EmblWriter(_InsdcWriter):
comment = record.annotations["comment"]
if isinstance(comment, basestring):
lines = comment.split("\n")
- elif isinstance(comment, list) or isinstance(comment, tuple):
+ elif isinstance(comment, (list, tuple)):
lines = comment
else:
raise ValueError("Could not understand comment annotation")
@@ -1067,7 +1132,30 @@ class EmblWriter(_InsdcWriter):
self._write_the_first_lines(record)
# PR line (0 or 1 lines only), project identifier
- for xref in record.dbxrefs:
+ #
+ # Assuming can't use 2 lines, we should prefer newer GenBank
+ # DBLINK BioProject:... entries over the older GenBank DBLINK
+ # Project:... lines.
+ #
+ # In either case, seems EMBL usess just "PR Project:..."
+ # regardless of the type of ID (old numeric only, or new
+ # with alpha prefix), e.g. for CP002497 NCBI now uses:
+ #
+ # DBLINK BioProject: PRJNA60715
+ # BioSample: SAMN03081426
+ #
+ # While EMBL uses:
+ #
+ # XX
+ # PR Project:PRJNA60715;
+ # XX
+ #
+ # Sorting ensures (new) BioProject:... is before old Project:...
+ for xref in sorted(record.dbxrefs):
+ if xref.startswith("BioProject:"):
+ self._write_single_line("PR", xref[3:] + ";")
+ handle.write("XX\n")
+ break
if xref.startswith("Project:"):
self._write_single_line("PR", xref + ";")
handle.write("XX\n")
@@ -1119,140 +1207,5 @@ class ImgtWriter(EmblWriter):
FEATURE_HEADER = "FH Key Location/Qualifiers\n"
if __name__ == "__main__":
- print("Quick self test")
- import os
- from Bio._py3k import StringIO
-
- def compare_record(old, new):
- if old.id != new.id and old.name != new.name:
- raise ValueError("'%s' or '%s' vs '%s' or '%s' records"
- % (old.id, old.name, new.id, new.name))
- if len(old.seq) != len(new.seq):
- raise ValueError("%i vs %i" % (len(old.seq), len(new.seq)))
- if str(old.seq).upper() != str(new.seq).upper():
- if len(old.seq) < 200:
- raise ValueError("'%s' vs '%s'" % (old.seq, new.seq))
- else:
- raise ValueError(
- "'%s...' vs '%s...'" % (old.seq[:100], new.seq[:100]))
- if old.features and new.features:
- return compare_features(old.features, new.features)
- # Just insist on at least one word in common:
- if (old.description or new.description) \
- and not set(old.description.split()).intersection(new.description.split()):
- raise ValueError("%s versus %s"
- % (repr(old.description), repr(new.description)))
- # TODO - check annotation
- if "contig" in old.annotations:
- assert old.annotations["contig"] == \
- new.annotations["contig"]
- return True
-
- def compare_records(old_list, new_list):
- """Check two lists of SeqRecords agree, raises a ValueError if mismatch."""
- if len(old_list) != len(new_list):
- raise ValueError(
- "%i vs %i records" % (len(old_list), len(new_list)))
- for old, new in zip(old_list, new_list):
- if not compare_record(old, new):
- return False
- return True
-
- def compare_feature(old, new, ignore_sub_features=False):
- """Check two SeqFeatures agree."""
- if old.type != new.type:
- raise ValueError("Type %s versus %s" % (old.type, new.type))
- if old.location.nofuzzy_start != new.location.nofuzzy_start \
- or old.location.nofuzzy_end != new.location.nofuzzy_end:
- raise ValueError("%s versus %s:\n%s\nvs:\n%s"
- % (old.location, new.location, str(old), str(new)))
- if old.strand != new.strand:
- raise ValueError(
- "Different strand:\n%s\nvs:\n%s" % (str(old), str(new)))
- if old.location.start != new.location.start:
- raise ValueError("Start %s versus %s:\n%s\nvs:\n%s"
- % (old.location.start, new.location.start, str(old), str(new)))
- if old.location.end != new.location.end:
- raise ValueError("End %s versus %s:\n%s\nvs:\n%s"
- % (old.location.end, new.location.end, str(old), str(new)))
- if not ignore_sub_features:
- if len(old.sub_features) != len(new.sub_features):
- raise ValueError("Different sub features")
- for a, b in zip(old.sub_features, new.sub_features):
- if not compare_feature(a, b):
- return False
- # This only checks key shared qualifiers
- # Would a white list be easier?
- # for key in ["name", "gene", "translation", "codon_table", "codon_start", "locus_tag"]:
- for key in set(old.qualifiers).intersection(new.qualifiers):
- if key in ["db_xref", "protein_id", "product", "note"]:
- # EMBL and GenBank files are use different references/notes/etc
- continue
- if old.qualifiers[key] != new.qualifiers[key]:
- raise ValueError("Qualifier mis-match for %s:\n%s\n%s"
- % (key, old.qualifiers[key], new.qualifiers[key]))
- return True
-
- def compare_features(old_list, new_list, ignore_sub_features=False):
- """Check two lists of SeqFeatures agree, raises a ValueError if mismatch."""
- if len(old_list) != len(new_list):
- raise ValueError(
- "%i vs %i features" % (len(old_list), len(new_list)))
- for old, new in zip(old_list, new_list):
- # This assumes they are in the same order
- if not compare_feature(old, new, ignore_sub_features):
- return False
- return True
-
- def check_genbank_writer(records):
- handle = StringIO()
- GenBankWriter(handle).write_file(records)
- handle.seek(0)
-
- records2 = list(GenBankIterator(handle))
- assert compare_records(records, records2)
-
- def check_embl_writer(records):
- handle = StringIO()
- try:
- EmblWriter(handle).write_file(records)
- except ValueError as err:
- print(err)
- return
- handle.seek(0)
-
- records2 = list(EmblIterator(handle))
- assert compare_records(records, records2)
-
- for filename in os.listdir("../../Tests/GenBank"):
- if not filename.endswith(".gbk") and not filename.endswith(".gb"):
- continue
- print(filename)
-
- with open("../../Tests/GenBank/%s" % filename) as handle:
- records = list(GenBankIterator(handle))
-
- check_genbank_writer(records)
- check_embl_writer(records)
-
- for filename in os.listdir("../../Tests/EMBL"):
- if not filename.endswith(".embl"):
- continue
- print(filename)
-
- with open("../../Tests/EMBL/%s" % filename) as handle:
- records = list(EmblIterator(handle))
-
- check_genbank_writer(records)
- check_embl_writer(records)
-
- from Bio import SeqIO
- for filename in os.listdir("../../Tests/SwissProt"):
- if not filename.startswith("sp"):
- continue
- print(filename)
-
- with open("../../Tests/SwissProt/%s" % filename) as handle:
- records = list(SeqIO.parse(handle, "swiss"))
-
- check_genbank_writer(records)
+ from Bio._utils import run_doctest
+ run_doctest(verbose=0)
diff --git a/Bio/SeqIO/Interfaces.py b/Bio/SeqIO/Interfaces.py
index 8e76930..6478837 100644
--- a/Bio/SeqIO/Interfaces.py
+++ b/Bio/SeqIO/Interfaces.py
@@ -16,8 +16,6 @@ from Bio.Alphabet import generic_alphabet
from Bio.Seq import Seq, MutableSeq
from Bio.SeqRecord import SeqRecord
-__docformat__ = "restructuredtext en"
-
class SequenceIterator(object):
"""Base class for building SeqRecord iterators.
diff --git a/Bio/SeqIO/PdbIO.py b/Bio/SeqIO/PdbIO.py
index 6066fbf..4286b4a 100644
--- a/Bio/SeqIO/PdbIO.py
+++ b/Bio/SeqIO/PdbIO.py
@@ -5,13 +5,12 @@
import collections
import warnings
+from Bio import BiopythonWarning
from Bio.Alphabet import generic_protein
from Bio.Seq import Seq
from Bio.SeqRecord import SeqRecord
from Bio.Data.SCOPData import protein_letters_3to1
-__docformat__ = "restructuredtext en"
-
def PdbSeqresIterator(handle):
"""Returns SeqRecord objects for each chain in a PDB file.
@@ -22,6 +21,30 @@ def PdbSeqresIterator(handle):
Specifically, these PDB records are handled: DBREF, SEQADV, SEQRES, MODRES
See: http://www.wwpdb.org/documentation/format23/sect3.html
+
+ This gets called internally via Bio.SeqIO for the SEQRES based interpretation
+ of the PDB file format:
+
+ >>> from Bio import SeqIO
+ >>> for record in SeqIO.parse("PDB/1A8O.pdb", "pdb-seqres"):
+ ... print("Record id %s, chain %s" % (record.id, record.annotations["chain"]))
+ ... print(record.dbxrefs)
+ ...
+ Record id 1A8O:A, chain A
+ ['UNP:P12497', 'UNP:POL_HV1N5']
+
+ Equivalently,
+
+ >>> with open("PDB/1A8O.pdb") as handle:
+ ... for record in PdbSeqresIterator(handle):
+ ... print("Record id %s, chain %s" % (record.id, record.annotations["chain"]))
+ ... print(record.dbxrefs)
+ ...
+ Record id 1A8O:A, chain A
+ ['UNP:P12497', 'UNP:POL_HV1N5']
+
+ Note the chain is recorded in the annotations dictionary, and any PDB DBREF
+ lines are recorded in the database cross-references list.
"""
# Late-binding import to avoid circular dependency on SeqIO in Bio.SeqUtils
from Bio.SeqUtils import seq1
@@ -120,7 +143,27 @@ def PdbAtomIterator(handle):
This function uses the Bio.PDB module to do most of the hard work. The
annotation information could be improved but this extra parsing should be
done in parse_pdb_header, not this module.
+
+ This gets called internally via Bio.SeqIO for the atom based interpretation
+ of the PDB file format:
+
+ >>> from Bio import SeqIO
+ >>> for record in SeqIO.parse("PDB/1A8O.pdb", "pdb-atom"):
+ ... print("Record id %s, chain %s" % (record.id, record.annotations["chain"]))
+ ...
+ Record id 1A8O:A, chain A
+
+ Equivalently,
+
+ >>> with open("PDB/1A8O.pdb") as handle:
+ ... for record in PdbAtomIterator(handle):
+ ... print("Record id %s, chain %s" % (record.id, record.annotations["chain"]))
+ ...
+ Record id 1A8O:A, chain A
+
"""
+ # TODO - Add record.annotations to the doctest, esp the residues (not working?)
+
# Only import PDB when needed, to avoid/delay NumPy dependency in SeqIO
from Bio.PDB import PDBParser
from Bio.SeqUtils import seq1
@@ -140,7 +183,8 @@ def PdbAtomIterator(handle):
if firstline.startswith("HEADER"):
pdb_id = firstline[62:66]
else:
- warnings.warn("First line is not a 'HEADER'; can't determine PDB ID")
+ warnings.warn("First line is not a 'HEADER'; can't determine PDB ID. "
+ "Line: %r" % firstline, BiopythonWarning)
pdb_id = '????'
struct = PDBParser().get_structure(pdb_id, undo_handle)
@@ -171,7 +215,7 @@ def PdbAtomIterator(handle):
res_out.append('X' * gapsize)
else:
warnings.warn("Ignoring out-of-order residues after a gap",
- UserWarning)
+ BiopythonWarning)
# Keep the normal part, drop the out-of-order segment
# (presumably modified or hetatm residues, e.g. 3BEG)
res_out.extend(restype(x) for x in residues[prev_idx:i])
@@ -209,11 +253,5 @@ def PdbAtomIterator(handle):
if __name__ == '__main__':
- # Test
- import sys
- from Bio import SeqIO
- for fname in sys.argv[1:]:
- for parser in (PdbSeqresIterator, PdbAtomIterator):
- with open(fname) as handle:
- records = parser(handle)
- SeqIO.write(records, sys.stdout, 'fasta')
+ from Bio._utils import run_doctest
+ run_doctest(verbose=0)
diff --git a/Bio/SeqIO/PhdIO.py b/Bio/SeqIO/PhdIO.py
index 5de62e1..92771e2 100644
--- a/Bio/SeqIO/PhdIO.py
+++ b/Bio/SeqIO/PhdIO.py
@@ -1,4 +1,4 @@
-# Copyright 2008-2010 by Peter Cock. All rights reserved.
+# Copyright 2008-2016 by Peter Cock. All rights reserved.
# Revisions copyright 2009 by Cymon J. Cox. All rights reserved.
#
# This code is part of the Biopython distribution and governed by its
@@ -59,8 +59,6 @@ from Bio.Sequencing import Phd
from Bio.SeqIO.Interfaces import SequentialSequenceWriter
from Bio.SeqIO import QualityIO
-__docformat__ = "restructuredtext en"
-
def PhdIterator(handle):
"""Returns SeqRecord objects from a PHD file.
@@ -106,7 +104,7 @@ class PhdWriter(SequentialSequenceWriter):
# This method returns the 'phred_quality' scores or converted
# 'solexa_quality' scores if present, else raises a value error
phred_qualities = QualityIO._get_phred_quality(record)
- peak_locations = record.letter_annotations.get("peak_location", None)
+ peak_locations = record.letter_annotations.get("peak_location")
assert len(record.seq) == len(phred_qualities), "Number of " + \
"phd quality scores does not match length of sequence"
if peak_locations:
@@ -123,14 +121,14 @@ class PhdWriter(SequentialSequenceWriter):
for annot in [k.lower() for k in Phd.CKEYWORDS]:
value = None
if annot == "trim":
- if record.annotations.get("trim", None):
+ if record.annotations.get("trim"):
value = "%s %s %.4f" % record.annotations["trim"]
elif annot == "trace_peak_area_ratio":
- if record.annotations.get("trace_peak_area_ratio", None):
+ if record.annotations.get("trace_peak_area_ratio"):
value = "%.4f" % record.annotations[
"trace_peak_area_ratio"]
else:
- value = record.annotations.get(annot, None)
+ value = record.annotations.get(annot)
if value or value == 0:
self.handle.write("%s: %s\n" % (annot.upper(), value))
diff --git a/Bio/SeqIO/PirIO.py b/Bio/SeqIO/PirIO.py
index 1ae5676..7fd58c9 100644
--- a/Bio/SeqIO/PirIO.py
+++ b/Bio/SeqIO/PirIO.py
@@ -1,4 +1,4 @@
-# Copyright 2008-2009 by Peter Cock. All rights reserved.
+# Copyright 2008-2015 by Peter Cock. All rights reserved.
# This code is part of the Biopython distribution and governed by its
# license. Please see the LICENSE file that should have been included
# as part of this package.
@@ -93,7 +93,6 @@ from Bio.Alphabet import single_letter_alphabet, generic_protein, \
from Bio.Seq import Seq
from Bio.SeqRecord import SeqRecord
-__docformat__ = "restructuredtext en"
_pir_alphabets = {"P1": generic_protein,
"F1": generic_protein,
@@ -121,6 +120,19 @@ def PirIterator(handle):
Note that use of title2ids matches that of Bio.Fasta.SequenceParser
but the defaults are slightly different.
+
+ Example:
+
+ >>> with open("NBRF/DMB_prot.pir") as handle:
+ ... for record in PirIterator(handle):
+ ... print("%s length %i" % (record.id, len(record)))
+ HLA:HLA00489 length 263
+ HLA:HLA00490 length 94
+ HLA:HLA00491 length 94
+ HLA:HLA00492 length 80
+ HLA:HLA00493 length 175
+ HLA:HLA01083 length 188
+
"""
# Skip any text before the first record (e.g. blank lines, comments)
while True:
@@ -171,22 +183,5 @@ def PirIterator(handle):
assert False, "Should not reach this line"
if __name__ == "__main__":
- print("Running quick self test")
-
- import os
-
- for name in ["clustalw", "DMA_nuc", "DMB_prot", "B_nuc", "Cw_prot"]:
- print(name)
- filename = "../../Tests/NBRF/%s.pir" % name
- if not os.path.isfile(filename):
- print("Missing %s" % filename)
- continue
-
- records = list(PirIterator(open(filename)))
- count = 0
- for record in records:
- count += 1
- parts = record.description.split()
- if "bases," in parts:
- assert len(record) == int(parts[parts.index("bases,") - 1])
- print("Could read %s (%i records)" % (name, count))
+ from Bio._utils import run_doctest
+ run_doctest(verbose=0)
diff --git a/Bio/SeqIO/QualityIO.py b/Bio/SeqIO/QualityIO.py
index f6754ee..93dac24 100644
--- a/Bio/SeqIO/QualityIO.py
+++ b/Bio/SeqIO/QualityIO.py
@@ -1,4 +1,4 @@
-# Copyright 2009-2010 by Peter Cock. All rights reserved.
+# Copyright 2009-2015 by Peter Cock. All rights reserved.
# This code is part of the Biopython distribution and governed by its
# license. Please see the LICENSE file that should have been included
# as part of this package.
@@ -259,7 +259,7 @@ using ("fastq" or "fastq-sanger" for the Sanger standard using PHRED values,
for the more recent variant), as this cannot be detected reliably
automatically.
-To illustrate this problem, let's consider an artifical example:
+To illustrate this problem, let's consider an artificial example:
>>> from Bio.Seq import Seq
>>> from Bio.Alphabet import generic_dna
@@ -363,8 +363,6 @@ are approximately equal.
"""
from __future__ import print_function
-__docformat__ = "restructuredtext en" # Don't just use plain text in epydoc API pages!
-
from Bio.Alphabet import single_letter_alphabet
from Bio.Seq import Seq, UnknownSeq
from Bio.SeqRecord import SeqRecord
@@ -469,8 +467,8 @@ def solexa_quality_from_phred(phred_quality):
# Special case, map to -5 as discussed in the docstring
return -5.0
else:
- raise ValueError("PHRED qualities must be positive (or zero), not %s"
- % repr(phred_quality))
+ raise ValueError("PHRED qualities must be positive (or zero), not %r"
+ % phred_quality)
def phred_quality_from_solexa(solexa_quality):
@@ -515,8 +513,8 @@ def phred_quality_from_solexa(solexa_quality):
# Assume None is used as some kind of NULL or NA value; return None
return None
if solexa_quality < -5:
- warnings.warn("Solexa quality less than -5 passed, %s"
- % repr(solexa_quality), BiopythonWarning)
+ warnings.warn("Solexa quality less than -5 passed, %r" % solexa_quality,
+ BiopythonWarning)
return 10 * log(10 ** (solexa_quality / 10.0) + 1, 10)
diff --git a/Bio/SeqIO/SeqXmlIO.py b/Bio/SeqIO/SeqXmlIO.py
index 31c45bd..3ea72c9 100644
--- a/Bio/SeqIO/SeqXmlIO.py
+++ b/Bio/SeqIO/SeqXmlIO.py
@@ -134,7 +134,7 @@ class SeqXmlIterator(XMLRecordIterator):
if "name" not in attr_dict:
raise ValueError("Malformed property element.")
- value = attr_dict.get("value", None)
+ value = attr_dict.get("value")
if attr_dict["name"] not in record.annotations:
record.annotations[attr_dict["name"]] = value
@@ -418,25 +418,3 @@ class SeqXmlWriter(SequentialSequenceWriter):
self.xml_generator.startElement(
"property", AttributesImpl(attr))
self.xml_generator.endElement("property")
-
-if __name__ == "__main__":
- print("Running quick self test")
-
- from Bio import SeqIO
- import sys
-
- with open("Tests/SeqXML/protein_example.xml", "r") as fileHandle:
- records = list(SeqIO.parse(fileHandle, "seqxml"))
-
- from Bio._py3k import StringIO
- stringHandle = StringIO()
-
- SeqIO.write(records, stringHandle, "seqxml")
- SeqIO.write(records, sys.stdout, "seqxml")
- print("")
-
- stringHandle.seek(0)
- records = list(SeqIO.parse(stringHandle, "seqxml"))
-
- SeqIO.write(records, sys.stdout, "seqxml")
- print("")
diff --git a/Bio/SeqIO/SffIO.py b/Bio/SeqIO/SffIO.py
index 353c6ba..b78f6cd 100644
--- a/Bio/SeqIO/SffIO.py
+++ b/Bio/SeqIO/SffIO.py
@@ -1,4 +1,4 @@
-# Copyright 2009-2010 by Peter Cock. All rights reserved.
+# Copyright 2009-2016 by Peter Cock. All rights reserved.
# Based on code contributed and copyright 2009 by Jose Blanca (COMAV-UPV).
#
# This code is part of the Biopython distribution and governed by its
@@ -251,8 +251,6 @@ _srt = b".srt"
_mft = b".mft"
_flag = b"\xff"
-__docformat__ = "restructuredtext en"
-
def _check_mode(handle):
"""Ensure handle not opened in text mode.
@@ -321,7 +319,7 @@ def _sff_file_header(handle):
data = handle.read(31)
if not data:
raise ValueError("Empty file.")
- elif len(data) < 13:
+ elif len(data) < 31:
raise ValueError("File too small to hold a valid SFF header.")
magic_number, ver0, ver1, ver2, ver3, index_offset, index_length, \
number_of_reads, header_length, key_length, number_of_flows_per_read, \
@@ -330,8 +328,7 @@ def _sff_file_header(handle):
# Probably user error, calling Bio.SeqIO.parse() twice!
raise ValueError("Handle seems to be at SFF index block, not start")
if magic_number != _sff: # 779314790
- raise ValueError("SFF file did not start '.sff', but %s"
- % repr(magic_number))
+ raise ValueError("SFF file did not start '.sff', but %r" % magic_number)
if (ver0, ver1, ver2, ver3) != (0, 0, 0, 1):
raise ValueError("Unsupported SFF version in header, %i.%i.%i.%i"
% (ver0, ver1, ver2, ver3))
@@ -400,8 +397,8 @@ def _sff_do_slow_index(handle):
clip_qual_right, clip_adapter_left, clip_adapter_right \
= struct.unpack(read_header_fmt, data)
if read_header_length < 10 or read_header_length % 8 != 0:
- raise ValueError("Malformed read header, says length is %i:\n%s"
- % (read_header_length, repr(data)))
+ raise ValueError("Malformed read header, says length is %i:\n%r"
+ % (read_header_length, data))
# now the name and any padding (remainder of header)
name = _bytes_to_string(handle.read(name_length))
padding = read_header_length - read_header_size - name_length
@@ -459,8 +456,8 @@ def _sff_find_roche_index(handle):
raise ValueError("Premature end of file? Expected index of size %i at offest %i, found nothing"
% (index_length, index_offset))
if len(data) < fmt_size:
- raise ValueError("Premature end of file? Expected index of size %i at offest %i, found %s"
- % (index_length, index_offset, repr(data)))
+ raise ValueError("Premature end of file? Expected index of size %i at offest %i, found %r"
+ % (index_length, index_offset, data))
magic_number, ver0, ver1, ver2, ver3 = struct.unpack(fmt, data)
if magic_number == _mft: # 778921588
# Roche 454 manifest index
@@ -500,8 +497,8 @@ def _sff_find_roche_index(handle):
raise ValueError("Hash table style indexes (.hsh) in SFF files are "
"not (yet) supported")
else:
- raise ValueError("Unknown magic number %s in SFF index header:\n%s"
- % (repr(magic_number), repr(data)))
+ raise ValueError("Unknown magic number %r in SFF index header:\n%r"
+ % (magic_number, data))
def ReadRocheXmlManifest(handle):
@@ -509,7 +506,7 @@ def ReadRocheXmlManifest(handle):
The SFF file format allows for multiple different index blocks, and Roche
took advantage of this to define their own index block which also embeds
- an XML manifest string. This is not a publically documented extension to
+ an XML manifest string. This is not a publicly documented extension to
the SFF file format, this was reverse engineered.
The handle should be to an SFF file opened in binary mode. This function
@@ -1311,77 +1308,14 @@ if __name__ == "__main__":
filename = "../../Tests/Roche/E3MFGYR02_random_10_reads.sff"
with open(filename, "rb") as handle:
metadata = ReadRocheXmlManifest(handle)
- with open(filename, "rb") as handle:
- index1 = sorted(_sff_read_roche_index(handle))
- with open(filename, "rb") as handle:
- index2 = sorted(_sff_do_slow_index(handle))
- assert index1 == index2
- with open(filename, "rb") as handle:
- assert len(index1) == len(list(SffIterator(handle)))
- from Bio._py3k import StringIO
+
from io import BytesIO
- with open(filename, "rb") as handle:
- assert len(index1) == len(list(SffIterator(BytesIO(handle.read()))))
-
- if sys.platform != "win32" and sys.version_info[0] < 3:
- # Can be lazy and treat as binary...
- with open(filename, "r") as handle:
- assert len(index1) == len(list(SffIterator(handle)))
- with open(filename) as handle:
- index2 = sorted(_sff_read_roche_index(handle))
- assert index1 == index2
- with open(filename, "r") as handle:
- index2 = sorted(_sff_do_slow_index(handle))
- assert index1 == index2
- with open(filename, "r") as handle:
- assert len(index1) == len(list(SffIterator(handle)))
- with open(filename, "r") as handle:
- assert len(index1) == len(list(SffIterator(BytesIO(handle.read()))))
with open(filename, "rb") as handle:
sff = list(SffIterator(handle))
-
- with open("../../Tests/Roche/E3MFGYR02_alt_index_at_end.sff", "rb") as handle:
- sff2 = list(SffIterator(handle))
- assert len(sff) == len(sff2)
- for old, new in zip(sff, sff2):
- assert old.id == new.id
- assert str(old.seq) == str(new.seq)
-
- with open("../../Tests/Roche/E3MFGYR02_alt_index_at_start.sff", "rb") as handle:
- sff2 = list(SffIterator(handle))
- assert len(sff) == len(sff2)
- for old, new in zip(sff, sff2):
- assert old.id == new.id
- assert str(old.seq) == str(new.seq)
-
- with open("../../Tests/Roche/E3MFGYR02_alt_index_in_middle.sff", "rb") as handle:
- sff2 = list(SffIterator(handle))
- assert len(sff) == len(sff2)
- for old, new in zip(sff, sff2):
- assert old.id == new.id
- assert str(old.seq) == str(new.seq)
-
- with open("../../Tests/Roche/E3MFGYR02_index_at_start.sff", "rb") as handle:
- sff2 = list(SffIterator(handle))
- assert len(sff) == len(sff2)
- for old, new in zip(sff, sff2):
- assert old.id == new.id
- assert str(old.seq) == str(new.seq)
-
- with open("../../Tests/Roche/E3MFGYR02_index_in_middle.sff", "rb") as handle:
- sff2 = list(SffIterator(handle))
- assert len(sff) == len(sff2)
- for old, new in zip(sff, sff2):
- assert old.id == new.id
- assert str(old.seq) == str(new.seq)
-
with open(filename, "rb") as handle:
sff_trim = list(SffIterator(handle, trim=True))
- with open(filename, "rb") as handle:
- print(ReadRocheXmlManifest(handle))
-
from Bio import SeqIO
filename = "../../Tests/Roche/E3MFGYR02_random_10_reads_no_trim.fasta"
fasta_no_trim = list(SeqIO.parse(filename, "fasta"))
diff --git a/Bio/SeqIO/SwissIO.py b/Bio/SeqIO/SwissIO.py
index 62d6208..ce4b2c6 100644
--- a/Bio/SeqIO/SwissIO.py
+++ b/Bio/SeqIO/SwissIO.py
@@ -23,8 +23,6 @@ from Bio import Alphabet
from Bio import SeqFeature
from Bio import SwissProt
-__docformat__ = "restructuredtext en"
-
def _make_position(location_string, offset=0):
"""Turn a Swiss location position into a SeqFeature position object (PRIVATE).
@@ -79,6 +77,9 @@ def SwissIterator(handle):
For consistency with BioPerl and EMBOSS we call this the "swiss"
format. See also the SeqIO support for "uniprot-xml" format.
+
+ Rather than calling it directly, you are expected to use this
+ parser via Bio.SeqIO.parse(..., format="swiss") instead.
"""
swiss_records = SwissProt.parse(handle)
for swiss_record in swiss_records:
@@ -143,24 +144,3 @@ def SwissIterator(handle):
if swiss_record.keywords:
record.annotations['keywords'] = swiss_record.keywords
yield record
-
-if __name__ == "__main__":
- print("Quick self test...")
-
- example_filename = "../../Tests/SwissProt/sp008"
-
- import os
- if not os.path.isfile(example_filename):
- print("Missing test file %s" % example_filename)
- else:
- # Try parsing it!
- with open(example_filename) as handle:
- records = SwissIterator(handle)
- for record in records:
- print(record.name)
- print(record.id)
- print(record.annotations['keywords'])
- print(repr(record.annotations['organism']))
- print(str(record.seq)[:20] + "...")
- for f in record.features:
- print(f)
diff --git a/Bio/SeqIO/TabIO.py b/Bio/SeqIO/TabIO.py
index 4e7e646..8deb96e 100644
--- a/Bio/SeqIO/TabIO.py
+++ b/Bio/SeqIO/TabIO.py
@@ -1,4 +1,4 @@
-# Copyright 2008-2010 by Peter Cock. All rights reserved.
+# Copyright 2008-2015 by Peter Cock. All rights reserved.
# This code is part of the Biopython distribution and governed by its
# license. Please see the LICENSE file that should have been included
# as part of this package.
@@ -38,8 +38,6 @@ from Bio.Seq import Seq
from Bio.SeqRecord import SeqRecord
from Bio.SeqIO.Interfaces import SequentialSequenceWriter
-__docformat__ = "restructuredtext en"
-
def TabIterator(handle, alphabet=single_letter_alphabet):
"""Iterates over tab separated lines (as SeqRecord objects).
@@ -55,6 +53,23 @@ def TabIterator(handle, alphabet=single_letter_alphabet):
any spaces within the text) and the second field is the sequence.
Any blank lines are ignored.
+
+ Example:
+
+ >>> with open("GenBank/NC_005816.tsv") as handle:
+ ... for record in TabIterator(handle):
+ ... print("%s length %i" % (record.id, len(record)))
+ gi|45478712|ref|NP_995567.1| length 340
+ gi|45478713|ref|NP_995568.1| length 260
+ gi|45478714|ref|NP_995569.1| length 64
+ gi|45478715|ref|NP_995570.1| length 123
+ gi|45478716|ref|NP_995571.1| length 145
+ gi|45478717|ref|NP_995572.1| length 357
+ gi|45478718|ref|NP_995573.1| length 138
+ gi|45478719|ref|NP_995574.1| length 312
+ gi|45478720|ref|NP_995575.1| length 99
+ gi|45478721|ref|NP_995576.1| length 90
+
"""
for line in handle:
try:
@@ -64,8 +79,8 @@ def TabIterator(handle, alphabet=single_letter_alphabet):
# It's a blank line, ignore it
continue
raise ValueError("Each line should have one tab separating the" +
- " title and sequence, this line has %i tabs: %s"
- % (line.count("\t"), repr(line)))
+ " title and sequence, this line has %i tabs: %r"
+ % (line.count("\t"), line))
title = title.strip()
seq = seq.strip() # removes the trailing new line
yield SeqRecord(Seq(seq, alphabet),
@@ -98,20 +113,5 @@ class TabWriter(SequentialSequenceWriter):
if __name__ == "__main__":
- print("Running quick self test")
- from Bio._py3k import StringIO
-
- # This example has a trailing blank line which should be ignored
- handle = StringIO("Alpha\tAAAAAAA\nBeta\tCCCCCCC\n\n")
- records = list(TabIterator(handle))
- assert len(records) == 2
-
- handle = StringIO("Alpha\tAAAAAAA\tExtra\nBeta\tCCCCCCC\n")
- try:
- records = list(TabIterator(handle))
- assert False, "Should have reject this invalid example!"
- except ValueError:
- # Good!
- pass
-
- print("Done")
+ from Bio._utils import run_doctest
+ run_doctest(verbose=0)
diff --git a/Bio/SeqIO/UniprotIO.py b/Bio/SeqIO/UniprotIO.py
index ea07fbb..504a3e6 100644
--- a/Bio/SeqIO/UniprotIO.py
+++ b/Bio/SeqIO/UniprotIO.py
@@ -1,5 +1,5 @@
# Copyright 2010 by Andrea Pierleoni
-# Revisions copyright 2010 by Peter Cock
+# Revisions copyright 2010, 2016 by Peter Cock
# All rights reserved.
#
# This code is part of the Biopython distribution and governed by its
@@ -23,8 +23,6 @@ from Bio import Alphabet
from Bio.SeqRecord import SeqRecord
from Bio._py3k import StringIO
-__docformat__ = "restructuredtext en"
-
# For speed try to use cElementTree rather than ElementTree
try:
@@ -273,7 +271,7 @@ class Parser(object):
else:
start = int(list(loc_element.getiterator(NS + 'begin'))[0].attrib['position']) - 1
end = int(list(loc_element.getiterator(NS + 'end'))[0].attrib['position'])
- except: # undefined positions or erroneously mapped
+ except ValueError: # undefined positions or erroneously mapped
pass
mass = element.attrib['mass']
method = element.attrib['method']
@@ -361,8 +359,8 @@ class Parser(object):
for person_element in cit_element:
authors.append(person_element.attrib['name'])
elif cit_element.tag == NS + 'dbReference':
- self.ParsedSeqRecord.dbxrefs.append(cit_element.attrib['type']
- + ':' + cit_element.attrib['id'])
+ self.ParsedSeqRecord.dbxrefs.append(cit_element.attrib['type'] +
+ ':' + cit_element.attrib['id'])
if cit_element.attrib['type'] == 'PubMed':
reference.pubmed_id = cit_element.attrib['id']
elif ref_element.attrib['type'] == 'MEDLINE':
@@ -439,7 +437,7 @@ class Parser(object):
else:
try:
feature.qualifiers[feature_element.tag.replace(NS, '')] = feature_element.text
- except:
+ except Exception: # TODO - Which exceptions?
pass # skip unparsable tag
self.ParsedSeqRecord.features.append(feature)
diff --git a/Bio/SeqIO/__init__.py b/Bio/SeqIO/__init__.py
index e5e8696..058f4fc 100644
--- a/Bio/SeqIO/__init__.py
+++ b/Bio/SeqIO/__init__.py
@@ -1,4 +1,4 @@
-# Copyright 2006-2010 by Peter Cock. All rights reserved.
+# Copyright 2006-2015 by Peter Cock. All rights reserved.
# This code is part of the Biopython distribution and governed by its
# license. Please see the LICENSE file that should have been included
# as part of this package.
@@ -313,8 +313,6 @@ making up each alignment as SeqRecords.
from __future__ import print_function
from Bio._py3k import basestring
-__docformat__ = "restructuredtext en" # not just plaintext
-
# TODO
# - define policy on reading aligned sequences with gaps in
# (e.g. - and . characters) including how the alphabet interacts
@@ -325,40 +323,37 @@ __docformat__ = "restructuredtext en" # not just plaintext
#
# - MSF multiple alignment format, aka GCG, aka PileUp format (*.msf)
# http://www.bioperl.org/wiki/MSF_multiple_alignment_format
-
-"""
-FAO BioPython Developers
-------------------------
-The way I envision this SeqIO system working as that for any sequence file
-format we have an iterator that returns SeqRecord objects.
-
-This also applies to interlaced file formats (like clustal - although that
-is now handled via Bio.AlignIO instead) where the file cannot be read record
-by record. You should still return an iterator, even if the implementation
-could just as easily return a list.
-
-These file format specific sequence iterators may be implemented as:
- - Classes which take a handle for __init__ and provide the __iter__ method
- - Functions that take a handle, and return an iterator object
- - Generator functions that take a handle, and yield SeqRecord objects
-
-It is then trivial to turn this iterator into a list of SeqRecord objects,
-an in memory dictionary, or a multiple sequence alignment object.
-
-For building the dictionary by default the id property of each SeqRecord is
-used as the key. You should always populate the id property, and it should
-be unique in most cases. For some file formats the accession number is a good
-choice. If the file itself contains ambiguous identifiers, don't try and
-dis-ambiguate them - return them as is.
-
-When adding a new file format, please use the same lower case format name
-as BioPerl, or if they have not defined one, try the names used by EMBOSS.
-
-See also http://biopython.org/wiki/SeqIO_dev
-
---Peter
-"""
-
+#
+# FAO BioPython Developers
+# ------------------------
+# The way I envision this SeqIO system working as that for any sequence file
+# format we have an iterator that returns SeqRecord objects.
+#
+# This also applies to interlaced file formats (like clustal - although that
+# is now handled via Bio.AlignIO instead) where the file cannot be read record
+# by record. You should still return an iterator, even if the implementation
+# could just as easily return a list.
+#
+# These file format specific sequence iterators may be implemented as:
+# - Classes which take a handle for __init__ and provide the __iter__ method
+# - Functions that take a handle, and return an iterator object
+# - Generator functions that take a handle, and yield SeqRecord objects
+#
+# It is then trivial to turn this iterator into a list of SeqRecord objects,
+# an in memory dictionary, or a multiple sequence alignment object.
+#
+# For building the dictionary by default the id property of each SeqRecord is
+# used as the key. You should always populate the id property, and it should
+# be unique in most cases. For some file formats the accession number is a good
+# choice. If the file itself contains ambiguous identifiers, don't try and
+# dis-ambiguate them - return them as is.
+#
+# When adding a new file format, please use the same lower case format name
+# as BioPerl, or if they have not defined one, try the names used by EMBOSS.
+#
+# See also http://biopython.org/wiki/SeqIO_dev
+#
+# --Peter
from Bio.File import as_handle
from Bio.SeqRecord import SeqRecord
@@ -488,7 +483,7 @@ def write(sequences, handle, format):
alignment_count = AlignIO.write([alignment], fp, format)
assert alignment_count == 1, \
"Internal error - the underlying writer " \
- " should have returned 1, not %s" % repr(alignment_count)
+ " should have returned 1, not %r" % alignment_count
count = len(alignment)
del alignment_count, alignment
elif format in _FormatToIterator or format in AlignIO._FormatToIterator:
@@ -498,8 +493,8 @@ def write(sequences, handle, format):
raise ValueError("Unknown format '%s'" % format)
assert isinstance(count, int), "Internal error - the underlying %s " \
- "writer should have returned the record count, not %s" \
- % (format, repr(count))
+ "writer should have returned the record count, not %r" \
+ % (format, count)
return count
@@ -578,7 +573,7 @@ def parse(handle, format, alphabet=None):
raise ValueError("Format string '%s' should be lower case" % format)
if alphabet is not None and not (isinstance(alphabet, Alphabet) or
isinstance(alphabet, AlphabetEncoder)):
- raise ValueError("Invalid alphabet, %s" % repr(alphabet))
+ raise ValueError("Invalid alphabet, %r" % alphabet)
with as_handle(handle, mode) as fp:
# Map the file format to a sequence iterator:
@@ -613,9 +608,9 @@ def _force_alphabet(record_iterator, alphabet):
record.seq.alphabet = alphabet
yield record
else:
- raise ValueError("Specified alphabet %s clashes with "
- "that determined from the file, %s"
- % (repr(alphabet), repr(record.seq.alphabet)))
+ raise ValueError("Specified alphabet %r clashes with "
+ "that determined from the file, %r"
+ % (alphabet, record.seq.alphabet))
def read(handle, format, alphabet=None):
@@ -857,7 +852,7 @@ def index(filename, format, alphabet=None, key_function=None):
raise ValueError("Format string '%s' should be lower case" % format)
if alphabet is not None and not (isinstance(alphabet, Alphabet) or
isinstance(alphabet, AlphabetEncoder)):
- raise ValueError("Invalid alphabet, %s" % repr(alphabet))
+ raise ValueError("Invalid alphabet, %r" % alphabet)
# Map the file format to a sequence iterator:
from ._index import _FormatToRandomAccess # Lazy import
@@ -937,7 +932,7 @@ def index_db(index_filename, filenames=None, format=None, alphabet=None,
raise ValueError("Format string '%s' should be lower case" % format)
if alphabet is not None and not (isinstance(alphabet, Alphabet) or
isinstance(alphabet, AlphabetEncoder)):
- raise ValueError("Invalid alphabet, %s" % repr(alphabet))
+ raise ValueError("Invalid alphabet, %r" % alphabet)
# Map the file format to a sequence iterator:
from ._index import _FormatToRandomAccess # Lazy import
diff --git a/Bio/SeqIO/_convert.py b/Bio/SeqIO/_convert.py
index a0b1813..cc5f183 100644
--- a/Bio/SeqIO/_convert.py
+++ b/Bio/SeqIO/_convert.py
@@ -1,4 +1,4 @@
-# Copyright 2009 by Peter Cock. All rights reserved.
+# Copyright 2009-2016 by Peter Cock. All rights reserved.
# This code is part of the Biopython distribution and governed by its
# license. Please see the LICENSE file that should have been included
# as part of this package.
@@ -24,11 +24,10 @@ means that using Bio.SeqIO.convert() may be faster, as well as more convenient.
All these file format specific optimisations are handled by this (private) module.
"""
+from Bio import BiopythonWarning
from Bio import SeqIO
# NOTE - Lots of lazy imports further on...
-__docformat__ = "restructuredtext en"
-
def _genbank_convert_fasta(in_handle, out_handle, alphabet=None):
"""Fast GenBank to FASTA (PRIVATE)."""
@@ -79,7 +78,7 @@ def _fastq_generic2(in_handle, out_handle, mapping, truncate_char, truncate_msg)
if truncate_char in qual:
qual = qual.replace(truncate_char, chr(126))
import warnings
- warnings.warn(truncate_msg)
+ warnings.warn(truncate_msg, BiopythonWarning)
out_handle.write("@%s\n%s\n+\n%s\n" % (title, seq, qual))
return count
@@ -94,9 +93,9 @@ def _fastq_sanger_convert_fastq_sanger(in_handle, out_handle, alphabet=None):
conversion.
"""
# Map unexpected chars to null
- mapping = "".join([chr(0) for ascii in range(0, 33)]
- + [chr(ascii) for ascii in range(33, 127)]
- + [chr(0) for ascii in range(127, 256)])
+ mapping = "".join([chr(0) for ascii in range(0, 33)] +
+ [chr(ascii) for ascii in range(33, 127)] +
+ [chr(0) for ascii in range(127, 256)])
assert len(mapping) == 256
return _fastq_generic(in_handle, out_handle, mapping)
@@ -110,9 +109,9 @@ def _fastq_solexa_convert_fastq_solexa(in_handle, out_handle, alphabet=None):
conversion.
"""
# Map unexpected chars to null
- mapping = "".join([chr(0) for ascii in range(0, 59)]
- + [chr(ascii) for ascii in range(59, 127)]
- + [chr(0) for ascii in range(127, 256)])
+ mapping = "".join([chr(0) for ascii in range(0, 59)] +
+ [chr(ascii) for ascii in range(59, 127)] +
+ [chr(0) for ascii in range(127, 256)])
assert len(mapping) == 256
return _fastq_generic(in_handle, out_handle, mapping)
@@ -126,9 +125,9 @@ def _fastq_illumina_convert_fastq_illumina(in_handle, out_handle, alphabet=None)
conversion.
"""
# Map unexpected chars to null
- mapping = "".join([chr(0) for ascii in range(0, 64)]
- + [chr(ascii) for ascii in range(64, 127)]
- + [chr(0) for ascii in range(127, 256)])
+ mapping = "".join([chr(0) for ascii in range(0, 64)] +
+ [chr(ascii) for ascii in range(64, 127)] +
+ [chr(0) for ascii in range(127, 256)])
assert len(mapping) == 256
return _fastq_generic(in_handle, out_handle, mapping)
@@ -140,9 +139,9 @@ def _fastq_illumina_convert_fastq_sanger(in_handle, out_handle, alphabet=None):
conversion.
"""
# Map unexpected chars to null
- mapping = "".join([chr(0) for ascii in range(0, 64)]
- + [chr(33 + q) for q in range(0, 62 + 1)]
- + [chr(0) for ascii in range(127, 256)])
+ mapping = "".join([chr(0) for ascii in range(0, 64)] +
+ [chr(33 + q) for q in range(0, 62 + 1)] +
+ [chr(0) for ascii in range(127, 256)])
assert len(mapping) == 256
return _fastq_generic(in_handle, out_handle, mapping)
@@ -156,10 +155,10 @@ def _fastq_sanger_convert_fastq_illumina(in_handle, out_handle, alphabet=None):
"""
# Map unexpected chars to null
trunc_char = chr(1)
- mapping = "".join([chr(0) for ascii in range(0, 33)]
- + [chr(64 + q) for q in range(0, 62 + 1)]
- + [trunc_char for ascii in range(96, 127)]
- + [chr(0) for ascii in range(127, 256)])
+ mapping = "".join([chr(0) for ascii in range(0, 33)] +
+ [chr(64 + q) for q in range(0, 62 + 1)] +
+ [trunc_char for ascii in range(96, 127)] +
+ [chr(0) for ascii in range(127, 256)])
assert len(mapping) == 256
return _fastq_generic2(in_handle, out_handle, mapping, trunc_char,
"Data loss - max PHRED quality 62 in Illumina 1.3+ FASTQ")
@@ -173,10 +172,10 @@ def _fastq_solexa_convert_fastq_sanger(in_handle, out_handle, alphabet=None):
"""
# Map unexpected chars to null
from Bio.SeqIO.QualityIO import phred_quality_from_solexa
- mapping = "".join([chr(0) for ascii in range(0, 59)]
- + [chr(33 + int(round(phred_quality_from_solexa(q))))
- for q in range(-5, 62 + 1)]
- + [chr(0) for ascii in range(127, 256)])
+ mapping = "".join([chr(0) for ascii in range(0, 59)] +
+ [chr(33 + int(round(phred_quality_from_solexa(q))))
+ for q in range(-5, 62 + 1)] +
+ [chr(0) for ascii in range(127, 256)])
assert len(mapping) == 256
return _fastq_generic(in_handle, out_handle, mapping)
@@ -191,11 +190,11 @@ def _fastq_sanger_convert_fastq_solexa(in_handle, out_handle, alphabet=None):
# Map unexpected chars to null
from Bio.SeqIO.QualityIO import solexa_quality_from_phred
trunc_char = chr(1)
- mapping = "".join([chr(0) for ascii in range(0, 33)]
- + [chr(64 + int(round(solexa_quality_from_phred(q))))
- for q in range(0, 62 + 1)]
- + [trunc_char for ascii in range(96, 127)]
- + [chr(0) for ascii in range(127, 256)])
+ mapping = "".join([chr(0) for ascii in range(0, 33)] +
+ [chr(64 + int(round(solexa_quality_from_phred(q))))
+ for q in range(0, 62 + 1)] +
+ [trunc_char for ascii in range(96, 127)] +
+ [chr(0) for ascii in range(127, 256)])
assert len(mapping) == 256
return _fastq_generic2(in_handle, out_handle, mapping, trunc_char,
"Data loss - max Solexa quality 62 in Solexa FASTQ")
@@ -209,10 +208,10 @@ def _fastq_solexa_convert_fastq_illumina(in_handle, out_handle, alphabet=None):
"""
# Map unexpected chars to null
from Bio.SeqIO.QualityIO import phred_quality_from_solexa
- mapping = "".join([chr(0) for ascii in range(0, 59)]
- + [chr(64 + int(round(phred_quality_from_solexa(q))))
- for q in range(-5, 62 + 1)]
- + [chr(0) for ascii in range(127, 256)])
+ mapping = "".join([chr(0) for ascii in range(0, 59)] +
+ [chr(64 + int(round(phred_quality_from_solexa(q))))
+ for q in range(-5, 62 + 1)] +
+ [chr(0) for ascii in range(127, 256)])
assert len(mapping) == 256
return _fastq_generic(in_handle, out_handle, mapping)
@@ -225,11 +224,10 @@ def _fastq_illumina_convert_fastq_solexa(in_handle, out_handle, alphabet=None):
"""
# Map unexpected chars to null
from Bio.SeqIO.QualityIO import solexa_quality_from_phred
- trunc_char = chr(1)
- mapping = "".join([chr(0) for ascii in range(0, 64)]
- + [chr(64 + int(round(solexa_quality_from_phred(q))))
- for q in range(0, 62 + 1)]
- + [chr(0) for ascii in range(127, 256)])
+ mapping = "".join([chr(0) for ascii in range(0, 64)] +
+ [chr(64 + int(round(solexa_quality_from_phred(q))))
+ for q in range(0, 62 + 1)] +
+ [chr(0) for ascii in range(127, 256)])
assert len(mapping) == 256
return _fastq_generic(in_handle, out_handle, mapping)
diff --git a/Bio/SeqIO/_index.py b/Bio/SeqIO/_index.py
index 2a523c6..3e5eeb9 100644
--- a/Bio/SeqIO/_index.py
+++ b/Bio/SeqIO/_index.py
@@ -33,8 +33,6 @@ from Bio import SeqIO
from Bio import Alphabet
from Bio.File import _IndexedSeqFileProxy, _open_for_random_access
-__docformat__ = "restructuredtext en"
-
class SeqFileRandomAccess(_IndexedSeqFileProxy):
def __init__(self, filename, format, alphabet):
@@ -143,6 +141,7 @@ class SffRandomAccess(SeqFileRandomAccess):
self._alphabet)
def get_raw(self, offset):
+ """Return the raw record from the file as a bytes string."""
handle = self._handle
handle.seek(offset)
return SeqIO.SffIO._sff_read_raw_record(handle, self._flows_per_read)
@@ -176,7 +175,6 @@ class SequentialSeqFileRandomAccess(SeqFileRandomAccess):
"phd": "BEGIN_SEQUENCE",
"pir": ">..;",
"qual": ">",
- "qual": ">",
"swiss": "ID ",
"uniprot-xml": "<entry ",
}[format]
@@ -214,7 +212,7 @@ class SequentialSeqFileRandomAccess(SeqFileRandomAccess):
assert not line, repr(line)
def get_raw(self, offset):
- """Similar to the get method, but returns the record as a raw string."""
+ """Return the raw record from the file as a bytes string."""
# For non-trivial file formats this must be over-ridden in the subclass
handle = self._handle
marker_re = self._marker_re
@@ -417,7 +415,7 @@ class UniprotRandomAccess(SequentialSeqFileRandomAccess):
assert not line, repr(line)
def get_raw(self, offset):
- """Similar to the get method, but returns the record as a raw string."""
+ """Return the raw record from the file as a bytes string."""
handle = self._handle
marker_re = self._marker_re
end_entry_marker = _as_bytes("</entry>")
@@ -482,6 +480,7 @@ class IntelliGeneticsRandomAccess(SeqFileRandomAccess):
break
def get_raw(self, offset):
+ """Return the raw record from the file as a bytes string."""
handle = self._handle
handle.seek(offset)
marker_re = self._marker_re
@@ -520,7 +519,7 @@ class TabRandomAccess(SeqFileRandomAccess):
yield _bytes_to_string(key), start_offset, len(line)
def get_raw(self, offset):
- """Like the get method, but returns the record as a raw string."""
+ """Return the raw record from the file as a bytes string."""
handle = self._handle
handle.seek(offset)
return handle.readline()
@@ -591,7 +590,7 @@ class FastqRandomAccess(SeqFileRandomAccess):
# print("EOF")
def get_raw(self, offset):
- """Similar to the get method, but returns the record as a raw string."""
+ """Return the raw record from the file as a bytes string."""
# TODO - Refactor this and the __init__ method to reduce code duplication?
handle = self._handle
handle.seek(offset)
diff --git a/Bio/SeqRecord.py b/Bio/SeqRecord.py
index 91e72d5..eba7d6e 100644
--- a/Bio/SeqRecord.py
+++ b/Bio/SeqRecord.py
@@ -10,13 +10,13 @@
from Bio._py3k import basestring
-__docformat__ = "restructuredtext en" # Simple markup to show doctests nicely
-
# NEEDS TO BE SYNCH WITH THE REST OF BIOPYTHON AND BIOPERL
# In particular, the SeqRecord and BioSQL.BioSeq.DBSeqRecord classes
# need to be in sync (this is the BioSQL "Database SeqRecord", see
# also BioSQL.BioSeq.DBSeq which is the "Database Seq" class)
+_NO_SEQRECORD_COMPARISON = "SeqRecord comparison is deliberately not implemented. Explicitly compare the attributes of interest."
+
class _RestrictedDict(dict):
"""Dict which only allows sequences of given length as values (PRIVATE).
@@ -357,8 +357,8 @@ class SeqRecord(object):
Number of features: 1
Per letter annotation for: secondary_structure
Seq('MAAGVKQLADDRTLLMAGVSHDLRTPLTRIRLATEMMSEQDGYLAESINKDIEE...YLR', IUPACProtein())
- >>> print(rec.letter_annotations["secondary_structure"])
- S SSSSSSHHHHHTTTHHHHHHHHHHHHHHHHHHHHHHTHHHHHHHHHHHHHHHHHHHHHTT
+ >>> rec.letter_annotations["secondary_structure"]
+ ' S SSSSSSHHHHHTTTHHHHHHHHHHHHHHHHHHHHHHTHHHHHHHHHHHHHHHHHHHHHTT '
>>> print(rec.features[0].location)
[20:21]
@@ -373,8 +373,8 @@ class SeqRecord(object):
Number of features: 1
Per letter annotation for: secondary_structure
Seq('RTLLMAGVSHDLRTPLTRIRLATEMMSEQD', IUPACProtein())
- >>> print(sub.letter_annotations["secondary_structure"])
- HHHHHTTTHHHHHHHHHHHHHHHHHHHHHH
+ >>> sub.letter_annotations["secondary_structure"]
+ 'HHHHHTTTHHHHHHHHHHHHHHHHHHHHHH'
>>> print(sub.features[0].location)
[9:10]
@@ -710,6 +710,27 @@ class SeqRecord(object):
"""
return len(self.seq)
+ def __lt__(self, other):
+ raise NotImplementedError(_NO_SEQRECORD_COMPARISON)
+
+ def __le___(self, other):
+ raise NotImplementedError(_NO_SEQRECORD_COMPARISON)
+
+ def __eq__(self, other):
+ raise NotImplementedError(_NO_SEQRECORD_COMPARISON)
+
+ def __ne__(self, other):
+ raise NotImplementedError(_NO_SEQRECORD_COMPARISON)
+
+ def __gt__(self, other):
+ raise NotImplementedError(_NO_SEQRECORD_COMPARISON)
+
+ def __ge__(self, other):
+ raise NotImplementedError(_NO_SEQRECORD_COMPARISON)
+
+ # Note Python 3 does not use __cmp__ and there is no need to
+ # define __cmp__ on Python 2 as have all of _lt__ etc defined.
+
# Python 3:
def __bool__(self):
"""Boolean value of an instance of this class (True).
diff --git a/Bio/SeqUtils/CheckSum.py b/Bio/SeqUtils/CheckSum.py
index dc9e90f..e35e4a3 100644
--- a/Bio/SeqUtils/CheckSum.py
+++ b/Bio/SeqUtils/CheckSum.py
@@ -15,11 +15,18 @@ from __future__ import print_function
from binascii import crc32 as _crc32
from Bio._py3k import _as_bytes
-__docformat__ = "restructuredtext en"
-
def crc32(seq):
- """Returns the crc32 checksum for a sequence (string or Seq object)."""
+ """Returns the crc32 checksum for a sequence (string or Seq object).
+
+ Note that the case is important:
+
+ >>> crc32("ACGTACGTACGT")
+ 20049947
+ >>> crc32("acgtACGTacgt")
+ 1688586483
+
+ """
# NOTE - On Python 2 returns a signed int, on Python 3 it is unsigned
# Docs suggest should use crc32(x) & 0xffffffff for consistency.
# TODO - Should we return crc32(x) & 0xffffffff here?
@@ -52,7 +59,16 @@ _table_h = _init_table_h()
def crc64(s):
- """Returns the crc64 checksum for a sequence (string or Seq object)."""
+ """Returns the crc64 checksum for a sequence (string or Seq object).
+
+ Note that the case is important:
+
+ >>> crc64("ACGTACGTACGT")
+ 'CRC-C4FBB762C4A87EBD'
+ >>> crc64("acgtACGTacgt")
+ 'CRC-DA4509DC64A87EBD'
+
+ """
crcl = 0
crch = 0
for c in s:
@@ -77,6 +93,12 @@ def gcg(seq):
with the help of John Lenton, Pablo Ziliani, and Gabriel Genellina.
All sequences are converted to uppercase.
+
+ >>> gcg("ACGTACGTACGT")
+ 5688
+ >>> gcg("acgtACGTacgt")
+ 5688
+
"""
try:
# Assume its a Seq object
@@ -100,6 +122,13 @@ def seguid(seq):
returns the SEGUID string (A SEquence Globally Unique IDentifier).
seq type = str.
+ Note that the case is not important:
+
+ >>> seguid("ACGTACGTACGT")
+ 'If6HIvcnRSQDVNiAoefAzySc6i4'
+ >>> seguid("acgtACGTacgt")
+ 'If6HIvcnRSQDVNiAoefAzySc6i4'
+
For more information about SEGUID, see:
http://bioinformatics.anl.gov/seguid/
DOI: 10.1002/pmic.200600032
@@ -124,20 +153,5 @@ def seguid(seq):
if __name__ == "__main__":
- print("Quick self test")
-
- str_light_chain_one = "QSALTQPASVSGSPGQSITISCTGTSSDVGSYNLVSWYQQHPGK" \
- + "APKLMIYEGSKRPSGVSNRFSGSKSGNTASLTISGLQAEDEADY" \
- + "YCSSYAGSSTLVFGGGTKLTVL"
-
- str_light_chain_two = "QSALTQPASVSGSPGQSITISCTGTSSDVGSYNLVSWYQQHPGK" \
- + "APKLMIYEGSKRPSGVSNRFSGSKSGNTASLTISGLQAEDEADY" \
- + "YCCSYAGSSTWVFGGGTKLTVL"
-
- assert crc64(str_light_chain_one) == crc64(str_light_chain_two)
- assert 'CRC-44CAAD88706CC153' == crc64(str_light_chain_one)
-
- assert 'BpBeDdcNUYNsdk46JoJdw7Pd3BI' == seguid(str_light_chain_one)
- assert 'X5XEaayob1nZLOc7eVT9qyczarY' == seguid(str_light_chain_two)
-
- print("Done")
+ from Bio._utils import run_doctest
+ run_doctest()
diff --git a/Bio/SeqUtils/CodonUsage.py b/Bio/SeqUtils/CodonUsage.py
index c123e67..e1a85f1 100644
--- a/Bio/SeqUtils/CodonUsage.py
+++ b/Bio/SeqUtils/CodonUsage.py
@@ -9,8 +9,6 @@ import math
from .CodonUsageIndices import SharpEcoliIndex
from Bio import SeqIO # To parse a FASTA file
-__docformat__ = "restructuredtext en"
-
CodonsDict = {'TTT': 0, 'TTC': 0, 'TTA': 0, 'TTG': 0, 'CTT': 0,
'CTC': 0, 'CTA': 0, 'CTG': 0, 'ATT': 0, 'ATC': 0,
diff --git a/Bio/SeqUtils/CodonUsageIndices.py b/Bio/SeqUtils/CodonUsageIndices.py
index 5f55e3e..23026f1 100644
--- a/Bio/SeqUtils/CodonUsageIndices.py
+++ b/Bio/SeqUtils/CodonUsageIndices.py
@@ -7,8 +7,6 @@ Currently this module only defines a single codon adaption index from
Sharp & Li, Nucleic Acids Res. 1987.
"""
-__docformat__ = "restructuredtext en"
-
SharpEcoliIndex = {
'GCA': 0.586, 'GCC': 0.122, 'GCG': 0.424, 'GCT': 1, 'AGA': 0.004, 'AGG': 0.002, 'CGA': 0.004,
diff --git a/Bio/SeqUtils/IsoelectricPoint.py b/Bio/SeqUtils/IsoelectricPoint.py
index a63d220..0c72c59 100644
--- a/Bio/SeqUtils/IsoelectricPoint.py
+++ b/Bio/SeqUtils/IsoelectricPoint.py
@@ -20,7 +20,6 @@ http://fields.scripps.edu/DTASelect/20010710-pI-Algorithm.pdf
"""
-__docformat__ = "restructuredtext en"
positive_pKs = {'Nterm': 7.5, 'K': 10.0, 'R': 12.0, 'H': 5.98}
negative_pKs = {'Cterm': 3.55, 'D': 4.05, 'E': 4.45, 'C': 9.0, 'Y': 10.0}
diff --git a/Bio/SeqUtils/MeltingTemp.py b/Bio/SeqUtils/MeltingTemp.py
index a953a05..9e4496a 100644
--- a/Bio/SeqUtils/MeltingTemp.py
+++ b/Bio/SeqUtils/MeltingTemp.py
@@ -25,14 +25,14 @@ with default parameters gives (essentially) the same results.
General parameters for most Tm methods:
- seq -- A Biopython sequence object or a string.
- - check -- Checks if the sequence is valid for the given method (default=True).
- In general, whitespaces and non-base characters are removed and
+ - check -- Checks if the sequence is valid for the given method (default=
+ True). In general, whitespaces and non-base characters are removed and
characters are converted to uppercase. RNA will be backtranscribed.
- - strict - Do not allow base characters or neighbor duplex keys (e.g. 'AT/NA').
- that could not or not unambigiously be evaluated for the respective
- method (default=True). Note that W (= A or T) and S (= C or G) are
- not ambiguous for Tm_Wallace and Tm_GC. If 'False', average values
- (if applicable) will be used.
+ - strict -- Do not allow base characters or neighbor duplex keys (e.g.
+ 'AT/NA') that could not or not unambigiously be evaluated for the respective
+ method (default=True). Note that W (= A or T) and S (= C or G) are not
+ ambiguous for Tm_Wallace and Tm_GC. If 'False', average values (if
+ applicable) will be used.
This module is not able to detect self-complementary and it will not use
alignment tools to align an oligonucleotide sequence to its target sequence.
@@ -106,21 +106,38 @@ to von Ahsen et al. (2001) or Owczarzy et al. (2008) (for Tm_NN and Tm_GC):
Dangling ends and mismatches, e.g.::
- Oligo: CGTTCCaAAGATGTGGGCATGAGCTTAC CGTTCCaAAGATGTGGGCATGAGCTTAC
- ::::::X::::::::::::::::::::: or ::::::X:::::::::::::::::::::
- Template: GCAAGGcTTCTACACCCGTACTCGAATG TGCAAGGcTTCTACACCCGTACTCGAATGC
+ Oligo: CGTTCCaAAGATGTGGGCATGAGCTTAC CGTTCCaAAGATGTGGGCATGAGCTTAC
+ ::::::X::::::::::::::::::::: or ::::::X:::::::::::::::::::::
+ Template: GCAAGGcTTCTACACCCGTACTCGAATG TGCAAGGcTTCTACACCCGTACTCGAATGC
Here:
>>> print('%0.2f' % mt.Tm_NN('CGTTCCAAAGATGTGGGCATGAGCTTAC'))
60.32
>>> print('%0.2f' % mt.Tm_NN('CGTTCCAAAGATGTGGGCATGAGCTTAC',
- ... c_seq='GCAAGGCTTCTACACCCGTACTCGAATG'))
+ ... c_seq='GCAAGGcTTCTACACCCGTACTCGAATG'))
55.39
>>> print('%0.2f' % mt.Tm_NN('CGTTCCAAAGATGTGGGCATGAGCTTAC', shift=1,
- ... c_seq='TGCAAGGCTTCTACACCCGTACTCGAATGC'))
+ ... c_seq='TGCAAGGcTTCTACACCCGTACTCGAATGC'))
55.69
+The same for RNA:
+
+ >>> print('%0.2f' % mt.Tm_NN('CGUUCCAAAGAUGUGGGCAUGAGCUUAC',
+ ... c_seq='UGCAAGGcUUCUACACCCGUACUCGAAUGC',
+ ... shift=1, nn_table=mt.RNA_NN3,
+ ... de_table=mt.RNA_DE1))
+ 73.00
+
+Note, that thermodynamic data are not available for all kind of mismatches,
+e.g. most double mismatches or terminal mismaches combined with danglind ends:
+
+ >>> print('%0.2f' % mt.Tm_NN('CGTTCCAAAGATGTGGGCATGAGCTTAC',
+ ... c_seq='TtCAAGGcTTCTACACCCGTACTCGAATGC',
+ ... shift=1))
+ Traceback (most recent call last):
+ ValueError: no thermodynamic data for neighbors '.C/TT' available
+
Make your own tables, or update/extend existing tables. E.g., add values for
locked nucleotides. Here, 'locked A' (and its complement) should be represented
by '1':
@@ -145,7 +162,6 @@ import warnings
from Bio import SeqUtils, Seq
from Bio import BiopythonWarning
-__docformat__ = "restructuredtext en"
# Thermodynamic lookup tables (dictionaries):
# Enthalpy (dH) and entropy (dS) values for nearest neighbors and initiation
@@ -333,30 +349,30 @@ DNA_DE1 = {
# Dangling ends table (RNA)
# Turner & Mathews (2010), Nucl Acids Res 38: D280-D282
RNA_DE1 = {
- 'AA/T.': (-4.9, -13.2), 'AC/T.': (-0.9, -1.3), 'AG/T.': (-5.5, -15.1),
- 'AT/T.': (-2.3, -5.5),
- 'CA/G.': (-9.0, -23.5), 'CC/G.': (-4.1, -10.6), 'CG/G.': (-8.6, -22.2),
- 'CT/G.': (-7.5, -20.31),
- 'GA/C.': (-7.4, -20.3), 'GC/C.': (-2.8, -7.7), 'GG/C.': (-6.4, -16.4),
- 'GT/C.': (-3.6, -9.7),
- 'GA/T.': (-4.9, -13.2), 'GC/T.': (-0.9, -1.3), 'GG/T.': (-5.5, -15.1),
- 'GT/T.': (-2.3, -5.5),
- 'TA/A.': (-5.7, -16.1), 'TC/A.': (-0.7, -1.9), 'TG/A.': (-5.8, -16.4),
- 'TT/A.': (-2.2, -6.8),
- 'TA/G.': (-5.7, -16.1), 'TC/G.': (-0.7, -1.9), 'TG/G.': (-5.8, -16.4),
- 'TT/G.': (-2.2, -6.8),
- 'A./TA': (-0.5, -0.6), 'A./TC': (6.9, 22.6), 'A./TG': (0.6, 2.6),
- 'A./TT': (0.6, 2.6),
- 'C./GA': (-1.6, -4.5), 'C./GC': (0.7, 3.2), 'C./GG': (-4.6, -14.8),
- 'C./GT': (-0.4, -1.3),
- 'G./CA': (-2.4, -6.1), 'G./CC': (3.3, 11.6), 'G./CG': (0.8, 3.2),
- 'G./CT': (-1.4, -4.2),
- 'G./TA': (-0.5, -0.6), 'G./TC': (6.9, 22.6), 'G./TG': (0.6, 2.6),
- 'G./TT': (0.6, 2.6),
- 'T./AA': (1.6, 6.1), 'T./AC': (2.2, 8.1), 'T./AG': (0.7, 3.5),
- 'T./AT': (3.1, 10.6),
- 'T./GA': (1.6, 6.1), 'T./GC': (2.2, 8.1), 'T./GG': (0.7, 3.5),
- 'T./GT': (3.1, 10.6)}
+ '.T/AA': (-4.9, -13.2), '.T/CA': (-0.9, -1.3), '.T/GA': (-5.5, -15.1),
+ '.T/TA': (-2.3, -5.5),
+ '.G/AC': (-9.0, -23.5), '.G/CC': (-4.1, -10.6), '.G/GC': (-8.6, -22.2),
+ '.G/TC': (-7.5, -20.31),
+ '.C/AG': (-7.4, -20.3), '.C/CG': (-2.8, -7.7), '.C/GG': (-6.4, -16.4),
+ '.C/TG': (-3.6, -9.7),
+ '.T/AG': (-4.9, -13.2), '.T/CG': (-0.9, -1.3), '.T/GG': (-5.5, -15.1),
+ '.T/TG': (-2.3, -5.5),
+ '.A/AT': (-5.7, -16.1), '.A/CT': (-0.7, -1.9), '.A/GT': (-5.8, -16.4),
+ '.A/TT': (-2.2, -6.8),
+ '.G/AT': (-5.7, -16.1), '.G/CT': (-0.7, -1.9), '.G/GT': (-5.8, -16.4),
+ '.G/TT': (-2.2, -6.8),
+ 'AT/.A': (-0.5, -0.6), 'CT/.A': (6.9, 22.6), 'GT/.A': (0.6, 2.6),
+ 'TT/.A': (0.6, 2.6),
+ 'AG/.C': (-1.6, -4.5), 'CG/.C': (0.7, 3.2), 'GG/.C': (-4.6, -14.8),
+ 'TG/.C': (-0.4, -1.3),
+ 'AC/.G': (-2.4, -6.1), 'CC/.G': (3.3, 11.6), 'GC/.G': (0.8, 3.2),
+ 'TC/.G': (-1.4, -4.2),
+ 'AT/.G': (-0.5, -0.6), 'CT/.G': (6.9, 22.6), 'GT/.G': (0.6, 2.6),
+ 'TT/.G': (0.6, 2.6),
+ 'AA/.T': (1.6, 6.1), 'CA/.T': (2.2, 8.1), 'GA/.T': (0.7, 3.5),
+ 'TA/.T': (3.1, 10.6),
+ 'AG/.T': (1.6, 6.1), 'CG/.T': (2.2, 8.1), 'GG/.T': (0.7, 3.5),
+ 'TG/.T': (3.1, 10.6)}
def make_table(oldtable=None, values=None):
@@ -395,13 +411,13 @@ def make_table(oldtable=None, values=None):
def _check(seq, method):
- """Return a sequence which fullfils the requirements of the given method (PRIVATE).
+ """Return a sequence which fullfils the requirements of the given method.
All Tm methods in this package require the sequence in uppercase format.
Most methods make use of the length of the sequence (directly or
indirectly), which can only be expressed as len(seq) if the sequence does
not contain whitespaces and other non-base characters. RNA sequences are
- backtranscribed to DNA. This method is private.
+ backtranscribed to DNA. This method is PRIVATE.
Arguments:
seq: The sequence as given by the user (passed as string).
@@ -436,8 +452,8 @@ def salt_correction(Na=0, K=0, Tris=0, Mg=0, dNTPs=0, method=1, seq=None):
- methods 6+7: Tm(new) = 1/(1/Tm(old) + corr)
Parameters:
- - Na, K, Tris, Mg, dNTPS: Millimolar concentration of respective ion. To have
- a simple 'salt correction', just pass Na. If any of K, Tris, Mg and
+ - Na, K, Tris, Mg, dNTPS: Millimolar concentration of respective ion. To
+ have a simple 'salt correction', just pass Na. If any of K, Tris, Mg and
dNTPS is non-zero, a 'sodium-equivalent' concentration is calculated
according to von Ahsen et al. (2001, Clin Chem 47: 1956-1961):
[Na_eq] = [Na+] + [K+] + [Tris]/2 + 120*([Mg2+] - [dNTPs])^0.5
@@ -486,11 +502,10 @@ def salt_correction(Na=0, K=0, Tris=0, Mg=0, dNTPs=0, method=1, seq=None):
Mon = Na + K + Tris / 2.0 # Note: all these values are millimolar
mg = Mg * 1e-3 # Lowercase ions (mg, mon, dntps) are molar
# Na equivalent according to von Ahsen et al. (2001):
- if sum((K, Mg, Tris, dNTPs)) > 0 and not method == 7:
- if dNTPs < Mg:
- # dNTPs bind Mg2+ strongly. If [dNTPs] is larger or equal than
- # [Mg2+], free Mg2+ is considered not to be relevant.
- Mon += 120 * math.sqrt(Mg - dNTPs)
+ if sum((K, Mg, Tris, dNTPs)) > 0 and not method == 7 and dNTPs < Mg:
+ # dNTPs bind Mg2+ strongly. If [dNTPs] is larger or equal than
+ # [Mg2+], free Mg2+ is considered not to be relevant.
+ Mon += 120 * math.sqrt(Mg - dNTPs)
mon = Mon * 1e-3
# Note: math.log = ln(), math.log10 = log()
if method in range(1, 7) and not mon:
@@ -507,7 +522,7 @@ def salt_correction(Na=0, K=0, Tris=0, Mg=0, dNTPs=0, method=1, seq=None):
if method == 5:
corr = 0.368 * (len(seq) - 1) * math.log(mon)
if method == 6:
- corr = (4.29 * SeqUtils.GC(seq) / 100 - 3.95) * 1e-5 * math.log(mon) + \
+ corr = (4.29 * SeqUtils.GC(seq) / 100 - 3.95) * 1e-5 * math.log(mon) +\
9.40e-6 * math.log(mon) ** 2
if method == 7:
a, b, c, d = 3.92, -0.911, 6.26, 1.42
@@ -517,7 +532,8 @@ def salt_correction(Na=0, K=0, Tris=0, Mg=0, dNTPs=0, method=1, seq=None):
ka = 3e4 # Dissociation constant for Mg:dNTP
# Free Mg2+ calculation:
mg = (-(ka * dntps - ka * mg + 1.0) +
- math.sqrt((ka * dntps - ka * mg + 1.0) ** 2 + 4.0 * ka * mg)) / (2.0 * ka)
+ math.sqrt((ka * dntps - ka * mg + 1.0) ** 2 +
+ 4.0 * ka * mg)) / (2.0 * ka)
if Mon > 0:
R = math.sqrt(mg) / mon
if R < 0.22:
@@ -538,20 +554,20 @@ def salt_correction(Na=0, K=0, Tris=0, Mg=0, dNTPs=0, method=1, seq=None):
return corr
-def chem_correction(Tm, DMSO=0, fmd=0, DMSOfactor=0.75, fmdfactor=0.65,
- fmdmethod=1, GC=None):
+def chem_correction(melting_temp, DMSO=0, fmd=0, DMSOfactor=0.75,
+ fmdfactor=0.65, fmdmethod=1, GC=None):
"""Correct a given Tm for DMSO and formamide.
Please note that these corrections are +/- rough approximations.
Arguments:
- - Tm: Melting temperature.
+ - melting_temp: Melting temperature.
- DMSO: Percent DMSO.
- - fmd: Formamide concentration in %(fmdmethod=1) or in molar (fmdmethod=2).
+ - fmd: Formamide concentration in %(fmdmethod=1) or molar (fmdmethod=2).
- DMSOfactor: How much should Tm decreases per percent DMSO. Default=0.65
(von Ahsen et al. 2001). Other published values are 0.5, 0.6 and 0.675.
- - fmdfactor: How much should Tm decrease per percent formamide. Default=0.65.
- Several papers report factors between 0.6 and 0.72.
+ - fmdfactor: How much should Tm decrease per percent formamide.
+ Default=0.65. Several papers report factors between 0.6 and 0.72.
- fmdmethod:
1. Tm = Tm - factor(%formamide) (Default)
@@ -577,19 +593,21 @@ def chem_correction(Tm, DMSO=0, fmd=0, DMSOfactor=0.75, fmdfactor=0.65,
"""
if DMSO:
- Tm -= DMSOfactor * DMSO
+ melting_temp -= DMSOfactor * DMSO
if fmd:
# McConaughy et al. (1969), Biochemistry 8: 3289-3295
if fmdmethod == 1:
- Tm -= fmdfactor * fmd # Note: fmd is percent
+ # Note: Here fmd is given in percent
+ melting_temp -= fmdfactor * fmd
# Blake & Delcourt (1996), Nucl Acids Res 11: 2095-2103
if fmdmethod == 2:
if GC is None or GC < 0:
raise ValueError('\'GC\' is missing or negative')
- Tm += (0.453 * (GC / 100.0) - 2.88) * fmd # Note: fmd is molar
+ # Note: Here fmd is given in molar
+ melting_temp += (0.453 * (GC / 100.0) - 2.88) * fmd
if fmdmethod not in (1, 2):
raise ValueError('\'fmdmethod\' must be 1 or 2')
- return Tm
+ return melting_temp
def Tm_Wallace(seq, check=True, strict=True):
@@ -618,7 +636,7 @@ def Tm_Wallace(seq, check=True, strict=True):
if check:
seq = _check(seq, 'Tm_Wallace')
- Tm = 2 * (sum(map(seq.count, ('A', 'T', 'W')))) + \
+ melting_temp = 2 * (sum(map(seq.count, ('A', 'T', 'W')))) + \
4 * (sum(map(seq.count, ('C', 'G', 'S'))))
# Intermediate values for ambiguous positions:
@@ -629,8 +647,8 @@ def Tm_Wallace(seq, check=True, strict=True):
raise ValueError('ambiguous bases B, D, H, K, M, N, R, V, Y not ' +
'allowed when strict=True')
else:
- Tm += tmp
- return Tm
+ melting_temp += tmp
+ return melting_temp
def Tm_GC(seq, check=True, strict=True, valueset=7, userset=None, Na=50, K=0,
@@ -655,17 +673,20 @@ def Tm_GC(seq, check=True, strict=True, valueset=7, userset=None, Na=50, K=0,
(Marmur & Doty 1962, J Mol Biol 5: 109-118; Chester & Marshak 1993),
Anal Biochem 209: 284-290)
2. Tm = 81.5 + 0.41(%GC) - 675/N - %mismatch
- 'QuikChange' formula. Recommended (by the manufacturer) for the design
- of primers for QuikChange mutagenesis.
+ 'QuikChange' formula. Recommended (by the manufacturer) for the
+ design of primers for QuikChange mutagenesis.
3. Tm = 81.5 + 0.41(%GC) - 675/N + 16.6 x log[Na+]
- (Marmur & Doty 1962, J Mol Biol 5: 109-118; Schildkraut & Lifson 1965,
- Biopolymers 3: 195-208)
- 4. Tm = 81.5 + 0.41(%GC) - 500/N + 16.6 x log([Na+]/(1.0 + 0.7 x [Na+])) - %mismatch
+ (Marmur & Doty 1962, J Mol Biol 5: 109-118; Schildkraut & Lifson
+ 1965, Biopolymers 3: 195-208)
+ 4. Tm = 81.5 + 0.41(%GC) - 500/N + 16.6 x log([Na+]/(1.0 + 0.7 x
+ [Na+])) - %mismatch
(Wetmur 1991, Crit Rev Biochem Mol Biol 126: 227-259). This is the
standard formula in approximative mode of MELTING 4.3.
- 5. Tm = 78 + 0.7(%GC) - 500/N + 16.6 x log([Na+]/(1.0 + 0.7 x [Na+])) - %mismatch
+ 5. Tm = 78 + 0.7(%GC) - 500/N + 16.6 x log([Na+]/(1.0 + 0.7 x [Na+]))
+ - %mismatch
(Wetmur 1991, Crit Rev Biochem Mol Biol 126: 227-259). For RNA.
- 6. Tm = 67 + 0.8(%GC) - 500/N + 16.6 x log([Na+]/(1.0 + 0.7 x [Na+])) - %mismatch
+ 6. Tm = 67 + 0.8(%GC) - 500/N + 16.6 x log([Na+]/(1.0 + 0.7 x [Na+]))
+ - %mismatch
(Wetmur 1991, Crit Rev Biochem Mol Biol 126: 227-259). For RNA/DNA
hybrids.
7. Tm = 81.5 + 0.41(%GC) - 600/N + 16.6 x log[Na+]
@@ -676,12 +697,12 @@ def Tm_GC(seq, check=True, strict=True, valueset=7, userset=None, Na=50, K=0,
- userset: Tuple of four values for A, B, C, and D. Usersets override
valuesets.
- - Na, K, Tris, Mg, dNTPs: Concentration of the respective ions [mM]. If any
- of K, Tris, Mg and dNTPS is non-zero, a 'sodium-equivalent'
+ - Na, K, Tris, Mg, dNTPs: Concentration of the respective ions [mM]. If
+ any of K, Tris, Mg and dNTPS is non-zero, a 'sodium-equivalent'
concentration is calculated and used for salt correction (von Ahsen et
al., 2001).
- - saltcorr: Type of salt correction (see method salt_correction). Default=5.
- 0 or None means no salt correction.
+ - saltcorr: Type of salt correction (see method salt_correction).
+ Default=5. 0 or None means no salt correction.
- mismatch: If 'True' (default) every 'X' in the sequence is counted as
mismatch.
@@ -692,7 +713,7 @@ def Tm_GC(seq, check=True, strict=True, valueset=7, userset=None, Na=50, K=0,
seq = str(seq)
if check:
seq = _check(seq, 'Tm_GC')
- pGC = SeqUtils.GC(seq)
+ percent_gc = SeqUtils.GC(seq)
# Ambiguous bases: add 0.5, 0.67 or 0.33% depending on G+C probability:
tmp = sum(map(seq.count, ('K', 'M', 'N', 'R', 'Y'))) * 50.0 / len(seq) + \
sum(map(seq.count, ('B', 'V'))) * 66.67 / len(seq) + \
@@ -701,7 +722,7 @@ def Tm_GC(seq, check=True, strict=True, valueset=7, userset=None, Na=50, K=0,
raise ValueError('ambiguous bases B, D, H, K, M, N, R, V, Y not ' +
'allowed when strict=True')
else:
- pGC += tmp
+ percent_gc += tmp
if userset:
A, B, C, D = userset
else:
@@ -732,13 +753,26 @@ def Tm_GC(seq, check=True, strict=True, valueset=7, userset=None, Na=50, K=0,
if valueset > 8:
raise ValueError('allowed values for parameter \'valueset\' are 0-8.')
- Tm = A + B * pGC - C / (len(seq) * 1.0)
+ melting_temp = A + B * percent_gc - C / (len(seq) * 1.0)
if saltcorr:
- Tm += salt_correction(Na=Na, K=K, Tris=Tris, Mg=Mg, dNTPs=dNTPs,
- seq=seq, method=saltcorr)
+ melting_temp += salt_correction(Na=Na, K=K, Tris=Tris, Mg=Mg,
+ dNTPs=dNTPs, seq=seq, method=saltcorr)
if mismatch:
- Tm -= D * (seq.count('X') * 100.0 / len(seq))
- return Tm
+ melting_temp -= D * (seq.count('X') * 100.0 / len(seq))
+ return melting_temp
+
+
+def _key_error(neighbors, strict):
+ """Throw an error or a warning if there is no data for the neighbors."""
+ # We haven't found the key in the tables
+ if strict:
+ raise ValueError('no thermodynamic data for neighbors \'' + neighbors +
+ '\' available')
+ else:
+ warnings.warn('no themodynamic data for neighbors \'' + neighbors +
+ '\' available. Calculation will be wrong',
+ BiopythonWarning)
+ return
def Tm_NN(seq, check=True, strict=True, c_seq=None, shift=0, nn_table=DNA_NN3,
@@ -748,14 +782,14 @@ def Tm_NN(seq, check=True, strict=True, c_seq=None, shift=0, nn_table=DNA_NN3,
"""Return the Tm using nearest neighbor thermodynamics.
Arguments:
- - seq: The primer/probe sequence as string or Biopython sequence object. For
- RNA/DNA hybridizations seq must be the RNA sequence.
+ - seq: The primer/probe sequence as string or Biopython sequence object.
+ For RNA/DNA hybridizations seq must be the RNA sequence.
- c_seq: Complementary sequence. The sequence of the template/target in
3'->5' direction. c_seq is necessary for mismatch correction and
dangling-ends correction. Both corrections will automatically be
applied if mismatches or dangling ends are present. Default=None.
- - shift: Shift of the primer/probe sequence on the template/target sequence,
- e.g.::
+ - shift: Shift of the primer/probe sequence on the template/target
+ sequence, e.g.::
shift=0 shift=1 shift= -1
Primer (seq): 5' ATGC... 5' ATGC... 5' ATGC...
@@ -783,8 +817,8 @@ def Tm_NN(seq, check=True, strict=True, c_seq=None, shift=0, nn_table=DNA_NN3,
Use the module's maketable method to make a new table or to update one
one of the implemented tables.
- - tmm_table: Thermodynamic values for terminal mismatches. Default: DNA_TMM1
- (SantaLucia & Peyret, 2001)
+ - tmm_table: Thermodynamic values for terminal mismatches.
+ Default: DNA_TMM1 (SantaLucia & Peyret, 2001)
- imm_table: Thermodynamic values for internal mismatches, may include
insosine mismatches. Default: DNA_IMM1 (Allawi & SantaLucia, 1997-1998;
Peyret et al., 1999; Watkins & SantaLucia, 2005)
@@ -793,18 +827,18 @@ def Tm_NN(seq, check=True, strict=True, c_seq=None, shift=0, nn_table=DNA_NN3,
- DNA_DE1: for DNA. Values from Bommarito et al. (2000). Default
- RNA_DE1: for RNA. Values from Turner & Mathews (2010)
- - dnac1: Concentration of the higher concentrated strand [nM]. Typically this
- will be the primer (for PCR) or the probe. Default=25.
- - dnac2: Concentration of the lower concentrated strand [nM]. In PCR this is
- the template strand which concentration is typically very low and may
+ - dnac1: Concentration of the higher concentrated strand [nM]. Typically
+ this will be the primer (for PCR) or the probe. Default=25.
+ - dnac2: Concentration of the lower concentrated strand [nM]. In PCR this
+ is the template strand which concentration is typically very low and may
be ignored (dnac2=0). In oligo/oligo hybridization experiments, dnac1
equals dnac1. Default=25.
MELTING and Primer3Plus use k = [Oligo(Total)]/4 by default. To mimic
this behaviour, you have to divide [Oligo(Total)] by 2 and assign this
concentration to dnac1 and dnac2. E.g., Total oligo concentration of
50 nM in Primer3Plus means dnac1=25, dnac2=25.
- - selfcomp: Is the sequence self-complementary? Default=False. If 'True' the
- primer is thought binding to itself, thus dnac2 is not considered.
+ - selfcomp: Is the sequence self-complementary? Default=False. If 'True'
+ the primer is thought binding to itself, thus dnac2 is not considered.
- Na, K, Tris, Mg, dNTPs: See method 'Tm_GC' for details. Defaults: Na=50,
K=0, Tris=0, Mg=0, dNTPs=0.
- saltcorr: See method 'Tm_GC'. Default=5. 0 means no salt correction.
@@ -819,57 +853,63 @@ def Tm_NN(seq, check=True, strict=True, c_seq=None, shift=0, nn_table=DNA_NN3,
if check:
seq = _check(seq, 'Tm_NN')
c_seq = _check(c_seq, 'Tm_NN')
- tmpseq = seq
+ tmp_seq = seq
tmp_cseq = c_seq
- deltaH = 0
- deltaS = 0
- dH = 0 # Names for indexes
- dS = 1 # 0 and 1
+ delta_h = 0
+ delta_s = 0
+ d_h = 0 # Names for indexes
+ d_s = 1 # 0 and 1
# Dangling ends?
if shift or len(seq) != len(c_seq):
# Align both sequences using the shift parameter
if shift > 0:
- tmpseq = '.' * shift + seq
+ tmp_seq = '.' * shift + seq
if shift < 0:
tmp_cseq = '.' * abs(shift) + c_seq
- if len(tmp_cseq) > len(tmpseq):
- tmpseq += (len(tmp_cseq) - len(tmpseq)) * '.'
- if len(tmp_cseq) < len(tmpseq):
- tmp_cseq += (len(tmpseq) - len(tmp_cseq)) * '.'
+ if len(tmp_cseq) > len(tmp_seq):
+ tmp_seq += (len(tmp_cseq) - len(tmp_seq)) * '.'
+ if len(tmp_cseq) < len(tmp_seq):
+ tmp_cseq += (len(tmp_seq) - len(tmp_cseq)) * '.'
# Remove 'over-dangling' ends
- while tmpseq.startswith('..') or tmp_cseq.startswith('..'):
- tmpseq = tmpseq[1:]
+ while tmp_seq.startswith('..') or tmp_cseq.startswith('..'):
+ tmp_seq = tmp_seq[1:]
tmp_cseq = tmp_cseq[1:]
- while tmpseq.endswith('..') or tmp_cseq.endswith('..'):
- tmpseq = tmpseq[:-1]
+ while tmp_seq.endswith('..') or tmp_cseq.endswith('..'):
+ tmp_seq = tmp_seq[:-1]
tmp_cseq = tmp_cseq[:-1]
# Now for the dangling ends
- if tmpseq.startswith('.') or tmp_cseq.startswith('.'):
- left_de = tmpseq[:2] + '/' + tmp_cseq[:2]
- deltaH += de_table[left_de][dH]
- deltaS += de_table[left_de][dS]
- tmpseq = tmpseq[1:]
+ if tmp_seq.startswith('.') or tmp_cseq.startswith('.'):
+ left_de = tmp_seq[:2] + '/' + tmp_cseq[:2]
+ try:
+ delta_h += de_table[left_de][d_h]
+ delta_s += de_table[left_de][d_s]
+ except KeyError:
+ _key_error(left_de, strict)
+ tmp_seq = tmp_seq[1:]
tmp_cseq = tmp_cseq[1:]
- if tmpseq.endswith('.') or tmp_cseq.endswith('.'):
- right_de = tmp_cseq[-2:][::-1] + '/' + tmpseq[-2:][::-1]
- deltaH += de_table[right_de][dH]
- deltaS += de_table[right_de][dS]
- tmpseq = tmpseq[:-1]
+ if tmp_seq.endswith('.') or tmp_cseq.endswith('.'):
+ right_de = tmp_cseq[-2:][::-1] + '/' + tmp_seq[-2:][::-1]
+ try:
+ delta_h += de_table[right_de][d_h]
+ delta_s += de_table[right_de][d_s]
+ except KeyError:
+ _key_error(right_de, strict)
+ tmp_seq = tmp_seq[:-1]
tmp_cseq = tmp_cseq[:-1]
# Now for terminal mismatches
- left_tmm = tmp_cseq[:2][::-1] + '/' + tmpseq[:2][::-1]
+ left_tmm = tmp_cseq[:2][::-1] + '/' + tmp_seq[:2][::-1]
if left_tmm in tmm_table:
- deltaH += tmm_table[left_tmm][dH]
- deltaS += tmm_table[left_tmm][dS]
- tmpseq = tmpseq[1:]
+ delta_h += tmm_table[left_tmm][d_h]
+ delta_s += tmm_table[left_tmm][d_s]
+ tmp_seq = tmp_seq[1:]
tmp_cseq = tmp_cseq[1:]
- right_tmm = tmpseq[-2:] + '/' + tmp_cseq[-2:]
+ right_tmm = tmp_seq[-2:] + '/' + tmp_cseq[-2:]
if right_tmm in tmm_table:
- deltaH += tmm_table[right_tmm][dH]
- deltaS += tmm_table[right_tmm][dS]
- tmpseq = tmpseq[:-1]
+ delta_h += tmm_table[right_tmm][d_h]
+ delta_s += tmm_table[right_tmm][d_s]
+ tmp_seq = tmp_seq[:-1]
tmp_cseq = tmp_cseq[:-1]
# Now everything 'unusual' at the ends is handled and removed and we can
@@ -877,84 +917,77 @@ def Tm_NN(seq, check=True, strict=True, c_seq=None, shift=0, nn_table=DNA_NN3,
# One or several of the following initiation types may apply:
# Type: General initiation value
- deltaH += nn_table['init'][dH]
- deltaS += nn_table['init'][dS]
+ delta_h += nn_table['init'][d_h]
+ delta_s += nn_table['init'][d_s]
# Type: Duplex with no (allA/T) or at least one (oneG/C) GC pair
if SeqUtils.GC(seq) == 0:
- deltaH += nn_table['init_allA/T'][dH]
- deltaS += nn_table['init_allA/T'][dS]
+ delta_h += nn_table['init_allA/T'][d_h]
+ delta_s += nn_table['init_allA/T'][d_s]
else:
- deltaH += nn_table['init_oneG/C'][dH]
- deltaS += nn_table['init_oneG/C'][dS]
+ delta_h += nn_table['init_oneG/C'][d_h]
+ delta_s += nn_table['init_oneG/C'][d_s]
# Type: Penalty if 5' end is T
if seq.startswith('T'):
- deltaH += nn_table['init_5T/A'][dH]
- deltaS += nn_table['init_5T/A'][dS]
+ delta_h += nn_table['init_5T/A'][d_h]
+ delta_s += nn_table['init_5T/A'][d_s]
if seq.endswith('A'):
- deltaH += nn_table['init_5T/A'][dH]
- deltaS += nn_table['init_5T/A'][dS]
+ delta_h += nn_table['init_5T/A'][d_h]
+ delta_s += nn_table['init_5T/A'][d_s]
# Type: Different values for G/C or A/T terminal basepairs
ends = seq[0] + seq[-1]
AT = ends.count('A') + ends.count('T')
GC = ends.count('G') + ends.count('C')
- deltaH += nn_table['init_A/T'][dH] * AT
- deltaS += nn_table['init_A/T'][dS] * AT
- deltaH += nn_table['init_G/C'][dH] * GC
- deltaS += nn_table['init_G/C'][dS] * GC
+ delta_h += nn_table['init_A/T'][d_h] * AT
+ delta_s += nn_table['init_A/T'][d_s] * AT
+ delta_h += nn_table['init_G/C'][d_h] * GC
+ delta_s += nn_table['init_G/C'][d_s] * GC
# Finally, the 'zipping'
- for basenumber in range(len(tmpseq) - 1):
- neighbors = tmpseq[basenumber:basenumber + 2] + '/' + \
+ for basenumber in range(len(tmp_seq) - 1):
+ neighbors = tmp_seq[basenumber:basenumber + 2] + '/' + \
tmp_cseq[basenumber:basenumber + 2]
if neighbors in imm_table:
- deltaH += imm_table[neighbors][dH]
- deltaS += imm_table[neighbors][dS]
+ delta_h += imm_table[neighbors][d_h]
+ delta_s += imm_table[neighbors][d_s]
elif neighbors[::-1] in imm_table:
- deltaH += imm_table[neighbors[::-1]][dH]
- deltaS += imm_table[neighbors[::-1]][dS]
+ delta_h += imm_table[neighbors[::-1]][d_h]
+ delta_s += imm_table[neighbors[::-1]][d_s]
elif neighbors in nn_table:
- deltaH += nn_table[neighbors][dH]
- deltaS += nn_table[neighbors][dS]
+ delta_h += nn_table[neighbors][d_h]
+ delta_s += nn_table[neighbors][d_s]
elif neighbors[::-1] in nn_table:
- deltaH += nn_table[neighbors[::-1]][dH]
- deltaS += nn_table[neighbors[::-1]][dS]
+ delta_h += nn_table[neighbors[::-1]][d_h]
+ delta_s += nn_table[neighbors[::-1]][d_s]
else:
# We haven't found the key...
- if strict:
- raise ValueError('no data for neighbors \'' + neighbors + '\'')
- else:
- warnings.warn('no data for neighbors \'' + neighbors +
- '\'. Calculation will be wrong',
- BiopythonWarning)
+ _key_error(neighbors, strict)
k = (dnac1 - (dnac2 / 2.0)) * 1e-9
if selfcomp:
k = dnac1 * 1e-9
- deltaH += nn_table['sym'][dH]
- deltaS += nn_table['sym'][dS]
+ delta_h += nn_table['sym'][d_h]
+ delta_s += nn_table['sym'][d_s]
R = 1.987 # universal gas constant in Cal/degrees C*Mol
if saltcorr:
corr = salt_correction(Na=Na, K=K, Tris=Tris, Mg=Mg, dNTPs=dNTPs,
method=saltcorr, seq=seq)
if saltcorr == 5:
- deltaS += corr
- Tm = (1000 * deltaH) / (deltaS + (R * (math.log(k)))) - 273.15
+ delta_s += corr
+ melting_temp = (1000 * delta_h) / (delta_s + (R * (math.log(k)))) - 273.15
if saltcorr in (1, 2, 3, 4):
- Tm += corr
+ melting_temp += corr
if saltcorr in (6, 7):
# Tm = 1/(1/Tm + corr)
- Tm = (1 / (1 / (Tm + 273.15) + corr) - 273.15)
-
- return Tm
+ melting_temp = (1 / (1 / (melting_temp + 273.15) + corr) - 273.15)
-__docformat__ = "restructuredtext en"
+ return melting_temp
def Tm_staluc(s, dnac=50, saltc=50, rna=0):
- """Returns DNA/DNA tm using nearest neighbor thermodynamics (OBSOLETE).
+ """Returns DNA/DNA Tm using nearest neighbor thermodynamics (OBSOLETE).
This method may be depreceated in the future. Use Tm_NN instead. Tm_NN
with default values gives the same result as Tm_staluc.
@@ -996,7 +1029,7 @@ def Tm_staluc(s, dnac=50, saltc=50, rna=0):
return Tm_NN(s, dnac1=dnac / 2.0, dnac2=dnac / 2.0, Na=saltc,
nn_table=RNA_NN2)
else:
- raise ValueError("rna=%r not supported" % rna)
+ raise ValueError("rna={0} not supported".format(rna))
def _test():
diff --git a/Bio/SeqUtils/ProtParam.py b/Bio/SeqUtils/ProtParam.py
index 5d91f40..be36ec5 100644
--- a/Bio/SeqUtils/ProtParam.py
+++ b/Bio/SeqUtils/ProtParam.py
@@ -28,8 +28,6 @@ from Bio.Alphabet import IUPAC
from Bio.Data import IUPACData
from Bio.SeqUtils import molecular_weight
-__docformat__ = "restructuredtext en"
-
class ProteinAnalysis(object):
"""Class containing methods for protein analysis.
diff --git a/Bio/SeqUtils/ProtParamData.py b/Bio/SeqUtils/ProtParamData.py
index e018a47..51071c4 100644
--- a/Bio/SeqUtils/ProtParamData.py
+++ b/Bio/SeqUtils/ProtParamData.py
@@ -1,7 +1,5 @@
"""This module contains indices to be used with ProtParam."""
-__docformat__ = "restructuredtext en"
-
# Kyte & Doolittle index of hydrophobicity
kd = {'A': 1.8, 'R':-4.5, 'N':-3.5, 'D':-3.5, 'C': 2.5,
@@ -39,7 +37,7 @@ ja = {'A': 0.28, 'R':-1.14, 'N':-0.55, 'D':-0.52, 'C': 0.97,
'S':-0.19, 'T':-0.32, 'W': 0.29, 'Y':-0.15, 'V': 0.60}
-# A two dimentional dictionary for calculating the instability index.
+# A two dimensional dictionary for calculating the instability index.
# Guruprasad K., Reddy B.V.B., Pandit M.W. Protein Engineering 4:155-161(1990).
# It is based on dipeptide values therefore the vale for the dipeptide DG is DIWV['D']['G'].
DIWV = {'A': {'A': 1.0, 'C': 44.94, 'E': 1.0, 'D': -7.49,
diff --git a/Bio/SeqUtils/__init__.py b/Bio/SeqUtils/__init__.py
index 61ffe38..59b25f5 100644
--- a/Bio/SeqUtils/__init__.py
+++ b/Bio/SeqUtils/__init__.py
@@ -3,7 +3,7 @@
# thomas at cbs.dtu.dk, Cecilia.Alsmark at ebc.uu.se
# Copyright 2001 by Thomas Sicheritz-Ponten and Cecilia Alsmark.
# Revisions copyright 2014 by Markus Piotrowski.
-# Revisions copyright 2014 by Peter Cock.
+# Revisions copyright 2014-2016 by Peter Cock.
# All rights reserved.
# This code is part of the Biopython distribution and governed by its
# license. Please see the LICENSE file that should have been included
@@ -21,7 +21,6 @@ from Bio import Alphabet
from Bio.Alphabet import IUPAC
from Bio.Data import IUPACData
-__docformat__ = "restructuredtext en"
######################################
# DNA
@@ -81,7 +80,7 @@ def GC123(seq):
try:
n = d['G'][i] + d['C'][i] + d['T'][i] + d['A'][i]
gc[i] = (d['G'][i] + d['C'][i]) * 100.0 / n
- except:
+ except Exception: # TODO - ValueError?
gc[i] = 0
gcall = gcall + d['G'][i] + d['C'][i]
@@ -209,18 +208,19 @@ def nt_search(seq, subseq):
# {{{
-def seq3(seq, custom_map={'*': 'Ter'}, undef_code='Xaa'):
+def seq3(seq, custom_map=None, undef_code='Xaa'):
"""Turn a one letter code protein sequence into one with three letter codes.
- The single input argument 'seq' should be a protein sequence using single
- letter codes, either as a python string or as a Seq or MutableSeq object.
+ The single required input argument 'seq' should be a protein sequence using
+ single letter codes, either as a python string or as a Seq or MutableSeq
+ object.
This function returns the amino acid sequence as a string using the three
letter amino acid codes. Output follows the IUPAC standard (including
ambiguous characters B for "Asx", J for "Xle" and X for "Xaa", and also U
for "Sel" and O for "Pyl") plus "Ter" for a terminator given as an asterisk.
Any unknown character (including possible gap characters), is changed into
- 'Xaa'.
+ 'Xaa' by default.
e.g.
@@ -229,7 +229,7 @@ def seq3(seq, custom_map={'*': 'Ter'}, undef_code='Xaa'):
'MetAlaIleValMetGlyArgTrpLysGlyAlaArgTer'
You can set a custom translation of the codon termination code using the
- "custom_map" argument, e.g.
+ dictionary "custom_map" argument (which defaults to {'*': 'Ter'}), e.g.
>>> seq3("MAIVMGRWKGAR*", custom_map={"*": "***"})
'MetAlaIleValMetGlyArgTrpLysGlyAlaArg***'
@@ -247,6 +247,8 @@ def seq3(seq, custom_map={'*': 'Ter'}, undef_code='Xaa'):
This function was inspired by BioPerl's seq3.
"""
+ if custom_map is None:
+ custom_map = {'*': 'Ter'}
# not doing .update() on IUPACData dict with custom_map dict
# to preserve its initial state (may be imported in other modules)
threecode = dict(list(IUPACData.protein_letters_1to3_extended.items()) +
@@ -256,18 +258,19 @@ def seq3(seq, custom_map={'*': 'Ter'}, undef_code='Xaa'):
return ''.join(threecode.get(aa, undef_code) for aa in seq)
-def seq1(seq, custom_map={'Ter': '*'}, undef_code='X'):
+def seq1(seq, custom_map=None, undef_code='X'):
"""Turns a three-letter code protein sequence into one with single letter codes.
- The single input argument 'seq' should be a protein sequence using three-
- letter codes, either as a python string or as a Seq or MutableSeq object.
+ The single required input argument 'seq' should be a protein sequence
+ using three-letter codes, either as a python string or as a Seq or
+ MutableSeq object.
This function returns the amino acid sequence as a string using the one
letter amino acid codes. Output follows the IUPAC standard (including
ambiguous characters "B" for "Asx", "J" for "Xle", "X" for "Xaa", "U" for
"Sel", and "O" for "Pyl") plus "*" for a terminator given the "Ter" code.
- Any unknown character (including possible gap characters), is changed into
- '-'.
+ Any unknown character (including possible gap characters), is changed
+ into '-' by default.
e.g.
@@ -282,7 +285,7 @@ def seq1(seq, custom_map={'Ter': '*'}, undef_code='X'):
'MAIVMGRWKGAR*'
You can set a custom translation of the codon termination code using the
- "custom_map" argument, e.g.
+ dictionary "custom_map" argument (defaulting to {'Ter': '*'}), e.g.
>>> seq1("MetAlaIleValMetGlyArgTrpLysGlyAlaArg***", custom_map={"***": "*"})
'MAIVMGRWKGAR*'
@@ -299,6 +302,8 @@ def seq1(seq, custom_map={'Ter': '*'}, undef_code='X'):
'MAIVMGRWKGAXXR*'
"""
+ if custom_map is None:
+ custom_map = {'Ter': '*'}
# reverse map of threecode
# upper() on all keys to enable caps-insensitive input seq handling
onecode = dict((k.upper(), v) for k, v in
@@ -376,7 +381,7 @@ def molecular_weight(seq, seq_type=None, double_stranded=False, circular=False,
# Find the alphabet type
tmp_type = ''
- if isinstance(seq, Seq) or isinstance(seq, MutableSeq):
+ if isinstance(seq, (Seq, MutableSeq)):
base_alphabet = Alphabet._get_base_alphabet(seq.alphabet)
if isinstance(base_alphabet, Alphabet.DNAAlphabet):
tmp_type = 'DNA'
@@ -516,56 +521,6 @@ def six_frame_translations(seq, genetic_code=1):
# }}}
-######################################
-# FASTA file utilities
-######################
-# {{{
-
-
-def quick_FASTA_reader(file):
- """Simple FASTA reader, returning a list of string tuples (DEPRECATED).
-
- The single argument 'file' should be the filename of a FASTA format file.
- This function will open and read in the entire file, constructing a list
- of all the records, each held as a tuple of strings (the sequence name or
- title, and its sequence).
-
- >>> seqs = quick_FASTA_reader("Fasta/dups.fasta")
- >>> for title, sequence in seqs:
- ... print("%s %s" % (title, sequence))
- alpha ACGTA
- beta CGTC
- gamma CCGCC
- alpha (again - this is a duplicate entry to test the indexing code) ACGTA
- delta CGCGC
-
- This function was is fast, but because it returns the data as a single in
- memory list, is unsuitable for large files where an iterator approach is
- preferable.
-
- You are generally encouraged to use Bio.SeqIO.parse(handle, "fasta") which
- allows you to iterate over the records one by one (avoiding having all the
- records in memory at once). Using Bio.SeqIO also makes it easy to switch
- between different input file formats. However, please note that rather
- than simple strings, Bio.SeqIO uses SeqRecord objects for each record.
-
- If you want to use simple strings, use the function SimpleFastaParser
- added to Bio.SeqIO.FastaIO in Biopython 1.61 instead.
- """
- import warnings
- from Bio import BiopythonDeprecationWarning
- warnings.warn("The quick_FASTA_reader has been deprecated and will be "
- "removed in a future release of Biopython. Please try "
- "function SimpleFastaParser from Bio.SeqIO.FastaIO "
- "instead.", BiopythonDeprecationWarning)
- from Bio.SeqIO.FastaIO import SimpleFastaParser
- with open(file) as handle:
- entries = list(SimpleFastaParser(handle))
- return entries
-
-
-# }}}
-
def _test():
"""Run the module's doctests (PRIVATE)."""
diff --git a/Bio/SeqUtils/lcc.py b/Bio/SeqUtils/lcc.py
index 4c51c22..c47e404 100644
--- a/Bio/SeqUtils/lcc.py
+++ b/Bio/SeqUtils/lcc.py
@@ -7,8 +7,6 @@
import math
-__docformat__ = "restructuredtext en"
-
def lcc_mult(seq, wsize):
"""Local Composition Complexity (LCC) values over sliding window.
@@ -146,21 +144,21 @@ def lcc_simp(seq):
term_a = 0
# Check to avoid calculating the log of 0.
else:
- term_a = ((upper.count('A')) / float(wsize)) * ((math.log((upper.count('A'))
- / float(wsize))) / l2)
+ term_a = ((upper.count('A')) / float(wsize)) * \
+ ((math.log((upper.count('A')) / float(wsize))) / l2)
if 'C' not in seq:
term_c = 0
else:
- term_c = ((upper.count('C')) / float(wsize)) * ((math.log((upper.count('C'))
- / float(wsize))) / l2)
+ term_c = ((upper.count('C')) / float(wsize)) * \
+ ((math.log((upper.count('C')) / float(wsize))) / l2)
if 'T' not in seq:
term_t = 0
else:
- term_t = ((upper.count('T')) / float(wsize)) * ((math.log((upper.count('T'))
- / float(wsize))) / l2)
+ term_t = ((upper.count('T')) / float(wsize)) * \
+ ((math.log((upper.count('T')) / float(wsize))) / l2)
if 'G' not in seq:
term_g = 0
else:
- term_g = ((upper.count('G')) / float(wsize)) * ((math.log((upper.count('G'))
- / float(wsize))) / l2)
+ term_g = ((upper.count('G')) / float(wsize)) * \
+ ((math.log((upper.count('G')) / float(wsize))) / l2)
return -(term_a + term_c + term_t + term_g)
diff --git a/Bio/Sequencing/Ace.py b/Bio/Sequencing/Ace.py
index fbbf32e..60a72ef 100644
--- a/Bio/Sequencing/Ace.py
+++ b/Bio/Sequencing/Ace.py
@@ -58,8 +58,6 @@ are needed, the 'read' function rather than the 'parse' function might be more a
from __future__ import print_function
from Bio._py3k import zip
-__docformat__ = "restructuredtext en"
-
class rd(object):
"""RD (reads), store a read with its name, sequence etc.
diff --git a/Bio/Sequencing/Applications/_Novoalign.py b/Bio/Sequencing/Applications/_Novoalign.py
index f22c44c..5a17fe1 100644
--- a/Bio/Sequencing/Applications/_Novoalign.py
+++ b/Bio/Sequencing/Applications/_Novoalign.py
@@ -176,12 +176,6 @@ class NovoalignCommandline(AbstractCommandline):
AbstractCommandline.__init__(self, cmd, **kwargs)
-def _test():
- """Run the module's doctests (PRIVATE)."""
- print("Running Novoalign doctests...")
- import doctest
- doctest.testmod()
- print("Done")
-
if __name__ == "__main__":
- _test()
+ from Bio._utils import run_doctest
+ run_doctest()
diff --git a/Bio/Sequencing/Applications/__init__.py b/Bio/Sequencing/Applications/__init__.py
index e458cf8..754c91f 100644
--- a/Bio/Sequencing/Applications/__init__.py
+++ b/Bio/Sequencing/Applications/__init__.py
@@ -16,6 +16,7 @@ from ._samtools import SamtoolsMpileupCommandline, SamtoolsPhaseCommandline
from ._samtools import SamtoolsReheaderCommandline, SamtoolsRmdupCommandline
from ._samtools import SamtoolsSortCommandline, SamtoolsTargetcutCommandline
+
# Make this explicit, then they show up in the API docs
__all__ = ["BwaIndexCommandline",
"BwaAlignCommandline",
diff --git a/Bio/Sequencing/Applications/_bwa.py b/Bio/Sequencing/Applications/_bwa.py
index a3eaa78..aa08659 100644
--- a/Bio/Sequencing/Applications/_bwa.py
+++ b/Bio/Sequencing/Applications/_bwa.py
@@ -9,8 +9,6 @@
from __future__ import print_function
from Bio._py3k import basestring
-__docformat__ = "restructuredtext en"
-
from Bio.Application import _Option, _Argument, _Switch, AbstractCommandline
from Bio.Application import _StaticArgument
@@ -379,13 +377,6 @@ class BwaBwaswCommandline(AbstractCommandline):
AbstractCommandline.__init__(self, cmd, **kwargs)
-def _test():
- """Run the module's doctests (PRIVATE)."""
- print("Running modules doctests...")
- import doctest
- doctest.testmod()
- print("Done")
-
-
if __name__ == "__main__":
- _test()
+ from Bio._utils import run_doctest
+ run_doctest()
diff --git a/Bio/Sequencing/Applications/_samtools.py b/Bio/Sequencing/Applications/_samtools.py
index 6ac1591..910f88a 100644
--- a/Bio/Sequencing/Applications/_samtools.py
+++ b/Bio/Sequencing/Applications/_samtools.py
@@ -4,7 +4,7 @@
# are missing from this wrapper
from __future__ import print_function
-__docformat__ = "restructuredtext en"
+
from Bio.Application import _Option, _Argument, _Switch
from Bio.Application import AbstractCommandline, _ArgumentList
from Bio.Application import _StaticArgument
@@ -731,12 +731,6 @@ class SamtoolsPhaseCommandline(AbstractCommandline):
AbstractCommandline.__init__(self, cmd, **kwargs)
-def _test():
- """Run the module's doctests (PRIVATE)."""
- print("Running modules doctests...")
- import doctest
- doctest.testmod()
- print("Done")
-
if __name__ == "__main__":
- _test()
+ from Bio._utils import run_doctest
+ run_doctest()
diff --git a/Bio/Sequencing/Phd.py b/Bio/Sequencing/Phd.py
index 8527825..9ae91a9 100644
--- a/Bio/Sequencing/Phd.py
+++ b/Bio/Sequencing/Phd.py
@@ -19,7 +19,6 @@ internally. This will give SeqRecord objects for each contig sequence.
from Bio import Seq
from Bio.Alphabet import generic_dna
-__docformat__ = "restructuredtext en"
CKEYWORDS = ['CHROMAT_FILE', 'ABI_THUMBPRINT', 'PHRED_VERSION', 'CALL_METHOD',
'QUALITY_LEVELS', 'TIME', 'TRACE_ARRAY_MIN_INDEX', 'TRACE_ARRAY_MAX_INDEX',
diff --git a/Bio/Statistics/lowess.py b/Bio/Statistics/lowess.py
index 569ef1e..18d513a 100644
--- a/Bio/Statistics/lowess.py
+++ b/Bio/Statistics/lowess.py
@@ -25,6 +25,7 @@ from Bio._py3k import range
import numpy
+
try:
from Bio.Cluster import median
# The function median in Bio.Cluster is faster than the function median
diff --git a/Bio/SubsMat/FreqTable.py b/Bio/SubsMat/FreqTable.py
index f2eefb8..077cda5 100644
--- a/Bio/SubsMat/FreqTable.py
+++ b/Bio/SubsMat/FreqTable.py
@@ -1,56 +1,53 @@
# This code is part of the Biopython distribution and governed by its
# license. Please see the LICENSE file that should have been included
# as part of this package.
-#
+# Copyright Iddo Friedberg idoerg at cc.huji.ac.il
+"""A class to handle frequency tables
+
+Methods to read a letter frequency or a letter count file:
+Example files for a DNA alphabet:
+
+A count file (whitespace separated):
+
+A 50
+C 37
+G 23
+T 58
+
+The same info as a frequency file:
+
+A 0.2976
+C 0.2202
+G 0.1369
+T 0.3452
+
+Functions:
+ read_count(f): read a count file from stream f. Then convert to
+ frequencies
+ read_freq(f): read a frequency data file from stream f. Of course, we then
+ don't have the counts, but it is usually the letter frquencies which are
+ interesting.
+
+Methods:
+ (all internal)
+Attributes:
+ alphabet: The IUPAC alphabet set (or any other) whose letters you are
+ using. Common sets are: IUPAC.protein (20-letter protein),
+ IUPAC.unambiguous_dna (4-letter DNA). See Bio/alphabet for more.
+ data: frequency dictionary.
+ count: count dictionary. Empty if no counts are provided.
+
+Example of use:
+ >>> from SubsMat import FreqTable
+ >>> ftab = FreqTable.FreqTable(my_frequency_dictionary,FreqTable.FREQ)
+ >>> ftab = FreqTable.FreqTable(my_count_dictionary,FreqTable.COUNT)
+ >>> ftab = FreqTable.read_count(open('myDNACountFile'))
+
+"""
from Bio import Alphabet
COUNT = 1
FREQ = 2
-##################################################################
-# A class to handle frequency tables
-# Copyright Iddo Friedberg idoerg at cc.huji.ac.il
-# Biopython (http://biopython.org) license applies
-# Methods to read a letter frequency or a letter count file:
-# Example files for a DNA alphabet:
-#
-# A count file (whitespace separated):
-#
-# A 50
-# C 37
-# G 23
-# T 58
-#
-# The same info as a frequency file:
-#
-# A 0.2976
-# C 0.2202
-# G 0.1369
-# T 0.3452
-#
-# Functions:
-# read_count(f): read a count file from stream f. Then convert to
-# frequencies
-# read_freq(f): read a frequency data file from stream f. Of course, we then
-# don't have the counts, but it is usually the letter frquencies which are
-# interesting.
-#
-# Methods:
-# (all internal)
-# Attributes:
-# alphabet: The IUPAC alphabet set (or any other) whose letters you are
-# using. Common sets are: IUPAC.protein (20-letter protein),
-# IUPAC.unambiguous_dna (4-letter DNA). See Bio/alphabet for more.
-# data: frequency dictionary.
-# count: count dictionary. Empty if no counts are provided.
-#
-# Example of use:
-# >>> from SubsMat import FreqTable
-# >>> ftab = FreqTable.FreqTable(my_frequency_dictionary,FreqTable.FREQ)
-# >>> ftab = FreqTable.FreqTable(my_count_dictionary,FreqTable.COUNT)
-# >>> ftab = FreqTable.read_count(open('myDNACountFile'))
-#
-#
-##################################################################
class FreqTable(dict):
diff --git a/Bio/SubsMat/MatrixInfo.py b/Bio/SubsMat/MatrixInfo.py
index 9e94058..e0e7f4d 100644
--- a/Bio/SubsMat/MatrixInfo.py
+++ b/Bio/SubsMat/MatrixInfo.py
@@ -13,7 +13,6 @@ You can view an archive copy of this webpage from 1999 here;
https://web.archive.org/web/19991014010917/http://www.embl-heidelberg.de/%7Evogt/matrices/mlist1.html
"""
-__docformat__ = "restructuredtext en"
# a list of all available substitution matrices
available_matrices = ['benner6', 'benner22', 'benner74', 'blosum100',
@@ -28,69 +27,69 @@ available_matrices = ['benner6', 'benner22', 'benner74', 'blosum100',
'pam90', 'rao', 'risler', 'structure'
]
-#NOTE - The only reason we define temp functions rather than assigning
-#the dictionaries directly to variables is to work around a JVM
-#limitation on code size if running under Jython. The temp functions
-#have an underscore to indicate they are private, and are in any case
-#deleted after use (partly to ensure we don't use any of them twice).
+# NOTE - The only reason we define temp functions rather than assigning
+# the dictionaries directly to variables is to work around a JVM
+# limitation on code size if running under Jython. The temp functions
+# have an underscore to indicate they are private, and are in any case
+# deleted after use (partly to ensure we don't use any of them twice).
# http://www.embl-heidelberg.de/~vogt/matrices/benner6.cmp
def _temp():
return {
-('W', 'F') : -1.6, ('L', 'R') : -3.2, ('I', 'I') : 4.4, ('Q', 'Q') : 5.3,
-('W', 'N') : -4.4, ('V', 'I') : 3.9, ('H', 'T') : -1.7, ('H', 'P') : -0.4,
-('W', 'V') : -4.8, ('Q', 'E') : 2.1, ('W', 'R') : 2.0, ('Q', 'A') : -1.7,
-('H', 'H') : 6.1, ('H', 'D') : 0.1, ('L', 'N') : -3.4, ('Y', 'M') : -3.6,
-('Y', 'I') : -3.3, ('Y', 'E') : -4.1, ('E', 'S') : -1.2, ('Y', 'A') : -4.0,
-('Y', 'Y') : 9.5, ('T', 'C') : -1.5, ('E', 'C') : -4.7, ('Y', 'Q') : -1.4,
-('E', 'G') : 0.5, ('V', 'A') : 0.7, ('C', 'C') : 12.1, ('M', 'R') : -3.0,
-('P', 'T') : 0.6, ('V', 'E') : -3.0, ('P', 'P') : 6.5, ('I', 'T') : 0.7,
-('K', 'S') : -1.2, ('R', 'G') : -0.1, ('I', 'P') : -2.0, ('R', 'C') : -0.4,
-('A', 'T') : 1.7, ('K', 'K') : 5.6, ('A', 'P') : 1.1, ('V', 'M') : 3.3,
-('I', 'D') : -4.2, ('K', 'C') : -2.8, ('K', 'G') : -1.4, ('R', 'S') : -0.9,
-('F', 'Q') : -4.4, ('F', 'A') : -3.2, ('V', 'V') : 4.0, ('M', 'N') : -2.5,
-('F', 'E') : -6.7, ('D', 'N') : 2.5, ('F', 'I') : 0.0, ('F', 'M') : -0.1,
-('M', 'S') : -1.3, ('S', 'S') : 2.1, ('L', 'Q') : -2.4, ('W', 'E') : -5.6,
-('W', 'A') : -4.3, ('W', 'M') : -4.4, ('H', 'S') : -0.9, ('W', 'I') : -5.0,
-('S', 'C') : 0.9, ('L', 'A') : -1.3, ('L', 'E') : -5.0, ('W', 'Q') : -2.6,
-('H', 'G') : -2.1, ('Q', 'N') : 0.1, ('H', 'C') : -1.2, ('L', 'M') : -2.9,
-('W', 'Y') : -0.3, ('Y', 'N') : -0.9, ('E', 'P') : -2.6, ('Y', 'F') : 5.6,
-('E', 'T') : -1.6, ('A', 'A') : 2.5, ('I', 'N') : -2.5, ('G', 'A') : 0.8,
-('Y', 'V') : -3.8, ('E', 'D') : 4.4, ('W', 'H') : -2.8, ('Y', 'R') : -2.6,
-('M', 'Q') : -3.1, ('P', 'S') : 1.4, ('R', 'H') : 1.8, ('A', 'C') : -1.7,
-('R', 'D') : -1.5, ('K', 'P') : -2.3, ('L', 'D') : -5.3, ('K', 'T') : -1.1,
-('V', 'N') : -2.4, ('M', 'A') : -0.2, ('K', 'H') : 0.9, ('V', 'R') : -3.7,
-('P', 'C') : -2.7, ('M', 'E') : -4.1, ('A', 'S') : 1.4, ('T', 'T') : 2.4,
-('R', 'T') : -1.3, ('I', 'G') : -3.4, ('R', 'P') : -1.3, ('K', 'D') : -0.2,
-('I', 'C') : -3.6, ('F', 'R') : -4.9, ('F', 'V') : -0.5, ('L', 'C') : -3.8,
-('F', 'F') : 8.3, ('D', 'A') : -0.6, ('F', 'N') : -3.5, ('W', 'D') : -6.3,
-('L', 'P') : -0.2, ('Q', 'S') : -1.4, ('N', 'C') : -1.6, ('N', 'G') : -0.1,
-('H', 'N') : 1.4, ('W', 'T') : -2.6, ('Q', 'G') : -1.6, ('W', 'P') : -4.8,
-('Q', 'C') : -3.2, ('N', 'S') : 1.2, ('L', 'H') : -2.2, ('L', 'L') : 4.8,
-('G', 'T') : -0.5, ('M', 'M') : 4.8, ('G', 'P') : -1.7, ('Y', 'K') : -4.0,
-('Y', 'G') : -4.9, ('Y', 'C') : 2.6, ('E', 'A') : -0.7, ('E', 'E') : 5.2,
-('Y', 'S') : -1.8, ('M', 'P') : -1.8, ('V', 'C') : -3.1, ('M', 'T') : 0.6,
-('V', 'G') : -2.3, ('R', 'E') : -0.4, ('V', 'K') : -3.8, ('K', 'Q') : 2.5,
-('R', 'A') : -1.7, ('I', 'R') : -3.8, ('N', 'A') : 0.0, ('V', 'S') : -0.9,
-('M', 'D') : -4.3, ('M', 'H') : -3.4, ('K', 'A') : -1.9, ('R', 'Q') : 2.5,
-('K', 'E') : 0.9, ('F', 'S') : -1.8, ('I', 'K') : -3.8, ('D', 'P') : -2.8,
-('D', 'T') : -1.2, ('I', 'M') : 4.0, ('F', 'C') : -0.1, ('W', 'L') : -3.0,
-('F', 'G') : -5.7, ('F', 'K') : -6.3, ('F', 'T') : -2.4, ('D', 'D') : 5.2,
-('Q', 'T') : -1.7, ('W', 'G') : -1.7, ('Q', 'P') : 0.1, ('W', 'C') : 1.6,
-('W', 'K') : -1.4, ('H', 'Q') : 3.2, ('Q', 'D') : 0.6, ('W', 'W') : 14.7,
-('V', 'L') : 1.9, ('L', 'G') : -4.6, ('W', 'S') : -2.9, ('L', 'K') : -4.1,
-('N', 'P') : -1.1, ('H', 'E') : -0.2, ('N', 'T') : 0.5, ('H', 'A') : -2.1,
-('Y', 'L') : -1.6, ('Y', 'H') : 4.4, ('G', 'S') : 0.8, ('Y', 'D') : -2.3,
-('V', 'Q') : -3.5, ('L', 'T') : -0.4, ('G', 'G') : 5.8, ('G', 'C') : -1.3,
-('E', 'N') : 1.1, ('Y', 'T') : -3.4, ('Y', 'P') : -3.8, ('R', 'N') : -0.1,
-('V', 'D') : -3.3, ('K', 'R') : 4.3, ('V', 'H') : -3.8, ('I', 'Q') : -3.8,
-('V', 'P') : -1.6, ('M', 'C') : -3.7, ('K', 'N') : 1.0, ('V', 'T') : 0.6,
-('M', 'G') : -3.7, ('T', 'S') : 1.5, ('I', 'E') : -4.1, ('M', 'K') : -2.9,
-('I', 'A') : 0.1, ('N', 'N') : 3.6, ('R', 'R') : 5.1, ('F', 'P') : -3.2,
-('L', 'I') : 2.4, ('I', 'S') : -1.2, ('D', 'S') : -0.4, ('L', 'S') : -1.5,
-('I', 'H') : -3.7, ('F', 'D') : -5.7, ('D', 'C') : -3.7, ('F', 'H') : 0.1,
-('D', 'G') : 0.8, ('F', 'L') : 2.4
+('W', 'F'): -1.6, ('L', 'R'): -3.2, ('I', 'I'): 4.4, ('Q', 'Q'): 5.3,
+('W', 'N'): -4.4, ('V', 'I'): 3.9, ('H', 'T'): -1.7, ('H', 'P'): -0.4,
+('W', 'V'): -4.8, ('Q', 'E'): 2.1, ('W', 'R'): 2.0, ('Q', 'A'): -1.7,
+('H', 'H'): 6.1, ('H', 'D'): 0.1, ('L', 'N'): -3.4, ('Y', 'M'): -3.6,
+('Y', 'I'): -3.3, ('Y', 'E'): -4.1, ('E', 'S'): -1.2, ('Y', 'A'): -4.0,
+('Y', 'Y'): 9.5, ('T', 'C'): -1.5, ('E', 'C'): -4.7, ('Y', 'Q'): -1.4,
+('E', 'G'): 0.5, ('V', 'A'): 0.7, ('C', 'C'): 12.1, ('M', 'R'): -3.0,
+('P', 'T'): 0.6, ('V', 'E'): -3.0, ('P', 'P'): 6.5, ('I', 'T'): 0.7,
+('K', 'S'): -1.2, ('R', 'G'): -0.1, ('I', 'P'): -2.0, ('R', 'C'): -0.4,
+('A', 'T'): 1.7, ('K', 'K'): 5.6, ('A', 'P'): 1.1, ('V', 'M'): 3.3,
+('I', 'D'): -4.2, ('K', 'C'): -2.8, ('K', 'G'): -1.4, ('R', 'S'): -0.9,
+('F', 'Q'): -4.4, ('F', 'A'): -3.2, ('V', 'V'): 4.0, ('M', 'N'): -2.5,
+('F', 'E'): -6.7, ('D', 'N'): 2.5, ('F', 'I'): 0.0, ('F', 'M'): -0.1,
+('M', 'S'): -1.3, ('S', 'S'): 2.1, ('L', 'Q'): -2.4, ('W', 'E'): -5.6,
+('W', 'A'): -4.3, ('W', 'M'): -4.4, ('H', 'S'): -0.9, ('W', 'I'): -5.0,
+('S', 'C'): 0.9, ('L', 'A'): -1.3, ('L', 'E'): -5.0, ('W', 'Q'): -2.6,
+('H', 'G'): -2.1, ('Q', 'N'): 0.1, ('H', 'C'): -1.2, ('L', 'M'): -2.9,
+('W', 'Y'): -0.3, ('Y', 'N'): -0.9, ('E', 'P'): -2.6, ('Y', 'F'): 5.6,
+('E', 'T'): -1.6, ('A', 'A'): 2.5, ('I', 'N'): -2.5, ('G', 'A'): 0.8,
+('Y', 'V'): -3.8, ('E', 'D'): 4.4, ('W', 'H'): -2.8, ('Y', 'R'): -2.6,
+('M', 'Q'): -3.1, ('P', 'S'): 1.4, ('R', 'H'): 1.8, ('A', 'C'): -1.7,
+('R', 'D'): -1.5, ('K', 'P'): -2.3, ('L', 'D'): -5.3, ('K', 'T'): -1.1,
+('V', 'N'): -2.4, ('M', 'A'): -0.2, ('K', 'H'): 0.9, ('V', 'R'): -3.7,
+('P', 'C'): -2.7, ('M', 'E'): -4.1, ('A', 'S'): 1.4, ('T', 'T'): 2.4,
+('R', 'T'): -1.3, ('I', 'G'): -3.4, ('R', 'P'): -1.3, ('K', 'D'): -0.2,
+('I', 'C'): -3.6, ('F', 'R'): -4.9, ('F', 'V'): -0.5, ('L', 'C'): -3.8,
+('F', 'F'): 8.3, ('D', 'A'): -0.6, ('F', 'N'): -3.5, ('W', 'D'): -6.3,
+('L', 'P'): -0.2, ('Q', 'S'): -1.4, ('N', 'C'): -1.6, ('N', 'G'): -0.1,
+('H', 'N'): 1.4, ('W', 'T'): -2.6, ('Q', 'G'): -1.6, ('W', 'P'): -4.8,
+('Q', 'C'): -3.2, ('N', 'S'): 1.2, ('L', 'H'): -2.2, ('L', 'L'): 4.8,
+('G', 'T'): -0.5, ('M', 'M'): 4.8, ('G', 'P'): -1.7, ('Y', 'K'): -4.0,
+('Y', 'G'): -4.9, ('Y', 'C'): 2.6, ('E', 'A'): -0.7, ('E', 'E'): 5.2,
+('Y', 'S'): -1.8, ('M', 'P'): -1.8, ('V', 'C'): -3.1, ('M', 'T'): 0.6,
+('V', 'G'): -2.3, ('R', 'E'): -0.4, ('V', 'K'): -3.8, ('K', 'Q'): 2.5,
+('R', 'A'): -1.7, ('I', 'R'): -3.8, ('N', 'A'): 0.0, ('V', 'S'): -0.9,
+('M', 'D'): -4.3, ('M', 'H'): -3.4, ('K', 'A'): -1.9, ('R', 'Q'): 2.5,
+('K', 'E'): 0.9, ('F', 'S'): -1.8, ('I', 'K'): -3.8, ('D', 'P'): -2.8,
+('D', 'T'): -1.2, ('I', 'M'): 4.0, ('F', 'C'): -0.1, ('W', 'L'): -3.0,
+('F', 'G'): -5.7, ('F', 'K'): -6.3, ('F', 'T'): -2.4, ('D', 'D'): 5.2,
+('Q', 'T'): -1.7, ('W', 'G'): -1.7, ('Q', 'P'): 0.1, ('W', 'C'): 1.6,
+('W', 'K'): -1.4, ('H', 'Q'): 3.2, ('Q', 'D'): 0.6, ('W', 'W'): 14.7,
+('V', 'L'): 1.9, ('L', 'G'): -4.6, ('W', 'S'): -2.9, ('L', 'K'): -4.1,
+('N', 'P'): -1.1, ('H', 'E'): -0.2, ('N', 'T'): 0.5, ('H', 'A'): -2.1,
+('Y', 'L'): -1.6, ('Y', 'H'): 4.4, ('G', 'S'): 0.8, ('Y', 'D'): -2.3,
+('V', 'Q'): -3.5, ('L', 'T'): -0.4, ('G', 'G'): 5.8, ('G', 'C'): -1.3,
+('E', 'N'): 1.1, ('Y', 'T'): -3.4, ('Y', 'P'): -3.8, ('R', 'N'): -0.1,
+('V', 'D'): -3.3, ('K', 'R'): 4.3, ('V', 'H'): -3.8, ('I', 'Q'): -3.8,
+('V', 'P'): -1.6, ('M', 'C'): -3.7, ('K', 'N'): 1.0, ('V', 'T'): 0.6,
+('M', 'G'): -3.7, ('T', 'S'): 1.5, ('I', 'E'): -4.1, ('M', 'K'): -2.9,
+('I', 'A'): 0.1, ('N', 'N'): 3.6, ('R', 'R'): 5.1, ('F', 'P'): -3.2,
+('L', 'I'): 2.4, ('I', 'S'): -1.2, ('D', 'S'): -0.4, ('L', 'S'): -1.5,
+('I', 'H'): -3.7, ('F', 'D'): -5.7, ('D', 'C'): -3.7, ('F', 'H'): 0.1,
+('D', 'G'): 0.8, ('F', 'L'): 2.4
}
benner6 = _temp()
@@ -98,59 +97,59 @@ benner6 = _temp()
# http://www.embl-heidelberg.de/~vogt/matrices/benner22.cmp
def _temp():
return {
-('W', 'F') : 0.5, ('L', 'R') : -2.9, ('I', 'I') : 4.2, ('Q', 'Q') : 4.2,
-('W', 'N') : -5.2, ('V', 'I') : 3.6, ('H', 'T') : -1.1, ('H', 'P') : -0.4,
-('W', 'V') : -4.5, ('Q', 'E') : 1.7, ('W', 'R') : -1.1, ('Q', 'A') : -0.9,
-('H', 'H') : 6.1, ('H', 'D') : 0.3, ('L', 'N') : -3.5, ('Y', 'M') : -1.8,
-('Y', 'I') : -2.2, ('Y', 'E') : -4.0, ('E', 'S') : -0.5, ('Y', 'A') : -3.5,
-('Y', 'Y') : 9.0, ('T', 'C') : -1.1, ('E', 'C') : -4.3, ('Y', 'Q') : -1.9,
-('E', 'G') : 0.5, ('V', 'A') : 0.4, ('C', 'C') : 12.6, ('M', 'R') : -2.1,
-('P', 'T') : 0.4, ('V', 'E') : -2.7, ('P', 'P') : 7.0, ('I', 'T') : 0.3,
-('K', 'S') : -0.4, ('R', 'G') : -0.7, ('I', 'P') : -2.3, ('R', 'C') : -1.6,
-('A', 'T') : 1.4, ('K', 'K') : 4.4, ('A', 'P') : 0.8, ('V', 'M') : 2.5,
-('I', 'D') : -4.0, ('K', 'C') : -3.3, ('K', 'G') : -1.0, ('R', 'S') : -0.5,
-('F', 'Q') : -3.6, ('F', 'A') : -3.1, ('V', 'V') : 3.7, ('M', 'N') : -2.6,
-('F', 'E') : -5.7, ('D', 'N') : 2.4, ('F', 'I') : 0.5, ('F', 'M') : 0.7,
-('M', 'S') : -1.5, ('S', 'S') : 2.0, ('L', 'Q') : -2.0, ('W', 'E') : -6.3,
-('W', 'A') : -5.5, ('W', 'M') : -2.8, ('H', 'S') : -0.5, ('W', 'I') : -4.4,
-('S', 'C') : 0.3, ('L', 'A') : -1.7, ('L', 'E') : -4.4, ('W', 'Q') : -3.3,
-('H', 'G') : -2.0, ('Q', 'N') : 0.5, ('H', 'C') : -1.5, ('L', 'M') : 3.2,
-('W', 'Y') : 1.5, ('Y', 'N') : -1.2, ('E', 'P') : -1.7, ('Y', 'F') : 5.9,
-('E', 'T') : -0.9, ('A', 'A') : 2.5, ('I', 'N') : -2.7, ('G', 'A') : 0.8,
-('Y', 'V') : -2.6, ('E', 'D') : 3.9, ('W', 'H') : -2.7, ('Y', 'R') : -2.7,
-('M', 'Q') : -1.7, ('P', 'S') : 1.1, ('R', 'H') : 1.5, ('A', 'C') : -1.2,
-('R', 'D') : -1.0, ('K', 'P') : -1.6, ('L', 'D') : -4.9, ('K', 'T') : -0.4,
-('V', 'N') : -2.3, ('M', 'A') : -0.8, ('K', 'H') : 0.8, ('V', 'R') : -2.9,
-('P', 'C') : -3.1, ('M', 'E') : -3.4, ('A', 'S') : 1.3, ('T', 'T') : 2.5,
-('R', 'T') : -0.7, ('I', 'G') : -3.8, ('R', 'P') : -1.2, ('K', 'D') : 0.2,
-('I', 'C') : -2.4, ('F', 'R') : -4.3, ('F', 'V') : -0.1, ('L', 'C') : -2.6,
-('F', 'F') : 7.7, ('D', 'A') : -0.2, ('F', 'N') : -3.5, ('W', 'D') : -6.4,
-('L', 'P') : -1.3, ('Q', 'S') : -0.6, ('N', 'C') : -1.9, ('N', 'G') : 0.4,
-('H', 'N') : 1.4, ('W', 'T') : -4.5, ('Q', 'G') : -1.4, ('W', 'P') : -5.8,
-('Q', 'C') : -3.3, ('N', 'S') : 1.1, ('L', 'H') : -2.1, ('L', 'L') : 4.6,
-('G', 'T') : -0.7, ('M', 'M') : 4.9, ('G', 'P') : -1.8, ('Y', 'K') : -3.6,
-('Y', 'G') : -4.8, ('Y', 'C') : 0.6, ('E', 'A') : -0.3, ('E', 'E') : 4.6,
-('Y', 'S') : -1.9, ('M', 'P') : -2.0, ('V', 'C') : -1.7, ('M', 'T') : 0.1,
-('V', 'G') : -2.5, ('R', 'E') : -0.1, ('V', 'K') : -2.7, ('K', 'Q') : 2.2,
-('R', 'A') : -1.2, ('I', 'R') : -3.2, ('N', 'A') : 0.0, ('V', 'S') : -0.9,
-('M', 'D') : -3.9, ('M', 'H') : -2.4, ('K', 'A') : -1.0, ('R', 'Q') : 2.2,
-('K', 'E') : 1.0, ('F', 'S') : -2.2, ('I', 'K') : -3.0, ('D', 'P') : -1.8,
-('D', 'T') : -0.7, ('I', 'M') : 3.1, ('F', 'C') : -0.1, ('W', 'L') : -1.8,
-('F', 'G') : -5.8, ('F', 'K') : -5.1, ('F', 'T') : -2.6, ('D', 'D') : 4.8,
-('Q', 'T') : -0.7, ('W', 'G') : -4.5, ('Q', 'P') : -0.1, ('W', 'C') : 0.5,
-('W', 'K') : -3.7, ('H', 'Q') : 2.4, ('Q', 'D') : 0.6, ('W', 'W') : 15.7,
-('V', 'L') : 2.0, ('L', 'G') : -4.9, ('W', 'S') : -3.9, ('L', 'K') : -3.3,
-('N', 'P') : -1.1, ('H', 'E') : -0.2, ('N', 'T') : 0.5, ('H', 'A') : -1.6,
-('Y', 'L') : -0.7, ('Y', 'H') : 3.7, ('G', 'S') : 0.6, ('Y', 'D') : -3.0,
-('V', 'Q') : -2.4, ('L', 'T') : -1.0, ('G', 'G') : 6.2, ('G', 'C') : -1.7,
-('E', 'N') : 1.2, ('Y', 'T') : -3.0, ('Y', 'P') : -3.5, ('R', 'N') : 0.4,
-('V', 'D') : -3.0, ('K', 'R') : 3.9, ('V', 'H') : -3.0, ('I', 'Q') : -2.7,
-('V', 'P') : -1.7, ('M', 'C') : -2.5, ('K', 'N') : 1.0, ('V', 'T') : 0.4,
-('M', 'G') : -3.8, ('T', 'S') : 1.5, ('I', 'E') : -3.6, ('M', 'K') : -2.0,
-('I', 'A') : -0.4, ('N', 'N') : 3.3, ('R', 'R') : 5.0, ('F', 'P') : -3.4,
-('L', 'I') : 2.7, ('I', 'S') : -1.4, ('D', 'S') : 0.1, ('L', 'S') : -2.1,
-('I', 'H') : -3.2, ('F', 'D') : -5.4, ('D', 'C') : -3.7, ('F', 'H') : 0.3,
-('D', 'G') : 0.7, ('F', 'L') : 2.2
+('W', 'F'): 0.5, ('L', 'R'): -2.9, ('I', 'I'): 4.2, ('Q', 'Q'): 4.2,
+('W', 'N'): -5.2, ('V', 'I'): 3.6, ('H', 'T'): -1.1, ('H', 'P'): -0.4,
+('W', 'V'): -4.5, ('Q', 'E'): 1.7, ('W', 'R'): -1.1, ('Q', 'A'): -0.9,
+('H', 'H'): 6.1, ('H', 'D'): 0.3, ('L', 'N'): -3.5, ('Y', 'M'): -1.8,
+('Y', 'I'): -2.2, ('Y', 'E'): -4.0, ('E', 'S'): -0.5, ('Y', 'A'): -3.5,
+('Y', 'Y'): 9.0, ('T', 'C'): -1.1, ('E', 'C'): -4.3, ('Y', 'Q'): -1.9,
+('E', 'G'): 0.5, ('V', 'A'): 0.4, ('C', 'C'): 12.6, ('M', 'R'): -2.1,
+('P', 'T'): 0.4, ('V', 'E'): -2.7, ('P', 'P'): 7.0, ('I', 'T'): 0.3,
+('K', 'S'): -0.4, ('R', 'G'): -0.7, ('I', 'P'): -2.3, ('R', 'C'): -1.6,
+('A', 'T'): 1.4, ('K', 'K'): 4.4, ('A', 'P'): 0.8, ('V', 'M'): 2.5,
+('I', 'D'): -4.0, ('K', 'C'): -3.3, ('K', 'G'): -1.0, ('R', 'S'): -0.5,
+('F', 'Q'): -3.6, ('F', 'A'): -3.1, ('V', 'V'): 3.7, ('M', 'N'): -2.6,
+('F', 'E'): -5.7, ('D', 'N'): 2.4, ('F', 'I'): 0.5, ('F', 'M'): 0.7,
+('M', 'S'): -1.5, ('S', 'S'): 2.0, ('L', 'Q'): -2.0, ('W', 'E'): -6.3,
+('W', 'A'): -5.5, ('W', 'M'): -2.8, ('H', 'S'): -0.5, ('W', 'I'): -4.4,
+('S', 'C'): 0.3, ('L', 'A'): -1.7, ('L', 'E'): -4.4, ('W', 'Q'): -3.3,
+('H', 'G'): -2.0, ('Q', 'N'): 0.5, ('H', 'C'): -1.5, ('L', 'M'): 3.2,
+('W', 'Y'): 1.5, ('Y', 'N'): -1.2, ('E', 'P'): -1.7, ('Y', 'F'): 5.9,
+('E', 'T'): -0.9, ('A', 'A'): 2.5, ('I', 'N'): -2.7, ('G', 'A'): 0.8,
+('Y', 'V'): -2.6, ('E', 'D'): 3.9, ('W', 'H'): -2.7, ('Y', 'R'): -2.7,
+('M', 'Q'): -1.7, ('P', 'S'): 1.1, ('R', 'H'): 1.5, ('A', 'C'): -1.2,
+('R', 'D'): -1.0, ('K', 'P'): -1.6, ('L', 'D'): -4.9, ('K', 'T'): -0.4,
+('V', 'N'): -2.3, ('M', 'A'): -0.8, ('K', 'H'): 0.8, ('V', 'R'): -2.9,
+('P', 'C'): -3.1, ('M', 'E'): -3.4, ('A', 'S'): 1.3, ('T', 'T'): 2.5,
+('R', 'T'): -0.7, ('I', 'G'): -3.8, ('R', 'P'): -1.2, ('K', 'D'): 0.2,
+('I', 'C'): -2.4, ('F', 'R'): -4.3, ('F', 'V'): -0.1, ('L', 'C'): -2.6,
+('F', 'F'): 7.7, ('D', 'A'): -0.2, ('F', 'N'): -3.5, ('W', 'D'): -6.4,
+('L', 'P'): -1.3, ('Q', 'S'): -0.6, ('N', 'C'): -1.9, ('N', 'G'): 0.4,
+('H', 'N'): 1.4, ('W', 'T'): -4.5, ('Q', 'G'): -1.4, ('W', 'P'): -5.8,
+('Q', 'C'): -3.3, ('N', 'S'): 1.1, ('L', 'H'): -2.1, ('L', 'L'): 4.6,
+('G', 'T'): -0.7, ('M', 'M'): 4.9, ('G', 'P'): -1.8, ('Y', 'K'): -3.6,
+('Y', 'G'): -4.8, ('Y', 'C'): 0.6, ('E', 'A'): -0.3, ('E', 'E'): 4.6,
+('Y', 'S'): -1.9, ('M', 'P'): -2.0, ('V', 'C'): -1.7, ('M', 'T'): 0.1,
+('V', 'G'): -2.5, ('R', 'E'): -0.1, ('V', 'K'): -2.7, ('K', 'Q'): 2.2,
+('R', 'A'): -1.2, ('I', 'R'): -3.2, ('N', 'A'): 0.0, ('V', 'S'): -0.9,
+('M', 'D'): -3.9, ('M', 'H'): -2.4, ('K', 'A'): -1.0, ('R', 'Q'): 2.2,
+('K', 'E'): 1.0, ('F', 'S'): -2.2, ('I', 'K'): -3.0, ('D', 'P'): -1.8,
+('D', 'T'): -0.7, ('I', 'M'): 3.1, ('F', 'C'): -0.1, ('W', 'L'): -1.8,
+('F', 'G'): -5.8, ('F', 'K'): -5.1, ('F', 'T'): -2.6, ('D', 'D'): 4.8,
+('Q', 'T'): -0.7, ('W', 'G'): -4.5, ('Q', 'P'): -0.1, ('W', 'C'): 0.5,
+('W', 'K'): -3.7, ('H', 'Q'): 2.4, ('Q', 'D'): 0.6, ('W', 'W'): 15.7,
+('V', 'L'): 2.0, ('L', 'G'): -4.9, ('W', 'S'): -3.9, ('L', 'K'): -3.3,
+('N', 'P'): -1.1, ('H', 'E'): -0.2, ('N', 'T'): 0.5, ('H', 'A'): -1.6,
+('Y', 'L'): -0.7, ('Y', 'H'): 3.7, ('G', 'S'): 0.6, ('Y', 'D'): -3.0,
+('V', 'Q'): -2.4, ('L', 'T'): -1.0, ('G', 'G'): 6.2, ('G', 'C'): -1.7,
+('E', 'N'): 1.2, ('Y', 'T'): -3.0, ('Y', 'P'): -3.5, ('R', 'N'): 0.4,
+('V', 'D'): -3.0, ('K', 'R'): 3.9, ('V', 'H'): -3.0, ('I', 'Q'): -2.7,
+('V', 'P'): -1.7, ('M', 'C'): -2.5, ('K', 'N'): 1.0, ('V', 'T'): 0.4,
+('M', 'G'): -3.8, ('T', 'S'): 1.5, ('I', 'E'): -3.6, ('M', 'K'): -2.0,
+('I', 'A'): -0.4, ('N', 'N'): 3.3, ('R', 'R'): 5.0, ('F', 'P'): -3.4,
+('L', 'I'): 2.7, ('I', 'S'): -1.4, ('D', 'S'): 0.1, ('L', 'S'): -2.1,
+('I', 'H'): -3.2, ('F', 'D'): -5.4, ('D', 'C'): -3.7, ('F', 'H'): 0.3,
+('D', 'G'): 0.7, ('F', 'L'): 2.2
}
benner22 = _temp()
del _temp
@@ -160,59 +159,59 @@ assert benner6 != benner22
# http://www.embl-heidelberg.de/~vogt/matrices/benner74.cmp
def _temp():
return {
-('W', 'F') : 3.0, ('L', 'R') : -2.4, ('I', 'I') : 4.0, ('Q', 'Q') : 3.0,
-('W', 'N') : -4.0, ('V', 'I') : 3.2, ('H', 'T') : -0.5, ('H', 'P') : -1.0,
-('W', 'V') : -2.9, ('Q', 'E') : 1.7, ('W', 'R') : -1.6, ('Q', 'A') : -0.3,
-('H', 'H') : 6.1, ('H', 'D') : 0.4, ('L', 'N') : -3.1, ('Y', 'M') : -0.5,
-('Y', 'I') : -1.0, ('Y', 'E') : -3.0, ('E', 'S') : 0.1, ('Y', 'A') : -2.6,
-('Y', 'Y') : 8.1, ('T', 'C') : -0.6, ('E', 'C') : -3.2, ('Y', 'Q') : -1.8,
-('E', 'G') : -0.5, ('V', 'A') : 0.1, ('C', 'C') : 11.8, ('M', 'R') : -1.8,
-('P', 'T') : 0.1, ('V', 'E') : -2.1, ('P', 'P') : 7.5, ('I', 'T') : -0.3,
-('K', 'S') : 0.0, ('R', 'G') : -1.0, ('I', 'P') : -2.6, ('R', 'C') : -2.2,
-('A', 'T') : 0.7, ('K', 'K') : 3.4, ('A', 'P') : 0.4, ('V', 'M') : 1.8,
-('I', 'D') : -3.9, ('K', 'C') : -2.9, ('K', 'G') : -1.1, ('R', 'S') : -0.2,
-('F', 'Q') : -2.8, ('F', 'A') : -2.6, ('V', 'V') : 3.4, ('M', 'N') : -2.2,
-('F', 'E') : -4.3, ('D', 'N') : 2.2, ('F', 'I') : 0.9, ('F', 'M') : 1.3,
-('M', 'S') : -1.4, ('S', 'S') : 2.1, ('L', 'Q') : -1.7, ('W', 'E') : -4.7,
-('W', 'A') : -4.1, ('W', 'M') : -1.3, ('H', 'S') : -0.3, ('W', 'I') : -2.3,
-('S', 'C') : 0.1, ('L', 'A') : -1.4, ('L', 'E') : -3.1, ('W', 'Q') : -2.8,
-('H', 'G') : -1.6, ('Q', 'N') : 0.7, ('H', 'C') : -1.3, ('L', 'M') : 2.9,
-('W', 'Y') : 3.6, ('Y', 'N') : -1.4, ('E', 'P') : -0.7, ('Y', 'F') : 5.3,
-('E', 'T') : -0.2, ('A', 'A') : 2.4, ('I', 'N') : -2.8, ('G', 'A') : 0.6,
-('Y', 'V') : -1.4, ('E', 'D') : 2.9, ('W', 'H') : -1.0, ('Y', 'R') : -2.0,
-('M', 'Q') : -1.0, ('P', 'S') : 0.5, ('R', 'H') : 1.0, ('A', 'C') : 0.3,
-('R', 'D') : -0.5, ('K', 'P') : -0.8, ('L', 'D') : -4.2, ('K', 'T') : 0.1,
-('V', 'N') : -2.2, ('M', 'A') : -0.8, ('K', 'H') : 0.6, ('V', 'R') : -2.2,
-('P', 'C') : -3.1, ('M', 'E') : -2.2, ('A', 'S') : 1.1, ('T', 'T') : 2.5,
-('R', 'T') : -0.3, ('I', 'G') : -4.3, ('R', 'P') : -0.1, ('K', 'D') : 0.4,
-('I', 'C') : -1.2, ('F', 'R') : -3.5, ('F', 'V') : 0.1, ('L', 'C') : -1.6,
-('F', 'F') : 7.2, ('D', 'A') : -0.3, ('F', 'N') : -3.2, ('W', 'D') : -5.5,
-('L', 'P') : -2.2, ('Q', 'S') : 0.1, ('N', 'C') : -1.8, ('N', 'G') : 0.4,
-('H', 'N') : 1.2, ('W', 'T') : -3.7, ('Q', 'G') : -1.1, ('W', 'P') : -5.2,
-('Q', 'C') : -2.6, ('N', 'S') : 0.9, ('L', 'H') : -1.9, ('L', 'L') : 4.2,
-('G', 'T') : -1.0, ('M', 'M') : 4.5, ('G', 'P') : -1.7, ('Y', 'K') : -2.4,
-('Y', 'G') : -4.3, ('Y', 'C') : -0.4, ('E', 'A') : -0.1, ('E', 'E') : 3.7,
-('Y', 'S') : -1.9, ('M', 'P') : -2.4, ('V', 'C') : -0.2, ('M', 'T') : -0.4,
-('V', 'G') : -3.1, ('R', 'E') : 0.3, ('V', 'K') : -1.9, ('K', 'Q') : 1.7,
-('R', 'A') : -0.8, ('I', 'R') : -2.6, ('N', 'A') : -0.2, ('V', 'S') : -1.0,
-('M', 'D') : -3.2, ('M', 'H') : -1.5, ('K', 'A') : -0.4, ('R', 'Q') : 1.6,
-('K', 'E') : 1.2, ('F', 'S') : -2.6, ('I', 'K') : -2.3, ('D', 'P') : -1.0,
-('D', 'T') : -0.2, ('I', 'M') : 2.6, ('F', 'C') : -0.7, ('W', 'L') : -0.9,
-('F', 'G') : -5.4, ('F', 'K') : -3.6, ('F', 'T') : -2.2, ('D', 'D') : 4.8,
-('Q', 'T') : -0.1, ('W', 'G') : -4.1, ('Q', 'P') : -0.2, ('W', 'C') : -0.9,
-('W', 'K') : -3.6, ('H', 'Q') : 1.4, ('Q', 'D') : 0.8, ('W', 'W') : 14.7,
-('V', 'L') : 1.9, ('L', 'G') : -4.6, ('W', 'S') : -3.4, ('L', 'K') : -2.4,
-('N', 'P') : -1.0, ('H', 'E') : 0.2, ('N', 'T') : 0.4, ('H', 'A') : -1.0,
-('Y', 'L') : -0.1, ('Y', 'H') : 2.5, ('G', 'S') : 0.4, ('Y', 'D') : -2.8,
-('V', 'Q') : -1.7, ('L', 'T') : -1.1, ('G', 'G') : 6.6, ('G', 'C') : -2.0,
-('E', 'N') : 1.0, ('Y', 'T') : -2.1, ('Y', 'P') : -3.4, ('R', 'N') : 0.3,
-('V', 'D') : -2.9, ('K', 'R') : 2.9, ('V', 'H') : -2.1, ('I', 'Q') : -2.0,
-('V', 'P') : -1.9, ('M', 'C') : -1.2, ('K', 'N') : 0.9, ('V', 'T') : 0.2,
-('M', 'G') : -3.5, ('T', 'S') : 1.4, ('I', 'E') : -2.9, ('M', 'K') : -1.5,
-('I', 'A') : -0.8, ('N', 'N') : 3.6, ('R', 'R') : 4.8, ('F', 'P') : -3.8,
-('L', 'I') : 2.8, ('I', 'S') : -1.8, ('D', 'S') : 0.4, ('L', 'S') : -2.2,
-('I', 'H') : -2.3, ('F', 'D') : -4.7, ('D', 'C') : -3.2, ('F', 'H') : 0.0,
-('D', 'G') : 0.2, ('F', 'L') : 2.1
+('W', 'F'): 3.0, ('L', 'R'): -2.4, ('I', 'I'): 4.0, ('Q', 'Q'): 3.0,
+('W', 'N'): -4.0, ('V', 'I'): 3.2, ('H', 'T'): -0.5, ('H', 'P'): -1.0,
+('W', 'V'): -2.9, ('Q', 'E'): 1.7, ('W', 'R'): -1.6, ('Q', 'A'): -0.3,
+('H', 'H'): 6.1, ('H', 'D'): 0.4, ('L', 'N'): -3.1, ('Y', 'M'): -0.5,
+('Y', 'I'): -1.0, ('Y', 'E'): -3.0, ('E', 'S'): 0.1, ('Y', 'A'): -2.6,
+('Y', 'Y'): 8.1, ('T', 'C'): -0.6, ('E', 'C'): -3.2, ('Y', 'Q'): -1.8,
+('E', 'G'): -0.5, ('V', 'A'): 0.1, ('C', 'C'): 11.8, ('M', 'R'): -1.8,
+('P', 'T'): 0.1, ('V', 'E'): -2.1, ('P', 'P'): 7.5, ('I', 'T'): -0.3,
+('K', 'S'): 0.0, ('R', 'G'): -1.0, ('I', 'P'): -2.6, ('R', 'C'): -2.2,
+('A', 'T'): 0.7, ('K', 'K'): 3.4, ('A', 'P'): 0.4, ('V', 'M'): 1.8,
+('I', 'D'): -3.9, ('K', 'C'): -2.9, ('K', 'G'): -1.1, ('R', 'S'): -0.2,
+('F', 'Q'): -2.8, ('F', 'A'): -2.6, ('V', 'V'): 3.4, ('M', 'N'): -2.2,
+('F', 'E'): -4.3, ('D', 'N'): 2.2, ('F', 'I'): 0.9, ('F', 'M'): 1.3,
+('M', 'S'): -1.4, ('S', 'S'): 2.1, ('L', 'Q'): -1.7, ('W', 'E'): -4.7,
+('W', 'A'): -4.1, ('W', 'M'): -1.3, ('H', 'S'): -0.3, ('W', 'I'): -2.3,
+('S', 'C'): 0.1, ('L', 'A'): -1.4, ('L', 'E'): -3.1, ('W', 'Q'): -2.8,
+('H', 'G'): -1.6, ('Q', 'N'): 0.7, ('H', 'C'): -1.3, ('L', 'M'): 2.9,
+('W', 'Y'): 3.6, ('Y', 'N'): -1.4, ('E', 'P'): -0.7, ('Y', 'F'): 5.3,
+('E', 'T'): -0.2, ('A', 'A'): 2.4, ('I', 'N'): -2.8, ('G', 'A'): 0.6,
+('Y', 'V'): -1.4, ('E', 'D'): 2.9, ('W', 'H'): -1.0, ('Y', 'R'): -2.0,
+('M', 'Q'): -1.0, ('P', 'S'): 0.5, ('R', 'H'): 1.0, ('A', 'C'): 0.3,
+('R', 'D'): -0.5, ('K', 'P'): -0.8, ('L', 'D'): -4.2, ('K', 'T'): 0.1,
+('V', 'N'): -2.2, ('M', 'A'): -0.8, ('K', 'H'): 0.6, ('V', 'R'): -2.2,
+('P', 'C'): -3.1, ('M', 'E'): -2.2, ('A', 'S'): 1.1, ('T', 'T'): 2.5,
+('R', 'T'): -0.3, ('I', 'G'): -4.3, ('R', 'P'): -0.1, ('K', 'D'): 0.4,
+('I', 'C'): -1.2, ('F', 'R'): -3.5, ('F', 'V'): 0.1, ('L', 'C'): -1.6,
+('F', 'F'): 7.2, ('D', 'A'): -0.3, ('F', 'N'): -3.2, ('W', 'D'): -5.5,
+('L', 'P'): -2.2, ('Q', 'S'): 0.1, ('N', 'C'): -1.8, ('N', 'G'): 0.4,
+('H', 'N'): 1.2, ('W', 'T'): -3.7, ('Q', 'G'): -1.1, ('W', 'P'): -5.2,
+('Q', 'C'): -2.6, ('N', 'S'): 0.9, ('L', 'H'): -1.9, ('L', 'L'): 4.2,
+('G', 'T'): -1.0, ('M', 'M'): 4.5, ('G', 'P'): -1.7, ('Y', 'K'): -2.4,
+('Y', 'G'): -4.3, ('Y', 'C'): -0.4, ('E', 'A'): -0.1, ('E', 'E'): 3.7,
+('Y', 'S'): -1.9, ('M', 'P'): -2.4, ('V', 'C'): -0.2, ('M', 'T'): -0.4,
+('V', 'G'): -3.1, ('R', 'E'): 0.3, ('V', 'K'): -1.9, ('K', 'Q'): 1.7,
+('R', 'A'): -0.8, ('I', 'R'): -2.6, ('N', 'A'): -0.2, ('V', 'S'): -1.0,
+('M', 'D'): -3.2, ('M', 'H'): -1.5, ('K', 'A'): -0.4, ('R', 'Q'): 1.6,
+('K', 'E'): 1.2, ('F', 'S'): -2.6, ('I', 'K'): -2.3, ('D', 'P'): -1.0,
+('D', 'T'): -0.2, ('I', 'M'): 2.6, ('F', 'C'): -0.7, ('W', 'L'): -0.9,
+('F', 'G'): -5.4, ('F', 'K'): -3.6, ('F', 'T'): -2.2, ('D', 'D'): 4.8,
+('Q', 'T'): -0.1, ('W', 'G'): -4.1, ('Q', 'P'): -0.2, ('W', 'C'): -0.9,
+('W', 'K'): -3.6, ('H', 'Q'): 1.4, ('Q', 'D'): 0.8, ('W', 'W'): 14.7,
+('V', 'L'): 1.9, ('L', 'G'): -4.6, ('W', 'S'): -3.4, ('L', 'K'): -2.4,
+('N', 'P'): -1.0, ('H', 'E'): 0.2, ('N', 'T'): 0.4, ('H', 'A'): -1.0,
+('Y', 'L'): -0.1, ('Y', 'H'): 2.5, ('G', 'S'): 0.4, ('Y', 'D'): -2.8,
+('V', 'Q'): -1.7, ('L', 'T'): -1.1, ('G', 'G'): 6.6, ('G', 'C'): -2.0,
+('E', 'N'): 1.0, ('Y', 'T'): -2.1, ('Y', 'P'): -3.4, ('R', 'N'): 0.3,
+('V', 'D'): -2.9, ('K', 'R'): 2.9, ('V', 'H'): -2.1, ('I', 'Q'): -2.0,
+('V', 'P'): -1.9, ('M', 'C'): -1.2, ('K', 'N'): 0.9, ('V', 'T'): 0.2,
+('M', 'G'): -3.5, ('T', 'S'): 1.4, ('I', 'E'): -2.9, ('M', 'K'): -1.5,
+('I', 'A'): -0.8, ('N', 'N'): 3.6, ('R', 'R'): 4.8, ('F', 'P'): -3.8,
+('L', 'I'): 2.8, ('I', 'S'): -1.8, ('D', 'S'): 0.4, ('L', 'S'): -2.2,
+('I', 'H'): -2.3, ('F', 'D'): -4.7, ('D', 'C'): -3.2, ('F', 'H'): 0.0,
+('D', 'G'): 0.2, ('F', 'L'): 2.1
}
benner74 = _temp()
del _temp
@@ -221,75 +220,75 @@ del _temp
# http://www.embl-heidelberg.de/~vogt/matrices/blosum100.cmp
def _temp():
return {
-('W', 'F') : 0, ('L', 'R') : -4, ('S', 'P') : -2, ('V', 'T') : -1,
-('Q', 'Q') : 7, ('N', 'A') : -2, ('Z', 'Y') : -4, ('W', 'R') : -4,
-('Q', 'A') : -1, ('S', 'D') : -1, ('H', 'H') : 9, ('S', 'H') : -2,
-('H', 'D') : -2, ('L', 'N') : -5, ('W', 'A') : -4, ('Y', 'M') : -3,
-('G', 'R') : -4, ('Y', 'I') : -3, ('Y', 'E') : -4, ('B', 'Y') : -4,
-('Y', 'A') : -4, ('V', 'D') : -5, ('B', 'S') : -1, ('Y', 'Y') : 8,
-('G', 'N') : -2, ('E', 'C') : -6, ('Y', 'Q') : -3, ('Z', 'Z') : 4,
-('V', 'A') : -1, ('C', 'C') : 9, ('M', 'R') : -2, ('V', 'E') : -3,
-('T', 'N') : -1, ('P', 'P') : 8, ('V', 'I') : 2, ('V', 'S') : -3,
-('Z', 'P') : -3, ('V', 'M') : 0, ('T', 'F') : -3, ('V', 'Q') : -3,
-('K', 'K') : 6, ('P', 'D') : -3, ('I', 'H') : -5, ('I', 'D') : -6,
-('T', 'R') : -2, ('P', 'L') : -4, ('K', 'G') : -3, ('M', 'N') : -4,
-('P', 'H') : -3, ('F', 'Q') : -4, ('Z', 'G') : -4, ('X', 'L') : -2,
-('T', 'M') : -2, ('Z', 'C') : -6, ('X', 'H') : -2, ('D', 'R') : -3,
-('B', 'W') : -6, ('X', 'D') : -3, ('Z', 'K') : 0, ('F', 'A') : -4,
-('Z', 'W') : -4, ('F', 'E') : -5, ('D', 'N') : 1, ('B', 'K') : -1,
-('X', 'X') : -2, ('F', 'I') : -1, ('B', 'G') : -2, ('X', 'T') : -1,
-('F', 'M') : -1, ('B', 'C') : -5, ('Z', 'I') : -4, ('Z', 'V') : -3,
-('S', 'S') : 6, ('L', 'Q') : -3, ('W', 'E') : -5, ('Q', 'R') : 0,
-('N', 'N') : 7, ('W', 'M') : -3, ('Q', 'C') : -5, ('W', 'I') : -4,
-('S', 'C') : -2, ('L', 'A') : -3, ('S', 'G') : -1, ('L', 'E') : -5,
-('W', 'Q') : -3, ('H', 'G') : -4, ('S', 'K') : -1, ('Q', 'N') : -1,
-('N', 'R') : -1, ('H', 'C') : -5, ('Y', 'N') : -3, ('G', 'Q') : -3,
-('Y', 'F') : 3, ('C', 'A') : -1, ('V', 'L') : 0, ('G', 'E') : -4,
-('G', 'A') : -1, ('K', 'R') : 2, ('E', 'D') : 1, ('Y', 'R') : -3,
-('M', 'Q') : -1, ('T', 'I') : -2, ('C', 'D') : -5, ('V', 'F') : -2,
-('T', 'A') : -1, ('T', 'P') : -3, ('B', 'P') : -3, ('T', 'E') : -2,
-('V', 'N') : -4, ('P', 'G') : -4, ('M', 'A') : -2, ('K', 'H') : -2,
-('V', 'R') : -4, ('P', 'C') : -5, ('M', 'E') : -4, ('K', 'L') : -4,
-('V', 'V') : 5, ('M', 'I') : 1, ('T', 'Q') : -2, ('I', 'G') : -6,
-('P', 'K') : -2, ('M', 'M') : 8, ('K', 'D') : -2, ('I', 'C') : -2,
-('Z', 'D') : 0, ('F', 'R') : -4, ('X', 'K') : -2, ('Q', 'D') : -2,
-('X', 'G') : -3, ('Z', 'L') : -4, ('X', 'C') : -3, ('Z', 'H') : -1,
-('B', 'L') : -5, ('B', 'H') : -1, ('F', 'F') : 7, ('X', 'W') : -4,
-('B', 'D') : 4, ('D', 'A') : -3, ('S', 'L') : -4, ('X', 'S') : -1,
-('F', 'N') : -5, ('S', 'R') : -2, ('W', 'D') : -7, ('V', 'Y') : -3,
-('W', 'L') : -4, ('H', 'R') : -1, ('W', 'H') : -3, ('H', 'N') : 0,
-('W', 'T') : -5, ('T', 'T') : 6, ('S', 'F') : -3, ('W', 'P') : -6,
-('L', 'D') : -6, ('B', 'I') : -5, ('L', 'H') : -4, ('S', 'N') : 0,
-('B', 'T') : -2, ('L', 'L') : 5, ('Y', 'K') : -4, ('E', 'Q') : 1,
-('Y', 'G') : -6, ('Z', 'S') : -1, ('Y', 'C') : -4, ('G', 'D') : -3,
-('B', 'V') : -5, ('E', 'A') : -2, ('Y', 'W') : 1, ('E', 'E') : 6,
-('Y', 'S') : -3, ('C', 'N') : -4, ('V', 'C') : -2, ('T', 'H') : -3,
-('P', 'R') : -3, ('V', 'G') : -5, ('T', 'L') : -3, ('V', 'K') : -4,
-('K', 'Q') : 1, ('R', 'A') : -2, ('I', 'R') : -4, ('T', 'D') : -2,
-('P', 'F') : -5, ('I', 'N') : -5, ('K', 'I') : -4, ('M', 'D') : -5,
-('V', 'W') : -4, ('W', 'W') : 11, ('M', 'H') : -3, ('P', 'N') : -4,
-('K', 'A') : -2, ('M', 'L') : 2, ('K', 'E') : 0, ('Z', 'E') : 5,
-('X', 'N') : -2, ('Z', 'A') : -2, ('Z', 'M') : -3, ('X', 'F') : -3,
-('K', 'C') : -5, ('B', 'Q') : -1, ('X', 'B') : -2, ('B', 'M') : -4,
-('F', 'C') : -3, ('Z', 'Q') : 3, ('X', 'Z') : -2, ('F', 'G') : -5,
-('B', 'E') : 0, ('X', 'V') : -2, ('F', 'K') : -4, ('B', 'A') : -3,
-('X', 'R') : -2, ('D', 'D') : 7, ('W', 'G') : -5, ('Z', 'F') : -5,
-('S', 'Q') : -1, ('W', 'C') : -5, ('W', 'K') : -5, ('H', 'Q') : 0,
-('L', 'C') : -3, ('W', 'N') : -6, ('S', 'A') : 1, ('L', 'G') : -5,
-('W', 'S') : -4, ('S', 'E') : -1, ('H', 'E') : -1, ('S', 'I') : -4,
-('H', 'A') : -3, ('S', 'M') : -3, ('Y', 'L') : -3, ('Y', 'H') : 1,
-('Y', 'D') : -5, ('E', 'R') : -2, ('X', 'P') : -3, ('G', 'G') : 6,
-('G', 'C') : -5, ('E', 'N') : -1, ('Y', 'T') : -3, ('Y', 'P') : -5,
-('T', 'K') : -2, ('A', 'A') : 5, ('P', 'Q') : -2, ('T', 'C') : -2,
-('V', 'H') : -5, ('T', 'G') : -3, ('I', 'Q') : -4, ('Z', 'T') : -2,
-('C', 'R') : -5, ('V', 'P') : -4, ('P', 'E') : -3, ('M', 'C') : -3,
-('K', 'N') : -1, ('I', 'I') : 5, ('P', 'A') : -1, ('M', 'G') : -5,
-('T', 'S') : 1, ('I', 'E') : -5, ('P', 'M') : -4, ('M', 'K') : -2,
-('I', 'A') : -3, ('P', 'I') : -4, ('R', 'R') : 7, ('X', 'M') : -2,
-('L', 'I') : 1, ('X', 'I') : -2, ('Z', 'B') : 1, ('X', 'E') : -2,
-('Z', 'N') : -1, ('X', 'A') : -1, ('B', 'R') : -2, ('B', 'N') : 4,
-('F', 'D') : -5, ('X', 'Y') : -3, ('Z', 'R') : -1, ('F', 'H') : -2,
-('B', 'F') : -5, ('F', 'L') : 0, ('X', 'Q') : -2, ('B', 'B') : 4
+('W', 'F'): 0, ('L', 'R'): -4, ('S', 'P'): -2, ('V', 'T'): -1,
+('Q', 'Q'): 7, ('N', 'A'): -2, ('Z', 'Y'): -4, ('W', 'R'): -4,
+('Q', 'A'): -1, ('S', 'D'): -1, ('H', 'H'): 9, ('S', 'H'): -2,
+('H', 'D'): -2, ('L', 'N'): -5, ('W', 'A'): -4, ('Y', 'M'): -3,
+('G', 'R'): -4, ('Y', 'I'): -3, ('Y', 'E'): -4, ('B', 'Y'): -4,
+('Y', 'A'): -4, ('V', 'D'): -5, ('B', 'S'): -1, ('Y', 'Y'): 8,
+('G', 'N'): -2, ('E', 'C'): -6, ('Y', 'Q'): -3, ('Z', 'Z'): 4,
+('V', 'A'): -1, ('C', 'C'): 9, ('M', 'R'): -2, ('V', 'E'): -3,
+('T', 'N'): -1, ('P', 'P'): 8, ('V', 'I'): 2, ('V', 'S'): -3,
+('Z', 'P'): -3, ('V', 'M'): 0, ('T', 'F'): -3, ('V', 'Q'): -3,
+('K', 'K'): 6, ('P', 'D'): -3, ('I', 'H'): -5, ('I', 'D'): -6,
+('T', 'R'): -2, ('P', 'L'): -4, ('K', 'G'): -3, ('M', 'N'): -4,
+('P', 'H'): -3, ('F', 'Q'): -4, ('Z', 'G'): -4, ('X', 'L'): -2,
+('T', 'M'): -2, ('Z', 'C'): -6, ('X', 'H'): -2, ('D', 'R'): -3,
+('B', 'W'): -6, ('X', 'D'): -3, ('Z', 'K'): 0, ('F', 'A'): -4,
+('Z', 'W'): -4, ('F', 'E'): -5, ('D', 'N'): 1, ('B', 'K'): -1,
+('X', 'X'): -2, ('F', 'I'): -1, ('B', 'G'): -2, ('X', 'T'): -1,
+('F', 'M'): -1, ('B', 'C'): -5, ('Z', 'I'): -4, ('Z', 'V'): -3,
+('S', 'S'): 6, ('L', 'Q'): -3, ('W', 'E'): -5, ('Q', 'R'): 0,
+('N', 'N'): 7, ('W', 'M'): -3, ('Q', 'C'): -5, ('W', 'I'): -4,
+('S', 'C'): -2, ('L', 'A'): -3, ('S', 'G'): -1, ('L', 'E'): -5,
+('W', 'Q'): -3, ('H', 'G'): -4, ('S', 'K'): -1, ('Q', 'N'): -1,
+('N', 'R'): -1, ('H', 'C'): -5, ('Y', 'N'): -3, ('G', 'Q'): -3,
+('Y', 'F'): 3, ('C', 'A'): -1, ('V', 'L'): 0, ('G', 'E'): -4,
+('G', 'A'): -1, ('K', 'R'): 2, ('E', 'D'): 1, ('Y', 'R'): -3,
+('M', 'Q'): -1, ('T', 'I'): -2, ('C', 'D'): -5, ('V', 'F'): -2,
+('T', 'A'): -1, ('T', 'P'): -3, ('B', 'P'): -3, ('T', 'E'): -2,
+('V', 'N'): -4, ('P', 'G'): -4, ('M', 'A'): -2, ('K', 'H'): -2,
+('V', 'R'): -4, ('P', 'C'): -5, ('M', 'E'): -4, ('K', 'L'): -4,
+('V', 'V'): 5, ('M', 'I'): 1, ('T', 'Q'): -2, ('I', 'G'): -6,
+('P', 'K'): -2, ('M', 'M'): 8, ('K', 'D'): -2, ('I', 'C'): -2,
+('Z', 'D'): 0, ('F', 'R'): -4, ('X', 'K'): -2, ('Q', 'D'): -2,
+('X', 'G'): -3, ('Z', 'L'): -4, ('X', 'C'): -3, ('Z', 'H'): -1,
+('B', 'L'): -5, ('B', 'H'): -1, ('F', 'F'): 7, ('X', 'W'): -4,
+('B', 'D'): 4, ('D', 'A'): -3, ('S', 'L'): -4, ('X', 'S'): -1,
+('F', 'N'): -5, ('S', 'R'): -2, ('W', 'D'): -7, ('V', 'Y'): -3,
+('W', 'L'): -4, ('H', 'R'): -1, ('W', 'H'): -3, ('H', 'N'): 0,
+('W', 'T'): -5, ('T', 'T'): 6, ('S', 'F'): -3, ('W', 'P'): -6,
+('L', 'D'): -6, ('B', 'I'): -5, ('L', 'H'): -4, ('S', 'N'): 0,
+('B', 'T'): -2, ('L', 'L'): 5, ('Y', 'K'): -4, ('E', 'Q'): 1,
+('Y', 'G'): -6, ('Z', 'S'): -1, ('Y', 'C'): -4, ('G', 'D'): -3,
+('B', 'V'): -5, ('E', 'A'): -2, ('Y', 'W'): 1, ('E', 'E'): 6,
+('Y', 'S'): -3, ('C', 'N'): -4, ('V', 'C'): -2, ('T', 'H'): -3,
+('P', 'R'): -3, ('V', 'G'): -5, ('T', 'L'): -3, ('V', 'K'): -4,
+('K', 'Q'): 1, ('R', 'A'): -2, ('I', 'R'): -4, ('T', 'D'): -2,
+('P', 'F'): -5, ('I', 'N'): -5, ('K', 'I'): -4, ('M', 'D'): -5,
+('V', 'W'): -4, ('W', 'W'): 11, ('M', 'H'): -3, ('P', 'N'): -4,
+('K', 'A'): -2, ('M', 'L'): 2, ('K', 'E'): 0, ('Z', 'E'): 5,
+('X', 'N'): -2, ('Z', 'A'): -2, ('Z', 'M'): -3, ('X', 'F'): -3,
+('K', 'C'): -5, ('B', 'Q'): -1, ('X', 'B'): -2, ('B', 'M'): -4,
+('F', 'C'): -3, ('Z', 'Q'): 3, ('X', 'Z'): -2, ('F', 'G'): -5,
+('B', 'E'): 0, ('X', 'V'): -2, ('F', 'K'): -4, ('B', 'A'): -3,
+('X', 'R'): -2, ('D', 'D'): 7, ('W', 'G'): -5, ('Z', 'F'): -5,
+('S', 'Q'): -1, ('W', 'C'): -5, ('W', 'K'): -5, ('H', 'Q'): 0,
+('L', 'C'): -3, ('W', 'N'): -6, ('S', 'A'): 1, ('L', 'G'): -5,
+('W', 'S'): -4, ('S', 'E'): -1, ('H', 'E'): -1, ('S', 'I'): -4,
+('H', 'A'): -3, ('S', 'M'): -3, ('Y', 'L'): -3, ('Y', 'H'): 1,
+('Y', 'D'): -5, ('E', 'R'): -2, ('X', 'P'): -3, ('G', 'G'): 6,
+('G', 'C'): -5, ('E', 'N'): -1, ('Y', 'T'): -3, ('Y', 'P'): -5,
+('T', 'K'): -2, ('A', 'A'): 5, ('P', 'Q'): -2, ('T', 'C'): -2,
+('V', 'H'): -5, ('T', 'G'): -3, ('I', 'Q'): -4, ('Z', 'T'): -2,
+('C', 'R'): -5, ('V', 'P'): -4, ('P', 'E'): -3, ('M', 'C'): -3,
+('K', 'N'): -1, ('I', 'I'): 5, ('P', 'A'): -1, ('M', 'G'): -5,
+('T', 'S'): 1, ('I', 'E'): -5, ('P', 'M'): -4, ('M', 'K'): -2,
+('I', 'A'): -3, ('P', 'I'): -4, ('R', 'R'): 7, ('X', 'M'): -2,
+('L', 'I'): 1, ('X', 'I'): -2, ('Z', 'B'): 1, ('X', 'E'): -2,
+('Z', 'N'): -1, ('X', 'A'): -1, ('B', 'R'): -2, ('B', 'N'): 4,
+('F', 'D'): -5, ('X', 'Y'): -3, ('Z', 'R'): -1, ('F', 'H'): -2,
+('B', 'F'): -5, ('F', 'L'): 0, ('X', 'Q'): -2, ('B', 'B'): 4
}
blosum100 = _temp()
del _temp
@@ -298,75 +297,75 @@ del _temp
# http://www.embl-heidelberg.de/~vogt/matrices/blosum30.cmp
def _temp():
return {
-('W', 'F') : 1, ('L', 'R') : -2, ('S', 'P') : -1, ('V', 'T') : 1,
-('Q', 'Q') : 8, ('N', 'A') : 0, ('Z', 'Y') : -2, ('W', 'R') : 0,
-('Q', 'A') : 1, ('S', 'D') : 0, ('H', 'H') : 14, ('S', 'H') : -1,
-('H', 'D') : -2, ('L', 'N') : -2, ('W', 'A') : -5, ('Y', 'M') : -1,
-('G', 'R') : -2, ('Y', 'I') : -1, ('Y', 'E') : -2, ('B', 'Y') : -3,
-('Y', 'A') : -4, ('V', 'D') : -2, ('B', 'S') : 0, ('Y', 'Y') : 9,
-('G', 'N') : 0, ('E', 'C') : 1, ('Y', 'Q') : -1, ('Z', 'Z') : 4,
-('V', 'A') : 1, ('C', 'C') : 17, ('M', 'R') : 0, ('V', 'E') : -3,
-('T', 'N') : 1, ('P', 'P') : 11, ('V', 'I') : 4, ('V', 'S') : -1,
-('Z', 'P') : 0, ('V', 'M') : 0, ('T', 'F') : -2, ('V', 'Q') : -3,
-('K', 'K') : 4, ('P', 'D') : -1, ('I', 'H') : -2, ('I', 'D') : -4,
-('T', 'R') : -3, ('P', 'L') : -3, ('K', 'G') : -1, ('M', 'N') : 0,
-('P', 'H') : 1, ('F', 'Q') : -3, ('Z', 'G') : -2, ('X', 'L') : 0,
-('T', 'M') : 0, ('Z', 'C') : 0, ('X', 'H') : -1, ('D', 'R') : -1,
-('B', 'W') : -5, ('X', 'D') : -1, ('Z', 'K') : 1, ('F', 'A') : -2,
-('Z', 'W') : -1, ('F', 'E') : -4, ('D', 'N') : 1, ('B', 'K') : 0,
-('X', 'X') : -1, ('F', 'I') : 0, ('B', 'G') : 0, ('X', 'T') : 0,
-('F', 'M') : -2, ('B', 'C') : -2, ('Z', 'I') : -3, ('Z', 'V') : -3,
-('S', 'S') : 4, ('L', 'Q') : -2, ('W', 'E') : -1, ('Q', 'R') : 3,
-('N', 'N') : 8, ('W', 'M') : -3, ('Q', 'C') : -2, ('W', 'I') : -3,
-('S', 'C') : -2, ('L', 'A') : -1, ('S', 'G') : 0, ('L', 'E') : -1,
-('W', 'Q') : -1, ('H', 'G') : -3, ('S', 'K') : 0, ('Q', 'N') : -1,
-('N', 'R') : -2, ('H', 'C') : -5, ('Y', 'N') : -4, ('G', 'Q') : -2,
-('Y', 'F') : 3, ('C', 'A') : -3, ('V', 'L') : 1, ('G', 'E') : -2,
-('G', 'A') : 0, ('K', 'R') : 1, ('E', 'D') : 1, ('Y', 'R') : 0,
-('M', 'Q') : -1, ('T', 'I') : 0, ('C', 'D') : -3, ('V', 'F') : 1,
-('T', 'A') : 1, ('T', 'P') : 0, ('B', 'P') : -2, ('T', 'E') : -2,
-('V', 'N') : -2, ('P', 'G') : -1, ('M', 'A') : 1, ('K', 'H') : -2,
-('V', 'R') : -1, ('P', 'C') : -3, ('M', 'E') : -1, ('K', 'L') : -2,
-('V', 'V') : 5, ('M', 'I') : 1, ('T', 'Q') : 0, ('I', 'G') : -1,
-('P', 'K') : 1, ('M', 'M') : 6, ('K', 'D') : 0, ('I', 'C') : -2,
-('Z', 'D') : 0, ('F', 'R') : -1, ('X', 'K') : 0, ('Q', 'D') : -1,
-('X', 'G') : -1, ('Z', 'L') : -1, ('X', 'C') : -2, ('Z', 'H') : 0,
-('B', 'L') : -1, ('B', 'H') : -2, ('F', 'F') : 10, ('X', 'W') : -2,
-('B', 'D') : 5, ('D', 'A') : 0, ('S', 'L') : -2, ('X', 'S') : 0,
-('F', 'N') : -1, ('S', 'R') : -1, ('W', 'D') : -4, ('V', 'Y') : 1,
-('W', 'L') : -2, ('H', 'R') : -1, ('W', 'H') : -5, ('H', 'N') : -1,
-('W', 'T') : -5, ('T', 'T') : 5, ('S', 'F') : -1, ('W', 'P') : -3,
-('L', 'D') : -1, ('B', 'I') : -2, ('L', 'H') : -1, ('S', 'N') : 0,
-('B', 'T') : 0, ('L', 'L') : 4, ('Y', 'K') : -1, ('E', 'Q') : 2,
-('Y', 'G') : -3, ('Z', 'S') : -1, ('Y', 'C') : -6, ('G', 'D') : -1,
-('B', 'V') : -2, ('E', 'A') : 0, ('Y', 'W') : 5, ('E', 'E') : 6,
-('Y', 'S') : -2, ('C', 'N') : -1, ('V', 'C') : -2, ('T', 'H') : -2,
-('P', 'R') : -1, ('V', 'G') : -3, ('T', 'L') : 0, ('V', 'K') : -2,
-('K', 'Q') : 0, ('R', 'A') : -1, ('I', 'R') : -3, ('T', 'D') : -1,
-('P', 'F') : -4, ('I', 'N') : 0, ('K', 'I') : -2, ('M', 'D') : -3,
-('V', 'W') : -3, ('W', 'W') : 20, ('M', 'H') : 2, ('P', 'N') : -3,
-('K', 'A') : 0, ('M', 'L') : 2, ('K', 'E') : 2, ('Z', 'E') : 5,
-('X', 'N') : 0, ('Z', 'A') : 0, ('Z', 'M') : -1, ('X', 'F') : -1,
-('K', 'C') : -3, ('B', 'Q') : -1, ('X', 'B') : -1, ('B', 'M') : -2,
-('F', 'C') : -3, ('Z', 'Q') : 4, ('X', 'Z') : 0, ('F', 'G') : -3,
-('B', 'E') : 0, ('X', 'V') : 0, ('F', 'K') : -1, ('B', 'A') : 0,
-('X', 'R') : -1, ('D', 'D') : 9, ('W', 'G') : 1, ('Z', 'F') : -4,
-('S', 'Q') : -1, ('W', 'C') : -2, ('W', 'K') : -2, ('H', 'Q') : 0,
-('L', 'C') : 0, ('W', 'N') : -7, ('S', 'A') : 1, ('L', 'G') : -2,
-('W', 'S') : -3, ('S', 'E') : 0, ('H', 'E') : 0, ('S', 'I') : -1,
-('H', 'A') : -2, ('S', 'M') : -2, ('Y', 'L') : 3, ('Y', 'H') : 0,
-('Y', 'D') : -1, ('E', 'R') : -1, ('X', 'P') : -1, ('G', 'G') : 8,
-('G', 'C') : -4, ('E', 'N') : -1, ('Y', 'T') : -1, ('Y', 'P') : -2,
-('T', 'K') : -1, ('A', 'A') : 4, ('P', 'Q') : 0, ('T', 'C') : -2,
-('V', 'H') : -3, ('T', 'G') : -2, ('I', 'Q') : -2, ('Z', 'T') : -1,
-('C', 'R') : -2, ('V', 'P') : -4, ('P', 'E') : 1, ('M', 'C') : -2,
-('K', 'N') : 0, ('I', 'I') : 6, ('P', 'A') : -1, ('M', 'G') : -2,
-('T', 'S') : 2, ('I', 'E') : -3, ('P', 'M') : -4, ('M', 'K') : 2,
-('I', 'A') : 0, ('P', 'I') : -3, ('R', 'R') : 8, ('X', 'M') : 0,
-('L', 'I') : 2, ('X', 'I') : 0, ('Z', 'B') : 0, ('X', 'E') : -1,
-('Z', 'N') : -1, ('X', 'A') : 0, ('B', 'R') : -2, ('B', 'N') : 4,
-('F', 'D') : -5, ('X', 'Y') : -1, ('Z', 'R') : 0, ('F', 'H') : -3,
-('B', 'F') : -3, ('F', 'L') : 2, ('X', 'Q') : 0, ('B', 'B') : 5
+('W', 'F'): 1, ('L', 'R'): -2, ('S', 'P'): -1, ('V', 'T'): 1,
+('Q', 'Q'): 8, ('N', 'A'): 0, ('Z', 'Y'): -2, ('W', 'R'): 0,
+('Q', 'A'): 1, ('S', 'D'): 0, ('H', 'H'): 14, ('S', 'H'): -1,
+('H', 'D'): -2, ('L', 'N'): -2, ('W', 'A'): -5, ('Y', 'M'): -1,
+('G', 'R'): -2, ('Y', 'I'): -1, ('Y', 'E'): -2, ('B', 'Y'): -3,
+('Y', 'A'): -4, ('V', 'D'): -2, ('B', 'S'): 0, ('Y', 'Y'): 9,
+('G', 'N'): 0, ('E', 'C'): 1, ('Y', 'Q'): -1, ('Z', 'Z'): 4,
+('V', 'A'): 1, ('C', 'C'): 17, ('M', 'R'): 0, ('V', 'E'): -3,
+('T', 'N'): 1, ('P', 'P'): 11, ('V', 'I'): 4, ('V', 'S'): -1,
+('Z', 'P'): 0, ('V', 'M'): 0, ('T', 'F'): -2, ('V', 'Q'): -3,
+('K', 'K'): 4, ('P', 'D'): -1, ('I', 'H'): -2, ('I', 'D'): -4,
+('T', 'R'): -3, ('P', 'L'): -3, ('K', 'G'): -1, ('M', 'N'): 0,
+('P', 'H'): 1, ('F', 'Q'): -3, ('Z', 'G'): -2, ('X', 'L'): 0,
+('T', 'M'): 0, ('Z', 'C'): 0, ('X', 'H'): -1, ('D', 'R'): -1,
+('B', 'W'): -5, ('X', 'D'): -1, ('Z', 'K'): 1, ('F', 'A'): -2,
+('Z', 'W'): -1, ('F', 'E'): -4, ('D', 'N'): 1, ('B', 'K'): 0,
+('X', 'X'): -1, ('F', 'I'): 0, ('B', 'G'): 0, ('X', 'T'): 0,
+('F', 'M'): -2, ('B', 'C'): -2, ('Z', 'I'): -3, ('Z', 'V'): -3,
+('S', 'S'): 4, ('L', 'Q'): -2, ('W', 'E'): -1, ('Q', 'R'): 3,
+('N', 'N'): 8, ('W', 'M'): -3, ('Q', 'C'): -2, ('W', 'I'): -3,
+('S', 'C'): -2, ('L', 'A'): -1, ('S', 'G'): 0, ('L', 'E'): -1,
+('W', 'Q'): -1, ('H', 'G'): -3, ('S', 'K'): 0, ('Q', 'N'): -1,
+('N', 'R'): -2, ('H', 'C'): -5, ('Y', 'N'): -4, ('G', 'Q'): -2,
+('Y', 'F'): 3, ('C', 'A'): -3, ('V', 'L'): 1, ('G', 'E'): -2,
+('G', 'A'): 0, ('K', 'R'): 1, ('E', 'D'): 1, ('Y', 'R'): 0,
+('M', 'Q'): -1, ('T', 'I'): 0, ('C', 'D'): -3, ('V', 'F'): 1,
+('T', 'A'): 1, ('T', 'P'): 0, ('B', 'P'): -2, ('T', 'E'): -2,
+('V', 'N'): -2, ('P', 'G'): -1, ('M', 'A'): 1, ('K', 'H'): -2,
+('V', 'R'): -1, ('P', 'C'): -3, ('M', 'E'): -1, ('K', 'L'): -2,
+('V', 'V'): 5, ('M', 'I'): 1, ('T', 'Q'): 0, ('I', 'G'): -1,
+('P', 'K'): 1, ('M', 'M'): 6, ('K', 'D'): 0, ('I', 'C'): -2,
+('Z', 'D'): 0, ('F', 'R'): -1, ('X', 'K'): 0, ('Q', 'D'): -1,
+('X', 'G'): -1, ('Z', 'L'): -1, ('X', 'C'): -2, ('Z', 'H'): 0,
+('B', 'L'): -1, ('B', 'H'): -2, ('F', 'F'): 10, ('X', 'W'): -2,
+('B', 'D'): 5, ('D', 'A'): 0, ('S', 'L'): -2, ('X', 'S'): 0,
+('F', 'N'): -1, ('S', 'R'): -1, ('W', 'D'): -4, ('V', 'Y'): 1,
+('W', 'L'): -2, ('H', 'R'): -1, ('W', 'H'): -5, ('H', 'N'): -1,
+('W', 'T'): -5, ('T', 'T'): 5, ('S', 'F'): -1, ('W', 'P'): -3,
+('L', 'D'): -1, ('B', 'I'): -2, ('L', 'H'): -1, ('S', 'N'): 0,
+('B', 'T'): 0, ('L', 'L'): 4, ('Y', 'K'): -1, ('E', 'Q'): 2,
+('Y', 'G'): -3, ('Z', 'S'): -1, ('Y', 'C'): -6, ('G', 'D'): -1,
+('B', 'V'): -2, ('E', 'A'): 0, ('Y', 'W'): 5, ('E', 'E'): 6,
+('Y', 'S'): -2, ('C', 'N'): -1, ('V', 'C'): -2, ('T', 'H'): -2,
+('P', 'R'): -1, ('V', 'G'): -3, ('T', 'L'): 0, ('V', 'K'): -2,
+('K', 'Q'): 0, ('R', 'A'): -1, ('I', 'R'): -3, ('T', 'D'): -1,
+('P', 'F'): -4, ('I', 'N'): 0, ('K', 'I'): -2, ('M', 'D'): -3,
+('V', 'W'): -3, ('W', 'W'): 20, ('M', 'H'): 2, ('P', 'N'): -3,
+('K', 'A'): 0, ('M', 'L'): 2, ('K', 'E'): 2, ('Z', 'E'): 5,
+('X', 'N'): 0, ('Z', 'A'): 0, ('Z', 'M'): -1, ('X', 'F'): -1,
+('K', 'C'): -3, ('B', 'Q'): -1, ('X', 'B'): -1, ('B', 'M'): -2,
+('F', 'C'): -3, ('Z', 'Q'): 4, ('X', 'Z'): 0, ('F', 'G'): -3,
+('B', 'E'): 0, ('X', 'V'): 0, ('F', 'K'): -1, ('B', 'A'): 0,
+('X', 'R'): -1, ('D', 'D'): 9, ('W', 'G'): 1, ('Z', 'F'): -4,
+('S', 'Q'): -1, ('W', 'C'): -2, ('W', 'K'): -2, ('H', 'Q'): 0,
+('L', 'C'): 0, ('W', 'N'): -7, ('S', 'A'): 1, ('L', 'G'): -2,
+('W', 'S'): -3, ('S', 'E'): 0, ('H', 'E'): 0, ('S', 'I'): -1,
+('H', 'A'): -2, ('S', 'M'): -2, ('Y', 'L'): 3, ('Y', 'H'): 0,
+('Y', 'D'): -1, ('E', 'R'): -1, ('X', 'P'): -1, ('G', 'G'): 8,
+('G', 'C'): -4, ('E', 'N'): -1, ('Y', 'T'): -1, ('Y', 'P'): -2,
+('T', 'K'): -1, ('A', 'A'): 4, ('P', 'Q'): 0, ('T', 'C'): -2,
+('V', 'H'): -3, ('T', 'G'): -2, ('I', 'Q'): -2, ('Z', 'T'): -1,
+('C', 'R'): -2, ('V', 'P'): -4, ('P', 'E'): 1, ('M', 'C'): -2,
+('K', 'N'): 0, ('I', 'I'): 6, ('P', 'A'): -1, ('M', 'G'): -2,
+('T', 'S'): 2, ('I', 'E'): -3, ('P', 'M'): -4, ('M', 'K'): 2,
+('I', 'A'): 0, ('P', 'I'): -3, ('R', 'R'): 8, ('X', 'M'): 0,
+('L', 'I'): 2, ('X', 'I'): 0, ('Z', 'B'): 0, ('X', 'E'): -1,
+('Z', 'N'): -1, ('X', 'A'): 0, ('B', 'R'): -2, ('B', 'N'): 4,
+('F', 'D'): -5, ('X', 'Y'): -1, ('Z', 'R'): 0, ('F', 'H'): -3,
+('B', 'F'): -3, ('F', 'L'): 2, ('X', 'Q'): 0, ('B', 'B'): 5
}
blosum30 = _temp()
del _temp
@@ -375,75 +374,75 @@ del _temp
# http://www.embl-heidelberg.de/~vogt/matrices/blosum35.cmp
def _temp():
return {
-('W', 'F') : 1, ('L', 'R') : -2, ('S', 'P') : -2, ('V', 'T') : 1,
-('Q', 'Q') : 7, ('N', 'A') : -1, ('Z', 'Y') : -1, ('W', 'R') : 0,
-('Q', 'A') : 0, ('S', 'D') : -1, ('H', 'H') : 12, ('S', 'H') : -1,
-('H', 'D') : 0, ('L', 'N') : -2, ('W', 'A') : -2, ('Y', 'M') : 0,
-('G', 'R') : -2, ('Y', 'I') : 0, ('Y', 'E') : -1, ('B', 'Y') : -2,
-('Y', 'A') : -1, ('V', 'D') : -2, ('B', 'S') : 0, ('Y', 'Y') : 8,
-('G', 'N') : 1, ('E', 'C') : -1, ('Y', 'Q') : 0, ('Z', 'Z') : 4,
-('V', 'A') : 0, ('C', 'C') : 15, ('M', 'R') : 0, ('V', 'E') : -2,
-('T', 'N') : 0, ('P', 'P') : 10, ('V', 'I') : 4, ('V', 'S') : -1,
-('Z', 'P') : 0, ('V', 'M') : 1, ('T', 'F') : -1, ('V', 'Q') : -3,
-('K', 'K') : 5, ('P', 'D') : -1, ('I', 'H') : -3, ('I', 'D') : -3,
-('T', 'R') : -2, ('P', 'L') : -3, ('K', 'G') : -1, ('M', 'N') : -1,
-('P', 'H') : -1, ('F', 'Q') : -4, ('Z', 'G') : -2, ('X', 'L') : 0,
-('T', 'M') : 0, ('Z', 'C') : -2, ('X', 'H') : -1, ('D', 'R') : -1,
-('B', 'W') : -3, ('X', 'D') : -1, ('Z', 'K') : 1, ('F', 'A') : -2,
-('Z', 'W') : -1, ('F', 'E') : -3, ('D', 'N') : 1, ('B', 'K') : 0,
-('X', 'X') : -1, ('F', 'I') : 1, ('B', 'G') : 0, ('X', 'T') : 0,
-('F', 'M') : 0, ('B', 'C') : -2, ('Z', 'I') : -3, ('Z', 'V') : -2,
-('S', 'S') : 4, ('L', 'Q') : -2, ('W', 'E') : -1, ('Q', 'R') : 2,
-('N', 'N') : 7, ('W', 'M') : 1, ('Q', 'C') : -3, ('W', 'I') : -1,
-('S', 'C') : -3, ('L', 'A') : -2, ('S', 'G') : 1, ('L', 'E') : -1,
-('W', 'Q') : -1, ('H', 'G') : -2, ('S', 'K') : 0, ('Q', 'N') : 1,
-('N', 'R') : -1, ('H', 'C') : -4, ('Y', 'N') : -2, ('G', 'Q') : -2,
-('Y', 'F') : 3, ('C', 'A') : -2, ('V', 'L') : 2, ('G', 'E') : -2,
-('G', 'A') : 0, ('K', 'R') : 2, ('E', 'D') : 2, ('Y', 'R') : 0,
-('M', 'Q') : -1, ('T', 'I') : -1, ('C', 'D') : -3, ('V', 'F') : 1,
-('T', 'A') : 0, ('T', 'P') : 0, ('B', 'P') : -1, ('T', 'E') : -1,
-('V', 'N') : -2, ('P', 'G') : -2, ('M', 'A') : 0, ('K', 'H') : -2,
-('V', 'R') : -1, ('P', 'C') : -4, ('M', 'E') : -2, ('K', 'L') : -2,
-('V', 'V') : 5, ('M', 'I') : 1, ('T', 'Q') : 0, ('I', 'G') : -3,
-('P', 'K') : 0, ('M', 'M') : 6, ('K', 'D') : -1, ('I', 'C') : -4,
-('Z', 'D') : 1, ('F', 'R') : -1, ('X', 'K') : 0, ('Q', 'D') : -1,
-('X', 'G') : -1, ('Z', 'L') : -2, ('X', 'C') : -2, ('Z', 'H') : -1,
-('B', 'L') : -2, ('B', 'H') : 0, ('F', 'F') : 8, ('X', 'W') : -1,
-('B', 'D') : 5, ('D', 'A') : -1, ('S', 'L') : -2, ('X', 'S') : 0,
-('F', 'N') : -1, ('S', 'R') : -1, ('W', 'D') : -3, ('V', 'Y') : 0,
-('W', 'L') : 0, ('H', 'R') : -1, ('W', 'H') : -4, ('H', 'N') : 1,
-('W', 'T') : -2, ('T', 'T') : 5, ('S', 'F') : -1, ('W', 'P') : -4,
-('L', 'D') : -2, ('B', 'I') : -2, ('L', 'H') : -2, ('S', 'N') : 0,
-('B', 'T') : -1, ('L', 'L') : 5, ('Y', 'K') : -1, ('E', 'Q') : 2,
-('Y', 'G') : -2, ('Z', 'S') : 0, ('Y', 'C') : -5, ('G', 'D') : -2,
-('B', 'V') : -2, ('E', 'A') : -1, ('Y', 'W') : 3, ('E', 'E') : 6,
-('Y', 'S') : -1, ('C', 'N') : -1, ('V', 'C') : -2, ('T', 'H') : -2,
-('P', 'R') : -2, ('V', 'G') : -3, ('T', 'L') : 0, ('V', 'K') : -2,
-('K', 'Q') : 0, ('R', 'A') : -1, ('I', 'R') : -3, ('T', 'D') : -1,
-('P', 'F') : -4, ('I', 'N') : -1, ('K', 'I') : -2, ('M', 'D') : -3,
-('V', 'W') : -2, ('W', 'W') : 16, ('M', 'H') : 1, ('P', 'N') : -2,
-('K', 'A') : 0, ('M', 'L') : 3, ('K', 'E') : 1, ('Z', 'E') : 5,
-('X', 'N') : 0, ('Z', 'A') : -1, ('Z', 'M') : -2, ('X', 'F') : -1,
-('K', 'C') : -2, ('B', 'Q') : 0, ('X', 'B') : -1, ('B', 'M') : -2,
-('F', 'C') : -4, ('Z', 'Q') : 4, ('X', 'Z') : 0, ('F', 'G') : -3,
-('B', 'E') : 0, ('X', 'V') : 0, ('F', 'K') : -1, ('B', 'A') : -1,
-('X', 'R') : -1, ('D', 'D') : 8, ('W', 'G') : -1, ('Z', 'F') : -3,
-('S', 'Q') : 0, ('W', 'C') : -5, ('W', 'K') : 0, ('H', 'Q') : -1,
-('L', 'C') : -2, ('W', 'N') : -2, ('S', 'A') : 1, ('L', 'G') : -3,
-('W', 'S') : -2, ('S', 'E') : 0, ('H', 'E') : -1, ('S', 'I') : -2,
-('H', 'A') : -2, ('S', 'M') : -1, ('Y', 'L') : 0, ('Y', 'H') : 0,
-('Y', 'D') : -2, ('E', 'R') : -1, ('X', 'P') : -1, ('G', 'G') : 7,
-('G', 'C') : -3, ('E', 'N') : -1, ('Y', 'T') : -2, ('Y', 'P') : -3,
-('T', 'K') : 0, ('A', 'A') : 5, ('P', 'Q') : 0, ('T', 'C') : -1,
-('V', 'H') : -4, ('T', 'G') : -2, ('I', 'Q') : -2, ('Z', 'T') : -1,
-('C', 'R') : -3, ('V', 'P') : -3, ('P', 'E') : 0, ('M', 'C') : -4,
-('K', 'N') : 0, ('I', 'I') : 5, ('P', 'A') : -2, ('M', 'G') : -1,
-('T', 'S') : 2, ('I', 'E') : -3, ('P', 'M') : -3, ('M', 'K') : 0,
-('I', 'A') : -1, ('P', 'I') : -1, ('R', 'R') : 8, ('X', 'M') : 0,
-('L', 'I') : 2, ('X', 'I') : 0, ('Z', 'B') : 0, ('X', 'E') : -1,
-('Z', 'N') : 0, ('X', 'A') : 0, ('B', 'R') : -1, ('B', 'N') : 4,
-('F', 'D') : -3, ('X', 'Y') : -1, ('Z', 'R') : 0, ('F', 'H') : -3,
-('B', 'F') : -2, ('F', 'L') : 2, ('X', 'Q') : -1, ('B', 'B') : 5
+('W', 'F'): 1, ('L', 'R'): -2, ('S', 'P'): -2, ('V', 'T'): 1,
+('Q', 'Q'): 7, ('N', 'A'): -1, ('Z', 'Y'): -1, ('W', 'R'): 0,
+('Q', 'A'): 0, ('S', 'D'): -1, ('H', 'H'): 12, ('S', 'H'): -1,
+('H', 'D'): 0, ('L', 'N'): -2, ('W', 'A'): -2, ('Y', 'M'): 0,
+('G', 'R'): -2, ('Y', 'I'): 0, ('Y', 'E'): -1, ('B', 'Y'): -2,
+('Y', 'A'): -1, ('V', 'D'): -2, ('B', 'S'): 0, ('Y', 'Y'): 8,
+('G', 'N'): 1, ('E', 'C'): -1, ('Y', 'Q'): 0, ('Z', 'Z'): 4,
+('V', 'A'): 0, ('C', 'C'): 15, ('M', 'R'): 0, ('V', 'E'): -2,
+('T', 'N'): 0, ('P', 'P'): 10, ('V', 'I'): 4, ('V', 'S'): -1,
+('Z', 'P'): 0, ('V', 'M'): 1, ('T', 'F'): -1, ('V', 'Q'): -3,
+('K', 'K'): 5, ('P', 'D'): -1, ('I', 'H'): -3, ('I', 'D'): -3,
+('T', 'R'): -2, ('P', 'L'): -3, ('K', 'G'): -1, ('M', 'N'): -1,
+('P', 'H'): -1, ('F', 'Q'): -4, ('Z', 'G'): -2, ('X', 'L'): 0,
+('T', 'M'): 0, ('Z', 'C'): -2, ('X', 'H'): -1, ('D', 'R'): -1,
+('B', 'W'): -3, ('X', 'D'): -1, ('Z', 'K'): 1, ('F', 'A'): -2,
+('Z', 'W'): -1, ('F', 'E'): -3, ('D', 'N'): 1, ('B', 'K'): 0,
+('X', 'X'): -1, ('F', 'I'): 1, ('B', 'G'): 0, ('X', 'T'): 0,
+('F', 'M'): 0, ('B', 'C'): -2, ('Z', 'I'): -3, ('Z', 'V'): -2,
+('S', 'S'): 4, ('L', 'Q'): -2, ('W', 'E'): -1, ('Q', 'R'): 2,
+('N', 'N'): 7, ('W', 'M'): 1, ('Q', 'C'): -3, ('W', 'I'): -1,
+('S', 'C'): -3, ('L', 'A'): -2, ('S', 'G'): 1, ('L', 'E'): -1,
+('W', 'Q'): -1, ('H', 'G'): -2, ('S', 'K'): 0, ('Q', 'N'): 1,
+('N', 'R'): -1, ('H', 'C'): -4, ('Y', 'N'): -2, ('G', 'Q'): -2,
+('Y', 'F'): 3, ('C', 'A'): -2, ('V', 'L'): 2, ('G', 'E'): -2,
+('G', 'A'): 0, ('K', 'R'): 2, ('E', 'D'): 2, ('Y', 'R'): 0,
+('M', 'Q'): -1, ('T', 'I'): -1, ('C', 'D'): -3, ('V', 'F'): 1,
+('T', 'A'): 0, ('T', 'P'): 0, ('B', 'P'): -1, ('T', 'E'): -1,
+('V', 'N'): -2, ('P', 'G'): -2, ('M', 'A'): 0, ('K', 'H'): -2,
+('V', 'R'): -1, ('P', 'C'): -4, ('M', 'E'): -2, ('K', 'L'): -2,
+('V', 'V'): 5, ('M', 'I'): 1, ('T', 'Q'): 0, ('I', 'G'): -3,
+('P', 'K'): 0, ('M', 'M'): 6, ('K', 'D'): -1, ('I', 'C'): -4,
+('Z', 'D'): 1, ('F', 'R'): -1, ('X', 'K'): 0, ('Q', 'D'): -1,
+('X', 'G'): -1, ('Z', 'L'): -2, ('X', 'C'): -2, ('Z', 'H'): -1,
+('B', 'L'): -2, ('B', 'H'): 0, ('F', 'F'): 8, ('X', 'W'): -1,
+('B', 'D'): 5, ('D', 'A'): -1, ('S', 'L'): -2, ('X', 'S'): 0,
+('F', 'N'): -1, ('S', 'R'): -1, ('W', 'D'): -3, ('V', 'Y'): 0,
+('W', 'L'): 0, ('H', 'R'): -1, ('W', 'H'): -4, ('H', 'N'): 1,
+('W', 'T'): -2, ('T', 'T'): 5, ('S', 'F'): -1, ('W', 'P'): -4,
+('L', 'D'): -2, ('B', 'I'): -2, ('L', 'H'): -2, ('S', 'N'): 0,
+('B', 'T'): -1, ('L', 'L'): 5, ('Y', 'K'): -1, ('E', 'Q'): 2,
+('Y', 'G'): -2, ('Z', 'S'): 0, ('Y', 'C'): -5, ('G', 'D'): -2,
+('B', 'V'): -2, ('E', 'A'): -1, ('Y', 'W'): 3, ('E', 'E'): 6,
+('Y', 'S'): -1, ('C', 'N'): -1, ('V', 'C'): -2, ('T', 'H'): -2,
+('P', 'R'): -2, ('V', 'G'): -3, ('T', 'L'): 0, ('V', 'K'): -2,
+('K', 'Q'): 0, ('R', 'A'): -1, ('I', 'R'): -3, ('T', 'D'): -1,
+('P', 'F'): -4, ('I', 'N'): -1, ('K', 'I'): -2, ('M', 'D'): -3,
+('V', 'W'): -2, ('W', 'W'): 16, ('M', 'H'): 1, ('P', 'N'): -2,
+('K', 'A'): 0, ('M', 'L'): 3, ('K', 'E'): 1, ('Z', 'E'): 5,
+('X', 'N'): 0, ('Z', 'A'): -1, ('Z', 'M'): -2, ('X', 'F'): -1,
+('K', 'C'): -2, ('B', 'Q'): 0, ('X', 'B'): -1, ('B', 'M'): -2,
+('F', 'C'): -4, ('Z', 'Q'): 4, ('X', 'Z'): 0, ('F', 'G'): -3,
+('B', 'E'): 0, ('X', 'V'): 0, ('F', 'K'): -1, ('B', 'A'): -1,
+('X', 'R'): -1, ('D', 'D'): 8, ('W', 'G'): -1, ('Z', 'F'): -3,
+('S', 'Q'): 0, ('W', 'C'): -5, ('W', 'K'): 0, ('H', 'Q'): -1,
+('L', 'C'): -2, ('W', 'N'): -2, ('S', 'A'): 1, ('L', 'G'): -3,
+('W', 'S'): -2, ('S', 'E'): 0, ('H', 'E'): -1, ('S', 'I'): -2,
+('H', 'A'): -2, ('S', 'M'): -1, ('Y', 'L'): 0, ('Y', 'H'): 0,
+('Y', 'D'): -2, ('E', 'R'): -1, ('X', 'P'): -1, ('G', 'G'): 7,
+('G', 'C'): -3, ('E', 'N'): -1, ('Y', 'T'): -2, ('Y', 'P'): -3,
+('T', 'K'): 0, ('A', 'A'): 5, ('P', 'Q'): 0, ('T', 'C'): -1,
+('V', 'H'): -4, ('T', 'G'): -2, ('I', 'Q'): -2, ('Z', 'T'): -1,
+('C', 'R'): -3, ('V', 'P'): -3, ('P', 'E'): 0, ('M', 'C'): -4,
+('K', 'N'): 0, ('I', 'I'): 5, ('P', 'A'): -2, ('M', 'G'): -1,
+('T', 'S'): 2, ('I', 'E'): -3, ('P', 'M'): -3, ('M', 'K'): 0,
+('I', 'A'): -1, ('P', 'I'): -1, ('R', 'R'): 8, ('X', 'M'): 0,
+('L', 'I'): 2, ('X', 'I'): 0, ('Z', 'B'): 0, ('X', 'E'): -1,
+('Z', 'N'): 0, ('X', 'A'): 0, ('B', 'R'): -1, ('B', 'N'): 4,
+('F', 'D'): -3, ('X', 'Y'): -1, ('Z', 'R'): 0, ('F', 'H'): -3,
+('B', 'F'): -2, ('F', 'L'): 2, ('X', 'Q'): -1, ('B', 'B'): 5
}
blosum35 = _temp()
del _temp
@@ -452,75 +451,75 @@ del _temp
# http://www.embl-heidelberg.de/~vogt/matrices/blosum40.cmp
def _temp():
return {
-('W', 'F') : 1, ('L', 'R') : -2, ('S', 'P') : -1, ('V', 'T') : 1,
-('Q', 'Q') : 8, ('N', 'A') : -1, ('Z', 'Y') : -2, ('W', 'R') : -2,
-('Q', 'A') : 0, ('S', 'D') : 0, ('H', 'H') : 13, ('S', 'H') : -1,
-('H', 'D') : 0, ('L', 'N') : -3, ('W', 'A') : -3, ('Y', 'M') : 1,
-('G', 'R') : -3, ('Y', 'I') : 0, ('Y', 'E') : -2, ('B', 'Y') : -3,
-('Y', 'A') : -2, ('V', 'D') : -3, ('B', 'S') : 0, ('Y', 'Y') : 9,
-('G', 'N') : 0, ('E', 'C') : -2, ('Y', 'Q') : -1, ('Z', 'Z') : 5,
-('V', 'A') : 0, ('C', 'C') : 16, ('M', 'R') : -1, ('V', 'E') : -3,
-('T', 'N') : 0, ('P', 'P') : 11, ('V', 'I') : 4, ('V', 'S') : -1,
-('Z', 'P') : -1, ('V', 'M') : 1, ('T', 'F') : -1, ('V', 'Q') : -3,
-('K', 'K') : 6, ('P', 'D') : -2, ('I', 'H') : -3, ('I', 'D') : -4,
-('T', 'R') : -2, ('P', 'L') : -4, ('K', 'G') : -2, ('M', 'N') : -2,
-('P', 'H') : -2, ('F', 'Q') : -4, ('Z', 'G') : -2, ('X', 'L') : -1,
-('T', 'M') : -1, ('Z', 'C') : -3, ('X', 'H') : -1, ('D', 'R') : -1,
-('B', 'W') : -4, ('X', 'D') : -1, ('Z', 'K') : 1, ('F', 'A') : -3,
-('Z', 'W') : -2, ('F', 'E') : -3, ('D', 'N') : 2, ('B', 'K') : 0,
-('X', 'X') : -1, ('F', 'I') : 1, ('B', 'G') : -1, ('X', 'T') : 0,
-('F', 'M') : 0, ('B', 'C') : -2, ('Z', 'I') : -4, ('Z', 'V') : -3,
-('S', 'S') : 5, ('L', 'Q') : -2, ('W', 'E') : -2, ('Q', 'R') : 2,
-('N', 'N') : 8, ('W', 'M') : -2, ('Q', 'C') : -4, ('W', 'I') : -3,
-('S', 'C') : -1, ('L', 'A') : -2, ('S', 'G') : 0, ('L', 'E') : -2,
-('W', 'Q') : -1, ('H', 'G') : -2, ('S', 'K') : 0, ('Q', 'N') : 1,
-('N', 'R') : 0, ('H', 'C') : -4, ('Y', 'N') : -2, ('G', 'Q') : -2,
-('Y', 'F') : 4, ('C', 'A') : -2, ('V', 'L') : 2, ('G', 'E') : -3,
-('G', 'A') : 1, ('K', 'R') : 3, ('E', 'D') : 2, ('Y', 'R') : -1,
-('M', 'Q') : -1, ('T', 'I') : -1, ('C', 'D') : -2, ('V', 'F') : 0,
-('T', 'A') : 0, ('T', 'P') : 0, ('B', 'P') : -2, ('T', 'E') : -1,
-('V', 'N') : -3, ('P', 'G') : -1, ('M', 'A') : -1, ('K', 'H') : -1,
-('V', 'R') : -2, ('P', 'C') : -5, ('M', 'E') : -2, ('K', 'L') : -2,
-('V', 'V') : 5, ('M', 'I') : 1, ('T', 'Q') : -1, ('I', 'G') : -4,
-('P', 'K') : -1, ('M', 'M') : 7, ('K', 'D') : 0, ('I', 'C') : -4,
-('Z', 'D') : 1, ('F', 'R') : -2, ('X', 'K') : -1, ('Q', 'D') : -1,
-('X', 'G') : -1, ('Z', 'L') : -2, ('X', 'C') : -2, ('Z', 'H') : 0,
-('B', 'L') : -3, ('B', 'H') : 0, ('F', 'F') : 9, ('X', 'W') : -2,
-('B', 'D') : 6, ('D', 'A') : -1, ('S', 'L') : -3, ('X', 'S') : 0,
-('F', 'N') : -3, ('S', 'R') : -1, ('W', 'D') : -5, ('V', 'Y') : -1,
-('W', 'L') : -1, ('H', 'R') : 0, ('W', 'H') : -5, ('H', 'N') : 1,
-('W', 'T') : -4, ('T', 'T') : 6, ('S', 'F') : -2, ('W', 'P') : -4,
-('L', 'D') : -3, ('B', 'I') : -3, ('L', 'H') : -2, ('S', 'N') : 1,
-('B', 'T') : 0, ('L', 'L') : 6, ('Y', 'K') : -1, ('E', 'Q') : 2,
-('Y', 'G') : -3, ('Z', 'S') : 0, ('Y', 'C') : -4, ('G', 'D') : -2,
-('B', 'V') : -3, ('E', 'A') : -1, ('Y', 'W') : 3, ('E', 'E') : 7,
-('Y', 'S') : -2, ('C', 'N') : -2, ('V', 'C') : -2, ('T', 'H') : -2,
-('P', 'R') : -3, ('V', 'G') : -4, ('T', 'L') : -1, ('V', 'K') : -2,
-('K', 'Q') : 1, ('R', 'A') : -2, ('I', 'R') : -3, ('T', 'D') : -1,
-('P', 'F') : -4, ('I', 'N') : -2, ('K', 'I') : -3, ('M', 'D') : -3,
-('V', 'W') : -3, ('W', 'W') : 19, ('M', 'H') : 1, ('P', 'N') : -2,
-('K', 'A') : -1, ('M', 'L') : 3, ('K', 'E') : 1, ('Z', 'E') : 5,
-('X', 'N') : -1, ('Z', 'A') : -1, ('Z', 'M') : -2, ('X', 'F') : -1,
-('K', 'C') : -3, ('B', 'Q') : 0, ('X', 'B') : -1, ('B', 'M') : -3,
-('F', 'C') : -2, ('Z', 'Q') : 4, ('X', 'Z') : -1, ('F', 'G') : -3,
-('B', 'E') : 1, ('X', 'V') : -1, ('F', 'K') : -3, ('B', 'A') : -1,
-('X', 'R') : -1, ('D', 'D') : 9, ('W', 'G') : -2, ('Z', 'F') : -4,
-('S', 'Q') : 1, ('W', 'C') : -6, ('W', 'K') : -2, ('H', 'Q') : 0,
-('L', 'C') : -2, ('W', 'N') : -4, ('S', 'A') : 1, ('L', 'G') : -4,
-('W', 'S') : -5, ('S', 'E') : 0, ('H', 'E') : 0, ('S', 'I') : -2,
-('H', 'A') : -2, ('S', 'M') : -2, ('Y', 'L') : 0, ('Y', 'H') : 2,
-('Y', 'D') : -3, ('E', 'R') : -1, ('X', 'P') : -2, ('G', 'G') : 8,
-('G', 'C') : -3, ('E', 'N') : -1, ('Y', 'T') : -1, ('Y', 'P') : -3,
-('T', 'K') : 0, ('A', 'A') : 5, ('P', 'Q') : -2, ('T', 'C') : -1,
-('V', 'H') : -4, ('T', 'G') : -2, ('I', 'Q') : -3, ('Z', 'T') : -1,
-('C', 'R') : -3, ('V', 'P') : -3, ('P', 'E') : 0, ('M', 'C') : -3,
-('K', 'N') : 0, ('I', 'I') : 6, ('P', 'A') : -2, ('M', 'G') : -2,
-('T', 'S') : 2, ('I', 'E') : -4, ('P', 'M') : -2, ('M', 'K') : -1,
-('I', 'A') : -1, ('P', 'I') : -2, ('R', 'R') : 9, ('X', 'M') : 0,
-('L', 'I') : 2, ('X', 'I') : -1, ('Z', 'B') : 2, ('X', 'E') : -1,
-('Z', 'N') : 0, ('X', 'A') : 0, ('B', 'R') : -1, ('B', 'N') : 4,
-('F', 'D') : -4, ('X', 'Y') : -1, ('Z', 'R') : 0, ('F', 'H') : -2,
-('B', 'F') : -3, ('F', 'L') : 2, ('X', 'Q') : -1, ('B', 'B') : 5
+('W', 'F'): 1, ('L', 'R'): -2, ('S', 'P'): -1, ('V', 'T'): 1,
+('Q', 'Q'): 8, ('N', 'A'): -1, ('Z', 'Y'): -2, ('W', 'R'): -2,
+('Q', 'A'): 0, ('S', 'D'): 0, ('H', 'H'): 13, ('S', 'H'): -1,
+('H', 'D'): 0, ('L', 'N'): -3, ('W', 'A'): -3, ('Y', 'M'): 1,
+('G', 'R'): -3, ('Y', 'I'): 0, ('Y', 'E'): -2, ('B', 'Y'): -3,
+('Y', 'A'): -2, ('V', 'D'): -3, ('B', 'S'): 0, ('Y', 'Y'): 9,
+('G', 'N'): 0, ('E', 'C'): -2, ('Y', 'Q'): -1, ('Z', 'Z'): 5,
+('V', 'A'): 0, ('C', 'C'): 16, ('M', 'R'): -1, ('V', 'E'): -3,
+('T', 'N'): 0, ('P', 'P'): 11, ('V', 'I'): 4, ('V', 'S'): -1,
+('Z', 'P'): -1, ('V', 'M'): 1, ('T', 'F'): -1, ('V', 'Q'): -3,
+('K', 'K'): 6, ('P', 'D'): -2, ('I', 'H'): -3, ('I', 'D'): -4,
+('T', 'R'): -2, ('P', 'L'): -4, ('K', 'G'): -2, ('M', 'N'): -2,
+('P', 'H'): -2, ('F', 'Q'): -4, ('Z', 'G'): -2, ('X', 'L'): -1,
+('T', 'M'): -1, ('Z', 'C'): -3, ('X', 'H'): -1, ('D', 'R'): -1,
+('B', 'W'): -4, ('X', 'D'): -1, ('Z', 'K'): 1, ('F', 'A'): -3,
+('Z', 'W'): -2, ('F', 'E'): -3, ('D', 'N'): 2, ('B', 'K'): 0,
+('X', 'X'): -1, ('F', 'I'): 1, ('B', 'G'): -1, ('X', 'T'): 0,
+('F', 'M'): 0, ('B', 'C'): -2, ('Z', 'I'): -4, ('Z', 'V'): -3,
+('S', 'S'): 5, ('L', 'Q'): -2, ('W', 'E'): -2, ('Q', 'R'): 2,
+('N', 'N'): 8, ('W', 'M'): -2, ('Q', 'C'): -4, ('W', 'I'): -3,
+('S', 'C'): -1, ('L', 'A'): -2, ('S', 'G'): 0, ('L', 'E'): -2,
+('W', 'Q'): -1, ('H', 'G'): -2, ('S', 'K'): 0, ('Q', 'N'): 1,
+('N', 'R'): 0, ('H', 'C'): -4, ('Y', 'N'): -2, ('G', 'Q'): -2,
+('Y', 'F'): 4, ('C', 'A'): -2, ('V', 'L'): 2, ('G', 'E'): -3,
+('G', 'A'): 1, ('K', 'R'): 3, ('E', 'D'): 2, ('Y', 'R'): -1,
+('M', 'Q'): -1, ('T', 'I'): -1, ('C', 'D'): -2, ('V', 'F'): 0,
+('T', 'A'): 0, ('T', 'P'): 0, ('B', 'P'): -2, ('T', 'E'): -1,
+('V', 'N'): -3, ('P', 'G'): -1, ('M', 'A'): -1, ('K', 'H'): -1,
+('V', 'R'): -2, ('P', 'C'): -5, ('M', 'E'): -2, ('K', 'L'): -2,
+('V', 'V'): 5, ('M', 'I'): 1, ('T', 'Q'): -1, ('I', 'G'): -4,
+('P', 'K'): -1, ('M', 'M'): 7, ('K', 'D'): 0, ('I', 'C'): -4,
+('Z', 'D'): 1, ('F', 'R'): -2, ('X', 'K'): -1, ('Q', 'D'): -1,
+('X', 'G'): -1, ('Z', 'L'): -2, ('X', 'C'): -2, ('Z', 'H'): 0,
+('B', 'L'): -3, ('B', 'H'): 0, ('F', 'F'): 9, ('X', 'W'): -2,
+('B', 'D'): 6, ('D', 'A'): -1, ('S', 'L'): -3, ('X', 'S'): 0,
+('F', 'N'): -3, ('S', 'R'): -1, ('W', 'D'): -5, ('V', 'Y'): -1,
+('W', 'L'): -1, ('H', 'R'): 0, ('W', 'H'): -5, ('H', 'N'): 1,
+('W', 'T'): -4, ('T', 'T'): 6, ('S', 'F'): -2, ('W', 'P'): -4,
+('L', 'D'): -3, ('B', 'I'): -3, ('L', 'H'): -2, ('S', 'N'): 1,
+('B', 'T'): 0, ('L', 'L'): 6, ('Y', 'K'): -1, ('E', 'Q'): 2,
+('Y', 'G'): -3, ('Z', 'S'): 0, ('Y', 'C'): -4, ('G', 'D'): -2,
+('B', 'V'): -3, ('E', 'A'): -1, ('Y', 'W'): 3, ('E', 'E'): 7,
+('Y', 'S'): -2, ('C', 'N'): -2, ('V', 'C'): -2, ('T', 'H'): -2,
+('P', 'R'): -3, ('V', 'G'): -4, ('T', 'L'): -1, ('V', 'K'): -2,
+('K', 'Q'): 1, ('R', 'A'): -2, ('I', 'R'): -3, ('T', 'D'): -1,
+('P', 'F'): -4, ('I', 'N'): -2, ('K', 'I'): -3, ('M', 'D'): -3,
+('V', 'W'): -3, ('W', 'W'): 19, ('M', 'H'): 1, ('P', 'N'): -2,
+('K', 'A'): -1, ('M', 'L'): 3, ('K', 'E'): 1, ('Z', 'E'): 5,
+('X', 'N'): -1, ('Z', 'A'): -1, ('Z', 'M'): -2, ('X', 'F'): -1,
+('K', 'C'): -3, ('B', 'Q'): 0, ('X', 'B'): -1, ('B', 'M'): -3,
+('F', 'C'): -2, ('Z', 'Q'): 4, ('X', 'Z'): -1, ('F', 'G'): -3,
+('B', 'E'): 1, ('X', 'V'): -1, ('F', 'K'): -3, ('B', 'A'): -1,
+('X', 'R'): -1, ('D', 'D'): 9, ('W', 'G'): -2, ('Z', 'F'): -4,
+('S', 'Q'): 1, ('W', 'C'): -6, ('W', 'K'): -2, ('H', 'Q'): 0,
+('L', 'C'): -2, ('W', 'N'): -4, ('S', 'A'): 1, ('L', 'G'): -4,
+('W', 'S'): -5, ('S', 'E'): 0, ('H', 'E'): 0, ('S', 'I'): -2,
+('H', 'A'): -2, ('S', 'M'): -2, ('Y', 'L'): 0, ('Y', 'H'): 2,
+('Y', 'D'): -3, ('E', 'R'): -1, ('X', 'P'): -2, ('G', 'G'): 8,
+('G', 'C'): -3, ('E', 'N'): -1, ('Y', 'T'): -1, ('Y', 'P'): -3,
+('T', 'K'): 0, ('A', 'A'): 5, ('P', 'Q'): -2, ('T', 'C'): -1,
+('V', 'H'): -4, ('T', 'G'): -2, ('I', 'Q'): -3, ('Z', 'T'): -1,
+('C', 'R'): -3, ('V', 'P'): -3, ('P', 'E'): 0, ('M', 'C'): -3,
+('K', 'N'): 0, ('I', 'I'): 6, ('P', 'A'): -2, ('M', 'G'): -2,
+('T', 'S'): 2, ('I', 'E'): -4, ('P', 'M'): -2, ('M', 'K'): -1,
+('I', 'A'): -1, ('P', 'I'): -2, ('R', 'R'): 9, ('X', 'M'): 0,
+('L', 'I'): 2, ('X', 'I'): -1, ('Z', 'B'): 2, ('X', 'E'): -1,
+('Z', 'N'): 0, ('X', 'A'): 0, ('B', 'R'): -1, ('B', 'N'): 4,
+('F', 'D'): -4, ('X', 'Y'): -1, ('Z', 'R'): 0, ('F', 'H'): -2,
+('B', 'F'): -3, ('F', 'L'): 2, ('X', 'Q'): -1, ('B', 'B'): 5
}
blosum40 = _temp()
del _temp
@@ -529,75 +528,75 @@ del _temp
# http://www.embl-heidelberg.de/~vogt/matrices/blosum45.cmp
def _temp():
return {
-('W', 'F') : 1, ('L', 'R') : -2, ('S', 'P') : -1, ('V', 'T') : 0,
-('Q', 'Q') : 6, ('N', 'A') : -1, ('Z', 'Y') : -2, ('W', 'R') : -2,
-('Q', 'A') : -1, ('S', 'D') : 0, ('H', 'H') : 10, ('S', 'H') : -1,
-('H', 'D') : 0, ('L', 'N') : -3, ('W', 'A') : -2, ('Y', 'M') : 0,
-('G', 'R') : -2, ('Y', 'I') : 0, ('Y', 'E') : -2, ('B', 'Y') : -2,
-('Y', 'A') : -2, ('V', 'D') : -3, ('B', 'S') : 0, ('Y', 'Y') : 8,
-('G', 'N') : 0, ('E', 'C') : -3, ('Y', 'Q') : -1, ('Z', 'Z') : 4,
-('V', 'A') : 0, ('C', 'C') : 12, ('M', 'R') : -1, ('V', 'E') : -3,
-('T', 'N') : 0, ('P', 'P') : 9, ('V', 'I') : 3, ('V', 'S') : -1,
-('Z', 'P') : -1, ('V', 'M') : 1, ('T', 'F') : -1, ('V', 'Q') : -3,
-('K', 'K') : 5, ('P', 'D') : -1, ('I', 'H') : -3, ('I', 'D') : -4,
-('T', 'R') : -1, ('P', 'L') : -3, ('K', 'G') : -2, ('M', 'N') : -2,
-('P', 'H') : -2, ('F', 'Q') : -4, ('Z', 'G') : -2, ('X', 'L') : -1,
-('T', 'M') : -1, ('Z', 'C') : -3, ('X', 'H') : -1, ('D', 'R') : -1,
-('B', 'W') : -4, ('X', 'D') : -1, ('Z', 'K') : 1, ('F', 'A') : -2,
-('Z', 'W') : -2, ('F', 'E') : -3, ('D', 'N') : 2, ('B', 'K') : 0,
-('X', 'X') : -1, ('F', 'I') : 0, ('B', 'G') : -1, ('X', 'T') : 0,
-('F', 'M') : 0, ('B', 'C') : -2, ('Z', 'I') : -3, ('Z', 'V') : -3,
-('S', 'S') : 4, ('L', 'Q') : -2, ('W', 'E') : -3, ('Q', 'R') : 1,
-('N', 'N') : 6, ('W', 'M') : -2, ('Q', 'C') : -3, ('W', 'I') : -2,
-('S', 'C') : -1, ('L', 'A') : -1, ('S', 'G') : 0, ('L', 'E') : -2,
-('W', 'Q') : -2, ('H', 'G') : -2, ('S', 'K') : -1, ('Q', 'N') : 0,
-('N', 'R') : 0, ('H', 'C') : -3, ('Y', 'N') : -2, ('G', 'Q') : -2,
-('Y', 'F') : 3, ('C', 'A') : -1, ('V', 'L') : 1, ('G', 'E') : -2,
-('G', 'A') : 0, ('K', 'R') : 3, ('E', 'D') : 2, ('Y', 'R') : -1,
-('M', 'Q') : 0, ('T', 'I') : -1, ('C', 'D') : -3, ('V', 'F') : 0,
-('T', 'A') : 0, ('T', 'P') : -1, ('B', 'P') : -2, ('T', 'E') : -1,
-('V', 'N') : -3, ('P', 'G') : -2, ('M', 'A') : -1, ('K', 'H') : -1,
-('V', 'R') : -2, ('P', 'C') : -4, ('M', 'E') : -2, ('K', 'L') : -3,
-('V', 'V') : 5, ('M', 'I') : 2, ('T', 'Q') : -1, ('I', 'G') : -4,
-('P', 'K') : -1, ('M', 'M') : 6, ('K', 'D') : 0, ('I', 'C') : -3,
-('Z', 'D') : 1, ('F', 'R') : -2, ('X', 'K') : -1, ('Q', 'D') : 0,
-('X', 'G') : -1, ('Z', 'L') : -2, ('X', 'C') : -2, ('Z', 'H') : 0,
-('B', 'L') : -3, ('B', 'H') : 0, ('F', 'F') : 8, ('X', 'W') : -2,
-('B', 'D') : 5, ('D', 'A') : -2, ('S', 'L') : -3, ('X', 'S') : 0,
-('F', 'N') : -2, ('S', 'R') : -1, ('W', 'D') : -4, ('V', 'Y') : -1,
-('W', 'L') : -2, ('H', 'R') : 0, ('W', 'H') : -3, ('H', 'N') : 1,
-('W', 'T') : -3, ('T', 'T') : 5, ('S', 'F') : -2, ('W', 'P') : -3,
-('L', 'D') : -3, ('B', 'I') : -3, ('L', 'H') : -2, ('S', 'N') : 1,
-('B', 'T') : 0, ('L', 'L') : 5, ('Y', 'K') : -1, ('E', 'Q') : 2,
-('Y', 'G') : -3, ('Z', 'S') : 0, ('Y', 'C') : -3, ('G', 'D') : -1,
-('B', 'V') : -3, ('E', 'A') : -1, ('Y', 'W') : 3, ('E', 'E') : 6,
-('Y', 'S') : -2, ('C', 'N') : -2, ('V', 'C') : -1, ('T', 'H') : -2,
-('P', 'R') : -2, ('V', 'G') : -3, ('T', 'L') : -1, ('V', 'K') : -2,
-('K', 'Q') : 1, ('R', 'A') : -2, ('I', 'R') : -3, ('T', 'D') : -1,
-('P', 'F') : -3, ('I', 'N') : -2, ('K', 'I') : -3, ('M', 'D') : -3,
-('V', 'W') : -3, ('W', 'W') : 15, ('M', 'H') : 0, ('P', 'N') : -2,
-('K', 'A') : -1, ('M', 'L') : 2, ('K', 'E') : 1, ('Z', 'E') : 4,
-('X', 'N') : -1, ('Z', 'A') : -1, ('Z', 'M') : -1, ('X', 'F') : -1,
-('K', 'C') : -3, ('B', 'Q') : 0, ('X', 'B') : -1, ('B', 'M') : -2,
-('F', 'C') : -2, ('Z', 'Q') : 4, ('X', 'Z') : -1, ('F', 'G') : -3,
-('B', 'E') : 1, ('X', 'V') : -1, ('F', 'K') : -3, ('B', 'A') : -1,
-('X', 'R') : -1, ('D', 'D') : 7, ('W', 'G') : -2, ('Z', 'F') : -3,
-('S', 'Q') : 0, ('W', 'C') : -5, ('W', 'K') : -2, ('H', 'Q') : 1,
-('L', 'C') : -2, ('W', 'N') : -4, ('S', 'A') : 1, ('L', 'G') : -3,
-('W', 'S') : -4, ('S', 'E') : 0, ('H', 'E') : 0, ('S', 'I') : -2,
-('H', 'A') : -2, ('S', 'M') : -2, ('Y', 'L') : 0, ('Y', 'H') : 2,
-('Y', 'D') : -2, ('E', 'R') : 0, ('X', 'P') : -1, ('G', 'G') : 7,
-('G', 'C') : -3, ('E', 'N') : 0, ('Y', 'T') : -1, ('Y', 'P') : -3,
-('T', 'K') : -1, ('A', 'A') : 5, ('P', 'Q') : -1, ('T', 'C') : -1,
-('V', 'H') : -3, ('T', 'G') : -2, ('I', 'Q') : -2, ('Z', 'T') : -1,
-('C', 'R') : -3, ('V', 'P') : -3, ('P', 'E') : 0, ('M', 'C') : -2,
-('K', 'N') : 0, ('I', 'I') : 5, ('P', 'A') : -1, ('M', 'G') : -2,
-('T', 'S') : 2, ('I', 'E') : -3, ('P', 'M') : -2, ('M', 'K') : -1,
-('I', 'A') : -1, ('P', 'I') : -2, ('R', 'R') : 7, ('X', 'M') : -1,
-('L', 'I') : 2, ('X', 'I') : -1, ('Z', 'B') : 2, ('X', 'E') : -1,
-('Z', 'N') : 0, ('X', 'A') : 0, ('B', 'R') : -1, ('B', 'N') : 4,
-('F', 'D') : -4, ('X', 'Y') : -1, ('Z', 'R') : 0, ('F', 'H') : -2,
-('B', 'F') : -3, ('F', 'L') : 1, ('X', 'Q') : -1, ('B', 'B') : 4
+('W', 'F'): 1, ('L', 'R'): -2, ('S', 'P'): -1, ('V', 'T'): 0,
+('Q', 'Q'): 6, ('N', 'A'): -1, ('Z', 'Y'): -2, ('W', 'R'): -2,
+('Q', 'A'): -1, ('S', 'D'): 0, ('H', 'H'): 10, ('S', 'H'): -1,
+('H', 'D'): 0, ('L', 'N'): -3, ('W', 'A'): -2, ('Y', 'M'): 0,
+('G', 'R'): -2, ('Y', 'I'): 0, ('Y', 'E'): -2, ('B', 'Y'): -2,
+('Y', 'A'): -2, ('V', 'D'): -3, ('B', 'S'): 0, ('Y', 'Y'): 8,
+('G', 'N'): 0, ('E', 'C'): -3, ('Y', 'Q'): -1, ('Z', 'Z'): 4,
+('V', 'A'): 0, ('C', 'C'): 12, ('M', 'R'): -1, ('V', 'E'): -3,
+('T', 'N'): 0, ('P', 'P'): 9, ('V', 'I'): 3, ('V', 'S'): -1,
+('Z', 'P'): -1, ('V', 'M'): 1, ('T', 'F'): -1, ('V', 'Q'): -3,
+('K', 'K'): 5, ('P', 'D'): -1, ('I', 'H'): -3, ('I', 'D'): -4,
+('T', 'R'): -1, ('P', 'L'): -3, ('K', 'G'): -2, ('M', 'N'): -2,
+('P', 'H'): -2, ('F', 'Q'): -4, ('Z', 'G'): -2, ('X', 'L'): -1,
+('T', 'M'): -1, ('Z', 'C'): -3, ('X', 'H'): -1, ('D', 'R'): -1,
+('B', 'W'): -4, ('X', 'D'): -1, ('Z', 'K'): 1, ('F', 'A'): -2,
+('Z', 'W'): -2, ('F', 'E'): -3, ('D', 'N'): 2, ('B', 'K'): 0,
+('X', 'X'): -1, ('F', 'I'): 0, ('B', 'G'): -1, ('X', 'T'): 0,
+('F', 'M'): 0, ('B', 'C'): -2, ('Z', 'I'): -3, ('Z', 'V'): -3,
+('S', 'S'): 4, ('L', 'Q'): -2, ('W', 'E'): -3, ('Q', 'R'): 1,
+('N', 'N'): 6, ('W', 'M'): -2, ('Q', 'C'): -3, ('W', 'I'): -2,
+('S', 'C'): -1, ('L', 'A'): -1, ('S', 'G'): 0, ('L', 'E'): -2,
+('W', 'Q'): -2, ('H', 'G'): -2, ('S', 'K'): -1, ('Q', 'N'): 0,
+('N', 'R'): 0, ('H', 'C'): -3, ('Y', 'N'): -2, ('G', 'Q'): -2,
+('Y', 'F'): 3, ('C', 'A'): -1, ('V', 'L'): 1, ('G', 'E'): -2,
+('G', 'A'): 0, ('K', 'R'): 3, ('E', 'D'): 2, ('Y', 'R'): -1,
+('M', 'Q'): 0, ('T', 'I'): -1, ('C', 'D'): -3, ('V', 'F'): 0,
+('T', 'A'): 0, ('T', 'P'): -1, ('B', 'P'): -2, ('T', 'E'): -1,
+('V', 'N'): -3, ('P', 'G'): -2, ('M', 'A'): -1, ('K', 'H'): -1,
+('V', 'R'): -2, ('P', 'C'): -4, ('M', 'E'): -2, ('K', 'L'): -3,
+('V', 'V'): 5, ('M', 'I'): 2, ('T', 'Q'): -1, ('I', 'G'): -4,
+('P', 'K'): -1, ('M', 'M'): 6, ('K', 'D'): 0, ('I', 'C'): -3,
+('Z', 'D'): 1, ('F', 'R'): -2, ('X', 'K'): -1, ('Q', 'D'): 0,
+('X', 'G'): -1, ('Z', 'L'): -2, ('X', 'C'): -2, ('Z', 'H'): 0,
+('B', 'L'): -3, ('B', 'H'): 0, ('F', 'F'): 8, ('X', 'W'): -2,
+('B', 'D'): 5, ('D', 'A'): -2, ('S', 'L'): -3, ('X', 'S'): 0,
+('F', 'N'): -2, ('S', 'R'): -1, ('W', 'D'): -4, ('V', 'Y'): -1,
+('W', 'L'): -2, ('H', 'R'): 0, ('W', 'H'): -3, ('H', 'N'): 1,
+('W', 'T'): -3, ('T', 'T'): 5, ('S', 'F'): -2, ('W', 'P'): -3,
+('L', 'D'): -3, ('B', 'I'): -3, ('L', 'H'): -2, ('S', 'N'): 1,
+('B', 'T'): 0, ('L', 'L'): 5, ('Y', 'K'): -1, ('E', 'Q'): 2,
+('Y', 'G'): -3, ('Z', 'S'): 0, ('Y', 'C'): -3, ('G', 'D'): -1,
+('B', 'V'): -3, ('E', 'A'): -1, ('Y', 'W'): 3, ('E', 'E'): 6,
+('Y', 'S'): -2, ('C', 'N'): -2, ('V', 'C'): -1, ('T', 'H'): -2,
+('P', 'R'): -2, ('V', 'G'): -3, ('T', 'L'): -1, ('V', 'K'): -2,
+('K', 'Q'): 1, ('R', 'A'): -2, ('I', 'R'): -3, ('T', 'D'): -1,
+('P', 'F'): -3, ('I', 'N'): -2, ('K', 'I'): -3, ('M', 'D'): -3,
+('V', 'W'): -3, ('W', 'W'): 15, ('M', 'H'): 0, ('P', 'N'): -2,
+('K', 'A'): -1, ('M', 'L'): 2, ('K', 'E'): 1, ('Z', 'E'): 4,
+('X', 'N'): -1, ('Z', 'A'): -1, ('Z', 'M'): -1, ('X', 'F'): -1,
+('K', 'C'): -3, ('B', 'Q'): 0, ('X', 'B'): -1, ('B', 'M'): -2,
+('F', 'C'): -2, ('Z', 'Q'): 4, ('X', 'Z'): -1, ('F', 'G'): -3,
+('B', 'E'): 1, ('X', 'V'): -1, ('F', 'K'): -3, ('B', 'A'): -1,
+('X', 'R'): -1, ('D', 'D'): 7, ('W', 'G'): -2, ('Z', 'F'): -3,
+('S', 'Q'): 0, ('W', 'C'): -5, ('W', 'K'): -2, ('H', 'Q'): 1,
+('L', 'C'): -2, ('W', 'N'): -4, ('S', 'A'): 1, ('L', 'G'): -3,
+('W', 'S'): -4, ('S', 'E'): 0, ('H', 'E'): 0, ('S', 'I'): -2,
+('H', 'A'): -2, ('S', 'M'): -2, ('Y', 'L'): 0, ('Y', 'H'): 2,
+('Y', 'D'): -2, ('E', 'R'): 0, ('X', 'P'): -1, ('G', 'G'): 7,
+('G', 'C'): -3, ('E', 'N'): 0, ('Y', 'T'): -1, ('Y', 'P'): -3,
+('T', 'K'): -1, ('A', 'A'): 5, ('P', 'Q'): -1, ('T', 'C'): -1,
+('V', 'H'): -3, ('T', 'G'): -2, ('I', 'Q'): -2, ('Z', 'T'): -1,
+('C', 'R'): -3, ('V', 'P'): -3, ('P', 'E'): 0, ('M', 'C'): -2,
+('K', 'N'): 0, ('I', 'I'): 5, ('P', 'A'): -1, ('M', 'G'): -2,
+('T', 'S'): 2, ('I', 'E'): -3, ('P', 'M'): -2, ('M', 'K'): -1,
+('I', 'A'): -1, ('P', 'I'): -2, ('R', 'R'): 7, ('X', 'M'): -1,
+('L', 'I'): 2, ('X', 'I'): -1, ('Z', 'B'): 2, ('X', 'E'): -1,
+('Z', 'N'): 0, ('X', 'A'): 0, ('B', 'R'): -1, ('B', 'N'): 4,
+('F', 'D'): -4, ('X', 'Y'): -1, ('Z', 'R'): 0, ('F', 'H'): -2,
+('B', 'F'): -3, ('F', 'L'): 1, ('X', 'Q'): -1, ('B', 'B'): 4
}
blosum45 = _temp()
del _temp
@@ -606,75 +605,75 @@ del _temp
# http://www.embl-heidelberg.de/~vogt/matrices/blosum50.cmp
def _temp():
return {
-('W', 'F') : 1, ('L', 'R') : -3, ('S', 'P') : -1, ('V', 'T') : 0,
-('Q', 'Q') : 7, ('N', 'A') : -1, ('Z', 'Y') : -2, ('W', 'R') : -3,
-('Q', 'A') : -1, ('S', 'D') : 0, ('H', 'H') : 10, ('S', 'H') : -1,
-('H', 'D') : -1, ('L', 'N') : -4, ('W', 'A') : -3, ('Y', 'M') : 0,
-('G', 'R') : -3, ('Y', 'I') : -1, ('Y', 'E') : -2, ('B', 'Y') : -3,
-('Y', 'A') : -2, ('V', 'D') : -4, ('B', 'S') : 0, ('Y', 'Y') : 8,
-('G', 'N') : 0, ('E', 'C') : -3, ('Y', 'Q') : -1, ('Z', 'Z') : 5,
-('V', 'A') : 0, ('C', 'C') : 13, ('M', 'R') : -2, ('V', 'E') : -3,
-('T', 'N') : 0, ('P', 'P') : 10, ('V', 'I') : 4, ('V', 'S') : -2,
-('Z', 'P') : -1, ('V', 'M') : 1, ('T', 'F') : -2, ('V', 'Q') : -3,
-('K', 'K') : 6, ('P', 'D') : -1, ('I', 'H') : -4, ('I', 'D') : -4,
-('T', 'R') : -1, ('P', 'L') : -4, ('K', 'G') : -2, ('M', 'N') : -2,
-('P', 'H') : -2, ('F', 'Q') : -4, ('Z', 'G') : -2, ('X', 'L') : -1,
-('T', 'M') : -1, ('Z', 'C') : -3, ('X', 'H') : -1, ('D', 'R') : -2,
-('B', 'W') : -5, ('X', 'D') : -1, ('Z', 'K') : 1, ('F', 'A') : -3,
-('Z', 'W') : -2, ('F', 'E') : -3, ('D', 'N') : 2, ('B', 'K') : 0,
-('X', 'X') : -1, ('F', 'I') : 0, ('B', 'G') : -1, ('X', 'T') : 0,
-('F', 'M') : 0, ('B', 'C') : -3, ('Z', 'I') : -3, ('Z', 'V') : -3,
-('S', 'S') : 5, ('L', 'Q') : -2, ('W', 'E') : -3, ('Q', 'R') : 1,
-('N', 'N') : 7, ('W', 'M') : -1, ('Q', 'C') : -3, ('W', 'I') : -3,
-('S', 'C') : -1, ('L', 'A') : -2, ('S', 'G') : 0, ('L', 'E') : -3,
-('W', 'Q') : -1, ('H', 'G') : -2, ('S', 'K') : 0, ('Q', 'N') : 0,
-('N', 'R') : -1, ('H', 'C') : -3, ('Y', 'N') : -2, ('G', 'Q') : -2,
-('Y', 'F') : 4, ('C', 'A') : -1, ('V', 'L') : 1, ('G', 'E') : -3,
-('G', 'A') : 0, ('K', 'R') : 3, ('E', 'D') : 2, ('Y', 'R') : -1,
-('M', 'Q') : 0, ('T', 'I') : -1, ('C', 'D') : -4, ('V', 'F') : -1,
-('T', 'A') : 0, ('T', 'P') : -1, ('B', 'P') : -2, ('T', 'E') : -1,
-('V', 'N') : -3, ('P', 'G') : -2, ('M', 'A') : -1, ('K', 'H') : 0,
-('V', 'R') : -3, ('P', 'C') : -4, ('M', 'E') : -2, ('K', 'L') : -3,
-('V', 'V') : 5, ('M', 'I') : 2, ('T', 'Q') : -1, ('I', 'G') : -4,
-('P', 'K') : -1, ('M', 'M') : 7, ('K', 'D') : -1, ('I', 'C') : -2,
-('Z', 'D') : 1, ('F', 'R') : -3, ('X', 'K') : -1, ('Q', 'D') : 0,
-('X', 'G') : -2, ('Z', 'L') : -3, ('X', 'C') : -2, ('Z', 'H') : 0,
-('B', 'L') : -4, ('B', 'H') : 0, ('F', 'F') : 8, ('X', 'W') : -3,
-('B', 'D') : 5, ('D', 'A') : -2, ('S', 'L') : -3, ('X', 'S') : -1,
-('F', 'N') : -4, ('S', 'R') : -1, ('W', 'D') : -5, ('V', 'Y') : -1,
-('W', 'L') : -2, ('H', 'R') : 0, ('W', 'H') : -3, ('H', 'N') : 1,
-('W', 'T') : -3, ('T', 'T') : 5, ('S', 'F') : -3, ('W', 'P') : -4,
-('L', 'D') : -4, ('B', 'I') : -4, ('L', 'H') : -3, ('S', 'N') : 1,
-('B', 'T') : 0, ('L', 'L') : 5, ('Y', 'K') : -2, ('E', 'Q') : 2,
-('Y', 'G') : -3, ('Z', 'S') : 0, ('Y', 'C') : -3, ('G', 'D') : -1,
-('B', 'V') : -4, ('E', 'A') : -1, ('Y', 'W') : 2, ('E', 'E') : 6,
-('Y', 'S') : -2, ('C', 'N') : -2, ('V', 'C') : -1, ('T', 'H') : -2,
-('P', 'R') : -3, ('V', 'G') : -4, ('T', 'L') : -1, ('V', 'K') : -3,
-('K', 'Q') : 2, ('R', 'A') : -2, ('I', 'R') : -4, ('T', 'D') : -1,
-('P', 'F') : -4, ('I', 'N') : -3, ('K', 'I') : -3, ('M', 'D') : -4,
-('V', 'W') : -3, ('W', 'W') : 15, ('M', 'H') : -1, ('P', 'N') : -2,
-('K', 'A') : -1, ('M', 'L') : 3, ('K', 'E') : 1, ('Z', 'E') : 5,
-('X', 'N') : -1, ('Z', 'A') : -1, ('Z', 'M') : -1, ('X', 'F') : -2,
-('K', 'C') : -3, ('B', 'Q') : 0, ('X', 'B') : -1, ('B', 'M') : -3,
-('F', 'C') : -2, ('Z', 'Q') : 4, ('X', 'Z') : -1, ('F', 'G') : -4,
-('B', 'E') : 1, ('X', 'V') : -1, ('F', 'K') : -4, ('B', 'A') : -2,
-('X', 'R') : -1, ('D', 'D') : 8, ('W', 'G') : -3, ('Z', 'F') : -4,
-('S', 'Q') : 0, ('W', 'C') : -5, ('W', 'K') : -3, ('H', 'Q') : 1,
-('L', 'C') : -2, ('W', 'N') : -4, ('S', 'A') : 1, ('L', 'G') : -4,
-('W', 'S') : -4, ('S', 'E') : -1, ('H', 'E') : 0, ('S', 'I') : -3,
-('H', 'A') : -2, ('S', 'M') : -2, ('Y', 'L') : -1, ('Y', 'H') : 2,
-('Y', 'D') : -3, ('E', 'R') : 0, ('X', 'P') : -2, ('G', 'G') : 8,
-('G', 'C') : -3, ('E', 'N') : 0, ('Y', 'T') : -2, ('Y', 'P') : -3,
-('T', 'K') : -1, ('A', 'A') : 5, ('P', 'Q') : -1, ('T', 'C') : -1,
-('V', 'H') : -4, ('T', 'G') : -2, ('I', 'Q') : -3, ('Z', 'T') : -1,
-('C', 'R') : -4, ('V', 'P') : -3, ('P', 'E') : -1, ('M', 'C') : -2,
-('K', 'N') : 0, ('I', 'I') : 5, ('P', 'A') : -1, ('M', 'G') : -3,
-('T', 'S') : 2, ('I', 'E') : -4, ('P', 'M') : -3, ('M', 'K') : -2,
-('I', 'A') : -1, ('P', 'I') : -3, ('R', 'R') : 7, ('X', 'M') : -1,
-('L', 'I') : 2, ('X', 'I') : -1, ('Z', 'B') : 2, ('X', 'E') : -1,
-('Z', 'N') : 0, ('X', 'A') : -1, ('B', 'R') : -1, ('B', 'N') : 4,
-('F', 'D') : -5, ('X', 'Y') : -1, ('Z', 'R') : 0, ('F', 'H') : -1,
-('B', 'F') : -4, ('F', 'L') : 1, ('X', 'Q') : -1, ('B', 'B') : 5
+('W', 'F'): 1, ('L', 'R'): -3, ('S', 'P'): -1, ('V', 'T'): 0,
+('Q', 'Q'): 7, ('N', 'A'): -1, ('Z', 'Y'): -2, ('W', 'R'): -3,
+('Q', 'A'): -1, ('S', 'D'): 0, ('H', 'H'): 10, ('S', 'H'): -1,
+('H', 'D'): -1, ('L', 'N'): -4, ('W', 'A'): -3, ('Y', 'M'): 0,
+('G', 'R'): -3, ('Y', 'I'): -1, ('Y', 'E'): -2, ('B', 'Y'): -3,
+('Y', 'A'): -2, ('V', 'D'): -4, ('B', 'S'): 0, ('Y', 'Y'): 8,
+('G', 'N'): 0, ('E', 'C'): -3, ('Y', 'Q'): -1, ('Z', 'Z'): 5,
+('V', 'A'): 0, ('C', 'C'): 13, ('M', 'R'): -2, ('V', 'E'): -3,
+('T', 'N'): 0, ('P', 'P'): 10, ('V', 'I'): 4, ('V', 'S'): -2,
+('Z', 'P'): -1, ('V', 'M'): 1, ('T', 'F'): -2, ('V', 'Q'): -3,
+('K', 'K'): 6, ('P', 'D'): -1, ('I', 'H'): -4, ('I', 'D'): -4,
+('T', 'R'): -1, ('P', 'L'): -4, ('K', 'G'): -2, ('M', 'N'): -2,
+('P', 'H'): -2, ('F', 'Q'): -4, ('Z', 'G'): -2, ('X', 'L'): -1,
+('T', 'M'): -1, ('Z', 'C'): -3, ('X', 'H'): -1, ('D', 'R'): -2,
+('B', 'W'): -5, ('X', 'D'): -1, ('Z', 'K'): 1, ('F', 'A'): -3,
+('Z', 'W'): -2, ('F', 'E'): -3, ('D', 'N'): 2, ('B', 'K'): 0,
+('X', 'X'): -1, ('F', 'I'): 0, ('B', 'G'): -1, ('X', 'T'): 0,
+('F', 'M'): 0, ('B', 'C'): -3, ('Z', 'I'): -3, ('Z', 'V'): -3,
+('S', 'S'): 5, ('L', 'Q'): -2, ('W', 'E'): -3, ('Q', 'R'): 1,
+('N', 'N'): 7, ('W', 'M'): -1, ('Q', 'C'): -3, ('W', 'I'): -3,
+('S', 'C'): -1, ('L', 'A'): -2, ('S', 'G'): 0, ('L', 'E'): -3,
+('W', 'Q'): -1, ('H', 'G'): -2, ('S', 'K'): 0, ('Q', 'N'): 0,
+('N', 'R'): -1, ('H', 'C'): -3, ('Y', 'N'): -2, ('G', 'Q'): -2,
+('Y', 'F'): 4, ('C', 'A'): -1, ('V', 'L'): 1, ('G', 'E'): -3,
+('G', 'A'): 0, ('K', 'R'): 3, ('E', 'D'): 2, ('Y', 'R'): -1,
+('M', 'Q'): 0, ('T', 'I'): -1, ('C', 'D'): -4, ('V', 'F'): -1,
+('T', 'A'): 0, ('T', 'P'): -1, ('B', 'P'): -2, ('T', 'E'): -1,
+('V', 'N'): -3, ('P', 'G'): -2, ('M', 'A'): -1, ('K', 'H'): 0,
+('V', 'R'): -3, ('P', 'C'): -4, ('M', 'E'): -2, ('K', 'L'): -3,
+('V', 'V'): 5, ('M', 'I'): 2, ('T', 'Q'): -1, ('I', 'G'): -4,
+('P', 'K'): -1, ('M', 'M'): 7, ('K', 'D'): -1, ('I', 'C'): -2,
+('Z', 'D'): 1, ('F', 'R'): -3, ('X', 'K'): -1, ('Q', 'D'): 0,
+('X', 'G'): -2, ('Z', 'L'): -3, ('X', 'C'): -2, ('Z', 'H'): 0,
+('B', 'L'): -4, ('B', 'H'): 0, ('F', 'F'): 8, ('X', 'W'): -3,
+('B', 'D'): 5, ('D', 'A'): -2, ('S', 'L'): -3, ('X', 'S'): -1,
+('F', 'N'): -4, ('S', 'R'): -1, ('W', 'D'): -5, ('V', 'Y'): -1,
+('W', 'L'): -2, ('H', 'R'): 0, ('W', 'H'): -3, ('H', 'N'): 1,
+('W', 'T'): -3, ('T', 'T'): 5, ('S', 'F'): -3, ('W', 'P'): -4,
+('L', 'D'): -4, ('B', 'I'): -4, ('L', 'H'): -3, ('S', 'N'): 1,
+('B', 'T'): 0, ('L', 'L'): 5, ('Y', 'K'): -2, ('E', 'Q'): 2,
+('Y', 'G'): -3, ('Z', 'S'): 0, ('Y', 'C'): -3, ('G', 'D'): -1,
+('B', 'V'): -4, ('E', 'A'): -1, ('Y', 'W'): 2, ('E', 'E'): 6,
+('Y', 'S'): -2, ('C', 'N'): -2, ('V', 'C'): -1, ('T', 'H'): -2,
+('P', 'R'): -3, ('V', 'G'): -4, ('T', 'L'): -1, ('V', 'K'): -3,
+('K', 'Q'): 2, ('R', 'A'): -2, ('I', 'R'): -4, ('T', 'D'): -1,
+('P', 'F'): -4, ('I', 'N'): -3, ('K', 'I'): -3, ('M', 'D'): -4,
+('V', 'W'): -3, ('W', 'W'): 15, ('M', 'H'): -1, ('P', 'N'): -2,
+('K', 'A'): -1, ('M', 'L'): 3, ('K', 'E'): 1, ('Z', 'E'): 5,
+('X', 'N'): -1, ('Z', 'A'): -1, ('Z', 'M'): -1, ('X', 'F'): -2,
+('K', 'C'): -3, ('B', 'Q'): 0, ('X', 'B'): -1, ('B', 'M'): -3,
+('F', 'C'): -2, ('Z', 'Q'): 4, ('X', 'Z'): -1, ('F', 'G'): -4,
+('B', 'E'): 1, ('X', 'V'): -1, ('F', 'K'): -4, ('B', 'A'): -2,
+('X', 'R'): -1, ('D', 'D'): 8, ('W', 'G'): -3, ('Z', 'F'): -4,
+('S', 'Q'): 0, ('W', 'C'): -5, ('W', 'K'): -3, ('H', 'Q'): 1,
+('L', 'C'): -2, ('W', 'N'): -4, ('S', 'A'): 1, ('L', 'G'): -4,
+('W', 'S'): -4, ('S', 'E'): -1, ('H', 'E'): 0, ('S', 'I'): -3,
+('H', 'A'): -2, ('S', 'M'): -2, ('Y', 'L'): -1, ('Y', 'H'): 2,
+('Y', 'D'): -3, ('E', 'R'): 0, ('X', 'P'): -2, ('G', 'G'): 8,
+('G', 'C'): -3, ('E', 'N'): 0, ('Y', 'T'): -2, ('Y', 'P'): -3,
+('T', 'K'): -1, ('A', 'A'): 5, ('P', 'Q'): -1, ('T', 'C'): -1,
+('V', 'H'): -4, ('T', 'G'): -2, ('I', 'Q'): -3, ('Z', 'T'): -1,
+('C', 'R'): -4, ('V', 'P'): -3, ('P', 'E'): -1, ('M', 'C'): -2,
+('K', 'N'): 0, ('I', 'I'): 5, ('P', 'A'): -1, ('M', 'G'): -3,
+('T', 'S'): 2, ('I', 'E'): -4, ('P', 'M'): -3, ('M', 'K'): -2,
+('I', 'A'): -1, ('P', 'I'): -3, ('R', 'R'): 7, ('X', 'M'): -1,
+('L', 'I'): 2, ('X', 'I'): -1, ('Z', 'B'): 2, ('X', 'E'): -1,
+('Z', 'N'): 0, ('X', 'A'): -1, ('B', 'R'): -1, ('B', 'N'): 4,
+('F', 'D'): -5, ('X', 'Y'): -1, ('Z', 'R'): 0, ('F', 'H'): -1,
+('B', 'F'): -4, ('F', 'L'): 1, ('X', 'Q'): -1, ('B', 'B'): 5
}
blosum50 = _temp()
del _temp
@@ -683,75 +682,75 @@ del _temp
# http://www.embl-heidelberg.de/~vogt/matrices/blosum55.cmp
def _temp():
return {
-('W', 'F') : 1, ('L', 'R') : -3, ('S', 'P') : -1, ('V', 'T') : 0,
-('Q', 'Q') : 7, ('N', 'A') : -1, ('Z', 'Y') : -2, ('W', 'R') : -3,
-('Q', 'A') : -1, ('S', 'D') : 0, ('H', 'H') : 10, ('S', 'H') : -1,
-('H', 'D') : -1, ('L', 'N') : -4, ('W', 'A') : -3, ('Y', 'M') : 0,
-('G', 'R') : -3, ('Y', 'I') : -1, ('Y', 'E') : -2, ('B', 'Y') : -3,
-('Y', 'A') : -2, ('V', 'D') : -4, ('B', 'S') : 0, ('Y', 'Y') : 8,
-('G', 'N') : 0, ('E', 'C') : -3, ('Y', 'Q') : -1, ('Z', 'Z') : 5,
-('V', 'A') : 0, ('C', 'C') : 13, ('M', 'R') : -2, ('V', 'E') : -3,
-('T', 'N') : 0, ('P', 'P') : 10, ('V', 'I') : 4, ('V', 'S') : -2,
-('Z', 'P') : -1, ('V', 'M') : 1, ('T', 'F') : -2, ('V', 'Q') : -3,
-('K', 'K') : 6, ('P', 'D') : -1, ('I', 'H') : -4, ('I', 'D') : -4,
-('T', 'R') : -1, ('P', 'L') : -4, ('K', 'G') : -2, ('M', 'N') : -2,
-('P', 'H') : -2, ('F', 'Q') : -4, ('Z', 'G') : -2, ('X', 'L') : -1,
-('T', 'M') : -1, ('Z', 'C') : -3, ('X', 'H') : -1, ('D', 'R') : -2,
-('B', 'W') : -5, ('X', 'D') : -1, ('Z', 'K') : 1, ('F', 'A') : -3,
-('Z', 'W') : -2, ('F', 'E') : -3, ('D', 'N') : 2, ('B', 'K') : 0,
-('X', 'X') : -1, ('F', 'I') : 0, ('B', 'G') : -1, ('X', 'T') : 0,
-('F', 'M') : 0, ('B', 'C') : -3, ('Z', 'I') : -3, ('Z', 'V') : -3,
-('S', 'S') : 5, ('L', 'Q') : -2, ('W', 'E') : -3, ('Q', 'R') : 1,
-('N', 'N') : 7, ('W', 'M') : -1, ('Q', 'C') : -3, ('W', 'I') : -3,
-('S', 'C') : -1, ('L', 'A') : -2, ('S', 'G') : 0, ('L', 'E') : -3,
-('W', 'Q') : -1, ('H', 'G') : -2, ('S', 'K') : 0, ('Q', 'N') : 0,
-('N', 'R') : -1, ('H', 'C') : -3, ('Y', 'N') : -2, ('G', 'Q') : -2,
-('Y', 'F') : 4, ('C', 'A') : -1, ('V', 'L') : 1, ('G', 'E') : -3,
-('G', 'A') : 0, ('K', 'R') : 3, ('E', 'D') : 2, ('Y', 'R') : -1,
-('M', 'Q') : 0, ('T', 'I') : -1, ('C', 'D') : -4, ('V', 'F') : -1,
-('T', 'A') : 0, ('T', 'P') : -1, ('B', 'P') : -2, ('T', 'E') : -1,
-('V', 'N') : -3, ('P', 'G') : -2, ('M', 'A') : -1, ('K', 'H') : 0,
-('V', 'R') : -3, ('P', 'C') : -4, ('M', 'E') : -2, ('K', 'L') : -3,
-('V', 'V') : 5, ('M', 'I') : 2, ('T', 'Q') : -1, ('I', 'G') : -4,
-('P', 'K') : -1, ('M', 'M') : 7, ('K', 'D') : -1, ('I', 'C') : -2,
-('Z', 'D') : 1, ('F', 'R') : -3, ('X', 'K') : -1, ('Q', 'D') : 0,
-('X', 'G') : -2, ('Z', 'L') : -3, ('X', 'C') : -2, ('Z', 'H') : 0,
-('B', 'L') : -4, ('B', 'H') : 0, ('F', 'F') : 8, ('X', 'W') : -3,
-('B', 'D') : 5, ('D', 'A') : -2, ('S', 'L') : -3, ('X', 'S') : -1,
-('F', 'N') : -4, ('S', 'R') : -1, ('W', 'D') : -5, ('V', 'Y') : -1,
-('W', 'L') : -2, ('H', 'R') : 0, ('W', 'H') : -3, ('H', 'N') : 1,
-('W', 'T') : -3, ('T', 'T') : 5, ('S', 'F') : -3, ('W', 'P') : -4,
-('L', 'D') : -4, ('B', 'I') : -4, ('L', 'H') : -3, ('S', 'N') : 1,
-('B', 'T') : 0, ('L', 'L') : 5, ('Y', 'K') : -2, ('E', 'Q') : 2,
-('Y', 'G') : -3, ('Z', 'S') : 0, ('Y', 'C') : -3, ('G', 'D') : -1,
-('B', 'V') : -4, ('E', 'A') : -1, ('Y', 'W') : 2, ('E', 'E') : 6,
-('Y', 'S') : -2, ('C', 'N') : -2, ('V', 'C') : -1, ('T', 'H') : -2,
-('P', 'R') : -3, ('V', 'G') : -4, ('T', 'L') : -1, ('V', 'K') : -3,
-('K', 'Q') : 2, ('R', 'A') : -2, ('I', 'R') : -4, ('T', 'D') : -1,
-('P', 'F') : -4, ('I', 'N') : -3, ('K', 'I') : -3, ('M', 'D') : -4,
-('V', 'W') : -3, ('W', 'W') : 15, ('M', 'H') : -1, ('P', 'N') : -2,
-('K', 'A') : -1, ('M', 'L') : 3, ('K', 'E') : 1, ('Z', 'E') : 5,
-('X', 'N') : -1, ('Z', 'A') : -1, ('Z', 'M') : -1, ('X', 'F') : -2,
-('K', 'C') : -3, ('B', 'Q') : 0, ('X', 'B') : -1, ('B', 'M') : -3,
-('F', 'C') : -2, ('Z', 'Q') : 4, ('X', 'Z') : -1, ('F', 'G') : -4,
-('B', 'E') : 1, ('X', 'V') : -1, ('F', 'K') : -4, ('B', 'A') : -2,
-('X', 'R') : -1, ('D', 'D') : 8, ('W', 'G') : -3, ('Z', 'F') : -4,
-('S', 'Q') : 0, ('W', 'C') : -5, ('W', 'K') : -3, ('H', 'Q') : 1,
-('L', 'C') : -2, ('W', 'N') : -4, ('S', 'A') : 1, ('L', 'G') : -4,
-('W', 'S') : -4, ('S', 'E') : -1, ('H', 'E') : 0, ('S', 'I') : -3,
-('H', 'A') : -2, ('S', 'M') : -2, ('Y', 'L') : -1, ('Y', 'H') : 2,
-('Y', 'D') : -3, ('E', 'R') : 0, ('X', 'P') : -2, ('G', 'G') : 8,
-('G', 'C') : -3, ('E', 'N') : 0, ('Y', 'T') : -2, ('Y', 'P') : -3,
-('T', 'K') : -1, ('A', 'A') : 5, ('P', 'Q') : -1, ('T', 'C') : -1,
-('V', 'H') : -4, ('T', 'G') : -2, ('I', 'Q') : -3, ('Z', 'T') : -1,
-('C', 'R') : -4, ('V', 'P') : -3, ('P', 'E') : -1, ('M', 'C') : -2,
-('K', 'N') : 0, ('I', 'I') : 5, ('P', 'A') : -1, ('M', 'G') : -3,
-('T', 'S') : 2, ('I', 'E') : -4, ('P', 'M') : -3, ('M', 'K') : -2,
-('I', 'A') : -1, ('P', 'I') : -3, ('R', 'R') : 7, ('X', 'M') : -1,
-('L', 'I') : 2, ('X', 'I') : -1, ('Z', 'B') : 2, ('X', 'E') : -1,
-('Z', 'N') : 0, ('X', 'A') : -1, ('B', 'R') : -1, ('B', 'N') : 4,
-('F', 'D') : -5, ('X', 'Y') : -1, ('Z', 'R') : 0, ('F', 'H') : -1,
-('B', 'F') : -4, ('F', 'L') : 1, ('X', 'Q') : -1, ('B', 'B') : 5
+('W', 'F'): 1, ('L', 'R'): -3, ('S', 'P'): -1, ('V', 'T'): 0,
+('Q', 'Q'): 7, ('N', 'A'): -1, ('Z', 'Y'): -2, ('W', 'R'): -3,
+('Q', 'A'): -1, ('S', 'D'): 0, ('H', 'H'): 10, ('S', 'H'): -1,
+('H', 'D'): -1, ('L', 'N'): -4, ('W', 'A'): -3, ('Y', 'M'): 0,
+('G', 'R'): -3, ('Y', 'I'): -1, ('Y', 'E'): -2, ('B', 'Y'): -3,
+('Y', 'A'): -2, ('V', 'D'): -4, ('B', 'S'): 0, ('Y', 'Y'): 8,
+('G', 'N'): 0, ('E', 'C'): -3, ('Y', 'Q'): -1, ('Z', 'Z'): 5,
+('V', 'A'): 0, ('C', 'C'): 13, ('M', 'R'): -2, ('V', 'E'): -3,
+('T', 'N'): 0, ('P', 'P'): 10, ('V', 'I'): 4, ('V', 'S'): -2,
+('Z', 'P'): -1, ('V', 'M'): 1, ('T', 'F'): -2, ('V', 'Q'): -3,
+('K', 'K'): 6, ('P', 'D'): -1, ('I', 'H'): -4, ('I', 'D'): -4,
+('T', 'R'): -1, ('P', 'L'): -4, ('K', 'G'): -2, ('M', 'N'): -2,
+('P', 'H'): -2, ('F', 'Q'): -4, ('Z', 'G'): -2, ('X', 'L'): -1,
+('T', 'M'): -1, ('Z', 'C'): -3, ('X', 'H'): -1, ('D', 'R'): -2,
+('B', 'W'): -5, ('X', 'D'): -1, ('Z', 'K'): 1, ('F', 'A'): -3,
+('Z', 'W'): -2, ('F', 'E'): -3, ('D', 'N'): 2, ('B', 'K'): 0,
+('X', 'X'): -1, ('F', 'I'): 0, ('B', 'G'): -1, ('X', 'T'): 0,
+('F', 'M'): 0, ('B', 'C'): -3, ('Z', 'I'): -3, ('Z', 'V'): -3,
+('S', 'S'): 5, ('L', 'Q'): -2, ('W', 'E'): -3, ('Q', 'R'): 1,
+('N', 'N'): 7, ('W', 'M'): -1, ('Q', 'C'): -3, ('W', 'I'): -3,
+('S', 'C'): -1, ('L', 'A'): -2, ('S', 'G'): 0, ('L', 'E'): -3,
+('W', 'Q'): -1, ('H', 'G'): -2, ('S', 'K'): 0, ('Q', 'N'): 0,
+('N', 'R'): -1, ('H', 'C'): -3, ('Y', 'N'): -2, ('G', 'Q'): -2,
+('Y', 'F'): 4, ('C', 'A'): -1, ('V', 'L'): 1, ('G', 'E'): -3,
+('G', 'A'): 0, ('K', 'R'): 3, ('E', 'D'): 2, ('Y', 'R'): -1,
+('M', 'Q'): 0, ('T', 'I'): -1, ('C', 'D'): -4, ('V', 'F'): -1,
+('T', 'A'): 0, ('T', 'P'): -1, ('B', 'P'): -2, ('T', 'E'): -1,
+('V', 'N'): -3, ('P', 'G'): -2, ('M', 'A'): -1, ('K', 'H'): 0,
+('V', 'R'): -3, ('P', 'C'): -4, ('M', 'E'): -2, ('K', 'L'): -3,
+('V', 'V'): 5, ('M', 'I'): 2, ('T', 'Q'): -1, ('I', 'G'): -4,
+('P', 'K'): -1, ('M', 'M'): 7, ('K', 'D'): -1, ('I', 'C'): -2,
+('Z', 'D'): 1, ('F', 'R'): -3, ('X', 'K'): -1, ('Q', 'D'): 0,
+('X', 'G'): -2, ('Z', 'L'): -3, ('X', 'C'): -2, ('Z', 'H'): 0,
+('B', 'L'): -4, ('B', 'H'): 0, ('F', 'F'): 8, ('X', 'W'): -3,
+('B', 'D'): 5, ('D', 'A'): -2, ('S', 'L'): -3, ('X', 'S'): -1,
+('F', 'N'): -4, ('S', 'R'): -1, ('W', 'D'): -5, ('V', 'Y'): -1,
+('W', 'L'): -2, ('H', 'R'): 0, ('W', 'H'): -3, ('H', 'N'): 1,
+('W', 'T'): -3, ('T', 'T'): 5, ('S', 'F'): -3, ('W', 'P'): -4,
+('L', 'D'): -4, ('B', 'I'): -4, ('L', 'H'): -3, ('S', 'N'): 1,
+('B', 'T'): 0, ('L', 'L'): 5, ('Y', 'K'): -2, ('E', 'Q'): 2,
+('Y', 'G'): -3, ('Z', 'S'): 0, ('Y', 'C'): -3, ('G', 'D'): -1,
+('B', 'V'): -4, ('E', 'A'): -1, ('Y', 'W'): 2, ('E', 'E'): 6,
+('Y', 'S'): -2, ('C', 'N'): -2, ('V', 'C'): -1, ('T', 'H'): -2,
+('P', 'R'): -3, ('V', 'G'): -4, ('T', 'L'): -1, ('V', 'K'): -3,
+('K', 'Q'): 2, ('R', 'A'): -2, ('I', 'R'): -4, ('T', 'D'): -1,
+('P', 'F'): -4, ('I', 'N'): -3, ('K', 'I'): -3, ('M', 'D'): -4,
+('V', 'W'): -3, ('W', 'W'): 15, ('M', 'H'): -1, ('P', 'N'): -2,
+('K', 'A'): -1, ('M', 'L'): 3, ('K', 'E'): 1, ('Z', 'E'): 5,
+('X', 'N'): -1, ('Z', 'A'): -1, ('Z', 'M'): -1, ('X', 'F'): -2,
+('K', 'C'): -3, ('B', 'Q'): 0, ('X', 'B'): -1, ('B', 'M'): -3,
+('F', 'C'): -2, ('Z', 'Q'): 4, ('X', 'Z'): -1, ('F', 'G'): -4,
+('B', 'E'): 1, ('X', 'V'): -1, ('F', 'K'): -4, ('B', 'A'): -2,
+('X', 'R'): -1, ('D', 'D'): 8, ('W', 'G'): -3, ('Z', 'F'): -4,
+('S', 'Q'): 0, ('W', 'C'): -5, ('W', 'K'): -3, ('H', 'Q'): 1,
+('L', 'C'): -2, ('W', 'N'): -4, ('S', 'A'): 1, ('L', 'G'): -4,
+('W', 'S'): -4, ('S', 'E'): -1, ('H', 'E'): 0, ('S', 'I'): -3,
+('H', 'A'): -2, ('S', 'M'): -2, ('Y', 'L'): -1, ('Y', 'H'): 2,
+('Y', 'D'): -3, ('E', 'R'): 0, ('X', 'P'): -2, ('G', 'G'): 8,
+('G', 'C'): -3, ('E', 'N'): 0, ('Y', 'T'): -2, ('Y', 'P'): -3,
+('T', 'K'): -1, ('A', 'A'): 5, ('P', 'Q'): -1, ('T', 'C'): -1,
+('V', 'H'): -4, ('T', 'G'): -2, ('I', 'Q'): -3, ('Z', 'T'): -1,
+('C', 'R'): -4, ('V', 'P'): -3, ('P', 'E'): -1, ('M', 'C'): -2,
+('K', 'N'): 0, ('I', 'I'): 5, ('P', 'A'): -1, ('M', 'G'): -3,
+('T', 'S'): 2, ('I', 'E'): -4, ('P', 'M'): -3, ('M', 'K'): -2,
+('I', 'A'): -1, ('P', 'I'): -3, ('R', 'R'): 7, ('X', 'M'): -1,
+('L', 'I'): 2, ('X', 'I'): -1, ('Z', 'B'): 2, ('X', 'E'): -1,
+('Z', 'N'): 0, ('X', 'A'): -1, ('B', 'R'): -1, ('B', 'N'): 4,
+('F', 'D'): -5, ('X', 'Y'): -1, ('Z', 'R'): 0, ('F', 'H'): -1,
+('B', 'F'): -4, ('F', 'L'): 1, ('X', 'Q'): -1, ('B', 'B'): 5
}
blosum55 = _temp()
del _temp
@@ -760,75 +759,75 @@ del _temp
# http://www.embl-heidelberg.de/~vogt/matrices/blosum60.cmp
def _temp():
return {
-('W', 'F') : 1, ('L', 'R') : -2, ('S', 'P') : -1, ('V', 'T') : 0,
-('Q', 'Q') : 5, ('N', 'A') : -1, ('Z', 'Y') : -2, ('W', 'R') : -3,
-('Q', 'A') : -1, ('S', 'D') : 0, ('H', 'H') : 7, ('S', 'H') : -1,
-('H', 'D') : -1, ('L', 'N') : -3, ('W', 'A') : -3, ('Y', 'M') : -1,
-('G', 'R') : -2, ('Y', 'I') : -1, ('Y', 'E') : -2, ('B', 'Y') : -2,
-('Y', 'A') : -2, ('V', 'D') : -3, ('B', 'S') : 0, ('Y', 'Y') : 6,
-('G', 'N') : 0, ('E', 'C') : -3, ('Y', 'Q') : -1, ('Z', 'Z') : 3,
-('V', 'A') : 0, ('C', 'C') : 9, ('M', 'R') : -1, ('V', 'E') : -2,
-('T', 'N') : 0, ('P', 'P') : 7, ('V', 'I') : 3, ('V', 'S') : -2,
-('Z', 'P') : -1, ('V', 'M') : 1, ('T', 'F') : -2, ('V', 'Q') : -2,
-('K', 'K') : 4, ('P', 'D') : -1, ('I', 'H') : -3, ('I', 'D') : -3,
-('T', 'R') : -1, ('P', 'L') : -3, ('K', 'G') : -1, ('M', 'N') : -2,
-('P', 'H') : -2, ('F', 'Q') : -3, ('Z', 'G') : -2, ('X', 'L') : -1,
-('T', 'M') : -1, ('Z', 'C') : -3, ('X', 'H') : -1, ('D', 'R') : -1,
-('B', 'W') : -4, ('X', 'D') : -1, ('Z', 'K') : 1, ('F', 'A') : -2,
-('Z', 'W') : -2, ('F', 'E') : -3, ('D', 'N') : 1, ('B', 'K') : 0,
-('X', 'X') : -1, ('F', 'I') : 0, ('B', 'G') : -1, ('X', 'T') : 0,
-('F', 'M') : 0, ('B', 'C') : -3, ('Z', 'I') : -3, ('Z', 'V') : -2,
-('S', 'S') : 4, ('L', 'Q') : -2, ('W', 'E') : -3, ('Q', 'R') : 1,
-('N', 'N') : 6, ('W', 'M') : -1, ('Q', 'C') : -3, ('W', 'I') : -2,
-('S', 'C') : -1, ('L', 'A') : -1, ('S', 'G') : 0, ('L', 'E') : -3,
-('W', 'Q') : -2, ('H', 'G') : -2, ('S', 'K') : 0, ('Q', 'N') : 0,
-('N', 'R') : 0, ('H', 'C') : -3, ('Y', 'N') : -2, ('G', 'Q') : -2,
-('Y', 'F') : 3, ('C', 'A') : 0, ('V', 'L') : 1, ('G', 'E') : -2,
-('G', 'A') : 0, ('K', 'R') : 2, ('E', 'D') : 2, ('Y', 'R') : -2,
-('M', 'Q') : 0, ('T', 'I') : -1, ('C', 'D') : -3, ('V', 'F') : -1,
-('T', 'A') : 0, ('T', 'P') : -1, ('B', 'P') : -2, ('T', 'E') : -1,
-('V', 'N') : -3, ('P', 'G') : -2, ('M', 'A') : -1, ('K', 'H') : -1,
-('V', 'R') : -2, ('P', 'C') : -3, ('M', 'E') : -2, ('K', 'L') : -2,
-('V', 'V') : 4, ('M', 'I') : 1, ('T', 'Q') : -1, ('I', 'G') : -3,
-('P', 'K') : -1, ('M', 'M') : 5, ('K', 'D') : -1, ('I', 'C') : -1,
-('Z', 'D') : 1, ('F', 'R') : -3, ('X', 'K') : -1, ('Q', 'D') : 0,
-('X', 'G') : -1, ('Z', 'L') : -2, ('X', 'C') : -2, ('Z', 'H') : 0,
-('B', 'L') : -3, ('B', 'H') : 0, ('F', 'F') : 6, ('X', 'W') : -2,
-('B', 'D') : 4, ('D', 'A') : -2, ('S', 'L') : -2, ('X', 'S') : 0,
-('F', 'N') : -3, ('S', 'R') : -1, ('W', 'D') : -4, ('V', 'Y') : -1,
-('W', 'L') : -2, ('H', 'R') : 0, ('W', 'H') : -2, ('H', 'N') : 1,
-('W', 'T') : -2, ('T', 'T') : 4, ('S', 'F') : -2, ('W', 'P') : -4,
-('L', 'D') : -3, ('B', 'I') : -3, ('L', 'H') : -3, ('S', 'N') : 1,
-('B', 'T') : 0, ('L', 'L') : 4, ('Y', 'K') : -2, ('E', 'Q') : 2,
-('Y', 'G') : -3, ('Z', 'S') : 0, ('Y', 'C') : -2, ('G', 'D') : -1,
-('B', 'V') : -3, ('E', 'A') : -1, ('Y', 'W') : 2, ('E', 'E') : 5,
-('Y', 'S') : -2, ('C', 'N') : -2, ('V', 'C') : -1, ('T', 'H') : -2,
-('P', 'R') : -2, ('V', 'G') : -3, ('T', 'L') : -1, ('V', 'K') : -2,
-('K', 'Q') : 1, ('R', 'A') : -1, ('I', 'R') : -3, ('T', 'D') : -1,
-('P', 'F') : -4, ('I', 'N') : -3, ('K', 'I') : -3, ('M', 'D') : -3,
-('V', 'W') : -3, ('W', 'W') : 10, ('M', 'H') : -1, ('P', 'N') : -2,
-('K', 'A') : -1, ('M', 'L') : 2, ('K', 'E') : 1, ('Z', 'E') : 4,
-('X', 'N') : -1, ('Z', 'A') : -1, ('Z', 'M') : -1, ('X', 'F') : -1,
-('K', 'C') : -3, ('B', 'Q') : 0, ('X', 'B') : -1, ('B', 'M') : -3,
-('F', 'C') : -2, ('Z', 'Q') : 3, ('X', 'Z') : -1, ('F', 'G') : -3,
-('B', 'E') : 1, ('X', 'V') : -1, ('F', 'K') : -3, ('B', 'A') : -2,
-('X', 'R') : -1, ('D', 'D') : 6, ('W', 'G') : -2, ('Z', 'F') : -3,
-('S', 'Q') : 0, ('W', 'C') : -2, ('W', 'K') : -3, ('H', 'Q') : 1,
-('L', 'C') : -1, ('W', 'N') : -4, ('S', 'A') : 1, ('L', 'G') : -4,
-('W', 'S') : -3, ('S', 'E') : 0, ('H', 'E') : 0, ('S', 'I') : -2,
-('H', 'A') : -2, ('S', 'M') : -1, ('Y', 'L') : -1, ('Y', 'H') : 2,
-('Y', 'D') : -3, ('E', 'R') : 0, ('X', 'P') : -2, ('G', 'G') : 6,
-('G', 'C') : -2, ('E', 'N') : 0, ('Y', 'T') : -2, ('Y', 'P') : -3,
-('T', 'K') : -1, ('A', 'A') : 4, ('P', 'Q') : -1, ('T', 'C') : -1,
-('V', 'H') : -3, ('T', 'G') : -2, ('I', 'Q') : -3, ('Z', 'T') : -1,
-('C', 'R') : -3, ('V', 'P') : -2, ('P', 'E') : -1, ('M', 'C') : -1,
-('K', 'N') : 0, ('I', 'I') : 4, ('P', 'A') : -1, ('M', 'G') : -2,
-('T', 'S') : 1, ('I', 'E') : -3, ('P', 'M') : -2, ('M', 'K') : -1,
-('I', 'A') : -1, ('P', 'I') : -3, ('R', 'R') : 5, ('X', 'M') : -1,
-('L', 'I') : 2, ('X', 'I') : -1, ('Z', 'B') : 1, ('X', 'E') : -1,
-('Z', 'N') : 0, ('X', 'A') : 0, ('B', 'R') : -1, ('B', 'N') : 3,
-('F', 'D') : -3, ('X', 'Y') : -1, ('Z', 'R') : 0, ('F', 'H') : -1,
-('B', 'F') : -3, ('F', 'L') : 0, ('X', 'Q') : -1, ('B', 'B') : 4
+('W', 'F'): 1, ('L', 'R'): -2, ('S', 'P'): -1, ('V', 'T'): 0,
+('Q', 'Q'): 5, ('N', 'A'): -1, ('Z', 'Y'): -2, ('W', 'R'): -3,
+('Q', 'A'): -1, ('S', 'D'): 0, ('H', 'H'): 7, ('S', 'H'): -1,
+('H', 'D'): -1, ('L', 'N'): -3, ('W', 'A'): -3, ('Y', 'M'): -1,
+('G', 'R'): -2, ('Y', 'I'): -1, ('Y', 'E'): -2, ('B', 'Y'): -2,
+('Y', 'A'): -2, ('V', 'D'): -3, ('B', 'S'): 0, ('Y', 'Y'): 6,
+('G', 'N'): 0, ('E', 'C'): -3, ('Y', 'Q'): -1, ('Z', 'Z'): 3,
+('V', 'A'): 0, ('C', 'C'): 9, ('M', 'R'): -1, ('V', 'E'): -2,
+('T', 'N'): 0, ('P', 'P'): 7, ('V', 'I'): 3, ('V', 'S'): -2,
+('Z', 'P'): -1, ('V', 'M'): 1, ('T', 'F'): -2, ('V', 'Q'): -2,
+('K', 'K'): 4, ('P', 'D'): -1, ('I', 'H'): -3, ('I', 'D'): -3,
+('T', 'R'): -1, ('P', 'L'): -3, ('K', 'G'): -1, ('M', 'N'): -2,
+('P', 'H'): -2, ('F', 'Q'): -3, ('Z', 'G'): -2, ('X', 'L'): -1,
+('T', 'M'): -1, ('Z', 'C'): -3, ('X', 'H'): -1, ('D', 'R'): -1,
+('B', 'W'): -4, ('X', 'D'): -1, ('Z', 'K'): 1, ('F', 'A'): -2,
+('Z', 'W'): -2, ('F', 'E'): -3, ('D', 'N'): 1, ('B', 'K'): 0,
+('X', 'X'): -1, ('F', 'I'): 0, ('B', 'G'): -1, ('X', 'T'): 0,
+('F', 'M'): 0, ('B', 'C'): -3, ('Z', 'I'): -3, ('Z', 'V'): -2,
+('S', 'S'): 4, ('L', 'Q'): -2, ('W', 'E'): -3, ('Q', 'R'): 1,
+('N', 'N'): 6, ('W', 'M'): -1, ('Q', 'C'): -3, ('W', 'I'): -2,
+('S', 'C'): -1, ('L', 'A'): -1, ('S', 'G'): 0, ('L', 'E'): -3,
+('W', 'Q'): -2, ('H', 'G'): -2, ('S', 'K'): 0, ('Q', 'N'): 0,
+('N', 'R'): 0, ('H', 'C'): -3, ('Y', 'N'): -2, ('G', 'Q'): -2,
+('Y', 'F'): 3, ('C', 'A'): 0, ('V', 'L'): 1, ('G', 'E'): -2,
+('G', 'A'): 0, ('K', 'R'): 2, ('E', 'D'): 2, ('Y', 'R'): -2,
+('M', 'Q'): 0, ('T', 'I'): -1, ('C', 'D'): -3, ('V', 'F'): -1,
+('T', 'A'): 0, ('T', 'P'): -1, ('B', 'P'): -2, ('T', 'E'): -1,
+('V', 'N'): -3, ('P', 'G'): -2, ('M', 'A'): -1, ('K', 'H'): -1,
+('V', 'R'): -2, ('P', 'C'): -3, ('M', 'E'): -2, ('K', 'L'): -2,
+('V', 'V'): 4, ('M', 'I'): 1, ('T', 'Q'): -1, ('I', 'G'): -3,
+('P', 'K'): -1, ('M', 'M'): 5, ('K', 'D'): -1, ('I', 'C'): -1,
+('Z', 'D'): 1, ('F', 'R'): -3, ('X', 'K'): -1, ('Q', 'D'): 0,
+('X', 'G'): -1, ('Z', 'L'): -2, ('X', 'C'): -2, ('Z', 'H'): 0,
+('B', 'L'): -3, ('B', 'H'): 0, ('F', 'F'): 6, ('X', 'W'): -2,
+('B', 'D'): 4, ('D', 'A'): -2, ('S', 'L'): -2, ('X', 'S'): 0,
+('F', 'N'): -3, ('S', 'R'): -1, ('W', 'D'): -4, ('V', 'Y'): -1,
+('W', 'L'): -2, ('H', 'R'): 0, ('W', 'H'): -2, ('H', 'N'): 1,
+('W', 'T'): -2, ('T', 'T'): 4, ('S', 'F'): -2, ('W', 'P'): -4,
+('L', 'D'): -3, ('B', 'I'): -3, ('L', 'H'): -3, ('S', 'N'): 1,
+('B', 'T'): 0, ('L', 'L'): 4, ('Y', 'K'): -2, ('E', 'Q'): 2,
+('Y', 'G'): -3, ('Z', 'S'): 0, ('Y', 'C'): -2, ('G', 'D'): -1,
+('B', 'V'): -3, ('E', 'A'): -1, ('Y', 'W'): 2, ('E', 'E'): 5,
+('Y', 'S'): -2, ('C', 'N'): -2, ('V', 'C'): -1, ('T', 'H'): -2,
+('P', 'R'): -2, ('V', 'G'): -3, ('T', 'L'): -1, ('V', 'K'): -2,
+('K', 'Q'): 1, ('R', 'A'): -1, ('I', 'R'): -3, ('T', 'D'): -1,
+('P', 'F'): -4, ('I', 'N'): -3, ('K', 'I'): -3, ('M', 'D'): -3,
+('V', 'W'): -3, ('W', 'W'): 10, ('M', 'H'): -1, ('P', 'N'): -2,
+('K', 'A'): -1, ('M', 'L'): 2, ('K', 'E'): 1, ('Z', 'E'): 4,
+('X', 'N'): -1, ('Z', 'A'): -1, ('Z', 'M'): -1, ('X', 'F'): -1,
+('K', 'C'): -3, ('B', 'Q'): 0, ('X', 'B'): -1, ('B', 'M'): -3,
+('F', 'C'): -2, ('Z', 'Q'): 3, ('X', 'Z'): -1, ('F', 'G'): -3,
+('B', 'E'): 1, ('X', 'V'): -1, ('F', 'K'): -3, ('B', 'A'): -2,
+('X', 'R'): -1, ('D', 'D'): 6, ('W', 'G'): -2, ('Z', 'F'): -3,
+('S', 'Q'): 0, ('W', 'C'): -2, ('W', 'K'): -3, ('H', 'Q'): 1,
+('L', 'C'): -1, ('W', 'N'): -4, ('S', 'A'): 1, ('L', 'G'): -4,
+('W', 'S'): -3, ('S', 'E'): 0, ('H', 'E'): 0, ('S', 'I'): -2,
+('H', 'A'): -2, ('S', 'M'): -1, ('Y', 'L'): -1, ('Y', 'H'): 2,
+('Y', 'D'): -3, ('E', 'R'): 0, ('X', 'P'): -2, ('G', 'G'): 6,
+('G', 'C'): -2, ('E', 'N'): 0, ('Y', 'T'): -2, ('Y', 'P'): -3,
+('T', 'K'): -1, ('A', 'A'): 4, ('P', 'Q'): -1, ('T', 'C'): -1,
+('V', 'H'): -3, ('T', 'G'): -2, ('I', 'Q'): -3, ('Z', 'T'): -1,
+('C', 'R'): -3, ('V', 'P'): -2, ('P', 'E'): -1, ('M', 'C'): -1,
+('K', 'N'): 0, ('I', 'I'): 4, ('P', 'A'): -1, ('M', 'G'): -2,
+('T', 'S'): 1, ('I', 'E'): -3, ('P', 'M'): -2, ('M', 'K'): -1,
+('I', 'A'): -1, ('P', 'I'): -3, ('R', 'R'): 5, ('X', 'M'): -1,
+('L', 'I'): 2, ('X', 'I'): -1, ('Z', 'B'): 1, ('X', 'E'): -1,
+('Z', 'N'): 0, ('X', 'A'): 0, ('B', 'R'): -1, ('B', 'N'): 3,
+('F', 'D'): -3, ('X', 'Y'): -1, ('Z', 'R'): 0, ('F', 'H'): -1,
+('B', 'F'): -3, ('F', 'L'): 0, ('X', 'Q'): -1, ('B', 'B'): 4
}
blosum60 = _temp()
del _temp
@@ -837,75 +836,75 @@ del _temp
# http://www.embl-heidelberg.de/~vogt/matrices/blosum62.cmp
def _temp():
return {
-('W', 'F') : 1, ('L', 'R') : -2, ('S', 'P') : -1, ('V', 'T') : 0,
-('Q', 'Q') : 5, ('N', 'A') : -2, ('Z', 'Y') : -2, ('W', 'R') : -3,
-('Q', 'A') : -1, ('S', 'D') : 0, ('H', 'H') : 8, ('S', 'H') : -1,
-('H', 'D') : -1, ('L', 'N') : -3, ('W', 'A') : -3, ('Y', 'M') : -1,
-('G', 'R') : -2, ('Y', 'I') : -1, ('Y', 'E') : -2, ('B', 'Y') : -3,
-('Y', 'A') : -2, ('V', 'D') : -3, ('B', 'S') : 0, ('Y', 'Y') : 7,
-('G', 'N') : 0, ('E', 'C') : -4, ('Y', 'Q') : -1, ('Z', 'Z') : 4,
-('V', 'A') : 0, ('C', 'C') : 9, ('M', 'R') : -1, ('V', 'E') : -2,
-('T', 'N') : 0, ('P', 'P') : 7, ('V', 'I') : 3, ('V', 'S') : -2,
-('Z', 'P') : -1, ('V', 'M') : 1, ('T', 'F') : -2, ('V', 'Q') : -2,
-('K', 'K') : 5, ('P', 'D') : -1, ('I', 'H') : -3, ('I', 'D') : -3,
-('T', 'R') : -1, ('P', 'L') : -3, ('K', 'G') : -2, ('M', 'N') : -2,
-('P', 'H') : -2, ('F', 'Q') : -3, ('Z', 'G') : -2, ('X', 'L') : -1,
-('T', 'M') : -1, ('Z', 'C') : -3, ('X', 'H') : -1, ('D', 'R') : -2,
-('B', 'W') : -4, ('X', 'D') : -1, ('Z', 'K') : 1, ('F', 'A') : -2,
-('Z', 'W') : -3, ('F', 'E') : -3, ('D', 'N') : 1, ('B', 'K') : 0,
-('X', 'X') : -1, ('F', 'I') : 0, ('B', 'G') : -1, ('X', 'T') : 0,
-('F', 'M') : 0, ('B', 'C') : -3, ('Z', 'I') : -3, ('Z', 'V') : -2,
-('S', 'S') : 4, ('L', 'Q') : -2, ('W', 'E') : -3, ('Q', 'R') : 1,
-('N', 'N') : 6, ('W', 'M') : -1, ('Q', 'C') : -3, ('W', 'I') : -3,
-('S', 'C') : -1, ('L', 'A') : -1, ('S', 'G') : 0, ('L', 'E') : -3,
-('W', 'Q') : -2, ('H', 'G') : -2, ('S', 'K') : 0, ('Q', 'N') : 0,
-('N', 'R') : 0, ('H', 'C') : -3, ('Y', 'N') : -2, ('G', 'Q') : -2,
-('Y', 'F') : 3, ('C', 'A') : 0, ('V', 'L') : 1, ('G', 'E') : -2,
-('G', 'A') : 0, ('K', 'R') : 2, ('E', 'D') : 2, ('Y', 'R') : -2,
-('M', 'Q') : 0, ('T', 'I') : -1, ('C', 'D') : -3, ('V', 'F') : -1,
-('T', 'A') : 0, ('T', 'P') : -1, ('B', 'P') : -2, ('T', 'E') : -1,
-('V', 'N') : -3, ('P', 'G') : -2, ('M', 'A') : -1, ('K', 'H') : -1,
-('V', 'R') : -3, ('P', 'C') : -3, ('M', 'E') : -2, ('K', 'L') : -2,
-('V', 'V') : 4, ('M', 'I') : 1, ('T', 'Q') : -1, ('I', 'G') : -4,
-('P', 'K') : -1, ('M', 'M') : 5, ('K', 'D') : -1, ('I', 'C') : -1,
-('Z', 'D') : 1, ('F', 'R') : -3, ('X', 'K') : -1, ('Q', 'D') : 0,
-('X', 'G') : -1, ('Z', 'L') : -3, ('X', 'C') : -2, ('Z', 'H') : 0,
-('B', 'L') : -4, ('B', 'H') : 0, ('F', 'F') : 6, ('X', 'W') : -2,
-('B', 'D') : 4, ('D', 'A') : -2, ('S', 'L') : -2, ('X', 'S') : 0,
-('F', 'N') : -3, ('S', 'R') : -1, ('W', 'D') : -4, ('V', 'Y') : -1,
-('W', 'L') : -2, ('H', 'R') : 0, ('W', 'H') : -2, ('H', 'N') : 1,
-('W', 'T') : -2, ('T', 'T') : 5, ('S', 'F') : -2, ('W', 'P') : -4,
-('L', 'D') : -4, ('B', 'I') : -3, ('L', 'H') : -3, ('S', 'N') : 1,
-('B', 'T') : -1, ('L', 'L') : 4, ('Y', 'K') : -2, ('E', 'Q') : 2,
-('Y', 'G') : -3, ('Z', 'S') : 0, ('Y', 'C') : -2, ('G', 'D') : -1,
-('B', 'V') : -3, ('E', 'A') : -1, ('Y', 'W') : 2, ('E', 'E') : 5,
-('Y', 'S') : -2, ('C', 'N') : -3, ('V', 'C') : -1, ('T', 'H') : -2,
-('P', 'R') : -2, ('V', 'G') : -3, ('T', 'L') : -1, ('V', 'K') : -2,
-('K', 'Q') : 1, ('R', 'A') : -1, ('I', 'R') : -3, ('T', 'D') : -1,
-('P', 'F') : -4, ('I', 'N') : -3, ('K', 'I') : -3, ('M', 'D') : -3,
-('V', 'W') : -3, ('W', 'W') : 11, ('M', 'H') : -2, ('P', 'N') : -2,
-('K', 'A') : -1, ('M', 'L') : 2, ('K', 'E') : 1, ('Z', 'E') : 4,
-('X', 'N') : -1, ('Z', 'A') : -1, ('Z', 'M') : -1, ('X', 'F') : -1,
-('K', 'C') : -3, ('B', 'Q') : 0, ('X', 'B') : -1, ('B', 'M') : -3,
-('F', 'C') : -2, ('Z', 'Q') : 3, ('X', 'Z') : -1, ('F', 'G') : -3,
-('B', 'E') : 1, ('X', 'V') : -1, ('F', 'K') : -3, ('B', 'A') : -2,
-('X', 'R') : -1, ('D', 'D') : 6, ('W', 'G') : -2, ('Z', 'F') : -3,
-('S', 'Q') : 0, ('W', 'C') : -2, ('W', 'K') : -3, ('H', 'Q') : 0,
-('L', 'C') : -1, ('W', 'N') : -4, ('S', 'A') : 1, ('L', 'G') : -4,
-('W', 'S') : -3, ('S', 'E') : 0, ('H', 'E') : 0, ('S', 'I') : -2,
-('H', 'A') : -2, ('S', 'M') : -1, ('Y', 'L') : -1, ('Y', 'H') : 2,
-('Y', 'D') : -3, ('E', 'R') : 0, ('X', 'P') : -2, ('G', 'G') : 6,
-('G', 'C') : -3, ('E', 'N') : 0, ('Y', 'T') : -2, ('Y', 'P') : -3,
-('T', 'K') : -1, ('A', 'A') : 4, ('P', 'Q') : -1, ('T', 'C') : -1,
-('V', 'H') : -3, ('T', 'G') : -2, ('I', 'Q') : -3, ('Z', 'T') : -1,
-('C', 'R') : -3, ('V', 'P') : -2, ('P', 'E') : -1, ('M', 'C') : -1,
-('K', 'N') : 0, ('I', 'I') : 4, ('P', 'A') : -1, ('M', 'G') : -3,
-('T', 'S') : 1, ('I', 'E') : -3, ('P', 'M') : -2, ('M', 'K') : -1,
-('I', 'A') : -1, ('P', 'I') : -3, ('R', 'R') : 5, ('X', 'M') : -1,
-('L', 'I') : 2, ('X', 'I') : -1, ('Z', 'B') : 1, ('X', 'E') : -1,
-('Z', 'N') : 0, ('X', 'A') : 0, ('B', 'R') : -1, ('B', 'N') : 3,
-('F', 'D') : -3, ('X', 'Y') : -1, ('Z', 'R') : 0, ('F', 'H') : -1,
-('B', 'F') : -3, ('F', 'L') : 0, ('X', 'Q') : -1, ('B', 'B') : 4
+('W', 'F'): 1, ('L', 'R'): -2, ('S', 'P'): -1, ('V', 'T'): 0,
+('Q', 'Q'): 5, ('N', 'A'): -2, ('Z', 'Y'): -2, ('W', 'R'): -3,
+('Q', 'A'): -1, ('S', 'D'): 0, ('H', 'H'): 8, ('S', 'H'): -1,
+('H', 'D'): -1, ('L', 'N'): -3, ('W', 'A'): -3, ('Y', 'M'): -1,
+('G', 'R'): -2, ('Y', 'I'): -1, ('Y', 'E'): -2, ('B', 'Y'): -3,
+('Y', 'A'): -2, ('V', 'D'): -3, ('B', 'S'): 0, ('Y', 'Y'): 7,
+('G', 'N'): 0, ('E', 'C'): -4, ('Y', 'Q'): -1, ('Z', 'Z'): 4,
+('V', 'A'): 0, ('C', 'C'): 9, ('M', 'R'): -1, ('V', 'E'): -2,
+('T', 'N'): 0, ('P', 'P'): 7, ('V', 'I'): 3, ('V', 'S'): -2,
+('Z', 'P'): -1, ('V', 'M'): 1, ('T', 'F'): -2, ('V', 'Q'): -2,
+('K', 'K'): 5, ('P', 'D'): -1, ('I', 'H'): -3, ('I', 'D'): -3,
+('T', 'R'): -1, ('P', 'L'): -3, ('K', 'G'): -2, ('M', 'N'): -2,
+('P', 'H'): -2, ('F', 'Q'): -3, ('Z', 'G'): -2, ('X', 'L'): -1,
+('T', 'M'): -1, ('Z', 'C'): -3, ('X', 'H'): -1, ('D', 'R'): -2,
+('B', 'W'): -4, ('X', 'D'): -1, ('Z', 'K'): 1, ('F', 'A'): -2,
+('Z', 'W'): -3, ('F', 'E'): -3, ('D', 'N'): 1, ('B', 'K'): 0,
+('X', 'X'): -1, ('F', 'I'): 0, ('B', 'G'): -1, ('X', 'T'): 0,
+('F', 'M'): 0, ('B', 'C'): -3, ('Z', 'I'): -3, ('Z', 'V'): -2,
+('S', 'S'): 4, ('L', 'Q'): -2, ('W', 'E'): -3, ('Q', 'R'): 1,
+('N', 'N'): 6, ('W', 'M'): -1, ('Q', 'C'): -3, ('W', 'I'): -3,
+('S', 'C'): -1, ('L', 'A'): -1, ('S', 'G'): 0, ('L', 'E'): -3,
+('W', 'Q'): -2, ('H', 'G'): -2, ('S', 'K'): 0, ('Q', 'N'): 0,
+('N', 'R'): 0, ('H', 'C'): -3, ('Y', 'N'): -2, ('G', 'Q'): -2,
+('Y', 'F'): 3, ('C', 'A'): 0, ('V', 'L'): 1, ('G', 'E'): -2,
+('G', 'A'): 0, ('K', 'R'): 2, ('E', 'D'): 2, ('Y', 'R'): -2,
+('M', 'Q'): 0, ('T', 'I'): -1, ('C', 'D'): -3, ('V', 'F'): -1,
+('T', 'A'): 0, ('T', 'P'): -1, ('B', 'P'): -2, ('T', 'E'): -1,
+('V', 'N'): -3, ('P', 'G'): -2, ('M', 'A'): -1, ('K', 'H'): -1,
+('V', 'R'): -3, ('P', 'C'): -3, ('M', 'E'): -2, ('K', 'L'): -2,
+('V', 'V'): 4, ('M', 'I'): 1, ('T', 'Q'): -1, ('I', 'G'): -4,
+('P', 'K'): -1, ('M', 'M'): 5, ('K', 'D'): -1, ('I', 'C'): -1,
+('Z', 'D'): 1, ('F', 'R'): -3, ('X', 'K'): -1, ('Q', 'D'): 0,
+('X', 'G'): -1, ('Z', 'L'): -3, ('X', 'C'): -2, ('Z', 'H'): 0,
+('B', 'L'): -4, ('B', 'H'): 0, ('F', 'F'): 6, ('X', 'W'): -2,
+('B', 'D'): 4, ('D', 'A'): -2, ('S', 'L'): -2, ('X', 'S'): 0,
+('F', 'N'): -3, ('S', 'R'): -1, ('W', 'D'): -4, ('V', 'Y'): -1,
+('W', 'L'): -2, ('H', 'R'): 0, ('W', 'H'): -2, ('H', 'N'): 1,
+('W', 'T'): -2, ('T', 'T'): 5, ('S', 'F'): -2, ('W', 'P'): -4,
+('L', 'D'): -4, ('B', 'I'): -3, ('L', 'H'): -3, ('S', 'N'): 1,
+('B', 'T'): -1, ('L', 'L'): 4, ('Y', 'K'): -2, ('E', 'Q'): 2,
+('Y', 'G'): -3, ('Z', 'S'): 0, ('Y', 'C'): -2, ('G', 'D'): -1,
+('B', 'V'): -3, ('E', 'A'): -1, ('Y', 'W'): 2, ('E', 'E'): 5,
+('Y', 'S'): -2, ('C', 'N'): -3, ('V', 'C'): -1, ('T', 'H'): -2,
+('P', 'R'): -2, ('V', 'G'): -3, ('T', 'L'): -1, ('V', 'K'): -2,
+('K', 'Q'): 1, ('R', 'A'): -1, ('I', 'R'): -3, ('T', 'D'): -1,
+('P', 'F'): -4, ('I', 'N'): -3, ('K', 'I'): -3, ('M', 'D'): -3,
+('V', 'W'): -3, ('W', 'W'): 11, ('M', 'H'): -2, ('P', 'N'): -2,
+('K', 'A'): -1, ('M', 'L'): 2, ('K', 'E'): 1, ('Z', 'E'): 4,
+('X', 'N'): -1, ('Z', 'A'): -1, ('Z', 'M'): -1, ('X', 'F'): -1,
+('K', 'C'): -3, ('B', 'Q'): 0, ('X', 'B'): -1, ('B', 'M'): -3,
+('F', 'C'): -2, ('Z', 'Q'): 3, ('X', 'Z'): -1, ('F', 'G'): -3,
+('B', 'E'): 1, ('X', 'V'): -1, ('F', 'K'): -3, ('B', 'A'): -2,
+('X', 'R'): -1, ('D', 'D'): 6, ('W', 'G'): -2, ('Z', 'F'): -3,
+('S', 'Q'): 0, ('W', 'C'): -2, ('W', 'K'): -3, ('H', 'Q'): 0,
+('L', 'C'): -1, ('W', 'N'): -4, ('S', 'A'): 1, ('L', 'G'): -4,
+('W', 'S'): -3, ('S', 'E'): 0, ('H', 'E'): 0, ('S', 'I'): -2,
+('H', 'A'): -2, ('S', 'M'): -1, ('Y', 'L'): -1, ('Y', 'H'): 2,
+('Y', 'D'): -3, ('E', 'R'): 0, ('X', 'P'): -2, ('G', 'G'): 6,
+('G', 'C'): -3, ('E', 'N'): 0, ('Y', 'T'): -2, ('Y', 'P'): -3,
+('T', 'K'): -1, ('A', 'A'): 4, ('P', 'Q'): -1, ('T', 'C'): -1,
+('V', 'H'): -3, ('T', 'G'): -2, ('I', 'Q'): -3, ('Z', 'T'): -1,
+('C', 'R'): -3, ('V', 'P'): -2, ('P', 'E'): -1, ('M', 'C'): -1,
+('K', 'N'): 0, ('I', 'I'): 4, ('P', 'A'): -1, ('M', 'G'): -3,
+('T', 'S'): 1, ('I', 'E'): -3, ('P', 'M'): -2, ('M', 'K'): -1,
+('I', 'A'): -1, ('P', 'I'): -3, ('R', 'R'): 5, ('X', 'M'): -1,
+('L', 'I'): 2, ('X', 'I'): -1, ('Z', 'B'): 1, ('X', 'E'): -1,
+('Z', 'N'): 0, ('X', 'A'): 0, ('B', 'R'): -1, ('B', 'N'): 3,
+('F', 'D'): -3, ('X', 'Y'): -1, ('Z', 'R'): 0, ('F', 'H'): -1,
+('B', 'F'): -3, ('F', 'L'): 0, ('X', 'Q'): -1, ('B', 'B'): 4
}
blosum62 = _temp()
del _temp
@@ -914,75 +913,75 @@ del _temp
# http://www.embl-heidelberg.de/~vogt/matrices/blosum65.cmp
def _temp():
return {
-('W', 'F') : 1, ('L', 'R') : -2, ('S', 'P') : -1, ('V', 'T') : 0,
-('Q', 'Q') : 6, ('N', 'A') : -2, ('Z', 'Y') : -2, ('W', 'R') : -3,
-('Q', 'A') : -1, ('S', 'D') : 0, ('H', 'H') : 8, ('S', 'H') : -1,
-('H', 'D') : -1, ('L', 'N') : -4, ('W', 'A') : -3, ('Y', 'M') : -1,
-('G', 'R') : -2, ('Y', 'I') : -1, ('Y', 'E') : -2, ('B', 'Y') : -3,
-('Y', 'A') : -2, ('V', 'D') : -3, ('B', 'S') : 0, ('Y', 'Y') : 7,
-('G', 'N') : -1, ('E', 'C') : -4, ('Y', 'Q') : -2, ('Z', 'Z') : 4,
-('V', 'A') : 0, ('C', 'C') : 9, ('M', 'R') : -2, ('V', 'E') : -3,
-('T', 'N') : 0, ('P', 'P') : 8, ('V', 'I') : 3, ('V', 'S') : -2,
-('Z', 'P') : -1, ('V', 'M') : 1, ('T', 'F') : -2, ('V', 'Q') : -2,
-('K', 'K') : 5, ('P', 'D') : -2, ('I', 'H') : -3, ('I', 'D') : -3,
-('T', 'R') : -1, ('P', 'L') : -3, ('K', 'G') : -2, ('M', 'N') : -2,
-('P', 'H') : -2, ('F', 'Q') : -3, ('Z', 'G') : -2, ('X', 'L') : -1,
-('T', 'M') : -1, ('Z', 'C') : -4, ('X', 'H') : -1, ('D', 'R') : -2,
-('B', 'W') : -4, ('X', 'D') : -1, ('Z', 'K') : 1, ('F', 'A') : -2,
-('Z', 'W') : -3, ('F', 'E') : -3, ('D', 'N') : 1, ('B', 'K') : 0,
-('X', 'X') : -1, ('F', 'I') : 0, ('B', 'G') : -1, ('X', 'T') : -1,
-('F', 'M') : 0, ('B', 'C') : -3, ('Z', 'I') : -3, ('Z', 'V') : -2,
-('S', 'S') : 4, ('L', 'Q') : -2, ('W', 'E') : -3, ('Q', 'R') : 1,
-('N', 'N') : 6, ('W', 'M') : -2, ('Q', 'C') : -3, ('W', 'I') : -2,
-('S', 'C') : -1, ('L', 'A') : -2, ('S', 'G') : 0, ('L', 'E') : -3,
-('W', 'Q') : -2, ('H', 'G') : -2, ('S', 'K') : 0, ('Q', 'N') : 0,
-('N', 'R') : 0, ('H', 'C') : -3, ('Y', 'N') : -2, ('G', 'Q') : -2,
-('Y', 'F') : 3, ('C', 'A') : 0, ('V', 'L') : 1, ('G', 'E') : -2,
-('G', 'A') : 0, ('K', 'R') : 2, ('E', 'D') : 2, ('Y', 'R') : -2,
-('M', 'Q') : 0, ('T', 'I') : -1, ('C', 'D') : -4, ('V', 'F') : -1,
-('T', 'A') : 0, ('T', 'P') : -1, ('B', 'P') : -2, ('T', 'E') : -1,
-('V', 'N') : -3, ('P', 'G') : -2, ('M', 'A') : -1, ('K', 'H') : -1,
-('V', 'R') : -3, ('P', 'C') : -3, ('M', 'E') : -2, ('K', 'L') : -3,
-('V', 'V') : 4, ('M', 'I') : 1, ('T', 'Q') : -1, ('I', 'G') : -4,
-('P', 'K') : -1, ('M', 'M') : 6, ('K', 'D') : -1, ('I', 'C') : -1,
-('Z', 'D') : 1, ('F', 'R') : -3, ('X', 'K') : -1, ('Q', 'D') : 0,
-('X', 'G') : -2, ('Z', 'L') : -3, ('X', 'C') : -2, ('Z', 'H') : 0,
-('B', 'L') : -4, ('B', 'H') : 0, ('F', 'F') : 6, ('X', 'W') : -2,
-('B', 'D') : 4, ('D', 'A') : -2, ('S', 'L') : -3, ('X', 'S') : -1,
-('F', 'N') : -3, ('S', 'R') : -1, ('W', 'D') : -5, ('V', 'Y') : -1,
-('W', 'L') : -2, ('H', 'R') : 0, ('W', 'H') : -2, ('H', 'N') : 1,
-('W', 'T') : -3, ('T', 'T') : 5, ('S', 'F') : -2, ('W', 'P') : -4,
-('L', 'D') : -4, ('B', 'I') : -3, ('L', 'H') : -3, ('S', 'N') : 1,
-('B', 'T') : -1, ('L', 'L') : 4, ('Y', 'K') : -2, ('E', 'Q') : 2,
-('Y', 'G') : -3, ('Z', 'S') : 0, ('Y', 'C') : -2, ('G', 'D') : -1,
-('B', 'V') : -3, ('E', 'A') : -1, ('Y', 'W') : 2, ('E', 'E') : 5,
-('Y', 'S') : -2, ('C', 'N') : -3, ('V', 'C') : -1, ('T', 'H') : -2,
-('P', 'R') : -2, ('V', 'G') : -3, ('T', 'L') : -1, ('V', 'K') : -2,
-('K', 'Q') : 1, ('R', 'A') : -1, ('I', 'R') : -3, ('T', 'D') : -1,
-('P', 'F') : -4, ('I', 'N') : -3, ('K', 'I') : -3, ('M', 'D') : -3,
-('V', 'W') : -3, ('W', 'W') : 10, ('M', 'H') : -2, ('P', 'N') : -2,
-('K', 'A') : -1, ('M', 'L') : 2, ('K', 'E') : 1, ('Z', 'E') : 4,
-('X', 'N') : -1, ('Z', 'A') : -1, ('Z', 'M') : -2, ('X', 'F') : -2,
-('K', 'C') : -3, ('B', 'Q') : 0, ('X', 'B') : -1, ('B', 'M') : -3,
-('F', 'C') : -2, ('Z', 'Q') : 3, ('X', 'Z') : -1, ('F', 'G') : -3,
-('B', 'E') : 1, ('X', 'V') : -1, ('F', 'K') : -3, ('B', 'A') : -2,
-('X', 'R') : -1, ('D', 'D') : 6, ('W', 'G') : -3, ('Z', 'F') : -3,
-('S', 'Q') : 0, ('W', 'C') : -2, ('W', 'K') : -3, ('H', 'Q') : 1,
-('L', 'C') : -1, ('W', 'N') : -4, ('S', 'A') : 1, ('L', 'G') : -4,
-('W', 'S') : -3, ('S', 'E') : 0, ('H', 'E') : 0, ('S', 'I') : -2,
-('H', 'A') : -2, ('S', 'M') : -2, ('Y', 'L') : -1, ('Y', 'H') : 2,
-('Y', 'D') : -3, ('E', 'R') : 0, ('X', 'P') : -2, ('G', 'G') : 6,
-('G', 'C') : -3, ('E', 'N') : 0, ('Y', 'T') : -2, ('Y', 'P') : -3,
-('T', 'K') : -1, ('A', 'A') : 4, ('P', 'Q') : -1, ('T', 'C') : -1,
-('V', 'H') : -3, ('T', 'G') : -2, ('I', 'Q') : -3, ('Z', 'T') : -1,
-('C', 'R') : -4, ('V', 'P') : -2, ('P', 'E') : -1, ('M', 'C') : -2,
-('K', 'N') : 0, ('I', 'I') : 4, ('P', 'A') : -1, ('M', 'G') : -3,
-('T', 'S') : 1, ('I', 'E') : -3, ('P', 'M') : -3, ('M', 'K') : -2,
-('I', 'A') : -1, ('P', 'I') : -3, ('R', 'R') : 6, ('X', 'M') : -1,
-('L', 'I') : 2, ('X', 'I') : -1, ('Z', 'B') : 1, ('X', 'E') : -1,
-('Z', 'N') : 0, ('X', 'A') : -1, ('B', 'R') : -1, ('B', 'N') : 3,
-('F', 'D') : -4, ('X', 'Y') : -1, ('Z', 'R') : 0, ('F', 'H') : -1,
-('B', 'F') : -3, ('F', 'L') : 0, ('X', 'Q') : -1, ('B', 'B') : 4
+('W', 'F'): 1, ('L', 'R'): -2, ('S', 'P'): -1, ('V', 'T'): 0,
+('Q', 'Q'): 6, ('N', 'A'): -2, ('Z', 'Y'): -2, ('W', 'R'): -3,
+('Q', 'A'): -1, ('S', 'D'): 0, ('H', 'H'): 8, ('S', 'H'): -1,
+('H', 'D'): -1, ('L', 'N'): -4, ('W', 'A'): -3, ('Y', 'M'): -1,
+('G', 'R'): -2, ('Y', 'I'): -1, ('Y', 'E'): -2, ('B', 'Y'): -3,
+('Y', 'A'): -2, ('V', 'D'): -3, ('B', 'S'): 0, ('Y', 'Y'): 7,
+('G', 'N'): -1, ('E', 'C'): -4, ('Y', 'Q'): -2, ('Z', 'Z'): 4,
+('V', 'A'): 0, ('C', 'C'): 9, ('M', 'R'): -2, ('V', 'E'): -3,
+('T', 'N'): 0, ('P', 'P'): 8, ('V', 'I'): 3, ('V', 'S'): -2,
+('Z', 'P'): -1, ('V', 'M'): 1, ('T', 'F'): -2, ('V', 'Q'): -2,
+('K', 'K'): 5, ('P', 'D'): -2, ('I', 'H'): -3, ('I', 'D'): -3,
+('T', 'R'): -1, ('P', 'L'): -3, ('K', 'G'): -2, ('M', 'N'): -2,
+('P', 'H'): -2, ('F', 'Q'): -3, ('Z', 'G'): -2, ('X', 'L'): -1,
+('T', 'M'): -1, ('Z', 'C'): -4, ('X', 'H'): -1, ('D', 'R'): -2,
+('B', 'W'): -4, ('X', 'D'): -1, ('Z', 'K'): 1, ('F', 'A'): -2,
+('Z', 'W'): -3, ('F', 'E'): -3, ('D', 'N'): 1, ('B', 'K'): 0,
+('X', 'X'): -1, ('F', 'I'): 0, ('B', 'G'): -1, ('X', 'T'): -1,
+('F', 'M'): 0, ('B', 'C'): -3, ('Z', 'I'): -3, ('Z', 'V'): -2,
+('S', 'S'): 4, ('L', 'Q'): -2, ('W', 'E'): -3, ('Q', 'R'): 1,
+('N', 'N'): 6, ('W', 'M'): -2, ('Q', 'C'): -3, ('W', 'I'): -2,
+('S', 'C'): -1, ('L', 'A'): -2, ('S', 'G'): 0, ('L', 'E'): -3,
+('W', 'Q'): -2, ('H', 'G'): -2, ('S', 'K'): 0, ('Q', 'N'): 0,
+('N', 'R'): 0, ('H', 'C'): -3, ('Y', 'N'): -2, ('G', 'Q'): -2,
+('Y', 'F'): 3, ('C', 'A'): 0, ('V', 'L'): 1, ('G', 'E'): -2,
+('G', 'A'): 0, ('K', 'R'): 2, ('E', 'D'): 2, ('Y', 'R'): -2,
+('M', 'Q'): 0, ('T', 'I'): -1, ('C', 'D'): -4, ('V', 'F'): -1,
+('T', 'A'): 0, ('T', 'P'): -1, ('B', 'P'): -2, ('T', 'E'): -1,
+('V', 'N'): -3, ('P', 'G'): -2, ('M', 'A'): -1, ('K', 'H'): -1,
+('V', 'R'): -3, ('P', 'C'): -3, ('M', 'E'): -2, ('K', 'L'): -3,
+('V', 'V'): 4, ('M', 'I'): 1, ('T', 'Q'): -1, ('I', 'G'): -4,
+('P', 'K'): -1, ('M', 'M'): 6, ('K', 'D'): -1, ('I', 'C'): -1,
+('Z', 'D'): 1, ('F', 'R'): -3, ('X', 'K'): -1, ('Q', 'D'): 0,
+('X', 'G'): -2, ('Z', 'L'): -3, ('X', 'C'): -2, ('Z', 'H'): 0,
+('B', 'L'): -4, ('B', 'H'): 0, ('F', 'F'): 6, ('X', 'W'): -2,
+('B', 'D'): 4, ('D', 'A'): -2, ('S', 'L'): -3, ('X', 'S'): -1,
+('F', 'N'): -3, ('S', 'R'): -1, ('W', 'D'): -5, ('V', 'Y'): -1,
+('W', 'L'): -2, ('H', 'R'): 0, ('W', 'H'): -2, ('H', 'N'): 1,
+('W', 'T'): -3, ('T', 'T'): 5, ('S', 'F'): -2, ('W', 'P'): -4,
+('L', 'D'): -4, ('B', 'I'): -3, ('L', 'H'): -3, ('S', 'N'): 1,
+('B', 'T'): -1, ('L', 'L'): 4, ('Y', 'K'): -2, ('E', 'Q'): 2,
+('Y', 'G'): -3, ('Z', 'S'): 0, ('Y', 'C'): -2, ('G', 'D'): -1,
+('B', 'V'): -3, ('E', 'A'): -1, ('Y', 'W'): 2, ('E', 'E'): 5,
+('Y', 'S'): -2, ('C', 'N'): -3, ('V', 'C'): -1, ('T', 'H'): -2,
+('P', 'R'): -2, ('V', 'G'): -3, ('T', 'L'): -1, ('V', 'K'): -2,
+('K', 'Q'): 1, ('R', 'A'): -1, ('I', 'R'): -3, ('T', 'D'): -1,
+('P', 'F'): -4, ('I', 'N'): -3, ('K', 'I'): -3, ('M', 'D'): -3,
+('V', 'W'): -3, ('W', 'W'): 10, ('M', 'H'): -2, ('P', 'N'): -2,
+('K', 'A'): -1, ('M', 'L'): 2, ('K', 'E'): 1, ('Z', 'E'): 4,
+('X', 'N'): -1, ('Z', 'A'): -1, ('Z', 'M'): -2, ('X', 'F'): -2,
+('K', 'C'): -3, ('B', 'Q'): 0, ('X', 'B'): -1, ('B', 'M'): -3,
+('F', 'C'): -2, ('Z', 'Q'): 3, ('X', 'Z'): -1, ('F', 'G'): -3,
+('B', 'E'): 1, ('X', 'V'): -1, ('F', 'K'): -3, ('B', 'A'): -2,
+('X', 'R'): -1, ('D', 'D'): 6, ('W', 'G'): -3, ('Z', 'F'): -3,
+('S', 'Q'): 0, ('W', 'C'): -2, ('W', 'K'): -3, ('H', 'Q'): 1,
+('L', 'C'): -1, ('W', 'N'): -4, ('S', 'A'): 1, ('L', 'G'): -4,
+('W', 'S'): -3, ('S', 'E'): 0, ('H', 'E'): 0, ('S', 'I'): -2,
+('H', 'A'): -2, ('S', 'M'): -2, ('Y', 'L'): -1, ('Y', 'H'): 2,
+('Y', 'D'): -3, ('E', 'R'): 0, ('X', 'P'): -2, ('G', 'G'): 6,
+('G', 'C'): -3, ('E', 'N'): 0, ('Y', 'T'): -2, ('Y', 'P'): -3,
+('T', 'K'): -1, ('A', 'A'): 4, ('P', 'Q'): -1, ('T', 'C'): -1,
+('V', 'H'): -3, ('T', 'G'): -2, ('I', 'Q'): -3, ('Z', 'T'): -1,
+('C', 'R'): -4, ('V', 'P'): -2, ('P', 'E'): -1, ('M', 'C'): -2,
+('K', 'N'): 0, ('I', 'I'): 4, ('P', 'A'): -1, ('M', 'G'): -3,
+('T', 'S'): 1, ('I', 'E'): -3, ('P', 'M'): -3, ('M', 'K'): -2,
+('I', 'A'): -1, ('P', 'I'): -3, ('R', 'R'): 6, ('X', 'M'): -1,
+('L', 'I'): 2, ('X', 'I'): -1, ('Z', 'B'): 1, ('X', 'E'): -1,
+('Z', 'N'): 0, ('X', 'A'): -1, ('B', 'R'): -1, ('B', 'N'): 3,
+('F', 'D'): -4, ('X', 'Y'): -1, ('Z', 'R'): 0, ('F', 'H'): -1,
+('B', 'F'): -3, ('F', 'L'): 0, ('X', 'Q'): -1, ('B', 'B'): 4
}
blosum65 = _temp()
del _temp
@@ -991,75 +990,75 @@ del _temp
# http://www.embl-heidelberg.de/~vogt/matrices/blosum70.cmp
def _temp():
return {
-('W', 'F') : 1, ('L', 'R') : -3, ('S', 'P') : -1, ('V', 'T') : 0,
-('Q', 'Q') : 6, ('N', 'A') : -2, ('Z', 'Y') : -2, ('W', 'R') : -3,
-('Q', 'A') : -1, ('S', 'D') : 0, ('H', 'H') : 8, ('S', 'H') : -1,
-('H', 'D') : -1, ('L', 'N') : -4, ('W', 'A') : -3, ('Y', 'M') : -1,
-('G', 'R') : -3, ('Y', 'I') : -1, ('Y', 'E') : -3, ('B', 'Y') : -3,
-('Y', 'A') : -2, ('V', 'D') : -4, ('B', 'S') : 0, ('Y', 'Y') : 7,
-('G', 'N') : -1, ('E', 'C') : -4, ('Y', 'Q') : -2, ('Z', 'Z') : 4,
-('V', 'A') : 0, ('C', 'C') : 9, ('M', 'R') : -2, ('V', 'E') : -3,
-('T', 'N') : 0, ('P', 'P') : 8, ('V', 'I') : 3, ('V', 'S') : -2,
-('Z', 'P') : -1, ('V', 'M') : 1, ('T', 'F') : -2, ('V', 'Q') : -2,
-('K', 'K') : 5, ('P', 'D') : -2, ('I', 'H') : -4, ('I', 'D') : -4,
-('T', 'R') : -1, ('P', 'L') : -3, ('K', 'G') : -2, ('M', 'N') : -2,
-('P', 'H') : -2, ('F', 'Q') : -3, ('Z', 'G') : -2, ('X', 'L') : -1,
-('T', 'M') : -1, ('Z', 'C') : -4, ('X', 'H') : -1, ('D', 'R') : -2,
-('B', 'W') : -4, ('X', 'D') : -2, ('Z', 'K') : 1, ('F', 'A') : -2,
-('Z', 'W') : -3, ('F', 'E') : -4, ('D', 'N') : 1, ('B', 'K') : -1,
-('X', 'X') : -1, ('F', 'I') : 0, ('B', 'G') : -1, ('X', 'T') : -1,
-('F', 'M') : 0, ('B', 'C') : -4, ('Z', 'I') : -3, ('Z', 'V') : -3,
-('S', 'S') : 4, ('L', 'Q') : -2, ('W', 'E') : -4, ('Q', 'R') : 1,
-('N', 'N') : 6, ('W', 'M') : -2, ('Q', 'C') : -3, ('W', 'I') : -3,
-('S', 'C') : -1, ('L', 'A') : -2, ('S', 'G') : -1, ('L', 'E') : -3,
-('W', 'Q') : -2, ('H', 'G') : -2, ('S', 'K') : 0, ('Q', 'N') : 0,
-('N', 'R') : -1, ('H', 'C') : -4, ('Y', 'N') : -2, ('G', 'Q') : -2,
-('Y', 'F') : 3, ('C', 'A') : -1, ('V', 'L') : 1, ('G', 'E') : -2,
-('G', 'A') : 0, ('K', 'R') : 2, ('E', 'D') : 1, ('Y', 'R') : -2,
-('M', 'Q') : 0, ('T', 'I') : -1, ('C', 'D') : -4, ('V', 'F') : -1,
-('T', 'A') : 0, ('T', 'P') : -1, ('B', 'P') : -2, ('T', 'E') : -1,
-('V', 'N') : -3, ('P', 'G') : -3, ('M', 'A') : -1, ('K', 'H') : -1,
-('V', 'R') : -3, ('P', 'C') : -3, ('M', 'E') : -2, ('K', 'L') : -3,
-('V', 'V') : 4, ('M', 'I') : 1, ('T', 'Q') : -1, ('I', 'G') : -4,
-('P', 'K') : -1, ('M', 'M') : 6, ('K', 'D') : -1, ('I', 'C') : -1,
-('Z', 'D') : 1, ('F', 'R') : -3, ('X', 'K') : -1, ('Q', 'D') : -1,
-('X', 'G') : -2, ('Z', 'L') : -3, ('X', 'C') : -2, ('Z', 'H') : 0,
-('B', 'L') : -4, ('B', 'H') : -1, ('F', 'F') : 6, ('X', 'W') : -3,
-('B', 'D') : 4, ('D', 'A') : -2, ('S', 'L') : -3, ('X', 'S') : -1,
-('F', 'N') : -3, ('S', 'R') : -1, ('W', 'D') : -5, ('V', 'Y') : -2,
-('W', 'L') : -2, ('H', 'R') : 0, ('W', 'H') : -2, ('H', 'N') : 0,
-('W', 'T') : -3, ('T', 'T') : 5, ('S', 'F') : -3, ('W', 'P') : -4,
-('L', 'D') : -4, ('B', 'I') : -4, ('L', 'H') : -3, ('S', 'N') : 0,
-('B', 'T') : -1, ('L', 'L') : 4, ('Y', 'K') : -2, ('E', 'Q') : 2,
-('Y', 'G') : -4, ('Z', 'S') : 0, ('Y', 'C') : -3, ('G', 'D') : -2,
-('B', 'V') : -3, ('E', 'A') : -1, ('Y', 'W') : 2, ('E', 'E') : 5,
-('Y', 'S') : -2, ('C', 'N') : -3, ('V', 'C') : -1, ('T', 'H') : -2,
-('P', 'R') : -2, ('V', 'G') : -4, ('T', 'L') : -2, ('V', 'K') : -3,
-('K', 'Q') : 1, ('R', 'A') : -2, ('I', 'R') : -3, ('T', 'D') : -1,
-('P', 'F') : -4, ('I', 'N') : -4, ('K', 'I') : -3, ('M', 'D') : -3,
-('V', 'W') : -3, ('W', 'W') : 11, ('M', 'H') : -2, ('P', 'N') : -2,
-('K', 'A') : -1, ('M', 'L') : 2, ('K', 'E') : 1, ('Z', 'E') : 4,
-('X', 'N') : -1, ('Z', 'A') : -1, ('Z', 'M') : -2, ('X', 'F') : -2,
-('K', 'C') : -4, ('B', 'Q') : 0, ('X', 'B') : -1, ('B', 'M') : -3,
-('F', 'C') : -2, ('Z', 'Q') : 3, ('X', 'Z') : -1, ('F', 'G') : -4,
-('B', 'E') : 1, ('X', 'V') : -1, ('F', 'K') : -3, ('B', 'A') : -2,
-('X', 'R') : -1, ('D', 'D') : 6, ('W', 'G') : -3, ('Z', 'F') : -4,
-('S', 'Q') : 0, ('W', 'C') : -3, ('W', 'K') : -3, ('H', 'Q') : 1,
-('L', 'C') : -2, ('W', 'N') : -4, ('S', 'A') : 1, ('L', 'G') : -4,
-('W', 'S') : -3, ('S', 'E') : 0, ('H', 'E') : 0, ('S', 'I') : -3,
-('H', 'A') : -2, ('S', 'M') : -2, ('Y', 'L') : -1, ('Y', 'H') : 2,
-('Y', 'D') : -4, ('E', 'R') : 0, ('X', 'P') : -2, ('G', 'G') : 6,
-('G', 'C') : -3, ('E', 'N') : 0, ('Y', 'T') : -2, ('Y', 'P') : -3,
-('T', 'K') : -1, ('A', 'A') : 4, ('P', 'Q') : -2, ('T', 'C') : -1,
-('V', 'H') : -3, ('T', 'G') : -2, ('I', 'Q') : -3, ('Z', 'T') : -1,
-('C', 'R') : -4, ('V', 'P') : -3, ('P', 'E') : -1, ('M', 'C') : -2,
-('K', 'N') : 0, ('I', 'I') : 4, ('P', 'A') : -1, ('M', 'G') : -3,
-('T', 'S') : 1, ('I', 'E') : -4, ('P', 'M') : -3, ('M', 'K') : -2,
-('I', 'A') : -2, ('P', 'I') : -3, ('R', 'R') : 6, ('X', 'M') : -1,
-('L', 'I') : 2, ('X', 'I') : -1, ('Z', 'B') : 0, ('X', 'E') : -1,
-('Z', 'N') : 0, ('X', 'A') : -1, ('B', 'R') : -1, ('B', 'N') : 3,
-('F', 'D') : -4, ('X', 'Y') : -2, ('Z', 'R') : 0, ('F', 'H') : -1,
-('B', 'F') : -4, ('F', 'L') : 0, ('X', 'Q') : -1, ('B', 'B') : 4
+('W', 'F'): 1, ('L', 'R'): -3, ('S', 'P'): -1, ('V', 'T'): 0,
+('Q', 'Q'): 6, ('N', 'A'): -2, ('Z', 'Y'): -2, ('W', 'R'): -3,
+('Q', 'A'): -1, ('S', 'D'): 0, ('H', 'H'): 8, ('S', 'H'): -1,
+('H', 'D'): -1, ('L', 'N'): -4, ('W', 'A'): -3, ('Y', 'M'): -1,
+('G', 'R'): -3, ('Y', 'I'): -1, ('Y', 'E'): -3, ('B', 'Y'): -3,
+('Y', 'A'): -2, ('V', 'D'): -4, ('B', 'S'): 0, ('Y', 'Y'): 7,
+('G', 'N'): -1, ('E', 'C'): -4, ('Y', 'Q'): -2, ('Z', 'Z'): 4,
+('V', 'A'): 0, ('C', 'C'): 9, ('M', 'R'): -2, ('V', 'E'): -3,
+('T', 'N'): 0, ('P', 'P'): 8, ('V', 'I'): 3, ('V', 'S'): -2,
+('Z', 'P'): -1, ('V', 'M'): 1, ('T', 'F'): -2, ('V', 'Q'): -2,
+('K', 'K'): 5, ('P', 'D'): -2, ('I', 'H'): -4, ('I', 'D'): -4,
+('T', 'R'): -1, ('P', 'L'): -3, ('K', 'G'): -2, ('M', 'N'): -2,
+('P', 'H'): -2, ('F', 'Q'): -3, ('Z', 'G'): -2, ('X', 'L'): -1,
+('T', 'M'): -1, ('Z', 'C'): -4, ('X', 'H'): -1, ('D', 'R'): -2,
+('B', 'W'): -4, ('X', 'D'): -2, ('Z', 'K'): 1, ('F', 'A'): -2,
+('Z', 'W'): -3, ('F', 'E'): -4, ('D', 'N'): 1, ('B', 'K'): -1,
+('X', 'X'): -1, ('F', 'I'): 0, ('B', 'G'): -1, ('X', 'T'): -1,
+('F', 'M'): 0, ('B', 'C'): -4, ('Z', 'I'): -3, ('Z', 'V'): -3,
+('S', 'S'): 4, ('L', 'Q'): -2, ('W', 'E'): -4, ('Q', 'R'): 1,
+('N', 'N'): 6, ('W', 'M'): -2, ('Q', 'C'): -3, ('W', 'I'): -3,
+('S', 'C'): -1, ('L', 'A'): -2, ('S', 'G'): -1, ('L', 'E'): -3,
+('W', 'Q'): -2, ('H', 'G'): -2, ('S', 'K'): 0, ('Q', 'N'): 0,
+('N', 'R'): -1, ('H', 'C'): -4, ('Y', 'N'): -2, ('G', 'Q'): -2,
+('Y', 'F'): 3, ('C', 'A'): -1, ('V', 'L'): 1, ('G', 'E'): -2,
+('G', 'A'): 0, ('K', 'R'): 2, ('E', 'D'): 1, ('Y', 'R'): -2,
+('M', 'Q'): 0, ('T', 'I'): -1, ('C', 'D'): -4, ('V', 'F'): -1,
+('T', 'A'): 0, ('T', 'P'): -1, ('B', 'P'): -2, ('T', 'E'): -1,
+('V', 'N'): -3, ('P', 'G'): -3, ('M', 'A'): -1, ('K', 'H'): -1,
+('V', 'R'): -3, ('P', 'C'): -3, ('M', 'E'): -2, ('K', 'L'): -3,
+('V', 'V'): 4, ('M', 'I'): 1, ('T', 'Q'): -1, ('I', 'G'): -4,
+('P', 'K'): -1, ('M', 'M'): 6, ('K', 'D'): -1, ('I', 'C'): -1,
+('Z', 'D'): 1, ('F', 'R'): -3, ('X', 'K'): -1, ('Q', 'D'): -1,
+('X', 'G'): -2, ('Z', 'L'): -3, ('X', 'C'): -2, ('Z', 'H'): 0,
+('B', 'L'): -4, ('B', 'H'): -1, ('F', 'F'): 6, ('X', 'W'): -3,
+('B', 'D'): 4, ('D', 'A'): -2, ('S', 'L'): -3, ('X', 'S'): -1,
+('F', 'N'): -3, ('S', 'R'): -1, ('W', 'D'): -5, ('V', 'Y'): -2,
+('W', 'L'): -2, ('H', 'R'): 0, ('W', 'H'): -2, ('H', 'N'): 0,
+('W', 'T'): -3, ('T', 'T'): 5, ('S', 'F'): -3, ('W', 'P'): -4,
+('L', 'D'): -4, ('B', 'I'): -4, ('L', 'H'): -3, ('S', 'N'): 0,
+('B', 'T'): -1, ('L', 'L'): 4, ('Y', 'K'): -2, ('E', 'Q'): 2,
+('Y', 'G'): -4, ('Z', 'S'): 0, ('Y', 'C'): -3, ('G', 'D'): -2,
+('B', 'V'): -3, ('E', 'A'): -1, ('Y', 'W'): 2, ('E', 'E'): 5,
+('Y', 'S'): -2, ('C', 'N'): -3, ('V', 'C'): -1, ('T', 'H'): -2,
+('P', 'R'): -2, ('V', 'G'): -4, ('T', 'L'): -2, ('V', 'K'): -3,
+('K', 'Q'): 1, ('R', 'A'): -2, ('I', 'R'): -3, ('T', 'D'): -1,
+('P', 'F'): -4, ('I', 'N'): -4, ('K', 'I'): -3, ('M', 'D'): -3,
+('V', 'W'): -3, ('W', 'W'): 11, ('M', 'H'): -2, ('P', 'N'): -2,
+('K', 'A'): -1, ('M', 'L'): 2, ('K', 'E'): 1, ('Z', 'E'): 4,
+('X', 'N'): -1, ('Z', 'A'): -1, ('Z', 'M'): -2, ('X', 'F'): -2,
+('K', 'C'): -4, ('B', 'Q'): 0, ('X', 'B'): -1, ('B', 'M'): -3,
+('F', 'C'): -2, ('Z', 'Q'): 3, ('X', 'Z'): -1, ('F', 'G'): -4,
+('B', 'E'): 1, ('X', 'V'): -1, ('F', 'K'): -3, ('B', 'A'): -2,
+('X', 'R'): -1, ('D', 'D'): 6, ('W', 'G'): -3, ('Z', 'F'): -4,
+('S', 'Q'): 0, ('W', 'C'): -3, ('W', 'K'): -3, ('H', 'Q'): 1,
+('L', 'C'): -2, ('W', 'N'): -4, ('S', 'A'): 1, ('L', 'G'): -4,
+('W', 'S'): -3, ('S', 'E'): 0, ('H', 'E'): 0, ('S', 'I'): -3,
+('H', 'A'): -2, ('S', 'M'): -2, ('Y', 'L'): -1, ('Y', 'H'): 2,
+('Y', 'D'): -4, ('E', 'R'): 0, ('X', 'P'): -2, ('G', 'G'): 6,
+('G', 'C'): -3, ('E', 'N'): 0, ('Y', 'T'): -2, ('Y', 'P'): -3,
+('T', 'K'): -1, ('A', 'A'): 4, ('P', 'Q'): -2, ('T', 'C'): -1,
+('V', 'H'): -3, ('T', 'G'): -2, ('I', 'Q'): -3, ('Z', 'T'): -1,
+('C', 'R'): -4, ('V', 'P'): -3, ('P', 'E'): -1, ('M', 'C'): -2,
+('K', 'N'): 0, ('I', 'I'): 4, ('P', 'A'): -1, ('M', 'G'): -3,
+('T', 'S'): 1, ('I', 'E'): -4, ('P', 'M'): -3, ('M', 'K'): -2,
+('I', 'A'): -2, ('P', 'I'): -3, ('R', 'R'): 6, ('X', 'M'): -1,
+('L', 'I'): 2, ('X', 'I'): -1, ('Z', 'B'): 0, ('X', 'E'): -1,
+('Z', 'N'): 0, ('X', 'A'): -1, ('B', 'R'): -1, ('B', 'N'): 3,
+('F', 'D'): -4, ('X', 'Y'): -2, ('Z', 'R'): 0, ('F', 'H'): -1,
+('B', 'F'): -4, ('F', 'L'): 0, ('X', 'Q'): -1, ('B', 'B'): 4
}
blosum70 = _temp()
del _temp
@@ -1068,75 +1067,75 @@ del _temp
# http://www.embl-heidelberg.de/~vogt/matrices/blosum75.cmp
def _temp():
return {
-('W', 'F') : 1, ('L', 'R') : -3, ('S', 'P') : -1, ('V', 'T') : 0,
-('Q', 'Q') : 6, ('N', 'A') : -2, ('Z', 'Y') : -3, ('W', 'R') : -3,
-('Q', 'A') : -1, ('S', 'D') : -1, ('H', 'H') : 8, ('S', 'H') : -1,
-('H', 'D') : -1, ('L', 'N') : -4, ('W', 'A') : -3, ('Y', 'M') : -2,
-('G', 'R') : -3, ('Y', 'I') : -2, ('Y', 'E') : -3, ('B', 'Y') : -3,
-('Y', 'A') : -2, ('V', 'D') : -4, ('B', 'S') : 0, ('Y', 'Y') : 7,
-('G', 'N') : -1, ('E', 'C') : -5, ('Y', 'Q') : -2, ('Z', 'Z') : 4,
-('V', 'A') : 0, ('C', 'C') : 9, ('M', 'R') : -2, ('V', 'E') : -3,
-('T', 'N') : 0, ('P', 'P') : 8, ('V', 'I') : 3, ('V', 'S') : -2,
-('Z', 'P') : -2, ('V', 'M') : 1, ('T', 'F') : -2, ('V', 'Q') : -2,
-('K', 'K') : 5, ('P', 'D') : -2, ('I', 'H') : -4, ('I', 'D') : -4,
-('T', 'R') : -1, ('P', 'L') : -3, ('K', 'G') : -2, ('M', 'N') : -3,
-('P', 'H') : -2, ('F', 'Q') : -4, ('Z', 'G') : -2, ('X', 'L') : -1,
-('T', 'M') : -1, ('Z', 'C') : -4, ('X', 'H') : -1, ('D', 'R') : -2,
-('B', 'W') : -5, ('X', 'D') : -2, ('Z', 'K') : 1, ('F', 'A') : -3,
-('Z', 'W') : -3, ('F', 'E') : -4, ('D', 'N') : 1, ('B', 'K') : -1,
-('X', 'X') : -1, ('F', 'I') : 0, ('B', 'G') : -1, ('X', 'T') : -1,
-('F', 'M') : 0, ('B', 'C') : -4, ('Z', 'I') : -4, ('Z', 'V') : -3,
-('S', 'S') : 5, ('L', 'Q') : -3, ('W', 'E') : -4, ('Q', 'R') : 1,
-('N', 'N') : 6, ('W', 'M') : -2, ('Q', 'C') : -3, ('W', 'I') : -3,
-('S', 'C') : -1, ('L', 'A') : -2, ('S', 'G') : -1, ('L', 'E') : -4,
-('W', 'Q') : -2, ('H', 'G') : -2, ('S', 'K') : 0, ('Q', 'N') : 0,
-('N', 'R') : -1, ('H', 'C') : -4, ('Y', 'N') : -3, ('G', 'Q') : -2,
-('Y', 'F') : 3, ('C', 'A') : -1, ('V', 'L') : 1, ('G', 'E') : -3,
-('G', 'A') : 0, ('K', 'R') : 2, ('E', 'D') : 1, ('Y', 'R') : -2,
-('M', 'Q') : 0, ('T', 'I') : -1, ('C', 'D') : -4, ('V', 'F') : -1,
-('T', 'A') : 0, ('T', 'P') : -1, ('B', 'P') : -2, ('T', 'E') : -1,
-('V', 'N') : -3, ('P', 'G') : -3, ('M', 'A') : -1, ('K', 'H') : -1,
-('V', 'R') : -3, ('P', 'C') : -4, ('M', 'E') : -2, ('K', 'L') : -3,
-('V', 'V') : 4, ('M', 'I') : 1, ('T', 'Q') : -1, ('I', 'G') : -5,
-('P', 'K') : -1, ('M', 'M') : 6, ('K', 'D') : -1, ('I', 'C') : -1,
-('Z', 'D') : 1, ('F', 'R') : -3, ('X', 'K') : -1, ('Q', 'D') : -1,
-('X', 'G') : -2, ('Z', 'L') : -3, ('X', 'C') : -2, ('Z', 'H') : 0,
-('B', 'L') : -4, ('B', 'H') : -1, ('F', 'F') : 6, ('X', 'W') : -3,
-('B', 'D') : 4, ('D', 'A') : -2, ('S', 'L') : -3, ('X', 'S') : -1,
-('F', 'N') : -4, ('S', 'R') : -1, ('W', 'D') : -5, ('V', 'Y') : -2,
-('W', 'L') : -2, ('H', 'R') : 0, ('W', 'H') : -2, ('H', 'N') : 0,
-('W', 'T') : -3, ('T', 'T') : 5, ('S', 'F') : -3, ('W', 'P') : -5,
-('L', 'D') : -4, ('B', 'I') : -4, ('L', 'H') : -3, ('S', 'N') : 0,
-('B', 'T') : -1, ('L', 'L') : 4, ('Y', 'K') : -2, ('E', 'Q') : 2,
-('Y', 'G') : -4, ('Z', 'S') : 0, ('Y', 'C') : -3, ('G', 'D') : -2,
-('B', 'V') : -4, ('E', 'A') : -1, ('Y', 'W') : 2, ('E', 'E') : 5,
-('Y', 'S') : -2, ('C', 'N') : -3, ('V', 'C') : -1, ('T', 'H') : -2,
-('P', 'R') : -2, ('V', 'G') : -4, ('T', 'L') : -2, ('V', 'K') : -3,
-('K', 'Q') : 1, ('R', 'A') : -2, ('I', 'R') : -3, ('T', 'D') : -1,
-('P', 'F') : -4, ('I', 'N') : -4, ('K', 'I') : -3, ('M', 'D') : -4,
-('V', 'W') : -3, ('W', 'W') : 11, ('M', 'H') : -2, ('P', 'N') : -3,
-('K', 'A') : -1, ('M', 'L') : 2, ('K', 'E') : 1, ('Z', 'E') : 4,
-('X', 'N') : -1, ('Z', 'A') : -1, ('Z', 'M') : -2, ('X', 'F') : -2,
-('K', 'C') : -4, ('B', 'Q') : 0, ('X', 'B') : -2, ('B', 'M') : -3,
-('F', 'C') : -2, ('Z', 'Q') : 3, ('X', 'Z') : -1, ('F', 'G') : -4,
-('B', 'E') : 1, ('X', 'V') : -1, ('F', 'K') : -4, ('B', 'A') : -2,
-('X', 'R') : -1, ('D', 'D') : 6, ('W', 'G') : -3, ('Z', 'F') : -4,
-('S', 'Q') : 0, ('W', 'C') : -3, ('W', 'K') : -4, ('H', 'Q') : 1,
-('L', 'C') : -2, ('W', 'N') : -4, ('S', 'A') : 1, ('L', 'G') : -4,
-('W', 'S') : -3, ('S', 'E') : 0, ('H', 'E') : 0, ('S', 'I') : -3,
-('H', 'A') : -2, ('S', 'M') : -2, ('Y', 'L') : -1, ('Y', 'H') : 2,
-('Y', 'D') : -4, ('E', 'R') : 0, ('X', 'P') : -2, ('G', 'G') : 6,
-('G', 'C') : -3, ('E', 'N') : -1, ('Y', 'T') : -2, ('Y', 'P') : -4,
-('T', 'K') : -1, ('A', 'A') : 4, ('P', 'Q') : -2, ('T', 'C') : -1,
-('V', 'H') : -4, ('T', 'G') : -2, ('I', 'Q') : -3, ('Z', 'T') : -1,
-('C', 'R') : -4, ('V', 'P') : -3, ('P', 'E') : -1, ('M', 'C') : -2,
-('K', 'N') : 0, ('I', 'I') : 4, ('P', 'A') : -1, ('M', 'G') : -3,
-('T', 'S') : 1, ('I', 'E') : -4, ('P', 'M') : -3, ('M', 'K') : -2,
-('I', 'A') : -2, ('P', 'I') : -3, ('R', 'R') : 6, ('X', 'M') : -1,
-('L', 'I') : 1, ('X', 'I') : -2, ('Z', 'B') : 0, ('X', 'E') : -1,
-('Z', 'N') : 0, ('X', 'A') : -1, ('B', 'R') : -1, ('B', 'N') : 3,
-('F', 'D') : -4, ('X', 'Y') : -2, ('Z', 'R') : 0, ('F', 'H') : -2,
-('B', 'F') : -4, ('F', 'L') : 0, ('X', 'Q') : -1, ('B', 'B') : 4
+('W', 'F'): 1, ('L', 'R'): -3, ('S', 'P'): -1, ('V', 'T'): 0,
+('Q', 'Q'): 6, ('N', 'A'): -2, ('Z', 'Y'): -3, ('W', 'R'): -3,
+('Q', 'A'): -1, ('S', 'D'): -1, ('H', 'H'): 8, ('S', 'H'): -1,
+('H', 'D'): -1, ('L', 'N'): -4, ('W', 'A'): -3, ('Y', 'M'): -2,
+('G', 'R'): -3, ('Y', 'I'): -2, ('Y', 'E'): -3, ('B', 'Y'): -3,
+('Y', 'A'): -2, ('V', 'D'): -4, ('B', 'S'): 0, ('Y', 'Y'): 7,
+('G', 'N'): -1, ('E', 'C'): -5, ('Y', 'Q'): -2, ('Z', 'Z'): 4,
+('V', 'A'): 0, ('C', 'C'): 9, ('M', 'R'): -2, ('V', 'E'): -3,
+('T', 'N'): 0, ('P', 'P'): 8, ('V', 'I'): 3, ('V', 'S'): -2,
+('Z', 'P'): -2, ('V', 'M'): 1, ('T', 'F'): -2, ('V', 'Q'): -2,
+('K', 'K'): 5, ('P', 'D'): -2, ('I', 'H'): -4, ('I', 'D'): -4,
+('T', 'R'): -1, ('P', 'L'): -3, ('K', 'G'): -2, ('M', 'N'): -3,
+('P', 'H'): -2, ('F', 'Q'): -4, ('Z', 'G'): -2, ('X', 'L'): -1,
+('T', 'M'): -1, ('Z', 'C'): -4, ('X', 'H'): -1, ('D', 'R'): -2,
+('B', 'W'): -5, ('X', 'D'): -2, ('Z', 'K'): 1, ('F', 'A'): -3,
+('Z', 'W'): -3, ('F', 'E'): -4, ('D', 'N'): 1, ('B', 'K'): -1,
+('X', 'X'): -1, ('F', 'I'): 0, ('B', 'G'): -1, ('X', 'T'): -1,
+('F', 'M'): 0, ('B', 'C'): -4, ('Z', 'I'): -4, ('Z', 'V'): -3,
+('S', 'S'): 5, ('L', 'Q'): -3, ('W', 'E'): -4, ('Q', 'R'): 1,
+('N', 'N'): 6, ('W', 'M'): -2, ('Q', 'C'): -3, ('W', 'I'): -3,
+('S', 'C'): -1, ('L', 'A'): -2, ('S', 'G'): -1, ('L', 'E'): -4,
+('W', 'Q'): -2, ('H', 'G'): -2, ('S', 'K'): 0, ('Q', 'N'): 0,
+('N', 'R'): -1, ('H', 'C'): -4, ('Y', 'N'): -3, ('G', 'Q'): -2,
+('Y', 'F'): 3, ('C', 'A'): -1, ('V', 'L'): 1, ('G', 'E'): -3,
+('G', 'A'): 0, ('K', 'R'): 2, ('E', 'D'): 1, ('Y', 'R'): -2,
+('M', 'Q'): 0, ('T', 'I'): -1, ('C', 'D'): -4, ('V', 'F'): -1,
+('T', 'A'): 0, ('T', 'P'): -1, ('B', 'P'): -2, ('T', 'E'): -1,
+('V', 'N'): -3, ('P', 'G'): -3, ('M', 'A'): -1, ('K', 'H'): -1,
+('V', 'R'): -3, ('P', 'C'): -4, ('M', 'E'): -2, ('K', 'L'): -3,
+('V', 'V'): 4, ('M', 'I'): 1, ('T', 'Q'): -1, ('I', 'G'): -5,
+('P', 'K'): -1, ('M', 'M'): 6, ('K', 'D'): -1, ('I', 'C'): -1,
+('Z', 'D'): 1, ('F', 'R'): -3, ('X', 'K'): -1, ('Q', 'D'): -1,
+('X', 'G'): -2, ('Z', 'L'): -3, ('X', 'C'): -2, ('Z', 'H'): 0,
+('B', 'L'): -4, ('B', 'H'): -1, ('F', 'F'): 6, ('X', 'W'): -3,
+('B', 'D'): 4, ('D', 'A'): -2, ('S', 'L'): -3, ('X', 'S'): -1,
+('F', 'N'): -4, ('S', 'R'): -1, ('W', 'D'): -5, ('V', 'Y'): -2,
+('W', 'L'): -2, ('H', 'R'): 0, ('W', 'H'): -2, ('H', 'N'): 0,
+('W', 'T'): -3, ('T', 'T'): 5, ('S', 'F'): -3, ('W', 'P'): -5,
+('L', 'D'): -4, ('B', 'I'): -4, ('L', 'H'): -3, ('S', 'N'): 0,
+('B', 'T'): -1, ('L', 'L'): 4, ('Y', 'K'): -2, ('E', 'Q'): 2,
+('Y', 'G'): -4, ('Z', 'S'): 0, ('Y', 'C'): -3, ('G', 'D'): -2,
+('B', 'V'): -4, ('E', 'A'): -1, ('Y', 'W'): 2, ('E', 'E'): 5,
+('Y', 'S'): -2, ('C', 'N'): -3, ('V', 'C'): -1, ('T', 'H'): -2,
+('P', 'R'): -2, ('V', 'G'): -4, ('T', 'L'): -2, ('V', 'K'): -3,
+('K', 'Q'): 1, ('R', 'A'): -2, ('I', 'R'): -3, ('T', 'D'): -1,
+('P', 'F'): -4, ('I', 'N'): -4, ('K', 'I'): -3, ('M', 'D'): -4,
+('V', 'W'): -3, ('W', 'W'): 11, ('M', 'H'): -2, ('P', 'N'): -3,
+('K', 'A'): -1, ('M', 'L'): 2, ('K', 'E'): 1, ('Z', 'E'): 4,
+('X', 'N'): -1, ('Z', 'A'): -1, ('Z', 'M'): -2, ('X', 'F'): -2,
+('K', 'C'): -4, ('B', 'Q'): 0, ('X', 'B'): -2, ('B', 'M'): -3,
+('F', 'C'): -2, ('Z', 'Q'): 3, ('X', 'Z'): -1, ('F', 'G'): -4,
+('B', 'E'): 1, ('X', 'V'): -1, ('F', 'K'): -4, ('B', 'A'): -2,
+('X', 'R'): -1, ('D', 'D'): 6, ('W', 'G'): -3, ('Z', 'F'): -4,
+('S', 'Q'): 0, ('W', 'C'): -3, ('W', 'K'): -4, ('H', 'Q'): 1,
+('L', 'C'): -2, ('W', 'N'): -4, ('S', 'A'): 1, ('L', 'G'): -4,
+('W', 'S'): -3, ('S', 'E'): 0, ('H', 'E'): 0, ('S', 'I'): -3,
+('H', 'A'): -2, ('S', 'M'): -2, ('Y', 'L'): -1, ('Y', 'H'): 2,
+('Y', 'D'): -4, ('E', 'R'): 0, ('X', 'P'): -2, ('G', 'G'): 6,
+('G', 'C'): -3, ('E', 'N'): -1, ('Y', 'T'): -2, ('Y', 'P'): -4,
+('T', 'K'): -1, ('A', 'A'): 4, ('P', 'Q'): -2, ('T', 'C'): -1,
+('V', 'H'): -4, ('T', 'G'): -2, ('I', 'Q'): -3, ('Z', 'T'): -1,
+('C', 'R'): -4, ('V', 'P'): -3, ('P', 'E'): -1, ('M', 'C'): -2,
+('K', 'N'): 0, ('I', 'I'): 4, ('P', 'A'): -1, ('M', 'G'): -3,
+('T', 'S'): 1, ('I', 'E'): -4, ('P', 'M'): -3, ('M', 'K'): -2,
+('I', 'A'): -2, ('P', 'I'): -3, ('R', 'R'): 6, ('X', 'M'): -1,
+('L', 'I'): 1, ('X', 'I'): -2, ('Z', 'B'): 0, ('X', 'E'): -1,
+('Z', 'N'): 0, ('X', 'A'): -1, ('B', 'R'): -1, ('B', 'N'): 3,
+('F', 'D'): -4, ('X', 'Y'): -2, ('Z', 'R'): 0, ('F', 'H'): -2,
+('B', 'F'): -4, ('F', 'L'): 0, ('X', 'Q'): -1, ('B', 'B'): 4
}
blosum75 = _temp()
del _temp
@@ -1145,75 +1144,75 @@ del _temp
# http://www.embl-heidelberg.de/~vogt/matrices/blosum80.cmp
def _temp():
return {
-('W', 'F') : 0, ('L', 'R') : -3, ('S', 'P') : -1, ('V', 'T') : 0,
-('Q', 'Q') : 6, ('N', 'A') : -2, ('Z', 'Y') : -3, ('W', 'R') : -4,
-('Q', 'A') : -1, ('S', 'D') : -1, ('H', 'H') : 8, ('S', 'H') : -1,
-('H', 'D') : -2, ('L', 'N') : -4, ('W', 'A') : -3, ('Y', 'M') : -2,
-('G', 'R') : -3, ('Y', 'I') : -2, ('Y', 'E') : -3, ('B', 'Y') : -3,
-('Y', 'A') : -2, ('V', 'D') : -4, ('B', 'S') : 0, ('Y', 'Y') : 7,
-('G', 'N') : -1, ('E', 'C') : -5, ('Y', 'Q') : -2, ('Z', 'Z') : 4,
-('V', 'A') : 0, ('C', 'C') : 9, ('M', 'R') : -2, ('V', 'E') : -3,
-('T', 'N') : 0, ('P', 'P') : 8, ('V', 'I') : 3, ('V', 'S') : -2,
-('Z', 'P') : -2, ('V', 'M') : 1, ('T', 'F') : -2, ('V', 'Q') : -3,
-('K', 'K') : 5, ('P', 'D') : -2, ('I', 'H') : -4, ('I', 'D') : -4,
-('T', 'R') : -1, ('P', 'L') : -3, ('K', 'G') : -2, ('M', 'N') : -3,
-('P', 'H') : -3, ('F', 'Q') : -4, ('Z', 'G') : -3, ('X', 'L') : -2,
-('T', 'M') : -1, ('Z', 'C') : -4, ('X', 'H') : -2, ('D', 'R') : -2,
-('B', 'W') : -5, ('X', 'D') : -2, ('Z', 'K') : 1, ('F', 'A') : -3,
-('Z', 'W') : -4, ('F', 'E') : -4, ('D', 'N') : 1, ('B', 'K') : -1,
-('X', 'X') : -1, ('F', 'I') : -1, ('B', 'G') : -1, ('X', 'T') : -1,
-('F', 'M') : 0, ('B', 'C') : -4, ('Z', 'I') : -4, ('Z', 'V') : -3,
-('S', 'S') : 5, ('L', 'Q') : -3, ('W', 'E') : -4, ('Q', 'R') : 1,
-('N', 'N') : 6, ('W', 'M') : -2, ('Q', 'C') : -4, ('W', 'I') : -3,
-('S', 'C') : -2, ('L', 'A') : -2, ('S', 'G') : -1, ('L', 'E') : -4,
-('W', 'Q') : -3, ('H', 'G') : -3, ('S', 'K') : -1, ('Q', 'N') : 0,
-('N', 'R') : -1, ('H', 'C') : -4, ('Y', 'N') : -3, ('G', 'Q') : -2,
-('Y', 'F') : 3, ('C', 'A') : -1, ('V', 'L') : 1, ('G', 'E') : -3,
-('G', 'A') : 0, ('K', 'R') : 2, ('E', 'D') : 1, ('Y', 'R') : -3,
-('M', 'Q') : 0, ('T', 'I') : -1, ('C', 'D') : -4, ('V', 'F') : -1,
-('T', 'A') : 0, ('T', 'P') : -2, ('B', 'P') : -2, ('T', 'E') : -1,
-('V', 'N') : -4, ('P', 'G') : -3, ('M', 'A') : -1, ('K', 'H') : -1,
-('V', 'R') : -3, ('P', 'C') : -4, ('M', 'E') : -2, ('K', 'L') : -3,
-('V', 'V') : 4, ('M', 'I') : 1, ('T', 'Q') : -1, ('I', 'G') : -5,
-('P', 'K') : -1, ('M', 'M') : 6, ('K', 'D') : -1, ('I', 'C') : -2,
-('Z', 'D') : 1, ('F', 'R') : -4, ('X', 'K') : -1, ('Q', 'D') : -1,
-('X', 'G') : -2, ('Z', 'L') : -3, ('X', 'C') : -3, ('Z', 'H') : 0,
-('B', 'L') : -4, ('B', 'H') : -1, ('F', 'F') : 6, ('X', 'W') : -3,
-('B', 'D') : 4, ('D', 'A') : -2, ('S', 'L') : -3, ('X', 'S') : -1,
-('F', 'N') : -4, ('S', 'R') : -1, ('W', 'D') : -6, ('V', 'Y') : -2,
-('W', 'L') : -2, ('H', 'R') : 0, ('W', 'H') : -3, ('H', 'N') : 0,
-('W', 'T') : -4, ('T', 'T') : 5, ('S', 'F') : -3, ('W', 'P') : -5,
-('L', 'D') : -5, ('B', 'I') : -4, ('L', 'H') : -3, ('S', 'N') : 0,
-('B', 'T') : -1, ('L', 'L') : 4, ('Y', 'K') : -3, ('E', 'Q') : 2,
-('Y', 'G') : -4, ('Z', 'S') : 0, ('Y', 'C') : -3, ('G', 'D') : -2,
-('B', 'V') : -4, ('E', 'A') : -1, ('Y', 'W') : 2, ('E', 'E') : 6,
-('Y', 'S') : -2, ('C', 'N') : -3, ('V', 'C') : -1, ('T', 'H') : -2,
-('P', 'R') : -2, ('V', 'G') : -4, ('T', 'L') : -2, ('V', 'K') : -3,
-('K', 'Q') : 1, ('R', 'A') : -2, ('I', 'R') : -3, ('T', 'D') : -1,
-('P', 'F') : -4, ('I', 'N') : -4, ('K', 'I') : -3, ('M', 'D') : -4,
-('V', 'W') : -3, ('W', 'W') : 11, ('M', 'H') : -2, ('P', 'N') : -3,
-('K', 'A') : -1, ('M', 'L') : 2, ('K', 'E') : 1, ('Z', 'E') : 4,
-('X', 'N') : -1, ('Z', 'A') : -1, ('Z', 'M') : -2, ('X', 'F') : -2,
-('K', 'C') : -4, ('B', 'Q') : 0, ('X', 'B') : -2, ('B', 'M') : -3,
-('F', 'C') : -3, ('Z', 'Q') : 3, ('X', 'Z') : -1, ('F', 'G') : -4,
-('B', 'E') : 1, ('X', 'V') : -1, ('F', 'K') : -4, ('B', 'A') : -2,
-('X', 'R') : -1, ('D', 'D') : 6, ('W', 'G') : -4, ('Z', 'F') : -4,
-('S', 'Q') : 0, ('W', 'C') : -3, ('W', 'K') : -4, ('H', 'Q') : 1,
-('L', 'C') : -2, ('W', 'N') : -4, ('S', 'A') : 1, ('L', 'G') : -4,
-('W', 'S') : -4, ('S', 'E') : 0, ('H', 'E') : 0, ('S', 'I') : -3,
-('H', 'A') : -2, ('S', 'M') : -2, ('Y', 'L') : -2, ('Y', 'H') : 2,
-('Y', 'D') : -4, ('E', 'R') : -1, ('X', 'P') : -2, ('G', 'G') : 6,
-('G', 'C') : -4, ('E', 'N') : -1, ('Y', 'T') : -2, ('Y', 'P') : -4,
-('T', 'K') : -1, ('A', 'A') : 5, ('P', 'Q') : -2, ('T', 'C') : -1,
-('V', 'H') : -4, ('T', 'G') : -2, ('I', 'Q') : -3, ('Z', 'T') : -1,
-('C', 'R') : -4, ('V', 'P') : -3, ('P', 'E') : -2, ('M', 'C') : -2,
-('K', 'N') : 0, ('I', 'I') : 5, ('P', 'A') : -1, ('M', 'G') : -4,
-('T', 'S') : 1, ('I', 'E') : -4, ('P', 'M') : -3, ('M', 'K') : -2,
-('I', 'A') : -2, ('P', 'I') : -4, ('R', 'R') : 6, ('X', 'M') : -1,
-('L', 'I') : 1, ('X', 'I') : -2, ('Z', 'B') : 0, ('X', 'E') : -1,
-('Z', 'N') : 0, ('X', 'A') : -1, ('B', 'R') : -2, ('B', 'N') : 4,
-('F', 'D') : -4, ('X', 'Y') : -2, ('Z', 'R') : 0, ('F', 'H') : -2,
-('B', 'F') : -4, ('F', 'L') : 0, ('X', 'Q') : -1, ('B', 'B') : 4
+('W', 'F'): 0, ('L', 'R'): -3, ('S', 'P'): -1, ('V', 'T'): 0,
+('Q', 'Q'): 6, ('N', 'A'): -2, ('Z', 'Y'): -3, ('W', 'R'): -4,
+('Q', 'A'): -1, ('S', 'D'): -1, ('H', 'H'): 8, ('S', 'H'): -1,
+('H', 'D'): -2, ('L', 'N'): -4, ('W', 'A'): -3, ('Y', 'M'): -2,
+('G', 'R'): -3, ('Y', 'I'): -2, ('Y', 'E'): -3, ('B', 'Y'): -3,
+('Y', 'A'): -2, ('V', 'D'): -4, ('B', 'S'): 0, ('Y', 'Y'): 7,
+('G', 'N'): -1, ('E', 'C'): -5, ('Y', 'Q'): -2, ('Z', 'Z'): 4,
+('V', 'A'): 0, ('C', 'C'): 9, ('M', 'R'): -2, ('V', 'E'): -3,
+('T', 'N'): 0, ('P', 'P'): 8, ('V', 'I'): 3, ('V', 'S'): -2,
+('Z', 'P'): -2, ('V', 'M'): 1, ('T', 'F'): -2, ('V', 'Q'): -3,
+('K', 'K'): 5, ('P', 'D'): -2, ('I', 'H'): -4, ('I', 'D'): -4,
+('T', 'R'): -1, ('P', 'L'): -3, ('K', 'G'): -2, ('M', 'N'): -3,
+('P', 'H'): -3, ('F', 'Q'): -4, ('Z', 'G'): -3, ('X', 'L'): -2,
+('T', 'M'): -1, ('Z', 'C'): -4, ('X', 'H'): -2, ('D', 'R'): -2,
+('B', 'W'): -5, ('X', 'D'): -2, ('Z', 'K'): 1, ('F', 'A'): -3,
+('Z', 'W'): -4, ('F', 'E'): -4, ('D', 'N'): 1, ('B', 'K'): -1,
+('X', 'X'): -1, ('F', 'I'): -1, ('B', 'G'): -1, ('X', 'T'): -1,
+('F', 'M'): 0, ('B', 'C'): -4, ('Z', 'I'): -4, ('Z', 'V'): -3,
+('S', 'S'): 5, ('L', 'Q'): -3, ('W', 'E'): -4, ('Q', 'R'): 1,
+('N', 'N'): 6, ('W', 'M'): -2, ('Q', 'C'): -4, ('W', 'I'): -3,
+('S', 'C'): -2, ('L', 'A'): -2, ('S', 'G'): -1, ('L', 'E'): -4,
+('W', 'Q'): -3, ('H', 'G'): -3, ('S', 'K'): -1, ('Q', 'N'): 0,
+('N', 'R'): -1, ('H', 'C'): -4, ('Y', 'N'): -3, ('G', 'Q'): -2,
+('Y', 'F'): 3, ('C', 'A'): -1, ('V', 'L'): 1, ('G', 'E'): -3,
+('G', 'A'): 0, ('K', 'R'): 2, ('E', 'D'): 1, ('Y', 'R'): -3,
+('M', 'Q'): 0, ('T', 'I'): -1, ('C', 'D'): -4, ('V', 'F'): -1,
+('T', 'A'): 0, ('T', 'P'): -2, ('B', 'P'): -2, ('T', 'E'): -1,
+('V', 'N'): -4, ('P', 'G'): -3, ('M', 'A'): -1, ('K', 'H'): -1,
+('V', 'R'): -3, ('P', 'C'): -4, ('M', 'E'): -2, ('K', 'L'): -3,
+('V', 'V'): 4, ('M', 'I'): 1, ('T', 'Q'): -1, ('I', 'G'): -5,
+('P', 'K'): -1, ('M', 'M'): 6, ('K', 'D'): -1, ('I', 'C'): -2,
+('Z', 'D'): 1, ('F', 'R'): -4, ('X', 'K'): -1, ('Q', 'D'): -1,
+('X', 'G'): -2, ('Z', 'L'): -3, ('X', 'C'): -3, ('Z', 'H'): 0,
+('B', 'L'): -4, ('B', 'H'): -1, ('F', 'F'): 6, ('X', 'W'): -3,
+('B', 'D'): 4, ('D', 'A'): -2, ('S', 'L'): -3, ('X', 'S'): -1,
+('F', 'N'): -4, ('S', 'R'): -1, ('W', 'D'): -6, ('V', 'Y'): -2,
+('W', 'L'): -2, ('H', 'R'): 0, ('W', 'H'): -3, ('H', 'N'): 0,
+('W', 'T'): -4, ('T', 'T'): 5, ('S', 'F'): -3, ('W', 'P'): -5,
+('L', 'D'): -5, ('B', 'I'): -4, ('L', 'H'): -3, ('S', 'N'): 0,
+('B', 'T'): -1, ('L', 'L'): 4, ('Y', 'K'): -3, ('E', 'Q'): 2,
+('Y', 'G'): -4, ('Z', 'S'): 0, ('Y', 'C'): -3, ('G', 'D'): -2,
+('B', 'V'): -4, ('E', 'A'): -1, ('Y', 'W'): 2, ('E', 'E'): 6,
+('Y', 'S'): -2, ('C', 'N'): -3, ('V', 'C'): -1, ('T', 'H'): -2,
+('P', 'R'): -2, ('V', 'G'): -4, ('T', 'L'): -2, ('V', 'K'): -3,
+('K', 'Q'): 1, ('R', 'A'): -2, ('I', 'R'): -3, ('T', 'D'): -1,
+('P', 'F'): -4, ('I', 'N'): -4, ('K', 'I'): -3, ('M', 'D'): -4,
+('V', 'W'): -3, ('W', 'W'): 11, ('M', 'H'): -2, ('P', 'N'): -3,
+('K', 'A'): -1, ('M', 'L'): 2, ('K', 'E'): 1, ('Z', 'E'): 4,
+('X', 'N'): -1, ('Z', 'A'): -1, ('Z', 'M'): -2, ('X', 'F'): -2,
+('K', 'C'): -4, ('B', 'Q'): 0, ('X', 'B'): -2, ('B', 'M'): -3,
+('F', 'C'): -3, ('Z', 'Q'): 3, ('X', 'Z'): -1, ('F', 'G'): -4,
+('B', 'E'): 1, ('X', 'V'): -1, ('F', 'K'): -4, ('B', 'A'): -2,
+('X', 'R'): -1, ('D', 'D'): 6, ('W', 'G'): -4, ('Z', 'F'): -4,
+('S', 'Q'): 0, ('W', 'C'): -3, ('W', 'K'): -4, ('H', 'Q'): 1,
+('L', 'C'): -2, ('W', 'N'): -4, ('S', 'A'): 1, ('L', 'G'): -4,
+('W', 'S'): -4, ('S', 'E'): 0, ('H', 'E'): 0, ('S', 'I'): -3,
+('H', 'A'): -2, ('S', 'M'): -2, ('Y', 'L'): -2, ('Y', 'H'): 2,
+('Y', 'D'): -4, ('E', 'R'): -1, ('X', 'P'): -2, ('G', 'G'): 6,
+('G', 'C'): -4, ('E', 'N'): -1, ('Y', 'T'): -2, ('Y', 'P'): -4,
+('T', 'K'): -1, ('A', 'A'): 5, ('P', 'Q'): -2, ('T', 'C'): -1,
+('V', 'H'): -4, ('T', 'G'): -2, ('I', 'Q'): -3, ('Z', 'T'): -1,
+('C', 'R'): -4, ('V', 'P'): -3, ('P', 'E'): -2, ('M', 'C'): -2,
+('K', 'N'): 0, ('I', 'I'): 5, ('P', 'A'): -1, ('M', 'G'): -4,
+('T', 'S'): 1, ('I', 'E'): -4, ('P', 'M'): -3, ('M', 'K'): -2,
+('I', 'A'): -2, ('P', 'I'): -4, ('R', 'R'): 6, ('X', 'M'): -1,
+('L', 'I'): 1, ('X', 'I'): -2, ('Z', 'B'): 0, ('X', 'E'): -1,
+('Z', 'N'): 0, ('X', 'A'): -1, ('B', 'R'): -2, ('B', 'N'): 4,
+('F', 'D'): -4, ('X', 'Y'): -2, ('Z', 'R'): 0, ('F', 'H'): -2,
+('B', 'F'): -4, ('F', 'L'): 0, ('X', 'Q'): -1, ('B', 'B'): 4
}
blosum80 = _temp()
del _temp
@@ -1222,75 +1221,75 @@ del _temp
# http://www.embl-heidelberg.de/~vogt/matrices/blosum85.cmp
def _temp():
return {
-('W', 'F') : 0, ('L', 'R') : -3, ('S', 'P') : -1, ('V', 'T') : 0,
-('Q', 'Q') : 6, ('N', 'A') : -2, ('Z', 'Y') : -3, ('W', 'R') : -4,
-('Q', 'A') : -1, ('S', 'D') : -1, ('H', 'H') : 8, ('S', 'H') : -1,
-('H', 'D') : -2, ('L', 'N') : -4, ('W', 'A') : -3, ('Y', 'M') : -2,
-('G', 'R') : -3, ('Y', 'I') : -2, ('Y', 'E') : -4, ('B', 'Y') : -4,
-('Y', 'A') : -3, ('V', 'D') : -4, ('B', 'S') : 0, ('Y', 'Y') : 7,
-('G', 'N') : -1, ('E', 'C') : -5, ('Y', 'Q') : -2, ('Z', 'Z') : 4,
-('V', 'A') : -1, ('C', 'C') : 9, ('M', 'R') : -2, ('V', 'E') : -3,
-('T', 'N') : 0, ('P', 'P') : 8, ('V', 'I') : 3, ('V', 'S') : -2,
-('Z', 'P') : -2, ('V', 'M') : 0, ('T', 'F') : -3, ('V', 'Q') : -3,
-('K', 'K') : 6, ('P', 'D') : -2, ('I', 'H') : -4, ('I', 'D') : -5,
-('T', 'R') : -2, ('P', 'L') : -4, ('K', 'G') : -2, ('M', 'N') : -3,
-('P', 'H') : -3, ('F', 'Q') : -4, ('Z', 'G') : -3, ('X', 'L') : -2,
-('T', 'M') : -1, ('Z', 'C') : -5, ('X', 'H') : -2, ('D', 'R') : -2,
-('B', 'W') : -5, ('X', 'D') : -2, ('Z', 'K') : 1, ('F', 'A') : -3,
-('Z', 'W') : -4, ('F', 'E') : -4, ('D', 'N') : 1, ('B', 'K') : -1,
-('X', 'X') : -2, ('F', 'I') : -1, ('B', 'G') : -1, ('X', 'T') : -1,
-('F', 'M') : -1, ('B', 'C') : -4, ('Z', 'I') : -4, ('Z', 'V') : -3,
-('S', 'S') : 5, ('L', 'Q') : -3, ('W', 'E') : -4, ('Q', 'R') : 1,
-('N', 'N') : 7, ('W', 'M') : -2, ('Q', 'C') : -4, ('W', 'I') : -3,
-('S', 'C') : -2, ('L', 'A') : -2, ('S', 'G') : -1, ('L', 'E') : -4,
-('W', 'Q') : -3, ('H', 'G') : -3, ('S', 'K') : -1, ('Q', 'N') : 0,
-('N', 'R') : -1, ('H', 'C') : -5, ('Y', 'N') : -3, ('G', 'Q') : -3,
-('Y', 'F') : 3, ('C', 'A') : -1, ('V', 'L') : 0, ('G', 'E') : -3,
-('G', 'A') : 0, ('K', 'R') : 2, ('E', 'D') : 1, ('Y', 'R') : -3,
-('M', 'Q') : 0, ('T', 'I') : -1, ('C', 'D') : -5, ('V', 'F') : -1,
-('T', 'A') : 0, ('T', 'P') : -2, ('B', 'P') : -3, ('T', 'E') : -1,
-('V', 'N') : -4, ('P', 'G') : -3, ('M', 'A') : -2, ('K', 'H') : -1,
-('V', 'R') : -3, ('P', 'C') : -4, ('M', 'E') : -3, ('K', 'L') : -3,
-('V', 'V') : 5, ('M', 'I') : 1, ('T', 'Q') : -1, ('I', 'G') : -5,
-('P', 'K') : -2, ('M', 'M') : 7, ('K', 'D') : -1, ('I', 'C') : -2,
-('Z', 'D') : 1, ('F', 'R') : -4, ('X', 'K') : -1, ('Q', 'D') : -1,
-('X', 'G') : -2, ('Z', 'L') : -4, ('X', 'C') : -3, ('Z', 'H') : 0,
-('B', 'L') : -5, ('B', 'H') : -1, ('F', 'F') : 7, ('X', 'W') : -3,
-('B', 'D') : 4, ('D', 'A') : -2, ('S', 'L') : -3, ('X', 'S') : -1,
-('F', 'N') : -4, ('S', 'R') : -1, ('W', 'D') : -6, ('V', 'Y') : -2,
-('W', 'L') : -3, ('H', 'R') : 0, ('W', 'H') : -3, ('H', 'N') : 0,
-('W', 'T') : -4, ('T', 'T') : 5, ('S', 'F') : -3, ('W', 'P') : -5,
-('L', 'D') : -5, ('B', 'I') : -5, ('L', 'H') : -3, ('S', 'N') : 0,
-('B', 'T') : -1, ('L', 'L') : 4, ('Y', 'K') : -3, ('E', 'Q') : 2,
-('Y', 'G') : -5, ('Z', 'S') : -1, ('Y', 'C') : -3, ('G', 'D') : -2,
-('B', 'V') : -4, ('E', 'A') : -1, ('Y', 'W') : 2, ('E', 'E') : 6,
-('Y', 'S') : -2, ('C', 'N') : -4, ('V', 'C') : -1, ('T', 'H') : -2,
-('P', 'R') : -2, ('V', 'G') : -4, ('T', 'L') : -2, ('V', 'K') : -3,
-('K', 'Q') : 1, ('R', 'A') : -2, ('I', 'R') : -4, ('T', 'D') : -2,
-('P', 'F') : -4, ('I', 'N') : -4, ('K', 'I') : -3, ('M', 'D') : -4,
-('V', 'W') : -3, ('W', 'W') : 11, ('M', 'H') : -3, ('P', 'N') : -3,
-('K', 'A') : -1, ('M', 'L') : 2, ('K', 'E') : 0, ('Z', 'E') : 4,
-('X', 'N') : -2, ('Z', 'A') : -1, ('Z', 'M') : -2, ('X', 'F') : -2,
-('K', 'C') : -4, ('B', 'Q') : -1, ('X', 'B') : -2, ('B', 'M') : -4,
-('F', 'C') : -3, ('Z', 'Q') : 4, ('X', 'Z') : -1, ('F', 'G') : -4,
-('B', 'E') : 0, ('X', 'V') : -1, ('F', 'K') : -4, ('B', 'A') : -2,
-('X', 'R') : -2, ('D', 'D') : 7, ('W', 'G') : -4, ('Z', 'F') : -4,
-('S', 'Q') : -1, ('W', 'C') : -4, ('W', 'K') : -5, ('H', 'Q') : 1,
-('L', 'C') : -2, ('W', 'N') : -5, ('S', 'A') : 1, ('L', 'G') : -5,
-('W', 'S') : -4, ('S', 'E') : -1, ('H', 'E') : -1, ('S', 'I') : -3,
-('H', 'A') : -2, ('S', 'M') : -2, ('Y', 'L') : -2, ('Y', 'H') : 2,
-('Y', 'D') : -4, ('E', 'R') : -1, ('X', 'P') : -2, ('G', 'G') : 6,
-('G', 'C') : -4, ('E', 'N') : -1, ('Y', 'T') : -2, ('Y', 'P') : -4,
-('T', 'K') : -1, ('A', 'A') : 5, ('P', 'Q') : -2, ('T', 'C') : -2,
-('V', 'H') : -4, ('T', 'G') : -2, ('I', 'Q') : -4, ('Z', 'T') : -1,
-('C', 'R') : -4, ('V', 'P') : -3, ('P', 'E') : -2, ('M', 'C') : -2,
-('K', 'N') : 0, ('I', 'I') : 5, ('P', 'A') : -1, ('M', 'G') : -4,
-('T', 'S') : 1, ('I', 'E') : -4, ('P', 'M') : -3, ('M', 'K') : -2,
-('I', 'A') : -2, ('P', 'I') : -4, ('R', 'R') : 6, ('X', 'M') : -1,
-('L', 'I') : 1, ('X', 'I') : -2, ('Z', 'B') : 0, ('X', 'E') : -1,
-('Z', 'N') : -1, ('X', 'A') : -1, ('B', 'R') : -2, ('B', 'N') : 4,
-('F', 'D') : -4, ('X', 'Y') : -2, ('Z', 'R') : 0, ('F', 'H') : -2,
-('B', 'F') : -4, ('F', 'L') : 0, ('X', 'Q') : -1, ('B', 'B') : 4
+('W', 'F'): 0, ('L', 'R'): -3, ('S', 'P'): -1, ('V', 'T'): 0,
+('Q', 'Q'): 6, ('N', 'A'): -2, ('Z', 'Y'): -3, ('W', 'R'): -4,
+('Q', 'A'): -1, ('S', 'D'): -1, ('H', 'H'): 8, ('S', 'H'): -1,
+('H', 'D'): -2, ('L', 'N'): -4, ('W', 'A'): -3, ('Y', 'M'): -2,
+('G', 'R'): -3, ('Y', 'I'): -2, ('Y', 'E'): -4, ('B', 'Y'): -4,
+('Y', 'A'): -3, ('V', 'D'): -4, ('B', 'S'): 0, ('Y', 'Y'): 7,
+('G', 'N'): -1, ('E', 'C'): -5, ('Y', 'Q'): -2, ('Z', 'Z'): 4,
+('V', 'A'): -1, ('C', 'C'): 9, ('M', 'R'): -2, ('V', 'E'): -3,
+('T', 'N'): 0, ('P', 'P'): 8, ('V', 'I'): 3, ('V', 'S'): -2,
+('Z', 'P'): -2, ('V', 'M'): 0, ('T', 'F'): -3, ('V', 'Q'): -3,
+('K', 'K'): 6, ('P', 'D'): -2, ('I', 'H'): -4, ('I', 'D'): -5,
+('T', 'R'): -2, ('P', 'L'): -4, ('K', 'G'): -2, ('M', 'N'): -3,
+('P', 'H'): -3, ('F', 'Q'): -4, ('Z', 'G'): -3, ('X', 'L'): -2,
+('T', 'M'): -1, ('Z', 'C'): -5, ('X', 'H'): -2, ('D', 'R'): -2,
+('B', 'W'): -5, ('X', 'D'): -2, ('Z', 'K'): 1, ('F', 'A'): -3,
+('Z', 'W'): -4, ('F', 'E'): -4, ('D', 'N'): 1, ('B', 'K'): -1,
+('X', 'X'): -2, ('F', 'I'): -1, ('B', 'G'): -1, ('X', 'T'): -1,
+('F', 'M'): -1, ('B', 'C'): -4, ('Z', 'I'): -4, ('Z', 'V'): -3,
+('S', 'S'): 5, ('L', 'Q'): -3, ('W', 'E'): -4, ('Q', 'R'): 1,
+('N', 'N'): 7, ('W', 'M'): -2, ('Q', 'C'): -4, ('W', 'I'): -3,
+('S', 'C'): -2, ('L', 'A'): -2, ('S', 'G'): -1, ('L', 'E'): -4,
+('W', 'Q'): -3, ('H', 'G'): -3, ('S', 'K'): -1, ('Q', 'N'): 0,
+('N', 'R'): -1, ('H', 'C'): -5, ('Y', 'N'): -3, ('G', 'Q'): -3,
+('Y', 'F'): 3, ('C', 'A'): -1, ('V', 'L'): 0, ('G', 'E'): -3,
+('G', 'A'): 0, ('K', 'R'): 2, ('E', 'D'): 1, ('Y', 'R'): -3,
+('M', 'Q'): 0, ('T', 'I'): -1, ('C', 'D'): -5, ('V', 'F'): -1,
+('T', 'A'): 0, ('T', 'P'): -2, ('B', 'P'): -3, ('T', 'E'): -1,
+('V', 'N'): -4, ('P', 'G'): -3, ('M', 'A'): -2, ('K', 'H'): -1,
+('V', 'R'): -3, ('P', 'C'): -4, ('M', 'E'): -3, ('K', 'L'): -3,
+('V', 'V'): 5, ('M', 'I'): 1, ('T', 'Q'): -1, ('I', 'G'): -5,
+('P', 'K'): -2, ('M', 'M'): 7, ('K', 'D'): -1, ('I', 'C'): -2,
+('Z', 'D'): 1, ('F', 'R'): -4, ('X', 'K'): -1, ('Q', 'D'): -1,
+('X', 'G'): -2, ('Z', 'L'): -4, ('X', 'C'): -3, ('Z', 'H'): 0,
+('B', 'L'): -5, ('B', 'H'): -1, ('F', 'F'): 7, ('X', 'W'): -3,
+('B', 'D'): 4, ('D', 'A'): -2, ('S', 'L'): -3, ('X', 'S'): -1,
+('F', 'N'): -4, ('S', 'R'): -1, ('W', 'D'): -6, ('V', 'Y'): -2,
+('W', 'L'): -3, ('H', 'R'): 0, ('W', 'H'): -3, ('H', 'N'): 0,
+('W', 'T'): -4, ('T', 'T'): 5, ('S', 'F'): -3, ('W', 'P'): -5,
+('L', 'D'): -5, ('B', 'I'): -5, ('L', 'H'): -3, ('S', 'N'): 0,
+('B', 'T'): -1, ('L', 'L'): 4, ('Y', 'K'): -3, ('E', 'Q'): 2,
+('Y', 'G'): -5, ('Z', 'S'): -1, ('Y', 'C'): -3, ('G', 'D'): -2,
+('B', 'V'): -4, ('E', 'A'): -1, ('Y', 'W'): 2, ('E', 'E'): 6,
+('Y', 'S'): -2, ('C', 'N'): -4, ('V', 'C'): -1, ('T', 'H'): -2,
+('P', 'R'): -2, ('V', 'G'): -4, ('T', 'L'): -2, ('V', 'K'): -3,
+('K', 'Q'): 1, ('R', 'A'): -2, ('I', 'R'): -4, ('T', 'D'): -2,
+('P', 'F'): -4, ('I', 'N'): -4, ('K', 'I'): -3, ('M', 'D'): -4,
+('V', 'W'): -3, ('W', 'W'): 11, ('M', 'H'): -3, ('P', 'N'): -3,
+('K', 'A'): -1, ('M', 'L'): 2, ('K', 'E'): 0, ('Z', 'E'): 4,
+('X', 'N'): -2, ('Z', 'A'): -1, ('Z', 'M'): -2, ('X', 'F'): -2,
+('K', 'C'): -4, ('B', 'Q'): -1, ('X', 'B'): -2, ('B', 'M'): -4,
+('F', 'C'): -3, ('Z', 'Q'): 4, ('X', 'Z'): -1, ('F', 'G'): -4,
+('B', 'E'): 0, ('X', 'V'): -1, ('F', 'K'): -4, ('B', 'A'): -2,
+('X', 'R'): -2, ('D', 'D'): 7, ('W', 'G'): -4, ('Z', 'F'): -4,
+('S', 'Q'): -1, ('W', 'C'): -4, ('W', 'K'): -5, ('H', 'Q'): 1,
+('L', 'C'): -2, ('W', 'N'): -5, ('S', 'A'): 1, ('L', 'G'): -5,
+('W', 'S'): -4, ('S', 'E'): -1, ('H', 'E'): -1, ('S', 'I'): -3,
+('H', 'A'): -2, ('S', 'M'): -2, ('Y', 'L'): -2, ('Y', 'H'): 2,
+('Y', 'D'): -4, ('E', 'R'): -1, ('X', 'P'): -2, ('G', 'G'): 6,
+('G', 'C'): -4, ('E', 'N'): -1, ('Y', 'T'): -2, ('Y', 'P'): -4,
+('T', 'K'): -1, ('A', 'A'): 5, ('P', 'Q'): -2, ('T', 'C'): -2,
+('V', 'H'): -4, ('T', 'G'): -2, ('I', 'Q'): -4, ('Z', 'T'): -1,
+('C', 'R'): -4, ('V', 'P'): -3, ('P', 'E'): -2, ('M', 'C'): -2,
+('K', 'N'): 0, ('I', 'I'): 5, ('P', 'A'): -1, ('M', 'G'): -4,
+('T', 'S'): 1, ('I', 'E'): -4, ('P', 'M'): -3, ('M', 'K'): -2,
+('I', 'A'): -2, ('P', 'I'): -4, ('R', 'R'): 6, ('X', 'M'): -1,
+('L', 'I'): 1, ('X', 'I'): -2, ('Z', 'B'): 0, ('X', 'E'): -1,
+('Z', 'N'): -1, ('X', 'A'): -1, ('B', 'R'): -2, ('B', 'N'): 4,
+('F', 'D'): -4, ('X', 'Y'): -2, ('Z', 'R'): 0, ('F', 'H'): -2,
+('B', 'F'): -4, ('F', 'L'): 0, ('X', 'Q'): -1, ('B', 'B'): 4
}
blosum85 = _temp()
del _temp
@@ -1299,75 +1298,75 @@ del _temp
# http://www.embl-heidelberg.de/~vogt/matrices/blosum90.cmp
def _temp():
return {
-('W', 'F') : 0, ('L', 'R') : -3, ('S', 'P') : -2, ('V', 'T') : -1,
-('Q', 'Q') : 7, ('N', 'A') : -2, ('Z', 'Y') : -3, ('W', 'R') : -4,
-('Q', 'A') : -1, ('S', 'D') : -1, ('H', 'H') : 8, ('S', 'H') : -2,
-('H', 'D') : -2, ('L', 'N') : -4, ('W', 'A') : -4, ('Y', 'M') : -2,
-('G', 'R') : -3, ('Y', 'I') : -2, ('Y', 'E') : -4, ('B', 'Y') : -4,
-('Y', 'A') : -3, ('V', 'D') : -5, ('B', 'S') : 0, ('Y', 'Y') : 8,
-('G', 'N') : -1, ('E', 'C') : -6, ('Y', 'Q') : -3, ('Z', 'Z') : 4,
-('V', 'A') : -1, ('C', 'C') : 9, ('M', 'R') : -2, ('V', 'E') : -3,
-('T', 'N') : 0, ('P', 'P') : 8, ('V', 'I') : 3, ('V', 'S') : -2,
-('Z', 'P') : -2, ('V', 'M') : 0, ('T', 'F') : -3, ('V', 'Q') : -3,
-('K', 'K') : 6, ('P', 'D') : -3, ('I', 'H') : -4, ('I', 'D') : -5,
-('T', 'R') : -2, ('P', 'L') : -4, ('K', 'G') : -2, ('M', 'N') : -3,
-('P', 'H') : -3, ('F', 'Q') : -4, ('Z', 'G') : -3, ('X', 'L') : -2,
-('T', 'M') : -1, ('Z', 'C') : -5, ('X', 'H') : -2, ('D', 'R') : -3,
-('B', 'W') : -6, ('X', 'D') : -2, ('Z', 'K') : 1, ('F', 'A') : -3,
-('Z', 'W') : -4, ('F', 'E') : -5, ('D', 'N') : 1, ('B', 'K') : -1,
-('X', 'X') : -2, ('F', 'I') : -1, ('B', 'G') : -2, ('X', 'T') : -1,
-('F', 'M') : -1, ('B', 'C') : -4, ('Z', 'I') : -4, ('Z', 'V') : -3,
-('S', 'S') : 5, ('L', 'Q') : -3, ('W', 'E') : -5, ('Q', 'R') : 1,
-('N', 'N') : 7, ('W', 'M') : -2, ('Q', 'C') : -4, ('W', 'I') : -4,
-('S', 'C') : -2, ('L', 'A') : -2, ('S', 'G') : -1, ('L', 'E') : -4,
-('W', 'Q') : -3, ('H', 'G') : -3, ('S', 'K') : -1, ('Q', 'N') : 0,
-('N', 'R') : -1, ('H', 'C') : -5, ('Y', 'N') : -3, ('G', 'Q') : -3,
-('Y', 'F') : 3, ('C', 'A') : -1, ('V', 'L') : 0, ('G', 'E') : -3,
-('G', 'A') : 0, ('K', 'R') : 2, ('E', 'D') : 1, ('Y', 'R') : -3,
-('M', 'Q') : 0, ('T', 'I') : -1, ('C', 'D') : -5, ('V', 'F') : -2,
-('T', 'A') : 0, ('T', 'P') : -2, ('B', 'P') : -3, ('T', 'E') : -1,
-('V', 'N') : -4, ('P', 'G') : -3, ('M', 'A') : -2, ('K', 'H') : -1,
-('V', 'R') : -3, ('P', 'C') : -4, ('M', 'E') : -3, ('K', 'L') : -3,
-('V', 'V') : 5, ('M', 'I') : 1, ('T', 'Q') : -1, ('I', 'G') : -5,
-('P', 'K') : -2, ('M', 'M') : 7, ('K', 'D') : -1, ('I', 'C') : -2,
-('Z', 'D') : 0, ('F', 'R') : -4, ('X', 'K') : -1, ('Q', 'D') : -1,
-('X', 'G') : -2, ('Z', 'L') : -4, ('X', 'C') : -3, ('Z', 'H') : 0,
-('B', 'L') : -5, ('B', 'H') : -1, ('F', 'F') : 7, ('X', 'W') : -3,
-('B', 'D') : 4, ('D', 'A') : -3, ('S', 'L') : -3, ('X', 'S') : -1,
-('F', 'N') : -4, ('S', 'R') : -1, ('W', 'D') : -6, ('V', 'Y') : -3,
-('W', 'L') : -3, ('H', 'R') : 0, ('W', 'H') : -3, ('H', 'N') : 0,
-('W', 'T') : -4, ('T', 'T') : 6, ('S', 'F') : -3, ('W', 'P') : -5,
-('L', 'D') : -5, ('B', 'I') : -5, ('L', 'H') : -4, ('S', 'N') : 0,
-('B', 'T') : -1, ('L', 'L') : 5, ('Y', 'K') : -3, ('E', 'Q') : 2,
-('Y', 'G') : -5, ('Z', 'S') : -1, ('Y', 'C') : -4, ('G', 'D') : -2,
-('B', 'V') : -4, ('E', 'A') : -1, ('Y', 'W') : 2, ('E', 'E') : 6,
-('Y', 'S') : -3, ('C', 'N') : -4, ('V', 'C') : -2, ('T', 'H') : -2,
-('P', 'R') : -3, ('V', 'G') : -5, ('T', 'L') : -2, ('V', 'K') : -3,
-('K', 'Q') : 1, ('R', 'A') : -2, ('I', 'R') : -4, ('T', 'D') : -2,
-('P', 'F') : -4, ('I', 'N') : -4, ('K', 'I') : -4, ('M', 'D') : -4,
-('V', 'W') : -3, ('W', 'W') : 11, ('M', 'H') : -3, ('P', 'N') : -3,
-('K', 'A') : -1, ('M', 'L') : 2, ('K', 'E') : 0, ('Z', 'E') : 4,
-('X', 'N') : -2, ('Z', 'A') : -1, ('Z', 'M') : -2, ('X', 'F') : -2,
-('K', 'C') : -4, ('B', 'Q') : -1, ('X', 'B') : -2, ('B', 'M') : -4,
-('F', 'C') : -3, ('Z', 'Q') : 4, ('X', 'Z') : -1, ('F', 'G') : -5,
-('B', 'E') : 0, ('X', 'V') : -2, ('F', 'K') : -4, ('B', 'A') : -2,
-('X', 'R') : -2, ('D', 'D') : 7, ('W', 'G') : -4, ('Z', 'F') : -4,
-('S', 'Q') : -1, ('W', 'C') : -4, ('W', 'K') : -5, ('H', 'Q') : 1,
-('L', 'C') : -2, ('W', 'N') : -5, ('S', 'A') : 1, ('L', 'G') : -5,
-('W', 'S') : -4, ('S', 'E') : -1, ('H', 'E') : -1, ('S', 'I') : -3,
-('H', 'A') : -2, ('S', 'M') : -2, ('Y', 'L') : -2, ('Y', 'H') : 1,
-('Y', 'D') : -4, ('E', 'R') : -1, ('X', 'P') : -2, ('G', 'G') : 6,
-('G', 'C') : -4, ('E', 'N') : -1, ('Y', 'T') : -2, ('Y', 'P') : -4,
-('T', 'K') : -1, ('A', 'A') : 5, ('P', 'Q') : -2, ('T', 'C') : -2,
-('V', 'H') : -4, ('T', 'G') : -3, ('I', 'Q') : -4, ('Z', 'T') : -1,
-('C', 'R') : -5, ('V', 'P') : -3, ('P', 'E') : -2, ('M', 'C') : -2,
-('K', 'N') : 0, ('I', 'I') : 5, ('P', 'A') : -1, ('M', 'G') : -4,
-('T', 'S') : 1, ('I', 'E') : -4, ('P', 'M') : -3, ('M', 'K') : -2,
-('I', 'A') : -2, ('P', 'I') : -4, ('R', 'R') : 6, ('X', 'M') : -1,
-('L', 'I') : 1, ('X', 'I') : -2, ('Z', 'B') : 0, ('X', 'E') : -2,
-('Z', 'N') : -1, ('X', 'A') : -1, ('B', 'R') : -2, ('B', 'N') : 4,
-('F', 'D') : -5, ('X', 'Y') : -2, ('Z', 'R') : 0, ('F', 'H') : -2,
-('B', 'F') : -4, ('F', 'L') : 0, ('X', 'Q') : -1, ('B', 'B') : 4
+('W', 'F'): 0, ('L', 'R'): -3, ('S', 'P'): -2, ('V', 'T'): -1,
+('Q', 'Q'): 7, ('N', 'A'): -2, ('Z', 'Y'): -3, ('W', 'R'): -4,
+('Q', 'A'): -1, ('S', 'D'): -1, ('H', 'H'): 8, ('S', 'H'): -2,
+('H', 'D'): -2, ('L', 'N'): -4, ('W', 'A'): -4, ('Y', 'M'): -2,
+('G', 'R'): -3, ('Y', 'I'): -2, ('Y', 'E'): -4, ('B', 'Y'): -4,
+('Y', 'A'): -3, ('V', 'D'): -5, ('B', 'S'): 0, ('Y', 'Y'): 8,
+('G', 'N'): -1, ('E', 'C'): -6, ('Y', 'Q'): -3, ('Z', 'Z'): 4,
+('V', 'A'): -1, ('C', 'C'): 9, ('M', 'R'): -2, ('V', 'E'): -3,
+('T', 'N'): 0, ('P', 'P'): 8, ('V', 'I'): 3, ('V', 'S'): -2,
+('Z', 'P'): -2, ('V', 'M'): 0, ('T', 'F'): -3, ('V', 'Q'): -3,
+('K', 'K'): 6, ('P', 'D'): -3, ('I', 'H'): -4, ('I', 'D'): -5,
+('T', 'R'): -2, ('P', 'L'): -4, ('K', 'G'): -2, ('M', 'N'): -3,
+('P', 'H'): -3, ('F', 'Q'): -4, ('Z', 'G'): -3, ('X', 'L'): -2,
+('T', 'M'): -1, ('Z', 'C'): -5, ('X', 'H'): -2, ('D', 'R'): -3,
+('B', 'W'): -6, ('X', 'D'): -2, ('Z', 'K'): 1, ('F', 'A'): -3,
+('Z', 'W'): -4, ('F', 'E'): -5, ('D', 'N'): 1, ('B', 'K'): -1,
+('X', 'X'): -2, ('F', 'I'): -1, ('B', 'G'): -2, ('X', 'T'): -1,
+('F', 'M'): -1, ('B', 'C'): -4, ('Z', 'I'): -4, ('Z', 'V'): -3,
+('S', 'S'): 5, ('L', 'Q'): -3, ('W', 'E'): -5, ('Q', 'R'): 1,
+('N', 'N'): 7, ('W', 'M'): -2, ('Q', 'C'): -4, ('W', 'I'): -4,
+('S', 'C'): -2, ('L', 'A'): -2, ('S', 'G'): -1, ('L', 'E'): -4,
+('W', 'Q'): -3, ('H', 'G'): -3, ('S', 'K'): -1, ('Q', 'N'): 0,
+('N', 'R'): -1, ('H', 'C'): -5, ('Y', 'N'): -3, ('G', 'Q'): -3,
+('Y', 'F'): 3, ('C', 'A'): -1, ('V', 'L'): 0, ('G', 'E'): -3,
+('G', 'A'): 0, ('K', 'R'): 2, ('E', 'D'): 1, ('Y', 'R'): -3,
+('M', 'Q'): 0, ('T', 'I'): -1, ('C', 'D'): -5, ('V', 'F'): -2,
+('T', 'A'): 0, ('T', 'P'): -2, ('B', 'P'): -3, ('T', 'E'): -1,
+('V', 'N'): -4, ('P', 'G'): -3, ('M', 'A'): -2, ('K', 'H'): -1,
+('V', 'R'): -3, ('P', 'C'): -4, ('M', 'E'): -3, ('K', 'L'): -3,
+('V', 'V'): 5, ('M', 'I'): 1, ('T', 'Q'): -1, ('I', 'G'): -5,
+('P', 'K'): -2, ('M', 'M'): 7, ('K', 'D'): -1, ('I', 'C'): -2,
+('Z', 'D'): 0, ('F', 'R'): -4, ('X', 'K'): -1, ('Q', 'D'): -1,
+('X', 'G'): -2, ('Z', 'L'): -4, ('X', 'C'): -3, ('Z', 'H'): 0,
+('B', 'L'): -5, ('B', 'H'): -1, ('F', 'F'): 7, ('X', 'W'): -3,
+('B', 'D'): 4, ('D', 'A'): -3, ('S', 'L'): -3, ('X', 'S'): -1,
+('F', 'N'): -4, ('S', 'R'): -1, ('W', 'D'): -6, ('V', 'Y'): -3,
+('W', 'L'): -3, ('H', 'R'): 0, ('W', 'H'): -3, ('H', 'N'): 0,
+('W', 'T'): -4, ('T', 'T'): 6, ('S', 'F'): -3, ('W', 'P'): -5,
+('L', 'D'): -5, ('B', 'I'): -5, ('L', 'H'): -4, ('S', 'N'): 0,
+('B', 'T'): -1, ('L', 'L'): 5, ('Y', 'K'): -3, ('E', 'Q'): 2,
+('Y', 'G'): -5, ('Z', 'S'): -1, ('Y', 'C'): -4, ('G', 'D'): -2,
+('B', 'V'): -4, ('E', 'A'): -1, ('Y', 'W'): 2, ('E', 'E'): 6,
+('Y', 'S'): -3, ('C', 'N'): -4, ('V', 'C'): -2, ('T', 'H'): -2,
+('P', 'R'): -3, ('V', 'G'): -5, ('T', 'L'): -2, ('V', 'K'): -3,
+('K', 'Q'): 1, ('R', 'A'): -2, ('I', 'R'): -4, ('T', 'D'): -2,
+('P', 'F'): -4, ('I', 'N'): -4, ('K', 'I'): -4, ('M', 'D'): -4,
+('V', 'W'): -3, ('W', 'W'): 11, ('M', 'H'): -3, ('P', 'N'): -3,
+('K', 'A'): -1, ('M', 'L'): 2, ('K', 'E'): 0, ('Z', 'E'): 4,
+('X', 'N'): -2, ('Z', 'A'): -1, ('Z', 'M'): -2, ('X', 'F'): -2,
+('K', 'C'): -4, ('B', 'Q'): -1, ('X', 'B'): -2, ('B', 'M'): -4,
+('F', 'C'): -3, ('Z', 'Q'): 4, ('X', 'Z'): -1, ('F', 'G'): -5,
+('B', 'E'): 0, ('X', 'V'): -2, ('F', 'K'): -4, ('B', 'A'): -2,
+('X', 'R'): -2, ('D', 'D'): 7, ('W', 'G'): -4, ('Z', 'F'): -4,
+('S', 'Q'): -1, ('W', 'C'): -4, ('W', 'K'): -5, ('H', 'Q'): 1,
+('L', 'C'): -2, ('W', 'N'): -5, ('S', 'A'): 1, ('L', 'G'): -5,
+('W', 'S'): -4, ('S', 'E'): -1, ('H', 'E'): -1, ('S', 'I'): -3,
+('H', 'A'): -2, ('S', 'M'): -2, ('Y', 'L'): -2, ('Y', 'H'): 1,
+('Y', 'D'): -4, ('E', 'R'): -1, ('X', 'P'): -2, ('G', 'G'): 6,
+('G', 'C'): -4, ('E', 'N'): -1, ('Y', 'T'): -2, ('Y', 'P'): -4,
+('T', 'K'): -1, ('A', 'A'): 5, ('P', 'Q'): -2, ('T', 'C'): -2,
+('V', 'H'): -4, ('T', 'G'): -3, ('I', 'Q'): -4, ('Z', 'T'): -1,
+('C', 'R'): -5, ('V', 'P'): -3, ('P', 'E'): -2, ('M', 'C'): -2,
+('K', 'N'): 0, ('I', 'I'): 5, ('P', 'A'): -1, ('M', 'G'): -4,
+('T', 'S'): 1, ('I', 'E'): -4, ('P', 'M'): -3, ('M', 'K'): -2,
+('I', 'A'): -2, ('P', 'I'): -4, ('R', 'R'): 6, ('X', 'M'): -1,
+('L', 'I'): 1, ('X', 'I'): -2, ('Z', 'B'): 0, ('X', 'E'): -2,
+('Z', 'N'): -1, ('X', 'A'): -1, ('B', 'R'): -2, ('B', 'N'): 4,
+('F', 'D'): -5, ('X', 'Y'): -2, ('Z', 'R'): 0, ('F', 'H'): -2,
+('B', 'F'): -4, ('F', 'L'): 0, ('X', 'Q'): -1, ('B', 'B'): 4
}
blosum90 = _temp()
del _temp
@@ -1376,75 +1375,75 @@ del _temp
# http://www.embl-heidelberg.de/~vogt/matrices/blosum95.cmp
def _temp():
return {
-('W', 'F') : 0, ('L', 'R') : -3, ('S', 'P') : -2, ('V', 'T') : -1,
-('Q', 'Q') : 7, ('N', 'A') : -2, ('Z', 'Y') : -4, ('W', 'R') : -4,
-('Q', 'A') : -1, ('S', 'D') : -1, ('H', 'H') : 9, ('S', 'H') : -2,
-('H', 'D') : -2, ('L', 'N') : -5, ('W', 'A') : -4, ('Y', 'M') : -3,
-('G', 'R') : -4, ('Y', 'I') : -2, ('Y', 'E') : -4, ('B', 'Y') : -4,
-('Y', 'A') : -3, ('V', 'D') : -5, ('B', 'S') : -1, ('Y', 'Y') : 8,
-('G', 'N') : -1, ('E', 'C') : -6, ('Y', 'Q') : -3, ('Z', 'Z') : 4,
-('V', 'A') : -1, ('C', 'C') : 9, ('M', 'R') : -2, ('V', 'E') : -3,
-('T', 'N') : -1, ('P', 'P') : 8, ('V', 'I') : 3, ('V', 'S') : -3,
-('Z', 'P') : -2, ('V', 'M') : 0, ('T', 'F') : -3, ('V', 'Q') : -3,
-('K', 'K') : 6, ('P', 'D') : -3, ('I', 'H') : -4, ('I', 'D') : -5,
-('T', 'R') : -2, ('P', 'L') : -4, ('K', 'G') : -3, ('M', 'N') : -3,
-('P', 'H') : -3, ('F', 'Q') : -4, ('Z', 'G') : -3, ('X', 'L') : -2,
-('T', 'M') : -1, ('Z', 'C') : -5, ('X', 'H') : -2, ('D', 'R') : -3,
-('B', 'W') : -6, ('X', 'D') : -2, ('Z', 'K') : 0, ('F', 'A') : -3,
-('Z', 'W') : -4, ('F', 'E') : -5, ('D', 'N') : 1, ('B', 'K') : -1,
-('X', 'X') : -2, ('F', 'I') : -1, ('B', 'G') : -2, ('X', 'T') : -1,
-('F', 'M') : -1, ('B', 'C') : -4, ('Z', 'I') : -4, ('Z', 'V') : -3,
-('S', 'S') : 5, ('L', 'Q') : -3, ('W', 'E') : -5, ('Q', 'R') : 0,
-('N', 'N') : 7, ('W', 'M') : -2, ('Q', 'C') : -4, ('W', 'I') : -4,
-('S', 'C') : -2, ('L', 'A') : -2, ('S', 'G') : -1, ('L', 'E') : -4,
-('W', 'Q') : -3, ('H', 'G') : -3, ('S', 'K') : -1, ('Q', 'N') : 0,
-('N', 'R') : -1, ('H', 'C') : -5, ('Y', 'N') : -3, ('G', 'Q') : -3,
-('Y', 'F') : 3, ('C', 'A') : -1, ('V', 'L') : 0, ('G', 'E') : -3,
-('G', 'A') : -1, ('K', 'R') : 2, ('E', 'D') : 1, ('Y', 'R') : -3,
-('M', 'Q') : -1, ('T', 'I') : -2, ('C', 'D') : -5, ('V', 'F') : -2,
-('T', 'A') : 0, ('T', 'P') : -2, ('B', 'P') : -3, ('T', 'E') : -2,
-('V', 'N') : -4, ('P', 'G') : -4, ('M', 'A') : -2, ('K', 'H') : -1,
-('V', 'R') : -4, ('P', 'C') : -5, ('M', 'E') : -3, ('K', 'L') : -3,
-('V', 'V') : 5, ('M', 'I') : 1, ('T', 'Q') : -1, ('I', 'G') : -6,
-('P', 'K') : -2, ('M', 'M') : 7, ('K', 'D') : -2, ('I', 'C') : -2,
-('Z', 'D') : 0, ('F', 'R') : -4, ('X', 'K') : -1, ('Q', 'D') : -1,
-('X', 'G') : -3, ('Z', 'L') : -4, ('X', 'C') : -3, ('Z', 'H') : 0,
-('B', 'L') : -5, ('B', 'H') : -1, ('F', 'F') : 7, ('X', 'W') : -4,
-('B', 'D') : 4, ('D', 'A') : -3, ('S', 'L') : -3, ('X', 'S') : -1,
-('F', 'N') : -4, ('S', 'R') : -2, ('W', 'D') : -6, ('V', 'Y') : -3,
-('W', 'L') : -3, ('H', 'R') : -1, ('W', 'H') : -3, ('H', 'N') : 0,
-('W', 'T') : -4, ('T', 'T') : 6, ('S', 'F') : -3, ('W', 'P') : -5,
-('L', 'D') : -5, ('B', 'I') : -5, ('L', 'H') : -4, ('S', 'N') : 0,
-('B', 'T') : -1, ('L', 'L') : 5, ('Y', 'K') : -3, ('E', 'Q') : 2,
-('Y', 'G') : -5, ('Z', 'S') : -1, ('Y', 'C') : -4, ('G', 'D') : -2,
-('B', 'V') : -5, ('E', 'A') : -1, ('Y', 'W') : 2, ('E', 'E') : 6,
-('Y', 'S') : -3, ('C', 'N') : -4, ('V', 'C') : -2, ('T', 'H') : -2,
-('P', 'R') : -3, ('V', 'G') : -5, ('T', 'L') : -2, ('V', 'K') : -3,
-('K', 'Q') : 1, ('R', 'A') : -2, ('I', 'R') : -4, ('T', 'D') : -2,
-('P', 'F') : -5, ('I', 'N') : -4, ('K', 'I') : -4, ('M', 'D') : -5,
-('V', 'W') : -3, ('W', 'W') : 11, ('M', 'H') : -3, ('P', 'N') : -3,
-('K', 'A') : -1, ('M', 'L') : 2, ('K', 'E') : 0, ('Z', 'E') : 4,
-('X', 'N') : -2, ('Z', 'A') : -1, ('Z', 'M') : -2, ('X', 'F') : -2,
-('K', 'C') : -5, ('B', 'Q') : -1, ('X', 'B') : -2, ('B', 'M') : -4,
-('F', 'C') : -3, ('Z', 'Q') : 4, ('X', 'Z') : -1, ('F', 'G') : -5,
-('B', 'E') : 0, ('X', 'V') : -2, ('F', 'K') : -4, ('B', 'A') : -3,
-('X', 'R') : -2, ('D', 'D') : 7, ('W', 'G') : -5, ('Z', 'F') : -4,
-('S', 'Q') : -1, ('W', 'C') : -4, ('W', 'K') : -5, ('H', 'Q') : 1,
-('L', 'C') : -3, ('W', 'N') : -5, ('S', 'A') : 1, ('L', 'G') : -5,
-('W', 'S') : -4, ('S', 'E') : -1, ('H', 'E') : -1, ('S', 'I') : -3,
-('H', 'A') : -3, ('S', 'M') : -3, ('Y', 'L') : -2, ('Y', 'H') : 1,
-('Y', 'D') : -5, ('E', 'R') : -1, ('X', 'P') : -3, ('G', 'G') : 6,
-('G', 'C') : -5, ('E', 'N') : -1, ('Y', 'T') : -3, ('Y', 'P') : -5,
-('T', 'K') : -1, ('A', 'A') : 5, ('P', 'Q') : -2, ('T', 'C') : -2,
-('V', 'H') : -4, ('T', 'G') : -3, ('I', 'Q') : -4, ('Z', 'T') : -2,
-('C', 'R') : -5, ('V', 'P') : -4, ('P', 'E') : -2, ('M', 'C') : -3,
-('K', 'N') : 0, ('I', 'I') : 5, ('P', 'A') : -1, ('M', 'G') : -4,
-('T', 'S') : 1, ('I', 'E') : -4, ('P', 'M') : -3, ('M', 'K') : -2,
-('I', 'A') : -2, ('P', 'I') : -4, ('R', 'R') : 7, ('X', 'M') : -2,
-('L', 'I') : 1, ('X', 'I') : -2, ('Z', 'B') : 0, ('X', 'E') : -2,
-('Z', 'N') : -1, ('X', 'A') : -1, ('B', 'R') : -2, ('B', 'N') : 4,
-('F', 'D') : -5, ('X', 'Y') : -2, ('Z', 'R') : -1, ('F', 'H') : -2,
-('B', 'F') : -5, ('F', 'L') : 0, ('X', 'Q') : -1, ('B', 'B') : 4
+('W', 'F'): 0, ('L', 'R'): -3, ('S', 'P'): -2, ('V', 'T'): -1,
+('Q', 'Q'): 7, ('N', 'A'): -2, ('Z', 'Y'): -4, ('W', 'R'): -4,
+('Q', 'A'): -1, ('S', 'D'): -1, ('H', 'H'): 9, ('S', 'H'): -2,
+('H', 'D'): -2, ('L', 'N'): -5, ('W', 'A'): -4, ('Y', 'M'): -3,
+('G', 'R'): -4, ('Y', 'I'): -2, ('Y', 'E'): -4, ('B', 'Y'): -4,
+('Y', 'A'): -3, ('V', 'D'): -5, ('B', 'S'): -1, ('Y', 'Y'): 8,
+('G', 'N'): -1, ('E', 'C'): -6, ('Y', 'Q'): -3, ('Z', 'Z'): 4,
+('V', 'A'): -1, ('C', 'C'): 9, ('M', 'R'): -2, ('V', 'E'): -3,
+('T', 'N'): -1, ('P', 'P'): 8, ('V', 'I'): 3, ('V', 'S'): -3,
+('Z', 'P'): -2, ('V', 'M'): 0, ('T', 'F'): -3, ('V', 'Q'): -3,
+('K', 'K'): 6, ('P', 'D'): -3, ('I', 'H'): -4, ('I', 'D'): -5,
+('T', 'R'): -2, ('P', 'L'): -4, ('K', 'G'): -3, ('M', 'N'): -3,
+('P', 'H'): -3, ('F', 'Q'): -4, ('Z', 'G'): -3, ('X', 'L'): -2,
+('T', 'M'): -1, ('Z', 'C'): -5, ('X', 'H'): -2, ('D', 'R'): -3,
+('B', 'W'): -6, ('X', 'D'): -2, ('Z', 'K'): 0, ('F', 'A'): -3,
+('Z', 'W'): -4, ('F', 'E'): -5, ('D', 'N'): 1, ('B', 'K'): -1,
+('X', 'X'): -2, ('F', 'I'): -1, ('B', 'G'): -2, ('X', 'T'): -1,
+('F', 'M'): -1, ('B', 'C'): -4, ('Z', 'I'): -4, ('Z', 'V'): -3,
+('S', 'S'): 5, ('L', 'Q'): -3, ('W', 'E'): -5, ('Q', 'R'): 0,
+('N', 'N'): 7, ('W', 'M'): -2, ('Q', 'C'): -4, ('W', 'I'): -4,
+('S', 'C'): -2, ('L', 'A'): -2, ('S', 'G'): -1, ('L', 'E'): -4,
+('W', 'Q'): -3, ('H', 'G'): -3, ('S', 'K'): -1, ('Q', 'N'): 0,
+('N', 'R'): -1, ('H', 'C'): -5, ('Y', 'N'): -3, ('G', 'Q'): -3,
+('Y', 'F'): 3, ('C', 'A'): -1, ('V', 'L'): 0, ('G', 'E'): -3,
+('G', 'A'): -1, ('K', 'R'): 2, ('E', 'D'): 1, ('Y', 'R'): -3,
+('M', 'Q'): -1, ('T', 'I'): -2, ('C', 'D'): -5, ('V', 'F'): -2,
+('T', 'A'): 0, ('T', 'P'): -2, ('B', 'P'): -3, ('T', 'E'): -2,
+('V', 'N'): -4, ('P', 'G'): -4, ('M', 'A'): -2, ('K', 'H'): -1,
+('V', 'R'): -4, ('P', 'C'): -5, ('M', 'E'): -3, ('K', 'L'): -3,
+('V', 'V'): 5, ('M', 'I'): 1, ('T', 'Q'): -1, ('I', 'G'): -6,
+('P', 'K'): -2, ('M', 'M'): 7, ('K', 'D'): -2, ('I', 'C'): -2,
+('Z', 'D'): 0, ('F', 'R'): -4, ('X', 'K'): -1, ('Q', 'D'): -1,
+('X', 'G'): -3, ('Z', 'L'): -4, ('X', 'C'): -3, ('Z', 'H'): 0,
+('B', 'L'): -5, ('B', 'H'): -1, ('F', 'F'): 7, ('X', 'W'): -4,
+('B', 'D'): 4, ('D', 'A'): -3, ('S', 'L'): -3, ('X', 'S'): -1,
+('F', 'N'): -4, ('S', 'R'): -2, ('W', 'D'): -6, ('V', 'Y'): -3,
+('W', 'L'): -3, ('H', 'R'): -1, ('W', 'H'): -3, ('H', 'N'): 0,
+('W', 'T'): -4, ('T', 'T'): 6, ('S', 'F'): -3, ('W', 'P'): -5,
+('L', 'D'): -5, ('B', 'I'): -5, ('L', 'H'): -4, ('S', 'N'): 0,
+('B', 'T'): -1, ('L', 'L'): 5, ('Y', 'K'): -3, ('E', 'Q'): 2,
+('Y', 'G'): -5, ('Z', 'S'): -1, ('Y', 'C'): -4, ('G', 'D'): -2,
+('B', 'V'): -5, ('E', 'A'): -1, ('Y', 'W'): 2, ('E', 'E'): 6,
+('Y', 'S'): -3, ('C', 'N'): -4, ('V', 'C'): -2, ('T', 'H'): -2,
+('P', 'R'): -3, ('V', 'G'): -5, ('T', 'L'): -2, ('V', 'K'): -3,
+('K', 'Q'): 1, ('R', 'A'): -2, ('I', 'R'): -4, ('T', 'D'): -2,
+('P', 'F'): -5, ('I', 'N'): -4, ('K', 'I'): -4, ('M', 'D'): -5,
+('V', 'W'): -3, ('W', 'W'): 11, ('M', 'H'): -3, ('P', 'N'): -3,
+('K', 'A'): -1, ('M', 'L'): 2, ('K', 'E'): 0, ('Z', 'E'): 4,
+('X', 'N'): -2, ('Z', 'A'): -1, ('Z', 'M'): -2, ('X', 'F'): -2,
+('K', 'C'): -5, ('B', 'Q'): -1, ('X', 'B'): -2, ('B', 'M'): -4,
+('F', 'C'): -3, ('Z', 'Q'): 4, ('X', 'Z'): -1, ('F', 'G'): -5,
+('B', 'E'): 0, ('X', 'V'): -2, ('F', 'K'): -4, ('B', 'A'): -3,
+('X', 'R'): -2, ('D', 'D'): 7, ('W', 'G'): -5, ('Z', 'F'): -4,
+('S', 'Q'): -1, ('W', 'C'): -4, ('W', 'K'): -5, ('H', 'Q'): 1,
+('L', 'C'): -3, ('W', 'N'): -5, ('S', 'A'): 1, ('L', 'G'): -5,
+('W', 'S'): -4, ('S', 'E'): -1, ('H', 'E'): -1, ('S', 'I'): -3,
+('H', 'A'): -3, ('S', 'M'): -3, ('Y', 'L'): -2, ('Y', 'H'): 1,
+('Y', 'D'): -5, ('E', 'R'): -1, ('X', 'P'): -3, ('G', 'G'): 6,
+('G', 'C'): -5, ('E', 'N'): -1, ('Y', 'T'): -3, ('Y', 'P'): -5,
+('T', 'K'): -1, ('A', 'A'): 5, ('P', 'Q'): -2, ('T', 'C'): -2,
+('V', 'H'): -4, ('T', 'G'): -3, ('I', 'Q'): -4, ('Z', 'T'): -2,
+('C', 'R'): -5, ('V', 'P'): -4, ('P', 'E'): -2, ('M', 'C'): -3,
+('K', 'N'): 0, ('I', 'I'): 5, ('P', 'A'): -1, ('M', 'G'): -4,
+('T', 'S'): 1, ('I', 'E'): -4, ('P', 'M'): -3, ('M', 'K'): -2,
+('I', 'A'): -2, ('P', 'I'): -4, ('R', 'R'): 7, ('X', 'M'): -2,
+('L', 'I'): 1, ('X', 'I'): -2, ('Z', 'B'): 0, ('X', 'E'): -2,
+('Z', 'N'): -1, ('X', 'A'): -1, ('B', 'R'): -2, ('B', 'N'): 4,
+('F', 'D'): -5, ('X', 'Y'): -2, ('Z', 'R'): -1, ('F', 'H'): -2,
+('B', 'F'): -5, ('F', 'L'): 0, ('X', 'Q'): -1, ('B', 'B'): 4
}
blosum95 = _temp()
del _temp
@@ -1453,59 +1452,59 @@ del _temp
# http://www.embl-heidelberg.de/~vogt/matrices/feng.cmp
def _temp():
return {
-('W', 'F') : 3, ('L', 'R') : 2, ('I', 'I') : 6, ('Q', 'Q') : 6,
-('W', 'N') : 0, ('V', 'I') : 5, ('H', 'T') : 2, ('H', 'P') : 3,
-('W', 'V') : 3, ('Q', 'E') : 4, ('W', 'R') : 2, ('Q', 'A') : 3,
-('H', 'H') : 6, ('H', 'D') : 3, ('L', 'N') : 1, ('Y', 'M') : 2,
-('Y', 'I') : 3, ('Y', 'E') : 1, ('E', 'S') : 3, ('Y', 'A') : 2,
-('Y', 'Y') : 6, ('T', 'C') : 2, ('E', 'C') : 0, ('Y', 'Q') : 2,
-('E', 'G') : 4, ('V', 'A') : 5, ('C', 'C') : 6, ('M', 'R') : 2,
-('P', 'T') : 4, ('V', 'E') : 4, ('P', 'P') : 6, ('I', 'T') : 3,
-('K', 'S') : 3, ('R', 'G') : 3, ('I', 'P') : 2, ('R', 'C') : 2,
-('A', 'T') : 5, ('K', 'K') : 6, ('A', 'P') : 5, ('V', 'M') : 4,
-('I', 'D') : 1, ('K', 'C') : 0, ('K', 'G') : 2, ('R', 'S') : 3,
-('F', 'Q') : 1, ('F', 'A') : 2, ('V', 'V') : 6, ('M', 'N') : 1,
-('F', 'E') : 0, ('D', 'N') : 5, ('F', 'I') : 4, ('F', 'M') : 2,
-('M', 'S') : 1, ('S', 'S') : 6, ('L', 'Q') : 2, ('W', 'E') : 1,
-('W', 'A') : 2, ('W', 'M') : 3, ('H', 'S') : 3, ('W', 'I') : 2,
-('S', 'C') : 4, ('L', 'A') : 2, ('L', 'E') : 1, ('W', 'Q') : 1,
-('H', 'G') : 1, ('Q', 'N') : 3, ('H', 'C') : 2, ('L', 'M') : 5,
-('W', 'Y') : 3, ('Y', 'N') : 3, ('E', 'P') : 3, ('Y', 'F') : 5,
-('E', 'T') : 3, ('A', 'A') : 6, ('I', 'N') : 2, ('G', 'A') : 5,
-('Y', 'V') : 3, ('E', 'D') : 5, ('W', 'H') : 1, ('Y', 'R') : 1,
-('M', 'Q') : 2, ('P', 'S') : 4, ('R', 'H') : 4, ('A', 'C') : 2,
-('R', 'D') : 2, ('K', 'P') : 2, ('L', 'D') : 1, ('K', 'T') : 4,
-('V', 'N') : 2, ('M', 'A') : 2, ('K', 'H') : 3, ('V', 'R') : 2,
-('P', 'C') : 2, ('M', 'E') : 1, ('A', 'S') : 5, ('T', 'T') : 6,
-('R', 'T') : 3, ('I', 'G') : 2, ('R', 'P') : 3, ('K', 'D') : 3,
-('I', 'C') : 2, ('F', 'R') : 1, ('F', 'V') : 4, ('L', 'C') : 2,
-('F', 'F') : 6, ('D', 'A') : 4, ('F', 'N') : 1, ('W', 'D') : 0,
-('L', 'P') : 3, ('Q', 'S') : 3, ('N', 'C') : 2, ('N', 'G') : 3,
-('H', 'N') : 4, ('W', 'T') : 1, ('Q', 'G') : 2, ('W', 'P') : 2,
-('Q', 'C') : 1, ('N', 'S') : 5, ('L', 'H') : 3, ('L', 'L') : 6,
-('G', 'T') : 2, ('M', 'M') : 6, ('G', 'P') : 3, ('Y', 'K') : 1,
-('Y', 'G') : 2, ('Y', 'C') : 3, ('E', 'A') : 4, ('E', 'E') : 6,
-('Y', 'S') : 3, ('M', 'P') : 2, ('V', 'C') : 2, ('M', 'T') : 3,
-('V', 'G') : 4, ('R', 'E') : 2, ('V', 'K') : 3, ('K', 'Q') : 4,
-('R', 'A') : 2, ('I', 'R') : 2, ('N', 'A') : 3, ('V', 'S') : 2,
-('M', 'D') : 0, ('M', 'H') : 1, ('K', 'A') : 3, ('R', 'Q') : 3,
-('K', 'E') : 4, ('F', 'S') : 3, ('I', 'K') : 2, ('D', 'P') : 2,
-('D', 'T') : 2, ('I', 'M') : 4, ('F', 'C') : 3, ('W', 'L') : 4,
-('F', 'G') : 1, ('F', 'K') : 0, ('F', 'T') : 1, ('D', 'D') : 6,
-('Q', 'T') : 3, ('W', 'G') : 3, ('Q', 'P') : 3, ('W', 'C') : 3,
-('W', 'K') : 1, ('H', 'Q') : 4, ('Q', 'D') : 4, ('W', 'W') : 6,
-('V', 'L') : 5, ('L', 'G') : 2, ('W', 'S') : 2, ('L', 'K') : 2,
-('N', 'P') : 2, ('H', 'E') : 2, ('N', 'T') : 4, ('H', 'A') : 2,
-('Y', 'L') : 3, ('Y', 'H') : 3, ('G', 'S') : 5, ('Y', 'D') : 2,
-('V', 'Q') : 2, ('L', 'T') : 2, ('G', 'G') : 6, ('G', 'C') : 3,
-('E', 'N') : 3, ('Y', 'T') : 2, ('Y', 'P') : 2, ('R', 'N') : 2,
-('V', 'D') : 3, ('K', 'R') : 5, ('V', 'H') : 1, ('I', 'Q') : 1,
-('V', 'P') : 3, ('M', 'C') : 2, ('K', 'N') : 4, ('V', 'T') : 3,
-('M', 'G') : 1, ('T', 'S') : 5, ('I', 'E') : 1, ('M', 'K') : 2,
-('I', 'A') : 2, ('N', 'N') : 6, ('R', 'R') : 6, ('F', 'P') : 2,
-('L', 'I') : 5, ('I', 'S') : 2, ('D', 'S') : 3, ('L', 'S') : 2,
-('I', 'H') : 1, ('F', 'D') : 1, ('D', 'C') : 1, ('F', 'H') : 2,
-('D', 'G') : 4, ('F', 'L') : 4
+('W', 'F'): 3, ('L', 'R'): 2, ('I', 'I'): 6, ('Q', 'Q'): 6,
+('W', 'N'): 0, ('V', 'I'): 5, ('H', 'T'): 2, ('H', 'P'): 3,
+('W', 'V'): 3, ('Q', 'E'): 4, ('W', 'R'): 2, ('Q', 'A'): 3,
+('H', 'H'): 6, ('H', 'D'): 3, ('L', 'N'): 1, ('Y', 'M'): 2,
+('Y', 'I'): 3, ('Y', 'E'): 1, ('E', 'S'): 3, ('Y', 'A'): 2,
+('Y', 'Y'): 6, ('T', 'C'): 2, ('E', 'C'): 0, ('Y', 'Q'): 2,
+('E', 'G'): 4, ('V', 'A'): 5, ('C', 'C'): 6, ('M', 'R'): 2,
+('P', 'T'): 4, ('V', 'E'): 4, ('P', 'P'): 6, ('I', 'T'): 3,
+('K', 'S'): 3, ('R', 'G'): 3, ('I', 'P'): 2, ('R', 'C'): 2,
+('A', 'T'): 5, ('K', 'K'): 6, ('A', 'P'): 5, ('V', 'M'): 4,
+('I', 'D'): 1, ('K', 'C'): 0, ('K', 'G'): 2, ('R', 'S'): 3,
+('F', 'Q'): 1, ('F', 'A'): 2, ('V', 'V'): 6, ('M', 'N'): 1,
+('F', 'E'): 0, ('D', 'N'): 5, ('F', 'I'): 4, ('F', 'M'): 2,
+('M', 'S'): 1, ('S', 'S'): 6, ('L', 'Q'): 2, ('W', 'E'): 1,
+('W', 'A'): 2, ('W', 'M'): 3, ('H', 'S'): 3, ('W', 'I'): 2,
+('S', 'C'): 4, ('L', 'A'): 2, ('L', 'E'): 1, ('W', 'Q'): 1,
+('H', 'G'): 1, ('Q', 'N'): 3, ('H', 'C'): 2, ('L', 'M'): 5,
+('W', 'Y'): 3, ('Y', 'N'): 3, ('E', 'P'): 3, ('Y', 'F'): 5,
+('E', 'T'): 3, ('A', 'A'): 6, ('I', 'N'): 2, ('G', 'A'): 5,
+('Y', 'V'): 3, ('E', 'D'): 5, ('W', 'H'): 1, ('Y', 'R'): 1,
+('M', 'Q'): 2, ('P', 'S'): 4, ('R', 'H'): 4, ('A', 'C'): 2,
+('R', 'D'): 2, ('K', 'P'): 2, ('L', 'D'): 1, ('K', 'T'): 4,
+('V', 'N'): 2, ('M', 'A'): 2, ('K', 'H'): 3, ('V', 'R'): 2,
+('P', 'C'): 2, ('M', 'E'): 1, ('A', 'S'): 5, ('T', 'T'): 6,
+('R', 'T'): 3, ('I', 'G'): 2, ('R', 'P'): 3, ('K', 'D'): 3,
+('I', 'C'): 2, ('F', 'R'): 1, ('F', 'V'): 4, ('L', 'C'): 2,
+('F', 'F'): 6, ('D', 'A'): 4, ('F', 'N'): 1, ('W', 'D'): 0,
+('L', 'P'): 3, ('Q', 'S'): 3, ('N', 'C'): 2, ('N', 'G'): 3,
+('H', 'N'): 4, ('W', 'T'): 1, ('Q', 'G'): 2, ('W', 'P'): 2,
+('Q', 'C'): 1, ('N', 'S'): 5, ('L', 'H'): 3, ('L', 'L'): 6,
+('G', 'T'): 2, ('M', 'M'): 6, ('G', 'P'): 3, ('Y', 'K'): 1,
+('Y', 'G'): 2, ('Y', 'C'): 3, ('E', 'A'): 4, ('E', 'E'): 6,
+('Y', 'S'): 3, ('M', 'P'): 2, ('V', 'C'): 2, ('M', 'T'): 3,
+('V', 'G'): 4, ('R', 'E'): 2, ('V', 'K'): 3, ('K', 'Q'): 4,
+('R', 'A'): 2, ('I', 'R'): 2, ('N', 'A'): 3, ('V', 'S'): 2,
+('M', 'D'): 0, ('M', 'H'): 1, ('K', 'A'): 3, ('R', 'Q'): 3,
+('K', 'E'): 4, ('F', 'S'): 3, ('I', 'K'): 2, ('D', 'P'): 2,
+('D', 'T'): 2, ('I', 'M'): 4, ('F', 'C'): 3, ('W', 'L'): 4,
+('F', 'G'): 1, ('F', 'K'): 0, ('F', 'T'): 1, ('D', 'D'): 6,
+('Q', 'T'): 3, ('W', 'G'): 3, ('Q', 'P'): 3, ('W', 'C'): 3,
+('W', 'K'): 1, ('H', 'Q'): 4, ('Q', 'D'): 4, ('W', 'W'): 6,
+('V', 'L'): 5, ('L', 'G'): 2, ('W', 'S'): 2, ('L', 'K'): 2,
+('N', 'P'): 2, ('H', 'E'): 2, ('N', 'T'): 4, ('H', 'A'): 2,
+('Y', 'L'): 3, ('Y', 'H'): 3, ('G', 'S'): 5, ('Y', 'D'): 2,
+('V', 'Q'): 2, ('L', 'T'): 2, ('G', 'G'): 6, ('G', 'C'): 3,
+('E', 'N'): 3, ('Y', 'T'): 2, ('Y', 'P'): 2, ('R', 'N'): 2,
+('V', 'D'): 3, ('K', 'R'): 5, ('V', 'H'): 1, ('I', 'Q'): 1,
+('V', 'P'): 3, ('M', 'C'): 2, ('K', 'N'): 4, ('V', 'T'): 3,
+('M', 'G'): 1, ('T', 'S'): 5, ('I', 'E'): 1, ('M', 'K'): 2,
+('I', 'A'): 2, ('N', 'N'): 6, ('R', 'R'): 6, ('F', 'P'): 2,
+('L', 'I'): 5, ('I', 'S'): 2, ('D', 'S'): 3, ('L', 'S'): 2,
+('I', 'H'): 1, ('F', 'D'): 1, ('D', 'C'): 1, ('F', 'H'): 2,
+('D', 'G'): 4, ('F', 'L'): 4
}
feng = _temp()
del _temp
@@ -1514,49 +1513,49 @@ del _temp
# http://www.embl-heidelberg.de/~vogt/matrices/fitch.cmp
def _temp():
return {
-('W', 'F') : 2, ('U', 'I') : 1, ('W', 'T') : 0, ('R', 'R') : 3,
-('N', 'M') : 1, ('U', 'M') : 2, ('U', 'O') : 1, ('N', 'A') : 2,
-('U', 'A') : 1, ('N', 'C') : 1, ('U', 'C') : 1, ('N', 'E') : 2,
-('U', 'E') : 1, ('W', 'H') : 1, ('W', 'R') : 1, ('W', 'V') : 2,
-('Q', 'E') : 1, ('S', 'S') : 3, ('T', 'T') : 3, ('S', 'F') : 2,
-('Q', 'A') : 1, ('Q', 'C') : 1, ('H', 'F') : 1, ('S', 'H') : 1,
-('L', 'H') : 1, ('S', 'N') : 2, ('Q', 'I') : 2, ('S', 'L') : 2,
-('L', 'L') : 3, ('Y', 'M') : 1, ('M', 'M') : 3, ('Y', 'I') : 1,
-('W', 'N') : 2, ('Y', 'R') : 2, ('Y', 'E') : 1, ('O', 'O') : 3,
-('Y', 'A') : 2, ('Y', 'C') : 2, ('U', 'S') : 2, ('Y', 'U') : 2,
-('Q', 'Q') : 3, ('E', 'A') : 1, ('N', 'L') : 1, ('E', 'C') : 1,
-('W', 'W') : 3, ('E', 'E') : 3, ('Y', 'S') : 2, ('V', 'A') : 2,
-('C', 'C') : 3, ('V', 'C') : 2, ('T', 'H') : 0, ('V', 'E') : 1,
-('T', 'N') : 0, ('O', 'I') : 1, ('R', 'E') : 2, ('O', 'E') : 1,
-('R', 'A') : 1, ('O', 'C') : 2, ('R', 'C') : 2, ('O', 'A') : 2,
-('V', 'Q') : 1, ('V', 'M') : 2, ('V', 'S') : 1, ('Q', 'O') : 2,
-('I', 'H') : 1, ('M', 'F') : 1, ('R', 'Q') : 2, ('M', 'H') : 0,
-('O', 'F') : 2, ('T', 'F') : 1, ('M', 'I') : 2, ('M', 'L') : 1,
-('V', 'T') : 1, ('Q', 'H') : 2, ('Q', 'N') : 1, ('T', 'M') : 1,
-('W', 'M') : 2, ('Y', 'Y') : 3, ('T', 'S') : 2, ('R', 'O') : 1,
-('T', 'R') : 2, ('H', 'H') : 3, ('F', 'A') : 1, ('F', 'C') : 2,
-('F', 'E') : 1, ('W', 'L') : 1, ('T', 'L') : 1, ('U', 'R') : 2,
-('U', 'H') : 2, ('N', 'H') : 2, ('U', 'Q') : 2, ('W', 'E') : 2,
-('U', 'L') : 1, ('W', 'C') : 1, ('U', 'N') : 1, ('W', 'A') : 1,
-('W', 'O') : 1, ('I', 'F') : 0, ('U', 'F') : 2, ('N', 'F') : 1,
-('L', 'C') : 1, ('S', 'C') : 2, ('L', 'A') : 2, ('S', 'A') : 1,
-('W', 'S') : 2, ('L', 'E') : 2, ('S', 'E') : 2, ('Q', 'L') : 1,
-('H', 'E') : 1, ('S', 'I') : 1, ('H', 'C') : 1, ('S', 'O') : 2,
-('H', 'A') : 2, ('S', 'M') : 1, ('Y', 'L') : 2, ('Y', 'N') : 1,
-('Y', 'H') : 1, ('O', 'M') : 0, ('Y', 'Q') : 1, ('Y', 'F') : 1,
-('W', 'I') : 1, ('C', 'A') : 1, ('R', 'I') : 2, ('Y', 'O') : 1,
-('Q', 'M') : 1, ('S', 'Q') : 2, ('U', 'T') : 2, ('Y', 'T') : 2,
-('Y', 'V') : 2, ('O', 'L') : 1, ('R', 'N') : 1, ('A', 'A') : 3,
-('N', 'I') : 2, ('R', 'L') : 1, ('T', 'I') : 1, ('L', 'F') : 1,
-('T', 'O') : 1, ('R', 'H') : 2, ('O', 'H') : 2, ('V', 'F') : 2,
-('T', 'C') : 2, ('V', 'H') : 1, ('T', 'A') : 0, ('R', 'F') : 1,
-('V', 'L') : 2, ('T', 'E') : 1, ('V', 'N') : 1, ('M', 'A') : 0,
-('Q', 'F') : 1, ('M', 'C') : 0, ('W', 'U') : 2, ('I', 'I') : 3,
-('V', 'V') : 3, ('O', 'N') : 2, ('I', 'E') : 2, ('T', 'Q') : 2,
-('I', 'A') : 1, ('N', 'N') : 3, ('I', 'C') : 0, ('S', 'R') : 2,
-('V', 'R') : 1, ('L', 'I') : 1, ('V', 'I') : 1, ('R', 'M') : 2,
-('Y', 'W') : 1, ('M', 'E') : 2, ('V', 'U') : 2, ('W', 'Q') : 0,
-('U', 'U') : 3, ('V', 'O') : 1, ('F', 'F') : 3
+('W', 'F'): 2, ('U', 'I'): 1, ('W', 'T'): 0, ('R', 'R'): 3,
+('N', 'M'): 1, ('U', 'M'): 2, ('U', 'O'): 1, ('N', 'A'): 2,
+('U', 'A'): 1, ('N', 'C'): 1, ('U', 'C'): 1, ('N', 'E'): 2,
+('U', 'E'): 1, ('W', 'H'): 1, ('W', 'R'): 1, ('W', 'V'): 2,
+('Q', 'E'): 1, ('S', 'S'): 3, ('T', 'T'): 3, ('S', 'F'): 2,
+('Q', 'A'): 1, ('Q', 'C'): 1, ('H', 'F'): 1, ('S', 'H'): 1,
+('L', 'H'): 1, ('S', 'N'): 2, ('Q', 'I'): 2, ('S', 'L'): 2,
+('L', 'L'): 3, ('Y', 'M'): 1, ('M', 'M'): 3, ('Y', 'I'): 1,
+('W', 'N'): 2, ('Y', 'R'): 2, ('Y', 'E'): 1, ('O', 'O'): 3,
+('Y', 'A'): 2, ('Y', 'C'): 2, ('U', 'S'): 2, ('Y', 'U'): 2,
+('Q', 'Q'): 3, ('E', 'A'): 1, ('N', 'L'): 1, ('E', 'C'): 1,
+('W', 'W'): 3, ('E', 'E'): 3, ('Y', 'S'): 2, ('V', 'A'): 2,
+('C', 'C'): 3, ('V', 'C'): 2, ('T', 'H'): 0, ('V', 'E'): 1,
+('T', 'N'): 0, ('O', 'I'): 1, ('R', 'E'): 2, ('O', 'E'): 1,
+('R', 'A'): 1, ('O', 'C'): 2, ('R', 'C'): 2, ('O', 'A'): 2,
+('V', 'Q'): 1, ('V', 'M'): 2, ('V', 'S'): 1, ('Q', 'O'): 2,
+('I', 'H'): 1, ('M', 'F'): 1, ('R', 'Q'): 2, ('M', 'H'): 0,
+('O', 'F'): 2, ('T', 'F'): 1, ('M', 'I'): 2, ('M', 'L'): 1,
+('V', 'T'): 1, ('Q', 'H'): 2, ('Q', 'N'): 1, ('T', 'M'): 1,
+('W', 'M'): 2, ('Y', 'Y'): 3, ('T', 'S'): 2, ('R', 'O'): 1,
+('T', 'R'): 2, ('H', 'H'): 3, ('F', 'A'): 1, ('F', 'C'): 2,
+('F', 'E'): 1, ('W', 'L'): 1, ('T', 'L'): 1, ('U', 'R'): 2,
+('U', 'H'): 2, ('N', 'H'): 2, ('U', 'Q'): 2, ('W', 'E'): 2,
+('U', 'L'): 1, ('W', 'C'): 1, ('U', 'N'): 1, ('W', 'A'): 1,
+('W', 'O'): 1, ('I', 'F'): 0, ('U', 'F'): 2, ('N', 'F'): 1,
+('L', 'C'): 1, ('S', 'C'): 2, ('L', 'A'): 2, ('S', 'A'): 1,
+('W', 'S'): 2, ('L', 'E'): 2, ('S', 'E'): 2, ('Q', 'L'): 1,
+('H', 'E'): 1, ('S', 'I'): 1, ('H', 'C'): 1, ('S', 'O'): 2,
+('H', 'A'): 2, ('S', 'M'): 1, ('Y', 'L'): 2, ('Y', 'N'): 1,
+('Y', 'H'): 1, ('O', 'M'): 0, ('Y', 'Q'): 1, ('Y', 'F'): 1,
+('W', 'I'): 1, ('C', 'A'): 1, ('R', 'I'): 2, ('Y', 'O'): 1,
+('Q', 'M'): 1, ('S', 'Q'): 2, ('U', 'T'): 2, ('Y', 'T'): 2,
+('Y', 'V'): 2, ('O', 'L'): 1, ('R', 'N'): 1, ('A', 'A'): 3,
+('N', 'I'): 2, ('R', 'L'): 1, ('T', 'I'): 1, ('L', 'F'): 1,
+('T', 'O'): 1, ('R', 'H'): 2, ('O', 'H'): 2, ('V', 'F'): 2,
+('T', 'C'): 2, ('V', 'H'): 1, ('T', 'A'): 0, ('R', 'F'): 1,
+('V', 'L'): 2, ('T', 'E'): 1, ('V', 'N'): 1, ('M', 'A'): 0,
+('Q', 'F'): 1, ('M', 'C'): 0, ('W', 'U'): 2, ('I', 'I'): 3,
+('V', 'V'): 3, ('O', 'N'): 2, ('I', 'E'): 2, ('T', 'Q'): 2,
+('I', 'A'): 1, ('N', 'N'): 3, ('I', 'C'): 0, ('S', 'R'): 2,
+('V', 'R'): 1, ('L', 'I'): 1, ('V', 'I'): 1, ('R', 'M'): 2,
+('Y', 'W'): 1, ('M', 'E'): 2, ('V', 'U'): 2, ('W', 'Q'): 0,
+('U', 'U'): 3, ('V', 'O'): 1, ('F', 'F'): 3
}
fitch = _temp()
del _temp
@@ -1565,59 +1564,59 @@ del _temp
# http://www.embl-heidelberg.de/~vogt/matrices/genetic.cmp
def _temp():
return {
-('W', 'F') : 0.0, ('L', 'R') : -0.4, ('I', 'I') : 4.1, ('Q', 'Q') : 5.5,
-('W', 'N') : -3.0, ('V', 'I') : 1.0, ('H', 'T') : -1.8, ('H', 'P') : 0.7,
-('W', 'V') : -2.1, ('Q', 'E') : 2.0, ('W', 'R') : 1.8, ('Q', 'A') : -2.1,
-('H', 'H') : 4.7, ('H', 'D') : 1.7, ('L', 'N') : -2.2, ('Y', 'M') : -2.9,
-('Y', 'I') : -1.6, ('Y', 'E') : -0.9, ('E', 'S') : -2.8, ('Y', 'A') : -2.4,
-('Y', 'Y') : 6.5, ('T', 'C') : -1.9, ('E', 'C') : -3.0, ('Y', 'Q') : -0.8,
-('E', 'G') : 1.4, ('V', 'A') : 1.0, ('C', 'C') : 5.5, ('M', 'R') : -0.4,
-('P', 'T') : 1.1, ('V', 'E') : 1.3, ('P', 'P') : 3.8, ('I', 'T') : 0.8,
-('K', 'S') : -1.5, ('R', 'G') : 0.8, ('I', 'P') : -1.6, ('R', 'C') : 0.7,
-('A', 'T') : 0.9, ('K', 'K') : 5.6, ('A', 'P') : 0.8, ('V', 'M') : 1.0,
-('I', 'D') : -2.1, ('K', 'C') : -3.2, ('K', 'G') : -2.2, ('R', 'S') : 0.3,
-('F', 'Q') : -2.1, ('F', 'A') : -2.4, ('V', 'V') : 4.1, ('M', 'N') : 0.1,
-('F', 'E') : -2.9, ('D', 'N') : 1.7, ('F', 'I') : 1.3, ('F', 'M') : 0.5,
-('M', 'S') : -1.3, ('S', 'S') : 2.6, ('L', 'Q') : 0.1, ('W', 'E') : -3.2,
-('W', 'A') : -2.2, ('W', 'M') : -2.0, ('H', 'S') : -1.6, ('W', 'I') : -2.2,
-('S', 'C') : 1.5, ('L', 'A') : -2.3, ('L', 'E') : -2.5, ('W', 'Q') : -2.3,
-('H', 'G') : -2.2, ('Q', 'N') : 0.4, ('H', 'C') : -1.6, ('L', 'M') : 1.5,
-('W', 'Y') : -0.5, ('Y', 'N') : 2.5, ('E', 'P') : -2.1, ('Y', 'F') : 2.0,
-('E', 'T') : -2.1, ('A', 'A') : 4.0, ('I', 'N') : 0.9, ('G', 'A') : 1.2,
-('Y', 'V') : -2.2, ('E', 'D') : 3.8, ('W', 'H') : -2.1, ('Y', 'R') : -1.9,
-('M', 'Q') : -1.2, ('P', 'S') : 0.4, ('R', 'H') : 3.6, ('A', 'C') : -1.9,
-('R', 'D') : -2.3, ('K', 'P') : -1.5, ('L', 'D') : -2.4, ('K', 'T') : 1.0,
-('V', 'N') : -2.2, ('M', 'A') : -2.0, ('K', 'H') : 0.6, ('V', 'R') : -2.1,
-('P', 'C') : -1.9, ('M', 'E') : -1.8, ('A', 'S') : 0.1, ('T', 'T') : 4.0,
-('R', 'T') : -0.6, ('I', 'G') : -2.5, ('R', 'P') : 0.3, ('K', 'D') : 0.3,
-('I', 'C') : -1.9, ('F', 'R') : -1.5, ('F', 'V') : 1.0, ('L', 'C') : -1.3,
-('F', 'F') : 4.5, ('D', 'A') : 1.0, ('F', 'N') : -1.3, ('W', 'D') : -2.9,
-('L', 'P') : 0.0, ('Q', 'S') : -2.3, ('N', 'C') : -1.5, ('N', 'G') : -2.6,
-('H', 'N') : 1.8, ('W', 'T') : -2.2, ('Q', 'G') : -2.1, ('W', 'P') : -1.6,
-('Q', 'C') : -3.1, ('N', 'S') : -0.3, ('L', 'H') : -0.1, ('L', 'L') : 3.4,
-('G', 'T') : -2.1, ('M', 'M') : 5.4, ('G', 'P') : -1.8, ('Y', 'K') : -0.8,
-('Y', 'G') : -1.8, ('Y', 'C') : 2.6, ('E', 'A') : 1.3, ('E', 'E') : 5.7,
-('Y', 'S') : 0.3, ('M', 'P') : -1.4, ('V', 'C') : -2.2, ('M', 'T') : 0.7,
-('V', 'G') : 1.1, ('R', 'E') : -2.0, ('V', 'K') : -2.1, ('K', 'Q') : 2.2,
-('R', 'A') : -1.6, ('I', 'R') : -1.2, ('N', 'A') : -1.7, ('V', 'S') : -2.2,
-('M', 'D') : -2.5, ('M', 'H') : -1.8, ('K', 'A') : -1.9, ('R', 'Q') : 0.3,
-('K', 'E') : 2.0, ('F', 'S') : 0.0, ('I', 'K') : 0.7, ('D', 'P') : -2.2,
-('D', 'T') : -2.1, ('I', 'M') : 3.3, ('F', 'C') : 1.8, ('W', 'L') : -0.3,
-('F', 'G') : -1.9, ('F', 'K') : -2.8, ('F', 'T') : -2.1, ('D', 'D') : 4.8,
-('Q', 'T') : -1.7, ('W', 'G') : 1.4, ('Q', 'P') : 1.0, ('W', 'C') : 4.1,
-('W', 'K') : -3.0, ('H', 'Q') : 3.6, ('Q', 'D') : 0.3, ('W', 'W') : 7.5,
-('V', 'L') : 1.1, ('L', 'G') : -2.2, ('W', 'S') : 0.8, ('L', 'K') : -2.0,
-('N', 'P') : -1.6, ('H', 'E') : 0.3, ('N', 'T') : 0.9, ('H', 'A') : -2.1,
-('Y', 'L') : -1.6, ('Y', 'H') : 2.3, ('G', 'S') : -0.6, ('Y', 'D') : 2.3,
-('V', 'Q') : -2.0, ('L', 'T') : -1.9, ('G', 'G') : 4.2, ('G', 'C') : 1.0,
-('E', 'N') : 0.3, ('Y', 'T') : -2.1, ('Y', 'P') : -2.3, ('R', 'N') : -1.5,
-('V', 'D') : 1.0, ('K', 'R') : -0.2, ('V', 'H') : -2.1, ('I', 'Q') : -1.9,
-('V', 'P') : -2.1, ('M', 'C') : -2.7, ('K', 'N') : 3.5, ('V', 'T') : -2.2,
-('M', 'G') : -2.3, ('T', 'S') : 1.0, ('I', 'E') : -2.3, ('M', 'K') : 1.6,
-('I', 'A') : -1.8, ('N', 'N') : 4.7, ('R', 'R') : 2.9, ('F', 'P') : -1.8,
-('L', 'I') : 1.2, ('I', 'S') : -0.5, ('D', 'S') : -2.1, ('L', 'S') : -1.2,
-('I', 'H') : -1.8, ('F', 'D') : -1.7, ('D', 'C') : -1.6, ('F', 'H') : -1.1,
-('D', 'G') : 1.1, ('F', 'L') : 2.2
+('W', 'F'): 0.0, ('L', 'R'): -0.4, ('I', 'I'): 4.1, ('Q', 'Q'): 5.5,
+('W', 'N'): -3.0, ('V', 'I'): 1.0, ('H', 'T'): -1.8, ('H', 'P'): 0.7,
+('W', 'V'): -2.1, ('Q', 'E'): 2.0, ('W', 'R'): 1.8, ('Q', 'A'): -2.1,
+('H', 'H'): 4.7, ('H', 'D'): 1.7, ('L', 'N'): -2.2, ('Y', 'M'): -2.9,
+('Y', 'I'): -1.6, ('Y', 'E'): -0.9, ('E', 'S'): -2.8, ('Y', 'A'): -2.4,
+('Y', 'Y'): 6.5, ('T', 'C'): -1.9, ('E', 'C'): -3.0, ('Y', 'Q'): -0.8,
+('E', 'G'): 1.4, ('V', 'A'): 1.0, ('C', 'C'): 5.5, ('M', 'R'): -0.4,
+('P', 'T'): 1.1, ('V', 'E'): 1.3, ('P', 'P'): 3.8, ('I', 'T'): 0.8,
+('K', 'S'): -1.5, ('R', 'G'): 0.8, ('I', 'P'): -1.6, ('R', 'C'): 0.7,
+('A', 'T'): 0.9, ('K', 'K'): 5.6, ('A', 'P'): 0.8, ('V', 'M'): 1.0,
+('I', 'D'): -2.1, ('K', 'C'): -3.2, ('K', 'G'): -2.2, ('R', 'S'): 0.3,
+('F', 'Q'): -2.1, ('F', 'A'): -2.4, ('V', 'V'): 4.1, ('M', 'N'): 0.1,
+('F', 'E'): -2.9, ('D', 'N'): 1.7, ('F', 'I'): 1.3, ('F', 'M'): 0.5,
+('M', 'S'): -1.3, ('S', 'S'): 2.6, ('L', 'Q'): 0.1, ('W', 'E'): -3.2,
+('W', 'A'): -2.2, ('W', 'M'): -2.0, ('H', 'S'): -1.6, ('W', 'I'): -2.2,
+('S', 'C'): 1.5, ('L', 'A'): -2.3, ('L', 'E'): -2.5, ('W', 'Q'): -2.3,
+('H', 'G'): -2.2, ('Q', 'N'): 0.4, ('H', 'C'): -1.6, ('L', 'M'): 1.5,
+('W', 'Y'): -0.5, ('Y', 'N'): 2.5, ('E', 'P'): -2.1, ('Y', 'F'): 2.0,
+('E', 'T'): -2.1, ('A', 'A'): 4.0, ('I', 'N'): 0.9, ('G', 'A'): 1.2,
+('Y', 'V'): -2.2, ('E', 'D'): 3.8, ('W', 'H'): -2.1, ('Y', 'R'): -1.9,
+('M', 'Q'): -1.2, ('P', 'S'): 0.4, ('R', 'H'): 3.6, ('A', 'C'): -1.9,
+('R', 'D'): -2.3, ('K', 'P'): -1.5, ('L', 'D'): -2.4, ('K', 'T'): 1.0,
+('V', 'N'): -2.2, ('M', 'A'): -2.0, ('K', 'H'): 0.6, ('V', 'R'): -2.1,
+('P', 'C'): -1.9, ('M', 'E'): -1.8, ('A', 'S'): 0.1, ('T', 'T'): 4.0,
+('R', 'T'): -0.6, ('I', 'G'): -2.5, ('R', 'P'): 0.3, ('K', 'D'): 0.3,
+('I', 'C'): -1.9, ('F', 'R'): -1.5, ('F', 'V'): 1.0, ('L', 'C'): -1.3,
+('F', 'F'): 4.5, ('D', 'A'): 1.0, ('F', 'N'): -1.3, ('W', 'D'): -2.9,
+('L', 'P'): 0.0, ('Q', 'S'): -2.3, ('N', 'C'): -1.5, ('N', 'G'): -2.6,
+('H', 'N'): 1.8, ('W', 'T'): -2.2, ('Q', 'G'): -2.1, ('W', 'P'): -1.6,
+('Q', 'C'): -3.1, ('N', 'S'): -0.3, ('L', 'H'): -0.1, ('L', 'L'): 3.4,
+('G', 'T'): -2.1, ('M', 'M'): 5.4, ('G', 'P'): -1.8, ('Y', 'K'): -0.8,
+('Y', 'G'): -1.8, ('Y', 'C'): 2.6, ('E', 'A'): 1.3, ('E', 'E'): 5.7,
+('Y', 'S'): 0.3, ('M', 'P'): -1.4, ('V', 'C'): -2.2, ('M', 'T'): 0.7,
+('V', 'G'): 1.1, ('R', 'E'): -2.0, ('V', 'K'): -2.1, ('K', 'Q'): 2.2,
+('R', 'A'): -1.6, ('I', 'R'): -1.2, ('N', 'A'): -1.7, ('V', 'S'): -2.2,
+('M', 'D'): -2.5, ('M', 'H'): -1.8, ('K', 'A'): -1.9, ('R', 'Q'): 0.3,
+('K', 'E'): 2.0, ('F', 'S'): 0.0, ('I', 'K'): 0.7, ('D', 'P'): -2.2,
+('D', 'T'): -2.1, ('I', 'M'): 3.3, ('F', 'C'): 1.8, ('W', 'L'): -0.3,
+('F', 'G'): -1.9, ('F', 'K'): -2.8, ('F', 'T'): -2.1, ('D', 'D'): 4.8,
+('Q', 'T'): -1.7, ('W', 'G'): 1.4, ('Q', 'P'): 1.0, ('W', 'C'): 4.1,
+('W', 'K'): -3.0, ('H', 'Q'): 3.6, ('Q', 'D'): 0.3, ('W', 'W'): 7.5,
+('V', 'L'): 1.1, ('L', 'G'): -2.2, ('W', 'S'): 0.8, ('L', 'K'): -2.0,
+('N', 'P'): -1.6, ('H', 'E'): 0.3, ('N', 'T'): 0.9, ('H', 'A'): -2.1,
+('Y', 'L'): -1.6, ('Y', 'H'): 2.3, ('G', 'S'): -0.6, ('Y', 'D'): 2.3,
+('V', 'Q'): -2.0, ('L', 'T'): -1.9, ('G', 'G'): 4.2, ('G', 'C'): 1.0,
+('E', 'N'): 0.3, ('Y', 'T'): -2.1, ('Y', 'P'): -2.3, ('R', 'N'): -1.5,
+('V', 'D'): 1.0, ('K', 'R'): -0.2, ('V', 'H'): -2.1, ('I', 'Q'): -1.9,
+('V', 'P'): -2.1, ('M', 'C'): -2.7, ('K', 'N'): 3.5, ('V', 'T'): -2.2,
+('M', 'G'): -2.3, ('T', 'S'): 1.0, ('I', 'E'): -2.3, ('M', 'K'): 1.6,
+('I', 'A'): -1.8, ('N', 'N'): 4.7, ('R', 'R'): 2.9, ('F', 'P'): -1.8,
+('L', 'I'): 1.2, ('I', 'S'): -0.5, ('D', 'S'): -2.1, ('L', 'S'): -1.2,
+('I', 'H'): -1.8, ('F', 'D'): -1.7, ('D', 'C'): -1.6, ('F', 'H'): -1.1,
+('D', 'G'): 1.1, ('F', 'L'): 2.2
}
genetic = _temp()
del _temp
@@ -1626,59 +1625,59 @@ del _temp
# http://www.embl-heidelberg.de/~vogt/matrices/gonnet.cmp
def _temp():
return {
-('W', 'F') : 3.6, ('L', 'R') : -2.2, ('I', 'I') : 4.0, ('Q', 'Q') : 2.7,
-('W', 'N') : -3.6, ('V', 'I') : 3.1, ('H', 'T') : -0.3, ('H', 'P') : -1.1,
-('W', 'V') : -2.6, ('Q', 'E') : 1.7, ('W', 'R') : -1.6, ('Q', 'A') : -0.2,
-('H', 'H') : 6.0, ('H', 'D') : 0.4, ('L', 'N') : -3.0, ('Y', 'M') : -0.2,
-('Y', 'I') : -0.7, ('Y', 'E') : -2.7, ('E', 'S') : 0.2, ('Y', 'A') : -2.2,
-('Y', 'Y') : 7.8, ('T', 'C') : -0.5, ('E', 'C') : -3.0, ('Y', 'Q') : -1.7,
-('E', 'G') : -0.8, ('V', 'A') : 0.1, ('C', 'C') : 11.5, ('M', 'R') : -1.7,
-('P', 'T') : 0.1, ('V', 'E') : -1.9, ('P', 'P') : 7.6, ('I', 'T') : -0.6,
-('K', 'S') : 0.1, ('R', 'G') : -1.0, ('I', 'P') : -2.6, ('R', 'C') : -2.2,
-('A', 'T') : 0.6, ('K', 'K') : 3.2, ('A', 'P') : 0.3, ('V', 'M') : 1.6,
-('I', 'D') : -3.8, ('K', 'C') : -2.8, ('K', 'G') : -1.1, ('R', 'S') : -0.2,
-('F', 'Q') : -2.6, ('F', 'A') : -2.3, ('V', 'V') : 3.4, ('M', 'N') : -2.2,
-('F', 'E') : -3.9, ('D', 'N') : 2.2, ('F', 'I') : 1.0, ('F', 'M') : 1.6,
-('M', 'S') : -1.4, ('S', 'S') : 2.2, ('L', 'Q') : -1.6, ('W', 'E') : -4.3,
-('W', 'A') : -3.6, ('W', 'M') : -1.0, ('H', 'S') : -0.2, ('W', 'I') : -1.8,
-('S', 'C') : 0.1, ('L', 'A') : -1.2, ('L', 'E') : -2.8, ('W', 'Q') : -2.7,
-('H', 'G') : -1.4, ('Q', 'N') : 0.7, ('H', 'C') : -1.3, ('L', 'M') : 2.8,
-('W', 'Y') : 4.1, ('Y', 'N') : -1.4, ('E', 'P') : -0.5, ('Y', 'F') : 5.1,
-('E', 'T') : -0.1, ('A', 'A') : 2.4, ('I', 'N') : -2.8, ('G', 'A') : 0.5,
-('Y', 'V') : -1.1, ('E', 'D') : 2.7, ('W', 'H') : -0.8, ('Y', 'R') : -1.8,
-('M', 'Q') : -1.0, ('P', 'S') : 0.4, ('R', 'H') : 0.6, ('A', 'C') : 0.5,
-('R', 'D') : -0.3, ('K', 'P') : -0.6, ('L', 'D') : -4.0, ('K', 'T') : 0.1,
-('V', 'N') : -2.2, ('M', 'A') : -0.7, ('K', 'H') : 0.6, ('V', 'R') : -2.0,
-('P', 'C') : -3.1, ('M', 'E') : -2.0, ('A', 'S') : 1.1, ('T', 'T') : 2.5,
-('R', 'T') : -0.2, ('I', 'G') : -4.5, ('R', 'P') : -0.9, ('K', 'D') : 0.5,
-('I', 'C') : -1.1, ('F', 'R') : -3.2, ('F', 'V') : 0.1, ('L', 'C') : -1.5,
-('F', 'F') : 7.0, ('D', 'A') : -0.3, ('F', 'N') : -3.1, ('W', 'D') : -5.2,
-('L', 'P') : -2.3, ('Q', 'S') : 0.2, ('N', 'C') : -1.8, ('N', 'G') : 0.4,
-('H', 'N') : 1.2, ('W', 'T') : -3.5, ('Q', 'G') : -1.0, ('W', 'P') : -5.0,
-('Q', 'C') : -2.4, ('N', 'S') : 0.9, ('L', 'H') : -1.9, ('L', 'L') : 4.0,
-('G', 'T') : -1.1, ('M', 'M') : 4.3, ('G', 'P') : -1.6, ('Y', 'K') : -2.1,
-('Y', 'G') : -4.0, ('Y', 'C') : -0.5, ('E', 'A') : 0.0, ('E', 'E') : 3.6,
-('Y', 'S') : -1.9, ('M', 'P') : -2.4, ('V', 'C') : 0.0, ('M', 'T') : -0.6,
-('V', 'G') : -3.3, ('R', 'E') : 0.4, ('V', 'K') : -1.7, ('K', 'Q') : 1.5,
-('R', 'A') : -0.6, ('I', 'R') : -2.4, ('N', 'A') : -0.3, ('V', 'S') : -1.0,
-('M', 'D') : -3.0, ('M', 'H') : -1.3, ('K', 'A') : -0.4, ('R', 'Q') : 1.5,
-('K', 'E') : 1.2, ('F', 'S') : -2.8, ('I', 'K') : -2.1, ('D', 'P') : -0.7,
-('D', 'T') : 0.0, ('I', 'M') : 2.5, ('F', 'C') : -0.8, ('W', 'L') : -0.7,
-('F', 'G') : -5.2, ('F', 'K') : -3.3, ('F', 'T') : -2.2, ('D', 'D') : 4.7,
-('Q', 'T') : 0.0, ('W', 'G') : -4.0, ('Q', 'P') : -0.2, ('W', 'C') : -1.0,
-('W', 'K') : -3.5, ('H', 'Q') : 1.2, ('Q', 'D') : 0.9, ('W', 'W') : 14.2,
-('V', 'L') : 1.8, ('L', 'G') : -4.4, ('W', 'S') : -3.3, ('L', 'K') : -2.1,
-('N', 'P') : -0.9, ('H', 'E') : 0.4, ('N', 'T') : 0.5, ('H', 'A') : -0.8,
-('Y', 'L') : 0.0, ('Y', 'H') : 2.2, ('G', 'S') : 0.4, ('Y', 'D') : -2.8,
-('V', 'Q') : -1.5, ('L', 'T') : -1.3, ('G', 'G') : 6.6, ('G', 'C') : -2.0,
-('E', 'N') : 0.9, ('Y', 'T') : -1.9, ('Y', 'P') : -3.1, ('R', 'N') : 0.3,
-('V', 'D') : -2.9, ('K', 'R') : 2.7, ('V', 'H') : -2.0, ('I', 'Q') : -1.9,
-('V', 'P') : -1.8, ('M', 'C') : -0.9, ('K', 'N') : 0.8, ('V', 'T') : 0.0,
-('M', 'G') : -3.5, ('T', 'S') : 1.5, ('I', 'E') : -2.7, ('M', 'K') : -1.4,
-('I', 'A') : -0.8, ('N', 'N') : 3.8, ('R', 'R') : 4.7, ('F', 'P') : -3.8,
-('L', 'I') : 2.8, ('I', 'S') : -1.8, ('D', 'S') : 0.5, ('L', 'S') : -2.1,
-('I', 'H') : -2.2, ('F', 'D') : -4.5, ('D', 'C') : -3.2, ('F', 'H') : -0.1,
-('D', 'G') : 0.1, ('F', 'L') : 2.0
+('W', 'F'): 3.6, ('L', 'R'): -2.2, ('I', 'I'): 4.0, ('Q', 'Q'): 2.7,
+('W', 'N'): -3.6, ('V', 'I'): 3.1, ('H', 'T'): -0.3, ('H', 'P'): -1.1,
+('W', 'V'): -2.6, ('Q', 'E'): 1.7, ('W', 'R'): -1.6, ('Q', 'A'): -0.2,
+('H', 'H'): 6.0, ('H', 'D'): 0.4, ('L', 'N'): -3.0, ('Y', 'M'): -0.2,
+('Y', 'I'): -0.7, ('Y', 'E'): -2.7, ('E', 'S'): 0.2, ('Y', 'A'): -2.2,
+('Y', 'Y'): 7.8, ('T', 'C'): -0.5, ('E', 'C'): -3.0, ('Y', 'Q'): -1.7,
+('E', 'G'): -0.8, ('V', 'A'): 0.1, ('C', 'C'): 11.5, ('M', 'R'): -1.7,
+('P', 'T'): 0.1, ('V', 'E'): -1.9, ('P', 'P'): 7.6, ('I', 'T'): -0.6,
+('K', 'S'): 0.1, ('R', 'G'): -1.0, ('I', 'P'): -2.6, ('R', 'C'): -2.2,
+('A', 'T'): 0.6, ('K', 'K'): 3.2, ('A', 'P'): 0.3, ('V', 'M'): 1.6,
+('I', 'D'): -3.8, ('K', 'C'): -2.8, ('K', 'G'): -1.1, ('R', 'S'): -0.2,
+('F', 'Q'): -2.6, ('F', 'A'): -2.3, ('V', 'V'): 3.4, ('M', 'N'): -2.2,
+('F', 'E'): -3.9, ('D', 'N'): 2.2, ('F', 'I'): 1.0, ('F', 'M'): 1.6,
+('M', 'S'): -1.4, ('S', 'S'): 2.2, ('L', 'Q'): -1.6, ('W', 'E'): -4.3,
+('W', 'A'): -3.6, ('W', 'M'): -1.0, ('H', 'S'): -0.2, ('W', 'I'): -1.8,
+('S', 'C'): 0.1, ('L', 'A'): -1.2, ('L', 'E'): -2.8, ('W', 'Q'): -2.7,
+('H', 'G'): -1.4, ('Q', 'N'): 0.7, ('H', 'C'): -1.3, ('L', 'M'): 2.8,
+('W', 'Y'): 4.1, ('Y', 'N'): -1.4, ('E', 'P'): -0.5, ('Y', 'F'): 5.1,
+('E', 'T'): -0.1, ('A', 'A'): 2.4, ('I', 'N'): -2.8, ('G', 'A'): 0.5,
+('Y', 'V'): -1.1, ('E', 'D'): 2.7, ('W', 'H'): -0.8, ('Y', 'R'): -1.8,
+('M', 'Q'): -1.0, ('P', 'S'): 0.4, ('R', 'H'): 0.6, ('A', 'C'): 0.5,
+('R', 'D'): -0.3, ('K', 'P'): -0.6, ('L', 'D'): -4.0, ('K', 'T'): 0.1,
+('V', 'N'): -2.2, ('M', 'A'): -0.7, ('K', 'H'): 0.6, ('V', 'R'): -2.0,
+('P', 'C'): -3.1, ('M', 'E'): -2.0, ('A', 'S'): 1.1, ('T', 'T'): 2.5,
+('R', 'T'): -0.2, ('I', 'G'): -4.5, ('R', 'P'): -0.9, ('K', 'D'): 0.5,
+('I', 'C'): -1.1, ('F', 'R'): -3.2, ('F', 'V'): 0.1, ('L', 'C'): -1.5,
+('F', 'F'): 7.0, ('D', 'A'): -0.3, ('F', 'N'): -3.1, ('W', 'D'): -5.2,
+('L', 'P'): -2.3, ('Q', 'S'): 0.2, ('N', 'C'): -1.8, ('N', 'G'): 0.4,
+('H', 'N'): 1.2, ('W', 'T'): -3.5, ('Q', 'G'): -1.0, ('W', 'P'): -5.0,
+('Q', 'C'): -2.4, ('N', 'S'): 0.9, ('L', 'H'): -1.9, ('L', 'L'): 4.0,
+('G', 'T'): -1.1, ('M', 'M'): 4.3, ('G', 'P'): -1.6, ('Y', 'K'): -2.1,
+('Y', 'G'): -4.0, ('Y', 'C'): -0.5, ('E', 'A'): 0.0, ('E', 'E'): 3.6,
+('Y', 'S'): -1.9, ('M', 'P'): -2.4, ('V', 'C'): 0.0, ('M', 'T'): -0.6,
+('V', 'G'): -3.3, ('R', 'E'): 0.4, ('V', 'K'): -1.7, ('K', 'Q'): 1.5,
+('R', 'A'): -0.6, ('I', 'R'): -2.4, ('N', 'A'): -0.3, ('V', 'S'): -1.0,
+('M', 'D'): -3.0, ('M', 'H'): -1.3, ('K', 'A'): -0.4, ('R', 'Q'): 1.5,
+('K', 'E'): 1.2, ('F', 'S'): -2.8, ('I', 'K'): -2.1, ('D', 'P'): -0.7,
+('D', 'T'): 0.0, ('I', 'M'): 2.5, ('F', 'C'): -0.8, ('W', 'L'): -0.7,
+('F', 'G'): -5.2, ('F', 'K'): -3.3, ('F', 'T'): -2.2, ('D', 'D'): 4.7,
+('Q', 'T'): 0.0, ('W', 'G'): -4.0, ('Q', 'P'): -0.2, ('W', 'C'): -1.0,
+('W', 'K'): -3.5, ('H', 'Q'): 1.2, ('Q', 'D'): 0.9, ('W', 'W'): 14.2,
+('V', 'L'): 1.8, ('L', 'G'): -4.4, ('W', 'S'): -3.3, ('L', 'K'): -2.1,
+('N', 'P'): -0.9, ('H', 'E'): 0.4, ('N', 'T'): 0.5, ('H', 'A'): -0.8,
+('Y', 'L'): 0.0, ('Y', 'H'): 2.2, ('G', 'S'): 0.4, ('Y', 'D'): -2.8,
+('V', 'Q'): -1.5, ('L', 'T'): -1.3, ('G', 'G'): 6.6, ('G', 'C'): -2.0,
+('E', 'N'): 0.9, ('Y', 'T'): -1.9, ('Y', 'P'): -3.1, ('R', 'N'): 0.3,
+('V', 'D'): -2.9, ('K', 'R'): 2.7, ('V', 'H'): -2.0, ('I', 'Q'): -1.9,
+('V', 'P'): -1.8, ('M', 'C'): -0.9, ('K', 'N'): 0.8, ('V', 'T'): 0.0,
+('M', 'G'): -3.5, ('T', 'S'): 1.5, ('I', 'E'): -2.7, ('M', 'K'): -1.4,
+('I', 'A'): -0.8, ('N', 'N'): 3.8, ('R', 'R'): 4.7, ('F', 'P'): -3.8,
+('L', 'I'): 2.8, ('I', 'S'): -1.8, ('D', 'S'): 0.5, ('L', 'S'): -2.1,
+('I', 'H'): -2.2, ('F', 'D'): -4.5, ('D', 'C'): -3.2, ('F', 'H'): -0.1,
+('D', 'G'): 0.1, ('F', 'L'): 2.0
}
gonnet = _temp()
del _temp
@@ -1687,59 +1686,59 @@ del _temp
# http://www.embl-heidelberg.de/~vogt/matrices/grant.cmp
def _temp():
return {
-('L', 'R') : 103, ('I', 'I') : 215, ('Q', 'Q') : 215, ('N', 'E') : 150,
-('Q', 'Y') : 128, ('H', 'P') : 138, ('W', 'V') : 186, ('Q', 'E') : 173,
-('H', 'L') : 129, ('W', 'R') : 118, ('H', 'H') : 215, ('N', 'Q') : 169,
-('U', 'S') : 70, ('N', 'U') : 62, ('G', 'V') : 94, ('G', 'R') : 161,
-('I', 'N') : 121, ('Y', 'E') : 156, ('E', 'S') : 157, ('G', 'F') : 75,
-('Y', 'Y') : 215, ('R', 'R') : 215, ('Y', 'U') : 77, ('C', 'C') : 215,
-('M', 'R') : 124, ('V', 'E') : 146, ('T', 'N') : 41, ('M', 'V') : 194,
-('P', 'P') : 215, ('A', 'L') : 89, ('V', 'S') : 91, ('O', 'E') : 123,
-('I', 'P') : 112, ('T', 'F') : 175, ('I', 'L') : 109, ('C', 'S') : 103,
-('A', 'P') : 107, ('C', 'W') : 17, ('M', 'F') : 187, ('O', 'Y') : 68,
-('T', 'R') : 114, ('O', 'U') : 179, ('V', 'R') : 119, ('U', 'R') : 113,
-('R', 'S') : 105, ('F', 'U') : 193, ('F', 'Y') : 62, ('M', 'N') : 73,
-('F', 'E') : 112, ('G', 'N') : 173, ('P', 'U') : 117, ('S', 'S') : 215,
-('Q', 'V') : 119, ('W', 'E') : 126, ('Q', 'R') : 172, ('N', 'N') : 215,
-('H', 'W') : 121, ('Q', 'H') : 191, ('H', 'S') : 126, ('N', 'F') : 57,
-('H', 'O') : 132, ('Q', 'F') : 99, ('W', 'U') : 210, ('L', 'E') : 157,
-('C', 'P') : 46, ('N', 'R') : 129, ('H', 'C') : 41, ('N', 'V') : 82,
-('G', 'U') : 77, ('A', 'S') : 150, ('Q', 'W') : 106, ('G', 'Q') : 186,
-('E', 'P') : 177, ('A', 'N') : 192, ('G', 'Y') : 117, ('A', 'A') : 215,
-('G', 'E') : 150, ('G', 'A') : 170, ('Y', 'V') : 106, ('Y', 'R') : 90,
-('G', 'I') : 159, ('C', 'F') : 10, ('M', 'Q') : 114, ('T', 'I') : 105,
-('P', 'S') : 141, ('M', 'U') : 200, ('T', 'M') : 148, ('A', 'C') : 61,
-('O', 'F') : 193, ('M', 'Y') : 88, ('T', 'A') : 34, ('A', 'O') : 55,
-('C', 'L') : 20, ('I', 'S') : 94, ('C', 'O') : 21, ('T', 'Y') : 31,
-('I', 'O') : 130, ('M', 'E') : 134, ('V', 'V') : 215, ('O', 'V') : 160,
-('M', 'I') : 120, ('T', 'Q') : 85, ('O', 'R') : 138, ('M', 'M') : 215,
-('T', 'U') : 154, ('I', 'C') : 13, ('F', 'R') : 118, ('A', 'W') : 47,
-('F', 'V') : 165, ('I', 'H') : 183, ('C', 'E') : 66, ('F', 'F') : 215,
-('M', 'A') : 55, ('Q', 'U') : 102, ('L', 'P') : 188, ('N', 'O') : 72,
-('Q', 'S') : 147, ('H', 'V') : 131, ('W', 'L') : 121, ('H', 'R') : 186,
-('W', 'P') : 120, ('Q', 'C') : 61, ('H', 'F') : 115, ('N', 'S') : 169,
-('Q', 'O') : 116, ('U', 'U') : 215, ('N', 'W') : 66, ('L', 'L') : 215,
-('G', 'P') : 122, ('A', 'U') : 43, ('E', 'U') : 123, ('T', 'P') : 68,
-('I', 'Q') : 162, ('G', 'L') : 108, ('E', 'E') : 215, ('G', 'H') : 175,
-('Y', 'S') : 159, ('M', 'P') : 128, ('O', 'O') : 215, ('A', 'F') : 38,
-('T', 'H') : 100, ('P', 'R') : 112, ('T', 'L') : 67, ('I', 'V') : 118,
-('I', 'R') : 189, ('A', 'V') : 63, ('A', 'R') : 119, ('C', 'U') : 17,
-('M', 'H') : 128, ('O', 'W') : 182, ('I', 'F') : 113, ('C', 'Y') : 56,
-('M', 'L') : 131, ('O', 'S') : 71, ('T', 'T') : 215, ('F', 'S') : 60,
-('F', 'W') : 194, ('L', 'S') : 116, ('N', 'H') : 147, ('A', 'H') : 134,
-('Q', 'P') : 139, ('N', 'L') : 104, ('H', 'Y') : 117, ('H', 'U') : 116,
-('A', 'I') : 114, ('W', 'W') : 215, ('W', 'S') : 73, ('Q', 'L') : 124,
-('N', 'P') : 124, ('H', 'E') : 168, ('L', 'U') : 119, ('I', 'W') : 113,
-('Y', 'L') : 155, ('G', 'W') : 81, ('G', 'S') : 135, ('E', 'R') : 144,
-('W', 'Y') : 80, ('G', 'G') : 215, ('T', 'V') : 127, ('G', 'C') : 45,
-('G', 'O') : 93, ('Y', 'P') : 173, ('A', 'E') : 130, ('O', 'L') : 103,
-('M', 'S') : 80, ('T', 'O') : 178, ('I', 'Y') : 88, ('M', 'W') : 205,
-('T', 'C') : 0, ('I', 'U') : 108, ('T', 'G') : 63, ('V', 'L') : 151,
-('C', 'R') : 35, ('V', 'P') : 147, ('M', 'C') : 19, ('C', 'V') : 23,
-('A', 'Q') : 154, ('M', 'G') : 89, ('T', 'S') : 38, ('I', 'E') : 137,
-('T', 'W') : 154, ('A', 'Y') : 121, ('O', 'P') : 105, ('M', 'O') : 179,
-('F', 'P') : 101, ('N', 'Y') : 135, ('T', 'E') : 87, ('V', 'U') : 183,
-('N', 'C') : 76, ('F', 'L') : 102
+('L', 'R'): 103, ('I', 'I'): 215, ('Q', 'Q'): 215, ('N', 'E'): 150,
+('Q', 'Y'): 128, ('H', 'P'): 138, ('W', 'V'): 186, ('Q', 'E'): 173,
+('H', 'L'): 129, ('W', 'R'): 118, ('H', 'H'): 215, ('N', 'Q'): 169,
+('U', 'S'): 70, ('N', 'U'): 62, ('G', 'V'): 94, ('G', 'R'): 161,
+('I', 'N'): 121, ('Y', 'E'): 156, ('E', 'S'): 157, ('G', 'F'): 75,
+('Y', 'Y'): 215, ('R', 'R'): 215, ('Y', 'U'): 77, ('C', 'C'): 215,
+('M', 'R'): 124, ('V', 'E'): 146, ('T', 'N'): 41, ('M', 'V'): 194,
+('P', 'P'): 215, ('A', 'L'): 89, ('V', 'S'): 91, ('O', 'E'): 123,
+('I', 'P'): 112, ('T', 'F'): 175, ('I', 'L'): 109, ('C', 'S'): 103,
+('A', 'P'): 107, ('C', 'W'): 17, ('M', 'F'): 187, ('O', 'Y'): 68,
+('T', 'R'): 114, ('O', 'U'): 179, ('V', 'R'): 119, ('U', 'R'): 113,
+('R', 'S'): 105, ('F', 'U'): 193, ('F', 'Y'): 62, ('M', 'N'): 73,
+('F', 'E'): 112, ('G', 'N'): 173, ('P', 'U'): 117, ('S', 'S'): 215,
+('Q', 'V'): 119, ('W', 'E'): 126, ('Q', 'R'): 172, ('N', 'N'): 215,
+('H', 'W'): 121, ('Q', 'H'): 191, ('H', 'S'): 126, ('N', 'F'): 57,
+('H', 'O'): 132, ('Q', 'F'): 99, ('W', 'U'): 210, ('L', 'E'): 157,
+('C', 'P'): 46, ('N', 'R'): 129, ('H', 'C'): 41, ('N', 'V'): 82,
+('G', 'U'): 77, ('A', 'S'): 150, ('Q', 'W'): 106, ('G', 'Q'): 186,
+('E', 'P'): 177, ('A', 'N'): 192, ('G', 'Y'): 117, ('A', 'A'): 215,
+('G', 'E'): 150, ('G', 'A'): 170, ('Y', 'V'): 106, ('Y', 'R'): 90,
+('G', 'I'): 159, ('C', 'F'): 10, ('M', 'Q'): 114, ('T', 'I'): 105,
+('P', 'S'): 141, ('M', 'U'): 200, ('T', 'M'): 148, ('A', 'C'): 61,
+('O', 'F'): 193, ('M', 'Y'): 88, ('T', 'A'): 34, ('A', 'O'): 55,
+('C', 'L'): 20, ('I', 'S'): 94, ('C', 'O'): 21, ('T', 'Y'): 31,
+('I', 'O'): 130, ('M', 'E'): 134, ('V', 'V'): 215, ('O', 'V'): 160,
+('M', 'I'): 120, ('T', 'Q'): 85, ('O', 'R'): 138, ('M', 'M'): 215,
+('T', 'U'): 154, ('I', 'C'): 13, ('F', 'R'): 118, ('A', 'W'): 47,
+('F', 'V'): 165, ('I', 'H'): 183, ('C', 'E'): 66, ('F', 'F'): 215,
+('M', 'A'): 55, ('Q', 'U'): 102, ('L', 'P'): 188, ('N', 'O'): 72,
+('Q', 'S'): 147, ('H', 'V'): 131, ('W', 'L'): 121, ('H', 'R'): 186,
+('W', 'P'): 120, ('Q', 'C'): 61, ('H', 'F'): 115, ('N', 'S'): 169,
+('Q', 'O'): 116, ('U', 'U'): 215, ('N', 'W'): 66, ('L', 'L'): 215,
+('G', 'P'): 122, ('A', 'U'): 43, ('E', 'U'): 123, ('T', 'P'): 68,
+('I', 'Q'): 162, ('G', 'L'): 108, ('E', 'E'): 215, ('G', 'H'): 175,
+('Y', 'S'): 159, ('M', 'P'): 128, ('O', 'O'): 215, ('A', 'F'): 38,
+('T', 'H'): 100, ('P', 'R'): 112, ('T', 'L'): 67, ('I', 'V'): 118,
+('I', 'R'): 189, ('A', 'V'): 63, ('A', 'R'): 119, ('C', 'U'): 17,
+('M', 'H'): 128, ('O', 'W'): 182, ('I', 'F'): 113, ('C', 'Y'): 56,
+('M', 'L'): 131, ('O', 'S'): 71, ('T', 'T'): 215, ('F', 'S'): 60,
+('F', 'W'): 194, ('L', 'S'): 116, ('N', 'H'): 147, ('A', 'H'): 134,
+('Q', 'P'): 139, ('N', 'L'): 104, ('H', 'Y'): 117, ('H', 'U'): 116,
+('A', 'I'): 114, ('W', 'W'): 215, ('W', 'S'): 73, ('Q', 'L'): 124,
+('N', 'P'): 124, ('H', 'E'): 168, ('L', 'U'): 119, ('I', 'W'): 113,
+('Y', 'L'): 155, ('G', 'W'): 81, ('G', 'S'): 135, ('E', 'R'): 144,
+('W', 'Y'): 80, ('G', 'G'): 215, ('T', 'V'): 127, ('G', 'C'): 45,
+('G', 'O'): 93, ('Y', 'P'): 173, ('A', 'E'): 130, ('O', 'L'): 103,
+('M', 'S'): 80, ('T', 'O'): 178, ('I', 'Y'): 88, ('M', 'W'): 205,
+('T', 'C'): 0, ('I', 'U'): 108, ('T', 'G'): 63, ('V', 'L'): 151,
+('C', 'R'): 35, ('V', 'P'): 147, ('M', 'C'): 19, ('C', 'V'): 23,
+('A', 'Q'): 154, ('M', 'G'): 89, ('T', 'S'): 38, ('I', 'E'): 137,
+('T', 'W'): 154, ('A', 'Y'): 121, ('O', 'P'): 105, ('M', 'O'): 179,
+('F', 'P'): 101, ('N', 'Y'): 135, ('T', 'E'): 87, ('V', 'U'): 183,
+('N', 'C'): 76, ('F', 'L'): 102
}
grant = _temp()
del _temp
@@ -1748,59 +1747,59 @@ del _temp
# http://www.embl-heidelberg.de/~vogt/matrices/ident.cmp
def _temp():
return {
-('W', 'F') : -1, ('L', 'R') : -1, ('I', 'I') : 6, ('Q', 'Q') : 6,
-('W', 'N') : -1, ('V', 'I') : -1, ('H', 'T') : -1, ('H', 'P') : -1,
-('W', 'V') : -1, ('Q', 'E') : -1, ('W', 'R') : -1, ('Q', 'A') : -1,
-('H', 'H') : 6, ('H', 'D') : -1, ('L', 'N') : -1, ('Y', 'M') : -1,
-('Y', 'I') : -1, ('Y', 'E') : -1, ('E', 'S') : -1, ('Y', 'A') : -1,
-('Y', 'Y') : 6, ('T', 'C') : -1, ('E', 'C') : -1, ('Y', 'Q') : -1,
-('E', 'G') : -1, ('V', 'A') : -1, ('C', 'C') : 6, ('M', 'R') : -1,
-('P', 'T') : -1, ('V', 'E') : -1, ('P', 'P') : 6, ('I', 'T') : -1,
-('K', 'S') : -1, ('R', 'G') : -1, ('I', 'P') : -1, ('R', 'C') : -1,
-('A', 'T') : -1, ('K', 'K') : 6, ('A', 'P') : -1, ('V', 'M') : -1,
-('I', 'D') : -1, ('K', 'C') : -1, ('K', 'G') : -1, ('R', 'S') : -1,
-('F', 'Q') : -1, ('F', 'A') : -1, ('V', 'V') : 6, ('M', 'N') : -1,
-('F', 'E') : -1, ('D', 'N') : -1, ('F', 'I') : -1, ('F', 'M') : -1,
-('M', 'S') : -1, ('S', 'S') : 6, ('L', 'Q') : -1, ('W', 'E') : -1,
-('W', 'A') : -1, ('W', 'M') : -1, ('H', 'S') : -1, ('W', 'I') : -1,
-('S', 'C') : -1, ('L', 'A') : -1, ('L', 'E') : -1, ('W', 'Q') : -1,
-('H', 'G') : -1, ('Q', 'N') : -1, ('H', 'C') : -1, ('L', 'M') : -1,
-('W', 'Y') : -1, ('Y', 'N') : -1, ('E', 'P') : -1, ('Y', 'F') : -1,
-('E', 'T') : -1, ('A', 'A') : 6, ('I', 'N') : -1, ('G', 'A') : -1,
-('Y', 'V') : -1, ('E', 'D') : -1, ('W', 'H') : -1, ('Y', 'R') : -1,
-('M', 'Q') : -1, ('P', 'S') : -1, ('R', 'H') : -1, ('A', 'C') : -1,
-('R', 'D') : -1, ('K', 'P') : -1, ('L', 'D') : -1, ('K', 'T') : -1,
-('V', 'N') : -1, ('M', 'A') : -1, ('K', 'H') : -1, ('V', 'R') : -1,
-('P', 'C') : -1, ('M', 'E') : -1, ('A', 'S') : -1, ('T', 'T') : 6,
-('R', 'T') : -1, ('I', 'G') : -1, ('R', 'P') : -1, ('K', 'D') : -1,
-('I', 'C') : -1, ('F', 'R') : -1, ('F', 'V') : -1, ('L', 'C') : -1,
-('F', 'F') : 6, ('D', 'A') : -1, ('F', 'N') : -1, ('W', 'D') : -1,
-('L', 'P') : -1, ('Q', 'S') : -1, ('N', 'C') : -1, ('N', 'G') : -1,
-('H', 'N') : -1, ('W', 'T') : -1, ('Q', 'G') : -1, ('W', 'P') : -1,
-('Q', 'C') : -1, ('N', 'S') : -1, ('L', 'H') : -1, ('L', 'L') : 6,
-('G', 'T') : -1, ('M', 'M') : 6, ('G', 'P') : -1, ('Y', 'K') : -1,
-('Y', 'G') : -1, ('Y', 'C') : -1, ('E', 'A') : -1, ('E', 'E') : 6,
-('Y', 'S') : -1, ('M', 'P') : -1, ('V', 'C') : -1, ('M', 'T') : -1,
-('V', 'G') : -1, ('R', 'E') : -1, ('V', 'K') : -1, ('K', 'Q') : -1,
-('R', 'A') : -1, ('I', 'R') : -1, ('N', 'A') : -1, ('V', 'S') : -1,
-('M', 'D') : -1, ('M', 'H') : -1, ('K', 'A') : -1, ('R', 'Q') : -1,
-('K', 'E') : -1, ('F', 'S') : -1, ('I', 'K') : -1, ('D', 'P') : -1,
-('D', 'T') : -1, ('I', 'M') : -1, ('F', 'C') : -1, ('W', 'L') : -1,
-('F', 'G') : -1, ('F', 'K') : -1, ('F', 'T') : -1, ('D', 'D') : 6,
-('Q', 'T') : -1, ('W', 'G') : -1, ('Q', 'P') : -1, ('W', 'C') : -1,
-('W', 'K') : -1, ('H', 'Q') : -1, ('Q', 'D') : -1, ('W', 'W') : 6,
-('V', 'L') : -1, ('L', 'G') : -1, ('W', 'S') : -1, ('L', 'K') : -1,
-('N', 'P') : -1, ('H', 'E') : -1, ('N', 'T') : -1, ('H', 'A') : -1,
-('Y', 'L') : -1, ('Y', 'H') : -1, ('G', 'S') : -1, ('Y', 'D') : -1,
-('V', 'Q') : -1, ('L', 'T') : -1, ('G', 'G') : 6, ('G', 'C') : -1,
-('E', 'N') : -1, ('Y', 'T') : -1, ('Y', 'P') : -1, ('R', 'N') : -1,
-('V', 'D') : -1, ('K', 'R') : -1, ('V', 'H') : -1, ('I', 'Q') : -1,
-('V', 'P') : -1, ('M', 'C') : -1, ('K', 'N') : -1, ('V', 'T') : -1,
-('M', 'G') : -1, ('T', 'S') : -1, ('I', 'E') : -1, ('M', 'K') : -1,
-('I', 'A') : -1, ('N', 'N') : 6, ('R', 'R') : 6, ('F', 'P') : -1,
-('L', 'I') : -1, ('I', 'S') : -1, ('D', 'S') : -1, ('L', 'S') : -1,
-('I', 'H') : -1, ('F', 'D') : -1, ('D', 'C') : -1, ('F', 'H') : -1,
-('D', 'G') : -1, ('F', 'L') : -1
+('W', 'F'): -1, ('L', 'R'): -1, ('I', 'I'): 6, ('Q', 'Q'): 6,
+('W', 'N'): -1, ('V', 'I'): -1, ('H', 'T'): -1, ('H', 'P'): -1,
+('W', 'V'): -1, ('Q', 'E'): -1, ('W', 'R'): -1, ('Q', 'A'): -1,
+('H', 'H'): 6, ('H', 'D'): -1, ('L', 'N'): -1, ('Y', 'M'): -1,
+('Y', 'I'): -1, ('Y', 'E'): -1, ('E', 'S'): -1, ('Y', 'A'): -1,
+('Y', 'Y'): 6, ('T', 'C'): -1, ('E', 'C'): -1, ('Y', 'Q'): -1,
+('E', 'G'): -1, ('V', 'A'): -1, ('C', 'C'): 6, ('M', 'R'): -1,
+('P', 'T'): -1, ('V', 'E'): -1, ('P', 'P'): 6, ('I', 'T'): -1,
+('K', 'S'): -1, ('R', 'G'): -1, ('I', 'P'): -1, ('R', 'C'): -1,
+('A', 'T'): -1, ('K', 'K'): 6, ('A', 'P'): -1, ('V', 'M'): -1,
+('I', 'D'): -1, ('K', 'C'): -1, ('K', 'G'): -1, ('R', 'S'): -1,
+('F', 'Q'): -1, ('F', 'A'): -1, ('V', 'V'): 6, ('M', 'N'): -1,
+('F', 'E'): -1, ('D', 'N'): -1, ('F', 'I'): -1, ('F', 'M'): -1,
+('M', 'S'): -1, ('S', 'S'): 6, ('L', 'Q'): -1, ('W', 'E'): -1,
+('W', 'A'): -1, ('W', 'M'): -1, ('H', 'S'): -1, ('W', 'I'): -1,
+('S', 'C'): -1, ('L', 'A'): -1, ('L', 'E'): -1, ('W', 'Q'): -1,
+('H', 'G'): -1, ('Q', 'N'): -1, ('H', 'C'): -1, ('L', 'M'): -1,
+('W', 'Y'): -1, ('Y', 'N'): -1, ('E', 'P'): -1, ('Y', 'F'): -1,
+('E', 'T'): -1, ('A', 'A'): 6, ('I', 'N'): -1, ('G', 'A'): -1,
+('Y', 'V'): -1, ('E', 'D'): -1, ('W', 'H'): -1, ('Y', 'R'): -1,
+('M', 'Q'): -1, ('P', 'S'): -1, ('R', 'H'): -1, ('A', 'C'): -1,
+('R', 'D'): -1, ('K', 'P'): -1, ('L', 'D'): -1, ('K', 'T'): -1,
+('V', 'N'): -1, ('M', 'A'): -1, ('K', 'H'): -1, ('V', 'R'): -1,
+('P', 'C'): -1, ('M', 'E'): -1, ('A', 'S'): -1, ('T', 'T'): 6,
+('R', 'T'): -1, ('I', 'G'): -1, ('R', 'P'): -1, ('K', 'D'): -1,
+('I', 'C'): -1, ('F', 'R'): -1, ('F', 'V'): -1, ('L', 'C'): -1,
+('F', 'F'): 6, ('D', 'A'): -1, ('F', 'N'): -1, ('W', 'D'): -1,
+('L', 'P'): -1, ('Q', 'S'): -1, ('N', 'C'): -1, ('N', 'G'): -1,
+('H', 'N'): -1, ('W', 'T'): -1, ('Q', 'G'): -1, ('W', 'P'): -1,
+('Q', 'C'): -1, ('N', 'S'): -1, ('L', 'H'): -1, ('L', 'L'): 6,
+('G', 'T'): -1, ('M', 'M'): 6, ('G', 'P'): -1, ('Y', 'K'): -1,
+('Y', 'G'): -1, ('Y', 'C'): -1, ('E', 'A'): -1, ('E', 'E'): 6,
+('Y', 'S'): -1, ('M', 'P'): -1, ('V', 'C'): -1, ('M', 'T'): -1,
+('V', 'G'): -1, ('R', 'E'): -1, ('V', 'K'): -1, ('K', 'Q'): -1,
+('R', 'A'): -1, ('I', 'R'): -1, ('N', 'A'): -1, ('V', 'S'): -1,
+('M', 'D'): -1, ('M', 'H'): -1, ('K', 'A'): -1, ('R', 'Q'): -1,
+('K', 'E'): -1, ('F', 'S'): -1, ('I', 'K'): -1, ('D', 'P'): -1,
+('D', 'T'): -1, ('I', 'M'): -1, ('F', 'C'): -1, ('W', 'L'): -1,
+('F', 'G'): -1, ('F', 'K'): -1, ('F', 'T'): -1, ('D', 'D'): 6,
+('Q', 'T'): -1, ('W', 'G'): -1, ('Q', 'P'): -1, ('W', 'C'): -1,
+('W', 'K'): -1, ('H', 'Q'): -1, ('Q', 'D'): -1, ('W', 'W'): 6,
+('V', 'L'): -1, ('L', 'G'): -1, ('W', 'S'): -1, ('L', 'K'): -1,
+('N', 'P'): -1, ('H', 'E'): -1, ('N', 'T'): -1, ('H', 'A'): -1,
+('Y', 'L'): -1, ('Y', 'H'): -1, ('G', 'S'): -1, ('Y', 'D'): -1,
+('V', 'Q'): -1, ('L', 'T'): -1, ('G', 'G'): 6, ('G', 'C'): -1,
+('E', 'N'): -1, ('Y', 'T'): -1, ('Y', 'P'): -1, ('R', 'N'): -1,
+('V', 'D'): -1, ('K', 'R'): -1, ('V', 'H'): -1, ('I', 'Q'): -1,
+('V', 'P'): -1, ('M', 'C'): -1, ('K', 'N'): -1, ('V', 'T'): -1,
+('M', 'G'): -1, ('T', 'S'): -1, ('I', 'E'): -1, ('M', 'K'): -1,
+('I', 'A'): -1, ('N', 'N'): 6, ('R', 'R'): 6, ('F', 'P'): -1,
+('L', 'I'): -1, ('I', 'S'): -1, ('D', 'S'): -1, ('L', 'S'): -1,
+('I', 'H'): -1, ('F', 'D'): -1, ('D', 'C'): -1, ('F', 'H'): -1,
+('D', 'G'): -1, ('F', 'L'): -1
}
ident = _temp()
del _temp
@@ -1809,59 +1808,59 @@ del _temp
# http://www.embl-heidelberg.de/~vogt/matrices/johnson.cmp
def _temp():
return {
-('W', 'F') : 3.4, ('S', 'P') : -1.0, ('N', 'M') : -3.7, ('Q', 'Q') : 9.0,
-('N', 'A') : -1.4, ('N', 'E') : -0.7, ('W', 'V') : -4.9, ('Q', 'E') : 2.4,
-('L', 'H') : -4.2, ('W', 'R') : -3.8, ('Q', 'A') : -0.6, ('S', 'D') : -0.2,
-('H', 'H') : 12.7, ('Q', 'M') : -0.6, ('S', 'H') : -2.6, ('H', 'D') : -0.7,
-('Q', 'I') : -7.0, ('S', 'L') : -5.2, ('Y', 'M') : -1.3, ('Y', 'I') : -2.5,
-('Y', 'E') : -3.7, ('Y', 'A') : -4.0, ('G', 'F') : -8.6, ('V', 'T') : -1.9,
-('Y', 'Y') : 10.5, ('V', 'H') : -3.9, ('E', 'C') : -6.9, ('Y', 'Q') : -5.1,
-('V', 'A') : -0.5, ('C', 'C') : 16.1, ('V', 'E') : -4.2, ('T', 'N') : 0.1,
-('R', 'K') : 3.2, ('P', 'P') : 10.3, ('V', 'I') : 3.9, ('R', 'G') : -2.8,
-('V', 'M') : 0.7, ('T', 'F') : -5.0, ('R', 'C') : -5.6, ('V', 'Q') : -3.6,
-('K', 'K') : 7.6, ('P', 'D') : -1.0, ('I', 'H') : -5.1, ('M', 'F') : -0.6,
-('I', 'D') : -4.8, ('K', 'C') : -8.7, ('P', 'L') : -2.8, ('K', 'G') : -3.5,
-('P', 'H') : -4.3, ('T', 'R') : -1.4, ('F', 'A') : -3.2, ('F', 'E') : -6.4,
-('S', 'S') : 5.8, ('W', 'E') : -7.6, ('N', 'N') : 8.0, ('W', 'M') : -0.9,
-('Q', 'C') : -6.9, ('N', 'F') : -3.8, ('S', 'C') : -7.7, ('L', 'A') : -3.3,
-('S', 'G') : -1.3, ('L', 'E') : -5.6, ('W', 'Q') : -8.2, ('H', 'G') : -3.2,
-('S', 'K') : -1.5, ('Q', 'N') : -0.8, ('V', 'D') : -5.2, ('H', 'C') : -8.2,
-('Y', 'N') : -1.3, ('Y', 'F') : 3.4, ('W', 'I') : -3.3, ('C', 'A') : -3.4,
-('G', 'E') : -2.5, ('G', 'A') : -0.5, ('Y', 'V') : -1.8, ('E', 'D') : 2.4,
-('W', 'H') : -4.0, ('Y', 'R') : -2.1, ('N', 'I') : -4.7, ('R', 'L') : -3.7,
-('T', 'I') : -3.2, ('Q', 'L') : -4.4, ('R', 'H') : 0.1, ('T', 'M') : -3.2,
-('V', 'F') : -1.3, ('R', 'D') : -3.4, ('T', 'A') : -0.8, ('T', 'P') : -2.0,
-('T', 'E') : -0.5, ('V', 'N') : -5.7, ('P', 'G') : -2.5, ('M', 'A') : -1.5,
-('K', 'H') : 0.1, ('V', 'R') : -4.9, ('P', 'C') : -8.9, ('M', 'E') : -2.8,
-('V', 'V') : 7.0, ('T', 'T') : 6.8, ('M', 'I') : 2.6, ('T', 'Q') : -0.4,
-('I', 'G') : -5.5, ('P', 'K') : -0.6, ('M', 'M') : 11.2, ('K', 'D') : -1.5,
-('I', 'C') : -7.7, ('L', 'C') : -8.7, ('F', 'F') : 10.4, ('D', 'A') : -1.6,
-('S', 'R') : -0.6, ('W', 'D') : -6.0, ('N', 'C') : -7.6, ('N', 'G') : -1.4,
-('W', 'T') : -9.3, ('Q', 'G') : -2.8, ('S', 'F') : -4.8, ('W', 'P') : -7.4,
-('L', 'D') : -8.0, ('H', 'F') : -1.7, ('Q', 'K') : 1.1, ('S', 'N') : 1.0,
-('L', 'L') : 7.3, ('Q', 'F') : -6.4, ('Y', 'K') : -3.7, ('Y', 'G') : -5.4,
-('Y', 'C') : -7.7, ('G', 'D') : -2.1, ('E', 'A') : -0.7, ('Y', 'W') : 2.3,
-('E', 'E') : 8.6, ('Y', 'S') : -3.4, ('R', 'M') : -4.2, ('V', 'C') : -4.8,
-('T', 'H') : -3.0, ('R', 'I') : -5.4, ('V', 'G') : -5.6, ('T', 'L') : -4.6,
-('R', 'E') : -0.2, ('V', 'K') : -3.7, ('R', 'Q') : 2.1, ('R', 'A') : -1.6,
-('T', 'D') : -1.8, ('P', 'F') : -5.0, ('V', 'S') : -4.3, ('K', 'I') : -4.7,
-('M', 'D') : -5.9, ('W', 'W') : 15.2, ('M', 'H') : -2.3, ('P', 'N') : -2.4,
-('I', 'F') : 0.5, ('K', 'A') : -0.9, ('M', 'L') : 4.4, ('K', 'E') : 1.1,
-('N', 'K') : 0.1, ('R', 'P') : -3.6, ('L', 'F') : 1.8, ('F', 'C') : -4.4,
-('W', 'G') : -6.3, ('W', 'L') : -1.0, ('D', 'D') : 8.5, ('N', 'H') : 1.7,
-('S', 'Q') : -1.2, ('Q', 'P') : -3.6, ('N', 'L') : -4.8, ('W', 'K') : -5.4,
-('Q', 'D') : -1.1, ('W', 'N') : -6.1, ('S', 'A') : 0.0, ('L', 'G') : -7.2,
-('W', 'S') : -6.2, ('S', 'E') : -2.2, ('L', 'K') : -3.4, ('H', 'E') : -2.3,
-('S', 'I') : -4.7, ('Q', 'H') : 1.4, ('H', 'A') : -3.1, ('S', 'M') : -4.8,
-('Y', 'L') : -2.4, ('Y', 'H') : -0.4, ('Y', 'D') : -3.8, ('G', 'G') : 8.0,
-('G', 'C') : -8.2, ('Y', 'T') : -2.7, ('W', 'C') : -9.1, ('Y', 'P') : -7.0,
-('T', 'K') : -0.2, ('R', 'N') : -1.5, ('A', 'A') : 6.0, ('W', 'A') : -5.8,
-('T', 'C') : -6.0, ('N', 'D') : 2.6, ('R', 'F') : -6.0, ('T', 'G') : -3.8,
-('V', 'L') : 1.8, ('V', 'P') : -5.2, ('P', 'E') : -1.5, ('M', 'C') : -4.4,
-('I', 'I') : 8.1, ('P', 'A') : -1.0, ('M', 'G') : -5.2, ('T', 'S') : 2.0,
-('I', 'E') : -4.8, ('P', 'M') : -9.8, ('M', 'K') : -1.9, ('K', 'F') : -5.6,
-('I', 'A') : -2.2, ('P', 'I') : -5.7, ('R', 'R') : 10.0, ('L', 'I') : 2.6,
-('F', 'D') : -7.0, ('D', 'C') : -9.7
+('W', 'F'): 3.4, ('S', 'P'): -1.0, ('N', 'M'): -3.7, ('Q', 'Q'): 9.0,
+('N', 'A'): -1.4, ('N', 'E'): -0.7, ('W', 'V'): -4.9, ('Q', 'E'): 2.4,
+('L', 'H'): -4.2, ('W', 'R'): -3.8, ('Q', 'A'): -0.6, ('S', 'D'): -0.2,
+('H', 'H'): 12.7, ('Q', 'M'): -0.6, ('S', 'H'): -2.6, ('H', 'D'): -0.7,
+('Q', 'I'): -7.0, ('S', 'L'): -5.2, ('Y', 'M'): -1.3, ('Y', 'I'): -2.5,
+('Y', 'E'): -3.7, ('Y', 'A'): -4.0, ('G', 'F'): -8.6, ('V', 'T'): -1.9,
+('Y', 'Y'): 10.5, ('V', 'H'): -3.9, ('E', 'C'): -6.9, ('Y', 'Q'): -5.1,
+('V', 'A'): -0.5, ('C', 'C'): 16.1, ('V', 'E'): -4.2, ('T', 'N'): 0.1,
+('R', 'K'): 3.2, ('P', 'P'): 10.3, ('V', 'I'): 3.9, ('R', 'G'): -2.8,
+('V', 'M'): 0.7, ('T', 'F'): -5.0, ('R', 'C'): -5.6, ('V', 'Q'): -3.6,
+('K', 'K'): 7.6, ('P', 'D'): -1.0, ('I', 'H'): -5.1, ('M', 'F'): -0.6,
+('I', 'D'): -4.8, ('K', 'C'): -8.7, ('P', 'L'): -2.8, ('K', 'G'): -3.5,
+('P', 'H'): -4.3, ('T', 'R'): -1.4, ('F', 'A'): -3.2, ('F', 'E'): -6.4,
+('S', 'S'): 5.8, ('W', 'E'): -7.6, ('N', 'N'): 8.0, ('W', 'M'): -0.9,
+('Q', 'C'): -6.9, ('N', 'F'): -3.8, ('S', 'C'): -7.7, ('L', 'A'): -3.3,
+('S', 'G'): -1.3, ('L', 'E'): -5.6, ('W', 'Q'): -8.2, ('H', 'G'): -3.2,
+('S', 'K'): -1.5, ('Q', 'N'): -0.8, ('V', 'D'): -5.2, ('H', 'C'): -8.2,
+('Y', 'N'): -1.3, ('Y', 'F'): 3.4, ('W', 'I'): -3.3, ('C', 'A'): -3.4,
+('G', 'E'): -2.5, ('G', 'A'): -0.5, ('Y', 'V'): -1.8, ('E', 'D'): 2.4,
+('W', 'H'): -4.0, ('Y', 'R'): -2.1, ('N', 'I'): -4.7, ('R', 'L'): -3.7,
+('T', 'I'): -3.2, ('Q', 'L'): -4.4, ('R', 'H'): 0.1, ('T', 'M'): -3.2,
+('V', 'F'): -1.3, ('R', 'D'): -3.4, ('T', 'A'): -0.8, ('T', 'P'): -2.0,
+('T', 'E'): -0.5, ('V', 'N'): -5.7, ('P', 'G'): -2.5, ('M', 'A'): -1.5,
+('K', 'H'): 0.1, ('V', 'R'): -4.9, ('P', 'C'): -8.9, ('M', 'E'): -2.8,
+('V', 'V'): 7.0, ('T', 'T'): 6.8, ('M', 'I'): 2.6, ('T', 'Q'): -0.4,
+('I', 'G'): -5.5, ('P', 'K'): -0.6, ('M', 'M'): 11.2, ('K', 'D'): -1.5,
+('I', 'C'): -7.7, ('L', 'C'): -8.7, ('F', 'F'): 10.4, ('D', 'A'): -1.6,
+('S', 'R'): -0.6, ('W', 'D'): -6.0, ('N', 'C'): -7.6, ('N', 'G'): -1.4,
+('W', 'T'): -9.3, ('Q', 'G'): -2.8, ('S', 'F'): -4.8, ('W', 'P'): -7.4,
+('L', 'D'): -8.0, ('H', 'F'): -1.7, ('Q', 'K'): 1.1, ('S', 'N'): 1.0,
+('L', 'L'): 7.3, ('Q', 'F'): -6.4, ('Y', 'K'): -3.7, ('Y', 'G'): -5.4,
+('Y', 'C'): -7.7, ('G', 'D'): -2.1, ('E', 'A'): -0.7, ('Y', 'W'): 2.3,
+('E', 'E'): 8.6, ('Y', 'S'): -3.4, ('R', 'M'): -4.2, ('V', 'C'): -4.8,
+('T', 'H'): -3.0, ('R', 'I'): -5.4, ('V', 'G'): -5.6, ('T', 'L'): -4.6,
+('R', 'E'): -0.2, ('V', 'K'): -3.7, ('R', 'Q'): 2.1, ('R', 'A'): -1.6,
+('T', 'D'): -1.8, ('P', 'F'): -5.0, ('V', 'S'): -4.3, ('K', 'I'): -4.7,
+('M', 'D'): -5.9, ('W', 'W'): 15.2, ('M', 'H'): -2.3, ('P', 'N'): -2.4,
+('I', 'F'): 0.5, ('K', 'A'): -0.9, ('M', 'L'): 4.4, ('K', 'E'): 1.1,
+('N', 'K'): 0.1, ('R', 'P'): -3.6, ('L', 'F'): 1.8, ('F', 'C'): -4.4,
+('W', 'G'): -6.3, ('W', 'L'): -1.0, ('D', 'D'): 8.5, ('N', 'H'): 1.7,
+('S', 'Q'): -1.2, ('Q', 'P'): -3.6, ('N', 'L'): -4.8, ('W', 'K'): -5.4,
+('Q', 'D'): -1.1, ('W', 'N'): -6.1, ('S', 'A'): 0.0, ('L', 'G'): -7.2,
+('W', 'S'): -6.2, ('S', 'E'): -2.2, ('L', 'K'): -3.4, ('H', 'E'): -2.3,
+('S', 'I'): -4.7, ('Q', 'H'): 1.4, ('H', 'A'): -3.1, ('S', 'M'): -4.8,
+('Y', 'L'): -2.4, ('Y', 'H'): -0.4, ('Y', 'D'): -3.8, ('G', 'G'): 8.0,
+('G', 'C'): -8.2, ('Y', 'T'): -2.7, ('W', 'C'): -9.1, ('Y', 'P'): -7.0,
+('T', 'K'): -0.2, ('R', 'N'): -1.5, ('A', 'A'): 6.0, ('W', 'A'): -5.8,
+('T', 'C'): -6.0, ('N', 'D'): 2.6, ('R', 'F'): -6.0, ('T', 'G'): -3.8,
+('V', 'L'): 1.8, ('V', 'P'): -5.2, ('P', 'E'): -1.5, ('M', 'C'): -4.4,
+('I', 'I'): 8.1, ('P', 'A'): -1.0, ('M', 'G'): -5.2, ('T', 'S'): 2.0,
+('I', 'E'): -4.8, ('P', 'M'): -9.8, ('M', 'K'): -1.9, ('K', 'F'): -5.6,
+('I', 'A'): -2.2, ('P', 'I'): -5.7, ('R', 'R'): 10.0, ('L', 'I'): 2.6,
+('F', 'D'): -7.0, ('D', 'C'): -9.7
}
johnson = _temp()
del _temp
@@ -1870,59 +1869,59 @@ del _temp
# http://www.embl-heidelberg.de/~vogt/matrices/levin.cmp
def _temp():
return {
-('W', 'F') : 0, ('L', 'R') : -1, ('S', 'P') : 0, ('I', 'I') : 2,
-('Q', 'Q') : 2, ('N', 'A') : 0, ('H', 'T') : 0, ('N', 'E') : 0,
-('H', 'P') : 0, ('W', 'V') : 0, ('Q', 'E') : 1, ('W', 'R') : 0,
-('Q', 'A') : 0, ('S', 'D') : 0, ('H', 'H') : 2, ('H', 'D') : 0,
-('L', 'N') : -1, ('W', 'A') : -1, ('Y', 'M') : 0, ('Y', 'I') : 0,
-('Y', 'E') : -1, ('Y', 'A') : -1, ('Y', 'Y') : 2, ('Y', 'Q') : -1,
-('E', 'G') : 0, ('A', 'D') : 0, ('C', 'C') : 2, ('M', 'R') : -1,
-('V', 'E') : -1, ('T', 'N') : 0, ('R', 'K') : 1, ('P', 'P') : 3,
-('I', 'T') : 0, ('K', 'S') : 0, ('R', 'G') : 0, ('I', 'P') : -1,
-('C', 'G') : 0, ('C', 'S') : 0, ('A', 'P') : -1, ('I', 'D') : -1,
-('M', 'I') : 0, ('K', 'G') : 0, ('M', 'N') : -1, ('F', 'Q') : -1,
-('I', 'V') : 1, ('F', 'A') : -1, ('V', 'V') : 2, ('F', 'E') : -1,
-('C', 'M') : 0, ('F', 'I') : 1, ('F', 'M') : 0, ('S', 'S') : 2,
-('L', 'Q') : -1, ('W', 'E') : -1, ('N', 'N') : 3, ('V', 'A') : 0,
-('C', 'K') : 0, ('W', 'M') : 0, ('H', 'S') : 0, ('L', 'V') : 1,
-('L', 'A') : 0, ('H', 'K') : 0, ('S', 'G') : 0, ('L', 'E') : -1,
-('W', 'Q') : -1, ('H', 'G') : 0, ('Q', 'N') : 1, ('T', 'A') : 0,
-('L', 'M') : 2, ('W', 'Y') : 0, ('Y', 'N') : -1, ('E', 'P') : -1,
-('Y', 'F') : 1, ('W', 'I') : 0, ('R', 'S') : 0, ('Y', 'V') : 0,
-('E', 'D') : 1, ('W', 'H') : -1, ('Y', 'R') : -1, ('M', 'Q') : -1,
-('A', 'G') : 0, ('C', 'D') : 0, ('R', 'D') : 0, ('C', 'H') : 0,
-('T', 'P') : 0, ('K', 'T') : 0, ('V', 'N') : -1, ('P', 'G') : 0,
-('M', 'A') : 0, ('C', 'P') : 0, ('V', 'R') : -1, ('M', 'V') : 0,
-('M', 'E') : -1, ('C', 'T') : 0, ('I', 'K') : -1, ('R', 'T') : 0,
-('T', 'Q') : 0, ('I', 'G') : -1, ('R', 'P') : 0, ('K', 'D') : 0,
-('F', 'R') : -1, ('F', 'V') : 0, ('K', 'P') : 0, ('L', 'C') : 0,
-('F', 'F') : 2, ('F', 'N') : -1, ('V', 'D') : -1, ('L', 'P') : -1,
-('W', 'K') : -1, ('L', 'T') : 0, ('I', 'N') : -1, ('I', 'S') : -1,
-('H', 'R') : 0, ('N', 'G') : 0, ('C', 'I') : 0, ('H', 'N') : 0,
-('W', 'T') : -1, ('Q', 'G') : 0, ('W', 'P') : -1, ('L', 'D') : -1,
-('L', 'H') : -1, ('S', 'N') : 0, ('L', 'L') : 2, ('M', 'M') : 2,
-('Y', 'K') : -1, ('Y', 'G') : -1, ('Y', 'C') : -1, ('E', 'E') : 2,
-('Y', 'S') : -1, ('M', 'P') : -1, ('C', 'A') : 0, ('M', 'T') : 0,
-('V', 'G') : -1, ('C', 'E') : 0, ('R', 'E') : 0, ('V', 'K') : -1,
-('K', 'Q') : 0, ('R', 'A') : 0, ('I', 'R') : -1, ('T', 'D') : 0,
-('V', 'S') : -1, ('C', 'Q') : 0, ('M', 'D') : -1, ('W', 'W') : 2,
-('M', 'H') : -1, ('T', 'G') : 0, ('K', 'A') : 0, ('R', 'Q') : 0,
-('T', 'T') : 2, ('F', 'S') : -1, ('D', 'P') : 0, ('F', 'C') : -1,
-('W', 'L') : 0, ('F', 'G') : -1, ('F', 'K') : -1, ('D', 'D') : 2,
-('L', 'S') : -1, ('W', 'G') : -1, ('S', 'Q') : 0, ('Q', 'P') : 0,
-('W', 'C') : -1, ('N', 'D') : 1, ('H', 'Q') : 0, ('Q', 'D') : 0,
-('W', 'N') : -1, ('S', 'A') : 1, ('L', 'G') : -1, ('W', 'S') : -1,
-('S', 'E') : 0, ('L', 'K') : -1, ('N', 'P') : 0, ('H', 'E') : 0,
-('H', 'A') : 0, ('Y', 'L') : 0, ('Y', 'H') : 0, ('Y', 'D') : -1,
-('V', 'Q') : -1, ('G', 'G') : 2, ('Y', 'T') : -1, ('R', 'N') : 0,
-('Y', 'P') : -1, ('A', 'E') : 1, ('C', 'V') : 0, ('M', 'S') : -1,
-('A', 'A') : 2, ('V', 'H') : -1, ('T', 'E') : 0, ('C', 'N') : 0,
-('I', 'Q') : -1, ('C', 'R') : 0, ('V', 'P') : -1, ('K', 'E') : 0,
-('K', 'N') : 1, ('V', 'T') : 0, ('M', 'G') : -1, ('T', 'S') : 0,
-('I', 'E') : -1, ('M', 'K') : -1, ('I', 'A') : 0, ('R', 'R') : 2,
-('F', 'P') : -1, ('L', 'I') : 0, ('W', 'D') : -1, ('F', 'T') : -1,
-('K', 'K') : 2, ('I', 'H') : -1, ('F', 'D') : -1, ('F', 'H') : -1,
-('D', 'G') : 0, ('F', 'L') : 0
+('W', 'F'): 0, ('L', 'R'): -1, ('S', 'P'): 0, ('I', 'I'): 2,
+('Q', 'Q'): 2, ('N', 'A'): 0, ('H', 'T'): 0, ('N', 'E'): 0,
+('H', 'P'): 0, ('W', 'V'): 0, ('Q', 'E'): 1, ('W', 'R'): 0,
+('Q', 'A'): 0, ('S', 'D'): 0, ('H', 'H'): 2, ('H', 'D'): 0,
+('L', 'N'): -1, ('W', 'A'): -1, ('Y', 'M'): 0, ('Y', 'I'): 0,
+('Y', 'E'): -1, ('Y', 'A'): -1, ('Y', 'Y'): 2, ('Y', 'Q'): -1,
+('E', 'G'): 0, ('A', 'D'): 0, ('C', 'C'): 2, ('M', 'R'): -1,
+('V', 'E'): -1, ('T', 'N'): 0, ('R', 'K'): 1, ('P', 'P'): 3,
+('I', 'T'): 0, ('K', 'S'): 0, ('R', 'G'): 0, ('I', 'P'): -1,
+('C', 'G'): 0, ('C', 'S'): 0, ('A', 'P'): -1, ('I', 'D'): -1,
+('M', 'I'): 0, ('K', 'G'): 0, ('M', 'N'): -1, ('F', 'Q'): -1,
+('I', 'V'): 1, ('F', 'A'): -1, ('V', 'V'): 2, ('F', 'E'): -1,
+('C', 'M'): 0, ('F', 'I'): 1, ('F', 'M'): 0, ('S', 'S'): 2,
+('L', 'Q'): -1, ('W', 'E'): -1, ('N', 'N'): 3, ('V', 'A'): 0,
+('C', 'K'): 0, ('W', 'M'): 0, ('H', 'S'): 0, ('L', 'V'): 1,
+('L', 'A'): 0, ('H', 'K'): 0, ('S', 'G'): 0, ('L', 'E'): -1,
+('W', 'Q'): -1, ('H', 'G'): 0, ('Q', 'N'): 1, ('T', 'A'): 0,
+('L', 'M'): 2, ('W', 'Y'): 0, ('Y', 'N'): -1, ('E', 'P'): -1,
+('Y', 'F'): 1, ('W', 'I'): 0, ('R', 'S'): 0, ('Y', 'V'): 0,
+('E', 'D'): 1, ('W', 'H'): -1, ('Y', 'R'): -1, ('M', 'Q'): -1,
+('A', 'G'): 0, ('C', 'D'): 0, ('R', 'D'): 0, ('C', 'H'): 0,
+('T', 'P'): 0, ('K', 'T'): 0, ('V', 'N'): -1, ('P', 'G'): 0,
+('M', 'A'): 0, ('C', 'P'): 0, ('V', 'R'): -1, ('M', 'V'): 0,
+('M', 'E'): -1, ('C', 'T'): 0, ('I', 'K'): -1, ('R', 'T'): 0,
+('T', 'Q'): 0, ('I', 'G'): -1, ('R', 'P'): 0, ('K', 'D'): 0,
+('F', 'R'): -1, ('F', 'V'): 0, ('K', 'P'): 0, ('L', 'C'): 0,
+('F', 'F'): 2, ('F', 'N'): -1, ('V', 'D'): -1, ('L', 'P'): -1,
+('W', 'K'): -1, ('L', 'T'): 0, ('I', 'N'): -1, ('I', 'S'): -1,
+('H', 'R'): 0, ('N', 'G'): 0, ('C', 'I'): 0, ('H', 'N'): 0,
+('W', 'T'): -1, ('Q', 'G'): 0, ('W', 'P'): -1, ('L', 'D'): -1,
+('L', 'H'): -1, ('S', 'N'): 0, ('L', 'L'): 2, ('M', 'M'): 2,
+('Y', 'K'): -1, ('Y', 'G'): -1, ('Y', 'C'): -1, ('E', 'E'): 2,
+('Y', 'S'): -1, ('M', 'P'): -1, ('C', 'A'): 0, ('M', 'T'): 0,
+('V', 'G'): -1, ('C', 'E'): 0, ('R', 'E'): 0, ('V', 'K'): -1,
+('K', 'Q'): 0, ('R', 'A'): 0, ('I', 'R'): -1, ('T', 'D'): 0,
+('V', 'S'): -1, ('C', 'Q'): 0, ('M', 'D'): -1, ('W', 'W'): 2,
+('M', 'H'): -1, ('T', 'G'): 0, ('K', 'A'): 0, ('R', 'Q'): 0,
+('T', 'T'): 2, ('F', 'S'): -1, ('D', 'P'): 0, ('F', 'C'): -1,
+('W', 'L'): 0, ('F', 'G'): -1, ('F', 'K'): -1, ('D', 'D'): 2,
+('L', 'S'): -1, ('W', 'G'): -1, ('S', 'Q'): 0, ('Q', 'P'): 0,
+('W', 'C'): -1, ('N', 'D'): 1, ('H', 'Q'): 0, ('Q', 'D'): 0,
+('W', 'N'): -1, ('S', 'A'): 1, ('L', 'G'): -1, ('W', 'S'): -1,
+('S', 'E'): 0, ('L', 'K'): -1, ('N', 'P'): 0, ('H', 'E'): 0,
+('H', 'A'): 0, ('Y', 'L'): 0, ('Y', 'H'): 0, ('Y', 'D'): -1,
+('V', 'Q'): -1, ('G', 'G'): 2, ('Y', 'T'): -1, ('R', 'N'): 0,
+('Y', 'P'): -1, ('A', 'E'): 1, ('C', 'V'): 0, ('M', 'S'): -1,
+('A', 'A'): 2, ('V', 'H'): -1, ('T', 'E'): 0, ('C', 'N'): 0,
+('I', 'Q'): -1, ('C', 'R'): 0, ('V', 'P'): -1, ('K', 'E'): 0,
+('K', 'N'): 1, ('V', 'T'): 0, ('M', 'G'): -1, ('T', 'S'): 0,
+('I', 'E'): -1, ('M', 'K'): -1, ('I', 'A'): 0, ('R', 'R'): 2,
+('F', 'P'): -1, ('L', 'I'): 0, ('W', 'D'): -1, ('F', 'T'): -1,
+('K', 'K'): 2, ('I', 'H'): -1, ('F', 'D'): -1, ('F', 'H'): -1,
+('D', 'G'): 0, ('F', 'L'): 0
}
levin = _temp()
del _temp
@@ -1931,59 +1930,59 @@ del _temp
# http://www.embl-heidelberg.de/~vogt/matrices/mclach.cmp
def _temp():
return {
-('N', 'I') : 1, ('K', 'V') : 2, ('S', 'P') : 3, ('N', 'M') : 2,
-('L', 'V') : 5, ('N', 'A') : 3, ('H', 'T') : 4, ('N', 'E') : 4,
-('Q', 'Y') : 1, ('H', 'P') : 3, ('W', 'V') : 2, ('H', 'L') : 2,
-('Q', 'A') : 3, ('H', 'H') : 8, ('N', 'Q') : 4, ('Q', 'M') : 3,
-('Q', 'I') : 0, ('S', 'L') : 2, ('G', 'V') : 2, ('Y', 'M') : 2,
-('K', 'L') : 2, ('Y', 'I') : 3, ('E', 'S') : 4, ('K', 'A') : 3,
-('E', 'W') : 1, ('G', 'F') : 0, ('E', 'K') : 4, ('Y', 'Y') : 9,
-('K', 'F') : 0, ('E', 'G') : 3, ('C', 'C') : 9, ('C', 'G') : 1,
-('M', 'V') : 4, ('P', 'P') : 8, ('A', 'L') : 2, ('K', 'S') : 3,
-('R', 'G') : 3, ('K', 'W') : 1, ('R', 'C') : 1, ('I', 'L') : 5,
-('C', 'S') : 2, ('C', 'W') : 2, ('K', 'C') : 0, ('R', 'W') : 3,
-('P', 'L') : 1, ('K', 'G') : 3, ('R', 'S') : 4, ('D', 'R') : 1,
-('D', 'V') : 1, ('D', 'N') : 5, ('F', 'I') : 3, ('F', 'M') : 5,
-('D', 'F') : 1, ('S', 'S') : 8, ('Q', 'V') : 2, ('S', 'W') : 3,
-('Q', 'R') : 5, ('N', 'N') : 8, ('H', 'W') : 3, ('W', 'M') : 1,
-('H', 'S') : 3, ('W', 'I') : 3, ('T', 'F') : 1, ('Q', 'F') : 0,
-('S', 'G') : 3, ('H', 'G') : 2, ('C', 'P') : 0, ('N', 'R') : 3,
-('H', 'C') : 3, ('N', 'V') : 1, ('E', 'P') : 4, ('Y', 'F') : 6,
-('E', 'T') : 4, ('G', 'Y') : 0, ('E', 'H') : 2, ('E', 'L') : 1,
-('Y', 'V') : 3, ('G', 'M') : 1, ('G', 'I') : 1, ('P', 'W') : 0,
-('R', 'L') : 2, ('T', 'I') : 3, ('A', 'G') : 3, ('R', 'H') : 5,
-('T', 'M') : 3, ('T', 'A') : 3, ('K', 'T') : 3, ('P', 'G') : 3,
-('T', 'Y') : 1, ('A', 'W') : 1, ('C', 'T') : 2, ('V', 'V') : 8,
-('R', 'T') : 3, ('R', 'P') : 3, ('D', 'Y') : 1, ('F', 'V') : 3,
-('D', 'Q') : 4, ('K', 'P') : 3, ('D', 'I') : 1, ('D', 'M') : 2,
-('F', 'F') : 9, ('D', 'A') : 3, ('D', 'E') : 5, ('N', 'K') : 4,
-('Q', 'W') : 2, ('S', 'V') : 2, ('Q', 'S') : 4, ('H', 'V') : 2,
-('W', 'L') : 3, ('N', 'G') : 3, ('Q', 'G') : 2, ('S', 'F') : 2,
-('Q', 'C') : 0, ('H', 'F') : 4, ('N', 'S') : 5, ('Q', 'K') : 4,
-('N', 'W') : 0, ('L', 'L') : 8, ('E', 'Y') : 2, ('M', 'M') : 8,
-('E', 'Q') : 5, ('E', 'I') : 1, ('E', 'M') : 1, ('E', 'A') : 4,
-('G', 'L') : 1, ('Y', 'W') : 6, ('E', 'E') : 8, ('R', 'M') : 1,
-('P', 'V') : 2, ('A', 'F') : 1, ('C', 'A') : 1, ('R', 'I') : 1,
-('T', 'L') : 3, ('I', 'V') : 5, ('C', 'I') : 1, ('R', 'A') : 2,
-('C', 'Y') : 1, ('C', 'M') : 3, ('P', 'F') : 1, ('A', 'V') : 3,
-('K', 'I') : 1, ('R', 'Y') : 2, ('K', 'M') : 1, ('K', 'H') : 4,
-('T', 'P') : 3, ('M', 'L') : 6, ('T', 'T') : 8, ('C', 'L') : 0,
-('D', 'P') : 3, ('N', 'F') : 0, ('K', 'Y') : 1, ('D', 'T') : 3,
-('D', 'H') : 4, ('D', 'L') : 1, ('K', 'K') : 8, ('D', 'D') : 8,
-('Q', 'T') : 3, ('N', 'H') : 4, ('Q', 'P') : 3, ('N', 'L') : 1,
-('H', 'Y') : 4, ('S', 'Y') : 3, ('W', 'W') : 9, ('H', 'M') : 3,
-('S', 'A') : 4, ('H', 'I') : 2, ('Q', 'L') : 3, ('N', 'P') : 1,
-('S', 'I') : 2, ('Q', 'H') : 4, ('N', 'T') : 3, ('H', 'A') : 3,
-('S', 'M') : 2, ('Y', 'L') : 3, ('G', 'W') : 1, ('E', 'R') : 3,
-('E', 'V') : 2, ('G', 'G') : 8, ('T', 'V') : 3, ('E', 'F') : 0,
-('C', 'F') : 0, ('A', 'A') : 8, ('K', 'R') : 5, ('A', 'M') : 3,
-('Q', 'Q') : 8, ('R', 'F') : 1, ('T', 'G') : 2, ('A', 'I') : 2,
-('P', 'Y') : 0, ('C', 'V') : 1, ('I', 'I') : 8, ('P', 'A') : 4,
-('T', 'S') : 5, ('P', 'M') : 1, ('R', 'V') : 2, ('T', 'W') : 2,
-('A', 'Y') : 1, ('P', 'I') : 1, ('R', 'R') : 8, ('N', 'Y') : 2,
-('D', 'S') : 3, ('D', 'W') : 0, ('M', 'I') : 5, ('D', 'K') : 3,
-('N', 'C') : 1, ('E', 'C') : 0, ('D', 'C') : 1, ('D', 'G') : 3,
-('F', 'L') : 5, ('W', 'F') : 6
+('N', 'I'): 1, ('K', 'V'): 2, ('S', 'P'): 3, ('N', 'M'): 2,
+('L', 'V'): 5, ('N', 'A'): 3, ('H', 'T'): 4, ('N', 'E'): 4,
+('Q', 'Y'): 1, ('H', 'P'): 3, ('W', 'V'): 2, ('H', 'L'): 2,
+('Q', 'A'): 3, ('H', 'H'): 8, ('N', 'Q'): 4, ('Q', 'M'): 3,
+('Q', 'I'): 0, ('S', 'L'): 2, ('G', 'V'): 2, ('Y', 'M'): 2,
+('K', 'L'): 2, ('Y', 'I'): 3, ('E', 'S'): 4, ('K', 'A'): 3,
+('E', 'W'): 1, ('G', 'F'): 0, ('E', 'K'): 4, ('Y', 'Y'): 9,
+('K', 'F'): 0, ('E', 'G'): 3, ('C', 'C'): 9, ('C', 'G'): 1,
+('M', 'V'): 4, ('P', 'P'): 8, ('A', 'L'): 2, ('K', 'S'): 3,
+('R', 'G'): 3, ('K', 'W'): 1, ('R', 'C'): 1, ('I', 'L'): 5,
+('C', 'S'): 2, ('C', 'W'): 2, ('K', 'C'): 0, ('R', 'W'): 3,
+('P', 'L'): 1, ('K', 'G'): 3, ('R', 'S'): 4, ('D', 'R'): 1,
+('D', 'V'): 1, ('D', 'N'): 5, ('F', 'I'): 3, ('F', 'M'): 5,
+('D', 'F'): 1, ('S', 'S'): 8, ('Q', 'V'): 2, ('S', 'W'): 3,
+('Q', 'R'): 5, ('N', 'N'): 8, ('H', 'W'): 3, ('W', 'M'): 1,
+('H', 'S'): 3, ('W', 'I'): 3, ('T', 'F'): 1, ('Q', 'F'): 0,
+('S', 'G'): 3, ('H', 'G'): 2, ('C', 'P'): 0, ('N', 'R'): 3,
+('H', 'C'): 3, ('N', 'V'): 1, ('E', 'P'): 4, ('Y', 'F'): 6,
+('E', 'T'): 4, ('G', 'Y'): 0, ('E', 'H'): 2, ('E', 'L'): 1,
+('Y', 'V'): 3, ('G', 'M'): 1, ('G', 'I'): 1, ('P', 'W'): 0,
+('R', 'L'): 2, ('T', 'I'): 3, ('A', 'G'): 3, ('R', 'H'): 5,
+('T', 'M'): 3, ('T', 'A'): 3, ('K', 'T'): 3, ('P', 'G'): 3,
+('T', 'Y'): 1, ('A', 'W'): 1, ('C', 'T'): 2, ('V', 'V'): 8,
+('R', 'T'): 3, ('R', 'P'): 3, ('D', 'Y'): 1, ('F', 'V'): 3,
+('D', 'Q'): 4, ('K', 'P'): 3, ('D', 'I'): 1, ('D', 'M'): 2,
+('F', 'F'): 9, ('D', 'A'): 3, ('D', 'E'): 5, ('N', 'K'): 4,
+('Q', 'W'): 2, ('S', 'V'): 2, ('Q', 'S'): 4, ('H', 'V'): 2,
+('W', 'L'): 3, ('N', 'G'): 3, ('Q', 'G'): 2, ('S', 'F'): 2,
+('Q', 'C'): 0, ('H', 'F'): 4, ('N', 'S'): 5, ('Q', 'K'): 4,
+('N', 'W'): 0, ('L', 'L'): 8, ('E', 'Y'): 2, ('M', 'M'): 8,
+('E', 'Q'): 5, ('E', 'I'): 1, ('E', 'M'): 1, ('E', 'A'): 4,
+('G', 'L'): 1, ('Y', 'W'): 6, ('E', 'E'): 8, ('R', 'M'): 1,
+('P', 'V'): 2, ('A', 'F'): 1, ('C', 'A'): 1, ('R', 'I'): 1,
+('T', 'L'): 3, ('I', 'V'): 5, ('C', 'I'): 1, ('R', 'A'): 2,
+('C', 'Y'): 1, ('C', 'M'): 3, ('P', 'F'): 1, ('A', 'V'): 3,
+('K', 'I'): 1, ('R', 'Y'): 2, ('K', 'M'): 1, ('K', 'H'): 4,
+('T', 'P'): 3, ('M', 'L'): 6, ('T', 'T'): 8, ('C', 'L'): 0,
+('D', 'P'): 3, ('N', 'F'): 0, ('K', 'Y'): 1, ('D', 'T'): 3,
+('D', 'H'): 4, ('D', 'L'): 1, ('K', 'K'): 8, ('D', 'D'): 8,
+('Q', 'T'): 3, ('N', 'H'): 4, ('Q', 'P'): 3, ('N', 'L'): 1,
+('H', 'Y'): 4, ('S', 'Y'): 3, ('W', 'W'): 9, ('H', 'M'): 3,
+('S', 'A'): 4, ('H', 'I'): 2, ('Q', 'L'): 3, ('N', 'P'): 1,
+('S', 'I'): 2, ('Q', 'H'): 4, ('N', 'T'): 3, ('H', 'A'): 3,
+('S', 'M'): 2, ('Y', 'L'): 3, ('G', 'W'): 1, ('E', 'R'): 3,
+('E', 'V'): 2, ('G', 'G'): 8, ('T', 'V'): 3, ('E', 'F'): 0,
+('C', 'F'): 0, ('A', 'A'): 8, ('K', 'R'): 5, ('A', 'M'): 3,
+('Q', 'Q'): 8, ('R', 'F'): 1, ('T', 'G'): 2, ('A', 'I'): 2,
+('P', 'Y'): 0, ('C', 'V'): 1, ('I', 'I'): 8, ('P', 'A'): 4,
+('T', 'S'): 5, ('P', 'M'): 1, ('R', 'V'): 2, ('T', 'W'): 2,
+('A', 'Y'): 1, ('P', 'I'): 1, ('R', 'R'): 8, ('N', 'Y'): 2,
+('D', 'S'): 3, ('D', 'W'): 0, ('M', 'I'): 5, ('D', 'K'): 3,
+('N', 'C'): 1, ('E', 'C'): 0, ('D', 'C'): 1, ('D', 'G'): 3,
+('F', 'L'): 5, ('W', 'F'): 6
}
mclach = _temp()
del _temp
@@ -1992,59 +1991,59 @@ del _temp
# http://www.embl-heidelberg.de/~vogt/matrices/miyata.cmp
def _temp():
return {
-('W', 'F') : 0.14, ('L', 'R') : -1.37, ('S', 'P') : 0.69, ('I', 'I') : 1.25,
-('Q', 'Q') : 1.25, ('N', 'A') : -0.53, ('H', 'T') : -0.07, ('N', 'E') : 0.4,
-('H', 'P') : -0.9, ('W', 'V') : -1.26, ('W', 'R') : -1.47, ('Q', 'A') : -0.67,
-('H', 'H') : 1.25, ('N', 'Q') : 0.26, ('H', 'D') : -0.47, ('L', 'N') : -2.24,
-('Y', 'M') : 0.32, ('Y', 'I') : 0.39, ('Y', 'E') : -1.97, ('E', 'S') : -0.81,
-('Y', 'A') : -1.93, ('Y', 'Y') : 1.25, ('E', 'C') : -2.01, ('Y', 'Q') : -1.23,
-('E', 'G') : -1.53, ('V', 'A') : -0.6, ('C', 'C') : 1.25, ('M', 'R') : -1.04,
-('V', 'E') : -1.72, ('R', 'K') : 0.85, ('P', 'P') : 1.25, ('I', 'T') : -0.89,
-('K', 'S') : -1.46, ('R', 'G') : -2.33, ('I', 'P') : -1.37, ('R', 'C') : -1.81,
-('V', 'Q') : -0.88, ('K', 'K') : 1.25, ('A', 'P') : 1.19, ('I', 'D') : -2.73,
-('K', 'C') : -2.02, ('M', 'I') : 0.96, ('K', 'G') : -2.29, ('R', 'S') : -1.49,
-('F', 'Q') : -1.56, ('I', 'V') : 0.4, ('M', 'V') : 0.63, ('F', 'A') : -1.98,
-('V', 'V') : 1.25, ('M', 'N') : -1.83, ('F', 'E') : -2.34, ('D', 'N') : 0.6,
-('F', 'I') : 0.64, ('F', 'M') : 0.43, ('M', 'S') : -1.42, ('S', 'S') : 1.25,
-('L', 'Q') : -1.45, ('W', 'E') : -2.83, ('W', 'A') : -2.98, ('W', 'M') : -0.64,
-('H', 'S') : -0.69, ('L', 'V') : 0.34, ('S', 'C') : -0.59, ('L', 'A') : -1.51,
-('S', 'G') : 0.4, ('L', 'E') : -2.28, ('W', 'Q') : -2.17, ('H', 'G') : -1.53,
-('H', 'C') : -1.31, ('W', 'Y') : 0.19, ('Y', 'N') : -2.17, ('E', 'P') : -1.23,
-('I', 'L') : 1.11, ('E', 'T') : -0.58, ('W', 'I') : -0.47, ('A', 'A') : 1.25,
-('I', 'N') : -2.12, ('G', 'A') : 0.34, ('Y', 'V') : -0.27, ('W', 'H') : -1.91,
-('Y', 'R') : -0.77, ('M', 'Q') : -1.05, ('R', 'H') : 0.43, ('A', 'C') : -0.14,
-('R', 'D') : -1.09, ('T', 'A') : 0.35, ('T', 'P') : 0.38, ('L', 'D') : -2.85,
-('K', 'T') : -0.85, ('V', 'N') : -1.51, ('M', 'A') : -1.17, ('K', 'H') : 0.46,
-('V', 'R') : -1.18, ('P', 'C') : -0.08, ('M', 'E') : -1.88, ('I', 'K') : -1.59,
-('T', 'T') : 1.25, ('R', 'T') : -0.78, ('I', 'G') : -2.35, ('R', 'P') : -1.65,
-('K', 'D') : -0.8, ('I', 'C') : -0.38, ('F', 'R') : -1.22, ('F', 'V') : -0.18,
-('D', 'Q') : -0.22, ('K', 'P') : -1.69, ('F', 'F') : 1.25, ('D', 'A') : -1.12,
-('D', 'E') : 0.35, ('F', 'N') : -2.45, ('W', 'D') : -3.63, ('L', 'P') : -1.45,
-('Q', 'S') : -0.4, ('N', 'C') : -1.58, ('N', 'G') : -0.71, ('H', 'N') : -0.04,
-('W', 'T') : -2.25, ('Q', 'G') : -1.23, ('W', 'P') : -2.92, ('Q', 'C') : -1.23,
-('N', 'S') : -0.06, ('L', 'H') : -1.34, ('L', 'L') : 1.25, ('M', 'M') : 1.25,
-('G', 'P') : 0.28, ('Y', 'K') : -1.17, ('E', 'Q') : 0.41, ('Y', 'G') : -2.83,
-('Y', 'C') : -1.13, ('E', 'A') : -1.21, ('E', 'E') : 1.25, ('Y', 'S') : -2.08,
-('M', 'P') : -1.11, ('V', 'C') : 0.39, ('M', 'T') : -0.61, ('V', 'G') : -1.51,
-('R', 'E') : -0.2, ('V', 'K') : -1.45, ('K', 'Q') : 0.19, ('R', 'A') : -1.67,
-('I', 'R') : -1.24, ('V', 'S') : -0.9, ('M', 'L') : 0.84, ('M', 'D') : -2.44,
-('W', 'W') : 1.25, ('M', 'H') : -0.94, ('K', 'A') : -1.71, ('R', 'Q') : 0.12,
-('K', 'E') : 0.11, ('F', 'S') : -2.2, ('D', 'P') : -1.15, ('D', 'T') : -0.8,
-('F', 'C') : -0.99, ('W', 'L') : -0.48, ('F', 'G') : -2.89, ('F', 'K') : -1.6,
-('F', 'T') : -1.35, ('D', 'D') : 1.25, ('Q', 'T') : 0.13, ('W', 'G') : -3.88,
-('Q', 'P') : -0.67, ('W', 'C') : -2.09, ('W', 'K') : -1.86, ('H', 'Q') : 0.93,
-('L', 'C') : -0.4, ('W', 'N') : -3.14, ('S', 'A') : 0.74, ('L', 'G') : -2.42,
-('W', 'S') : -3.13, ('L', 'K') : -1.73, ('N', 'P') : -0.55, ('H', 'E') : 0.29,
-('N', 'T') : -0.15, ('H', 'A') : -0.92, ('Y', 'L') : 0.31, ('Y', 'H') : -1.02,
-('Y', 'D') : -2.7, ('L', 'T') : -1.0, ('G', 'G') : 1.25, ('G', 'C') : -0.97,
-('Y', 'T') : -1.2, ('Y', 'P') : -1.87, ('R', 'N') : -0.79, ('V', 'D') : -2.15,
-('T', 'C') : -0.2, ('V', 'H') : -0.86, ('T', 'G') : -0.45, ('I', 'Q') : -1.32,
-('V', 'P') : -0.54, ('M', 'C') : -0.21, ('K', 'N') : -0.59, ('V', 'T') : -0.17,
-('M', 'G') : -2.09, ('T', 'S') : 0.36, ('I', 'E') : -2.14, ('M', 'K') : -1.38,
-('I', 'A') : -1.44, ('N', 'N') : 1.25, ('R', 'R') : 1.25, ('F', 'P') : -1.92,
-('I', 'S') : -1.7, ('D', 'S') : -0.62, ('Y', 'F') : 0.77, ('L', 'S') : -1.79,
-('I', 'H') : -1.2, ('F', 'D') : -3.02, ('D', 'C') : -2.23, ('F', 'H') : -1.38,
-('D', 'G') : -1.12, ('F', 'L') : 0.62
+('W', 'F'): 0.14, ('L', 'R'): -1.37, ('S', 'P'): 0.69, ('I', 'I'): 1.25,
+('Q', 'Q'): 1.25, ('N', 'A'): -0.53, ('H', 'T'): -0.07, ('N', 'E'): 0.4,
+('H', 'P'): -0.9, ('W', 'V'): -1.26, ('W', 'R'): -1.47, ('Q', 'A'): -0.67,
+('H', 'H'): 1.25, ('N', 'Q'): 0.26, ('H', 'D'): -0.47, ('L', 'N'): -2.24,
+('Y', 'M'): 0.32, ('Y', 'I'): 0.39, ('Y', 'E'): -1.97, ('E', 'S'): -0.81,
+('Y', 'A'): -1.93, ('Y', 'Y'): 1.25, ('E', 'C'): -2.01, ('Y', 'Q'): -1.23,
+('E', 'G'): -1.53, ('V', 'A'): -0.6, ('C', 'C'): 1.25, ('M', 'R'): -1.04,
+('V', 'E'): -1.72, ('R', 'K'): 0.85, ('P', 'P'): 1.25, ('I', 'T'): -0.89,
+('K', 'S'): -1.46, ('R', 'G'): -2.33, ('I', 'P'): -1.37, ('R', 'C'): -1.81,
+('V', 'Q'): -0.88, ('K', 'K'): 1.25, ('A', 'P'): 1.19, ('I', 'D'): -2.73,
+('K', 'C'): -2.02, ('M', 'I'): 0.96, ('K', 'G'): -2.29, ('R', 'S'): -1.49,
+('F', 'Q'): -1.56, ('I', 'V'): 0.4, ('M', 'V'): 0.63, ('F', 'A'): -1.98,
+('V', 'V'): 1.25, ('M', 'N'): -1.83, ('F', 'E'): -2.34, ('D', 'N'): 0.6,
+('F', 'I'): 0.64, ('F', 'M'): 0.43, ('M', 'S'): -1.42, ('S', 'S'): 1.25,
+('L', 'Q'): -1.45, ('W', 'E'): -2.83, ('W', 'A'): -2.98, ('W', 'M'): -0.64,
+('H', 'S'): -0.69, ('L', 'V'): 0.34, ('S', 'C'): -0.59, ('L', 'A'): -1.51,
+('S', 'G'): 0.4, ('L', 'E'): -2.28, ('W', 'Q'): -2.17, ('H', 'G'): -1.53,
+('H', 'C'): -1.31, ('W', 'Y'): 0.19, ('Y', 'N'): -2.17, ('E', 'P'): -1.23,
+('I', 'L'): 1.11, ('E', 'T'): -0.58, ('W', 'I'): -0.47, ('A', 'A'): 1.25,
+('I', 'N'): -2.12, ('G', 'A'): 0.34, ('Y', 'V'): -0.27, ('W', 'H'): -1.91,
+('Y', 'R'): -0.77, ('M', 'Q'): -1.05, ('R', 'H'): 0.43, ('A', 'C'): -0.14,
+('R', 'D'): -1.09, ('T', 'A'): 0.35, ('T', 'P'): 0.38, ('L', 'D'): -2.85,
+('K', 'T'): -0.85, ('V', 'N'): -1.51, ('M', 'A'): -1.17, ('K', 'H'): 0.46,
+('V', 'R'): -1.18, ('P', 'C'): -0.08, ('M', 'E'): -1.88, ('I', 'K'): -1.59,
+('T', 'T'): 1.25, ('R', 'T'): -0.78, ('I', 'G'): -2.35, ('R', 'P'): -1.65,
+('K', 'D'): -0.8, ('I', 'C'): -0.38, ('F', 'R'): -1.22, ('F', 'V'): -0.18,
+('D', 'Q'): -0.22, ('K', 'P'): -1.69, ('F', 'F'): 1.25, ('D', 'A'): -1.12,
+('D', 'E'): 0.35, ('F', 'N'): -2.45, ('W', 'D'): -3.63, ('L', 'P'): -1.45,
+('Q', 'S'): -0.4, ('N', 'C'): -1.58, ('N', 'G'): -0.71, ('H', 'N'): -0.04,
+('W', 'T'): -2.25, ('Q', 'G'): -1.23, ('W', 'P'): -2.92, ('Q', 'C'): -1.23,
+('N', 'S'): -0.06, ('L', 'H'): -1.34, ('L', 'L'): 1.25, ('M', 'M'): 1.25,
+('G', 'P'): 0.28, ('Y', 'K'): -1.17, ('E', 'Q'): 0.41, ('Y', 'G'): -2.83,
+('Y', 'C'): -1.13, ('E', 'A'): -1.21, ('E', 'E'): 1.25, ('Y', 'S'): -2.08,
+('M', 'P'): -1.11, ('V', 'C'): 0.39, ('M', 'T'): -0.61, ('V', 'G'): -1.51,
+('R', 'E'): -0.2, ('V', 'K'): -1.45, ('K', 'Q'): 0.19, ('R', 'A'): -1.67,
+('I', 'R'): -1.24, ('V', 'S'): -0.9, ('M', 'L'): 0.84, ('M', 'D'): -2.44,
+('W', 'W'): 1.25, ('M', 'H'): -0.94, ('K', 'A'): -1.71, ('R', 'Q'): 0.12,
+('K', 'E'): 0.11, ('F', 'S'): -2.2, ('D', 'P'): -1.15, ('D', 'T'): -0.8,
+('F', 'C'): -0.99, ('W', 'L'): -0.48, ('F', 'G'): -2.89, ('F', 'K'): -1.6,
+('F', 'T'): -1.35, ('D', 'D'): 1.25, ('Q', 'T'): 0.13, ('W', 'G'): -3.88,
+('Q', 'P'): -0.67, ('W', 'C'): -2.09, ('W', 'K'): -1.86, ('H', 'Q'): 0.93,
+('L', 'C'): -0.4, ('W', 'N'): -3.14, ('S', 'A'): 0.74, ('L', 'G'): -2.42,
+('W', 'S'): -3.13, ('L', 'K'): -1.73, ('N', 'P'): -0.55, ('H', 'E'): 0.29,
+('N', 'T'): -0.15, ('H', 'A'): -0.92, ('Y', 'L'): 0.31, ('Y', 'H'): -1.02,
+('Y', 'D'): -2.7, ('L', 'T'): -1.0, ('G', 'G'): 1.25, ('G', 'C'): -0.97,
+('Y', 'T'): -1.2, ('Y', 'P'): -1.87, ('R', 'N'): -0.79, ('V', 'D'): -2.15,
+('T', 'C'): -0.2, ('V', 'H'): -0.86, ('T', 'G'): -0.45, ('I', 'Q'): -1.32,
+('V', 'P'): -0.54, ('M', 'C'): -0.21, ('K', 'N'): -0.59, ('V', 'T'): -0.17,
+('M', 'G'): -2.09, ('T', 'S'): 0.36, ('I', 'E'): -2.14, ('M', 'K'): -1.38,
+('I', 'A'): -1.44, ('N', 'N'): 1.25, ('R', 'R'): 1.25, ('F', 'P'): -1.92,
+('I', 'S'): -1.7, ('D', 'S'): -0.62, ('Y', 'F'): 0.77, ('L', 'S'): -1.79,
+('I', 'H'): -1.2, ('F', 'D'): -3.02, ('D', 'C'): -2.23, ('F', 'H'): -1.38,
+('D', 'G'): -1.12, ('F', 'L'): 0.62
}
miyata = _temp()
del _temp
@@ -2053,70 +2052,70 @@ del _temp
# http://www.embl-heidelberg.de/~vogt/matrices/nwsgappep.cmp
def _temp():
return {
-('W', 'F') : 1.3, ('S', 'P') : 0.4, ('W', 'B') : -0.7, ('N', 'N') : 1.5,
-('N', 'A') : 0.2, ('N', 'E') : 0.5, ('Z', 'Y') : -0.6, ('W', 'V') : -0.8,
-('L', 'B') : -0.5, ('W', 'R') : 1.4, ('Q', 'A') : 0.2, ('S', 'D') : 0.2,
-('H', 'H') : 1.5, ('Q', 'M') : 0.0, ('S', 'H') : -0.2, ('H', 'D') : 0.4,
-('Q', 'I') : -0.3, ('S', 'L') : -0.4, ('Y', 'M') : -0.1, ('Q', 'E') : 0.7,
-('Y', 'I') : 0.1, ('Y', 'E') : -0.5, ('Y', 'A') : -0.3, ('G', 'F') : -0.6,
-('V', 'T') : 0.2, ('G', 'B') : 0.6, ('Y', 'Y') : 1.5, ('N', 'L') : -0.4,
-('E', 'C') : -0.6, ('Y', 'Q') : -0.6, ('Z', 'Z') : 1.1, ('V', 'A') : 0.2,
-('C', 'C') : 1.5, ('V', 'E') : -0.2, ('T', 'N') : 0.2, ('R', 'K') : 0.8,
-('P', 'P') : 1.5, ('V', 'I') : 1.1, ('T', 'B') : 0.2, ('R', 'G') : -0.3,
-('V', 'M') : 0.6, ('T', 'F') : -0.3, ('R', 'C') : -0.3, ('V', 'Q') : -0.2,
-('K', 'K') : 1.5, ('M', 'B') : -0.3, ('P', 'D') : 0.1, ('I', 'H') : -0.3,
-('M', 'F') : 0.5, ('I', 'D') : -0.2, ('K', 'C') : -0.6, ('L', 'L') : 1.5,
-('K', 'G') : -0.1, ('P', 'H') : 0.2, ('Z', 'G') : 0.3, ('W', 'M') : -0.3,
-('Z', 'C') : -0.6, ('T', 'R') : -0.1, ('Z', 'K') : 0.4, ('F', 'A') : -0.5,
-('Z', 'W') : -0.8, ('F', 'E') : -0.7, ('Z', 'S') : 0.0, ('D', 'B') : 1.1,
-('S', 'S') : 1.5, ('W', 'E') : -1.1, ('W', 'A') : -0.8, ('N', 'B') : 1.1,
-('Q', 'C') : -0.6, ('Z', 'Q') : 1.1, ('N', 'F') : -0.5, ('S', 'C') : 0.7,
-('Q', 'F') : -0.8, ('S', 'G') : 0.6, ('Q', 'B') : 0.5, ('W', 'Q') : -0.5,
-('H', 'G') : -0.2, ('S', 'K') : 0.2, ('L', 'I') : 0.8, ('V', 'D') : -0.2,
-('H', 'C') : -0.1, ('L', 'E') : -0.3, ('Y', 'N') : -0.1, ('Y', 'F') : 1.4,
-('W', 'I') : -0.5, ('C', 'A') : 0.3, ('G', 'E') : 0.5, ('G', 'A') : 0.7,
-('Y', 'V') : -0.1, ('E', 'D') : 1.0, ('W', 'H') : -0.1, ('Y', 'R') : -0.6,
-('N', 'I') : -0.3, ('R', 'L') : -0.4, ('T', 'I') : 0.2, ('V', 'B') : -0.2,
-('R', 'H') : 0.5, ('T', 'M') : 0.0, ('V', 'F') : 0.2, ('R', 'D') : 0.0,
-('T', 'A') : 0.4, ('T', 'P') : 0.3, ('T', 'E') : 0.2, ('V', 'N') : -0.3,
-('P', 'G') : 0.3, ('M', 'A') : 0.0, ('K', 'H') : 0.1, ('V', 'R') : -0.3,
-('P', 'C') : 0.1, ('M', 'E') : -0.2, ('V', 'V') : 1.5, ('T', 'T') : 1.5,
-('M', 'I') : 0.6, ('T', 'Q') : -0.1, ('I', 'G') : -0.3, ('P', 'K') : 0.1,
-('M', 'M') : 1.5, ('K', 'D') : 0.3, ('I', 'C') : 0.2, ('Z', 'D') : 0.9,
-('Y', 'W') : 1.1, ('Z', 'L') : -0.2, ('P', 'L') : -0.3, ('Z', 'I') : -0.2,
-('Z', 'T') : 0.1, ('L', 'C') : -0.8, ('F', 'B') : -0.7, ('Z', 'P') : 0.2,
-('F', 'F') : 1.5, ('D', 'A') : 0.3, ('S', 'R') : 0.1, ('W', 'D') : -1.1,
-('R', 'R') : 1.5, ('W', 'K') : 0.1, ('N', 'M') : -0.3, ('N', 'C') : -0.3,
-('N', 'G') : 0.4, ('S', 'B') : 0.3, ('W', 'T') : -0.6, ('Q', 'G') : 0.2,
-('S', 'F') : -0.3, ('W', 'P') : -0.8, ('L', 'D') : -0.5, ('H', 'F') : -0.1,
-('L', 'H') : -0.2, ('S', 'N') : 0.3, ('H', 'B') : 0.4, ('Q', 'K') : 0.4,
-('R', 'P') : 0.3, ('Y', 'K') : -0.6, ('Y', 'B') : -0.3, ('Y', 'G') : -0.7,
-('Y', 'C') : 1.0, ('G', 'D') : 0.7, ('E', 'A') : 0.3, ('T', 'S') : 0.3,
-('E', 'E') : 1.5, ('Y', 'S') : -0.4, ('R', 'M') : 0.2, ('V', 'C') : 0.2,
-('T', 'H') : -0.1, ('R', 'I') : -0.3, ('V', 'S') : -0.1, ('V', 'G') : 0.2,
-('T', 'L') : -0.1, ('R', 'E') : 0.0, ('V', 'K') : -0.2, ('R', 'Q') : 0.4,
-('R', 'A') : -0.3, ('Z', 'H') : 0.5, ('T', 'D') : 0.2, ('P', 'F') : -0.7,
-('L', 'A') : -0.1, ('K', 'I') : -0.2, ('M', 'D') : -0.4, ('P', 'B') : 0.1,
-('W', 'W') : 1.5, ('M', 'H') : -0.3, ('P', 'N') : 0.0, ('I', 'F') : 0.7,
-('K', 'A') : 0.0, ('M', 'L') : 1.3, ('I', 'B') : -0.2, ('K', 'E') : 0.3,
-('Z', 'E') : 1.1, ('Q', 'N') : 0.4, ('Z', 'A') : 0.2, ('Z', 'M') : -0.1,
-('L', 'F') : 1.2, ('F', 'C') : -0.1, ('W', 'G') : -1.0, ('W', 'L') : 0.5,
-('C', 'B') : -0.4, ('B', 'A') : 0.2, ('D', 'D') : 1.5, ('N', 'H') : 0.5,
-('S', 'Q') : -0.1, ('Q', 'P') : 0.3, ('W', 'C') : -1.2, ('N', 'D') : 0.7,
-('Q', 'D') : 0.7, ('W', 'N') : -0.3, ('S', 'A') : 0.4, ('L', 'G') : -0.5,
-('W', 'S') : 0.3, ('S', 'E') : 0.2, ('L', 'K') : -0.3, ('H', 'E') : 0.4,
-('S', 'I') : -0.1, ('Q', 'H') : 0.7, ('H', 'A') : -0.1, ('S', 'M') : -0.3,
-('Y', 'L') : 0.3, ('Y', 'H') : 0.3, ('Y', 'D') : -0.5, ('G', 'G') : 1.5,
-('G', 'C') : 0.2, ('Y', 'T') : -0.3, ('E', 'B') : 0.7, ('Y', 'P') : -0.8,
-('T', 'K') : 0.2, ('R', 'N') : 0.1, ('A', 'A') : 1.5, ('N', 'K') : 0.4,
-('T', 'C') : 0.2, ('V', 'H') : -0.3, ('Q', 'Q') : 1.5, ('R', 'F') : -0.5,
-('T', 'G') : 0.4, ('V', 'L') : 0.8, ('R', 'B') : 0.1, ('V', 'P') : 0.1,
-('P', 'E') : 0.1, ('M', 'C') : -0.6, ('I', 'I') : 1.5, ('P', 'A') : 0.5,
-('M', 'G') : -0.3, ('K', 'B') : 0.4, ('I', 'E') : -0.2, ('P', 'M') : -0.2,
-('M', 'K') : 0.2, ('K', 'F') : -0.7, ('I', 'A') : 0.0, ('P', 'I') : -0.2,
-('Q', 'L') : -0.1, ('Z', 'F') : -0.7, ('Z', 'B') : 0.6, ('Z', 'N') : 0.4,
-('Z', 'V') : -0.2, ('F', 'D') : -1.0, ('Z', 'R') : 0.2, ('D', 'C') : -0.5,
-('B', 'B') : 1.1
+('W', 'F'): 1.3, ('S', 'P'): 0.4, ('W', 'B'): -0.7, ('N', 'N'): 1.5,
+('N', 'A'): 0.2, ('N', 'E'): 0.5, ('Z', 'Y'): -0.6, ('W', 'V'): -0.8,
+('L', 'B'): -0.5, ('W', 'R'): 1.4, ('Q', 'A'): 0.2, ('S', 'D'): 0.2,
+('H', 'H'): 1.5, ('Q', 'M'): 0.0, ('S', 'H'): -0.2, ('H', 'D'): 0.4,
+('Q', 'I'): -0.3, ('S', 'L'): -0.4, ('Y', 'M'): -0.1, ('Q', 'E'): 0.7,
+('Y', 'I'): 0.1, ('Y', 'E'): -0.5, ('Y', 'A'): -0.3, ('G', 'F'): -0.6,
+('V', 'T'): 0.2, ('G', 'B'): 0.6, ('Y', 'Y'): 1.5, ('N', 'L'): -0.4,
+('E', 'C'): -0.6, ('Y', 'Q'): -0.6, ('Z', 'Z'): 1.1, ('V', 'A'): 0.2,
+('C', 'C'): 1.5, ('V', 'E'): -0.2, ('T', 'N'): 0.2, ('R', 'K'): 0.8,
+('P', 'P'): 1.5, ('V', 'I'): 1.1, ('T', 'B'): 0.2, ('R', 'G'): -0.3,
+('V', 'M'): 0.6, ('T', 'F'): -0.3, ('R', 'C'): -0.3, ('V', 'Q'): -0.2,
+('K', 'K'): 1.5, ('M', 'B'): -0.3, ('P', 'D'): 0.1, ('I', 'H'): -0.3,
+('M', 'F'): 0.5, ('I', 'D'): -0.2, ('K', 'C'): -0.6, ('L', 'L'): 1.5,
+('K', 'G'): -0.1, ('P', 'H'): 0.2, ('Z', 'G'): 0.3, ('W', 'M'): -0.3,
+('Z', 'C'): -0.6, ('T', 'R'): -0.1, ('Z', 'K'): 0.4, ('F', 'A'): -0.5,
+('Z', 'W'): -0.8, ('F', 'E'): -0.7, ('Z', 'S'): 0.0, ('D', 'B'): 1.1,
+('S', 'S'): 1.5, ('W', 'E'): -1.1, ('W', 'A'): -0.8, ('N', 'B'): 1.1,
+('Q', 'C'): -0.6, ('Z', 'Q'): 1.1, ('N', 'F'): -0.5, ('S', 'C'): 0.7,
+('Q', 'F'): -0.8, ('S', 'G'): 0.6, ('Q', 'B'): 0.5, ('W', 'Q'): -0.5,
+('H', 'G'): -0.2, ('S', 'K'): 0.2, ('L', 'I'): 0.8, ('V', 'D'): -0.2,
+('H', 'C'): -0.1, ('L', 'E'): -0.3, ('Y', 'N'): -0.1, ('Y', 'F'): 1.4,
+('W', 'I'): -0.5, ('C', 'A'): 0.3, ('G', 'E'): 0.5, ('G', 'A'): 0.7,
+('Y', 'V'): -0.1, ('E', 'D'): 1.0, ('W', 'H'): -0.1, ('Y', 'R'): -0.6,
+('N', 'I'): -0.3, ('R', 'L'): -0.4, ('T', 'I'): 0.2, ('V', 'B'): -0.2,
+('R', 'H'): 0.5, ('T', 'M'): 0.0, ('V', 'F'): 0.2, ('R', 'D'): 0.0,
+('T', 'A'): 0.4, ('T', 'P'): 0.3, ('T', 'E'): 0.2, ('V', 'N'): -0.3,
+('P', 'G'): 0.3, ('M', 'A'): 0.0, ('K', 'H'): 0.1, ('V', 'R'): -0.3,
+('P', 'C'): 0.1, ('M', 'E'): -0.2, ('V', 'V'): 1.5, ('T', 'T'): 1.5,
+('M', 'I'): 0.6, ('T', 'Q'): -0.1, ('I', 'G'): -0.3, ('P', 'K'): 0.1,
+('M', 'M'): 1.5, ('K', 'D'): 0.3, ('I', 'C'): 0.2, ('Z', 'D'): 0.9,
+('Y', 'W'): 1.1, ('Z', 'L'): -0.2, ('P', 'L'): -0.3, ('Z', 'I'): -0.2,
+('Z', 'T'): 0.1, ('L', 'C'): -0.8, ('F', 'B'): -0.7, ('Z', 'P'): 0.2,
+('F', 'F'): 1.5, ('D', 'A'): 0.3, ('S', 'R'): 0.1, ('W', 'D'): -1.1,
+('R', 'R'): 1.5, ('W', 'K'): 0.1, ('N', 'M'): -0.3, ('N', 'C'): -0.3,
+('N', 'G'): 0.4, ('S', 'B'): 0.3, ('W', 'T'): -0.6, ('Q', 'G'): 0.2,
+('S', 'F'): -0.3, ('W', 'P'): -0.8, ('L', 'D'): -0.5, ('H', 'F'): -0.1,
+('L', 'H'): -0.2, ('S', 'N'): 0.3, ('H', 'B'): 0.4, ('Q', 'K'): 0.4,
+('R', 'P'): 0.3, ('Y', 'K'): -0.6, ('Y', 'B'): -0.3, ('Y', 'G'): -0.7,
+('Y', 'C'): 1.0, ('G', 'D'): 0.7, ('E', 'A'): 0.3, ('T', 'S'): 0.3,
+('E', 'E'): 1.5, ('Y', 'S'): -0.4, ('R', 'M'): 0.2, ('V', 'C'): 0.2,
+('T', 'H'): -0.1, ('R', 'I'): -0.3, ('V', 'S'): -0.1, ('V', 'G'): 0.2,
+('T', 'L'): -0.1, ('R', 'E'): 0.0, ('V', 'K'): -0.2, ('R', 'Q'): 0.4,
+('R', 'A'): -0.3, ('Z', 'H'): 0.5, ('T', 'D'): 0.2, ('P', 'F'): -0.7,
+('L', 'A'): -0.1, ('K', 'I'): -0.2, ('M', 'D'): -0.4, ('P', 'B'): 0.1,
+('W', 'W'): 1.5, ('M', 'H'): -0.3, ('P', 'N'): 0.0, ('I', 'F'): 0.7,
+('K', 'A'): 0.0, ('M', 'L'): 1.3, ('I', 'B'): -0.2, ('K', 'E'): 0.3,
+('Z', 'E'): 1.1, ('Q', 'N'): 0.4, ('Z', 'A'): 0.2, ('Z', 'M'): -0.1,
+('L', 'F'): 1.2, ('F', 'C'): -0.1, ('W', 'G'): -1.0, ('W', 'L'): 0.5,
+('C', 'B'): -0.4, ('B', 'A'): 0.2, ('D', 'D'): 1.5, ('N', 'H'): 0.5,
+('S', 'Q'): -0.1, ('Q', 'P'): 0.3, ('W', 'C'): -1.2, ('N', 'D'): 0.7,
+('Q', 'D'): 0.7, ('W', 'N'): -0.3, ('S', 'A'): 0.4, ('L', 'G'): -0.5,
+('W', 'S'): 0.3, ('S', 'E'): 0.2, ('L', 'K'): -0.3, ('H', 'E'): 0.4,
+('S', 'I'): -0.1, ('Q', 'H'): 0.7, ('H', 'A'): -0.1, ('S', 'M'): -0.3,
+('Y', 'L'): 0.3, ('Y', 'H'): 0.3, ('Y', 'D'): -0.5, ('G', 'G'): 1.5,
+('G', 'C'): 0.2, ('Y', 'T'): -0.3, ('E', 'B'): 0.7, ('Y', 'P'): -0.8,
+('T', 'K'): 0.2, ('R', 'N'): 0.1, ('A', 'A'): 1.5, ('N', 'K'): 0.4,
+('T', 'C'): 0.2, ('V', 'H'): -0.3, ('Q', 'Q'): 1.5, ('R', 'F'): -0.5,
+('T', 'G'): 0.4, ('V', 'L'): 0.8, ('R', 'B'): 0.1, ('V', 'P'): 0.1,
+('P', 'E'): 0.1, ('M', 'C'): -0.6, ('I', 'I'): 1.5, ('P', 'A'): 0.5,
+('M', 'G'): -0.3, ('K', 'B'): 0.4, ('I', 'E'): -0.2, ('P', 'M'): -0.2,
+('M', 'K'): 0.2, ('K', 'F'): -0.7, ('I', 'A'): 0.0, ('P', 'I'): -0.2,
+('Q', 'L'): -0.1, ('Z', 'F'): -0.7, ('Z', 'B'): 0.6, ('Z', 'N'): 0.4,
+('Z', 'V'): -0.2, ('F', 'D'): -1.0, ('Z', 'R'): 0.2, ('D', 'C'): -0.5,
+('B', 'B'): 1.1
}
nwsgappep = _temp()
del _temp
@@ -2125,75 +2124,75 @@ del _temp
# http://www.embl-heidelberg.de/~vogt/matrices/pam120.cmp
def _temp():
return {
-('W', 'F') : -1, ('L', 'R') : -4, ('S', 'P') : 1, ('V', 'T') : 0,
-('Q', 'Q') : 6, ('N', 'A') : -1, ('Z', 'Y') : -5, ('W', 'R') : 1,
-('Q', 'A') : -1, ('S', 'D') : 0, ('H', 'H') : 7, ('S', 'H') : -2,
-('H', 'D') : 0, ('L', 'N') : -4, ('W', 'A') : -7, ('Y', 'M') : -4,
-('G', 'R') : -4, ('Y', 'I') : -2, ('Y', 'E') : -5, ('B', 'Y') : -3,
-('Y', 'A') : -4, ('V', 'D') : -3, ('B', 'S') : 0, ('Y', 'Y') : 8,
-('G', 'N') : 0, ('E', 'C') : -7, ('Y', 'Q') : -5, ('Z', 'Z') : 4,
-('V', 'A') : 0, ('C', 'C') : 9, ('M', 'R') : -1, ('V', 'E') : -3,
-('T', 'N') : 0, ('P', 'P') : 6, ('V', 'I') : 3, ('V', 'S') : -2,
-('Z', 'P') : -1, ('V', 'M') : 1, ('T', 'F') : -4, ('V', 'Q') : -3,
-('K', 'K') : 5, ('P', 'D') : -3, ('I', 'H') : -4, ('I', 'D') : -3,
-('T', 'R') : -2, ('P', 'L') : -3, ('K', 'G') : -3, ('M', 'N') : -3,
-('P', 'H') : -1, ('F', 'Q') : -6, ('Z', 'G') : -2, ('X', 'L') : -2,
-('T', 'M') : -1, ('Z', 'C') : -7, ('X', 'H') : -2, ('D', 'R') : -3,
-('B', 'W') : -6, ('X', 'D') : -2, ('Z', 'K') : -1, ('F', 'A') : -4,
-('Z', 'W') : -7, ('F', 'E') : -7, ('D', 'N') : 2, ('B', 'K') : 0,
-('X', 'X') : -2, ('F', 'I') : 0, ('B', 'G') : 0, ('X', 'T') : -1,
-('F', 'M') : -1, ('B', 'C') : -6, ('Z', 'I') : -3, ('Z', 'V') : -3,
-('S', 'S') : 3, ('L', 'Q') : -2, ('W', 'E') : -8, ('Q', 'R') : 1,
-('N', 'N') : 4, ('W', 'M') : -6, ('Q', 'C') : -7, ('W', 'I') : -6,
-('S', 'C') : 0, ('L', 'A') : -3, ('S', 'G') : 1, ('L', 'E') : -4,
-('W', 'Q') : -6, ('H', 'G') : -4, ('S', 'K') : -1, ('Q', 'N') : 0,
-('N', 'R') : -1, ('H', 'C') : -4, ('Y', 'N') : -2, ('G', 'Q') : -3,
-('Y', 'F') : 4, ('C', 'A') : -3, ('V', 'L') : 1, ('G', 'E') : -1,
-('G', 'A') : 1, ('K', 'R') : 2, ('E', 'D') : 3, ('Y', 'R') : -5,
-('M', 'Q') : -1, ('T', 'I') : 0, ('C', 'D') : -7, ('V', 'F') : -3,
-('T', 'A') : 1, ('T', 'P') : -1, ('B', 'P') : -2, ('T', 'E') : -2,
-('V', 'N') : -3, ('P', 'G') : -2, ('M', 'A') : -2, ('K', 'H') : -2,
-('V', 'R') : -3, ('P', 'C') : -4, ('M', 'E') : -3, ('K', 'L') : -4,
-('V', 'V') : 5, ('M', 'I') : 1, ('T', 'Q') : -2, ('I', 'G') : -4,
-('P', 'K') : -2, ('M', 'M') : 8, ('K', 'D') : -1, ('I', 'C') : -3,
-('Z', 'D') : 3, ('F', 'R') : -5, ('X', 'K') : -2, ('Q', 'D') : 1,
-('X', 'G') : -2, ('Z', 'L') : -3, ('X', 'C') : -4, ('Z', 'H') : 1,
-('B', 'L') : -4, ('B', 'H') : 1, ('F', 'F') : 8, ('X', 'W') : -5,
-('B', 'D') : 4, ('D', 'A') : 0, ('S', 'L') : -4, ('X', 'S') : -1,
-('F', 'N') : -4, ('S', 'R') : -1, ('W', 'D') : -8, ('V', 'Y') : -3,
-('W', 'L') : -3, ('H', 'R') : 1, ('W', 'H') : -3, ('H', 'N') : 2,
-('W', 'T') : -6, ('T', 'T') : 4, ('S', 'F') : -3, ('W', 'P') : -7,
-('L', 'D') : -5, ('B', 'I') : -3, ('L', 'H') : -3, ('S', 'N') : 1,
-('B', 'T') : 0, ('L', 'L') : 5, ('Y', 'K') : -5, ('E', 'Q') : 2,
-('Y', 'G') : -6, ('Z', 'S') : -1, ('Y', 'C') : -1, ('G', 'D') : 0,
-('B', 'V') : -3, ('E', 'A') : 0, ('Y', 'W') : -2, ('E', 'E') : 5,
-('Y', 'S') : -3, ('C', 'N') : -5, ('V', 'C') : -3, ('T', 'H') : -3,
-('P', 'R') : -1, ('V', 'G') : -2, ('T', 'L') : -3, ('V', 'K') : -4,
-('K', 'Q') : 0, ('R', 'A') : -3, ('I', 'R') : -2, ('T', 'D') : -1,
-('P', 'F') : -5, ('I', 'N') : -2, ('K', 'I') : -3, ('M', 'D') : -4,
-('V', 'W') : -8, ('W', 'W') : 12, ('M', 'H') : -4, ('P', 'N') : -2,
-('K', 'A') : -2, ('M', 'L') : 3, ('K', 'E') : -1, ('Z', 'E') : 4,
-('X', 'N') : -1, ('Z', 'A') : -1, ('Z', 'M') : -2, ('X', 'F') : -3,
-('K', 'C') : -7, ('B', 'Q') : 0, ('X', 'B') : -1, ('B', 'M') : -4,
-('F', 'C') : -6, ('Z', 'Q') : 4, ('X', 'Z') : -1, ('F', 'G') : -5,
-('B', 'E') : 3, ('X', 'V') : -1, ('F', 'K') : -7, ('B', 'A') : 0,
-('X', 'R') : -2, ('D', 'D') : 5, ('W', 'G') : -8, ('Z', 'F') : -6,
-('S', 'Q') : -2, ('W', 'C') : -8, ('W', 'K') : -5, ('H', 'Q') : 3,
-('L', 'C') : -7, ('W', 'N') : -4, ('S', 'A') : 1, ('L', 'G') : -5,
-('W', 'S') : -2, ('S', 'E') : -1, ('H', 'E') : -1, ('S', 'I') : -2,
-('H', 'A') : -3, ('S', 'M') : -2, ('Y', 'L') : -2, ('Y', 'H') : -1,
-('Y', 'D') : -5, ('E', 'R') : -3, ('X', 'P') : -2, ('G', 'G') : 5,
-('G', 'C') : -4, ('E', 'N') : 1, ('Y', 'T') : -3, ('Y', 'P') : -6,
-('T', 'K') : -1, ('A', 'A') : 3, ('P', 'Q') : 0, ('T', 'C') : -3,
-('V', 'H') : -3, ('T', 'G') : -1, ('I', 'Q') : -3, ('Z', 'T') : -2,
-('C', 'R') : -4, ('V', 'P') : -2, ('P', 'E') : -2, ('M', 'C') : -6,
-('K', 'N') : 1, ('I', 'I') : 6, ('P', 'A') : 1, ('M', 'G') : -4,
-('T', 'S') : 2, ('I', 'E') : -3, ('P', 'M') : -3, ('M', 'K') : 0,
-('I', 'A') : -1, ('P', 'I') : -3, ('R', 'R') : 6, ('X', 'M') : -2,
-('L', 'I') : 1, ('X', 'I') : -1, ('Z', 'B') : 2, ('X', 'E') : -1,
-('Z', 'N') : 0, ('X', 'A') : -1, ('B', 'R') : -2, ('B', 'N') : 3,
-('F', 'D') : -7, ('X', 'Y') : -3, ('Z', 'R') : -1, ('F', 'H') : -3,
-('B', 'F') : -5, ('F', 'L') : 0, ('X', 'Q') : -1, ('B', 'B') : 4
+('W', 'F'): -1, ('L', 'R'): -4, ('S', 'P'): 1, ('V', 'T'): 0,
+('Q', 'Q'): 6, ('N', 'A'): -1, ('Z', 'Y'): -5, ('W', 'R'): 1,
+('Q', 'A'): -1, ('S', 'D'): 0, ('H', 'H'): 7, ('S', 'H'): -2,
+('H', 'D'): 0, ('L', 'N'): -4, ('W', 'A'): -7, ('Y', 'M'): -4,
+('G', 'R'): -4, ('Y', 'I'): -2, ('Y', 'E'): -5, ('B', 'Y'): -3,
+('Y', 'A'): -4, ('V', 'D'): -3, ('B', 'S'): 0, ('Y', 'Y'): 8,
+('G', 'N'): 0, ('E', 'C'): -7, ('Y', 'Q'): -5, ('Z', 'Z'): 4,
+('V', 'A'): 0, ('C', 'C'): 9, ('M', 'R'): -1, ('V', 'E'): -3,
+('T', 'N'): 0, ('P', 'P'): 6, ('V', 'I'): 3, ('V', 'S'): -2,
+('Z', 'P'): -1, ('V', 'M'): 1, ('T', 'F'): -4, ('V', 'Q'): -3,
+('K', 'K'): 5, ('P', 'D'): -3, ('I', 'H'): -4, ('I', 'D'): -3,
+('T', 'R'): -2, ('P', 'L'): -3, ('K', 'G'): -3, ('M', 'N'): -3,
+('P', 'H'): -1, ('F', 'Q'): -6, ('Z', 'G'): -2, ('X', 'L'): -2,
+('T', 'M'): -1, ('Z', 'C'): -7, ('X', 'H'): -2, ('D', 'R'): -3,
+('B', 'W'): -6, ('X', 'D'): -2, ('Z', 'K'): -1, ('F', 'A'): -4,
+('Z', 'W'): -7, ('F', 'E'): -7, ('D', 'N'): 2, ('B', 'K'): 0,
+('X', 'X'): -2, ('F', 'I'): 0, ('B', 'G'): 0, ('X', 'T'): -1,
+('F', 'M'): -1, ('B', 'C'): -6, ('Z', 'I'): -3, ('Z', 'V'): -3,
+('S', 'S'): 3, ('L', 'Q'): -2, ('W', 'E'): -8, ('Q', 'R'): 1,
+('N', 'N'): 4, ('W', 'M'): -6, ('Q', 'C'): -7, ('W', 'I'): -6,
+('S', 'C'): 0, ('L', 'A'): -3, ('S', 'G'): 1, ('L', 'E'): -4,
+('W', 'Q'): -6, ('H', 'G'): -4, ('S', 'K'): -1, ('Q', 'N'): 0,
+('N', 'R'): -1, ('H', 'C'): -4, ('Y', 'N'): -2, ('G', 'Q'): -3,
+('Y', 'F'): 4, ('C', 'A'): -3, ('V', 'L'): 1, ('G', 'E'): -1,
+('G', 'A'): 1, ('K', 'R'): 2, ('E', 'D'): 3, ('Y', 'R'): -5,
+('M', 'Q'): -1, ('T', 'I'): 0, ('C', 'D'): -7, ('V', 'F'): -3,
+('T', 'A'): 1, ('T', 'P'): -1, ('B', 'P'): -2, ('T', 'E'): -2,
+('V', 'N'): -3, ('P', 'G'): -2, ('M', 'A'): -2, ('K', 'H'): -2,
+('V', 'R'): -3, ('P', 'C'): -4, ('M', 'E'): -3, ('K', 'L'): -4,
+('V', 'V'): 5, ('M', 'I'): 1, ('T', 'Q'): -2, ('I', 'G'): -4,
+('P', 'K'): -2, ('M', 'M'): 8, ('K', 'D'): -1, ('I', 'C'): -3,
+('Z', 'D'): 3, ('F', 'R'): -5, ('X', 'K'): -2, ('Q', 'D'): 1,
+('X', 'G'): -2, ('Z', 'L'): -3, ('X', 'C'): -4, ('Z', 'H'): 1,
+('B', 'L'): -4, ('B', 'H'): 1, ('F', 'F'): 8, ('X', 'W'): -5,
+('B', 'D'): 4, ('D', 'A'): 0, ('S', 'L'): -4, ('X', 'S'): -1,
+('F', 'N'): -4, ('S', 'R'): -1, ('W', 'D'): -8, ('V', 'Y'): -3,
+('W', 'L'): -3, ('H', 'R'): 1, ('W', 'H'): -3, ('H', 'N'): 2,
+('W', 'T'): -6, ('T', 'T'): 4, ('S', 'F'): -3, ('W', 'P'): -7,
+('L', 'D'): -5, ('B', 'I'): -3, ('L', 'H'): -3, ('S', 'N'): 1,
+('B', 'T'): 0, ('L', 'L'): 5, ('Y', 'K'): -5, ('E', 'Q'): 2,
+('Y', 'G'): -6, ('Z', 'S'): -1, ('Y', 'C'): -1, ('G', 'D'): 0,
+('B', 'V'): -3, ('E', 'A'): 0, ('Y', 'W'): -2, ('E', 'E'): 5,
+('Y', 'S'): -3, ('C', 'N'): -5, ('V', 'C'): -3, ('T', 'H'): -3,
+('P', 'R'): -1, ('V', 'G'): -2, ('T', 'L'): -3, ('V', 'K'): -4,
+('K', 'Q'): 0, ('R', 'A'): -3, ('I', 'R'): -2, ('T', 'D'): -1,
+('P', 'F'): -5, ('I', 'N'): -2, ('K', 'I'): -3, ('M', 'D'): -4,
+('V', 'W'): -8, ('W', 'W'): 12, ('M', 'H'): -4, ('P', 'N'): -2,
+('K', 'A'): -2, ('M', 'L'): 3, ('K', 'E'): -1, ('Z', 'E'): 4,
+('X', 'N'): -1, ('Z', 'A'): -1, ('Z', 'M'): -2, ('X', 'F'): -3,
+('K', 'C'): -7, ('B', 'Q'): 0, ('X', 'B'): -1, ('B', 'M'): -4,
+('F', 'C'): -6, ('Z', 'Q'): 4, ('X', 'Z'): -1, ('F', 'G'): -5,
+('B', 'E'): 3, ('X', 'V'): -1, ('F', 'K'): -7, ('B', 'A'): 0,
+('X', 'R'): -2, ('D', 'D'): 5, ('W', 'G'): -8, ('Z', 'F'): -6,
+('S', 'Q'): -2, ('W', 'C'): -8, ('W', 'K'): -5, ('H', 'Q'): 3,
+('L', 'C'): -7, ('W', 'N'): -4, ('S', 'A'): 1, ('L', 'G'): -5,
+('W', 'S'): -2, ('S', 'E'): -1, ('H', 'E'): -1, ('S', 'I'): -2,
+('H', 'A'): -3, ('S', 'M'): -2, ('Y', 'L'): -2, ('Y', 'H'): -1,
+('Y', 'D'): -5, ('E', 'R'): -3, ('X', 'P'): -2, ('G', 'G'): 5,
+('G', 'C'): -4, ('E', 'N'): 1, ('Y', 'T'): -3, ('Y', 'P'): -6,
+('T', 'K'): -1, ('A', 'A'): 3, ('P', 'Q'): 0, ('T', 'C'): -3,
+('V', 'H'): -3, ('T', 'G'): -1, ('I', 'Q'): -3, ('Z', 'T'): -2,
+('C', 'R'): -4, ('V', 'P'): -2, ('P', 'E'): -2, ('M', 'C'): -6,
+('K', 'N'): 1, ('I', 'I'): 6, ('P', 'A'): 1, ('M', 'G'): -4,
+('T', 'S'): 2, ('I', 'E'): -3, ('P', 'M'): -3, ('M', 'K'): 0,
+('I', 'A'): -1, ('P', 'I'): -3, ('R', 'R'): 6, ('X', 'M'): -2,
+('L', 'I'): 1, ('X', 'I'): -1, ('Z', 'B'): 2, ('X', 'E'): -1,
+('Z', 'N'): 0, ('X', 'A'): -1, ('B', 'R'): -2, ('B', 'N'): 3,
+('F', 'D'): -7, ('X', 'Y'): -3, ('Z', 'R'): -1, ('F', 'H'): -3,
+('B', 'F'): -5, ('F', 'L'): 0, ('X', 'Q'): -1, ('B', 'B'): 4
}
pam120 = _temp()
del _temp
@@ -2202,75 +2201,75 @@ del _temp
# http://www.embl-heidelberg.de/~vogt/matrices/pam180.cmp
def _temp():
return {
-('W', 'F') : 0, ('L', 'R') : -4, ('S', 'P') : 1, ('V', 'T') : 0,
-('Q', 'Q') : 6, ('N', 'A') : 0, ('Z', 'Y') : -6, ('W', 'R') : 2,
-('Q', 'A') : -1, ('S', 'D') : 0, ('H', 'H') : 8, ('S', 'H') : -2,
-('H', 'D') : 0, ('L', 'N') : -4, ('W', 'A') : -8, ('Y', 'M') : -4,
-('G', 'R') : -4, ('Y', 'I') : -2, ('Y', 'E') : -6, ('B', 'Y') : -4,
-('Y', 'A') : -5, ('V', 'D') : -3, ('B', 'S') : 1, ('Y', 'Y') : 11,
-('G', 'N') : 0, ('E', 'C') : -7, ('Y', 'Q') : -6, ('Z', 'Z') : 5,
-('V', 'A') : 0, ('C', 'C') : 13, ('M', 'R') : -1, ('V', 'E') : -3,
-('T', 'N') : 0, ('P', 'P') : 8, ('V', 'I') : 5, ('V', 'S') : -2,
-('Z', 'P') : -1, ('V', 'M') : 2, ('T', 'F') : -4, ('V', 'Q') : -3,
-('K', 'K') : 6, ('P', 'D') : -2, ('I', 'H') : -4, ('I', 'D') : -3,
-('T', 'R') : -2, ('P', 'L') : -4, ('K', 'G') : -3, ('M', 'N') : -3,
-('P', 'H') : -1, ('F', 'Q') : -6, ('Z', 'G') : -1, ('X', 'L') : -2,
-('T', 'M') : -1, ('Z', 'C') : -7, ('X', 'H') : -1, ('D', 'R') : -3,
-('B', 'W') : -7, ('X', 'D') : -1, ('Z', 'K') : 0, ('F', 'A') : -5,
-('Z', 'W') : -8, ('F', 'E') : -7, ('D', 'N') : 3, ('B', 'K') : 0,
-('X', 'X') : -1, ('F', 'I') : 1, ('B', 'G') : 0, ('X', 'T') : -1,
-('F', 'M') : 0, ('B', 'C') : -6, ('Z', 'I') : -3, ('Z', 'V') : -3,
-('S', 'S') : 3, ('L', 'Q') : -2, ('W', 'E') : -9, ('Q', 'R') : 1,
-('N', 'N') : 4, ('W', 'M') : -6, ('Q', 'C') : -7, ('W', 'I') : -7,
-('S', 'C') : 0, ('L', 'A') : -3, ('S', 'G') : 1, ('L', 'E') : -5,
-('W', 'Q') : -6, ('H', 'G') : -3, ('S', 'K') : -1, ('Q', 'N') : 0,
-('N', 'R') : -1, ('H', 'C') : -4, ('Y', 'N') : -2, ('G', 'Q') : -2,
-('Y', 'F') : 7, ('C', 'A') : -3, ('V', 'L') : 2, ('G', 'E') : 0,
-('G', 'A') : 1, ('K', 'R') : 4, ('E', 'D') : 4, ('Y', 'R') : -6,
-('M', 'Q') : -1, ('T', 'I') : 0, ('C', 'D') : -7, ('V', 'F') : -2,
-('T', 'A') : 2, ('T', 'P') : 0, ('B', 'P') : -2, ('T', 'E') : -1,
-('V', 'N') : -3, ('P', 'G') : -1, ('M', 'A') : -2, ('K', 'H') : -1,
-('V', 'R') : -4, ('P', 'C') : -4, ('M', 'E') : -3, ('K', 'L') : -4,
-('V', 'V') : 6, ('M', 'I') : 2, ('T', 'Q') : -2, ('I', 'G') : -4,
-('P', 'K') : -2, ('M', 'M') : 9, ('K', 'D') : 0, ('I', 'C') : -3,
-('Z', 'D') : 3, ('F', 'R') : -6, ('X', 'K') : -1, ('Q', 'D') : 2,
-('X', 'G') : -2, ('Z', 'L') : -3, ('X', 'C') : -4, ('Z', 'H') : 2,
-('B', 'L') : -5, ('B', 'H') : 1, ('F', 'F') : 10, ('X', 'W') : -6,
-('B', 'D') : 4, ('D', 'A') : 0, ('S', 'L') : -4, ('X', 'S') : 0,
-('F', 'N') : -5, ('S', 'R') : -1, ('W', 'D') : -9, ('V', 'Y') : -4,
-('W', 'L') : -3, ('H', 'R') : 2, ('W', 'H') : -4, ('H', 'N') : 2,
-('W', 'T') : -7, ('T', 'T') : 4, ('S', 'F') : -4, ('W', 'P') : -7,
-('L', 'D') : -6, ('B', 'I') : -3, ('L', 'H') : -3, ('S', 'N') : 1,
-('B', 'T') : 0, ('L', 'L') : 7, ('Y', 'K') : -6, ('E', 'Q') : 3,
-('Y', 'G') : -7, ('Z', 'S') : -1, ('Y', 'C') : 0, ('G', 'D') : 0,
-('B', 'V') : -3, ('E', 'A') : 0, ('Y', 'W') : -1, ('E', 'E') : 5,
-('Y', 'S') : -4, ('C', 'N') : -5, ('V', 'C') : -3, ('T', 'H') : -2,
-('P', 'R') : -1, ('V', 'G') : -2, ('T', 'L') : -3, ('V', 'K') : -4,
-('K', 'Q') : 0, ('R', 'A') : -3, ('I', 'R') : -3, ('T', 'D') : -1,
-('P', 'F') : -6, ('I', 'N') : -3, ('K', 'I') : -3, ('M', 'D') : -4,
-('V', 'W') : -8, ('W', 'W') : 18, ('M', 'H') : -3, ('P', 'N') : -1,
-('K', 'A') : -2, ('M', 'L') : 4, ('K', 'E') : -1, ('Z', 'E') : 5,
-('X', 'N') : -1, ('Z', 'A') : 0, ('Z', 'M') : -2, ('X', 'F') : -3,
-('K', 'C') : -7, ('B', 'Q') : 1, ('X', 'B') : -1, ('B', 'M') : -3,
-('F', 'C') : -6, ('Z', 'Q') : 5, ('X', 'Z') : -1, ('F', 'G') : -6,
-('B', 'E') : 3, ('X', 'V') : -1, ('F', 'K') : -7, ('B', 'A') : 0,
-('X', 'R') : -2, ('D', 'D') : 5, ('W', 'G') : -9, ('Z', 'F') : -7,
-('S', 'Q') : -1, ('W', 'C') : -10, ('W', 'K') : -5, ('H', 'Q') : 4,
-('L', 'C') : -8, ('W', 'N') : -5, ('S', 'A') : 1, ('L', 'G') : -6,
-('W', 'S') : -3, ('S', 'E') : -1, ('H', 'E') : 0, ('S', 'I') : -2,
-('H', 'A') : -2, ('S', 'M') : -2, ('Y', 'L') : -2, ('Y', 'H') : 0,
-('Y', 'D') : -6, ('E', 'R') : -2, ('X', 'P') : -1, ('G', 'G') : 6,
-('G', 'C') : -5, ('E', 'N') : 2, ('Y', 'T') : -4, ('Y', 'P') : -7,
-('T', 'K') : 0, ('A', 'A') : 3, ('P', 'Q') : 0, ('T', 'C') : -3,
-('V', 'H') : -3, ('T', 'G') : -1, ('I', 'Q') : -3, ('Z', 'T') : -1,
-('C', 'R') : -5, ('V', 'P') : -2, ('P', 'E') : -1, ('M', 'C') : -7,
-('K', 'N') : 1, ('I', 'I') : 6, ('P', 'A') : 1, ('M', 'G') : -4,
-('T', 'S') : 2, ('I', 'E') : -3, ('P', 'M') : -3, ('M', 'K') : 1,
-('I', 'A') : -1, ('P', 'I') : -3, ('R', 'R') : 8, ('X', 'M') : -1,
-('L', 'I') : 2, ('X', 'I') : -1, ('Z', 'B') : 3, ('X', 'E') : -1,
-('Z', 'N') : 1, ('X', 'A') : -1, ('B', 'R') : -2, ('B', 'N') : 3,
-('F', 'D') : -8, ('X', 'Y') : -3, ('Z', 'R') : 0, ('F', 'H') : -3,
-('B', 'F') : -6, ('F', 'L') : 1, ('X', 'Q') : -1, ('B', 'B') : 4
+('W', 'F'): 0, ('L', 'R'): -4, ('S', 'P'): 1, ('V', 'T'): 0,
+('Q', 'Q'): 6, ('N', 'A'): 0, ('Z', 'Y'): -6, ('W', 'R'): 2,
+('Q', 'A'): -1, ('S', 'D'): 0, ('H', 'H'): 8, ('S', 'H'): -2,
+('H', 'D'): 0, ('L', 'N'): -4, ('W', 'A'): -8, ('Y', 'M'): -4,
+('G', 'R'): -4, ('Y', 'I'): -2, ('Y', 'E'): -6, ('B', 'Y'): -4,
+('Y', 'A'): -5, ('V', 'D'): -3, ('B', 'S'): 1, ('Y', 'Y'): 11,
+('G', 'N'): 0, ('E', 'C'): -7, ('Y', 'Q'): -6, ('Z', 'Z'): 5,
+('V', 'A'): 0, ('C', 'C'): 13, ('M', 'R'): -1, ('V', 'E'): -3,
+('T', 'N'): 0, ('P', 'P'): 8, ('V', 'I'): 5, ('V', 'S'): -2,
+('Z', 'P'): -1, ('V', 'M'): 2, ('T', 'F'): -4, ('V', 'Q'): -3,
+('K', 'K'): 6, ('P', 'D'): -2, ('I', 'H'): -4, ('I', 'D'): -3,
+('T', 'R'): -2, ('P', 'L'): -4, ('K', 'G'): -3, ('M', 'N'): -3,
+('P', 'H'): -1, ('F', 'Q'): -6, ('Z', 'G'): -1, ('X', 'L'): -2,
+('T', 'M'): -1, ('Z', 'C'): -7, ('X', 'H'): -1, ('D', 'R'): -3,
+('B', 'W'): -7, ('X', 'D'): -1, ('Z', 'K'): 0, ('F', 'A'): -5,
+('Z', 'W'): -8, ('F', 'E'): -7, ('D', 'N'): 3, ('B', 'K'): 0,
+('X', 'X'): -1, ('F', 'I'): 1, ('B', 'G'): 0, ('X', 'T'): -1,
+('F', 'M'): 0, ('B', 'C'): -6, ('Z', 'I'): -3, ('Z', 'V'): -3,
+('S', 'S'): 3, ('L', 'Q'): -2, ('W', 'E'): -9, ('Q', 'R'): 1,
+('N', 'N'): 4, ('W', 'M'): -6, ('Q', 'C'): -7, ('W', 'I'): -7,
+('S', 'C'): 0, ('L', 'A'): -3, ('S', 'G'): 1, ('L', 'E'): -5,
+('W', 'Q'): -6, ('H', 'G'): -3, ('S', 'K'): -1, ('Q', 'N'): 0,
+('N', 'R'): -1, ('H', 'C'): -4, ('Y', 'N'): -2, ('G', 'Q'): -2,
+('Y', 'F'): 7, ('C', 'A'): -3, ('V', 'L'): 2, ('G', 'E'): 0,
+('G', 'A'): 1, ('K', 'R'): 4, ('E', 'D'): 4, ('Y', 'R'): -6,
+('M', 'Q'): -1, ('T', 'I'): 0, ('C', 'D'): -7, ('V', 'F'): -2,
+('T', 'A'): 2, ('T', 'P'): 0, ('B', 'P'): -2, ('T', 'E'): -1,
+('V', 'N'): -3, ('P', 'G'): -1, ('M', 'A'): -2, ('K', 'H'): -1,
+('V', 'R'): -4, ('P', 'C'): -4, ('M', 'E'): -3, ('K', 'L'): -4,
+('V', 'V'): 6, ('M', 'I'): 2, ('T', 'Q'): -2, ('I', 'G'): -4,
+('P', 'K'): -2, ('M', 'M'): 9, ('K', 'D'): 0, ('I', 'C'): -3,
+('Z', 'D'): 3, ('F', 'R'): -6, ('X', 'K'): -1, ('Q', 'D'): 2,
+('X', 'G'): -2, ('Z', 'L'): -3, ('X', 'C'): -4, ('Z', 'H'): 2,
+('B', 'L'): -5, ('B', 'H'): 1, ('F', 'F'): 10, ('X', 'W'): -6,
+('B', 'D'): 4, ('D', 'A'): 0, ('S', 'L'): -4, ('X', 'S'): 0,
+('F', 'N'): -5, ('S', 'R'): -1, ('W', 'D'): -9, ('V', 'Y'): -4,
+('W', 'L'): -3, ('H', 'R'): 2, ('W', 'H'): -4, ('H', 'N'): 2,
+('W', 'T'): -7, ('T', 'T'): 4, ('S', 'F'): -4, ('W', 'P'): -7,
+('L', 'D'): -6, ('B', 'I'): -3, ('L', 'H'): -3, ('S', 'N'): 1,
+('B', 'T'): 0, ('L', 'L'): 7, ('Y', 'K'): -6, ('E', 'Q'): 3,
+('Y', 'G'): -7, ('Z', 'S'): -1, ('Y', 'C'): 0, ('G', 'D'): 0,
+('B', 'V'): -3, ('E', 'A'): 0, ('Y', 'W'): -1, ('E', 'E'): 5,
+('Y', 'S'): -4, ('C', 'N'): -5, ('V', 'C'): -3, ('T', 'H'): -2,
+('P', 'R'): -1, ('V', 'G'): -2, ('T', 'L'): -3, ('V', 'K'): -4,
+('K', 'Q'): 0, ('R', 'A'): -3, ('I', 'R'): -3, ('T', 'D'): -1,
+('P', 'F'): -6, ('I', 'N'): -3, ('K', 'I'): -3, ('M', 'D'): -4,
+('V', 'W'): -8, ('W', 'W'): 18, ('M', 'H'): -3, ('P', 'N'): -1,
+('K', 'A'): -2, ('M', 'L'): 4, ('K', 'E'): -1, ('Z', 'E'): 5,
+('X', 'N'): -1, ('Z', 'A'): 0, ('Z', 'M'): -2, ('X', 'F'): -3,
+('K', 'C'): -7, ('B', 'Q'): 1, ('X', 'B'): -1, ('B', 'M'): -3,
+('F', 'C'): -6, ('Z', 'Q'): 5, ('X', 'Z'): -1, ('F', 'G'): -6,
+('B', 'E'): 3, ('X', 'V'): -1, ('F', 'K'): -7, ('B', 'A'): 0,
+('X', 'R'): -2, ('D', 'D'): 5, ('W', 'G'): -9, ('Z', 'F'): -7,
+('S', 'Q'): -1, ('W', 'C'): -10, ('W', 'K'): -5, ('H', 'Q'): 4,
+('L', 'C'): -8, ('W', 'N'): -5, ('S', 'A'): 1, ('L', 'G'): -6,
+('W', 'S'): -3, ('S', 'E'): -1, ('H', 'E'): 0, ('S', 'I'): -2,
+('H', 'A'): -2, ('S', 'M'): -2, ('Y', 'L'): -2, ('Y', 'H'): 0,
+('Y', 'D'): -6, ('E', 'R'): -2, ('X', 'P'): -1, ('G', 'G'): 6,
+('G', 'C'): -5, ('E', 'N'): 2, ('Y', 'T'): -4, ('Y', 'P'): -7,
+('T', 'K'): 0, ('A', 'A'): 3, ('P', 'Q'): 0, ('T', 'C'): -3,
+('V', 'H'): -3, ('T', 'G'): -1, ('I', 'Q'): -3, ('Z', 'T'): -1,
+('C', 'R'): -5, ('V', 'P'): -2, ('P', 'E'): -1, ('M', 'C'): -7,
+('K', 'N'): 1, ('I', 'I'): 6, ('P', 'A'): 1, ('M', 'G'): -4,
+('T', 'S'): 2, ('I', 'E'): -3, ('P', 'M'): -3, ('M', 'K'): 1,
+('I', 'A'): -1, ('P', 'I'): -3, ('R', 'R'): 8, ('X', 'M'): -1,
+('L', 'I'): 2, ('X', 'I'): -1, ('Z', 'B'): 3, ('X', 'E'): -1,
+('Z', 'N'): 1, ('X', 'A'): -1, ('B', 'R'): -2, ('B', 'N'): 3,
+('F', 'D'): -8, ('X', 'Y'): -3, ('Z', 'R'): 0, ('F', 'H'): -3,
+('B', 'F'): -6, ('F', 'L'): 1, ('X', 'Q'): -1, ('B', 'B'): 4
}
pam180 = _temp()
del _temp
@@ -2279,75 +2278,75 @@ del _temp
# http://www.embl-heidelberg.de/~vogt/matrices/pam250.cmp
def _temp():
return {
-('W', 'F') : 0, ('L', 'R') : -3, ('S', 'P') : 1, ('V', 'T') : 0,
-('Q', 'Q') : 4, ('N', 'A') : 0, ('Z', 'Y') : -4, ('W', 'R') : 2,
-('Q', 'A') : 0, ('S', 'D') : 0, ('H', 'H') : 6, ('S', 'H') : -1,
-('H', 'D') : 1, ('L', 'N') : -3, ('W', 'A') : -6, ('Y', 'M') : -2,
-('G', 'R') : -3, ('Y', 'I') : -1, ('Y', 'E') : -4, ('B', 'Y') : -3,
-('Y', 'A') : -3, ('V', 'D') : -2, ('B', 'S') : 0, ('Y', 'Y') : 10,
-('G', 'N') : 0, ('E', 'C') : -5, ('Y', 'Q') : -4, ('Z', 'Z') : 3,
-('V', 'A') : 0, ('C', 'C') : 12, ('M', 'R') : 0, ('V', 'E') : -2,
-('T', 'N') : 0, ('P', 'P') : 6, ('V', 'I') : 4, ('V', 'S') : -1,
-('Z', 'P') : 0, ('V', 'M') : 2, ('T', 'F') : -3, ('V', 'Q') : -2,
-('K', 'K') : 5, ('P', 'D') : -1, ('I', 'H') : -2, ('I', 'D') : -2,
-('T', 'R') : -1, ('P', 'L') : -3, ('K', 'G') : -2, ('M', 'N') : -2,
-('P', 'H') : 0, ('F', 'Q') : -5, ('Z', 'G') : 0, ('X', 'L') : -1,
-('T', 'M') : -1, ('Z', 'C') : -5, ('X', 'H') : -1, ('D', 'R') : -1,
-('B', 'W') : -5, ('X', 'D') : -1, ('Z', 'K') : 0, ('F', 'A') : -3,
-('Z', 'W') : -6, ('F', 'E') : -5, ('D', 'N') : 2, ('B', 'K') : 1,
-('X', 'X') : -1, ('F', 'I') : 1, ('B', 'G') : 0, ('X', 'T') : 0,
-('F', 'M') : 0, ('B', 'C') : -4, ('Z', 'I') : -2, ('Z', 'V') : -2,
-('S', 'S') : 2, ('L', 'Q') : -2, ('W', 'E') : -7, ('Q', 'R') : 1,
-('N', 'N') : 2, ('W', 'M') : -4, ('Q', 'C') : -5, ('W', 'I') : -5,
-('S', 'C') : 0, ('L', 'A') : -2, ('S', 'G') : 1, ('L', 'E') : -3,
-('W', 'Q') : -5, ('H', 'G') : -2, ('S', 'K') : 0, ('Q', 'N') : 1,
-('N', 'R') : 0, ('H', 'C') : -3, ('Y', 'N') : -2, ('G', 'Q') : -1,
-('Y', 'F') : 7, ('C', 'A') : -2, ('V', 'L') : 2, ('G', 'E') : 0,
-('G', 'A') : 1, ('K', 'R') : 3, ('E', 'D') : 3, ('Y', 'R') : -4,
-('M', 'Q') : -1, ('T', 'I') : 0, ('C', 'D') : -5, ('V', 'F') : -1,
-('T', 'A') : 1, ('T', 'P') : 0, ('B', 'P') : -1, ('T', 'E') : 0,
-('V', 'N') : -2, ('P', 'G') : 0, ('M', 'A') : -1, ('K', 'H') : 0,
-('V', 'R') : -2, ('P', 'C') : -3, ('M', 'E') : -2, ('K', 'L') : -3,
-('V', 'V') : 4, ('M', 'I') : 2, ('T', 'Q') : -1, ('I', 'G') : -3,
-('P', 'K') : -1, ('M', 'M') : 6, ('K', 'D') : 0, ('I', 'C') : -2,
-('Z', 'D') : 3, ('F', 'R') : -4, ('X', 'K') : -1, ('Q', 'D') : 2,
-('X', 'G') : -1, ('Z', 'L') : -3, ('X', 'C') : -3, ('Z', 'H') : 2,
-('B', 'L') : -3, ('B', 'H') : 1, ('F', 'F') : 9, ('X', 'W') : -4,
-('B', 'D') : 3, ('D', 'A') : 0, ('S', 'L') : -3, ('X', 'S') : 0,
-('F', 'N') : -3, ('S', 'R') : 0, ('W', 'D') : -7, ('V', 'Y') : -2,
-('W', 'L') : -2, ('H', 'R') : 2, ('W', 'H') : -3, ('H', 'N') : 2,
-('W', 'T') : -5, ('T', 'T') : 3, ('S', 'F') : -3, ('W', 'P') : -6,
-('L', 'D') : -4, ('B', 'I') : -2, ('L', 'H') : -2, ('S', 'N') : 1,
-('B', 'T') : 0, ('L', 'L') : 6, ('Y', 'K') : -4, ('E', 'Q') : 2,
-('Y', 'G') : -5, ('Z', 'S') : 0, ('Y', 'C') : 0, ('G', 'D') : 1,
-('B', 'V') : -2, ('E', 'A') : 0, ('Y', 'W') : 0, ('E', 'E') : 4,
-('Y', 'S') : -3, ('C', 'N') : -4, ('V', 'C') : -2, ('T', 'H') : -1,
-('P', 'R') : 0, ('V', 'G') : -1, ('T', 'L') : -2, ('V', 'K') : -2,
-('K', 'Q') : 1, ('R', 'A') : -2, ('I', 'R') : -2, ('T', 'D') : 0,
-('P', 'F') : -5, ('I', 'N') : -2, ('K', 'I') : -2, ('M', 'D') : -3,
-('V', 'W') : -6, ('W', 'W') : 17, ('M', 'H') : -2, ('P', 'N') : 0,
-('K', 'A') : -1, ('M', 'L') : 4, ('K', 'E') : 0, ('Z', 'E') : 3,
-('X', 'N') : 0, ('Z', 'A') : 0, ('Z', 'M') : -2, ('X', 'F') : -2,
-('K', 'C') : -5, ('B', 'Q') : 1, ('X', 'B') : -1, ('B', 'M') : -2,
-('F', 'C') : -4, ('Z', 'Q') : 3, ('X', 'Z') : -1, ('F', 'G') : -5,
-('B', 'E') : 3, ('X', 'V') : -1, ('F', 'K') : -5, ('B', 'A') : 0,
-('X', 'R') : -1, ('D', 'D') : 4, ('W', 'G') : -7, ('Z', 'F') : -5,
-('S', 'Q') : -1, ('W', 'C') : -8, ('W', 'K') : -3, ('H', 'Q') : 3,
-('L', 'C') : -6, ('W', 'N') : -4, ('S', 'A') : 1, ('L', 'G') : -4,
-('W', 'S') : -2, ('S', 'E') : 0, ('H', 'E') : 1, ('S', 'I') : -1,
-('H', 'A') : -1, ('S', 'M') : -2, ('Y', 'L') : -1, ('Y', 'H') : 0,
-('Y', 'D') : -4, ('E', 'R') : -1, ('X', 'P') : -1, ('G', 'G') : 5,
-('G', 'C') : -3, ('E', 'N') : 1, ('Y', 'T') : -3, ('Y', 'P') : -5,
-('T', 'K') : 0, ('A', 'A') : 2, ('P', 'Q') : 0, ('T', 'C') : -2,
-('V', 'H') : -2, ('T', 'G') : 0, ('I', 'Q') : -2, ('Z', 'T') : -1,
-('C', 'R') : -4, ('V', 'P') : -1, ('P', 'E') : -1, ('M', 'C') : -5,
-('K', 'N') : 1, ('I', 'I') : 5, ('P', 'A') : 1, ('M', 'G') : -3,
-('T', 'S') : 1, ('I', 'E') : -2, ('P', 'M') : -2, ('M', 'K') : 0,
-('I', 'A') : -1, ('P', 'I') : -2, ('R', 'R') : 6, ('X', 'M') : -1,
-('L', 'I') : 2, ('X', 'I') : -1, ('Z', 'B') : 2, ('X', 'E') : -1,
-('Z', 'N') : 1, ('X', 'A') : 0, ('B', 'R') : -1, ('B', 'N') : 2,
-('F', 'D') : -6, ('X', 'Y') : -2, ('Z', 'R') : 0, ('F', 'H') : -2,
-('B', 'F') : -4, ('F', 'L') : 2, ('X', 'Q') : -1, ('B', 'B') : 3
+('W', 'F'): 0, ('L', 'R'): -3, ('S', 'P'): 1, ('V', 'T'): 0,
+('Q', 'Q'): 4, ('N', 'A'): 0, ('Z', 'Y'): -4, ('W', 'R'): 2,
+('Q', 'A'): 0, ('S', 'D'): 0, ('H', 'H'): 6, ('S', 'H'): -1,
+('H', 'D'): 1, ('L', 'N'): -3, ('W', 'A'): -6, ('Y', 'M'): -2,
+('G', 'R'): -3, ('Y', 'I'): -1, ('Y', 'E'): -4, ('B', 'Y'): -3,
+('Y', 'A'): -3, ('V', 'D'): -2, ('B', 'S'): 0, ('Y', 'Y'): 10,
+('G', 'N'): 0, ('E', 'C'): -5, ('Y', 'Q'): -4, ('Z', 'Z'): 3,
+('V', 'A'): 0, ('C', 'C'): 12, ('M', 'R'): 0, ('V', 'E'): -2,
+('T', 'N'): 0, ('P', 'P'): 6, ('V', 'I'): 4, ('V', 'S'): -1,
+('Z', 'P'): 0, ('V', 'M'): 2, ('T', 'F'): -3, ('V', 'Q'): -2,
+('K', 'K'): 5, ('P', 'D'): -1, ('I', 'H'): -2, ('I', 'D'): -2,
+('T', 'R'): -1, ('P', 'L'): -3, ('K', 'G'): -2, ('M', 'N'): -2,
+('P', 'H'): 0, ('F', 'Q'): -5, ('Z', 'G'): 0, ('X', 'L'): -1,
+('T', 'M'): -1, ('Z', 'C'): -5, ('X', 'H'): -1, ('D', 'R'): -1,
+('B', 'W'): -5, ('X', 'D'): -1, ('Z', 'K'): 0, ('F', 'A'): -3,
+('Z', 'W'): -6, ('F', 'E'): -5, ('D', 'N'): 2, ('B', 'K'): 1,
+('X', 'X'): -1, ('F', 'I'): 1, ('B', 'G'): 0, ('X', 'T'): 0,
+('F', 'M'): 0, ('B', 'C'): -4, ('Z', 'I'): -2, ('Z', 'V'): -2,
+('S', 'S'): 2, ('L', 'Q'): -2, ('W', 'E'): -7, ('Q', 'R'): 1,
+('N', 'N'): 2, ('W', 'M'): -4, ('Q', 'C'): -5, ('W', 'I'): -5,
+('S', 'C'): 0, ('L', 'A'): -2, ('S', 'G'): 1, ('L', 'E'): -3,
+('W', 'Q'): -5, ('H', 'G'): -2, ('S', 'K'): 0, ('Q', 'N'): 1,
+('N', 'R'): 0, ('H', 'C'): -3, ('Y', 'N'): -2, ('G', 'Q'): -1,
+('Y', 'F'): 7, ('C', 'A'): -2, ('V', 'L'): 2, ('G', 'E'): 0,
+('G', 'A'): 1, ('K', 'R'): 3, ('E', 'D'): 3, ('Y', 'R'): -4,
+('M', 'Q'): -1, ('T', 'I'): 0, ('C', 'D'): -5, ('V', 'F'): -1,
+('T', 'A'): 1, ('T', 'P'): 0, ('B', 'P'): -1, ('T', 'E'): 0,
+('V', 'N'): -2, ('P', 'G'): 0, ('M', 'A'): -1, ('K', 'H'): 0,
+('V', 'R'): -2, ('P', 'C'): -3, ('M', 'E'): -2, ('K', 'L'): -3,
+('V', 'V'): 4, ('M', 'I'): 2, ('T', 'Q'): -1, ('I', 'G'): -3,
+('P', 'K'): -1, ('M', 'M'): 6, ('K', 'D'): 0, ('I', 'C'): -2,
+('Z', 'D'): 3, ('F', 'R'): -4, ('X', 'K'): -1, ('Q', 'D'): 2,
+('X', 'G'): -1, ('Z', 'L'): -3, ('X', 'C'): -3, ('Z', 'H'): 2,
+('B', 'L'): -3, ('B', 'H'): 1, ('F', 'F'): 9, ('X', 'W'): -4,
+('B', 'D'): 3, ('D', 'A'): 0, ('S', 'L'): -3, ('X', 'S'): 0,
+('F', 'N'): -3, ('S', 'R'): 0, ('W', 'D'): -7, ('V', 'Y'): -2,
+('W', 'L'): -2, ('H', 'R'): 2, ('W', 'H'): -3, ('H', 'N'): 2,
+('W', 'T'): -5, ('T', 'T'): 3, ('S', 'F'): -3, ('W', 'P'): -6,
+('L', 'D'): -4, ('B', 'I'): -2, ('L', 'H'): -2, ('S', 'N'): 1,
+('B', 'T'): 0, ('L', 'L'): 6, ('Y', 'K'): -4, ('E', 'Q'): 2,
+('Y', 'G'): -5, ('Z', 'S'): 0, ('Y', 'C'): 0, ('G', 'D'): 1,
+('B', 'V'): -2, ('E', 'A'): 0, ('Y', 'W'): 0, ('E', 'E'): 4,
+('Y', 'S'): -3, ('C', 'N'): -4, ('V', 'C'): -2, ('T', 'H'): -1,
+('P', 'R'): 0, ('V', 'G'): -1, ('T', 'L'): -2, ('V', 'K'): -2,
+('K', 'Q'): 1, ('R', 'A'): -2, ('I', 'R'): -2, ('T', 'D'): 0,
+('P', 'F'): -5, ('I', 'N'): -2, ('K', 'I'): -2, ('M', 'D'): -3,
+('V', 'W'): -6, ('W', 'W'): 17, ('M', 'H'): -2, ('P', 'N'): 0,
+('K', 'A'): -1, ('M', 'L'): 4, ('K', 'E'): 0, ('Z', 'E'): 3,
+('X', 'N'): 0, ('Z', 'A'): 0, ('Z', 'M'): -2, ('X', 'F'): -2,
+('K', 'C'): -5, ('B', 'Q'): 1, ('X', 'B'): -1, ('B', 'M'): -2,
+('F', 'C'): -4, ('Z', 'Q'): 3, ('X', 'Z'): -1, ('F', 'G'): -5,
+('B', 'E'): 3, ('X', 'V'): -1, ('F', 'K'): -5, ('B', 'A'): 0,
+('X', 'R'): -1, ('D', 'D'): 4, ('W', 'G'): -7, ('Z', 'F'): -5,
+('S', 'Q'): -1, ('W', 'C'): -8, ('W', 'K'): -3, ('H', 'Q'): 3,
+('L', 'C'): -6, ('W', 'N'): -4, ('S', 'A'): 1, ('L', 'G'): -4,
+('W', 'S'): -2, ('S', 'E'): 0, ('H', 'E'): 1, ('S', 'I'): -1,
+('H', 'A'): -1, ('S', 'M'): -2, ('Y', 'L'): -1, ('Y', 'H'): 0,
+('Y', 'D'): -4, ('E', 'R'): -1, ('X', 'P'): -1, ('G', 'G'): 5,
+('G', 'C'): -3, ('E', 'N'): 1, ('Y', 'T'): -3, ('Y', 'P'): -5,
+('T', 'K'): 0, ('A', 'A'): 2, ('P', 'Q'): 0, ('T', 'C'): -2,
+('V', 'H'): -2, ('T', 'G'): 0, ('I', 'Q'): -2, ('Z', 'T'): -1,
+('C', 'R'): -4, ('V', 'P'): -1, ('P', 'E'): -1, ('M', 'C'): -5,
+('K', 'N'): 1, ('I', 'I'): 5, ('P', 'A'): 1, ('M', 'G'): -3,
+('T', 'S'): 1, ('I', 'E'): -2, ('P', 'M'): -2, ('M', 'K'): 0,
+('I', 'A'): -1, ('P', 'I'): -2, ('R', 'R'): 6, ('X', 'M'): -1,
+('L', 'I'): 2, ('X', 'I'): -1, ('Z', 'B'): 2, ('X', 'E'): -1,
+('Z', 'N'): 1, ('X', 'A'): 0, ('B', 'R'): -1, ('B', 'N'): 2,
+('F', 'D'): -6, ('X', 'Y'): -2, ('Z', 'R'): 0, ('F', 'H'): -2,
+('B', 'F'): -4, ('F', 'L'): 2, ('X', 'Q'): -1, ('B', 'B'): 3
}
pam250 = _temp()
del _temp
@@ -2356,75 +2355,75 @@ del _temp
# http://www.embl-heidelberg.de/~vogt/matrices/pam30.cmp
def _temp():
return {
-('W', 'F') : -4, ('L', 'R') : -8, ('S', 'P') : -2, ('V', 'T') : -3,
-('Q', 'Q') : 8, ('N', 'A') : -4, ('Z', 'Y') : -9, ('W', 'R') : -2,
-('Q', 'A') : -4, ('S', 'D') : -4, ('H', 'H') : 9, ('S', 'H') : -6,
-('H', 'D') : -4, ('L', 'N') : -7, ('W', 'A') : -13, ('Y', 'M') : -11,
-('G', 'R') : -9, ('Y', 'I') : -6, ('Y', 'E') : -8, ('B', 'Y') : -6,
-('Y', 'A') : -8, ('V', 'D') : -8, ('B', 'S') : -1, ('Y', 'Y') : 10,
-('G', 'N') : -3, ('E', 'C') : -14, ('Y', 'Q') : -12, ('Z', 'Z') : 6,
-('V', 'A') : -2, ('C', 'C') : 10, ('M', 'R') : -4, ('V', 'E') : -6,
-('T', 'N') : -2, ('P', 'P') : 8, ('V', 'I') : 2, ('V', 'S') : -6,
-('Z', 'P') : -4, ('V', 'M') : -1, ('T', 'F') : -9, ('V', 'Q') : -7,
-('K', 'K') : 7, ('P', 'D') : -8, ('I', 'H') : -9, ('I', 'D') : -7,
-('T', 'R') : -6, ('P', 'L') : -7, ('K', 'G') : -7, ('M', 'N') : -9,
-('P', 'H') : -4, ('F', 'Q') : -13, ('Z', 'G') : -5, ('X', 'L') : -6,
-('T', 'M') : -4, ('Z', 'C') : -14, ('X', 'H') : -5, ('D', 'R') : -10,
-('B', 'W') : -10, ('X', 'D') : -5, ('Z', 'K') : -4, ('F', 'A') : -8,
-('Z', 'W') : -14, ('F', 'E') : -14, ('D', 'N') : 2, ('B', 'K') : -2,
-('X', 'X') : -5, ('F', 'I') : -2, ('B', 'G') : -3, ('X', 'T') : -4,
-('F', 'M') : -4, ('B', 'C') : -12, ('Z', 'I') : -6, ('Z', 'V') : -6,
-('S', 'S') : 6, ('L', 'Q') : -5, ('W', 'E') : -17, ('Q', 'R') : -2,
-('N', 'N') : 8, ('W', 'M') : -13, ('Q', 'C') : -14, ('W', 'I') : -14,
-('S', 'C') : -3, ('L', 'A') : -6, ('S', 'G') : -2, ('L', 'E') : -9,
-('W', 'Q') : -13, ('H', 'G') : -9, ('S', 'K') : -4, ('Q', 'N') : -3,
-('N', 'R') : -6, ('H', 'C') : -7, ('Y', 'N') : -4, ('G', 'Q') : -7,
-('Y', 'F') : 2, ('C', 'A') : -6, ('V', 'L') : -2, ('G', 'E') : -4,
-('G', 'A') : -2, ('K', 'R') : 0, ('E', 'D') : 2, ('Y', 'R') : -10,
-('M', 'Q') : -4, ('T', 'I') : -2, ('C', 'D') : -14, ('V', 'F') : -8,
-('T', 'A') : -1, ('T', 'P') : -4, ('B', 'P') : -7, ('T', 'E') : -6,
-('V', 'N') : -8, ('P', 'G') : -6, ('M', 'A') : -5, ('K', 'H') : -6,
-('V', 'R') : -8, ('P', 'C') : -8, ('M', 'E') : -7, ('K', 'L') : -8,
-('V', 'V') : 7, ('M', 'I') : -1, ('T', 'Q') : -5, ('I', 'G') : -11,
-('P', 'K') : -6, ('M', 'M') : 11, ('K', 'D') : -4, ('I', 'C') : -6,
-('Z', 'D') : 1, ('F', 'R') : -9, ('X', 'K') : -5, ('Q', 'D') : -2,
-('X', 'G') : -5, ('Z', 'L') : -7, ('X', 'C') : -9, ('Z', 'H') : -1,
-('B', 'L') : -9, ('B', 'H') : -1, ('F', 'F') : 9, ('X', 'W') : -11,
-('B', 'D') : 6, ('D', 'A') : -3, ('S', 'L') : -8, ('X', 'S') : -3,
-('F', 'N') : -9, ('S', 'R') : -3, ('W', 'D') : -15, ('V', 'Y') : -7,
-('W', 'L') : -6, ('H', 'R') : -2, ('W', 'H') : -7, ('H', 'N') : 0,
-('W', 'T') : -13, ('T', 'T') : 7, ('S', 'F') : -6, ('W', 'P') : -14,
-('L', 'D') : -12, ('B', 'I') : -6, ('L', 'H') : -6, ('S', 'N') : 0,
-('B', 'T') : -3, ('L', 'L') : 7, ('Y', 'K') : -9, ('E', 'Q') : 1,
-('Y', 'G') : -14, ('Z', 'S') : -5, ('Y', 'C') : -4, ('G', 'D') : -3,
-('B', 'V') : -8, ('E', 'A') : -2, ('Y', 'W') : -5, ('E', 'E') : 8,
-('Y', 'S') : -7, ('C', 'N') : -11, ('V', 'C') : -6, ('T', 'H') : -7,
-('P', 'R') : -4, ('V', 'G') : -5, ('T', 'L') : -7, ('V', 'K') : -9,
-('K', 'Q') : -3, ('R', 'A') : -7, ('I', 'R') : -5, ('T', 'D') : -5,
-('P', 'F') : -10, ('I', 'N') : -5, ('K', 'I') : -6, ('M', 'D') : -11,
-('V', 'W') : -15, ('W', 'W') : 13, ('M', 'H') : -10, ('P', 'N') : -6,
-('K', 'A') : -7, ('M', 'L') : 1, ('K', 'E') : -4, ('Z', 'E') : 6,
-('X', 'N') : -3, ('Z', 'A') : -3, ('Z', 'M') : -5, ('X', 'F') : -8,
-('K', 'C') : -14, ('B', 'Q') : -3, ('X', 'B') : -5, ('B', 'M') : -10,
-('F', 'C') : -13, ('Z', 'Q') : 6, ('X', 'Z') : -5, ('F', 'G') : -9,
-('B', 'E') : 1, ('X', 'V') : -5, ('F', 'K') : -14, ('B', 'A') : -3,
-('X', 'R') : -6, ('D', 'D') : 8, ('W', 'G') : -15, ('Z', 'F') : -13,
-('S', 'Q') : -5, ('W', 'C') : -15, ('W', 'K') : -12, ('H', 'Q') : 1,
-('L', 'C') : -15, ('W', 'N') : -8, ('S', 'A') : 0, ('L', 'G') : -10,
-('W', 'S') : -5, ('S', 'E') : -4, ('H', 'E') : -5, ('S', 'I') : -7,
-('H', 'A') : -7, ('S', 'M') : -5, ('Y', 'L') : -7, ('Y', 'H') : -3,
-('Y', 'D') : -11, ('E', 'R') : -9, ('X', 'P') : -5, ('G', 'G') : 6,
-('G', 'C') : -9, ('E', 'N') : -2, ('Y', 'T') : -6, ('Y', 'P') : -13,
-('T', 'K') : -3, ('A', 'A') : 6, ('P', 'Q') : -3, ('T', 'C') : -8,
-('V', 'H') : -6, ('T', 'G') : -6, ('I', 'Q') : -8, ('Z', 'T') : -6,
-('C', 'R') : -8, ('V', 'P') : -6, ('P', 'E') : -5, ('M', 'C') : -13,
-('K', 'N') : -1, ('I', 'I') : 8, ('P', 'A') : -2, ('M', 'G') : -8,
-('T', 'S') : 0, ('I', 'E') : -5, ('P', 'M') : -8, ('M', 'K') : -2,
-('I', 'A') : -5, ('P', 'I') : -8, ('R', 'R') : 8, ('X', 'M') : -5,
-('L', 'I') : -1, ('X', 'I') : -5, ('Z', 'B') : 0, ('X', 'E') : -5,
-('Z', 'N') : -3, ('X', 'A') : -3, ('B', 'R') : -7, ('B', 'N') : 6,
-('F', 'D') : -15, ('X', 'Y') : -7, ('Z', 'R') : -4, ('F', 'H') : -6,
-('B', 'F') : -10, ('F', 'L') : -3, ('X', 'Q') : -5, ('B', 'B') : 6
+('W', 'F'): -4, ('L', 'R'): -8, ('S', 'P'): -2, ('V', 'T'): -3,
+('Q', 'Q'): 8, ('N', 'A'): -4, ('Z', 'Y'): -9, ('W', 'R'): -2,
+('Q', 'A'): -4, ('S', 'D'): -4, ('H', 'H'): 9, ('S', 'H'): -6,
+('H', 'D'): -4, ('L', 'N'): -7, ('W', 'A'): -13, ('Y', 'M'): -11,
+('G', 'R'): -9, ('Y', 'I'): -6, ('Y', 'E'): -8, ('B', 'Y'): -6,
+('Y', 'A'): -8, ('V', 'D'): -8, ('B', 'S'): -1, ('Y', 'Y'): 10,
+('G', 'N'): -3, ('E', 'C'): -14, ('Y', 'Q'): -12, ('Z', 'Z'): 6,
+('V', 'A'): -2, ('C', 'C'): 10, ('M', 'R'): -4, ('V', 'E'): -6,
+('T', 'N'): -2, ('P', 'P'): 8, ('V', 'I'): 2, ('V', 'S'): -6,
+('Z', 'P'): -4, ('V', 'M'): -1, ('T', 'F'): -9, ('V', 'Q'): -7,
+('K', 'K'): 7, ('P', 'D'): -8, ('I', 'H'): -9, ('I', 'D'): -7,
+('T', 'R'): -6, ('P', 'L'): -7, ('K', 'G'): -7, ('M', 'N'): -9,
+('P', 'H'): -4, ('F', 'Q'): -13, ('Z', 'G'): -5, ('X', 'L'): -6,
+('T', 'M'): -4, ('Z', 'C'): -14, ('X', 'H'): -5, ('D', 'R'): -10,
+('B', 'W'): -10, ('X', 'D'): -5, ('Z', 'K'): -4, ('F', 'A'): -8,
+('Z', 'W'): -14, ('F', 'E'): -14, ('D', 'N'): 2, ('B', 'K'): -2,
+('X', 'X'): -5, ('F', 'I'): -2, ('B', 'G'): -3, ('X', 'T'): -4,
+('F', 'M'): -4, ('B', 'C'): -12, ('Z', 'I'): -6, ('Z', 'V'): -6,
+('S', 'S'): 6, ('L', 'Q'): -5, ('W', 'E'): -17, ('Q', 'R'): -2,
+('N', 'N'): 8, ('W', 'M'): -13, ('Q', 'C'): -14, ('W', 'I'): -14,
+('S', 'C'): -3, ('L', 'A'): -6, ('S', 'G'): -2, ('L', 'E'): -9,
+('W', 'Q'): -13, ('H', 'G'): -9, ('S', 'K'): -4, ('Q', 'N'): -3,
+('N', 'R'): -6, ('H', 'C'): -7, ('Y', 'N'): -4, ('G', 'Q'): -7,
+('Y', 'F'): 2, ('C', 'A'): -6, ('V', 'L'): -2, ('G', 'E'): -4,
+('G', 'A'): -2, ('K', 'R'): 0, ('E', 'D'): 2, ('Y', 'R'): -10,
+('M', 'Q'): -4, ('T', 'I'): -2, ('C', 'D'): -14, ('V', 'F'): -8,
+('T', 'A'): -1, ('T', 'P'): -4, ('B', 'P'): -7, ('T', 'E'): -6,
+('V', 'N'): -8, ('P', 'G'): -6, ('M', 'A'): -5, ('K', 'H'): -6,
+('V', 'R'): -8, ('P', 'C'): -8, ('M', 'E'): -7, ('K', 'L'): -8,
+('V', 'V'): 7, ('M', 'I'): -1, ('T', 'Q'): -5, ('I', 'G'): -11,
+('P', 'K'): -6, ('M', 'M'): 11, ('K', 'D'): -4, ('I', 'C'): -6,
+('Z', 'D'): 1, ('F', 'R'): -9, ('X', 'K'): -5, ('Q', 'D'): -2,
+('X', 'G'): -5, ('Z', 'L'): -7, ('X', 'C'): -9, ('Z', 'H'): -1,
+('B', 'L'): -9, ('B', 'H'): -1, ('F', 'F'): 9, ('X', 'W'): -11,
+('B', 'D'): 6, ('D', 'A'): -3, ('S', 'L'): -8, ('X', 'S'): -3,
+('F', 'N'): -9, ('S', 'R'): -3, ('W', 'D'): -15, ('V', 'Y'): -7,
+('W', 'L'): -6, ('H', 'R'): -2, ('W', 'H'): -7, ('H', 'N'): 0,
+('W', 'T'): -13, ('T', 'T'): 7, ('S', 'F'): -6, ('W', 'P'): -14,
+('L', 'D'): -12, ('B', 'I'): -6, ('L', 'H'): -6, ('S', 'N'): 0,
+('B', 'T'): -3, ('L', 'L'): 7, ('Y', 'K'): -9, ('E', 'Q'): 1,
+('Y', 'G'): -14, ('Z', 'S'): -5, ('Y', 'C'): -4, ('G', 'D'): -3,
+('B', 'V'): -8, ('E', 'A'): -2, ('Y', 'W'): -5, ('E', 'E'): 8,
+('Y', 'S'): -7, ('C', 'N'): -11, ('V', 'C'): -6, ('T', 'H'): -7,
+('P', 'R'): -4, ('V', 'G'): -5, ('T', 'L'): -7, ('V', 'K'): -9,
+('K', 'Q'): -3, ('R', 'A'): -7, ('I', 'R'): -5, ('T', 'D'): -5,
+('P', 'F'): -10, ('I', 'N'): -5, ('K', 'I'): -6, ('M', 'D'): -11,
+('V', 'W'): -15, ('W', 'W'): 13, ('M', 'H'): -10, ('P', 'N'): -6,
+('K', 'A'): -7, ('M', 'L'): 1, ('K', 'E'): -4, ('Z', 'E'): 6,
+('X', 'N'): -3, ('Z', 'A'): -3, ('Z', 'M'): -5, ('X', 'F'): -8,
+('K', 'C'): -14, ('B', 'Q'): -3, ('X', 'B'): -5, ('B', 'M'): -10,
+('F', 'C'): -13, ('Z', 'Q'): 6, ('X', 'Z'): -5, ('F', 'G'): -9,
+('B', 'E'): 1, ('X', 'V'): -5, ('F', 'K'): -14, ('B', 'A'): -3,
+('X', 'R'): -6, ('D', 'D'): 8, ('W', 'G'): -15, ('Z', 'F'): -13,
+('S', 'Q'): -5, ('W', 'C'): -15, ('W', 'K'): -12, ('H', 'Q'): 1,
+('L', 'C'): -15, ('W', 'N'): -8, ('S', 'A'): 0, ('L', 'G'): -10,
+('W', 'S'): -5, ('S', 'E'): -4, ('H', 'E'): -5, ('S', 'I'): -7,
+('H', 'A'): -7, ('S', 'M'): -5, ('Y', 'L'): -7, ('Y', 'H'): -3,
+('Y', 'D'): -11, ('E', 'R'): -9, ('X', 'P'): -5, ('G', 'G'): 6,
+('G', 'C'): -9, ('E', 'N'): -2, ('Y', 'T'): -6, ('Y', 'P'): -13,
+('T', 'K'): -3, ('A', 'A'): 6, ('P', 'Q'): -3, ('T', 'C'): -8,
+('V', 'H'): -6, ('T', 'G'): -6, ('I', 'Q'): -8, ('Z', 'T'): -6,
+('C', 'R'): -8, ('V', 'P'): -6, ('P', 'E'): -5, ('M', 'C'): -13,
+('K', 'N'): -1, ('I', 'I'): 8, ('P', 'A'): -2, ('M', 'G'): -8,
+('T', 'S'): 0, ('I', 'E'): -5, ('P', 'M'): -8, ('M', 'K'): -2,
+('I', 'A'): -5, ('P', 'I'): -8, ('R', 'R'): 8, ('X', 'M'): -5,
+('L', 'I'): -1, ('X', 'I'): -5, ('Z', 'B'): 0, ('X', 'E'): -5,
+('Z', 'N'): -3, ('X', 'A'): -3, ('B', 'R'): -7, ('B', 'N'): 6,
+('F', 'D'): -15, ('X', 'Y'): -7, ('Z', 'R'): -4, ('F', 'H'): -6,
+('B', 'F'): -10, ('F', 'L'): -3, ('X', 'Q'): -5, ('B', 'B'): 6
}
pam30 = _temp()
del _temp
@@ -2433,75 +2432,75 @@ del _temp
# http://www.embl-heidelberg.de/~vogt/matrices/pam300.cmp
def _temp():
return {
-('W', 'F') : 1, ('L', 'R') : -3, ('S', 'P') : 1, ('V', 'T') : 0,
-('Q', 'Q') : 4, ('N', 'A') : 0, ('Z', 'Y') : -5, ('W', 'R') : 3,
-('Q', 'A') : 0, ('S', 'D') : 0, ('H', 'H') : 7, ('S', 'H') : -1,
-('H', 'D') : 1, ('L', 'N') : -3, ('W', 'A') : -6, ('Y', 'M') : -2,
-('G', 'R') : -2, ('Y', 'I') : -1, ('Y', 'E') : -5, ('B', 'Y') : -4,
-('Y', 'A') : -4, ('V', 'D') : -2, ('B', 'S') : 1, ('Y', 'Y') : 12,
-('G', 'N') : 1, ('E', 'C') : -6, ('Y', 'Q') : -4, ('Z', 'Z') : 3,
-('V', 'A') : 0, ('C', 'C') : 15, ('M', 'R') : 0, ('V', 'E') : -2,
-('T', 'N') : 0, ('P', 'P') : 6, ('V', 'I') : 4, ('V', 'S') : -1,
-('Z', 'P') : 0, ('V', 'M') : 2, ('T', 'F') : -3, ('V', 'Q') : -2,
-('K', 'K') : 5, ('P', 'D') : -1, ('I', 'H') : -2, ('I', 'D') : -2,
-('T', 'R') : -1, ('P', 'L') : -3, ('K', 'G') : -2, ('M', 'N') : -2,
-('P', 'H') : 0, ('F', 'Q') : -5, ('Z', 'G') : 0, ('X', 'L') : -1,
-('T', 'M') : -1, ('Z', 'C') : -6, ('X', 'H') : 0, ('D', 'R') : -1,
-('B', 'W') : -6, ('X', 'D') : -1, ('Z', 'K') : 1, ('F', 'A') : -4,
-('Z', 'W') : -6, ('F', 'E') : -6, ('D', 'N') : 2, ('B', 'K') : 1,
-('X', 'X') : -1, ('F', 'I') : 1, ('B', 'G') : 1, ('X', 'T') : 0,
-('F', 'M') : 1, ('B', 'C') : -5, ('Z', 'I') : -2, ('Z', 'V') : -2,
-('S', 'S') : 1, ('L', 'Q') : -2, ('W', 'E') : -8, ('Q', 'R') : 2,
-('N', 'N') : 2, ('W', 'M') : -5, ('Q', 'C') : -6, ('W', 'I') : -6,
-('S', 'C') : 0, ('L', 'A') : -2, ('S', 'G') : 1, ('L', 'E') : -4,
-('W', 'Q') : -5, ('H', 'G') : -2, ('S', 'K') : 0, ('Q', 'N') : 1,
-('N', 'R') : 0, ('H', 'C') : -4, ('Y', 'N') : -2, ('G', 'Q') : -1,
-('Y', 'F') : 9, ('C', 'A') : -2, ('V', 'L') : 2, ('G', 'E') : 0,
-('G', 'A') : 2, ('K', 'R') : 4, ('E', 'D') : 4, ('Y', 'R') : -5,
-('M', 'Q') : -1, ('T', 'I') : 0, ('C', 'D') : -6, ('V', 'F') : -1,
-('T', 'A') : 1, ('T', 'P') : 1, ('B', 'P') : 0, ('T', 'E') : 0,
-('V', 'N') : -2, ('P', 'G') : 0, ('M', 'A') : -1, ('K', 'H') : 0,
-('V', 'R') : -3, ('P', 'C') : -3, ('M', 'E') : -2, ('K', 'L') : -3,
-('V', 'V') : 5, ('M', 'I') : 3, ('T', 'Q') : -1, ('I', 'G') : -3,
-('P', 'K') : -1, ('M', 'M') : 6, ('K', 'D') : 0, ('I', 'C') : -3,
-('Z', 'D') : 3, ('F', 'R') : -5, ('X', 'K') : -1, ('Q', 'D') : 2,
-('X', 'G') : -1, ('Z', 'L') : -3, ('X', 'C') : -3, ('Z', 'H') : 2,
-('B', 'L') : -4, ('B', 'H') : 1, ('F', 'F') : 11, ('X', 'W') : -4,
-('B', 'D') : 3, ('D', 'A') : 0, ('S', 'L') : -3, ('X', 'S') : 0,
-('F', 'N') : -4, ('S', 'R') : 0, ('W', 'D') : -7, ('V', 'Y') : -3,
-('W', 'L') : -2, ('H', 'R') : 2, ('W', 'H') : -3, ('H', 'N') : 2,
-('W', 'T') : -6, ('T', 'T') : 2, ('S', 'F') : -4, ('W', 'P') : -6,
-('L', 'D') : -4, ('B', 'I') : -2, ('L', 'H') : -2, ('S', 'N') : 1,
-('B', 'T') : 0, ('L', 'L') : 7, ('Y', 'K') : -5, ('E', 'Q') : 3,
-('Y', 'G') : -6, ('Z', 'S') : 0, ('Y', 'C') : 1, ('G', 'D') : 1,
-('B', 'V') : -2, ('E', 'A') : 0, ('Y', 'W') : 0, ('E', 'E') : 4,
-('Y', 'S') : -3, ('C', 'N') : -4, ('V', 'C') : -2, ('T', 'H') : -1,
-('P', 'R') : 0, ('V', 'G') : -1, ('T', 'L') : -2, ('V', 'K') : -2,
-('K', 'Q') : 1, ('R', 'A') : -1, ('I', 'R') : -2, ('T', 'D') : 0,
-('P', 'F') : -5, ('I', 'N') : -2, ('K', 'I') : -2, ('M', 'D') : -3,
-('V', 'W') : -7, ('W', 'W') : 22, ('M', 'H') : -2, ('P', 'N') : 0,
-('K', 'A') : -1, ('M', 'L') : 4, ('K', 'E') : 0, ('Z', 'E') : 3,
-('X', 'N') : 0, ('Z', 'A') : 0, ('Z', 'M') : -2, ('X', 'F') : -2,
-('K', 'C') : -6, ('B', 'Q') : 2, ('X', 'B') : 0, ('B', 'M') : -2,
-('F', 'C') : -5, ('Z', 'Q') : 3, ('X', 'Z') : -1, ('F', 'G') : -5,
-('B', 'E') : 3, ('X', 'V') : 0, ('F', 'K') : -6, ('B', 'A') : 0,
-('X', 'R') : -1, ('D', 'D') : 4, ('W', 'G') : -8, ('Z', 'F') : -5,
-('S', 'Q') : 0, ('W', 'C') : -9, ('W', 'K') : -4, ('H', 'Q') : 3,
-('L', 'C') : -7, ('W', 'N') : -5, ('S', 'A') : 1, ('L', 'G') : -4,
-('W', 'S') : -3, ('S', 'E') : 0, ('H', 'E') : 1, ('S', 'I') : -1,
-('H', 'A') : -1, ('S', 'M') : -2, ('Y', 'L') : 0, ('Y', 'H') : 0,
-('Y', 'D') : -5, ('E', 'R') : -1, ('X', 'P') : -1, ('G', 'G') : 5,
-('G', 'C') : -4, ('E', 'N') : 2, ('Y', 'T') : -3, ('Y', 'P') : -5,
-('T', 'K') : 0, ('A', 'A') : 2, ('P', 'Q') : 0, ('T', 'C') : -2,
-('V', 'H') : -2, ('T', 'G') : 0, ('I', 'Q') : -2, ('Z', 'T') : 0,
-('C', 'R') : -4, ('V', 'P') : -1, ('P', 'E') : 0, ('M', 'C') : -6,
-('K', 'N') : 1, ('I', 'I') : 5, ('P', 'A') : 1, ('M', 'G') : -3,
-('T', 'S') : 1, ('I', 'E') : -2, ('P', 'M') : -2, ('M', 'K') : 0,
-('I', 'A') : 0, ('P', 'I') : -2, ('R', 'R') : 7, ('X', 'M') : -1,
-('L', 'I') : 3, ('X', 'I') : -1, ('Z', 'B') : 2, ('X', 'E') : -1,
-('Z', 'N') : 1, ('X', 'A') : 0, ('B', 'R') : 0, ('B', 'N') : 2,
-('F', 'D') : -6, ('X', 'Y') : -2, ('Z', 'R') : 0, ('F', 'H') : -2,
-('B', 'F') : -5, ('F', 'L') : 3, ('X', 'Q') : 0, ('B', 'B') : 3
+('W', 'F'): 1, ('L', 'R'): -3, ('S', 'P'): 1, ('V', 'T'): 0,
+('Q', 'Q'): 4, ('N', 'A'): 0, ('Z', 'Y'): -5, ('W', 'R'): 3,
+('Q', 'A'): 0, ('S', 'D'): 0, ('H', 'H'): 7, ('S', 'H'): -1,
+('H', 'D'): 1, ('L', 'N'): -3, ('W', 'A'): -6, ('Y', 'M'): -2,
+('G', 'R'): -2, ('Y', 'I'): -1, ('Y', 'E'): -5, ('B', 'Y'): -4,
+('Y', 'A'): -4, ('V', 'D'): -2, ('B', 'S'): 1, ('Y', 'Y'): 12,
+('G', 'N'): 1, ('E', 'C'): -6, ('Y', 'Q'): -4, ('Z', 'Z'): 3,
+('V', 'A'): 0, ('C', 'C'): 15, ('M', 'R'): 0, ('V', 'E'): -2,
+('T', 'N'): 0, ('P', 'P'): 6, ('V', 'I'): 4, ('V', 'S'): -1,
+('Z', 'P'): 0, ('V', 'M'): 2, ('T', 'F'): -3, ('V', 'Q'): -2,
+('K', 'K'): 5, ('P', 'D'): -1, ('I', 'H'): -2, ('I', 'D'): -2,
+('T', 'R'): -1, ('P', 'L'): -3, ('K', 'G'): -2, ('M', 'N'): -2,
+('P', 'H'): 0, ('F', 'Q'): -5, ('Z', 'G'): 0, ('X', 'L'): -1,
+('T', 'M'): -1, ('Z', 'C'): -6, ('X', 'H'): 0, ('D', 'R'): -1,
+('B', 'W'): -6, ('X', 'D'): -1, ('Z', 'K'): 1, ('F', 'A'): -4,
+('Z', 'W'): -6, ('F', 'E'): -6, ('D', 'N'): 2, ('B', 'K'): 1,
+('X', 'X'): -1, ('F', 'I'): 1, ('B', 'G'): 1, ('X', 'T'): 0,
+('F', 'M'): 1, ('B', 'C'): -5, ('Z', 'I'): -2, ('Z', 'V'): -2,
+('S', 'S'): 1, ('L', 'Q'): -2, ('W', 'E'): -8, ('Q', 'R'): 2,
+('N', 'N'): 2, ('W', 'M'): -5, ('Q', 'C'): -6, ('W', 'I'): -6,
+('S', 'C'): 0, ('L', 'A'): -2, ('S', 'G'): 1, ('L', 'E'): -4,
+('W', 'Q'): -5, ('H', 'G'): -2, ('S', 'K'): 0, ('Q', 'N'): 1,
+('N', 'R'): 0, ('H', 'C'): -4, ('Y', 'N'): -2, ('G', 'Q'): -1,
+('Y', 'F'): 9, ('C', 'A'): -2, ('V', 'L'): 2, ('G', 'E'): 0,
+('G', 'A'): 2, ('K', 'R'): 4, ('E', 'D'): 4, ('Y', 'R'): -5,
+('M', 'Q'): -1, ('T', 'I'): 0, ('C', 'D'): -6, ('V', 'F'): -1,
+('T', 'A'): 1, ('T', 'P'): 1, ('B', 'P'): 0, ('T', 'E'): 0,
+('V', 'N'): -2, ('P', 'G'): 0, ('M', 'A'): -1, ('K', 'H'): 0,
+('V', 'R'): -3, ('P', 'C'): -3, ('M', 'E'): -2, ('K', 'L'): -3,
+('V', 'V'): 5, ('M', 'I'): 3, ('T', 'Q'): -1, ('I', 'G'): -3,
+('P', 'K'): -1, ('M', 'M'): 6, ('K', 'D'): 0, ('I', 'C'): -3,
+('Z', 'D'): 3, ('F', 'R'): -5, ('X', 'K'): -1, ('Q', 'D'): 2,
+('X', 'G'): -1, ('Z', 'L'): -3, ('X', 'C'): -3, ('Z', 'H'): 2,
+('B', 'L'): -4, ('B', 'H'): 1, ('F', 'F'): 11, ('X', 'W'): -4,
+('B', 'D'): 3, ('D', 'A'): 0, ('S', 'L'): -3, ('X', 'S'): 0,
+('F', 'N'): -4, ('S', 'R'): 0, ('W', 'D'): -7, ('V', 'Y'): -3,
+('W', 'L'): -2, ('H', 'R'): 2, ('W', 'H'): -3, ('H', 'N'): 2,
+('W', 'T'): -6, ('T', 'T'): 2, ('S', 'F'): -4, ('W', 'P'): -6,
+('L', 'D'): -4, ('B', 'I'): -2, ('L', 'H'): -2, ('S', 'N'): 1,
+('B', 'T'): 0, ('L', 'L'): 7, ('Y', 'K'): -5, ('E', 'Q'): 3,
+('Y', 'G'): -6, ('Z', 'S'): 0, ('Y', 'C'): 1, ('G', 'D'): 1,
+('B', 'V'): -2, ('E', 'A'): 0, ('Y', 'W'): 0, ('E', 'E'): 4,
+('Y', 'S'): -3, ('C', 'N'): -4, ('V', 'C'): -2, ('T', 'H'): -1,
+('P', 'R'): 0, ('V', 'G'): -1, ('T', 'L'): -2, ('V', 'K'): -2,
+('K', 'Q'): 1, ('R', 'A'): -1, ('I', 'R'): -2, ('T', 'D'): 0,
+('P', 'F'): -5, ('I', 'N'): -2, ('K', 'I'): -2, ('M', 'D'): -3,
+('V', 'W'): -7, ('W', 'W'): 22, ('M', 'H'): -2, ('P', 'N'): 0,
+('K', 'A'): -1, ('M', 'L'): 4, ('K', 'E'): 0, ('Z', 'E'): 3,
+('X', 'N'): 0, ('Z', 'A'): 0, ('Z', 'M'): -2, ('X', 'F'): -2,
+('K', 'C'): -6, ('B', 'Q'): 2, ('X', 'B'): 0, ('B', 'M'): -2,
+('F', 'C'): -5, ('Z', 'Q'): 3, ('X', 'Z'): -1, ('F', 'G'): -5,
+('B', 'E'): 3, ('X', 'V'): 0, ('F', 'K'): -6, ('B', 'A'): 0,
+('X', 'R'): -1, ('D', 'D'): 4, ('W', 'G'): -8, ('Z', 'F'): -5,
+('S', 'Q'): 0, ('W', 'C'): -9, ('W', 'K'): -4, ('H', 'Q'): 3,
+('L', 'C'): -7, ('W', 'N'): -5, ('S', 'A'): 1, ('L', 'G'): -4,
+('W', 'S'): -3, ('S', 'E'): 0, ('H', 'E'): 1, ('S', 'I'): -1,
+('H', 'A'): -1, ('S', 'M'): -2, ('Y', 'L'): 0, ('Y', 'H'): 0,
+('Y', 'D'): -5, ('E', 'R'): -1, ('X', 'P'): -1, ('G', 'G'): 5,
+('G', 'C'): -4, ('E', 'N'): 2, ('Y', 'T'): -3, ('Y', 'P'): -5,
+('T', 'K'): 0, ('A', 'A'): 2, ('P', 'Q'): 0, ('T', 'C'): -2,
+('V', 'H'): -2, ('T', 'G'): 0, ('I', 'Q'): -2, ('Z', 'T'): 0,
+('C', 'R'): -4, ('V', 'P'): -1, ('P', 'E'): 0, ('M', 'C'): -6,
+('K', 'N'): 1, ('I', 'I'): 5, ('P', 'A'): 1, ('M', 'G'): -3,
+('T', 'S'): 1, ('I', 'E'): -2, ('P', 'M'): -2, ('M', 'K'): 0,
+('I', 'A'): 0, ('P', 'I'): -2, ('R', 'R'): 7, ('X', 'M'): -1,
+('L', 'I'): 3, ('X', 'I'): -1, ('Z', 'B'): 2, ('X', 'E'): -1,
+('Z', 'N'): 1, ('X', 'A'): 0, ('B', 'R'): 0, ('B', 'N'): 2,
+('F', 'D'): -6, ('X', 'Y'): -2, ('Z', 'R'): 0, ('F', 'H'): -2,
+('B', 'F'): -5, ('F', 'L'): 3, ('X', 'Q'): 0, ('B', 'B'): 3
}
pam300 = _temp()
del _temp
@@ -2510,75 +2509,75 @@ del _temp
# http://www.embl-heidelberg.de/~vogt/matrices/pam60.cmp
def _temp():
return {
-('W', 'F') : -3, ('L', 'R') : -6, ('S', 'P') : 0, ('V', 'T') : -1,
-('Q', 'Q') : 7, ('N', 'A') : -2, ('Z', 'Y') : -7, ('W', 'R') : 0,
-('Q', 'A') : -3, ('S', 'D') : -2, ('H', 'H') : 8, ('S', 'H') : -4,
-('H', 'D') : -2, ('L', 'N') : -5, ('W', 'A') : -10, ('Y', 'M') : -7,
-('G', 'R') : -7, ('Y', 'I') : -4, ('Y', 'E') : -7, ('B', 'Y') : -5,
-('Y', 'A') : -6, ('V', 'D') : -6, ('B', 'S') : 0, ('Y', 'Y') : 9,
-('G', 'N') : -1, ('E', 'C') : -10, ('Y', 'Q') : -8, ('Z', 'Z') : 5,
-('V', 'A') : -1, ('C', 'C') : 9, ('M', 'R') : -2, ('V', 'E') : -4,
-('T', 'N') : -1, ('P', 'P') : 7, ('V', 'I') : 3, ('V', 'S') : -4,
-('Z', 'P') : -2, ('V', 'M') : 0, ('T', 'F') : -6, ('V', 'Q') : -5,
-('K', 'K') : 6, ('P', 'D') : -5, ('I', 'H') : -6, ('I', 'D') : -5,
-('T', 'R') : -4, ('P', 'L') : -5, ('K', 'G') : -5, ('M', 'N') : -6,
-('P', 'H') : -2, ('F', 'Q') : -9, ('Z', 'G') : -3, ('X', 'L') : -4,
-('T', 'M') : -2, ('Z', 'C') : -10, ('X', 'H') : -3, ('D', 'R') : -6,
-('B', 'W') : -8, ('X', 'D') : -3, ('Z', 'K') : -2, ('F', 'A') : -6,
-('Z', 'W') : -11, ('F', 'E') : -10, ('D', 'N') : 2, ('B', 'K') : -1,
-('X', 'X') : -3, ('F', 'I') : -1, ('B', 'G') : -2, ('X', 'T') : -2,
-('F', 'M') : -2, ('B', 'C') : -9, ('Z', 'I') : -4, ('Z', 'V') : -5,
-('S', 'S') : 5, ('L', 'Q') : -3, ('W', 'E') : -12, ('Q', 'R') : 0,
-('N', 'N') : 6, ('W', 'M') : -9, ('Q', 'C') : -10, ('W', 'I') : -10,
-('S', 'C') : -1, ('L', 'A') : -4, ('S', 'G') : 0, ('L', 'E') : -7,
-('W', 'Q') : -9, ('H', 'G') : -6, ('S', 'K') : -2, ('Q', 'N') : -2,
-('N', 'R') : -3, ('H', 'C') : -6, ('Y', 'N') : -3, ('G', 'Q') : -5,
-('Y', 'F') : 3, ('C', 'A') : -5, ('V', 'L') : -1, ('G', 'E') : -2,
-('G', 'A') : 0, ('K', 'R') : 2, ('E', 'D') : 3, ('Y', 'R') : -8,
-('M', 'Q') : -2, ('T', 'I') : -1, ('C', 'D') : -10, ('V', 'F') : -5,
-('T', 'A') : 1, ('T', 'P') : -2, ('B', 'P') : -4, ('T', 'E') : -4,
-('V', 'N') : -5, ('P', 'G') : -4, ('M', 'A') : -3, ('K', 'H') : -4,
-('V', 'R') : -5, ('P', 'C') : -6, ('M', 'E') : -5, ('K', 'L') : -6,
-('V', 'V') : 6, ('M', 'I') : 1, ('T', 'Q') : -4, ('I', 'G') : -7,
-('P', 'K') : -4, ('M', 'M') : 10, ('K', 'D') : -2, ('I', 'C') : -4,
-('Z', 'D') : 2, ('F', 'R') : -7, ('X', 'K') : -3, ('Q', 'D') : -1,
-('X', 'G') : -3, ('Z', 'L') : -5, ('X', 'C') : -6, ('Z', 'H') : 0,
-('B', 'L') : -7, ('B', 'H') : 0, ('F', 'F') : 8, ('X', 'W') : -8,
-('B', 'D') : 5, ('D', 'A') : -2, ('S', 'L') : -6, ('X', 'S') : -2,
-('F', 'N') : -6, ('S', 'R') : -2, ('W', 'D') : -11, ('V', 'Y') : -5,
-('W', 'L') : -4, ('H', 'R') : 0, ('W', 'H') : -5, ('H', 'N') : 1,
-('W', 'T') : -9, ('T', 'T') : 6, ('S', 'F') : -5, ('W', 'P') : -10,
-('L', 'D') : -9, ('B', 'I') : -4, ('L', 'H') : -4, ('S', 'N') : 1,
-('B', 'T') : -2, ('L', 'L') : 6, ('Y', 'K') : -7, ('E', 'Q') : 2,
-('Y', 'G') : -10, ('Z', 'S') : -3, ('Y', 'C') : -2, ('G', 'D') : -2,
-('B', 'V') : -5, ('E', 'A') : -1, ('Y', 'W') : -3, ('E', 'E') : 7,
-('Y', 'S') : -5, ('C', 'N') : -7, ('V', 'C') : -4, ('T', 'H') : -5,
-('P', 'R') : -2, ('V', 'G') : -4, ('T', 'L') : -5, ('V', 'K') : -6,
-('K', 'Q') : -1, ('R', 'A') : -5, ('I', 'R') : -4, ('T', 'D') : -3,
-('P', 'F') : -7, ('I', 'N') : -4, ('K', 'I') : -4, ('M', 'D') : -7,
-('V', 'W') : -11, ('W', 'W') : 13, ('M', 'H') : -7, ('P', 'N') : -4,
-('K', 'A') : -5, ('M', 'L') : 2, ('K', 'E') : -3, ('Z', 'E') : 5,
-('X', 'N') : -2, ('Z', 'A') : -2, ('Z', 'M') : -4, ('X', 'F') : -5,
-('K', 'C') : -10, ('B', 'Q') : -1, ('X', 'B') : -3, ('B', 'M') : -6,
-('F', 'C') : -9, ('Z', 'Q') : 6, ('X', 'Z') : -3, ('F', 'G') : -7,
-('B', 'E') : 2, ('X', 'V') : -3, ('F', 'K') : -10, ('B', 'A') : -2,
-('X', 'R') : -4, ('D', 'D') : 7, ('W', 'G') : -11, ('Z', 'F') : -10,
-('S', 'Q') : -3, ('W', 'C') : -12, ('W', 'K') : -8, ('H', 'Q') : 2,
-('L', 'C') : -11, ('W', 'N') : -6, ('S', 'A') : 1, ('L', 'G') : -8,
-('W', 'S') : -4, ('S', 'E') : -2, ('H', 'E') : -3, ('S', 'I') : -4,
-('H', 'A') : -5, ('S', 'M') : -4, ('Y', 'L') : -5, ('Y', 'H') : -2,
-('Y', 'D') : -8, ('E', 'R') : -6, ('X', 'P') : -3, ('G', 'G') : 6,
-('G', 'C') : -7, ('E', 'N') : 0, ('Y', 'T') : -5, ('Y', 'P') : -10,
-('T', 'K') : -2, ('A', 'A') : 5, ('P', 'Q') : -1, ('T', 'C') : -5,
-('V', 'H') : -5, ('T', 'G') : -3, ('I', 'Q') : -5, ('Z', 'T') : -4,
-('C', 'R') : -6, ('V', 'P') : -4, ('P', 'E') : -3, ('M', 'C') : -10,
-('K', 'N') : 0, ('I', 'I') : 7, ('P', 'A') : 0, ('M', 'G') : -6,
-('T', 'S') : 1, ('I', 'E') : -4, ('P', 'M') : -6, ('M', 'K') : 0,
-('I', 'A') : -3, ('P', 'I') : -6, ('R', 'R') : 8, ('X', 'M') : -3,
-('L', 'I') : 0, ('X', 'I') : -3, ('Z', 'B') : 1, ('X', 'E') : -3,
-('Z', 'N') : -1, ('X', 'A') : -2, ('B', 'R') : -5, ('B', 'N') : 5,
-('F', 'D') : -11, ('X', 'Y') : -5, ('Z', 'R') : -2, ('F', 'H') : -4,
-('B', 'F') : -8, ('F', 'L') : -1, ('X', 'Q') : -3, ('B', 'B') : 5
+('W', 'F'): -3, ('L', 'R'): -6, ('S', 'P'): 0, ('V', 'T'): -1,
+('Q', 'Q'): 7, ('N', 'A'): -2, ('Z', 'Y'): -7, ('W', 'R'): 0,
+('Q', 'A'): -3, ('S', 'D'): -2, ('H', 'H'): 8, ('S', 'H'): -4,
+('H', 'D'): -2, ('L', 'N'): -5, ('W', 'A'): -10, ('Y', 'M'): -7,
+('G', 'R'): -7, ('Y', 'I'): -4, ('Y', 'E'): -7, ('B', 'Y'): -5,
+('Y', 'A'): -6, ('V', 'D'): -6, ('B', 'S'): 0, ('Y', 'Y'): 9,
+('G', 'N'): -1, ('E', 'C'): -10, ('Y', 'Q'): -8, ('Z', 'Z'): 5,
+('V', 'A'): -1, ('C', 'C'): 9, ('M', 'R'): -2, ('V', 'E'): -4,
+('T', 'N'): -1, ('P', 'P'): 7, ('V', 'I'): 3, ('V', 'S'): -4,
+('Z', 'P'): -2, ('V', 'M'): 0, ('T', 'F'): -6, ('V', 'Q'): -5,
+('K', 'K'): 6, ('P', 'D'): -5, ('I', 'H'): -6, ('I', 'D'): -5,
+('T', 'R'): -4, ('P', 'L'): -5, ('K', 'G'): -5, ('M', 'N'): -6,
+('P', 'H'): -2, ('F', 'Q'): -9, ('Z', 'G'): -3, ('X', 'L'): -4,
+('T', 'M'): -2, ('Z', 'C'): -10, ('X', 'H'): -3, ('D', 'R'): -6,
+('B', 'W'): -8, ('X', 'D'): -3, ('Z', 'K'): -2, ('F', 'A'): -6,
+('Z', 'W'): -11, ('F', 'E'): -10, ('D', 'N'): 2, ('B', 'K'): -1,
+('X', 'X'): -3, ('F', 'I'): -1, ('B', 'G'): -2, ('X', 'T'): -2,
+('F', 'M'): -2, ('B', 'C'): -9, ('Z', 'I'): -4, ('Z', 'V'): -5,
+('S', 'S'): 5, ('L', 'Q'): -3, ('W', 'E'): -12, ('Q', 'R'): 0,
+('N', 'N'): 6, ('W', 'M'): -9, ('Q', 'C'): -10, ('W', 'I'): -10,
+('S', 'C'): -1, ('L', 'A'): -4, ('S', 'G'): 0, ('L', 'E'): -7,
+('W', 'Q'): -9, ('H', 'G'): -6, ('S', 'K'): -2, ('Q', 'N'): -2,
+('N', 'R'): -3, ('H', 'C'): -6, ('Y', 'N'): -3, ('G', 'Q'): -5,
+('Y', 'F'): 3, ('C', 'A'): -5, ('V', 'L'): -1, ('G', 'E'): -2,
+('G', 'A'): 0, ('K', 'R'): 2, ('E', 'D'): 3, ('Y', 'R'): -8,
+('M', 'Q'): -2, ('T', 'I'): -1, ('C', 'D'): -10, ('V', 'F'): -5,
+('T', 'A'): 1, ('T', 'P'): -2, ('B', 'P'): -4, ('T', 'E'): -4,
+('V', 'N'): -5, ('P', 'G'): -4, ('M', 'A'): -3, ('K', 'H'): -4,
+('V', 'R'): -5, ('P', 'C'): -6, ('M', 'E'): -5, ('K', 'L'): -6,
+('V', 'V'): 6, ('M', 'I'): 1, ('T', 'Q'): -4, ('I', 'G'): -7,
+('P', 'K'): -4, ('M', 'M'): 10, ('K', 'D'): -2, ('I', 'C'): -4,
+('Z', 'D'): 2, ('F', 'R'): -7, ('X', 'K'): -3, ('Q', 'D'): -1,
+('X', 'G'): -3, ('Z', 'L'): -5, ('X', 'C'): -6, ('Z', 'H'): 0,
+('B', 'L'): -7, ('B', 'H'): 0, ('F', 'F'): 8, ('X', 'W'): -8,
+('B', 'D'): 5, ('D', 'A'): -2, ('S', 'L'): -6, ('X', 'S'): -2,
+('F', 'N'): -6, ('S', 'R'): -2, ('W', 'D'): -11, ('V', 'Y'): -5,
+('W', 'L'): -4, ('H', 'R'): 0, ('W', 'H'): -5, ('H', 'N'): 1,
+('W', 'T'): -9, ('T', 'T'): 6, ('S', 'F'): -5, ('W', 'P'): -10,
+('L', 'D'): -9, ('B', 'I'): -4, ('L', 'H'): -4, ('S', 'N'): 1,
+('B', 'T'): -2, ('L', 'L'): 6, ('Y', 'K'): -7, ('E', 'Q'): 2,
+('Y', 'G'): -10, ('Z', 'S'): -3, ('Y', 'C'): -2, ('G', 'D'): -2,
+('B', 'V'): -5, ('E', 'A'): -1, ('Y', 'W'): -3, ('E', 'E'): 7,
+('Y', 'S'): -5, ('C', 'N'): -7, ('V', 'C'): -4, ('T', 'H'): -5,
+('P', 'R'): -2, ('V', 'G'): -4, ('T', 'L'): -5, ('V', 'K'): -6,
+('K', 'Q'): -1, ('R', 'A'): -5, ('I', 'R'): -4, ('T', 'D'): -3,
+('P', 'F'): -7, ('I', 'N'): -4, ('K', 'I'): -4, ('M', 'D'): -7,
+('V', 'W'): -11, ('W', 'W'): 13, ('M', 'H'): -7, ('P', 'N'): -4,
+('K', 'A'): -5, ('M', 'L'): 2, ('K', 'E'): -3, ('Z', 'E'): 5,
+('X', 'N'): -2, ('Z', 'A'): -2, ('Z', 'M'): -4, ('X', 'F'): -5,
+('K', 'C'): -10, ('B', 'Q'): -1, ('X', 'B'): -3, ('B', 'M'): -6,
+('F', 'C'): -9, ('Z', 'Q'): 6, ('X', 'Z'): -3, ('F', 'G'): -7,
+('B', 'E'): 2, ('X', 'V'): -3, ('F', 'K'): -10, ('B', 'A'): -2,
+('X', 'R'): -4, ('D', 'D'): 7, ('W', 'G'): -11, ('Z', 'F'): -10,
+('S', 'Q'): -3, ('W', 'C'): -12, ('W', 'K'): -8, ('H', 'Q'): 2,
+('L', 'C'): -11, ('W', 'N'): -6, ('S', 'A'): 1, ('L', 'G'): -8,
+('W', 'S'): -4, ('S', 'E'): -2, ('H', 'E'): -3, ('S', 'I'): -4,
+('H', 'A'): -5, ('S', 'M'): -4, ('Y', 'L'): -5, ('Y', 'H'): -2,
+('Y', 'D'): -8, ('E', 'R'): -6, ('X', 'P'): -3, ('G', 'G'): 6,
+('G', 'C'): -7, ('E', 'N'): 0, ('Y', 'T'): -5, ('Y', 'P'): -10,
+('T', 'K'): -2, ('A', 'A'): 5, ('P', 'Q'): -1, ('T', 'C'): -5,
+('V', 'H'): -5, ('T', 'G'): -3, ('I', 'Q'): -5, ('Z', 'T'): -4,
+('C', 'R'): -6, ('V', 'P'): -4, ('P', 'E'): -3, ('M', 'C'): -10,
+('K', 'N'): 0, ('I', 'I'): 7, ('P', 'A'): 0, ('M', 'G'): -6,
+('T', 'S'): 1, ('I', 'E'): -4, ('P', 'M'): -6, ('M', 'K'): 0,
+('I', 'A'): -3, ('P', 'I'): -6, ('R', 'R'): 8, ('X', 'M'): -3,
+('L', 'I'): 0, ('X', 'I'): -3, ('Z', 'B'): 1, ('X', 'E'): -3,
+('Z', 'N'): -1, ('X', 'A'): -2, ('B', 'R'): -5, ('B', 'N'): 5,
+('F', 'D'): -11, ('X', 'Y'): -5, ('Z', 'R'): -2, ('F', 'H'): -4,
+('B', 'F'): -8, ('F', 'L'): -1, ('X', 'Q'): -3, ('B', 'B'): 5
}
pam60 = _temp()
del _temp
@@ -2587,70 +2586,70 @@ del _temp
# http://www.embl-heidelberg.de/~vogt/matrices/pam90.cmp
def _temp():
return {
-('W', 'F') : -2, ('L', 'R') : -5, ('S', 'P') : 0, ('V', 'T') : -1,
-('Q', 'Q') : 6, ('N', 'A') : -1, ('Z', 'Y') : -6, ('W', 'R') : 0,
-('Q', 'A') : -2, ('S', 'D') : -1, ('H', 'H') : 8, ('S', 'H') : -3,
-('H', 'D') : -1, ('L', 'N') : -4, ('W', 'A') : -8, ('Y', 'M') : -6,
-('G', 'R') : -5, ('Y', 'I') : -3, ('Y', 'E') : -6, ('B', 'Y') : -4,
-('Y', 'A') : -5, ('V', 'D') : -4, ('B', 'S') : 0, ('Y', 'Y') : 9,
-('G', 'N') : -1, ('E', 'C') : -8, ('Y', 'Q') : -6, ('Z', 'Z') : 5,
-('V', 'A') : 0, ('C', 'C') : 9, ('M', 'R') : -2, ('V', 'E') : -3,
-('T', 'N') : 0, ('P', 'P') : 7, ('V', 'I') : 3, ('V', 'S') : -3,
-('Z', 'P') : -2, ('V', 'M') : 1, ('T', 'F') : -5, ('V', 'Q') : -4,
-('K', 'K') : 5, ('P', 'D') : -4, ('I', 'H') : -5, ('I', 'D') : -4,
-('T', 'R') : -3, ('P', 'L') : -4, ('K', 'G') : -4, ('M', 'N') : -4,
-('P', 'H') : -2, ('F', 'Q') : -7, ('Z', 'G') : -2, ('T', 'M') : -2,
-('Z', 'C') : -8, ('D', 'R') : -5, ('B', 'W') : -7, ('Z', 'K') : -1,
-('F', 'A') : -5, ('Z', 'W') : -8, ('F', 'E') : -8, ('D', 'N') : 3,
-('B', 'K') : 0, ('F', 'I') : 0, ('B', 'G') : -1, ('F', 'M') : -1,
-('B', 'C') : -7, ('Z', 'I') : -3, ('Z', 'V') : -3, ('S', 'S') : 4,
-('L', 'Q') : -3, ('W', 'E') : -10, ('Q', 'R') : 0, ('N', 'N') : 5,
-('W', 'M') : -7, ('Q', 'C') : -8, ('W', 'I') : -8, ('S', 'C') : -1,
-('L', 'A') : -3, ('S', 'G') : 0, ('L', 'E') : -5, ('W', 'Q') : -7,
-('H', 'G') : -5, ('S', 'K') : -1, ('L', 'I') : 1, ('N', 'R') : -2,
-('H', 'C') : -5, ('Y', 'N') : -2, ('G', 'Q') : -3, ('Y', 'F') : 4,
-('C', 'A') : -3, ('V', 'L') : 0, ('G', 'E') : -1, ('G', 'A') : 0,
-('K', 'R') : 2, ('E', 'D') : 4, ('Y', 'R') : -6, ('M', 'Q') : -2,
-('T', 'I') : 0, ('C', 'D') : -8, ('V', 'F') : -4, ('T', 'A') : 1,
-('T', 'P') : -1, ('B', 'P') : -3, ('T', 'E') : -2, ('V', 'N') : -4,
-('P', 'G') : -3, ('M', 'A') : -2, ('K', 'H') : -2, ('V', 'R') : -4,
-('P', 'C') : -5, ('M', 'E') : -4, ('K', 'L') : -5, ('V', 'V') : 6,
-('M', 'I') : 1, ('T', 'Q') : -3, ('I', 'G') : -5, ('P', 'K') : -3,
-('M', 'M') : 9, ('K', 'D') : -2, ('I', 'C') : -3, ('Z', 'D') : 3,
-('F', 'R') : -6, ('Q', 'D') : 0, ('Z', 'L') : -4, ('Z', 'H') : 1,
-('B', 'L') : -5, ('B', 'H') : 1, ('F', 'F') : 8, ('B', 'D') : 5,
-('D', 'A') : -1, ('S', 'L') : -5, ('F', 'N') : -5, ('S', 'R') : -1,
-('W', 'D') : -9, ('W', 'L') : -3, ('H', 'R') : 1, ('W', 'H') : -4,
-('H', 'N') : 2, ('W', 'T') : -7, ('T', 'T') : 5, ('S', 'F') : -4,
-('W', 'P') : -8, ('L', 'D') : -7, ('B', 'I') : -3, ('L', 'H') : -3,
-('S', 'N') : 1, ('B', 'T') : -1, ('L', 'L') : 6, ('Y', 'K') : -6,
-('E', 'Q') : 2, ('Y', 'G') : -8, ('Z', 'S') : -2, ('Y', 'C') : -1,
-('G', 'D') : -1, ('B', 'V') : -4, ('E', 'A') : 0, ('Y', 'W') : -2,
-('E', 'E') : 6, ('Y', 'S') : -4, ('C', 'N') : -6, ('V', 'C') : -3,
-('T', 'H') : -3, ('P', 'R') : -1, ('V', 'G') : -3, ('T', 'L') : -3,
-('V', 'K') : -5, ('K', 'Q') : -1, ('R', 'A') : -4, ('I', 'R') : -3,
-('T', 'D') : -2, ('P', 'F') : -6, ('I', 'N') : -3, ('K', 'I') : -3,
-('M', 'D') : -5, ('V', 'W') : -9, ('W', 'W') : 13, ('M', 'H') : -5,
-('P', 'N') : -2, ('K', 'A') : -3, ('M', 'L') : 2, ('K', 'E') : -2,
-('Z', 'E') : 5, ('Q', 'N') : -1, ('Z', 'A') : -1, ('Z', 'M') : -3,
-('K', 'C') : -8, ('B', 'Q') : 0, ('B', 'M') : -5, ('F', 'C') : -7,
-('Z', 'Q') : 5, ('F', 'G') : -6, ('B', 'E') : 2, ('F', 'K') : -8,
-('B', 'A') : -1, ('D', 'D') : 6, ('W', 'G') : -9, ('S', 'Q') : -2,
-('W', 'C') : -10, ('W', 'K') : -6, ('H', 'Q') : 2, ('L', 'C') : -9,
-('W', 'N') : -5, ('S', 'A') : 1, ('L', 'G') : -6, ('W', 'S') : -3,
-('S', 'E') : -2, ('H', 'E') : -1, ('S', 'I') : -3, ('H', 'A') : -4,
-('S', 'M') : -3, ('Y', 'L') : -3, ('Y', 'H') : -1, ('Y', 'D') : -6,
-('E', 'R') : -4, ('G', 'G') : 5, ('G', 'C') : -5, ('E', 'N') : 0,
-('Y', 'T') : -4, ('Y', 'P') : -8, ('T', 'K') : -1, ('A', 'A') : 4,
-('P', 'Q') : -1, ('T', 'C') : -4, ('V', 'H') : -4, ('T', 'G') : -2,
-('I', 'Q') : -4, ('Z', 'T') : -2, ('C', 'R') : -5, ('V', 'P') : -3,
-('P', 'E') : -2, ('M', 'C') : -8, ('K', 'N') : 1, ('I', 'I') : 6,
-('P', 'A') : 0, ('M', 'G') : -5, ('T', 'S') : 2, ('I', 'E') : -3,
-('P', 'M') : -4, ('M', 'K') : 0, ('I', 'A') : -2, ('P', 'I') : -4,
-('R', 'R') : 7, ('Z', 'F') : -8, ('Z', 'B') : 2, ('Z', 'N') : 0,
-('B', 'R') : -3, ('B', 'N') : 4, ('F', 'D') : -8, ('Z', 'R') : -1,
-('F', 'H') : -3, ('B', 'F') : -6, ('F', 'L') : 0, ('V', 'Y') : -4,
-('B', 'B') : 4
+('W', 'F'): -2, ('L', 'R'): -5, ('S', 'P'): 0, ('V', 'T'): -1,
+('Q', 'Q'): 6, ('N', 'A'): -1, ('Z', 'Y'): -6, ('W', 'R'): 0,
+('Q', 'A'): -2, ('S', 'D'): -1, ('H', 'H'): 8, ('S', 'H'): -3,
+('H', 'D'): -1, ('L', 'N'): -4, ('W', 'A'): -8, ('Y', 'M'): -6,
+('G', 'R'): -5, ('Y', 'I'): -3, ('Y', 'E'): -6, ('B', 'Y'): -4,
+('Y', 'A'): -5, ('V', 'D'): -4, ('B', 'S'): 0, ('Y', 'Y'): 9,
+('G', 'N'): -1, ('E', 'C'): -8, ('Y', 'Q'): -6, ('Z', 'Z'): 5,
+('V', 'A'): 0, ('C', 'C'): 9, ('M', 'R'): -2, ('V', 'E'): -3,
+('T', 'N'): 0, ('P', 'P'): 7, ('V', 'I'): 3, ('V', 'S'): -3,
+('Z', 'P'): -2, ('V', 'M'): 1, ('T', 'F'): -5, ('V', 'Q'): -4,
+('K', 'K'): 5, ('P', 'D'): -4, ('I', 'H'): -5, ('I', 'D'): -4,
+('T', 'R'): -3, ('P', 'L'): -4, ('K', 'G'): -4, ('M', 'N'): -4,
+('P', 'H'): -2, ('F', 'Q'): -7, ('Z', 'G'): -2, ('T', 'M'): -2,
+('Z', 'C'): -8, ('D', 'R'): -5, ('B', 'W'): -7, ('Z', 'K'): -1,
+('F', 'A'): -5, ('Z', 'W'): -8, ('F', 'E'): -8, ('D', 'N'): 3,
+('B', 'K'): 0, ('F', 'I'): 0, ('B', 'G'): -1, ('F', 'M'): -1,
+('B', 'C'): -7, ('Z', 'I'): -3, ('Z', 'V'): -3, ('S', 'S'): 4,
+('L', 'Q'): -3, ('W', 'E'): -10, ('Q', 'R'): 0, ('N', 'N'): 5,
+('W', 'M'): -7, ('Q', 'C'): -8, ('W', 'I'): -8, ('S', 'C'): -1,
+('L', 'A'): -3, ('S', 'G'): 0, ('L', 'E'): -5, ('W', 'Q'): -7,
+('H', 'G'): -5, ('S', 'K'): -1, ('L', 'I'): 1, ('N', 'R'): -2,
+('H', 'C'): -5, ('Y', 'N'): -2, ('G', 'Q'): -3, ('Y', 'F'): 4,
+('C', 'A'): -3, ('V', 'L'): 0, ('G', 'E'): -1, ('G', 'A'): 0,
+('K', 'R'): 2, ('E', 'D'): 4, ('Y', 'R'): -6, ('M', 'Q'): -2,
+('T', 'I'): 0, ('C', 'D'): -8, ('V', 'F'): -4, ('T', 'A'): 1,
+('T', 'P'): -1, ('B', 'P'): -3, ('T', 'E'): -2, ('V', 'N'): -4,
+('P', 'G'): -3, ('M', 'A'): -2, ('K', 'H'): -2, ('V', 'R'): -4,
+('P', 'C'): -5, ('M', 'E'): -4, ('K', 'L'): -5, ('V', 'V'): 6,
+('M', 'I'): 1, ('T', 'Q'): -3, ('I', 'G'): -5, ('P', 'K'): -3,
+('M', 'M'): 9, ('K', 'D'): -2, ('I', 'C'): -3, ('Z', 'D'): 3,
+('F', 'R'): -6, ('Q', 'D'): 0, ('Z', 'L'): -4, ('Z', 'H'): 1,
+('B', 'L'): -5, ('B', 'H'): 1, ('F', 'F'): 8, ('B', 'D'): 5,
+('D', 'A'): -1, ('S', 'L'): -5, ('F', 'N'): -5, ('S', 'R'): -1,
+('W', 'D'): -9, ('W', 'L'): -3, ('H', 'R'): 1, ('W', 'H'): -4,
+('H', 'N'): 2, ('W', 'T'): -7, ('T', 'T'): 5, ('S', 'F'): -4,
+('W', 'P'): -8, ('L', 'D'): -7, ('B', 'I'): -3, ('L', 'H'): -3,
+('S', 'N'): 1, ('B', 'T'): -1, ('L', 'L'): 6, ('Y', 'K'): -6,
+('E', 'Q'): 2, ('Y', 'G'): -8, ('Z', 'S'): -2, ('Y', 'C'): -1,
+('G', 'D'): -1, ('B', 'V'): -4, ('E', 'A'): 0, ('Y', 'W'): -2,
+('E', 'E'): 6, ('Y', 'S'): -4, ('C', 'N'): -6, ('V', 'C'): -3,
+('T', 'H'): -3, ('P', 'R'): -1, ('V', 'G'): -3, ('T', 'L'): -3,
+('V', 'K'): -5, ('K', 'Q'): -1, ('R', 'A'): -4, ('I', 'R'): -3,
+('T', 'D'): -2, ('P', 'F'): -6, ('I', 'N'): -3, ('K', 'I'): -3,
+('M', 'D'): -5, ('V', 'W'): -9, ('W', 'W'): 13, ('M', 'H'): -5,
+('P', 'N'): -2, ('K', 'A'): -3, ('M', 'L'): 2, ('K', 'E'): -2,
+('Z', 'E'): 5, ('Q', 'N'): -1, ('Z', 'A'): -1, ('Z', 'M'): -3,
+('K', 'C'): -8, ('B', 'Q'): 0, ('B', 'M'): -5, ('F', 'C'): -7,
+('Z', 'Q'): 5, ('F', 'G'): -6, ('B', 'E'): 2, ('F', 'K'): -8,
+('B', 'A'): -1, ('D', 'D'): 6, ('W', 'G'): -9, ('S', 'Q'): -2,
+('W', 'C'): -10, ('W', 'K'): -6, ('H', 'Q'): 2, ('L', 'C'): -9,
+('W', 'N'): -5, ('S', 'A'): 1, ('L', 'G'): -6, ('W', 'S'): -3,
+('S', 'E'): -2, ('H', 'E'): -1, ('S', 'I'): -3, ('H', 'A'): -4,
+('S', 'M'): -3, ('Y', 'L'): -3, ('Y', 'H'): -1, ('Y', 'D'): -6,
+('E', 'R'): -4, ('G', 'G'): 5, ('G', 'C'): -5, ('E', 'N'): 0,
+('Y', 'T'): -4, ('Y', 'P'): -8, ('T', 'K'): -1, ('A', 'A'): 4,
+('P', 'Q'): -1, ('T', 'C'): -4, ('V', 'H'): -4, ('T', 'G'): -2,
+('I', 'Q'): -4, ('Z', 'T'): -2, ('C', 'R'): -5, ('V', 'P'): -3,
+('P', 'E'): -2, ('M', 'C'): -8, ('K', 'N'): 1, ('I', 'I'): 6,
+('P', 'A'): 0, ('M', 'G'): -5, ('T', 'S'): 2, ('I', 'E'): -3,
+('P', 'M'): -4, ('M', 'K'): 0, ('I', 'A'): -2, ('P', 'I'): -4,
+('R', 'R'): 7, ('Z', 'F'): -8, ('Z', 'B'): 2, ('Z', 'N'): 0,
+('B', 'R'): -3, ('B', 'N'): 4, ('F', 'D'): -8, ('Z', 'R'): -1,
+('F', 'H'): -3, ('B', 'F'): -6, ('F', 'L'): 0, ('V', 'Y'): -4,
+('B', 'B'): 4
}
pam90 = _temp()
del _temp
@@ -2659,120 +2658,120 @@ del _temp
# http://www.embl-heidelberg.de/~vogt/matrices/rao.cmp
def _temp():
return {
-('W', 'F') : 11, ('S', 'P') : 10, ('N', 'M') : 6, ('Q', 'Q') : 16,
-('N', 'A') : 9, ('N', 'E') : 10, ('W', 'V') : 11, ('Q', 'E') : 11,
-('L', 'H') : 10, ('W', 'R') : 7, ('Q', 'A') : 11, ('S', 'D') : 10,
-('H', 'H') : 16, ('Q', 'M') : 9, ('S', 'H') : 10, ('H', 'D') : 9,
-('Q', 'I') : 6, ('S', 'L') : 8, ('Y', 'M') : 8, ('Y', 'I') : 10,
-('Y', 'E') : 6, ('Y', 'A') : 9, ('G', 'F') : 7, ('V', 'T') : 10,
-('Y', 'Y') : 16, ('V', 'H') : 9, ('E', 'C') : 9, ('Y', 'Q') : 8,
-('V', 'A') : 9, ('C', 'C') : 16, ('V', 'E') : 4, ('T', 'N') : 10,
-('R', 'K') : 11, ('P', 'P') : 16, ('V', 'I') : 12, ('R', 'G') : 7,
-('V', 'M') : 9, ('T', 'F') : 10, ('R', 'C') : 8, ('V', 'Q') : 6,
-('K', 'K') : 16, ('P', 'D') : 8, ('I', 'H') : 8, ('M', 'F') : 10,
-('I', 'D') : 3, ('K', 'C') : 9, ('P', 'L') : 4, ('K', 'G') : 7,
-('P', 'H') : 5, ('T', 'R') : 9, ('F', 'A') : 10, ('F', 'E') : 6,
-('S', 'S') : 16, ('W', 'E') : 7, ('N', 'N') : 16, ('W', 'M') : 10,
-('Q', 'C') : 10, ('N', 'F') : 6, ('S', 'C') : 10, ('L', 'A') : 11,
-('S', 'G') : 11, ('L', 'E') : 7, ('W', 'Q') : 9, ('H', 'G') : 7,
-('S', 'K') : 10, ('Q', 'N') : 11, ('V', 'D') : 3, ('H', 'C') : 10,
-('Y', 'N') : 8, ('Y', 'F') : 10, ('W', 'I') : 11, ('C', 'A') : 11,
-('G', 'E') : 6, ('G', 'A') : 8, ('Y', 'V') : 10, ('E', 'D') : 11,
-('W', 'H') : 10, ('Y', 'R') : 7, ('N', 'I') : 5, ('R', 'L') : 6,
-('T', 'I') : 10, ('Q', 'L') : 9, ('R', 'H') : 10, ('T', 'M') : 8,
-('V', 'F') : 11, ('R', 'D') : 10, ('T', 'A') : 10, ('T', 'P') : 8,
-('T', 'E') : 8, ('V', 'N') : 5, ('P', 'G') : 11, ('M', 'A') : 11,
-('K', 'H') : 11, ('V', 'R') : 5, ('P', 'C') : 7, ('M', 'E') : 8,
-('V', 'V') : 16, ('T', 'T') : 16, ('M', 'I') : 9, ('T', 'Q') : 10,
-('I', 'G') : 6, ('P', 'K') : 6, ('M', 'M') : 16, ('K', 'D') : 11,
-('I', 'C') : 8, ('L', 'C') : 11, ('F', 'F') : 16, ('D', 'A') : 9,
-('S', 'R') : 9, ('W', 'D') : 6, ('N', 'C') : 9, ('N', 'G') : 10,
-('W', 'T') : 11, ('Q', 'G') : 8, ('S', 'F') : 8, ('W', 'P') : 6,
-('L', 'D') : 6, ('H', 'F') : 9, ('Q', 'K') : 12, ('S', 'N') : 11,
-('L', 'L') : 16, ('Q', 'F') : 7, ('Y', 'K') : 7, ('Y', 'G') : 10,
-('Y', 'C') : 10, ('G', 'D') : 9, ('E', 'A') : 10, ('Y', 'W') : 11,
-('E', 'E') : 16, ('Y', 'S') : 11, ('R', 'M') : 6, ('V', 'C') : 8,
-('T', 'H') : 10, ('R', 'I') : 4, ('V', 'G') : 6, ('T', 'L') : 9,
-('R', 'E') : 9, ('V', 'K') : 5, ('R', 'Q') : 10, ('R', 'A') : 8,
-('T', 'D') : 9, ('P', 'F') : 4, ('V', 'S') : 8, ('K', 'I') : 4,
-('M', 'D') : 5, ('W', 'W') : 16, ('M', 'H') : 10, ('P', 'N') : 9,
-('I', 'F') : 12, ('K', 'A') : 10, ('M', 'L') : 11, ('K', 'E') : 11,
-('N', 'K') : 11, ('R', 'P') : 6, ('L', 'F') : 11, ('F', 'C') : 10,
-('W', 'G') : 8, ('W', 'L') : 11, ('D', 'D') : 16, ('N', 'H') : 10,
-('S', 'Q') : 10, ('Q', 'P') : 7, ('N', 'L') : 7, ('W', 'K') : 7,
-('Q', 'D') : 11, ('W', 'N') : 8, ('S', 'A') : 10, ('L', 'G') : 6,
-('W', 'S') : 10, ('S', 'E') : 9, ('L', 'K') : 7, ('H', 'E') : 11,
-('S', 'I') : 8, ('Q', 'H') : 11, ('H', 'A') : 11, ('S', 'M') : 7,
-('Y', 'L') : 9, ('Y', 'H') : 9, ('Y', 'D') : 7, ('G', 'G') : 16,
-('G', 'C') : 8, ('Y', 'T') : 11, ('W', 'C') : 11, ('Y', 'P') : 8,
-('T', 'K') : 9, ('R', 'N') : 10, ('A', 'A') : 16, ('W', 'A') : 11,
-('T', 'C') : 10, ('N', 'D') : 11, ('R', 'F') : 5, ('T', 'G') : 10,
-('V', 'L') : 10, ('V', 'P') : 3, ('P', 'E') : 5, ('M', 'C') : 10,
-('I', 'I') : 16, ('P', 'A') : 6, ('M', 'G') : 4, ('T', 'S') : 11,
-('I', 'E') : 4, ('P', 'M') : 2, ('M', 'K') : 8, ('K', 'F') : 6,
-('I', 'A') : 9, ('P', 'I') : 3, ('R', 'R') : 16, ('L', 'I') : 10,
-('F', 'D') : 4, ('D', 'C') : 8
+('W', 'F'): 11, ('S', 'P'): 10, ('N', 'M'): 6, ('Q', 'Q'): 16,
+('N', 'A'): 9, ('N', 'E'): 10, ('W', 'V'): 11, ('Q', 'E'): 11,
+('L', 'H'): 10, ('W', 'R'): 7, ('Q', 'A'): 11, ('S', 'D'): 10,
+('H', 'H'): 16, ('Q', 'M'): 9, ('S', 'H'): 10, ('H', 'D'): 9,
+('Q', 'I'): 6, ('S', 'L'): 8, ('Y', 'M'): 8, ('Y', 'I'): 10,
+('Y', 'E'): 6, ('Y', 'A'): 9, ('G', 'F'): 7, ('V', 'T'): 10,
+('Y', 'Y'): 16, ('V', 'H'): 9, ('E', 'C'): 9, ('Y', 'Q'): 8,
+('V', 'A'): 9, ('C', 'C'): 16, ('V', 'E'): 4, ('T', 'N'): 10,
+('R', 'K'): 11, ('P', 'P'): 16, ('V', 'I'): 12, ('R', 'G'): 7,
+('V', 'M'): 9, ('T', 'F'): 10, ('R', 'C'): 8, ('V', 'Q'): 6,
+('K', 'K'): 16, ('P', 'D'): 8, ('I', 'H'): 8, ('M', 'F'): 10,
+('I', 'D'): 3, ('K', 'C'): 9, ('P', 'L'): 4, ('K', 'G'): 7,
+('P', 'H'): 5, ('T', 'R'): 9, ('F', 'A'): 10, ('F', 'E'): 6,
+('S', 'S'): 16, ('W', 'E'): 7, ('N', 'N'): 16, ('W', 'M'): 10,
+('Q', 'C'): 10, ('N', 'F'): 6, ('S', 'C'): 10, ('L', 'A'): 11,
+('S', 'G'): 11, ('L', 'E'): 7, ('W', 'Q'): 9, ('H', 'G'): 7,
+('S', 'K'): 10, ('Q', 'N'): 11, ('V', 'D'): 3, ('H', 'C'): 10,
+('Y', 'N'): 8, ('Y', 'F'): 10, ('W', 'I'): 11, ('C', 'A'): 11,
+('G', 'E'): 6, ('G', 'A'): 8, ('Y', 'V'): 10, ('E', 'D'): 11,
+('W', 'H'): 10, ('Y', 'R'): 7, ('N', 'I'): 5, ('R', 'L'): 6,
+('T', 'I'): 10, ('Q', 'L'): 9, ('R', 'H'): 10, ('T', 'M'): 8,
+('V', 'F'): 11, ('R', 'D'): 10, ('T', 'A'): 10, ('T', 'P'): 8,
+('T', 'E'): 8, ('V', 'N'): 5, ('P', 'G'): 11, ('M', 'A'): 11,
+('K', 'H'): 11, ('V', 'R'): 5, ('P', 'C'): 7, ('M', 'E'): 8,
+('V', 'V'): 16, ('T', 'T'): 16, ('M', 'I'): 9, ('T', 'Q'): 10,
+('I', 'G'): 6, ('P', 'K'): 6, ('M', 'M'): 16, ('K', 'D'): 11,
+('I', 'C'): 8, ('L', 'C'): 11, ('F', 'F'): 16, ('D', 'A'): 9,
+('S', 'R'): 9, ('W', 'D'): 6, ('N', 'C'): 9, ('N', 'G'): 10,
+('W', 'T'): 11, ('Q', 'G'): 8, ('S', 'F'): 8, ('W', 'P'): 6,
+('L', 'D'): 6, ('H', 'F'): 9, ('Q', 'K'): 12, ('S', 'N'): 11,
+('L', 'L'): 16, ('Q', 'F'): 7, ('Y', 'K'): 7, ('Y', 'G'): 10,
+('Y', 'C'): 10, ('G', 'D'): 9, ('E', 'A'): 10, ('Y', 'W'): 11,
+('E', 'E'): 16, ('Y', 'S'): 11, ('R', 'M'): 6, ('V', 'C'): 8,
+('T', 'H'): 10, ('R', 'I'): 4, ('V', 'G'): 6, ('T', 'L'): 9,
+('R', 'E'): 9, ('V', 'K'): 5, ('R', 'Q'): 10, ('R', 'A'): 8,
+('T', 'D'): 9, ('P', 'F'): 4, ('V', 'S'): 8, ('K', 'I'): 4,
+('M', 'D'): 5, ('W', 'W'): 16, ('M', 'H'): 10, ('P', 'N'): 9,
+('I', 'F'): 12, ('K', 'A'): 10, ('M', 'L'): 11, ('K', 'E'): 11,
+('N', 'K'): 11, ('R', 'P'): 6, ('L', 'F'): 11, ('F', 'C'): 10,
+('W', 'G'): 8, ('W', 'L'): 11, ('D', 'D'): 16, ('N', 'H'): 10,
+('S', 'Q'): 10, ('Q', 'P'): 7, ('N', 'L'): 7, ('W', 'K'): 7,
+('Q', 'D'): 11, ('W', 'N'): 8, ('S', 'A'): 10, ('L', 'G'): 6,
+('W', 'S'): 10, ('S', 'E'): 9, ('L', 'K'): 7, ('H', 'E'): 11,
+('S', 'I'): 8, ('Q', 'H'): 11, ('H', 'A'): 11, ('S', 'M'): 7,
+('Y', 'L'): 9, ('Y', 'H'): 9, ('Y', 'D'): 7, ('G', 'G'): 16,
+('G', 'C'): 8, ('Y', 'T'): 11, ('W', 'C'): 11, ('Y', 'P'): 8,
+('T', 'K'): 9, ('R', 'N'): 10, ('A', 'A'): 16, ('W', 'A'): 11,
+('T', 'C'): 10, ('N', 'D'): 11, ('R', 'F'): 5, ('T', 'G'): 10,
+('V', 'L'): 10, ('V', 'P'): 3, ('P', 'E'): 5, ('M', 'C'): 10,
+('I', 'I'): 16, ('P', 'A'): 6, ('M', 'G'): 4, ('T', 'S'): 11,
+('I', 'E'): 4, ('P', 'M'): 2, ('M', 'K'): 8, ('K', 'F'): 6,
+('I', 'A'): 9, ('P', 'I'): 3, ('R', 'R'): 16, ('L', 'I'): 10,
+('F', 'D'): 4, ('D', 'C'): 8
}
rao = _temp()
del _temp
# http://www.embl-heidelberg.de/~vogt/matrices/risler.cmp
-def _temp() :
+def _temp():
return {
-('W', 'F') : -0.9, ('S', 'P') : -0.3, ('N', 'M') : 0.0, ('Q', 'Q') : 2.2,
-('N', 'A') : 1.3, ('N', 'E') : 1.4, ('W', 'V') : -0.7, ('Q', 'E') : 2.1,
-('L', 'H') : -0.9, ('W', 'R') : -0.8, ('Q', 'A') : 1.8, ('S', 'D') : 0.7,
-('H', 'H') : 2.2, ('Q', 'M') : 1.2, ('S', 'H') : -0.4, ('H', 'D') : -1.3,
-('Q', 'I') : 1.4, ('S', 'L') : 1.3, ('Y', 'M') : -0.2, ('Y', 'I') : 0.4,
-('Y', 'E') : 0.2, ('Y', 'A') : 0.2, ('G', 'F') : -0.4, ('V', 'T') : 1.6,
-('Y', 'Y') : 2.2, ('V', 'H') : -0.7, ('E', 'C') : -1.5, ('Y', 'Q') : 0.5,
-('V', 'A') : 2.0, ('C', 'C') : 2.2, ('V', 'E') : 1.6, ('T', 'N') : 1.1,
-('R', 'K') : 2.1, ('P', 'P') : 2.2, ('V', 'I') : 2.2, ('R', 'G') : 0.1,
-('V', 'M') : 0.8, ('T', 'F') : 0.3, ('R', 'C') : -1.5, ('V', 'Q') : 1.5,
-('K', 'K') : 2.2, ('P', 'D') : -1.2, ('I', 'H') : -0.8, ('M', 'F') : -0.2,
-('I', 'D') : 0.0, ('K', 'C') : -1.6, ('P', 'L') : -0.8, ('K', 'G') : -0.1,
-('P', 'H') : -1.6, ('T', 'R') : 1.9, ('F', 'A') : 0.6, ('F', 'E') : 0.6,
-('S', 'S') : 2.2, ('W', 'E') : -1.0, ('N', 'N') : 2.2, ('W', 'M') : -1.3,
-('Q', 'C') : -1.4, ('N', 'F') : 0.4, ('S', 'C') : -1.3, ('L', 'A') : 1.3,
-('S', 'G') : 0.7, ('L', 'E') : 0.9, ('W', 'Q') : -1.0, ('H', 'G') : -1.2,
-('S', 'K') : 1.4, ('Q', 'N') : 1.6, ('V', 'D') : 0.0, ('H', 'C') : -1.8,
-('Y', 'N') : -0.1, ('Y', 'F') : 2.0, ('W', 'I') : -0.7, ('C', 'A') : -1.5,
-('G', 'E') : 0.3, ('G', 'A') : 0.6, ('Y', 'V') : 0.3, ('E', 'D') : 1.0,
-('W', 'H') : -1.7, ('Y', 'R') : 0.8, ('N', 'I') : 0.9, ('R', 'L') : 1.2,
-('T', 'I') : 1.6, ('Q', 'L') : 1.1, ('R', 'H') : -0.4, ('T', 'M') : 0.8,
-('V', 'F') : 0.8, ('R', 'D') : -0.1, ('T', 'A') : 1.9, ('T', 'P') : -0.5,
-('T', 'E') : 1.6, ('V', 'N') : 1.1, ('P', 'G') : -1.2, ('M', 'A') : 1.0,
-('K', 'H') : -1.0, ('V', 'R') : 1.5, ('P', 'C') : -1.8, ('M', 'E') : 0.6,
-('V', 'V') : 2.2, ('T', 'T') : 2.2, ('M', 'I') : 0.9, ('T', 'Q') : 1.7,
-('I', 'G') : 0.0, ('P', 'K') : -0.7, ('M', 'M') : 2.2, ('K', 'D') : 0.1,
-('I', 'C') : -1.6, ('L', 'C') : -1.5, ('F', 'F') : 2.2, ('D', 'A') : 0.2,
-('S', 'R') : 2.0, ('W', 'D') : -1.4, ('N', 'C') : -1.6, ('N', 'G') : 0.2,
-('W', 'T') : -1.0, ('Q', 'G') : 0.2, ('S', 'F') : 0.5, ('W', 'P') : -1.6,
-('L', 'D') : -0.2, ('H', 'F') : -1.1, ('Q', 'K') : 1.7, ('S', 'N') : 1.9,
-('L', 'L') : 2.2, ('Q', 'F') : 0.7, ('Y', 'K') : 0.5, ('Y', 'G') : -0.2,
-('Y', 'C') : -1.1, ('G', 'D') : -0.4, ('E', 'A') : 1.7, ('Y', 'W') : -0.6,
-('E', 'E') : 2.2, ('Y', 'S') : 0.4, ('R', 'M') : 1.1, ('V', 'C') : -1.4,
-('T', 'H') : -0.9, ('R', 'I') : 1.4, ('V', 'G') : 0.1, ('T', 'L') : 1.2,
-('R', 'E') : 1.9, ('V', 'K') : 1.2, ('R', 'Q') : 2.0, ('R', 'A') : 1.5,
-('T', 'D') : 0.0, ('P', 'F') : -1.1, ('V', 'S') : 1.8, ('K', 'I') : 1.0,
-('M', 'D') : -0.5, ('W', 'W') : 2.2, ('M', 'H') : -1.2, ('P', 'N') : -1.0,
-('I', 'F') : 1.0, ('K', 'A') : 1.4, ('M', 'L') : 1.8, ('K', 'E') : 1.4,
-('N', 'K') : 1.0, ('R', 'P') : -0.3, ('L', 'F') : 1.0, ('F', 'C') : -1.6,
-('W', 'G') : -1.3, ('W', 'L') : -0.8, ('D', 'D') : 2.2, ('N', 'H') : -0.3,
-('S', 'Q') : 1.8, ('Q', 'P') : -0.6, ('N', 'L') : 0.8, ('W', 'K') : -1.1,
-('Q', 'D') : 0.6, ('W', 'N') : -1.1, ('S', 'A') : 2.0, ('L', 'G') : -0.2,
-('W', 'S') : -0.8, ('S', 'E') : 1.8, ('L', 'K') : 0.7, ('H', 'E') : -0.6,
-('S', 'I') : 1.6, ('Q', 'H') : -0.5, ('H', 'A') : -0.6, ('S', 'M') : 0.6,
-('Y', 'L') : 0.5, ('Y', 'H') : -0.8, ('Y', 'D') : -0.4, ('G', 'G') : 2.2,
-('G', 'C') : -1.7, ('Y', 'T') : 0.3, ('W', 'C') : -1.8, ('Y', 'P') : -1.2,
-('T', 'K') : 1.2, ('R', 'N') : 1.2, ('A', 'A') : 2.2, ('W', 'A') : -0.9,
-('T', 'C') : -1.4, ('N', 'D') : 0.8, ('R', 'F') : 0.4, ('T', 'G') : 0.2,
-('V', 'L') : 2.0, ('V', 'P') : -0.6, ('P', 'E') : -0.1, ('M', 'C') : -1.6,
-('I', 'I') : 2.2, ('P', 'A') : -0.2, ('M', 'G') : -0.4, ('T', 'S') : 2.1,
-('I', 'E') : 1.5, ('P', 'M') : -1.2, ('M', 'K') : 0.4, ('K', 'F') : 0.1,
-('I', 'A') : 1.7, ('P', 'I') : -0.6, ('R', 'R') : 2.2, ('L', 'I') : 2.1,
-('F', 'D') : -0.3, ('D', 'C') : -1.7
+('W', 'F'): -0.9, ('S', 'P'): -0.3, ('N', 'M'): 0.0, ('Q', 'Q'): 2.2,
+('N', 'A'): 1.3, ('N', 'E'): 1.4, ('W', 'V'): -0.7, ('Q', 'E'): 2.1,
+('L', 'H'): -0.9, ('W', 'R'): -0.8, ('Q', 'A'): 1.8, ('S', 'D'): 0.7,
+('H', 'H'): 2.2, ('Q', 'M'): 1.2, ('S', 'H'): -0.4, ('H', 'D'): -1.3,
+('Q', 'I'): 1.4, ('S', 'L'): 1.3, ('Y', 'M'): -0.2, ('Y', 'I'): 0.4,
+('Y', 'E'): 0.2, ('Y', 'A'): 0.2, ('G', 'F'): -0.4, ('V', 'T'): 1.6,
+('Y', 'Y'): 2.2, ('V', 'H'): -0.7, ('E', 'C'): -1.5, ('Y', 'Q'): 0.5,
+('V', 'A'): 2.0, ('C', 'C'): 2.2, ('V', 'E'): 1.6, ('T', 'N'): 1.1,
+('R', 'K'): 2.1, ('P', 'P'): 2.2, ('V', 'I'): 2.2, ('R', 'G'): 0.1,
+('V', 'M'): 0.8, ('T', 'F'): 0.3, ('R', 'C'): -1.5, ('V', 'Q'): 1.5,
+('K', 'K'): 2.2, ('P', 'D'): -1.2, ('I', 'H'): -0.8, ('M', 'F'): -0.2,
+('I', 'D'): 0.0, ('K', 'C'): -1.6, ('P', 'L'): -0.8, ('K', 'G'): -0.1,
+('P', 'H'): -1.6, ('T', 'R'): 1.9, ('F', 'A'): 0.6, ('F', 'E'): 0.6,
+('S', 'S'): 2.2, ('W', 'E'): -1.0, ('N', 'N'): 2.2, ('W', 'M'): -1.3,
+('Q', 'C'): -1.4, ('N', 'F'): 0.4, ('S', 'C'): -1.3, ('L', 'A'): 1.3,
+('S', 'G'): 0.7, ('L', 'E'): 0.9, ('W', 'Q'): -1.0, ('H', 'G'): -1.2,
+('S', 'K'): 1.4, ('Q', 'N'): 1.6, ('V', 'D'): 0.0, ('H', 'C'): -1.8,
+('Y', 'N'): -0.1, ('Y', 'F'): 2.0, ('W', 'I'): -0.7, ('C', 'A'): -1.5,
+('G', 'E'): 0.3, ('G', 'A'): 0.6, ('Y', 'V'): 0.3, ('E', 'D'): 1.0,
+('W', 'H'): -1.7, ('Y', 'R'): 0.8, ('N', 'I'): 0.9, ('R', 'L'): 1.2,
+('T', 'I'): 1.6, ('Q', 'L'): 1.1, ('R', 'H'): -0.4, ('T', 'M'): 0.8,
+('V', 'F'): 0.8, ('R', 'D'): -0.1, ('T', 'A'): 1.9, ('T', 'P'): -0.5,
+('T', 'E'): 1.6, ('V', 'N'): 1.1, ('P', 'G'): -1.2, ('M', 'A'): 1.0,
+('K', 'H'): -1.0, ('V', 'R'): 1.5, ('P', 'C'): -1.8, ('M', 'E'): 0.6,
+('V', 'V'): 2.2, ('T', 'T'): 2.2, ('M', 'I'): 0.9, ('T', 'Q'): 1.7,
+('I', 'G'): 0.0, ('P', 'K'): -0.7, ('M', 'M'): 2.2, ('K', 'D'): 0.1,
+('I', 'C'): -1.6, ('L', 'C'): -1.5, ('F', 'F'): 2.2, ('D', 'A'): 0.2,
+('S', 'R'): 2.0, ('W', 'D'): -1.4, ('N', 'C'): -1.6, ('N', 'G'): 0.2,
+('W', 'T'): -1.0, ('Q', 'G'): 0.2, ('S', 'F'): 0.5, ('W', 'P'): -1.6,
+('L', 'D'): -0.2, ('H', 'F'): -1.1, ('Q', 'K'): 1.7, ('S', 'N'): 1.9,
+('L', 'L'): 2.2, ('Q', 'F'): 0.7, ('Y', 'K'): 0.5, ('Y', 'G'): -0.2,
+('Y', 'C'): -1.1, ('G', 'D'): -0.4, ('E', 'A'): 1.7, ('Y', 'W'): -0.6,
+('E', 'E'): 2.2, ('Y', 'S'): 0.4, ('R', 'M'): 1.1, ('V', 'C'): -1.4,
+('T', 'H'): -0.9, ('R', 'I'): 1.4, ('V', 'G'): 0.1, ('T', 'L'): 1.2,
+('R', 'E'): 1.9, ('V', 'K'): 1.2, ('R', 'Q'): 2.0, ('R', 'A'): 1.5,
+('T', 'D'): 0.0, ('P', 'F'): -1.1, ('V', 'S'): 1.8, ('K', 'I'): 1.0,
+('M', 'D'): -0.5, ('W', 'W'): 2.2, ('M', 'H'): -1.2, ('P', 'N'): -1.0,
+('I', 'F'): 1.0, ('K', 'A'): 1.4, ('M', 'L'): 1.8, ('K', 'E'): 1.4,
+('N', 'K'): 1.0, ('R', 'P'): -0.3, ('L', 'F'): 1.0, ('F', 'C'): -1.6,
+('W', 'G'): -1.3, ('W', 'L'): -0.8, ('D', 'D'): 2.2, ('N', 'H'): -0.3,
+('S', 'Q'): 1.8, ('Q', 'P'): -0.6, ('N', 'L'): 0.8, ('W', 'K'): -1.1,
+('Q', 'D'): 0.6, ('W', 'N'): -1.1, ('S', 'A'): 2.0, ('L', 'G'): -0.2,
+('W', 'S'): -0.8, ('S', 'E'): 1.8, ('L', 'K'): 0.7, ('H', 'E'): -0.6,
+('S', 'I'): 1.6, ('Q', 'H'): -0.5, ('H', 'A'): -0.6, ('S', 'M'): 0.6,
+('Y', 'L'): 0.5, ('Y', 'H'): -0.8, ('Y', 'D'): -0.4, ('G', 'G'): 2.2,
+('G', 'C'): -1.7, ('Y', 'T'): 0.3, ('W', 'C'): -1.8, ('Y', 'P'): -1.2,
+('T', 'K'): 1.2, ('R', 'N'): 1.2, ('A', 'A'): 2.2, ('W', 'A'): -0.9,
+('T', 'C'): -1.4, ('N', 'D'): 0.8, ('R', 'F'): 0.4, ('T', 'G'): 0.2,
+('V', 'L'): 2.0, ('V', 'P'): -0.6, ('P', 'E'): -0.1, ('M', 'C'): -1.6,
+('I', 'I'): 2.2, ('P', 'A'): -0.2, ('M', 'G'): -0.4, ('T', 'S'): 2.1,
+('I', 'E'): 1.5, ('P', 'M'): -1.2, ('M', 'K'): 0.4, ('K', 'F'): 0.1,
+('I', 'A'): 1.7, ('P', 'I'): -0.6, ('R', 'R'): 2.2, ('L', 'I'): 2.1,
+('F', 'D'): -0.3, ('D', 'C'): -1.7
}
risler = _temp()
del _temp
@@ -2781,59 +2780,59 @@ del _temp
# http://www.embl-heidelberg.de/~vogt/matrices/str.cmp
def _temp():
return {
-('W', 'F') : 2, ('L', 'R') : -3, ('I', 'I') : 6, ('Q', 'Q') : 6,
-('W', 'N') : -5, ('V', 'I') : 2, ('H', 'T') : -2, ('H', 'P') : -3,
-('W', 'V') : -4, ('Q', 'E') : 2, ('W', 'R') : -2, ('Q', 'A') : 0,
-('H', 'H') : 8, ('H', 'D') : 0, ('L', 'N') : -3, ('Y', 'M') : -1,
-('Y', 'I') : -1, ('Y', 'E') : -2, ('E', 'S') : -1, ('Y', 'A') : -3,
-('Y', 'Y') : 7, ('T', 'C') : -5, ('E', 'C') : -3, ('Y', 'Q') : -3,
-('E', 'G') : -2, ('V', 'A') : 0, ('C', 'C') : 11, ('M', 'R') : -4,
-('P', 'T') : -1, ('V', 'E') : -2, ('P', 'P') : 7, ('I', 'T') : -2,
-('K', 'S') : -1, ('R', 'G') : -2, ('I', 'P') : -4, ('R', 'C') : -2,
-('A', 'T') : -1, ('K', 'K') : 5, ('A', 'P') : -1, ('V', 'M') : 0,
-('I', 'D') : -3, ('K', 'C') : -4, ('K', 'G') : -3, ('R', 'S') : 0,
-('F', 'Q') : -4, ('F', 'A') : -3, ('V', 'V') : 5, ('M', 'N') : -2,
-('F', 'E') : -4, ('D', 'N') : 2, ('F', 'I') : 1, ('F', 'M') : 0,
-('M', 'S') : -4, ('S', 'S') : 4, ('L', 'Q') : -3, ('W', 'E') : -6,
-('W', 'A') : -3, ('W', 'M') : -2, ('H', 'S') : -2, ('W', 'I') : -2,
-('S', 'C') : -4, ('L', 'A') : -2, ('L', 'E') : -4, ('W', 'Q') : -5,
-('H', 'G') : -3, ('Q', 'N') : 0, ('H', 'C') : -6, ('L', 'M') : 3,
-('W', 'Y') : 2, ('Y', 'N') : -1, ('E', 'P') : -1, ('Y', 'F') : 3,
-('E', 'T') : 0, ('A', 'A') : 4, ('I', 'N') : -3, ('G', 'A') : 0,
-('Y', 'V') : -1, ('E', 'D') : 2, ('W', 'H') : -3, ('Y', 'R') : -1,
-('M', 'Q') : 1, ('P', 'S') : -1, ('R', 'H') : 0, ('A', 'C') : -2,
-('R', 'D') : -2, ('K', 'P') : -1, ('L', 'D') : -6, ('K', 'T') : 0,
-('V', 'N') : -4, ('M', 'A') : 0, ('K', 'H') : 0, ('V', 'R') : -3,
-('P', 'C') : -8, ('M', 'E') : -2, ('A', 'S') : 0, ('T', 'T') : 5,
-('R', 'T') : -1, ('I', 'G') : -5, ('R', 'P') : -2, ('K', 'D') : -1,
-('I', 'C') : -4, ('F', 'R') : -4, ('F', 'V') : -1, ('L', 'C') : -6,
-('F', 'F') : 7, ('D', 'A') : -1, ('F', 'N') : -3, ('W', 'D') : -6,
-('L', 'P') : -3, ('Q', 'S') : -1, ('N', 'C') : -6, ('N', 'G') : -1,
-('H', 'N') : 2, ('W', 'T') : -5, ('Q', 'G') : -2, ('W', 'P') : -4,
-('Q', 'C') : -3, ('N', 'S') : 0, ('L', 'H') : -3, ('L', 'L') : 5,
-('G', 'T') : -3, ('M', 'M') : 8, ('G', 'P') : -2, ('Y', 'K') : -2,
-('Y', 'G') : -3, ('Y', 'C') : -6, ('E', 'A') : 0, ('E', 'E') : 5,
-('Y', 'S') : -2, ('M', 'P') : -6, ('V', 'C') : -4, ('M', 'T') : -2,
-('V', 'G') : -4, ('R', 'E') : 0, ('V', 'K') : -3, ('K', 'Q') : 1,
-('R', 'A') : -1, ('I', 'R') : -3, ('N', 'A') : -1, ('V', 'S') : -3,
-('M', 'D') : -4, ('M', 'H') : -2, ('K', 'A') : -1, ('R', 'Q') : 1,
-('K', 'E') : 1, ('F', 'S') : -3, ('I', 'K') : -3, ('D', 'P') : -1,
-('D', 'T') : -1, ('I', 'M') : 1, ('F', 'C') : -2, ('W', 'L') : -1,
-('F', 'G') : -6, ('F', 'K') : -3, ('F', 'T') : -3, ('D', 'D') : 6,
-('Q', 'T') : 0, ('W', 'G') : -4, ('Q', 'P') : -2, ('W', 'C') : -6,
-('W', 'K') : -3, ('H', 'Q') : 0, ('Q', 'D') : 0, ('W', 'W') : 10,
-('V', 'L') : 1, ('L', 'G') : -5, ('W', 'S') : -5, ('L', 'K') : -2,
-('N', 'P') : -2, ('H', 'E') : -2, ('N', 'T') : 0, ('H', 'A') : -2,
-('Y', 'L') : -2, ('Y', 'H') : 0, ('G', 'S') : -1, ('Y', 'D') : -3,
-('V', 'Q') : -2, ('L', 'T') : -3, ('G', 'G') : 5, ('G', 'C') : -6,
-('E', 'N') : 0, ('Y', 'T') : -2, ('Y', 'P') : -6, ('R', 'N') : -1,
-('V', 'D') : -4, ('K', 'R') : 2, ('V', 'H') : -2, ('I', 'Q') : -5,
-('V', 'P') : -4, ('M', 'C') : -5, ('K', 'N') : 0, ('V', 'T') : -1,
-('M', 'G') : -4, ('T', 'S') : 1, ('I', 'E') : -3, ('M', 'K') : -1,
-('I', 'A') : -2, ('N', 'N') : 5, ('R', 'R') : 7, ('F', 'P') : -5,
-('L', 'I') : 2, ('I', 'S') : -3, ('D', 'S') : 0, ('L', 'S') : -4,
-('I', 'H') : -5, ('F', 'D') : -5, ('D', 'C') : -7, ('F', 'H') : -2,
-('D', 'G') : -1, ('F', 'L') : 2
+('W', 'F'): 2, ('L', 'R'): -3, ('I', 'I'): 6, ('Q', 'Q'): 6,
+('W', 'N'): -5, ('V', 'I'): 2, ('H', 'T'): -2, ('H', 'P'): -3,
+('W', 'V'): -4, ('Q', 'E'): 2, ('W', 'R'): -2, ('Q', 'A'): 0,
+('H', 'H'): 8, ('H', 'D'): 0, ('L', 'N'): -3, ('Y', 'M'): -1,
+('Y', 'I'): -1, ('Y', 'E'): -2, ('E', 'S'): -1, ('Y', 'A'): -3,
+('Y', 'Y'): 7, ('T', 'C'): -5, ('E', 'C'): -3, ('Y', 'Q'): -3,
+('E', 'G'): -2, ('V', 'A'): 0, ('C', 'C'): 11, ('M', 'R'): -4,
+('P', 'T'): -1, ('V', 'E'): -2, ('P', 'P'): 7, ('I', 'T'): -2,
+('K', 'S'): -1, ('R', 'G'): -2, ('I', 'P'): -4, ('R', 'C'): -2,
+('A', 'T'): -1, ('K', 'K'): 5, ('A', 'P'): -1, ('V', 'M'): 0,
+('I', 'D'): -3, ('K', 'C'): -4, ('K', 'G'): -3, ('R', 'S'): 0,
+('F', 'Q'): -4, ('F', 'A'): -3, ('V', 'V'): 5, ('M', 'N'): -2,
+('F', 'E'): -4, ('D', 'N'): 2, ('F', 'I'): 1, ('F', 'M'): 0,
+('M', 'S'): -4, ('S', 'S'): 4, ('L', 'Q'): -3, ('W', 'E'): -6,
+('W', 'A'): -3, ('W', 'M'): -2, ('H', 'S'): -2, ('W', 'I'): -2,
+('S', 'C'): -4, ('L', 'A'): -2, ('L', 'E'): -4, ('W', 'Q'): -5,
+('H', 'G'): -3, ('Q', 'N'): 0, ('H', 'C'): -6, ('L', 'M'): 3,
+('W', 'Y'): 2, ('Y', 'N'): -1, ('E', 'P'): -1, ('Y', 'F'): 3,
+('E', 'T'): 0, ('A', 'A'): 4, ('I', 'N'): -3, ('G', 'A'): 0,
+('Y', 'V'): -1, ('E', 'D'): 2, ('W', 'H'): -3, ('Y', 'R'): -1,
+('M', 'Q'): 1, ('P', 'S'): -1, ('R', 'H'): 0, ('A', 'C'): -2,
+('R', 'D'): -2, ('K', 'P'): -1, ('L', 'D'): -6, ('K', 'T'): 0,
+('V', 'N'): -4, ('M', 'A'): 0, ('K', 'H'): 0, ('V', 'R'): -3,
+('P', 'C'): -8, ('M', 'E'): -2, ('A', 'S'): 0, ('T', 'T'): 5,
+('R', 'T'): -1, ('I', 'G'): -5, ('R', 'P'): -2, ('K', 'D'): -1,
+('I', 'C'): -4, ('F', 'R'): -4, ('F', 'V'): -1, ('L', 'C'): -6,
+('F', 'F'): 7, ('D', 'A'): -1, ('F', 'N'): -3, ('W', 'D'): -6,
+('L', 'P'): -3, ('Q', 'S'): -1, ('N', 'C'): -6, ('N', 'G'): -1,
+('H', 'N'): 2, ('W', 'T'): -5, ('Q', 'G'): -2, ('W', 'P'): -4,
+('Q', 'C'): -3, ('N', 'S'): 0, ('L', 'H'): -3, ('L', 'L'): 5,
+('G', 'T'): -3, ('M', 'M'): 8, ('G', 'P'): -2, ('Y', 'K'): -2,
+('Y', 'G'): -3, ('Y', 'C'): -6, ('E', 'A'): 0, ('E', 'E'): 5,
+('Y', 'S'): -2, ('M', 'P'): -6, ('V', 'C'): -4, ('M', 'T'): -2,
+('V', 'G'): -4, ('R', 'E'): 0, ('V', 'K'): -3, ('K', 'Q'): 1,
+('R', 'A'): -1, ('I', 'R'): -3, ('N', 'A'): -1, ('V', 'S'): -3,
+('M', 'D'): -4, ('M', 'H'): -2, ('K', 'A'): -1, ('R', 'Q'): 1,
+('K', 'E'): 1, ('F', 'S'): -3, ('I', 'K'): -3, ('D', 'P'): -1,
+('D', 'T'): -1, ('I', 'M'): 1, ('F', 'C'): -2, ('W', 'L'): -1,
+('F', 'G'): -6, ('F', 'K'): -3, ('F', 'T'): -3, ('D', 'D'): 6,
+('Q', 'T'): 0, ('W', 'G'): -4, ('Q', 'P'): -2, ('W', 'C'): -6,
+('W', 'K'): -3, ('H', 'Q'): 0, ('Q', 'D'): 0, ('W', 'W'): 10,
+('V', 'L'): 1, ('L', 'G'): -5, ('W', 'S'): -5, ('L', 'K'): -2,
+('N', 'P'): -2, ('H', 'E'): -2, ('N', 'T'): 0, ('H', 'A'): -2,
+('Y', 'L'): -2, ('Y', 'H'): 0, ('G', 'S'): -1, ('Y', 'D'): -3,
+('V', 'Q'): -2, ('L', 'T'): -3, ('G', 'G'): 5, ('G', 'C'): -6,
+('E', 'N'): 0, ('Y', 'T'): -2, ('Y', 'P'): -6, ('R', 'N'): -1,
+('V', 'D'): -4, ('K', 'R'): 2, ('V', 'H'): -2, ('I', 'Q'): -5,
+('V', 'P'): -4, ('M', 'C'): -5, ('K', 'N'): 0, ('V', 'T'): -1,
+('M', 'G'): -4, ('T', 'S'): 1, ('I', 'E'): -3, ('M', 'K'): -1,
+('I', 'A'): -2, ('N', 'N'): 5, ('R', 'R'): 7, ('F', 'P'): -5,
+('L', 'I'): 2, ('I', 'S'): -3, ('D', 'S'): 0, ('L', 'S'): -4,
+('I', 'H'): -5, ('F', 'D'): -5, ('D', 'C'): -7, ('F', 'H'): -2,
+('D', 'G'): -1, ('F', 'L'): 2
}
structure = _temp()
del _temp
diff --git a/Bio/SubsMat/__init__.py b/Bio/SubsMat/__init__.py
index e4c668e..287c150 100644
--- a/Bio/SubsMat/__init__.py
+++ b/Bio/SubsMat/__init__.py
@@ -122,7 +122,6 @@ import Bio
from Bio import Alphabet
from Bio.SubsMat import FreqTable
-__docformat__ = "restructuredtext en"
log = math.log
# Matrix types
diff --git a/Bio/SwissProt/KeyWList.py b/Bio/SwissProt/KeyWList.py
index cff8770..4736f25 100644
--- a/Bio/SwissProt/KeyWList.py
+++ b/Bio/SwissProt/KeyWList.py
@@ -22,8 +22,6 @@ Functions:
from __future__ import print_function
-__docformat__ = "restructuredtext en"
-
class Record(dict):
"""
diff --git a/Bio/SwissProt/__init__.py b/Bio/SwissProt/__init__.py
index d43633b..93cffbd 100644
--- a/Bio/SwissProt/__init__.py
+++ b/Bio/SwissProt/__init__.py
@@ -26,8 +26,6 @@ from __future__ import print_function
from Bio._py3k import _as_string
-__docformat__ = "restructuredtext en"
-
class Record(object):
"""Holds information from a SwissProt record.
@@ -131,7 +129,10 @@ def read(handle):
if not record:
raise ValueError("No SwissProt record found")
# We should have reached the end of the record by now
- remainder = handle.read()
+ # Used to check with handle.read() but that breaks on Python 3.5
+ # due to http://bugs.python.org/issue26499 and could download
+ # lot of data needlessly if there were more records.
+ remainder = handle.readline()
if remainder:
raise ValueError("More than one SwissProt record found")
return record
@@ -307,7 +308,7 @@ def _read_dt(record, line):
# find where the version information will be located
# This is needed for when you have cases like IPI where
- # the release verison is in a different spot:
+ # the release version is in a different spot:
# DT 08-JAN-2002 (IPI Human rel. 2.3, Created)
uprcols = uprline.split()
rel_index = -1
@@ -429,6 +430,7 @@ def _read_rn(reference, rn):
assert evidence.startswith('{') and evidence.endswith('}'), "Missing braces %s" % evidence
reference.evidence = evidence[1:-1].split('|')
+
def _read_rc(reference, value):
cols = value.split(';')
if value[-1] == ';':
diff --git a/Bio/TogoWS/__init__.py b/Bio/TogoWS/__init__.py
index 1b4f7a4..3c393f1 100644
--- a/Bio/TogoWS/__init__.py
+++ b/Bio/TogoWS/__init__.py
@@ -39,7 +39,6 @@ from Bio._py3k import _binary_to_string_handle, _as_bytes
from Bio._py3k import urlopen as _urlopen
from Bio._py3k import quote as _quote
-__docformat__ = "restructuredtext en"
# Constant
_BASE_URL = "http://togows.dbcls.jp"
@@ -317,7 +316,7 @@ def _open(url, post=None):
Open a handle to TogoWS, will raise an IOError if it encounters an error.
- In the absense of clear guidelines, this function enforces a limit of
+ In the absence of clear guidelines, this function enforces a limit of
"up to three queries per second" to avoid abusing the TogoWS servers.
"""
delay = 0.333333333 # one third of a second
diff --git a/Bio/UniGene/__init__.py b/Bio/UniGene/__init__.py
index 3c4f19b..d881bac 100644
--- a/Bio/UniGene/__init__.py
+++ b/Bio/UniGene/__init__.py
@@ -78,8 +78,6 @@ Here is an overview of the flat file format that this parser deals with:
NCBI Trace Archive
"""
-__docformat__ = "restructuredtext en"
-
class SequenceLine(object):
"""Store the information for one SEQUENCE line from a Unigene file
@@ -87,7 +85,7 @@ class SequenceLine(object):
Initialize with the text part of the SEQUENCE line, or nothing.
Attributes and descriptions (access as LOWER CASE):
-
+
- ACC= GenBank/EMBL/DDBJ accession number of sequence
- NID= Unique nucleotide sequence identifier (gi)
- PID= Unique protein sequence identifier (used for non-ESTs)
diff --git a/Bio/UniProt/GOA.py b/Bio/UniProt/GOA.py
index b53a591..75c9b72 100644
--- a/Bio/UniProt/GOA.py
+++ b/Bio/UniProt/GOA.py
@@ -23,6 +23,7 @@ import sys
from Bio._py3k import zip
+
# GAF: GO Annotation Format
#
# GAF version 2.0
diff --git a/Bio/Wise/__init__.py b/Bio/Wise/__init__.py
index 1008ed6..bed9637 100755
--- a/Bio/Wise/__init__.py
+++ b/Bio/Wise/__init__.py
@@ -3,13 +3,15 @@
# license. Please see the LICENSE file that should have been included
# as part of this package.
#
-# Bio.Wise contains modules for running and processing the output of
-# some of the models in the Wise2 package by Ewan Birney available from:
-# ftp://ftp.ebi.ac.uk/pub/software/unix/wise2/
-# http://www.ebi.ac.uk/Wise2/
-#
-# Bio.Wise.psw is for protein Smith-Waterman alignments
-# Bio.Wise.dnal is for Smith-Waterman DNA alignments
+"""
+Bio.Wise contains modules for running and processing the output of
+some of the models in the Wise2 package by Ewan Birney available from:
+ftp://ftp.ebi.ac.uk/pub/software/unix/wise2/
+http://www.ebi.ac.uk/Wise2/
+
+Bio.Wise.psw is for protein Smith-Waterman alignments
+Bio.Wise.dnal is for Smith-Waterman DNA alignments
+"""
from __future__ import print_function
diff --git a/Bio/Wise/dnal.py b/Bio/Wise/dnal.py
index 5b7d284..e34f831 100755
--- a/Bio/Wise/dnal.py
+++ b/Bio/Wise/dnal.py
@@ -3,13 +3,15 @@
# license. Please see the LICENSE file that should have been included
# as part of this package.
#
-# Bio.Wise contains modules for running and processing the output of
-# some of the models in the Wise2 package by Ewan Birney available from:
-# ftp://ftp.ebi.ac.uk/pub/software/unix/wise2/
-# http://www.ebi.ac.uk/Wise2/
-#
-# Bio.Wise.psw is for protein Smith-Waterman alignments
-# Bio.Wise.dnal is for Smith-Waterman DNA alignments
+"""
+Bio.Wise contains modules for running and processing the output of
+some of the models in the Wise2 package by Ewan Birney available from:
+ftp://ftp.ebi.ac.uk/pub/software/unix/wise2/
+http://www.ebi.ac.uk/Wise2/
+
+Bio.Wise.psw is for protein Smith-Waterman alignments
+Bio.Wise.dnal is for Smith-Waterman DNA alignments
+"""
from __future__ import print_function
@@ -22,6 +24,7 @@ from Bio._py3k import map
from Bio import Wise
+
_SCORE_MATCH = 4
_SCORE_MISMATCH = -1
_SCORE_GAP_START = -5
diff --git a/Bio/Wise/psw.py b/Bio/Wise/psw.py
index b381f02..c2d6612 100755
--- a/Bio/Wise/psw.py
+++ b/Bio/Wise/psw.py
@@ -3,13 +3,15 @@
# license. Please see the LICENSE file that should have been included
# as part of this package.
#
-# Bio.Wise contains modules for running and processing the output of
-# some of the models in the Wise2 package by Ewan Birney available from:
-# ftp://ftp.ebi.ac.uk/pub/software/unix/wise2/
-# http://www.ebi.ac.uk/Wise2/
-#
-# Bio.Wise.psw is for protein Smith-Waterman alignments
-# Bio.Wise.dnal is for Smith-Waterman DNA alignments
+"""
+Bio.Wise contains modules for running and processing the output of
+some of the models in the Wise2 package by Ewan Birney available from:
+ftp://ftp.ebi.ac.uk/pub/software/unix/wise2/
+http://www.ebi.ac.uk/Wise2/
+
+Bio.Wise.psw is for protein Smith-Waterman alignments
+Bio.Wise.dnal is for Smith-Waterman DNA alignments
+"""
from __future__ import print_function
@@ -19,6 +21,7 @@ import sys
from Bio import Wise
+
_CMDLINE_PSW = ["psw", "-l", "-F"]
_OPTION_GAP_START = "-g"
_OPTION_GAP_EXTENSION = "-e"
diff --git a/Bio/__init__.py b/Bio/__init__.py
index bcb4744..ed35488 100644
--- a/Bio/__init__.py
+++ b/Bio/__init__.py
@@ -10,9 +10,7 @@ of freely available Python tools for computational molecular biology.
http://biopython.org
"""
-__docformat__ = "restructuredtext en" # not just plaintext
-
-__version__ = "1.66"
+__version__ = "1.67"
class MissingExternalDependencyError(Exception):
@@ -90,7 +88,7 @@ class BiopythonExperimentalWarning(BiopythonWarning):
Biopython uses this warning for experimental code ('alpha' or 'beta'
level code) which is released as part of the standard releases to mark
- sub-modules or functions for early adopters to test & give feedback..
+ sub-modules or functions for early adopters to test & give feedback.
Code issuing this warning is likely to change (or even be removed) in
a subsequent release of Biopython. Such code should NOT be used for
@@ -99,9 +97,9 @@ class BiopythonExperimentalWarning(BiopythonWarning):
- You are running the latest release of Biopython, or ideally the
latest code from our repository.
- You are subscribed to the biopython-dev mailing list to provide
- feedback on this code, and to be alterted to changes to it.
+ feedback on this code, and to be alerted of changes to it.
If all goes well, experimental code would be promoted to stable in
- a subsequence release, and this warning removed from it.
+ a subsequent release, and this warning removed from it.
"""
pass
diff --git a/Bio/_py3k/__init__.py b/Bio/_py3k/__init__.py
index 04e464f..4d7f4a4 100644
--- a/Bio/_py3k/__init__.py
+++ b/Bio/_py3k/__init__.py
@@ -15,7 +15,7 @@ There is no similar option for range yet, other than::
range = xrange
input = raw_input
-or::
+or:
from __builtin__ import xrange as range
from __builtin__ import raw_input as input
@@ -25,11 +25,11 @@ importing of built in functions like open changes from Python 2::
from __builtin__ import open
-to this under Python 3::
+To do this under Python 3:
from builtins import open
-Instead, we can do this under either Python 2 or 3::
+Instead, we can do this under either Python 2 or 3:
from Bio._py3k import open
from Bio._py3k import zip
@@ -39,6 +39,7 @@ go away.
"""
import sys
+
if sys.version_info[0] >= 3:
# Code for Python 3
from builtins import open, zip, map, filter, range, input
@@ -89,59 +90,70 @@ if sys.version_info[0] >= 3:
import io
- def _binary_to_string_handle(handle):
- """Treat a binary (bytes) handle like a text (unicode) handle."""
- # TODO, once drop all of Python 3.0 - 3.3, replace this with just:
- #
- # return io.TextIOWrapper(io.BufferedReader(handle))
- #
- # See also http://bugs.python.org/issue5628
- # and http://bugs.python.org/issue13541
- # and http://bugs.python.org/issue13464 which should be fixed in Python 3.3
- #
- # However, still have problems under Python 3.3.0, e.g.
- #
- # $ python3.3 test_SeqIO_online.py
- # test_nuccore_X52960 (__main__.EntrezTests)
- # Bio.Entrez.efetch('nuccore', id='X52960', ...) ... ERROR
- # test_nucleotide_6273291 (__main__.EntrezTests)
- # Bio.Entrez.efetch('nucleotide', id='6273291', ...) ... ERROR
- # test_protein_16130152 (__main__.EntrezTests)
- # Bio.Entrez.efetch('protein', id='16130152', ...) ... ERROR
- # test_get_sprot_raw (__main__.ExPASyTests)
- # Bio.ExPASy.get_sprot_raw("O23729") ... ok
- # ..
- # ValueError: I/O operation on closed file.
- #
- class EvilHandleHack(object):
- def __init__(self, handle):
- self._handle = handle
- try:
- # If wrapping an online handle, this this is nice to have:
- self.url = handle.url
- except AttributeError:
- pass
-
- def read(self, length=None):
- return _as_string(self._handle.read(length))
-
- def readline(self):
- return _as_string(self._handle.readline())
-
- def __iter__(self):
- for line in self._handle:
- yield _as_string(line)
-
- def close(self):
- return self._handle.close()
-
- def seek(self, pos):
- return self._handle.seek(pos)
-
- def tell(self):
- return self._handle.tell()
-
- return EvilHandleHack(handle)
+ if sys.version_info[:2] <= (3, 3):
+ def _binary_to_string_handle(handle):
+ """Treat a binary (bytes) handle like a text (unicode) handle."""
+ # TODO, once drop all of Python 3.0 - 3.3, remove this!
+ #
+ # See also http://bugs.python.org/issue5628
+ # and http://bugs.python.org/issue13541
+ # and http://bugs.python.org/issue13464 which should be fixed in Python 3.3
+ #
+ # However, still have problems under Python 3.3.0, e.g.
+ #
+ # $ python3.3 test_SeqIO_online.py
+ # test_nuccore_X52960 (__main__.EntrezTests)
+ # Bio.Entrez.efetch('nuccore', id='X52960', ...) ... ERROR
+ # test_nucleotide_6273291 (__main__.EntrezTests)
+ # Bio.Entrez.efetch('nucleotide', id='6273291', ...) ... ERROR
+ # test_protein_16130152 (__main__.EntrezTests)
+ # Bio.Entrez.efetch('protein', id='16130152', ...) ... ERROR
+ # test_get_sprot_raw (__main__.ExPASyTests)
+ # Bio.ExPASy.get_sprot_raw("O23729") ... ok
+ # ..
+ # ValueError: I/O operation on closed file.
+ #
+ class EvilHandleHack(object):
+ """Biopython internal class to work around bugs in early versions of Python 3."""
+ def __init__(self, handle):
+ self._handle = handle
+ try:
+ # If wrapping an online handle, this this is nice to have:
+ self.url = handle.url
+ except AttributeError:
+ pass
+
+ def read(self, length=None):
+ return _as_string(self._handle.read(length))
+
+ def readline(self):
+ return _as_string(self._handle.readline())
+
+ def __iter__(self):
+ for line in self._handle:
+ yield _as_string(line)
+
+ def close(self):
+ return self._handle.close()
+
+ def seek(self, pos):
+ return self._handle.seek(pos)
+
+ def tell(self):
+ return self._handle.tell()
+
+ return EvilHandleHack(handle)
+ else:
+ # Python 3.4 onwards, the standard library wrappers should work:
+ def _binary_to_string_handle(handle):
+ """Treat a binary (bytes) handle like a text (unicode) handle."""
+ wrapped = io.TextIOWrapper(io.BufferedReader(handle))
+ try:
+ # If wrapping an online handle, this this is nice to have:
+ wrapped.url = handle.url
+ except AttributeError:
+ pass
+ return wrapped
# This is to avoid the deprecation warning from open(filename, "rU")
_universal_read_mode = "r" # text mode does universal new lines
diff --git a/Bio/_py3k/_ordereddict.py b/Bio/_py3k/_ordereddict.py
index 2d1d813..f5e02f6 100644
--- a/Bio/_py3k/_ordereddict.py
+++ b/Bio/_py3k/_ordereddict.py
@@ -19,6 +19,7 @@
# WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
# OTHER DEALINGS IN THE SOFTWARE.
+"""Python 3 compatibility tools (PRIVATE)."""
from UserDict import DictMixin
diff --git a/Bio/_utils.py b/Bio/_utils.py
index f0270f3..ef798e0 100644
--- a/Bio/_utils.py
+++ b/Bio/_utils.py
@@ -106,7 +106,7 @@ def run_doctest(target_dir=None, *args, **kwargs):
cur_dir = os.path.abspath(os.curdir)
- print("Runing doctests...")
+ print("Running doctests...")
try:
os.chdir(find_test_dir(target_dir))
doctest.testmod(*args, **kwargs)
diff --git a/Bio/bgzf.py b/Bio/bgzf.py
index 168d30e..eb5d3d0 100755
--- a/Bio/bgzf.py
+++ b/Bio/bgzf.py
@@ -1,5 +1,5 @@
#!/usr/bin/env python
-# Copyright 2010-2013 by Peter Cock.
+# Copyright 2010-2015 by Peter Cock.
# All rights reserved.
# This code is part of the Biopython distribution and governed by its
# license. Please see the LICENSE file that should have been included
@@ -227,14 +227,13 @@ NC_000932.1
from __future__ import print_function
-import sys # to detect when under Python 2
+import sys
import zlib
import struct
from Bio._py3k import _as_bytes, _as_string
from Bio._py3k import open as _open
-__docformat__ = "restructuredtext en"
# For Python 2 can just use: _bgzf_magic = '\x1f\x8b\x08\x04'
# but need to use bytes on Python 3
@@ -588,7 +587,7 @@ class BgzfReader(object):
def tell(self):
"""Returns a 64-bit unsigned BGZF virtual offset."""
- if 0 < self._within_block_offset == len(self._buffer):
+ if 0 < self._within_block_offset and self._within_block_offset == len(self._buffer):
# Special case where we're right at the end of a (non empty) block.
# For non-maximal blocks could give two possible virtual offsets,
# but for a maximal block can't use 65536 as the within block
@@ -824,7 +823,6 @@ class BgzfWriter(object):
if __name__ == "__main__":
- import sys
if len(sys.argv) > 1:
print("Call this with no arguments and pipe uncompressed data in on stdin")
print("and it will produce BGZF compressed data on stdout. e.g.")
diff --git a/Bio/codonalign/__init__.py b/Bio/codonalign/__init__.py
index cf9ce0a..baedcba 100644
--- a/Bio/codonalign/__init__.py
+++ b/Bio/codonalign/__init__.py
@@ -7,15 +7,10 @@
"""Code for dealing with Codon Alignment.
"""
from __future__ import print_function
-__docformat__ = "restructuredtext en" # Don't just use plain text in epydoc API pages!
+from Bio import BiopythonWarning
from Bio import BiopythonExperimentalWarning
-import warnings
-warnings.warn('Bio.codonalign is an experimental module which may undergo '
- 'significant changes prior to its future official release.',
- BiopythonExperimentalWarning)
-
try:
from itertools import izip
except ImportError:
@@ -30,6 +25,11 @@ from Bio.codonalign.codonalphabet import CodonAlphabet
from Bio.codonalign.codonalphabet import default_codon_table, default_codon_alphabet
from Bio.codonalign.codonalphabet import get_codon_alphabet as _get_codon_alphabet
+import warnings
+warnings.warn('Bio.codonalign is an experimental module which may undergo '
+ 'significant changes prior to its future official release.',
+ BiopythonExperimentalWarning)
+
def build(pro_align, nucl_seqs, corr_dict=None, gap_char='-', unknown='X',
codon_table=default_codon_table, alphabet=None,
@@ -40,12 +40,12 @@ def build(pro_align, nucl_seqs, corr_dict=None, gap_char='-', unknown='X',
Arguments:
- pro_align - a protein MultipleSeqAlignment object
- nucl_align - an object returned by SeqIO.parse or SeqIO.index
- or a colloction of SeqRecord.
+ or a collection of SeqRecord.
- alphabet - alphabet for the returned codon alignment
- corr_dict - a dict that maps protein id to nucleotide id
- complete_protein - whether the sequence begins with a start
codon
- - frameshift - whether to appply frameshift detection
+ - frameshift - whether to apply frameshift detection
Return a CodonAlignment object
@@ -93,12 +93,12 @@ def build(pro_align, nucl_seqs, corr_dict=None, gap_char='-', unknown='X',
nucl_seqs = tuple(nucl_seqs)
nucl_num = len(nucl_seqs)
if pro_num > nucl_num:
- raise ValueError("More Number of SeqRecords in Protein Alignment "
+ raise ValueError("Higher Number of SeqRecords in Protein Alignment "
"({0}) than the Number of Nucleotide SeqRecords "
"({1}) are found!".format(pro_num, nucl_num))
# Determine the protein sequences and nucl sequences
- # correspondance. If nucl_seqs is a list, tuple or read by
+ # correspondence. If nucl_seqs is a list, tuple or read by
# SeqIO.parse(), we assume the order of sequences in pro_align
# and nucl_seqs are the same. If nucl_seqs is a dict or read by
# SeqIO.index(), we match seqs in pro_align and those in
@@ -109,7 +109,7 @@ def build(pro_align, nucl_seqs, corr_dict=None, gap_char='-', unknown='X',
corr_method = 0
else:
raise TypeError("Nucl Sequences Error, Unknown type to assign "
- "correspondance method")
+ "correspondence method")
else:
if not isinstance(corr_dict, dict):
raise TypeError("corr_dict should be a dict that corresponds "
@@ -134,7 +134,7 @@ def build(pro_align, nucl_seqs, corr_dict=None, gap_char='-', unknown='X',
"than number of protein records "
"({1})".format(len(corr_dict), pro_num))
- # set up pro-nucl correspondance based on corr_method
+ # set up pro-nucl correspondence based on corr_method
# corr_method = 0, consecutive pairing
if corr_method == 0:
pro_nucl_pair = izip(pro_align, nucl_seqs)
@@ -164,9 +164,9 @@ def build(pro_align, nucl_seqs, corr_dict=None, gap_char='-', unknown='X',
pro_nucl_pair.append((pro_rec, nucl_seqs[nucl_id]))
codon_aln = []
- shift = None
+ shift = False
for pair in pro_nucl_pair:
- # Beaware that the following span corresponds to an ungapped
+ # Beware that the following span corresponds to an ungapped
# nucleotide sequence.
corr_span = _check_corr(pair[0], pair[1], gap_char=gap_char,
codon_table=codon_table,
@@ -184,7 +184,7 @@ def build(pro_align, nucl_seqs, corr_dict=None, gap_char='-', unknown='X',
codon_aln.append(codon_rec)
if corr_span[1] == 2:
shift = True
- if shift is True:
+ if shift:
return CodonAlignment(_align_shift_recs(codon_aln), alphabet=alphabet)
else:
return CodonAlignment(codon_aln, alphabet=alphabet)
@@ -204,7 +204,7 @@ def _codons2re(codons):
def _get_aa_regex(codon_table, stop='*', unknown='X'):
"""Set up the regular expression of a given CodonTable for
- futher use.
+ further use.
>>> from Bio.Data.CodonTable import generic_by_id
>>> p = generic_by_id[1]
@@ -245,8 +245,8 @@ def _check_corr(pro, nucl, gap_char='-', codon_table=default_codon_table,
import re
from Bio.Alphabet import NucleotideAlphabet
- if not all([isinstance(pro, SeqRecord), isinstance(nucl, SeqRecord)]):
- raise TypeError("_check_corr accept two SeqRecord object. Please "
+ if not isinstance(pro, SeqRecord) or not isinstance(nucl, SeqRecord):
+ raise TypeError("_check_corr accepts two SeqRecord object. Please "
"check your input.")
def get_alpha(alpha):
@@ -299,7 +299,7 @@ def _check_corr(pro, nucl, gap_char='-', codon_table=default_codon_table,
# pro_re
if this_anchor_len == anchor_len:
for aa in anchor:
- if complete_protein is True and i == 0:
+ if complete_protein and i == 0:
qcodon += _codons2re(codon_table.start_codons)
fncodon += aa2re['X']
continue
@@ -336,7 +336,7 @@ def _check_corr(pro, nucl, gap_char='-', codon_table=default_codon_table,
first_anchor = True
shift_id_pos = 0
# check the first anchor
- if first_anchor is True and anchor_pos[0][2] != 0:
+ if first_anchor and anchor_pos[0][2] != 0:
shift_val_lst = [1, 2, 3 * anchor_len - 2, 3 * anchor_len - 1, 0]
sh_anc = anchors[0]
for shift_val in shift_val_lst:
@@ -364,7 +364,7 @@ def _check_corr(pro, nucl, gap_char='-', codon_table=default_codon_table,
break
if qcodon == -1:
warnings.warn("first frameshift detection failed for "
- "{0}".format(nucl.id))
+ "{0}".format(nucl.id), BiopythonWarning)
# check anchors in the middle
for i in range(len(anchor_pos) - 1):
shift_val = (anchor_pos[i + 1][0] - anchor_pos[i][0]) % \
@@ -383,7 +383,7 @@ def _check_corr(pro, nucl, gap_char='-', codon_table=default_codon_table,
qcodon = None
elif qcodon == -1:
warnings.warn("middle frameshift detection failed for "
- "{0}".format(nucl.id))
+ "{0}".format(nucl.id), BiopythonWarning)
# check the last anchor
if anchor_pos[-1][2] + 1 == len(anchors) - 1:
sh_anc = anchors[-1]
@@ -414,7 +414,7 @@ def _check_corr(pro, nucl, gap_char='-', codon_table=default_codon_table,
break
if qcodon == -1:
warnings.warn("last frameshift detection failed for "
- "{0}".format(nucl.id))
+ "{0}".format(nucl.id), BiopythonWarning)
# try global match
full_pro_re = "".join(pro_re)
match = re.search(full_pro_re, nucl_seq)
@@ -561,7 +561,7 @@ def _get_codon_rec(pro, nucl, span_mode, alphabet, gap_char="-",
for aa in pro.seq:
if aa == "-":
codon_seq += "---"
- elif complete_protein is True and aa_num == 0:
+ elif complete_protein and aa_num == 0:
this_codon = nucl_seq._data[span[0]:span[0] + 3]
if not re.search(_codons2re[codon_table.start_codons],
this_codon.upper()):
@@ -569,8 +569,8 @@ def _get_codon_rec(pro, nucl, span_mode, alphabet, gap_char="-",
warnings.warn("start codon of {0} ({1} {2}) does not "
"correspond to {3} "
"({4})".format(pro.id, aa, aa_num,
- nucl.id, this_codon)
- )
+ nucl.id, this_codon),
+ BiopythonWarning)
if max_score == 0:
raise RuntimeError("max_score reached for {0}! Please "
"raise up the tolerance to get an "
@@ -583,7 +583,8 @@ def _get_codon_rec(pro, nucl, span_mode, alphabet, gap_char="-",
if not str(Seq(this_codon.upper()).translate(table=codon_table)) == aa:
max_score -= 1
warnings.warn("%s(%s %d) does not correspond to %s(%s)"
- % (pro.id, aa, aa_num, nucl.id, this_codon))
+ % (pro.id, aa, aa_num, nucl.id, this_codon),
+ BiopythonWarning)
if max_score == 0:
raise RuntimeError("max_score reached for {0}! Please "
"raise up the tolerance to get an "
@@ -623,15 +624,15 @@ def _get_codon_rec(pro, nucl, span_mode, alphabet, gap_char="-",
for aa in pro.seq:
if aa == "-":
codon_seq += "---"
- elif complete_protein is True and aa_num == 0:
+ elif complete_protein and aa_num == 0:
this_codon = nucl_seq._data[rf_table[0]:rf_table[0] + 3]
if not re.search(_codons2re[codon_table.start_codons],
this_codon.upper()):
max_score -= 1
warnings.warn("start codon of {0}({1} {2}) does not "
"correspond to {3}({4})".format(
- pro.id, aa, aa_num, nucl.id, this_codon)
- )
+ pro.id, aa, aa_num, nucl.id, this_codon),
+ BiopythonWarning)
codon_seq += this_codon
aa_num += 1
else:
@@ -658,8 +659,8 @@ def _get_codon_rec(pro, nucl, span_mode, alphabet, gap_char="-",
warnings.warn("Codon of {0}({1} {2}) does not "
"correspond to {3}({4})".format(
pro.id, aa, aa_num, nucl.id,
- this_codon)
- )
+ this_codon),
+ BiopythonWarning)
if max_score == 0:
raise RuntimeError("max_score reached for {0}! Please "
"raise up the tolerance to get an "
diff --git a/Bio/codonalign/chisq.py b/Bio/codonalign/chisq.py
index 077dd7a..1a4f130 100644
--- a/Bio/codonalign/chisq.py
+++ b/Bio/codonalign/chisq.py
@@ -4,9 +4,257 @@ Adapted from SciPy: scipy/special/cephes/{chdtr,igam}.
"""
import math
-import sys
-__docformat__ = "restructuredtext en"
+try:
+ from math import lgamma as _lgamma
+except ImportError:
+ # Missing in Python 2.6, using Python Python recipe from
+ # http://code.activestate.com/recipes/576393/
+ # with PEP8 whitespace and minor import changes
+ #
+ # * ====================================================
+ # * Copyright (C) 1993 by Sun Microsystems, Inc. All rights reserved.
+ # *
+ # * Developed at SunPro, a Sun Microsystems, Inc. business.
+ # * Permission to use, copy, modify, and distribute this
+ # * software is freely granted, provided that this notice
+ # * is preserved.
+ # * ====================================================
+ two52 = 4.50359962737049600000e+15
+ half = 5.00000000000000000000e-01
+ one = 1.00000000000000000000e+00
+ pi = 3.14159265358979311600e+00
+ a0 = 7.72156649015328655494e-02
+ a1 = 3.22467033424113591611e-01
+ a2 = 6.73523010531292681824e-02
+ a3 = 2.05808084325167332806e-02
+ a4 = 7.38555086081402883957e-03
+ a5 = 2.89051383673415629091e-03
+ a6 = 1.19270763183362067845e-03
+ a7 = 5.10069792153511336608e-04
+ a8 = 2.20862790713908385557e-04
+ a9 = 1.08011567247583939954e-04
+ a10 = 2.52144565451257326939e-05
+ a11 = 4.48640949618915160150e-05
+ tc = 1.46163214496836224576e+00
+ tf = -1.21486290535849611461e-01
+ # /* tt = -(tail of tf) */
+ tt = -3.63867699703950536541e-18
+ t0 = 4.83836122723810047042e-01
+ t1 = -1.47587722994593911752e-01
+ t2 = 6.46249402391333854778e-02
+ t3 = -3.27885410759859649565e-02
+ t4 = 1.79706750811820387126e-02
+ t5 = -1.03142241298341437450e-02
+ t6 = 6.10053870246291332635e-03
+ t7 = -3.68452016781138256760e-03
+ t8 = 2.25964780900612472250e-03
+ t9 = -1.40346469989232843813e-03
+ t10 = 8.81081882437654011382e-04
+ t11 = -5.38595305356740546715e-04
+ t12 = 3.15632070903625950361e-04
+ t13 = -3.12754168375120860518e-04
+ t14 = 3.35529192635519073543e-04
+ u0 = -7.72156649015328655494e-02
+ u1 = 6.32827064025093366517e-01
+ u2 = 1.45492250137234768737e+00
+ u3 = 9.77717527963372745603e-01
+ u4 = 2.28963728064692451092e-01
+ u5 = 1.33810918536787660377e-02
+ v1 = 2.45597793713041134822e+00
+ v2 = 2.12848976379893395361e+00
+ v3 = 7.69285150456672783825e-01
+ v4 = 1.04222645593369134254e-01
+ v5 = 3.21709242282423911810e-03
+ s0 = -7.72156649015328655494e-02
+ s1 = 2.14982415960608852501e-01
+ s2 = 3.25778796408930981787e-01
+ s3 = 1.46350472652464452805e-01
+ s4 = 2.66422703033638609560e-02
+ s5 = 1.84028451407337715652e-03
+ s6 = 3.19475326584100867617e-05
+ r1 = 1.39200533467621045958e+00
+ r2 = 7.21935547567138069525e-01
+ r3 = 1.71933865632803078993e-01
+ r4 = 1.86459191715652901344e-02
+ r5 = 7.77942496381893596434e-04
+ r6 = 7.32668430744625636189e-06
+ w0 = 4.18938533204672725052e-01
+ w1 = 8.33333333333329678849e-02
+ w2 = -2.77777777728775536470e-03
+ w3 = 7.93650558643019558500e-04
+ w4 = -5.95187557450339963135e-04
+ w5 = 8.36339918996282139126e-04
+ w6 = -1.63092934096575273989e-03
+ zero = 0.00000000000000000000e+00
+
+ # inf = float('inf')
+ # nan = float('nan')
+ inf = float(9e999)
+
+ def _sin_pi(x):
+ x = float(x)
+ e, ix = math.frexp(x)
+ if(abs(x) < 0.25):
+ return -math.sin(pi * x)
+ y = -x # /* x is assume negative */
+
+ # * argument reduction, make sure inexact flag not raised if input
+ # * is an integer
+ z = floor(y)
+ if(z != y):
+ y *= 0.5
+ y = 2.0 * (y - floor(y)) # /* y = |x| mod 2.0 */
+ n = int(y * 4.0)
+ else:
+ if(abs(ix) >= 53):
+ y = zero
+ n = 0 # /* y must be even */
+ else:
+ if(abs(ix) < 52):
+ z = y + two52 # /* exact */
+ e, n = math.frexp(z)
+ n &= 1
+ y = n
+ n <<= 2
+
+ if n == 0:
+ y = sin(pi * y)
+ elif (n == 1 or n == 2):
+ y = cos(pi * (0.5 - y))
+ elif (n == 3 or n == 4):
+ y = sin(pi * (one - y))
+ elif (n == 5 or n == 6):
+ y = -cos(pi * (y - 1.5))
+ else:
+ y = sin(pi * (y - 2.0))
+
+ z = cos(pi * (z + 1.0))
+ return -y * z
+
+ def _lgamma(x):
+ """Natural logarithm of gamma function of x
+
+ raise ValueError if x is negative integer."""
+ x = float(x)
+
+ # /* purge off +-inf, NaN, +-0, and negative arguments */
+ if ((x == inf) or (x == -inf)):
+ return inf
+ # if (x is nan):
+ # return nan
+
+ e, ix = math.frexp(x)
+ nadj = 0
+ signgamp = 1
+
+ if ((e == 0.0) and (ix == 0)):
+ return inf
+
+ if (ix > 1020):
+ return inf
+
+ if ((e != 0.0) and (ix < -71)):
+ if (x < 0):
+ return -math.log(-x)
+ else:
+ return -math.log(x)
+
+ if e < 0:
+ if ix > 52:
+ return inf # one/zero
+ t = sin_pi(x)
+ if t == zero:
+ # return inf
+ raise ValueError('gamma not defined for negative integer')
+ nadj = math.log(pi / fabs(t * x))
+ if t < zero:
+ signgamp = -1
+ x = -x
+
+ # /* purge off 1 and 2 */
+ if x == 2.0 or x == 1.0:
+ r = 0.0
+
+ # /* for x < 2.0 */
+ elif ix < 2:
+ if x <= 0.9: # /* lgamma(x) = lgamma(x+1)-log(x) */
+ r = -math.log(x)
+ if x >= 0.7316:
+ y = one - x
+ z = y * y
+ p1 = a0 + z * (a2 + z * (a4 + z * (a6 + z * (a8 + z * a10))))
+ p2 = z * (a1 + z * (a3 + z * (a5 + z * (a7 + z * (a9 + z * a11)))))
+ p = y * p1 + p2
+ r += (p - 0.5 * y)
+ elif (x >= 0.23164):
+ y = x - (tc - one)
+ z = y * y
+ w = z * y
+ p1 = t0 + w * (t3 + w * (t6 + w * (t9 + w * t12))) # /* parallel comp */
+ p2 = t1 + w * (t4 + w * (t7 + w * (t10 + w * t13)))
+ p3 = t2 + w * (t5 + w * (t8 + w * (t11 + w * t14)))
+ p = z * p1 - (tt - w * (p2 + y * p3))
+ r += (tf + p)
+ else:
+ y = x
+ p1 = y * (u0 + y * (u1 + y * (u2 + y * (u3 + y * (u4 + y * u5)))))
+ p2 = one + y * (v1 + y * (v2 + y * (v3 + y * (v4 + y * v5))))
+ r += (-0.5 * y + p1 / p2)
+ else:
+ r = zero
+ if(x >= 1.7316):
+ y = 2.0 - x # /* [1.7316,2] */
+ z = y * y
+ p1 = a0 + z * (a2 + z * (a4 + z * (a6 + z * (a8 + z * a10))))
+ p2 = z * (a1 + z * (a3 + z * (a5 + z * (a7 + z * (a9 + z * a11)))))
+ p = y * p1 + p2
+ r += (p - 0.5 * y)
+ elif(x >= 1.23164):
+ y = x - tc # /* [1.23,1.73] */
+ z = y * y
+ w = z * y
+ p1 = t0 + w * (t3 + w * (t6 + w * (t9 + w * t12))) # /* parallel comp */
+ p2 = t1 + w * (t4 + w * (t7 + w * (t10 + w * t13)))
+ p3 = t2 + w * (t5 + w * (t8 + w * (t11 + w * t14)))
+ p = z * p1 - (tt - w * (p2 + y * p3))
+ r += (tf + p)
+ else:
+ y = x - one
+ p1 = y * (u0 + y * (u1 + y * (u2 + y * (u3 + y * (u4 + y * u5)))))
+ p2 = one + y * (v1 + y * (v2 + y * (v3 + y * (v4 + y * v5))))
+ r += (-0.5 * y + p1 / p2)
+
+ # /* x < 8.0 */
+ elif(ix < 4):
+ i = int(x)
+ t = zero
+ y = x - i
+ p = y * (s0 + y * (s1 + y * (s2 + y * (s3 + y * (s4 + y * (s5 + y * s6))))))
+ q = one + y * (r1 + y * (r2 + y * (r3 + y * (r4 + y * (r5 + y * r6)))))
+ r = half * y + p / q
+ z = one # /* lgamma(1+s) = log(s) + lgamma(s) */
+ while (i > 2):
+ i -= 1
+ z *= (y + i)
+ r += log(z)
+
+ # /* 8.0 <= x < 2**58 */
+ elif (ix < 58):
+ t = log(x)
+ z = one / x
+ y = z * z
+ w = w0 + z * (w1 + y * (w2 + y * (w3 + y * (w4 + y * (w5 + y * w6)))))
+ r = (x - half) * (t - one) + w
+
+ # /* 2**58 <= x <= inf */
+ else:
+ r = x * (log(x) - one)
+
+ if (e < 0):
+ r = nadj - r
+ return signgamp * r
+
# Cephes Math Library Release 2.0: April, 1987
# Copyright 1985, 1987 by Stephen L. Moshier
@@ -74,7 +322,8 @@ def _igamc(a, x):
values of a and x.
"""
# Compute x**a * exp(-x) / Gamma(a)
- ax = math.exp(a * math.log(x) - x - math.lgamma(a))
+ # TODO: Return this to math.lgamma once drop Python 2.6
+ ax = math.exp(a * math.log(x) - x - _lgamma(a))
# Continued fraction
y = 1.0 - a
diff --git a/Bio/codonalign/codonalignment.py b/Bio/codonalign/codonalignment.py
index 5796883..f75ae77 100644
--- a/Bio/codonalign/codonalignment.py
+++ b/Bio/codonalign/codonalignment.py
@@ -5,13 +5,11 @@
# as part of this package.
"""Code for dealing with Codon Alignment.
-CodonAlignment class is interited from MultipleSeqAlignment class. This is
+CodonAlignment class is inherited from MultipleSeqAlignment class. This is
the core class to deal with codon alignment in biopython.
"""
from __future__ import division, print_function
-__docformat__ = "restructuredtext en" # Don't just use plain text in epydoc API pages!
-
from Bio.Align import MultipleSeqAlignment
from Bio.SeqRecord import SeqRecord
@@ -43,7 +41,7 @@ class CodonAlignment(MultipleSeqAlignment):
# check the type of the alignment to be nucleotide
for rec in self:
if not isinstance(rec.seq, CodonSeq):
- raise TypeError("CodonSeq object are expected in each "
+ raise TypeError("CodonSeq objects are expected in each "
"SeqRecord in CodonAlignment")
assert self.get_alignment_length() % 3 == 0, \
@@ -155,7 +153,7 @@ class CodonAlignment(MultipleSeqAlignment):
dn_tree = dn_constructor.nj(dn_dm)
ds_tree = ds_constructor.nj(ds_dm)
else:
- raise RuntimeError("Unkown tree method ({0}). Only NJ and UPGMA "
+ raise RuntimeError("Unknown tree method ({0}). Only NJ and UPGMA "
"are accepted.".format(tree_method))
return dn_tree, ds_tree
@@ -182,8 +180,8 @@ def mktest(codon_alns, codon_table=default_codon_table, alpha=0.05):
Return the p-value of test result
"""
import copy
- if not all([isinstance(i, CodonAlignment) for i in codon_alns]):
- raise TypeError("mktest accept CodonAlignment list.")
+ if not all(isinstance(i, CodonAlignment) for i in codon_alns):
+ raise TypeError("mktest accepts CodonAlignment list.")
codon_aln_len = [i.get_alignment_length() for i in codon_alns]
if len(set(codon_aln_len)) != 1:
raise RuntimeError("CodonAlignment object for mktest should be of"
@@ -212,7 +210,7 @@ def mktest(codon_alns, codon_table=default_codon_table, alpha=0.05):
all_codon = i[0].union(*i[1:])
if '-' in all_codon or len(all_codon) == 1:
continue
- fix_or_not = all([len(k) == 1 for k in i])
+ fix_or_not = all(len(k) == 1 for k in i)
if fix_or_not:
# fixed
nonsyn_subgraph = _get_subgraph(all_codon, nonsyn_G)
@@ -233,7 +231,7 @@ def mktest(codon_alns, codon_table=default_codon_table, alpha=0.05):
def _get_codon2codon_matrix(codon_table=default_codon_table):
- """Function to get codon codon subsitution matrix. Elements
+ """Function to get codon codon substitution matrix. Elements
in the matrix are number of synonymous and nonsynonymous
substitutions required for the substitution (PRIVATE).
"""
@@ -287,7 +285,7 @@ def _dijkstra(graph, start, end):
Dijkstra's algorithm Python implementation.
Algorithm adapted from
http://thomas.pelletier.im/2010/02/dijkstras-algorithm-python-implementation/.
- However, an abvious bug in::
+ However, an obvious bug in::
if D[child_node] >(<) D[node] + child_value:
@@ -296,12 +294,12 @@ def _dijkstra(graph, start, end):
Arguments:
- - graph: Dictionnary of dictionnary (keys are vertices).
+ - graph: Dictionary of dictionary (keys are vertices).
- start: Start vertex.
- end: End vertex.
Output:
- List of vertices from the beggining to the end.
+ List of vertices from the beginning to the end.
"""
D = {} # Final distances dict
P = {} # Predecessor dict
@@ -419,7 +417,7 @@ def _G_test(site_counts):
- site_counts - [syn_fix, nonsyn_fix, syn_poly, nonsyn_poly]
- >>> round(_G_test([17, 7, 42, 2]), 7)
+ >>> print("%0.6f" % _G_test([17, 7, 42, 2]))
0.004924
"""
# TODO:
diff --git a/Bio/codonalign/codonalphabet.py b/Bio/codonalign/codonalphabet.py
index e45033d..4426284 100644
--- a/Bio/codonalign/codonalphabet.py
+++ b/Bio/codonalign/codonalphabet.py
@@ -5,11 +5,9 @@
# as part of this package.
"""Code for Codon Alphabet.
-CodonAlphabet class is interited from Alphabet class. It is an
+CodonAlphabet class is inherited from Alphabet class. It is an
alphabet for CodonSeq class.
"""
-__docformat__ = "restructuredtext en" # Don't just use plain text in epydoc API pages!
-
import copy
try:
@@ -17,9 +15,7 @@ try:
except ImportError:
izip = zip
-from Bio.Seq import Seq
-from Bio.SeqRecord import SeqRecord
-from Bio.Alphabet import IUPAC, Gapped, HasStopCodon, Alphabet, generic_dna
+from Bio.Alphabet import IUPAC, Gapped, HasStopCodon, Alphabet
from Bio.Data.CodonTable import generic_by_id
default_codon_table = copy.deepcopy(generic_by_id[1])
@@ -27,8 +23,8 @@ default_codon_table = copy.deepcopy(generic_by_id[1])
def get_codon_alphabet(alphabet, gap="-", stop="*"):
"""Gets alignment alphabet for codon alignment.
-
- Only nucleotide alphabet is accepted. Raise an error when the type of
+
+ Only nucleotide alphabet is accepted. Raise an error when the type of
alphabet is incompatible.
"""
from Bio.Alphabet import NucleotideAlphabet
diff --git a/Bio/codonalign/codonseq.py b/Bio/codonalign/codonseq.py
index f01435e..0afe6e3 100644
--- a/Bio/codonalign/codonseq.py
+++ b/Bio/codonalign/codonseq.py
@@ -1,11 +1,11 @@
-# Copyright 2013 by Zheng Ruan (zruan1991 at gmai.com).
+# Copyright 2013 by Zheng Ruan (zruan1991 at gmail.com).
# All rights reserved.
# This code is part of the Biopython distribution and governed by its
# license. Please see the LICENSE file that should have been included
# as part of this package.
"""Code for dealing with coding sequence.
-CodonSeq class is interited from Seq class. This is the core class to
+CodonSeq class is inherited from Seq class. This is the core class to
deal with sequences in CodonAlignment in biopython.
"""
@@ -15,14 +15,10 @@ from math import log
from Bio.Seq import Seq
from Bio.SeqRecord import SeqRecord
-from Bio.Alphabet import IUPAC, Gapped, HasStopCodon, Alphabet
from Bio.Alphabet import generic_dna, _ungap
-from Bio.Data.CodonTable import generic_by_id
from Bio.codonalign.codonalphabet import default_codon_alphabet, default_codon_table
-__docformat__ = "restructuredtext en"
-
class CodonSeq(Seq):
"""CodonSeq is designed to be within the SeqRecords of a CodonAlignment class.
@@ -116,7 +112,7 @@ class CodonSeq(Seq):
raise RuntimeError("frameshift detected. "
"CodonSeq object is not able to deal "
"with codon sequence with frameshift. "
- "Plase use normal slice option.")
+ "Please use normal slice option.")
if isinstance(index, int):
if index != -1:
return self._data[index * 3:(index + 1) * 3]
@@ -156,7 +152,7 @@ class CodonSeq(Seq):
purpose.
"""
amino_acids = []
- if ungap_seq is True:
+ if ungap_seq:
tr_seq = self._data.replace(self.gap_char, "")
else:
tr_seq = self._data
@@ -188,8 +184,8 @@ class CodonSeq(Seq):
try:
amino_acids.append(codon_table.forward_table[codon])
except KeyError:
- raise RuntimeError("Unknown codon detected ({0}). Do you "
- "forget to speficy ungap_seq "
+ raise RuntimeError("Unknown codon detected ({0}). Did you "
+ "forget to specify the ungap_seq "
"argument?".format(codon))
return "".join(amino_acids)
@@ -231,7 +227,7 @@ class CodonSeq(Seq):
try:
this_len = len(self._data[i:i + 3].replace("-", ""))
relative_pos[codon_num] -= this_len
- except:
+ except Exception: # TODO: IndexError?
# we probably reached the last codon
pass
return full_rf_table
@@ -290,7 +286,7 @@ def _get_codon_list(codonseq):
elif str(codonseq[int(k):int(k) + 3]) == "---":
codon_lst.append("---")
else:
- # this may be problematic, as normally no codon shoud
+ # this may be problematic, as normally no codon should
# fall into this condition
codon_lst.append(codonseq[int(k):int(k) + 3])
return codon_lst
@@ -314,16 +310,14 @@ def cal_dn_ds(codon_seq1, codon_seq2, method="NG86",
Arguments:
- codon_seq1 - CodonSeq or or SeqRecord that contains a CodonSeq
- codon_seq2 - CodonSeq or or SeqRecord that contains a CodonSeq
- - w - transition/transvertion ratio
+ - w - transition/transversion ratio
- cfreq - Current codon frequency vector can only be specified
when you are using ML method. Possible ways of
getting cfreq are: F1x4, F3x4 and F61.
"""
- if all([isinstance(codon_seq1, CodonSeq),
- isinstance(codon_seq2, CodonSeq)]):
+ if isinstance(codon_seq1, CodonSeq) and isinstance(codon_seq2, CodonSeq):
pass
- elif all([isinstance(codon_seq1, SeqRecord),
- isinstance(codon_seq2, SeqRecord)]):
+ elif isinstance(codon_seq1, SeqRecord) and isinstance(codon_seq2, SeqRecord):
codon_seq1 = codon_seq1.seq
codon_seq2 = codon_seq2.seq
else:
@@ -459,12 +453,10 @@ def _count_diff_NG86(codon1, codon2, codon_table=default_codon_table):
The function will take multiple pathways from codon1 to codon2
into account.
"""
- if not all([isinstance(codon1, str), isinstance(codon2, str)]):
- raise TypeError("_count_diff_NG86 accept string object to represent "
- "codon ({0}, {1} detected)".format(
- type(codon1),
- type(codon2))
- )
+ if not isinstance(codon1, str) or not isinstance(codon2, str):
+ raise TypeError("_count_diff_NG86 accepts string object "
+ "to represent codon ({0}, {1} "
+ "detected)".format(type(codon1), type(codon2)))
if len(codon1) != 3 or len(codon2) != 3:
raise RuntimeError("codon should be three letter string ({0}, {1} "
"detected)".format(len(codon1), len(codon2)))
@@ -472,13 +464,13 @@ def _count_diff_NG86(codon1, codon2, codon_table=default_codon_table):
if codon1 == '---' or codon2 == '---':
return SN
base_tuple = ('A', 'C', 'G', 'T')
- if not all([i in base_tuple for i in codon1]):
+ if not all(i in base_tuple for i in codon1):
raise RuntimeError("Unrecognized character detected in codon1 {0} "
- "(Codon is consist of "
+ "(Codons consist of "
"A, T, C or G)".format(codon1))
- if not all([i in base_tuple for i in codon2]):
+ if not all(i in base_tuple for i in codon2):
raise RuntimeError("Unrecognized character detected in codon2 {0} "
- "(Codon is consist of "
+ "(Codons consist of "
"A, T, C or G)".format(codon2))
if codon1 == codon2:
return SN
@@ -650,8 +642,8 @@ def _diff_codon(codon1, codon2, fold_dict):
P4 += 1
else:
raise RuntimeError("Unexpected fold_num %d" % fold_num[n])
- if i != j and ((i in purine and j in pyrimidine)
- or (i in pyrimidine and j in purine)):
+ if i != j and ((i in purine and j in pyrimidine) or
+ (i in pyrimidine and j in purine)):
if fold_num[n] == '0':
Q0 += 1
elif fold_num[n] == '2':
@@ -750,7 +742,7 @@ def _yn00(seq1, seq2, k, codon_table):
codon_table.stop_codons if 'U' not in i]
Q = _get_Q(pi, kappa, w, codon_lst, codon_table)
P = expm(Q * t)
- TV = [0, 0, 0, 0] # synonymous/nonsynonymous transition/transvertion
+ TV = [0, 0, 0, 0] # synonymous/nonsynonymous transition/transversion
sites = [0, 0]
codon_npath = {}
for i, j in zip(seq1, seq2):
@@ -896,30 +888,28 @@ def _count_diff_YN00(codon1, codon2, P, codon_lst,
The function will weighted multiple pathways from codon1 to codon2
according to P matrix of codon substitution. The proportion
- of transition and transvertion (TV) will also be calculated in
+ of transition and transversion (TV) will also be calculated in
the function.
"""
- if not all([isinstance(codon1, str), isinstance(codon2, str)]):
- raise TypeError("_count_diff_YN00 accept string object to represent "
- "codon ({0}, {1} detected)".format(
- type(codon1),
- type(codon2))
- )
+ if not isinstance(codon1, str) or not isinstance(codon2, str):
+ raise TypeError("_count_diff_YN00 accepts string object "
+ "to represent codon ({0}, {1} "
+ "detected)".format(type(codon1), type(codon2)))
if len(codon1) != 3 or len(codon2) != 3:
raise RuntimeError("codon should be three letter string ({0}, {1} "
"detected)".format(len(codon1), len(codon2)))
- TV = [0, 0, 0, 0] # transition and transvertion counts (synonymous and nonsynonymous)
+ TV = [0, 0, 0, 0] # transition and transversion counts (synonymous and nonsynonymous)
site = 0
if codon1 == '---' or codon2 == '---':
return TV
base_tuple = ('A', 'C', 'G', 'T')
- if not all([i in base_tuple for i in codon1]):
+ if not all(i in base_tuple for i in codon1):
raise RuntimeError("Unrecognized character detected in codon1 {0} "
- "(Codon is consist of "
+ "(Codons consist of "
"A, T, C or G)".format(codon1))
- if not all([i in base_tuple for i in codon2]):
+ if not all(i in base_tuple for i in codon2):
raise RuntimeError("Unrecognized character detected in codon2 {0} "
- "(Codon is consist of "
+ "(Codons consist of "
"A, T, C or G)".format(codon2))
if codon1 == codon2:
return TV
diff --git a/Bio/kNN.py b/Bio/kNN.py
index fc000a6..e5d0f65 100644
--- a/Bio/kNN.py
+++ b/Bio/kNN.py
@@ -33,14 +33,12 @@ Weighting Functions:
import numpy
-__docformat__ = "restructuredtext en"
-
class kNN(object):
"""Holds information necessary to do nearest neighbors classification.
Members:
-
+
- classes Set of the possible classes.
- xs List of the neighbors.
- ys List of the classes that the neighbors belong to.
diff --git a/Bio/motifs/__init__.py b/Bio/motifs/__init__.py
index 2ade664..4118fe2 100644
--- a/Bio/motifs/__init__.py
+++ b/Bio/motifs/__init__.py
@@ -19,8 +19,6 @@ from Bio._py3k import range
import math
-__docformat__ = "restructuredtext en"
-
def create(instances, alphabet=None):
instances = Instances(instances, alphabet)
@@ -440,6 +438,16 @@ class Motif(object):
"""
from Bio._py3k import urlopen, urlencode, Request
+ from Bio import Alphabet
+
+ if isinstance(self.alphabet, Alphabet.ProteinAlphabet):
+ alpha = "alphabet_protein"
+ elif isinstance(self.alphabet, Alphabet.RNAAlphabet):
+ alpha = "alphabet_rna"
+ elif isinstance(self.alphabet, Alphabet.DNAAlphabet):
+ alpha = "alphabet_dna"
+ else:
+ alpha = "auto"
frequencies = self.format('transfac')
url = 'http://weblogo.threeplusone.com/create.cgi'
@@ -447,7 +455,7 @@ class Motif(object):
'format': format.lower(),
'stack_width': 'medium',
'stack_per_line': '40',
- 'alphabet': 'alphabet_dna',
+ 'alphabet': alpha,
'ignore_lower_case': True,
'unit_name': "bits",
'first_index': '1',
diff --git a/Bio/motifs/applications/__init__.py b/Bio/motifs/applications/__init__.py
index 243d576..c2743e0 100644
--- a/Bio/motifs/applications/__init__.py
+++ b/Bio/motifs/applications/__init__.py
@@ -4,6 +4,4 @@
# license. Please see the LICENSE file that should have been included
# as part of this package.
"""Motif command line tool wrappers."""
-from ._alignace import AlignAceCommandline
-from ._alignace import CompareAceCommandline
from ._xxmotif import XXmotifCommandline
diff --git a/Bio/motifs/applications/_alignace.py b/Bio/motifs/applications/_alignace.py
deleted file mode 100644
index 37ea10d..0000000
--- a/Bio/motifs/applications/_alignace.py
+++ /dev/null
@@ -1,146 +0,0 @@
-# Copyright 2003-2009 by Bartek Wilczynski. All rights reserved.
-# Revisions copyright 2009 by Peter Cock.
-# This code is part of the Biopython distribution and governed by its
-# license. Please see the LICENSE file that should have been included
-# as part of this package.
-"""This module provides code to work with the standalone version of AlignACE,
-for motif search in DNA sequences.
-
-AlignACE homepage:
-
-http://arep.med.harvard.edu/mrnadata/mrnasoft.html
-
-AlignACE Citations:
-
-Computational identification of cis-regulatory elements associated with
-groups of functionally related genes in Saccharomyces cerevisiae,
-Hughes, JD, Estep, PW, Tavazoie S, & GM Church, Journal of Molecular
-Biology 2000 Mar 10;296(5):1205-14.
-
-Finding DNA Regulatory Motifs within Unaligned Non-Coding Sequences
-Clustered by Whole-Genome mRNA Quantitation,
-Roth, FR, Hughes, JD, Estep, PE & GM Church, Nature Biotechnology
-1998 Oct;16(10):939-45.
-
-"""
-from __future__ import print_function
-
-from Bio.Application import AbstractCommandline, _Option, _Argument
-
-import warnings
-from Bio import BiopythonDeprecationWarning
-
-
-class AlignAceCommandline(AbstractCommandline):
- """Create a commandline for the AlignAce program (DEPRECATED).
-
- Example:
-
- >>> from Bio.motifs.applications import AlignAceCommandline
- >>> in_file = "sequences.fasta"
- >>> alignace_cline = AlignAceCommandline(infile=in_file, gcback=0.55)
- >>> print(alignace_cline)
- AlignACE -i sequences.fasta -gcback 0.55
-
- You would typically run the command line with alignace_cline() or via
- the Python subprocess module, as described in the Biopython tutorial.
- """
- def __init__(self, cmd="AlignACE", **kwargs):
- warnings.warn("""The AlignACE application wrapper is deprecated and
- is likely to be removed in a future release of Biopython,
- since an up to date version of the AlignACE software
- cannot be obtained anymore. If you have a copy of
- AlignACE 4, please consider contacting the Biopython
- developers.""", BiopythonDeprecationWarning)
- self.parameters = \
- [
- _Option(["-i", "infile"],
- "Input Sequence file in FASTA format.",
- checker_function=lambda x: isinstance(x, str),
- equate=False,
- filename=True),
-
- _Option(["-numcols", "numcols"],
- "Number of columns to align",
- equate=False,
- checker_function=lambda x: isinstance(x, int)),
-
- _Option(["-expect", "expect"],
- "number of sites expected in model",
- equate=False,
- checker_function=lambda x: isinstance(x, int)),
-
- _Option(["-gcback", "gcback"],
- "background fractional GC content of input sequence",
- equate=False,
- checker_function=lambda x: isinstance(x, float)),
-
- _Option(["-minpass", "minpass"],
- "minimum number of non-improved passes in phase 1",
- equate=False,
- checker_function=lambda x: isinstance(x, int)),
-
- _Option(["-seed", "seed"],
- "set seed for random number generator (time)",
- equate=False,
- checker_function=lambda x: isinstance(x, int)),
-
- _Option(["-undersample", "undersample"],
- "possible sites / (expect * numcols * seedings)",
- equate=False,
- checker_function=lambda x: isinstance(x, int)),
-
- _Option(["-oversample", "oversample"],
- "1/undersample",
- equate=False,
- checker_function=lambda x: isinstance(x, int)),
- ]
- AbstractCommandline.__init__(self, cmd, **kwargs)
-
-
-class CompareAceCommandline(AbstractCommandline):
- """Create a commandline for the CompareAce program.
-
- Example:
-
- >>> from Bio.motifs.applications import CompareAceCommandline
- >>> m1_file = "sequences1.fasta"
- >>> m2_file = "sequences2.fasta"
- >>> compareace_cline = CompareAceCommandline(motif1=m1_file, motif2=m2_file)
- >>> print(compareace_cline)
- CompareACE sequences1.fasta sequences2.fasta
-
- You would typically run the command line with compareace_cline() or via
- the Python subprocess module, as described in the Biopython tutorial.
- """
- def __init__(self, cmd="CompareACE", **kwargs):
- warnings.warn("""The CompareACE application wrapper is deprecated and
- is likely to be removed in a future release of Biopython,
- since an up to date version of the AlignACE software
- cannot be obtained anymore. If you have a copy of
- AlignACE 4, please consider contacting the Biopython
- developers.""", BiopythonDeprecationWarning)
- self.parameters = \
- [
- _Argument(["motif1"],
- "name of file containing motif 1",
- checker_function=lambda x: isinstance(x, str),
- filename=True),
- _Argument(["motif2"],
- "name of file containing motif 2",
- checker_function=lambda x: isinstance(x, str),
- filename=True),
- ]
- AbstractCommandline.__init__(self, cmd, **kwargs)
-
-
-def _test():
- """Run the module's doctests (PRIVATE)."""
- print("Running alignace doctests...")
- import doctest
- doctest.testmod()
- print("Done")
-
-
-if __name__ == "__main__":
- _test()
diff --git a/Bio/motifs/applications/_xxmotif.py b/Bio/motifs/applications/_xxmotif.py
index 2da96bc..f6c0ea7 100644
--- a/Bio/motifs/applications/_xxmotif.py
+++ b/Bio/motifs/applications/_xxmotif.py
@@ -177,13 +177,6 @@ class XXmotifCommandline(AbstractCommandline):
AbstractCommandline.__init__(self, cmd, **kwargs)
-def _test():
- """Run the module's doctests (PRIVATE)."""
- print("Running XXmotif doctests...")
- import doctest
- doctest.testmod()
- print("Done")
-
-
if __name__ == "__main__":
- _test()
+ from Bio._utils import run_doctest
+ run_doctest()
diff --git a/Bio/motifs/jaspar/__init__.py b/Bio/motifs/jaspar/__init__.py
index 1233e25..ff4f549 100644
--- a/Bio/motifs/jaspar/__init__.py
+++ b/Bio/motifs/jaspar/__init__.py
@@ -127,7 +127,7 @@ class Record(list):
return "\n".join(str(the_motif) for the_motif in self)
def to_dict(self):
- """Return the list of matrices as a dictionnary of matrices."""
+ """Return the list of matrices as a dictionary of matrices."""
dic = {}
for motif in self:
dic[motif.matrix_id] = motif
diff --git a/Bio/motifs/jaspar/db.py b/Bio/motifs/jaspar/db.py
index 90b47f3..e50085c 100644
--- a/Bio/motifs/jaspar/db.py
+++ b/Bio/motifs/jaspar/db.py
@@ -76,7 +76,6 @@ except:
from Bio.Alphabet.IUPAC import unambiguous_dna as dna
from Bio.motifs import jaspar, matrix
-__docformat__ = "restructuredtext en"
JASPAR_DFLT_COLLECTION = 'CORE'
@@ -235,7 +234,7 @@ class JASPAR5(object):
('ChIP-seq', 'PBM', 'SELEX' etc.) are returned.
NOTE - must match exactly as stored in the database.
pazar_id - Only motifs with the given PAZAR TF ID are returned.
- medline - Only motifs with the given medline (PubmMed IDs) are
+ medline - Only motifs with the given medline (PubmMed IDs) are
returned.
min_ic - Only motifs whose profile matrices have at least this
information content (specificty) are returned.
@@ -388,7 +387,7 @@ class JASPAR5(object):
# Many JASPAR motifs (especially those not in the CORE collection)
# do not have taxonomy IDs. So this warning would get annoying.
- #if not tax_ids:
+ # if not tax_ids:
# warnings.warn("Could not fetch any taxonomy IDs for JASPAR motif {0}".format(motif.matrix_id), BiopythonWarning)
motif.species = tax_ids
diff --git a/Bio/motifs/matrix.py b/Bio/motifs/matrix.py
index 8df895a..1308627 100644
--- a/Bio/motifs/matrix.py
+++ b/Bio/motifs/matrix.py
@@ -7,34 +7,13 @@ and position-specific scoring matrices.
"""
import math
+import platform
from Bio._py3k import range
from Bio.Seq import Seq
from Bio.Alphabet import IUPAC
-
-# Hack for Python 2.5, isnan and isinf were new in Python 2.6
-try:
- from math import isnan as _isnan
-except ImportError:
- def _isnan(value):
- # This is tricky due to cross platform float differences
- if str(value).lower() == "nan":
- return True
- return value != value
-try:
- from math import isinf as _isinf
-except ImportError:
- def _isinf(value):
- # This is tricky due to cross platform float differences
- if str(value).lower().endswith("inf"):
- return True
- return False
-# Hack for Python 2.5 on Windows:
-try:
- _nan = float("nan")
-except ValueError:
- _nan = 1e1000 / 1e1000
+from Bio import Alphabet
class GenericPositionMatrix(dict):
@@ -221,9 +200,17 @@ class GenericPositionMatrix(dict):
key = "".join(sorted(nucleotides[:3]))
else:
key = "ACGT"
- nucleotide = degenerate_nucleotide[key]
+ nucleotide = degenerate_nucleotide.get(key, key)
sequence += nucleotide
- return Seq(sequence, alphabet=IUPAC.ambiguous_dna)
+ if isinstance(self.alphabet, Alphabet.DNAAlphabet):
+ alpha = IUPAC.ambiguous_dna
+ elif isinstance(self.alphabet, Alphabet.RNAAlphabet):
+ alpha = IUPAC.ambiguous_rna
+ elif isinstance(self.alphabet, Alphabet.ProteinAlphabet):
+ alpha = IUPAC.protein
+ else:
+ raise Exception("Unknown alphabet")
+ return Seq(sequence, alphabet=alpha)
@property
def gc_content(self):
@@ -328,7 +315,7 @@ class PositionWeightMatrix(GenericPositionMatrix):
if p > 0:
logodds = float("inf")
else:
- logodds = _nan
+ logodds = float("nan")
values[letter].append(logodds)
pssm = PositionSpecificScoringMatrix(alphabet, values)
return pssm
@@ -336,6 +323,34 @@ class PositionWeightMatrix(GenericPositionMatrix):
class PositionSpecificScoringMatrix(GenericPositionMatrix):
+ # Make sure that we use C-accelerated PWM calculations if running under CPython.
+ # Fall back to the slower Python implementation if Jython or IronPython.
+ try:
+ from . import _pwm
+
+ def _calculate(self, sequence, m, n):
+ logodds = [[self[letter][i] for letter in "ACGT"] for i in range(m)]
+ return self._pwm.calculate(sequence, logodds)
+ except ImportError:
+ if platform.python_implementation() == 'CPython':
+ raise
+ else:
+ def _calculate(self, sequence, m, n):
+ # The C code handles mixed case so Python version must too:
+ sequence = sequence.upper()
+ scores = []
+ for i in range(n - m + 1):
+ score = 0.0
+ for position in range(m):
+ letter = sequence[i + position]
+ try:
+ score += self[letter][position]
+ except KeyError:
+ score = float("nan")
+ break
+ scores.append(score)
+ return scores
+
def calculate(self, sequence):
"""Returns the PWM score for a given sequence for all positions.
@@ -361,29 +376,8 @@ class PositionSpecificScoringMatrix(GenericPositionMatrix):
m = self.length
n = len(sequence)
- scores = []
- # check if the fast C code can be used
- try:
- import _pwm
- except ImportError:
- # use the slower Python code otherwise
- # The C code handles mixed case so Python version must too:
- sequence = sequence.upper()
- for i in range(n - m + 1):
- score = 0.0
- for position in range(m):
- letter = sequence[i + position]
- try:
- score += self[letter][position]
- except KeyError:
- score = _nan
- break
- scores.append(score)
- else:
- # get the log-odds matrix into a proper shape
- # (each row contains sorted (ACGT) log-odds values)
- logodds = [[self[letter][i] for letter in "ACGT"] for i in range(m)]
- scores = _pwm.calculate(sequence, logodds)
+ scores = self._calculate(sequence, m, n)
+
if len(scores) == 1:
return scores[0]
else:
@@ -451,9 +445,9 @@ class PositionSpecificScoringMatrix(GenericPositionMatrix):
for i in range(self.length):
for letter in self._letters:
logodds = self[letter, i]
- if _isnan(logodds):
+ if math.isnan(logodds):
continue
- if _isinf(logodds) and logodds < 0:
+ if math.isinf(logodds) and logodds < 0:
continue
b = background[letter]
p = b * math.pow(2, logodds)
@@ -475,9 +469,9 @@ class PositionSpecificScoringMatrix(GenericPositionMatrix):
sxx = 0.0
for letter in self._letters:
logodds = self[letter, i]
- if _isnan(logodds):
+ if math.isnan(logodds):
continue
- if _isinf(logodds) and logodds < 0:
+ if math.isinf(logodds) and logodds < 0:
continue
b = background[letter]
p = b * math.pow(2, logodds)
diff --git a/Bio/motifs/meme.py b/Bio/motifs/meme.py
index 0f8e2c2..9f23cbc 100644
--- a/Bio/motifs/meme.py
+++ b/Bio/motifs/meme.py
@@ -47,7 +47,7 @@ def read(handle):
motif.evalue = evalue
motif.name = name
record.append(motif)
- assert len(record)==motif_number
+ assert len(record) == motif_number
__skip_unused_lines(handle)
try:
line = next(handle)
@@ -220,18 +220,18 @@ def __read_motif_statistics(line):
# or like
# MOTIF 1 MEME width = 19 sites = 3 llr = 43 E-value = 6.9e-002
words = line.split()
- assert words[0]=='MOTIF'
+ assert words[0] == 'MOTIF'
motif_number = int(words[1])
- if words[2]=='MEME':
+ if words[2] == 'MEME':
key_values = words[3:]
else:
key_values = words[2:]
keys = key_values[::3]
equal_signs = key_values[1::3]
values = key_values[2::3]
- assert keys==['width', 'sites', 'llr', 'E-value']
+ assert keys == ['width', 'sites', 'llr', 'E-value']
for equal_sign in equal_signs:
- assert equal_sign=='='
+ assert equal_sign == '='
length = int(values[0])
num_occurrences = int(values[1])
evalue = float(values[3])
diff --git a/Bio/motifs/thresholds.py b/Bio/motifs/thresholds.py
index c16713b..1d6de80 100644
--- a/Bio/motifs/thresholds.py
+++ b/Bio/motifs/thresholds.py
@@ -10,9 +10,9 @@
class ScoreDistribution(object):
""" Class representing approximate score distribution for a given motif.
- Utilizes a dynamic programming approch to calculate the distribution of
+ Utilizes a dynamic programming approach to calculate the distribution of
scores with a predefined precision. Provides a number of methods for calculating
- thresholds for motif occurences.
+ thresholds for motif occurrences.
"""
def __init__(self, motif=None, precision=10 ** 3, pssm=None, background=None):
if pssm is None:
diff --git a/Bio/motifs/transfac.py b/Bio/motifs/transfac.py
index 6b8e1ca..164805d 100644
--- a/Bio/motifs/transfac.py
+++ b/Bio/motifs/transfac.py
@@ -9,8 +9,6 @@
from Bio import motifs
from Bio.Alphabet import IUPAC
-__docformat__ = "restructuredtext en"
-
class Motif(motifs.Motif, dict):
"""A Bio.motifs.transfac.Motif stores the information in one TRANSFAC
@@ -185,17 +183,20 @@ XX
if length == 0:
continue
sequence = motif.degenerate_consensus
- line = "P0 A C G T"
+ letters = sorted(motif.alphabet.letters)
+ line = " ".join(["P0"] + letters)
+
lines.append(line)
for i in range(length):
- line = "%02.d %6.20g %6.20g %6.20g %6.20g %s" % (
- i + 1,
- motif.counts['A'][i],
- motif.counts['C'][i],
- motif.counts['G'][i],
- motif.counts['T'][i],
- sequence[i],
- )
+ line = " ".join(
+ ["%02.d"] +
+ ["%6.20g" for l in letters]) + \
+ " %s"
+ line = line % tuple(
+ [i + 1] +
+ [motif.counts[l][i] for l in letters] +
+ [sequence[i]]
+ )
lines.append(line)
blank = True
else:
diff --git a/Bio/pairwise2.py b/Bio/pairwise2.py
index 103a575..73d1bd0 100644
--- a/Bio/pairwise2.py
+++ b/Bio/pairwise2.py
@@ -155,7 +155,6 @@ type help(pairwise2.align.localds) at the Python prompt.
from __future__ import print_function
-__docformat__ = "restructuredtext en"
MAX_ALIGNMENTS = 1000 # maximum alignments recovered in traceback
@@ -163,7 +162,7 @@ MAX_ALIGNMENTS = 1000 # maximum alignments recovered in traceback
class align(object):
"""This class provides functions that do alignments."""
- class alignment_function:
+ class alignment_function(object):
"""This class is callable impersonates an alignment function.
The constructor takes the name of the function. This class
will decode the name of the function to figure out how to
diff --git a/Bio/phenotype/__init__.py b/Bio/phenotype/__init__.py
new file mode 100644
index 0000000..f3ed9de
--- /dev/null
+++ b/Bio/phenotype/__init__.py
@@ -0,0 +1,244 @@
+# Copyright 2014-2016 by Marco Galardini. All rights reserved.
+# This code is part of the Biopython distribution and governed by its
+# license. Please see the LICENSE file that should have been included
+# as part of this package.
+#
+
+r"""phenotype data input/output.
+
+Input
+=====
+The main function is Bio.phenotype.parse(...) which takes an input file,
+and format string. This returns an iterator giving PlateRecord objects:
+
+ >>> from Bio import phenotype
+ >>> for record in phenotype.parse("plates.csv", "pm-csv"):
+ ... print("%s %i" % (record.id, len(record)))
+ ...
+ PM01 96
+ PM01 96
+ PM09 96
+ PM09 96
+
+Note that the parse() function will invoke the relevant parser for the
+format with its default settings. You may want more control, in which case
+you need to create a format specific sequence iterator directly.
+
+Input - Single Records
+======================
+If you expect your file to contain one-and-only-one record, then we provide
+the following 'helper' function which will return a single PlateRecord, or
+raise an exception if there are no records or more than one record:
+
+ >>> from Bio import phenotype
+ >>> record = phenotype.read("plate.json", "pm-json")
+ >>> print("%s %i" % (record.id, len(record)))
+ PM01 96
+
+This style is useful when you expect a single record only (and would
+consider multiple records an error). For example, when dealing with PM
+JSON files saved by the opm library.
+
+However, if you just want the first record from a file containing multiple
+record, use the next() function on the iterator (or under Python 2, the
+iterator's next() method):
+
+ >>> from Bio import phenotype
+ >>> record = next(phenotype.parse("plates.csv", "pm-csv"))
+ >>> print("%s %i" % (record.id, len(record)))
+ PM01 96
+
+The above code will work as long as the file contains at least one record.
+Note that if there is more than one record, the remaining records will be
+silently ignored.
+
+Output
+======
+Use the function Bio.phenotype.write(...), which takes a complete set of
+PlateRecord objects (either as a list, or an iterator), an output file handle
+(or in recent versions of Biopython an output filename as a string) and of
+course the file format:
+
+ >>> from Bio import phenotype
+ >>> records = ...
+ >>> phenotype.write(records, "example.json", "pm-json")
+
+Or, using a handle::
+
+ >>> from Bio import phenotype
+ >>> records = ...
+ >>> with open("example.json", "w") as handle:
+ >>> phenotype.write(records, handle, "pm-json")
+
+You are expected to call this function once (with all your records) and if
+using a handle, make sure you close it to flush the data to the hard disk.
+
+
+File Formats
+============
+When specifying the file format, use lowercase strings.
+
+ - pm-json - Phenotype Microarray plates in JSON format.
+ - pm-csv - Phenotype Microarray plates in CSV format, which is the
+ machine vendor format
+
+Note that while Bio.phenotype can read the above file formats, it can only
+write in JSON format.
+"""
+
+from __future__ import print_function
+from Bio._py3k import basestring
+
+from Bio import BiopythonExperimentalWarning
+from Bio.File import as_handle
+from . import phen_micro
+
+import warnings
+
+__docformat__ = "epytext en" # not just plaintext
+
+warnings.warn('Bio.phenotype is an experimental submodule which may undergo '
+ 'significant changes prior to its future official release.',
+ BiopythonExperimentalWarning)
+
+# Convention for format names is "mainname-format" in lower case.
+
+_FormatToIterator = {"pm-csv": phen_micro.CsvIterator,
+ "pm-json": phen_micro.JsonIterator,
+ }
+
+_FormatToWriter = {"pm-json": phen_micro.JsonWriter,
+ }
+
+
+def write(plates, handle, format):
+ """Write complete set of PlateRecords to a file.
+
+ - plates - A list (or iterator) of PlateRecord objects.
+ - handle - File handle object to write to, or filename as string
+ (note older versions of Biopython only took a handle).
+ - format - lower case string describing the file format to write.
+
+ You should close the handle after calling this function.
+
+ Returns the number of records written (as an integer).
+ """
+ # Try and give helpful error messages:
+ if not isinstance(format, basestring):
+ raise TypeError("Need a string for the file format (lower case)")
+ if not format:
+ raise ValueError("Format required (lower case string)")
+ if format != format.lower():
+ raise ValueError("Format string '%s' should be lower case" % format)
+
+ if isinstance(plates, phen_micro.PlateRecord):
+ plates = [plates]
+
+ with as_handle(handle, 'w') as fp:
+ # Map the file format to a writer class
+ if format in _FormatToWriter:
+ writer_class = _FormatToWriter[format]
+ count = writer_class(plates).write(fp)
+ else:
+ raise ValueError("Unknown format '%s'" % format)
+
+ assert isinstance(count, int), "Internal error - the underlying %s " \
+ "writer should have returned the record count, not %s" \
+ % (format, repr(count))
+
+ return count
+
+
+def parse(handle, format):
+ """Turns a phenotype file into an iterator returning PlateRecords.
+
+ - handle - handle to the file, or the filename as a string
+ (note older versions of Biopython only took a handle).
+ - format - lower case string describing the file format.
+
+ Typical usage, opening a file to read in, and looping over the record(s):
+
+ >>> from Bio import phenotype
+ >>> filename = "plates.csv"
+ >>> for record in phenotype.parse(filename, "pm-csv"):
+ ... print("ID %s" % record.id)
+ ... print("Number of wells %i" % len(record))
+ ...
+ ID PM01
+ Number of wells 96
+
+ Use the Bio.phenotype.read(...) function when you expect a single record
+ only.
+ """
+ # Try and give helpful error messages:
+ if not isinstance(format, basestring):
+ raise TypeError("Need a string for the file format (lower case)")
+ if not format:
+ raise ValueError("Format required (lower case string)")
+ if format != format.lower():
+ raise ValueError("Format string '%s' should be lower case" % format)
+
+ with as_handle(handle, 'rU') as fp:
+ # Map the file format to a sequence iterator:
+ if format in _FormatToIterator:
+ iterator_generator = _FormatToIterator[format]
+ i = iterator_generator(fp)
+ else:
+ raise ValueError("Unknown format '%s'" % format)
+ # This imposes some overhead... wait until we drop Python 2.4 to fix it
+ for r in i:
+ yield r
+
+
+def read(handle, format):
+ """Turns a phenotype file into a single PlateRecord.
+
+ - handle - handle to the file, or the filename as a string
+ (note older versions of Biopython only took a handle).
+ - format - string describing the file format.
+
+ This function is for use parsing phenotype files containing
+ exactly one record. For example, reading a PM JSON file:
+
+ >>> from Bio import phenotype
+ >>> record = phenotype.read("plate.json", "pm-json")
+ >>> print("ID %s" % record.id)
+ PM09
+ >>> print("Number of wells %i" % len(record))
+ Number of wells 96
+
+ If the handle contains no records, or more than one record,
+ an exception is raised. For example:
+
+ >>> from Bio import phenotype
+ >>> record = phenotype.read("plates.csv", "pm-csv")
+ Traceback (most recent call last):
+ ...
+ ValueError: More than one record found in handle
+
+ If however you want the first record from a file containing
+ multiple records this function would raise an exception (as
+ shown in the example above). Instead use:
+
+ >>> from Bio import phenotype
+ >>> record = next(phenotype.parse("plates.csv", "pm-csv"))
+ >>> print("First record's ID %s" % record.id)
+ First record's ID PM09
+
+ Use the Bio.phenotype.parse(handle, format) function if you want
+ to read multiple records from the handle.
+ """
+ iterator = parse(handle, format)
+ try:
+ first = next(iterator)
+ except StopIteration:
+ first = None
+ if first is None:
+ raise ValueError("No records found in handle")
+ try:
+ second = next(iterator)
+ except StopIteration:
+ second = None
+ if second is not None:
+ raise ValueError("More than one record found in handle")
+ return first
diff --git a/Bio/phenotype/phen_micro.py b/Bio/phenotype/phen_micro.py
new file mode 100644
index 0000000..864a615
--- /dev/null
+++ b/Bio/phenotype/phen_micro.py
@@ -0,0 +1,1131 @@
+# Copyright 2014-2016 by Marco Galardini. All rights reserved.
+# This code is part of the Biopython distribution and governed by its
+# license. Please see the LICENSE file that should have been included
+# as part of this package.
+"""This module provides classes to work with Phenotype Microarray data.
+
+More information on the single plates can be found here: http://www.biolog.com/
+
+Classes:
+
+ - PlateRecord - Object that contain time course data on each well of the
+ plate, as well as metadata (if any).
+ - WellRecord - Object that contains the time course data of a single well
+ - JsonWriter - Writer of PlateRecord objects in JSON format.
+
+Functions:
+
+ - JsonIterator - Incremental PM JSON parser, this is an iterator that returns
+ PlateRecord objects.
+ - CsvIterator - Incremental PM CSV parser, this is an iterator that returns
+ PlateRecord objects.
+ - _toOPM - Used internally by JsonWriter, converts PlateRecord objects in
+ dictionaries ready to be serialized in JSON format.
+
+"""
+
+import warnings
+import json
+import csv
+import numpy as np
+
+from Bio._py3k import basestring
+from Bio._py3k import _is_int_or_long
+from Bio import BiopythonParserWarning
+
+# Private csv headers - hardcoded because this are supposedly never changed
+_datafile = 'Data File'
+_plate = 'Plate Type'
+_strainType = 'Strain Type'
+_sample = 'Sample Number'
+_strainName = 'Strain Name'
+_strainNumber = 'Strain Number'
+_other = 'Other'
+_hour = 'Hour'
+_file = 'File'
+_position = 'Position'
+_setupTime = 'Setup Time'
+
+_platesPrefix = 'PM'
+_platesPrefixMammalian = 'PM-M'
+#
+
+# Json identifiers - hardcoded as they are set by the creators of opm
+_csvData = 'csv_data'
+_measurements = 'measurements'
+#
+
+
+class PlateRecord(object):
+ """PlateRecord object for storing Phenotype Microarray plates data.
+
+ A PlateRecord stores all the wells of a particular phenotype
+ Microarray plate, along with metadata (if any). The single wells can be
+ accessed calling their id as an index or iterating on the PlateRecord:
+
+ >>> from Bio import phenotype
+ >>> plate = phenotype.read("plate.csv", "pm-csv")
+ >>> well = plate['A05']
+ >>> for well in plate:
+ ... print("%s" % well.id)
+ ...
+ A01
+ A02
+ ...
+
+ The plate rows and columns can be queried with an indexing system similar
+ to NumPy and other matrices:
+
+ >>> print(plate[1])
+ PlateRecord('WellRecord['B01'], WellRecord['B02'], WellRecord['B03'], ..., WellRecord['B12']')
+
+ >>> print(plate[:,1])
+ PlateRecord('WellRecord['A02'], WellRecord['B02'], WellRecord['C02'], ..., WellRecord['H02']')
+
+ Single WellRecord objects can be accessed using this indexing system:
+
+ >>> print(plate[1,2])
+ WellRecord('(0.0, 11.0), (0.25, 11.0), (0.5, 11.0), (0.75, 11.0), (1.0, 11.0), ..., (95.75, 11.0)')
+
+ The presence of a particular well can be inspected with the "in" keyword:
+ >>> 'A01' in plate
+ True
+
+ All the wells belonging to a "row" (identified by the first charachter of
+ the well id) in the plate can be obtained:
+
+ >>> for well in plate.get_row('H'):
+ ... print("%s" % well.id)
+ ...
+ H01
+ H02
+ H03
+ ...
+
+ All the wells belonging to a "column" (identified by the number of the well)
+ in the plate can be obtained:
+
+ >>> for well in plate.get_column(12):
+ ... print("%s" % well.id)
+ ...
+ A01
+ B12
+ C12
+ ...
+
+ Two PlateRecord objects can be compared: if all their wells are equal the
+ two plates are considered equal:
+
+ >>> plate2 = phenotype.read("plate.json", "pm-json")
+ >>> plate == plate2
+ True
+
+ Two PlateRecord object can be summed up or subracted from each other: the
+ the signals of each well will be summed up or subtracted. The id of the
+ left operand will be kept:
+
+ >>> plate3 = plate + plate2
+ >>> print(plate3.id)
+ PM01
+
+ Many Phenotype Microarray plate have a "negative control" well, which can
+ be subtracted to all wells:
+
+ >>> subplate = plate.subtract_control()
+ """
+
+ def __init__(self, plateid, wells=None):
+ self.id = plateid
+
+ if wells is None:
+ wells = []
+
+ # Similar behaviour as GenBank
+ # Contains all the attributes
+ self.qualifiers = {}
+
+ # Well_id --> WellRecord objects
+ self._wells = {}
+ try:
+ for w in wells:
+ self._is_well(w)
+ self[w.id] = w
+ except TypeError:
+ raise TypeError('You must provide an iterator-like object ' +
+ 'containing the single wells')
+
+ self._update()
+
+ def _update(self):
+ """Update the rows and columns string identifiers."""
+ self._rows = sorted(set(x[0] for x in self._wells))
+ self._columns = sorted(set(x[1:] for x in self._wells))
+
+ def _is_well(self, obj):
+ """Check if the given object is a WellRecord object
+
+ Used both for the class constructor and the __setitem__ method
+ """
+ # Value should be of WellRecord type
+ if not isinstance(obj, WellRecord):
+ raise ValueError('A WellRecord type object is needed as value' +
+ ' (got %s)' % type(obj))
+
+ def __getitem__(self, index):
+ """Access part of the plate.
+
+ Depending on the indices, you can get a WellRecord object
+ (representing a single well of the plate),
+ or another plate
+ (representing some part or all of the original plate).
+
+ plate[wid] gives a WellRecord (if wid is a WellRecord id)
+ plate[r,c] gives a WellRecord
+ plate[r] gives a row as a PlateRecord
+ plate[r,:] gives a row as a PlateRecord
+ plate[:,c] gives a column as a PlateRecord
+
+ plate[:] and plate[:,:] give a copy of the plate
+
+ Anything else gives a subset of the original plate, e.g.
+ plate[0:2] or plate[0:2,:] uses only row 0 and 1
+ plate[:,1:3] uses only columns 1 and 2
+ plate[0:2,1:3] uses only rows 0 & 1 and only cols 1 & 2
+
+ >>> from Bio import phenotype
+ >>> plate = phenotype.read("plate.csv", "pm-csv")
+
+ You can access a well of the plate, using its id.
+
+ >>> w = plate['A01']
+
+ You can access a row of the plate as a PlateRecord using an integer
+ index:
+
+ >>> first_row = plate[0]
+ >>> print(first_row)
+ PlateRecord('WellRecord['A01'], WellRecord['A02'], WellRecord['A03'], ..., WellRecord['A12']')
+ >>> last_row = plate[-1]
+ >>> print(last_row)
+ PlateRecord('WellRecord['H01'], WellRecord['H02'], WellRecord['H03'], ..., WellRecord['H12']')
+
+ You can also access use python's slice notation to sub-plates
+ containing only some of the plate rows:
+
+ >>> sub_plate = plate[2:5]
+ >>> print(sub_plate)
+ PlateRecord('WellRecord['C01'], WellRecord['C02'], WellRecord['C03'], ..., WellRecord['E12']')
+
+ This includes support for a step, i.e. plate[start:end:step], which
+ can be used to select every second row:
+
+ >>> sub_plate = plate[::2]
+
+ You can also use two indices to specify both rows and columns.
+ Using simple integers gives you the single wells. e.g.
+
+ >>> w = plate[3, 4]
+ >>> print(w.id)
+ 'D05'
+
+ To get a single column use this syntax:
+
+ >>> sub_plate = plate[:, 4]
+ >>> print(sub_plate)
+ PlateRecord('WellRecord['A05'], WellRecord['B05'], WellRecord['C05'], ..., WellRecord['H05']')
+
+ Or, to get part of a column,
+
+ >>> sub_plate = plate[1:3, 4]
+ >>> print(sub_plate)
+ PlateRecord(WellRecord['B05'], WellRecord['C05'])
+
+ However, in general you get a sub-plate,
+
+ >>> print(align[1:5, 3:6])
+ PlateRecord('WellRecord['B04'], WellRecord['B05'], WellRecord['B06'], ..., WellRecord['E06']')
+
+ This should all seem familiar to anyone who has used the NumPy
+ array or matrix objects.
+ """
+
+ # Well identifier access
+ if isinstance(index, basestring):
+ try:
+ return self._wells[index]
+ except KeyError:
+ raise KeyError('Well %s not found!' % index)
+
+ # Integer index
+ elif isinstance(index, int):
+ try:
+ row = self._rows[index]
+ except IndexError:
+ raise IndexError('Row %d not found!' % index)
+ return PlateRecord(self.id,
+ filter(lambda x: x.id.startswith(row),
+ self._wells.values()))
+
+ # Slice
+ elif isinstance(index, slice):
+ rows = self._rows[index]
+ return PlateRecord(self.id,
+ filter(lambda x: x.id[0] in rows,
+ self._wells.values()))
+
+ # Other access
+ elif len(index) != 2:
+ raise TypeError('Invalid index type.')
+
+ row_index, col_index = index
+ if isinstance(row_index, int) and isinstance(col_index, int):
+ # Return a single WellRecord
+ try:
+ row = self._rows[row_index]
+ except IndexError:
+ raise IndexError('Row %d not found!' % row_index)
+ try:
+ col = self._columns[col_index]
+ except IndexError:
+ raise IndexError('Column %d not found!' % col_index)
+
+ return self._wells[row + col]
+
+ elif isinstance(row_index, int):
+ try:
+ row = self._rows[row_index]
+ except IndexError:
+ raise IndexError('Row %d not found!' % row_index)
+ cols = self._columns[col_index]
+
+ return PlateRecord(self.id,
+ filter(lambda x: x.id.startswith(row) and
+ x.id[1:] in cols,
+ self._wells.values()))
+
+ elif isinstance(col_index, int):
+ try:
+ col = self._columns[col_index]
+ except IndexError:
+ raise IndexError('Columns %d not found!' % col_index)
+ rows = self._rows[row_index]
+
+ return PlateRecord(self.id,
+ filter(lambda x: x.id.endswith(col) and
+ x.id[0] in rows,
+ self._wells.values()))
+
+ else:
+ rows = self._rows[row_index]
+ cols = self._columns[col_index]
+
+ return PlateRecord(self.id,
+ filter(lambda x: x.id[0] in rows and
+ x.id[1:] in cols,
+ self._wells.values()))
+
+ def __setitem__(self, key, value):
+ if not isinstance(key, basestring):
+ raise ValueError('Well identifier should be string-like')
+ self._is_well(value)
+ # Provided key and well ID should be the same
+ if value.id != key:
+ raise ValueError('WellRecord ID and provided key are different' +
+ ' (got "%s" and "%s")' % (type(value.id), type(key)))
+ self._wells[key] = value
+
+ self._update()
+
+ def __delitem__(self, key):
+ if not isinstance(key, basestring):
+ raise ValueError('Well identifier should be string-like')
+ del self._wells[key]
+
+ self._update()
+
+ def __iter__(self):
+ for well in sorted(self._wells):
+ yield self._wells[well]
+
+ def __contains__(self, wellid):
+ if wellid in self._wells:
+ return True
+ return False
+
+ def __len__(self):
+ """Returns the number of wells in this plate"""
+ return len(self._wells)
+
+ def __eq__(self, other):
+ if isinstance(other, self.__class__):
+ return self._wells == other._wells
+ else:
+ return False
+
+ def __ne__(self, other):
+ return not self.__eq__(other)
+
+ def __add__(self, plate):
+ """Add another PlateRecord object
+
+ The wells in both plates must be the same
+
+ A new PlateRecord object is returned, having the same id as the
+ left operand
+ """
+ if not isinstance(plate, PlateRecord):
+ raise TypeError('Expecting a PlateRecord object')
+
+ if set(x.id for x in self) != set(x.id for x in plate):
+ raise ValueError('The two plates have different wells')
+
+ wells = []
+
+ for w in self:
+ wells.append(w + plate[w.id])
+
+ newp = PlateRecord(self.id, wells=wells)
+
+ return newp
+
+ def __sub__(self, plate):
+ """Subtract another PlateRecord object
+
+ The wells in both plates must be the same
+
+ A new PlateRecord object is returned, having the same id as the
+ left operand
+ """
+ if not isinstance(plate, PlateRecord):
+ raise TypeError('Expecting a PlateRecord object')
+
+ if set(x.id for x in self) != set(x.id for x in plate):
+ raise ValueError('The two plates have different wells')
+
+ wells = []
+
+ for w in self:
+ wells.append(w - plate[w.id])
+
+ newp = PlateRecord(self.id, wells=wells)
+
+ return newp
+
+ def get_row(self, row):
+ """Get all the wells of a given row
+
+ A row is identified with a letter (e.g. 'A')
+ """
+ # Key is casted to str implicitly
+ try:
+ row = str(row)
+ except Exception:
+ # Is it even possible to get an exception here?
+ raise ValueError('Row identifier should be string-like')
+ if len(row) > 1:
+ raise ValueError('Row identifier must be of maximum one letter')
+
+ for w in sorted(filter(lambda x: x.startswith(row), self._wells)):
+ yield self._wells[w]
+
+ def get_column(self, column):
+ """Get all the wells of a given column
+
+ A column is identified with a number (e.g. '6')
+ """
+ # Column is casted to int implicitly
+ try:
+ column = int(column)
+ except Exception:
+ raise ValueError('Column identifier should be a number')
+
+ # A 96-well plate has well numbers in two digits
+ for w in sorted(filter(lambda x: x.endswith('%02d' % column),
+ self._wells)):
+ yield self._wells[w]
+
+ def subtract_control(self, control='A01', wells=None):
+ """Subtract a 'control' well from the other plates wells
+
+ By default the control is subtracted to all wells, unless
+ a list of well ID is provided
+
+ The control well should belong to the plate
+ A new PlateRecord object is returned
+ """
+ if control not in self:
+ raise ValueError('Control well not present in plate')
+ wcontrol = self[control]
+
+ if wells is None:
+ wells = self._wells.keys()
+
+ missing = set(w for w in wells if w not in self)
+ if missing:
+ raise ValueError('Some wells to be subtracted are not present')
+
+ nwells = []
+
+ for w in self:
+ if w.id in wells:
+ nwells.append(w - wcontrol)
+ else:
+ nwells.append(w)
+
+ newp = PlateRecord(self.id, wells=nwells)
+
+ return newp
+
+ def __repr__(self):
+ """Returns a (truncated) representation of the plate for debugging."""
+ if len(self._wells) > 4:
+ # Show the last well and the first three
+ return "%s('%s, ..., %s')" % (self.__class__.__name__,
+ ', '.join(
+ ["%s['%s']" % (str(self[x].__class__.__name__), self[x].id)
+ for x in sorted(self._wells.keys())[:3]]),
+ "%s['%s']" % (
+ self[
+ sorted(self._wells.keys())[-1]].__class__.__name__,
+ self[sorted(self._wells.keys())[-1]].id))
+ else:
+ return "%s(%s)" % (self.__class__.__name__,
+ ', '.join(
+ ["%s['%s']" % (str(self[x].__class__.__name__), self[x].id)
+ for x in sorted(self._wells.keys())]
+ ))
+
+ def __str__(self):
+ """A human readable summary of the record (string).
+
+ The python built in function str works by calling the object's ___str__
+ method. e.g.
+
+ >>> from Bio import phenotype
+ >>> record = phenotype.read("plates.csv", "pm-csv")
+ >>> print(record)
+ Plate ID: PM09
+ Well: 96
+ Rows: 8
+ Columns: 12
+ PlateRecord('WellRecord['A01'], WellRecord['A02'], WellRecord['A03'], WellRecord['A04']...WellRecord['H12']')
+
+ Note that long well lists are shown truncated.
+ """
+ lines = []
+ if self.id:
+ lines.append("Plate ID: %s" % self.id)
+ lines.append("Well: %i" % len(self))
+ # Here we assume that all well ID start with a char
+ lines.append("Rows: %d" %
+ len(set(x.id[0] for x in self)))
+ # Here we assume that well number is a two-digit number
+ lines.append("Columns: %d" %
+ len(set(x.id[1:3] for x in self)))
+ lines.append(repr(self))
+ return "\n".join(lines)
+
+
+class WellRecord(object):
+ """WellRecord object stores all the time course signals of a phenotype
+ Microarray well.
+
+ The single time points and signals can be
+ accessed iterating on the WellRecord or using lists indeces or slices:
+
+ >>> from Bio import phenotype
+ >>> plate = phenotype.read("plate.csv", "pm-csv")
+ >>> well = plate['A05']
+ >>> for time, signal in well:
+ ... print("Time: %f, Signal: %f" % (time, signal))
+ ...
+ Time: 0.0, Signal: 10.0
+ Time: 0.25, Signal: 14.0
+ Time: 0.5, Signal: 19.0
+ Time: 24.25, Signal: 32.0
+ ...
+ >>> well[1]
+ 23.0
+ >>> well[1:5]
+ [23.0, 23.0, 26.0, 26.0]
+ >>> well[1:5:0.5]
+ [23.0, 23.0, 23.0, 26.0, 26.0, 26.0, 26.0, 26.0]
+
+ If a time point was not present in the input file but it's between the
+ minimum and maximum time point, the interpolated signal is returned,
+ otherwise a nan value:
+
+ >>> well[1.3]
+ 23.0
+ >>> well[1250]
+ nan
+
+ Two WellRecord objects can be compared: if their input time/signal pairs
+ are exactly the same, the two records are considered equal:
+
+ >>> well2 = plate['H12']
+ >>> well == well2
+ False
+
+ Two WellRecord objects can be summed up or subtracted from each other: a new
+ WellRecord object is returned, having the left operand id.
+
+ >>> well2 = well + well1
+ >>> print(well2.id)
+ A05
+
+ If SciPy is installed, a sigmoid function can be fitted to the PM curve,
+ in order to extract some parameters; three sigmoid functions are available:
+ * gompertz
+ * logistic
+ * richards
+ The functions are described in Zwietering et al., 1990 (PMID: 16348228)
+
+ >>> well.fit()
+ >>> print(well.slope, well.model)
+ (61.853516785566917, 'logistic')
+
+ If not sigmoid function is specified, the first one that is succesfully
+ fitted is used. The user can also specify a specific function.
+
+ >>> well.fit('gompertz')
+ >>> print(well.slope, well.model)
+ (127.94630059171354, 'gompertz')
+
+ If no function can be fitted, the parameters are left as None, except for
+ the max, min, average_height and area.
+ """
+
+ def __init__(self, wellid, plate=None, signals=None):
+ if plate is None:
+ self.plate = PlateRecord(None)
+ else:
+ self.plate = plate
+
+ self.id = wellid
+
+ # Curve parameters (to be calculated with the "fit" function)
+ # Parameters that don't need scipy
+ self.max = None
+ self.min = None
+ self.average_height = None
+
+ # Parameters that need scipy
+ self.area = None
+ self.plateau = None
+ self.slope = None
+ self.lag = None
+ self.v = None
+ self.y0 = None
+ self.model = None
+
+ # Original signals (private)
+ if signals is None:
+ self._signals = {}
+ else:
+ self._signals = signals
+
+ def _interpolate(self, time):
+ """Private method to get a linear interpolation of the signals
+ at certain time points.
+ """
+ times = sorted(self._signals.keys())
+
+ return np.interp(time,
+ times,
+ [self._signals[x] for x in times],
+ left=np.nan, right=np.nan)
+
+ def __setitem__(self, time, signal):
+ """Assign a signal at a certain time point.
+ """
+ try:
+ time = float(time)
+ except ValueError:
+ raise ValueError('Time point should be a number')
+ try:
+ signal = float(signal)
+ except ValueError:
+ raise ValueError('Signal should be a number')
+
+ self._signals[time] = signal
+
+ def __getitem__(self, time):
+ """Returns a subset of signals or a single signal.
+ """
+ if isinstance(time, slice):
+ # Fix the missing values in the slice
+ if time.start is None:
+ start = 0
+ else:
+ start = time.start
+
+ if time.stop is None:
+ stop = max(self.get_times())
+ else:
+ stop = time.stop
+
+ time = np.arange(start, stop, time.step)
+ return list(self._interpolate(time))
+
+ elif _is_int_or_long(time) or isinstance(time, float):
+ return self._interpolate(time)
+
+ raise ValueError('Invalid index')
+
+ def __iter__(self):
+ for time in sorted(self._signals.keys()):
+ yield time, self._signals[time]
+
+ def __eq__(self, other):
+ if isinstance(other, self.__class__):
+ if list(self._signals.keys()) != list(other._signals.keys()):
+ return False
+ # Account for the presence of NaNs
+ for k in self._signals:
+ if np.isnan(self[k]) and np.isnan(other[k]):
+ continue
+ elif self[k] != other[k]:
+ return False
+ return True
+ else:
+ return False
+
+ def __ne__(self, other):
+ return not self.__eq__(other)
+
+ def __add__(self, well):
+ """Add another WellRecord object
+
+ A new WellRecord object is returned, having the same id as the
+ left operand
+ """
+ if not isinstance(well, WellRecord):
+ raise TypeError('Expecting a WellRecord object')
+
+ signals = {}
+
+ times = set(self._signals.keys()).union(set(well._signals.keys()))
+ for t in sorted(times):
+ signals[t] = self[t] + well[t]
+
+ neww = WellRecord(self.id, signals=signals)
+
+ return neww
+
+ def __sub__(self, well):
+ """Subtract another WellRecord object
+
+ A new WellRecord object is returned, having the same id as the
+ left operand
+ """
+ if not isinstance(well, WellRecord):
+ raise TypeError('Expecting a WellRecord object')
+
+ signals = {}
+
+ times = set(self._signals.keys()).union(set(well._signals.keys()))
+ for t in sorted(times):
+ signals[t] = self[t] - well[t]
+
+ neww = WellRecord(self.id, signals=signals)
+
+ return neww
+
+ def __len__(self):
+ """Returns the number of time points sampled"""
+ return len(self._signals)
+
+ def __repr__(self):
+ """Returns a (truncated) representation of the signals for debugging."""
+ if len(self) > 7:
+ # Shows the last time point and the first five
+ return "%s('%s, ..., %s')" % (self.__class__.__name__,
+ ', '.join([str(x)
+ for x in self.get_raw()[:5]]),
+ str(self.get_raw()[-1]))
+ else:
+ return "%s(%s)" % (self.__class__.__name__,
+ ', '.join([str(x) for x in self.get_raw()]))
+
+ def __str__(self):
+ """A human readable summary of the record (string).
+
+ The python built in function str works by calling the object's ___str__
+ method. e.g.
+
+ >>> from Bio import phenotype
+ >>> plate = phenotype.read("plates.csv", "pm-csv")
+ >>> record = plate['A05']
+ >>> print(record)
+ Plate ID: PM09
+ Well ID: A05
+ Time points: 288
+ Minum signal 0.00 at time 10.00
+ Maximum signal 7.50 at time 32.00
+ WellRecord('(0.0, 10.0), (0.25, 14.0), (0.5, 19.0), (0.75, 19.0), (1.0, 23.0)...(71.75, 32.0)')
+
+ Note that long time spans are shown truncated.
+ """
+ lines = []
+ if self.plate and self.plate.id:
+ lines.append("Plate ID: %s" % self.plate.id)
+ if self.id:
+ lines.append("Well ID: %s" % self.id)
+ lines.append("Time points: %i" % len(self))
+ lines.append("Minum signal %.2f at time %.2f" %
+ min(self, key=lambda x: x[1]))
+ lines.append("Maximum signal %.2f at time %.2f" %
+ max(self, key=lambda x: x[1]))
+ lines.append(repr(self))
+ return "\n".join(lines)
+
+ def get_raw(self):
+ """Get a list of time/signal pairs"""
+ return [(t, self._signals[t]) for t in sorted(self._signals.keys())]
+
+ def get_times(self):
+ """Get a list of the recorded time points"""
+ return sorted(self._signals.keys())
+
+ def get_signals(self):
+ """Get a list of the recorded signals (ordered by collection time)"""
+ return [self._signals[t] for t in sorted(self._signals.keys())]
+
+ def fit(self, function=("gompertz", "logistic", "richards")):
+ """Fit a sigmoid function to this well and extract curve parameters.
+
+ If function is None or an empty tuple/list, then no fitting is done.
+ Only the object's ``.min``, ``.max`` and ``.average_height`` are
+ calculated.
+
+ By default the following fitting functions will be used in order:
+ * gompertz
+ * logistic
+ * richards
+
+ The first function that is succesfuly fitted to the signals will
+ be used to extract the curve parameters and update ``.area`` and
+ ``.model``. If no function can be fitted an exception is raised.
+
+ The function argument should be a tuple or list of any of these three
+ function names as strings.
+
+ There is no return value.
+ """
+ avail_func = ('gompertz', 'logistic', 'richards', )
+
+ # Parameters not dependent on curve fitting
+ self.max = max(self, key=lambda x: x[1])[1]
+ self.min = min(self, key=lambda x: x[1])[1]
+
+ self.average_height = np.array(self.get_signals()).mean()
+
+ if not function:
+ self.area = None
+ self.model = None
+ return
+ for sigmoid_func in function:
+ if sigmoid_func not in avail_func:
+ raise ValueError("Fitting function %r not supported" % sigmoid_func)
+
+ # Parameters that depend on scipy curve_fit
+ from .pm_fitting import fit, get_area
+ from .pm_fitting import logistic, gompertz, richards
+ function_map = {'logistic': logistic,
+ 'gompertz': gompertz,
+ 'richards': richards}
+
+ self.area = get_area(self.get_signals(), self.get_times())
+
+ self.model = None
+ for sigmoid_func in function:
+ func = function_map[sigmoid_func]
+ try:
+ (self.plateau, self.slope,
+ self.lag, self.v, self.y0), pcov = fit(func,
+ self.get_times(),
+ self.get_signals())
+
+ self.model = sigmoid_func
+ return
+ except RuntimeError:
+ continue
+ raise RuntimeError("Could not fit any sigmoid function")
+
+
+def JsonIterator(handle):
+ """Generator function to iterate over PM json records (as PlateRecord
+ objects).
+
+ handle - input file
+ """
+ try:
+ data = json.load(handle)
+ except ValueError:
+ raise ValueError('Could not parse JSON file')
+
+ # We can have one single plate or several
+ # we need to discriminate
+ if hasattr(data, 'keys'):
+ data = [data]
+
+ for pobj in data:
+ try:
+ plateID = pobj[_csvData][_plate]
+ except TypeError:
+ raise TypeError('Malformed JSON input')
+ except KeyError:
+ raise KeyError('Could not retrieve plate id')
+
+ # Parse also non-standard plate IDs
+ if not plateID.startswith(_platesPrefix) and not plateID.startswith(_platesPrefixMammalian):
+ warnings.warn('Non-standard plate ID found (%s)' % plateID,
+ BiopythonParserWarning)
+ else:
+ # Simplify the plates IDs, removing letters, as opm does
+ if plateID.startswith(_platesPrefixMammalian):
+ pID = plateID[len(_platesPrefixMammalian):]
+ else:
+ pID = plateID[len(_platesPrefix):]
+ while len(pID) > 0:
+ try:
+ int(pID)
+ break
+ except ValueError:
+ pID = pID[:-1]
+
+ # No luck
+ if len(pID) == 0:
+ warnings.warn('Non-standard plate ID found (%s)' % plateID,
+ BiopythonParserWarning)
+ elif int(pID) < 0:
+ warnings.warn('Non-standard plate ID found (%s), using %s' %
+ (plateID, _platesPrefix + abs(int(pID))))
+ plateID = _platesPrefix + abs(int(pID))
+ else:
+ if plateID.startswith(_platesPrefixMammalian):
+ plateID = _platesPrefixMammalian + '%02d' % int(pID)
+ else:
+ plateID = _platesPrefix + '%02d' % int(pID)
+
+ try:
+ times = pobj[_measurements][_hour]
+ except KeyError:
+ raise KeyError('Could not retrieve the time points')
+
+ plate = PlateRecord(plateID)
+
+ for k in pobj[_measurements]:
+ # Skip the time points
+ if k == _hour:
+ continue
+
+ plate[k] = WellRecord(k, plate=plate,
+ signals=dict([(times[i], pobj[_measurements][k][i])
+ for i in range(len(times))]))
+
+ # Remove the measurements and assign the other qualifiers
+ del pobj['measurements']
+ plate.qualifiers = pobj
+
+ yield plate
+
+
+def CsvIterator(handle):
+ """Generator function to iterate over PM csv records (as PlateRecord
+ objects).
+
+ handle - input file
+ """
+ plate = None
+ data = False
+ qualifiers = {}
+ idx = {}
+ wells = {}
+
+ tblreader = csv.reader(handle, delimiter=',',
+ quotechar='"')
+ for line in tblreader:
+ if len(line) < 2:
+ continue
+
+ elif _datafile in line[0].strip():
+ # Do we have a previous plate?
+ if plate is not None:
+ qualifiers[_csvData][_datafile] = line[1].strip()
+ plate = PlateRecord(plate.id)
+ for k, v in wells.items():
+ plate[k] = WellRecord(k, plate, v)
+ plate.qualifiers = qualifiers
+ yield plate
+ plate = PlateRecord(None)
+ data = False
+ qualifiers[_csvData] = {}
+ idx = {}
+ wells = {}
+
+ elif _plate in line[0].strip():
+ plateID = line[1].strip()
+
+ qualifiers[_csvData][_plate] = plateID
+
+ # Parse also non-standard plate IDs
+ if not plateID.startswith(_platesPrefix) and not plateID.startswith(_platesPrefixMammalian):
+ warnings.warn('Non-standard plate ID found (%s)' % plateID,
+ BiopythonParserWarning)
+ else:
+ # Simplify the plates IDs, removing letters, as opm does
+ if plateID.startswith(_platesPrefixMammalian):
+ pID = plateID[len(_platesPrefixMammalian):]
+ else:
+ pID = plateID[len(_platesPrefix):]
+ while len(pID) > 0:
+ try:
+ int(pID)
+ break
+ except ValueError:
+ pID = pID[:-1]
+
+ # No luck
+ if len(pID) == 0:
+ warnings.warn('Non-standard plate ID found (%s)' % plateID,
+ BiopythonParserWarning)
+ elif int(pID) < 0:
+ warnings.warn('Non-standard plate ID found (%s), using %s' %
+ (plateID, _platesPrefix + abs(int(pID))))
+ plateID = _platesPrefix + abs(int(pID))
+ else:
+ if plateID.startswith(_platesPrefixMammalian):
+ plateID = _platesPrefixMammalian + '%02d' % int(pID)
+ else:
+ plateID = _platesPrefix + '%02d' % int(pID)
+
+ plate.id = plateID
+
+ elif _strainType in line[0].strip():
+ if plate is None:
+ continue
+ qualifiers[_csvData][_strainType] = line[1].strip()
+
+ elif _sample in line[0].strip():
+ if plate is None:
+ continue
+ qualifiers[_csvData][_sample] = line[1].strip()
+
+ elif _strainNumber in line[0].strip():
+ if plate is None:
+ continue
+ qualifiers[_csvData][_strainNumber] = line[1].strip()
+
+ elif _strainName in line[0].strip():
+ if plate is None:
+ continue
+ qualifiers[_csvData][_strainName] = line[1].strip()
+
+ elif _other in line[0].strip():
+ if plate is None:
+ continue
+ qualifiers[_csvData][_other] = line[1].strip()
+
+ elif _file in line[0].strip():
+ if plate is None:
+ continue
+ qualifiers[_csvData][_file] = line[1].strip()
+
+ elif _position in line[0].strip():
+ if plate is None:
+ continue
+ qualifiers[_csvData][_position] = line[1].strip()
+
+ elif _other in line[0].strip():
+ if plate is None:
+ continue
+ qualifiers[_csvData][_setupTime] = line[1].strip()
+
+ elif _hour in line[0].strip():
+ if plate is None:
+ continue
+ data = True
+ for i in range(1, len(line)):
+ x = line[i]
+ if x == '':
+ continue
+ wells[x.strip()] = {}
+ idx[i] = x.strip()
+
+ elif data:
+ if plate is None:
+ continue
+
+ # Workaround for bad-formatted files
+ try:
+ float(line[0])
+ except ValueError:
+ continue
+
+ time = float(line[0])
+
+ for i in range(1, len(line)):
+ x = line[i]
+
+ try:
+ signal = float(x)
+ except ValueError:
+ continue
+
+ well = idx[i]
+ wells[well][time] = signal
+
+ if plate is not None and plate.id is not None:
+ plate = PlateRecord(plate.id)
+ for k, v in wells.items():
+ plate[k] = WellRecord(k, plate, v)
+ plate.qualifiers = qualifiers
+ yield plate
+
+
+def _toOPM(plate):
+ """Transform a PlateRecord object into a dictionary (PRIVATE)."""
+ d = dict(plate.qualifiers.items())
+
+ d[_csvData] = {}
+ d[_csvData][_plate] = plate.id
+ d[_measurements] = {}
+ d[_measurements][_hour] = []
+ times = set()
+ for wid, w in plate._wells.items():
+ d[_measurements][wid] = []
+ for hour in w._signals:
+ times.add(hour)
+
+ for hour in sorted(times):
+ d[_measurements][_hour].append(hour)
+ for wid, w in plate._wells.items():
+ if hour in w._signals:
+ d[_measurements][wid].append(w[hour])
+ # This shouldn't happen
+ else:
+ d[_measurements][wid].append(float('nan'))
+
+ return d
+
+
+class JsonWriter(object):
+ """Class to write PM Json format files."""
+
+ def __init__(self, plates):
+ self.plates = plates
+
+ def write(self, handle):
+ """Write this instance's plates to a file handle."""
+ out = []
+ for plate in self.plates:
+ try:
+ out.append(_toOPM(plate))
+ except ValueError:
+ raise ValueError('Could not export plate(s) in JSON format')
+
+ handle.write(json.dumps(out) + '\n')
+
+ return len(out)
diff --git a/Bio/phenotype/pm_fitting.py b/Bio/phenotype/pm_fitting.py
new file mode 100644
index 0000000..3a2aa1e
--- /dev/null
+++ b/Bio/phenotype/pm_fitting.py
@@ -0,0 +1,133 @@
+# Copyright 2014-2016 by Marco Galardini. All rights reserved.
+# This code is part of the Biopython distribution and governed by its
+# license. Please see the LICENSE file that should have been included
+# as part of this package.
+"""Growth curves fitting and parameters extraction for phenotype data
+
+This module provides functions to perform sigmoid functions fitting to
+Phenotype Microarray data. This module depends on scipy curve_fit function.
+If not available, a warning is raised.
+
+Functions:
+logistic Logistic growth model.
+gompertz Gompertz growth model.
+richards Richards growth model.
+guess_plateau Guess the plateau point to improve sigmoid fitting.
+guess_lag Guess the lag point to improve sigmoid fitting.
+fit Sigmoid functions fit.
+get_area Calculate the area under the PM curve."""
+
+import numpy as np
+
+try:
+ from scipy.optimize.minpack import curve_fit
+ from scipy.integrate import trapz
+except ImportError:
+ from Bio import MissingPythonDependencyError
+ raise MissingPythonDependencyError(
+ 'Install scipy to extract curve parameters.')
+
+
+def logistic(x, A, u, d, v, y0):
+ """Logistic growth model
+
+ Proposed in Zwietering et al., 1990 (PMID: 16348228)
+ """
+ y = (A / (1 + np.exp((((4 * u) / A) * (d - x)) + 2))) + y0
+ return y
+
+
+def gompertz(x, A, u, d, v, y0):
+ """Gompertz growth model
+
+ Proposed in Zwietering et al., 1990 (PMID: 16348228)
+ """
+ y = (A * np.exp(-np.exp((((u * np.e) / A) * (d - x)) + 1))) + y0
+ return y
+
+
+def richards(x, A, u, d, v, y0):
+ """Gompertz growth model (equivalent to Stannard)
+
+ Proposed in Zwietering et al., 1990 (PMID: 16348228)
+ """
+ y = (A * pow(1 + (v + (np.exp(1 + v) * np.exp((u / A) *
+ (1 + v) * (1 + (1 / v)) * (d - x)))), -(1 / v))) + y0
+ return y
+
+
+def guess_lag(x, y):
+ """Given two axes returns a guess of the lag point.
+
+ The lag point is defined as the x point where the difference in y
+ with the next point is higher then the mean differences between
+ the points plus one standard deviation. If such point is not found
+ or x and y have different lengths the function returns zero.
+ """
+ if len(x) != len(y):
+ return 0
+
+ diffs = []
+ indexes = range(len(x))
+
+ for i in indexes:
+ if i + 1 not in indexes:
+ continue
+ diffs.append(y[i + 1] - y[i])
+ diffs = np.array(diffs)
+
+ flex = x[-1]
+ for i in indexes:
+ if i + 1 not in indexes:
+ continue
+ if (y[i + 1] - y[i]) > (diffs.mean() + (diffs.std())):
+ flex = x[i]
+ break
+
+ return flex
+
+
+def guess_plateau(x, y):
+ """Given two axes returns a guess of the plateau point.
+
+ The plateau point is defined as the x point where the y point
+ is near one standard deviation of the differences between the y points to
+ the maximum y value. If such point is not found or x and y have
+ different lengths the function returns zero.
+ """
+ if len(x) != len(y):
+ return 0
+
+ diffs = []
+ indexes = range(len(y))
+
+ for i in indexes:
+ if i + 1 not in indexes:
+ continue
+ diffs.append(y[i + 1] - y[i])
+ diffs = np.array(diffs)
+
+ ymax = y[-1]
+ for i in indexes:
+ if y[i] > (ymax - diffs.std()) and y[i] < (ymax + diffs.std()):
+ ymax = y[i]
+ break
+
+ return ymax
+
+
+def fit(function, x, y):
+ """Fit the provided functrion to the x and y values.
+
+ The function parameters and the parameters covariance."""
+ # Compute guesses for the parameters
+ # This is necessary to get significant fits
+ p0 = [guess_plateau(x, y), 4.0, guess_lag(x, y), 0.1, min(y)]
+
+ params, pcov = curve_fit(function, x, y, p0=p0)
+ return params, pcov
+
+
+def get_area(y, x):
+ """Get the area under the curve"""
+ return trapz(y=y, x=x)
diff --git a/Bio/triefind.py b/Bio/triefind.py
index fc1e766..e0c1019 100644
--- a/Bio/triefind.py
+++ b/Bio/triefind.py
@@ -16,6 +16,7 @@ find Find keys in a trie matching anywhere in a string.
find_words Find keys in a trie matching whole words in a string.
"""
+
import string
import re
diff --git a/BioSQL/BioSeq.py b/BioSQL/BioSeq.py
index 0d8c175..d430c17 100644
--- a/BioSQL/BioSeq.py
+++ b/BioSQL/BioSeq.py
@@ -137,6 +137,17 @@ class DBSeq(Seq):
return other + self.toseq()
+def _retrieve_seq_len(adaptor, primary_id):
+ # The database schema ensures there will be only one matching row
+ seqs = adaptor.execute_and_fetchall(
+ "SELECT length FROM biosequence WHERE bioentry_id = %s", (primary_id,))
+ if not seqs:
+ return None
+ assert len(seqs) == 1
+ given_length, = seqs[0]
+ return int(given_length)
+
+
def _retrieve_seq(adaptor, primary_id):
# The database schema ensures there will be only one matching
# row in the table.
@@ -510,11 +521,8 @@ class DBSeqRecord(SeqRecord):
# We don't yet record any per-letter-annotations in the
# BioSQL database, but we should set this property up
# for completeness (and the __str__ method).
- try:
- length = len(self.seq)
- except:
- # Could be no sequence in the database!
- length = 0
+ # We do NOT want to load the sequence from the DB here!
+ length = _retrieve_seq_len(adaptor, primary_id)
self._per_letter_annotations = _RestrictedDict(length=length)
def __get_seq(self):
@@ -523,6 +531,7 @@ class DBSeqRecord(SeqRecord):
return self._seq
def __set_seq(self, seq):
+ # TODO - Check consistent with self._per_letter_annotations
self._seq = seq
def __del_seq(self):
diff --git a/BioSQL/BioSeqDatabase.py b/BioSQL/BioSeqDatabase.py
index 35d8f58..72cfc3f 100644
--- a/BioSQL/BioSeqDatabase.py
+++ b/BioSQL/BioSeqDatabase.py
@@ -1,5 +1,5 @@
# Copyright 2002 by Andrew Dalke. All rights reserved.
-# Revisions 2007-2014 copyright by Peter Cock. All rights reserved.
+# Revisions 2007-2016 copyright by Peter Cock. All rights reserved.
# Revisions 2009 copyright by Cymon J. Cox. All rights reserved.
# Revisions 2013-2014 copyright by Tiago Antao. All rights reserved.
# This code is part of the Biopython distribution and governed by its
@@ -24,6 +24,7 @@ from . import BioSeq
from . import Loader
from . import DBUtils
+
_POSTGRES_RULES_PRESENT = False # Hack for BioSQL Bug 2839
@@ -112,17 +113,23 @@ def open_database(driver="MySQLdb", **kwargs):
if server.adaptor.execute_and_fetchall(sql):
import warnings
from Bio import BiopythonWarning
- warnings.warn("Your BioSQL PostgreSQL schema includes some "
- "rules currently required for bioperl-db but "
- "which may cause problems loading data using "
- "Biopython (see BioSQL Bug 2839). If you do not "
- "use BioPerl, please remove these rules. "
- "Biopython should cope with the rules present, "
- "but with a performance penalty when loading "
- "new records.", BiopythonWarning)
+ warnings.warn("Your BioSQL PostgreSQL schema includes some rules "
+ "currently required for bioperl-db but which may"
+ "cause problems loading data using Biopython (see "
+ "BioSQL's RedMine Bug 2839 aka GitHub Issue 4 "
+ "https://github.com/biosql/biosql/issues/4). "
+ "If you do not use BioPerl, please remove these "
+ "rules. Biopython should cope with the rules "
+ "present, but with a performance penalty when "
+ "loading new records.", BiopythonWarning)
global _POSTGRES_RULES_PRESENT
_POSTGRES_RULES_PRESENT = True
+ elif driver == 'sqlite3':
+ # Tell SQLite that we want to use foreign keys
+ # https://www.sqlite.org/foreignkeys.html#fk_enable
+ server.adaptor.execute('PRAGMA foreign_keys = ON')
+
return server
@@ -304,9 +311,18 @@ class _CursorWrapper(object):
self.real_cursor = real_cursor
def execute(self, operation, params=None, multi=False):
+ """Execute a sql statement
+ """
self.real_cursor.execute(operation, params, multi)
+ def executemany(self, operation, params):
+ """Execute many sql statements
+ """
+ self.real_cursor.executemany(operation, params)
+
def _convert_tuple(self, tuple_):
+ """Decode any bytestrings present in the row
+ """
tuple_list = list(tuple_)
for i, elem in enumerate(tuple_list):
if type(elem) is bytes:
@@ -482,6 +498,13 @@ class Adaptor(object):
sql = sql.replace("%s", "?")
self.dbutils.execute(self.cursor, sql, args)
+ def executemany(self, sql, args):
+ """Execute many sql commands.
+ """
+ if os.name == "java":
+ sql = sql.replace("%s", "?")
+ self.dbutils.executemany(self.cursor, sql, args)
+
def get_subseq_as_string(self, seqid, start, end):
length = end - start
# XXX Check this on MySQL and PostgreSQL. substr should be general,
@@ -499,10 +522,14 @@ class Adaptor(object):
(start + 1, length, seqid))[0])
def execute_and_fetch_col0(self, sql, args=None):
+ """Return a list of values from the first column in the row
+ """
self.execute(sql, args or ())
return [field[0] for field in self.cursor.fetchall()]
def execute_and_fetchall(self, sql, args=None):
+ """Return a list of tuples of all rows
+ """
self.execute(sql, args or ())
return self.cursor.fetchall()
@@ -622,12 +649,15 @@ class BioSeqDatabase(object):
return list(self.keys())
def __getitem__(self, key):
- return BioSeq.DBSeqRecord(self.adaptor, key)
+ record = BioSeq.DBSeqRecord(self.adaptor, key)
+ if record._biodatabase_id != self.dbid:
+ raise KeyError("Entry %r does exist, but not in current name space" % key)
+ return record
def __delitem__(self, key):
"""Remove an entry and all its annotation."""
if key not in self:
- raise KeyError(key)
+ raise KeyError("Entry %r cannot be deleted. It was not found or is invalid" % key)
# Assuming this will automatically cascade to the other tables...
sql = "DELETE FROM bioentry " + \
"WHERE biodatabase_id=%s AND bioentry_id=%s;"
@@ -765,7 +795,7 @@ class BioSeqDatabase(object):
else:
accession = cur_record.id
version = 0
- gi = cur_record.annotations.get("gi", None)
+ gi = cur_record.annotations.get("gi")
sql = "SELECT bioentry_id FROM bioentry WHERE (identifier " + \
"= '%s' AND biodatabase_id = '%s') OR (accession = " + \
"'%s' AND version = '%s' AND biodatabase_id = '%s')"
diff --git a/BioSQL/DBUtils.py b/BioSQL/DBUtils.py
index 8c64457..5e16a5d 100644
--- a/BioSQL/DBUtils.py
+++ b/BioSQL/DBUtils.py
@@ -10,6 +10,7 @@
# available and licensed separately. Please consult www.biosql.org
import os
+
_dbutils = {}
@@ -38,6 +39,11 @@ class Generic_dbutils(object):
"""
cursor.execute(sql, args or ())
+ def executemany(self, cursor, sql, seq):
+ """Execute many sql commands
+ """
+ cursor.executemany(sql, seq)
+
def autocommit(self, conn, y=1):
# Let's hope it was not really needed
pass
@@ -46,10 +52,21 @@ class Generic_dbutils(object):
class Sqlite_dbutils(Generic_dbutils):
"""Custom database utilities for SQLite."""
+ def _sub_placeholder(self, sql):
+ """Format the argument placeholders for sqlite
+ """
+ return sql.replace("%s", "?")
+
def execute(self, cursor, sql, args=None):
"""Execute SQL command, replacing %s with ? for variable substitution in sqlite3.
"""
- cursor.execute(sql.replace("%s", "?"), args or ())
+ sql = self._sub_placeholder(sql)
+ cursor.execute(sql, args or ())
+
+ def executemany(self, cursor, sql, seq):
+ sql = self._sub_placeholder(sql)
+ cursor.executemany(sql, seq)
+
_dbutils["sqlite3"] = Sqlite_dbutils
diff --git a/BioSQL/Loader.py b/BioSQL/Loader.py
index 7b0952c..0f08f37 100644
--- a/BioSQL/Loader.py
+++ b/BioSQL/Loader.py
@@ -1,5 +1,5 @@
# Copyright 2002 by Andrew Dalke. All rights reserved.
-# Revisions 2007-2009 copyright by Peter Cock. All rights reserved.
+# Revisions 2007-2016 copyright by Peter Cock. All rights reserved.
# Revisions 2008 copyright by Cymon J. Cox. All rights reserved.
# This code is part of the Biopython distribution and governed by its
# license. Please see the LICENSE file that should have been included
@@ -30,8 +30,6 @@ from Bio._py3k import _is_int_or_long
from Bio._py3k import range
from Bio._py3k import basestring
-__docformat__ = "restructuredtext en"
-
class DatabaseLoader(object):
"""Object used to load SeqRecord objects into a BioSQL database."""
@@ -329,6 +327,45 @@ class DatabaseLoader(object):
assert answer == answer.lower()
return answer
+ def _update_left_right_taxon_values(self, left_value):
+ """update the left and right values in the table
+ """
+ if not left_value:
+ return
+ # Due to the UNIQUE constraint on the left and right values in the taxon
+ # table we cannot simply update them through an SQL statement as we risk
+ # colliding values. Instead we must select all of the rows that we want to
+ # update, modify the values in python and then update the rows
+ # self.adaptor.execute("UPDATE taxon SET right_value = right_value + 2 WHERE right_value >= %s", (left_value,))
+ # self.adaptor.execute("UPDATE taxon SET left_value = left_value + 2 WHERE left_value > %s", (left_value,))
+
+ rows = self.adaptor.execute_and_fetchall(
+ "SELECT left_value, right_value, taxon_id FROM taxon WHERE right_value >= %s or left_value > %s",
+ # "SELECT left_value, right_value, taxon_id FROM taxon",
+ (left_value, left_value)
+ )
+
+ right_rows = []
+ left_rows = []
+ for row in rows:
+ new_right = row[1]
+ new_left = row[0]
+ if new_right >= left_value:
+ new_right += 2
+
+ if new_left > left_value:
+ new_left += 2
+ right_rows.append((new_right, row[2]))
+ left_rows.append((new_left, row[2]))
+
+ # sort the rows based on the value from largest to smallest
+ # should ensure no overlaps
+ right_rows = sorted(right_rows, key=lambda x: x[0], reverse=True)
+ left_rows = sorted(left_rows, key=lambda x: x[0], reverse=True)
+
+ self.adaptor.executemany("UPDATE taxon SET left_value = %s WHERE taxon_id = %s", left_rows)
+ self.adaptor.executemany("UPDATE taxon SET right_value = %s WHERE taxon_id = %s", right_rows)
+
def _get_taxon_id_from_ncbi_taxon_id(self, ncbi_taxon_id,
scientific_name=None,
common_name=None):
@@ -372,6 +409,10 @@ class DatabaseLoader(object):
rank = "species"
genetic_code = None
mito_genetic_code = None
+ parent_left_value = None
+ parent_right_value = None
+ left_value = None
+ right_value = None
species_names = []
if scientific_name:
species_names.append(("scientific name", scientific_name))
@@ -387,14 +428,22 @@ class DatabaseLoader(object):
assert taxonomic_record[0]["TaxId"] == str(ncbi_taxon_id), \
"%s versus %s" % (taxonomic_record[0]["TaxId"],
ncbi_taxon_id)
- parent_taxon_id = self._get_taxon_id_from_ncbi_lineage(
+
+ parent_taxon_id, parent_left_value, parent_right_value = self._get_taxon_id_from_ncbi_lineage(
taxonomic_record[0]["LineageEx"])
- rank = taxonomic_record[0]["Rank"]
- genetic_code = taxonomic_record[0]["GeneticCode"]["GCId"]
- mito_genetic_code = taxonomic_record[
- 0]["MitoGeneticCode"]["MGCId"]
+
+ left_value = parent_right_value
+ right_value = parent_right_value + 1
+
+ rank = str(taxonomic_record[0]["Rank"])
+
+ genetic_code = int(taxonomic_record[0]["GeneticCode"]["GCId"])
+
+ mito_genetic_code = int(taxonomic_record[
+ 0]["MitoGeneticCode"]["MGCId"])
+
species_names = [("scientific name",
- taxonomic_record[0]["ScientificName"])]
+ str(taxonomic_record[0]["ScientificName"]))]
try:
for name_class, names in taxonomic_record[0]["OtherNames"].items():
name_class = self._fix_name_class(name_class)
@@ -422,6 +471,8 @@ class DatabaseLoader(object):
# is in the record annotation as a list of names, as we won't
# know the NCBI taxon IDs for these parent nodes.
+ self._update_left_right_taxon_values(left_value)
+
self.adaptor.execute(
"INSERT INTO taxon(parent_taxon_id, ncbi_taxon_id, node_rank,"
" genetic_code, mito_genetic_code, left_value, right_value)"
@@ -430,8 +481,9 @@ class DatabaseLoader(object):
rank,
genetic_code,
mito_genetic_code,
- None,
- None))
+ left_value,
+ right_value))
+
taxon_id = self.adaptor.last_id("taxon")
# Record the scientific name, common name, etc
@@ -458,46 +510,59 @@ class DatabaseLoader(object):
This method will record all the lineage given, returning the taxon id
(database key, not NCBI taxon id) of the final entry (the species).
"""
- ncbi_taxon_id = taxonomic_lineage[-1]["TaxId"]
-
+ ncbi_taxon_id = int(taxonomic_lineage[-1]["TaxId"])
+ left_value = None
+ right_value = None
+ parent_left_value = None
+ parent_right_value = None
# Is this in the database already? Check the taxon table...
- taxon_id = self.adaptor.execute_and_fetch_col0(
- "SELECT taxon_id FROM taxon"
+ rows = self.adaptor.execute_and_fetchall(
+ "SELECT taxon_id, left_value, right_value FROM taxon"
" WHERE ncbi_taxon_id=%s" % ncbi_taxon_id)
- if taxon_id:
+ if rows:
# we could verify that the Scientific Name etc in the database
# is the same and update it or print a warning if not...
- if isinstance(taxon_id, list):
- assert len(taxon_id) == 1
- return taxon_id[0]
- else:
- return taxon_id
+ assert len(rows) == 1
+ return rows[0]
# We have to record this.
if len(taxonomic_lineage) > 1:
# Use recursion to find out the taxon id (database key) of the
# parent.
- parent_taxon_id = self._get_taxon_id_from_ncbi_lineage(
+ parent_taxon_id, parent_left_value, parent_right_value = self._get_taxon_id_from_ncbi_lineage(
taxonomic_lineage[:-1])
+ left_value = parent_right_value
+ right_value = parent_right_value + 1
assert _is_int_or_long(parent_taxon_id), repr(parent_taxon_id)
else:
+ # we have reached the top of the lineage but no current taxonomy
+ # id has been found
parent_taxon_id = None
+ left_value = self.adaptor.execute_one(
+ "SELECT MAX(left_value) FROM taxon")[0]
+ if not left_value:
+ left_value = 0
+
+ right_value = left_value + 1
+
+ self._update_left_right_taxon_values(left_value)
# INSERT new taxon
- rank = taxonomic_lineage[-1].get("Rank", None)
+ rank = str(taxonomic_lineage[-1].get("Rank"))
self.adaptor.execute(
- "INSERT INTO taxon(ncbi_taxon_id, parent_taxon_id, node_rank)"
- " VALUES (%s, %s, %s)", (ncbi_taxon_id, parent_taxon_id, rank))
+ "INSERT INTO taxon(ncbi_taxon_id, parent_taxon_id, node_rank, left_value, right_value)"
+ " VALUES (%s, %s, %s, %s, %s)", (ncbi_taxon_id, parent_taxon_id, rank, left_value, right_value))
+
taxon_id = self.adaptor.last_id("taxon")
- assert isinstance(taxon_id, (int, long)), repr(taxon_id)
+ # assert isinstance(taxon_id, int), repr(taxon_id)
# ... and its name in taxon_name
- scientific_name = taxonomic_lineage[-1].get("ScientificName", None)
+ scientific_name = taxonomic_lineage[-1].get("ScientificName")
if scientific_name:
self.adaptor.execute(
"INSERT INTO taxon_name(taxon_id, name, name_class)"
" VALUES (%s, %s, 'scientific name')", (taxon_id,
scientific_name[:255]))
- return taxon_id
+ return taxon_id, left_value, right_value
def _load_bioentry_table(self, record):
"""Fill the bioentry table with sequence information (PRIVATE).
@@ -536,7 +601,7 @@ class DatabaseLoader(object):
# Allow description and division to default to NULL as in BioPerl.
description = getattr(record, 'description', None)
- division = record.annotations.get("data_file_division", None)
+ division = record.annotations.get("data_file_division")
sql = """
INSERT INTO bioentry (
@@ -668,10 +733,10 @@ class DatabaseLoader(object):
# Handled separately
continue
term_id = self._get_term_id(key, ontology_id=tag_ontology_id)
- if isinstance(value, list) or isinstance(value, tuple):
+ if isinstance(value, (list, tuple)):
rank = 0
for entry in value:
- if isinstance(entry, str) or isinstance(entry, int):
+ if isinstance(entry, (str, int)):
# Easy case
rank += 1
self.adaptor.execute(many_sql,
@@ -680,7 +745,7 @@ class DatabaseLoader(object):
pass
# print "Ignoring annotation '%s' sub-entry of type '%s'" \
# % (key, str(type(entry)))
- elif isinstance(value, str) or isinstance(value, int):
+ elif isinstance(value, (str, int)):
# Have a simple single entry, leave rank as the DB default
self.adaptor.execute(mono_sql,
(bioentry_id, term_id, str(value)))
diff --git a/CONTRIB b/CONTRIB
index 4cef19e..d45870a 100644
--- a/CONTRIB
+++ b/CONTRIB
@@ -16,11 +16,14 @@ Paul T. Bathen
Yair Benita <Y.Benita at domain pharm.uu.nl>
Peter Bienstman <Peter.Bienstman at domain rug.ac.be>
Jose Blanca
+Kai Blin < https://github.com/kblin >
+Steve Bond < https://github.com/biologyguy >
Bob Bussell <rgb2003 at domain med.cornell.edu>
John Bradley < https://github.com/johnbradley >
Diego Brouard <diego at domain conysis.com>
Christian Brueffer <christian at domain brueffer.de>
David Cain <gmail, david joseph cain>
+Christiam Camacho < https://github.com/christiam >
Franco Caramia < https://github.com/fcaramia >
James Casbon <j.a.casbon at domain qmul.ac.uk>
Hye-Shik Chang <perky at domain fallin.lv>
@@ -30,6 +33,7 @@ Saket Choudhary < https://github.com/saketkc >
Peter Cock <p.j.a.cock at googlemail dot com / https://github.com/peterjc >
Marc Colosimo <mcolosimo at domain mitre.org>
Andres Colubri <andres dot colubri at gmail dot com>
+Joe Cora < https://github.com/JoeCora >
Cymon J Cox <cymon at domain duke.edu>
Gavin E Crooks <gec at domain compbio.berkeley.edu>
Andrew Dalke <dalke at domain acm.org>
@@ -42,14 +46,17 @@ Nathan J. Edwards <nje5 at edu domain georgetown>
Kyle Ellrott
Gokcen Eraslan < https://github.com/gokceneraslan >
Tarcisio Fedrizzi < https://github.com/hcraT >
+João D Ferreira < https://github.com/jdferreira >
Jeffrey Finkelstein <jeffrey.finkelstein at domain gmail.com>
Konrad Förstner < https://github.com/konrad >
Iddo Friedberg <idoerg at domain burnham.org>
Bertrand Frottier <bertrand.frottier at domain free.fr>
Ben Fulton < https://github.com/benfulton >
+Marco Galardini < https://github.com/mgalardini >
Aaron Gallagher <habnabit at gmail>
Phillip Garland <pgarland at gmail>
Walter Gillett < https://github.com/wgillett >
+Chaitanya Gupta < https://github.com/iCHAIT >
Melissa Gymrek < https://github.com/mgymrek >
Frederik Gwinner
Jason A. Hackney <jhackney at domain stanford.edu>
@@ -63,9 +70,11 @@ Jeff Hussmann <first dot last at gmail dot com>
Kevin Jacobs <jacobs at bioinformed dot com>
Diana Jaunzeikare
Sunhwan Jo < https://github.com/sunhwan >
+Terry Jones < https://github.com/terrycojones >
Joanna & Dominik Kasprzak
Frank Kauff <fkauff at domain duke.edu>
Siong Kong
+David Koppstein < https://github.com/dkoppstein >
Andreas Kuntzagk <andreas.kuntzagk at domain mdc-berlin.de>
Michal Kurowski <michal at domain genesilico.pl>
Gleb Kuznetsov < https://github.com/glebkuznetsov >
@@ -85,12 +94,15 @@ Alan Medlar < https://github.com/ajm >
Tarjei Mikkelsen <tarjei at domain genome.wi.mit.edu>
Chris Mitchell < https://github.com/chrismit >
Ben Morris
+Nader Morshed < https://github.com/naderm >
+Barbara Mühlemann < https://github.com/bamueh >
Bertrand Néron < https://github.com/bneron >
David Nicholson < https://github.com/danich1 >
Kozo Nishida < https://github.com/kozo2 >
Emmanuel Noutahi < https://github.com/maclandrol >
Konstantin Okonechnikov <k.okonechnikov at domain gmail.com>
Cheng Soon Ong <chengsoon.ong at tuebingen.mpg.de>
+Brian Osborne < https://github.com/bosborne >
Anne Pajon <ap one two at sanger ac uk>
Alessio Papini <first dot last at unifi dot it>
Claude Paroz <claude at two (as digit) xlibre dot net>
@@ -98,36 +110,44 @@ Carlos Pena < https://github.com/carlosp420 >
Lenna Peterson <ark first-name at gmail dot com>
Andrea Pierleoni <andrea at the Italian domain biocomp dot unibo>
Markus Piotrowski < https://github.com/MarkusPiotrowski >
+Anders Pitman < https://github.com/anderspitman >
Mike Poidinger <Michael.Poidinger at domain eBioinformatics.com>
Leighton Pritchard <lpritc at domain scri.sari.ac.uk>
Leszek Pryszcz < https://github.com/lpryszcz >
Eric Rasche < https://github.com/erasche >
Andrea Rizzi < https://github.com/andrrizzi >
Joao Rodrigues <anaryin at the domain gmail dot com>
+Aaron Rosenfeld < https://github.com/arosenfeld >
Thomas Rosleff Soerensen <rosleff at domain mpiz-koeln.mpg.de>
+Matt Ruffalo < https://github.com/mruffalo >
Thomas Schmitt <Thomas dot Schmitt at Swedish domain sbc.su>
Wolfgang Schueler <wolfgang at domain proceryon.at>
Anuj Sharma < https://github.com/xulesc >
Matt Shirley < https://github.com/mdshw5 >
+Thomas Sicheritz-Ponten <thomas at domain cbs.dtu.dk>
Seth Sims <seth.sims at gmail>
Lucas Sinclair < https://github.com/xapple >
+Connor T. Skennerton < https://github.com/ctSkennerton >
Peter Slickers <piet at domain clondiag.com>
Kamil Slowikowski <k no dot slowikowski at gmail dot com>
-Thomas Sicheritz-Ponten <thomas at domain cbs.dtu.dk>
-Connor T. Skennerton < https://github.com/ctSkennerton >
+Jacek Śmietański < https://github.com/dadoskawina >
Frederic Sohm <fsms at domain users.sourceforge.net>
+Owen Solberg < https://github.com/odoublewen >
+Matteo Sticco < https://github.com/sticken88/ >
Nate Sutton
Anuj Sharma
-Eric Talevich <eric.talevich at domain gmail.com>
+Eric Talevich <etal at domain uga.edu / https://github.com/etal>
Bartosz Telenczuk <bartosz.telenczuk at domain gmail.com>
Bogdan T. (bogdan at pearlgen dot com)
Tyghe Vallard < https://github.com/necrolyte2 >
Carlos Rios Vera <crosvera at domain gmail.com>
Johann Visagie <wjv at domain cityip.co.za>
Dan Vogel <dmv at domain andrew.cmu.edu>
+Chris Warth < https://github.com/cswarth >
David Weisman <david.weisman at domain acm.org>
Bartek Wilczynski <bartek at domain rezolwenta.eu.org>
David Winter <david dot winter at gmail dot com>
+Ben Woodcroft < https://github.com/wwood >
Chunlei Wu < https://github.com/newgene/ >
Kevin Wu < https://github.com/kevinwuhoo >
Yanbo Ye < https://github.com/lijax >
diff --git a/DEPRECATED b/DEPRECATED
index 2b4598c..2721a1a 100644
--- a/DEPRECATED
+++ b/DEPRECATED
@@ -28,11 +28,29 @@ Never officially supported, these triggered a warning in Release 1.62
recommending Python 3.3 or later. As of Biopython Release 1.63 onwards,
installation simply aborts with a error message.
+Python 3.3
+==========
+Still suported but deprecated as of Release 1.67, triggering a warning
+at installation time.
+
Bio.CodonAlign
==============
This new experimental module included in Biopython 1.64 was renamed to
Bio.codonalign in Biopython 1.65 to follow PEP8 module naming rules.
+Bio.SeqRecord equality
+======================
+As of Release 1.67, the SeqRecord objects (and their subclasses) no longer use
+the default Python object comparison. Instead they will raise an exception if
+you try to compare them.
+
+For backward compatibility and/or to explicitly use object comparison, please
+use id(record1) == id(record2) instead.
+
+Otherwise please test whichever specific attributes you are interested in
+explicitly, for example record1.id == record2.id or record1.seq == record.seq
+(see also the note below about sequence equality).
+
Bio.Seq sequence equality
=========================
As of Release 1.65, the Seq and MutableSeq objects (and their subclasses)
@@ -88,12 +106,14 @@ attribute sub_features was deprecated.
Bio.Motif
=========
Declared obsolete with a PendingDeprecationWarning in Release 1.61, formally
-deprecated in Release 1.62. Please use the newer Bio.motifs module instead.
+deprecated in Release 1.62, removed in Release 1.67. Please use the newer
+Bio.motifs module instead.
AlignAceCommandline and CompareAceCommandline
=============================================
-Deprecated in release 1.62. An up to date version of the software cannot
-be obtained anymore (affects Bio.Motif and its replacement Bio.motifs).
+Deprecated in release 1.62, removed in Release 1.67. An up to date version of
+the software cannot be obtained anymore (affects Bio.Motif and its replacement
+Bio.motifs).
Bio.SeqIO.Interfaces
====================
@@ -529,10 +549,10 @@ Functions GC_Frame, fasta_uniqids, apply_on_multi_fasta, and
quicker_apply_on_multi_fasta were deprecated in Release 1.55, and removed
in Release 1.58.
-Function quick_FASTA_reader was declared obsolete in Release 1.61, and
-deprecated in Release 1.64. Use function list(SimpleFastaParser(handle))
-from Bio.SeqIO.FastaIO instead (but ideally convert your code to using
-an iterator approach).
+Function quick_FASTA_reader was declared obsolete in Release 1.61,
+deprecated in Release 1.64, and removed in Release 1.67. Use function
+list(SimpleFastaParser(handle)) from Bio.SeqIO.FastaIO instead (but
+ideally convert your code to using an iterator approach).
Bio.GFF (for accessing a MySQL database created with BioPerl, etc)
=======
@@ -650,7 +670,7 @@ method get_seq_by_num were deprecated in Release 1.57.
Bio.ParserSupport
=================
Bio.ParserSupport was declared obsolete in Release 1.59, and deprecated in
-Release 1.63.
+Release 1.63. The Martel specific EventGenerator was removed in Release 1.67..
Bio.ParserSupport.SGMLStrippingConsumer was deprecated in Release 1.59, and
removed in Release 1.61.
diff --git a/Doc/Tutorial.html b/Doc/Tutorial.html
deleted file mode 100644
index 8612f99..0000000
--- a/Doc/Tutorial.html
+++ /dev/null
@@ -1,10952 +0,0 @@
-<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.0 Transitional//EN"
- "http://www.w3.org/TR/REC-html40/loose.dtd">
-<HTML>
-<HEAD>
-<TITLE>Biopython Tutorial and Cookbook
-</TITLE>
-
-<META http-equiv="Content-Type" content="text/html; charset=US-ASCII">
-<META name="GENERATOR" content="hevea 1.10+9 of 2008-12-17">
-<STYLE type="text/css">
-.li-itemize{margin:1ex 0ex;}
-.li-enumerate{margin:1ex 0ex;}
-.dd-description{margin:0ex 0ex 1ex 4ex;}
-.dt-description{margin:0ex;}
-.footnotetext{margin:0ex; padding 0ex;}
-DIV.footnotetext P{margin:0px; text-indent:1em;}
-.toc{list-style:none;}
-.thefootnotes{text-align:left;margin:0ex;}
-.dt-thefootnotes{margin:0em;}
-.dd-thefootnotes{margin:0em 0em 0em 2em;}
-.footnoterule{margin:1em auto 1em 0px;width:50%;}
-.caption{padding-left:2ex; padding-right:2ex; margin-left:auto; margin-right:auto}
-.title{margin:2ex auto;text-align:center}
-.center{text-align:center;margin-left:auto;margin-right:auto;}
-.flushleft{text-align:left;margin-left:0ex;margin-right:auto;}
-.flushright{text-align:right;margin-left:auto;margin-right:0ex;}
-DIV TABLE{margin-left:inherit;margin-right:inherit;}
-PRE{text-align:left;margin-left:0ex;margin-right:auto;}
-BLOCKQUOTE{margin-left:4ex;margin-right:4ex;text-align:left;}
-TD P{margin:0px;}
-.boxed{border:1px solid black}
-.textboxed{border:1px solid black}
-.vbar{border:none;width:2px;background-color:black;}
-.hbar{border:none;height:2px;width:100%;background-color:black;}
-.hfill{border:none;height:1px;width:200%;background-color:black;}
-.vdisplay{border-collapse:separate;border-spacing:2px;width:auto; empty-cells:show; border:2px solid red;}
-.vdcell{white-space:nowrap;padding:0px;width:auto; border:2px solid green;}
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-.marginparright{float:right; margin-left:1ex; margin-right:0ex;}
-.theorem{text-align:left;margin:1ex auto 1ex 0ex;}
-.part{margin:2ex auto;text-align:center}
-</STYLE>
-</HEAD>
-<BODY >
-<!--HEVEA command line is: hevea -fix Tutorial.tex -->
-<!--CUT DEF chapter 1 --><P>
-<P ALIGN="center">
-<IMG ALIGN="center" SRC="images/biopython.jpg" TITLE="Biopython Logo" ALT="[Biopython Logo]" width="1024" height="288" />
-</p>
-</P><TABLE CLASS="title"><TR><TD><H1 CLASS="titlemain">Biopython Tutorial and Cookbook</H1><H3 CLASS="titlerest">Jeff Chang, Brad Chapman, Iddo Friedberg, Thomas Hamelryck,<BR>
-Michiel de Hoon, Peter Cock, Tiago Antao, Eric Talevich, Bartek Wilczyński</H3><H3 CLASS="titlerest">Last Update – 21 October 2015 (Biopython 1.66)</H3></TD></TR>
-</TABLE><!--TOC chapter Contents-->
-<H1 CLASS="chapter"><!--SEC ANCHOR -->Contents</H1><!--SEC END --><UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc1">Chapter 1  Introduction</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc2">1.1  What is Biopython?</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc3">1.2  What can I find in the Biopython package</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc4">1.3  Installing Biopython</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc5">1.4  Frequently Asked Questions (FAQ)</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc6">Chapter 2  Quick Start – What can you do with Biopython?</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc7">2.1  General overview of what Biopython provides</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc8">2.2  Working with sequences</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc9">2.3  A usage example</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc10">2.4  Parsing sequence file formats</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc11">2.4.1  Simple FASTA parsing example</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc12">2.4.2  Simple GenBank parsing example</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc13">2.4.3  I love parsing – please don’t stop talking about it!</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc14">2.5  Connecting with biological databases</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc15">2.6  What to do next</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc16">Chapter 3  Sequence objects</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc17">3.1  Sequences and Alphabets</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc18">3.2  Sequences act like strings</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc19">3.3  Slicing a sequence</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc20">3.4  Turning Seq objects into strings</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc21">3.5  Concatenating or adding sequences</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc22">3.6  Changing case</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc23">3.7  Nucleotide sequences and (reverse) complements</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc24">3.8  Transcription</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc25">3.9  Translation</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc26">3.10  Translation Tables</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc27">3.11  Comparing Seq objects</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc28">3.12  MutableSeq objects</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc29">3.13  UnknownSeq objects</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc30">3.14  Working with strings directly</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc31">Chapter 4  Sequence annotation objects</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc32">4.1  The SeqRecord object</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc33">4.2  Creating a SeqRecord</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc34">4.2.1  SeqRecord objects from scratch</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc35">4.2.2  SeqRecord objects from FASTA files</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc36">4.2.3  SeqRecord objects from GenBank files</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc37">4.3  Feature, location and position objects</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc38">4.3.1  SeqFeature objects</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc39">4.3.2  Positions and locations</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc40">4.3.3  Sequence described by a feature or location</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc41">4.4  References</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc42">4.5  The format method</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc43">4.6  Slicing a SeqRecord</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc44">4.7  Adding SeqRecord objects</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc45">4.8  Reverse-complementing SeqRecord objects</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc46">Chapter 5  Sequence Input/Output</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc47">5.1  Parsing or Reading Sequences</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc48">5.1.1  Reading Sequence Files</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc49">5.1.2  Iterating over the records in a sequence file</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc50">5.1.3  Getting a list of the records in a sequence file</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc51">5.1.4  Extracting data</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc52">5.2  Parsing sequences from compressed files</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc53">5.3  Parsing sequences from the net</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc54">5.3.1  Parsing GenBank records from the net</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc55">5.3.2  Parsing SwissProt sequences from the net</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc56">5.4  Sequence files as Dictionaries</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc57">5.4.1  Sequence files as Dictionaries – In memory</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc58">5.4.2  Sequence files as Dictionaries – Indexed files</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc59">5.4.3  Sequence files as Dictionaries – Database indexed files</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc60">5.4.4  Indexing compressed files</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc61">5.4.5  Discussion</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc62">5.5  Writing Sequence Files</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc63">5.5.1  Round trips</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc64">5.5.2  Converting between sequence file formats</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc65">5.5.3  Converting a file of sequences to their reverse complements</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc66">5.5.4  Getting your SeqRecord objects as formatted strings</A>
-</LI></UL>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc67">Chapter 6  Multiple Sequence Alignment objects</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc68">6.1  Parsing or Reading Sequence Alignments</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc69">6.1.1  Single Alignments</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc70">6.1.2  Multiple Alignments</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc71">6.1.3  Ambiguous Alignments</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc72">6.2  Writing Alignments</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc73">6.2.1  Converting between sequence alignment file formats</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc74">6.2.2  Getting your alignment objects as formatted strings</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc75">6.3  Manipulating Alignments</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc76">6.3.1  Slicing alignments</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc77">6.3.2  Alignments as arrays</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc78">6.4  Alignment Tools</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc79">6.4.1  ClustalW</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc80">6.4.2  MUSCLE</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc81">6.4.3  MUSCLE using stdout</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc82">6.4.4  MUSCLE using stdin and stdout</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc83">6.4.5  EMBOSS needle and water</A>
-</LI></UL>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc84">Chapter 7  BLAST</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc85">7.1  Running BLAST over the Internet</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc86">7.2  Running BLAST locally</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc87">7.2.1  Introduction</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc88">7.2.2  Standalone NCBI BLAST+</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc89">7.2.3  Other versions of BLAST</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc90">7.3  Parsing BLAST output</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc91">7.4  The BLAST record class</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc92">7.5  Deprecated BLAST parsers</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc93">7.5.1  Parsing plain-text BLAST output</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc94">7.5.2  Parsing a plain-text BLAST file full of BLAST runs</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc95">7.5.3  Finding a bad record somewhere in a huge plain-text BLAST file</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc96">7.6  Dealing with PSI-BLAST</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc97">7.7  Dealing with RPS-BLAST</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc98">Chapter 8  BLAST and other sequence search tools (<I>experimental code</I>)</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc99">8.1  The SearchIO object model</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc100">8.1.1  QueryResult</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc101">8.1.2  Hit</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc102">8.1.3  HSP</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc103">8.1.4  HSPFragment</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc104">8.2  A note about standards and conventions</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc105">8.3  Reading search output files</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc106">8.4  Dealing with large search output files with indexing</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc107">8.5  Writing and converting search output files</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc108">Chapter 9  Accessing NCBI’s Entrez databases</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc109">9.1  Entrez Guidelines</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc110">9.2  EInfo: Obtaining information about the Entrez databases</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc111">9.3  ESearch: Searching the Entrez databases</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc112">9.4  EPost: Uploading a list of identifiers</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc113">9.5  ESummary: Retrieving summaries from primary IDs</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc114">9.6  EFetch: Downloading full records from Entrez</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc115">9.7  ELink: Searching for related items in NCBI Entrez</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc116">9.8  EGQuery: Global Query - counts for search terms</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc117">9.9  ESpell: Obtaining spelling suggestions</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc118">9.10  Parsing huge Entrez XML files</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc119">9.11  Handling errors</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc120">9.12  Specialized parsers</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc121">9.12.1  Parsing Medline records</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc122">9.12.2  Parsing GEO records</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc123">9.12.3  Parsing UniGene records</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc124">9.13  Using a proxy</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc125">9.14  Examples</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc126">9.14.1  PubMed and Medline</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc127">9.14.2  Searching, downloading, and parsing Entrez Nucleotide records</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc128">9.14.3  Searching, downloading, and parsing GenBank records</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc129">9.14.4  Finding the lineage of an organism</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc130">9.15  Using the history and WebEnv</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc131">9.15.1  Searching for and downloading sequences using the history</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc132">9.15.2  Searching for and downloading abstracts using the history</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc133">9.15.3  Searching for citations</A>
-</LI></UL>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc134">Chapter 10  Swiss-Prot and ExPASy</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc135">10.1  Parsing Swiss-Prot files</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc136">10.1.1  Parsing Swiss-Prot records</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc137">10.1.2  Parsing the Swiss-Prot keyword and category list</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc138">10.2  Parsing Prosite records</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc139">10.3  Parsing Prosite documentation records</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc140">10.4  Parsing Enzyme records</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc141">10.5  Accessing the ExPASy server</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc142">10.5.1  Retrieving a Swiss-Prot record</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc143">10.5.2  Searching Swiss-Prot</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc144">10.5.3  Retrieving Prosite and Prosite documentation records</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc145">10.6  Scanning the Prosite database</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc146">Chapter 11  Bio.PopGen: Population genetics</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc147">11.1  GenePop</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc148">11.2  Coalescent simulation</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc149">11.2.1  Creating scenarios</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc150">11.2.2  Running Fastsimcoal2</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc151">11.3  Other applications</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc152">11.3.1  FDist: Detecting selection and molecular adaptation</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc153">11.4  Future Developments</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc154">Chapter 12  Phylogenetics with Bio.Phylo</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc155">12.1  Demo: What’s in a Tree?</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc156">12.1.1  Coloring branches within a tree</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc157">12.2  I/O functions</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc158">12.3  View and export trees</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc159">12.4  Using Tree and Clade objects</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc160">12.4.1  Search and traversal methods</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc161">12.4.2  Information methods</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc162">12.4.3  Modification methods</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc163">12.4.4  Features of PhyloXML trees</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc164">12.5  Running external applications</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc165">12.6  PAML integration</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc166">12.7  Future plans</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc167">Chapter 13  Sequence motif analysis using Bio.motifs</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc168">13.1  Motif objects</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc169">13.1.1  Creating a motif from instances</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc170">13.1.2  Creating a sequence logo</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc171">13.2  Reading motifs</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc172">13.2.1  JASPAR</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc173">13.2.2  MEME</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc174">13.2.3  TRANSFAC</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc175">13.3  Writing motifs</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc176">13.4  Position-Weight Matrices</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc177">13.5  Position-Specific Scoring Matrices</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc178">13.6  Searching for instances</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc179">13.6.1  Searching for exact matches</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc180">13.6.2  Searching for matches using the PSSM score</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc181">13.6.3  Selecting a score threshold</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc182">13.7  Each motif object has an associated Position-Specific Scoring Matrix</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc183">13.8  Comparing motifs</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc184">13.9  <EM>De novo</EM> motif finding</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc185">13.9.1  MEME</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc186">13.9.2  AlignAce</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc187">13.10  Useful links </A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc188">Chapter 14  Cluster analysis</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc189">14.1  Distance functions</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc190">14.2  Calculating cluster properties</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc191">14.3  Partitioning algorithms</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc192">14.4  Hierarchical clustering</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc193">14.5  Self-Organizing Maps</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc194">14.6  Principal Component Analysis</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc195">14.7  Handling Cluster/TreeView-type files</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc196">14.8  Example calculation</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc197">14.9  Auxiliary functions</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc198">Chapter 15  Supervised learning methods</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc199">15.1  The Logistic Regression Model</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc200">15.1.1  Background and Purpose</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc201">15.1.2  Training the logistic regression model</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc202">15.1.3  Using the logistic regression model for classification</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc203">15.1.4  Logistic Regression, Linear Discriminant Analysis, and Support Vector Machines</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc204">15.2  <I>k</I>-Nearest Neighbors</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc205">15.2.1  Background and purpose</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc206">15.2.2  Initializing a <I>k</I>-nearest neighbors model</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc207">15.2.3  Using a <I>k</I>-nearest neighbors model for classification</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc208">15.3  Naïve Bayes</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc209">15.4  Maximum Entropy</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc210">15.5  Markov Models</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc211">Chapter 16  Graphics including GenomeDiagram</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc212">16.1  GenomeDiagram</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc213">16.1.1  Introduction</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc214">16.1.2  Diagrams, tracks, feature-sets and features</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc215">16.1.3  A top down example</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc216">16.1.4  A bottom up example</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc217">16.1.5  Features without a SeqFeature</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc218">16.1.6  Feature captions</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc219">16.1.7  Feature sigils</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc220">16.1.8  Arrow sigils</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc221">16.1.9  A nice example</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc222">16.1.10  Multiple tracks</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc223">16.1.11  Cross-Links between tracks</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc224">16.1.12  Further options</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc225">16.1.13  Converting old code</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc226">16.2  Chromosomes</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc227">16.2.1  Simple Chromosomes</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc228">16.2.2  Annotated Chromosomes</A>
-</LI></UL>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc229">Chapter 17  KEGG</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc230">17.1  Parsing KEGG records</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc231">17.2  Querying the KEGG API</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc232">Chapter 18  Cookbook – Cool things to do with it</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc233">18.1  Working with sequence files</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc234">18.1.1  Filtering a sequence file</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc235">18.1.2  Producing randomised genomes</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc236">18.1.3  Translating a FASTA file of CDS entries</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc237">18.1.4  Making the sequences in a FASTA file upper case</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc238">18.1.5  Sorting a sequence file</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc239">18.1.6  Simple quality filtering for FASTQ files</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc240">18.1.7  Trimming off primer sequences</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc241">18.1.8  Trimming off adaptor sequences</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc242">18.1.9  Converting FASTQ files</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc243">18.1.10  Converting FASTA and QUAL files into FASTQ files</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc244">18.1.11  Indexing a FASTQ file</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc245">18.1.12  Converting SFF files</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc246">18.1.13  Identifying open reading frames</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc247">18.2  Sequence parsing plus simple plots</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc248">18.2.1  Histogram of sequence lengths</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc249">18.2.2  Plot of sequence GC%</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc250">18.2.3  Nucleotide dot plots</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc251">18.2.4  Plotting the quality scores of sequencing read data</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc252">18.3  Dealing with alignments</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc253">18.3.1  Calculating summary information</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc254">18.3.2  Calculating a quick consensus sequence</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc255">18.3.3  Position Specific Score Matrices</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc256">18.3.4  Information Content</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc257">18.4  Substitution Matrices</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc258">18.4.1  Using common substitution matrices</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc259">18.4.2  Creating your own substitution matrix from an alignment</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc260">18.5  BioSQL – storing sequences in a relational database</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc261">Chapter 19  The Biopython testing framework</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc262">19.1  Running the tests</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc263">19.1.1  Running the tests using Tox</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc264">19.2  Writing tests</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc265">19.2.1  Writing a print-and-compare test</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc266">19.2.2  Writing a unittest-based test</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc267">19.3  Writing doctests</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc268">Chapter 20  Advanced</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc269">20.1  Parser Design</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc270">20.2  Substitution Matrices</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc271">20.2.1  SubsMat</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc272">20.2.2  FreqTable</A>
-</LI></UL>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc273">Chapter 21  Where to go from here – contributing to Biopython</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc274">21.1  Bug Reports + Feature Requests</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc275">21.2  Mailing lists and helping newcomers</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc276">21.3  Contributing Documentation</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc277">21.4  Contributing cookbook examples</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc278">21.5  Maintaining a distribution for a platform</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc279">21.6  Contributing Unit Tests</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc280">21.7  Contributing Code</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc281">Chapter 22  Appendix: Useful stuff about Python</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc282">22.1  What the heck is a handle?</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc283">22.1.1  Creating a handle from a string</A>
-</LI></UL>
-</LI></UL>
-</LI></UL><!--TOC chapter Introduction-->
-<H1 CLASS="chapter"><!--SEC ANCHOR --><A NAME="htoc1">Chapter 1</A>  Introduction</H1><!--SEC END --><P>
-<A NAME="chapter:introduction"></A></P><!--TOC section What is Biopython?-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc2">1.1</A>  What is Biopython?</H2><!--SEC END --><P>The Biopython Project is an international association of developers of freely available Python (<A HREF="http://www.python.org"><TT>http://www.python.org</TT></A>) tools for computational molecular biology. Python is an object oriented, interpreted, flexible language that is becoming increasingly popular for scientific computing. Python is easy to learn, has a very clear synt [...]
-an online resource for modules, scripts, and web links for developers
-of Python-based software for bioinformatics use and research. Basically,
-the goal of Biopython is to make it as easy as possible to use Python
-for bioinformatics by creating high-quality, reusable modules and
-classes. Biopython features include parsers for various Bioinformatics
-file formats (BLAST, Clustalw, FASTA, Genbank,...), access to online
-services (NCBI, Expasy,...), interfaces to common and not-so-common
-programs (Clustalw, DSSP, MSMS...), a standard sequence class, various
-clustering modules, a KD tree data structure etc. and even documentation.</P><P>Basically, we just like to program in Python and want to make it as easy as possible to use Python for bioinformatics by creating high-quality, reusable modules and scripts.</P><!--TOC section What can I find in the Biopython package-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc3">1.2</A>  What can I find in the Biopython package</H2><!--SEC END --><P>The main Biopython releases have lots of functionality, including:</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-The ability to parse bioinformatics files into Python utilizable data structures, including support for the following formats:<UL CLASS="itemize"><LI CLASS="li-itemize">
-Blast output – both from standalone and WWW Blast
-</LI><LI CLASS="li-itemize">Clustalw
-</LI><LI CLASS="li-itemize">FASTA
-</LI><LI CLASS="li-itemize">GenBank
-</LI><LI CLASS="li-itemize">PubMed and Medline
-</LI><LI CLASS="li-itemize">ExPASy files, like Enzyme and Prosite
-</LI><LI CLASS="li-itemize">SCOP, including ‘dom’ and ‘lin’ files
-</LI><LI CLASS="li-itemize">UniGene
-</LI><LI CLASS="li-itemize">SwissProt
-</LI></UL></LI><LI CLASS="li-itemize">Files in the supported formats can be iterated over record by record or indexed and accessed via a Dictionary interface.</LI><LI CLASS="li-itemize">Code to deal with popular on-line bioinformatics destinations such as:<UL CLASS="itemize"><LI CLASS="li-itemize">
-NCBI – Blast, Entrez and PubMed services
-</LI><LI CLASS="li-itemize">ExPASy – Swiss-Prot and Prosite entries, as well as Prosite searches
-</LI></UL></LI><LI CLASS="li-itemize">Interfaces to common bioinformatics programs such as:<UL CLASS="itemize"><LI CLASS="li-itemize">
-Standalone Blast from NCBI
-</LI><LI CLASS="li-itemize">Clustalw alignment program
-</LI><LI CLASS="li-itemize">EMBOSS command line tools
-</LI></UL></LI><LI CLASS="li-itemize">A standard sequence class that deals with sequences, ids on sequences, and sequence features.</LI><LI CLASS="li-itemize">Tools for performing common operations on sequences, such as translation, transcription and weight calculations.</LI><LI CLASS="li-itemize">Code to perform classification of data using k Nearest Neighbors, Naive Bayes or Support Vector Machines.</LI><LI CLASS="li-itemize">Code for dealing with alignments, including a standard way t [...]
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc4">1.3</A>  Installing Biopython</H2><!--SEC END --><P>All of the installation information for Biopython was separated from
-this document to make it easier to keep updated.</P><P>The short version is go to our downloads page (<A HREF="http://biopython.org/wiki/Download"><TT>http://biopython.org/wiki/Download</TT></A>),
-download and install the listed dependencies, then download and install Biopython.
-Biopython runs on many platforms (Windows, Mac, and on the various flavors of Linux and Unix).
-For Windows we provide pre-compiled click-and-run installers, while for Unix and other
-operating systems you must install from source as described in the included README file.
-This is usually as simple as the standard commands:</P><PRE CLASS="verbatim">python setup.py build
-python setup.py test
-sudo python setup.py install
-</PRE><P>(You can in fact skip the build and test, and go straight to the install –
-but its better to make sure everything seems to be working.)</P><P>The longer version of our installation instructions covers
-installation of Python, Biopython dependencies and Biopython itself.
-It is available in PDF
-(<A HREF="http://biopython.org/DIST/docs/install/Installation.pdf"><TT>http://biopython.org/DIST/docs/install/Installation.pdf</TT></A>)
-and HTML formats
-(<A HREF="http://biopython.org/DIST/docs/install/Installation.html"><TT>http://biopython.org/DIST/docs/install/Installation.html</TT></A>).</P><!--TOC section Frequently Asked Questions (FAQ)-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc5">1.4</A>  Frequently Asked Questions (FAQ)</H2><!--SEC END --><OL CLASS="enumerate" type=1><LI CLASS="li-enumerate"><EM>How do I cite Biopython in a scientific publication?</EM><BR>
- Please cite our application note [<A HREF="#cock2009">1</A>, Cock <I>et al.</I>, 2009]
-as the main Biopython reference.
-In addition, please cite any publications from the following list if appropriate, in particular as a reference for specific modules within Biopython (more information can be found on our website):
-<UL CLASS="itemize"><LI CLASS="li-itemize">
-For the official project announcement: [<A HREF="#chapman2000">13</A>, Chapman and Chang, 2000];
-</LI><LI CLASS="li-itemize">For <CODE>Bio.PDB</CODE>: [<A HREF="#hamelryck2003a">18</A>, Hamelryck and Manderick, 2003];
-</LI><LI CLASS="li-itemize">For <CODE>Bio.Cluster</CODE>: [<A HREF="#dehoon2004">14</A>, De Hoon <I>et al.</I>, 2004];
-</LI><LI CLASS="li-itemize">For <CODE>Bio.Graphics.GenomeDiagram</CODE>: [<A HREF="#pritchard2006">2</A>, Pritchard <I>et al.</I>, 2006];
-</LI><LI CLASS="li-itemize">For <CODE>Bio.Phylo</CODE> and <CODE>Bio.Phylo.PAML</CODE>: [<A HREF="#talevich2012">9</A>, Talevich <I>et al.</I>, 2012];
-</LI><LI CLASS="li-itemize">For the FASTQ file format as supported in Biopython, BioPerl, BioRuby, BioJava, and EMBOSS: [<A HREF="#cock2010">7</A>, Cock <I>et al.</I>, 2010].
-</LI></UL></LI><LI CLASS="li-enumerate"><EM>How should I capitalize “Biopython”? Is “BioPython” OK?</EM><BR>
- The correct capitalization is “Biopython”, not “BioPython” (even though
-that would have matched BioPerl, BioJava and BioRuby).</LI><LI CLASS="li-enumerate"><EM>What is going wrong with my print commands?</EM><BR>
- This tutorial now uses the Python 3 style print <EM>function</EM>.
-As of Biopython 1.62, we support both Python 2 and Python 3.
-The most obvious language difference is the print <EM>statement</EM>
-in Python 2 became a print <EM>function</EM> in Python 3.<P>For example, this will only work under Python 2:</P><PRE CLASS="verbatim">>>> print "Hello World!"
-Hello World!
-</PRE><P>If you try that on Python 3 you’ll get a <CODE>SyntaxError</CODE>.
-Under Python 3 you must write:</P><PRE CLASS="verbatim">>>> print("Hello World!")
-Hello World!
-</PRE><P>Surprisingly that will also work on Python 2 – but only for simple
-examples printing one thing. In general you need to add this magic
-line to the start of your Python scripts to use the print function
-under Python 2.6 and 2.7:</P><PRE CLASS="verbatim">from __future__ import print_function
-</PRE><P>If you forget to add this magic import, under Python 2 you’ll see
-extra brackets produced by trying to use the print function when
-Python 2 is interpreting it as a print statement and a tuple.</P></LI><LI CLASS="li-enumerate"><EM>How do I find out what version of Biopython I have installed?</EM><BR>
- Use this:
-<PRE CLASS="verbatim"> >>> import Bio
- >>> print(Bio.__version__)
- ...
- </PRE>If the “<CODE>import Bio</CODE>” line fails, Biopython is not installed.
-Note that those are double underscores before and after version.
-If the second line fails, your version is <EM>very</EM> out of date.
-If the version string ends with a plus, you don’t have an official
-release, but a snapshot of the in development code.</LI><LI CLASS="li-enumerate"><EM>Where is the latest version of this document?</EM><BR>
- If you download a Biopython source code archive, it will include the
-relevant version in both HTML and PDF formats. The latest published
-version of this document (updated at each release) is online:
-<UL CLASS="itemize"><LI CLASS="li-itemize">
-<A HREF="http://biopython.org/DIST/docs/tutorial/Tutorial.html"><TT>http://biopython.org/DIST/docs/tutorial/Tutorial.html</TT></A>
-</LI><LI CLASS="li-itemize"><A HREF="http://biopython.org/DIST/docs/tutorial/Tutorial.pdf"><TT>http://biopython.org/DIST/docs/tutorial/Tutorial.pdf</TT></A>
-</LI></UL>
-If you are using the very latest unreleased code from our repository
-you can find copies of the in-progress tutorial here:
-<UL CLASS="itemize"><LI CLASS="li-itemize">
-<A HREF="http://biopython.org/DIST/docs/tutorial/Tutorial-dev.html"><TT>http://biopython.org/DIST/docs/tutorial/Tutorial-dev.html</TT></A>
-</LI><LI CLASS="li-itemize"><A HREF="http://biopython.org/DIST/docs/tutorial/Tutorial-dev.pdf"><TT>http://biopython.org/DIST/docs/tutorial/Tutorial-dev.pdf</TT></A>
-</LI></UL></LI><LI CLASS="li-enumerate"><EM>What is wrong with my sequence comparisons?</EM><BR>
- There was a major change in Biopython 1.65 making the <CODE>Seq</CODE> and
-<CODE>MutableSeq</CODE> classes (and subclasses) use simple string-based
-comparison (ignoring the alphabet other than if giving a warning),
-which you can do explicitly with <CODE>str(seq1) == str(seq2)</CODE>.<P>Older versions of Biopython would use instance-based comparison
-for <CODE>Seq</CODE> objects which you can do explicitly with
-<CODE>id(seq1) == id(seq2)</CODE>.</P><P>If you still need to support old versions of Biopython, use these
-explicit forms to avoid problems. See Section <A HREF="#sec:seq-comparison">3.11</A>.</P></LI><LI CLASS="li-enumerate"><EM>Why is the</EM> <CODE>Seq</CODE> <EM>object missing the upper & lower methods described in this Tutorial?</EM><BR>
- You need Biopython 1.53 or later. Alternatively, use <CODE>str(my_seq).upper()</CODE> to get an upper case string.
-If you need a Seq object, try <CODE>Seq(str(my_seq).upper())</CODE> but be careful about blindly re-using the same alphabet.</LI><LI CLASS="li-enumerate"><EM>Why doesn’t the</EM> <CODE>Seq</CODE> <EM>object translation method support the</EM> <CODE>cds</CODE> <EM>option described in this Tutorial?</EM><BR>
- You need Biopython 1.51 or later.</LI><LI CLASS="li-enumerate"><EM>What file formats do</EM> <CODE>Bio.SeqIO</CODE> <EM>and</EM> <CODE>Bio.AlignIO</CODE> <EM>read and write?</EM><BR>
- Check the built in docstrings (<TT>from Bio import SeqIO</TT>, then <TT>help(SeqIO)</TT>), or see <A HREF="http://biopython.org/wiki/SeqIO"><TT>http://biopython.org/wiki/SeqIO</TT></A> and <A HREF="http://biopython.org/wiki/AlignIO"><TT>http://biopython.org/wiki/AlignIO</TT></A> on the wiki for the latest listing.</LI><LI CLASS="li-enumerate"><EM>Why won’t the </EM> <CODE>Bio.SeqIO</CODE> <EM>and</EM> <CODE>Bio.AlignIO</CODE> <EM>functions</EM> <CODE>parse</CODE><EM>,</EM> <CODE> [...]
- You need Biopython 1.54 or later, or just use handles explicitly (see Section <A HREF="#sec:appendix-handles">22.1</A>).
-It is especially important to remember to close output handles explicitly after writing your data.</LI><LI CLASS="li-enumerate"><EM>Why won’t the </EM> <CODE>Bio.SeqIO.write()</CODE> <EM>and</EM> <CODE>Bio.AlignIO.write()</CODE> <EM>functions accept a single record or alignment? They insist on a list or iterator!</EM><BR>
- You need Biopython 1.54 or later, or just wrap the item with <CODE>[...]</CODE> to create a list of one element.</LI><LI CLASS="li-enumerate"><EM>Why doesn’t</EM> <CODE>str(...)</CODE> <EM>give me the full sequence of a</EM> <CODE>Seq</CODE> <EM>object?</EM><BR>
- You need Biopython 1.45 or later.</LI><LI CLASS="li-enumerate"><EM>Why doesn’t</EM> <CODE>Bio.Blast</CODE> <EM>work with the latest plain text NCBI blast output?</EM><BR>
- The NCBI keep tweaking the plain text output from the BLAST tools, and keeping our parser up to date is/was an ongoing struggle.
-If you aren’t using the latest version of Biopython, you could try upgrading.
-However, we (and the NCBI) recommend you use the XML output instead, which is designed to be read by a computer program.</LI><LI CLASS="li-enumerate"><EM>Why doesn’t</EM> <CODE>Bio.Entrez.parse()</CODE> <EM>work? The module imports fine but there is no parse function!</EM><BR>
- You need Biopython 1.52 or later.</LI><LI CLASS="li-enumerate"><EM>Why has my script using</EM> <CODE>Bio.Entrez.efetch()</CODE> <EM>stopped working?</EM><BR>
- This could be due to NCBI changes in February 2012 introducing EFetch 2.0.
-First, they changed the default return modes - you probably want to add <CODE>retmode="text"</CODE> to
-your call.
-Second, they are now stricter about how to provide a list of IDs – Biopython 1.59 onwards
-turns a list into a comma separated string automatically.</LI><LI CLASS="li-enumerate"><EM>Why doesn’t</EM> <CODE>Bio.Blast.NCBIWWW.qblast()</CODE> <EM>give the same results as the NCBI BLAST website?</EM><BR>
- You need to specify the same options – the NCBI often adjust the default settings on the website,
-and they do not match the QBLAST defaults anymore. Check things like the gap penalties and expectation threshold.</LI><LI CLASS="li-enumerate"><EM>Why doesn’t</EM> <CODE>Bio.Blast.NCBIXML.read()</CODE> <EM>work? The module imports but there is no read function!</EM><BR>
- You need Biopython 1.50 or later. Or, use <TT>next(Bio.Blast.NCBIXML.parse(...))</TT> instead.</LI><LI CLASS="li-enumerate"><EM>Why doesn’t my</EM> <CODE>SeqRecord</CODE> <EM>object have a</EM> <CODE>letter_annotations</CODE> <EM>attribute?</EM><BR>
- Per-letter-annotation support was added in Biopython 1.50.</LI><LI CLASS="li-enumerate"><EM>Why can’t I slice my</EM> <CODE>SeqRecord</CODE> <EM>to get a sub-record?</EM><BR>
- You need Biopython 1.50 or later.</LI><LI CLASS="li-enumerate"><EM>Why can’t I add</EM> <CODE>SeqRecord</CODE> <EM>objects together?</EM><BR>
- You need Biopython 1.53 or later.</LI><LI CLASS="li-enumerate"><EM>Why doesn’t</EM> <CODE>Bio.SeqIO.convert()</CODE> <EM>or</EM> <CODE>Bio.AlignIO.convert()</CODE> <EM>work? The modules import fine but there is no convert function!</EM><BR>
- You need Biopython 1.52 or later. Alternatively, combine the <CODE>parse</CODE> and <CODE>write</CODE>
-functions as described in this tutorial (see Sections <A HREF="#sec:SeqIO-conversion">5.5.2</A> and <A HREF="#sec:converting-alignments">6.2.1</A>).</LI><LI CLASS="li-enumerate"><EM>Why doesn’t</EM> <CODE>Bio.SeqIO.index()</CODE> <EM>work? The module imports fine but there is no index function!</EM><BR>
- You need Biopython 1.52 or later.</LI><LI CLASS="li-enumerate"><EM>Why doesn’t</EM> <CODE>Bio.SeqIO.index_db()</CODE> <EM>work? The module imports fine but there is no </EM><EM><TT>index_db</TT></EM><EM> function!</EM><BR>
- You need Biopython 1.57 or later (and a Python with SQLite3 support).</LI><LI CLASS="li-enumerate"><EM>Where is the</EM> <CODE>MultipleSeqAlignment</CODE> <EM>object? The</EM> <CODE>Bio.Align</CODE> <EM>module imports fine but this class isn’t there!</EM><BR>
- You need Biopython 1.54 or later. Alternatively, the older <CODE>Bio.Align.Generic.Alignment</CODE> class supports some of its functionality, but using this is now discouraged.</LI><LI CLASS="li-enumerate"><EM>Why can’t I run command line tools directly from the application wrappers?</EM><BR>
- You need Biopython 1.55 or later. Alternatively, use the Python <CODE>subprocess</CODE> module directly.</LI><LI CLASS="li-enumerate"><EM>I looked in a directory for code, but I couldn’t find the code that does something. Where’s it hidden?</EM><BR>
- One thing to know is that we put code in <CODE>__init__.py</CODE> files. If you are not used to looking for code in this file this can be confusing. The reason we do this is to make the imports easier for users. For instance, instead of having to do a “repetitive” import like <CODE>from Bio.GenBank import GenBank</CODE>, you can just use <CODE>from Bio import GenBank</CODE>.</LI><LI CLASS="li-enumerate"><EM>Why does the code from CVS seem out of date?</EM><BR>
- In late September 2009, just after the release of Biopython 1.52, we switched from using CVS to git, a distributed version control system. The old CVS server will remain available as a static and read only backup, but if you want to grab the latest code, you’ll need to use git instead. See our website for more details.</LI><LI CLASS="li-enumerate"><EM>Why doesn’t</EM> <CODE>Bio.Fasta</CODE> <EM>work?</EM><BR>
- We deprecated the <CODE>Bio.Fasta</CODE> module in Biopython 1.51 (August 2009) and removed it in Biopython 1.55 (August 2010). There is a brief example showing how to convert old code to use <CODE>Bio.SeqIO</CODE> instead in the <A HREF="http://biopython.org/SRC/biopython/DEPRECATED">DEPRECATED</A> file.</LI></OL><P>For more general questions, the Python FAQ pages <A HREF="http://www.python.org/doc/faq/"><TT>http://www.python.org/doc/faq/</TT></A> may be useful.</P><!--TOC chapter Quic [...]
-<H1 CLASS="chapter"><!--SEC ANCHOR --><A NAME="htoc6">Chapter 2</A>  Quick Start – What can you do with Biopython?</H1><!--SEC END --><P>
-<A NAME="chapter:quick-start"></A></P><P>This section is designed to get you started quickly with Biopython, and to give a general overview of what is available and how to use it. All of the examples in this section assume that you have some general working knowledge of Python, and that you have successfully installed Biopython on your system. If you think you need to brush up on your Python, the main Python web site provides quite a bit of free documentation to get started with (<A HREF [...]
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc7">2.1</A>  General overview of what Biopython provides</H2><!--SEC END --><P>As mentioned in the introduction, Biopython is a set of libraries to provide the ability to deal with “things” of interest to biologists working on the computer. In general this means that you will need to have at least some programming experience (in Python, of course!) or at least an interest in learning to program. Biopython’s [...]
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc8">2.2</A>  Working with sequences</H2><!--SEC END --><P>
-<A NAME="sec:sequences"></A></P><P>Disputably (of course!), the central object in bioinformatics is the sequence. Thus, we’ll start with a quick introduction to the Biopython mechanisms for dealing with sequences, the <CODE>Seq</CODE> object, which we’ll discuss in more detail in Chapter <A HREF="#chapter:Bio.Seq">3</A>.</P><P>Most of the time when we think about sequences we have in my mind a string of letters like ‘<CODE>AGTACACTGGT</CODE>’. You can cre [...]
->>> my_seq = Seq("AGTACACTGGT")
->>> my_seq
-Seq('AGTACACTGGT', Alphabet())
->>> print(my_seq)
-AGTACACTGGT
->>> my_seq.alphabet
-Alphabet()
-</PRE><P>What we have here is a sequence object with a <EM>generic</EM> alphabet - reflecting the fact we have <EM>not</EM> specified if this is a DNA or protein sequence (okay, a protein with a lot of Alanines, Glycines, Cysteines and Threonines!). We’ll talk more about alphabets in Chapter <A HREF="#chapter:Bio.Seq">3</A>.</P><P>In addition to having an alphabet, the <CODE>Seq</CODE> object differs from the Python string in the methods it supports. You can’t do this [...]
-Seq('AGTACACTGGT', Alphabet())
->>> my_seq.complement()
-Seq('TCATGTGACCA', Alphabet())
->>> my_seq.reverse_complement()
-Seq('ACCAGTGTACT', Alphabet())
-</PRE><P>The next most important class is the <CODE>SeqRecord</CODE> or Sequence Record. This holds a sequence (as a <CODE>Seq</CODE> object) with additional annotation including an identifier, name and description. The <CODE>Bio.SeqIO</CODE> module for reading and writing sequence file formats works with <CODE>SeqRecord</CODE> objects, which will be introduced below and covered in more detail by Chapter <A HREF="#chapter:Bio.SeqIO">5</A>.</P><P>This covers the basic features and us [...]
-Now that you’ve got some idea of what it is like to interact with the Biopython libraries, it’s time to delve into the fun, fun world of dealing with biological file formats!</P><!--TOC section A usage example-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc9">2.3</A>  A usage example</H2><!--SEC END --><P>
-<A NAME="sec:orchids"></A></P><P>Before we jump right into parsers and everything else to do with Biopython, let’s set up an example to motivate everything we do and make life more interesting. After all, if there wasn’t any biology in this tutorial, why would you want you read it?</P><P>Since I love plants, I think we’re just going to have to have a plant based example (sorry to all the fans of other organisms out there!). Having just completed a recent trip to our [...]
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc10">2.4</A>  Parsing sequence file formats</H2><!--SEC END --><P>
-<A NAME="sec:sequence-parsing"></A></P><P>A large part of much bioinformatics work involves dealing with the many types of file formats designed to hold biological data. These files are loaded with interesting biological data, and a special challenge is parsing these files into a format so that you can manipulate them with some kind of programming language. However the task of parsing these files can be frustrated by the fact that the formats can change quite regularly, and that formats [...]
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc11">2.4.1</A>  Simple FASTA parsing example</H3><!--SEC END --><P>
-<A NAME="sec:fasta-parsing"></A></P><P>If you open the lady slipper orchids FASTA file <A HREF="http://biopython.org/DIST/docs/tutorial/examples/ls_orchid.fasta"><TT>ls_orchid.fasta</TT></A> in your favourite text editor, you’ll see that the file starts like this:</P><PRE CLASS="verbatim">>gi|2765658|emb|Z78533.1|CIZ78533 C.irapeanum 5.8S rRNA gene and ITS1 and ITS2 DNA
-CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGATGAGACCGTGGAATAAACGATCGAGTG
-AATCCGGAGGACCGGTGTACTCAGCTCACCGGGGGCATTGCTCCCGTGGTGACCCTGATTTGTTGTTGGG
-...
-</PRE><P>It contains 94 records, each has a line starting with “<CODE>></CODE>” (greater-than symbol) followed by the sequence on one or more lines. Now try this in Python:</P><PRE CLASS="verbatim">from Bio import SeqIO
-for seq_record in SeqIO.parse("ls_orchid.fasta", "fasta"):
- print(seq_record.id)
- print(repr(seq_record.seq))
- print(len(seq_record))
-</PRE><P>You should get something like this on your screen:</P><PRE CLASS="verbatim">gi|2765658|emb|Z78533.1|CIZ78533
-Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGATGAGACCGTGG...CGC', SingleLetterAlphabet())
-740
-...
-gi|2765564|emb|Z78439.1|PBZ78439
-Seq('CATTGTTGAGATCACATAATAATTGATCGAGTTAATCTGGAGGATCTGTTTACT...GCC', SingleLetterAlphabet())
-592
-</PRE><P>Notice that the FASTA format does not specify the alphabet, so <CODE>Bio.SeqIO</CODE> has defaulted to the rather generic <CODE>SingleLetterAlphabet()</CODE> rather than something DNA specific.</P><!--TOC subsection Simple GenBank parsing example-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc12">2.4.2</A>  Simple GenBank parsing example</H3><!--SEC END --><P>Now let’s load the GenBank file <A HREF="http://biopython.org/DIST/docs/tutorial/examples/ls_orchid.gbk"><TT>ls_orchid.gbk</TT></A> instead - notice that the code to do this is almost identical to the snippet used above for the FASTA file - the only difference is we change the filename and the format string:</P><PRE CLASS="verbatim">from Bio import SeqIO
-for seq_record in SeqIO.parse("ls_orchid.gbk", "genbank"):
- print(seq_record.id)
- print(repr(seq_record.seq))
- print(len(seq_record))
-</PRE><P>This should give:</P><PRE CLASS="verbatim">Z78533.1
-Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGATGAGACCGTGG...CGC', IUPACAmbiguousDNA())
-740
-...
-Z78439.1
-Seq('CATTGTTGAGATCACATAATAATTGATCGAGTTAATCTGGAGGATCTGTTTACT...GCC', IUPACAmbiguousDNA())
-592
-</PRE><P>This time <CODE>Bio.SeqIO</CODE> has been able to choose a sensible alphabet, IUPAC Ambiguous DNA. You’ll also notice that a shorter string has been used as the <CODE>seq_record.id</CODE> in this case.</P><!--TOC subsection I love parsing – please don’t stop talking about it!-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc13">2.4.3</A>  I love parsing – please don’t stop talking about it!</H3><!--SEC END --><P>Biopython has a lot of parsers, and each has its own little special niches based on the sequence format it is parsing and all of that. Chapter <A HREF="#chapter:Bio.SeqIO">5</A> covers <CODE>Bio.SeqIO</CODE> in more detail, while Chapter <A HREF="#chapter:Bio.AlignIO">6</A> introduces <CODE>Bio.AlignIO</CODE> for [...]
-Please also check the wiki pages <A HREF="http://biopython.org/wiki/SeqIO"><TT>http://biopython.org/wiki/SeqIO</TT></A> and <A HREF="http://biopython.org/wiki/AlignIO"><TT>http://biopython.org/wiki/AlignIO</TT></A> for the latest information, or ask on the mailing list. The wiki pages should include an up to date list of supported file types, and some additional examples.</P><P>The next place to look for information about specific parsers and how to do cool things with them is in the Coo [...]
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc14">2.5</A>  Connecting with biological databases</H2><!--SEC END --><P>
-<A NAME="sec:connecting-with-biological-databases"></A></P><P>One of the very common things that you need to do in bioinformatics is extract information from biological databases. It can be quite tedious to access these databases manually, especially if you have a lot of repetitive work to do. Biopython attempts to save you time and energy by making some on-line databases available from Python scripts. Currently, Biopython has code to extract information from the following databases:</P> [...]
-<A HREF="http://www.ncbi.nlm.nih.gov/Entrez/">Entrez</A> (and <A HREF="http://www.ncbi.nlm.nih.gov/PubMed/">PubMed</A>) from the NCBI – See Chapter <A HREF="#chapter:entrez">9</A>.
-</LI><LI CLASS="li-itemize"><A HREF="http://www.expasy.org/">ExPASy</A> – See Chapter <A HREF="#chapter:swiss_prot">10</A>.
-</LI><LI CLASS="li-itemize"><A HREF="http://scop.mrc-lmb.cam.ac.uk/scop/">SCOP</A> – See the <CODE>Bio.SCOP.search()</CODE> function.
-</LI></UL><P>The code in these modules basically makes it easy to write Python code that interact with the CGI scripts on these pages, so that you can get results in an easy to deal with format. In some cases, the results can be tightly integrated with the Biopython parsers to make it even easier to extract information.</P><!--TOC section What to do next-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc15">2.6</A>  What to do next</H2><!--SEC END --><P>Now that you’ve made it this far, you hopefully have a good understanding of the basics of Biopython and are ready to start using it for doing useful work. The best thing to do now is finish reading this tutorial, and then if you want start snooping around in the source code, and looking at the automatically generated documentation.</P><P>Once you get a picture of what y [...]
-<H1 CLASS="chapter"><!--SEC ANCHOR --><A NAME="htoc16">Chapter 3</A>  Sequence objects</H1><!--SEC END --><P>
-<A NAME="chapter:Bio.Seq"></A></P><P>Biological sequences are arguably the central object in Bioinformatics, and in this chapter we’ll introduce the Biopython mechanism for dealing with sequences, the <CODE>Seq</CODE> object.
-Chapter <A HREF="#chapter:SeqRecord">4</A> will introduce the related <CODE>SeqRecord</CODE> object, which combines the sequence information with any annotation, used again in Chapter <A HREF="#chapter:Bio.SeqIO">5</A> for Sequence Input/Output.</P><P>Sequences are essentially strings of letters like <CODE>AGTACACTGGT</CODE>, which seems very natural since this is the most common way that sequences are seen in biological file formats.</P><P>There are two important differences b [...]
-First of all, they have different methods. Although the <CODE>Seq</CODE> object supports many of the same methods as a plain string, its <CODE>translate()</CODE> method differs by doing biological translation, and there are also additional biologically relevant methods like <CODE>reverse_complement()</CODE>.
-Secondly, the <CODE>Seq</CODE> object has an important attribute, <CODE>alphabet</CODE>, which is an object describing what the individual characters making up the sequence string “mean”, and how they should be interpreted. For example, is <CODE>AGTACACTGGT</CODE> a DNA sequence, or just a protein sequence that happens to be rich in Alanines, Glycines, Cysteines
-and Threonines?</P><!--TOC section Sequences and Alphabets-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc17">3.1</A>  Sequences and Alphabets</H2><!--SEC END --><P>The alphabet object is perhaps the important thing that makes the <CODE>Seq</CODE> object more than just a string. The currently available alphabets for Biopython are defined in the <CODE>Bio.Alphabet</CODE> module. We’ll use the IUPAC alphabets (<A HREF="http://www.chem.qmw.ac.uk/iupac/"><TT>http://www.chem.qmw.ac.uk/iupac/</TT></A>) here to deal with some of ou [...]
-You can create an ambiguous sequence with the default generic alphabet like this:</P><PRE CLASS="verbatim">>>> from Bio.Seq import Seq
->>> my_seq = Seq("AGTACACTGGT")
->>> my_seq
-Seq('AGTACACTGGT', Alphabet())
->>> my_seq.alphabet
-Alphabet()
-</PRE><P>However, where possible you should specify the alphabet explicitly when creating your sequence objects - in this case an unambiguous DNA alphabet object:</P><PRE CLASS="verbatim">>>> from Bio.Seq import Seq
->>> from Bio.Alphabet import IUPAC
->>> my_seq = Seq("AGTACACTGGT", IUPAC.unambiguous_dna)
->>> my_seq
-Seq('AGTACACTGGT', IUPACUnambiguousDNA())
->>> my_seq.alphabet
-IUPACUnambiguousDNA()
-</PRE><P>Unless of course, this really is an amino acid sequence:</P><PRE CLASS="verbatim">>>> from Bio.Seq import Seq
->>> from Bio.Alphabet import IUPAC
->>> my_prot = Seq("AGTACACTGGT", IUPAC.protein)
->>> my_prot
-Seq('AGTACACTGGT', IUPACProtein())
->>> my_prot.alphabet
-IUPACProtein()
-</PRE><!--TOC section Sequences act like strings-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc18">3.2</A>  Sequences act like strings</H2><!--SEC END --><P>In many ways, we can deal with Seq objects as if they were normal Python strings, for example getting the length, or iterating over the elements:</P><PRE CLASS="verbatim">>>> from Bio.Seq import Seq
->>> from Bio.Alphabet import IUPAC
->>> my_seq = Seq("GATCG", IUPAC.unambiguous_dna)
->>> for index, letter in enumerate(my_seq):
-... print("%i %s" % (index, letter))
-0 G
-1 A
-2 T
-3 C
-4 G
->>> print(len(my_seq))
-5
-</PRE><P>You can access elements of the sequence in the same way as for strings (but remember, Python counts from zero!):</P><PRE CLASS="verbatim">>>> print(my_seq[0]) #first letter
-G
->>> print(my_seq[2]) #third letter
-T
->>> print(my_seq[-1]) #last letter
-G
-</PRE><P>The <CODE>Seq</CODE> object has a <CODE>.count()</CODE> method, just like a string.
-Note that this means that like a Python string, this gives a
-<EM>non-overlapping</EM> count:</P><PRE CLASS="verbatim">>>> from Bio.Seq import Seq
->>> "AAAA".count("AA")
-2
->>> Seq("AAAA").count("AA")
-2
-</PRE><P>For some biological uses, you may actually want an overlapping count
-(i.e. 3 in this trivial example). When searching for single letters, this
-makes no difference:</P><PRE CLASS="verbatim">>>> from Bio.Seq import Seq
->>> from Bio.Alphabet import IUPAC
->>> my_seq = Seq('GATCGATGGGCCTATATAGGATCGAAAATCGC', IUPAC.unambiguous_dna)
->>> len(my_seq)
-32
->>> my_seq.count("G")
-9
->>> 100 * float(my_seq.count("G") + my_seq.count("C")) / len(my_seq)
-46.875
-</PRE><P>While you could use the above snippet of code to calculate a GC%, note that the <CODE>Bio.SeqUtils</CODE> module has several GC functions already built. For example:</P><PRE CLASS="verbatim">>>> from Bio.Seq import Seq
->>> from Bio.Alphabet import IUPAC
->>> from Bio.SeqUtils import GC
->>> my_seq = Seq('GATCGATGGGCCTATATAGGATCGAAAATCGC', IUPAC.unambiguous_dna)
->>> GC(my_seq)
-46.875
-</PRE><P>Note that using the <CODE>Bio.SeqUtils.GC()</CODE> function should automatically cope with mixed case sequences and the ambiguous nucleotide S which means G or C.</P><P>Also note that just like a normal Python string, the <CODE>Seq</CODE> object is in some ways “read-only”. If you need to edit your sequence, for example simulating a point mutation, look at the Section <A HREF="#sec:mutable-seq">3.12</A> below which talks about the <CODE>MutableSeq</CODE> objec [...]
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc19">3.3</A>  Slicing a sequence</H2><!--SEC END --><P>A more complicated example, let’s get a slice of the sequence:</P><PRE CLASS="verbatim">>>> from Bio.Seq import Seq
->>> from Bio.Alphabet import IUPAC
->>> my_seq = Seq("GATCGATGGGCCTATATAGGATCGAAAATCGC", IUPAC.unambiguous_dna)
->>> my_seq[4:12]
-Seq('GATGGGCC', IUPACUnambiguousDNA())
-</PRE><P>Two things are interesting to note. First, this follows the normal conventions for Python strings. So the first element of the sequence is 0 (which is normal for computer science, but not so normal for biology). When you do a slice the first item is included (i.e. 4 in this case) and the last is excluded (12 in this case), which is the way things work in Python, but of course not necessarily the way everyone in the world would expect. The main goal is to stay consistent wit [...]
-Seq('GCTGTAGTAAG', IUPACUnambiguousDNA())
->>> my_seq[1::3]
-Seq('AGGCATGCATC', IUPACUnambiguousDNA())
->>> my_seq[2::3]
-Seq('TAGCTAAGAC', IUPACUnambiguousDNA())
-</PRE><P>Another stride trick you might have seen with a Python string is the use of a -1 stride to reverse the string. You can do this with a <CODE>Seq</CODE> object too:</P><PRE CLASS="verbatim">>>> my_seq[::-1]
-Seq('CGCTAAAAGCTAGGATATATCCGGGTAGCTAG', IUPACUnambiguousDNA())
-</PRE><!--TOC section Turning Seq objects into strings-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc20">3.4</A>  Turning Seq objects into strings</H2><!--SEC END --><P>
-<A NAME="sec:seq-to-string"></A></P><P>If you really do just need a plain string, for example to write to a file, or insert into a database, then this is very easy to get:
-</P><PRE CLASS="verbatim">>>> str(my_seq)
-'GATCGATGGGCCTATATAGGATCGAAAATCGC'
-</PRE><P>Since calling <CODE>str()</CODE> on a <CODE>Seq</CODE> object returns the full sequence as a string,
-you often don’t actually have to do this conversion explicitly.
-Python does this automatically in the print function
-(and the print statement under Python 2):
-</P><PRE CLASS="verbatim">>>> print(my_seq)
-GATCGATGGGCCTATATAGGATCGAAAATCGC
-</PRE><P>You can also use the <CODE>Seq</CODE> object directly with a <CODE>%s</CODE> placeholder when using the Python string formatting or interpolation operator (<CODE>%</CODE>):
-</P><PRE CLASS="verbatim">>>> fasta_format_string = ">Name\n%s\n" % my_seq
->>> print(fasta_format_string)
->Name
-GATCGATGGGCCTATATAGGATCGAAAATCGC
-<BLANKLINE>
-</PRE><P>This line of code constructs a simple FASTA format record (without worrying about line wrapping).
-Section <A HREF="#sec:SeqRecord-format">4.5</A> describes a neat way to get a FASTA formatted
-string from a <CODE>SeqRecord</CODE> object, while the more general topic of reading and
-writing FASTA format sequence files is covered in Chapter <A HREF="#chapter:Bio.SeqIO">5</A>.</P><PRE CLASS="verbatim">>>> str(my_seq)
-'GATCGATGGGCCTATATAGGATCGAAAATCGC'
-</PRE><!--TOC section Concatenating or adding sequences-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc21">3.5</A>  Concatenating or adding sequences</H2><!--SEC END --><P>Naturally, you can in principle add any two Seq objects together - just like you can with Python strings to concatenate them. However, you can’t add sequences with incompatible alphabets, such as a protein sequence and a DNA sequence:</P><PRE CLASS="verbatim">>>> from Bio.Alphabet import IUPAC
->>> from Bio.Seq import Seq
->>> protein_seq = Seq("EVRNAK", IUPAC.protein)
->>> dna_seq = Seq("ACGT", IUPAC.unambiguous_dna)
->>> protein_seq + dna_seq
-Traceback (most recent call last):
-...
-TypeError: Incompatible alphabets IUPACProtein() and IUPACUnambiguousDNA()
-</PRE><P>If you <EM>really</EM> wanted to do this, you’d have to first give both sequences generic alphabets:</P><PRE CLASS="verbatim">>>> from Bio.Alphabet import generic_alphabet
->>> protein_seq.alphabet = generic_alphabet
->>> dna_seq.alphabet = generic_alphabet
->>> protein_seq + dna_seq
-Seq('EVRNAKACGT', Alphabet())
-</PRE><P>Here is an example of adding a generic nucleotide sequence to an unambiguous IUPAC DNA sequence, resulting in an ambiguous nucleotide sequence:</P><PRE CLASS="verbatim">>>> from Bio.Seq import Seq
->>> from Bio.Alphabet import generic_nucleotide
->>> from Bio.Alphabet import IUPAC
->>> nuc_seq = Seq("GATCGATGC", generic_nucleotide)
->>> dna_seq = Seq("ACGT", IUPAC.unambiguous_dna)
->>> nuc_seq
-Seq('GATCGATGC', NucleotideAlphabet())
->>> dna_seq
-Seq('ACGT', IUPACUnambiguousDNA())
->>> nuc_seq + dna_seq
-Seq('GATCGATGCACGT', NucleotideAlphabet())
-</PRE><P>You may often have many sequences to add together, which can be done with a for loop like this:</P><PRE CLASS="verbatim">>>> from Bio.Seq import Seq
->>> from Bio.Alphabet import generic_dna
->>> list_of_seqs = [Seq("ACGT", generic_dna), Seq("AACC", generic_dna), Seq("GGTT", generic_dna)]
->>> concatenated = Seq("", generic_dna)
->>> for s in list_of_seqs:
-... concatenated += s
-...
->>> concatenated
-Seq('ACGTAACCGGTT', DNAAlphabet())
-</PRE><P>Or, a more elegant approach is to the use built in <CODE>sum</CODE> function with its optional start value argument (which otherwise defaults to zero):</P><PRE CLASS="verbatim">>>> from Bio.Seq import Seq
->>> from Bio.Alphabet import generic_dna
->>> list_of_seqs = [Seq("ACGT", generic_dna), Seq("AACC", generic_dna), Seq("GGTT", generic_dna)]
->>> sum(list_of_seqs, Seq("", generic_dna))
-Seq('ACGTAACCGGTT', DNAAlphabet())
-</PRE><P>Unlike the Python string, the Biopython <CODE>Seq</CODE> does not (currently) have a <CODE>.join</CODE> method.</P><!--TOC section Changing case-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc22">3.6</A>  Changing case</H2><!--SEC END --><P>Python strings have very useful <CODE>upper</CODE> and <CODE>lower</CODE> methods for changing the case.
-As of Biopython 1.53, the <CODE>Seq</CODE> object gained similar methods which are alphabet aware.
-For example,</P><PRE CLASS="verbatim">>>> from Bio.Seq import Seq
->>> from Bio.Alphabet import generic_dna
->>> dna_seq = Seq("acgtACGT", generic_dna)
->>> dna_seq
-Seq('acgtACGT', DNAAlphabet())
->>> dna_seq.upper()
-Seq('ACGTACGT', DNAAlphabet())
->>> dna_seq.lower()
-Seq('acgtacgt', DNAAlphabet())
-</PRE><P>These are useful for doing case insensitive matching:</P><PRE CLASS="verbatim">>>> "GTAC" in dna_seq
-False
->>> "GTAC" in dna_seq.upper()
-True
-</PRE><P>Note that strictly speaking the IUPAC alphabets are for upper case
-sequences only, thus:</P><PRE CLASS="verbatim">>>> from Bio.Seq import Seq
->>> from Bio.Alphabet import IUPAC
->>> dna_seq = Seq("ACGT", IUPAC.unambiguous_dna)
->>> dna_seq
-Seq('ACGT', IUPACUnambiguousDNA())
->>> dna_seq.lower()
-Seq('acgt', DNAAlphabet())
-</PRE><!--TOC section Nucleotide sequences and (reverse) complements-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc23">3.7</A>  Nucleotide sequences and (reverse) complements</H2><!--SEC END --><P>
-<A NAME="sec:seq-reverse-complement"></A></P><P>For nucleotide sequences, you can easily obtain the complement or reverse
-complement of a <CODE>Seq</CODE> object using its built-in methods:</P><PRE CLASS="verbatim">>>> from Bio.Seq import Seq
->>> from Bio.Alphabet import IUPAC
->>> my_seq = Seq("GATCGATGGGCCTATATAGGATCGAAAATCGC", IUPAC.unambiguous_dna)
->>> my_seq
-Seq('GATCGATGGGCCTATATAGGATCGAAAATCGC', IUPACUnambiguousDNA())
->>> my_seq.complement()
-Seq('CTAGCTACCCGGATATATCCTAGCTTTTAGCG', IUPACUnambiguousDNA())
->>> my_seq.reverse_complement()
-Seq('GCGATTTTCGATCCTATATAGGCCCATCGATC', IUPACUnambiguousDNA())
-</PRE><P>As mentioned earlier, an easy way to just reverse a <CODE>Seq</CODE> object (or a
-Python string) is slice it with -1 step:</P><PRE CLASS="verbatim">>>> my_seq[::-1]
-Seq('CGCTAAAAGCTAGGATATATCCGGGTAGCTAG', IUPACUnambiguousDNA())
-</PRE><P>In all of these operations, the alphabet property is maintained. This is very
-useful in case you accidentally end up trying to do something weird like take
-the (reverse)complement of a protein sequence:</P><PRE CLASS="verbatim">>>> from Bio.Seq import Seq
->>> from Bio.Alphabet import IUPAC
->>> protein_seq = Seq("EVRNAK", IUPAC.protein)
->>> protein_seq.complement()
-Traceback (most recent call last):
-...
-ValueError: Proteins do not have complements!
-</PRE><P>The example in Section <A HREF="#sec:SeqIO-reverse-complement">5.5.3</A> combines the <CODE>Seq</CODE>
-object’s reverse complement method with <CODE>Bio.SeqIO</CODE> for sequence input/output.</P><!--TOC section Transcription-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc24">3.8</A>  Transcription</H2><!--SEC END --><P>
-Before talking about transcription, I want to try to clarify the strand issue.
-Consider the following (made up) stretch of double stranded DNA which
-encodes a short peptide:</P><TABLE CELLSPACING=6 CELLPADDING=0><TR><TD ALIGN=right NOWRAP> </TD></TR>
-<TR><TD ALIGN=right NOWRAP> </TD><TD ALIGN=center NOWRAP>DNA coding strand (aka Crick strand, strand +1)</TD><TD ALIGN=left NOWRAP> </TD></TR>
-<TR><TD ALIGN=right NOWRAP>5’</TD><TD ALIGN=center NOWRAP><TT>ATGGCCATTGTAATGGGCCGCTGAAAGGGTGCCCGATAG</TT></TD><TD ALIGN=left NOWRAP>3’</TD></TR>
-<TR><TD ALIGN=right NOWRAP> </TD><TD ALIGN=center NOWRAP><TT>|||||||||||||||||||||||||||||||||||||||</TT></TD><TD ALIGN=left NOWRAP> </TD></TR>
-<TR><TD ALIGN=right NOWRAP>3’</TD><TD ALIGN=center NOWRAP><TT>TACCGGTAACATTACCCGGCGACTTTCCCACGGGCTATC</TT></TD><TD ALIGN=left NOWRAP>5’</TD></TR>
-<TR><TD ALIGN=right NOWRAP> </TD><TD ALIGN=center NOWRAP>DNA template strand (aka Watson strand, strand −1)</TD><TD ALIGN=left NOWRAP> </TD></TR>
-<TR><TD ALIGN=right NOWRAP> </TD></TR>
-<TR><TD ALIGN=right NOWRAP> </TD><TD ALIGN=center NOWRAP><FONT SIZE=5>|</FONT></TD><TD ALIGN=left NOWRAP> </TD></TR>
-<TR><TD ALIGN=right NOWRAP> </TD><TD ALIGN=center NOWRAP>Transcription</TD><TD ALIGN=left NOWRAP> </TD></TR>
-<TR><TD ALIGN=right NOWRAP> </TD><TD ALIGN=center NOWRAP><FONT SIZE=5>↓</FONT></TD><TD ALIGN=left NOWRAP> </TD></TR>
-<TR><TD ALIGN=right NOWRAP> </TD></TR>
-<TR><TD ALIGN=right NOWRAP>5’</TD><TD ALIGN=center NOWRAP><TT>AUGGCCAUUGUAAUGGGCCGCUGAAAGGGUGCCCGAUAG</TT></TD><TD ALIGN=left NOWRAP>3’</TD></TR>
-<TR><TD ALIGN=right NOWRAP> </TD><TD ALIGN=center NOWRAP>Single stranded messenger RNA</TD><TD ALIGN=left NOWRAP> </TD></TR>
-<TR><TD ALIGN=right NOWRAP> </TD></TR>
-</TABLE><P>The actual biological transcription process works from the template strand, doing a reverse complement (TCAG → CUGA) to give the mRNA. However, in Biopython and bioinformatics in general, we typically work directly with the coding strand because this means we can get the mRNA sequence just by switching T → U.</P><P>Now let’s actually get down to doing a transcription in Biopython. First, let’s create <CODE>Seq</CODE> objects for the coding and templ [...]
-</P><PRE CLASS="verbatim">>>> from Bio.Seq import Seq
->>> from Bio.Alphabet import IUPAC
->>> coding_dna = Seq("ATGGCCATTGTAATGGGCCGCTGAAAGGGTGCCCGATAG", IUPAC.unambiguous_dna)
->>> coding_dna
-Seq('ATGGCCATTGTAATGGGCCGCTGAAAGGGTGCCCGATAG', IUPACUnambiguousDNA())
->>> template_dna = coding_dna.reverse_complement()
->>> template_dna
-Seq('CTATCGGGCACCCTTTCAGCGGCCCATTACAATGGCCAT', IUPACUnambiguousDNA())
-</PRE><P>These should match the figure above - remember by convention nucleotide sequences are normally read from the 5’ to 3’ direction, while in the figure the template strand is shown reversed.</P><P>Now let’s transcribe the coding strand into the corresponding mRNA, using the <CODE>Seq</CODE> object’s built in <CODE>transcribe</CODE> method:
-</P><PRE CLASS="verbatim">>>> coding_dna
-Seq('ATGGCCATTGTAATGGGCCGCTGAAAGGGTGCCCGATAG', IUPACUnambiguousDNA())
->>> messenger_rna = coding_dna.transcribe()
->>> messenger_rna
-Seq('AUGGCCAUUGUAAUGGGCCGCUGAAAGGGUGCCCGAUAG', IUPACUnambiguousRNA())
-</PRE><P>As you can see, all this does is switch T → U, and adjust the alphabet.</P><P>If you do want to do a true biological transcription starting with the template strand, then this becomes a two-step process:
-</P><PRE CLASS="verbatim">>>> template_dna.reverse_complement().transcribe()
-Seq('AUGGCCAUUGUAAUGGGCCGCUGAAAGGGUGCCCGAUAG', IUPACUnambiguousRNA())
-</PRE><P>The <CODE>Seq</CODE> object also includes a back-transcription method for going from the mRNA to the coding strand of the DNA. Again, this is a simple U → T substitution and associated change of alphabet:
-</P><PRE CLASS="verbatim">>>> from Bio.Seq import Seq
->>> from Bio.Alphabet import IUPAC
->>> messenger_rna = Seq("AUGGCCAUUGUAAUGGGCCGCUGAAAGGGUGCCCGAUAG", IUPAC.unambiguous_rna)
->>> messenger_rna
-Seq('AUGGCCAUUGUAAUGGGCCGCUGAAAGGGUGCCCGAUAG', IUPACUnambiguousRNA())
->>> messenger_rna.back_transcribe()
-Seq('ATGGCCATTGTAATGGGCCGCTGAAAGGGTGCCCGATAG', IUPACUnambiguousDNA())
-</PRE><P><EM>Note:</EM> The <CODE>Seq</CODE> object’s <CODE>transcribe</CODE> and <CODE>back_transcribe</CODE> methods
-were added in Biopython 1.49. For older releases you would have to use the <CODE>Bio.Seq</CODE>
-module’s functions instead, see Section <A HREF="#sec:seq-module-functions">3.14</A>.</P><!--TOC section Translation-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc25">3.9</A>  Translation</H2><!--SEC END --><P>
-<A NAME="sec:translation"></A>
-Sticking with the same example discussed in the transcription section above,
-now let’s translate this mRNA into the corresponding protein sequence - again taking
-advantage of one of the <CODE>Seq</CODE> object’s biological methods:</P><PRE CLASS="verbatim">>>> from Bio.Seq import Seq
->>> from Bio.Alphabet import IUPAC
->>> messenger_rna = Seq("AUGGCCAUUGUAAUGGGCCGCUGAAAGGGUGCCCGAUAG", IUPAC.unambiguous_rna)
->>> messenger_rna
-Seq('AUGGCCAUUGUAAUGGGCCGCUGAAAGGGUGCCCGAUAG', IUPACUnambiguousRNA())
->>> messenger_rna.translate()
-Seq('MAIVMGR*KGAR*', HasStopCodon(IUPACProtein(), '*'))
-</PRE><P>You can also translate directly from the coding strand DNA sequence:
-</P><PRE CLASS="verbatim">>>> from Bio.Seq import Seq
->>> from Bio.Alphabet import IUPAC
->>> coding_dna = Seq("ATGGCCATTGTAATGGGCCGCTGAAAGGGTGCCCGATAG", IUPAC.unambiguous_dna)
->>> coding_dna
-Seq('ATGGCCATTGTAATGGGCCGCTGAAAGGGTGCCCGATAG', IUPACUnambiguousDNA())
->>> coding_dna.translate()
-Seq('MAIVMGR*KGAR*', HasStopCodon(IUPACProtein(), '*'))
-</PRE><P>You should notice in the above protein sequences that in addition to the end stop character, there is an internal stop as well. This was a deliberate choice of example, as it gives an excuse to talk about some optional arguments, including different translation tables (Genetic Codes).</P><P>The translation tables available in Biopython are based on those <A HREF="http://www.ncbi.nlm.nih.gov/Taxonomy/Utils/wprintgc.cgi">from the NCBI</A> (see the next section of this tutorial). B [...]
-Suppose we are dealing with a mitochondrial sequence. We need to tell the translation function to use the relevant genetic code instead:
-</P><PRE CLASS="verbatim">>>> coding_dna.translate(table="Vertebrate Mitochondrial")
-Seq('MAIVMGRWKGAR*', HasStopCodon(IUPACProtein(), '*'))
-</PRE><P>You can also specify the table using the NCBI table number which is shorter, and often included in the feature annotation of GenBank files:
-</P><PRE CLASS="verbatim">>>> coding_dna.translate(table=2)
-Seq('MAIVMGRWKGAR*', HasStopCodon(IUPACProtein(), '*'))
-</PRE><P>Now, you may want to translate the nucleotides up to the first in frame stop codon,
-and then stop (as happens in nature):
-</P><PRE CLASS="verbatim">>>> coding_dna.translate()
-Seq('MAIVMGR*KGAR*', HasStopCodon(IUPACProtein(), '*'))
->>> coding_dna.translate(to_stop=True)
-Seq('MAIVMGR', IUPACProtein())
->>> coding_dna.translate(table=2)
-Seq('MAIVMGRWKGAR*', HasStopCodon(IUPACProtein(), '*'))
->>> coding_dna.translate(table=2, to_stop=True)
-Seq('MAIVMGRWKGAR', IUPACProtein())
-</PRE><P>Notice that when you use the <CODE>to_stop</CODE> argument, the stop codon itself
-is not translated - and the stop symbol is not included at the end of your protein
-sequence.</P><P>You can even specify the stop symbol if you don’t like the default asterisk:
-</P><PRE CLASS="verbatim">>>> coding_dna.translate(table=2, stop_symbol="@")
-Seq('MAIVMGRWKGAR@', HasStopCodon(IUPACProtein(), '@'))
-</PRE><P>Now, suppose you have a complete coding sequence CDS, which is to say a
-nucleotide sequence (e.g. mRNA – after any splicing) which is a whole number
-of codons (i.e. the length is a multiple of three), commences with a start
-codon, ends with a stop codon, and has no internal in-frame stop codons.
-In general, given a complete CDS, the default translate method will do what
-you want (perhaps with the <CODE>to_stop</CODE> option). However, what if your
-sequence uses a non-standard start codon? This happens a lot in bacteria –
-for example the gene yaaX in <TT>E. coli</TT> K12:</P><PRE CLASS="verbatim">>>> from Bio.Seq import Seq
->>> from Bio.Alphabet import generic_dna
->>> gene = Seq("GTGAAAAAGATGCAATCTATCGTACTCGCACTTTCCCTGGTTCTGGTCGCTCCCATGGCA" + \
-... "GCACAGGCTGCGGAAATTACGTTAGTCCCGTCAGTAAAATTACAGATAGGCGATCGTGAT" + \
-... "AATCGTGGCTATTACTGGGATGGAGGTCACTGGCGCGACCACGGCTGGTGGAAACAACAT" + \
-... "TATGAATGGCGAGGCAATCGCTGGCACCTACACGGACCGCCGCCACCGCCGCGCCACCAT" + \
-... "AAGAAAGCTCCTCATGATCATCACGGCGGTCATGGTCCAGGCAAACATCACCGCTAA",
-... generic_dna)
->>> gene.translate(table="Bacterial")
-Seq('VKKMQSIVLALSLVLVAPMAAQAAEITLVPSVKLQIGDRDNRGYYWDGGHWRDH...HR*',
-HasStopCodon(ExtendedIUPACProtein(), '*')
->>> gene.translate(table="Bacterial", to_stop=True)
-Seq('VKKMQSIVLALSLVLVAPMAAQAAEITLVPSVKLQIGDRDNRGYYWDGGHWRDH...HHR',
-ExtendedIUPACProtein())
-</PRE><P>In the bacterial genetic code <TT>GTG</TT> is a valid start codon,
-and while it does <EM>normally</EM> encode Valine, if used as a start codon it
-should be translated as methionine. This happens if you tell Biopython your
-sequence is a complete CDS:</P><PRE CLASS="verbatim">>>> gene.translate(table="Bacterial", cds=True)
-Seq('MKKMQSIVLALSLVLVAPMAAQAAEITLVPSVKLQIGDRDNRGYYWDGGHWRDH...HHR',
-ExtendedIUPACProtein())
-</PRE><P>In addition to telling Biopython to translate an alternative start codon as
-methionine, using this option also makes sure your sequence really is a valid
-CDS (you’ll get an exception if not).</P><P>The example in Section <A HREF="#sec:SeqIO-translate">18.1.3</A> combines the <CODE>Seq</CODE> object’s
-translate method with <CODE>Bio.SeqIO</CODE> for sequence input/output.</P><!--TOC section Translation Tables-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc26">3.10</A>  Translation Tables</H2><!--SEC END --><P>In the previous sections we talked about the <CODE>Seq</CODE> object translation method (and mentioned the equivalent function in the <CODE>Bio.Seq</CODE> module – see
-Section <A HREF="#sec:seq-module-functions">3.14</A>).
-Internally these use codon table objects derived from the NCBI information at
-<A HREF="ftp://ftp.ncbi.nlm.nih.gov/entrez/misc/data/gc.prt"><TT>ftp://ftp.ncbi.nlm.nih.gov/entrez/misc/data/gc.prt</TT></A>, also shown on
-<A HREF="http://www.ncbi.nlm.nih.gov/Taxonomy/Utils/wprintgc.cgi"><TT>http://www.ncbi.nlm.nih.gov/Taxonomy/Utils/wprintgc.cgi</TT></A> in a much more readable layout.</P><P>As before, let’s just focus on two choices: the Standard translation table, and the
-translation table for Vertebrate Mitochondrial DNA.</P><PRE CLASS="verbatim">>>> from Bio.Data import CodonTable
->>> standard_table = CodonTable.unambiguous_dna_by_name["Standard"]
->>> mito_table = CodonTable.unambiguous_dna_by_name["Vertebrate Mitochondrial"]
-</PRE><P>Alternatively, these tables are labeled with ID numbers 1 and 2, respectively:
-</P><PRE CLASS="verbatim">>>> from Bio.Data import CodonTable
->>> standard_table = CodonTable.unambiguous_dna_by_id[1]
->>> mito_table = CodonTable.unambiguous_dna_by_id[2]
-</PRE><P>You can compare the actual tables visually by printing them:
-</P><PRE CLASS="verbatim">>>> print(standard_table)
-Table 1 Standard, SGC0
-
- | T | C | A | G |
---+---------+---------+---------+---------+--
-T | TTT F | TCT S | TAT Y | TGT C | T
-T | TTC F | TCC S | TAC Y | TGC C | C
-T | TTA L | TCA S | TAA Stop| TGA Stop| A
-T | TTG L(s)| TCG S | TAG Stop| TGG W | G
---+---------+---------+---------+---------+--
-C | CTT L | CCT P | CAT H | CGT R | T
-C | CTC L | CCC P | CAC H | CGC R | C
-C | CTA L | CCA P | CAA Q | CGA R | A
-C | CTG L(s)| CCG P | CAG Q | CGG R | G
---+---------+---------+---------+---------+--
-A | ATT I | ACT T | AAT N | AGT S | T
-A | ATC I | ACC T | AAC N | AGC S | C
-A | ATA I | ACA T | AAA K | AGA R | A
-A | ATG M(s)| ACG T | AAG K | AGG R | G
---+---------+---------+---------+---------+--
-G | GTT V | GCT A | GAT D | GGT G | T
-G | GTC V | GCC A | GAC D | GGC G | C
-G | GTA V | GCA A | GAA E | GGA G | A
-G | GTG V | GCG A | GAG E | GGG G | G
---+---------+---------+---------+---------+--
-</PRE><P>and:
-</P><PRE CLASS="verbatim">>>> print(mito_table)
-Table 2 Vertebrate Mitochondrial, SGC1
-
- | T | C | A | G |
---+---------+---------+---------+---------+--
-T | TTT F | TCT S | TAT Y | TGT C | T
-T | TTC F | TCC S | TAC Y | TGC C | C
-T | TTA L | TCA S | TAA Stop| TGA W | A
-T | TTG L | TCG S | TAG Stop| TGG W | G
---+---------+---------+---------+---------+--
-C | CTT L | CCT P | CAT H | CGT R | T
-C | CTC L | CCC P | CAC H | CGC R | C
-C | CTA L | CCA P | CAA Q | CGA R | A
-C | CTG L | CCG P | CAG Q | CGG R | G
---+---------+---------+---------+---------+--
-A | ATT I(s)| ACT T | AAT N | AGT S | T
-A | ATC I(s)| ACC T | AAC N | AGC S | C
-A | ATA M(s)| ACA T | AAA K | AGA Stop| A
-A | ATG M(s)| ACG T | AAG K | AGG Stop| G
---+---------+---------+---------+---------+--
-G | GTT V | GCT A | GAT D | GGT G | T
-G | GTC V | GCC A | GAC D | GGC G | C
-G | GTA V | GCA A | GAA E | GGA G | A
-G | GTG V(s)| GCG A | GAG E | GGG G | G
---+---------+---------+---------+---------+--
-</PRE><P>You may find these following properties useful – for example if you are trying
-to do your own gene finding:
-</P><PRE CLASS="verbatim">>>> mito_table.stop_codons
-['TAA', 'TAG', 'AGA', 'AGG']
->>> mito_table.start_codons
-['ATT', 'ATC', 'ATA', 'ATG', 'GTG']
->>> mito_table.forward_table["ACG"]
-'T'
-</PRE><!--TOC section Comparing Seq objects-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc27">3.11</A>  Comparing Seq objects</H2><!--SEC END --><P>
-<A NAME="sec:seq-comparison"></A></P><P>Sequence comparison is actually a very complicated topic, and there is no easy
-way to decide if two sequences are equal. The basic problem is the meaning of
-the letters in a sequence are context dependent - the letter “A” could be part
-of a DNA, RNA or protein sequence. Biopython uses alphabet objects as part of
-each <CODE>Seq</CODE> object to try to capture this information - so comparing two
-<CODE>Seq</CODE> objects could mean considering both the sequence strings <EM>and</EM>
-the alphabets.</P><P>For example, you might argue that the two DNA <CODE>Seq</CODE> objects
-<TT>Seq("ACGT", IUPAC.unambiguous_dna)</TT> and
-<TT>Seq("ACGT", IUPAC.ambiguous_dna)</TT> should be equal, even though
-they do have different alphabets. Depending on the context this could be
-important.</P><P>This gets worse – suppose you think <TT>Seq("ACGT",
-IUPAC.unambiguous_dna)</TT> and <TT>Seq("ACGT")</TT> (i.e. the default generic
-alphabet) should be equal. Then, logically, <TT>Seq("ACGT", IUPAC.protein)</TT>
-and <TT>Seq("ACGT")</TT> should also be equal. Now, in logic if <I>A</I>=<I>B</I> and
-<I>B</I>=<I>C</I>, by transitivity we expect <I>A</I>=<I>C</I>. So for logical consistency we’d
-require <TT>Seq("ACGT", IUPAC.unambiguous_dna)</TT> and <TT>Seq("ACGT",
-IUPAC.protein)</TT> to be equal – which most people would agree is just not right.
-This transitivity also has implications for using <CODE>Seq</CODE> objects as
-Python dictionary keys.</P><P>Now, in everyday use, your sequences will probably all have the same
-alphabet, or at least all be the same type of sequence (all DNA, all RNA, or
-all protein). What you probably want is to just compare the sequences as
-strings – which you can do explicitly:
-</P><PRE CLASS="verbatim">>>> from Bio.Seq import Seq
->>> from Bio.Alphabet import IUPAC
->>> seq1 = Seq("ACGT", IUPAC.unambiguous_dna)
->>> seq2 = Seq("ACGT", IUPAC.ambiguous_dna)
->>> str(seq1) == str(seq2)
-True
->>> str(seq1) == str(seq1)
-True
-</PRE><P>So, what does Biopython do? Well, as of Biopython 1.65, sequence comparison
-only looks at the sequence, essentially ignoring the alphabet:</P><PRE CLASS="verbatim">>>> seq1 == seq2
-True
->>> seq1 == "ACGT"
-True
-</PRE><P>As an extension to this, using sequence objects as keys in a Python dictionary
-is now equivalent to using the sequence as a plain string for the key.
-See also Section <A HREF="#sec:seq-to-string">3.4</A>.</P><P>Note if you compare sequences with incompatible alphabets (e.g. DNA vs RNA,
-or nucleotide versus protein), then you will get a warning but for the
-comparison itself only the string of letters in the sequence is used:
-</P><PRE CLASS="verbatim">>>> from Bio.Seq import Seq
->>> from Bio.Alphabet import generic_dna, generic_protein
->>> dna_seq = Seq("ACGT", generic_dna)
->>> prot_seq = Seq(``ACGT'', generic_protein)
->>> dna_seq == prot_seq
-BiopythonWarning: Incompatible alphabets DNAAlphabet() and ProteinAlphabet()
-True
-</PRE><P><EM>WARNING:</EM> Older versions of Biopython instead used to check if the
-<CODE>Seq</CODE> objects were the same object in memory.
-This is important if you need to support scripts on both old and new
-versions of Biopython. Here make the comparison explicit by wrapping
-your sequence objects with either <CODE>str(...)</CODE> for string based
-comparison or <CODE>id(...)</CODE> for object instance based comparison.</P><!--TOC section MutableSeq objects-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc28">3.12</A>  MutableSeq objects</H2><!--SEC END --><P>
-<A NAME="sec:mutable-seq"></A></P><P>Just like the normal Python string, the <CODE>Seq</CODE> object is “read only”, or in Python terminology, immutable. Apart from wanting the <CODE>Seq</CODE> object to act like a string, this is also a useful default since in many biological applications you want to ensure you are not changing your sequence data:</P><PRE CLASS="verbatim">>>> from Bio.Seq import Seq
->>> from Bio.Alphabet import IUPAC
->>> my_seq = Seq("GCCATTGTAATGGGCCGCTGAAAGGGTGCCCGA", IUPAC.unambiguous_dna)
-</PRE><P>Observe what happens if you try to edit the sequence:
-</P><PRE CLASS="verbatim">>>> my_seq[5] = "G"
-Traceback (most recent call last):
-...
-TypeError: 'Seq' object does not support item assignment
-</PRE><P>However, you can convert it into a mutable sequence (a <CODE>MutableSeq</CODE> object) and do pretty much anything you want with it:</P><PRE CLASS="verbatim">>>> mutable_seq = my_seq.tomutable()
->>> mutable_seq
-MutableSeq('GCCATTGTAATGGGCCGCTGAAAGGGTGCCCGA', IUPACUnambiguousDNA())
-</PRE><P>Alternatively, you can create a <CODE>MutableSeq</CODE> object directly from a string:
-</P><PRE CLASS="verbatim">>>> from Bio.Seq import MutableSeq
->>> from Bio.Alphabet import IUPAC
->>> mutable_seq = MutableSeq("GCCATTGTAATGGGCCGCTGAAAGGGTGCCCGA", IUPAC.unambiguous_dna)
-</PRE><P>Either way will give you a sequence object which can be changed:
-</P><PRE CLASS="verbatim">>>> mutable_seq
-MutableSeq('GCCATTGTAATGGGCCGCTGAAAGGGTGCCCGA', IUPACUnambiguousDNA())
->>> mutable_seq[5] = "C"
->>> mutable_seq
-MutableSeq('GCCATCGTAATGGGCCGCTGAAAGGGTGCCCGA', IUPACUnambiguousDNA())
->>> mutable_seq.remove("T")
->>> mutable_seq
-MutableSeq('GCCACGTAATGGGCCGCTGAAAGGGTGCCCGA', IUPACUnambiguousDNA())
->>> mutable_seq.reverse()
->>> mutable_seq
-MutableSeq('AGCCCGTGGGAAAGTCGCCGGGTAATGCACCG', IUPACUnambiguousDNA())
-</PRE><P>Do note that unlike the <CODE>Seq</CODE> object, the <CODE>MutableSeq</CODE> object’s methods like <CODE>reverse_complement()</CODE> and <CODE>reverse()</CODE> act in-situ!</P><P>An important technical difference between mutable and immutable objects in Python means that you can’t use a <CODE>MutableSeq</CODE> object as a dictionary key, but you can use a Python string or a <CODE>Seq</CODE> object in this way.</P><P>Once you have finished editing your a <CODE>Mutab [...]
->>> new_seq
-Seq('AGCCCGTGGGAAAGTCGCCGGGTAATGCACCG', IUPACUnambiguousDNA())
-</PRE><P>You can also get a string from a <CODE>MutableSeq</CODE> object just like from a <CODE>Seq</CODE> object (Section <A HREF="#sec:seq-to-string">3.4</A>).</P><!--TOC section UnknownSeq objects-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc29">3.13</A>  UnknownSeq objects</H2><!--SEC END --><P>
-The <CODE>UnknownSeq</CODE> object is a subclass of the basic <CODE>Seq</CODE> object
-and its purpose is to represent a
-sequence where we know the length, but not the actual letters making it up.
-You could of course use a normal <CODE>Seq</CODE> object in this situation, but it wastes
-rather a lot of memory to hold a string of a million “N” characters when you could
-just store a single letter “N” and the desired length as an integer.</P><PRE CLASS="verbatim">>>> from Bio.Seq import UnknownSeq
->>> unk = UnknownSeq(20)
->>> unk
-UnknownSeq(20, alphabet = Alphabet(), character = '?')
->>> print(unk)
-????????????????????
->>> len(unk)
-20
-</PRE><P>You can of course specify an alphabet, meaning for nucleotide sequences
-the letter defaults to “N” and for proteins “X”, rather than just “?”.</P><PRE CLASS="verbatim">>>> from Bio.Seq import UnknownSeq
->>> from Bio.Alphabet import IUPAC
->>> unk_dna = UnknownSeq(20, alphabet=IUPAC.ambiguous_dna)
->>> unk_dna
-UnknownSeq(20, alphabet = IUPACAmbiguousDNA(), character = 'N')
->>> print(unk_dna)
-NNNNNNNNNNNNNNNNNNNN
-</PRE><P>You can use all the usual <CODE>Seq</CODE> object methods too, note these give back
-memory saving <CODE>UnknownSeq</CODE> objects where appropriate as you might expect:</P><PRE CLASS="verbatim">>>> unk_dna
-UnknownSeq(20, alphabet = IUPACAmbiguousDNA(), character = 'N')
->>> unk_dna.complement()
-UnknownSeq(20, alphabet = IUPACAmbiguousDNA(), character = 'N')
->>> unk_dna.reverse_complement()
-UnknownSeq(20, alphabet = IUPACAmbiguousDNA(), character = 'N')
->>> unk_dna.transcribe()
-UnknownSeq(20, alphabet = IUPACAmbiguousRNA(), character = 'N')
->>> unk_protein = unk_dna.translate()
->>> unk_protein
-UnknownSeq(6, alphabet = ProteinAlphabet(), character = 'X')
->>> print(unk_protein)
-XXXXXX
->>> len(unk_protein)
-6
-</PRE><P>You may be able to find a use for the <CODE>UnknownSeq</CODE> object in your own
-code, but it is more likely that you will first come across them in a
-<CODE>SeqRecord</CODE> object created by <CODE>Bio.SeqIO</CODE>
-(see Chapter <A HREF="#chapter:Bio.SeqIO">5</A>).
-Some sequence file formats don’t always include the actual sequence, for
-example GenBank and EMBL files may include a list of features but for the
-sequence just present the contig information. Alternatively, the QUAL files
-used in sequencing work hold quality scores but they <EM>never</EM> contain a
-sequence – instead there is a partner FASTA file which <EM>does</EM> have the
-sequence.</P><!--TOC section Working with strings directly-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc30">3.14</A>  Working with strings directly</H2><!--SEC END --><P>
-<A NAME="sec:seq-module-functions"></A>
-To close this chapter, for those you who <EM>really</EM> don’t want to use the sequence
-objects (or who prefer a functional programming style to an object orientated one),
-there are module level functions in <CODE>Bio.Seq</CODE> will accept plain Python strings,
-<CODE>Seq</CODE> objects (including <CODE>UnknownSeq</CODE> objects) or <CODE>MutableSeq</CODE> objects:</P><PRE CLASS="verbatim">>>> from Bio.Seq import reverse_complement, transcribe, back_transcribe, translate
->>> my_string = "GCTGTTATGGGTCGTTGGAAGGGTGGTCGTGCTGCTGGTTAG"
->>> reverse_complement(my_string)
-'CTAACCAGCAGCACGACCACCCTTCCAACGACCCATAACAGC'
->>> transcribe(my_string)
-'GCUGUUAUGGGUCGUUGGAAGGGUGGUCGUGCUGCUGGUUAG'
->>> back_transcribe(my_string)
-'GCTGTTATGGGTCGTTGGAAGGGTGGTCGTGCTGCTGGTTAG'
->>> translate(my_string)
-'AVMGRWKGGRAAG*'
-</PRE><P>You are, however, encouraged to work with <CODE>Seq</CODE> objects by default.</P><!--TOC chapter Sequence annotation objects-->
-<H1 CLASS="chapter"><!--SEC ANCHOR --><A NAME="htoc31">Chapter 4</A>  Sequence annotation objects</H1><!--SEC END --><P>
-<A NAME="chapter:SeqRecord"></A></P><P>Chapter <A HREF="#chapter:Bio.Seq">3</A> introduced the sequence classes. Immediately “above” the <CODE>Seq</CODE> class is the Sequence Record or <CODE>SeqRecord</CODE> class, defined in the <CODE>Bio.SeqRecord</CODE> module. This class allows higher level features such as identifiers and features (as <CODE>SeqFeature</CODE> objects) to be associated with the sequence, and is used throughout the sequence input/output interface <C [...]
-for now. If on the other hand you are going to be using richly annotated sequence data, say from GenBank
-or EMBL files, this information is quite important.</P><P>While this chapter should cover most things to do with the <CODE>SeqRecord</CODE> and <CODE>SeqFeature</CODE> objects in this chapter, you may also want to read the <CODE>SeqRecord</CODE> wiki page (<A HREF="http://biopython.org/wiki/SeqRecord"><TT>http://biopython.org/wiki/SeqRecord</TT></A>), and the built in documentation (also online – <A HREF="http://biopython.org/DIST/docs/api/Bio.SeqRecord.SeqRecord-class.html">SeqRe [...]
->>> help(SeqRecord)
-...
-</PRE><!--TOC section The SeqRecord object-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc32">4.1</A>  The SeqRecord object</H2><!--SEC END --><P>
-<A NAME="sec:SeqRecord"></A></P><P>The <CODE>SeqRecord</CODE> (Sequence Record) class is defined in the <CODE>Bio.SeqRecord</CODE> module. This class allows higher level features such as identifiers and features to be associated with a sequence (see Chapter <A HREF="#chapter:Bio.Seq">3</A>), and is the basic data type for the <CODE>Bio.SeqIO</CODE> sequence input/output interface (see Chapter <A HREF="#chapter:Bio.SeqIO">5</A>).</P><P>The <CODE>SeqRecord</CODE> class itself is [...]
-<B>.seq</B></DT><DD CLASS="dd-description"> – The sequence itself, typically a <CODE>Seq</CODE> object.</DD><DT CLASS="dt-description"><B>.id</B></DT><DD CLASS="dd-description"> – The primary ID used to identify the sequence – a string. In most cases this is something like an accession number.</DD><DT CLASS="dt-description"><B>.name</B></DT><DD CLASS="dd-description"> – A “common” name/id for the sequence – a string. In some cases this wil [...]
-</DD></DL><!--TOC section Creating a SeqRecord-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc33">4.2</A>  Creating a SeqRecord</H2><!--SEC END --><P>Using a <CODE>SeqRecord</CODE> object is not very complicated, since all of the
-information is presented as attributes of the class. Usually you won’t create
-a <CODE>SeqRecord</CODE> “by hand”, but instead use <CODE>Bio.SeqIO</CODE> to read in a
-sequence file for you (see Chapter <A HREF="#chapter:Bio.SeqIO">5</A> and the examples
-below). However, creating <CODE>SeqRecord</CODE> can be quite simple.</P><!--TOC subsection SeqRecord objects from scratch-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc34">4.2.1</A>  SeqRecord objects from scratch</H3><!--SEC END --><P>To create a <CODE>SeqRecord</CODE> at a minimum you just need a <CODE>Seq</CODE> object:</P><PRE CLASS="verbatim">>>> from Bio.Seq import Seq
->>> simple_seq = Seq("GATC")
->>> from Bio.SeqRecord import SeqRecord
->>> simple_seq_r = SeqRecord(simple_seq)
-</PRE><P>Additionally, you can also pass the id, name and description to the initialization function, but if not they will be set as strings indicating they are unknown, and can be modified subsequently:</P><PRE CLASS="verbatim">>>> simple_seq_r.id
-'<unknown id>'
->>> simple_seq_r.id = "AC12345"
->>> simple_seq_r.description = "Made up sequence I wish I could write a paper about"
->>> print(simple_seq_r.description)
-Made up sequence I wish I could write a paper about
->>> simple_seq_r.seq
-Seq('GATC', Alphabet())
-</PRE><P>Including an identifier is very important if you want to output your <CODE>SeqRecord</CODE> to a file. You would normally include this when creating the object:</P><PRE CLASS="verbatim">>>> from Bio.Seq import Seq
->>> simple_seq = Seq("GATC")
->>> from Bio.SeqRecord import SeqRecord
->>> simple_seq_r = SeqRecord(simple_seq, id="AC12345")
-</PRE><P>As mentioned above, the <CODE>SeqRecord</CODE> has an dictionary attribute <CODE>annotations</CODE>. This is used
-for any miscellaneous annotations that doesn’t fit under one of the other more specific attributes.
-Adding annotations is easy, and just involves dealing directly with the annotation dictionary:</P><PRE CLASS="verbatim">>>> simple_seq_r.annotations["evidence"] = "None. I just made it up."
->>> print(simple_seq_r.annotations)
-{'evidence': 'None. I just made it up.'}
->>> print(simple_seq_r.annotations["evidence"])
-None. I just made it up.
-</PRE><P>Working with per-letter-annotations is similar, <CODE>letter_annotations</CODE> is a
-dictionary like attribute which will let you assign any Python sequence (i.e.
-a string, list or tuple) which has the same length as the sequence:</P><PRE CLASS="verbatim">>>> simple_seq_r.letter_annotations["phred_quality"] = [40, 40, 38, 30]
->>> print(simple_seq_r.letter_annotations)
-{'phred_quality': [40, 40, 38, 30]}
->>> print(simple_seq_r.letter_annotations["phred_quality"])
-[40, 40, 38, 30]
-</PRE><P>The <CODE>dbxrefs</CODE> and <CODE>features</CODE> attributes are just Python lists, and
-should be used to store strings and <CODE>SeqFeature</CODE> objects (discussed later
-in this chapter) respectively.</P><!--TOC subsection SeqRecord objects from FASTA files-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc35">4.2.2</A>  SeqRecord objects from FASTA files</H3><!--SEC END --><P>This example uses a fairly large FASTA file containing the whole sequence for <I>Yersinia pestis biovar Microtus</I> str. 91001 plasmid pPCP1, originally downloaded from the NCBI. This file is included with the Biopython unit tests under the GenBank folder, or online <A HREF="http://biopython.org/SRC/biopython/Tests/GenBank/NC_005816.fna"><TT>NC_005816.f [...]
-TGTAACGAACGGTGCAATAGTGATCCACACCCAACGCCTGAAATCAGATCCAGGGGGTAATCTGCTCTCC
-...
-</PRE><P>Back in Chapter <A HREF="#chapter:quick-start">2</A> you will have seen the function <CODE>Bio.SeqIO.parse(...)</CODE>
-used to loop over all the records in a file as <CODE>SeqRecord</CODE> objects. The <CODE>Bio.SeqIO</CODE> module
-has a sister function for use on files which contain just one record which we’ll use here (see Chapter <A HREF="#chapter:Bio.SeqIO">5</A> for details):</P><PRE CLASS="verbatim">>>> from Bio import SeqIO
->>> record = SeqIO.read("NC_005816.fna", "fasta")
->>> record
-SeqRecord(seq=Seq('TGTAACGAACGGTGCAATAGTGATCCACACCCAACGCCTGAAATCAGATCCAGG...CTG',
-SingleLetterAlphabet()), id='gi|45478711|ref|NC_005816.1|', name='gi|45478711|ref|NC_005816.1|',
-description='gi|45478711|ref|NC_005816.1| Yersinia pestis biovar Microtus ... sequence',
-dbxrefs=[])
-</PRE><P>Now, let’s have a look at the key attributes of this <CODE>SeqRecord</CODE>
-individually – starting with the <CODE>seq</CODE> attribute which gives you a
-<CODE>Seq</CODE> object:</P><PRE CLASS="verbatim">>>> record.seq
-Seq('TGTAACGAACGGTGCAATAGTGATCCACACCCAACGCCTGAAATCAGATCCAGG...CTG', SingleLetterAlphabet())
-</PRE><P>Here <CODE>Bio.SeqIO</CODE> has defaulted to a generic alphabet, rather
-than guessing that this is DNA. If you know in advance what kind of sequence
-your FASTA file contains, you can tell <CODE>Bio.SeqIO</CODE> which alphabet to use
-(see Chapter <A HREF="#chapter:Bio.SeqIO">5</A>).</P><P>Next, the identifiers and description:</P><PRE CLASS="verbatim">>>> record.id
-'gi|45478711|ref|NC_005816.1|'
->>> record.name
-'gi|45478711|ref|NC_005816.1|'
->>> record.description
-'gi|45478711|ref|NC_005816.1| Yersinia pestis biovar Microtus ... pPCP1, complete sequence'
-</PRE><P>As you can see above, the first word of the FASTA record’s title line (after
-removing the greater than symbol) is used for both the <CODE>id</CODE> and
-<CODE>name</CODE> attributes. The whole title line (after removing the greater than
-symbol) is used for the record description. This is deliberate, partly for
-backwards compatibility reasons, but it also makes sense if you have a FASTA
-file like this:</P><PRE CLASS="verbatim">>Yersinia pestis biovar Microtus str. 91001 plasmid pPCP1
-TGTAACGAACGGTGCAATAGTGATCCACACCCAACGCCTGAAATCAGATCCAGGGGGTAATCTGCTCTCC
-...
-</PRE><P>Note that none of the other annotation attributes get populated when reading a
-FASTA file:</P><PRE CLASS="verbatim">>>> record.dbxrefs
-[]
->>> record.annotations
-{}
->>> record.letter_annotations
-{}
->>> record.features
-[]
-</PRE><P>In this case our example FASTA file was from the NCBI, and they have a fairly well defined set of conventions for formatting their FASTA lines. This means it would be possible to parse this information and extract the GI number and accession for example. However, FASTA files from other sources vary, so this isn’t possible in general.</P><!--TOC subsection SeqRecord objects from GenBank files-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc36">4.2.3</A>  SeqRecord objects from GenBank files</H3><!--SEC END --><P>As in the previous example, we’re going to look at the whole sequence for <I>Yersinia pestis biovar Microtus</I> str. 91001 plasmid pPCP1, originally downloaded from the NCBI, but this time as a GenBank file.
-Again, this file is included with the Biopython unit tests under the GenBank folder, or online <A HREF="http://biopython.org/SRC/biopython/Tests/GenBank/NC_005816.gb"><TT>NC_005816.gb</TT></A> from our website.</P><P>This file contains a single record (i.e. only one LOCUS line) and starts:
-</P><PRE CLASS="verbatim">LOCUS NC_005816 9609 bp DNA circular BCT 21-JUL-2008
-DEFINITION Yersinia pestis biovar Microtus str. 91001 plasmid pPCP1, complete
- sequence.
-ACCESSION NC_005816
-VERSION NC_005816.1 GI:45478711
-PROJECT GenomeProject:10638
-...
-</PRE><P>Again, we’ll use <CODE>Bio.SeqIO</CODE> to read this file in, and the code is almost identical to that for used above for the FASTA file (see Chapter <A HREF="#chapter:Bio.SeqIO">5</A> for details):</P><PRE CLASS="verbatim">>>> from Bio import SeqIO
->>> record = SeqIO.read("NC_005816.gb", "genbank")
->>> record
-SeqRecord(seq=Seq('TGTAACGAACGGTGCAATAGTGATCCACACCCAACGCCTGAAATCAGATCCAGG...CTG',
-IUPACAmbiguousDNA()), id='NC_005816.1', name='NC_005816',
-description='Yersinia pestis biovar Microtus str. 91001 plasmid pPCP1, complete sequence.',
-dbxrefs=['Project:10638'])
-</PRE><P>You should be able to spot some differences already! But taking the attributes individually,
-the sequence string is the same as before, but this time <CODE>Bio.SeqIO</CODE> has been able to automatically assign a more specific alphabet (see Chapter <A HREF="#chapter:Bio.SeqIO">5</A> for details):</P><PRE CLASS="verbatim">>>> record.seq
-Seq('TGTAACGAACGGTGCAATAGTGATCCACACCCAACGCCTGAAATCAGATCCAGG...CTG', IUPACAmbiguousDNA())
-</PRE><P>The <CODE>name</CODE> comes from the LOCUS line, while the <CODE>id</CODE> includes the version suffix.
-The description comes from the DEFINITION line:</P><PRE CLASS="verbatim">>>> record.id
-'NC_005816.1'
->>> record.name
-'NC_005816'
->>> record.description
-'Yersinia pestis biovar Microtus str. 91001 plasmid pPCP1, complete sequence.'
-</PRE><P>GenBank files don’t have any per-letter annotations:</P><PRE CLASS="verbatim">>>> record.letter_annotations
-{}
-</PRE><P>Most of the annotations information gets recorded in the <CODE>annotations</CODE> dictionary, for example:</P><PRE CLASS="verbatim">>>> len(record.annotations)
-11
->>> record.annotations["source"]
-'Yersinia pestis biovar Microtus str. 91001'
-</PRE><P>The <CODE>dbxrefs</CODE> list gets populated from any PROJECT or DBLINK lines:</P><PRE CLASS="verbatim">>>> record.dbxrefs
-['Project:10638']
-</PRE><P>Finally, and perhaps most interestingly, all the entries in the features table (e.g. the genes or CDS features) get recorded as <CODE>SeqFeature</CODE> objects in the <CODE>features</CODE> list.</P><PRE CLASS="verbatim">>>> len(record.features)
-29
-</PRE><P>We’ll talk about <CODE>SeqFeature</CODE> objects next, in
-Section <A HREF="#sec:seq_features">4.3</A>.</P><!--TOC section Feature, location and position objects-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc37">4.3</A>  Feature, location and position objects</H2><!--SEC END --><P>
-<A NAME="sec:seq_features"></A></P><!--TOC subsection SeqFeature objects-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc38">4.3.1</A>  SeqFeature objects</H3><!--SEC END --><P>Sequence features are an essential part of describing a sequence. Once you get beyond the sequence itself, you need some way to organize and easily get at the more “abstract” information that is known about the sequence. While it is probably impossible to develop a general sequence feature class that will cover everything, the Biopython <CODE>SeqFeature</C [...]
-<B>.type</B></DT><DD CLASS="dd-description"> – This is a textual description of the type of feature (for instance, this will be something like ‘CDS’ or ‘gene’).</DD><DT CLASS="dt-description"><B>.location</B></DT><DD CLASS="dd-description"> – The location of the <CODE>SeqFeature</CODE> on the sequence
-that you are dealing with, see Section <A HREF="#sec:locations">4.3.2</A> below. The
-<CODE>SeqFeature</CODE> delegates much of its functionality to the location
-object, and includes a number of shortcut attributes for properties
-of the location:<DL CLASS="description"><DT CLASS="dt-description">
-<B>.ref</B></DT><DD CLASS="dd-description"> – shorthand for <CODE>.location.ref</CODE> – any (different)
-reference sequence the location is referring to. Usually just None.</DD><DT CLASS="dt-description"><B>.ref_db</B></DT><DD CLASS="dd-description"> – shorthand for <CODE>.location.ref_db</CODE> – specifies
-the database any identifier in <CODE>.ref</CODE> refers to. Usually just None.</DD><DT CLASS="dt-description"><B>.strand</B></DT><DD CLASS="dd-description"> – shorthand for <CODE>.location.strand</CODE> – the strand on
-the sequence that the feature is located on. For double stranded nucleotide
-sequence this may either be 1 for the top strand, −1 for the bottom
-strand, 0 if the strand is important but is unknown, or <TT>None</TT>
-if it doesn’t matter. This is None for proteins, or single stranded sequences.
-</DD></DL></DD><DT CLASS="dt-description"><B>.qualifiers</B></DT><DD CLASS="dd-description"> – This is a Python dictionary of additional information about the feature. The key is some kind of terse one-word description of what the information contained in the value is about, and the value is the actual information. For example, a common key for a qualifier might be “evidence” and the value might be “computational (non-experimental).” This is just a way [...]
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc39">4.3.2</A>  Positions and locations</H3><!--SEC END --><P>
-<A NAME="sec:locations"></A></P><P>The key idea about each <CODE>SeqFeature</CODE> object is to describe a
-region on a parent sequence, for which we use a location object,
-typically describing a range between two positions. Two try to
-clarify the terminology we’re using:</P><DL CLASS="description"><DT CLASS="dt-description">
-<B>position</B></DT><DD CLASS="dd-description"> – This refers to a single position on a sequence,
-which may be fuzzy or not. For instance, 5, 20, <CODE><100</CODE> and
-<CODE>>200</CODE> are all positions.</DD><DT CLASS="dt-description"><B>location</B></DT><DD CLASS="dd-description"> – A location is region of sequence bounded by
-some positions. For instance 5..20 (i. e. 5 to 20) is a location.
-</DD></DL><P>I just mention this because sometimes I get confused between the two.</P><!--TOC subsubsection FeatureLocation object-->
-<H4 CLASS="subsubsection"><!--SEC ANCHOR -->4.3.2.1  FeatureLocation object</H4><!--SEC END --><P>Unless you work with eukaryotic genes, most <CODE>SeqFeature</CODE> locations are
-extremely simple - you just need start and end coordinates and a strand.
-That’s essentially all the basic <CODE>FeatureLocation</CODE> object does.</P><P>In practise of course, things can be more complicated. First of all
-we have to handle compound locations made up of several regions.
-Secondly, the positions themselves may be fuzzy (inexact).</P><!--TOC subsubsection CompoundLocation object-->
-<H4 CLASS="subsubsection"><!--SEC ANCHOR -->4.3.2.2  CompoundLocation object</H4><!--SEC END --><P>Biopython 1.62 introduced the <CODE>CompoundLocation</CODE> as part of
-a restructuring of how complex locations made up of multiple regions
-are represented.
-The main usage is for handling ‘join’ locations in EMBL/GenBank files.</P><!--TOC subsubsection Fuzzy Positions-->
-<H4 CLASS="subsubsection"><!--SEC ANCHOR -->4.3.2.3  Fuzzy Positions</H4><!--SEC END --><P>So far we’ve only used simple positions. One complication in dealing
-with feature locations comes in the positions themselves.
-In biology many times things aren’t entirely certain
-(as much as us wet lab biologists try to make them certain!). For
-instance, you might do a dinucleotide priming experiment and discover
-that the start of mRNA transcript starts at one of two sites. This
-is very useful information, but the complication comes in how to
-represent this as a position. To help us deal with this, we have
-the concept of fuzzy positions. Basically there are several types
-of fuzzy positions, so we have five classes do deal with them:</P><DL CLASS="description"><DT CLASS="dt-description">
-<B>ExactPosition</B></DT><DD CLASS="dd-description"> – As its name suggests, this class represents a position which is specified as exact along the sequence. This is represented as just a number, and you can get the position by looking at the <CODE>position</CODE> attribute of the object.</DD><DT CLASS="dt-description"><B>BeforePosition</B></DT><DD CLASS="dd-description"> – This class represents a fuzzy position
-that occurs prior to some specified site. In GenBank/EMBL notation,
-this is represented as something like <CODE>`<13'</CODE>, signifying that
-the real position is located somewhere less than 13. To get
-the specified upper boundary, look at the <CODE>position</CODE>
-attribute of the object.</DD><DT CLASS="dt-description"><B>AfterPosition</B></DT><DD CLASS="dd-description"> – Contrary to <CODE>BeforePosition</CODE>, this
-class represents a position that occurs after some specified site.
-This is represented in GenBank as <CODE>`>13'</CODE>, and like
-<CODE>BeforePosition</CODE>, you get the boundary number by looking
-at the <CODE>position</CODE> attribute of the object.</DD><DT CLASS="dt-description"><B>WithinPosition</B></DT><DD CLASS="dd-description"> – Occasionally used for GenBank/EMBL locations,
-this class models a position which occurs somewhere between two
-specified nucleotides. In GenBank/EMBL notation, this would be
-represented as ‘(1.5)’, to represent that the position is somewhere
-within the range 1 to 5. To get the information in this class you
-have to look at two attributes. The <CODE>position</CODE> attribute
-specifies the lower boundary of the range we are looking at, so in
-our example case this would be one. The <CODE>extension</CODE> attribute
-specifies the range to the higher boundary, so in this case it
-would be 4. So <CODE>object.position</CODE> is the lower boundary and
-<CODE>object.position + object.extension</CODE> is the upper boundary.</DD><DT CLASS="dt-description"><B>OneOfPosition</B></DT><DD CLASS="dd-description"> – Occasionally used for GenBank/EMBL locations,
-this class deals with a position where several possible values exist,
-for instance you could use this if the start codon was unclear and
-there where two candidates for the start of the gene. Alternatively,
-that might be handled explicitly as two related gene features.</DD><DT CLASS="dt-description"><B>UnknownPosition</B></DT><DD CLASS="dd-description"> – This class deals with a position of unknown
-location. This is not used in GenBank/EMBL, but corresponds to the ‘?’
-feature coordinate used in UniProt.</DD></DL><P>Here’s an example where we create a location with fuzzy end points:</P><PRE CLASS="verbatim">>>> from Bio import SeqFeature
->>> start_pos = SeqFeature.AfterPosition(5)
->>> end_pos = SeqFeature.BetweenPosition(9, left=8, right=9)
->>> my_location = SeqFeature.FeatureLocation(start_pos, end_pos)
-</PRE><P>Note that the details of some of the fuzzy-locations changed in Biopython 1.59,
-in particular for BetweenPosition and WithinPosition you must now make it explicit
-which integer position should be used for slicing etc. For a start position this
-is generally the lower (left) value, while for an end position this would generally
-be the higher (right) value.</P><P>If you print out a <CODE>FeatureLocation</CODE> object, you can get a nice representation of the information:</P><PRE CLASS="verbatim">>>> print(my_location)
-[>5:(8^9)]
-</PRE><P>We can access the fuzzy start and end positions using the start and end attributes of the location:</P><PRE CLASS="verbatim">>>> my_location.start
-AfterPosition(5)
->>> print(my_location.start)
->5
->>> my_location.end
-BetweenPosition(9, left=8, right=9)
->>> print(my_location.end)
-(8^9)
-</PRE><P>If you don’t want to deal with fuzzy positions and just want numbers,
-they are actually subclasses of integers so should work like integers:</P><PRE CLASS="verbatim">>>> int(my_location.start)
-5
->>> int(my_location.end)
-9
-</PRE><P>For compatibility with older versions of Biopython you can ask for the
-<CODE>nofuzzy_start</CODE> and <CODE>nofuzzy_end</CODE> attributes of the location
-which are plain integers:</P><PRE CLASS="verbatim">>>> my_location.nofuzzy_start
-5
->>> my_location.nofuzzy_end
-9
-</PRE><P>Notice that this just gives you back the position attributes of the fuzzy locations.</P><P>Similarly, to make it easy to create a position without worrying about fuzzy positions, you can just pass in numbers to the <CODE>FeaturePosition</CODE> constructors, and you’ll get back out <CODE>ExactPosition</CODE> objects:</P><PRE CLASS="verbatim">>>> exact_location = SeqFeature.FeatureLocation(5, 9)
->>> print(exact_location)
-[5:9]
->>> exact_location.start
-ExactPosition(5)
->>> int(exact_location.start)
-5
->>> exact_location.nofuzzy_start
-5
-</PRE><P>That is most of the nitty gritty about dealing with fuzzy positions in Biopython.
-It has been designed so that dealing with fuzziness is not that much more
-complicated than dealing with exact positions, and hopefully you find that true!</P><!--TOC subsubsection Location testing-->
-<H4 CLASS="subsubsection"><!--SEC ANCHOR -->4.3.2.4  Location testing</H4><!--SEC END --><P>You can use the Python keyword <CODE>in</CODE> with a <CODE>SeqFeature</CODE> or location
-object to see if the base/residue for a parent coordinate is within the
-feature/location or not.</P><P>For example, suppose you have a SNP of interest and you want to know which
-features this SNP is within, and lets suppose this SNP is at index 4350
-(Python counting!). Here is a simple brute force solution where we just
-check all the features one by one in a loop:</P><PRE CLASS="verbatim">>>> from Bio import SeqIO
->>> my_snp = 4350
->>> record = SeqIO.read("NC_005816.gb", "genbank")
->>> for feature in record.features:
-... if my_snp in feature:
-... print("%s %s" % (feature.type, feature.qualifiers.get('db_xref')))
-...
-source ['taxon:229193']
-gene ['GeneID:2767712']
-CDS ['GI:45478716', 'GeneID:2767712']
-</PRE><P>Note that gene and CDS features from GenBank or EMBL files defined with joins
-are the union of the exons – they do not cover any introns.</P><!--TOC subsection Sequence described by a feature or location-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc40">4.3.3</A>  Sequence described by a feature or location</H3><!--SEC END --><P>A <CODE>SeqFeature</CODE> or location object doesn’t directly contain a sequence, instead the location (see Section <A HREF="#sec:locations">4.3.2</A>) describes how to get this from the parent sequence. For example consider a (short) gene sequence with location 5:18 on the reverse strand, which in GenBank/EMBL notation using 1-based [...]
->>> from Bio.SeqFeature import SeqFeature, FeatureLocation
->>> example_parent = Seq("ACCGAGACGGCAAAGGCTAGCATAGGTATGAGACTTCCTTCCTGCCAGTGCTGAGGAACTGGGAGCCTAC")
->>> example_feature = SeqFeature(FeatureLocation(5, 18), type="gene", strand=-1)
-</PRE><P>You could take the parent sequence, slice it to extract 5:18, and then take the reverse complement.
-If you are using Biopython 1.59 or later, the feature location’s start and end are integer like so this works:</P><PRE CLASS="verbatim">>>> feature_seq = example_parent[example_feature.location.start:example_feature.location.end].reverse_complement()
->>> print(feature_seq)
-AGCCTTTGCCGTC
-</PRE><P>This is a simple example so this isn’t too bad – however once you have to deal with compound features (joins) this is rather messy. Instead, the <CODE>SeqFeature</CODE> object has an <CODE>extract</CODE> method to take care of all this:</P><PRE CLASS="verbatim">>>> feature_seq = example_feature.extract(example_parent)
->>> print(feature_seq)
-AGCCTTTGCCGTC
-</PRE><P>The length of a <CODE>SeqFeature</CODE> or location matches
-that of the region of sequence it describes.</P><PRE CLASS="verbatim">>>> print(example_feature.extract(example_parent))
-AGCCTTTGCCGTC
->>> print(len(example_feature.extract(example_parent)))
-13
->>> print(len(example_feature))
-13
->>> print(len(example_feature.location))
-13
-</PRE><P>For simple <CODE>FeatureLocation</CODE> objects the length is just
-the difference between the start and end positions. However,
-for a <CODE>CompoundLocation</CODE> the length is the sum of the
-constituent regions.</P><!--TOC section References-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc41">4.4</A>  References</H2><!--SEC END --><P>Another common annotation related to a sequence is a reference to a journal or other published work dealing with the sequence. We have a fairly simple way of representing a Reference in Biopython – we have a <CODE>Bio.SeqFeature.Reference</CODE> class that stores the relevant information about a reference as attributes of an object.</P><P>The attributes include things that yo [...]
-That’s all there is too it. References are meant to be easy to deal with, and hopefully general enough to cover lots of usage cases.</P><!--TOC section The format method-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc42">4.5</A>  The format method</H2><!--SEC END --><P>
-<A NAME="sec:SeqRecord-format"></A></P><P>The <CODE>format()</CODE> method of the <CODE>SeqRecord</CODE> class gives a string
-containing your record formatted using one of the output file formats
-supported by <CODE>Bio.SeqIO</CODE>, such as FASTA:</P><PRE CLASS="verbatim">from Bio.Seq import Seq
-from Bio.SeqRecord import SeqRecord
-from Bio.Alphabet import generic_protein
-
-record = SeqRecord(Seq("MMYQQGCFAGGTVLRLAKDLAENNRGARVLVVCSEITAVTFRGPSETHLDSMVGQALFGD" \
- +"GAGAVIVGSDPDLSVERPLYELVWTGATLLPDSEGAIDGHLREVGLTFHLLKDVPGLISK" \
- +"NIEKSLKEAFTPLGISDWNSTFWIAHPGGPAILDQVEAKLGLKEEKMRATREVLSEYGNM" \
- +"SSAC", generic_protein),
- id="gi|14150838|gb|AAK54648.1|AF376133_1",
- description="chalcone synthase [Cucumis sativus]")
-
-print(record.format("fasta"))
-</PRE><P>which should give:
-</P><PRE CLASS="verbatim">>gi|14150838|gb|AAK54648.1|AF376133_1 chalcone synthase [Cucumis sativus]
-MMYQQGCFAGGTVLRLAKDLAENNRGARVLVVCSEITAVTFRGPSETHLDSMVGQALFGD
-GAGAVIVGSDPDLSVERPLYELVWTGATLLPDSEGAIDGHLREVGLTFHLLKDVPGLISK
-NIEKSLKEAFTPLGISDWNSTFWIAHPGGPAILDQVEAKLGLKEEKMRATREVLSEYGNM
-SSAC
-</PRE><P>This <CODE>format</CODE> method takes a single mandatory argument, a lower case string which is
-supported by <CODE>Bio.SeqIO</CODE> as an output format (see Chapter <A HREF="#chapter:Bio.SeqIO">5</A>).
-However, some of the file formats <CODE>Bio.SeqIO</CODE> can write to <EM>require</EM> more than
-one record (typically the case for multiple sequence alignment formats), and thus won’t
-work via this <CODE>format()</CODE> method. See also Section <A HREF="#sec:Bio.SeqIO-and-StringIO">5.5.4</A>.</P><!--TOC section Slicing a SeqRecord-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc43">4.6</A>  Slicing a SeqRecord</H2><!--SEC END --><P>
-<A NAME="sec:SeqRecord-slicing"></A></P><P>You can slice a <CODE>SeqRecord</CODE>, to give you a new <CODE>SeqRecord</CODE> covering just
-part of the sequence. What is important
-here is that any per-letter annotations are also sliced, and any features which fall
-completely within the new sequence are preserved (with their locations adjusted).</P><P>For example, taking the same GenBank file used earlier:</P><PRE CLASS="verbatim">>>> from Bio import SeqIO
->>> record = SeqIO.read("NC_005816.gb", "genbank")
-</PRE><PRE CLASS="verbatim">>>> record
-SeqRecord(seq=Seq('TGTAACGAACGGTGCAATAGTGATCCACACCCAACGCCTGAAATCAGATCCAGG...CTG',
-IUPACAmbiguousDNA()), id='NC_005816.1', name='NC_005816',
-description='Yersinia pestis biovar Microtus str. 91001 plasmid pPCP1, complete sequence.',
-dbxrefs=['Project:10638'])
-</PRE><PRE CLASS="verbatim">>>> len(record)
-9609
->>> len(record.features)
-41
-</PRE><P>For this example we’re going to focus in on the <CODE>pim</CODE> gene, <CODE>YP_pPCP05</CODE>.
-If you have a look at the GenBank file directly you’ll find this gene/CDS has
-location string <TT>4343..4780</TT>, or in Python counting <TT>4342:4780</TT>.
-From looking at the file you can work out that these are the twelfth and
-thirteenth entries in the file, so in Python zero-based counting they are
-entries 11 and 12 in the <TT>features</TT> list:</P><PRE CLASS="verbatim">>>> print(record.features[20])
-type: gene
-location: [4342:4780](+)
-qualifiers:
- Key: db_xref, Value: ['GeneID:2767712']
- Key: gene, Value: ['pim']
- Key: locus_tag, Value: ['YP_pPCP05']
-<BLANKLINE>
-</PRE><PRE CLASS="verbatim">>>> print(record.features[21])
-type: CDS
-location: [4342:4780](+)
-qualifiers:
- Key: codon_start, Value: ['1']
- Key: db_xref, Value: ['GI:45478716', 'GeneID:2767712']
- Key: gene, Value: ['pim']
- Key: locus_tag, Value: ['YP_pPCP05']
- Key: note, Value: ['similar to many previously sequenced pesticin immunity ...']
- Key: product, Value: ['pesticin immunity protein']
- Key: protein_id, Value: ['NP_995571.1']
- Key: transl_table, Value: ['11']
- Key: translation, Value: ['MGGGMISKLFCLALIFLSSSGLAEKNTYTAKDILQNLELNTFGNSLSH...']
-</PRE><P>Let’s slice this parent record from 4300 to 4800 (enough to include the <CODE>pim</CODE>
-gene/CDS), and see how many features we get:</P><PRE CLASS="verbatim">>>> sub_record = record[4300:4800]
-</PRE><PRE CLASS="verbatim">>>> sub_record
-SeqRecord(seq=Seq('ATAAATAGATTATTCCAAATAATTTATTTATGTAAGAACAGGATGGGAGGGGGA...TTA',
-IUPACAmbiguousDNA()), id='NC_005816.1', name='NC_005816',
-description='Yersinia pestis biovar Microtus str. 91001 plasmid pPCP1, complete sequence.',
-dbxrefs=[])
-</PRE><PRE CLASS="verbatim">>>> len(sub_record)
-500
->>> len(sub_record.features)
-2
-</PRE><P>Our sub-record just has two features, the gene and CDS entries for <CODE>YP_pPCP05</CODE>:</P><PRE CLASS="verbatim">>>> print(sub_record.features[0])
-type: gene
-location: [42:480](+)
-qualifiers:
- Key: db_xref, Value: ['GeneID:2767712']
- Key: gene, Value: ['pim']
- Key: locus_tag, Value: ['YP_pPCP05']
-<BLANKLINE>
-</PRE><PRE CLASS="verbatim">>>> print(sub_record.features[20])
-type: CDS
-location: [42:480](+)
-qualifiers:
- Key: codon_start, Value: ['1']
- Key: db_xref, Value: ['GI:45478716', 'GeneID:2767712']
- Key: gene, Value: ['pim']
- Key: locus_tag, Value: ['YP_pPCP05']
- Key: note, Value: ['similar to many previously sequenced pesticin immunity ...']
- Key: product, Value: ['pesticin immunity protein']
- Key: protein_id, Value: ['NP_995571.1']
- Key: transl_table, Value: ['11']
- Key: translation, Value: ['MGGGMISKLFCLALIFLSSSGLAEKNTYTAKDILQNLELNTFGNSLSH...']
-</PRE><P>Notice that their locations have been adjusted to reflect the new parent sequence!</P><P>While Biopython has done something sensible and hopefully intuitive with the features
-(and any per-letter annotation), for the other annotation it is impossible to know if
-this still applies to the sub-sequence or not. To avoid guessing, the <TT>annotations</TT>
-and <TT>dbxrefs</TT> are omitted from the sub-record, and it is up to you to transfer
-any relevant information as appropriate.</P><PRE CLASS="verbatim">>>> sub_record.annotations
-{}
->>> sub_record.dbxrefs
-[]
-</PRE><P>The same point could be made about the record <TT>id</TT>, <TT>name</TT>
-and <TT>description</TT>, but for practicality these are preserved:</P><PRE CLASS="verbatim">>>> sub_record.id
-'NC_005816.1'
->>> sub_record.name
-'NC_005816'
->>> sub_record.description
-'Yersinia pestis biovar Microtus str. 91001 plasmid pPCP1, complete sequence.'
-</PRE><P>This illustrates the problem nicely though, our new sub-record is
-<EM>not</EM> the complete sequence of the plasmid, so the description is wrong!
-Let’s fix this and then view the sub-record as a reduced GenBank file using
-the <TT>format</TT> method described above in Section <A HREF="#sec:SeqRecord-format">4.5</A>:</P><PRE CLASS="verbatim">>>> sub_record.description = "Yersinia pestis biovar Microtus str. 91001 plasmid pPCP1, partial."
->>> print(sub_record.format("genbank"))
-...
-</PRE><P>See Sections <A HREF="#sec:FASTQ-slicing-off-primer">18.1.7</A>
-and <A HREF="#sec:FASTQ-slicing-off-adaptor">18.1.8</A> for some FASTQ examples where the
-per-letter annotations (the read quality scores) are also sliced.</P><!--TOC section Adding SeqRecord objects-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc44">4.7</A>  Adding SeqRecord objects</H2><!--SEC END --><P>
-<A NAME="sec:SeqRecord-addition"></A></P><P>You can add <CODE>SeqRecord</CODE> objects together, giving a new <CODE>SeqRecord</CODE>.
-What is important here is that any common
-per-letter annotations are also added, all the features are preserved (with their
-locations adjusted), and any other common annotation is also kept (like the id, name
-and description).</P><P>For an example with per-letter annotation, we’ll use the first record in a
-FASTQ file. Chapter <A HREF="#chapter:Bio.SeqIO">5</A> will explain the <CODE>SeqIO</CODE> functions:</P><PRE CLASS="verbatim">>>> from Bio import SeqIO
->>> record = next(SeqIO.parse("example.fastq", "fastq"))
->>> len(record)
-25
->>> print(record.seq)
-CCCTTCTTGTCTTCAGCGTTTCTCC
-</PRE><PRE CLASS="verbatim">>>> print(record.letter_annotations["phred_quality"])
-[26, 26, 18, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 22, 26, 26, 26, 26,
-26, 26, 26, 23, 23]
-</PRE><P>Let’s suppose this was Roche 454 data, and that from other information
-you think the <TT>TTT</TT> should be only <TT>TT</TT>. We can make a new edited
-record by first slicing the <CODE>SeqRecord</CODE> before and after the “extra”
-third <TT>T</TT>:</P><PRE CLASS="verbatim">>>> left = record[:20]
->>> print(left.seq)
-CCCTTCTTGTCTTCAGCGTT
->>> print(left.letter_annotations["phred_quality"])
-[26, 26, 18, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 22, 26, 26, 26, 26]
->>> right = record[21:]
->>> print(right.seq)
-CTCC
->>> print(right.letter_annotations["phred_quality"])
-[26, 26, 23, 23]
-</PRE><P>Now add the two parts together:</P><PRE CLASS="verbatim">>>> edited = left + right
->>> len(edited)
-24
->>> print(edited.seq)
-CCCTTCTTGTCTTCAGCGTTCTCC
-</PRE><PRE CLASS="verbatim">>>> print(edited.letter_annotations["phred_quality"])
-[26, 26, 18, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 22, 26, 26, 26, 26,
-26, 26, 23, 23]
-</PRE><P>Easy and intuitive? We hope so! You can make this shorter with just:</P><PRE CLASS="verbatim">>>> edited = record[:20] + record[21:]
-</PRE><P>Now, for an example with features, we’ll use a GenBank file.
-Suppose you have a circular genome:</P><PRE CLASS="verbatim">>>> from Bio import SeqIO
->>> record = SeqIO.read("NC_005816.gb", "genbank")
-</PRE><PRE CLASS="verbatim">>>> record
-SeqRecord(seq=Seq('TGTAACGAACGGTGCAATAGTGATCCACACCCAACGCCTGAAATCAGATCCAGG...CTG',
-IUPACAmbiguousDNA()), id='NC_005816.1', name='NC_005816',
-description='Yersinia pestis biovar Microtus str. 91001 plasmid pPCP1, complete sequence.',
-dbxrefs=['Project:10638'])
-</PRE><PRE CLASS="verbatim">>>> len(record)
-9609
->>> len(record.features)
-41
->>> record.dbxrefs
-['Project:58037']
-</PRE><PRE CLASS="verbatim">>>> record.annotations.keys()
-['comment', 'sequence_version', 'source', 'taxonomy', 'keywords', 'references',
-'accessions', 'data_file_division', 'date', 'organism', 'gi']
-</PRE><P>You can shift the origin like this:</P><PRE CLASS="verbatim">>>> shifted = record[2000:] + record[:2000]
-</PRE><PRE CLASS="verbatim">>>> shifted
-SeqRecord(seq=Seq('GATACGCAGTCATATTTTTTACACAATTCTCTAATCCCGACAAGGTCGTAGGTC...GGA',
-IUPACAmbiguousDNA()), id='NC_005816.1', name='NC_005816',
-description='Yersinia pestis biovar Microtus str. 91001 plasmid pPCP1, complete sequence.',
-dbxrefs=[])
-</PRE><PRE CLASS="verbatim">>>> len(shifted)
-9609
-</PRE><P>Note that this isn’t perfect in that some annotation like the database cross references
-and one of the features (the source feature) have been lost:</P><PRE CLASS="verbatim">>>> len(shifted.features)
-40
->>> shifted.dbxrefs
-[]
->>> shifted.annotations.keys()
-[]
-</PRE><P>This is because the <CODE>SeqRecord</CODE> slicing step is cautious in what annotation
-it preserves (erroneously propagating annotation can cause major problems). If
-you want to keep the database cross references or the annotations dictionary,
-this must be done explicitly:</P><PRE CLASS="verbatim">>>> shifted.dbxrefs = record.dbxrefs[:]
->>> shifted.annotations = record.annotations.copy()
->>> shifted.dbxrefs
-['Project:10638']
->>> shifted.annotations.keys()
-['comment', 'sequence_version', 'source', 'taxonomy', 'keywords', 'references',
-'accessions', 'data_file_division', 'date', 'organism', 'gi']
-</PRE><P>Also note that in an example like this, you should probably change the record
-identifiers since the NCBI references refer to the <EM>original</EM> unmodified
-sequence.</P><!--TOC section Reverse-complementing SeqRecord objects-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc45">4.8</A>  Reverse-complementing SeqRecord objects</H2><!--SEC END --><P>
-<A NAME="sec:SeqRecord-reverse-complement"></A></P><P>One of the new features in Biopython 1.57 was the <CODE>SeqRecord</CODE> object’s
-<CODE>reverse_complement</CODE> method. This tries to balance easy of use with worries
-about what to do with the annotation in the reverse complemented record.</P><P>For the sequence, this uses the Seq object’s reverse complement method. Any
-features are transferred with the location and strand recalculated. Likewise
-any per-letter-annotation is also copied but reversed (which makes sense for
-typical examples like quality scores). However, transfer of most annotation
-is problematical.</P><P>For instance, if the record ID was an accession, that accession should not really
-apply to the reverse complemented sequence, and transferring the identifier by
-default could easily cause subtle data corruption in downstream analysis.
-Therefore by default, the <CODE>SeqRecord</CODE>’s id, name, description, annotations
-and database cross references are all <EM>not</EM> transferred by default.</P><P>The <CODE>SeqRecord</CODE> object’s <CODE>reverse_complement</CODE> method takes a number
-of optional arguments corresponding to properties of the record. Setting these
-arguments to <CODE>True</CODE> means copy the old values, while <CODE>False</CODE> means
-drop the old values and use the default value. You can alternatively provide
-the new desired value instead.</P><P>Consider this example record:</P><PRE CLASS="verbatim">>>> from Bio import SeqIO
->>> record = SeqIO.read("NC_005816.gb", "genbank")
->>> print("%s %i %i %i %i" % (record.id, len(record), len(record.features), len(record.dbxrefs), len(record.annotations)))
-NC_005816.1 9609 41 1 11
-</PRE><P>Here we take the reverse complement and specify a new identifier – but notice
-how most of the annotation is dropped (but not the features):</P><PRE CLASS="verbatim">>>> rc = record.reverse_complement(id="TESTING")
->>> print("%s %i %i %i %i" % (rc.id, len(rc), len(rc.features), len(rc.dbxrefs), len(rc.annotations)))
-TESTING 9609 41 0 0
-</PRE><!--TOC chapter Sequence Input/Output-->
-<H1 CLASS="chapter"><!--SEC ANCHOR --><A NAME="htoc46">Chapter 5</A>  Sequence Input/Output</H1><!--SEC END --><P>
-<A NAME="chapter:Bio.SeqIO"></A></P><P>In this chapter we’ll discuss in more detail the <CODE>Bio.SeqIO</CODE> module, which was briefly introduced in Chapter <A HREF="#chapter:quick-start">2</A> and also used in Chapter <A HREF="#chapter:SeqRecord">4</A>. This aims to provide a simple interface for working with assorted sequence file formats in a uniform way.
-See also the <CODE>Bio.SeqIO</CODE> wiki page (<A HREF="http://biopython.org/wiki/SeqIO"><TT>http://biopython.org/wiki/SeqIO</TT></A>), and the built in documentation (also <A HREF="http://biopython.org/DIST/docs/api/Bio.SeqIO-module.html">online</A>):</P><PRE CLASS="verbatim">>>> from Bio import SeqIO
->>> help(SeqIO)
-...
-</PRE><P>The “catch” is that you have to work with <CODE>SeqRecord</CODE> objects (see Chapter <A HREF="#chapter:SeqRecord">4</A>), which contain a <CODE>Seq</CODE> object (see Chapter <A HREF="#chapter:Bio.Seq">3</A>) plus annotation like an identifier and description.</P><!--TOC section Parsing or Reading Sequences-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc47">5.1</A>  Parsing or Reading Sequences</H2><!--SEC END --><P>
-<A NAME="sec:Bio.SeqIO-input"></A></P><P>The workhorse function <CODE>Bio.SeqIO.parse()</CODE> is used to read in sequence data as SeqRecord objects. This function expects two arguments:</P><OL CLASS="enumerate" type=1><LI CLASS="li-enumerate">
-The first argument is a <I>handle</I> to read the data from, or a filename. A handle is typically a file opened for reading, but could be the output from a command line program, or data downloaded from the internet (see Section <A HREF="#sec:SeqIO_Online">5.3</A>). See Section <A HREF="#sec:appendix-handles">22.1</A> for more about handles.
-</LI><LI CLASS="li-enumerate">The second argument is a lower case string specifying sequence format – we don’t try and guess the file format for you! See <A HREF="http://biopython.org/wiki/SeqIO"><TT>http://biopython.org/wiki/SeqIO</TT></A> for a full listing of supported formats.
-</LI></OL><P>There is an optional argument <CODE>alphabet</CODE> to specify the alphabet to be used. This is useful for file formats like FASTA where otherwise <CODE>Bio.SeqIO</CODE> will default to a generic alphabet.</P><P>The <CODE>Bio.SeqIO.parse()</CODE> function returns an <I>iterator</I> which gives <CODE>SeqRecord</CODE> objects. Iterators are typically used in a for loop as shown below.</P><P>Sometimes you’ll find yourself dealing with files which contain only a single re [...]
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc48">5.1.1</A>  Reading Sequence Files</H3><!--SEC END --><P>In general <CODE>Bio.SeqIO.parse()</CODE> is used to read in sequence files as <CODE>SeqRecord</CODE> objects, and is typically used with a for loop like this:</P><PRE CLASS="verbatim">from Bio import SeqIO
-for seq_record in SeqIO.parse("ls_orchid.fasta", "fasta"):
- print(seq_record.id)
- print(repr(seq_record.seq))
- print(len(seq_record))
-</PRE><P>The above example is repeated from the introduction in Section <A HREF="#sec:sequence-parsing">2.4</A>, and will load the orchid DNA sequences in the FASTA format file <A HREF="http://biopython.org/DIST/docs/tutorial/examples/ls_orchid.fasta">ls_orchid.fasta</A>. If instead you wanted to load a GenBank format file like <A HREF="http://biopython.org/DIST/docs/tutorial/examples/ls_orchid.gbk">ls_orchid.gbk</A> then all you need to do is change the filename and the format stri [...]
-for seq_record in SeqIO.parse("ls_orchid.gbk", "genbank"):
- print(seq_record.id)
- print(seq_record.seq)
- print(len(seq_record))
-</PRE><P>Similarly, if you wanted to read in a file in another file format, then assuming <CODE>Bio.SeqIO.parse()</CODE> supports it you would just need to change the format string as appropriate, for example “swiss” for SwissProt files or “embl” for EMBL text files. There is a full listing on the wiki page (<A HREF="http://biopython.org/wiki/SeqIO"><TT>http://biopython.org/wiki/SeqIO</TT></A>) and in the built in documentation (also <A HREF="http://biopython. [...]
-a generator expression). For example, if all you wanted to extract from the file was
-a list of the record identifiers we can easily do this with the following list comprehension:</P><PRE CLASS="verbatim">>>> from Bio import SeqIO
->>> identifiers = [seq_record.id for seq_record in SeqIO.parse("ls_orchid.gbk", "genbank")]
->>> identifiers
-['Z78533.1', 'Z78532.1', 'Z78531.1', 'Z78530.1', 'Z78529.1', 'Z78527.1', ..., 'Z78439.1']
-</PRE><P>There are more examples using <CODE>SeqIO.parse()</CODE> in a list
-comprehension like this in Section <A HREF="#seq:sequence-parsing-plus-pylab">18.2</A>
-(e.g. for plotting sequence lengths or GC%).</P><!--TOC subsection Iterating over the records in a sequence file-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc49">5.1.2</A>  Iterating over the records in a sequence file</H3><!--SEC END --><P>In the above examples, we have usually used a for loop to iterate over all the records one by one. You can use the for loop with all sorts of Python objects (including lists, tuples and strings) which support the iteration interface.</P><P>The object returned by <CODE>Bio.SeqIO</CODE> is actually an iterator which returns <CODE>SeqRecord</CODE [...]
-record_iterator = SeqIO.parse("ls_orchid.fasta", "fasta")
-
-first_record = next(record_iterator)
-print(first_record.id)
-print(first_record.description)
-
-second_record = next(record_iterator)
-print(second_record.id)
-print(second_record.description)
-</PRE><P>Note that if you try to use <CODE>next()</CODE> and there are no more results, you’ll get the special <CODE>StopIteration</CODE> exception.</P><P>One special case to consider is when your sequence files have multiple records, but you only want the first one. In this situation the following code is very concise:</P><PRE CLASS="verbatim">from Bio import SeqIO
-first_record = next(SeqIO.parse("ls_orchid.gbk", "genbank"))
-</PRE><P>A word of warning here – using the <CODE>next()</CODE> function like this will silently ignore any additional records in the file.
-If your files have <I>one and only one</I> record, like some of the online examples later in this chapter, or a GenBank file for a single chromosome, then use the new <CODE>Bio.SeqIO.read()</CODE> function instead.
-This will check there are no extra unexpected records present.</P><!--TOC subsection Getting a list of the records in a sequence file-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc50">5.1.3</A>  Getting a list of the records in a sequence file</H3><!--SEC END --><P>In the previous section we talked about the fact that <CODE>Bio.SeqIO.parse()</CODE> gives you a <CODE>SeqRecord</CODE> iterator, and that you get the records one by one. Very often you need to be able to access the records in any order. The Python <CODE>list</CODE> data type is perfect for this, and we can turn the record iterator into a l [...]
-records = list(SeqIO.parse("ls_orchid.gbk", "genbank"))
-
-print("Found %i records" % len(records))
-
-print("The last record")
-last_record = records[-1] #using Python's list tricks
-print(last_record.id)
-print(repr(last_record.seq))
-print(len(last_record))
-
-print("The first record")
-first_record = records[0] #remember, Python counts from zero
-print(first_record.id)
-print(repr(first_record.seq))
-print(len(first_record))
-</PRE><P>Giving:</P><PRE CLASS="verbatim">Found 94 records
-The last record
-Z78439.1
-Seq('CATTGTTGAGATCACATAATAATTGATCGAGTTAATCTGGAGGATCTGTTTACT...GCC', IUPACAmbiguousDNA())
-592
-The first record
-Z78533.1
-Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGATGAGACCGTGG...CGC', IUPACAmbiguousDNA())
-740
-</PRE><P>You can of course still use a for loop with a list of <CODE>SeqRecord</CODE> objects. Using a list is much more flexible than an iterator (for example, you can determine the number of records from the length of the list), but does need more memory because it will hold all the records in memory at once.</P><!--TOC subsection Extracting data-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc51">5.1.4</A>  Extracting data</H3><!--SEC END --><P>The <CODE>SeqRecord</CODE> object and its annotation structures are described more fully in
-Chapter <A HREF="#chapter:SeqRecord">4</A>. As an example of how annotations are stored, we’ll look at the output from parsing the first record in the GenBank file <A HREF="http://biopython.org/DIST/docs/tutorial/examples/ls_orchid.gbk">ls_orchid.gbk</A>.</P><PRE CLASS="verbatim">from Bio import SeqIO
-record_iterator = SeqIO.parse("ls_orchid.gbk", "genbank")
-first_record = next(record_iterator)
-print(first_record)
-</PRE><P>That should give something like this:</P><PRE CLASS="verbatim">ID: Z78533.1
-Name: Z78533
-Description: C.irapeanum 5.8S rRNA gene and ITS1 and ITS2 DNA.
-Number of features: 5
-/sequence_version=1
-/source=Cypripedium irapeanum
-/taxonomy=['Eukaryota', 'Viridiplantae', 'Streptophyta', ..., 'Cypripedium']
-/keywords=['5.8S ribosomal RNA', '5.8S rRNA gene', ..., 'ITS1', 'ITS2']
-/references=[...]
-/accessions=['Z78533']
-/data_file_division=PLN
-/date=30-NOV-2006
-/organism=Cypripedium irapeanum
-/gi=2765658
-Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGATGAGACCGTGG...CGC', IUPACAmbiguousDNA())
-</PRE><P>This gives a human readable summary of most of the annotation data for the <CODE>SeqRecord</CODE>.
-For this example we’re going to use the <CODE>.annotations</CODE> attribute which is just a Python dictionary.
-The contents of this annotations dictionary were shown when we printed the record above.
-You can also print them out directly:
-</P><PRE CLASS="verbatim">print(first_record.annotations)
-</PRE><P>Like any Python dictionary, you can easily get a list of the keys:
-</P><PRE CLASS="verbatim">print(first_record.annotations.keys())
-</PRE><P>or values:
-</P><PRE CLASS="verbatim">print(first_record.annotations.values())
-</PRE><P>In general, the annotation values are strings, or lists of strings. One special case is any references in the file get stored as reference objects.</P><P>Suppose you wanted to extract a list of the species from the <A HREF="http://biopython.org/DIST/docs/tutorial/examples/ls_orchid.gbk">ls_orchid.gbk</A> GenBank file. The information we want, <EM>Cypripedium irapeanum</EM>, is held in the annotations dictionary under ‘source’ and ‘organism’, which we [...]
-Cypripedium irapeanum
-</PRE><P>or:</P><PRE CLASS="verbatim">>>> print(first_record.annotations["organism"])
-Cypripedium irapeanum
-</PRE><P>In general, ‘organism’ is used for the scientific name (in Latin, e.g. <I>Arabidopsis thaliana</I>),
-while ‘source’ will often be the common name (e.g. thale cress). In this example, as is often the case,
-the two fields are identical.</P><P>Now let’s go through all the records, building up a list of the species each orchid sequence is from:</P><PRE CLASS="verbatim">from Bio import SeqIO
-all_species = []
-for seq_record in SeqIO.parse("ls_orchid.gbk", "genbank"):
- all_species.append(seq_record.annotations["organism"])
-print(all_species)
-</PRE><P>Another way of writing this code is to use a list comprehension:</P><PRE CLASS="verbatim">from Bio import SeqIO
-all_species = [seq_record.annotations["organism"] for seq_record in \
- SeqIO.parse("ls_orchid.gbk", "genbank")]
-print(all_species)
-</PRE><P>In either case, the result is:</P><PRE CLASS="verbatim">['Cypripedium irapeanum', 'Cypripedium californicum', ..., 'Paphiopedilum barbatum']
-</PRE><P>Great. That was pretty easy because GenBank files are annotated in a standardised way.</P><P>Now, let’s suppose you wanted to extract a list of the species from a FASTA file, rather than the GenBank file. The bad news is you will have to write some code to extract the data you want from the record’s description line - if the information is in the file in the first place! Our example FASTA format file <A HREF="http://biopython.org/DIST/docs/tutorial/examples/ls_orch [...]
-CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGATGAGACCGTGGAATAAACGATCGAGTG
-AATCCGGAGGACCGGTGTACTCAGCTCACCGGGGGCATTGCTCCCGTGGTGACCCTGATTTGTTGTTGGG
-...
-</PRE><P>You can check by hand, but for every record the species name is in the description line as the second word. This means if we break up each record’s <CODE>.description</CODE> at the spaces, then the species is there as field number one (field zero is the record identifier). That means we can do this:</P><PRE CLASS="verbatim">from Bio import SeqIO
-all_species = []
-for seq_record in SeqIO.parse("ls_orchid.fasta", "fasta"):
- all_species.append(seq_record.description.split()[1])
-print(all_species)
-</PRE><P>This gives:</P><PRE CLASS="verbatim">['C.irapeanum', 'C.californicum', 'C.fasciculatum', 'C.margaritaceum', ..., 'P.barbatum']
-</PRE><P>The concise alternative using list comprehensions would be:</P><PRE CLASS="verbatim">from Bio import SeqIO
-all_species == [seq_record.description.split()[1] for seq_record in \
- SeqIO.parse("ls_orchid.fasta", "fasta")]
-print(all_species)
-</PRE><P>In general, extracting information from the FASTA description line is not very nice.
-If you can get your sequences in a well annotated file format like GenBank or EMBL,
-then this sort of annotation information is much easier to deal with.</P><!--TOC section Parsing sequences from compressed files-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc52">5.2</A>  Parsing sequences from compressed files</H2><!--SEC END --><P>
-<A NAME="sec:SeqIO_compressed"></A>
-In the previous section, we looked at parsing sequence data from a file.
-Instead of using a filename, you can give <CODE>Bio.SeqIO</CODE> a handle
-(see Section <A HREF="#sec:appendix-handles">22.1</A>), and in this section
-we’ll use handles to parse sequence from compressed files.</P><P>As you’ll have seen above, we can use <CODE>Bio.SeqIO.read()</CODE> or
-<CODE>Bio.SeqIO.parse()</CODE> with a filename - for instance this quick
-example calculates the total length of the sequences in a multiple
-record GenBank file using a generator expression:</P><PRE CLASS="verbatim">>>> from Bio import SeqIO
->>> print(sum(len(r) for r in SeqIO.parse("ls_orchid.gbk", "gb")))
-67518
-</PRE><P>Here we use a file handle instead, using the <CODE>with</CODE> statement
-to close the handle automatically:</P><PRE CLASS="verbatim">>>> from Bio import SeqIO
->>> with open("ls_orchid.gbk") as handle:
-... print(sum(len(r) for r in SeqIO.parse(handle, "gb")))
-67518
-</PRE><P>Or, the old fashioned way where you manually close the handle:</P><PRE CLASS="verbatim">>>> from Bio import SeqIO
->>> handle = open("ls_orchid.gbk")
->>> print(sum(len(r) for r in SeqIO.parse(handle, "gb")))
-67518
->>> handle.close()
-</PRE><P>Now, suppose we have a gzip compressed file instead? These are very
-commonly used on Linux. We can use Python’s <CODE>gzip</CODE> module to open
-the compressed file for reading - which gives us a handle object:</P><PRE CLASS="verbatim">>>> import gzip
->>> from Bio import SeqIO
->>> handle = gzip.open("ls_orchid.gbk.gz", "r")
->>> print(sum(len(r) for r in SeqIO.parse(handle, "gb")))
-67518
->>> handle.close()
-</PRE><P>Similarly if we had a bzip2 compressed file (sadly the function name isn’t
-quite as consistent):</P><PRE CLASS="verbatim">>>> import bz2
->>> from Bio import SeqIO
->>> handle = bz2.BZ2File("ls_orchid.gbk.bz2", "r")
->>> print(sum(len(r) for r in SeqIO.parse(handle, "gb")))
-67518
->>> handle.close()
-</PRE><P>If you are using Python 2.7 or later, the <CODE>with</CODE>-version works for
-gzip and bz2 as well. Unfortunately this is broken on older versions of
-Python (<A HREF="http://bugs.python.org/issue3860">Issue 3860</A>) and you’d
-get an <CODE>AttributeError</CODE> about <CODE>__exit__</CODE> being missing.</P><P>There is a gzip (GNU Zip) variant called BGZF (Blocked GNU Zip Format),
-which can be treated like an ordinary gzip file for reading, but has
-advantages for random access later which we’ll talk about later in
-Section <A HREF="#sec:SeqIO-index-bgzf">5.4.4</A>.</P><!--TOC section Parsing sequences from the net-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc53">5.3</A>  Parsing sequences from the net</H2><!--SEC END --><P>
-<A NAME="sec:SeqIO_Online"></A>
-In the previous sections, we looked at parsing sequence data from a file
-(using a filename or handle), and from compressed files (using a handle).
-Here we’ll use <CODE>Bio.SeqIO</CODE> with another type of handle, a network
-connection, to download and parse sequences from the internet.</P><P>Note that just because you <EM>can</EM> download sequence data and parse it into
-a <CODE>SeqRecord</CODE> object in one go doesn’t mean this is a good idea.
-In general, you should probably download sequences <EM>once</EM> and save them to
-a file for reuse.</P><!--TOC subsection Parsing GenBank records from the net-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc54">5.3.1</A>  Parsing GenBank records from the net</H3><!--SEC END --><P>
-<A NAME="sec:SeqIO_GenBank_Online"></A>
-Section <A HREF="#sec:efetch">9.6</A> talks about the Entrez EFetch interface in more detail,
-but for now let’s just connect to the NCBI and get a few <I>Opuntia</I> (prickly-pear)
-sequences from GenBank using their GI numbers.</P><P>First of all, let’s fetch just one record. If you don’t care about the
-annotations and features downloading a FASTA file is a good choice as these
-are compact. Now remember, when you expect the handle to contain one and
-only one record, use the <CODE>Bio.SeqIO.read()</CODE> function:</P><PRE CLASS="verbatim">from Bio import Entrez
-from Bio import SeqIO
-Entrez.email = "A.N.Other at example.com"
-handle = Entrez.efetch(db="nucleotide", rettype="fasta", retmode="text", id="6273291")
-seq_record = SeqIO.read(handle, "fasta")
-handle.close()
-print("%s with %i features" % (seq_record.id, len(seq_record.features)))
-</PRE><P>Expected output:</P><PRE CLASS="verbatim">gi|6273291|gb|AF191665.1|AF191665 with 0 features
-</PRE><P>The NCBI will also let you ask for the file in other formats, in particular as
-a GenBank file. Until Easter 2009, the Entrez EFetch API let you use “genbank”
-as the return type, however the NCBI now insist on using the official
-return types of “gb” (or “gp” for proteins) as described on
-<A HREF="http://www.ncbi.nlm.nih.gov/entrez/query/static/efetchseq_help.html">EFetch for Sequence and other Molecular Biology Databases</A>.
-As a result, in Biopython 1.50 onwards, we support “gb” as an
-alias for “genbank” in <CODE>Bio.SeqIO</CODE>.</P><PRE CLASS="verbatim">from Bio import Entrez
-from Bio import SeqIO
-Entrez.email = "A.N.Other at example.com"
-handle = Entrez.efetch(db="nucleotide", rettype="gb", retmode="text", id="6273291")
-seq_record = SeqIO.read(handle, "gb") #using "gb" as an alias for "genbank"
-handle.close()
-print("%s with %i features" % (seq_record.id, len(seq_record.features)))
-</PRE><P>The expected output of this example is:</P><PRE CLASS="verbatim">AF191665.1 with 3 features
-</PRE><P>Notice this time we have three features.</P><P>Now let’s fetch several records. This time the handle contains multiple records,
-so we must use the <CODE>Bio.SeqIO.parse()</CODE> function:</P><PRE CLASS="verbatim">from Bio import Entrez
-from Bio import SeqIO
-Entrez.email = "A.N.Other at example.com"
-handle = Entrez.efetch(db="nucleotide", rettype="gb", retmode="text", \
- id="6273291,6273290,6273289")
-for seq_record in SeqIO.parse(handle, "gb"):
- print seq_record.id, seq_record.description[:50] + "..."
- print "Sequence length %i," % len(seq_record),
- print "%i features," % len(seq_record.features),
- print "from: %s" % seq_record.annotations["source"]
-handle.close()
-</PRE><P>That should give the following output:</P><PRE CLASS="verbatim">AF191665.1 Opuntia marenae rpl16 gene; chloroplast gene for c...
-Sequence length 902, 3 features, from: chloroplast Opuntia marenae
-AF191664.1 Opuntia clavata rpl16 gene; chloroplast gene for c...
-Sequence length 899, 3 features, from: chloroplast Grusonia clavata
-AF191663.1 Opuntia bradtiana rpl16 gene; chloroplast gene for...
-Sequence length 899, 3 features, from: chloroplast Opuntia bradtianaa
-</PRE><P>See Chapter <A HREF="#chapter:entrez">9</A> for more about the <CODE>Bio.Entrez</CODE> module, and make sure to read about the NCBI guidelines for using Entrez (Section <A HREF="#sec:entrez-guidelines">9.1</A>).</P><!--TOC subsection Parsing SwissProt sequences from the net-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc55">5.3.2</A>  Parsing SwissProt sequences from the net</H3><!--SEC END --><P>
-<A NAME="sec:SeqIO_ExPASy_and_SwissProt"></A>
-Now let’s use a handle to download a SwissProt file from ExPASy,
-something covered in more depth in Chapter <A HREF="#chapter:swiss_prot">10</A>.
-As mentioned above, when you expect the handle to contain one and only one record,
-use the <CODE>Bio.SeqIO.read()</CODE> function:</P><PRE CLASS="verbatim">from Bio import ExPASy
-from Bio import SeqIO
-handle = ExPASy.get_sprot_raw("O23729")
-seq_record = SeqIO.read(handle, "swiss")
-handle.close()
-print(seq_record.id)
-print(seq_record.name)
-print(seq_record.description)
-print(repr(seq_record.seq))
-print("Length %i" % len(seq_record))
-print(seq_record.annotations["keywords"])
-</PRE><P>Assuming your network connection is OK, you should get back:</P><PRE CLASS="verbatim">O23729
-CHS3_BROFI
-RecName: Full=Chalcone synthase 3; EC=2.3.1.74; AltName: Full=Naringenin-chalcone synthase 3;
-Seq('MAPAMEEIRQAQRAEGPAAVLAIGTSTPPNALYQADYPDYYFRITKSEHLTELK...GAE', ProteinAlphabet())
-Length 394
-['Acyltransferase', 'Flavonoid biosynthesis', 'Transferase']
-</PRE><!--TOC section Sequence files as Dictionaries-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc56">5.4</A>  Sequence files as Dictionaries</H2><!--SEC END --><P>We’re now going to introduce three related functions in the <CODE>Bio.SeqIO</CODE>
-module which allow dictionary like random access to a multi-sequence file.
-There is a trade off here between flexibility and memory usage. In summary:
-</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-<CODE>Bio.SeqIO.to_dict()</CODE> is the most flexible but also the most
-memory demanding option (see Section <A HREF="#SeqIO:to_dict">5.4.1</A>). This is basically
-a helper function to build a normal Python <CODE>dictionary</CODE> with each entry
-held as a <CODE>SeqRecord</CODE> object in memory, allowing you to modify the
-records.
-</LI><LI CLASS="li-itemize"><CODE>Bio.SeqIO.index()</CODE> is a useful middle ground, acting like a
-read only dictionary and parsing sequences into <CODE>SeqRecord</CODE> objects
-on demand (see Section <A HREF="#sec:SeqIO-index">5.4.2</A>).
-</LI><LI CLASS="li-itemize"><CODE>Bio.SeqIO.index_db()</CODE> also acts like a read only dictionary
-but stores the identifiers and file offsets in a file on disk (as an
-SQLite3 database), meaning it has very low memory requirements (see
-Section <A HREF="#sec:SeqIO-index-db">5.4.3</A>), but will be a little bit slower.
-</LI></UL><P>
-See the discussion for an broad overview
-(Section <A HREF="#sec:SeqIO-indexing-discussion">5.4.5</A>).</P><!--TOC subsection Sequence files as Dictionaries – In memory-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc57">5.4.1</A>  Sequence files as Dictionaries – In memory</H3><!--SEC END --><P>
-<A NAME="SeqIO:to_dict"></A></P><P>The next thing that we’ll do with our ubiquitous orchid files is to show how
-to index them and access them like a database using the Python <CODE>dictionary</CODE>
-data type (like a hash in Perl). This is very useful for moderately large files
-where you only need to access certain elements of the file, and makes for a nice
-quick ’n dirty database. For dealing with larger files where memory becomes a
-problem, see Section <A HREF="#sec:SeqIO-index">5.4.2</A> below.</P><P>You can use the function <CODE>Bio.SeqIO.to_dict()</CODE> to make a SeqRecord dictionary
-(in memory). By default this will use each record’s identifier (i.e. the <CODE>.id</CODE>
-attribute) as the key. Let’s try this using our GenBank file:</P><PRE CLASS="verbatim">>>> from Bio import SeqIO
->>> orchid_dict = SeqIO.to_dict(SeqIO.parse("ls_orchid.gbk", "genbank"))
-</PRE><P>There is just one required argument for <CODE>Bio.SeqIO.to_dict()</CODE>, a list or
-generator giving <CODE>SeqRecord</CODE> objects. Here we have just used the output
-from the <CODE>SeqIO.parse</CODE> function. As the name suggests, this returns a
-Python dictionary.</P><P>Since this variable <CODE>orchid_dict</CODE> is an ordinary Python dictionary,
-we can look at all of the keys we have available:</P><PRE CLASS="verbatim">>>> len(orchid_dict)
-94
-</PRE><PRE CLASS="verbatim">>>> list(orchid_dict.keys())
-['Z78484.1', 'Z78464.1', 'Z78455.1', 'Z78442.1', 'Z78532.1', 'Z78453.1', ..., 'Z78471.1']
-</PRE><P>You can leave out the “list(...)“ bit if you are still using Python 2.
-Under Python 3 the dictionary methods like “.keys()“ and “.values()“
-are iterators rather than lists.</P><P>If you really want to, you can even look at all the records at once:
-</P><PRE CLASS="verbatim">>>> list(orchid_dict.values()) #lots of output!
-...
-</PRE><P>We can access a single <CODE>SeqRecord</CODE> object via the keys and manipulate the object as normal:</P><PRE CLASS="verbatim">>>> seq_record = orchid_dict["Z78475.1"]
->>> print(seq_record.description)
-P.supardii 5.8S rRNA gene and ITS1 and ITS2 DNA.
->>> print(repr(seq_record.seq))
-Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCACAT...GGT', IUPACAmbiguousDNA())
-</PRE><P>So, it is very easy to create an in memory “database” of our GenBank records. Next we’ll try this for the FASTA file instead.</P><P>Note that those of you with prior Python experience should all be able to construct a dictionary like this “by hand”. However, typical dictionary construction methods will not deal with the case of repeated keys very nicely. Using the <CODE>Bio.SeqIO.to_dict()</CODE> will explicitly check for duplicate keys, and ra [...]
-<H4 CLASS="subsubsection"><!--SEC ANCHOR -->5.4.1.1  Specifying the dictionary keys</H4><!--SEC END --><P>
-<A NAME="seq:seqio-todict-functionkey"></A></P><P>Using the same code as above, but for the FASTA file instead:</P><PRE CLASS="verbatim">from Bio import SeqIO
-orchid_dict = SeqIO.to_dict(SeqIO.parse("ls_orchid.fasta", "fasta"))
-print(orchid_dict.keys())
-</PRE><P>This time the keys are:</P><PRE CLASS="verbatim">['gi|2765596|emb|Z78471.1|PDZ78471', 'gi|2765646|emb|Z78521.1|CCZ78521', ...
- ..., 'gi|2765613|emb|Z78488.1|PTZ78488', 'gi|2765583|emb|Z78458.1|PHZ78458']
-</PRE><P>You should recognise these strings from when we parsed the FASTA file earlier in Section <A HREF="#sec:fasta-parsing">2.4.1</A>. Suppose you would rather have something else as the keys - like the accession numbers. This brings us nicely to <CODE>SeqIO.to_dict()</CODE>’s optional argument <CODE>key_function</CODE>, which lets you define what to use as the dictionary key for your records.</P><P>First you must write your own function to return the key you want (as a st [...]
- """"Given a SeqRecord, return the accession number as a string.
-
- e.g. "gi|2765613|emb|Z78488.1|PTZ78488" -> "Z78488.1"
- """
- parts = record.id.split("|")
- assert len(parts) == 5 and parts[0] == "gi" and parts[2] == "emb"
- return parts[3]
-</PRE><P>Then we can give this function to the <CODE>SeqIO.to_dict()</CODE> function to use in building the dictionary:</P><PRE CLASS="verbatim">from Bio import SeqIO
-orchid_dict = SeqIO.to_dict(SeqIO.parse("ls_orchid.fasta", "fasta"), key_function=get_accession)
-print(orchid_dict.keys())
-</PRE><P>Finally, as desired, the new dictionary keys:</P><PRE CLASS="verbatim">>>> print(orchid_dict.keys())
-['Z78484.1', 'Z78464.1', 'Z78455.1', 'Z78442.1', 'Z78532.1', 'Z78453.1', ..., 'Z78471.1']
-</PRE><P>Not too complicated, I hope!</P><!--TOC subsubsection Indexing a dictionary using the SEGUID checksum-->
-<H4 CLASS="subsubsection"><!--SEC ANCHOR -->5.4.1.2  Indexing a dictionary using the SEGUID checksum</H4><!--SEC END --><P>To give another example of working with dictionaries of <CODE>SeqRecord</CODE> objects, we’ll use the SEGUID checksum function. This is a relatively recent checksum, and collisions should be very rare (i.e. two different sequences with the same checksum), an improvement on the CRC64 checksum.</P><P>Once again, working with the orchids GenBank file:</ [...]
-from Bio.SeqUtils.CheckSum import seguid
-for record in SeqIO.parse("ls_orchid.gbk", "genbank"):
- print(record.id, seguid(record.seq))
-</PRE><P>This should give:</P><PRE CLASS="verbatim">Z78533.1 JUEoWn6DPhgZ9nAyowsgtoD9TTo
-Z78532.1 MN/s0q9zDoCVEEc+k/IFwCNF2pY
-...
-Z78439.1 H+JfaShya/4yyAj7IbMqgNkxdxQ
-</PRE><P>Now, recall the <CODE>Bio.SeqIO.to_dict()</CODE> function’s <CODE>key_function</CODE> argument expects a function which turns a <CODE>SeqRecord</CODE> into a string. We can’t use the <CODE>seguid()</CODE> function directly because it expects to be given a <CODE>Seq</CODE> object (or a string). However, we can use Python’s <CODE>lambda</CODE> feature to create a “one off” function to give to <CODE>Bio.SeqIO.to_dict()</CODE> instead:</P><PRE CLAS [...]
->>> from Bio.SeqUtils.CheckSum import seguid
->>> seguid_dict = SeqIO.to_dict(SeqIO.parse("ls_orchid.gbk", "genbank"),
-... lambda rec : seguid(rec.seq))
->>> record = seguid_dict["MN/s0q9zDoCVEEc+k/IFwCNF2pY"]
->>> print(record.id)
-Z78532.1
->>> print(record.description)
-C.californicum 5.8S rRNA gene and ITS1 and ITS2 DNA.
-</PRE><P>That should have retrieved the record <TT>Z78532.1</TT>, the second entry in the file.</P><!--TOC subsection Sequence files as Dictionaries – Indexed files-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc58">5.4.2</A>  Sequence files as Dictionaries – Indexed files</H3><!--SEC END --><P>
-<A NAME="sec:SeqIO-index"></A></P><P>As the previous couple of examples tried to illustrate, using
-<CODE>Bio.SeqIO.to_dict()</CODE> is very flexible. However, because it holds
-everything in memory, the size of file you can work with is limited by your
-computer’s RAM. In general, this will only work on small to medium files.</P><P>For larger files you should consider
-<CODE>Bio.SeqIO.index()</CODE>, which works a little differently. Although
-it still returns a dictionary like object, this does <EM>not</EM> keep
-<EM>everything</EM> in memory. Instead, it just records where each record
-is within the file – when you ask for a particular record, it then parses
-it on demand.</P><P>As an example, let’s use the same GenBank file as before:</P><PRE CLASS="verbatim">>>> from Bio import SeqIO
->>> orchid_dict = SeqIO.index("ls_orchid.gbk", "genbank")
->>> len(orchid_dict)
-94
-</PRE><PRE CLASS="verbatim">>>> orchid_dict.keys()
-['Z78484.1', 'Z78464.1', 'Z78455.1', 'Z78442.1', 'Z78532.1', 'Z78453.1', ..., 'Z78471.1']
-</PRE><PRE CLASS="verbatim">>>> seq_record = orchid_dict["Z78475.1"]
->>> print(seq_record.description)
-P.supardii 5.8S rRNA gene and ITS1 and ITS2 DNA.
->>> seq_record.seq
-Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCACAT...GGT', IUPACAmbiguousDNA())
->>> orchid_dict.close()
-</PRE><P>Note that <CODE>Bio.SeqIO.index()</CODE> won’t take a handle,
-but only a filename. There are good reasons for this, but it is a little
-technical. The second argument is the file format (a lower case string as
-used in the other <CODE>Bio.SeqIO</CODE> functions). You can use many other
-simple file formats, including FASTA and FASTQ files (see the example in
-Section <A HREF="#sec:fastq-indexing">18.1.11</A>). However, alignment
-formats like PHYLIP or Clustal are not supported. Finally as an optional
-argument you can supply an alphabet, or a key function.</P><P>Here is the same example using the FASTA file - all we change is the
-filename and the format name:</P><PRE CLASS="verbatim">>>> from Bio import SeqIO
->>> orchid_dict = SeqIO.index("ls_orchid.fasta", "fasta")
->>> len(orchid_dict)
-94
->>> orchid_dict.keys()
-['gi|2765596|emb|Z78471.1|PDZ78471', 'gi|2765646|emb|Z78521.1|CCZ78521', ...
- ..., 'gi|2765613|emb|Z78488.1|PTZ78488', 'gi|2765583|emb|Z78458.1|PHZ78458']
-</PRE><!--TOC subsubsection Specifying the dictionary keys-->
-<H4 CLASS="subsubsection"><!--SEC ANCHOR -->5.4.2.1  Specifying the dictionary keys</H4><!--SEC END --><P>
-<A NAME="seq:seqio-index-functionkey"></A></P><P>Suppose you want to use the same keys as before? Much like with the
-<CODE>Bio.SeqIO.to_dict()</CODE> example in Section <A HREF="#seq:seqio-todict-functionkey">5.4.1.1</A>,
-you’ll need to write a tiny function to map from the FASTA identifier
-(as a string) to the key you want:</P><PRE CLASS="verbatim">def get_acc(identifier):
- """"Given a SeqRecord identifier string, return the accession number as a string.
-
- e.g. "gi|2765613|emb|Z78488.1|PTZ78488" -> "Z78488.1"
- """
- parts = identifier.split("|")
- assert len(parts) == 5 and parts[0] == "gi" and parts[2] == "emb"
- return parts[3]
-</PRE><P>Then we can give this function to the <CODE>Bio.SeqIO.index()</CODE>
-function to use in building the dictionary:</P><PRE CLASS="verbatim">>>> from Bio import SeqIO
->>> orchid_dict = SeqIO.index("ls_orchid.fasta", "fasta", key_function=get_acc)
->>> print(orchid_dict.keys())
-['Z78484.1', 'Z78464.1', 'Z78455.1', 'Z78442.1', 'Z78532.1', 'Z78453.1', ..., 'Z78471.1']
-</PRE><P>Easy when you know how?</P><!--TOC subsubsection Getting the raw data for a record-->
-<H4 CLASS="subsubsection"><!--SEC ANCHOR -->5.4.2.2  Getting the raw data for a record</H4><!--SEC END --><P>
-<A NAME="sec:seqio-index-getraw"></A></P><P>The dictionary-like object from <CODE>Bio.SeqIO.index()</CODE> gives you each
-entry as a <CODE>SeqRecord</CODE> object. However, it is sometimes useful to
-be able to get the original raw data straight from the file. For this
-use the <CODE>get_raw()</CODE> method which takes a
-single argument (the record identifier) and returns a string (extracted
-from the file without modification).</P><P>A motivating example is extracting a subset of a records from a large
-file where either <CODE>Bio.SeqIO.write()</CODE> does not (yet) support the
-output file format (e.g. the plain text SwissProt file format) or
-where you need to preserve the text exactly (e.g. GenBank or EMBL
-output from Biopython does not yet preserve every last bit of
-annotation).</P><P>Let’s suppose you have download the whole of UniProt in the plain
-text SwissPort file format from their FTP site
-(<A HREF="ftp://ftp.uniprot.org/pub/databases/uniprot/current_release/knowledgebase/complete/uniprot_sprot.dat.gz"><TT>ftp://ftp.uniprot.org/pub/databases/uniprot/current_release/knowledgebase/complete/uniprot_sprot.dat.gz</TT></A>)
-and uncompressed it as the file <CODE>uniprot_sprot.dat</CODE>, and you
-want to extract just a few records from it:</P><PRE CLASS="verbatim">>>> from Bio import SeqIO
->>> uniprot = SeqIO.index("uniprot_sprot.dat", "swiss")
->>> handle = open("selected.dat", "w")
->>> for acc in ["P33487", "P19801", "P13689", "Q8JZQ5", "Q9TRC7"]:
-... handle.write(uniprot.get_raw(acc))
->>> handle.close()
-</PRE><P>There is a longer example in Section <A HREF="#sec:SeqIO-sort">18.1.5</A> using the
-<CODE>SeqIO.index()</CODE> function to sort a large sequence file (without
-loading everything into memory at once).</P><!--TOC subsection Sequence files as Dictionaries – Database indexed files-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc59">5.4.3</A>  Sequence files as Dictionaries – Database indexed files</H3><!--SEC END --><P>
-<A NAME="sec:SeqIO-index-db"></A></P><P>Biopython 1.57 introduced an alternative, <CODE>Bio.SeqIO.index_db()</CODE>, which
-can work on even extremely large files since it stores the record information
-as a file on disk (using an SQLite3 database) rather than in memory. Also,
-you can index multiple files together (providing all the record identifiers
-are unique).</P><P>The <CODE>Bio.SeqIO.index()</CODE> function takes three required arguments:
-</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-Index filename, we suggest using something ending <TT>.idx</TT>.
-This index file is actually an SQLite3 database.
-</LI><LI CLASS="li-itemize">List of sequence filenames to index (or a single filename)
-</LI><LI CLASS="li-itemize">File format (lower case string as used in the rest of the
-<CODE>SeqIO</CODE> module).
-</LI></UL><P>As an example, consider the GenBank flat file releases from the NCBI FTP site,
-<A HREF="ftp://ftp.ncbi.nih.gov/genbank/"><TT>ftp://ftp.ncbi.nih.gov/genbank/</TT></A>, which are gzip compressed GenBank files.
-As of GenBank release 182, there are 16 files making up the viral sequences,
-<TT>gbvrl1.seq</TT>, …, <TT>gbvrl16.seq</TT>, containing in total almost
-one million records. You can index them like this:</P><PRE CLASS="verbatim">>>> from Bio import SeqIO
->>> files = ["gbvrl%i.seq" % (i+1) for i in range(16)]
->>> gb_vrl = SeqIO.index_db("gbvrl.idx", files, "genbank")
->>> print("%i sequences indexed" % len(gb_vrl))
-958086 sequences indexed
-</PRE><P>That takes about two minutes to run on my machine. If you rerun it then the
-index file (here <TT>gbvrl.idx</TT>) is reloaded in under a second. You can
-use the index as a read only Python dictionary - without having to worry
-about which file the sequence comes from, e.g.</P><PRE CLASS="verbatim">>>> print(gb_vrl["GQ333173.1"].description)
-HIV-1 isolate F12279A1 from Uganda gag protein (gag) gene, partial cds.
-</PRE><!--TOC subsubsection Getting the raw data for a record-->
-<H4 CLASS="subsubsection"><!--SEC ANCHOR -->5.4.3.1  Getting the raw data for a record</H4><!--SEC END --><P>Just as with the <CODE>Bio.SeqIO.index()</CODE> function discussed above in
-Section <A HREF="#sec:seqio-index-getraw">5.4.2.2</A>, the dictionary like object also lets you
-get at the raw text of each record:</P><PRE CLASS="verbatim">>>> print(gb_vrl.get_raw("GQ333173.1"))
-LOCUS GQ333173 459 bp DNA linear VRL 21-OCT-2009
-DEFINITION HIV-1 isolate F12279A1 from Uganda gag protein (gag) gene, partial
- cds.
-ACCESSION GQ333173
-...
-//
-</PRE><!--TOC subsection Indexing compressed files-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc60">5.4.4</A>  Indexing compressed files</H3><!--SEC END --><P>
-<A NAME="sec:SeqIO-index-bgzf"></A></P><P>Very often when you are indexing a sequence file it can be quite large – so
-you may want to compress it on disk. Unfortunately efficient random access
-is difficult with the more common file formats like gzip and bzip2. In this
-setting, BGZF (Blocked GNU Zip Format) can be very helpful. This is a variant
-of gzip (and can be decompressed using standard gzip tools) popularised by
-the BAM file format, <A HREF="http://samtools.sourceforge.net/">samtools</A>, and
-<A HREF="http://samtools.sourceforge.net/tabix.shtml">tabix</A>.</P><P>To create a BGZF compressed file you can use the command line tool <CODE>bgzip</CODE>
-which comes with samtools. In our examples we use a filename extension
-<CODE>*.bgz</CODE>, so they can be distinguished from normal gzipped files (named
-<CODE>*.gz</CODE>). You can also use the <CODE>Bio.bgzf</CODE> module to read and write
-BGZF files from within Python.</P><P>The <CODE>Bio.SeqIO.index()</CODE> and <CODE>Bio.SeqIO.index_db()</CODE> can both be
-used with BGZF compressed files. For example, if you started with an
-uncompressed GenBank file:</P><PRE CLASS="verbatim">>>> from Bio import SeqIO
->>> orchid_dict = SeqIO.index("ls_orchid.gbk", "genbank")
->>> len(orchid_dict)
-94
->>> orchid_dict.close()
-</PRE><P>You could compress this (while keeping the original file) at the command
-line using the following command – but don’t worry, the compressed file
-is already included with the other example files:</P><PRE CLASS="verbatim">$ bgzip -c ls_orchid.gbk > ls_orchid.gbk.bgz
-</PRE><P>You can use the compressed file in exactly the same way:</P><PRE CLASS="verbatim">>>> from Bio import SeqIO
->>> orchid_dict = SeqIO.index("ls_orchid.gbk.bgz", "genbank")
->>> len(orchid_dict)
-94
->>> orchid_dict.close()
-</PRE><P>or:</P><PRE CLASS="verbatim">>>> from Bio import SeqIO
->>> orchid_dict = SeqIO.index_db("ls_orchid.gbk.bgz.idx", "ls_orchid.gbk.bgz", "genbank")
->>> len(orchid_dict)
-94
->>> orchid_dict.close()
-</PRE><P>The <CODE>SeqIO</CODE> indexing automatically detects the BGZF compression. Note
-that you can’t use the same index file for the uncompressed and compressed files.</P><!--TOC subsection Discussion-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc61">5.4.5</A>  Discussion</H3><!--SEC END --><P>
-<A NAME="sec:SeqIO-indexing-discussion"></A></P><P>So, which of these methods should you use and why? It depends on what you are
-trying to do (and how much data you are dealing with). However, in general
-picking <CODE>Bio.SeqIO.index()</CODE> is a good starting point. If you are dealing
-with millions of records, multiple files, or repeated analyses, then look at
-<CODE>Bio.SeqIO.index_db()</CODE>.</P><P>Reasons to choose <CODE>Bio.SeqIO.to_dict()</CODE> over either
-<CODE>Bio.SeqIO.index()</CODE> or <CODE>Bio.SeqIO.index_db()</CODE> boil down to a need
-for flexibility despite its high memory needs. The advantage of storing the
-<CODE>SeqRecord</CODE> objects in memory is they can be changed, added to, or
-removed at will. In addition to the downside of high memory consumption,
-indexing can also take longer because all the records must be fully parsed.</P><P>Both <CODE>Bio.SeqIO.index()</CODE> and <CODE>Bio.SeqIO.index_db()</CODE> only parse
-records on demand. When indexing, they scan the file once looking for the
-start of each record and do as little work as possible to extract the
-identifier.</P><P>Reasons to choose <CODE>Bio.SeqIO.index()</CODE> over <CODE>Bio.SeqIO.index_db()</CODE>
-include:
-</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-Faster to build the index (more noticeable in simple file formats)
-</LI><LI CLASS="li-itemize">Slightly faster access as SeqRecord objects (but the difference is only
-really noticeable for simple to parse file formats).
-</LI><LI CLASS="li-itemize">Can use any immutable Python object as the dictionary keys (e.g. a
-tuple of strings, or a frozen set) not just strings.
-</LI><LI CLASS="li-itemize">Don’t need to worry about the index database being out of date if the
-sequence file being indexed has changed.
-</LI></UL><P>Reasons to choose <CODE>Bio.SeqIO.index_db()</CODE> over <CODE>Bio.SeqIO.index()</CODE>
-include:
-</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-Not memory limited – this is already important with files from second
-generation sequencing where 10s of millions of sequences are common, and
-using <CODE>Bio.SeqIO.index()</CODE> can require more than 4GB of RAM and therefore
-a 64bit version of Python.
-</LI><LI CLASS="li-itemize">Because the index is kept on disk, it can be reused. Although building
-the index database file takes longer, if you have a script which will be
-rerun on the same datafiles in future, this could save time in the long run.
-</LI><LI CLASS="li-itemize">Indexing multiple files together
-</LI><LI CLASS="li-itemize">The <CODE>get_raw()</CODE> method can be much faster, since for most file
-formats the length of each record is stored as well as its offset.
-</LI></UL><!--TOC section Writing Sequence Files-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc62">5.5</A>  Writing Sequence Files</H2><!--SEC END --><P>We’ve talked about using <CODE>Bio.SeqIO.parse()</CODE> for sequence input (reading files), and now we’ll look at <CODE>Bio.SeqIO.write()</CODE> which is for sequence output (writing files). This is a function taking three arguments: some <CODE>SeqRecord</CODE> objects, a handle or filename to write to, and a sequence format.</P><P>Here is an example, where [...]
-from Bio.SeqRecord import SeqRecord
-from Bio.Alphabet import generic_protein
-
-rec1 = SeqRecord(Seq("MMYQQGCFAGGTVLRLAKDLAENNRGARVLVVCSEITAVTFRGPSETHLDSMVGQALFGD" \
- +"GAGAVIVGSDPDLSVERPLYELVWTGATLLPDSEGAIDGHLREVGLTFHLLKDVPGLISK" \
- +"NIEKSLKEAFTPLGISDWNSTFWIAHPGGPAILDQVEAKLGLKEEKMRATREVLSEYGNM" \
- +"SSAC", generic_protein),
- id="gi|14150838|gb|AAK54648.1|AF376133_1",
- description="chalcone synthase [Cucumis sativus]")
-
-rec2 = SeqRecord(Seq("YPDYYFRITNREHKAELKEKFQRMCDKSMIKKRYMYLTEEILKENPSMCEYMAPSLDARQ" \
- +"DMVVVEIPKLGKEAAVKAIKEWGQ", generic_protein),
- id="gi|13919613|gb|AAK33142.1|",
- description="chalcone synthase [Fragaria vesca subsp. bracteata]")
-
-rec3 = SeqRecord(Seq("MVTVEEFRRAQCAEGPATVMAIGTATPSNCVDQSTYPDYYFRITNSEHKVELKEKFKRMC" \
- +"EKSMIKKRYMHLTEEILKENPNICAYMAPSLDARQDIVVVEVPKLGKEAAQKAIKEWGQP" \
- +"KSKITHLVFCTTSGVDMPGCDYQLTKLLGLRPSVKRFMMYQQGCFAGGTVLRMAKDLAEN" \
- +"NKGARVLVVCSEITAVTFRGPNDTHLDSLVGQALFGDGAAAVIIGSDPIPEVERPLFELV" \
- +"SAAQTLLPDSEGAIDGHLREVGLTFHLLKDVPGLISKNIEKSLVEAFQPLGISDWNSLFW" \
- +"IAHPGGPAILDQVELKLGLKQEKLKATRKVLSNYGNMSSACVLFILDEMRKASAKEGLGT" \
- +"TGEGLEWGVLFGFGPGLTVETVVLHSVAT", generic_protein),
- id="gi|13925890|gb|AAK49457.1|",
- description="chalcone synthase [Nicotiana tabacum]")
-
-my_records = [rec1, rec2, rec3]
-</PRE><P>Now we have a list of <CODE>SeqRecord</CODE> objects, we’ll write them to a FASTA format file:</P><PRE CLASS="verbatim">from Bio import SeqIO
-SeqIO.write(my_records, "my_example.faa", "fasta")
-</PRE><P>And if you open this file in your favourite text editor it should look like this:</P><PRE CLASS="verbatim">>gi|14150838|gb|AAK54648.1|AF376133_1 chalcone synthase [Cucumis sativus]
-MMYQQGCFAGGTVLRLAKDLAENNRGARVLVVCSEITAVTFRGPSETHLDSMVGQALFGD
-GAGAVIVGSDPDLSVERPLYELVWTGATLLPDSEGAIDGHLREVGLTFHLLKDVPGLISK
-NIEKSLKEAFTPLGISDWNSTFWIAHPGGPAILDQVEAKLGLKEEKMRATREVLSEYGNM
-SSAC
->gi|13919613|gb|AAK33142.1| chalcone synthase [Fragaria vesca subsp. bracteata]
-YPDYYFRITNREHKAELKEKFQRMCDKSMIKKRYMYLTEEILKENPSMCEYMAPSLDARQ
-DMVVVEIPKLGKEAAVKAIKEWGQ
->gi|13925890|gb|AAK49457.1| chalcone synthase [Nicotiana tabacum]
-MVTVEEFRRAQCAEGPATVMAIGTATPSNCVDQSTYPDYYFRITNSEHKVELKEKFKRMC
-EKSMIKKRYMHLTEEILKENPNICAYMAPSLDARQDIVVVEVPKLGKEAAQKAIKEWGQP
-KSKITHLVFCTTSGVDMPGCDYQLTKLLGLRPSVKRFMMYQQGCFAGGTVLRMAKDLAEN
-NKGARVLVVCSEITAVTFRGPNDTHLDSLVGQALFGDGAAAVIIGSDPIPEVERPLFELV
-SAAQTLLPDSEGAIDGHLREVGLTFHLLKDVPGLISKNIEKSLVEAFQPLGISDWNSLFW
-IAHPGGPAILDQVELKLGLKQEKLKATRKVLSNYGNMSSACVLFILDEMRKASAKEGLGT
-TGEGLEWGVLFGFGPGLTVETVVLHSVAT
-</PRE><P>Suppose you wanted to know how many records the <CODE>Bio.SeqIO.write()</CODE> function wrote to the handle?
-If your records were in a list you could just use <CODE>len(my_records)</CODE>, however you can’t do that when your records come from a generator/iterator. The <CODE>Bio.SeqIO.write()</CODE> function returns the number of <CODE>SeqRecord</CODE> objects written to the file.</P><P><EM>Note</EM> - If you tell the <CODE>Bio.SeqIO.write()</CODE> function to write to a file that already exists, the old file will be overwritten without any warning.</P><!--TOC subsection Round trips-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc63">5.5.1</A>  Round trips</H3><!--SEC END --><P>Some people like their parsers to be “round-tripable”, meaning if you read in
-a file and write it back out again it is unchanged. This requires that the parser
-must extract enough information to reproduce the original file <EM>exactly</EM>.
-<CODE>Bio.SeqIO</CODE> does <EM>not</EM> aim to do this.</P><P>As a trivial example, any line wrapping of the sequence data in FASTA files is
-allowed. An identical <CODE>SeqRecord</CODE> would be given from parsing the following
-two examples which differ only in their line breaks:</P><PRE CLASS="verbatim">>YAL068C-7235.2170 Putative promoter sequence
-TACGAGAATAATTTCTCATCATCCAGCTTTAACACAAAATTCGCACAGTTTTCGTTAAGA
-GAACTTAACATTTTCTTATGACGTAAATGAAGTTTATATATAAATTTCCTTTTTATTGGA
-
->YAL068C-7235.2170 Putative promoter sequence
-TACGAGAATAATTTCTCATCATCCAGCTTTAACACAAAATTCGCA
-CAGTTTTCGTTAAGAGAACTTAACATTTTCTTATGACGTAAATGA
-AGTTTATATATAAATTTCCTTTTTATTGGA
-</PRE><P>To make a round-tripable FASTA parser you would need to keep track of where the
-sequence line breaks occurred, and this extra information is usually pointless.
-Instead Biopython uses a default line wrapping of 60 characters on output.
-The same problem with white space applies in many other file formats too.
-Another issue in some cases is that Biopython does not (yet) preserve every
-last bit of annotation (e.g. GenBank and EMBL).</P><P>Occasionally preserving the original layout (with any quirks it may have) is
-important. See Section <A HREF="#sec:seqio-index-getraw">5.4.2.2</A> about the <CODE>get_raw()</CODE>
-method of the <CODE>Bio.SeqIO.index()</CODE> dictionary-like object for one potential
-solution.</P><!--TOC subsection Converting between sequence file formats-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc64">5.5.2</A>  Converting between sequence file formats</H3><!--SEC END --><P>
-<A NAME="sec:SeqIO-conversion"></A></P><P>In previous example we used a list of <CODE>SeqRecord</CODE> objects as input to the <CODE>Bio.SeqIO.write()</CODE> function, but it will also accept a <CODE>SeqRecord</CODE> iterator like we get from <CODE>Bio.SeqIO.parse()</CODE> – this lets us do file conversion by combining these two functions.</P><P>For this example we’ll read in the GenBank format file <A HREF="http://biopython.org/DIST/docs/tutorial/examples/ls_orchid.gbk">ls [...]
-records = SeqIO.parse("ls_orchid.gbk", "genbank")
-count = SeqIO.write(records, "my_example.fasta", "fasta")
-print("Converted %i records" % count)
-</PRE><P>Still, that is a little bit complicated. So, because file conversion is such a
-common task, there is a helper function letting you replace that with just:</P><PRE CLASS="verbatim">from Bio import SeqIO
-count = SeqIO.convert("ls_orchid.gbk", "genbank", "my_example.fasta", "fasta")
-print("Converted %i records" % count)
-</PRE><P>The <CODE>Bio.SeqIO.convert()</CODE> function will take handles <EM>or</EM> filenames.
-Watch out though – if the output file already exists, it will overwrite it!
-To find out more, see the built in help:</P><PRE CLASS="verbatim">>>> from Bio import SeqIO
->>> help(SeqIO.convert)
-...
-</PRE><P>In principle, just by changing the filenames and the format names, this code
-could be used to convert between any file formats available in Biopython.
-However, writing some formats requires information (e.g. quality scores) which
-other files formats don’t contain. For example, while you can turn a FASTQ
-file into a FASTA file, you can’t do the reverse. See also
-Sections <A HREF="#sec:SeqIO-fastq-conversion">18.1.9</A> and <A HREF="#sec:SeqIO-fasta-qual-conversion">18.1.10</A>
-in the cookbook chapter which looks at inter-converting between different FASTQ formats.</P><P>Finally, as an added incentive for using the <CODE>Bio.SeqIO.convert()</CODE> function
-(on top of the fact your code will be shorter), doing it this way may also be
-faster! The reason for this is the convert function can take advantage of
-several file format specific optimisations and tricks.</P><!--TOC subsection Converting a file of sequences to their reverse complements-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc65">5.5.3</A>  Converting a file of sequences to their reverse complements</H3><!--SEC END --><P>
-<A NAME="sec:SeqIO-reverse-complement"></A></P><P>Suppose you had a file of nucleotide sequences, and you wanted to turn it into a file containing their reverse complement sequences. This time a little bit of work is required to transform the <CODE>SeqRecord</CODE> objects we get from our input file into something suitable for saving to our output file.</P><P>To start with, we’ll use <CODE>Bio.SeqIO.parse()</CODE> to load some nucleotide
-sequences from a file, then print out their reverse complements using
-the <CODE>Seq</CODE> object’s built in <CODE>.reverse_complement()</CODE> method (see Section <A HREF="#sec:seq-reverse-complement">3.7</A>):</P><PRE CLASS="verbatim">>>> from Bio import SeqIO
->>> for record in SeqIO.parse("ls_orchid.gbk", "genbank"):
-... print(record.id)
-... print(record.seq.reverse_complement())
-</PRE><P>Now, if we want to save these reverse complements to a file, we’ll need to make <CODE>SeqRecord</CODE> objects.
-We can use the <CODE>SeqRecord</CODE> object’s built in <CODE>.reverse_complement()</CODE> method (see Section <A HREF="#sec:SeqRecord-reverse-complement">4.8</A>) but we must decide how to name our new records.</P><P>This is an excellent place to demonstrate the power of list comprehensions which make a list in memory:
-</P><PRE CLASS="verbatim">>>> from Bio import SeqIO
->>> records = [rec.reverse_complement(id="rc_"+rec.id, description = "reverse complement") \
-... for rec in SeqIO.parse("ls_orchid.fasta", "fasta")]
->>> len(records)
-94
-</PRE><P>Now list comprehensions have a nice trick up their sleeves, you can add a conditional statement:</P><PRE CLASS="verbatim">>>> records = [rec.reverse_complement(id="rc_"+rec.id, description = "reverse complement") \
-... for rec in SeqIO.parse("ls_orchid.fasta", "fasta") if len(rec)<700]
->>> len(records)
-18
-</PRE><P>That would create an in memory list of reverse complement records where the sequence length was under 700 base pairs. However, we can do exactly the same with a generator expression - but with the advantage that this does not create a list of all the records in memory at once:</P><PRE CLASS="verbatim">>>> records = (rec.reverse_complement(id="rc_"+rec.id, description = "reverse complement") \
-... for rec in SeqIO.parse("ls_orchid.fasta", "fasta") if len(rec)<700)
-</PRE><P>As a complete example:</P><PRE CLASS="verbatim">>>> from Bio import SeqIO
->>> records = (rec.reverse_complement(id="rc_"+rec.id, description = "reverse complement") \
-... for rec in SeqIO.parse("ls_orchid.fasta", "fasta") if len(rec)<700)
->>> SeqIO.write(records, "rev_comp.fasta", "fasta")
-18
-</PRE><P>There is a related example in Section <A HREF="#sec:SeqIO-translate">18.1.3</A>, translating each
-record in a FASTA file from nucleotides to amino acids.</P><!--TOC subsection Getting your SeqRecord objects as formatted strings-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc66">5.5.4</A>  Getting your SeqRecord objects as formatted strings</H3><!--SEC END --><P>
-<A NAME="sec:Bio.SeqIO-and-StringIO"></A>
-Suppose that you don’t really want to write your records to a file or handle – instead you want a string containing the records in a particular file format. The <CODE>Bio.SeqIO</CODE> interface is based on handles, but Python has a useful built in module which provides a string based handle.</P><P>For an example of how you might use this, let’s load in a bunch of <CODE>SeqRecord</CODE> objects from our orchids GenBank file, and create a string containing the records [...]
-from StringIO import StringIO
-records = SeqIO.parse("ls_orchid.gbk", "genbank")
-out_handle = StringIO()
-SeqIO.write(records, out_handle, "fasta")
-fasta_data = out_handle.getvalue()
-print(fasta_data)
-</PRE><P>This isn’t entirely straightforward the first time you see it! On the bright side, for the special case where you would like a string containing a <EM>single</EM> record in a particular file format, use the the <CODE>SeqRecord</CODE> class’ <CODE>format()</CODE> method (see Section <A HREF="#sec:SeqRecord-format">4.5</A>).</P><P>Note that although we don’t encourage it, you <EM>can</EM> use the <CODE>format()</CODE> method to write to a file, for exampl [...]
-</P><PRE CLASS="verbatim">from Bio import SeqIO
-out_handle = open("ls_orchid_long.tab", "w")
-for record in SeqIO.parse("ls_orchid.gbk", "genbank"):
- if len(record) > 100:
- out_handle.write(record.format("tab"))
-out_handle.close()
-</PRE><P>While this style of code will work for a simple sequential file format like FASTA or the simple tab separated format used here, it will <EM>not</EM> work for more complex or interlaced file formats. This is why we still recommend using <CODE>Bio.SeqIO.write()</CODE>, as in the following example:
-</P><PRE CLASS="verbatim">from Bio import SeqIO
-records = (rec for rec in SeqIO.parse("ls_orchid.gbk", "genbank") if len(rec) > 100)
-SeqIO.write(records, "ls_orchid.tab", "tab")
-</PRE><P>Making a single call to <CODE>SeqIO.write(...)</CODE> is also much quicker than
-multiple calls to the <CODE>SeqRecord.format(...)</CODE> method.</P><!--TOC chapter Multiple Sequence Alignment objects-->
-<H1 CLASS="chapter"><!--SEC ANCHOR --><A NAME="htoc67">Chapter 6</A>  Multiple Sequence Alignment objects</H1><!--SEC END --><P>
-<A NAME="chapter:Bio.AlignIO"></A></P><P>This chapter is about Multiple Sequence Alignments, by which we mean a collection of
-multiple sequences which have been aligned together – usually with the insertion of gap
-characters, and addition of leading or trailing gaps – such that all the sequence
-strings are the same length. Such an alignment can be regarded as a matrix of letters,
-where each row is held as a <CODE>SeqRecord</CODE> object internally.</P><P>We will introduce the <CODE>MultipleSeqAlignment</CODE> object which holds this kind of data,
-and the <CODE>Bio.AlignIO</CODE> module for reading and writing them as various file formats
-(following the design of the <CODE>Bio.SeqIO</CODE> module from the previous chapter).
-Note that both <CODE>Bio.SeqIO</CODE> and <CODE>Bio.AlignIO</CODE> can read and write sequence
-alignment files. The appropriate choice will depend largely on what you want to do
-with the data.</P><P>The final part of this chapter is about our command line wrappers for common multiple
-sequence alignment tools like ClustalW and MUSCLE.</P><!--TOC section Parsing or Reading Sequence Alignments-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc68">6.1</A>  Parsing or Reading Sequence Alignments</H2><!--SEC END --><P>We have two functions for reading in sequence alignments, <CODE>Bio.AlignIO.read()</CODE> and <CODE>Bio.AlignIO.parse()</CODE> which following the convention introduced in <CODE>Bio.SeqIO</CODE> are for files containing one or multiple alignments respectively.</P><P>Using <CODE>Bio.AlignIO.parse()</CODE> will return an <I>iterator</I> which gives <CODE>Mu [...]
-The first argument is a <I>handle</I> to read the data from, typically an open file (see Section <A HREF="#sec:appendix-handles">22.1</A>), or a filename.
-</LI><LI CLASS="li-enumerate">The second argument is a lower case string specifying the alignment format. As in <CODE>Bio.SeqIO</CODE> we don’t try and guess the file format for you! See <A HREF="http://biopython.org/wiki/AlignIO"><TT>http://biopython.org/wiki/AlignIO</TT></A> for a full listing of supported formats.
-</LI></OL><P>There is also an optional <CODE>seq_count</CODE> argument which is discussed in Section <A HREF="#sec:AlignIO-count-argument">6.1.3</A> below for dealing with ambiguous file formats which may contain more than one alignment.</P><P>A further optional <CODE>alphabet</CODE> argument allowing you to specify the expected alphabet. This can be useful as many alignment file formats do not explicitly label the sequences as RNA, DNA or protein – which means <CODE>Bio.Alig [...]
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc69">6.1.1</A>  Single Alignments</H3><!--SEC END --><P>
-As an example, consider the following annotation rich protein alignment in the PFAM or Stockholm file format:</P><PRE CLASS="verbatim"># STOCKHOLM 1.0
-#=GS COATB_BPIKE/30-81 AC P03620.1
-#=GS COATB_BPIKE/30-81 DR PDB; 1ifl ; 1-52;
-#=GS Q9T0Q8_BPIKE/1-52 AC Q9T0Q8.1
-#=GS COATB_BPI22/32-83 AC P15416.1
-#=GS COATB_BPM13/24-72 AC P69541.1
-#=GS COATB_BPM13/24-72 DR PDB; 2cpb ; 1-49;
-#=GS COATB_BPM13/24-72 DR PDB; 2cps ; 1-49;
-#=GS COATB_BPZJ2/1-49 AC P03618.1
-#=GS Q9T0Q9_BPFD/1-49 AC Q9T0Q9.1
-#=GS Q9T0Q9_BPFD/1-49 DR PDB; 1nh4 A; 1-49;
-#=GS COATB_BPIF1/22-73 AC P03619.2
-#=GS COATB_BPIF1/22-73 DR PDB; 1ifk ; 1-50;
-COATB_BPIKE/30-81 AEPNAATNYATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIRLFKKFSSKA
-#=GR COATB_BPIKE/30-81 SS -HHHHHHHHHHHHHH--HHHHHHHH--HHHHHHHHHHHHHHHHHHHHH----
-Q9T0Q8_BPIKE/1-52 AEPNAATNYATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIKLFKKFVSRA
-COATB_BPI22/32-83 DGTSTATSYATEAMNSLKTQATDLIDQTWPVVTSVAVAGLAIRLFKKFSSKA
-COATB_BPM13/24-72 AEGDDP...AKAAFNSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFTSKA
-#=GR COATB_BPM13/24-72 SS ---S-T...CHCHHHHCCCCTCCCTTCHHHHHHHHHHHHHHHHHHHHCTT--
-COATB_BPZJ2/1-49 AEGDDP...AKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFASKA
-Q9T0Q9_BPFD/1-49 AEGDDP...AKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFTSKA
-#=GR Q9T0Q9_BPFD/1-49 SS ------...-HHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHH--
-COATB_BPIF1/22-73 FAADDATSQAKAAFDSLTAQATEMSGYAWALVVLVVGATVGIKLFKKFVSRA
-#=GR COATB_BPIF1/22-73 SS XX-HHHH--HHHHHH--HHHHHHH--HHHHHHHHHHHHHHHHHHHHHHH---
-#=GC SS_cons XHHHHHHHHHHHHHHHCHHHHHHHHCHHHHHHHHHHHHHHHHHHHHHHHC--
-#=GC seq_cons AEssss...AptAhDSLpspAT-hIu.sWshVsslVsAsluIKLFKKFsSKA
-//
-</PRE><P>This is the seed alignment for the Phage_Coat_Gp8 (PF05371) PFAM entry, downloaded from a now out of date release of PFAM from <A HREF="http://pfam.sanger.ac.uk/"><TT>http://pfam.sanger.ac.uk/</TT></A>. We can load this file as follows (assuming it has been saved to disk as “PF05371_seed.sth” in the current working directory):</P><PRE CLASS="verbatim">>>> from Bio import AlignIO
->>> alignment = AlignIO.read("PF05371_seed.sth", "stockholm")
-</PRE><P>This code will print out a summary of the alignment:</P><PRE CLASS="verbatim">>>> print(alignment)
-SingleLetterAlphabet() alignment with 7 rows and 52 columns
-AEPNAATNYATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIRL...SKA COATB_BPIKE/30-81
-AEPNAATNYATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIKL...SRA Q9T0Q8_BPIKE/1-52
-DGTSTATSYATEAMNSLKTQATDLIDQTWPVVTSVAVAGLAIRL...SKA COATB_BPI22/32-83
-AEGDDP---AKAAFNSLQASATEYIGYAWAMVVVIVGATIGIKL...SKA COATB_BPM13/24-72
-AEGDDP---AKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKL...SKA COATB_BPZJ2/1-49
-AEGDDP---AKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKL...SKA Q9T0Q9_BPFD/1-49
-FAADDATSQAKAAFDSLTAQATEMSGYAWALVVLVVGATVGIKL...SRA COATB_BPIF1/22-73
-</PRE><P>You’ll notice in the above output the sequences have been truncated. We could instead write our own code to format this as we please by iterating over the rows as <CODE>SeqRecord</CODE> objects:</P><PRE CLASS="verbatim">>>> from Bio import AlignIO
->>> alignment = AlignIO.read("PF05371_seed.sth", "stockholm")
->>> print("Alignment length %i" % alignment.get_alignment_length())
-Alignment length 52
->>> for record in alignment:
-... print("%s - %s" % (record.seq, record.id))
-AEPNAATNYATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIRLFKKFSSKA - COATB_BPIKE/30-81
-AEPNAATNYATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIKLFKKFVSRA - Q9T0Q8_BPIKE/1-52
-DGTSTATSYATEAMNSLKTQATDLIDQTWPVVTSVAVAGLAIRLFKKFSSKA - COATB_BPI22/32-83
-AEGDDP---AKAAFNSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFTSKA - COATB_BPM13/24-72
-AEGDDP---AKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFASKA - COATB_BPZJ2/1-49
-AEGDDP---AKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFTSKA - Q9T0Q9_BPFD/1-49
-FAADDATSQAKAAFDSLTAQATEMSGYAWALVVLVVGATVGIKLFKKFVSRA - COATB_BPIF1/22-73
-</PRE><P>You could also use the alignment object’s <CODE>format</CODE> method to show it in a particular file format – see Section <A HREF="#sec:alignment-format-method">6.2.2</A> for details.</P><P>Did you notice in the raw file above that several of the sequences include database cross-references to the PDB and the associated known secondary structure? Try this:</P><PRE CLASS="verbatim">>>> for record in alignment:
-... if record.dbxrefs:
-... print("%s %s" % (record.id, record.dbxrefs))
-COATB_BPIKE/30-81 ['PDB; 1ifl ; 1-52;']
-COATB_BPM13/24-72 ['PDB; 2cpb ; 1-49;', 'PDB; 2cps ; 1-49;']
-Q9T0Q9_BPFD/1-49 ['PDB; 1nh4 A; 1-49;']
-COATB_BPIF1/22-73 ['PDB; 1ifk ; 1-50;']
-</PRE><P>To have a look at all the sequence annotation, try this:</P><PRE CLASS="verbatim">>>> for record in alignment:
-... print(record)
-</PRE><P>Sanger provide a nice web interface at <A HREF="http://pfam.sanger.ac.uk/family?acc=PF05371"><TT>http://pfam.sanger.ac.uk/family?acc=PF05371</TT></A> which will actually let you download this alignment in several other formats. This is what the file looks like in the FASTA file format:</P><PRE CLASS="verbatim">>COATB_BPIKE/30-81
-AEPNAATNYATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIRLFKKFSSKA
->Q9T0Q8_BPIKE/1-52
-AEPNAATNYATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIKLFKKFVSRA
->COATB_BPI22/32-83
-DGTSTATSYATEAMNSLKTQATDLIDQTWPVVTSVAVAGLAIRLFKKFSSKA
->COATB_BPM13/24-72
-AEGDDP---AKAAFNSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFTSKA
->COATB_BPZJ2/1-49
-AEGDDP---AKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFASKA
->Q9T0Q9_BPFD/1-49
-AEGDDP---AKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFTSKA
->COATB_BPIF1/22-73
-FAADDATSQAKAAFDSLTAQATEMSGYAWALVVLVVGATVGIKLFKKFVSRA
-</PRE><P>Note the website should have an option about showing gaps as periods (dots) or dashes, we’ve shown dashes above. Assuming you download and save this as file “PF05371_seed.faa” then you can load it with almost exactly the same code:</P><PRE CLASS="verbatim">from Bio import AlignIO
-alignment = AlignIO.read("PF05371_seed.faa", "fasta")
-print(alignment)
-</PRE><P>All that has changed in this code is the filename and the format string. You’ll get the same output as before, the sequences and record identifiers are the same.
-However, as you should expect, if you check each <CODE>SeqRecord</CODE> there is no annotation nor database cross-references because these are not included in the FASTA file format.</P><P>Note that rather than using the Sanger website, you could have used <CODE>Bio.AlignIO</CODE> to convert the original Stockholm format file into a FASTA file yourself (see below).</P><P>With any supported file format, you can load an alignment in exactly the same way just by changing the format string. F [...]
->>> help(AlignIO)
-...
-</PRE><!--TOC subsection Multiple Alignments-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc70">6.1.2</A>  Multiple Alignments</H3><!--SEC END --><P>The previous section focused on reading files containing a single alignment. In general however, files can contain more than one alignment, and to read these files we must use the <CODE>Bio.AlignIO.parse()</CODE> function.</P><P>Suppose you have a small alignment in PHYLIP format:</P><PRE CLASS="verbatim"> 5 6
-Alpha AACAAC
-Beta AACCCC
-Gamma ACCAAC
-Delta CCACCA
-Epsilon CCAAAC
-</PRE><P>If you wanted to bootstrap a phylogenetic tree using the PHYLIP tools, one of the steps would be to create a set of many resampled alignments using the tool <CODE>bootseq</CODE>. This would give output something like this, which has been abbreviated for conciseness:</P><PRE CLASS="verbatim"> 5 6
-Alpha AAACCA
-Beta AAACCC
-Gamma ACCCCA
-Delta CCCAAC
-Epsilon CCCAAA
- 5 6
-Alpha AAACAA
-Beta AAACCC
-Gamma ACCCAA
-Delta CCCACC
-Epsilon CCCAAA
- 5 6
-Alpha AAAAAC
-Beta AAACCC
-Gamma AACAAC
-Delta CCCCCA
-Epsilon CCCAAC
-...
- 5 6
-Alpha AAAACC
-Beta ACCCCC
-Gamma AAAACC
-Delta CCCCAA
-Epsilon CAAACC
-</PRE><P>If you wanted to read this in using <CODE>Bio.AlignIO</CODE> you could use:</P><PRE CLASS="verbatim">from Bio import AlignIO
-alignments = AlignIO.parse("resampled.phy", "phylip")
-for alignment in alignments:
- print(alignment)
- print("")
-</PRE><P>This would give the following output, again abbreviated for display:</P><PRE CLASS="verbatim">SingleLetterAlphabet() alignment with 5 rows and 6 columns
-AAACCA Alpha
-AAACCC Beta
-ACCCCA Gamma
-CCCAAC Delta
-CCCAAA Epsilon
-
-SingleLetterAlphabet() alignment with 5 rows and 6 columns
-AAACAA Alpha
-AAACCC Beta
-ACCCAA Gamma
-CCCACC Delta
-CCCAAA Epsilon
-
-SingleLetterAlphabet() alignment with 5 rows and 6 columns
-AAAAAC Alpha
-AAACCC Beta
-AACAAC Gamma
-CCCCCA Delta
-CCCAAC Epsilon
-
-...
-
-SingleLetterAlphabet() alignment with 5 rows and 6 columns
-AAAACC Alpha
-ACCCCC Beta
-AAAACC Gamma
-CCCCAA Delta
-CAAACC Epsilon
-</PRE><P>As with the function <CODE>Bio.SeqIO.parse()</CODE>, using <CODE>Bio.AlignIO.parse()</CODE> returns an iterator.
-If you want to keep all the alignments in memory at once, which will allow you to access them in any order, then turn the iterator into a list:</P><PRE CLASS="verbatim">from Bio import AlignIO
-alignments = list(AlignIO.parse("resampled.phy", "phylip"))
-last_align = alignments[-1]
-first_align = alignments[0]
-</PRE><!--TOC subsection Ambiguous Alignments-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc71">6.1.3</A>  Ambiguous Alignments</H3><!--SEC END --><P>
-<A NAME="sec:AlignIO-count-argument"></A>
-Many alignment file formats can explicitly store more than one alignment, and the division between each alignment is clear. However, when a general sequence file format has been used there is no such block structure. The most common such situation is when alignments have been saved in the FASTA file format. For example consider the following:</P><PRE CLASS="verbatim">>Alpha
-ACTACGACTAGCTCAG--G
->Beta
-ACTACCGCTAGCTCAGAAG
->Gamma
-ACTACGGCTAGCACAGAAG
->Alpha
-ACTACGACTAGCTCAGG--
->Beta
-ACTACCGCTAGCTCAGAAG
->Gamma
-ACTACGGCTAGCACAGAAG
-</PRE><P>This could be a single alignment containing six sequences (with repeated identifiers). Or, judging from the identifiers, this is probably two different alignments each with three sequences, which happen to all have the same length.</P><P>What about this next example?</P><PRE CLASS="verbatim">>Alpha
-ACTACGACTAGCTCAG--G
->Beta
-ACTACCGCTAGCTCAGAAG
->Alpha
-ACTACGACTAGCTCAGG--
->Gamma
-ACTACGGCTAGCACAGAAG
->Alpha
-ACTACGACTAGCTCAGG--
->Delta
-ACTACGGCTAGCACAGAAG
-</PRE><P>Again, this could be a single alignment with six sequences. However this time based on the identifiers we might guess this is three pairwise alignments which by chance have all got the same lengths.</P><P>This final example is similar:</P><PRE CLASS="verbatim">>Alpha
-ACTACGACTAGCTCAG--G
->XXX
-ACTACCGCTAGCTCAGAAG
->Alpha
-ACTACGACTAGCTCAGG
->YYY
-ACTACGGCAAGCACAGG
->Alpha
---ACTACGAC--TAGCTCAGG
->ZZZ
-GGACTACGACAATAGCTCAGG
-</PRE><P>In this third example, because of the differing lengths, this cannot be treated as a single alignment containing all six records. However, it could be three pairwise alignments.</P><P>Clearly trying to store more than one alignment in a FASTA file is not ideal. However, if you are forced to deal with these as input files <CODE>Bio.AlignIO</CODE> can cope with the most common situation where all the alignments have the same number of records.
-One example of this is a collection of pairwise alignments, which can be produced by the EMBOSS tools <CODE>needle</CODE> and <CODE>water</CODE> – although in this situation, <CODE>Bio.AlignIO</CODE> should be able to understand their native output using “emboss” as the format string.</P><P>To interpret these FASTA examples as several separate alignments, we can use <CODE>Bio.AlignIO.parse()</CODE> with the optional <CODE>seq_count</CODE> argument which specifies how [...]
-For example, using the third example as the input data:</P><PRE CLASS="verbatim">for alignment in AlignIO.parse(handle, "fasta", seq_count=2):
- print("Alignment length %i" % alignment.get_alignment_length())
- for record in alignment:
- print("%s - %s" % (record.seq, record.id))
- print("")
-</PRE><P>giving:</P><PRE CLASS="verbatim">Alignment length 19
-ACTACGACTAGCTCAG--G - Alpha
-ACTACCGCTAGCTCAGAAG - XXX
-
-Alignment length 17
-ACTACGACTAGCTCAGG - Alpha
-ACTACGGCAAGCACAGG - YYY
-
-Alignment length 21
---ACTACGAC--TAGCTCAGG - Alpha
-GGACTACGACAATAGCTCAGG - ZZZ
-</PRE><P>Using <CODE>Bio.AlignIO.read()</CODE> or <CODE>Bio.AlignIO.parse()</CODE> without the <CODE>seq_count</CODE> argument would give a single alignment containing all six records for the first two examples. For the third example, an exception would be raised because the lengths differ preventing them being turned into a single alignment.</P><P>If the file format itself has a block structure allowing <CODE>Bio.AlignIO</CODE> to determine the number of sequences in each alignment dire [...]
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc72">6.2</A>  Writing Alignments</H2><!--SEC END --><P>We’ve talked about using <CODE>Bio.AlignIO.read()</CODE> and <CODE>Bio.AlignIO.parse()</CODE> for alignment input (reading files), and now we’ll look at <CODE>Bio.AlignIO.write()</CODE> which is for alignment output (writing files). This is a function taking three arguments: some <CODE>MultipleSeqAlignment</CODE> objects (or for backwards compatibility the obso [...]
-Note we create some <CODE>SeqRecord</CODE> objects to construct the alignment from.</P><PRE CLASS="verbatim">from Bio.Alphabet import generic_dna
-from Bio.Seq import Seq
-from Bio.SeqRecord import SeqRecord
-from Bio.Align import MultipleSeqAlignment
-
-align1 = MultipleSeqAlignment([
- SeqRecord(Seq("ACTGCTAGCTAG", generic_dna), id="Alpha"),
- SeqRecord(Seq("ACT-CTAGCTAG", generic_dna), id="Beta"),
- SeqRecord(Seq("ACTGCTAGDTAG", generic_dna), id="Gamma"),
- ])
-
-align2 = MultipleSeqAlignment([
- SeqRecord(Seq("GTCAGC-AG", generic_dna), id="Delta"),
- SeqRecord(Seq("GACAGCTAG", generic_dna), id="Epsilon"),
- SeqRecord(Seq("GTCAGCTAG", generic_dna), id="Zeta"),
- ])
-
-align3 = MultipleSeqAlignment([
- SeqRecord(Seq("ACTAGTACAGCTG", generic_dna), id="Eta"),
- SeqRecord(Seq("ACTAGTACAGCT-", generic_dna), id="Theta"),
- SeqRecord(Seq("-CTACTACAGGTG", generic_dna), id="Iota"),
- ])
-
-my_alignments = [align1, align2, align3]
-</PRE><P>Now we have a list of <CODE>Alignment</CODE> objects, we’ll write them to a PHYLIP format file:</P><PRE CLASS="verbatim">from Bio import AlignIO
-AlignIO.write(my_alignments, "my_example.phy", "phylip")
-</PRE><P>And if you open this file in your favourite text editor it should look like this:</P><PRE CLASS="verbatim"> 3 12
-Alpha ACTGCTAGCT AG
-Beta ACT-CTAGCT AG
-Gamma ACTGCTAGDT AG
- 3 9
-Delta GTCAGC-AG
-Epislon GACAGCTAG
-Zeta GTCAGCTAG
- 3 13
-Eta ACTAGTACAG CTG
-Theta ACTAGTACAG CT-
-Iota -CTACTACAG GTG
-</PRE><P>Its more common to want to load an existing alignment, and save that, perhaps after some simple manipulation like removing certain rows or columns.</P><P>Suppose you wanted to know how many alignments the <CODE>Bio.AlignIO.write()</CODE> function wrote to the handle? If your alignments were in a list like the example above, you could just use <CODE>len(my_alignments)</CODE>, however you can’t do that when your records come from a generator/iterator. Therefore the <CODE>Bi [...]
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc73">6.2.1</A>  Converting between sequence alignment file formats</H3><!--SEC END --><P>
-<A NAME="sec:converting-alignments"></A></P><P>Converting between sequence alignment file formats with <CODE>Bio.AlignIO</CODE> works
-in the same way as converting between sequence file formats with <CODE>Bio.SeqIO</CODE>
-(Section <A HREF="#sec:SeqIO-conversion">5.5.2</A>). We load generally the alignment(s) using
-<CODE>Bio.AlignIO.parse()</CODE> and then save them using the <CODE>Bio.AlignIO.write()</CODE>
-– or just use the <CODE>Bio.AlignIO.convert()</CODE> helper function.</P><P>For this example, we’ll load the PFAM/Stockholm format file used earlier and save it as a Clustal W format file:</P><PRE CLASS="verbatim">from Bio import AlignIO
-count = AlignIO.convert("PF05371_seed.sth", "stockholm", "PF05371_seed.aln", "clustal")
-print("Converted %i alignments" % count)
-</PRE><P>Or, using <CODE>Bio.AlignIO.parse()</CODE> and <CODE>Bio.AlignIO.write()</CODE>:</P><PRE CLASS="verbatim">from Bio import AlignIO
-alignments = AlignIO.parse("PF05371_seed.sth", "stockholm")
-count = AlignIO.write(alignments, "PF05371_seed.aln", "clustal")
-print("Converted %i alignments" % count)
-</PRE><P>The <CODE>Bio.AlignIO.write()</CODE> function expects to be given multiple alignment objects. In the example above we gave it the alignment iterator returned by <CODE>Bio.AlignIO.parse()</CODE>.</P><P>In this case, we know there is only one alignment in the file so we could have used <CODE>Bio.AlignIO.read()</CODE> instead, but notice we have to pass this alignment to <CODE>Bio.AlignIO.write()</CODE> as a single element list:</P><PRE CLASS="verbatim">from Bio import AlignIO
-alignment = AlignIO.read("PF05371_seed.sth", "stockholm")
-AlignIO.write([alignment], "PF05371_seed.aln", "clustal")
-</PRE><P>Either way, you should end up with the same new Clustal W format file “PF05371_seed.aln” with the following content:</P><PRE CLASS="verbatim">CLUSTAL X (1.81) multiple sequence alignment
-
-
-COATB_BPIKE/30-81 AEPNAATNYATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIRLFKKFSS
-Q9T0Q8_BPIKE/1-52 AEPNAATNYATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIKLFKKFVS
-COATB_BPI22/32-83 DGTSTATSYATEAMNSLKTQATDLIDQTWPVVTSVAVAGLAIRLFKKFSS
-COATB_BPM13/24-72 AEGDDP---AKAAFNSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFTS
-COATB_BPZJ2/1-49 AEGDDP---AKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFAS
-Q9T0Q9_BPFD/1-49 AEGDDP---AKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFTS
-COATB_BPIF1/22-73 FAADDATSQAKAAFDSLTAQATEMSGYAWALVVLVVGATVGIKLFKKFVS
-
-COATB_BPIKE/30-81 KA
-Q9T0Q8_BPIKE/1-52 RA
-COATB_BPI22/32-83 KA
-COATB_BPM13/24-72 KA
-COATB_BPZJ2/1-49 KA
-Q9T0Q9_BPFD/1-49 KA
-COATB_BPIF1/22-73 RA
-</PRE><P>Alternatively, you could make a PHYLIP format file which we’ll name “PF05371_seed.phy”:</P><PRE CLASS="verbatim">from Bio import AlignIO
-AlignIO.convert("PF05371_seed.sth", "stockholm", "PF05371_seed.phy", "phylip")
-</PRE><P>This time the output looks like this:</P><PRE CLASS="verbatim"> 7 52
-COATB_BPIK AEPNAATNYA TEAMDSLKTQ AIDLISQTWP VVTTVVVAGL VIRLFKKFSS
-Q9T0Q8_BPI AEPNAATNYA TEAMDSLKTQ AIDLISQTWP VVTTVVVAGL VIKLFKKFVS
-COATB_BPI2 DGTSTATSYA TEAMNSLKTQ ATDLIDQTWP VVTSVAVAGL AIRLFKKFSS
-COATB_BPM1 AEGDDP---A KAAFNSLQAS ATEYIGYAWA MVVVIVGATI GIKLFKKFTS
-COATB_BPZJ AEGDDP---A KAAFDSLQAS ATEYIGYAWA MVVVIVGATI GIKLFKKFAS
-Q9T0Q9_BPF AEGDDP---A KAAFDSLQAS ATEYIGYAWA MVVVIVGATI GIKLFKKFTS
-COATB_BPIF FAADDATSQA KAAFDSLTAQ ATEMSGYAWA LVVLVVGATV GIKLFKKFVS
-
- KA
- RA
- KA
- KA
- KA
- KA
- RA
-</PRE><P>One of the big handicaps of the original PHYLIP alignment file format is
-that the sequence identifiers are strictly truncated at ten characters.
-In this example, as you can see the resulting names are still unique -
-but they are not very readable. As a result, a more relaxed variant of
-the original PHYLIP format is now quite widely used:</P><PRE CLASS="verbatim">from Bio import AlignIO
-AlignIO.convert("PF05371_seed.sth", "stockholm", "PF05371_seed.phy", "phylip-relaxed")
-</PRE><P>This time the output looks like this, using a longer indentation to
-allow all the identifers to be given in full::</P><PRE CLASS="verbatim"> 7 52
-COATB_BPIKE/30-81 AEPNAATNYA TEAMDSLKTQ AIDLISQTWP VVTTVVVAGL VIRLFKKFSS
-Q9T0Q8_BPIKE/1-52 AEPNAATNYA TEAMDSLKTQ AIDLISQTWP VVTTVVVAGL VIKLFKKFVS
-COATB_BPI22/32-83 DGTSTATSYA TEAMNSLKTQ ATDLIDQTWP VVTSVAVAGL AIRLFKKFSS
-COATB_BPM13/24-72 AEGDDP---A KAAFNSLQAS ATEYIGYAWA MVVVIVGATI GIKLFKKFTS
-COATB_BPZJ2/1-49 AEGDDP---A KAAFDSLQAS ATEYIGYAWA MVVVIVGATI GIKLFKKFAS
-Q9T0Q9_BPFD/1-49 AEGDDP---A KAAFDSLQAS ATEYIGYAWA MVVVIVGATI GIKLFKKFTS
-COATB_BPIF1/22-73 FAADDATSQA KAAFDSLTAQ ATEMSGYAWA LVVLVVGATV GIKLFKKFVS
-
- KA
- RA
- KA
- KA
- KA
- KA
- RA
-</PRE><P>If you have to work with the original strict PHYLIP format, then you may need to
-compress the identifers somehow – or assign your own names or numbering system.
-This following bit of code manipulates the record identifiers before saving the output:</P><PRE CLASS="verbatim">from Bio import AlignIO
-alignment = AlignIO.read("PF05371_seed.sth", "stockholm")
-name_mapping = {}
-for i, record in enumerate(alignment):
- name_mapping[i] = record.id
- record.id = "seq%i" % i
-print(name_mapping)
-
-AlignIO.write([alignment], "PF05371_seed.phy", "phylip")
-</PRE><P>This code used a Python dictionary to record a simple mapping from the new sequence system to the original identifier:
-</P><PRE CLASS="verbatim">{0: 'COATB_BPIKE/30-81', 1: 'Q9T0Q8_BPIKE/1-52', 2: 'COATB_BPI22/32-83', ...}
-</PRE><P>Here is the new (strict) PHYLIP format output:
-</P><PRE CLASS="verbatim"> 7 52
-seq0 AEPNAATNYA TEAMDSLKTQ AIDLISQTWP VVTTVVVAGL VIRLFKKFSS
-seq1 AEPNAATNYA TEAMDSLKTQ AIDLISQTWP VVTTVVVAGL VIKLFKKFVS
-seq2 DGTSTATSYA TEAMNSLKTQ ATDLIDQTWP VVTSVAVAGL AIRLFKKFSS
-seq3 AEGDDP---A KAAFNSLQAS ATEYIGYAWA MVVVIVGATI GIKLFKKFTS
-seq4 AEGDDP---A KAAFDSLQAS ATEYIGYAWA MVVVIVGATI GIKLFKKFAS
-seq5 AEGDDP---A KAAFDSLQAS ATEYIGYAWA MVVVIVGATI GIKLFKKFTS
-seq6 FAADDATSQA KAAFDSLTAQ ATEMSGYAWA LVVLVVGATV GIKLFKKFVS
-
- KA
- RA
- KA
- KA
- KA
- KA
- RA
-</PRE><P>In general, because of the identifier limitation, working with
-<I>strict</I> PHYLIP file formats shouldn’t be your first choice.
-Using the PFAM/Stockholm format on the other hand allows you to record a lot of additional annotation too.</P><!--TOC subsection Getting your alignment objects as formatted strings-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc74">6.2.2</A>  Getting your alignment objects as formatted strings</H3><!--SEC END --><P>
-<A NAME="sec:alignment-format-method"></A>
-The <CODE>Bio.AlignIO</CODE> interface is based on handles, which means if you want to get your alignment(s) into a string in a particular file format you need to do a little bit more work (see below).
-However, you will probably prefer to take advantage of the alignment object’s <CODE>format()</CODE> method.
-This takes a single mandatory argument, a lower case string which is supported by <CODE>Bio.AlignIO</CODE> as an output format. For example:</P><PRE CLASS="verbatim">from Bio import AlignIO
-alignment = AlignIO.read("PF05371_seed.sth", "stockholm")
-print(alignment.format("clustal"))
-</PRE><P>As described in Section <A HREF="#sec:SeqRecord-format">4.5</A>, the <CODE>SeqRecord</CODE> object has a similar method using output formats supported by <CODE>Bio.SeqIO</CODE>.</P><P>Internally the <CODE>format()</CODE> method is using the <CODE>StringIO</CODE> string based handle and calling
-<CODE>Bio.AlignIO.write()</CODE>. You can do this in your own code if for example you are using an
-older version of Biopython:</P><PRE CLASS="verbatim">from Bio import AlignIO
-from StringIO import StringIO
-
-alignments = AlignIO.parse("PF05371_seed.sth", "stockholm")
-
-out_handle = StringIO()
-AlignIO.write(alignments, out_handle, "clustal")
-clustal_data = out_handle.getvalue()
-
-print(clustal_data)
-</PRE><!--TOC section Manipulating Alignments-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc75">6.3</A>  Manipulating Alignments</H2><!--SEC END --><P>
-<A NAME="sec:manipulating-alignments"></A></P><P>Now that we’ve covered loading and saving alignments, we’ll look at what else you can do
-with them.</P><!--TOC subsection Slicing alignments-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc76">6.3.1</A>  Slicing alignments</H3><!--SEC END --><P>
-First of all, in some senses the alignment objects act like a Python <CODE>list</CODE> of
-<CODE>SeqRecord</CODE> objects (the rows). With this model in mind hopefully the actions
-of <CODE>len()</CODE> (the number of rows) and iteration (each row as a <CODE>SeqRecord</CODE>)
-make sense:</P><PRE CLASS="verbatim">>>> from Bio import AlignIO
->>> alignment = AlignIO.read("PF05371_seed.sth", "stockholm")
->>> print("Number of rows: %i" % len(alignment))
-Number of rows: 7
->>> for record in alignment:
-... print("%s - %s" % (record.seq, record.id))
-AEPNAATNYATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIRLFKKFSSKA - COATB_BPIKE/30-81
-AEPNAATNYATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIKLFKKFVSRA - Q9T0Q8_BPIKE/1-52
-DGTSTATSYATEAMNSLKTQATDLIDQTWPVVTSVAVAGLAIRLFKKFSSKA - COATB_BPI22/32-83
-AEGDDP---AKAAFNSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFTSKA - COATB_BPM13/24-72
-AEGDDP---AKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFASKA - COATB_BPZJ2/1-49
-AEGDDP---AKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFTSKA - Q9T0Q9_BPFD/1-49
-FAADDATSQAKAAFDSLTAQATEMSGYAWALVVLVVGATVGIKLFKKFVSRA - COATB_BPIF1/22-73
-</PRE><P>You can also use the list-like <CODE>append</CODE> and <CODE>extend</CODE> methods to add
-more rows to the alignment (as <CODE>SeqRecord</CODE> objects). Keeping the list
-metaphor in mind, simple slicing of the alignment should also make sense -
-it selects some of the rows giving back another alignment object:</P><PRE CLASS="verbatim">>>> print(alignment)
-SingleLetterAlphabet() alignment with 7 rows and 52 columns
-AEPNAATNYATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIRL...SKA COATB_BPIKE/30-81
-AEPNAATNYATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIKL...SRA Q9T0Q8_BPIKE/1-52
-DGTSTATSYATEAMNSLKTQATDLIDQTWPVVTSVAVAGLAIRL...SKA COATB_BPI22/32-83
-AEGDDP---AKAAFNSLQASATEYIGYAWAMVVVIVGATIGIKL...SKA COATB_BPM13/24-72
-AEGDDP---AKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKL...SKA COATB_BPZJ2/1-49
-AEGDDP---AKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKL...SKA Q9T0Q9_BPFD/1-49
-FAADDATSQAKAAFDSLTAQATEMSGYAWALVVLVVGATVGIKL...SRA COATB_BPIF1/22-73
->>> print(alignment[3:7])
-SingleLetterAlphabet() alignment with 4 rows and 52 columns
-AEGDDP---AKAAFNSLQASATEYIGYAWAMVVVIVGATIGIKL...SKA COATB_BPM13/24-72
-AEGDDP---AKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKL...SKA COATB_BPZJ2/1-49
-AEGDDP---AKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKL...SKA Q9T0Q9_BPFD/1-49
-FAADDATSQAKAAFDSLTAQATEMSGYAWALVVLVVGATVGIKL...SRA COATB_BPIF1/22-73
-</PRE><P>What if you wanted to select by column? Those of you who have used the NumPy
-matrix or array objects won’t be surprised at this - you use a double index.</P><PRE CLASS="verbatim">>>> print(alignment[2, 6])
-T
-</PRE><P>Using two integer indices pulls out a single letter, short hand for this:</P><PRE CLASS="verbatim">>>> print(alignment[2].seq[6])
-T
-</PRE><P>You can pull out a single column as a string like this:</P><PRE CLASS="verbatim">>>> print(alignment[:, 6])
-TTT---T
-</PRE><P>You can also select a range of columns. For example, to pick out those same
-three rows we extracted earlier, but take just their first six columns:</P><PRE CLASS="verbatim">>>> print(alignment[3:6, :6])
-SingleLetterAlphabet() alignment with 3 rows and 6 columns
-AEGDDP COATB_BPM13/24-72
-AEGDDP COATB_BPZJ2/1-49
-AEGDDP Q9T0Q9_BPFD/1-49
-</PRE><P>Leaving the first index as <CODE>:</CODE> means take all the rows:</P><PRE CLASS="verbatim">>>> print(alignment[:, :6])
-SingleLetterAlphabet() alignment with 7 rows and 6 columns
-AEPNAA COATB_BPIKE/30-81
-AEPNAA Q9T0Q8_BPIKE/1-52
-DGTSTA COATB_BPI22/32-83
-AEGDDP COATB_BPM13/24-72
-AEGDDP COATB_BPZJ2/1-49
-AEGDDP Q9T0Q9_BPFD/1-49
-FAADDA COATB_BPIF1/22-73
-</PRE><P>This brings us to a neat way to remove a section. Notice columns
-7, 8 and 9 which are gaps in three of the seven sequences:</P><PRE CLASS="verbatim">>>> print(alignment[:, 6:9])
-SingleLetterAlphabet() alignment with 7 rows and 3 columns
-TNY COATB_BPIKE/30-81
-TNY Q9T0Q8_BPIKE/1-52
-TSY COATB_BPI22/32-83
---- COATB_BPM13/24-72
---- COATB_BPZJ2/1-49
---- Q9T0Q9_BPFD/1-49
-TSQ COATB_BPIF1/22-73
-</PRE><P>Again, you can slice to get everything after the ninth column:</P><PRE CLASS="verbatim">>>> print(alignment[:, 9:])
-SingleLetterAlphabet() alignment with 7 rows and 43 columns
-ATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIRLFKKFSSKA COATB_BPIKE/30-81
-ATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIKLFKKFVSRA Q9T0Q8_BPIKE/1-52
-ATEAMNSLKTQATDLIDQTWPVVTSVAVAGLAIRLFKKFSSKA COATB_BPI22/32-83
-AKAAFNSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFTSKA COATB_BPM13/24-72
-AKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFASKA COATB_BPZJ2/1-49
-AKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFTSKA Q9T0Q9_BPFD/1-49
-AKAAFDSLTAQATEMSGYAWALVVLVVGATVGIKLFKKFVSRA COATB_BPIF1/22-73
-</PRE><P>Now, the interesting thing is that addition of alignment objects works
-by column. This lets you do this as a way to remove a block of columns:</P><PRE CLASS="verbatim">>>> edited = alignment[:, :6] + alignment[:, 9:]
->>> print(edited)
-SingleLetterAlphabet() alignment with 7 rows and 49 columns
-AEPNAAATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIRLFKKFSSKA COATB_BPIKE/30-81
-AEPNAAATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIKLFKKFVSRA Q9T0Q8_BPIKE/1-52
-DGTSTAATEAMNSLKTQATDLIDQTWPVVTSVAVAGLAIRLFKKFSSKA COATB_BPI22/32-83
-AEGDDPAKAAFNSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFTSKA COATB_BPM13/24-72
-AEGDDPAKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFASKA COATB_BPZJ2/1-49
-AEGDDPAKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFTSKA Q9T0Q9_BPFD/1-49
-FAADDAAKAAFDSLTAQATEMSGYAWALVVLVVGATVGIKLFKKFVSRA COATB_BPIF1/22-73
-</PRE><P>Another common use of alignment addition would be to combine alignments for
-several different genes into a meta-alignment. Watch out though - the identifiers
-need to match up (see Section <A HREF="#sec:SeqRecord-addition">4.7</A> for how adding
-<CODE>SeqRecord</CODE> objects works). You may find it helpful to first sort the
-alignment rows alphabetically by id:</P><PRE CLASS="verbatim">>>> edited.sort()
->>> print(edited)
-SingleLetterAlphabet() alignment with 7 rows and 49 columns
-DGTSTAATEAMNSLKTQATDLIDQTWPVVTSVAVAGLAIRLFKKFSSKA COATB_BPI22/32-83
-FAADDAAKAAFDSLTAQATEMSGYAWALVVLVVGATVGIKLFKKFVSRA COATB_BPIF1/22-73
-AEPNAAATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIRLFKKFSSKA COATB_BPIKE/30-81
-AEGDDPAKAAFNSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFTSKA COATB_BPM13/24-72
-AEGDDPAKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFASKA COATB_BPZJ2/1-49
-AEPNAAATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIKLFKKFVSRA Q9T0Q8_BPIKE/1-52
-AEGDDPAKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFTSKA Q9T0Q9_BPFD/1-49
-</PRE><P>Note that you can only add two alignments together if they
-have the same number of rows.</P><!--TOC subsection Alignments as arrays-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc77">6.3.2</A>  Alignments as arrays</H3><!--SEC END --><P>
-Depending on what you are doing, it can be more useful to turn the alignment
-object into an array of letters – and you can do this with NumPy:</P><PRE CLASS="verbatim">>>> import numpy as np
->>> from Bio import AlignIO
->>> alignment = AlignIO.read("PF05371_seed.sth", "stockholm")
->>> align_array = np.array([list(rec) for rec in alignment], np.character)
->>> print("Array shape %i by %i" % align_array.shape)
-Array shape 7 by 52
-</PRE><P>If you will be working heavily with the columns, you can tell NumPy to store
-the array by column (as in Fortran) rather then its default of by row (as in C):</P><PRE CLASS="verbatim">>>> align_array = np.array([list(rec) for rec in alignment], np.character, order="F")
-</PRE><P>Note that this leaves the original Biopython alignment object and the NumPy array
-in memory as separate objects - editing one will not update the other!</P><!--TOC section Alignment Tools-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc78">6.4</A>  Alignment Tools</H2><!--SEC END --><P>
-<A NAME="sec:alignment-tools"></A></P><P>There are <EM>lots</EM> of algorithms out there for aligning sequences, both pairwise alignments
-and multiple sequence alignments. These calculations are relatively slow, and you generally
-wouldn’t want to write such an algorithm in Python. Instead, you can use Biopython to invoke
-a command line tool on your behalf. Normally you would:
-</P><OL CLASS="enumerate" type=1><LI CLASS="li-enumerate">
-Prepare an input file of your unaligned sequences, typically this will be a FASTA file
-which you might create using <CODE>Bio.SeqIO</CODE> (see Chapter <A HREF="#chapter:Bio.SeqIO">5</A>).
-</LI><LI CLASS="li-enumerate">Call the command line tool to process this input file, typically via one of Biopython’s
-command line wrappers (which we’ll discuss here).
-</LI><LI CLASS="li-enumerate">Read the output from the tool, i.e. your aligned sequences, typically using
-<CODE>Bio.AlignIO</CODE> (see earlier in this chapter).
-</LI></OL><P>All the command line wrappers we’re going to talk about in this chapter follow the same style.
-You create a command line object specifying the options (e.g. the input filename and the
-output filename), then invoke this command line via a Python operating system call (e.g.
-using the <TT>subprocess</TT> module).</P><P>Most of these wrappers are defined in the <CODE>Bio.Align.Applications</CODE> module:</P><PRE CLASS="verbatim">>>> import Bio.Align.Applications
->>> dir(Bio.Align.Applications)
-...
-['ClustalwCommandline', 'DialignCommandline', 'MafftCommandline', 'MuscleCommandline',
-'PrankCommandline', 'ProbconsCommandline', 'TCoffeeCommandline' ...]
-</PRE><P>(Ignore the entries starting with an underscore – these have
-special meaning in Python.)
-The module <CODE>Bio.Emboss.Applications</CODE> has wrappers for some of the
-<A HREF="http://emboss.sourceforge.net/">EMBOSS suite</A>, including
-<TT>needle</TT> and <TT>water</TT>, which are described below in
-Section <A HREF="#seq:emboss-needle-water">6.4.5</A>, and wrappers for the EMBOSS
-packaged versions of the PHYLIP tools (which EMBOSS refer to as one
-of their EMBASSY packages - third party tools with an EMBOSS style
-interface).
-We won’t explore all these alignment tools here in the section, just a
-sample, but the same principles apply.</P><!--TOC subsection ClustalW-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc79">6.4.1</A>  ClustalW</H3><!--SEC END --><P>
-<A NAME="sec:align_clustal"></A>
-ClustalW is a popular command line tool for multiple sequence alignment
-(there is also a graphical interface called ClustalX). Biopython’s
-<CODE>Bio.Align.Applications</CODE> module has a wrapper for this alignment tool
-(and several others).</P><P>Before trying to use ClustalW from within Python, you should first try running
-the ClustalW tool yourself by hand at the command line, to familiarise
-yourself the other options. You’ll find the Biopython wrapper is very
-faithful to the actual command line API:</P><PRE CLASS="verbatim">>>> from Bio.Align.Applications import ClustalwCommandline
->>> help(ClustalwCommandline)
-...
-</PRE><P>For the most basic usage, all you need is to have a FASTA input file, such as
-<A HREF="http://biopython.org/DIST/docs/tutorial/examples/opuntia.fasta">opuntia.fasta</A>
-(available online or in the Doc/examples subdirectory of the Biopython source
-code). This is a small FASTA file containing seven prickly-pear DNA sequences
-(from the cactus family <I>Opuntia</I>).</P><P>By default ClustalW will generate an alignment and guide tree file with names
-based on the input FASTA file, in this case <TT>opuntia.aln</TT> and
-<TT>opuntia.dnd</TT>, but you can override this or make it explicit:</P><PRE CLASS="verbatim">>>> from Bio.Align.Applications import ClustalwCommandline
->>> cline = ClustalwCommandline("clustalw2", infile="opuntia.fasta")
->>> print(cline)
-clustalw2 -infile=opuntia.fasta
-</PRE><P>Notice here we have given the executable name as <TT>clustalw2</TT>,
-indicating we have version two installed, which has a different filename to
-version one (<TT>clustalw</TT>, the default). Fortunately both versions
-support the same set of arguments at the command line (and indeed, should be
-functionally identical).</P><P>You may find that even though you have ClustalW installed, the above command
-doesn’t work – you may get a message about “command not found” (especially
-on Windows). This indicated that the ClustalW executable is not on your PATH
-(an environment variable, a list of directories to be searched). You can
-either update your PATH setting to include the location of your copy of
-ClustalW tools (how you do this will depend on your OS), or simply type in
-the full path of the tool. For example:</P><PRE CLASS="verbatim">>>> import os
->>> from Bio.Align.Applications import ClustalwCommandline
->>> clustalw_exe = r"C:\Program Files\new clustal\clustalw2.exe"
->>> clustalw_cline = ClustalwCommandline(clustalw_exe, infile="opuntia.fasta")
-</PRE><PRE CLASS="verbatim">>>> assert os.path.isfile(clustalw_exe), "Clustal W executable missing"
->>> stdout, stderr = clustalw_cline()
-</PRE><P>Remember, in Python strings <CODE>\n</CODE> and <CODE>\t</CODE> are by default
-interpreted as a new line and a tab – which is why we’re put a letter
-“r” at the start for a raw string that isn’t translated in this way.
-This is generally good practice when specifying a Windows style file name.</P><P>Internally this uses the
-<CODE>subprocess</CODE> module which is now the recommended way to run another
-program in Python. This replaces older options like the <CODE>os.system()</CODE>
-and the <CODE>os.popen*</CODE> functions.</P><P>Now, at this point it helps to know about how command line tools “work”.
-When you run a tool at the command line, it will often print text output
-directly to screen. This text can be captured or redirected, via
-two “pipes”, called standard output (the normal results) and standard
-error (for error messages and debug messages). There is also standard
-input, which is any text fed into the tool. These names get shortened
-to stdin, stdout and stderr. When the tool finishes, it has a return
-code (an integer), which by convention is zero for success.</P><P>When you run the command line tool like this via the Biopython wrapper,
-it will wait for it to finish, and check the return code. If this is
-non zero (indicating an error), an exception is raised. The wrapper
-then returns two strings, stdout and stderr.</P><P>In the case of ClustalW, when run at the command line all the important
-output is written directly to the output files. Everything normally printed to
-screen while you wait (via stdout or stderr) is boring and can be
-ignored (assuming it worked).</P><P>What we care about are the two output files, the alignment and the guide
-tree. We didn’t tell ClustalW what filenames to use, but it defaults to
-picking names based on the input file. In this case the output should be
-in the file <CODE>opuntia.aln</CODE>.
-You should be able to work out how to read in the alignment using
-<CODE>Bio.AlignIO</CODE> by now:</P><PRE CLASS="verbatim">>>> from Bio import AlignIO
->>> align = AlignIO.read("opuntia.aln", "clustal")
->>> print(align)
-SingleLetterAlphabet() alignment with 7 rows and 906 columns
-TATACATTAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273285|gb|AF191659.1|AF191
-TATACATTAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273284|gb|AF191658.1|AF191
-TATACATTAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273287|gb|AF191661.1|AF191
-TATACATAAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273286|gb|AF191660.1|AF191
-TATACATTAAAGGAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273290|gb|AF191664.1|AF191
-TATACATTAAAGGAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273289|gb|AF191663.1|AF191
-TATACATTAAAGGAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273291|gb|AF191665.1|AF191
-</PRE><P>In case you are interested (and this is an aside from the main thrust of this
-chapter), the <TT>opuntia.dnd</TT> file ClustalW creates is just a standard
-Newick tree file, and <CODE>Bio.Phylo</CODE> can parse these:</P><PRE CLASS="verbatim">>>> from Bio import Phylo
->>> tree = Phylo.read("opuntia.dnd", "newick")
->>> Phylo.draw_ascii(tree)
- _______________ gi|6273291|gb|AF191665.1|AF191665
- __________________________|
- | | ______ gi|6273290|gb|AF191664.1|AF191664
- | |__|
- | |_____ gi|6273289|gb|AF191663.1|AF191663
- |
-_|_________________ gi|6273287|gb|AF191661.1|AF191661
- |
- |__________ gi|6273286|gb|AF191660.1|AF191660
- |
- | __ gi|6273285|gb|AF191659.1|AF191659
- |___|
- | gi|6273284|gb|AF191658.1|AF191658
-<BLANKLINE>
-</PRE><P>Chapter <A HREF="#sec:Phylo">12</A> covers Biopython’s support for phylogenetic trees in more
-depth.</P><!--TOC subsection MUSCLE-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc80">6.4.2</A>  MUSCLE</H3><!--SEC END --><P>
-MUSCLE is a more recent multiple sequence alignment tool than ClustalW, and
-Biopython also has a wrapper for it under the <CODE>Bio.Align.Applications</CODE>
-module. As before, we recommend you try using MUSCLE from the command line before
-trying it from within Python, as the Biopython wrapper is very faithful to the
-actual command line API:</P><PRE CLASS="verbatim">>>> from Bio.Align.Applications import MuscleCommandline
->>> help(MuscleCommandline)
-...
-</PRE><P>For the most basic usage, all you need is to have a FASTA input file, such as
-<A HREF="http://biopython.org/DIST/docs/tutorial/examples/opuntia.fasta">opuntia.fasta</A>
-(available online or in the Doc/examples subdirectory of the Biopython source
-code). You can then tell MUSCLE to read in this FASTA file, and write the
-alignment to an output file:</P><PRE CLASS="verbatim">>>> from Bio.Align.Applications import MuscleCommandline
->>> cline = MuscleCommandline(input="opuntia.fasta", out="opuntia.txt")
->>> print(cline)
-muscle -in opuntia.fasta -out opuntia.txt
-</PRE><P>Note that MUSCLE uses “-in” and “-out” but in Biopython we have to use
-“input” and “out” as the keyword arguments or property names. This is
-because “in” is a reserved word in Python.</P><P>By default MUSCLE will output the alignment as a FASTA file (using gapped
-sequences). The <CODE>Bio.AlignIO</CODE> module should be able to read this
-alignment using <TT>format="fasta"</TT>.
-You can also ask for ClustalW-like output:</P><PRE CLASS="verbatim">>>> from Bio.Align.Applications import MuscleCommandline
->>> cline = MuscleCommandline(input="opuntia.fasta", out="opuntia.aln", clw=True)
->>> print(cline)
-muscle -in opuntia.fasta -out opuntia.aln -clw
-</PRE><P>Or, strict ClustalW output where the original ClustalW header line is
-used for maximum compatibility:</P><PRE CLASS="verbatim">>>> from Bio.Align.Applications import MuscleCommandline
->>> cline = MuscleCommandline(input="opuntia.fasta", out="opuntia.aln", clwstrict=True)
->>> print(cline)
-muscle -in opuntia.fasta -out opuntia.aln -clwstrict
-</PRE><P>The <CODE>Bio.AlignIO</CODE> module should be able to read these alignments
-using <TT>format="clustal"</TT>.</P><P>MUSCLE can also output in GCG MSF format (using the <TT>msf</TT> argument), but
-Biopython can’t currently parse that, or using HTML which would give a human
-readable web page (not suitable for parsing).</P><P>You can also set the other optional parameters, for example the maximum number
-of iterations. See the built in help for details.</P><P>You would then run MUSCLE command line string as described above for
-ClustalW, and parse the output using <CODE>Bio.AlignIO</CODE> to get an
-alignment object.</P><!--TOC subsection MUSCLE using stdout-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc81">6.4.3</A>  MUSCLE using stdout</H3><!--SEC END --><P>Using a MUSCLE command line as in the examples above will write the alignment
-to a file. This means there will be no important information written to the
-standard out (stdout) or standard error (stderr) handles. However, by default
-MUSCLE will write the alignment to standard output (stdout). We can take
-advantage of this to avoid having a temporary output file! For example:</P><PRE CLASS="verbatim">>>> from Bio.Align.Applications import MuscleCommandline
->>> muscle_cline = MuscleCommandline(input="opuntia.fasta")
->>> print(muscle_cline)
-muscle -in opuntia.fasta
-</PRE><P>If we run this via the wrapper, we get back the output as a string. In order
-to parse this we can use <CODE>StringIO</CODE> to turn it into a handle.
-Remember that MUSCLE defaults to using FASTA as the output format:</P><PRE CLASS="verbatim">>>> from Bio.Align.Applications import MuscleCommandline
->>> muscle_cline = MuscleCommandline(input="opuntia.fasta")
->>> stdout, stderr = muscle_cline()
->>> from StringIO import StringIO
->>> from Bio import AlignIO
->>> align = AlignIO.read(StringIO(stdout), "fasta")
->>> print(align)
-SingleLetterAlphabet() alignment with 7 rows and 906 columns
-TATACATTAAAGGAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273289|gb|AF191663.1|AF191663
-TATACATTAAAGGAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273291|gb|AF191665.1|AF191665
-TATACATTAAAGGAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273290|gb|AF191664.1|AF191664
-TATACATTAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273287|gb|AF191661.1|AF191661
-TATACATAAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273286|gb|AF191660.1|AF191660
-TATACATTAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273285|gb|AF191659.1|AF191659
-TATACATTAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273284|gb|AF191658.1|AF191658
-</PRE><P>The above approach is fairly simple, but if you are dealing with very large output
-text the fact that all of stdout and stderr is loaded into memory as a string can
-be a potential drawback. Using the <CODE>subprocess</CODE> module we can work directly
-with handles instead:</P><PRE CLASS="verbatim">>>> import subprocess
->>> from Bio.Align.Applications import MuscleCommandline
->>> muscle_cline = MuscleCommandline(input="opuntia.fasta")
->>> child = subprocess.Popen(str(muscle_cline),
-... stdout=subprocess.PIPE,
-... stderr=subprocess.PIPE,
-... shell=(sys.platform!="win32"))
->>> from Bio import AlignIO
->>> align = AlignIO.read(child.stdout, "fasta")
->>> print(align)
-SingleLetterAlphabet() alignment with 7 rows and 906 columns
-TATACATTAAAGGAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273289|gb|AF191663.1|AF191663
-TATACATTAAAGGAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273291|gb|AF191665.1|AF191665
-TATACATTAAAGGAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273290|gb|AF191664.1|AF191664
-TATACATTAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273287|gb|AF191661.1|AF191661
-TATACATAAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273286|gb|AF191660.1|AF191660
-TATACATTAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273285|gb|AF191659.1|AF191659
-TATACATTAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273284|gb|AF191658.1|AF191658
-</PRE><!--TOC subsection MUSCLE using stdin and stdout-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc82">6.4.4</A>  MUSCLE using stdin and stdout</H3><!--SEC END --><P>We don’t actually <EM>need</EM> to have our FASTA input sequences prepared in a file,
-because by default MUSCLE will read in the input sequence from standard input!
-Note this is a bit more advanced and fiddly, so don’t bother with this technique
-unless you need to.</P><P>First, we’ll need some unaligned sequences in memory as <CODE>SeqRecord</CODE> objects.
-For this demonstration I’m going to use a filtered version of the original FASTA
-file (using a generator expression), taking just six of the seven sequences:</P><PRE CLASS="verbatim">>>> from Bio import SeqIO
->>> records = (r for r in SeqIO.parse("opuntia.fasta", "fasta") if len(r) < 900)
-</PRE><P>Then we create the MUSCLE command line, leaving the input and output to their
-defaults (stdin and stdout). I’m also going to ask for strict ClustalW format
-as for the output.</P><PRE CLASS="verbatim">>>> from Bio.Align.Applications import MuscleCommandline
->>> muscle_cline = MuscleCommandline(clwstrict=True)
->>> print(muscle_cline)
-muscle -clwstrict
-</PRE><P>Now for the fiddly bits using the <CODE>subprocess</CODE> module, stdin and stdout:</P><PRE CLASS="verbatim">>>> import subprocess
->>> import sys
->>> child = subprocess.Popen(str(cline),
-... stdin=subprocess.PIPE,
-... stdout=subprocess.PIPE,
-... stderr=subprocess.PIPE,
-... universal_newlines=True,
-... shell=(sys.platform!="win32"))
-</PRE><P>That should start MUSCLE, but it will be sitting waiting for its FASTA input
-sequences, which we must supply via its stdin handle:</P><PRE CLASS="verbatim">>>> SeqIO.write(records, child.stdin, "fasta")
-6
->>> child.stdin.close()
-</PRE><P>After writing the six sequences to the handle, MUSCLE will still be waiting
-to see if that is all the FASTA sequences or not – so we must signal that
-this is all the input data by closing the handle. At that point MUSCLE should
-start to run, and we can ask for the output:</P><PRE CLASS="verbatim">>>> from Bio import AlignIO
->>> align = AlignIO.read(child.stdout, "clustal")
->>> print(align)
-SingleLetterAlphabet() alignment with 6 rows and 900 columns
-TATACATTAAAGGAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273290|gb|AF191664.1|AF19166
-TATACATTAAAGGAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273289|gb|AF191663.1|AF19166
-TATACATTAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273287|gb|AF191661.1|AF19166
-TATACATAAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273286|gb|AF191660.1|AF19166
-TATACATTAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273285|gb|AF191659.1|AF19165
-TATACATTAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273284|gb|AF191658.1|AF19165
-</PRE><P>Wow! There we are with a new alignment of just the six records, without having created
-a temporary FASTA input file, or a temporary alignment output file. However, a word of
-caution: Dealing with errors with this style of calling external programs is much more
-complicated.
-It also becomes far harder to diagnose problems, because you can’t try running MUSCLE
-manually outside of Biopython (because you don’t have the input file to supply).
-There can also be subtle cross platform issues (e.g. Windows versus Linux,
-Python 2 versus Python 3), and how
-you run your script can have an impact (e.g. at the command line, from IDLE or an
-IDE, or as a GUI script). These are all generic Python issues though, and not
-specific to Biopython.</P><P>If you find working directly with <TT>subprocess</TT> like this scary, there is an
-alternative. If you execute the tool with <TT>muscle_cline()</TT> you can supply
-any standard input as a big string, <TT>muscle_cline(stdin=...)</TT>. So,
-provided your data isn’t very big, you can prepare the FASTA input in memory as
-a string using <TT>StringIO</TT> (see Section <A HREF="#sec:appendix-handles">22.1</A>):</P><PRE CLASS="verbatim">>>> from Bio import SeqIO
->>> records = (r for r in SeqIO.parse("opuntia.fasta", "fasta") if len(r) < 900)
->>> from StringIO import StringIO
->>> handle = StringIO()
->>> SeqIO.write(records, handle, "fasta")
-6
->>> data = handle.getvalue()
-</PRE><P>You can then run the tool and parse the alignment as follows:</P><PRE CLASS="verbatim">>>> stdout, stderr = muscle_cline(stdin=data)
->>> from Bio import AlignIO
->>> align = AlignIO.read(StringIO(stdout), "clustal")
->>> print(align)
-SingleLetterAlphabet() alignment with 6 rows and 900 columns
-TATACATTAAAGGAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273290|gb|AF191664.1|AF19166
-TATACATTAAAGGAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273289|gb|AF191663.1|AF19166
-TATACATTAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273287|gb|AF191661.1|AF19166
-TATACATAAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273286|gb|AF191660.1|AF19166
-TATACATTAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273285|gb|AF191659.1|AF19165
-TATACATTAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273284|gb|AF191658.1|AF19165
-</PRE><P>You might find this easier, but it does require more memory (RAM) for the strings
-used for the input FASTA and output Clustal formatted data.</P><!--TOC subsection EMBOSS needle and water-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc83">6.4.5</A>  EMBOSS needle and water</H3><!--SEC END --><P>
-<A NAME="seq:emboss-needle-water"></A>
-The <A HREF="http://emboss.sourceforge.net/">EMBOSS</A> suite includes the <TT>water</TT> and
-<TT>needle</TT> tools for Smith-Waterman algorithm local alignment, and Needleman-Wunsch
-global alignment. The tools share the same style interface, so switching between the two
-is trivial – we’ll just use <TT>needle</TT> here.</P><P>Suppose you want to do a global pairwise alignment between two sequences, prepared in
-FASTA format as follows:</P><PRE CLASS="verbatim">>HBA_HUMAN
-MVLSPADKTNVKAAWGKVGAHAGEYGAEALERMFLSFPTTKTYFPHFDLSHGSAQVKGHG
-KKVADALTNAVAHVDDMPNALSALSDLHAHKLRVDPVNFKLLSHCLLVTLAAHLPAEFTP
-AVHASLDKFLASVSTVLTSKYR
-</PRE><P>in a file <TT>alpha.fasta</TT>, and secondly in a file <TT>beta.fasta</TT>:</P><PRE CLASS="verbatim">>HBB_HUMAN
-MVHLTPEEKSAVTALWGKVNVDEVGGEALGRLLVVYPWTQRFFESFGDLSTPDAVMGNPK
-VKAHGKKVLGAFSDGLAHLDNLKGTFATLSELHCDKLHVDPENFRLLGNVLVCVLAHHFG
-KEFTPPVQAAYQKVVAGVANALAHKYH
-</PRE><P>Let’s start by creating a complete <TT>needle</TT> command line object in one go:</P><PRE CLASS="verbatim">>>> from Bio.Emboss.Applications import NeedleCommandline
->>> needle_cline = NeedleCommandline(asequence="alpha.faa", bsequence="beta.faa",
-... gapopen=10, gapextend=0.5, outfile="needle.txt")
->>> print(needle_cline)
-needle -outfile=needle.txt -asequence=alpha.faa -bsequence=beta.faa -gapopen=10 -gapextend=0.5
-</PRE><P>Why not try running this by hand at the command prompt? You should see it does a
-pairwise comparison and records the output in the file <TT>needle.txt</TT> (in the
-default EMBOSS alignment file format).</P><P>Even if you have EMBOSS installed, running this command may not work – you
-might get a message about “command not found” (especially on Windows). This
-probably means that the EMBOSS tools are not on your PATH environment
-variable. You can either update your PATH setting, or simply tell Biopython
-the full path to the tool, for example:</P><PRE CLASS="verbatim">>>> from Bio.Emboss.Applications import NeedleCommandline
->>> needle_cline = NeedleCommandline(r"C:\EMBOSS\needle.exe",
-... asequence="alpha.faa", bsequence="beta.faa",
-... gapopen=10, gapextend=0.5, outfile="needle.txt")
-</PRE><P>Remember in Python that for a default string <CODE>\n</CODE> or <CODE>\t</CODE> means a
-new line or a tab – which is why we’re put a letter “r” at the start for a raw string.</P><P>At this point it might help to try running the EMBOSS tools yourself by hand at the
-command line, to familiarise yourself the other options and compare them to the
-Biopython help text:</P><PRE CLASS="verbatim">>>> from Bio.Emboss.Applications import NeedleCommandline
->>> help(NeedleCommandline)
-...
-</PRE><P>Note that you can also specify (or change or look at) the settings like this:</P><PRE CLASS="verbatim">>>> from Bio.Emboss.Applications import NeedleCommandline
->>> needle_cline = NeedleCommandline()
->>> needle_cline.asequence="alpha.faa"
->>> needle_cline.bsequence="beta.faa"
->>> needle_cline.gapopen=10
->>> needle_cline.gapextend=0.5
->>> needle_cline.outfile="needle.txt"
->>> print(needle_cline)
-needle -outfile=needle.txt -asequence=alpha.faa -bsequence=beta.faa -gapopen=10 -gapextend=0.5
->>> print(needle_cline.outfile)
-needle.txt
-</PRE><P>Next we want to use Python to run this command for us. As explained above,
-for full control, we recommend you use the built in Python <TT>subprocess</TT>
-module, but for simple usage the wrapper object usually suffices:</P><PRE CLASS="verbatim">>>> stdout, stderr = needle_cline()
->>> print(stdout + stderr)
-Needleman-Wunsch global alignment of two sequences
-</PRE><P>Next we can load the output file with <CODE>Bio.AlignIO</CODE> as
-discussed earlier in this chapter, as the <TT>emboss</TT> format:</P><PRE CLASS="verbatim">>>> from Bio import AlignIO
->>> align = AlignIO.read("needle.txt", "emboss")
->>> print(align)
-SingleLetterAlphabet() alignment with 2 rows and 149 columns
-MV-LSPADKTNVKAAWGKVGAHAGEYGAEALERMFLSFPTTKTY...KYR HBA_HUMAN
-MVHLTPEEKSAVTALWGKV--NVDEVGGEALGRLLVVYPWTQRF...KYH HBB_HUMAN
-</PRE><P>In this example, we told EMBOSS to write the output to a file, but you
-<EM>can</EM> tell it to write the output to stdout instead (useful if you
-don’t want a temporary output file to get rid of – use
-<TT>stdout=True</TT> rather than the <TT>outfile</TT> argument), and
-also to read <EM>one</EM> of the one of the inputs from stdin (e.g.
-<TT>asequence="stdin"</TT>, much like in the MUSCLE example in the
-section above).</P><P>This has only scratched the surface of what you can do with <TT>needle</TT>
-and <TT>water</TT>. One useful trick is that the second file can contain
-multiple sequences (say five), and then EMBOSS will do five pairwise
-alignments.</P><P>Note - Biopython includes its own pairwise alignment code in the <CODE>Bio.pairwise2</CODE>
-module (written in C for speed, but with a pure Python fallback available too). This
-doesn’t work with alignment objects, so we have not covered it within this chapter.
-See the module’s docstring (built in help) for details.</P><!--TOC chapter BLAST-->
-<H1 CLASS="chapter"><!--SEC ANCHOR --><A NAME="htoc84">Chapter 7</A>  BLAST</H1><!--SEC END --><P>
-<A NAME="chapter:blast"></A>
-Hey, everybody loves BLAST right? I mean, geez, how can it get any easier to do comparisons between one of your sequences and every other sequence in the known world? But, of course, this section isn’t about how cool BLAST is, since we already know that. It is about the problem with BLAST – it can be really difficult to deal with the volume of data generated by large runs, and to automate BLAST runs in general.</P><P>Fortunately, the Biopython folks know this only too well, [...]
-Firstly, running BLAST for your query sequence(s), and getting some output.
-Secondly, parsing the BLAST output in Python for further analysis.</P><P>Your first introduction to running BLAST was probably via the NCBI web-service.
-In fact, there are lots of ways you can run BLAST, which can be categorised in several ways.
-The most important distinction is running BLAST locally (on your own machine),
-and running BLAST remotely (on another machine, typically the NCBI servers).
-We’re going to start this chapter by invoking the NCBI online BLAST service
-from within a Python script.</P><P><EM>NOTE</EM>: The following Chapter <A HREF="#chapter:searchio">8</A> describes
-<CODE>Bio.SearchIO</CODE>, an <EM>experimental</EM> module in Biopython. We
-intend this to ultimately replace the older <CODE>Bio.Blast</CODE> module, as it
-provides a more general framework handling other related sequence
-searching tools as well. However, until that is declared stable, for
-production code please continue to use the <CODE>Bio.Blast</CODE> module
-for dealing with NCBI BLAST.</P><!--TOC section Running BLAST over the Internet-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc85">7.1</A>  Running BLAST over the Internet</H2><!--SEC END --><P>
-<A NAME="sec:running-www-blast"></A></P><P>We use the function <CODE>qblast()</CODE> in the <CODE>Bio.Blast.NCBIWWW</CODE> module
-to call the online version of BLAST. This has three non-optional arguments:
-</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-The first argument is the blast program to use for the search, as a
-lower case string. The options and descriptions of the programs are
-available at <A HREF="http://www.ncbi.nlm.nih.gov/BLAST/blast_program.shtml"><TT>http://www.ncbi.nlm.nih.gov/BLAST/blast_program.shtml</TT></A>.
-Currently <CODE>qblast</CODE> only works with blastn, blastp, blastx, tblast
-and tblastx.
-</LI><LI CLASS="li-itemize">The second argument specifies the databases to search against. Again,
-the options for this are available on the NCBI web pages at
-<A HREF="http://www.ncbi.nlm.nih.gov/BLAST/blast_databases.shtml"><TT>http://www.ncbi.nlm.nih.gov/BLAST/blast_databases.shtml</TT></A>.
-</LI><LI CLASS="li-itemize">The third argument is a string containing your query sequence. This
-can either be the sequence itself, the sequence in fasta format,
-or an identifier like a GI number.
-</LI></UL><P>The <CODE>qblast</CODE> function also take a number of other option arguments
-which are basically analogous to the different parameters you can set
-on the BLAST web page. We’ll just highlight a few of them here:</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-The <CODE>qblast</CODE> function can return the BLAST results in various
-formats, which you can choose with the optional <CODE>format_type</CODE> keyword:
-<CODE>"HTML"</CODE>, <CODE>"Text"</CODE>, <CODE>"ASN.1"</CODE>, or <CODE>"XML"</CODE>.
-The default is <CODE>"XML"</CODE>, as that is the format expected by the parser,
-described in section <A HREF="#sec:parsing-blast">7.3</A> below.
-</LI><LI CLASS="li-itemize">The argument <CODE>expect</CODE> sets the expectation or e-value threshold.
-</LI></UL><P>For more about the optional BLAST arguments, we refer you to the NCBI’s own
-documentation, or that built into Biopython:</P><PRE CLASS="verbatim">>>> from Bio.Blast import NCBIWWW
->>> help(NCBIWWW.qblast)
-...
-</PRE><P>Note that the default settings on the NCBI BLAST website are not quite
-the same as the defaults on QBLAST. If you get different results, you’ll
-need to check the parameters (e.g., the expectation value threshold and
-the gap values).</P><P>For example, if you have a nucleotide sequence you want to search against
-the nucleotide database (nt) using BLASTN, and you know the GI number of your
-query sequence, you can use:</P><PRE CLASS="verbatim">>>> from Bio.Blast import NCBIWWW
->>> result_handle = NCBIWWW.qblast("blastn", "nt", "8332116")
-</PRE><P>Alternatively, if we have our query sequence already in a FASTA formatted
-file, we just need to open the file and read in this record as a string,
-and use that as the query argument:</P><PRE CLASS="verbatim">>>> from Bio.Blast import NCBIWWW
->>> fasta_string = open("m_cold.fasta").read()
->>> result_handle = NCBIWWW.qblast("blastn", "nt", fasta_string)
-</PRE><P>We could also have read in the FASTA file as a <CODE>SeqRecord</CODE> and then
-supplied just the sequence itself:</P><PRE CLASS="verbatim">>>> from Bio.Blast import NCBIWWW
->>> from Bio import SeqIO
->>> record = SeqIO.read("m_cold.fasta", format="fasta")
->>> result_handle = NCBIWWW.qblast("blastn", "nt", record.seq)
-</PRE><P>Supplying just the sequence means that BLAST will assign an identifier
-for your sequence automatically. You might prefer to use the
-<CODE>SeqRecord</CODE> object’s format method to make a FASTA string
-(which will include the existing identifier):</P><PRE CLASS="verbatim">>>> from Bio.Blast import NCBIWWW
->>> from Bio import SeqIO
->>> record = SeqIO.read("m_cold.fasta", format="fasta")
->>> result_handle = NCBIWWW.qblast("blastn", "nt", record.format("fasta"))
-</PRE><P>This approach makes more sense if you have your sequence(s) in a
-non-FASTA file format which you can extract using <CODE>Bio.SeqIO</CODE>
-(see Chapter <A HREF="#chapter:Bio.SeqIO">5</A>).</P><P>Whatever arguments you give the <CODE>qblast()</CODE> function, you should
-get back your results in a handle object (by default in XML format).
-The next step would be to parse the XML output into Python objects
-representing the search results (Section <A HREF="#sec:parsing-blast">7.3</A>),
-but you might want to save a local copy of the output file first.
-I find this especially useful when debugging my code that extracts
-info from the BLAST results (because re-running the online search
-is slow and wastes the NCBI computer time).</P><P><A NAME="sec:saving-blast-output"></A></P><P>We need to be a bit careful since we can use <CODE>result_handle.read()</CODE> to
-read the BLAST output only once – calling <CODE>result_handle.read()</CODE> again
-returns an empty string.</P><PRE CLASS="verbatim">>>> save_file = open("my_blast.xml", "w")
->>> save_file.write(result_handle.read())
->>> save_file.close()
->>> result_handle.close()
-</PRE><P>After doing this, the results are in the file <CODE>my_blast.xml</CODE> and the
-original handle has had all its data extracted (so we closed it). However,
-the <CODE>parse</CODE> function of the BLAST parser (described
-in <A HREF="#sec:parsing-blast">7.3</A>) takes a file-handle-like object, so
-we can just open the saved file for input:</P><PRE CLASS="verbatim">>>> result_handle = open("my_blast.xml")
-</PRE><P>Now that we’ve got the BLAST results back into a handle again, we are ready
-to do something with them, so this leads us right into the parsing section
-(see Section <A HREF="#sec:parsing-blast">7.3</A> below). You may want to jump ahead to
-that now ….</P><!--TOC section Running BLAST locally-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc86">7.2</A>  Running BLAST locally</H2><!--SEC END --><P>
-<A NAME="sec:running-local-blast"></A></P><!--TOC subsection Introduction-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc87">7.2.1</A>  Introduction</H3><!--SEC END --><P>Running BLAST locally (as opposed to over the internet, see
-Section <A HREF="#sec:running-www-blast">7.1</A>) has at least major two advantages:
-</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-Local BLAST may be faster than BLAST over the internet;
-</LI><LI CLASS="li-itemize">Local BLAST allows you to make your own database to search for sequences against.
-</LI></UL><P>
-Dealing with proprietary or unpublished sequence data can be another reason to run BLAST
-locally. You may not be allowed to redistribute the sequences, so submitting them to the
-NCBI as a BLAST query would not be an option.</P><P>Unfortunately, there are some major drawbacks too – installing all the bits and getting
-it setup right takes some effort:
-</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-Local BLAST requires command line tools to be installed.
-</LI><LI CLASS="li-itemize">Local BLAST requires (large) BLAST databases to be setup (and potentially kept up to date).
-</LI></UL><P>To further confuse matters there are several different BLAST packages available,
-and there are also other tools which can produce imitation BLAST output files, such as BLAT.</P><!--TOC subsection Standalone NCBI BLAST+-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc88">7.2.2</A>  Standalone NCBI BLAST+</H3><!--SEC END --><P>The “new”
-<A HREF="http://blast.ncbi.nlm.nih.gov/Blast.cgi?CMD=Web&PAGE_TYPE=BlastDocs&DOC_TYPE=Download">NCBI BLAST+</A> suite was released in 2009. This replaces the old NCBI “legacy” BLAST
-package (see below).</P><P>This section will show briefly how to use these tools from within Python. If you have
-already read or tried the alignment tool examples in Section <A HREF="#sec:alignment-tools">6.4</A>
-this should all seem quite straightforward. First, we construct a command line string
-(as you would type in at the command line prompt if running standalone BLAST by hand).
-Then we can execute this command from within Python.</P><P>For example, taking a FASTA file of gene nucleotide sequences, you might want to
-run a BLASTX (translation) search against the non-redundant (NR) protein database.
-Assuming you (or your systems administrator) has downloaded and installed the NR
-database, you might run:</P><PRE CLASS="verbatim">blastx -query opuntia.fasta -db nr -out opuntia.xml -evalue 0.001 -outfmt 5
-</PRE><P>This should run BLASTX against the NR database, using an expectation cut-off value
-of 0.001 and produce XML output to the specified file (which we can then parse).
-On my computer this takes about six minutes - a good reason to save the output
-to a file so you can repeat any analysis as needed.</P><P>From within Biopython we can use the NCBI BLASTX wrapper from the
-<CODE>Bio.Blast.Applications</CODE> module to build the command line string,
-and run it:</P><PRE CLASS="verbatim">>>> from Bio.Blast.Applications import NcbiblastxCommandline
->>> help(NcbiblastxCommandline)
-...
->>> blastx_cline = NcbiblastxCommandline(query="opuntia.fasta", db="nr", evalue=0.001,
-... outfmt=5, out="opuntia.xml")
->>> blastx_cline
-NcbiblastxCommandline(cmd='blastx', out='opuntia.xml', outfmt=5, query='opuntia.fasta',
-db='nr', evalue=0.001)
->>> print(blastx_cline)
-blastx -out opuntia.xml -outfmt 5 -query opuntia.fasta -db nr -evalue 0.001
->>> stdout, stderr = blastx_cline()
-</PRE><P>In this example there shouldn’t be any output from BLASTX to the terminal,
-so stdout and stderr should be empty. You may want to check the output file
-<CODE>opuntia.xml</CODE> has been created.</P><P>As you may recall from earlier examples in the tutorial, the <CODE>opuntia.fasta</CODE>
-contains seven sequences, so the BLAST XML output should contain multiple results.
-Therefore use <CODE>Bio.Blast.NCBIXML.parse()</CODE> to parse it as described below in
-Section <A HREF="#sec:parsing-blast">7.3</A>.</P><!--TOC subsection Other versions of BLAST-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc89">7.2.3</A>  Other versions of BLAST</H3><!--SEC END --><P>NCBI BLAST+ (written in C++) was first released in 2009 as a replacement for
-the original NCBI “legacy” BLAST (written in C) which is no longer being updated.
-There were a lot of changes – the old version had a single core command line
-tool <CODE>blastall</CODE> which covered multiple different BLAST search types (which
-are now separate commands in BLAST+), and all the command line options
-were renamed.
-Biopython’s wrappers for the NCBI “legacy” BLAST tools have been deprecated
-and will be removed in a future release.
-To try to avoid confusion, we do not cover calling these old tools from Biopython
-in this tutorial.</P><P>You may also come across <A HREF="http://blast.wustl.edu/">Washington University BLAST</A>
-(WU-BLAST), and its successor, <A HREF="http://blast.advbiocomp.com">Advanced Biocomputing
-BLAST</A> (AB-BLAST, released in 2009, not free/open source). These packages include
-the command line tools <CODE>wu-blastall</CODE> and <CODE>ab-blastall</CODE>, which mimicked
-<CODE>blastall</CODE> from the NCBI “legacy” BLAST suite.
-Biopython does not currently provide wrappers for calling these tools, but should be able
-to parse any NCBI compatible output from them.</P><!--TOC section Parsing BLAST output-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc90">7.3</A>  Parsing BLAST output</H2><!--SEC END --><P>
-<A NAME="sec:parsing-blast"></A></P><P>As mentioned above, BLAST can generate output in various formats, such as
-XML, HTML, and plain text. Originally, Biopython had parsers for BLAST
-plain text and HTML output, as these were the only output formats offered
-at the time. Unfortunately, the BLAST output in these formats kept changing,
-each time breaking the Biopython parsers. Our HTML BLAST parser has been
-removed, but the plain text BLAST parser is still available (see
-Section <A HREF="#sec:parsing-blast-deprecated">7.5</A>). Use it at your own risk,
-it may or may not work, depending on which BLAST version you’re using.</P><P>As keeping up with changes in BLAST
-became a hopeless endeavor, especially with users running different BLAST
-versions, we now recommend to parse the output in XML format, which can be
-generated by recent versions of BLAST. Not only is the XML output more stable
-than the plain text and HTML output, it is also much easier to parse
-automatically, making Biopython a whole lot more stable.</P><P>You can get BLAST output in XML format in various ways. For the parser, it
-doesn’t matter how the output was generated, as long as it is in the XML format.
-</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-You can use Biopython to run BLAST over the internet, as described in
-section <A HREF="#sec:running-www-blast">7.1</A>.
-</LI><LI CLASS="li-itemize">You can use Biopython to run BLAST locally, as described in
-section <A HREF="#sec:running-local-blast">7.2</A>.
-</LI><LI CLASS="li-itemize">You can do the BLAST search yourself on the NCBI site through your
-web browser, and then save the results. You need to choose XML as the format
-in which to receive the results, and save the final BLAST page you get
-(you know, the one with all of the interesting results!) to a file.
-</LI><LI CLASS="li-itemize">You can also run BLAST locally without using Biopython, and save
-the output in a file. Again, you need to choose XML as the format in which
-to receive the results.
-</LI></UL><P>
-The important point is that you do not have to use Biopython
-scripts to fetch the data in order to be able to parse it.
-Doing things in one of these ways, you then need to get a handle
-to the results. In Python, a handle is just a nice general way of
-describing input to any info source so that the info can be retrieved
-using <CODE>read()</CODE> and <CODE>readline()</CODE> functions
-(see Section sec:appendix-handles).</P><P>If you followed the code above for interacting with BLAST through a
-script, then you already have <CODE>result_handle</CODE>, the handle to the
-BLAST results. For example, using a GI number to do an online search:</P><PRE CLASS="verbatim">>>> from Bio.Blast import NCBIWWW
->>> result_handle = NCBIWWW.qblast("blastn", "nt", "8332116")
-</PRE><P>If instead you ran BLAST some other way, and have the
-BLAST output (in XML format) in the file <CODE>my_blast.xml</CODE>, all you
-need to do is to open the file for reading:</P><PRE CLASS="verbatim">>>> result_handle = open("my_blast.xml")
-</PRE><P>Now that we’ve got a handle, we are ready to parse the output. The
-code to parse it is really quite small. If you expect a single
-BLAST result (i.e., you used a single query):</P><PRE CLASS="verbatim">>>> from Bio.Blast import NCBIXML
->>> blast_record = NCBIXML.read(result_handle)
-</PRE><P>or, if you have lots of results (i.e., multiple query sequences):</P><PRE CLASS="verbatim">>>> from Bio.Blast import NCBIXML
->>> blast_records = NCBIXML.parse(result_handle)
-</PRE><P>Just like <CODE>Bio.SeqIO</CODE> and <CODE>Bio.AlignIO</CODE>
-(see Chapters <A HREF="#chapter:Bio.SeqIO">5</A> and <A HREF="#chapter:Bio.AlignIO">6</A>),
-we have a pair of input functions, <CODE>read</CODE> and <CODE>parse</CODE>, where
-<CODE>read</CODE> is for when you have exactly one object, and <CODE>parse</CODE>
-is an iterator for when you can have lots of objects – but instead of
-getting <CODE>SeqRecord</CODE> or <CODE>MultipleSeqAlignment</CODE> objects, we
-get BLAST record objects.</P><P>To be able to handle the situation where the BLAST file may be huge,
-containing thousands of results, <CODE>NCBIXML.parse()</CODE> returns an
-iterator. In plain English, an iterator allows you to step through
-the BLAST output, retrieving BLAST records one by one for each BLAST
-search result:</P><PRE CLASS="verbatim">>>> from Bio.Blast import NCBIXML
->>> blast_records = NCBIXML.parse(result_handle)
->>> blast_record = next(blast_records)
-# ... do something with blast_record
->>> blast_record = next(blast_records)
-# ... do something with blast_record
->>> blast_record = next(blast_records)
-# ... do something with blast_record
->>> blast_record = next(blast_records)
-Traceback (most recent call last):
- File "<stdin>", line 1, in <module>
-StopIteration
-# No further records
-</PRE><P>Or, you can use a <CODE>for</CODE>-loop:
-</P><PRE CLASS="verbatim">>>> for blast_record in blast_records:
-... # Do something with blast_record
-</PRE><P>Note though that you can step through the BLAST records only once.
-Usually, from each BLAST record you would save the information that
-you are interested in. If you want to save all returned BLAST records,
-you can convert the iterator into a list:
-</P><PRE CLASS="verbatim">>>> blast_records = list(blast_records)
-</PRE><P>Now you can access each BLAST record in the list with an index as usual.
-If your BLAST file is huge though, you may run into memory problems trying to
-save them all in a list.</P><P>Usually, you’ll be running one BLAST search at a time. Then, all you need
-to do is to pick up the first (and only) BLAST record in <CODE>blast_records</CODE>:
-</P><PRE CLASS="verbatim">>>> from Bio.Blast import NCBIXML
->>> blast_records = NCBIXML.parse(result_handle)
->>> blast_record = next(blast_records)
-</PRE><P>or more elegantly:
-</P><PRE CLASS="verbatim">>>> from Bio.Blast import NCBIXML
->>> blast_record = NCBIXML.read(result_handle)
-</PRE><P>I guess by now you’re wondering what is in a BLAST record.</P><!--TOC section The BLAST record class-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc91">7.4</A>  The BLAST record class</H2><!--SEC END --><P>A BLAST Record contains everything you might ever want to extract from the
-BLAST output. Right now we’ll just show
-an example of how to get some info out of the BLAST report, but if you
-want something in particular that is not described here, look at the
-info on the record class in detail, and take a gander into the code or
-automatically generated documentation – the docstrings have lots of
-good info about what is stored in each piece of information.</P><P>To continue with our example, let’s just print out some summary info
-about all hits in our blast report greater than a particular
-threshold. The following code does this:</P><PRE CLASS="verbatim">>>> E_VALUE_THRESH = 0.04
-
->>> for alignment in blast_record.alignments:
-... for hsp in alignment.hsps:
-... if hsp.expect < E_VALUE_THRESH:
-... print('****Alignment****')
-... print('sequence:', alignment.title)
-... print('length:', alignment.length)
-... print('e value:', hsp.expect)
-... print(hsp.query[0:75] + '...')
-... print(hsp.match[0:75] + '...')
-... print(hsp.sbjct[0:75] + '...')
-</PRE><P>This will print out summary reports like the following:</P><PRE CLASS="verbatim">****Alignment****
-sequence: >gb|AF283004.1|AF283004 Arabidopsis thaliana cold acclimation protein WCOR413-like protein
-alpha form mRNA, complete cds
-length: 783
-e value: 0.034
-tacttgttgatattggatcgaacaaactggagaaccaacatgctcacgtcacttttagtcccttacatattcctc...
-||||||||| | ||||||||||| || |||| || || |||||||| |||||| | | |||||||| ||| ||...
-tacttgttggtgttggatcgaaccaattggaagacgaatatgctcacatcacttctcattccttacatcttcttc...
-</PRE><P>Basically, you can do anything you want to with the info in the BLAST
-report once you have parsed it. This will, of course, depend on what
-you want to use it for, but hopefully this helps you get started on
-doing what you need to do!</P><P>An important consideration for extracting information from a BLAST report is the type of objects that the information is stored in. In Biopython, the parsers return <CODE>Record</CODE> objects, either <CODE>Blast</CODE> or <CODE>PSIBlast</CODE> depending on what you are parsing. These objects are defined in <CODE>Bio.Blast.Record</CODE> and are quite complete.</P><P>Here are my attempts at UML class diagrams for the <CODE>Blast</CODE> and <CODE>PSIBlast</ [...]
-<A NAME="fig:blastrecord"></A>
-<IMG SRC="images/BlastRecord.png" width=650, height=750>
-</P><P>The PSIBlast record object is similar, but has support for the rounds that are used in the iteration steps of PSIBlast. The class diagram for PSIBlast is shown in Figure <A HREF="#fig:psiblastrecord">7.4</A>.</P><P>
-<A NAME="fig:psiblastrecord"></A>
-<IMG SRC="images/PSIBlastRecord.png" width=650, height=750>
-</P><!--TOC section Deprecated BLAST parsers-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc92">7.5</A>  Deprecated BLAST parsers</H2><!--SEC END --><P>
-<A NAME="sec:parsing-blast-deprecated"></A></P><P>Older versions of Biopython had parsers for BLAST output in plain text or HTML
-format. Over the years, we discovered that it is very hard to maintain these
-parsers in working order. Basically, any small change to the BLAST output in
-newly released BLAST versions tends to cause the plain text and HTML parsers
-to break. We therefore recommend parsing BLAST output in XML format, as
-described in section <A HREF="#sec:parsing-blast">7.3</A>.</P><P>Depending on which BLAST versions or programs you’re using, our plain text BLAST parser may or may not work. Use it at your own risk!</P><!--TOC subsection Parsing plain-text BLAST output-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc93">7.5.1</A>  Parsing plain-text BLAST output</H3><!--SEC END --><P>The plain text BLAST parser is located in <CODE>Bio.Blast.NCBIStandalone</CODE>.</P><P>As with the XML parser, we need to have a handle object that we can pass to the parser. The handle must implement the <CODE>readline()</CODE> method and do this properly. The common ways to get such a handle are to either use the provided <CODE>blastall</CODE> or <CODE>bl [...]
-</PRE><P>Well, now that we’ve got a handle (which we’ll call <CODE>result_handle</CODE>),
-we are ready to parse it. This can be done with the following code:</P><PRE CLASS="verbatim">>>> from Bio.Blast import NCBIStandalone
->>> blast_parser = NCBIStandalone.BlastParser()
->>> blast_record = blast_parser.parse(result_handle)
-</PRE><P>This will parse the BLAST report into a Blast Record class (either a Blast or a PSIBlast record, depending on what you are parsing) so that you can extract the information from it. In our case, let’s just print out a quick summary of all of the alignments greater than some threshold value.</P><PRE CLASS="verbatim">>>> E_VALUE_THRESH = 0.04
->>> for alignment in blast_record.alignments:
-... for hsp in alignment.hsps:
-... if hsp.expect < E_VALUE_THRESH:
-... print('****Alignment****')
-... print('sequence:', alignment.title)
-... print('length:', alignment.length)
-... print('e value:', hsp.expect)
-... print(hsp.query[0:75] + '...')
-... print(hsp.match[0:75] + '...')
-... print(hsp.sbjct[0:75] + '...')
-</PRE><P>If you also read the section <A HREF="#sec:parsing-blast">7.3</A> on parsing BLAST XML output, you’ll notice that the above code is identical to what is found in that section. Once you parse something into a record class you can deal with it independent of the format of the original BLAST info you were parsing. Pretty snazzy!</P><P>Sure, parsing one record is great, but I’ve got a BLAST file with tons of records – how can I parse them all? Well, fear no [...]
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc94">7.5.2</A>  Parsing a plain-text BLAST file full of BLAST runs</H3><!--SEC END --><P>We can do this using the blast iterator. To set up an iterator, we first set up a parser, to parse our blast reports in Blast Record objects:</P><PRE CLASS="verbatim">>>> from Bio.Blast import NCBIStandalone
->>> blast_parser = NCBIStandalone.BlastParser()
-</PRE><P>Then we will assume we have a handle to a bunch of blast records, which we’ll call <CODE>result_handle</CODE>. Getting a handle is described in full detail above in the blast parsing sections.</P><P>Now that we’ve got a parser and a handle, we are ready to set up the iterator with the following command:</P><PRE CLASS="verbatim">>>> blast_iterator = NCBIStandalone.Iterator(result_handle, blast_parser)
-</PRE><P>The second option, the parser, is optional. If we don’t supply a parser, then the iterator will just return the raw BLAST reports one at a time.</P><P>Now that we’ve got an iterator, we start retrieving blast records (generated by our parser) using <CODE>next()</CODE>:</P><PRE CLASS="verbatim">>>> blast_record = next(blast_iterator)
-</PRE><P>Each call to next will return a new record that we can deal with. Now we can iterate through these records and generate our old favorite, a nice little blast report:</P><PRE CLASS="verbatim">>>> for blast_record in blast_iterator:
-... E_VALUE_THRESH = 0.04
-... for alignment in blast_record.alignments:
-... for hsp in alignment.hsps:
-... if hsp.expect < E_VALUE_THRESH:
-... print('****Alignment****')
-... print('sequence:', alignment.title)
-... print('length:', alignment.length)
-... print('e value:', hsp.expect)
-... if len(hsp.query) > 75:
-... dots = '...'
-... else:
-... dots = ''
-... print(hsp.query[0:75] + dots)
-... print(hsp.match[0:75] + dots)
-... print(hsp.sbjct[0:75] + dots)
-</PRE><P>The iterator allows you to deal with huge blast records without any memory problems, since things are read in one at a time. I have parsed tremendously huge files without any problems using this.</P><!--TOC subsection Finding a bad record somewhere in a huge plain-text BLAST file-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc95">7.5.3</A>  Finding a bad record somewhere in a huge plain-text BLAST file</H3><!--SEC END --><P>One really ugly problem that happens to me is that I’ll be parsing a huge blast file for a while, and the parser will bomb out with a ValueError. This is a serious problem, since you can’t tell if the ValueError is due to a parser problem, or a problem with the BLAST. To make it even worse, you have no idea where [...]
->>> blast_file = os.path.join(os.getcwd(), "blast_out", "big_blast.out")
->>> error_file = os.path.join(os.getcwd(), "blast_out", "big_blast.problems")
-</PRE><P>Now we want to get a <CODE>BlastErrorParser</CODE>:</P><PRE CLASS="verbatim">>>> from Bio.Blast import NCBIStandalone
->>> error_handle = open(error_file, "w")
->>> blast_error_parser = NCBIStandalone.BlastErrorParser(error_handle)
-</PRE><P>Notice that the parser take an optional argument of a handle. If a handle is passed, then the parser will write any blast records which generate a ValueError to this handle. Otherwise, these records will not be recorded.</P><P>Now we can use the <CODE>BlastErrorParser</CODE> just like a regular blast parser. Specifically, we might want to make an iterator that goes through our blast records one at a time and parses them with the error parser:</P><PRE CLASS="verbatim">>>> [...]
->>> iterator = NCBIStandalone.Iterator(result_handle, blast_error_parser)
-</PRE><P>We can read these records one a time, but now we can catch and deal with errors that are due to problems with Blast (and not with the parser itself):</P><PRE CLASS="verbatim">>>> try:
-... next_record = next(iterator)
-... except NCBIStandalone.LowQualityBlastError as info:
-... print("LowQualityBlastError detected in id %s" % info[1])
-</PRE><P>The <CODE>next()</CODE> functionality is normally called indirectly via a <CODE>for</CODE>-loop.
-Right now the <CODE>BlastErrorParser</CODE> can generate the following errors:</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-<CODE>ValueError</CODE> – This is the same error generated by the regular BlastParser, and is due to the parser not being able to parse a specific file. This is normally either due to a bug in the parser, or some kind of discrepancy between the version of BLAST you are using and the versions the parser is able to handle.</LI><LI CLASS="li-itemize"><CODE>LowQualityBlastError</CODE> – When BLASTing a sequence that is of really bad quality (for example, a short sequence that i [...]
-<UL CLASS="itemize"><LI CLASS="li-itemize">
-<CODE>item[0]</CODE> – The error message
-</LI><LI CLASS="li-itemize"><CODE>item[1]</CODE> – The id of the input record that caused the error. This is really useful if you want to record all of the records that are causing problems.
-</LI></UL>
-</LI></UL><P>As mentioned, with each error generated, the BlastErrorParser will write the offending record to the specified <CODE>error_handle</CODE>. You can then go ahead and look and these and deal with them as you see fit. Either you will be able to debug the parser with a single blast report, or will find out problems in your blast runs. Either way, it will definitely be a useful experience!</P><P>Hopefully the <CODE>BlastErrorParser</CODE> will make it much easier to debug and deal [...]
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc96">7.6</A>  Dealing with PSI-BLAST</H2><!--SEC END --><P>You can run the standalone version of PSI-BLAST (the legacy NCBI command line
-tool <CODE>blastpgp</CODE>, or its replacement <CODE>psiblast</CODE>) using the wrappers
-in <CODE>Bio.Blast.Applications</CODE> module.</P><P>At the time of writing, the NCBI do not appear to support tools running a
-PSI-BLAST search via the internet.</P><P>Note that the <CODE>Bio.Blast.NCBIXML</CODE> parser can read the XML output from
-current versions of PSI-BLAST, but information like which sequences in each
-iteration is new or reused isn’t present in the XML file.
-If you care about this information you may have more joy with the plain text
-output and the <CODE>PSIBlastParser</CODE> in <CODE>Bio.Blast.NCBIStandalone</CODE>.</P><!--TOC section Dealing with RPS-BLAST-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc97">7.7</A>  Dealing with RPS-BLAST</H2><!--SEC END --><P>You can run the standalone version of RPS-BLAST (either the legacy NCBI
-command line tool <CODE>rpsblast</CODE>, or its replacement with the same name)
-using the wrappers in <CODE>Bio.Blast.Applications</CODE> module.</P><P>At the time of writing, the NCBI do not appear to support tools running an
-RPS-BLAST search via the internet.</P><P>You can use the <CODE>Bio.Blast.NCBIXML</CODE> parser to read the XML output from
-current versions of RPS-BLAST.</P><!--TOC chapter BLAST and other sequence search tools (<I>experimental code</I>)-->
-<H1 CLASS="chapter"><!--SEC ANCHOR --><A NAME="htoc98">Chapter 8</A>  BLAST and other sequence search tools (<I>experimental code</I>)</H1><!--SEC END --><P>
-<A NAME="chapter:searchio"></A></P><P><EM>WARNING</EM>: This chapter of the Tutorial describes an <EM>experimental</EM>
-module in Biopython. It is being included in Biopython and documented
-here in the tutorial in a pre-final state to allow a period of feedback
-and refinement before we declare it stable. Until then the details will
-probably change, and any scripts using the current <CODE>Bio.SearchIO</CODE>
-would need to be updated. Please keep this in mind! For stable code
-working with NCBI BLAST, please continue to use Bio.Blast described
-in the preceding Chapter <A HREF="#chapter:blast">7</A>.</P><P>Biological sequence identification is an integral part of bioinformatics.
-Several tools are available for this, each with their own algorithms and
-approaches, such as BLAST (arguably the most popular), FASTA, HMMER, and many
-more. In general, these tools usually use your sequence to search a database of
-potential matches. With the growing number of known sequences (hence the
-growing number of potential matches), interpreting the results becomes
-increasingly hard as there could be hundreds or even thousands of potential
-matches. Naturally, manual interpretation of these searches’ results is out of
-the question. Moreover, you often need to work with several sequence search
-tools, each with its own statistics, conventions, and output format. Imagine how
-daunting it would be when you need to work with multiple sequences using
-multiple search tools.</P><P>We know this too well ourselves, which is why we created the <CODE>Bio.SearchIO</CODE>
-submodule in Biopython. <CODE>Bio.SearchIO</CODE> allows you to extract information
-from your search results in a convenient way, while also dealing with the
-different standards and conventions used by different search tools.
-The name <CODE>SearchIO</CODE> is a homage to BioPerl’s module of the same name.</P><P>In this chapter, we’ll go through the main features of <CODE>Bio.SearchIO</CODE> to
-show what it can do for you. We’ll use two popular search tools along the way:
-BLAST and BLAT. They are used merely for illustrative purposes, and you should
-be able to adapt the workflow to any other search tools supported by
-<CODE>Bio.SearchIO</CODE> in a breeze. You’re very welcome to follow along with the
-search output files we’ll be using. The BLAST output file can be downloaded
-<A HREF="http://biopython.org/SRC/Tests/Tutorial/my_blast.xml">here</A>,
-and the BLAT output file
-<A HREF="http://biopython.org/SRC/Tests/Tutorial/my_blat.psl">here</A>.
-Both output files were generated using this sequence:</P><PRE CLASS="verbatim">>mystery_seq
-CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAGGG
-</PRE><P>The BLAST result is an XML file generated using <CODE>blastn</CODE> against the NCBI
-<CODE>refseq_rna</CODE> database. For BLAT, the sequence database was the February 2009
-<CODE>hg19</CODE> human genome draft and the output format is PSL.</P><P>We’ll start from an introduction to the <CODE>Bio.SearchIO</CODE> object model. The
-model is the representation of your search results, thus it is core to
-<CODE>Bio.SearchIO</CODE> itself. After that, we’ll check out the main functions in
-<CODE>Bio.SearchIO</CODE> that you may often use.</P><P>Now that we’re all set, let’s go to the first step: introducing the core
-object model.</P><!--TOC section The SearchIO object model-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc99">8.1</A>  The SearchIO object model</H2><!--SEC END --><P>
-<A NAME="sec:searchio-model"></A></P><P>Despite the wildly differing output styles among many sequence search tools,
-it turns out that their underlying concept is similar:</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-The output file may contain results from one or more search queries.
-</LI><LI CLASS="li-itemize">In each search query, you will see one or more hits from the given
-search database.
-</LI><LI CLASS="li-itemize">In each database hit, you will see one or more regions containing the
-actual sequence alignment between your query sequence and the database
-sequence.
-</LI><LI CLASS="li-itemize">Some programs like BLAT or Exonerate may further split these regions into
-several alignment fragments (or blocks in BLAT and possibly exons in
-exonerate). This is not something you always see, as programs like BLAST and
-HMMER do not do this.
-</LI></UL><P>Realizing this generality, we decided use it as base for creating the
-<CODE>Bio.SearchIO</CODE> object model. The object model consists of a nested
-hierarchy of Python objects, each one representing one concept outlined above.
-These objects are:</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-<CODE>QueryResult</CODE>, to represent a single search query.
-</LI><LI CLASS="li-itemize"><CODE>Hit</CODE>, to represent a single database hit. <CODE>Hit</CODE> objects are
-contained within <CODE>QueryResult</CODE> and in each <CODE>QueryResult</CODE> there is
-zero or more <CODE>Hit</CODE> objects.
-</LI><LI CLASS="li-itemize"><CODE>HSP</CODE> (short for high-scoring pair), to represent region(s) of
-significant alignments between query and hit sequences. <CODE>HSP</CODE> objects
-are contained within <CODE>Hit</CODE> objects and each <CODE>Hit</CODE> has one or more
-<CODE>HSP</CODE> objects.
-</LI><LI CLASS="li-itemize"><CODE>HSPFragment</CODE>, to represent a single contiguous alignment between
-query and hit sequences. <CODE>HSPFragment</CODE> objects are contained within
-<CODE>HSP</CODE> objects. Most sequence search tools like BLAST and HMMER unify
-<CODE>HSP</CODE> and <CODE>HSPFragment</CODE> objects as each <CODE>HSP</CODE> will only have
-a single <CODE>HSPFragment</CODE>. However there are tools like BLAT and Exonerate
-that produce <CODE>HSP</CODE> containing multiple <CODE>HSPFragment</CODE>. Don’t worry
-if this seems a tad confusing now, we’ll elaborate more on these two objects
-later on.
-</LI></UL><P>These four objects are the ones you will interact with when you use
-<CODE>Bio.SearchIO</CODE>. They are created using one of the main <CODE>Bio.SearchIO</CODE>
-methods: <CODE>read</CODE>, <CODE>parse</CODE>, <CODE>index</CODE>, or <CODE>index_db</CODE>. The
-details of these methods are provided in later sections. For this section, we’ll
-only be using read and parse. These functions behave similarly to their
-<CODE>Bio.SeqIO</CODE> and <CODE>Bio.AlignIO</CODE> counterparts:</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-<CODE>read</CODE> is used for search output files with a single query and
-returns a <CODE>QueryResult</CODE> object
-</LI><LI CLASS="li-itemize"><CODE>parse</CODE> is used for search output files with multiple queries and
-returns a generator that yields <CODE>QueryResult</CODE> objects
-</LI></UL><P>With that settled, let’s start probing each <CODE>Bio.SearchIO</CODE> object,
-beginning with <CODE>QueryResult</CODE>.</P><!--TOC subsection QueryResult-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc100">8.1.1</A>  QueryResult</H3><!--SEC END --><P>
-<A NAME="sec:searchio-qresult"></A></P><P>The QueryResult object represents a single search query and contains zero or
-more Hit objects. Let’s see what it looks like using the BLAST file we have:</P><PRE CLASS="verbatim">>>> from Bio import SearchIO
->>> blast_qresult = SearchIO.read('my_blast.xml', 'blast-xml')
->>> print(blast_qresult)
-Program: blastn (2.2.27+)
- Query: 42291 (61)
- mystery_seq
- Target: refseq_rna
- Hits: ---- ----- ----------------------------------------------------------
- # # HSP ID + description
- ---- ----- ----------------------------------------------------------
- 0 1 gi|262205317|ref|NR_030195.1| Homo sapiens microRNA 52...
- 1 1 gi|301171311|ref|NR_035856.1| Pan troglodytes microRNA...
- 2 1 gi|270133242|ref|NR_032573.1| Macaca mulatta microRNA ...
- 3 2 gi|301171322|ref|NR_035857.1| Pan troglodytes microRNA...
- 4 1 gi|301171267|ref|NR_035851.1| Pan troglodytes microRNA...
- 5 2 gi|262205330|ref|NR_030198.1| Homo sapiens microRNA 52...
- 6 1 gi|262205302|ref|NR_030191.1| Homo sapiens microRNA 51...
- 7 1 gi|301171259|ref|NR_035850.1| Pan troglodytes microRNA...
- 8 1 gi|262205451|ref|NR_030222.1| Homo sapiens microRNA 51...
- 9 2 gi|301171447|ref|NR_035871.1| Pan troglodytes microRNA...
- 10 1 gi|301171276|ref|NR_035852.1| Pan troglodytes microRNA...
- 11 1 gi|262205290|ref|NR_030188.1| Homo sapiens microRNA 51...
- 12 1 gi|301171354|ref|NR_035860.1| Pan troglodytes microRNA...
- 13 1 gi|262205281|ref|NR_030186.1| Homo sapiens microRNA 52...
- 14 2 gi|262205298|ref|NR_030190.1| Homo sapiens microRNA 52...
- 15 1 gi|301171394|ref|NR_035865.1| Pan troglodytes microRNA...
- 16 1 gi|262205429|ref|NR_030218.1| Homo sapiens microRNA 51...
- 17 1 gi|262205423|ref|NR_030217.1| Homo sapiens microRNA 52...
- 18 1 gi|301171401|ref|NR_035866.1| Pan troglodytes microRNA...
- 19 1 gi|270133247|ref|NR_032574.1| Macaca mulatta microRNA ...
- 20 1 gi|262205309|ref|NR_030193.1| Homo sapiens microRNA 52...
- 21 2 gi|270132717|ref|NR_032716.1| Macaca mulatta microRNA ...
- 22 2 gi|301171437|ref|NR_035870.1| Pan troglodytes microRNA...
- 23 2 gi|270133306|ref|NR_032587.1| Macaca mulatta microRNA ...
- 24 2 gi|301171428|ref|NR_035869.1| Pan troglodytes microRNA...
- 25 1 gi|301171211|ref|NR_035845.1| Pan troglodytes microRNA...
- 26 2 gi|301171153|ref|NR_035838.1| Pan troglodytes microRNA...
- 27 2 gi|301171146|ref|NR_035837.1| Pan troglodytes microRNA...
- 28 2 gi|270133254|ref|NR_032575.1| Macaca mulatta microRNA ...
- 29 2 gi|262205445|ref|NR_030221.1| Homo sapiens microRNA 51...
- ~~~
- 97 1 gi|356517317|ref|XM_003527287.1| PREDICTED: Glycine ma...
- 98 1 gi|297814701|ref|XM_002875188.1| Arabidopsis lyrata su...
- 99 1 gi|397513516|ref|XM_003827011.1| PREDICTED: Pan panisc...
-</PRE><P>We’ve just begun to scratch the surface of the object model, but you can see that
-there’s already some useful information. By invoking <CODE>print</CODE> on the
-<CODE>QueryResult</CODE> object, you can see:</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-The program name and version (blastn version 2.2.27+)
-</LI><LI CLASS="li-itemize">The query ID, description, and its sequence length (ID is 42291,
-description is ‘mystery_seq’, and it is 61 nucleotides long)
-</LI><LI CLASS="li-itemize">The target database to search against (refseq_rna)
-</LI><LI CLASS="li-itemize">A quick overview of the resulting hits. For our query sequence, there are
-100 potential hits (numbered 0–99 in the table). For each hit, we can also see
-how many HSPs it contains, its ID, and a snippet of its description. Notice
-here that <CODE>Bio.SearchIO</CODE> truncates the hit table overview, by showing
-only hits numbered 0–29, and then 97–99.
-</LI></UL><P>Now let’s check our BLAT results using the same procedure as above:</P><PRE CLASS="verbatim">>>> blat_qresult = SearchIO.read('my_blat.psl', 'blat-psl')
->>> print(blat_qresult)
-Program: blat (<unknown version>)
- Query: mystery_seq (61)
- <unknown description>
- Target: <unknown target>
- Hits: ---- ----- ----------------------------------------------------------
- # # HSP ID + description
- ---- ----- ----------------------------------------------------------
- 0 17 chr19 <unknown description>
-</PRE><P>You’ll immediately notice that there are some differences. Some of these are
-caused by the way PSL format stores its details, as you’ll see. The rest are
-caused by the genuine program and target database differences between our BLAST
-and BLAT searches:</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-The program name and version. <CODE>Bio.SearchIO</CODE> knows that the program
-is BLAT, but in the output file there is no information regarding the
-program version so it defaults to ‘<unknown version>’.
-</LI><LI CLASS="li-itemize">The query ID, description, and its sequence length. Notice here that these
-details are slightly different from the ones we saw in BLAST. The ID is
-‘mystery_seq’ instead of 42991, there is no known description, but the query
-length is still 61. This is actually a difference introduced by the file
-formats themselves. BLAST sometimes creates its own query IDs and uses your
-original ID as the sequence description.
-</LI><LI CLASS="li-itemize">The target database is not known, as it is not stated in the BLAT output
-file.
-</LI><LI CLASS="li-itemize">And finally, the list of hits we have is completely different. Here, we
-see that our query sequence only hits the ‘chr19’ database entry, but in it
-we see 17 HSP regions. This should not be surprising however, given that we
-are using a different program, each with its own target database.
-</LI></UL><P>All the details you saw when invoking the <CODE>print</CODE> method can be accessed
-individually using Python’s object attribute access notation (a.k.a. the dot
-notation). There are also other format-specific attributes that you can access
-using the same method.</P><PRE CLASS="verbatim">>>> print("%s %s" % (blast_qresult.program, blast_qresult.version))
-blastn 2.2.27+
->>> print("%s %s" % (blat_qresult.program, blat_qresult.version))
-blat <unknown version>
->>> blast_qresult.param_evalue_threshold # blast-xml specific
-10.0
-</PRE><P>For a complete list of accessible attributes, you can check each format-specific
-documentation. Here are the ones
-<A HREF="http://biopython.org/DIST/docs/api/Bio.SearchIO.BlastIO-module.html">for BLAST</A>
-and for
-<A HREF="http://biopython.org/DIST/docs/api/Bio.SearchIO.BlatIO-module.html">BLAT</A>.</P><P>Having looked at using <CODE>print</CODE> on <CODE>QueryResult</CODE> objects, let’s drill
-down deeper. What exactly is a <CODE>QueryResult</CODE>? In terms of Python objects,
-<CODE>QueryResult</CODE> is a hybrid between a list and a dictionary. In other words,
-it is a container object with all the convenient features of lists and
-dictionaries.</P><P>Like Python lists and dictionaries, <CODE>QueryResult</CODE> objects are iterable.
-Each iteration returns a <CODE>Hit</CODE> object:</P><PRE CLASS="verbatim">>>> for hit in blast_qresult:
-... hit
-Hit(id='gi|262205317|ref|NR_030195.1|', query_id='42291', 1 hsps)
-Hit(id='gi|301171311|ref|NR_035856.1|', query_id='42291', 1 hsps)
-Hit(id='gi|270133242|ref|NR_032573.1|', query_id='42291', 1 hsps)
-Hit(id='gi|301171322|ref|NR_035857.1|', query_id='42291', 2 hsps)
-Hit(id='gi|301171267|ref|NR_035851.1|', query_id='42291', 1 hsps)
-...
-</PRE><P>To check how many items (hits) a <CODE>QueryResult</CODE> has, you can simply invoke
-Python’s <CODE>len</CODE> method:</P><PRE CLASS="verbatim">>>> len(blast_qresult)
-100
->>> len(blat_qresult)
-1
-</PRE><P>Like Python lists, you can retrieve items (hits) from a <CODE>QueryResult</CODE> using
-the slice notation:</P><PRE CLASS="verbatim">>>> blast_qresult[0] # retrieves the top hit
-Hit(id='gi|262205317|ref|NR_030195.1|', query_id='42291', 1 hsps)
->>> blast_qresult[-1] # retrieves the last hit
-Hit(id='gi|397513516|ref|XM_003827011.1|', query_id='42291', 1 hsps)
-</PRE><P>To retrieve multiple hits, you can slice <CODE>QueryResult</CODE> objects using the
-slice notation as well. In this case, the slice will return a new
-<CODE>QueryResult</CODE> object containing only the sliced hits:</P><PRE CLASS="verbatim">>>> blast_slice = blast_qresult[:3] # slices the first three hits
->>> print(blast_slice)
-Program: blastn (2.2.27+)
- Query: 42291 (61)
- mystery_seq
- Target: refseq_rna
- Hits: ---- ----- ----------------------------------------------------------
- # # HSP ID + description
- ---- ----- ----------------------------------------------------------
- 0 1 gi|262205317|ref|NR_030195.1| Homo sapiens microRNA 52...
- 1 1 gi|301171311|ref|NR_035856.1| Pan troglodytes microRNA...
- 2 1 gi|270133242|ref|NR_032573.1| Macaca mulatta microRNA ...
-</PRE><P>Like Python dictionaries, you can also retrieve hits using the hit’s ID. This is
-particularly useful if you know a given hit ID exists within a search query
-results:</P><PRE CLASS="verbatim">>>> blast_qresult['gi|262205317|ref|NR_030195.1|']
-Hit(id='gi|262205317|ref|NR_030195.1|', query_id='42291', 1 hsps)
-</PRE><P>You can also get a full list of <CODE>Hit</CODE> objects using <CODE>hits</CODE> and a full
-list of <CODE>Hit</CODE> IDs using <CODE>hit_keys</CODE>:</P><PRE CLASS="verbatim">>>> blast_qresult.hits
-[...] # list of all hits
->>> blast_qresult.hit_keys
-[...] # list of all hit IDs
-</PRE><P>What if you just want to check whether a particular hit is present in the query
-results? You can do a simple Python membership test using the <CODE>in</CODE> keyword:</P><PRE CLASS="verbatim">>>> 'gi|262205317|ref|NR_030195.1|' in blast_qresult
-True
->>> 'gi|262205317|ref|NR_030194.1|' in blast_qresult
-False
-</PRE><P>Sometimes, knowing whether a hit is present is not enough; you also want to know
-the rank of the hit. Here, the <CODE>index</CODE> method comes to the rescue:</P><PRE CLASS="verbatim">>>> blast_qresult.index('gi|301171437|ref|NR_035870.1|')
-22
-</PRE><P>Remember that we’re using Python’s indexing style here, which is zero-based.
-This means our hit above is ranked at no. 23, not 22.</P><P>Also, note that the hit rank you see here is based on the native hit ordering
-present in the original search output file. Different search tools may order
-these hits based on different criteria.</P><P>If the native hit ordering doesn’t suit your taste, you can use the <CODE>sort</CODE>
-method of the <CODE>QueryResult</CODE> object. It is very similar to Python’s
-<CODE>list.sort</CODE> method, with the addition of an option to create a new sorted
-<CODE>QueryResult</CODE> object or not.</P><P>Here is an example of using <CODE>QueryResult.sort</CODE> to sort the hits based on
-each hit’s full sequence length. For this particular sort, we’ll set the
-<CODE>in_place</CODE> flag to <CODE>False</CODE> so that sorting will return a new
-<CODE>QueryResult</CODE> object and leave our initial object unsorted. We’ll also set
-the <CODE>reverse</CODE> flag to <CODE>True</CODE> so that we sort in descending order.</P><PRE CLASS="verbatim">>>> for hit in blast_qresult[:5]: # id and sequence length of the first five hits
-... print("%s %i" % (hit.id, hit.seq_len))
-...
-gi|262205317|ref|NR_030195.1| 61
-gi|301171311|ref|NR_035856.1| 60
-gi|270133242|ref|NR_032573.1| 85
-gi|301171322|ref|NR_035857.1| 86
-gi|301171267|ref|NR_035851.1| 80
-
->>> sort_key = lambda hit: hit.seq_len
->>> sorted_qresult = blast_qresult.sort(key=sort_key, reverse=True, in_place=False)
->>> for hit in sorted_qresult[:5]:
-... print("%s %i" % (hit.id, hit.seq_len))
-...
-gi|397513516|ref|XM_003827011.1| 6002
-gi|390332045|ref|XM_776818.2| 4082
-gi|390332043|ref|XM_003723358.1| 4079
-gi|356517317|ref|XM_003527287.1| 3251
-gi|356543101|ref|XM_003539954.1| 2936
-</PRE><P>The advantage of having the <CODE>in_place</CODE> flag here is that we’re preserving
-the native ordering, so we may use it again later. You should note that this is
-not the default behavior of <CODE>QueryResult.sort</CODE>, however, which is why we
-needed to set the <CODE>in_place</CODE> flag to <CODE>True</CODE> explicitly.</P><P>At this point, you’ve known enough about <CODE>QueryResult</CODE> objects to make it
-work for you. But before we go on to the next object in the <CODE>Bio.SearchIO</CODE>
-model, let’s take a look at two more sets of methods that could make it even
-easier to work with <CODE>QueryResult</CODE> objects: the <CODE>filter</CODE> and <CODE>map</CODE>
-methods.</P><P>If you’re familiar with Python’s list comprehensions, generator expressions
-or the built in <CODE>filter</CODE> and <CODE>map</CODE> functions,
-you’ll know how useful they are for working with list-like objects (if you’re
-not, check them out!). You can use these built in methods to manipulate
-<CODE>QueryResult</CODE> objects, but you’ll end up with regular Python lists and lose
-the ability to do more interesting manipulations.</P><P>That’s why, <CODE>QueryResult</CODE> objects provide its own flavor of
-<CODE>filter</CODE> and <CODE>map</CODE> methods. Analogous to <CODE>filter</CODE>, there are
-<CODE>hit_filter</CODE> and <CODE>hsp_filter</CODE> methods. As their name implies, these
-methods filter its <CODE>QueryResult</CODE> object either on its <CODE>Hit</CODE> objects
-or <CODE>HSP</CODE> objects. Similarly, analogous to <CODE>map</CODE>, <CODE>QueryResult</CODE>
-objects also provide the <CODE>hit_map</CODE> and <CODE>hsp_map</CODE> methods. These
-methods apply a given function to all hits or HSPs in a <CODE>QueryResult</CODE>
-object, respectively.</P><P>Let’s see these methods in action, beginning with <CODE>hit_filter</CODE>. This method
-accepts a callback function that checks whether a given <CODE>Hit</CODE> object passes
-the condition you set or not. In other words, the function must accept as its
-argument a single <CODE>Hit</CODE> object and returns <CODE>True</CODE> or <CODE>False</CODE>.</P><P>Here is an example of using <CODE>hit_filter</CODE> to filter out <CODE>Hit</CODE> objects
-that only have one HSP:</P><PRE CLASS="verbatim">>>> filter_func = lambda hit: len(hit.hsps) > 1 # the callback function
->>> len(blast_qresult) # no. of hits before filtering
-100
->>> filtered_qresult = blast_qresult.hit_filter(filter_func)
->>> len(filtered_qresult) # no. of hits after filtering
-37
->>> for hit in filtered_qresult[:5]: # quick check for the hit lengths
-... print("%s %i" % (hit.id, len(hit.hsps)))
-gi|301171322|ref|NR_035857.1| 2
-gi|262205330|ref|NR_030198.1| 2
-gi|301171447|ref|NR_035871.1| 2
-gi|262205298|ref|NR_030190.1| 2
-gi|270132717|ref|NR_032716.1| 2
-</PRE><P><CODE>hsp_filter</CODE> works the same as <CODE>hit_filter</CODE>, only instead of looking
-at the <CODE>Hit</CODE> objects, it performs filtering on the <CODE>HSP</CODE> objects in
-each hits.</P><P>As for the <CODE>map</CODE> methods, they too accept a callback function as their
-arguments. However, instead of returning <CODE>True</CODE> or <CODE>False</CODE>, the
-callback function must return the modified <CODE>Hit</CODE> or <CODE>HSP</CODE> object
-(depending on whether you’re using <CODE>hit_map</CODE> or <CODE>hsp_map</CODE>).</P><P>Let’s see an example where we’re using <CODE>hit_map</CODE> to rename the hit IDs:</P><PRE CLASS="verbatim">>>> def map_func(hit):
-... hit.id = hit.id.split('|')[3] # renames 'gi|301171322|ref|NR_035857.1|' to 'NR_035857.1'
-... return hit
-...
->>> mapped_qresult = blast_qresult.hit_map(map_func)
->>> for hit in mapped_qresult[:5]:
-... print(hit.id)
-NR_030195.1
-NR_035856.1
-NR_032573.1
-NR_035857.1
-NR_035851.1
-</PRE><P>Again, <CODE>hsp_map</CODE> works the same as <CODE>hit_map</CODE>, but on <CODE>HSP</CODE>
-objects instead of <CODE>Hit</CODE> objects.</P><!--TOC subsection Hit-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc101">8.1.2</A>  Hit</H3><!--SEC END --><P>
-<A NAME="sec:searchio-hit"></A></P><P><CODE>Hit</CODE> objects represent all query results from a single database entry.
-They are the second-level container in the <CODE>Bio.SearchIO</CODE> object hierarchy.
-You’ve seen that they are contained by <CODE>QueryResult</CODE> objects, but they
-themselves contain <CODE>HSP</CODE> objects.</P><P>Let’s see what they look like, beginning with our BLAST search:</P><PRE CLASS="verbatim">>>> from Bio import SearchIO
->>> blast_qresult = SearchIO.read('my_blast.xml', 'blast-xml')
->>> blast_hit = blast_qresult[3] # fourth hit from the query result
->>> print(blast_hit)
-Query: 42291
- mystery_seq
- Hit: gi|301171322|ref|NR_035857.1| (86)
- Pan troglodytes microRNA mir-520c (MIR520C), microRNA
- HSPs: ---- -------- --------- ------ --------------- ---------------------
- # E-value Bit score Span Query range Hit range
- ---- -------- --------- ------ --------------- ---------------------
- 0 8.9e-20 100.47 60 [1:61] [13:73]
- 1 3.3e-06 55.39 60 [0:60] [13:73]
-</PRE><P>You see that we’ve got the essentials covered here:</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-The query ID and description is present. A hit is always tied to a query,
-so we want to keep track of the originating query as well. These values can
-be accessed from a hit using the <CODE>query_id</CODE> and
-<CODE>query_description</CODE> attributes.
-</LI><LI CLASS="li-itemize">We also have the unique hit ID, description, and full sequence lengths.
-They can be accessed using <CODE>id</CODE>, <CODE>description</CODE>, and
-<CODE>seq_len</CODE>, respectively.
-</LI><LI CLASS="li-itemize">Finally, there’s a table containing quick information about the HSPs this
-hit contains. In each row, we’ve got the important HSP details listed: the
-HSP index, its e-value, its bit score, its span (the alignment length
-including gaps), its query coordinates, and its hit coordinates.
-</LI></UL><P>Now let’s contrast this with the BLAT search. Remember that in the BLAT search we
-had one hit with 17 HSPs.</P><PRE CLASS="verbatim">>>> blat_qresult = SearchIO.read('my_blat.psl', 'blat-psl')
->>> blat_hit = blat_qresult[0] # the only hit
->>> print(blat_hit)
-Query: mystery_seq
- <unknown description>
- Hit: chr19 (59128983)
- <unknown description>
- HSPs: ---- -------- --------- ------ --------------- ---------------------
- # E-value Bit score Span Query range Hit range
- ---- -------- --------- ------ --------------- ---------------------
- 0 ? ? ? [0:61] [54204480:54204541]
- 1 ? ? ? [0:61] [54233104:54264463]
- 2 ? ? ? [0:61] [54254477:54260071]
- 3 ? ? ? [1:61] [54210720:54210780]
- 4 ? ? ? [0:60] [54198476:54198536]
- 5 ? ? ? [0:61] [54265610:54265671]
- 6 ? ? ? [0:61] [54238143:54240175]
- 7 ? ? ? [0:60] [54189735:54189795]
- 8 ? ? ? [0:61] [54185425:54185486]
- 9 ? ? ? [0:60] [54197657:54197717]
- 10 ? ? ? [0:61] [54255662:54255723]
- 11 ? ? ? [0:61] [54201651:54201712]
- 12 ? ? ? [8:60] [54206009:54206061]
- 13 ? ? ? [10:61] [54178987:54179038]
- 14 ? ? ? [8:61] [54212018:54212071]
- 15 ? ? ? [8:51] [54234278:54234321]
- 16 ? ? ? [8:61] [54238143:54238196]
-</PRE><P>Here, we’ve got a similar level of detail as with the BLAST hit we saw earlier.
-There are some differences worth explaining, though:</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-The e-value and bit score column values. As BLAT HSPs do not have e-values
-and bit scores, the display defaults to ‘?’.
-</LI><LI CLASS="li-itemize">What about the span column? The span values is meant to display the
-complete alignment length, which consists of all residues and any gaps that
-may be present. The PSL format do not have this information readily available
-and <CODE>Bio.SearchIO</CODE> does not attempt to try guess what it is, so we get a
-‘?’ similar to the e-value and bit score columns.
-</LI></UL><P>In terms of Python objects, <CODE>Hit</CODE> behaves almost the same as Python lists,
-but contain <CODE>HSP</CODE> objects exclusively. If you’re familiar with lists, you
-should encounter no difficulties working with the <CODE>Hit</CODE> object.</P><P>Just like Python lists, <CODE>Hit</CODE> objects are iterable, and each iteration
-returns one <CODE>HSP</CODE> object it contains:</P><PRE CLASS="verbatim">>>> for hsp in blast_hit:
-... hsp
-HSP(hit_id='gi|301171322|ref|NR_035857.1|', query_id='42291', 1 fragments)
-HSP(hit_id='gi|301171322|ref|NR_035857.1|', query_id='42291', 1 fragments)
-</PRE><P>You can invoke <CODE>len</CODE> on a <CODE>Hit</CODE> to see how many <CODE>HSP</CODE> objects it
-has:</P><PRE CLASS="verbatim">>>> len(blast_hit)
-2
->>> len(blat_hit)
-17
-</PRE><P>You can use the slice notation on <CODE>Hit</CODE> objects, whether to retrieve single
-<CODE>HSP</CODE> or multiple <CODE>HSP</CODE> objects. Like <CODE>QueryResult</CODE>, if you slice
-for multiple <CODE>HSP</CODE>, a new <CODE>Hit</CODE> object will be returned containing
-only the sliced <CODE>HSP</CODE> objects:</P><PRE CLASS="verbatim">>>> blat_hit[0] # retrieve single items
-HSP(hit_id='chr19', query_id='mystery_seq', 1 fragments)
->>> sliced_hit = blat_hit[4:9] # retrieve multiple items
->>> len(sliced_hit)
-5
->>> print(sliced_hit)
-Query: mystery_seq
- <unknown description>
- Hit: chr19 (59128983)
- <unknown description>
- HSPs: ---- -------- --------- ------ --------------- ---------------------
- # E-value Bit score Span Query range Hit range
- ---- -------- --------- ------ --------------- ---------------------
- 0 ? ? ? [0:60] [54198476:54198536]
- 1 ? ? ? [0:61] [54265610:54265671]
- 2 ? ? ? [0:61] [54238143:54240175]
- 3 ? ? ? [0:60] [54189735:54189795]
- 4 ? ? ? [0:61] [54185425:54185486]
-</PRE><P>You can also sort the <CODE>HSP</CODE> inside a <CODE>Hit</CODE>, using the exact same
-arguments like the sort method you saw in the <CODE>QueryResult</CODE> object.</P><P>Finally, there are also the <CODE>filter</CODE> and <CODE>map</CODE> methods you can use
-on <CODE>Hit</CODE> objects. Unlike in the <CODE>QueryResult</CODE> object, <CODE>Hit</CODE>
-objects only have one variant of <CODE>filter</CODE> (<CODE>Hit.filter</CODE>) and one
-variant of <CODE>map</CODE> (<CODE>Hit.map</CODE>). Both of <CODE>Hit.filter</CODE> and
-<CODE>Hit.map</CODE> work on the <CODE>HSP</CODE> objects a <CODE>Hit</CODE> has.</P><!--TOC subsection HSP-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc102">8.1.3</A>  HSP</H3><!--SEC END --><P>
-<A NAME="sec:searchio-hsp"></A></P><P><CODE>HSP</CODE> (high-scoring pair) represents region(s) in the hit sequence that
-contains significant alignment(s) to the query sequence. It contains the actual
-match between your query sequence and a database entry. As this match is
-determined by the sequence search tool’s algorithms, the <CODE>HSP</CODE> object
-contains the bulk of the statistics computed by the search tool. This also makes
-the distinction between <CODE>HSP</CODE> objects from different search tools more
-apparent compared to the differences you’ve seen in <CODE>QueryResult</CODE> or
-<CODE>Hit</CODE> objects.</P><P>Let’s see some examples from our BLAST and BLAT searches. We’ll look at the
-BLAST HSP first:</P><PRE CLASS="verbatim">>>> from Bio import SearchIO
->>> blast_qresult = SearchIO.read('my_blast.xml', 'blast-xml')
->>> blast_hsp = blast_qresult[0][0] # first hit, first hsp
->>> print(blast_hsp)
- Query: 42291 mystery_seq
- Hit: gi|262205317|ref|NR_030195.1| Homo sapiens microRNA 520b (MIR520...
-Query range: [0:61] (1)
- Hit range: [0:61] (1)
-Quick stats: evalue 4.9e-23; bitscore 111.29
- Fragments: 1 (61 columns)
- Query - CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAGGG
- |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
- Hit - CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAGGG
-</PRE><P>Just like <CODE>QueryResult</CODE> and <CODE>Hit</CODE>, invoking <CODE>print</CODE> on an
-<CODE>HSP</CODE> shows its general details:
-</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-There are the query and hit IDs and descriptions. We need these to
-identify our <CODE>HSP</CODE>.
-</LI><LI CLASS="li-itemize">We’ve also got the matching range of the query and hit sequences. The
-slice notation we’re using here is an indication that the range is displayed
-using Python’s indexing style (zero-based, half open). The number inside the
-parenthesis denotes the strand. In this case, both sequences have the plus
-strand.
-</LI><LI CLASS="li-itemize">Some quick statistics are available: the e-value and bitscore.
-</LI><LI CLASS="li-itemize">There is information about the HSP fragments. Ignore this for now; it will
-be explained later on.
-</LI><LI CLASS="li-itemize">And finally, we have the query and hit sequence alignment itself.
-</LI></UL><P>These details can be accessed on their own using the dot notation, just like in
-<CODE>QueryResult</CODE> and <CODE>Hit</CODE>:</P><PRE CLASS="verbatim">>>> blast_hsp.query_range
-(0, 61)
-</PRE><PRE CLASS="verbatim">>>> blast_hsp.evalue
-4.91307e-23
-</PRE><P>They’re not the only attributes available, though. <CODE>HSP</CODE> objects come with
-a default set of properties that makes it easy to probe their various
-details. Here are some examples:</P><PRE CLASS="verbatim">>>> blast_hsp.hit_start # start coordinate of the hit sequence
-0
->>> blast_hsp.query_span # how many residues in the query sequence
-61
->>> blast_hsp.aln_span # how long the alignment is
-61
-</PRE><P>Check out the <CODE>HSP</CODE>
-<A HREF="http://biopython.org/DIST/docs/api/Bio.SearchIO._model.hsp-module.html">documentation</A>
-for a full list of these predefined properties.</P><P>Furthermore, each sequence search tool usually computes its own statistics /
-details for its <CODE>HSP</CODE> objects. For example, an XML BLAST search also
-outputs the number of gaps and identical residues. These attributes can be
-accessed like so:</P><PRE CLASS="verbatim">>>> blast_hsp.gap_num # number of gaps
-0
->>> blast_hsp.ident_num # number of identical residues
-61
-</PRE><P>These details are format-specific; they may not be present in other formats.
-To see which details are available for a given sequence search tool, you
-should check the format’s documentation in <CODE>Bio.SearchIO</CODE>. Alternatively,
-you may also use <CODE>.__dict__.keys()</CODE> for a quick list of what’s available:</P><PRE CLASS="verbatim">>>> blast_hsp.__dict__.keys()
-['bitscore', 'evalue', 'ident_num', 'gap_num', 'bitscore_raw', 'pos_num', '_items']
-</PRE><P>Finally, you may have noticed that the <CODE>query</CODE> and <CODE>hit</CODE> attributes
-of our HSP are not just regular strings:</P><PRE CLASS="verbatim">>>> blast_hsp.query
-SeqRecord(seq=Seq('CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTT...GGG', DNAAlphabet()), id='42291', name='aligned query sequence', description='mystery_seq', dbxrefs=[])
->>> blast_hsp.hit
-SeqRecord(seq=Seq('CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTT...GGG', DNAAlphabet()), id='gi|262205317|ref|NR_030195.1|', name='aligned hit sequence', description='Homo sapiens microRNA 520b (MIR520B), microRNA', dbxrefs=[])
-</PRE><P>They are <CODE>SeqRecord</CODE> objects you saw earlier in
-Section <A HREF="#chapter:SeqRecord">4</A>! This means that you can do all sorts of
-interesting things you can do with <CODE>SeqRecord</CODE> objects on <CODE>HSP.query</CODE>
-and/or <CODE>HSP.hit</CODE>.</P><P>It should not surprise you now that the <CODE>HSP</CODE> object has an
-<CODE>alignment</CODE> property which is a <CODE>MultipleSeqAlignment</CODE> object:</P><PRE CLASS="verbatim">>>> print(blast_hsp.aln)
-DNAAlphabet() alignment with 2 rows and 61 columns
-CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAG...GGG 42291
-CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAG...GGG gi|262205317|ref|NR_030195.1|
-</PRE><P>Having probed the BLAST HSP, let’s now take a look at HSPs from our BLAT
-results for a different kind of HSP. As usual, we’ll begin by invoking
-<CODE>print</CODE> on it:</P><PRE CLASS="verbatim">>>> blat_qresult = SearchIO.read('my_blat.psl', 'blat-psl')
->>> blat_hsp = blat_qresult[0][0] # first hit, first hsp
->>> print(blat_hsp)
- Query: mystery_seq <unknown description>
- Hit: chr19 <unknown description>
-Query range: [0:61] (1)
- Hit range: [54204480:54204541] (1)
-Quick stats: evalue ?; bitscore ?
- Fragments: 1 (? columns)
-</PRE><P>Some of the outputs you may have already guessed. We have the query and hit IDs
-and descriptions and the sequence coordinates. Values for evalue and bitscore is
-‘?’ as BLAT HSPs do not have these attributes. But The biggest difference here
-is that you don’t see any sequence alignments displayed. If you look closer, PSL
-formats themselves do not have any hit or query sequences, so
-<CODE>Bio.SearchIO</CODE> won’t create any sequence or alignment objects. What happens
-if you try to access <CODE>HSP.query</CODE>, <CODE>HSP.hit</CODE>, or <CODE>HSP.aln</CODE>?
-You’ll get the default values for these attributes, which is <CODE>None</CODE>:</P><PRE CLASS="verbatim">>>> blat_hsp.hit is None
-True
->>> blat_hsp.query is None
-True
->>> blat_hsp.aln is None
-True
-</PRE><P>This does not affect other attributes, though. For example, you can still
-access the length of the query or hit alignment. Despite not displaying any
-attributes, the PSL format still have this information so <CODE>Bio.SearchIO</CODE>
-can extract them:</P><PRE CLASS="verbatim">>>> blat_hsp.query_span # length of query match
-61
->>> blat_hsp.hit_span # length of hit match
-61
-</PRE><P>Other format-specific attributes are still present as well:</P><PRE CLASS="verbatim">>>> blat_hsp.score # PSL score
-61
->>> blat_hsp.mismatch_num # the mismatch column
-0
-</PRE><P>So far so good? Things get more interesting when you look at another ‘variant’
-of HSP present in our BLAT results. You might recall that in BLAT searches,
-sometimes we get our results separated into ‘blocks’. These blocks are
-essentially alignment fragments that may have some intervening sequence between
-them.</P><P>Let’s take a look at a BLAT HSP that contains multiple blocks to see how
-<CODE>Bio.SearchIO</CODE> deals with this:</P><PRE CLASS="verbatim">>>> blat_hsp2 = blat_qresult[0][1] # first hit, second hsp
->>> print(blat_hsp2)
- Query: mystery_seq <unknown description>
- Hit: chr19 <unknown description>
-Query range: [0:61] (1)
- Hit range: [54233104:54264463] (1)
-Quick stats: evalue ?; bitscore ?
- Fragments: --- -------------- ---------------------- ----------------------
- # Span Query range Hit range
- --- -------------- ---------------------- ----------------------
- 0 ? [0:18] [54233104:54233122]
- 1 ? [18:61] [54264420:54264463]
-</PRE><P>What’s happening here? We still some essential details covered: the IDs and
-descriptions, the coordinates, and the quick statistics are similar to what
-you’ve seen before. But the fragments detail is all different. Instead of
-showing ‘Fragments: 1’, we now have a table with two data rows.</P><P>This is how <CODE>Bio.SearchIO</CODE> deals with HSPs having multiple fragments. As
-mentioned before, an HSP alignment may be separated by intervening sequences
-into fragments. The intervening sequences are not part of the query-hit match,
-so they should not be considered part of query nor hit sequence. However, they
-do affect how we deal with sequence coordinates, so we can’t ignore them.</P><P>Take a look at the hit coordinate of the HSP above. In the <CODE>Hit range:</CODE> field,
-we see that the coordinate is <CODE>[54233104:54264463]</CODE>. But looking at the
-table rows, we see that not the entire region spanned by this coordinate matches
-our query. Specifically, the intervening region spans from <CODE>54233122</CODE> to
-<CODE>54264420</CODE>.</P><P>Why then, is the query coordinates seem to be contiguous, you ask? This is
-perfectly fine. In this case it means that the query match is contiguous (no
-intervening regions), while the hit match is not.</P><P>All these attributes are accessible from the HSP directly, by the way:</P><PRE CLASS="verbatim">>>> blat_hsp2.hit_range # hit start and end coordinates of the entire HSP
-(54233104, 54264463)
->>> blat_hsp2.hit_range_all # hit start and end coordinates of each fragment
-[(54233104, 54233122), (54264420, 54264463)]
->>> blat_hsp2.hit_span # hit span of the entire HSP
-31359
->>> blat_hsp2.hit_span_all # hit span of each fragment
-[18, 43]
->>> blat_hsp2.hit_inter_ranges # start and end coordinates of intervening regions in the hit sequence
-[(54233122, 54264420)]
->>> blat_hsp2.hit_inter_spans # span of intervening regions in the hit sequence
-[31298]
-</PRE><P>Most of these attributes are not readily available from the PSL file we have,
-but <CODE>Bio.SearchIO</CODE> calculates them for you on the fly when you parse the
-PSL file. All it needs are the start and end coordinates of each fragment.</P><P>What about the <CODE>query</CODE>, <CODE>hit</CODE>, and <CODE>aln</CODE> attributes? If the
-HSP has multiple fragments, you won’t be able to use these attributes as they
-only fetch single <CODE>SeqRecord</CODE> or <CODE>MultipleSeqAlignment</CODE> objects.
-However, you can use their <CODE>*_all</CODE> counterparts: <CODE>query_all</CODE>,
-<CODE>hit_all</CODE>, and <CODE>aln_all</CODE>. These properties will return a list containing
-<CODE>SeqRecord</CODE> or <CODE>MultipleSeqAlignment</CODE> objects from each of the HSP
-fragment. There are other attributes that behave similarly, i.e. they only work
-for HSPs with one fragment. Check out the <CODE>HSP</CODE> <A HREF="http://biopython.org/DIST/docs/api/Bio.SearchIO._model.hsp-module.html">documentation</A>
-for a full list.</P><P>Finally, to check whether you have multiple fragments or not, you can use the
-<CODE>is_fragmented</CODE> property like so:</P><PRE CLASS="verbatim">>>> blat_hsp2.is_fragmented # BLAT HSP with 2 fragments
-True
->>> blat_hsp.is_fragmented # BLAT HSP from earlier, with one fragment
-False
-</PRE><P>Before we move on, you should also know that we can use the slice notation on
-<CODE>HSP</CODE> objects, just like <CODE>QueryResult</CODE> or <CODE>Hit</CODE> objects. When
-you use this notation, you’ll get an <CODE>HSPFragment</CODE> object in return, the
-last component of the object model.</P><!--TOC subsection HSPFragment-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc103">8.1.4</A>  HSPFragment</H3><!--SEC END --><P>
-<A NAME="sec:searchio-hspfragment"></A></P><P><CODE>HSPFragment</CODE> represents a single, contiguous match between the query and
-hit sequences. You could consider it the core of the object model and search
-result, since it is the presence of these fragments that determine whether your
-search have results or not.</P><P>In most cases, you don’t have to deal with <CODE>HSPFragment</CODE> objects directly
-since not that many sequence search tools fragment their HSPs. When you do have
-to deal with them, what you should remember is that <CODE>HSPFragment</CODE> objects
-were written with to be as compact as possible. In most cases, they only contain
-attributes directly related to sequences: strands, reading frames, alphabets,
-coordinates, the sequences themselves, and their IDs and descriptions.</P><P>These attributes are readily shown when you invoke <CODE>print</CODE> on an
-<CODE>HSPFragment</CODE>. Here’s an example, taken from our BLAST search:</P><PRE CLASS="verbatim">>>> from Bio import SearchIO
->>> blast_qresult = SearchIO.read('my_blast.xml', 'blast-xml')
->>> blast_frag = blast_qresult[0][0][0] # first hit, first hsp, first fragment
->>> print(blast_frag)
- Query: 42291 mystery_seq
- Hit: gi|262205317|ref|NR_030195.1| Homo sapiens microRNA 520b (MIR520...
-Query range: [0:61] (1)
- Hit range: [0:61] (1)
- Fragments: 1 (61 columns)
- Query - CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAGGG
- |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
- Hit - CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAGGG
-</PRE><P>At this level, the BLAT fragment looks quite similar to the BLAST fragment, save
-for the query and hit sequences which are not present:</P><PRE CLASS="verbatim">>>> blat_qresult = SearchIO.read('my_blat.psl', 'blat-psl')
->>> blat_frag = blat_qresult[0][0][0] # first hit, first hsp, first fragment
->>> print(blat_frag)
- Query: mystery_seq <unknown description>
- Hit: chr19 <unknown description>
-Query range: [0:61] (1)
- Hit range: [54204480:54204541] (1)
- Fragments: 1 (? columns)
-</PRE><P>In all cases, these attributes are accessible using our favorite dot notation.
-Some examples:</P><PRE CLASS="verbatim">>>> blast_frag.query_start # query start coordinate
-0
->>> blast_frag.hit_strand # hit sequence strand
-1
->>> blast_frag.hit # hit sequence, as a SeqRecord object
-SeqRecord(seq=Seq('CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTT...GGG', DNAAlphabet()), id='gi|262205317|ref|NR_030195.1|', name='aligned hit sequence', description='Homo sapiens microRNA 520b (MIR520B), microRNA', dbxrefs=[])
-</PRE><!--TOC section A note about standards and conventions-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc104">8.2</A>  A note about standards and conventions</H2><!--SEC END --><P>
-<A NAME="sec:searchio-standards"></A></P><P>Before we move on to the main functions, there is something you ought to know
-about the standards <CODE>Bio.SearchIO</CODE> uses. If you’ve worked with multiple
-sequence search tools, you might have had to deal with the many different ways
-each program deals with things like sequence coordinates. It might not have been
-a pleasant experience as these search tools usually have their own standards.
-For example, one tools might use one-based coordinates, while the other uses
-zero-based coordinates. Or, one program might reverse the start and end
-coordinates if the strand is minus, while others don’t. In short, these often
-creates unnecessary mess must be dealt with.</P><P>We realize this problem ourselves and we intend to address it in
-<CODE>Bio.SearchIO</CODE>. After all, one of the goals of <CODE>Bio.SearchIO</CODE> is to
-create a common, easy to use interface to deal with various search output files.
-This means creating standards that extend beyond the object model you just saw.</P><P>Now, you might complain, "Not another standard!". Well, eventually we have to
-choose one convention or the other, so this is necessary. Plus, we’re not
-creating something entirely new here; just adopting a standard we think is best
-for a Python programmer (it is Biopython, after all).</P><P>There are three implicit standards that you can expect when working with
-<CODE>Bio.SearchIO</CODE>:</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-The first one pertains to sequence coordinates. In <CODE>Bio.SearchIO</CODE>,
-all sequence coordinates follows Python’s coordinate style: zero-based and
-half open. For example, if in a BLAST XML output file the start and end
-coordinates of an HSP are 10 and 28, they would become 9 and 28 in
-<CODE>Bio.SearchIO</CODE>. The start coordinate becomes 9 because Python indices
-start from zero, while the end coordinate remains 28 as Python slices omit
-the last item in an interval.
-</LI><LI CLASS="li-itemize">The second is on sequence coordinate orders. In <CODE>Bio.SearchIO</CODE>, start
-coordinates are always less than or equal to end coordinates. This isn’t
-always the case with all sequence search tools, as some of them have larger
-start coordinates when the sequence strand is minus.
-</LI><LI CLASS="li-itemize">The last one is on strand and reading frame values. For strands, there are
-only four valid choices: <CODE>1</CODE> (plus strand), <CODE>-1</CODE> (minus strand),
-<CODE>0</CODE> (protein sequences), and <CODE>None</CODE> (no strand). For reading
-frames, the valid choices are integers from <CODE>-3</CODE> to <CODE>3</CODE> and
-<CODE>None</CODE>.
-</LI></UL><P>Note that these standards only exist in <CODE>Bio.SearchIO</CODE> objects. If you
-write <CODE>Bio.SearchIO</CODE> objects into an output format, <CODE>Bio.SearchIO</CODE>
-will use the format’s standard for the output. It does not force its standard
-over to your output file.</P><!--TOC section Reading search output files-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc105">8.3</A>  Reading search output files</H2><!--SEC END --><P>
-<A NAME="sec:searchio-input"></A></P><P>There are two functions you can use for reading search output files into
-<CODE>Bio.SearchIO</CODE> objects: <CODE>read</CODE> and <CODE>parse</CODE>. They’re essentially
-similar to <CODE>read</CODE> and <CODE>parse</CODE> functions in other submodules like
-<CODE>Bio.SeqIO</CODE> or <CODE>Bio.AlignIO</CODE>. In both cases, you need to supply the
-search output file name and the file format name, both as Python strings. You
-can check the documentation for a list of format names <CODE>Bio.SearchIO</CODE>
-recognizes.</P><P><CODE>Bio.SearchIO.read</CODE> is used for reading search output files with only one
-query and returns a <CODE>QueryResult</CODE> object. You’ve seen <CODE>read</CODE> used in
-our previous examples. What you haven’t seen is that <CODE>read</CODE> may also accept
-additional keyword arguments, depending on the file format.</P><P>Here are some examples. In the first one, we use <CODE>read</CODE> just like
-previously to read a BLAST tabular output file. In the second one, we use a
-keyword argument to modify so it parses the BLAST tabular variant with comments
-in it:</P><PRE CLASS="verbatim">>>> from Bio import SearchIO
->>> qresult = SearchIO.read('tab_2226_tblastn_003.txt', 'blast-tab')
->>> qresult
-QueryResult(id='gi|16080617|ref|NP_391444.1|', 3 hits)
->>> qresult2 = SearchIO.read('tab_2226_tblastn_007.txt', 'blast-tab', comments=True)
->>> qresult2
-QueryResult(id='gi|16080617|ref|NP_391444.1|', 3 hits)
-</PRE><P>These keyword arguments differs among file formats. Check the format
-documentation to see if it has keyword arguments that modifies its parser’s
-behavior.</P><P>As for the <CODE>Bio.SearchIO.parse</CODE>, it is used for reading search output
-files with any number of queries. The function returns a generator object that
-yields a <CODE>QueryResult</CODE> object in each iteration. Like
-<CODE>Bio.SearchIO.read</CODE>, it also accepts format-specific keyword arguments:</P><PRE CLASS="verbatim">>>> from Bio import SearchIO
->>> qresults = SearchIO.parse('tab_2226_tblastn_001.txt', 'blast-tab')
->>> for qresult in qresults:
-... print(qresult.id)
-gi|16080617|ref|NP_391444.1|
-gi|11464971:4-101
->>> qresults2 = SearchIO.parse('tab_2226_tblastn_005.txt', 'blast-tab', comments=True)
->>> for qresult in qresults2:
-... print(qresult.id)
-random_s00
-gi|16080617|ref|NP_391444.1|
-gi|11464971:4-101
-</PRE><!--TOC section Dealing with large search output files with indexing-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc106">8.4</A>  Dealing with large search output files with indexing</H2><!--SEC END --><P>
-<A NAME="sec:searchio-index"></A></P><P>Sometimes, you’re handed a search output file containing hundreds or thousands
-of queries that you need to parse. You can of course use
-<CODE>Bio.SearchIO.parse</CODE> for this file, but that would be grossly inefficient
-if you need to access only a few of the queries. This is because <CODE>parse</CODE>
-will parse all queries it sees before it fetches your query of interest.</P><P>In this case, the ideal choice would be to index the file using
-<CODE>Bio.SearchIO.index</CODE> or <CODE>Bio.SearchIO.index_db</CODE>. If the names sound
-familiar, it’s because you’ve seen them before in Section <A HREF="#sec:SeqIO-index">5.4.2</A>.
-These functions also behave similarly to their <CODE>Bio.SeqIO</CODE> counterparts,
-with the addition of format-specific keyword arguments.</P><P>Here are some examples. You can use <CODE>index</CODE> with just the filename and
-format name:</P><PRE CLASS="verbatim">>>> from Bio import SearchIO
->>> idx = SearchIO.index('tab_2226_tblastn_001.txt', 'blast-tab')
->>> sorted(idx.keys())
-['gi|11464971:4-101', 'gi|16080617|ref|NP_391444.1|']
->>> idx['gi|16080617|ref|NP_391444.1|']
-QueryResult(id='gi|16080617|ref|NP_391444.1|', 3 hits)
->>> idx.close()
-</PRE><P>Or also with the format-specific keyword argument:</P><PRE CLASS="verbatim">>>> idx = SearchIO.index('tab_2226_tblastn_005.txt', 'blast-tab', comments=True)
->>> sorted(idx.keys())
-['gi|11464971:4-101', 'gi|16080617|ref|NP_391444.1|', 'random_s00']
->>> idx['gi|16080617|ref|NP_391444.1|']
-QueryResult(id='gi|16080617|ref|NP_391444.1|', 3 hits)
->>> idx.close()
-</PRE><P>Or with the <CODE>key_function</CODE> argument, as in <CODE>Bio.SeqIO</CODE>:</P><PRE CLASS="verbatim">>>> key_function = lambda id: id.upper() # capitalizes the keys
->>> idx = SearchIO.index('tab_2226_tblastn_001.txt', 'blast-tab', key_function=key_function)
->>> sorted(idx.keys())
-['GI|11464971:4-101', 'GI|16080617|REF|NP_391444.1|']
->>> idx['GI|16080617|REF|NP_391444.1|']
-QueryResult(id='gi|16080617|ref|NP_391444.1|', 3 hits)
->>> idx.close()
-</PRE><P><CODE>Bio.SearchIO.index_db</CODE> works like as <CODE>index</CODE>, only it writes the
-query offsets into an SQLite database file.</P><!--TOC section Writing and converting search output files-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc107">8.5</A>  Writing and converting search output files</H2><!--SEC END --><P>
-<A NAME="sec:searchio-write"></A></P><P>It is occasionally useful to be able to manipulate search results from an output
-file and write it again to a new file. <CODE>Bio.SearchIO</CODE> provides a
-<CODE>write</CODE> function that lets you do exactly this. It takes as its arguments
-an iterable returning <CODE>QueryResult</CODE> objects, the output filename to write
-to, the format name to write to, and optionally some format-specific keyword
-arguments. It returns a four-item tuple, which denotes the number or
-<CODE>QueryResult</CODE>, <CODE>Hit</CODE>, <CODE>HSP</CODE>, and <CODE>HSPFragment</CODE> objects that
-were written.</P><PRE CLASS="verbatim">>>> from Bio import SearchIO
->>> qresults = SearchIO.parse('mirna.xml', 'blast-xml') # read XML file
->>> SearchIO.write(qresults, 'results.tab', 'blast-tab') # write to tabular file
-(3, 239, 277, 277)
-</PRE><P>You should note different file formats require different attributes of the
-<CODE>QueryResult</CODE>, <CODE>Hit</CODE>, <CODE>HSP</CODE> and <CODE>HSPFragment</CODE> objects. If
-these attributes are not present, writing won’t work. In other words, you can’t
-always write to the output format that you want. For example, if you read a
-BLAST XML file, you wouldn’t be able to write the results to a PSL file as PSL
-files require attributes not calculated by BLAST (e.g. the number of repeat
-matches). You can always set these attributes manually, if you really want to
-write to PSL, though.</P><P>Like <CODE>read</CODE>, <CODE>parse</CODE>, <CODE>index</CODE>, and <CODE>index_db</CODE>, <CODE>write</CODE>
-also accepts format-specific keyword arguments. Check out the documentation for
-a complete list of formats <CODE>Bio.SearchIO</CODE> can write to and their arguments.</P><P>Finally, <CODE>Bio.SearchIO</CODE> also provides a <CODE>convert</CODE> function, which is
-simply a shortcut for <CODE>Bio.SearchIO.parse</CODE> and <CODE>Bio.SearchIO.write</CODE>.
-Using the convert function, our example above would be:</P><PRE CLASS="verbatim">>>> from Bio import SearchIO
->>> SearchIO.convert('mirna.xml', 'blast-xml', 'results.tab', 'blast-tab')
-(3, 239, 277, 277)
-</PRE><P>As <CODE>convert</CODE> uses <CODE>write</CODE>, it is only limited to format conversions
-that have all the required attributes. Here, the BLAST XML file provides all the
-default values a BLAST tabular file requires, so it works just fine. However,
-other format conversions are less likely to work since you need to manually
-assign the required attributes first.</P><!--TOC chapter Accessing NCBI’s Entrez databases-->
-<H1 CLASS="chapter"><!--SEC ANCHOR --><A NAME="htoc108">Chapter 9</A>  Accessing NCBI’s Entrez databases</H1><!--SEC END --><P>
-<A NAME="chapter:entrez"></A></P><P>Entrez (<A HREF="http://www.ncbi.nlm.nih.gov/Entrez"><TT>http://www.ncbi.nlm.nih.gov/Entrez</TT></A>) is a data retrieval system that provides users access to NCBI’s databases such as PubMed, GenBank, GEO, and many others. You can access Entrez from a web browser to manually enter queries, or you can use Biopython’s <CODE>Bio.Entrez</CODE> module for programmatic access to Entrez. The latter allows you for example to search PubMed or down [...]
-Each of these tools corresponds to one Python function in the <CODE>Bio.Entrez</CODE> module, as described in the sections below. This module makes sure that the correct URL is used for the queries, and that not more than one request is made every three seconds, as required by NCBI.</P><P>The output returned by the Entrez Programming Utilities is typically in XML format. To parse such output, you have several options:
-</P><OL CLASS="enumerate" type=1><LI CLASS="li-enumerate">
-Use <CODE>Bio.Entrez</CODE>’s parser to parse the XML output into a Python object;
-</LI><LI CLASS="li-enumerate">Use the DOM (Document Object Model) parser in Python’s standard library;
-</LI><LI CLASS="li-enumerate">Use the SAX (Simple API for XML) parser in Python’s standard library;
-</LI><LI CLASS="li-enumerate">Read the XML output as raw text, and parse it by string searching and manipulation.
-</LI></OL><P>
-For the DOM and SAX parsers, see the Python documentation. The parser in <CODE>Bio.Entrez</CODE> is discussed below.</P><P>NCBI uses DTD (Document Type Definition) files to describe the structure of the information contained in XML files. Most of the DTD files used by NCBI are included in the Biopython distribution. The <CODE>Bio.Entrez</CODE> parser makes use of the DTD files when parsing an XML file returned by NCBI Entrez.</P><P>Occasionally, you may find that the DTD file associated [...]
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc109">9.1</A>  Entrez Guidelines</H2><!--SEC END --><P>
-<A NAME="sec:entrez-guidelines"></A>
-Before using Biopython to access the NCBI’s online resources (via <CODE>Bio.Entrez</CODE> or some of the other modules), please read the
-<A HREF="http://www.ncbi.nlm.nih.gov/books/NBK25497/#chapter2.Usage_Guidelines_and_Requiremen">NCBI’s Entrez User Requirements</A>.
-If the NCBI finds you are abusing their systems, they can and will ban your access!</P><P>To paraphrase:</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-For any series of more than 100 requests, do this at weekends or outside USA peak times. This is up to you to obey.
-</LI><LI CLASS="li-itemize">Use the <A HREF="http://eutils.ncbi.nlm.nih.gov"><TT>http://eutils.ncbi.nlm.nih.gov</TT></A> address, not the standard NCBI Web address. Biopython uses this web address.
-</LI><LI CLASS="li-itemize">Make no more than three requests every seconds (relaxed from at most one request every three seconds in early 2009). This is automatically enforced by Biopython.
-</LI><LI CLASS="li-itemize">Use the optional email parameter so the NCBI can contact you if there is a problem. You can either explicitly set this as a parameter with each call to Entrez (e.g. include <TT>email="A.N.Other at example.com"</TT> in the argument list), or you can set a global email address:
-<PRE CLASS="verbatim">>>> from Bio import Entrez
->>> Entrez.email = "A.N.Other at example.com"
-</PRE><TT>Bio.Entrez</TT> will then use this email address with each call to Entrez. The <TT>example.com</TT> address is a reserved domain name specifically for documentation (RFC 2606). Please DO NOT use a random email – it’s better not to give an email at all. The email parameter will be mandatory from June 1, 2010. In case of excessive usage, NCBI will attempt to contact a user at the e-mail address provided prior to blocking access to the E-utilities.
-</LI><LI CLASS="li-itemize">If you are using Biopython within some larger software suite, use the tool parameter to specify this. You can either explicitly set the tool name as a parameter with each call to Entrez (e.g. include <TT>tool="MyLocalScript"</TT> in the argument list), or you can set a global tool name:
-<PRE CLASS="verbatim">>>> from Bio import Entrez
->>> Entrez.tool = "MyLocalScript"
-</PRE>The tool parameter will default to Biopython.
-</LI><LI CLASS="li-itemize">For large queries, the NCBI also recommend using their session history feature (the WebEnv session cookie string, see Section <A HREF="#sec:entrez-webenv">9.15</A>). This is only slightly more complicated.
-</LI></UL><P>In conclusion, be sensible with your usage levels. If you plan to download lots of data, consider other options. For example, if you want easy access to all the human genes, consider fetching each chromosome by FTP as a GenBank file, and importing these into your own BioSQL database (see Section <A HREF="#sec:BioSQL">18.5</A>).</P><!--TOC section EInfo: Obtaining information about the Entrez databases-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc110">9.2</A>  EInfo: Obtaining information about the Entrez databases</H2><!--SEC END --><P>
-<A NAME="sec:entrez-einfo"></A>
-EInfo provides field index term counts, last update, and available links for each of NCBI’s databases. In addition, you can use EInfo to obtain a list of all database names accessible through the Entrez utilities:
-</P><PRE CLASS="verbatim">>>> from Bio import Entrez
->>> Entrez.email = "A.N.Other at example.com" # Always tell NCBI who you are
->>> handle = Entrez.einfo()
->>> result = handle.read()
-</PRE><P>The variable <CODE>result</CODE> now contains a list of databases in XML format:
-</P><PRE CLASS="verbatim">>>> print(result)
-<?xml version="1.0"?>
-<!DOCTYPE eInfoResult PUBLIC "-//NLM//DTD eInfoResult, 11 May 2002//EN"
- "http://www.ncbi.nlm.nih.gov/entrez/query/DTD/eInfo_020511.dtd">
-<eInfoResult>
-<DbList>
- <DbName>pubmed</DbName>
- <DbName>protein</DbName>
- <DbName>nucleotide</DbName>
- <DbName>nuccore</DbName>
- <DbName>nucgss</DbName>
- <DbName>nucest</DbName>
- <DbName>structure</DbName>
- <DbName>genome</DbName>
- <DbName>books</DbName>
- <DbName>cancerchromosomes</DbName>
- <DbName>cdd</DbName>
- <DbName>gap</DbName>
- <DbName>domains</DbName>
- <DbName>gene</DbName>
- <DbName>genomeprj</DbName>
- <DbName>gensat</DbName>
- <DbName>geo</DbName>
- <DbName>gds</DbName>
- <DbName>homologene</DbName>
- <DbName>journals</DbName>
- <DbName>mesh</DbName>
- <DbName>ncbisearch</DbName>
- <DbName>nlmcatalog</DbName>
- <DbName>omia</DbName>
- <DbName>omim</DbName>
- <DbName>pmc</DbName>
- <DbName>popset</DbName>
- <DbName>probe</DbName>
- <DbName>proteinclusters</DbName>
- <DbName>pcassay</DbName>
- <DbName>pccompound</DbName>
- <DbName>pcsubstance</DbName>
- <DbName>snp</DbName>
- <DbName>taxonomy</DbName>
- <DbName>toolkit</DbName>
- <DbName>unigene</DbName>
- <DbName>unists</DbName>
-</DbList>
-</eInfoResult>
-</PRE><P>Since this is a fairly simple XML file, we could extract the information it contains simply by string searching. Using <CODE>Bio.Entrez</CODE>’s parser instead, we can directly parse this XML file into a Python object:
-</P><PRE CLASS="verbatim">>>> from Bio import Entrez
->>> handle = Entrez.einfo()
->>> record = Entrez.read(handle)
-</PRE><P>Now <CODE>record</CODE> is a dictionary with exactly one key:
-</P><PRE CLASS="verbatim">>>> record.keys()
-[u'DbList']
-</PRE><P>The values stored in this key is the list of database names shown in the XML above:
-</P><PRE CLASS="verbatim">>>> record["DbList"]
-['pubmed', 'protein', 'nucleotide', 'nuccore', 'nucgss', 'nucest',
- 'structure', 'genome', 'books', 'cancerchromosomes', 'cdd', 'gap',
- 'domains', 'gene', 'genomeprj', 'gensat', 'geo', 'gds', 'homologene',
- 'journals', 'mesh', 'ncbisearch', 'nlmcatalog', 'omia', 'omim', 'pmc',
- 'popset', 'probe', 'proteinclusters', 'pcassay', 'pccompound',
- 'pcsubstance', 'snp', 'taxonomy', 'toolkit', 'unigene', 'unists']
-</PRE><P>For each of these databases, we can use EInfo again to obtain more information:
-</P><PRE CLASS="verbatim">>>> handle = Entrez.einfo(db="pubmed")
->>> record = Entrez.read(handle)
->>> record["DbInfo"]["Description"]
-'PubMed bibliographic record'
->>> record["DbInfo"]["Count"]
-'17989604'
->>> record["DbInfo"]["LastUpdate"]
-'2008/05/24 06:45'
-</PRE><P>Try <CODE>record["DbInfo"].keys()</CODE> for other information stored in this record.
-One of the most useful is a list of possible search fields for use with ESearch:</P><PRE CLASS="verbatim">>>> for field in record["DbInfo"]["FieldList"]:
-... print("%(Name)s, %(FullName)s, %(Description)s" % field)
-ALL, All Fields, All terms from all searchable fields
-UID, UID, Unique number assigned to publication
-FILT, Filter, Limits the records
-TITL, Title, Words in title of publication
-WORD, Text Word, Free text associated with publication
-MESH, MeSH Terms, Medical Subject Headings assigned to publication
-MAJR, MeSH Major Topic, MeSH terms of major importance to publication
-AUTH, Author, Author(s) of publication
-JOUR, Journal, Journal abbreviation of publication
-AFFL, Affiliation, Author's institutional affiliation and address
-...
-</PRE><P>That’s a long list, but indirectly this tells you that for the PubMed
-database, you can do things like <TT>Jones[AUTH]</TT> to search the
-author field, or <TT>Sanger[AFFL]</TT> to restrict to authors at the
-Sanger Centre. This can be very handy - especially if you are not so
-familiar with a particular database.</P><!--TOC section ESearch: Searching the Entrez databases-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc111">9.3</A>  ESearch: Searching the Entrez databases</H2><!--SEC END --><P>
-<A NAME="sec:entrez-esearch"></A>
-To search any of these databases, we use <CODE>Bio.Entrez.esearch()</CODE>. For example, let’s search in PubMed for publications related to Biopython:
-</P><PRE CLASS="verbatim">>>> from Bio import Entrez
->>> Entrez.email = "A.N.Other at example.com" # Always tell NCBI who you are
->>> handle = Entrez.esearch(db="pubmed", term="biopython")
->>> record = Entrez.read(handle)
->>> record["IdList"]
-['19304878', '18606172', '16403221', '16377612', '14871861', '14630660', '12230038']
-</PRE><P>In this output, you see seven PubMed IDs (including 19304878 which is the PMID for the Biopython application note), which can be retrieved by EFetch (see section <A HREF="#sec:efetch">9.6</A>).</P><P>You can also use ESearch to search GenBank. Here we’ll do a quick
-search for the <EM>matK</EM> gene in <EM>Cypripedioideae</EM> orchids
-(see Section <A HREF="#sec:entrez-einfo">9.2</A> about EInfo for one way to
-find out which fields you can search in each Entrez database):</P><PRE CLASS="verbatim">>>> handle = Entrez.esearch(db="nucleotide", term="Cypripedioideae[Orgn] AND matK[Gene]")
->>> record = Entrez.read(handle)
->>> record["Count"]
-'25'
->>> record["IdList"]
-['126789333', '37222967', '37222966', '37222965', ..., '61585492']
-</PRE><P>Each of the IDs (126789333, 37222967, 37222966, …) is a GenBank identifier.
-See section <A HREF="#sec:efetch">9.6</A> for information on how to actually download these GenBank records.</P><P>Note that instead of a species name like <TT>Cypripedioideae[Orgn]</TT>, you can restrict the search using an NCBI taxon identifier, here this would be <TT>txid158330[Orgn]</TT>. This isn’t currently documented on the ESearch help page - the NCBI explained this in reply to an email query. You can often deduce the search term formatting by playing with the Entrez [...]
-</P><PRE CLASS="verbatim">>>> handle = Entrez.esearch(db="journals", term="computational")
->>> record = Entrez.read(handle)
->>> record["Count"]
-'16'
->>> record["IdList"]
-['30367', '33843', '33823', '32989', '33190', '33009', '31986',
- '34502', '8799', '22857', '32675', '20258', '33859', '32534',
- '32357', '32249']
-</PRE><P>Again, we could use EFetch to obtain more information for each of these journal IDs.</P><P>ESearch has many useful options — see the <A HREF="http://www.ncbi.nlm.nih.gov/entrez/query/static/esearch_help.html">ESearch help page</A> for more information.</P><!--TOC section EPost: Uploading a list of identifiers-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc112">9.4</A>  EPost: Uploading a list of identifiers</H2><!--SEC END --><P>
-EPost uploads a list of UIs for use in subsequent search strategies; see the
-<A HREF="http://www.ncbi.nlm.nih.gov/entrez/query/static/epost_help.html">EPost help page</A> for more information. It is available from Biopython through
-the <CODE>Bio.Entrez.epost()</CODE> function.</P><P>To give an example of when this is useful, suppose you have a long list of IDs
-you want to download using EFetch (maybe sequences, maybe citations –
-anything). When you make a request with EFetch your list of IDs, the database
-etc, are all turned into a long URL sent to the server. If your list of IDs is
-long, this URL gets long, and long URLs can break (e.g. some proxies don’t
-cope well).</P><P>Instead, you can break this up into two steps, first uploading the list of IDs
-using EPost (this uses an “HTML post” internally, rather than an “HTML get”,
-getting round the long URL problem). With the history support, you can then
-refer to this long list of IDs, and download the associated data with EFetch.</P><P>Let’s look at a simple example to see how EPost works – uploading some PubMed identifiers:
-</P><PRE CLASS="verbatim">>>> from Bio import Entrez
->>> Entrez.email = "A.N.Other at example.com" # Always tell NCBI who you are
->>> id_list = ["19304878", "18606172", "16403221", "16377612", "14871861", "14630660"]
->>> print(Entrez.epost("pubmed", id=",".join(id_list)).read())
-<?xml version="1.0"?>
-<!DOCTYPE ePostResult PUBLIC "-//NLM//DTD ePostResult, 11 May 2002//EN"
- "http://www.ncbi.nlm.nih.gov/entrez/query/DTD/ePost_020511.dtd">
-<ePostResult>
- <QueryKey>1</QueryKey>
- <WebEnv>NCID_01_206841095_130.14.22.101_9001_1242061629</WebEnv>
-</ePostResult>
-</PRE><P>The returned XML includes two important strings, <CODE>QueryKey</CODE> and <CODE>WebEnv</CODE> which together define your history session.
-You would extract these values for use with another Entrez call such as EFetch:
-</P><PRE CLASS="verbatim">>>> from Bio import Entrez
->>> Entrez.email = "A.N.Other at example.com" # Always tell NCBI who you are
->>> id_list = ["19304878", "18606172", "16403221", "16377612", "14871861", "14630660"]
->>> search_results = Entrez.read(Entrez.epost("pubmed", id=",".join(id_list)))
->>> webenv = search_results["WebEnv"]
->>> query_key = search_results["QueryKey"]
-</PRE><P>Section <A HREF="#sec:entrez-webenv">9.15</A> shows how to use the history feature.</P><!--TOC section ESummary: Retrieving summaries from primary IDs-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc113">9.5</A>  ESummary: Retrieving summaries from primary IDs</H2><!--SEC END --><P>
-ESummary retrieves document summaries from a list of primary IDs (see the <A HREF="http://www.ncbi.nlm.nih.gov/entrez/query/static/esummary_help.html">ESummary help page</A> for more information). In Biopython, ESummary is available as <CODE>Bio.Entrez.esummary()</CODE>. Using the search result above, we can for example find out more about the journal with ID 30367:
-</P><PRE CLASS="verbatim">>>> from Bio import Entrez
->>> Entrez.email = "A.N.Other at example.com" # Always tell NCBI who you are
->>> handle = Entrez.esummary(db="journals", id="30367")
->>> record = Entrez.read(handle)
->>> record[0]["Id"]
-'30367'
->>> record[0]["Title"]
-'Computational biology and chemistry'
->>> record[0]["Publisher"]
-'Pergamon,'
-</PRE><!--TOC section EFetch: Downloading full records from Entrez-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc114">9.6</A>  EFetch: Downloading full records from Entrez</H2><!--SEC END --><P>
-<A NAME="sec:efetch"></A></P><P>EFetch is what you use when you want to retrieve a full record from Entrez.
-This covers several possible databases, as described on the main <A HREF="http://eutils.ncbi.nlm.nih.gov/entrez/query/static/efetch_help.html">EFetch Help page</A>.</P><P>For most of their databases, the NCBI support several different file formats. Requesting a specific file format from Entrez using <CODE>Bio.Entrez.efetch()</CODE> requires specifying the <CODE>rettype</CODE> and/or <CODE>retmode</CODE> optional arguments. The different combinations are described for each database type o [...]
->>> Entrez.email = "A.N.Other at example.com" # Always tell NCBI who you are
->>> handle = Entrez.efetch(db="nucleotide", id="186972394", rettype="gb", retmode="text")
->>> print(handle.read())
-LOCUS EU490707 1302 bp DNA linear PLN 05-MAY-2008
-DEFINITION Selenipedium aequinoctiale maturase K (matK) gene, partial cds;
- chloroplast.
-ACCESSION EU490707
-VERSION EU490707.1 GI:186972394
-KEYWORDS .
-SOURCE chloroplast Selenipedium aequinoctiale
- ORGANISM Selenipedium aequinoctiale
- Eukaryota; Viridiplantae; Streptophyta; Embryophyta; Tracheophyta;
- Spermatophyta; Magnoliophyta; Liliopsida; Asparagales; Orchidaceae;
- Cypripedioideae; Selenipedium.
-REFERENCE 1 (bases 1 to 1302)
- AUTHORS Neubig,K.M., Whitten,W.M., Carlsward,B.S., Blanco,M.A.,
- Endara,C.L., Williams,N.H. and Moore,M.J.
- TITLE Phylogenetic utility of ycf1 in orchids
- JOURNAL Unpublished
-REFERENCE 2 (bases 1 to 1302)
- AUTHORS Neubig,K.M., Whitten,W.M., Carlsward,B.S., Blanco,M.A.,
- Endara,C.L., Williams,N.H. and Moore,M.J.
- TITLE Direct Submission
- JOURNAL Submitted (14-FEB-2008) Department of Botany, University of
- Florida, 220 Bartram Hall, Gainesville, FL 32611-8526, USA
-FEATURES Location/Qualifiers
- source 1..1302
- /organism="Selenipedium aequinoctiale"
- /organelle="plastid:chloroplast"
- /mol_type="genomic DNA"
- /specimen_voucher="FLAS:Blanco 2475"
- /db_xref="taxon:256374"
- gene <1..>1302
- /gene="matK"
- CDS <1..>1302
- /gene="matK"
- /codon_start=1
- /transl_table=11
- /product="maturase K"
- /protein_id="ACC99456.1"
- /db_xref="GI:186972395"
- /translation="IFYEPVEIFGYDNKSSLVLVKRLITRMYQQNFLISSVNDSNQKG
- FWGHKHFFSSHFSSQMVSEGFGVILEIPFSSQLVSSLEEKKIPKYQNLRSIHSIFPFL
- EDKFLHLNYVSDLLIPHPIHLEILVQILQCRIKDVPSLHLLRLLFHEYHNLNSLITSK
- KFIYAFSKRKKRFLWLLYNSYVYECEYLFQFLRKQSSYLRSTSSGVFLERTHLYVKIE
- HLLVVCCNSFQRILCFLKDPFMHYVRYQGKAILASKGTLILMKKWKFHLVNFWQSYFH
- FWSQPYRIHIKQLSNYSFSFLGYFSSVLENHLVVRNQMLENSFIINLLTKKFDTIAPV
- ISLIGSLSKAQFCTVLGHPISKPIWTDFSDSDILDRFCRICRNLCRYHSGSSKKQVLY
- RIKYILRLSCARTLARKHKSTVRTFMRRLGSGLLEEFFMEEE"
-ORIGIN
- 1 attttttacg aacctgtgga aatttttggt tatgacaata aatctagttt agtacttgtg
- 61 aaacgtttaa ttactcgaat gtatcaacag aattttttga tttcttcggt taatgattct
- 121 aaccaaaaag gattttgggg gcacaagcat tttttttctt ctcatttttc ttctcaaatg
- 181 gtatcagaag gttttggagt cattctggaa attccattct cgtcgcaatt agtatcttct
- 241 cttgaagaaa aaaaaatacc aaaatatcag aatttacgat ctattcattc aatatttccc
- 301 tttttagaag acaaattttt acatttgaat tatgtgtcag atctactaat accccatccc
- 361 atccatctgg aaatcttggt tcaaatcctt caatgccgga tcaaggatgt tccttctttg
- 421 catttattgc gattgctttt ccacgaatat cataatttga atagtctcat tacttcaaag
- 481 aaattcattt acgccttttc aaaaagaaag aaaagattcc tttggttact atataattct
- 541 tatgtatatg aatgcgaata tctattccag tttcttcgta aacagtcttc ttatttacga
- 601 tcaacatctt ctggagtctt tcttgagcga acacatttat atgtaaaaat agaacatctt
- 661 ctagtagtgt gttgtaattc ttttcagagg atcctatgct ttctcaagga tcctttcatg
- 721 cattatgttc gatatcaagg aaaagcaatt ctggcttcaa agggaactct tattctgatg
- 781 aagaaatgga aatttcatct tgtgaatttt tggcaatctt attttcactt ttggtctcaa
- 841 ccgtatagga ttcatataaa gcaattatcc aactattcct tctcttttct ggggtatttt
- 901 tcaagtgtac tagaaaatca tttggtagta agaaatcaaa tgctagagaa ttcatttata
- 961 ataaatcttc tgactaagaa attcgatacc atagccccag ttatttctct tattggatca
- 1021 ttgtcgaaag ctcaattttg tactgtattg ggtcatccta ttagtaaacc gatctggacc
- 1081 gatttctcgg attctgatat tcttgatcga ttttgccgga tatgtagaaa tctttgtcgt
- 1141 tatcacagcg gatcctcaaa aaaacaggtt ttgtatcgta taaaatatat acttcgactt
- 1201 tcgtgtgcta gaactttggc acggaaacat aaaagtacag tacgcacttt tatgcgaaga
- 1261 ttaggttcgg gattattaga agaattcttt atggaagaag aa
-//
-</PRE><P>The arguments <CODE>rettype="gb"</CODE> and <CODE>retmode="text"</CODE> let us download this record in the GenBank format.</P><P>Note that until Easter 2009, the Entrez EFetch API let you use “genbank” as the
-return type, however the NCBI now insist on using the official return types of
-“gb” or “gbwithparts” (or “gp” for proteins) as described on online.
-Also not that until Feb 2012, the Entrez EFetch API would default to returning
-plain text files, but now defaults to XML.</P><P>Alternatively, you could for example use <CODE>rettype="fasta"</CODE> to get the Fasta-format; see the <A HREF="http://www.ncbi.nlm.nih.gov/entrez/query/static/efetchseq_help.html">EFetch Sequences Help page</A> for other options. Remember – the available formats depend on which database you are downloading from - see the main <A HREF="http://eutils.ncbi.nlm.nih.gov/entrez/query/static/efetch_help.html">EFetch Help page</A>.</P><P>I [...]
->>> handle = Entrez.efetch(db="nucleotide", id="186972394", rettype="gb", retmode="text")
->>> record = SeqIO.read(handle, "genbank")
->>> handle.close()
->>> print(record)
-ID: EU490707.1
-Name: EU490707
-Description: Selenipedium aequinoctiale maturase K (matK) gene, partial cds; chloroplast.
-Number of features: 3
-...
-Seq('ATTTTTTACGAACCTGTGGAAATTTTTGGTTATGACAATAAATCTAGTTTAGTA...GAA', IUPACAmbiguousDNA())
-</PRE><P>Note that a more typical use would be to save the sequence data to a local file, and <EM>then</EM> parse it with <CODE>Bio.SeqIO</CODE>. This can save you having to re-download the same file repeatedly while working on your script, and places less load on the NCBI’s servers. For example:</P><PRE CLASS="verbatim">import os
-from Bio import SeqIO
-from Bio import Entrez
-Entrez.email = "A.N.Other at example.com" # Always tell NCBI who you are
-filename = "gi_186972394.gbk"
-if not os.path.isfile(filename):
- # Downloading...
- net_handle = Entrez.efetch(db="nucleotide",id="186972394",rettype="gb", retmode="text")
- out_handle = open(filename, "w")
- out_handle.write(net_handle.read())
- out_handle.close()
- net_handle.close()
- print("Saved")
-
-print("Parsing...")
-record = SeqIO.read(filename, "genbank")
-print(record)
-</PRE><P>To get the output in XML format, which you can parse using the <CODE>Bio.Entrez.read()</CODE> function, use <CODE>retmode="xml"</CODE>:</P><PRE CLASS="verbatim">>>> from Bio import Entrez
->>> handle = Entrez.efetch(db="nucleotide", id="186972394", retmode="xml")
->>> record = Entrez.read(handle)
->>> handle.close()
->>> record[0]["GBSeq_definition"]
-'Selenipedium aequinoctiale maturase K (matK) gene, partial cds; chloroplast'
->>> record[0]["GBSeq_source"]
-'chloroplast Selenipedium aequinoctiale'
-</PRE><P>So, that dealt with sequences. For examples of parsing file formats specific to the other databases (e.g. the <CODE>MEDLINE</CODE> format used in PubMed), see Section <A HREF="#sec:entrez-specialized-parsers">9.12</A>.</P><P>If you want to perform a search with <CODE>Bio.Entrez.esearch()</CODE>, and then download the records with <CODE>Bio.Entrez.efetch()</CODE>, you should use the WebEnv history feature – see Section <A HREF="#sec:entrez-webenv">9.15</A>.</P><! [...]
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc115">9.7</A>  ELink: Searching for related items in NCBI Entrez</H2><!--SEC END --><P>
-<A NAME="sec:elink"></A></P><P>ELink, available from Biopython as <CODE>Bio.Entrez.elink()</CODE>, can be used to find related items in the NCBI Entrez databases. For example, you can us this to find nucleotide entries for an entry in the gene database,
-and other cool stuff.</P><P>Let’s use ELink to find articles related to the Biopython application note published in <I>Bioinformatics</I> in 2009. The PubMed ID of this article is 19304878:</P><PRE CLASS="verbatim">>>> from Bio import Entrez
->>> Entrez.email = "A.N.Other at example.com"
->>> pmid = "19304878"
->>> record = Entrez.read(Entrez.elink(dbfrom="pubmed", id=pmid))
-</PRE><P>The <CODE>record</CODE> variable consists of a Python list, one for each database in which we searched. Since we specified only one PubMed ID to search for, <CODE>record</CODE> contains only one item. This item is a dictionary containing information about our search term, as well as all the related items that were found:</P><PRE CLASS="verbatim">>>> record[0]["DbFrom"]
-'pubmed'
->>> record[0]["IdList"]
-['19304878']
-</PRE><P>The <CODE>"LinkSetDb"</CODE> key contains the search results, stored as a list consisting of one item for each target database. In our search results, we only find hits in the PubMed database (although sub-divided into categories):</P><PRE CLASS="verbatim">>>> len(record[0]["LinkSetDb"])
-5
->>> for linksetdb in record[0]["LinkSetDb"]:
-... print(linksetdb["DbTo"], linksetdb["LinkName"], len(linksetdb["Link"]))
-...
-pubmed pubmed_pubmed 110
-pubmed pubmed_pubmed_combined 6
-pubmed pubmed_pubmed_five 6
-pubmed pubmed_pubmed_reviews 5
-pubmed pubmed_pubmed_reviews_five 5
-</PRE><P>The actual search results are stored as under the <CODE>"Link"</CODE> key. In total, 110 items were found under
-standard search.
-Let’s now at the first search result:
-</P><PRE CLASS="verbatim">>>> record[0]["LinkSetDb"][0]["Link"][0]
-{u'Id': '19304878'}
-</PRE><P>This is the article we searched for, which doesn’t help us much, so let’s look at the second search result:</P><PRE CLASS="verbatim">>>> record[0]["LinkSetDb"][0]["Link"][1]
-{u'Id': '14630660'}
-</PRE><P>This paper, with PubMed ID 14630660, is about the Biopython PDB parser.</P><P>We can use a loop to print out all PubMed IDs:
-</P><PRE CLASS="verbatim">>>> for link in record[0]["LinkSetDb"][0]["Link"]:
-... print(link["Id"])
-19304878
-14630660
-18689808
-17121776
-16377612
-12368254
-......
-</PRE><P>Now that was nice, but personally I am often more interested to find out if a paper has been cited.
-Well, ELink can do that too – at least for journals in Pubmed Central (see Section <A HREF="#sec:elink-citations">9.15.3</A>).</P><P>For help on ELink, see the <A HREF="http://www.ncbi.nlm.nih.gov/entrez/query/static/elink_help.html">ELink help page</A>.
-There is an entire sub-page just for the <A HREF="http://eutils.ncbi.nlm.nih.gov/corehtml/query/static/entrezlinks.html">link names</A>, describing how different databases can be cross referenced.</P><!--TOC section EGQuery: Global Query - counts for search terms-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc116">9.8</A>  EGQuery: Global Query - counts for search terms</H2><!--SEC END --><P>
-EGQuery provides counts for a search term in each of the Entrez databases (i.e. a global query). This is particularly useful to find out how many items your search terms would find in each database without actually performing lots of separate searches with ESearch (see the example in <A HREF="#subsec:entrez_example_genbank">9.14.2</A> below).</P><P>In this example, we use <CODE>Bio.Entrez.egquery()</CODE> to obtain the counts for “Biopython”:</P><PRE CLASS="verbatim">>&g [...]
->>> Entrez.email = "A.N.Other at example.com" # Always tell NCBI who you are
->>> handle = Entrez.egquery(term="biopython")
->>> record = Entrez.read(handle)
->>> for row in record["eGQueryResult"]:
-... print(row["DbName"], row["Count"])
-...
-pubmed 6
-pmc 62
-journals 0
-...
-</PRE><P>See the <A HREF="http://www.ncbi.nlm.nih.gov/entrez/query/static/egquery_help.html">EGQuery help page</A> for more information.</P><!--TOC section ESpell: Obtaining spelling suggestions-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc117">9.9</A>  ESpell: Obtaining spelling suggestions</H2><!--SEC END --><P>
-ESpell retrieves spelling suggestions. In this example, we use <CODE>Bio.Entrez.espell()</CODE> to obtain the correct spelling of Biopython:</P><PRE CLASS="verbatim">>>> from Bio import Entrez
->>> Entrez.email = "A.N.Other at example.com" # Always tell NCBI who you are
->>> handle = Entrez.espell(term="biopythooon")
->>> record = Entrez.read(handle)
->>> record["Query"]
-'biopythooon'
->>> record["CorrectedQuery"]
-'biopython'
-</PRE><P>See the <A HREF="http://www.ncbi.nlm.nih.gov/entrez/query/static/espell_help.html">ESpell help page</A> for more information.
-The main use of this is for GUI tools to provide automatic suggestions for search terms.</P><!--TOC section Parsing huge Entrez XML files-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc118">9.10</A>  Parsing huge Entrez XML files</H2><!--SEC END --><P>The <CODE>Entrez.read</CODE> function reads the entire XML file returned by Entrez into a single Python object, which is kept in memory. To parse Entrez XML files too large to fit in memory, you can use the function <CODE>Entrez.parse</CODE>. This is a generator function that reads records in the XML file one by one. This function is only useful if the XML file [...]
-</PRE><P>The resulting XML file has a size of 6.1 GB. Attempting <CODE>Entrez.read</CODE> on this file will result in a <CODE>MemoryError</CODE> on many computers.</P><P>The XML file <CODE>Homo_sapiens.xml</CODE> consists of a list of Entrez gene records, each corresponding to one Entrez gene in human. <CODE>Entrez.parse</CODE> retrieves these gene records one by one. You can then print out or store the relevant information in each record by iterating over the records. For example, this [...]
->>> handle = open("Homo_sapiens.xml")
->>> records = Entrez.parse(handle)
-
->>> for record in records:
-... status = record['Entrezgene_track-info']['Gene-track']['Gene-track_status']
-... if status.attributes['value']=='discontinued':
-... continue
-... geneid = record['Entrezgene_track-info']['Gene-track']['Gene-track_geneid']
-... genename = record['Entrezgene_gene']['Gene-ref']['Gene-ref_locus']
-... print(geneid, genename)
-</PRE><P>This will print:
-</P><PRE CLASS="verbatim">1 A1BG
-2 A2M
-3 A2MP
-8 AA
-9 NAT1
-10 NAT2
-11 AACP
-12 SERPINA3
-13 AADAC
-14 AAMP
-15 AANAT
-16 AARS
-17 AAVS1
-...
-</PRE><!--TOC section Handling errors-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc119">9.11</A>  Handling errors</H2><!--SEC END --><P>Three things can go wrong when parsing an XML file:
-</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-The file may not be an XML file to begin with;
-</LI><LI CLASS="li-itemize">The file may end prematurely or otherwise be corrupted;
-</LI><LI CLASS="li-itemize">The file may be correct XML, but contain items that are not represented in the associated DTD.
-</LI></UL><P>The first case occurs if, for example, you try to parse a Fasta file as if it were an XML file:
-</P><PRE CLASS="verbatim">>>> from Bio import Entrez
->>> handle = open("NC_005816.fna") # a Fasta file
->>> record = Entrez.read(handle)
-Traceback (most recent call last):
- File "<stdin>", line 1, in <module>
- File "/usr/local/lib/python2.7/site-packages/Bio/Entrez/__init__.py", line 257, in read
- record = handler.read(handle)
- File "/usr/local/lib/python2.7/site-packages/Bio/Entrez/Parser.py", line 164, in read
- raise NotXMLError(e)
-Bio.Entrez.Parser.NotXMLError: Failed to parse the XML data (syntax error: line 1, column 0). Please make sure that the input data are in XML format.
-</PRE><P>Here, the parser didn’t find the <CODE><?xml ...</CODE> tag with which an XML file is supposed to start, and therefore decides (correctly) that the file is not an XML file.</P><P>When your file is in the XML format but is corrupted (for example, by ending prematurely), the parser will raise a CorruptedXMLError.
-Here is an example of an XML file that ends prematurely:
-</P><PRE CLASS="verbatim"><?xml version="1.0"?>
-<!DOCTYPE eInfoResult PUBLIC "-//NLM//DTD eInfoResult, 11 May 2002//EN" "http://www.ncbi.nlm.nih.gov/entrez/query/DTD/eInfo_020511.dtd">
-<eInfoResult>
-<DbList>
- <DbName>pubmed</DbName>
- <DbName>protein</DbName>
- <DbName>nucleotide</DbName>
- <DbName>nuccore</DbName>
- <DbName>nucgss</DbName>
- <DbName>nucest</DbName>
- <DbName>structure</DbName>
- <DbName>genome</DbName>
- <DbName>books</DbName>
- <DbName>cancerchromosomes</DbName>
- <DbName>cdd</DbName>
-</PRE><P>which will generate the following traceback:
-</P><PRE CLASS="verbatim">>>> Entrez.read(handle)
-Traceback (most recent call last):
- File "<stdin>", line 1, in <module>
- File "/usr/local/lib/python2.7/site-packages/Bio/Entrez/__init__.py", line 257, in read
- record = handler.read(handle)
- File "/usr/local/lib/python2.7/site-packages/Bio/Entrez/Parser.py", line 160, in read
- raise CorruptedXMLError(e)
-Bio.Entrez.Parser.CorruptedXMLError: Failed to parse the XML data (no element found: line 16, column 0). Please make sure that the input data are not corrupted.
-
->>>
-</PRE><P>Note that the error message tells you at what point in the XML file the error was detected.</P><P>The third type of error occurs if the XML file contains tags that do not have a description in the corresponding DTD file. This is an example of such an XML file:</P><PRE CLASS="verbatim"><?xml version="1.0"?>
-<!DOCTYPE eInfoResult PUBLIC "-//NLM//DTD eInfoResult, 11 May 2002//EN" "http://www.ncbi.nlm.nih.gov/entrez/query/DTD/eInfo_020511.dtd">
-<eInfoResult>
- <DbInfo>
- <DbName>pubmed</DbName>
- <MenuName>PubMed</MenuName>
- <Description>PubMed bibliographic record</Description>
- <Count>20161961</Count>
- <LastUpdate>2010/09/10 04:52</LastUpdate>
- <FieldList>
- <Field>
-...
- </Field>
- </FieldList>
- <DocsumList>
- <Docsum>
- <DsName>PubDate</DsName>
- <DsType>4</DsType>
- <DsTypeName>string</DsTypeName>
- </Docsum>
- <Docsum>
- <DsName>EPubDate</DsName>
-...
- </DbInfo>
-</eInfoResult>
-</PRE><P>In this file, for some reason the tag <CODE><DocsumList></CODE> (and several others) are not listed in the DTD file <CODE>eInfo_020511.dtd</CODE>, which is specified on the second line as the DTD for this XML file. By default, the parser will stop and raise a ValidationError if it cannot find some tag in the DTD:</P><PRE CLASS="verbatim">>>> from Bio import Entrez
->>> handle = open("einfo3.xml")
->>> record = Entrez.read(handle)
-Traceback (most recent call last):
- File "<stdin>", line 1, in <module>
- File "/usr/local/lib/python2.7/site-packages/Bio/Entrez/__init__.py", line 257, in read
- record = handler.read(handle)
- File "/usr/local/lib/python2.7/site-packages/Bio/Entrez/Parser.py", line 154, in read
- self.parser.ParseFile(handle)
- File "/usr/local/lib/python2.7/site-packages/Bio/Entrez/Parser.py", line 246, in startElementHandler
- raise ValidationError(name)
-Bio.Entrez.Parser.ValidationError: Failed to find tag 'DocsumList' in the DTD. To skip all tags that are not represented in the DTD, please call Bio.Entrez.read or Bio.Entrez.parse with validate=False.
-</PRE><P>Optionally, you can instruct the parser to skip such tags instead of raising a ValidationError. This is done by calling <CODE>Entrez.read</CODE> or <CODE>Entrez.parse</CODE> with the argument <CODE>validate</CODE> equal to False:
-</P><PRE CLASS="verbatim">>>> from Bio import Entrez
->>> handle = open("einfo3.xml")
->>> record = Entrez.read(handle, validate=False)
->>>
-</PRE><P>Of course, the information contained in the XML tags that are not in the DTD are not present in the record returned by <CODE>Entrez.read</CODE>.</P><!--TOC section Specialized parsers-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc120">9.12</A>  Specialized parsers</H2><!--SEC END --><P>
-<A NAME="sec:entrez-specialized-parsers"></A></P><P>The <CODE>Bio.Entrez.read()</CODE> function can parse most (if not all) XML output returned by Entrez. Entrez typically allows you to retrieve records in other formats, which may have some advantages compared to the XML format in terms of readability (or download size).</P><P>To request a specific file format from Entrez using <CODE>Bio.Entrez.efetch()</CODE> requires specifying the <CODE>rettype</CODE> and/or <CODE>retmode</CODE> optio [...]
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc121">9.12.1</A>  Parsing Medline records</H3><!--SEC END --><P>
-<A NAME="subsec:entrez-and-medline"></A>
-You can find the Medline parser in <CODE>Bio.Medline</CODE>. Suppose we want to parse the file <CODE>pubmed_result1.txt</CODE>, containing one Medline record. You can find this file in Biopython’s <CODE>Tests\Medline</CODE> directory. The file looks like this:</P><PRE CLASS="verbatim">PMID- 12230038
-OWN - NLM
-STAT- MEDLINE
-DA - 20020916
-DCOM- 20030606
-LR - 20041117
-PUBM- Print
-IS - 1467-5463 (Print)
-VI - 3
-IP - 3
-DP - 2002 Sep
-TI - The Bio* toolkits--a brief overview.
-PG - 296-302
-AB - Bioinformatics research is often difficult to do with commercial software. The
- Open Source BioPerl, BioPython and Biojava projects provide toolkits with
-...
-</PRE><P>We first open the file and then parse it:
-</P><PRE CLASS="verbatim">>>> from Bio import Medline
->>> with open("pubmed_result1.txt") as handle:
-... record = Medline.read(handle)
-...
-</PRE><P>The <CODE>record</CODE> now contains the Medline record as a Python dictionary:
-</P><PRE CLASS="verbatim">>>> record["PMID"]
-'12230038'
-</PRE><PRE CLASS="verbatim">>>> record["AB"]
-'Bioinformatics research is often difficult to do with commercial software.
-The Open Source BioPerl, BioPython and Biojava projects provide toolkits with
-multiple functionality that make it easier to create customised pipelines or
-analysis. This review briefly compares the quirks of the underlying languages
-and the functionality, documentation, utility and relative advantages of the
-Bio counterparts, particularly from the point of view of the beginning
-biologist programmer.'
-</PRE><P>The key names used in a Medline record can be rather obscure; use
-</P><PRE CLASS="verbatim">>>> help(record)
-</PRE><P>for a brief summary.</P><P>To parse a file containing multiple Medline records, you can use the <CODE>parse</CODE> function instead:
-</P><PRE CLASS="verbatim">>>> from Bio import Medline
->>> with open("pubmed_result2.txt") as handle:
-... for record in Medline.parse(handle):
-... print(record["TI"])
-...
-A high level interface to SCOP and ASTRAL implemented in python.
-GenomeDiagram: a python package for the visualization of large-scale genomic data.
-Open source clustering software.
-PDB file parser and structure class implemented in Python.
-</PRE><P>Instead of parsing Medline records stored in files, you can also parse Medline records downloaded by <CODE>Bio.Entrez.efetch</CODE>. For example, let’s look at all Medline records in PubMed related to Biopython:
-</P><PRE CLASS="verbatim">>>> from Bio import Entrez
->>> Entrez.email = "A.N.Other at example.com" # Always tell NCBI who you are
->>> handle = Entrez.esearch(db="pubmed", term="biopython")
->>> record = Entrez.read(handle)
->>> record["IdList"]
-['19304878', '18606172', '16403221', '16377612', '14871861', '14630660', '12230038']
-</PRE><P>We now use <CODE>Bio.Entrez.efetch</CODE> to download these Medline records:
-</P><PRE CLASS="verbatim">>>> idlist = record["IdList"]
->>> handle = Entrez.efetch(db="pubmed", id=idlist, rettype="medline", retmode="text")
-</PRE><P>Here, we specify <CODE>rettype="medline", retmode="text"</CODE> to obtain the Medline records in plain-text Medline format. Now we use <CODE>Bio.Medline</CODE> to parse these records:
-</P><PRE CLASS="verbatim">>>> from Bio import Medline
->>> records = Medline.parse(handle)
->>> for record in records:
-... print(record["AU"])
-['Cock PJ', 'Antao T', 'Chang JT', 'Chapman BA', 'Cox CJ', 'Dalke A', ..., 'de Hoon MJ']
-['Munteanu CR', 'Gonzalez-Diaz H', 'Magalhaes AL']
-['Casbon JA', 'Crooks GE', 'Saqi MA']
-['Pritchard L', 'White JA', 'Birch PR', 'Toth IK']
-['de Hoon MJ', 'Imoto S', 'Nolan J', 'Miyano S']
-['Hamelryck T', 'Manderick B']
-['Mangalam H']
-</PRE><P>For comparison, here we show an example using the XML format:
-</P><PRE CLASS="verbatim">>>> idlist = record["IdList"]
->>> handle = Entrez.efetch(db="pubmed", id=idlist, rettype="medline", retmode="xml")
->>> records = Entrez.read(handle)
->>> for record in records:
-... print(record["MedlineCitation"]["Article"]["ArticleTitle"])
-Biopython: freely available Python tools for computational molecular biology and
- bioinformatics.
-Enzymes/non-enzymes classification model complexity based on composition, sequence,
- 3D and topological indices.
-A high level interface to SCOP and ASTRAL implemented in python.
-GenomeDiagram: a python package for the visualization of large-scale genomic data.
-Open source clustering software.
-PDB file parser and structure class implemented in Python.
-The Bio* toolkits--a brief overview.
-</PRE><P>Note that in both of these examples, for simplicity we have naively combined ESearch and EFetch.
-In this situation, the NCBI would expect you to use their history feature,
-as illustrated in Section <A HREF="#sec:entrez-webenv">9.15</A>.</P><!--TOC subsection Parsing GEO records-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc122">9.12.2</A>  Parsing GEO records</H3><!--SEC END --><P>GEO (<A HREF="http://www.ncbi.nlm.nih.gov/geo/">Gene Expression Omnibus</A>)
-is a data repository of high-throughput gene expression and hybridization
-array data. The <CODE>Bio.Geo</CODE> module can be used to parse GEO-formatted
-data.</P><P>The following code fragment shows how to parse the example GEO file
-<CODE>GSE16.txt</CODE> into a record and print the record:</P><PRE CLASS="verbatim">>>> from Bio import Geo
->>> handle = open("GSE16.txt")
->>> records = Geo.parse(handle)
->>> for record in records:
-... print(record)
-</PRE><P>You can search the “gds” database (GEO datasets) with ESearch:</P><PRE CLASS="verbatim">>>> from Bio import Entrez
->>> Entrez.email = "A.N.Other at example.com" # Always tell NCBI who you are
->>> handle = Entrez.esearch(db="gds", term="GSE16")
->>> record = Entrez.read(handle)
->>> record["Count"]
-2
->>> record["IdList"]
-['200000016', '100000028']
-</PRE><P>From the Entrez website, UID “200000016” is GDS16 while the other hit
-“100000028” is for the associated platform, GPL28. Unfortunately, at the
-time of writing the NCBI don’t seem to support downloading GEO files using
-Entrez (not as XML, nor in the <I>Simple Omnibus Format in Text</I> (SOFT)
-format).</P><P>However, it is actually pretty straight forward to download the GEO files by FTP
-from <A HREF="ftp://ftp.ncbi.nih.gov/pub/geo/"><TT>ftp://ftp.ncbi.nih.gov/pub/geo/</TT></A> instead. In this case you might want
-<A HREF="ftp://ftp.ncbi.nih.gov/pub/geo/DATA/SOFT/by_series/GSE16/GSE16_family.soft.gz"><TT>ftp://ftp.ncbi.nih.gov/pub/geo/DATA/SOFT/by_series/GSE16/GSE16_family.soft.gz</TT></A>
-(a compressed file, see the Python module gzip).</P><!--TOC subsection Parsing UniGene records-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc123">9.12.3</A>  Parsing UniGene records</H3><!--SEC END --><P>UniGene is an NCBI database of the transcriptome, with each UniGene record showing the set of transcripts that are associated with a particular gene in a specific organism. A typical UniGene record looks like this:</P><PRE CLASS="verbatim">ID Hs.2
-TITLE N-acetyltransferase 2 (arylamine N-acetyltransferase)
-GENE NAT2
-CYTOBAND 8p22
-GENE_ID 10
-LOCUSLINK 10
-HOMOL YES
-EXPRESS bone| connective tissue| intestine| liver| liver tumor| normal| soft tissue/muscle tissue tumor| adult
-RESTR_EXPR adult
-CHROMOSOME 8
-STS ACC=PMC310725P3 UNISTS=272646
-STS ACC=WIAF-2120 UNISTS=44576
-STS ACC=G59899 UNISTS=137181
-...
-STS ACC=GDB:187676 UNISTS=155563
-PROTSIM ORG=10090; PROTGI=6754794; PROTID=NP_035004.1; PCT=76.55; ALN=288
-PROTSIM ORG=9796; PROTGI=149742490; PROTID=XP_001487907.1; PCT=79.66; ALN=288
-PROTSIM ORG=9986; PROTGI=126722851; PROTID=NP_001075655.1; PCT=76.90; ALN=288
-...
-PROTSIM ORG=9598; PROTGI=114619004; PROTID=XP_519631.2; PCT=98.28; ALN=288
-
-SCOUNT 38
-SEQUENCE ACC=BC067218.1; NID=g45501306; PID=g45501307; SEQTYPE=mRNA
-SEQUENCE ACC=NM_000015.2; NID=g116295259; PID=g116295260; SEQTYPE=mRNA
-SEQUENCE ACC=D90042.1; NID=g219415; PID=g219416; SEQTYPE=mRNA
-SEQUENCE ACC=D90040.1; NID=g219411; PID=g219412; SEQTYPE=mRNA
-SEQUENCE ACC=BC015878.1; NID=g16198419; PID=g16198420; SEQTYPE=mRNA
-SEQUENCE ACC=CR407631.1; NID=g47115198; PID=g47115199; SEQTYPE=mRNA
-SEQUENCE ACC=BG569293.1; NID=g13576946; CLONE=IMAGE:4722596; END=5'; LID=6989; SEQTYPE=EST; TRACE=44157214
-...
-SEQUENCE ACC=AU099534.1; NID=g13550663; CLONE=HSI08034; END=5'; LID=8800; SEQTYPE=EST
-//
-</PRE><P>This particular record shows the set of transcripts (shown in the <CODE>SEQUENCE</CODE> lines) that originate from the human gene NAT2, encoding en N-acetyltransferase. The <CODE>PROTSIM</CODE> lines show proteins with significant similarity to NAT2, whereas the <CODE>STS</CODE> lines show the corresponding sequence-tagged sites in the genome.</P><P>To parse UniGene files, use the <CODE>Bio.UniGene</CODE> module:
-</P><PRE CLASS="verbatim">>>> from Bio import UniGene
->>> input = open("myunigenefile.data")
->>> record = UniGene.read(input)
-</PRE><P>The <CODE>record</CODE> returned by <CODE>UniGene.read</CODE> is a Python object with attributes corresponding to the fields in the UniGene record. For example,
-</P><PRE CLASS="verbatim">>>> record.ID
-"Hs.2"
->>> record.title
-"N-acetyltransferase 2 (arylamine N-acetyltransferase)"
-</PRE><P>The <CODE>EXPRESS</CODE> and <CODE>RESTR_EXPR</CODE> lines are stored as Python lists of strings:
-</P><PRE CLASS="verbatim">['bone', 'connective tissue', 'intestine', 'liver', 'liver tumor', 'normal', 'soft tissue/muscle tissue tumor', 'adult']
-</PRE><P>Specialized objects are returned for the <CODE>STS</CODE>, <CODE>PROTSIM</CODE>, and <CODE>SEQUENCE</CODE> lines, storing the keys shown in each line as attributes:
-</P><PRE CLASS="verbatim">>>> record.sts[0].acc
-'PMC310725P3'
->>> record.sts[0].unists
-'272646'
-</PRE><P>and similarly for the <CODE>PROTSIM</CODE> and <CODE>SEQUENCE</CODE> lines.</P><P>To parse a file containing more than one UniGene record, use the <CODE>parse</CODE> function in <CODE>Bio.UniGene</CODE>:</P><PRE CLASS="verbatim">>>> from Bio import UniGene
->>> input = open("unigenerecords.data")
->>> records = UniGene.parse(input)
->>> for record in records:
-... print(record.ID)
-</PRE><!--TOC section Using a proxy-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc124">9.13</A>  Using a proxy</H2><!--SEC END --><P>Normally you won’t have to worry about using a proxy, but if this is an issue
-on your network here is how to deal with it. Internally, <CODE>Bio.Entrez</CODE>
-uses the standard Python library <CODE>urllib</CODE> for accessing the NCBI servers.
-This will check an environment variable called <CODE>http_proxy</CODE> to configure
-any simple proxy automatically. Unfortunately this module does not support
-the use of proxies which require authentication.</P><P>You may choose to set the <CODE>http_proxy</CODE> environment variable once (how you
-do this will depend on your operating system). Alternatively you can set this
-within Python at the start of your script, for example:</P><PRE CLASS="verbatim">import os
-os.environ["http_proxy"] = "http://proxyhost.example.com:8080"
-</PRE><P>See the <A HREF="http://www.python.org/doc/lib/module-urllib.html">urllib documentation</A> for more details.</P><!--TOC section Examples-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc125">9.14</A>  Examples</H2><!--SEC END --><P>
-<A NAME="sec:entrez_examples"></A></P><!--TOC subsection PubMed and Medline-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc126">9.14.1</A>  PubMed and Medline</H3><!--SEC END --><P>
-<A NAME="subsec:pub_med"></A></P><P>If you are in the medical field or interested in human issues (and many times even if you are not!), PubMed (<A HREF="http://www.ncbi.nlm.nih.gov/PubMed/"><TT>http://www.ncbi.nlm.nih.gov/PubMed/</TT></A>) is an excellent source of all kinds of goodies. So like other things, we’d like to be able to grab information from it and use it in Python scripts.</P><P>In this example, we will query PubMed for all articles having to do with orchids (see sec [...]
->>> Entrez.email = "A.N.Other at example.com" # Always tell NCBI who you are
->>> handle = Entrez.egquery(term="orchid")
->>> record = Entrez.read(handle)
->>> for row in record["eGQueryResult"]:
-... if row["DbName"]=="pubmed":
-... print(row["Count"])
-463
-</PRE><P>Now we use the <CODE>Bio.Entrez.efetch</CODE> function to download the PubMed IDs of these 463 articles:
-</P><PRE CLASS="verbatim">>>> handle = Entrez.esearch(db="pubmed", term="orchid", retmax=463)
->>> record = Entrez.read(handle)
->>> idlist = record["IdList"]
->>> print(idlist)
-</PRE><P>This returns a Python list containing all of the PubMed IDs of articles related to orchids:
-</P><PRE CLASS="verbatim">['18680603', '18665331', '18661158', '18627489', '18627452', '18612381',
-'18594007', '18591784', '18589523', '18579475', '18575811', '18575690',
-...
-</PRE><P>Now that we’ve got them, we obviously want to get the corresponding Medline records and extract the information from them. Here, we’ll download the Medline records in the Medline flat-file format, and use the <CODE>Bio.Medline</CODE> module to parse them:
-</P><PRE CLASS="verbatim">>>> from Bio import Medline
->>> handle = Entrez.efetch(db="pubmed", id=idlist, rettype="medline",
- retmode="text")
->>> records = Medline.parse(handle)
-</PRE><P>NOTE - We’ve just done a separate search and fetch here, the NCBI much prefer you to take advantage of their history support in this situation. See Section <A HREF="#sec:entrez-webenv">9.15</A>.</P><P>Keep in mind that <CODE>records</CODE> is an iterator, so you can iterate through the records only once. If you want to save the records, you can convert them to a list:
-</P><PRE CLASS="verbatim">>>> records = list(records)
-</PRE><P>Let’s now iterate over the records to print out some information about each record:
-</P><PRE CLASS="verbatim">>>> for record in records:
-... print("title:", record.get("TI", "?"))
-... print("authors:", record.get("AU", "?"))
-... print("source:", record.get("SO", "?"))
-... print("")
-</PRE><P>The output for this looks like:
-</P><PRE CLASS="verbatim">title: Sex pheromone mimicry in the early spider orchid (ophrys sphegodes):
-patterns of hydrocarbons as the key mechanism for pollination by sexual
-deception [In Process Citation]
-authors: ['Schiestl FP', 'Ayasse M', 'Paulus HF', 'Lofstedt C', 'Hansson BS',
-'Ibarra F', 'Francke W']
-source: J Comp Physiol [A] 2000 Jun;186(6):567-74
-</PRE><P>Especially interesting to note is the list of authors, which is returned as a standard Python list. This makes it easy to manipulate and search using standard Python tools. For instance, we could loop through a whole bunch of entries searching for a particular author with code like the following:
-</P><PRE CLASS="verbatim">>>> search_author = "Waits T"
-
->>> for record in records:
-... if not "AU" in record:
-... continue
-... if search_author in record["AU"]:
-... print("Author %s found: %s" % (search_author, record["SO"]))
-</PRE><P>Hopefully this section gave you an idea of the power and flexibility of the Entrez and Medline interfaces and how they can be used together.</P><!--TOC subsection Searching, downloading, and parsing Entrez Nucleotide records-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc127">9.14.2</A>  Searching, downloading, and parsing Entrez Nucleotide records</H3><!--SEC END --><P>
-<A NAME="subsec:entrez_example_genbank"></A></P><P>Here we’ll show a simple example of performing a remote Entrez query. In section <A HREF="#sec:orchids">2.3</A> of the parsing examples, we talked about using NCBI’s Entrez website to search the NCBI nucleotide databases for info on Cypripedioideae, our friends the lady slipper orchids. Now, we’ll look at how to automate that process using a Python script. In this example, we’ll just show how to connect, [...]
-</P><PRE CLASS="verbatim">>>> from Bio import Entrez
->>> Entrez.email = "A.N.Other at example.com" # Always tell NCBI who you are
->>> handle = Entrez.egquery(term="Cypripedioideae")
->>> record = Entrez.read(handle)
->>> for row in record["eGQueryResult"]:
-... if row["DbName"]=="nuccore":
-... print(row["Count"])
-814
-</PRE><P>So, we expect to find 814 Entrez Nucleotide records (this is the number I obtained in 2008; it is likely to increase in the future). If you find some ridiculously high number of hits, you may want to reconsider if you really want to download all of them, which is our next step:
-</P><PRE CLASS="verbatim">>>> from Bio import Entrez
->>> handle = Entrez.esearch(db="nucleotide", term="Cypripedioideae", retmax=814)
->>> record = Entrez.read(handle)
-</PRE><P>Here, <CODE>record</CODE> is a Python dictionary containing the search results and some auxiliary information. Just for information, let’s look at what is stored in this dictionary:
-</P><PRE CLASS="verbatim">>>> print(record.keys())
-[u'Count', u'RetMax', u'IdList', u'TranslationSet', u'RetStart', u'QueryTranslation']
-</PRE><P>First, let’s check how many results were found:
-</P><PRE CLASS="verbatim">>>> print(record["Count"])
-'814'
-</PRE><P>which is the number we expected. The 814 results are stored in <CODE>record['IdList']</CODE>:
-</P><PRE CLASS="verbatim">>>> len(record["IdList"])
-814
-</PRE><P>Let’s look at the first five results:
-</P><PRE CLASS="verbatim">>>> record["IdList"][:5]
-['187237168', '187372713', '187372690', '187372688', '187372686']
-</PRE><P><A NAME="sec:entrez-batched-efetch"></A>
-We can download these records using <CODE>efetch</CODE>.
-While you could download these records one by one, to reduce the load on NCBI’s servers, it is better to fetch a bunch of records at the same time, shown below.
-However, in this situation you should ideally be using the history feature described later in Section <A HREF="#sec:entrez-webenv">9.15</A>.</P><PRE CLASS="verbatim">>>> idlist = ",".join(record["IdList"][:5])
->>> print(idlist)
-187237168,187372713,187372690,187372688,187372686
->>> handle = Entrez.efetch(db="nucleotide", id=idlist, retmode="xml")
->>> records = Entrez.read(handle)
->>> len(records)
-5
-</PRE><P>Each of these records corresponds to one GenBank record.
-</P><PRE CLASS="verbatim">>>> print(records[0].keys())
-[u'GBSeq_moltype', u'GBSeq_source', u'GBSeq_sequence',
- u'GBSeq_primary-accession', u'GBSeq_definition', u'GBSeq_accession-version',
- u'GBSeq_topology', u'GBSeq_length', u'GBSeq_feature-table',
- u'GBSeq_create-date', u'GBSeq_other-seqids', u'GBSeq_division',
- u'GBSeq_taxonomy', u'GBSeq_references', u'GBSeq_update-date',
- u'GBSeq_organism', u'GBSeq_locus', u'GBSeq_strandedness']
-
->>> print(records[0]["GBSeq_primary-accession"])
-DQ110336
-
->>> print(records[0]["GBSeq_other-seqids"])
-['gb|DQ110336.1|', 'gi|187237168']
-
->>> print(records[0]["GBSeq_definition"])
-Cypripedium calceolus voucher Davis 03-03 A maturase (matR) gene, partial cds;
-mitochondrial
-
->>> print(records[0]["GBSeq_organism"])
-Cypripedium calceolus
-</PRE><P>You could use this to quickly set up searches – but for heavy usage, see Section <A HREF="#sec:entrez-webenv">9.15</A>.</P><!--TOC subsection Searching, downloading, and parsing GenBank records-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc128">9.14.3</A>  Searching, downloading, and parsing GenBank records</H3><!--SEC END --><P>
-<A NAME="sec:entrez-search-fetch-genbank"></A></P><P>The GenBank record format is a very popular method of holding information about sequences, sequence features, and other associated sequence information. The format is a good way to get information from the NCBI databases at <A HREF="http://www.ncbi.nlm.nih.gov/"><TT>http://www.ncbi.nlm.nih.gov/</TT></A>.</P><P>In this example we’ll show how to query the NCBI databases,to retrieve the records from the query, and then parse them u [...]
-For simplicity, this example <EM>does not</EM> take advantage of the WebEnv history feature – see Section <A HREF="#sec:entrez-webenv">9.15</A> for this.</P><P>First, we want to make a query and find out the ids of the records to retrieve. Here we’ll do a quick search for one of our favorite organisms, <EM>Opuntia</EM> (prickly-pear cacti). We can do quick search and get back the GIs (GenBank identifiers) for all of the corresponding records. First we check how many re [...]
->>> Entrez.email = "A.N.Other at example.com" # Always tell NCBI who you are
->>> handle = Entrez.egquery(term="Opuntia AND rpl16")
->>> record = Entrez.read(handle)
->>> for row in record["eGQueryResult"]:
-... if row["DbName"]=="nuccore":
-... print(row["Count"])
-...
-9
-</PRE><P>Now we download the list of GenBank identifiers:
-</P><PRE CLASS="verbatim">>>> handle = Entrez.esearch(db="nuccore", term="Opuntia AND rpl16")
->>> record = Entrez.read(handle)
->>> gi_list = record["IdList"]
->>> gi_list
-['57240072', '57240071', '6273287', '6273291', '6273290', '6273289', '6273286',
-'6273285', '6273284']
-</PRE><P>Now we use these GIs to download the GenBank records - note that with older versions of Biopython you had to supply a comma separated list of GI numbers to Entrez, as of Biopython 1.59 you can pass a list and this is converted for you:</P><PRE CLASS="verbatim">>>> gi_str = ",".join(gi_list)
->>> handle = Entrez.efetch(db="nuccore", id=gi_str, rettype="gb", retmode="text")
-</PRE><P>If you want to look at the raw GenBank files, you can read from this handle and print out the result:</P><PRE CLASS="verbatim">>>> text = handle.read()
->>> print(text)
-LOCUS AY851612 892 bp DNA linear PLN 10-APR-2007
-DEFINITION Opuntia subulata rpl16 gene, intron; chloroplast.
-ACCESSION AY851612
-VERSION AY851612.1 GI:57240072
-KEYWORDS .
-SOURCE chloroplast Austrocylindropuntia subulata
- ORGANISM Austrocylindropuntia subulata
- Eukaryota; Viridiplantae; Streptophyta; Embryophyta; Tracheophyta;
- Spermatophyta; Magnoliophyta; eudicotyledons; core eudicotyledons;
- Caryophyllales; Cactaceae; Opuntioideae; Austrocylindropuntia.
-REFERENCE 1 (bases 1 to 892)
- AUTHORS Butterworth,C.A. and Wallace,R.S.
-...
-</PRE><P>In this case, we are just getting the raw records. To get the records in a more Python-friendly form, we can use <CODE>Bio.SeqIO</CODE> to parse the GenBank data into <CODE>SeqRecord</CODE> objects, including <CODE>SeqFeature</CODE> objects (see Chapter <A HREF="#chapter:Bio.SeqIO">5</A>):</P><PRE CLASS="verbatim">>>> from Bio import SeqIO
->>> handle = Entrez.efetch(db="nuccore", id=gi_str, rettype="gb", retmode="text")
->>> records = SeqIO.parse(handle, "gb")
-</PRE><P>We can now step through the records and look at the information we are interested in:
-</P><PRE CLASS="verbatim">>>> for record in records:
->>> ... print("%s, length %i, with %i features" \
->>> ... % (record.name, len(record), len(record.features)))
-AY851612, length 892, with 3 features
-AY851611, length 881, with 3 features
-AF191661, length 895, with 3 features
-AF191665, length 902, with 3 features
-AF191664, length 899, with 3 features
-AF191663, length 899, with 3 features
-AF191660, length 893, with 3 features
-AF191659, length 894, with 3 features
-AF191658, length 896, with 3 features
-</PRE><P>Using these automated query retrieval functionality is a big plus over doing things by hand. Although the module should obey the NCBI’s max three queries per second rule, the NCBI have other recommendations like avoiding peak hours. See Section <A HREF="#sec:entrez-guidelines">9.1</A>.
-In particular, please note that for simplicity, this example does not use the WebEnv history feature. You should use this for any non-trivial search and download work, see Section <A HREF="#sec:entrez-webenv">9.15</A>.</P><P>Finally, if plan to repeat your analysis, rather than downloading the files from the NCBI and parsing them immediately (as shown in this example), you should just download the records <EM>once</EM> and save them to your hard disk, and then parse the local file.< [...]
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc129">9.14.4</A>  Finding the lineage of an organism</H3><!--SEC END --><P>Staying with a plant example, let’s now find the lineage of the Cypripedioideae orchid family. First, we search the Taxonomy database for Cypripedioideae, which yields exactly one NCBI taxonomy identifier:
-</P><PRE CLASS="verbatim">>>> from Bio import Entrez
->>> Entrez.email = "A.N.Other at example.com" # Always tell NCBI who you are
->>> handle = Entrez.esearch(db="Taxonomy", term="Cypripedioideae")
->>> record = Entrez.read(handle)
->>> record["IdList"]
-['158330']
->>> record["IdList"][0]
-'158330'
-</PRE><P>Now, we use <CODE>efetch</CODE> to download this entry in the Taxonomy database, and then parse it:
-</P><PRE CLASS="verbatim">>>> handle = Entrez.efetch(db="Taxonomy", id="158330", retmode="xml")
->>> records = Entrez.read(handle)
-</PRE><P>Again, this record stores lots of information:
-</P><PRE CLASS="verbatim">>>> records[0].keys()
-[u'Lineage', u'Division', u'ParentTaxId', u'PubDate', u'LineageEx',
- u'CreateDate', u'TaxId', u'Rank', u'GeneticCode', u'ScientificName',
- u'MitoGeneticCode', u'UpdateDate']
-</PRE><P>We can get the lineage directly from this record:
-</P><PRE CLASS="verbatim">>>> records[0]["Lineage"]
-'cellular organisms; Eukaryota; Viridiplantae; Streptophyta; Streptophytina;
- Embryophyta; Tracheophyta; Euphyllophyta; Spermatophyta; Magnoliophyta;
- Liliopsida; Asparagales; Orchidaceae'
-</PRE><P>The record data contains much more than just the information shown here - for example look under <TT>"LineageEx"</TT> instead of <TT>"Lineage"</TT> and you’ll get the NCBI taxon identifiers of the lineage entries too.</P><!--TOC section Using the history and WebEnv-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc130">9.15</A>  Using the history and WebEnv</H2><!--SEC END --><P>
-<A NAME="sec:entrez-webenv"></A></P><P>Often you will want to make a series of linked queries. Most typically,
-running a search, perhaps refining the search, and then retrieving detailed
-search results. You <EM>can</EM> do this by making a series of separate calls
-to Entrez. However, the NCBI prefer you to take advantage of their history
-support - for example combining ESearch and EFetch.</P><P>Another typical use of the history support would be to combine EPost and
-EFetch. You use EPost to upload a list of identifiers, which starts a new
-history session. You then download the records with EFetch by referring
-to the session (instead of the identifiers).</P><!--TOC subsection Searching for and downloading sequences using the history-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc131">9.15.1</A>  Searching for and downloading sequences using the history</H3><!--SEC END --><P>
-Suppose we want to search and download all the <I>Opuntia</I> rpl16
-nucleotide sequences, and store them in a FASTA file. As shown in
-Section <A HREF="#sec:entrez-search-fetch-genbank">9.14.3</A>, we can naively combine
-<CODE>Bio.Entrez.esearch()</CODE> to get a list of GI numbers, and then call
-<CODE>Bio.Entrez.efetch()</CODE> to download them all.</P><P>However, the approved approach is to run the search with the history
-feature. Then, we can fetch the results by reference to the search
-results - which the NCBI can anticipate and cache.</P><P>To do this, call <CODE>Bio.Entrez.esearch()</CODE> as normal, but with the
-additional argument of <CODE>usehistory="y"</CODE>,</P><PRE CLASS="verbatim">>>> from Bio import Entrez
->>> Entrez.email = "history.user at example.com"
->>> search_handle = Entrez.esearch(db="nucleotide",term="Opuntia[orgn] and rpl16",
- usehistory="y")
->>> search_results = Entrez.read(search_handle)
->>> search_handle.close()
-</PRE><P>When you get the XML output back, it will still include the usual search results:</P><PRE CLASS="verbatim">>>> gi_list = search_results["IdList"]
->>> count = int(search_results["Count"])
->>> assert count == len(gi_list)
-</PRE><P>However, you also get given two additional pieces of information, the <TT>WebEnv</TT> session cookie, and the <TT>QueryKey</TT>:</P><PRE CLASS="verbatim">>>> webenv = search_results["WebEnv"]
->>> query_key = search_results["QueryKey"]
-</PRE><P>Having stored these values in variables <TT>session_cookie</TT> and <TT>query_key</TT> we can use them as parameters to <CODE>Bio.Entrez.efetch()</CODE> instead of giving the GI numbers as identifiers.</P><P>While for small searches you might be OK downloading everything at once, it is better to download in batches. You use the <TT>retstart</TT> and <TT>retmax</TT> parameters to specify which range of search results you want returned (starting entry using zero-based counting, an [...]
-For example,</P><PRE CLASS="verbatim">from Bio import Entrez
-import time
-try:
- from urllib.error import HTTPError # for Python 3
-except ImportError:
- from urllib2 import HTTPError # for Python 2
-batch_size = 3
-out_handle = open("orchid_rpl16.fasta", "w")
-for start in range(0, count, batch_size):
- end = min(count, start+batch_size)
- print("Going to download record %i to %i" % (start+1, end))
- attempt = 1
- while attempt <= 3:
- try:
- fetch_handle = Entrez.efetch(db="nucleotide", rettype="fasta", retmode="text",
- retstart=start, retmax=batch_size,
- webenv=webenv, query_key=query_key)
- except HTTPError as err:
- if 500 <= err.code <= 599:
- print("Received error from server %s" % err)
- print("Attempt %i of 3" % attempt)
- attempt += 1
- time.sleep(15)
- else:
- raise
- data = fetch_handle.read()
- fetch_handle.close()
- out_handle.write(data)
-out_handle.close()
-</PRE><P>For illustrative purposes, this example downloaded the FASTA records in batches of three. Unless you are downloading genomes or chromosomes, you would normally pick a larger batch size.</P><!--TOC subsection Searching for and downloading abstracts using the history-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc132">9.15.2</A>  Searching for and downloading abstracts using the history</H3><!--SEC END --><P>
-Here is another history example, searching for papers published in the last year about the <I>Opuntia</I>, and then downloading them into a file in MedLine format:</P><PRE CLASS="verbatim">from Bio import Entrez
-import time
-try:
- from urllib.error import HTTPError # for Python 3
-except ImportError:
- from urllib2 import HTTPError # for Python 2
-Entrez.email = "history.user at example.com"
-search_results = Entrez.read(Entrez.esearch(db="pubmed",
- term="Opuntia[ORGN]",
- reldate=365, datetype="pdat",
- usehistory="y"))
-count = int(search_results["Count"])
-print("Found %i results" % count)
-
-batch_size = 10
-out_handle = open("recent_orchid_papers.txt", "w")
-for start in range(0,count,batch_size):
- end = min(count, start+batch_size)
- print("Going to download record %i to %i" % (start+1, end))
- attempt = 1
- while attempt <= 3:
- try:
- fetch_handle = Entrez.efetch(db="pubmed",rettype="medline",
- retmode="text",retstart=start,
- retmax=batch_size,
- webenv=search_results["WebEnv"],
- query_key=search_results["QueryKey"])
- except HTTPError as err:
- if 500 <= err.code <= 599:
- print("Received error from server %s" % err)
- print("Attempt %i of 3" % attempt)
- attempt += 1
- time.sleep(15)
- else:
- raise
- data = fetch_handle.read()
- fetch_handle.close()
- out_handle.write(data)
-out_handle.close()
-</PRE><P>At the time of writing, this gave 28 matches - but because this is a date dependent search, this will of course vary. As described in Section <A HREF="#subsec:entrez-and-medline">9.12.1</A> above, you can then use <CODE>Bio.Medline</CODE> to parse the saved records.</P><!--TOC subsection Searching for citations-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc133">9.15.3</A>  Searching for citations</H3><!--SEC END --><P>
-<A NAME="sec:elink-citations"></A></P><P>Back in Section <A HREF="#sec:elink">9.7</A> we mentioned ELink can be used to search for citations of a given paper.
-Unfortunately this only covers journals indexed for PubMed Central
-(doing it for all the journals in PubMed would mean a lot more work for the NIH).
-Let’s try this for the Biopython PDB parser paper, PubMed ID 14630660:</P><PRE CLASS="verbatim">>>> from Bio import Entrez
->>> Entrez.email = "A.N.Other at example.com"
->>> pmid = "14630660"
->>> results = Entrez.read(Entrez.elink(dbfrom="pubmed", db="pmc",
-... LinkName="pubmed_pmc_refs", from_uid=pmid))
->>> pmc_ids = [link["Id"] for link in results[0]["LinkSetDb"][0]["Link"]]
->>> pmc_ids
-['2744707', '2705363', '2682512', ..., '1190160']
-</PRE><P>Great - eleven articles. But why hasn’t the Biopython application note been
-found (PubMed ID 19304878)? Well, as you might have guessed from the variable
-names, there are not actually PubMed IDs, but PubMed Central IDs. Our
-application note is the third citing paper in that list, PMCID 2682512.</P><P>So, what if (like me) you’d rather get back a list of PubMed IDs? Well we
-can call ELink again to translate them. This becomes a two step process,
-so by now you should expect to use the history feature to accomplish it
-(Section <A HREF="#sec:entrez-webenv">9.15</A>).</P><P>But first, taking the more straightforward approach of making a second
-(separate) call to ELink:</P><PRE CLASS="verbatim">>>> results2 = Entrez.read(Entrez.elink(dbfrom="pmc", db="pubmed", LinkName="pmc_pubmed",
-... from_uid=",".join(pmc_ids)))
->>> pubmed_ids = [link["Id"] for link in results2[0]["LinkSetDb"][0]["Link"]]
->>> pubmed_ids
-['19698094', '19450287', '19304878', ..., '15985178']
-</PRE><P>This time you can immediately spot the Biopython application note
-as the third hit (PubMed ID 19304878).</P><P>Now, let’s do that all again but with the history …<I>TODO</I>.</P><P>And finally, don’t forget to include your <EM>own</EM> email address in the Entrez calls.
-</P><!--TOC chapter Swiss-Prot and ExPASy-->
-<H1 CLASS="chapter"><!--SEC ANCHOR --><A NAME="htoc134">Chapter 10</A>  Swiss-Prot and ExPASy</H1><!--SEC END --><P>
-<A NAME="chapter:swiss_prot"></A></P><!--TOC section Parsing Swiss-Prot files-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc135">10.1</A>  Parsing Swiss-Prot files</H2><!--SEC END --><P>Swiss-Prot (<A HREF="http://www.expasy.org/sprot"><TT>http://www.expasy.org/sprot</TT></A>) is a hand-curated database of protein sequences. Biopython can parse the “plain text” Swiss-Prot file format, which is still used for the UniProt Knowledgebase which combined Swiss-Prot, TrEMBL and PIR-PSD. We do not (yet) support the UniProtKB XML file format.</ [...]
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc136">10.1.1</A>  Parsing Swiss-Prot records</H3><!--SEC END --><P>In Section <A HREF="#sec:SeqIO_ExPASy_and_SwissProt">5.3.2</A>, we described how to extract the sequence of a Swiss-Prot record as a <CODE>SeqRecord</CODE> object. Alternatively, you can store the Swiss-Prot record in a <CODE>Bio.SwissProt.Record</CODE> object, which in fact stores the complete information contained in the Swiss-Prot record. In this secti [...]
-</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-Open a Swiss-Prot file locally: <BR>
-<CODE>>>> handle = open("myswissprotfile.dat")</CODE>
-</LI><LI CLASS="li-itemize">Open a gzipped Swiss-Prot file:
-<PRE CLASS="verbatim">>>> import gzip
->>> handle = gzip.open("myswissprotfile.dat.gz")
-</PRE></LI><LI CLASS="li-itemize">Open a Swiss-Prot file over the internet:
-<PRE CLASS="verbatim">>>> import urllib
->>> handle = urllib.urlopen("http://www.somelocation.org/data/someswissprotfile.dat")
-</PRE></LI><LI CLASS="li-itemize">Open a Swiss-Prot file over the internet from the ExPASy database
-(see section <A HREF="#subsec:expasy_swissprot">10.5.1</A>):
-<PRE CLASS="verbatim">>>> from Bio import ExPASy
->>> handle = ExPASy.get_sprot_raw(myaccessionnumber)
-</PRE></LI></UL><P>
-The key point is that for the parser, it doesn’t matter how the handle was created, as long as it points to data in the Swiss-Prot format.</P><P>We can use <CODE>Bio.SeqIO</CODE> as described in Section <A HREF="#sec:SeqIO_ExPASy_and_SwissProt">5.3.2</A> to get file format agnostic <CODE>SeqRecord</CODE> objects. Alternatively, we can use <CODE>Bio.SwissProt</CODE> get <CODE>Bio.SwissProt.Record</CODE> objects, which are a much closer match to the underlying file format.</P>< [...]
-</P><PRE CLASS="verbatim">>>> from Bio import SwissProt
->>> record = SwissProt.read(handle)
-</PRE><P>This function should be used if the handle points to exactly one Swiss-Prot record. It raises a <CODE>ValueError</CODE> if no Swiss-Prot record was found, and also if more than one record was found.</P><P>We can now print out some information about this record:
-</P><PRE CLASS="verbatim">>>> print(record.description)
-'RecName: Full=Chalcone synthase 3; EC=2.3.1.74; AltName: Full=Naringenin-chalcone synthase 3;'
->>> for ref in record.references:
-... print("authors:", ref.authors)
-... print("title:", ref.title)
-...
-authors: Liew C.F., Lim S.H., Loh C.S., Goh C.J.;
-title: "Molecular cloning and sequence analysis of chalcone synthase cDNAs of
-Bromheadia finlaysoniana.";
->>> print(record.organism_classification)
-['Eukaryota', 'Viridiplantae', 'Streptophyta', 'Embryophyta', ..., 'Bromheadia']
-</PRE><P>To parse a file that contains more than one Swiss-Prot record, we use the <CODE>parse</CODE> function instead. This function allows us to iterate over the records in the file.</P><P>For example, let’s parse the full Swiss-Prot database and collect all the descriptions.
-You can download this from the <A HREF="ftp://ftp.expasy.org/databases/uniprot/current_release/knowledgebase/complete/uniprot_sprot.dat.gz">ExPAYs FTP site</A> as a single gzipped-file <CODE>uniprot_sprot.dat.gz</CODE> (about 300MB). This is a compressed file containing a single file, <CODE>uniprot_sprot.dat</CODE> (over 1.5GB).</P><P>As described at the start of this section, you can use the Python library <CODE>gzip</CODE> to open and uncompress a <TT>.gz</TT> file, like this:</P><PRE [...]
->>> handle = gzip.open("uniprot_sprot.dat.gz")
-</PRE><P>However, uncompressing a large file takes time, and each time you open the file for reading in this way, it has to be decompressed on the fly. So, if you can spare the disk space you’ll save time in the long run if you first decompress the file to disk, to get the <CODE>uniprot_sprot.dat</CODE> file inside. Then you can open the file for reading as usual:</P><PRE CLASS="verbatim">>>> handle = open("uniprot_sprot.dat")
-</PRE><P>As of June 2009, the full Swiss-Prot database downloaded from ExPASy contained 468851 Swiss-Prot records. One concise way to build up a list of the record descriptions is with a list comprehension:
-</P><PRE CLASS="verbatim">>>> from Bio import SwissProt
->>> handle = open("uniprot_sprot.dat")
->>> descriptions = [record.description for record in SwissProt.parse(handle)]
->>> len(descriptions)
-468851
->>> descriptions[:5]
-['RecName: Full=Protein MGF 100-1R;',
- 'RecName: Full=Protein MGF 100-1R;',
- 'RecName: Full=Protein MGF 100-1R;',
- 'RecName: Full=Protein MGF 100-1R;',
- 'RecName: Full=Protein MGF 100-2L;']
-
-</PRE><P>Or, using a for loop over the record iterator:
-</P><PRE CLASS="verbatim">>>> from Bio import SwissProt
->>> descriptions = []
->>> handle = open("uniprot_sprot.dat")
->>> for record in SwissProt.parse(handle):
-... descriptions.append(record.description)
-...
->>> len(descriptions)
-468851
-</PRE><P>Because this is such a large input file, either way takes about eleven minutes on my new desktop computer (using the uncompressed <CODE>uniprot_sprot.dat</CODE> file as input).</P><P>It is equally easy to extract any kind of information you’d like from Swiss-Prot records. To see the members of a Swiss-Prot record, use
-</P><PRE CLASS="verbatim">>>> dir(record)
-['__doc__', '__init__', '__module__', 'accessions', 'annotation_update',
-'comments', 'created', 'cross_references', 'data_class', 'description',
-'entry_name', 'features', 'gene_name', 'host_organism', 'keywords',
-'molecule_type', 'organelle', 'organism', 'organism_classification',
-'references', 'seqinfo', 'sequence', 'sequence_length',
-'sequence_update', 'taxonomy_id']
-</PRE><!--TOC subsection Parsing the Swiss-Prot keyword and category list-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc137">10.1.2</A>  Parsing the Swiss-Prot keyword and category list</H3><!--SEC END --><P>Swiss-Prot also distributes a file <CODE>keywlist.txt</CODE>, which lists the keywords and categories used in Swiss-Prot. The file contains entries in the following form:</P><PRE CLASS="verbatim">ID 2Fe-2S.
-AC KW-0001
-DE Protein which contains at least one 2Fe-2S iron-sulfur cluster: 2 iron
-DE atoms complexed to 2 inorganic sulfides and 4 sulfur atoms of
-DE cysteines from the protein.
-SY Fe2S2; [2Fe-2S] cluster; [Fe2S2] cluster; Fe2/S2 (inorganic) cluster;
-SY Di-mu-sulfido-diiron; 2 iron, 2 sulfur cluster binding.
-GO GO:0051537; 2 iron, 2 sulfur cluster binding
-HI Ligand: Iron; Iron-sulfur; 2Fe-2S.
-HI Ligand: Metal-binding; 2Fe-2S.
-CA Ligand.
-//
-ID 3D-structure.
-AC KW-0002
-DE Protein, or part of a protein, whose three-dimensional structure has
-DE been resolved experimentally (for example by X-ray crystallography or
-DE NMR spectroscopy) and whose coordinates are available in the PDB
-DE database. Can also be used for theoretical models.
-HI Technical term: 3D-structure.
-CA Technical term.
-//
-ID 3Fe-4S.
-...
-</PRE><P>The entries in this file can be parsed by the <CODE>parse</CODE> function in the <CODE>Bio.SwissProt.KeyWList</CODE> module. Each entry is then stored as a <CODE>Bio.SwissProt.KeyWList.Record</CODE>, which is a Python dictionary.</P><PRE CLASS="verbatim">>>> from Bio.SwissProt import KeyWList
->>> handle = open("keywlist.txt")
->>> records = KeyWList.parse(handle)
->>> for record in records:
-... print(record['ID'])
-... print(record['DE'])
-</PRE><P>This prints
-</P><PRE CLASS="verbatim">2Fe-2S.
-Protein which contains at least one 2Fe-2S iron-sulfur cluster: 2 iron atoms
-complexed to 2 inorganic sulfides and 4 sulfur atoms of cysteines from the
-protein.
-...
-</PRE><!--TOC section Parsing Prosite records-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc138">10.2</A>  Parsing Prosite records</H2><!--SEC END --><P>Prosite is a database containing protein domains, protein families, functional sites, as well as the patterns and profiles to recognize them. Prosite was developed in parallel with Swiss-Prot. In Biopython, a Prosite record is represented by the <CODE>Bio.ExPASy.Prosite.Record</CODE> class, whose members correspond to the different fields in a Prosite record.</P><P>In [...]
->>> handle = open("myprositefile.dat")
->>> records = Prosite.parse(handle)
-</PRE><P>We can now take the records one at a time and print out some information. For example, using the file containing the complete Prosite database, we’d find
-</P><PRE CLASS="verbatim">>>> from Bio.ExPASy import Prosite
->>> handle = open("prosite.dat")
->>> records = Prosite.parse(handle)
->>> record = next(records)
->>> record.accession
-'PS00001'
->>> record.name
-'ASN_GLYCOSYLATION'
->>> record.pdoc
-'PDOC00001'
->>> record = next(records)
->>> record.accession
-'PS00004'
->>> record.name
-'CAMP_PHOSPHO_SITE'
->>> record.pdoc
-'PDOC00004'
->>> record = next(records)
->>> record.accession
-'PS00005'
->>> record.name
-'PKC_PHOSPHO_SITE'
->>> record.pdoc
-'PDOC00005'
-</PRE><P>and so on. If you’re interested in how many Prosite records there are, you could use
-</P><PRE CLASS="verbatim">>>> from Bio.ExPASy import Prosite
->>> handle = open("prosite.dat")
->>> records = Prosite.parse(handle)
->>> n = 0
->>> for record in records: n+=1
-...
->>> n
-2073
-</PRE><P>To read exactly one Prosite from the handle, you can use the <CODE>read</CODE> function:
-</P><PRE CLASS="verbatim">>>> from Bio.ExPASy import Prosite
->>> handle = open("mysingleprositerecord.dat")
->>> record = Prosite.read(handle)
-</PRE><P>This function raises a ValueError if no Prosite record is found, and also if more than one Prosite record is found.</P><!--TOC section Parsing Prosite documentation records-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc139">10.3</A>  Parsing Prosite documentation records</H2><!--SEC END --><P>In the Prosite example above, the <CODE>record.pdoc</CODE> accession numbers <CODE>'PDOC00001'</CODE>, <CODE>'PDOC00004'</CODE>, <CODE>'PDOC00005'</CODE> and so on refer to Prosite documentation. The Prosite documentation records are available from ExPASy as individual files, and as one file (<CODE>prosite.doc</CODE>) containing all Prosite documentation [...]
->>> handle = open("prosite.doc")
->>> records = Prodoc.parse(handle)
->>> accessions = [record.accession for record in records]
-</PRE><P>Again a <CODE>read()</CODE> function is provided to read exactly one Prosite documentation record from the handle.</P><!--TOC section Parsing Enzyme records-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc140">10.4</A>  Parsing Enzyme records</H2><!--SEC END --><P>ExPASy’s Enzyme database is a repository of information on enzyme nomenclature. A typical Enzyme record looks as follows:</P><PRE CLASS="verbatim">ID 3.1.1.34
-DE Lipoprotein lipase.
-AN Clearing factor lipase.
-AN Diacylglycerol lipase.
-AN Diglyceride lipase.
-CA Triacylglycerol + H(2)O = diacylglycerol + a carboxylate.
-CC -!- Hydrolyzes triacylglycerols in chylomicrons and very low-density
-CC lipoproteins (VLDL).
-CC -!- Also hydrolyzes diacylglycerol.
-PR PROSITE; PDOC00110;
-DR P11151, LIPL_BOVIN ; P11153, LIPL_CAVPO ; P11602, LIPL_CHICK ;
-DR P55031, LIPL_FELCA ; P06858, LIPL_HUMAN ; P11152, LIPL_MOUSE ;
-DR O46647, LIPL_MUSVI ; P49060, LIPL_PAPAN ; P49923, LIPL_PIG ;
-DR Q06000, LIPL_RAT ; Q29524, LIPL_SHEEP ;
-//
-</PRE><P>In this example, the first line shows the EC (Enzyme Commission) number of lipoprotein lipase (second line). Alternative names of lipoprotein lipase are "clearing factor lipase", "diacylglycerol lipase", and "diglyceride lipase" (lines 3 through 5). The line starting with "CA" shows the catalytic activity of this enzyme. Comment lines start with "CC". The "PR" line shows references to the Prosite Documentation records, and the "DR" lines show references to Swiss-Prot records. No [...]
->>> with open("lipoprotein.txt") as handle:
-... record = Enzyme.read(handle)
-...
->>> record["ID"]
-'3.1.1.34'
->>> record["DE"]
-'Lipoprotein lipase.'
->>> record["AN"]
-['Clearing factor lipase.', 'Diacylglycerol lipase.', 'Diglyceride lipase.']
->>> record["CA"]
-'Triacylglycerol + H(2)O = diacylglycerol + a carboxylate.'
->>> record["PR"]
-['PDOC00110']
-</PRE><PRE CLASS="verbatim">>>> record["CC"]
-['Hydrolyzes triacylglycerols in chylomicrons and very low-density lipoproteins
-(VLDL).', 'Also hydrolyzes diacylglycerol.']
->>> record["DR"]
-[['P11151', 'LIPL_BOVIN'], ['P11153', 'LIPL_CAVPO'], ['P11602', 'LIPL_CHICK'],
-['P55031', 'LIPL_FELCA'], ['P06858', 'LIPL_HUMAN'], ['P11152', 'LIPL_MOUSE'],
-['O46647', 'LIPL_MUSVI'], ['P49060', 'LIPL_PAPAN'], ['P49923', 'LIPL_PIG'],
-['Q06000', 'LIPL_RAT'], ['Q29524', 'LIPL_SHEEP']]
-</PRE><P>The <CODE>read</CODE> function raises a ValueError if no Enzyme record is found, and also if more than one Enzyme record is found.</P><P>The full set of Enzyme records can be downloaded as a single file (<CODE>enzyme.dat</CODE>) from the <A HREF="ftp://ftp.expasy.org/databases/enzyme/enzyme.dat">ExPASy FTP site</A>, containing 4877 records (release of 3 March 2009). To parse such a file containing multiple Enzyme records, use the <CODE>parse</CODE> function in <CODE>Bio.ExPASy.E [...]
->>> handle = open("enzyme.dat")
->>> records = Enzyme.parse(handle)
-</PRE><P>We can now iterate over the records one at a time. For example, we can make a list of all EC numbers for which an Enzyme record is available:
-</P><PRE CLASS="verbatim">>>> ecnumbers = [record["ID"] for record in records]
-</PRE><!--TOC section Accessing the ExPASy server-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc141">10.5</A>  Accessing the ExPASy server</H2><!--SEC END --><P>Swiss-Prot, Prosite, and Prosite documentation records can be downloaded from the ExPASy web server at <A HREF="http://www.expasy.org"><TT>http://www.expasy.org</TT></A>. Six kinds of queries are available from ExPASy:
-</P><DL CLASS="description"><DT CLASS="dt-description">
-<B>get_prodoc_entry</B></DT><DD CLASS="dd-description">To download a Prosite documentation record in HTML format
-</DD><DT CLASS="dt-description"><B>get_prosite_entry</B></DT><DD CLASS="dd-description">To download a Prosite record in HTML format
-</DD><DT CLASS="dt-description"><B>get_prosite_raw</B></DT><DD CLASS="dd-description">To download a Prosite or Prosite documentation record in raw format
-</DD><DT CLASS="dt-description"><B>get_sprot_raw</B></DT><DD CLASS="dd-description">To download a Swiss-Prot record in raw format
-</DD><DT CLASS="dt-description"><B>sprot_search_ful</B></DT><DD CLASS="dd-description">To search for a Swiss-Prot record
-</DD><DT CLASS="dt-description"><B>sprot_search_de</B></DT><DD CLASS="dd-description">To search for a Swiss-Prot record
-</DD></DL><P>
-To access this web server from a Python script, we use the <CODE>Bio.ExPASy</CODE> module.</P><!--TOC subsection Retrieving a Swiss-Prot record-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc142">10.5.1</A>  Retrieving a Swiss-Prot record</H3><!--SEC END --><P>
-<A NAME="subsec:expasy_swissprot"></A></P><P>Let’s say we are looking at chalcone synthases for Orchids (see section <A HREF="#sec:orchids">2.3</A> for some justification for looking for interesting things about orchids). Chalcone synthase is involved in flavanoid biosynthesis in plants, and flavanoids make lots of cool things like pigment colors and UV protectants.</P><P>If you do a search on Swiss-Prot, you can find three orchid proteins for Chalcone Synthase, id numbers O2 [...]
->>> from Bio import SwissProt
-
->>> accessions = ["O23729", "O23730", "O23731"]
->>> records = []
-
->>> for accession in accessions:
-... handle = ExPASy.get_sprot_raw(accession)
-... record = SwissProt.read(handle)
-... records.append(record)
-</PRE><P>If the accession number you provided to <CODE>ExPASy.get_sprot_raw</CODE> does not exist, then <CODE>SwissProt.read(handle)</CODE> will raise a <CODE>ValueError</CODE>. You can catch <CODE>ValueException</CODE> exceptions to detect invalid accession numbers:</P><PRE CLASS="verbatim">>>> for accession in accessions:
-... handle = ExPASy.get_sprot_raw(accession)
-... try:
-... record = SwissProt.read(handle)
-... except ValueException:
-... print("WARNING: Accession %s not found" % accession)
-... records.append(record)
-</PRE><!--TOC subsection Searching Swiss-Prot-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc143">10.5.2</A>  Searching Swiss-Prot</H3><!--SEC END --><P>Now, you may remark that I knew the records’ accession numbers
-beforehand. Indeed, <CODE>get_sprot_raw()</CODE> needs either the entry name
-or an accession number. When you don’t have them handy, you can use
-one of the <CODE>sprot_search_de()</CODE> or <CODE>sprot_search_ful()</CODE>
-functions.</P><P><CODE>sprot_search_de()</CODE> searches in the ID, DE, GN, OS and OG lines;
-<CODE>sprot_search_ful()</CODE> searches in (nearly) all the fields. They
-are detailed on
-<A HREF="http://www.expasy.org/cgi-bin/sprot-search-de"><TT>http://www.expasy.org/cgi-bin/sprot-search-de</TT></A> and
-<A HREF="http://www.expasy.org/cgi-bin/sprot-search-ful"><TT>http://www.expasy.org/cgi-bin/sprot-search-ful</TT></A>
-respectively. Note that they don’t search in TrEMBL by default
-(argument <CODE>trembl</CODE>). Note also that they return HTML pages;
-however, accession numbers are quite easily extractable:</P><PRE CLASS="verbatim">>>> from Bio import ExPASy
->>> import re
-
->>> handle = ExPASy.sprot_search_de("Orchid Chalcone Synthase")
->>> # or:
->>> # handle = ExPASy.sprot_search_ful("Orchid and {Chalcone Synthase}")
->>> html_results = handle.read()
->>> if "Number of sequences found" in html_results:
-... ids = re.findall(r'HREF="/uniprot/(\w+)"', html_results)
-... else:
-... ids = re.findall(r'href="/cgi-bin/niceprot\.pl\?(\w+)"', html_results)
-</PRE><!--TOC subsection Retrieving Prosite and Prosite documentation records-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc144">10.5.3</A>  Retrieving Prosite and Prosite documentation records</H3><!--SEC END --><P>Prosite and Prosite documentation records can be retrieved either in HTML format, or in raw format. To parse Prosite and Prosite documentation records with Biopython, you should retrieve the records in raw format. For other purposes, however, you may be interested in these records in HTML format.</P><P>To retrieve a Prosite or Prosite [...]
->>> handle = ExPASy.get_prosite_raw('PS00001')
->>> text = handle.read()
->>> print(text)
-</PRE><P>To retrieve a Prosite record and parse it into a <CODE>Bio.Prosite.Record</CODE> object, use</P><PRE CLASS="verbatim">>>> from Bio import ExPASy
->>> from Bio import Prosite
->>> handle = ExPASy.get_prosite_raw('PS00001')
->>> record = Prosite.read(handle)
-</PRE><P>The same function can be used to retrieve a Prosite documentation record and parse it into a <CODE>Bio.ExPASy.Prodoc.Record</CODE> object:</P><PRE CLASS="verbatim">>>> from Bio import ExPASy
->>> from Bio.ExPASy import Prodoc
->>> handle = ExPASy.get_prosite_raw('PDOC00001')
->>> record = Prodoc.read(handle)
-</PRE><P>For non-existing accession numbers, <CODE>ExPASy.get_prosite_raw</CODE> returns a handle to an emptry string. When faced with an empty string, <CODE>Prosite.read</CODE> and <CODE>Prodoc.read</CODE> will raise a ValueError. You can catch these exceptions to detect invalid accession numbers.</P><P>The functions <CODE>get_prosite_entry()</CODE> and <CODE>get_prodoc_entry()</CODE> are used to download Prosite and Prosite documentation records in HTML format. To create a web page sho [...]
->>> handle = ExPASy.get_prosite_entry('PS00001')
->>> html = handle.read()
->>> output = open("myprositerecord.html", "w")
->>> output.write(html)
->>> output.close()
-</PRE><P>and similarly for a Prosite documentation record:</P><PRE CLASS="verbatim">>>> from Bio import ExPASy
->>> handle = ExPASy.get_prodoc_entry('PDOC00001')
->>> html = handle.read()
->>> output = open("myprodocrecord.html", "w")
->>> output.write(html)
->>> output.close()
-</PRE><P>For these functions, an invalid accession number returns an error message in HTML format.</P><!--TOC section Scanning the Prosite database-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc145">10.6</A>  Scanning the Prosite database</H2><!--SEC END --><P><A HREF="http://www.expasy.org/tools/scanprosite/">ScanProsite</A> allows you to scan protein sequences online against the Prosite database by providing a UniProt or PDB sequence identifier or the sequence itself. For more information about ScanProsite, please see the <A HREF="http://www.expasy.org/tools/scanprosite/scanprosite-doc.html">ScanProsite documentatio [...]
-CRAFQYHSKEQQCVIMAENRKSSIIIRMRDVVLFEKKVYLSECKTGNGKNYRGTMSKTKN
-</PRE><P>you can use the following code:</P><PRE CLASS="verbatim">>>> sequence = "MEHKEVVLLLLLFLKSGQGEPLDDYVNTQGASLFSVTKKQLGAGSIEECAAKCEEDEEFT
-CRAFQYHSKEQQCVIMAENRKSSIIIRMRDVVLFEKKVYLSECKTGNGKNYRGTMSKTKN"
->>> from Bio.ExPASy import ScanProsite
->>> handle = ScanProsite.scan(seq=sequence)
-</PRE><P>By executing <CODE>handle.read()</CODE>, you can obtain the search results in raw XML format. Instead, let’s use <CODE>Bio.ExPASy.ScanProsite.read</CODE> to parse the raw XML into a Python object:</P><PRE CLASS="verbatim">>>> result = ScanProsite.read(handle)
->>> type(result)
-<class 'Bio.ExPASy.ScanProsite.Record'>
-</PRE><P>A <CODE>Bio.ExPASy.ScanProsite.Record</CODE> object is derived from a list, with each element in the list storing one ScanProsite hit. This object also stores the number of hits, as well as the number of search sequences, as returned by ScanProsite. This ScanProsite search resulted in six hits:</P><PRE CLASS="verbatim">>>> result.n_seq
-1
->>> result.n_match
-6
->>> len(result)
-6
->>> result[0]
-{'signature_ac': u'PS50948', 'level': u'0', 'stop': 98, 'sequence_ac': u'USERSEQ1', 'start': 16, 'score': u'8.873'}
->>> result[1]
-{'start': 37, 'stop': 39, 'sequence_ac': u'USERSEQ1', 'signature_ac': u'PS00005'}
->>> result[2]
-{'start': 45, 'stop': 48, 'sequence_ac': u'USERSEQ1', 'signature_ac': u'PS00006'}
->>> result[3]
-{'start': 60, 'stop': 62, 'sequence_ac': u'USERSEQ1', 'signature_ac': u'PS00005'}
->>> result[4]
-{'start': 80, 'stop': 83, 'sequence_ac': u'USERSEQ1', 'signature_ac': u'PS00004'}
->>> result[5]
-{'start': 106, 'stop': 111, 'sequence_ac': u'USERSEQ1', 'signature_ac': u'PS00008'}
-</PRE><P>Other ScanProsite parameters can be passed as keyword arguments; see the <A HREF="http://www.expasy.org/tools/scanprosite/ScanPrositeREST.html">documentation for programmatic access of ScanProsite</A> for more information. As an example, passing <CODE>lowscore=1</CODE> to include matches with low level scores lets use find one additional hit:</P><PRE CLASS="verbatim">>>> handle = ScanProsite.scan(seq=sequence, lowscore=1)
->>> result = ScanProsite.read(handle)
->>> result.n_match
-7
-</PRE><!--TOC chapter Bio.PopGen: Population genetics-->
-<H1 CLASS="chapter"><!--SEC ANCHOR --><A NAME="htoc146">Chapter 11</A>  Bio.PopGen: Population genetics</H1><!--SEC END --><P>Bio.PopGen is a Biopython module supporting population genetics,
-available in Biopython 1.44 onwards.</P><P>The medium term objective for the module is to support widely used data
-formats, applications and databases. This module is currently under intense
-development and support for new features should appear at a rather fast pace.
-Unfortunately this might also entail some instability on the API, especially
-if you are using a development version. APIs that are made available on
-our official public releases should be much more stable.</P><!--TOC section GenePop-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc147">11.1</A>  GenePop</H2><!--SEC END --><P>GenePop (<A HREF="http://genepop.curtin.edu.au/"><TT>http://genepop.curtin.edu.au/</TT></A>) is a popular population
-genetics software package supporting Hardy-Weinberg tests, linkage
-desiquilibrium, population diferentiation, basic statistics, <I>F</I><SUB><I>st</I></SUB> and
-migration estimates, among others. GenePop does not supply sequence
-based statistics as it doesn’t handle sequence data.
-The GenePop file format is supported by a wide range of other population
-genetic software applications, thus making it a relevant format in the
-population genetics field.</P><P>Bio.PopGen provides a parser and generator of GenePop file format.
-Utilities to manipulate the content of a record are also provided.
-Here is an example on how to read a GenePop file (you can find
-example GenePop data files in the Test/PopGen directory of Biopython):</P><PRE CLASS="verbatim">from Bio.PopGen import GenePop
-
-handle = open("example.gen")
-rec = GenePop.read(handle)
-handle.close()
-</PRE><P>This will read a file called example.gen and parse it. If you
-do print rec, the record will be output again, in GenePop format.</P><P>The most important information in rec will be the loci names and
-population information (but there is more – use help(GenePop.Record)
-to check the API documentation). Loci names can be found on rec.loci_list.
-Population information can be found on rec.populations.
-Populations is a list with one element per population. Each element is itself
-a list of individuals, each individual is a pair composed by individual
-name and a list of alleles (2 per marker), here is an example for
-rec.populations:</P><PRE CLASS="verbatim">[
- [
- ('Ind1', [(1, 2), (3, 3), (200, 201)],
- ('Ind2', [(2, None), (3, 3), (None, None)],
- ],
- [
- ('Other1', [(1, 1), (4, 3), (200, 200)],
- ]
-]
-</PRE><P>So we have two populations, the first with two individuals, the
-second with only one. The first individual of the first
-population is called Ind1, allelic information for each of
-the 3 loci follows. Please note that for any locus, information
-might be missing (see as an example, Ind2 above).</P><P>A few utility functions to manipulate GenePop records are made
-available, here is an example:</P><PRE CLASS="verbatim">from Bio.PopGen import GenePop
-
-#Imagine that you have loaded rec, as per the code snippet above...
-
-rec.remove_population(pos)
-#Removes a population from a record, pos is the population position in
-# rec.populations, remember that it starts on position 0.
-# rec is altered.
-
-rec.remove_locus_by_position(pos)
-#Removes a locus by its position, pos is the locus position in
-# rec.loci_list, remember that it starts on position 0.
-# rec is altered.
-
-rec.remove_locus_by_name(name)
-#Removes a locus by its name, name is the locus name as in
-# rec.loci_list. If the name doesn't exist the function fails
-# silently.
-# rec is altered.
-
-rec_loci = rec.split_in_loci()
-#Splits a record in loci, that is, for each loci, it creates a new
-# record, with a single loci and all populations.
-# The result is returned in a dictionary, being each key the locus name.
-# The value is the GenePop record.
-# rec is not altered.
-
-rec_pops = rec.split_in_pops(pop_names)
-#Splits a record in populations, that is, for each population, it creates
-# a new record, with a single population and all loci.
-# The result is returned in a dictionary, being each key
-# the population name. As population names are not available in GenePop,
-# they are passed in array (pop_names).
-# The value of each dictionary entry is the GenePop record.
-# rec is not altered.
-</PRE><P>GenePop does not support population names, a limitation which can be
-cumbersome at times. Functionality to enable population names is currently
-being planned for Biopython. These extensions won’t break compatibility in
-any way with the standard format. In the medium term, we would also like to
-support the GenePop web service.</P><!--TOC section Coalescent simulation-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc148">11.2</A>  Coalescent simulation</H2><!--SEC END --><P>A coalescent simulation is a backward model of population genetics with relation to
-time. A simulation of ancestry is done until the Most Recent Common Ancestor (MRCA) is found.
-This ancestry relationship starting on the MRCA and ending on the current generation
-sample is sometimes called a genealogy. Simple cases assume a population of constant
-size in time, haploidy, no population structure, and simulate the alleles of a single
-locus under no selection pressure.</P><P>Coalescent theory is used in many fields like selection detection, estimation of
-demographic parameters of real populations or disease gene mapping.</P><P>The strategy followed in the Biopython implementation of the coalescent was not
-to create a new, built-in, simulator from scratch but to use an existing one,
-Fastsimcoal2 (<A HREF="http://cmpg.unibe.ch/software/fastsimcoal2/"><TT>http://cmpg.unibe.ch/software/fastsimcoal2/</TT></A>). Fastsimcoal2 allows for,
-among others, population structure, multiple demographic events, simulation
-of multiple types of loci (SNPs, sequences, STRs/microsatellites and RFLPs)
-with recombination, diploidy multiple chromosomes or ascertainment bias. Notably
-Fastsimcoal2 doesn’t support any selection model. We recommend reading
-Fastsimcoal2’s
-documentation, available in the link above.</P><P>The input for Fastsimcoal2 is a file specifying the desired demography and genome,
-the output is a set of files (typically around 1000) with the simulated genomes
-of a sample of individuals per subpopulation. This set of files can be used
-in many ways, like to compute confidence intervals where which certain
-statistics (e.g., <I>F</I><SUB><I>st</I></SUB> or Tajima D) are expected to lie. Real population
-genetics datasets statistics can then be compared to those confidence intervals.</P><P>Biopython coalescent code allows to create demographic scenarios and genomes and
-to run Fastsimcoal2.</P><!--TOC subsection Creating scenarios-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc149">11.2.1</A>  Creating scenarios</H3><!--SEC END --><P>Creating a scenario involves both creating a demography and a chromosome structure.
-In many cases (e.g. when doing Approximate Bayesian Computations – ABC) it is
-important to test many parameter variations (e.g. vary the effective population size,
-<I>N</I><SUB><I>e</I></SUB>, between 10, 50, 500 and 1000 individuals). The code provided allows for
-the simulation of scenarios with different demographic parameters very easily.</P><P>Below we see how we can create scenarios and then how simulate them.</P><!--TOC subsubsection Demography-->
-<H4 CLASS="subsubsection"><!--SEC ANCHOR -->11.2.1.1  Demography</H4><!--SEC END --><P>A few predefined demographies are built-in, all have two shared parameters: sample size
-(called sample_size on the template, see below for its use) per deme and deme size, i.e.
-subpopulation size (pop_size). All demographies are available as templates where all
-parameters can be varied, each template has a system name. The prefedined
-demographies/templates are:</P><DL CLASS="description"><DT CLASS="dt-description">
-<B>Single population, constant size</B></DT><DD CLASS="dd-description"> The standard parameters are enough to specify
-it. Template name: simple.
-</DD><DT CLASS="dt-description"><B>Single population, bottleneck</B></DT><DD CLASS="dd-description"> As seen on figure <A HREF="#fig:bottle">11.2.1.1</A>. The parameters
-are current population size (pop_size on template ne3 on figure), time of expansion,
-given as the generation in the past when it occurred (expand_gen),
-effective population size during bottleneck (ne2), time of contraction
-(contract_gen) and original size in the remote past (ne3). Template name: bottle.
-</DD><DT CLASS="dt-description"><B>Island model</B></DT><DD CLASS="dd-description"> The typical island model. The total number of demes is specified
-by total_demes and the migration rate by mig. Template name island.
-</DD><DT CLASS="dt-description"><B>Stepping stone model - 1 dimension</B></DT><DD CLASS="dd-description"> The stepping stone model in 1 dimension,
-extremes disconnected. The total number of demes is total_demes, migration rate
-is mig. Template name is ssm_1d.
-</DD><DT CLASS="dt-description"><B>Stepping stone model - 2 dimensions</B></DT><DD CLASS="dd-description"> The stepping stone model in 2 dimensions,
-extremes disconnected. The parameters are x for the horizontal dimension and y
-for the vertical (being the total number of demes x times y), migration rate is mig.
-Template name is ssm_2d.
-</DD></DL><P>
-<A NAME="fig:bottle"></A>
-<IMG SRC="images/bottle.png">
-</P><P>In our first example, we will generate a template for a single population, constant size
-model with a sample size of 30 and a deme size of 500. The code for this is:</P><PRE CLASS="verbatim">from Bio.PopGen.SimCoal.Template import generate_simcoal_from_template
-
-generate_simcoal_from_template('simple',
- [(1, [('SNP', [24, 0.0005, 0.0])])],
- [('sample_size', [30]),
- ('pop_size', [100])])
-</PRE><P>Executing this code snippet will generate a file on the current directory called
-simple_100_300.par this file can be given as input to Fastsimcoal2 to simulate the
-demography (below we will see how Biopython can take care of calling
-Fastsimcoal2).</P><P>This code consists of a single function call, let’s discuss it parameter by parameter.</P><P>The first parameter is the template id (from the list above). We are using the id
-’simple’ which is the template for a single population of constant size along time.</P><P>The second parameter is the chromosome structure. Please ignore it for now, it will be
-explained in the next section.</P><P>The third parameter is a list of all required parameters (recall that the simple model
-only needs sample_size and pop_size) and possible values (in this case each
-parameter only has a possible value).</P><P>Now, let’s consider an example where we want to generate several island models, and we
-are interested in varying the number of demes: 10, 50 and 100 with a migration
-rate of 1%. Sample size and deme
-size will be the same as before. Here is the code:</P><PRE CLASS="verbatim">from Bio.PopGen.SimCoal.Template import generate_simcoal_from_template
-
-generate_simcoal_from_template('island',
- [(1, [('SNP', [24, 0.0005, 0.0])])],
- [('sample_size', [30]),
- ('pop_size', [100]),
- ('mig', [0.01]),
- ('total_demes', [10, 50, 100])])
-</PRE><P>In this case, 3 files will be generated: island_100_0.01_100_30.par,
-island_10_0.01_100_30.par and island_50_0.01_100_30.par. Notice the
-rule to make file names: template name, followed by parameter values in
-reverse order.</P><P>A few, arguably more esoteric template demographies exist (please check the
-Bio/PopGen/SimCoal/data directory on Biopython source tree). Furthermore it is possible
-for the user to create new templates. That functionality will be discussed in a future
-version of this document.</P><!--TOC subsubsection Chromosome structure-->
-<H4 CLASS="subsubsection"><!--SEC ANCHOR -->11.2.1.2  Chromosome structure</H4><!--SEC END --><P>We strongly recommend reading Fastsimcoal2 documentation to understand the full potential
-available in modeling chromosome structures. In this subsection we only discuss how
-to implement chromosome structures using the Biopython interface, not the underlying
-Fastsimcoal2 capabilities.</P><P>We will start by implementing a single chromosome, with 24 SNPs with
-a recombination rate immediately on the right of each locus of 0.0005 and a
-minimum frequency of the minor allele of 0. This will be specified by the
-following list (to be passed as second parameter to the function
-generate_simcoal_from_template):</P><PRE CLASS="verbatim">[(1, [('SNP', [24, 0.0005, 0.0])])]
-</PRE><P>This is actually the chromosome structure used in the above examples.</P><P>The chromosome structure is represented by a list of chromosomes,
-each chromosome (i.e., each element in the list)
-is composed by a tuple (a pair): the first element
-is the number of times the chromosome is to be repeated (as there
-might be interest in repeating the same chromosome many times).
-The second element is a list of the actual components of the chromosome.
-Each element is again a pair, the first member is the locus type and
-the second element the parameters for that locus type. Confused?
-Before showing more examples let’s review the example above: We have
-a list with one element (thus one chromosome), the chromosome is
-a single instance (therefore not to be repeated), it is composed
-of 24 SNPs, with a recombination rate of 0.0005 between each
-consecutive SNP, the minimum frequency of the minor allele is
-0.0 (i.e, it can be absent from a certain population).</P><P>Let’s see a more complicated example:</P><PRE CLASS="verbatim">[
- (5, [
- ('SNP', [24, 0.0005, 0.0])
- ]
- ),
- (2, [
- ('DNA', [10, 0.0, 0.00005, 0.33]),
- ('RFLP', [1, 0.0, 0.0001]),
- ('MICROSAT', [1, 0.0, 0.001, 0.0, 0.0])
- ]
- )
-]
-</PRE><P>We start by having 5 chromosomes with the same structure as
-above (i.e., 24 SNPs). We then have 2 chromosomes which
-have a DNA sequence with 10 nucleotides, 0.0 recombination rate,
-0.0005 mutation rate, and a transition rate of 0.33. Then we
-have an RFLP with 0.0 recombination rate to the next locus and
-a 0.0001 mutation rate. Finally we have a microsatellite (or STR),
-with 0.0 recombination rate to the next locus (note, that as this
-is a single microsatellite which has no loci following, this
-recombination rate here is irrelevant), with a mutation rate
-of 0.001, geometric parameter of 0.0 and a range constraint
-of 0.0 (for information about this parameters please consult
-the Fastsimcoal2 documentation, you can use them to simulate
-various mutation models, including the typical – for microsatellites –
-stepwise mutation model among others).</P><!--TOC subsection Running Fastsimcoal2-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc150">11.2.2</A>  Running Fastsimcoal2</H3><!--SEC END --><P>We now discuss how to run Fastsimcoal2 from inside Biopython. It is required
-that the binary for Fastsimcoal2 is called fastsimcoal21 (or fastsimcoal21.exe on Windows
-based platforms), please note that the typical name when downloading the
-program is in the format fastsimcoal2_x_y. As such, when installing
-Fastsimcoal2
-you will need to rename of the downloaded executable so that Biopython can
-find it.</P><P>It is possible to run Fastsimcoal2 on files that were not generated using the method
-above (e.g., writing a parameter file by hand), but we will show an
-example by creating a model using the framework presented above.</P><PRE CLASS="verbatim">from Bio.PopGen.SimCoal.Template import generate_simcoal_from_template
-from Bio.PopGen.SimCoal.Controller import FastSimCoalController
-
-
-generate_simcoal_from_template('simple',
- [
- (5, [
- ('SNP', [24, 0.0005, 0.0])
- ]
- ),
- (2, [
- ('DNA', [10, 0.0, 0.00005, 0.33]),
- ('RFLP', [1, 0.0, 0.0001]),
- ('MICROSAT', [1, 0.0, 0.001, 0.0, 0.0])
- ]
- )
- ],
- [('sample_size', [30]),
- ('pop_size', [100])])
-
-ctrl = FastSimCoalController()
-ctrl.run_fastsimcoal('simple_100_30.par', 50)
-</PRE><P>The lines of interest are the last two (plus the new import).
-Firstly a controller for the
-application is created. The directory where the binary is located has
-to be specified.</P><P>The simulator is then run on the last line: we know, from the rules explained
-above, that the input file name is simple_100_30.par for the
-simulation parameter file created. We then specify
-that we want to run 50 independent simulations, by default Biopython
-requests a simulation of diploid data, but a third parameter can
-be added to simulate haploid data (adding as a parameter the
-string ’0’). Fastsimcoal2 will now run (please
-note that this can take quite a lot of time) and will create a directory
-with the simulation results. The results can now be analysed (typically
-studying the data with Arlequin3). In the future Biopython might support
-reading the Arlequin3 format and thus allowing for the analysis of
-Fastsimcoal2
-data inside Biopython.</P><!--TOC section Other applications-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc151">11.3</A>  Other applications</H2><!--SEC END --><P>Here we discuss interfaces and utilities to deal with population genetics’
-applications which arguably have a smaller user base.</P><!--TOC subsection FDist: Detecting selection and molecular adaptation-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc152">11.3.1</A>  FDist: Detecting selection and molecular adaptation</H3><!--SEC END --><P>FDist is a selection detection application suite based on computing
-(i.e. simulating) a “neutral” confidence interval based on <I>F</I><SUB><I>st</I></SUB> and
-heterozygosity. Markers (which can be SNPs, microsatellites, AFLPs
-among others) which lie outside the “neutral” interval are to be
-considered as possible candidates for being under selection.</P><P>FDist is mainly used when the number of markers is considered enough
-to estimate an average <I>F</I><SUB><I>st</I></SUB>, but not enough to either have outliers
-calculated from the dataset directly or, with even more markers for
-which the relative positions in the genome are known, to use
-approaches based on, e.g., Extended Haplotype Heterozygosity (EHH).</P><P>The typical usage pattern for FDist is as follows:</P><OL CLASS="enumerate" type=1><LI CLASS="li-enumerate">
-Import a dataset from an external format into FDist format.
-</LI><LI CLASS="li-enumerate">Compute average <I>F</I><SUB><I>st</I></SUB>. This is done by datacal inside FDist.
-</LI><LI CLASS="li-enumerate">Simulate “neutral” markers based on the
-average <I>F</I><SUB><I>st</I></SUB> and expected number of total populations.
-This is the core operation, done by fdist inside FDist.
-</LI><LI CLASS="li-enumerate">Calculate the confidence interval, based on the desired
-confidence boundaries (typically 95% or 99%). This is done by
-cplot and is mainly used to plot the interval.
-</LI><LI CLASS="li-enumerate">Assess each marker status against the simulation “neutral”
-confidence interval. Done
-by pv. This is used to detect the outlier status of each marker
-against the simulation.
-</LI></OL><P>We will now discuss each step with illustrating example code
-(for this example to work FDist binaries have to be on the
-executable PATH).</P><P>The FDist data format is application specific and is not used at
-all by other applications, as such you will probably have to convert
-your data for use with FDist. Biopython can help you do this.
-Here is an example converting from GenePop format to FDist format
-(along with imports that will be needed on examples further below):</P><PRE CLASS="verbatim">from Bio.PopGen import GenePop
-from Bio.PopGen import FDist
-from Bio.PopGen.FDist import Controller
-from Bio.PopGen.FDist.Utils import convert_genepop_to_fdist
-
-gp_rec = GenePop.read(open("example.gen"))
-fd_rec = convert_genepop_to_fdist(gp_rec)
-in_file = open("infile", "w")
-in_file.write(str(fd_rec))
-in_file.close()
-</PRE><P>In this code we simply parse a GenePop file and convert it to a FDist
-record.</P><P>Printing an FDist record will generate
-a string that can be directly saved to a file and supplied to FDist. FDist
-requires the input file to be called infile, therefore we save the record on
-a file with that name.</P><P>The most important fields on a FDist record are: num_pops, the number of
-populations; num_loci, the number of loci and loci_data with the marker
-data itself. Most probably the details of the record are of no interest
-to the user, as the record only purpose is to be passed to FDist.</P><P>The next step is to calculate the average <I>F</I><SUB><I>st</I></SUB> of the dataset (along
-with the sample size):</P><PRE CLASS="verbatim">ctrl = Controller.FDistController()
-fst, samp_size = ctrl.run_datacal()
-</PRE><P>On the first line we create an object to control the call of FDist
-suite, this object will be used further on in order to call other
-suite applications.</P><P>On the second line we call the datacal application which computes the
-average <I>F</I><SUB><I>st</I></SUB>
-and the sample size. It is worth noting that the <I>F</I><SUB><I>st</I></SUB> computed by
-datacal is a <EM>variation</EM> of Weir and Cockerham’s θ.</P><P>We can now call the main fdist application in order to simulate neutral
-markers.</P><PRE CLASS="verbatim">sim_fst = ctrl.run_fdist(npops = 15, nsamples = fd_rec.num_pops, fst = fst,
- sample_size = samp_size, mut = 0, num_sims = 40000)
-</PRE><DL CLASS="description"><DT CLASS="dt-description">
-<B>npops</B></DT><DD CLASS="dd-description"> Number of populations existing in nature. This is really a
-“guestimate”. Has to be lower than 100.
-</DD><DT CLASS="dt-description"><B>nsamples</B></DT><DD CLASS="dd-description"> Number of populations sampled, has to be lower than npops.
-</DD><DT CLASS="dt-description"><B>fst</B></DT><DD CLASS="dd-description"> Average <I>F</I><SUB><I>st</I></SUB>.
-</DD><DT CLASS="dt-description"><B>sample_size</B></DT><DD CLASS="dd-description"> Average number of individuals sampled on each population.
-</DD><DT CLASS="dt-description"><B>mut</B></DT><DD CLASS="dd-description"> Mutation model: 0 - Infinite alleles; 1 - Stepwise mutations
-</DD><DT CLASS="dt-description"><B>num_sims</B></DT><DD CLASS="dd-description"> Number of simulations to perform. Typically a number around
-40000 will be OK, but if you get a confidence interval that looks sharp
-(this can be detected when plotting the confidence interval computed
-below) the value can be increased (a suggestion would be steps of 10000
-simulations).
-</DD></DL><P>The confusion in wording between number of samples and sample size
-stems from the original application.</P><P>A file named out.dat will be created with the simulated heterozygosities
-and <I>F</I><SUB><I>st</I></SUB>s, it will have as many lines as the number of simulations
-requested.</P><P>Note that fdist returns the average <I>F</I><SUB><I>st</I></SUB> that it was <EM>capable</EM> of
-simulating, for more details about this issue please read below the paragraph
-on approximating the desired average <I>F</I><SUB><I>st</I></SUB>.</P><P>The next (optional) step is to calculate the confidence interval:</P><PRE CLASS="verbatim">cpl_interval = ctrl.run_cplot(ci=0.99)
-</PRE><P>You can only call cplot after having run fdist.</P><P>This will calculate the confidence intervals (99% in this case)
-for a previous fdist run. A list of quadruples is returned. The
-first element represents the heterozygosity, the second the lower
-bound of <I>F</I><SUB><I>st</I></SUB> confidence interval for that heterozygosity,
-the third the average and the fourth the upper bound. This can
-be used to trace the confidence interval contour. This list
-is also written to a file, out.cpl.</P><P>The main purpose of this step is return a set of points which can
-be easily used to plot a confidence interval. It can be skipped
-if the objective is only to assess the status of each marker against
-the simulation, which is the next step...</P><PRE CLASS="verbatim">pv_data = ctrl.run_pv()
-</PRE><P>You can only call cplot after having run datacal and fdist.</P><P>This will use the simulated markers to assess the status of each
-individual real marker. A list, in the same order than the loci_list
-that is on the FDist record (which is in the same order that the GenePop
-record) is returned. Each element in the list is a quadruple, the
-fundamental member of each quadruple is the last element (regarding the
-other elements, please refer to the pv documentation – for the
-sake of simplicity we will not discuss them here) which returns the
-probability of the simulated <I>F</I><SUB><I>st</I></SUB> being lower than the marker <I>F</I><SUB><I>st</I></SUB>.
-Higher values would indicate a stronger candidate for positive selection,
-lower values a candidate for balancing selection, and intermediate values
-a possible neutral marker. What is “higher”, “lower” or “intermediate”
-is really a subjective issue, but taking a “confidence interval” approach
-and considering a 95% confidence interval, “higher” would be between 0.95
-and 1.0, “lower” between 0.0 and 0.05 and “intermediate” between 0.05 and
-0.95.</P><!--TOC subsubsection Approximating the desired average <I>F</I><SUB><I>st</I></SUB>-->
-<H4 CLASS="subsubsection"><!--SEC ANCHOR -->11.3.1.1  Approximating the desired average <I>F</I><SUB><I>st</I></SUB></H4><!--SEC END --><P>Fdist tries to approximate the desired average <I>F</I><SUB><I>st</I></SUB> by doing a
-coalescent simulation using migration rates based on the formula</P><TABLE CLASS="display dcenter"><TR VALIGN="middle"><TD CLASS="dcell"><I>N</I><SUB><I>m</I></SUB> = </TD><TD CLASS="dcell"><TABLE CLASS="display"><TR><TD CLASS="dcell" ALIGN="center">1 − <I>F</I><SUB><I>st</I></SUB></TD></TR>
-<TR><TD CLASS="hbar"></TD></TR>
-<TR><TD CLASS="dcell" ALIGN="center">4<I>F</I><SUB><I>st</I></SUB></TD></TR>
-</TABLE></TD><TD CLASS="dcell"> </TD></TR>
-</TABLE><P>This formula assumes a few premises like an infinite number of populations.</P><P>In practice, when the number of populations is low, the mutation model
-is stepwise and the sample size increases, fdist will not be able to
-simulate an acceptable approximate average <I>F</I><SUB><I>st</I></SUB>.</P><P>To address that, a function is provided to iteratively approach the desired
-value by running several fdists in sequence. This approach is computationally
-more intensive than running a single fdist run, but yields good results.
-The following code runs fdist approximating the desired <I>F</I><SUB><I>st</I></SUB>:</P><PRE CLASS="verbatim">sim_fst = ctrl.run_fdist_force_fst(npops = 15, nsamples = fd_rec.num_pops,
- fst = fst, sample_size = samp_size, mut = 0, num_sims = 40000,
- limit = 0.05)
-</PRE><P>The only new optional parameter, when comparing with run_fdist, is limit
-which is the desired maximum error. run_fdist can (and probably should)
-be safely replaced with run_fdist_force_fst.</P><!--TOC subsubsection Final notes-->
-<H4 CLASS="subsubsection"><!--SEC ANCHOR -->11.3.1.2  Final notes</H4><!--SEC END --><P>The process to determine the average <I>F</I><SUB><I>st</I></SUB> can be more sophisticated than
-the one presented here. For more information we refer you to the FDist
-README file. Biopython’s code can be used to implement more sophisticated
-approaches.</P><!--TOC section Future Developments-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc153">11.4</A>  Future Developments</H2><!--SEC END --><P>The most desired future developments would be the ones you add yourself ;) .</P><P>That being said, already existing fully functional code is currently being
-incorporated in Bio.PopGen, that code covers the applications FDist and
-SimCoal2, the HapMap and UCSC Table Browser databases and some simple statistics
-like <I>F</I><SUB><I>st</I></SUB>, or allele counts.</P><!--TOC chapter Phylogenetics with Bio.Phylo-->
-<H1 CLASS="chapter"><!--SEC ANCHOR --><A NAME="htoc154">Chapter 12</A>  Phylogenetics with Bio.Phylo</H1><!--SEC END --><P>
-<A NAME="sec:Phylo"></A></P><P>The Bio.Phylo module was introduced in Biopython 1.54. Following the lead of SeqIO and AlignIO,
-it aims to provide a common way to work with phylogenetic trees independently of the source data
-format, as well as a consistent API for I/O operations.</P><P>Bio.Phylo is described in an open-access journal article [<A HREF="#talevich2012">9</A>, Talevich
-<I>et al.</I>, 2012], which you might also find helpful.</P><!--TOC section Demo: What’s in a Tree?-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc155">12.1</A>  Demo: What’s in a Tree?</H2><!--SEC END --><P>To get acquainted with the module, let’s start with a tree that we’ve already constructed, and
-inspect it a few different ways. Then we’ll colorize the branches, to use a special phyloXML
-feature, and finally save it.</P><P>Create a simple Newick file named <TT>simple.dnd</TT> using your favorite text editor,
-or use <A HREF="http://biopython.org/SRC/biopython/Doc/examples/simple.dnd"><TT>simple.dnd</TT></A>
-provided with the Biopython source code:</P><PRE CLASS="verbatim">(((A,B),(C,D)),(E,F,G));
-</PRE><P>This tree has no branch lengths, only a topology and labelled terminals. (If you have a real
-tree file available, you can follow this demo using that instead.)</P><P>Launch the Python interpreter of your choice:</P><PRE CLASS="verbatim">% ipython -pylab
-</PRE><P>For interactive work, launching the IPython interpreter with the <CODE>-pylab</CODE> flag enables
-<B>matplotlib</B> integration, so graphics will pop up automatically. We’ll use that during
-this demo.</P><P>Now, within Python, read the tree file, giving the file name and the name of the format.</P><PRE CLASS="verbatim">>>> from Bio import Phylo
->>> tree = Phylo.read("simple.dnd", "newick")
-</PRE><P>Printing the tree object as a string gives us a look at the entire object hierarchy.</P><PRE CLASS="verbatim">>>> print(tree)
-Tree(rooted=False, weight=1.0)
- Clade()
- Clade()
- Clade()
- Clade(name='A')
- Clade(name='B')
- Clade()
- Clade(name='C')
- Clade(name='D')
- Clade()
- Clade(name='E')
- Clade(name='F')
- Clade(name='G')
-</PRE><P>The <TT>Tree</TT> object contains global information about the tree, such as whether it’s
-rooted or unrooted. It has one root clade, and under that, it’s nested lists of clades all the
-way down to the tips.</P><P>The function <CODE>draw_ascii</CODE> creates a simple ASCII-art (plain text) dendrogram. This is a
-convenient visualization for interactive exploration, in case better graphical tools aren’t
-available.</P><PRE CLASS="verbatim">>>> from Bio import Phylo
->>> tree = Phylo.read("simple.dnd", "newick")
->>> Phylo.draw_ascii(tree)
- ________________________ A
- ________________________|
- | |________________________ B
- ________________________|
- | | ________________________ C
- | |________________________|
-_| |________________________ D
- |
- | ________________________ E
- | |
- |________________________|________________________ F
- |
- |________________________ G
-<BLANKLINE>
-</PRE><P>If you have <B>matplotlib</B> or <B>pylab</B> installed, you can create a graphic
-using the <CODE>draw</CODE> function (see Fig. <A HREF="#fig:phylo-simple-draw">12.1</A>):</P><PRE CLASS="verbatim">>>> tree.rooted = True
->>> Phylo.draw(tree)
-</PRE><P>
-<IMG SRC="images/phylo-simple-draw.png" width=666, height=530>
-<A NAME="fig:phylo-simple-draw"></A>
-</P><!--TOC subsection Coloring branches within a tree-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc156">12.1.1</A>  Coloring branches within a tree</H3><!--SEC END --><P>The functions <CODE>draw</CODE> and <CODE>draw_graphviz</CODE> support the display of different
-colors and branch widths in a tree.
-As of Biopython 1.59, the <CODE>color</CODE> and <CODE>width</CODE> attributes are available on the
-basic Clade object and there’s nothing extra required to use them.
-Both attributes refer to the branch leading the given clade, and apply recursively, so
-all descendent branches will also inherit the assigned width and color values during
-display.</P><P>In earlier versions of Biopython, these were special features of PhyloXML trees, and
-using the attributes required first converting the tree to a subclass of the basic tree
-object called Phylogeny, from the Bio.Phylo.PhyloXML module.</P><P>In Biopython 1.55 and later, this is a convenient tree method:</P><PRE CLASS="verbatim">>>> tree = tree.as_phyloxml()
-</PRE><P>In Biopython 1.54, you can accomplish the same thing with one extra import:</P><PRE CLASS="verbatim">>>> from Bio.Phylo.PhyloXML import Phylogeny
->>> tree = Phylogeny.from_tree(tree)
-</PRE><P>Note that the file formats Newick and Nexus don’t support branch colors or widths, so
-if you use these attributes in Bio.Phylo, you will only be able to save the values in
-PhyloXML format. (You can still save a tree as Newick or Nexus, but the color and width
-values will be skipped in the output file.)</P><P>Now we can begin assigning colors.
-First, we’ll color the root clade gray. We can do that by assigning the 24-bit color
-value as an RGB triple, an HTML-style hex string, or the name of one of the predefined
-colors.</P><PRE CLASS="verbatim">>>> tree.root.color = (128, 128, 128)
-</PRE><P>Or:</P><PRE CLASS="verbatim">>>> tree.root.color = "#808080"
-</PRE><P>Or:</P><PRE CLASS="verbatim">>>> tree.root.color = "gray"
-</PRE><P>Colors for a clade are treated as cascading down through the entire clade, so when we colorize
-the root here, it turns the whole tree gray. We can override that by assigning a different
-color lower down on the tree.</P><P>Let’s target the most recent common ancestor (MRCA) of the nodes named “E” and “F”. The
-<CODE>common_ancestor</CODE> method returns a reference to that clade in the original tree, so when
-we color that clade “salmon”, the color will show up in the original tree.</P><PRE CLASS="verbatim">>>> mrca = tree.common_ancestor({"name": "E"}, {"name": "F"})
->>> mrca.color = "salmon"
-</PRE><P>If we happened to know exactly where a certain clade is in the tree, in terms of nested list
-entries, we can jump directly to that position in the tree by indexing it. Here, the index
-<CODE>[0,1]</CODE> refers to the second child of the first child of the root.</P><PRE CLASS="verbatim">>>> tree.clade[0, 1].color = "blue"
-</PRE><P>Finally, show our work (see Fig. <A HREF="#fig:phylo-color-draw">12.1.1</A>):</P><PRE CLASS="verbatim">>>> Phylo.draw(tree)
-</PRE><P>
-<IMG SRC="images/phylo-color-draw.png" width=666, height=530>
-<A NAME="fig:phylo-color-draw"></A>
-</P><P>Note that a clade’s color includes the branch leading to that clade, as well as its
-descendents. The common ancestor of E and F turns out to be just under the root, and with this
-coloring we can see exactly where the root of the tree is.</P><P>My, we’ve accomplished a lot! Let’s take a break here and save our work. Call the
-<TT>write</TT> function with a file name or handle — here we use standard output, to see what
-would be written — and the format <TT>phyloxml</TT>. PhyloXML saves the colors we assigned,
-so you can open this phyloXML file in another tree viewer like Archaeopteryx, and the colors
-will show up there, too.</P><PRE CLASS="verbatim">>>> import sys
->>> Phylo.write(tree, sys.stdout, "phyloxml")
-
-<phy:phyloxml xmlns:phy="http://www.phyloxml.org">
- <phy:phylogeny rooted="true">
- <phy:clade>
- <phy:branch_length>1.0</phy:branch_length>
- <phy:color>
- <phy:red>128</phy:red>
- <phy:green>128</phy:green>
- <phy:blue>128</phy:blue>
- </phy:color>
- <phy:clade>
- <phy:branch_length>1.0</phy:branch_length>
- <phy:clade>
- <phy:branch_length>1.0</phy:branch_length>
- <phy:clade>
- <phy:name>A</phy:name>
- ...
-</PRE><P>The rest of this chapter covers the core functionality of Bio.Phylo in greater detail. For more
-examples of using Bio.Phylo, see the cookbook page on Biopython.org:</P><P><A HREF="http://biopython.org/wiki/Phylo_cookbook"><TT>http://biopython.org/wiki/Phylo_cookbook</TT></A></P><!--TOC section I/O functions-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc157">12.2</A>  I/O functions</H2><!--SEC END --><P>Like SeqIO and AlignIO, Phylo handles file input and output through four functions:
-<CODE>parse</CODE>, <CODE>read</CODE>, <CODE>write</CODE> and <CODE>convert</CODE>,
-all of which support the tree file formats Newick, NEXUS, phyloXML and NeXML, as
-well as the Comparative Data Analysis Ontology (CDAO).</P><P>The <CODE>read</CODE> function parses a single tree in the given file and returns it. Careful; it
-will raise an error if the file contains more than one tree, or no trees.</P><PRE CLASS="verbatim">>>> from Bio import Phylo
->>> tree = Phylo.read("Tests/Nexus/int_node_labels.nwk", "newick")
->>> print(tree)
-</PRE><P>(Example files are available in the <TT>Tests/Nexus/</TT> and <TT>Tests/PhyloXML/</TT>
-directories of the Biopython distribution.)</P><P>To handle multiple (or an unknown number of) trees, use the <CODE>parse</CODE> function iterates
-through each of the trees in the given file:</P><PRE CLASS="verbatim">>>> trees = Phylo.parse("../../Tests/PhyloXML/phyloxml_examples.xml", "phyloxml")
->>> for tree in trees:
-... print(tree)
-</PRE><P>Write a tree or iterable of trees back to file with the <CODE>write</CODE> function:</P><PRE CLASS="verbatim">>>> trees = list(Phylo.parse("../../Tests/PhyloXML/phyloxml_examples.xml", "phyloxml"))
->>> tree1 = trees[0]
->>> others = trees[1:]
->>> Phylo.write(tree1, "tree1.nwk", "newick")
-1
->>> Phylo.write(others, "other_trees.nwk", "newick")
-12
-</PRE><P>Convert files between any of the supported formats with the <CODE>convert</CODE> function:</P><PRE CLASS="verbatim">>>> Phylo.convert("tree1.nwk", "newick", "tree1.xml", "nexml")
-1
->>> Phylo.convert("other_trees.xml", "phyloxml", "other_trees.nex", "nexus")
-12
-</PRE><P>To use strings as input or output instead of actual files, use <CODE>StringIO</CODE> as you would
-with SeqIO and AlignIO:</P><PRE CLASS="verbatim">>>> from Bio import Phylo
->>> from StringIO import StringIO
->>> handle = StringIO("(((A,B),(C,D)),(E,F,G));")
->>> tree = Phylo.read(handle, "newick")
-</PRE><!--TOC section View and export trees-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc158">12.3</A>  View and export trees</H2><!--SEC END --><P>The simplest way to get an overview of a <CODE>Tree</CODE> object is to <CODE>print</CODE> it:</P><PRE CLASS="verbatim">>>> from Bio import Phylo
->>> tree = Phylo.read("PhyloXML/example.xml", "phyloxml")
->>> print(tree)
-Phylogeny(description='phyloXML allows to use either a "branch_length" attribute...', name='example from Prof. Joe Felsenstein's book "Inferring Phyl...', rooted=True)
- Clade()
- Clade(branch_length=0.06)
- Clade(branch_length=0.102, name='A')
- Clade(branch_length=0.23, name='B')
- Clade(branch_length=0.4, name='C')
-</PRE><P>This is essentially an outline of the object hierarchy Biopython uses to represent a tree. But
-more likely, you’d want to see a drawing of the tree. There are three functions to do this.</P><P>As we saw in the demo, <CODE>draw_ascii</CODE> prints an ascii-art drawing of the tree (a
-rooted phylogram) to standard output, or an open file handle if given. Not all of the
-available information about the tree is shown, but it provides a way to quickly view the
-tree without relying on any external dependencies.</P><PRE CLASS="verbatim">>>> tree = Phylo.read("example.xml", "phyloxml")
->>> Phylo.draw_ascii(tree)
- __________________ A
- __________|
-_| |___________________________________________ B
- |
- |___________________________________________________________________________ C
-
-</PRE><P>The <CODE>draw</CODE> function draws a more attractive image using the matplotlib
-library. See the API documentation for details on the arguments it accepts to
-customize the output.</P><PRE CLASS="verbatim">>>> tree = Phylo.read("example.xml", "phyloxml")
->>> Phylo.draw(tree, branch_labels=lambda c: c.branch_length)
-</PRE><P>
-<IMG SRC="images/phylo-draw-example.png" width=701, height=465>
-<A NAME="fig:phylo-draw-example"></A>
-</P><P><CODE>draw_graphviz</CODE> draws an unrooted cladogram, but requires that you have Graphviz,
-PyDot or PyGraphviz, NetworkX, and matplotlib (or pylab) installed. Using the same example as
-above, and the <CODE>dot</CODE> program included with Graphviz, let’s draw a rooted tree (see
-Fig. <A HREF="#fig:phylo-dot">12.3</A>):</P><PRE CLASS="verbatim">>>> tree = Phylo.read("example.xml", "phyloxml")
->>> Phylo.draw_graphviz(tree, prog='dot')
->>> import pylab
->>> pylab.show() # Displays the tree in an interactive viewer
->>> pylab.savefig('phylo-dot.png') # Creates a PNG file of the same graphic
-</PRE><P>
-<IMG SRC="images/phylo-dot.png" width=400, height=312>
-<A NAME="fig:phylo-dot"></A>
-</P><P>(Tip: If you execute IPython with the <CODE>-pylab</CODE> option, calling <CODE>draw_graphviz</CODE> causes
-the matplotlib viewer to launch automatically without manually calling <CODE>show()</CODE>.)</P><P>This exports the tree object to a NetworkX graph, uses Graphviz to lay out the nodes, and
-displays it using matplotlib.
-There are a number of keyword arguments that can modify the resulting diagram, including
-most of those accepted by the NetworkX functions <CODE>networkx.draw</CODE> and
-<CODE>networkx.draw_graphviz</CODE>.</P><P>The display is also affected by the <CODE>rooted</CODE> attribute of the given tree object.
-Rooted trees are shown with a “head” on each branch indicating direction (see
-Fig. <A HREF="#fig:phylo-rooted">12.3</A>):</P><PRE CLASS="verbatim">>>> tree = Phylo.read("simple.dnd", "newick")
->>> tree.rooted = True
->>> Phylo.draw_graphviz(tree)
-</PRE><P>
-<IMG SRC="images/phylo-rooted.png" width=432, height=368>
-<A NAME="fig:phylo-rooted"></A>
-</P><P>The “prog” argument specifies the Graphviz engine used for layout. The default,
-<CODE>twopi</CODE>, behaves well for any size tree, reliably avoiding crossed branches. The
-<CODE>neato</CODE> program may draw more attractive moderately-sized trees, but sometimes will
-cross branches (see Fig. <A HREF="#fig:phylo-color">12.3</A>). The <CODE>dot</CODE> program may be useful
-with small trees, but tends to do surprising things with the layout of larger trees.</P><PRE CLASS="verbatim">>>> Phylo.draw_graphviz(tree, prog="neato")
-</PRE><P>
-<IMG SRC="images/phylo-color.png" width=499, height=348>
-<A NAME="fig:phylo-color"></A>
-</P><P>This viewing mode is particularly handy for exploring larger trees, because the matplotlib
-viewer can zoom in on a selected region, thinning out a cluttered graphic.
-</P><PRE CLASS="verbatim">>>> tree = Phylo.read("apaf.xml", "phyloxml")
->>> Phylo.draw_graphviz(tree, prog="neato", node_size=0)
-</PRE><P>
-<IMG SRC="images/phylo-apaf.png" width=519, height=400>
-<A NAME="fig:phylo-apaf"></A>
-<IMG SRC="images/phylo-apaf-zoom.png" width=506, height=400>
-<A NAME="fig:phylo-apaf-zoom"></A>
-</P><P>Note that branch lengths are not displayed accurately, because Graphviz ignores them when
-creating the node layouts. The branch lengths are retained when exporting a tree as a NetworkX
-graph object (<CODE>to_networkx</CODE>), however.</P><P>See the Phylo page on the Biopython wiki (<A HREF="http://biopython.org/wiki/Phylo"><TT>http://biopython.org/wiki/Phylo</TT></A>) for
-descriptions and examples of the more advanced functionality in <CODE>draw_ascii</CODE>,
-<CODE>draw_graphviz</CODE> and <CODE>to_networkx</CODE>.</P><!--TOC section Using Tree and Clade objects-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc159">12.4</A>  Using Tree and Clade objects</H2><!--SEC END --><P>The <CODE>Tree</CODE> objects produced by <CODE>parse</CODE> and <CODE>read</CODE> are containers for recursive
-sub-trees, attached to the <CODE>Tree</CODE> object at the <CODE>root</CODE> attribute (whether or not the
-phylogenic tree is actually considered rooted). A <CODE>Tree</CODE> has globally applied information
-for the phylogeny, such as rootedness, and a reference to a single <CODE>Clade</CODE>; a
-<CODE>Clade</CODE> has node- and clade-specific information, such as branch length, and a list of
-its own descendent <CODE>Clade</CODE> instances, attached at the <CODE>clades</CODE> attribute.</P><P>So there is a distinction between <CODE>tree</CODE> and <CODE>tree.root</CODE>. In practice, though, you
-rarely need to worry about it. To smooth over the difference, both <CODE>Tree</CODE> and
-<CODE>Clade</CODE> inherit from <CODE>TreeMixin</CODE>, which contains the implementations for methods
-that would be commonly used to search, inspect or modify a tree or any of its clades. This
-means that almost all of the methods supported by <CODE>tree</CODE> are also available on
-<CODE>tree.root</CODE> and any clade below it. (<CODE>Clade</CODE> also has a <CODE>root</CODE> property, which
-returns the clade object itself.)</P><!--TOC subsection Search and traversal methods-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc160">12.4.1</A>  Search and traversal methods</H3><!--SEC END --><P>For convenience, we provide a couple of simplified methods that return all external or internal
-nodes directly as a list:</P><DL CLASS="description"><DT CLASS="dt-description">
-<B><TT>get_terminals</TT></B></DT><DD CLASS="dd-description"> makes a list of all of this tree’s terminal (leaf) nodes.
-</DD><DT CLASS="dt-description"><B><TT>get_nonterminals</TT></B></DT><DD CLASS="dd-description"> makes a list of all of this tree’s nonterminal (internal)
-nodes.
-</DD></DL><P>These both wrap a method with full control over tree traversal, <CODE>find_clades</CODE>. Two more
-traversal methods, <CODE>find_elements</CODE> and <CODE>find_any</CODE>, rely on the same core
-functionality and accept the same arguments, which we’ll call a “target specification” for
-lack of a better description. These specify which objects in the tree will be matched and
-returned during iteration. The first argument can be any of the following types:</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-A <B>TreeElement instance</B>, which tree elements will match by identity — so
-searching with a Clade instance as the target will find that clade in the tree;</LI><LI CLASS="li-itemize">A <B>string</B>, which matches tree elements’ string representation — in
-particular, a clade’s <CODE>name</CODE> <I>(added in Biopython 1.56)</I>;</LI><LI CLASS="li-itemize">A <B>class</B> or <B>type</B>, where every tree element of the same type (or
-sub-type) will be matched;</LI><LI CLASS="li-itemize">A <B>dictionary</B> where keys are tree element attributes and values are matched to the
-corresponding attribute of each tree element. This one gets even more elaborate:<UL CLASS="itemize"><LI CLASS="li-itemize">
-If an <TT>int</TT> is given, it matches numerically equal attributes, e.g. 1 will
-match 1 or 1.0</LI><LI CLASS="li-itemize">If a boolean is given (True or False), the corresponding attribute value is
-evaluated as a boolean and checked for the same</LI><LI CLASS="li-itemize"><TT>None</TT> matches <TT>None</TT></LI><LI CLASS="li-itemize">If a string is given, the value is treated as a regular expression (which must
-match the whole string in the corresponding element attribute, not just a prefix). A
-given string without special regex characters will match string attributes exactly, so
-if you don’t use regexes, don’t worry about it. For example, in a tree with clade
-names Foo1, Foo2 and Foo3, <CODE>tree.find_clades({"name": "Foo1"})</CODE> matches Foo1,
-<CODE>{"name": "Foo.*"}</CODE> matches all three clades, and <CODE>{"name": "Foo"}</CODE> doesn’t
-match anything.</LI></UL><P>Since floating-point arithmetic can produce some strange behavior, we don’t support
-matching <TT>float</TT>s directly. Instead, use the boolean <TT>True</TT> to match every
-element with a nonzero value in the specified attribute, then filter on that attribute
-manually with an inequality (or exact number, if you like living dangerously).</P><P>If the dictionary contains multiple entries, a matching element must match each of the
-given attribute values — think “and”, not “or”.</P></LI><LI CLASS="li-itemize">A <B>function</B> taking a single argument (it will be applied to each element in the
-tree), returning True or False. For convenience, LookupError, AttributeError and ValueError
-are silenced, so this provides another safe way to search for floating-point values in the
-tree, or some more complex characteristic.</LI></UL><P>After the target, there are two optional keyword arguments:</P><DL CLASS="description"><DT CLASS="dt-description">
-<B>terminal</B></DT><DD CLASS="dd-description"> — A boolean value to select for or against terminal clades (a.k.a. leaf
-nodes): True searches for only terminal clades, False for non-terminal (internal) clades,
-and the default, None, searches both terminal and non-terminal clades, as well as any tree
-elements lacking the <CODE>is_terminal</CODE> method.</DD><DT CLASS="dt-description"><B>order</B></DT><DD CLASS="dd-description"> — Tree traversal order: <TT>"preorder"</TT> (default) is depth-first search,
-<TT>"postorder"</TT> is DFS with child nodes preceding parents, and <TT>"level"</TT> is
-breadth-first search.</DD></DL><P>Finally, the methods accept arbitrary keyword arguments which are treated the same way as a
-dictionary target specification: keys indicate the name of the element attribute to search for,
-and the argument value (string, integer, None or boolean) is compared to the value of each
-attribute found. If no keyword arguments are given, then any TreeElement types are matched.
-The code for this is generally shorter than passing a dictionary as the target specification:
-<CODE>tree.find_clades({"name": "Foo1"})</CODE> can be shortened to
-<CODE>tree.find_clades(name="Foo1")</CODE>.</P><P>(In Biopython 1.56 or later, this can be even shorter: <CODE>tree.find_clades("Foo1")</CODE>)</P><P>Now that we’ve mastered target specifications, here are the methods used to traverse a tree:</P><DL CLASS="description"><DT CLASS="dt-description">
-<B><TT>find_clades</TT></B></DT><DD CLASS="dd-description">
-Find each clade containing a matching element. That is, find each element as with
-<CODE>find_elements</CODE>, but return the corresponding clade object. (This is usually what you
-want.)<P>The result is an iterable through all matching objects, searching depth-first by default.
-This is not necessarily the same order as the elements appear in the Newick, Nexus or XML
-source file!</P></DD><DT CLASS="dt-description"><B><TT>find_elements</TT></B></DT><DD CLASS="dd-description">
-Find all tree elements matching the given attributes, and return the matching elements
-themselves. Simple Newick trees don’t have complex sub-elements, so this behaves the same
-as <CODE>find_clades</CODE> on them. PhyloXML trees often do have complex objects attached to
-clades, so this method is useful for extracting those.</DD><DT CLASS="dt-description"><B><TT>find_any</TT></B></DT><DD CLASS="dd-description">
-Return the first element found by <CODE>find_elements()</CODE>, or None. This is also useful for
-checking whether any matching element exists in the tree, and can be used in a conditional.</DD></DL><P>Two more methods help navigating between nodes in the tree:</P><DL CLASS="description"><DT CLASS="dt-description">
-<B><TT>get_path</TT></B></DT><DD CLASS="dd-description">
-List the clades directly between the tree root (or current clade) and the given target.
-Returns a list of all clade objects along this path, ending with the given target, but
-excluding the root clade.</DD><DT CLASS="dt-description"><B><TT>trace</TT></B></DT><DD CLASS="dd-description">
-List of all clade object between two targets in this tree. Excluding start, including
-finish.</DD></DL><!--TOC subsection Information methods-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc161">12.4.2</A>  Information methods</H3><!--SEC END --><P>These methods provide information about the whole tree (or any clade).</P><DL CLASS="description"><DT CLASS="dt-description">
-<B><TT>common_ancestor</TT></B></DT><DD CLASS="dd-description">
-Find the most recent common ancestor of all the given targets. (This will be a Clade object).
-If no target is given, returns the root of the current clade (the one this method is called
-from); if 1 target is given, this returns the target itself. However, if any of the
-specified targets are not found in the current tree (or clade), an exception is raised.</DD><DT CLASS="dt-description"><B><TT>count_terminals</TT></B></DT><DD CLASS="dd-description">
-Counts the number of terminal (leaf) nodes within the tree.</DD><DT CLASS="dt-description"><B><TT>depths</TT></B></DT><DD CLASS="dd-description">
-Create a mapping of tree clades to depths. The result is a dictionary where the keys are
-all of the Clade instances in the tree, and the values are the distance from the root to
-each clade (including terminals). By default the distance is the cumulative branch length
-leading to the clade, but with the <CODE>unit_branch_lengths=True</CODE> option, only the number
-of branches (levels in the tree) is counted.</DD><DT CLASS="dt-description"><B><TT>distance</TT></B></DT><DD CLASS="dd-description">
-Calculate the sum of the branch lengths between two targets. If only one target is
-specified, the other is the root of this tree.</DD><DT CLASS="dt-description"><B><TT>total_branch_length</TT></B></DT><DD CLASS="dd-description">
-Calculate the sum of all the branch lengths in this tree. This is usually just called the
-“length” of the tree in phylogenetics, but we use a more explicit name to avoid confusion
-with Python terminology.</DD></DL><P>The rest of these methods are boolean checks:</P><DL CLASS="description"><DT CLASS="dt-description">
-<B><TT>is_bifurcating</TT></B></DT><DD CLASS="dd-description">
-True if the tree is strictly bifurcating; i.e. all nodes have either 2 or 0 children
-(internal or external, respectively). The root may have 3 descendents and still be
-considered part of a bifurcating tree.</DD><DT CLASS="dt-description"><B><TT>is_monophyletic</TT></B></DT><DD CLASS="dd-description">
-Test if all of the given targets comprise a complete subclade — i.e., there
-exists a clade such that its terminals are the same set as the given targets. The targets
-should be terminals of the tree. For convenience, this method returns the common ancestor
-(MCRA) of the targets if they are monophyletic (instead of the value <CODE>True</CODE>), and
-<CODE>False</CODE> otherwise.</DD><DT CLASS="dt-description"><B><TT>is_parent_of</TT></B></DT><DD CLASS="dd-description"> True if target is a descendent of this tree — not required
-to be a direct descendent. To check direct descendents of a clade, simply use list
-membership testing: <CODE>if subclade in clade: ...</CODE></DD><DT CLASS="dt-description"><B><TT>is_preterminal</TT></B></DT><DD CLASS="dd-description"> True if all direct descendents are terminal; False if any
-direct descendent is not terminal.</DD></DL><!--TOC subsection Modification methods-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc162">12.4.3</A>  Modification methods</H3><!--SEC END --><P>These methods modify the tree in-place. If you want to keep the original tree intact, make a
-complete copy of the tree first, using Python’s <TT>copy</TT> module:</P><PRE CLASS="verbatim">tree = Phylo.read('example.xml', 'phyloxml')
-import copy
-newtree = copy.deepcopy(tree)
-</PRE><DL CLASS="description"><DT CLASS="dt-description">
-<B><TT>collapse</TT></B></DT><DD CLASS="dd-description">
-Deletes the target from the tree, relinking its children to its parent.</DD><DT CLASS="dt-description"><B><TT>collapse_all</TT></B></DT><DD CLASS="dd-description">
-Collapse all the descendents of this tree, leaving only terminals. Branch lengths are
-preserved, i.e. the distance to each terminal stays the same. With a target specification
-(see above), collapses only the internal nodes matching the specification.</DD><DT CLASS="dt-description"><B><TT>ladderize</TT></B></DT><DD CLASS="dd-description">
-Sort clades in-place according to the number of terminal nodes. Deepest clades are placed
-last by default. Use <CODE>reverse=True</CODE> to sort clades deepest-to-shallowest.</DD><DT CLASS="dt-description"><B><TT>prune</TT></B></DT><DD CLASS="dd-description">
-Prunes a terminal clade from the tree. If taxon is from a bifurcation, the connecting node
-will be collapsed and its branch length added to remaining terminal node. This might no
-longer be a meaningful value.</DD><DT CLASS="dt-description"><B><TT>root_with_outgroup</TT></B></DT><DD CLASS="dd-description">
-Reroot this tree with the outgroup clade containing the given targets, i.e. the common
-ancestor of the outgroup. This method is only available on Tree objects, not Clades.<P>If the outgroup is identical to self.root, no change occurs. If the outgroup clade is
-terminal (e.g. a single terminal node is given as the outgroup), a new bifurcating root
-clade is created with a 0-length branch to the given outgroup. Otherwise, the internal node
-at the base of the outgroup becomes a trifurcating root for the whole tree. If the original
-root was bifurcating, it is dropped from the tree.</P><P>In all cases, the total branch length of the tree stays the same.</P></DD><DT CLASS="dt-description"><B><TT>root_at_midpoint</TT></B></DT><DD CLASS="dd-description">
-Reroot this tree at the calculated midpoint between the two most distant
-tips of the tree. (This uses <CODE>root_with_outgroup</CODE> under the hood.)</DD><DT CLASS="dt-description"><B><TT>split</TT></B></DT><DD CLASS="dd-description">
-Generate <I>n</I> (default 2) new descendants. In a species tree, this is a speciation
-event. New clades have the given <CODE>branch_length</CODE> and the same name as this clade’s
-root plus an integer suffix (counting from 0) — for example, splitting a clade named
-“A” produces the sub-clades “A0” and “A1”.</DD></DL><P>See the Phylo page on the Biopython wiki (<A HREF="http://biopython.org/wiki/Phylo"><TT>http://biopython.org/wiki/Phylo</TT></A>) for
-more examples of using the available methods.</P><!--TOC subsection Features of PhyloXML trees-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc163">12.4.4</A>  Features of PhyloXML trees</H3><!--SEC END --><P>
-<A NAME="sec:PhyloXML"></A></P><P>The phyloXML file format includes fields for annotating trees with additional data types and
-visual cues.</P><P>See the PhyloXML page on the Biopython wiki (<A HREF="http://biopython.org/wiki/PhyloXML"><TT>http://biopython.org/wiki/PhyloXML</TT></A>) for
-descriptions and examples of using the additional annotation features provided by PhyloXML.</P><!--TOC section Running external applications-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc164">12.5</A>  Running external applications</H2><!--SEC END --><P>
-<A NAME="sec:PhyloApps"></A></P><P>While Bio.Phylo doesn’t infer trees from alignments itself, there are third-party
-programs available that do. These are supported through the module
-<TT>Bio.Phylo.Applications</TT>, using the same general framework as
-<TT>Bio.Emboss.Applications</TT>, <TT>Bio.Align.Applications</TT> and others.</P><P>Biopython 1.58 introduced a wrapper for PhyML
-(<A HREF="http://www.atgc-montpellier.fr/phyml/"><TT>http://www.atgc-montpellier.fr/phyml/</TT></A>). The program accepts an input alignment in
-<TT>phylip-relaxed</TT> format (that’s Phylip format, but without the 10-character limit
-on taxon names) and a variety of options. A quick example:</P><PRE CLASS="verbatim">>>> from Bio import Phylo
->>> from Bio.Phylo.Applications import PhymlCommandline
->>> cmd = PhymlCommandline(input='Tests/Phylip/random.phy')
->>> out_log, err_log = cmd()
-</PRE><P>This generates a tree file and a stats file with the names
-[<I>input filename</I>]<CODE>_phyml_tree.txt</CODE> and
-[<I>input filename</I>]<CODE>_phyml_stats.txt</CODE>. The tree file is in Newick format:</P><PRE CLASS="verbatim">>>> tree = Phylo.read('Tests/Phylip/random.phy_phyml_tree.txt', 'newick')
->>> Phylo.draw_ascii(tree)
-</PRE><P>A similar wrapper for RAxML (<A HREF="http://sco.h-its.org/exelixis/software.html"><TT>http://sco.h-its.org/exelixis/software.html</TT></A>)
-was added in Biopython 1.60, and FastTree
-(<A HREF="http://www.microbesonline.org/fasttree/"><TT>http://www.microbesonline.org/fasttree/</TT></A>) in Biopython 1.62.</P><P>Note that some popular Phylip programs, including <TT>dnaml</TT> and <TT>protml</TT>,
-are already available through the EMBOSS wrappers in <TT>Bio.Emboss.Applications</TT> if
-you have the Phylip extensions to EMBOSS installed on your system.
-See Section <A HREF="#sec:alignment-tools">6.4</A> for some examples and clues on how to use
-programs like these.</P><!--TOC section PAML integration-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc165">12.6</A>  PAML integration</H2><!--SEC END --><P>
-<A NAME="sec:PhyloPAML"></A></P><P>Biopython 1.58 brought support for PAML
-(<A HREF="http://abacus.gene.ucl.ac.uk/software/paml.html"><TT>http://abacus.gene.ucl.ac.uk/software/paml.html</TT></A>), a suite of programs for
-phylogenetic analysis by maximum likelihood. Currently the programs codeml, baseml and
-yn00 are implemented. Due to PAML’s usage of control files rather than command line
-arguments to control runtime options, usage of this wrapper strays from the format of
-other application wrappers in Biopython.</P><P>A typical workflow would be to initialize a PAML object, specifying an alignment file, a
-tree file, an output file and a working directory. Next, runtime options are set via the
-<TT>set_options()</TT> method or by reading an existing control file. Finally, the
-program is run via the <TT>run()</TT> method and the output file is automatically parsed
-to a results dictionary.</P><P>Here is an example of typical usage of codeml:
-</P><PRE CLASS="verbatim">>>> from Bio.Phylo.PAML import codeml
->>> cml = codeml.Codeml()
->>> cml.alignment = "Tests/PAML/alignment.phylip"
->>> cml.tree = "Tests/PAML/species.tree"
->>> cml.out_file = "results.out"
->>> cml.working_dir = "./scratch"
->>> cml.set_options(seqtype=1,
-... verbose=0,
-... noisy=0,
-... RateAncestor=0,
-... model=0,
-... NSsites=[0, 1, 2],
-... CodonFreq=2,
-... cleandata=1,
-... fix_alpha=1,
-... kappa=4.54006)
->>> results = cml.run()
->>> ns_sites = results.get("NSsites")
->>> m0 = ns_sites.get(0)
->>> m0_params = m0.get("parameters")
->>> print(m0_params.get("omega"))
-</PRE><P>Existing output files may be parsed as well using a module’s <TT>read()</TT> function:
-</P><PRE CLASS="verbatim">>>> results = codeml.read("Tests/PAML/Results/codeml/codeml_NSsites_all.out")
->>> print(results.get("lnL max"))
-</PRE><P>Detailed documentation for this new module currently lives on the Biopython wiki:
-<A HREF="http://biopython.org/wiki/PAML"><TT>http://biopython.org/wiki/PAML</TT></A></P><!--TOC section Future plans-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc166">12.7</A>  Future plans</H2><!--SEC END --><P>
-<A NAME="sec:PhyloFuture"></A></P><P>Bio.Phylo is under active development. Here are some features we might add in future
-releases:</P><DL CLASS="description"><DT CLASS="dt-description">
-<B>New methods</B></DT><DD CLASS="dd-description">
-Generally useful functions for operating on Tree or Clade objects appear on the Biopython
-wiki first, so that casual users can test them and decide if they’re useful before we add
-them to Bio.Phylo:<P><A HREF="http://biopython.org/wiki/Phylo_cookbook"><TT>http://biopython.org/wiki/Phylo_cookbook</TT></A></P></DD><DT CLASS="dt-description"><B>Bio.Nexus port</B></DT><DD CLASS="dd-description">
-Much of this module was written during Google Summer of Code 2009, under the auspices of
-NESCent, as a project to implement Python support for the phyloXML data format (see
-<A HREF="#sec:PhyloXML">12.4.4</A>). Support for Newick and Nexus formats was added by porting part of the
-existing Bio.Nexus module to the new classes used by Bio.Phylo.<P>Currently, Bio.Nexus contains some useful features that have not yet been ported to
-Bio.Phylo classes — notably, calculating a consensus tree. If you find some functionality
-lacking in Bio.Phylo, try poking throught Bio.Nexus to see if it’s there instead.</P></DD></DL><P>We’re open to any suggestions for improving the functionality and usability of this module;
-just let us know on the mailing list or our bug database.</P><P>Finally, if you need additional functionality not yet included in the Phylo
-module, check if it’s available in another of the high-quality Python libraries
-for phylogenetics such as DendroPy (<A HREF="http://pythonhosted.org/DendroPy/"><TT>http://pythonhosted.org/DendroPy/</TT></A>) or
-PyCogent (<A HREF="http://pycogent.org/"><TT>http://pycogent.org/</TT></A>). Since these libraries also support
-standard file formats for phylogenetic trees, you can easily transfer data
-between libraries by writing to a temporary file or StringIO object.</P><!--TOC chapter Sequence motif analysis using Bio.motifs-->
-<H1 CLASS="chapter"><!--SEC ANCHOR --><A NAME="htoc167">Chapter 13</A>  Sequence motif analysis using Bio.motifs</H1><!--SEC END --><P>This chapter gives an overview of the functionality of the
-<CODE>Bio.motifs</CODE> package included in Biopython. It is intended
-for people who are involved in the analysis of sequence motifs, so I’ll
-assume that you are familiar with basic notions of motif analysis. In
-case something is unclear, please look at Section <A HREF="#sec:links">13.10</A>
-for some relevant links.</P><P>Most of this chapter describes the new <CODE>Bio.motifs</CODE> package included
-in Biopython 1.61 onwards, which is replacing the older <CODE>Bio.Motif</CODE> package
-introduced with Biopython 1.50, which was in turn based on two older former
-Biopython modules, <CODE>Bio.AlignAce</CODE> and <CODE>Bio.MEME</CODE>. It provides
-most of their functionality with a unified motif object implementation.</P><P>Speaking of other libraries, if you are reading this you might be
-interested in <A HREF="http://fraenkel.mit.edu/TAMO/">TAMO</A>, another python library
-designed to deal with sequence motifs. It supports more <EM>de-novo</EM>
-motif finders, but it is not a part of Biopython and has some restrictions
-on commercial use.</P><!--TOC section Motif objects-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc168">13.1</A>  Motif objects</H2><!--SEC END --><P>
-<A NAME="sec:object"></A></P><P>Since we are interested in motif analysis, we need to take a look at
-<CODE>Motif</CODE> objects in the first place. For that we need to import
-the Bio.motifs library:
-</P><PRE CLASS="verbatim">>>> from Bio import motifs
-</PRE><P>and we can start creating our first motif objects. We can either create
-a <CODE>Motif</CODE> object from a list of instances of the motif, or we can
-obtain a <CODE>Motif</CODE> object by parsing a file from a motif database
-or motif finding software.</P><!--TOC subsection Creating a motif from instances-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc169">13.1.1</A>  Creating a motif from instances</H3><!--SEC END --><P>Suppose we have these instances of a DNA motif:
-</P><PRE CLASS="verbatim">>>> from Bio.Seq import Seq
->>> instances = [Seq("TACAA"),
-... Seq("TACGC"),
-... Seq("TACAC"),
-... Seq("TACCC"),
-... Seq("AACCC"),
-... Seq("AATGC"),
-... Seq("AATGC"),
-... ]
-</PRE><P>then we can create a Motif object as follows:
-</P><PRE CLASS="verbatim">>>> m = motifs.create(instances)
-</PRE><P>The instances are saved in an attribute <CODE>m.instances</CODE>, which is essentially a Python list with some added functionality, as described below.
-Printing out the Motif object shows the instances from which it was constructed:
-</P><PRE CLASS="verbatim">>>> print(m)
-TACAA
-TACGC
-TACAC
-TACCC
-AACCC
-AATGC
-AATGC
-<BLANKLINE>
-</PRE><P>The length of the motif is defined as the sequence length, which should be the same for all instances:
-</P><PRE CLASS="verbatim">>>> len(m)
-5
-</PRE><P>The Motif object has an attribute <CODE>.counts</CODE> containing the counts of each
-nucleotide at each position. Printing this counts matrix shows it in an easily readable format:
-</P><PRE CLASS="verbatim">>>> print(m.counts)
- 0 1 2 3 4
-A: 3.00 7.00 0.00 2.00 1.00
-C: 0.00 0.00 5.00 2.00 6.00
-G: 0.00 0.00 0.00 3.00 0.00
-T: 4.00 0.00 2.00 0.00 0.00
-<BLANKLINE>
-</PRE><P>You can access these counts as a dictionary:
-</P><PRE CLASS="verbatim">>>> m.counts['A']
-[3, 7, 0, 2, 1]
-</PRE><P>but you can also think of it as a 2D array with the nucleotide as the first
-dimension and the position as the second dimension:
-</P><PRE CLASS="verbatim">>>> m.counts['T', 0]
-4
->>> m.counts['T', 2]
-2
->>> m.counts['T', 3]
-0
-</PRE><P>You can also directly access columns of the counts matrix
-</P><PRE CLASS="verbatim">>>> m.counts[:, 3]
-{'A': 2, 'C': 2, 'T': 0, 'G': 3}
-</PRE><P>Instead of the nucleotide itself, you can also use the index of the nucleotide
-in the sorted letters in the alphabet of the motif:
-</P><PRE CLASS="verbatim">>>> m.alphabet
-IUPACUnambiguousDNA()
->>> m.alphabet.letters
-'GATC'
->>> sorted(m.alphabet.letters)
-['A', 'C', 'G', 'T']
->>> m.counts['A',:]
-(3, 7, 0, 2, 1)
->>> m.counts[0,:]
-(3, 7, 0, 2, 1)
-</PRE><P>The motif has an associated consensus sequence, defined as the sequence of
-letters along the positions of the motif for which the largest value in the
-corresponding columns of the <CODE>.counts</CODE> matrix is obtained:
-</P><PRE CLASS="verbatim">>>> m.consensus
-Seq('TACGC', IUPACUnambiguousDNA())
-</PRE><P>as well as an anticonsensus sequence, corresponding to the smallest values in
-the columns of the <CODE>.counts</CODE> matrix:
-</P><PRE CLASS="verbatim">>>> m.anticonsensus
-Seq('GGGTG', IUPACUnambiguousDNA())
-</PRE><P>You can also ask for a degenerate consensus sequence, in which ambiguous
-nucleotides are used for positions where there are multiple nucleotides with
-high counts:
-</P><PRE CLASS="verbatim">>>> m.degenerate_consensus
-Seq('WACVC', IUPACAmbiguousDNA())
-</PRE><P>Here, W and R follow the IUPAC nucleotide ambiguity codes: W is either A or T,
-and V is A, C, or G [<A HREF="#cornish1985">10</A>]. The degenerate consensus sequence is
-constructed following the rules specified by Cavener [<A HREF="#cavener1987">11</A>].</P><P>We can also get the reverse complement of a motif:
-</P><PRE CLASS="verbatim">>>> r = m.reverse_complement()
->>> r.consensus
-Seq('GCGTA', IUPACUnambiguousDNA())
->>> r.degenerate_consensus
-Seq('GBGTW', IUPACAmbiguousDNA())
->>> print(r)
-TTGTA
-GCGTA
-GTGTA
-GGGTA
-GGGTT
-GCATT
-GCATT
-<BLANKLINE>
-</PRE><P>The reverse complement and the degenerate consensus sequence are
-only defined for DNA motifs.</P><!--TOC subsection Creating a sequence logo-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc170">13.1.2</A>  Creating a sequence logo</H3><!--SEC END --><P>
-If we have internet access, we can create a <A HREF="http://weblogo.berkeley.edu">weblogo</A>:
-</P><PRE CLASS="verbatim">>>> m.weblogo("mymotif.png")
-</PRE><P>We should get our logo saved as a PNG in the specified file.</P><!--TOC section Reading motifs-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc171">13.2</A>  Reading motifs</H2><!--SEC END --><P>
-<A NAME="sec:io"></A></P><P>Creating motifs from instances by hand is a bit boring, so it’s
-useful to have some I/O functions for reading and writing
-motifs. There are not any really well established standards for storing
-motifs, but there are a couple of formats that are more used than
-others.</P><!--TOC subsection JASPAR-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc172">13.2.1</A>  JASPAR</H3><!--SEC END --><P>
-One of the most popular motif databases is <A HREF="http://jaspar.genereg.net">JASPAR</A>. In addition to the motif sequence information, the JASPAR database stores a lot of meta-information for each motif. The module <CODE>Bio.motifs</CODE> contains a specialized class <CODE>jaspar.Motif</CODE> in which this meta-information is represented as attributes:
-</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-<CODE>matrix_id</CODE> - the unique JASPAR motif ID, e.g. ’MA0004.1’
-</LI><LI CLASS="li-itemize"><CODE>name</CODE> - the name of the TF, e.g. ’Arnt’
-</LI><LI CLASS="li-itemize"><CODE>collection</CODE> - the JASPAR collection to which the motif belongs, e.g. ’CORE’
-</LI><LI CLASS="li-itemize"><CODE>tf_class</CODE> - the structual class of this TF, e.g. ’Zipper-Type’
-</LI><LI CLASS="li-itemize"><CODE>tf_family</CODE> - the family to which this TF belongs, e.g. ’Helix-Loop-Helix’
-</LI><LI CLASS="li-itemize"><CODE>species</CODE> - the species to which this TF belongs, may have multiple values, these are specified as taxonomy IDs, e.g. 10090
-</LI><LI CLASS="li-itemize"><CODE>tax_group</CODE> - the taxonomic supergroup to which this motif belongs, e.g. ’vertebrates’
-</LI><LI CLASS="li-itemize"><CODE>acc</CODE> - the accession number of the TF protein, e.g. ’P53762’
-</LI><LI CLASS="li-itemize"><CODE>data_type</CODE> - the type of data used to construct this motif, e.g. ’SELEX’
-</LI><LI CLASS="li-itemize"><CODE>medline</CODE> - the Pubmed ID of literature supporting this motif, may be multiple values, e.g. 7592839
-</LI><LI CLASS="li-itemize"><CODE>pazar_id</CODE> - external reference to the TF in the <A HREF="http://pazar.info">PAZAR</A> database, e.g. ’TF0000003’
-</LI><LI CLASS="li-itemize"><CODE>comment</CODE> - free form text containing notes about the construction of the motif
-</LI></UL><P>The <CODE>jaspar.Motif</CODE> class inherits from the generic <CODE>Motif</CODE> class and therefore provides all the facilities of any of the motif formats — reading motifs, writing motifs, scanning sequences for motif instances etc.</P><P>JASPAR stores motifs in several different ways including three different flat file formats and as an SQL database. All of these formats facilitate the construction of a counts matrix. However, the amount of meta information describ [...]
-<H4 CLASS="subsubsection"><!--SEC ANCHOR -->The JASPAR <TT>sites</TT> format</H4><!--SEC END --><P>The first of the three flat file formats contains a list of instances. As an example, these are the beginning and ending lines of the JASPAR <CODE>Arnt.sites</CODE> file showing known binding sites of the mouse helix-loop-helix transcription factor Arnt.
-</P><PRE CLASS="verbatim">>MA0004 ARNT 1
-CACGTGatgtcctc
->MA0004 ARNT 2
-CACGTGggaggtac
->MA0004 ARNT 3
-CACGTGccgcgcgc
-...
->MA0004 ARNT 18
-AACGTGacagccctcc
->MA0004 ARNT 19
-AACGTGcacatcgtcc
->MA0004 ARNT 20
-aggaatCGCGTGc
-</PRE><P>The parts of the sequence in capital letters are the motif instances that were found to align to each other.</P><P>We can create a <CODE>Motif</CODE> object from these instances as follows:
-</P><PRE CLASS="verbatim">>>> from Bio import motifs
->>> with open("Arnt.sites") as handle:
-... arnt = motifs.read(handle, "sites")
-...
-</PRE><P>The instances from which this motif was created is stored in the <CODE>.instances</CODE> property:
-</P><PRE CLASS="verbatim">>>> print(arnt.instances[:3])
-[Seq('CACGTG', IUPACUnambiguousDNA()), Seq('CACGTG', IUPACUnambiguousDNA()), Seq('CACGTG', IUPACUnambiguousDNA())]
->>> for instance in arnt.instances:
-... print(instance)
-...
-CACGTG
-CACGTG
-CACGTG
-CACGTG
-CACGTG
-CACGTG
-CACGTG
-CACGTG
-CACGTG
-CACGTG
-CACGTG
-CACGTG
-CACGTG
-CACGTG
-CACGTG
-AACGTG
-AACGTG
-AACGTG
-AACGTG
-CGCGTG
-</PRE><P>The counts matrix of this motif is automatically calculated from the instances:
-</P><PRE CLASS="verbatim">>>> print(arnt.counts)
- 0 1 2 3 4 5
-A: 4.00 19.00 0.00 0.00 0.00 0.00
-C: 16.00 0.00 20.00 0.00 0.00 0.00
-G: 0.00 1.00 0.00 20.00 0.00 20.00
-T: 0.00 0.00 0.00 0.00 20.00 0.00
-<BLANKLINE>
-</PRE><P>This format does not store any meta information.</P><!--TOC subsubsection The JASPAR <TT>pfm</TT> format-->
-<H4 CLASS="subsubsection"><!--SEC ANCHOR -->The JASPAR <TT>pfm</TT> format</H4><!--SEC END --><P>JASPAR also makes motifs available directly as a count matrix,
-without the instances from which it was created. This <CODE>pfm</CODE> format only
-stores the counts matrix for a single motif.
-For example, this is the JASPAR file <CODE>SRF.pfm</CODE> containing the counts matrix for the human SRF transcription factor:
-</P><PRE CLASS="verbatim"> 2 9 0 1 32 3 46 1 43 15 2 2
- 1 33 45 45 1 1 0 0 0 1 0 1
-39 2 1 0 0 0 0 0 0 0 44 43
- 4 2 0 0 13 42 0 45 3 30 0 0
-</PRE><P>We can create a motif for this count matrix as follows:
-</P><PRE CLASS="verbatim">>>> with open("SRF.pfm") as handle:
-... srf = motifs.read(handle, "pfm")
-...
->>> print(srf.counts)
- 0 1 2 3 4 5 6 7 8 9 10 11
-A: 2.00 9.00 0.00 1.00 32.00 3.00 46.00 1.00 43.00 15.00 2.00 2.00
-C: 1.00 33.00 45.00 45.00 1.00 1.00 0.00 0.00 0.00 1.00 0.00 1.00
-G: 39.00 2.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 44.00 43.00
-T: 4.00 2.00 0.00 0.00 13.00 42.00 0.00 45.00 3.00 30.00 0.00 0.00
-<BLANKLINE>
-</PRE><P>As this motif was created from the counts matrix directly, it has no instances associated with it:
-</P><PRE CLASS="verbatim">>>> print(srf.instances)
-None
-</PRE><P>We can now ask for the consensus sequence of these two motifs:
-</P><PRE CLASS="verbatim">>>> print(arnt.counts.consensus)
-CACGTG
->>> print(srf.counts.consensus)
-GCCCATATATGG
-</PRE><P>As with the instances file, no meta information is stored in this format.</P><!--TOC subsubsection The JASPAR format <TT>jaspar</TT>-->
-<H4 CLASS="subsubsection"><!--SEC ANCHOR -->The JASPAR format <TT>jaspar</TT></H4><!--SEC END --><P>The <CODE>jaspar</CODE> file format allows multiple motifs to be specified in a single file. In this format each of the motif records consist of a header line followed by four lines defining the counts matrix. The header line begins with a <CODE>></CODE> character (similar to the Fasta file format) and is followed by the unique JASPAR matrix ID and the TF name. The following example sho [...]
-</P><PRE CLASS="verbatim">>MA0004.1 Arnt
-A [ 4 19 0 0 0 0 ]
-C [16 0 20 0 0 0 ]
-G [ 0 1 0 20 0 20 ]
-T [ 0 0 0 0 20 0 ]
->MA0002.1 RUNX1
-A [10 12 4 1 2 2 0 0 0 8 13 ]
-C [ 2 2 7 1 0 8 0 0 1 2 2 ]
-G [ 3 1 1 0 23 0 26 26 0 0 4 ]
-T [11 11 14 24 1 16 0 0 25 16 7 ]
->MA0052.1 MEF2A
-A [ 1 0 57 2 9 6 37 2 56 6 ]
-C [50 0 1 1 0 0 0 0 0 0 ]
-G [ 0 0 0 0 0 0 0 0 2 50 ]
-T [ 7 58 0 55 49 52 21 56 0 2 ]
-</PRE><P>The motifs are read as follows:
-</P><PRE CLASS="verbatim">>>> fh = open("jaspar_motifs.txt")
->>> for m in motifs.parse(fh, "jaspar"))
-... print(m)
-TF name Arnt
-Matrix ID MA0004.1
-Matrix:
- 0 1 2 3 4 5
-A: 4.00 19.00 0.00 0.00 0.00 0.00
-C: 16.00 0.00 20.00 0.00 0.00 0.00
-G: 0.00 1.00 0.00 20.00 0.00 20.00
-T: 0.00 0.00 0.00 0.00 20.00 0.00
-
-
-
-TF name RUNX1
-Matrix ID MA0002.1
-Matrix:
- 0 1 2 3 4 5 6 7 8 9 10
-A: 10.00 12.00 4.00 1.00 2.00 2.00 0.00 0.00 0.00 8.00 13.00
-C: 2.00 2.00 7.00 1.00 0.00 8.00 0.00 0.00 1.00 2.00 2.00
-G: 3.00 1.00 1.00 0.00 23.00 0.00 26.00 26.00 0.00 0.00 4.00
-T: 11.00 11.00 14.00 24.00 1.00 16.00 0.00 0.00 25.00 16.00 7.00
-
-
-
-TF name MEF2A
-Matrix ID MA0052.1
-Matrix:
- 0 1 2 3 4 5 6 7 8 9
-A: 1.00 0.00 57.00 2.00 9.00 6.00 37.00 2.00 56.00 6.00
-C: 50.00 0.00 1.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00
-G: 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2.00 50.00
-T: 7.00 58.00 0.00 55.00 49.00 52.00 21.00 56.00 0.00 2.00
-</PRE><P>Note that printing a JASPAR motif yields both the counts data and the available meta-information.</P><!--TOC subsubsection Accessing the JASPAR database-->
-<H4 CLASS="subsubsection"><!--SEC ANCHOR -->Accessing the JASPAR database</H4><!--SEC END --><P>In addition to parsing these flat file formats, we can also retrieve motifs from a JASPAR SQL database. Unlike the flat file formats, a JASPAR database allows storing of all possible meta information defined in the JASPAR <CODE>Motif</CODE> class. It is beyond the scope of this document to describe how to set up a JASPAR database (please see the main <A HREF="http://jaspar.genereg.net">JASPAR< [...]
-</P><PRE CLASS="verbatim">>>> from Bio.motifs.jaspar.db import JASPAR5
->>>
->>> JASPAR_DB_HOST = <hostname>
->>> JASPAR_DB_NAME = <db_name>
->>> JASPAR_DB_USER = <user>
->>> JASPAR_DB_PASS = <passord>
->>>
->>> jdb = JASPAR5(
-... host=JASPAR_DB_HOST,
-... name=JASPAR_DB_NAME,
-... user=JASPAR_DB_USER,
-... password=JASPAR_DB_PASS
-... )
-</PRE><P>Now we can fetch a single motif by its unique JASPAR ID with the <CODE>fetch_motif_by_id</CODE> method. Note that a JASPAR ID conists of a base ID and a version number seperated by a decimal point, e.g. ’MA0004.1’. The <CODE>fetch_motif_by_id</CODE> method allows you to use either the fully specified ID or just the base ID. If only the base ID is provided, the latest version of the motif is returned.
-</P><PRE CLASS="verbatim">>>> arnt = jdb.fetch_motif_by_id("MA0004")
-</PRE><P>Printing the motif reveals that the JASPAR SQL database stores much more meta-information than the flat files:
-</P><PRE CLASS="verbatim">>>> print(arnt)
-TF name Arnt
-Matrix ID MA0004.1
-Collection CORE
-TF class Zipper-Type
-TF family Helix-Loop-Helix
-Species 10090
-Taxonomic group vertebrates
-Accession ['P53762']
-Data type used SELEX
-Medline 7592839
-PAZAR ID TF0000003
-Comments -
-Matrix:
- 0 1 2 3 4 5
-A: 4.00 19.00 0.00 0.00 0.00 0.00
-C: 16.00 0.00 20.00 0.00 0.00 0.00
-G: 0.00 1.00 0.00 20.00 0.00 20.00
-T: 0.00 0.00 0.00 0.00 20.00 0.00
-
-
-</PRE><P>We can also fetch motifs by name. The name must be an exact match (partial matches or database wildcards are not currently supported). Note that as the name is not guaranteed to be unique, the <CODE>fetch_motifs_by_name</CODE> method actually returns a list.
-</P><PRE CLASS="verbatim">>>> motifs = jdb.fetch_motifs_by_name("Arnt")
->>> print(motifs[0])
-TF name Arnt
-Matrix ID MA0004.1
-Collection CORE
-TF class Zipper-Type
-TF family Helix-Loop-Helix
-Species 10090
-Taxonomic group vertebrates
-Accession ['P53762']
-Data type used SELEX
-Medline 7592839
-PAZAR ID TF0000003
-Comments -
-Matrix:
- 0 1 2 3 4 5
-A: 4.00 19.00 0.00 0.00 0.00 0.00
-C: 16.00 0.00 20.00 0.00 0.00 0.00
-G: 0.00 1.00 0.00 20.00 0.00 20.00
-T: 0.00 0.00 0.00 0.00 20.00 0.00
-
-
-</PRE><P>The <CODE>fetch_motifs</CODE> method allows you to fetch motifs which match a specified set of criteria. These criteria include any of the above described meta information as well as certain matrix properties such as the minimum information content (<CODE>min_ic</CODE> in the example below), the minimum length of the matrix or the minimum number of sites used to construct the matrix. Only motifs which pass ALL the specified criteria are returned. Note that selection criteria whi [...]
-</P><PRE CLASS="verbatim">>>> motifs = jdb.fetch_motifs(
-... collection = 'CORE',
-... tax_group = ['vertebrates', 'insects'],
-... tf_class = 'Winged Helix-Turn-Helix',
-... tf_family = ['Forkhead', 'Ets'],
-... min_ic = 12
-... )
->>> for motif in motifs:
-... pass # do something with the motif
-</PRE><!--TOC subsubsection Compatibility with Perl TFBS modules-->
-<H4 CLASS="subsubsection"><!--SEC ANCHOR -->Compatibility with Perl TFBS modules</H4><!--SEC END --><P>An important thing to note is that the JASPAR <CODE>Motif</CODE> class was designed to be compatible with the popular <A HREF="http://tfbs.genereg.net/">Perl TFBS modules</A>. Therefore some specifics about the choice of defaults for background and pseudocounts as well as how information content is computed and sequences searched for instances is based on this compatibility criteria. Th [...]
-<B>Choice of background:</B><BR>
-The Perl <CODE>TFBS</CODE> modules appear to allow a choice of custom background probabilities (although the documentation states that uniform background is assumed). However the default is to use a uniform background. Therefore it is recommended that you use a uniform background for computing the position-specific scoring matrix (PSSM). This is the default when using the Biopython <CODE>motifs</CODE> module.
-</LI><LI CLASS="li-itemize"><B>Choice of pseudocounts:</B><BR>
-By default, the Perl <CODE>TFBS</CODE> modules use a pseudocount equal to √<SPAN style="text-decoration:overline"><I>N</I></SPAN> * bg[nucleotide], where <I>N</I> represents the total number of sequences used to construct the matrix. To apply this same pseudocount formula, set the motif <CODE>pseudocounts</CODE> attribute using the <CODE>jaspar.calculate\_pseudcounts()</CODE> function:
-<PRE CLASS="verbatim">>>> motif.pseudocounts = motifs.jaspar.calculate_pseudocounts(motif)
-</PRE>Note that it is possible for the counts matrix to have an unequal number of sequences making up the columns. The pseudocount computation uses the average number of sequences making up the matrix. However, when <CODE>normalize</CODE> is called on the counts matrix, each count value in a column is divided by the total number of sequences making up that specific column, not by the average number of sequences. This differs from the Perl <CODE>TFBS</CODE> modules because the normalizati [...]
-</LI><LI CLASS="li-itemize"><B>Computation of matrix information content:</B><BR>
-The information content (IC) or specificity of a matrix is computed using the <CODE>mean</CODE> method of the <CODE>PositionSpecificScoringMatrix</CODE> class. However of note, in the Perl <CODE>TFBS</CODE> modules the default behaviour is to compute the IC without first applying pseudocounts, even though by default the PSSMs are computed using pseudocounts as described above.
-</LI><LI CLASS="li-itemize"><B>Searching for instances:</B><BR>
-Searching for instances with the Perl <CODE>TFBS</CODE> motifs was usually performed using a relative score threshold, i.e. a score in the range 0 to 1. In order to compute the absolute PSSM score corresponding to a relative score one can use the equation:
-<PRE CLASS="verbatim">>>> abs_score = (pssm.max - pssm.min) * rel_score + pssm.min
-</PRE>To convert the absolute score of an instance back to a relative score, one can use the equation:
-<PRE CLASS="verbatim">>>> rel_score = (abs_score - pssm.min) / (pssm.max - pssm.min)
-</PRE>For example, using the Arnt motif before, let’s search a sequence with a relative score threshold of 0.8.
-<PRE CLASS="verbatim">>>> test_seq=Seq("TAAGCGTGCACGCGCAACACGTGCATTA", unambiguous_dna)
->>> arnt.pseudocounts = motifs.jaspar.calculate_pseudocounts(arnt)
->>> pssm = arnt.pssm
->>> max_score = pssm.max
->>> min_score = pssm.min
->>> abs_score_threshold = (max_score - min_score) * 0.8 + min_score
->>> for position, score in pssm.search(test_seq,
- threshold=abs_score_threshold):
-... rel_score = (score - min_score) / (max_score - min_score)
-... print("Position %d: score = %5.3f, rel. score = %5.3f" % (
- position, score, rel_score))
-...
-Position 2: score = 5.362, rel. score = 0.801
-Position 8: score = 6.112, rel. score = 0.831
-Position -20: score = 7.103, rel. score = 0.870
-Position 17: score = 10.351, rel. score = 1.000
-Position -11: score = 10.351, rel. score = 1.000
-</PRE></LI></UL><!--TOC subsection MEME-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc173">13.2.2</A>  MEME</H3><!--SEC END --><P>MEME [<A HREF="#bailey1994">12</A>] is a tool for discovering motifs in a group of related
-DNA or protein sequences. It takes as input a group of DNA or protein sequences
-and outputs as many motifs as requested. Therefore, in contrast to JASPAR
-files, MEME output files typically contain multiple motifs. This is an example.</P><P>At the top of an output file generated by MEME shows some background information
-about the MEME and the version of MEME used:
-</P><PRE CLASS="verbatim">********************************************************************************
-MEME - Motif discovery tool
-********************************************************************************
-MEME version 3.0 (Release date: 2004/08/18 09:07:01)
-...
-</PRE><P>Further down, the input set of training sequences is recapitulated:
-</P><PRE CLASS="verbatim">********************************************************************************
-TRAINING SET
-********************************************************************************
-DATAFILE= INO_up800.s
-ALPHABET= ACGT
-Sequence name Weight Length Sequence name Weight Length
-------------- ------ ------ ------------- ------ ------
-CHO1 1.0000 800 CHO2 1.0000 800
-FAS1 1.0000 800 FAS2 1.0000 800
-ACC1 1.0000 800 INO1 1.0000 800
-OPI3 1.0000 800
-********************************************************************************
-</PRE><P>and the exact command line that was used:
-</P><PRE CLASS="verbatim">********************************************************************************
-COMMAND LINE SUMMARY
-********************************************************************************
-This information can also be useful in the event you wish to report a
-problem with the MEME software.
-
-command: meme -mod oops -dna -revcomp -nmotifs 2 -bfile yeast.nc.6.freq INO_up800.s
-...
-</PRE><P>Next is detailed information on each motif that was found:
-</P><PRE CLASS="verbatim">********************************************************************************
-MOTIF 1 width = 12 sites = 7 llr = 95 E-value = 2.0e-001
-********************************************************************************
---------------------------------------------------------------------------------
- Motif 1 Description
---------------------------------------------------------------------------------
-Simplified A :::9:a::::3:
-pos.-specific C ::a:9:11691a
-probability G ::::1::94:4:
-matrix T aa:1::9::11:
-</PRE><P>To parse this file (stored as <CODE>meme.dna.oops.txt</CODE>), use
-</P><PRE CLASS="verbatim">>>> handle = open("meme.dna.oops.txt")
->>> record = motifs.parse(handle, "meme")
->>> handle.close()
-</PRE><P>The <CODE>motifs.parse</CODE> command reads the complete file directly, so you can
-close the file after calling <CODE>motifs.parse</CODE>.
-The header information is stored in attributes:
-</P><PRE CLASS="verbatim">>>> record.version
-'3.0'
->>> record.datafile
-'INO_up800.s'
->>> record.command
-'meme -mod oops -dna -revcomp -nmotifs 2 -bfile yeast.nc.6.freq INO_up800.s'
->>> record.alphabet
-IUPACUnambiguousDNA()
->>> record.sequences
-['CHO1', 'CHO2', 'FAS1', 'FAS2', 'ACC1', 'INO1', 'OPI3']
-</PRE><P>The record is an object of the <CODE>Bio.motifs.meme.Record</CODE> class.
-The class inherits from list, and you can think of <CODE>record</CODE> as a list of Motif objects:
-</P><PRE CLASS="verbatim">>>> len(record)
-2
->>> motif = record[0]
->>> print(motif.consensus)
-TTCACATGCCGC
->>> print(motif.degenerate_consensus)
-TTCACATGSCNC
-</PRE><P>In addition to these generic motif attributes, each motif also stores its
-specific information as calculated by MEME. For example,
-</P><PRE CLASS="verbatim">>>> motif.num_occurrences
-7
->>> motif.length
-12
->>> evalue = motif.evalue
->>> print("%3.1g" % evalue)
-0.2
->>> motif.name
-'Motif 1'
-</PRE><P>In addition to using an index into the record, as we did above,
-you can also find it by its name:
-</P><PRE CLASS="verbatim">>>> motif = record['Motif 1']
-</PRE><P>Each motif has an attribute <CODE>.instances</CODE> with the sequence instances
-in which the motif was found, providing some information on each instance:
-</P><PRE CLASS="verbatim">>>> len(motif.instances)
-7
->>> motif.instances[0]
-Instance('TTCACATGCCGC', IUPACUnambiguousDNA())
->>> motif.instances[0].motif_name
-'Motif 1'
->>> motif.instances[0].sequence_name
-'INO1'
->>> motif.instances[0].start
-620
->>> motif.instances[0].strand
-'-'
->>> motif.instances[0].length
-12
->>> pvalue = motif.instances[0].pvalue
->>> print("%5.3g" % pvalue)
-1.85e-08
-</PRE><!--TOC subsubsection MAST-->
-<H4 CLASS="subsubsection"><!--SEC ANCHOR -->MAST</H4><!--SEC END --><!--TOC subsection TRANSFAC-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc174">13.2.3</A>  TRANSFAC</H3><!--SEC END --><P>TRANSFAC is a manually curated database of transcription factors, together
-with their genomic binding sites and DNA binding profiles [<A HREF="#matys2003">27</A>].
-While the file format used in the TRANSFAC database is nowadays also used
-by others, we will refer to it as the TRANSFAC file format.</P><P>A minimal file in the TRANSFAC format looks as follows:
-</P><PRE CLASS="verbatim">ID motif1
-P0 A C G T
-01 1 2 2 0 S
-02 2 1 2 0 R
-03 3 0 1 1 A
-04 0 5 0 0 C
-05 5 0 0 0 A
-06 0 0 4 1 G
-07 0 1 4 0 G
-08 0 0 0 5 T
-09 0 0 5 0 G
-10 0 1 2 2 K
-11 0 2 0 3 Y
-12 1 0 3 1 G
-//
-</PRE><P>This file shows the frequency matrix of motif <CODE>motif1</CODE> of 12 nucleotides.
-In general, one file in the TRANSFAC format can contain multiple motifs. For
-example, this is the contents of the example TRANSFAC file <CODE>transfac.dat</CODE>:
-</P><PRE CLASS="verbatim">VV EXAMPLE January 15, 2013
-XX
-//
-ID motif1
-P0 A C G T
-01 1 2 2 0 S
-02 2 1 2 0 R
-03 3 0 1 1 A
-...
-11 0 2 0 3 Y
-12 1 0 3 1 G
-//
-ID motif2
-P0 A C G T
-01 2 1 2 0 R
-02 1 2 2 0 S
-...
-09 0 0 0 5 T
-10 0 2 0 3 Y
-//
-</PRE><P>To parse a TRANSFAC file, use
-</P><PRE CLASS="verbatim">>>> handle = open("transfac.dat")
->>> record = motifs.parse(handle, "TRANSFAC")
->>> handle.close()
-</PRE><P>The overall version number, if available, is stored as <CODE>record.version</CODE>:
-</P><PRE CLASS="verbatim">>>> record.version
-'EXAMPLE January 15, 2013'
-</PRE><P>Each motif in <CODE>record</CODE> is in instance of the <CODE>Bio.motifs.transfac.Motif</CODE>
-class, which inherits both from the <CODE>Bio.motifs.Motif</CODE> class and
-from a Python dictionary. The dictionary uses the two-letter keys to
-store any additional information about the motif:
-</P><PRE CLASS="verbatim">>>> motif = record[0]
->>> motif.degenerate_consensus # Using the Bio.motifs.Motif method
-Seq('SRACAGGTGKYG', IUPACAmbiguousDNA())
->>> motif['ID'] # Using motif as a dictionary
-'motif1'
-</PRE><P>TRANSFAC files are typically much more elaborate than this example, containing
-lots of additional information about the motif. Table <A HREF="#table:transfaccodes">13.2.3</A>
-lists the two-letter field codes that are commonly found in TRANSFAC files:
-</P><BLOCKQUOTE CLASS="table"><DIV CLASS="center"><HR WIDTH="80%" SIZE=2></DIV>
-<A NAME="table:transfaccodes"></A>
-<DIV CLASS="center">
-<DIV CLASS="caption"><TABLE CELLSPACING=6 CELLPADDING=0><TR><TD VALIGN=top ALIGN=left>Table 13.1: Fields commonly found in TRANSFAC files</TD></TR>
-</TABLE></DIV>
-<TABLE BORDER=1 CELLSPACING=0 CELLPADDING=1><TR><TD ALIGN=left NOWRAP><CODE>AC</CODE></TD><TD ALIGN=left NOWRAP>Accession number</TD></TR>
-<TR><TD ALIGN=left NOWRAP><CODE>AS</CODE></TD><TD ALIGN=left NOWRAP>Accession numbers, secondary</TD></TR>
-<TR><TD ALIGN=left NOWRAP><CODE>BA</CODE></TD><TD ALIGN=left NOWRAP>Statistical basis</TD></TR>
-<TR><TD ALIGN=left NOWRAP><CODE>BF</CODE></TD><TD ALIGN=left NOWRAP>Binding factors</TD></TR>
-<TR><TD ALIGN=left NOWRAP><CODE>BS</CODE></TD><TD ALIGN=left NOWRAP>Factor binding sites underlying the matrix</TD></TR>
-<TR><TD ALIGN=left NOWRAP><CODE>CC</CODE></TD><TD ALIGN=left NOWRAP>Comments</TD></TR>
-<TR><TD ALIGN=left NOWRAP><CODE>CO</CODE></TD><TD ALIGN=left NOWRAP>Copyright notice</TD></TR>
-<TR><TD ALIGN=left NOWRAP><CODE>DE</CODE></TD><TD ALIGN=left NOWRAP>Short factor description</TD></TR>
-<TR><TD ALIGN=left NOWRAP><CODE>DR</CODE></TD><TD ALIGN=left NOWRAP>External databases</TD></TR>
-<TR><TD ALIGN=left NOWRAP><CODE>DT</CODE></TD><TD ALIGN=left NOWRAP>Date created/updated</TD></TR>
-<TR><TD ALIGN=left NOWRAP><CODE>HC</CODE></TD><TD ALIGN=left NOWRAP>Subfamilies</TD></TR>
-<TR><TD ALIGN=left NOWRAP><CODE>HP</CODE></TD><TD ALIGN=left NOWRAP>Superfamilies</TD></TR>
-<TR><TD ALIGN=left NOWRAP><CODE>ID</CODE></TD><TD ALIGN=left NOWRAP>Identifier</TD></TR>
-<TR><TD ALIGN=left NOWRAP><CODE>NA</CODE></TD><TD ALIGN=left NOWRAP>Name of the binding factor</TD></TR>
-<TR><TD ALIGN=left NOWRAP><CODE>OC</CODE></TD><TD ALIGN=left NOWRAP>Taxonomic classification</TD></TR>
-<TR><TD ALIGN=left NOWRAP><CODE>OS</CODE></TD><TD ALIGN=left NOWRAP>Species/Taxon</TD></TR>
-<TR><TD ALIGN=left NOWRAP><CODE>OV</CODE></TD><TD ALIGN=left NOWRAP>Older version</TD></TR>
-<TR><TD ALIGN=left NOWRAP><CODE>PV</CODE></TD><TD ALIGN=left NOWRAP>Preferred version</TD></TR>
-<TR><TD ALIGN=left NOWRAP><CODE>TY</CODE></TD><TD ALIGN=left NOWRAP>Type</TD></TR>
-<TR><TD ALIGN=left NOWRAP><CODE>XX</CODE></TD><TD ALIGN=left NOWRAP>Empty line; these are not stored in the Record.</TD></TR>
-</TABLE>
-</DIV>
-<DIV CLASS="center"><HR WIDTH="80%" SIZE=2></DIV></BLOCKQUOTE><P>Each motif also has an attribute <CODE>.references</CODE> containing the
-references associated with the motif, using these two-letter keys:</P><BLOCKQUOTE CLASS="table"><DIV CLASS="center"><HR WIDTH="80%" SIZE=2></DIV>
-<DIV CLASS="center">
-<DIV CLASS="caption"><TABLE CELLSPACING=6 CELLPADDING=0><TR><TD VALIGN=top ALIGN=left>Table 13.2: Fields used to store references in TRANSFAC files</TD></TR>
-</TABLE></DIV>
-<TABLE BORDER=1 CELLSPACING=0 CELLPADDING=1><TR><TD ALIGN=left NOWRAP><CODE>RN</CODE></TD><TD ALIGN=left NOWRAP>Reference number</TD></TR>
-<TR><TD ALIGN=left NOWRAP><CODE>RA</CODE></TD><TD ALIGN=left NOWRAP>Reference authors</TD></TR>
-<TR><TD ALIGN=left NOWRAP><CODE>RL</CODE></TD><TD ALIGN=left NOWRAP>Reference data</TD></TR>
-<TR><TD ALIGN=left NOWRAP><CODE>RT</CODE></TD><TD ALIGN=left NOWRAP>Reference title</TD></TR>
-<TR><TD ALIGN=left NOWRAP><CODE>RX</CODE></TD><TD ALIGN=left NOWRAP>PubMed ID</TD></TR>
-</TABLE>
-</DIV>
-<DIV CLASS="center"><HR WIDTH="80%" SIZE=2></DIV></BLOCKQUOTE><P>Printing the motifs writes them out in their native TRANSFAC format:
-</P><PRE CLASS="verbatim">>>> print(record)
-VV EXAMPLE January 15, 2013
-XX
-//
-ID motif1
-XX
-P0 A C G T
-01 1 2 2 0 S
-02 2 1 2 0 R
-03 3 0 1 1 A
-04 0 5 0 0 C
-05 5 0 0 0 A
-06 0 0 4 1 G
-07 0 1 4 0 G
-08 0 0 0 5 T
-09 0 0 5 0 G
-10 0 1 2 2 K
-11 0 2 0 3 Y
-12 1 0 3 1 G
-XX
-//
-ID motif2
-XX
-P0 A C G T
-01 2 1 2 0 R
-02 1 2 2 0 S
-03 0 5 0 0 C
-04 3 0 1 1 A
-05 0 0 4 1 G
-06 5 0 0 0 A
-07 0 1 4 0 G
-08 0 0 5 0 G
-09 0 0 0 5 T
-10 0 2 0 3 Y
-XX
-//
-<BLANKLINE>
-</PRE><P>You can export the motifs in the TRANSFAC format by capturing this output
-in a string and saving it in a file:
-</P><PRE CLASS="verbatim">>>> text = str(record)
->>> handle = open("mytransfacfile.dat", 'w')
->>> handle.write(text)
->>> handle.close()
-</PRE><!--TOC section Writing motifs-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc175">13.3</A>  Writing motifs</H2><!--SEC END --><P>Speaking of exporting, let’s look at export functions in general.
-We can use the <CODE>format</CODE> method to write the motif in the simple JASPAR <CODE>pfm</CODE> format:
-</P><PRE CLASS="verbatim">>>> print(arnt.format("pfm"))
- 4.00 19.00 0.00 0.00 0.00 0.00
- 16.00 0.00 20.00 0.00 0.00 0.00
- 0.00 1.00 0.00 20.00 0.00 20.00
- 0.00 0.00 0.00 0.00 20.00 0.00
-</PRE><P>Similarly, we can use <CODE>format</CODE> to write the motif in the JASPAR <CODE>jaspar</CODE> format:
-</P><PRE CLASS="verbatim">>>> print(arnt.format("jaspar"))
->MA0004.1 Arnt
-A [ 4.00 19.00 0.00 0.00 0.00 0.00]
-C [ 16.00 0.00 20.00 0.00 0.00 0.00]
-G [ 0.00 1.00 0.00 20.00 0.00 20.00]
-T [ 0.00 0.00 0.00 0.00 20.00 0.00]
-</PRE><P>To write the motif in a TRANSFAC-like matrix format, use
-</P><PRE CLASS="verbatim">>>> print(m.format("transfac"))
-P0 A C G T
-01 3 0 0 4 W
-02 7 0 0 0 A
-03 0 5 0 2 C
-04 2 2 3 0 V
-05 1 6 0 0 C
-XX
-//
-<BLANKLINE>
-</PRE><P>To write out multiple motifs, you can use <CODE>motifs.write</CODE>.
-This function can be used regardless of whether the motifs originated from a TRANSFAC file. For example,
-</P><PRE CLASS="verbatim">>>> two_motifs = [arnt, srf]
->>> print(motifs.write(two_motifs, 'transfac'))
-P0 A C G T
-01 4 16 0 0 C
-02 19 0 1 0 A
-03 0 20 0 0 C
-04 0 0 20 0 G
-05 0 0 0 20 T
-06 0 0 20 0 G
-XX
-//
-P0 A C G T
-01 2 1 39 4 G
-02 9 33 2 2 C
-03 0 45 1 0 C
-04 1 45 0 0 C
-05 32 1 0 13 A
-06 3 1 0 42 T
-07 46 0 0 0 A
-08 1 0 0 45 T
-09 43 0 0 3 A
-10 15 1 0 30 T
-11 2 0 44 0 G
-12 2 1 43 0 G
-XX
-//
-<BLANKLINE>
-</PRE><P>Or, to write multiple motifs in the <CODE>jaspar</CODE> format:
-</P><PRE CLASS="verbatim">>>> two_motifs = [arnt, mef2a]
->>> print(motifs.write(two_motifs, "jaspar"))
->MA0004.1 Arnt
-A [ 4.00 19.00 0.00 0.00 0.00 0.00]
-C [ 16.00 0.00 20.00 0.00 0.00 0.00]
-G [ 0.00 1.00 0.00 20.00 0.00 20.00]
-T [ 0.00 0.00 0.00 0.00 20.00 0.00]
->MA0052.1 MEF2A
-A [ 1.00 0.00 57.00 2.00 9.00 6.00 37.00 2.00 56.00 6.00]
-C [ 50.00 0.00 1.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00]
-G [ 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2.00 50.00]
-T [ 7.00 58.00 0.00 55.00 49.00 52.00 21.00 56.00 0.00 2.00]
-</PRE><!--TOC section Position-Weight Matrices-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc176">13.4</A>  Position-Weight Matrices</H2><!--SEC END --><P>The <CODE>.counts</CODE> attribute of a Motif object shows how often each
-nucleotide appeared at each position along the alignment. We can
-normalize this matrix by dividing by the number of instances in the
-alignment, resulting in the probability of each nucleotide at each
-position along the alignment. We refer to these probabilities as
-the position-weight matrix. However, beware that in the literature
-this term may also be used to refer to the position-specific scoring
-matrix, which we discuss below.</P><P>Usually, pseudocounts are added to each position before normalizing.
-This avoids overfitting of the position-weight matrix to the limited
-number of motif instances in the alignment, and can also prevent
-probabilities from becoming zero. To add a fixed pseudocount to all
-nucleotides at all positions, specify a number for the
-<CODE>pseudocounts</CODE> argument:
-</P><PRE CLASS="verbatim">>>> pwm = m.counts.normalize(pseudocounts=0.5)
->>> print(pwm)
- 0 1 2 3 4
-A: 0.39 0.83 0.06 0.28 0.17
-C: 0.06 0.06 0.61 0.28 0.72
-G: 0.06 0.06 0.06 0.39 0.06
-T: 0.50 0.06 0.28 0.06 0.06
-<BLANKLINE>
-</PRE><P>Alternatively, <CODE>pseudocounts</CODE> can be a dictionary specifying the
-pseudocounts for each nucleotide. For example, as the GC content of
-the human genome is about 40%, you may want to choose the
-pseudocounts accordingly:
-</P><PRE CLASS="verbatim">>>> pwm = m.counts.normalize(pseudocounts={'A':0.6, 'C': 0.4, 'G': 0.4, 'T': 0.6})
->>> print(pwm)
- 0 1 2 3 4
-A: 0.40 0.84 0.07 0.29 0.18
-C: 0.04 0.04 0.60 0.27 0.71
-G: 0.04 0.04 0.04 0.38 0.04
-T: 0.51 0.07 0.29 0.07 0.07
-<BLANKLINE>
-</PRE><P>The position-weight matrix has its own methods to calculate the
-consensus, anticonsensus, and degenerate consensus sequences:
-</P><PRE CLASS="verbatim">>>> pwm.consensus
-Seq('TACGC', IUPACUnambiguousDNA())
->>> pwm.anticonsensus
-Seq('GGGTG', IUPACUnambiguousDNA())
->>> pwm.degenerate_consensus
-Seq('WACNC', IUPACAmbiguousDNA())
-</PRE><P>Note that due to the pseudocounts, the degenerate consensus sequence
-calculated from the position-weight matrix is slightly different
-from the degenerate consensus sequence calculated from the instances
-in the motif:
-</P><PRE CLASS="verbatim">>>> m.degenerate_consensus
-Seq('WACVC', IUPACAmbiguousDNA())
-</PRE><P>The reverse complement of the position-weight matrix can be calculated directly from the <CODE>pwm</CODE>:
-</P><PRE CLASS="verbatim">>>> rpwm = pwm.reverse_complement()
->>> print(rpwm)
- 0 1 2 3 4
-A: 0.07 0.07 0.29 0.07 0.51
-C: 0.04 0.38 0.04 0.04 0.04
-G: 0.71 0.27 0.60 0.04 0.04
-T: 0.18 0.29 0.07 0.84 0.40
-<BLANKLINE>
-</PRE><!--TOC section Position-Specific Scoring Matrices-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc177">13.5</A>  Position-Specific Scoring Matrices</H2><!--SEC END --><P>Using the background distribution and PWM with pseudo-counts added,
-it’s easy to compute the log-odds ratios, telling us what are the log
-odds of a particular symbol to be coming from a motif against the
-background. We can use the <CODE>.log_odds()</CODE> method on the position-weight
-matrix:
-</P><PRE CLASS="verbatim">>>> pssm = pwm.log_odds()
->>> print(pssm)
- 0 1 2 3 4
-A: 0.68 1.76 -1.91 0.21 -0.49
-C: -2.49 -2.49 1.26 0.09 1.51
-G: -2.49 -2.49 -2.49 0.60 -2.49
-T: 1.03 -1.91 0.21 -1.91 -1.91
-<BLANKLINE>
-</PRE><P>Here we can see positive values for symbols more frequent in the motif
-than in the background and negative for symbols more frequent in the
-background. 0.0 means that it’s equally likely to see a symbol in the
-background and in the motif.</P><P>This assumes that A, C, G, and T are equally likely in the background. To
-calculate the position-specific scoring matrix against a background with
-unequal probabilities for A, C, G, T, use the <CODE>background</CODE> argument.
-For example, against a background with a 40% GC content, use
-</P><PRE CLASS="verbatim">>>> background = {'A':0.3,'C':0.2,'G':0.2,'T':0.3}
->>> pssm = pwm.log_odds(background)
->>> print(pssm)
- 0 1 2 3 4
-A: 0.42 1.49 -2.17 -0.05 -0.75
-C: -2.17 -2.17 1.58 0.42 1.83
-G: -2.17 -2.17 -2.17 0.92 -2.17
-T: 0.77 -2.17 -0.05 -2.17 -2.17
-<BLANKLINE>
-</PRE><P>The maximum and minimum score obtainable from the PSSM are stored in the
-<CODE>.max</CODE> and <CODE>.min</CODE> properties:
-</P><PRE CLASS="verbatim">>>> print("%4.2f" % pssm.max)
-6.59
->>> print("%4.2f" % pssm.min)
--10.85
-</PRE><P>The mean and standard deviation of the PSSM scores with respect to a specific
-background are calculated by the <CODE>.mean</CODE> and <CODE>.std</CODE> methods.
-</P><PRE CLASS="verbatim">>>> mean = pssm.mean(background)
->>> std = pssm.std(background)
->>> print("mean = %0.2f, standard deviation = %0.2f" % (mean, std))
-mean = 3.21, standard deviation = 2.59
-</PRE><P>A uniform background is used if <CODE>background</CODE> is not specified.
-The mean is particularly important, as its value is equal to the
-Kullback-Leibler divergence or relative entropy, and is a measure for the
-information content of the motif compared to the background. As in Biopython
-the base-2 logarithm is used in the calculation of the log-odds scores, the
-information content has units of bits.</P><P>The <CODE>.reverse_complement</CODE>, <CODE>.consensus</CODE>, <CODE>.anticonsensus</CODE>, and
-<CODE>.degenerate_consensus</CODE> methods can be applied directly to PSSM objects.</P><!--TOC section Searching for instances-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc178">13.6</A>  Searching for instances</H2><!--SEC END --><P>
-<A NAME="sec:search"></A></P><P>The most frequent use for a motif is to find its instances in some
-sequence. For the sake of this section, we will use an artificial sequence like this:</P><PRE CLASS="verbatim">>>> test_seq=Seq("TACACTGCATTACAACCCAAGCATTA", m.alphabet)
->>> len(test_seq)
-26
-</PRE><!--TOC subsection Searching for exact matches-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc179">13.6.1</A>  Searching for exact matches</H3><!--SEC END --><P>The simplest way to find instances, is to look for exact matches of
-the true instances of the motif:
-</P><PRE CLASS="verbatim">>>> for pos, seq in m.instances.search(test_seq):
-... print("%i %s" % (pos, seq))
-...
-0 TACAC
-10 TACAA
-13 AACCC
-</PRE><P>We can do the same with the reverse complement (to find instances on the complementary strand):
-</P><PRE CLASS="verbatim">>>> for pos, seq in r.instances.search(test_seq):
-... print("%i %s" % (pos, seq))
-...
-6 GCATT
-20 GCATT
-</PRE><!--TOC subsection Searching for matches using the PSSM score-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc180">13.6.2</A>  Searching for matches using the PSSM score</H3><!--SEC END --><P>It’s just as easy to look for positions, giving rise to high log-odds scores against our motif:
-</P><PRE CLASS="verbatim">>>> for position, score in pssm.search(test_seq, threshold=3.0):
-... print("Position %d: score = %5.3f" % (position, score))
-...
-Position 0: score = 5.622
-Position -20: score = 4.601
-Position 10: score = 3.037
-Position 13: score = 5.738
-Position -6: score = 4.601
-</PRE><P>The negative positions refer to instances of the motif found on the
-reverse strand of the test sequence, and follow the Python convention
-on negative indices. Therefore, the instance of the motif at <CODE>pos</CODE>
-is located at <CODE>test_seq[pos:pos+len(m)]</CODE> both for positive and for
-negative values of <CODE>pos</CODE>.</P><P>You may notice the threshold parameter, here set arbitrarily to
-3.0. This is in <I>log</I><SUB>2</SUB>, so we are now looking only for words, which
-are eight times more likely to occur under the motif model than in the
-background. The default threshold is 0.0, which selects everything
-that looks more like the motif than the background.</P><P>You can also calculate the scores at all positions along the sequence:
-</P><PRE CLASS="verbatim">>>> pssm.calculate(test_seq)
-array([ 5.62230396, -5.6796999 , -3.43177247, 0.93827754,
- -6.84962511, -2.04066086, -10.84962463, -3.65614533,
- -0.03370807, -3.91102552, 3.03734159, -2.14918518,
- -0.6016975 , 5.7381525 , -0.50977498, -3.56422281,
- -8.73414803, -0.09919716, -0.6016975 , -2.39429784,
- -10.84962463, -3.65614533], dtype=float32)
-</PRE><P>In general, this is the fastest way to calculate PSSM scores.
-The scores returned by <CODE>pssm.calculate</CODE> are for the forward strand
-only. To obtain the scores on the reverse strand, you can take the reverse
-complement of the PSSM:
-</P><PRE CLASS="verbatim">>>> rpssm = pssm.reverse_complement()
->>> rpssm.calculate(test_seq)
-array([ -9.43458748, -3.06172252, -7.18665981, -7.76216221,
- -2.04066086, -4.26466274, 4.60124254, -4.2480607 ,
- -8.73414803, -2.26503372, -6.49598789, -5.64668512,
- -8.73414803, -10.84962463, -4.82356262, -4.82356262,
- -5.64668512, -8.73414803, -4.15613794, -5.6796999 ,
- 4.60124254, -4.2480607 ], dtype=float32)
-</PRE><!--TOC subsection Selecting a score threshold-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc181">13.6.3</A>  Selecting a score threshold</H3><!--SEC END --><P>If you want to use a less arbitrary way of selecting thresholds, you
-can explore the distribution of PSSM scores. Since the space for a score
-distribution grows exponentially with motif length, we are using an
-approximation with a given precision to keep computation cost manageable:
-</P><PRE CLASS="verbatim">>>> distribution = pssm.distribution(background=background, precision=10**4)
-</PRE><P>The <CODE>distribution</CODE> object can be used to determine a number of different thresholds.
-We can specify the requested false-positive rate (probability of “finding” a motif instance in background generated sequence):
-</P><PRE CLASS="verbatim">>>> threshold = distribution.threshold_fpr(0.01)
->>> print("%5.3f" % threshold)
-4.009
-</PRE><P>or the false-negative rate (probability of “not finding” an instance generated from the motif):
-</P><PRE CLASS="verbatim">>>> threshold = distribution.threshold_fnr(0.1)
->>> print("%5.3f" % threshold)
--0.510
-</PRE><P>or a threshold (approximately) satisfying some relation between the false-positive rate and the false-negative rate (fnr/fpr≃ <I>t</I>):
-</P><PRE CLASS="verbatim">>>> threshold = distribution.threshold_balanced(1000)
->>> print("%5.3f" % threshold)
-6.241
-</PRE><P>or a threshold satisfying (roughly) the equality between the
-false-positive rate and the −<I>log</I> of the information content (as used
-in patser software by Hertz and Stormo):
-</P><PRE CLASS="verbatim">>>> threshold = distribution.threshold_patser()
->>> print("%5.3f" % threshold)
-0.346
-</PRE><P>For example, in case of our motif, you can get the threshold giving
-you exactly the same results (for this sequence) as searching for
-instances with balanced threshold with rate of 1000.
-</P><PRE CLASS="verbatim">>>> threshold = distribution.threshold_fpr(0.01)
->>> print("%5.3f" % threshold)
-4.009
->>> for position, score in pssm.search(test_seq, threshold=threshold):
-... print("Position %d: score = %5.3f" % (position, score))
-...
-Position 0: score = 5.622
-Position -20: score = 4.601
-Position 13: score = 5.738
-Position -6: score = 4.601
-</PRE><!--TOC section Each motif object has an associated Position-Specific Scoring Matrix-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc182">13.7</A>  Each motif object has an associated Position-Specific Scoring Matrix</H2><!--SEC END --><P>To facilitate searching for potential TFBSs using PSSMs, both the position-weight matrix and the position-specific scoring matrix are associated with each motif. Using the Arnt motif as an example:
-</P><PRE CLASS="verbatim">>>> from Bio import motifs
->>> with open("Arnt.sites") as handle:
-... motif = motifs.read(handle, 'sites')
-...
->>> print(motif.counts)
- 0 1 2 3 4 5
-A: 4.00 19.00 0.00 0.00 0.00 0.00
-C: 16.00 0.00 20.00 0.00 0.00 0.00
-G: 0.00 1.00 0.00 20.00 0.00 20.00
-T: 0.00 0.00 0.00 0.00 20.00 0.00
-<BLANKLINE>
->>> print(motif.pwm)
- 0 1 2 3 4 5
-A: 0.20 0.95 0.00 0.00 0.00 0.00
-C: 0.80 0.00 1.00 0.00 0.00 0.00
-G: 0.00 0.05 0.00 1.00 0.00 1.00
-T: 0.00 0.00 0.00 0.00 1.00 0.00
-<BLANKLINE>
-</PRE><PRE CLASS="verbatim">>>> print(motif.pssm)
- 0 1 2 3 4 5
-A: -0.32 1.93 -inf -inf -inf -inf
-C: 1.68 -inf 2.00 -inf -inf -inf
-G: -inf -2.32 -inf 2.00 -inf 2.00
-T: -inf -inf -inf -inf 2.00 -inf
-<BLANKLINE>
-</PRE><P>The negative infinities appear here because the corresponding entry in the frequency matrix is 0, and we are using zero pseudocounts by default:
-</P><PRE CLASS="verbatim">>>> for letter in "ACGT":
-... print("%s: %4.2f" % (letter, motif.pseudocounts[letter]))
-...
-A: 0.00
-C: 0.00
-G: 0.00
-T: 0.00
-</PRE><P>If you change the <CODE>.pseudocounts</CODE> attribute, the position-frequency matrix and the position-specific scoring matrix are recalculated automatically:
-</P><PRE CLASS="verbatim">>>> motif.pseudocounts = 3.0
->>> for letter in "ACGT":
-... print("%s: %4.2f" % (letter, motif.pseudocounts[letter]))
-...
-A: 3.00
-C: 3.00
-G: 3.00
-T: 3.00
-</PRE><PRE CLASS="verbatim">>>> print(motif.pwm)
- 0 1 2 3 4 5
-A: 0.22 0.69 0.09 0.09 0.09 0.09
-C: 0.59 0.09 0.72 0.09 0.09 0.09
-G: 0.09 0.12 0.09 0.72 0.09 0.72
-T: 0.09 0.09 0.09 0.09 0.72 0.09
-<BLANKLINE>
-</PRE><PRE CLASS="verbatim">>>> print(motif.pssm)
- 0 1 2 3 4 5
-A: -0.19 1.46 -1.42 -1.42 -1.42 -1.42
-C: 1.25 -1.42 1.52 -1.42 -1.42 -1.42
-G: -1.42 -1.00 -1.42 1.52 -1.42 1.52
-T: -1.42 -1.42 -1.42 -1.42 1.52 -1.42
-<BLANKLINE>
-</PRE><P>You can also set the <CODE>.pseudocounts</CODE> to a dictionary over the four nucleotides if you want to use different pseudocounts for them. Setting <CODE>motif.pseudocounts</CODE> to <CODE>None</CODE> resets it to its default value of zero.</P><P>The position-specific scoring matrix depends on the background distribution, which is uniform by default:
-</P><PRE CLASS="verbatim">>>> for letter in "ACGT":
-... print("%s: %4.2f" % (letter, motif.background[letter]))
-...
-A: 0.25
-C: 0.25
-G: 0.25
-T: 0.25
-</PRE><P>Again, if you modify the background distribution, the position-specific scoring matrix is recalculated:
-</P><PRE CLASS="verbatim">>>> motif.background = {'A': 0.2, 'C': 0.3, 'G': 0.3, 'T': 0.2}
->>> print(motif.pssm)
- 0 1 2 3 4 5
-A: 0.13 1.78 -1.09 -1.09 -1.09 -1.09
-C: 0.98 -1.68 1.26 -1.68 -1.68 -1.68
-G: -1.68 -1.26 -1.68 1.26 -1.68 1.26
-T: -1.09 -1.09 -1.09 -1.09 1.85 -1.09
-<BLANKLINE>
-</PRE><P>Setting <CODE>motif.background</CODE> to <CODE>None</CODE> resets it to a uniform distribution:
-</P><PRE CLASS="verbatim">>>> motif.background = None
->>> for letter in "ACGT":
-... print("%s: %4.2f" % (letter, motif.background[letter]))
-...
-A: 0.25
-C: 0.25
-G: 0.25
-T: 0.25
-</PRE><P>If you set <CODE>motif.background</CODE> equal to a single value, it will be interpreted as the GC content:
-</P><PRE CLASS="verbatim">>>> motif.background = 0.8
->>> for letter in "ACGT":
-... print("%s: %4.2f" % (letter, motif.background[letter]))
-...
-A: 0.10
-C: 0.40
-G: 0.40
-T: 0.10
-</PRE><P>Note that you can now calculate the mean of the PSSM scores over the background against which it was computed:
-</P><PRE CLASS="verbatim">>>> print("%f" % motif.pssm.mean(motif.background))
-4.703928
-</PRE><P>as well as its standard deviation:
-</P><PRE CLASS="verbatim">>>> print("%f" % motif.pssm.std(motif.background))
-3.290900
-</PRE><P>and its distribution:
-</P><PRE CLASS="verbatim">>>> distribution = motif.pssm.distribution(background=motif.background)
->>> threshold = distribution.threshold_fpr(0.01)
->>> print("%f" % threshold)
-3.854375
-</PRE><P>Note that the position-weight matrix and the position-specific scoring matrix are recalculated each time you call <CODE>motif.pwm</CODE> or <CODE>motif.pssm</CODE>, respectively. If speed is an issue and you want to use the PWM or PSSM repeatedly, you can save them as a variable, as in
-</P><PRE CLASS="verbatim">>>> pssm = motif.pssm
-</PRE><!--TOC section Comparing motifs-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc183">13.8</A>  Comparing motifs</H2><!--SEC END --><P>
-<A NAME="sec:comp"></A>
-Once we have more than one motif, we might want to compare them.</P><P>Before we start comparing motifs, I should point out that motif
-boundaries are usually quite arbitrary. This means we often need
-to compare motifs of different lengths, so comparison needs to involve
-some kind of alignment. This means we have to take into account two things:
-</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-alignment of motifs
-</LI><LI CLASS="li-itemize">some function to compare aligned motifs
-</LI></UL><P>
-To align the motifs, we use ungapped alignment of PSSMs and substitute zeros
-for any missing columns at the beginning and end of the matrices. This means
-that effectively we are using the background distribution for columns missing
-from the PSSM.
-The distance function then returns the minimal distance between motifs, as
-well as the corresponding offset in their alignment.</P><P>To give an example, let us first load another motif,
-which is similar to our test motif <CODE>m</CODE>:
-</P><PRE CLASS="verbatim">>>> with open("REB1.pfm") as handle:
-... m_reb1 = motifs.read(handle, "pfm")
-...
->>> m_reb1.consensus
-Seq('GTTACCCGG', IUPACUnambiguousDNA())
->>> print(m_reb1.counts)
- 0 1 2 3 4 5 6 7 8
-A: 30.00 0.00 0.00 100.00 0.00 0.00 0.00 0.00 15.00
-C: 10.00 0.00 0.00 0.00 100.00 100.00 100.00 0.00 15.00
-G: 50.00 0.00 0.00 0.00 0.00 0.00 0.00 60.00 55.00
-T: 10.00 100.00 100.00 0.00 0.00 0.00 0.00 40.00 15.00
-<BLANKLINE>
-</PRE><P>To make the motifs comparable, we choose the same values for the pseudocounts and the background distribution as our motif <CODE>m</CODE>:
-</P><PRE CLASS="verbatim">>>> m_reb1.pseudocounts = {'A':0.6, 'C': 0.4, 'G': 0.4, 'T': 0.6}
->>> m_reb1.background = {'A':0.3,'C':0.2,'G':0.2,'T':0.3}
->>> pssm_reb1 = m_reb1.pssm
->>> print(pssm_reb1)
- 0 1 2 3 4 5 6 7 8
-A: 0.00 -5.67 -5.67 1.72 -5.67 -5.67 -5.67 -5.67 -0.97
-C: -0.97 -5.67 -5.67 -5.67 2.30 2.30 2.30 -5.67 -0.41
-G: 1.30 -5.67 -5.67 -5.67 -5.67 -5.67 -5.67 1.57 1.44
-T: -1.53 1.72 1.72 -5.67 -5.67 -5.67 -5.67 0.41 -0.97
-<BLANKLINE>
-</PRE><P>We’ll compare these motifs using the Pearson correlation.
-Since we want it to resemble a distance measure, we actually take
-1−<I>r</I>, where <I>r</I> is the Pearson correlation coefficient (PCC):
-</P><PRE CLASS="verbatim">>>> distance, offset = pssm.dist_pearson(pssm_reb1)
->>> print("distance = %5.3g" % distance)
-distance = 0.239
->>> print(offset)
--2
-</PRE><P>This means that the best PCC between motif <CODE>m</CODE> and <CODE>m_reb1</CODE> is obtained with the following alignment:
-</P><PRE CLASS="verbatim">m: bbTACGCbb
-m_reb1: GTTACCCGG
-</PRE><P>where <CODE>b</CODE> stands for background distribution. The PCC itself is
-roughly 1−0.239=0.761.</P><!--TOC section <EM>De novo</EM> motif finding-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc184">13.9</A>  <EM>De novo</EM> motif finding</H2><!--SEC END --><P>
-<A NAME="sec:find"></A></P><P>Currently, Biopython has only limited support for <EM>de novo</EM> motif
-finding. Namely, we support running and parsing of AlignAce and
-MEME. Since the number of motif finding tools is growing rapidly,
-contributions of new parsers are welcome.</P><!--TOC subsection MEME-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc185">13.9.1</A>  MEME</H3><!--SEC END --><P>
-<A NAME="sec:meme"></A></P><P>Let’s assume, you have run MEME on sequences of your choice with your
-favorite parameters and saved the output in the file
-<CODE>meme.out</CODE>. You can retrieve the motifs reported by MEME by
-running the following piece of code:</P><PRE CLASS="verbatim">>>> from Bio import motifs
->>> with open("meme.out") as handle:
-... motifsM = motifs.parse(handle, "meme")
-...
-</PRE><PRE CLASS="verbatim">>>> motifsM
-[<Bio.motifs.meme.Motif object at 0xc356b0>]
-</PRE><P>Besides the most wanted list of motifs, the result object contains more useful information, accessible through properties with self-explanatory names:
-</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-<CODE>.alphabet</CODE>
-</LI><LI CLASS="li-itemize"><CODE>.datafile</CODE>
-</LI><LI CLASS="li-itemize"><CODE>.sequence_names</CODE>
-</LI><LI CLASS="li-itemize"><CODE>.version</CODE>
-</LI><LI CLASS="li-itemize"><CODE>.command</CODE>
-</LI></UL><P>The motifs returned by the MEME Parser can be treated exactly like regular
-Motif objects (with instances), they also provide some extra
-functionality, by adding additional information about the instances.</P><PRE CLASS="verbatim">>>> motifsM[0].consensus
-Seq('CTCAATCGTA', IUPACUnambiguousDNA())
->>> motifsM[0].instances[0].sequence_name
-'SEQ10;'
->>> motifsM[0].instances[0].start
-3
->>> motifsM[0].instances[0].strand
-'+'
-</PRE><PRE CLASS="verbatim">>>> motifsM[0].instances[0].pvalue
-8.71e-07
-</PRE><!--TOC subsection AlignAce-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc186">13.9.2</A>  AlignAce</H3><!--SEC END --><P>
-<A NAME="sec:alignace"></A></P><P>We can do very similar things with the AlignACE program. Assume, you have
-your output in the file <CODE>alignace.out</CODE>. You can parse your output
-with the following code:</P><PRE CLASS="verbatim">>>> from Bio import motifs
->>> with open("alignace.out") as handle:
-... motifsA = motifs.parse(handle, "alignace")
-...
-</PRE><P>Again, your motifs behave as they should:
-</P><PRE CLASS="verbatim">>>> motifsA[0].consensus
-Seq('TCTACGATTGAG', IUPACUnambiguousDNA())
-</PRE><P>In fact you can even see, that AlignAce found a very similar motif as
-MEME. It is just a longer version of a reverse complement of the MEME
-motif:
-</P><PRE CLASS="verbatim">>>> motifsM[0].reverse_complement().consensus
-Seq('TACGATTGAG', IUPACUnambiguousDNA())
-</PRE><P>If you have AlignAce installed on the same machine, you can also run
-it directly from Biopython. A short example of how this can be done is
-shown below (other parameters can be specified as keyword parameters):</P><PRE CLASS="verbatim">>>> command="/opt/bin/AlignACE"
->>> input_file="test.fa"
->>> from Bio.motifs.applications import AlignAceCommandline
->>> cmd = AlignAceCommandline(cmd=command, input=input_file, gcback=0.6, numcols=10)
->>> stdout, stderr= cmd()
-</PRE><P>Since AlignAce prints all of its output to standard output, you can get
-to your motifs by parsing the first part of the result:
-</P><PRE CLASS="verbatim">>>> motifs = motifs.parse(stdout, "alignace")
-</PRE><!--TOC section Useful links -->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc187">13.10</A>  Useful links </H2><!--SEC END --><P>
-<A NAME="sec:links"></A></P><UL CLASS="itemize"><LI CLASS="li-itemize">
-<A HREF="http://en.wikipedia.org/wiki/Sequence_motif">Sequence motif</A> in wikipedia
-</LI><LI CLASS="li-itemize"><A HREF="http://en.wikipedia.org/wiki/Position_weight_matrix">PWM</A> in wikipedia
-</LI><LI CLASS="li-itemize"><A HREF="http://en.wikipedia.org/wiki/Consensus_sequence">Consensus sequence</A> in wikipedia
-</LI><LI CLASS="li-itemize"><A HREF="http://bio.cs.washington.edu/assessment/">Comparison of different motif finding programs</A>
-</LI></UL><!--TOC chapter Cluster analysis-->
-<H1 CLASS="chapter"><!--SEC ANCHOR --><A NAME="htoc188">Chapter 14</A>  Cluster analysis</H1><!--SEC END --><P>Cluster analysis is the grouping of items into clusters based on the similarity of the items to each other. In bioinformatics, clustering is widely used in gene expression data analysis to find groups of genes with similar gene expression profiles. This may identify functionally related genes, as well as suggest the function of presently unknown genes.</P><P>The B [...]
-</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-Hierarchical clustering (pairwise centroid-, single-, complete-, and average-linkage);
-</LI><LI CLASS="li-itemize"><I>k</I>-means, <I>k</I>-medians, and <I>k</I>-medoids clustering;
-</LI><LI CLASS="li-itemize">Self-Organizing Maps;
-</LI><LI CLASS="li-itemize">Principal Component Analysis.
-</LI></UL><!--TOC subsection Data representation-->
-<H3 CLASS="subsection"><!--SEC ANCHOR -->Data representation</H3><!--SEC END --><P>The data to be clustered are represented by a <I>n</I> × <I>m</I> Numerical Python array <CODE>data</CODE>. Within the context of gene expression data clustering, typically the rows correspond to different genes whereas the columns correspond to different experimental conditions. The clustering algorithms in <CODE>Bio.Cluster</CODE> can be applied both to rows (genes) and to columns (experiments).</P> [...]
-<H3 CLASS="subsection"><!--SEC ANCHOR -->Missing values</H3><!--SEC END --><P>Often in microarray experiments, some of the data values are missing, which is indicated by an additional <I>n</I> × <I>m</I> Numerical Python integer array <CODE>mask</CODE>. If <CODE>mask[i,j]==0</CODE>, then <CODE>data[i,j]</CODE> is missing and is ignored in the analysis.</P><!--TOC subsection Random number generator-->
-<H3 CLASS="subsection"><!--SEC ANCHOR -->Random number generator</H3><!--SEC END --><P>The <I>k</I>-means/medians/medoids clustering algorithms and Self-Organizing Maps (SOMs) include the use of a random number generator. The uniform random number generator in <CODE>Bio.Cluster</CODE> is based on the algorithm by L’Ecuyer [<A HREF="#lecuyer1988">25</A>], while random numbers following the binomial distribution are generated using the BTPE algorithm by Kachitvichyanukul and Schmeis [...]
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc189">14.1</A>  Distance functions</H2><!--SEC END --><P>
-<A NAME="sec:distancefunctions"></A></P><P>In order to cluster items into groups based on their similarity, we should first define what exactly we mean by <EM>similar</EM>. <CODE>Bio.Cluster</CODE> provides eight distance functions, indicated by a single character, to measure similarity, or conversely, distance:
-</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-<CODE>'e'</CODE>:
-Euclidean distance;
-</LI><LI CLASS="li-itemize"><CODE>'b'</CODE>:
-City-block distance.
-</LI><LI CLASS="li-itemize"><CODE>'c'</CODE>:
-Pearson correlation coefficient;
-</LI><LI CLASS="li-itemize"><CODE>'a'</CODE>:
-Absolute value of the Pearson correlation coefficient;
-</LI><LI CLASS="li-itemize"><CODE>'u'</CODE>:
-Uncentered Pearson correlation (equivalent to the cosine of the angle between two data vectors);
-</LI><LI CLASS="li-itemize"><CODE>'x'</CODE>:
-Absolute uncentered Pearson correlation;
-</LI><LI CLASS="li-itemize"><CODE>'s'</CODE>:
-Spearman’s rank correlation;
-</LI><LI CLASS="li-itemize"><CODE>'k'</CODE>:
-Kendall’s τ.
-</LI></UL><P>
-The first two are true distance functions that satisfy the triangle inequality:
-</P><TABLE CLASS="display dcenter"><TR VALIGN="middle"><TD CLASS="dcell"><I>d</I></TD><TD CLASS="dcell">⎛<BR>
-⎜<BR>
-⎝</TD><TD CLASS="dcell"><TABLE border=0 cellspacing=1 cellpadding=0><TR><TD ALIGN=center NOWRAP><I>u</I></TD></TR>
-<TR><TD CLASS="hbar"></TD></TR>
-</TABLE></TD><TD CLASS="dcell">,</TD><TD CLASS="dcell"><TABLE border=0 cellspacing=1 cellpadding=0><TR><TD ALIGN=center NOWRAP><I>v</I></TD></TR>
-<TR><TD CLASS="hbar"></TD></TR>
-</TABLE></TD><TD CLASS="dcell">⎞<BR>
-⎟<BR>
-⎠</TD><TD CLASS="dcell">≤ <I>d</I></TD><TD CLASS="dcell">⎛<BR>
-⎜<BR>
-⎝</TD><TD CLASS="dcell"><TABLE border=0 cellspacing=1 cellpadding=0><TR><TD ALIGN=center NOWRAP><I>u</I></TD></TR>
-<TR><TD CLASS="hbar"></TD></TR>
-</TABLE></TD><TD CLASS="dcell">,</TD><TD CLASS="dcell"><TABLE border=0 cellspacing=1 cellpadding=0><TR><TD ALIGN=center NOWRAP><I>w</I></TD></TR>
-<TR><TD CLASS="hbar"></TD></TR>
-</TABLE></TD><TD CLASS="dcell">⎞<BR>
-⎟<BR>
-⎠</TD><TD CLASS="dcell">+ <I>d</I></TD><TD CLASS="dcell">⎛<BR>
-⎜<BR>
-⎝</TD><TD CLASS="dcell"><TABLE border=0 cellspacing=1 cellpadding=0><TR><TD ALIGN=center NOWRAP><I>w</I></TD></TR>
-<TR><TD CLASS="hbar"></TD></TR>
-</TABLE></TD><TD CLASS="dcell">,</TD><TD CLASS="dcell"><TABLE border=0 cellspacing=1 cellpadding=0><TR><TD ALIGN=center NOWRAP><I>v</I></TD></TR>
-<TR><TD CLASS="hbar"></TD></TR>
-</TABLE></TD><TD CLASS="dcell">⎞<BR>
-⎟<BR>
-⎠</TD><TD CLASS="dcell"> for all  </TD><TD CLASS="dcell"><TABLE border=0 cellspacing=1 cellpadding=0><TR><TD ALIGN=center NOWRAP><I>u</I></TD></TR>
-<TR><TD CLASS="hbar"></TD></TR>
-</TABLE></TD><TD CLASS="dcell">, </TD><TD CLASS="dcell"><TABLE border=0 cellspacing=1 cellpadding=0><TR><TD ALIGN=center NOWRAP><I>v</I></TD></TR>
-<TR><TD CLASS="hbar"></TD></TR>
-</TABLE></TD><TD CLASS="dcell">, </TD><TD CLASS="dcell"><TABLE border=0 cellspacing=1 cellpadding=0><TR><TD ALIGN=center NOWRAP><I>w</I></TD></TR>
-<TR><TD CLASS="hbar"></TD></TR>
-</TABLE></TD><TD CLASS="dcell">,</TD></TR>
-</TABLE><P>
-and are therefore refered to as <EM>metrics</EM>. In everyday language, this means that the shortest distance between two points is a straight line.</P><P>The remaining six distance measures are related to the correlation coefficient, where the distance <I>d</I> is defined in terms of the correlation <I>r</I> by <I>d</I>=1−<I>r</I>. Note that these distance functions are <EM>semi-metrics</EM> that do not satisfy the triangle inequality. For example, for
-</P><TABLE CLASS="display dcenter"><TR VALIGN="middle"><TD CLASS="dcell"><TABLE border=0 cellspacing=1 cellpadding=0><TR><TD ALIGN=center NOWRAP><I>u</I></TD></TR>
-<TR><TD CLASS="hbar"></TD></TR>
-</TABLE></TD><TD CLASS="dcell">=</TD><TD CLASS="dcell">⎛<BR>
-⎝</TD><TD CLASS="dcell">1,0,−1</TD><TD CLASS="dcell">⎞<BR>
-⎠</TD><TD CLASS="dcell">;</TD></TR>
-</TABLE><TABLE CLASS="display dcenter"><TR VALIGN="middle"><TD CLASS="dcell"><TABLE border=0 cellspacing=1 cellpadding=0><TR><TD ALIGN=center NOWRAP><I>v</I></TD></TR>
-<TR><TD CLASS="hbar"></TD></TR>
-</TABLE></TD><TD CLASS="dcell">=</TD><TD CLASS="dcell">⎛<BR>
-⎝</TD><TD CLASS="dcell">1,1,0</TD><TD CLASS="dcell">⎞<BR>
-⎠</TD><TD CLASS="dcell">;</TD></TR>
-</TABLE><TABLE CLASS="display dcenter"><TR VALIGN="middle"><TD CLASS="dcell"><TABLE border=0 cellspacing=1 cellpadding=0><TR><TD ALIGN=center NOWRAP><I>w</I></TD></TR>
-<TR><TD CLASS="hbar"></TD></TR>
-</TABLE></TD><TD CLASS="dcell">=</TD><TD CLASS="dcell">⎛<BR>
-⎝</TD><TD CLASS="dcell">0,1,1</TD><TD CLASS="dcell">⎞<BR>
-⎠</TD><TD CLASS="dcell">;</TD></TR>
-</TABLE><P>
-we find a Pearson distance
-<I>d</I>(<U><I>u</I></U>,<U><I>w</I></U>) = 1.8660, while
-<I>d</I>(<U><I>u</I></U>,<U><I>v</I></U>)+<I>d</I>(<U><I>v</I></U>,<U><I>w</I></U>) = 1.6340.</P><!--TOC subsection Euclidean distance-->
-<H3 CLASS="subsection"><!--SEC ANCHOR -->Euclidean distance</H3><!--SEC END --><P>In <CODE>Bio.Cluster</CODE>, we define the Euclidean distance as
-</P><TABLE CLASS="display dcenter"><TR VALIGN="middle"><TD CLASS="dcell"><I>d</I> = </TD><TD CLASS="dcell"><TABLE CLASS="display"><TR><TD CLASS="dcell" ALIGN="center">1 </TD></TR>
-<TR><TD CLASS="hbar"></TD></TR>
-<TR><TD CLASS="dcell" ALIGN="center"><I>n</I></TD></TR>
-</TABLE></TD><TD CLASS="dcell"> </TD><TD CLASS="dcell"><TABLE CLASS="display"><TR><TD CLASS="dcell" ALIGN="center"><I>n</I></TD></TR>
-<TR><TD CLASS="dcell" ALIGN="center"><FONT SIZE=6>∑</FONT></TD></TR>
-<TR><TD CLASS="dcell" ALIGN="center"><I>i</I>=1</TD></TR>
-</TABLE></TD><TD CLASS="dcell"> </TD><TD CLASS="dcell">⎛<BR>
-⎝</TD><TD CLASS="dcell"><I>x</I><SUB><I>i</I></SUB>−<I>y</I><SUB><I>i</I></SUB></TD><TD CLASS="dcell">⎞<BR>
-⎠</TD><TD CLASS="dcell"><SUP>2</SUP>.</TD></TR>
-</TABLE><P>
-Only those terms are included in the summation for which both
-<I>x</I><SUB><I>i</I></SUB> and <I>y</I><SUB><I>i</I></SUB> are present, and the denominator <I>n</I> is chosen accordingly.
-As the expression data <I>x</I><SUB><I>i</I></SUB> and <I>y</I><SUB><I>i</I></SUB> are subtracted directly from each other, we should make sure that the expression data are properly normalized when using the Euclidean distance.</P><!--TOC subsection City-block distance-->
-<H3 CLASS="subsection"><!--SEC ANCHOR -->City-block distance</H3><!--SEC END --><P>The city-block distance, alternatively known as the Manhattan distance, is related to the Euclidean distance. Whereas the Euclidean distance corresponds to the length of the shortest path between two points, the city-block distance is the sum of distances along each dimension. As gene expression data tend to have missing values, in <CODE>Bio.Cluster</CODE> we define the city-block distance as the sum of di [...]
-</P><TABLE CLASS="display dcenter"><TR VALIGN="middle"><TD CLASS="dcell"><I>d</I> = </TD><TD CLASS="dcell"><TABLE CLASS="display"><TR><TD CLASS="dcell" ALIGN="center">1 </TD></TR>
-<TR><TD CLASS="hbar"></TD></TR>
-<TR><TD CLASS="dcell" ALIGN="center"><I>n</I></TD></TR>
-</TABLE></TD><TD CLASS="dcell"> </TD><TD CLASS="dcell"><TABLE CLASS="display"><TR><TD CLASS="dcell" ALIGN="center"><I>n</I></TD></TR>
-<TR><TD CLASS="dcell" ALIGN="center"><FONT SIZE=6>∑</FONT></TD></TR>
-<TR><TD CLASS="dcell" ALIGN="center"><I>i</I>=1</TD></TR>
-</TABLE></TD><TD CLASS="dcell"> </TD><TD CLASS="dcell">⎪<BR>
-⎪</TD><TD CLASS="dcell"><I>x</I><SUB><I>i</I></SUB>−<I>y</I><SUB><I>i</I></SUB></TD><TD CLASS="dcell">⎪<BR>
-⎪</TD><TD CLASS="dcell">.</TD></TR>
-</TABLE><P>
-This is equal to the distance you would have to walk between two points in a city, where you have to walk along city blocks. As for the Euclidean distance,
-the expression data are subtracted directly from each other, and we should therefore make sure that they are properly normalized.</P><!--TOC subsection The Pearson correlation coefficient-->
-<H3 CLASS="subsection"><!--SEC ANCHOR -->The Pearson correlation coefficient</H3><!--SEC END --><P>The Pearson correlation coefficient is defined as
-</P><TABLE CLASS="display dcenter"><TR VALIGN="middle"><TD CLASS="dcell"><I>r</I> = </TD><TD CLASS="dcell"><TABLE CLASS="display"><TR><TD CLASS="dcell" ALIGN="center">1</TD></TR>
-<TR><TD CLASS="hbar"></TD></TR>
-<TR><TD CLASS="dcell" ALIGN="center"><I>n</I></TD></TR>
-</TABLE></TD><TD CLASS="dcell"> </TD><TD CLASS="dcell"><TABLE CLASS="display"><TR><TD CLASS="dcell" ALIGN="center"><I>n</I></TD></TR>
-<TR><TD CLASS="dcell" ALIGN="center"><FONT SIZE=6>∑</FONT></TD></TR>
-<TR><TD CLASS="dcell" ALIGN="center"><I>i</I>=1</TD></TR>
-</TABLE></TD><TD CLASS="dcell"> </TD><TD CLASS="dcell">⎛<BR>
-⎜<BR>
-⎜<BR>
-⎝</TD><TD CLASS="dcell"><TABLE CLASS="display"><TR><TD CLASS="dcell" ALIGN="center"><I>x</I><SUB><I>i</I></SUB> −<SPAN style="text-decoration:overline">x</SPAN></TD></TR>
-<TR><TD CLASS="hbar"></TD></TR>
-<TR><TD CLASS="dcell" ALIGN="center">σ<SUB><I>x</I></SUB></TD></TR>
-</TABLE></TD><TD CLASS="dcell"> </TD><TD CLASS="dcell">⎞<BR>
-⎟<BR>
-⎟<BR>
-⎠</TD><TD CLASS="dcell">⎛<BR>
-⎜<BR>
-⎜<BR>
-⎝</TD><TD CLASS="dcell"><TABLE CLASS="display"><TR><TD CLASS="dcell" ALIGN="center"><I>y</I><SUB><I>i</I></SUB> −ȳ</TD></TR>
-<TR><TD CLASS="hbar"></TD></TR>
-<TR><TD CLASS="dcell" ALIGN="center">σ<SUB><I>y</I></SUB></TD></TR>
-</TABLE></TD><TD CLASS="dcell"> </TD><TD CLASS="dcell">⎞<BR>
-⎟<BR>
-⎟<BR>
-⎠</TD><TD CLASS="dcell">,</TD></TR>
-</TABLE><P>
-in which
-<SPAN style="text-decoration:overline">x</SPAN>, ȳ
-are the sample mean of <I>x</I> and <I>y</I> respectively, and
-σ<SUB><I>x</I></SUB>, σ<SUB><I>y</I></SUB>
-are the sample standard deviation of <I>x</I> and <I>y</I>.
-The Pearson correlation coefficient is a measure for how well a straight line can be fitted to a scatterplot of <I>x</I> and <I>y</I>.
-If all the points in the scatterplot lie on a straight line, the Pearson correlation coefficient is either +1 or -1, depending on whether the slope of line is positive or negative. If the Pearson correlation coefficient is equal to zero, there is no correlation between <I>x</I> and <I>y</I>.</P><P>The <EM>Pearson distance</EM> is then defined as
-</P><TABLE CLASS="display dcenter"><TR VALIGN="middle"><TD CLASS="dcell"><I>d</I><SUB>P</SUB> ≡ 1 − <I>r</I>.</TD></TR>
-</TABLE><P>
-As the Pearson correlation coefficient lies between -1 and 1, the Pearson distance lies between 0 and 2.</P><!--TOC subsection Absolute Pearson correlation-->
-<H3 CLASS="subsection"><!--SEC ANCHOR -->Absolute Pearson correlation</H3><!--SEC END --><P>By taking the absolute value of the Pearson correlation, we find a number between 0 and 1. If the absolute value is 1, all the points in the scatter plot lie on a straight line with either a positive or a negative slope. If the absolute value is equal to zero, there is no correlation between <I>x</I> and <I>y</I>.</P><P>The corresponding distance is defined as
-</P><TABLE CLASS="display dcenter"><TR VALIGN="middle"><TD CLASS="dcell"><I>d</I><SUB>A</SUB> ≡ 1 − </TD><TD CLASS="dcell">⎪<BR>
-⎪</TD><TD CLASS="dcell"><I>r</I></TD><TD CLASS="dcell">⎪<BR>
-⎪</TD><TD CLASS="dcell">,</TD></TR>
-</TABLE><P>
-where <I>r</I> is the Pearson correlation coefficient. As the absolute value of the Pearson correlation coefficient lies between 0 and 1, the corresponding distance lies between 0 and 1 as well.</P><P>In the context of gene expression experiments, the absolute correlation is equal to 1 if the gene expression profiles of two genes are either exactly the same or exactly opposite. The absolute correlation coefficient should therefore be used with care.</P><!--TOC subsection Uncentered corre [...]
-<H3 CLASS="subsection"><!--SEC ANCHOR -->Uncentered correlation (cosine of the angle)</H3><!--SEC END --><P>In some cases, it may be preferable to use the <EM>uncentered correlation</EM> instead of the regular Pearson correlation coefficient. The uncentered correlation is defined as
-</P><TABLE CLASS="display dcenter"><TR VALIGN="middle"><TD CLASS="dcell"><I>r</I><SUB>U</SUB> = </TD><TD CLASS="dcell"><TABLE CLASS="display"><TR><TD CLASS="dcell" ALIGN="center">1</TD></TR>
-<TR><TD CLASS="hbar"></TD></TR>
-<TR><TD CLASS="dcell" ALIGN="center"><I>n</I></TD></TR>
-</TABLE></TD><TD CLASS="dcell"> </TD><TD CLASS="dcell"><TABLE CLASS="display"><TR><TD CLASS="dcell" ALIGN="center"><I>n</I></TD></TR>
-<TR><TD CLASS="dcell" ALIGN="center"><FONT SIZE=6>∑</FONT></TD></TR>
-<TR><TD CLASS="dcell" ALIGN="center"><I>i</I>=1</TD></TR>
-</TABLE></TD><TD CLASS="dcell"> </TD><TD CLASS="dcell">⎛<BR>
-⎜<BR>
-⎜<BR>
-⎝</TD><TD CLASS="dcell"><TABLE CLASS="display"><TR><TD CLASS="dcell" ALIGN="center"><I>x</I><SUB><I>i</I></SUB></TD></TR>
-<TR><TD CLASS="hbar"></TD></TR>
-<TR><TD CLASS="dcell" ALIGN="center">σ<SUB><I>x</I></SUB><SUP>(0)</SUP></TD></TR>
-</TABLE></TD><TD CLASS="dcell"> </TD><TD CLASS="dcell">⎞<BR>
-⎟<BR>
-⎟<BR>
-⎠</TD><TD CLASS="dcell">⎛<BR>
-⎜<BR>
-⎜<BR>
-⎝</TD><TD CLASS="dcell"><TABLE CLASS="display"><TR><TD CLASS="dcell" ALIGN="center"><I>y</I><SUB><I>i</I></SUB></TD></TR>
-<TR><TD CLASS="hbar"></TD></TR>
-<TR><TD CLASS="dcell" ALIGN="center">σ<SUB><I>y</I></SUB><SUP>(0)</SUP></TD></TR>
-</TABLE></TD><TD CLASS="dcell"> </TD><TD CLASS="dcell">⎞<BR>
-⎟<BR>
-⎟<BR>
-⎠</TD><TD CLASS="dcell">,</TD></TR>
-</TABLE><P>
-where
-</P><TABLE CLASS="display dcenter"><TR VALIGN="middle"><TD CLASS="dcell">
-
-
-     
-
-</TD><TD CLASS="dcell"><TABLE CELLSPACING=6 CELLPADDING=0><TR><TD ALIGN=right NOWRAP>σ<SUB><I>x</I></SUB><SUP>(0)</SUP></TD><TD ALIGN=center NOWRAP> =</TD><TD ALIGN=left NOWRAP><TABLE CLASS="display"><TR VALIGN="middle"><TD CLASS="dcell"> </TD><TD CLASS="dcell"><TABLE CELLSPACING=0 CELLPADDING=0><TR><TD ALIGN="right"><DIV CLASS="vbar" STYLE="height:2em;"></DIV></TD></TR>
-<TR><TD><FONT SIZE=7>√</FONT></TD></TR>
-</TABLE></TD><TD CLASS="dcell"><TABLE border=0 cellspacing=1 cellpadding=0><TR><TD CLASS="hbar"></TD></TR>
-<TR><TD ALIGN=center NOWRAP><TABLE CLASS="display"><TR VALIGN="middle"><TD CLASS="dcell"><TABLE CLASS="display"><TR><TD CLASS="dcell" ALIGN="center">1</TD></TR>
-<TR><TD CLASS="hbar"></TD></TR>
-<TR><TD CLASS="dcell" ALIGN="center"><I>n</I></TD></TR>
-</TABLE></TD><TD CLASS="dcell"> </TD><TD CLASS="dcell"><TABLE CLASS="display"><TR><TD CLASS="dcell" ALIGN="center"><I>n</I></TD></TR>
-<TR><TD CLASS="dcell" ALIGN="center"><FONT SIZE=6>∑</FONT></TD></TR>
-<TR><TD CLASS="dcell" ALIGN="center"><I>i</I>=1</TD></TR>
-</TABLE></TD><TD CLASS="dcell"><I>x</I><SUB><I>i</I></SUB><SUP>2</SUP></TD></TR>
-</TABLE></TD></TR>
-</TABLE></TD><TD CLASS="dcell">;  </TD></TR>
-</TABLE></TD><TD ALIGN=right NOWRAP> </TD></TR>
-<TR><TD ALIGN=right NOWRAP>σ<SUB><I>y</I></SUB><SUP>(0)</SUP></TD><TD ALIGN=center NOWRAP> =</TD><TD ALIGN=left NOWRAP><TABLE CLASS="display"><TR VALIGN="middle"><TD CLASS="dcell"> </TD><TD CLASS="dcell"><TABLE CELLSPACING=0 CELLPADDING=0><TR><TD ALIGN="right"><DIV CLASS="vbar" STYLE="height:2em;"></DIV></TD></TR>
-<TR><TD><FONT SIZE=7>√</FONT></TD></TR>
-</TABLE></TD><TD CLASS="dcell"><TABLE border=0 cellspacing=1 cellpadding=0><TR><TD CLASS="hbar"></TD></TR>
-<TR><TD ALIGN=center NOWRAP><TABLE CLASS="display"><TR VALIGN="middle"><TD CLASS="dcell"><TABLE CLASS="display"><TR><TD CLASS="dcell" ALIGN="center">1</TD></TR>
-<TR><TD CLASS="hbar"></TD></TR>
-<TR><TD CLASS="dcell" ALIGN="center"><I>n</I></TD></TR>
-</TABLE></TD><TD CLASS="dcell"> </TD><TD CLASS="dcell"><TABLE CLASS="display"><TR><TD CLASS="dcell" ALIGN="center"><I>n</I></TD></TR>
-<TR><TD CLASS="dcell" ALIGN="center"><FONT SIZE=6>∑</FONT></TD></TR>
-<TR><TD CLASS="dcell" ALIGN="center"><I>i</I>=1</TD></TR>
-</TABLE></TD><TD CLASS="dcell"><I>y</I><SUB><I>i</I></SUB><SUP>2</SUP></TD></TR>
-</TABLE></TD></TR>
-</TABLE></TD><TD CLASS="dcell">. 
-</TD></TR>
-</TABLE></TD><TD ALIGN=right NOWRAP> </TD></TR>
-</TABLE></TD></TR>
-</TABLE><P>
-This is the same expression as for the regular Pearson correlation coefficient, except that the sample means
-<SPAN style="text-decoration:overline">x</SPAN>, ȳ
-are set equal to zero. The uncentered correlation may be appropriate if there is a zero reference state. For instance, in the case of gene expression data given in terms of log-ratios, a log-ratio equal to zero corresponds to the green and red signal being equal, which means that the experimental manipulation did not affect the gene expression.</P><P>The distance corresponding to the uncentered correlation coefficient is defined as
-</P><TABLE CLASS="display dcenter"><TR VALIGN="middle"><TD CLASS="dcell"><I>d</I><SUB>U</SUB> ≡ 1 − <I>r</I><SUB>U</SUB>,</TD></TR>
-</TABLE><P>
-where
-<I>r</I><SUB>U</SUB>
-is the uncentered correlation.
-As the uncentered correlation coefficient lies between -1 and 1, the corresponding distance lies between 0 and 2.</P><P>The uncentered correlation is equal to the cosine of the angle of the two data vectors in <I>n</I>-dimensional space, and is often referred to as such.</P><!--TOC subsection Absolute uncentered correlation-->
-<H3 CLASS="subsection"><!--SEC ANCHOR -->Absolute uncentered correlation</H3><!--SEC END --><P>As for the regular Pearson correlation, we can define a distance measure using the absolute value of the uncentered correlation:
-</P><TABLE CLASS="display dcenter"><TR VALIGN="middle"><TD CLASS="dcell"><I>d</I><SUB>AU</SUB> ≡ 1 − </TD><TD CLASS="dcell">⎪<BR>
-⎪</TD><TD CLASS="dcell"><I>r</I><SUB>U</SUB></TD><TD CLASS="dcell">⎪<BR>
-⎪</TD><TD CLASS="dcell">,</TD></TR>
-</TABLE><P>
-where
-<I>r</I><SUB>U</SUB>
-is the uncentered correlation coefficient. As the absolute value of the uncentered correlation coefficient lies between 0 and 1, the corresponding distance lies between 0 and 1 as well.</P><P>Geometrically, the absolute value of the uncentered correlation is equal to the cosine between the supporting lines of the two data vectors (i.e., the angle without taking the direction of the vectors into consideration).</P><!--TOC subsection Spearman rank correlation-->
-<H3 CLASS="subsection"><!--SEC ANCHOR -->Spearman rank correlation</H3><!--SEC END --><P>The Spearman rank correlation is an example of a non-parametric similarity measure, and tends to be more robust against outliers than the Pearson correlation.</P><P>To calculate the Spearman rank correlation, we replace each data value by their rank if we would order the data in each vector by their value. We then calculate the Pearson correlation between the two rank vectors instead of the data vect [...]
-</P><TABLE CLASS="display dcenter"><TR VALIGN="middle"><TD CLASS="dcell"><I>d</I><SUB>S</SUB> ≡ 1 − <I>r</I><SUB>S</SUB>,</TD></TR>
-</TABLE><P>
-where
-<I>r</I><SUB>S</SUB>
-is the Spearman rank correlation.</P><!--TOC subsection Kendall’s τ-->
-<H3 CLASS="subsection"><!--SEC ANCHOR -->Kendall’s τ</H3><!--SEC END --><P>Kendall’s τ
-is another example of a non-parametric similarity measure. It is similar to the Spearman rank correlation, but instead of the ranks themselves only the relative ranks are used to calculate τ (see Snedecor & Cochran [<A HREF="#snedecor1989">29</A>]).</P><P>We can define a distance measure corresponding to Kendall’s τ
-as </P><TABLE CLASS="display dcenter"><TR VALIGN="middle"><TD CLASS="dcell"><I>d</I><SUB>K</SUB> ≡ 1 − τ.</TD></TR>
-</TABLE><P>
-As Kendall’s τ is always between -1 and 1, the corresponding distance will be between 0 and 2.</P><!--TOC subsection Weighting-->
-<H3 CLASS="subsection"><!--SEC ANCHOR -->Weighting</H3><!--SEC END --><P>For most of the distance functions available in <CODE>Bio.Cluster</CODE>, a weight vector can be applied. The weight vector contains weights for the items in the data vector. If the weight for item <I>i</I> is <I>w</I><SUB><I>i</I></SUB>, then that item is treated as if it occurred <I>w</I><SUB><I>i</I></SUB> times in the data. The weight do not have to be integers.
-For the Spearman rank correlation and Kendall’s
-τ,
-weights do not have a well-defined meaning and are therefore not implemented.</P><!--TOC subsection Calculating the distance matrix-->
-<H3 CLASS="subsection"><!--SEC ANCHOR -->Calculating the distance matrix</H3><!--SEC END --><P>
-<A NAME="subsec:distancematrix"></A></P><P>The distance matrix is a square matrix with all pairwise distances between the items in <CODE>data</CODE>, and can be calculated by the function <CODE>distancematrix</CODE> in the <CODE>Bio.Cluster</CODE> module:
-</P><PRE CLASS="verbatim">>>> from Bio.Cluster import distancematrix
->>> matrix = distancematrix(data)
-</PRE><P>where the following arguments are defined:
-</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-<CODE>data</CODE> (required)<BR>
-Array containing the data for the items.
-</LI><LI CLASS="li-itemize"><CODE>mask</CODE> (default: <CODE>None</CODE>)<BR>
-Array of integers showing which data are missing. If <CODE>mask[i,j]==0</CODE>, then <CODE>data[i,j]</CODE> is missing. If <CODE>mask==None</CODE>, then all data are present.
-</LI><LI CLASS="li-itemize"><CODE>weight</CODE> (default: <CODE>None</CODE>)<BR>
-The weights to be used when calculating distances. If <CODE>weight==None</CODE>, then equal weights are assumed.
-</LI><LI CLASS="li-itemize"><CODE>transpose</CODE> (default: <CODE>0</CODE>)<BR>
-Determines if the distances between the rows of <CODE>data</CODE> are to be calculated (<CODE>transpose==0</CODE>), or between the columns of <CODE>data</CODE> (<CODE>transpose==1</CODE>).
-</LI><LI CLASS="li-itemize"><CODE>dist</CODE> (default: <CODE>'e'</CODE>, Euclidean distance)<BR>
-Defines the distance function to be used (see <A HREF="#sec:distancefunctions">14.1</A>).
-</LI></UL><P>To save memory, the distance matrix is returned as a list of 1D arrays.
-The number of columns in each row is equal to the row number. Hence, the first row has zero elements. An example of the return value is
-</P><PRE CLASS="verbatim">[array([]),
- array([1.]),
- array([7., 3.]),
- array([4., 2., 6.])]
-</PRE><P>This corresponds to the distance matrix
-</P><TABLE CLASS="display dcenter"><TR VALIGN="middle"><TD CLASS="dcell">⎛<BR>
-⎜<BR>
-⎜<BR>
-⎜<BR>
-⎝</TD><TD CLASS="dcell">
-</TD><TD CLASS="dcell"><TABLE CELLSPACING=6 CELLPADDING=0><TR><TD ALIGN=center NOWRAP>0</TD><TD ALIGN=center NOWRAP>1</TD><TD ALIGN=center NOWRAP>7</TD><TD ALIGN=center NOWRAP>4  </TD></TR>
-<TR><TD ALIGN=center NOWRAP>1</TD><TD ALIGN=center NOWRAP>0</TD><TD ALIGN=center NOWRAP>3</TD><TD ALIGN=center NOWRAP>2  </TD></TR>
-<TR><TD ALIGN=center NOWRAP>7</TD><TD ALIGN=center NOWRAP>3</TD><TD ALIGN=center NOWRAP>0</TD><TD ALIGN=center NOWRAP>6  </TD></TR>
-<TR><TD ALIGN=center NOWRAP>4</TD><TD ALIGN=center NOWRAP>2</TD><TD ALIGN=center NOWRAP>6</TD><TD ALIGN=center NOWRAP>0</TD></TR>
-</TABLE></TD><TD CLASS="dcell">
-</TD><TD CLASS="dcell">⎞<BR>
-⎟<BR>
-⎟<BR>
-⎟<BR>
-⎠</TD><TD CLASS="dcell">.
-</TD></TR>
-</TABLE><!--TOC section Calculating cluster properties-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc190">14.2</A>  Calculating cluster properties</H2><!--SEC END --><!--TOC subsection Calculating the cluster centroids-->
-<H3 CLASS="subsection"><!--SEC ANCHOR -->Calculating the cluster centroids</H3><!--SEC END --><P>
-<A NAME="subsec:clustercentroids"></A></P><P>The centroid of a cluster can be defined either as the mean or as the median of each dimension over all cluster items. The function <CODE>clustercentroids</CODE> in <CODE>Bio.Cluster</CODE> can be used to calculate either:</P><PRE CLASS="verbatim">>>> from Bio.Cluster import clustercentroids
->>> cdata, cmask = clustercentroids(data)
-</PRE><P>where the following arguments are defined:
-</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-<CODE>data</CODE> (required)<BR>
-Array containing the data for the items.
-</LI><LI CLASS="li-itemize"><CODE>mask</CODE> (default: <CODE>None</CODE>)<BR>
-Array of integers showing which data are missing. If <CODE>mask[i,j]==0</CODE>, then <CODE>data[i,j]</CODE> is missing. If <CODE>mask==None</CODE>, then all data are present.
-</LI><LI CLASS="li-itemize"><CODE>clusterid</CODE> (default: <CODE>None</CODE>)<BR>
-Vector of integers showing to which cluster each item belongs. If <CODE>clusterid</CODE> is <CODE>None</CODE>, then all items are assumed to belong to the same cluster.
-</LI><LI CLASS="li-itemize"><CODE>method</CODE> (default: <CODE>'a'</CODE>)<BR>
-Specifies whether the arithmetic mean (<CODE>method=='a'</CODE>) or the median (<CODE>method=='m'</CODE>) is used to calculate the cluster center.
-</LI><LI CLASS="li-itemize"><CODE>transpose</CODE> (default: <CODE>0</CODE>)<BR>
-Determines if the centroids of the rows of <CODE>data</CODE> are to be calculated (<CODE>transpose==0</CODE>), or the centroids of the columns of <CODE>data</CODE> (<CODE>transpose==1</CODE>).
-</LI></UL><P>This function returns the tuple <CODE>(cdata, cmask)</CODE>. The centroid data are stored in the 2D Numerical Python array <CODE>cdata</CODE>, with missing data indicated by the 2D Numerical Python integer array <CODE>cmask</CODE>. The dimensions of these arrays are (number of clusters, number of columns) if <CODE>transpose</CODE> is <CODE>0</CODE>, or (number of rows, number of clusters) if <CODE>transpose</CODE> is <CODE>1</CODE>. Each row (if <CODE>transpose</CODE> is <CO [...]
-<H3 CLASS="subsection"><!--SEC ANCHOR -->Calculating the distance between clusters</H3><!--SEC END --><P>Given a distance function between <EM>items</EM>, we can define the distance between two <EM>clusters</EM> in several ways. The distance between the arithmetic means of the two clusters is used in pairwise centroid-linkage clustering and in <I>k</I>-means clustering. In <I>k</I>-medoids clustering, the distance between the medians of the two clusters is used instead. The shortest pair [...]
-</P><PRE CLASS="verbatim">>>> from Bio.Cluster import clusterdistance
->>> distance = clusterdistance(data)
-</PRE><P>where the following arguments are defined:
-</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-<CODE>data</CODE> (required)<BR>
-Array containing the data for the items.
-</LI><LI CLASS="li-itemize"><CODE>mask</CODE> (default: <CODE>None</CODE>)<BR>
-Array of integers showing which data are missing. If <CODE>mask[i,j]==0</CODE>, then <CODE>data[i,j]</CODE> is missing. If <CODE>mask==None</CODE>, then all data are present.
-</LI><LI CLASS="li-itemize"><CODE>weight</CODE> (default: <CODE>None</CODE>)<BR>
-The weights to be used when calculating distances. If <CODE>weight==None</CODE>, then equal weights are assumed.
-</LI><LI CLASS="li-itemize"><CODE>index1</CODE> (default: <CODE>0</CODE>)<BR>
-A list containing the indices of the items belonging to the first cluster. A cluster containing only one item <I>i</I> can be represented either as a list <CODE>[i]</CODE>, or as an integer <CODE>i</CODE>.
-</LI><LI CLASS="li-itemize"><CODE>index2</CODE> (default: <CODE>0</CODE>)<BR>
-A list containing the indices of the items belonging to the second cluster. A cluster containing only one items <I>i</I> can be represented either as a list <CODE>[i]</CODE>, or as an integer <CODE>i</CODE>.
-</LI><LI CLASS="li-itemize"><CODE>method</CODE> (default: <CODE>'a'</CODE>)<BR>
-Specifies how the distance between clusters is defined:
-<UL CLASS="itemize"><LI CLASS="li-itemize">
-<CODE>'a'</CODE>: Distance between the two cluster centroids (arithmetic mean);
-</LI><LI CLASS="li-itemize"><CODE>'m'</CODE>: Distance between the two cluster centroids (median);
-</LI><LI CLASS="li-itemize"><CODE>'s'</CODE>: Shortest pairwise distance between items in the two clusters;
-</LI><LI CLASS="li-itemize"><CODE>'x'</CODE>: Longest pairwise distance between items in the two clusters;
-</LI><LI CLASS="li-itemize"><CODE>'v'</CODE>: Average over the pairwise distances between items in the two clusters.
-</LI></UL>
-</LI><LI CLASS="li-itemize"><CODE>dist</CODE> (default: <CODE>'e'</CODE>, Euclidean distance)<BR>
-Defines the distance function to be used (see <A HREF="#sec:distancefunctions">14.1</A>).
-</LI><LI CLASS="li-itemize"><CODE>transpose</CODE> (default: <CODE>0</CODE>)<BR>
-If <CODE>transpose==0</CODE>, calculate the distance between the rows of <CODE>data</CODE>. If <CODE>transpose==1</CODE>, calculate the distance between the columns of <CODE>data</CODE>.
-</LI></UL><!--TOC section Partitioning algorithms-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc191">14.3</A>  Partitioning algorithms</H2><!--SEC END --><P>Partitioning algorithms divide items into <I>k</I> clusters such that the sum of distances over the items to their cluster centers is minimal.
-The number of clusters <I>k</I> is specified by the user.
-Three partitioning algorithms are available in <CODE>Bio.Cluster</CODE>:
-</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-<I>k</I>-means clustering
-</LI><LI CLASS="li-itemize"><I>k</I>-medians clustering
-</LI><LI CLASS="li-itemize"><I>k</I>-medoids clustering
-</LI></UL><P>
-These algorithms differ in how the cluster center is defined. In <I>k</I>-means clustering, the cluster center is defined as the mean data vector averaged over all items in the cluster. Instead of the mean, in <I>k</I>-medians clustering the median is calculated for each dimension in the data vector. Finally, in <I>k</I>-medoids clustering the cluster center is defined as the item which has the smallest sum of distances to the other items in the cluster. This clustering algorithm is suit [...]
-In the initialization of the EM algorithm, we randomly assign items to clusters. To ensure that no empty clusters are produced, we use the binomial distribution to randomly choose the number of items in each cluster to be one or more. We then randomly permute the cluster assignments to items such that each item has an equal probability to be in any cluster. Each cluster is thus guaranteed to contain at least one item.</P><P>We then iterate:
-</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-Calculate the centroid of each cluster, defined as either the mean, the median, or the medoid of the cluster;
-</LI><LI CLASS="li-itemize">Calculate the distances of each item to the cluster centers;
-</LI><LI CLASS="li-itemize">For each item, determine which cluster centroid is closest;
-</LI><LI CLASS="li-itemize">Reassign each item to its closest cluster, or stop the iteration if no further item reassignments take place.
-</LI></UL><P>To avoid clusters becoming empty during the iteration, in <I>k</I>-means and <I>k</I>-medians clustering the algorithm keeps track of the number of items in each cluster, and prohibits the last remaining item in a cluster from being reassigned to a different cluster. For <I>k</I>-medoids clustering, such a check is not needed, as the item that functions as the cluster centroid has a zero distance to itself, and will therefore never be closer to a different cluster.</P><P>As [...]
-To find the optimal clustering solution, the <I>k</I>-means algorithm is repeated many times, each time starting from a different initial random clustering. The sum of distances of the items to their cluster center is saved for each run, and the solution with the smallest value of this sum will be returned as the overall clustering solution.</P><P>How often the EM algorithm should be run depends on the number of items being clustered. As a rule of thumb, we can consider how often the opt [...]
-<H3 CLASS="subsection"><!--SEC ANCHOR --><I>k</I>-means and <I>k</I>-medians</H3><!--SEC END --><P>The <I>k</I>-means and <I>k</I>-medians algorithms are implemented as the function <CODE>kcluster</CODE> in <CODE>Bio.Cluster</CODE>:</P><PRE CLASS="verbatim">>>> from Bio.Cluster import kcluster
->>> clusterid, error, nfound = kcluster(data)
-</PRE><P>where the following arguments are defined:
-</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-<CODE>data</CODE> (required)<BR>
-Array containing the data for the items.
-</LI><LI CLASS="li-itemize"><CODE>nclusters</CODE> (default: <CODE>2</CODE>)<BR>
-The number of clusters <I>k</I>.
-</LI><LI CLASS="li-itemize"><CODE>mask</CODE> (default: <CODE>None</CODE>)<BR>
-Array of integers showing which data are missing. If <CODE>mask[i,j]==0</CODE>, then <CODE>data[i,j]</CODE> is missing. If <CODE>mask==None</CODE>, then all data are present.
-</LI><LI CLASS="li-itemize"><CODE>weight</CODE> (default: <CODE>None</CODE>)<BR>
-The weights to be used when calculating distances. If <CODE>weight==None</CODE>, then equal weights are assumed.
-</LI><LI CLASS="li-itemize"><CODE>transpose</CODE> (default: <CODE>0</CODE>)<BR>
-Determines if rows (<CODE>transpose</CODE> is <CODE>0</CODE>) or columns (<CODE>transpose</CODE> is <CODE>1</CODE>) are to be clustered.
-</LI><LI CLASS="li-itemize"><CODE>npass</CODE> (default: <CODE>1</CODE>)<BR>
-The number of times the <I>k</I>-means/-medians clustering algorithm is performed, each time with a different (random) initial condition. If <CODE>initialid</CODE> is given, the value of <CODE>npass</CODE> is ignored and the clustering algorithm is run only once, as it behaves deterministically in that case.
-</LI><LI CLASS="li-itemize"><CODE>method</CODE> (default: <CODE>a</CODE>)<BR>
-describes how the center of a cluster is found:
-<UL CLASS="itemize"><LI CLASS="li-itemize">
-<CODE>method=='a'</CODE>: arithmetic mean (<I>k</I>-means clustering);
-</LI><LI CLASS="li-itemize"><CODE>method=='m'</CODE>: median (<I>k</I>-medians clustering).
-</LI></UL>
-For other values of <CODE>method</CODE>, the arithmetic mean is used.
-</LI><LI CLASS="li-itemize"><CODE>dist</CODE> (default: <CODE>'e'</CODE>, Euclidean distance)<BR>
-Defines the distance function to be used (see <A HREF="#sec:distancefunctions">14.1</A>).
-Whereas all eight distance measures are accepted by <CODE>kcluster</CODE>, from a theoretical viewpoint it is best to use the Euclidean distance for the <I>k</I>-means algorithm, and the city-block distance for <I>k</I>-medians.
-</LI><LI CLASS="li-itemize"><CODE>initialid</CODE> (default: <CODE>None</CODE>)<BR>
-Specifies the initial clustering to be used for the EM algorithm. If <CODE>initialid==None</CODE>, then a different random initial clustering is used for each of the <CODE>npass</CODE> runs of the EM algorithm. If <CODE>initialid</CODE> is not <CODE>None</CODE>, then it should be equal to a 1D array containing the cluster number (between <CODE>0</CODE> and <CODE>nclusters-1</CODE>) for each item. Each cluster should contain at least one item. With the initial clustering specified, the EM [...]
-</LI></UL><P>This function returns a tuple <CODE>(clusterid, error, nfound)</CODE>, where <CODE>clusterid</CODE> is an integer array containing the number of the cluster to which each row or cluster was assigned, <CODE>error</CODE> is the within-cluster sum of distances for the optimal clustering solution, and <CODE>nfound</CODE> is the number of times this optimal solution was found.</P><!--TOC subsection <I>k</I>-medoids clustering-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><I>k</I>-medoids clustering</H3><!--SEC END --><P>The <CODE>kmedoids</CODE> routine performs <I>k</I>-medoids clustering on a given set of items, using the distance matrix and the number of clusters passed by the user:
-</P><PRE CLASS="verbatim">>>> from Bio.Cluster import kmedoids
->>> clusterid, error, nfound = kmedoids(distance)
-</PRE><P>where the following arguments are defined:
-, nclusters=2, npass=1, initialid=None)|</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-<CODE>distance</CODE> (required)<BR>
-The matrix containing the distances between the items; this matrix can be specified in three ways:
-<UL CLASS="itemize"><LI CLASS="li-itemize">
-as a 2D Numerical Python array (in which only the left-lower part of the array will be accessed):
-<PRE CLASS="verbatim">distance = array([[0.0, 1.1, 2.3],
- [1.1, 0.0, 4.5],
- [2.3, 4.5, 0.0]])
-</PRE></LI><LI CLASS="li-itemize">as a 1D Numerical Python array containing consecutively the distances in the left-lower part of the distance matrix:
-<PRE CLASS="verbatim">distance = array([1.1, 2.3, 4.5])
-</PRE></LI><LI CLASS="li-itemize">as a list containing the rows of the left-lower part of the distance matrix:
-<PRE CLASS="verbatim">distance = [array([]|,
- array([1.1]),
- array([2.3, 4.5])
- ]
-</PRE></LI></UL>
-These three expressions correspond to the same distance matrix.
-</LI><LI CLASS="li-itemize"><CODE>nclusters</CODE> (default: <CODE>2</CODE>)<BR>
-The number of clusters <I>k</I>.
-</LI><LI CLASS="li-itemize"><CODE>npass</CODE> (default: <CODE>1</CODE>)<BR>
-The number of times the <I>k</I>-medoids clustering algorithm is performed, each time with a different (random) initial condition. If <CODE>initialid</CODE> is given, the value of <CODE>npass</CODE> is ignored, as the clustering algorithm behaves deterministically in that case.
-</LI><LI CLASS="li-itemize"><CODE>initialid</CODE> (default: <CODE>None</CODE>)<BR>
-Specifies the initial clustering to be used for the EM algorithm. If <CODE>initialid==None</CODE>, then a different random initial clustering is used for each of the <CODE>npass</CODE> runs of the EM algorithm. If <CODE>initialid</CODE> is not <CODE>None</CODE>, then it should be equal to a 1D array containing the cluster number (between <CODE>0</CODE> and <CODE>nclusters-1</CODE>) for each item. Each cluster should contain at least one item. With the initial clustering specified, the EM [...]
-</LI></UL><P>This function returns a tuple <CODE>(clusterid, error, nfound)</CODE>, where <CODE>clusterid</CODE> is an array containing the number of the cluster to which each item was assigned, <CODE>error</CODE> is the within-cluster sum of distances for the optimal <I>k</I>-medoids clustering solution, and <CODE>nfound</CODE> is the number of times the optimal solution was found. Note that the cluster number in <CODE>clusterid</CODE> is defined as the item number of the item represent [...]
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc192">14.4</A>  Hierarchical clustering</H2><!--SEC END --><P>Hierarchical clustering methods are inherently different from the <I>k</I>-means clustering method. In hierarchical clustering, the similarity in the expression profile between genes or experimental conditions are represented in the form of a tree structure. This tree structure can be shown graphically by programs such as Treeview and Java Treeview, which has contribu [...]
-In pairwise single-linkage clustering, the distance between two nodes is defined as the shortest distance among the pairwise distances between the members of the two nodes.
-</LI><LI CLASS="li-itemize">In pairwise maximum-linkage clustering, alternatively known as pairwise complete-linkage clustering, the distance between two nodes is defined as the longest distance among the pairwise distances between the members of the two nodes.
-</LI><LI CLASS="li-itemize">In pairwise average-linkage clustering, the distance between two nodes is defined as the average over all pairwise distances between the items of the two nodes.
-</LI><LI CLASS="li-itemize">In pairwise centroid-linkage clustering, the distance between two nodes is defined as the distance between their centroids. The centroids are calculated by taking the mean over all the items in a cluster. As the distance from each newly formed node to existing nodes and items need to be calculated at each step, the computing time of pairwise centroid-linkage clustering may be significantly longer than for the other hierarchical clustering methods. Another pecu [...]
-</LI></UL><P>For pairwise single-, complete-, and average-linkage clustering, the distance between two nodes can be found directly from the distances between the individual items. Therefore, the clustering algorithm does not need access to the original gene expression data, once the distance matrix is known. For pairwise centroid-linkage clustering, however, the centroids of newly formed subnodes can only be calculated from the original data and not from the distance matrix.</P><P>The im [...]
-<H3 CLASS="subsection"><!--SEC ANCHOR -->Representing a hierarchical clustering solution</H3><!--SEC END --><P>The result of hierarchical clustering consists of a tree of nodes, in which each node joins two items or subnodes. Usually, we are not only interested in which items or subnodes are joined at each node, but also in their similarity (or distance) as they are joined. To store one node in the hierarchical clustering tree, we make use of the class <CODE>Node</CODE>, which defined in [...]
-</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-<CODE>left</CODE>
-</LI><LI CLASS="li-itemize"><CODE>right</CODE>
-</LI><LI CLASS="li-itemize"><CODE>distance</CODE>
-</LI></UL><P>
-Here, <CODE>left</CODE> and <CODE>right</CODE> are integers referring to the two items or subnodes that are joined at this node, and <CODE>distance</CODE> is the distance between them. The items being clustered are numbered from 0 to (number of items − 1), while clusters are numbered from -1 to −(number of items−1). Note that the number of nodes is one less than the number of items.</P><P>To create a new <CODE>Node</CODE> object, we need to specify <CODE>left</CODE> [...]
->>> Node(2, 3)
-(2, 3): 0
->>> Node(2, 3, 0.91)
-(2, 3): 0.91
-</PRE><P>The attributes <CODE>left</CODE>, <CODE>right</CODE>, and <CODE>distance</CODE> of an existing <CODE>Node</CODE> object can be modified directly:</P><PRE CLASS="verbatim">>>> node = Node(4, 5)
->>> node.left = 6
->>> node.right = 2
->>> node.distance = 0.73
->>> node
-(6, 2): 0.73
-</PRE><P>An error is raised if <CODE>left</CODE> and <CODE>right</CODE> are not integers, or if <CODE>distance</CODE> cannot be converted to a floating-point value.</P><P>The Python class <CODE>Tree</CODE> represents a full hierarchical clustering solution. A <CODE>Tree</CODE> object can be created from a list of <CODE>Node</CODE> objects:</P><PRE CLASS="verbatim">>>> from Bio.Cluster import Node, Tree
->>> nodes = [Node(1, 2, 0.2), Node(0, 3, 0.5), Node(-2, 4, 0.6), Node(-1, -3, 0.9)]
->>> tree = Tree(nodes)
->>> print(tree)
-(1, 2): 0.2
-(0, 3): 0.5
-(-2, 4): 0.6
-(-1, -3): 0.9
-</PRE><P>The <CODE>Tree</CODE> initializer checks if the list of nodes is a valid hierarchical clustering result:</P><PRE CLASS="verbatim">>>> nodes = [Node(1, 2, 0.2), Node(0, 2, 0.5)]
->>> Tree(nodes)
-Traceback (most recent call last):
- File "<stdin>", line 1, in ?
-ValueError: Inconsistent tree
-</PRE><P>Individual nodes in a <CODE>Tree</CODE> object can be accessed using square brackets:</P><PRE CLASS="verbatim">>>> nodes = [Node(1, 2, 0.2), Node(0, -1, 0.5)]
->>> tree = Tree(nodes)
->>> tree[0]
-(1, 2): 0.2
->>> tree[1]
-(0, -1): 0.5
->>> tree[-1]
-(0, -1): 0.5
-</PRE><P>As a <CODE>Tree</CODE> object is read-only, we cannot change individual nodes in a <CODE>Tree</CODE> object. However, we can convert the tree to a list of nodes, modify this list, and create a new tree from this list:</P><PRE CLASS="verbatim">>>> tree = Tree([Node(1, 2, 0.1), Node(0, -1, 0.5), Node(-2, 3, 0.9)])
->>> print(tree)
-(1, 2): 0.1
-(0, -1): 0.5
-(-2, 3): 0.9
->>> nodes = tree[:]
->>> nodes[0] = Node(0, 1, 0.2)
->>> nodes[1].left = 2
->>> tree = Tree(nodes)
->>> print(tree)
-(0, 1): 0.2
-(2, -1): 0.5
-(-2, 3): 0.9
-</PRE><P>This guarantees that any <CODE>Tree</CODE> object is always well-formed.</P><P>To display a hierarchical clustering solution with visualization programs such as Java Treeview, it is better to scale all node distances such that they are between zero and one. This can be accomplished by calling the <CODE>scale</CODE> method on an existing <CODE>Tree</CODE> object:
-</P><PRE CLASS="verbatim">>>> tree.scale()
-</PRE><P>This method takes no arguments, and returns <CODE>None</CODE>.</P><P>After hierarchical clustering, the items can be grouped into <I>k</I> clusters based on the tree structure stored in the <CODE>Tree</CODE> object by cutting the tree:
-</P><PRE CLASS="verbatim">>>> clusterid = tree.cut(nclusters=1)
-</PRE><P>where <CODE>nclusters</CODE> (defaulting to <CODE>1</CODE>) is the desired number of clusters <I>k</I>.
-This method ignores the top <I>k</I>−1 linking events in the tree structure, resulting in <I>k</I> separated clusters of items. The number of clusters <I>k</I> should be positive, and less than or equal to the number of items.
-This method returns an array <CODE>clusterid</CODE> containing the number of the cluster to which each item is assigned.</P><!--TOC subsection Performing hierarchical clustering-->
-<H3 CLASS="subsection"><!--SEC ANCHOR -->Performing hierarchical clustering</H3><!--SEC END --><P>To perform hierarchical clustering, use the <CODE>treecluster</CODE> function in <CODE>Bio.Cluster</CODE>.
-</P><PRE CLASS="verbatim">>>> from Bio.Cluster import treecluster
->>> tree = treecluster(data)
-</PRE><P>where the following arguments are defined:</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-<CODE>data</CODE><BR>
-Array containing the data for the items.
-</LI><LI CLASS="li-itemize"><CODE>mask</CODE> (default: <CODE>None</CODE>)<BR>
-Array of integers showing which data are missing. If <CODE>mask[i,j]==0</CODE>, then <CODE>data[i,j]</CODE> is missing. If <CODE>mask==None</CODE>, then all data are present.
-</LI><LI CLASS="li-itemize"><CODE>weight</CODE> (default: <CODE>None</CODE>)<BR>
-The weights to be used when calculating distances. If <CODE>weight==None</CODE>, then equal weights are assumed.
-</LI><LI CLASS="li-itemize"><CODE>transpose</CODE> (default: <CODE>0</CODE>)<BR>
-Determines if rows (<CODE>transpose==0</CODE>) or columns (<CODE>transpose==1</CODE>) are to be clustered.
-</LI><LI CLASS="li-itemize"><CODE>method</CODE> (default: <CODE>'m'</CODE>)<BR>
-defines the linkage method to be used:
-<UL CLASS="itemize"><LI CLASS="li-itemize">
-<CODE>method=='s'</CODE>: pairwise single-linkage clustering
-</LI><LI CLASS="li-itemize"><CODE>method=='m'</CODE>: pairwise maximum- (or complete-) linkage clustering
-</LI><LI CLASS="li-itemize"><CODE>method=='c'</CODE>: pairwise centroid-linkage clustering
-</LI><LI CLASS="li-itemize"><CODE>method=='a'</CODE>: pairwise average-linkage clustering
-</LI></UL>
-</LI><LI CLASS="li-itemize"><CODE>dist</CODE> (default: <CODE>'e'</CODE>, Euclidean distance)<BR>
-Defines the distance function to be used (see <A HREF="#sec:distancefunctions">14.1</A>).
-</LI></UL><P>To apply hierarchical clustering on a precalculated distance matrix, specify the <CODE>distancematrix</CODE> argument when calling <CODE>treecluster</CODE> function instead of the <CODE>data</CODE> argument:
-</P><PRE CLASS="verbatim">>>> from Bio.Cluster import treecluster
->>> tree = treecluster(distancematrix=distance)
-</PRE><P>In this case, the following arguments are defined:
-</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-<CODE>distancematrix</CODE><BR>
-The distance matrix, which can be specified in three ways:
-<UL CLASS="itemize"><LI CLASS="li-itemize">
-as a 2D Numerical Python array (in which only the left-lower part of the array will be accessed):
-<PRE CLASS="verbatim">distance = array([[0.0, 1.1, 2.3],
- [1.1, 0.0, 4.5],
- [2.3, 4.5, 0.0]])
-</PRE></LI><LI CLASS="li-itemize">as a 1D Numerical Python array containing consecutively the distances in the left-lower part of the distance matrix:
-<PRE CLASS="verbatim">distance = array([1.1, 2.3, 4.5])
-</PRE></LI><LI CLASS="li-itemize">as a list containing the rows of the left-lower part of the distance matrix:
-<PRE CLASS="verbatim">distance = [array([]),
- array([1.1]),
- array([2.3, 4.5])
-</PRE></LI></UL>
-These three expressions correspond to the same distance matrix.
-As <CODE>treecluster</CODE> may shuffle the values in the distance matrix as part of the clustering algorithm, be sure to save this array in a different variable before calling <CODE>treecluster</CODE> if you need it later.
-</LI><LI CLASS="li-itemize"><CODE>method</CODE><BR>
-The linkage method to be used:
-<UL CLASS="itemize"><LI CLASS="li-itemize">
-<CODE>method=='s'</CODE>: pairwise single-linkage clustering
-</LI><LI CLASS="li-itemize"><CODE>method=='m'</CODE>: pairwise maximum- (or complete-) linkage clustering
-</LI><LI CLASS="li-itemize"><CODE>method=='a'</CODE>: pairwise average-linkage clustering
-</LI></UL>
-While pairwise single-, maximum-, and average-linkage clustering can be calculated from the distance matrix alone, pairwise centroid-linkage cannot.
-</LI></UL><P>When calling <CODE>treecluster</CODE>, either <CODE>data</CODE> or <CODE>distancematrix</CODE> should be <CODE>None</CODE>.</P><P>This function returns a <CODE>Tree</CODE> object. This object contains (number of items − 1) nodes, where the number of items is the number of rows if rows were clustered, or the number of columns if columns were clustered. Each node describes a pairwise linking event, where the node attributes <CODE>left</CODE> and <CODE>right</CODE> each [...]
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc193">14.5</A>  Self-Organizing Maps</H2><!--SEC END --><P>Self-Organizing Maps (SOMs) were invented by Kohonen to describe neural networks (see for instance Kohonen, 1997 [<A HREF="#kohonen1997">24</A>]). Tamayo (1999) first applied Self-Organizing Maps to gene expression data [<A HREF="#tamayo1999">30</A>].</P><P>SOMs organize items into clusters that are situated in some topology. Usually a rectangular topology is chosen. The [...]
-</P><TABLE CLASS="display dcenter"><TR VALIGN="middle"><TD CLASS="dcell">Δ </TD><TD CLASS="dcell"><TABLE border=0 cellspacing=1 cellpadding=0><TR><TD ALIGN=center NOWRAP><I>x</I></TD></TR>
-<TR><TD CLASS="hbar"></TD></TR>
-</TABLE></TD><TD CLASS="dcell"><SUB>cell</SUB> = τ · </TD><TD CLASS="dcell">⎛<BR>
-⎜<BR>
-⎝</TD><TD CLASS="dcell"><TABLE border=0 cellspacing=1 cellpadding=0><TR><TD ALIGN=center NOWRAP><I>x</I></TD></TR>
-<TR><TD CLASS="hbar"></TD></TR>
-</TABLE></TD><TD CLASS="dcell"><SUB>row</SUB> − </TD><TD CLASS="dcell"><TABLE border=0 cellspacing=1 cellpadding=0><TR><TD ALIGN=center NOWRAP><I>x</I></TD></TR>
-<TR><TD CLASS="hbar"></TD></TR>
-</TABLE></TD><TD CLASS="dcell"><SUB>cell</SUB> </TD><TD CLASS="dcell">⎞<BR>
-⎟<BR>
-⎠</TD><TD CLASS="dcell">.</TD></TR>
-</TABLE><P>
-The parameter
-τ
-is a parameter that decreases at each iteration step. We have used a simple linear function of the iteration step:
-</P><TABLE CLASS="display dcenter"><TR VALIGN="middle"><TD CLASS="dcell">τ = τ<SUB>init</SUB> · </TD><TD CLASS="dcell">⎛<BR>
-⎜<BR>
-⎜<BR>
-⎝</TD><TD CLASS="dcell">1 − </TD><TD CLASS="dcell"><TABLE CLASS="display"><TR><TD CLASS="dcell" ALIGN="center"><I>i</I> </TD></TR>
-<TR><TD CLASS="hbar"></TD></TR>
-<TR><TD CLASS="dcell" ALIGN="center"><I>n</I></TD></TR>
-</TABLE></TD><TD CLASS="dcell">⎞<BR>
-⎟<BR>
-⎟<BR>
-⎠</TD><TD CLASS="dcell">,</TD></TR>
-</TABLE><P>
-τ<SUB>init</SUB>
-is the initial value of τ as specified by the user, <I>i</I> is the number of the current iteration step, and <I>n</I> is the total number of iteration steps to be performed. While changes are made rapidly in the beginning of the iteration, at the end of iteration only small changes are made.</P><P>All clusters within a radius <I>R</I> are adjusted to the gene under consideration. This radius decreases as the calculation progresses as
-</P><TABLE CLASS="display dcenter"><TR VALIGN="middle"><TD CLASS="dcell"><I>R</I> = <I>R</I><SUB>max</SUB> · </TD><TD CLASS="dcell">⎛<BR>
-⎜<BR>
-⎜<BR>
-⎝</TD><TD CLASS="dcell">1 − </TD><TD CLASS="dcell"><TABLE CLASS="display"><TR><TD CLASS="dcell" ALIGN="center"><I>i</I> </TD></TR>
-<TR><TD CLASS="hbar"></TD></TR>
-<TR><TD CLASS="dcell" ALIGN="center"><I>n</I></TD></TR>
-</TABLE></TD><TD CLASS="dcell">⎞<BR>
-⎟<BR>
-⎟<BR>
-⎠</TD><TD CLASS="dcell">,</TD></TR>
-</TABLE><P>
-in which the maximum radius is defined as
-</P><TABLE CLASS="display dcenter"><TR VALIGN="middle"><TD CLASS="dcell"><I>R</I><SUB>max</SUB> = </TD><TD CLASS="dcell"><FONT SIZE=5>√</FONT></TD><TD CLASS="dcell"><TABLE border=0 cellspacing=1 cellpadding=0><TR><TD CLASS="hbar"></TD></TR>
-<TR><TD ALIGN=center NOWRAP><I>N</I><SUB><I>x</I></SUB><SUP>2</SUP> + <I>N</I><SUB><I>y</I></SUB><SUP>2</SUP></TD></TR>
-</TABLE></TD><TD CLASS="dcell">,</TD></TR>
-</TABLE><P>
-where
-(<I>N</I><SUB><I>x</I></SUB>, <I>N</I><SUB><I>y</I></SUB>)
-are the dimensions of the rectangle defining the topology.</P><P>The function <CODE>somcluster</CODE> implements the complete algorithm to calculate a Self-Organizing Map on a rectangular grid. First it initializes the random number generator. The node data are then initialized using the random number generator. The order in which genes or microarrays are used to modify the SOM is also randomized. The total number of iterations in the SOM algorithm is specified by the user.</P><P>To run [...]
-</P><PRE CLASS="verbatim">>>> from Bio.Cluster import somcluster
->>> clusterid, celldata = somcluster(data)
-</PRE><P>where the following arguments are defined:
-</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-<CODE>data</CODE> (required)<BR>
-Array containing the data for the items.
-</LI><LI CLASS="li-itemize"><CODE>mask</CODE> (default: <CODE>None</CODE>)<BR>
-Array of integers showing which data are missing. If <CODE>mask[i,j]==0</CODE>, then <CODE>data[i,j]</CODE> is missing. If <CODE>mask==None</CODE>, then all data are present.
-</LI><LI CLASS="li-itemize"><CODE>weight</CODE> (default: <CODE>None</CODE>)<BR>
-contains the weights to be used when calculating distances. If <CODE>weight==None</CODE>, then equal weights are assumed.
-</LI><LI CLASS="li-itemize"><CODE>transpose</CODE> (default: <CODE>0</CODE>)<BR>
-Determines if rows (<CODE>transpose</CODE> is <CODE>0</CODE>) or columns (<CODE>transpose</CODE> is <CODE>1</CODE>) are to be clustered.
-</LI><LI CLASS="li-itemize"><CODE>nxgrid, nygrid</CODE> (default: <CODE>2, 1</CODE>)<BR>
-The number of cells horizontally and vertically in the rectangular grid on which the Self-Organizing Map is calculated.
-</LI><LI CLASS="li-itemize"><CODE>inittau</CODE> (default: <CODE>0.02</CODE>)<BR>
-The initial value for the parameter τ that is used in the SOM algorithm. The default value for <CODE>inittau</CODE> is 0.02, which was used in Michael Eisen’s Cluster/TreeView program.
-</LI><LI CLASS="li-itemize"><CODE>niter</CODE> (default: <CODE>1</CODE>)<BR>
-The number of iterations to be performed.
-</LI><LI CLASS="li-itemize"><CODE>dist</CODE> (default: <CODE>'e'</CODE>, Euclidean distance)<BR>
-Defines the distance function to be used (see <A HREF="#sec:distancefunctions">14.1</A>).
-</LI></UL><P>This function returns the tuple <CODE>(clusterid, celldata)</CODE>:
-</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-<CODE>clusterid</CODE>:<BR>
-An array with two columns, where the number of rows is equal to the number of items that were clustered. Each row contains the <I>x</I> and <I>y</I> coordinates of the cell in the rectangular SOM grid to which the item was assigned.
-</LI><LI CLASS="li-itemize"><CODE>celldata</CODE>:<BR>
-An array with dimensions (<CODE>nxgrid</CODE>, <CODE>nygrid</CODE>, number of columns) if rows are being clustered, or (<CODE>nxgrid</CODE>, <CODE>nygrid</CODE>, number of rows) if columns are being clustered. Each element <CODE>[ix][iy]</CODE> of this array is a 1D vector containing the gene expression data for the centroid of the cluster in the grid cell with coordinates <CODE>[ix][iy]</CODE>.
-</LI></UL><!--TOC section Principal Component Analysis-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc194">14.6</A>  Principal Component Analysis</H2><!--SEC END --><P>Principal Component Analysis (PCA) is a widely used technique for analyzing multivariate data. A practical example of applying Principal Component Analysis to gene expression data is presented by Yeung and Ruzzo (2001) [<A HREF="#yeung2001">33</A>].</P><P>In essence, PCA is a coordinate transformation in which each row in the data matrix is written as a linear su [...]
-</P><PRE CLASS="verbatim">>>> from Bio.Cluster import pca
->>> columnmean, coordinates, components, eigenvalues = pca(data)
-</PRE><P>This function returns a tuple <CODE>columnmean, coordinates, components, eigenvalues</CODE>:
-</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-<CODE>columnmean</CODE><BR>
-Array containing the mean over each column in <CODE>data</CODE>.
-</LI><LI CLASS="li-itemize"><CODE>coordinates</CODE><BR>
-The coordinates of each row in <CODE>data</CODE> with respect to the principal components.
-</LI><LI CLASS="li-itemize"><CODE>components</CODE><BR>
-The principal components.
-</LI><LI CLASS="li-itemize"><CODE>eigenvalues</CODE><BR>
-The eigenvalues corresponding to each of the principal components.
-</LI></UL><P>
-The original matrix <CODE>data</CODE> can be recreated by calculating <CODE>columnmean + dot(coordinates, components)</CODE>.</P><!--TOC section Handling Cluster/TreeView-type files-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc195">14.7</A>  Handling Cluster/TreeView-type files</H2><!--SEC END --><P>Cluster/TreeView are GUI-based codes for clustering gene expression data. They were originally written by <A HREF="http://rana.lbl.gov">Michael Eisen</A> while at Stanford University. <CODE>Bio.Cluster</CODE> contains functions for reading and writing data files that correspond to the format specified for Cluster/TreeView. In particular, by saving a clust [...]
->>> handle = open("mydatafile.txt")
->>> record = Cluster.read(handle)
->>> handle.close()
-</PRE><P>This two-step process gives you some flexibility in the source of the data.
-For example, you can use</P><PRE CLASS="verbatim">>>> import gzip # Python standard library
->>> handle = gzip.open("mydatafile.txt.gz")
-</PRE><P>to open a gzipped file, or
-</P><PRE CLASS="verbatim">>>> import urllib # Python standard library
->>> handle = urllib.urlopen("http://somewhere.org/mydatafile.txt")
-</PRE><P>to open a file stored on the Internet before calling <CODE>read</CODE>.</P><P>The <CODE>read</CODE> command reads the tab-delimited text file <CODE>mydatafile.txt</CODE> containing gene expression data in the format specified for Michael Eisen’s Cluster/TreeView program. For a description of this file format, see the manual to Cluster/TreeView. It is available at <A HREF="http://rana.lbl.gov/manuals/ClusterTreeView.pdf">Michael Eisen’s lab website</A> and at <A HRE [...]
-<CODE>data</CODE><BR>
-The data array containing the gene expression data. Genes are stored row-wise, while microarrays are stored column-wise.</LI><LI CLASS="li-itemize"><CODE>mask</CODE><BR>
-This array shows which elements in the <CODE>data</CODE> array, if any, are missing. If <CODE>mask[i,j]==0</CODE>, then <CODE>data[i,j]</CODE> is missing. If no data were found to be missing, <CODE>mask</CODE> is set to <CODE>None</CODE>.</LI><LI CLASS="li-itemize"><CODE>geneid</CODE><BR>
-This is a list containing a unique description for each gene (i.e., ORF numbers).</LI><LI CLASS="li-itemize"><CODE>genename</CODE><BR>
-This is a list containing a description for each gene (i.e., gene name). If not present in the data file, <CODE>genename</CODE> is set to <CODE>None</CODE>.</LI><LI CLASS="li-itemize"><CODE>gweight</CODE><BR>
-The weights that are to be used to calculate the distance in expression profile between genes. If not present in the data file, <CODE>gweight</CODE> is set to <CODE>None</CODE>.</LI><LI CLASS="li-itemize"><CODE>gorder</CODE><BR>
-The preferred order in which genes should be stored in an output file. If not present in the data file, <CODE>gorder</CODE> is set to <CODE>None</CODE>.</LI><LI CLASS="li-itemize"><CODE>expid</CODE><BR>
-This is a list containing a description of each microarray, e.g. experimental condition.</LI><LI CLASS="li-itemize"><CODE>eweight</CODE><BR>
-The weights that are to be used to calculate the distance in expression profile between microarrays. If not present in the data file, <CODE>eweight</CODE> is set to <CODE>None</CODE>.</LI><LI CLASS="li-itemize"><CODE>eorder</CODE><BR>
-The preferred order in which microarrays should be stored in an output file. If not present in the data file, <CODE>eorder</CODE> is set to <CODE>None</CODE>.</LI><LI CLASS="li-itemize"><CODE>uniqid</CODE><BR>
-The string that was used instead of UNIQID in the data file.
-</LI></UL><P>After loading a <CODE>Record</CODE> object, each of these attributes can be accessed and modified directly. For example, the data can be log-transformed by taking the logarithm of <CODE>record.data</CODE>.</P><!--TOC subsection Calculating the distance matrix-->
-<H3 CLASS="subsection"><!--SEC ANCHOR -->Calculating the distance matrix</H3><!--SEC END --><P>To calculate the distance matrix between the items stored in the record, use
-</P><PRE CLASS="verbatim">>>> matrix = record.distancematrix()
-</PRE><P>where the following arguments are defined:
-</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-<CODE>transpose</CODE> (default: <CODE>0</CODE>)<BR>
-Determines if the distances between the rows of <CODE>data</CODE> are to be calculated (<CODE>transpose==0</CODE>), or between the columns of <CODE>data</CODE> (<CODE>transpose==1</CODE>).
-</LI><LI CLASS="li-itemize"><CODE>dist</CODE> (default: <CODE>'e'</CODE>, Euclidean distance)<BR>
-Defines the distance function to be used (see <A HREF="#sec:distancefunctions">14.1</A>).
-</LI></UL><P>This function returns the distance matrix as a list of rows, where the number of columns of each row is equal to the row number (see section <A HREF="#subsec:distancematrix">14.1</A>).</P><!--TOC subsection Calculating the cluster centroids-->
-<H3 CLASS="subsection"><!--SEC ANCHOR -->Calculating the cluster centroids</H3><!--SEC END --><P>To calculate the centroids of clusters of items stored in the record, use
-</P><PRE CLASS="verbatim">>>> cdata, cmask = record.clustercentroids()
-</PRE><UL CLASS="itemize"><LI CLASS="li-itemize">
-<CODE>clusterid</CODE> (default: <CODE>None</CODE>)<BR>
-Vector of integers showing to which cluster each item belongs. If <CODE>clusterid</CODE> is not given, then all items are assumed to belong to the same cluster.
-</LI><LI CLASS="li-itemize"><CODE>method</CODE> (default: <CODE>'a'</CODE>)<BR>
-Specifies whether the arithmetic mean (<CODE>method=='a'</CODE>) or the median (<CODE>method=='m'</CODE>) is used to calculate the cluster center.
-</LI><LI CLASS="li-itemize"><CODE>transpose</CODE> (default: <CODE>0</CODE>)<BR>
-Determines if the centroids of the rows of <CODE>data</CODE> are to be calculated (<CODE>transpose==0</CODE>), or the centroids of the columns of <CODE>data</CODE> (<CODE>transpose==1</CODE>).
-</LI></UL><P>This function returns the tuple <CODE>cdata, cmask</CODE>; see section <A HREF="#subsec:clustercentroids">14.2</A> for a description.</P><!--TOC subsection Calculating the distance between clusters-->
-<H3 CLASS="subsection"><!--SEC ANCHOR -->Calculating the distance between clusters</H3><!--SEC END --><P>
-To calculate the distance between clusters of items stored in the record, use
-</P><PRE CLASS="verbatim">>>> distance = record.clusterdistance()
-</PRE><P>where the following arguments are defined:
-</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-<CODE>index1</CODE> (default: <CODE>0</CODE>)<BR>
-A list containing the indices of the items belonging to the first cluster. A cluster containing only one item <I>i</I> can be represented either as a list <CODE>[i]</CODE>, or as an integer <CODE>i</CODE>.
-</LI><LI CLASS="li-itemize"><CODE>index2</CODE> (default: <CODE>0</CODE>)<BR>
-A list containing the indices of the items belonging to the second cluster. A cluster containing only one item <I>i</I> can be represented either as a list <CODE>[i]</CODE>, or as an integer <CODE>i</CODE>.
-</LI><LI CLASS="li-itemize"><CODE>method</CODE> (default: <CODE>'a'</CODE>)<BR>
-Specifies how the distance between clusters is defined:
-<UL CLASS="itemize"><LI CLASS="li-itemize">
-<CODE>'a'</CODE>: Distance between the two cluster centroids (arithmetic mean);
-</LI><LI CLASS="li-itemize"><CODE>'m'</CODE>: Distance between the two cluster centroids (median);
-</LI><LI CLASS="li-itemize"><CODE>'s'</CODE>: Shortest pairwise distance between items in the two clusters;
-</LI><LI CLASS="li-itemize"><CODE>'x'</CODE>: Longest pairwise distance between items in the two clusters;
-</LI><LI CLASS="li-itemize"><CODE>'v'</CODE>: Average over the pairwise distances between items in the two clusters.
-</LI></UL>
-</LI><LI CLASS="li-itemize"><CODE>dist</CODE> (default: <CODE>'e'</CODE>, Euclidean distance)<BR>
-Defines the distance function to be used (see <A HREF="#sec:distancefunctions">14.1</A>).
-</LI><LI CLASS="li-itemize"><CODE>transpose</CODE> (default: <CODE>0</CODE>)<BR>
-If <CODE>transpose==0</CODE>, calculate the distance between the rows of <CODE>data</CODE>. If <CODE>transpose==1</CODE>, calculate the distance between the columns of <CODE>data</CODE>.
-</LI></UL><!--TOC subsection Performing hierarchical clustering-->
-<H3 CLASS="subsection"><!--SEC ANCHOR -->Performing hierarchical clustering</H3><!--SEC END --><P>To perform hierarchical clustering on the items stored in the record, use
-</P><PRE CLASS="verbatim">>>> tree = record.treecluster()
-</PRE><P>where the following arguments are defined:
-</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-<CODE>transpose</CODE> (default: <CODE>0</CODE>)<BR>
-Determines if rows (<CODE>transpose==0</CODE>) or columns (<CODE>transpose==1</CODE>) are to be clustered.
-</LI><LI CLASS="li-itemize"><CODE>method</CODE> (default: <CODE>'m'</CODE>)<BR>
-defines the linkage method to be used:
-<UL CLASS="itemize"><LI CLASS="li-itemize">
-<CODE>method=='s'</CODE>: pairwise single-linkage clustering
-</LI><LI CLASS="li-itemize"><CODE>method=='m'</CODE>: pairwise maximum- (or complete-) linkage clustering
-</LI><LI CLASS="li-itemize"><CODE>method=='c'</CODE>: pairwise centroid-linkage clustering
-</LI><LI CLASS="li-itemize"><CODE>method=='a'</CODE>: pairwise average-linkage clustering
-</LI></UL>
-</LI><LI CLASS="li-itemize"><CODE>dist</CODE> (default: <CODE>'e'</CODE>, Euclidean distance)<BR>
-Defines the distance function to be used (see <A HREF="#sec:distancefunctions">14.1</A>).
-</LI><LI CLASS="li-itemize"><CODE>transpose</CODE><BR>
-Determines if genes or microarrays are being clustered. If <CODE>transpose==0</CODE>, genes (rows) are being clustered. If <CODE>transpose==1</CODE>, microarrays (columns) are clustered.
-</LI></UL><P>This function returns a <CODE>Tree</CODE> object. This object contains (number of items − 1) nodes, where the number of items is the number of rows if rows were clustered, or the number of columns if columns were clustered. Each node describes a pairwise linking event, where the node attributes <CODE>left</CODE> and <CODE>right</CODE> each contain the number of one item or subnode, and <CODE>distance</CODE> the distance between them. Items are numbered from 0 to (numb [...]
-<H3 CLASS="subsection"><!--SEC ANCHOR -->Performing <I>k</I>-means or <I>k</I>-medians clustering</H3><!--SEC END --><P>To perform <I>k</I>-means or <I>k</I>-medians clustering on the items stored in the record, use
-</P><PRE CLASS="verbatim">>>> clusterid, error, nfound = record.kcluster()
-</PRE><P>where the following arguments are defined:
-</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-<CODE>nclusters</CODE> (default: <CODE>2</CODE>)<BR>
-The number of clusters <I>k</I>.
-</LI><LI CLASS="li-itemize"><CODE>transpose</CODE> (default: <CODE>0</CODE>)<BR>
-Determines if rows (<CODE>transpose</CODE> is <CODE>0</CODE>) or columns (<CODE>transpose</CODE> is <CODE>1</CODE>) are to be clustered.
-</LI><LI CLASS="li-itemize"><CODE>npass</CODE> (default: <CODE>1</CODE>)<BR>
-The number of times the <I>k</I>-means/-medians clustering algorithm is performed, each time with a different (random) initial condition. If <CODE>initialid</CODE> is given, the value of <CODE>npass</CODE> is ignored and the clustering algorithm is run only once, as it behaves deterministically in that case.
-</LI><LI CLASS="li-itemize"><CODE>method</CODE> (default: <CODE>a</CODE>)<BR>
-describes how the center of a cluster is found:
-<UL CLASS="itemize"><LI CLASS="li-itemize">
-<CODE>method=='a'</CODE>: arithmetic mean (<I>k</I>-means clustering);
-</LI><LI CLASS="li-itemize"><CODE>method=='m'</CODE>: median (<I>k</I>-medians clustering).
-</LI></UL>
-For other values of <CODE>method</CODE>, the arithmetic mean is used.
-</LI><LI CLASS="li-itemize"><CODE>dist</CODE> (default: <CODE>'e'</CODE>, Euclidean distance)<BR>
-Defines the distance function to be used (see <A HREF="#sec:distancefunctions">14.1</A>).
-</LI></UL><P>This function returns a tuple <CODE>(clusterid, error, nfound)</CODE>, where <CODE>clusterid</CODE> is an integer array containing the number of the cluster to which each row or cluster was assigned, <CODE>error</CODE> is the within-cluster sum of distances for the optimal clustering solution, and <CODE>nfound</CODE> is the number of times this optimal solution was found.</P><!--TOC subsection Calculating a Self-Organizing Map-->
-<H3 CLASS="subsection"><!--SEC ANCHOR -->Calculating a Self-Organizing Map</H3><!--SEC END --><P>To calculate a Self-Organizing Map of the items stored in the record, use
-</P><PRE CLASS="verbatim">>>> clusterid, celldata = record.somcluster()
-</PRE><P>where the following arguments are defined:
-</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-<CODE>transpose</CODE> (default: <CODE>0</CODE>)<BR>
-Determines if rows (<CODE>transpose</CODE> is <CODE>0</CODE>) or columns (<CODE>transpose</CODE> is <CODE>1</CODE>) are to be clustered.
-</LI><LI CLASS="li-itemize"><CODE>nxgrid, nygrid</CODE> (default: <CODE>2, 1</CODE>)<BR>
-The number of cells horizontally and vertically in the rectangular grid on which the Self-Organizing Map is calculated.
-</LI><LI CLASS="li-itemize"><CODE>inittau</CODE> (default: <CODE>0.02</CODE>)<BR>
-The initial value for the parameter τ that is used in the SOM algorithm. The default value for <CODE>inittau</CODE> is 0.02, which was used in Michael Eisen’s Cluster/TreeView program.
-</LI><LI CLASS="li-itemize"><CODE>niter</CODE> (default: <CODE>1</CODE>)<BR>
-The number of iterations to be performed.
-</LI><LI CLASS="li-itemize"><CODE>dist</CODE> (default: <CODE>'e'</CODE>, Euclidean distance)<BR>
-Defines the distance function to be used (see <A HREF="#sec:distancefunctions">14.1</A>).
-</LI></UL><P>This function returns the tuple <CODE>(clusterid, celldata)</CODE>:
-</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-<CODE>clusterid</CODE>:<BR>
-An array with two columns, where the number of rows is equal to the number of items that were clustered. Each row contains the <I>x</I> and <I>y</I> coordinates of the cell in the rectangular SOM grid to which the item was assigned.
-</LI><LI CLASS="li-itemize"><CODE>celldata</CODE>:<BR>
-An array with dimensions (<CODE>nxgrid</CODE>, <CODE>nygrid</CODE>, number of columns) if rows are being clustered, or (<CODE>nxgrid</CODE>, <CODE>nygrid</CODE>, number of rows) if columns are being clustered. Each element <CODE>[ix][iy]</CODE> of this array is a 1D vector containing the gene expression data for the centroid of the cluster in the grid cell with coordinates <CODE>[ix][iy]</CODE>.
-</LI></UL><!--TOC subsection Saving the clustering result-->
-<H3 CLASS="subsection"><!--SEC ANCHOR -->Saving the clustering result</H3><!--SEC END --><P>To save the clustering result, use
-</P><PRE CLASS="verbatim">>>> record.save(jobname, geneclusters, expclusters)
-</PRE><P>where the following arguments are defined:
-</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-<CODE>jobname</CODE><BR>
-The string <CODE>jobname</CODE> is used as the base name for names of the files that are to be saved.
-</LI><LI CLASS="li-itemize"><CODE>geneclusters</CODE><BR>
-This argument describes the gene (row-wise) clustering result. In case of <I>k</I>-means clustering, this is a 1D array containing the number of the cluster each gene belongs to. It can be calculated using <CODE>kcluster</CODE>. In case of hierarchical clustering, <CODE>geneclusters</CODE> is a <CODE>Tree</CODE> object.
-</LI><LI CLASS="li-itemize"><CODE>expclusters</CODE><BR>
-This argument describes the (column-wise) clustering result for the experimental conditions. In case of <I>k</I>-means clustering, this is a 1D array containing the number of the cluster each experimental condition belongs to. It can be calculated using <CODE>kcluster</CODE>. In case of hierarchical clustering, <CODE>expclusters</CODE> is a <CODE>Tree</CODE> object.
-</LI></UL><P>This method writes the text file <CODE>jobname.cdt</CODE>, <CODE>jobname.gtr</CODE>, <CODE>jobname.atr</CODE>, <CODE>jobname*.kgg</CODE>, and/or <CODE>jobname*.kag</CODE> for subsequent reading by the Java TreeView program. If <CODE>geneclusters</CODE> and <CODE>expclusters</CODE> are both <CODE>None</CODE>, this method only writes the text file <CODE>jobname.cdt</CODE>; this file can subsequently be read into a new <CODE>Record</CODE> object.
-</P><!--TOC section Example calculation-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc196">14.8</A>  Example calculation</H2><!--SEC END --><P>This is an example of a hierarchical clustering calculation, using single linkage clustering for genes and maximum linkage clustering for experimental conditions. As the Euclidean distance is being used for gene clustering, it is necessary to scale the node distances <CODE>genetree</CODE> such that they are all between zero and one. This is needed for the Java TreeView co [...]
->>> handle = open("cyano.txt")
->>> record = Cluster.read(handle)
->>> handle.close()
->>> genetree = record.treecluster(method='s')
->>> genetree.scale()
->>> exptree = record.treecluster(dist='u', transpose=1)
->>> record.save("cyano_result", genetree, exptree)
-</PRE><P>This will create the files <CODE>cyano_result.cdt</CODE>, <CODE>cyano_result.gtr</CODE>, and <CODE>cyano_result.atr</CODE>.</P><P>Similarly, we can save a <I>k</I>-means clustering solution:</P><PRE CLASS="verbatim">>>> from Bio import Cluster
->>> handle = open("cyano.txt")
->>> record = Cluster.read(handle)
->>> handle.close()
->>> (geneclusters, error, ifound) = record.kcluster(nclusters=5, npass=1000)
->>> (expclusters, error, ifound) = record.kcluster(nclusters=2, npass=100, transpose=1)
->>> record.save("cyano_result", geneclusters, expclusters)
-</PRE><P>This will create the files <CODE>cyano_result_K_G2_A2.cdt</CODE>, <CODE>cyano_result_K_G2.kgg</CODE>, and <CODE>cyano_result_K_A2.kag</CODE>.</P><!--TOC section Auxiliary functions-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc197">14.9</A>  Auxiliary functions</H2><!--SEC END --><P><CODE>median(data)</CODE>
-returns the median of the 1D array <CODE>data</CODE>.</P><P><CODE>mean(data)</CODE>
-returns the mean of the 1D array <CODE>data</CODE>.</P><P><CODE>version()</CODE>
-returns the version number of the underlying C Clustering Library as a string.</P><!--TOC chapter Supervised learning methods-->
-<H1 CLASS="chapter"><!--SEC ANCHOR --><A NAME="htoc198">Chapter 15</A>  Supervised learning methods</H1><!--SEC END --><P>Note the supervised learning methods described in this chapter all require Numerical Python (numpy) to be installed.</P><!--TOC section The Logistic Regression Model-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc199">15.1</A>  The Logistic Regression Model</H2><!--SEC END --><P>
-<A NAME="sec:LogisticRegression"></A></P><!--TOC subsection Background and Purpose-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc200">15.1.1</A>  Background and Purpose</H3><!--SEC END --><P>Logistic regression is a supervised learning approach that attempts to distinguish <I>K</I> classes from each other using a weighted sum of some predictor variables <I>x</I><SUB><I>i</I></SUB>. The logistic regression model is used to calculate the weights β<SUB><I>i</I></SUB> of the predictor variables. In Biopython, the logistic regression model is current [...]
-</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-<I>x</I><SUB>1</SUB>: the number of base pairs between them;
-</LI><LI CLASS="li-itemize"><I>x</I><SUB>2</SUB>: their similarity in expression profile.
-</LI></UL><P>In a logistic regression model, we use a weighted sum of these two predictors to calculate a joint score <I>S</I>:
-</P><TABLE CLASS="display dcenter"><TR VALIGN="middle"><TD CLASS="dcell">
-<I>S</I> = β<SUB>0</SUB> + β<SUB>1</SUB> <I>x</I><SUB>1</SUB> + β<SUB>2</SUB> <I>x</I><SUB>2</SUB>.
-    (15.1)</TD></TR>
-</TABLE><P>
-The logistic regression model gives us appropriate values for the parameters β<SUB>0</SUB>, β<SUB>1</SUB>, β<SUB>2</SUB> using two sets of example genes:
-</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-OP: Adjacent genes, on the same strand of DNA, known to belong to the same operon;
-</LI><LI CLASS="li-itemize">NOP: Adjacent genes, on the same strand of DNA, known to belong to different operons.
-</LI></UL><P>In the logistic regression model, the probability of belonging to a class depends on the score via the logistic function. For the two classes OP and NOP, we can write this as
-</P><TABLE CLASS="display dcenter"><TR VALIGN="middle"><TD CLASS="dcell">
-
-
-     
-
-</TD><TD CLASS="dcell"><TABLE CELLSPACING=6 CELLPADDING=0><TR><TD ALIGN=right NOWRAP>Pr(<I>OP</I>|<I>x</I><SUB>1</SUB>, <I>x</I><SUB>2</SUB>)</TD><TD ALIGN=center NOWRAP> =</TD><TD ALIGN=left NOWRAP><TABLE CLASS="display"><TR VALIGN="middle"><TD CLASS="dcell"> </TD><TD CLASS="dcell"><TABLE CLASS="display"><TR><TD CLASS="dcell" ALIGN="center">exp(β<SUB>0</SUB> + β<SUB>1</SUB> <I>x</I><SUB>1</SUB> + β<SUB>2</SUB> <I>x</I><SUB>2 [...]
-<TR><TD CLASS="hbar"></TD></TR>
-<TR><TD CLASS="dcell" ALIGN="center">1+exp(β<SUB>0</SUB> + β<SUB>1</SUB> <I>x</I><SUB>1</SUB> + β<SUB>2</SUB> <I>x</I><SUB>2</SUB>)</TD></TR>
-</TABLE></TD><TD CLASS="dcell"> <A NAME="eq:OP"> </A> </TD></TR>
-</TABLE></TD><TD ALIGN=right NOWRAP>    (15.2)</TD></TR>
-<TR><TD ALIGN=right NOWRAP>Pr(<I>NOP</I>|<I>x</I><SUB>1</SUB>, <I>x</I><SUB>2</SUB>)</TD><TD ALIGN=center NOWRAP> =</TD><TD ALIGN=left NOWRAP><TABLE CLASS="display"><TR VALIGN="middle"><TD CLASS="dcell"> </TD><TD CLASS="dcell"><TABLE CLASS="display"><TR><TD CLASS="dcell" ALIGN="center">1</TD></TR>
-<TR><TD CLASS="hbar"></TD></TR>
-<TR><TD CLASS="dcell" ALIGN="center">1+exp(β<SUB>0</SUB> + β<SUB>1</SUB> <I>x</I><SUB>1</SUB> + β<SUB>2</SUB> <I>x</I><SUB>2</SUB>)</TD></TR>
-</TABLE></TD><TD CLASS="dcell"> <A NAME="eq:NOP"> </A>
-</TD></TR>
-</TABLE></TD><TD ALIGN=right NOWRAP>    (15.3)</TD></TR>
-</TABLE></TD></TR>
-</TABLE><P>
-Using a set of gene pairs for which it is known whether they belong to the same operon (class OP) or to different operons (class NOP), we can calculate the weights β<SUB>0</SUB>, β<SUB>1</SUB>, β<SUB>2</SUB> by maximizing the log-likelihood corresponding to the probability functions (<A HREF="#eq:OP">15.2</A>) and (<A HREF="#eq:NOP">15.3</A>).</P><!--TOC subsection Training the logistic regression model-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc201">15.1.2</A>  Training the logistic regression model</H3><!--SEC END --><P>
-<A NAME="subsec:LogisticRegressionTraining"></A></P><BLOCKQUOTE CLASS="table"><DIV CLASS="center"><HR WIDTH="80%" SIZE=2></DIV>
-<DIV CLASS="center">
-<DIV CLASS="caption"><TABLE CELLSPACING=6 CELLPADDING=0><TR><TD VALIGN=top ALIGN=left>Table 15.1: Adjacent gene pairs known to belong to the same operon (class OP) or to different operons (class NOP). Intergene distances are negative if the two genes overlap.</TD></TR>
-</TABLE></DIV>
-<TABLE BORDER=1 CELLSPACING=0 CELLPADDING=1><TR><TD ALIGN=center NOWRAP>Gene pair</TD><TD ALIGN=center NOWRAP>Intergene distance (<I>x</I><SUB>1</SUB>)</TD><TD ALIGN=center NOWRAP>Gene expression score (<I>x</I><SUB>2</SUB>)</TD><TD ALIGN=center NOWRAP>Class</TD></TR>
-<TR><TD ALIGN=center NOWRAP><I>cotJA</I> — <I>cotJB</I></TD><TD ALIGN=center NOWRAP>-53</TD><TD ALIGN=center NOWRAP>-200.78</TD><TD ALIGN=center NOWRAP>OP</TD></TR>
-<TR><TD ALIGN=center NOWRAP><I>yesK</I> — <I>yesL</I></TD><TD ALIGN=center NOWRAP>117</TD><TD ALIGN=center NOWRAP>-267.14</TD><TD ALIGN=center NOWRAP>OP</TD></TR>
-<TR><TD ALIGN=center NOWRAP><I>lplA</I> — <I>lplB</I></TD><TD ALIGN=center NOWRAP>57</TD><TD ALIGN=center NOWRAP>-163.47</TD><TD ALIGN=center NOWRAP>OP</TD></TR>
-<TR><TD ALIGN=center NOWRAP><I>lplB</I> — <I>lplC</I></TD><TD ALIGN=center NOWRAP>16</TD><TD ALIGN=center NOWRAP>-190.30</TD><TD ALIGN=center NOWRAP>OP</TD></TR>
-<TR><TD ALIGN=center NOWRAP><I>lplC</I> — <I>lplD</I></TD><TD ALIGN=center NOWRAP>11</TD><TD ALIGN=center NOWRAP>-220.94</TD><TD ALIGN=center NOWRAP>OP</TD></TR>
-<TR><TD ALIGN=center NOWRAP><I>lplD</I> — <I>yetF</I></TD><TD ALIGN=center NOWRAP>85</TD><TD ALIGN=center NOWRAP>-193.94</TD><TD ALIGN=center NOWRAP>OP</TD></TR>
-<TR><TD ALIGN=center NOWRAP><I>yfmT</I> — <I>yfmS</I></TD><TD ALIGN=center NOWRAP>16</TD><TD ALIGN=center NOWRAP>-182.71</TD><TD ALIGN=center NOWRAP>OP</TD></TR>
-<TR><TD ALIGN=center NOWRAP><I>yfmF</I> — <I>yfmE</I></TD><TD ALIGN=center NOWRAP>15</TD><TD ALIGN=center NOWRAP>-180.41</TD><TD ALIGN=center NOWRAP>OP</TD></TR>
-<TR><TD ALIGN=center NOWRAP><I>citS</I> — <I>citT</I></TD><TD ALIGN=center NOWRAP>-26</TD><TD ALIGN=center NOWRAP>-181.73</TD><TD ALIGN=center NOWRAP>OP</TD></TR>
-<TR><TD ALIGN=center NOWRAP><I>citM</I> — <I>yflN</I></TD><TD ALIGN=center NOWRAP>58</TD><TD ALIGN=center NOWRAP>-259.87</TD><TD ALIGN=center NOWRAP>OP</TD></TR>
-<TR><TD ALIGN=center NOWRAP><I>yfiI</I> — <I>yfiJ</I></TD><TD ALIGN=center NOWRAP>126</TD><TD ALIGN=center NOWRAP>-414.53</TD><TD ALIGN=center NOWRAP>NOP</TD></TR>
-<TR><TD ALIGN=center NOWRAP><I>lipB</I> — <I>yfiQ</I></TD><TD ALIGN=center NOWRAP>191</TD><TD ALIGN=center NOWRAP>-249.57</TD><TD ALIGN=center NOWRAP>NOP</TD></TR>
-<TR><TD ALIGN=center NOWRAP><I>yfiU</I> — <I>yfiV</I></TD><TD ALIGN=center NOWRAP>113</TD><TD ALIGN=center NOWRAP>-265.28</TD><TD ALIGN=center NOWRAP>NOP</TD></TR>
-<TR><TD ALIGN=center NOWRAP><I>yfhH</I> — <I>yfhI</I></TD><TD ALIGN=center NOWRAP>145</TD><TD ALIGN=center NOWRAP>-312.99</TD><TD ALIGN=center NOWRAP>NOP</TD></TR>
-<TR><TD ALIGN=center NOWRAP><I>cotY</I> — <I>cotX</I></TD><TD ALIGN=center NOWRAP>154</TD><TD ALIGN=center NOWRAP>-213.83</TD><TD ALIGN=center NOWRAP>NOP</TD></TR>
-<TR><TD ALIGN=center NOWRAP><I>yjoB</I> — <I>rapA</I></TD><TD ALIGN=center NOWRAP>147</TD><TD ALIGN=center NOWRAP>-380.85</TD><TD ALIGN=center NOWRAP>NOP</TD></TR>
-<TR><TD ALIGN=center NOWRAP><I>ptsI</I> — <I>splA</I></TD><TD ALIGN=center NOWRAP>93</TD><TD ALIGN=center NOWRAP>-291.13</TD><TD ALIGN=center NOWRAP>NOP</TD></TR>
-</TABLE>
-<A NAME="table:training"></A>
-</DIV>
-<DIV CLASS="center"><HR WIDTH="80%" SIZE=2></DIV></BLOCKQUOTE><P>Table <A HREF="#table:training">15.1</A> lists some of the <I>Bacillus subtilis</I> gene pairs for which the operon structure is known.
-Let’s calculate the logistic regression model from these data:</P><PRE CLASS="verbatim">>>> from Bio import LogisticRegression
->>> xs = [[-53, -200.78],
- [117, -267.14],
- [57, -163.47],
- [16, -190.30],
- [11, -220.94],
- [85, -193.94],
- [16, -182.71],
- [15, -180.41],
- [-26, -181.73],
- [58, -259.87],
- [126, -414.53],
- [191, -249.57],
- [113, -265.28],
- [145, -312.99],
- [154, -213.83],
- [147, -380.85],
- [93, -291.13]]
->>> ys = [1,
- 1,
- 1,
- 1,
- 1,
- 1,
- 1,
- 1,
- 1,
- 1,
- 0,
- 0,
- 0,
- 0,
- 0,
- 0,
- 0]
->>> model = LogisticRegression.train(xs, ys)
-</PRE><P>Here, <CODE>xs</CODE> and <CODE>ys</CODE> are the training data: <CODE>xs</CODE> contains the predictor variables for each gene pair, and <CODE>ys</CODE> specifies if the gene pair belongs to the same operon (<CODE>1</CODE>, class OP) or different operons (<CODE>0</CODE>, class NOP). The resulting logistic regression model is stored in <CODE>model</CODE>, which contains the weights β<SUB>0</SUB>, β<SUB>1</SUB>, and β<SUB>2</SUB>:</P><PRE CLASS="verbatim">>&g [...]
-[8.9830290157144681, -0.035968960444850887, 0.02181395662983519]
-</PRE><P>Note that β<SUB>1</SUB> is negative, as gene pairs with a shorter intergene distance have a higher probability of belonging to the same operon (class OP). On the other hand, β<SUB>2</SUB> is positive, as gene pairs belonging to the same operon typically have a higher similarity score of their gene expression profiles.
-The parameter β<SUB>0</SUB> is positive due to the higher prevalence of operon gene pairs than non-operon gene pairs in the training data.</P><P>The function <CODE>train</CODE> has two optional arguments: <CODE>update_fn</CODE> and <CODE>typecode</CODE>. The <CODE>update_fn</CODE> can be used to specify a callback function, taking as arguments the iteration number and the log-likelihood. With the callback function, we can for example track the progress of the model calculation (whi [...]
- print("Iteration:", iteration, "Log-likelihood function:", loglikelihood)
->>>
->>> model = LogisticRegression.train(xs, ys, update_fn=show_progress)
-Iteration: 0 Log-likelihood function: -11.7835020695
-Iteration: 1 Log-likelihood function: -7.15886767672
-Iteration: 2 Log-likelihood function: -5.76877209868
-Iteration: 3 Log-likelihood function: -5.11362294338
-Iteration: 4 Log-likelihood function: -4.74870642433
-Iteration: 5 Log-likelihood function: -4.50026077146
-Iteration: 6 Log-likelihood function: -4.31127773737
-Iteration: 7 Log-likelihood function: -4.16015043396
-Iteration: 8 Log-likelihood function: -4.03561719785
-Iteration: 9 Log-likelihood function: -3.93073282192
-Iteration: 10 Log-likelihood function: -3.84087660929
-Iteration: 11 Log-likelihood function: -3.76282560605
-Iteration: 12 Log-likelihood function: -3.69425027154
-Iteration: 13 Log-likelihood function: -3.6334178602
-Iteration: 14 Log-likelihood function: -3.57900855837
-Iteration: 15 Log-likelihood function: -3.52999671386
-Iteration: 16 Log-likelihood function: -3.48557145163
-Iteration: 17 Log-likelihood function: -3.44508206139
-Iteration: 18 Log-likelihood function: -3.40799948447
-Iteration: 19 Log-likelihood function: -3.3738885624
-Iteration: 20 Log-likelihood function: -3.3423876581
-Iteration: 21 Log-likelihood function: -3.31319343769
-Iteration: 22 Log-likelihood function: -3.2860493346
-Iteration: 23 Log-likelihood function: -3.2607366863
-Iteration: 24 Log-likelihood function: -3.23706784091
-Iteration: 25 Log-likelihood function: -3.21488073614
-Iteration: 26 Log-likelihood function: -3.19403459259
-Iteration: 27 Log-likelihood function: -3.17440646052
-Iteration: 28 Log-likelihood function: -3.15588842703
-Iteration: 29 Log-likelihood function: -3.13838533947
-Iteration: 30 Log-likelihood function: -3.12181293595
-Iteration: 31 Log-likelihood function: -3.10609629966
-Iteration: 32 Log-likelihood function: -3.09116857282
-Iteration: 33 Log-likelihood function: -3.07696988017
-Iteration: 34 Log-likelihood function: -3.06344642288
-Iteration: 35 Log-likelihood function: -3.05054971191
-Iteration: 36 Log-likelihood function: -3.03823591619
-Iteration: 37 Log-likelihood function: -3.02646530573
-Iteration: 38 Log-likelihood function: -3.01520177394
-Iteration: 39 Log-likelihood function: -3.00441242601
-Iteration: 40 Log-likelihood function: -2.99406722296
-Iteration: 41 Log-likelihood function: -2.98413867259
-</PRE><P>The iteration stops once the increase in the log-likelihood function is less than 0.01. If no convergence is reached after 500 iterations, the <CODE>train</CODE> function returns with an <CODE>AssertionError</CODE>.</P><P>The optional keyword <CODE>typecode</CODE> can almost always be ignored. This keyword allows the user to choose the type of Numeric matrix to use. In particular, to avoid memory problems for very large problems, it may be necessary to use single-precision float [...]
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc202">15.1.3</A>  Using the logistic regression model for classification</H3><!--SEC END --><P>Classification is performed by calling the <CODE>classify</CODE> function. Given a logistic regression model and the values for <I>x</I><SUB>1</SUB> and <I>x</I><SUB>2</SUB> (e.g. for a gene pair of unknown operon structure), the <CODE>classify</CODE> function returns <CODE>1</CODE> or <CODE>0</CODE>, corresponding to class OP and c [...]
-<DIV CLASS="center">
-<DIV CLASS="caption"><TABLE CELLSPACING=6 CELLPADDING=0><TR><TD VALIGN=top ALIGN=left>Table 15.2: Adjacent gene pairs of unknown operon status.</TD></TR>
-</TABLE></DIV>
-<TABLE BORDER=1 CELLSPACING=0 CELLPADDING=1><TR><TD ALIGN=center NOWRAP>Gene pair</TD><TD ALIGN=center NOWRAP>Intergene distance <I>x</I><SUB>1</SUB></TD><TD ALIGN=center NOWRAP>Gene expression score <I>x</I><SUB>2</SUB></TD></TR>
-<TR><TD ALIGN=center NOWRAP><I>yxcE</I> — <I>yxcD</I></TD><TD ALIGN=center NOWRAP>6</TD><TD ALIGN=center NOWRAP>-173.143442352</TD></TR>
-<TR><TD ALIGN=center NOWRAP><I>yxiB</I> — <I>yxiA</I></TD><TD ALIGN=center NOWRAP>309</TD><TD ALIGN=center NOWRAP>-271.005880394</TD></TR>
-</TABLE>
-</DIV>
-<DIV CLASS="center"><HR WIDTH="80%" SIZE=2></DIV></BLOCKQUOTE><P>The logistic regression model classifies <I>yxcE</I>, <I>yxcD</I> as belonging to the same operon (class OP), while <I>yxiB</I>, <I>yxiA</I> are predicted to belong to different operons:
-</P><PRE CLASS="verbatim">>>> print("yxcE, yxcD:", LogisticRegression.classify(model, [6, -173.143442352]))
-yxcE, yxcD: 1
->>> print("yxiB, yxiA:", LogisticRegression.classify(model, [309, -271.005880394]))
-yxiB, yxiA: 0
-</PRE><P>(which, by the way, agrees with the biological literature).</P><P>To find out how confident we can be in these predictions, we can call the <CODE>calculate</CODE> function to obtain the probabilities (equations (<A HREF="#eq:OP">15.2</A>) and <A HREF="#eq:NOP">15.3</A>) for class OP and NOP. For <I>yxcE</I>, <I>yxcD</I> we find
-</P><PRE CLASS="verbatim">>>> q, p = LogisticRegression.calculate(model, [6, -173.143442352])
->>> print("class OP: probability =", p, "class NOP: probability =", q)
-class OP: probability = 0.993242163503 class NOP: probability = 0.00675783649744
-</PRE><P>and for <I>yxiB</I>, <I>yxiA</I>
-</P><PRE CLASS="verbatim">>>> q, p = LogisticRegression.calculate(model, [309, -271.005880394])
->>> print("class OP: probability =", p, "class NOP: probability =", q)
-class OP: probability = 0.000321211251817 class NOP: probability = 0.999678788748
-</PRE><P>To get some idea of the prediction accuracy of the logistic regression model, we can apply it to the training data:
-</P><PRE CLASS="verbatim">>>> for i in range(len(ys)):
- print("True:", ys[i], "Predicted:", LogisticRegression.classify(model, xs[i]))
-True: 1 Predicted: 1
-True: 1 Predicted: 0
-True: 1 Predicted: 1
-True: 1 Predicted: 1
-True: 1 Predicted: 1
-True: 1 Predicted: 1
-True: 1 Predicted: 1
-True: 1 Predicted: 1
-True: 1 Predicted: 1
-True: 1 Predicted: 1
-True: 0 Predicted: 0
-True: 0 Predicted: 0
-True: 0 Predicted: 0
-True: 0 Predicted: 0
-True: 0 Predicted: 0
-True: 0 Predicted: 0
-True: 0 Predicted: 0
-</PRE><P>showing that the prediction is correct for all but one of the gene pairs. A more reliable estimate of the prediction accuracy can be found from a leave-one-out analysis, in which the model is recalculated from the training data after removing the gene to be predicted:
-</P><PRE CLASS="verbatim">>>> for i in range(len(ys)):
- model = LogisticRegression.train(xs[:i]+xs[i+1:], ys[:i]+ys[i+1:])
- print("True:", ys[i], "Predicted:", LogisticRegression.classify(model, xs[i]))
-True: 1 Predicted: 1
-True: 1 Predicted: 0
-True: 1 Predicted: 1
-True: 1 Predicted: 1
-True: 1 Predicted: 1
-True: 1 Predicted: 1
-True: 1 Predicted: 1
-True: 1 Predicted: 1
-True: 1 Predicted: 1
-True: 1 Predicted: 1
-True: 0 Predicted: 0
-True: 0 Predicted: 0
-True: 0 Predicted: 0
-True: 0 Predicted: 0
-True: 0 Predicted: 1
-True: 0 Predicted: 0
-True: 0 Predicted: 0
-</PRE><P>The leave-one-out analysis shows that the prediction of the logistic regression model is incorrect for only two of the gene pairs, which corresponds to a prediction accuracy of 88%.</P><!--TOC subsection Logistic Regression, Linear Discriminant Analysis, and Support Vector Machines-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc203">15.1.4</A>  Logistic Regression, Linear Discriminant Analysis, and Support Vector Machines</H3><!--SEC END --><P>The logistic regression model is similar to linear discriminant analysis. In linear discriminant analysis, the class probabilities also follow equations (<A HREF="#eq:OP">15.2</A>) and (<A HREF="#eq:NOP">15.3</A>). However, instead of estimating the coefficients β directly, we first fit a normal distrib [...]
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc204">15.2</A>  <I>k</I>-Nearest Neighbors</H2><!--SEC END --><!--TOC subsection Background and purpose-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc205">15.2.1</A>  Background and purpose</H3><!--SEC END --><P>The <I>k</I>-nearest neighbors method is a supervised learning approach that does not need to fit a model to the data. Instead, data points are classified based on the categories of the <I>k</I> nearest neighbors in the training data set.</P><P>In Biopython, the <I>k</I>-nearest neighbors method is available in <CODE>Bio.kNN</CODE>. To illustrate the use of the <I [...]
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc206">15.2.2</A>  Initializing a <I>k</I>-nearest neighbors model</H3><!--SEC END --><P>Using the data in Table <A HREF="#table:training">15.1</A>, we create and initialize a <I>k</I>-nearest neighbors model as follows:</P><PRE CLASS="verbatim">>>> from Bio import kNN
->>> k = 3
->>> model = kNN.train(xs, ys, k)
-</PRE><P>where <CODE>xs</CODE> and <CODE>ys</CODE> are the same as in Section <A HREF="#subsec:LogisticRegressionTraining">15.1.2</A>. Here, <CODE>k</CODE> is the number of neighbors <I>k</I> that will be considered for the classification. For classification into two classes, choosing an odd number for <I>k</I> lets you avoid tied votes. The function name <CODE>train</CODE> is a bit of a misnomer, since no model training is done: this function simply stores <CODE>xs</CODE>, <CODE>ys</COD [...]
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc207">15.2.3</A>  Using a <I>k</I>-nearest neighbors model for classification</H3><!--SEC END --><P>To classify new data using the <I>k</I>-nearest neighbors model, we use the <CODE>classify</CODE> function. This function takes a data point (<I>x</I><SUB>1</SUB>,<I>x</I><SUB>2</SUB>) and finds the <I>k</I>-nearest neighbors in the training data set <CODE>xs</CODE>. The data point (<I>x</I><SUB>1</SUB>, <I>x</I><SUB>2</SUB>) i [...]
-</P><PRE CLASS="verbatim">>>> x = [6, -173.143442352]
->>> print("yxcE, yxcD:", kNN.classify(model, x))
-yxcE, yxcD: 1
->>> x = [309, -271.005880394]
->>> print("yxiB, yxiA:", kNN.classify(model, x))
-yxiB, yxiA: 0
-</PRE><P>In agreement with the logistic regression model, <I>yxcE</I>, <I>yxcD</I> are classified as belonging to the same operon (class OP), while <I>yxiB</I>, <I>yxiA</I> are predicted to belong to different operons.</P><P>The <CODE>classify</CODE> function lets us specify both a distance function and a weight function as optional arguments. The distance function affects which <I>k</I> neighbors are chosen as the nearest neighbors, as these are defined as the neighbors with the smalles [...]
-... assert len(x1)==2
-... assert len(x2)==2
-... distance = abs(x1[0]-x2[0]) + abs(x1[1]-x2[1])
-... return distance
-...
->>> x = [6, -173.143442352]
->>> print("yxcE, yxcD:", kNN.classify(model, x, distance_fn = cityblock))
-yxcE, yxcD: 1
-</PRE><P>The weight function can be used for weighted voting. For example, we may want to give closer neighbors a higher weight than neighbors that are further away:</P><PRE CLASS="verbatim">>>> def weight(x1, x2):
-... assert len(x1)==2
-... assert len(x2)==2
-... return exp(-abs(x1[0]-x2[0]) - abs(x1[1]-x2[1]))
-...
->>> x = [6, -173.143442352]
->>> print("yxcE, yxcD:", kNN.classify(model, x, weight_fn = weight))
-yxcE, yxcD: 1
-</PRE><P>By default, all neighbors are given an equal weight.</P><P>To find out how confident we can be in these predictions, we can call the <CODE>calculate</CODE> function, which will calculate the total weight assigned to the classes OP and NOP. For the default weighting scheme, this reduces to the number of neighbors in each category. For <I>yxcE</I>, <I>yxcD</I>, we find
-</P><PRE CLASS="verbatim">>>> x = [6, -173.143442352]
->>> weight = kNN.calculate(model, x)
->>> print("class OP: weight =", weight[0], "class NOP: weight =", weight[1])
-class OP: weight = 0.0 class NOP: weight = 3.0
-</PRE><P>which means that all three neighbors of <CODE>x1</CODE>, <CODE>x2</CODE> are in the NOP class. As another example, for <I>yesK</I>, <I>yesL</I> we find</P><PRE CLASS="verbatim">>>> x = [117, -267.14]
->>> weight = kNN.calculate(model, x)
->>> print("class OP: weight =", weight[0], "class NOP: weight =", weight[1])
-class OP: weight = 2.0 class NOP: weight = 1.0
-</PRE><P>which means that two neighbors are operon pairs and one neighbor is a non-operon pair.</P><P>To get some idea of the prediction accuracy of the <I>k</I>-nearest neighbors approach, we can apply it to the training data:
-</P><PRE CLASS="verbatim">>>> for i in range(len(ys)):
- print("True:", ys[i], "Predicted:", kNN.classify(model, xs[i]))
-True: 1 Predicted: 1
-True: 1 Predicted: 0
-True: 1 Predicted: 1
-True: 1 Predicted: 1
-True: 1 Predicted: 1
-True: 1 Predicted: 1
-True: 1 Predicted: 1
-True: 1 Predicted: 1
-True: 1 Predicted: 1
-True: 1 Predicted: 0
-True: 0 Predicted: 0
-True: 0 Predicted: 0
-True: 0 Predicted: 0
-True: 0 Predicted: 0
-True: 0 Predicted: 0
-True: 0 Predicted: 0
-True: 0 Predicted: 0
-</PRE><P>showing that the prediction is correct for all but two of the gene pairs. A more reliable estimate of the prediction accuracy can be found from a leave-one-out analysis, in which the model is recalculated from the training data after removing the gene to be predicted:
-</P><PRE CLASS="verbatim">>>> for i in range(len(ys)):
- model = kNN.train(xs[:i]+xs[i+1:], ys[:i]+ys[i+1:])
- print("True:", ys[i], "Predicted:", kNN.classify(model, xs[i]))
-True: 1 Predicted: 1
-True: 1 Predicted: 0
-True: 1 Predicted: 1
-True: 1 Predicted: 1
-True: 1 Predicted: 1
-True: 1 Predicted: 1
-True: 1 Predicted: 1
-True: 1 Predicted: 1
-True: 1 Predicted: 1
-True: 1 Predicted: 0
-True: 0 Predicted: 0
-True: 0 Predicted: 0
-True: 0 Predicted: 1
-True: 0 Predicted: 0
-True: 0 Predicted: 0
-True: 0 Predicted: 0
-True: 0 Predicted: 1
-</PRE><P>The leave-one-out analysis shows that <I>k</I>-nearest neighbors model is correct for 13 out of 17 gene pairs, which corresponds to a prediction accuracy of 76%.</P><!--TOC section Naïve Bayes-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc208">15.3</A>  Naïve Bayes</H2><!--SEC END --><P>This section will describe the <CODE>Bio.NaiveBayes</CODE> module.</P><!--TOC section Maximum Entropy-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc209">15.4</A>  Maximum Entropy</H2><!--SEC END --><P>This section will describe the <CODE>Bio.MaximumEntropy</CODE> module.</P><!--TOC section Markov Models-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc210">15.5</A>  Markov Models</H2><!--SEC END --><P>This section will describe the <CODE>Bio.MarkovModel</CODE> and/or <CODE>Bio.HMM.MarkovModel</CODE> modules.</P><!--TOC chapter Graphics including GenomeDiagram-->
-<H1 CLASS="chapter"><!--SEC ANCHOR --><A NAME="htoc211">Chapter 16</A>  Graphics including GenomeDiagram</H1><!--SEC END --><P>
-<A NAME="chapter:graphics"></A></P><P>The <CODE>Bio.Graphics</CODE> module depends on the third party Python library
-<A HREF="http://www.reportlab.org">ReportLab</A>. Although focused on producing PDF files,
-ReportLab can also create encapsulated postscript (EPS) and (SVG) files. In addition
-to these vector based images, provided certain further dependencies such as the
-<A HREF="http://www.pythonware.com/products/pil/">Python Imaging Library (PIL)</A> are
-installed, ReportLab can also output bitmap images (including JPEG, PNG, GIF, BMP
-and PICT formats).</P><!--TOC section GenomeDiagram-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc212">16.1</A>  GenomeDiagram</H2><!--SEC END --><P>
-<A NAME="sec:genomediagram"></A>
-</P><!--TOC subsection Introduction-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc213">16.1.1</A>  Introduction</H3><!--SEC END --><P>The <CODE>Bio.Graphics.GenomeDiagram</CODE> module was added to Biopython 1.50,
-having previously been available as a separate Python module dependent on Biopython.
-GenomeDiagram is described in the Bioinformatics journal publication by Pritchard et al. (2006) [<A HREF="#pritchard2006">2</A>],
-which includes some examples images. There is a PDF copy of the old manual here,
-<A HREF="http://biopython.org/DIST/docs/GenomeDiagram/userguide.pdf"><TT>http://biopython.org/DIST/docs/GenomeDiagram/userguide.pdf</TT></A> which has some
-more examples.
-</P><P>As the name might suggest, GenomeDiagram was designed for drawing whole genomes, in
-particular prokaryotic genomes, either as linear diagrams (optionally broken up into
-fragments to fit better) or as circular wheel diagrams. Have a look at Figure 2 in
-Toth <I>et al.</I> (2006) [<A HREF="#toth2006">3</A>]
-for a good example. It proved also well suited to drawing quite detailed figures for
-smaller genomes such as phage, plasmids or mitochrondia, for example see Figures 1
-and 2 in Van der Auwera <I>et al.</I> (2009) [<A HREF="#vanderauwera2009">4</A>]
-(shown with additional manual editing).</P><P>This module is easiest to use if you have your genome loaded as a <CODE>SeqRecord</CODE>
-object containing lots of <CODE>SeqFeature</CODE> objects - for example as loaded from a
-GenBank file (see Chapters <A HREF="#chapter:SeqRecord">4</A> and <A HREF="#chapter:Bio.SeqIO">5</A>).</P><!--TOC subsection Diagrams, tracks, feature-sets and features-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc214">16.1.2</A>  Diagrams, tracks, feature-sets and features</H3><!--SEC END --><P>GenomeDiagram uses a nested set of objects. At the top level, you have a diagram
-object representing a sequence (or sequence region) along the horizontal axis (or
-circle). A diagram can contain one or more tracks, shown stacked vertically (or
-radially on circular diagrams). These will typically all have the same length
-and represent the same sequence region. You might use one track to show the gene
-locations, another to show regulatory regions, and a third track to show the GC
-percentage. </P><P>The most commonly used type of track will contain features, bundled together in
-feature-sets. You might choose to use one feature-set for all your CDS features,
-and another for tRNA features. This isn’t required - they can all go in the same
-feature-set, but it makes it easier to update the properties of just selected
-features (e.g. make all the tRNA features red).</P><P>There are two main ways to build up a complete diagram. Firstly, the top down
-approach where you create a diagram object, and then using its methods add
-track(s), and use the track methods to add feature-set(s), and use their
-methods to add the features. Secondly, you can create the individual objects
-separately (in whatever order suits your code), and then combine them.</P><!--TOC subsection A top down example-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc215">16.1.3</A>  A top down example</H3><!--SEC END --><P>
-<A NAME="sec:gd_top_down"></A></P><P>We’re going to draw a whole genome from a <CODE>SeqRecord</CODE> object read in from
-a GenBank file (see Chapter <A HREF="#chapter:Bio.SeqIO">5</A>). This example uses the
-pPCP1 plasmid from <I>Yersinia pestis biovar Microtus</I>, the file is
-included with the Biopython unit tests under the GenBank folder, or online
-<A HREF="http://biopython.org/SRC/biopython/Tests/GenBank/NC_005816.gb"><TT>NC_005816.gb</TT></A> from our website.</P><PRE CLASS="verbatim">from reportlab.lib import colors
-from reportlab.lib.units import cm
-from Bio.Graphics import GenomeDiagram
-from Bio import SeqIO
-record = SeqIO.read("NC_005816.gb", "genbank")
-</PRE><P>We’re using a top down approach, so after loading in our sequence we next
-create an empty diagram, then add an (empty) track, and to that add an
-(empty) feature set:</P><PRE CLASS="verbatim">gd_diagram = GenomeDiagram.Diagram("Yersinia pestis biovar Microtus plasmid pPCP1")
-gd_track_for_features = gd_diagram.new_track(1, name="Annotated Features")
-gd_feature_set = gd_track_for_features.new_set()
-</PRE><P>Now the fun part - we take each gene <CODE>SeqFeature</CODE> object in our
-<CODE>SeqRecord</CODE>, and use it to generate a feature on the diagram. We’re
-going to color them blue, alternating between a dark blue and a light blue.
-</P><PRE CLASS="verbatim">for feature in record.features:
- if feature.type != "gene":
- #Exclude this feature
- continue
- if len(gd_feature_set) % 2 == 0:
- color = colors.blue
- else:
- color = colors.lightblue
- gd_feature_set.add_feature(feature, color=color, label=True)
-</PRE><P>Now we come to actually making the output file. This happens in two steps,
-first we call the <CODE>draw</CODE> method, which creates all the shapes using
-ReportLab objects. Then we call the <CODE>write</CODE> method which renders these
-to the requested file format. Note you can output in multiple file formats:</P><PRE CLASS="verbatim">gd_diagram.draw(format="linear", orientation="landscape", pagesize='A4',
- fragments=4, start=0, end=len(record))
-gd_diagram.write("plasmid_linear.pdf", "PDF")
-gd_diagram.write("plasmid_linear.eps", "EPS")
-gd_diagram.write("plasmid_linear.svg", "SVG")
-</PRE><P>Also, provided you have the dependencies installed, you can also do bitmaps,
-for example:</P><PRE CLASS="verbatim">gd_diagram.write("plasmid_linear.png", "PNG")
-</PRE><P>
-<IMG SRC="images/plasmid_linear.png" width=550, height=400></P><P>
-
-Notice that the <CODE>fragments</CODE> argument which we set to four controls how
-many pieces the genome gets broken up into.</P><P>If you want to do a circular figure, then try this:</P><PRE CLASS="verbatim">gd_diagram.draw(format="circular", circular=True, pagesize=(20*cm,20*cm),
- start=0, end=len(record), circle_core=0.7)
-gd_diagram.write("plasmid_circular.pdf", "PDF")
-</PRE><P>
-<IMG SRC="images/plasmid_circular.png" width=400, height=400></P><P>
-
-These figures are not very exciting, but we’ve only just got started.</P><!--TOC subsection A bottom up example-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc216">16.1.4</A>  A bottom up example</H3><!--SEC END --><P>
-Now let’s produce exactly the same figures, but using the bottom up approach.
-This means we create the different objects directly (and this can be done in
-almost any order) and then combine them.</P><PRE CLASS="verbatim">from reportlab.lib import colors
-from reportlab.lib.units import cm
-from Bio.Graphics import GenomeDiagram
-from Bio import SeqIO
-record = SeqIO.read("NC_005816.gb", "genbank")
-
-#Create the feature set and its feature objects,
-gd_feature_set = GenomeDiagram.FeatureSet()
-for feature in record.features:
- if feature.type != "gene":
- #Exclude this feature
- continue
- if len(gd_feature_set) % 2 == 0:
- color = colors.blue
- else:
- color = colors.lightblue
- gd_feature_set.add_feature(feature, color=color, label=True)
-#(this for loop is the same as in the previous example)
-
-#Create a track, and a diagram
-gd_track_for_features = GenomeDiagram.Track(name="Annotated Features")
-gd_diagram = GenomeDiagram.Diagram("Yersinia pestis biovar Microtus plasmid pPCP1")
-
-#Now have to glue the bits together...
-gd_track_for_features.add_set(gd_feature_set)
-gd_diagram.add_track(gd_track_for_features, 1)
-</PRE><P>You can now call the <CODE>draw</CODE> and <CODE>write</CODE> methods as before to produce
-a linear or circular diagram, using the code at the end of the top-down example
-above. The figures should be identical.</P><!--TOC subsection Features without a SeqFeature-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc217">16.1.5</A>  Features without a SeqFeature</H3><!--SEC END --><P>
-<A NAME="sec:gd_features_without_seqfeatures"></A></P><P>In the above example we used a <CODE>SeqRecord</CODE>’s <CODE>SeqFeature</CODE> objects
-to build our diagram (see also Section <A HREF="#sec:seq_features">4.3</A>).
-Sometimes you won’t have <CODE>SeqFeature</CODE> objects,
-but just the coordinates for a feature you want to draw. You have to create
-minimal <CODE>SeqFeature</CODE> object, but this is easy:</P><PRE CLASS="verbatim">from Bio.SeqFeature import SeqFeature, FeatureLocation
-my_seq_feature = SeqFeature(FeatureLocation(50,100),strand=+1)
-</PRE><P>For strand, use <TT>+1</TT> for the forward strand, <TT>-1</TT> for the
-reverse strand, and <TT>None</TT> for both. Here is a short self contained
-example:</P><PRE CLASS="verbatim">from Bio.SeqFeature import SeqFeature, FeatureLocation
-from Bio.Graphics import GenomeDiagram
-from reportlab.lib.units import cm
-
-gdd = GenomeDiagram.Diagram('Test Diagram')
-gdt_features = gdd.new_track(1, greytrack=False)
-gds_features = gdt_features.new_set()
-
-#Add three features to show the strand options,
-feature = SeqFeature(FeatureLocation(25, 125), strand=+1)
-gds_features.add_feature(feature, name="Forward", label=True)
-feature = SeqFeature(FeatureLocation(150, 250), strand=None)
-gds_features.add_feature(feature, name="Strandless", label=True)
-feature = SeqFeature(FeatureLocation(275, 375), strand=-1)
-gds_features.add_feature(feature, name="Reverse", label=True)
-
-gdd.draw(format='linear', pagesize=(15*cm,4*cm), fragments=1,
- start=0, end=400)
-gdd.write("GD_labels_default.pdf", "pdf")
-</PRE><P>
-The top part of the image in the next subsection shows the output
-
-
-(in the default feature color, pale green).</P><P>Notice that we have used the <TT>name</TT> argument here to specify the
-caption text for these features. This is discussed in more detail next.</P><!--TOC subsection Feature captions-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc218">16.1.6</A>  Feature captions</H3><!--SEC END --><P>
-<A NAME="sec:gd_feature_captions"></A></P><P>Recall we used the following (where <TT>feature</TT> was a
-<CODE>SeqFeature</CODE> object) to add a feature to the diagram:</P><PRE CLASS="verbatim">gd_feature_set.add_feature(feature, color=color, label=True)
-</PRE><P>In the example above the <CODE>SeqFeature</CODE> annotation was used to pick a
-sensible caption for the features. By default the following possible entries
-under the <CODE>SeqFeature</CODE> object’s qualifiers dictionary are used:
-<TT>gene</TT>, <TT>label</TT>, <TT>name</TT>, <TT>locus_tag</TT>, and
-<TT>product</TT>. More simply, you can specify a name directly:</P><PRE CLASS="verbatim">gd_feature_set.add_feature(feature, color=color, label=True, name="My Gene")
-</PRE><P>In addition to the caption text for each feature’s label, you can also choose
-the font, position (this defaults to the start of the sigil, you can also
-choose the middle or at the end) and orientation (for linear diagrams only,
-where this defaults to rotated by 45 degrees):</P><PRE CLASS="verbatim">#Large font, parallel with the track
-gd_feature_set.add_feature(feature, label=True, color="green",
- label_size=25, label_angle=0)
-
-#Very small font, perpendicular to the track (towards it)
-gd_feature_set.add_feature(feature, label=True, color="purple",
- label_position="end",
- label_size=4, label_angle=90)
-
-#Small font, perpendicular to the track (away from it)
-gd_feature_set.add_feature(feature, label=True, color="blue",
- label_position="middle",
- label_size=6, label_angle=-90)
-</PRE><P>Combining each of these three fragments with the complete example
-in the previous section should give something like
-
-this:</P><P><IMG SRC="images/GD_sigil_labels.png" width=600, height=700>
-<A NAME="fig:gd_sigil_labels"></A></P><P>We’ve not shown it here, but you can also set <TT>label_color</TT> to
-control the label’s color (used in Section <A HREF="#sec:gd_nice_example">16.1.9</A>).</P><P>You’ll notice the default font is quite small - this makes sense because
-you will usually be drawing many (small) features on a page, not just a
-few large ones as shown here.</P><!--TOC subsection Feature sigils-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc219">16.1.7</A>  Feature sigils</H3><!--SEC END --><P>
-<A NAME="sec:gd_sigils"></A></P><P>The examples above have all just used the default sigil for the feature, a
-plain box, which was all that was available in the last publicly released standalone version of GenomeDiagram. Arrow sigils were included when
-GenomeDiagram was added to Biopython 1.50:</P><PRE CLASS="verbatim">#Default uses a BOX sigil
-gd_feature_set.add_feature(feature)
-
-#You can make this explicit:
-gd_feature_set.add_feature(feature, sigil="BOX")
-
-#Or opt for an arrow:
-gd_feature_set.add_feature(feature, sigil="ARROW")
-</PRE><P>Biopython 1.61 added three more sigils,</P><PRE CLASS="verbatim">#Box with corners cut off (making it an octagon)
-gd_feature_set.add_feature(feature, sigil="OCTO")
-
-#Box with jagged edges (useful for showing breaks in contains)
-gd_feature_set.add_feature(feature, sigil="JAGGY")
-
-#Arrow which spans the axis with strand used only for direction
-gd_feature_set.add_feature(feature, sigil="BIGARROW")
-</PRE><P>These are shown
-
-below.
-
-
-Most sigils fit into a bounding box (as given by the default BOX sigil),
-either above or below the axis for the forward or reverse strand, or
-straddling it (double the height) for strand-less features.
-The BIGARROW sigil is different, always straddling the axis with the
-direction taken from the feature’s stand.</P><P>
-<IMG SRC="images/GD_sigils.png" width=425, height=600>
-
-</P><!--TOC subsection Arrow sigils-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc220">16.1.8</A>  Arrow sigils</H3><!--SEC END --><P>
-<A NAME="sec:gd_arrow_sigils"></A></P><P>We introduced the arrow sigils in the previous section.
-There are two additional options to adjust the shapes of the arrows, firstly
-the thickness of the arrow shaft, given as a proportion of the height of the
-bounding box:</P><PRE CLASS="verbatim">#Full height shafts, giving pointed boxes:
-gd_feature_set.add_feature(feature, sigil="ARROW", color="brown",
- arrowshaft_height=1.0)
-#Or, thin shafts:
-gd_feature_set.add_feature(feature, sigil="ARROW", color="teal",
- arrowshaft_height=0.2)
-#Or, very thin shafts:
-gd_feature_set.add_feature(feature, sigil="ARROW", color="darkgreen",
- arrowshaft_height=0.1)
-</PRE><P>
-The results are shown below:</P><P><IMG SRC="images/GD_sigil_arrow_shafts.png" width=600, height=700></P><P>Secondly, the length of the arrow head - given as a proportion of the height
-of the bounding box (defaulting to 0.5, or 50%):</P><PRE CLASS="verbatim">#Short arrow heads:
-gd_feature_set.add_feature(feature, sigil="ARROW", color="blue",
- arrowhead_length=0.25)
-#Or, longer arrow heads:
-gd_feature_set.add_feature(feature, sigil="ARROW", color="orange",
- arrowhead_length=1)
-#Or, very very long arrow heads (i.e. all head, no shaft, so triangles):
-gd_feature_set.add_feature(feature, sigil="ARROW", color="red",
- arrowhead_length=10000)
-</PRE><P>
-The results are shown below:</P><P><IMG SRC="images/GD_sigil_arrow_heads.png" width=600, height=700></P><P>Biopython 1.61 adds a new <CODE>BIGARROW</CODE> sigil which always stradles
-the axis, pointing left for the reverse strand or right otherwise:</P><PRE CLASS="verbatim">#A large arrow straddling the axis:
-gd_feature_set.add_feature(feature, sigil="BIGARROW")
-</PRE><P>All the shaft and arrow head options shown above for the
-<CODE>ARROW</CODE> sigil can be used for the <CODE>BIGARROW</CODE> sigil too.</P><!--TOC subsection A nice example-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc221">16.1.9</A>  A nice example</H3><!--SEC END --><P>
-<A NAME="sec:gd_nice_example"></A></P><P>Now let’s return to the pPCP1 plasmid from <I>Yersinia pestis biovar
-Microtus</I>, and the top down approach used in Section <A HREF="#sec:gd_top_down">16.1.3</A>,
-but take advantage of the sigil options we’ve now discussed. This time
-we’ll use arrows for the genes, and overlay them with strand-less features
-(as plain boxes) showing the position of some restriction digest sites.</P><PRE CLASS="verbatim">from reportlab.lib import colors
-from reportlab.lib.units import cm
-from Bio.Graphics import GenomeDiagram
-from Bio import SeqIO
-from Bio.SeqFeature import SeqFeature, FeatureLocation
-
-record = SeqIO.read("NC_005816.gb", "genbank")
-
-gd_diagram = GenomeDiagram.Diagram(record.id)
-gd_track_for_features = gd_diagram.new_track(1, name="Annotated Features")
-gd_feature_set = gd_track_for_features.new_set()
-
-for feature in record.features:
- if feature.type != "gene":
- #Exclude this feature
- continue
- if len(gd_feature_set) % 2 == 0:
- color = colors.blue
- else:
- color = colors.lightblue
- gd_feature_set.add_feature(feature, sigil="ARROW",
- color=color, label=True,
- label_size = 14, label_angle=0)
-
-#I want to include some strandless features, so for an example
-#will use EcoRI recognition sites etc.
-for site, name, color in [("GAATTC","EcoRI",colors.green),
- ("CCCGGG","SmaI",colors.orange),
- ("AAGCTT","HindIII",colors.red),
- ("GGATCC","BamHI",colors.purple)]:
- index = 0
- while True:
- index = record.seq.find(site, start=index)
- if index == -1 : break
- feature = SeqFeature(FeatureLocation(index, index+len(site)))
- gd_feature_set.add_feature(feature, color=color, name=name,
- label=True, label_size = 10,
- label_color=color)
- index += len(site)
-
-gd_diagram.draw(format="linear", pagesize='A4', fragments=4,
- start=0, end=len(record))
-gd_diagram.write("plasmid_linear_nice.pdf", "PDF")
-gd_diagram.write("plasmid_linear_nice.eps", "EPS")
-gd_diagram.write("plasmid_linear_nice.svg", "SVG")
-
-gd_diagram.draw(format="circular", circular=True, pagesize=(20*cm,20*cm),
- start=0, end=len(record), circle_core = 0.5)
-gd_diagram.write("plasmid_circular_nice.pdf", "PDF")
-gd_diagram.write("plasmid_circular_nice.eps", "EPS")
-gd_diagram.write("plasmid_circular_nice.svg", "SVG")
-</PRE><P>
-And the output:</P><P><IMG SRC="images/plasmid_linear_nice.png" width=550, height=400></P><P><IMG SRC="images/plasmid_circular_nice.png" width=591, height=591></P><!--TOC subsection Multiple tracks-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc222">16.1.10</A>  Multiple tracks</H3><!--SEC END --><P>
-<A NAME="sec:gd_multiple_tracks"></A></P><P>All the examples so far have used a single track, but you can have more than
-one track – for example show the genes on one, and repeat regions on another.
-In this example we’re going to show three phage genomes side by side to scale,
-inspired by Figure 6 in Proux <I>e</I>t al. (2002) [<A HREF="#proux2002">5</A>].
-We’ll need the GenBank files for the following three phage:
-</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-<CODE>NC_002703</CODE> – Lactococcus phage Tuc2009, complete genome (38347 bp)
-</LI><LI CLASS="li-itemize"><CODE>AF323668</CODE> – Bacteriophage bIL285, complete genome (35538 bp)
-</LI><LI CLASS="li-itemize"><CODE>NC_003212</CODE> – <I>Listeria innocua</I> Clip11262, complete genome,
-of which we are focussing only on integrated prophage 5 (similar length).
-</LI></UL><P>You can download these using Entrez if you like, see Section <A HREF="#sec:efetch">9.6</A>
-for more details. For the third record we’ve worked out where the phage is
-integrated into the genome, and slice the record to extract it (with the
-features preserved, see Section <A HREF="#sec:SeqRecord-slicing">4.6</A>), and must also
-reverse complement to match the orientation of the first two phage (again
-preserving the features, see Section <A HREF="#sec:SeqRecord-reverse-complement">4.8</A>):</P><PRE CLASS="verbatim">from Bio import SeqIO
-
-A_rec = SeqIO.read("NC_002703.gbk", "gb")
-B_rec = SeqIO.read("AF323668.gbk", "gb")
-C_rec = SeqIO.read("NC_003212.gbk", "gb")[2587879:2625807].reverse_complement(name=True)
-</PRE><P>The figure we are imitating used different colors for different gene functions.
-One way to do this is to edit the GenBank file to record color preferences for
-each feature - something <A HREF="http://www.sanger.ac.uk/resources/software/artemis/">Sanger’s Artemis editor</A> does, and which GenomeDiagram should understand. Here
-however, we’ll just hard code three lists of colors.</P><P>Note that the annotation in the GenBank files doesn’t exactly match that shown
-in Proux <I>et al.</I>, they have drawn some unannotated genes.</P><PRE CLASS="verbatim">from reportlab.lib.colors import red, grey, orange, green, brown, blue, lightblue, purple
-
-A_colors = [red]*5 + [grey]*7 + [orange]*2 + [grey]*2 + [orange] + [grey]*11 + [green]*4 \
- + [grey] + [green]*2 + [grey, green] + [brown]*5 + [blue]*4 + [lightblue]*5 \
- + [grey, lightblue] + [purple]*2 + [grey]
-B_colors = [red]*6 + [grey]*8 + [orange]*2 + [grey] + [orange] + [grey]*21 + [green]*5 \
- + [grey] + [brown]*4 + [blue]*3 + [lightblue]*3 + [grey]*5 + [purple]*2
-C_colors = [grey]*30 + [green]*5 + [brown]*4 + [blue]*2 + [grey, blue] + [lightblue]*2 \
- + [grey]*5
-</PRE><P>Now to draw them – this time we add three tracks to the diagram, and also notice they
-are given different start/end values to reflect their different lengths (this requires
-Biopython 1.59 or later).</P><PRE CLASS="verbatim">from Bio.Graphics import GenomeDiagram
-
-name = "Proux Fig 6"
-gd_diagram = GenomeDiagram.Diagram(name)
-max_len = 0
-for record, gene_colors in zip([A_rec, B_rec, C_rec], [A_colors, B_colors, C_colors]):
- max_len = max(max_len, len(record))
- gd_track_for_features = gd_diagram.new_track(1,
- name=record.name,
- greytrack=True,
- start=0, end=len(record))
- gd_feature_set = gd_track_for_features.new_set()
-
- i = 0
- for feature in record.features:
- if feature.type != "gene":
- #Exclude this feature
- continue
- gd_feature_set.add_feature(feature, sigil="ARROW",
- color=gene_colors[i], label=True,
- name = str(i+1),
- label_position="start",
- label_size = 6, label_angle=0)
- i+=1
-
-gd_diagram.draw(format="linear", pagesize='A4', fragments=1,
- start=0, end=max_len)
-gd_diagram.write(name + ".pdf", "PDF")
-gd_diagram.write(name + ".eps", "EPS")
-gd_diagram.write(name + ".svg", "SVG")
-</PRE><P>
-The result:</P><P><IMG SRC="images/three_track_simple.png" width=565, height=400></P><P>
-
-I did wonder why in the original manuscript there were no red or orange genes
-marked in the bottom phage. Another important point is here the phage are
-shown with different lengths - this is because they are all drawn to the same
-scale (they <EM>are</EM> different lengths).</P><P>The key difference from the published figure is they have color-coded links
-between similar proteins – which is what we will do in the next section.</P><!--TOC subsection Cross-Links between tracks-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc223">16.1.11</A>  Cross-Links between tracks</H3><!--SEC END --><P>
-<A NAME="sec:gd_cross_links"></A></P><P>Biopython 1.59 added the ability to draw cross links between tracks - both
-simple linear diagrams as we will show here, but also linear diagrams split
-into fragments and circular diagrams.</P><P>Continuing the example from the previous section inspired by Figure 6 from
-Proux <I>et al.</I> 2002 [<A HREF="#proux2002">5</A>],
-we would need a list of cross links between pairs of genes, along with a score
-or color to use. Realistically you might extract this from a BLAST file
-computationally, but here I have manually typed them in.</P><P>My naming convention continues to refer to the three phage as A, B and C.
-Here are the links we want to show between A and B, given as a list of
-tuples (percentage similarity score, gene in A, gene in B).</P><PRE CLASS="verbatim">#Tuc2009 (NC_002703) vs bIL285 (AF323668)
-A_vs_B = [
- (99, "Tuc2009_01", "int"),
- (33, "Tuc2009_03", "orf4"),
- (94, "Tuc2009_05", "orf6"),
- (100,"Tuc2009_06", "orf7"),
- (97, "Tuc2009_07", "orf8"),
- (98, "Tuc2009_08", "orf9"),
- (98, "Tuc2009_09", "orf10"),
- (100,"Tuc2009_10", "orf12"),
- (100,"Tuc2009_11", "orf13"),
- (94, "Tuc2009_12", "orf14"),
- (87, "Tuc2009_13", "orf15"),
- (94, "Tuc2009_14", "orf16"),
- (94, "Tuc2009_15", "orf17"),
- (88, "Tuc2009_17", "rusA"),
- (91, "Tuc2009_18", "orf20"),
- (93, "Tuc2009_19", "orf22"),
- (71, "Tuc2009_20", "orf23"),
- (51, "Tuc2009_22", "orf27"),
- (97, "Tuc2009_23", "orf28"),
- (88, "Tuc2009_24", "orf29"),
- (26, "Tuc2009_26", "orf38"),
- (19, "Tuc2009_46", "orf52"),
- (77, "Tuc2009_48", "orf54"),
- (91, "Tuc2009_49", "orf55"),
- (95, "Tuc2009_52", "orf60"),
-]
-</PRE><P>Likewise for B and C:</P><PRE CLASS="verbatim">#bIL285 (AF323668) vs Listeria innocua prophage 5 (in NC_003212)
-B_vs_C = [
- (42, "orf39", "lin2581"),
- (31, "orf40", "lin2580"),
- (49, "orf41", "lin2579"), #terL
- (54, "orf42", "lin2578"), #portal
- (55, "orf43", "lin2577"), #protease
- (33, "orf44", "lin2576"), #mhp
- (51, "orf46", "lin2575"),
- (33, "orf47", "lin2574"),
- (40, "orf48", "lin2573"),
- (25, "orf49", "lin2572"),
- (50, "orf50", "lin2571"),
- (48, "orf51", "lin2570"),
- (24, "orf52", "lin2568"),
- (30, "orf53", "lin2567"),
- (28, "orf54", "lin2566"),
-]
-</PRE><P>For the first and last phage these identifiers are locus tags, for the middle
-phage there are no locus tags so I’ve used gene names instead. The following
-little helper function lets us lookup a feature using either a locus tag or
-gene name:</P><PRE CLASS="verbatim">def get_feature(features, id, tags=["locus_tag", "gene"]):
- """Search list of SeqFeature objects for an identifier under the given tags."""
- for f in features:
- for key in tags:
- #tag may not be present in this feature
- for x in f.qualifiers.get(key, []):
- if x == id:
- return f
- raise KeyError(id)
-</PRE><P>We can now turn those list of identifier pairs into SeqFeature pairs, and thus
-find their location co-ordinates. We can now add all that code and the following
-snippet to the previous example (just before the <CODE>gd_diagram.draw(...)</CODE>
-line – see the finished example script
-<A HREF="http://biopython.org/SRC/biopython/Doc/examples/Proux_et_al_2002_Figure_6.py">Proux_et_al_2002_Figure_6.py</A>
-included in the <TT>Doc/examples</TT> folder of the Biopython source code)
-to add cross links to the figure:</P><PRE CLASS="verbatim">from Bio.Graphics.GenomeDiagram import CrossLink
-from reportlab.lib import colors
-#Note it might have been clearer to assign the track numbers explicitly...
-for rec_X, tn_X, rec_Y, tn_Y, X_vs_Y in [(A_rec, 3, B_rec, 2, A_vs_B),
- (B_rec, 2, C_rec, 1, B_vs_C)]:
- track_X = gd_diagram.tracks[tn_X]
- track_Y = gd_diagram.tracks[tn_Y]
- for score, id_X, id_Y in X_vs_Y:
- feature_X = get_feature(rec_X.features, id_X)
- feature_Y = get_feature(rec_Y.features, id_Y)
- color = colors.linearlyInterpolatedColor(colors.white, colors.firebrick, 0, 100, score)
- link_xy = CrossLink((track_X, feature_X.location.start, feature_X.location.end),
- (track_Y, feature_Y.location.start, feature_Y.location.end),
- color, colors.lightgrey)
- gd_diagram.cross_track_links.append(link_xy)
-</PRE><P>There are several important pieces to this code. First the <CODE>GenomeDiagram</CODE> object
-has a <CODE>cross_track_links</CODE> attribute which is just a list of <CODE>CrossLink</CODE> objects.
-Each <CODE>CrossLink</CODE> object takes two sets of track-specific co-ordinates (here given
-as tuples, you can alternatively use a <CODE>GenomeDiagram.Feature</CODE> object instead).
-You can optionally supply a colour, border color, and say if this link should be drawn
-flipped (useful for showing inversions).</P><P>You can also see how we turn the BLAST percentage identity score into a colour,
-interpolating between white (0%) and a dark red (100%). In this example
-we don’t have any problems with overlapping cross-links. One way to tackle that
-is to use transparency in ReportLab, by using colors with their alpha channel set.
-However, this kind of shaded color scheme combined with overlap transparency
-would be difficult to interpret.
-
-The result:</P><P><IMG SRC="images/three_track_cl.png" width=565, height=400></P><P>There is still a lot more that can be done within Biopython to help
-improve this figure. First of all, the cross links in this case are
-between proteins which are drawn in a strand specific manor. It can
-help to add a background region (a feature using the ‘BOX’ sigil) on the
-feature track to extend the cross link. Also, we could reduce the vertical
-height of the feature tracks to allocate more to the links instead – one
-way to do that is to allocate space for empty tracks. Furthermore,
-in cases like this where there are no large gene overlaps, we can use
-the axis-straddling <CODE>BIGARROW</CODE> sigil, which allows us to further
-reduce the vertical space needed for the track. These improvements
-are demonstrated in the example script
-<A HREF="http://biopython.org/SRC/biopython/Doc/examples/Proux_et_al_2002_Figure_6.py">Proux_et_al_2002_Figure_6.py</A>
-included in the <TT>Doc/examples</TT> folder of the Biopython source code.
-
-The result:</P><P><IMG SRC="images/three_track_cl2a.png" width=565, height=400></P><P>Beyond that, finishing touches you might want to do manually in a vector
-image editor include fine tuning the placement of gene labels, and adding
-other custom annotation such as highlighting particular regions.</P><P>Although not really necessary in this example since none of the cross-links
-overlap, using a transparent color in ReportLab is a very useful technique
-for superimposing multiple links. However, in this case a shaded color
-scheme should be avoided.</P><!--TOC subsection Further options-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc224">16.1.12</A>  Further options</H3><!--SEC END --><P>You can control the tick marks to show the scale – after all every graph
-should show its units, and the number of the grey-track labels.</P><P>Also, we have only used the <CODE>FeatureSet</CODE> so far. GenomeDiagram also has
-a <CODE>GraphSet</CODE> which can be used for show line graphs, bar charts and heat
-plots (e.g. to show plots of GC% on a track parallel to the features).</P><P>These options are not covered here yet, so for now we refer you to the
-<A HREF="http://biopython.org/DIST/docs/GenomeDiagram/userguide.pdf">User Guide (PDF)</A> included with the standalone version of GenomeDiagram (but
-please read the next section first), and the docstrings.</P><!--TOC subsection Converting old code-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc225">16.1.13</A>  Converting old code</H3><!--SEC END --><P>If you have old code written using the standalone version of GenomeDiagram, and
-you want to switch it over to using the new version included with Biopython then
-you will have to make a few changes - most importantly to your import statements.</P><P>Also, the older version of GenomeDiagram used only the UK spellings of color and
-center (colour and centre). You will need to change to the American spellings,
-although for several years the Biopython version of GenomeDiagram supported both.</P><P>For example, if you used to have:
-</P><PRE CLASS="verbatim">from GenomeDiagram import GDFeatureSet, GDDiagram
-gdd = GDDiagram("An example")
-...
-</PRE><P>you could just switch the import statements like this:
-</P><PRE CLASS="verbatim">from Bio.Graphics.GenomeDiagram import FeatureSet as GDFeatureSet, Diagram as GDDiagram
-gdd = GDDiagram("An example")
-...
-</PRE><P>and hopefully that should be enough. In the long term you might want to
-switch to the new names, but you would have to change more of your code:
-</P><PRE CLASS="verbatim">from Bio.Graphics.GenomeDiagram import FeatureSet, Diagram
-gdd = Diagram("An example")
-...
-</PRE><P>or:
-</P><PRE CLASS="verbatim">from Bio.Graphics import GenomeDiagram
-gdd = GenomeDiagram.Diagram("An example")
-...
-</PRE><P>If you run into difficulties, please ask on the Biopython mailing list for
-advice. One catch is that we have not included the old module
-<CODE>GenomeDiagram.GDUtilities</CODE> yet. This included a number of
-GC% related functions, which will probably be merged under
-<CODE>Bio.SeqUtils</CODE> later on.
-</P><!--TOC section Chromosomes-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc226">16.2</A>  Chromosomes</H2><!--SEC END --><P>The <CODE>Bio.Graphics.BasicChromosome</CODE> module allows drawing of chromosomes.
-There is an example in Jupe <I>et al.</I> (2012) [<A HREF="#jupe2012">6</A>]
-(open access) using colors to highlight different gene families.</P><!--TOC subsection Simple Chromosomes-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc227">16.2.1</A>  Simple Chromosomes</H3><!--SEC END --><P>
-Here is a very simple example - for which we’ll use <I>Arabidopsis thaliana</I>.</P><P>You can skip this bit, but first I downloaded the five sequenced chromosomes
-from the NCBI’s FTP site
-<A HREF="ftp://ftp.ncbi.nlm.nih.gov/genomes/Arabidopsis_thaliana"><TT>ftp://ftp.ncbi.nlm.nih.gov/genomes/Arabidopsis_thaliana</TT></A> and then parsed
-them with <CODE>Bio.SeqIO</CODE> to find out their lengths. You could use the
-GenBank files for this, but it is faster to use the FASTA files for the
-whole chromosomes:</P><PRE CLASS="verbatim">from Bio import SeqIO
-entries = [("Chr I", "CHR_I/NC_003070.fna"),
- ("Chr II", "CHR_II/NC_003071.fna"),
- ("Chr III", "CHR_III/NC_003074.fna"),
- ("Chr IV", "CHR_IV/NC_003075.fna"),
- ("Chr V", "CHR_V/NC_003076.fna")]
-for (name, filename) in entries:
- record = SeqIO.read(filename,"fasta")
- print(name, len(record))
-</PRE><P>This gave the lengths of the five chromosomes, which we’ll now use in
-the following short demonstration of the <CODE>BasicChromosome</CODE> module:</P><PRE CLASS="verbatim">from reportlab.lib.units import cm
-from Bio.Graphics import BasicChromosome
-
-entries = [("Chr I", 30432563),
- ("Chr II", 19705359),
- ("Chr III", 23470805),
- ("Chr IV", 18585042),
- ("Chr V", 26992728)]
-
-max_len = 30432563 #Could compute this
-telomere_length = 1000000 #For illustration
-
-chr_diagram = BasicChromosome.Organism()
-chr_diagram.page_size = (29.7*cm, 21*cm) #A4 landscape
-
-for name, length in entries:
- cur_chromosome = BasicChromosome.Chromosome(name)
- #Set the scale to the MAXIMUM length plus the two telomeres in bp,
- #want the same scale used on all five chromosomes so they can be
- #compared to each other
- cur_chromosome.scale_num = max_len + 2 * telomere_length
-
- #Add an opening telomere
- start = BasicChromosome.TelomereSegment()
- start.scale = telomere_length
- cur_chromosome.add(start)
-
- #Add a body - using bp as the scale length here.
- body = BasicChromosome.ChromosomeSegment()
- body.scale = length
- cur_chromosome.add(body)
-
- #Add a closing telomere
- end = BasicChromosome.TelomereSegment(inverted=True)
- end.scale = telomere_length
- cur_chromosome.add(end)
-
- #This chromosome is done
- chr_diagram.add(cur_chromosome)
-
-chr_diagram.draw("simple_chrom.pdf", "Arabidopsis thaliana")
-</PRE><P>This should create a very simple PDF file, shown
-
-here:</P><P><IMG SRC="images/simple_chrom.png" width=650, height=460></P><P>
-
-This example is deliberately short and sweet. The next example shows the
-location of features of interest.</P><!--TOC subsection Annotated Chromosomes-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc228">16.2.2</A>  Annotated Chromosomes</H3><!--SEC END --><P>Continuing from the previous example, let’s also show the tRNA genes.
-We’ll get their locations by parsing the GenBank files for the five
-<I>Arabidopsis thaliana</I> chromosomes. You’ll need to download these
-files from the NCBI FTP site
-<A HREF="ftp://ftp.ncbi.nlm.nih.gov/genomes/Arabidopsis_thaliana"><TT>ftp://ftp.ncbi.nlm.nih.gov/genomes/Arabidopsis_thaliana</TT></A>,
-and preserve the subdirectory names or edit the paths below:</P><PRE CLASS="verbatim">from reportlab.lib.units import cm
-from Bio import SeqIO
-from Bio.Graphics import BasicChromosome
-
-entries = [("Chr I", "CHR_I/NC_003070.gbk"),
- ("Chr II", "CHR_II/NC_003071.gbk"),
- ("Chr III", "CHR_III/NC_003074.gbk"),
- ("Chr IV", "CHR_IV/NC_003075.gbk"),
- ("Chr V", "CHR_V/NC_003076.gbk")]
-
-max_len = 30432563 #Could compute this
-telomere_length = 1000000 #For illustration
-
-chr_diagram = BasicChromosome.Organism()
-chr_diagram.page_size = (29.7*cm, 21*cm) #A4 landscape
-
-for index, (name, filename) in enumerate(entries):
- record = SeqIO.read(filename,"genbank")
- length = len(record)
- features = [f for f in record.features if f.type=="tRNA"]
- #Record an Artemis style integer color in the feature's qualifiers,
- #1 = Black, 2 = Red, 3 = Green, 4 = blue, 5 =cyan, 6 = purple
- for f in features: f.qualifiers["color"] = [index+2]
-
- cur_chromosome = BasicChromosome.Chromosome(name)
- #Set the scale to the MAXIMUM length plus the two telomeres in bp,
- #want the same scale used on all five chromosomes so they can be
- #compared to each other
- cur_chromosome.scale_num = max_len + 2 * telomere_length
-
- #Add an opening telomere
- start = BasicChromosome.TelomereSegment()
- start.scale = telomere_length
- cur_chromosome.add(start)
-
- #Add a body - again using bp as the scale length here.
- body = BasicChromosome.AnnotatedChromosomeSegment(length, features)
- body.scale = length
- cur_chromosome.add(body)
-
- #Add a closing telomere
- end = BasicChromosome.TelomereSegment(inverted=True)
- end.scale = telomere_length
- cur_chromosome.add(end)
-
- #This chromosome is done
- chr_diagram.add(cur_chromosome)
-
-chr_diagram.draw("tRNA_chrom.pdf", "Arabidopsis thaliana")
-</PRE><P>It might warn you about the labels being too close together - have a look
-at the forward strand (right hand side) of Chr I, but it should create a
-colorful PDF file, shown
-
-here:</P><P><IMG SRC="images/tRNA_chrom.png" width=650, height=460></P><!--TOC chapter KEGG-->
-<H1 CLASS="chapter"><!--SEC ANCHOR --><A NAME="htoc229">Chapter 17</A>  KEGG</H1><!--SEC END --><P>
-<A NAME="chap:kegg"></A></P><P>KEGG (<A HREF="http://www.kegg.jp/"><TT>http://www.kegg.jp/</TT></A>) is a database resource for understanding high-level functions and utilities of the biological system, such as the cell, the organism and the ecosystem, from molecular-level information, especially large-scale molecular datasets generated by genome sequencing and other high-throughput experimental technologies.</P><P>Please note that the KEGG parser implementation in Biopython is incomplet [...]
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc230">17.1</A>  Parsing KEGG records</H2><!--SEC END --><P>
-Parsing a KEGG record is as simple as using any other file format parser in Biopython.
-(Before running the following codes, please open <A HREF="http://rest.kegg.jp/get/ec:5.4.2.2"><TT>http://rest.kegg.jp/get/ec:5.4.2.2</TT></A> with your web browser and save it as <CODE>ec_5.4.2.2.txt</CODE>.)</P><PRE CLASS="verbatim">>>> from Bio.KEGG import Enzyme
->>> records = Enzyme.parse(open("ec_5.4.2.2.txt"))
->>> record = list(records)[0]
->>> record.classname
-['Isomerases;', 'Intramolecular transferases;', 'Phosphotransferases (phosphomutases)']
->>> record.entry
-'5.4.2.2'
-</PRE><P>The following section will shows how to download the above enzyme using the KEGG api as well as how to use the generic parser with data that does not have a custom parser implemented.</P><!--TOC section Querying the KEGG API-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc231">17.2</A>  Querying the KEGG API</H2><!--SEC END --><P>Biopython has full support for the querying of the KEGG api. Querying all KEGG endpoints are supported; all methods documented by KEGG (<A HREF="http://www.kegg.jp/kegg/rest/keggapi.html"><TT>http://www.kegg.jp/kegg/rest/keggapi.html</TT></A>) are supported. The interface has some validation of queries which follow rules defined on the KEGG site. However, invalid querie [...]
->>> from Bio.KEGG import Enzyme
->>> request = REST.kegg_get("ec:5.4.2.2")
->>> open("ec_5.4.2.2.txt", 'w').write(request.read())
->>> records = Enzyme.parse(open("ec_5.4.2.2.txt"))
->>> record = list(records)[0]
->>> record.classname
-['Isomerases;', 'Intramolecular transferases;', 'Phosphotransferases (phosphomutases)']
->>> record.entry
-'5.4.2.2'
-</PRE><P>Now, here’s a more realistic example which shows a combination of querying the KEGG API. This will demonstrate how to extract a unique set of all human pathway gene symbols which relate to DNA repair. The steps that need to be taken to do so are as follows. First, we need to get a list of all human pathways. Secondly, we need to filter those for ones which relate to "repair". Lastly, we need to get a list of all the gene symbols in all repair pathways.</P><PRE CLASS="verb [...]
-
-human_pathways = REST.kegg_list("pathway", "hsa").read()
-
-# Filter all human pathways for repair pathways
-repair_pathways = []
-for line in human_pathways.rstrip().split("\n"):
- entry, description = line.split("\t")
- if "repair" in description:
- repair_pathways.append(entry)
-
-# Get the genes for pathways and add them to a list
-repair_genes = []
-for pathway in repair_pathways:
- pathway_file = REST.kegg_get(pathway).read() # query and read each pathway
-
- # iterate through each KEGG pathway file, keeping track of which section
- # of the file we're in, only read the gene in each pathway
- current_section = None
- for line in pathway_file.rstrip().split("\n"):
- section = line[:12].strip() # section names are within 12 columns
- if not section == "":
- current_section = section
-
- if current_section == "GENE":
- gene_identifiers, gene_description = line[12:].split("; ")
- gene_id, gene_symbol = gene_identifiers.split()
-
- if not gene_symbol in repair_genes:
- repair_genes.append(gene_symbol)
-
-print "There are %d repair pathways and %d repair genes. The genes are:" % \
- (len(repair_pathways), len(repair_genes))
-print ", ".join(repair_genes)
-</PRE><P>The KEGG API wrapper is compatible with all endpoints. Usage is essentially replacing all slashes in the url with commas and using that list as arguments to the corresponding method in the KEGG module. Here are a few examples from the api documentation (<A HREF="http://www.kegg.jp/kegg/docs/keggapi.html"><TT>http://www.kegg.jp/kegg/docs/keggapi.html</TT></A>).</P><PRE CLASS="verbatim">/list/hsa:10458+ece:Z5100 -> REST.kegg_list(["hsa:10458", "ece:Z5100"])
-/find/compound/300-310/mol_weight -> REST.kegg_find("compound", "300-310", "mol_weight")
-/get/hsa:10458+ece:Z5100/aaseq -> REST.kegg_get(["hsa:10458", "ece:Z5100"], "aaseq")
-</PRE><!--TOC chapter Cookbook – Cool things to do with it-->
-<H1 CLASS="chapter"><!--SEC ANCHOR --><A NAME="htoc232">Chapter 18</A>  Cookbook – Cool things to do with it</H1><!--SEC END --><P>
-<A NAME="chapter:cookbook"></A></P><P>Biopython now has two collections of “cookbook” examples – this chapter
-(which has been included in this tutorial for many years and has gradually
-grown), and <A HREF="http://biopython.org/wiki/Category:Cookbook"><TT>http://biopython.org/wiki/Category:Cookbook</TT></A> which is a
-user contributed collection on our wiki.</P><P>We’re trying to encourage Biopython users to contribute their own examples
-to the wiki. In addition to helping the community, one direct benefit of
-sharing an example like this is that you could also get some feedback on
-the code from other Biopython users and developers - which could help you
-improve all your Python code.</P><P>In the long term, we may end up moving all of the examples in this chapter
-to the wiki, or elsewhere within the tutorial.</P><!--TOC section Working with sequence files-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc233">18.1</A>  Working with sequence files</H2><!--SEC END --><P>
-<A NAME="seq:cookbook-sequences"></A></P><P>This section shows some more examples of sequence input/output, using the
-<CODE>Bio.SeqIO</CODE> module described in Chapter <A HREF="#chapter:Bio.SeqIO">5</A>.</P><!--TOC subsection Filtering a sequence file-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc234">18.1.1</A>  Filtering a sequence file</H3><!--SEC END --><P>Often you’ll have a large file with many sequences in it (e.g. FASTA file
-or genes, or a FASTQ or SFF file of reads), a separate shorter list of
-the IDs for a subset of sequences of interest, and want to make a new
-sequence file for this subset.</P><P>Let’s say the list of IDs is in a simple text file, as the first word on
-each line. This could be a tabular file where the first column is the ID.
-Try something like this:</P><PRE CLASS="verbatim">from Bio import SeqIO
-input_file = "big_file.sff"
-id_file = "short_list.txt"
-output_file = "short_list.sff"
-wanted = set(line.rstrip("\n").split(None,1)[0] for line in open(id_file))
-print("Found %i unique identifiers in %s" % (len(wanted), id_file))
-records = (r for r in SeqIO.parse(input_file, "sff") if r.id in wanted)
-count = SeqIO.write(records, output_file, "sff")
-print("Saved %i records from %s to %s" % (count, input_file, output_file))
-if count < len(wanted):
- print("Warning %i IDs not found in %s" % (len(wanted)-count, input_file))
-</PRE><P>Note that we use a Python <CODE>set</CODE> rather than a <CODE>list</CODE>, this makes
-testing membership faster.</P><!--TOC subsection Producing randomised genomes-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc235">18.1.2</A>  Producing randomised genomes</H3><!--SEC END --><P>Let’s suppose you are looking at genome sequence, hunting for some sequence
-feature – maybe extreme local GC% bias, or possible restriction digest sites.
-Once you’ve got your Python code working on the real genome it may be sensible
-to try running the same search on randomised versions of the same genome for
-statistical analysis (after all, any “features” you’ve found could just be
-there just by chance).</P><P>For this discussion, we’ll use the GenBank file for the pPCP1 plasmid from
-<I>Yersinia pestis biovar Microtus</I>. The file is included with the
-Biopython unit tests under the GenBank folder, or you can get it from our
-website, <A HREF="http://biopython.org/SRC/biopython/Tests/GenBank/NC_005816.gb"><TT>NC_005816.gb</TT></A>.
-This file contains one and only one record, so we can read it in as a
-<CODE>SeqRecord</CODE> using the <CODE>Bio.SeqIO.read()</CODE> function:</P><PRE CLASS="verbatim">>>> from Bio import SeqIO
->>> original_rec = SeqIO.read("NC_005816.gb", "genbank")
-</PRE><P>So, how can we generate a shuffled versions of the original sequence? I would
-use the built in Python <CODE>random</CODE> module for this, in particular the function
-<CODE>random.shuffle</CODE> – but this works on a Python list. Our sequence is a
-<CODE>Seq</CODE> object, so in order to shuffle it we need to turn it into a list:</P><PRE CLASS="verbatim">>>> import random
->>> nuc_list = list(original_rec.seq)
->>> random.shuffle(nuc_list) #acts in situ!
-</PRE><P>Now, in order to use <CODE>Bio.SeqIO</CODE> to output the shuffled sequence, we need
-to construct a new <CODE>SeqRecord</CODE> with a new <CODE>Seq</CODE> object using this
-shuffled list. In order to do this, we need to turn the list of nucleotides
-(single letter strings) into a long string – the standard Python way to do
-this is with the string object’s join method.</P><PRE CLASS="verbatim">>>> from Bio.Seq import Seq
->>> from Bio.SeqRecord import SeqRecord
->>> shuffled_rec = SeqRecord(Seq("".join(nuc_list), original_rec.seq.alphabet),
-... id="Shuffled", description="Based on %s" % original_rec.id)
-</PRE><P>Let’s put all these pieces together to make a complete Python script which
-generates a single FASTA file containing 30 randomly shuffled versions of
-the original sequence.</P><P>This first version just uses a big for loop and writes out the records one by one
-(using the <CODE>SeqRecord</CODE>’s format method described in
-Section <A HREF="#sec:Bio.SeqIO-and-StringIO">5.5.4</A>):</P><PRE CLASS="verbatim">import random
-from Bio.Seq import Seq
-from Bio.SeqRecord import SeqRecord
-from Bio import SeqIO
-
-original_rec = SeqIO.read("NC_005816.gb","genbank")
-
-handle = open("shuffled.fasta", "w")
-for i in range(30):
- nuc_list = list(original_rec.seq)
- random.shuffle(nuc_list)
- shuffled_rec = SeqRecord(Seq("".join(nuc_list), original_rec.seq.alphabet), \
- id="Shuffled%i" % (i+1), \
- description="Based on %s" % original_rec.id)
- handle.write(shuffled_rec.format("fasta"))
-handle.close()
-</PRE><P>Personally I prefer the following version using a function to shuffle the record
-and a generator expression instead of the for loop:</P><PRE CLASS="verbatim">import random
-from Bio.Seq import Seq
-from Bio.SeqRecord import SeqRecord
-from Bio import SeqIO
-
-def make_shuffle_record(record, new_id):
- nuc_list = list(record.seq)
- random.shuffle(nuc_list)
- return SeqRecord(Seq("".join(nuc_list), record.seq.alphabet), \
- id=new_id, description="Based on %s" % original_rec.id)
-
-original_rec = SeqIO.read("NC_005816.gb","genbank")
-shuffled_recs = (make_shuffle_record(original_rec, "Shuffled%i" % (i+1)) \
- for i in range(30))
-handle = open("shuffled.fasta", "w")
-SeqIO.write(shuffled_recs, handle, "fasta")
-handle.close()
-</PRE><!--TOC subsection Translating a FASTA file of CDS entries-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc236">18.1.3</A>  Translating a FASTA file of CDS entries</H3><!--SEC END --><P>
-<A NAME="sec:SeqIO-translate"></A>
-Suppose you’ve got an input file of CDS entries for some organism, and you
-want to generate a new FASTA file containing their protein sequences. i.e.
-Take each nucleotide sequence from the original file, and translate it.
-Back in Section <A HREF="#sec:translation">3.9</A> we saw how to use the <CODE>Seq</CODE>
-object’s <CODE>translate method</CODE>, and the optional <CODE>cds</CODE> argument
-which enables correct translation of alternative start codons.</P><P>We can combine this with <CODE>Bio.SeqIO</CODE> as
-shown in the reverse complement example in Section <A HREF="#sec:SeqIO-reverse-complement">5.5.3</A>.
-The key point is that for each nucleotide <CODE>SeqRecord</CODE>, we need to create
-a protein <CODE>SeqRecord</CODE> - and take care of naming it.</P><P>You can write you own function to do this, choosing suitable protein identifiers
-for your sequences, and the appropriate genetic code. In this example we just
-use the default table and add a prefix to the identifier:</P><PRE CLASS="verbatim">from Bio.SeqRecord import SeqRecord
-def make_protein_record(nuc_record):
- """Returns a new SeqRecord with the translated sequence (default table)."""
- return SeqRecord(seq = nuc_record.seq.translate(cds=True), \
- id = "trans_" + nuc_record.id, \
- description = "translation of CDS, using default table")
-</PRE><P>We can then use this function to turn the input nucleotide records into protein
-records ready for output. An elegant way and memory efficient way to do this
-is with a generator expression:</P><PRE CLASS="verbatim">from Bio import SeqIO
-proteins = (make_protein_record(nuc_rec) for nuc_rec in \
- SeqIO.parse("coding_sequences.fasta", "fasta"))
-SeqIO.write(proteins, "translations.fasta", "fasta")
-</PRE><P>This should work on any FASTA file of complete coding sequences.
-If you are working on partial coding sequences, you may prefer to use
-<CODE>nuc_record.seq.translate(to_stop=True)</CODE> in the example above, as
-this wouldn’t check for a valid start codon etc.</P><!--TOC subsection Making the sequences in a FASTA file upper case-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc237">18.1.4</A>  Making the sequences in a FASTA file upper case</H3><!--SEC END --><P>Often you’ll get data from collaborators as FASTA files, and sometimes the
-sequences can be in a mixture of upper and lower case. In some cases this is
-deliberate (e.g. lower case for poor quality regions), but usually it is not
-important. You may want to edit the file to make everything consistent (e.g.
-all upper case), and you can do this easily using the <CODE>upper()</CODE> method
-of the <CODE>SeqRecord</CODE> object (added in Biopython 1.55):</P><PRE CLASS="verbatim">from Bio import SeqIO
-records = (rec.upper() for rec in SeqIO.parse("mixed.fas", "fasta"))
-count = SeqIO.write(records, "upper.fas", "fasta")
-print("Converted %i records to upper case" % count)
-</PRE><P>How does this work? The first line is just importing the <CODE>Bio.SeqIO</CODE>
-module. The second line is the interesting bit – this is a Python
-generator expression which gives an upper case version of each record
-parsed from the input file (<TT>mixed.fas</TT>). In the third line we give
-this generator expression to the <CODE>Bio.SeqIO.write()</CODE> function and it
-saves the new upper cases records to our output file (<TT>upper.fas</TT>).</P><P>The reason we use a generator expression (rather than a list or list
-comprehension) is this means only one record is kept in memory at a time.
-This can be really important if you are dealing with large files with
-millions of entries.</P><!--TOC subsection Sorting a sequence file-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc238">18.1.5</A>  Sorting a sequence file</H3><!--SEC END --><P>
-<A NAME="sec:SeqIO-sort"></A></P><P>Suppose you wanted to sort a sequence file by length (e.g. a set of
-contigs from an assembly), and you are working with a file format like
-FASTA or FASTQ which <CODE>Bio.SeqIO</CODE> can read, write (and index).</P><P>If the file is small enough, you can load it all into memory at once
-as a list of <CODE>SeqRecord</CODE> objects, sort the list, and save it:</P><PRE CLASS="verbatim">from Bio import SeqIO
-records = list(SeqIO.parse("ls_orchid.fasta","fasta"))
-records.sort(cmp=lambda x,y: cmp(len(x),len(y)))
-SeqIO.write(records, "sorted_orchids.fasta", "fasta")
-</PRE><P>The only clever bit is specifying a comparison function for how to
-sort the records (here we sort them by length). If you wanted the
-longest records first, you could flip the comparison or use the
-reverse argument:</P><PRE CLASS="verbatim">from Bio import SeqIO
-records = list(SeqIO.parse("ls_orchid.fasta","fasta"))
-records.sort(cmp=lambda x,y: cmp(len(y),len(x)))
-SeqIO.write(records, "sorted_orchids.fasta", "fasta")
-</PRE><P>Now that’s pretty straight forward - but what happens if you have a
-very large file and you can’t load it all into memory like this?
-For example, you might have some next-generation sequencing reads
-to sort by length. This can be solved using the
-<CODE>Bio.SeqIO.index()</CODE> function.</P><PRE CLASS="verbatim">from Bio import SeqIO
-#Get the lengths and ids, and sort on length
-len_and_ids = sorted((len(rec), rec.id) for rec in \
- SeqIO.parse("ls_orchid.fasta","fasta"))
-ids = reversed([id for (length, id) in len_and_ids])
-del len_and_ids #free this memory
-record_index = SeqIO.index("ls_orchid.fasta", "fasta")
-records = (record_index[id] for id in ids)
-SeqIO.write(records, "sorted.fasta", "fasta")
-</PRE><P>First we scan through the file once using <CODE>Bio.SeqIO.parse()</CODE>,
-recording the record identifiers and their lengths in a list of tuples.
-We then sort this list to get them in length order, and discard the lengths.
-Using this sorted list of identifiers <CODE>Bio.SeqIO.index()</CODE> allows us to
-retrieve the records one by one, and we pass them to <CODE>Bio.SeqIO.write()</CODE>
-for output.</P><P>These examples all use <CODE>Bio.SeqIO</CODE> to parse the records into
-<CODE>SeqRecord</CODE> objects which are output using <CODE>Bio.SeqIO.write()</CODE>.
-What if you want to sort a file format which <CODE>Bio.SeqIO.write()</CODE> doesn’t
-support, like the plain text SwissProt format? Here is an alternative
-solution using the <CODE>get_raw()</CODE> method added to <CODE>Bio.SeqIO.index()</CODE>
-in Biopython 1.54 (see Section <A HREF="#sec:seqio-index-getraw">5.4.2.2</A>).</P><PRE CLASS="verbatim">from Bio import SeqIO
-#Get the lengths and ids, and sort on length
-len_and_ids = sorted((len(rec), rec.id) for rec in \
- SeqIO.parse("ls_orchid.fasta","fasta"))
-ids = reversed([id for (length, id) in len_and_ids])
-del len_and_ids #free this memory
-record_index = SeqIO.index("ls_orchid.fasta", "fasta")
-handle = open("sorted.fasta", "w")
-for id in ids:
- handle.write(record_index.get_raw(id))
-handle.close()
-</PRE><P>As a bonus, because it doesn’t parse the data into <CODE>SeqRecord</CODE> objects
-a second time it should be faster.</P><!--TOC subsection Simple quality filtering for FASTQ files-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc239">18.1.6</A>  Simple quality filtering for FASTQ files</H3><!--SEC END --><P>
-<A NAME="sec:FASTQ-filtering-example"></A></P><P>The FASTQ file format was introduced at Sanger and is now widely used for
-holding nucleotide sequencing reads together with their quality scores.
-FASTQ files (and the related QUAL files) are an excellent example of
-per-letter-annotation, because for each nucleotide in the sequence there is
-an associated quality score. Any per-letter-annotation is held in a
-<CODE>SeqRecord</CODE> in the <CODE>letter_annotations</CODE> dictionary as a list,
-tuple or string (with the same number of elements as the sequence length).</P><P>One common task is taking a large set of sequencing reads and filtering them
-(or cropping them) based on their quality scores.
-The following example is very simplistic, but should illustrate the basics of
-working with quality data in a <CODE>SeqRecord</CODE> object. All we are going to
-do here is read in a file of FASTQ data, and filter it to pick out only those
-records whose PHRED quality scores are all above some threshold (here 20).</P><P>For this example we’ll use some real data downloaded from the ENA sequence
-read archive,
-<A HREF="ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR020/SRR020192/SRR020192.fastq.gz"><TT>ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR020/SRR020192/SRR020192.fastq.gz</TT></A>
-(2MB) which unzips to a 19MB file <TT>SRR020192.fastq</TT>. This is some
-Roche 454 GS FLX single end data from virus infected California sea lions
-(see <A HREF="http://www.ebi.ac.uk/ena/data/view/SRS004476"><TT>http://www.ebi.ac.uk/ena/data/view/SRS004476</TT></A> for details).</P><P>First, let’s count the reads:</P><PRE CLASS="verbatim">from Bio import SeqIO
-count = 0
-for rec in SeqIO.parse("SRR020192.fastq", "fastq"):
- count += 1
-print("%i reads" % count)
-</PRE><P>Now let’s do a simple filtering for a minimum PHRED quality of 20:</P><PRE CLASS="verbatim">from Bio import SeqIO
-good_reads = (rec for rec in \
- SeqIO.parse("SRR020192.fastq", "fastq") \
- if min(rec.letter_annotations["phred_quality"]) >= 20)
-count = SeqIO.write(good_reads, "good_quality.fastq", "fastq")
-print("Saved %i reads" % count)
-</PRE><P>This pulled out only 14580 reads out of the 41892 present.
-A more sensible thing to do would be to quality trim the reads, but this
-is intended as an example only.</P><P>FASTQ files can contain millions of entries, so it is best to avoid loading
-them all into memory at once. This example uses a generator expression, which
-means only one <CODE>SeqRecord</CODE> is created at a time - avoiding any memory
-limitations.</P><!--TOC subsection Trimming off primer sequences-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc240">18.1.7</A>  Trimming off primer sequences</H3><!--SEC END --><P>
-<A NAME="sec:FASTQ-slicing-off-primer"></A></P><P>For this example we’re going to pretend that <TT>GATGACGGTGT</TT> is a 5’ primer
-sequence we want to look for in some FASTQ formatted read data. As in the example
-above, we’ll use the <TT>SRR020192.fastq</TT> file downloaded from the ENA
-(<A HREF="ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR020/SRR020192/SRR020192.fastq.gz"><TT>ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR020/SRR020192/SRR020192.fastq.gz</TT></A>).
-The same approach would work with any other supported file format (e.g. FASTA files).</P><P>This code uses <CODE>Bio.SeqIO</CODE> with a generator expression (to avoid loading
-all the sequences into memory at once), and the <CODE>Seq</CODE> object’s
-<CODE>startswith</CODE> method to see if the read starts with the primer sequence:</P><PRE CLASS="verbatim">from Bio import SeqIO
-primer_reads = (rec for rec in \
- SeqIO.parse("SRR020192.fastq", "fastq") \
- if rec.seq.startswith("GATGACGGTGT"))
-count = SeqIO.write(primer_reads, "with_primer.fastq", "fastq")
-print("Saved %i reads" % count)
-</PRE><P>That should find 13819 reads from <TT>SRR014849.fastq</TT> and save them to
-a new FASTQ file, <TT>with_primer.fastq</TT>.</P><P>Now suppose that instead you wanted to make a FASTQ file containing these reads
-but with the primer sequence removed? That’s just a small change as we can slice the
-<CODE>SeqRecord</CODE> (see Section <A HREF="#sec:SeqRecord-slicing">4.6</A>) to remove the first eleven
-letters (the length of our primer):</P><PRE CLASS="verbatim">from Bio import SeqIO
-trimmed_primer_reads = (rec[11:] for rec in \
- SeqIO.parse("SRR020192.fastq", "fastq") \
- if rec.seq.startswith("GATGACGGTGT"))
-count = SeqIO.write(trimmed_primer_reads, "with_primer_trimmed.fastq", "fastq")
-print("Saved %i reads" % count)
-</PRE><P>Again, that should pull out the 13819 reads from <TT>SRR020192.fastq</TT>,
-but this time strip off the first ten characters, and save them to another new
-FASTQ file, <TT>with_primer_trimmed.fastq</TT>.</P><P>Finally, suppose you want to create a new FASTQ file where these reads have
-their primer removed, but all the other reads are kept as they were?
-If we want to still use a generator expression, it is probably clearest to
-define our own trim function:</P><PRE CLASS="verbatim">from Bio import SeqIO
-def trim_primer(record, primer):
- if record.seq.startswith(primer):
- return record[len(primer):]
- else:
- return record
-
-trimmed_reads = (trim_primer(record, "GATGACGGTGT") for record in \
- SeqIO.parse("SRR020192.fastq", "fastq"))
-count = SeqIO.write(trimmed_reads, "trimmed.fastq", "fastq")
-print("Saved %i reads" % count)
-</PRE><P>This takes longer, as this time the output file contains all 41892 reads.
-Again, we’re used a generator expression to avoid any memory problems.
-You could alternatively use a generator function rather than a generator
-expression.</P><PRE CLASS="verbatim">from Bio import SeqIO
-def trim_primers(records, primer):
- """Removes perfect primer sequences at start of reads.
-
- This is a generator function, the records argument should
- be a list or iterator returning SeqRecord objects.
- """
- len_primer = len(primer) #cache this for later
- for record in records:
- if record.seq.startswith(primer):
- yield record[len_primer:]
- else:
- yield record
-
-original_reads = SeqIO.parse("SRR020192.fastq", "fastq")
-trimmed_reads = trim_primers(original_reads, "GATGACGGTGT")
-count = SeqIO.write(trimmed_reads, "trimmed.fastq", "fastq")
-print("Saved %i reads" % count)
-</PRE><P>This form is more flexible if you want to do something more complicated
-where only some of the records are retained – as shown in the next example.</P><!--TOC subsection Trimming off adaptor sequences-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc241">18.1.8</A>  Trimming off adaptor sequences</H3><!--SEC END --><P>
-<A NAME="sec:FASTQ-slicing-off-adaptor"></A></P><P>This is essentially a simple extension to the previous example. We are going
-to going to pretend <TT>GATGACGGTGT</TT> is an adaptor sequence in some FASTQ
-formatted read data, again the <TT>SRR020192.fastq</TT> file from the NCBI
-(<A HREF="ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR020/SRR020192/SRR020192.fastq.gz"><TT>ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR020/SRR020192/SRR020192.fastq.gz</TT></A>).</P><P>This time however, we will look for the sequence <EM>anywhere</EM> in the reads,
-not just at the very beginning:</P><PRE CLASS="verbatim">from Bio import SeqIO
-
-def trim_adaptors(records, adaptor):
- """Trims perfect adaptor sequences.
-
- This is a generator function, the records argument should
- be a list or iterator returning SeqRecord objects.
- """
- len_adaptor = len(adaptor) #cache this for later
- for record in records:
- index = record.seq.find(adaptor)
- if index == -1:
- #adaptor not found, so won't trim
- yield record
- else:
- #trim off the adaptor
- yield record[index+len_adaptor:]
-
-original_reads = SeqIO.parse("SRR020192.fastq", "fastq")
-trimmed_reads = trim_adaptors(original_reads, "GATGACGGTGT")
-count = SeqIO.write(trimmed_reads, "trimmed.fastq", "fastq")
-print("Saved %i reads" % count)
-</PRE><P>Because we are using a FASTQ input file in this example, the <CODE>SeqRecord</CODE>
-objects have per-letter-annotation for the quality scores. By slicing the
-<CODE>SeqRecord</CODE> object the appropriate scores are used on the trimmed
-records, so we can output them as a FASTQ file too.</P><P>Compared to the output of the previous example where we only looked for
-a primer/adaptor at the start of each read, you may find some of the
-trimmed reads are quite short after trimming (e.g. if the adaptor was
-found in the middle rather than near the start). So, let’s add a minimum
-length requirement as well:</P><PRE CLASS="verbatim">from Bio import SeqIO
-
-def trim_adaptors(records, adaptor, min_len):
- """Trims perfect adaptor sequences, checks read length.
-
- This is a generator function, the records argument should
- be a list or iterator returning SeqRecord objects.
- """
- len_adaptor = len(adaptor) #cache this for later
- for record in records:
- len_record = len(record) #cache this for later
- if len(record) < min_len:
- #Too short to keep
- continue
- index = record.seq.find(adaptor)
- if index == -1:
- #adaptor not found, so won't trim
- yield record
- elif len_record - index - len_adaptor >= min_len:
- #after trimming this will still be long enough
- yield record[index+len_adaptor:]
-
-original_reads = SeqIO.parse("SRR020192.fastq", "fastq")
-trimmed_reads = trim_adaptors(original_reads, "GATGACGGTGT", 100)
-count = SeqIO.write(trimmed_reads, "trimmed.fastq", "fastq")
-print("Saved %i reads" % count)
-</PRE><P>By changing the format names, you could apply this to FASTA files instead.
-This code also could be extended to do a fuzzy match instead of an exact
-match (maybe using a pairwise alignment, or taking into account the read
-quality scores), but that will be much slower.</P><!--TOC subsection Converting FASTQ files-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc242">18.1.9</A>  Converting FASTQ files</H3><!--SEC END --><P>
-<A NAME="sec:SeqIO-fastq-conversion"></A></P><P>Back in Section <A HREF="#sec:SeqIO-conversion">5.5.2</A> we showed how to use
-<CODE>Bio.SeqIO</CODE> to convert between two file formats. Here we’ll go into a
-little more detail regarding FASTQ files which are used in second generation
-DNA sequencing. Please refer to Cock <I>et al.</I> (2009) [<A HREF="#cock2010">7</A>]
-for a longer description. FASTQ files store both the DNA sequence (as a string)
-and the associated read qualities.</P><P>PHRED scores (used in most FASTQ files, and also in QUAL files, ACE files
-and SFF files) have become a <I>de facto</I> standard for representing
-the probability of a sequencing error (here denoted by <I>P</I><SUB><I>e</I></SUB>) at a given
-base using a simple base ten log transformation:</P><TABLE CLASS="display dcenter"><TR VALIGN="middle"><TD CLASS="dcell">
-<I>Q</I><SUB>PHRED</SUB> = − 10 × log<SUB>10</SUB> ( <I>P</I><SUB><I>e</I></SUB> )
-    (18.1)</TD></TR>
-</TABLE><P>This means a wrong read (<I>P</I><SUB><I>e</I></SUB> = 1) gets a PHRED quality of 0, while a very
-good read like <I>P</I><SUB><I>e</I></SUB> = 0.00001 gets a PHRED quality of 50. While for raw
-sequencing data qualities higher than this are rare, with post processing
-such as read mapping or assembly, qualities of up to about 90 are possible
-(indeed, the MAQ tool allows for PHRED scores in the range 0 to 93 inclusive).</P><P>The FASTQ format has the potential to become a <I>de facto</I> standard for
-storing the letters and quality scores for a sequencing read in a single plain
-text file. The only fly in the ointment is that there are at least three
-versions of the FASTQ format which are incompatible and difficult to
-distinguish...</P><OL CLASS="enumerate" type=1><LI CLASS="li-enumerate">
-The original Sanger FASTQ format uses PHRED qualities encoded with an
-ASCII offset of 33. The NCBI are using this format in their Short Read
-Archive. We call this the <TT>fastq</TT> (or <TT>fastq-sanger</TT>) format
-in <CODE>Bio.SeqIO</CODE>.
-</LI><LI CLASS="li-enumerate">Solexa (later bought by Illumina) introduced their own version using
-Solexa qualities encoded with an ASCII offset of 64. We call this the
-<TT>fastq-solexa</TT> format.
-</LI><LI CLASS="li-enumerate">Illumina pipeline 1.3 onwards produces FASTQ files with PHRED qualities
-(which is more consistent), but encoded with an ASCII offset of 64. We call
-this the <TT>fastq-illumina</TT> format.
-</LI></OL><P>The Solexa quality scores are defined using a different log transformation:</P><TABLE CLASS="display dcenter"><TR VALIGN="middle"><TD CLASS="dcell">
-<I>Q</I><SUB>Solexa</SUB> = − 10 × log<SUB>10</SUB> </TD><TD CLASS="dcell">⎛<BR>
-⎜<BR>
-⎜<BR>
-⎝</TD><TD CLASS="dcell"><TABLE CLASS="display"><TR><TD CLASS="dcell" ALIGN="center"><I>P</I><SUB><I>e</I></SUB></TD></TR>
-<TR><TD CLASS="hbar"></TD></TR>
-<TR><TD CLASS="dcell" ALIGN="center">1−<I>P</I><SUB><I>e</I></SUB></TD></TR>
-</TABLE></TD><TD CLASS="dcell"> </TD><TD CLASS="dcell">⎞<BR>
-⎟<BR>
-⎟<BR>
-⎠</TD><TD CLASS="dcell">
-    (18.2)</TD></TR>
-</TABLE><P>Given Solexa/Illumina have now moved to using PHRED scores in version 1.3 of
-their pipeline, the Solexa quality scores will gradually fall out of use.
-If you equate the error estimates (<I>P</I><SUB><I>e</I></SUB>) these two equations allow conversion
-between the two scoring systems - and Biopython includes functions to do this
-in the <CODE>Bio.SeqIO.QualityIO</CODE> module, which are called if you use
-<CODE>Bio.SeqIO</CODE> to convert an old Solexa/Illumina file into a standard Sanger
-FASTQ file:</P><PRE CLASS="verbatim">from Bio import SeqIO
-SeqIO.convert("solexa.fastq", "fastq-solexa", "standard.fastq", "fastq")
-</PRE><P>If you want to convert a new Illumina 1.3+ FASTQ file, all that gets changed
-is the ASCII offset because although encoded differently the scores are all
-PHRED qualities:</P><PRE CLASS="verbatim">from Bio import SeqIO
-SeqIO.convert("illumina.fastq", "fastq-illumina", "standard.fastq", "fastq")
-</PRE><P>Note that using <CODE>Bio.SeqIO.convert()</CODE> like this is <EM>much</EM> faster
-than combining <CODE>Bio.SeqIO.parse()</CODE> and <CODE>Bio.SeqIO.write()</CODE>
-because optimised code is used for converting between FASTQ variants
-(and also for FASTQ to FASTA conversion).</P><P>For good quality reads, PHRED and Solexa scores are approximately equal,
-which means since both the <TT>fasta-solexa</TT> and <TT>fastq-illumina</TT>
-formats use an ASCII offset of 64 the files are almost the same. This was a
-deliberate design choice by Illumina, meaning applications expecting the old
-<TT>fasta-solexa</TT> style files will probably be OK using the newer
-<TT>fastq-illumina</TT> files (on good data). Of course, both variants are
-very different from the original FASTQ standard as used by Sanger,
-the NCBI, and elsewhere (format name <TT>fastq</TT> or <TT>fastq-sanger</TT>).</P><P>For more details, see the built in help (also <A HREF="http://www.biopython.org/DIST/docs/api/Bio.SeqIO.QualityIO-module.html">online</A>):</P><PRE CLASS="verbatim">>>> from Bio.SeqIO import QualityIO
->>> help(QualityIO)
-...
-</PRE><!--TOC subsection Converting FASTA and QUAL files into FASTQ files-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc243">18.1.10</A>  Converting FASTA and QUAL files into FASTQ files</H3><!--SEC END --><P>
-<A NAME="sec:SeqIO-fasta-qual-conversion"></A></P><P>FASTQ files hold <EM>both</EM> sequences and their quality strings.
-FASTA files hold <EM>just</EM> sequences, while QUAL files hold <EM>just</EM>
-the qualities. Therefore a single FASTQ file can be converted to or from
-<EM>paired</EM> FASTA and QUAL files.</P><P>Going from FASTQ to FASTA is easy:</P><PRE CLASS="verbatim">from Bio import SeqIO
-SeqIO.convert("example.fastq", "fastq", "example.fasta", "fasta")
-</PRE><P>Going from FASTQ to QUAL is also easy:</P><PRE CLASS="verbatim">from Bio import SeqIO
-SeqIO.convert("example.fastq", "fastq", "example.qual", "qual")
-</PRE><P>However, the reverse is a little more tricky. You can use <CODE>Bio.SeqIO.parse()</CODE>
-to iterate over the records in a <EM>single</EM> file, but in this case we have
-two input files. There are several strategies possible, but assuming that the
-two files are really paired the most memory efficient way is to loop over both
-together. The code is a little fiddly, so we provide a function called
-<CODE>PairedFastaQualIterator</CODE> in the <CODE>Bio.SeqIO.QualityIO</CODE> module to do
-this. This takes two handles (the FASTA file and the QUAL file) and returns
-a <CODE>SeqRecord</CODE> iterator:</P><PRE CLASS="verbatim">from Bio.SeqIO.QualityIO import PairedFastaQualIterator
-for record in PairedFastaQualIterator(open("example.fasta"), open("example.qual")):
- print(record)
-</PRE><P>This function will check that the FASTA and QUAL files are consistent (e.g.
-the records are in the same order, and have the same sequence length).
-You can combine this with the <CODE>Bio.SeqIO.write()</CODE> function to convert a
-pair of FASTA and QUAL files into a single FASTQ files:</P><PRE CLASS="verbatim">from Bio import SeqIO
-from Bio.SeqIO.QualityIO import PairedFastaQualIterator
-handle = open("temp.fastq", "w") #w=write
-records = PairedFastaQualIterator(open("example.fasta"), open("example.qual"))
-count = SeqIO.write(records, handle, "fastq")
-handle.close()
-print("Converted %i records" % count)
-</PRE><!--TOC subsection Indexing a FASTQ file-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc244">18.1.11</A>  Indexing a FASTQ file</H3><!--SEC END --><P>
-<A NAME="sec:fastq-indexing"></A></P><P>FASTQ files are often very large, with millions of reads in them. Due to the
-sheer amount of data, you can’t load all the records into memory at once.
-This is why the examples above (filtering and trimming) iterate over the file
-looking at just one <CODE>SeqRecord</CODE> at a time.</P><P>However, sometimes you can’t use a big loop or an iterator - you may need
-random access to the reads. Here the <CODE>Bio.SeqIO.index()</CODE> function
-may prove very helpful, as it allows you to access any read in the FASTQ file
-by its name (see Section <A HREF="#sec:SeqIO-index">5.4.2</A>).</P><P>Again we’ll use the <TT>SRR020192.fastq</TT> file from the ENA
-(<A HREF="ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR020/SRR020192/SRR020192.fastq.gz"><TT>ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR020/SRR020192/SRR020192.fastq.gz</TT></A>),
-although this is actually quite a small FASTQ file with less than 50,000 reads:</P><PRE CLASS="verbatim">>>> from Bio import SeqIO
->>> fq_dict = SeqIO.index("SRR020192.fastq", "fastq")
->>> len(fq_dict)
-41892
->>> fq_dict.keys()[:4]
-['SRR020192.38240', 'SRR020192.23181', 'SRR020192.40568', 'SRR020192.23186']
->>> fq_dict["SRR020192.23186"].seq
-Seq('GTCCCAGTATTCGGATTTGTCTGCCAAAACAATGAAATTGACACAGTTTACAAC...CCG', SingleLetterAlphabet())
-</PRE><P>When testing this on a FASTQ file with seven million reads,
-indexing took about a minute, but record access was almost instant.</P><P>The example in Section <A HREF="#sec:SeqIO-sort">18.1.5</A> show how you can use the
-<CODE>Bio.SeqIO.index()</CODE> function to sort a large FASTA file – this
-could also be used on FASTQ files.</P><!--TOC subsection Converting SFF files-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc245">18.1.12</A>  Converting SFF files</H3><!--SEC END --><P>
-<A NAME="sec:SeqIO-sff-conversion"></A></P><P>If you work with 454 (Roche) sequence data, you will probably have access
-to the raw data as a Standard Flowgram Format (SFF) file. This contains
-the sequence reads (called bases) with quality scores and the original
-flow information.</P><P>A common task is to convert from SFF to a pair of FASTA and QUAL files,
-or to a single FASTQ file. These operations are trivial using the
-<CODE>Bio.SeqIO.convert()</CODE> function (see Section <A HREF="#sec:SeqIO-conversion">5.5.2</A>):</P><PRE CLASS="verbatim">>>> from Bio import SeqIO
->>> SeqIO.convert("E3MFGYR02_random_10_reads.sff", "sff", "reads.fasta", "fasta")
-10
->>> SeqIO.convert("E3MFGYR02_random_10_reads.sff", "sff", "reads.qual", "qual")
-10
->>> SeqIO.convert("E3MFGYR02_random_10_reads.sff", "sff", "reads.fastq", "fastq")
-10
-</PRE><P>Remember the convert function returns the number of records, in
-this example just ten. This will give you the <EM>untrimmed</EM> reads, where
-the leading and trailing poor quality sequence or adaptor will be in lower
-case. If you want the <EM>trimmed</EM> reads (using the clipping information
-recorded within the SFF file) use this:</P><PRE CLASS="verbatim">>>> from Bio import SeqIO
->>> SeqIO.convert("E3MFGYR02_random_10_reads.sff", "sff-trim", "trimmed.fasta", "fasta")
-10
->>> SeqIO.convert("E3MFGYR02_random_10_reads.sff", "sff-trim", "trimmed.qual", "qual")
-10
->>> SeqIO.convert("E3MFGYR02_random_10_reads.sff", "sff-trim", "trimmed.fastq", "fastq")
-10
-</PRE><P>If you run Linux, you could ask Roche for a copy of their “off instrument”
-tools (often referred to as the Newbler tools). This offers an alternative way to
-do SFF to FASTA or QUAL conversion at the command line (but currently FASTQ output
-is not supported), e.g.</P><PRE CLASS="verbatim">$ sffinfo -seq -notrim E3MFGYR02_random_10_reads.sff > reads.fasta
-$ sffinfo -qual -notrim E3MFGYR02_random_10_reads.sff > reads.qual
-$ sffinfo -seq -trim E3MFGYR02_random_10_reads.sff > trimmed.fasta
-$ sffinfo -qual -trim E3MFGYR02_random_10_reads.sff > trimmed.qual
-</PRE><P>The way Biopython uses mixed case sequence strings to represent
-the trimming points deliberately mimics what the Roche tools do.</P><P>For more information on the Biopython SFF support, consult the built in help:</P><PRE CLASS="verbatim">>>> from Bio.SeqIO import SffIO
->>> help(SffIO)
-...
-</PRE><!--TOC subsection Identifying open reading frames-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc246">18.1.13</A>  Identifying open reading frames</H3><!--SEC END --><P>A very simplistic first step at identifying possible genes is to look for
-open reading frames (ORFs). By this we mean look in all six frames for long
-regions without stop codons – an ORF is just a region of nucleotides with
-no in frame stop codons.</P><P>Of course, to find a gene you would also need to worry about locating a start
-codon, possible promoters – and in Eukaryotes there are introns to worry about
-too. However, this approach is still useful in viruses and Prokaryotes.</P><P>To show how you might approach this with Biopython, we’ll need a sequence to
-search, and as an example we’ll again use the bacterial plasmid – although
-this time we’ll start with a plain FASTA file with no pre-marked genes:
-<A HREF="http://biopython.org/SRC/biopython/Tests/GenBank/NC_005816.fna"><TT>NC_005816.fna</TT></A>. This is a bacterial sequence, so we’ll want to use
-NCBI codon table 11 (see Section <A HREF="#sec:translation">3.9</A> about translation).</P><PRE CLASS="verbatim">>>> from Bio import SeqIO
->>> record = SeqIO.read("NC_005816.fna", "fasta")
->>> table = 11
->>> min_pro_len = 100
-</PRE><P>Here is a neat trick using the <CODE>Seq</CODE> object’s <CODE>split</CODE> method to
-get a list of all the possible ORF translations in the six reading frames:</P><PRE CLASS="verbatim">>>> for strand, nuc in [(+1, record.seq), (-1, record.seq.reverse_complement())]:
-... for frame in range(3):
-... length = 3 * ((len(record)-frame) // 3) #Multiple of three
-... for pro in nuc[frame:frame+length].translate(table).split("*"):
-... if len(pro) >= min_pro_len:
-... print("%s...%s - length %i, strand %i, frame %i" \
-... % (pro[:30], pro[-3:], len(pro), strand, frame))
-GCLMKKSSIVATIITILSGSANAASSQLIP...YRF - length 315, strand 1, frame 0
-KSGELRQTPPASSTLHLRLILQRSGVMMEL...NPE - length 285, strand 1, frame 1
-GLNCSFFSICNWKFIDYINRLFQIIYLCKN...YYH - length 176, strand 1, frame 1
-VKKILYIKALFLCTVIKLRRFIFSVNNMKF...DLP - length 165, strand 1, frame 1
-NQIQGVICSPDSGEFMVTFETVMEIKILHK...GVA - length 355, strand 1, frame 2
-RRKEHVSKKRRPQKRPRRRRFFHRLRPPDE...PTR - length 128, strand 1, frame 2
-TGKQNSCQMSAIWQLRQNTATKTRQNRARI...AIK - length 100, strand 1, frame 2
-QGSGYAFPHASILSGIAMSHFYFLVLHAVK...CSD - length 114, strand -1, frame 0
-IYSTSEHTGEQVMRTLDEVIASRSPESQTR...FHV - length 111, strand -1, frame 0
-WGKLQVIGLSMWMVLFSQRFDDWLNEQEDA...ESK - length 125, strand -1, frame 1
-RGIFMSDTMVVNGSGGVPAFLFSGSTLSSY...LLK - length 361, strand -1, frame 1
-WDVKTVTGVLHHPFHLTFSLCPEGATQSGR...VKR - length 111, strand -1, frame 1
-LSHTVTDFTDQMAQVGLCQCVNVFLDEVTG...KAA - length 107, strand -1, frame 2
-RALTGLSAPGIRSQTSCDRLRELRYVPVSL...PLQ - length 119, strand -1, frame 2
-</PRE><P>Note that here we are counting the frames from the 5’ end (start) of
-<EM>each</EM> strand. It is sometimes easier to always count from the 5’ end
-(start) of the <EM>forward</EM> strand.</P><P>You could easily edit the above loop based code to build up a list of the
-candidate proteins, or convert this to a list comprehension. Now, one thing
-this code doesn’t do is keep track of where the proteins are.</P><P>You could tackle this in several ways. For example, the following code tracks
-the locations in terms of the protein counting, and converts back to the
-parent sequence by multiplying by three, then adjusting for the frame and
-strand:</P><PRE CLASS="verbatim">from Bio import SeqIO
-record = SeqIO.read("NC_005816.gb","genbank")
-table = 11
-min_pro_len = 100
-
-def find_orfs_with_trans(seq, trans_table, min_protein_length):
- answer = []
- seq_len = len(seq)
- for strand, nuc in [(+1, seq), (-1, seq.reverse_complement())]:
- for frame in range(3):
- trans = str(nuc[frame:].translate(trans_table))
- trans_len = len(trans)
- aa_start = 0
- aa_end = 0
- while aa_start < trans_len:
- aa_end = trans.find("*", aa_start)
- if aa_end == -1:
- aa_end = trans_len
- if aa_end-aa_start >= min_protein_length:
- if strand == 1:
- start = frame+aa_start*3
- end = min(seq_len,frame+aa_end*3+3)
- else:
- start = seq_len-frame-aa_end*3-3
- end = seq_len-frame-aa_start*3
- answer.append((start, end, strand,
- trans[aa_start:aa_end]))
- aa_start = aa_end+1
- answer.sort()
- return answer
-
-orf_list = find_orfs_with_trans(record.seq, table, min_pro_len)
-for start, end, strand, pro in orf_list:
- print("%s...%s - length %i, strand %i, %i:%i" \
- % (pro[:30], pro[-3:], len(pro), strand, start, end))
-</PRE><P>And the output:</P><PRE CLASS="verbatim">NQIQGVICSPDSGEFMVTFETVMEIKILHK...GVA - length 355, strand 1, 41:1109
-WDVKTVTGVLHHPFHLTFSLCPEGATQSGR...VKR - length 111, strand -1, 491:827
-KSGELRQTPPASSTLHLRLILQRSGVMMEL...NPE - length 285, strand 1, 1030:1888
-RALTGLSAPGIRSQTSCDRLRELRYVPVSL...PLQ - length 119, strand -1, 2830:3190
-RRKEHVSKKRRPQKRPRRRRFFHRLRPPDE...PTR - length 128, strand 1, 3470:3857
-GLNCSFFSICNWKFIDYINRLFQIIYLCKN...YYH - length 176, strand 1, 4249:4780
-RGIFMSDTMVVNGSGGVPAFLFSGSTLSSY...LLK - length 361, strand -1, 4814:5900
-VKKILYIKALFLCTVIKLRRFIFSVNNMKF...DLP - length 165, strand 1, 5923:6421
-LSHTVTDFTDQMAQVGLCQCVNVFLDEVTG...KAA - length 107, strand -1, 5974:6298
-GCLMKKSSIVATIITILSGSANAASSQLIP...YRF - length 315, strand 1, 6654:7602
-IYSTSEHTGEQVMRTLDEVIASRSPESQTR...FHV - length 111, strand -1, 7788:8124
-WGKLQVIGLSMWMVLFSQRFDDWLNEQEDA...ESK - length 125, strand -1, 8087:8465
-TGKQNSCQMSAIWQLRQNTATKTRQNRARI...AIK - length 100, strand 1, 8741:9044
-QGSGYAFPHASILSGIAMSHFYFLVLHAVK...CSD - length 114, strand -1, 9264:9609
-</PRE><P>If you comment out the sort statement, then the protein sequences will be
-shown in the same order as before, so you can check this is doing the same
-thing. Here we have sorted them by location to make it easier to compare
-to the actual annotation in the GenBank file (as visualised in
-Section <A HREF="#sec:gd_nice_example">16.1.9</A>).</P><P>If however all you want to find are the locations of the open reading frames,
-then it is a waste of time to translate every possible codon, including doing
-the reverse complement to search the reverse strand too. All you need to do
-is search for the possible stop codons (and their reverse complements). Using
-regular expressions is an obvious approach here (see the Python module
-<CODE>re</CODE>). These are an extremely powerful (but rather complex) way of
-describing search strings, which are supported in lots of programming
-languages and also command line tools like <TT>grep</TT> as well). You can
-find whole books about this topic!</P><!--TOC section Sequence parsing plus simple plots-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc247">18.2</A>  Sequence parsing plus simple plots</H2><!--SEC END --><P>
-<A NAME="seq:sequence-parsing-plus-pylab"></A></P><P>This section shows some more examples of sequence parsing, using the <CODE>Bio.SeqIO</CODE>
-module described in Chapter <A HREF="#chapter:Bio.SeqIO">5</A>, plus the Python library matplotlib’s <CODE>pylab</CODE> plotting interface (see <A HREF="http://matplotlib.sourceforge.net/">the matplotlib website for a tutorial</A>). Note that to follow these examples you will need matplotlib installed - but without it you can still try the data parsing bits.</P><!--TOC subsection Histogram of sequence lengths-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc248">18.2.1</A>  Histogram of sequence lengths</H3><!--SEC END --><P>There are lots of times when you might want to visualise the distribution of sequence
-lengths in a dataset – for example the range of contig sizes in a genome assembly
-project. In this example we’ll reuse our orchid FASTA file <A HREF="http://biopython.org/DIST/docs/tutorial/examples/ls_orchid.fasta"><TT>ls_orchid.fasta</TT></A> which has only 94 sequences.</P><P>First of all, we will use <CODE>Bio.SeqIO</CODE> to parse the FASTA file and compile a list
-of all the sequence lengths. You could do this with a for loop, but I find a list
-comprehension more pleasing:</P><PRE CLASS="verbatim">>>> from Bio import SeqIO
->>> sizes = [len(rec) for rec in SeqIO.parse("ls_orchid.fasta", "fasta")]
->>> len(sizes), min(sizes), max(sizes)
-(94, 572, 789)
->>> sizes
-[740, 753, 748, 744, 733, 718, 730, 704, 740, 709, 700, 726, ..., 592]
-</PRE><P>Now that we have the lengths of all the genes (as a list of integers), we can use the
-matplotlib histogram function to display it.</P><PRE CLASS="verbatim">from Bio import SeqIO
-sizes = [len(rec) for rec in SeqIO.parse("ls_orchid.fasta", "fasta")]
-
-import pylab
-pylab.hist(sizes, bins=20)
-pylab.title("%i orchid sequences\nLengths %i to %i" \
- % (len(sizes),min(sizes),max(sizes)))
-pylab.xlabel("Sequence length (bp)")
-pylab.ylabel("Count")
-pylab.show()
-</PRE><P>
-That should pop up a new window containing the following graph:</P><P><IMG SRC="images/hist_plot.png" width=600, height=450></P><P>
-
-Notice that most of these orchid sequences are about 740 bp long, and there could be
-two distinct classes of sequence here with a subset of shorter sequences.</P><P><EM>Tip:</EM> Rather than using <CODE>pylab.show()</CODE> to show the plot in a window, you can also use <CODE>pylab.savefig(...)</CODE> to save the figure to a file (e.g. as a PNG or PDF).</P><!--TOC subsection Plot of sequence GC%-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc249">18.2.2</A>  Plot of sequence GC%</H3><!--SEC END --><P>Another easily calculated quantity of a nucleotide sequence is the GC%. You might
-want to look at the GC% of all the genes in a bacterial genome for example, and
-investigate any outliers which could have been recently acquired by horizontal gene
-transfer. Again, for this example we’ll reuse our orchid FASTA file <A HREF="http://biopython.org/DIST/docs/tutorial/examples/ls_orchid.fasta"><TT>ls_orchid.fasta</TT></A>.</P><P>First of all, we will use <CODE>Bio.SeqIO</CODE> to parse the FASTA file and compile a list
-of all the GC percentages. Again, you could do this with a for loop, but I prefer this:</P><PRE CLASS="verbatim">from Bio import SeqIO
-from Bio.SeqUtils import GC
-
-gc_values = sorted(GC(rec.seq) for rec in SeqIO.parse("ls_orchid.fasta", "fasta"))
-</PRE><P>Having read in each sequence and calculated the GC%, we then sorted them into ascending
-order. Now we’ll take this list of floating point values and plot them with matplotlib:</P><PRE CLASS="verbatim">import pylab
-pylab.plot(gc_values)
-pylab.title("%i orchid sequences\nGC%% %0.1f to %0.1f" \
- % (len(gc_values),min(gc_values),max(gc_values)))
-pylab.xlabel("Genes")
-pylab.ylabel("GC%")
-pylab.show()
-</PRE><P>
-As in the previous example, that should pop up a new window containing a graph:</P><P><IMG SRC="images/gc_plot.png" width=600, height=450></P><P>
-
-If you tried this on the full set of genes from one organism, you’d probably get a much
-smoother plot than this.</P><!--TOC subsection Nucleotide dot plots-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc250">18.2.3</A>  Nucleotide dot plots</H3><!--SEC END --><P>
-A dot plot is a way of visually comparing two nucleotide sequences for similarity to
-each other. A sliding window is used to compare short sub-sequences to each other,
-often with a mis-match threshold. Here for simplicity we’ll only look for perfect
-matches (shown in black
-
-
-in the plot below).
-</P><P>To start off, we’ll need two sequences. For the sake of argument, we’ll just take
-the first two from our orchid FASTA file <A HREF="http://biopython.org/DIST/docs/tutorial/examples/ls_orchid.fasta"><TT>ls_orchid.fasta</TT></A>:</P><PRE CLASS="verbatim">from Bio import SeqIO
-handle = open("ls_orchid.fasta")
-record_iterator = SeqIO.parse(handle, "fasta")
-rec_one = next(record_iterator)
-rec_two = next(record_iterator)
-handle.close()
-</PRE><P>We’re going to show two approaches. Firstly, a simple naive implementation
-which compares all the window sized sub-sequences to each other to compiles a
-similarity matrix. You could construct a matrix or array object, but here we
-just use a list of lists of booleans created with a nested list
-comprehension:</P><PRE CLASS="verbatim">window = 7
-seq_one = str(rec_one.seq).upper()
-seq_two = str(rec_two.seq).upper()
-data = [[(seq_one[i:i+window] <> seq_two[j:j+window]) \
- for j in range(len(seq_one)-window)] \
- for i in range(len(seq_two)-window)]
-</PRE><P>Note that we have <EM>not</EM> checked for reverse complement matches here.
-Now we’ll use the matplotlib’s <CODE>pylab.imshow()</CODE> function to display this
-data, first requesting the gray color scheme so this is done in black and
-white:</P><PRE CLASS="verbatim">import pylab
-pylab.gray()
-pylab.imshow(data)
-pylab.xlabel("%s (length %i bp)" % (rec_one.id, len(rec_one)))
-pylab.ylabel("%s (length %i bp)" % (rec_two.id, len(rec_two)))
-pylab.title("Dot plot using window size %i\n(allowing no mis-matches)" % window)
-pylab.show()
-</PRE><P>
-That should pop up a new window containing a graph like this:</P><P><IMG SRC="images/dot_plot.png" width=600, height=450></P><P>
-
-As you might have expected, these two sequences are very similar with a
-partial line of window sized matches along the diagonal. There are no off
-diagonal matches which would be indicative of inversions or other interesting
-events.</P><P>The above code works fine on small examples, but there are two problems
-applying this to larger sequences, which we will address below.
-First off all, this brute force approach to the all against all comparisons
-is very slow. Instead, we’ll compile dictionaries mapping the window sized
-sub-sequences to their locations, and then take the set intersection to find
-those sub-sequences found in both sequences. This uses more memory, but is
-<EM>much</EM> faster. Secondly, the <CODE>pylab.imshow()</CODE> function is limited
-in the size of matrix it can display. As an alternative, we’ll use the
-<CODE>pylab.scatter()</CODE> function.</P><P>We start by creating dictionaries mapping the window-sized sub-sequences to locations:
-</P><PRE CLASS="verbatim">window = 7
-dict_one = {}
-dict_two = {}
-for (seq, section_dict) in [(str(rec_one.seq).upper(), dict_one),
- (str(rec_two.seq).upper(), dict_two)]:
- for i in range(len(seq)-window):
- section = seq[i:i+window]
- try:
- section_dict[section].append(i)
- except KeyError:
- section_dict[section] = [i]
-#Now find any sub-sequences found in both sequences
-#(Python 2.3 would require slightly different code here)
-matches = set(dict_one).intersection(dict_two)
-print("%i unique matches" % len(matches))
-</PRE><P>In order to use the <CODE>pylab.scatter()</CODE> we need separate lists for the <I>x</I> and <I>y</I> co-ordinates:
-</P><PRE CLASS="verbatim">#Create lists of x and y co-ordinates for scatter plot
-x = []
-y = []
-for section in matches:
- for i in dict_one[section]:
- for j in dict_two[section]:
- x.append(i)
- y.append(j)
-</PRE><P>We are now ready to draw the revised dot plot as a scatter plot:
-</P><PRE CLASS="verbatim">import pylab
-pylab.cla() #clear any prior graph
-pylab.gray()
-pylab.scatter(x,y)
-pylab.xlim(0, len(rec_one)-window)
-pylab.ylim(0, len(rec_two)-window)
-pylab.xlabel("%s (length %i bp)" % (rec_one.id, len(rec_one)))
-pylab.ylabel("%s (length %i bp)" % (rec_two.id, len(rec_two)))
-pylab.title("Dot plot using window size %i\n(allowing no mis-matches)" % window)
-pylab.show()
-</PRE><P>
-That should pop up a new window containing a graph like this:</P><P><IMG SRC="images/dot_plot_scatter.png" width=600, height=450></P><P>
-
-Personally I find this second plot much easier to read!
-Again note that we have <EM>not</EM> checked for reverse complement matches here
-– you could extend this example to do this, and perhaps plot the forward
-matches in one color and the reverse matches in another.</P><!--TOC subsection Plotting the quality scores of sequencing read data-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc251">18.2.4</A>  Plotting the quality scores of sequencing read data</H3><!--SEC END --><P>If you are working with second generation sequencing data, you may want to try plotting
-the quality data. Here is an example using two FASTQ files containing paired end reads,
-<TT>SRR001666_1.fastq</TT> for the forward reads, and <TT>SRR001666_2.fastq</TT> for
-the reverse reads. These were downloaded from the ENA sequence read archive FTP site
-(<A HREF="ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR001/SRR001666/SRR001666_1.fastq.gz"><TT>ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR001/SRR001666/SRR001666_1.fastq.gz</TT></A> and
-<A HREF="ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR001/SRR001666/SRR001666_2.fastq.gz"><TT>ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR001/SRR001666/SRR001666_2.fastq.gz</TT></A>), and
-are from <I>E. coli</I> – see <A HREF="http://www.ebi.ac.uk/ena/data/view/SRR001666"><TT>http://www.ebi.ac.uk/ena/data/view/SRR001666</TT></A>
-for details.
-</P><P>In the following code the <CODE>pylab.subplot(...)</CODE> function is used in order to show
-the forward and reverse qualities on two subplots, side by side. There is also a little
-bit of code to only plot the first fifty reads.</P><PRE CLASS="verbatim">import pylab
-from Bio import SeqIO
-for subfigure in [1,2]:
- filename = "SRR001666_%i.fastq" % subfigure
- pylab.subplot(1, 2, subfigure)
- for i,record in enumerate(SeqIO.parse(filename, "fastq")):
- if i >= 50 : break #trick!
- pylab.plot(record.letter_annotations["phred_quality"])
- pylab.ylim(0,45)
- pylab.ylabel("PHRED quality score")
- pylab.xlabel("Position")
-pylab.savefig("SRR001666.png")
-print("Done")
-</PRE><P>You should note that we are using the <CODE>Bio.SeqIO</CODE> format name <TT>fastq</TT>
-here because the NCBI has saved these reads using the standard Sanger FASTQ format
-with PHRED scores. However, as you might guess from the read lengths, this data was
-from an Illumina Genome Analyzer and was probably originally in one of the two
-Solexa/Illumina FASTQ variant file formats instead.</P><P>This example uses the <CODE>pylab.savefig(...)</CODE> function instead of
-<CODE>pylab.show(...)</CODE>, but as mentioned before both are useful.
-
-
-Here is the result:</P><P><IMG SRC="images/SRR001666.png" width=600, height=600></P><!--TOC section Dealing with alignments-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc252">18.3</A>  Dealing with alignments</H2><!--SEC END --><P>This section can been seen as a follow on to Chapter <A HREF="#chapter:Bio.AlignIO">6</A>.</P><!--TOC subsection Calculating summary information-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc253">18.3.1</A>  Calculating summary information</H3><!--SEC END --><P>
-<A NAME="sec:summary_info"></A></P><P>Once you have an alignment, you are very likely going to want to find out information about it. Instead of trying to have all of the functions that can generate information about an alignment in the alignment object itself, we’ve tried to separate out the functionality into separate classes, which act on the alignment.</P><P>Getting ready to calculate summary information about an object is quick to do. Let’s say we’ve got an alig [...]
-summary_align = AlignInfo.SummaryInfo(alignment)
-</PRE><P>The <CODE>summary_align</CODE> object is very useful, and will do the following neat things for you:</P><OL CLASS="enumerate" type=1><LI CLASS="li-enumerate">
-Calculate a quick consensus sequence – see section <A HREF="#sec:consensus">18.3.2</A>
-</LI><LI CLASS="li-enumerate">Get a position specific score matrix for the alignment – see section <A HREF="#sec:pssm">18.3.3</A>
-</LI><LI CLASS="li-enumerate">Calculate the information content for the alignment – see section <A HREF="#sec:getting_info_content">18.3.4</A>
-</LI><LI CLASS="li-enumerate">Generate information on substitutions in the alignment – section <A HREF="#sec:sub_matrix">18.4</A> details using this to generate a substitution matrix.
-</LI></OL><!--TOC subsection Calculating a quick consensus sequence-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc254">18.3.2</A>  Calculating a quick consensus sequence</H3><!--SEC END --><P>
-<A NAME="sec:consensus"></A></P><P>The <CODE>SummaryInfo</CODE> object, described in section <A HREF="#sec:summary_info">18.3.1</A>, provides functionality to calculate a quick consensus of an alignment. Assuming we’ve got a <CODE>SummaryInfo</CODE> object called <CODE>summary_align</CODE> we can calculate a consensus by doing:</P><PRE CLASS="verbatim">consensus = summary_align.dumb_consensus()
-</PRE><P>As the name suggests, this is a really simple consensus calculator, and will just add up all of the residues at each point in the consensus, and if the most common value is higher than some threshold value will add the common residue to the consensus. If it doesn’t reach the threshold, it adds an ambiguity character to the consensus. The returned consensus object is Seq object whose alphabet is inferred from the alphabets of the sequences making up the consensus. So doing [...]
-...', IUPACAmbiguousDNA())
-</PRE><P>You can adjust how <CODE>dumb_consensus</CODE> works by passing optional parameters:</P><DL CLASS="description"><DT CLASS="dt-description">
-<B>the threshold</B></DT><DD CLASS="dd-description"> This is the threshold specifying how common a particular residue has to be at a position before it is added. The default is 0.7 (meaning 70%).</DD><DT CLASS="dt-description"><B>the ambiguous character</B></DT><DD CLASS="dd-description"> This is the ambiguity character to use. The default is ’N’.</DD><DT CLASS="dt-description"><B>the consensus alphabet</B></DT><DD CLASS="dd-description"> This is the alphabet to use for the [...]
-</DD></DL><!--TOC subsection Position Specific Score Matrices-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc255">18.3.3</A>  Position Specific Score Matrices</H3><!--SEC END --><P>
-<A NAME="sec:pssm"></A></P><P>Position specific score matrices (PSSMs) summarize the alignment information in a different way than a consensus, and may be useful for different tasks. Basically, a PSSM is a count matrix. For each column in the alignment, the number of each alphabet letters is counted and totaled. The totals are displayed relative to some representative sequence along the left axis. This sequence may be the consesus sequence, but can also be any sequence in the alignment. [...]
-AT--C
-CTGTC
-</PRE><P>the PSSM is:</P><PRE CLASS="verbatim"> G A T C
- G 1 1 0 1
- T 0 0 3 0
- A 1 1 0 0
- T 0 0 2 0
- C 0 0 0 3
-</PRE><P>Let’s assume we’ve got an alignment object called <CODE>c_align</CODE>. To get a PSSM with the consensus sequence along the side we first get a summary object and calculate the consensus sequence:</P><PRE CLASS="verbatim">summary_align = AlignInfo.SummaryInfo(c_align)
-consensus = summary_align.dumb_consensus()
-</PRE><P>Now, we want to make the PSSM, but ignore any <CODE>N</CODE> ambiguity residues when calculating this:</P><PRE CLASS="verbatim">my_pssm = summary_align.pos_specific_score_matrix(consensus,
- chars_to_ignore = ['N'])
-</PRE><P>Two notes should be made about this:</P><OL CLASS="enumerate" type=1><LI CLASS="li-enumerate">
-To maintain strictness with the alphabets, you can only include characters along the top of the PSSM that are in the alphabet of the alignment object. Gaps are not included along the top axis of the PSSM.</LI><LI CLASS="li-enumerate">The sequence passed to be displayed along the left side of the axis does not need to be the consensus. For instance, if you wanted to display the second sequence in the alignment along this axis, you would need to do:<PRE CLASS="verbatim">second_seq = alignm [...]
-my_pssm = summary_align.pos_specific_score_matrix(second_seq
- chars_to_ignore = ['N'])
-</PRE></LI></OL><P>The command above returns a <CODE>PSSM</CODE> object.
-To print out the PSSM as shown above,
-we simply need to do a <CODE>print(my_pssm)</CODE>, which gives:</P><PRE CLASS="verbatim"> A C G T
-T 0.0 0.0 0.0 7.0
-A 7.0 0.0 0.0 0.0
-T 0.0 0.0 0.0 7.0
-A 7.0 0.0 0.0 0.0
-C 0.0 7.0 0.0 0.0
-A 7.0 0.0 0.0 0.0
-T 0.0 0.0 0.0 7.0
-T 1.0 0.0 0.0 6.0
-...
-</PRE><P>You can access any element of the PSSM by subscripting like <CODE>your_pssm[sequence_number][residue_count_name]</CODE>. For instance, to get the counts for the ’A’ residue in the second element of the above PSSM you would do:</P><PRE CLASS="verbatim">>>> print(my_pssm[1]["A"])
-7.0
-</PRE><P>The structure of the PSSM class hopefully makes it easy both to access elements and to pretty print the matrix.</P><!--TOC subsection Information Content-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc256">18.3.4</A>  Information Content</H3><!--SEC END --><P>
-<A NAME="sec:getting_info_content"></A></P><P>A potentially useful measure of evolutionary conservation is the information content of a sequence.</P><P>A useful introduction to information theory targeted towards molecular biologists can be found at <A HREF="http://www.lecb.ncifcrf.gov/~toms/paper/primer/"><TT>http://www.lecb.ncifcrf.gov/~toms/paper/primer/</TT></A>. For our purposes, we will be looking at the information content of a consesus sequence, or a portion of a consensus sequen [...]
-<TR><TD CLASS="dcell" ALIGN="center"><FONT SIZE=6>∑</FONT></TD></TR>
-<TR><TD CLASS="dcell" ALIGN="center"><I>i</I>=1</TD></TR>
-</TABLE></TD><TD CLASS="dcell"> <I>P</I><SUB><I>ij</I></SUB> <I>log</I></TD><TD CLASS="dcell">⎛<BR>
-⎜<BR>
-⎜<BR>
-⎝</TD><TD CLASS="dcell"><TABLE CLASS="display"><TR><TD CLASS="dcell" ALIGN="center"><I>P</I><SUB><I>ij</I></SUB></TD></TR>
-<TR><TD CLASS="hbar"></TD></TR>
-<TR><TD CLASS="dcell" ALIGN="center"><I>Q</I><SUB><I>i</I></SUB></TD></TR>
-</TABLE></TD><TD CLASS="dcell">⎞<BR>
-⎟<BR>
-⎟<BR>
-⎠</TD></TR>
-</TABLE><P>where:</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-<I>IC</I><SUB><I>j</I></SUB> – The information content for the <I>j</I>-th column in an alignment.
-</LI><LI CLASS="li-itemize"><I>N</I><SUB><I>a</I></SUB> – The number of letters in the alphabet.
-</LI><LI CLASS="li-itemize"><I>P</I><SUB><I>ij</I></SUB> – The frequency of a particular letter <I>i</I> in the <I>j</I>-th column (i. e. if G occurred 3 out of 6 times in an aligment column, this would be 0.5)
-</LI><LI CLASS="li-itemize"><I>Q</I><SUB><I>i</I></SUB> – The expected frequency of a letter <I>i</I>. This is an
-optional argument, usage of which is left at the user’s
-discretion. By default, it is automatically assigned to 0.05 = 1/20 for a
-protein alphabet, and 0.25 = 1/4 for a nucleic acid alphabet. This is for
-geting the information content without any assumption of prior
-distributions. When assuming priors, or when using a non-standard
-alphabet, you should supply the values for <I>Q</I><SUB><I>i</I></SUB>.
-</LI></UL><P>Well, now that we have an idea what information content is being calculated in Biopython, let’s look at how to get it for a particular region of the alignment.</P><P>First, we need to use our alignment to get an alignment summary object, which we’ll assume is called <CODE>summary_align</CODE> (see section <A HREF="#sec:summary_info">18.3.1</A>) for instructions on how to get this. Once we’ve got this object, calculating the information content for a [...]
- chars_to_ignore = ['N'])
-</PRE><P>Wow, that was much easier then the formula above made it look! The variable <CODE>info_content</CODE> now contains a float value specifying the information content over the specified region (from 5 to 30 of the alignment). We specifically ignore the ambiguity residue ’N’ when calculating the information content, since this value is not included in our alphabet (so we shouldn’t be interested in looking at it!).</P><P>As mentioned above, we can also calculate [...]
- 'A' : .3,
- 'G' : .2,
- 'T' : .3,
- 'C' : .2}
-</PRE><P>The expected should not be passed as a raw dictionary, but instead by passed as a <CODE>SubsMat.FreqTable</CODE> object (see section <A HREF="#sec:freq_table">20.2.2</A> for more information about FreqTables). The FreqTable object provides a standard for associating the dictionary with an Alphabet, similar to how the Biopython Seq class works.</P><P>To create a FreqTable object, from the frequency dictionary you just need to do:</P><PRE CLASS="verbatim">from Bio.Alphabet im [...]
-from Bio.SubsMat import FreqTable
-
-e_freq_table = FreqTable.FreqTable(expect_freq, FreqTable.FREQ,
- IUPAC.unambiguous_dna)
-</PRE><P>Now that we’ve got that, calculating the relative information content for our region of the alignment is as simple as:</P><PRE CLASS="verbatim">info_content = summary_align.information_content(5, 30,
- e_freq_table = e_freq_table,
- chars_to_ignore = ['N'])
-</PRE><P>Now, <CODE>info_content</CODE> will contain the relative information content over the region in relation to the expected frequencies.</P><P>The value return is calculated using base 2 as the logarithm base in the formula above. You can modify this by passing the parameter <CODE>log_base</CODE> as the base you want:</P><PRE CLASS="verbatim">info_content = summary_align.information_content(5, 30, log_base = 10,
- chars_to_ignore = ['N'])
-</PRE><P>Well, now you are ready to calculate information content. If you want to try applying this to some real life problems, it would probably be best to dig into the literature on information content to get an idea of how it is used. Hopefully your digging won’t reveal any mistakes made in coding this function!</P><!--TOC section Substitution Matrices-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc257">18.4</A>  Substitution Matrices</H2><!--SEC END --><P>
-<A NAME="sec:sub_matrix"></A></P><P>Substitution matrices are an extremely important part of everyday bioinformatics work. They provide the scoring terms for classifying how likely two different residues are to substitute for each other. This is essential in doing sequence comparisons. The book “Biological Sequence Analysis” by Durbin et al. provides a really nice introduction to Substitution Matrices and their uses. Some famous substitution matrices are the PAM and BLOSUM [...]
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc258">18.4.1</A>  Using common substitution matrices</H3><!--SEC END --><!--TOC subsection Creating your own substitution matrix from an alignment-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc259">18.4.2</A>  Creating your own substitution matrix from an alignment</H3><!--SEC END --><P>
-<A NAME="sec:subs_mat_ex"></A></P><P>A very cool thing that you can do easily with the substitution matrix
-classes is to create your own substitution matrix from an
-alignment. In practice, this is normally done with protein
-alignments. In this example, we’ll first get a Biopython alignment
-object and then get a summary object to calculate info about the
-alignment. The file containing <A HREF="examples/protein.aln">protein.aln</A>
-(also available online
-<A HREF="http://biopython.org/DIST/docs/tutorial/examples/protein.aln">here</A>)
-contains the Clustalw alignment output.</P><PRE CLASS="verbatim">>>> from Bio import AlignIO
->>> from Bio import Alphabet
->>> from Bio.Alphabet import IUPAC
->>> from Bio.Align import AlignInfo
->>> filename = "protein.aln"
->>> alpha = Alphabet.Gapped(IUPAC.protein)
->>> c_align = AlignIO.read(filename, "clustal", alphabet=alpha)
->>> summary_align = AlignInfo.SummaryInfo(c_align)
-</PRE><P>Sections <A HREF="#sec:align_clustal">6.4.1</A> and <A HREF="#sec:summary_info">18.3.1</A> contain
-more information on doing this.</P><P>Now that we’ve got our <CODE>summary_align</CODE> object, we want to use it
-to find out the number of times different residues substitute for each
-other. To make the example more readable, we’ll focus on only amino
-acids with polar charged side chains. Luckily, this can be done easily
-when generating a replacement dictionary, by passing in all of the
-characters that should be ignored. Thus we’ll create a dictionary of
-replacements for only charged polar amino acids using:</P><PRE CLASS="verbatim">>>> replace_info = summary_align.replacement_dictionary(["G", "A", "V", "L", "I",
-... "M", "P", "F", "W", "S",
-... "T", "N", "Q", "Y", "C"])
-</PRE><P>This information about amino acid replacements is represented as a
-python dictionary which will look something like (the order can vary):</P><PRE CLASS="verbatim">{('R', 'R'): 2079.0, ('R', 'H'): 17.0, ('R', 'K'): 103.0, ('R', 'E'): 2.0,
-('R', 'D'): 2.0, ('H', 'R'): 0, ('D', 'H'): 15.0, ('K', 'K'): 3218.0,
-('K', 'H'): 24.0, ('H', 'K'): 8.0, ('E', 'H'): 15.0, ('H', 'H'): 1235.0,
-('H', 'E'): 18.0, ('H', 'D'): 0, ('K', 'D'): 0, ('K', 'E'): 9.0,
-('D', 'R'): 48.0, ('E', 'R'): 2.0, ('D', 'K'): 1.0, ('E', 'K'): 45.0,
-('K', 'R'): 130.0, ('E', 'D'): 241.0, ('E', 'E'): 3305.0,
-('D', 'E'): 270.0, ('D', 'D'): 2360.0}
-</PRE><P>This information gives us our accepted number of replacements, or how
-often we expect different things to substitute for each other. It
-turns out, amazingly enough, that this is all of the information we
-need to go ahead and create a substitution matrix. First, we use the
-replacement dictionary information to create an Accepted Replacement
-Matrix (ARM):</P><PRE CLASS="verbatim">>>> from Bio import SubsMat
->>> my_arm = SubsMat.SeqMat(replace_info)
-</PRE><P>With this accepted replacement matrix, we can go right ahead and
-create our log odds matrix (i. e. a standard type Substitution Matrix):</P><PRE CLASS="verbatim">>>> my_lom = SubsMat.make_log_odds_matrix(my_arm)
-</PRE><P>The log odds matrix you create is customizable with the following
-optional arguments:</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-<CODE>exp_freq_table</CODE> – You can pass a table of expected
-frequencies for each alphabet. If supplied, this will be used
-instead of the passed accepted replacement matrix when calculate
-expected replacments.</LI><LI CLASS="li-itemize"><CODE>logbase</CODE> - The base of the logarithm taken to create the
-log odd matrix. Defaults to base 10.</LI><LI CLASS="li-itemize"><CODE>factor</CODE> - The factor to multiply each matrix entry
-by. This defaults to 10, which normally makes the matrix numbers
-easy to work with.</LI><LI CLASS="li-itemize"><CODE>round_digit</CODE> - The digit to round to in the matrix. This
-defaults to 0 (i. e. no digits).</LI></UL><P>Once you’ve got your log odds matrix, you can display it prettily
-using the function <CODE>print_mat</CODE>. Doing this on our created matrix
-gives:</P><PRE CLASS="verbatim">>>> my_lom.print_mat()
-D 2
-E -1 1
-H -5 -4 3
-K -10 -5 -4 1
-R -4 -8 -4 -2 2
- D E H K R
-</PRE><P>Very nice. Now we’ve got our very own substitution matrix to play with!</P><!--TOC section BioSQL – storing sequences in a relational database-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc260">18.5</A>  BioSQL – storing sequences in a relational database</H2><!--SEC END --><P>
-<A NAME="sec:BioSQL"></A>
-<A HREF="http://www.biosql.org/">BioSQL</A> is a joint effort between the
-<A HREF="http://open-bio.org/">OBF</A> projects (BioPerl, BioJava etc) to support a
-shared database schema for storing sequence data. In theory, you could load a
-GenBank file into the database with BioPerl, then using Biopython extract this
-from the database as a record object with features - and get more or less the same
-thing as if you had loaded the GenBank file directly as a SeqRecord using
-<CODE>Bio.SeqIO</CODE> (Chapter <A HREF="#chapter:Bio.SeqIO">5</A>).</P><P>Biopython’s BioSQL module is currently documented at
-<A HREF="http://biopython.org/wiki/BioSQL"><TT>http://biopython.org/wiki/BioSQL</TT></A> which is part of our wiki pages.</P><!--TOC chapter The Biopython testing framework-->
-<H1 CLASS="chapter"><!--SEC ANCHOR --><A NAME="htoc261">Chapter 19</A>  The Biopython testing framework</H1><!--SEC END --><P>
-<A NAME="sec:regr_test"></A></P><P>Biopython has a regression testing framework (the file
-<CODE>run_tests.py</CODE>) based on
-<A HREF="http://docs.python.org/library/unittest.html">unittest</A>,
-the standard unit testing framework for Python. Providing comprehensive
-tests for modules is one of the most important aspects of making sure that
-the Biopython code is as bug-free as possible before going out.
-It also tends to be one of the most undervalued aspects of contributing.
-This chapter is designed to make running the Biopython tests and
-writing good test code as easy as possible.
-Ideally, every module that goes into Biopython
-should have a test (and should also have documentation!).
-All our developers, and anyone installing Biopython from source,
-are strongly encouraged to run the unit tests.</P><!--TOC section Running the tests-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc262">19.1</A>  Running the tests</H2><!--SEC END --><P>When you download the Biopython source code, or check it out from
-our source code repository, you should find a subdirectory call
-<CODE>Tests</CODE>. This contains the key script <CODE>run_tests.py</CODE>,
-lots of individual scripts named <CODE>test_XXX.py</CODE>, a subdirectory
-called <CODE>output</CODE> and lots of other subdirectories which
-contain input files for the test suite.</P><P>As part of building and installing Biopython you will typically
-run the full test suite at the command line from the Biopython
-source top level directory using the following:
-</P><PRE CLASS="verbatim">python setup.py test
-</PRE><P>This is actually equivalent to going to the <CODE>Tests</CODE>
-subdirectory and running:
-</P><PRE CLASS="verbatim">python run_tests.py
-</PRE><P>You’ll often want to run just some of the tests, and this is done
-like this:
-</P><PRE CLASS="verbatim">python run_tests.py test_SeqIO.py test_AlignIO.py
-</PRE><P>When giving the list of tests, the <CODE>.py</CODE> extension is optional,
-so you can also just type:
-</P><PRE CLASS="verbatim">python run_tests.py test_SeqIO test_AlignIO
-</PRE><P>To run the docstring tests (see section <A HREF="#section:doctest">19.3</A>), you can use
-</P><PRE CLASS="verbatim">python run_tests.py doctest
-</PRE><P>By default, <CODE>run_tests.py</CODE> runs all tests, including the docstring tests.</P><P>If an individual test is failing, you can also try running it
-directly, which may give you more information.</P><P>Importantly, note that the individual unit tests come in two types:
-</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-Simple print-and-compare scripts. These unit tests are
-essentially short example Python programs, which print out
-various output text. For a test file named <CODE>test_XXX.py</CODE>
-there will be a matching text file called <CODE>test_XXX</CODE> under
-the <CODE>output</CODE> subdirectory which contains the expected
-output. All that the test framework does to is run the script,
-and check the output agrees.
-</LI><LI CLASS="li-itemize">Standard <CODE>unittest</CODE>- based tests. These will <CODE>import unittest</CODE>
-and then define <CODE>unittest.TestCase</CODE> classes, each with one
-or more sub-tests as methods starting with <CODE>test_</CODE> which
-check some specific aspect of the code.
-These tests should not print any output directly.
-</LI></UL><P>
-Currently, about half of the Biopython tests are <CODE>unittest</CODE>-style tests, and half are print-and-compare tests.</P><P>Running a simple print-and-compare test directly will usually give lots
-of output on screen, but does not check the output matches the expected
-output. If the test is failing with an exception error, it should be
-very easy to locate where exactly the script is failing.
-For an example of a print-and-compare test, try:
-</P><PRE CLASS="verbatim">python test_SeqIO.py
-</PRE><P>The <CODE>unittest</CODE>-based tests instead show you exactly which sub-section(s) of
-the test are failing. For example,
-</P><PRE CLASS="verbatim">python test_Cluster.py
-</PRE><!--TOC subsection Running the tests using Tox-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc263">19.1.1</A>  Running the tests using Tox</H3><!--SEC END --><P>Like most Python projects, you can also use
-<A HREF="http://tox.readthedocs.org/en/latest/">Tox</A> to run the tests on multiple
-Python versions, provided they are already installed in your system.</P><P>We do not provide the configuration <TT>tox.ini</TT> file in our code base because
-of difficulties pinning down user-specific settings (e.g. executable names of the
-Python versions). You may also only be interested in testing Biopython only against
-a subset of the Python versions that we support.</P><P>If you are interested in using Tox, you may start with the example <TT>tox.ini</TT>
-shown below:</P><PRE CLASS="verbatim">[tox]
-envlist = py26, py27, pypy, py33, py34, jython
-
-[testenv]
-changedir = Tests
-commands = {envpython} run_tests.py --offline
-deps =
- numpy
-</PRE><P>Using the template above, executing <TT>tox</TT> will test your Biopython code against
-Python2.6, Python2.7, PyPy, Python3.3, Python3.4, and Jython. It assumes that those
-Pythons’ executables are named accordingly: python2.6 for Python2.6, and so on.</P><!--TOC section Writing tests-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc264">19.2</A>  Writing tests</H2><!--SEC END --><P>Let’s say you want to write some tests for a module called <CODE>Biospam</CODE>.
-This can be a module you wrote, or an existing module that doesn’t have
-any tests yet. In the examples below, we assume that
-<CODE>Biospam</CODE> is a module that does simple math.</P><P>Each Biopython test can have three important files and directories involved with it:</P><OL CLASS="enumerate" type=1><LI CLASS="li-enumerate">
-<CODE>test_Biospam.py</CODE> – The actual test code for your module.
-</LI><LI CLASS="li-enumerate"><CODE>Biospam</CODE> [optional]– A directory where any necessary input files
-will be located. Any output files that will be generated should also
-be written here (and preferably cleaned up after the tests are
-done) to prevent clogging up the main Tests directory.
-</LI><LI CLASS="li-enumerate"><CODE>output/Biospam</CODE> – [for print-and-compare tests only] This
-file contains the expected output from running <CODE>test_Biospam.py</CODE>.
-This file is not needed for <CODE>unittest</CODE>-style tests, since there
-the validation is done in the test script <CODE>test_Biospam.py</CODE> itself.
-</LI></OL><P>It’s up to you to decide whether you want to write a print-and-compare test script or a <CODE>unittest</CODE>-style test script. The important thing is that you cannot mix these two styles in a single test script. Particularly, don’t use <CODE>unittest</CODE> features in a print-and-compare test.</P><P>Any script with a <CODE>test_</CODE> prefix in the <CODE>Tests</CODE> directory will be found and run by <CODE>run_tests.py</CODE>. Below, we show an example tes [...]
-</P><PRE CLASS="verbatim">$ python run_tests.py
-test_Ace ... ok
-test_AlignIO ... ok
-test_BioSQL ... ok
-test_BioSQL_SeqIO ... ok
-test_Biospam ... ok
-test_CAPS ... ok
-test_Clustalw ... ok
-</PRE><P>…</P><PRE CLASS="verbatim">----------------------------------------------------------------------
-Ran 107 tests in 86.127 seconds
-</PRE><!--TOC subsection Writing a print-and-compare test-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc265">19.2.1</A>  Writing a print-and-compare test</H3><!--SEC END --><P>A print-and-compare style test should be much simpler for beginners
-or novices to write - essentially it is just an example script using
-your new module.</P><P>Here is what you should do to make a print-and-compare test for the
-<CODE>Biospam</CODE> module.</P><OL CLASS="enumerate" type=1><LI CLASS="li-enumerate">
-Write a script called <CODE>test_Biospam.py</CODE><UL CLASS="itemize"><LI CLASS="li-itemize">This script should live in the Tests directory</LI><LI CLASS="li-itemize">The script should test all of the important functionality
-of the module (the more you test the better your test is, of course!).</LI><LI CLASS="li-itemize">Try to avoid anything which might be platform specific,
-such as printing floating point numbers without using an explicit
-formatting string to avoid having too many decimal places
-(different platforms can give very slightly different values).</LI></UL></LI><LI CLASS="li-enumerate">If the script requires files to do the testing, these should go in
-the directory Tests/Biospam (if you just need something generic, like
-a FASTA sequence file, or a GenBank record, try and use an existing
-sample input file instead).</LI><LI CLASS="li-enumerate">Write out the test output and verify the output to be correct.<P>There are two ways to do this:</P><OL CLASS="enumerate" type=a><LI CLASS="li-enumerate">
-The long way:<UL CLASS="itemize"><LI CLASS="li-itemize">Run the script and write its output to a file. On UNIX (including
-Linux and Mac OS X) machines, you would do something like:
-<CODE>python test_Biospam.py > test_Biospam</CODE> which would write the
-output to the file <CODE>test_Biospam</CODE>.</LI><LI CLASS="li-itemize">Manually look at the file <CODE>test_Biospam</CODE> to make sure the output is correct. When you are sure it is all right and there are no bugs, you need to quickly edit the <CODE>test_Biospam</CODE> file so that the first line is: ‘<CODE>test_Biospam</CODE>’ (no quotes).</LI><LI CLASS="li-itemize">copy the <CODE>test_Biospam</CODE> file to the directory Tests/output</LI></UL></LI><LI CLASS="li-enumera [...]
-Run <CODE>python run_tests.py -g test_Biospam.py</CODE>. The
-regression testing framework is nifty enough that it’ll put
-the output in the right place in just the way it likes it.</LI><LI CLASS="li-itemize">Go to the output (which should be in <CODE>Tests/output/test_Biospam</CODE>) and double check the output to make sure it is all correct.</LI></UL></LI></OL></LI><LI CLASS="li-enumerate">Now change to the Tests directory and run the regression tests
-with <CODE>python run_tests.py</CODE>. This will run all of the tests, and
-you should see your test run (and pass!).</LI><LI CLASS="li-enumerate">That’s it! Now you’ve got a nice test for your module ready to check in,
-or submit to Biopython. Congratulations!
-</LI></OL><P>As an example, the <CODE>test_Biospam.py</CODE> test script to test the
-<CODE>addition</CODE> and <CODE>multiplication</CODE> functions in the <CODE>Biospam</CODE>
-module could look as follows:</P><PRE CLASS="verbatim">from __future__ import print_function
-from Bio import Biospam
-
-print("2 + 3 =", Biospam.addition(2, 3))
-print("9 - 1 =", Biospam.addition(9, -1))
-print("2 * 3 =", Biospam.multiplication(2, 3))
-print("9 * (- 1) =", Biospam.multiplication(9, -1))
-</PRE><P>We generate the corresponding output with <CODE>python run_tests.py -g test_Biospam.py</CODE>, and check the output file <CODE>output/test_Biospam</CODE>:</P><PRE CLASS="verbatim">test_Biospam
-2 + 3 = 5
-9 - 1 = 8
-2 * 3 = 6
-9 * (- 1) = -9
-</PRE><P>Often, the difficulty with larger print-and-compare tests is to keep track which line in the output corresponds to which command in the test script. For this purpose, it is important to print out some markers to help you match lines in the input script with the generated output.</P><!--TOC subsection Writing a unittest-based test-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc266">19.2.2</A>  Writing a unittest-based test</H3><!--SEC END --><P>We want all the modules in Biopython to have unit tests, and a simple
-print-and-compare test is better than no test at all. However, although
-there is a steeper learning curve, using the <CODE>unittest</CODE> framework
-gives a more structured result, and if there is a test failure this can
-clearly pinpoint which part of the test is going wrong. The sub-tests can
-also be run individually which is helpful for testing or debugging.</P><P>The <CODE>unittest</CODE>-framework has been included with Python since version
-2.1, and is documented in the Python Library Reference (which I know you
-are keeping under your pillow, as recommended). There is also
-<A HREF="http://docs.python.org/library/unittest.html">online documentaion
-for unittest</A>.
-If you are familiar with the <CODE>unittest</CODE> system (or something similar
-like the nose test framework), you shouldn’t have any trouble. You may
-find looking at the existing example within Biopython helpful too.</P><P>Here’s a minimal <CODE>unittest</CODE>-style test script for <CODE>Biospam</CODE>,
-which you can copy and paste to get started:</P><PRE CLASS="verbatim">import unittest
-from Bio import Biospam
-
-class BiospamTestAddition(unittest.TestCase):
-
- def test_addition1(self):
- result = Biospam.addition(2, 3)
- self.assertEqual(result, 5)
-
- def test_addition2(self):
- result = Biospam.addition(9, -1)
- self.assertEqual(result, 8)
-
-class BiospamTestDivision(unittest.TestCase):
-
- def test_division1(self):
- result = Biospam.division(3.0, 2.0)
- self.assertAlmostEqual(result, 1.5)
-
- def test_division2(self):
- result = Biospam.division(10.0, -2.0)
- self.assertAlmostEqual(result, -5.0)
-
-
-if __name__ == "__main__":
- runner = unittest.TextTestRunner(verbosity = 2)
- unittest.main(testRunner=runner)
-</PRE><P>In the division tests, we use <CODE>assertAlmostEqual</CODE> instead of <CODE>assertEqual</CODE> to avoid tests failing due to roundoff errors; see the <CODE>unittest</CODE> chapter in the Python documentation for details and for other functionality available in <CODE>unittest</CODE> (<A HREF="http://docs.python.org/library/unittest.html">online reference</A>).</P><P>These are the key points of <CODE>unittest</CODE>-based tests:</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-Test cases are stored in classes that derive from
-<CODE>unittest.TestCase</CODE> and cover one basic aspect of your code</LI><LI CLASS="li-itemize">You can use methods <CODE>setUp</CODE> and <CODE>tearDown</CODE> for any repeated
-code which should be run before and after each test method. For example,
-the <CODE>setUp</CODE> method might be used to create an instance of the object
-you are testing, or open a file handle. The <CODE>tearDown</CODE> should do any
-“tidying up”, for example closing the file handle.</LI><LI CLASS="li-itemize">The tests are prefixed with <CODE>test_</CODE> and each test should cover
-one specific part of what you are trying to test. You can have as
-many tests as you want in a class.</LI><LI CLASS="li-itemize">At the end of the test script, you can use
-<PRE CLASS="verbatim">if __name__ == "__main__":
- runner = unittest.TextTestRunner(verbosity = 2)
- unittest.main(testRunner=runner)
-</PRE>to execute the tests when the script is run by itself (rather than
-imported from <CODE>run_tests.py</CODE>).
-If you run this script, then you’ll see something like the following:<PRE CLASS="verbatim">$ python test_BiospamMyModule.py
-test_addition1 (__main__.TestAddition) ... ok
-test_addition2 (__main__.TestAddition) ... ok
-test_division1 (__main__.TestDivision) ... ok
-test_division2 (__main__.TestDivision) ... ok
-
-----------------------------------------------------------------------
-Ran 4 tests in 0.059s
-
-OK
-</PRE></LI><LI CLASS="li-itemize">To indicate more clearly what each test is doing, you can add
-docstrings to each test. These are shown when running the tests,
-which can be useful information if a test is failing.<PRE CLASS="verbatim">import unittest
-from Bio import Biospam
-
-class BiospamTestAddition(unittest.TestCase):
-
- def test_addition1(self):
- """An addition test"""
- result = Biospam.addition(2, 3)
- self.assertEqual(result, 5)
-
- def test_addition2(self):
- """A second addition test"""
- result = Biospam.addition(9, -1)
- self.assertEqual(result, 8)
-
-class BiospamTestDivision(unittest.TestCase):
-
- def test_division1(self):
- """Now let's check division"""
- result = Biospam.division(3.0, 2.0)
- self.assertAlmostEqual(result, 1.5)
-
- def test_division2(self):
- """A second division test"""
- result = Biospam.division(10.0, -2.0)
- self.assertAlmostEqual(result, -5.0)
-
-
-if __name__ == "__main__":
- runner = unittest.TextTestRunner(verbosity = 2)
- unittest.main(testRunner=runner)
-</PRE><P>Running the script will now show you:</P><PRE CLASS="verbatim">$ python test_BiospamMyModule.py
-An addition test ... ok
-A second addition test ... ok
-Now let's check division ... ok
-A second division test ... ok
-
-----------------------------------------------------------------------
-Ran 4 tests in 0.001s
-
-OK
-</PRE></LI></UL><P>If your module contains docstring tests (see section <A HREF="#section:doctest">19.3</A>),
-you may want to include those in the tests to be run. You can do so as
-follows by modifying the code under <CODE>if __name__ == "__main__":</CODE>
-to look like this:</P><PRE CLASS="verbatim">if __name__ == "__main__":
- unittest_suite = unittest.TestLoader().loadTestsFromName("test_Biospam")
- doctest_suite = doctest.DocTestSuite(Biospam)
- suite = unittest.TestSuite((unittest_suite, doctest_suite))
- runner = unittest.TextTestRunner(sys.stdout, verbosity = 2)
- runner.run(suite)
-</PRE><P>This is only relevant if you want to run the docstring tests when you
-execute <CODE>python test_Biospam.py</CODE>; with
-<CODE>python run_tests.py</CODE>, the docstring tests are run automatically
-(assuming they are included in the list of docstring tests in
-<CODE>run_tests.py</CODE>, see the section below).</P><!--TOC section Writing doctests-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc267">19.3</A>  Writing doctests</H2><!--SEC END --><P>
-<A NAME="section:doctest"></A></P><P>Python modules, classes and functions support built in documentation using
-docstrings. The <A HREF="http://docs.python.org/library/doctest.html">doctest
-framework</A> (included with Python) allows the developer to embed working
-examples in the docstrings, and have these examples automatically tested.</P><P>Currently only a small part of Biopython includes doctests. The
-<CODE>run_tests.py</CODE> script takes care of running the doctests.
-For this purpose, at the top of the <CODE>run_tests.py</CODE> script is a
-manually compiled list of modules to test, which
-allows us to skip modules with optional external dependencies which may
-not be installed (e.g. the Reportlab and NumPy libraries). So, if you’ve
-added some doctests to the docstrings in a Biopython module, in order to
-have them included in the Biopython test suite, you must update
-<CODE>run_tests.py</CODE> to include your module. Currently, the relevant part
-of <CODE>run_tests.py</CODE> looks as follows:</P><PRE CLASS="verbatim"># This is the list of modules containing docstring tests.
-# If you develop docstring tests for other modules, please add
-# those modules here.
-DOCTEST_MODULES = ["Bio.Seq",
- "Bio.SeqRecord",
- "Bio.SeqIO",
- "...",
- ]
-#Silently ignore any doctests for modules requiring numpy!
-try:
- import numpy
- DOCTEST_MODULES.extend(["Bio.Statistics.lowess"])
-except ImportError:
- pass
-</PRE><P>Note that we regard doctests primarily as documentation, so you should
-stick to typical usage. Generally complicated examples dealing with error
-conditions and the like would be best left to a dedicated unit test.</P><P>Note that if you want to write doctests involving file parsing, defining
-the file location complicates matters. Ideally use relative paths assuming
-the code will be run from the <CODE>Tests</CODE> directory, see the
-<CODE>Bio.SeqIO</CODE> doctests for an example of this.</P><P>To run the docstring tests only, use
-</P><PRE CLASS="verbatim">$ python run_tests.py doctest
-</PRE><!--TOC chapter Advanced-->
-<H1 CLASS="chapter"><!--SEC ANCHOR --><A NAME="htoc268">Chapter 20</A>  Advanced</H1><!--SEC END --><P>
-<A NAME="chapter:advanced"></A></P><!--TOC section Parser Design-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc269">20.1</A>  Parser Design</H2><!--SEC END --><P>Many of the older Biopython parsers were built around an event-oriented
-design that includes Scanner and Consumer objects.</P><P>Scanners take input from a data source and analyze it line by line,
-sending off an event whenever it recognizes some information in the
-data. For example, if the data includes information about an organism
-name, the scanner may generate an <CODE>organism_name</CODE> event whenever it
-encounters a line containing the name.</P><P>Consumers are objects that receive the events generated by Scanners.
-Following the previous example, the consumer receives the
-<CODE>organism_name</CODE> event, and the processes it in whatever manner
-necessary in the current application.</P><P>This is a very flexible framework, which is advantageous if you want to
-be able to parse a file format into more than one representation. For
-example, the <CODE>Bio.GenBank</CODE> module uses this to construct either
-<CODE>SeqRecord</CODE> objects or file-format-specific record objects.</P><P>More recently, many of the parsers added for <CODE>Bio.SeqIO</CODE> and
-<CODE>Bio.AlignIO</CODE> take a much simpler approach, but only generate a
-single object representation (<CODE>SeqRecord</CODE> and
-<CODE>MultipleSeqAlignment</CODE> objects respectively). In some cases the
-<CODE>Bio.SeqIO</CODE> parsers actually wrap
-another Biopython parser - for example, the <CODE>Bio.SwissProt</CODE> parser
-produces SwissProt format specific record objects, which get converted
-into <CODE>SeqRecord</CODE> objects.</P><!--TOC section Substitution Matrices-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc270">20.2</A>  Substitution Matrices</H2><!--SEC END --><!--TOC subsection SubsMat-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc271">20.2.1</A>  SubsMat</H3><!--SEC END --><P>This module provides a class and a few routines for generating substitution matrices, similar to BLOSUM or PAM matrices, but based on user-provided data. Additionally, you may select a matrix from MatrixInfo.py, a collection of established substitution matrices. The <CODE>SeqMat</CODE> class derives from a dictionary:
-</P><PRE CLASS="verbatim">class SeqMat(dict)
-</PRE><P>The dictionary is of the form <CODE>{(i1,j1):n1, (i1,j2):n2,...,(ik,jk):nk}</CODE> where i, j are alphabet letters, and n is a value.</P><OL CLASS="enumerate" type=1><LI CLASS="li-enumerate">
-Attributes
-<OL CLASS="enumerate" type=a><LI CLASS="li-enumerate">
-<CODE>self.alphabet</CODE>: a class as defined in Bio.Alphabet</LI><LI CLASS="li-enumerate"><CODE>self.ab_list</CODE>: a list of the alphabet’s letters, sorted. Needed mainly for internal purposes
-</LI></OL></LI><LI CLASS="li-enumerate">Methods<OL CLASS="enumerate" type=a><LI CLASS="li-enumerate"><PRE CLASS="verbatim">__init__(self,data=None,alphabet=None, mat_name='', build_later=0):
-</PRE><OL CLASS="enumerate" type=i><LI CLASS="li-enumerate"><CODE>data</CODE>: can be either a dictionary, or another SeqMat instance.
-</LI><LI CLASS="li-enumerate"><CODE>alphabet</CODE>: a Bio.Alphabet instance. If not provided, construct an alphabet from data.</LI><LI CLASS="li-enumerate"><CODE>mat_name</CODE>: matrix name, such as "BLOSUM62" or "PAM250"</LI><LI CLASS="li-enumerate"><CODE>build_later</CODE>: default false. If true, user may supply only alphabet and empty dictionary, if intending to build the matrix later. this skips the sanity check of alphabet size vs. matrix size.</LI></OL></LI><LI CLASS="li-enumera [...]
-</PRE><OL CLASS="enumerate" type=i><LI CLASS="li-enumerate">
-<CODE>obs_freq_mat</CODE>: an observed frequency matrix. Returns the matrix’s entropy, based on the frequency in <CODE>obs_freq_mat</CODE>. The matrix instance should be LO or SUBS.
-</LI></OL></LI><LI CLASS="li-enumerate"><PRE CLASS="verbatim">sum(self)
-</PRE>Calculates the sum of values for each letter in the matrix’s alphabet, and returns it as a dictionary of the form <CODE>{i1: s1, i2: s2,...,in:sn}</CODE>, where:
-<UL CLASS="itemize"><LI CLASS="li-itemize">
-i: an alphabet letter;
-</LI><LI CLASS="li-itemize">s: sum of all values in a half-matrix for that letter;
-</LI><LI CLASS="li-itemize">n: number of letters in alphabet.
-</LI></UL></LI><LI CLASS="li-enumerate"><PRE CLASS="verbatim">print_mat(self,f,format="%4d",bottomformat="%4s",alphabet=None)
-</PRE><P>prints the matrix to file handle f. <CODE>format</CODE> is the format field for the matrix values; <CODE>bottomformat</CODE> is the format field for the bottom row, containing matrix letters. Example output for a 3-letter alphabet matrix:</P><PRE CLASS="verbatim">A 23
-B 12 34
-C 7 22 27
- A B C
-</PRE><P>The <CODE>alphabet</CODE> optional argument is a string of all characters in the alphabet. If supplied, the order of letters along the axes is taken from the string, rather than by alphabetical order.</P></LI></OL></LI><LI CLASS="li-enumerate">Usage<P>The following section is laid out in the order by which most people wish to generate a log-odds matrix. Of course, interim matrices can be generated and
-investigated. Most people just want a log-odds matrix, that’s all.</P><OL CLASS="enumerate" type=a><LI CLASS="li-enumerate">Generating an Accepted Replacement Matrix<P>Initially, you should generate an accepted replacement matrix (ARM) from your data. The values in ARM are the counted number of replacements according to your data. The data could be a set of pairs or multiple alignments. So for instance if Alanine was replaced by Cysteine 10 times, and Cysteine by Alanine 12 times, [...]
-</PRE><P>as order doesn’t matter, user can already provide only one entry:</P><PRE CLASS="verbatim">('A','C'): 22
-</PRE><P>A SeqMat instance may be initialized with either a full (first method of counting: 10, 12) or half (the latter method, 22) matrices. A full protein
-alphabet matrix would be of the size 20x20 = 400. A half matrix of that alphabet would be 20x20/2 + 20/2 = 210. That is because same-letter entries don’t
-change. (The matrix diagonal). Given an alphabet size of N:</P><OL CLASS="enumerate" type=i><LI CLASS="li-enumerate">
-Full matrix size: N*N</LI><LI CLASS="li-enumerate">Half matrix size: N(N+1)/2
-</LI></OL><P>The SeqMat constructor automatically generates a half-matrix, if a full matrix is passed. If a half matrix is passed, letters in the key should be provided in alphabetical order: (’A’,’C’) and not (’C’,A’).</P><P>At this point, if all you wish to do is generate a log-odds matrix, please go to the section titled Example of Use. The following text describes the nitty-gritty of internal functions, to be used by people who wish to [...]
-</P><PRE CLASS="verbatim">OFM = SubsMat._build_obs_freq_mat(ARM)
-</PRE><P>The OFM is generated from the ARM, only instead of replacement counts, it contains replacement frequencies.</P></LI><LI CLASS="li-enumerate">Generating an expected frequency matrix (EFM)<P>Use:</P><PRE CLASS="verbatim">EFM = SubsMat._build_exp_freq_mat(OFM,exp_freq_table)
-</PRE><OL CLASS="enumerate" type=i><LI CLASS="li-enumerate">
-<CODE>exp_freq_table</CODE>: should be a FreqTable instance. See section <A HREF="#sec:freq_table">20.2.2</A> for detailed information on FreqTable. Briefly, the expected frequency table has the frequencies of appearance for each member of the alphabet. It is
-implemented as a dictionary with the alphabet letters as keys, and each letter’s frequency as a value. Values sum to 1.
-</LI></OL><P>The expected frequency table can (and generally should) be generated from the observed frequency matrix. So in most cases you will generate <CODE>exp_freq_table</CODE> using:</P><PRE CLASS="verbatim">>>> exp_freq_table = SubsMat._exp_freq_table_from_obs_freq(OFM)
->>> EFM = SubsMat._build_exp_freq_mat(OFM, exp_freq_table)
-</PRE><P>But you can supply your own <CODE>exp_freq_table</CODE>, if you wish</P></LI><LI CLASS="li-enumerate">Generating a substitution frequency matrix (SFM)<P>Use:</P><PRE CLASS="verbatim">SFM = SubsMat._build_subs_mat(OFM,EFM)
-</PRE><P>Accepts an OFM, EFM. Provides the division product of the corresponding values.</P></LI><LI CLASS="li-enumerate">Generating a log-odds matrix (LOM)<P>Use:
-</P><PRE CLASS="verbatim">LOM=SubsMat._build_log_odds_mat(SFM[,logbase=10,factor=10.0,round_digit=1])
-</PRE><OL CLASS="enumerate" type=i><LI CLASS="li-enumerate">
-Accepts an SFM.</LI><LI CLASS="li-enumerate"><CODE>logbase</CODE>: base of the logarithm used to generate the log-odds values.</LI><LI CLASS="li-enumerate"><CODE>factor</CODE>: factor used to multiply the log-odds values. Each entry is generated by log(LOM[key])*factor And rounded to the <CODE>round_digit</CODE> place after the decimal point, if required.</LI></OL></LI></OL></LI><LI CLASS="li-enumerate">Example of use<P>As most people would want to generate a log-odds matrix, with minimu [...]
- factor=10.0,round_digit=0):
-</PRE><OL CLASS="enumerate" type=a><LI CLASS="li-enumerate">
-<CODE>acc_rep_mat</CODE>: user provided accepted replacements matrix
-</LI><LI CLASS="li-enumerate"><CODE>exp_freq_table</CODE>: expected frequencies table. Used if provided, if not, generated from the <CODE>acc_rep_mat</CODE>.
-</LI><LI CLASS="li-enumerate"><CODE>logbase</CODE>: base of logarithm for the log-odds matrix. Default base 10.
-</LI><LI CLASS="li-enumerate"><CODE>round_digit</CODE>: number after decimal digit to which result should be rounded. Default zero.
-</LI></OL></LI></OL><!--TOC subsection FreqTable-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc272">20.2.2</A>  FreqTable</H3><!--SEC END --><P>
-<A NAME="sec:freq_table"></A></P><PRE CLASS="verbatim">FreqTable.FreqTable(UserDict.UserDict)
-</PRE><OL CLASS="enumerate" type=1><LI CLASS="li-enumerate">Attributes:<OL CLASS="enumerate" type=a><LI CLASS="li-enumerate">
-<CODE>alphabet</CODE>: A Bio.Alphabet instance.
-</LI><LI CLASS="li-enumerate"><CODE>data</CODE>: frequency dictionary
-</LI><LI CLASS="li-enumerate"><CODE>count</CODE>: count dictionary (in case counts are provided).
-</LI></OL></LI><LI CLASS="li-enumerate">Functions:
-<OL CLASS="enumerate" type=a><LI CLASS="li-enumerate">
-<CODE>read_count(f)</CODE>: read a count file from stream f. Then convert to frequencies.
-</LI><LI CLASS="li-enumerate"><CODE>read_freq(f)</CODE>: read a frequency data file from stream f. Of course, we then don’t have the counts, but it is usually the letter frequencies which are interesting.
-</LI></OL></LI><LI CLASS="li-enumerate">Example of use:
-The expected count of the residues in the database is sitting in a file, whitespace delimited, in the following format (example given for a 3-letter alphabet):<PRE CLASS="verbatim">A 35
-B 65
-C 100
-</PRE><P>And will be read using the <CODE>FreqTable.read_count(file_handle)</CODE> function.</P><P>An equivalent frequency file:</P><PRE CLASS="verbatim">A 0.175
-B 0.325
-C 0.5
-</PRE><P>Conversely, the residue frequencies or counts can be passed as a dictionary.
-Example of a count dictionary (3-letter alphabet):</P><PRE CLASS="verbatim">{'A': 35, 'B': 65, 'C': 100}
-</PRE><P>Which means that an expected data count would give a 0.5 frequency
-for ’C’, a 0.325 probability of ’B’ and a 0.175 probability of ’A’
-out of 200 total, sum of A, B and C)</P><P>A frequency dictionary for the same data would be:</P><PRE CLASS="verbatim">{'A': 0.175, 'B': 0.325, 'C': 0.5}
-</PRE><P>Summing up to 1.</P><P>When passing a dictionary as an argument, you should indicate whether it is a count or a frequency dictionary. Therefore the FreqTable class constructor requires two arguments: the dictionary itself, and FreqTable.COUNT or FreqTable.FREQ indicating counts or frequencies, respectively.</P><P>Read expected counts. readCount will already generate the frequencies
-Any one of the following may be done to geerate the frequency table (ftab):</P><PRE CLASS="verbatim">>>> from SubsMat import *
->>> ftab = FreqTable.FreqTable(my_frequency_dictionary, FreqTable.FREQ)
->>> ftab = FreqTable.FreqTable(my_count_dictionary, FreqTable.COUNT)
->>> ftab = FreqTable.read_count(open('myCountFile'))
->>> ftab = FreqTable.read_frequency(open('myFrequencyFile'))
-</PRE></LI></OL><!--TOC chapter Where to go from here – contributing to Biopython-->
-<H1 CLASS="chapter"><!--SEC ANCHOR --><A NAME="htoc273">Chapter 21</A>  Where to go from here – contributing to Biopython</H1><!--SEC END --><!--TOC section Bug Reports + Feature Requests-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc274">21.1</A>  Bug Reports + Feature Requests</H2><!--SEC END --><P>Getting feedback on the Biopython modules is very important to us. Open-source projects like this benefit greatly from feedback, bug-reports (and patches!) from a wide variety of contributors.</P><P>The main forums for discussing feature requests and potential bugs are the
-<A HREF="http://biopython.org/wiki/Mailing_lists">Biopython mailing lists</A>:</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-<A HREF="mailto:biopython at biopython.org">biopython at biopython.org</A> – An unmoderated list for discussion of anything to do with Biopython.</LI><LI CLASS="li-itemize"><A HREF="mailto:biopython-dev at biopython.org">biopython-dev at biopython.org</A> – A more development oriented list that is mainly used by developers (but anyone is free to contribute!).
-</LI></UL><P>Additionally, if you think you’ve found a new bug, you can submit it to
-our issue tracker at <A HREF="https://github.com/biopython/biopython/issues"><TT>https://github.com/biopython/biopython/issues</TT></A>
-(this has replaced the older tracker hosted at
-<A HREF="http://redmine.open-bio.org/projects/biopython"><TT>http://redmine.open-bio.org/projects/biopython</TT></A>).
-This way, it won’t get buried in anyone’s Inbox and forgotten about.</P><!--TOC section Mailing lists and helping newcomers-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc275">21.2</A>  Mailing lists and helping newcomers</H2><!--SEC END --><P>We encourage all our uses to sign up to the main Biopython mailing list.
-Once you’ve got the hang of an area of Biopython, we’d encourage you to
-help answer questions from beginners. After all, you were a beginner once.</P><!--TOC section Contributing Documentation-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc276">21.3</A>  Contributing Documentation</H2><!--SEC END --><P>We’re happy to take feedback or contributions - either via a bug-report or on the Mailing List.
-While reading this tutorial, perhaps you noticed some topics you were interested in which were missing, or not clearly explained. There is also Biopython’s built in documentation (the docstrings, these are also
-<A HREF="http://biopython.org/DIST/docs/api">online</A>), where again, you may be able to help fill in any blanks.</P><!--TOC section Contributing cookbook examples-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc277">21.4</A>  Contributing cookbook examples</H2><!--SEC END --><P>
-As explained in Chapter <A HREF="#chapter:cookbook">18</A>, Biopython now has a wiki
-collection of user contributed “cookbook” examples,
-<A HREF="http://biopython.org/wiki/Category:Cookbook"><TT>http://biopython.org/wiki/Category:Cookbook</TT></A> – maybe you can add
-to this?</P><!--TOC section Maintaining a distribution for a platform-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc278">21.5</A>  Maintaining a distribution for a platform</H2><!--SEC END --><P>
-<A NAME="sec:maintain_dist"></A></P><P>We currently provide source code archives (suitable for any OS, if you have the right build tools installed), and Windows Installers which are just click and run. This covers all the major operating systems.</P><P>Most major Linux distributions have volunteers who take these source code releases, and compile them into packages for Linux users to easily install (taking care of dependencies etc). This is really great and we are of course very grateful [...]
-</PRE><P>Now you’ve got a Windows installer. Congrats! At the moment we have no trouble shipping installers built on 32 bit windows. If anyone would like to look into supporting 64 bit Windows that would be great.</P></DD><DT CLASS="dt-description"><B>RPMs</B></DT><DD CLASS="dd-description"> – RPMs are pretty popular package systems on some Linux platforms. There is lots of documentation on RPMs available at <A HREF="http://www.rpm.org"><TT>http://www.rpm.org</TT></A> to he [...]
-</PRE><P>This will create an RPM for your specific platform and a source RPM in the directory <CODE>dist</CODE>. This RPM should be good and ready to go, so this is all you need to do! Nice and easy.</P></DD><DT CLASS="dt-description"><B>Macintosh</B></DT><DD CLASS="dd-description"> – Since Apple moved to Mac OS X, things have become much easier on the Mac. We generally
-treat it as just another Unix variant, and installing Biopython from source is just as easy as on Linux.
-The easiest way to get all the GCC compilers etc installed is to install Apple’s X-Code.
-We might be able to provide click and run installers for Mac OS X, but to date there hasn’t been any demand.</DD></DL><P>Once you’ve got a package, please test it on your system to make sure it installs everything in a good way and seems to work properly. Once you feel good about it, send it off to one of the Biopython developers (write to our main mailing list at biopython at biopython.org if you’re not sure who to send it to) and you’ve done it. Thanks!</P><!-- [...]
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc279">21.6</A>  Contributing Unit Tests</H2><!--SEC END --><P>Even if you don’t have any new functionality to add to Biopython, but you want to write some code, please
-consider extending our unit test coverage. We’ve devoted all of Chapter <A HREF="#sec:regr_test">19</A> to this topic.</P><!--TOC section Contributing Code-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc280">21.7</A>  Contributing Code</H2><!--SEC END --><P>There are no barriers to joining Biopython code development other
-than an interest in creating biology-related code in Python. The
-best place to express an interest is on the Biopython mailing lists
-– just let us know you are interested in coding and what kind of
-stuff you want to work on. Normally, we try to have some discussion
-on modules before coding them, since that helps generate good ideas
-– then just feel free to jump right in and start coding!</P><P>The main Biopython release tries to be fairly uniform and interworkable,
-to make it easier for users. You can read about some of (fairly
-informal) coding style guidelines we try to use in Biopython in the
-contributing documentation at
-<A HREF="http://biopython.org/wiki/Contributing"><TT>http://biopython.org/wiki/Contributing</TT></A>. We also try to add code to the distribution along with tests (see Chapter <A HREF="#sec:regr_test">19</A> for more info on the regression testing framework) and documentation, so that everything can stay as workable and well documented as possible (including docstrings). This is, of course, the most ideal situation, under many situations you’ll be able to find other people on [...]
-distribution, but that you want to make available, we maintain Script
-Central (<A HREF="http://biopython.org/wiki/Scriptcentral"><TT>http://biopython.org/wiki/Scriptcentral</TT></A>)
-which has pointers to freely available code in Python for bioinformatics.</P><P>Hopefully this documentation has got you excited enough about
-Biopython to try it out (and most importantly, contribute!). Thanks
-for reading all the way through!</P><!--TOC chapter Appendix: Useful stuff about Python-->
-<H1 CLASS="chapter"><!--SEC ANCHOR --><A NAME="htoc281">Chapter 22</A>  Appendix: Useful stuff about Python</H1><!--SEC END --><P>
-<A NAME="sec:appendix"></A></P><P>If you haven’t spent a lot of time programming in Python, many
-questions and problems that come up in using Biopython are often
-related to Python itself. This section tries to present some ideas and
-code that come up often (at least for us!) while using the Biopython
-libraries. If you have any suggestions for useful pointers that could
-go here, please contribute!</P><!--TOC section What the heck is a handle?-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc282">22.1</A>  What the heck is a handle?</H2><!--SEC END --><P>
-<A NAME="sec:appendix-handles"></A></P><P>Handles are mentioned quite frequently throughout this documentation,
-and are also fairly confusing (at least to me!). Basically, you can
-think of a handle as being a “wrapper” around text information.</P><P>Handles provide (at least) two benefits over plain text information:</P><OL CLASS="enumerate" type=1><LI CLASS="li-enumerate">
-They provide a standard way to deal with information stored in
-different ways. The text information can be in a file, or in a
-string stored in memory, or the output from a command line program,
-or at some remote website, but the handle provides a common way of
-dealing with information in all of these formats.</LI><LI CLASS="li-enumerate">They allow text information to be read incrementally, instead
-of all at once. This is really important when you are dealing with
-huge text files which would use up all of your memory if you had to
-load them all.
-</LI></OL><P>Handles can deal with text information that is being read (e. g. reading
-from a file) or written (e. g. writing information to a file). In the
-case of a “read” handle, commonly used functions are <CODE>read()</CODE>,
-which reads the entire text information from the handle, and
-<CODE>readline()</CODE>, which reads information one line at a time. For
-“write” handles, the function <CODE>write()</CODE> is regularly used.</P><P>The most common usage for handles is reading information from a file,
-which is done using the built-in Python function <CODE>open</CODE>. Here, we open a
-handle to the file <A HREF="examples/m_cold.fasta">m_cold.fasta</A>
-(also available online
-<A HREF="http://biopython.org/DIST/docs/tutorial/examples/m_cold.fasta">here</A>):</P><PRE CLASS="verbatim">>>> handle = open("m_cold.fasta", "r")
->>> handle.readline()
-">gi|8332116|gb|BE037100.1|BE037100 MP14H09 MP Mesembryanthemum ...\n"
-</PRE><P>Handles are regularly used in Biopython for passing information to parsers.
-For example, since Biopython 1.54 the main functions in <CODE>Bio.SeqIO</CODE>
-and <CODE>Bio.AlignIO</CODE> have allowed you to use a filename instead of a
-handle:</P><PRE CLASS="verbatim">from Bio import SeqIO
-for record in SeqIO.parse("m_cold.fasta", "fasta"):
- print(record.id, len(record))
-</PRE><P>On older versions of Biopython you had to use a handle, e.g.</P><PRE CLASS="verbatim">from Bio import SeqIO
-handle = open("m_cold.fasta", "r")
-for record in SeqIO.parse(handle, "fasta"):
- print(record.id, len(record))
-handle.close()
-</PRE><P>This pattern is still useful - for example suppose you have a gzip
-compressed FASTA file you want to parse:</P><PRE CLASS="verbatim">import gzip
-from Bio import SeqIO
-handle = gzip.open("m_cold.fasta.gz")
-for record in SeqIO.parse(handle, "fasta"):
- print(record.id, len(record))
-handle.close()
-</PRE><P>See Section <A HREF="#sec:SeqIO_compressed">5.2</A> for more examples like this,
-including reading bzip2 compressed files.</P><!--TOC subsection Creating a handle from a string-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc283">22.1.1</A>  Creating a handle from a string</H3><!--SEC END --><P>One useful thing is to be able to turn information contained in a
-string into a handle. The following example shows how to do this using
-<CODE>cStringIO</CODE> from the Python standard library:</P><PRE CLASS="verbatim">>>> my_info = 'A string\n with multiple lines.'
->>> print(my_info)
-A string
- with multiple lines.
->>> from StringIO import StringIO
->>> my_info_handle = StringIO(my_info)
->>> first_line = my_info_handle.readline()
->>> print(first_line)
-A string
-<BLANKLINE>
->>> second_line = my_info_handle.readline()
->>> print(second_line)
- with multiple lines.
-</PRE><!--TOC chapter References-->
-<H1 CLASS="chapter"><!--SEC ANCHOR -->References</H1><!--SEC END --><DL CLASS="thebibliography"><DT CLASS="dt-thebibliography">
-
-<A NAME="cock2009"><FONT COLOR=purple>[1]</FONT></A></DT><DD CLASS="dd-thebibliography">
-Peter J. A. Cock, Tiago Antao, Jeffrey T. Chang, Brad A. Chapman, Cymon J. Cox, Andrew Dalke, Iddo Friedberg, Thomas Hamelryck, Frank Kauff, Bartek Wilczynski, Michiel J. L. de Hoon: “Biopython: freely available Python tools for computational molecular biology and bioinformatics”. <I>Bioinformatics</I> <B>25</B> (11), 1422–1423 (2009). <A HREF="http://dx.doi.org/10.1093/bioinformatics/btp163">doi:10.1093/bioinformatics/btp163</A>,
-</DD><DT CLASS="dt-thebibliography"><A NAME="pritchard2006"><FONT COLOR=purple>[2]</FONT></A></DT><DD CLASS="dd-thebibliography">
-Leighton Pritchard, Jennifer A. White, Paul R.J. Birch, Ian K. Toth: “GenomeDiagram: a python package for the visualization of large-scale genomic data”. <I>Bioinformatics</I> <B>22</B> (5): 616–617 (2006).
-<A HREF="http://dx.doi.org/10.1093/bioinformatics/btk021">doi:10.1093/bioinformatics/btk021</A>,
-</DD><DT CLASS="dt-thebibliography"><A NAME="toth2006"><FONT COLOR=purple>[3]</FONT></A></DT><DD CLASS="dd-thebibliography">
-Ian K. Toth, Leighton Pritchard, Paul R. J. Birch: “Comparative genomics reveals what makes an enterobacterial plant pathogen”. <I>Annual Review of Phytopathology</I> <B>44</B>: 305–336 (2006).
-<A HREF="http://dx.doi.org/10.1146/annurev.phyto.44.070505.143444">doi:10.1146/annurev.phyto.44.070505.143444</A>,
-</DD><DT CLASS="dt-thebibliography"><A NAME="vanderauwera2009"><FONT COLOR=purple>[4]</FONT></A></DT><DD CLASS="dd-thebibliography">
-Géraldine A. van der Auwera, Jaroslaw E. Król, Haruo Suzuki, Brian Foster, Rob van Houdt, Celeste J. Brown, Max Mergeay, Eva M. Top: “Plasmids captured in C. metallidurans CH34: defining the PromA family of broad-host-range plasmids”.
-<I>Antonie van Leeuwenhoek</I> <B>96</B> (2): 193–204 (2009).
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-Caroline Proux, Douwe van Sinderen, Juan Suarez, Pilar Garcia, Victor Ladero, Gerald F. Fitzgerald, Frank Desiere, Harald Brüssow:
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-</DD><DT CLASS="dt-thebibliography"><A NAME="jupe2012"><FONT COLOR=purple>[6]</FONT></A></DT><DD CLASS="dd-thebibliography">
-Florian Jupe, Leighton Pritchard, Graham J. Etherington, Katrin MacKenzie, Peter JA Cock, Frank Wright, Sanjeev Kumar Sharma1, Dan Bolser, Glenn J Bryan, Jonathan DG Jones, Ingo Hein: “Identification and localisation of the NB-LRR gene family within the potato genome”. <I>BMC Genomics</I> <B>13</B>: 75 (2012).
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-</DD><DT CLASS="dt-thebibliography"><A NAME="cock2010"><FONT COLOR=purple>[7]</FONT></A></DT><DD CLASS="dd-thebibliography">
-Peter J. A. Cock, Christopher J. Fields, Naohisa Goto, Michael L. Heuer, Peter M. Rice: “The Sanger FASTQ file format for sequences with quality scores, and the Solexa/Illumina FASTQ variants”. <I>Nucleic Acids Research</I> <B>38</B> (6): 1767–1771 (2010). <A HREF="http://dx.doi.org/10.1093/nar/gkp1137">doi:10.1093/nar/gkp1137</A>
-</DD><DT CLASS="dt-thebibliography"><A NAME="brown1999"><FONT COLOR=purple>[8]</FONT></A></DT><DD CLASS="dd-thebibliography">
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-Timothy L. Bailey and Charles Elkan: “Fitting a mixture model by expectation maximization to discover motifs in biopolymers”, <I>Proceedings of the Second International Conference on Intelligent Systems for Molecular Biology</I> 28–36. AAAI Press, Menlo Park, California (1994).
-</DD><DT CLASS="dt-thebibliography"><A NAME="chapman2000"><FONT COLOR=purple>[13]</FONT></A></DT><DD CLASS="dd-thebibliography">
-Brad Chapman and Jeff Chang: “Biopython: Python tools for computational biology”. <I>ACM SIGBIO Newsletter</I> <B>20</B> (2): 15–19 (August 2000).
-</DD><DT CLASS="dt-thebibliography"><A NAME="dehoon2004"><FONT COLOR=purple>[14]</FONT></A></DT><DD CLASS="dd-thebibliography">
-Michiel J. L. de Hoon, Seiya Imoto, John Nolan, Satoru Miyano: “Open source clustering software”. <I>Bioinformatics</I> <B>20</B> (9): 1453–1454 (2004). <A HREF="http://dx.doi.org/10.1093/bioinformatics/bth078">doi:10.1093/bioinformatics/bth078</A>
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-Gene H. Golub, Christian Reinsch: “Singular value decomposition and least squares solutions”. In <I>Handbook for Automatic Computation</I>, <B>2</B>, (Linear Algebra) (J. H. Wilkinson and C. Reinsch, eds), 134–151. New York: Springer-Verlag (1971).
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-Gene H. Golub, Charles F. Van Loan: <I>Matrix computations</I>, 2nd edition (1989).
-</DD><DT CLASS="dt-thebibliography"><A NAME="hamelryck2003a"><FONT COLOR=purple>[18]</FONT></A></DT><DD CLASS="dd-thebibliography">
-Thomas Hamelryck and Bernard Manderick: 11PDB parser and structure class
-implemented in Python”. <I>Bioinformatics</I>, <B>19</B> (17): 2308–2310 (2003) <A HREF="http://dx.doi.org/10.1093/bioinformatics/btg299">doi: 10.1093/bioinformatics/btg299</A>.
-</DD><DT CLASS="dt-thebibliography"><A NAME="hamelryck2003b"><FONT COLOR=purple>[19]</FONT></A></DT><DD CLASS="dd-thebibliography">
-Thomas Hamelryck: “Efficient identification of side-chain patterns using a multidimensional index tree”. <I>Proteins</I> <B>51</B> (1): 96–108 (2003). <A HREF="http://dx.doi.org/10.1002/prot.10338">doi:10.1002/prot.10338</A>
-</DD><DT CLASS="dt-thebibliography"><A NAME="hamelryck2005"><FONT COLOR=purple>[20]</FONT></A></DT><DD CLASS="dd-thebibliography">
-Thomas Hamelryck: “An amino acid has two sides; A new 2D measure provides a different view of solvent exposure”. <I>Proteins</I> <B>59</B> (1): 29–48 (2005). <A HREF="http://dx.doi.org/10.1002/prot.20379">doi:10.1002/prot.20379</A>.
-</DD><DT CLASS="dt-thebibliography"><A NAME="hartigan1975"><FONT COLOR=purple>[21]</FONT></A></DT><DD CLASS="dd-thebibliography">
-John A. Hartiga. <I>Clustering algorithms</I>. New York: Wiley (1975).
-</DD><DT CLASS="dt-thebibliography"><A NAME="jain1988"><FONT COLOR=purple>[22]</FONT></A></DT><DD CLASS="dd-thebibliography">
-Anil L. Jain, Richard C. Dubes: <I>Algorithms for clustering data</I>. Englewood Cliffs, N.J.: Prentice Hall (1988).
-</DD><DT CLASS="dt-thebibliography"><A NAME="kachitvichyanukul1988"><FONT COLOR=purple>[23]</FONT></A></DT><DD CLASS="dd-thebibliography">
-Voratas Kachitvichyanukul, Bruce W. Schmeiser: Binomial Random Variate Generation. <I>Communications of the ACM</I> <B>31</B> (2): 216–222 (1988). <A HREF="http://dx.doi.org/10.1145/42372.42381">doi:10.1145/42372.42381</A>
-</DD><DT CLASS="dt-thebibliography"><A NAME="kohonen1997"><FONT COLOR=purple>[24]</FONT></A></DT><DD CLASS="dd-thebibliography">
-Teuvo Kohonen: “Self-organizing maps”, 2nd Edition. Berlin; New York: Springer-Verlag (1997).
-</DD><DT CLASS="dt-thebibliography"><A NAME="lecuyer1988"><FONT COLOR=purple>[25]</FONT></A></DT><DD CLASS="dd-thebibliography">
-Pierre L’Ecuyer: “Efficient and Portable Combined Random Number Generators.”
-<I>Communications of the ACM</I> <B>31</B> (6): 742–749,774 (1988). <A HREF="http://dx.doi.org/10.1145/62959.62969">doi:10.1145/62959.62969</A>
-</DD><DT CLASS="dt-thebibliography"><A NAME="majumdar2005"><FONT COLOR=purple>[26]</FONT></A></DT><DD CLASS="dd-thebibliography">
-Indraneel Majumdar, S. Sri Krishna, Nick V. Grishin: “PALSSE: A program to delineate linear secondary structural elements from protein structures.” <I>BMC Bioinformatics</I>, <B>6</B>: 202 (2005). <A HREF="http://dx.doi.org/10.1186/1471-2105-6-202">doi:10.1186/1471-2105-6-202</A>.
-</DD><DT CLASS="dt-thebibliography"><A NAME="matys2003"><FONT COLOR=purple>[27]</FONT></A></DT><DD CLASS="dd-thebibliography">
-V. Matys, E. Fricke, R. Geffers, E. Gössling, M. Haubrock, R. Hehl, K. Hornischer, D. Karas, A.E. Kel, O.V. Kel-Margoulis, D.U. Kloos, S. Land, B. Lewicki-Potapov, H. Michael, R. Münch, I. Reuter, S. Rotert, H. Saxel, M. Scheer, S. Thiele, E. Wingender E: “TRANSFAC: transcriptional regulation, from patterns to profiles.” Nucleic Acids Research <B>31</B> (1): 374–378 (2003). <A HREF="http://dx.doi.org/10.1093/nar/gkg108">doi:10.1093/nar/gkg108</A>
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-Robin Sibson: “SLINK: An optimally efficient algorithm for the single-link cluster method”. <I>The Computer Journal</I> <B>16</B> (1): 30–34 (1973). <A HREF="http://dx.doi.org/10.1093/comjnl/16.1.30">doi:10.1093/comjnl/16.1.30</A>
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-George W. Snedecor, William G. Cochran: <I>Statistical methods</I>. Ames, Iowa: Iowa State University Press (1989).
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-</DD></DL><!--CUT END -->
-<!--HTMLFOOT-->
-<!--ENDHTML-->
-<!--FOOTER-->
-<HR SIZE=2><BLOCKQUOTE CLASS="quote"><EM>This document was translated from L<sup>A</sup>T<sub>E</sub>X by
-</EM><A HREF="http://hevea.inria.fr/index.html"><EM>H</EM><EM><FONT SIZE=2><sup>E</sup></FONT></EM><EM>V</EM><EM><FONT SIZE=2><sup>E</sup></FONT></EM><EM>A</EM></A><EM>.</EM></BLOCKQUOTE></BODY>
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-<< /S /GoTo /D (chapter.1) >>
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-trailer
-<< /Size 3786
-/Root 3784 0 R
-/Info 3785 0 R
-/ID [<5170E867C7B1397C94B9D520831636F9> <5170E867C7B1397C94B9D520831636F9>] >>
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-%%EOF
diff --git a/Doc/Tutorial.tex b/Doc/Tutorial.tex
index ac345c8..d6db866 100644
--- a/Doc/Tutorial.tex
+++ b/Doc/Tutorial.tex
@@ -80,7 +80,7 @@ Biopython Tutorial and Cookbook}
\author{Jeff Chang, Brad Chapman, Iddo Friedberg, Thomas Hamelryck, \\
Michiel de Hoon, Peter Cock, Tiago Antao, Eric Talevich, Bartek Wilczy\'{n}ski}
-\date{Last Update -- 21 October 2015 (Biopython 1.66)}
+\date{Last Update -- 8 June 2016 (Biopython 1.67)}
%Hack to get the logo at the start of the HTML front page:
%(hopefully this isn't going to be too wide for most people)
@@ -134,7 +134,7 @@ Michiel de Hoon, Peter Cock, Tiago Antao, Eric Talevich, Bartek Wilczy\'{n}ski}
\include{Tutorial/chapter_uniprot}
%\chapter{Going 3D: The PDB module}
-\include{Tutorial/chapter_pdf}
+\include{Tutorial/chapter_pdb}
%\chapter{Bio.PopGen: Population genetics}
\include{Tutorial/chapter_popgen}
@@ -160,6 +160,10 @@ Michiel de Hoon, Peter Cock, Tiago Antao, Eric Talevich, Bartek Wilczy\'{n}ski}
%\label{chap:kegg}
\include{Tutorial/chapter_kegg}
+%\chapter{Bio.phenotype: analyse phenotypic data}
+%\label{chap:phenotype}
+\include{Tutorial/chapter_phenotype}
+
%\chapter{Cookbook -- Cool things to do with it}
%\label{chapter:cookbook}
\include{Tutorial/chapter_cookbook}
diff --git a/Doc/Tutorial/chapter_align.tex b/Doc/Tutorial/chapter_align.tex
index 3c4b47d..b0dbd13 100644
--- a/Doc/Tutorial/chapter_align.tex
+++ b/Doc/Tutorial/chapter_align.tex
@@ -893,7 +893,7 @@ faithful to the actual command line API:
\end{verbatim}
For the most basic usage, all you need is to have a FASTA input file, such as
-\href{http://biopython.org/DIST/docs/tutorial/examples/opuntia.fasta}{opuntia.fasta}
+\href{https://raw.githubusercontent.com/biopython/biopython/master/Doc/examples/opuntia.fasta}{opuntia.fasta}
(available online or in the Doc/examples subdirectory of the Biopython source
code). This is a small FASTA file containing seven prickly-pear DNA sequences
(from the cactus family \textit{Opuntia}).
@@ -1031,7 +1031,7 @@ actual command line API:
\end{verbatim}
For the most basic usage, all you need is to have a FASTA input file, such as
-\href{http://biopython.org/DIST/docs/tutorial/examples/opuntia.fasta}{opuntia.fasta}
+\href{https://raw.githubusercontent.com/biopython/biopython/master/Doc/examples/opuntia.fasta}{opuntia.fasta}
(available online or in the Doc/examples subdirectory of the Biopython source
code). You can then tell MUSCLE to read in this FASTA file, and write the
alignment to an output file:
@@ -1136,6 +1136,7 @@ with handles instead:
>>> child = subprocess.Popen(str(muscle_cline),
... stdout=subprocess.PIPE,
... stderr=subprocess.PIPE,
+... universal_newlines=True,
... shell=(sys.platform!="win32"))
>>> from Bio import AlignIO
>>> align = AlignIO.read(child.stdout, "fasta")
diff --git a/Doc/Tutorial/chapter_appendix.tex b/Doc/Tutorial/chapter_appendix.tex
index 0a41ac4..7795914 100644
--- a/Doc/Tutorial/chapter_appendix.tex
+++ b/Doc/Tutorial/chapter_appendix.tex
@@ -38,10 +38,10 @@ which reads the entire text information from the handle, and
``write'' handles, the function \verb|write()| is regularly used.
The most common usage for handles is reading information from a file,
-which is done using the built-in Python function \verb|open|. Here, we open a
-handle to the file \href{examples/m\_cold.fasta}{m\_cold.fasta}
-(also available online
-\href{http://biopython.org/DIST/docs/tutorial/examples/m\_cold.fasta}{here}):
+which is done using the built-in Python function \verb|open|. Here,
+we handle to the file {\tt m\_cold.fasta} which you can download
+\href{https://raw.githubusercontent.com/biopython/biopython/master/Doc/examples/m\_cold.fasta}{here}
+(or find included in the Biopython source code as {\tt Doc/examples/m\_cold.fasta}).
\begin{verbatim}
>>> handle = open("m_cold.fasta", "r")
@@ -107,86 +107,3 @@ A string
>>> print(second_line)
with multiple lines.
\end{verbatim}
-
-\begin{thebibliography}{99}
-\bibitem{cock2009}
-Peter J. A. Cock, Tiago Antao, Jeffrey T. Chang, Brad A. Chapman, Cymon J. Cox, Andrew Dalke, Iddo Friedberg, Thomas Hamelryck, Frank Kauff, Bartek Wilczynski, Michiel J. L. de Hoon: ``Biopython: freely available Python tools for computational molecular biology and bioinformatics''. {\it Bioinformatics} {\bf 25} (11), 1422--1423 (2009). \href{http://dx.doi.org/10.1093/bioinformatics/btp163}{doi:10.1093/bioinformatics/btp163},
-\bibitem{pritchard2006}
-Leighton Pritchard, Jennifer A. White, Paul R.J. Birch, Ian K. Toth: ``GenomeDiagram: a python package for the visualization of large-scale genomic data''. {\it Bioinformatics} {\bf 22} (5): 616--617 (2006).
-\href{http://dx.doi.org/10.1093/bioinformatics/btk021}{doi:10.1093/bioinformatics/btk021},
-\bibitem{toth2006}
-Ian K. Toth, Leighton Pritchard, Paul R. J. Birch: ``Comparative genomics reveals what makes an enterobacterial plant pathogen''. {\it Annual Review of Phytopathology} {\bf 44}: 305--336 (2006).
-\href{http://dx.doi.org/10.1146/annurev.phyto.44.070505.143444}{doi:10.1146/annurev.phyto.44.070505.143444},
-\bibitem{vanderauwera2009}
-G\'eraldine A. van der Auwera, Jaroslaw E. Kr\'ol, Haruo Suzuki, Brian Foster, Rob van Houdt, Celeste J. Brown, Max Mergeay, Eva M. Top: ``Plasmids captured in C. metallidurans CH34: defining the PromA family of broad-host-range plasmids''.
-\textit{Antonie van Leeuwenhoek} {\bf 96} (2): 193--204 (2009).
-\href{http://dx.doi.org/10.1007/s10482-009-9316-9}{doi:10.1007/s10482-009-9316-9}
-\bibitem{proux2002}
-Caroline Proux, Douwe van Sinderen, Juan Suarez, Pilar Garcia, Victor Ladero, Gerald F. Fitzgerald, Frank Desiere, Harald Br\"ussow:
-``The dilemma of phage taxonomy illustrated by comparative genomics of Sfi21-Like Siphoviridae in lactic acid bacteria''. \textit{Journal of Bacteriology} {\bf 184} (21): 6026--6036 (2002).
-\href{http://dx.doi.org/10.1128/JB.184.21.6026-6036.2002}{http://dx.doi.org/10.1128/JB.184.21.6026-6036.2002}
-\bibitem{jupe2012}
-Florian Jupe, Leighton Pritchard, Graham J. Etherington, Katrin MacKenzie, Peter JA Cock, Frank Wright, Sanjeev Kumar Sharma1, Dan Bolser, Glenn J Bryan, Jonathan DG Jones, Ingo Hein: ``Identification and localisation of the NB-LRR gene family within the potato genome''. \textit{BMC Genomics} {\bf 13}: 75 (2012).
-\href{http://dx.doi.org/10.1186/1471-2164-13-75}{http://dx.doi.org/10.1186/1471-2164-13-75}
-\bibitem{cock2010}
-Peter J. A. Cock, Christopher J. Fields, Naohisa Goto, Michael L. Heuer, Peter M. Rice: ``The Sanger FASTQ file format for sequences with quality scores, and the Solexa/Illumina FASTQ variants''. \textit{Nucleic Acids Research} {\bf 38} (6): 1767--1771 (2010). \href{http://dx.doi.org/10.1093/nar/gkp1137}{doi:10.1093/nar/gkp1137}
-\bibitem{brown1999}
-Patrick O. Brown, David Botstein: ``Exploring the new world of the genome with DNA microarrays''. \textit{Nature Genetics} {\bf 21} (Supplement 1), 33--37 (1999). \href{http://dx.doi.org/10.1038/4462}{doi:10.1038/4462}
-\bibitem{talevich2012}
-Eric Talevich, Brandon M. Invergo, Peter J.A. Cock, Brad A. Chapman: ``Bio.Phylo: A unified toolkit for processing, analyzing and visualizing phylogenetic trees in Biopython''. \textit{BMC Bioinformatics} {\bf 13}: 209 (2012). \href{http://dx.doi.org/10.1186/1471-2105-13-209}{doi:10.1186/1471-2105-13-209}
-\bibitem{cornish1985}
-Athel Cornish-Bowden: ``Nomenclature for incompletely specified bases in nucleic acid sequences: Recommendations 1984.'' \textit{Nucleic Acids Research} {\bf 13} (9): 3021--3030 (1985). \href{http://dx.doi.org/10.1093/nar/13.9.3021}{doi:10.1093/nar/13.9.3021}
-\bibitem{cavener1987}
-Douglas R. Cavener: ``Comparison of the consensus sequence flanking translational start sites in Drosophila and vertebrates.'' \textit{Nucleic Acids Research} {\bf 15} (4): 1353--1361 (1987). \href{http://dx.doi.org/10.1093/nar/15.4.1353}{doi:10.1093/nar/15.4.1353}
-\bibitem{bailey1994}
-Timothy L. Bailey and Charles Elkan: ``Fitting a mixture model by expectation maximization to discover motifs in biopolymers'', \textit{Proceedings of the Second International Conference on Intelligent Systems for Molecular Biology} 28--36. AAAI Press, Menlo Park, California (1994).
-\bibitem{chapman2000}
-Brad Chapman and Jeff Chang: ``Biopython: Python tools for computational biology''. \textit{ACM SIGBIO Newsletter} {\bf 20} (2): 15--19 (August 2000).
-\bibitem{dehoon2004}
-Michiel J. L. de Hoon, Seiya Imoto, John Nolan, Satoru Miyano: ``Open source clustering software''. \textit{Bioinformatics} {\bf 20} (9): 1453--1454 (2004). \href{http://dx.doi.org/10.1093/bioinformatics/bth078}{doi:10.1093/bioinformatics/bth078}
-\bibitem{eisen1998}
-Michiel B. Eisen, Paul T. Spellman, Patrick O. Brown, David Botstein: ``Cluster analysis and display of genome-wide expression patterns''. \textit{Proceedings of the National Academy of Science USA} {\bf 95} (25): 14863--14868 (1998). \href{http://dx.doi.org/10.1073/pnas.96.19.10943-c}{doi:10.1073/pnas.96.19.10943-c}
-\bibitem{golub1971}
-Gene H. Golub, Christian Reinsch: ``Singular value decomposition and least squares solutions''. In \textit{Handbook for Automatic Computation}, {\bf 2}, (Linear Algebra) (J. H. Wilkinson and C. Reinsch, eds), 134--151. New York: Springer-Verlag (1971).
-\bibitem{golub1989}
-Gene H. Golub, Charles F. Van Loan: \textit{Matrix computations}, 2nd edition (1989).
-\bibitem{hamelryck2003a}
-Thomas Hamelryck and Bernard Manderick: 11PDB parser and structure class
-implemented in Python''. \textit{Bioinformatics}, \textbf{19} (17): 2308--2310 (2003) \href{http://dx.doi.org/10.1093/bioinformatics/btg299}{doi: 10.1093/bioinformatics/btg299}.
-\bibitem{hamelryck2003b}
-Thomas Hamelryck: ``Efficient identification of side-chain patterns using a multidimensional index tree''. \textit{Proteins} {\bf 51} (1): 96--108 (2003). \href{http://dx.doi.org/10.1002/prot.10338}{doi:10.1002/prot.10338}
-\bibitem{hamelryck2005}
-Thomas Hamelryck: ``An amino acid has two sides; A new 2D measure provides a different view of solvent exposure''. \textit{Proteins} {\bf 59} (1): 29--48 (2005). \href{http://dx.doi.org/10.1002/prot.20379}{doi:10.1002/prot.20379}.
-\bibitem{hartigan1975}
-John A. Hartiga. \textit{Clustering algorithms}. New York: Wiley (1975).
-\bibitem{jain1988}
-Anil L. Jain, Richard C. Dubes: \textit{Algorithms for clustering data}. Englewood Cliffs, N.J.: Prentice Hall (1988).
-\bibitem{kachitvichyanukul1988}
-Voratas Kachitvichyanukul, Bruce W. Schmeiser: Binomial Random Variate Generation. \textit{Communications of the ACM} {\bf 31} (2): 216--222 (1988). \href{http://dx.doi.org/10.1145/42372.42381}{doi:10.1145/42372.42381}
-\bibitem{kohonen1997}
-Teuvo Kohonen: ``Self-organizing maps'', 2nd Edition. Berlin; New York: Springer-Verlag (1997).
-\bibitem{lecuyer1988}
-Pierre L'Ecuyer: ``Efficient and Portable Combined Random Number Generators.''
-\textit{Communications of the ACM} {\bf 31} (6): 742--749,774 (1988). \href{http://dx.doi.org/10.1145/62959.62969}{doi:10.1145/62959.62969}
-\bibitem{majumdar2005}
-Indraneel Majumdar, S. Sri Krishna, Nick V. Grishin: ``PALSSE: A program to delineate linear secondary structural elements from protein structures.'' \textit{BMC Bioinformatics}, {\bf 6}: 202 (2005). \href{http://dx.doi.org/10.1186/1471-2105-6-202}{doi:10.1186/1471-2105-6-202}.
-\bibitem{matys2003}
-V. Matys, E. Fricke, R. Geffers, E. G\"ossling, M. Haubrock, R. Hehl, K. Hornischer, D. Karas, A.E. Kel, O.V. Kel-Margoulis, D.U. Kloos, S. Land, B. Lewicki-Potapov, H. Michael, R. M\"unch, I. Reuter, S. Rotert, H. Saxel, M. Scheer, S. Thiele, E. Wingender E: ``TRANSFAC: transcriptional regulation, from patterns to profiles.'' Nucleic Acids Research {\bf 31} (1): 374--378 (2003). \href{http://dx.doi.org/10.1093/nar/gkg108}{doi:10.1093/nar/gkg108}
-\bibitem{sibson1973}
-Robin Sibson: ``SLINK: An optimally efficient algorithm for the single-link cluster method''. \textit{The Computer Journal} {\bf 16} (1): 30--34 (1973). \href{http://dx.doi.org/10.1093/comjnl/16.1.30}{doi:10.1093/comjnl/16.1.30}
-\bibitem{snedecor1989}
-George W. Snedecor, William G. Cochran: \textit{Statistical methods}. Ames, Iowa: Iowa State University Press (1989).
-\bibitem{tamayo1999}
-Pablo Tamayo, Donna Slonim, Jill Mesirov, Qing Zhu, Sutisak Kitareewan, Ethan Dmitrovsky, Eric S. Lander, Todd R. Golub: ``Interpreting patterns of gene expression with self-organizing maps: Methods and application to hematopoietic differentiation''. \textit{Proceedings of the National Academy of Science USA} {\bf 96} (6): 2907--2912 (1999). \href{http://dx.doi.org/10.1073/pnas.96.6.2907}{doi:10.1073/pnas.96.6.2907}
-\bibitem{tryon1970}
-Robert C. Tryon, Daniel E. Bailey: \textit{Cluster analysis}. New York: McGraw-Hill (1970).
-\bibitem{tukey1977}
-John W. Tukey: ``Exploratory data analysis''. Reading, Mass.: Addison-Wesley Pub. Co. (1977).
-\bibitem{yeung2001}
-Ka Yee Yeung, Walter L. Ruzzo: ``Principal Component Analysis for clustering gene expression data''. \textit{Bioinformatics} {\bf 17} (9): 763--774 (2001). \href{http://dx.doi.org/10.1093/bioinformatics/17.9.763}{doi:10.1093/bioinformatics/17.9.763}
-\bibitem{saldanha2004}
-Alok Saldanha: ``Java Treeview---extensible visualization of microarray data''. \textit{Bioinformatics} {\bf 20} (17): 3246--3248 (2004).
-\href{http://dx.doi.org/10.1093/bioinformatics/bth349}{http://dx.doi.org/10.1093/bioinformatics/bth349}
-\end{thebibliography}
-\end{document}
-
diff --git a/Doc/Tutorial/chapter_blast.tex b/Doc/Tutorial/chapter_blast.tex
index 692d16c..66e41b4 100644
--- a/Doc/Tutorial/chapter_blast.tex
+++ b/Doc/Tutorial/chapter_blast.tex
@@ -47,6 +47,10 @@ which are basically analogous to the different parameters you can set
on the BLAST web page. We'll just highlight a few of them here:
\begin{itemize}
+\item The argument \verb|url_base| sets the base URL for running BLAST over the
+internet. By default it connects to the NCBI, but one can use this to connect
+to an instance of NCBI BLAST running in the cloud. Please refer to the documentation
+for the \verb|qblast| function for further details.
\item The \verb|qblast| function can return the BLAST results in various
formats, which you can choose with the optional \verb|format_type| keyword:
\verb|"HTML"|, \verb|"Text"|, \verb|"ASN.1"|, or \verb|"XML"|.
diff --git a/Doc/Tutorial/chapter_cluster.tex b/Doc/Tutorial/chapter_cluster.tex
index 9690c9c..fb7684f 100644
--- a/Doc/Tutorial/chapter_cluster.tex
+++ b/Doc/Tutorial/chapter_cluster.tex
@@ -695,7 +695,44 @@ to open a gzipped file, or
\end{verbatim}
to open a file stored on the Internet before calling \verb|read|.
-The \verb|read| command reads the tab-delimited text file \verb|mydatafile.txt| containing gene expression data in the format specified for Michael Eisen's Cluster/TreeView program. For a description of this file format, see the manual to Cluster/TreeView. It is available at \href{http://rana.lbl.gov/manuals/ClusterTreeView.pdf}{Michael Eisen's lab website} and at \href{http://bonsai.ims.u-tokyo.ac.jp/~mdehoon/software/cluster/cluster3.pdf}{our website}.
+The \verb|read| command reads the tab-delimited text file \verb|mydatafile.txt| containing gene expression data in the format specified for Michael Eisen's Cluster/TreeView program. In this file format, rows represent genes and columns represent samples or observations. For a simple time course, a minimal input file would look like this:
+
+\begin{table}[!htbp]
+\begin{center}
+\begin{tabular}{|l|r|r|r|r|r|}
+\hline
+YORF & 0 minutes & 30 minutes & 1 hour & 2 hours & 4 hours\\
+YAL001C & 1 & 1.3 & 2.4 & 5.8 & 2.4 \\
+YAL002W & 0.9 & 0.8 & 0.7 & 0.5 & 0.2 \\
+YAL003W & 0.8 & 2.1 & 4.2 & 10.1 & 10.1 \\
+YAL005C & 1.1 & 1.3 & 0.8 & & 0.4 \\
+YAL010C & 1.2 & 1 & 1.1 & 4.5 & 8.3 \\
+\hline
+\end{tabular}
+\end{center}
+\end{table}
+
+\noindent Each row (gene) has an identifier that always goes in the first column. In this example, we are using yeast open reading frame codes. Each column (sample) has a label in the first row. In this example, the labels describe the time at which a sample was taken. The first column of the first row contains a special field that tells the program what kind of objects are in each row. In this case, YORF stands for yeast open reading frame. This field can be any alpha-numeric value. The [...]
+
+The input file may contain additional information. A maximal input file would look like this:
+
+\begin{table}[!htbp]
+\begin{center}
+\begin{tabular}{|l|l|r|r|r|r|r|r|r|}
+\hline
+YORF & NAME & GWEIGHT & GORDER & 0 & 30 & 1 & 2 & 4 \\
+EWEIGHT & & & & 1 & 1 & 1 & 1 & 0 \\
+EORDER & & & & 5 & 3 & 2 & 1 & 1 \\
+YAL001C & TFIIIC 138 KD SUBUNIT & 1 & 1 & 1 & 1.3 & 2.4 & 5.8 & 2.4 \\
+YAL002W & UNKNOWN & 0.4 & 3 & 0.9 & 0.8 & 0.7 & 0.5 & 0.2 \\
+YAL003W & ELONGATION FACTOR EF1-BETA & 0.4 & 2 & 0.8 & 2.1 & 4.2 & 10.1 & 10.1 \\
+YAL005C & CYTOSOLIC HSP70 & 0.4 & 5 & 1.1 & 1.3 & 0.8 & & 0.4 \\
+\hline
+\end{tabular}
+\end{center}
+\end{table}
+
+\noindent The added columns NAME, GWEIGHT, and GORDER and rows EWEIGHT and EORDER are optional. The NAME column allows you to specify a label for each gene that is distinct from the ID in column 1.
A \verb|Record| object has the following attributes:
diff --git a/Doc/Tutorial/chapter_cookbook.tex b/Doc/Tutorial/chapter_cookbook.tex
index 9716e2d..6ef94e1 100644
--- a/Doc/Tutorial/chapter_cookbook.tex
+++ b/Doc/Tutorial/chapter_cookbook.tex
@@ -258,11 +258,11 @@ to sort by length. This can be solved using the
\begin{verbatim}
from Bio import SeqIO
-#Get the lengths and ids, and sort on length
+# Get the lengths and ids, and sort on length
len_and_ids = sorted((len(rec), rec.id) for rec in \
SeqIO.parse("ls_orchid.fasta","fasta"))
ids = reversed([id for (length, id) in len_and_ids])
-del len_and_ids #free this memory
+del len_and_ids # free this memory
record_index = SeqIO.index("ls_orchid.fasta", "fasta")
records = (record_index[id] for id in ids)
SeqIO.write(records, "sorted.fasta", "fasta")
@@ -284,18 +284,21 @@ in Biopython 1.54 (see Section~\ref{sec:seqio-index-getraw}).
\begin{verbatim}
from Bio import SeqIO
-#Get the lengths and ids, and sort on length
+# Get the lengths and ids, and sort on length
len_and_ids = sorted((len(rec), rec.id) for rec in \
SeqIO.parse("ls_orchid.fasta","fasta"))
ids = reversed([id for (length, id) in len_and_ids])
-del len_and_ids #free this memory
+del len_and_ids # free this memory
record_index = SeqIO.index("ls_orchid.fasta", "fasta")
-handle = open("sorted.fasta", "w")
+handle = open("sorted.fasta", "wb")
for id in ids:
handle.write(record_index.get_raw(id))
handle.close()
\end{verbatim}
+Note with Python 3 onwards, we have to open the file for writing in
+binary mode because the \verb|get_raw()| method returns bytes strings.
+
As a bonus, because it doesn't parse the data into \verb|SeqRecord| objects
a second time it should be faster.
@@ -912,14 +915,21 @@ find whole books about this topic!
\section{Sequence parsing plus simple plots}
\label{seq:sequence-parsing-plus-pylab}
-This section shows some more examples of sequence parsing, using the \verb|Bio.SeqIO|
-module described in Chapter~\ref{chapter:Bio.SeqIO}, plus the Python library matplotlib's \verb|pylab| plotting interface (see \href{http://matplotlib.sourceforge.net/}{the matplotlib website for a tutorial}). Note that to follow these examples you will need matplotlib installed - but without it you can still try the data parsing bits.
+This section shows some more examples of sequence parsing, using the
+\verb|Bio.SeqIO| module described in Chapter~\ref{chapter:Bio.SeqIO},
+plus the Python library matplotlib's \verb|pylab| plotting interface
+(see \href{http://matplotlib.sourceforge.net/}{the matplotlib website
+for a tutorial}). Note that to follow these examples you will need
+matplotlib installed - but without it you can still try the data
+parsing bits.
\subsection{Histogram of sequence lengths}
There are lots of times when you might want to visualise the distribution of sequence
lengths in a dataset -- for example the range of contig sizes in a genome assembly
-project. In this example we'll reuse our orchid FASTA file \href{http://biopython.org/DIST/docs/tutorial/examples/ls_orchid.fasta}{\tt ls\_orchid.fasta} which has only 94 sequences.
+project. In this example we'll reuse our orchid FASTA file
+\href{https://raw.githubusercontent.com/biopython/biopython/master/Doc/examples/ls_orchid.fasta}{\tt ls\_orchid.fasta}
+which has only 94 sequences.
First of all, we will use \verb|Bio.SeqIO| to parse the FASTA file and compile a list
of all the sequence lengths. You could do this with a for loop, but I find a list
@@ -979,14 +989,17 @@ shown in Figure~\ref{fig:seq-len-hist}.
Notice that most of these orchid sequences are about $740$ bp long, and there could be
two distinct classes of sequence here with a subset of shorter sequences.
-\emph{Tip:} Rather than using \verb|pylab.show()| to show the plot in a window, you can also use \verb|pylab.savefig(...)| to save the figure to a file (e.g. as a PNG or PDF).
+\emph{Tip:} Rather than using \verb|pylab.show()| to show the plot in a window,
+you can also use \verb|pylab.savefig(...)| to save the figure to a file
+(e.g. as a PNG or PDF).
\subsection{Plot of sequence GC\%}
Another easily calculated quantity of a nucleotide sequence is the GC\%. You might
want to look at the GC\% of all the genes in a bacterial genome for example, and
investigate any outliers which could have been recently acquired by horizontal gene
-transfer. Again, for this example we'll reuse our orchid FASTA file \href{http://biopython.org/DIST/docs/tutorial/examples/ls_orchid.fasta}{\tt ls\_orchid.fasta}.
+transfer. Again, for this example we'll reuse our orchid FASTA file
+\href{https://raw.githubusercontent.com/biopython/biopython/master/Doc/examples/ls_orchid.fasta}{\tt ls\_orchid.fasta}.
First of all, we will use \verb|Bio.SeqIO| to parse the FASTA file and compile a list
of all the GC percentages. Again, you could do this with a for loop, but I prefer this:
@@ -1065,7 +1078,7 @@ in the plot below).
\end{latexonly}
To start off, we'll need two sequences. For the sake of argument, we'll just take
-the first two from our orchid FASTA file \href{http://biopython.org/DIST/docs/tutorial/examples/ls_orchid.fasta}{\tt ls\_orchid.fasta}:
+the first two from our orchid FASTA file \href{https://raw.githubusercontent.com/biopython/biopython/master/Doc/examples/ls_orchid.fasta}{\tt ls\_orchid.fasta}:
\begin{verbatim}
from Bio import SeqIO
@@ -1486,7 +1499,7 @@ alignments. In this example, we'll first get a Biopython alignment
object and then get a summary object to calculate info about the
alignment. The file containing \href{examples/protein.aln}{protein.aln}
(also available online
-\href{http://biopython.org/DIST/docs/tutorial/examples/protein.aln}{here})
+\href{https://raw.githubusercontent.com/biopython/biopython/master/Doc/examples/protein.aln}{here})
contains the Clustalw alignment output.
%doctest examples
diff --git a/Doc/Tutorial/chapter_entrez.tex b/Doc/Tutorial/chapter_entrez.tex
index 20a7324..336fb12 100644
--- a/Doc/Tutorial/chapter_entrez.tex
+++ b/Doc/Tutorial/chapter_entrez.tex
@@ -200,18 +200,19 @@ Note that instead of a species name like \texttt{Cypripedioideae[Orgn]}, you can
As a final example, let's get a list of computational journal titles:
\begin{verbatim}
->>> handle = Entrez.esearch(db="journals", term="computational")
+>>> handle = Entrez.esearch(db="nlmcatalog", term="computational[Journal]", retmax='20')
>>> record = Entrez.read(handle)
->>> record["Count"]
-'16'
->>> record["IdList"]
-['30367', '33843', '33823', '32989', '33190', '33009', '31986',
- '34502', '8799', '22857', '32675', '20258', '33859', '32534',
- '32357', '32249']
+>>> print("{} computational journals found".format(record["Count"]))
+117 computational Journals found
+>>> print("The first 20 are\n{}".format(record['IdList']))
+['101660833', '101664671', '101661657', '101659814', '101657941',
+ '101653734', '101669877', '101649614', '101647835', '101639023',
+ '101627224', '101647801', '101589678', '101585369', '101645372',
+ '101586429', '101582229', '101574747', '101564639', '101671907']
\end{verbatim}
Again, we could use EFetch to obtain more information for each of these journal IDs.
-ESearch has many useful options --- see the \href{http://www.ncbi.nlm.nih.gov/entrez/query/static/esearch\_help.html}{ESearch help page} for more information.
+ESearch has many useful options --- see the \href{https://www.ncbi.nlm.nih.gov/books/NBK25499/#chapter4.ESearch}{ESearch help page} for more information.
\section{EPost: Uploading a list of identifiers}
EPost uploads a list of UIs for use in subsequent search strategies; see the
@@ -261,14 +262,13 @@ ESummary retrieves document summaries from a list of primary IDs (see the \href
\begin{verbatim}
>>> from Bio import Entrez
>>> Entrez.email = "A.N.Other at example.com" # Always tell NCBI who you are
->>> handle = Entrez.esummary(db="journals", id="30367")
+>>> handle = Entrez.esummary(db="nlmcatalog", id="101660833")
>>> record = Entrez.read(handle)
->>> record[0]["Id"]
-'30367'
->>> record[0]["Title"]
-'Computational biology and chemistry'
->>> record[0]["Publisher"]
-'Pergamon,'
+>>> info = record[0]['TitleMainList'][0]
+>>> print("Journal info\nid: {}\nTitle: {}".format(record[0]["Id"], info["Title"]))
+Journal info
+id: 101660833
+Title: IEEE transactions on computational imaging.
\end{verbatim}
\section{EFetch: Downloading full records from Entrez}
@@ -390,11 +390,11 @@ Note that a more typical use would be to save the sequence data to a local file,
import os
from Bio import SeqIO
from Bio import Entrez
-Entrez.email = "A.N.Other at example.com" # Always tell NCBI who you are
+Entrez.email = "A.N.Other at example.com" # Always tell NCBI who you are
filename = "gi_186972394.gbk"
if not os.path.isfile(filename):
# Downloading...
- net_handle = Entrez.efetch(db="nucleotide",id="186972394",rettype="gb", retmode="text")
+ net_handle = Entrez.efetch(db="nucleotide", id="186972394", rettype="gb", retmode="text")
out_handle = open(filename, "w")
out_handle.write(net_handle.read())
out_handle.close()
@@ -1409,7 +1409,7 @@ Let's try this for the Biopython PDB parser paper, PubMed ID 14630660:
>>> Entrez.email = "A.N.Other at example.com"
>>> pmid = "14630660"
>>> results = Entrez.read(Entrez.elink(dbfrom="pubmed", db="pmc",
-... LinkName="pubmed_pmc_refs", from_uid=pmid))
+... LinkName="pubmed_pmc_refs", id=pmid))
>>> pmc_ids = [link["Id"] for link in results[0]["LinkSetDb"][0]["Link"]]
>>> pmc_ids
['2744707', '2705363', '2682512', ..., '1190160']
@@ -1430,7 +1430,7 @@ But first, taking the more straightforward approach of making a second
\begin{verbatim}
>>> results2 = Entrez.read(Entrez.elink(dbfrom="pmc", db="pubmed", LinkName="pmc_pubmed",
-... from_uid=",".join(pmc_ids)))
+... id=",".join(pmc_ids)))
>>> pubmed_ids = [link["Id"] for link in results2[0]["LinkSetDb"][0]["Link"]]
>>> pubmed_ids
['19698094', '19450287', '19304878', ..., '15985178']
diff --git a/Doc/Tutorial/chapter_graphics.tex b/Doc/Tutorial/chapter_graphics.tex
index 96bf4f2..d2b857e 100644
--- a/Doc/Tutorial/chapter_graphics.tex
+++ b/Doc/Tutorial/chapter_graphics.tex
@@ -867,7 +867,6 @@ plots (e.g. to show plots of GC\% on a track parallel to the features).
These options are not covered here yet, so for now we refer you to the
\href{http://biopython.org/DIST/docs/GenomeDiagram/userguide.pdf}
-%\href{http://bioinf.scri.ac.uk/lp/downloads/programs/genomediagram/userguide.pdf}
{User Guide (PDF)} included with the standalone version of GenomeDiagram (but
please read the next section first), and the docstrings.
diff --git a/Doc/Tutorial/chapter_kegg.tex b/Doc/Tutorial/chapter_kegg.tex
index 9301c47..c128352 100644
--- a/Doc/Tutorial/chapter_kegg.tex
+++ b/Doc/Tutorial/chapter_kegg.tex
@@ -74,9 +74,9 @@ for pathway in repair_pathways:
if not gene_symbol in repair_genes:
repair_genes.append(gene_symbol)
-print "There are %d repair pathways and %d repair genes. The genes are:" % \
- (len(repair_pathways), len(repair_genes))
-print ", ".join(repair_genes)
+print("There are %d repair pathways and %d repair genes. The genes are:" % \
+ (len(repair_pathways), len(repair_genes)))
+print(", ".join(repair_genes))
\end{verbatim}
The KEGG API wrapper is compatible with all endpoints. Usage is essentially replacing all slashes in the url with commas and using that list as arguments to the corresponding method in the KEGG module. Here are a few examples from the api documentation (\url{http://www.kegg.jp/kegg/docs/keggapi.html}).
diff --git a/Doc/Tutorial/chapter_learning.tex b/Doc/Tutorial/chapter_learning.tex
index 9785173..1415d5b 100644
--- a/Doc/Tutorial/chapter_learning.tex
+++ b/Doc/Tutorial/chapter_learning.tex
@@ -379,8 +379,9 @@ True: 0 Predicted: 0
\end{verbatim}
showing that the prediction is correct for all but two of the gene pairs. A more reliable estimate of the prediction accuracy can be found from a leave-one-out analysis, in which the model is recalculated from the training data after removing the gene to be predicted:
\begin{verbatim}
+>>> k = 3
>>> for i in range(len(ys)):
- model = kNN.train(xs[:i]+xs[i+1:], ys[:i]+ys[i+1:])
+ model = kNN.train(xs[:i]+xs[i+1:], ys[:i]+ys[i+1:], k)
print("True:", ys[i], "Predicted:", kNN.classify(model, xs[i]))
True: 1 Predicted: 1
True: 1 Predicted: 0
diff --git a/Doc/Tutorial/chapter_motifs.tex b/Doc/Tutorial/chapter_motifs.tex
index 4c38fb9..8ff7ec1 100644
--- a/Doc/Tutorial/chapter_motifs.tex
+++ b/Doc/Tutorial/chapter_motifs.tex
@@ -1178,9 +1178,9 @@ or a threshold (approximately) satisfying some relation between the false-positi
>>> print("%5.3f" % threshold)
6.241
\end{verbatim}
-or a threshold satisfying (roughly) the equality between the
-false-positive rate and the $-log$ of the information content (as used
-in patser software by Hertz and Stormo):
+or a threshold satisfying (roughly) the equality between the $-log$ of the
+false-positive rate and the information content (as used in patser software by
+Hertz and Stormo):
%cont-doctest
\begin{verbatim}
>>> threshold = distribution.threshold_patser()
@@ -1434,7 +1434,7 @@ roughly $1-0.239=0.761$.
\label{sec:find}
Currently, Biopython has only limited support for \emph{de novo} motif
-finding. Namely, we support running and parsing of AlignAce and
+finding. Namely, we support running \verb|xxmotif| and also parsing of
MEME. Since the number of motif finding tools is growing rapidly,
contributions of new parsers are welcome.
@@ -1488,58 +1488,7 @@ Seq('CTCAATCGTA', IUPACUnambiguousDNA())
\end{verbatim}
-\subsection{AlignAce}
-\label{sec:alignace}
-
-We can do very similar things with the AlignACE program. Assume, you have
-your output in the file \verb|alignace.out|. You can parse your output
-with the following code:
-
-%cont-doctest
-\begin{verbatim}
->>> from Bio import motifs
->>> with open("alignace.out") as handle:
-... motifsA = motifs.parse(handle, "alignace")
-...
-\end{verbatim}
-
-Again, your motifs behave as they should:
-%cont-doctest
-\begin{verbatim}
->>> motifsA[0].consensus
-Seq('TCTACGATTGAG', IUPACUnambiguousDNA())
-\end{verbatim}
-
-In fact you can even see, that AlignAce found a very similar motif as
-MEME. It is just a longer version of a reverse complement of the MEME
-motif:
-%cont-doctest
-\begin{verbatim}
->>> motifsM[0].reverse_complement().consensus
-Seq('TACGATTGAG', IUPACUnambiguousDNA())
-\end{verbatim}
-
-If you have AlignAce installed on the same machine, you can also run
-it directly from Biopython. A short example of how this can be done is
-shown below (other parameters can be specified as keyword parameters):
-
-\begin{verbatim}
->>> command="/opt/bin/AlignACE"
->>> input_file="test.fa"
->>> from Bio.motifs.applications import AlignAceCommandline
->>> cmd = AlignAceCommandline(cmd=command, input=input_file, gcback=0.6, numcols=10)
->>> stdout, stderr= cmd()
-\end{verbatim}
-
-Since AlignAce prints all of its output to standard output, you can get
-to your motifs by parsing the first part of the result:
-\begin{verbatim}
->>> motifs = motifs.parse(stdout, "alignace")
-\end{verbatim}
-
-
-
-\section{Useful links }
+\section{Useful links}
\label{sec:links}
diff --git a/Doc/Tutorial/chapter_phenotype.tex b/Doc/Tutorial/chapter_phenotype.tex
new file mode 100644
index 0000000..6495fe8
--- /dev/null
+++ b/Doc/Tutorial/chapter_phenotype.tex
@@ -0,0 +1,271 @@
+\chapter{Bio.phenotype: analyse phenotypic data}
+\label{chapter:phenotype}
+
+This chapter gives an overview of the functionalities of the
+\verb|Bio.phenotype| package included in Biopython. The scope of this
+package is the analysis of phenotypic data, which means parsing and
+analysing growth measurements of cell cultures.
+In its current state the package is focused on the analysis of
+high-throughput phenotypic experiments produced by the
+\href{https://en.wikipedia.org/wiki/Phenotype_microarray}{Phenotype Microarray technology},
+but future developments may include other platforms and formats.
+
+\section{Phenotype Microarrays}
+\label{sec:phenotypemicroarrays}
+
+The \href{https://en.wikipedia.org/wiki/Phenotype_microarray}{Phenotype Microarray}
+is a technology that measures the metabolism of bacterial
+and eukaryotic cells on roughly 2000 chemicals, divided in twenty 96-well
+plates.
+The technology measures the reduction of a tetrazolium dye by
+NADH, whose production by the cell is used as a proxy for cell metabolism;
+color development due to the reduction of this dye is typically measured
+once every 15 minutes.
+When cells are grown in a media that sustains cell metabolism, the
+recorded phenotypic data resembles a sigmoid growth curve, from which a
+series of growth parameters can be retrieved.
+
+\subsection{Parsing Phenotype Microarray data}
+
+The \verb|Bio.phenotype| package can parse two different formats of
+Phenotype Microarray data: the
+\href{https://en.wikipedia.org/wiki/Comma-separated_values}{CSV}
+(comma separated values) files produced by the machine's proprietary
+software and \href{https://en.wikipedia.org/wiki/JSON}{JSON}
+files produced by analysis software, like
+\href{https://www.dsmz.de/research/microorganisms/projects/analysis-of-omnilog-phenotype-microarray-data.html}{opm}
+or \href{http://combogenomics.github.io/DuctApe/}{DuctApe}.
+The parser will return one or a generator of PlateRecord objects, depending
+on whether the read or parse method is being used.
+You can test the parse function by using the \href{http://biopython.org/SRC/biopython/Doc/examples/Plates.csv}{\texttt{Plates.csv}} file provided with the Biopython source code.
+
+%doctest examples lib:numpy
+\begin{verbatim}
+>>> from Bio import phenotype
+>>> for record in phenotype.parse("Plates.csv", "pm-csv"):
+... print("%s %i" % (record.id, len(record)))
+...
+PM01 96
+PM01 96
+PM09 96
+PM09 96
+\end{verbatim}
+
+The parser returns a series of PlateRecord objects, each one containing a series of WellRecord objects
+(holding each well's experimental data) arranged in 8 rows and 12 columns; each row is indicated by
+a uppercase character from A to H, while columns are indicated by a two digit number, from 01 to 12.
+There are several ways to access WellRecord objects from a PlateRecord objects:
+
+\begin{description}
+ \item[Well identifier]
+ If you know the well identifier (row + column identifiers) you can access the desired well directly.
+ \begin{verbatim}
+ >>> record['A02']
+ \end{verbatim}
+
+ \item[Well plate coordinates]
+ The same well can be retrieved by using the row and columns numbers (0-based index).
+
+%doctest examples lib:numpy
+\begin{verbatim}
+>>> from Bio import phenotype
+>>> record = list(phenotype.parse("Plates.csv", "pm-csv"))[-1]
+>>> print(record[0, 1].id)
+A02
+\end{verbatim}
+
+ \item[Row or column coordinates]
+ A series of WellRecord objects contiguous to each other in the plate can be retrieved in bulk by
+ using the python list slicing syntax on PlateRecord objects; rows and columns are numbered with
+ a 0-based index.
+
+%cont-doctest
+\begin{verbatim}
+>>> print(record[0])
+Plate ID: PM09
+Well: 12
+Rows: 1
+Columns: 12
+PlateRecord('WellRecord['A01'], WellRecord['A02'], WellRecord['A03'], ..., WellRecord['A12']')
+>>> print(record[:, 0])
+Plate ID: PM09
+Well: 8
+Rows: 8
+Columns: 1
+PlateRecord('WellRecord['A01'], WellRecord['B01'], WellRecord['C01'], ..., WellRecord['H01']')
+>>> print(record[:3, :3])
+Plate ID: PM09
+Well: 9
+Rows: 3
+Columns: 3
+PlateRecord('WellRecord['A01'], WellRecord['A02'], WellRecord['A03'], ..., WellRecord['C03']')
+\end{verbatim}
+
+\end{description}
+
+\subsection{Manipulating Phenotype Microarray data}
+
+\subsubsection{Accessing raw data}
+The raw data extracted from the PM files is comprised of a series of tuples for each well,
+containing the time (in hours) and the colorimetric measure (in arbitrary units).
+Usually the instrument collects data every fifteen minutes, but that can vary between
+experiments. The raw data can be accessed by iterating on a WellRecord object;
+in the example below only the first ten time points are shown.
+
+%doctest examples lib:numpy
+\begin{verbatim}
+>>> from Bio import phenotype
+>>> record = list(phenotype.parse("Plates.csv", "pm-csv"))[-1]
+>>> well = record['A02']
+\end{verbatim}
+%rest of code snippet output is truncated
+\begin{verbatim}
+>>> for time, signal in well:
+... print(time, signal)
+...
+(0.0, 12.0)
+(0.25, 18.0)
+(0.5, 27.0)
+(0.75, 35.0)
+(1.0, 37.0)
+(1.25, 41.0)
+(1.5, 44.0)
+(1.75, 44.0)
+(2.0, 44.0)
+(2.25, 44.0)
+[...]
+\end{verbatim}
+
+This method, while providing a way to access the raw data, doesn't allow a direct
+comparison between different WellRecord objects, which may have measurements at
+different time points.
+
+\subsubsection{Accessing interpolated data}
+To make it easier to compare different experiments and in general to allow a more intuitive handling
+of the phenotypic data, the module allows to define a custom slicing of the time points that are present
+in the WellRecord object. Colorimetric data for time points that have not been directly measured are
+derived through a linear interpolation of the available data, otherwise a NaN is returned.
+This method only works in the time interval where actual data is available.
+Time intervals can be defined with the same syntax as list
+indexing; the default time interval is therefore one hour.
+
+%cont-doctest
+\begin{verbatim}
+>>> well[:10]
+[12.0, 37.0, 44.0, 44.0, 44.0, 44.0, 44.0, 44.0, 44.0, 44.0]
+\end{verbatim}
+
+Different time intervals can be used, for instance five minutes:
+%Rounding makes this tricky as a doctest...
+\begin{verbatim}
+>>> well[63:64:0.083]
+[12.0, 37.0, 44.0, 44.0, 44.0, 44.0, 44.0, 44.0, 44.0, 44.0]
+>>> well[9.55]
+44.0
+>>> well[63.33:73.33]
+[113.31999999999999,
+ 117.0,
+ 120.31999999999999,
+ 128.0,
+ 129.63999999999999,
+ 132.95999999999998,
+ 136.95999999999998,
+ 140.0,
+ 142.0,
+ nan]
+\end{verbatim}
+
+\subsubsection{Control well subtraction}
+Many Phenotype Microarray plates contain a control well (usually A01), that is a well where the media shouldn't support
+any growth; the low signal produced by this well can be subtracted from the other wells.
+The PlateRecord objects have a dedicated function for that, which returns another PlateRecord object
+with the corrected data.
+
+%cont-doctest
+\begin{verbatim}
+>>> corrected = record.subtract_control(control='A01')
+>>> record['A01'][63]
+336.0
+>>> corrected['A01'][63]
+0.0
+\end{verbatim}
+
+\subsubsection{Parameters extraction}
+Those wells where metabolic activity is observed show a sigmoid behavior for the colorimetric data.
+To allow an easier way to compare different experiments a sigmoid curve can be fitted onto the data,
+so that a series of summary parameters can be extracted and used for comparisons.
+The parameters that can be extracted from the curve are:
+
+\begin{itemize}
+ \item Minimum (\textbf{min}) and maximum (\textbf{max}) signal;
+
+ \item Average height (\textbf{average\_height});
+
+ \item Area under the curve (\textbf{area});
+
+ \item Curve plateau point (\textbf{plateau});
+
+ \item Curve slope during exponential metabolic activity (\textbf{slope});
+
+ \item Curve lag time (\textbf{lag}).
+\end{itemize}
+
+All the parameters (except \textbf{min}, \textbf{max} and \textbf{average\_height}) require the
+\href{http://www.scipy.org/}{scipy library} to be installed.
+
+The fit function uses three sigmoid functions:
+
+\begin{description}
+ \item[Gompertz] $Ae^{-e^{(\frac{\mu_{m}e}{A}(\lambda - t) + 1)}} + y0$
+
+ \item[Logistic] $\frac{A}{1+e^{(\frac{4\mu_{m}}{A}(\lambda - t) + 2)}} + y_{0}$
+
+ \item[Richards] $A(1 + ve^{1 + v} + e^{\frac{\mu_{m}}{A}(1 + v)(1 + \frac{1}{v})(\lambda - t)})^{-\frac{1}{v}} + y0$
+
+\end{description}
+
+Where:
+\begin{itemize}
+ \item[\textbf{A}] corresponds to the \textbf{plateau}
+
+ \item[\textbf{$\mu_{m}$}] corresponds to the \textbf{slope}
+
+ \item[\textbf{$\lambda$}] corresponds to the \textbf{lag}
+
+\end{itemize}
+
+These functions have been derived from \href{http://www.ncbi.nlm.nih.gov/pubmed/16348228}{this publication}.
+The fit method by default tries first to fit the gompertz function: if it fails it will then try to fit
+the logistic and then the richards function. The user can also specify one of the three functions to be applied.
+
+%TODO: enable with doctest examples
+\begin{verbatim}
+>>> from Bio import phenotype
+>>> record = list(phenotype.parse("Plates.csv", "pm-csv"))[-1]
+>>> well = record['A02']
+>>> well.fit()
+>>> print("Function fitted: %s" % well.model)
+Function fitted: gompertz
+>>> for param in ["area", "average_height", "lag", "max", "min",
+... "plateau", "slope"]:
+... print("%s\t%.2f" % (param, getattr(well, param)))
+...
+area 4414.38
+average_height 61.58
+lag 48.60
+max 143.00
+min 12.00
+plateau 120.02
+slope 4.99
+\end{verbatim}
+
+\subsection{Writing Phenotype Microarray data}
+PlateRecord objects can be written to file in the form of
+\href{https://en.wikipedia.org/wiki/JSON}{JSON}
+files, a format compatible with other software packages such as
+\href{https://www.dsmz.de/research/microorganisms/projects/analysis-of-omnilog-phenotype-microarray-data.html}{opm}
+or \href{http://combogenomics.github.io/DuctApe/}{DuctApe}.
+\begin{verbatim}
+>>> phenotype.write(record, "out.json", "pm-json")
+1
+\end{verbatim}
diff --git a/Doc/Tutorial/chapter_quick_start.tex b/Doc/Tutorial/chapter_quick_start.tex
index ce967a3..119fdaa 100644
--- a/Doc/Tutorial/chapter_quick_start.tex
+++ b/Doc/Tutorial/chapter_quick_start.tex
@@ -75,7 +75,7 @@ A large part of much bioinformatics work involves dealing with the many types of
We are now going to briefly introduce the \verb|Bio.SeqIO| module -- you can find out more in Chapter~\ref{chapter:Bio.SeqIO}. We'll start with an online search for our friends, the lady slipper orchids. To keep this introduction simple, we're just using the NCBI website by hand. Let's just take a look through the nucleotide databases at NCBI, using an Entrez online search (\url{http://www.ncbi.nlm.nih.gov:80/entrez/query.fcgi?db=Nucleotide}) for everything mentioning the text Cypriped [...]
-When this tutorial was originally written, this search gave us only 94 hits, which we saved as a FASTA formatted text file and as a GenBank formatted text file (files \href{http://biopython.org/DIST/docs/tutorial/examples/ls_orchid.fasta}{\tt ls\_orchid.fasta} and \href{http://biopython.org/DIST/docs/tutorial/examples/ls_orchid.gbk}{\tt ls\_orchid.gbk}, also included with the Biopython source code under {\tt docs/tutorial/examples/}).
+When this tutorial was originally written, this search gave us only 94 hits, which we saved as a FASTA formatted text file and as a GenBank formatted text file (files \href{https://raw.githubusercontent.com/biopython/biopython/master/Doc/examples/ls_orchid.fasta}{\tt ls\_orchid.fasta} and \href{https://raw.githubusercontent.com/biopython/biopython/master/Doc/examples/ls_orchid.gbk}{\tt ls\_orchid.gbk}, also included with the Biopython source code under {\tt docs/tutorial/examples/}).
% The GenBank version is actually new - it was created by filtering out the original 94 hits from the
% latest search of over 400 hits.
@@ -85,7 +85,7 @@ If you run the search today, you'll get hundreds of results! When following the
\subsection{Simple FASTA parsing example}
\label{sec:fasta-parsing}
-If you open the lady slipper orchids FASTA file \href{http://biopython.org/DIST/docs/tutorial/examples/ls_orchid.fasta}{\tt ls\_orchid.fasta} in your favourite text editor, you'll see that the file starts like this:
+If you open the lady slipper orchids FASTA file \href{https://raw.githubusercontent.com/biopython/biopython/master/Doc/examples/ls_orchid.fasta}{\tt ls\_orchid.fasta} in your favourite text editor, you'll see that the file starts like this:
\begin{verbatim}
>gi|2765658|emb|Z78533.1|CIZ78533 C.irapeanum 5.8S rRNA gene and ITS1 and ITS2 DNA
@@ -120,7 +120,7 @@ Notice that the FASTA format does not specify the alphabet, so \verb|Bio.SeqIO|
\subsection{Simple GenBank parsing example}
-Now let's load the GenBank file \href{http://biopython.org/DIST/docs/tutorial/examples/ls_orchid.gbk}{\tt ls\_orchid.gbk} instead - notice that the code to do this is almost identical to the snippet used above for the FASTA file - the only difference is we change the filename and the format string:
+Now let's load the GenBank file \href{https://raw.githubusercontent.com/biopython/biopython/master/Doc/examples/ls_orchid.gbk}{\tt ls\_orchid.gbk} instead - notice that the code to do this is almost identical to the snippet used above for the FASTA file - the only difference is we change the filename and the format string:
\begin{verbatim}
from Bio import SeqIO
diff --git a/Doc/Tutorial/chapter_searchio.tex b/Doc/Tutorial/chapter_searchio.tex
index 1835d69..7d1d552 100644
--- a/Doc/Tutorial/chapter_searchio.tex
+++ b/Doc/Tutorial/chapter_searchio.tex
@@ -35,10 +35,11 @@ BLAST and BLAT. They are used merely for illustrative purposes, and you should
be able to adapt the workflow to any other search tools supported by
\verb|Bio.SearchIO| in a breeze. You're very welcome to follow along with the
search output files we'll be using. The BLAST output file can be downloaded
-\href{http://biopython.org/SRC/Tests/Tutorial/my_blast.xml}{here},
+\href{http://biopython.org/SRC/biopython/Doc/examples/my_blast.xml}{here},
and the BLAT output file
-\href{http://biopython.org/SRC/Tests/Tutorial/my_blat.psl}{here}.
-Both output files were generated using this sequence:
+\href{http://biopython.org/SRC/biopython/Doc/examples/my_blat.psl}{here}
+or are included with the Biopython source code under the \verb|Doc/examples/|
+folder. Both output files were generated using this sequence:
\begin{verbatim}
>mystery_seq
diff --git a/Doc/Tutorial/chapter_seq_annot.tex b/Doc/Tutorial/chapter_seq_annot.tex
index 235aaf6..f6da0fe 100644
--- a/Doc/Tutorial/chapter_seq_annot.tex
+++ b/Doc/Tutorial/chapter_seq_annot.tex
@@ -574,6 +574,63 @@ the difference between the start and end positions. However,
for a \verb|CompoundLocation| the length is the sum of the
constituent regions.
+\section{Comparison}
+
+The \verb|SeqRecord| objects can be very complex, but here's a simple example:
+
+%doctest
+\begin{verbatim}
+>>> from Bio.Seq import Seq
+>>> from Bio.SeqRecord import SeqRecord
+>>> record1 = SeqRecord(Seq("ACGT"), id="test")
+>>> record2 = SeqRecord(Seq("ACGT"), id="test")
+\end{verbatim}
+
+What happens when you try to compare these ``identical'' records?
+
+%this is not a doctest:
+\begin{verbatim}
+>>> record1 == record2
+...
+\end{verbatim}
+
+Perhaps surprisingly older versions of Biopython would use Python's default object
+comparison for the \verb|SeqRecord|, meaning \verb|record1 == record2| would
+only return \verb|True| if these variables pointed at the same object in memory.
+In this example, \verb|record1 == record2| would have returned \verb|False|
+here!
+
+%this is not a doctest:
+\begin{verbatim}
+>>> record1 == record2 # on old versions of Biopython!
+False
+\end{verbatim}
+
+As of Biopython 1.67, \verb|SeqRecord| comparison like \verb|record1 == record2|
+will instead raise an explicit error to avoid people being caught out by this:
+
+%cont-doctest
+\begin{verbatim}
+>>> record1 == record2
+Traceback (most recent call last):
+...
+NotImplementedError: SeqRecord comparison is deliberately not implemented. Explicitly compare the attributes of interest.
+\end{verbatim}
+
+Instead you should check the attributes you are interested in, for example the
+identifier and the sequence:
+
+%cont-doctest
+\begin{verbatim}
+>>> record1.id == record2.id
+True
+>>> record1.seq == record2.seq
+True
+\end{verbatim}
+
+Beware that comparing complex objects quickly gets complicated (see also
+Section~\ref{sec:seq-comparison}).
+
\section{References}
Another common annotation related to a sequence is a reference to a journal or other published work dealing with the sequence. We have a fairly simple way of representing a Reference in Biopython -- we have a \verb|Bio.SeqFeature.Reference| class that stores the relevant information about a reference as attributes of an object.
@@ -724,8 +781,9 @@ qualifiers:
Key: locus_tag, Value: ['YP_pPCP05']
<BLANKLINE>
\end{verbatim}
+%As above, output is truncated so cannot test this as a doctest:
\begin{verbatim}
->>> print(sub_record.features[20])
+>>> print(sub_record.features[1])
type: CDS
location: [42:480](+)
qualifiers:
diff --git a/Doc/Tutorial/chapter_seqio.tex b/Doc/Tutorial/chapter_seqio.tex
index dd7990c..e544878 100644
--- a/Doc/Tutorial/chapter_seqio.tex
+++ b/Doc/Tutorial/chapter_seqio.tex
@@ -40,7 +40,7 @@ for seq_record in SeqIO.parse("ls_orchid.fasta", "fasta"):
print(len(seq_record))
\end{verbatim}
-The above example is repeated from the introduction in Section~\ref{sec:sequence-parsing}, and will load the orchid DNA sequences in the FASTA format file \href{http://biopython.org/DIST/docs/tutorial/examples/ls_orchid.fasta}{ls\_orchid.fasta}. If instead you wanted to load a GenBank format file like \href{http://biopython.org/DIST/docs/tutorial/examples/ls_orchid.gbk}{ls\_orchid.gbk} then all you need to do is change the filename and the format string:
+The above example is repeated from the introduction in Section~\ref{sec:sequence-parsing}, and will load the orchid DNA sequences in the FASTA format file \href{https://raw.githubusercontent.com/biopython/biopython/master/Doc/examples/ls_orchid.fasta}{ls\_orchid.fasta}. If instead you wanted to load a GenBank format file like \href{https://raw.githubusercontent.com/biopython/biopython/master/Doc/examples/ls_orchid.gbk}{ls\_orchid.gbk} then all you need to do is change the filename and t [...]
\begin{verbatim}
from Bio import SeqIO
@@ -143,7 +143,7 @@ You can of course still use a for loop with a list of \verb|SeqRecord| objects.
\subsection{Extracting data}
The \verb|SeqRecord| object and its annotation structures are described more fully in
-Chapter~\ref{chapter:SeqRecord}. As an example of how annotations are stored, we'll look at the output from parsing the first record in the GenBank file \href{http://biopython.org/DIST/docs/tutorial/examples/ls_orchid.gbk}{ls\_orchid.gbk}.
+Chapter~\ref{chapter:SeqRecord}. As an example of how annotations are stored, we'll look at the output from parsing the first record in the GenBank file \href{https://raw.githubusercontent.com/biopython/biopython/master/Doc/examples/ls_orchid.gbk}{ls\_orchid.gbk}.
\begin{verbatim}
from Bio import SeqIO
@@ -190,7 +190,7 @@ print(first_record.annotations.values())
In general, the annotation values are strings, or lists of strings. One special case is any references in the file get stored as reference objects.
-Suppose you wanted to extract a list of the species from the \href{http://biopython.org/DIST/docs/tutorial/examples/ls_orchid.gbk}{ls\_orchid.gbk} GenBank file. The information we want, \emph{Cypripedium irapeanum}, is held in the annotations dictionary under `source' and `organism', which we can access like this:
+Suppose you wanted to extract a list of the species from the \href{https://raw.githubusercontent.com/biopython/biopython/master/Doc/examples/ls_orchid.gbk}{ls\_orchid.gbk} GenBank file. The information we want, \emph{Cypripedium irapeanum}, is held in the annotations dictionary under `source' and `organism', which we can access like this:
\begin{verbatim}
>>> print(first_record.annotations["source"])
@@ -236,7 +236,7 @@ print(all_species)
Great. That was pretty easy because GenBank files are annotated in a standardised way.
-Now, let's suppose you wanted to extract a list of the species from a FASTA file, rather than the GenBank file. The bad news is you will have to write some code to extract the data you want from the record's description line - if the information is in the file in the first place! Our example FASTA format file \href{http://biopython.org/DIST/docs/tutorial/examples/ls_orchid.fasta}{ls\_orchid.fasta} starts like this:
+Now, let's suppose you wanted to extract a list of the species from a FASTA file, rather than the GenBank file. The bad news is you will have to write some code to extract the data you want from the record's description line - if the information is in the file in the first place! Our example FASTA format file \href{https://raw.githubusercontent.com/biopython/biopython/master/Doc/examples/ls_orchid.fasta}{ls\_orchid.fasta} starts like this:
\begin{verbatim}
>gi|2765658|emb|Z78533.1|CIZ78533 C.irapeanum 5.8S rRNA gene and ITS1 and ITS2 DNA
@@ -428,13 +428,12 @@ so we must use the \verb|Bio.SeqIO.parse()| function:
from Bio import Entrez
from Bio import SeqIO
Entrez.email = "A.N.Other at example.com"
-handle = Entrez.efetch(db="nucleotide", rettype="gb", retmode="text", \
+handle = Entrez.efetch(db="nucleotide", rettype="gb", retmode="text",
id="6273291,6273290,6273289")
for seq_record in SeqIO.parse(handle, "gb"):
- print seq_record.id, seq_record.description[:50] + "..."
- print "Sequence length %i," % len(seq_record),
- print "%i features," % len(seq_record.features),
- print "from: %s" % seq_record.annotations["source"]
+ print("%s %s..." % (seq_record.id, seq_record.description[:50]))
+ print("Sequence length %i, %i features, from: %s"
+ % (len(seq_record), len(seq_record.features), seq_record.annotations["source"]))
handle.close()
\end{verbatim}
@@ -758,8 +757,8 @@ The dictionary-like object from \verb|Bio.SeqIO.index()| gives you each
entry as a \verb|SeqRecord| object. However, it is sometimes useful to
be able to get the original raw data straight from the file. For this
use the \verb|get_raw()| method which takes a
-single argument (the record identifier) and returns a string (extracted
-from the file without modification).
+single argument (the record identifier) and returns a bytes string
+(extracted from the file without modification).
A motivating example is extracting a subset of a records from a large
file where either \verb|Bio.SeqIO.write()| does not (yet) support the
@@ -777,12 +776,15 @@ want to extract just a few records from it:
\begin{verbatim}
>>> from Bio import SeqIO
>>> uniprot = SeqIO.index("uniprot_sprot.dat", "swiss")
->>> handle = open("selected.dat", "w")
+>>> handle = open("selected.dat", "wb")
>>> for acc in ["P33487", "P19801", "P13689", "Q8JZQ5", "Q9TRC7"]:
... handle.write(uniprot.get_raw(acc))
>>> handle.close()
\end{verbatim}
+Note with Python 3 onwards, we have to open the file for writing in
+binary mode because the \verb|get_raw()| method returns bytes strings.
+
There is a longer example in Section~\ref{sec:SeqIO-sort} using the
\verb|SeqIO.index()| function to sort a large sequence file (without
loading everything into memory at once).
@@ -807,40 +809,77 @@ This index file is actually an SQLite3 database.
As an example, consider the GenBank flat file releases from the NCBI FTP site,
\url{ftp://ftp.ncbi.nih.gov/genbank/}, which are gzip compressed GenBank files.
-As of GenBank release $182$, there are $16$ files making up the viral sequences,
-\texttt{gbvrl1.seq}, \ldots, \texttt{gbvrl16.seq}, containing in total almost
-one million records. You can index them like this:
+
+As of GenBank release $210$, there are $38$ files making up the viral sequences,
+\texttt{gbvrl1.seq}, \ldots, \texttt{gbvrl38.seq}, taking about 8GB on disk once
+decompressed, and containing in total nearly two million records.
+
+If you were interested in the viruses, you could download all the virus files
+from the command line very easily with the \texttt{rsync} command, and then
+decompress them with \texttt{gunzip}:
+
+\begin{verbatim}
+# For illustration only, see reduced example below
+$ rsync -avP "ftp.ncbi.nih.gov::genbank/gbvrl*.seq.gz" .
+$ gunzip gbvrl*.seq.gz
+\end{verbatim}
+
+Unless you care about viruses, that's a lot of data to download just for this
+example - so let's download \emph{just} the first four chunks (about 25MB each
+compressed), and decompress them (taking in all about 1GB of space):
\begin{verbatim}
+# Reduced example, download only the first four chunks
+$ curl -O ftp://ftp.ncbi.nih.gov/genbank/gbvrl1.seq.gz
+$ curl -O ftp://ftp.ncbi.nih.gov/genbank/gbvrl2.seq.gz
+$ curl -O ftp://ftp.ncbi.nih.gov/genbank/gbvrl3.seq.gz
+$ curl -O ftp://ftp.ncbi.nih.gov/genbank/gbvrl4.seq.gz
+$ gunzip gbvrl*.seq.gz
+\end{verbatim}
+
+Now, in Python, index these GenBank files as follows:
+
+\begin{verbatim}
+>>> import glob
>>> from Bio import SeqIO
->>> files = ["gbvrl%i.seq" % (i+1) for i in range(16)]
+>>> files = glob.glob("gbvrl*.seq")
+>>> print("%i files to index" % len(files))
+4
>>> gb_vrl = SeqIO.index_db("gbvrl.idx", files, "genbank")
>>> print("%i sequences indexed" % len(gb_vrl))
-958086 sequences indexed
+272960 sequences indexed
\end{verbatim}
-That takes about two minutes to run on my machine. If you rerun it then the
-index file (here \texttt{gbvrl.idx}) is reloaded in under a second. You can
-use the index as a read only Python dictionary - without having to worry
+Indexing the full set of virus GenBank files took about ten minutes on my machine,
+just the first four files took about a minute or so.
+
+However, once done, repeating this will reload the index file \verb|gbvrl.idx|
+in a fraction of a second.
+
+You can use the index as a read only Python dictionary - without having to worry
about which file the sequence comes from, e.g.
\begin{verbatim}
->>> print(gb_vrl["GQ333173.1"].description)
-HIV-1 isolate F12279A1 from Uganda gag protein (gag) gene, partial cds.
+>>> print(gb_vrl[``AB811634.1''].description)
+Equine encephalosis virus NS3 gene, complete cds, isolate: Kimron1.
\end{verbatim}
\subsubsection{Getting the raw data for a record}
Just as with the \verb|Bio.SeqIO.index()| function discussed above in
Section~\ref{sec:seqio-index-getraw}, the dictionary like object also lets you
-get at the raw text of each record:
-
-\begin{verbatim}
->>> print(gb_vrl.get_raw("GQ333173.1"))
-LOCUS GQ333173 459 bp DNA linear VRL 21-OCT-2009
-DEFINITION HIV-1 isolate F12279A1 from Uganda gag protein (gag) gene, partial
- cds.
-ACCESSION GQ333173
+get at the raw bytes of each record:
+
+% TODO - Under Python 3 you'd get the bytes string representation with
+% leading b single-quote, escaped newlines, and closing single-quote:
+%
+% >>> print(gb_vrl.get_raw(``GQ333173.1''))
+% b'LOCUS GQ333173 459 bp DNA linear VRL 21-OCT-2009\nDEFINITION...'
+\begin{verbatim}
+>>> print(gb_vrl.get_raw(``AB811634.1''))
+LOCUS AB811634 723 bp RNA linear VRL 17-JUN-2015
+DEFINITION Equine encephalosis virus NS3 gene, complete cds, isolate: Kimron1.
+ACCESSION AB811634
...
//
\end{verbatim}
@@ -1065,7 +1104,7 @@ solution.
In previous example we used a list of \verb|SeqRecord| objects as input to the \verb|Bio.SeqIO.write()| function, but it will also accept a \verb|SeqRecord| iterator like we get from \verb|Bio.SeqIO.parse()| -- this lets us do file conversion by combining these two functions.
-For this example we'll read in the GenBank format file \href{http://biopython.org/DIST/docs/tutorial/examples/ls_orchid.gbk}{ls\_orchid.gbk} and write it out in FASTA format:
+For this example we'll read in the GenBank format file \href{https://raw.githubusercontent.com/biopython/biopython/master/Doc/examples/ls_orchid.gbk}{ls\_orchid.gbk} and write it out in FASTA format:
\begin{verbatim}
from Bio import SeqIO
diff --git a/Doc/biopdb_faq.lyx b/Doc/biopdb_faq.lyx
new file mode 100644
index 0000000..3f8a79c
--- /dev/null
+++ b/Doc/biopdb_faq.lyx
@@ -0,0 +1,2724 @@
+#LyX 1.3 created this file. For more info see http://www.lyx.org/
+\lyxformat 221
+\textclass article
+\begin_preamble
+% header
+\usepackage{fancyhdr}
+\pagestyle{fancy}
+\lhead{Structural Biopython FAQ}
+\rhead{}
+
+% remove date
+\date{}
+
+% make everything have section numbers
+% Make links between references
+\usepackage{hyperref}
+
+\newif\ifpdf
+\ifx\pdfoutput\undefined
+ \pdffalse
+\else
+ \pdfoutput=1
+ \pdftrue
+\fi
+\ifpdf
+ \hypersetup{colorlinks=true, hyperindex=true, citecolor=red, urlcolor=blue}
+\fi
+\end_preamble
+\language english
+\inputencoding auto
+\fontscheme bookman
+\graphics default
+\paperfontsize default
+\spacing single
+\papersize a4paper
+\paperpackage a4
+\use_geometry 1
+\use_amsmath 0
+\use_natbib 0
+\use_numerical_citations 0
+\paperorientation portrait
+\topmargin 20mm
+\bottommargin 20mm
+\secnumdepth 3
+\tocdepth 3
+\paragraph_separation indent
+\defskip medskip
+\quotes_language english
+\quotes_times 1
+\papercolumns 1
+\papersides 1
+\paperpagestyle default
+
+\layout Title
+
+
+\size huge
+The Biopython
+\newline
+Structural Bioinformatics FAQ
+\layout Author
+
+Thomas Hamelryck
+\layout Author
+
+
+\size normal
+Bioinformatics center
+\newline
+Institute of Molecular Biology
+\newline
+University of Copenhagen
+\newline
+Universitetsparken 15, Bygning 10
+\newline
+DK-2100 K�benhavn �
+\newline
+Denmark
+\newline
+thamelry at binf.ku.dk
+\size default
+
+\newline
+
+\begin_inset LatexCommand \url{http://www.binf.ku.dk/users/thamelry/}
+
+\end_inset
+
+
+\layout Section
+
+Introduction
+\layout Standard
+
+The Biopython Project is an international association of developers of freely
+ available Python (
+\begin_inset LatexCommand \url{http://www.python.org}
+
+\end_inset
+
+) tools for computational molecular biology.
+ Python is an object oriented, interpreted, flexible language that is becoming
+ increasingly popular for scientific computing.
+ Python is easy to learn, has a very clear syntax and can easily be extended
+ with modules written in C, C++ or FORTRAN.
+\layout Standard
+
+The Biopython web site (
+\begin_inset LatexCommand \url{http://www.biopython.org}
+
+\end_inset
+
+) provides an online resource for modules, scripts, and web links for developers
+ of Python-based software for bioinformatics use and research.
+ Basically, the goal of biopython is to make it as easy as possible to use
+ python for bioinformatics by creating high-quality, reusable modules and
+ classes.
+ Biopython features include parsers for various Bioinformatics file formats
+ (BLAST, Clustalw, FASTA, Genbank,...), access to online services (NCBI, Expasy,...),
+ interfaces to common and not-so-common programs (Clustalw, DSSP, MSMS...),
+ a standard sequence class, various clustering modules, a KD tree data structure
+ etc.
+ and even documentation.
+
+\layout Standard
+
+Bio.PDB is a biopython module that focuses on working with crystal structures
+ of biological macromolecules.
+ This document gives a fairly complete overview of Bio.PDB.
+\layout Section
+
+Bio.PDB's installation
+\layout Standard
+
+Bio.PDB is automatically installed as part of Biopython.
+ Biopython can be obtained from
+\begin_inset LatexCommand \url{http://www.biopython.org}
+
+\end_inset
+
+.
+ It runs on many platforms (Linux/Unix, windows, Mac,...).
+\layout Section
+
+Who's using Bio.PDB?
+\layout Standard
+
+Bio.PDB was used in the construction of DISEMBL, a web server that predicts
+ disordered regions in proteins (
+\begin_inset LatexCommand \url{http://dis.embl.de/}
+
+\end_inset
+
+), and COLUMBA, a website that provides annotated protein structures (
+\begin_inset LatexCommand \url{http://www.columba-db.de/}
+
+\end_inset
+
+).
+ Bio.PDB has also been used to perform a large scale search for active sites
+ similarities between protein structures in the PDB (see
+\shape italic
+Proteins Struct.
+ Func.
+ Gen.
+\shape default
+,
+\series bold
+2003
+\series default
+, 51, 96-108), and to develop a new algorithm that identifies linear secondary
+ structure elements (
+\emph on
+BMC Bioinformatics
+\emph default
+,
+\series bold
+2005
+\series default
+, 6, 202,
+\begin_inset LatexCommand \url{http://www.biomedcentral.com/1471-2105/6/202}
+
+\end_inset
+
+).
+
+\layout Standard
+
+Judging from requests for features and information, Bio.PDB is also used
+ by several LPCs (Large Pharmaceutical Companies :-).
+\layout Section
+
+Is there a Bio.PDB reference?
+\layout Standard
+
+Yes, and I'd appreciate it if you would refer to Bio.PDB in publications
+ if you make use of it.
+ The reference is:
+\layout Quote
+
+Hamelryck, T., Manderick, B.
+ (2003) PDB parser and structure class implemented in Python.
+
+\shape italic
+Bioinformatics
+\shape default
+,
+\series bold
+19
+\series default
+, 2308-2310.
+
+\layout Standard
+
+The article can be freely downloaded via the Bioinformatics journal website
+ (
+\begin_inset LatexCommand \url{http://www.binf.ku.dk/users/thamelry/references.html}
+
+\end_inset
+
+).
+ I welcome e-mails telling me what you are using Bio.PDB for.
+ Feature requests are welcome too.
+\layout Section
+
+How well tested is Bio.PDB?
+\layout Standard
+
+Pretty well, actually.
+ Bio.PDB has been extensively tested on nearly 5500 structures from the PDB
+ - all structures seemed to be parsed correctly.
+ More details can be found in the Bio.PDB Bioinformatics article.
+ Bio.PDB has been used/is being used in many research projects as a reliable
+ tool.
+ In fact, I'm using Bio.PDB almost daily for research purposes and continue
+ working on improving it and adding new features.
+\layout Section
+
+How fast is it?
+\layout Standard
+
+The
+\family typewriter
+PDBParser
+\family default
+ performance was tested on about 800 structures (each belonging to a unique
+ SCOP superfamily).
+ This takes about 20 minutes, or on average 1.5 seconds per structure.
+ Parsing the structure of the large ribosomal subunit (1FKK), which contains
+ about 64000 atoms, takes 10 seconds on a 1000 MHz PC.
+ In short: it's more than fast enough for many applications.
+\layout Section
+
+Why should I use Bio.PDB?
+\layout Standard
+
+Bio.PDB might be exactly what you want, and then again it might not.
+ If you are interested in data mining the PDB header, you might want to
+ look elsewhere because there is only limited support for this.
+ If you look for a powerful, complete data structure to access the atomic
+ data Bio.PDB is probably for you.
+
+\layout Section
+
+Usage
+\layout Subsection
+
+General questions
+\layout Subsubsection*
+
+Importing Bio.PDB
+\layout Standard
+
+That's simple:
+\layout LyX-Code
+
+from Bio.PDB import *
+\layout Subsubsection*
+
+Is there support for molecular graphics?
+\layout Standard
+
+Not directly, mostly since there are quite a few Python based/Python aware
+ solutions already, that can potentially be used with Bio.PDB.
+ My choice is Pymol, BTW (I've used this successfully with Bio.PDB, and there
+ will probably be specific PyMol modules in Bio.PDB soon/some day).
+ Python based/aware molecular graphics solutions include:
+\layout Itemize
+
+PyMol:
+\begin_inset LatexCommand \url{http://pymol.sourceforge.net/}
+
+\end_inset
+
+
+\layout Itemize
+
+Chimera:
+\begin_inset LatexCommand \url{http://www.cgl.ucsf.edu/chimera/}
+
+\end_inset
+
+
+\layout Itemize
+
+PMV:
+\begin_inset LatexCommand \url{http://www.scripps.edu/~sanner/python/}
+
+\end_inset
+
+
+\layout Itemize
+
+Coot:
+\begin_inset LatexCommand \url{http://www.ysbl.york.ac.uk/~emsley/coot/}
+
+\end_inset
+
+
+\layout Itemize
+
+CCP4mg:
+\begin_inset LatexCommand \url{http://www.ysbl.york.ac.uk/~lizp/molgraphics.html}
+
+\end_inset
+
+
+\layout Itemize
+
+mmLib:
+\begin_inset LatexCommand \url{http://pymmlib.sourceforge.net/}
+
+\end_inset
+
+
+\layout Itemize
+
+VMD:
+\begin_inset LatexCommand \url{http://www.ks.uiuc.edu/Research/vmd/}
+
+\end_inset
+
+
+\layout Itemize
+
+MMTK:
+\begin_inset LatexCommand \url{http://starship.python.net/crew/hinsen/MMTK/}
+
+\end_inset
+
+
+\layout Standard
+
+I'd be crazy to write another molecular graphics application (been there
+ - done that, actually :-).
+\layout Subsection
+
+Input/output
+\layout Subsubsection*
+
+How do I create a structure object from a PDB file?
+\layout Standard
+
+First, create a
+\family typewriter
+ PDBParser
+\family default
+object:
+\layout LyX-Code
+
+parser=PDBParser()
+\layout Standard
+
+Then, create a structure object from a PDB file in the following way (the
+ PDB file in this case is called '1FAT.pdb', 'PHA-L' is a user defined name
+ for the structure):
+\layout LyX-Code
+
+structure=parser.get_structure('PHA-L', '1FAT.pdb')
+\layout Subsubsection*
+
+How do I create a structure object from an mmCIF file?
+\layout Standard
+
+Similarly to the case the case of PDB files, first create an
+\family typewriter
+MMCIFParser
+\family default
+ object:
+\layout LyX-Code
+
+parser=MMCIFParser()
+\layout Standard
+
+Then use this parser to create a structure object from the mmCIF file:
+\layout LyX-Code
+
+structure=parser.get_structure('PHA-L', '1FAT.cif')
+\layout Subsubsection*
+
+...and what about the new PDB XML format?
+\layout Standard
+
+That's not yet supported, but I'm definitely planning to support that in
+ the future (it's not a lot of work).
+ Contact me if you need this, it might encourage me :-).
+\layout Subsubsection*
+
+I'd like to have some more low level access to an mmCIF file...
+\layout Standard
+
+You got it.
+ You can create a python dictionary that maps all mmCIF tags in an mmCIF
+ file to their values.
+ If there are multiple values (like in the case of tag
+\family typewriter
+_atom_site.Cartn_y
+\family default
+, which holds the y coordinates of all atoms), the tag is mapped to a list
+ of values.
+ The dictionary is created from the mmCIF file as follows:
+\layout LyX-Code
+
+mmcif_dict=MMCIF2Dict('1FAT.cif')
+\layout Standard
+
+Example: get the solvent content from an mmCIF file:
+\layout LyX-Code
+
+sc=mmcif_dict['_exptl_crystal.density_percent_sol']
+\layout Standard
+
+Example: get the list of the y coordinates of all atoms
+\layout LyX-Code
+
+y_list=mmcif_dict['_atom_site.Cartn_y']
+\layout Subsubsection*
+
+Can I access the header information?
+\layout Standard
+
+Thanks to Christian Rother you can access some information from the PDB
+ header.
+ Note however that many PDB files contain headers with incomplete or erroneous
+ information.
+ Many of the errors have been fixed in the equivalent mmCIF files.
+
+\emph on
+Hence, if you are interested in the header information, it is a good idea
+ to extract information from mmCIF files using the
+\family typewriter
+MMCIF2Dict
+\family default
+ tool described above, instead of parsing the PDB header.
+
+\layout Standard
+
+Now that is clarified, let's return to parsing the PDB header.
+ The structure object has an attribute called
+\family typewriter
+header
+\family default
+ which is a python dictionary that maps header records to their values.
+\layout Standard
+
+Example:
+\layout LyX-Code
+
+resolution=structure.header['resolution']
+\layout LyX-Code
+
+keywords=structure.header['keywords']
+\layout Standard
+
+The available keys are
+\family typewriter
+name, head, deposition_\SpecialChar \-
+date, release_\SpecialChar \-
+date, structure_\SpecialChar \-
+method, resolution,
+ structure_\SpecialChar \-
+reference
+\family default
+(maps to a list of references),
+\family typewriter
+journal_\SpecialChar \-
+reference, author
+\family default
+ and
+\family typewriter
+compound
+\family default
+ (maps to a dictionary with various information about the crystallized compound).
+\layout Standard
+
+The dictionary can also be created without creating a
+\family typewriter
+Structure
+\family default
+ object, ie.
+ directly from the PDB file:
+\layout LyX-Code
+
+file=open(filename,'r')
+\layout LyX-Code
+
+header_dict=parse_pdb_header(file)
+\layout LyX-Code
+
+file.close()
+\layout Subsubsection*
+
+Can I use Bio.PDB with NMR structures (ie.
+ with more than one model)?
+\layout Standard
+
+Sure.
+ Many PDB parsers assume that there is only one model, making them all but
+ useless for NMR structures.
+ The design of the
+\family typewriter
+Structure
+\family default
+ object makes it easy to handle PDB files with more than one model (see
+ section
+\begin_inset LatexCommand \ref{sub:The-Structure-object}
+
+\end_inset
+
+).
+
+\layout Subsubsection*
+
+How do I download structures from the PDB?
+\layout Standard
+
+This can be done using the
+\family typewriter
+PDBList
+\family default
+ object, using the
+\family typewriter
+retrieve_pdb_file
+\family default
+ method.
+ The argument for this method is the PDB identifier of the structure.
+\layout LyX-Code
+
+pdbl=PDBList()
+\layout LyX-Code
+
+pdbl.retrieve_pdb_file('1FAT')
+\layout Standard
+
+The
+\family typewriter
+PDBList
+\family default
+ class can also be used as a command-line tool:
+\layout LyX-Code
+
+python PDBList.py 1fat
+\layout Standard
+
+The downloaded file will be called
+\family typewriter
+pdb1fat.ent
+\family default
+ and stored in the current working directory.
+ Note that the
+\family typewriter
+retrieve_pdb_file
+\family default
+ method also has an optional argument
+\family typewriter
+pdir
+\family default
+ that specifies a specific directory in which to store the downloaded PDB
+ files.
+
+\layout Standard
+
+The
+\family typewriter
+retrieve_pdb_file
+\family default
+ method also has some options to specify the compression format used for
+ the download, and the program used for local decompression (default
+\family typewriter
+.Z
+\family default
+ format and
+\family typewriter
+gunzip
+\family default
+).
+ In addition, the PDB ftp site can be specified upon creation of the
+\family typewriter
+PDBList
+\family default
+ object.
+ By default, the RCSB PDB server (
+\begin_inset LatexCommand \url{ftp://ftp.rcsb.org/pub/pdb/data/structures/divided/pdb/}
+
+\end_inset
+
+) is used.
+ See the API documentation for more details.
+ Thanks again to Kristian Rother for donating this module.
+\layout Subsubsection*
+
+How do I download the entire PDB?
+\layout Standard
+
+The following commands will store all PDB files in the
+\family typewriter
+/data/pdb
+\family default
+ directory:
+\layout LyX-Code
+
+python PDBList.py all /data/pdb
+\layout LyX-Code
+
+python PDBList.py all /data/pdb -d
+\layout Standard
+\noindent
+The API method for this is called
+\family typewriter
+download_entire_pdb
+\family default
+.
+ Adding the
+\family typewriter
+-d
+\family default
+ option will store all files in the same directory.
+ Otherwise, they are sorted into PDB-style subdirectories according to their
+ PDB ID's.
+ Depending on the traffic, a complete download will take 2-4 days.
+
+\layout Subsubsection*
+
+How do I keep a local copy of the PDB up-to-date?
+\layout Standard
+
+This can also be done using the
+\family typewriter
+PDBList
+\family default
+ object.
+ One simply creates a
+\family typewriter
+PDBList
+\family default
+ object (specifying the directory where the local copy of the PDB is present)
+ and calls the
+\family typewriter
+update_pdb
+\family default
+ method:
+\layout LyX-Code
+
+pl=PDBList(pdb='/data/pdb')
+\layout LyX-Code
+
+pl.update_pdb()
+\layout Standard
+
+One can of course make a weekly
+\family typewriter
+cronjob
+\family default
+out of this to keep the local copy automatically up-to-date.
+ The PDB ftp site can also be specified (see API documentation).
+\layout Standard
+
+
+\family typewriter
+PDBList
+\family default
+ has some additional methods that can be of use.
+ The
+\family typewriter
+get_all_obsolete
+\family default
+ method can be used to get a list of all obsolete PDB entries.
+ The
+\family typewriter
+changed_this_week
+\family default
+ method can be used to obtain the entries that were added, modified or obsoleted
+ during the current week.
+ For more info on the possibilities of
+\family typewriter
+PDBList
+\family default
+, see the API documentation.
+\layout Subsubsection*
+
+What about all those buggy PDB files?
+\layout Standard
+
+It is well known that many PDB files contain semantic errors (I'm not talking
+ about the structures themselves know, but their representation in PDB files).
+ Bio.PDB tries to handle this in two ways.
+ The PDBParser object can behave in two ways: a restrictive way and a permissive
+ way (THIS IS NOW THE DEFAULT).
+ The restrictive way used to be the default, but people seemed to think
+ that Bio.PDB 'crashed' due to a bug (hah!), so I changed it.
+ If you ever encounter a real bug, please tell me immediately!
+\layout Standard
+
+Example:
+\layout LyX-Code
+
+# Permissive parser
+\layout LyX-Code
+
+parser=PDBParser(PERMISSIVE=1)
+\layout LyX-Code
+
+parser=PDBParser() # The same (default)
+\layout LyX-Code
+
+# Strict parser
+\layout LyX-Code
+
+strict_parser=PDBParser(PERMISSIVE=0)
+\layout Standard
+
+In the permissive state (DEFAULT), PDB files that obviously contain errors
+ are 'corrected' (ie.
+ some residues or atoms are left out).
+ These errors include:
+\layout Itemize
+
+Multiple residues with the same identifier
+\layout Itemize
+
+Multiple atoms with the same identifier (taking into account the altloc
+ identifier)
+\layout Standard
+
+These errors indicate real problems in the PDB file (for details see the
+ Bioinformatics article).
+ In the restrictive state, PDB files with errors cause an exception to occur.
+ This is useful to find errors in PDB files.
+\layout Standard
+
+Some errors however are automatically corrected.
+ Normally each disordered atom should have a non-blanc altloc identifier.
+ However, there are many structures that do not follow this convention,
+ and have a blank and a non-blank identifier for two disordered positions
+ of the same atom.
+ This is automatically interpreted in the right way.
+\layout Standard
+
+Sometimes a structure contains a list of residues belonging to chain A,
+ followed by residues belonging to chain B, and again followed by residues
+ belonging to chain A, i.e.
+ the chains are 'broken'.
+ This is also correctly interpreted.
+\layout Subsubsection*
+
+Can I write PDB files?
+\layout Standard
+
+Use the PDBIO class for this.
+ It's easy to write out specific parts of a structure too, of course.
+\layout Standard
+
+Example: saving a structure
+\layout LyX-Code
+
+io=PDBIO()
+\layout LyX-Code
+
+io.set_structure(s)
+\layout LyX-Code
+
+io.save('out.pdb')
+\layout Standard
+
+If you want to write out a part of the structure, make use of the
+\family typewriter
+Select
+\family default
+class (also in
+\family typewriter
+PDBIO
+\family default
+).
+ Select has four methods:
+\layout LyX-Code
+
+accept_model(model)
+\layout LyX-Code
+
+accept_chain(chain)
+\layout LyX-Code
+
+accept_residue(residue)
+\layout LyX-Code
+
+accept_atom(atom)
+\layout Standard
+
+By default, every method returns 1 (which means the model/\SpecialChar \-
+chain/\SpecialChar \-
+residue/\SpecialChar \-
+atom
+ is included in the output).
+ By subclassing
+\family typewriter
+Select
+\family default
+ and returning 0 when appropriate you can exclude models, chains, etc.
+ from the output.
+ Cumbersome maybe, but very powerful.
+ The following code only writes out glycine residues:
+\layout LyX-Code
+
+class GlySelect(Select):
+\layout LyX-Code
+
+ def accept_residue(self, residue):
+\layout LyX-Code
+
+ if residue.get_name()=='GLY':
+\layout LyX-Code
+
+ return 1
+\layout LyX-Code
+
+ else:
+\layout LyX-Code
+
+ return 0
+\layout LyX-Code
+
+\layout LyX-Code
+
+io=PDBIO()
+\layout LyX-Code
+
+io.set_structure(s)
+\layout LyX-Code
+
+io.save('gly_only.pdb', GlySelect())
+\layout Standard
+
+If this is all too complicated for you, the
+\family typewriter
+Dice
+\family default
+module contains a handy
+\family typewriter
+extract
+\family default
+ function that writes out all residues in a chain between a start and end
+ residue.
+
+\layout Subsubsection*
+
+Can I write mmCIF files?
+\layout Standard
+
+No, and I also don't have plans to add that functionality soon (or ever
+ - I don't need it at all, and it's a lot of work, plus no-one has ever
+ asked for it).
+ People who want to add this can contact me.
+\layout Subsection
+
+The Structure object
+\begin_inset LatexCommand \label{sub:The-Structure-object}
+
+\end_inset
+
+
+\layout Subsubsection*
+
+What's the overall layout of a Structure object?
+\layout Standard
+
+The
+\family typewriter
+Structure
+\family default
+ object follows the so-called
+\family typewriter
+SMCRA
+\family default
+ (Structure/\SpecialChar \-
+Model/\SpecialChar \-
+Chain/\SpecialChar \-
+Residue/\SpecialChar \-
+Atom) architecture :
+\layout Itemize
+
+A structure consists of models
+\layout Itemize
+
+A model consists of chains
+\layout Itemize
+
+A chain consists of residues
+\layout Itemize
+
+A residue consists of atoms
+\layout Standard
+
+This is the way many structural biologists/bioinformaticians think about
+ structure, and provides a simple but efficient way to deal with structure.
+ Additional stuff is essentially added when needed.
+ A UML diagram of the
+\family typewriter
+Structure
+\family default
+ object (forget about the
+\family typewriter
+Disordered
+\family default
+ classes for now) is shown in Fig.
+
+\begin_inset LatexCommand \ref{cap:SMCRA}
+
+\end_inset
+
+.
+\layout Standard
+
+
+\begin_inset Float figure
+placement tbh
+wide false
+collapsed false
+
+\layout Standard
+\align center
+
+\begin_inset Graphics
+ filename images/smcra.png
+ lyxscale 50
+ width 100mm
+ keepAspectRatio
+
+\end_inset
+
+
+\layout Caption
+
+
+\begin_inset LatexCommand \label{cap:SMCRA}
+
+\end_inset
+
+UML diagram of SMCRA architecture of the
+\family typewriter
+Structure
+\family default
+object.
+ Full lines with diamonds denote aggregation, full lines with arrows denote
+ referencing, full lines with triangles denote inheritance and dashed lines
+ with triangles denote interface realization.
+
+\end_inset
+
+
+\layout Subsubsection*
+
+How do I navigate through a Structure object?
+\layout Standard
+
+The following code iterates through all atoms of a structure:
+\layout LyX-Code
+
+p=PDBParser()
+\layout LyX-Code
+
+structure=p.get_structure('X', 'pdb1fat.ent')
+\layout LyX-Code
+
+for model in structure:
+\layout LyX-Code
+
+ for chain in model:
+\layout LyX-Code
+
+ for residue in chain:
+\layout LyX-Code
+
+ for atom in residue:
+\layout LyX-Code
+
+ print atom
+\layout Standard
+
+There are also some shortcuts:
+\layout LyX-Code
+
+# Iterate over all atoms in a structure
+\layout LyX-Code
+
+for atom in structure.get_atoms():
+\layout LyX-Code
+
+ print atom
+\layout LyX-Code
+
+# Iterate over all residues in a model
+\layout LyX-Code
+
+for residue in model.get_residues():
+\layout LyX-Code
+
+ print residue
+\layout Standard
+
+Structures, models, chains, residues and atoms are called
+\family typewriter
+Entities
+\family default
+ in Biopython.
+ You can always get a parent
+\family typewriter
+Entity
+\family default
+ from a child
+\family typewriter
+Entity
+\family default
+, eg.:
+\layout LyX-Code
+
+residue=atom.get_parent()
+\layout LyX-Code
+
+chain=residue.get_parent()
+\layout Standard
+
+You can also test wether an
+\family typewriter
+Entity
+\family default
+ has a certain child using the
+\family typewriter
+has_id
+\family default
+ method.
+\layout Subsubsection*
+
+Can I do that a bit more conveniently?
+\layout Standard
+
+You can do things like:
+\layout LyX-Code
+
+atoms=structure.get_atoms()
+\layout LyX-Code
+
+residue=structure.get_residues()
+\layout LyX-Code
+
+atoms=chain.get_atoms()
+\layout Standard
+
+You can also use the
+\family typewriter
+Selection.unfold_entities
+\family default
+ function:
+\layout LyX-Code
+
+# Get all residues from a structure
+\layout LyX-Code
+
+res_list=Selection.unfold_entities(structure, 'R')
+\layout LyX-Code
+
+# Get all atoms from a chain
+\layout LyX-Code
+
+atom_list=Selection.unfold_entities(chain, 'A')
+\layout Standard
+
+Obviously,
+\family typewriter
+A=atom, R=residue, C=chain, M=model, S=structure
+\family default
+.
+ You can use this to go up in the hierarchy, eg.
+\begin_inset ERT
+status Collapsed
+
+\layout Standard
+
+\backslash
+
+\end_inset
+
+ to get a list of (unique)
+\family typewriter
+Residue
+\family default
+ or
+\family typewriter
+Chain
+\family default
+ parents from a list of
+\family typewriter
+Atoms
+\family default
+:
+\layout LyX-Code
+
+residue_list=Selection.unfold_entities(atom_list, 'R')
+\layout LyX-Code
+
+chain_list=Selection.unfold_entities(atom_list, 'C')
+\layout Standard
+
+For more info, see the API documentation.
+\layout Subsubsection*
+
+How do I extract a specific
+\family typewriter
+Atom/\SpecialChar \-
+Residue/\SpecialChar \-
+Chain/\SpecialChar \-
+Model
+\family default
+ from a Structure?
+\layout Standard
+
+Easy.
+ Here are some examples:
+\layout LyX-Code
+
+model=structure[0]
+\layout LyX-Code
+
+chain=model['A']
+\layout LyX-Code
+
+residue=chain[100]
+\layout LyX-Code
+
+atom=residue['CA']
+\layout Standard
+
+Note that you can use a shortcut:
+\layout LyX-Code
+
+atom=structure[0]['A'][100]['CA']
+\layout Subsubsection*
+
+What is a model id?
+\layout Standard
+
+The model id is an integer which denotes the rank of the model in the PDB/mmCIF
+ file.
+ The model is starts at 0.
+ Crystal structures generally have only one model (with id 0), while NMR
+ files usually have several models.
+\layout Subsubsection*
+
+What is a chain id?
+\layout Standard
+
+The chain id is specified in the PDB/mmCIF file, and is a single character
+ (typically a letter).
+
+\layout Subsubsection*
+
+What is a residue id?
+\layout Standard
+
+This is a bit more complicated, due to the clumsy PDB format.
+ A residue id is a tuple with three elements:
+\layout Itemize
+
+The
+\series bold
+hetero-flag
+\series default
+: this is
+\family typewriter
+ 'H_'
+\family default
+ plus the name of the hetero-residue (eg.
+
+\family typewriter
+ 'H_GLC'
+\family default
+ in the case of a glucose molecule), or
+\family typewriter
+'W'
+\family default
+ in the case of a water molecule.
+\layout Itemize
+
+The
+\series bold
+sequence identifier
+\series default
+ in the chain, eg.
+ 100
+\layout Itemize
+
+The
+\series bold
+insertion code
+\series default
+, eg.
+ 'A'.
+ The insertion code is sometimes used to preserve a certain desirable residue
+ numbering scheme.
+ A Ser 80 insertion mutant (inserted e.g.
+ between a Thr 80 and an Asn 81 residue) could e.g.
+ have sequence identifiers and insertion codes as follows: Thr 80 A, Ser
+ 80 B, Asn 81.
+ In this way the residue numbering scheme stays in tune with that of the
+ wild type structure.
+\layout Standard
+
+The id of the above glucose residue would thus be
+\family typewriter
+('H_GLC', 100, 'A')
+\family default
+.
+ If the hetero-flag and insertion code are blanc, the sequence identifier
+ alone can be used:
+\layout LyX-Code
+
+# Full id
+\layout LyX-Code
+
+residue=chain[(' ', 100, ' ')]
+\layout LyX-Code
+
+# Shortcut id
+\layout LyX-Code
+
+residue=chain[100]
+\layout Standard
+
+The reason for the hetero-flag is that many, many PDB files use the same
+ sequence identifier for an amino acid and a hetero-residue or a water,
+ which would create obvious problems if the hetero-flag was not used.
+
+\layout Subsubsection*
+
+What is an atom id?
+\layout Standard
+
+The atom id is simply the atom name (eg.
+
+\family typewriter
+'CA'
+\family default
+).
+ In practice, the atom name is created by stripping all spaces from the
+ atom name in the PDB file.
+
+\layout Standard
+
+However, in PDB files, a space can be part of an atom name.
+ Often, calcium atoms are called
+\family typewriter
+'CA..'
+\family default
+ in order to distinguish them from C
+\begin_inset Formula $\alpha$
+\end_inset
+
+ atoms (which are called
+\family typewriter
+'.CA.'
+\family default
+).
+ In cases were stripping the spaces would create problems (ie.
+ two atoms called
+\family typewriter
+ 'CA'
+\family default
+ in the same residue) the spaces are kept.
+\layout Subsubsection*
+
+How is disorder handled?
+\layout Standard
+
+This is one of the strong points of Bio.PDB.
+ It can handle both disordered atoms and point mutations (ie.
+ a Gly and an Ala residue in the same position).
+
+\layout Standard
+
+Disorder should be dealt with from two points of view: the atom and the
+ residue points of view.
+ In general, I have tried to encapsulate all the complexity that arises
+ from disorder.
+ If you just want to loop over all C
+\begin_inset Formula $\alpha$
+\end_inset
+
+ atoms, you do not care that some residues have a disordered side chain.
+ On the other hand it should also be possible to represent disorder completely
+ in the data structure.
+ Therefore, disordered atoms or residues are stored in special objects that
+ behave as if there is no disorder.
+ This is done by only representing a subset of the disordered atoms or residues.
+ Which subset is picked (e.g.
+ which of the two disordered OG side chain atom positions of a Ser residue
+ is used) can be specified by the user.
+\layout Standard
+
+
+\series bold
+Disordered atom positions
+\series default
+ are represented by ordinary
+\family typewriter
+Atom
+\family default
+ objects, but all
+\family typewriter
+Atom
+\family default
+ objects that represent the same physical atom are stored in a
+\family typewriter
+Disordered\SpecialChar \-
+Atom
+\family default
+ object (see Fig.
+
+\begin_inset LatexCommand \ref{cap:SMCRA}
+
+\end_inset
+
+).
+ Each
+\family typewriter
+Atom
+\family default
+ object in a
+\family typewriter
+Disordered\SpecialChar \-
+Atom
+\family default
+ object can be uniquely indexed using its altloc specifier.
+ The
+\family typewriter
+Disordered\SpecialChar \-
+Atom
+\family default
+ object forwards all uncaught method calls to the selected Atom object,
+ by default the one that represents the atom with the highest occupancy.
+ The user can of course change the selected
+\family typewriter
+Atom
+\family default
+object, making use of its altloc specifier.
+ In this way atom disorder is represented correctly without much additional
+ complexity.
+ In other words, if you are not interested in atom disorder, you will not
+ be bothered by it.
+\layout Standard
+
+Each disordered atom has a characteristic altloc identifier.
+ You can specify that a
+\family typewriter
+Disordered\SpecialChar \-
+Atom
+\family default
+ object should behave like the
+\family typewriter
+Atom
+\family default
+ object associated with a specific altloc identifier:
+\layout LyX-Code
+
+atom.disordered_select('A') # select altloc A atom
+\layout LyX-Code
+
+atom.disordered_select('B') # select altloc B atom
+\layout Standard
+
+A special case arises when disorder is due to
+\series bold
+point mutations
+\series default
+, i.e.
+ when two or more point mutants of a polypeptide are present in the crystal.
+ An example of this can be found in PDB structure 1EN2.
+\layout Standard
+
+Since these residues belong to a different residue type (e.g.
+ let's say Ser 60 and Cys 60) they should not be stored in a single
+\family typewriter
+Residue
+\family default
+ object as in the common case.
+ In this case, each residue is represented by one
+\family typewriter
+Residue
+\family default
+ object, and both
+\family typewriter
+Residue
+\family default
+ objects are stored in a single
+\family typewriter
+Disordered\SpecialChar \-
+Residue
+\family default
+ object (see Fig.
+
+\begin_inset LatexCommand \ref{cap:SMCRA}
+
+\end_inset
+
+).
+\layout Standard
+
+The
+\family typewriter
+Dis\SpecialChar \-
+ordered\SpecialChar \-
+Residue
+\family default
+ object forwards all un\SpecialChar \-
+caught methods to the selected
+\family typewriter
+Residue
+\family default
+ object (by default the last
+\family typewriter
+Residue
+\family default
+ object added), and thus behaves like an ordinary residue.
+ Each
+\family typewriter
+Residue
+\family default
+ object in a
+\family typewriter
+Disordered\SpecialChar \-
+Residue
+\family default
+ object can be uniquely identified by its residue name.
+ In the above example, residue Ser 60 would have id 'SER' in the
+\family typewriter
+Disordered\SpecialChar \-
+Residue
+\family default
+ object, while residue Cys 60 would have id 'CYS'.
+ The user can select the active
+\family typewriter
+Residue
+\family default
+ object in a
+\family typewriter
+Disordered\SpecialChar \-
+Residue
+\family default
+ object via this id.
+\layout Standard
+
+Example: suppose that a chain has a point mutation at position 10, consisting
+ of a Ser and a Cys residue.
+ Make sure that residue 10 of this chain behaves as the Cys residue.
+\layout LyX-Code
+
+residue=chain[10]
+\layout LyX-Code
+
+residue.disordered_select('CYS')
+\layout Standard
+
+In addition, you can get a list of all
+\family typewriter
+Atom
+\family default
+ objects (ie.
+ all
+\family typewriter
+DisorderedAtom
+\family default
+ objects are 'unpacked' to their individual
+\family typewriter
+Atom
+\family default
+ objects) using the
+\family typewriter
+get_unpacked_list
+\family default
+ method of a
+\family typewriter
+(Disordered)\SpecialChar \-
+Residue
+\family default
+ object.
+\layout Subsubsection*
+
+Can I sort residues in a chain somehow?
+\layout Standard
+
+Yes, kinda, but I'm waiting for a request for this feature to finish it
+ :-).
+\layout Subsubsection*
+
+How are ligands and solvent handled?
+\layout Standard
+
+See 'What is a residue id?'.
+\layout Subsubsection*
+
+What about B factors?
+\layout Standard
+
+Well, yes! Bio.PDB supports isotropic and anisotropic B factors, and also
+ deals with standard deviations of anisotropic B factor if present (see
+
+\begin_inset LatexCommand \ref{sub:Analysis}
+
+\end_inset
+
+).
+\layout Subsubsection*
+
+What about standard deviation of atomic positions?
+\layout Standard
+
+Yup, supported.
+ See section
+\begin_inset LatexCommand \ref{sub:Analysis}
+
+\end_inset
+
+.
+\layout Subsubsection*
+
+I think the SMCRA data structure is not flexible/\SpecialChar \-
+sexy/\SpecialChar \-
+whatever enough...
+\layout Standard
+
+Sure, sure.
+ Everybody is always coming up with (mostly vaporware or partly implemented)
+ data structures that handle all possible situations and are extensible
+ in all thinkable (and unthinkable) ways.
+ The prosaic truth however is that 99.9% of people using (and I mean really
+ using!) crystal structures think in terms of models, chains, residues and
+ atoms.
+ The philosophy of Bio.PDB is to provide a reasonably fast, clean, simple,
+ but complete data structure to access structure data.
+ The proof of the pudding is in the eating.
+\layout Standard
+
+Moreover, it is quite easy to build more specialised data structures on
+ top of the
+\family typewriter
+Structure
+\family default
+ class (eg.
+ there's a
+\family typewriter
+Polypeptide
+\family default
+ class).
+ On the other hand, the
+\family typewriter
+Structure
+\family default
+object is built using a Parser/\SpecialChar \-
+Consumer approach (called
+\family typewriter
+PDBParser/\SpecialChar \-
+MMCIFParser
+\family default
+ and
+\family typewriter
+Structure\SpecialChar \-
+Builder
+\family default
+, respectively).
+ One can easily re-use the PDB/mmCIF parsers by implementing a specialised
+
+\family typewriter
+Structure\SpecialChar \-
+Builder
+\family default
+ class.
+ It is of course also trivial to add support for new file formats by writing
+ new parsers.
+\layout Subsection
+
+
+\begin_inset LatexCommand \label{sub:Analysis}
+
+\end_inset
+
+Analysis
+\layout Subsubsection*
+
+How do I extract information from an
+\family typewriter
+Atom
+\family default
+ object?
+\layout Standard
+
+Using the following methods:
+\layout LyX-Code
+
+a.get_name() # atom name (spaces stripped, e.g.
+ 'CA')
+\layout LyX-Code
+
+a.get_id() # id (equals atom name)
+\layout LyX-Code
+
+a.get_coord() # atomic coordinates
+\layout LyX-Code
+
+a.get_vector() # atomic coordinates as Vector object
+\layout LyX-Code
+
+a.get_bfactor() # isotropic B factor
+\layout LyX-Code
+
+a.get_occupancy() # occupancy
+\layout LyX-Code
+
+a.get_altloc() # alternative location specifier
+\layout LyX-Code
+
+a.get_sigatm() # std.
+ dev.
+ of atomic parameters
+\layout LyX-Code
+
+a.get_siguij() # std.
+ dev.
+ of anisotropic B factor
+\layout LyX-Code
+
+a.get_anisou() # anisotropic B factor
+\layout LyX-Code
+
+a.get_fullname() # atom name (with spaces, e.g.
+ '.CA.')
+\layout Subsubsection*
+
+How do I extract information from a
+\family typewriter
+Residue
+\family default
+ object?
+\layout Standard
+
+Using the following methods:
+\layout LyX-Code
+
+r.get_resname() # return the residue name (eg.
+ 'GLY')
+\layout LyX-Code
+
+r.is_disordered() # 1 if the residue has disordered atoms
+\layout LyX-Code
+
+r.get_segid() # return the SEGID
+\layout LyX-Code
+
+r.has_id(name) # test if a residue has a certain atom
+\layout Subsubsection*
+
+How do I measure distances?
+\layout Standard
+
+That's simple: the minus operator for atoms has been overloaded to return
+ the distance between two atoms.
+
+\layout Standard
+
+Example:
+\layout LyX-Code
+
+# Get some atoms
+\layout LyX-Code
+
+ca1=residue1['CA']
+\layout LyX-Code
+
+ca2=residue2['CA']
+\layout LyX-Code
+
+# Simply subtract the atoms to get their distance
+\layout LyX-Code
+
+distance=ca1-ca2
+\layout Subsubsection*
+
+How do I measure angles?
+\layout Standard
+
+This can easily be done via the vector representation of the atomic coordinates,
+ and the
+\family typewriter
+calc_angle
+\family default
+ function from the
+\family typewriter
+Vector
+\family default
+ module:
+\layout LyX-Code
+
+vector1=atom1.get_vector()
+\layout LyX-Code
+
+vector2=atom2.get_vector()
+\layout LyX-Code
+
+vector3=atom3.get_vector()
+\layout LyX-Code
+
+angle=calc_angle(vector1, vector2, vector3)
+\layout Subsubsection*
+
+How do I measure torsion angles?
+\layout Standard
+
+Again, this can easily be done via the vector representation of the atomic
+ coordinates, this time using the
+\family typewriter
+calc_dihedral
+\family default
+ function from the
+\family typewriter
+Vector
+\family default
+ module:
+\layout LyX-Code
+
+vector1=atom1.get_vector()
+\layout LyX-Code
+
+vector2=atom2.get_vector()
+\layout LyX-Code
+
+vector3=atom3.get_vector()
+\layout LyX-Code
+
+vector4=atom4.get_vector()
+\layout LyX-Code
+
+angle=calc_dihedral(vector1, vector2, vector3, vector4)
+\layout Subsubsection*
+
+How do I determine atom-atom contacts?
+\layout Standard
+
+Use
+\family typewriter
+NeighborSearch
+\family default
+.
+ This uses a KD tree data structure coded in C++ behind the screens, so
+ it's pretty darn fast (see
+\family typewriter
+Bio.KDTree
+\family default
+).
+\layout Subsubsection*
+
+How do I extract polypeptides from a
+\family typewriter
+Structure
+\family default
+ object?
+\layout Standard
+
+Use
+\family typewriter
+PolypeptideBuilder
+\family default
+.
+ You can use the resulting
+\family typewriter
+Polypeptide
+\family default
+ object to get the sequence as a
+\family typewriter
+Seq
+\family default
+ object or to get a list of C
+\begin_inset Formula $\alpha$
+\end_inset
+
+ atoms as well.
+ Polypeptides can be built using a C-N or a C
+\begin_inset Formula $\alpha$
+\end_inset
+
+-C
+\begin_inset Formula $\alpha$
+\end_inset
+
+ distance criterion.
+\layout Standard
+
+Example:
+\layout LyX-Code
+
+# Using C-N
+\layout LyX-Code
+
+ppb=PPBuilder()
+\layout LyX-Code
+
+for pp in ppb.build_peptides(structure):
+\layout LyX-Code
+
+ print pp.get_sequence()
+\layout LyX-Code
+
+# Using CA-CA
+\layout LyX-Code
+
+ppb=CaPPBuilder()
+\layout LyX-Code
+
+for pp in ppb.build_peptides(structure):
+\layout LyX-Code
+
+ print pp.get_sequence()
+\layout Standard
+
+Note that in the above case only model 0 of the structure is considered
+ by
+\family typewriter
+PolypeptideBuilder
+\family default
+.
+ However, it is possible to use
+\family typewriter
+PolypeptideBuilder
+\family default
+ to build
+\family typewriter
+Polypeptide
+\family default
+ objects from
+\family typewriter
+Model
+\family default
+ and
+\family typewriter
+Chain
+\family default
+ objects as well.
+\layout Subsubsection*
+
+How do I get the sequence of a structure?
+\layout Standard
+
+The first thing to do is to extract all polypeptides from the structure
+ (see previous entry).
+ The sequence of each polypeptide can then easily be obtained from the
+\family typewriter
+Polypeptide
+\family default
+objects.
+ The sequence is represented as a Biopython
+\family typewriter
+Seq
+\family default
+object, and its alphabet is defined by a
+\family typewriter
+ProteinAlphabet
+\family default
+object.
+\layout Standard
+
+Example:
+\layout LyX-Code
+
+>>> seq=polypeptide.get_sequence()
+\layout LyX-Code
+
+>>> print seq
+\layout LyX-Code
+
+Seq('SNVVE...', <class Bio.Alphabet.ProteinAlphabet>)
+\layout Subsubsection*
+
+How do I determine secondary structure?
+\layout Standard
+
+For this functionality, you need to install DSSP (and obtain a license for
+ it - free for academic use, see
+\begin_inset LatexCommand \url{http://www.cmbi.kun.nl/gv/dssp/}
+
+\end_inset
+
+).
+ Then use the
+\family typewriter
+DSSP
+\family default
+ class, which maps
+\family typewriter
+Residue
+\family default
+ objects to their secondary structure (and accessible surface area).
+ The DSSP codes are listed in Table
+\begin_inset LatexCommand \ref{cap:DSSP-codes}
+
+\end_inset
+
+.
+ Note that DSSP (the program, and thus by consequence the class) cannot
+ handle multiple models!
+\layout Standard
+
+
+\begin_inset Float table
+wide false
+collapsed false
+
+\layout Subsubsection*
+
+
+\begin_inset Tabular
+<lyxtabular version="3" rows="9" columns="2">
+<features>
+<column alignment="center" valignment="top" leftline="true" width="0">
+<column alignment="center" valignment="top" leftline="true" rightline="true" width="0">
+<row topline="true" bottomline="true">
+<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
+\begin_inset Text
+
+\layout Standard
+
+Code
+\end_inset
+</cell>
+<cell alignment="center" valignment="top" topline="true" leftline="true" rightline="true" usebox="none">
+\begin_inset Text
+
+\layout Standard
+
+Secondary structure
+\end_inset
+</cell>
+</row>
+<row topline="true">
+<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
+\begin_inset Text
+
+\layout Standard
+
+H
+\end_inset
+</cell>
+<cell alignment="center" valignment="top" topline="true" leftline="true" rightline="true" usebox="none">
+\begin_inset Text
+
+\layout Standard
+
+
+\begin_inset Formula $\alpha$
+\end_inset
+
+-helix
+\end_inset
+</cell>
+</row>
+<row topline="true">
+<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
+\begin_inset Text
+
+\layout Standard
+
+B
+\end_inset
+</cell>
+<cell alignment="center" valignment="top" topline="true" leftline="true" rightline="true" usebox="none">
+\begin_inset Text
+
+\layout Standard
+
+
+\family roman
+\series medium
+\shape up
+\size normal
+\emph off
+\bar no
+\noun off
+\color none
+Isolated
+\family default
+\series default
+\shape default
+\size default
+\emph default
+\bar default
+\noun default
+\color default
+
+\begin_inset Formula $\beta$
+\end_inset
+
+
+\family roman
+\series medium
+\shape up
+\size normal
+\emph off
+\bar no
+\noun off
+\color none
+-bridge resid
+\family default
+\series default
+\shape default
+\size default
+\emph default
+\bar default
+\noun default
+\color default
+ue
+\end_inset
+</cell>
+</row>
+<row topline="true">
+<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
+\begin_inset Text
+
+\layout Standard
+
+E
+\end_inset
+</cell>
+<cell alignment="center" valignment="top" topline="true" leftline="true" rightline="true" usebox="none">
+\begin_inset Text
+
+\layout Standard
+
+
+\family roman
+\series medium
+\shape up
+\size normal
+\emph off
+\bar no
+\noun off
+\color none
+Strand
+\end_inset
+</cell>
+</row>
+<row topline="true">
+<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
+\begin_inset Text
+
+\layout Standard
+
+G
+\end_inset
+</cell>
+<cell alignment="center" valignment="top" topline="true" leftline="true" rightline="true" usebox="none">
+\begin_inset Text
+
+\layout Standard
+
+
+\family roman
+\series medium
+\shape up
+\size normal
+\emph off
+\bar no
+\noun off
+\color none
+3-10 helix
+\end_inset
+</cell>
+</row>
+<row topline="true">
+<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
+\begin_inset Text
+
+\layout Standard
+
+I
+\end_inset
+</cell>
+<cell alignment="center" valignment="top" topline="true" leftline="true" rightline="true" usebox="none">
+\begin_inset Text
+
+\layout Standard
+
+
+\begin_inset Formula $\Pi$
+\end_inset
+
+-
+\family roman
+\series medium
+\shape up
+\size normal
+\emph off
+\bar no
+\noun off
+\color none
+helix
+\end_inset
+</cell>
+</row>
+<row topline="true">
+<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
+\begin_inset Text
+
+\layout Standard
+
+T
+\end_inset
+</cell>
+<cell alignment="center" valignment="top" topline="true" leftline="true" rightline="true" usebox="none">
+\begin_inset Text
+
+\layout Standard
+
+
+\family roman
+\series medium
+\shape up
+\size normal
+\emph off
+\bar no
+\noun off
+\color none
+Turn
+\end_inset
+</cell>
+</row>
+<row topline="true">
+<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
+\begin_inset Text
+
+\layout Standard
+
+S
+\end_inset
+</cell>
+<cell alignment="center" valignment="top" topline="true" leftline="true" rightline="true" usebox="none">
+\begin_inset Text
+
+\layout Standard
+
+
+\family roman
+\series medium
+\shape up
+\size normal
+\emph off
+\bar no
+\noun off
+\color none
+Bend
+\end_inset
+</cell>
+</row>
+<row topline="true" bottomline="true">
+<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
+\begin_inset Text
+
+\layout Standard
+
+-
+\end_inset
+</cell>
+<cell alignment="center" valignment="top" topline="true" leftline="true" rightline="true" usebox="none">
+\begin_inset Text
+
+\layout Standard
+
+
+\family roman
+\series medium
+\shape up
+\size normal
+\emph off
+\bar no
+\noun off
+\color none
+Other
+\end_inset
+</cell>
+</row>
+</lyxtabular>
+
+\end_inset
+
+
+\layout Caption
+
+
+\begin_inset LatexCommand \label{cap:DSSP-codes}
+
+\end_inset
+
+DSSP codes in Bio.PDB.
+\end_inset
+
+
+\layout Subsubsection*
+
+How do I calculate the accessible surface area of a residue?
+\layout Standard
+
+Use the
+\family typewriter
+DSSP
+\family default
+class (see also previous entry).
+ But see also next entry.
+\layout Subsubsection*
+
+How do I calculate residue depth?
+\layout Standard
+
+Residue depth is the average distance of a residue's atoms from the solvent
+ accessible surface.
+ It's a fairly new and very powerful parameterization of solvent accessibility.
+ For this functionality, you need to install Michel Sanner's MSMS program
+ (
+\begin_inset LatexCommand \url{http://www.scripps.edu/pub/olson-web/people/sanner/html/msms_home.html}
+
+\end_inset
+
+).
+ Then use the
+\family typewriter
+ResidueDepth
+\family default
+ class.
+ This class behaves as a dictionary which maps
+\family typewriter
+Residue
+\family default
+ objects to corresponding (residue depth, C
+\begin_inset Formula $\alpha$
+\end_inset
+
+ depth) tuples.
+ The C
+\begin_inset Formula $\alpha$
+\end_inset
+
+ depth is the distance of a residue's C
+\begin_inset Formula $\alpha$
+\end_inset
+
+ atom to the solvent accessible surface.
+
+\layout Standard
+
+Example:
+\layout LyX-Code
+
+model=structure[0]
+\layout LyX-Code
+
+rd=ResidueDepth(model, pdb_file)
+\layout LyX-Code
+
+residue_depth, ca_depth=rd[some_residue]
+\layout Standard
+
+You can also get access to the molecular surface itself (via the
+\family typewriter
+get_surface
+\family default
+ function), in the form of a Numeric python array with the surface points.
+\layout Subsubsection*
+
+How do I calculate Half Sphere Exposure?
+\layout Standard
+
+Half Sphere Exposure (HSE) is a new, 2D measure of solvent exposure.
+ Basically, it counts the number of C
+\begin_inset Formula $\alpha$
+\end_inset
+
+ atoms around a residue in the direction of its side chain, and in the opposite
+ direction (within a radius of 13 �).
+ Despite its simplicity, it outperforms many other measures of solvent exposure.
+ An article describing this novel 2D measure has been submitted.
+\layout Standard
+
+HSE comes in two flavors: HSE
+\begin_inset Formula $\alpha$
+\end_inset
+
+ and HSE
+\begin_inset Formula $\beta$
+\end_inset
+
+.
+ The former only uses the C
+\begin_inset Formula $\alpha$
+\end_inset
+
+ atom positions, while the latter uses the C
+\begin_inset Formula $\alpha$
+\end_inset
+
+ and C
+\begin_inset Formula $\beta$
+\end_inset
+
+ atom positions.
+ The HSE measure is calculated by the
+\family typewriter
+HSExposure
+\family default
+ class, which can also calculate the contact number.
+ The latter class has methods which return dictionaries that map a
+\family typewriter
+Residue
+\family default
+ object to its corresponding HSE
+\begin_inset Formula $\alpha$
+\end_inset
+
+, HSE
+\begin_inset Formula $\beta$
+\end_inset
+
+ and contact number values.
+\layout Standard
+
+Example:
+\layout LyX-Code
+
+model=structure[0]
+\layout LyX-Code
+
+hse=HSExposure()
+\layout LyX-Code
+
+# Calculate HSEalpha
+\layout LyX-Code
+
+exp_ca=hse.calc_hs_exposure(model, option='CA3')
+\layout LyX-Code
+
+# Calculate HSEbeta
+\layout LyX-Code
+
+exp_cb=hse.calc_hs_exposure(model, option='CB')
+\layout LyX-Code
+
+# Calculate classical coordination number exp_fs=hse.calc_fs_exposure(model)
+\layout LyX-Code
+
+# Print HSEalpha for a residue
+\layout LyX-Code
+
+print exp_ca[some_residue]
+\layout Subsubsection*
+
+How do I map the residues of two related structures onto each other?
+\layout Standard
+
+First, create an alignment file in FASTA format, then use the
+\family typewriter
+StructureAlignment
+\family default
+ class.
+ This class can also be used for alignments with more than two structures.
+\layout Subsubsection*
+
+How do I test if a Residue object is an amino acid?
+\layout Standard
+
+Use
+\family typewriter
+is_aa(residue)
+\family default
+.
+\layout Subsubsection*
+
+Can I do vector operations on atomic coordinates?
+\layout Standard
+
+
+\family typewriter
+Atom
+\family default
+ objects return a
+\family typewriter
+Vector
+\family default
+ object representation of the coordinates with the
+\family typewriter
+get_vector
+\family default
+ method.
+
+\family typewriter
+Vector
+\family default
+ implements the full set of 3D vector operations, matrix multiplication
+ (left and right) and some advanced rotation-related operations as well.
+ See also next question.
+\layout Subsubsection*
+
+How do I put a virtual C
+\begin_inset Formula $\beta$
+\end_inset
+
+ on a Gly residue?
+\layout Standard
+
+OK, I admit, this example is only present to show off the possibilities
+ of Bio.PDB's
+\family typewriter
+Vector
+\family default
+ module (though this code is actually used in the
+\family typewriter
+HSExposure
+\family default
+ module, which contains a novel way to parametrize residue exposure - publicatio
+n underway).
+ Suppose that you would like to find the position of a Gly residue's C
+\begin_inset Formula $\beta$
+\end_inset
+
+ atom, if it had one.
+ How would you do that? Well, rotating the N atom of the Gly residue along
+ the C
+\begin_inset Formula $\alpha$
+\end_inset
+
+-C bond over -120 degrees roughly puts it in the position of a virtual C
+\begin_inset Formula $\beta$
+\end_inset
+
+ atom.
+ Here's how to do it, making use of the
+\family typewriter
+rotaxis
+\family default
+ method (which can be used to construct a rotation around a certain axis)
+ of the
+\family typewriter
+Vector
+\family default
+ module:
+\layout LyX-Code
+
+# get atom coordinates as vectors
+\layout LyX-Code
+
+n=residue['N'].get_vector()
+\layout LyX-Code
+
+c=residue['C'].get_vector()
+\layout LyX-Code
+
+ca=residue['CA'].get_vector()
+\layout LyX-Code
+
+# center at origin
+\layout LyX-Code
+
+n=n-ca
+\layout LyX-Code
+
+c=c-ca
+\layout LyX-Code
+
+# find rotation matrix that rotates n
+\layout LyX-Code
+
+# -120 degrees along the ca-c vector
+\layout LyX-Code
+
+rot=rotaxis(-pi*120.0/180.0, c)
+\layout LyX-Code
+
+# apply rotation to ca-n vector
+\layout LyX-Code
+
+cb_at_origin=n.left_multiply(rot)
+\layout LyX-Code
+
+# put on top of ca atom
+\layout LyX-Code
+
+cb=cb_at_origin+ca
+\layout Standard
+
+This example shows that it's possible to do some quite nontrivial vector
+ operations on atomic data, which can be quite useful.
+ In addition to all the usual vector operations (cross (use
+\family typewriter
+**
+\family default
+), and dot (use
+\family typewriter
+*
+\family default
+) product, angle, norm, etc.) and the above mentioned
+\family typewriter
+rotaxis
+\family default
+ function, the
+\family typewriter
+Vector
+\family default
+ module also has methods to rotate (
+\family typewriter
+rotmat
+\family default
+) or reflect (
+\family typewriter
+refmat
+\family default
+) one vector on top of another.
+\layout Subsection
+
+Manipulating the structure
+\layout Subsubsection*
+
+How do I superimpose two structures?
+\layout Standard
+
+Surprisingly, this is done using the
+\family typewriter
+Superimposer
+\family default
+ object.
+ This object calculates the rotation and translation matrix that rotates
+ two lists of atoms on top of each other in such a way that their RMSD is
+ minimized.
+ Of course, the two lists need to contain the same amount of atoms.
+ The
+\family typewriter
+Superimposer
+\family default
+ object can also apply the rotation/translation to a list of atoms.
+ The rotation and translation are stored as a tuple in the
+\family typewriter
+rotran
+\family default
+ attribute of the
+\family typewriter
+Superimposer
+\family default
+ object (note that the rotation is right multiplying!).
+ The RMSD is stored in the
+\family typewriter
+rmsd
+\family default
+ attribute.
+\layout Standard
+
+The algorithm used by
+\family typewriter
+Superimposer
+\family default
+ comes from
+\shape italic
+Matrix computations, 2nd ed.
+ Golub, G.
+ & Van Loan (1989)
+\shape default
+ and makes use of singular value decomposition (this is implemented in the
+ general
+\family typewriter
+Bio.\SpecialChar \-
+SVDSuperimposer
+\family default
+ module).
+\layout Standard
+
+Example:
+\layout LyX-Code
+
+sup=Superimposer()
+\layout LyX-Code
+
+# Specify the atom lists
+\layout LyX-Code
+
+# 'fixed' and 'moving' are lists of Atom objects
+\layout LyX-Code
+
+# The moving atoms will be put on the fixed atoms
+\layout LyX-Code
+
+sup.set_atoms(fixed, moving)
+\layout LyX-Code
+
+# Print rotation/translation/rmsd
+\layout LyX-Code
+
+print sup.rotran
+\layout LyX-Code
+
+print sup.rms
+\layout LyX-Code
+
+# Apply rotation/translation to the moving atoms
+\layout LyX-Code
+
+sup.apply(moving)
+\layout Subsubsection*
+
+How do I superimpose two structures based on their active sites?
+\layout Standard
+
+Pretty easily.
+ Use the active site atoms to calculate the rotation/translation matrices
+ (see above), and apply these to the whole molecule.
+\layout Subsubsection*
+
+Can I manipulate the atomic coordinates?
+\layout Standard
+
+Yes, using the
+\family typewriter
+transform
+\family default
+ method of the
+\family typewriter
+Atom
+\family default
+ object, or directly using the
+\family typewriter
+set_coord
+\family default
+ method.
+\layout Section
+
+Other Structural Bioinformatics modules
+\layout Subsubsection*
+
+Bio.SCOP
+\layout Standard
+
+Info coming soon.
+\layout Subsubsection*
+
+Bio.FSSP
+\layout Standard
+
+Info coming soon.
+\layout Section
+
+You haven't answered my question yet!
+\layout Standard
+
+Woah! It's late and I'm tired, and a glass of excellent
+\shape italic
+Pedro Ximenez
+\shape default
+ sherry is waiting for me.
+ Just drop me a mail, and I'll answer you in the morning (with a bit of
+ luck...).
+\layout Section
+
+Contributors
+\layout Standard
+
+The main author/maintainer of Bio.PDB is yours truly.
+ Kristian Rother donated code to interact with the PDB database, and to
+ parse the PDB header.
+ Indraneel Majumdar sent in some bug reports and assisted in coding the
+
+\family typewriter
+Polypeptide
+\family default
+module.
+ Many thanks to Brad Chapman, Jeffrey Chang, Andrew Dalke and Iddo Friedberg
+ for suggestions, comments, help and/or biting criticism :-).
+\layout Section
+
+Can I contribute?
+\layout Standard
+
+Yes, yes, yes! Just send me an e-mail (thamelry at binf.ku.dk) if you have something
+ useful to contribute! Eternal fame awaits!
+\layout Section
+
+Biopython License Agreement
+\layout Standard
+
+Permission to use, copy, modify, and distribute this software and its documentat
+ion with or without modifications and for any purpose and without fee is
+ hereby granted, provided that any copyright notices appear in all copies
+ and that both those copyright notices and this permission notice appear
+ in supporting documentation, and that the names of the contributors or
+ copyright holders not be used in advertising or publicity pertaining to
+ distribution of the software without specific prior permission.
+\layout Standard
+
+THE CONTRIBUTORS AND COPYRIGHT HOLDERS OF THIS SOFTWARE DISCLAIM ALL WARRANTIES
+ WITH REGARD TO THIS SOFTWARE, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILI
+TY AND FITNESS, IN NO EVENT SHALL THE CONTRIBUTORS OR COPYRIGHT HOLDERS
+ BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES
+ WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION
+ OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN
+ CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE.
+
+\the_end
diff --git a/Doc/biopdb_faq.pdf b/Doc/biopdb_faq.pdf
deleted file mode 100644
index 23b44c4..0000000
Binary files a/Doc/biopdb_faq.pdf and /dev/null differ
diff --git a/Doc/cookbook/README b/Doc/cookbook/README
new file mode 100644
index 0000000..926ad6b
--- /dev/null
+++ b/Doc/cookbook/README
@@ -0,0 +1,13 @@
+This is a directory to hold "cookbook" style documentation -- small,
+single-purpose documents that describe a particular aspect of
+Biopython. This is meant as an alternative to the large Tutorial
+style documentation which can be daunting to maintain and keep up
+to date.
+
+Since these are all independent -- they can be written in any
+style you find convenient, as long as it can be used to produce
+something displayable on the web (HTML or PDF).
+
+We welcome contributions about any aspect of Biopython. Please feel
+free to discuss on our mailing list (biopython at biopython.org).
+
diff --git a/Doc/cookbook/Restriction/Restriction.html b/Doc/cookbook/Restriction/Restriction.html
index e957159..ecb7b4d 100644
--- a/Doc/cookbook/Restriction/Restriction.html
+++ b/Doc/cookbook/Restriction/Restriction.html
@@ -1,854 +1,1096 @@
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+<html xmlns="http://www.w3.org/1999/xhtml">
<head>
- <meta content="text/html; charset=ISO-8859-1"
- http-equiv="content-type">
- <title>Restriction.html</title>
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+code > span.cv { color: #60a0b0; font-weight: bold; font-style: italic; } /* CommentVar */
+code > span.in { color: #60a0b0; font-weight: bold; font-style: italic; } /* Information */
+ </style>
</head>
-<body vlink="#ff0000" alink="#000088" link="#0000ff"
- style="color: rgb(0, 0, 0); background-color: rgb(255, 255, 255);">
-<h2><a class="mozTocH2" name="mozTocId419365"></a><span class="mozTocH2"></span>
-Working
-with restriction enzymes</h2>
-<big style="font-weight: bold;"><br>
-Table of content<br>
-</big>
-<ul id="mozToc">
-<!--mozToc h1 1 h2 2 h3 3 h4 4 h5 5 h6 6--><li><a href="#mozTocId419365">
-Working
-with restriction enzymes</a>
- <ul>
- <li>
- <ul>
- <li><a href="#mozTocId101269">1 The
-restriction enzymes classes </a>
- <ul>
- <li><a href="#mozTocId59051">1.1
-Importing the enzymes</a></li>
- <li><a href="#mozTocId868359">1.2
-Naming convention </a></li>
- <li><a href="#mozTocId447698">1.3
-Searching for restriction sites</a></li>
- <li><a href="#mozTocId29937">1.4
-Retrieving the sequences produced by a digestion</a></li>
- <li><a href="#mozTocId255275">1.5
-Analysing circular sequences</a></li>
- <li><a href="#mozTocId25220">1.6 Comparing enzymes with
-each others </a></li>
- <li><a href="#mozTocId724099">1.7
-Other
-facilities provided by the enzyme classes</a></li>
- </ul>
- </li>
- <li><a href="#mozTocId343319">2
-The
-RestrictionBatch class : a class to deal with several enzymes </a>
- <ul>
- <li><a href="#mozTocId32850">2.1
-Creating a RestrictionBatch </a></li>
- <li><a href="#mozTocId672810">2.2
-Restricting a RestrictionBatch to a particular supplier</a></li>
- <li><a href="#mozTocId436678">2.3
-Adding enzymes to a RestrictionBatch </a></li>
- <li><a href="#mozTocId432981">2.4
-Removing enzymes from a RestrictionBatch</a></li>
- <li><a href="#mozTocId479498">2.5
-Manipulating RestrictionBatch</a></li>
- <li><a href="#mozTocId919079">2.6
-Analysing sequences with a RestrictionBatch </a></li>
- <li><a href="#mozTocId114148">2.7
-Other
-RestrictionBatch methods </a></li>
- </ul>
- </li>
- <li><a href="#mozTocId569237">3 AllEnzymes
-and CommOnly : two
-preconfigured RestrictionBatches </a></li>
- <li><a href="#mozTocId960354">4 The Analysis
-class : even simpler
-restriction analysis </a>
- <ul>
- <li><a href="#mozTocId994414">4.1 Setting up an
-Analysis</a></li>
- <li><a href="#mozTocId129244">4.2 Full restriction
-analysis </a></li>
- <li><a href="#mozTocId441271">4.3 Changing the title </a></li>
- <li><a href="#mozTocId450266">4.4 Customising the
-output </a></li>
- <li><a href="#mozTocId864311">4.5 Fancier
-restriction analysis </a></li>
- <li><a href="#mozTocId289477">4.6 More complex
-analysis </a></li>
- </ul>
- </li>
- <li><a href="#mozTocId343319">5
-Advanced features : the FormattedSeq class </a>
- <ul>
- <li><a href="#mozTocId74092">5.1 Creating a
-FormattedSeq </a></li>
- <li><a href="#mozTocId143837">5.2 Unlike Bio.Seq,
-FormattedSeq retains information about their shape </a></li>
- <li><a href="#mozTocId123270">5.3 Changing the
-shape of a FormattedSeq </a></li>
- <li><a href="#mozTocId710376">5.4 Using / and //
-operators with FormattedSeq </a></li>
- </ul>
- </li>
- <li><a href="#mozTocId80946">6 More advanced
-features </a>
- <ul>
- <li><a href="#mozTocId546339">6.1 Updating the
-enzymes from Rebase : rebase_update.py </a></li>
- <li><a href="#mozTocId49285">6.2 Compiling a new
-dictionary : the script ranacompiler.py </a></li>
- <li><a href="#mozTocId968583">6.3 Subclassing
-the class Analysis </a></li>
- </ul>
- </li>
- <li><a href="#mozTocId114111">7 Limitation
-and caveat </a>
- <ul>
- <li><a href="#mozTocId218594">7.1 All DNA are non
-methylated </a></li>
- <li><a href="#mozTocId790484">7.2 No support for
-star activity </a></li>
- <li><a href="#mozTocId218334">7.3 Safe to use with
-degenerated DNA </a></li>
- <li><a href="#mozTocId49392">7.4 Non standard bases
-in DNA are not
-allowed </a></li>
- <li><a href="#mozTocId395117">7.5 Sites found at
-the edge of linear
-DNA might not be accessible in a real digestion </a></li>
- <li><a href="#mozTocId112206">7.6 Restriction
-reports cutting sites
-not enzyme recognition sites </a></li>
- </ul>
- </li>
- <li><a href="#mozTocId386540">8 Annexe :
-modifying dir() to use
-with from Bio.Restriction import * </a></li>
- </ul>
- </li>
- </ul>
- </li>
-</ul>
-<big style="font-weight: bold;"><br>
-</big><br>
-<br>
-<h3><a class="mozTocH3" name="mozTocId101269"></a>1 The
-restriction enzymes classes<br>
-</h3>
-<br>
-The restriction enzyme
-package is situated in Bio/Restriction. This package will allow you to
-work with restriction enzymes and realise restriction analysis on your
-sequence. Restriction make use of the facilities offered by Rebase and
-contains classes for more than 600 restriction enzymes. This chapter
-will lead you through a quick overview of the facilities offered by the
-Restriction package of biopython. The chapter is constructed as an
-interactive python session and the best way to read it is with a python
-shell open alongside you.<br>
-<h4><a class="mozTocH4" name="mozTocId59051"></a><span class="mozTocH4"></span>1.1
-Importing the enzymes</h4>
-To import the enzymes, open a python shell and type :<br>
-<pre>>>> from Bio import Restriction<br>>>> dir()<br>['Restriction', '__builtins__', '__doc__', '__name__']<br>>>> Restriction.EcoRI<br>EcoRI<br>>>> Restriction.EcoRI.site<br>'GAATTC'<br>>>><br></pre>
-You will certainly notice that the package is quite slow to load. This
-is normal as each enzyme possess its own class and there is a lot of
-them. This will not affect the speed of python after the initial
-import. <br>
-<br>
-I don't know for you but I find it quite cumbersome to have to prefix
-each operation with <span style="font-family: monospace;">Restriction.</span>,
-so here is another way to import
-the package.<br>
-<pre>>>> from Bio.Restriction import *<br>>>> EcoRI<br>EcoRI<br>>>> EcoRI.site<br>'GAATTC'<br>>>></pre>
-However, this method has one big disadvantage :<br>
-It
-is almost impossible to use the command 'dir()' anymore as there is
-so much enzymes the results is hardly readable. A workaround is
-provided at the end of this tutorial. I let you decide which method you
-prefer. But in this tutorial I
-will use the second. If you prefer the first method you will need to
-prefix each call to a restriction enzyme with 'Restriction.' in the
-remaining of the tutorial.<br>
-<h4><a class="mozTocH4" name="mozTocId868359"></a><span class="mozTocH4"></span>1.2
-Naming convention<br>
-</h4>
-To access an Enzyme simply enter
-it's name. You must respect the usual naming convention with the upper
-case letters and Latin numbering (in upper case as well):<br>
-<pre>>>> EcoRI <br>EcoRI<br>>>> ecori<br><br>Traceback (most recent call last):<br> File "<pyshell#25>", line 1, in -toplevel-<br> ecori<br>NameError: name 'ecori' is not defined<br>>>> EcoR1<br><br>Traceback (most recent call last):<br> File "<pyshell#26>", line 1, in -toplevel-<br> EcoR1<br>NameError: name 'EcoR1' is not defined<br>>>> KpnI<br>KpnI<br>>>><br></pre>
-ecori or EcoR1 are not enzymes, EcoRI and KpnI are.<br>
-<h4><a class="mozTocH4" name="mozTocId447698"></a><span class="mozTocH4"></span>1.3
-Searching for restriction sites</h4>
-So what can we do with these restriction enzymes. To see that we
-will
-need a DNA sequence. Restriction enzymes support both <span
- style="font-family: monospace;">Bio.Seq.MutableSeq</span> and <span
- style="font-family: monospace;">Bio.Seq.Seq</span> objects. You can
-use any DNA
-alphabet which complies with the IUPAC
-alphabet.
-<pre>>>> from Bio.Seq import Seq<br>>>> from Bio.Alphabet.IUPAC import IUPACAmbiguousDNA<br>>>> amb = IUPACAmbiguousDNA()<br>>>> my_seq = Seq('AAAAAAAAAAAAAA', amb)<br>>>> my_seq<br>Seq('AAAAAAAAAAAAAA', IUPACAmbiguousDNA())<br></pre>
-Searching a sequence for the presence of restriction site for your
-preferred enzyme is as simple as :<br>
-<pre>>>> EcoRI.search(my_seq)<br>[]<br></pre>
-The results is a list. Here the list is empty since there is
-obviously no site EcoRI site in my_seq. Let's try to get a
-sequence
-with a EcoRI site.<br>
-<pre>>>> ecoseq = my_seq + Seq(EcoRI.site, amb) + my_seq<br>>>> ecoseq<br>Seq('AAAAAAAAAAAAAAGAATTCAAAAAAAAAAAAAA', IUPACAmbiguousDNA())<br>>>> EcoRI.search(ecoseq)<br>[16]<br></pre>
-We therefore have a site at position 16 of the sequence ecoseq. The
-position returned by the method search is the first base of the
-downstream segment produced by a restriction (i.e. the first base after
-the position where the enzyme will cut). The Restriction
-package follows biological convention (the first base of a sequence is
-base 1). No need to make difficult conversions between your recorded
-biological data and the results produced by the enzymes in this
-package.<br>
-<h4><a class="mozTocH4" name="mozTocId29937"></a><span class="mozTocH4"></span>1.4
-Retrieving the sequences produced by a digestion</h4>
-Seq objects as all python sequence, have different conventions and the
-first base of a sequence is base 0. Therefore to get the sequences
-produced by an EcoRI digestion of ecoseq, one should do the following :<br>
-<pre>>>> ecoseq[:15], ecoseq[15:] <br>(Seq('AAAAAAAAAAAAAAG', IUPACAmbiguousDNA()), Seq('AATTCAAAAAAAAAAAAAA', IUPACAmbiguousDNA())) <br></pre>
-I hear you thinking "this is a cumbersome and error prone method to get
-these sequences". To simplify your life, enzymes provide another
-method to get theses sequences
-without hassle : <span style="font-family: monospace;">catalyse</span>.
-This method will return a tuple containing
-all the fragments produced by a complete digestion of the sequence.
-Using it is as simple as before :<br>
-<pre>>>> EcoRI.catalyse(ecoseq)<br>(Seq('AAAAAAAAAAAAAAG', IUPACAmbiguousDNA()), Seq('AATTCAAAAAAAAAAAAAA', IUPACAmbiguousDNA()))</pre>
-<h4><a class="mozTocH4" name="mozTocId255275"></a><span class="mozTocH4"></span>1.5
-Analysing circular sequences</h4>
-Now, if you have entered the previous command in your shell you may
-have noticed that both 'search' and 'catalyse' can take a second
-argument 'linear' which default to True. Using this will allow you to
-simulate circular sequences such as plasmids. Setting linear to False
-inform the enzyme to make the search over a circular sequence and to
-search for potential sites spanning over the boundaries of the sequence.<br>
-<pre>>>> EcoRI.search(ecoseq, linear=False)<br>[16]<br>>>> EcoRI.catalyse(ecoseq, linear=False)<br>(Seq('AATTCAAAAAAAAAAAAAAAAAAAAAAAAAAAAG', IUPACAmbiguousDNA()),)<br>>>> ecoseq # for memory<br>Seq('AAAAAAAAAAAAAAGAATTCAAAAAAAAAAAAAA', IUPACAmbiguousDNA())</pre>
-OK, this is quite a difference, we only get one fragment, which
-correspond to the linearised sequence. The beginning sequence has been
-shifted to take this fact into account. Moreover we can see another
-difference :<br>
-<pre>>>> new_seq = Seq('TTCAAAAAAAAAAAAAAAAAAAAAAAAAAAAGAA', IUPACAmbiguousDNA())<br>>>> EcoRI.search(new_seq)<br>[]<br>>>> EcoRI.search(new_seq, linear=False)<br>[33]<br></pre>
-As you can see using 'linear=False', make a site appears in the
-sequence new_seq. This site does not exist in a linear sequence as the
-EcoRI site is split into two halves at the start and the end of the
-sequence. In a circular sequence however, the site is effectively
-present when the beginning and end of the sequence are joined.<br>
-<h4><a class="mozTocH4" name="mozTocId25220"></a>1.6 Comparing enzymes
-with each others<br>
-</h4>
-Restriction Enzymes define 4 comparative operators ==, !=, >> and
-%. All these operator compares two enzymes together and either return
-True or False.<br>
-<br>
-== :<br>
-<br>
-It will return True if the two sides of the operator are the same. same
-is defined as : same name, same site, same overhang (i.e. the only
-thing which is equal to EcoRI is EcoRI).<br>
-<br>
-!= :<br>
-<br>
-It will return True if the two sides of the operator are different. Two
-enzymes are not different if the result produced by one enzyme will
-always be the same as the result produced by the other (i.e. True
-isoschizomers will not being the same enzymes, are not different since
-they are interchangeable).<br>
-<br>
->> :<br>
-<br>
-True if the enzymes recognise the same site, but cut it in a different
-way (i.e. the enzymes are neoschizomers).<br>
-<br>
-% :<br>
-<br>
-Test the compatibility of the ending produced by the enzymes. (will be
-True if the fragments produced with one of the enzyme can directly be
-ligated to fragments produced by the other).<br>
-<br>
-Let's use Acc65I and its isoschizomers as example :<br>
-<pre>>>> Acc65I.isoschizomers()<br>[Asp718I, KpnI]<br>>>> Acc65I.elucidate()<br>'G^GTAC_C'<br>>>> Asp718I.elucidate()<br>'G^GTAC_C'<br>>>> KpnI.elucidate()<br>'G_GTAC^C'<br>>>> # Asp718I and Acc65I are True isoschizomers, <br>>>> # they recognise the same site and cut it the <br>>>> # same way.<br>>>> # KpnI is a neoschizomers of the 2 others.<br>>>> # here is the results of the 4 operator<br>>>> # [...]
-<h4><a class="mozTocH4" name="mozTocId724099"></a>1.7
-Other
-facilities provided by the enzyme classes</h4>
-The restriction enzymes class provided quite a number of others
-methods. We will not go through all of them, but only have a quick look
-to the most useful ones. <br>
-<br>
-Not all enzymes possess the same properties when it comes to the way
-they digest a DNA. If you want to know more about the way a particular
-enzyme cut you can use the three following methods. They are fairly
-straightforward to understand and refer to the ends that the enzyme
-produces blunt, 5' overhanging (also called 3' recessed) sticky end and
-3' overhanging (or 5' recessed) sticky end.<br>
-<pre>>>> EcoRI.is_blunt()<br>False<br>>>> EcoRI.is_5overhang()<br>True<br>>>> EcoRI.is_3overhang()<br>False<br></pre>
-A more detailled view of the restriction site can be produced using the
-<span style="font-family: monospace;">elucidate()</span> method. the
-"^" refers to the position of the cut in the
-sense strand of the sequence. "_" to the cut on the antisense or
-complementary strand. "^_" means blunt.
-<pre>>>> EcoRI.elucidate()<br>'G^AATT_C'<br>>>> KpnI.elucidate()<br>'G_GTAC^C'<br>>>> EcoRV.elucidate()<br>'GAT^_ATC'<br></pre>
-The method frequency will give you the statiscal <span
- style="font-family: monospace;">frequency()</span> of the
-enzyme site. <br>
-<pre>>>> EcoRI.frequency()<br>4096<br>>>> XhoII.elucidate()<br>'R^GATC_Y'<br>>>> XhoII.frequency()<br>1024 </pre>
-To get the length of a the recognition sequence of an enzyme use the
-built-in function <span style="font-family: monospace;">len()</span> :<br>
-<pre>>>> len(EcoRI)<br>6<br>>>> BstXI.elucidate()<br>'CCAN_NNNN^NTGG'<br>>>> len(BstXI)<br>12<br>>>> FokI.site<br>'GGATG'<br>>>> FokI.elucidate() # FokI cut well outside its recognition site<br>'GGATGNNNNNNNNN^NNNN_N'<br>>>> len(FokI) # its length is the length of the recognition site <br>5</pre>
-Also interesting are the methods dealing with isoschizomers.
-For memory, two enzymes are isoschizomers if they share a same
-recognition site. <br>
-A further division is made between isoschizomers (same name, recognise
-the same
-sequence and cut the same way) and neoschizomers which cut at different
-positions. equischizomer is an arbitrary choice to design
-"isoschizomers_that_are_not_neoschizomers" as this last one was a bit
-long.<br>
-Another set of method <span style="font-family: monospace;">one_enzyme.is_*schizomers(one_other_enzyme)</span>,
-allow to test 2
-enzymes against each other.<br>
-<pre>>>> Acc65I.isoschizomers()<br>[Asp718I, KpnI]<br>>>> Acc65I.neoschizomers()<br>[KpnI]<br>>>> Acc65I.equischizomers()<br>[Asp718I]<br>>>> KpnI.elucidate()<br>'G_GTAC^C'<br>>>> Acc65I.elucidate()<br>'G^GTAC_C'<br>>>> KpnI.is_neoschizomer(Acc65I)<br>True<br>>>> KpnI.is_neoschizomer(KpnI)<br>False<br>>>> KpnI.is_isoschizomer(Acc65I)<br>True<br>>>> KpnI.is_isoschizomer(KpnI)<br>True<br>>>> KpnI.is_e [...]
-<span style="font-family: monospace;">suppliers()</span> will get you
-the list of all the suppliers of the enzyme. <span
- style="font-family: monospace;">all_suppliers()</span> will give you
-all the suppliers in the database.<br>
-<br>
-<h3><a class="mozTocH3" name="mozTocId343319"></a><span class="mozTocH3"></span>2
-The
-RestrictionBatch class : a class to deal with several enzymes<br>
-</h3>
-If you want to
-make a restriction map of a sequence, using individual enzymes can
-become tedious and will endures a big overhead due to the repetitive
-conversion of the sequence to a FormattedSeq. Restriction provides a
-class to make easier the use of
-large number of enzymes in one go : RestrictionBatch.<br>
-RestrictionBatch will help you to manipulate lots of enzymes with a
-single command. Moreover all the enzymes in the restrictionBatch will
-share the same converted sequence, reducing the overhead to <br>
-<h4><a class="mozTocH4" name="mozTocId32850"></a><span class="mozTocH4"></span>2.1
-Creating a RestrictionBatch<br>
-</h4>
-You can initiate a
-restriction batch by passing it a list of enzymes or enzymes name as
-argument.
-<pre>>>> rb = RestrictionBatch([EcoRI])<br>>>> rb<br>RestrictionBatch(['EcoRI'])<br>>>> rb2 = RestrictionBatch(['EcoRI'])<br>RestrictionBatch(['EcoRI'])<br>>>> rb == rb2<br>True<br></pre>
-Adding a new enzyme to a restriction batch is easy :<br>
-<pre>>>> rb.add(KpnI)<br>>>> rb<br>RestrictionBatch(['EcoRI', 'KpnI'])<br>>>> rb += EcoRV<br>>>> rb<br>RestrictionBatch(['EcoRI', 'EcoRV', 'KpnI'])])<br></pre>
-Another way to create a RestrictionBatch is by simply adding
-restriction enzymes together, this is particularly useful for small
-batches :<br>
-<pre>>>> rb3 = EcoRI + KpnI + EcoRV<br>>>> rb3<br>RestrictionBatch(['EcoRI', 'EcoRV', 'KpnI'])<br></pre>
-<h4><a class="mozTocH4" name="mozTocId672810"></a><span class="mozTocH4"></span>2.2
-Restricting a RestrictionBatch to a particular supplier</h4>
-The Restriction package is based upon the Rebase database. This
-database gives a list of suppliers for each enzyme. It would be a shame
-not to make use of this facility. You can Produce a RestrictionBatch
-containing only enzymes from one or a few supplier(s). Here is how to
-do it :<br>
-<pre>>>> rb_supp = RestrictionBatch(first=[], suppliers=['A','C','E','G','F','I','H','K','J','M','O','N','Q','P','S','R','U','V','X'])<br>>>> # This will create a RestrictionBatch with the all enzymes which possess a supplier. </pre>
-The argument 'suppliers' take a list of one or several single letter
-codes corresponding to the supplier(s). The codes are the same as
-defined in Rebase. As it would be a pain to have to remember each
-supplier code, RestrictionBatch provides a method which show the pair
-code <=> supplier :<br>
-<pre>>>> RestrictionBatch.show_codes() # as of july 2004 Rebase release.<br>A = Amersham Pharmacia Biotech<br>C = Minotech Biotechnology<br>E = Stratagene<br>G = Qbiogene<br>F = Fermentas AB<br>I = SibEnzyme Ltd.<br>H = American Allied Biochemical, Inc.<br>K = Takara Shuzo Co. Ltd.<br>J = Nippon Gene Co., Ltd.<br>M = Roche Applied Science<br>O = Toyobo Biochemicals<br>N = New England Biolabs<br>Q = CHIMERx<br>P = Megabase Research Products<br>S = Sigma Chemical Corporation<br>R [...]
-This way of producing a RestrictionBatch can drastically reduce the
-amount of useless output from a restriction analysis, limiting the
-search to enzymes that you can get hold of and limiting the risks of
-nervous breakdown. Nothing is more frustrating than to get the perfect
-enzyme for a sub-cloning only to find it's not commercially available.<br>
-<h4><a class="mozTocH4" name="mozTocId436678"></a><span class="mozTocH4"></span>2.3
-Adding enzymes to a RestrictionBatch<br>
-</h4>
-Adding an enzyme to a batch if the enzyme is already present will not
-raise an exception, but will have no effects. Sometimes you want to get
-an enzyme from a RestrictionBatch or add it to the batch if it is not
-present.<br>
-You will use the get method setting the second argument to True.<br>
-<pre>>>> rb3<br>RestrictionBatch(['EcoRI', 'EcoRV', 'KpnI'])<br>>>> rb3.add(EcoRI)<br>>>> rb3<br>RestrictionBatch(['EcoRI', 'EcoRV', 'KpnI'])<br>>>> rb3.get(EcoRI)<br>EcoRI<br>>>> rb3.get(SmaI)<br><br>Traceback (most recent call last):<br> File "<pyshell#4>", line 1, in -toplevel-<br> rb3.get(SmaI)<br> File "/usr/lib/python2.3/site-packages/Bio/Restriction/Restriction.py", line 1800, in get<br> raise ValueError, 'enzyme %s is not [...]
-<h4><a class="mozTocH4" name="mozTocId432981"></a><span class="mozTocH4"></span>2.4
-Removing enzymes from a RestrictionBatch</h4>
-Removing enzymes from a Batch is done using the <span
- style="font-family: monospace;">remove()</span> method. If the
-enzyme is not present in the batch this will raise a KeyError. If the
-value you want to remove is not an enzyme this will raise a ValueError.<br>
-<pre>>>> rb3.remove(EcoRI)<br>>>> rb3<br>RestrictionBatch(['EcoRV', 'KpnI', 'SmaI'])<br>>>> rb3.remove(EcoRI)<br><br>Traceback (most recent call last):<br> File "<pyshell#14>", line 1, in -toplevel-<br> rb3.remove('EcoRI')<br> File "/usr/lib/python2.3/site-packages/Bio/Restriction/Restriction.py", line 1839, in remove<br> return Set.remove(self, self.format(other))<br> File "/usr/lib/python2.3/sets.py", line 534, in remove<br> del self._data[e [...]
-<h4><a class="mozTocH4" name="mozTocId479498"></a><span class="mozTocH4"></span>2.5
-Manipulating RestrictionBatch</h4>
-You can not however add batch together, as they are python sets. You
-must use the operator | instead. You can find the intersection between
-2 batches using & (see the python documentation about sets for more
-information (or import sets; help(sets)).<br>
-<pre>>>> rb3 = EcoRI+KpnI+EcoRV<br>>>> rb3<br>RestrictionBatch(['EcoRI', 'EcoRV', 'KpnI'])<br>>>> rb4 = SmaI + PstI<br>>>> rb4<br>RestrictionBatch(['PstI', 'SmaI'])<br>>>> rb3 + rb4<br><br>Traceback (most recent call last):<br> File "<pyshell#23>", line 1, in -toplevel-<br> rb3 + rb4<br> File "/usr/lib/python2.3/site-packages/Bio/Restriction/Restriction.py", line 1829, in __add__<br> new.add(other)<br> File "/usr/lib/python2.3/s [...]
-<h4><a class="mozTocH4" name="mozTocId919079"></a><span class="mozTocH4"></span>2.6
-Analysing sequences with a RestrictionBatch<br>
-</h4>
-To analyse a sequence for potential site, you can use the search
-method of the batch, the same way you did for restriction enzymes. The
-results is no longer a list however, but a dictionary. The keys of the
-dictionary are the names of the enzymes and the value a list of
-position site. RestrictionBatch does not implement a catalyse method,
-as it would not have a real meaning when used with large batch.<br>
-<pre>>>> new_seq = Seq('TTCAAAAAAAAAAAAAAAAAAAAAAAAAAAAGAA', IUPACAmbiguousDNA())<br>>>> rb.search(new_seq)<br>{'KpnI': [], 'EcoRV': [], 'EcoRI': []}<br>>>> rb.search(new_seq, linear=False)<br>{'KpnI': [], 'EcoRV': [], 'EcoRI': [33]}</pre>
-<h4><a class="mozTocH4" name="mozTocId114148"></a><span class="mozTocH4"></span>2.7
-Other
-RestrictionBatch methods<br>
-</h4>
-Amongst the other methods provided by RestrictionBatch <span
- style="font-family: monospace;">elements()</span> which
-return a list of all the element names
-alphabetically sorted is certainly the most useful.<br>
-<pre>>>> rb = EcoRI + KpnI + EcoRV<br>>>> rb.elements()<br>['EcoRI', 'EcoRV', 'KpnI']</pre>
-If you don't care about the alphabetical order use the method <span
- style="font-family: monospace;">as_string()</span>, to get the same
-thing a bit faster. The list is not sorted.
-The order is random as python sets are dictionary.<br>
-<pre>>>> rb = EcoRI + KpnI + EcoRV <br>>>> rb.as_string()<br>['EcoRI', 'KpnI', 'EcoRV']</pre>
-Other RestrictionBatch methods are generally used for particular
-purposes and will not be discussed here. See the source if you are
-interested.<span style="font-family: monospace;"></span><br>
-<br>
-<h3><a class="mozTocH3" name="mozTocId569237"></a>3 AllEnzymes
-and CommOnly : two
-preconfigured RestrictionBatches<br>
-</h3>
-While it is sometime practical to produce a RestrictionBatch of your
-own you will certainly more frequently use the two batches provided
-with the Restriction packages : AllEnzymes and CommOnly. These two
-batches contain respectively all the enzymes in the database and only
-the enzymes which have a commercial supplier. They are rather big, but
-that's what make them useful. With these batch you can produce a full
-description of a sequence with a single command. You can use these two
-batch as any other batch.<br>
-<pre>>>> len(AllEnzymes)<br>671<br>>>> len(CommOnly)<br>589<br>>>> AllEnzymes.search(new_seq) ...</pre>
-There is not a lot to say about them apart the fact that they are
-present. They are really normal batches, and you can use them as any
-other batch.
-<h3><a class="mozTocH3" name="mozTocId960354"></a>4 The Analysis
-class : even simpler
-restriction analysis<br>
-</h3>
-RestrictionBatch can give you a dictionary with the sites for all the
-enzymes in a RestrictionBatch. However, it is sometime nice to
-get something a bit easier to read than a python dictionary. Complex
-restriction analysis are not easy with RestrictionBatch. Some
-refinements in the way to search a sequence for restriction sites will
-help. Analysis
-provides a serie of command to customise the results obtained from a
-pair RestrictionBatch/sequence and some facilities to make the output
-sligthly more human readable.<br>
-<h4><a class="mozTocH4" name="mozTocId994414"></a>4.1 Setting up an
-Analysis</h4>
-To build a Restriction Analysis you will need a
-RestrictionBatch and a sequence and to tell it if the sequence is
-linear or circular. The first argument Analysis take is the
-RestrictionBatch, the second is the sequence. If the third argument is
-not provided Analysis will assume the sequence is linear.<br>
-<pre>>>> new_seq = Seq('TTCAAAAAAAAAAAAAAAAAAAAAAAAAAAAGAA', IUPACAmbiguousDNA())<br>>>> rb = RestrictionBatch([EcoRI, KpnI, EcoRV])<br>>>> Ana = Analysis(rb, new_seq, linear=False)<br>>>> Ana<br>Analysis(RestrictionBatch(['EcoRI', 'EcoRV', 'KpnI']),Seq('TTCAAAAAAAAAAAAAAAAAAAAAAAAAAAAGAA', IUPACAmbiguousDNA()),False)<br></pre>
-<h4><a class="mozTocH4" name="mozTocId129244"></a>4.2 Full restriction
-analysis<br>
-</h4>
-Once you have created your new Analysis, you can use it to get a
-restriction analysis of your sequence. The way to make a full
-restriction analysis of the sequence is :<br>
-<pre>>>> Ana.full()<br>{'KpnI': [], 'EcoRV': [], 'EcoRI': [33]}<br></pre>
-This is much the same as the output of a RestrictionBatch.search
-method. You will get a more easy to read output with "print_that" used
-without argument :<br>
-<pre>>>> <br>>>> # let's create a something a bit more complex to analyse.<br>>>><br>>>> rb = RestrictionBatch([], ['A']) # we will explain the meaning of the<br>>>> # double list argument later. <br>>>><br>>>> multi_site = Seq('AAA' + EcoRI.site +'G' + KpnI.site + EcoRV.site + 'CT' +\ <br>SmaI.site + 'GT' + FokI.site + 'GAAAGGGC' + EcoRI.site + 'ACGT', IUPACAmbiguousDNA())<br>>>><br>>>> Analong = Analy [...]
-Much clearer, is'nt ? The output is optimised for a shell 80 columns
-wide. If the output seems odd, check that the width of your shell
-is at least 80 columns.<br>
-<h4><a class="mozTocH4" name="mozTocId441271"></a>4.3 Changing the title<br>
-</h4>
-You can provide a title to the analysis and modify the sentence
-<span style="font-family: monospace;">'Enzymes which do not cut the
-sequence'</span>, by setting the two optional
-arguments of <span style="font-family: monospace;">print_that</span>
-"title" and "s1". No formating will be
-done on these strings so if you have to include the newline (<span
- style="font-family: monospace;">'\n'</span>) as
-you see fit :<br>
-<pre>>>> Analong.print_that(None, title='sequence = multi_site\n\n')<br><br>sequence = multi_site<br><br>AfaI : 14.<br>AvaI : 26.<br>EcoRI : 5, 47.<br>EcoRV : 20.<br>FokI : 47.<br>HapII : 27.<br>KpnI : 16.<br>MspI : 27.<br>SmaI : 28.<br><br> Enzymes which do not cut the sequence.<br><br>AatII AccI AccII AflII AluI Aor51HI ApaI ApaLI <br>AvaII BalI BamHI BanII BbeI [...]
-<pre>>>> Analong.print_that(None, <br> title = 'sequence = multi_site\n\n',<br> s1 = '\n no site :\n\n')<br><br>sequence = multi_site<br><br>AfaI : 14.<br>AvaI : 26.<br>EcoRI : 5, 47.<br>EcoRV : 20.<br>FokI : 47.<br>HapII : 27.<br>KpnI : 16.<br>MspI : 27.<br>SmaI : 28.<br><br> no site :<br><br>AatII AccI AccII AflII AluI Aor51HI ApaI ApaLI <br>AvaII BalI BamHI [...]
-<h4><a class="mozTocH4" name="mozTocId450266"></a>4.4 Customising the
-output <br>
-</h4>
-You can modify some aspects of the output interactively. There is three
-main type of output,
-two listing types (alphabetically sorted and sorted by number of
-site) and map-like type. To change the output, use the method <span
- style="font-family: monospace;">print_as()</span> of Analysis. The
-change of output is permanent for the
-instance of Analysis (that is
-until the next time you use <span style="font-family: monospace;">print_as()</span>).
-The argument of <span style="font-family: monospace;">print_as()</span>
-are
-strings : <span style="font-family: monospace;">'map'</span>, <span
- style="font-family: monospace;">'number'</span> or <span
- style="font-family: monospace;">'alpha'</span>. As you have seen
-previously the
-default behaviour is an
-alphabetical list (<span style="font-family: monospace;">'alpha'</span>).<br>
-<pre>>>> Analong.print_as('map') <br>>>> Analong.print_that()<br><br> 5 EcoRI<br> | <br> | 14 AfaI<br> | | <br> | | 16 KpnI<br> | | | <br> | | | 20 EcoRV<br> | | | | <br> | | | | 26 AvaI<br> | | [...]
-To come back to the previous behaviour :<br>
-<pre>>>> Analong.print_as('alpha')<br>>>> Analong.print_that()<br><br>AfaI : 14.<br>AvaI : 26.<br>EcoRI : 5, 47.<br>EcoRV : 20.<br>etc ...</pre>
-<h4><a class="mozTocH4" name="mozTocId864311"></a>4.5 Fancier
-restriction analysis<br>
-</h4>
-I will not go into the detail for each single method, here are all the
-functions that are available. Most are perfectly self explanatory and
-the others are fairly well documented (use <span
- style="font-family: monospace;">help('Analysis.command_name')</span>).
-The methods are :<br>
-<pre> full(self,linear=True) <br> blunt(self,dct = None) <br> overhang5(self, dct=None) <br> overhang3(self, dct=None) <br> defined(self,dct=None) <br> with_sites(self, dct=None) <br> without_site(self, dct=None) <br> with_N_sites(self, N, dct=None) <br> with_number_list(self, list, dct=None) <br> with_name(self, names, dct=None) <br> with_site_size(self, site_size, dct=None) [...]
-Using these methods is simple :<br>
-<pre>>>> new_seq = Seq('TTCAAAAAAAAAAAAAAAAAAAAAAAAAAAAGAA', IUPACAmbiguousDNA())<br>>>> rb = RestrictionBatch([EcoRI, KpnI, EcoRV])<br>>>> Ana = Analysis(rb, new_seq, linear=False)<br>>>> Ana<br>Analysis(RestrictionBatch(['EcoRI', 'EcoRV', 'KpnI']),Seq('TTCAAAAAAAAAAAAAAAAAAAAAAAAAAAAGAA', IUPACAmbiguousDNA()),False)<br>>>> Ana.blunt() # output only the result for enzymes which cut blunt<br>{'EcoRV': []}<br>>>> Ana.full() # all [...]
-To get a nice output, you still use <span
- style="font-family: monospace;">print_that</span> but this time with
-the
-command you want executed as argument.<br>
-<pre>>>> Ana.print_that(Ana.blunt())<br><br><br> Enzymes which do not cut the sequence.<br><br>EcoRV <br><br>>>> pt = Ana.print_that<br>>>> pt(Ana.with_sites())<br><br>EcoRI : 33.<br><br>>>> pt(Ana.without_site())<br><br><br> Enzymes which do not cut the sequence.<br><br>EcoRV KpnI <br><br>>>> # etc ...<br></pre>
-<h4><a class="mozTocH4" name="mozTocId289477"></a>4.6 More complex
-analysis<br>
-</h4>
-All of these methods (except <span style="font-family: monospace;">full()</span>
-which, well ... do a full
-restriction analysis) can be supplied with an additional dictionary. <br>
-If no
-dictionary is supplied a full
-restriction analysis is used as starting point. Otherwise the
-dictionary provided by the argument dct is used. The dictionary must be
-formatted as the result of <span style="font-family: monospace;">RestrictionBatch.search</span>.
-Therefore of
-the form <span style="font-family: monospace;">{'enzyme_name' :
-[position1, position2],...}</span>, where
-position1 and position2 are integer. All methods list previously output
-such dictionaries and can be used as starting point.<br>
-<br>
-Using this method you can build really complex query by
-chaining several method one after the other. For example if
-you want all the enzymes which are 5' overhang and cut the sequence
-only once, you have two ways to go :<br>
-<br>
-The hard way consist to build a
-RestrictionBatch containing only 5' overhang enzymes and use this batch
-to create a new analysis instance and then use the method <span
- style="font-family: monospace;">with_N_sites()</span> as follow :<br>
-<pre>>>> rbov5 = RestrictionBatch([x for x in rb if x.is_5overhang()])<br>>>> Anaov5 = Analysis(rbov5, new_seq, linear=False)<br>>>> Anaov5.with_N_sites(1)<br>{'EcoRI' : [33]}<br></pre>
-The easy solution is to chain several Analysis methods. This is
-possible since each method return a dictionary as results and is able
-to take a dictionary as input:<br>
-<pre>>>> Ana.with_N_sites(1, Ana.overhang5())<br>{'EcoRI': [33]}<br></pre>
-The dictionary is always the last argument whatever the command you use.<br>
-<br>
-The way to prefer certainly depends of the conditions you will use your
-Analysis instance. If
-you are likely to frequently reuse the same batch with different
-sequences, using a dedicated RestrictionBatch might be faster as the
-batch is likely to be smaller. Chaining methods is
-generally quicker when working with an interactive shell. In a
-script, the extended syntax may be easier to understand in a few
-months.
-<br>
-<br>
-<h3><a class="mozTocH3" name="mozTocId343319"></a><span class="mozTocH3"></span>5
-Advanced features : the FormattedSeq class<br>
-</h3>
-Restriction enzymes require a much more strict formatting of the DNA
-sequences than Bio.Seq object provides. For example, the restriction
-enzymes expect to find an ungapped (no space) upper-case sequence,
-while Bio.Seq object allow sequences to be in lower-case separated by
-spaces. Therefore when a restriction enzyme analyse a Bio.Seq
-object (be it a Seq or a MutableSeq), the object undergoes a
-conversion. The class FormattedSeq ensure the smooth conversion from
-a Bio.Seq object to something which can be safely be used by the
-enzyme.<br>
-<br>
-While this conversion is done automatically by the enzymes if you
-provide them with a Seq or a MutableSeq, there is time where it
-will be more efficient to realise the conversion before hand. Each time
-a Seq object is passed to an enzyme for analysis you pay a overhead due
-to the conversion. When analysing the same sequence over and over, it
-will be faster to convert the sequence, store the conversion and then
-use only the converted sequence. <br>
-<h4><a class="mozTocH4" name="mozTocId74092"></a>5.1 Creating a
-FormattedSeq<br>
-</h4>
-Creating a FormattedSeq from a Bio.Seq object is simple. The first
-argument of FormattedSeq is the sequence you wish to convert. You can
-specify a shape with the second argument linear, if you don't the
-FormattedSeq will be linear :
-<pre>>>> from Bio.Restriction import *<br>>>> from Bio.Seq import Seq<br>>>> seq = Seq('TTCAAAAAAAAAAGAATTCAAAAGAA')<br>>>> linear_fseq = FormattedSeq(seq, linear=True) <br>>>> default_fseq = FormattedSeq(seq)<br>>>> circular_fseq = FormattedSeq(seq, linear=False)<br>>>> linear_fseq<br>FormattedSeq(Seq('TTCAAAAAAAAAAGAATTCAAAAGAA', Alphabet()), linear=True)<br>>>> linear_fseq.is_linear()<br>True<br>>>> defau [...]
-<h4><a class="mozTocH4" name="mozTocId143837"></a>5.2 Unlike Bio.Seq,
-FormattedSeq retains information about their shape<br>
-</h4>
-FormattedSeq retains information about the shape of the sequence.
-Therefore unlike with Seq and MutableSeq you don't need to specify the
-shape of the sequence when using <span style="font-family: monospace;">search()</span>
-or <span style="font-family: monospace;">catalyse()</span>:<br>
-<pre>>>> EcoRI.search(linear_fseq)<br>[15]<br>>>> EcoRI.search(circular_fseq) # no need to specify the shape<br>[15, 25]<br></pre>
-In fact, the shape of a FormattedSeq is not altered by the second
-argument of the commands search() and catalyse() :<br>
-<pre>>>> # In fact the shape is blocked.<br>>>> # The 3 following commands give the same results<br>>>> # which correspond to a circular sequence<br>>>> EcoRI.search(circular_fseq) <br>[15, 25]<br>>>> EcoRI.search(circular_fseq, linear=True)<br>[15, 25]<br>>>> EcoRI.search(circular_fseq, linear=False)<br>[15, 25]<br>>>><br></pre>
-<h4><a class="mozTocH4" name="mozTocId123270"></a>5.3 Changing the
-shape of a FormattedSeq<br>
-</h4>
-You can however change the shape of the FormattedSeq. The command to
-use are :<br>
-<pre>FormattedSeq.to_circular() => new FormattedSeq, shape will be circular. <br>FormattedSeq.to_linear() => new FormattedSeq, shape will be linear<br>FormattedSeq.circularise() => change the shape of FormattedShape to circular<br>FormattedSeq.linearise() => change the shape of FormattedShape to linear<br><br>>>> circular_fseq<br>FormatedSeq(Seq('TTCAAAAAAAAAAGAATTCAAAAGAA', Alphabet()), linear=False)<br>>>> circular_fseq.is_linear()<br>False<br>>> [...]
-<h4><a class="mozTocH4" name="mozTocId710376"></a>5.4 Using / and //
-operators with FormattedSeq<br>
-</h4>
-Not having to specify the shape of the sequence to analyse gives you
-the opportunity to use the shorthand '/' and '//' with restriction
-enzymes :<br>
-<pre>>>> EcoRI/linear_fseq # <=> EcoRI.search(linear_fseq)<br>[15]<br>>>> linear_fseq/EcoRI # <=> EcoRI.search(linear_fseq)<br>[15]<br>>>> EcoRI//linear_fseq # <=> linear_fseq//EcoRI <=> EcoRI.catalyse(linear_fseq)<br>(Seq('TTCAAAAAAAAAAG', Alphabet()), Seq('AATTCAAAAGAA', Alphabet()))<br></pre>
-Another way to avoid the overhead due to a repetitive conversion from a
-Seq object to a FormattedSeq is to use a RestrictionBatch. <br>
-<br>
-To conclude, the performance gain achieved when using a FormattedSeq
-instead of a Seq is not huge. The analysis of a 10 kb sequence by all
-the enzymes in AllEnzymes (<span style="font-family: monospace;">for x
-in AllEnzymes : x.search(seq), </span>671 enzymes)<span
- style="font-family: monospace;"></span> is 7 % faster when using a
-FormattedSeq than a Seq. Using a RestrictionBatch (<span
- style="font-family: monospace;">AllEnzymes.search(seq)</span>) is
-about as fast as using a FormattedSeq the first time the search is run.
-This however is dramatically reduced in subsequent runs with the same
-sequence (RestrictionBatch keep in memory the result of their last run
-while the sequence is not changed).<br>
-<h3><a class="mozTocH3" name="mozTocId80946"></a>6 More advanced
-features <br>
-</h3>
-This chapter addresses some more advanced features of the packages,
-most
-users can safely ignore it.<br>
-<h4><a class="mozTocH4" name="mozTocId546339"></a>6.1 Updating the
-enzymes from Rebase : rebase_update.py<br>
-</h4>
-Most people will certainly not need to update the enzymes. The
-restriction enzyme package will be updated in with each new release of
-biopython. But if you wish to get an update in between
-biopython-releases here is how to do it. Each month, Rebase release a
-new compilation of data about restriction enzymes. While the enzymes do
-not change so frequently, you may wish to update the restriction
-enzymes classes. The first thing to do is to get the last rebase file.
-You can find the release of Rebase at <span
- style="font-family: monospace;">http://rebase.neb.com/rebase.files.html</span>.
-The file you are interested in are in the EMBOSS format. You can
-download the files directly from the rebase ftp server using your
-browser. The file are situated at <span style="font-family: monospace;">ftp://ftp.neb.com/pub/rebase</span>.
-<br>
-You will have to download 3 files :<br>
- <span
- style="font-family: monospace;">emboss_e.###</span><br>
- <span
- style="font-family: monospace;">emboss_r.###</span><br>
- <span
- style="font-family: monospace;">emboss_s.###</span><br>
-The <span style="font-family: monospace;">###</span> is a 3 digit
-number corresponding to the year and month of the release. The first
-digit is the year, the two last are the month : so July 2004 will be :
-407; October 2005 : 510, etc... Download the three file corresponding
-to the current month and place them in a folder. <br>
-<br>
-Another way to do the same thing is to use the <span
- style="font-family: monospace;">rebase_update.py</span>. script
-provided in the package. The script is in
-biopython/Bio/Restriction/Scripts. It will connect directly to the
-rebase ftp server and download the last batch of emboss files. From a
-DOS or Unix shell do the following :<br>
-<pre>$ cd path_to_/Scripts<br>$ ls<br>ranacompiler.py rebase_update.py<br>$ ./rebase_update.py -m your_e_mail at my_address.com -p http://www.somewhere.com:8000<br><br>Please wait, trying to connect to Rebase<br><br><br>copying ftp://ftp.neb.com/pub/rebase/emboss_e.407<br>to /cvsroot/biopython/Bio/Restriction/Scripts/emboss_e.407<br>copying ftp://ftp.neb.com/pub/rebase/emboss_s.407<br>to /cvsroot/biopython/Bio/Restriction/Scripts/emboss_s.407<br>copying ftp://ftp.neb.com/pub/rebase/emboss_ [...]
-Some explanation are needed : <span style="font-family: monospace;">-m</span>
-stands for e-mail, in order to connect to the ftp server you need to
-provide a your e-mail address. So replace <span
- style="font-family: monospace;">your_e_mail at your_address.com</span>
-with your e-mail address. <span style="font-family: monospace;">-p</span>
-is the switch to indicate to the script you are using a proxy. If you
-use a ftp proxy enter its address and the connection port after the '<span
- style="font-family: monospace;">:</span>'.<br>
-<span style="font-family: monospace;"></span>
-<h4><a class="mozTocH4" name="mozTocId49285"></a>6.2 Compiling a new
-dictionary : the script ranacompiler.py<br>
-</h4>
-Once you have got a the last serie of emboss files you can compile a
-new module containing the data necessary to create restriction enzyme.
-You will need to get out of the python shell and open either a DOS
-shell on windows, or your prefered Unix shell for the others.<br>
-<br>
-Note : if the emboss files are not present in the current directory or
-if they are not up to date, <span style="font-family: monospace;">ranacompiler.py</span>
-will invoke the script rebase_update. You will need to use the same
-options as before (ie <span style="font-family: monospace;">-m</span>
-and <span style="font-family: monospace;">-p</span>). See the previous
-paragraph on <span style="font-family: monospace;">rebase_update.py</span>
-for more details.<br>
-<br>
-For
-simplicity let's assume we have put the emboss files in the same folder
-as the files which contains the script <span
- style="font-family: monospace;">ranacompiler.py</span>.<br>
-<pre><br>$ cd path_to_/Scripts<br>$ ls<br>emboss_e.407 emboss_r.407 emboss_s.407 ranacompiler.py rebase_update.py<br><br></pre>
-We will use the script <span style="font-family: monospace;">ranacompiler.py</span>.
-You may have the change the mode of the file to make it executable :<br>
-<pre>$ chmod '+x' ranacompiler.py<br></pre>
-Now execute the script :<br>
-<pre>$ python ranacompiler.py # or ./ranacompiler.py</pre>
-You get normally the following message :<br>
-<pre>$ ./ranacompiler.py<br><br> Using the files : emboss_e.407, emboss_r.407, emboss_s.407<br><br>WARNING : HaeIV cut twice with different overhang length each time.<br> Unable to deal with this behaviour.<br> This enzyme will not be included in the database. Sorry.<br> Checking : Anyway, HaeIV is not commercially available.<br><br><br>WARNING : TaqII has two different sites.<br><br>WARNING : It seems that AspCNI is both commercially available<br> and its characteri [...]
-The first line indicate which emboss files have been used for the
-present compilation. You can safely ignore the warnings as long as the<span
- style="font-family: monospace;"> compilation of the new
-dictionary : OK.</span>
-is present in the last part of the output. They are here for debugging
-purpose. The number of enzymes in the new module is indicated as
-well as a list of the dictionary which have been compiled. The last
-part indicate that the module has been succesfully created but not
-installed. To finish the update you must copy the file<span
- style="font-family: monospace;">
-/cvsroot/biopython/Bio/Restriction/Scripts/Restriction_Dictionary.py</span>
-into the folder <span style="font-family: monospace;">/usr/lib/python2.3/site-packages/Bio/Restriction/</span>
-as indicated by the script. Looking into the present folder, you will
-see to new files : the newly created dictionary <span
- style="font-family: monospace;">Restriction_Dictionary.py </span>and <span
- style="font-family: monospace;">Restriction_Dictionary.old</span>
-. This last file containing the old dictionary to which you can revert
-in case anything the new file is corrupted (this should not happen
-since the script is happy enough the new dictionary is good, but if
-there is a problem it is always nice to know you can revert to the
-previous setting without having to reinstall the whole thing.<br>
-<pre>$ ls<br>emboss_e.407 ranacompiler.py* Restriction_Dictionary.py<br>emboss_r.407 rebase_update.py*<br>emboss_s.407 Restriction_Dictionary.old<br>$<br>$ # complete the installation by copying the new dictionary to the Bio/Restriction/ folder.<br>$ # You may have to become root to do this :<br>$<br>$ su -c "cp Restriction_Dictionary.py <span
- style="font-family: monospace;">/usr/lib/python2.3/site-packages/Bio/Restriction/"<br>password :<br></span></pre>
-Enter your password and that's it. If you whish, the script may install
-the folder for you as well,
-but you will have to run it as root if your normal user has no write
-access to your python installation (and it should'nt). Use the command <span
- style="font-family: monospace;">ranacompiler.py -i</span> or <span
- style="font-family: monospace;">ranacompiler.py --install</span>.<br>
-<pre>$ su -c "./ranacompiler.py -i"<br>password :<br><br> Using the files : emboss_e.407, emboss_r.407, emboss_s.407<br><br>WARNING : HaeIV cut twice with different overhang length each time.<br> Unable to deal with this behaviour.<br> This enzyme will not be included in the database. Sorry.<br> Checking : Anyway, HaeIV is not commercially available.<br><br><br>WARNING : TaqII has two different sites.<br><br>WARNING : It seems that AspCNI is both commercially availab [...]
-Much of the same really, but this time the module has directly been
-installed with your other python modules, you don't need to do anything
-more. If anything goes wrong (you have no write access to the
-destination folder for example) the script will let you know it did not
-perform the installation. It will however still save the new module in
-the current directory :<br>
-<pre>$ ./ranacompiler.py -i<br><br> Using the files : emboss_e.407, emboss_r.407, emboss_s.407<br><br>WARNING : HaeIV cut twice with different overhang length each time.<br> Unable to deal with this behaviour.<br> This enzyme will not be included in the database. Sorry.<br> Checking : Anyway, HaeIV is not commercially available.<br><br><br>WARNING : TaqII has two different sites.<br><br>WARNING : It seems that AspCNI is both commercially available<br> and its [...]
-As you can see the script is not very bright and will redo the
-compilation each time it is invoked, no matter if a previous version of
-the module is already present. <br>
-<h4><a class="mozTocH4" name="mozTocId968583"></a>6.3 Subclassing
-the class Analysis<br>
-</h4>
-As seen previously, you can modify some aspects of the Analysis output
-interactively. However if you want to write your own Analysis class,
-you may wish to provide others output facilities than is given in this
-package. Depending on what you want to do you may get away with simply
-changing the <span style="font-family: monospace;">make_format</span>
-method of your derived class or you will need
-to provide new methods. Rather than get into a long explanation, here
-is the implementation of a rather useless Analysis class :<br>
-<pre>>>> class UselessAnalysis(Analysis) :<br><br> def __init__(self, rb=RestrictionBatch(), seq=Seq(''), lin=True) :<br> """UselessAnalysis -> A class that waste your time"""<br> #<br> # Unless you want to do something more fancy all<br> # you need to do here is instantiate Analysis.<br> # Don't forget the self in __init__<br> #<br> Analysis.__init__(self, rb, seq, lin)<br><br> def make_format(self, cut=[], t='', nc=[], s='') :<br> """not funny"""<br> # [...]
-<br>
-Using this example, as a template you should now be able to subclass
-Analysis as you wish. You will found more implementation details in the
-module <span style="font-family: monospace;">Bio.Restriction.PrintFormat</span>
-which contains the class providing all the <span
- style="font-family: monospace;">_make_*</span> methods.<br>
-<pre> </pre>
-<h3><a class="mozTocH3" name="mozTocId114111"></a>7 Limitation
-and caveat <br>
-</h3>
-You must be aware that Restriction is a fairly young package.
-Particularly, the class Analysis is a quick and
-dirty implementation based on the facilities furnished by the package.
-Please check your
-results and report any fault.<br>
-<br>
-On a more general basis, Restriction have some other limitations :<br>
-<h4><a class="mozTocH4" name="mozTocId218594"></a>7.1 All DNA are non
-methylated<br>
-</h4>
-No facility to work with methylated DNA has been implemented yet. As
-far as the enzyme classes are concerned all DNA is non methylated DNA.
-Implementation of methylation sensibility will certainly occur in the
-future. But for now, if your sequence is methylated, you will have to
-check if the site is methylated using other means. <br>
-<h4><a class="mozTocH4" name="mozTocId790484"></a>7.2 No support for
-star activity<br>
-</h4>
-As before no support has been yet implemented to find site
-mis-recognised by enzymes under high salt concentration conditions, the
-so-called star activity. This will be implemented as soon as I can get
-a good source of information for that.<br>
-<h4><a class="mozTocH4" name="mozTocId218334"></a>7.3 Safe to use with
-degenerated DNA<br>
-</h4>
-It is safe to use degenerated DNA as input for the query. You will not
-be flooded with meaningless results. But this come at a price : GAA<span
- style="text-decoration: underline;">N</span>TC
-will not be recognised as a potential EcoRI site for example, in fact
-it will not
-be recognised at all. Degenerated sequences will not be analysed. If
-your sequence is not fully sequenced, you will certainly miss
-restriction sites :<br>
-<pre>>>> a = Seq('nnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnGAATTCrrrrrrrrrrr', IUPACAmbiguousDNA())<br>>>> EcoRI.search(a)<br>[36]<br>>>> b = Seq('nnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnGAAnTCrrrrrrrrrrr', IUPACAmbiguousDNA())<br>>>> EcoRI.search(b)<br>[]<br></pre>
-<h4><a class="mozTocH4" name="mozTocId49392"></a>7.4 Non standard bases
-in DNA are not
-allowed<br>
-</h4>
-While you can use degenerated DNA, using non standard base alphabet
-will make the enzymes choke, even if Bio.Seq.Seq accepts them. However,
-space-like characters (' ', '\n', '\t', ...) and digit will be removed
-but will not stop the enzyme analysing the sequence. You can use them
-but the fragments produced by catalyse will have lost any formatting.
-Catalyse try to keep the original case of the sequence (i.e lower case
-sequences will generate lower case fragments, upper case sequences
-upper case fragments), but mixed case will return upper case fragments :<br>
-<pre>>>> c = Seq('xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxGAANTCrrrrrrrrrrr', IUPACAmbiguousDNA())<br>>>> EcoRI.search(c)<br><br>Traceback (most recent call last):<br> File "<pyshell#110>", line 1, in -toplevel-<br> EcoRI.search(b)<br> File "/usr/lib/python2.3/site-packages/Bio/Restriction/Restriction.py", line 396, in search<br> cls.dna = FormatedSeq(dna, linear)<br> File "/usr/lib/python2.3/site-packages/Bio/Restriction/Restriction.py", line 137, in __init__< [...]
-Not allowing other letters than IUPAC might seems drastic but this is
-really to limit errors. It is not
-totally fool proof but it does help. <br>
-<br>
-<h4><a class="mozTocH4" name="mozTocId395117"></a>7.5 Sites found at
-the edge of linear
-DNA might not be accessible in a real digestion<br>
-</h4>
-While sites clearly outsides a sequence will not be reported, nothing
-has been done to try to determine if a restriction site at the end of a
-linear sequence is valid :<br>
-<pre>>>> d = Seq('GAATTCAAAAAAAAAAAAAAAAAAAAAAAAAAGGATG', IUPACAmbiguousDNA())<br>>>> FokI.site # site present<br>'GGATG'<br>>>> FokI.elucidate() # but cut outside the sequence <br>'GGATGNNNNNNNNN^NNNN_N'<br>>>> FokI.search(d) # therefore no site found<br>[]<br>>>> EcoRI.search(d)<br>[2] <br></pre>
-EcoRI finds a site at position 2 even if it is highly unlikely that
-EcoRI accepts to cut this site in a tube. It is generally considered
-that at about 5 nucleotides must separate the site from the edge of the
-sequence to be reasonably sure the enzyme will work correctly. This
-"security margin" is variable from one enzyme to the other. In doubt
-consult the documentation for the enzyme.<br>
-<h4><a class="mozTocH4" name="mozTocId112206"></a>7.6 Restriction
-reports cutting sites
-not enzyme recognition sites<br>
-</h4>
-Some enzymes will cut twice each time they encounter a restriction
-site. The enzymes in this package report both cut not the site. Other
-software may only reports restriction sites. Therefore the output given
-for some enzymes might seems to be the double when compared with the
-results of these software. It is not a bug.<br>
-<pre>>>> AloI.cut_twice()<br>True<br>>>> AloI.fst5 # first cut<br>-7<br>>>> AloI.scd5 # second cut <br>25<br>>>> AloI.site<br>'GAACNNNNNNTCC'<br>>>> b = Seq('AAAAAAAAAAA'+ AloI.site + 'AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA')<br>>>> b<br>Seq('AAAAAAAAAAAGAACNNNNNNTCCAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA', Alphabet())<br>>>> AloI.search(b) # one site, two cuts -> two positions<br>[5, 37]<br><br><br><br></pre>
-<h3><a class="mozTocH3" name="mozTocId386540"></a>8 Annexe :
-modifying dir() to use
-with from Bio.Restriction import *<br>
-</h3>
-Having all the enzymes imported directly in the shell is useful when
-working in an interactive shell (even if it is not recommended by the
-purists). Here is a little hack to get some sanity back when using
-dir() in those conditions :<br>
-<pre>>>> # we will change the builtin dir() function to get ride of the enzyme names.<br>>>> import sys<br>>>> def dir(object=None) :<br> """dir([object]) -> list of string.<br><br> over-ride the built-in function to get some clarity."""<br> if object :<br> # we only want to modify dir(),<br> # so here we return the result of the builtin function.<br> return __builtins__.dir(object)<br> else :<br> # now the part we want to modify.<br> # All the enzymes [...]
+<body>
+<h1 id="working-with-restriction-enzymes">Working with restriction enzymes</h1>
+<h2 id="table-of-contents">Table of contents</h2>
+<ol style="list-style-type: decimal">
+<li><a href="#1">The restriction enzymes classes</a>
+<ol style="list-style-type: decimal">
+<li><a href="#1.1">Importing the enzymes</a></li>
+<li><a href="#1.2">Naming convention</a></li>
+<li><a href="#1.3">Searching for restriction sites</a></li>
+<li><a href="#1.4">Retrieving the sequences produced by a digestion</a></li>
+<li><a href="#1.5">Analysing circular sequences</a></li>
+<li><a href="#1.6">Comparing enzymes with each others</a></li>
+<li><a href="#1.7">Other facilities provided by the enzyme classes</a></li>
+</ol></li>
+<li><a href="#2">The RestrictionBatch class: a class to deal with several enzymes</a>
+<ol style="list-style-type: decimal">
+<li><a href="#2.1">Creating a RestrictionBatch</a></li>
+<li><a href="#2.2">Restricting a RestrictionBatch to a particular supplier</a></li>
+<li><a href="#2.3">Adding enzymes to a RestrictionBatch</a></li>
+<li><a href="#2.4">Removing enzymes from a RestrictionBatch</a></li>
+<li><a href="#2.5">Manipulating RestrictionBatch</a></li>
+<li><a href="#2.6">Analysing sequences with a RestrictionBatch</a></li>
+<li><a href="#2.7">Other RestrictionBatch methods</a></li>
+</ol></li>
+<li><a href="#3">AllEnzymes and CommOnly: two preconfigured RestrictionBatches</a></li>
+<li><a href="#4">The Analysis class: even simpler restriction analysis</a>
+<ol style="list-style-type: decimal">
+<li><a href="#4.1">Setting up an Analysis</a></li>
+<li><a href="#4.2">Full restriction analysis</a></li>
+<li><a href="#4.3">Changing the title</a></li>
+<li><a href="#4.4">Customising the output</a></li>
+<li><a href="#4.5">Fancier restriction analysis</a></li>
+<li><a href="#4.6">More complex analysis</a></li>
+</ol></li>
+<li><a href="#5">Advanced features: the FormattedSeq class</a>
+<ol style="list-style-type: decimal">
+<li><a href="#5.1">Creating a FormattedSeq</a></li>
+<li><a href="#5.2">Unlike Bio.Seq, FormattedSeq retains information about their shape</a></li>
+<li><a href="#5.3">Changing the shape of a FormattedSeq</a></li>
+<li><a href="#5.4">Using / and // operators with FormattedSeq</a></li>
+</ol></li>
+<li><a href="#6">More advanced features</a>
+<ol style="list-style-type: decimal">
+<li><a href="#6.1">Updating the enzymes from Rebase</a>
+<ol style="list-style-type: decimal">
+<li><a href="#6.1.1">Fetching the recent enzyme files manually from Rebase</a></li>
+<li><a href="#6.1.2">Fetching the recent enzyme files with rebase_update.py</a></li>
+<li><a href="#6.1.3">Compiling a new dictionary with ranacompiler.py</a></li>
+</ol></li>
+<li><a href="#6.2">Subclassing the class Analysis</a></li>
+</ol></li>
+<li><a href="#7">Limitation and caveat</a>
+<ol style="list-style-type: decimal">
+<li><a href="#7.1">All DNA are non methylated</a></li>
+<li><a href="#7.2">No support for star activity</a></li>
+<li><a href="#7.3">Safe to use with degenerated DNA</a></li>
+<li><a href="#7.4">Non standard bases in DNA are not allowed</a></li>
+<li><a href="#7.5">Sites found at the edge of linear DNA might not be accessible in a real digestion</a></li>
+<li><a href="#7.6">Restriction reports cutting sites not enzyme recognition sites</a></li>
+</ol></li>
+<li><a href="#8">Annexe: modifying dir() to use with from Bio.Restriction import *</a></li>
+</ol>
+<h3 id="the-restriction-enzymes-classes"><a name="1"></a>1. The restriction enzymes classes</h3>
+<p>The restriction enzyme package is situated in <code class="inline">Bio.Restriction</code>. This package will allow you to work with restriction enzymes and realise restriction analysis on your sequence. Restriction make use of the facilities offered by <strong>REBASE</strong> and contains classes for more than 800 restriction enzymes. This chapter will lead you through a quick overview of the facilities offered by the <code class="inline">Restriction</code> package of Biopython. The c [...]
+<h4 id="importing-the-enzymes"><a name="1.1"></a> 1.1 Importing the enzymes</h4>
+<p>To import the enzymes, open a Python shell and type:</p>
+<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python"><span class="op">>>></span> <span class="im">from</span> Bio <span class="im">import</span> Restriction
+<span class="op">>>></span> <span class="bu">dir</span>()
+[<span class="st">'Restriction'</span>, <span class="st">'__builtins__'</span>, <span class="st">'__doc__'</span>, <span class="st">'__name__'</span>, <span class="st">'__package__'</span>]
+<span class="op">>>></span> Restriction.EcoRI
+EcoRI
+<span class="op">>>></span> Restriction.EcoRI.site
+<span class="co">'GAATTC'</span>
+<span class="op">>>></span></code></pre></div>
+<p>You will certainly notice that the package is quite slow to load. This is normal as each enzyme possess its own class and there is a lot of them. This will not affect the speed of Python after the initial import.</p>
+<p>I don't know for you but I find it quite cumbersome to have to prefix each operation with <code class="inline">Restriction.</code>, so here is another way to import the package.</p>
+<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python"><span class="op">>>></span> <span class="im">from</span> Bio.Restriction <span class="im">import</span> <span class="op">*</span>
+<span class="op">>>></span> EcoRI
+EcoRI
+<span class="op">>>></span> EcoRI.site
+<span class="co">'GAATTC'</span>
+<span class="op">>>></span></code></pre></div>
+<p>However, this method has one big disadvantage: It is almost impossible to use the command <code class="inline">dir()</code> anymore as there is so much enzymes the results is hardly readable. A workaround is provided at the end of this tutorial. I let you decide which method you prefer. But in this tutorial I will use the second. If you prefer the first method you will need to prefix each call to a restriction enzyme with <code class="inline">Restriction.</code> in the remaining of th [...]
+<h4 id="naming-convention"><a name="1.2"></a>1.2 Naming convention</h4>
+<p>To access an enzyme simply enter it's name. You must respect the usual naming convention with the upper case letters and Latin numbering (in upper case as well):</p>
+<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python"><span class="op">>>></span> EcoRI
+EcoRI
+<span class="op">>>></span> ecori
+
+Traceback (most recent call last):
+ File <span class="st">"<pyshell#25>"</span>, line <span class="dv">1</span>, <span class="op">in</span> <span class="op">-</span>toplevel<span class="op">-</span>
+ ecori
+<span class="pp">NameError</span>: name <span class="st">'ecori'</span> <span class="op">is</span> <span class="op">not</span> defined
+<span class="op">>>></span> EcoR1
+
+Traceback (most recent call last):
+ File <span class="st">"<pyshell#26>"</span>, line <span class="dv">1</span>, <span class="op">in</span> <span class="op">-</span>toplevel<span class="op">-</span>
+ EcoR1
+<span class="pp">NameError</span>: name <span class="st">'EcoR1'</span> <span class="op">is</span> <span class="op">not</span> defined
+<span class="op">>>></span> KpnI
+KpnI
+<span class="op">>>></span></code></pre></div>
+<p><code class="inline">ecori</code> or <code class="inline">EcoR1</code> are not enzymes, <code class="inline">EcoRI</code> and <code class="inline">KpnI</code> are.</p>
+<h4 id="searching-for-restriction-sites"><a name="1.3"></a>1.3 Searching for restriction sites</h4>
+<p>So what can we do with these restriction enzymes? To see that we will need a DNA sequence. Restriction enzymes support both <code class="inline">Bio.Seq.MutableSeq</code>and <code class="inline">Bio.Seq.Seq</code> objects. You can use any DNA alphabet which complies with the IUPAC alphabet.</p>
+<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python"><span class="op">>>></span> <span class="im">from</span> Bio.Seq <span class="im">import</span> Seq
+<span class="op">>>></span> <span class="im">from</span> Bio.Alphabet.IUPAC <span class="im">import</span> IUPACAmbiguousDNA
+<span class="op">>>></span> amb <span class="op">=</span> IUPACAmbiguousDNA()
+<span class="op">>>></span> my_seq <span class="op">=</span> Seq(<span class="st">'AAAAAAAAAAAAAA'</span>, amb)
+<span class="op">>>></span> my_seq
+Seq(<span class="st">'AAAAAAAAAAAAAA'</span>, IUPACAmbiguousDNA())</code></pre></div>
+<p>Searching a sequence for the presence of restriction site for your preferred enzyme is as simple as:</p>
+<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python"><span class="op">>>></span> EcoRI.search(my_seq)
+[]</code></pre></div>
+<p>The results is a list. Here the list is empty since there is obviously no EcoRI site in <em>my_seq</em>. Let's try to get a sequence with an EcoRI site.</p>
+<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python"><span class="op">>>></span> ecoseq <span class="op">=</span> my_seq <span class="op">+</span> Seq(EcoRI.site, amb) <span class="op">+</span> my_seq
+<span class="op">>>></span> ecoseq
+Seq(<span class="st">'AAAAAAAAAAAAAAGAATTCAAAAAAAAAAAAAA'</span>, IUPACAmbiguousDNA())
+<span class="op">>>></span> EcoRI.search(ecoseq)
+[<span class="dv">16</span>]</code></pre></div>
+<p>We therefore have a site at position 16 of the sequence <em>ecoseq</em>. The position returned by the method search is the first base of the downstream segment produced by a restriction (i.e. the first base after the position where the enzyme will cut). The <code class="inline">Restriction</code> package follows biological convention (the first base of a sequence is base 1). No need to make difficult conversions between your recorded biological data and the results produced by the enz [...]
+<h4 id="retrieving-the-sequences-produced-by-a-digestion"><a name="1.4"></a>1.4 Retrieving the sequences produced by a digestion</h4>
+<p><code class="inline">Seq</code> objects as all Python sequences, have different conventions and the first base of a sequence is base 0. Therefore to get the sequences produced by an EcoRI digestion of <em>ecoseq</em>, one should do the following:</p>
+<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python"><span class="op">>>></span> ecoseq[:<span class="dv">15</span>], ecoseq[<span class="dv">15</span>:]
+(Seq(<span class="st">'AAAAAAAAAAAAAAG'</span>, IUPACAmbiguousDNA()), Seq(<span class="st">'AATTCAAAAAAAAAAAAAA'</span>, IUPACAm
+biguousDNA()))</code></pre></div>
+<p>I hear you thinking "this is a cumbersome and error prone method to get these sequences". To simplify your life, <code class="inline">Restriction</code> provides another method to get these sequences without hassle: <code class="inline">catalyse</code>. This method will return a tuple containing all the fragments produced by a complete digestion of the sequence. Using it is as simple as before:</p>
+<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python"><span class="op">>>></span> EcoRI.catalyse(ecoseq)
+(Seq(<span class="st">'AAAAAAAAAAAAAAG'</span>, IUPACAmbiguousDNA()), Seq(<span class="st">'AATTCAAAAAAAAAAAAAA'</span>, IUPACAm
+biguousDNA()))</code></pre></div>
+<p>BTW, you can also use spell it the American way <code class="inline">catalyze</code>:</p>
+<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python"><span class="op">>>></span> EcoRI.catalyze(ecoseq)
+(Seq(<span class="st">'AAAAAAAAAAAAAAG'</span>, IUPACAmbiguousDNA()), Seq(<span class="st">'AATTCAAAAAAAAAAAAAA'</span>, IUPACAm
+biguousDNA()))</code></pre></div>
+<h4 id="analysing-circular-sequences"><a name="1.5"></a>1.5 Analysing circular sequences</h4>
+<p>Now, if you have entered the previous command in your shell you may have noticed that both <code class="inline">search</code> and <code class="inline">catalyse</code> can take a second argument <code class="inline">linear</code> which defaults to <code class="inline">True</code>. Using this will allow you to simulate circular sequences such as plasmids. Setting <code class="inline">linear</code> to <code class="inline">False</code> informs the enzyme to make the search over a circular [...]
+<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python"><span class="op">>>></span> EcoRI.search(ecoseq, linear<span class="op">=</span><span class="va">False</span>)
+[<span class="dv">16</span>]
+<span class="op">>>></span> EcoRI.catalyse(ecoseq, linear<span class="op">=</span><span class="va">False</span>)
+(Seq(<span class="st">'AATTCAAAAAAAAAAAAAAAAAAAAAAAAAAAAG'</span>, IUPACAmbiguousDNA()),)
+<span class="op">>>></span> ecoseq <span class="co"># for memory</span>
+Seq(<span class="st">'AAAAAAAAAAAAAAGAATTCAAAAAAAAAAAAAA'</span>, IUPACAmbiguousDNA())</code></pre></div>
+<p>OK, this is quite a difference, we only get one fragment, which correspond to the linearised sequence. The beginning sequence has been shifted to take this fact into account. Moreover we can see another difference:</p>
+<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python"><span class="op">>>></span> new_seq <span class="op">=</span> Seq(<span class="st">'TTCAAAAAAAAAAAAAAAAAAAAAAAAAAAAGAA'</span>, IUPACAmbiguousDNA())
+<span class="op">>>></span> EcoRI.search(new_seq)
+[]
+<span class="op">>>></span> EcoRI.search(new_seq, linear<span class="op">=</span><span class="va">False</span>)
+[<span class="dv">33</span>]</code></pre></div>
+<p>As you can see using <code class="inline">linear=False</code>, make a site appearing in the sequence <em>new_seq</em>. This site does not exist in a linear sequence as the EcoRI site is split into two halves at the start and the end of the sequence. In a circular sequence however, the site is effectively present when the beginning and end of the sequence are joined.</p>
+<h4 id="comparing-enzymes-with-each-others"><a name="1.6"></a>1.6 Comparing enzymes with each others</h4>
+<p><code class="inline">Restriction</code> enzymes define 4 comparative operators <code class="inline">==</code>, <code class="inline">!=</code>, <code class="inline">>></code> and <code class="inline">%</code>. All these operator compares two enzymes together and either return <code class="inline">True</code> or <code class="inline">False</code>.</p>
+<dl>
+<dt><code class="inline">==</code> (test identity)</dt>
+<dd>It will return <code class="inline">True</code> if the two sides of the operator are the same. *Same" is defined as: same name, same site, same overhang (i.e. the only thing which is equal to <code class="inline">EcoRI</code> is <code class="inline">EcoRI</code>).
+</dd>
+<dt><code class="inline">!=</code> (test for different site or cutting)</dt>
+<dd>It will return <code class="inline">True</code> if the two sides of the operator are different. Two enzymes are not different if the result produced by one enzyme will always be the same as the result produced by the other (i.e. true isoschizomers will not being the same enzymes, are not different since they are interchangeable).
+</dd>
+<dt><code class="inline">>></code> (test for neoschizomer)</dt>
+<dd><code class="inline">True</code> if the enzymes recognise the same site, but cut it in a different way (i.e. the enzymes are neoschizomers).
+</dd>
+<dt><code class="inline">%</code> (test compatibilty)</dt>
+<dd>Test the compatibility of the ending produced by the enzymes (will be <code class="inline">True</code> if the fragments produced with one of the enzyme can directly be ligated to fragments produced by the other).
+</dd>
+</dl>
+<p>Let's use <code class="inline">Acc65I</code> and its isoschizomers as example:</p>
+<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python"><span class="op">>>></span> Acc65I.isoschizomers()
+[Asp718I, KpnI]
+<span class="op">>>></span> Acc65I.elucidate()
+<span class="co">'G^GTAC_C'</span>
+<span class="op">>>></span> Asp718I.elucidate()
+<span class="co">'G^GTAC_C'</span>
+<span class="op">>>></span> KpnI.elucidate()
+<span class="co">'G_GTAC^C'</span>
+<span class="op">>>></span> <span class="co"># Asp718I and Acc65I are true isoschizomers,</span>
+<span class="op">>>></span> <span class="co"># they recognise the same site and cut it the</span>
+<span class="op">>>></span> <span class="co"># same way.</span>
+<span class="op">>>></span> <span class="co"># KpnI is a neoschizomers of the 2 others.</span>
+<span class="op">>>></span> <span class="co"># Here are the results of the 4 operators</span>
+<span class="op">>>></span> <span class="co"># for each pair of enzymes:</span>
+<span class="op">>>></span>
+<span class="op">>>></span> <span class="co">############# x == y (x is y)</span>
+<span class="op">>>></span> Acc65I <span class="op">==</span> Acc65I <span class="co"># same enzyme => True</span>
+<span class="va">True</span>
+<span class="op">>>></span> Acc65I <span class="op">==</span> KpnI <span class="co"># all other cases => False</span>
+<span class="va">False</span>
+<span class="op">>>></span> Acc65I <span class="op">==</span> Asp718I
+<span class="va">False</span>
+<span class="op">>>></span> Acc65I <span class="op">==</span> EcoRI
+<span class="va">False</span>
+<span class="op">>>></span> <span class="co">############ x != y (x and y are not true isoschizomers)</span>
+<span class="op">>>></span> Acc65I <span class="op">!=</span> Acc65I <span class="co"># same enzyme => False</span>
+<span class="va">False</span>
+<span class="op">>>></span> Acc65I <span class="op">!=</span> Asp718I <span class="co"># different enzymes, but cut same manner => False</span>
+<span class="va">False</span>
+<span class="op">>>></span> Acc65I <span class="op">!=</span> KpnI <span class="co"># all other cases => True</span>
+<span class="va">True</span>
+<span class="op">>>></span> Acc65I <span class="op">!=</span> EcoRI
+<span class="va">True</span>
+<span class="op">>>></span> <span class="co">########### x >> y (x is neoschizomer of y)</span>
+<span class="op">>>></span> Acc65I <span class="op">>></span> Acc65I <span class="co"># same enzyme => False</span>
+<span class="va">False</span>
+<span class="op">>>></span> Acc65I <span class="op">>></span> Asp718I <span class="co"># same site, same cut => False</span>
+<span class="va">False</span>
+<span class="op">>>></span> Acc65I <span class="op">>></span> EcoRI <span class="co"># different site => False</span>
+<span class="va">False</span>
+<span class="op">>>></span> Acc65I <span class="op">>></span> KpnI <span class="co"># same site, different cut => True</span>
+<span class="va">True</span>
+<span class="op">>>></span> <span class="co">########### x % y (fragments produced by x and fragments produced by y</span>
+<span class="op">>>></span> <span class="co"># can be directly ligated to each other)</span>
+<span class="op">>>></span> Acc65I <span class="op">%</span> Asp718I
+<span class="va">True</span>
+<span class="op">>>></span> Acc65I <span class="op">%</span> Acc65I
+<span class="va">True</span>
+<span class="op">>>></span> Acc65I <span class="op">%</span> KpnI <span class="co"># KpnI -> '3 overhang, Acc65I-> 5' overhang => False</span>
+<span class="va">False</span>
+<span class="op">>>></span>
+<span class="op">>>></span> SunI.elucidate()
+<span class="co">'C^GTAC_G'</span>
+<span class="op">>>></span> SunI <span class="op">==</span> Acc65I
+<span class="va">False</span>
+<span class="op">>>></span> SunI <span class="op">!=</span> Acc65I
+<span class="va">True</span>
+<span class="op">>>></span> SunI <span class="op">>></span> Acc65I
+<span class="va">False</span>
+<span class="op">>>></span> SunI <span class="op">%</span> Acc65I <span class="co"># different site, same overhang (5' GTAC) => True</span>
+<span class="va">True</span>
+<span class="op">>>></span> SmaI <span class="op">%</span> EcoRV <span class="co"># 2 Blunt enzymes, all blunt enzymes are compatible => True</span>
+<span class="va">True</span></code></pre></div>
+<h4 id="other-facilities-provided-by-the-enzyme-classes"><a name="1.7"></a>1.7 Other facilities provided by the enzyme classes</h4>
+<p>The <code class="inline">Restriction</code> class provides quite a number of others methods. We will not go through all of them, but only have a quick look to the most useful ones.</p>
+<p>Not all enzymes possess the same properties when it comes to the way they digest a DNA. If you want to know more about the way a particular enzyme cut you can use the three following methods. They are fairly straightforward to understand and refer to the ends that the enzyme produces: blunt, 5' overhanging (also called 3' recessed) sticky end and 3' overhanging (or 5' recessed) sticky end.</p>
+<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python"><span class="op">>>></span> EcoRI.is_blunt()
+<span class="va">False</span>
+<span class="op">>>></span> EcoRI.is_5overhang()
+<span class="va">True</span>
+<span class="op">>>></span> EcoRI.is_3overhang()
+<span class="va">False</span></code></pre></div>
+<p>A more detailled view of the restriction site can be produced using the <code class="inline">elucidate()</code> method. The <code class="inline">^</code> refers to the position of the cut in the sense strand of the sequence, <code class="inline">_</code> to the cut on the antisense or complementary strand. <code class="inline">^_</code> means blunt.</p>
+<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python"><span class="op">>>></span> EcoRI.elucidate()
+<span class="co">'G^AATT_C'</span>
+<span class="op">>>></span> KpnI.elucidate()
+<span class="co">'G_GTAC^C'</span>
+<span class="op">>>></span> EcoRV.elucidate()
+<span class="co">'GAT^_ATC'</span></code></pre></div>
+<p>The method <code class="inline">frequency()</code> will give you the statistical frequency of the enzyme site.</p>
+<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python"><span class="op">>>></span> EcoRI.frequency()
+<span class="dv">4096</span>
+<span class="op">>>></span> XhoII.elucidate()
+<span class="co">'R^GATC_Y'</span>
+<span class="op">>>></span> XhoII.frequency()
+<span class="dv">1024</span></code></pre></div>
+<p>To get the length of a the recognition sequence of an enzyme use the built-in function <code class="inline">len()</code>:</p>
+<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python"><span class="op">>>></span> <span class="bu">len</span>(EcoRI)
+<span class="dv">6</span>
+<span class="op">>>></span> BstXI.elucidate()
+<span class="co">'CCAN_NNNN^NTGG'</span>
+<span class="op">>>></span> <span class="bu">len</span>(BstXI)
+<span class="dv">12</span>
+<span class="op">>>></span> FokI.site
+<span class="co">'GGATG'</span>
+<span class="op">>>></span> FokI.elucidate() <span class="co"># FokI cut well outside its recognition site</span>
+<span class="co">'GGATGNNNNNNNNN^NNNN_N'</span>
+<span class="op">>>></span> <span class="bu">len</span>(FokI) <span class="co"># its length is the length of the recognition site</span>
+<span class="dv">5</span></code></pre></div>
+<p>Also interesting are the methods dealing with isoschizomers. For memory, two enzymes are <em>isoschizomers</em> if they share a same recognition site. A further division is made between isoschizomers (same name, recognise the same sequence and cut the same way) and <em>neoschizomers</em> which cut at different positions. <em>Equischizomer</em> is an arbitrary choice to design "isoschizomers_that_are_not_neoschizomers" as this last one was a bit long. Another set of method <c [...]
+<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python"><span class="op">>>></span> Acc65I.isoschizomers()
+[Asp718I, KpnI]
+<span class="op">>>></span> Acc65I.neoschizomers()
+[KpnI]
+<span class="op">>>></span> Acc65I.equischizomers()
+[Asp718I]
+<span class="op">>>></span> KpnI.elucidate()
+<span class="co">'G_GTAC^C'</span>
+<span class="op">>>></span> Acc65I.elucidate()
+<span class="co">'G^GTAC_C'</span>
+<span class="op">>>></span> KpnI.is_neoschizomer(Acc65I)
+<span class="va">True</span>
+<span class="op">>>></span> KpnI.is_neoschizomer(KpnI)
+<span class="va">False</span>
+<span class="op">>>></span> KpnI.is_isoschizomer(Acc65I)
+<span class="va">True</span>
+<span class="op">>>></span> KpnI.is_isoschizomer(KpnI)
+<span class="va">True</span>
+<span class="op">>>></span> KpnI.is_equischizomer(Acc65I)
+<span class="va">False</span>
+<span class="op">>>></span> KpnI.is_equischizomer(KpnI)
+<span class="va">True</span></code></pre></div>
+<p><code class="inline">suppliers()</code> will get you the list of all the suppliers of the enzyme. <code class="inline">all_suppliers()</code> will give you all the suppliers in the database.</p>
+<h3 id="the-restrictionbatch-class-a-class-to-deal-with-several-enzymes"><a name="2"></a>2. The RestrictionBatch class: a class to deal with several enzymes</h3>
+<p>If you want to make a restriction map of a sequence, using individual enzymes can become tedious and will endures a big overhead due to the repetitive conversion of the sequence to a <code class="inline">FormattedSeq</code> (see <a href="#5">Chapter 5</a>). <code class="inline">Restriction</code> provides a class to make easier the use of large number of enzymes in one go: <code class="inline">RestrictionBatch</code>. <code class="inline">RestrictionBatch</code> will help you to manip [...]
+<h4 id="creating-a-restrictionbatch"><a name="2.1"></a><span class="mozTocH4"></span>2.1 Creating a RestrictionBatch</h4>
+<p>You can initiate a restriction batch by passing it a list of enzymes or enzyme names as argument.</p>
+<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python"><span class="op">>>></span> rb <span class="op">=</span> RestrictionBatch([EcoRI])
+<span class="op">>>></span> rb
+RestrictionBatch([<span class="st">'EcoRI'</span>])
+<span class="op">>>></span> rb2 <span class="op">=</span> RestrictionBatch([<span class="st">'EcoRI'</span>])
+<span class="op">>>></span> rb2
+RestrictionBatch([<span class="st">'EcoRI'</span>])
+<span class="op">>>></span> rb <span class="op">==</span> rb2
+<span class="va">True</span></code></pre></div>
+<p>Adding a new enzyme to a restriction batch is easy:</p>
+<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python"><span class="op">>>></span> rb.add(KpnI)
+<span class="op">>>></span> rb
+RestrictionBatch([<span class="st">'EcoRI'</span>, <span class="st">'KpnI'</span>])
+<span class="op">>>></span> rb <span class="op">+=</span> EcoRV
+<span class="op">>>></span> rb
+RestrictionBatch([<span class="st">'EcoRI'</span>, <span class="st">'EcoRV'</span>, <span class="st">'KpnI'</span>])])</code></pre></div>
+<p>Another way to create a RestrictionBatch is by simply adding restriction enzymes together, this is particularly useful for small batches:</p>
+<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python"><span class="op">>>></span> rb3 <span class="op">=</span> EcoRI <span class="op">+</span> KpnI <span class="op">+</span> EcoRV
+<span class="op">>>></span> rb3
+RestrictionBatch([<span class="st">'EcoRI'</span>, <span class="st">'EcoRV'</span>, <span class="st">'KpnI'</span>])</code></pre></div>
+<h4 id="restricting-a-restrictionbatch-to-a-particular-supplier"><a name="2.2"></a>2.2 Restricting a RestrictionBatch to a particular supplier</h4>
+<p>The Restriction package is based upon the <strong>REBASE</strong> database. This database gives a list of suppliers for each enzyme. It would be a shame not to make use of this facility. You can produce a <code class="inline">RestrictionBatch</code> containing only enzymes from one or a few supplier(s). Here is how to do it:</p>
+<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python"><span class="op">>>></span> rb_supp <span class="op">=</span> RestrictionBatch(first<span class="op">=</span>[], suppliers<span class="op">=</span>[<span class="st">'C'</span>,<span class="st">'B'</span>,<span class="st">'E'</span>,<span class="st">'I'</span>,<span class="st">'K'</span>,<span class="st">'J'</span>,<span class="st">'M'</span>,
+<span class="co">'O'</span>,<span class="st">'N'</span>,<span class="st">'Q'</span>,<span class="st">'S'</span>,<span class="st">'R'</span>,<span class="st">'V'</span>,<span class="st">'Y'</span>,<span class="st">'X'</span>])
+<span class="op">>>></span> <span class="co"># This will create a RestrictionBatch with the all enzymes which possess a s</span>
+upplier.
+<span class="op">>>></span> <span class="bu">len</span>(rb_supp) <span class="co"># May 2016</span>
+<span class="dv">622</span></code></pre></div>
+<p>The argument <code class="inline">suppliers</code> take a list of one or several single letter codes corresponding to the supplier(s). The codes are the same as defined in REBASE. As it would be a pain to have to remember each supplier code, <code class="inline">RestrictionBatch</code> provides a method which show the pair code <=> supplier:</p>
+<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python"><span class="op">>>></span> RestrictionBatch.show_codes() <span class="co"># as of May 2016 REBASE release.</span>
+C <span class="op">=</span> Minotech Biotechnology
+B <span class="op">=</span> Life Technologies
+E <span class="op">=</span> Agilent Technologies
+I <span class="op">=</span> SibEnzyme Ltd.
+K <span class="op">=</span> Takara Bio Inc.
+J <span class="op">=</span> Nippon Gene Co., Ltd.
+M <span class="op">=</span> Roche Applied Science
+O <span class="op">=</span> Toyobo Biochemicals
+N <span class="op">=</span> New England Biolabs
+Q <span class="op">=</span> Molecular Biology Resources <span class="op">-</span> CHIMERx
+S <span class="op">=</span> Sigma Chemical Corporation
+R <span class="op">=</span> Promega Corporation
+V <span class="op">=</span> Vivantis Technologies
+Y <span class="op">=</span> SinaClon BioScience Co.
+X <span class="op">=</span> EURx Ltd.
+<span class="op">>>></span> <span class="co"># You can now choose a code and built your RestrictionBatch</span></code></pre></div>
+<p>This way of producing a <code class="inline">RestrictionBatch</code> can drastically reduce the amount of useless output from a restriction analysis, limiting the search to enzymes that you can get hold of and limiting the risks of nervous breakdown. Nothing is more frustrating than to get the perfect enzyme for a sub-cloning only to find it's not commercially available.</p>
+<h4 id="adding-enzymes-to-a-restrictionbatch"><a name="2.3"></a>2.3 Adding enzymes to a RestrictionBatch</h4>
+<p>Adding an enzyme to a batch if the enzyme is already present will not raise an exception, but will have no effects. Sometimes you want to get an enzyme from a <code class="inline">RestrictionBatch</code> or add it to the batch if it is not present. You will use the <code class="inline">get</code> method setting the second argument <code class="inline">add</code> to <code class="inline">True</code>.</p>
+<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python"><span class="op">>>></span> rb3
+RestrictionBatch([<span class="st">'EcoRI'</span>, <span class="st">'EcoRV'</span>, <span class="st">'KpnI'</span>])
+<span class="op">>>></span> rb3.add(EcoRI)
+<span class="op">>>></span> rb3
+RestrictionBatch([<span class="st">'EcoRI'</span>, <span class="st">'EcoRV'</span>, <span class="st">'KpnI'</span>])
+<span class="op">>>></span> rb3.get(EcoRI)
+EcoRI
+<span class="op">>>></span> rb3.get(SmaI)
+
+Traceback (most recent call last):
+ File <span class="st">"<pyshell#4>"</span>, line <span class="dv">1</span>, <span class="op">in</span> <span class="op">-</span>toplevel<span class="op">-</span>
+ rb3.get(SmaI)
+ File <span class="st">"/usr/lib/Python2.3/site-packages/Bio/Restriction/Restriction.py"</span>, line <span class="dv">1800</span>, <span class="op">in</span> get
+ <span class="cf">raise</span> <span class="pp">ValueError</span>, <span class="st">'enzyme </span><span class="sc">%s</span><span class="st"> is not in RestrictionBatch'</span><span class="op">%</span>e.<span class="va">__name__</span>
+<span class="pp">ValueError</span>: enzyme SmaI <span class="op">is</span> <span class="op">not</span> <span class="op">in</span> RestrictionBatch
+<span class="op">>>></span> rb3.get(SmaI, add<span class="op">=</span><span class="va">True</span>)
+SmaI
+<span class="op">>>></span> rb3
+RestrictionBatch([<span class="st">'EcoRI'</span>, <span class="st">'EcoRV'</span>, <span class="st">'KpnI'</span>, <span class="st">'SmaI'</span>])</code></pre></div>
+<h4 id="removing-enzymes-from-a-restrictionbatch"><a name="2.4"></a>2.4 Removing enzymes from a RestrictionBatch</h4>
+<p>Removing enzymes from a batch is done using the <code class="inline">remove()</code> method. If the enzyme is not present in the batch this will raise a <code class="inline">KeyError</code>. If the value you want to remove is not an enzyme this will raise a <code class="inline">ValueError</code>.</p>
+<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python"><span class="op">>>></span> rb3.remove(EcoRI)
+<span class="op">>>></span> rb3
+RestrictionBatch([<span class="st">'EcoRV'</span>, <span class="st">'KpnI'</span>, <span class="st">'SmaI'</span>])
+<span class="op">>>></span> rb3.remove(EcoRI)
+
+Traceback (most recent call last):
+ File <span class="st">"<pyshell#14>"</span>, line <span class="dv">1</span>, <span class="op">in</span> <span class="op">-</span>toplevel<span class="op">-</span>
+ rb3.remove(<span class="st">'EcoRI'</span>)
+ File <span class="st">"/usr/lib/Python2.3/site-packages/Bio/Restriction/Restriction.py"</span>, line <span class="dv">1839</span>, <span class="op">in</span> remove
+ <span class="cf">return</span> Set.remove(<span class="va">self</span>, <span class="va">self</span>.<span class="bu">format</span>(other))
+ File <span class="st">"/usr/lib/Python2.3/sets.py"</span>, line <span class="dv">534</span>, <span class="op">in</span> remove
+ <span class="kw">del</span> <span class="va">self</span>._data[element]
+<span class="pp">KeyError</span>: EcoRI
+<span class="op">>>></span> rb3 <span class="op">+=</span> EcoRI
+<span class="op">>>></span> rb3
+RestrictionBatch([<span class="st">'EcoRI'</span>, <span class="st">'EcoRV'</span>, <span class="st">'KpnI'</span>, <span class="st">'SmaI'</span>])
+<span class="op">>>></span> rb3.remove(<span class="st">'EcoRI'</span>)
+<span class="op">>>></span> rb3
+RestrictionBatch([<span class="st">'EcoRV'</span>, <span class="st">'KpnI'</span>, <span class="st">'SmaI'</span>])
+<span class="op">>>></span> rb3.remove(<span class="st">'spam'</span>)
+
+Traceback (most recent call last):
+ File <span class="st">"<pyshell#18>"</span>, line <span class="dv">1</span>, <span class="op">in</span> <span class="op">-</span>toplevel<span class="op">-</span>
+ rb3.remove(<span class="st">'spam'</span>)
+ File <span class="st">"/usr/lib/Python2.3/site-packages/Bio/Restriction/Restriction.py"</span>, line <span class="dv">1839</span>, <span class="op">in</span> remove
+ <span class="cf">return</span> Set.remove(<span class="va">self</span>, <span class="va">self</span>.<span class="bu">format</span>(other))
+ File <span class="st">"/usr/lib/Python2.3/site-packages/Bio/Restriction/Restriction.py"</span>, line <span class="dv">1871</span>, <span class="op">in</span> <span class="bu">format</span>
+ <span class="cf">raise</span> <span class="pp">ValueError</span>, <span class="st">'</span><span class="sc">%s</span><span class="st"> is not a RestrictionType'</span><span class="op">%</span>y.__class__
+<span class="pp">ValueError</span>: <span class="op"><</span><span class="bu">type</span> <span class="st">'str'</span><span class="op">></span> <span class="op">is</span> <span class="op">not</span> a RestrictionType</code></pre></div>
+<h4 id="manipulating-restrictionbatch"><a name="2.5"></a>2.5 Manipulating RestrictionBatch</h4>
+<p>You can not, however, add batches together, as they are Python <code class="inline">sets</code>. You must use the pipe operator <code class="inline">|</code> instead. You can find the intersection between 2 batches using <code class="inline">&</code> (see the Python documentation about <code class="inline">sets</code> for more information.</p>
+<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python"><span class="op">>>></span> rb3 <span class="op">=</span> EcoRI <span class="op">+</span> KpnI <span class="op">+</span> EcoRV
+<span class="op">>>></span> rb3
+RestrictionBatch([<span class="st">'EcoRI'</span>, <span class="st">'EcoRV'</span>, <span class="st">'KpnI'</span>])
+<span class="op">>>></span> rb4 <span class="op">=</span> SmaI <span class="op">+</span> PstI
+<span class="op">>>></span> rb4
+RestrictionBatch([<span class="st">'PstI'</span>, <span class="st">'SmaI'</span>])
+<span class="op">>>></span> rb3 <span class="op">+</span> rb4
+
+Traceback (most recent call last):
+ File <span class="st">"<pyshell#23>"</span>, line <span class="dv">1</span>, <span class="op">in</span> <span class="op">-</span>toplevel<span class="op">-</span>
+ rb3 <span class="op">+</span> rb4
+ File <span class="st">"/usr/lib/Python2.3/site-packages/Bio/Restriction/Restriction.py"</span>, line <span class="dv">1829</span>, <span class="op">in</span> <span class="fu">__add__</span>
+ new.add(other)
+ File <span class="st">"/usr/lib/Python2.3/site-packages/Bio/Restriction/Restriction.py"</span>, line <span class="dv">1848</span>, <span class="op">in</span> add
+ <span class="cf">return</span> Set.add(<span class="va">self</span>, <span class="va">self</span>.<span class="bu">format</span>(other))
+ File <span class="st">"/usr/lib/Python2.3/site-packages/Bio/Restriction/Restriction.py"</span>, line <span class="dv">1871</span>, <span class="op">in</span> <span class="bu">format</span>
+ <span class="cf">raise</span> <span class="pp">ValueError</span>, <span class="st">'</span><span class="sc">%s</span><span class="st"> is not a RestrictionType'</span><span class="op">%</span>y.__class__
+<span class="pp">ValueError</span>: <span class="op"><</span><span class="kw">class</span> <span class="st">'Bio.Restriction.Restriction.RestrictionBatch'</span><span class="op">></span> <span class="op">is</span> <span class="op">not</span> a RestrictionType
+<span class="op">>>></span> rb3 <span class="op">|</span> rb4
+RestrictionBatch([<span class="st">'EcoRI'</span>, <span class="st">'EcoRV'</span>, <span class="st">'KpnI'</span>, <span class="st">'PstI'</span>, <span class="st">'SmaI'</span>])
+<span class="op">>>></span> rb3 <span class="op">&</span> rb4
+RestrictionBatch([])
+<span class="op">>>></span> rb4 <span class="op">+=</span> EcoRI
+<span class="op">>>></span> rb4
+RestrictionBatch([<span class="st">'EcoRI'</span>, <span class="st">'PstI'</span>, <span class="st">'SmaI'</span>])
+<span class="op">>>></span> rb3 <span class="op">&</span> rb4
+RestrictionBatch([<span class="st">'EcoRI'</span>])</code></pre></div>
+<h4 id="analysing-sequences-with-a-restrictionbatch"><a name="2.6"></a>2.6 Analysing sequences with a RestrictionBatch</h4>
+<p>To analyse a sequence for potential site, you can use the <code class="inline">search</code> method of the batch, the same way you did for restriction enzymes. The results is no longer a list however, but a dictionary. The keys of the dictionary are the names of the enzymes and the value a list of position site. <code class="inline">RestrictionBatch</code> does not implement a <code class="inline">catalyse</code> method, as it would not have a real meaning when used with large batch.</p>
+<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python"><span class="op">>>></span> new_seq <span class="op">=</span> Seq(<span class="st">'TTCAAAAAAAAAAAAAAAAAAAAAAAAAAAAGAA'</span>, IUPACAmbiguousDNA())
+<span class="op">>>></span> rb.search(new_seq)
+{<span class="st">'KpnI'</span>: [], <span class="st">'EcoRV'</span>: [], <span class="st">'EcoRI'</span>: []}
+<span class="op">>>></span> rb.search(new_seq, linear<span class="op">=</span><span class="va">False</span>)
+{<span class="st">'KpnI'</span>: [], <span class="st">'EcoRV'</span>: [], <span class="st">'EcoRI'</span>: [<span class="dv">33</span>]}</code></pre></div>
+<h4 id="other-restrictionbatch-methods"><a name="2.7"></a>2.7 Other RestrictionBatch methods</h4>
+<p>Amongst the other methods provided by <code class="inline">RestrictionBatch</code>, <code class="inline">elements()</code> which return a list of all the element names alphabetically sorted, is certainly the most useful.</p>
+<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python"><span class="op">>>></span> rb <span class="op">=</span> EcoRI <span class="op">+</span> KpnI <span class="op">+</span> EcoRV
+<span class="op">>>></span> rb.elements()
+[<span class="st">'EcoRI'</span>, <span class="st">'EcoRV'</span>, <span class="st">'KpnI'</span>]</code></pre></div>
+<p>If you don't care about the alphabetical order use the method <code class="inline">as_string()</code>, to get the same thing a bit faster. The list is not sorted. The order is random as Python sets are dictionary.</p>
+<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python"><span class="op">>>></span> rb <span class="op">=</span> EcoRI <span class="op">+</span> KpnI <span class="op">+</span> EcoRV
+<span class="op">>>></span> rb.as_string()
+[<span class="st">'EcoRI'</span>, <span class="st">'KpnI'</span>, <span class="st">'EcoRV'</span>]</code></pre></div>
+<p>Other <code class="inline">RestrictionBatch</code> methods are generally used for particular purposes and will not be discussed here. See the <a href="https://github.com/biopython/biopython/tree/master/Bio/Restriction">source</a> if you are interested.</p>
+<h3 id="allenzymes-and-commonly-two-preconfigured-restrictionbatches"><a name="3"></a>3. AllEnzymes and CommOnly: two preconfigured RestrictionBatches</h3>
+<p>While it is sometime practical to produce a <code class="inline">RestrictionBatch</code> of your own you will certainly more frequently use the two batches provided with the <code class="inline">Restriction</code> packages: <code class="inline">AllEnzymes</code> and <code class="inline">CommOnly</code>. These two batches contain respectively all the enzymes in the database and only the enzymes which have a commercial supplier. They are rather big, but that's what make them useful. Wit [...]
+<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python"><span class="op">>>></span> <span class="bu">len</span>(AllEnzymes)
+<span class="dv">778</span>
+<span class="op">>>></span> <span class="bu">len</span>(CommOnly)
+<span class="dv">622</span>
+<span class="op">>>></span> AllEnzymes.search(new_seq) ...</code></pre></div>
+<p>There is not a lot to say about them apart the fact that they are present. They are really normal batches, and you can use them as any other batch.</p>
+<h3 id="the-analysis-class-even-simpler-restriction-analysis"><a name="4"></a>4. The Analysis class: even simpler restriction analysis</h3>
+<p><code class="inline">RestrictionBatch</code> can give you a dictionary with the sites for all the enzymes in a batch. However, it is sometime nice to get something a bit easier to read than a Python dictionary. Complex restriction analysis are not easy with <code class="inline">RestrictionBatch</code>. Some refinements in the way to search a sequence for restriction sites will help. <code class="inline">Analysis</code> provides a serie of command to customise the results obtained from [...]
+<h4 id="setting-up-an-analysis"><a name="4.1"></a>4.1 Setting up an Analysis</h4>
+<p>To build a restriction analysis you will need a <code class="inline">RestrictionBatch</code> and a sequence and to tell it if the sequence is linear or circular. The first argument <code class="inline">Analysis</code> takes is the restriction batch, the second is the sequence. If the third argument is not provided, <code class="inline">Analysis</code> will assume the sequence is linear.</p>
+<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python"><span class="op">>>></span> new_seq <span class="op">=</span> Seq(<span class="st">'TTCAAAAAAAAAAAAAAAAAAAAAAAAAAAAGAA'</span>, IUPACAmbiguousDNA())
+<span class="op">>>></span> rb <span class="op">=</span> RestrictionBatch([EcoRI, KpnI, EcoRV])
+<span class="op">>>></span> Ana <span class="op">=</span> Analysis(rb, new_seq, linear<span class="op">=</span><span class="va">False</span>)
+<span class="op">>>></span> Ana
+Analysis(RestrictionBatch([<span class="st">'EcoRI'</span>, <span class="st">'EcoRV'</span>, <span class="st">'KpnI'</span>]),Seq(<span class="st">'TTCAAAAAAAAAAAAAAAAAA</span>
+<span class="st">AAAAAAAAAAGAA'</span>, IUPACAmbiguousDNA()),<span class="va">False</span>)</code></pre></div>
+<h4 id="full-restriction-analysis"><a name="4.2"></a>4.2 Full restriction analysis</h4>
+<p>Once you have created your new <code class="inline">Analysis</code>, you can use it to get a restriction analysis of your sequence. The way to make a full restriction analysis of the sequence is:</p>
+<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python"><span class="op">>>></span> Ana.full()
+{<span class="st">'KpnI'</span>: [], <span class="st">'EcoRV'</span>: [], <span class="st">'EcoRI'</span>: [<span class="dv">33</span>]}</code></pre></div>
+<p>This is much the same as the output of a <code class="inline">RestrictionBatch.search</code> method. You will get a more easy to read output with <code class="inline">print_that</code> used without argument:</p>
+<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python"><span class="op">>>></span> <span class="co"># let's create a something a bit more complex to analyse.</span>
+<span class="op">>>></span>
+<span class="op">>>></span> rb <span class="op">=</span> RestrictionBatch([], [<span class="st">'C'</span>]) <span class="co"># we will explain the meaning of the</span>
+<span class="op">>>></span> <span class="co"># double list argument later.</span>
+<span class="op">>>></span>
+<span class="op">>>></span> multi_site <span class="op">=</span> Seq.Seq(<span class="st">'AAA'</span> <span class="op">+</span> EcoRI.site <span class="op">+</span> <span class="st">'G'</span> <span class="op">+</span> KpnI.site <span class="op">+</span> EcoRV.site <span class="op">+</span>
+ <span class="co">'CT'</span> <span class="op">+</span> SmaI.site <span class="op">+</span> <span class="st">'GT'</span> <span class="op">+</span> FokI.site <span class="op">+</span> <span class="st">'GAAAGGGC'</span> <span class="op">+</span>
+ EcoRI.site <span class="op">+</span> <span class="st">'ACGT'</span>, IUPACAmbiguousDNA())
+<span class="op">>>></span> Analong <span class="op">=</span> Analysis(rb, multi_site)
+<span class="op">>>></span> Analong.full()
+{BglI: [], BstEII: [], AsuII: [], HinfI: [], SfiI: [], PspPI: [], BsiSI: [<span class="dv">27</span>], S
+alI: [], SlaI: [], NcoI: [], NotI: [], PstI: [], StyI: [], BseBI: [], PvuII: [],
+HindIII: [], BglII: [], ApaLI: [], TaqI: [], BssAI: [], AluI: [], SstI: [], Bse
+CI: [], Sau3AI: [], HpaI: [], SnaBI: [], NheI: [], BclI: [], KpnI: [<span class="dv">16</span>], NruI: [
+], MspCI: [], BshFI: [], CspAI: [], RsaI: [<span class="dv">14</span>], EcoRV: [<span class="dv">20</span>], SphI: [], BamHI: []
+, MboI: [], SgrBI: [], SspI: [], ScaI: [], XbaI: [], SseBI: [], NaeI: [], EcoRI:
+[<span class="dv">5</span>, <span class="dv">47</span>], SmaI: [<span class="dv">28</span>], BseAI: []}
+<span class="op">>>></span>
+<span class="op">>>></span> <span class="co"># The results are here but it is difficult to read. let's try print_that</span>
+<span class="op">>>></span>
+<span class="op">>>></span> Analong.print_that()
+
+BsiSI : <span class="dv">27</span>.
+RsaI : <span class="dv">14</span>.
+EcoRI : <span class="dv">5</span>, <span class="dv">47</span>.
+EcoRV : <span class="dv">20</span>.
+KpnI : <span class="dv">16</span>.
+SmaI : <span class="dv">28</span>.
+
+ Enzymes which do <span class="op">not</span> cut the sequence.
+
+AluI BshFI MboI Sau3AI TaqI BseBI HinfI PspPI
+ApaLI AsuII BamHI BclI BglII BseAI BseCI BssAI
+CspAI HindIII HpaI MspCI NaeI NcoI NheI NruI
+PstI PvuII SalI ScaI SgrBI SlaI SnaBI SphI
+SseBI SspI SstI StyI XbaI BstEII NotI BglI
+SfiI</code></pre></div>
+<p>Much clearer, is'nt ? The output is optimised for a shell 80 columns wide. If the output seems odd, check that the width of your shell is at least 80 columns.</p>
+<h4 id="changing-the-title"><a name="4.3"></a>4.3 Changing the title</h4>
+<p>You can provide a title to the analysis and modify the sentence 'Enzymes which do not cut the sequence', by setting the two optional arguments of <code class="inline">print_that</code>, <code class="inline">title</code> and <code class="inline">s1</code>. No formatting will be done on these strings so if you have to include the newline (<code class="inline">\n</code>) as you see fit:</p>
+<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python"><span class="op">>>></span> Analong.print_that(<span class="va">None</span>, title<span class="op">=</span><span class="st">'sequence = multi_site</span><span class="ch">\n\n</span><span class="st">'</span>)
+
+sequence <span class="op">=</span> multi_site
+
+BsiSI : <span class="dv">27</span>.
+RsaI : <span class="dv">14</span>.
+EcoRI : <span class="dv">5</span>, <span class="dv">47</span>.
+EcoRV : <span class="dv">20</span>.
+KpnI : <span class="dv">16</span>.
+SmaI : <span class="dv">28</span>.
+
+ Enzymes which do <span class="op">not</span> cut the sequence.
+
+AluI BshFI MboI Sau3AI TaqI BseBI HinfI PspPI
+ApaLI AsuII BamHI BclI BglII BseAI BseCI BssAI
+CspAI HindIII HpaI MspCI NaeI NcoI NheI NruI
+PstI PvuII SalI ScaI SgrBI SlaI SnaBI SphI
+SseBI SspI SstI StyI XbaI BstEII NotI BglI
+SfiI
+
+<span class="op">>>></span> Analong.print_that(<span class="va">None</span>, title<span class="op">=</span><span class="st">'sequence = multi_site</span><span class="ch">\n\n</span><span class="st">'</span>,
+ s1<span class="op">=</span><span class="st">'</span><span class="ch">\n</span><span class="st"> no site:</span><span class="ch">\n\n</span><span class="st">'</span>)
+
+sequence <span class="op">=</span> multi_site
+
+BsiSI : <span class="dv">27</span>.
+RsaI : <span class="dv">14</span>.
+EcoRI : <span class="dv">5</span>, <span class="dv">47</span>.
+EcoRV : <span class="dv">20</span>.
+KpnI : <span class="dv">16</span>.
+SmaI : <span class="dv">28</span>.
+
+ no site:
+
+AluI BshFI MboI Sau3AI TaqI BseBI HinfI PspPI
+ApaLI AsuII BamHI BclI BglII BseAI BseCI BssAI
+CspAI HindIII HpaI MspCI NaeI NcoI NheI NruI
+PstI PvuII SalI ScaI SgrBI SlaI SnaBI SphI
+SseBI SspI SstI StyI XbaI BstEII NotI BglI
+SfiI</code></pre></div>
+<h4 id="customising-the-output"><a name="4.4"></a>4.4 Customising the output</h4>
+<p>You can modify some aspects of the output interactively. There is three main type of output, two listing types (alphabetically sorted and sorted by number of site) and map-like type. To change the output, use the method <code class="inline">print_as()</code> of <code class="inline">Analysis</code>. The change of output is permanent for the instance of <code class="inline">Analysis</code> (that is until the next time you use <code class="inline">print_as()</code>). The argument of <cod [...]
+<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python"><span class="op">>>></span> Analong.print_as(<span class="st">'map'</span>)
+<span class="op">>>></span> Analong.print_that()
+
+ <span class="dv">5</span> EcoRI
+ <span class="op">|</span>
+ <span class="op">|</span> <span class="dv">14</span> RsaI
+ <span class="op">|</span> <span class="op">|</span>
+ <span class="op">|</span> <span class="op">|</span> <span class="dv">16</span> KpnI
+ <span class="op">|</span> <span class="op">|</span> <span class="op">|</span>
+ <span class="op">|</span> <span class="op">|</span> <span class="op">|</span> <span class="dv">20</span> EcoRV
+ <span class="op">|</span> <span class="op">|</span> <span class="op">|</span> <span class="op">|</span>
+ <span class="op">|</span> <span class="op">|</span> <span class="op">|</span> <span class="op">|</span> <span class="dv">27</span> BsiSI
+ <span class="op">|</span> <span class="op">|</span> <span class="op">|</span> <span class="op">|</span> <span class="op">|</span>
+ <span class="op">|</span> <span class="op">|</span> <span class="op">|</span> <span class="op">|</span> <span class="op">|</span><span class="dv">28</span> SmaI
+ <span class="op">|</span> <span class="op">|</span> <span class="op">|</span> <span class="op">|</span> <span class="op">||</span>
+ <span class="op">|</span> <span class="op">|</span> <span class="op">|</span> <span class="op">|</span> <span class="op">||</span> <span class="dv">47</span> EcoRI
+ <span class="op">|</span> <span class="op">|</span> <span class="op">|</span> <span class="op">|</span> <span class="op">||</span> <span class="op">|</span>
+AAAGAATTCGGGTACCGATATCCTCCCGGGGTGGATGGAAAGGGCGAATTCACGT
+<span class="op">|||||||||||||||||||||||||||||||||||||||||||||||||||||||</span>
+TTTCTTAAGCCCATGGCTATAGGAGGGCCCCACCTACCTTTCCCGCTTAAGTGCA
+<span class="dv">1</span> <span class="dv">55</span>
+
+
+ Enzymes which do <span class="op">not</span> cut the sequence.
+
+AluI BshFI MboI Sau3AI TaqI BseBI HinfI PspPI
+ApaLI AsuII BamHI BclI BglII BseAI BseCI BssAI
+CspAI HindIII HpaI MspCI NaeI NcoI NheI NruI
+PstI PvuII SalI ScaI SgrBI SlaI SnaBI SphI
+SseBI SspI SstI StyI XbaI BstEII NotI BglI
+SfiI
+
+<span class="op">>>></span> Analong.print_as(<span class="st">'number'</span>)
+<span class="op">>>></span> Analong.print_that()
+
+
+
+enzymes which cut <span class="dv">1</span> times :
+
+BsiSI : <span class="dv">27</span>.
+RsaI : <span class="dv">14</span>.
+EcoRV : <span class="dv">20</span>.
+KpnI : <span class="dv">16</span>.
+SmaI : <span class="dv">28</span>.
+
+
+enzymes which cut <span class="dv">2</span> times :
+
+EcoRI : <span class="dv">5</span>, <span class="dv">47</span>.
+
+ Enzymes which do <span class="op">not</span> cut the sequence.
+
+AluI BshFI MboI Sau3AI TaqI BseBI HinfI PspPI
+ApaLI AsuII BamHI BclI BglII BseAI BseCI BssAI
+CspAI HindIII HpaI MspCI NaeI NcoI NheI NruI
+PstI PvuII SalI ScaI SgrBI SlaI SnaBI SphI
+SseBI SspI SstI StyI XbaI BstEII NotI BglI
+SfiI
+
+<span class="op">>>></span></code></pre></div>
+<p>To come back to the previous behaviour:</p>
+<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python"><span class="op">>>></span> Analong.print_as(<span class="st">'alpha'</span>)
+<span class="op">>>></span> Analong.print_that()
+
+BsiSI : <span class="dv">27</span>.
+RsaI : <span class="dv">14</span>.
+EcoRI : <span class="dv">5</span>, <span class="dv">47</span>.
+EcoRV : <span class="dv">20</span>.
+etc ...</code></pre></div>
+<h4 id="fancier-restriction-analysis"><a name="4.5"></a>4.5 Fancier restriction analysis</h4>
+<p>I will not go into the detail for each single method, here are all the functions that are available. Most are perfectly self explanatory and the others are fairly well documented (use <code class="inline">help('Analysis.command_name')</code>). The methods are:</p>
+<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python">full(<span class="va">self</span>,linear<span class="op">=</span><span class="va">True</span>)
+blunt(<span class="va">self</span>,dct <span class="op">=</span> <span class="va">None</span>)
+overhang5(<span class="va">self</span>, dct<span class="op">=</span><span class="va">None</span>)
+overhang3(<span class="va">self</span>, dct<span class="op">=</span><span class="va">None</span>)
+defined(<span class="va">self</span>,dct<span class="op">=</span><span class="va">None</span>)
+with_sites(<span class="va">self</span>, dct<span class="op">=</span><span class="va">None</span>)
+without_site(<span class="va">self</span>, dct<span class="op">=</span><span class="va">None</span>)
+with_N_sites(<span class="va">self</span>, N, dct<span class="op">=</span><span class="va">None</span>)
+with_number_list(<span class="va">self</span>, <span class="bu">list</span>, dct<span class="op">=</span><span class="va">None</span>)
+with_name(<span class="va">self</span>, names, dct<span class="op">=</span><span class="va">None</span>)
+with_site_size(<span class="va">self</span>, site_size, dct<span class="op">=</span><span class="va">None</span>)
+only_between(<span class="va">self</span>, start, end, dct<span class="op">=</span><span class="va">None</span>)
+between(<span class="va">self</span>,start, end, dct<span class="op">=</span><span class="va">None</span>)
+show_only_between(<span class="va">self</span>, start, end, dct<span class="op">=</span><span class="va">None</span>)
+only_outside(<span class="va">self</span>, start, end, dct <span class="op">=</span><span class="va">None</span>)
+outside(<span class="va">self</span>, start, end, dct<span class="op">=</span><span class="va">None</span>)
+do_not_cut(<span class="va">self</span>, start, end, dct <span class="op">=</span><span class="va">None</span>)</code></pre></div>
+<p>Using these methods is simple:</p>
+<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python"><span class="op">>>></span> new_seq <span class="op">=</span> Seq(<span class="st">'TTCAAAAAAAAAAAAAAAAAAAAAAAAAAAAGAA'</span>, IUPACAmbiguousDNA())
+<span class="op">>>></span> rb <span class="op">=</span> RestrictionBatch([EcoRI, KpnI, EcoRV])
+<span class="op">>>></span> Ana <span class="op">=</span> Analysis(rb, new_seq, linear<span class="op">=</span><span class="va">False</span>)
+<span class="op">>>></span> Ana
+Analysis(RestrictionBatch([<span class="st">'EcoRI'</span>, <span class="st">'EcoRV'</span>, <span class="st">'KpnI'</span>]),Seq(<span class="st">'TTCAAAAAAAAAAAAAAAAAA</span>
+<span class="st">AAAAAAAAAAGAA'</span>, IUPACAmbiguousDNA()),<span class="va">False</span>)
+<span class="op">>>></span> Ana.blunt() <span class="co"># output only the result for enzymes which cut blunt</span>
+{<span class="st">'EcoRV'</span>: []}
+<span class="op">>>></span> Ana.full() <span class="co"># all the enzymes in the RestrictionBatch</span>
+{<span class="st">'KpnI'</span>: [], <span class="st">'EcoRV'</span>: [], <span class="st">'EcoRI'</span>: [<span class="dv">33</span>]}
+<span class="op">>>></span> Ana.with_sites() <span class="co"># output only the result for enzymes which have a site</span>
+{<span class="st">'EcoRI'</span>: [<span class="dv">33</span>]}
+<span class="op">>>></span> Ana.without_site() <span class="co"># output only the enzymes which have no site</span>
+{<span class="st">'KpnI'</span>: [], <span class="st">'EcoRV'</span>: []}
+<span class="op">>>></span> Ana.only_between(<span class="dv">1</span>, <span class="dv">20</span>) <span class="co"># the enzymes which cut between position 1 and 20</span>
+{}
+<span class="op">>>></span> Ana.only_between(<span class="dv">20</span>, <span class="dv">34</span>) <span class="co"># etc...</span>
+{<span class="st">'EcoRI'</span>: [<span class="dv">33</span>]}
+<span class="op">>>></span> Ana.only_outside(<span class="dv">20</span>, <span class="dv">34</span>)
+{}
+<span class="op">>>></span> Ana.with_name([EcoRI])
+{<span class="st">'EcoRI'</span>: [<span class="dv">33</span>]}
+<span class="op">>>></span></code></pre></div>
+<p>To get a nice output, you still use <code class="inline">print_that</code> but this time with the command you want executed as argument.</p>
+<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python"><span class="op">>>></span> Ana.print_that(Ana.blunt())
+
+ Enzymes which do <span class="op">not</span> cut the sequence.
+
+EcoRV
+
+<span class="op">>>></span> pt <span class="op">=</span> Ana.print_that
+<span class="op">>>></span> pt(Ana.with_sites())
+
+EcoRI : <span class="dv">33</span>.
+
+<span class="op">>>></span> pt(Ana.without_site())
+
+ Enzymes which do <span class="op">not</span> cut the sequence.
+
+EcoRV KpnI
+
+<span class="op">>>></span> <span class="co"># etc ...</span></code></pre></div>
+<h4 id="more-complex-analysis"><a name="4.6"></a>4.6 More complex analysis</h4>
+<p>All of these methods (except <code class="inline">full()</code> which, well ... do a full restriction analysis) can be supplied with an additional dictionary. If no dictionary is supplied a full restriction analysis is used as starting point. Otherwise the dictionary provided by the argument <code class="inline">dct</code> is used. The dictionary must be formatted as the result of <code class="inline">RestrictionBatch.search</code>. Therefore of the form <code class="inline">{'enzyme_ [...]
+<p>Using this method you can build really complex query by chaining several method one after the other. For example if you want all the enzymes which are 5' overhang and cut the sequence only once, you have two ways to go:</p>
+<p>The hard way consist to build a restriction batch containing only 5' overhang enzymes and use this batch to create a new <code class="inline">Analysis</code> instance and then use the method <code class="inline">with_N_sites()</code> as follow:</p>
+<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python"><span class="op">>>></span> rbov5 <span class="op">=</span> RestrictionBatch([x <span class="cf">for</span> x <span class="op">in</span> rb <span class="cf">if</span> x.is_5overhang()])
+<span class="op">>>></span> Anaov5 <span class="op">=</span> Analysis(rbov5, new_seq, linear<span class="op">=</span><span class="va">False</span>)
+<span class="op">>>></span> Anaov5.with_N_sites(<span class="dv">1</span>)
+{<span class="st">'EcoRI'</span> : [<span class="dv">33</span>]}</code></pre></div>
+<p>The easy solution is to chain several <code class="inline">Analysis</code> methods. This is possible since each method return a dictionary as results and is able to take a dictionary as input:</p>
+<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python"><span class="op">>>></span> Ana.with_N_sites(<span class="dv">1</span>, Ana.overhang5())
+{<span class="st">'EcoRI'</span>: [<span class="dv">33</span>]}</code></pre></div>
+<p>The dictionary is always the last argument whatever the command you use.</p>
+<p>The way to prefer certainly depends of the conditions you will use your <code class="inline">Analysis</code> instance. If you are likely to frequently reuse the same batch with different sequences, using a dedicated <code class="inline">RestrictionBatch</code> might be faster as the batch is likely to be smaller. Chaining methods is generally quicker when working with an interactive shell. In a script, the extended syntax may be easier to understand in a few months.</p>
+<h3 id="advanced-features-the-formattedseq-class"><a name="5"></a>5. Advanced features: the FormattedSeq class</h3>
+<p>Restriction enzymes require a much more strict formatting of the DNA sequences than <code class="inline">Bio.Seq</code> object provides. For example, the restriction enzymes expect to find an ungapped (no space) upper-case sequence, while <code class="inline">Bio.Seq</code> object allow sequences to be in lower-case separated by spaces. Therefore when a restriction enzyme analyse a <code class="inline">Bio.Seq</code> object (be it a <code class="inline">Seq</code> or a <code class="in [...]
+<p>While this conversion is done automatically by the enzymes if you provide them with a <code class="inline">Seq</code> or a <code class="inline">MutableSeq</code>, there is time where it will be more efficient to realise the conversion before hand. Each time a <code class="inline">Seq</code> object is passed to an enzyme for analysis you pay a overhead due to the conversion. When analysing the same sequence over and over, it will be faster to convert the sequence, store the conversion [...]
+<h4 id="creating-a-formattedseq"><a name="5.1"></a>5.1 Creating a FormattedSeq</h4>
+<p>Creating a <code class="inline">FormattedSeq</code> from a <code class="inline">Bio.Seq</code> object is simple. The first argument of <code class="inline">FormattedSeq</code> is the sequence you wish to convert. You can specify a shape with the second argument <code class="inline">linear</code>, if you don't the <code class="inline">FormattedSeq</code> will be linear:</p>
+<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python"><span class="op">>>></span> <span class="im">from</span> Bio.Restriction <span class="im">import</span> <span class="op">*</span>
+<span class="op">>>></span> <span class="im">from</span> Bio.Seq <span class="im">import</span> Seq
+<span class="op">>>></span> seq <span class="op">=</span> Seq(<span class="st">'TTCAAAAAAAAAAGAATTCAAAAGAA'</span>)
+<span class="op">>>></span> linear_fseq <span class="op">=</span> FormattedSeq(seq, linear<span class="op">=</span><span class="va">True</span>)
+<span class="op">>>></span> default_fseq <span class="op">=</span> FormattedSeq(seq)
+<span class="op">>>></span> circular_fseq <span class="op">=</span> FormattedSeq(seq, linear<span class="op">=</span><span class="va">False</span>)
+<span class="op">>>></span> linear_fseq
+FormattedSeq(Seq(<span class="st">'TTCAAAAAAAAAAGAATTCAAAAGAA'</span>, Alphabet()), linear<span class="op">=</span><span class="va">True</span>)
+<span class="op">>>></span> linear_fseq.is_linear()
+<span class="va">True</span>
+<span class="op">>>></span> default_fseq.is_linear()
+<span class="va">True</span>
+<span class="op">>>></span> circular_fseq.is_linear()
+<span class="va">False</span>
+<span class="op">>>></span> circular_fseq
+FormattedSeq(Seq(<span class="st">'TTCAAAAAAAAAAGAATTCAAAAGAA'</span>, Alphabet()), linear<span class="op">=</span><span class="va">False</span>)</code></pre></div>
+<h4 id="unlike-bio.seq-formattedseq-retains-information-about-their-shape"><a name="5.2"></a>5.2 Unlike Bio.Seq, FormattedSeq retains information about their shape</h4>
+<p><code class="inline">FormattedSeq</code> retains information about the shape of the sequence. Therefore unlike with <code class="inline">Seq</code> and <code class="inline">MutableSeq</code> you don't need to specify the shape of the sequence when using <code class="inline">search()</code> or <code class="inline">catalyse()</code>:</p>
+<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python"><span class="op">>>></span> EcoRI.search(linear_fseq)
+[<span class="dv">15</span>]
+<span class="op">>>></span> EcoRI.search(circular_fseq) <span class="co"># no need to specify the shape</span>
+[<span class="dv">15</span>, <span class="dv">25</span>]</code></pre></div>
+<p>In fact, the shape of a FormattedSeq is not altered by the second argument of the commands <code class="inline">search()</code> and <code class="inline">catalyse()</code>:</p>
+<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python"><span class="op">>>></span> <span class="co"># In fact the shape is blocked.</span>
+<span class="op">>>></span> <span class="co"># The 3 following commands give the same results</span>
+<span class="op">>>></span> <span class="co"># which correspond to a circular sequence</span>
+<span class="op">>>></span> EcoRI.search(circular_fseq)
+[<span class="dv">15</span>, <span class="dv">25</span>]
+<span class="op">>>></span> EcoRI.search(circular_fseq, linear<span class="op">=</span><span class="va">True</span>)
+[<span class="dv">15</span>, <span class="dv">25</span>]
+<span class="op">>>></span> EcoRI.search(circular_fseq, linear<span class="op">=</span><span class="va">False</span>)
+[<span class="dv">15</span>, <span class="dv">25</span>]</code></pre></div>
+<h4 id="changing-the-shape-of-a-formattedseq"><a name="5.3"></a>5.3 Changing the shape of a FormattedSeq</h4>
+<p>You can however change the shape of the <code class="inline">FormattedSeq</code>. The command to use are:</p>
+<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python">FormattedSeq.to_circular() <span class="op">=></span> new FormattedSeq, shape will be circular.
+FormattedSeq.to_linear() <span class="op">=></span> new FormattedSeq, shape will be linear
+FormattedSeq.circularise() <span class="op">=></span> change the shape of FormattedShape to circular
+FormattedSeq.linearise() <span class="op">=></span> change the shape of FormattedShape to linear</code></pre></div>
+<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python"><span class="op">>>></span> circular_fseq
+FormatedSeq(Seq(<span class="st">'TTCAAAAAAAAAAGAATTCAAAAGAA'</span>, Alphabet()), linear<span class="op">=</span><span class="va">False</span>)
+<span class="op">>>></span> circular_fseq.is_linear()
+<span class="va">False</span>
+<span class="op">>>></span> circular_fseq <span class="op">==</span> linear_fseq
+<span class="va">False</span>
+<span class="op">>>></span> newseq <span class="op">=</span> circular_fseq.to_linear()
+<span class="op">>>></span> circular_fseq
+FormatedSeq(Seq(<span class="st">'TTCAAAAAAAAAAGAATTCAAAAGAA'</span>, Alphabet()), linear<span class="op">=</span><span class="va">False</span>)
+<span class="op">>>></span> newseq
+FormatedSeq(Seq(<span class="st">'TTCAAAAAAAAAAGAATTCAAAAGAA'</span>, Alphabet()), linear<span class="op">=</span><span class="va">True</span>)
+<span class="op">>>></span> circular_fseq.linearise()
+<span class="op">>>></span> circular_fseq
+FormatedSeq(Seq(<span class="st">'TTCAAAAAAAAAAGAATTCAAAAGAA'</span>, Alphabet()), linear<span class="op">=</span><span class="va">True</span>)
+<span class="op">>>></span> circular_fseq.is_linear()
+<span class="va">True</span>
+<span class="op">>>></span> circular_fseq <span class="op">==</span> linear_fseq
+<span class="va">True</span>
+<span class="op">>>></span> EcoRI.search(circular_fseq) <span class="co"># which is now linear</span>
+[<span class="dv">15</span>]</code></pre></div>
+<h4 id="using-and-operators-with-formattedseq"><a name="5.4"></a>5.4 Using / and // operators with FormattedSeq</h4>
+<p>Not having to specify the shape of the sequence to analyse gives you the opportunity to use the shorthand '/' and '//' with restriction enzymes:</p>
+<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python"><span class="op">>>></span> EcoRI<span class="op">/</span>linear_fseq <span class="co"># <=> EcoRI.search(linear_fseq)</span>
+[<span class="dv">15</span>]
+<span class="op">>>></span> linear_fseq<span class="op">/</span>EcoRI <span class="co"># <=> EcoRI.search(linear_fseq)</span>
+[<span class="dv">15</span>]
+<span class="op">>>></span> EcoRI<span class="op">//</span>linear_fseq <span class="co"># <=> linear_fseq//EcoRI <=> EcoRI.catalyse(linear_fseq)</span>
+(Seq(<span class="st">'TTCAAAAAAAAAAG'</span>, Alphabet()), Seq(<span class="st">'AATTCAAAAGAA'</span>, Alphabet()))</code></pre></div>
+<p>Another way to avoid the overhead due to a repetitive conversion from a <code class="inline">Seq</code> object to a <code class="inline">FormattedSeq</code> is to use a <a href="#2"><code class="inline">RestrictionBatch</code></a>.</p>
+<p>To conclude, the performance gain achieved when using a <code class="inline">FormattedSeq</code> instead of a <code class="inline">Seq</code> is not huge. The analysis of a 10 kb sequence by all the enzymes in <code class="inline">AllEnzymes</code> (<code class="inline">for x in AllEnzymes: x.search(seq)</code>, 867 enzymes) is 7 % faster when using a <code class="inline">FormattedSeq</code> than a <code class="inline">Seq</code>. Using a <code class="inline">RestrictionBatch</code> ( [...]
+<h3 id="more-advanced-features"><a name="6"></a>6. More advanced features</h3>
+<p>This chapter addresses some more advanced features of the packages, most users can safely ignore it.</p>
+<h4 id="updating-the-enzymes-from-rebase"><a name="6.1"></a>6.1 Updating the enzymes from REBASE</h4>
+<p>Most people will certainly not need to update the enzymes. The restriction enzyme package will be updated in with each new release of Biopython. But if you wish to get an update in between Biopython-releases here is how to do it.</p>
+<p>First, you have to download the two scripts <code class="inline">rebase_update.py</code> and <code class="inline">ranacompile.py</code>: Go to <a href="https://github.com/biopython/biopython/tree/master/Scripts/Restriction">https://github.com/biopython/biopython/tree/master/Scripts/Restriction</a>, click on the respective file and press the '<strong>Raw</strong>' button on the top right of the code window. Then, with right-click, save the file. Both scripts must be in the same directory.</p>
+<h5 id="fetching-the-recent-enzyme-files-manually-from-rebase"><a name="6.1.1"></a>6.1.1 Fetching the recent enzyme files manually from REBASE</h5>
+<p>Each month, <a href="http://rebase.neb.com/rebase/rebase.html">REBASE</a> release a new compilation of data about restriction enzymes. While the enzymes do not change so frequently, you may wish to update the restriction enzymes classes. The first thing to do is to get the last rebase file. You can find the release of REBASE at <a href="http://rebase.neb.com/rebase/rebase.files.html">http://rebase.neb.com/rebase/rebase.files.html</a>. The file you are interested in are in the EMBOSS f [...]
+<h5 id="fetching-the-recent-enzyme-files-with-rebase_update.py"><a name="6.1.2"></a>6.1.2 Fetching the recent enzyme files with rebase_update.py</h5>
+<p>Another way to do the same thing is to use the <code class="inline">rebase_update.py</code> script. It will connect directly to the rebase ftp server and download the last batch of emboss files. From a DOS or Unix shell do the following:</p>
+<div class="sourceCode"><pre class="sourceCode bash"><code class="sourceCode bash">$ <span class="kw">cd</span> path_to_the_update_script
+$ <span class="kw">rebase_update.py</span> -p http://www.somewhere.com:8000
+
+<span class="kw">Please</span> wait, trying to connect to Rebase
+
+<span class="kw">copying</span> ftp://ftp.neb.com/pub/rebase/emboss_e.407
+<span class="kw">to</span> /cvsroot/bioPython/Bio/Restriction/Scripts/emboss_e.407
+<span class="kw">copying</span> ftp://ftp.neb.com/pub/rebase/emboss_s.407
+<span class="kw">to</span> /cvsroot/bioPython/Bio/Restriction/Scripts/emboss_s.407
+<span class="kw">copying</span> ftp://ftp.neb.com/pub/rebase/emboss_r.407
+<span class="kw">to</span> /cvsroot/bioPython/Bio/Restriction/Scripts/emboss_r.407</code></pre></div>
+<p>Some explanation are needed: <code class="inline">-p</code> is the switch to indicate to the script you are using a proxy. If you use a ftp proxy enter its address and the connection port after the '<code class="inline">:</code>'.</p>
+<h5 id="compiling-a-new-dictionary-with-ranacompiler.py"><a name="6.1.3"></a>6.1.3 Compiling a new dictionary with ranacompiler.py</h5>
+<p>Once you have got the recent emboss files you can compile a new module containing the data necessary to create restriction enzyme.</p>
+<p>Note: if the emboss files are not present in the current directory or if they are not up to date, <code class="inline">ranacompiler.py</code> will invoke the script <a href="#6.1.2"><code class="inline">rebase_update.py</code></a>, which needs to be installed in the same folder. You will need to use the same options as before (ie <code class="inline">-m</code> and <code class="inline">-p</code>). See the previous paragraph on <a href="#6.1.2"><code class="inline">rebase_update.py</cod [...]
+<p>For simplicity let's assume we have put the emboss files in the same folder as the files which contains the script <code class="inline">ranacompiler.py</code>. You may have the change the mode of the file to make it executable:</p>
+<div class="sourceCode"><pre class="sourceCode bash"><code class="sourceCode bash">$ <span class="kw">cd</span> path_to_the_ranacompiler_script
+$ <span class="kw">chmod</span> <span class="st">'+x'</span> ranacompiler.py</code></pre></div>
+<p>Now execute the script:</p>
+<div class="sourceCode"><pre class="sourceCode bash"><code class="sourceCode bash">$ <span class="kw">Python</span> ranacompiler.py <span class="co"># or ./ranacompiler.py</span></code></pre></div>
+<p>You get normally the following message:</p>
+<div class="sourceCode"><pre class="sourceCode bash"><code class="sourceCode bash">$ <span class="kw">./ranacompiler.py</span>
+
+ <span class="kw">Using</span> the files : emboss_e.407, emboss_r.407, emboss_s.407
+
+<span class="kw">WARNING</span> : HaeIV cut twice with different overhang length each time.
+ <span class="kw">Unable</span> to deal with this behaviour.
+ <span class="kw">This</span> enzyme will not be included in the database. Sorry.
+ <span class="kw">Checking</span> :
+<span class="kw">Anyway</span>, HaeIV is not commercially available.
+
+<span class="kw">WARNING</span> : HpyUM037X has two different sites.
+
+
+<span class="kw">The</span> new database contains 867 enzymes.
+
+<span class="kw">Writing</span> the dictionary containing the new Restriction classes...
+<span class="kw">OK.</span>
+
+<span class="kw">Writing</span> the dictionary containing the suppliers datas...
+<span class="kw">OK.</span>
+
+<span class="kw">Writing</span> the dictionary containing the Restriction types....
+<span class="kw">OK.</span>
+
+ <span class="kw">******************************************************************************</span>
+
+ <span class="kw">Compilation</span> of the new dictionary : OK.
+ <span class="kw">Installation</span> : No.
+
+ <span class="kw">You</span> will find the newly created <span class="st">'Restriction_Dictionary.py'</span> file
+ <span class="kw">in</span> <span class="kw">the</span> :
+
+ <span class="kw">/path/where/you/run/ranacompiler.py</span>
+
+ <span class="kw">Make</span> a copy of <span class="st">'Restriction_Dictionary.py'</span> and place it with
+ <span class="kw">the</span> other Restriction libraries.
+
+ <span class="kw">note</span> :
+ <span class="kw">This</span> folder should be :
+
+ <span class="kw">path_to_python/site-packages/Bio/Restriction</span>
+
+ <span class="kw">******************************************************************************</span></code></pre></div>
+<p>The first line indicate which emboss files have been used for the present compilation. You can safely ignore the warnings as long as the <code class="inline">compilation of the new dictionary : OK.</code> is present in the last part of the output. They are here for debugging purpose. The number of enzymes in the new module is indicated as well as a list of the dictionary which have been compiled. The last part indicate that the module has been succesfully created but not installed. To [...]
+<p>If you whish, the script may install the folder for you as well, but you will have to run it as root if your normal user has no write access to your Python installation (and it should'nt). Use the command <code class="inline">ranacompiler.py -i</code> or <code class="inline">ranacompiler.py --install</code> for this.</p>
+<p>If anything goes wrong (you have no write access to the destination folder for example) the script will let you know it did not perform the installation. It will however still save the new module in the current directory.</p>
+<p>As you can see the script is not very bright and will redo the compilation each time it is invoked, no matter if a previous version of the module is already present.</p>
+<h4 id="subclassing-the-class-analysis"><a name="6.2"></a>6.2 Subclassing the class Analysis</h4>
+<p>As seen previously, you can modify some aspects of the <code class="inline">Analysis</code> output interactively. However if you want to write your own <code class="inline">Analysis</code> class, you may wish to provide others output facilities than is given in this package. Depending on what you want to do you may get away with simply changing the <code class="inline">make_format</code> method of your derived class or you will need to provide new methods. Rather than get into a long [...]
+<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python"><span class="op">>>></span> <span class="kw">class</span> UselessAnalysis(Analysis):
+
+ <span class="kw">def</span> <span class="fu">__init__</span>(<span class="va">self</span>, rb<span class="op">=</span>RestrictionBatch(), seq<span class="op">=</span>Seq(<span class="st">''</span>), lin<span class="op">=</span><span class="va">True</span>):
+ <span class="co">"""UselessAnalysis -> A class that waste your time"""</span>
+ <span class="co">#</span>
+ <span class="co"># Unless you want to do something more fancy all</span>
+ <span class="co"># you need to do here is instantiate Analysis.</span>
+ <span class="co"># Don't forget the self in __init__</span>
+ <span class="co">#</span>
+ Analysis.<span class="fu">__init__</span>(<span class="va">self</span>, rb, seq, lin)
+
+ <span class="kw">def</span> make_format(<span class="va">self</span>, cut<span class="op">=</span>[], t<span class="op">=</span><span class="st">''</span>, nc<span class="op">=</span>[], s<span class="op">=</span><span class="st">''</span>):
+ <span class="co">"""not funny"""</span>
+ <span class="co">#</span>
+ <span class="co"># Generally, you don't need to do anything else here</span>
+ <span class="co"># This will tell to your new class to default to the</span>
+ <span class="co"># _make_joke format.</span>
+ <span class="co">#</span>
+ <span class="cf">return</span> <span class="va">self</span>._make_joke(cut, t, nc, s)
+
+ <span class="kw">def</span> print_as(<span class="va">self</span>, what<span class="op">=</span><span class="st">'joke'</span>):
+ <span class="co">"""Never know somebody might want to change the behaviour of</span>
+<span class="co"> this class."""</span>
+ <span class="co">#</span>
+ <span class="co"># add your new option to print_as</span>
+ <span class="co">#</span>
+ <span class="cf">if</span> what <span class="op">==</span> <span class="st">'joke'</span>:
+ <span class="va">self</span>.make_format <span class="op">=</span> <span class="va">self</span>._make_joke
+ <span class="cf">return</span>
+ <span class="cf">else</span>:
+ <span class="co">#</span>
+ <span class="co"># The other options will be treated as before</span>
+ <span class="co">#</span>
+ <span class="cf">return</span> Analysis.print_as(<span class="va">self</span>, what)
+
+ <span class="kw">def</span> _make_joke(<span class="va">self</span>, cut<span class="op">=</span>[], title<span class="op">=</span><span class="st">''</span>, nc<span class="op">=</span>[], s1<span class="op">=</span><span class="st">''</span>):
+ <span class="co">"""UA._make_joke(cut, t, nc, s) -> new analysis output"""</span>
+ <span class="co">#</span>
+ <span class="co"># starting your new method with '_make_'</span>
+ <span class="co"># will give a hint to what it is suppose to do</span>
+ <span class="co">#</span>
+ <span class="co"># We will not process the non-cutting enzymes</span>
+ <span class="co"># Their names are in nc</span>
+ <span class="co"># s1 is the string printed before them</span>
+ <span class="co">#</span>
+ <span class="cf">if</span> <span class="op">not</span> title:
+ title <span class="op">=</span> <span class="st">'</span><span class="ch">\n</span><span class="st">You have guessed right the following enzymes:</span><span class="ch">\n\n</span><span class="st">'</span>
+ <span class="cf">for</span> name, sites <span class="op">in</span> cut:
+ <span class="co">#</span>
+ <span class="co"># cut contains:</span>
+ <span class="co"># - the name of the enzymes which cut the sequence (name)</span>
+ <span class="co"># - a list of the site positions (sites)</span>
+ <span class="co">#</span>
+ guess <span class="op">=</span> <span class="bu">raw_input</span>(<span class="st">"next enzyme is </span><span class="sc">%s</span><span class="st">, Guess how many sites ?</span><span class="ch">\n</span><span class="st">>>> "</span><span class="op">%</span>name)
+ <span class="cf">try</span>:
+ guess <span class="op">=</span> <span class="bu">int</span>(guess)
+ <span class="cf">except</span>:
+ guess <span class="op">=</span> <span class="va">None</span>
+ <span class="cf">if</span> guess <span class="op">==</span> <span class="bu">len</span>(sites):
+ <span class="bu">print</span> <span class="st">'You did guess right. Good. Next.'</span>
+ result <span class="op">=</span> <span class="st">'</span><span class="sc">%i</span><span class="st"> site'</span> <span class="op">%</span> guess
+ <span class="cf">if</span> guess <span class="op">></span> <span class="dv">1</span>:
+ result <span class="op">+=</span> <span class="st">'s'</span>
+
+ <span class="co">#</span>
+ <span class="co"># now we format the line. See the PrintFormat module</span>
+ <span class="co"># for some examples</span>
+ <span class="co"># PrintFormat.__section_list and _make_map are good start.</span>
+ <span class="co">#</span>
+ title<span class="op">=</span><span class="st">''</span>.join((title, <span class="bu">str</span>(name).ljust(<span class="va">self</span>.NameWidth),
+ <span class="st">' : '</span>, result, <span class="st">'.</span><span class="ch">\n</span><span class="st">'</span>))
+ <span class="bu">print</span> <span class="st">'</span><span class="ch">\n</span><span class="st">No more enzyme.'</span>
+ <span class="cf">return</span> title
+ <span class="co">#</span>
+ <span class="co"># I you want to print the non cutting enzymes use</span>
+ <span class="co"># the following return instead of the previous one:</span>
+ <span class="co">#</span>
+ <span class="co">#return title + t + self._make_nocut_only(nc,s1)</span>
+
+<span class="op">>>></span> <span class="co"># You initiate and use it as before</span>
+<span class="op">>>></span> rb <span class="op">=</span> RestrictionBatch([], [<span class="st">'A'</span>])
+<span class="op">>>></span> multi_site <span class="op">=</span> Seq(<span class="st">'AAA'</span> <span class="op">+</span> EcoRI.site <span class="op">+</span><span class="st">'G'</span> <span class="op">+</span> KpnI.site <span class="op">+</span> EcoRV.site <span class="op">+</span> <span class="st">'CT'</span> <span class="op">+\</span>
+SmaI.site <span class="op">+</span> <span class="st">'GT'</span> <span class="op">+</span> FokI.site <span class="op">+</span> <span class="st">'GAAAGGGC'</span> <span class="op">+</span> EcoRI.site <span class="op">+</span> <span class="st">'ACGT'</span>, IUPACAmbiguousDNA())
+<span class="op">>>></span>
+<span class="op">>>></span> b <span class="op">=</span> UselessAnalysis(rb, multi_site)
+<span class="op">>>></span> b.print_that() <span class="co"># Well, I let you discover if you haven't already guessed</span></code></pre></div>
+<p>Using this example, as a template you should now be able to subclass <code class="inline">Analysis</code> as you wish. You will found more implementation details in the module <code class="inline">Bio.Restriction.PrintFormat</code> which contains the class providing all the <code class="inline">_make_*</code> methods.</p>
+<h3 id="limitation-and-caveat"><a name="7"></a>7. Limitation and caveat</h3>
+<p>Particularly, the class <code class="inline">Analysis</code> is a quick and dirty implementation based on the facilities furnished by the package. Please check your results and report any fault.</p>
+<p>On a more general basis, <code class="inline">Restriction</code> have some other limitations:</p>
+<h4 id="all-dna-are-non-methylated"><a name="7.1"></a>7.1 All DNA are non methylated</h4>
+<p>No facility to work with methylated DNA has been implemented yet. As far as the enzyme classes are concerned all DNA is non methylated DNA. Implementation of methylation sensibility will possibly occur in the future. But for now, if your sequence is methylated, you will have to check if the site is methylated using other means.</p>
+<h4 id="no-support-for-star-activity"><a name="7.2"></a>7.2 No support for star activity</h4>
+<p>As before no support has been yet implemented to find site mis-recognised by enzymes under high salt concentration conditions, the so-called star activity. This will be implemented as soon as I can get a good source of information for that.</p>
+<h4 id="safe-to-use-with-degenerated-dna"><a name="7.3"></a>7.3 Safe to use with degenerated DNA</h4>
+<p>It is safe to use degenerated DNA as input for the query. You will not be flooded with meaningless results. But this come at a price: GAA<strong><em>N</em></strong>TC will not be recognised as a potential EcoRI site for example, in fact it will not be recognised at all. Degenerated sequences will not be analysed. If your sequence is not fully sequenced, you will certainly miss restriction sites:</p>
+<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python"><span class="op">>>></span> a <span class="op">=</span> Seq(<span class="st">'nnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnGAATTCrrrrrrrrrrr'</span>, IUPACAmbiguou
+sDNA())
+<span class="op">>>></span> EcoRI.search(a)
+[<span class="dv">36</span>]
+<span class="op">>>></span> b <span class="op">=</span> Seq(<span class="st">'nnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnGAAnTCrrrrrrrrrrr'</span>, IUPACAmbiguou
+sDNA())
+<span class="op">>>></span> EcoRI.search(b)
+[]</code></pre></div>
+<h4 id="non-standard-bases-in-dna-are-not-allowed"><a name="7.4"></a>7.4 Non standard bases in DNA are not allowed</h4>
+<p>While you can use degenerated DNA, using non standard base alphabet will make the enzymes choke, even if <code class="inline">Bio.Seq.Seq</code> accepts them. However, space-like characters (' ', '', '', ...) and digit will be removed but will not stop the enzyme analysing the sequence. You can use them but the fragments produced by <code class="inline">catalyse</code> will have lost any formatting. <code class="inline">catalyse</code> tries to keep the original case of the sequence ( [...]
+<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python"><span class="op">>>></span> c <span class="op">=</span> Seq(<span class="st">'xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxGAANTCrrrrrrrrrrr'</span>, IUPACAmbiguou
+sDNA())
+<span class="op">>>></span> EcoRI.search(c)
+
+Traceback (most recent call last):
+ File <span class="st">"<pyshell#110>"</span>, line <span class="dv">1</span>, <span class="op">in</span> <span class="op">-</span>toplevel<span class="op">-</span>
+ EcoRI.search(b)
+ File <span class="st">"/usr/lib/Python2.3/site-packages/Bio/Restriction/Restriction.py"</span>, line <span class="dv">3</span>
+ <span class="dv">96</span>, <span class="op">in</span> search
+ cls.dna <span class="op">=</span> FormatedSeq(dna, linear)
+ File <span class="st">"/usr/lib/Python2.3/site-packages/Bio/Restriction/Restriction.py"</span>, line <span class="dv">1</span>
+ <span class="dv">37</span>, <span class="op">in</span> <span class="fu">__init__</span>
+ <span class="va">self</span>.<span class="bu">format</span>()
+ File <span class="st">"/usr/lib/Python2.3/site-packages/Bio/Restriction/Restriction.py"</span>, line <span class="dv">1</span>
+ <span class="dv">53</span>, <span class="op">in</span> <span class="bu">format</span>
+ <span class="cf">raise</span> AlphabetError, <span class="st">" '</span><span class="sc">%s</span><span class="st">' is not in the IUPAC alphabet"</span> <span class="op">%</span> s
+AlphabetError: <span class="st">'X'</span> <span class="op">is</span> <span class="op">not</span> <span class="op">in</span> the IUPAC alphabet
+<span class="op">>>></span> d <span class="op">=</span> Seq(<span class="st">'1 nnnnn nnnnn nnnnn nnnnn nnnnn </span><span class="ch">\n</span><span class="st">\</span>
+<span class="st">26 nnnnn nnnnG AATTC rrrrr rrrrr </span><span class="ch">\n</span><span class="st">\</span>
+<span class="st">51 r'</span>, IUPACAmbiguousDNA())
+<span class="op">>>></span> d
+Seq(<span class="st">'1 nnnnn nnnnn nnnnn nnnnn nnnnn </span><span class="ch">\n</span><span class="st">26 nnnnn nnnnG AATTC rrrrr rrrrr </span><span class="ch">\n</span><span class="st">51 r'</span>,
+ IUPACAmbiguousDNA())
+<span class="op">>>></span> EcoRI.search(d)
+[<span class="dv">36</span>]
+<span class="op">>>></span> EcoRI.catalyse(d)
+(Seq(<span class="st">'AATTCRRRRRRRRRRR'</span>, IUPACAmbiguousDNA()), Seq(<span class="st">'NNNNNNNNNNNNNNNNNNNNNNNNNNNN</span>
+<span class="st">NNNNNNG'</span>, IUPACAmbiguousDNA()))
+<span class="op">>>></span> e <span class="op">=</span> Seq(<span class="st">'nnnnGAATTCrr'</span>, IUPACAmbiguousDNA())
+<span class="op">>>></span> f <span class="op">=</span> Seq(<span class="st">'NNNNGAATTCRR'</span>, IUPACAmbiguousDNA())
+<span class="op">>>></span> g <span class="op">=</span> Seq(<span class="st">'nnnngaattcrr'</span>, IUPACAmbiguousDNA())
+<span class="op">>>></span> EcoRI.catalyse(e)
+(Seq(<span class="st">'NNNNG'</span>, IUPACAmbiguousDNA()), Seq(<span class="st">'AATTCRR'</span>, IUPACAmbiguousDNA()))
+<span class="op">>>></span> EcoRI.catalyse(f)
+(Seq(<span class="st">'NNNNG'</span>, IUPACAmbiguousDNA()), Seq(<span class="st">'AATTCRR'</span>, IUPACAmbiguousDNA()))
+<span class="op">>>></span> EcoRI.catalyse(g)
+(Seq(<span class="st">'nnnng'</span>, IUPACAmbiguousDNA()), Seq(<span class="st">'aattcrr'</span>, IUPACAmbiguousDNA()))</code></pre></div>
+<p>Not allowing other letters than IUPAC might seems drastic but this is really to limit errors. It is not totally fool proof but it does help.</p>
+<h4 id="sites-found-at-the-edge-of-linear-dna-might-not-be-accessible-in-a-real-digestion"><a name="7.5"></a>7.5 Sites found at the edge of linear DNA might not be accessible in a real digestion</h4>
+<p>While sites clearly outsides a sequence will not be reported, nothing has been done to try to determine if a restriction site at the end of a linear sequence is valid:</p>
+<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python"><span class="op">>>></span> d <span class="op">=</span> Seq(<span class="st">'GAATTCAAAAAAAAAAAAAAAAAAAAAAAAAAGGATG'</span>, IUPACAmbiguousDNA())
+<span class="op">>>></span> FokI.site <span class="co"># site present</span>
+<span class="co">'GGATG'</span>
+<span class="op">>>></span> FokI.elucidate() <span class="co"># but cut outside the sequence</span>
+<span class="co">'GGATGNNNNNNNNN^NNNN_N'</span>
+<span class="op">>>></span> FokI.search(d) <span class="co"># therefore no site found</span>
+[]
+<span class="op">>>></span> EcoRI.search(d)
+[<span class="dv">2</span>]</code></pre></div>
+<p><code class="inline">EcoRI</code> finds a site at position 2 even if it is highly unlikely that EcoRI accepts to cut this site in a tube. It is generally considered that at about 5 nucleotides must separate the site from the edge of the sequence to be reasonably sure the enzyme will work correctly. This "security margin" is variable from one enzyme to the other. In doubt consult the documentation for the enzyme.</p>
+<h4 id="restriction-reports-cutting-sites-not-enzyme-recognition-sites"><a name="7.6"></a>7.6 Restriction reports cutting sites not enzyme recognition sites</h4>
+<p>Some enzymes will cut twice each time they encounter a restriction site. The enzymes in this package report both cut not the site. Other software may only reports restriction sites. Therefore the output given for some enzymes might seems to be the double when compared with the results of these software. It is not a bug.</p>
+<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python"><span class="op">>>></span> AloI.cut_twice()
+<span class="va">True</span>
+<span class="op">>>></span> AloI.fst5 <span class="co"># first cut</span>
+<span class="op">-</span><span class="dv">7</span>
+<span class="op">>>></span> AloI.scd5 <span class="co"># second cut</span>
+<span class="dv">25</span>
+<span class="op">>>></span> AloI.site
+<span class="co">'GAACNNNNNNTCC'</span>
+<span class="op">>>></span> b <span class="op">=</span> Seq(<span class="st">'AAAAAAAAAAA'</span><span class="op">+</span> AloI.site <span class="op">+</span> <span class="st">'AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA'</span>)
+<span class="op">>>></span> b
+Seq(<span class="st">'AAAAAAAAAAAGAACNNNNNNTCCAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA'</span>, Alphabet())
+<span class="op">>>></span> AloI.search(b) <span class="co"># one site, two cuts -> two positions</span>
+[<span class="dv">5</span>, <span class="dv">37</span>]</code></pre></div>
+<h3 id="annex-modifying-dir-to-use-with-from-bio.restriction-import"><a name="8"></a>8. Annex: modifying dir() to use with from Bio.Restriction import *</h3>
+<p>Having all the enzymes imported directly in the shell is useful when working in an interactive shell (even if it is not recommended by the purists). Here is a little hack to get some sanity back when using dir() in those conditions:</p>
+<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python"><span class="op">>>></span> <span class="co"># we will change the builtin dir() function to get ride of the enzyme names.</span>
+<span class="op">>>></span> <span class="im">import</span> sys
+<span class="op">>>></span> <span class="kw">def</span> <span class="bu">dir</span>(<span class="bu">object</span><span class="op">=</span><span class="va">None</span>):
+ <span class="co">"""dir([object]) -> list of string.</span>
+
+<span class="co"> over-ride the built-in function to get some clarity."""</span>
+ <span class="cf">if</span> <span class="bu">object</span>:
+ <span class="co"># we only want to modify dir(),</span>
+ <span class="co"># so here we return the result of the builtin function.</span>
+ <span class="cf">return</span> __builtins__.<span class="bu">dir</span>(<span class="bu">object</span>)
+ <span class="cf">else</span>:
+ <span class="co"># now the part we want to modify.</span>
+ <span class="co"># All the enzymes are in a RestrictionBatch (we will talk about</span>
+ <span class="co"># that later, for the moment simply believe me).</span>
+ <span class="co"># So if we remove from the results of dir() everything which is</span>
+ <span class="co"># in AllEnzymes we will get a much shorter list when we do dir()</span>
+ <span class="co">#</span>
+ <span class="co"># the current level is __main__ ie dir() is equivalent to</span>
+ <span class="co"># ask what's in __main__ at the moment.</span>
+ <span class="co"># we can't access __main__ directly.</span>
+ <span class="co"># so we will use sys.modules['__main__'] to reach it.</span>
+ <span class="co"># the following list comprehension remove from the result of</span>
+ <span class="co"># dir() everything which is also present in AllEnzymes.</span>
+ <span class="co">#</span>
+ <span class="cf">return</span> [x <span class="cf">for</span> x <span class="op">in</span> __builtins__.<span class="bu">dir</span>(sys.modules[<span class="st">'__main__'</span>])
+ <span class="cf">if</span> <span class="op">not</span> x <span class="op">in</span> AllEnzymes]
+
+<span class="op">>>></span> <span class="co"># now let's see if it works.</span>
+<span class="op">>>></span> <span class="bu">dir</span>()
+[<span class="st">'AllEnzymes'</span>, <span class="st">'Analysis'</span>, <span class="st">'CommOnly'</span>, <span class="st">'NonComm'</span>, <span class="st">'PrintFormat'</span>, <span class="st">'RanaConfig'</span>,
+ <span class="co">'Restriction'</span>, <span class="st">'RestrictionBatch'</span>, <span class="st">'Restriction_Dictionary'</span>, <span class="st">'__builtins__'</span>,
+ <span class="co">'__doc__'</span>, <span class="st">'__name__'</span>, <span class="st">'dir'</span>, <span class="st">'sys'</span>]
+<span class="op">>>></span> <span class="co"># ok that's much better.</span>
+<span class="op">>>></span> <span class="co"># The enzymes are still there</span>
+<span class="op">>>></span> EcoRI.site
+<span class="co">'GAATTC'</span></code></pre></div>
</body>
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diff --git a/Doc/cookbook/Restriction/Restriction.md b/Doc/cookbook/Restriction/Restriction.md
new file mode 100644
index 0000000..15ae168
--- /dev/null
+++ b/Doc/cookbook/Restriction/Restriction.md
@@ -0,0 +1,1625 @@
+Working with restriction enzymes
+================================
+
+## Table of contents
+
+1. [The restriction enzymes classes](#1)
+ 1. [Importing the enzymes](#1.1)
+ 2. [Naming convention](#1.2)
+ 3. [Searching for restriction sites](#1.3)
+ 4. [Retrieving the sequences produced by a digestion](#1.4)
+ 5. [Analysing circular sequences](#1.5)
+ 6. [Comparing enzymes with each others](#1.6)
+ 7. [Other facilities provided by the enzyme classes](#1.7)
+2. [The RestrictionBatch class: a class to deal with several enzymes](#2)
+ 1. [Creating a RestrictionBatch](#2.1)
+ 2. [Restricting a RestrictionBatch to a particular supplier](#2.2)
+ 3. [Adding enzymes to a RestrictionBatch](#2.3)
+ 4. [Removing enzymes from a RestrictionBatch](#2.4)
+ 5. [Manipulating RestrictionBatch](#2.5)
+ 6. [Analysing sequences with a RestrictionBatch](#2.6)
+ 7. [Other RestrictionBatch methods](#2.7)
+3. [AllEnzymes and CommOnly: two preconfigured RestrictionBatches](#3)
+4. [The Analysis class: even simpler restriction analysis](#4)
+ 1. [Setting up an Analysis](#4.1)
+ 2. [Full restriction analysis](#4.2)
+ 3. [Changing the title](#4.3)
+ 4. [Customising the output](#4.4)
+ 5. [Fancier restriction analysis](#4.5)
+ 6. [More complex analysis](#4.6)
+5. [Advanced features: the FormattedSeq class](#5)
+ 1. [Creating a FormattedSeq](#5.1)
+ 2. [Unlike Bio.Seq, FormattedSeq retains information about their shape](#5.2)
+ 3. [Changing the shape of a FormattedSeq](#5.3)
+ 4. [Using / and // operators with FormattedSeq](#5.4)
+6. [More advanced features](#6)
+ 1. [Updating the enzymes from Rebase](#6.1)
+ 1. [Fetching the recent enzyme files manually from Rebase](#6.1.1)
+ 2. [Fetching the recent enzyme files with rebase_update.py](#6.1.2)
+ 3. [Compiling a new dictionary with ranacompiler.py](#6.1.3)
+ 2. [Subclassing the class Analysis](#6.2)
+7. [Limitation and caveat](#7)
+ 1. [All DNA are non methylated](#7.1)
+ 2. [No support for star activity](#7.2)
+ 3. [Safe to use with degenerated DNA](#7.3)
+ 4. [Non standard bases in DNA are not allowed](#7.4)
+ 5. [Sites found at the edge of linear DNA might not be accessible in a real digestion](#7.5)
+ 6. [Restriction reports cutting sites not enzyme recognition sites](#7.6)
+8. [Annexe: modifying dir() to use with from Bio.Restriction import *](#8)
+
+### <a name="1"></a>1. The restriction enzymes classes
+The restriction enzyme package is situated in `Bio.Restriction`. This package
+will allow you to work with restriction enzymes and realise restriction
+analysis on your sequence. Restriction make use of the facilities offered
+by **REBASE** and contains classes for more than 800 restriction enzymes.
+This chapter will lead you through a quick overview of the facilities offered
+by the `Restriction` package of Biopython. The chapter is constructed as an
+interactive Python session and the best way to read it is with a Python shell
+open alongside you.
+
+#### <a name="1.1"></a> 1.1 Importing the enzymes
+To import the enzymes, open a Python shell and type:
+
+``` python
+>>> from Bio import Restriction
+>>> dir()
+['Restriction', '__builtins__', '__doc__', '__name__', '__package__']
+>>> Restriction.EcoRI
+EcoRI
+>>> Restriction.EcoRI.site
+'GAATTC'
+>>>
+```
+
+You will certainly notice that the package is quite slow to load. This is normal
+as each enzyme possess its own class and there is a lot of them. This will not
+affect the speed of Python after the initial import.
+
+I don't know for you but I find it quite cumbersome to have to prefix each
+operation with `Restriction.`, so here is another way to import the package.
+
+``` python
+>>> from Bio.Restriction import *
+>>> EcoRI
+EcoRI
+>>> EcoRI.site
+'GAATTC'
+>>>
+```
+
+However, this method has one big disadvantage:
+It is almost impossible to use the command `dir()` anymore as there is so much
+enzymes the results is hardly readable. A workaround is provided at the end of
+this tutorial. I let you decide which method you prefer. But in this tutorial I
+will use the second. If you prefer the first method you will need to prefix
+each call to a restriction enzyme with `Restriction.` in the remaining of the
+tutorial.
+
+#### <a name="1.2"></a>1.2 Naming convention
+
+To access an enzyme simply enter it's name. You must respect the usual naming
+convention with the upper case letters and Latin numbering (in upper case as
+well):
+
+``` python
+>>> EcoRI
+EcoRI
+>>> ecori
+
+Traceback (most recent call last):
+ File "<pyshell#25>", line 1, in -toplevel-
+ ecori
+NameError: name 'ecori' is not defined
+>>> EcoR1
+
+Traceback (most recent call last):
+ File "<pyshell#26>", line 1, in -toplevel-
+ EcoR1
+NameError: name 'EcoR1' is not defined
+>>> KpnI
+KpnI
+>>>
+```
+
+`ecori` or `EcoR1` are not enzymes, `EcoRI` and `KpnI` are.
+
+#### <a name="1.3"></a>1.3 Searching for restriction sites
+
+So what can we do with these restriction enzymes? To see that we will need a
+DNA sequence. Restriction enzymes support both `Bio.Seq.MutableSeq`and
+`Bio.Seq.Seq` objects. You can use any DNA alphabet which complies with the
+IUPAC alphabet.
+
+``` python
+>>> from Bio.Seq import Seq
+>>> from Bio.Alphabet.IUPAC import IUPACAmbiguousDNA
+>>> amb = IUPACAmbiguousDNA()
+>>> my_seq = Seq('AAAAAAAAAAAAAA', amb)
+>>> my_seq
+Seq('AAAAAAAAAAAAAA', IUPACAmbiguousDNA())
+```
+
+Searching a sequence for the presence of restriction site for your preferred
+enzyme is as simple as:
+
+``` python
+>>> EcoRI.search(my_seq)
+[]
+```
+
+The results is a list. Here the list is empty since there is obviously no EcoRI
+site in *my_seq*. Let's try to get a sequence with an EcoRI site.
+
+``` python
+>>> ecoseq = my_seq + Seq(EcoRI.site, amb) + my_seq
+>>> ecoseq
+Seq('AAAAAAAAAAAAAAGAATTCAAAAAAAAAAAAAA', IUPACAmbiguousDNA())
+>>> EcoRI.search(ecoseq)
+[16]
+```
+
+We therefore have a site at position 16 of the sequence *ecoseq*. The position
+returned by the method search is the first base of the downstream segment
+produced by a restriction (i.e. the first base after the position where the
+enzyme will cut). The `Restriction` package follows biological convention (the
+first base of a sequence is base 1). No need to make difficult conversions
+between your recorded biological data and the results produced by the enzymes
+in this package.
+
+#### <a name="1.4"></a>1.4 Retrieving the sequences produced by a digestion
+
+`Seq` objects as all Python sequences, have different conventions and the first
+base of a sequence is base 0. Therefore to get the sequences produced by an
+EcoRI digestion of *ecoseq*, one should do the following:
+
+``` python
+>>> ecoseq[:15], ecoseq[15:]
+(Seq('AAAAAAAAAAAAAAG', IUPACAmbiguousDNA()), Seq('AATTCAAAAAAAAAAAAAA', IUPACAm
+biguousDNA()))
+```
+
+I hear you thinking "this is a cumbersome and error prone method to get these
+sequences". To simplify your life, `Restriction` provides another method to get
+these sequences without hassle: `catalyse`. This method will return a tuple
+containing all the fragments produced by a complete digestion of the sequence.
+Using it is as simple as before:
+
+``` python
+>>> EcoRI.catalyse(ecoseq)
+(Seq('AAAAAAAAAAAAAAG', IUPACAmbiguousDNA()), Seq('AATTCAAAAAAAAAAAAAA', IUPACAm
+biguousDNA()))
+```
+
+BTW, you can also use spell it the American way `catalyze`:
+
+``` python
+>>> EcoRI.catalyze(ecoseq)
+(Seq('AAAAAAAAAAAAAAG', IUPACAmbiguousDNA()), Seq('AATTCAAAAAAAAAAAAAA', IUPACAm
+biguousDNA()))
+```
+
+#### <a name="1.5"></a>1.5 Analysing circular sequences
+
+Now, if you have entered the previous command in your shell you may have noticed
+that both `search` and `catalyse` can take a second argument `linear` which
+defaults to `True`. Using this will allow you to simulate circular sequences
+such as plasmids. Setting `linear` to `False` informs the enzyme to make the
+search over a circular sequence and to search for potential sites spanning over
+the boundaries of the sequence.
+
+``` python
+>>> EcoRI.search(ecoseq, linear=False)
+[16]
+>>> EcoRI.catalyse(ecoseq, linear=False)
+(Seq('AATTCAAAAAAAAAAAAAAAAAAAAAAAAAAAAG', IUPACAmbiguousDNA()),)
+>>> ecoseq # for memory
+Seq('AAAAAAAAAAAAAAGAATTCAAAAAAAAAAAAAA', IUPACAmbiguousDNA())
+```
+
+OK, this is quite a difference, we only get one fragment, which correspond to
+the linearised sequence. The beginning sequence has been shifted to take this
+fact into account. Moreover we can see another difference:
+
+``` python
+>>> new_seq = Seq('TTCAAAAAAAAAAAAAAAAAAAAAAAAAAAAGAA', IUPACAmbiguousDNA())
+>>> EcoRI.search(new_seq)
+[]
+>>> EcoRI.search(new_seq, linear=False)
+[33]
+```
+
+As you can see using `linear=False`, make a site appearing in the sequence
+*new_seq*. This site does not exist in a linear sequence as the EcoRI site is
+split into two halves at the start and the end of the sequence. In a circular
+sequence however, the site is effectively present when the beginning and end of
+the sequence are joined.
+
+#### <a name="1.6"></a>1.6 Comparing enzymes with each others
+
+`Restriction` enzymes define 4 comparative operators `==`, `!=`, `>>` and `%`.
+All these operator compares two enzymes together and either return `True` or
+`False`.
+
+`==` (test identity)
+: It will return `True` if the two sides of the operator are the same. *Same"
+is defined as: same name, same site, same overhang (i.e. the only thing which
+is equal to `EcoRI` is `EcoRI`).
+
+`!=` (test for different site or cutting)
+: It will return `True` if the two sides of the operator are different. Two
+enzymes are not different if the result produced by one enzyme will always be
+the same as the result produced by the other (i.e. true isoschizomers will not
+being the same enzymes, are not different since they are interchangeable).
+
+`>>` (test for neoschizomer)
+: `True` if the enzymes recognise the same site, but cut it in a different way
+(i.e. the enzymes are neoschizomers).
+
+`%` (test compatibilty)
+: Test the compatibility of the ending produced by the enzymes (will be `True`
+if the fragments produced with one of the enzyme can directly be ligated to
+fragments produced by the other).
+
+Let's use `Acc65I` and its isoschizomers as example:
+
+``` python
+>>> Acc65I.isoschizomers()
+[Asp718I, KpnI]
+>>> Acc65I.elucidate()
+'G^GTAC_C'
+>>> Asp718I.elucidate()
+'G^GTAC_C'
+>>> KpnI.elucidate()
+'G_GTAC^C'
+>>> # Asp718I and Acc65I are true isoschizomers,
+>>> # they recognise the same site and cut it the
+>>> # same way.
+>>> # KpnI is a neoschizomers of the 2 others.
+>>> # Here are the results of the 4 operators
+>>> # for each pair of enzymes:
+>>>
+>>> ############# x == y (x is y)
+>>> Acc65I == Acc65I # same enzyme => True
+True
+>>> Acc65I == KpnI # all other cases => False
+False
+>>> Acc65I == Asp718I
+False
+>>> Acc65I == EcoRI
+False
+>>> ############ x != y (x and y are not true isoschizomers)
+>>> Acc65I != Acc65I # same enzyme => False
+False
+>>> Acc65I != Asp718I # different enzymes, but cut same manner => False
+False
+>>> Acc65I != KpnI # all other cases => True
+True
+>>> Acc65I != EcoRI
+True
+>>> ########### x >> y (x is neoschizomer of y)
+>>> Acc65I >> Acc65I # same enzyme => False
+False
+>>> Acc65I >> Asp718I # same site, same cut => False
+False
+>>> Acc65I >> EcoRI # different site => False
+False
+>>> Acc65I >> KpnI # same site, different cut => True
+True
+>>> ########### x % y (fragments produced by x and fragments produced by y
+>>> # can be directly ligated to each other)
+>>> Acc65I % Asp718I
+True
+>>> Acc65I % Acc65I
+True
+>>> Acc65I % KpnI # KpnI -> '3 overhang, Acc65I-> 5' overhang => False
+False
+>>>
+>>> SunI.elucidate()
+'C^GTAC_G'
+>>> SunI == Acc65I
+False
+>>> SunI != Acc65I
+True
+>>> SunI >> Acc65I
+False
+>>> SunI % Acc65I # different site, same overhang (5' GTAC) => True
+True
+>>> SmaI % EcoRV # 2 Blunt enzymes, all blunt enzymes are compatible => True
+True
+```
+
+#### <a name="1.7"></a>1.7 Other facilities provided by the enzyme classes
+
+The `Restriction` class provides quite a number of others methods. We will not
+go through all of them, but only have a quick look to the most useful ones.
+
+Not all enzymes possess the same properties when it comes to the way they digest
+a DNA. If you want to know more about the way a particular enzyme cut you can
+use the three following methods. They are fairly straightforward to understand
+and refer to the ends that the enzyme produces: blunt, 5' overhanging (also
+called 3' recessed) sticky end and 3' overhanging (or 5' recessed) sticky end.
+
+``` python
+>>> EcoRI.is_blunt()
+False
+>>> EcoRI.is_5overhang()
+True
+>>> EcoRI.is_3overhang()
+False
+```
+
+A more detailled view of the restriction site can be produced using the
+ `elucidate()` method. The `^` refers to the position of the cut in the sense
+strand of the sequence, `_` to the cut on the antisense or complementary strand.
+`^_` means blunt.
+
+``` python
+>>> EcoRI.elucidate()
+'G^AATT_C'
+>>> KpnI.elucidate()
+'G_GTAC^C'
+>>> EcoRV.elucidate()
+'GAT^_ATC'
+```
+
+The method `frequency()` will give you the statistical frequency of the enzyme
+site.
+
+``` python
+>>> EcoRI.frequency()
+4096
+>>> XhoII.elucidate()
+'R^GATC_Y'
+>>> XhoII.frequency()
+1024
+```
+
+To get the length of a the recognition sequence of an enzyme use the built-in
+function `len()`:
+
+``` python
+>>> len(EcoRI)
+6
+>>> BstXI.elucidate()
+'CCAN_NNNN^NTGG'
+>>> len(BstXI)
+12
+>>> FokI.site
+'GGATG'
+>>> FokI.elucidate() # FokI cut well outside its recognition site
+'GGATGNNNNNNNNN^NNNN_N'
+>>> len(FokI) # its length is the length of the recognition site
+5
+```
+
+Also interesting are the methods dealing with isoschizomers. For memory, two
+enzymes are *isoschizomers* if they share a same recognition site.
+A further division is made between isoschizomers (same name, recognise the same
+sequence and cut the same way) and *neoschizomers* which cut at different
+positions. *Equischizomer* is an arbitrary choice to design
+"isoschizomers_that_are_not_neoschizomers" as this last one was a bit long.
+Another set of method `one_enzyme.is_*schizomers(one_other_enzyme)`, allow to
+test 2 enzymes against each other.
+
+``` python
+>>> Acc65I.isoschizomers()
+[Asp718I, KpnI]
+>>> Acc65I.neoschizomers()
+[KpnI]
+>>> Acc65I.equischizomers()
+[Asp718I]
+>>> KpnI.elucidate()
+'G_GTAC^C'
+>>> Acc65I.elucidate()
+'G^GTAC_C'
+>>> KpnI.is_neoschizomer(Acc65I)
+True
+>>> KpnI.is_neoschizomer(KpnI)
+False
+>>> KpnI.is_isoschizomer(Acc65I)
+True
+>>> KpnI.is_isoschizomer(KpnI)
+True
+>>> KpnI.is_equischizomer(Acc65I)
+False
+>>> KpnI.is_equischizomer(KpnI)
+True
+```
+
+`suppliers()` will get you the list of all the suppliers of the enzyme.
+`all_suppliers()` will give you all the suppliers in the database.
+
+### <a name="2"></a>2. The RestrictionBatch class: a class to deal with several enzymes
+
+If you want to make a restriction map of a sequence, using individual enzymes
+can become tedious and will endures a big overhead due to the repetitive
+conversion of the sequence to a `FormattedSeq` (see [Chapter 5](#5)).
+`Restriction` provides a class to make easier the use of large number of enzymes
+in one go: `RestrictionBatch`.
+`RestrictionBatch` will help you to manipulate lots of enzymes with a single
+command. Moreover all the enzymes in the restriction batch will share the same
+converted sequence, reducing the overhead.
+
+#### <a name="2.1"></a><span class="mozTocH4"></span>2.1 Creating a RestrictionBatch
+
+You can initiate a restriction batch by passing it a list of enzymes or enzyme
+names as argument.
+
+``` python
+>>> rb = RestrictionBatch([EcoRI])
+>>> rb
+RestrictionBatch(['EcoRI'])
+>>> rb2 = RestrictionBatch(['EcoRI'])
+>>> rb2
+RestrictionBatch(['EcoRI'])
+>>> rb == rb2
+True
+```
+
+Adding a new enzyme to a restriction batch is easy:
+
+``` python
+>>> rb.add(KpnI)
+>>> rb
+RestrictionBatch(['EcoRI', 'KpnI'])
+>>> rb += EcoRV
+>>> rb
+RestrictionBatch(['EcoRI', 'EcoRV', 'KpnI'])])
+```
+
+Another way to create a RestrictionBatch is by simply adding restriction enzymes
+together, this is particularly useful for small batches:
+
+``` python
+>>> rb3 = EcoRI + KpnI + EcoRV
+>>> rb3
+RestrictionBatch(['EcoRI', 'EcoRV', 'KpnI'])
+```
+
+#### <a name="2.2"></a>2.2 Restricting a RestrictionBatch to a particular supplier
+
+The Restriction package is based upon the **REBASE** database. This database
+gives a list of suppliers for each enzyme. It would be a shame not to make use
+of this facility. You can produce a `RestrictionBatch` containing only enzymes
+from one or a few supplier(s). Here is how to do it:
+
+``` python
+>>> rb_supp = RestrictionBatch(first=[], suppliers=['C','B','E','I','K','J','M',
+'O','N','Q','S','R','V','Y','X'])
+>>> # This will create a RestrictionBatch with the all enzymes which possess a s
+upplier.
+>>> len(rb_supp) # May 2016
+622
+```
+
+The argument `suppliers` take a list of one or several single letter codes
+corresponding to the supplier(s). The codes are the same as defined in REBASE.
+As it would be a pain to have to remember each supplier code, `RestrictionBatch`
+provides a method which show the pair code <=> supplier:
+
+``` python
+>>> RestrictionBatch.show_codes() # as of May 2016 REBASE release.
+C = Minotech Biotechnology
+B = Life Technologies
+E = Agilent Technologies
+I = SibEnzyme Ltd.
+K = Takara Bio Inc.
+J = Nippon Gene Co., Ltd.
+M = Roche Applied Science
+O = Toyobo Biochemicals
+N = New England Biolabs
+Q = Molecular Biology Resources - CHIMERx
+S = Sigma Chemical Corporation
+R = Promega Corporation
+V = Vivantis Technologies
+Y = SinaClon BioScience Co.
+X = EURx Ltd.
+>>> # You can now choose a code and built your RestrictionBatch
+```
+
+This way of producing a `RestrictionBatch` can drastically reduce the amount of
+useless output from a restriction analysis, limiting the search to enzymes that
+you can get hold of and limiting the risks of nervous breakdown. Nothing is more
+frustrating than to get the perfect enzyme for a sub-cloning only to find it's
+not commercially available.
+
+#### <a name="2.3"></a>2.3 Adding enzymes to a RestrictionBatch
+
+Adding an enzyme to a batch if the enzyme is already present will not raise an
+exception, but will have no effects. Sometimes you want to get an enzyme from a
+`RestrictionBatch` or add it to the batch if it is not present.
+You will use the `get` method setting the second argument `add` to `True`.
+
+``` python
+>>> rb3
+RestrictionBatch(['EcoRI', 'EcoRV', 'KpnI'])
+>>> rb3.add(EcoRI)
+>>> rb3
+RestrictionBatch(['EcoRI', 'EcoRV', 'KpnI'])
+>>> rb3.get(EcoRI)
+EcoRI
+>>> rb3.get(SmaI)
+
+Traceback (most recent call last):
+ File "<pyshell#4>", line 1, in -toplevel-
+ rb3.get(SmaI)
+ File "/usr/lib/Python2.3/site-packages/Bio/Restriction/Restriction.py", line 1800, in get
+ raise ValueError, 'enzyme %s is not in RestrictionBatch'%e.__name__
+ValueError: enzyme SmaI is not in RestrictionBatch
+>>> rb3.get(SmaI, add=True)
+SmaI
+>>> rb3
+RestrictionBatch(['EcoRI', 'EcoRV', 'KpnI', 'SmaI'])
+```
+
+#### <a name="2.4"></a>2.4 Removing enzymes from a RestrictionBatch
+
+Removing enzymes from a batch is done using the `remove()` method. If the enzyme
+is not present in the batch this will raise a `KeyError`. If the value you want
+to remove is not an enzyme this will raise a `ValueError`.
+
+``` python
+>>> rb3.remove(EcoRI)
+>>> rb3
+RestrictionBatch(['EcoRV', 'KpnI', 'SmaI'])
+>>> rb3.remove(EcoRI)
+
+Traceback (most recent call last):
+ File "<pyshell#14>", line 1, in -toplevel-
+ rb3.remove('EcoRI')
+ File "/usr/lib/Python2.3/site-packages/Bio/Restriction/Restriction.py", line 1839, in remove
+ return Set.remove(self, self.format(other))
+ File "/usr/lib/Python2.3/sets.py", line 534, in remove
+ del self._data[element]
+KeyError: EcoRI
+>>> rb3 += EcoRI
+>>> rb3
+RestrictionBatch(['EcoRI', 'EcoRV', 'KpnI', 'SmaI'])
+>>> rb3.remove('EcoRI')
+>>> rb3
+RestrictionBatch(['EcoRV', 'KpnI', 'SmaI'])
+>>> rb3.remove('spam')
+
+Traceback (most recent call last):
+ File "<pyshell#18>", line 1, in -toplevel-
+ rb3.remove('spam')
+ File "/usr/lib/Python2.3/site-packages/Bio/Restriction/Restriction.py", line 1839, in remove
+ return Set.remove(self, self.format(other))
+ File "/usr/lib/Python2.3/site-packages/Bio/Restriction/Restriction.py", line 1871, in format
+ raise ValueError, '%s is not a RestrictionType'%y.__class__
+ValueError: <type 'str'> is not a RestrictionType
+```
+
+#### <a name="2.5"></a>2.5 Manipulating RestrictionBatch
+
+You can not, however, add batches together, as they are Python `sets`. You must
+use the pipe operator `|` instead. You can find the intersection between 2
+batches using `&` (see the Python documentation about `sets` for more
+information.
+
+``` python
+>>> rb3 = EcoRI + KpnI + EcoRV
+>>> rb3
+RestrictionBatch(['EcoRI', 'EcoRV', 'KpnI'])
+>>> rb4 = SmaI + PstI
+>>> rb4
+RestrictionBatch(['PstI', 'SmaI'])
+>>> rb3 + rb4
+
+Traceback (most recent call last):
+ File "<pyshell#23>", line 1, in -toplevel-
+ rb3 + rb4
+ File "/usr/lib/Python2.3/site-packages/Bio/Restriction/Restriction.py", line 1829, in __add__
+ new.add(other)
+ File "/usr/lib/Python2.3/site-packages/Bio/Restriction/Restriction.py", line 1848, in add
+ return Set.add(self, self.format(other))
+ File "/usr/lib/Python2.3/site-packages/Bio/Restriction/Restriction.py", line 1871, in format
+ raise ValueError, '%s is not a RestrictionType'%y.__class__
+ValueError: <class 'Bio.Restriction.Restriction.RestrictionBatch'> is not a RestrictionType
+>>> rb3 | rb4
+RestrictionBatch(['EcoRI', 'EcoRV', 'KpnI', 'PstI', 'SmaI'])
+>>> rb3 & rb4
+RestrictionBatch([])
+>>> rb4 += EcoRI
+>>> rb4
+RestrictionBatch(['EcoRI', 'PstI', 'SmaI'])
+>>> rb3 & rb4
+RestrictionBatch(['EcoRI'])
+```
+
+#### <a name="2.6"></a>2.6 Analysing sequences with a RestrictionBatch
+
+To analyse a sequence for potential site, you can use the `search` method of the
+batch, the same way you did for restriction enzymes. The results is no longer a
+list however, but a dictionary. The keys of the dictionary are the names of the
+enzymes and the value a list of position site. `RestrictionBatch` does not
+implement a `catalyse` method, as it would not have a real meaning when used
+with large batch.
+
+``` python
+>>> new_seq = Seq('TTCAAAAAAAAAAAAAAAAAAAAAAAAAAAAGAA', IUPACAmbiguousDNA())
+>>> rb.search(new_seq)
+{'KpnI': [], 'EcoRV': [], 'EcoRI': []}
+>>> rb.search(new_seq, linear=False)
+{'KpnI': [], 'EcoRV': [], 'EcoRI': [33]}
+```
+
+#### <a name="2.7"></a>2.7 Other RestrictionBatch methods
+
+Amongst the other methods provided by `RestrictionBatch`, `elements()` which
+return a list of all the element names alphabetically sorted, is certainly the
+most useful.
+
+``` python
+>>> rb = EcoRI + KpnI + EcoRV
+>>> rb.elements()
+['EcoRI', 'EcoRV', 'KpnI']
+```
+
+If you don't care about the alphabetical order use the method `as_string()`, to
+get the same thing a bit faster. The list is not sorted. The order is random as
+Python sets are dictionary.
+
+``` python
+>>> rb = EcoRI + KpnI + EcoRV
+>>> rb.as_string()
+['EcoRI', 'KpnI', 'EcoRV']
+```
+
+Other `RestrictionBatch` methods are generally used for particular purposes and
+will not be discussed here. See the
+[source](https://github.com/biopython/biopython/tree/master/Bio/Restriction)
+if you are interested.
+
+### <a name="3"></a>3. AllEnzymes and CommOnly: two preconfigured RestrictionBatches
+
+While it is sometime practical to produce a `RestrictionBatch` of your own you
+will certainly more frequently use the two batches provided with the
+`Restriction` packages: `AllEnzymes` and `CommOnly`. These two batches contain
+respectively all the enzymes in the database and only the enzymes which have a
+commercial supplier. They are rather big, but that's what make them useful. With
+these batch you can produce a full description of a sequence with a single
+command. You can use these two batch as any other batch.
+
+``` python
+>>> len(AllEnzymes)
+778
+>>> len(CommOnly)
+622
+>>> AllEnzymes.search(new_seq) ...
+```
+
+There is not a lot to say about them apart the fact that they are present. They
+are really normal batches, and you can use them as any other batch.
+
+### <a name="4"></a>4. The Analysis class: even simpler restriction analysis
+
+`RestrictionBatch` can give you a dictionary with the sites for all the enzymes
+in a batch. However, it is sometime nice to get something a bit easier to read
+than a Python dictionary. Complex restriction analysis are not easy with
+`RestrictionBatch`. Some refinements in the way to search a sequence for
+restriction sites will help. `Analysis` provides a serie of command to customise
+the results obtained from a pair restriction batch/sequence and some facilities
+to make the output sligthly more human readable.
+
+#### <a name="4.1"></a>4.1 Setting up an Analysis
+
+To build a restriction analysis you will need a `RestrictionBatch` and a
+sequence and to tell it if the sequence is linear or circular. The first
+argument `Analysis` takes is the restriction batch, the second is the sequence.
+If the third argument is not provided, `Analysis` will assume the sequence is
+linear.
+
+``` python
+>>> new_seq = Seq('TTCAAAAAAAAAAAAAAAAAAAAAAAAAAAAGAA', IUPACAmbiguousDNA())
+>>> rb = RestrictionBatch([EcoRI, KpnI, EcoRV])
+>>> Ana = Analysis(rb, new_seq, linear=False)
+>>> Ana
+Analysis(RestrictionBatch(['EcoRI', 'EcoRV', 'KpnI']),Seq('TTCAAAAAAAAAAAAAAAAAA
+AAAAAAAAAAGAA', IUPACAmbiguousDNA()),False)
+```
+
+#### <a name="4.2"></a>4.2 Full restriction analysis
+
+Once you have created your new `Analysis`, you can use it to get a restriction
+analysis of your sequence. The way to make a full restriction analysis of the
+sequence is:
+
+``` python
+>>> Ana.full()
+{'KpnI': [], 'EcoRV': [], 'EcoRI': [33]}
+```
+
+This is much the same as the output of a `RestrictionBatch.search` method. You
+will get a more easy to read output with `print_that` used without argument:
+
+``` python
+>>> # let's create a something a bit more complex to analyse.
+>>>
+>>> rb = RestrictionBatch([], ['C']) # we will explain the meaning of the
+>>> # double list argument later.
+>>>
+>>> multi_site = Seq.Seq('AAA' + EcoRI.site + 'G' + KpnI.site + EcoRV.site +
+ 'CT' + SmaI.site + 'GT' + FokI.site + 'GAAAGGGC' +
+ EcoRI.site + 'ACGT', IUPACAmbiguousDNA())
+>>> Analong = Analysis(rb, multi_site)
+>>> Analong.full()
+{BglI: [], BstEII: [], AsuII: [], HinfI: [], SfiI: [], PspPI: [], BsiSI: [27], S
+alI: [], SlaI: [], NcoI: [], NotI: [], PstI: [], StyI: [], BseBI: [], PvuII: [],
+HindIII: [], BglII: [], ApaLI: [], TaqI: [], BssAI: [], AluI: [], SstI: [], Bse
+CI: [], Sau3AI: [], HpaI: [], SnaBI: [], NheI: [], BclI: [], KpnI: [16], NruI: [
+], MspCI: [], BshFI: [], CspAI: [], RsaI: [14], EcoRV: [20], SphI: [], BamHI: []
+, MboI: [], SgrBI: [], SspI: [], ScaI: [], XbaI: [], SseBI: [], NaeI: [], EcoRI:
+[5, 47], SmaI: [28], BseAI: []}
+>>>
+>>> # The results are here but it is difficult to read. let's try print_that
+>>>
+>>> Analong.print_that()
+
+BsiSI : 27.
+RsaI : 14.
+EcoRI : 5, 47.
+EcoRV : 20.
+KpnI : 16.
+SmaI : 28.
+
+ Enzymes which do not cut the sequence.
+
+AluI BshFI MboI Sau3AI TaqI BseBI HinfI PspPI
+ApaLI AsuII BamHI BclI BglII BseAI BseCI BssAI
+CspAI HindIII HpaI MspCI NaeI NcoI NheI NruI
+PstI PvuII SalI ScaI SgrBI SlaI SnaBI SphI
+SseBI SspI SstI StyI XbaI BstEII NotI BglI
+SfiI
+```
+
+Much clearer, is'nt ? The output is optimised for a shell 80 columns wide. If
+the output seems odd, check that the width of your shell is at least 80 columns.
+
+#### <a name="4.3"></a>4.3 Changing the title
+
+You can provide a title to the analysis and modify the sentence 'Enzymes which
+do not cut the sequence', by setting the two optional arguments of `print_that`,
+`title` and `s1`. No formatting will be done on these strings so if you have to
+include the newline (`\n`) as you see fit:
+
+``` python
+>>> Analong.print_that(None, title='sequence = multi_site\n\n')
+
+sequence = multi_site
+
+BsiSI : 27.
+RsaI : 14.
+EcoRI : 5, 47.
+EcoRV : 20.
+KpnI : 16.
+SmaI : 28.
+
+ Enzymes which do not cut the sequence.
+
+AluI BshFI MboI Sau3AI TaqI BseBI HinfI PspPI
+ApaLI AsuII BamHI BclI BglII BseAI BseCI BssAI
+CspAI HindIII HpaI MspCI NaeI NcoI NheI NruI
+PstI PvuII SalI ScaI SgrBI SlaI SnaBI SphI
+SseBI SspI SstI StyI XbaI BstEII NotI BglI
+SfiI
+
+>>> Analong.print_that(None, title='sequence = multi_site\n\n',
+ s1='\n no site:\n\n')
+
+sequence = multi_site
+
+BsiSI : 27.
+RsaI : 14.
+EcoRI : 5, 47.
+EcoRV : 20.
+KpnI : 16.
+SmaI : 28.
+
+ no site:
+
+AluI BshFI MboI Sau3AI TaqI BseBI HinfI PspPI
+ApaLI AsuII BamHI BclI BglII BseAI BseCI BssAI
+CspAI HindIII HpaI MspCI NaeI NcoI NheI NruI
+PstI PvuII SalI ScaI SgrBI SlaI SnaBI SphI
+SseBI SspI SstI StyI XbaI BstEII NotI BglI
+SfiI
+```
+
+#### <a name="4.4"></a>4.4 Customising the output
+
+You can modify some aspects of the output interactively. There is three main
+type of output, two listing types (alphabetically sorted and sorted by number
+of site) and map-like type. To change the output, use the method `print_as()` of
+`Analysis`. The change of output is permanent for the instance of `Analysis`
+(that is until the next time you use `print_as()`). The argument of `print_as()`
+are strings: `'map'`, `'number'` or `'alpha'`. As you have seen previously the
+default behaviour is an alphabetical list (`'alpha'`).
+
+``` python
+>>> Analong.print_as('map')
+>>> Analong.print_that()
+
+ 5 EcoRI
+ |
+ | 14 RsaI
+ | |
+ | | 16 KpnI
+ | | |
+ | | | 20 EcoRV
+ | | | |
+ | | | | 27 BsiSI
+ | | | | |
+ | | | | |28 SmaI
+ | | | | ||
+ | | | | || 47 EcoRI
+ | | | | || |
+AAAGAATTCGGGTACCGATATCCTCCCGGGGTGGATGGAAAGGGCGAATTCACGT
+|||||||||||||||||||||||||||||||||||||||||||||||||||||||
+TTTCTTAAGCCCATGGCTATAGGAGGGCCCCACCTACCTTTCCCGCTTAAGTGCA
+1 55
+
+
+ Enzymes which do not cut the sequence.
+
+AluI BshFI MboI Sau3AI TaqI BseBI HinfI PspPI
+ApaLI AsuII BamHI BclI BglII BseAI BseCI BssAI
+CspAI HindIII HpaI MspCI NaeI NcoI NheI NruI
+PstI PvuII SalI ScaI SgrBI SlaI SnaBI SphI
+SseBI SspI SstI StyI XbaI BstEII NotI BglI
+SfiI
+
+>>> Analong.print_as('number')
+>>> Analong.print_that()
+
+
+
+enzymes which cut 1 times :
+
+BsiSI : 27.
+RsaI : 14.
+EcoRV : 20.
+KpnI : 16.
+SmaI : 28.
+
+
+enzymes which cut 2 times :
+
+EcoRI : 5, 47.
+
+ Enzymes which do not cut the sequence.
+
+AluI BshFI MboI Sau3AI TaqI BseBI HinfI PspPI
+ApaLI AsuII BamHI BclI BglII BseAI BseCI BssAI
+CspAI HindIII HpaI MspCI NaeI NcoI NheI NruI
+PstI PvuII SalI ScaI SgrBI SlaI SnaBI SphI
+SseBI SspI SstI StyI XbaI BstEII NotI BglI
+SfiI
+
+>>>
+```
+
+To come back to the previous behaviour:
+
+``` python
+>>> Analong.print_as('alpha')
+>>> Analong.print_that()
+
+BsiSI : 27.
+RsaI : 14.
+EcoRI : 5, 47.
+EcoRV : 20.
+etc ...
+```
+
+#### <a name="4.5"></a>4.5 Fancier restriction analysis
+
+I will not go into the detail for each single method, here are all the functions
+that are available. Most are perfectly self explanatory and the others are
+fairly well documented (use `help('Analysis.command_name')`). The methods are:
+
+``` python
+full(self,linear=True)
+blunt(self,dct = None)
+overhang5(self, dct=None)
+overhang3(self, dct=None)
+defined(self,dct=None)
+with_sites(self, dct=None)
+without_site(self, dct=None)
+with_N_sites(self, N, dct=None)
+with_number_list(self, list, dct=None)
+with_name(self, names, dct=None)
+with_site_size(self, site_size, dct=None)
+only_between(self, start, end, dct=None)
+between(self,start, end, dct=None)
+show_only_between(self, start, end, dct=None)
+only_outside(self, start, end, dct =None)
+outside(self, start, end, dct=None)
+do_not_cut(self, start, end, dct =None)
+```
+
+Using these methods is simple:
+
+``` python
+>>> new_seq = Seq('TTCAAAAAAAAAAAAAAAAAAAAAAAAAAAAGAA', IUPACAmbiguousDNA())
+>>> rb = RestrictionBatch([EcoRI, KpnI, EcoRV])
+>>> Ana = Analysis(rb, new_seq, linear=False)
+>>> Ana
+Analysis(RestrictionBatch(['EcoRI', 'EcoRV', 'KpnI']),Seq('TTCAAAAAAAAAAAAAAAAAA
+AAAAAAAAAAGAA', IUPACAmbiguousDNA()),False)
+>>> Ana.blunt() # output only the result for enzymes which cut blunt
+{'EcoRV': []}
+>>> Ana.full() # all the enzymes in the RestrictionBatch
+{'KpnI': [], 'EcoRV': [], 'EcoRI': [33]}
+>>> Ana.with_sites() # output only the result for enzymes which have a site
+{'EcoRI': [33]}
+>>> Ana.without_site() # output only the enzymes which have no site
+{'KpnI': [], 'EcoRV': []}
+>>> Ana.only_between(1, 20) # the enzymes which cut between position 1 and 20
+{}
+>>> Ana.only_between(20, 34) # etc...
+{'EcoRI': [33]}
+>>> Ana.only_outside(20, 34)
+{}
+>>> Ana.with_name([EcoRI])
+{'EcoRI': [33]}
+>>>
+```
+
+To get a nice output, you still use `print_that` but this time with the command
+you want executed as argument.
+
+``` python
+>>> Ana.print_that(Ana.blunt())
+
+ Enzymes which do not cut the sequence.
+
+EcoRV
+
+>>> pt = Ana.print_that
+>>> pt(Ana.with_sites())
+
+EcoRI : 33.
+
+>>> pt(Ana.without_site())
+
+ Enzymes which do not cut the sequence.
+
+EcoRV KpnI
+
+>>> # etc ...
+```
+
+#### <a name="4.6"></a>4.6 More complex analysis
+
+All of these methods (except `full()` which, well ... do a full restriction
+analysis) can be supplied with an additional dictionary. If no dictionary is
+supplied a full restriction analysis is used as starting point. Otherwise the
+dictionary provided by the argument `dct` is used. The dictionary must be
+formatted as the result of `RestrictionBatch.search`. Therefore of the form
+`{'enzyme_name': [position1, position2],...}`, where *position1* and
+*position2* are integers. All methods list previously output such dictionaries
+and can be used as starting point.
+
+Using this method you can build really complex query by chaining several method
+one after the other. For example if you want all the enzymes which are 5'
+overhang and cut the sequence only once, you have two ways to go:
+
+The hard way consist to build a restriction batch containing only 5' overhang
+enzymes and use this batch to create a new `Analysis` instance and then use the
+method `with_N_sites()` as follow:
+
+``` python
+>>> rbov5 = RestrictionBatch([x for x in rb if x.is_5overhang()])
+>>> Anaov5 = Analysis(rbov5, new_seq, linear=False)
+>>> Anaov5.with_N_sites(1)
+{'EcoRI' : [33]}
+```
+
+The easy solution is to chain several `Analysis` methods. This is possible since
+each method return a dictionary as results and is able to take a dictionary as
+input:
+
+``` python
+>>> Ana.with_N_sites(1, Ana.overhang5())
+{'EcoRI': [33]}
+```
+
+The dictionary is always the last argument whatever the command you use.
+
+The way to prefer certainly depends of the conditions you will use your
+`Analysis` instance. If you are likely to frequently reuse the same batch with
+different sequences, using a dedicated `RestrictionBatch` might be faster as the
+batch is likely to be smaller. Chaining methods is generally quicker when
+working with an interactive shell. In a script, the extended syntax may be
+easier to understand in a few months.
+
+### <a name="5"></a>5. Advanced features: the FormattedSeq class
+
+Restriction enzymes require a much more strict formatting of the DNA sequences
+than `Bio.Seq` object provides. For example, the restriction enzymes expect to
+find an ungapped (no space) upper-case sequence, while `Bio.Seq` object allow
+sequences to be in lower-case separated by spaces. Therefore when a restriction
+enzyme analyse a `Bio.Seq` object (be it a `Seq` or a `MutableSeq`), the object
+undergoes a conversion. The class `FormattedSeq` ensure the smooth conversion
+from a `Bio.Seq` object to something which can be safely be used by the enzyme.
+
+While this conversion is done automatically by the enzymes if you provide them
+with a `Seq` or a `MutableSeq`, there is time where it will be more efficient to
+realise the conversion before hand. Each time a `Seq` object is passed to an
+enzyme for analysis you pay a overhead due to the conversion. When analysing the
+same sequence over and over, it will be faster to convert the sequence, store
+the conversion and then use only the converted sequence.
+
+#### <a name="5.1"></a>5.1 Creating a FormattedSeq
+
+Creating a `FormattedSeq` from a `Bio.Seq` object is simple. The first argument
+of `FormattedSeq` is the sequence you wish to convert. You can specify a shape
+with the second argument `linear`, if you don't the `FormattedSeq` will be
+linear:
+
+``` python
+>>> from Bio.Restriction import *
+>>> from Bio.Seq import Seq
+>>> seq = Seq('TTCAAAAAAAAAAGAATTCAAAAGAA')
+>>> linear_fseq = FormattedSeq(seq, linear=True)
+>>> default_fseq = FormattedSeq(seq)
+>>> circular_fseq = FormattedSeq(seq, linear=False)
+>>> linear_fseq
+FormattedSeq(Seq('TTCAAAAAAAAAAGAATTCAAAAGAA', Alphabet()), linear=True)
+>>> linear_fseq.is_linear()
+True
+>>> default_fseq.is_linear()
+True
+>>> circular_fseq.is_linear()
+False
+>>> circular_fseq
+FormattedSeq(Seq('TTCAAAAAAAAAAGAATTCAAAAGAA', Alphabet()), linear=False)
+```
+
+#### <a name="5.2"></a>5.2 Unlike Bio.Seq, FormattedSeq retains information about their shape
+
+`FormattedSeq` retains information about the shape of the sequence. Therefore
+unlike with `Seq` and `MutableSeq` you don't need to specify the shape of the
+sequence when using `search()` or `catalyse()`:
+
+``` python
+>>> EcoRI.search(linear_fseq)
+[15]
+>>> EcoRI.search(circular_fseq) # no need to specify the shape
+[15, 25]
+```
+
+In fact, the shape of a FormattedSeq is not altered by the second argument of
+the commands `search()` and `catalyse()`:
+
+``` python
+>>> # In fact the shape is blocked.
+>>> # The 3 following commands give the same results
+>>> # which correspond to a circular sequence
+>>> EcoRI.search(circular_fseq)
+[15, 25]
+>>> EcoRI.search(circular_fseq, linear=True)
+[15, 25]
+>>> EcoRI.search(circular_fseq, linear=False)
+[15, 25]
+```
+
+#### <a name="5.3"></a>5.3 Changing the shape of a FormattedSeq
+
+You can however change the shape of the `FormattedSeq`. The command to use are:
+
+``` python
+FormattedSeq.to_circular() => new FormattedSeq, shape will be circular.
+FormattedSeq.to_linear() => new FormattedSeq, shape will be linear
+FormattedSeq.circularise() => change the shape of FormattedShape to circular
+FormattedSeq.linearise() => change the shape of FormattedShape to linear
+```
+
+``` python
+>>> circular_fseq
+FormatedSeq(Seq('TTCAAAAAAAAAAGAATTCAAAAGAA', Alphabet()), linear=False)
+>>> circular_fseq.is_linear()
+False
+>>> circular_fseq == linear_fseq
+False
+>>> newseq = circular_fseq.to_linear()
+>>> circular_fseq
+FormatedSeq(Seq('TTCAAAAAAAAAAGAATTCAAAAGAA', Alphabet()), linear=False)
+>>> newseq
+FormatedSeq(Seq('TTCAAAAAAAAAAGAATTCAAAAGAA', Alphabet()), linear=True)
+>>> circular_fseq.linearise()
+>>> circular_fseq
+FormatedSeq(Seq('TTCAAAAAAAAAAGAATTCAAAAGAA', Alphabet()), linear=True)
+>>> circular_fseq.is_linear()
+True
+>>> circular_fseq == linear_fseq
+True
+>>> EcoRI.search(circular_fseq) # which is now linear
+[15]
+```
+
+#### <a name="5.4"></a>5.4 Using / and // operators with FormattedSeq
+
+Not having to specify the shape of the sequence to analyse gives you the
+opportunity to use the shorthand '/' and '//' with restriction enzymes:
+
+``` python
+>>> EcoRI/linear_fseq # <=> EcoRI.search(linear_fseq)
+[15]
+>>> linear_fseq/EcoRI # <=> EcoRI.search(linear_fseq)
+[15]
+>>> EcoRI//linear_fseq # <=> linear_fseq//EcoRI <=> EcoRI.catalyse(linear_fseq)
+(Seq('TTCAAAAAAAAAAG', Alphabet()), Seq('AATTCAAAAGAA', Alphabet()))
+```
+
+Another way to avoid the overhead due to a repetitive conversion from a `Seq`
+object to a `FormattedSeq` is to use a [`RestrictionBatch`](#2).
+
+To conclude, the performance gain achieved when using a `FormattedSeq` instead
+of a `Seq` is not huge. The analysis of a 10 kb sequence by all the enzymes in
+`AllEnzymes` (`for x in AllEnzymes: x.search(seq)`, 867 enzymes) is 7 % faster
+when using a `FormattedSeq` than a `Seq`. Using a `RestrictionBatch`
+(`AllEnzymes.search(seq)`) is about as fast as using a `FormattedSeq` the first
+time the search is run. This however is dramatically reduced in subsequent runs
+with the same sequence (`RestrictionBatch` keeps in memory the result of their
+last run while the sequence is not changed).
+
+### <a name="6"></a>6. More advanced features
+
+This chapter addresses some more advanced features of the packages, most users
+can safely ignore it.
+
+#### <a name="6.1"></a>6.1 Updating the enzymes from REBASE
+
+Most people will certainly not need to update the enzymes. The restriction
+enzyme package will be updated in with each new release of Biopython. But if you
+wish to get an update in between Biopython-releases here is how to do it.
+
+First, you have to download the two scripts `rebase_update.py` and
+`ranacompile.py`:
+Go to https://github.com/biopython/biopython/tree/master/Scripts/Restriction,
+click on the respective file and press the '**Raw**' button on the top right of
+the code window. Then, with right-click, save the file. Both scripts must be in
+the same directory.
+
+##### <a name="6.1.1"></a>6.1.1 Fetching the recent enzyme files manually from REBASE
+
+Each month, [REBASE](http://rebase.neb.com/rebase/rebase.html) release a new
+compilation of data about restriction enzymes. While the enzymes do not change
+so frequently, you may wish to update the restriction enzymes classes. The first
+thing to do is to get the last rebase file. You can find the release of REBASE
+at http://rebase.neb.com/rebase/rebase.files.html. The file you are interested
+in are in the EMBOSS format. You can download the files directly from the REBASE
+ftp server using your browser. The file are situated at
+ftp://ftp.neb.com/pub/rebase.
+You will have to download 3 files: `emboss_e.###`, `emboss_r.###`, and
+`emboss_s.###`.
+The `###` is a 3 digit number corresponding to the year and month of the
+release. The first digit is the year, the two last are the month: so July 2015
+will be: 507; October 2016: 610, etc... Download the three file corresponding to
+the current month and place them in the same folder as your `rebase_update.py`
+and `ranacompiler.py` scripts.
+
+##### <a name="6.1.2"></a>6.1.2 Fetching the recent enzyme files with rebase_update.py
+
+Another way to do the same thing is to use the `rebase_update.py` script. It
+will connect directly to the rebase ftp server and download the last batch of
+emboss files. From a DOS or Unix shell do the following:
+
+``` bash
+$ cd path_to_the_update_script
+$ rebase_update.py -p http://www.somewhere.com:8000
+
+Please wait, trying to connect to Rebase
+
+copying ftp://ftp.neb.com/pub/rebase/emboss_e.407
+to /cvsroot/bioPython/Bio/Restriction/Scripts/emboss_e.407
+copying ftp://ftp.neb.com/pub/rebase/emboss_s.407
+to /cvsroot/bioPython/Bio/Restriction/Scripts/emboss_s.407
+copying ftp://ftp.neb.com/pub/rebase/emboss_r.407
+to /cvsroot/bioPython/Bio/Restriction/Scripts/emboss_r.407
+```
+
+Some explanation are needed: `-p` is the switch to indicate to the script you
+are using a proxy. If you use a ftp proxy enter its address and the connection
+port after the '`:`'.
+
+
+##### <a name="6.1.3"></a>6.1.3 Compiling a new dictionary with ranacompiler.py
+
+Once you have got the recent emboss files you can compile a new module
+containing the data necessary to create restriction enzyme.
+
+Note: if the emboss files are not present in the current directory or if they
+are not up to date, `ranacompiler.py` will invoke the script
+[`rebase_update.py`](#6.1.2), which needs to be installed in the same folder.
+You will need to use the same options as before (ie `-m` and `-p`). See the
+previous paragraph on [`rebase_update.py`](#6.1.2) for more details.
+
+For simplicity let's assume we have put the emboss files in the same folder as
+the files which contains the script `ranacompiler.py`. You may have the change
+the mode of the file to make it executable:
+
+``` bash
+$ cd path_to_the_ranacompiler_script
+$ chmod '+x' ranacompiler.py
+```
+
+Now execute the script:
+
+``` bash
+$ Python ranacompiler.py # or ./ranacompiler.py
+```
+
+You get normally the following message:
+
+``` bash
+$ ./ranacompiler.py
+
+ Using the files : emboss_e.407, emboss_r.407, emboss_s.407
+
+WARNING : HaeIV cut twice with different overhang length each time.
+ Unable to deal with this behaviour.
+ This enzyme will not be included in the database. Sorry.
+ Checking :
+Anyway, HaeIV is not commercially available.
+
+WARNING : HpyUM037X has two different sites.
+
+
+The new database contains 867 enzymes.
+
+Writing the dictionary containing the new Restriction classes...
+OK.
+
+Writing the dictionary containing the suppliers datas...
+OK.
+
+Writing the dictionary containing the Restriction types....
+OK.
+
+ ******************************************************************************
+
+ Compilation of the new dictionary : OK.
+ Installation : No.
+
+ You will find the newly created 'Restriction_Dictionary.py' file
+ in the :
+
+ /path/where/you/run/ranacompiler.py
+
+ Make a copy of 'Restriction_Dictionary.py' and place it with
+ the other Restriction libraries.
+
+ note :
+ This folder should be :
+
+ path_to_python/site-packages/Bio/Restriction
+
+ ******************************************************************************
+```
+
+The first line indicate which emboss files have been used for the present
+compilation. You can safely ignore the warnings as long as the
+`compilation of the new dictionary : OK.` is present in the last part of the
+output. They are here for debugging purpose. The number of enzymes in the new
+module is indicated as well as a list of the dictionary which have been
+compiled. The last part indicate that the module has been succesfully created
+but not installed. To finish the update you must copy the file
+`Restriction_Dictionary.py` into the folder
+`/your_python_path/site-packages/Bio/Restriction/` as indicated by the script.
+Looking into the present folder, you will see to new files: the newly created
+dictionary `Restriction_Dictionary.py` and `Restriction_Dictionary.old`. This
+last file containing the old dictionary to which you can revert in case anything
+the new file is corrupted (this should not happen since the script is happy
+enough the new dictionary is good, but if there is a problem it is always nice
+to know you can revert to the previous setting without having to reinstall the
+whole thing.
+
+If you whish, the script may install the folder for you as well, but you will
+have to run it as root if your normal user has no write access to your Python
+installation (and it should'nt). Use the command `ranacompiler.py -i` or
+`ranacompiler.py --install` for this.
+
+If anything goes wrong (you have no write access to the destination folder for
+example) the script will let you know it did not perform the installation. It
+will however still save the new module in the current directory.
+
+As you can see the script is not very bright and will redo the compilation each
+time it is invoked, no matter if a previous version of the module is already
+present.
+
+#### <a name="6.2"></a>6.2 Subclassing the class Analysis
+
+As seen previously, you can modify some aspects of the `Analysis` output
+interactively. However if you want to write your own `Analysis` class, you may
+wish to provide others output facilities than is given in this package.
+Depending on what you want to do you may get away with simply changing the
+`make_format` method of your derived class or you will need to provide new
+methods. Rather than get into a long explanation, here is the implementation of
+a rather useless `Analysis` class:
+
+``` python
+>>> class UselessAnalysis(Analysis):
+
+ def __init__(self, rb=RestrictionBatch(), seq=Seq(''), lin=True):
+ """UselessAnalysis -> A class that waste your time"""
+ #
+ # Unless you want to do something more fancy all
+ # you need to do here is instantiate Analysis.
+ # Don't forget the self in __init__
+ #
+ Analysis.__init__(self, rb, seq, lin)
+
+ def make_format(self, cut=[], t='', nc=[], s=''):
+ """not funny"""
+ #
+ # Generally, you don't need to do anything else here
+ # This will tell to your new class to default to the
+ # _make_joke format.
+ #
+ return self._make_joke(cut, t, nc, s)
+
+ def print_as(self, what='joke'):
+ """Never know somebody might want to change the behaviour of
+ this class."""
+ #
+ # add your new option to print_as
+ #
+ if what == 'joke':
+ self.make_format = self._make_joke
+ return
+ else:
+ #
+ # The other options will be treated as before
+ #
+ return Analysis.print_as(self, what)
+
+ def _make_joke(self, cut=[], title='', nc=[], s1=''):
+ """UA._make_joke(cut, t, nc, s) -> new analysis output"""
+ #
+ # starting your new method with '_make_'
+ # will give a hint to what it is suppose to do
+ #
+ # We will not process the non-cutting enzymes
+ # Their names are in nc
+ # s1 is the string printed before them
+ #
+ if not title:
+ title = '\nYou have guessed right the following enzymes:\n\n'
+ for name, sites in cut:
+ #
+ # cut contains:
+ # - the name of the enzymes which cut the sequence (name)
+ # - a list of the site positions (sites)
+ #
+ guess = raw_input("next enzyme is %s, Guess how many sites ?\n>>> "%name)
+ try:
+ guess = int(guess)
+ except:
+ guess = None
+ if guess == len(sites):
+ print 'You did guess right. Good. Next.'
+ result = '%i site' % guess
+ if guess > 1:
+ result += 's'
+
+ #
+ # now we format the line. See the PrintFormat module
+ # for some examples
+ # PrintFormat.__section_list and _make_map are good start.
+ #
+ title=''.join((title, str(name).ljust(self.NameWidth),
+ ' : ', result, '.\n'))
+ print '\nNo more enzyme.'
+ return title
+ #
+ # I you want to print the non cutting enzymes use
+ # the following return instead of the previous one:
+ #
+ #return title + t + self._make_nocut_only(nc,s1)
+
+>>> # You initiate and use it as before
+>>> rb = RestrictionBatch([], ['A'])
+>>> multi_site = Seq('AAA' + EcoRI.site +'G' + KpnI.site + EcoRV.site + 'CT' +\
+SmaI.site + 'GT' + FokI.site + 'GAAAGGGC' + EcoRI.site + 'ACGT', IUPACAmbiguousDNA())
+>>>
+>>> b = UselessAnalysis(rb, multi_site)
+>>> b.print_that() # Well, I let you discover if you haven't already guessed
+```
+
+Using this example, as a template you should now be able to subclass `Analysis`
+as you wish. You will found more implementation details in the module
+`Bio.Restriction.PrintFormat` which contains the class providing all the
+`_make_*` methods.
+
+### <a name="7"></a>7. Limitation and caveat
+
+Particularly, the class `Analysis` is a quick and dirty implementation based on
+the facilities furnished by the package. Please check your results and report
+any fault.
+
+On a more general basis, `Restriction` have some other limitations:
+
+#### <a name="7.1"></a>7.1 All DNA are non methylated
+
+No facility to work with methylated DNA has been implemented yet. As far as the
+enzyme classes are concerned all DNA is non methylated DNA. Implementation of
+methylation sensibility will possibly occur in the future. But for now, if your
+sequence is methylated, you will have to check if the site is methylated using
+other means.
+
+#### <a name="7.2"></a>7.2 No support for star activity
+
+As before no support has been yet implemented to find site mis-recognised by
+enzymes under high salt concentration conditions, the so-called star activity.
+This will be implemented as soon as I can get a good source of information for
+that.
+
+#### <a name="7.3"></a>7.3 Safe to use with degenerated DNA
+
+It is safe to use degenerated DNA as input for the query. You will not be
+flooded with meaningless results. But this come at a price: GAA***N***TC will
+not be recognised as a potential EcoRI site for example, in fact it will not be
+recognised at all. Degenerated sequences will not be analysed. If your sequence
+is not fully sequenced, you will certainly miss restriction sites:
+
+``` python
+>>> a = Seq('nnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnGAATTCrrrrrrrrrrr', IUPACAmbiguou
+sDNA())
+>>> EcoRI.search(a)
+[36]
+>>> b = Seq('nnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnGAAnTCrrrrrrrrrrr', IUPACAmbiguou
+sDNA())
+>>> EcoRI.search(b)
+[]
+```
+
+#### <a name="7.4"></a>7.4 Non standard bases in DNA are not allowed
+
+While you can use degenerated DNA, using non standard base alphabet will make
+the enzymes choke, even if `Bio.Seq.Seq` accepts them. However, space-like
+characters (' ', '\n', '\t', ...) and digit will be removed but will not stop
+the enzyme analysing the sequence. You can use them but the fragments produced
+by `catalyse` will have lost any formatting. `catalyse` tries to keep the
+original case of the sequence (i.e lower case sequences will generate lower case
+fragments, upper case sequences upper case fragments), but mixed case will
+return upper case fragments:
+
+``` python
+>>> c = Seq('xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxGAANTCrrrrrrrrrrr', IUPACAmbiguou
+sDNA())
+>>> EcoRI.search(c)
+
+Traceback (most recent call last):
+ File "<pyshell#110>", line 1, in -toplevel-
+ EcoRI.search(b)
+ File "/usr/lib/Python2.3/site-packages/Bio/Restriction/Restriction.py", line 3
+ 96, in search
+ cls.dna = FormatedSeq(dna, linear)
+ File "/usr/lib/Python2.3/site-packages/Bio/Restriction/Restriction.py", line 1
+ 37, in __init__
+ self.format()
+ File "/usr/lib/Python2.3/site-packages/Bio/Restriction/Restriction.py", line 1
+ 53, in format
+ raise AlphabetError, " '%s' is not in the IUPAC alphabet" % s
+AlphabetError: 'X' is not in the IUPAC alphabet
+>>> d = Seq('1 nnnnn nnnnn nnnnn nnnnn nnnnn \n\
+26 nnnnn nnnnG AATTC rrrrr rrrrr \n\
+51 r', IUPACAmbiguousDNA())
+>>> d
+Seq('1 nnnnn nnnnn nnnnn nnnnn nnnnn \n26 nnnnn nnnnG AATTC rrrrr rrrrr \n51 r',
+ IUPACAmbiguousDNA())
+>>> EcoRI.search(d)
+[36]
+>>> EcoRI.catalyse(d)
+(Seq('AATTCRRRRRRRRRRR', IUPACAmbiguousDNA()), Seq('NNNNNNNNNNNNNNNNNNNNNNNNNNNN
+NNNNNNG', IUPACAmbiguousDNA()))
+>>> e = Seq('nnnnGAATTCrr', IUPACAmbiguousDNA())
+>>> f = Seq('NNNNGAATTCRR', IUPACAmbiguousDNA())
+>>> g = Seq('nnnngaattcrr', IUPACAmbiguousDNA())
+>>> EcoRI.catalyse(e)
+(Seq('NNNNG', IUPACAmbiguousDNA()), Seq('AATTCRR', IUPACAmbiguousDNA()))
+>>> EcoRI.catalyse(f)
+(Seq('NNNNG', IUPACAmbiguousDNA()), Seq('AATTCRR', IUPACAmbiguousDNA()))
+>>> EcoRI.catalyse(g)
+(Seq('nnnng', IUPACAmbiguousDNA()), Seq('aattcrr', IUPACAmbiguousDNA()))
+```
+
+Not allowing other letters than IUPAC might seems drastic but this is really to
+limit errors. It is not totally fool proof but it does help.
+
+#### <a name="7.5"></a>7.5 Sites found at the edge of linear DNA might not be accessible in a real digestion
+
+While sites clearly outsides a sequence will not be reported, nothing has been
+done to try to determine if a restriction site at the end of a linear sequence
+is valid:
+
+``` python
+>>> d = Seq('GAATTCAAAAAAAAAAAAAAAAAAAAAAAAAAGGATG', IUPACAmbiguousDNA())
+>>> FokI.site # site present
+'GGATG'
+>>> FokI.elucidate() # but cut outside the sequence
+'GGATGNNNNNNNNN^NNNN_N'
+>>> FokI.search(d) # therefore no site found
+[]
+>>> EcoRI.search(d)
+[2]
+```
+
+`EcoRI` finds a site at position 2 even if it is highly unlikely that EcoRI
+accepts to cut this site in a tube. It is generally considered that at about 5
+nucleotides must separate the site from the edge of the sequence to be
+reasonably sure the enzyme will work correctly. This "security margin" is
+variable from one enzyme to the other. In doubt consult the documentation for
+the enzyme.
+
+#### <a name="7.6"></a>7.6 Restriction reports cutting sites not enzyme recognition sites
+
+Some enzymes will cut twice each time they encounter a restriction site. The
+enzymes in this package report both cut not the site. Other software may only
+reports restriction sites. Therefore the output given for some enzymes might
+seems to be the double when compared with the results of these software. It is
+not a bug.
+
+``` python
+>>> AloI.cut_twice()
+True
+>>> AloI.fst5 # first cut
+-7
+>>> AloI.scd5 # second cut
+25
+>>> AloI.site
+'GAACNNNNNNTCC'
+>>> b = Seq('AAAAAAAAAAA'+ AloI.site + 'AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA')
+>>> b
+Seq('AAAAAAAAAAAGAACNNNNNNTCCAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA', Alphabet())
+>>> AloI.search(b) # one site, two cuts -> two positions
+[5, 37]
+```
+
+### <a name="8"></a>8. Annex: modifying dir() to use with from Bio.Restriction import *
+
+Having all the enzymes imported directly in the shell is useful when working in
+an interactive shell (even if it is not recommended by the purists). Here is a
+little hack to get some sanity back when using dir() in those conditions:
+
+``` python
+>>> # we will change the builtin dir() function to get ride of the enzyme names.
+>>> import sys
+>>> def dir(object=None):
+ """dir([object]) -> list of string.
+
+ over-ride the built-in function to get some clarity."""
+ if object:
+ # we only want to modify dir(),
+ # so here we return the result of the builtin function.
+ return __builtins__.dir(object)
+ else:
+ # now the part we want to modify.
+ # All the enzymes are in a RestrictionBatch (we will talk about
+ # that later, for the moment simply believe me).
+ # So if we remove from the results of dir() everything which is
+ # in AllEnzymes we will get a much shorter list when we do dir()
+ #
+ # the current level is __main__ ie dir() is equivalent to
+ # ask what's in __main__ at the moment.
+ # we can't access __main__ directly.
+ # so we will use sys.modules['__main__'] to reach it.
+ # the following list comprehension remove from the result of
+ # dir() everything which is also present in AllEnzymes.
+ #
+ return [x for x in __builtins__.dir(sys.modules['__main__'])
+ if not x in AllEnzymes]
+
+>>> # now let's see if it works.
+>>> dir()
+['AllEnzymes', 'Analysis', 'CommOnly', 'NonComm', 'PrintFormat', 'RanaConfig',
+ 'Restriction', 'RestrictionBatch', 'Restriction_Dictionary', '__builtins__',
+ '__doc__', '__name__', 'dir', 'sys']
+>>> # ok that's much better.
+>>> # The enzymes are still there
+>>> EcoRI.site
+'GAATTC'
+```
diff --git a/Doc/cookbook/motif/Makefile b/Doc/cookbook/motif/Makefile
deleted file mode 100644
index f82d0d7..0000000
--- a/Doc/cookbook/motif/Makefile
+++ /dev/null
@@ -1,30 +0,0 @@
-# create pdf, txt and html documentation
-
-FILENAME=motif
-
-all: ${FILENAME}.html ${FILENAME}.pdf ${FILENAME}.txt
-
-clean:
- rm -f ${FILENAME}.aux
- rm -f ${FILENAME}.log
- rm -f ${FILENAME}.out
- rm -f ${FILENAME}.toc
- rm -f ${FILENAME}.haux
- rm -f ${FILENAME}.htoc
-
-distclean: clean
- rm -f ${FILENAME}.pdf
- rm -f ${FILENAME}.html
- rm -f ${FILENAME}.txt
-
-${FILENAME}.pdf: ${FILENAME}.tex
- pdflatex ${FILENAME}.tex
- pdflatex ${FILENAME}.tex
- pdflatex ${FILENAME}.tex
-
-${FILENAME}.html: ${FILENAME}.tex
- hevea -fix ${FILENAME}.tex
-
-${FILENAME}.txt: ${FILENAME}.tex
- hevea -fix -text ${FILENAME}.tex
-
diff --git a/Doc/cookbook/motif/motif.tex b/Doc/cookbook/motif/motif.tex
deleted file mode 100644
index 6b0a426..0000000
--- a/Doc/cookbook/motif/motif.tex
+++ /dev/null
@@ -1,590 +0,0 @@
-% Documentation describing writing Tests for Biopython modules
-\documentclass{article}
-\usepackage{url}
-\usepackage{fullpage}
-\usepackage{hevea}
-\usepackage{graphicx}
-
-% Make links between references
-\usepackage{hyperref}
-\newif\ifpdf
-\ifx\pdfoutput\undefined
- \pdffalse
-\else
- \pdfoutput=1
- \pdftrue
-\fi
-\ifpdf
- \hypersetup{colorlinks=true, hyperindex=true, citecolor=red, urlcolor=blue}
-\fi
-
-\begin{document}
-
-\title{Sequence motif analysis using Biopython}
-\author{Bartek Wilczynski (bartek at mimuw.edu.pl)}
-
-\maketitle
-\tableofcontents
-
-\section{Short introduction}
-\label{sec:intro}
-This short tutorial describes some of the functionality of the
-\verb|Bio.Motif| package included in Biopython distribution. It is intended
-for people who are involved in analysis of sequence motif, so I'll
-assume that you are familiar with basic notions of motif analysis. In
-case something is unclear, please look into Section \ref{sec:links}
-for some relevant links.
-
-It should be also noted, that \verb|Bio.Motif| is based on two
-Biopython modules, \verb|Bio.AlignAce| and \verb|Bio.MEME| and is
-meant to replace them. It provides (almost) all of the functionality
-of these modules and unifies the basic motif object implementation.
-
-Speaking of other libraries, if you are reading this you might be
-interested in the TAMO \cite{tamo}
-(\url{http://fraenkel.mit.edu/TAMO/}), which is another python library
-designed to deal with sequence motifs. It supports more \emph{de-novo}
-motif finders, but it is not a part of biopython (so requires a bit of
-work) and has some restrictions on commercial use.
-
-\section{Motif objects}
-\label{sec:object}
-Since we are interested in motif analysis, we need to take a look at
-\verb|Motif| objects in the first place. For that we need to import
-the Motif library:
-\begin{verbatim}
-from Bio import Motif
-\end{verbatim}
-and we can start creating our first motif objects. Let's create a DNA motif:
-\begin{verbatim}
-from Bio.Alphabet import IUPAC
-m=Motif.Motif(alphabet=IUPAC.unambiguous_dna)
-\end{verbatim}
-This is for now just an empty container, so let's add some sequences to our newly created motif:
-\begin{verbatim}
-from Bio.Seq import Seq
-m.add_instance(Seq("TATAA", m.alphabet))
-m.add_instance(Seq("TATTA", m.alphabet))
-m.add_instance(Seq("TATAA", m.alphabet))
-m.add_instance(Seq("TATAA", m.alphabet))
-\end{verbatim}
-Now we have a full \verb|Motif| instance, so we can try to get some
-basic information about it. Let's start with length and consensus
-sequence:
-\begin{verbatim}
->>> m.length
-5
->>> m.consensus
-Seq('TATAA', IUPACUnambiguousDNA())
-\end{verbatim}
-In case of DNA motifs, we can also get a reverse complement of a motif:
-\begin{verbatim}
->>> m.reverse_complement()
-<Bio.Motif.Motif.Motif object at 0xb39890>
->>> m.reverse_complement().consensus
-Seq('TTATA', IUPACUnambiguousDNA())
->>> m.reverse_complement().instances
-[Seq('TTATA', IUPACUnambiguousDNA()),
- Seq('TAATA', IUPACUnambiguousDNA()),
- Seq('TTATA', IUPACUnambiguousDNA()),
- Seq('TTATA', IUPACUnambiguousDNA())]
-\end{verbatim}
-
-We can also calculate the information content of a motif with a simple call:
-\begin{verbatim}
->>> m.ic()
-5.2735010263278932
-\end{verbatim}
-This gives us a number of bits of information provided by the motif,
-which tells us how much we are different from background.
-
-The most common representation of a motif is a PWM (Position Weight
-Matrix). It summarizes the probabilities of finding any symbol (in
-this case nucleotide) in any position of a motif. It can be computed by calling the \verb|.pwm()| method:
-\begin{verbatim}
->>> m.pwm()
-[{'A': 0.05, 'C': 0.05, 'T': 0.85, 'G': 0.05},
- {'A': 0.85, 'C': 0.05, 'T': 0.05, 'G': 0.05},
- {'A': 0.05, 'C': 0.05, 'T': 0.85, 'G': 0.05},
- {'A': 0.65, 'C': 0.05, 'T': 0.25, 'G': 0.05},
- {'A': 0.85, 'C': 0.05, 'T': 0.05, 'G': 0.05}]
-\end{verbatim}
-The probabilities in the motif's PWM are based on the counts in the
-instances, but we can see, that even though there were no Gs and no Cs
-in the instances, we still have non-zero probabilities assigned to
-them. These come from pseudo-counts which are, roughly speaking, a
-commonly used way to acknowledge the incompleteness of our knowledge
-and avoid technical problems with calculating logarithms of $0$.
-
-We can control the way that pseudo-counts are added with two
-properties of Motif objects \verb|.background| is the probability
-distribution over all symbols in the alphabet that we assume is found
-in background (usually based on the GC content of the respective
-genome). It is by default set to a uniform distribution upon creation of a motif:
-\begin{verbatim}
->>> m.background
-{'A': 0.25, 'C': 0.25, 'T': 0.25, 'G': 0.25}
-\end{verbatim}
-The other parameter is \verb|.beta|, which states the amount of
-pseudo-counts we should add to the PWM. By default it is set to $1.0$,
-\begin{verbatim}
->>> m.beta
-1.0
-\end{verbatim}
-so that the total input of pseudo-counts is equal to that of one instance.
-
-Using the background distribution and pwm with pseudo-counts added,
-it's easy to compute the log-odds ratios, telling us what are the log
-odds of a particular symbol to be coming from a motif against the
-background. We can use the \verb|.log_odds()| method:
-
-\begin{verbatim}
->>> m.log_odds()
-[{'A': -2.3219280948873622,
- 'C': -2.3219280948873622,
- 'T': 1.7655347463629771,
- 'G': -2.3219280948873622},
- {'A': 1.7655347463629771,
- 'C': -2.3219280948873622,
- 'T': -2.3219280948873622,
- 'G': -2.3219280948873622},
- {'A': -2.3219280948873622,
- 'C': -2.3219280948873622,
- 'T': 1.7655347463629771,
- 'G': -2.3219280948873622},
- {'A': 1.3785116232537298,
- 'C': -2.3219280948873622,
- 'T': 0.0,
- 'G': -2.3219280948873622},
- {'A': 1.7655347463629771,
- 'C': -2.3219280948873622,
- 'T': -2.3219280948873622,
- 'G': -2.3219280948873622}
-]
-\end{verbatim}
-Here we can see positive values for symbols more frequent in the motif
-than in the background and negative for symbols more frequent in the
-background. $0.0$ means that it's equally likely to see a symbol in
-background and in the motif (e.g. 'T' in the second-last position).
-
-\subsection{Reading and writing}
-\label{sec:io}
-
-Creating motifs from instances by hand is useful but boring, so it's
-useful to have some I/O functions for reading and writing
-motifs. There are no really well established standards for storing
-motifs, but there's a couple of formats which are more used than
-others. The most important distinction is whether the motif
-representation is based on instances or on some version of PWM matrix.
-On of the most popular motif databases JASPAR
-(\url{http://jaspar.genereg.net}) stores motifs in both formats, so
-let's look at how we can import JASPAR motifs from instances:
-\begin{verbatim}
-arnt = Motif.read(open("Arnt.sites"), "jaspar-sites")
-\end{verbatim}
-and from a count matrix:
-\begin{verbatim}
-srf = Motif.read(open("SRF.pfm"), "jaspar-pfm")
-\end{verbatim}
-
-The \verb|arnt| and \verb|srf| motifs can both do the same things for
-us, but they use different internal representations of the motif. We
-can tell that by inspecting the \verb|has_counts| and
-\verb|has_instances| properties:
-\begin{verbatim}
->>> arnt.has_instances
-True
->>> srf.has_instances
-False
->>> srf.has_counts
-True
->>> srf.counts
-{'A': [2, 9, 0, 1, 32, 3, 46, 1, 43, 15, 2, 2],
- 'C': [1, 33, 45, 45, 1, 1, 0, 0, 0, 1, 0, 1],
- 'G': [39, 2, 1, 0, 0, 0, 0, 0, 0, 0, 44, 43],
- 'T': [4, 2, 0, 0, 13, 42, 0, 45, 3, 30, 0, 0]}
-\end{verbatim}
-
-There are conversion functions, which can help us convert between
-different representations:
-\begin{verbatim}
->>> arnt.make_counts_from_instances()
-{'A': [8, 38, 0, 0, 0, 0],
- 'C': [32, 0, 40, 0, 0, 0],
- 'G': [0, 2, 0, 40, 0, 40],
- 'T': [0, 0, 0, 0, 40, 0]}
-
->>> srf.make_instances_from_counts()
-[Seq('GGGAAAAAAAGG', IUPACUnambiguousDNA()),
- Seq('GGCCAAATAAGG', IUPACUnambiguousDNA()),
- Seq('GACCAAATAAGG', IUPACUnambiguousDNA()),
-....
-\end{verbatim}
-The important thing to remember here is that the method
-\verb|make_instances_from_counts()| creates fake instances, because
-usually there are very many possible sets of instances which give rise
-to the same pwm, and if we have only the count matrix, we cannot
-reconstruct the original one. This does not make any difference if we
-are using the PWM as the representation of the motif, but one should
-be careful with exporting instances from count-based motifs.
-
-Speaking of exporting, let's look at export functions. We can export to fasta:
-\begin{verbatim}
->>> print(m.format("fasta"))
-> instance 0
-TATAA
-> instance 1
-TATTA
-> instance 2
-TATAA
-> instance 3
-TATAA
-\end{verbatim}
-or to TRANSFAC-like matrix format (used by some motif processing software)
-\begin{verbatim}
->>> print(m.format("transfac"))
-XX
-TY Motif
-ID
-BF undef
-P0 G A T C
-01 0 0 4 0
-02 0 4 0 0
-03 0 0 4 0
-04 0 3 1 0
-05 0 4 0 0
-XX
-\end{verbatim}
-
-Finally, if we have internet access, we can create a weblogo using a nice service at \url{http://weblogo.berkeley.edu} by Crooks et al. \cite{crooks2004}:
-\begin{verbatim}
->>> arnt.weblogo("Arnt.png")
-\end{verbatim}
-We should get our logo saved as a png in the specified file.
-
-\subsection{Searching for instances}
-\label{sec:search}
-
-The most frequent use for a motif is to find its instances in some
-sequence. For the sake of this section, we will use an artificial sequence like this:
-
-\begin{verbatim}
-test_seq=Seq("TATGATGTAGTATAATATAATTATAA",m.alphabet)
-\end{verbatim}
-
-The simplest way to find instances, is to look for exact matches of
-the true instances of the motif:
-\begin{verbatim}
->>> for pos, seq in m.search_instances(test_seq):
-... print("%i %s" % (pos, seq))
-...
-10 TATAA
-15 TATAA
-21 TATAA
-\end{verbatim}
-We can do the same with the reverse complement (to find instances on the complementary strand):
-\begin{verbatim}
->>> for pos, seq in m.reverse_complement().search_instances(test_seq):
-... print("%i %s" % (pos, seq))
-...
-12 TAATA
-20 TTATA
-\end{verbatim}
-
-It's just as easy to look for positions, giving rise to high log-odds scores against our motif:
-\begin{verbatim}
->>> for pos, score in m.search(test_seq,threshold=5.0):
-... print("%i %f" % (pos, score))
-...
-10 8.44065060871
--12 7.06213898545
-15 8.44065060871
--20 8.44065060871
-21 8.44065060871
-\end{verbatim}
-You may notice the threshold parameter, here set arbitrarily to
-$5.0$. This is in $log_2$, so we are now looking only for words, which
-are 32 times more likely to occur under the motif model than in the
-background. The default threshold is $0.0$, which selects everything
-that looks more like the motif, than the background.
-
-If you want to use a less arbitrary way of selecting thresholds, you
-can explore the \verb|Motif.score_distribution| class implementing an
-distribution of scores for a given motif. Since the space for a score
-distribution grows exponentially with motif.length, we are using an
-approximation with a given precision to keep computation cost manageable:
-\begin{verbatim}
->>> sd = Motif.score_distribution(m,precision=10**4)
-\end{verbatim}
-The \verb+sd+ object can be used to determine a number of different thresholds.
-
-We can specify the requested false-positive rate (probability of ``finding'' a motif instance in background generated sequence):
-\begin{verbatim}
->>> sd.threshold_fpr(0.01)
-4.3535838726139886
-\end{verbatim}
-
-or the false-negative rate (probability of ``not finding'' an instance generated from the motif):
-\begin{verbatim}
->>> sd.threshold_fnr(0.1)
-0.26651713652234044
-\end{verbatim}
-
-or a threshold (approximately) satisfying some relation between fpr
-and fnr ($\frac{fnr}{fpr}$, as proposed by Rahmann \cite{Rahmann2003}):
-\begin{verbatim}
->>> sd.threshold_balanced(1000)
-8.4406506087056368
-\end{verbatim}
-
-or a threshold satisfying (roughly) the equality between the
-false-positive rate and the $-log$ of the information content (as used
-in patser software by Hertz and Stormo).
-
-For example, in case of our motif, you can get the threshold giving
-you exactly the same results (for this sequence) as searching for
-instances with balanced threshold with rate of $1000$.
-\begin{verbatim}
->>> threshold = sd.threshold_balanced(1000)
->>> for pos, score in m.search(test_seq, threshold=threshold):
-... print("%s %s" % (pos, score))
-...
-10 8.44065060871
-15 8.44065060871
--20 8.44065060871
-21 8.44065060871
-\end{verbatim}
-
-\subsection{Comparing motifs}
-\label{sec:comp}
-Once we have more than one motif, we might want to compare them. For
-that, we have currently three different methods of \verb|Bio.Motif|
-objects.
-
-Before we start comparing motifs, I should point out that motif
-boundaries are usually quite arbitrary. This means, that we often need
-to compare motifs of different lengths, so comparison needs to involve
-some kind of alignment. This means, that we have to take into account two things:
-\begin{itemize}
-\item alignment of motifs
-\item some function to compare aligned motifs
-\end{itemize}
-In \verb|Bio.Motif| we have 3 different functions for motif
-comparison, which are based on the same idea behind motif alignment,
-but use different functions to compare aligned motifs. Briefly
-speaking, we are using ungapped alignment of PWMs and substitute the
-missing columns at the beginning and end of the matrices with
-background distribution. All three comparison functions are written in
-such a way, that they can be interpreted as distance measures, however
-only one (\verb|dist_dpq|) satisfies the triangle inequality. All of
-them return the minimal distance and the corresponding offset between
-motifs.
-
-To show how these functions work, let us first load another motif,
-which is similar to our test motif \verb|m|:
-\begin{verbatim}
->>> ubx=Motif.read(open("Ubx.pfm"), "jaspar-pfm")
-<Bio.Motif.Motif.Motif object at 0xc29b90>
->>> ubx.consensus
-Seq('TAAT', IUPACUnambiguousDNA())
-\end{verbatim}
-
-The first function we'll use to compare these motifs is based on
-Pearson correlation. Since we want it to resemble a distance
-measure. we actually take $1-r$, where r is the Pearson correlation
-coefficient (PCC):
-\begin{verbatim}
->>> m.dist_pearson(ubx)
-(0.41740393308237722, 2)
-\end{verbatim}
-This means, that the best PCC between motif \verb|m| and \verb|Ubx| is obtained with the following alignment:
-\begin{verbatim}
-bbTAAT
-TATAAb
-\end{verbatim}
-where \verb|b| stands for background distribution. The PCC itself is
-roughly $1-0.42=0.58$. If we try the reverse complement of the Ubx motif:
-
-\begin{verbatim}
->>> m.dist_pearson(ubx.reverse_complement())
-(0.25784180151584823, 1)
-\end{verbatim}
-We can see that the PCC is better (almost $0.75$), and the alignment is also different:
-\begin{verbatim}
-bATTA
-TATAA
-\end{verbatim}
-
-There are two other functions \verb|dist_dpq|, which is a true metric based on the Kullback-Leibler divergence and proposed by Endres and Sendelin \cite{Endres2003}
-\begin{verbatim}
->>> m.dist_dpq(ubx.reverse_complement())
-(0.49292358382899853, 1)
-\end{verbatim}
-
-In case you need yet another way of comparing motifs, you can use the
-\verb|dist_product| method, which is based on the product of
-probabilities which can be interpreted as the probability of
-independently generating the same instance by both motifs.
-
-\begin{verbatim}
->>> m.dist_product(ubx.reverse_complement())
-(0.16224587301064275, 1)
-\end{verbatim}
-
-\section{\emph{De novo} motif finding}
-\label{sec:find}
-
-Currently, biopython has only limited support for \emph{de novo} motif
-finding. Namely, we support running and parsing of AlignAce \cite{Hughes2000} and
-MEME\cite{Bailey1994}. Since the number of motif finding tools is growing rapidly,
-contributions of new parsers are welcome.
-
-\subsection{MEME}
-\label{sec:meme}
-
-Let's assume, you have run MEME on sequences of your choice with your
-favorite parameters and saved the output in the file
-\verb|meme.out|. You can retrieve the motifs reported by MEME by
-running the following piece of code:
-
-\begin{verbatim}
->>> motifsM = list(Motif.parse(open("meme.out"), "MEME"))
->>> motifsM
-[<Bio.Motif.MEMEMotif.MEMEMotif object at 0xc356b0>]
-\end{verbatim}
-
-Besides the most wanted list of motifs, the result object contains more useful information, accessible through properties with self-explanatory names:
-\begin{itemize}
-\item \verb|.alphabet|
-\item \verb|.datafile|
-\item \verb|.sequence_names|
-\item \verb|.version|
-\item \verb|.command|
-\end{itemize}
-
-The motifs returned by MEMEParser can be treated exactly like regular
-Motif objects (with instances), they also provide some extra
-functionality, by adding additional information about the instances.
-
-\begin{verbatim}
->>> motifsM[0].consensus
-Seq('CTCAATCGTA', IUPACUnambiguousDNA())
-
->>> motifsM[0].instances[0].pvalue
-8.71e-07
->>> motifsM[0].instances[0].sequence_name
-'SEQ10;'
->>> motifsM[0].instances[0].start
-3
->>> motifsM[0].instances[0].strand
-'+'
-\end{verbatim}
-
-
-\subsection{AlignAce}
-\label{sec:alignace}
-
-We can do very similar things with AlignACE program. Assume, you have
-your output in the file \verb|alignace.out|. You can parse your output
-with the following code:
-
-\begin{verbatim}
->>> motifsA = list(Motif.parse(open("alignace.out"), "AlignAce"))
-\end{verbatim}
-
-Again, your motifs behave as they should:
-\begin{verbatim}
->>> motifsA[0].consensus
-Seq('TCTACGATTGAG', IUPACUnambiguousDNA())
-\end{verbatim}
-
-In fact you can even see, that AlignAce found a very similar motif as
-MEME, it is just a longer version of a reverse complement of MEME
-motif:
-\begin{verbatim}
->>> motifsM[0].reverse_complement().consensus
-Seq('TACGATTGAG', IUPACUnambiguousDNA())
-\end{verbatim}
-
-If you have AlignAce installed on the same machine, you can also run
-it directly from Biopython. Short example of how this can be done is
-shown below (other parameters can be specified as keyword parameters):
-
-\begin{verbatim}
->>> command = "/opt/bin/AlignACE"
->>> input_file = "test.fa"
->>> result = Motif.AlignAce(input_file, cmd=command, gcback=0.6, numcols=10)
->>> result
-(<Bio.Application.ApplicationResult instance at 0xf49d78>,
- <Bio.File.UndoHandle instance at 0xf49d00>,
- <Bio.File.UndoHandle instance at 0xf49dc8>)
-\end{verbatim}
-
-Since AlignAce prints all its output to standard output, you can get
-to your motifs by parsing the second member of the result:
-\begin{verbatim}
-motifs = list(Motif.parse(result[1], "AlignAce"))
-\end{verbatim}
-
-
-
-\section{Useful links }
-\label{sec:links}
-
-Wikipedia definitions:
-
-\begin{itemize}
-\item \url{http://en.wikipedia.org/wiki/Sequence_motif}
-\item \url{http://en.wikipedia.org/wiki/Position_weight_matrix}
-\item \url{http://en.wikipedia.org/wiki/Consensus_sequence}
-\end{itemize}
-
-
-Motif finding and comparison methods:
-
-\begin{itemize}
-\item AlignAce and CompareAce\url{http://www.psc.edu/general/software/packages/alignace/}
-\item MEME and MAST \url{http://meme.sdsc.edu/meme/}
-\item BioProspector \url{http://bioprospector.stanford.edu/}
-\item MDScan \url{http://ai.stanford.edu/~xsliu/MDscan/}
-\item Weeder
-\item STAMP \url{http://www.benoslab.pitt.edu/stamp/}
-\item WebMotifs \url{http://fraenkel.mit.edu/webmotifs/}
-\item Comparison of different programs \url{http://bio.cs.washington.edu/assessment/}
-\end{itemize}
-
-\begin{thebibliography}{10}
-
-\bibitem{tamo}
-Gordon DB, Nekludova L, McCallum S, Fraenkel E: \textbf{TAMO: a flexible,
- object-oriented framework for analyzing transcriptional regulation using
- DNA-sequence motifs.} \emph{Bioinformatics} 2005, \textbf{21}(14):3164--5.
-
-\bibitem{crooks2004}
-Crooks GE, Hon G, Chandonia JM, steven E~Brenner: \textbf{WebLogo: A Sequence
- Logo Generator}. \emph{Genome Research} 2004, \textbf{14}:1188--1190.
-
-\bibitem{Rahmann2003}
-Rahmann S, Mueller T, Vingron M: \textbf{On the power of profiles for
- transcription factor binding site detection.} \emph{Stat Appl Genet Mol Biol}
- 2003, \textbf{2}:Article7.
-
-\bibitem{Endres2003}
-Endres D, Schindelin J: \textbf{A new metric for probability distributions}.
- \emph{IEEE transactions on Information Theory} 2003,
- \textbf{49}(7):1858--1860.
-
-\bibitem{Bailey1994}
-Bailey TL, Elkan C: \textbf{{Fitting a mixture model by expectation
- maximization to discover motifs in biopolymers}}. \emph{Proc Int Conf Intell
- Syst Mol Biol} 1994, \textbf{2}:28--36.
-
-\bibitem{Hughes2000}
-Hughes JD, Estep PW, Tavazoie S, Church GM: \textbf{{Computational
- identification of cis-regulatory elements associated with groups of
- functionally related genes in Saccharomyces cerevisiae}}. \emph{J Mol Biol}
- 2000, \textbf{296}(5):1205--1214.
-
-\end{thebibliography}
-
-\end{document}
diff --git a/Doc/doc.rst b/Doc/doc.rst
new file mode 100644
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--- /dev/null
+++ b/Doc/doc.rst
@@ -0,0 +1,508 @@
+
+Chapter XXX Codon Alignment
+==============================================
+
+This chapter is about Codon Alignments, which is a special case of nucleotide
+alignment in which the trinucleotides correspond directly to amino acids in
+the translated protein product. Codon Alignment carries information that can
+be used for many evolutionary analysis.
+
+This chapter has been divided into four parts to explain the codon alignment
+support in Biopython. First, a general introduction about the basic classes
+in ``Bio.CodonAlign`` will be given. Then, a typical procedure of how to
+obtain a codon alignment within Biopython is then discussed. Next, some
+simple applications of codon alignment, such as dN/dS ratio estimation and
+neutrality test and so forth will be covered. Finally, IO support of codon
+alignment will help user to conduct analysis that cannot be done within
+Biopython.
+
+
+X.1 ``CodonSeq`` Class
+-------------------------------------------
+
+``Bio.CodonAlign.CodonSeq`` object is the base object in Codon Alignment. It
+is similar to ``Bio.Seq`` but with some extra attributes. To obtain a simple
+``CodonSeq`` object, you just need to give a ``str`` object of nucleotide
+sequence whose length is a multiple of 3 (This can be violated if you have
+``rf_table`` argument). For example:
+
+.. code:: verbatim
+
+ >>> from Bio.CodonAlign import CodonSeq
+ >>> codon_seq = CodonSeq("AAATTTCCCGGG")
+ >>> codon_seq
+ CodonSeq('AAATTTCCCGGG', Gapped(CodonAlphabet(), '-'))
+
+An error will raise up if the input sequence is not a multiple of 3.
+
+.. code:: verbatim
+
+ >>> codon_seq = CodonSeq("AAATTTCCCGG")
+ Traceback (most recent call last):
+ File "<stdin>", line 1, in <module>
+ File "/biopython/Bio/CodonAlign/CodonSeq.py", line 81, in __init__
+ assert len(self) % 3 == 0, "Sequence length is not a triple number"
+ AssertionError: Sequence length is not a triple number
+
+By default, ``Bio.CodonAlign.default_codon_alphabet`` will be assigned to
+``CodonSeq`` object if you don't specify any Alphabet. This
+``default_codon_alphabet`` is gapped universal genetic code, which will work
+in most cases. However, if you are analyzing data from mitochondria, for
+instance, and are in need of assigning an special codon alphabet by yourself,
+``Bio.CodonAlign.CodonAlphabet`` also provides you an easy solution. All you
+need is to pick up a ``CodonTable`` object that is correct for your data.
+For example:
+
+.. code:: verbatim
+
+ >>> from Bio.CodonAlign import CodonSeq
+ >>> from Bio.CodonAlign.CodonAlphabet import get_codon_alphabet
+ >>> from Bio.Data.CodonTable import generic_by_id
+ # vertebrate mitochondria alphabet
+ >>> codon_alphabet = get_codon_alphabet(generic_by_id[2], gap_char="-")
+ >>> codon_seq1 = CodonSeq("AAA---CCCGGG", alphabet=codon_alphabet)
+ >>> codon_seq1
+ CodonSeq('AAA---CCCGGG', CodonAlphabet(Vertebrate Mitochondrial))
+
+The slice of ``CodonSeq`` is exactly the same with ``Seq`` and it will always
+return a ``Seq`` object if you sliced a ``CodonSeq``. For example:
+
+.. code:: verbatim
+
+ >>> codon_seq1
+ CodonSeq('AAA---CCCGGG', CodonAlphabet(Vertebrate Mitochondrial))
+ >>> codon_seq1[:6]
+ Seq('AAA---', DNAAlphabet())
+ >>> codon_seq1[1:5]
+ Seq('AA--', DNAAlphabet())
+
+As you might imagine, ``CodonSeq`` is able to be translated into amino acid
+sequence based on the ``CodonAlphabet`` within it. In fact, ``CodonSeq`` does
+more than this. ``CodonSeq`` object has a ``rf_table`` attribute that dictates
+how the ``CodonSeq`` will be translated (``rf_table`` will indicate the
+starting position of each codon in the sequence). This is useful if you
+sequence is known to have frameshift events or pseudogene that has insertion
+or deletion. You might notice that in the previous example, you haven't
+specify the ``rf_table`` when initiate a ``CodonSeq`` object. In fact,
+``CodonSeq`` object will automatically assign a ``rf_table`` to the
+``CodonSeq`` if you don't say anything about it.
+
+.. code:: verbatim
+
+ >>> codon_seq1 = CodonSeq("AAACCCGGG")
+ >>> codon_seq1
+ CodonSeq('AAACCCGGG', CodonAlphabet(Standard))
+ >>> codon_seq1.rf_table
+ [0, 3, 6]
+ >>> codon_seq1.translate()
+ 'KPG'
+ >>> codon_seq2 = CodonSeq("AAACCCGG", rf_table=[0, 3, 5])
+ >>> codon_seq2.rf_table
+ [0, 3, 5]
+ >>> codon_seq2.translate()
+ 'KPR'
+
+In the example, we didn't assign ``rf_table`` to ``codon_seq1``. By default,
+``CodonSeq`` will automatically generate a ``rf_table`` to the coding sequence
+assuming no frameshift events. In this case, it is ``[0, 3, 6]``, which means
+the first codon in the sequence starts at position 0, the second codon in the
+sequence starts at position 3, and the third codon in the sequence starts at
+position 6. In ``codon_seq2``, we only have 8 nucleotides in the sequence, but
+with ``rf_table`` option specified. In this case, the third codon starts at
+the 5th position of the sequence rather than the 6th. And the ``translate()``
+function will use the ``rf_table`` to get the translated amino acid sequence.
+
+Another thing to keep in mind is that ``rf_table`` will only be applied to
+ungapped nucleotide sequence. This makes ``rf_table`` to be interchangeable
+between ``CodonSeq`` with the same sequence but different gaps inserted. For
+example,
+
+.. code:: verbatim
+
+ >>> codon_seq1 = CodonSeq("AAACCC---GGG")
+ >>> codon_seq1.rf_table
+ [0, 3, 6]
+ >>> codon_seq1.translate()
+ 'KPG'
+ >>> codon_seq1.full_translate()
+ 'KP-G'
+
+We can see that the ``rf_table`` of ``codon_seq1`` is still ``[0, 3, 6]``,
+even though we have gaps added. The ``translate()`` function will skip the
+gaps and return the ungapped amino acid sequence. If gapped protein sequence
+is what you need, ``full_translate()`` comes to help.
+
+It is also easy to convert ``Seq`` object to ``CodonSeq`` object, but it is
+the user's responsibility to ensure all the necessary information is correct
+for a ``CodonSeq`` (mainly ``rf_table``).
+
+.. code:: verbatim
+
+ >>> from Bio.Seq import Seq
+ >>> codon_seq = CodonSeq()
+ >>> seq = Seq('AAAAAA')
+ >>> codon_seq.from_seq(seq)
+ CodonSeq('AAAAAA', CodonAlphabet(Standard))
+ >>> seq = Seq('AAAAA')
+ >>> codon_seq.from_seq(seq)
+ Traceback (most recent call last):
+ File "<stdin>", line 1, in <module>
+ File "/biopython/Bio/CodonAlign/CodonSeq.py", line 264, in from_seq
+ return cls(seq._data, alphabet=alphabet)
+ File "/biopython/Bio/CodonAlign/CodonSeq.py", line 80, in __init__
+ assert len(self) % 3 == 0, "Sequence length is not a triple number"
+ AssertionError: Sequence length is not a triple number
+ >>> codon_seq.from_seq(seq, rf_table=(0, 2))
+ CodonSeq('AAAAA', CodonAlphabet(Standard))
+
+
+X.2 ``CodonAlignment`` Class
+-------------------------------------------
+
+The ``CodonAlignment`` class is another new class in ``Codon.Align``. It's
+aim is to store codon alignment data and apply various analysis upon it.
+Similar to ``MultipleSeqAlignment``, you can use numpy style slice to a
+``CodonAlignment``. However, once you sliced, the returned result will
+always be a ``MultipleSeqAlignment`` object.
+
+.. code:: verbatim
+
+ >>> from Bio.CodonAlign import default_codon_alphabet, CodonSeq, CodonAlignment
+ >>> from Bio.Alphabet import generic_dna
+ >>> from Bio.SeqRecord import SeqRecord
+ >>> from Bio.Alphabet import IUPAC, Gapped
+ >>> a = SeqRecord(CodonSeq("AAAACGTCG", alphabet=default_codon_alphabet), id="Alpha")
+ >>> b = SeqRecord(CodonSeq("AAA---TCG", alphabet=default_codon_alphabet), id="Beta")
+ >>> c = SeqRecord(CodonSeq("AAAAGGTGG", alphabet=default_codon_alphabet), id="Gamma")
+ >>> codon_aln = CodonAlignment([a, b, c])
+ >>> print codon_aln
+ CodonAlphabet(Standard) CodonAlignment with 3 rows and 9 columns (3 codons)
+ AAAACGTCG Alpha
+ AAA---TCG Beta
+ AAAAGGTGG Gamma
+ >>> codon_aln[0]
+ ID: Alpha
+ Name: <unknown name>
+ Description: <unknown description>
+ Number of features: 0
+ CodonSeq('AAAACGTCG', CodonAlphabet(Standard))
+ >>> print codon_aln[:, 3]
+ A-A
+ >>> print codon_aln[1:, 3:10]
+ CodonAlphabet(Standard) alignment with 2 rows and 6 columns
+ ---TCG Beta
+ AGGTGG Gamma
+
+You can write out ``CodonAlignment`` object just as what you do with
+``MultipleSeqAlignment``.
+
+.. code:: verbatim
+
+ >>> from Bio import AlignIO
+ >>> AlignIO.write(codon_aln, 'example.aln', 'clustal')
+
+An alignment file called ``example.aln`` can then be found in your current
+working directory. You can write ``CodonAlignment`` out in any MSA format
+that Biopython supports.
+
+Currently, you are not able to read MSA data as a ``CodonAlignment`` object
+directly (because of dealing with ``rf_table`` issue for each sequence).
+However, you can read the alignment data in as a ``MultipleSeqAlignment``
+object and convert them into ``CodonAlignment`` object using ``from_msa()``
+class method. For example,
+
+.. code:: verbatim
+
+ >>> aln = AlignIO.read('example.aln', 'clustal')
+ >>> codon_aln = CodonAlignment()
+ >>> print codon_aln.from_msa(aln)
+ CodonAlphabet(Standard) CodonAlignment with 3 rows and 9 columns (3 codons)
+ AAAACGTCG Alpha
+ AAA---TCG Beta
+ AAAAGGTGG Gamma
+
+Note, the ``from_msa()`` method assume there is no frameshift events occurs
+in your alignment. Its behavior is not guaranteed if your sequence contain
+frameshift events!!
+
+There is a couple of methods that can be applied to ``CodonAlignment`` class
+for evolutionary analysis. We will cover them more in X.4.
+
+
+X.3 Build a Codon Alignment
+-------------------------------------------
+
+Building a codon alignment is the first step of many evolutionary anaysis.
+But how to do that? ``Bio.CodonAlign`` provides you an easy funciton
+``build()`` to achieve all. The data you need to prepare in advance is a
+protein alignment and a set of DNA sequences that can be translated into the
+protein sequences in the alignment.
+
+``CodonAlign.build`` method requires two mandatory arguments. The first one
+should be a protein ``MultipleSeqAlignment`` object and the second one is a
+list of nucleotide ``SeqRecord`` object. By default, ``CodonAlign.build``
+assumes the order of the alignment and nucleotide sequences are in the same.
+For example:
+
+.. code:: verbatim
+
+ >>> from Bio import CodonAlign
+ >>> from Bio.Alphabet import IUPAC
+ >>> from Bio.Align import MultipleSeqAlignment
+ >>> from Bio.SeqRecord import SeqRecord
+ >>> from Bio.Seq import Seq
+ >>> nucl1 = SeqRecord(Seq('AAATTTCCCGGG', alphabet=IUPAC.IUPACUnambiguousDNA()), id='nucl1')
+ >>> nucl2 = SeqRecord(Seq('AAATTACCCGCG', alphabet=IUPAC.IUPACUnambiguousDNA()), id='nucl2')
+ >>> nucl3 = SeqRecord(Seq('ATATTACCCGGG', alphabet=IUPAC.IUPACUnambiguousDNA()), id='nucl3')
+ >>> prot1 = SeqRecord(nucl1.seq.translate(), id='prot1')
+ >>> prot2 = SeqRecord(nucl2.seq.translate(), id='prot2')
+ >>> prot3 = SeqRecord(nucl3.seq.translate(), id='prot3')
+ >>> aln = MultipleSeqAlignment([prot1, prot2, prot3])
+ >>> codon_aln = CodonAlign.build(aln, [nucl1, nucl2, nucl3])
+ >>> print codon_aln
+ CodonAlphabet(Standard) CodonAlignment with 3 rows and 12 columns (4 codons)
+ AAATTTCCCGGG nucl1
+ AAATTACCCGCG nucl2
+ ATATTACCCGGG nucl3
+
+In the above example, ``CodonAlign.build`` will try to match ``nucl1`` with
+``prot1``, ``nucl2`` with ``prot2`` and ``nucl3`` with ``prot3``, i.e.,
+assuming the order of records in ``aln`` and ``[nucl1, nucl2, nucl3]`` is the
+same.
+
+``CodonAlign.build`` method is also able to handle key match. In this case,
+records with same id are paired. For example:
+
+.. code:: verbatim
+
+ >>> nucl1 = SeqRecord(Seq('AAATTTCCCGGG', alphabet=IUPAC.IUPACUnambiguousDNA()), id='nucl1')
+ >>> nucl2 = SeqRecord(Seq('AAATTACCCGCG', alphabet=IUPAC.IUPACUnambiguousDNA()), id='nucl2')
+ >>> nucl3 = SeqRecord(Seq('ATATTACCCGGG', alphabet=IUPAC.IUPACUnambiguousDNA()), id='nucl3')
+ >>> prot1 = SeqRecord(nucl1.seq.translate(), id='prot1')
+ >>> prot2 = SeqRecord(nucl2.seq.translate(), id='prot2')
+ >>> prot3 = SeqRecord(nucl3.seq.translate(), id='prot3')
+ >>> aln = MultipleSeqAlignment([prot1, prot2, prot3])
+ >>> nucl = {'prot1': nucl1, 'prot2': nucl2, 'prot3': nucl3}
+ >>> codon_aln = CodonAlign.build(aln, nucl)
+ >>> print codon_aln
+ CodonAlphabet(Standard) CodonAlignment with 3 rows and 12 columns (4 codons)
+ AAATTTCCCGGG nucl1
+ AAATTACCCGCG nucl2
+ ATATTACCCGGG nucl3
+
+This option is handleful if you read nucleotide sequences using ``SeqIO.index``
+method, in which case the nucleotide dict with be generated automatically.
+
+Sometimes, you are neither not able to ensure the same order or the same id.
+``CodonAlign.build`` method provides you an manual approach to tell the
+program nucleotide sequence and protein sequence correspondance by generating
+a ``corr_dict``. ``corr_dict`` should be a dictionary that uses protein record
+id as key and nucleotide record id as item. Let's look at an example:
+
+.. code:: verbatim
+
+ >>> nucl1 = SeqRecord(Seq('AAATTTCCCGGG', alphabet=IUPAC.IUPACUnambiguousDNA()), id='nucl1')
+ >>> nucl2 = SeqRecord(Seq('AAATTACCCGCG', alphabet=IUPAC.IUPACUnambiguousDNA()), id='nucl2')
+ >>> nucl3 = SeqRecord(Seq('ATATTACCCGGG', alphabet=IUPAC.IUPACUnambiguousDNA()), id='nucl3')
+ >>> prot1 = SeqRecord(nucl1.seq.translate(), id='prot1')
+ >>> prot2 = SeqRecord(nucl2.seq.translate(), id='prot2')
+ >>> prot3 = SeqRecord(nucl3.seq.translate(), id='prot3')
+ >>> aln = MultipleSeqAlignment([prot1, prot2, prot3])
+ >>> corr_dict = {'prot1': 'nucl1', 'prot2': 'nucl2', 'prot3': 'nucl3'}
+ >>> codon_aln = CodonAlign.build(aln, [nucl3, nucl1, nucl2], corr_dict=corr_dict)
+ >>> print codon_aln
+ CodonAlphabet(Standard) CodonAlignment with 3 rows and 12 columns (4 codons)
+ AAATTTCCCGGG nucl1
+ AAATTACCCGCG nucl2
+ ATATTACCCGGG nucl3
+
+We can see, even though the second argument of ``CodonAlign.build`` is not in
+the same order with ``aln`` in the above example, the ``corr_dict`` tells the
+program to pair protein records and nucleotide records. And we are still able
+to obtain the correct ``CodonAlignment`` object.
+
+The underlying algorithm of ``CodonAlign.build`` method is very similar to
+``pal2nal`` (a very famous perl script to build codon alignment).
+``CodonAlign.build`` will first translate protein sequences into a long
+degenerate regular expression and tries to find a match in its corresponding
+nucleotide sequence. When translation fails, it divide protein sequence into
+several small anchors and tries to match each anchor to the nucleotide sequence
+to figure out where the mismatch and frameshift events lie. Other options
+available for ``CodonAlign.build`` includes ``anchor_len`` (default 10) and
+``max_score`` (maximum tolerance of unexpected events, default 10). You may
+want to refer the Biopython build-in help to get more information about these
+options.
+
+Now let's look at a real example of building codon alignment. Here we will use
+epidermal growth factor (EGFR) gene to demonstrate how to obtain codon
+alignment. To reduce your effort, we have already collected EGFR sequences for
+`Homo sapiens`, `Bos taurus`, `Rattus norvegicus`, `Sus scrofa` and
+`Drosophila melanogaster`. You can download them from
+`here <http://zruanweb.com/egfr.zip>`_.
+Uncomressing the ``.zip``, you will see three files. ``egfr_nucl.fa`` is
+nucleotide sequences of EGFR and ``egfr_pro.aln`` is EGFR protein sequence
+alignment in ``clustal`` format. The ``egfr_id`` contains id correspondance
+between protein records and nucleotide records. You can then try the following
+code (make sure the files are in your current python working directory):
+
+.. code:: verbatim
+
+ >>> from Bio import SeqIO, AlignIO
+ >>> nucl = SeqIO.parse('egfr_nucl.fa', 'fasta', alphabet=IUPAC.IUPACUnambiguousDNA())
+ >>> prot = AlignIO.read('egfr_pro.aln', 'clustal', alphabet=IUPAC.protein)
+ >>> id_corr = {i.split()[0]: i.split()[1] for i in open('egfr_id').readlines()}
+ >>> aln = CodonAlign.build(prot, nucl, corr_dict=id_corr, alphabet=CodonAlign.default_codon_alphabet)
+ /biopython/Bio/CodonAlign/__init__.py:568: UserWarning: gi|47522840|ref|NP_999172.1|(L 449) does not correspond to gi|47522839|ref|NM_214007.1|(ATG)
+ % (pro.id, aa, aa_num, nucl.id, this_codon))
+ >>> print aln
+ CodonAlphabet(Standard) CodonAlignment with 6 rows and 4446 columns (1482 codons)
+ ATGATGATTATCAGCATGTGGATGAGCATATCGCGAGGATTGTGGGACAGCAGCTCC...GTG gi|24657088|ref|NM_057410.3|
+ ---------------------ATGCTGCTGCGACGGCGCAACGGCCCCTGCCCCTTC...GTG gi|24657104|ref|NM_057411.3|
+ ------------------------------ATGAAAAAGCACGAG------------...GCC gi|302179500|gb|HM749883.1|
+ ------------------------------ATGCGACGCTCCTGGGCGGGCGGCGCC...GCA gi|47522839|ref|NM_214007.1|
+ ------------------------------ATGCGACCCTCCGGGACGGCCGGGGCA...GCA gi|41327737|ref|NM_005228.3|
+ ------------------------------ATGCGACCCTCAGGGACTGCGAGAACC...GCA gi|6478867|gb|M37394.2|RATEGFR
+
+We can see, while building the codon alignment a mismatch event is found. And
+this is shown as a UserWarning.
+
+
+X.4 Codon Alignment Application
+-------------------------------------------
+
+The most important application of codon alignment is to estimate
+nonsynonymous substitutions per site (dN) and synonymous substitutions per
+site (dS). ``CodonAlign`` currently support three counting based methods
+(NG86, LWL85, YN00) and maximum likelihood method to estimate dN and dS.
+The function to conduct dN, dS estimation is called ``cal_dn_ds``. When you
+obtained a codon alignment, it is quite easy to calculate dN and dS. For
+example (assuming you have EGFR codon alignmnet in the python working
+space):
+
+.. code:: verbatim
+
+ >>> from Bio.CodonAlign.CodonSeq import cal_dn_ds
+ >>> print aln
+ CodonAlphabet(Standard) CodonAlignment with 6 rows and 4446 columns (1482 codons)
+ ATGATGATTATCAGCATGTGGATGAGCATATCGCGAGGATTGTGGGACAGCAGCTCC...GTG gi|24657088|ref|NM_057410.3|
+ ---------------------ATGCTGCTGCGACGGCGCAACGGCCCCTGCCCCTTC...GTG gi|24657104|ref|NM_057411.3|
+ ------------------------------ATGAAAAAGCACGAG------------...GCC gi|302179500|gb|HM749883.1|
+ ------------------------------ATGCGACGCTCCTGGGCGGGCGGCGCC...GCA gi|47522839|ref|NM_214007.1|
+ ------------------------------ATGCGACCCTCCGGGACGGCCGGGGCA...GCA gi|41327737|ref|NM_005228.3|
+ ------------------------------ATGCGACCCTCAGGGACTGCGAGAACC...GCA gi|6478867|gb|M37394.2|RATEGFR
+ >>> dN, dS = cal_dn_ds(aln[0], aln[1], method='NG86')
+ >>> print dN, dS
+ 0.0209078305058 0.0178371876389
+ >>> dN, dS = cal_dn_ds(aln[0], aln[1], method='LWL95')
+ >>> print dN, dS
+ 0.0203061425453 0.0163935691992
+ >>> dN, dS = cal_dn_ds(aln[0], aln[1], method='YN00')
+ >>> print dN, dS
+ 0.0198195580321 0.0221560648799
+ >>> dN, dS = cal_dn_ds(aln[0], aln[1], method='ML')
+ >>> print dN, dS
+ 0.0193877676103 0.0217247139962
+
+If you are using maximum likelihood methdo to estimate dN and dS, you are
+also able to specify equilibrium codon frequency to ``cfreq`` argument.
+Available options include ``F1x4``, ``F3x4`` and ``F61``.
+
+It is also possible to get dN and dS matrix or a tree from a ``CodonAlignment`` object.
+
+.. code:: verbatim
+
+ >>> dn_matrix, ds_matrix = aln.get_dn_ds_matrxi()
+ >>> print dn_matrix
+ gi|24657088|ref|NM_057410.3| 0
+ gi|24657104|ref|NM_057411.3| 0.0209078305058 0
+ gi|302179500|gb|HM749883.1| 0.611523924924 0.61022032668 0
+ gi|47522839|ref|NM_214007.1| 0.614035083563 0.60401686212 0.0411803504059 0
+ gi|41327737|ref|NM_005228.3| 0.61415325314 0.60182631356 0.0670105144563 0.0614703609541 0
+ gi|6478867|gb|M37394.2|RATEGFR 0.61870883409 0.606868724887 0.0738690303483 0.0735789092792 0.0517984707257 0
+ gi|24657088|ref|NM_057410.3| gi|24657104|ref|NM_057411.3| gi|302179500|gb|HM749883.1| gi|47522839|ref|NM_214007.1| gi|41327737|ref|NM_005228.3| gi|6478867|gb|M37394.2|RATEGFR
+ >>> dn_tree, ds_tree = aln.get_dn_ds_tree()
+ >>> print dn_tree
+ Tree(rooted=True)
+ Clade(branch_length=0, name='Inner5')
+ Clade(branch_length=0.279185347322, name='Inner4')
+ Clade(branch_length=0.00859186651689, name='Inner3')
+ Clade(branch_length=0.0258992353629, name='gi|6478867|gb|M37394.2|RATEGFR')
+ Clade(branch_length=0.0258992353629, name='gi|41327737|ref|NM_005228.3|')
+ Clade(branch_length=0.0139009266768, name='Inner2')
+ Clade(branch_length=0.020590175203, name='gi|47522839|ref|NM_214007.1|')
+ Clade(branch_length=0.020590175203, name='gi|302179500|gb|HM749883.1|')
+ Clade(branch_length=0.294630667432, name='Inner1')
+ Clade(branch_length=0.0104539152529, name='gi|24657104|ref|NM_057411.3|')
+ Clade(branch_length=0.0104539152529, name='gi|24657088|ref|NM_057410.3|')
+
+Another application of codon alignment that ``CodonAlign`` supports is
+Mcdonald-Kreitman test. This test compares the within species synonymous
+substitutions and nonsynonymous substitutions and between species synonymous
+substitutions and nonsynonymous substitutions to see if they are from the same
+evolutionary process. The test requires gene sequences sampled from different
+individuals of the same species. In the following example, we will use `Adh`
+gene from fluit fly to demonstrate how to conduct the test. The data includes
+11 individuals from `D. melanogaster`, 4 individuals from `D. simulans` and
+12 individuals from `D. yakuba`. The data is available from
+`here <http://zruanweb.com/adh.zip>`_. A function called ``mktest`` will be
+used for the test.
+
+.. code:: verbatim
+
+ >>> from Bio import SeqIO, AlignIO
+ >>> from Bio.Alphabet import IUPAC
+ >>> from Bio.CodonAlign import build
+ >>> from Bio.CodonAlign.CodonAlignment import mktest
+
+ >>> pro_aln = AlignIO.read('adh.aln', 'clustal', alphabet=IUPAC.protein)
+ >>> p = SeqIO.index('drosophilla.fasta', 'fasta', alphabet=IUPAC.IUPACUnambiguousDNA())
+ >>> codon_aln = build(pro_aln, p)
+ >>> print codon_aln
+ CodonAlphabet(Standard) CodonAlignment with 27 rows and 768 columns (256 codons)
+ ATGGCGTTTACCTTGACCAACAAGAACGTGGTTTTCGTGGCCGGTCTGGGAGGCATT...ATC gi|9217|emb|X57365.1|
+ ATGGCGTTTACCTTGACCAACAAGAACGTGGTTTTCGTGGCCGGTCTGGGAGGCATT...ATC gi|9219|emb|X57366.1|
+ ATGGCGTTTACCTTGACCAACAAGAACGTGGTTTTCGTGGCCGGTCTGGGAGGCATT...ATC gi|9221|emb|X57367.1|
+ ATGGCGTTTACCTTGACCAACAAGAACGTGGTTTTCGTGGCCGGTCTGGGAGGCATT...ATC gi|9223|emb|X57368.1|
+ ATGGCGTTTACCTTGACCAACAAGAACGTGGTTTTCGTGGCCGGTCTGGGAGGCATT...ATC gi|9225|emb|X57369.1|
+ ATGGCGTTTACCTTGACCAACAAGAACGTGGTTTTCGTGGCCGGTCTGGGAGGCATT...ATC gi|9227|emb|X57370.1|
+ ATGGCGTTTACCTTGACCAACAAGAACGTGGTTTTCGTGGCCGGTCTGGGAGGCATT...ATC gi|9229|emb|X57371.1|
+ ATGGCGTTTACCTTGACCAACAAGAACGTGGTTTTCGTGGCCGGTCTGGGAGGCATT...ATC gi|9231|emb|X57372.1|
+ ATGGCGTTTACCTTGACCAACAAGAACGTGGTTTTCGTGGCCGGTCTGGGAGGCATT...ATC gi|9233|emb|X57373.1|
+ ATGGCGTTTACCTTGACCAACAAGAACGTGGTTTTCGTGGCCGGTCTGGGAGGCATT...ATC gi|9235|emb|X57374.1|
+ ATGGCGTTTACCTTGACCAACAAGAACGTGGTTTTCGTGGCCGGTCTGGGAGGCATT...ATC gi|9237|emb|X57375.1|
+ ATGGCGTTTACCTTGACCAACAAGAACGTGGTTTTCGTGGCCGGTCTGGGAGGCATT...ATC gi|9239|emb|X57376.1|
+ ATGGCGTTTACTTTGACCAACAAGAACGTGATTTTCGTTGCCGGTCTGGGAGGCATT...ATC gi|9097|emb|X57361.1|
+ ATGGCGTTTACTTTGACCAACAAGAACGTGATTTTCGTTGCCGGTCTGGGAGGCATT...ATC gi|9099|emb|X57362.1|
+ ATGGCGTTTACTTTGACCAACAAGAACGTGATTTTCGTTGCCGGTCTGGGAGGCATT...ATC gi|9101|emb|X57363.1|
+ ATGGCGTTTACTTTGACCAACAAGAACGTGATTTTCGTTGCCGGTCTGGGAGGCATC...ATC gi|9103|emb|X57364.1|
+ ATGTCGTTTACTTTGACCAACAAGAACGTGATTTTCGTGGCCGGTCTGGGAGGCATT...ATC gi|156879|gb|M17837.1|DROADHCK
+ ATGTCGTTTACTTTGACCAACAAGAACGTGATTTTCGTGGCCGGTCTGGGAGGCATT...ATC gi|156877|gb|M17836.1|DROADHCJ
+ ATGTCGTTTACTTTGACCAACAAGAACGTGATTTTCGTGGCCGGTCTGGGAGGCATT...ATC gi|156875|gb|M17835.1|DROADHCI
+ ATGTCGTTTACTTTGACCAACAAGAACGTGATTTTCGTGGCCGGTCTGGGAGGCATT...ATC gi|156873|gb|M17834.1|DROADHCH
+ ATGTCGTTTACTTTGACCAACAAGAACGTGATTTTCGTGGCCGGTCTGGGAGGCATT...ATC gi|156871|gb|M17833.1|DROADHCG
+ ATGTCGTTTACTTTGACCAACAAGAACGTGATTTTCGTTGCCGGTCTGGGAGGCATT...ATC gi|156863|gb|M19547.1|DROADHCC
+ ATGTCGTTTACTTTGACCAACAAGAACGTGATTTTCGTGGCCGGTCTGGGAGGCATT...ATC gi|156869|gb|M17832.1|DROADHCF
+ ATGTCGTTTACTTTGACCAACAAGAACGTGATTTTCGTGGCCGGTCTGGGAGGCATT...ATC gi|156867|gb|M17831.1|DROADHCE
+ ATGTCGTTTACTTTGACCAACAAGAACGTGATTTTCGTTGCCGGTCTGGGAGGCATT...ATC gi|156865|gb|M17830.1|DROADHCD
+ ATGTCGTTTACTTTGACCAACAAGAACGTGATTTTCGTTGCCGGTCTGGGAGGCATT...ATC gi|156861|gb|M17828.1|DROADHCB
+ ATGTCGTTTACTTTGACCAACAAGAACGTGATTTTCGTTGCCGGTCTGGGAGGCATT...ATC gi|156859|gb|M17827.1|DROADHCA
+
+ >>> print mktest([codon_aln[1:12], codon_aln[12:16], codon_aln[16:]])
+ 0.00206457257254
+
+In the above example, ``codon_aln[1:12]`` belongs to `D. melanogaster`,
+``codon_aln[12:16]`` belongs to `D. simulans` and ``codon_aln[16:]`` belongs
+to `D. yakuba`. ``mktest`` will return the p-value of the test. We can see
+in this case, 0.00206 << 0.01, therefore, the gene is under strong negative
+selection according to MK test.
+
+X.4 Future Development
+-------------------------------------------
+
+Because of the limited time frame for Google Summer of Code project, some of
+the functions in ``CodonAlign`` is not tested comprehensively. In the
+following days, I will continue perfect the code and several new features
+will be added. I am always welcome to hear your suggestions and feature
+request. You are also highly encouraged to contribute to the existing code.
+Please do not hesitable to email me (zruan1991 at gmail dot com) when you have
+novel ideas that can make the code better.
diff --git a/Doc/examples/ACT_example.py b/Doc/examples/ACT_example.py
index 863f22f..7ed10bf 100644
--- a/Doc/examples/ACT_example.py
+++ b/Doc/examples/ACT_example.py
@@ -7,11 +7,9 @@ from __future__ import print_function
import sys
import os
-import time
from reportlab.lib import colors
from reportlab.lib.units import cm
-from Bio.Blast.Applications import NcbitblastxCommandline
from Bio.Graphics.GenomeDiagram import Diagram, CrossLink
from Bio.SeqFeature import SeqFeature, FeatureLocation
from Bio import SeqIO
@@ -40,7 +38,7 @@ genomes = [
comparisons = [os.path.join(input_folder, file_a_vs_b)]
# Create diagram with tracks, each with a feature set
-assert len(genomes) >= 2 and len(genomes) == len(comparisons)+1
+assert len(genomes) >= 2 and len(genomes) == len(comparisons) + 1
gd_diagram = Diagram(name, track_size=0.35, circular=False)
tracks = dict()
feature_sets = dict()
@@ -55,53 +53,52 @@ for f, format in genomes:
print("Drawing matches...")
for i, crunch_file in enumerate(comparisons):
- q = genomes[i+1][0] # query file
+ q = genomes[i + 1][0] # query file
s = genomes[i][0] # subject file
q_set = feature_sets[q]
s_set = feature_sets[s]
- handle = open(crunch_file)
- for line in handle:
- if line[0]=="#":
- continue
- parts = line.rstrip("\n").split(None, 7)
- # 0 = score
- # 1 = id
- # 2 = S1
- # 3 = E1
- # 4 = seq1
- # 5 = S2
- # 6 = E2
- # 7 = seq2
- try:
- q_start, q_end = int(parts[2]), int(parts[3])
- s_start, s_end = int(parts[5]), int(parts[6])
- except IndexError:
- sys.stderr.write(repr(line) + "\n")
- sys.stderr.write(repr(parts) + "\n")
- raise
- flip = False
- if q_start > q_end:
- flip = not flip
- q_start, q_end = q_end, q_start
- if s_start > s_end:
- flip = not flip
- s_start, s_end = s_end, s_start
- if flip:
- c = colors.Color(0, 0, 1, alpha=0.25)
- b = False
- else:
- c = colors.Color(1, 0, 0, alpha=0.25)
- b = False
- q_feature = q_set.add_feature(SeqFeature(FeatureLocation(q_start-1, q_end)),
- color=c, border=b)
- s_feature = s_set.add_feature(SeqFeature(FeatureLocation(s_start-1, s_end)),
- color=c, border=b)
- gd_diagram.cross_track_links.append(CrossLink(q_feature, s_feature, c, b))
- # NOTE: We are using the same colour for all the matches,
- # with transparency. This means overlayed matches will appear darker.
- # It also means the drawing order not very important.
- # Note ACT puts long hits at the back, and colours by hit score
- handle.close()
+ with open(crunch_file) as handle:
+ for line in handle:
+ if line[0] == "#":
+ continue
+ parts = line.rstrip("\n").split(None, 7)
+ # 0 = score
+ # 1 = id
+ # 2 = S1
+ # 3 = E1
+ # 4 = seq1
+ # 5 = S2
+ # 6 = E2
+ # 7 = seq2
+ try:
+ q_start, q_end = int(parts[2]), int(parts[3])
+ s_start, s_end = int(parts[5]), int(parts[6])
+ except IndexError:
+ sys.stderr.write(repr(line) + "\n")
+ sys.stderr.write(repr(parts) + "\n")
+ raise
+ flip = False
+ if q_start > q_end:
+ flip = not flip
+ q_start, q_end = q_end, q_start
+ if s_start > s_end:
+ flip = not flip
+ s_start, s_end = s_end, s_start
+ if flip:
+ c = colors.Color(0, 0, 1, alpha=0.25)
+ b = False
+ else:
+ c = colors.Color(1, 0, 0, alpha=0.25)
+ b = False
+ q_feature = q_set.add_feature(SeqFeature(FeatureLocation(q_start - 1, q_end)),
+ color=c, border=b)
+ s_feature = s_set.add_feature(SeqFeature(FeatureLocation(s_start - 1, s_end)),
+ color=c, border=b)
+ gd_diagram.cross_track_links.append(CrossLink(q_feature, s_feature, c, b))
+ # NOTE: We are using the same colour for all the matches,
+ # with transparency. This means overlayed matches will appear darker.
+ # It also means the drawing order not very important.
+ # Note ACT puts long hits at the back, and colours by hit score
print("Drawing CDS features...")
for f, format in genomes:
@@ -116,9 +113,9 @@ for f, format in genomes:
border=colors.blue)
gd_diagram.draw(format="linear", fragments=3,
- orientation="landscape", pagesize=(20*cm, 10*cm))
+ orientation="landscape", pagesize=(20 * cm, 10 * cm))
gd_diagram.write(name + ".pdf", "PDF")
gd_diagram.draw(format="circular",
- orientation="landscape", pagesize=(20*cm, 20*cm))
+ orientation="landscape", pagesize=(20 * cm, 20 * cm))
gd_diagram.write(name + "_c.pdf", "PDF")
diff --git a/Doc/examples/Plates.csv b/Doc/examples/Plates.csv
new file mode 100644
index 0000000..c5087ab
--- /dev/null
+++ b/Doc/examples/Plates.csv
@@ -0,0 +1,1202 @@
+"Data File "," C:\Documents and Settings\Standard\Desktop\esperimenti e lavori in corso\Phenotype MicroArray\dati PM\2007\10\smel_Rm1021___1_30_IDS_207_Octx01x2007_A_25A.oka",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Set up Time ","Oct 01 2007 3:12 PM",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Position ","25-A",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Plate Type ","PM 1-",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Strain Type ","NOT APPLICABLE",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Sample Number","Rm1021",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Strain Name ","Rm1021",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Strain Number","Rm1021",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Other ",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Hour"," A01"," A02"," A03"," A04"," A05"," A06"," A07"," A08"," A09"," A10"," A11"," A12"," B01"," B02"," B03"," B04"," B05"," B06"," B07"," B08"," B09"," B10"," B11"," B12"," C01"," C02"," C03"," C04"," C05"," C06"," C07"," C08"," C09"," C10"," C11"," C12"," D01"," D02"," D03"," D04"," D05"," D06"," D07"," D08"," D09"," D10"," D11"," D12"," E01"," E02"," E03"," E04"," E05"," E06"," E07"," E08"," E09"," E10"," E11"," E12"," F0 [...]
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+,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Data File "," C:\Documents and Settings\Standard\Desktop\esperimenti e lavori in corso\Phenotype MicroArray\dati PM\2007\04\smel_Rmx1021___1_30_IDS_207_Aprx12x2007_B_24A.oka",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Set up Time ","Apr 12 2007 12:25 PM",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Position ","24-A",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Plate Type ","PM 1-",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Strain Type ","NOT APPLICABLE",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Sample Number","Rm1021",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Strain Name ","Rm1021",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Strain Number","Rm 1021",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Other ",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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+,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Data File "," C:\Documents and Settings\Standard\Desktop\esperimenti e lavori in corso\Phenotype MicroArray\dati PM\2007\10\smel_Rm1021___9_30_IDS_207_Octx01x2007_A_23A.oka",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Set up Time ","Oct 01 2007 3:12 PM",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Position ","23-A",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Plate Type ","PM 9-",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Strain Type ","NOT APPLICABLE",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Sample Number","Rm1021",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Strain Name ","Rm1021",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Strain Number","Rm1021",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Other ",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Hour"," A01"," A02"," A03"," A04"," A05"," A06"," A07"," A08"," A09"," A10"," A11"," A12"," B01"," B02"," B03"," B04"," B05"," B06"," B07"," B08"," B09"," B10"," B11"," B12"," C01"," C02"," C03"," C04"," C05"," C06"," C07"," C08"," C09"," C10"," C11"," C12"," D01"," D02"," D03"," D04"," D05"," D06"," D07"," D08"," D09"," D10"," D11"," D12"," E01"," E02"," E03"," E04"," E05"," E06"," E07"," E08"," E09"," E10"," E11"," E12"," F0 [...]
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+1.25,0,52,0,15,0,5,18,1,10,8,30,31,24,7,15,13,0,5,3,0,3,7,8,23,6,2,11,11,15,11,9,4,11,7,10,11,8,1,7,26,0,4,0,15,0,0,0,0,33,6,17,29,14,19,0,0,0,0,0,2,26,0,1,17,5,0,7,10,0,0,7,27,3,21,26,28,0,0,1,8,0,0,21,31,0,1,7,14,0,14,17,1,14,17,5,22
+1.5,2,52,0,17,0,5,18,1,10,11,30,31,24,7,15,22,0,5,5,3,3,8,8,23,6,2,12,11,15,11,9,5,11,7,10,15,8,1,7,26,0,4,0,15,0,0,0,0,35,6,17,35,14,19,0,0,0,0,0,2,26,0,1,17,5,0,11,16,0,0,7,30,3,21,40,28,0,0,3,8,0,0,23,34,0,7,9,14,0,21,17,1,14,17,5,22
+1.75,2,52,0,17,1,5,18,1,12,11,30,31,24,8,15,22,0,6,5,3,6,8,8,27,6,2,12,11,15,14,9,5,11,7,13,15,8,1,7,26,0,4,0,17,0,0,0,0,35,6,17,35,23,19,0,0,0,0,0,2,28,0,1,17,5,0,11,16,2,0,7,30,3,21,40,31,0,0,3,10,0,0,23,34,0,7,9,14,0,21,17,1,14,17,6,23
+2,2,52,0,17,1,7,18,1,12,13,31,31,24,8,15,22,0,6,7,7,9,8,8,27,11,2,17,11,15,19,11,5,11,7,16,15,8,1,7,26,0,13,4,17,0,0,0,0,35,6,17,35,23,19,0,0,0,0,0,3,29,1,1,17,9,0,11,16,2,0,7,31,3,21,40,31,0,0,3,10,0,0,24,34,0,7,9,14,0,21,17,1,14,17,7,25
+2.25,6,52,3,17,1,13,18,1,12,13,33,31,24,10,15,22,0,9,7,7,9,8,12,27,11,2,17,17,15,19,11,5,11,7,16,15,8,1,7,26,0,13,4,17,0,0,0,0,35,6,17,35,25,19,0,0,0,0,0,7,32,1,1,17,9,0,11,16,2,0,9,35,3,21,40,31,0,0,3,10,0,6,28,42,0,7,9,14,0,23,17,1,14,17,7,29
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+2.75,9,52,3,17,4,13,18,1,12,16,38,31,24,10,17,22,0,15,7,7,9,8,14,27,22,2,17,17,15,22,13,5,11,7,16,21,8,1,7,26,0,13,4,17,0,0,0,0,39,6,17,35,25,19,0,0,0,0,0,7,34,1,1,17,9,0,11,24,12,0,9,38,6,21,40,31,0,0,3,13,0,6,28,42,0,7,9,14,0,32,17,1,14,17,7,29
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+3.25,9,52,3,17,4,13,18,1,14,16,38,31,24,10,17,22,0,15,7,10,12,8,17,30,25,2,18,17,15,25,13,5,14,7,16,22,9,1,7,26,0,17,9,17,0,0,0,0,39,6,17,35,26,19,0,0,0,0,0,7,34,1,1,17,12,0,11,24,12,0,9,38,6,21,40,31,0,0,3,13,0,6,28,42,0,7,11,14,0,32,17,1,14,17,7,31
+3.5,15,54,3,17,4,13,18,1,20,16,38,31,24,10,24,22,0,15,8,10,12,8,17,37,25,2,18,17,15,25,13,5,15,7,16,22,9,1,7,26,0,17,9,17,0,0,0,0,39,6,17,35,26,19,0,0,0,0,0,7,34,1,1,17,13,0,11,25,12,0,9,38,6,21,40,31,0,0,3,13,0,6,28,43,0,7,11,14,0,35,17,1,16,17,7,31
+3.75,15,54,5,17,4,13,18,1,20,16,40,31,24,12,24,22,0,16,8,10,17,8,19,37,25,2,18,20,15,25,13,5,20,7,16,22,9,1,7,26,0,17,9,17,0,0,0,0,39,6,17,35,28,19,0,0,0,0,0,7,34,1,1,17,13,0,17,25,12,0,9,38,6,21,40,31,0,0,3,13,0,6,28,43,0,7,11,14,0,35,17,1,16,17,7,34
+4,15,54,5,17,8,13,18,1,20,16,40,31,24,16,24,22,0,21,8,10,17,8,19,37,25,2,18,20,15,25,13,5,20,7,16,22,9,1,7,26,0,17,9,17,0,0,0,0,39,6,17,35,28,19,0,0,0,0,0,7,34,1,1,17,13,0,22,25,12,0,9,38,6,21,40,31,0,0,3,13,0,6,28,43,0,7,11,14,0,35,17,1,16,17,7,34
+4.25,15,54,5,17,8,13,18,1,20,16,40,31,24,16,24,22,0,24,8,10,17,14,23,37,25,2,18,20,15,25,13,5,20,7,16,22,9,1,7,26,0,17,9,17,0,0,0,0,39,6,17,35,30,19,0,0,0,0,0,7,34,1,1,17,13,0,63,25,12,0,9,38,6,21,40,31,0,0,3,13,0,6,28,48,0,7,11,14,0,35,17,1,16,17,7,34
+4.5,15,55,5,17,8,13,18,1,20,16,40,31,24,16,24,22,0,27,8,10,17,14,23,38,25,2,18,20,15,25,13,5,20,7,16,22,9,1,7,26,0,17,9,17,0,0,0,0,39,6,17,35,30,19,0,0,0,0,0,7,34,1,1,17,13,0,63,25,12,0,9,38,6,21,40,31,0,0,3,13,0,6,28,48,0,7,11,14,0,35,17,1,16,17,7,34
+4.75,15,55,5,17,8,13,18,1,20,16,40,31,24,16,24,22,0,28,8,10,17,14,23,38,25,2,18,20,15,25,13,5,23,7,16,22,9,1,7,26,0,17,9,17,0,0,0,0,39,6,17,35,30,19,0,0,0,0,0,7,34,1,1,17,13,0,65,25,12,0,9,38,6,21,40,31,0,0,3,13,0,6,28,48,0,7,11,14,0,35,18,1,16,17,7,37
+5,15,55,5,17,8,13,18,1,21,18,40,31,24,16,24,22,0,32,9,10,17,15,23,38,26,2,18,20,15,25,13,5,23,7,16,22,9,1,7,26,0,17,11,17,0,0,0,0,39,6,17,35,30,19,0,0,0,0,0,7,34,1,1,17,13,0,66,25,13,0,9,38,6,21,40,31,0,0,3,13,0,6,28,48,0,7,11,14,0,35,18,1,16,17,7,37
+5.25,15,55,5,17,8,13,18,1,21,18,40,34,24,18,24,22,0,32,9,10,17,15,23,38,26,2,18,20,15,25,13,5,23,7,16,22,12,1,7,26,0,17,11,17,0,0,0,0,39,6,17,35,30,19,0,0,0,0,0,7,34,1,1,17,13,1,66,25,13,0,9,38,6,21,40,31,0,0,3,13,0,6,28,48,0,7,11,14,0,35,18,1,16,17,7,37
+5.5,15,55,5,17,8,13,18,2,23,18,40,34,24,18,24,22,0,32,9,10,17,15,25,38,26,2,18,20,15,25,13,5,23,7,16,22,12,1,7,26,0,17,12,17,0,0,0,0,39,6,17,35,30,19,0,0,0,0,0,7,34,1,1,17,13,1,66,26,13,0,9,38,6,21,40,31,0,0,3,13,0,6,28,49,0,7,11,14,0,35,18,1,16,17,7,37
+5.75,15,55,5,17,8,13,18,3,24,18,42,34,24,18,24,22,0,33,9,10,17,15,25,38,26,2,18,25,15,25,13,5,23,7,16,28,12,1,7,26,0,17,12,17,0,0,0,0,39,6,17,35,30,19,0,0,0,0,0,7,34,1,1,17,13,1,66,26,21,0,9,38,6,21,40,31,0,0,3,13,0,6,28,49,0,7,11,14,0,35,18,2,16,17,7,37
+6,15,55,5,17,8,13,18,3,24,21,44,34,24,18,28,22,0,33,9,10,17,15,25,42,26,2,18,25,15,28,15,5,23,7,16,29,12,2,7,26,0,17,13,17,0,0,0,0,39,6,17,35,30,19,0,0,0,0,0,7,34,1,1,17,13,1,66,26,21,0,9,38,6,21,40,31,0,0,3,13,0,6,28,49,0,7,11,14,0,35,18,2,16,17,7,37
+6.25,15,56,5,17,8,13,18,3,24,21,44,34,24,18,28,22,0,33,9,10,17,17,25,42,26,2,18,25,15,28,15,5,23,7,16,29,12,2,7,26,0,17,13,17,0,0,0,0,39,6,17,35,30,19,0,0,0,0,0,7,34,1,1,17,13,6,66,26,21,0,9,38,6,21,40,31,0,0,3,13,0,6,28,49,0,7,11,14,0,35,18,2,16,17,7,37
+6.5,15,56,5,17,8,13,18,3,24,21,44,34,24,18,28,22,0,33,9,10,17,17,25,42,26,2,18,25,15,29,16,5,23,7,16,29,12,2,7,26,0,17,15,17,0,0,0,0,39,6,17,35,30,19,0,0,0,0,0,7,34,1,1,17,13,6,66,26,22,0,9,38,6,21,40,31,0,0,3,13,0,6,28,49,2,7,11,14,0,35,18,2,16,17,7,37
+6.75,15,56,6,17,8,13,18,3,24,23,44,34,24,18,28,22,0,33,9,10,19,17,25,42,26,2,18,25,15,32,16,5,23,7,16,29,12,2,7,26,0,17,17,17,0,0,0,0,39,6,17,35,30,19,0,0,0,0,0,7,34,1,1,17,13,6,66,26,22,0,9,38,6,21,40,31,0,0,3,13,0,6,28,49,2,7,11,14,0,35,18,2,16,17,7,37
+7,15,56,6,17,8,13,18,3,24,23,44,37,24,18,28,22,0,33,9,10,19,18,25,42,26,3,18,26,15,32,16,5,23,7,16,29,12,2,7,26,0,17,17,17,0,0,0,0,39,6,17,35,30,19,0,0,0,0,0,10,34,1,1,17,13,6,66,26,22,0,9,38,6,21,40,31,0,0,3,13,0,6,28,49,2,7,11,14,0,35,18,2,16,17,7,37
+7.25,15,56,6,17,8,15,18,3,24,23,44,37,24,18,28,22,0,33,9,10,20,18,25,42,27,3,18,26,15,32,16,5,23,7,16,29,12,2,7,26,0,17,17,17,0,0,0,0,39,6,17,35,30,19,0,0,0,0,0,10,34,1,1,17,13,6,66,26,22,0,9,38,6,21,40,31,0,0,3,13,0,6,28,49,2,7,12,14,0,35,18,2,16,17,7,37
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+7.75,15,56,6,17,8,15,18,4,25,23,44,37,24,18,28,22,0,33,9,10,21,18,25,42,27,3,18,26,15,32,16,5,23,7,16,29,12,2,7,26,0,17,17,17,0,0,0,0,39,6,17,35,30,19,0,0,0,0,0,10,34,1,1,17,13,6,66,26,22,0,9,38,6,21,40,31,0,0,3,13,0,6,28,49,2,7,12,14,0,35,18,2,17,17,7,37
+8,15,56,6,17,8,15,18,4,25,23,44,37,24,18,28,22,0,33,9,10,21,18,25,42,27,4,18,26,15,32,16,5,23,7,16,29,12,2,7,26,0,17,17,17,0,0,0,0,39,6,17,35,30,19,0,0,0,0,0,10,34,1,1,17,13,6,66,26,22,0,9,38,6,21,40,31,0,0,3,13,0,6,28,49,2,7,12,14,0,35,18,2,17,17,7,37
+8.25,15,58,6,17,8,15,18,4,25,23,44,38,24,18,28,22,0,33,9,10,21,18,25,42,27,4,18,26,15,32,16,5,23,7,16,29,12,2,7,26,0,17,17,17,0,0,0,0,39,6,17,35,30,19,0,0,0,0,0,10,34,1,1,17,13,6,66,26,22,0,9,38,6,21,40,31,0,0,3,13,0,6,28,49,2,7,12,14,0,35,18,2,17,17,7,37
+8.5,15,59,6,17,8,15,18,4,25,23,44,38,24,18,28,22,0,33,9,10,21,18,25,42,27,4,18,26,15,32,16,5,23,7,16,29,12,2,7,26,0,17,17,17,0,0,0,0,39,6,17,35,30,19,0,0,0,0,0,10,34,1,1,17,13,6,66,26,22,0,9,38,6,21,40,31,0,0,3,13,0,6,28,49,2,7,12,14,0,35,18,2,17,17,7,37
+8.75,15,59,6,17,8,15,18,5,25,23,44,39,24,18,28,22,0,33,9,10,21,18,25,42,27,5,18,26,15,32,16,5,23,7,16,29,12,2,7,26,0,17,17,17,0,0,0,0,39,6,17,35,30,19,0,0,0,0,0,10,34,1,1,17,13,6,66,26,22,0,9,38,6,21,40,31,0,0,3,13,0,6,28,49,2,7,12,14,0,35,18,2,17,17,7,37
+9,15,59,6,17,8,15,18,6,25,23,44,42,24,18,28,22,0,33,9,10,21,18,25,42,27,5,18,26,15,32,16,5,23,7,16,29,12,2,7,26,0,17,17,17,0,0,0,0,39,6,17,35,30,19,0,0,0,0,0,10,34,1,1,17,13,6,66,26,22,0,9,38,6,21,40,31,0,0,3,13,0,6,28,49,2,7,12,14,0,35,18,2,17,17,7,37
+9.25,15,62,6,17,8,15,18,6,25,23,44,42,24,18,28,22,0,33,9,10,21,18,25,42,27,6,18,26,15,32,16,5,23,7,16,29,12,2,7,26,0,17,17,17,0,0,0,0,39,6,17,35,30,19,0,0,0,0,0,10,34,1,1,17,13,6,66,26,22,0,9,38,6,21,40,31,0,0,3,13,0,6,28,49,2,7,12,14,0,35,19,2,17,17,7,37
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+,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Data File "," C:\Documents and Settings\Standard\Desktop\esperimenti e lavori in corso\Phenotype MicroArray\dati PM\2007\04\smel_Rmx1021___9_30_IDS_207_Aprx12x2007_B_24B.oka",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Set up Time ","Apr 12 2007 12:25 PM",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Position ","24-B",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Plate Type ","PM 9-",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Strain Type ","NOT APPLICABLE",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Sample Number","Rm1021",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Strain Name ","Rm1021",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Strain Number","Rm 1021",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Other ",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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diff --git a/Doc/examples/Proux_et_al_2002_Figure_6.py b/Doc/examples/Proux_et_al_2002_Figure_6.py
index 6bf3a0d..095dbcf 100644
--- a/Doc/examples/Proux_et_al_2002_Figure_6.py
+++ b/Doc/examples/Proux_et_al_2002_Figure_6.py
@@ -12,11 +12,8 @@ which produces a GenomeDiagram figure close to Proux et al 2002 Figure 6.
See http://dx.doi.org/10.1128/JB.184.21.6026-6036.2002
"""
-import os
-
from reportlab.lib import colors
from reportlab.lib.colors import red, grey, orange, green, brown, blue, lightblue, purple
-from reportlab.lib.units import cm
from Bio.Graphics import GenomeDiagram
from Bio.Graphics.GenomeDiagram import CrossLink
@@ -104,7 +101,7 @@ B_vs_C = [
]
-def get_feature(features, id, tags=["locus_tag", "gene", "old_locus_tag"]):
+def get_feature(features, id, tags=("locus_tag", "gene", "old_locus_tag")):
"""Search list of SeqFeature objects for an identifier under the given tags."""
for f in features:
for key in tags:
diff --git a/Doc/examples/clustal_run.py b/Doc/examples/clustal_run.py
index b0b8a0c..4ca087e 100644
--- a/Doc/examples/clustal_run.py
+++ b/Doc/examples/clustal_run.py
@@ -7,7 +7,6 @@ and parse the results into an object that can be dealt with easily."""
from __future__ import print_function
-import os
import sys
import subprocess
diff --git a/Doc/examples/getgene.py b/Doc/examples/getgene.py
index ed25cb1..16cc848 100755
--- a/Doc/examples/getgene.py
+++ b/Doc/examples/getgene.py
@@ -42,44 +42,43 @@ except ImportError:
from dbm import gnu as gdbm # Python 3
-class DB_Index:
+class DB_Index(object):
def __init__(self, open=1):
if open:
self.Open()
def Create(self, infile, outfile):
db = gdbm.open(outfile, 'n')
- fid = open(infile)
+ with open(infile) as fid:
- db['datafile'] = os.path.abspath(infile)
+ db['datafile'] = os.path.abspath(infile)
- while True:
- line = fid.readline()
- if not line or not len(line):
- break
+ while True:
+ line = fid.readline()
+ if not line or not len(line):
+ break
+
+ if line[:3] == 'ID ':
+ id = string.split(line)[1]
+ start = fid.tell() - len(line)
+
+ elif line[:3] == 'AC ':
+ acc = string.split(line)[1]
+ if acc[-1] == ';':
+ acc = acc[:-1]
+
+ elif line[:2] == '//':
+ stop = fid.tell()
+ try:
+ value = '%d %d' % (start, stop)
+ db[id] = value
+ db[acc] = value
+ id, acc, start, stop = None, None, None, None
+ except:
+ print("AARRGGGG %d %d %s %s" % (start, stop, type(start), type(stop)))
+ print("%s %s" % (id, acc))
- if line[:3] == 'ID ':
- id = string.split(line)[1]
- start = fid.tell() - len(line)
-
- elif line[:3] == 'AC ':
- acc = string.split(line)[1]
- if acc[-1] == ';':
- acc = acc[:-1]
-
- elif line[:2] == '//':
- stop = fid.tell()
- try:
- value = '%d %d' % (start, stop)
- db[id] = value
- db[acc] = value
- id, acc, start, stop = None, None, None, None
- except:
- print("AARRGGGG %d %d %s %s" % (start, stop, type(start), type(stop)))
- print("%s %s" % (id, acc))
-
- db.close()
- fid.close()
+ db.close()
def Open(self, indexfile=None):
if not indexfile:
@@ -115,7 +114,7 @@ class DB_Index:
return OS
if line[0:2] == "//":
break
- return OS
+ return None
def FixOS(self, os):
os = string.split(os, ',')[0]
@@ -138,7 +137,7 @@ class DB_Index:
def Get_Kingdom(self, id):
res = self.Get_Taxonomy(id)
- #print("%s %s" % (id, res))
+ # print("%s %s" % (id, res))
if not res:
return "U"
kd = string.strip(string.split(res, ";")[0])
@@ -273,7 +272,7 @@ def help(exit=0):
sys.exit(0)
if __name__ == '__main__':
- pyphy_home = os.environ.get('PYPHY', None)
+ pyphy_home = os.environ.get('PYPHY')
if len(sys.argv) == 1:
help(exit=1)
diff --git a/Doc/examples/nmr/simplepredict.py b/Doc/examples/nmr/simplepredict.py
index 5102210..558937e 100755
--- a/Doc/examples/nmr/simplepredict.py
+++ b/Doc/examples/nmr/simplepredict.py
@@ -45,14 +45,12 @@
# ***** LOAD MODULES *****
from __future__ import print_function
-import getopt
import string
-import sys
# -- don't need to modify sys.path with the *tools in Biopython
# -- just need Biopython installed somewhere in the PYTHONPATH
-#sys.path=[sys.path,"./"]
-#sys.path=[sys.path,"/usr/people/robert/development/xpktools"]
+# sys.path=[sys.path,"./"]
+# sys.path=[sys.path,"/usr/people/robert/development/xpktools"]
from Bio.NMR import xpktools # Contains data classes and functions for .xpk files
from Bio.NMR import NOEtools # A module specific for generate NOE predictions
@@ -67,69 +65,68 @@ detectatom = "H1" # Directly detected atom
relayatom = "N15" # J-coupling from here to detected atom
fromatom = "15N2" # The other labelled nucleus
-# *-*-* First the peaklist is read into a data class from xpktools
-# *-*-* that contains methods for easily extracting information from
-# *-*-* the peaklist file
+# First the peaklist is read into a data class from xpktools
+# that contains methods for easily extracting information from
+# the peaklist file
peaklist = xpktools.Peaklist(infn) # infn is the name of the xpk file
-# *-*-* The class attribute residue_dict allows the data lines
-# *-*-* to be separated from the header and returned here to
-# *-*-* the dictionary <dict> as a list indexed by the assignment
-# *-*-* of any of the nuclei in the file -- here, the detected atom
-# *-*-* is used
+# The class attribute residue_dict allows the data lines
+# to be separated from the header and returned here to
+# the dictionary <dict> as a list indexed by the assignment
+# of any of the nuclei in the file -- here, the detected atom
+# is used
dict = peaklist.residue_dict(detectatom)
-# *-*-* As well as the data, the dictionary contains two other entries,
-# *-*-* corresponding to the maximum and minimum residues indexed
+# As well as the data, the dictionary contains two other entries,
+# corresponding to the maximum and minimum residues indexed
MAXRES = dict["maxres"]
MINRES = dict["minres"]
-#****** CALCULATE AND WRITE CROSSPEAK PEAKLIST *****
+# ****** CALCULATE AND WRITE CROSSPEAK PEAKLIST *****
-# *-*-* The peaklist class has a method for writing out the header
-# *-*-* information in a format recognizable by nmrview
+# The peaklist class has a method for writing out the header
+# information in a format recognizable by nmrview
peaklist.write_header(outfn) # Write the header to the output file
-# *-*-* Predict the i->i+inc and i->i-inc noe positions if possible
-# *-*-* Write each one to the output file as they are calculated
+# Predict the i->i+inc and i->i-inc noe positions if possible
+# Write each one to the output file as they are calculated
count = 0 # A counter that number the output data lines in order
res = MINRES # minimum residue number in the set
outlist = [] # Holds the output data
while (res <= MAXRES):
-
-# *-*-* Predicting the NOE positions based on peak assignment data
-# *-*-* is done by supplying the peaklist to and specifying the label
-# *-*-* of the origin and detected atom in the NOE transfer as well as
-# *-*-* the residues between which the NOE transfer takes places.
+ # Predicting the NOE positions based on peak assignment data
+ # is done by supplying the peaklist to and specifying the label
+ # of the origin and detected atom in the NOE transfer as well as
+ # the residues between which the NOE transfer takes places.
noe1 = NOEtools.predictNOE(peaklist, "15N2", "H1", res, res + inc)
noe2 = NOEtools.predictNOE(peaklist, "15N2", "H1", res, res - inc)
-# *-*-* The output of predictNOE is in the form of an xpk entry line
-# *-*-* suitable for printing to an output file
-# *-*-* Additionally, it is possible to extract information easily from
-# *-*-* these output lines by using the xpktools.XpkEntry class
+ # The output of predictNOE is in the form of an xpk entry line
+ # suitable for printing to an output file
+ # Additionally, it is possible to extract information easily from
+ # these output lines by using the xpktools.XpkEntry class
entry1 = xpktools.XpkEntry(noe1, peaklist.datalabels)
if noe1 != "":
- # *-*-* Here I'm using the XpkEntry class to gain access to
- # *-*-* specific fields in the that make the information
- # *-*-* more readable and suitable for creating data tables
- # *-*-* This output will be printed to the screen.
- # *-*-* The data table contains the assignment, coordinates and
- # *-*-* intensity of the resonance.
+ # Here I'm using the XpkEntry class to gain access to
+ # specific fields in the that make the information
+ # more readable and suitable for creating data tables
+ # This output will be printed to the screen.
+ # The data table contains the assignment, coordinates and
+ # intensity of the resonance.
- print(string.split(entry1.fields["15N2.L"], ".")[0], "-->", \
- string.split(entry1.fields["N15.L"], ".")[0], "\t", \
- entry1.fields["H1.P"], entry1.fields["N15.P"], \
+ print(string.split(entry1.fields["15N2.L"], ".")[0], "-->",
+ string.split(entry1.fields["N15.L"], ".")[0], "\t",
+ entry1.fields["H1.P"], entry1.fields["N15.P"],
entry1.fields["15N2.P"], entry1.fields["int"])
noe1 = noe1 + "\012"
@@ -145,6 +142,5 @@ while (res <= MAXRES):
res += 1
# Open the output file and write the data
-outfile = open(outfn, 'a')
-outfile.writelines(outlist) # Write the output lines to the file
-outfile.close()
+with open(outfn, 'a') as outfile:
+ outfile.writelines(outlist) # Write the output lines to the file
diff --git a/Doc/examples/www_blast.py b/Doc/examples/www_blast.py
index 14be037..54d3599 100644
--- a/Doc/examples/www_blast.py
+++ b/Doc/examples/www_blast.py
@@ -24,10 +24,9 @@ print('Doing the BLAST and retrieving the results...')
result_handle = NCBIWWW.qblast('blastn', 'nr', f_record.format('fasta'))
# save the results for later, in case we want to look at it
-save_file = open('m_cold_blast.out', 'w')
-blast_results = result_handle.read()
-save_file.write(blast_results)
-save_file.close()
+with open('m_cold_blast.out', 'w') as save_file:
+ blast_results = result_handle.read()
+ save_file.write(blast_results)
print('Parsing the results and extracting info...')
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diff --git a/Doc/images/BlastRecord.png b/Doc/images/BlastRecord.png
index db323d1..7086e33 100644
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diff --git a/Doc/images/smcra.eps b/Doc/images/smcra.eps
new file mode 100644
index 0000000..0cf1387
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-<!--HEVEA command line is: hevea -fix Installation.tex -->
-<!--CUT DEF section 1 --><TABLE CLASS="title"><TR><TD><H1 CLASS="titlemain">Biopython Installation</H1><H3 CLASS="titlerest">Brad Chapman, with other contributors</H3></TD></TR>
-</TABLE><!--TOC section Contents-->
-<H2 CLASS="section"><!--SEC ANCHOR -->Contents</H2><!--SEC END --><UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc1">1  Purpose and Assumptions</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc2">2  C Compiler</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc3">2.1  Unix</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc4">2.2  Mac OS X</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc5">2.3  Windows</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc6">3  Installing Python</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc7">3.1  Python installation on UNIX systems</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc8">3.1.1  RPM and other Package Manager Installation</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc9">3.2  Python installation on Windows</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc10">3.3  Python installation on Mac OS X</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc11">4  Installing Biopython dependencies</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc12">4.1  Numerical Python (NumPy) (strongly recommended)</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc13">4.1.1  UNIX and Mac OS X systems</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc14">4.1.2  Windows systems</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc15">4.1.3  Making sure it installed correctly</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc16">4.2  ReportLab (optional)</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc17">4.2.1  UNIX and Mac OS X systems</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc18">4.2.2  Windows systems</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc19">4.2.3  Making sure it installed correctly</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc20">4.3  Database Access (MySQLdb, ...) (optional)</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc21">4.4  mxTextTools (no longer needed)</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc22">5  Installing Biopython</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc23">5.1  Obtaining Biopython</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc24">5.2  Installing on UNIX and Mac OS X</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc25">5.2.1  Installation from source on UNIX and Mac OS X</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc26">5.2.2  Using the Python package index</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc27">5.2.3  Installation on Mac OS X using the fink package manager</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc28">5.2.4  Installation on UNIX systems using RPMs</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc29">5.3  Installing with a Windows Installer</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc30">5.4  Installing from source on Windows</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc31">6  Making sure everything worked</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc32">7  Third Party Tools</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc33">8  Notes for installing with non-administrator permissions</A>
-</LI></UL><!--TOC section Purpose and Assumptions-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc1">1</A>  Purpose and Assumptions</H2><!--SEC END --><P>For those of you familiar with installing python packages and who don’t
-care for following details instructions can try going to
-<A HREF="http://biopython.org/wiki/Download"><TT>http://biopython.org/wiki/Download</TT></A>, installing
-the relevant prerequisites, and Biopython.</P><P>This document describes installing Biopython on your computer. To make
-things as simple as possible, it basically assumes you have nothing
-related to Python or Biopython on your computer and want to end up with
-a working installation of Biopython when you are finished following
-through this documentation. </P><P>Biopython should work on just any operating system where Python works,
-so these instructions contain directions for installation on UNIX/Linux,
-Windows and Macintosh machines. The directions assume
-that you have permission to install programs on the machine
-(root access on UNIX and Administrator privileges on Windows or Mac
-machines). While it is certainly possible to install things without
-these privileges, this is a serious pain and all the tedious workarounds
-aren’t something that I’ll go into very much in this documentation.</P><P>With all this said, hopefully these directions will make it
-straightforward to get Biopython installed on your machine so you can
-begin using it as quick as possible.</P><!--TOC section C Compiler-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc2">2</A>  C Compiler</H2><!--SEC END --><P>Although mostly written in Python, Biopython (and some of its dependencies)
-include C code, which must be compiled to run. If you are going to be
-installing from source you will therefore need a C compiler. However,
-in many cases this can be avoided by using pre-compiled packages (which
-is what we recommend on Windows).</P><!--TOC subsection Unix-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc3">2.1</A>  Unix</H3><!--SEC END --><P>
-We recommend GCC as the C compiler, this is usually available as part
-of the standard set of packages on any Unix or Linux system.</P><!--TOC subsection Mac OS X-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc4">2.2</A>  Mac OS X</H3><!--SEC END --><P>
-Please install Apple’s XCode suite from the App Store, and then from
-the Xcode options also install the optional command line utilities.</P><!--TOC subsection Windows-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc5">2.3</A>  Windows</H3><!--SEC END --><P>
-We recommend you install Biopython and its dependencies using the
-provided pre-compiled Windows Installers. i.e. You don’t <EM>need</EM>
-a C compiler. See Section <A HREF="#sec:windows_install">5.4</A> for more details.</P><!--TOC section Installing Python-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc6">3</A>  Installing Python</H2><!--SEC END --><P>Python is a interpreting, interactive object-oriented programming
-language and the home for all things python is
-<A HREF="http://www.python.org"><TT>http://www.python.org</TT></A>. Presumedly you have some idea of
-python and what it can do if you are interested in Biopython, but if not
-the python website contains tons of documentation and reasons to learn
-to program in python.</P><P>Biopython is designed to work with Python 2.5 to 2.7 inclusive.
-Python 2.7 is the final 2.x series release, and this would be our
-recommended version (assuming all other Python libraries you plan
-to use support it).</P><P>Upgrading bug-fix releases (for example. 2.6.1 to 2.6.2)
-is incredibly easy and won’t require any re-installation of libraries.</P><P>Upgrading between versions (e.g. 2.6 to 2.7) is more time consuming since you
-need to re-install all libraries you have added to python.</P><P>As of Biopython 1.62 we officially support Python 3, specifically Python 3.3.
-Python 3.0, 3.1 and 3.2 will not be supported.</P><P>Let’s get started with installation on various platforms.</P><!--TOC subsection Python installation on UNIX systems-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc7">3.1</A>  Python installation on UNIX systems</H3><!--SEC END --><P>First, you should go the main python web site and head over to the information
-page for the latest python release. At the time of this writing the
-latest stable Python 2 release is 2.7.5, which is available from
-<A HREF="http://www.python.org/download/releases/2.7.5/"><TT>http://www.python.org/download/releases/2.7.5/</TT></A>. This page contains links
-to all released files for the given release. For UNIX, we’ll want to use
-the tarred and gzipped file, which is called <CODE>Python-2.7.5.tgz</CODE> at
-the time of this writing.</P><P>Download this file and then unpack it with the following command:</P><PRE CLASS="verbatim">$ tar -zxvf Python-2.7.5.tar.gz
-</PRE><P>Then enter into the created directory:</P><PRE CLASS="verbatim">$ cd Python-2.7
-</PRE><P>Now, start the build process by configuring everything to your system:</P><PRE CLASS="verbatim">$ ./configure
-</PRE><P>Build all of the files with:</P><PRE CLASS="verbatim">$ make
-</PRE><P>Finally, you’ll need to have root permissions on the system and then
-install everything:</P><PRE CLASS="verbatim">$ make install
-</PRE><P>If there were no errors and everything worked correctly, you should now
-be able to type <CODE>python</CODE> at a command prompt and enter into the
-python interpreter:</P><PRE CLASS="verbatim">$ python
-Python 2.7.5 (...)
-...
-Type "help", "copyright", "credits" or "license" for more information.
->>>
-</PRE><P>(The precise version text and details will depend on the version you installed and your operating system.)</P><!--TOC subsubsection RPM and other Package Manager Installation-->
-<H4 CLASS="subsubsection"><!--SEC ANCHOR --><A NAME="htoc8">3.1.1</A>  RPM and other Package Manager Installation</H4><!--SEC END --><P>There are a multitude of package manager systems out there for which
-python is available. One popular one is the RPM (RedHat Package Manager)
-system. Each of these package managing systems has its own quirks and
-tricks and I certainly can’t pretend to understand them all so I won’t
-try to describe them all here.</P><P>While these package repositories may include Biopython all ready to install,
-you will typically want to install Biopython from source to get the very
-latest version.</P><P>However, there is one general point which it is important to remember
-when installing from any of these systems: you need to download and
-install the development packages for python. A number of distributions
-contain a "basic" python which contains libraries and enough stuff to
-run simple python programs. However, they do not contain the python
-libraries necessary to build third-party python applications (like
-Biopython and it’s dependencies). You’ll need to install these libraries
-and header files, which are often found in a separate package called
-<CODE>python-devel</CODE> or something similar. </P><!--TOC subsection Python installation on Windows-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc9">3.2</A>  Python installation on Windows</H3><!--SEC END --><P>Installation on Windows is most easily done using handy windows
-installers. As described above in the UNIX section, you should go to the
-webpage for the current stable version of Python to download this
-installer. At the current time, you’d go to
-<A HREF="http://www.python.org/download/releases/2.7.5/"><TT>http://www.python.org/download/releases/2.7.5/</TT></A> and download
-<CODE>Python-2.7.5.msi</CODE>. </P><P>The installer is an executable program, so you only need to double click
-it to run it. Then just follow the friendly instructions. On all newer Windows
-machines you’ll need to have Administrator privileges to do this
-installation.</P><!--TOC subsection Python installation on Mac OS X-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc10">3.3</A>  Python installation on Mac OS X</H3><!--SEC END --><P>Apple includes python on Mac OS X, and while you can use this many people have
-preferred to install the latest version of python as well in parallel. We refer
-you to the <A HREF="http://www.python.org"><TT>http://www.python.org</TT></A> for more details, although
-otherwise the UNIX instructions apply.</P><P>(See note above about installing XCode to get the compiler tools.)</P><!--TOC section Installing Biopython dependencies-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc11">4</A>  Installing Biopython dependencies</H2><!--SEC END --><P>Once python is installed, the next step is getting the dependencies
-for Biopython installed. Since not all functionality is included in the
-main python installation, Biopython needs some support libraries to save
-us a lot of work re-writing code that already exists. We try to keep
-as few dependencies as possible to make installation as easy as
-possible.</P><!--TOC subsection Numerical Python (NumPy) (strongly recommended)-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc12">4.1</A>  Numerical Python (NumPy) (strongly recommended)</H3><!--SEC END --><P>The Numerical Python distribution is a fast implementation of arrays and
-associated array functionality. This is important for a number of Biopython
-modules that deal with number processing (e.g. Bio.Cluster and Bio.PDB).</P><P>As of release 1.49, Biopython supports the standard NumPy distribution.
-Previous releases instead used the older Numeric module (which is no
-longer being maintained).</P><P>The main web site for NumPy is:
-<A HREF="http://numpy.scipy.org/"><TT>http://numpy.scipy.org/</TT></A>.</P><!--TOC subsubsection UNIX and Mac OS X systems-->
-<H4 CLASS="subsubsection"><!--SEC ANCHOR --><A NAME="htoc13">4.1.1</A>  UNIX and Mac OS X systems</H4><!--SEC END --><P>You should download the <CODE>tar.gz</CODE> file, and follow the standard python
-build process. Note you will need a C compiler installed (see above):</P><PRE CLASS="verbatim">$ tar -zxvf numpy-1.7.1.tar.gz
-$ cd numpy-1.7.1/
-$ python setup.py build
-</PRE><P>Once it is built, you should become root, and then install it:</P><PRE CLASS="verbatim">$ python setup.py install
-</PRE><P>One important note if you use an package system and not installing
-NumPy from source: you may also need to
-install the header files which are not included with some
-packages. As with the main python distribution, this means
-you’ll need to look for something like <CODE>python-numpy-devel</CODE>
-and make sure to install this as well as the basic package.</P><!--TOC subsubsection Windows systems-->
-<H4 CLASS="subsubsection"><!--SEC ANCHOR --><A NAME="htoc14">4.1.2</A>  Windows systems</H4><!--SEC END --><P>We recommend using the NumPy provided windows installers for your installed
-version of python. For Python 2.7, at the current time this would be
-<CODE>numpy-1.7.1-win32-superpack-python2.7.exe</CODE>. You should follow the
-now-standard procedure of downloading the installer, double
-clicking it and then following the installation instructions. As before,
-you will need to have administrator permissions to do this.</P><!--TOC subsubsection Making sure it installed correctly-->
-<H4 CLASS="subsubsection"><!--SEC ANCHOR --><A NAME="htoc15">4.1.3</A>  Making sure it installed correctly</H4><!--SEC END --><P>To make sure everything went okay during the install, fire up the python
-interpreter and ensure you can import NumPy without any errors:</P><PRE CLASS="verbatim">$ python2.7
-Python 2.7.4 (default, Apr 8 2013, 15:01:09)
-[GCC 4.4.6 20120305 (Red Hat 4.4.6-4)] on linux2
-Type "help", "copyright", "credits" or "license" for more information.
->>> import numpy
->>>
-</PRE><P>Note that for the import statement, NumPy should be in lower case!</P><!--TOC subsection ReportLab (optional)-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc16">4.2</A>  ReportLab (optional)</H3><!--SEC END --><P>The ReportLab package is a library for generating PDF documents. It is
-used in the Biopython Graphics modules, which contains basic
-functionality for drawing biological objects like chromosomes. If you
-are not planning on using this, installing ReportLab is not necessary.
-ReportLab in itself is very useful for a number of tasks besides just
-Biopython, so you may want to check out
-<A HREF="http://www.reportlab.org"><TT>http://www.reportlab.org</TT></A> before making your decision.</P><P>The main download page for ReportLab is
-<A HREF="http://www.reportlab.org/downloads.html"><TT>http://www.reportlab.org/downloads.html</TT></A>. The ReportLab
-company has some commercial products as well, but just scroll down their
-page to the Open Source software section for the base ReportLab
-downloads.</P><P>If you want to generate bitmap images, you will also need the ReportLab
-module renderPM. This in turn requires the
-<A HREF="http://www.pythonware.com/products/pil/">Python Imaging Library (PIL)</A>.</P><!--TOC subsubsection UNIX and Mac OS X systems-->
-<H4 CLASS="subsubsection"><!--SEC ANCHOR --><A NAME="htoc17">4.2.1</A>  UNIX and Mac OS X systems</H4><!--SEC END --><P>For UNIX installs, you should download the tarred and gzipped version of
-the ReportLab distribution. At the time of this writing, this is called
-<CODE>ReportLab_2_3.tar.gz</CODE>. First, unpack the distribution and change
-into the created directory:</P><PRE CLASS="verbatim">$ gunzip ReportLab_2_3.tar.gz
-$ tar -xvpf ReportLab_2_3.tar
-$ cd reportlab_2_3/
-</PRE><P>Once again, ReportLab uses the standard python installation system which
-you are probably feeling really comfortable with by now. So, first build
-the package:</P><PRE CLASS="verbatim">$ python setup.py build
-</PRE><P>Now become root, and install it:</P><PRE CLASS="verbatim">$ python setup.py install
-</PRE><!--TOC subsubsection Windows systems-->
-<H4 CLASS="subsubsection"><!--SEC ANCHOR --><A NAME="htoc18">4.2.2</A>  Windows systems</H4><!--SEC END --><P>ReportLab now has graphical windows installers. Nice and easy.</P><!--TOC subsubsection Making sure it installed correctly-->
-<H4 CLASS="subsubsection"><!--SEC ANCHOR --><A NAME="htoc19">4.2.3</A>  Making sure it installed correctly</H4><!--SEC END --><P>If reportlab is installed correctly, you should be able to do the
-following:</P><PRE CLASS="verbatim">$ python2.7
-Python 2.7.4 (default, Apr 8 2013, 15:01:09)
-[GCC 4.4.6 20120305 (Red Hat 4.4.6-4)] on linux2
-Type "help", "copyright", "credits" or "license" for more information.
->>> from reportlab.graphics import renderPDF
->>>
-</PRE><P>Depending on your version of python and what you have installed, you may
-get the following warning message:
-<CODE>Warn: Python Imaging Library not available</CODE>. This isn’t anything
-to worry about unless you want to produce bitmap images, since the
-Biopython parts that use ReportLab will work just fine without it.</P><!--TOC subsection Database Access (MySQLdb, ...) (optional)-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc20">4.3</A>  Database Access (MySQLdb, ...) (optional)</H3><!--SEC END --><P>The MySQLdb package is a library for accessing MySQL databases.
-Biopython includes an accessory module, DocSQL, which provides a convenient
-interface to MySQLdb.
-If you are not planning on using Bio.DocSQL, installing
-MySQLdb is not necessary.</P><P>Additionally, both MySQLdb and psycopg (a PostgreSQL database adaptor)
-can be used for accessing BioSQL databases through Biopython
-(see <A HREF="http://biopython.org/wiki/BioSQL"><TT>http://biopython.org/wiki/BioSQL</TT></A>). Again if
-you are not going to use BioSQL, there shouldn’t be any need to install
-these modules.</P><!--TOC subsection mxTextTools (no longer needed)-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc21">4.4</A>  mxTextTools (no longer needed)</H3><!--SEC END --><P>Historically this was an important Biopython dependency, used extensively
-in a number of parsers. However, we have gradually phased out its use,
-and as of Biopython 1.50, mxTextTools is no longer used at all.</P><P>mxTextTools is available along with the entire mx-base system (which
-contains a number of other useful utilities as well) and the latest
-version is available for download at:
-<A HREF="http://www.egenix.com/products/python/mxBase/mxTextTools/"><TT>http://www.egenix.com/products/python/mxBase/mxTextTools/</TT></A>.</P><!--TOC section Installing Biopython-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc22">5</A>  Installing Biopython</H2><!--SEC END --><!--TOC subsection Obtaining Biopython-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc23">5.1</A>  Obtaining Biopython</H3><!--SEC END --><P>
-Biopython’s internet home is at, naturally enough,
-<A HREF="http://www.biopython.org"><TT>http://www.biopython.org</TT></A>. This is the home of all things
-Biopython, so it is the best place to start looking around.
-You have two choices for obtaining Biopython:</P><OL CLASS="enumerate" type=1><LI CLASS="li-enumerate">Release code – We made available releases on the download page
-(<A HREF="http://biopython.org/wiki/Download"><TT>http://biopython.org/wiki/Download</TT></A>).
-The releases are also available both as source and as installers
-(windows installers right now), so you have some choices to pick from
-on releases if you prefer not to deal with source code directly.</LI><LI CLASS="li-enumerate">git – The current working copy of the Biopython sources is available
-via git hosted on github – <A HREF="http://github.com/biopython/biopython"><TT>http://github.com/biopython/biopython</TT></A>).
-Concise instructions for accessing this copy are available at
-<A HREF="http://biopython.org/wiki/SourceCode"><TT>http://biopython.org/wiki/SourceCode</TT></A>. Our code in git
-is normally quite stable but there is always the caveat that the code
-there is under development.</LI></OL><P>Based on which way you choose, you’ll need to follow one of the following installation options. Read on for the platform you are working on.</P><!--TOC subsection Installing on UNIX and Mac OS X-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc24">5.2</A>  Installing on UNIX and Mac OS X</H3><!--SEC END --><P>
-<A NAME="sec:unix_install"></A></P><!--TOC subsubsection Installation from source on UNIX and Mac OS X-->
-<H4 CLASS="subsubsection"><!--SEC ANCHOR --><A NAME="htoc25">5.2.1</A>  Installation from source on UNIX and Mac OS X</H4><!--SEC END --><P>Biopython uses Distutils, the standard python installation package, for
-its installation. If you read the install instructions above you are
-already quite familiar with its workings. Distutils comes standard with
-Python 1.6 and beyond.</P><P>Now that we’ve got what we need, let’s get into the installation:</P><OL CLASS="enumerate" type=1><LI CLASS="li-enumerate">First you need to unpack the distribution. If you got the git version, you are all set to go and can skip on ahead. Otherwise, you’ll need to unpack it. On UN*X machines, a tar.gz package is provided, which you can unpack with <CODE>tar -xzvpf biopython-X.X.tar.gz</CODE>. A zip file is also provided for other platform [...]
-some other packaging system, this means you’ll also have to install the
-header files. This requires installing the python development libraries
-as well (normally called something like <CODE>python-devel-2.5.rpm</CODE>).</LI><LI CLASS="li-enumerate">The distutils setup process allows you to do some customization of your install so you don’t have to stick everything in the default location (in case you don’t have write permissions there, or just want to test Biopython out). You have quite a few choices, which are covered in detail in the distutils installation manual (<A HREF="http://www.python.org/sigs/distutils-sig [...]
-<H4 CLASS="subsubsection"><!--SEC ANCHOR --><A NAME="htoc26">5.2.2</A>  Using the Python package index</H4><!--SEC END --><P>Another simple option is to use the Python package index
-(<A HREF="http://pypi.python.org/pypi"><TT>http://pypi.python.org/pypi</TT></A>) with the <CODE>easy_install</CODE>
-command:</P><PRE CLASS="verbatim">$ easy_install -f http://biopython.org/DIST/ biopython
-</PRE><P>If Python is installed in the standard location, you will need administrator
-privileges to do this; the <CODE>sudo</CODE> command works well:</P><PRE CLASS="verbatim">$ sudo easy_install -f http://biopython.org/DIST/ biopython
-</PRE><!--TOC subsubsection Installation on Mac OS X using the fink package manager-->
-<H4 CLASS="subsubsection"><!--SEC ANCHOR --><A NAME="htoc27">5.2.3</A>  Installation on Mac OS X using the fink package manager</H4><!--SEC END --><P>Instead of installing from source, on Mac OS X you can also use the fink package manager, see <A HREF="http://fink.sf.net"><TT>http://fink.sf.net</TT></A>. Fink should take care of downloading the source code and installing all needed packages for Biopython, including Python itself. Once you have installed fink, you can install bi [...]
-</PRE><P>where XX is the python version you would like to use. Currently, python 2.4, 2.5, and 2.6 are available through fink on Mac OS X 10.4, so you would have to replace XX with 24, 25, or 26, respectively. Most likely, you will have to enable the unstable tree of fink in order to install the most recent versions of the package, see also this item in the Fink FAQ: <A HREF="http://fink.sourceforge.net/faq/usage-fink.php#unstable"><TT>http://fink.sourceforge.net/faq/usage-fink.php#unsta [...]
-<H4 CLASS="subsubsection"><!--SEC ANCHOR --><A NAME="htoc28">5.2.4</A>  Installation on UNIX systems using RPMs</H4><!--SEC END --><P>Warning. Right now we’re not making RPMs for biopython (because I
-stopped using an RPM system, basically). If anyone wants to pick this
-up, or feels especially strongly that they’d like RPMs, please let us
-know.</P><P>To simplify things for people running RPM-based systems, biopython can
-also be installed via the RPM system. Additionally, this saves the
-necessity of having a C compiler to install biopython. </P><P>Installing Biopython from a RPM package should be much the same process as used for other RPMs. If you need general information about how RPMs work, the best place to go is <A HREF="http://www.rpm.org"><TT>http://www.rpm.org</TT></A>.</P><P>To install it, you should just need to do:</P><PRE CLASS="verbatim">$ rpm -i your_biopython.rpm
-</PRE><P>To see what you installed try doing <CODE>rpm -qpl your_biopython.rpm</CODE> which will list all of the installed files.</P><P>RPMs do not install the documentation, tests, or example code, so you might want to also grab a source distribution, so you can use these resources (and also look at the source code if you want to).</P><!--TOC subsection Installing with a Windows Installer-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc29">5.3</A>  Installing with a Windows Installer</H3><!--SEC END --><P>Installing things on Windows with the installer should be really easy (hey, that’s why they’ve got graphical installers, right?). You should just need to download the <CODE>Biopython-version.exe</CODE> installer from biopython web site. Then you just need to double click and voila, a nice little installer will come up and you can stick the l [...]
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc30">5.4</A>  Installing from source on Windows</H3><!--SEC END --><P>
-<A NAME="sec:windows_install"></A></P><P>This section deals with installing the source (i. e. from git or from a source zip file) on a Windows machine. Much of the information from the UNIX install applies here, so it would be good to read section <A HREF="#sec:unix_install">5.2</A> before starting. You will need a suitable C compiler.
-What you choose may depend on your version of Python.</P><P>For Python 2.6 we currently use Microsoft’s free VC++ 2008 Express Edition from <A HREF="http://www.microsoft.com/express/download/"><TT>http://www.microsoft.com/express/download/</TT></A>, installation of this is pretty simple. Then go to the Biopython source directory and run:</P><PRE CLASS="verbatim">c:\python26\python setup.py build
-c:\python26\python setup.py test
-c:\python26\python setup.py install
-</PRE><P>For older versions of Python, we use mingw32 installed from cygwin (<A HREF="http://www.cygwin.com"><TT>http://www.cygwin.com</TT></A>). Once everything is setup (which is a bit complicated), you would again get the source, and from that directory run:</P><PRE CLASS="verbatim">c:\python25\python setup.py build --compiler=mingw32
-c:\python25\python setup.py test
-c:\python25\python setup.py install
-</PRE><P>Previously (back on Python 2.0), Brad has also managed to use Borland’s free C++ compiler (available from <A HREF="http://www.inprise.com/bcppbuilder/freecompiler/"><TT>http://www.inprise.com/bcppbuilder/freecompiler/</TT></A>), but this required extra work.</P><P>Now that you’ve got everything installed, carry on ahead to section <A HREF="#sec:is_working">6</A> to make sure everything worked.</P><!--TOC section Making sure everything worked-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc31">6</A>  Making sure everything worked</H2><!--SEC END --><P>
-<A NAME="sec:is_working"></A></P><P>First, we’ll just do a quick test to make sure Biopython is installed correctly. The most important thing is that python can find the biopython installation. Biopython installs into top level <CODE>Bio</CODE> and <CODE>BioSQL</CODE> directories, so you’ll want to make sure these directories are located in a directory specified
-in your<CODE> $PYTHONPATH</CODE> environmental variable. If you used the default install, this shouldn’t be a problem, but if not, you’ll need to set the <CODE>PYTHONPATH</CODE> with something like <CODE>export PYTHONPATH = $PYTHONPATH':/directory/where/you/put/Biopython'</CODE> (on UNIX). Now that we think we are ready, fire up your python interpreter and follow along with the following code:</P><PRE CLASS="verbatim">$ python
-Python 2.5 (r25:51908, Nov 23 2006, 18:40:28)
-[GCC 4.1.1 20061011 (Red Hat 4.1.1-30)] on linux2
-Type "help", "copyright", "credits" or "license" for more information.
->>> from Bio.Seq import Seq
->>> from Bio.Alphabet.IUPAC import unambiguous_dna
->>> new_seq = Seq('GATCAGAAG', unambiguous_dna)
->>> new_seq[0:2]
-Seq('GA', IUPACUnambiguousDNA())
->>> new_seq.translate()
-Seq('DQK', IUPACProtein())
->>>
-</PRE><P>If this worked properly, then it looks like Biopython is in a happy place where python can find it, so now you might want to do some more rigorous tests. The <CODE>Tests</CODE> directory inside the distribution contains a number of tests you can run to make sure all of the different parts of biopython are working. These should all work just by running <CODE>python test_WhateverTheTestIs.py</CODE>. </P><P>If you didn’t do this earlier, you should also all of our tests. To [...]
-</PRE><P>You can also run them by typing <CODE>python run_tests.py</CODE> in the Tests sub directory.
-See the main Tutorial for further details (there is a whole chapter on the test framework).</P><P>If you’ve made it this far, you’ve gotten Biopython installed and running.
-Congratulations!</P><!--TOC section Third Party Tools-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc32">7</A>  Third Party Tools</H2><!--SEC END --><P>Note that Biopython includes support for interfacing with or parsing the output from a number of third party command line tools. These are not required to install Biopython, but may be of interest. This includes:</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-NCBI Standalone BLAST, which can used with the <CODE>Bio.Blast</CODE> module and parsed with the <CODE>Bio.SearchIO</CODE> module.
-</LI><LI CLASS="li-itemize">EMBOSS tools, which can be invoked using the <CODE>Bio.Emboss</CODE> module. The <CODE>Bio.AlignIO</CODE> module can also parse some alignment formats output by the EMBOSS suite.
-</LI><LI CLASS="li-itemize">ClustalW, which can be parsed using <CODE>Bio.AlignIO</CODE> and invoked using the <CODE>Bio.Align.Applications</CODE> module.
-</LI><LI CLASS="li-itemize">SIMCOAL2 and FDist tools for population genetics can be used via the <CODE>Bio.PopGen</CODE> module.
-</LI><LI CLASS="li-itemize">Bill Pearson’s FASTA tools output can be parsed using the <CODE>Bio.AlignIO</CODE> and <CODE>Bio.SearchIO</CODE> module.
-</LI><LI CLASS="li-itemize">Wise2 includes the useful tool dnal.
-</LI></UL><P>See also the listing on <A HREF="http://biopython.org/wiki/Download"><TT>http://biopython.org/wiki/Download</TT></A> which should include URLs for these tools, and may also be more up to date.</P><!--TOC section Notes for installing with non-administrator permissions-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc33">8</A>  Notes for installing with non-administrator permissions</H2><!--SEC END --><P>Although I mentioned above that I wouldn’t go much into installing in
-non-root directories, if you are stuck installing
-Biopython and it’s dependencies into your home directory here are a
-few notes and tricks to keep you going:</P><UL CLASS="itemize"><LI CLASS="li-itemize">Building some C modules, such as <CODE>Bio.Cluster</CODE> require that
-the NumPy include files (normally installed in
-<CODE>your_dir/include/python/Numeric</CODE>) be available. If the
-compiler can’t find these directories you’ll normally get an error
-like:<PRE CLASS="verbatim">Bio/Cluster/clustermodule.c:2: NumpPy/arrayobject.h: No such file or directory
- </PRE><P>Followed by a long messy list of syntax errors. To fix this, you’ll
-have to edit the <CODE>setup.py</CODE> file to let it know where the
-include directories are located. Look for the line in
-<CODE>setup.py</CODE> that looks like:</P><PRE CLASS="verbatim"> include_dirs=["Bio/Cluster"]
- </PRE><P>and adjust it so that it includes the include directory where the
-NumPy libraries were installed:</P><PRE CLASS="verbatim"> include_dirs=["Bio/Cluster", "your_dir/include/python"]
- </PRE><P>Then you should be able to install everything happily.</P></LI></UL><P>Yes, it’s a bit of a mess installing lots of packages in non-standard
-locations. The best solution is to talk with your friendly system
-administrator and get them to assist with the installation of at least
-the required packages (they are generally quite useful for any python
-install) before going ahead with Biopython installation.</P><!--CUT END -->
-<!--HTMLFOOT-->
-<!--ENDHTML-->
-<!--FOOTER-->
-<HR SIZE=2><BLOCKQUOTE CLASS="quote"><EM>This document was translated from L<sup>A</sup>T<sub>E</sub>X by
-</EM><A HREF="http://hevea.inria.fr/index.html"><EM>H</EM><EM><FONT SIZE=2><sup>E</sup></FONT></EM><EM>V</EM><EM><FONT SIZE=2><sup>E</sup></FONT></EM><EM>A</EM></A><EM>.</EM></BLOCKQUOTE></BODY>
-</HTML>
diff --git a/Doc/install/Installation.pdf b/Doc/install/Installation.pdf
deleted file mode 100644
index d299ee5..0000000
Binary files a/Doc/install/Installation.pdf and /dev/null differ
diff --git a/MANIFEST.in b/MANIFEST.in
index b28d80c..53f1c4a 100644
--- a/MANIFEST.in
+++ b/MANIFEST.in
@@ -27,20 +27,22 @@ recursive-include Doc/examples *.bgz
recursive-include Doc/examples *.xml
recursive-include Doc/examples *.psl
recursive-include Doc/examples *.dnd
+recursive-include Doc/examples *.csv
recursive-include Doc/images *.png
recursive-include Doc/images *.jpg # e.g. the logo for the Tutorial
-# Include Bio.Cluster, Bio.Affy and Bio.KDTree
+# Include Bio.Cluster, Bio.Affy, Bio.KDTree, Bio.phenotype
# Note setup.py will only install these if Numpy is installed.
recursive-include Bio/Cluster *.py
recursive-include Bio/Affy *.py
recursive-include Bio/KDTree *.py
+recursive-include Bio/phenotype *.py
exclude Tests/Graphics/* # Exclude test files generated by reportlab.
include Tests/Graphics/README # But include README file to make directory!
-recursive-exclude * .cvsignore
-recursive-exclude * *.pyc # e.g. from running the unit tests
+# e.g. from running the unit tests, or PyPy, etc
+global-exclude *.pyc *.pyo *.py{} *.py-e
# Make sure the required data files are included.
include Bio/Entrez/DTDs/*.dtd # Include DTD files for Entrez
diff --git a/NEWS b/NEWS
index 4d95b45..ab69ac9 100644
--- a/NEWS
+++ b/NEWS
@@ -10,8 +10,112 @@ The latest news is at the top of this file.
===================================================================
+8 June 2016: Biopython 1.67
+
+This release of Biopython supports Python 2.6, 2.7, 3.3, 3.4 and 3.5, but
+support for Python 2.6 is considered to be deprecated. It has also been
+tested on PyPy 5.0, PyPy3 version 2.4, and Jython 2.7.
+
+Comparison of SeqRecord objects until now has used the default Python object
+comparison (are they the same instance in memory?). This can be surprising, but
+comparing all of the attributes would be too complex. As of this release
+attempting to compare SeqRecord objects should raise an exception instead. If
+you want the old behaviour, use id(record1) == id(record2) instead.
+
+New experimental module Bio.phenotype is for working with Phenotype Microarray
+plates in JSON and the machine vendor's CSV format (contributed by Marco
+Galardini).
+
+Following the convention used elsewhere in Biopython, there is a new function
+Bio.KEGG.read(...) for parsing KEGG files expected to contain a single record
+only - the existing function Bio.KEGG.parse(...) is intended to be used to
+iterate over multi-record files.
+
+When a gap character is defined, Bio.Seq will now translate gap codons
+(e.g. "---") into a single gap ("-") in the protein sequence. The gap character
+is inferred from the Seq object's alphabet, but it can also be passed as an
+argument to the translate method.
+
+The new NCBI genetic code table 25, covering Candidate Division SR1 and
+Gracilibacteria, has been added to Bio.Data (and the translation
+functionality).
+
+The Bio.Entrez interface will automatically use an HTTP POST rather than
+HTTP GET if the URL would exceed 1000 characters. This is based on NCBI
+guidelines and the fact that very long queries like complex searches can
+otherwise trigger an HTTP Error 414 Request URI too long.
+
+Foreign keys are now used when creating BioSQL databases with SQLite3 (this
+was not possible until SQLite version 3.6.19). The BioSQL taxonomy code now
+updates the taxon table left/right keys when updating the taxonomy.
+
+There have been some fixes to the MMCIF structure parser which now uses
+identifiers which better match results from the PDB structure parse.
+
+The restriction enzyme list in Bio.Restriction has been updated to the
+May 2016 release of REBASE.
+
+The mmCIF parser in Bio.PDB.MMCIFParser has been joined by a second version
+which only looks at the ATOM and HETATM lines and can be much faster.
+
+The Bio.KEGG.REST will now return unicode text-based handles, except for
+images which remain as binary bytes-based handles, making it easier to use
+with the mostly text-based parsers in Biopython.
+
+Note that the BioSQL test configuration information is now in a new file
+Tests/biosql.ini rather than directly in Tests/test_BioSQL_*.py as before.
+You can make a copy of the provided example file Tests/biosql.ini.sample
+as Tests/biosql.ini and edit this if you wish to run the BioSQL tests.
+
+Additionally, a number of small bugs have been fixed with further additions
+to the test suite, and there has been further work to follow the Python PEP8
+standard coding style, and in converting our docstring documentation to use
+the reStructuredText markup style.
+
+Many thanks to the Biopython developers and community for making this release
+possible, especially the following contributors:
+
+Aaron Rosenfeld (first contribution)
+Anders Pitman (first contribution)
+Barbara Mühlemann (first contribution)
+Ben Fulton
+Ben Woodcroft (first contribution)
+Brandon Invergo
+Brian Osborne (first contribution)
+Carlos Pena
+Chaitanya Gupta (first contribution)
+Chris Warth (first contribution)
+Christiam Camacho (first contribution)
+Connor T. Skennerton
+David Koppstein (first contribution)
+Eric Talevich
+Jacek Śmietański (first contribution)
+João D Ferreira (first contribution)
+João Rodrigues
+Joe Cora (first contribution)
+Kai Blin
+Leighton Pritchard
+Lenna Peterson
+Marco Galardini (first contribution)
+Markus Piotrowski
+Matt Ruffalo (first contribution)
+Matteo Sticco (first contribution)
+Nader Morshed (first contribution)
+Owen Solberg (first contribution)
+Peter Cock
+Steve Bond (first contribution)
+Terry Jones (first contribution)
+Vincent Davis
+Zheng Ruan
+
+===================================================================
+
21 October 2015: Biopython 1.66
+This release of Biopython supports Python 2.6, 2.7, 3.3, 3.4 and 3.5, but
+support for Python 2.6 is considered to be deprecated. It has also been
+tested on PyPy 2.4 to 2.6, PyPy3 version 2.4, and Jython 2.7.
+
Further work on the Bio.KEGG and Bio.Graphics modules now allows drawing KGML
pathways with transparency.
@@ -71,6 +175,7 @@ Leighton Pritchard
Lucas Sinclair
Michiel de Hoon
Peter Cock
+Saket Choudhary
Sunhwan Jo (first contribution)
Tarcisio Fedrizzi (first contribution)
Tiago Antao
@@ -404,6 +509,10 @@ The Bio.Motif module has been updated and reorganized. To allow for a clean
deprecation of the old code, the new motif code is stored in a new module
Bio.motifs, and a PendingDeprecationWarning was added to Bio.Motif.
+A faster low level string FASTA based parser SimpleFastaParser has been added
+to Bio.SeqIO.FastaIO which like its sister function for FASTQ files does not
+have the overhead of constructing SeqRecord objects.
+
Additionally there have been other minor bug fixes and more unit tests.
Finally, we are phasing out support for Python 2.5. We will continue support
diff --git a/PKG-INFO b/PKG-INFO
deleted file mode 100644
index 2e8ddf5..0000000
--- a/PKG-INFO
+++ /dev/null
@@ -1,11 +0,0 @@
-Metadata-Version: 1.1
-Name: biopython
-Version: 1.66
-Summary: Freely available tools for computational molecular biology.
-Home-page: http://www.biopython.org/
-Author: The Biopython Contributors
-Author-email: biopython at biopython.org
-License: UNKNOWN
-Download-URL: http://biopython.org/DIST/
-Description: UNKNOWN
-Platform: UNKNOWN
diff --git a/README b/README
index 69e9b6d..133390d 100644
--- a/README
+++ b/README
@@ -30,15 +30,23 @@ http://dx.doi.org/10.1093/bioinformatics/btp163 pmid:19304878
For the impatient
=================
-To build and install Biopython, download and unzip the source code, go to this
-directory at the command line, and type::
+Windows users are currently recommended to use the installation packages provided
+on our website, http://biopython.org -- further instructions are given below.
+
+Python 2.7.9 onwards, and Python 3.4 onwards, include the package management
+system "pip" which should allow you to install Biopython with just::
+
+ pip install numpy
+ pip install biopython
+
+Otherwise you may have to build and install Biopython. Download and unzip the
+source code, go to this directory at the command line, and type::
python setup.py build
python setup.py test
sudo python setup.py install
-Windows users are instead recommended to use the installation packages provided
-on our website, http://biopython.org -- further instructions are given below.
+Here you can replace ``python`` with a specific version, e.g. ``python3.5``.
Python Requirements
@@ -53,7 +61,7 @@ implementations:
This is the primary development platform for Biopython.
-- PyPy 2.4, 2.5, 2.6 and also PyPy3 2.4 -- see http://www.pypy.org
+- PyPy 5.0.1 and also PyPy3 2.4 -- see http://www.pypy.org
Aside from modules with C code or dependent on NumPy, everything should
work. PyPy's NumPy reimplementation NumPyPy is still in progress.
@@ -64,7 +72,8 @@ implementations:
or dependent on SQLite3 or NumPy, everything should work. There are some
known issues with test failures which have not yet been resolved.
-Please note that support for Python 2.6 is deprecated as of Biopython 1.66.
+Please note that support for Python 3.3 is deprecated as of Biopython 1.67,
+and that support for Python 2.6 is deprecated as of Biopython 1.66.
Biopython 1.62 was our final release to support Python 2.5 and Jython 2.5.
diff --git a/README.rst b/README.rst
new file mode 120000
index 0000000..100b938
--- /dev/null
+++ b/README.rst
@@ -0,0 +1 @@
+README
\ No newline at end of file
diff --git a/Scripts/GenBank/check_output.py b/Scripts/GenBank/check_output.py
index 9431d56..18a9b23 100644
--- a/Scripts/GenBank/check_output.py
+++ b/Scripts/GenBank/check_output.py
@@ -16,9 +16,9 @@ import os
import gzip
try:
- from StringIO import StringIO # Python 2
+ from StringIO import StringIO # Python 2
except ImportError:
- from io import StringIO # Python 3
+ from io import StringIO # Python 3
# biopython
from Bio import GenBank
diff --git a/Scripts/GenBank/check_output_simple.py b/Scripts/GenBank/check_output_simple.py
index ff009aa..c4153a6 100644
--- a/Scripts/GenBank/check_output_simple.py
+++ b/Scripts/GenBank/check_output_simple.py
@@ -17,38 +17,37 @@ import sys
# GenBank stuff to test
from Bio import GenBank
+
if len(sys.argv) != 2:
print("Usage ./check_output.py <GenBank file to parse>")
sys.exit()
parser = GenBank.FeatureParser(debug_level=2)
-handle = open(sys.argv[1], 'r')
-
-iterator = GenBank.Iterator(handle, parser)
-
-while True:
- cur_record = next(iterator)
-
- if not(cur_record):
- break
-
- print("***Record")
- print("Seq: %s" % cur_record.seq)
- print("Id: %s" % cur_record.id)
- print("Name: %s" % cur_record.name)
- print("Description: %s" % cur_record.description)
- print("Annotations****")
- for annotation_key in cur_record.annotations:
- if annotation_key != 'references':
- print("Key: %s" % annotation_key)
- print("Value: %s" % cur_record.annotations[annotation_key])
- else:
- print("References*")
- for reference in cur_record.annotations[annotation_key]:
- print(str(reference))
- print("Feaures")
- for feature in cur_record.features:
- print(feature)
-
-handle.close()
+with open(sys.argv[1], 'r') as handle:
+
+ iterator = GenBank.Iterator(handle, parser)
+
+ while True:
+ cur_record = next(iterator)
+
+ if not cur_record:
+ break
+
+ print("***Record")
+ print("Seq: %s" % cur_record.seq)
+ print("Id: %s" % cur_record.id)
+ print("Name: %s" % cur_record.name)
+ print("Description: %s" % cur_record.description)
+ print("Annotations****")
+ for annotation_key in cur_record.annotations:
+ if annotation_key != 'references':
+ print("Key: %s" % annotation_key)
+ print("Value: %s" % cur_record.annotations[annotation_key])
+ else:
+ print("References*")
+ for reference in cur_record.annotations[annotation_key]:
+ print(str(reference))
+ print("Feaures")
+ for feature in cur_record.features:
+ print(feature)
diff --git a/Scripts/Performance/biosql_performance_load.py b/Scripts/Performance/biosql_performance_load.py
index 663342d..464f2a6 100644
--- a/Scripts/Performance/biosql_performance_load.py
+++ b/Scripts/Performance/biosql_performance_load.py
@@ -7,6 +7,9 @@ import time
# set up the connection
from Bio import GenBank
from BioSQL import BioSeqDatabase
+
+__docformat__ = "restructuredtext en"
+
server = BioSeqDatabase.open_database(host="192.168.0.192", user="root",
passwd="", db="pythonloadtest")
@@ -30,5 +33,5 @@ num_records = db.load(iterator)
end_time = time.time()
elapsed_time = end_time - start_time
print("Loading")
-print("\tDid %s records in %s seconds for\n\t%f records per second" % \
+print("\tDid %s records in %s seconds for\n\t%f records per second" %
(num_records, elapsed_time, float(num_records) / float(elapsed_time)))
diff --git a/Scripts/Performance/biosql_performance_read.py b/Scripts/Performance/biosql_performance_read.py
index b5c89d3..db4668b 100644
--- a/Scripts/Performance/biosql_performance_read.py
+++ b/Scripts/Performance/biosql_performance_read.py
@@ -6,6 +6,9 @@ from __future__ import print_function
import time
# set up the connection
from BioSQL import BioSeqDatabase
+
+__docformat__ = "restructuredtext en"
+
server = BioSeqDatabase.open_database(host="192.168.0.192", user="root",
passwd="", db="test_biosql")
db = server["embl_rod"]
@@ -23,7 +26,7 @@ for junk_id, record in db.items():
end_time = time.time()
elapsed_time = end_time - start_time
print("Fasta")
-print("\tDid %s records in %s seconds for\n\t%f records per second" % \
+print("\tDid %s records in %s seconds for\n\t%f records per second" %
(num_records, elapsed_time, float(num_records) / float(elapsed_time)))
# -- do the "EMBL" timing part
@@ -43,5 +46,5 @@ for junk_id, record in db.items():
end_time = time.time()
elapsed_time = end_time - start_time
print("EMBL")
-print("\tDid %s records in %s seconds for\n\t%f records per second" % \
+print("\tDid %s records in %s seconds for\n\t%f records per second" %
(num_records, elapsed_time, float(num_records) / float(elapsed_time)))
diff --git a/Scripts/Restriction/ranacompiler.py b/Scripts/Restriction/ranacompiler.py
index 8893497..b380ea6 100644
--- a/Scripts/Restriction/ranacompiler.py
+++ b/Scripts/Restriction/ranacompiler.py
@@ -6,25 +6,20 @@
# This code is part of the Biopython distribution and governed by its
# license. Please see the LICENSE file that should have been included
# as part of this package.
-#
-# this script is used to produce the dictionary which will contains the data
-# about the restriction enzymes from the Emboss/Rebase data files
-# namely
+# This script is used to produce the dictionary which will contains the data
+# about the restriction enzymes from the Emboss/Rebase data files, namely:
# emboss_e.### (description of the sites),
# emboss_r.### (origin, methylation, references)
# emboss_s.### (suppliers)
-# where ### is a number of three digits : 1 for the year two for the month
-#
-# very dirty implementation but it does the job, so...
-# Not very quick either but you are not supposed to use it frequently.
-#
+# Where ### is a number of three digits : 1 for the year two for the month
# The results are stored in
# path/to/site-packages/Bio/Restriction/Restriction_Dictionary.py
# the file contains two dictionary:
# 'rest_dict' which contains the data for the enzymes
# and
# 'suppliers' which map the name of the suppliers to their abbreviation.
-#
+# very dirty implementation but it does the job, so...
+# Not very quick either but you are not supposed to use it frequently.
"""Convert a series of Rebase files into a Restriction_Dictionary.py module.
@@ -43,8 +38,8 @@ should allow automated fetching (the the update code and RanaConfig.py).
In addition there are links on this HTML page which requires manual download
and renaming of the files: http://rebase.neb.com/rebase/rebase.f37.html
-This Python file is intended to be used via the scripts Scripts/Restriction/*.py
-only.
+This Python file is intended to be used via the scripts in
+Scripts/Restriction/*.py only.
"""
from __future__ import print_function
@@ -61,33 +56,23 @@ from functools import reduce
from Bio.Seq import Seq
from Bio.Alphabet import generic_dna
+from Bio.Data.IUPACData import ambiguous_dna_values as amb_dna
import Bio.Restriction.Restriction
-from Bio.Restriction.Restriction import AbstractCut, RestrictionType, NoCut, OneCut
-from Bio.Restriction.Restriction import TwoCuts, Meth_Dep, Meth_Undep, Palindromic
-from Bio.Restriction.Restriction import NonPalindromic, Unknown, Blunt, Ov5, Ov3
+from Bio.Restriction.Restriction import AbstractCut, RestrictionType, NoCut
+from Bio.Restriction.Restriction import OneCut, TwoCuts, Meth_Dep, Meth_Undep
+from Bio.Restriction.Restriction import Palindromic, NonPalindromic, Unknown
+from Bio.Restriction.Restriction import Blunt, Ov5, Ov3
from Bio.Restriction.Restriction import NotDefined, Defined, Ambiguous
from Bio.Restriction.Restriction import Commercially_available, Not_available
import Bio.Restriction.RanaConfig as config
-from Bio.Restriction._Update.Update import RebaseUpdate
+from rebase_update import RebaseUpdate
from Bio.Restriction.Restriction import *
-dna_alphabet = {'A':'A', 'C':'C', 'G':'G', 'T':'T',
- 'R':'AG', 'Y':'CT', 'W':'AT', 'S':'CG', 'M':'AC', 'K':'GT',
- 'H':'ACT', 'B':'CGT', 'V':'ACG', 'D':'AGT',
- 'N':'ACGT',
- 'a': 'a', 'c': 'c', 'g': 'g', 't': 't',
- 'r':'ag', 'y':'ct', 'w':'at', 's':'cg', 'm':'ac', 'k':'gt',
- 'h':'act', 'b':'cgt', 'v':'acg', 'd':'agt',
- 'n':'acgt'}
-
-
-complement_alphabet = {'A':'T', 'T':'A', 'C':'G', 'G':'C','R':'Y', 'Y':'R',
- 'W':'W', 'S':'S', 'M':'K', 'K':'M', 'H':'D', 'D':'H',
- 'B':'V', 'V':'B', 'N':'N','a':'t', 'c':'g', 'g':'c',
- 't':'a', 'r':'y', 'y':'r', 'w':'w', 's':'s','m':'k',
- 'k':'m', 'h':'d', 'd':'h', 'b':'v', 'v':'b', 'n':'n'}
+__docformat__ = "restructuredtext en"
+
+
enzymedict = {}
suppliersdict = {}
classdict = {}
@@ -99,25 +84,13 @@ class OverhangError(ValueError):
pass
-def BaseExpand(base):
- """BaseExpand(base) -> string.
-
- given a degenerated base, returns its meaning in IUPAC alphabet.
-
- i.e:
- b= 'A' -> 'A'
- b= 'N' -> 'ACGT'
- etc..."""
- base = base.upper()
- return dna_alphabet[base]
-
-
def regex(site):
"""regex(site) -> string.
Construct a regular expression from a DNA sequence.
i.e.:
- site = 'ABCGN' -> 'A[CGT]CG.'"""
+ site = 'ABCGN' -> 'A[CGT]CG.'
+ """
reg_ex = str(site)
for base in reg_ex:
if base in ('A', 'T', 'C', 'G', 'a', 'c', 'g', 't'):
@@ -126,7 +99,7 @@ def regex(site):
reg_ex = '.'.join(reg_ex.split('N'))
reg_ex = '.'.join(reg_ex.split('n'))
if base in ('R', 'Y', 'W', 'M', 'S', 'K', 'H', 'D', 'B', 'V'):
- expand = '['+ str(BaseExpand(base))+']'
+ expand = '[' + amb_dna[base.upper()] + ']'
reg_ex = expand.join(reg_ex.split(base))
return reg_ex
@@ -135,7 +108,8 @@ def is_palindrom(sequence):
"""is_palindrom(sequence) -> bool.
True is the sequence is a palindrom.
- sequence is a Seq object."""
+ sequence is a Seq object.
+ """
return str(sequence) == str(sequence.reverse_complement())
@@ -143,13 +117,14 @@ def LocalTime():
"""LocalTime() -> string.
LocalTime calculate the extension for emboss file for the current year and
- month."""
+ month.
+ """
t = time.gmtime()
year = str(t.tm_year)[-1]
month = str(t.tm_mon)
if len(month) == 1:
month = '0' + month
- return year+month
+ return year + month
class newenzyme(object):
@@ -325,7 +300,7 @@ class TypeCompiler(object):
types = [(p, c, o, d, m, co, baT[0], baT[1])
for p in paT for c in cuT for o in ovT
for d in deT for m in meT for co in coT]
- n= 1
+ n = 1
for ty in types:
dct = {}
for t in ty:
@@ -333,8 +308,9 @@ class TypeCompiler(object):
#
# here we need to customize the dictionary.
# i.e. types deriving from OneCut have always scd5 and scd3
- # equal to None. No need therefore to store that in a specific
- # enzyme of this type. but it then need to be in the type.
+ # equal to None. No need therefore to store that in a
+ # specific enzyme of this type. but it then need to be in the
+ # type.
#
dct['results'] = []
dct['substrat'] = 'DNA'
@@ -348,55 +324,55 @@ class TypeCompiler(object):
class klass(type):
def __new__(cls):
- return type.__new__(cls, 'type%i'%n, ty, dct)
+ return type.__new__(cls, 'type%i' % n, ty, dct)
def __init__(cls):
- super(klass, cls).__init__('type%i'%n, ty, dct)
+ super(klass, cls).__init__('type%i' % n, ty, dct)
yield klass()
- n+=1
+ n += 1
-start = '\n\
-#!/usr/bin/env python\n\
-#\n\
-# Restriction Analysis Libraries.\n\
-# Copyright (C) 2004. Frederic Sohm.\n\
-#\n\
-# This code is part of the Biopython distribution and governed by its\n\
-# license. Please see the LICENSE file that should have been included\n\
+start = """#!/usr/bin/env python
+#
+# Restriction Analysis Libraries.
+# Copyright (C) 2004. Frederic Sohm.
+#
+# This code is part of the Biopython distribution and governed by its
+# license. Please see the LICENSE file that should have been included
# as part of this package.\n\
#\n\
-# This file is automatically generated - do not edit it by hand! Instead,\n\
-# use the tool Scripts/Restriction/ranacompiler.py which in turn uses\n\
-# Bio/Restriction/_Update/RestrictionCompiler.py\n\
+# This file is automatically generated - do not edit it by hand! Instead,
+# use the tool Scripts/Restriction/ranacompiler.py which in turn uses
+# Bio/Restriction/_Update/RestrictionCompiler.py
#\n\
-# The following dictionaries used to be defined in one go, but that does\n\
-# not work on Jython due to JVM limitations. Therefore we break this up\n\
-# into steps, using temporary functions to avoid the JVM limits.\n\
-\n\n'
+# The following dictionaries used to be defined in one go, but that does
+# not work on Jython due to JVM limitations. Therefore we break this up
+# into steps, using temporary functions to avoid the JVM limits.
+#
+# Used REBASE emboss files version {} ({}).
+
+""".format(LocalTime(), time.gmtime().tm_year)
class DictionaryBuilder(object):
- def __init__(self, e_mail='', ftp_proxy=''):
- """DictionaryBuilder([e_mail[, ftp_proxy]) -> DictionaryBuilder instance.
+ def __init__(self, ftp_proxy=''):
+ """DictionaryBuilder([ftp_proxy])->DictionaryBuilder instance.
If the emboss files used for the construction need to be updated this
class will download them if the ftp connection is correctly set.
either in RanaConfig.py or given at run time.
- e_mail is the e-mail address used as password for the anonymous
- ftp connection.
-
- proxy is the ftp_proxy to use if any."""
- self.rebase_pass = e_mail or config.Rebase_password
+ proxy is the ftp_proxy to use if any.
+ """
self.proxy = ftp_proxy or config.ftp_proxy
def build_dict(self):
"""DB.build_dict() -> None.
Construct the dictionary and build the files containing the new
- dictionaries."""
+ dictionaries.
+ """
#
# first parse the emboss files.
#
@@ -413,8 +389,8 @@ class DictionaryBuilder(object):
#
tdct = {}
for klass in TypeCompiler().buildtype():
- exec(klass.__name__ +'= klass')
- exec("tdct['"+klass.__name__+"'] = klass")
+ exec(klass.__name__ + '= klass')
+ exec("tdct['" + klass.__name__ + "'] = klass")
#
# Now we build the enzymes from enzymedict
@@ -448,7 +424,7 @@ class DictionaryBuilder(object):
dct = dict(cls.__dict__)
del dct['bases']
del dct['__bases__']
- del dct['__name__']# no need to keep that, it's already in the type.
+ del dct['__name__'] # no need to keep, it's already in the type.
classdict[name] = dct
commonattr = ['fst5', 'fst3', 'scd5', 'scd3', 'substrat',
@@ -456,9 +432,10 @@ class DictionaryBuilder(object):
if typename in typedict:
typedict[typename][1].append(name)
else:
- enzlst= []
+ enzlst = []
tydct = dict(typestuff.__dict__)
- tydct = dict([(k, v) for k, v in tydct.items() if k in commonattr])
+ tydct = dict([(k, v) for k, v in tydct.items()
+ if k in commonattr])
enzlst.append(name)
typedict[typename] = (bases, enzlst)
for letter in cls.__dict__['suppl']:
@@ -479,15 +456,18 @@ class DictionaryBuilder(object):
# update = config.updatefolder
update = os.getcwd()
- with open(os.path.join(update, 'Restriction_Dictionary.py'), 'w') as results:
- print('Writing the dictionary containing the new Restriction classes...')
+ with open(os.path.join(update, 'Restriction_Dictionary.py'),
+ 'w') as results:
+ print('Writing the dictionary containing the new Restriction ' +
+ 'classes...')
results.write(start)
results.write('rest_dict = {}\n')
for name in sorted(classdict):
results.write("def _temp():\n")
results.write(" return {\n")
for key, value in classdict[name].items():
- results.write(" %s: %s,\n" % (repr(key), repr(value)))
+ results.write(" %s: %s,\n" %
+ (repr(key), repr(value)))
results.write(" }\n")
results.write("rest_dict[%s] = _temp()\n" % repr(name))
results.write("\n")
@@ -513,9 +493,9 @@ class DictionaryBuilder(object):
results.write(" )\n")
results.write("typedict[%s] = _temp()\n" % repr(name))
results.write("\n")
- # I had wanted to do "del _temp" at each stage (just for clarity), but
- # that pushed the code size just over the Jython JVM limit. We include
- # one the final "del _temp" to clean up the namespace.
+ # I had wanted to do "del _temp" at each stage (just for clarity),
+ # but that pushed the code size just over the Jython JVM limit. We
+ # include one the final "del _temp" to clean up the namespace.
results.write("del _temp\n")
results.write("\n")
print('OK.\n')
@@ -526,8 +506,9 @@ class DictionaryBuilder(object):
Install the newly created dictionary in the site-packages folder.
- May need super user privilege on some architectures."""
- print('\n ' +'*'*78 + ' \n')
+ May need super user privilege on some architectures.
+ """
+ print('\n ' + '*' * 78 + ' \n')
print('\n\t\tInstalling Restriction_Dictionary.py')
try:
import Bio.Restriction.Restriction_Dictionary as rd
@@ -554,17 +535,17 @@ class DictionaryBuilder(object):
print('\
\n\tThe new file seems ok. Proceeding with the installation.')
except SyntaxError:
- print('\
- \n The new dictionary file is corrupted. Aborting the installation.')
+ print('\n The new dictionary file is corrupted. Aborting the ' +
+ 'installation.')
return
try:
shutil.copyfile(new, old)
print('\n\t Everything ok. If you need it a version of the old\
\n\t dictionary have been saved in the Updates folder under\
\n\t the name Restriction_Dictionary.old.')
- print('\n ' +'*'*78 + ' \n')
+ print('\n ' + '*' * 78 + ' \n')
except IOError:
- print('\n ' +'*'*78 + ' \n')
+ print('\n ' + '*' * 78 + ' \n')
print('\
\n\t WARNING : Impossible to install the new dictionary.\
\n\t Are you sure you have write permission to the folder :\n\
@@ -575,8 +556,9 @@ class DictionaryBuilder(object):
def no_install(self):
"""BD.no_install() -> None.
- build the new dictionary but do not install the dictionary."""
- print('\n ' +'*'*78 + '\n')
+ build the new dictionary but do not install the dictionary.
+ """
+ print('\n ' + '*' * 78 + '\n')
# update = config.updatefolder
try:
import Bio.Restriction.Restriction_Dictionary as rd
@@ -591,7 +573,8 @@ class DictionaryBuilder(object):
old = os.path.join(os.path.split(rd.__file__)[0],
'Restriction_Dictionary.py')
update = os.getcwd()
- shutil.copyfile(old, os.path.join(update, 'Restriction_Dictionary.old'))
+ shutil.copyfile(old, os.path.join(update,
+ 'Restriction_Dictionary.old'))
places = update, os.path.split(Bio.Restriction.Restriction.__file__)[0]
print("\t\tCompilation of the new dictionary : OK.\
\n\t\tInstallation : No.\n\
@@ -603,13 +586,13 @@ class DictionaryBuilder(object):
\n note : \
\n This folder should be :\n\
\n\t%s\n" % places)
- print('\n ' +'*'*78 + '\n')
+ print('\n ' + '*' * 78 + '\n')
return
def lastrebasefile(self):
"""BD.lastrebasefile() -> None.
- Check the emboss files are up to date and download them if they are not.
+ Check the emboss files are up to date and download them if not.
"""
embossnames = ('emboss_e', 'emboss_r', 'emboss_s')
#
@@ -619,7 +602,7 @@ class DictionaryBuilder(object):
update_needed = False
# dircontent = os.listdir(config.Rebase) # local database content
dircontent = os.listdir(os.getcwd())
- base = os.getcwd() # added for biopython current directory
+ base = os.getcwd() # added for biopython current directory
for name in emboss_now:
if name in dircontent:
pass
@@ -630,7 +613,7 @@ class DictionaryBuilder(object):
#
# nothing to be done
#
- print('\n Using the files : %s'% ', '.join(emboss_now))
+ print('\n Using the files : %s' % ', '.join(emboss_now))
return tuple(open(os.path.join(base, n)) for n in emboss_now)
else:
#
@@ -640,12 +623,12 @@ class DictionaryBuilder(object):
\n Would you like to update them before proceeding?(y/n)')
r = _input(' update [n] >>> ')
if r in ['y', 'yes', 'Y', 'Yes']:
- updt = RebaseUpdate(self.rebase_pass, self.proxy)
+ updt = RebaseUpdate(self.proxy)
updt.openRebase()
updt.getfiles()
updt.close()
print('\n Update complete. Creating the dictionaries.\n')
- print('\n Using the files : %s'% ', '.join(emboss_now))
+ print('\n Using the files : %s' % ', '.join(emboss_now))
return tuple(open(os.path.join(base, n)) for n in emboss_now)
else:
#
@@ -663,7 +646,8 @@ class DictionaryBuilder(object):
pass
raise NotFoundError
except NotFoundError:
- print("\nNo %s file found. Upgrade is impossible.\n"%name)
+ print("\nNo %s file found. Upgrade is impossible.\n" %
+ name)
sys.exit()
continue
pass
@@ -689,24 +673,28 @@ class DictionaryBuilder(object):
last[0], last[-1] = last[-1], last[0]
for number in last:
- files = [(name, name+'.%s'%number) for name in embossnames]
+ files = [(name, name + '.%s' % number) for name in embossnames]
strmess = '\nLast EMBOSS files found are :\n'
try:
for name, file in files:
if os.path.isfile(os.path.join(base, file)):
- strmess += '\t%s.\n'%file
+ strmess += '\t%s.\n' % file
else:
raise ValueError
print(strmess)
- emboss_e = open(os.path.join(base, 'emboss_e.%s'%number), 'r')
- emboss_r = open(os.path.join(base, 'emboss_r.%s'%number), 'r')
- emboss_s = open(os.path.join(base, 'emboss_s.%s'%number), 'r')
+ emboss_e = open(os.path.join(base, 'emboss_e.%s' % number),
+ 'r')
+ emboss_r = open(os.path.join(base, 'emboss_r.%s' % number),
+ 'r')
+ emboss_s = open(os.path.join(base, 'emboss_s.%s' % number),
+ 'r')
return emboss_e, emboss_r, emboss_s
except ValueError:
continue
def parseline(self, line):
- line = [line[0]]+[line[1].upper()]+[int(i) for i in line[2:9]]+line[9:]
+ line = [line[0]] + [line[1].upper()] + [int(i) for i in line[2:9]] + \
+ line[9:]
name = line[0].replace("-", "_").replace(".", "_")
site = line[1] # sequence of the recognition site
dna = Seq(site, generic_dna)
@@ -753,7 +741,8 @@ class DictionaryBuilder(object):
print('\
\nWARNING : %s cut twice with different overhang length each time.\
\n\tUnable to deal with this behaviour. \
- \n\tThis enzyme will not be included in the database. Sorry.' %name)
+ \n\tThis enzyme will not be included in the database. Sorry.' %
+ name)
print('\tChecking...')
raise OverhangError
if 0 <= fst5 <= size and 0 <= fst3 <= size:
@@ -830,12 +819,12 @@ class DictionaryBuilder(object):
#
# 3' overhang. site is included.
#
- ovhgseq = abs(fst3)*'N' + site + (fst5-size)*'N'
- elif fst5 < 0 and size <fst3:
+ ovhgseq = abs(fst3) * 'N' + site + (fst5 - size) * 'N'
+ elif fst5 < 0 and size < fst3:
#
# 5' overhang. site is included.
#
- ovhgseq = abs(fst5)*'N' + site + (fst3-size)*'N'
+ ovhgseq = abs(fst5) * 'N' + site + (fst3 - size) * 'N'
else:
#
# 5' and 3' outside of the site
@@ -888,8 +877,8 @@ class DictionaryBuilder(object):
#
# exact frequency of the site. (ie freq(N) == 1, ...)
#
- f = [4/len(dna_alphabet[l]) for l in site.upper()]
- freq = reduce(lambda x, y: x*y, f)
+ f = [4 / len(amb_dna[l]) for l in site.upper()]
+ freq = reduce(lambda x, y: x * y, f)
line.append(freq)
#
# append regex and ovhg1, they have not been appended before not to
@@ -904,15 +893,17 @@ class DictionaryBuilder(object):
#
# remove the heading of the file.
#
- return [l for l in itertools.dropwhile(lambda l:l.startswith('#'), file)]
+ return [l for l in itertools.dropwhile(lambda l:l.startswith('#'),
+ file)]
def getblock(self, file, index):
#
# emboss_r.txt, separation between blocks is //
#
take = itertools.takewhile
- block = [l for l in take(lambda l: not l.startswith('//'), file[index:])]
- index += len(block)+1
+ block = [l for l in take(lambda l: not l.startswith('//'),
+ file[index:])]
+ index += len(block) + 1
return block, index
def get(self, block):
@@ -937,7 +928,7 @@ class DictionaryBuilder(object):
sitefile = self.removestart(file2)
supplier = self.removestart(file3)
- i1, i2= 0, 0
+ i1, i2 = 0, 0
try:
while True:
block, i1 = self.getblock(methfile, i1)
@@ -962,13 +953,16 @@ class DictionaryBuilder(object):
except OverhangError: # overhang error
n = name # do not include the enzyme
if not bl[2]:
- print('Anyway, %s is not commercially available.\n' %n)
+ print('Anyway, %s is not commercially available.\n' %
+ n)
else:
- print('Unfortunately, %s is commercially available.\n'%n)
+ print('Unfortunately, %s is commercially ' % n +
+ ' available.\n')
continue
# Hyphens and dots can't be used as a Python name, nor as a
- # group name in a regular expression. e.g. 'CviKI-1', 'R2.BceSIV'
+ # group name in a regular expression. e.g. 'CviKI-1',
+ # 'R2.BceSIV'
name = name.replace("-", "_").replace(".", "_")
if name in enzymedict:
#
@@ -982,8 +976,9 @@ class DictionaryBuilder(object):
dna = Seq(enzymedict[other][0], generic_dna)
sense2 = regex(dna)
antisense2 = regex(dna.reverse_complement())
- sense = '(?P<'+other+'>'+sense1+'|'+sense2+')'
- antisense = '(?P<'+other+'_as>'+antisense1+'|'+antisense2 + ')'
+ sense = '(?P<' + other + '>' + sense1 + '|' + sense2 + ')'
+ antisense = '(?P<' + other + '_as>' + antisense1 + '|' + \
+ antisense2 + ')'
reg = sense + '|' + antisense
line[1] = line[1] + '|' + enzymedict[other][0]
line[-1] = reg
@@ -1014,12 +1009,6 @@ def standalone():
help="compile and install the newly created file. "
"default behaviour (without switch): "
"Compile the enzymes and store them in the Updates folder")
- add('-m', '--e-mail',
- action="store",
- dest='rebase_password',
- default='',
- help="set the e-mail address to be used as password for the"
- "anonymous ftp connection to Rebase.")
add('-p', '--proxy',
action="store",
dest='ftp_proxy',
@@ -1030,7 +1019,7 @@ def standalone():
if __name__ == '__main__':
options, args = standalone()
- Builder = DictionaryBuilder(options.rebase_password, options.ftp_proxy)
+ Builder = DictionaryBuilder(options.ftp_proxy)
Builder.build_dict()
if options.i:
Builder.install_dict()
diff --git a/Scripts/Restriction/rebase_update.py b/Scripts/Restriction/rebase_update.py
index e147f35..510315d 100644
--- a/Scripts/Restriction/rebase_update.py
+++ b/Scripts/Restriction/rebase_update.py
@@ -18,6 +18,7 @@ import sys
import time
import optparse
+
try:
from urllib import FancyURLopener
except ImportError:
@@ -29,26 +30,21 @@ from Bio.Restriction.RanaConfig import *
class RebaseUpdate(FancyURLopener):
- def __init__(self, e_mail='', ftpproxy=''):
- """RebaseUpdate([e_mail[, ftpproxy]]) -> new RebaseUpdate instance.
+ def __init__(self, ftpproxy=''):
+ """RebaseUpdate([ftpproxy]]) -> new RebaseUpdate instance.
- if e_mail and ftpproxy are not given RebaseUpdate uses the corresponding
+ if ftpproxy is not given RebaseUpdate uses the corresponding
variable from RanaConfig.
- e_mail is the password for the anonymous ftp connection to Rebase.
- ftpproxy is the proxy to use if any."""
+ ftpproxy is the proxy to use if any.
+ """
proxy = {'ftp': ftpproxy or ftp_proxy}
- global Rebase_password
- Rebase_password = e_mail or Rebase_password
- if not Rebase_password:
- raise FtpPasswordError('Rebase')
if not Rebase_name:
raise FtpNameError('Rebase')
+ if not proxy['ftp']:
+ proxy = {}
FancyURLopener.__init__(self, proxy)
- def prompt_user_passwd(self, host, realm):
- return (Rebase_name, Rebase_password)
-
def openRebase(self, name=ftp_Rebase):
print('\n Please wait, trying to connect to Rebase\n')
try:
@@ -74,7 +70,7 @@ class RebaseUpdate(FancyURLopener):
month = str(t.tm_mon)
if len(month) == 1:
month = '0' + month
- return year+month
+ return year + month
def update(self, *files):
if not files:
@@ -98,16 +94,6 @@ class FtpNameError(ValueError):
sys.exit()
-class FtpPasswordError(ValueError):
-
- def __init__(self, which_server):
- print("\n\
- \n In order to connect to %s ftp server, you must provide a password.\
- \n Use the --e-mail switch to enter your e-mail address.\
- \n\n" % which_server)
- sys.exit()
-
-
class ConnectionError(IOError):
def __init__(self, which_server):
@@ -124,12 +110,6 @@ if __name__ == '__main__':
parser = optparse.OptionParser()
add = parser.add_option
- add('-m', '--e-mail',
- action="store",
- dest='rebase_password',
- default='',
- help="set the e-mail address to be used as password for the"
- "anonymous ftp connection to Rebase.")
add('-p', '--proxy',
action="store",
dest='ftp_proxy',
@@ -138,7 +118,7 @@ if __name__ == '__main__':
(option, args) = parser.parse_args()
- Getfiles = RebaseUpdate(option.rebase_password, option.ftp_proxy)
+ Getfiles = RebaseUpdate(option.ftp_proxy)
Getfiles.openRebase()
Getfiles.getfiles()
Getfiles.close()
diff --git a/Scripts/SeqGui/SeqGui.py b/Scripts/SeqGui/SeqGui.py
index 35718bd..a054971 100644
--- a/Scripts/SeqGui/SeqGui.py
+++ b/Scripts/SeqGui/SeqGui.py
@@ -8,6 +8,7 @@ from __future__ import print_function
from Bio.Seq import translate, transcribe, back_transcribe
import wx
+
ID_APPLY = 101
ID_CLEAR = 102
ID_EXIT = 103
diff --git a/Scripts/Structure/hsexpo b/Scripts/Structure/hsexpo
index 6ac693c..5a1e378 100755
--- a/Scripts/Structure/hsexpo
+++ b/Scripts/Structure/hsexpo
@@ -10,6 +10,9 @@ from optparse import OptionParser
from Bio.PDB import *
import sys
+__docformat__ = "restructuredtext en"
+
+
__doc__ = """
This program calculates solvent exposure for all amino
acids in a PDB file using 5 different methods:
@@ -25,7 +28,7 @@ See --help for all options.
"""
if len(sys.argv) == 1:
- print __doc__
+ print(__doc__)
sys.exit()
# Get the user's options
@@ -102,7 +105,7 @@ elif options.exp == "RDA":
k = 'EXP_RD_CA'
format = "%4.1f"
else:
- print "ERROR: Unknown option."
+ print("ERROR: Unknown option.")
sys.exit()
residue_list = Selection.unfold_entities(m, 'R')
@@ -125,7 +128,7 @@ for r in residue_list:
resname = r.get_resname()
- print (("%s %4i %c\t" + format) % (resname, resseq, icode, exposure))
+ print(("%s %4i %c\t" + format) % (resname, resseq, icode, exposure))
else:
exposure = 0.0
diff --git a/Scripts/debug/debug_blast_parser.py b/Scripts/debug/debug_blast_parser.py
index 87bb8f9..c2d4a8e 100755
--- a/Scripts/debug/debug_blast_parser.py
+++ b/Scripts/debug/debug_blast_parser.py
@@ -16,6 +16,7 @@ import traceback
from Bio import ParserSupport
from Bio.Blast import NCBIStandalone
+
CONTEXT = 5 # show 5 lines of context around the error in the format file
USAGE = """%s [-h] [-v] [-p] [-n] [-o] <testfile>
@@ -37,7 +38,7 @@ OPTIONS:
""" % sys.argv[0]
-class DebuggingConsumer:
+class DebuggingConsumer(object):
def __init__(self, decorated=None):
self.linenum = 0
if decorated is None:
diff --git a/Scripts/xbbtools/nextorf.py b/Scripts/xbbtools/nextorf.py
index 4d9e868..b2faebb 100755
--- a/Scripts/xbbtools/nextorf.py
+++ b/Scripts/xbbtools/nextorf.py
@@ -30,7 +30,7 @@ class ProteinX(Alphabet.ProteinAlphabet):
proteinX = ProteinX()
-class MissingTable:
+class MissingTable(object):
def __init__(self, table):
self._table = table
@@ -50,7 +50,7 @@ def makeTableX(table):
table.stop_codons)
-class NextOrf:
+class NextOrf(object):
def __init__(self, file, options):
self.options = options
self.file = file
@@ -104,9 +104,9 @@ class NextOrf:
d[nt] = [0, 0, 0]
for i in range(0, l, 3):
- codon = seq[i:i+3]
+ codon = seq[i:i + 3]
if len(codon) < 3:
- codon = codon + ' '
+ codon += ' '
for pos in range(0, 3):
for nt in ['A', 'T', 'G', 'C']:
if codon[pos] == nt:
@@ -123,7 +123,7 @@ class NextOrf:
gc[i] = 0
gcall = gcall + d['G'][i] + d['C'][i]
- nall = nall + n
+ nall += n
gcall = 100.0 * gcall / nall
res = '%.1f%%, %.1f%%, %.1f%%, %.1f%%' % (gcall, gc[0], gc[1], gc[2])
@@ -143,7 +143,7 @@ class NextOrf:
for frame in range(0, 3):
coordinates = []
for i in range(0 + frame, n - n % 3, 3):
- codon = s[i:i+3]
+ codon = s[i:i + 3]
if codon in start_codons:
coordinates.append((i + 1, 1, codon))
elif codon in stop_codons:
@@ -160,7 +160,7 @@ class NextOrf:
CDS = []
f = 0
for frame in frame_coordinates:
- f+=1
+ f += 1
start_site = 0
if nostart == '1':
start_site = 1
@@ -180,9 +180,9 @@ class NextOrf:
if nostart == '1' and start_site == 1:
start_site = start_site + f - 1
if codon == 'XXX':
- stop = start_site + 3*((int((stop-1)-start_site)/3))
- s = seq[start_site-1:stop]
- CDS.append((start_site, stop, length, s, strand*f))
+ stop = start_site + 3 * ((int((stop - 1) - start_site) // 3))
+ s = seq[start_site - 1:stop]
+ CDS.append((start_site, stop, length, s, strand * f))
start_site = 0
if nostart == '1':
start_site = stop + 1
@@ -202,7 +202,7 @@ class NextOrf:
if strand > 0:
head = 'orf_%s:%s:%d:%d:%d' % (self.counter, self.header, strand, start, stop)
if strand < 0:
- head = 'orf_%s:%s:%d:%d:%d' % (self.counter, self.header, strand, n-stop+1, n-start+1)
+ head = 'orf_%s:%s:%d:%d:%d' % (self.counter, self.header, strand, n - stop + 1, n - start + 1)
if self.options['gc']:
head = '%s:%s' % (head, self.Gc2(subs.data))
diff --git a/Scripts/xbbtools/xbb_blast.py b/Scripts/xbbtools/xbb_blast.py
index cace2b9..de8d36c 100644
--- a/Scripts/xbbtools/xbb_blast.py
+++ b/Scripts/xbbtools/xbb_blast.py
@@ -12,9 +12,9 @@ import sys
from threading import *
try:
- from Tkinter import * # Python 2
+ from Tkinter import * # Python 2
except ImportError:
- from tkinter import * # Python 3
+ from tkinter import * # Python 3
import Pmw
sys.path.insert(0, '.')
@@ -23,7 +23,7 @@ from xbb_utils import NotePad
import xbb_blastbg
-class BlastIt:
+class BlastIt(object):
def __init__(self, seq, parent=None):
self.seq = seq
self.parent = parent
@@ -115,7 +115,7 @@ class BlastIt:
self.Update()
print(self.command)
- self.pipe = posix.popen(self.command)
+ self.pipe = os.popen(self.command)
while True:
try:
char = self.pipe.read(1)
@@ -150,7 +150,7 @@ class BlastIt:
try:
self.pipe.close()
- del(pipe)
+ del(self.pipe)
except:
pass
self.notepad.destroy()
diff --git a/Scripts/xbbtools/xbb_blastbg.py b/Scripts/xbbtools/xbb_blastbg.py
index d49b72b..35041c8 100644
--- a/Scripts/xbbtools/xbb_blastbg.py
+++ b/Scripts/xbbtools/xbb_blastbg.py
@@ -6,29 +6,27 @@
from __future__ import print_function
-import posix
-import posixpath
import os
import sys
sys.path.insert(0, '.')
try:
- import Queue as queue # Python 2
+ import Queue as queue # Python 2
except ImportError:
- import queue # Python 3
+ import queue # Python 3
import tempfile
import threading
try:
- from Tkinter import * # Python 2
+ from Tkinter import * # Python 2
except ImportError:
- from tkinter import * # Python 3
+ from tkinter import * # Python 3
from xbb_utils import NotePad
-class BlastDisplayer:
+class BlastDisplayer(object):
def __init__(self, command, text_id=None):
self.command = command
self.tid = text_id
@@ -37,8 +35,8 @@ class BlastDisplayer:
self.outfile = tempfile.mktemp()
# make sure outfile exists and is empty
- fid = open(self.outfile, 'w+')
- fid.close()
+ with open(self.outfile, 'w+') as fid:
+ pass
com = '%s > %s' % (self.command, self.outfile)
@@ -48,23 +46,22 @@ class BlastDisplayer:
def UpdateResults(self):
# open the oufile and displays new appended text
- fid = open(self.outfile)
- size = 0
- while True:
- if self.worker.finished:
- break
- fid.seek(size)
- txt = fid.read()
- size = os.stat(self.outfile)[6]
- try:
- self.tid.insert(END, txt)
- self.tid.update()
- except:
- # text widget is detroyed, we assume the search
- # has been cancelled
- break
-
- fid.close()
+ with open(self.outfile) as fid:
+ size = 0
+ while True:
+ if self.worker.finished:
+ break
+ fid.seek(size)
+ txt = fid.read()
+ size = os.stat(self.outfile)[6]
+ try:
+ self.tid.insert(END, txt)
+ self.tid.update()
+ except:
+ # text widget is detroyed, we assume the search
+ # has been cancelled
+ break
+
self.Exit()
def Exit(self):
diff --git a/Scripts/xbbtools/xbb_help.py b/Scripts/xbbtools/xbb_help.py
index a5516bd..4949e62 100644
--- a/Scripts/xbbtools/xbb_help.py
+++ b/Scripts/xbbtools/xbb_help.py
@@ -5,14 +5,14 @@
# File: xbb_help.py
try:
- from Tkinter import * # Python 2
+ from Tkinter import * # Python 2
except ImportError:
- from tkinter import * # Python 3
+ from tkinter import * # Python 3
try:
- import ScrolledText as scrolledtext # Python 2
+ import ScrolledText as scrolledtext # Python 2
except ImportError:
- from tkinter import scrolledtext # Python 3
+ from tkinter import scrolledtext # Python 3
class xbbtools_help(Toplevel):
diff --git a/Scripts/xbbtools/xbb_search.py b/Scripts/xbbtools/xbb_search.py
index d7f6f41..40d4d5e 100644
--- a/Scripts/xbbtools/xbb_search.py
+++ b/Scripts/xbbtools/xbb_search.py
@@ -7,23 +7,24 @@
import os
import re
import sys
+
sys.path.insert(0, '.')
try:
- from Tkinter import * # Python 2
+ from Tkinter import * # Python 2
except ImportError:
- from tkinter import * # Python 3
+ from tkinter import * # Python 3
try:
- import tkColorChooser as colorchooser # Python 2
+ import tkColorChooser as colorchooser # Python 2
except ImportError:
- from tkinter import colorchooser # Python 3
+ from tkinter import colorchooser # Python 3
from Bio.Data.IUPACData import ambiguous_dna_values
from Bio.Seq import reverse_complement
-class DNAsearch:
+class DNAsearch(object):
def __init__(self):
self.init_alphabet()
self.sequence = ''
@@ -123,7 +124,7 @@ class XDNAsearch(Toplevel, DNAsearch):
if not color:
try:
color = colorchooser.askcolor()[1]
- except:
+ except Exception: # TODO - Which exceptions?
color = 'cyan'
self.current_color = color
self.current_tag = 'searched_%s' % self.current_color
diff --git a/Scripts/xbbtools/xbb_translations.py b/Scripts/xbbtools/xbb_translations.py
index 332fe35..705de05 100644
--- a/Scripts/xbbtools/xbb_translations.py
+++ b/Scripts/xbbtools/xbb_translations.py
@@ -12,16 +12,16 @@ import time
sys.path.insert(0, '.')
try:
- from Tkinter import * # Python 2
+ from Tkinter import * # Python 2
except ImportError:
- from tkinter import * # Python 3
+ from tkinter import * # Python 3
from Bio.Seq import reverse_complement, translate
from Bio.SeqUtils import GC
-class xbb_translations:
+class xbb_translations(object):
def __init__(self):
""
@@ -71,10 +71,10 @@ class xbb_translations:
protein = self.frame(seq, frame, translation_table)
res = self.header_nice('Plus one frame translation', seq)
for i in range(0, length, 60):
- subseq = seq[i:i+60]
- p = i/3
- res += '%d/%d\n' % (i+1, i/3+1)
- res += ' '.join(protein[p:p+20]) + '\n'
+ subseq = seq[i:i + 60]
+ p = i // 3
+ res += '%d/%d\n' % (i + 1, i // 3 + 1)
+ res += ' '.join(protein[p:p + 20]) + '\n'
# seq
res += subseq.lower() + '%5d %%\n' % int(self.gc(subseq))
@@ -91,27 +91,27 @@ class xbb_translations:
length = len(seq)
frames = {}
for i in range(0, 3):
- frames[i+1] = self.frame1(seq[i:], translation_table)
- frames[-(i+1)] = self.reverse(self.frame1(anti[i:], translation_table))
+ frames[i + 1] = self.frame1(seq[i:], translation_table)
+ frames[-(i + 1)] = self.reverse(self.frame1(anti[i:], translation_table))
res = self.header_nice('GCFrame', seq)
for i in range(0, length, 60):
- subseq = seq[i:i+60]
- csubseq = comp[i:i+60]
- p = i/3
+ subseq = seq[i:i + 60]
+ csubseq = comp[i:i + 60]
+ p = i // 3
# + frames
- res += '%d/%d\n' % (i+1, i/3+1)
- res += ' ' + ' '.join(frames[3][p:p+20]) + '\n'
- res += ' ' + ' '.join(frames[2][p:p+20]) + '\n'
- res += ' '.join(frames[1][p:p+20]) + '\n'
+ res += '%d/%d\n' % (i + 1, i // 3 + 1)
+ res += ' ' + ' '.join(frames[3][p:p + 20]) + '\n'
+ res += ' ' + ' '.join(frames[2][p:p + 20]) + '\n'
+ res += ' '.join(frames[1][p:p + 20]) + '\n'
# seq
res += subseq.lower() + '%5d %%\n' % int(self.gc(subseq))
res += csubseq.lower() + '\n'
# - frames
- res += ' '.join(frames[-2][p:p+20]) + ' \n'
- res += ' ' + ' '.join(frames[-1][p:p+20]) + '\n'
- res += ' ' + ' '.join(frames[-3][p:p+20]) + '\n\n'
+ res += ' '.join(frames[-2][p:p + 20]) + ' \n'
+ res += ' ' + ' '.join(frames[-1][p:p + 20]) + '\n'
+ res += ' ' + ' '.join(frames[-3][p:p + 20]) + '\n\n'
return res
diff --git a/Scripts/xbbtools/xbb_utils.py b/Scripts/xbbtools/xbb_utils.py
index 04419e7..34834c5 100644
--- a/Scripts/xbbtools/xbb_utils.py
+++ b/Scripts/xbbtools/xbb_utils.py
@@ -5,17 +5,18 @@
# File: xbb_utils.py
import sys
+
sys.path.insert(0, '.')
try:
- from Tkinter import * # Python 2
+ from Tkinter import * # Python 2
except ImportError:
- from tkinter import * # Python 3
+ from tkinter import * # Python 3
try:
- import tkFileDialog as filedialog # Python 2
+ import tkFileDialog as filedialog # Python 2
except ImportError:
- from tkinter import filedialog # Python 3
+ from tkinter import filedialog # Python 3
class NotePad(Toplevel):
@@ -45,6 +46,5 @@ class NotePad(Toplevel):
fd = filedialog.SaveFileDialog(self)
file = fd.go(key="test")
if file:
- fid = open(file, 'w')
- fid.write(self.tid.get(0.0, END))
- fid.close()
+ with open(file, 'w') as fid:
+ fid.write(self.tid.get(0.0, END))
diff --git a/Scripts/xbbtools/xbb_widget.py b/Scripts/xbbtools/xbb_widget.py
index 7fcd9f3..200f867 100644
--- a/Scripts/xbbtools/xbb_widget.py
+++ b/Scripts/xbbtools/xbb_widget.py
@@ -16,14 +16,14 @@ import sys
import time
try:
- from Tkinter import * # Python 2
+ from Tkinter import * # Python 2
except ImportError:
- from tkinter import * # Python 3
+ from tkinter import * # Python 3
try:
- import tkFileDialog as filedialog # Python 2
+ import tkFileDialog as filedialog # Python 2
except ImportError:
- from tkinter import filedialog # Python 3
+ from tkinter import filedialog # Python 3
sys.path.insert(0, '.')
from xbb_utils import *
@@ -35,7 +35,7 @@ from Bio.Data import CodonTable
from Bio.SeqIO.FastaIO import SimpleFastaParser
-class xbb_widget:
+class xbb_widget(object):
def __init__(self, parent=None):
self.is_a_master = (parent is None)
self.parent = parent
@@ -296,7 +296,7 @@ class xbb_widget:
try:
return w.selection_get()
# return string.upper(w.get(sel.first, sel.last))
- except:
+ except Exception: # TODO - Which exceptions?
return ''
def get_self_selection(self):
@@ -306,7 +306,7 @@ class xbb_widget:
return w.selection_get()
# return string.upper(w.get(sel.first, sel.last))
# return string.upper(w.selection_own_get())
- except:
+ except Exception: # TODO - Which exceptions?
return ''
def count_selection(self, event):
@@ -326,7 +326,7 @@ class xbb_widget:
for nt in ['A', 'C', 'G', 'T']:
n = seq.count(nt)
self.statistics_ids[nt].configure(text='%s=%d' % (nt, n))
- except:
+ except Exception: # TODO - Which exceptions?
pass
def position(self, event):
@@ -429,7 +429,7 @@ GC=%f
w.selection_own()
try:
start, stop = w.tag_ranges(SEL)
- except:
+ except Exception: # TODO - Which exceptions?
start, stop = 1.0, self.sequence_id.index(END)
seq = w.get(start, stop)
@@ -448,7 +448,7 @@ GC=%f
w.selection_own()
try:
start, stop = w.tag_ranges(SEL)
- except:
+ except Exception: # Which exceptions?
start, stop = 1.0, self.sequence_id.index(END)
seq = w.get(start, stop)
@@ -467,7 +467,7 @@ GC=%f
w.selection_own()
try:
start, stop = w.tag_ranges(SEL)
- except:
+ except Exception: # TODO - Which exceptions?
start, stop = 1.0, self.sequence_id.index(END)
seq = w.get(start, stop)
@@ -488,14 +488,14 @@ GC=%f
pos = self.goto_entry.get()
try:
pos = int(pos) - 1
- except:
+ except ValueError:
try:
start, stop = pos.split(':')
start = int(start) - 1
stop = int(stop)
self.mark(start, stop)
return
- except:
+ except Exception: # TODO - which exceptions?
import traceback
traceback.print_exc()
diff --git a/Scripts/xbbtools/xbbtools.py b/Scripts/xbbtools/xbbtools.py
index 9e192e5..3409559 100755
--- a/Scripts/xbbtools/xbbtools.py
+++ b/Scripts/xbbtools/xbbtools.py
@@ -10,12 +10,13 @@
# as part of this package.
import sys
+
sys.path.insert(0, '.')
try:
- from Tkinter import * # Python 2
+ from Tkinter import * # Python 2
except ImportError:
- from tkinter import * # Python 3
+ from tkinter import * # Python 3
from xbb_widget import xbb_widget
@@ -25,7 +26,7 @@ xbbtools.main_frame.option_add('*frame.background', 'dimgrey')
try:
xbbtools.open(sys.argv[1])
-except:
+except Exception: # TODO - Which exceptions?
pass
win.mainloop()
diff --git a/Tests/.cvsignore b/Tests/.cvsignore
new file mode 100644
index 0000000..532bb5b
--- /dev/null
+++ b/Tests/.cvsignore
@@ -0,0 +1,2 @@
+dump
+*.pyc
diff --git a/Tests/BioSQL/biosqldb-pg.sql b/Tests/BioSQL/biosqldb-pg.sql
index 9e9468a..0ec220c 100644
--- a/Tests/BioSQL/biosqldb-pg.sql
+++ b/Tests/BioSQL/biosqldb-pg.sql
@@ -768,6 +768,12 @@ ALTER TABLE location_qualifier_value ADD CONSTRAINT FKterm_locqual
-- code that you use and hence add unnecessary overhead.
--
+
+/* Commenting out these rules intended for BioPerl only, in order
+to silence the Biopython test warning from open_database function.
+See: https://redmine.open-bio.org/issues/2839
+aka https://github.com/biosql/biosql/issues/4
+
CREATE RULE rule_bioentry_i1
AS ON INSERT TO bioentry
WHERE (
@@ -1127,6 +1133,9 @@ CREATE RULE rule_term_synonym_i
DO INSTEAD NOTHING
;
+-- End of commented out rules
+*/
+
--
-- Functions that may be used as an API by applications, e.g. load scripts etc.
--
diff --git a/Tests/BioSQL/biosqldb-sqlite.sql b/Tests/BioSQL/biosqldb-sqlite.sql
index 1620e78..f570b6d 100644
--- a/Tests/BioSQL/biosqldb-sqlite.sql
+++ b/Tests/BioSQL/biosqldb-sqlite.sql
@@ -61,7 +61,8 @@ CREATE TABLE taxon_name (
taxon_id INTEGER,
name VARCHAR(255) NOT NULL,
name_class VARCHAR(32) NOT NULL,
- UNIQUE (taxon_id,name,name_class)
+ UNIQUE (taxon_id,name,name_class),
+ FOREIGN KEY ( taxon_id ) REFERENCES taxon ( taxon_id ) ON DELETE CASCADE
);
CREATE INDEX taxnametaxonid ON taxon_name(taxon_id);
@@ -82,7 +83,8 @@ CREATE TABLE term (
is_obsolete CHAR(1),
ontology_id INTEGER,
UNIQUE (identifier),
- UNIQUE (name,ontology_id,is_obsolete)
+ UNIQUE (name,ontology_id,is_obsolete),
+ FOREIGN KEY ( ontology_id ) REFERENCES ontology ( ontology_id ) ON DELETE CASCADE
);
CREATE INDEX term_ont ON term(ontology_id);
@@ -90,14 +92,19 @@ CREATE INDEX term_ont ON term(ontology_id);
CREATE TABLE term_synonym (
synonym VARCHAR(255) NOT NULL,
term_id INTEGER,
- PRIMARY KEY (term_id,synonym)
+ PRIMARY KEY (term_id,synonym),
+
+ FOREIGN KEY ( term_id ) REFERENCES term ( term_id ) ON DELETE CASCADE
);
CREATE TABLE term_dbxref (
term_id INTEGER,
dbxref_id INTEGER,
rank SMALLINT,
- PRIMARY KEY (term_id, dbxref_id)
+ PRIMARY KEY (term_id, dbxref_id),
+
+ FOREIGN KEY ( dbxref_id ) REFERENCES dbxref ( dbxref_id ) ON DELETE CASCADE,
+ FOREIGN KEY ( term_id ) REFERENCES term ( term_id ) ON DELETE CASCADE
);
CREATE INDEX trmdbxref_dbxrefid ON term_dbxref(dbxref_id);
@@ -108,7 +115,13 @@ CREATE TABLE term_relationship (
predicate_term_id INTEGER,
object_term_id INTEGER,
ontology_id INTEGER,
- UNIQUE (subject_term_id,predicate_term_id,object_term_id,ontology_id)
+ UNIQUE (subject_term_id,predicate_term_id,object_term_id,ontology_id),
+
+ FOREIGN KEY ( subject_term_id ) REFERENCES term ( term_id ) ON DELETE CASCADE ,
+ FOREIGN KEY ( predicate_term_id ) REFERENCES term ( term_id ) ON DELETE CASCADE ,
+ FOREIGN KEY ( object_term_id ) REFERENCES term ( term_id ) ON DELETE CASCADE ,
+ FOREIGN KEY ( ontology_id ) REFERENCES ontology ( ontology_id ) ON DELETE CASCADE
+
);
CREATE INDEX trmrel_predicateid ON term_relationship(predicate_term_id);
@@ -118,7 +131,10 @@ CREATE INDEX trmrel_ontid ON term_relationship(ontology_id);
CREATE TABLE term_relationship_term (
term_relationship_id INTEGER PRIMARY KEY,
term_id INTEGER,
- UNIQUE ( term_id )
+ UNIQUE ( term_id ),
+
+ FOREIGN KEY (term_relationship_id) REFERENCES term_relationship(term_relationship_id) ON DELETE CASCADE,
+ FOREIGN KEY (term_id) REFERENCES term(term_id) ON DELETE CASCADE
);
CREATE TABLE term_path (
@@ -128,7 +144,11 @@ CREATE TABLE term_path (
object_term_id INTEGER,
ontology_id INTEGER,
distance INT(10) ,
- UNIQUE (subject_term_id,predicate_term_id,object_term_id,ontology_id,distance)
+ UNIQUE (subject_term_id,predicate_term_id,object_term_id,ontology_id,distance),
+ FOREIGN KEY ( subject_term_id ) REFERENCES term ( term_id ) ON DELETE CASCADE ,
+ FOREIGN KEY ( predicate_term_id ) REFERENCES term ( term_id ) ON DELETE CASCADE ,
+ FOREIGN KEY ( object_term_id ) REFERENCES term ( term_id ) ON DELETE CASCADE ,
+ FOREIGN KEY ( ontology_id ) REFERENCES ontology ( ontology_id ) ON DELETE CASCADE
);
CREATE INDEX trmpath_predicateid ON term_path(predicate_term_id);
@@ -146,7 +166,10 @@ CREATE TABLE bioentry (
description TEXT,
version SMALLINT NOT NULL,
UNIQUE (accession,biodatabase_id,version),
- UNIQUE (identifier, biodatabase_id)
+ UNIQUE (identifier, biodatabase_id),
+
+ FOREIGN KEY ( taxon_id ) REFERENCES taxon ( taxon_id ),
+ FOREIGN KEY ( biodatabase_id ) REFERENCES biodatabase ( biodatabase_id )
);
CREATE INDEX bioentry_name ON bioentry(name);
@@ -159,7 +182,10 @@ CREATE TABLE bioentry_relationship (
subject_bioentry_id INTEGER,
term_id INTEGER,
rank INT(5),
- UNIQUE (object_bioentry_id,subject_bioentry_id,term_id)
+ UNIQUE (object_bioentry_id,subject_bioentry_id,term_id),
+ FOREIGN KEY ( term_id ) REFERENCES term ( term_id ),
+ FOREIGN KEY ( object_bioentry_id ) REFERENCES bioentry ( bioentry_id ) ON DELETE CASCADE,
+ FOREIGN KEY ( subject_bioentry_id ) REFERENCES bioentry ( bioentry_id ) ON DELETE CASCADE
);
CREATE INDEX bioentryrel_trm ON bioentry_relationship(term_id);
@@ -170,7 +196,11 @@ CREATE TABLE bioentry_path (
subject_bioentry_id INTEGER,
term_id INTEGER,
distance INT(10) ,
- UNIQUE (object_bioentry_id,subject_bioentry_id,term_id,distance)
+ UNIQUE (object_bioentry_id,subject_bioentry_id,term_id,distance),
+
+ FOREIGN KEY ( term_id ) REFERENCES term ( term_id )
+ FOREIGN KEY ( object_bioentry_id ) REFERENCES bioentry ( bioentry_id ) ON DELETE CASCADE,
+ FOREIGN KEY ( subject_bioentry_id ) REFERENCES bioentry ( bioentry_id ) ON DELETE CASCADE
);
CREATE INDEX bioentrypath_trm ON bioentry_path(term_id);
@@ -181,7 +211,8 @@ CREATE TABLE biosequence (
version SMALLINT,
length INT(10),
alphabet VARCHAR(10),
- seq LONGTEXT
+ seq LONGTEXT,
+ FOREIGN KEY ( bioentry_id ) REFERENCES bioentry ( bioentry_id ) ON DELETE CASCADE
);
CREATE TABLE dbxref (
@@ -199,7 +230,10 @@ CREATE TABLE dbxref_qualifier_value (
term_id INTEGER,
rank SMALLINT NOT NULL DEFAULT 0,
value TEXT,
- PRIMARY KEY (dbxref_id,term_id,rank)
+ PRIMARY KEY (dbxref_id,term_id,rank),
+
+ FOREIGN KEY ( term_id ) REFERENCES term ( term_id ),
+ FOREIGN KEY ( dbxref_id ) REFERENCES dbxref ( dbxref_id ) ON DELETE CASCADE
);
CREATE INDEX dbxrefqual_dbx ON dbxref_qualifier_value(dbxref_id);
@@ -209,7 +243,10 @@ CREATE TABLE bioentry_dbxref (
bioentry_id INTEGER,
dbxref_id INTEGER,
rank SMALLINT,
- PRIMARY KEY (bioentry_id,dbxref_id)
+ PRIMARY KEY (bioentry_id,dbxref_id),
+ FOREIGN KEY ( bioentry_id ) REFERENCES bioentry ( bioentry_id ) ON DELETE CASCADE,
+ FOREIGN KEY ( dbxref_id ) REFERENCES dbxref ( dbxref_id ) ON DELETE CASCADE
+
);
CREATE INDEX dblink_dbx ON bioentry_dbxref(dbxref_id);
@@ -222,7 +259,8 @@ CREATE TABLE reference (
authors TEXT,
crc VARCHAR(32),
UNIQUE (dbxref_id),
- UNIQUE (crc)
+ UNIQUE (crc),
+ FOREIGN KEY ( dbxref_id ) REFERENCES dbxref ( dbxref_id )
);
CREATE TABLE bioentry_reference (
@@ -231,7 +269,9 @@ CREATE TABLE bioentry_reference (
start_pos INT(10),
end_pos INT(10),
rank SMALLINT NOT NULL DEFAULT 0,
- PRIMARY KEY(bioentry_id,reference_id,rank)
+ PRIMARY KEY(bioentry_id,reference_id,rank),
+ FOREIGN KEY ( bioentry_id ) REFERENCES bioentry ( bioentry_id ) ON DELETE CASCADE
+ FOREIGN KEY ( reference_id ) REFERENCES reference ( reference_id ) ON DELETE CASCADE
);
CREATE INDEX bioentryref_ref ON bioentry_reference(reference_id);
@@ -241,7 +281,8 @@ CREATE TABLE comment (
bioentry_id INTEGER,
comment_text TEXT NOT NULL,
rank SMALLINT NOT NULL DEFAULT 0,
- UNIQUE(bioentry_id, rank)
+ UNIQUE(bioentry_id, rank),
+ FOREIGN KEY ( bioentry_id ) REFERENCES bioentry ( bioentry_id ) ON DELETE CASCADE
);
CREATE TABLE bioentry_qualifier_value (
@@ -249,7 +290,9 @@ CREATE TABLE bioentry_qualifier_value (
term_id INTEGER,
value TEXT,
rank INT(5) NOT NULL DEFAULT 0,
- UNIQUE (bioentry_id,term_id,rank)
+ UNIQUE (bioentry_id,term_id,rank),
+ FOREIGN KEY ( bioentry_id ) REFERENCES bioentry ( bioentry_id ) ON DELETE CASCADE,
+ FOREIGN KEY ( term_id ) REFERENCES term ( term_id )
);
CREATE INDEX bioentryqual_trm ON bioentry_qualifier_value(term_id);
@@ -261,7 +304,10 @@ CREATE TABLE seqfeature (
source_term_id INTEGER,
display_name VARCHAR(64),
rank SMALLINT NOT NULL DEFAULT 0,
- UNIQUE (bioentry_id,type_term_id,source_term_id,rank)
+ UNIQUE (bioentry_id,type_term_id,source_term_id,rank),
+ FOREIGN KEY ( type_term_id ) REFERENCES term ( term_id ),
+ FOREIGN KEY ( source_term_id ) REFERENCES term ( term_id ),
+ FOREIGN KEY ( bioentry_id ) REFERENCES bioentry ( bioentry_id ) ON DELETE CASCADE
);
CREATE INDEX seqfeature_trm ON seqfeature(type_term_id);
@@ -273,7 +319,10 @@ CREATE TABLE seqfeature_relationship (
subject_seqfeature_id INTEGER,
term_id INTEGER,
rank INT(5),
- UNIQUE (object_seqfeature_id,subject_seqfeature_id,term_id)
+ UNIQUE (object_seqfeature_id,subject_seqfeature_id,term_id),
+ FOREIGN KEY ( term_id ) REFERENCES term ( term_id ),
+ FOREIGN KEY ( object_seqfeature_id ) REFERENCES seqfeature ( seqfeature_id ) ON DELETE CASCADE,
+ FOREIGN KEY ( subject_seqfeature_id ) REFERENCES seqfeature ( seqfeature_id ) ON DELETE CASCADE
);
CREATE INDEX seqfeaturerel_trm ON seqfeature_relationship(term_id);
@@ -284,7 +333,10 @@ CREATE TABLE seqfeature_path (
subject_seqfeature_id INTEGER,
term_id INTEGER,
distance INT(10) ,
- UNIQUE (object_seqfeature_id,subject_seqfeature_id,term_id,distance)
+ UNIQUE (object_seqfeature_id,subject_seqfeature_id,term_id,distance),
+ FOREIGN KEY ( term_id ) REFERENCES term ( term_id ),
+ FOREIGN KEY ( object_seqfeature_id ) REFERENCES seqfeature ( seqfeature_id ) ON DELETE CASCADE,
+ FOREIGN KEY ( subject_seqfeature_id ) REFERENCES seqfeature ( seqfeature_id ) ON DELETE CASCADE
);
CREATE INDEX seqfeaturepath_trm ON seqfeature_path(term_id);
@@ -295,7 +347,9 @@ CREATE TABLE seqfeature_qualifier_value (
term_id INTEGER,
rank SMALLINT NOT NULL DEFAULT 0,
value TEXT NOT NULL,
- PRIMARY KEY (seqfeature_id,term_id,rank)
+ PRIMARY KEY (seqfeature_id,term_id,rank),
+ FOREIGN KEY ( term_id ) REFERENCES term ( term_id ),
+ FOREIGN KEY ( seqfeature_id ) REFERENCES seqfeature ( seqfeature_id ) ON DELETE CASCADE
);
CREATE INDEX seqfeaturequal_trm ON seqfeature_qualifier_value(term_id);
@@ -304,7 +358,9 @@ CREATE TABLE seqfeature_dbxref (
seqfeature_id INTEGER,
dbxref_id INTEGER,
rank SMALLINT,
- PRIMARY KEY (seqfeature_id,dbxref_id)
+ PRIMARY KEY (seqfeature_id,dbxref_id),
+ FOREIGN KEY ( seqfeature_id ) REFERENCES seqfeature ( seqfeature_id ) ON DELETE CASCADE,
+ FOREIGN KEY ( dbxref_id ) REFERENCES dbxref ( dbxref_id ) ON DELETE CASCADE
);
CREATE INDEX feadblink_dbx ON seqfeature_dbxref(dbxref_id);
@@ -318,7 +374,10 @@ CREATE TABLE location (
end_pos INT(10),
strand TINYINT NOT NULL DEFAULT 0,
rank SMALLINT NOT NULL DEFAULT 0,
- UNIQUE (seqfeature_id, rank)
+ UNIQUE (seqfeature_id, rank),
+ FOREIGN KEY ( seqfeature_id ) REFERENCES seqfeature ( seqfeature_id ) ON DELETE CASCADE,
+ FOREIGN KEY ( dbxref_id ) REFERENCES dbxref ( dbxref_id ),
+ FOREIGN KEY ( term_id ) REFERENCES term ( term_id )
);
CREATE INDEX seqfeatureloc_start ON location(start_pos, end_pos);
@@ -330,14 +389,9 @@ CREATE TABLE location_qualifier_value (
term_id INTEGER,
value VARCHAR(255) NOT NULL,
int_value INT(10),
- PRIMARY KEY (location_id,term_id)
+ PRIMARY KEY (location_id,term_id),
+ FOREIGN KEY ( location_id ) REFERENCES location ( location_id ) ON DELETE CASCADE,
+ FOREIGN KEY ( term_id ) REFERENCES term ( term_id )
);
CREATE INDEX locationqual_trm ON location_qualifier_value(term_id);
-
--- SQLite does not enforce foreign key constraints. There are some trigger
--- based ways to replicate this:
---
--- http://www.sqlite.org/cvstrac/wiki?p=ForeignKeyTriggers
---
--- Currently no foreign key constraints are added.
diff --git a/Tests/EMBL/DS830848.embl b/Tests/EMBL/DS830848.embl
new file mode 100644
index 0000000..d0d7b59
--- /dev/null
+++ b/Tests/EMBL/DS830848.embl
@@ -0,0 +1,86 @@
+ID DS830848; SV 1; linear; genomic DNA; CON; INV; 1311 BP.
+XX
+AC DS830848;
+XX
+PR Project:PRJNA16232;
+XX
+DT 21-APR-2008 (Rel. 95, Created)
+DT 18-NOV-2008 (Rel. 97, Last updated, Version 3)
+XX
+DE Ixodes scapularis 1108462666300 genomic scaffold, whole genome shotgun
+DE sequence.
+XX
+KW .
+XX
+OS Ixodes scapularis (black-legged tick)
+OC Eukaryota; Metazoa; Ecdysozoa; Arthropoda; Chelicerata; Arachnida; Acari;
+OC Parasitiformes; Ixodida; Ixodoidea; Ixodidae; Ixodinae; Ixodes.
+XX
+RN [1]
+RP 1-1311
+RG Ixodes scapularis Genome Project Consortium
+RA Caler E., Hannick L.I., Bidwell S., Joardar V., Thiagarajan M., Amedeo P.,
+RA Galinsky K.J., Schobel S., Inman J., Hostetler J., Miller J., Hammond M.,
+RA Megy K., Lawson D., Kodira C., Sutton G., Meyer J., Hill C.A., Birren B.,
+RA Nene V., Collins F., Alarcon-Chaidez F., Wikel S., Strausberg R.;
+RT "Annotation of Ixodes scapularis";
+RL Unpublished.
+XX
+RN [2]
+RP 1-1311
+RG Ixodes scapularis Genome Project Consortium
+RA Nene V.;
+RT ;
+RL Submitted (18-MAR-2008) to the INSDC.
+RL J. Craig Venter Institute, 9704 Medical Center Drive, Rockville, MD 20850,
+RL USA
+XX
+DR MD5; 387e72e4f7ae804780d06f875ab3bc41.
+DR ENA; ABJB010000000; SET.
+DR ENA; ABJB000000000; SET.
+DR BioSample; SAMN03004382.
+XX
+CC This Wikel tick colony was established in 1996 using field
+CC collected ticks from New York, Oklahoma and a Lyme Disease endemic
+CC area of Connecticut. This colony has been continuously in-bred
+CC since establishment and has not been supplemented with field
+CC collected material. The colony is known to be a competent vector
+CC of various Borrelia and Babesia isolates. Genomic DNA was prepared
+CC from embryonated eggs.
+CC The Ixodes scapularis Genome Project (IGP) is a community-based
+CC effort initiated in early 2004 to sequence the I. scapularis
+CC genome. The IGP is funded by the National Institute of Allergy and
+CC Infectious Diseases (NIAID)/National Institute of Health as part of
+CC the NIAID-funded Microbial Sequencing Centers (MSCs) at The J.
+CC Craig Venter Institute (http://www.jcvi.org) and The Broad
+CC Institute (http://www.broad.mit.edu/). The genome sequencing was
+CC performed at both MSCs and the assembly at JCVI. The sum of all
+CC reads equals 6X based on an estimated genome size of 2.1 Gbp, while
+CC the coverage on assembled contigs is 3.8X. The scaffolds are
+CC comprised of 570637 contigs; the other 570958 contigs in the 01
+CC version of this project represent degenerate contigs that were not
+CC incorporated into scaffolds. The annotation for this assembly will
+CC be produced jointly by the MSCs and VectorBase
+CC (http://www.vectorbase.org). Data release and use follow NIAID
+CC guidelines
+CC (http://www.niaid.nih.gov/dmid/genomes/mscs/data_release.htm).
+CC VectorBase (www.vectorbase.org), a NIAID Bioinformatics Resource
+CC Center, is responsible for the long-term curation of the assembled
+CC genome sequence and its annotation. For more information, contact
+CC info at vectorbase.org.
+CC Assembly name: JCVI_ISG_i3_1.0
+CC Coverage: 3.8x on assembled contigs; 6x based on an estimated
+CC genome size of 2.1 Gbp
+CC Annotation was added to the scaffolds in December 2008.
+XX
+FH Key Location/Qualifiers
+FH
+FT source 1..1311
+FT /organism="Ixodes scapularis"
+FT /strain="Wikel colony"
+FT /mol_type="genomic DNA"
+FT /dev_stage="embryonated eggs"
+FT /db_xref="taxon:6945"
+XX
+CO join(ABJB010667125.1:1..1311)
+//
diff --git a/Tests/GenBank/.cvsignore b/Tests/GenBank/.cvsignore
new file mode 100644
index 0000000..61debbd
--- /dev/null
+++ b/Tests/GenBank/.cvsignore
@@ -0,0 +1 @@
+cor6_6.idx
diff --git a/Tests/GenBank/DS830848.gb b/Tests/GenBank/DS830848.gb
new file mode 100644
index 0000000..e300dac
--- /dev/null
+++ b/Tests/GenBank/DS830848.gb
@@ -0,0 +1,73 @@
+LOCUS DS830848 1311 bp DNA linear CON 13-NOV-2008
+DEFINITION Ixodes scapularis 1108462666300 genomic scaffold, whole genome
+ shotgun sequence.
+ACCESSION DS830848 ABJB010000000
+VERSION DS830848.1 GI:183757675
+DBLINK BioProject: PRJNA16232
+ BioSample: SAMN03004382
+KEYWORDS WGS.
+SOURCE Ixodes scapularis (black-legged tick)
+ ORGANISM Ixodes scapularis
+ Eukaryota; Metazoa; Ecdysozoa; Arthropoda; Chelicerata; Arachnida;
+ Acari; Parasitiformes; Ixodida; Ixodoidea; Ixodidae; Ixodinae;
+ Ixodes.
+REFERENCE 1 (bases 1 to 1311)
+ AUTHORS Caler,E., Hannick,L.I., Bidwell,S., Joardar,V., Thiagarajan,M.,
+ Amedeo,P., Galinsky,K.J., Schobel,S., Inman,J., Hostetler,J.,
+ Miller,J., Hammond,M., Megy,K., Lawson,D., Kodira,C., Sutton,G.,
+ Meyer,J., Hill,C.A., Birren,B., Nene,V., Collins,F.,
+ Alarcon-Chaidez,F., Wikel,S. and Strausberg,R.
+ CONSRTM Ixodes scapularis Genome Project Consortium
+ TITLE Annotation of Ixodes scapularis
+ JOURNAL Unpublished
+REFERENCE 2 (bases 1 to 1311)
+ AUTHORS Nene,V.
+ CONSRTM Ixodes scapularis Genome Project Consortium
+ TITLE Direct Submission
+ JOURNAL Submitted (18-MAR-2008) J. Craig Venter Institute, 9704 Medical
+ Center Drive, Rockville, MD 20850, USA
+COMMENT
+ This Wikel tick colony was established in 1996 using field
+ collected ticks from New York, Oklahoma and a Lyme Disease endemic
+ area of Connecticut. This colony has been continuously in-bred
+ since establishment and has not been supplemented with field
+ collected material. The colony is known to be a competent vector
+ of various Borrelia and Babesia isolates. Genomic DNA was prepared
+ from embryonated eggs.
+
+ The Ixodes scapularis Genome Project (IGP) is a community-based
+ effort initiated in early 2004 to sequence the I. scapularis
+ genome. The IGP is funded by the National Institute of Allergy and
+ Infectious Diseases (NIAID)/National Institute of Health as part of
+ the NIAID-funded Microbial Sequencing Centers (MSCs) at The J.
+ Craig Venter Institute (http://www.jcvi.org) and The Broad
+ Institute (http://www.broad.mit.edu/). The genome sequencing was
+ performed at both MSCs and the assembly at JCVI. The sum of all
+ reads equals 6X based on an estimated genome size of 2.1 Gbp, while
+ the coverage on assembled contigs is 3.8X. The scaffolds are
+ comprised of 570637 contigs; the other 570958 contigs in the 01
+ version of this project represent degenerate contigs that were not
+ incorporated into scaffolds. The annotation for this assembly will
+ be produced jointly by the MSCs and VectorBase
+ (http://www.vectorbase.org). Data release and use follow NIAID
+ guidelines
+ (http://www.niaid.nih.gov/dmid/genomes/mscs/data_release.htm).
+
+ VectorBase (www.vectorbase.org), a NIAID Bioinformatics Resource
+ Center, is responsible for the long-term curation of the assembled
+ genome sequence and its annotation. For more information, contact
+ info at vectorbase.org.
+ Assembly name: JCVI_ISG_i3_1.0
+ Coverage: 3.8x on assembled contigs; 6x based on an estimated
+ genome size of 2.1 Gbp
+ Annotation was added to the scaffolds in December 2008.
+FEATURES Location/Qualifiers
+ source 1..1311
+ /organism="Ixodes scapularis"
+ /mol_type="genomic DNA"
+ /strain="Wikel colony"
+ /db_xref="taxon:6945"
+ /dev_stage="embryonated eggs"
+CONTIG join(ABJB010667125.1:1..1311)
+//
+
diff --git a/Tests/GenBank/EU851978.gbk b/Tests/GenBank/EU851978.gbk
new file mode 100644
index 0000000..c5b6bb5
--- /dev/null
+++ b/Tests/GenBank/EU851978.gbk
@@ -0,0 +1,87 @@
+LOCUS EU851978 1698 bp cRNA linear VRL 24-OCT-2008
+DEFINITION Influenza A virus (A/Wisconsin/36/2007(H1N1)) segment 4
+ hemagglutinin (HA) gene, complete cds.
+ACCESSION EU851978
+VERSION EU851978.1 GI:193084886
+KEYWORDS .
+SOURCE Influenza A virus (A/Wisconsin/36/2007(H1N1))
+ ORGANISM Influenza A virus (A/Wisconsin/36/2007(H1N1))
+ Viruses; ssRNA viruses; ssRNA negative-strand viruses;
+ Orthomyxoviridae; Influenzavirus A.
+REFERENCE 1 (bases 1 to 1698)
+ AUTHORS Garten,R., Barnes,J., Myrick,A., Smith,C., Xu,X. and Klimov,A.
+ TITLE Direct Submission
+ JOURNAL Submitted (25-JUN-2008) NCIRD/Influenza Division/VSDB, Centers for
+ Disease Control and Prevention, 1600 Clifton Road, NE, MS-G16,
+ Atlanta, GA 30333, USA
+COMMENT ##FluData-START##
+ LabID :: 2008704957
+ Antigen_Character :: A/Solomon Islands/03/2006-like (H1N1)
+ Passage_Details :: SPFCK3E1
+ Drug Sensitivity :: adamantane-sensitive, oseltamivir-sensitive
+ LANL Accession :: ISDN306605
+ ##FluData-END##
+FEATURES Location/Qualifiers
+ source 1..1698
+ /organism="Influenza A virus (A/Wisconsin/36/2007(H1N1))"
+ /mol_type="viral cRNA"
+ /strain="A/Wisconsin/36/2007"
+ /serotype="H1N1"
+ /isolate="2008704957"
+ /host="Homo sapiens"
+ /db_xref="taxon:535875"
+ /segment="4"
+ /country="USA"
+ /collection_date="30-Dec-2007"
+ gene 1..1698
+ /gene="HA"
+ CDS 1..1698
+ /gene="HA"
+ /codon_start=1
+ /product="hemagglutinin"
+ /protein_id="ACF10333.1"
+ /db_xref="GI:193084887"
+ /translation="MKVKLLVLLCTFTATYADTICIGYHANNSTDTVDTVLEKNVTVT
+ HSVNLLENSHNGKLCLLKGIAPLQLGNCSVAGWILGNPECELLISKESWSYIVEKPNP
+ ENGTCYPGHFADYEELREQLSSVSSFERFEIFPKESSWPNHTVTGVSASCSHNGESSF
+ YRNLLWLTGKNGLYPNLSKSYANNKEKEVLVLWGVHHPPNIGGQKALYHTENAYVSVV
+ SSHYSRKFTPEIAKRPKVRDQEGRINYYWTLLEPGDTIIFEANGNLIAPRYAFALSRG
+ FGSGIINSNAPMDKCDAKCQTPQGAINSSLPFQNVHPVTIGECPKYVRSAKLRMVTGL
+ RNIPSIQSRGLFGAIAGFIEGGWTGMVDGWYGYHHQNEQGSGYAADQKSTQNAINGIT
+ NKVNSVIEKMNTQFTAVGKEFNKLERRMENLNKKVDDGFIDIWTYNAELLVLLENERT
+ LDFHDSNVKNLYEKVKSQLKNNAKEIGNGCFEFYHKCNDECMESVKNGTYDYPKYSEE
+ SKLNREKIDGVKLESMGVYQILAIYSTVASSLVLLVSLGAISFWMCSNGSLQCRICI"
+ misc_feature 1..1698
+ /gene="HA"
+ /note="ISDN306605"
+ORIGIN
+ 1 atgaaagtaa aactactggt cctgttatgc acattcacag ctacatatgc agacacaata
+ 61 tgtataggct accatgctaa caactcgacc gacactgttg acacagtact tgaaaagaat
+ 121 gtgacagtga cacactctgt caacctgctt gagaacagtc acaatggaaa actatgtcta
+ 181 ttaaaaggaa tagccccact acaattgggt aactgcagcg ttgccgggtg gatcttagga
+ 241 aacccagaat gcgaattact gatttccaag gagtcatggt cctacattgt agaaaaacca
+ 301 aatcctgaga atggaacatg ttacccaggg catttcgctg actatgagga actgagggag
+ 361 caattgagtt cagtatcttc atttgagagg ttcgaaatat tccccaaaga aagctcatgg
+ 421 cccaaccaca ccgtaaccgg agtgtcagca tcatgctccc ataatgggga aagtagtttt
+ 481 tacagaaatt tgctatggct gacggggaag aatggtttgt acccaaacct gagcaagtcc
+ 541 tatgcaaaca acaaagaaaa agaagtcctt gtactatggg gtgttcatca cccgccaaac
+ 601 ataggtggcc aaaaggccct ctatcataca gaaaatgctt atgtttctgt agtgtcttca
+ 661 cattatagca gaaaattcac cccagaaata gccaaaagac ccaaagtaag agatcaagaa
+ 721 ggaagaatca actactactg gactctgctt gaacccgggg atacaataat atttgaggca
+ 781 aatggaaatc taatagcgcc aagatatgct ttcgcactga gtagaggctt tggatcagga
+ 841 atcatcaact caaatgcacc aatggataaa tgtgatgcga agtgccaaac acctcaggga
+ 901 gctataaaca gcagtcttcc tttccagaac gtacacccag tcacaatagg agagtgtcca
+ 961 aagtatgtca ggagtgcaaa attaaggatg gttacaggac taaggaacat cccatccatt
+ 1021 caatccagag gtttgtttgg agccattgcc ggtttcattg aaggggggtg gactggaatg
+ 1081 gtagatggtt ggtatggtta tcatcatcag aatgagcaag gatctggcta tgctgcagat
+ 1141 caaaaaagca cacaaaatgc cattaatgga attacaaaca aggtgaattc tgtaattgag
+ 1201 aaaatgaaca ctcaattcac agccgtgggc aaagaattca acaaattgga aagaaggatg
+ 1261 gaaaacttga ataaaaaagt tgatgatggg tttatagaca tctggacata taatgcagaa
+ 1321 ctgttggttc tactggaaaa tgaaaggact ttggatttcc atgactccaa tgtgaagaat
+ 1381 ctgtatgaga aagtaaaaag ccagttaaag aataatgcta aagaaatagg aaatgggtgt
+ 1441 tttgaattct atcacaagtg taacgatgaa tgcatggaga gtgtaaagaa tggaacttat
+ 1501 gactatccaa aatattccga agaatcaaag ttaaacaggg agaaaattga tggagtgaaa
+ 1561 ttggaatcaa tgggagtcta tcagattctg gcgatctact caacagtcgc cagttctctg
+ 1621 gttcttttgg tctccctggg ggcaatcagc ttctggatgt gttccaatgg gtctttgcag
+ 1681 tgtagaatat gcatctaa
+//
diff --git a/Tests/GenBank/HM138502.gbk b/Tests/GenBank/HM138502.gbk
new file mode 100644
index 0000000..3a1a8bf
--- /dev/null
+++ b/Tests/GenBank/HM138502.gbk
@@ -0,0 +1,79 @@
+LOCUS HM138502 1410 bp cRNA linear VRL 22-APR-2010
+DEFINITION Influenza A virus (A/California/07/2009(H1N1)) segment 6
+ neuraminidase (NA) gene, complete cds.
+ACCESSION HM138502
+VERSION HM138502.1 GI:295002854
+DBLINK BioProject: PRJNA37813
+KEYWORDS .
+SOURCE Influenza A virus (A/California/07/2009(H1N1))
+ ORGANISM Influenza A virus (A/California/07/2009(H1N1))
+ Viruses; ssRNA negative-strand viruses; Orthomyxoviridae;
+ Influenzavirus A.
+REFERENCE 1 (bases 1 to 1410)
+ AUTHORS Starick,E.
+ TITLE Direct Submission
+ JOURNAL Submitted (21-APR-2010) Friedrich-Loeffler-Institut, Suedufer 10,
+ Greifswald-Insel Riems 17493, Germany
+COMMENT Swine influenza A (H1N1) virus isolated during human swine flu
+ outbreak of 2009.
+
+ ##GISAID_EpiFlu(TM)Data-START##
+ Isolate :: A/California/07/09
+ Subtype :: H1N1
+ Lineage :: swl
+ ##GISAID_EpiFlu(TM)Data-END##
+FEATURES Location/Qualifiers
+ source 1..1410
+ /organism="Influenza A virus (A/California/07/2009(H1N1))"
+ /mol_type="viral cRNA"
+ /strain="A/California/07/2009"
+ /serotype="H1N1"
+ /host="Homo sapiens"
+ /db_xref="taxon:641809"
+ /segment="6"
+ /country="USA"
+ /collection_date="09-Apr-2009"
+ /note="lineage: swl"
+ gene 1..1410
+ /gene="NA"
+ CDS 1..1410
+ /gene="NA"
+ /codon_start=1
+ /product="neuraminidase"
+ /protein_id="ADF58339.1"
+ /db_xref="GI:295002855"
+ /translation="MNPNQKIITIGSVCMTIGMANLILQIGNIISIWISHSIQLGNQN
+ QIETCNQSVITYENNTWVNQTYVNISNTNFAAGQSVVSVKLAGNSSLCPVSGWAIYSK
+ DNSVRIGSKGDVFVIREPFISCSPLECRTFFLTQGALLNDKHSNGTIKDRSPYRTLMS
+ CPIGEVPSPYNSRFESVAWSASACHDGINWLTIGISGPDNGAVAVLKYNGIITDTIKS
+ WRNNILRTQESECACVNGSCFTVMTDGPSNGQASYKIFRIEKGKIVKSVEMNAPNYHY
+ EECSCYPDSSEITCVCRDNWHGSNRPWVSFNQNLEYQIGYICSGIFGDNPRPNDKTGS
+ CGPVSSNGANGVKGFSFKYGNGVWIGRTKSISSRNGFEMIWDPNGWTGTDNNFSIKQD
+ IVGINEWSGYSGSFVQHPELTGLDCIRPCFWVELIRGRPKENTIWTSGSSISFCGVNS
+ DTVGWSWPDGAELPFTIDK"
+ORIGIN
+ 1 atgaatccaa accaaaagat aataaccatt ggttcggtct gtatgacaat tggaatggct
+ 61 aacttaatat tacaaattgg aaacataatc tcaatatgga ttagccactc aattcaactt
+ 121 gggaatcaaa atcagattga aacatgcaat caaagcgtca ttacttatga aaacaacact
+ 181 tgggtaaatc agacatatgt taacatcagc aacaccaact ttgctgctgg acagtcagtg
+ 241 gtttccgtga aattagcagg caattcctct ctctgccctg ttagtggatg ggctatatac
+ 301 agtaaagaca acagtgtaag aatcggttcc aagggggatg tgtttgtcat aagggaacca
+ 361 ttcatatcat gctccccctt ggaatgcaga accttcttct tgactcaagg ggccttgcta
+ 421 aatgacaaac attccaatgg aaccattaaa gacaggagcc catatcgaac cctaatgagc
+ 481 tgtcctattg gtgaagttcc ctctccatac aactcaagat ttgagtcagt cgcttggtca
+ 541 gcaagtgctt gtcatgatgg catcaattgg ctaacaattg gaatttctgg cccagacaat
+ 601 ggggcagtgg ctgtgttaaa gtacaacggc ataataacag acactatcaa gagttggaga
+ 661 aacaatatat tgagaacaca agagtctgaa tgtgcatgtg taaatggttc ttgctttact
+ 721 gtaatgaccg atggaccaag taatggacag gcctcataca agatcttcag aatagaaaag
+ 781 ggaaagatag tcaaatcagt cgaaatgaat gcccctaatt atcactatga ggaatgctcc
+ 841 tgttatcctg attctagtga aatcacatgt gtgtgcaggg ataactggca tggctcgaat
+ 901 cgaccgtggg tgtctttcaa ccagaatctg gaatatcaga taggatacat atgcagtggg
+ 961 attttcggag acaatccacg ccctaatgat aagacaggca gttgtggtcc agtatcgtct
+ 1021 aatggagcaa atggagtaaa agggttttca ttcaaatacg gcaatggtgt ttggataggg
+ 1081 agaactaaaa gcattagttc aagaaacggt tttgagatga tttgggatcc gaacggatgg
+ 1141 actgggacag acaataactt ctcaataaag caagatatcg taggaataaa tgagtggtca
+ 1201 ggatatagcg ggagttttgt tcagcatcca gaactaacag ggctggattg tataagacct
+ 1261 tgcttctggg ttgaactaat cagagggcga cccaaagaga acacaatctg gactagcggg
+ 1321 agcagcatat ccttttgtgg tgtaaacagt gacactgtgg gttggtcttg gccagacggt
+ 1381 gctgagttgc catttaccat tgacaagtaa
+//
diff --git a/Tests/GenBank/KF527485.gbk b/Tests/GenBank/KF527485.gbk
new file mode 100644
index 0000000..c64eb23
--- /dev/null
+++ b/Tests/GenBank/KF527485.gbk
@@ -0,0 +1,82 @@
+LOCUS KF527485 1444 bp cRNA linear VRL 02-OCT-2013
+DEFINITION Influenza A virus (A/California/07/2009(H1N1)) segment 5
+ nucleocapsid protein (NP) gene, partial cds.
+ACCESSION KF527485
+VERSION KF527485.1 GI:537446968
+KEYWORDS .
+SOURCE Influenza A virus (A/California/07/2009(H1N1))
+ ORGANISM Influenza A virus (A/California/07/2009(H1N1))
+ Viruses; ssRNA negative-strand viruses; Orthomyxoviridae;
+ Influenzavirus A.
+REFERENCE 1 (bases 1 to 1444)
+ AUTHORS Le,V.L., Courtney,C.L., Steel,J. and Compans,R.W.
+ TITLE Closely Related Influenza Viruses Induce Contrasting Respiratory
+ Tract Immunopathology
+ JOURNAL PLoS ONE 8 (9), E76708 (2013)
+ PUBMED 24086762
+ REMARK Publication Status: Online-Only
+REFERENCE 2 (bases 1 to 1444)
+ AUTHORS Le,V.L., Courtney,C.L., Steel,J. and Compans,R.W.
+ TITLE Direct Submission
+ JOURNAL Submitted (07-AUG-2013) Microbiology and Immunology, Emory
+ University, 1518 Clifton Rd Suite 5000, Atlanta, GA 30322, USA
+COMMENT ##Assembly-Data-START##
+ Assembly Method :: Lasergene v. 10
+ Sequencing Technology :: ABI37XL; Sanger dideoxy sequencing
+ ##Assembly-Data-END##
+FEATURES Location/Qualifiers
+ source 1..1444
+ /organism="Influenza A virus (A/California/07/2009(H1N1))"
+ /mol_type="viral cRNA"
+ /strain="A/California/07/2009"
+ /serotype="H1N1"
+ /host="Homo sapiens"
+ /db_xref="taxon:641809"
+ /segment="5"
+ /country="USA"
+ /collection_date="09-Apr-2009"
+ /note="passage details: MDCK 6"
+ gene <1..>1444
+ /gene="NP"
+ CDS <1..>1444
+ /gene="NP"
+ /codon_start=2
+ /product="nucleocapsid protein"
+ /protein_id="AGU69828.1"
+ /db_xref="GI:537446969"
+ /translation="ASQGTKRSYEQMETGGERQDATEIRASVGRMIGGIGRFYIQMCT
+ ELKLSDYDGRLIQNSITIERMVLSAFDERRNKYLEEHPSAGKDPKKTGGPIYRRVGGK
+ WMRELILYDKEEIRRVWRQANNGEDATAGLTHIMIWHSNLNDATYQRTRALVRTGMDP
+ RMCSLMQGSTLPRRSGAAGAAVKGVGTIAMELIRMIKRGINDRNFWRGENGRRTRVAY
+ ERMCNILKGKFQTAAQRAMMDQVRESRNPGNAEIEDLIFLARSALILRGSVAHKSCLP
+ ACVYGLAVASGHDFEREGYSLVGIDPFKLLQNSQVVSLMRPNENPAHKSQLVWMACHS
+ AAFEDLRVSSFIRGKKVIPRGKLSTRGVQIASNENVETMDSNTLELRSRYWAIRTRSG
+ GNTNQQKASAGQISVQPTFSVQRNLPFERATVMAAFSGNNEGRTSDMRTEVIRMMESA
+ KPEDLSFQGRGVFELSDEKATNPIVPSFDMS"
+ORIGIN
+ 1 ggcgtctcaa ggcaccaaac gatcatatga acaaatggag actggtgggg agcgccagga
+ 61 tgccacagaa atcagagcat ctgtcggaag aatgattggt ggaatcggga gattctacat
+ 121 ccaaatgtgc actgaactca aactcagtga ttatgatgga cgactaatcc agaatagcat
+ 181 aacaatagag aggatggtgc tttctgcttt tgatgagaga agaaataaat acctagaaga
+ 241 gcatcccagt gctgggaagg accctaagaa aacaggagga cccatatata gaagagtagg
+ 301 cggaaagtgg atgagagaac tcatccttta tgacaaagaa gaaataagga gagtttggcg
+ 361 ccaagcaaac aatggcgaag atgcaacagc aggtcttact catatcatga tttggcattc
+ 421 caacctgaat gatgccacat atcagagaac aagagcgctt gttcgcaccg gaatggatcc
+ 481 cagaatgtgc tctctaatgc aaggttcaac acttcccaga aggtctggtg ccgcaggtgc
+ 541 tgcggtgaaa ggagttggaa caatagcaat ggagttaatc agaatgatca aacgtggaat
+ 601 caatgaccga aatttctgga ggggtgaaaa tggacgaagg acaagggttg cttatgaaag
+ 661 aatgtgcaat atcctcaaag gaaaatttca aacagctgcc cagagggcaa tgatggatca
+ 721 agtaagagaa agtcgaaacc caggaaacgc tgagattgaa gacctcattt tcctggcacg
+ 781 gtcagcactc attctgaggg gatcagttgc acataaatcc tgcctgcctg cttgtgtgta
+ 841 tgggcttgca gtagcaagtg ggcatgactt tgaaagggaa gggtactcac tggtcgggat
+ 901 agacccattc aaattactcc aaaacagcca agtggtcagc ctgatgagac caaatgaaaa
+ 961 cccagctcac aagagtcaat tggtgtggat ggcatgccac tctgctgcat ttgaagattt
+ 1021 aagagtatca agtttcataa gaggaaagaa agtgattcca agaggaaagc tttccacaag
+ 1081 aggggtccag attgcttcaa atgagaatgt ggaaaccatg gactccaata ccctggaact
+ 1141 gagaagcaga tactgggcca taaggaccag gagtggagga aataccaatc aacaaaaggc
+ 1201 atccgcaggc cagatcagtg tgcagcctac attctcagtg cagcggaatc tcccttttga
+ 1261 aagagcaacc gttatggcag cattcagcgg gaacaatgaa ggacggacat ccgacatgcg
+ 1321 aacagaagtt ataagaatga tggaaagtgc aaagccagaa gatttgtcct tccaggggcg
+ 1381 gggagtcttc gagctctcgg acgaaaaggc aacgaacccg atcgtgcctt cctttgacat
+ 1441 gagt
+//
\ No newline at end of file
diff --git a/Tests/Graphics/.cvsignore b/Tests/Graphics/.cvsignore
new file mode 100644
index 0000000..e17b15c
--- /dev/null
+++ b/Tests/Graphics/.cvsignore
@@ -0,0 +1,6 @@
+multi_bar.pdf
+organism.eps
+organism.pdf
+random_organism.pdf
+scatter_test.pdf
+simple_bar.pdf
diff --git a/Tests/NeuralNetwork/.cvsignore b/Tests/NeuralNetwork/.cvsignore
new file mode 100644
index 0000000..77ebef4
--- /dev/null
+++ b/Tests/NeuralNetwork/.cvsignore
@@ -0,0 +1 @@
+patternio.txt
diff --git a/Tests/Nexus/vSysLab_Ganaspidium_multistate.nex b/Tests/Nexus/vSysLab_Ganaspidium_multistate.nex
new file mode 100644
index 0000000..cc26391
--- /dev/null
+++ b/Tests/Nexus/vSysLab_Ganaspidium_multistate.nex
@@ -0,0 +1,48 @@
+#NEXUS
+
+BEGIN TAXA;
+ DIMENSIONS NTAX=6;
+ TAXLABELS
+ 'Ganaspidium didionae'
+ 'Ganaspidium eldiablo'
+ 'Ganaspidium flemingi'
+ 'Ganaspidium kolmaci'
+ 'Ganaspidium konzaense'
+ 'Ganaspidium pusillae'
+ ;
+END;
+
+BEGIN CHARACTERS;
+ DIMENSIONS NCHAR=14;
+ FORMAT DATATYPE=STANDARD MISSING=? GAP=-
+ SYMBOLS="012345"
+ ;
+ CHARSET 'Head' = 1 2 3 4;
+ CHARSET 'Mesosoma' = 5 6 7 8 9 10 11 12;
+ CHARSET 'Forewing' = 13;
+ CHARSET 'Metasoma' = 14;
+ CHARSTATELABELS
+ 1 'Malar sulcus'/'simple' 'compound',
+ 2 'Malar space'/'smooth' 'partially striate, striations extending 1/2 to 2/3 distance from ventral margin of malar space to base of compound eye' 'completely striate from ventral margin of malar space to base of compound eye' 'partially striate, striations extending to 1/4 distance from ventral margin of malar space to base of compound eye',
+ 3 'Malar protuberance'/'smooth, elongate, extending beyond length of ventral margin of malar space' 'striate, elongate, extending beyond length of ventral margin of malar space' 'smooth, short, not extending beyond length of ventral margin of malar space' 'striate, short, not extending beyond length of ventral margin of malar space',
+ 4 'Clypeal protuberance'/'elongate, overhanging anterior margin of clypeus' 'short, not overhanging anterior margin of clypeus',
+ 5 'Tubercles of scutellar plate'/'present, extremely reduced, hardly distinguishable' 'present, distinct, small, length of tuber less than 1/2 width of tubercle base' 'present, distinct, large, length of tubercle equal to or greater than width of tubercle base' 'absent',
+ 6 'Dorsal surface of scutellar plate'/'flat, smooth, setal bearing pits present surrounding midpit' 'flat, horizontally striate, setal bearing pits present surrounding midpit' 'gently convex, smooth, setal bearing pits surrounding midpit' 'concave, smooth, setal bearing pits surrounding midpit' 'concave around midpit, two setal bearing pits at base of each tubercle' 'concave, radially striate, setal bearing pits surrounding midpit',
+ 7 'Carina along posterior margin of scutellum'/'absent' 'present, delicate, defining transition from dorsal surface of scutellum from posterior surface' 'present, distinctly cleft, defining transition from dorsal surface of scutellum from posterior surface',
+ 8 'Dorsal surface of scutellum'/'entirely smooth' 'smooth except for faint wrinkles along posterior carina' 'smooth anteriorly, reticular/strigate posteriorly' 'smooth anteriorly, longitudinally striate posteriorly' 'entirely rugulose/wrinkled' 'smooth anteriorly, gently striate posteriorly',
+ 9 'Midpit of scutellar plate'/'in center of plate; plate large, obscuring dorsal surface of scutellum when viewed dorsally' 'in center of plate; plate small, revealing dorsal surface of scutellum when viewed dorsally' 'in posterior half of plate; plate small, revealing dorsal surface of scutellum when viewed dorsally',
+ 10 'Mesopopleura setal patch'/'present' 'absent',
+ 11 'Mesopleuron'/'entirely smooth' 'smooth anteriorly, striate along the postero-dorsal margin' 'distinctly striate along the anterior, posterior margins; often entire sclerite striate' 'striate within confines of mesopleural triangle, remainder of sclerite smooth',
+ 12 'Lateral aspect of pronotum'/'covered in long, thin, white setae' 'anteriorly with some short setae, remainder glabrous',
+ 13 'Marginal cell of forewing'/'as deep as long' 'distinctly deeper than long',
+ 14 'Size of metasoma (in lateral view)'/'sub-equal in size to mesosoma' 'distinctly larger than mesosoma (longer and deeper)'
+ ;
+ MATRIX
+ 'Ganaspidium didionae' 1100242421(01)110
+ 'Ganaspidium eldiablo' 10000405210110
+ 'Ganaspidium flemingi' 10(02)(01)1411210110
+ 'Ganaspidium kolmaci' 010000(01)221(01)100
+ 'Ganaspidium konzaense' 12002424212100
+ 'Ganaspidium pusillae' 01(02)(01)141221(01)110
+ ;
+END;
diff --git a/Tests/Nexus/vSysLab_Heptascelio_no-states_10+chars.nex b/Tests/Nexus/vSysLab_Heptascelio_no-states_10+chars.nex
new file mode 100644
index 0000000..a79283e
--- /dev/null
+++ b/Tests/Nexus/vSysLab_Heptascelio_no-states_10+chars.nex
@@ -0,0 +1,105 @@
+#NEXUS
+
+BEGIN TAXA;
+ DIMENSIONS NTAX=19;
+ TAXLABELS
+ 'Heptascelio albipes'
+ 'Heptascelio anthonyi'
+ 'Heptascelio aquilinus'
+ 'Heptascelio bivius'
+ 'Heptascelio castor'
+ 'Heptascelio dayi'
+ 'Heptascelio dispar'
+ 'Heptascelio hamatus'
+ 'Heptascelio lateralis'
+ 'Heptascelio noyesi'
+ 'Heptascelio orarius'
+ 'Heptascelio paralugens'
+ 'Heptascelio strigatus'
+ 'Heptascelio teres'
+ 'Heptascelio watshami'
+ 'Heptascelio lugens'
+ 'Heptascelio sicarius'
+ 'Heptascelio striatosternus'
+ 'A'
+ ;
+END;
+
+BEGIN CHARACTERS;
+ DIMENSIONS NCHAR=43;
+ FORMAT DATATYPE=STANDARD MISSING=? GAP=-
+ SYMBOLS="0123456789A"
+ ;
+ CHARSET 'Head' = 1 2 3 4 5 6 7 8 9 10;
+ CHARSET 'Antenna' = 11 12;
+ CHARSET 'Mesosoma' = 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31;
+ CHARSET 'Legs' = 32 33;
+ CHARSET 'Wings' = 34 35 36;
+ CHARSET 'Metasoma' = 37 38 39 40 41 42 43;
+ CHARSTATELABELS
+ 1 'body color of female'/'entirely dark, dark brown to black' 'bicolored: head and mesosoma dark brown, metasoma yellow',
+ 2 'body color of male'/'entirely black' 'entirely dark brown' 'head and metasoma black, mesosoma reddish brown' 'head and mesosoma black, metasoma dark brown',
+ 3 'sculpture of occiput and posterior vertex'/'areolate-rugose' 'with strong transverse rugae' 'coarsely punctate' 'irregularly rugulose' 'areolate rugose, with distinct transverse tendency' 'with strong arched rugae',
+ 4 'sculpture of frons below ocellus in female'/'transversely striate' 'areolate rugose' 'coarsely punctate' 'dorsoventrally striate',
+ 5 'shape of dorsal margin of frontal scrobe'/'evenly arcuate, weakly produced' 'distinctly incised medially, strongly produced anteriorly' 'ecarinate, rounded onto dorsal portion of frons',
+ 6 'sculpture of frontal depression in female'/'transversely striate ventrally, with smooth field dorsomedially' 'entirely smooth' 'with short transverse striae on sides, medially entirely smooth' 'irregularly areolate ventrally, smooth dorsally and medially' 'coarsely punctate ventrally and laterally, otherwise smooth' 'transversely rugose throughout' 'diagonally striate laterally, smooth medially' 'coarsely punctate' 'punctate, with broad smooth spaces between punctures',
+ 7 'sculpture of gena'/'with irregular dorsoventral rugae' 'with irregular, nearly longitudinal rugulae' 'coarsely punctate' 'areolate rugose, with distinct dorsoventral tendency',
+ 8 'setation of gena'/'with short, uniform setae, with few short bristles interspersed' 'with numerous strong, erect, dark bristles amid shorter setation' 'with short, uniform setae, without bristles',
+ 9 'shape of mandibles'/'normal length, crossed transversely below head when closed, tips overlapping' 'elongate, extending ventrally below head, tips meeting apically',
+ 10 'sculpture of male mesoscutellum'/'coarsely reticulate' 'coarsely reticulate, with distinct longitudinal elements' 'areolate rugose' 'coarsely longitudinally areolate rugose' 'longitudinally strigose',
+ 11 'color of female antenna'/'A1-A2 pale brown, A3-A6 brown, A7-A12 dark brown' 'A1 brown, A2-A5 light brown, A6-A12 dark brown' 'dark brown to black throughout' 'dark brown to black throughout, extremities of A1, A2 lighter in color' 'brown to dark brown throughout' 'A1 brownish yellow, A2-A5 yellow, A6-A12 dark brown to black' 'A1 dark brown, A2-A5 off-white, A6-A12 dark brown to black' 'A1 dark brown, A2-A4 brownish yellow, A5-A12 dark brown to black' 'A1 brown, A2-A5 yellow, A6-A12 [...]
+ 12 'color of male antenna'/'light brown throughout' 'brown to dark brown throughout' 'brown' 'A1-A5 brown, A6-A12 pale brown',
+ 13 'sculpture of dorsal pronotum'/'longitudinally aciculate, with smooth interspaces' 'areolate rugose' 'coarsely reticulate' 'coarsely punctate' 'areolate rugose, with distinct longitudinal tendency' 'longitudinally strigose',
+ 14 'notaulus',
+ 15 'sculpture of mesoscutum in female'/'coarsely areolate-rugose' 'finely longitudinally aciculate with smooth interspaces' 'coarsely longitudinally strigose with smooth interspaces' 'longitudinally strigose, with strong transverse sculpture in interspaces' 'sparsely punctate, with broad smooth areas between punctures' 'longitudinally strigose with fine sculpture in interspaces' 'sparsely punctate, with broad smooth areas between punctures, with weak indications of longitudinal rugulae [...]
+ 16 'sculpture of mesoscutum in male'/'with dense umbilicate punctures' 'finely longitudinally aciculate' 'coarsely areolate rugose' 'sparsely punctate, largely smooth' 'longitudinally strigose, with strong transverse sculpture in interspaces' 'sparsely punctate, with broad smooth areas between punctures, with weak indications of longitudinal rugulae' 'coarsely areolate rugose with distinct longitudinal tendency' 'coarsely longitudinally areolate rugose',
+ 17 'parapsidal line'/'present, clearly impressed' 'absent' 'present, evanescent',
+ 18 'mesoscutellum shape'/'distinctly narrowed apically, with median longitudinal depression' 'broad, weakly convex, apex broadly rounded' 'nearly parallel-sided, apex weakly excavate, with weak median longitudinal impression' 'semicircular, posteriorly distinctly sloping ventrad, without distinct median longitudinal furrow' 'roughly trapezoidal, sides weakly converging apically, apex excavate, with distinct median longitudinal furrow' 'roughly semicircular, with distinct median longitu [...]
+ 19 'sculpture of female mesoscutellum'/'coarsely reticulate, with distinct longitudinal elements' 'areolate rugose' 'longitudinally strigate' 'coarsely punctate',
+ 20 'mesoscutellar points of female'/'absent, scutellum with rounded lobes laterally' 'broad, short, acute' 'narrow, elongate, acute' 'forming short, acute teeth' 'forming short, sharp angles, not protruding' 'flattened, broadly rounded apically' 'moderately elongate, narrow, acute, curving medially',
+ 21 'mesoscutellar points of male'/'apex of scutellum transverse, posterolateral corners very weakly produced' 'apex of scutellum rounded, points entirely absent' 'narrow, short, acute' 'flattened, broadly rounded apically' 'broad, short, acute',
+ 22 'posterior surface of propodeum'/'with distinct straight longitudinal paramedian keel arising from apex of inner propodeal projection' 'with medial area covered by elongate areolae, bounded laterally by irregular carinae' 'with central row of quadrate areolae above large smooth central area, bounded laterally by carinae' 'irregularly areolate, without distinct paramedian keel' 'with medial area with elongate areolae, bounded laterally by distinct paramedial carinae' 'with sinuous pa [...]
+ 23 'length of outer propodeal projection in female'/'extending beyond apex of T1' 'extending at most to midlength of T1' 'rather short, extending at most to midlength of T1, distinctly longer than inner propodeal projection' 'moderately elongate, not reaching midpoint of length of T1, subequal in length to inner propodeal projection' 'elongate, extending near or beyond apex of T1, distinctly longer than inner propodeal projection' 'moderately elongate, extending to midpoint of length o [...]
+ 24 'sculpture of propodeum between inner and outer propodeal projections'/'areolate rugose' 'coarsely punctate' 'largely smooth, with variably developed diagonal carinae' 'longitudinally carinate with punctures in interspaces' 'areolate rugose, with distinct longitudinal tendency',
+ 25 'netrion shape'/'moderately wide, parallel-sided in ventral half, open' 'strongly narrowed, nearly linear, foveae on surface nearly as high as wide' 'narrow, gradually widened ventrally, foveae on surface distinctly wider than high' 'moderately wide, nearly parallel-sided, with two columns of large punctures' 'moderately wide, nearly parallel-sided, weakly widened ventrally, with single column of large foveae' 'moderately wide, weakly fusiform, with column of elongate foveae' 'moder [...]
+ 26 'netrion setation'/'glabrous' 'densely setose',
+ 27 'sculpture of lateral pronotum posterior to epomial carina'/'smooth' 'coarsely sculptured' 'with large smooth field dorsally, elsewhere finely rugulose' 'with smooth field dorsally, elsewhere areolate rugose' 'with small smooth field near posterior margin, elsewhere finely rugulose' 'coarsely punctate' 'coarsely rugulose throughout' 'coarsely foveolate immediately behind epomial carina, elsewhere sparsely punctate',
+ 28 'setation of posterior half of lateral pronotum'/'largely glabrous, setae limited to small patch near spiracle' 'with numerous scattered setae, densest near spiracle',
+ 29 'sculpture of mesopleural scrobe'/'smooth' 'coarsely areolate to rugose' 'finely microreticulate to smooth' 'coarsely punctate' 'with narrow smooth field medially, elsewhere diagonally striate' 'smooth medially, elsewhere rugose',
+ 30 'sculpture of lower mesepisternum'/'smooth' 'rugulose to punctate' 'finely microreticulate, nearly smooth, sparsely punctate' 'with distinctly separated, coarse punctures' 'largely smooth, with 2-3 longitudinal lines of punctures',
+ 31 'sculpture of metapleuron'/'areolate rugose' 'irregularly longitudinally rugose, with smooth field medially' 'largely smooth' 'areolate rugose above, with smooth or finely sculptured field ventrally' 'coarsely punctate' 'with smooth field in posteroventral quadrant, elsewhere areolate rugose' 'coarsely areolate to rugose throughout' 'with smooth field medially, elsewhere coarsely punctate',
+ 32 'color of legs'/'coxae and femora dark brown, tibiae and tarsi yellow' 'coxae dark brown, femora largely brown, legs otherwise brownish yellow' 'tarsi light brown, otherwise brown to dark brown throughout' 'coxae dark brown to black, legs otherwise brown to dark brown' 'coxae, femora dark brown, tarsi brown, tibiae pale, contrasting with femora and tibiae' 'coxae and femora dark brown, otherwise brown' 'coxae dark brown, otherwise yellowish brown throughout' 'coxae dark brown to bla [...]
+ 33 'long dark bristles on legs'/'present on all femora, tibiae' 'absent' 'present on hind femur only',
+ 34 'color of wing membrane'/'generally hyaline throughout' 'with faint brown infuscation throughout, with strong dark streak below submarginal vein' 'hyaline basally, infuscate in apical two-thirds, streak below submarginal vein weak' 'with weak general brown infuscation, longitudinal dark streak below submarginal vein moderately developed' 'with weak general brown infuscation, longitudinal streak below submarginal vein weak' 'moderately infuscate throughout, streak below submarginal v [...]
+ 35 'fore wing venation'/'well-developed, with R, r-rs clearly visible' 'reduced to basal stub of R, with pale pseudostigma in wing disk',
+ 36 'submarginal vein bristles'/'with 1-2 dark bristles near base of fore wing' 'with dark bristles extending length of submarginal vein' 'with dark bristles in basal half of submarginal vein' 'absent',
+ 37 'T1 depression'/'glabrous or sparsely setose' 'moderately to densely setose',
+ 38 'sublateral lamella on T1'/'distinctly raised more or less perpendicular to T1' 'indicated as low carina' 'absent',
+ 39 'sculpture of T2-T4'/'longitudinally striate, with fine cross striae, punctures, smooth or finely punctulate transverse band apically' 'longitudinally striate with cross striae laterally, sculpture effaced medially',
+ 40 'setation of laterotergites'/'glabrous' 'setose' 'very sparsely setose',
+ 41 'sculpture of S2, S3 of female'/'strongly longitudinally rugose' 'nearly smooth, with scattered small punctures' 'irregularly longitudinally rugulose, apex of S3 nearly smooth' 'longitudinally rugulose laterally, nearly smooth medially, with scattered small punctures',
+ 42 'sculpture of S2, S3 of male'/'longitudinal rugulae largely effaced, strongest laterally, otherwise smooth with scattered setigerous punctures' 'strongly longitudinally rugose' 'nearly smooth, with scattered setigerous punctures',
+ 43 'distribution of felt fields'/'present on S2-S3' 'present on S3-S4' 'present on S3 only'
+ ;
+ MATRIX
+ 'Heptascelio albipes' 001(01)0(06)0004(46)15?5413224(04)4(23)(12)030(45)1(16)(47)1101010(12)320
+ 'Heptascelio anthonyi' 0?1103000?1?1?3?1012?02010310111120101020?0
+ 'Heptascelio aquilinus' ?130161110?12??301??11?020402202001001002?2
+ 'Heptascelio bivius' 0?2221200?4?3?7?1134?23051713049130310013?0
+ 'Heptascelio castor' 0321053002324?341213203241610151130110010?0
+ 'Heptascelio dayi' 0?2224220?2?3?4?1234?33131510343110210010?0
+ 'Heptascelio dispar' 1113100101000?111?0500201020213000100200121
+ 'Heptascelio hamatus' 0(03)010(0137)0002311?02141(13)(24)33(01)41(56)1(01)1(03)(26)(12)30110010(01)0
+ 'Heptascelio lateralis' 0?0102300?5?4?0?1510?(15)5040312101120102013?0
+ 'Heptascelio noyesi' 0(03)(05)3010002225?26060320441000002115010111120
+ 'Heptascelio orarius' 0201010002431?0?163333404000002803010111?20
+ 'Heptascelio paralugens' 0022282002224?43121113516151036614011001310
+ 'Heptascelio strigatus' 0011000003725?2(27)132230402021116916010202120
+ 'Heptascelio teres' 0?4301000?8?3?4?0630?6645000002A150201010?0
+ 'Heptascelio watshami' 0001000002901?32161330401020016922010101020
+ 'Heptascelio lugens' 0022043002315?6502162350(05)1(16)1013114011001010
+ 'Heptascelio sicarius' 0?2301220?9?3?4?2230?7403030047A160101022?0
+ 'Heptascelio striatosternus' 0041000102434?34141330312121017503011001000
+ 'A' ???????????????????????????????????????????
+ ;
+END;
diff --git a/Tests/Nexus/vSysLab_Oreiscelio_discrete+continuous.nex b/Tests/Nexus/vSysLab_Oreiscelio_discrete+continuous.nex
new file mode 100644
index 0000000..ed45ef8
--- /dev/null
+++ b/Tests/Nexus/vSysLab_Oreiscelio_discrete+continuous.nex
@@ -0,0 +1,162 @@
+#NEXUS
+
+BEGIN TAXA;
+ DIMENSIONS NTAX=19;
+ TAXLABELS
+ 'Oreiscelio zulu'
+ 'Oreiscelio aequalis'
+ 'Oreiscelio alluaudi'
+ 'Oreiscelio badius'
+ 'Oreiscelio coracinus'
+ 'Oreiscelio cultrarius'
+ 'Oreiscelio gryphus'
+ 'Oreiscelio iommii'
+ 'Oreiscelio magnipennis'
+ 'Oreiscelio majikununuensis'
+ 'Oreiscelio megadontus'
+ 'Oreiscelio naevus'
+ 'Oreiscelio paradoxus'
+ 'Oreiscelio rostratus'
+ 'Oreiscelio rugosus'
+ 'Oreiscelio scapularis'
+ 'Oreiscelio sechellensis'
+ 'Oreiscelio turneri'
+ 'Oreiscelio zuzkae'
+ ;
+END;
+
+BEGIN CHARACTERS;
+ DIMENSIONS NCHAR=68;
+ FORMAT DATATYPE=STANDARD MISSING=? GAP=-
+ SYMBOLS="0123456789"
+ ;
+ CHARSET 'General' = 1;
+ CHARSET 'Antenna' = 2 3 4;
+ CHARSET 'Head' = 5 6 7 8 9 10 11 12 13 14 15 16 17 18;
+ CHARSET 'Legs' = 19;
+ CHARSET 'Mesosoma' = 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42;
+ CHARSET 'Metasoma' = 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68;
+ CHARSTATELABELS
+ 1 'body color'/'dark brown to black' 'black' 'head and mesosoma black; metasoma dark brown to black' 'metasoma often lighter than head and mesosoma',
+ 2 'color of antennae in female'/'dark brown to black throughout' 'A1-A6 yellow; A7-A12 dark brown to black' 'A1-A6 pale brown; A7-A12 dark brown' 'pale brown throughout' 'A1, A3-A6 dark brown; A2 yellow; A7-A12 black' 'A1 black, A2-A6 pale brown, A7-A12 black' 'A1 dark brown, A2-A6 yellow, A7-A12 dark brown' 'A1-A6 brown; A7-A12 dark brown to black' 'A1 brown, A2-A6 yellow, A7-A12 brown' 'A1 brown, A2 pale brown, A3-A12 brown',
+ 3 'color of antennae in male'/'A1-A2 brown; A3-A12 variably becoming yellow apically, antennomeres often yellow ventrally' 'A1 brown; A2-A12 yellow' 'brown' 'A1-A2 brown; A3-A12 yellow',
+ 4 'seta on pedicel of antenna in female'/'present, reaching past apex of A3' 'present, not reaching apex of A3' 'present, reaching to apex of A3',
+ 5 'color of setae on the head'/'white' 'brown' 'yellow',
+ 6 'sculpture of dorsal frons between frontal carina and median ocellus'/'coarsely foveate' 'areolate' 'areolate, effaced medially' 'foveate with contiguous cells' 'transversely rugose',
+ 7 'preocellar pit in females'/'absent' 'indicated by a small round depression' 'present as a distinct pit',
+ 8 'transverse rugae at vertex'/'absent' 'present',
+ 9 'sculpture of posterior vertex'/'areolate' 'coarsely foveate' 'transversely strigose',
+ 10 'sculpture of occipital rim'/'areolate' 'coarsely foveate, cells often contiguous.',
+ 11 'interantennal process'/'extended dorsally into medial flange, flange reaching from near apex of interantennal process to frons' 'diverging into two carinae that extend laterally along surface of frons' 'simple' 'with semiparallel carinae dorsally that do not extend laterally onto frons',
+ 12 'number of anteriorly projecting setae on anteclypeus'/'6' '8',
+ 13 'color of mandibles in male'/'yellow throughout, teeth brown' 'basal half black; apical half pale brown' 'black throughout' 'black throughout with brown teeth' 'basal half black, apical half brown' 'basal half black, apical half brown, teeth dark brown to black',
+ 14 'color of mandibles in female'/'brown throughout with teeth black' 'basal half black; apical half dark brown' 'yellow throughout with teeth brown' 'dark brown throughout' 'black throughout' 'basal half black; apical half pale brown' 'brown throughout' 'basal half black; apical half yellow' 'pale brown with teeth dark brown',
+ 15 'mandibular teeth in female'/'dorsal tooth larger' 'teeth of equal size' 'ventral tooth larger, but less than 2x as long as dorsal tooth' 'ventral tooth at least twice as long and wide at base than dorsal tooth',
+ 16 'sculpture of ventral gena anterior to genal carina'/'row of areolae' 'row of shallow areolae with reduced ridges between cells' 'row of contiguous punctures' 'row of well separated punctures' 'dorsoventrally rugose',
+ 17 'sculpture of ventral gena posterior to genal carina'/'areolate' 'smooth' 'mostly smooth, with sparse large punctures' 'mostly smooth, with sparse small punctures' 'coarsely and irregularly punctate' 'irregularly rugose',
+ 18 'shape of anteclypeus'/'striplike, straight to slightly convex' 'concave medially, with points laterally',
+ 19 'color of legs'/'entirely yellow' 'dark brown, tarsi and distal portion of tibiae light brown' 'coxae brown, legs otherwise yellow' 'coxae dark brown, femora light brown, tibiae and tarsi yellow' 'brown, tarsi and distal portion of tibiae yellow' 'brown, distal portion of tibiae and tarsomeres 1-4 yellow, apical tarsomere brown' 'coxae and femora dark brown, trochanters, tibia and tarsi yellowish-brown' 'coxae black; femora brown; trochanters, tibia and tarsomeres 1-4 yellowish-brow [...]
+ 20 'sculpture of dorsal pronotum'/'areolate' 'areolate with expanded ridges' 'shallowly areolate',
+ 21 'color of setae on the dorsal mesosoma'/'white' 'brown' 'yellow',
+ 22 'sculpture of medial mesoscutum'/'coarsely foveate' 'areolate' 'areolate, with pronounced longitudinal ridges' 'shallowly areolate, becoming smooth posteriorly' 'longitudinally strigose throughout' 'partially to mostly effaced',
+ 23 'sculpture of lateral mesoscutum'/'same as medial mesoscutum with a subtle confusion of sculptural elements' 'smooth throughout' 'with an elevated patch of smoother sculpture, bounded by mesoscutal humeral sulcus laterally, extending to scuto-scutellar sulcus posteriorly' 'with small patch of smoother sculpture, formed by fusion of sculptural ridges, smooth patch not extending to scuto-scutellar sulcus' 'areolate',
+ 24 'notaulus in female'/'present' 'absent',
+ 25 'parapsidal line'/'delimited' 'indistinguishable',
+ 26 'sculpture of scutellum'/'coarsely foveate' 'areolate',
+ 27 'median furrow on scutellum'/'absent' 'present as a weak indentation' 'present as a distinct furrow reaching from posterior to anterior margin',
+ 28 'posterior margin of scutellum'/'emarginate' 'deeply notched' 'convex',
+ 29 'outer projection of the propodeum'/'present as distinct corner or posteriorly projecting spine' 'rounded',
+ 30 'posterior margin of propodeal shelf between inner and outer propodeal projections in female'/'nearly straight, perpendicular to lateral margin of propodeal shelf' 'concave' 'nearly straight, strongly oblique',
+ 31 'posterior margin of propodeal shelf between inner and outer propodeal projections in male'/'nearly straight' 'concave',
+ 32 'sculpture of ventral pronotum'/'foveate reticulate, becoming smooth at anterolateral corner' 'obliterated' 'mostly smooth with longitudinal rugulae ventrally' 'mostly smooth with small foveolae around antespiracular patch',
+ 33 'anterior pronotal pit'/'absent' 'small' 'large',
+ 34 'pronotal cervical sulcus'/'poorly defined' 'well defined by contiguous punctures',
+ 35 'mesepimeral sulcus'/'indicated by dorsoventral line of foveae' 'crenulate' 'indicated by a row of faint and shallow depressions',
+ 36 'sculpture of femoral depression in female'/'smooth throughout' 'transversely striate throughout' 'anterior half smooth, posterior half finely foveate' 'finely foveate throughout' 'anterior half smooth, posterior half longitudinally striate',
+ 37 'sculpture of femoral depression in male'/'smooth throughout' 'smooth medially, bounded by small foveae' 'finely foveate throughout' 'with weak transverse striae throughout' 'anterior half smooth, posterior half finely foveate' 'anterior half smooth, posterior half longitudinally striate',
+ 38 'mesopleural carina'/'indicated by one or two rows of punctures' 'present as one or two carinae' 'absent' 'weakly defined by fine ridges',
+ 39 'sculpture of ventral mesepisternum'/'smooth' 'foveate' 'with smooth area surrounded by small foveae' 'irregularly areolate',
+ 40 'pilosity of metapleural triangle in female'/'present as many short fine white setae' 'glabrous or with at most 3 small setae',
+ 41 'pilosity of metapleural triangle in male'/'absent' 'present as many short fine white setae' 'sparsely setose',
+ 42 'sculpture of ventral area of metapleuron'/'smooth' 'foveate reticulate' 'foveate reticulate, with slightly appressed ridges',
+ 43 'submarginal ridge on T1'/'percurrent, well-defined' 'indicated by an irregular ridge' 'absent' 'partially fused with marginal carina',
+ 44 'sublateral carina on T2'/'percurrent' 'absent' 'present in anterior half',
+ 45 'sculpture of T2-T3'/'reticulate rugose' 'longitudinally striate' 'longitudinally strigose with rugose interstices' 'weakly rugulose',
+ 46 'sculpture of T4'/'reticulate with finely rugulose interstices' 'longitudinally strigose' 'weakly rugulose' 'longitudinally striate' 'reticulate rugose',
+ 47 'sculpure of T5'/'longitudinally strigose' 'weakly rugulose' 'reticulate rugose',
+ 48 'shape of subapical T6 in female in dorsal view'/'semicircular, width along anterior margin twice the length.' 'short, width along anterior margin at least three times the length' 'trapezoidal, basal width twice the apical width',
+ 49 'apex of T7 in male'/'slightly convex' 'bispinose' 'straight to slightly concave, with small points laterally',
+ 50 'longitudinal medial keel on S1'/'absent' 'present',
+ 51 'sculpture of medial S2 in female'/'punctate reticulate' 'reticulate rugose' 'foveolate' 'punctate',
+ 52 'sculpture of medial S3 in female'/'longitudinally strigose' 'foveate' 'effaced to smooth',
+ 53 'sculpture of medial S4 in female'/'longitudinally strigose' 'effaced to smooth',
+ 54 'sculpture of medial S5 in female'/'reticulate rugose' 'longitudinally strigose' 'effaced to smooth',
+ 55 'sculpture of lateral S3 in female'/'reticulate rugose' 'foveolate' 'longitudinally strigose' 'smooth' 'punctate crenulate' 'rugulose',
+ 56 'sculpture of lateral S4 in female'/'reticulate rugose' 'longitudinally strigose' 'foveolate' 'smooth' 'rugulose' 'punctate crenulate',
+ 57 'sculpture of lateral S5 in female'/'reticulate rugose' 'foveate' 'longitudinally strigose' 'punctate crenulate',
+ 58 'sculpture of marginal depression of S2 in female'/'mostly smooth, with scattered punctures or shallow rugulae' 'rugulose' 'finely punctate throughout' 'smooth',
+ 59 'sculpture of marginal depression of S3 in female'/'mostly smooth, with scattered punctures or shallow rugulae' 'smooth' 'finely punctate throughout' 'rugulose',
+ 60 'sculpture of marginal depression of S4 in female'/'mostly smooth with scattered punctures or shallow rugulae' 'finely punctate throughout' 'rugulose',
+ 61 'sculpture of marginal depression of S5 in female'/'rugulose' 'mostly smooth with scattered punctures' 'finely punctate throughout',
+ 62 'apical spine on S6 in female'/'not extending beyond apex of T6 and not visible in dorsal view' 'extending beyond apex of T6 and visible in dorsal view',
+ 63 'sculpture of medial S2 in male'/'punctate reticulate' 'longitudinally strigose' 'foveolate' 'punctate' 'reticulate rugose',
+ 64 'sculpture of medial S3 in male'/'smooth' 'foveate' 'longitudinally reticulate' 'weakly longitudinally strigose',
+ 65 'sculpture of lateral S3 in male'/'smooth' 'reticulate rugose' 'longitudinally strigose' 'foveolate' 'rugulose',
+ 66 'sculpture of lateral S4 in male'/'smooth' 'rugulose' 'longitudinally strigose' 'foveolate' 'reticulate rugose',
+ 67 'sculpture of medial S5 in male'/'smooth' 'foveate' 'longitudinally strigose',
+ 68 'sculpture of lateral S5 in male'/'foveolate' 'longitudinally strigose' 'smooth' 'reticulate rugose' 'rugulose'
+ ;
+ MATRIX
+ 'Oreiscelio zulu' 1000010100003511(05)06001(04)111110110111(13)312022200421100(12)114531320(01)031221
+ 'Oreiscelio aequalis' 25?021210010?12000402101011001?021(01)(13)?110?1002102?0021245310011??????
+ 'Oreiscelio alluaudi' 1(17)1020(12)11020020(02)(24)0012020010(02)01102102(23)01102210421010212121(02)(02)200003300
+ 'Oreiscelio badius' (03)3000(12)(01)1002012(12)(04)(02)0(45)00(12)0111(01)(02)010(02)(01)(01)(01)4(03)(13)2010(12)10101201212(02)(01)013201002101
+ 'Oreiscelio coracinus' 0000(012)1(012)10010(23)(145)(12)0(04)0(15)0(012)(12)01(01)1(01)00110(12)1(01)(0123)(023)1(12)01(012)0(01)0(14)01100(012)11(015)(01)(02)(012)(02)(02)01(02)1(034)(013)(01)(013)
+ 'Oreiscelio cultrarius' 2(01)?000(12)11030?(23)(12)140(23)0010101(01)001?0210(03)?(13)10?20(12)0101?00212(14)(15)(02)00(02)(01)1??????
+ 'Oreiscelio gryphus' 10?011210010?1(12)04010110111(01)001?01103?130?1010101?002120001(03)201??????
+ 'Oreiscelio iommii' 10?021110000?11130122311010101?10020?200?0023211?0021134222201??????
+ 'Oreiscelio magnipennis' 16?011(12)11010?61200701(12)01011001?01111?110?2022101?002120(01)(02)13201??????
+ 'Oreiscelio majikununuensis' 04?22(01)000021?031(04)160210101(01)000?0(12)002?0(13)0?0000401?00(12)121(02)0(03)0201??????
+ 'Oreiscelio megadontus' 11?020211021?23130202231010001?01100?020?0300101?0021012031201??????
+ 'Oreiscelio naevus' 10000(01)01(01)02002(012)04030012(01)01(01)(02)011(02)110(03)(34)10010210(04)21003212(01)(02)(01)(12)(23)(12)(02)0003(134)(01)(03)
+ 'Oreiscelio paradoxus' 10?101211010?5(12)150(14)00(12)01010212?0100(013)?3(02)0?1000101?00(02)(01)(12)4120(01)001??????
+ 'Oreiscelio rostratus' 1(01)002(01)01(01)000020(23)(24)00(01)20200002011321000021(01)(02)201121010212(01)2(01)(13)(13)200(04)(02)(13)(34)0(03)
+ 'Oreiscelio rugosus' 20202(14)(12)1(02)0201(1567)1110502(1245)(013)1(01)1(01)(02)011(012)(01)(01)(01)4(35)(13)201(02)(0123)(12)0(14)0100221(12)(03)(03)0(13)(13)20100(04)1(02)(14)
+ 'Oreiscelio scapularis' 11002(03)011120020(23)(24)001202000020110210010210020130000?(02)(01)(12)21231(02)001(03)22(02)1
+ 'Oreiscelio sechellensis' 12312011102046112020213101(12)1011001004(01)201(12)010(14)(02)120321233(03)302(01)0300002
+ 'Oreiscelio turneri' (03)(05689)(02)221(01)100105(168)3(12)(04)0402(12)0111(01)00110(01)0(01)(012)(0234)1(12)01(012)(012)(12)0(14)(02)11(01)1212(45)(145)011001(02)(01)(23)(123)(01)(0134)
+ 'Oreiscelio zuzkae' 11?101(012)10020?221(45)020(02)100012101?021(01)(123)?1(12)0?(012)(01)2(02)(13)01?01(12)11(01)(02)(02)(01)00(01)0??????
+ ;
+END;
+
+BEGIN CHARACTERS;
+ DIMENSIONS NCHAR=2;
+ FORMAT DATATYPE=CONTINUOUS MISSING=?
+ ITEMS=(MIN MAX MEDIAN AVERAGE VARIANCE SAMPLESIZE)
+ ;
+ CHARSTATELABELS
+ 1 'body length of female'
+ 2 'body length of male'
+ ;
+ MATRIX
+ 'Oreiscelio zulu' (2.40 2.50 ? 2.45 0.0025 3)?
+ 'Oreiscelio aequalis' (3.30 3.30 ? 3.30 ? 1)?
+ 'Oreiscelio alluaudi' (3.20 3.80 ? 3.50 ? 10)(3.01 3.01 ? 3.01 ? 1)
+ 'Oreiscelio badius' (2.20 2.40 ? 2.30 0.0036 20)(2.10 2.10 ? 2.10 ? 1)
+ 'Oreiscelio coracinus' (2.30 3.10 ? 2.60 0.0400 20)(2.30 2.90 ? 2.70 0.0400 20)
+ 'Oreiscelio cultrarius' (3.00 3.20 ? 3.10 0.0025 5)?
+ 'Oreiscelio gryphus' (2.90 3.30 2.90 3.00 0.0289 5)?
+ 'Oreiscelio iommii' (2.42 2.42 2.42 2.42 ? 1)?
+ 'Oreiscelio magnipennis' (3.10 3.20 ? 3.20 0.0100 2)?
+ 'Oreiscelio majikununuensis' ??
+ 'Oreiscelio megadontus' (4.10 4.10 4.10 4.10 ? 4.1)?
+ 'Oreiscelio naevus' (2.20 3.00 ? 2.70 0.0900 20)(2.20 3.00 ? 2.70 0.0900 20)
+ 'Oreiscelio paradoxus' (3.00 3.00 3.00 3.00 0.0000 2)?
+ 'Oreiscelio rostratus' (3.30 3.70 ? 3.50 0.0100 20)(3.40 3.90 ? 3.60 0.0400 5)
+ 'Oreiscelio rugosus' (2.10 2.70 ? 2.40 0.0100 20)(2.30 2.50 ? 2.50 ? 4)
+ 'Oreiscelio scapularis' (3.10 4.10 ? 3.70 0.0400 15)(3.60 3.80 ? 3.70 0.0100 6)
+ 'Oreiscelio sechellensis' (3.10 3.40 ? 3.20 0.0081 9)(? ? ? 3.00 ? 1)
+ 'Oreiscelio turneri' (2.30 2.90 ? 2.60 0.0400 20)(2.20 2.70 ? 2.40 0.0100 20)
+ 'Oreiscelio zuzkae' (3.30 3.90 ? 3.70 0.0225 14)?
+ ;
+END;
diff --git a/Tests/PAML/Control_files/codeml/m2a_rel.ctl b/Tests/PAML/Control_files/codeml/m2a_rel.ctl
new file mode 100644
index 0000000..129ba49
--- /dev/null
+++ b/Tests/PAML/Control_files/codeml/m2a_rel.ctl
@@ -0,0 +1,25 @@
+seqfile = ../alignment.phylip
+outfile = temp.out
+treefile = ../species.tree
+verbose = 0
+CodonFreq = 2
+cleandata = 1
+fix_blength = 1
+NSsites = 22
+fix_omega = 0
+clock = 0
+runmode = 0
+fix_kappa = 0
+fix_alpha = 1
+alpha = 0
+method = 0
+Malpha = 0
+RateAncestor = 0
+icode = 0
+seqtype = 1
+omega = 1
+getSE = 0
+noisy = 0
+Mgene = 0
+kappa = 4.54006
+model = 0
diff --git a/Tests/PAML/Results/codeml/m2a_rel/m2a_rel-4_6.out b/Tests/PAML/Results/codeml/m2a_rel/m2a_rel-4_6.out
new file mode 100644
index 0000000..afab33d
--- /dev/null
+++ b/Tests/PAML/Results/codeml/m2a_rel/m2a_rel-4_6.out
@@ -0,0 +1,145 @@
+CODONML (in paml version 4.6, August 2012) ../Alignments/alignment.phylip
+Model: One dN/dS ratio for branches,
+Codon frequency model: F3x4
+Site-class models: M2a_rel (3 categories)
+ns = 5 ls = 74
+
+Codon usage in sequences
+--------------------------------------------------------------------------------------------------
+Phe TTT 0 0 0 0 0 | Ser TCT 1 1 1 1 1 | Tyr TAT 0 0 1 0 0 | Cys TGT 3 2 3 3 3
+ TTC 1 1 1 1 1 | TCC 1 1 1 1 1 | TAC 1 1 0 1 1 | TGC 0 1 0 0 0
+Leu TTA 0 0 0 0 0 | TCA 1 1 1 1 1 | *** TAA 0 0 0 0 0 | *** TGA 0 0 0 0 0
+ TTG 1 1 1 1 1 | TCG 0 0 0 0 0 | TAG 0 0 0 0 0 | Trp TGG 0 0 0 0 0
+--------------------------------------------------------------------------------------------------
+Leu CTT 0 0 0 0 0 | Pro CCT 0 0 0 0 0 | His CAT 0 0 0 0 0 | Arg CGT 0 0 0 0 0
+ CTC 2 2 2 2 2 | CCC 1 1 1 1 1 | CAC 0 0 0 0 0 | CGC 0 0 0 0 0
+ CTA 1 1 1 1 1 | CCA 3 3 3 3 3 | Gln CAA 0 0 0 0 0 | CGA 1 1 1 1 1
+ CTG 3 3 3 3 3 | CCG 0 0 0 0 0 | CAG 1 1 1 1 1 | CGG 0 0 0 0 0
+--------------------------------------------------------------------------------------------------
+Ile ATT 2 2 2 2 2 | Thr ACT 0 0 0 0 0 | Asn AAT 2 2 2 2 2 | Ser AGT 0 0 0 0 0
+ ATC 2 2 2 2 2 | ACC 0 0 0 0 0 | AAC 0 0 0 0 0 | AGC 0 0 0 0 0
+ ATA 0 0 0 0 0 | ACA 2 2 2 2 2 | Lys AAA 5 5 5 5 5 | Arg AGA 2 2 2 2 2
+Met ATG 3 3 3 3 3 | ACG 0 0 0 0 0 | AAG 5 5 5 5 5 | AGG 0 0 0 0 0
+--------------------------------------------------------------------------------------------------
+Val GTT 3 3 3 3 1 | Ala GCT 0 0 0 0 0 | Asp GAT 2 2 2 2 2 | Gly GGT 0 0 0 0 0
+ GTC 0 0 0 0 1 | GCC 0 0 0 0 0 | GAC 4 4 4 4 4 | GGC 3 3 3 3 3
+ GTA 3 3 3 3 4 | GCA 0 0 0 0 0 | Glu GAA 8 8 8 8 8 | GGA 1 1 1 1 1
+ GTG 2 2 2 2 2 | GCG 0 0 0 0 0 | GAG 4 4 4 4 4 | GGG 0 0 0 0 0
+--------------------------------------------------------------------------------------------------
+
+Codon position x base (3x4) table for each sequence.
+
+#1: Homo_sapie
+position 1: T:0.12162 C:0.16216 A:0.31081 G:0.40541
+position 2: T:0.31081 C:0.12162 A:0.43243 G:0.13514
+position 3: T:0.17568 C:0.20270 A:0.36486 G:0.25676
+Average T:0.20270 C:0.16216 A:0.36937 G:0.26577
+
+#2: Pan_troglo
+position 1: T:0.12162 C:0.16216 A:0.31081 G:0.40541
+position 2: T:0.31081 C:0.12162 A:0.43243 G:0.13514
+position 3: T:0.16216 C:0.21622 A:0.36486 G:0.25676
+Average T:0.19820 C:0.16667 A:0.36937 G:0.26577
+
+#3: Gorilla_go
+position 1: T:0.12162 C:0.16216 A:0.31081 G:0.40541
+position 2: T:0.31081 C:0.12162 A:0.43243 G:0.13514
+position 3: T:0.18919 C:0.18919 A:0.36486 G:0.25676
+Average T:0.20721 C:0.15766 A:0.36937 G:0.26577
+
+#4: Pongo_pygm
+position 1: T:0.12162 C:0.16216 A:0.31081 G:0.40541
+position 2: T:0.31081 C:0.12162 A:0.43243 G:0.13514
+position 3: T:0.17568 C:0.20270 A:0.36486 G:0.25676
+Average T:0.20270 C:0.16216 A:0.36937 G:0.26577
+
+#5: Macaca_mul
+position 1: T:0.12162 C:0.16216 A:0.31081 G:0.40541
+position 2: T:0.31081 C:0.12162 A:0.43243 G:0.13514
+position 3: T:0.14865 C:0.21622 A:0.37838 G:0.25676
+Average T:0.19369 C:0.16667 A:0.37387 G:0.26577
+
+Sums of codon usage counts
+------------------------------------------------------------------------------
+Phe F TTT 0 | Ser S TCT 5 | Tyr Y TAT 1 | Cys C TGT 14
+ TTC 5 | TCC 5 | TAC 4 | TGC 1
+Leu L TTA 0 | TCA 5 | *** * TAA 0 | *** * TGA 0
+ TTG 5 | TCG 0 | TAG 0 | Trp W TGG 0
+------------------------------------------------------------------------------
+Leu L CTT 0 | Pro P CCT 0 | His H CAT 0 | Arg R CGT 0
+ CTC 10 | CCC 5 | CAC 0 | CGC 0
+ CTA 5 | CCA 15 | Gln Q CAA 0 | CGA 5
+ CTG 15 | CCG 0 | CAG 5 | CGG 0
+------------------------------------------------------------------------------
+Ile I ATT 10 | Thr T ACT 0 | Asn N AAT 10 | Ser S AGT 0
+ ATC 10 | ACC 0 | AAC 0 | AGC 0
+ ATA 0 | ACA 10 | Lys K AAA 25 | Arg R AGA 10
+Met M ATG 15 | ACG 0 | AAG 25 | AGG 0
+------------------------------------------------------------------------------
+Val V GTT 13 | Ala A GCT 0 | Asp D GAT 10 | Gly G GGT 0
+ GTC 1 | GCC 0 | GAC 20 | GGC 15
+ GTA 16 | GCA 0 | Glu E GAA 40 | GGA 5
+ GTG 10 | GCG 0 | GAG 20 | GGG 0
+------------------------------------------------------------------------------
+
+
+Codon position x base (3x4) table, overall
+
+position 1: T:0.12162 C:0.16216 A:0.31081 G:0.40541
+position 2: T:0.31081 C:0.12162 A:0.43243 G:0.13514
+position 3: T:0.17027 C:0.20541 A:0.36757 G:0.25676
+Average T:0.20090 C:0.16306 A:0.37027 G:0.26577
+
+
+Nei & Gojobori 1986. dN/dS (dN, dS)
+(Note: This matrix is not used in later ML. analysis.
+Use runmode = -2 for ML pairwise comparison.)
+
+Homo_sapie
+Pan_troglo -1.0000 (0.0000 0.0207)
+Gorilla_go -1.0000 (0.0000 0.0207)-1.0000 (0.0000 0.0421)
+Pongo_pygm -1.0000 (0.0000 0.0000)-1.0000 (0.0000 0.0207)-1.0000 (0.0000 0.0207)
+Macaca_mul -1.0000 (0.0000 0.0421)-1.0000 (0.0000 0.0640)-1.0000 (0.0000 0.0640)-1.0000 (0.0000 0.0421)
+
+
+TREE # 1: (((1, 2), 3), 4, 5); MP score: 4
+check convergence..
+lnL(ntime: 7 np: 12): -308.031579 +0.000000
+ 6..7 7..8 8..1 8..2 7..3 6..4 6..5
+ 0.000004 0.000004 0.000004 0.013438 0.013431 0.000004 0.027839 1.518248 0.999999 0.000000 0.000001 0.000001
+
+Note: Branch length is defined as number of nucleotide substitutions per codon (not per neucleotide site).
+
+tree length = 0.05472
+
+(((1: 0.000004, 2: 0.013438): 0.000004, 3: 0.013431): 0.000004, 4: 0.000004, 5: 0.027839);
+
+(((Homo_sapie: 0.000004, Pan_troglo: 0.013438): 0.000004, Gorilla_go: 0.013431): 0.000004, Pongo_pygm: 0.000004, Macaca_mul: 0.027839);
+
+Detailed output identifying parameters
+
+kappa (ts/tv) = 1.51825
+
+Parameters in M22 (M2a_rel):
+
+
+dN/dS (w) for site classes (K=3)
+
+p: 1.00000 0.00000 0.00000
+w: 0.00000 1.00000 0.00000
+
+dN & dS for each branch
+
+ branch t N S dN/dS dN dS N*dN S*dS
+
+ 6..7 0.000 167.7 54.3 0.0000 0.0000 0.0000 0.0 0.0
+ 7..8 0.000 167.7 54.3 0.0000 0.0000 0.0000 0.0 0.0
+ 8..1 0.000 167.7 54.3 0.0000 0.0000 0.0000 0.0 0.0
+ 8..2 0.013 167.7 54.3 0.0000 0.0000 0.0183 0.0 1.0
+ 7..3 0.013 167.7 54.3 0.0000 0.0000 0.0183 0.0 1.0
+ 6..4 0.000 167.7 54.3 0.0000 0.0000 0.0000 0.0 0.0
+ 6..5 0.028 167.7 54.3 0.0000 0.0000 0.0379 0.0 2.1
+
+
+Naive Empirical Bayes (NEB) analysis
+Time used: 0:05
diff --git a/Tests/PAML/Results/codeml/m2a_rel/m2a_rel-4_7.out b/Tests/PAML/Results/codeml/m2a_rel/m2a_rel-4_7.out
new file mode 100644
index 0000000..94df814
--- /dev/null
+++ b/Tests/PAML/Results/codeml/m2a_rel/m2a_rel-4_7.out
@@ -0,0 +1,145 @@
+CODONML (in paml version 4.7, January 2013) ../Alignments/alignment.phylip
+Model: One dN/dS ratio for branches,
+Codon frequency model: F3x4
+Site-class models: M2a_rel (3 categories)
+ns = 5 ls = 74
+
+Codon usage in sequences
+--------------------------------------------------------------------------------------------------
+Phe TTT 0 0 0 0 0 | Ser TCT 1 1 1 1 1 | Tyr TAT 0 0 1 0 0 | Cys TGT 3 2 3 3 3
+ TTC 1 1 1 1 1 | TCC 1 1 1 1 1 | TAC 1 1 0 1 1 | TGC 0 1 0 0 0
+Leu TTA 0 0 0 0 0 | TCA 1 1 1 1 1 | *** TAA 0 0 0 0 0 | *** TGA 0 0 0 0 0
+ TTG 1 1 1 1 1 | TCG 0 0 0 0 0 | TAG 0 0 0 0 0 | Trp TGG 0 0 0 0 0
+--------------------------------------------------------------------------------------------------
+Leu CTT 0 0 0 0 0 | Pro CCT 0 0 0 0 0 | His CAT 0 0 0 0 0 | Arg CGT 0 0 0 0 0
+ CTC 2 2 2 2 2 | CCC 1 1 1 1 1 | CAC 0 0 0 0 0 | CGC 0 0 0 0 0
+ CTA 1 1 1 1 1 | CCA 3 3 3 3 3 | Gln CAA 0 0 0 0 0 | CGA 1 1 1 1 1
+ CTG 3 3 3 3 3 | CCG 0 0 0 0 0 | CAG 1 1 1 1 1 | CGG 0 0 0 0 0
+--------------------------------------------------------------------------------------------------
+Ile ATT 2 2 2 2 2 | Thr ACT 0 0 0 0 0 | Asn AAT 2 2 2 2 2 | Ser AGT 0 0 0 0 0
+ ATC 2 2 2 2 2 | ACC 0 0 0 0 0 | AAC 0 0 0 0 0 | AGC 0 0 0 0 0
+ ATA 0 0 0 0 0 | ACA 2 2 2 2 2 | Lys AAA 5 5 5 5 5 | Arg AGA 2 2 2 2 2
+Met ATG 3 3 3 3 3 | ACG 0 0 0 0 0 | AAG 5 5 5 5 5 | AGG 0 0 0 0 0
+--------------------------------------------------------------------------------------------------
+Val GTT 3 3 3 3 1 | Ala GCT 0 0 0 0 0 | Asp GAT 2 2 2 2 2 | Gly GGT 0 0 0 0 0
+ GTC 0 0 0 0 1 | GCC 0 0 0 0 0 | GAC 4 4 4 4 4 | GGC 3 3 3 3 3
+ GTA 3 3 3 3 4 | GCA 0 0 0 0 0 | Glu GAA 8 8 8 8 8 | GGA 1 1 1 1 1
+ GTG 2 2 2 2 2 | GCG 0 0 0 0 0 | GAG 4 4 4 4 4 | GGG 0 0 0 0 0
+--------------------------------------------------------------------------------------------------
+
+Codon position x base (3x4) table for each sequence.
+
+#1: Homo_sapie
+position 1: T:0.12162 C:0.16216 A:0.31081 G:0.40541
+position 2: T:0.31081 C:0.12162 A:0.43243 G:0.13514
+position 3: T:0.17568 C:0.20270 A:0.36486 G:0.25676
+Average T:0.20270 C:0.16216 A:0.36937 G:0.26577
+
+#2: Pan_troglo
+position 1: T:0.12162 C:0.16216 A:0.31081 G:0.40541
+position 2: T:0.31081 C:0.12162 A:0.43243 G:0.13514
+position 3: T:0.16216 C:0.21622 A:0.36486 G:0.25676
+Average T:0.19820 C:0.16667 A:0.36937 G:0.26577
+
+#3: Gorilla_go
+position 1: T:0.12162 C:0.16216 A:0.31081 G:0.40541
+position 2: T:0.31081 C:0.12162 A:0.43243 G:0.13514
+position 3: T:0.18919 C:0.18919 A:0.36486 G:0.25676
+Average T:0.20721 C:0.15766 A:0.36937 G:0.26577
+
+#4: Pongo_pygm
+position 1: T:0.12162 C:0.16216 A:0.31081 G:0.40541
+position 2: T:0.31081 C:0.12162 A:0.43243 G:0.13514
+position 3: T:0.17568 C:0.20270 A:0.36486 G:0.25676
+Average T:0.20270 C:0.16216 A:0.36937 G:0.26577
+
+#5: Macaca_mul
+position 1: T:0.12162 C:0.16216 A:0.31081 G:0.40541
+position 2: T:0.31081 C:0.12162 A:0.43243 G:0.13514
+position 3: T:0.14865 C:0.21622 A:0.37838 G:0.25676
+Average T:0.19369 C:0.16667 A:0.37387 G:0.26577
+
+Sums of codon usage counts
+------------------------------------------------------------------------------
+Phe F TTT 0 | Ser S TCT 5 | Tyr Y TAT 1 | Cys C TGT 14
+ TTC 5 | TCC 5 | TAC 4 | TGC 1
+Leu L TTA 0 | TCA 5 | *** * TAA 0 | *** * TGA 0
+ TTG 5 | TCG 0 | TAG 0 | Trp W TGG 0
+------------------------------------------------------------------------------
+Leu L CTT 0 | Pro P CCT 0 | His H CAT 0 | Arg R CGT 0
+ CTC 10 | CCC 5 | CAC 0 | CGC 0
+ CTA 5 | CCA 15 | Gln Q CAA 0 | CGA 5
+ CTG 15 | CCG 0 | CAG 5 | CGG 0
+------------------------------------------------------------------------------
+Ile I ATT 10 | Thr T ACT 0 | Asn N AAT 10 | Ser S AGT 0
+ ATC 10 | ACC 0 | AAC 0 | AGC 0
+ ATA 0 | ACA 10 | Lys K AAA 25 | Arg R AGA 10
+Met M ATG 15 | ACG 0 | AAG 25 | AGG 0
+------------------------------------------------------------------------------
+Val V GTT 13 | Ala A GCT 0 | Asp D GAT 10 | Gly G GGT 0
+ GTC 1 | GCC 0 | GAC 20 | GGC 15
+ GTA 16 | GCA 0 | Glu E GAA 40 | GGA 5
+ GTG 10 | GCG 0 | GAG 20 | GGG 0
+------------------------------------------------------------------------------
+
+
+Codon position x base (3x4) table, overall
+
+position 1: T:0.12162 C:0.16216 A:0.31081 G:0.40541
+position 2: T:0.31081 C:0.12162 A:0.43243 G:0.13514
+position 3: T:0.17027 C:0.20541 A:0.36757 G:0.25676
+Average T:0.20090 C:0.16306 A:0.37027 G:0.26577
+
+
+Nei & Gojobori 1986. dN/dS (dN, dS)
+(Note: This matrix is not used in later ML. analysis.
+Use runmode = -2 for ML pairwise comparison.)
+
+Homo_sapie
+Pan_troglo -1.0000 (0.0000 0.0207)
+Gorilla_go -1.0000 (0.0000 0.0207)-1.0000 (0.0000 0.0421)
+Pongo_pygm -1.0000 (0.0000 0.0000)-1.0000 (0.0000 0.0207)-1.0000 (0.0000 0.0207)
+Macaca_mul -1.0000 (0.0000 0.0421)-1.0000 (0.0000 0.0640)-1.0000 (0.0000 0.0640)-1.0000 (0.0000 0.0421)
+
+
+TREE # 1: (((1, 2), 3), 4, 5); MP score: 4
+check convergence..
+lnL(ntime: 7 np: 12): -308.031579 +0.000000
+ 6..7 7..8 8..1 8..2 7..3 6..4 6..5
+ 0.000004 0.000004 0.000004 0.013438 0.013431 0.000004 0.027839 1.518244 1.000000 0.000000 0.000001 0.000001
+
+Note: Branch length is defined as number of nucleotide substitutions per codon (not per neucleotide site).
+
+tree length = 0.05472
+
+(((1: 0.00000, 2: 0.01344): 0.00000, 3: 0.01343): 0.00000, 4: 0.00000, 5: 0.02784);
+
+(((Homo_sapie: 0.00000, Pan_troglo: 0.01344): 0.00000, Gorilla_go: 0.01343): 0.00000, Pongo_pygm: 0.00000, Macaca_mul: 0.02784);
+
+Detailed output identifying parameters
+
+kappa (ts/tv) = 1.51824
+
+Parameters in M22 (M2a_rel):
+
+
+dN/dS (w) for site classes (K=3)
+
+p: 1.00000 0.00000 0.00000
+w: 0.00000 1.00000 0.00000
+
+dN & dS for each branch
+
+ branch t N S dN/dS dN dS N*dN S*dS
+
+ 6..7 0.000 167.7 54.3 0.0000 0.0000 0.0000 0.0 0.0
+ 7..8 0.000 167.7 54.3 0.0000 0.0000 0.0000 0.0 0.0
+ 8..1 0.000 167.7 54.3 0.0000 0.0000 0.0000 0.0 0.0
+ 8..2 0.013 167.7 54.3 0.0000 0.0000 0.0183 0.0 1.0
+ 7..3 0.013 167.7 54.3 0.0000 0.0000 0.0183 0.0 1.0
+ 6..4 0.000 167.7 54.3 0.0000 0.0000 0.0000 0.0 0.0
+ 6..5 0.028 167.7 54.3 0.0000 0.0000 0.0379 0.0 2.1
+
+
+Naive Empirical Bayes (NEB) analysis
+Time used: 0:05
diff --git a/Tests/PAML/Results/codeml/m2a_rel/m2a_rel-4_8.out b/Tests/PAML/Results/codeml/m2a_rel/m2a_rel-4_8.out
new file mode 100644
index 0000000..f3957e5
--- /dev/null
+++ b/Tests/PAML/Results/codeml/m2a_rel/m2a_rel-4_8.out
@@ -0,0 +1,145 @@
+CODONML (in paml version 4.8, March 2014) ../Alignments/alignment.phylip
+Model: One dN/dS ratio for branches,
+Codon frequency model: F3x4
+Site-class models: M2a_rel (3 categories)
+ns = 5 ls = 74
+
+Codon usage in sequences
+----------------------------------------------------------------------------------------------------------------------
+Phe TTT 0 0 0 0 0 | Ser TCT 1 1 1 1 1 | Tyr TAT 0 0 1 0 0 | Cys TGT 3 2 3 3 3
+ TTC 1 1 1 1 1 | TCC 1 1 1 1 1 | TAC 1 1 0 1 1 | TGC 0 1 0 0 0
+Leu TTA 0 0 0 0 0 | TCA 1 1 1 1 1 | *** TAA 0 0 0 0 0 | *** TGA 0 0 0 0 0
+ TTG 1 1 1 1 1 | TCG 0 0 0 0 0 | TAG 0 0 0 0 0 | Trp TGG 0 0 0 0 0
+----------------------------------------------------------------------------------------------------------------------
+Leu CTT 0 0 0 0 0 | Pro CCT 0 0 0 0 0 | His CAT 0 0 0 0 0 | Arg CGT 0 0 0 0 0
+ CTC 2 2 2 2 2 | CCC 1 1 1 1 1 | CAC 0 0 0 0 0 | CGC 0 0 0 0 0
+ CTA 1 1 1 1 1 | CCA 3 3 3 3 3 | Gln CAA 0 0 0 0 0 | CGA 1 1 1 1 1
+ CTG 3 3 3 3 3 | CCG 0 0 0 0 0 | CAG 1 1 1 1 1 | CGG 0 0 0 0 0
+----------------------------------------------------------------------------------------------------------------------
+Ile ATT 2 2 2 2 2 | Thr ACT 0 0 0 0 0 | Asn AAT 2 2 2 2 2 | Ser AGT 0 0 0 0 0
+ ATC 2 2 2 2 2 | ACC 0 0 0 0 0 | AAC 0 0 0 0 0 | AGC 0 0 0 0 0
+ ATA 0 0 0 0 0 | ACA 2 2 2 2 2 | Lys AAA 5 5 5 5 5 | Arg AGA 2 2 2 2 2
+Met ATG 3 3 3 3 3 | ACG 0 0 0 0 0 | AAG 5 5 5 5 5 | AGG 0 0 0 0 0
+----------------------------------------------------------------------------------------------------------------------
+Val GTT 3 3 3 3 1 | Ala GCT 0 0 0 0 0 | Asp GAT 2 2 2 2 2 | Gly GGT 0 0 0 0 0
+ GTC 0 0 0 0 1 | GCC 0 0 0 0 0 | GAC 4 4 4 4 4 | GGC 3 3 3 3 3
+ GTA 3 3 3 3 4 | GCA 0 0 0 0 0 | Glu GAA 8 8 8 8 8 | GGA 1 1 1 1 1
+ GTG 2 2 2 2 2 | GCG 0 0 0 0 0 | GAG 4 4 4 4 4 | GGG 0 0 0 0 0
+----------------------------------------------------------------------------------------------------------------------
+
+Codon position x base (3x4) table for each sequence.
+
+#1: Homo_sapie
+position 1: T:0.12162 C:0.16216 A:0.31081 G:0.40541
+position 2: T:0.31081 C:0.12162 A:0.43243 G:0.13514
+position 3: T:0.17568 C:0.20270 A:0.36486 G:0.25676
+Average T:0.20270 C:0.16216 A:0.36937 G:0.26577
+
+#2: Pan_troglo
+position 1: T:0.12162 C:0.16216 A:0.31081 G:0.40541
+position 2: T:0.31081 C:0.12162 A:0.43243 G:0.13514
+position 3: T:0.16216 C:0.21622 A:0.36486 G:0.25676
+Average T:0.19820 C:0.16667 A:0.36937 G:0.26577
+
+#3: Gorilla_go
+position 1: T:0.12162 C:0.16216 A:0.31081 G:0.40541
+position 2: T:0.31081 C:0.12162 A:0.43243 G:0.13514
+position 3: T:0.18919 C:0.18919 A:0.36486 G:0.25676
+Average T:0.20721 C:0.15766 A:0.36937 G:0.26577
+
+#4: Pongo_pygm
+position 1: T:0.12162 C:0.16216 A:0.31081 G:0.40541
+position 2: T:0.31081 C:0.12162 A:0.43243 G:0.13514
+position 3: T:0.17568 C:0.20270 A:0.36486 G:0.25676
+Average T:0.20270 C:0.16216 A:0.36937 G:0.26577
+
+#5: Macaca_mul
+position 1: T:0.12162 C:0.16216 A:0.31081 G:0.40541
+position 2: T:0.31081 C:0.12162 A:0.43243 G:0.13514
+position 3: T:0.14865 C:0.21622 A:0.37838 G:0.25676
+Average T:0.19369 C:0.16667 A:0.37387 G:0.26577
+
+Sums of codon usage counts
+------------------------------------------------------------------------------
+Phe F TTT 0 | Ser S TCT 5 | Tyr Y TAT 1 | Cys C TGT 14
+ TTC 5 | TCC 5 | TAC 4 | TGC 1
+Leu L TTA 0 | TCA 5 | *** * TAA 0 | *** * TGA 0
+ TTG 5 | TCG 0 | TAG 0 | Trp W TGG 0
+------------------------------------------------------------------------------
+Leu L CTT 0 | Pro P CCT 0 | His H CAT 0 | Arg R CGT 0
+ CTC 10 | CCC 5 | CAC 0 | CGC 0
+ CTA 5 | CCA 15 | Gln Q CAA 0 | CGA 5
+ CTG 15 | CCG 0 | CAG 5 | CGG 0
+------------------------------------------------------------------------------
+Ile I ATT 10 | Thr T ACT 0 | Asn N AAT 10 | Ser S AGT 0
+ ATC 10 | ACC 0 | AAC 0 | AGC 0
+ ATA 0 | ACA 10 | Lys K AAA 25 | Arg R AGA 10
+Met M ATG 15 | ACG 0 | AAG 25 | AGG 0
+------------------------------------------------------------------------------
+Val V GTT 13 | Ala A GCT 0 | Asp D GAT 10 | Gly G GGT 0
+ GTC 1 | GCC 0 | GAC 20 | GGC 15
+ GTA 16 | GCA 0 | Glu E GAA 40 | GGA 5
+ GTG 10 | GCG 0 | GAG 20 | GGG 0
+------------------------------------------------------------------------------
+
+
+Codon position x base (3x4) table, overall
+
+position 1: T:0.12162 C:0.16216 A:0.31081 G:0.40541
+position 2: T:0.31081 C:0.12162 A:0.43243 G:0.13514
+position 3: T:0.17027 C:0.20541 A:0.36757 G:0.25676
+Average T:0.20090 C:0.16306 A:0.37027 G:0.26577
+
+
+Nei & Gojobori 1986. dN/dS (dN, dS)
+(Note: This matrix is not used in later ML. analysis.
+Use runmode = -2 for ML pairwise comparison.)
+
+Homo_sapie
+Pan_troglo -1.0000 (0.0000 0.0207)
+Gorilla_go -1.0000 (0.0000 0.0207)-1.0000 (0.0000 0.0421)
+Pongo_pygm -1.0000 (0.0000 0.0000)-1.0000 (0.0000 0.0207)-1.0000 (0.0000 0.0207)
+Macaca_mul -1.0000 (0.0000 0.0421)-1.0000 (0.0000 0.0640)-1.0000 (0.0000 0.0640)-1.0000 (0.0000 0.0421)
+
+
+TREE # 1: (((1, 2), 3), 4, 5); MP score: 4
+check convergence..
+lnL(ntime: 7 np: 12): -308.031579 +0.000000
+ 6..7 7..8 8..1 8..2 7..3 6..4 6..5
+ 0.000004 0.000004 0.000004 0.013438 0.013431 0.000004 0.027839 1.518244 1.000000 0.000000 0.000001 0.000001
+
+Note: Branch length is defined as number of nucleotide substitutions per codon (not per neucleotide site).
+
+tree length = 0.05472
+
+(((1: 0.000004, 2: 0.013438): 0.000004, 3: 0.013431): 0.000004, 4: 0.000004, 5: 0.027839);
+
+(((Homo_sapie: 0.000004, Pan_troglo: 0.013438): 0.000004, Gorilla_go: 0.013431): 0.000004, Pongo_pygm: 0.000004, Macaca_mul: 0.027839);
+
+Detailed output identifying parameters
+
+kappa (ts/tv) = 1.51824
+
+Parameters in M22 (M2a_rel):
+
+
+dN/dS (w) for site classes (K=3)
+
+p: 1.00000 0.00000 0.00000
+w: 0.00000 1.00000 0.00000
+
+dN & dS for each branch
+
+ branch t N S dN/dS dN dS N*dN S*dS
+
+ 6..7 0.000 167.7 54.3 0.0000 0.0000 0.0000 0.0 0.0
+ 7..8 0.000 167.7 54.3 0.0000 0.0000 0.0000 0.0 0.0
+ 8..1 0.000 167.7 54.3 0.0000 0.0000 0.0000 0.0 0.0
+ 8..2 0.013 167.7 54.3 0.0000 0.0000 0.0183 0.0 1.0
+ 7..3 0.013 167.7 54.3 0.0000 0.0000 0.0183 0.0 1.0
+ 6..4 0.000 167.7 54.3 0.0000 0.0000 0.0000 0.0 0.0
+ 6..5 0.028 167.7 54.3 0.0000 0.0000 0.0379 0.0 2.1
+
+
+Naive Empirical Bayes (NEB) analysis
+Time used: 0:04
diff --git a/Tests/PAML/Results/codeml/m2a_rel/m2a_rel-4_9a.out b/Tests/PAML/Results/codeml/m2a_rel/m2a_rel-4_9a.out
new file mode 100644
index 0000000..dd19dfa
--- /dev/null
+++ b/Tests/PAML/Results/codeml/m2a_rel/m2a_rel-4_9a.out
@@ -0,0 +1,144 @@
+CODONML (in paml version 4.9, March 2015) ../Alignments/alignment.phylip
+Model: One dN/dS ratio for branches,
+Codon frequency model: F3x4
+Site-class models: M2a_rel (3 categories)
+ns = 5 ls = 74
+
+Codon usage in sequences
+----------------------------------------------------------------------------------------------------------------------
+Phe TTT 0 0 0 0 0 | Ser TCT 1 1 1 1 1 | Tyr TAT 0 0 1 0 0 | Cys TGT 3 2 3 3 3
+ TTC 1 1 1 1 1 | TCC 1 1 1 1 1 | TAC 1 1 0 1 1 | TGC 0 1 0 0 0
+Leu TTA 0 0 0 0 0 | TCA 1 1 1 1 1 | *** TAA 0 0 0 0 0 | *** TGA 0 0 0 0 0
+ TTG 1 1 1 1 1 | TCG 0 0 0 0 0 | TAG 0 0 0 0 0 | Trp TGG 0 0 0 0 0
+----------------------------------------------------------------------------------------------------------------------
+Leu CTT 0 0 0 0 0 | Pro CCT 0 0 0 0 0 | His CAT 0 0 0 0 0 | Arg CGT 0 0 0 0 0
+ CTC 2 2 2 2 2 | CCC 1 1 1 1 1 | CAC 0 0 0 0 0 | CGC 0 0 0 0 0
+ CTA 1 1 1 1 1 | CCA 3 3 3 3 3 | Gln CAA 0 0 0 0 0 | CGA 1 1 1 1 1
+ CTG 3 3 3 3 3 | CCG 0 0 0 0 0 | CAG 1 1 1 1 1 | CGG 0 0 0 0 0
+----------------------------------------------------------------------------------------------------------------------
+Ile ATT 2 2 2 2 2 | Thr ACT 0 0 0 0 0 | Asn AAT 2 2 2 2 2 | Ser AGT 0 0 0 0 0
+ ATC 2 2 2 2 2 | ACC 0 0 0 0 0 | AAC 0 0 0 0 0 | AGC 0 0 0 0 0
+ ATA 0 0 0 0 0 | ACA 2 2 2 2 2 | Lys AAA 5 5 5 5 5 | Arg AGA 2 2 2 2 2
+Met ATG 3 3 3 3 3 | ACG 0 0 0 0 0 | AAG 5 5 5 5 5 | AGG 0 0 0 0 0
+----------------------------------------------------------------------------------------------------------------------
+Val GTT 3 3 3 3 1 | Ala GCT 0 0 0 0 0 | Asp GAT 2 2 2 2 2 | Gly GGT 0 0 0 0 0
+ GTC 0 0 0 0 1 | GCC 0 0 0 0 0 | GAC 4 4 4 4 4 | GGC 3 3 3 3 3
+ GTA 3 3 3 3 4 | GCA 0 0 0 0 0 | Glu GAA 8 8 8 8 8 | GGA 1 1 1 1 1
+ GTG 2 2 2 2 2 | GCG 0 0 0 0 0 | GAG 4 4 4 4 4 | GGG 0 0 0 0 0
+----------------------------------------------------------------------------------------------------------------------
+
+Codon position x base (3x4) table for each sequence.
+
+#1: Homo_sapie
+position 1: T:0.12162 C:0.16216 A:0.31081 G:0.40541
+position 2: T:0.31081 C:0.12162 A:0.43243 G:0.13514
+position 3: T:0.17568 C:0.20270 A:0.36486 G:0.25676
+Average T:0.20270 C:0.16216 A:0.36937 G:0.26577
+
+#2: Pan_troglo
+position 1: T:0.12162 C:0.16216 A:0.31081 G:0.40541
+position 2: T:0.31081 C:0.12162 A:0.43243 G:0.13514
+position 3: T:0.16216 C:0.21622 A:0.36486 G:0.25676
+Average T:0.19820 C:0.16667 A:0.36937 G:0.26577
+
+#3: Gorilla_go
+position 1: T:0.12162 C:0.16216 A:0.31081 G:0.40541
+position 2: T:0.31081 C:0.12162 A:0.43243 G:0.13514
+position 3: T:0.18919 C:0.18919 A:0.36486 G:0.25676
+Average T:0.20721 C:0.15766 A:0.36937 G:0.26577
+
+#4: Pongo_pygm
+position 1: T:0.12162 C:0.16216 A:0.31081 G:0.40541
+position 2: T:0.31081 C:0.12162 A:0.43243 G:0.13514
+position 3: T:0.17568 C:0.20270 A:0.36486 G:0.25676
+Average T:0.20270 C:0.16216 A:0.36937 G:0.26577
+
+#5: Macaca_mul
+position 1: T:0.12162 C:0.16216 A:0.31081 G:0.40541
+position 2: T:0.31081 C:0.12162 A:0.43243 G:0.13514
+position 3: T:0.14865 C:0.21622 A:0.37838 G:0.25676
+Average T:0.19369 C:0.16667 A:0.37387 G:0.26577
+
+Sums of codon usage counts
+------------------------------------------------------------------------------
+Phe F TTT 0 | Ser S TCT 5 | Tyr Y TAT 1 | Cys C TGT 14
+ TTC 5 | TCC 5 | TAC 4 | TGC 1
+Leu L TTA 0 | TCA 5 | *** * TAA 0 | *** * TGA 0
+ TTG 5 | TCG 0 | TAG 0 | Trp W TGG 0
+------------------------------------------------------------------------------
+Leu L CTT 0 | Pro P CCT 0 | His H CAT 0 | Arg R CGT 0
+ CTC 10 | CCC 5 | CAC 0 | CGC 0
+ CTA 5 | CCA 15 | Gln Q CAA 0 | CGA 5
+ CTG 15 | CCG 0 | CAG 5 | CGG 0
+------------------------------------------------------------------------------
+Ile I ATT 10 | Thr T ACT 0 | Asn N AAT 10 | Ser S AGT 0
+ ATC 10 | ACC 0 | AAC 0 | AGC 0
+ ATA 0 | ACA 10 | Lys K AAA 25 | Arg R AGA 10
+Met M ATG 15 | ACG 0 | AAG 25 | AGG 0
+------------------------------------------------------------------------------
+Val V GTT 13 | Ala A GCT 0 | Asp D GAT 10 | Gly G GGT 0
+ GTC 1 | GCC 0 | GAC 20 | GGC 15
+ GTA 16 | GCA 0 | Glu E GAA 40 | GGA 5
+ GTG 10 | GCG 0 | GAG 20 | GGG 0
+------------------------------------------------------------------------------
+
+
+Codon position x base (3x4) table, overall
+
+position 1: T:0.12162 C:0.16216 A:0.31081 G:0.40541
+position 2: T:0.31081 C:0.12162 A:0.43243 G:0.13514
+position 3: T:0.17027 C:0.20541 A:0.36757 G:0.25676
+Average T:0.20090 C:0.16306 A:0.37027 G:0.26577
+
+
+Nei & Gojobori 1986. dN/dS (dN, dS)
+(Note: This matrix is not used in later ML. analysis.
+Use runmode = -2 for ML pairwise comparison.)
+
+Homo_sapie
+Pan_troglo -1.0000 (0.0000 0.0207)
+Gorilla_go -1.0000 (0.0000 0.0207)-1.0000 (0.0000 0.0421)
+Pongo_pygm -1.0000 (0.0000 0.0000)-1.0000 (0.0000 0.0207)-1.0000 (0.0000 0.0207)
+Macaca_mul -1.0000 (0.0000 0.0421)-1.0000 (0.0000 0.0640)-1.0000 (0.0000 0.0640)-1.0000 (0.0000 0.0421)
+
+
+TREE # 1: (((1, 2), 3), 4, 5); MP score: 4
+lnL(ntime: 7 np: 12): -308.031579 +0.000000
+ 6..7 7..8 8..1 8..2 7..3 6..4 6..5
+ 0.000004 0.000004 0.000004 0.013438 0.013431 0.000004 0.027839 1.518250 0.999999 0.000000 0.000001 0.000001
+
+Note: Branch length is defined as number of nucleotide substitutions per codon (not per neucleotide site).
+
+tree length = 0.05472
+
+(((1: 0.000004, 2: 0.013438): 0.000004, 3: 0.013431): 0.000004, 4: 0.000004, 5: 0.027839);
+
+(((Homo_sapie: 0.000004, Pan_troglo: 0.013438): 0.000004, Gorilla_go: 0.013431): 0.000004, Pongo_pygm: 0.000004, Macaca_mul: 0.027839);
+
+Detailed output identifying parameters
+
+kappa (ts/tv) = 1.51825
+
+Parameters in M22 (M2a_rel):
+
+
+dN/dS (w) for site classes (K=3)
+
+p: 1.00000 0.00000 0.00000
+w: 0.00000 1.00000 0.00000
+
+dN & dS for each branch
+
+ branch t N S dN/dS dN dS N*dN S*dS
+
+ 6..7 0.000 167.7 54.3 0.0000 0.0000 0.0000 0.0 0.0
+ 7..8 0.000 167.7 54.3 0.0000 0.0000 0.0000 0.0 0.0
+ 8..1 0.000 167.7 54.3 0.0000 0.0000 0.0000 0.0 0.0
+ 8..2 0.013 167.7 54.3 0.0000 0.0000 0.0183 0.0 1.0
+ 7..3 0.013 167.7 54.3 0.0000 0.0000 0.0183 0.0 1.0
+ 6..4 0.000 167.7 54.3 0.0000 0.0000 0.0000 0.0 0.0
+ 6..5 0.028 167.7 54.3 0.0000 0.0000 0.0379 0.0 2.1
+
+
+Naive Empirical Bayes (NEB) analysis
+Time used: 0:06
diff --git a/Tests/PAML/gen_results.py b/Tests/PAML/gen_results.py
index 88af40d..9872980 100644
--- a/Tests/PAML/gen_results.py
+++ b/Tests/PAML/gen_results.py
@@ -1,4 +1,4 @@
-# Copyright (C) 2012, 2013 by Brandon Invergo (b.invergo at gmail.com)
+# Copyright (C) 2012, 2013, 2016 by Brandon Invergo (b.invergo at gmail.com)
# This code is part of the Biopython distribution and governed by its
# license. Please see the LICENSE file that should have been included
# as part of this package.
@@ -11,7 +11,7 @@ import sys
from Bio._py3k import range
-VERSIONS = ["4_1", "4_3", "4_4", "4_4c", "4_5", "4_6", "4_7"]
+VERSIONS = ["4_1", "4_3", "4_4", "4_4c", "4_5", "4_6", "4_7", "4_8", "4_9a"]
def codeml(vers=None, verbose=False):
@@ -29,7 +29,8 @@ def codeml(vers=None, verbose=False):
("ngene2_mgene02", "lysinYangSwanson2002.nuc", "lysin.trees"),
("ngene2_mgene34", "lysinYangSwanson2002.nuc", "lysin.trees"),
("pairwise", "alignment.phylip", "species.tree"),
- ("SE", "alignment.phylip", "species.tree")]
+ ("SE", "alignment.phylip", "species.tree"),
+ ("m2a_rel", "alignment.phylip", "species.tree")]
for test in tests:
print(test[0])
@@ -44,6 +45,9 @@ def codeml(vers=None, verbose=False):
cml.alignment = alignment
cml.tree = tree
for version in versions:
+ # M2a_rel (NSsites 22) was introduced in PAML 4.6
+ if test[0] == "m2a_rel" and int(version.split("_")[1][0]) < 6:
+ continue
print("\t{0}".format(version.replace('_', '.')))
if test[0] in ["ngene2_mgene02", "ngene2_mgene34"] and \
version == "4_6":
diff --git a/Tests/PDB/1LCD.pdb b/Tests/PDB/1LCD.pdb
new file mode 100644
index 0000000..4c0d82a
--- /dev/null
+++ b/Tests/PDB/1LCD.pdb
@@ -0,0 +1,3884 @@
+TITLE STRUCTURE OF THE COMPLEX OF LAC REPRESSOR HEADPIECE AND AN
+TITLE 2 11 BASE-PAIR HALF-OPERATOR DETERMINED BY NUCLEAR MAGNETIC
+TITLE 3 RESONANCE SPECTROSCOPY AND RESTRAINED MOLECULAR DYNAMICS
+COMPND MOL_ID: 1;
+COMPND 2 MOLECULE: DNA (5'-D(*AP*AP*TP*TP*GP*TP*GP*AP*GP*CP*G)-3');
+COMPND 3 CHAIN: B;
+COMPND 4 ENGINEERED: YES;
+COMPND 5 MOL_ID: 2;
+COMPND 6 MOLECULE: DNA (5'-D(*CP*GP*CP*TP*CP*AP*CP*AP*AP*TP*T)-3');
+COMPND 7 CHAIN: C;
+COMPND 8 ENGINEERED: YES;
+COMPND 9 MOL_ID: 3;
+COMPND 10 MOLECULE: LAC REPRESSOR;
+COMPND 11 CHAIN: A;
+COMPND 12 ENGINEERED: YES
+SOURCE MOL_ID: 1;
+SOURCE 2 SYNTHETIC: YES;
+SOURCE 3 MOL_ID: 2;
+SOURCE 4 SYNTHETIC: YES;
+SOURCE 5 MOL_ID: 3;
+SOURCE 6 ORGANISM_SCIENTIFIC: ESCHERICHIA COLI;
+SOURCE 7 ORGANISM_TAXID: 562;
+SOURCE 8 EXPRESSION_SYSTEM_VECTOR_TYPE: LAC
+KEYWDS GENE REGULATION/DNA
+EXPDTA SOLUTION NMR
+NUMMDL 3
+AUTHOR V.P.CHUPRINA,J.A.C.RULLMANN,R.M.J.N.LAMERICHS,J.H.VAN BOOM,
+AUTHOR 2 R.BOELENS,R.KAPTEIN
+REVDAT 3 24-FEB-09 1LCD 1 VERSN
+REVDAT 2 01-APR-03 1LCD 1 JRNL
+REVDAT 1 31-JAN-94 1LCD 0
+JRNL AUTH V.P.CHUPRINA,J.A.RULLMANN,R.M.LAMERICHS,
+JRNL AUTH 2 J.H.VAN BOOM,R.BOELENS,R.KAPTEIN
+JRNL TITL STRUCTURE OF THE COMPLEX OF LAC REPRESSOR
+JRNL TITL 2 HEADPIECE AND AN 11 BASE-PAIR HALF-OPERATOR
+JRNL TITL 3 DETERMINED BY NUCLEAR MAGNETIC RESONANCE
+JRNL TITL 4 SPECTROSCOPY AND RESTRAINED MOLECULAR DYNAMICS.
+JRNL REF J.MOL.BIOL. V. 234 446 1993
+JRNL REFN ISSN 0022-2836
+JRNL PMID 8230225
+JRNL DOI 10.1006/JMBI.1993.1598
+REMARK 1
+REMARK 1 REFERENCE 1
+REMARK 1 AUTH R.M.J.N.LAMERICHS,R.BOELENS,G.A.VAN DER MAREL,
+REMARK 1 AUTH 2 J.H.VAN BOOM,R.KAPTEIN
+REMARK 1 TITL ASSIGNMENT OF THE 1H-NMR SPECTRUM OF A LAC
+REMARK 1 TITL 2 REPRESSOR HEADPIECE-OPERATOR COMPLEX IN H2O AND
+REMARK 1 TITL 3 IDENTIFICATION OF NOES. CONSEQUENCES FOR
+REMARK 1 TITL 4 PROTEIN-DNA INTERACTION
+REMARK 1 REF EUR.J.BIOCHEM. V. 194 629 1990
+REMARK 1 REFN ISSN 0014-2956
+REMARK 1 REFERENCE 2
+REMARK 1 AUTH R.KAPTEIN,R.M.J.N.LAMERICHS,R.BOELENS,
+REMARK 1 AUTH 2 J.A.C.RULLMANN
+REMARK 1 TITL TWO-DIMENSIONAL NMR STUDY OF A PROTEIN-DNA
+REMARK 1 TITL 2 COMPLEX. LAC REPRESSOR HEADPIECE-OPERATOR
+REMARK 1 TITL 3 INTERACTION
+REMARK 1 REF BIOCHEM.PHARM. V. 40 89 1990
+REMARK 1 REFN ISSN 0006-2952
+REMARK 1 REFERENCE 3
+REMARK 1 AUTH R.M.J.N.LAMERICHS,R.BOELENS,G.A.VAN DER MAREL,
+REMARK 1 AUTH 2 J.H.VAN BOOM,R.KAPTEIN,F.BUCK,B.FERA,H.RUETERJANS
+REMARK 1 TITL 1H NMR STUDY OF A COMPLEX BETWEEN THE LAC
+REMARK 1 TITL 2 REPRESSOR HEADPIECE AND A 22 BASE PAIR SYMMETRIC
+REMARK 1 TITL 3 LAC OPERATOR
+REMARK 1 REF BIOCHEMISTRY V. 28 2985 1989
+REMARK 1 REFN ISSN 0006-2960
+REMARK 1 REFERENCE 4
+REMARK 1 AUTH J.A.C.RULLMANN,R.BOELENS,R.KAPTEIN
+REMARK 1 TITL NMR BASED DOCKING STUDIES OF LAC REPRESSOR
+REMARK 1 TITL 2 HEADPIECE ON A LAC OPERATOR FRAGMENT
+REMARK 1 REF UCLA SYMP.MOL.CELL.BIOL., V. 95 11 1989
+REMARK 1 REF 2 NEW SER.
+REMARK 1 REFN ISSN 0735-9543
+REMARK 1 REFERENCE 5
+REMARK 1 AUTH R.BOELENS,R.M.J.N.LAMERICHS,J.A.C.RULLMANN,
+REMARK 1 AUTH 2 J.H.VAN BOOM,R.KAPTEIN
+REMARK 1 TITL THE INTERACTION OF LAC REPRESSOR HEADPIECE WITH
+REMARK 1 TITL 2 ITS OPERATOR: AN NMR VIEW
+REMARK 1 REF PROTEIN SEQ.DATA ANAL. V. 1 487 1988
+REMARK 1 REFN ISSN 0931-9506
+REMARK 1 REFERENCE 6
+REMARK 1 AUTH J.DE VLIEG,R.M.SCHEEK,W.F.VAN GUNSTEREN,
+REMARK 1 AUTH 2 H.J.C.BERENDSEN,R.KAPTEIN,J.THOMASON
+REMARK 1 TITL COMBINED PROCEDURE OF DISTANCE GEOMETRY AND
+REMARK 1 TITL 2 RESTRAINED MOLECULAR DYNAMICS TECHNIQUES FOR
+REMARK 1 TITL 3 PROTEIN STRUCTURE DETERMINATION FROM NUCLEAR
+REMARK 1 TITL 4 MAGNETIC RESONANCE DATA: APPLICATION TO THE DNA
+REMARK 1 TITL 5 BINDING DOMAIN OF LAC REPRESSOR FROM ESCHERICHIA
+REMARK 1 TITL 6 COLI
+REMARK 1 REF PROTEINS V. 3 209 1988
+REMARK 1 REFN ISSN 0887-3585
+REMARK 1 REFERENCE 7
+REMARK 1 AUTH R.BOELENS,R.M.SCHEEK,R.M.J.N.LAMERICHS,J.DE VLIEG,
+REMARK 1 AUTH 2 J.H.VAN BOOM,R.KAPTEIN
+REMARK 1 TITL A TWO-DIMENSIONAL NMR STUDY OF THE COMPLEX OF LAC
+REMARK 1 TITL 2 REPRESSOR HEADPIECE WITH A 14 BASE PAIR LAC
+REMARK 1 TITL 3 OPERATOR FRAGMENT
+REMARK 1 REF NATO ASI SER.,SER.A V. 137 191 1987
+REMARK 1 REFN ISSN 0161-0449
+REMARK 1 REFERENCE 8
+REMARK 1 AUTH R.BOELENS,R.M.SCHEEK,J.H.VAN BOOM,R.KAPTEIN
+REMARK 1 TITL COMPLEX OF LAC REPRESSOR HEADPIECE WITH A 14 BASE
+REMARK 1 TITL 2 PAIR LAC OPERATOR FRAGMENT STUDIED BY
+REMARK 1 TITL 3 TWO-DIMENSIONAL NUCLEAR MAGNETIC RESONANCE
+REMARK 1 REF J.MOL.BIOL. V. 193 213 1987
+REMARK 1 REFN ISSN 0022-2836
+REMARK 1 REFERENCE 9
+REMARK 1 AUTH J.DE VLIEG,R.BOELENS,R.M.SCHEEK,R.KAPTEIN,
+REMARK 1 AUTH 2 W.F.VAN GUNSTEREN
+REMARK 1 TITL RESTRAINED MOLECULAR DYNAMICS PROCEDURE FOR
+REMARK 1 TITL 2 PROTEIN TERTIARY STRUCTURE DETERMINATION FROM NMR
+REMARK 1 TITL 3 DATA: A LAC REPRESSOR HEADPIECE STRUCTURE BASED ON
+REMARK 1 TITL 4 INFORMATION ON J-COUPLING AND FROM PRESENCE AND
+REMARK 1 TITL 5 ABSENCE OF NOES
+REMARK 1 REF ISR.J.CHEM. V. 27 181 1986
+REMARK 1 REFN ISSN 0021-2148
+REMARK 2
+REMARK 2 RESOLUTION. NOT APPLICABLE.
+REMARK 3
+REMARK 3 REFINEMENT.
+REMARK 3 PROGRAM : NULL
+REMARK 3 AUTHORS : NULL
+REMARK 3
+REMARK 3 OTHER REFINEMENT REMARKS: NULL
+REMARK 4
+REMARK 4 1LCD COMPLIES WITH FORMAT V. 3.15, 01-DEC-08
+REMARK 100
+REMARK 100 THIS ENTRY HAS BEEN PROCESSED BY BNL.
+REMARK 210
+REMARK 210 EXPERIMENTAL DETAILS
+REMARK 210 EXPERIMENT TYPE : NMR
+REMARK 210 TEMPERATURE (KELVIN) : NULL
+REMARK 210 PH : NULL
+REMARK 210 IONIC STRENGTH : NULL
+REMARK 210 PRESSURE : NULL
+REMARK 210 SAMPLE CONTENTS : NULL
+REMARK 210
+REMARK 210 NMR EXPERIMENTS CONDUCTED : NULL
+REMARK 210 SPECTROMETER FIELD STRENGTH : NULL
+REMARK 210 SPECTROMETER MODEL : NULL
+REMARK 210 SPECTROMETER MANUFACTURER : NULL
+REMARK 210
+REMARK 210 STRUCTURE DETERMINATION.
+REMARK 210 SOFTWARE USED : NULL
+REMARK 210 METHOD USED : NULL
+REMARK 210
+REMARK 210 CONFORMERS, NUMBER CALCULATED : NULL
+REMARK 210 CONFORMERS, NUMBER SUBMITTED : 3
+REMARK 210 CONFORMERS, SELECTION CRITERIA : NULL
+REMARK 210
+REMARK 210 BEST REPRESENTATIVE CONFORMER IN THIS ENSEMBLE : NULL
+REMARK 210
+REMARK 210 REMARK: NULL
+REMARK 215
+REMARK 215 NMR STUDY
+REMARK 215 THE COORDINATES IN THIS ENTRY WERE GENERATED FROM SOLUTION
+REMARK 215 NMR DATA. PROTEIN DATA BANK CONVENTIONS REQUIRE THAT
+REMARK 215 CRYST1 AND SCALE RECORDS BE INCLUDED, BUT THE VALUES ON
+REMARK 215 THESE RECORDS ARE MEANINGLESS.
+REMARK 500
+REMARK 500 GEOMETRY AND STEREOCHEMISTRY
+REMARK 500 SUBTOPIC: COVALENT BOND LENGTHS
+REMARK 500
+REMARK 500 THE STEREOCHEMICAL PARAMETERS OF THE FOLLOWING RESIDUES
+REMARK 500 HAVE VALUES WHICH DEVIATE FROM EXPECTED VALUES BY MORE
+REMARK 500 THAN 6*RMSD (M=MODEL NUMBER; RES=RESIDUE NAME; C=CHAIN
+REMARK 500 IDENTIFIER; SSEQ=SEQUENCE NUMBER; I=INSERTION CODE).
+REMARK 500
+REMARK 500 STANDARD TABLE:
+REMARK 500 FORMAT: (10X,I3,1X,2(A3,1X,A1,I4,A1,1X,A4,3X),1X,F6.3)
+REMARK 500
+REMARK 500 EXPECTED VALUES PROTEIN: ENGH AND HUBER, 1999
+REMARK 500 EXPECTED VALUES NUCLEIC ACID: CLOWNEY ET AL 1996
+REMARK 500
+REMARK 500 M RES CSSEQI ATM1 RES CSSEQI ATM2 DEVIATION
+REMARK 500 1 DA B 1 C5 DA B 1 N7 -0.052
+REMARK 500 1 DA B 1 N9 DA B 1 C4 -0.041
+REMARK 500 1 DA B 2 C5 DA B 2 N7 -0.060
+REMARK 500 1 DA B 2 N9 DA B 2 C4 -0.051
+REMARK 500 1 DT B 3 C5 DT B 3 C6 0.055
+REMARK 500 1 DT B 3 C6 DT B 3 N1 0.042
+REMARK 500 1 DT B 4 C5 DT B 4 C6 0.062
+REMARK 500 1 DG B 5 C5 DG B 5 N7 -0.051
+REMARK 500 1 DG B 5 N7 DG B 5 C8 0.040
+REMARK 500 1 DG B 5 C8 DG B 5 N9 -0.053
+REMARK 500 1 DG B 5 N9 DG B 5 C4 -0.056
+REMARK 500 1 DG B 7 C5 DG B 7 N7 -0.050
+REMARK 500 1 DG B 7 C8 DG B 7 N9 -0.050
+REMARK 500 1 DG B 7 N9 DG B 7 C4 -0.055
+REMARK 500 1 DA B 8 C5 DA B 8 N7 -0.070
+REMARK 500 1 DA B 8 N9 DA B 8 C4 -0.042
+REMARK 500 1 DG B 9 C5 DG B 9 N7 -0.063
+REMARK 500 1 DG B 9 C8 DG B 9 N9 -0.052
+REMARK 500 1 DC B 10 C5 DC B 10 C6 0.064
+REMARK 500 1 DG B 11 C5 DG B 11 N7 -0.052
+REMARK 500 1 DG B 11 N9 DG B 11 C4 -0.051
+REMARK 500 1 DC C 1 N1 DC C 1 C6 0.041
+REMARK 500 1 DC C 1 C5 DC C 1 C6 0.052
+REMARK 500 1 DG C 2 C5 DG C 2 N7 -0.043
+REMARK 500 1 DC C 3 N1 DC C 3 C6 0.043
+REMARK 500 1 DT C 4 C5 DT C 4 C6 0.064
+REMARK 500 1 DC C 5 N1 DC C 5 C6 0.048
+REMARK 500 1 DC C 5 C5 DC C 5 C6 0.057
+REMARK 500 1 DA C 6 C5 DA C 6 N7 -0.058
+REMARK 500 1 DA C 6 C8 DA C 6 N9 -0.054
+REMARK 500 1 DA C 6 N9 DA C 6 C4 -0.045
+REMARK 500 1 DC C 7 N1 DC C 7 C6 0.043
+REMARK 500 1 DC C 7 C5 DC C 7 C6 0.061
+REMARK 500 1 DA C 8 C5 DA C 8 N7 -0.048
+REMARK 500 1 DA C 8 C8 DA C 8 N9 -0.051
+REMARK 500 1 DA C 8 N9 DA C 8 C4 -0.040
+REMARK 500 1 DA C 9 C5 DA C 9 N7 -0.059
+REMARK 500 1 DA C 9 C8 DA C 9 N9 -0.050
+REMARK 500 1 DA C 9 N9 DA C 9 C4 -0.046
+REMARK 500 1 DT C 10 C5 DT C 10 C6 0.049
+REMARK 500 1 DT C 10 C5 DT C 10 C7 0.036
+REMARK 500 1 DT C 11 C5 DT C 11 C6 0.051
+REMARK 500 2 DA B 1 C5 DA B 1 N7 -0.051
+REMARK 500 2 DA B 1 C8 DA B 1 N9 -0.060
+REMARK 500 2 DA B 1 N9 DA B 1 C4 -0.048
+REMARK 500 2 DA B 2 C5 DA B 2 N7 -0.049
+REMARK 500 2 DA B 2 C8 DA B 2 N9 -0.053
+REMARK 500 2 DA B 2 N9 DA B 2 C4 -0.054
+REMARK 500 2 DT B 3 C5 DT B 3 C6 0.058
+REMARK 500 2 DT B 3 C5 DT B 3 C7 0.038
+REMARK 500
+REMARK 500 THIS ENTRY HAS 125 BOND DEVIATIONS.
+REMARK 500
+REMARK 500 REMARK: NULL
+REMARK 500
+REMARK 500 GEOMETRY AND STEREOCHEMISTRY
+REMARK 500 SUBTOPIC: COVALENT BOND ANGLES
+REMARK 500
+REMARK 500 THE STEREOCHEMICAL PARAMETERS OF THE FOLLOWING RESIDUES
+REMARK 500 HAVE VALUES WHICH DEVIATE FROM EXPECTED VALUES BY MORE
+REMARK 500 THAN 6*RMSD (M=MODEL NUMBER; RES=RESIDUE NAME; C=CHAIN
+REMARK 500 IDENTIFIER; SSEQ=SEQUENCE NUMBER; I=INSERTION CODE).
+REMARK 500
+REMARK 500 STANDARD TABLE:
+REMARK 500 FORMAT: (10X,I3,1X,A3,1X,A1,I4,A1,3(1X,A4,2X),12X,F5.1)
+REMARK 500
+REMARK 500 EXPECTED VALUES PROTEIN: ENGH AND HUBER, 1999
+REMARK 500 EXPECTED VALUES NUCLEIC ACID: CLOWNEY ET AL 1996
+REMARK 500
+REMARK 500 M RES CSSEQI ATM1 ATM2 ATM3
+REMARK 500 1 DA B 1 C3' - C2' - C1' ANGL. DEV. = -4.9 DEGREES
+REMARK 500 1 DA B 1 N1 - C2 - N3 ANGL. DEV. = -9.7 DEGREES
+REMARK 500 1 DA B 1 C2 - N3 - C4 ANGL. DEV. = 11.8 DEGREES
+REMARK 500 1 DA B 1 N3 - C4 - C5 ANGL. DEV. = -7.9 DEGREES
+REMARK 500 1 DA B 1 C5 - C6 - N1 ANGL. DEV. = 4.0 DEGREES
+REMARK 500 1 DA B 1 N3 - C4 - N9 ANGL. DEV. = 7.4 DEGREES
+REMARK 500 1 DA B 2 N1 - C2 - N3 ANGL. DEV. = -9.7 DEGREES
+REMARK 500 1 DA B 2 C2 - N3 - C4 ANGL. DEV. = 10.4 DEGREES
+REMARK 500 1 DA B 2 N3 - C4 - C5 ANGL. DEV. = -5.5 DEGREES
+REMARK 500 1 DA B 2 C5 - C6 - N1 ANGL. DEV. = 4.3 DEGREES
+REMARK 500 1 DA B 2 C5 - N7 - C8 ANGL. DEV. = 3.2 DEGREES
+REMARK 500 1 DA B 2 N7 - C8 - N9 ANGL. DEV. = -4.1 DEGREES
+REMARK 500 1 DA B 2 C8 - N9 - C4 ANGL. DEV. = 2.7 DEGREES
+REMARK 500 1 DA B 2 N3 - C4 - N9 ANGL. DEV. = 4.8 DEGREES
+REMARK 500 1 DA B 2 C6 - C5 - N7 ANGL. DEV. = 4.2 DEGREES
+REMARK 500 1 DT B 3 C5' - C4' - O4' ANGL. DEV. = 7.7 DEGREES
+REMARK 500 1 DT B 3 C1' - O4' - C4' ANGL. DEV. = -10.6 DEGREES
+REMARK 500 1 DT B 3 O4' - C1' - N1 ANGL. DEV. = 3.9 DEGREES
+REMARK 500 1 DT B 3 N1 - C2 - N3 ANGL. DEV. = 4.8 DEGREES
+REMARK 500 1 DT B 3 C2 - N3 - C4 ANGL. DEV. = -6.7 DEGREES
+REMARK 500 1 DT B 3 N3 - C4 - C5 ANGL. DEV. = 6.0 DEGREES
+REMARK 500 1 DT B 3 N3 - C2 - O2 ANGL. DEV. = -7.0 DEGREES
+REMARK 500 1 DT B 3 N3 - C4 - O4 ANGL. DEV. = -4.8 DEGREES
+REMARK 500 1 DT B 4 O4' - C1' - C2' ANGL. DEV. = 3.0 DEGREES
+REMARK 500 1 DT B 4 N1 - C2 - N3 ANGL. DEV. = 5.7 DEGREES
+REMARK 500 1 DT B 4 C2 - N3 - C4 ANGL. DEV. = -6.7 DEGREES
+REMARK 500 1 DT B 4 N3 - C4 - C5 ANGL. DEV. = 5.0 DEGREES
+REMARK 500 1 DT B 4 N3 - C2 - O2 ANGL. DEV. = -5.7 DEGREES
+REMARK 500 1 DT B 4 N3 - C4 - O4 ANGL. DEV. = -4.8 DEGREES
+REMARK 500 1 DT B 4 C5 - C4 - O4 ANGL. DEV. = -4.5 DEGREES
+REMARK 500 1 DG B 5 C6 - N1 - C2 ANGL. DEV. = -6.1 DEGREES
+REMARK 500 1 DG B 5 C2 - N3 - C4 ANGL. DEV. = 9.4 DEGREES
+REMARK 500 1 DG B 5 N3 - C4 - C5 ANGL. DEV. = -7.2 DEGREES
+REMARK 500 1 DG B 5 C5 - C6 - N1 ANGL. DEV. = 8.0 DEGREES
+REMARK 500 1 DG B 5 C4 - C5 - N7 ANGL. DEV. = -4.5 DEGREES
+REMARK 500 1 DG B 5 C5 - N7 - C8 ANGL. DEV. = 3.3 DEGREES
+REMARK 500 1 DG B 5 N7 - C8 - N9 ANGL. DEV. = -3.2 DEGREES
+REMARK 500 1 DG B 5 N9 - C4 - C5 ANGL. DEV. = 2.5 DEGREES
+REMARK 500 1 DG B 5 N3 - C4 - N9 ANGL. DEV. = 4.6 DEGREES
+REMARK 500 1 DG B 5 C6 - C5 - N7 ANGL. DEV. = 5.6 DEGREES
+REMARK 500 1 DG B 5 C5 - C6 - O6 ANGL. DEV. = -6.3 DEGREES
+REMARK 500 1 DT B 6 C2 - N3 - C4 ANGL. DEV. = -7.2 DEGREES
+REMARK 500 1 DT B 6 N3 - C4 - C5 ANGL. DEV. = 6.9 DEGREES
+REMARK 500 1 DT B 6 N3 - C2 - O2 ANGL. DEV. = -4.8 DEGREES
+REMARK 500 1 DT B 6 C6 - C5 - C7 ANGL. DEV. = -6.7 DEGREES
+REMARK 500 1 DG B 7 O4' - C1' - C2' ANGL. DEV. = -5.5 DEGREES
+REMARK 500 1 DG B 7 C6 - N1 - C2 ANGL. DEV. = -6.1 DEGREES
+REMARK 500 1 DG B 7 C2 - N3 - C4 ANGL. DEV. = 9.5 DEGREES
+REMARK 500 1 DG B 7 N3 - C4 - C5 ANGL. DEV. = -7.2 DEGREES
+REMARK 500 1 DG B 7 C5 - C6 - N1 ANGL. DEV. = 8.1 DEGREES
+REMARK 500
+REMARK 500 THIS ENTRY HAS 465 ANGLE DEVIATIONS.
+REMARK 500
+REMARK 500 REMARK: NULL
+REMARK 500
+REMARK 500 GEOMETRY AND STEREOCHEMISTRY
+REMARK 500 SUBTOPIC: TORSION ANGLES
+REMARK 500
+REMARK 500 TORSION ANGLES OUTSIDE THE EXPECTED RAMACHANDRAN REGIONS:
+REMARK 500 (M=MODEL NUMBER; RES=RESIDUE NAME; C=CHAIN IDENTIFIER;
+REMARK 500 SSEQ=SEQUENCE NUMBER; I=INSERTION CODE).
+REMARK 500
+REMARK 500 STANDARD TABLE:
+REMARK 500 FORMAT:(10X,I3,1X,A3,1X,A1,I4,A1,4X,F7.2,3X,F7.2)
+REMARK 500
+REMARK 500 EXPECTED VALUES: GJ KLEYWEGT AND TA JONES (1996). PHI/PSI-
+REMARK 500 CHOLOGY: RAMACHANDRAN REVISITED. STRUCTURE 4, 1395 - 1400
+REMARK 500
+REMARK 500 M RES CSSEQI PSI PHI
+REMARK 500 1 LYS A 2 109.85 -39.18
+REMARK 500 1 SER A 28 144.36 -175.28
+REMARK 500 1 MET A 42 -31.64 -39.56
+REMARK 500 1 ASN A 50 -15.91 91.14
+REMARK 500 2 HIS A 29 69.75 66.50
+REMARK 500 2 ASN A 46 38.34 -82.06
+REMARK 500 3 TYR A 17 -50.84 -22.99
+REMARK 500 3 SER A 28 147.83 -173.11
+REMARK 500 3 HIS A 29 72.40 59.78
+REMARK 500 3 ASN A 46 -36.46 -19.68
+REMARK 500 3 TYR A 47 124.90 53.23
+REMARK 500
+REMARK 500 REMARK: NULL
+REMARK 500
+REMARK 500 GEOMETRY AND STEREOCHEMISTRY
+REMARK 500 SUBTOPIC: PLANAR GROUPS
+REMARK 500
+REMARK 500 PLANAR GROUPS IN THE FOLLOWING RESIDUES HAVE A TOTAL
+REMARK 500 RMS DISTANCE OF ALL ATOMS FROM THE BEST-FIT PLANE
+REMARK 500 BY MORE THAN AN EXPECTED VALUE OF 6*RMSD, WITH AN
+REMARK 500 RMSD 0.02 ANGSTROMS, OR AT LEAST ONE ATOM HAS
+REMARK 500 AN RMSD GREATER THAN THIS VALUE
+REMARK 500 (M=MODEL NUMBER; RES=RESIDUE NAME; C=CHAIN IDENTIFIER;
+REMARK 500 SSEQ=SEQUENCE NUMBER; I=INSERTION CODE).
+REMARK 500
+REMARK 500 M RES CSSEQI RMS TYPE
+REMARK 500 1 DA B 1 0.08 SIDE_CHAIN
+REMARK 500 1 DA B 2 0.07 SIDE_CHAIN
+REMARK 500 1 DT B 3 0.24 SIDE_CHAIN
+REMARK 500 1 DT B 4 0.23 SIDE_CHAIN
+REMARK 500 1 DG B 5 0.15 SIDE_CHAIN
+REMARK 500 1 DT B 6 0.11 SIDE_CHAIN
+REMARK 500 1 DG B 7 0.16 SIDE_CHAIN
+REMARK 500 1 DA B 8 0.07 SIDE_CHAIN
+REMARK 500 1 DG B 11 0.12 SIDE_CHAIN
+REMARK 500 1 DC C 1 0.11 SIDE_CHAIN
+REMARK 500 1 DG C 2 0.11 SIDE_CHAIN
+REMARK 500 1 DC C 3 0.08 SIDE_CHAIN
+REMARK 500 1 DC C 5 0.14 SIDE_CHAIN
+REMARK 500 1 DA C 6 0.17 SIDE_CHAIN
+REMARK 500 1 DC C 7 0.08 SIDE_CHAIN
+REMARK 500 1 DA C 9 0.13 SIDE_CHAIN
+REMARK 500 1 DT C 10 0.15 SIDE_CHAIN
+REMARK 500 1 DT C 11 0.20 SIDE_CHAIN
+REMARK 500 1 TYR A 7 0.14 SIDE_CHAIN
+REMARK 500 1 TYR A 12 0.07 SIDE_CHAIN
+REMARK 500 1 TYR A 17 0.08 SIDE_CHAIN
+REMARK 500 1 TYR A 47 0.09 SIDE_CHAIN
+REMARK 500 2 DA B 1 0.07 SIDE_CHAIN
+REMARK 500 2 DA B 2 0.09 SIDE_CHAIN
+REMARK 500 2 DT B 3 0.09 SIDE_CHAIN
+REMARK 500 2 DT B 4 0.10 SIDE_CHAIN
+REMARK 500 2 DG B 5 0.10 SIDE_CHAIN
+REMARK 500 2 DT B 6 0.09 SIDE_CHAIN
+REMARK 500 2 DG B 7 0.12 SIDE_CHAIN
+REMARK 500 2 DA B 8 0.07 SIDE_CHAIN
+REMARK 500 2 DG B 9 0.09 SIDE_CHAIN
+REMARK 500 2 DC B 10 0.07 SIDE_CHAIN
+REMARK 500 2 DG B 11 0.12 SIDE_CHAIN
+REMARK 500 2 DC C 1 0.13 SIDE_CHAIN
+REMARK 500 2 DC C 3 0.11 SIDE_CHAIN
+REMARK 500 2 DT C 4 0.09 SIDE_CHAIN
+REMARK 500 2 DC C 5 0.07 SIDE_CHAIN
+REMARK 500 2 DA C 6 0.08 SIDE_CHAIN
+REMARK 500 2 DC C 7 0.08 SIDE_CHAIN
+REMARK 500 2 DA C 8 0.08 SIDE_CHAIN
+REMARK 500 2 DA C 9 0.07 SIDE_CHAIN
+REMARK 500 2 DT C 10 0.09 SIDE_CHAIN
+REMARK 500 2 DT C 11 0.11 SIDE_CHAIN
+REMARK 500 2 TYR A 7 0.09 SIDE_CHAIN
+REMARK 500 2 TYR A 12 0.11 SIDE_CHAIN
+REMARK 500 2 TYR A 47 0.14 SIDE_CHAIN
+REMARK 500 3 DT B 3 0.12 SIDE_CHAIN
+REMARK 500 3 DT B 4 0.10 SIDE_CHAIN
+REMARK 500 3 DG B 5 0.12 SIDE_CHAIN
+REMARK 500 3 DT B 6 0.17 SIDE_CHAIN
+REMARK 500 3 DG B 7 0.11 SIDE_CHAIN
+REMARK 500 3 DG B 9 0.07 SIDE_CHAIN
+REMARK 500 3 DC B 10 0.10 SIDE_CHAIN
+REMARK 500 3 DG B 11 0.08 SIDE_CHAIN
+REMARK 500 3 DC C 1 0.14 SIDE_CHAIN
+REMARK 500 3 DG C 2 0.12 SIDE_CHAIN
+REMARK 500 3 DT C 4 0.10 SIDE_CHAIN
+REMARK 500 3 DC C 5 0.13 SIDE_CHAIN
+REMARK 500 3 DA C 6 0.11 SIDE_CHAIN
+REMARK 500 3 DA C 8 0.10 SIDE_CHAIN
+REMARK 500 3 DA C 9 0.10 SIDE_CHAIN
+REMARK 500 3 DT C 10 0.09 SIDE_CHAIN
+REMARK 500 3 DT C 11 0.11 SIDE_CHAIN
+REMARK 500 3 TYR A 7 0.12 SIDE_CHAIN
+REMARK 500 3 TYR A 12 0.12 SIDE_CHAIN
+REMARK 500 3 TYR A 17 0.15 SIDE_CHAIN
+REMARK 500
+REMARK 500 REMARK: NULL
+REMARK 500
+REMARK 500 GEOMETRY AND STEREOCHEMISTRY
+REMARK 500 SUBTOPIC: MAIN CHAIN PLANARITY
+REMARK 500
+REMARK 500 THE FOLLOWING RESIDUES HAVE A PSEUDO PLANARITY
+REMARK 500 TORSION, C(I) - CA(I) - N(I+1) - O(I), GREATER
+REMARK 500 10.0 DEGREES. (M=MODEL NUMBER; RES=RESIDUE NAME;
+REMARK 500 C=CHAIN IDENTIFIER; SSEQ=SEQUENCE NUMBER;
+REMARK 500 I=INSERTION CODE).
+REMARK 500
+REMARK 500 M RES CSSEQI ANGLE
+REMARK 500 1 TYR A 7 -10.45
+REMARK 500 2 GLU A 11 -10.29
+REMARK 500 2 ALA A 27 12.13
+REMARK 500 2 HIS A 29 -12.13
+REMARK 500 2 PRO A 49 12.22
+REMARK 500 3 THR A 19 -10.04
+REMARK 500
+REMARK 500 REMARK: NULL
+REMARK 620
+REMARK 620 METAL COORDINATION
+REMARK 620 (M=MODEL NUMBER; RES=RESIDUE NAME; C=CHAIN IDENTIFIER;
+REMARK 620 SSEQ=SEQUENCE NUMBER; I=INSERTION CODE):
+REMARK 620
+REMARK 620 COORDINATION ANGLES FOR: M RES CSSEQI METAL
+REMARK 620 1 NA C 12 NA
+REMARK 620 N RES CSSEQI ATOM
+REMARK 620 1 DT C 4 OP1
+REMARK 620 2 HOH A 53 O 100.4
+REMARK 620 3 HOH A 57 O 102.6 84.0
+REMARK 620 4 HOH C 923 O 87.5 144.8 128.0
+REMARK 620 N 1 2 3
+REMARK 800
+REMARK 800 SITE
+REMARK 800 SITE_IDENTIFIER: AC1
+REMARK 800 EVIDENCE_CODE: SOFTWARE
+REMARK 800 SITE_DESCRIPTION: BINDING SITE FOR RESIDUE NA C 12
+REMARK 900
+REMARK 900 RELATED ENTRIES
+REMARK 900 RELATED ID: 1LCC RELATED DB: PDB
+DBREF 1LCD A 1 51 UNP P03023 LACI_ECOLI 1 51
+DBREF 1LCD B 1 11 PDB 1LCD 1LCD 1 11
+DBREF 1LCD C 1 11 PDB 1LCD 1LCD 1 11
+SEQRES 1 B 11 DA DA DT DT DG DT DG DA DG DC DG
+SEQRES 1 C 11 DC DG DC DT DC DA DC DA DA DT DT
+SEQRES 1 A 51 MET LYS PRO VAL THR LEU TYR ASP VAL ALA GLU TYR ALA
+SEQRES 2 A 51 GLY VAL SER TYR GLN THR VAL SER ARG VAL VAL ASN GLN
+SEQRES 3 A 51 ALA SER HIS VAL SER ALA LYS THR ARG GLU LYS VAL GLU
+SEQRES 4 A 51 ALA ALA MET ALA GLU LEU ASN TYR ILE PRO ASN ARG
+HET NA C 12 1
+HETNAM NA SODIUM ION
+FORMUL 4 NA NA 1+
+FORMUL 5 HOH *49(H2 O)
+HELIX 1 1 THR A 5 GLY A 14 1 10
+HELIX 2 2 SER A 16 ASN A 25 1 10
+HELIX 3 3 SER A 31 LEU A 45 1 15
+LINK NA NA C 12 OP1 DT C 4 1555 1555 2.52
+LINK NA NA C 12 O HOH A 53 1555 1555 2.10
+LINK NA NA C 12 O HOH A 57 1555 1555 2.22
+LINK NA NA C 12 O HOH C 923 1555 1555 2.13
+SITE 1 AC1 6 VAL A 24 HOH A 53 HOH A 57 DC C 3
+SITE 2 AC1 6 DT C 4 HOH C 923
+CRYST1 1.000 1.000 1.000 90.00 90.00 90.00 P 1 1
+ORIGX1 1.000000 0.000000 0.000000 0.00000
+ORIGX2 0.000000 1.000000 0.000000 0.00000
+ORIGX3 0.000000 0.000000 1.000000 0.00000
+SCALE1 1.000000 0.000000 0.000000 0.00000
+SCALE2 0.000000 1.000000 0.000000 0.00000
+SCALE3 0.000000 0.000000 1.000000 0.00000
+MODEL 1
+ATOM 1 O5' DA B 1 8.090 29.550 48.440 1.00 0.00 O
+ATOM 2 C5' DA B 1 8.340 29.590 47.030 1.00 0.00 C
+ATOM 3 C4' DA B 1 9.490 30.500 46.610 1.00 0.00 C
+ATOM 4 O4' DA B 1 10.740 30.100 47.230 1.00 0.00 O
+ATOM 5 C3' DA B 1 9.700 30.280 45.130 1.00 0.00 C
+ATOM 6 O3' DA B 1 9.870 31.540 44.460 1.00 0.00 O
+ATOM 7 C2' DA B 1 10.890 29.370 45.060 1.00 0.00 C
+ATOM 8 C1' DA B 1 11.710 30.030 46.160 1.00 0.00 C
+ATOM 9 N9 DA B 1 12.880 29.240 46.620 1.00 0.00 N
+ATOM 10 C8 DA B 1 12.750 27.970 46.990 1.00 0.00 C
+ATOM 11 N7 DA B 1 13.820 27.550 47.660 1.00 0.00 N
+ATOM 12 C5 DA B 1 14.670 28.580 47.680 1.00 0.00 C
+ATOM 13 C6 DA B 1 15.950 28.780 48.190 1.00 0.00 C
+ATOM 14 N6 DA B 1 16.660 27.750 48.610 1.00 0.00 N
+ATOM 15 N1 DA B 1 16.580 29.960 48.070 1.00 0.00 N
+ATOM 16 C2 DA B 1 16.010 30.970 47.410 1.00 0.00 C
+ATOM 17 N3 DA B 1 14.790 30.810 46.880 1.00 0.00 N
+ATOM 18 C4 DA B 1 14.090 29.650 47.000 1.00 0.00 C
+ATOM 19 H61 DA B 1 17.580 27.900 48.940 1.00 0.00 H
+ATOM 20 H62 DA B 1 16.240 26.840 48.580 1.00 0.00 H
+ATOM 21 HO5' DA B 1 7.710 30.440 48.770 1.00 0.00 H
+ATOM 22 P DA B 2 9.650 31.670 42.870 1.00 0.00 P
+ATOM 23 OP1 DA B 2 8.790 32.860 42.710 1.00 0.00 O
+ATOM 24 OP2 DA B 2 9.140 30.390 42.310 1.00 0.00 O
+ATOM 25 O5' DA B 2 11.160 31.910 42.400 1.00 0.00 O
+ATOM 26 C5' DA B 2 11.610 33.140 41.820 1.00 0.00 C
+ATOM 27 C4' DA B 2 13.070 33.060 41.360 1.00 0.00 C
+ATOM 28 O4' DA B 2 13.910 32.250 42.210 1.00 0.00 O
+ATOM 29 C3' DA B 2 13.170 32.460 39.980 1.00 0.00 C
+ATOM 30 O3' DA B 2 14.220 33.040 39.190 1.00 0.00 O
+ATOM 31 C2' DA B 2 13.470 31.010 40.230 1.00 0.00 C
+ATOM 32 C1' DA B 2 14.430 31.170 41.390 1.00 0.00 C
+ATOM 33 N9 DA B 2 14.490 29.960 42.220 1.00 0.00 N
+ATOM 34 C8 DA B 2 13.430 29.300 42.690 1.00 0.00 C
+ATOM 35 N7 DA B 2 13.830 28.360 43.550 1.00 0.00 N
+ATOM 36 C5 DA B 2 15.150 28.460 43.650 1.00 0.00 C
+ATOM 37 C6 DA B 2 16.170 27.780 44.320 1.00 0.00 C
+ATOM 38 N6 DA B 2 15.860 26.880 45.230 1.00 0.00 N
+ATOM 39 N1 DA B 2 17.470 28.040 44.090 1.00 0.00 N
+ATOM 40 C2 DA B 2 17.840 28.970 43.210 1.00 0.00 C
+ATOM 41 N3 DA B 2 16.900 29.680 42.560 1.00 0.00 N
+ATOM 42 C4 DA B 2 15.580 29.450 42.770 1.00 0.00 C
+ATOM 43 H61 DA B 2 16.600 26.370 45.680 1.00 0.00 H
+ATOM 44 H62 DA B 2 14.910 26.770 45.540 1.00 0.00 H
+ATOM 45 P DT B 3 13.840 33.560 37.730 1.00 0.00 P
+ATOM 46 OP1 DT B 3 13.260 34.900 37.950 1.00 0.00 O
+ATOM 47 OP2 DT B 3 13.020 32.550 37.010 1.00 0.00 O
+ATOM 48 O5' DT B 3 15.250 33.720 36.970 1.00 0.00 O
+ATOM 49 C5' DT B 3 15.730 32.690 36.110 1.00 0.00 C
+ATOM 50 C4' DT B 3 16.570 31.730 36.930 1.00 0.00 C
+ATOM 51 O4' DT B 3 15.880 30.830 37.810 1.00 0.00 O
+ATOM 52 C3' DT B 3 17.360 30.810 36.060 1.00 0.00 C
+ATOM 53 O3' DT B 3 18.400 31.530 35.350 1.00 0.00 O
+ATOM 54 C2' DT B 3 17.950 29.920 37.130 1.00 0.00 C
+ATOM 55 C1' DT B 3 17.000 30.070 38.300 1.00 0.00 C
+ATOM 56 N1 DT B 3 16.580 28.780 38.910 1.00 0.00 N
+ATOM 57 C2 DT B 3 17.500 28.110 39.760 1.00 0.00 C
+ATOM 58 O2 DT B 3 18.680 28.010 39.470 1.00 0.00 O
+ATOM 59 N3 DT B 3 17.030 27.170 40.670 1.00 0.00 N
+ATOM 60 C4 DT B 3 15.670 26.860 40.730 1.00 0.00 C
+ATOM 61 O4 DT B 3 15.300 26.340 41.780 1.00 0.00 O
+ATOM 62 C5 DT B 3 14.760 27.410 39.820 1.00 0.00 C
+ATOM 63 C7 DT B 3 13.320 26.900 39.770 1.00 0.00 C
+ATOM 64 C6 DT B 3 15.220 28.370 38.920 1.00 0.00 C
+ATOM 65 H3 DT B 3 17.670 26.650 41.230 1.00 0.00 H
+ATOM 66 P DT B 4 19.180 30.910 34.100 1.00 0.00 P
+ATOM 67 OP1 DT B 4 19.750 32.080 33.400 1.00 0.00 O
+ATOM 68 OP2 DT B 4 18.370 29.930 33.350 1.00 0.00 O
+ATOM 69 O5' DT B 4 20.360 30.080 34.770 1.00 0.00 O
+ATOM 70 C5' DT B 4 21.250 30.730 35.690 1.00 0.00 C
+ATOM 71 C4' DT B 4 22.180 29.780 36.430 1.00 0.00 C
+ATOM 72 O4' DT B 4 21.470 28.850 37.270 1.00 0.00 O
+ATOM 73 C3' DT B 4 23.050 28.950 35.520 1.00 0.00 C
+ATOM 74 O3' DT B 4 24.330 28.890 36.150 1.00 0.00 O
+ATOM 75 C2' DT B 4 22.440 27.580 35.450 1.00 0.00 C
+ATOM 76 C1' DT B 4 21.850 27.520 36.840 1.00 0.00 C
+ATOM 77 N1 DT B 4 20.620 26.700 36.820 1.00 0.00 N
+ATOM 78 C2 DT B 4 20.420 25.770 37.840 1.00 0.00 C
+ATOM 79 O2 DT B 4 21.330 25.330 38.530 1.00 0.00 O
+ATOM 80 N3 DT B 4 19.150 25.280 38.100 1.00 0.00 N
+ATOM 81 C4 DT B 4 18.050 25.720 37.350 1.00 0.00 C
+ATOM 82 O4 DT B 4 16.970 25.710 37.950 1.00 0.00 O
+ATOM 83 C5 DT B 4 18.240 26.560 36.250 1.00 0.00 C
+ATOM 84 C7 DT B 4 17.100 26.890 35.290 1.00 0.00 C
+ATOM 85 C6 DT B 4 19.530 27.040 35.990 1.00 0.00 C
+ATOM 86 H3 DT B 4 19.030 24.650 38.860 1.00 0.00 H
+ATOM 87 P DG B 5 25.670 28.660 35.310 1.00 0.00 P
+ATOM 88 OP1 DG B 5 26.720 29.390 36.060 1.00 0.00 O
+ATOM 89 OP2 DG B 5 25.490 29.100 33.910 1.00 0.00 O
+ATOM 90 O5' DG B 5 25.890 27.070 35.350 1.00 0.00 O
+ATOM 91 C5' DG B 5 26.310 26.520 36.610 1.00 0.00 C
+ATOM 92 C4' DG B 5 25.780 25.140 36.960 1.00 0.00 C
+ATOM 93 O4' DG B 5 24.370 25.060 36.690 1.00 0.00 O
+ATOM 94 C3' DG B 5 26.460 24.060 36.140 1.00 0.00 C
+ATOM 95 O3' DG B 5 27.350 23.330 37.020 1.00 0.00 O
+ATOM 96 C2' DG B 5 25.330 23.250 35.550 1.00 0.00 C
+ATOM 97 C1' DG B 5 24.110 23.690 36.370 1.00 0.00 C
+ATOM 98 N9 DG B 5 22.830 23.580 35.630 1.00 0.00 N
+ATOM 99 C8 DG B 5 22.500 24.250 34.540 1.00 0.00 C
+ATOM 100 N7 DG B 5 21.170 24.190 34.350 1.00 0.00 N
+ATOM 101 C5 DG B 5 20.650 23.470 35.350 1.00 0.00 C
+ATOM 102 C6 DG B 5 19.370 23.000 35.700 1.00 0.00 C
+ATOM 103 O6 DG B 5 18.340 23.450 35.190 1.00 0.00 O
+ATOM 104 N1 DG B 5 19.230 22.120 36.780 1.00 0.00 N
+ATOM 105 C2 DG B 5 20.360 21.740 37.490 1.00 0.00 C
+ATOM 106 N2 DG B 5 20.290 20.690 38.240 1.00 0.00 N
+ATOM 107 N3 DG B 5 21.560 22.240 37.190 1.00 0.00 N
+ATOM 108 C4 DG B 5 21.730 23.070 36.150 1.00 0.00 C
+ATOM 109 H1 DG B 5 18.350 21.740 37.040 1.00 0.00 H
+ATOM 110 H21 DG B 5 19.430 20.240 38.490 1.00 0.00 H
+ATOM 111 H22 DG B 5 21.150 20.370 38.710 1.00 0.00 H
+ATOM 112 P DT B 6 28.440 22.240 36.580 1.00 0.00 P
+ATOM 113 OP1 DT B 6 29.640 22.540 37.390 1.00 0.00 O
+ATOM 114 OP2 DT B 6 28.600 22.170 35.110 1.00 0.00 O
+ATOM 115 O5' DT B 6 27.770 20.840 36.990 1.00 0.00 O
+ATOM 116 C5' DT B 6 27.150 20.610 38.270 1.00 0.00 C
+ATOM 117 C4' DT B 6 26.100 19.500 38.220 1.00 0.00 C
+ATOM 118 O4' DT B 6 24.820 19.860 37.670 1.00 0.00 O
+ATOM 119 C3' DT B 6 26.630 18.340 37.420 1.00 0.00 C
+ATOM 120 O3' DT B 6 26.850 17.200 38.260 1.00 0.00 O
+ATOM 121 C2' DT B 6 25.580 18.060 36.390 1.00 0.00 C
+ATOM 122 C1' DT B 6 24.330 18.670 37.030 1.00 0.00 C
+ATOM 123 N1 DT B 6 23.310 19.090 36.040 1.00 0.00 N
+ATOM 124 C2 DT B 6 21.970 18.630 36.090 1.00 0.00 C
+ATOM 125 O2 DT B 6 21.630 17.600 36.660 1.00 0.00 O
+ATOM 126 N3 DT B 6 21.030 19.250 35.260 1.00 0.00 N
+ATOM 127 C4 DT B 6 21.440 20.230 34.350 1.00 0.00 C
+ATOM 128 O4 DT B 6 20.560 20.760 33.680 1.00 0.00 O
+ATOM 129 C5 DT B 6 22.780 20.610 34.220 1.00 0.00 C
+ATOM 130 C7 DT B 6 23.350 21.050 32.880 1.00 0.00 C
+ATOM 131 C6 DT B 6 23.680 20.080 35.120 1.00 0.00 C
+ATOM 132 H3 DT B 6 20.050 19.070 35.400 1.00 0.00 H
+ATOM 133 P DG B 7 27.660 15.900 37.780 1.00 0.00 P
+ATOM 134 OP1 DG B 7 28.440 15.370 38.930 1.00 0.00 O
+ATOM 135 OP2 DG B 7 28.390 16.180 36.510 1.00 0.00 O
+ATOM 136 O5' DG B 7 26.470 14.880 37.420 1.00 0.00 O
+ATOM 137 C5' DG B 7 25.230 14.820 38.170 1.00 0.00 C
+ATOM 138 C4' DG B 7 23.990 14.520 37.310 1.00 0.00 C
+ATOM 139 O4' DG B 7 23.700 15.530 36.320 1.00 0.00 O
+ATOM 140 C3' DG B 7 24.080 13.240 36.500 1.00 0.00 C
+ATOM 141 O3' DG B 7 23.060 12.310 36.920 1.00 0.00 O
+ATOM 142 C2' DG B 7 23.820 13.640 35.070 1.00 0.00 C
+ATOM 143 C1' DG B 7 22.950 14.870 35.300 1.00 0.00 C
+ATOM 144 N9 DG B 7 22.780 15.770 34.140 1.00 0.00 N
+ATOM 145 C8 DG B 7 23.740 16.420 33.500 1.00 0.00 C
+ATOM 146 N7 DG B 7 23.210 17.320 32.660 1.00 0.00 N
+ATOM 147 C5 DG B 7 21.880 17.240 32.780 1.00 0.00 C
+ATOM 148 C6 DG B 7 20.780 17.890 32.200 1.00 0.00 C
+ATOM 149 O6 DG B 7 20.910 18.890 31.490 1.00 0.00 O
+ATOM 150 N1 DG B 7 19.480 17.490 32.540 1.00 0.00 N
+ATOM 151 C2 DG B 7 19.310 16.470 33.460 1.00 0.00 C
+ATOM 152 N2 DG B 7 18.240 15.750 33.330 1.00 0.00 N
+ATOM 153 N3 DG B 7 20.360 15.890 34.040 1.00 0.00 N
+ATOM 154 C4 DG B 7 21.620 16.240 33.720 1.00 0.00 C
+ATOM 155 H1 DG B 7 18.690 17.770 32.010 1.00 0.00 H
+ATOM 156 H21 DG B 7 17.410 16.060 32.860 1.00 0.00 H
+ATOM 157 H22 DG B 7 18.300 14.810 33.650 1.00 0.00 H
+ATOM 158 P DA B 8 23.280 10.730 36.760 1.00 0.00 P
+ATOM 159 OP1 DA B 8 22.960 10.160 38.090 1.00 0.00 O
+ATOM 160 OP2 DA B 8 24.620 10.480 36.190 1.00 0.00 O
+ATOM 161 O5' DA B 8 22.240 10.280 35.630 1.00 0.00 O
+ATOM 162 C5' DA B 8 20.800 10.330 35.740 1.00 0.00 C
+ATOM 163 C4' DA B 8 20.150 10.660 34.400 1.00 0.00 C
+ATOM 164 O4' DA B 8 20.330 12.020 33.900 1.00 0.00 O
+ATOM 165 C3' DA B 8 20.620 9.740 33.280 1.00 0.00 C
+ATOM 166 O3' DA B 8 19.570 9.230 32.450 1.00 0.00 O
+ATOM 167 C2' DA B 8 21.400 10.660 32.390 1.00 0.00 C
+ATOM 168 C1' DA B 8 20.520 11.890 32.470 1.00 0.00 C
+ATOM 169 N9 DA B 8 21.090 13.090 31.800 1.00 0.00 N
+ATOM 170 C8 DA B 8 22.390 13.340 31.660 1.00 0.00 C
+ATOM 171 N7 DA B 8 22.590 14.370 30.850 1.00 0.00 N
+ATOM 172 C5 DA B 8 21.400 14.790 30.470 1.00 0.00 C
+ATOM 173 C6 DA B 8 20.930 15.870 29.720 1.00 0.00 C
+ATOM 174 N6 DA B 8 21.770 16.580 28.990 1.00 0.00 N
+ATOM 175 N1 DA B 8 19.610 16.080 29.570 1.00 0.00 N
+ATOM 176 C2 DA B 8 18.710 15.260 30.110 1.00 0.00 C
+ATOM 177 N3 DA B 8 19.120 14.230 30.850 1.00 0.00 N
+ATOM 178 C4 DA B 8 20.430 13.980 31.060 1.00 0.00 C
+ATOM 179 H61 DA B 8 21.530 17.480 28.620 1.00 0.00 H
+ATOM 180 H62 DA B 8 22.640 16.150 28.730 1.00 0.00 H
+ATOM 181 P DG B 9 18.370 8.320 33.000 1.00 0.00 P
+ATOM 182 OP1 DG B 9 17.340 9.180 33.650 1.00 0.00 O
+ATOM 183 OP2 DG B 9 18.890 7.140 33.720 1.00 0.00 O
+ATOM 184 O5' DG B 9 17.780 7.940 31.560 1.00 0.00 O
+ATOM 185 C5' DG B 9 16.960 8.920 30.890 1.00 0.00 C
+ATOM 186 C4' DG B 9 17.520 9.480 29.610 1.00 0.00 C
+ATOM 187 O4' DG B 9 18.570 10.450 29.690 1.00 0.00 O
+ATOM 188 C3' DG B 9 18.030 8.360 28.740 1.00 0.00 C
+ATOM 189 O3' DG B 9 17.220 8.330 27.560 1.00 0.00 O
+ATOM 190 C2' DG B 9 19.410 8.790 28.390 1.00 0.00 C
+ATOM 191 C1' DG B 9 19.150 10.290 28.390 1.00 0.00 C
+ATOM 192 N9 DG B 9 20.330 11.140 28.190 1.00 0.00 N
+ATOM 193 C8 DG B 9 21.500 10.910 28.760 1.00 0.00 C
+ATOM 194 N7 DG B 9 22.340 11.900 28.480 1.00 0.00 N
+ATOM 195 C5 DG B 9 21.680 12.770 27.730 1.00 0.00 C
+ATOM 196 C6 DG B 9 22.030 13.970 27.090 1.00 0.00 C
+ATOM 197 O6 DG B 9 23.130 14.490 27.270 1.00 0.00 O
+ATOM 198 N1 DG B 9 21.100 14.630 26.280 1.00 0.00 N
+ATOM 199 C2 DG B 9 19.820 14.100 26.120 1.00 0.00 C
+ATOM 200 N2 DG B 9 18.930 14.710 25.370 1.00 0.00 N
+ATOM 201 N3 DG B 9 19.490 12.960 26.760 1.00 0.00 N
+ATOM 202 C4 DG B 9 20.380 12.300 27.530 1.00 0.00 C
+ATOM 203 H1 DG B 9 21.350 15.470 25.810 1.00 0.00 H
+ATOM 204 H21 DG B 9 19.140 15.560 24.870 1.00 0.00 H
+ATOM 205 H22 DG B 9 17.990 14.360 25.280 1.00 0.00 H
+ATOM 206 P DC B 10 17.220 7.100 26.550 1.00 0.00 P
+ATOM 207 OP1 DC B 10 16.070 6.260 26.960 1.00 0.00 O
+ATOM 208 OP2 DC B 10 18.580 6.520 26.410 1.00 0.00 O
+ATOM 209 O5' DC B 10 16.910 7.900 25.210 1.00 0.00 O
+ATOM 210 C5' DC B 10 15.770 8.770 25.150 1.00 0.00 C
+ATOM 211 C4' DC B 10 15.960 9.890 24.130 1.00 0.00 C
+ATOM 212 O4' DC B 10 17.160 10.640 24.440 1.00 0.00 O
+ATOM 213 C3' DC B 10 16.180 9.370 22.720 1.00 0.00 C
+ATOM 214 O3' DC B 10 15.420 10.200 21.780 1.00 0.00 O
+ATOM 215 C2' DC B 10 17.680 9.360 22.560 1.00 0.00 C
+ATOM 216 C1' DC B 10 17.940 10.680 23.230 1.00 0.00 C
+ATOM 217 N1 DC B 10 19.380 10.880 23.540 1.00 0.00 N
+ATOM 218 C2 DC B 10 20.060 12.050 23.150 1.00 0.00 C
+ATOM 219 O2 DC B 10 19.590 12.820 22.310 1.00 0.00 O
+ATOM 220 N3 DC B 10 21.310 12.260 23.590 1.00 0.00 N
+ATOM 221 C4 DC B 10 21.930 11.370 24.390 1.00 0.00 C
+ATOM 222 N4 DC B 10 23.190 11.660 24.600 1.00 0.00 N
+ATOM 223 C5 DC B 10 21.320 10.170 24.790 1.00 0.00 C
+ATOM 224 C6 DC B 10 20.020 9.930 24.320 1.00 0.00 C
+ATOM 225 H41 DC B 10 23.560 12.450 24.110 1.00 0.00 H
+ATOM 226 H42 DC B 10 23.760 11.110 25.220 1.00 0.00 H
+ATOM 227 P DG B 11 15.250 9.800 20.220 1.00 0.00 P
+ATOM 228 OP1 DG B 11 13.950 10.240 19.670 1.00 0.00 O
+ATOM 229 OP2 DG B 11 15.530 8.360 20.050 1.00 0.00 O
+ATOM 230 O5' DG B 11 16.490 10.570 19.540 1.00 0.00 O
+ATOM 231 C5' DG B 11 16.380 11.850 18.890 1.00 0.00 C
+ATOM 232 C4' DG B 11 17.750 12.330 18.410 1.00 0.00 C
+ATOM 233 O4' DG B 11 18.730 12.430 19.470 1.00 0.00 O
+ATOM 234 C3' DG B 11 18.350 11.430 17.340 1.00 0.00 C
+ATOM 235 O3' DG B 11 18.000 11.770 15.980 1.00 0.00 O
+ATOM 236 C2' DG B 11 19.840 11.660 17.530 1.00 0.00 C
+ATOM 237 C1' DG B 11 19.940 12.600 18.720 1.00 0.00 C
+ATOM 238 N9 DG B 11 21.120 12.260 19.550 1.00 0.00 N
+ATOM 239 C8 DG B 11 21.240 11.120 20.230 1.00 0.00 C
+ATOM 240 N7 DG B 11 22.430 11.070 20.840 1.00 0.00 N
+ATOM 241 C5 DG B 11 23.080 12.190 20.510 1.00 0.00 C
+ATOM 242 C6 DG B 11 24.360 12.670 20.770 1.00 0.00 C
+ATOM 243 O6 DG B 11 24.960 12.380 21.810 1.00 0.00 O
+ATOM 244 N1 DG B 11 24.790 13.820 20.110 1.00 0.00 N
+ATOM 245 C2 DG B 11 23.950 14.500 19.260 1.00 0.00 C
+ATOM 246 N2 DG B 11 24.340 15.690 18.900 1.00 0.00 N
+ATOM 247 N3 DG B 11 22.690 14.080 19.080 1.00 0.00 N
+ATOM 248 C4 DG B 11 22.250 12.940 19.670 1.00 0.00 C
+ATOM 249 HO3' DG B 11 17.020 11.980 15.720 1.00 0.00 H
+ATOM 250 H1 DG B 11 25.650 14.240 20.390 1.00 0.00 H
+ATOM 251 H21 DG B 11 25.300 15.940 18.950 1.00 0.00 H
+ATOM 252 H22 DG B 11 23.660 16.430 18.850 1.00 0.00 H
+TER 253 DG B 11
+ATOM 254 O5' DC C 1 33.280 17.670 18.830 1.00 0.00 O
+ATOM 255 C5' DC C 1 32.290 18.610 19.290 1.00 0.00 C
+ATOM 256 C4' DC C 1 30.880 18.030 19.290 1.00 0.00 C
+ATOM 257 O4' DC C 1 30.650 16.820 20.030 1.00 0.00 O
+ATOM 258 C3' DC C 1 29.780 18.990 19.780 1.00 0.00 C
+ATOM 259 O3' DC C 1 29.280 19.700 18.650 1.00 0.00 O
+ATOM 260 C2' DC C 1 28.700 18.090 20.320 1.00 0.00 C
+ATOM 261 C1' DC C 1 29.210 16.720 20.000 1.00 0.00 C
+ATOM 262 N1 DC C 1 28.720 15.740 20.990 1.00 0.00 N
+ATOM 263 C2 DC C 1 27.870 14.690 20.590 1.00 0.00 C
+ATOM 264 O2 DC C 1 27.350 14.690 19.480 1.00 0.00 O
+ATOM 265 N3 DC C 1 27.350 13.880 21.540 1.00 0.00 N
+ATOM 266 C4 DC C 1 27.590 14.080 22.850 1.00 0.00 C
+ATOM 267 N4 DC C 1 27.250 13.050 23.590 1.00 0.00 N
+ATOM 268 C5 DC C 1 28.350 15.160 23.320 1.00 0.00 C
+ATOM 269 C6 DC C 1 28.960 15.960 22.360 1.00 0.00 C
+ATOM 270 H41 DC C 1 26.800 12.320 23.090 1.00 0.00 H
+ATOM 271 H42 DC C 1 27.400 13.020 24.590 1.00 0.00 H
+ATOM 272 HO5' DC C 1 34.030 17.610 19.560 1.00 0.00 H
+ATOM 273 P DG C 2 28.590 21.140 18.770 1.00 0.00 P
+ATOM 274 OP1 DG C 2 28.990 21.790 17.500 1.00 0.00 O
+ATOM 275 OP2 DG C 2 28.960 21.760 20.070 1.00 0.00 O
+ATOM 276 O5' DG C 2 27.050 20.730 18.870 1.00 0.00 O
+ATOM 277 C5' DG C 2 26.230 20.390 17.740 1.00 0.00 C
+ATOM 278 C4' DG C 2 24.850 19.980 18.240 1.00 0.00 C
+ATOM 279 O4' DG C 2 24.820 18.730 18.970 1.00 0.00 O
+ATOM 280 C3' DG C 2 24.300 21.080 19.140 1.00 0.00 C
+ATOM 281 O3' DG C 2 23.290 21.870 18.460 1.00 0.00 O
+ATOM 282 C2' DG C 2 23.880 20.350 20.400 1.00 0.00 C
+ATOM 283 C1' DG C 2 23.780 18.900 19.940 1.00 0.00 C
+ATOM 284 N9 DG C 2 24.020 17.920 21.030 1.00 0.00 N
+ATOM 285 C8 DG C 2 25.110 17.840 21.800 1.00 0.00 C
+ATOM 286 N7 DG C 2 24.990 16.830 22.660 1.00 0.00 N
+ATOM 287 C5 DG C 2 23.800 16.240 22.450 1.00 0.00 C
+ATOM 288 C6 DG C 2 23.120 15.140 22.980 1.00 0.00 C
+ATOM 289 O6 DG C 2 23.400 14.630 24.060 1.00 0.00 O
+ATOM 290 N1 DG C 2 21.900 14.760 22.410 1.00 0.00 N
+ATOM 291 C2 DG C 2 21.360 15.480 21.360 1.00 0.00 C
+ATOM 292 N2 DG C 2 20.090 15.460 21.080 1.00 0.00 N
+ATOM 293 N3 DG C 2 22.010 16.550 20.890 1.00 0.00 N
+ATOM 294 C4 DG C 2 23.200 16.940 21.400 1.00 0.00 C
+ATOM 295 H1 DG C 2 21.380 14.010 22.830 1.00 0.00 H
+ATOM 296 H21 DG C 2 19.490 14.680 21.210 1.00 0.00 H
+ATOM 297 H22 DG C 2 19.730 16.340 20.770 1.00 0.00 H
+ATOM 298 P DC C 3 22.460 23.100 19.110 1.00 0.00 P
+ATOM 299 OP1 DC C 3 21.830 23.940 18.060 1.00 0.00 O
+ATOM 300 OP2 DC C 3 23.220 23.800 20.160 1.00 0.00 O
+ATOM 301 O5' DC C 3 21.290 22.350 19.920 1.00 0.00 O
+ATOM 302 C5' DC C 3 20.300 21.520 19.290 1.00 0.00 C
+ATOM 303 C4' DC C 3 19.570 20.630 20.280 1.00 0.00 C
+ATOM 304 O4' DC C 3 20.380 19.640 20.960 1.00 0.00 O
+ATOM 305 C3' DC C 3 18.850 21.440 21.320 1.00 0.00 C
+ATOM 306 O3' DC C 3 17.440 21.410 21.060 1.00 0.00 O
+ATOM 307 C2' DC C 3 19.310 20.840 22.640 1.00 0.00 C
+ATOM 308 C1' DC C 3 19.730 19.430 22.230 1.00 0.00 C
+ATOM 309 N1 DC C 3 20.670 18.760 23.150 1.00 0.00 N
+ATOM 310 C2 DC C 3 20.300 17.590 23.840 1.00 0.00 C
+ATOM 311 O2 DC C 3 19.140 17.180 23.850 1.00 0.00 O
+ATOM 312 N3 DC C 3 21.140 17.090 24.780 1.00 0.00 N
+ATOM 313 C4 DC C 3 22.270 17.730 25.110 1.00 0.00 C
+ATOM 314 N4 DC C 3 23.070 17.160 25.980 1.00 0.00 N
+ATOM 315 C5 DC C 3 22.690 18.890 24.460 1.00 0.00 C
+ATOM 316 C6 DC C 3 21.890 19.400 23.450 1.00 0.00 C
+ATOM 317 H41 DC C 3 23.180 16.170 25.990 1.00 0.00 H
+ATOM 318 H42 DC C 3 23.830 17.710 26.330 1.00 0.00 H
+ATOM 319 P DT C 4 16.390 22.240 21.940 1.00 0.00 P
+ATOM 320 OP1 DT C 4 15.330 22.670 21.000 1.00 0.00 O
+ATOM 321 OP2 DT C 4 17.130 23.300 22.650 1.00 0.00 O
+ATOM 322 O5' DT C 4 15.920 21.110 22.960 1.00 0.00 O
+ATOM 323 C5' DT C 4 15.210 19.950 22.490 1.00 0.00 C
+ATOM 324 C4' DT C 4 14.910 18.940 23.590 1.00 0.00 C
+ATOM 325 O4' DT C 4 16.100 18.580 24.310 1.00 0.00 O
+ATOM 326 C3' DT C 4 13.940 19.420 24.650 1.00 0.00 C
+ATOM 327 O3' DT C 4 12.880 18.440 24.650 1.00 0.00 O
+ATOM 328 C2' DT C 4 14.720 19.600 25.910 1.00 0.00 C
+ATOM 329 C1' DT C 4 15.700 18.460 25.690 1.00 0.00 C
+ATOM 330 N1 DT C 4 16.980 18.620 26.400 1.00 0.00 N
+ATOM 331 C2 DT C 4 17.640 17.540 27.010 1.00 0.00 C
+ATOM 332 O2 DT C 4 17.080 16.470 27.260 1.00 0.00 O
+ATOM 333 N3 DT C 4 18.940 17.730 27.480 1.00 0.00 N
+ATOM 334 C4 DT C 4 19.580 18.960 27.330 1.00 0.00 C
+ATOM 335 O4 DT C 4 20.640 19.100 27.950 1.00 0.00 O
+ATOM 336 C5 DT C 4 18.940 20.040 26.720 1.00 0.00 C
+ATOM 337 C7 DT C 4 19.610 21.390 26.510 1.00 0.00 C
+ATOM 338 C6 DT C 4 17.620 19.860 26.280 1.00 0.00 C
+ATOM 339 H3 DT C 4 19.310 17.060 28.120 1.00 0.00 H
+ATOM 340 P DC C 5 11.390 18.780 25.110 1.00 0.00 P
+ATOM 341 OP1 DC C 5 10.500 17.910 24.310 1.00 0.00 O
+ATOM 342 OP2 DC C 5 11.150 20.240 25.020 1.00 0.00 O
+ATOM 343 O5' DC C 5 11.450 18.330 26.650 1.00 0.00 O
+ATOM 344 C5' DC C 5 11.530 16.940 27.010 1.00 0.00 C
+ATOM 345 C4' DC C 5 12.200 16.730 28.370 1.00 0.00 C
+ATOM 346 O4' DC C 5 13.580 17.100 28.380 1.00 0.00 O
+ATOM 347 C3' DC C 5 11.540 17.480 29.520 1.00 0.00 C
+ATOM 348 O3' DC C 5 10.530 16.640 30.140 1.00 0.00 O
+ATOM 349 C2' DC C 5 12.680 17.700 30.470 1.00 0.00 C
+ATOM 350 C1' DC C 5 13.900 17.110 29.780 1.00 0.00 C
+ATOM 351 N1 DC C 5 15.070 17.970 29.990 1.00 0.00 N
+ATOM 352 C2 DC C 5 16.200 17.470 30.670 1.00 0.00 C
+ATOM 353 O2 DC C 5 16.090 16.630 31.550 1.00 0.00 O
+ATOM 354 N3 DC C 5 17.310 18.220 30.700 1.00 0.00 N
+ATOM 355 C4 DC C 5 17.320 19.450 30.160 1.00 0.00 C
+ATOM 356 N4 DC C 5 18.490 20.040 30.250 1.00 0.00 N
+ATOM 357 C5 DC C 5 16.220 20.050 29.560 1.00 0.00 C
+ATOM 358 C6 DC C 5 15.070 19.270 29.430 1.00 0.00 C
+ATOM 359 H41 DC C 5 19.260 19.610 30.730 1.00 0.00 H
+ATOM 360 H42 DC C 5 18.640 20.930 29.790 1.00 0.00 H
+ATOM 361 P DA C 6 9.400 17.170 31.160 1.00 0.00 P
+ATOM 362 OP1 DA C 6 8.370 16.100 31.200 1.00 0.00 O
+ATOM 363 OP2 DA C 6 8.980 18.530 30.770 1.00 0.00 O
+ATOM 364 O5' DA C 6 10.120 17.210 32.590 1.00 0.00 O
+ATOM 365 C5' DA C 6 10.380 15.930 33.190 1.00 0.00 C
+ATOM 366 C4' DA C 6 11.470 15.890 34.240 1.00 0.00 C
+ATOM 367 O4' DA C 6 12.650 16.580 33.790 1.00 0.00 O
+ATOM 368 C3' DA C 6 11.030 16.660 35.470 1.00 0.00 C
+ATOM 369 O3' DA C 6 11.220 15.800 36.620 1.00 0.00 O
+ATOM 370 C2' DA C 6 11.780 17.950 35.430 1.00 0.00 C
+ATOM 371 C1' DA C 6 13.080 17.340 34.940 1.00 0.00 C
+ATOM 372 N9 DA C 6 14.050 18.370 34.560 1.00 0.00 N
+ATOM 373 C8 DA C 6 13.800 19.240 33.600 1.00 0.00 C
+ATOM 374 N7 DA C 6 14.930 19.770 33.120 1.00 0.00 N
+ATOM 375 C5 DA C 6 15.900 19.300 33.900 1.00 0.00 C
+ATOM 376 C6 DA C 6 17.230 19.670 34.120 1.00 0.00 C
+ATOM 377 N6 DA C 6 17.980 20.160 33.160 1.00 0.00 N
+ATOM 378 N1 DA C 6 17.910 19.260 35.200 1.00 0.00 N
+ATOM 379 C2 DA C 6 17.360 18.410 36.070 1.00 0.00 C
+ATOM 380 N3 DA C 6 16.100 18.000 35.880 1.00 0.00 N
+ATOM 381 C4 DA C 6 15.350 18.420 34.830 1.00 0.00 C
+ATOM 382 H61 DA C 6 18.920 20.410 33.450 1.00 0.00 H
+ATOM 383 H62 DA C 6 17.780 20.030 32.190 1.00 0.00 H
+ATOM 384 P DC C 7 10.910 16.270 38.120 1.00 0.00 P
+ATOM 385 OP1 DC C 7 10.370 15.100 38.830 1.00 0.00 O
+ATOM 386 OP2 DC C 7 10.110 17.530 38.140 1.00 0.00 O
+ATOM 387 O5' DC C 7 12.390 16.590 38.640 1.00 0.00 O
+ATOM 388 C5' DC C 7 13.430 15.590 38.730 1.00 0.00 C
+ATOM 389 C4' DC C 7 14.710 16.090 39.400 1.00 0.00 C
+ATOM 390 O4' DC C 7 15.320 17.220 38.740 1.00 0.00 O
+ATOM 391 C3' DC C 7 14.330 16.540 40.780 1.00 0.00 C
+ATOM 392 O3' DC C 7 15.200 15.970 41.760 1.00 0.00 O
+ATOM 393 C2' DC C 7 14.230 18.050 40.660 1.00 0.00 C
+ATOM 394 C1' DC C 7 15.380 18.310 39.700 1.00 0.00 C
+ATOM 395 N1 DC C 7 15.290 19.540 38.870 1.00 0.00 N
+ATOM 396 C2 DC C 7 16.460 20.280 38.590 1.00 0.00 C
+ATOM 397 O2 DC C 7 17.520 20.080 39.180 1.00 0.00 O
+ATOM 398 N3 DC C 7 16.440 21.240 37.640 1.00 0.00 N
+ATOM 399 C4 DC C 7 15.300 21.530 36.980 1.00 0.00 C
+ATOM 400 N4 DC C 7 15.430 22.440 36.040 1.00 0.00 N
+ATOM 401 C5 DC C 7 14.090 20.860 37.210 1.00 0.00 C
+ATOM 402 C6 DC C 7 14.100 19.850 38.180 1.00 0.00 C
+ATOM 403 H41 DC C 7 16.320 22.760 35.740 1.00 0.00 H
+ATOM 404 H42 DC C 7 14.610 22.770 35.520 1.00 0.00 H
+ATOM 405 P DA C 8 14.710 15.840 43.280 1.00 0.00 P
+ATOM 406 OP1 DA C 8 14.800 14.410 43.660 1.00 0.00 O
+ATOM 407 OP2 DA C 8 13.420 16.550 43.430 1.00 0.00 O
+ATOM 408 O5' DA C 8 15.830 16.650 44.070 1.00 0.00 O
+ATOM 409 C5' DA C 8 17.200 16.260 43.870 1.00 0.00 C
+ATOM 410 C4' DA C 8 18.150 17.450 43.910 1.00 0.00 C
+ATOM 411 O4' DA C 8 17.720 18.440 42.970 1.00 0.00 O
+ATOM 412 C3' DA C 8 18.090 18.090 45.280 1.00 0.00 C
+ATOM 413 O3' DA C 8 19.410 18.330 45.850 1.00 0.00 O
+ATOM 414 C2' DA C 8 17.350 19.380 45.030 1.00 0.00 C
+ATOM 415 C1' DA C 8 17.960 19.680 43.670 1.00 0.00 C
+ATOM 416 N9 DA C 8 17.250 20.680 42.850 1.00 0.00 N
+ATOM 417 C8 DA C 8 15.930 20.730 42.800 1.00 0.00 C
+ATOM 418 N7 DA C 8 15.510 21.640 41.920 1.00 0.00 N
+ATOM 419 C5 DA C 8 16.620 22.190 41.410 1.00 0.00 C
+ATOM 420 C6 DA C 8 16.870 23.060 40.340 1.00 0.00 C
+ATOM 421 N6 DA C 8 15.910 23.710 39.710 1.00 0.00 N
+ATOM 422 N1 DA C 8 18.120 23.270 39.890 1.00 0.00 N
+ATOM 423 C2 DA C 8 19.160 22.650 40.460 1.00 0.00 C
+ATOM 424 N3 DA C 8 18.970 21.830 41.490 1.00 0.00 N
+ATOM 425 C4 DA C 8 17.730 21.570 41.980 1.00 0.00 C
+ATOM 426 H61 DA C 8 16.160 24.270 38.920 1.00 0.00 H
+ATOM 427 H62 DA C 8 14.950 23.630 40.000 1.00 0.00 H
+ATOM 428 P DA C 9 20.360 17.180 46.460 1.00 0.00 P
+ATOM 429 OP1 DA C 9 20.670 16.150 45.440 1.00 0.00 O
+ATOM 430 OP2 DA C 9 19.790 16.750 47.760 1.00 0.00 O
+ATOM 431 O5' DA C 9 21.720 17.920 46.880 1.00 0.00 O
+ATOM 432 C5' DA C 9 22.600 18.420 45.860 1.00 0.00 C
+ATOM 433 C4' DA C 9 22.560 19.940 45.740 1.00 0.00 C
+ATOM 434 O4' DA C 9 21.390 20.640 45.260 1.00 0.00 O
+ATOM 435 C3' DA C 9 22.970 20.600 47.040 1.00 0.00 C
+ATOM 436 O3' DA C 9 24.390 20.790 46.990 1.00 0.00 O
+ATOM 437 C2' DA C 9 22.150 21.850 47.110 1.00 0.00 C
+ATOM 438 C1' DA C 9 21.630 22.000 45.680 1.00 0.00 C
+ATOM 439 N9 DA C 9 20.350 22.740 45.660 1.00 0.00 N
+ATOM 440 C8 DA C 9 19.280 22.400 46.360 1.00 0.00 C
+ATOM 441 N7 DA C 9 18.180 22.970 45.850 1.00 0.00 N
+ATOM 442 C5 DA C 9 18.570 23.670 44.790 1.00 0.00 C
+ATOM 443 C6 DA C 9 17.970 24.430 43.780 1.00 0.00 C
+ATOM 444 N6 DA C 9 16.670 24.580 43.650 1.00 0.00 N
+ATOM 445 N1 DA C 9 18.680 25.030 42.800 1.00 0.00 N
+ATOM 446 C2 DA C 9 20.010 24.930 42.790 1.00 0.00 C
+ATOM 447 N3 DA C 9 20.640 24.210 43.720 1.00 0.00 N
+ATOM 448 C4 DA C 9 19.960 23.570 44.700 1.00 0.00 C
+ATOM 449 H61 DA C 9 16.330 25.200 42.940 1.00 0.00 H
+ATOM 450 H62 DA C 9 16.040 24.190 44.340 1.00 0.00 H
+ATOM 451 P DT C 10 25.260 21.500 48.140 1.00 0.00 P
+ATOM 452 OP1 DT C 10 26.600 20.870 48.060 1.00 0.00 O
+ATOM 453 OP2 DT C 10 24.530 21.440 49.430 1.00 0.00 O
+ATOM 454 O5' DT C 10 25.350 23.040 47.690 1.00 0.00 O
+ATOM 455 C5' DT C 10 25.880 23.400 46.400 1.00 0.00 C
+ATOM 456 C4' DT C 10 25.410 24.710 45.790 1.00 0.00 C
+ATOM 457 O4' DT C 10 23.980 24.760 45.630 1.00 0.00 O
+ATOM 458 C3' DT C 10 25.810 25.930 46.610 1.00 0.00 C
+ATOM 459 O3' DT C 10 26.740 26.780 45.900 1.00 0.00 O
+ATOM 460 C2' DT C 10 24.510 26.650 46.890 1.00 0.00 C
+ATOM 461 C1' DT C 10 23.640 26.140 45.770 1.00 0.00 C
+ATOM 462 N1 DT C 10 22.190 26.230 46.090 1.00 0.00 N
+ATOM 463 C2 DT C 10 21.300 27.000 45.310 1.00 0.00 C
+ATOM 464 O2 DT C 10 21.610 27.350 44.180 1.00 0.00 O
+ATOM 465 N3 DT C 10 19.940 27.040 45.660 1.00 0.00 N
+ATOM 466 C4 DT C 10 19.460 26.270 46.720 1.00 0.00 C
+ATOM 467 O4 DT C 10 18.260 25.990 46.660 1.00 0.00 O
+ATOM 468 C5 DT C 10 20.340 25.540 47.520 1.00 0.00 C
+ATOM 469 C7 DT C 10 19.930 24.920 48.860 1.00 0.00 C
+ATOM 470 C6 DT C 10 21.690 25.510 47.200 1.00 0.00 C
+ATOM 471 H3 DT C 10 19.340 27.740 45.270 1.00 0.00 H
+ATOM 472 P DT C 11 27.460 28.020 46.650 1.00 0.00 P
+ATOM 473 OP1 DT C 11 28.720 28.370 45.940 1.00 0.00 O
+ATOM 474 OP2 DT C 11 27.610 27.680 48.080 1.00 0.00 O
+ATOM 475 O5' DT C 11 26.350 29.190 46.650 1.00 0.00 O
+ATOM 476 C5' DT C 11 25.900 29.910 45.480 1.00 0.00 C
+ATOM 477 C4' DT C 11 24.540 30.590 45.670 1.00 0.00 C
+ATOM 478 O4' DT C 11 23.540 29.650 46.090 1.00 0.00 O
+ATOM 479 C3' DT C 11 24.460 31.700 46.710 1.00 0.00 C
+ATOM 480 O3' DT C 11 24.740 33.020 46.220 1.00 0.00 O
+ATOM 481 C2' DT C 11 22.990 31.740 47.080 1.00 0.00 C
+ATOM 482 C1' DT C 11 22.430 30.480 46.430 1.00 0.00 C
+ATOM 483 N1 DT C 11 21.560 29.760 47.390 1.00 0.00 N
+ATOM 484 C2 DT C 11 20.170 29.920 47.310 1.00 0.00 C
+ATOM 485 O2 DT C 11 19.620 29.940 46.210 1.00 0.00 O
+ATOM 486 N3 DT C 11 19.380 29.510 48.380 1.00 0.00 N
+ATOM 487 C4 DT C 11 19.940 28.820 49.460 1.00 0.00 C
+ATOM 488 O4 DT C 11 19.170 28.020 49.990 1.00 0.00 O
+ATOM 489 C5 DT C 11 21.320 28.650 49.550 1.00 0.00 C
+ATOM 490 C7 DT C 11 21.990 28.060 50.790 1.00 0.00 C
+ATOM 491 C6 DT C 11 22.120 29.110 48.510 1.00 0.00 C
+ATOM 492 HO3' DT C 11 25.720 33.030 45.940 1.00 0.00 H
+ATOM 493 H3 DT C 11 18.440 29.840 48.450 1.00 0.00 H
+TER 494 DT C 11
+ATOM 495 N MET A 1 27.960 27.500 6.070 1.00 0.00 N
+ATOM 496 CA MET A 1 27.910 28.670 6.970 1.00 0.00 C
+ATOM 497 C MET A 1 28.300 28.280 8.410 1.00 0.00 C
+ATOM 498 O MET A 1 28.040 27.180 8.890 1.00 0.00 O
+ATOM 499 CB MET A 1 26.500 29.280 6.980 1.00 0.00 C
+ATOM 500 CG MET A 1 26.140 29.930 5.650 1.00 0.00 C
+ATOM 501 SD MET A 1 26.900 31.580 5.430 1.00 0.00 S
+ATOM 502 CE MET A 1 26.360 31.940 3.770 1.00 0.00 C
+ATOM 503 H1 MET A 1 27.340 26.800 6.430 1.00 0.00 H
+ATOM 504 H2 MET A 1 27.690 27.730 5.140 1.00 0.00 H
+ATOM 505 H3 MET A 1 28.910 27.170 6.080 1.00 0.00 H
+ATOM 506 N LYS A 2 28.880 29.280 9.060 1.00 0.00 N
+ATOM 507 CA LYS A 2 29.320 29.300 10.470 1.00 0.00 C
+ATOM 508 C LYS A 2 28.280 28.560 11.340 1.00 0.00 C
+ATOM 509 O LYS A 2 27.140 29.030 11.440 1.00 0.00 O
+ATOM 510 CB LYS A 2 29.500 30.750 10.940 1.00 0.00 C
+ATOM 511 CG LYS A 2 29.910 30.840 12.410 1.00 0.00 C
+ATOM 512 CD LYS A 2 30.340 32.240 12.840 1.00 0.00 C
+ATOM 513 CE LYS A 2 31.460 32.180 13.890 1.00 0.00 C
+ATOM 514 NZ LYS A 2 31.090 31.610 15.200 1.00 0.00 N
+ATOM 515 H LYS A 2 28.960 30.170 8.570 1.00 0.00 H
+ATOM 516 HZ1 LYS A 2 30.240 32.000 15.540 1.00 0.00 H
+ATOM 517 HZ2 LYS A 2 30.940 30.620 15.150 1.00 0.00 H
+ATOM 518 HZ3 LYS A 2 31.820 31.770 15.890 1.00 0.00 H
+ATOM 519 N PRO A 3 28.630 27.380 11.860 1.00 0.00 N
+ATOM 520 CA PRO A 3 27.650 26.540 12.560 1.00 0.00 C
+ATOM 521 C PRO A 3 27.460 27.020 14.000 1.00 0.00 C
+ATOM 522 O PRO A 3 28.120 26.570 14.930 1.00 0.00 O
+ATOM 523 CB PRO A 3 28.160 25.110 12.370 1.00 0.00 C
+ATOM 524 CG PRO A 3 29.670 25.240 12.200 1.00 0.00 C
+ATOM 525 CD PRO A 3 29.900 26.650 11.650 1.00 0.00 C
+ATOM 526 N VAL A 4 26.560 27.990 14.090 1.00 0.00 N
+ATOM 527 CA VAL A 4 26.220 28.700 15.340 1.00 0.00 C
+ATOM 528 C VAL A 4 25.270 27.810 16.160 1.00 0.00 C
+ATOM 529 O VAL A 4 24.060 27.820 15.940 1.00 0.00 O
+ATOM 530 CB VAL A 4 25.540 30.060 15.050 1.00 0.00 C
+ATOM 531 CG1 VAL A 4 25.300 30.850 16.350 1.00 0.00 C
+ATOM 532 CG2 VAL A 4 26.340 30.940 14.090 1.00 0.00 C
+ATOM 533 H VAL A 4 26.040 28.300 13.290 1.00 0.00 H
+ATOM 534 N THR A 5 25.870 27.230 17.190 1.00 0.00 N
+ATOM 535 CA THR A 5 25.170 26.410 18.210 1.00 0.00 C
+ATOM 536 C THR A 5 24.430 27.300 19.210 1.00 0.00 C
+ATOM 537 O THR A 5 24.770 28.470 19.370 1.00 0.00 O
+ATOM 538 CB THR A 5 26.120 25.530 19.050 1.00 0.00 C
+ATOM 539 OG1 THR A 5 27.230 26.240 19.620 1.00 0.00 O
+ATOM 540 CG2 THR A 5 26.530 24.250 18.320 1.00 0.00 C
+ATOM 541 H THR A 5 26.800 27.540 17.440 1.00 0.00 H
+ATOM 542 HG1 THR A 5 27.650 26.900 18.950 1.00 0.00 H
+ATOM 543 N LEU A 6 23.560 26.690 20.020 1.00 0.00 N
+ATOM 544 CA LEU A 6 23.050 27.360 21.240 1.00 0.00 C
+ATOM 545 C LEU A 6 24.160 27.970 22.110 1.00 0.00 C
+ATOM 546 O LEU A 6 24.040 29.130 22.510 1.00 0.00 O
+ATOM 547 CB LEU A 6 22.240 26.420 22.130 1.00 0.00 C
+ATOM 548 CG LEU A 6 20.920 25.970 21.480 1.00 0.00 C
+ATOM 549 CD1 LEU A 6 20.340 24.850 22.340 1.00 0.00 C
+ATOM 550 CD2 LEU A 6 19.910 27.100 21.290 1.00 0.00 C
+ATOM 551 H LEU A 6 23.120 25.810 19.790 1.00 0.00 H
+ATOM 552 N TYR A 7 25.260 27.230 22.280 1.00 0.00 N
+ATOM 553 CA TYR A 7 26.380 27.660 23.160 1.00 0.00 C
+ATOM 554 C TYR A 7 26.920 29.050 22.800 1.00 0.00 C
+ATOM 555 O TYR A 7 26.920 29.930 23.660 1.00 0.00 O
+ATOM 556 CB TYR A 7 27.560 26.680 23.180 1.00 0.00 C
+ATOM 557 CG TYR A 7 27.260 25.290 23.760 1.00 0.00 C
+ATOM 558 CD1 TYR A 7 26.860 24.290 22.850 1.00 0.00 C
+ATOM 559 CD2 TYR A 7 27.750 24.960 25.050 1.00 0.00 C
+ATOM 560 CE1 TYR A 7 26.990 22.940 23.200 1.00 0.00 C
+ATOM 561 CE2 TYR A 7 27.890 23.600 25.410 1.00 0.00 C
+ATOM 562 CZ TYR A 7 27.520 22.610 24.470 1.00 0.00 C
+ATOM 563 OH TYR A 7 27.760 21.300 24.760 1.00 0.00 O
+ATOM 564 H TYR A 7 25.380 26.360 21.810 1.00 0.00 H
+ATOM 565 HH TYR A 7 26.910 20.920 25.220 1.00 0.00 H
+ATOM 566 N ASP A 8 26.960 29.290 21.490 1.00 0.00 N
+ATOM 567 CA ASP A 8 27.420 30.510 20.800 1.00 0.00 C
+ATOM 568 C ASP A 8 26.540 31.740 21.040 1.00 0.00 C
+ATOM 569 O ASP A 8 27.060 32.820 21.320 1.00 0.00 O
+ATOM 570 CB ASP A 8 27.420 30.230 19.310 1.00 0.00 C
+ATOM 571 CG ASP A 8 28.580 29.330 18.890 1.00 0.00 C
+ATOM 572 OD1 ASP A 8 29.730 29.710 19.180 1.00 0.00 O
+ATOM 573 OD2 ASP A 8 28.330 28.340 18.180 1.00 0.00 O
+ATOM 574 H ASP A 8 26.650 28.600 20.840 1.00 0.00 H
+ATOM 575 N VAL A 9 25.240 31.570 20.800 1.00 0.00 N
+ATOM 576 CA VAL A 9 24.270 32.640 21.130 1.00 0.00 C
+ATOM 577 C VAL A 9 24.240 32.930 22.630 1.00 0.00 C
+ATOM 578 O VAL A 9 24.470 34.060 23.040 1.00 0.00 O
+ATOM 579 CB VAL A 9 22.840 32.430 20.570 1.00 0.00 C
+ATOM 580 CG1 VAL A 9 22.700 33.140 19.230 1.00 0.00 C
+ATOM 581 CG2 VAL A 9 22.380 30.970 20.490 1.00 0.00 C
+ATOM 582 H VAL A 9 24.860 30.690 20.500 1.00 0.00 H
+ATOM 583 N ALA A 10 24.300 31.850 23.400 1.00 0.00 N
+ATOM 584 CA ALA A 10 24.270 31.910 24.870 1.00 0.00 C
+ATOM 585 C ALA A 10 25.460 32.720 25.450 1.00 0.00 C
+ATOM 586 O ALA A 10 25.250 33.730 26.140 1.00 0.00 O
+ATOM 587 CB ALA A 10 24.260 30.500 25.460 1.00 0.00 C
+ATOM 588 H ALA A 10 24.410 30.940 23.000 1.00 0.00 H
+ATOM 589 N GLU A 11 26.660 32.350 25.010 1.00 0.00 N
+ATOM 590 CA GLU A 11 27.930 33.050 25.340 1.00 0.00 C
+ATOM 591 C GLU A 11 27.930 34.560 25.060 1.00 0.00 C
+ATOM 592 O GLU A 11 28.080 35.350 26.000 1.00 0.00 O
+ATOM 593 CB GLU A 11 29.130 32.410 24.630 1.00 0.00 C
+ATOM 594 CG GLU A 11 29.550 31.070 25.250 1.00 0.00 C
+ATOM 595 CD GLU A 11 30.460 30.260 24.320 1.00 0.00 C
+ATOM 596 OE1 GLU A 11 31.520 30.790 23.920 1.00 0.00 O
+ATOM 597 OE2 GLU A 11 30.140 29.070 24.110 1.00 0.00 O
+ATOM 598 H GLU A 11 26.770 31.430 24.620 1.00 0.00 H
+ATOM 599 N TYR A 12 27.480 34.910 23.850 1.00 0.00 N
+ATOM 600 CA TYR A 12 27.310 36.280 23.350 1.00 0.00 C
+ATOM 601 C TYR A 12 26.210 37.070 24.090 1.00 0.00 C
+ATOM 602 O TYR A 12 26.340 38.270 24.310 1.00 0.00 O
+ATOM 603 CB TYR A 12 26.900 36.090 21.880 1.00 0.00 C
+ATOM 604 CG TYR A 12 26.840 37.380 21.060 1.00 0.00 C
+ATOM 605 CD1 TYR A 12 28.050 37.900 20.570 1.00 0.00 C
+ATOM 606 CD2 TYR A 12 25.600 38.020 20.840 1.00 0.00 C
+ATOM 607 CE1 TYR A 12 28.040 39.100 19.850 1.00 0.00 C
+ATOM 608 CE2 TYR A 12 25.590 39.220 20.120 1.00 0.00 C
+ATOM 609 CZ TYR A 12 26.800 39.740 19.620 1.00 0.00 C
+ATOM 610 OH TYR A 12 26.750 40.740 18.710 1.00 0.00 O
+ATOM 611 H TYR A 12 27.240 34.220 23.170 1.00 0.00 H
+ATOM 612 HH TYR A 12 27.690 40.830 18.270 1.00 0.00 H
+ATOM 613 N ALA A 13 25.120 36.380 24.390 1.00 0.00 N
+ATOM 614 CA ALA A 13 23.940 37.010 25.030 1.00 0.00 C
+ATOM 615 C ALA A 13 24.160 37.330 26.510 1.00 0.00 C
+ATOM 616 O ALA A 13 23.950 38.470 26.910 1.00 0.00 O
+ATOM 617 CB ALA A 13 22.690 36.150 24.870 1.00 0.00 C
+ATOM 618 H ALA A 13 24.990 35.430 24.080 1.00 0.00 H
+ATOM 619 N GLY A 14 24.790 36.410 27.240 1.00 0.00 N
+ATOM 620 CA GLY A 14 24.990 36.520 28.700 1.00 0.00 C
+ATOM 621 C GLY A 14 24.240 35.450 29.520 1.00 0.00 C
+ATOM 622 O GLY A 14 23.980 35.620 30.720 1.00 0.00 O
+ATOM 623 H GLY A 14 25.280 35.650 26.790 1.00 0.00 H
+ATOM 624 N VAL A 15 24.090 34.300 28.890 1.00 0.00 N
+ATOM 625 CA VAL A 15 23.310 33.140 29.380 1.00 0.00 C
+ATOM 626 C VAL A 15 24.000 31.790 29.080 1.00 0.00 C
+ATOM 627 O VAL A 15 24.890 31.670 28.240 1.00 0.00 O
+ATOM 628 CB VAL A 15 21.840 33.090 28.950 1.00 0.00 C
+ATOM 629 CG1 VAL A 15 20.980 33.770 30.020 1.00 0.00 C
+ATOM 630 CG2 VAL A 15 21.520 33.690 27.590 1.00 0.00 C
+ATOM 631 H VAL A 15 24.640 34.100 28.070 1.00 0.00 H
+ATOM 632 N SER A 16 23.540 30.790 29.820 1.00 0.00 N
+ATOM 633 CA SER A 16 23.980 29.400 29.670 1.00 0.00 C
+ATOM 634 C SER A 16 23.200 28.720 28.530 1.00 0.00 C
+ATOM 635 O SER A 16 22.030 29.050 28.290 1.00 0.00 O
+ATOM 636 CB SER A 16 23.770 28.680 31.010 1.00 0.00 C
+ATOM 637 OG SER A 16 22.420 28.260 31.230 1.00 0.00 O
+ATOM 638 H SER A 16 22.890 30.940 30.580 1.00 0.00 H
+ATOM 639 HG SER A 16 21.820 29.050 31.450 1.00 0.00 H
+ATOM 640 N TYR A 17 23.860 27.800 27.840 1.00 0.00 N
+ATOM 641 CA TYR A 17 23.270 27.060 26.700 1.00 0.00 C
+ATOM 642 C TYR A 17 21.930 26.390 27.050 1.00 0.00 C
+ATOM 643 O TYR A 17 21.060 26.250 26.190 1.00 0.00 O
+ATOM 644 CB TYR A 17 24.330 26.080 26.170 1.00 0.00 C
+ATOM 645 CG TYR A 17 24.160 24.590 26.480 1.00 0.00 C
+ATOM 646 CD1 TYR A 17 23.380 23.870 25.560 1.00 0.00 C
+ATOM 647 CD2 TYR A 17 24.730 23.960 27.620 1.00 0.00 C
+ATOM 648 CE1 TYR A 17 23.130 22.510 25.790 1.00 0.00 C
+ATOM 649 CE2 TYR A 17 24.480 22.590 27.850 1.00 0.00 C
+ATOM 650 CZ TYR A 17 23.660 21.890 26.930 1.00 0.00 C
+ATOM 651 OH TYR A 17 23.140 20.660 27.200 1.00 0.00 O
+ATOM 652 H TYR A 17 24.770 27.470 28.110 1.00 0.00 H
+ATOM 653 HH TYR A 17 23.290 20.370 28.170 1.00 0.00 H
+ATOM 654 N GLN A 18 21.760 26.020 28.320 1.00 0.00 N
+ATOM 655 CA GLN A 18 20.500 25.480 28.870 1.00 0.00 C
+ATOM 656 C GLN A 18 19.280 26.410 28.700 1.00 0.00 C
+ATOM 657 O GLN A 18 18.160 25.960 28.440 1.00 0.00 O
+ATOM 658 CB GLN A 18 20.760 25.220 30.350 1.00 0.00 C
+ATOM 659 CG GLN A 18 19.990 24.020 30.910 1.00 0.00 C
+ATOM 660 CD GLN A 18 20.040 22.790 30.010 1.00 0.00 C
+ATOM 661 OE1 GLN A 18 19.080 22.500 29.320 1.00 0.00 O
+ATOM 662 NE2 GLN A 18 21.060 21.960 30.090 1.00 0.00 N
+ATOM 663 H GLN A 18 22.550 25.950 28.940 1.00 0.00 H
+ATOM 664 HE21 GLN A 18 21.950 22.290 30.490 1.00 0.00 H
+ATOM 665 HE22 GLN A 18 21.100 21.150 29.510 1.00 0.00 H
+ATOM 666 N THR A 19 19.530 27.640 29.120 1.00 0.00 N
+ATOM 667 CA THR A 19 18.600 28.790 29.200 1.00 0.00 C
+ATOM 668 C THR A 19 18.180 29.210 27.780 1.00 0.00 C
+ATOM 669 O THR A 19 17.000 29.220 27.460 1.00 0.00 O
+ATOM 670 CB THR A 19 19.350 29.910 29.950 1.00 0.00 C
+ATOM 671 OG1 THR A 19 19.920 29.400 31.160 1.00 0.00 O
+ATOM 672 CG2 THR A 19 18.510 31.130 30.310 1.00 0.00 C
+ATOM 673 H THR A 19 20.430 27.830 29.490 1.00 0.00 H
+ATOM 674 HG1 THR A 19 19.250 29.530 31.930 1.00 0.00 H
+ATOM 675 N VAL A 20 19.160 29.340 26.890 1.00 0.00 N
+ATOM 676 CA VAL A 20 18.940 29.560 25.440 1.00 0.00 C
+ATOM 677 C VAL A 20 18.220 28.370 24.760 1.00 0.00 C
+ATOM 678 O VAL A 20 17.330 28.580 23.940 1.00 0.00 O
+ATOM 679 CB VAL A 20 20.290 30.030 24.870 1.00 0.00 C
+ATOM 680 CG1 VAL A 20 20.470 30.050 23.360 1.00 0.00 C
+ATOM 681 CG2 VAL A 20 20.440 31.470 25.320 1.00 0.00 C
+ATOM 682 H VAL A 20 20.120 29.440 27.170 1.00 0.00 H
+ATOM 683 N SER A 21 18.480 27.170 25.260 1.00 0.00 N
+ATOM 684 CA SER A 21 17.640 25.970 24.980 1.00 0.00 C
+ATOM 685 C SER A 21 16.190 26.120 25.450 1.00 0.00 C
+ATOM 686 O SER A 21 15.300 26.040 24.620 1.00 0.00 O
+ATOM 687 CB SER A 21 18.250 24.690 25.560 1.00 0.00 C
+ATOM 688 OG SER A 21 17.380 23.580 25.340 1.00 0.00 O
+ATOM 689 H SER A 21 19.360 26.990 25.730 1.00 0.00 H
+ATOM 690 HG SER A 21 17.280 23.470 24.320 1.00 0.00 H
+ATOM 691 N ARG A 22 15.980 26.470 26.720 1.00 0.00 N
+ATOM 692 CA ARG A 22 14.660 26.790 27.310 1.00 0.00 C
+ATOM 693 C ARG A 22 14.040 28.170 26.960 1.00 0.00 C
+ATOM 694 O ARG A 22 13.220 28.730 27.680 1.00 0.00 O
+ATOM 695 CB ARG A 22 14.840 26.590 28.810 1.00 0.00 C
+ATOM 696 CG ARG A 22 15.160 25.140 29.160 1.00 0.00 C
+ATOM 697 CD ARG A 22 14.710 24.980 30.600 1.00 0.00 C
+ATOM 698 NE ARG A 22 15.140 23.680 31.120 1.00 0.00 N
+ATOM 699 CZ ARG A 22 16.170 23.490 31.960 1.00 0.00 C
+ATOM 700 NH1 ARG A 22 16.900 24.520 32.400 1.00 0.00 N
+ATOM 701 NH2 ARG A 22 16.430 22.240 32.340 1.00 0.00 N
+ATOM 702 H ARG A 22 16.680 26.290 27.420 1.00 0.00 H
+ATOM 703 HE ARG A 22 14.490 22.940 30.980 1.00 0.00 H
+ATOM 704 HH11 ARG A 22 16.800 25.410 31.940 1.00 0.00 H
+ATOM 705 HH12 ARG A 22 17.570 24.420 33.130 1.00 0.00 H
+ATOM 706 HH21 ARG A 22 15.770 21.530 32.100 1.00 0.00 H
+ATOM 707 HH22 ARG A 22 17.200 21.980 32.930 1.00 0.00 H
+ATOM 708 N VAL A 23 14.620 28.800 25.950 1.00 0.00 N
+ATOM 709 CA VAL A 23 13.950 29.820 25.110 1.00 0.00 C
+ATOM 710 C VAL A 23 13.140 29.160 23.970 1.00 0.00 C
+ATOM 711 O VAL A 23 11.990 29.500 23.700 1.00 0.00 O
+ATOM 712 CB VAL A 23 15.070 30.780 24.640 1.00 0.00 C
+ATOM 713 CG1 VAL A 23 14.740 31.620 23.400 1.00 0.00 C
+ATOM 714 CG2 VAL A 23 15.450 31.700 25.790 1.00 0.00 C
+ATOM 715 H VAL A 23 15.610 28.870 25.930 1.00 0.00 H
+ATOM 716 N VAL A 24 13.750 28.110 23.430 1.00 0.00 N
+ATOM 717 CA VAL A 24 13.250 27.320 22.290 1.00 0.00 C
+ATOM 718 C VAL A 24 12.370 26.160 22.780 1.00 0.00 C
+ATOM 719 O VAL A 24 11.160 26.140 22.550 1.00 0.00 O
+ATOM 720 CB VAL A 24 14.540 26.920 21.540 1.00 0.00 C
+ATOM 721 CG1 VAL A 24 14.380 25.780 20.550 1.00 0.00 C
+ATOM 722 CG2 VAL A 24 15.180 28.150 20.870 1.00 0.00 C
+ATOM 723 H VAL A 24 14.620 27.760 23.790 1.00 0.00 H
+ATOM 724 N ASN A 25 12.970 25.240 23.540 1.00 0.00 N
+ATOM 725 CA ASN A 25 12.330 24.020 24.060 1.00 0.00 C
+ATOM 726 C ASN A 25 12.630 23.770 25.540 1.00 0.00 C
+ATOM 727 O ASN A 25 13.700 24.100 26.060 1.00 0.00 O
+ATOM 728 CB ASN A 25 12.780 22.830 23.210 1.00 0.00 C
+ATOM 729 CG ASN A 25 11.850 22.640 22.020 1.00 0.00 C
+ATOM 730 OD1 ASN A 25 10.640 22.470 22.140 1.00 0.00 O
+ATOM 731 ND2 ASN A 25 12.400 22.510 20.830 1.00 0.00 N
+ATOM 732 H ASN A 25 13.940 25.330 23.800 1.00 0.00 H
+ATOM 733 HD21 ASN A 25 13.410 22.470 20.780 1.00 0.00 H
+ATOM 734 HD22 ASN A 25 11.840 22.490 20.010 1.00 0.00 H
+ATOM 735 N GLN A 26 11.600 23.240 26.200 1.00 0.00 N
+ATOM 736 CA GLN A 26 11.420 23.150 27.660 1.00 0.00 C
+ATOM 737 C GLN A 26 11.400 24.560 28.290 1.00 0.00 C
+ATOM 738 O GLN A 26 11.720 24.750 29.470 1.00 0.00 O
+ATOM 739 CB GLN A 26 12.440 22.210 28.330 1.00 0.00 C
+ATOM 740 CG GLN A 26 11.770 21.520 29.510 1.00 0.00 C
+ATOM 741 CD GLN A 26 12.480 21.800 30.840 1.00 0.00 C
+ATOM 742 OE1 GLN A 26 13.500 21.210 31.160 1.00 0.00 O
+ATOM 743 NE2 GLN A 26 12.110 22.820 31.580 1.00 0.00 N
+ATOM 744 H GLN A 26 10.810 22.890 25.690 1.00 0.00 H
+ATOM 745 HE21 GLN A 26 11.500 23.540 31.230 1.00 0.00 H
+ATOM 746 HE22 GLN A 26 12.540 22.920 32.480 1.00 0.00 H
+ATOM 747 N ALA A 27 10.780 25.490 27.570 1.00 0.00 N
+ATOM 748 CA ALA A 27 10.810 26.920 27.900 1.00 0.00 C
+ATOM 749 C ALA A 27 10.290 27.300 29.280 1.00 0.00 C
+ATOM 750 O ALA A 27 9.120 27.120 29.630 1.00 0.00 O
+ATOM 751 CB ALA A 27 10.130 27.710 26.780 1.00 0.00 C
+ATOM 752 H ALA A 27 10.130 25.260 26.840 1.00 0.00 H
+ATOM 753 N SER A 28 11.260 27.760 30.060 1.00 0.00 N
+ATOM 754 CA SER A 28 11.120 28.100 31.480 1.00 0.00 C
+ATOM 755 C SER A 28 12.450 28.700 31.950 1.00 0.00 C
+ATOM 756 O SER A 28 13.530 28.250 31.570 1.00 0.00 O
+ATOM 757 CB SER A 28 10.870 26.820 32.270 1.00 0.00 C
+ATOM 758 OG SER A 28 10.560 27.170 33.610 1.00 0.00 O
+ATOM 759 H SER A 28 12.180 27.940 29.690 1.00 0.00 H
+ATOM 760 HG SER A 28 9.550 27.300 33.660 1.00 0.00 H
+ATOM 761 N HIS A 29 12.340 29.670 32.840 1.00 0.00 N
+ATOM 762 CA HIS A 29 13.460 30.330 33.540 1.00 0.00 C
+ATOM 763 C HIS A 29 14.480 31.030 32.620 1.00 0.00 C
+ATOM 764 O HIS A 29 15.660 30.710 32.560 1.00 0.00 O
+ATOM 765 CB HIS A 29 14.110 29.370 34.550 1.00 0.00 C
+ATOM 766 CG HIS A 29 13.130 29.030 35.670 1.00 0.00 C
+ATOM 767 ND1 HIS A 29 12.560 29.900 36.510 1.00 0.00 N
+ATOM 768 CD2 HIS A 29 12.530 27.860 35.800 1.00 0.00 C
+ATOM 769 CE1 HIS A 29 11.600 29.240 37.150 1.00 0.00 C
+ATOM 770 NE2 HIS A 29 11.570 27.990 36.700 1.00 0.00 N
+ATOM 771 H HIS A 29 11.430 29.970 33.170 1.00 0.00 H
+ATOM 772 HD1 HIS A 29 12.850 30.860 36.670 1.00 0.00 H
+ATOM 773 HE2 HIS A 29 10.920 27.250 36.990 1.00 0.00 H
+ATOM 774 N VAL A 30 13.920 31.970 31.870 1.00 0.00 N
+ATOM 775 CA VAL A 30 14.670 32.930 31.040 1.00 0.00 C
+ATOM 776 C VAL A 30 14.070 34.320 31.210 1.00 0.00 C
+ATOM 777 O VAL A 30 13.060 34.730 30.620 1.00 0.00 O
+ATOM 778 CB VAL A 30 14.830 32.400 29.590 1.00 0.00 C
+ATOM 779 CG1 VAL A 30 13.550 32.440 28.740 1.00 0.00 C
+ATOM 780 CG2 VAL A 30 15.960 33.210 28.960 1.00 0.00 C
+ATOM 781 H VAL A 30 12.930 32.160 31.940 1.00 0.00 H
+ATOM 782 N SER A 31 14.800 35.030 32.050 1.00 0.00 N
+ATOM 783 CA SER A 31 14.280 36.190 32.770 1.00 0.00 C
+ATOM 784 C SER A 31 14.460 37.440 31.910 1.00 0.00 C
+ATOM 785 O SER A 31 15.030 37.370 30.830 1.00 0.00 O
+ATOM 786 CB SER A 31 15.010 36.310 34.110 1.00 0.00 C
+ATOM 787 OG SER A 31 14.150 35.780 35.110 1.00 0.00 O
+ATOM 788 H SER A 31 15.800 35.060 31.930 1.00 0.00 H
+ATOM 789 HG SER A 31 13.780 34.880 34.770 1.00 0.00 H
+ATOM 790 N ALA A 32 13.930 38.550 32.370 1.00 0.00 N
+ATOM 791 CA ALA A 32 13.670 39.730 31.520 1.00 0.00 C
+ATOM 792 C ALA A 32 14.830 40.190 30.620 1.00 0.00 C
+ATOM 793 O ALA A 32 14.680 40.200 29.390 1.00 0.00 O
+ATOM 794 CB ALA A 32 13.160 40.910 32.340 1.00 0.00 C
+ATOM 795 H ALA A 32 13.600 38.620 33.340 1.00 0.00 H
+ATOM 796 N LYS A 33 15.920 40.640 31.220 1.00 0.00 N
+ATOM 797 CA LYS A 33 17.060 41.170 30.450 1.00 0.00 C
+ATOM 798 C LYS A 33 17.850 40.050 29.760 1.00 0.00 C
+ATOM 799 O LYS A 33 18.200 40.170 28.600 1.00 0.00 O
+ATOM 800 CB LYS A 33 17.920 42.050 31.350 1.00 0.00 C
+ATOM 801 CG LYS A 33 19.010 42.820 30.600 1.00 0.00 C
+ATOM 802 CD LYS A 33 19.470 43.990 31.460 1.00 0.00 C
+ATOM 803 CE LYS A 33 20.500 44.840 30.720 1.00 0.00 C
+ATOM 804 NZ LYS A 33 20.830 46.030 31.500 1.00 0.00 N
+ATOM 805 H LYS A 33 16.080 40.470 32.200 1.00 0.00 H
+ATOM 806 HZ1 LYS A 33 21.100 45.810 32.450 1.00 0.00 H
+ATOM 807 HZ2 LYS A 33 21.560 46.540 31.060 1.00 0.00 H
+ATOM 808 HZ3 LYS A 33 20.030 46.630 31.550 1.00 0.00 H
+ATOM 809 N THR A 34 17.920 38.880 30.400 1.00 0.00 N
+ATOM 810 CA THR A 34 18.630 37.710 29.850 1.00 0.00 C
+ATOM 811 C THR A 34 17.950 37.180 28.570 1.00 0.00 C
+ATOM 812 O THR A 34 18.620 36.970 27.560 1.00 0.00 O
+ATOM 813 CB THR A 34 18.800 36.600 30.890 1.00 0.00 C
+ATOM 814 OG1 THR A 34 17.550 36.200 31.440 1.00 0.00 O
+ATOM 815 CG2 THR A 34 19.770 37.040 31.990 1.00 0.00 C
+ATOM 816 H THR A 34 17.450 38.710 31.270 1.00 0.00 H
+ATOM 817 HG1 THR A 34 17.750 35.500 32.170 1.00 0.00 H
+ATOM 818 N ARG A 35 16.630 37.100 28.610 1.00 0.00 N
+ATOM 819 CA ARG A 35 15.740 36.850 27.460 1.00 0.00 C
+ATOM 820 C ARG A 35 15.770 37.970 26.410 1.00 0.00 C
+ATOM 821 O ARG A 35 15.990 37.670 25.240 1.00 0.00 O
+ATOM 822 CB ARG A 35 14.330 36.580 27.990 1.00 0.00 C
+ATOM 823 CG ARG A 35 13.380 35.960 26.960 1.00 0.00 C
+ATOM 824 CD ARG A 35 11.990 35.740 27.560 1.00 0.00 C
+ATOM 825 NE ARG A 35 11.350 37.010 27.920 1.00 0.00 N
+ATOM 826 CZ ARG A 35 10.930 37.400 29.130 1.00 0.00 C
+ATOM 827 NH1 ARG A 35 11.110 36.700 30.250 1.00 0.00 N
+ATOM 828 NH2 ARG A 35 10.330 38.570 29.220 1.00 0.00 N
+ATOM 829 H ARG A 35 16.170 36.970 29.500 1.00 0.00 H
+ATOM 830 HE ARG A 35 11.110 37.600 27.130 1.00 0.00 H
+ATOM 831 HH11 ARG A 35 11.760 35.940 30.320 1.00 0.00 H
+ATOM 832 HH12 ARG A 35 10.680 37.060 31.090 1.00 0.00 H
+ATOM 833 HH21 ARG A 35 10.050 39.080 28.400 1.00 0.00 H
+ATOM 834 HH22 ARG A 35 10.020 38.890 30.120 1.00 0.00 H
+ATOM 835 N GLU A 36 15.790 39.240 26.840 1.00 0.00 N
+ATOM 836 CA GLU A 36 16.070 40.360 25.910 1.00 0.00 C
+ATOM 837 C GLU A 36 17.410 40.210 25.190 1.00 0.00 C
+ATOM 838 O GLU A 36 17.470 40.370 23.970 1.00 0.00 O
+ATOM 839 CB GLU A 36 16.030 41.740 26.550 1.00 0.00 C
+ATOM 840 CG GLU A 36 14.600 42.290 26.600 1.00 0.00 C
+ATOM 841 CD GLU A 36 14.630 43.810 26.530 1.00 0.00 C
+ATOM 842 OE1 GLU A 36 15.010 44.330 25.450 1.00 0.00 O
+ATOM 843 OE2 GLU A 36 14.360 44.440 27.570 1.00 0.00 O
+ATOM 844 H GLU A 36 15.630 39.480 27.810 1.00 0.00 H
+ATOM 845 N LYS A 37 18.440 39.740 25.900 1.00 0.00 N
+ATOM 846 CA LYS A 37 19.760 39.430 25.320 1.00 0.00 C
+ATOM 847 C LYS A 37 19.700 38.300 24.280 1.00 0.00 C
+ATOM 848 O LYS A 37 20.490 38.330 23.340 1.00 0.00 O
+ATOM 849 CB LYS A 37 20.740 39.020 26.420 1.00 0.00 C
+ATOM 850 CG LYS A 37 21.100 40.090 27.460 1.00 0.00 C
+ATOM 851 CD LYS A 37 22.020 41.130 26.840 1.00 0.00 C
+ATOM 852 CE LYS A 37 22.390 42.220 27.840 1.00 0.00 C
+ATOM 853 NZ LYS A 37 23.200 43.190 27.080 1.00 0.00 N
+ATOM 854 H LYS A 37 18.390 39.650 26.910 1.00 0.00 H
+ATOM 855 HZ1 LYS A 37 22.830 43.250 26.150 1.00 0.00 H
+ATOM 856 HZ2 LYS A 37 23.190 44.090 27.520 1.00 0.00 H
+ATOM 857 HZ3 LYS A 37 24.140 42.850 27.010 1.00 0.00 H
+ATOM 858 N VAL A 38 18.860 37.280 24.500 1.00 0.00 N
+ATOM 859 CA VAL A 38 18.670 36.150 23.560 1.00 0.00 C
+ATOM 860 C VAL A 38 17.890 36.590 22.300 1.00 0.00 C
+ATOM 861 O VAL A 38 18.290 36.230 21.190 1.00 0.00 O
+ATOM 862 CB VAL A 38 17.980 34.920 24.210 1.00 0.00 C
+ATOM 863 CG1 VAL A 38 18.160 33.690 23.320 1.00 0.00 C
+ATOM 864 CG2 VAL A 38 18.590 34.570 25.570 1.00 0.00 C
+ATOM 865 H VAL A 38 18.390 37.180 25.380 1.00 0.00 H
+ATOM 866 N GLU A 39 16.920 37.480 22.510 1.00 0.00 N
+ATOM 867 CA GLU A 39 16.140 38.140 21.440 1.00 0.00 C
+ATOM 868 C GLU A 39 17.010 39.070 20.580 1.00 0.00 C
+ATOM 869 O GLU A 39 16.880 39.080 19.360 1.00 0.00 O
+ATOM 870 CB GLU A 39 14.960 38.930 22.000 1.00 0.00 C
+ATOM 871 CG GLU A 39 13.860 38.000 22.520 1.00 0.00 C
+ATOM 872 CD GLU A 39 12.600 38.760 22.950 1.00 0.00 C
+ATOM 873 OE1 GLU A 39 12.590 39.360 24.040 1.00 0.00 O
+ATOM 874 OE2 GLU A 39 11.580 38.610 22.250 1.00 0.00 O
+ATOM 875 H GLU A 39 16.660 37.750 23.430 1.00 0.00 H
+ATOM 876 N ALA A 40 17.990 39.710 21.230 1.00 0.00 N
+ATOM 877 CA ALA A 40 19.080 40.440 20.580 1.00 0.00 C
+ATOM 878 C ALA A 40 19.950 39.480 19.740 1.00 0.00 C
+ATOM 879 O ALA A 40 19.930 39.520 18.510 1.00 0.00 O
+ATOM 880 CB ALA A 40 19.840 41.120 21.720 1.00 0.00 C
+ATOM 881 H ALA A 40 18.030 39.750 22.240 1.00 0.00 H
+ATOM 882 N ALA A 41 20.640 38.580 20.440 1.00 0.00 N
+ATOM 883 CA ALA A 41 21.480 37.480 19.900 1.00 0.00 C
+ATOM 884 C ALA A 41 20.930 36.720 18.690 1.00 0.00 C
+ATOM 885 O ALA A 41 21.650 36.590 17.710 1.00 0.00 O
+ATOM 886 CB ALA A 41 21.820 36.470 21.000 1.00 0.00 C
+ATOM 887 H ALA A 41 20.720 38.730 21.420 1.00 0.00 H
+ATOM 888 N MET A 42 19.720 36.160 18.790 1.00 0.00 N
+ATOM 889 CA MET A 42 19.010 35.560 17.630 1.00 0.00 C
+ATOM 890 C MET A 42 19.150 36.340 16.310 1.00 0.00 C
+ATOM 891 O MET A 42 19.170 35.720 15.250 1.00 0.00 O
+ATOM 892 CB MET A 42 17.540 35.330 17.980 1.00 0.00 C
+ATOM 893 CG MET A 42 17.410 34.130 18.920 1.00 0.00 C
+ATOM 894 SD MET A 42 18.490 32.710 18.500 1.00 0.00 S
+ATOM 895 CE MET A 42 18.800 32.080 20.130 1.00 0.00 C
+ATOM 896 H MET A 42 19.280 35.990 19.670 1.00 0.00 H
+ATOM 897 N ALA A 43 19.270 37.660 16.410 1.00 0.00 N
+ATOM 898 CA ALA A 43 19.540 38.540 15.260 1.00 0.00 C
+ATOM 899 C ALA A 43 21.040 38.850 15.050 1.00 0.00 C
+ATOM 900 O ALA A 43 21.590 38.580 13.980 1.00 0.00 O
+ATOM 901 CB ALA A 43 18.690 39.820 15.360 1.00 0.00 C
+ATOM 902 H ALA A 43 19.260 38.110 17.310 1.00 0.00 H
+ATOM 903 N GLU A 44 21.710 39.300 16.110 1.00 0.00 N
+ATOM 904 CA GLU A 44 23.170 39.550 16.110 1.00 0.00 C
+ATOM 905 C GLU A 44 24.120 38.400 15.720 1.00 0.00 C
+ATOM 906 O GLU A 44 25.210 38.670 15.220 1.00 0.00 O
+ATOM 907 CB GLU A 44 23.600 40.190 17.430 1.00 0.00 C
+ATOM 908 CG GLU A 44 23.720 41.710 17.230 1.00 0.00 C
+ATOM 909 CD GLU A 44 22.390 42.460 17.270 1.00 0.00 C
+ATOM 910 OE1 GLU A 44 21.650 42.290 18.260 1.00 0.00 O
+ATOM 911 OE2 GLU A 44 22.190 43.330 16.390 1.00 0.00 O
+ATOM 912 H GLU A 44 21.230 39.670 16.910 1.00 0.00 H
+ATOM 913 N LEU A 45 23.720 37.170 16.020 1.00 0.00 N
+ATOM 914 CA LEU A 45 24.250 35.940 15.390 1.00 0.00 C
+ATOM 915 C LEU A 45 23.070 35.230 14.690 1.00 0.00 C
+ATOM 916 O LEU A 45 21.930 35.250 15.150 1.00 0.00 O
+ATOM 917 CB LEU A 45 24.890 34.970 16.400 1.00 0.00 C
+ATOM 918 CG LEU A 45 25.900 35.590 17.380 1.00 0.00 C
+ATOM 919 CD1 LEU A 45 26.410 34.540 18.370 1.00 0.00 C
+ATOM 920 CD2 LEU A 45 27.110 36.240 16.700 1.00 0.00 C
+ATOM 921 H LEU A 45 23.000 37.000 16.700 1.00 0.00 H
+ATOM 922 N ASN A 46 23.360 34.600 13.560 1.00 0.00 N
+ATOM 923 CA ASN A 46 22.330 33.850 12.810 1.00 0.00 C
+ATOM 924 C ASN A 46 22.370 32.340 13.120 1.00 0.00 C
+ATOM 925 O ASN A 46 23.110 31.550 12.540 1.00 0.00 O
+ATOM 926 CB ASN A 46 22.310 34.200 11.310 1.00 0.00 C
+ATOM 927 CG ASN A 46 23.640 34.000 10.600 1.00 0.00 C
+ATOM 928 OD1 ASN A 46 23.930 32.960 10.020 1.00 0.00 O
+ATOM 929 ND2 ASN A 46 24.500 34.970 10.720 1.00 0.00 N
+ATOM 930 H ASN A 46 24.320 34.540 13.250 1.00 0.00 H
+ATOM 931 HD21 ASN A 46 24.250 35.780 11.280 1.00 0.00 H
+ATOM 932 HD22 ASN A 46 25.370 34.900 10.220 1.00 0.00 H
+ATOM 933 N TYR A 47 21.760 32.080 14.270 1.00 0.00 N
+ATOM 934 CA TYR A 47 21.600 30.740 14.850 1.00 0.00 C
+ATOM 935 C TYR A 47 21.060 29.670 13.890 1.00 0.00 C
+ATOM 936 O TYR A 47 19.910 29.760 13.460 1.00 0.00 O
+ATOM 937 CB TYR A 47 20.750 30.930 16.110 1.00 0.00 C
+ATOM 938 CG TYR A 47 20.450 29.650 16.890 1.00 0.00 C
+ATOM 939 CD1 TYR A 47 21.490 28.830 17.370 1.00 0.00 C
+ATOM 940 CD2 TYR A 47 19.120 29.200 16.820 1.00 0.00 C
+ATOM 941 CE1 TYR A 47 21.200 27.500 17.720 1.00 0.00 C
+ATOM 942 CE2 TYR A 47 18.810 27.880 17.200 1.00 0.00 C
+ATOM 943 CZ TYR A 47 19.860 27.050 17.640 1.00 0.00 C
+ATOM 944 OH TYR A 47 19.550 25.830 18.160 1.00 0.00 O
+ATOM 945 H TYR A 47 21.640 32.820 14.930 1.00 0.00 H
+ATOM 946 HH TYR A 47 20.430 25.360 18.360 1.00 0.00 H
+ATOM 947 N ILE A 48 21.780 28.540 13.950 1.00 0.00 N
+ATOM 948 CA ILE A 48 21.520 27.310 13.180 1.00 0.00 C
+ATOM 949 C ILE A 48 21.020 26.220 14.160 1.00 0.00 C
+ATOM 950 O ILE A 48 21.830 25.610 14.860 1.00 0.00 O
+ATOM 951 CB ILE A 48 22.690 26.740 12.360 1.00 0.00 C
+ATOM 952 CG1 ILE A 48 23.290 27.810 11.440 1.00 0.00 C
+ATOM 953 CG2 ILE A 48 22.190 25.530 11.530 1.00 0.00 C
+ATOM 954 CD1 ILE A 48 24.590 27.340 10.790 1.00 0.00 C
+ATOM 955 H ILE A 48 22.490 28.450 14.670 1.00 0.00 H
+ATOM 956 N PRO A 49 19.710 25.990 14.170 1.00 0.00 N
+ATOM 957 CA PRO A 49 19.060 24.890 14.910 1.00 0.00 C
+ATOM 958 C PRO A 49 19.640 23.510 14.600 1.00 0.00 C
+ATOM 959 O PRO A 49 19.760 23.140 13.430 1.00 0.00 O
+ATOM 960 CB PRO A 49 17.610 24.990 14.460 1.00 0.00 C
+ATOM 961 CG PRO A 49 17.390 26.490 14.310 1.00 0.00 C
+ATOM 962 CD PRO A 49 18.690 26.910 13.630 1.00 0.00 C
+ATOM 963 N ASN A 50 20.220 22.920 15.650 1.00 0.00 N
+ATOM 964 CA ASN A 50 20.850 21.580 15.700 1.00 0.00 C
+ATOM 965 C ASN A 50 22.360 21.540 15.390 1.00 0.00 C
+ATOM 966 O ASN A 50 22.980 20.500 15.630 1.00 0.00 O
+ATOM 967 CB ASN A 50 20.150 20.440 14.940 1.00 0.00 C
+ATOM 968 CG ASN A 50 18.800 20.030 15.540 1.00 0.00 C
+ATOM 969 OD1 ASN A 50 17.740 20.340 15.010 1.00 0.00 O
+ATOM 970 ND2 ASN A 50 18.790 19.260 16.610 1.00 0.00 N
+ATOM 971 H ASN A 50 20.430 23.470 16.470 1.00 0.00 H
+ATOM 972 HD21 ASN A 50 19.610 19.040 17.130 1.00 0.00 H
+ATOM 973 HD22 ASN A 50 17.880 18.990 16.950 1.00 0.00 H
+ATOM 974 N ARG A 51 22.930 22.590 14.800 1.00 0.00 N
+ATOM 975 CA ARG A 51 24.390 22.580 14.560 1.00 0.00 C
+ATOM 976 C ARG A 51 25.100 23.850 15.040 1.00 0.00 C
+ATOM 977 O ARG A 51 26.320 23.720 15.290 1.00 0.00 O
+ATOM 978 CB ARG A 51 24.730 22.340 13.080 1.00 0.00 C
+ATOM 979 CG ARG A 51 24.750 20.870 12.630 1.00 0.00 C
+ATOM 980 CD ARG A 51 25.740 19.980 13.390 1.00 0.00 C
+ATOM 981 NE ARG A 51 27.140 20.390 13.220 1.00 0.00 N
+ATOM 982 CZ ARG A 51 28.150 19.870 13.950 1.00 0.00 C
+ATOM 983 NH1 ARG A 51 27.950 18.830 14.750 1.00 0.00 N
+ATOM 984 NH2 ARG A 51 29.390 20.340 13.830 1.00 0.00 N
+ATOM 985 OXT ARG A 51 24.530 24.930 14.790 1.00 0.00 O
+ATOM 986 H ARG A 51 22.470 23.450 14.570 1.00 0.00 H
+ATOM 987 HE ARG A 51 27.330 21.010 12.450 1.00 0.00 H
+ATOM 988 HH11 ARG A 51 27.040 18.440 14.940 1.00 0.00 H
+ATOM 989 HH12 ARG A 51 28.740 18.400 15.210 1.00 0.00 H
+ATOM 990 HH21 ARG A 51 29.650 21.100 13.230 1.00 0.00 H
+ATOM 991 HH22 ARG A 51 30.100 19.980 14.450 1.00 0.00 H
+TER 992 ARG A 51
+HETATM 993 NA NA C 12 16.260 23.720 18.910 1.00 0.00 NA
+HETATM 994 O HOH B 835 19.850 26.680 33.020 1.00 0.00 O
+HETATM 995 H1 HOH B 835 20.310 27.550 32.820 1.00 0.00 H
+HETATM 996 H2 HOH B 835 20.450 26.110 33.580 1.00 0.00 H
+HETATM 997 O HOH B1026 8.460 29.650 39.060 1.00 0.00 O
+HETATM 998 H1 HOH B1026 7.740 30.120 38.550 1.00 0.00 H
+HETATM 999 H2 HOH B1026 8.660 30.160 39.900 1.00 0.00 H
+HETATM 1000 O HOH B1417 11.340 36.480 36.160 1.00 0.00 O
+HETATM 1001 H1 HOH B1417 11.830 35.700 36.560 1.00 0.00 H
+HETATM 1002 H2 HOH B1417 11.000 37.060 36.900 1.00 0.00 H
+HETATM 1003 O HOH B1632 9.240 32.250 36.410 1.00 0.00 O
+HETATM 1004 H1 HOH B1632 8.260 32.430 36.340 1.00 0.00 H
+HETATM 1005 H2 HOH B1632 9.630 32.830 37.120 1.00 0.00 H
+HETATM 1006 O HOH B1924 17.300 15.020 16.580 1.00 0.00 O
+HETATM 1007 H1 HOH B1924 17.310 15.610 15.770 1.00 0.00 H
+HETATM 1008 H2 HOH B1924 18.220 14.670 16.760 1.00 0.00 H
+HETATM 1009 O HOH B2488 25.040 25.940 32.620 1.00 0.00 O
+HETATM 1010 H1 HOH B2488 25.500 26.460 33.340 1.00 0.00 H
+HETATM 1011 H2 HOH B2488 25.510 25.070 32.490 1.00 0.00 H
+HETATM 1012 O HOH B2504 10.230 33.610 38.760 1.00 0.00 O
+HETATM 1013 H1 HOH B2504 11.090 33.110 38.900 1.00 0.00 H
+HETATM 1014 H2 HOH B2504 10.230 34.420 39.350 1.00 0.00 H
+HETATM 1015 O HOH B2561 24.170 31.850 33.870 1.00 0.00 O
+HETATM 1016 H1 HOH B2561 24.070 30.860 33.800 1.00 0.00 H
+HETATM 1017 H2 HOH B2561 25.120 32.080 34.090 1.00 0.00 H
+HETATM 1018 O HOH B2639 20.330 34.690 34.970 1.00 0.00 O
+HETATM 1019 H1 HOH B2639 19.610 35.290 34.620 1.00 0.00 H
+HETATM 1020 H2 HOH B2639 20.310 33.820 34.480 1.00 0.00 H
+HETATM 1021 O HOH B2869 20.450 15.490 17.620 1.00 0.00 O
+HETATM 1022 H1 HOH B2869 19.830 16.270 17.740 1.00 0.00 H
+HETATM 1023 H2 HOH B2869 21.050 15.420 18.410 1.00 0.00 H
+HETATM 1024 O HOH B3222 27.210 30.220 31.760 1.00 0.00 O
+HETATM 1025 H1 HOH B3222 26.570 29.800 32.400 1.00 0.00 H
+HETATM 1026 H2 HOH B3222 27.970 30.630 32.260 1.00 0.00 H
+HETATM 1027 O HOH B3293 17.620 35.240 36.380 1.00 0.00 O
+HETATM 1028 H1 HOH B3293 16.710 34.810 36.390 1.00 0.00 H
+HETATM 1029 H2 HOH B3293 18.310 34.550 36.540 1.00 0.00 H
+HETATM 1030 O HOH C 327 19.190 17.630 19.270 1.00 0.00 O
+HETATM 1031 H1 HOH C 327 18.230 17.890 19.180 1.00 0.00 H
+HETATM 1032 H2 HOH C 327 19.660 18.260 19.890 1.00 0.00 H
+HETATM 1033 O HOH C 827 10.030 19.550 21.880 1.00 0.00 O
+HETATM 1034 H1 HOH C 827 9.960 18.970 22.690 1.00 0.00 H
+HETATM 1035 H2 HOH C 827 10.360 20.450 22.140 1.00 0.00 H
+HETATM 1036 O HOH C 923 17.630 24.770 20.160 1.00 0.00 O
+HETATM 1037 H1 HOH C 923 18.510 25.250 20.070 1.00 0.00 H
+HETATM 1038 H2 HOH C 923 17.540 24.430 21.090 1.00 0.00 H
+HETATM 1039 O HOH C1041 9.720 20.420 33.120 1.00 0.00 O
+HETATM 1040 H1 HOH C1041 10.090 19.830 32.400 1.00 0.00 H
+HETATM 1041 H2 HOH C1041 9.200 19.860 33.760 1.00 0.00 H
+HETATM 1042 O HOH C1046 13.770 23.410 34.090 1.00 0.00 O
+HETATM 1043 H1 HOH C1046 13.830 24.400 34.050 1.00 0.00 H
+HETATM 1044 H2 HOH C1046 12.840 23.150 34.380 1.00 0.00 H
+HETATM 1045 O HOH C1830 29.220 23.780 15.410 1.00 0.00 O
+HETATM 1046 H1 HOH C1830 29.420 23.320 16.270 1.00 0.00 H
+HETATM 1047 H2 HOH C1830 28.230 23.840 15.280 1.00 0.00 H
+HETATM 1048 O HOH C1959 26.820 17.410 25.340 1.00 0.00 O
+HETATM 1049 H1 HOH C1959 26.870 16.650 24.690 1.00 0.00 H
+HETATM 1050 H2 HOH C1959 26.640 17.060 26.250 1.00 0.00 H
+HETATM 1051 O HOH C1993 30.970 23.530 22.110 1.00 0.00 O
+HETATM 1052 H1 HOH C1993 30.730 24.140 21.360 1.00 0.00 H
+HETATM 1053 H2 HOH C1993 30.670 22.600 21.890 1.00 0.00 H
+HETATM 1054 O HOH C2471 22.920 22.570 22.780 1.00 0.00 O
+HETATM 1055 H1 HOH C2471 23.070 23.190 22.010 1.00 0.00 H
+HETATM 1056 H2 HOH C2471 22.310 21.830 22.500 1.00 0.00 H
+HETATM 1057 O HOH C2518 8.990 20.940 27.510 1.00 0.00 O
+HETATM 1058 H1 HOH C2518 8.800 19.970 27.630 1.00 0.00 H
+HETATM 1059 H2 HOH C2518 9.700 21.060 26.820 1.00 0.00 H
+HETATM 1060 O HOH C2570 26.760 20.210 21.950 1.00 0.00 O
+HETATM 1061 H1 HOH C2570 27.690 20.310 22.290 1.00 0.00 H
+HETATM 1062 H2 HOH C2570 26.710 20.540 21.000 1.00 0.00 H
+HETATM 1063 O HOH A 52 21.650 18.570 17.310 1.00 0.00 O
+HETATM 1064 H1 HOH A 52 21.560 18.800 18.280 1.00 0.00 H
+HETATM 1065 H2 HOH A 52 22.420 19.070 16.930 1.00 0.00 H
+HETATM 1066 O HOH A 53 14.510 23.890 17.760 1.00 0.00 O
+HETATM 1067 H1 HOH A 53 14.790 24.370 16.920 1.00 0.00 H
+HETATM 1068 H2 HOH A 53 13.570 24.160 17.990 1.00 0.00 H
+HETATM 1069 O HOH A 54 10.250 25.780 40.110 1.00 0.00 O
+HETATM 1070 H1 HOH A 54 10.970 25.150 40.390 1.00 0.00 H
+HETATM 1071 H2 HOH A 54 10.190 26.530 40.770 1.00 0.00 H
+HETATM 1072 O HOH A 55 16.680 22.390 27.940 1.00 0.00 O
+HETATM 1073 H1 HOH A 55 17.560 22.710 28.300 1.00 0.00 H
+HETATM 1074 H2 HOH A 55 16.640 22.580 26.960 1.00 0.00 H
+HETATM 1075 O HOH A 56 9.310 22.490 24.900 1.00 0.00 O
+HETATM 1076 H1 HOH A 56 8.430 22.400 24.440 1.00 0.00 H
+HETATM 1077 H2 HOH A 56 9.960 21.840 24.510 1.00 0.00 H
+HETATM 1078 O HOH A 57 16.660 21.960 17.620 1.00 0.00 O
+HETATM 1079 H1 HOH A 57 17.390 21.990 16.940 1.00 0.00 H
+HETATM 1080 H2 HOH A 57 16.500 21.010 17.900 1.00 0.00 H
+HETATM 1081 O HOH A 58 16.590 18.980 18.620 1.00 0.00 O
+HETATM 1082 H1 HOH A 58 15.840 18.530 18.130 1.00 0.00 H
+HETATM 1083 H2 HOH A 58 16.290 19.200 19.550 1.00 0.00 H
+HETATM 1084 O HOH A 59 17.560 33.900 32.820 1.00 0.00 O
+HETATM 1085 H1 HOH A 59 16.850 33.260 33.120 1.00 0.00 H
+HETATM 1086 H2 HOH A 59 18.430 33.410 32.720 1.00 0.00 H
+HETATM 1087 O HOH A 60 16.400 27.600 32.270 1.00 0.00 O
+HETATM 1088 H1 HOH A 60 15.860 28.370 31.940 1.00 0.00 H
+HETATM 1089 H2 HOH A 60 17.170 27.950 32.820 1.00 0.00 H
+HETATM 1090 O HOH A 61 11.430 22.110 34.630 1.00 0.00 O
+HETATM 1091 H1 HOH A 61 11.250 21.740 35.540 1.00 0.00 H
+HETATM 1092 H2 HOH A 61 10.950 21.550 33.950 1.00 0.00 H
+HETATM 1093 O HOH A 62 13.340 25.120 37.320 1.00 0.00 O
+HETATM 1094 H1 HOH A 62 14.120 25.130 36.690 1.00 0.00 H
+HETATM 1095 H2 HOH A 62 12.500 25.170 36.790 1.00 0.00 H
+HETATM 1096 O HOH A 63 12.950 33.600 34.310 1.00 0.00 O
+HETATM 1097 H1 HOH A 63 12.010 33.640 33.970 1.00 0.00 H
+HETATM 1098 H2 HOH A 63 12.950 33.200 35.230 1.00 0.00 H
+HETATM 1099 O HOH A 64 30.670 19.500 16.170 1.00 0.00 O
+HETATM 1100 H1 HOH A 64 30.080 20.050 16.760 1.00 0.00 H
+HETATM 1101 H2 HOH A 64 30.480 18.530 16.320 1.00 0.00 H
+HETATM 1102 O HOH A 65 33.140 20.790 16.330 1.00 0.00 O
+HETATM 1103 H1 HOH A 65 32.310 20.230 16.410 1.00 0.00 H
+HETATM 1104 H2 HOH A 65 32.960 21.700 16.700 1.00 0.00 H
+HETATM 1105 O HOH A 66 25.770 17.510 15.790 1.00 0.00 O
+HETATM 1106 H1 HOH A 66 25.390 18.010 16.580 1.00 0.00 H
+HETATM 1107 H2 HOH A 66 25.370 16.590 15.770 1.00 0.00 H
+HETATM 1108 O HOH A 67 25.870 20.080 26.020 1.00 0.00 O
+HETATM 1109 H1 HOH A 67 25.250 20.440 25.330 1.00 0.00 H
+HETATM 1110 H2 HOH A 67 26.210 19.180 25.720 1.00 0.00 H
+HETATM 1111 O HOH A 68 23.280 19.710 29.590 1.00 0.00 O
+HETATM 1112 H1 HOH A 68 24.260 19.560 29.690 1.00 0.00 H
+HETATM 1113 H2 HOH A 68 22.850 19.660 30.500 1.00 0.00 H
+HETATM 1114 O HOH A 69 13.660 26.110 33.530 1.00 0.00 O
+HETATM 1115 H1 HOH A 69 12.660 26.140 33.590 1.00 0.00 H
+HETATM 1116 H2 HOH A 69 13.990 26.950 33.090 1.00 0.00 H
+HETATM 1117 O HOH A 70 23.390 23.170 30.810 1.00 0.00 O
+HETATM 1118 H1 HOH A 70 23.570 24.040 30.350 1.00 0.00 H
+HETATM 1119 H2 HOH A 70 24.080 23.030 31.520 1.00 0.00 H
+HETATM 1120 O HOH A 71 22.150 31.070 32.160 1.00 0.00 O
+HETATM 1121 H1 HOH A 71 21.270 31.180 32.640 1.00 0.00 H
+HETATM 1122 H2 HOH A 71 22.860 31.560 32.660 1.00 0.00 H
+HETATM 1123 O HOH A 72 30.140 25.470 20.070 1.00 0.00 O
+HETATM 1124 H1 HOH A 72 30.270 26.220 20.720 1.00 0.00 H
+HETATM 1125 H2 HOH A 72 29.180 25.430 19.800 1.00 0.00 H
+HETATM 1126 O HOH A 73 29.660 20.790 22.690 1.00 0.00 O
+HETATM 1127 H1 HOH A 73 29.290 21.250 21.880 1.00 0.00 H
+HETATM 1128 H2 HOH A 73 29.030 20.930 23.460 1.00 0.00 H
+HETATM 1129 O HOH A 74 24.720 20.740 23.650 1.00 0.00 O
+HETATM 1130 H1 HOH A 74 25.450 20.660 22.980 1.00 0.00 H
+HETATM 1131 H2 HOH A 74 24.130 21.510 23.410 1.00 0.00 H
+HETATM 1132 O HOH A 75 11.330 24.730 35.270 1.00 0.00 O
+HETATM 1133 H1 HOH A 75 11.210 23.740 35.300 1.00 0.00 H
+HETATM 1134 H2 HOH A 75 10.450 25.170 35.090 1.00 0.00 H
+HETATM 1135 O HOH A 76 16.550 37.270 38.760 1.00 0.00 O
+HETATM 1136 H1 HOH A 76 15.770 37.560 38.210 1.00 0.00 H
+HETATM 1137 H2 HOH A 76 16.690 36.290 38.640 1.00 0.00 H
+HETATM 1138 O HOH A 77 14.290 37.710 37.220 1.00 0.00 O
+HETATM 1139 H1 HOH A 77 13.730 37.200 37.870 1.00 0.00 H
+HETATM 1140 H2 HOH A 77 14.340 37.220 36.350 1.00 0.00 H
+ENDMDL
+MODEL 2
+ATOM 1 O5' DA B 1 7.900 34.300 47.200 1.00 0.00 O
+ATOM 2 C5' DA B 1 8.090 33.680 45.920 1.00 0.00 C
+ATOM 3 C4' DA B 1 9.560 33.570 45.550 1.00 0.00 C
+ATOM 4 O4' DA B 1 10.240 32.680 46.480 1.00 0.00 O
+ATOM 5 C3' DA B 1 9.700 32.900 44.190 1.00 0.00 C
+ATOM 6 O3' DA B 1 10.560 33.600 43.250 1.00 0.00 O
+ATOM 7 C2' DA B 1 10.150 31.500 44.510 1.00 0.00 C
+ATOM 8 C1' DA B 1 11.070 31.870 45.640 1.00 0.00 C
+ATOM 9 N9 DA B 1 11.670 30.720 46.360 1.00 0.00 N
+ATOM 10 C8 DA B 1 10.990 29.700 46.830 1.00 0.00 C
+ATOM 11 N7 DA B 1 11.810 28.780 47.370 1.00 0.00 N
+ATOM 12 C5 DA B 1 13.050 29.250 47.200 1.00 0.00 C
+ATOM 13 C6 DA B 1 14.350 28.810 47.490 1.00 0.00 C
+ATOM 14 N6 DA B 1 14.560 27.820 48.320 1.00 0.00 N
+ATOM 15 N1 DA B 1 15.430 29.500 47.080 1.00 0.00 N
+ATOM 16 C2 DA B 1 15.300 30.660 46.440 1.00 0.00 C
+ATOM 17 N3 DA B 1 14.080 31.150 46.200 1.00 0.00 N
+ATOM 18 C4 DA B 1 12.960 30.480 46.550 1.00 0.00 C
+ATOM 19 H61 DA B 1 15.510 27.610 48.570 1.00 0.00 H
+ATOM 20 H62 DA B 1 13.810 27.230 48.630 1.00 0.00 H
+ATOM 21 HO5' DA B 1 8.540 35.110 47.220 1.00 0.00 H
+ATOM 22 P DA B 2 9.940 34.790 42.370 1.00 0.00 P
+ATOM 23 OP1 DA B 2 9.750 35.990 43.220 1.00 0.00 O
+ATOM 24 OP2 DA B 2 8.760 34.230 41.670 1.00 0.00 O
+ATOM 25 O5' DA B 2 10.990 35.100 41.200 1.00 0.00 O
+ATOM 26 C5' DA B 2 12.310 35.610 41.490 1.00 0.00 C
+ATOM 27 C4' DA B 2 13.370 34.550 41.300 1.00 0.00 C
+ATOM 28 O4' DA B 2 13.240 33.470 42.230 1.00 0.00 O
+ATOM 29 C3' DA B 2 13.320 33.940 39.900 1.00 0.00 C
+ATOM 30 O3' DA B 2 14.290 34.540 38.990 1.00 0.00 O
+ATOM 31 C2' DA B 2 13.530 32.470 40.140 1.00 0.00 C
+ATOM 32 C1' DA B 2 14.020 32.470 41.580 1.00 0.00 C
+ATOM 33 N9 DA B 2 13.910 31.210 42.320 1.00 0.00 N
+ATOM 34 C8 DA B 2 12.820 30.480 42.470 1.00 0.00 C
+ATOM 35 N7 DA B 2 13.150 29.270 42.920 1.00 0.00 N
+ATOM 36 C5 DA B 2 14.480 29.250 43.070 1.00 0.00 C
+ATOM 37 C6 DA B 2 15.470 28.330 43.460 1.00 0.00 C
+ATOM 38 N6 DA B 2 15.220 27.080 43.710 1.00 0.00 N
+ATOM 39 N1 DA B 2 16.780 28.670 43.520 1.00 0.00 N
+ATOM 40 C2 DA B 2 17.180 29.880 43.150 1.00 0.00 C
+ATOM 41 N3 DA B 2 16.280 30.780 42.730 1.00 0.00 N
+ATOM 42 C4 DA B 2 14.960 30.500 42.690 1.00 0.00 C
+ATOM 43 H61 DA B 2 15.980 26.440 43.790 1.00 0.00 H
+ATOM 44 H62 DA B 2 14.300 26.840 44.050 1.00 0.00 H
+ATOM 45 P DT B 3 13.810 35.070 37.550 1.00 0.00 P
+ATOM 46 OP1 DT B 3 13.210 36.410 37.690 1.00 0.00 O
+ATOM 47 OP2 DT B 3 13.000 33.990 36.940 1.00 0.00 O
+ATOM 48 O5' DT B 3 15.170 35.260 36.720 1.00 0.00 O
+ATOM 49 C5' DT B 3 15.760 34.190 35.960 1.00 0.00 C
+ATOM 50 C4' DT B 3 16.530 33.120 36.740 1.00 0.00 C
+ATOM 51 O4' DT B 3 15.910 32.240 37.690 1.00 0.00 O
+ATOM 52 C3' DT B 3 17.220 32.220 35.750 1.00 0.00 C
+ATOM 53 O3' DT B 3 18.440 32.850 35.280 1.00 0.00 O
+ATOM 54 C2' DT B 3 17.520 30.980 36.550 1.00 0.00 C
+ATOM 55 C1' DT B 3 17.040 31.360 37.930 1.00 0.00 C
+ATOM 56 N1 DT B 3 16.710 30.160 38.750 1.00 0.00 N
+ATOM 57 C2 DT B 3 17.730 29.450 39.420 1.00 0.00 C
+ATOM 58 O2 DT B 3 18.910 29.610 39.150 1.00 0.00 O
+ATOM 59 N3 DT B 3 17.410 28.320 40.170 1.00 0.00 N
+ATOM 60 C4 DT B 3 16.090 27.860 40.200 1.00 0.00 C
+ATOM 61 O4 DT B 3 15.910 26.770 40.740 1.00 0.00 O
+ATOM 62 C5 DT B 3 15.060 28.540 39.540 1.00 0.00 C
+ATOM 63 C7 DT B 3 13.590 28.120 39.660 1.00 0.00 C
+ATOM 64 C6 DT B 3 15.380 29.700 38.830 1.00 0.00 C
+ATOM 65 H3 DT B 3 18.080 27.970 40.840 1.00 0.00 H
+ATOM 66 P DT B 4 19.340 32.370 34.040 1.00 0.00 P
+ATOM 67 OP1 DT B 4 20.000 33.580 33.490 1.00 0.00 O
+ATOM 68 OP2 DT B 4 18.630 31.460 33.110 1.00 0.00 O
+ATOM 69 O5' DT B 4 20.470 31.440 34.690 1.00 0.00 O
+ATOM 70 C5' DT B 4 21.550 32.010 35.460 1.00 0.00 C
+ATOM 71 C4' DT B 4 22.290 30.950 36.270 1.00 0.00 C
+ATOM 72 O4' DT B 4 21.350 30.090 36.940 1.00 0.00 O
+ATOM 73 C3' DT B 4 23.170 29.980 35.480 1.00 0.00 C
+ATOM 74 O3' DT B 4 24.410 29.890 36.230 1.00 0.00 O
+ATOM 75 C2' DT B 4 22.440 28.670 35.400 1.00 0.00 C
+ATOM 76 C1' DT B 4 21.760 28.720 36.750 1.00 0.00 C
+ATOM 77 N1 DT B 4 20.540 27.900 36.770 1.00 0.00 N
+ATOM 78 C2 DT B 4 20.340 26.960 37.810 1.00 0.00 C
+ATOM 79 O2 DT B 4 21.210 26.640 38.620 1.00 0.00 O
+ATOM 80 N3 DT B 4 19.110 26.330 37.920 1.00 0.00 N
+ATOM 81 C4 DT B 4 18.070 26.630 37.030 1.00 0.00 C
+ATOM 82 O4 DT B 4 16.970 26.210 37.340 1.00 0.00 O
+ATOM 83 C5 DT B 4 18.260 27.540 35.980 1.00 0.00 C
+ATOM 84 C7 DT B 4 17.150 27.860 34.980 1.00 0.00 C
+ATOM 85 C6 DT B 4 19.500 28.160 35.850 1.00 0.00 C
+ATOM 86 H3 DT B 4 19.010 25.610 38.610 1.00 0.00 H
+ATOM 87 P DG B 5 25.700 29.160 35.640 1.00 0.00 P
+ATOM 88 OP1 DG B 5 26.910 29.640 36.350 1.00 0.00 O
+ATOM 89 OP2 DG B 5 25.710 29.280 34.170 1.00 0.00 O
+ATOM 90 O5' DG B 5 25.450 27.610 35.940 1.00 0.00 O
+ATOM 91 C5' DG B 5 25.510 27.120 37.290 1.00 0.00 C
+ATOM 92 C4' DG B 5 24.950 25.710 37.370 1.00 0.00 C
+ATOM 93 O4' DG B 5 23.540 25.570 37.200 1.00 0.00 O
+ATOM 94 C3' DG B 5 25.640 24.800 36.380 1.00 0.00 C
+ATOM 95 O3' DG B 5 26.510 23.920 37.090 1.00 0.00 O
+ATOM 96 C2' DG B 5 24.530 24.010 35.740 1.00 0.00 C
+ATOM 97 C1' DG B 5 23.370 24.230 36.700 1.00 0.00 C
+ATOM 98 N9 DG B 5 22.110 24.240 35.950 1.00 0.00 N
+ATOM 99 C8 DG B 5 21.820 25.160 35.050 1.00 0.00 C
+ATOM 100 N7 DG B 5 20.510 25.190 34.800 1.00 0.00 N
+ATOM 101 C5 DG B 5 19.960 24.220 35.530 1.00 0.00 C
+ATOM 102 C6 DG B 5 18.680 23.650 35.550 1.00 0.00 C
+ATOM 103 O6 DG B 5 17.730 24.150 34.950 1.00 0.00 O
+ATOM 104 N1 DG B 5 18.480 22.470 36.270 1.00 0.00 N
+ATOM 105 C2 DG B 5 19.550 21.900 36.940 1.00 0.00 C
+ATOM 106 N2 DG B 5 19.400 20.810 37.640 1.00 0.00 N
+ATOM 107 N3 DG B 5 20.770 22.460 36.920 1.00 0.00 N
+ATOM 108 C4 DG B 5 20.990 23.600 36.250 1.00 0.00 C
+ATOM 109 H1 DG B 5 17.570 22.080 36.400 1.00 0.00 H
+ATOM 110 H21 DG B 5 18.500 20.400 37.820 1.00 0.00 H
+ATOM 111 H22 DG B 5 20.220 20.410 38.070 1.00 0.00 H
+ATOM 112 P DT B 6 27.570 22.980 36.320 1.00 0.00 P
+ATOM 113 OP1 DT B 6 28.840 23.090 37.070 1.00 0.00 O
+ATOM 114 OP2 DT B 6 27.550 23.320 34.880 1.00 0.00 O
+ATOM 115 O5' DT B 6 26.970 21.500 36.460 1.00 0.00 O
+ATOM 116 C5' DT B 6 26.660 21.010 37.780 1.00 0.00 C
+ATOM 117 C4' DT B 6 25.480 20.060 37.830 1.00 0.00 C
+ATOM 118 O4' DT B 6 24.270 20.590 37.250 1.00 0.00 O
+ATOM 119 C3' DT B 6 25.870 18.830 37.050 1.00 0.00 C
+ATOM 120 O3' DT B 6 26.180 17.750 37.970 1.00 0.00 O
+ATOM 121 C2' DT B 6 24.730 18.570 36.110 1.00 0.00 C
+ATOM 122 C1' DT B 6 23.620 19.470 36.650 1.00 0.00 C
+ATOM 123 N1 DT B 6 22.770 19.970 35.540 1.00 0.00 N
+ATOM 124 C2 DT B 6 21.450 19.520 35.410 1.00 0.00 C
+ATOM 125 O2 DT B 6 21.040 18.520 35.990 1.00 0.00 O
+ATOM 126 N3 DT B 6 20.620 20.080 34.450 1.00 0.00 N
+ATOM 127 C4 DT B 6 21.110 21.080 33.610 1.00 0.00 C
+ATOM 128 O4 DT B 6 20.250 21.610 32.900 1.00 0.00 O
+ATOM 129 C5 DT B 6 22.430 21.530 33.690 1.00 0.00 C
+ATOM 130 C7 DT B 6 23.010 22.560 32.720 1.00 0.00 C
+ATOM 131 C6 DT B 6 23.260 20.950 34.660 1.00 0.00 C
+ATOM 132 H3 DT B 6 19.690 19.760 34.330 1.00 0.00 H
+ATOM 133 P DG B 7 26.940 16.410 37.520 1.00 0.00 P
+ATOM 134 OP1 DG B 7 27.300 15.670 38.750 1.00 0.00 O
+ATOM 135 OP2 DG B 7 27.980 16.660 36.490 1.00 0.00 O
+ATOM 136 O5' DG B 7 25.760 15.610 36.810 1.00 0.00 O
+ATOM 137 C5' DG B 7 24.810 14.940 37.670 1.00 0.00 C
+ATOM 138 C4' DG B 7 23.600 14.400 36.950 1.00 0.00 C
+ATOM 139 O4' DG B 7 23.010 15.470 36.210 1.00 0.00 O
+ATOM 140 C3' DG B 7 24.030 13.350 35.960 1.00 0.00 C
+ATOM 141 O3' DG B 7 23.420 12.100 36.330 1.00 0.00 O
+ATOM 142 C2' DG B 7 23.660 13.880 34.610 1.00 0.00 C
+ATOM 143 C1' DG B 7 22.540 14.850 35.010 1.00 0.00 C
+ATOM 144 N9 DG B 7 22.230 15.850 33.980 1.00 0.00 N
+ATOM 145 C8 DG B 7 23.070 16.740 33.480 1.00 0.00 C
+ATOM 146 N7 DG B 7 22.410 17.670 32.800 1.00 0.00 N
+ATOM 147 C5 DG B 7 21.130 17.310 32.800 1.00 0.00 C
+ATOM 148 C6 DG B 7 20.000 17.790 32.140 1.00 0.00 C
+ATOM 149 O6 DG B 7 19.960 18.950 31.720 1.00 0.00 O
+ATOM 150 N1 DG B 7 18.810 17.070 32.210 1.00 0.00 N
+ATOM 151 C2 DG B 7 18.740 15.960 33.030 1.00 0.00 C
+ATOM 152 N2 DG B 7 17.560 15.540 33.390 1.00 0.00 N
+ATOM 153 N3 DG B 7 19.820 15.510 33.670 1.00 0.00 N
+ATOM 154 C4 DG B 7 21.010 16.150 33.560 1.00 0.00 C
+ATOM 155 H1 DG B 7 18.110 17.200 31.510 1.00 0.00 H
+ATOM 156 H21 DG B 7 16.720 15.890 32.970 1.00 0.00 H
+ATOM 157 H22 DG B 7 17.510 14.780 34.050 1.00 0.00 H
+ATOM 158 P DA B 8 23.780 10.720 35.610 1.00 0.00 P
+ATOM 159 OP1 DA B 8 23.750 9.670 36.660 1.00 0.00 O
+ATOM 160 OP2 DA B 8 25.000 10.920 34.800 1.00 0.00 O
+ATOM 161 O5' DA B 8 22.510 10.570 34.640 1.00 0.00 O
+ATOM 162 C5' DA B 8 21.190 10.840 35.170 1.00 0.00 C
+ATOM 163 C4' DA B 8 20.100 11.090 34.130 1.00 0.00 C
+ATOM 164 O4' DA B 8 20.340 12.330 33.460 1.00 0.00 O
+ATOM 165 C3' DA B 8 20.100 10.000 33.080 1.00 0.00 C
+ATOM 166 O3' DA B 8 18.760 9.510 32.870 1.00 0.00 O
+ATOM 167 C2' DA B 8 20.780 10.680 31.900 1.00 0.00 C
+ATOM 168 C1' DA B 8 20.130 12.040 32.070 1.00 0.00 C
+ATOM 169 N9 DA B 8 20.790 13.120 31.320 1.00 0.00 N
+ATOM 170 C8 DA B 8 22.080 13.410 31.300 1.00 0.00 C
+ATOM 171 N7 DA B 8 22.300 14.550 30.630 1.00 0.00 N
+ATOM 172 C5 DA B 8 21.110 14.940 30.180 1.00 0.00 C
+ATOM 173 C6 DA B 8 20.640 15.950 29.340 1.00 0.00 C
+ATOM 174 N6 DA B 8 21.420 16.930 28.940 1.00 0.00 N
+ATOM 175 N1 DA B 8 19.370 15.960 28.910 1.00 0.00 N
+ATOM 176 C2 DA B 8 18.490 15.020 29.280 1.00 0.00 C
+ATOM 177 N3 DA B 8 18.880 14.080 30.130 1.00 0.00 N
+ATOM 178 C4 DA B 8 20.160 14.020 30.580 1.00 0.00 C
+ATOM 179 H61 DA B 8 21.040 17.710 28.440 1.00 0.00 H
+ATOM 180 H62 DA B 8 22.400 16.850 29.120 1.00 0.00 H
+ATOM 181 P DG B 9 18.500 8.090 32.190 1.00 0.00 P
+ATOM 182 OP1 DG B 9 17.370 7.460 32.890 1.00 0.00 O
+ATOM 183 OP2 DG B 9 19.770 7.340 32.050 1.00 0.00 O
+ATOM 184 O5' DG B 9 17.940 8.490 30.750 1.00 0.00 O
+ATOM 185 C5' DG B 9 16.660 9.140 30.600 1.00 0.00 C
+ATOM 186 C4' DG B 9 16.600 9.790 29.230 1.00 0.00 C
+ATOM 187 O4' DG B 9 17.530 10.880 29.010 1.00 0.00 O
+ATOM 188 C3' DG B 9 16.920 8.750 28.180 1.00 0.00 C
+ATOM 189 O3' DG B 9 15.740 8.540 27.370 1.00 0.00 O
+ATOM 190 C2' DG B 9 18.230 9.180 27.600 1.00 0.00 C
+ATOM 191 C1' DG B 9 18.080 10.690 27.680 1.00 0.00 C
+ATOM 192 N9 DG B 9 19.340 11.420 27.560 1.00 0.00 N
+ATOM 193 C8 DG B 9 20.420 11.200 28.300 1.00 0.00 C
+ATOM 194 N7 DG B 9 21.340 12.130 28.060 1.00 0.00 N
+ATOM 195 C5 DG B 9 20.830 12.960 27.150 1.00 0.00 C
+ATOM 196 C6 DG B 9 21.240 14.180 26.620 1.00 0.00 C
+ATOM 197 O6 DG B 9 22.390 14.590 26.760 1.00 0.00 O
+ATOM 198 N1 DG B 9 20.360 14.940 25.860 1.00 0.00 N
+ATOM 199 C2 DG B 9 19.090 14.440 25.580 1.00 0.00 C
+ATOM 200 N2 DG B 9 18.250 15.210 24.940 1.00 0.00 N
+ATOM 201 N3 DG B 9 18.710 13.240 26.040 1.00 0.00 N
+ATOM 202 C4 DG B 9 19.540 12.510 26.820 1.00 0.00 C
+ATOM 203 H1 DG B 9 20.570 15.910 25.730 1.00 0.00 H
+ATOM 204 H21 DG B 9 18.610 16.090 24.620 1.00 0.00 H
+ATOM 205 H22 DG B 9 17.280 14.980 24.850 1.00 0.00 H
+ATOM 206 P DC B 10 15.730 7.600 26.080 1.00 0.00 P
+ATOM 207 OP1 DC B 10 14.330 7.280 25.720 1.00 0.00 O
+ATOM 208 OP2 DC B 10 16.670 6.470 26.320 1.00 0.00 O
+ATOM 209 O5' DC B 10 16.290 8.620 24.970 1.00 0.00 O
+ATOM 210 C5' DC B 10 15.340 9.470 24.320 1.00 0.00 C
+ATOM 211 C4' DC B 10 15.820 10.070 23.010 1.00 0.00 C
+ATOM 212 O4' DC B 10 16.890 11.010 23.240 1.00 0.00 O
+ATOM 213 C3' DC B 10 16.310 8.970 22.090 1.00 0.00 C
+ATOM 214 O3' DC B 10 15.690 9.130 20.800 1.00 0.00 O
+ATOM 215 C2' DC B 10 17.810 9.110 22.140 1.00 0.00 C
+ATOM 216 C1' DC B 10 17.940 10.620 22.340 1.00 0.00 C
+ATOM 217 N1 DC B 10 19.210 10.960 23.030 1.00 0.00 N
+ATOM 218 C2 DC B 10 19.840 12.200 22.840 1.00 0.00 C
+ATOM 219 O2 DC B 10 19.430 12.980 21.980 1.00 0.00 O
+ATOM 220 N3 DC B 10 20.970 12.490 23.530 1.00 0.00 N
+ATOM 221 C4 DC B 10 21.520 11.600 24.370 1.00 0.00 C
+ATOM 222 N4 DC B 10 22.740 11.850 24.800 1.00 0.00 N
+ATOM 223 C5 DC B 10 20.940 10.340 24.620 1.00 0.00 C
+ATOM 224 C6 DC B 10 19.790 10.030 23.910 1.00 0.00 C
+ATOM 225 H41 DC B 10 23.290 12.590 24.420 1.00 0.00 H
+ATOM 226 H42 DC B 10 23.190 11.230 25.480 1.00 0.00 H
+ATOM 227 P DG B 11 15.990 8.210 19.520 1.00 0.00 P
+ATOM 228 OP1 DG B 11 14.760 8.240 18.690 1.00 0.00 O
+ATOM 229 OP2 DG B 11 16.470 6.880 19.990 1.00 0.00 O
+ATOM 230 O5' DG B 11 17.250 8.920 18.800 1.00 0.00 O
+ATOM 231 C5' DG B 11 17.210 9.990 17.830 1.00 0.00 C
+ATOM 232 C4' DG B 11 18.630 10.490 17.530 1.00 0.00 C
+ATOM 233 O4' DG B 11 19.330 10.890 18.730 1.00 0.00 O
+ATOM 234 C3' DG B 11 19.540 9.440 16.910 1.00 0.00 C
+ATOM 235 O3' DG B 11 19.610 9.540 15.490 1.00 0.00 O
+ATOM 236 C2' DG B 11 20.930 9.760 17.400 1.00 0.00 C
+ATOM 237 C1' DG B 11 20.690 10.950 18.310 1.00 0.00 C
+ATOM 238 N9 DG B 11 21.650 10.960 19.440 1.00 0.00 N
+ATOM 239 C8 DG B 11 21.820 10.040 20.380 1.00 0.00 C
+ATOM 240 N7 DG B 11 22.680 10.500 21.300 1.00 0.00 N
+ATOM 241 C5 DG B 11 23.100 11.700 20.890 1.00 0.00 C
+ATOM 242 C6 DG B 11 24.120 12.570 21.280 1.00 0.00 C
+ATOM 243 O6 DG B 11 24.870 12.330 22.220 1.00 0.00 O
+ATOM 244 N1 DG B 11 24.460 13.650 20.470 1.00 0.00 N
+ATOM 245 C2 DG B 11 23.760 13.860 19.280 1.00 0.00 C
+ATOM 246 N2 DG B 11 24.220 14.690 18.390 1.00 0.00 N
+ATOM 247 N3 DG B 11 22.760 13.050 18.930 1.00 0.00 N
+ATOM 248 C4 DG B 11 22.430 11.990 19.700 1.00 0.00 C
+ATOM 249 HO3' DG B 11 20.280 8.910 15.000 1.00 0.00 H
+ATOM 250 H1 DG B 11 25.240 14.220 20.700 1.00 0.00 H
+ATOM 251 H21 DG B 11 25.030 15.270 18.540 1.00 0.00 H
+ATOM 252 H22 DG B 11 23.670 14.920 17.570 1.00 0.00 H
+TER 253 DG B 11
+ATOM 254 O5' DC C 1 33.320 17.310 20.690 1.00 0.00 O
+ATOM 255 C5' DC C 1 32.290 18.300 20.720 1.00 0.00 C
+ATOM 256 C4' DC C 1 31.090 17.790 19.950 1.00 0.00 C
+ATOM 257 O4' DC C 1 30.630 16.570 20.570 1.00 0.00 O
+ATOM 258 C3' DC C 1 29.960 18.800 20.020 1.00 0.00 C
+ATOM 259 O3' DC C 1 29.530 19.110 18.680 1.00 0.00 O
+ATOM 260 C2' DC C 1 28.910 18.110 20.820 1.00 0.00 C
+ATOM 261 C1' DC C 1 29.200 16.680 20.500 1.00 0.00 C
+ATOM 262 N1 DC C 1 28.600 15.780 21.490 1.00 0.00 N
+ATOM 263 C2 DC C 1 27.590 14.850 21.150 1.00 0.00 C
+ATOM 264 O2 DC C 1 26.910 15.000 20.140 1.00 0.00 O
+ATOM 265 N3 DC C 1 27.060 14.100 22.140 1.00 0.00 N
+ATOM 266 C4 DC C 1 27.430 14.280 23.410 1.00 0.00 C
+ATOM 267 N4 DC C 1 26.640 13.680 24.250 1.00 0.00 N
+ATOM 268 C5 DC C 1 28.380 15.230 23.830 1.00 0.00 C
+ATOM 269 C6 DC C 1 28.990 15.960 22.830 1.00 0.00 C
+ATOM 270 H41 DC C 1 25.710 13.520 23.930 1.00 0.00 H
+ATOM 271 H42 DC C 1 26.780 13.740 25.260 1.00 0.00 H
+ATOM 272 HO5' DC C 1 34.100 17.690 21.200 1.00 0.00 H
+ATOM 273 P DG C 2 28.950 20.560 18.340 1.00 0.00 P
+ATOM 274 OP1 DG C 2 29.030 20.690 16.870 1.00 0.00 O
+ATOM 275 OP2 DG C 2 29.690 21.550 19.160 1.00 0.00 O
+ATOM 276 O5' DG C 2 27.440 20.410 18.850 1.00 0.00 O
+ATOM 277 C5' DG C 2 26.370 20.150 17.910 1.00 0.00 C
+ATOM 278 C4' DG C 2 25.070 19.960 18.660 1.00 0.00 C
+ATOM 279 O4' DG C 2 25.120 18.750 19.420 1.00 0.00 O
+ATOM 280 C3' DG C 2 24.780 21.120 19.620 1.00 0.00 C
+ATOM 281 O3' DG C 2 23.740 21.980 19.110 1.00 0.00 O
+ATOM 282 C2' DG C 2 24.370 20.440 20.910 1.00 0.00 C
+ATOM 283 C1' DG C 2 24.180 19.010 20.460 1.00 0.00 C
+ATOM 284 N9 DG C 2 24.460 18.080 21.560 1.00 0.00 N
+ATOM 285 C8 DG C 2 25.530 18.060 22.360 1.00 0.00 C
+ATOM 286 N7 DG C 2 25.390 17.080 23.260 1.00 0.00 N
+ATOM 287 C5 DG C 2 24.210 16.500 23.040 1.00 0.00 C
+ATOM 288 C6 DG C 2 23.490 15.490 23.660 1.00 0.00 C
+ATOM 289 O6 DG C 2 23.980 14.750 24.510 1.00 0.00 O
+ATOM 290 N1 DG C 2 22.210 15.160 23.210 1.00 0.00 N
+ATOM 291 C2 DG C 2 21.690 15.840 22.110 1.00 0.00 C
+ATOM 292 N2 DG C 2 20.600 15.390 21.560 1.00 0.00 N
+ATOM 293 N3 DG C 2 22.360 16.830 21.520 1.00 0.00 N
+ATOM 294 C4 DG C 2 23.610 17.150 21.960 1.00 0.00 C
+ATOM 295 H1 DG C 2 21.710 14.430 23.660 1.00 0.00 H
+ATOM 296 H21 DG C 2 20.220 14.510 21.850 1.00 0.00 H
+ATOM 297 H22 DG C 2 20.070 15.980 20.940 1.00 0.00 H
+ATOM 298 P DC C 3 23.090 23.250 19.860 1.00 0.00 P
+ATOM 299 OP1 DC C 3 22.510 24.150 18.830 1.00 0.00 O
+ATOM 300 OP2 DC C 3 24.020 23.820 20.840 1.00 0.00 O
+ATOM 301 O5' DC C 3 21.920 22.640 20.770 1.00 0.00 O
+ATOM 302 C5' DC C 3 20.840 21.960 20.140 1.00 0.00 C
+ATOM 303 C4' DC C 3 20.060 21.050 21.060 1.00 0.00 C
+ATOM 304 O4' DC C 3 20.890 20.160 21.830 1.00 0.00 O
+ATOM 305 C3' DC C 3 19.230 21.870 22.030 1.00 0.00 C
+ATOM 306 O3' DC C 3 17.830 21.700 21.770 1.00 0.00 O
+ATOM 307 C2' DC C 3 19.690 21.370 23.390 1.00 0.00 C
+ATOM 308 C1' DC C 3 20.150 19.960 23.050 1.00 0.00 C
+ATOM 309 N1 DC C 3 20.980 19.350 24.090 1.00 0.00 N
+ATOM 310 C2 DC C 3 20.530 18.250 24.870 1.00 0.00 C
+ATOM 311 O2 DC C 3 19.390 17.790 24.760 1.00 0.00 O
+ATOM 312 N3 DC C 3 21.310 17.780 25.860 1.00 0.00 N
+ATOM 313 C4 DC C 3 22.510 18.330 26.120 1.00 0.00 C
+ATOM 314 N4 DC C 3 23.390 17.580 26.740 1.00 0.00 N
+ATOM 315 C5 DC C 3 22.990 19.440 25.410 1.00 0.00 C
+ATOM 316 C6 DC C 3 22.220 19.930 24.380 1.00 0.00 C
+ATOM 317 H41 DC C 3 23.210 16.610 26.920 1.00 0.00 H
+ATOM 318 H42 DC C 3 24.280 17.960 27.030 1.00 0.00 H
+ATOM 319 P DT C 4 16.730 22.600 22.510 1.00 0.00 P
+ATOM 320 OP1 DT C 4 15.940 23.190 21.420 1.00 0.00 O
+ATOM 321 OP2 DT C 4 17.390 23.530 23.450 1.00 0.00 O
+ATOM 322 O5' DT C 4 15.940 21.460 23.310 1.00 0.00 O
+ATOM 323 C5' DT C 4 15.200 20.450 22.600 1.00 0.00 C
+ATOM 324 C4' DT C 4 14.530 19.450 23.540 1.00 0.00 C
+ATOM 325 O4' DT C 4 15.580 18.840 24.320 1.00 0.00 O
+ATOM 326 C3' DT C 4 13.590 20.020 24.600 1.00 0.00 C
+ATOM 327 O3' DT C 4 12.530 19.040 24.730 1.00 0.00 O
+ATOM 328 C2' DT C 4 14.410 20.280 25.820 1.00 0.00 C
+ATOM 329 C1' DT C 4 15.270 19.030 25.700 1.00 0.00 C
+ATOM 330 N1 DT C 4 16.570 19.160 26.390 1.00 0.00 N
+ATOM 331 C2 DT C 4 17.090 18.020 27.020 1.00 0.00 C
+ATOM 332 O2 DT C 4 16.400 17.030 27.240 1.00 0.00 O
+ATOM 333 N3 DT C 4 18.340 18.120 27.610 1.00 0.00 N
+ATOM 334 C4 DT C 4 19.070 19.310 27.600 1.00 0.00 C
+ATOM 335 O4 DT C 4 19.980 19.380 28.420 1.00 0.00 O
+ATOM 336 C5 DT C 4 18.570 20.450 26.950 1.00 0.00 C
+ATOM 337 C7 DT C 4 19.320 21.780 26.980 1.00 0.00 C
+ATOM 338 C6 DT C 4 17.300 20.360 26.350 1.00 0.00 C
+ATOM 339 H3 DT C 4 18.710 17.320 28.090 1.00 0.00 H
+ATOM 340 P DC C 5 11.090 19.350 25.360 1.00 0.00 P
+ATOM 341 OP1 DC C 5 10.040 18.610 24.610 1.00 0.00 O
+ATOM 342 OP2 DC C 5 10.920 20.800 25.570 1.00 0.00 O
+ATOM 343 O5' DC C 5 11.160 18.700 26.830 1.00 0.00 O
+ATOM 344 C5' DC C 5 11.450 17.300 27.010 1.00 0.00 C
+ATOM 345 C4' DC C 5 12.190 17.020 28.310 1.00 0.00 C
+ATOM 346 O4' DC C 5 13.490 17.630 28.380 1.00 0.00 O
+ATOM 347 C3' DC C 5 11.430 17.570 29.490 1.00 0.00 C
+ATOM 348 O3' DC C 5 10.670 16.480 30.030 1.00 0.00 O
+ATOM 349 C2' DC C 5 12.460 18.170 30.410 1.00 0.00 C
+ATOM 350 C1' DC C 5 13.780 17.670 29.790 1.00 0.00 C
+ATOM 351 N1 DC C 5 14.900 18.610 29.950 1.00 0.00 N
+ATOM 352 C2 DC C 5 16.100 18.220 30.570 1.00 0.00 C
+ATOM 353 O2 DC C 5 16.230 17.100 31.070 1.00 0.00 O
+ATOM 354 N3 DC C 5 17.120 19.100 30.670 1.00 0.00 N
+ATOM 355 C4 DC C 5 16.970 20.360 30.230 1.00 0.00 C
+ATOM 356 N4 DC C 5 18.070 21.070 30.130 1.00 0.00 N
+ATOM 357 C5 DC C 5 15.810 20.840 29.610 1.00 0.00 C
+ATOM 358 C6 DC C 5 14.780 19.920 29.440 1.00 0.00 C
+ATOM 359 H41 DC C 5 18.940 20.560 30.090 1.00 0.00 H
+ATOM 360 H42 DC C 5 17.980 22.060 29.950 1.00 0.00 H
+ATOM 361 P DA C 6 9.320 16.700 30.860 1.00 0.00 P
+ATOM 362 OP1 DA C 6 8.590 15.430 30.730 1.00 0.00 O
+ATOM 363 OP2 DA C 6 8.630 17.980 30.570 1.00 0.00 O
+ATOM 364 O5' DA C 6 9.900 16.830 32.340 1.00 0.00 O
+ATOM 365 C5' DA C 6 10.480 15.640 32.900 1.00 0.00 C
+ATOM 366 C4' DA C 6 11.440 15.990 34.010 1.00 0.00 C
+ATOM 367 O4' DA C 6 12.550 16.780 33.560 1.00 0.00 O
+ATOM 368 C3' DA C 6 10.750 16.800 35.070 1.00 0.00 C
+ATOM 369 O3' DA C 6 10.150 15.830 35.940 1.00 0.00 O
+ATOM 370 C2' DA C 6 11.790 17.780 35.560 1.00 0.00 C
+ATOM 371 C1' DA C 6 13.030 17.410 34.760 1.00 0.00 C
+ATOM 372 N9 DA C 6 13.800 18.580 34.290 1.00 0.00 N
+ATOM 373 C8 DA C 6 13.320 19.620 33.620 1.00 0.00 C
+ATOM 374 N7 DA C 6 14.310 20.410 33.210 1.00 0.00 N
+ATOM 375 C5 DA C 6 15.450 19.880 33.660 1.00 0.00 C
+ATOM 376 C6 DA C 6 16.810 20.230 33.710 1.00 0.00 C
+ATOM 377 N6 DA C 6 17.380 21.210 33.020 1.00 0.00 N
+ATOM 378 N1 DA C 6 17.700 19.520 34.430 1.00 0.00 N
+ATOM 379 C2 DA C 6 17.320 18.460 35.130 1.00 0.00 C
+ATOM 380 N3 DA C 6 16.040 18.060 35.100 1.00 0.00 N
+ATOM 381 C4 DA C 6 15.110 18.730 34.380 1.00 0.00 C
+ATOM 382 H61 DA C 6 18.360 21.360 33.170 1.00 0.00 H
+ATOM 383 H62 DA C 6 17.010 21.550 32.170 1.00 0.00 H
+ATOM 384 P DC C 7 9.770 16.120 37.460 1.00 0.00 P
+ATOM 385 OP1 DC C 7 9.000 14.970 37.960 1.00 0.00 O
+ATOM 386 OP2 DC C 7 9.160 17.460 37.550 1.00 0.00 O
+ATOM 387 O5' DC C 7 11.200 16.200 38.190 1.00 0.00 O
+ATOM 388 C5' DC C 7 12.190 15.160 38.260 1.00 0.00 C
+ATOM 389 C4' DC C 7 13.530 15.800 38.650 1.00 0.00 C
+ATOM 390 O4' DC C 7 14.030 16.930 37.890 1.00 0.00 O
+ATOM 391 C3' DC C 7 13.490 16.250 40.080 1.00 0.00 C
+ATOM 392 O3' DC C 7 14.470 15.440 40.750 1.00 0.00 O
+ATOM 393 C2' DC C 7 13.730 17.750 40.060 1.00 0.00 C
+ATOM 394 C1' DC C 7 14.630 17.850 38.850 1.00 0.00 C
+ATOM 395 N1 DC C 7 14.790 19.210 38.280 1.00 0.00 N
+ATOM 396 C2 DC C 7 16.080 19.660 37.950 1.00 0.00 C
+ATOM 397 O2 DC C 7 17.070 19.030 38.300 1.00 0.00 O
+ATOM 398 N3 DC C 7 16.250 20.780 37.200 1.00 0.00 N
+ATOM 399 C4 DC C 7 15.190 21.500 36.780 1.00 0.00 C
+ATOM 400 N4 DC C 7 15.430 22.550 36.010 1.00 0.00 N
+ATOM 401 C5 DC C 7 13.870 21.130 37.100 1.00 0.00 C
+ATOM 402 C6 DC C 7 13.680 19.970 37.870 1.00 0.00 C
+ATOM 403 H41 DC C 7 16.340 22.900 35.780 1.00 0.00 H
+ATOM 404 H42 DC C 7 14.630 23.080 35.670 1.00 0.00 H
+ATOM 405 P DA C 8 14.530 15.310 42.350 1.00 0.00 P
+ATOM 406 OP1 DA C 8 15.100 13.980 42.700 1.00 0.00 O
+ATOM 407 OP2 DA C 8 13.200 15.660 42.910 1.00 0.00 O
+ATOM 408 O5' DA C 8 15.620 16.420 42.710 1.00 0.00 O
+ATOM 409 C5' DA C 8 16.960 16.100 42.280 1.00 0.00 C
+ATOM 410 C4' DA C 8 18.070 17.130 42.440 1.00 0.00 C
+ATOM 411 O4' DA C 8 17.960 18.250 41.550 1.00 0.00 O
+ATOM 412 C3' DA C 8 18.180 17.680 43.840 1.00 0.00 C
+ATOM 413 O3' DA C 8 19.530 17.380 44.280 1.00 0.00 O
+ATOM 414 C2' DA C 8 17.950 19.160 43.670 1.00 0.00 C
+ATOM 415 C1' DA C 8 18.490 19.400 42.270 1.00 0.00 C
+ATOM 416 N9 DA C 8 17.880 20.580 41.590 1.00 0.00 N
+ATOM 417 C8 DA C 8 16.560 20.710 41.530 1.00 0.00 C
+ATOM 418 N7 DA C 8 16.210 21.700 40.710 1.00 0.00 N
+ATOM 419 C5 DA C 8 17.340 22.210 40.250 1.00 0.00 C
+ATOM 420 C6 DA C 8 17.630 23.370 39.530 1.00 0.00 C
+ATOM 421 N6 DA C 8 16.630 24.080 39.070 1.00 0.00 N
+ATOM 422 N1 DA C 8 18.890 23.840 39.400 1.00 0.00 N
+ATOM 423 C2 DA C 8 19.900 23.190 39.990 1.00 0.00 C
+ATOM 424 N3 DA C 8 19.670 22.060 40.680 1.00 0.00 N
+ATOM 425 C4 DA C 8 18.420 21.540 40.830 1.00 0.00 C
+ATOM 426 H61 DA C 8 16.760 24.970 38.610 1.00 0.00 H
+ATOM 427 H62 DA C 8 15.700 23.730 39.150 1.00 0.00 H
+ATOM 428 P DA C 9 20.060 17.430 45.800 1.00 0.00 P
+ATOM 429 OP1 DA C 9 20.990 16.280 45.900 1.00 0.00 O
+ATOM 430 OP2 DA C 9 18.920 17.430 46.730 1.00 0.00 O
+ATOM 431 O5' DA C 9 20.840 18.830 45.970 1.00 0.00 O
+ATOM 432 C5' DA C 9 22.080 19.110 45.300 1.00 0.00 C
+ATOM 433 C4' DA C 9 22.540 20.570 45.260 1.00 0.00 C
+ATOM 434 O4' DA C 9 21.590 21.340 44.490 1.00 0.00 O
+ATOM 435 C3' DA C 9 22.680 21.250 46.620 1.00 0.00 C
+ATOM 436 O3' DA C 9 24.020 21.810 46.760 1.00 0.00 O
+ATOM 437 C2' DA C 9 21.470 22.140 46.660 1.00 0.00 C
+ATOM 438 C1' DA C 9 21.360 22.570 45.190 1.00 0.00 C
+ATOM 439 N9 DA C 9 20.000 23.020 44.810 1.00 0.00 N
+ATOM 440 C8 DA C 9 18.900 22.330 45.060 1.00 0.00 C
+ATOM 441 N7 DA C 9 17.840 22.900 44.470 1.00 0.00 N
+ATOM 442 C5 DA C 9 18.300 23.970 43.840 1.00 0.00 C
+ATOM 443 C6 DA C 9 17.750 24.930 42.980 1.00 0.00 C
+ATOM 444 N6 DA C 9 16.510 24.780 42.580 1.00 0.00 N
+ATOM 445 N1 DA C 9 18.500 25.860 42.350 1.00 0.00 N
+ATOM 446 C2 DA C 9 19.820 25.870 42.530 1.00 0.00 C
+ATOM 447 N3 DA C 9 20.400 24.990 43.360 1.00 0.00 N
+ATOM 448 C4 DA C 9 19.680 24.040 44.020 1.00 0.00 C
+ATOM 449 H61 DA C 9 16.130 25.440 41.930 1.00 0.00 H
+ATOM 450 H62 DA C 9 16.030 23.950 42.850 1.00 0.00 H
+ATOM 451 P DT C 10 24.700 22.330 48.140 1.00 0.00 P
+ATOM 452 OP1 DT C 10 26.170 22.130 48.190 1.00 0.00 O
+ATOM 453 OP2 DT C 10 23.930 21.860 49.310 1.00 0.00 O
+ATOM 454 O5' DT C 10 24.490 23.900 47.970 1.00 0.00 O
+ATOM 455 C5' DT C 10 25.050 24.590 46.850 1.00 0.00 C
+ATOM 456 C4' DT C 10 24.180 25.810 46.560 1.00 0.00 C
+ATOM 457 O4' DT C 10 22.790 25.540 46.310 1.00 0.00 O
+ATOM 458 C3' DT C 10 24.220 26.890 47.630 1.00 0.00 C
+ATOM 459 O3' DT C 10 25.030 27.970 47.110 1.00 0.00 O
+ATOM 460 C2' DT C 10 22.790 27.360 47.730 1.00 0.00 C
+ATOM 461 C1' DT C 10 22.240 26.870 46.430 1.00 0.00 C
+ATOM 462 N1 DT C 10 20.750 26.810 46.490 1.00 0.00 N
+ATOM 463 C2 DT C 10 19.920 27.750 45.860 1.00 0.00 C
+ATOM 464 O2 DT C 10 20.360 28.690 45.200 1.00 0.00 O
+ATOM 465 N3 DT C 10 18.530 27.580 45.900 1.00 0.00 N
+ATOM 466 C4 DT C 10 17.980 26.480 46.580 1.00 0.00 C
+ATOM 467 O4 DT C 10 16.810 26.210 46.330 1.00 0.00 O
+ATOM 468 C5 DT C 10 18.800 25.570 47.260 1.00 0.00 C
+ATOM 469 C7 DT C 10 18.260 24.440 48.130 1.00 0.00 C
+ATOM 470 C6 DT C 10 20.180 25.750 47.190 1.00 0.00 C
+ATOM 471 H3 DT C 10 17.940 28.160 45.340 1.00 0.00 H
+ATOM 472 P DT C 11 25.820 29.030 48.030 1.00 0.00 P
+ATOM 473 OP1 DT C 11 26.890 29.650 47.210 1.00 0.00 O
+ATOM 474 OP2 DT C 11 26.200 28.380 49.300 1.00 0.00 O
+ATOM 475 O5' DT C 11 24.720 30.140 48.340 1.00 0.00 O
+ATOM 476 C5' DT C 11 24.140 30.860 47.240 1.00 0.00 C
+ATOM 477 C4' DT C 11 22.750 31.380 47.550 1.00 0.00 C
+ATOM 478 O4' DT C 11 21.760 30.360 47.680 1.00 0.00 O
+ATOM 479 C3' DT C 11 22.740 32.100 48.870 1.00 0.00 C
+ATOM 480 O3' DT C 11 22.750 33.520 48.640 1.00 0.00 O
+ATOM 481 C2' DT C 11 21.450 31.730 49.550 1.00 0.00 C
+ATOM 482 C1' DT C 11 20.740 31.060 48.390 1.00 0.00 C
+ATOM 483 N1 DT C 11 19.750 30.060 48.830 1.00 0.00 N
+ATOM 484 C2 DT C 11 18.380 30.370 48.750 1.00 0.00 C
+ATOM 485 O2 DT C 11 17.980 31.340 48.110 1.00 0.00 O
+ATOM 486 N3 DT C 11 17.470 29.460 49.270 1.00 0.00 N
+ATOM 487 C4 DT C 11 17.890 28.240 49.790 1.00 0.00 C
+ATOM 488 O4 DT C 11 17.040 27.350 49.810 1.00 0.00 O
+ATOM 489 C5 DT C 11 19.250 27.940 49.910 1.00 0.00 C
+ATOM 490 C7 DT C 11 19.740 26.800 50.820 1.00 0.00 C
+ATOM 491 C6 DT C 11 20.170 28.870 49.430 1.00 0.00 C
+ATOM 492 HO3' DT C 11 22.760 33.950 49.590 1.00 0.00 H
+ATOM 493 H3 DT C 11 16.540 29.780 49.510 1.00 0.00 H
+TER 494 DT C 11
+ATOM 495 N MET A 1 32.840 26.300 6.980 1.00 0.00 N
+ATOM 496 CA MET A 1 32.290 27.380 7.830 1.00 0.00 C
+ATOM 497 C MET A 1 32.050 26.990 9.300 1.00 0.00 C
+ATOM 498 O MET A 1 31.550 25.910 9.630 1.00 0.00 O
+ATOM 499 CB MET A 1 30.980 27.820 7.190 1.00 0.00 C
+ATOM 500 CG MET A 1 31.140 28.320 5.740 1.00 0.00 C
+ATOM 501 SD MET A 1 32.060 29.890 5.590 1.00 0.00 S
+ATOM 502 CE MET A 1 31.010 30.940 6.560 1.00 0.00 C
+ATOM 503 H1 MET A 1 33.660 25.890 7.390 1.00 0.00 H
+ATOM 504 H2 MET A 1 32.130 25.600 6.880 1.00 0.00 H
+ATOM 505 H3 MET A 1 33.070 26.700 6.090 1.00 0.00 H
+ATOM 506 N LYS A 2 32.520 27.880 10.170 1.00 0.00 N
+ATOM 507 CA LYS A 2 32.550 27.760 11.640 1.00 0.00 C
+ATOM 508 C LYS A 2 31.120 27.820 12.230 1.00 0.00 C
+ATOM 509 O LYS A 2 30.510 28.900 12.200 1.00 0.00 O
+ATOM 510 CB LYS A 2 33.400 28.950 12.100 1.00 0.00 C
+ATOM 511 CG LYS A 2 34.030 28.730 13.460 1.00 0.00 C
+ATOM 512 CD LYS A 2 33.750 29.870 14.440 1.00 0.00 C
+ATOM 513 CE LYS A 2 34.470 29.480 15.720 1.00 0.00 C
+ATOM 514 NZ LYS A 2 33.960 30.250 16.850 1.00 0.00 N
+ATOM 515 H LYS A 2 32.940 28.740 9.830 1.00 0.00 H
+ATOM 516 HZ1 LYS A 2 32.960 30.190 16.880 1.00 0.00 H
+ATOM 517 HZ2 LYS A 2 34.320 29.900 17.710 1.00 0.00 H
+ATOM 518 HZ3 LYS A 2 34.220 31.230 16.770 1.00 0.00 H
+ATOM 519 N PRO A 3 30.580 26.720 12.790 1.00 0.00 N
+ATOM 520 CA PRO A 3 29.180 26.690 13.240 1.00 0.00 C
+ATOM 521 C PRO A 3 28.980 27.400 14.580 1.00 0.00 C
+ATOM 522 O PRO A 3 29.920 27.540 15.370 1.00 0.00 O
+ATOM 523 CB PRO A 3 28.830 25.210 13.360 1.00 0.00 C
+ATOM 524 CG PRO A 3 30.170 24.570 13.740 1.00 0.00 C
+ATOM 525 CD PRO A 3 31.220 25.390 12.980 1.00 0.00 C
+ATOM 526 N VAL A 4 27.750 27.800 14.830 1.00 0.00 N
+ATOM 527 CA VAL A 4 27.430 28.430 16.110 1.00 0.00 C
+ATOM 528 C VAL A 4 26.360 27.620 16.860 1.00 0.00 C
+ATOM 529 O VAL A 4 25.190 27.640 16.500 1.00 0.00 O
+ATOM 530 CB VAL A 4 27.060 29.900 15.840 1.00 0.00 C
+ATOM 531 CG1 VAL A 4 26.430 30.610 17.050 1.00 0.00 C
+ATOM 532 CG2 VAL A 4 28.280 30.690 15.360 1.00 0.00 C
+ATOM 533 H VAL A 4 27.040 27.890 14.130 1.00 0.00 H
+ATOM 534 N THR A 5 26.890 27.040 17.940 1.00 0.00 N
+ATOM 535 CA THR A 5 26.190 26.210 18.950 1.00 0.00 C
+ATOM 536 C THR A 5 25.400 27.100 19.920 1.00 0.00 C
+ATOM 537 O THR A 5 25.630 28.310 19.970 1.00 0.00 O
+ATOM 538 CB THR A 5 27.220 25.450 19.820 1.00 0.00 C
+ATOM 539 OG1 THR A 5 28.110 26.350 20.480 1.00 0.00 O
+ATOM 540 CG2 THR A 5 27.990 24.370 19.050 1.00 0.00 C
+ATOM 541 H THR A 5 27.850 27.230 18.150 1.00 0.00 H
+ATOM 542 HG1 THR A 5 28.900 26.570 19.860 1.00 0.00 H
+ATOM 543 N LEU A 6 24.590 26.490 20.780 1.00 0.00 N
+ATOM 544 CA LEU A 6 24.010 27.210 21.940 1.00 0.00 C
+ATOM 545 C LEU A 6 25.100 27.830 22.840 1.00 0.00 C
+ATOM 546 O LEU A 6 25.030 29.020 23.120 1.00 0.00 O
+ATOM 547 CB LEU A 6 23.150 26.370 22.880 1.00 0.00 C
+ATOM 548 CG LEU A 6 21.800 25.900 22.330 1.00 0.00 C
+ATOM 549 CD1 LEU A 6 21.280 24.920 23.370 1.00 0.00 C
+ATOM 550 CD2 LEU A 6 20.780 27.020 22.090 1.00 0.00 C
+ATOM 551 H LEU A 6 24.320 25.520 20.670 1.00 0.00 H
+ATOM 552 N TYR A 7 26.180 27.080 23.090 1.00 0.00 N
+ATOM 553 CA TYR A 7 27.320 27.520 23.910 1.00 0.00 C
+ATOM 554 C TYR A 7 27.890 28.830 23.370 1.00 0.00 C
+ATOM 555 O TYR A 7 27.820 29.850 24.050 1.00 0.00 O
+ATOM 556 CB TYR A 7 28.420 26.460 23.950 1.00 0.00 C
+ATOM 557 CG TYR A 7 27.930 25.140 24.560 1.00 0.00 C
+ATOM 558 CD1 TYR A 7 27.170 24.280 23.740 1.00 0.00 C
+ATOM 559 CD2 TYR A 7 28.070 24.930 25.940 1.00 0.00 C
+ATOM 560 CE1 TYR A 7 26.510 23.200 24.330 1.00 0.00 C
+ATOM 561 CE2 TYR A 7 27.410 23.830 26.530 1.00 0.00 C
+ATOM 562 CZ TYR A 7 26.650 22.960 25.710 1.00 0.00 C
+ATOM 563 OH TYR A 7 26.370 21.720 26.200 1.00 0.00 O
+ATOM 564 H TYR A 7 26.350 26.230 22.580 1.00 0.00 H
+ATOM 565 HH TYR A 7 26.040 21.150 25.390 1.00 0.00 H
+ATOM 566 N ASP A 8 28.140 28.840 22.060 1.00 0.00 N
+ATOM 567 CA ASP A 8 28.650 30.020 21.340 1.00 0.00 C
+ATOM 568 C ASP A 8 27.800 31.280 21.570 1.00 0.00 C
+ATOM 569 O ASP A 8 28.260 32.210 22.230 1.00 0.00 O
+ATOM 570 CB ASP A 8 28.730 29.720 19.840 1.00 0.00 C
+ATOM 571 CG ASP A 8 29.900 28.830 19.410 1.00 0.00 C
+ATOM 572 OD1 ASP A 8 31.060 29.240 19.590 1.00 0.00 O
+ATOM 573 OD2 ASP A 8 29.640 27.820 18.730 1.00 0.00 O
+ATOM 574 H ASP A 8 28.000 28.020 21.480 1.00 0.00 H
+ATOM 575 N VAL A 9 26.520 31.220 21.210 1.00 0.00 N
+ATOM 576 CA VAL A 9 25.620 32.410 21.350 1.00 0.00 C
+ATOM 577 C VAL A 9 25.410 32.860 22.800 1.00 0.00 C
+ATOM 578 O VAL A 9 25.400 34.050 23.120 1.00 0.00 O
+ATOM 579 CB VAL A 9 24.250 32.260 20.670 1.00 0.00 C
+ATOM 580 CG1 VAL A 9 24.370 32.660 19.200 1.00 0.00 C
+ATOM 581 CG2 VAL A 9 23.580 30.890 20.860 1.00 0.00 C
+ATOM 582 H VAL A 9 26.080 30.350 20.970 1.00 0.00 H
+ATOM 583 N ALA A 10 25.370 31.840 23.650 1.00 0.00 N
+ATOM 584 CA ALA A 10 25.290 31.940 25.120 1.00 0.00 C
+ATOM 585 C ALA A 10 26.490 32.680 25.750 1.00 0.00 C
+ATOM 586 O ALA A 10 26.320 33.600 26.540 1.00 0.00 O
+ATOM 587 CB ALA A 10 25.170 30.570 25.790 1.00 0.00 C
+ATOM 588 H ALA A 10 25.310 30.910 23.280 1.00 0.00 H
+ATOM 589 N GLU A 11 27.690 32.310 25.310 1.00 0.00 N
+ATOM 590 CA GLU A 11 28.940 33.020 25.670 1.00 0.00 C
+ATOM 591 C GLU A 11 28.910 34.480 25.200 1.00 0.00 C
+ATOM 592 O GLU A 11 28.840 35.370 26.050 1.00 0.00 O
+ATOM 593 CB GLU A 11 30.190 32.330 25.130 1.00 0.00 C
+ATOM 594 CG GLU A 11 30.450 30.980 25.810 1.00 0.00 C
+ATOM 595 CD GLU A 11 31.550 30.180 25.090 1.00 0.00 C
+ATOM 596 OE1 GLU A 11 32.740 30.480 25.290 1.00 0.00 O
+ATOM 597 OE2 GLU A 11 31.200 29.230 24.360 1.00 0.00 O
+ATOM 598 H GLU A 11 27.800 31.450 24.800 1.00 0.00 H
+ATOM 599 N TYR A 12 28.570 34.680 23.920 1.00 0.00 N
+ATOM 600 CA TYR A 12 28.560 36.020 23.290 1.00 0.00 C
+ATOM 601 C TYR A 12 27.530 36.980 23.920 1.00 0.00 C
+ATOM 602 O TYR A 12 27.690 38.200 23.860 1.00 0.00 O
+ATOM 603 CB TYR A 12 28.340 35.890 21.770 1.00 0.00 C
+ATOM 604 CG TYR A 12 28.800 37.120 20.950 1.00 0.00 C
+ATOM 605 CD1 TYR A 12 28.010 38.290 20.930 1.00 0.00 C
+ATOM 606 CD2 TYR A 12 29.930 37.000 20.120 1.00 0.00 C
+ATOM 607 CE1 TYR A 12 28.340 39.350 20.070 1.00 0.00 C
+ATOM 608 CE2 TYR A 12 30.270 38.050 19.240 1.00 0.00 C
+ATOM 609 CZ TYR A 12 29.450 39.200 19.220 1.00 0.00 C
+ATOM 610 OH TYR A 12 29.580 40.070 18.190 1.00 0.00 O
+ATOM 611 H TYR A 12 28.260 33.910 23.350 1.00 0.00 H
+ATOM 612 HH TYR A 12 30.200 39.590 17.490 1.00 0.00 H
+ATOM 613 N ALA A 13 26.400 36.450 24.370 1.00 0.00 N
+ATOM 614 CA ALA A 13 25.420 37.240 25.150 1.00 0.00 C
+ATOM 615 C ALA A 13 25.800 37.490 26.620 1.00 0.00 C
+ATOM 616 O ALA A 13 25.430 38.520 27.200 1.00 0.00 O
+ATOM 617 CB ALA A 13 24.060 36.560 25.090 1.00 0.00 C
+ATOM 618 H ALA A 13 26.050 35.580 24.000 1.00 0.00 H
+ATOM 619 N GLY A 14 26.590 36.590 27.200 1.00 0.00 N
+ATOM 620 CA GLY A 14 26.840 36.560 28.660 1.00 0.00 C
+ATOM 621 C GLY A 14 25.650 35.910 29.360 1.00 0.00 C
+ATOM 622 O GLY A 14 24.830 36.590 29.970 1.00 0.00 O
+ATOM 623 H GLY A 14 27.200 35.990 26.660 1.00 0.00 H
+ATOM 624 N VAL A 15 25.570 34.610 29.090 1.00 0.00 N
+ATOM 625 CA VAL A 15 24.550 33.650 29.560 1.00 0.00 C
+ATOM 626 C VAL A 15 25.070 32.200 29.510 1.00 0.00 C
+ATOM 627 O VAL A 15 26.070 31.880 28.880 1.00 0.00 O
+ATOM 628 CB VAL A 15 23.210 33.730 28.790 1.00 0.00 C
+ATOM 629 CG1 VAL A 15 22.240 34.640 29.540 1.00 0.00 C
+ATOM 630 CG2 VAL A 15 23.270 34.170 27.330 1.00 0.00 C
+ATOM 631 H VAL A 15 26.210 34.210 28.430 1.00 0.00 H
+ATOM 632 N SER A 16 24.320 31.340 30.180 1.00 0.00 N
+ATOM 633 CA SER A 16 24.430 29.880 30.100 1.00 0.00 C
+ATOM 634 C SER A 16 23.660 29.380 28.870 1.00 0.00 C
+ATOM 635 O SER A 16 22.730 30.010 28.370 1.00 0.00 O
+ATOM 636 CB SER A 16 23.870 29.220 31.360 1.00 0.00 C
+ATOM 637 OG SER A 16 23.780 27.810 31.130 1.00 0.00 O
+ATOM 638 H SER A 16 23.570 31.680 30.780 1.00 0.00 H
+ATOM 639 HG SER A 16 23.790 27.330 32.060 1.00 0.00 H
+ATOM 640 N TYR A 17 24.070 28.200 28.420 1.00 0.00 N
+ATOM 641 CA TYR A 17 23.430 27.400 27.380 1.00 0.00 C
+ATOM 642 C TYR A 17 22.050 26.900 27.830 1.00 0.00 C
+ATOM 643 O TYR A 17 21.180 26.690 26.980 1.00 0.00 O
+ATOM 644 CB TYR A 17 24.410 26.280 26.960 1.00 0.00 C
+ATOM 645 CG TYR A 17 24.100 24.840 27.390 1.00 0.00 C
+ATOM 646 CD1 TYR A 17 24.440 24.360 28.680 1.00 0.00 C
+ATOM 647 CD2 TYR A 17 23.350 24.050 26.490 1.00 0.00 C
+ATOM 648 CE1 TYR A 17 24.000 23.080 29.070 1.00 0.00 C
+ATOM 649 CE2 TYR A 17 22.900 22.780 26.880 1.00 0.00 C
+ATOM 650 CZ TYR A 17 23.220 22.310 28.170 1.00 0.00 C
+ATOM 651 OH TYR A 17 22.790 21.070 28.480 1.00 0.00 O
+ATOM 652 H TYR A 17 24.850 27.740 28.900 1.00 0.00 H
+ATOM 653 HH TYR A 17 23.270 20.760 29.350 1.00 0.00 H
+ATOM 654 N GLN A 18 21.830 26.780 29.140 1.00 0.00 N
+ATOM 655 CA GLN A 18 20.490 26.450 29.660 1.00 0.00 C
+ATOM 656 C GLN A 18 19.540 27.630 29.420 1.00 0.00 C
+ATOM 657 O GLN A 18 18.470 27.460 28.830 1.00 0.00 O
+ATOM 658 CB GLN A 18 20.570 26.050 31.140 1.00 0.00 C
+ATOM 659 CG GLN A 18 19.270 25.460 31.720 1.00 0.00 C
+ATOM 660 CD GLN A 18 18.770 24.160 31.090 1.00 0.00 C
+ATOM 661 OE1 GLN A 18 17.590 23.830 31.120 1.00 0.00 O
+ATOM 662 NE2 GLN A 18 19.690 23.320 30.650 1.00 0.00 N
+ATOM 663 H GLN A 18 22.490 27.100 29.820 1.00 0.00 H
+ATOM 664 HE21 GLN A 18 20.640 23.490 30.920 1.00 0.00 H
+ATOM 665 HE22 GLN A 18 19.470 22.460 30.210 1.00 0.00 H
+ATOM 666 N THR A 19 19.930 28.820 29.880 1.00 0.00 N
+ATOM 667 CA THR A 19 19.270 30.110 29.590 1.00 0.00 C
+ATOM 668 C THR A 19 18.930 30.310 28.090 1.00 0.00 C
+ATOM 669 O THR A 19 17.770 30.560 27.750 1.00 0.00 O
+ATOM 670 CB THR A 19 20.210 31.200 30.110 1.00 0.00 C
+ATOM 671 OG1 THR A 19 20.740 30.790 31.380 1.00 0.00 O
+ATOM 672 CG2 THR A 19 19.510 32.550 30.250 1.00 0.00 C
+ATOM 673 H THR A 19 20.510 28.900 30.690 1.00 0.00 H
+ATOM 674 HG1 THR A 19 20.030 30.960 32.100 1.00 0.00 H
+ATOM 675 N VAL A 20 19.890 30.000 27.220 1.00 0.00 N
+ATOM 676 CA VAL A 20 19.740 30.060 25.750 1.00 0.00 C
+ATOM 677 C VAL A 20 18.840 28.940 25.150 1.00 0.00 C
+ATOM 678 O VAL A 20 17.940 29.240 24.380 1.00 0.00 O
+ATOM 679 CB VAL A 20 21.170 30.240 25.180 1.00 0.00 C
+ATOM 680 CG1 VAL A 20 21.350 30.030 23.690 1.00 0.00 C
+ATOM 681 CG2 VAL A 20 21.640 31.660 25.470 1.00 0.00 C
+ATOM 682 H VAL A 20 20.820 29.830 27.550 1.00 0.00 H
+ATOM 683 N SER A 21 19.000 27.690 25.600 1.00 0.00 N
+ATOM 684 CA SER A 21 18.010 26.600 25.390 1.00 0.00 C
+ATOM 685 C SER A 21 16.580 27.040 25.720 1.00 0.00 C
+ATOM 686 O SER A 21 15.740 27.080 24.830 1.00 0.00 O
+ATOM 687 CB SER A 21 18.350 25.360 26.240 1.00 0.00 C
+ATOM 688 OG SER A 21 17.410 24.310 26.020 1.00 0.00 O
+ATOM 689 H SER A 21 19.890 27.370 25.960 1.00 0.00 H
+ATOM 690 HG SER A 21 17.470 24.020 25.020 1.00 0.00 H
+ATOM 691 N ARG A 22 16.390 27.600 26.920 1.00 0.00 N
+ATOM 692 CA ARG A 22 15.110 28.220 27.340 1.00 0.00 C
+ATOM 693 C ARG A 22 14.810 29.650 26.860 1.00 0.00 C
+ATOM 694 O ARG A 22 13.950 30.370 27.380 1.00 0.00 O
+ATOM 695 CB ARG A 22 14.910 28.030 28.850 1.00 0.00 C
+ATOM 696 CG ARG A 22 14.530 26.580 29.140 1.00 0.00 C
+ATOM 697 CD ARG A 22 14.340 26.450 30.640 1.00 0.00 C
+ATOM 698 NE ARG A 22 14.270 25.050 31.100 1.00 0.00 N
+ATOM 699 CZ ARG A 22 14.760 24.610 32.260 1.00 0.00 C
+ATOM 700 NH1 ARG A 22 15.680 25.320 32.930 1.00 0.00 N
+ATOM 701 NH2 ARG A 22 14.600 23.340 32.620 1.00 0.00 N
+ATOM 702 H ARG A 22 17.100 27.480 27.630 1.00 0.00 H
+ATOM 703 HE ARG A 22 13.780 24.410 30.490 1.00 0.00 H
+ATOM 704 HH11 ARG A 22 16.030 26.170 32.510 1.00 0.00 H
+ATOM 705 HH12 ARG A 22 16.090 24.990 33.770 1.00 0.00 H
+ATOM 706 HH21 ARG A 22 14.110 22.710 32.030 1.00 0.00 H
+ATOM 707 HH22 ARG A 22 14.900 23.040 33.520 1.00 0.00 H
+ATOM 708 N VAL A 23 15.400 29.990 25.720 1.00 0.00 N
+ATOM 709 CA VAL A 23 15.020 31.120 24.860 1.00 0.00 C
+ATOM 710 C VAL A 23 14.470 30.480 23.570 1.00 0.00 C
+ATOM 711 O VAL A 23 13.490 30.990 23.010 1.00 0.00 O
+ATOM 712 CB VAL A 23 16.210 32.090 24.660 1.00 0.00 C
+ATOM 713 CG1 VAL A 23 16.270 32.800 23.310 1.00 0.00 C
+ATOM 714 CG2 VAL A 23 16.180 33.120 25.790 1.00 0.00 C
+ATOM 715 H VAL A 23 16.110 29.400 25.340 1.00 0.00 H
+ATOM 716 N VAL A 24 15.180 29.460 23.090 1.00 0.00 N
+ATOM 717 CA VAL A 24 14.790 28.610 21.950 1.00 0.00 C
+ATOM 718 C VAL A 24 13.510 27.800 22.260 1.00 0.00 C
+ATOM 719 O VAL A 24 12.450 28.100 21.710 1.00 0.00 O
+ATOM 720 CB VAL A 24 16.000 27.750 21.490 1.00 0.00 C
+ATOM 721 CG1 VAL A 24 15.590 26.760 20.400 1.00 0.00 C
+ATOM 722 CG2 VAL A 24 17.170 28.560 20.920 1.00 0.00 C
+ATOM 723 H VAL A 24 16.090 29.250 23.460 1.00 0.00 H
+ATOM 724 N ASN A 25 13.620 26.840 23.180 1.00 0.00 N
+ATOM 725 CA ASN A 25 12.570 25.880 23.570 1.00 0.00 C
+ATOM 726 C ASN A 25 12.320 25.730 25.070 1.00 0.00 C
+ATOM 727 O ASN A 25 13.260 25.790 25.870 1.00 0.00 O
+ATOM 728 CB ASN A 25 13.010 24.500 23.070 1.00 0.00 C
+ATOM 729 CG ASN A 25 12.490 24.320 21.650 1.00 0.00 C
+ATOM 730 OD1 ASN A 25 11.330 24.560 21.360 1.00 0.00 O
+ATOM 731 ND2 ASN A 25 13.240 23.720 20.770 1.00 0.00 N
+ATOM 732 H ASN A 25 14.490 26.700 23.660 1.00 0.00 H
+ATOM 733 HD21 ASN A 25 14.140 23.320 21.000 1.00 0.00 H
+ATOM 734 HD22 ASN A 25 12.900 23.690 19.820 1.00 0.00 H
+ATOM 735 N GLN A 26 11.090 25.340 25.420 1.00 0.00 N
+ATOM 736 CA GLN A 26 10.650 25.130 26.820 1.00 0.00 C
+ATOM 737 C GLN A 26 10.990 26.360 27.700 1.00 0.00 C
+ATOM 738 O GLN A 26 11.370 26.290 28.870 1.00 0.00 O
+ATOM 739 CB GLN A 26 11.350 23.860 27.300 1.00 0.00 C
+ATOM 740 CG GLN A 26 11.040 23.330 28.700 1.00 0.00 C
+ATOM 741 CD GLN A 26 12.100 22.300 29.060 1.00 0.00 C
+ATOM 742 OE1 GLN A 26 12.890 22.480 29.990 1.00 0.00 O
+ATOM 743 NE2 GLN A 26 12.280 21.330 28.180 1.00 0.00 N
+ATOM 744 H GLN A 26 10.340 25.370 24.750 1.00 0.00 H
+ATOM 745 HE21 GLN A 26 11.750 21.340 27.320 1.00 0.00 H
+ATOM 746 HE22 GLN A 26 12.960 20.630 28.340 1.00 0.00 H
+ATOM 747 N ALA A 27 10.840 27.520 27.070 1.00 0.00 N
+ATOM 748 CA ALA A 27 11.220 28.840 27.590 1.00 0.00 C
+ATOM 749 C ALA A 27 10.520 29.220 28.890 1.00 0.00 C
+ATOM 750 O ALA A 27 9.400 28.800 29.150 1.00 0.00 O
+ATOM 751 CB ALA A 27 11.000 29.880 26.500 1.00 0.00 C
+ATOM 752 H ALA A 27 10.520 27.540 26.100 1.00 0.00 H
+ATOM 753 N SER A 28 11.420 29.560 29.800 1.00 0.00 N
+ATOM 754 CA SER A 28 11.100 30.120 31.120 1.00 0.00 C
+ATOM 755 C SER A 28 12.340 30.790 31.720 1.00 0.00 C
+ATOM 756 O SER A 28 13.470 30.440 31.360 1.00 0.00 O
+ATOM 757 CB SER A 28 10.630 29.000 32.060 1.00 0.00 C
+ATOM 758 OG SER A 28 11.730 28.200 32.530 1.00 0.00 O
+ATOM 759 H SER A 28 12.380 29.290 29.700 1.00 0.00 H
+ATOM 760 HG SER A 28 11.660 27.260 32.130 1.00 0.00 H
+ATOM 761 N HIS A 29 12.110 31.760 32.610 1.00 0.00 N
+ATOM 762 CA HIS A 29 13.140 32.420 33.430 1.00 0.00 C
+ATOM 763 C HIS A 29 14.150 33.270 32.650 1.00 0.00 C
+ATOM 764 O HIS A 29 15.360 33.220 32.850 1.00 0.00 O
+ATOM 765 CB HIS A 29 13.860 31.390 34.320 1.00 0.00 C
+ATOM 766 CG HIS A 29 12.960 30.880 35.430 1.00 0.00 C
+ATOM 767 ND1 HIS A 29 12.970 31.330 36.680 1.00 0.00 N
+ATOM 768 CD2 HIS A 29 12.210 29.780 35.390 1.00 0.00 C
+ATOM 769 CE1 HIS A 29 12.270 30.460 37.410 1.00 0.00 C
+ATOM 770 NE2 HIS A 29 11.780 29.520 36.610 1.00 0.00 N
+ATOM 771 H HIS A 29 11.180 32.070 32.790 1.00 0.00 H
+ATOM 772 HD1 HIS A 29 13.230 32.280 36.940 1.00 0.00 H
+ATOM 773 HE2 HIS A 29 11.070 28.840 36.860 1.00 0.00 H
+ATOM 774 N VAL A 30 13.580 34.360 32.160 1.00 0.00 N
+ATOM 775 CA VAL A 30 14.300 35.300 31.270 1.00 0.00 C
+ATOM 776 C VAL A 30 13.720 36.710 31.340 1.00 0.00 C
+ATOM 777 O VAL A 30 12.590 37.010 30.930 1.00 0.00 O
+ATOM 778 CB VAL A 30 14.440 34.680 29.850 1.00 0.00 C
+ATOM 779 CG1 VAL A 30 13.090 34.470 29.150 1.00 0.00 C
+ATOM 780 CG2 VAL A 30 15.370 35.530 29.000 1.00 0.00 C
+ATOM 781 H VAL A 30 12.770 34.760 32.610 1.00 0.00 H
+ATOM 782 N SER A 31 14.600 37.600 31.780 1.00 0.00 N
+ATOM 783 CA SER A 31 14.250 38.980 32.170 1.00 0.00 C
+ATOM 784 C SER A 31 14.760 39.970 31.120 1.00 0.00 C
+ATOM 785 O SER A 31 15.750 39.700 30.430 1.00 0.00 O
+ATOM 786 CB SER A 31 14.960 39.380 33.460 1.00 0.00 C
+ATOM 787 OG SER A 31 14.920 38.370 34.470 1.00 0.00 O
+ATOM 788 H SER A 31 15.580 37.430 31.680 1.00 0.00 H
+ATOM 789 HG SER A 31 13.940 38.220 34.760 1.00 0.00 H
+ATOM 790 N ALA A 32 14.290 41.200 31.300 1.00 0.00 N
+ATOM 791 CA ALA A 32 14.600 42.380 30.470 1.00 0.00 C
+ATOM 792 C ALA A 32 16.070 42.510 29.990 1.00 0.00 C
+ATOM 793 O ALA A 32 16.300 42.570 28.790 1.00 0.00 O
+ATOM 794 CB ALA A 32 14.170 43.600 31.280 1.00 0.00 C
+ATOM 795 H ALA A 32 13.730 41.400 32.100 1.00 0.00 H
+ATOM 796 N LYS A 33 17.050 42.410 30.890 1.00 0.00 N
+ATOM 797 CA LYS A 33 18.480 42.630 30.570 1.00 0.00 C
+ATOM 798 C LYS A 33 19.050 41.520 29.670 1.00 0.00 C
+ATOM 799 O LYS A 33 19.440 41.780 28.530 1.00 0.00 O
+ATOM 800 CB LYS A 33 19.340 42.810 31.820 1.00 0.00 C
+ATOM 801 CG LYS A 33 20.770 43.260 31.480 1.00 0.00 C
+ATOM 802 CD LYS A 33 20.760 44.750 31.120 1.00 0.00 C
+ATOM 803 CE LYS A 33 21.980 45.230 30.350 1.00 0.00 C
+ATOM 804 NZ LYS A 33 21.650 46.580 29.880 1.00 0.00 N
+ATOM 805 H LYS A 33 16.840 42.210 31.860 1.00 0.00 H
+ATOM 806 HZ1 LYS A 33 20.790 46.540 29.340 1.00 0.00 H
+ATOM 807 HZ2 LYS A 33 21.480 47.180 30.660 1.00 0.00 H
+ATOM 808 HZ3 LYS A 33 22.380 46.980 29.310 1.00 0.00 H
+ATOM 809 N THR A 34 19.010 40.290 30.190 1.00 0.00 N
+ATOM 810 CA THR A 34 19.380 39.060 29.470 1.00 0.00 C
+ATOM 811 C THR A 34 18.680 38.860 28.120 1.00 0.00 C
+ATOM 812 O THR A 34 19.360 38.650 27.110 1.00 0.00 O
+ATOM 813 CB THR A 34 19.130 37.830 30.340 1.00 0.00 C
+ATOM 814 OG1 THR A 34 17.850 37.920 30.990 1.00 0.00 O
+ATOM 815 CG2 THR A 34 20.320 37.640 31.280 1.00 0.00 C
+ATOM 816 H THR A 34 18.740 40.150 31.150 1.00 0.00 H
+ATOM 817 HG1 THR A 34 17.870 37.600 31.970 1.00 0.00 H
+ATOM 818 N ARG A 35 17.390 39.200 28.050 1.00 0.00 N
+ATOM 819 CA ARG A 35 16.590 39.100 26.810 1.00 0.00 C
+ATOM 820 C ARG A 35 17.210 39.920 25.660 1.00 0.00 C
+ATOM 821 O ARG A 35 17.510 39.360 24.600 1.00 0.00 O
+ATOM 822 CB ARG A 35 15.140 39.510 27.080 1.00 0.00 C
+ATOM 823 CG ARG A 35 14.270 39.330 25.830 1.00 0.00 C
+ATOM 824 CD ARG A 35 12.800 39.100 26.170 1.00 0.00 C
+ATOM 825 NE ARG A 35 12.280 40.320 26.800 1.00 0.00 N
+ATOM 826 CZ ARG A 35 11.550 40.410 27.920 1.00 0.00 C
+ATOM 827 NH1 ARG A 35 10.920 39.380 28.490 1.00 0.00 N
+ATOM 828 NH2 ARG A 35 11.300 41.620 28.420 1.00 0.00 N
+ATOM 829 H ARG A 35 16.900 39.530 28.870 1.00 0.00 H
+ATOM 830 HE ARG A 35 12.550 41.180 26.350 1.00 0.00 H
+ATOM 831 HH11 ARG A 35 10.830 38.480 28.040 1.00 0.00 H
+ATOM 832 HH12 ARG A 35 10.460 39.550 29.350 1.00 0.00 H
+ATOM 833 HH21 ARG A 35 11.670 42.440 27.970 1.00 0.00 H
+ATOM 834 HH22 ARG A 35 10.700 41.730 29.210 1.00 0.00 H
+ATOM 835 N GLU A 36 17.520 41.180 25.950 1.00 0.00 N
+ATOM 836 CA GLU A 36 18.250 42.060 25.010 1.00 0.00 C
+ATOM 837 C GLU A 36 19.590 41.440 24.540 1.00 0.00 C
+ATOM 838 O GLU A 36 19.830 41.320 23.340 1.00 0.00 O
+ATOM 839 CB GLU A 36 18.490 43.410 25.700 1.00 0.00 C
+ATOM 840 CG GLU A 36 18.920 44.500 24.720 1.00 0.00 C
+ATOM 841 CD GLU A 36 19.500 45.730 25.410 1.00 0.00 C
+ATOM 842 OE1 GLU A 36 18.760 46.690 25.720 1.00 0.00 O
+ATOM 843 OE2 GLU A 36 20.740 45.770 25.530 1.00 0.00 O
+ATOM 844 H GLU A 36 17.210 41.630 26.790 1.00 0.00 H
+ATOM 845 N LYS A 37 20.380 40.940 25.500 1.00 0.00 N
+ATOM 846 CA LYS A 37 21.690 40.280 25.280 1.00 0.00 C
+ATOM 847 C LYS A 37 21.650 39.080 24.320 1.00 0.00 C
+ATOM 848 O LYS A 37 22.460 38.980 23.400 1.00 0.00 O
+ATOM 849 CB LYS A 37 22.270 39.770 26.600 1.00 0.00 C
+ATOM 850 CG LYS A 37 22.670 40.870 27.570 1.00 0.00 C
+ATOM 851 CD LYS A 37 23.780 41.690 26.960 1.00 0.00 C
+ATOM 852 CE LYS A 37 24.220 42.680 28.010 1.00 0.00 C
+ATOM 853 NZ LYS A 37 25.240 43.500 27.380 1.00 0.00 N
+ATOM 854 H LYS A 37 20.090 41.000 26.460 1.00 0.00 H
+ATOM 855 HZ1 LYS A 37 25.960 42.900 27.020 1.00 0.00 H
+ATOM 856 HZ2 LYS A 37 24.850 44.050 26.640 1.00 0.00 H
+ATOM 857 HZ3 LYS A 37 25.670 44.110 28.060 1.00 0.00 H
+ATOM 858 N VAL A 38 20.740 38.160 24.620 1.00 0.00 N
+ATOM 859 CA VAL A 38 20.480 36.970 23.790 1.00 0.00 C
+ATOM 860 C VAL A 38 20.100 37.380 22.350 1.00 0.00 C
+ATOM 861 O VAL A 38 20.750 36.910 21.420 1.00 0.00 O
+ATOM 862 CB VAL A 38 19.390 36.040 24.390 1.00 0.00 C
+ATOM 863 CG1 VAL A 38 19.290 34.740 23.580 1.00 0.00 C
+ATOM 864 CG2 VAL A 38 19.680 35.680 25.850 1.00 0.00 C
+ATOM 865 H VAL A 38 20.300 38.160 25.530 1.00 0.00 H
+ATOM 866 N GLU A 39 19.200 38.350 22.200 1.00 0.00 N
+ATOM 867 CA GLU A 39 18.860 38.870 20.860 1.00 0.00 C
+ATOM 868 C GLU A 39 20.040 39.500 20.130 1.00 0.00 C
+ATOM 869 O GLU A 39 20.280 39.210 18.950 1.00 0.00 O
+ATOM 870 CB GLU A 39 17.630 39.790 20.860 1.00 0.00 C
+ATOM 871 CG GLU A 39 16.380 38.920 20.860 1.00 0.00 C
+ATOM 872 CD GLU A 39 15.080 39.680 20.600 1.00 0.00 C
+ATOM 873 OE1 GLU A 39 14.840 40.100 19.450 1.00 0.00 O
+ATOM 874 OE2 GLU A 39 14.340 39.880 21.580 1.00 0.00 O
+ATOM 875 H GLU A 39 18.690 38.740 22.970 1.00 0.00 H
+ATOM 876 N ALA A 40 20.820 40.320 20.850 1.00 0.00 N
+ATOM 877 CA ALA A 40 22.090 40.880 20.360 1.00 0.00 C
+ATOM 878 C ALA A 40 23.010 39.770 19.790 1.00 0.00 C
+ATOM 879 O ALA A 40 23.350 39.820 18.610 1.00 0.00 O
+ATOM 880 CB ALA A 40 22.760 41.620 21.520 1.00 0.00 C
+ATOM 881 H ALA A 40 20.570 40.640 21.760 1.00 0.00 H
+ATOM 882 N ALA A 41 23.210 38.690 20.560 1.00 0.00 N
+ATOM 883 CA ALA A 41 24.020 37.530 20.130 1.00 0.00 C
+ATOM 884 C ALA A 41 23.420 36.630 19.040 1.00 0.00 C
+ATOM 885 O ALA A 41 24.130 36.240 18.110 1.00 0.00 O
+ATOM 886 CB ALA A 41 24.440 36.670 21.320 1.00 0.00 C
+ATOM 887 H ALA A 41 22.940 38.700 21.530 1.00 0.00 H
+ATOM 888 N MET A 42 22.110 36.400 19.100 1.00 0.00 N
+ATOM 889 CA MET A 42 21.380 35.640 18.060 1.00 0.00 C
+ATOM 890 C MET A 42 21.590 36.220 16.650 1.00 0.00 C
+ATOM 891 O MET A 42 21.820 35.460 15.700 1.00 0.00 O
+ATOM 892 CB MET A 42 19.890 35.550 18.360 1.00 0.00 C
+ATOM 893 CG MET A 42 19.560 34.620 19.530 1.00 0.00 C
+ATOM 894 SD MET A 42 20.160 32.910 19.300 1.00 0.00 S
+ATOM 895 CE MET A 42 19.560 32.180 20.800 1.00 0.00 C
+ATOM 896 H MET A 42 21.550 36.670 19.900 1.00 0.00 H
+ATOM 897 N ALA A 43 21.390 37.530 16.500 1.00 0.00 N
+ATOM 898 CA ALA A 43 21.580 38.230 15.210 1.00 0.00 C
+ATOM 899 C ALA A 43 23.050 38.400 14.750 1.00 0.00 C
+ATOM 900 O ALA A 43 23.310 38.360 13.550 1.00 0.00 O
+ATOM 901 CB ALA A 43 20.830 39.560 15.190 1.00 0.00 C
+ATOM 902 H ALA A 43 20.920 38.060 17.200 1.00 0.00 H
+ATOM 903 N GLU A 44 23.950 38.710 15.680 1.00 0.00 N
+ATOM 904 CA GLU A 44 25.400 38.790 15.360 1.00 0.00 C
+ATOM 905 C GLU A 44 26.070 37.480 14.910 1.00 0.00 C
+ATOM 906 O GLU A 44 26.910 37.540 14.020 1.00 0.00 O
+ATOM 907 CB GLU A 44 26.250 39.480 16.440 1.00 0.00 C
+ATOM 908 CG GLU A 44 26.760 40.880 16.040 1.00 0.00 C
+ATOM 909 CD GLU A 44 25.780 42.030 16.330 1.00 0.00 C
+ATOM 910 OE1 GLU A 44 24.970 42.330 15.440 1.00 0.00 O
+ATOM 911 OE2 GLU A 44 25.930 42.700 17.380 1.00 0.00 O
+ATOM 912 H GLU A 44 23.680 39.210 16.510 1.00 0.00 H
+ATOM 913 N LEU A 45 25.660 36.310 15.410 1.00 0.00 N
+ATOM 914 CA LEU A 45 26.270 35.030 15.020 1.00 0.00 C
+ATOM 915 C LEU A 45 25.290 34.150 14.250 1.00 0.00 C
+ATOM 916 O LEU A 45 24.110 34.060 14.590 1.00 0.00 O
+ATOM 917 CB LEU A 45 26.760 34.320 16.280 1.00 0.00 C
+ATOM 918 CG LEU A 45 27.990 35.000 16.910 1.00 0.00 C
+ATOM 919 CD1 LEU A 45 28.300 34.340 18.250 1.00 0.00 C
+ATOM 920 CD2 LEU A 45 29.220 34.860 16.010 1.00 0.00 C
+ATOM 921 H LEU A 45 25.020 36.210 16.180 1.00 0.00 H
+ATOM 922 N ASN A 46 25.860 33.310 13.400 1.00 0.00 N
+ATOM 923 CA ASN A 46 25.120 32.510 12.410 1.00 0.00 C
+ATOM 924 C ASN A 46 24.500 31.210 12.970 1.00 0.00 C
+ATOM 925 O ASN A 46 24.530 30.160 12.330 1.00 0.00 O
+ATOM 926 CB ASN A 46 26.070 32.230 11.230 1.00 0.00 C
+ATOM 927 CG ASN A 46 27.380 31.550 11.650 1.00 0.00 C
+ATOM 928 OD1 ASN A 46 27.440 30.380 11.990 1.00 0.00 O
+ATOM 929 ND2 ASN A 46 28.440 32.320 11.750 1.00 0.00 N
+ATOM 930 H ASN A 46 26.850 33.150 13.400 1.00 0.00 H
+ATOM 931 HD21 ASN A 46 28.370 33.290 11.440 1.00 0.00 H
+ATOM 932 HD22 ASN A 46 29.300 31.940 12.080 1.00 0.00 H
+ATOM 933 N TYR A 47 24.000 31.310 14.190 1.00 0.00 N
+ATOM 934 CA TYR A 47 23.300 30.230 14.920 1.00 0.00 C
+ATOM 935 C TYR A 47 22.340 29.440 14.030 1.00 0.00 C
+ATOM 936 O TYR A 47 21.390 29.980 13.480 1.00 0.00 O
+ATOM 937 CB TYR A 47 22.530 30.820 16.100 1.00 0.00 C
+ATOM 938 CG TYR A 47 21.870 29.750 16.960 1.00 0.00 C
+ATOM 939 CD1 TYR A 47 22.750 28.990 17.760 1.00 0.00 C
+ATOM 940 CD2 TYR A 47 20.530 29.340 16.750 1.00 0.00 C
+ATOM 941 CE1 TYR A 47 22.320 27.770 18.300 1.00 0.00 C
+ATOM 942 CE2 TYR A 47 20.090 28.120 17.300 1.00 0.00 C
+ATOM 943 CZ TYR A 47 21.010 27.340 18.040 1.00 0.00 C
+ATOM 944 OH TYR A 47 20.670 26.070 18.410 1.00 0.00 O
+ATOM 945 H TYR A 47 24.110 32.170 14.700 1.00 0.00 H
+ATOM 946 HH TYR A 47 21.500 25.560 18.740 1.00 0.00 H
+ATOM 947 N ILE A 48 22.460 28.130 14.190 1.00 0.00 N
+ATOM 948 CA ILE A 48 21.710 27.130 13.410 1.00 0.00 C
+ATOM 949 C ILE A 48 21.100 26.210 14.480 1.00 0.00 C
+ATOM 950 O ILE A 48 21.860 25.650 15.280 1.00 0.00 O
+ATOM 951 CB ILE A 48 22.670 26.240 12.600 1.00 0.00 C
+ATOM 952 CG1 ILE A 48 23.790 27.000 11.900 1.00 0.00 C
+ATOM 953 CG2 ILE A 48 21.910 25.360 11.590 1.00 0.00 C
+ATOM 954 CD1 ILE A 48 25.000 27.130 12.840 1.00 0.00 C
+ATOM 955 H ILE A 48 23.140 27.720 14.800 1.00 0.00 H
+ATOM 956 N PRO A 49 19.780 25.980 14.420 1.00 0.00 N
+ATOM 957 CA PRO A 49 19.140 25.030 15.340 1.00 0.00 C
+ATOM 958 C PRO A 49 19.820 23.670 15.140 1.00 0.00 C
+ATOM 959 O PRO A 49 20.240 23.350 14.030 1.00 0.00 O
+ATOM 960 CB PRO A 49 17.670 25.050 14.920 1.00 0.00 C
+ATOM 961 CG PRO A 49 17.490 26.410 14.260 1.00 0.00 C
+ATOM 962 CD PRO A 49 18.800 26.600 13.510 1.00 0.00 C
+ATOM 963 N ASN A 50 20.330 23.150 16.260 1.00 0.00 N
+ATOM 964 CA ASN A 50 20.960 21.810 16.320 1.00 0.00 C
+ATOM 965 C ASN A 50 22.370 21.680 15.710 1.00 0.00 C
+ATOM 966 O ASN A 50 22.830 20.580 15.410 1.00 0.00 O
+ATOM 967 CB ASN A 50 20.060 20.740 15.690 1.00 0.00 C
+ATOM 968 CG ASN A 50 18.850 20.510 16.570 1.00 0.00 C
+ATOM 969 OD1 ASN A 50 17.810 21.150 16.470 1.00 0.00 O
+ATOM 970 ND2 ASN A 50 19.020 19.610 17.510 1.00 0.00 N
+ATOM 971 H ASN A 50 20.450 23.710 17.080 1.00 0.00 H
+ATOM 972 HD21 ASN A 50 19.930 19.190 17.630 1.00 0.00 H
+ATOM 973 HD22 ASN A 50 18.280 19.400 18.150 1.00 0.00 H
+ATOM 974 N ARG A 51 23.070 22.800 15.610 1.00 0.00 N
+ATOM 975 CA ARG A 51 24.480 22.760 15.180 1.00 0.00 C
+ATOM 976 C ARG A 51 25.400 23.530 16.120 1.00 0.00 C
+ATOM 977 O ARG A 51 25.050 24.700 16.380 1.00 0.00 O
+ATOM 978 CB ARG A 51 24.660 23.260 13.750 1.00 0.00 C
+ATOM 979 CG ARG A 51 24.250 22.200 12.710 1.00 0.00 C
+ATOM 980 CD ARG A 51 25.170 20.970 12.740 1.00 0.00 C
+ATOM 981 NE ARG A 51 26.580 21.360 12.540 1.00 0.00 N
+ATOM 982 CZ ARG A 51 27.660 20.860 13.150 1.00 0.00 C
+ATOM 983 NH1 ARG A 51 27.580 19.940 14.110 1.00 0.00 N
+ATOM 984 NH2 ARG A 51 28.840 21.320 12.750 1.00 0.00 N
+ATOM 985 OXT ARG A 51 26.530 23.030 16.340 1.00 0.00 O
+ATOM 986 H ARG A 51 22.730 23.700 15.910 1.00 0.00 H
+ATOM 987 HE ARG A 51 26.750 22.050 11.820 1.00 0.00 H
+ATOM 988 HH11 ARG A 51 26.710 19.690 14.530 1.00 0.00 H
+ATOM 989 HH12 ARG A 51 28.420 19.520 14.450 1.00 0.00 H
+ATOM 990 HH21 ARG A 51 28.890 21.900 11.940 1.00 0.00 H
+ATOM 991 HH22 ARG A 51 29.700 21.010 13.170 1.00 0.00 H
+TER 992 ARG A 51
+HETATM 993 NA NA C 12 16.870 24.560 19.270 1.00 0.00 NA
+HETATM 994 O HOH B1032 10.310 28.620 40.780 1.00 0.00 O
+HETATM 995 H1 HOH B1032 10.500 28.310 41.720 1.00 0.00 H
+HETATM 996 H2 HOH B1032 10.350 29.620 40.750 1.00 0.00 H
+HETATM 997 O HOH B1584 20.350 36.610 34.330 1.00 0.00 O
+HETATM 998 H1 HOH B1584 20.260 35.680 33.950 1.00 0.00 H
+HETATM 999 H2 HOH B1584 21.170 37.040 33.970 1.00 0.00 H
+HETATM 1000 O HOH B1619 17.340 37.240 36.200 1.00 0.00 O
+HETATM 1001 H1 HOH B1619 17.030 37.910 36.870 1.00 0.00 H
+HETATM 1002 H2 HOH B1619 16.640 36.540 36.090 1.00 0.00 H
+HETATM 1003 O HOH B1904 24.200 17.380 30.380 1.00 0.00 O
+HETATM 1004 H1 HOH B1904 24.750 17.450 29.550 1.00 0.00 H
+HETATM 1005 H2 HOH B1904 23.620 18.190 30.460 1.00 0.00 H
+HETATM 1006 O HOH B2484 25.960 22.390 31.380 1.00 0.00 O
+HETATM 1007 H1 HOH B2484 26.820 22.410 31.890 1.00 0.00 H
+HETATM 1008 H2 HOH B2484 26.150 22.240 30.410 1.00 0.00 H
+HETATM 1009 O HOH B2496 26.950 27.600 32.130 1.00 0.00 O
+HETATM 1010 H1 HOH B2496 26.510 28.190 32.810 1.00 0.00 H
+HETATM 1011 H2 HOH B2496 26.560 26.680 32.180 1.00 0.00 H
+HETATM 1012 O HOH B2514 25.640 24.890 32.750 1.00 0.00 O
+HETATM 1013 H1 HOH B2514 26.270 24.580 33.470 1.00 0.00 H
+HETATM 1014 H2 HOH B2514 25.480 24.140 32.110 1.00 0.00 H
+HETATM 1015 O HOH B2963 10.310 33.430 38.870 1.00 0.00 O
+HETATM 1016 H1 HOH B2963 10.550 34.070 39.600 1.00 0.00 H
+HETATM 1017 H2 HOH B2963 9.320 33.440 38.740 1.00 0.00 H
+HETATM 1018 O HOH B3222 25.390 32.260 33.890 1.00 0.00 O
+HETATM 1019 H1 HOH B3222 24.600 32.300 33.280 1.00 0.00 H
+HETATM 1020 H2 HOH B3222 25.560 31.310 34.150 1.00 0.00 H
+HETATM 1021 O HOH B3303 10.450 31.430 41.030 1.00 0.00 O
+HETATM 1022 H1 HOH B3303 10.580 32.020 41.830 1.00 0.00 H
+HETATM 1023 H2 HOH B3303 10.360 32.000 40.210 1.00 0.00 H
+HETATM 1024 O HOH C 317 9.180 20.100 28.640 1.00 0.00 O
+HETATM 1025 H1 HOH C 317 10.000 19.740 28.200 1.00 0.00 H
+HETATM 1026 H2 HOH C 317 8.780 19.390 29.230 1.00 0.00 H
+HETATM 1027 O HOH C 327 18.700 15.530 18.720 1.00 0.00 O
+HETATM 1028 H1 HOH C 327 18.930 16.340 19.260 1.00 0.00 H
+HETATM 1029 H2 HOH C 327 18.290 15.810 17.860 1.00 0.00 H
+HETATM 1030 O HOH C 328 19.370 17.630 20.250 1.00 0.00 O
+HETATM 1031 H1 HOH C 328 18.660 17.720 20.950 1.00 0.00 H
+HETATM 1032 H2 HOH C 328 20.180 18.150 20.540 1.00 0.00 H
+HETATM 1033 O HOH C 827 7.540 20.510 25.950 1.00 0.00 O
+HETATM 1034 H1 HOH C 827 8.410 20.890 26.280 1.00 0.00 H
+HETATM 1035 H2 HOH C 827 7.680 19.540 25.710 1.00 0.00 H
+HETATM 1036 O HOH C 845 28.930 16.400 17.250 1.00 0.00 O
+HETATM 1037 H1 HOH C 845 29.030 17.390 17.300 1.00 0.00 H
+HETATM 1038 H2 HOH C 845 29.790 15.970 17.510 1.00 0.00 H
+HETATM 1039 O HOH C 923 18.220 25.060 20.920 1.00 0.00 O
+HETATM 1040 H1 HOH C 923 17.680 24.720 21.690 1.00 0.00 H
+HETATM 1041 H2 HOH C 923 19.190 24.890 21.090 1.00 0.00 H
+HETATM 1042 O HOH C 952 14.110 25.370 38.310 1.00 0.00 O
+HETATM 1043 H1 HOH C 952 13.490 24.600 38.150 1.00 0.00 H
+HETATM 1044 H2 HOH C 952 13.870 26.120 37.690 1.00 0.00 H
+HETATM 1045 O HOH C1002 10.170 22.880 23.630 1.00 0.00 O
+HETATM 1046 H1 HOH C1002 10.500 22.380 22.820 1.00 0.00 H
+HETATM 1047 H2 HOH C1002 10.420 22.370 24.450 1.00 0.00 H
+HETATM 1048 O HOH C1419 15.760 19.980 19.340 1.00 0.00 O
+HETATM 1049 H1 HOH C1419 15.850 20.300 20.290 1.00 0.00 H
+HETATM 1050 H2 HOH C1419 14.920 20.360 18.950 1.00 0.00 H
+HETATM 1051 O HOH C1771 31.010 19.980 14.940 1.00 0.00 O
+HETATM 1052 H1 HOH C1771 30.230 20.380 15.430 1.00 0.00 H
+HETATM 1053 H2 HOH C1771 31.470 19.330 15.540 1.00 0.00 H
+HETATM 1054 O HOH C1817 29.700 22.480 21.810 1.00 0.00 O
+HETATM 1055 H1 HOH C1817 30.090 23.400 21.780 1.00 0.00 H
+HETATM 1056 H2 HOH C1817 29.600 22.130 20.880 1.00 0.00 H
+HETATM 1057 O HOH C1958 29.040 19.060 23.620 1.00 0.00 O
+HETATM 1058 H1 HOH C1958 28.510 19.640 23.000 1.00 0.00 H
+HETATM 1059 H2 HOH C1958 28.590 19.020 24.510 1.00 0.00 H
+HETATM 1060 O HOH C1961 25.720 18.250 28.220 1.00 0.00 O
+HETATM 1061 H1 HOH C1961 26.120 19.150 28.410 1.00 0.00 H
+HETATM 1062 H2 HOH C1961 26.430 17.660 27.830 1.00 0.00 H
+HETATM 1063 O HOH C2011 30.890 23.360 17.030 1.00 0.00 O
+HETATM 1064 H1 HOH C2011 30.500 22.880 17.830 1.00 0.00 H
+HETATM 1065 H2 HOH C2011 30.150 23.640 16.430 1.00 0.00 H
+HETATM 1066 O HOH C2471 23.550 22.330 23.470 1.00 0.00 O
+HETATM 1067 H1 HOH C2471 22.610 22.570 23.220 1.00 0.00 H
+HETATM 1068 H2 HOH C2471 24.180 22.730 22.810 1.00 0.00 H
+HETATM 1069 O HOH C2570 27.480 20.700 21.920 1.00 0.00 O
+HETATM 1070 H1 HOH C2570 27.300 20.490 20.960 1.00 0.00 H
+HETATM 1071 H2 HOH C2570 28.120 21.470 21.970 1.00 0.00 H
+HETATM 1072 O HOH A 52 21.460 18.730 18.280 1.00 0.00 O
+HETATM 1073 H1 HOH A 52 22.360 19.090 18.550 1.00 0.00 H
+HETATM 1074 H2 HOH A 52 21.140 18.090 18.970 1.00 0.00 H
+HETATM 1075 O HOH A 53 17.180 27.410 31.630 1.00 0.00 O
+HETATM 1076 H1 HOH A 53 16.940 28.270 32.080 1.00 0.00 H
+HETATM 1077 H2 HOH A 53 16.760 27.390 30.720 1.00 0.00 H
+HETATM 1078 O HOH A 54 15.310 23.600 27.950 1.00 0.00 O
+HETATM 1079 H1 HOH A 54 16.070 23.800 27.330 1.00 0.00 H
+HETATM 1080 H2 HOH A 54 14.460 23.920 27.530 1.00 0.00 H
+HETATM 1081 O HOH A 55 18.030 22.820 18.820 1.00 0.00 O
+HETATM 1082 H1 HOH A 55 18.390 22.540 19.710 1.00 0.00 H
+HETATM 1083 H2 HOH A 55 18.080 22.050 18.190 1.00 0.00 H
+HETATM 1084 O HOH A 56 17.510 34.670 31.650 1.00 0.00 O
+HETATM 1085 H1 HOH A 56 16.780 34.430 32.300 1.00 0.00 H
+HETATM 1086 H2 HOH A 56 18.280 34.030 31.770 1.00 0.00 H
+HETATM 1087 O HOH A 57 12.480 37.730 35.190 1.00 0.00 O
+HETATM 1088 H1 HOH A 57 11.660 38.290 35.330 1.00 0.00 H
+HETATM 1089 H2 HOH A 57 12.660 37.180 36.010 1.00 0.00 H
+HETATM 1090 O HOH A 58 16.160 29.790 32.320 1.00 0.00 O
+HETATM 1091 H1 HOH A 58 15.800 30.370 31.600 1.00 0.00 H
+HETATM 1092 H2 HOH A 58 16.960 30.230 32.740 1.00 0.00 H
+HETATM 1093 O HOH A 59 10.310 32.330 35.810 1.00 0.00 O
+HETATM 1094 H1 HOH A 59 10.750 33.150 36.170 1.00 0.00 H
+HETATM 1095 H2 HOH A 59 9.390 32.250 36.210 1.00 0.00 H
+HETATM 1096 O HOH A 60 13.440 27.020 35.920 1.00 0.00 O
+HETATM 1097 H1 HOH A 60 12.570 26.630 35.610 1.00 0.00 H
+HETATM 1098 H2 HOH A 60 13.880 27.490 35.150 1.00 0.00 H
+HETATM 1099 O HOH A 61 11.610 22.860 32.990 1.00 0.00 O
+HETATM 1100 H1 HOH A 61 11.460 23.770 32.600 1.00 0.00 H
+HETATM 1101 H2 HOH A 61 11.240 22.170 32.370 1.00 0.00 H
+HETATM 1102 O HOH A 62 25.560 17.840 14.880 1.00 0.00 O
+HETATM 1103 H1 HOH A 62 25.370 17.600 15.830 1.00 0.00 H
+HETATM 1104 H2 HOH A 62 25.980 17.060 14.410 1.00 0.00 H
+HETATM 1105 O HOH A 63 13.250 24.000 34.990 1.00 0.00 O
+HETATM 1106 H1 HOH A 63 12.720 24.250 35.790 1.00 0.00 H
+HETATM 1107 H2 HOH A 63 12.620 23.810 34.230 1.00 0.00 H
+HETATM 1108 O HOH A 64 24.050 20.340 30.530 1.00 0.00 O
+HETATM 1109 H1 HOH A 64 24.730 19.610 30.440 1.00 0.00 H
+HETATM 1110 H2 HOH A 64 24.450 21.110 31.030 1.00 0.00 H
+HETATM 1111 O HOH A 65 14.320 28.110 33.490 1.00 0.00 O
+HETATM 1112 H1 HOH A 65 15.000 28.780 33.200 1.00 0.00 H
+HETATM 1113 H2 HOH A 65 13.460 28.280 33.010 1.00 0.00 H
+HETATM 1114 O HOH A 66 23.030 32.690 32.190 1.00 0.00 O
+HETATM 1115 H1 HOH A 66 22.060 32.740 32.400 1.00 0.00 H
+HETATM 1116 H2 HOH A 66 23.430 33.610 32.220 1.00 0.00 H
+HETATM 1117 O HOH A 67 23.740 26.650 33.310 1.00 0.00 O
+HETATM 1118 H1 HOH A 67 24.190 27.140 34.060 1.00 0.00 H
+HETATM 1119 H2 HOH A 67 24.280 25.840 33.070 1.00 0.00 H
+HETATM 1120 O HOH A 68 25.600 20.490 24.090 1.00 0.00 O
+HETATM 1121 H1 HOH A 68 26.180 20.550 23.270 1.00 0.00 H
+HETATM 1122 H2 HOH A 68 24.750 21.000 23.930 1.00 0.00 H
+HETATM 1123 O HOH A 69 15.450 38.970 37.560 1.00 0.00 O
+HETATM 1124 H1 HOH A 69 15.090 39.190 38.470 1.00 0.00 H
+HETATM 1125 H2 HOH A 69 14.710 38.630 36.980 1.00 0.00 H
+HETATM 1126 O HOH A 70 10.170 21.260 31.180 1.00 0.00 O
+HETATM 1127 H1 HOH A 70 9.830 20.890 30.320 1.00 0.00 H
+HETATM 1128 H2 HOH A 70 10.190 20.530 31.870 1.00 0.00 H
+ENDMDL
+MODEL 3
+ATOM 1 O5' DA B 1 7.850 31.870 48.800 1.00 0.00 O
+ATOM 2 C5' DA B 1 7.900 31.760 47.370 1.00 0.00 C
+ATOM 3 C4' DA B 1 9.300 31.550 46.800 1.00 0.00 C
+ATOM 4 O4' DA B 1 10.200 30.670 47.500 1.00 0.00 O
+ATOM 5 C3' DA B 1 9.200 31.020 45.380 1.00 0.00 C
+ATOM 6 O3' DA B 1 9.670 32.030 44.480 1.00 0.00 O
+ATOM 7 C2' DA B 1 10.030 29.770 45.340 1.00 0.00 C
+ATOM 8 C1' DA B 1 11.000 30.210 46.410 1.00 0.00 C
+ATOM 9 N9 DA B 1 11.900 29.180 46.870 1.00 0.00 N
+ATOM 10 C8 DA B 1 11.620 27.890 47.020 1.00 0.00 C
+ATOM 11 N7 DA B 1 12.680 27.270 47.540 1.00 0.00 N
+ATOM 12 C5 DA B 1 13.620 28.200 47.710 1.00 0.00 C
+ATOM 13 C6 DA B 1 14.900 28.250 48.260 1.00 0.00 C
+ATOM 14 N6 DA B 1 15.490 27.150 48.660 1.00 0.00 N
+ATOM 15 N1 DA B 1 15.610 29.400 48.300 1.00 0.00 N
+ATOM 16 C2 DA B 1 15.110 30.540 47.820 1.00 0.00 C
+ATOM 17 N3 DA B 1 13.880 30.540 47.310 1.00 0.00 N
+ATOM 18 C4 DA B 1 13.130 29.410 47.260 1.00 0.00 C
+ATOM 19 H61 DA B 1 16.330 27.200 49.200 1.00 0.00 H
+ATOM 20 H62 DA B 1 15.260 26.290 48.200 1.00 0.00 H
+ATOM 21 HO5' DA B 1 7.700 32.850 49.090 1.00 0.00 H
+ATOM 22 P DA B 2 9.150 32.050 42.970 1.00 0.00 P
+ATOM 23 OP1 DA B 2 7.990 32.960 42.850 1.00 0.00 O
+ATOM 24 OP2 DA B 2 8.900 30.660 42.510 1.00 0.00 O
+ATOM 25 O5' DA B 2 10.470 32.550 42.230 1.00 0.00 O
+ATOM 26 C5' DA B 2 10.870 33.930 42.340 1.00 0.00 C
+ATOM 27 C4' DA B 2 12.350 34.160 42.010 1.00 0.00 C
+ATOM 28 O4' DA B 2 13.130 33.300 42.870 1.00 0.00 O
+ATOM 29 C3' DA B 2 12.670 33.760 40.570 1.00 0.00 C
+ATOM 30 O3' DA B 2 13.520 34.620 39.790 1.00 0.00 O
+ATOM 31 C2' DA B 2 13.250 32.370 40.700 1.00 0.00 C
+ATOM 32 C1' DA B 2 13.920 32.390 42.070 1.00 0.00 C
+ATOM 33 N9 DA B 2 13.900 31.060 42.730 1.00 0.00 N
+ATOM 34 C8 DA B 2 12.800 30.340 42.990 1.00 0.00 C
+ATOM 35 N7 DA B 2 13.130 29.220 43.650 1.00 0.00 N
+ATOM 36 C5 DA B 2 14.450 29.210 43.810 1.00 0.00 C
+ATOM 37 C6 DA B 2 15.420 28.420 44.460 1.00 0.00 C
+ATOM 38 N6 DA B 2 15.240 27.200 44.920 1.00 0.00 N
+ATOM 39 N1 DA B 2 16.710 28.800 44.550 1.00 0.00 N
+ATOM 40 C2 DA B 2 17.120 29.950 44.010 1.00 0.00 C
+ATOM 41 N3 DA B 2 16.250 30.710 43.350 1.00 0.00 N
+ATOM 42 C4 DA B 2 14.930 30.390 43.240 1.00 0.00 C
+ATOM 43 H61 DA B 2 16.000 26.710 45.370 1.00 0.00 H
+ATOM 44 H62 DA B 2 14.350 26.740 44.860 1.00 0.00 H
+ATOM 45 P DT B 3 13.250 34.690 38.200 1.00 0.00 P
+ATOM 46 OP1 DT B 3 12.620 36.010 37.990 1.00 0.00 O
+ATOM 47 OP2 DT B 3 12.510 33.480 37.750 1.00 0.00 O
+ATOM 48 O5' DT B 3 14.700 34.750 37.510 1.00 0.00 O
+ATOM 49 C5' DT B 3 15.270 33.610 36.840 1.00 0.00 C
+ATOM 50 C4' DT B 3 16.480 32.990 37.540 1.00 0.00 C
+ATOM 51 O4' DT B 3 16.200 32.300 38.780 1.00 0.00 O
+ATOM 52 C3' DT B 3 17.140 31.960 36.660 1.00 0.00 C
+ATOM 53 O3' DT B 3 17.930 32.620 35.660 1.00 0.00 O
+ATOM 54 C2' DT B 3 18.050 31.310 37.690 1.00 0.00 C
+ATOM 55 C1' DT B 3 17.330 31.440 39.020 1.00 0.00 C
+ATOM 56 N1 DT B 3 16.880 30.140 39.610 1.00 0.00 N
+ATOM 57 C2 DT B 3 17.790 29.340 40.350 1.00 0.00 C
+ATOM 58 O2 DT B 3 19.000 29.370 40.150 1.00 0.00 O
+ATOM 59 N3 DT B 3 17.330 28.240 41.060 1.00 0.00 N
+ATOM 60 C4 DT B 3 15.990 27.880 40.970 1.00 0.00 C
+ATOM 61 O4 DT B 3 15.610 26.960 41.690 1.00 0.00 O
+ATOM 62 C5 DT B 3 15.070 28.610 40.210 1.00 0.00 C
+ATOM 63 C7 DT B 3 13.700 28.010 39.860 1.00 0.00 C
+ATOM 64 C6 DT B 3 15.510 29.770 39.560 1.00 0.00 C
+ATOM 65 H3 DT B 3 17.920 27.790 41.730 1.00 0.00 H
+ATOM 66 P DT B 4 18.620 31.900 34.410 1.00 0.00 P
+ATOM 67 OP1 DT B 4 19.280 33.010 33.700 1.00 0.00 O
+ATOM 68 OP2 DT B 4 17.640 31.110 33.640 1.00 0.00 O
+ATOM 69 O5' DT B 4 19.770 30.920 34.990 1.00 0.00 O
+ATOM 70 C5' DT B 4 20.960 31.410 35.650 1.00 0.00 C
+ATOM 71 C4' DT B 4 21.880 30.340 36.230 1.00 0.00 C
+ATOM 72 O4' DT B 4 21.240 29.420 37.140 1.00 0.00 O
+ATOM 73 C3' DT B 4 22.590 29.450 35.230 1.00 0.00 C
+ATOM 74 O3' DT B 4 23.970 29.340 35.680 1.00 0.00 O
+ATOM 75 C2' DT B 4 21.790 28.180 35.160 1.00 0.00 C
+ATOM 76 C1' DT B 4 21.220 28.110 36.560 1.00 0.00 C
+ATOM 77 N1 DT B 4 19.800 27.680 36.640 1.00 0.00 N
+ATOM 78 C2 DT B 4 19.450 26.970 37.790 1.00 0.00 C
+ATOM 79 O2 DT B 4 20.270 26.460 38.540 1.00 0.00 O
+ATOM 80 N3 DT B 4 18.110 26.850 38.130 1.00 0.00 N
+ATOM 81 C4 DT B 4 17.130 27.410 37.330 1.00 0.00 C
+ATOM 82 O4 DT B 4 15.980 27.200 37.710 1.00 0.00 O
+ATOM 83 C5 DT B 4 17.440 28.110 36.160 1.00 0.00 C
+ATOM 84 C7 DT B 4 16.330 28.690 35.280 1.00 0.00 C
+ATOM 85 C6 DT B 4 18.790 28.230 35.810 1.00 0.00 C
+ATOM 86 H3 DT B 4 17.860 26.190 38.840 1.00 0.00 H
+ATOM 87 P DG B 5 25.160 28.750 34.770 1.00 0.00 P
+ATOM 88 OP1 DG B 5 26.460 29.280 35.250 1.00 0.00 O
+ATOM 89 OP2 DG B 5 24.810 28.960 33.350 1.00 0.00 O
+ATOM 90 O5' DG B 5 25.080 27.200 35.150 1.00 0.00 O
+ATOM 91 C5' DG B 5 25.230 26.820 36.530 1.00 0.00 C
+ATOM 92 C4' DG B 5 24.650 25.440 36.790 1.00 0.00 C
+ATOM 93 O4' DG B 5 23.220 25.260 36.700 1.00 0.00 O
+ATOM 94 C3' DG B 5 25.250 24.380 35.910 1.00 0.00 C
+ATOM 95 O3' DG B 5 26.070 23.620 36.830 1.00 0.00 O
+ATOM 96 C2' DG B 5 24.100 23.700 35.210 1.00 0.00 C
+ATOM 97 C1' DG B 5 22.990 23.900 36.250 1.00 0.00 C
+ATOM 98 N9 DG B 5 21.610 23.780 35.750 1.00 0.00 N
+ATOM 99 C8 DG B 5 21.120 24.570 34.820 1.00 0.00 C
+ATOM 100 N7 DG B 5 19.790 24.440 34.750 1.00 0.00 N
+ATOM 101 C5 DG B 5 19.440 23.520 35.650 1.00 0.00 C
+ATOM 102 C6 DG B 5 18.240 22.870 35.960 1.00 0.00 C
+ATOM 103 O6 DG B 5 17.200 23.150 35.380 1.00 0.00 O
+ATOM 104 N1 DG B 5 18.240 21.820 36.870 1.00 0.00 N
+ATOM 105 C2 DG B 5 19.430 21.420 37.460 1.00 0.00 C
+ATOM 106 N2 DG B 5 19.410 20.230 38.000 1.00 0.00 N
+ATOM 107 N3 DG B 5 20.570 22.060 37.180 1.00 0.00 N
+ATOM 108 C4 DG B 5 20.610 23.090 36.290 1.00 0.00 C
+ATOM 109 H1 DG B 5 17.380 21.380 37.140 1.00 0.00 H
+ATOM 110 H21 DG B 5 18.520 19.800 38.230 1.00 0.00 H
+ATOM 111 H22 DG B 5 20.250 19.730 38.160 1.00 0.00 H
+ATOM 112 P DT B 6 27.020 22.390 36.430 1.00 0.00 P
+ATOM 113 OP1 DT B 6 28.060 22.220 37.470 1.00 0.00 O
+ATOM 114 OP2 DT B 6 27.370 22.520 35.010 1.00 0.00 O
+ATOM 115 O5' DT B 6 26.040 21.150 36.590 1.00 0.00 O
+ATOM 116 C5' DT B 6 25.450 20.770 37.840 1.00 0.00 C
+ATOM 117 C4' DT B 6 24.400 19.730 37.510 1.00 0.00 C
+ATOM 118 O4' DT B 6 23.340 20.260 36.700 1.00 0.00 O
+ATOM 119 C3' DT B 6 25.000 18.610 36.690 1.00 0.00 C
+ATOM 120 O3' DT B 6 25.280 17.510 37.570 1.00 0.00 O
+ATOM 121 C2' DT B 6 24.060 18.330 35.550 1.00 0.00 C
+ATOM 122 C1' DT B 6 22.840 19.110 36.010 1.00 0.00 C
+ATOM 123 N1 DT B 6 21.990 19.580 34.910 1.00 0.00 N
+ATOM 124 C2 DT B 6 20.620 19.290 35.020 1.00 0.00 C
+ATOM 125 O2 DT B 6 20.230 18.240 35.520 1.00 0.00 O
+ATOM 126 N3 DT B 6 19.700 20.060 34.320 1.00 0.00 N
+ATOM 127 C4 DT B 6 20.130 21.060 33.460 1.00 0.00 C
+ATOM 128 O4 DT B 6 19.250 21.750 32.960 1.00 0.00 O
+ATOM 129 C5 DT B 6 21.500 21.310 33.260 1.00 0.00 C
+ATOM 130 C7 DT B 6 21.980 22.170 32.080 1.00 0.00 C
+ATOM 131 C6 DT B 6 22.420 20.600 34.030 1.00 0.00 C
+ATOM 132 H3 DT B 6 18.730 19.870 34.420 1.00 0.00 H
+ATOM 133 P DG B 7 26.020 16.170 37.090 1.00 0.00 P
+ATOM 134 OP1 DG B 7 26.480 15.600 38.380 1.00 0.00 O
+ATOM 135 OP2 DG B 7 27.020 16.510 36.050 1.00 0.00 O
+ATOM 136 O5' DG B 7 24.810 15.370 36.420 1.00 0.00 O
+ATOM 137 C5' DG B 7 23.610 15.000 37.140 1.00 0.00 C
+ATOM 138 C4' DG B 7 22.560 14.450 36.190 1.00 0.00 C
+ATOM 139 O4' DG B 7 22.010 15.510 35.390 1.00 0.00 O
+ATOM 140 C3' DG B 7 23.160 13.480 35.180 1.00 0.00 C
+ATOM 141 O3' DG B 7 22.310 12.320 35.050 1.00 0.00 O
+ATOM 142 C2' DG B 7 23.300 14.280 33.920 1.00 0.00 C
+ATOM 143 C1' DG B 7 21.900 14.920 34.090 1.00 0.00 C
+ATOM 144 N9 DG B 7 21.510 15.970 33.140 1.00 0.00 N
+ATOM 145 C8 DG B 7 22.350 16.810 32.550 1.00 0.00 C
+ATOM 146 N7 DG B 7 21.660 17.780 31.950 1.00 0.00 N
+ATOM 147 C5 DG B 7 20.380 17.500 32.110 1.00 0.00 C
+ATOM 148 C6 DG B 7 19.210 18.060 31.590 1.00 0.00 C
+ATOM 149 O6 DG B 7 19.250 19.070 30.890 1.00 0.00 O
+ATOM 150 N1 DG B 7 17.980 17.450 31.860 1.00 0.00 N
+ATOM 151 C2 DG B 7 17.940 16.300 32.640 1.00 0.00 C
+ATOM 152 N2 DG B 7 16.920 15.490 32.620 1.00 0.00 N
+ATOM 153 N3 DG B 7 19.070 15.780 33.140 1.00 0.00 N
+ATOM 154 C4 DG B 7 20.270 16.350 32.880 1.00 0.00 C
+ATOM 155 H1 DG B 7 17.150 17.800 31.420 1.00 0.00 H
+ATOM 156 H21 DG B 7 16.020 15.750 32.260 1.00 0.00 H
+ATOM 157 H22 DG B 7 17.130 14.540 32.900 1.00 0.00 H
+ATOM 158 P DA B 8 23.020 10.950 34.640 1.00 0.00 P
+ATOM 159 OP1 DA B 8 24.010 10.640 35.700 1.00 0.00 O
+ATOM 160 OP2 DA B 8 23.480 11.010 33.240 1.00 0.00 O
+ATOM 161 O5' DA B 8 21.910 9.780 34.740 1.00 0.00 O
+ATOM 162 C5' DA B 8 20.490 10.020 34.890 1.00 0.00 C
+ATOM 163 C4' DA B 8 19.700 10.400 33.640 1.00 0.00 C
+ATOM 164 O4' DA B 8 19.970 11.710 33.080 1.00 0.00 O
+ATOM 165 C3' DA B 8 19.780 9.400 32.480 1.00 0.00 C
+ATOM 166 O3' DA B 8 18.520 8.720 32.270 1.00 0.00 O
+ATOM 167 C2' DA B 8 20.250 10.250 31.320 1.00 0.00 C
+ATOM 168 C1' DA B 8 19.640 11.580 31.690 1.00 0.00 C
+ATOM 169 N9 DA B 8 20.230 12.720 30.950 1.00 0.00 N
+ATOM 170 C8 DA B 8 21.530 12.940 30.770 1.00 0.00 C
+ATOM 171 N7 DA B 8 21.730 14.080 30.110 1.00 0.00 N
+ATOM 172 C5 DA B 8 20.520 14.590 29.840 1.00 0.00 C
+ATOM 173 C6 DA B 8 20.030 15.720 29.190 1.00 0.00 C
+ATOM 174 N6 DA B 8 20.820 16.630 28.660 1.00 0.00 N
+ATOM 175 N1 DA B 8 18.700 15.900 29.030 1.00 0.00 N
+ATOM 176 C2 DA B 8 17.820 15.020 29.500 1.00 0.00 C
+ATOM 177 N3 DA B 8 18.250 13.940 30.160 1.00 0.00 N
+ATOM 178 C4 DA B 8 19.570 13.710 30.360 1.00 0.00 C
+ATOM 179 H61 DA B 8 20.490 17.490 28.260 1.00 0.00 H
+ATOM 180 H62 DA B 8 21.820 16.420 28.620 1.00 0.00 H
+ATOM 181 P DG B 9 18.310 7.590 31.130 1.00 0.00 P
+ATOM 182 OP1 DG B 9 17.390 6.530 31.600 1.00 0.00 O
+ATOM 183 OP2 DG B 9 19.630 7.150 30.640 1.00 0.00 O
+ATOM 184 O5' DG B 9 17.550 8.360 29.930 1.00 0.00 O
+ATOM 185 C5' DG B 9 16.230 8.930 30.070 1.00 0.00 C
+ATOM 186 C4' DG B 9 15.680 9.660 28.830 1.00 0.00 C
+ATOM 187 O4' DG B 9 16.430 10.860 28.510 1.00 0.00 O
+ATOM 188 C3' DG B 9 15.770 8.800 27.580 1.00 0.00 C
+ATOM 189 O3' DG B 9 14.640 8.970 26.690 1.00 0.00 O
+ATOM 190 C2' DG B 9 17.090 9.170 27.000 1.00 0.00 C
+ATOM 191 C1' DG B 9 16.960 10.680 27.180 1.00 0.00 C
+ATOM 192 N9 DG B 9 18.250 11.390 27.130 1.00 0.00 N
+ATOM 193 C8 DG B 9 19.360 11.020 27.750 1.00 0.00 C
+ATOM 194 N7 DG B 9 20.310 11.940 27.560 1.00 0.00 N
+ATOM 195 C5 DG B 9 19.770 12.920 26.820 1.00 0.00 C
+ATOM 196 C6 DG B 9 20.230 14.150 26.340 1.00 0.00 C
+ATOM 197 O6 DG B 9 21.420 14.420 26.270 1.00 0.00 O
+ATOM 198 N1 DG B 9 19.350 15.010 25.670 1.00 0.00 N
+ATOM 199 C2 DG B 9 18.040 14.610 25.490 1.00 0.00 C
+ATOM 200 N2 DG B 9 17.290 15.310 24.700 1.00 0.00 N
+ATOM 201 N3 DG B 9 17.610 13.400 25.890 1.00 0.00 N
+ATOM 202 C4 DG B 9 18.450 12.570 26.560 1.00 0.00 C
+ATOM 203 H1 DG B 9 19.710 15.710 25.070 1.00 0.00 H
+ATOM 204 H21 DG B 9 17.490 16.270 24.440 1.00 0.00 H
+ATOM 205 H22 DG B 9 16.500 14.850 24.290 1.00 0.00 H
+ATOM 206 P DC B 10 14.360 7.930 25.490 1.00 0.00 P
+ATOM 207 OP1 DC B 10 12.950 7.480 25.480 1.00 0.00 O
+ATOM 208 OP2 DC B 10 15.390 6.870 25.420 1.00 0.00 O
+ATOM 209 O5' DC B 10 14.640 8.880 24.240 1.00 0.00 O
+ATOM 210 C5' DC B 10 13.620 9.790 23.810 1.00 0.00 C
+ATOM 211 C4' DC B 10 14.050 10.610 22.610 1.00 0.00 C
+ATOM 212 O4' DC B 10 15.140 11.500 22.950 1.00 0.00 O
+ATOM 213 C3' DC B 10 14.530 9.640 21.550 1.00 0.00 C
+ATOM 214 O3' DC B 10 13.940 9.880 20.270 1.00 0.00 O
+ATOM 215 C2' DC B 10 16.010 9.830 21.550 1.00 0.00 C
+ATOM 216 C1' DC B 10 16.120 11.300 21.920 1.00 0.00 C
+ATOM 217 N1 DC B 10 17.390 11.610 22.610 1.00 0.00 N
+ATOM 218 C2 DC B 10 18.030 12.860 22.480 1.00 0.00 C
+ATOM 219 O2 DC B 10 17.680 13.660 21.620 1.00 0.00 O
+ATOM 220 N3 DC B 10 19.260 13.030 23.030 1.00 0.00 N
+ATOM 221 C4 DC B 10 19.890 12.020 23.650 1.00 0.00 C
+ATOM 222 N4 DC B 10 21.130 12.210 24.020 1.00 0.00 N
+ATOM 223 C5 DC B 10 19.320 10.750 23.810 1.00 0.00 C
+ATOM 224 C6 DC B 10 18.060 10.570 23.280 1.00 0.00 C
+ATOM 225 H41 DC B 10 21.570 13.100 23.930 1.00 0.00 H
+ATOM 226 H42 DC B 10 21.670 11.460 24.420 1.00 0.00 H
+ATOM 227 P DG B 11 14.040 8.750 19.120 1.00 0.00 P
+ATOM 228 OP1 DG B 11 12.850 8.930 18.260 1.00 0.00 O
+ATOM 229 OP2 DG B 11 14.430 7.440 19.690 1.00 0.00 O
+ATOM 230 O5' DG B 11 15.380 9.170 18.360 1.00 0.00 O
+ATOM 231 C5' DG B 11 15.320 10.350 17.570 1.00 0.00 C
+ATOM 232 C4' DG B 11 16.690 10.940 17.350 1.00 0.00 C
+ATOM 233 O4' DG B 11 17.450 11.130 18.560 1.00 0.00 O
+ATOM 234 C3' DG B 11 17.560 10.110 16.420 1.00 0.00 C
+ATOM 235 O3' DG B 11 17.340 10.500 15.050 1.00 0.00 O
+ATOM 236 C2' DG B 11 18.970 10.410 16.910 1.00 0.00 C
+ATOM 237 C1' DG B 11 18.760 11.420 18.040 1.00 0.00 C
+ATOM 238 N9 DG B 11 19.800 11.300 19.090 1.00 0.00 N
+ATOM 239 C8 DG B 11 19.950 10.230 19.860 1.00 0.00 C
+ATOM 240 N7 DG B 11 21.030 10.350 20.640 1.00 0.00 N
+ATOM 241 C5 DG B 11 21.620 11.500 20.300 1.00 0.00 C
+ATOM 242 C6 DG B 11 22.840 12.100 20.630 1.00 0.00 C
+ATOM 243 O6 DG B 11 23.510 11.720 21.590 1.00 0.00 O
+ATOM 244 N1 DG B 11 23.250 13.250 19.940 1.00 0.00 N
+ATOM 245 C2 DG B 11 22.420 13.800 18.980 1.00 0.00 C
+ATOM 246 N2 DG B 11 22.830 14.820 18.260 1.00 0.00 N
+ATOM 247 N3 DG B 11 21.240 13.240 18.700 1.00 0.00 N
+ATOM 248 C4 DG B 11 20.830 12.110 19.320 1.00 0.00 C
+ATOM 249 HO3' DG B 11 17.930 10.160 14.300 1.00 0.00 H
+ATOM 250 H1 DG B 11 24.150 13.640 20.140 1.00 0.00 H
+ATOM 251 H21 DG B 11 23.720 15.230 18.440 1.00 0.00 H
+ATOM 252 H22 DG B 11 22.290 15.120 17.470 1.00 0.00 H
+TER 253 DG B 11
+ATOM 254 O5' DC C 1 31.690 17.510 19.400 1.00 0.00 O
+ATOM 255 C5' DC C 1 30.410 18.150 19.490 1.00 0.00 C
+ATOM 256 C4' DC C 1 29.450 17.860 18.330 1.00 0.00 C
+ATOM 257 O4' DC C 1 28.900 16.550 18.620 1.00 0.00 O
+ATOM 258 C3' DC C 1 28.270 18.840 18.350 1.00 0.00 C
+ATOM 259 O3' DC C 1 27.840 19.190 17.010 1.00 0.00 O
+ATOM 260 C2' DC C 1 27.230 18.170 19.180 1.00 0.00 C
+ATOM 261 C1' DC C 1 27.490 16.720 18.840 1.00 0.00 C
+ATOM 262 N1 DC C 1 27.110 15.780 19.910 1.00 0.00 N
+ATOM 263 C2 DC C 1 26.380 14.630 19.550 1.00 0.00 C
+ATOM 264 O2 DC C 1 25.650 14.710 18.560 1.00 0.00 O
+ATOM 265 N3 DC C 1 26.070 13.730 20.500 1.00 0.00 N
+ATOM 266 C4 DC C 1 26.370 13.960 21.790 1.00 0.00 C
+ATOM 267 N4 DC C 1 25.790 13.160 22.670 1.00 0.00 N
+ATOM 268 C5 DC C 1 27.060 15.110 22.220 1.00 0.00 C
+ATOM 269 C6 DC C 1 27.460 16.010 21.250 1.00 0.00 C
+ATOM 270 H41 DC C 1 25.110 12.480 22.370 1.00 0.00 H
+ATOM 271 H42 DC C 1 25.950 13.310 23.650 1.00 0.00 H
+ATOM 272 HO5' DC C 1 31.600 16.480 19.370 1.00 0.00 H
+ATOM 273 P DG C 2 27.280 20.660 16.670 1.00 0.00 P
+ATOM 274 OP1 DG C 2 27.280 20.790 15.190 1.00 0.00 O
+ATOM 275 OP2 DG C 2 28.090 21.620 17.460 1.00 0.00 O
+ATOM 276 O5' DG C 2 25.790 20.710 17.270 1.00 0.00 O
+ATOM 277 C5' DG C 2 24.620 20.460 16.470 1.00 0.00 C
+ATOM 278 C4' DG C 2 23.430 20.150 17.350 1.00 0.00 C
+ATOM 279 O4' DG C 2 23.710 19.020 18.190 1.00 0.00 O
+ATOM 280 C3' DG C 2 23.030 21.290 18.270 1.00 0.00 C
+ATOM 281 O3' DG C 2 21.910 22.040 17.730 1.00 0.00 O
+ATOM 282 C2' DG C 2 22.750 20.580 19.590 1.00 0.00 C
+ATOM 283 C1' DG C 2 22.710 19.110 19.200 1.00 0.00 C
+ATOM 284 N9 DG C 2 23.020 18.110 20.240 1.00 0.00 N
+ATOM 285 C8 DG C 2 24.070 18.090 21.060 1.00 0.00 C
+ATOM 286 N7 DG C 2 23.910 17.110 21.960 1.00 0.00 N
+ATOM 287 C5 DG C 2 22.740 16.520 21.720 1.00 0.00 C
+ATOM 288 C6 DG C 2 21.950 15.580 22.400 1.00 0.00 C
+ATOM 289 O6 DG C 2 22.400 14.840 23.280 1.00 0.00 O
+ATOM 290 N1 DG C 2 20.640 15.370 21.980 1.00 0.00 N
+ATOM 291 C2 DG C 2 20.150 16.050 20.880 1.00 0.00 C
+ATOM 292 N2 DG C 2 18.870 16.050 20.650 1.00 0.00 N
+ATOM 293 N3 DG C 2 20.930 16.890 20.190 1.00 0.00 N
+ATOM 294 C4 DG C 2 22.180 17.150 20.610 1.00 0.00 C
+ATOM 295 H1 DG C 2 20.030 14.750 22.480 1.00 0.00 H
+ATOM 296 H21 DG C 2 18.260 15.460 21.180 1.00 0.00 H
+ATOM 297 H22 DG C 2 18.500 16.610 19.890 1.00 0.00 H
+ATOM 298 P DC C 3 21.270 23.340 18.420 1.00 0.00 P
+ATOM 299 OP1 DC C 3 20.460 24.060 17.410 1.00 0.00 O
+ATOM 300 OP2 DC C 3 22.300 24.060 19.200 1.00 0.00 O
+ATOM 301 O5' DC C 3 20.280 22.650 19.480 1.00 0.00 O
+ATOM 302 C5' DC C 3 19.160 21.860 19.040 1.00 0.00 C
+ATOM 303 C4' DC C 3 18.510 21.060 20.160 1.00 0.00 C
+ATOM 304 O4' DC C 3 19.370 20.130 20.820 1.00 0.00 O
+ATOM 305 C3' DC C 3 17.900 21.930 21.240 1.00 0.00 C
+ATOM 306 O3' DC C 3 16.480 21.840 21.140 1.00 0.00 O
+ATOM 307 C2' DC C 3 18.450 21.340 22.530 1.00 0.00 C
+ATOM 308 C1' DC C 3 18.740 19.910 22.090 1.00 0.00 C
+ATOM 309 N1 DC C 3 19.640 19.180 22.980 1.00 0.00 N
+ATOM 310 C2 DC C 3 19.130 18.240 23.900 1.00 0.00 C
+ATOM 311 O2 DC C 3 17.970 17.870 23.890 1.00 0.00 O
+ATOM 312 N3 DC C 3 19.930 17.770 24.880 1.00 0.00 N
+ATOM 313 C4 DC C 3 21.210 18.160 24.970 1.00 0.00 C
+ATOM 314 N4 DC C 3 21.930 17.380 25.750 1.00 0.00 N
+ATOM 315 C5 DC C 3 21.780 19.090 24.100 1.00 0.00 C
+ATOM 316 C6 DC C 3 20.980 19.610 23.080 1.00 0.00 C
+ATOM 317 H41 DC C 3 21.560 16.540 26.160 1.00 0.00 H
+ATOM 318 H42 DC C 3 22.890 17.590 25.930 1.00 0.00 H
+ATOM 319 P DT C 4 15.430 22.610 22.080 1.00 0.00 P
+ATOM 320 OP1 DT C 4 14.400 23.180 21.180 1.00 0.00 O
+ATOM 321 OP2 DT C 4 16.090 23.500 23.070 1.00 0.00 O
+ATOM 322 O5' DT C 4 14.770 21.410 22.920 1.00 0.00 O
+ATOM 323 C5' DT C 4 13.940 20.410 22.290 1.00 0.00 C
+ATOM 324 C4' DT C 4 13.570 19.260 23.210 1.00 0.00 C
+ATOM 325 O4' DT C 4 14.720 18.590 23.800 1.00 0.00 O
+ATOM 326 C3' DT C 4 12.700 19.740 24.360 1.00 0.00 C
+ATOM 327 O3' DT C 4 11.480 18.980 24.310 1.00 0.00 O
+ATOM 328 C2' DT C 4 13.570 19.670 25.580 1.00 0.00 C
+ATOM 329 C1' DT C 4 14.540 18.550 25.240 1.00 0.00 C
+ATOM 330 N1 DT C 4 15.850 18.750 25.910 1.00 0.00 N
+ATOM 331 C2 DT C 4 16.380 17.750 26.740 1.00 0.00 C
+ATOM 332 O2 DT C 4 15.650 16.890 27.240 1.00 0.00 O
+ATOM 333 N3 DT C 4 17.610 17.960 27.350 1.00 0.00 N
+ATOM 334 C4 DT C 4 18.290 19.170 27.200 1.00 0.00 C
+ATOM 335 O4 DT C 4 19.400 19.240 27.720 1.00 0.00 O
+ATOM 336 C5 DT C 4 17.760 20.200 26.400 1.00 0.00 C
+ATOM 337 C7 DT C 4 18.490 21.530 26.190 1.00 0.00 C
+ATOM 338 C6 DT C 4 16.520 19.970 25.770 1.00 0.00 C
+ATOM 339 H3 DT C 4 18.100 17.170 27.730 1.00 0.00 H
+ATOM 340 P DC C 5 10.100 19.520 24.960 1.00 0.00 P
+ATOM 341 OP1 DC C 5 9.010 18.860 24.200 1.00 0.00 O
+ATOM 342 OP2 DC C 5 10.120 21.000 25.060 1.00 0.00 O
+ATOM 343 O5' DC C 5 10.250 18.890 26.430 1.00 0.00 O
+ATOM 344 C5' DC C 5 10.100 17.490 26.680 1.00 0.00 C
+ATOM 345 C4' DC C 5 10.860 17.030 27.920 1.00 0.00 C
+ATOM 346 O4' DC C 5 12.230 17.460 27.940 1.00 0.00 O
+ATOM 347 C3' DC C 5 10.270 17.510 29.240 1.00 0.00 C
+ATOM 348 O3' DC C 5 9.620 16.380 29.860 1.00 0.00 O
+ATOM 349 C2' DC C 5 11.430 17.860 30.110 1.00 0.00 C
+ATOM 350 C1' DC C 5 12.630 17.360 29.320 1.00 0.00 C
+ATOM 351 N1 DC C 5 13.760 18.270 29.580 1.00 0.00 N
+ATOM 352 C2 DC C 5 14.900 17.850 30.290 1.00 0.00 C
+ATOM 353 O2 DC C 5 15.010 16.710 30.750 1.00 0.00 O
+ATOM 354 N3 DC C 5 15.920 18.710 30.460 1.00 0.00 N
+ATOM 355 C4 DC C 5 15.870 19.950 29.950 1.00 0.00 C
+ATOM 356 N4 DC C 5 17.050 20.470 29.770 1.00 0.00 N
+ATOM 357 C5 DC C 5 14.760 20.470 29.270 1.00 0.00 C
+ATOM 358 C6 DC C 5 13.700 19.580 29.070 1.00 0.00 C
+ATOM 359 H41 DC C 5 17.850 19.950 30.110 1.00 0.00 H
+ATOM 360 H42 DC C 5 17.170 21.390 29.370 1.00 0.00 H
+ATOM 361 P DA C 6 8.520 16.510 31.020 1.00 0.00 P
+ATOM 362 OP1 DA C 6 7.410 15.610 30.650 1.00 0.00 O
+ATOM 363 OP2 DA C 6 8.150 17.920 31.270 1.00 0.00 O
+ATOM 364 O5' DA C 6 9.220 16.040 32.380 1.00 0.00 O
+ATOM 365 C5' DA C 6 9.910 14.790 32.480 1.00 0.00 C
+ATOM 366 C4' DA C 6 11.110 14.960 33.390 1.00 0.00 C
+ATOM 367 O4' DA C 6 12.020 15.980 32.930 1.00 0.00 O
+ATOM 368 C3' DA C 6 10.670 15.340 34.790 1.00 0.00 C
+ATOM 369 O3' DA C 6 10.710 14.140 35.610 1.00 0.00 O
+ATOM 370 C2' DA C 6 11.560 16.490 35.170 1.00 0.00 C
+ATOM 371 C1' DA C 6 12.660 16.480 34.130 1.00 0.00 C
+ATOM 372 N9 DA C 6 13.200 17.830 33.880 1.00 0.00 N
+ATOM 373 C8 DA C 6 12.530 18.880 33.440 1.00 0.00 C
+ATOM 374 N7 DA C 6 13.350 19.920 33.240 1.00 0.00 N
+ATOM 375 C5 DA C 6 14.560 19.510 33.600 1.00 0.00 C
+ATOM 376 C6 DA C 6 15.820 20.100 33.720 1.00 0.00 C
+ATOM 377 N6 DA C 6 16.080 21.240 33.080 1.00 0.00 N
+ATOM 378 N1 DA C 6 16.830 19.450 34.340 1.00 0.00 N
+ATOM 379 C2 DA C 6 16.670 18.210 34.820 1.00 0.00 C
+ATOM 380 N3 DA C 6 15.500 17.580 34.660 1.00 0.00 N
+ATOM 381 C4 DA C 6 14.460 18.190 34.050 1.00 0.00 C
+ATOM 382 H61 DA C 6 16.980 21.660 33.120 1.00 0.00 H
+ATOM 383 H62 DA C 6 15.330 21.730 32.630 1.00 0.00 H
+ATOM 384 P DC C 7 10.390 14.130 37.180 1.00 0.00 P
+ATOM 385 OP1 DC C 7 10.450 12.740 37.700 1.00 0.00 O
+ATOM 386 OP2 DC C 7 9.190 14.940 37.450 1.00 0.00 O
+ATOM 387 O5' DC C 7 11.670 14.870 37.800 1.00 0.00 O
+ATOM 388 C5' DC C 7 12.960 14.210 37.790 1.00 0.00 C
+ATOM 389 C4' DC C 7 14.060 15.070 38.400 1.00 0.00 C
+ATOM 390 O4' DC C 7 14.390 16.260 37.670 1.00 0.00 O
+ATOM 391 C3' DC C 7 13.730 15.520 39.810 1.00 0.00 C
+ATOM 392 O3' DC C 7 14.840 15.130 40.660 1.00 0.00 O
+ATOM 393 C2' DC C 7 13.570 17.020 39.690 1.00 0.00 C
+ATOM 394 C1' DC C 7 14.660 17.280 38.670 1.00 0.00 C
+ATOM 395 N1 DC C 7 14.610 18.600 38.030 1.00 0.00 N
+ATOM 396 C2 DC C 7 15.760 19.430 37.940 1.00 0.00 C
+ATOM 397 O2 DC C 7 16.800 19.200 38.540 1.00 0.00 O
+ATOM 398 N3 DC C 7 15.670 20.600 37.300 1.00 0.00 N
+ATOM 399 C4 DC C 7 14.510 21.010 36.760 1.00 0.00 C
+ATOM 400 N4 DC C 7 14.520 22.250 36.360 1.00 0.00 N
+ATOM 401 C5 DC C 7 13.330 20.270 36.810 1.00 0.00 C
+ATOM 402 C6 DC C 7 13.390 19.040 37.470 1.00 0.00 C
+ATOM 403 H41 DC C 7 15.290 22.840 36.570 1.00 0.00 H
+ATOM 404 H42 DC C 7 13.690 22.640 35.900 1.00 0.00 H
+ATOM 405 P DA C 8 14.940 15.420 42.240 1.00 0.00 P
+ATOM 406 OP1 DA C 8 15.710 14.320 42.860 1.00 0.00 O
+ATOM 407 OP2 DA C 8 13.650 15.780 42.860 1.00 0.00 O
+ATOM 408 O5' DA C 8 15.800 16.760 42.230 1.00 0.00 O
+ATOM 409 C5' DA C 8 17.180 16.700 41.850 1.00 0.00 C
+ATOM 410 C4' DA C 8 17.930 17.940 42.290 1.00 0.00 C
+ATOM 411 O4' DA C 8 17.620 18.990 41.360 1.00 0.00 O
+ATOM 412 C3' DA C 8 17.500 18.410 43.680 1.00 0.00 C
+ATOM 413 O3' DA C 8 18.590 18.780 44.570 1.00 0.00 O
+ATOM 414 C2' DA C 8 16.730 19.660 43.340 1.00 0.00 C
+ATOM 415 C1' DA C 8 17.530 20.180 42.160 1.00 0.00 C
+ATOM 416 N9 DA C 8 16.800 21.080 41.240 1.00 0.00 N
+ATOM 417 C8 DA C 8 15.520 20.960 40.940 1.00 0.00 C
+ATOM 418 N7 DA C 8 15.150 21.910 40.080 1.00 0.00 N
+ATOM 419 C5 DA C 8 16.240 22.640 39.830 1.00 0.00 C
+ATOM 420 C6 DA C 8 16.530 23.820 39.130 1.00 0.00 C
+ATOM 421 N6 DA C 8 15.590 24.660 38.750 1.00 0.00 N
+ATOM 422 N1 DA C 8 17.760 24.350 39.160 1.00 0.00 N
+ATOM 423 C2 DA C 8 18.750 23.820 39.870 1.00 0.00 C
+ATOM 424 N3 DA C 8 18.510 22.700 40.560 1.00 0.00 N
+ATOM 425 C4 DA C 8 17.290 22.100 40.560 1.00 0.00 C
+ATOM 426 H61 DA C 8 15.810 25.500 38.240 1.00 0.00 H
+ATOM 427 H62 DA C 8 14.630 24.500 39.030 1.00 0.00 H
+ATOM 428 P DA C 9 18.980 18.030 45.940 1.00 0.00 P
+ATOM 429 OP1 DA C 9 19.160 16.580 45.690 1.00 0.00 O
+ATOM 430 OP2 DA C 9 18.040 18.390 47.030 1.00 0.00 O
+ATOM 431 O5' DA C 9 20.370 18.690 46.380 1.00 0.00 O
+ATOM 432 C5' DA C 9 21.570 18.480 45.610 1.00 0.00 C
+ATOM 433 C4' DA C 9 22.280 19.750 45.150 1.00 0.00 C
+ATOM 434 O4' DA C 9 21.370 20.590 44.390 1.00 0.00 O
+ATOM 435 C3' DA C 9 22.840 20.620 46.280 1.00 0.00 C
+ATOM 436 O3' DA C 9 24.220 20.950 45.980 1.00 0.00 O
+ATOM 437 C2' DA C 9 21.890 21.770 46.330 1.00 0.00 C
+ATOM 438 C1' DA C 9 21.500 21.960 44.870 1.00 0.00 C
+ATOM 439 N9 DA C 9 20.210 22.660 44.800 1.00 0.00 N
+ATOM 440 C8 DA C 9 19.080 22.180 45.320 1.00 0.00 C
+ATOM 441 N7 DA C 9 18.040 22.870 44.860 1.00 0.00 N
+ATOM 442 C5 DA C 9 18.520 23.820 44.070 1.00 0.00 C
+ATOM 443 C6 DA C 9 17.970 24.910 43.380 1.00 0.00 C
+ATOM 444 N6 DA C 9 16.670 24.960 43.240 1.00 0.00 N
+ATOM 445 N1 DA C 9 18.750 25.810 42.750 1.00 0.00 N
+ATOM 446 C2 DA C 9 20.090 25.670 42.750 1.00 0.00 C
+ATOM 447 N3 DA C 9 20.650 24.640 43.380 1.00 0.00 N
+ATOM 448 C4 DA C 9 19.910 23.710 44.040 1.00 0.00 C
+ATOM 449 H61 DA C 9 16.200 25.730 42.790 1.00 0.00 H
+ATOM 450 H62 DA C 9 16.150 24.150 43.570 1.00 0.00 H
+ATOM 451 P DT C 10 25.190 21.850 46.910 1.00 0.00 P
+ATOM 452 OP1 DT C 10 26.550 21.700 46.350 1.00 0.00 O
+ATOM 453 OP2 DT C 10 25.010 21.450 48.320 1.00 0.00 O
+ATOM 454 O5' DT C 10 24.680 23.350 46.620 1.00 0.00 O
+ATOM 455 C5' DT C 10 25.190 24.110 45.510 1.00 0.00 C
+ATOM 456 C4' DT C 10 24.580 25.490 45.370 1.00 0.00 C
+ATOM 457 O4' DT C 10 23.150 25.400 45.280 1.00 0.00 O
+ATOM 458 C3' DT C 10 24.870 26.450 46.520 1.00 0.00 C
+ATOM 459 O3' DT C 10 25.520 27.610 45.970 1.00 0.00 O
+ATOM 460 C2' DT C 10 23.530 26.880 47.090 1.00 0.00 C
+ATOM 461 C1' DT C 10 22.700 26.620 45.860 1.00 0.00 C
+ATOM 462 N1 DT C 10 21.270 26.460 46.210 1.00 0.00 N
+ATOM 463 C2 DT C 10 20.320 27.320 45.650 1.00 0.00 C
+ATOM 464 O2 DT C 10 20.640 28.400 45.160 1.00 0.00 O
+ATOM 465 N3 DT C 10 18.970 27.060 45.840 1.00 0.00 N
+ATOM 466 C4 DT C 10 18.560 25.980 46.630 1.00 0.00 C
+ATOM 467 O4 DT C 10 17.350 25.790 46.710 1.00 0.00 O
+ATOM 468 C5 DT C 10 19.510 25.150 47.260 1.00 0.00 C
+ATOM 469 C7 DT C 10 19.150 24.250 48.440 1.00 0.00 C
+ATOM 470 C6 DT C 10 20.860 25.370 47.010 1.00 0.00 C
+ATOM 471 H3 DT C 10 18.290 27.680 45.470 1.00 0.00 H
+ATOM 472 P DT C 11 26.140 28.760 46.900 1.00 0.00 P
+ATOM 473 OP1 DT C 11 27.220 29.420 46.130 1.00 0.00 O
+ATOM 474 OP2 DT C 11 26.450 28.240 48.250 1.00 0.00 O
+ATOM 475 O5' DT C 11 24.880 29.730 47.090 1.00 0.00 O
+ATOM 476 C5' DT C 11 24.470 30.660 46.070 1.00 0.00 C
+ATOM 477 C4' DT C 11 23.280 31.490 46.500 1.00 0.00 C
+ATOM 478 O4' DT C 11 22.080 30.720 46.640 1.00 0.00 O
+ATOM 479 C3' DT C 11 23.510 32.230 47.810 1.00 0.00 C
+ATOM 480 O3' DT C 11 24.130 33.520 47.640 1.00 0.00 O
+ATOM 481 C2' DT C 11 22.110 32.380 48.390 1.00 0.00 C
+ATOM 482 C1' DT C 11 21.240 31.500 47.500 1.00 0.00 C
+ATOM 483 N1 DT C 11 20.470 30.500 48.280 1.00 0.00 N
+ATOM 484 C2 DT C 11 19.070 30.510 48.180 1.00 0.00 C
+ATOM 485 O2 DT C 11 18.400 31.470 47.780 1.00 0.00 O
+ATOM 486 N3 DT C 11 18.360 29.420 48.660 1.00 0.00 N
+ATOM 487 C4 DT C 11 19.000 28.350 49.280 1.00 0.00 C
+ATOM 488 O4 DT C 11 18.300 27.370 49.530 1.00 0.00 O
+ATOM 489 C5 DT C 11 20.390 28.330 49.420 1.00 0.00 C
+ATOM 490 C7 DT C 11 21.140 27.220 50.160 1.00 0.00 C
+ATOM 491 C6 DT C 11 21.110 29.400 48.900 1.00 0.00 C
+ATOM 492 HO3' DT C 11 25.060 33.370 47.220 1.00 0.00 H
+ATOM 493 H3 DT C 11 17.360 29.430 48.580 1.00 0.00 H
+TER 494 DT C 11
+ATOM 495 N MET A 1 34.170 31.500 11.380 1.00 0.00 N
+ATOM 496 CA MET A 1 33.550 30.380 10.640 1.00 0.00 C
+ATOM 497 C MET A 1 33.130 29.230 11.590 1.00 0.00 C
+ATOM 498 O MET A 1 33.730 28.160 11.640 1.00 0.00 O
+ATOM 499 CB MET A 1 34.600 29.880 9.650 1.00 0.00 C
+ATOM 500 CG MET A 1 34.840 30.790 8.450 1.00 0.00 C
+ATOM 501 SD MET A 1 33.500 30.720 7.210 1.00 0.00 S
+ATOM 502 CE MET A 1 33.700 29.030 6.670 1.00 0.00 C
+ATOM 503 H1 MET A 1 34.930 31.130 11.920 1.00 0.00 H
+ATOM 504 H2 MET A 1 34.510 32.180 10.730 1.00 0.00 H
+ATOM 505 H3 MET A 1 33.520 31.940 12.010 1.00 0.00 H
+ATOM 506 N LYS A 2 32.100 29.510 12.380 1.00 0.00 N
+ATOM 507 CA LYS A 2 31.700 28.620 13.480 1.00 0.00 C
+ATOM 508 C LYS A 2 30.170 28.440 13.530 1.00 0.00 C
+ATOM 509 O LYS A 2 29.450 29.430 13.670 1.00 0.00 O
+ATOM 510 CB LYS A 2 32.260 29.170 14.800 1.00 0.00 C
+ATOM 511 CG LYS A 2 33.170 28.150 15.500 1.00 0.00 C
+ATOM 512 CD LYS A 2 33.810 28.700 16.790 1.00 0.00 C
+ATOM 513 CE LYS A 2 32.830 28.960 17.940 1.00 0.00 C
+ATOM 514 NZ LYS A 2 33.470 29.840 18.940 1.00 0.00 N
+ATOM 515 H LYS A 2 31.490 30.280 12.190 1.00 0.00 H
+ATOM 516 HZ1 LYS A 2 34.390 29.480 19.180 1.00 0.00 H
+ATOM 517 HZ2 LYS A 2 33.590 30.760 18.560 1.00 0.00 H
+ATOM 518 HZ3 LYS A 2 32.950 29.910 19.800 1.00 0.00 H
+ATOM 519 N PRO A 3 29.680 27.230 13.210 1.00 0.00 N
+ATOM 520 CA PRO A 3 28.260 26.870 13.400 1.00 0.00 C
+ATOM 521 C PRO A 3 27.770 27.260 14.800 1.00 0.00 C
+ATOM 522 O PRO A 3 28.400 26.930 15.810 1.00 0.00 O
+ATOM 523 CB PRO A 3 28.220 25.370 13.150 1.00 0.00 C
+ATOM 524 CG PRO A 3 29.320 25.130 12.130 1.00 0.00 C
+ATOM 525 CD PRO A 3 30.380 26.180 12.450 1.00 0.00 C
+ATOM 526 N VAL A 4 26.860 28.230 14.770 1.00 0.00 N
+ATOM 527 CA VAL A 4 26.260 28.810 15.990 1.00 0.00 C
+ATOM 528 C VAL A 4 25.230 27.860 16.620 1.00 0.00 C
+ATOM 529 O VAL A 4 24.160 27.610 16.070 1.00 0.00 O
+ATOM 530 CB VAL A 4 25.690 30.230 15.750 1.00 0.00 C
+ATOM 531 CG1 VAL A 4 25.380 30.890 17.100 1.00 0.00 C
+ATOM 532 CG2 VAL A 4 26.670 31.160 15.030 1.00 0.00 C
+ATOM 533 H VAL A 4 26.690 28.720 13.930 1.00 0.00 H
+ATOM 534 N THR A 5 25.730 27.220 17.680 1.00 0.00 N
+ATOM 535 CA THR A 5 24.990 26.290 18.580 1.00 0.00 C
+ATOM 536 C THR A 5 24.260 27.050 19.700 1.00 0.00 C
+ATOM 537 O THR A 5 24.670 28.170 20.030 1.00 0.00 O
+ATOM 538 CB THR A 5 25.930 25.300 19.300 1.00 0.00 C
+ATOM 539 OG1 THR A 5 26.930 25.980 20.060 1.00 0.00 O
+ATOM 540 CG2 THR A 5 26.530 24.250 18.360 1.00 0.00 C
+ATOM 541 H THR A 5 26.690 27.390 17.930 1.00 0.00 H
+ATOM 542 HG1 THR A 5 27.440 26.640 19.440 1.00 0.00 H
+ATOM 543 N LEU A 6 23.390 26.360 20.450 1.00 0.00 N
+ATOM 544 CA LEU A 6 22.740 26.930 21.650 1.00 0.00 C
+ATOM 545 C LEU A 6 23.770 27.430 22.680 1.00 0.00 C
+ATOM 546 O LEU A 6 23.640 28.530 23.220 1.00 0.00 O
+ATOM 547 CB LEU A 6 21.780 25.930 22.310 1.00 0.00 C
+ATOM 548 CG LEU A 6 20.400 25.890 21.640 1.00 0.00 C
+ATOM 549 CD1 LEU A 6 19.520 24.900 22.380 1.00 0.00 C
+ATOM 550 CD2 LEU A 6 19.700 27.250 21.660 1.00 0.00 C
+ATOM 551 H LEU A 6 23.120 25.410 20.250 1.00 0.00 H
+ATOM 552 N TYR A 7 24.830 26.650 22.840 1.00 0.00 N
+ATOM 553 CA TYR A 7 26.040 27.020 23.600 1.00 0.00 C
+ATOM 554 C TYR A 7 26.670 28.370 23.220 1.00 0.00 C
+ATOM 555 O TYR A 7 26.960 29.160 24.110 1.00 0.00 O
+ATOM 556 CB TYR A 7 27.150 25.990 23.420 1.00 0.00 C
+ATOM 557 CG TYR A 7 26.810 24.620 23.980 1.00 0.00 C
+ATOM 558 CD1 TYR A 7 26.860 24.460 25.380 1.00 0.00 C
+ATOM 559 CD2 TYR A 7 26.780 23.530 23.090 1.00 0.00 C
+ATOM 560 CE1 TYR A 7 26.930 23.160 25.910 1.00 0.00 C
+ATOM 561 CE2 TYR A 7 26.840 22.230 23.620 1.00 0.00 C
+ATOM 562 CZ TYR A 7 26.930 22.070 25.010 1.00 0.00 C
+ATOM 563 OH TYR A 7 27.110 20.820 25.510 1.00 0.00 O
+ATOM 564 H TYR A 7 24.770 25.680 22.580 1.00 0.00 H
+ATOM 565 HH TYR A 7 26.980 20.190 24.730 1.00 0.00 H
+ATOM 566 N ASP A 8 26.910 28.580 21.930 1.00 0.00 N
+ATOM 567 CA ASP A 8 27.590 29.780 21.400 1.00 0.00 C
+ATOM 568 C ASP A 8 26.820 31.050 21.790 1.00 0.00 C
+ATOM 569 O ASP A 8 27.310 31.820 22.620 1.00 0.00 O
+ATOM 570 CB ASP A 8 27.800 29.640 19.880 1.00 0.00 C
+ATOM 571 CG ASP A 8 28.730 28.480 19.480 1.00 0.00 C
+ATOM 572 OD1 ASP A 8 29.860 28.390 19.990 1.00 0.00 O
+ATOM 573 OD2 ASP A 8 28.330 27.680 18.620 1.00 0.00 O
+ATOM 574 H ASP A 8 26.610 27.930 21.230 1.00 0.00 H
+ATOM 575 N VAL A 9 25.530 31.060 21.480 1.00 0.00 N
+ATOM 576 CA VAL A 9 24.590 32.150 21.880 1.00 0.00 C
+ATOM 577 C VAL A 9 24.400 32.310 23.400 1.00 0.00 C
+ATOM 578 O VAL A 9 24.280 33.420 23.910 1.00 0.00 O
+ATOM 579 CB VAL A 9 23.230 32.110 21.150 1.00 0.00 C
+ATOM 580 CG1 VAL A 9 23.330 32.920 19.870 1.00 0.00 C
+ATOM 581 CG2 VAL A 9 22.640 30.710 20.900 1.00 0.00 C
+ATOM 582 H VAL A 9 25.110 30.300 20.980 1.00 0.00 H
+ATOM 583 N ALA A 10 24.390 31.170 24.090 1.00 0.00 N
+ATOM 584 CA ALA A 10 24.450 31.120 25.560 1.00 0.00 C
+ATOM 585 C ALA A 10 25.710 31.810 26.130 1.00 0.00 C
+ATOM 586 O ALA A 10 25.610 32.890 26.720 1.00 0.00 O
+ATOM 587 CB ALA A 10 24.360 29.650 25.970 1.00 0.00 C
+ATOM 588 H ALA A 10 24.210 30.280 23.650 1.00 0.00 H
+ATOM 589 N GLU A 11 26.890 31.250 25.850 1.00 0.00 N
+ATOM 590 CA GLU A 11 28.230 31.820 26.120 1.00 0.00 C
+ATOM 591 C GLU A 11 28.300 33.360 25.970 1.00 0.00 C
+ATOM 592 O GLU A 11 28.470 34.040 26.980 1.00 0.00 O
+ATOM 593 CB GLU A 11 29.260 31.180 25.180 1.00 0.00 C
+ATOM 594 CG GLU A 11 30.120 30.090 25.820 1.00 0.00 C
+ATOM 595 CD GLU A 11 31.300 29.720 24.910 1.00 0.00 C
+ATOM 596 OE1 GLU A 11 31.070 29.060 23.880 1.00 0.00 O
+ATOM 597 OE2 GLU A 11 32.440 30.130 25.220 1.00 0.00 O
+ATOM 598 H GLU A 11 26.940 30.320 25.450 1.00 0.00 H
+ATOM 599 N TYR A 12 27.950 33.850 24.780 1.00 0.00 N
+ATOM 600 CA TYR A 12 28.020 35.270 24.390 1.00 0.00 C
+ATOM 601 C TYR A 12 27.170 36.210 25.230 1.00 0.00 C
+ATOM 602 O TYR A 12 27.600 37.300 25.610 1.00 0.00 O
+ATOM 603 CB TYR A 12 27.560 35.390 22.940 1.00 0.00 C
+ATOM 604 CG TYR A 12 27.920 36.750 22.360 1.00 0.00 C
+ATOM 605 CD1 TYR A 12 29.250 36.890 21.940 1.00 0.00 C
+ATOM 606 CD2 TYR A 12 26.920 37.660 21.950 1.00 0.00 C
+ATOM 607 CE1 TYR A 12 29.600 37.880 21.020 1.00 0.00 C
+ATOM 608 CE2 TYR A 12 27.260 38.670 21.030 1.00 0.00 C
+ATOM 609 CZ TYR A 12 28.590 38.750 20.570 1.00 0.00 C
+ATOM 610 OH TYR A 12 28.950 39.760 19.750 1.00 0.00 O
+ATOM 611 H TYR A 12 27.670 33.250 24.030 1.00 0.00 H
+ATOM 612 HH TYR A 12 30.010 39.790 19.700 1.00 0.00 H
+ATOM 613 N ALA A 13 25.920 35.810 25.400 1.00 0.00 N
+ATOM 614 CA ALA A 13 24.980 36.610 26.210 1.00 0.00 C
+ATOM 615 C ALA A 13 25.210 36.430 27.720 1.00 0.00 C
+ATOM 616 O ALA A 13 24.610 37.140 28.540 1.00 0.00 O
+ATOM 617 CB ALA A 13 23.550 36.280 25.820 1.00 0.00 C
+ATOM 618 H ALA A 13 25.590 34.940 25.040 1.00 0.00 H
+ATOM 619 N GLY A 14 26.110 35.510 28.060 1.00 0.00 N
+ATOM 620 CA GLY A 14 26.550 35.210 29.430 1.00 0.00 C
+ATOM 621 C GLY A 14 25.400 34.630 30.260 1.00 0.00 C
+ATOM 622 O GLY A 14 25.090 35.130 31.340 1.00 0.00 O
+ATOM 623 H GLY A 14 26.460 34.880 27.360 1.00 0.00 H
+ATOM 624 N VAL A 15 24.770 33.630 29.640 1.00 0.00 N
+ATOM 625 CA VAL A 15 23.490 33.010 30.070 1.00 0.00 C
+ATOM 626 C VAL A 15 23.530 31.510 29.720 1.00 0.00 C
+ATOM 627 O VAL A 15 24.340 31.050 28.910 1.00 0.00 O
+ATOM 628 CB VAL A 15 22.260 33.760 29.560 1.00 0.00 C
+ATOM 629 CG1 VAL A 15 22.030 35.050 30.350 1.00 0.00 C
+ATOM 630 CG2 VAL A 15 22.280 34.000 28.060 1.00 0.00 C
+ATOM 631 H VAL A 15 25.180 33.200 28.830 1.00 0.00 H
+ATOM 632 N SER A 16 22.760 30.760 30.490 1.00 0.00 N
+ATOM 633 CA SER A 16 22.730 29.300 30.460 1.00 0.00 C
+ATOM 634 C SER A 16 22.080 28.750 29.200 1.00 0.00 C
+ATOM 635 O SER A 16 21.190 29.340 28.600 1.00 0.00 O
+ATOM 636 CB SER A 16 21.960 28.770 31.650 1.00 0.00 C
+ATOM 637 OG SER A 16 21.790 27.350 31.580 1.00 0.00 O
+ATOM 638 H SER A 16 22.100 31.180 31.120 1.00 0.00 H
+ATOM 639 HG SER A 16 22.340 26.920 32.360 1.00 0.00 H
+ATOM 640 N TYR A 17 22.750 27.700 28.750 1.00 0.00 N
+ATOM 641 CA TYR A 17 22.240 26.640 27.870 1.00 0.00 C
+ATOM 642 C TYR A 17 20.720 26.480 27.900 1.00 0.00 C
+ATOM 643 O TYR A 17 20.080 26.610 26.850 1.00 0.00 O
+ATOM 644 CB TYR A 17 22.960 25.390 28.370 1.00 0.00 C
+ATOM 645 CG TYR A 17 22.900 24.220 27.400 1.00 0.00 C
+ATOM 646 CD1 TYR A 17 23.100 24.430 26.020 1.00 0.00 C
+ATOM 647 CD2 TYR A 17 22.890 22.940 27.980 1.00 0.00 C
+ATOM 648 CE1 TYR A 17 23.340 23.320 25.190 1.00 0.00 C
+ATOM 649 CE2 TYR A 17 23.140 21.830 27.170 1.00 0.00 C
+ATOM 650 CZ TYR A 17 23.380 22.040 25.790 1.00 0.00 C
+ATOM 651 OH TYR A 17 23.940 21.010 25.110 1.00 0.00 O
+ATOM 652 H TYR A 17 23.750 27.690 28.870 1.00 0.00 H
+ATOM 653 HH TYR A 17 24.280 20.320 25.810 1.00 0.00 H
+ATOM 654 N GLN A 18 20.190 26.370 29.120 1.00 0.00 N
+ATOM 655 CA GLN A 18 18.760 26.290 29.480 1.00 0.00 C
+ATOM 656 C GLN A 18 17.950 27.600 29.390 1.00 0.00 C
+ATOM 657 O GLN A 18 16.940 27.590 28.700 1.00 0.00 O
+ATOM 658 CB GLN A 18 18.740 25.660 30.870 1.00 0.00 C
+ATOM 659 CG GLN A 18 18.020 24.300 30.910 1.00 0.00 C
+ATOM 660 CD GLN A 18 18.360 23.320 29.780 1.00 0.00 C
+ATOM 661 OE1 GLN A 18 17.640 23.250 28.790 1.00 0.00 O
+ATOM 662 NE2 GLN A 18 19.180 22.320 30.020 1.00 0.00 N
+ATOM 663 H GLN A 18 20.790 26.220 29.900 1.00 0.00 H
+ATOM 664 HE21 GLN A 18 19.470 22.110 30.960 1.00 0.00 H
+ATOM 665 HE22 GLN A 18 19.440 21.710 29.280 1.00 0.00 H
+ATOM 666 N THR A 19 18.500 28.720 29.860 1.00 0.00 N
+ATOM 667 CA THR A 19 17.900 30.070 29.700 1.00 0.00 C
+ATOM 668 C THR A 19 17.700 30.380 28.200 1.00 0.00 C
+ATOM 669 O THR A 19 16.590 30.710 27.800 1.00 0.00 O
+ATOM 670 CB THR A 19 18.800 31.150 30.340 1.00 0.00 C
+ATOM 671 OG1 THR A 19 19.260 30.630 31.590 1.00 0.00 O
+ATOM 672 CG2 THR A 19 18.070 32.470 30.580 1.00 0.00 C
+ATOM 673 H THR A 19 19.350 28.740 30.390 1.00 0.00 H
+ATOM 674 HG1 THR A 19 18.570 30.840 32.320 1.00 0.00 H
+ATOM 675 N VAL A 20 18.660 29.880 27.410 1.00 0.00 N
+ATOM 676 CA VAL A 20 18.710 30.020 25.940 1.00 0.00 C
+ATOM 677 C VAL A 20 17.840 28.950 25.230 1.00 0.00 C
+ATOM 678 O VAL A 20 16.990 29.300 24.430 1.00 0.00 O
+ATOM 679 CB VAL A 20 20.180 30.140 25.510 1.00 0.00 C
+ATOM 680 CG1 VAL A 20 20.450 30.090 24.000 1.00 0.00 C
+ATOM 681 CG2 VAL A 20 20.700 31.490 26.000 1.00 0.00 C
+ATOM 682 H VAL A 20 19.410 29.340 27.820 1.00 0.00 H
+ATOM 683 N SER A 21 17.910 27.690 25.670 1.00 0.00 N
+ATOM 684 CA SER A 21 16.900 26.650 25.330 1.00 0.00 C
+ATOM 685 C SER A 21 15.430 27.040 25.610 1.00 0.00 C
+ATOM 686 O SER A 21 14.580 26.780 24.770 1.00 0.00 O
+ATOM 687 CB SER A 21 17.260 25.350 26.060 1.00 0.00 C
+ATOM 688 OG SER A 21 16.270 24.350 25.790 1.00 0.00 O
+ATOM 689 H SER A 21 18.760 27.310 26.060 1.00 0.00 H
+ATOM 690 HG SER A 21 16.100 24.340 24.780 1.00 0.00 H
+ATOM 691 N ARG A 22 15.140 27.790 26.670 1.00 0.00 N
+ATOM 692 CA ARG A 22 13.760 28.280 26.950 1.00 0.00 C
+ATOM 693 C ARG A 22 13.280 29.270 25.890 1.00 0.00 C
+ATOM 694 O ARG A 22 12.190 29.150 25.320 1.00 0.00 O
+ATOM 695 CB ARG A 22 13.710 28.980 28.320 1.00 0.00 C
+ATOM 696 CG ARG A 22 14.140 28.210 29.570 1.00 0.00 C
+ATOM 697 CD ARG A 22 13.130 27.220 30.150 1.00 0.00 C
+ATOM 698 NE ARG A 22 12.970 26.100 29.230 1.00 0.00 N
+ATOM 699 CZ ARG A 22 13.820 25.100 29.000 1.00 0.00 C
+ATOM 700 NH1 ARG A 22 15.030 25.000 29.570 1.00 0.00 N
+ATOM 701 NH2 ARG A 22 13.470 24.230 28.070 1.00 0.00 N
+ATOM 702 H ARG A 22 15.840 28.010 27.360 1.00 0.00 H
+ATOM 703 HE ARG A 22 12.190 26.200 28.600 1.00 0.00 H
+ATOM 704 HH11 ARG A 22 15.260 25.470 30.430 1.00 0.00 H
+ATOM 705 HH12 ARG A 22 15.670 24.330 29.200 1.00 0.00 H
+ATOM 706 HH21 ARG A 22 12.740 24.480 27.430 1.00 0.00 H
+ATOM 707 HH22 ARG A 22 13.980 23.380 27.890 1.00 0.00 H
+ATOM 708 N VAL A 23 14.190 30.180 25.560 1.00 0.00 N
+ATOM 709 CA VAL A 23 14.050 31.110 24.410 1.00 0.00 C
+ATOM 710 C VAL A 23 13.650 30.360 23.110 1.00 0.00 C
+ATOM 711 O VAL A 23 12.830 30.840 22.320 1.00 0.00 O
+ATOM 712 CB VAL A 23 15.360 31.920 24.320 1.00 0.00 C
+ATOM 713 CG1 VAL A 23 15.530 32.640 22.980 1.00 0.00 C
+ATOM 714 CG2 VAL A 23 15.490 32.890 25.490 1.00 0.00 C
+ATOM 715 H VAL A 23 15.000 30.310 26.130 1.00 0.00 H
+ATOM 716 N VAL A 24 14.140 29.130 22.970 1.00 0.00 N
+ATOM 717 CA VAL A 24 13.780 28.270 21.830 1.00 0.00 C
+ATOM 718 C VAL A 24 12.460 27.510 22.080 1.00 0.00 C
+ATOM 719 O VAL A 24 11.530 27.630 21.270 1.00 0.00 O
+ATOM 720 CB VAL A 24 15.000 27.400 21.460 1.00 0.00 C
+ATOM 721 CG1 VAL A 24 14.700 26.430 20.320 1.00 0.00 C
+ATOM 722 CG2 VAL A 24 16.180 28.290 21.030 1.00 0.00 C
+ATOM 723 H VAL A 24 14.800 28.740 23.610 1.00 0.00 H
+ATOM 724 N ASN A 25 12.320 26.870 23.240 1.00 0.00 N
+ATOM 725 CA ASN A 25 11.140 26.090 23.630 1.00 0.00 C
+ATOM 726 C ASN A 25 10.990 25.860 25.140 1.00 0.00 C
+ATOM 727 O ASN A 25 11.960 25.840 25.900 1.00 0.00 O
+ATOM 728 CB ASN A 25 11.220 24.700 22.980 1.00 0.00 C
+ATOM 729 CG ASN A 25 10.670 24.730 21.550 1.00 0.00 C
+ATOM 730 OD1 ASN A 25 9.520 25.050 21.270 1.00 0.00 O
+ATOM 731 ND2 ASN A 25 11.520 24.370 20.630 1.00 0.00 N
+ATOM 732 H ASN A 25 13.090 26.800 23.890 1.00 0.00 H
+ATOM 733 HD21 ASN A 25 12.460 24.140 20.890 1.00 0.00 H
+ATOM 734 HD22 ASN A 25 11.210 24.350 19.680 1.00 0.00 H
+ATOM 735 N GLN A 26 9.720 25.700 25.530 1.00 0.00 N
+ATOM 736 CA GLN A 26 9.280 25.380 26.910 1.00 0.00 C
+ATOM 737 C GLN A 26 9.680 26.530 27.860 1.00 0.00 C
+ATOM 738 O GLN A 26 10.110 26.330 28.990 1.00 0.00 O
+ATOM 739 CB GLN A 26 9.880 24.020 27.280 1.00 0.00 C
+ATOM 740 CG GLN A 26 9.290 23.300 28.490 1.00 0.00 C
+ATOM 741 CD GLN A 26 10.290 22.230 28.930 1.00 0.00 C
+ATOM 742 OE1 GLN A 26 11.200 22.450 29.720 1.00 0.00 O
+ATOM 743 NE2 GLN A 26 10.440 21.200 28.140 1.00 0.00 N
+ATOM 744 H GLN A 26 8.980 25.790 24.870 1.00 0.00 H
+ATOM 745 HE21 GLN A 26 10.150 21.260 27.180 1.00 0.00 H
+ATOM 746 HE22 GLN A 26 11.020 20.450 28.420 1.00 0.00 H
+ATOM 747 N ALA A 27 9.530 27.760 27.360 1.00 0.00 N
+ATOM 748 CA ALA A 27 9.930 28.980 28.090 1.00 0.00 C
+ATOM 749 C ALA A 27 9.310 29.130 29.490 1.00 0.00 C
+ATOM 750 O ALA A 27 8.110 28.910 29.670 1.00 0.00 O
+ATOM 751 CB ALA A 27 9.720 30.200 27.180 1.00 0.00 C
+ATOM 752 H ALA A 27 9.110 27.900 26.460 1.00 0.00 H
+ATOM 753 N SER A 28 10.170 29.410 30.460 1.00 0.00 N
+ATOM 754 CA SER A 28 9.900 29.590 31.910 1.00 0.00 C
+ATOM 755 C SER A 28 11.170 30.070 32.620 1.00 0.00 C
+ATOM 756 O SER A 28 12.290 29.670 32.280 1.00 0.00 O
+ATOM 757 CB SER A 28 9.470 28.290 32.590 1.00 0.00 C
+ATOM 758 OG SER A 28 10.510 27.320 32.430 1.00 0.00 O
+ATOM 759 H SER A 28 11.090 29.730 30.210 1.00 0.00 H
+ATOM 760 HG SER A 28 10.040 26.400 32.340 1.00 0.00 H
+ATOM 761 N HIS A 29 10.980 30.870 33.660 1.00 0.00 N
+ATOM 762 CA HIS A 29 12.070 31.430 34.500 1.00 0.00 C
+ATOM 763 C HIS A 29 13.080 32.300 33.730 1.00 0.00 C
+ATOM 764 O HIS A 29 14.280 32.050 33.700 1.00 0.00 O
+ATOM 765 CB HIS A 29 12.770 30.340 35.330 1.00 0.00 C
+ATOM 766 CG HIS A 29 11.980 30.000 36.590 1.00 0.00 C
+ATOM 767 ND1 HIS A 29 11.850 30.810 37.640 1.00 0.00 N
+ATOM 768 CD2 HIS A 29 11.310 28.870 36.820 1.00 0.00 C
+ATOM 769 CE1 HIS A 29 11.130 30.130 38.540 1.00 0.00 C
+ATOM 770 NE2 HIS A 29 10.770 28.950 38.030 1.00 0.00 N
+ATOM 771 H HIS A 29 10.060 31.200 33.880 1.00 0.00 H
+ATOM 772 HD1 HIS A 29 12.030 31.810 37.640 1.00 0.00 H
+ATOM 773 HE2 HIS A 29 10.240 28.230 38.520 1.00 0.00 H
+ATOM 774 N VAL A 30 12.570 33.470 33.380 1.00 0.00 N
+ATOM 775 CA VAL A 30 13.260 34.430 32.500 1.00 0.00 C
+ATOM 776 C VAL A 30 12.940 35.880 32.880 1.00 0.00 C
+ATOM 777 O VAL A 30 11.800 36.340 32.910 1.00 0.00 O
+ATOM 778 CB VAL A 30 12.980 34.060 31.020 1.00 0.00 C
+ATOM 779 CG1 VAL A 30 11.540 34.300 30.560 1.00 0.00 C
+ATOM 780 CG2 VAL A 30 13.990 34.790 30.130 1.00 0.00 C
+ATOM 781 H VAL A 30 11.770 33.860 33.860 1.00 0.00 H
+ATOM 782 N SER A 31 14.040 36.510 33.260 1.00 0.00 N
+ATOM 783 CA SER A 31 14.110 37.930 33.670 1.00 0.00 C
+ATOM 784 C SER A 31 14.310 38.840 32.450 1.00 0.00 C
+ATOM 785 O SER A 31 14.840 38.440 31.420 1.00 0.00 O
+ATOM 786 CB SER A 31 15.280 38.160 34.630 1.00 0.00 C
+ATOM 787 OG SER A 31 14.980 37.670 35.940 1.00 0.00 O
+ATOM 788 H SER A 31 14.920 36.060 33.110 1.00 0.00 H
+ATOM 789 HG SER A 31 14.950 36.660 35.930 1.00 0.00 H
+ATOM 790 N ALA A 32 13.900 40.080 32.640 1.00 0.00 N
+ATOM 791 CA ALA A 32 13.980 41.150 31.620 1.00 0.00 C
+ATOM 792 C ALA A 32 15.360 41.330 30.990 1.00 0.00 C
+ATOM 793 O ALA A 32 15.500 41.110 29.790 1.00 0.00 O
+ATOM 794 CB ALA A 32 13.430 42.460 32.180 1.00 0.00 C
+ATOM 795 H ALA A 32 13.640 40.410 33.560 1.00 0.00 H
+ATOM 796 N LYS A 33 16.360 41.570 31.840 1.00 0.00 N
+ATOM 797 CA LYS A 33 17.740 41.830 31.390 1.00 0.00 C
+ATOM 798 C LYS A 33 18.240 40.650 30.540 1.00 0.00 C
+ATOM 799 O LYS A 33 18.690 40.850 29.420 1.00 0.00 O
+ATOM 800 CB LYS A 33 18.680 42.030 32.580 1.00 0.00 C
+ATOM 801 CG LYS A 33 19.930 42.900 32.340 1.00 0.00 C
+ATOM 802 CD LYS A 33 19.650 44.400 32.300 1.00 0.00 C
+ATOM 803 CE LYS A 33 20.970 45.180 32.160 1.00 0.00 C
+ATOM 804 NZ LYS A 33 21.600 44.930 30.850 1.00 0.00 N
+ATOM 805 H LYS A 33 16.220 41.630 32.830 1.00 0.00 H
+ATOM 806 HZ1 LYS A 33 21.570 43.960 30.600 1.00 0.00 H
+ATOM 807 HZ2 LYS A 33 21.120 45.450 30.140 1.00 0.00 H
+ATOM 808 HZ3 LYS A 33 22.560 45.230 30.890 1.00 0.00 H
+ATOM 809 N THR A 34 18.160 39.440 31.080 1.00 0.00 N
+ATOM 810 CA THR A 34 18.670 38.250 30.370 1.00 0.00 C
+ATOM 811 C THR A 34 18.000 37.930 29.040 1.00 0.00 C
+ATOM 812 O THR A 34 18.690 37.680 28.060 1.00 0.00 O
+ATOM 813 CB THR A 34 18.570 36.990 31.220 1.00 0.00 C
+ATOM 814 OG1 THR A 34 17.220 36.870 31.640 1.00 0.00 O
+ATOM 815 CG2 THR A 34 19.580 37.020 32.370 1.00 0.00 C
+ATOM 816 H THR A 34 18.010 39.300 32.070 1.00 0.00 H
+ATOM 817 HG1 THR A 34 17.110 36.060 32.280 1.00 0.00 H
+ATOM 818 N ARG A 35 16.670 38.010 29.000 1.00 0.00 N
+ATOM 819 CA ARG A 35 15.890 37.850 27.760 1.00 0.00 C
+ATOM 820 C ARG A 35 16.290 38.870 26.670 1.00 0.00 C
+ATOM 821 O ARG A 35 16.510 38.510 25.510 1.00 0.00 O
+ATOM 822 CB ARG A 35 14.420 37.910 28.190 1.00 0.00 C
+ATOM 823 CG ARG A 35 13.440 37.800 27.020 1.00 0.00 C
+ATOM 824 CD ARG A 35 11.980 38.020 27.440 1.00 0.00 C
+ATOM 825 NE ARG A 35 11.870 39.220 28.300 1.00 0.00 N
+ATOM 826 CZ ARG A 35 11.390 39.260 29.540 1.00 0.00 C
+ATOM 827 NH1 ARG A 35 10.770 38.220 30.130 1.00 0.00 N
+ATOM 828 NH2 ARG A 35 11.390 40.420 30.170 1.00 0.00 N
+ATOM 829 H ARG A 35 16.140 38.100 29.850 1.00 0.00 H
+ATOM 830 HE ARG A 35 12.370 40.040 27.990 1.00 0.00 H
+ATOM 831 HH11 ARG A 35 10.370 37.500 29.560 1.00 0.00 H
+ATOM 832 HH12 ARG A 35 10.520 38.240 31.100 1.00 0.00 H
+ATOM 833 HH21 ARG A 35 11.770 41.260 29.760 1.00 0.00 H
+ATOM 834 HH22 ARG A 35 11.040 40.450 31.120 1.00 0.00 H
+ATOM 835 N GLU A 36 16.580 40.110 27.080 1.00 0.00 N
+ATOM 836 CA GLU A 36 17.270 41.110 26.240 1.00 0.00 C
+ATOM 837 C GLU A 36 18.640 40.630 25.710 1.00 0.00 C
+ATOM 838 O GLU A 36 18.920 40.660 24.510 1.00 0.00 O
+ATOM 839 CB GLU A 36 17.360 42.340 27.150 1.00 0.00 C
+ATOM 840 CG GLU A 36 18.130 43.550 26.610 1.00 0.00 C
+ATOM 841 CD GLU A 36 17.190 44.750 26.460 1.00 0.00 C
+ATOM 842 OE1 GLU A 36 16.120 44.570 25.840 1.00 0.00 O
+ATOM 843 OE2 GLU A 36 17.450 45.810 27.080 1.00 0.00 O
+ATOM 844 H GLU A 36 16.190 40.490 27.930 1.00 0.00 H
+ATOM 845 N LYS A 37 19.470 40.090 26.610 1.00 0.00 N
+ATOM 846 CA LYS A 37 20.810 39.580 26.280 1.00 0.00 C
+ATOM 847 C LYS A 37 20.790 38.520 25.170 1.00 0.00 C
+ATOM 848 O LYS A 37 21.460 38.660 24.150 1.00 0.00 O
+ATOM 849 CB LYS A 37 21.410 38.970 27.550 1.00 0.00 C
+ATOM 850 CG LYS A 37 22.010 40.020 28.470 1.00 0.00 C
+ATOM 851 CD LYS A 37 23.430 40.350 28.010 1.00 0.00 C
+ATOM 852 CE LYS A 37 23.910 41.630 28.700 1.00 0.00 C
+ATOM 853 NZ LYS A 37 25.190 42.140 28.170 1.00 0.00 N
+ATOM 854 H LYS A 37 19.200 39.980 27.570 1.00 0.00 H
+ATOM 855 HZ1 LYS A 37 25.250 42.080 27.170 1.00 0.00 H
+ATOM 856 HZ2 LYS A 37 25.270 43.110 28.430 1.00 0.00 H
+ATOM 857 HZ3 LYS A 37 25.980 41.660 28.570 1.00 0.00 H
+ATOM 858 N VAL A 38 19.950 37.520 25.400 1.00 0.00 N
+ATOM 859 CA VAL A 38 19.790 36.350 24.490 1.00 0.00 C
+ATOM 860 C VAL A 38 19.200 36.750 23.140 1.00 0.00 C
+ATOM 861 O VAL A 38 19.700 36.290 22.120 1.00 0.00 O
+ATOM 862 CB VAL A 38 18.970 35.170 25.060 1.00 0.00 C
+ATOM 863 CG1 VAL A 38 19.170 33.930 24.180 1.00 0.00 C
+ATOM 864 CG2 VAL A 38 19.360 34.860 26.500 1.00 0.00 C
+ATOM 865 H VAL A 38 19.420 37.510 26.250 1.00 0.00 H
+ATOM 866 N GLU A 39 18.280 37.710 23.140 1.00 0.00 N
+ATOM 867 CA GLU A 39 17.770 38.270 21.870 1.00 0.00 C
+ATOM 868 C GLU A 39 18.870 38.970 21.050 1.00 0.00 C
+ATOM 869 O GLU A 39 19.130 38.580 19.910 1.00 0.00 O
+ATOM 870 CB GLU A 39 16.560 39.170 22.140 1.00 0.00 C
+ATOM 871 CG GLU A 39 15.360 38.290 22.510 1.00 0.00 C
+ATOM 872 CD GLU A 39 14.070 39.070 22.780 1.00 0.00 C
+ATOM 873 OE1 GLU A 39 13.720 39.940 21.960 1.00 0.00 O
+ATOM 874 OE2 GLU A 39 13.380 38.730 23.770 1.00 0.00 O
+ATOM 875 H GLU A 39 17.810 38.030 23.970 1.00 0.00 H
+ATOM 876 N ALA A 40 19.680 39.780 21.740 1.00 0.00 N
+ATOM 877 CA ALA A 40 20.870 40.440 21.160 1.00 0.00 C
+ATOM 878 C ALA A 40 21.940 39.420 20.730 1.00 0.00 C
+ATOM 879 O ALA A 40 22.630 39.640 19.740 1.00 0.00 O
+ATOM 880 CB ALA A 40 21.450 41.450 22.150 1.00 0.00 C
+ATOM 881 H ALA A 40 19.500 40.020 22.700 1.00 0.00 H
+ATOM 882 N ALA A 41 22.080 38.340 21.500 1.00 0.00 N
+ATOM 883 CA ALA A 41 22.950 37.180 21.190 1.00 0.00 C
+ATOM 884 C ALA A 41 22.540 36.460 19.900 1.00 0.00 C
+ATOM 885 O ALA A 41 23.330 36.490 18.960 1.00 0.00 O
+ATOM 886 CB ALA A 41 22.950 36.190 22.360 1.00 0.00 C
+ATOM 887 H ALA A 41 21.740 38.330 22.450 1.00 0.00 H
+ATOM 888 N MET A 42 21.260 36.080 19.820 1.00 0.00 N
+ATOM 889 CA MET A 42 20.600 35.540 18.610 1.00 0.00 C
+ATOM 890 C MET A 42 20.790 36.400 17.350 1.00 0.00 C
+ATOM 891 O MET A 42 20.680 35.910 16.230 1.00 0.00 O
+ATOM 892 CB MET A 42 19.090 35.360 18.780 1.00 0.00 C
+ATOM 893 CG MET A 42 18.770 34.160 19.660 1.00 0.00 C
+ATOM 894 SD MET A 42 19.640 32.630 19.180 1.00 0.00 S
+ATOM 895 CE MET A 42 19.010 31.680 20.550 1.00 0.00 C
+ATOM 896 H MET A 42 20.670 36.080 20.640 1.00 0.00 H
+ATOM 897 N ALA A 43 20.960 37.700 17.570 1.00 0.00 N
+ATOM 898 CA ALA A 43 21.120 38.680 16.480 1.00 0.00 C
+ATOM 899 C ALA A 43 22.580 38.780 16.000 1.00 0.00 C
+ATOM 900 O ALA A 43 22.910 38.270 14.930 1.00 0.00 O
+ATOM 901 CB ALA A 43 20.520 40.010 16.940 1.00 0.00 C
+ATOM 902 H ALA A 43 21.080 38.040 18.500 1.00 0.00 H
+ATOM 903 N GLU A 44 23.490 39.250 16.860 1.00 0.00 N
+ATOM 904 CA GLU A 44 24.940 39.280 16.560 1.00 0.00 C
+ATOM 905 C GLU A 44 25.530 37.940 16.130 1.00 0.00 C
+ATOM 906 O GLU A 44 26.290 37.870 15.160 1.00 0.00 O
+ATOM 907 CB GLU A 44 25.690 39.900 17.750 1.00 0.00 C
+ATOM 908 CG GLU A 44 25.740 41.430 17.590 1.00 0.00 C
+ATOM 909 CD GLU A 44 24.340 42.050 17.410 1.00 0.00 C
+ATOM 910 OE1 GLU A 44 23.550 41.990 18.360 1.00 0.00 O
+ATOM 911 OE2 GLU A 44 24.010 42.460 16.270 1.00 0.00 O
+ATOM 912 H GLU A 44 23.230 39.780 17.670 1.00 0.00 H
+ATOM 913 N LEU A 45 25.000 36.880 16.730 1.00 0.00 N
+ATOM 914 CA LEU A 45 25.190 35.500 16.280 1.00 0.00 C
+ATOM 915 C LEU A 45 23.860 34.990 15.710 1.00 0.00 C
+ATOM 916 O LEU A 45 22.970 34.550 16.430 1.00 0.00 O
+ATOM 917 CB LEU A 45 25.610 34.630 17.460 1.00 0.00 C
+ATOM 918 CG LEU A 45 26.930 35.050 18.120 1.00 0.00 C
+ATOM 919 CD1 LEU A 45 27.140 34.200 19.370 1.00 0.00 C
+ATOM 920 CD2 LEU A 45 28.130 34.860 17.180 1.00 0.00 C
+ATOM 921 H LEU A 45 24.490 36.950 17.600 1.00 0.00 H
+ATOM 922 N ASN A 46 23.730 35.180 14.400 1.00 0.00 N
+ATOM 923 CA ASN A 46 22.700 34.550 13.550 1.00 0.00 C
+ATOM 924 C ASN A 46 21.970 33.270 14.040 1.00 0.00 C
+ATOM 925 O ASN A 46 20.770 33.180 13.810 1.00 0.00 O
+ATOM 926 CB ASN A 46 23.330 34.310 12.180 1.00 0.00 C
+ATOM 927 CG ASN A 46 24.330 33.150 12.170 1.00 0.00 C
+ATOM 928 OD1 ASN A 46 23.980 31.990 11.990 1.00 0.00 O
+ATOM 929 ND2 ASN A 46 25.540 33.380 12.590 1.00 0.00 N
+ATOM 930 H ASN A 46 24.210 35.940 13.950 1.00 0.00 H
+ATOM 931 HD21 ASN A 46 25.820 34.230 13.080 1.00 0.00 H
+ATOM 932 HD22 ASN A 46 26.250 32.690 12.410 1.00 0.00 H
+ATOM 933 N TYR A 47 22.640 32.350 14.740 1.00 0.00 N
+ATOM 934 CA TYR A 47 22.080 31.050 15.200 1.00 0.00 C
+ATOM 935 C TYR A 47 21.430 30.160 14.140 1.00 0.00 C
+ATOM 936 O TYR A 47 20.420 30.490 13.510 1.00 0.00 O
+ATOM 937 CB TYR A 47 21.170 31.230 16.420 1.00 0.00 C
+ATOM 938 CG TYR A 47 20.650 29.910 16.990 1.00 0.00 C
+ATOM 939 CD1 TYR A 47 21.540 28.900 17.430 1.00 0.00 C
+ATOM 940 CD2 TYR A 47 19.260 29.690 16.960 1.00 0.00 C
+ATOM 941 CE1 TYR A 47 21.030 27.630 17.800 1.00 0.00 C
+ATOM 942 CE2 TYR A 47 18.750 28.450 17.350 1.00 0.00 C
+ATOM 943 CZ TYR A 47 19.630 27.420 17.750 1.00 0.00 C
+ATOM 944 OH TYR A 47 19.010 26.260 18.120 1.00 0.00 O
+ATOM 945 H TYR A 47 23.500 32.610 15.200 1.00 0.00 H
+ATOM 946 HH TYR A 47 19.530 25.440 17.810 1.00 0.00 H
+ATOM 947 N ILE A 48 21.950 28.940 14.100 1.00 0.00 N
+ATOM 948 CA ILE A 48 21.440 27.940 13.150 1.00 0.00 C
+ATOM 949 C ILE A 48 20.990 26.690 13.920 1.00 0.00 C
+ATOM 950 O ILE A 48 21.840 26.010 14.490 1.00 0.00 O
+ATOM 951 CB ILE A 48 22.420 27.570 12.020 1.00 0.00 C
+ATOM 952 CG1 ILE A 48 23.240 28.750 11.490 1.00 0.00 C
+ATOM 953 CG2 ILE A 48 21.640 26.940 10.850 1.00 0.00 C
+ATOM 954 CD1 ILE A 48 24.520 28.880 12.330 1.00 0.00 C
+ATOM 955 H ILE A 48 22.690 28.630 14.710 1.00 0.00 H
+ATOM 956 N PRO A 49 19.670 26.420 13.950 1.00 0.00 N
+ATOM 957 CA PRO A 49 19.120 25.200 14.570 1.00 0.00 C
+ATOM 958 C PRO A 49 19.730 23.960 13.910 1.00 0.00 C
+ATOM 959 O PRO A 49 19.940 23.910 12.700 1.00 0.00 O
+ATOM 960 CB PRO A 49 17.620 25.250 14.300 1.00 0.00 C
+ATOM 961 CG PRO A 49 17.350 26.740 14.130 1.00 0.00 C
+ATOM 962 CD PRO A 49 18.590 27.260 13.410 1.00 0.00 C
+ATOM 963 N ASN A 50 20.230 23.100 14.800 1.00 0.00 N
+ATOM 964 CA ASN A 50 20.870 21.790 14.540 1.00 0.00 C
+ATOM 965 C ASN A 50 22.250 21.900 13.900 1.00 0.00 C
+ATOM 966 O ASN A 50 22.870 20.930 13.470 1.00 0.00 O
+ATOM 967 CB ASN A 50 19.970 20.810 13.760 1.00 0.00 C
+ATOM 968 CG ASN A 50 18.660 20.550 14.490 1.00 0.00 C
+ATOM 969 OD1 ASN A 50 18.250 21.200 15.450 1.00 0.00 O
+ATOM 970 ND2 ASN A 50 17.980 19.500 14.120 1.00 0.00 N
+ATOM 971 H ASN A 50 20.320 23.380 15.770 1.00 0.00 H
+ATOM 972 HD21 ASN A 50 18.410 18.670 13.730 1.00 0.00 H
+ATOM 973 HD22 ASN A 50 17.050 19.450 14.490 1.00 0.00 H
+ATOM 974 N ARG A 51 22.720 23.140 13.920 1.00 0.00 N
+ATOM 975 CA ARG A 51 24.150 23.480 13.800 1.00 0.00 C
+ATOM 976 C ARG A 51 24.730 24.080 15.100 1.00 0.00 C
+ATOM 977 O ARG A 51 25.970 24.100 15.180 1.00 0.00 O
+ATOM 978 CB ARG A 51 24.410 24.400 12.610 1.00 0.00 C
+ATOM 979 CG ARG A 51 24.100 23.730 11.260 1.00 0.00 C
+ATOM 980 CD ARG A 51 24.620 22.300 11.070 1.00 0.00 C
+ATOM 981 NE ARG A 51 26.080 22.300 11.080 1.00 0.00 N
+ATOM 982 CZ ARG A 51 26.870 22.150 10.010 1.00 0.00 C
+ATOM 983 NH1 ARG A 51 26.470 21.770 8.800 1.00 0.00 N
+ATOM 984 NH2 ARG A 51 28.170 22.230 10.200 1.00 0.00 N
+ATOM 985 OXT ARG A 51 23.920 24.460 15.970 1.00 0.00 O
+ATOM 986 H ARG A 51 22.090 23.920 14.030 1.00 0.00 H
+ATOM 987 HE ARG A 51 26.510 22.440 11.980 1.00 0.00 H
+ATOM 988 HH11 ARG A 51 25.550 21.440 8.590 1.00 0.00 H
+ATOM 989 HH12 ARG A 51 27.140 21.830 8.050 1.00 0.00 H
+ATOM 990 HH21 ARG A 51 28.480 22.470 11.120 1.00 0.00 H
+ATOM 991 HH22 ARG A 51 28.800 22.020 9.460 1.00 0.00 H
+TER 992 ARG A 51
+HETATM 993 NA NA C 52 14.830 25.040 17.790 1.00 0.00 NA
+HETATM 994 O HOH B 853 11.350 37.460 35.810 1.00 0.00 O
+HETATM 995 H1 HOH B 853 11.630 37.130 36.710 1.00 0.00 H
+HETATM 996 H2 HOH B 853 12.070 37.250 35.150 1.00 0.00 H
+HETATM 997 O HOH B 902 21.100 16.580 16.780 1.00 0.00 O
+HETATM 998 H1 HOH B 902 20.760 17.510 16.610 1.00 0.00 H
+HETATM 999 H2 HOH B 902 21.120 16.410 17.760 1.00 0.00 H
+HETATM 1000 O HOH B 965 14.600 37.540 39.970 1.00 0.00 O
+HETATM 1001 H1 HOH B 965 13.990 36.990 39.400 1.00 0.00 H
+HETATM 1002 H2 HOH B 965 14.720 37.090 40.850 1.00 0.00 H
+HETATM 1003 O HOH B1584 20.250 35.110 35.540 1.00 0.00 O
+HETATM 1004 H1 HOH B1584 21.230 34.940 35.520 1.00 0.00 H
+HETATM 1005 H2 HOH B1584 19.770 34.400 35.030 1.00 0.00 H
+HETATM 1006 O HOH B2184 17.420 25.760 33.560 1.00 0.00 O
+HETATM 1007 H1 HOH B2184 16.610 25.460 34.070 1.00 0.00 H
+HETATM 1008 H2 HOH B2184 18.250 25.440 34.020 1.00 0.00 H
+HETATM 1009 O HOH B2490 17.170 36.180 37.910 1.00 0.00 O
+HETATM 1010 H1 HOH B2490 16.250 35.770 37.920 1.00 0.00 H
+HETATM 1011 H2 HOH B2490 17.220 36.900 38.610 1.00 0.00 H
+HETATM 1012 O HOH B2582 25.550 27.840 30.820 1.00 0.00 O
+HETATM 1013 H1 HOH B2582 25.050 28.210 31.610 1.00 0.00 H
+HETATM 1014 H2 HOH B2582 25.550 26.840 30.880 1.00 0.00 H
+HETATM 1015 O HOH B2691 8.400 30.640 39.650 1.00 0.00 O
+HETATM 1016 H1 HOH B2691 8.700 30.650 40.610 1.00 0.00 H
+HETATM 1017 H2 HOH B2691 8.420 31.570 39.290 1.00 0.00 H
+HETATM 1018 O HOH B3222 24.800 31.820 32.830 1.00 0.00 O
+HETATM 1019 H1 HOH B3222 24.810 30.820 32.840 1.00 0.00 H
+HETATM 1020 H2 HOH B3222 23.850 32.140 32.840 1.00 0.00 H
+HETATM 1021 O HOH B3303 9.650 27.990 42.380 1.00 0.00 O
+HETATM 1022 H1 HOH B3303 9.440 28.940 42.580 1.00 0.00 H
+HETATM 1023 H2 HOH B3303 9.760 27.870 41.390 1.00 0.00 H
+HETATM 1024 O HOH C 223 10.800 20.840 21.340 1.00 0.00 O
+HETATM 1025 H1 HOH C 223 11.080 21.420 22.110 1.00 0.00 H
+HETATM 1026 H2 HOH C 223 9.910 21.150 21.000 1.00 0.00 H
+HETATM 1027 O HOH C 703 12.240 20.830 18.770 1.00 0.00 O
+HETATM 1028 H1 HOH C 703 11.940 20.590 19.690 1.00 0.00 H
+HETATM 1029 H2 HOH C 703 12.070 20.060 18.160 1.00 0.00 H
+HETATM 1030 O HOH C 895 13.700 23.370 18.470 1.00 0.00 O
+HETATM 1031 H1 HOH C 895 14.020 23.310 19.420 1.00 0.00 H
+HETATM 1032 H2 HOH C 895 12.970 22.690 18.320 1.00 0.00 H
+HETATM 1033 O HOH C1046 14.110 22.870 31.590 1.00 0.00 O
+HETATM 1034 H1 HOH C1046 13.340 22.260 31.420 1.00 0.00 H
+HETATM 1035 H2 HOH C1046 13.800 23.680 32.090 1.00 0.00 H
+HETATM 1036 O HOH C1817 28.090 19.320 21.810 1.00 0.00 O
+HETATM 1037 H1 HOH C1817 28.700 20.080 22.020 1.00 0.00 H
+HETATM 1038 H2 HOH C1817 27.370 19.640 21.190 1.00 0.00 H
+HETATM 1039 O HOH C2471 21.860 22.600 22.100 1.00 0.00 O
+HETATM 1040 H1 HOH C2471 22.240 22.840 21.210 1.00 0.00 H
+HETATM 1041 H2 HOH C2471 20.860 22.610 22.050 1.00 0.00 H
+HETATM 1042 O HOH C2520 28.710 21.930 20.330 1.00 0.00 O
+HETATM 1043 H1 HOH C2520 28.560 21.890 19.340 1.00 0.00 H
+HETATM 1044 H2 HOH C2520 27.830 22.030 20.800 1.00 0.00 H
+HETATM 1045 O HOH C2570 26.030 20.710 20.620 1.00 0.00 O
+HETATM 1046 H1 HOH C2570 25.380 20.910 21.350 1.00 0.00 H
+HETATM 1047 H2 HOH C2570 25.610 20.900 19.740 1.00 0.00 H
+HETATM 1048 O HOH A 53 20.010 19.420 17.130 1.00 0.00 O
+HETATM 1049 H1 HOH A 53 19.310 19.970 16.670 1.00 0.00 H
+HETATM 1050 H2 HOH A 53 20.560 20.010 17.710 1.00 0.00 H
+HETATM 1051 O HOH A 54 25.390 19.890 13.150 1.00 0.00 O
+HETATM 1052 H1 HOH A 54 24.510 20.310 13.390 1.00 0.00 H
+HETATM 1053 H2 HOH A 54 26.040 20.030 13.900 1.00 0.00 H
+HETATM 1054 O HOH A 55 17.420 17.010 16.310 1.00 0.00 O
+HETATM 1055 H1 HOH A 55 17.200 16.050 16.450 1.00 0.00 H
+HETATM 1056 H2 HOH A 55 18.130 17.080 15.610 1.00 0.00 H
+HETATM 1057 O HOH A 56 10.520 34.570 35.210 1.00 0.00 O
+HETATM 1058 H1 HOH A 56 10.930 35.380 35.630 1.00 0.00 H
+HETATM 1059 H2 HOH A 56 9.770 34.850 34.610 1.00 0.00 H
+HETATM 1060 O HOH A 57 28.810 17.600 23.950 1.00 0.00 O
+HETATM 1061 H1 HOH A 57 28.350 18.310 23.410 1.00 0.00 H
+HETATM 1062 H2 HOH A 57 29.220 16.940 23.330 1.00 0.00 H
+HETATM 1063 O HOH A 58 15.950 27.370 31.920 1.00 0.00 O
+HETATM 1064 H1 HOH A 58 16.640 27.080 32.580 1.00 0.00 H
+HETATM 1065 H2 HOH A 58 15.980 28.370 31.840 1.00 0.00 H
+HETATM 1066 O HOH A 59 27.850 18.090 12.160 1.00 0.00 O
+HETATM 1067 H1 HOH A 59 27.430 17.450 11.520 1.00 0.00 H
+HETATM 1068 H2 HOH A 59 27.160 18.700 12.530 1.00 0.00 H
+HETATM 1069 O HOH A 60 14.640 22.250 26.360 1.00 0.00 O
+HETATM 1070 H1 HOH A 60 15.280 22.990 26.190 1.00 0.00 H
+HETATM 1071 H2 HOH A 60 14.390 21.830 25.490 1.00 0.00 H
+HETATM 1072 O HOH A 61 16.780 34.950 33.190 1.00 0.00 O
+HETATM 1073 H1 HOH A 61 17.380 34.160 33.070 1.00 0.00 H
+HETATM 1074 H2 HOH A 61 17.030 35.430 34.040 1.00 0.00 H
+HETATM 1075 O HOH A 62 16.580 24.210 18.810 1.00 0.00 O
+HETATM 1076 H1 HOH A 62 16.670 23.230 18.990 1.00 0.00 H
+HETATM 1077 H2 HOH A 62 17.440 24.670 19.050 1.00 0.00 H
+HETATM 1078 O HOH A 63 10.010 26.900 39.580 1.00 0.00 O
+HETATM 1079 H1 HOH A 63 10.550 26.570 40.360 1.00 0.00 H
+HETATM 1080 H2 HOH A 63 9.500 26.130 39.180 1.00 0.00 H
+HETATM 1081 O HOH A 64 15.400 30.150 31.860 1.00 0.00 O
+HETATM 1082 H1 HOH A 64 14.610 30.670 32.180 1.00 0.00 H
+HETATM 1083 H2 HOH A 64 16.220 30.470 32.350 1.00 0.00 H
+HETATM 1084 O HOH A 65 8.220 23.410 24.280 1.00 0.00 O
+HETATM 1085 H1 HOH A 65 8.680 22.570 24.560 1.00 0.00 H
+HETATM 1086 H2 HOH A 65 7.430 23.190 23.710 1.00 0.00 H
+HETATM 1087 O HOH A 66 9.720 32.220 36.490 1.00 0.00 O
+HETATM 1088 H1 HOH A 66 10.130 33.070 36.160 1.00 0.00 H
+HETATM 1089 H2 HOH A 66 9.210 32.410 37.330 1.00 0.00 H
+HETATM 1090 O HOH A 67 12.620 24.920 32.910 1.00 0.00 O
+HETATM 1091 H1 HOH A 67 11.670 25.170 32.700 1.00 0.00 H
+HETATM 1092 H2 HOH A 67 13.160 25.760 33.030 1.00 0.00 H
+HETATM 1093 O HOH A 68 28.570 21.930 12.850 1.00 0.00 O
+HETATM 1094 H1 HOH A 68 29.330 22.500 13.170 1.00 0.00 H
+HETATM 1095 H2 HOH A 68 28.070 21.580 13.640 1.00 0.00 H
+HETATM 1096 O HOH A 69 25.970 18.840 23.510 1.00 0.00 O
+HETATM 1097 H1 HOH A 69 24.990 18.960 23.340 1.00 0.00 H
+HETATM 1098 H2 HOH A 69 26.490 19.110 22.710 1.00 0.00 H
+HETATM 1099 O HOH A 70 25.880 16.840 25.510 1.00 0.00 O
+HETATM 1100 H1 HOH A 70 25.380 15.980 25.480 1.00 0.00 H
+HETATM 1101 H2 HOH A 70 25.970 17.200 24.580 1.00 0.00 H
+HETATM 1102 O HOH A 71 29.420 24.050 15.940 1.00 0.00 O
+HETATM 1103 H1 HOH A 71 29.400 23.390 16.690 1.00 0.00 H
+HETATM 1104 H2 HOH A 71 28.640 24.670 16.030 1.00 0.00 H
+HETATM 1105 O HOH A 72 24.720 19.130 26.650 1.00 0.00 O
+HETATM 1106 H1 HOH A 72 25.330 19.390 27.410 1.00 0.00 H
+HETATM 1107 H2 HOH A 72 25.030 18.260 26.260 1.00 0.00 H
+HETATM 1108 O HOH A 73 13.710 27.300 33.780 1.00 0.00 O
+HETATM 1109 H1 HOH A 73 14.570 27.330 33.270 1.00 0.00 H
+HETATM 1110 H2 HOH A 73 13.270 28.200 33.740 1.00 0.00 H
+HETATM 1111 O HOH A 74 22.010 32.300 32.770 1.00 0.00 O
+HETATM 1112 H1 HOH A 74 21.130 31.860 32.940 1.00 0.00 H
+HETATM 1113 H2 HOH A 74 21.940 33.270 32.990 1.00 0.00 H
+HETATM 1114 O HOH A 75 23.180 26.070 33.300 1.00 0.00 O
+HETATM 1115 H1 HOH A 75 23.810 26.310 34.040 1.00 0.00 H
+HETATM 1116 H2 HOH A 75 23.530 25.270 32.820 1.00 0.00 H
+HETATM 1117 O HOH A 76 24.200 21.220 22.450 1.00 0.00 O
+HETATM 1118 H1 HOH A 76 24.270 21.120 23.440 1.00 0.00 H
+HETATM 1119 H2 HOH A 76 23.410 21.800 22.230 1.00 0.00 H
+HETATM 1120 O HOH A 77 17.820 36.560 35.230 1.00 0.00 O
+HETATM 1121 H1 HOH A 77 17.450 36.580 36.160 1.00 0.00 H
+HETATM 1122 H2 HOH A 77 18.690 36.060 35.240 1.00 0.00 H
+HETATM 1123 O HOH A 78 24.880 21.920 30.570 1.00 0.00 O
+HETATM 1124 H1 HOH A 78 24.540 21.580 31.450 1.00 0.00 H
+HETATM 1125 H2 HOH A 78 25.870 22.040 30.610 1.00 0.00 H
+ENDMDL
+CONECT 320 993
+CONECT 993 320 1036 1066 1078
+CONECT 1036 993
+CONECT 1066 993
+CONECT 1078 993
+MASTER 408 0 1 3 0 0 2 6 3384 9 5 6
+END
diff --git a/Tests/PDB/1MOT.pdb b/Tests/PDB/1MOT.pdb
deleted file mode 100644
index fe8dc7a..0000000
--- a/Tests/PDB/1MOT.pdb
+++ /dev/null
@@ -1,8220 +0,0 @@
-MODEL 1
-ATOM 1 N ALA A 249 3.679 -9.708 -12.388 1.00 3.15 N
-ATOM 2 CA ALA A 249 3.984 -8.784 -11.262 1.00 2.35 C
-ATOM 3 C ALA A 249 2.849 -8.759 -10.242 1.00 1.70 C
-ATOM 4 O ALA A 249 1.696 -9.031 -10.576 1.00 2.08 O
-ATOM 5 CB ALA A 249 4.257 -7.381 -11.774 1.00 2.86 C
-ATOM 6 H1 ALA A 249 4.412 -10.440 -12.454 1.00 3.49 H
-ATOM 7 H2 ALA A 249 3.649 -9.185 -13.286 1.00 3.56 H
-ATOM 8 H3 ALA A 249 2.756 -10.168 -12.235 1.00 3.34 H
-ATOM 9 HA ALA A 249 4.878 -9.138 -10.771 1.00 2.13 H
-ATOM 10 HB1 ALA A 249 3.739 -6.669 -11.146 1.00 3.20 H
-ATOM 11 HB2 ALA A 249 3.906 -7.289 -12.790 1.00 3.15 H
-ATOM 12 HB3 ALA A 249 5.318 -7.185 -11.737 1.00 3.20 H
-ATOM 13 N PRO A 250 3.167 -8.435 -8.976 1.00 1.01 N
-ATOM 14 CA PRO A 250 2.181 -8.373 -7.892 1.00 0.87 C
-ATOM 15 C PRO A 250 1.189 -7.218 -8.067 1.00 0.66 C
-ATOM 16 O PRO A 250 1.533 -6.049 -7.868 1.00 0.58 O
-ATOM 17 CB PRO A 250 3.050 -8.158 -6.639 1.00 1.33 C
-ATOM 18 CG PRO A 250 4.432 -8.514 -7.058 1.00 1.35 C
-ATOM 19 CD PRO A 250 4.509 -8.097 -8.487 1.00 1.22 C
-ATOM 20 HA PRO A 250 1.638 -9.302 -7.799 1.00 1.37 H
-ATOM 21 HB2 PRO A 250 2.995 -7.124 -6.335 1.00 1.56 H
-ATOM 22 HB3 PRO A 250 2.707 -8.796 -5.844 1.00 1.83 H
-ATOM 23 HG2 PRO A 250 5.153 -7.972 -6.462 1.00 1.90 H
-ATOM 24 HG3 PRO A 250 4.585 -9.579 -6.967 1.00 1.40 H
-ATOM 25 HD2 PRO A 250 4.688 -7.034 -8.559 1.00 1.66 H
-ATOM 26 HD3 PRO A 250 5.266 -8.655 -9.006 1.00 1.40 H
-ATOM 27 N ALA A 251 -0.045 -7.554 -8.441 1.00 0.72 N
-ATOM 28 CA ALA A 251 -1.089 -6.550 -8.651 1.00 0.67 C
-ATOM 29 C ALA A 251 -1.385 -5.764 -7.372 1.00 0.51 C
-ATOM 30 O ALA A 251 -1.542 -4.544 -7.408 1.00 0.51 O
-ATOM 31 CB ALA A 251 -2.356 -7.213 -9.166 1.00 0.89 C
-ATOM 32 H ALA A 251 -0.256 -8.500 -8.586 1.00 0.89 H
-ATOM 33 HA ALA A 251 -0.740 -5.862 -9.407 1.00 0.69 H
-ATOM 34 HB1 ALA A 251 -2.832 -7.753 -8.363 1.00 1.31 H
-ATOM 35 HB2 ALA A 251 -2.104 -7.897 -9.963 1.00 1.26 H
-ATOM 36 HB3 ALA A 251 -3.033 -6.457 -9.541 1.00 1.54 H
-ATOM 37 N ARG A 252 -1.451 -6.472 -6.248 1.00 0.47 N
-ATOM 38 CA ARG A 252 -1.723 -5.848 -4.954 1.00 0.42 C
-ATOM 39 C ARG A 252 -0.639 -4.820 -4.608 1.00 0.34 C
-ATOM 40 O ARG A 252 -0.940 -3.706 -4.171 1.00 0.36 O
-ATOM 41 CB ARG A 252 -1.801 -6.927 -3.869 1.00 0.54 C
-ATOM 42 CG ARG A 252 -2.814 -6.638 -2.769 1.00 0.71 C
-ATOM 43 CD ARG A 252 -2.473 -5.375 -1.993 1.00 0.83 C
-ATOM 44 NE ARG A 252 -3.440 -5.099 -0.935 1.00 1.18 N
-ATOM 45 CZ ARG A 252 -3.429 -4.005 -0.207 1.00 1.47 C
-ATOM 46 NH1 ARG A 252 -2.547 -3.067 -0.440 1.00 1.50 N
-ATOM 47 NH2 ARG A 252 -4.311 -3.844 0.745 1.00 1.81 N
-ATOM 48 H ARG A 252 -1.314 -7.439 -6.289 1.00 0.57 H
-ATOM 49 HA ARG A 252 -2.673 -5.344 -5.019 1.00 0.51 H
-ATOM 50 HB2 ARG A 252 -2.069 -7.864 -4.335 1.00 0.66 H
-ATOM 51 HB3 ARG A 252 -0.827 -7.031 -3.413 1.00 0.60 H
-ATOM 52 HG2 ARG A 252 -3.789 -6.516 -3.216 1.00 0.86 H
-ATOM 53 HG3 ARG A 252 -2.834 -7.475 -2.085 1.00 0.84 H
-ATOM 54 HD2 ARG A 252 -1.495 -5.495 -1.550 1.00 0.87 H
-ATOM 55 HD3 ARG A 252 -2.455 -4.541 -2.680 1.00 0.81 H
-ATOM 56 HE ARG A 252 -4.122 -5.774 -0.757 1.00 1.28 H
-ATOM 57 HH11 ARG A 252 -1.882 -3.182 -1.174 1.00 1.30 H
-ATOM 58 HH12 ARG A 252 -2.541 -2.240 0.115 1.00 1.77 H
-ATOM 59 HH21 ARG A 252 -4.992 -4.552 0.919 1.00 1.87 H
-ATOM 60 HH22 ARG A 252 -4.302 -3.015 1.298 1.00 2.07 H
-ATOM 61 N VAL A 253 0.619 -5.196 -4.821 1.00 0.36 N
-ATOM 62 CA VAL A 253 1.744 -4.305 -4.547 1.00 0.40 C
-ATOM 63 C VAL A 253 1.707 -3.089 -5.470 1.00 0.36 C
-ATOM 64 O VAL A 253 1.822 -1.952 -5.014 1.00 0.37 O
-ATOM 65 CB VAL A 253 3.093 -5.038 -4.703 1.00 0.54 C
-ATOM 66 CG1 VAL A 253 4.263 -4.080 -4.519 1.00 0.68 C
-ATOM 67 CG2 VAL A 253 3.181 -6.188 -3.712 1.00 0.67 C
-ATOM 68 H VAL A 253 0.792 -6.087 -5.181 1.00 0.42 H
-ATOM 69 HA VAL A 253 1.658 -3.968 -3.527 1.00 0.44 H
-ATOM 70 HB VAL A 253 3.144 -5.447 -5.701 1.00 0.59 H
-ATOM 71 HG11 VAL A 253 3.895 -3.127 -4.169 1.00 1.28 H
-ATOM 72 HG12 VAL A 253 4.769 -3.946 -5.464 1.00 1.24 H
-ATOM 73 HG13 VAL A 253 4.952 -4.488 -3.796 1.00 1.23 H
-ATOM 74 HG21 VAL A 253 4.013 -6.025 -3.044 1.00 0.96 H
-ATOM 75 HG22 VAL A 253 3.324 -7.115 -4.248 1.00 1.04 H
-ATOM 76 HG23 VAL A 253 2.265 -6.240 -3.141 1.00 0.95 H
-ATOM 77 N GLY A 254 1.518 -3.343 -6.767 1.00 0.39 N
-ATOM 78 CA GLY A 254 1.438 -2.263 -7.742 1.00 0.43 C
-ATOM 79 C GLY A 254 0.325 -1.285 -7.410 1.00 0.38 C
-ATOM 80 O GLY A 254 0.535 -0.068 -7.382 1.00 0.38 O
-ATOM 81 H GLY A 254 1.416 -4.276 -7.061 1.00 0.44 H
-ATOM 82 HA2 GLY A 254 2.380 -1.733 -7.759 1.00 0.47 H
-ATOM 83 HA3 GLY A 254 1.254 -2.686 -8.718 1.00 0.51 H
-ATOM 84 N LEU A 255 -0.858 -1.824 -7.122 1.00 0.39 N
-ATOM 85 CA LEU A 255 -2.008 -1.009 -6.749 1.00 0.41 C
-ATOM 86 C LEU A 255 -1.726 -0.229 -5.474 1.00 0.35 C
-ATOM 87 O LEU A 255 -2.018 0.964 -5.380 1.00 0.39 O
-ATOM 88 CB LEU A 255 -3.236 -1.887 -6.536 1.00 0.49 C
-ATOM 89 CG LEU A 255 -4.175 -1.965 -7.729 1.00 0.68 C
-ATOM 90 CD1 LEU A 255 -5.352 -2.865 -7.404 1.00 0.79 C
-ATOM 91 CD2 LEU A 255 -4.645 -0.569 -8.111 1.00 0.84 C
-ATOM 92 H LEU A 255 -0.953 -2.805 -7.140 1.00 0.41 H
-ATOM 93 HA LEU A 255 -2.208 -0.318 -7.554 1.00 0.48 H
-ATOM 94 HB2 LEU A 255 -2.902 -2.886 -6.298 1.00 0.54 H
-ATOM 95 HB3 LEU A 255 -3.789 -1.498 -5.695 1.00 0.53 H
-ATOM 96 HG LEU A 255 -3.648 -2.387 -8.571 1.00 0.76 H
-ATOM 97 HD11 LEU A 255 -6.272 -2.315 -7.535 1.00 1.31 H
-ATOM 98 HD12 LEU A 255 -5.275 -3.201 -6.379 1.00 1.26 H
-ATOM 99 HD13 LEU A 255 -5.345 -3.719 -8.064 1.00 1.35 H
-ATOM 100 HD21 LEU A 255 -4.894 -0.017 -7.216 1.00 1.29 H
-ATOM 101 HD22 LEU A 255 -5.514 -0.640 -8.745 1.00 1.36 H
-ATOM 102 HD23 LEU A 255 -3.853 -0.056 -8.639 1.00 1.41 H
-ATOM 103 N GLY A 256 -1.156 -0.919 -4.492 1.00 0.29 N
-ATOM 104 CA GLY A 256 -0.839 -0.295 -3.227 1.00 0.29 C
-ATOM 105 C GLY A 256 0.156 0.850 -3.358 1.00 0.28 C
-ATOM 106 O GLY A 256 -0.111 1.954 -2.886 1.00 0.33 O
-ATOM 107 H GLY A 256 -0.951 -1.871 -4.631 1.00 0.31 H
-ATOM 108 HA2 GLY A 256 -1.752 0.087 -2.796 1.00 0.35 H
-ATOM 109 HA3 GLY A 256 -0.428 -1.042 -2.568 1.00 0.31 H
-ATOM 110 N ILE A 257 1.299 0.602 -4.010 1.00 0.28 N
-ATOM 111 CA ILE A 257 2.304 1.652 -4.188 1.00 0.34 C
-ATOM 112 C ILE A 257 1.745 2.807 -5.023 1.00 0.35 C
-ATOM 113 O ILE A 257 2.004 3.973 -4.725 1.00 0.40 O
-ATOM 114 CB ILE A 257 3.631 1.124 -4.801 1.00 0.45 C
-ATOM 115 CG1 ILE A 257 3.423 0.497 -6.184 1.00 0.48 C
-ATOM 116 CG2 ILE A 257 4.280 0.121 -3.861 1.00 0.54 C
-ATOM 117 CD1 ILE A 257 3.744 1.429 -7.336 1.00 0.58 C
-ATOM 118 H ILE A 257 1.463 -0.299 -4.380 1.00 0.29 H
-ATOM 119 HA ILE A 257 2.531 2.037 -3.203 1.00 0.37 H
-ATOM 120 HB ILE A 257 4.305 1.964 -4.898 1.00 0.52 H
-ATOM 121 HG12 ILE A 257 4.056 -0.372 -6.277 1.00 0.55 H
-ATOM 122 HG13 ILE A 257 2.389 0.193 -6.281 1.00 0.48 H
-ATOM 123 HG21 ILE A 257 4.372 0.556 -2.876 1.00 1.15 H
-ATOM 124 HG22 ILE A 257 5.261 -0.139 -4.233 1.00 1.10 H
-ATOM 125 HG23 ILE A 257 3.669 -0.768 -3.805 1.00 1.25 H
-ATOM 126 HD11 ILE A 257 4.644 1.981 -7.111 1.00 1.22 H
-ATOM 127 HD12 ILE A 257 2.925 2.118 -7.480 1.00 1.17 H
-ATOM 128 HD13 ILE A 257 3.891 0.851 -8.236 1.00 1.18 H
-ATOM 129 N THR A 258 0.948 2.482 -6.046 1.00 0.37 N
-ATOM 130 CA THR A 258 0.330 3.507 -6.888 1.00 0.44 C
-ATOM 131 C THR A 258 -0.622 4.364 -6.054 1.00 0.42 C
-ATOM 132 O THR A 258 -0.635 5.590 -6.164 1.00 0.47 O
-ATOM 133 CB THR A 258 -0.451 2.887 -8.069 1.00 0.53 C
-ATOM 134 OG1 THR A 258 0.422 2.078 -8.867 1.00 0.58 O
-ATOM 135 CG2 THR A 258 -1.076 3.968 -8.942 1.00 0.66 C
-ATOM 136 H THR A 258 0.758 1.530 -6.226 1.00 0.38 H
-ATOM 137 HA THR A 258 1.114 4.135 -7.285 1.00 0.49 H
-ATOM 138 HB THR A 258 -1.241 2.266 -7.672 1.00 0.53 H
-ATOM 139 HG1 THR A 258 0.540 1.216 -8.440 1.00 0.54 H
-ATOM 140 HG21 THR A 258 -1.991 3.594 -9.378 1.00 0.96 H
-ATOM 141 HG22 THR A 258 -0.387 4.239 -9.729 1.00 1.06 H
-ATOM 142 HG23 THR A 258 -1.294 4.837 -8.340 1.00 1.16 H
-ATOM 143 N THR A 259 -1.400 3.704 -5.197 1.00 0.40 N
-ATOM 144 CA THR A 259 -2.340 4.395 -4.317 1.00 0.43 C
-ATOM 145 C THR A 259 -1.586 5.314 -3.360 1.00 0.37 C
-ATOM 146 O THR A 259 -1.917 6.493 -3.226 1.00 0.42 O
-ATOM 147 CB THR A 259 -3.193 3.398 -3.497 1.00 0.47 C
-ATOM 148 OG1 THR A 259 -3.872 2.488 -4.374 1.00 0.53 O
-ATOM 149 CG2 THR A 259 -4.217 4.129 -2.640 1.00 0.59 C
-ATOM 150 H THR A 259 -1.326 2.725 -5.143 1.00 0.39 H
-ATOM 151 HA THR A 259 -3.002 4.989 -4.931 1.00 0.50 H
-ATOM 152 HB THR A 259 -2.538 2.837 -2.847 1.00 0.44 H
-ATOM 153 HG1 THR A 259 -3.230 1.876 -4.766 1.00 0.51 H
-ATOM 154 HG21 THR A 259 -5.196 3.707 -2.810 1.00 1.12 H
-ATOM 155 HG22 THR A 259 -4.226 5.177 -2.904 1.00 1.21 H
-ATOM 156 HG23 THR A 259 -3.956 4.023 -1.597 1.00 1.24 H
-ATOM 157 N VAL A 260 -0.550 4.771 -2.718 1.00 0.33 N
-ATOM 158 CA VAL A 260 0.275 5.545 -1.793 1.00 0.33 C
-ATOM 159 C VAL A 260 0.906 6.740 -2.511 1.00 0.33 C
-ATOM 160 O VAL A 260 0.813 7.874 -2.038 1.00 0.34 O
-ATOM 161 CB VAL A 260 1.384 4.676 -1.159 1.00 0.38 C
-ATOM 162 CG1 VAL A 260 2.288 5.515 -0.267 1.00 0.51 C
-ATOM 163 CG2 VAL A 260 0.776 3.528 -0.369 1.00 0.44 C
-ATOM 164 H VAL A 260 -0.325 3.825 -2.884 1.00 0.32 H
-ATOM 165 HA VAL A 260 -0.365 5.910 -1.003 1.00 0.38 H
-ATOM 166 HB VAL A 260 1.987 4.259 -1.953 1.00 0.41 H
-ATOM 167 HG11 VAL A 260 1.890 6.515 -0.191 1.00 1.14 H
-ATOM 168 HG12 VAL A 260 3.279 5.553 -0.695 1.00 1.20 H
-ATOM 169 HG13 VAL A 260 2.337 5.069 0.715 1.00 1.14 H
-ATOM 170 HG21 VAL A 260 0.221 2.886 -1.038 1.00 1.10 H
-ATOM 171 HG22 VAL A 260 0.111 3.921 0.385 1.00 1.12 H
-ATOM 172 HG23 VAL A 260 1.563 2.959 0.104 1.00 1.14 H
-ATOM 173 N LEU A 261 1.517 6.479 -3.671 1.00 0.36 N
-ATOM 174 CA LEU A 261 2.133 7.536 -4.473 1.00 0.41 C
-ATOM 175 C LEU A 261 1.098 8.606 -4.827 1.00 0.41 C
-ATOM 176 O LEU A 261 1.384 9.801 -4.782 1.00 0.45 O
-ATOM 177 CB LEU A 261 2.740 6.955 -5.747 1.00 0.50 C
-ATOM 178 CG LEU A 261 3.623 7.924 -6.529 1.00 0.61 C
-ATOM 179 CD1 LEU A 261 4.730 8.466 -5.636 1.00 0.67 C
-ATOM 180 CD2 LEU A 261 4.204 7.236 -7.750 1.00 0.77 C
-ATOM 181 H LEU A 261 1.537 5.551 -4.005 1.00 0.37 H
-ATOM 182 HA LEU A 261 2.921 7.988 -3.890 1.00 0.44 H
-ATOM 183 HB2 LEU A 261 3.333 6.091 -5.479 1.00 0.52 H
-ATOM 184 HB3 LEU A 261 1.936 6.634 -6.393 1.00 0.53 H
-ATOM 185 HG LEU A 261 3.024 8.758 -6.864 1.00 0.66 H
-ATOM 186 HD11 LEU A 261 4.294 9.047 -4.836 1.00 1.13 H
-ATOM 187 HD12 LEU A 261 5.389 9.093 -6.217 1.00 1.19 H
-ATOM 188 HD13 LEU A 261 5.290 7.643 -5.216 1.00 1.36 H
-ATOM 189 HD21 LEU A 261 4.051 6.169 -7.667 1.00 1.33 H
-ATOM 190 HD22 LEU A 261 5.262 7.445 -7.810 1.00 1.31 H
-ATOM 191 HD23 LEU A 261 3.712 7.603 -8.637 1.00 1.28 H
-ATOM 192 N THR A 262 -0.112 8.159 -5.150 1.00 0.43 N
-ATOM 193 CA THR A 262 -1.210 9.064 -5.481 1.00 0.49 C
-ATOM 194 C THR A 262 -1.583 9.921 -4.269 1.00 0.45 C
-ATOM 195 O THR A 262 -1.714 11.140 -4.371 1.00 0.50 O
-ATOM 196 CB THR A 262 -2.461 8.279 -5.938 1.00 0.57 C
-ATOM 197 OG1 THR A 262 -2.158 7.492 -7.098 1.00 0.66 O
-ATOM 198 CG2 THR A 262 -3.616 9.219 -6.245 1.00 0.69 C
-ATOM 199 H THR A 262 -0.276 7.188 -5.149 1.00 0.43 H
-ATOM 200 HA THR A 262 -0.891 9.708 -6.289 1.00 0.54 H
-ATOM 201 HB THR A 262 -2.761 7.618 -5.137 1.00 0.54 H
-ATOM 202 HG1 THR A 262 -1.575 6.758 -6.847 1.00 0.62 H
-ATOM 203 HG21 THR A 262 -3.416 9.746 -7.164 1.00 1.24 H
-ATOM 204 HG22 THR A 262 -3.722 9.930 -5.438 1.00 1.28 H
-ATOM 205 HG23 THR A 262 -4.526 8.648 -6.345 1.00 1.23 H
-ATOM 206 N MET A 263 -1.752 9.264 -3.124 1.00 0.40 N
-ATOM 207 CA MET A 263 -2.110 9.951 -1.880 1.00 0.43 C
-ATOM 208 C MET A 263 -1.038 10.955 -1.438 1.00 0.38 C
-ATOM 209 O MET A 263 -1.358 12.087 -1.071 1.00 0.43 O
-ATOM 210 CB MET A 263 -2.354 8.934 -0.761 1.00 0.48 C
-ATOM 211 CG MET A 263 -3.543 8.021 -1.016 1.00 0.59 C
-ATOM 212 SD MET A 263 -5.082 8.932 -1.243 1.00 0.75 S
-ATOM 213 CE MET A 263 -5.255 9.719 0.356 1.00 0.86 C
-ATOM 214 H MET A 263 -1.636 8.287 -3.117 1.00 0.38 H
-ATOM 215 HA MET A 263 -3.027 10.489 -2.060 1.00 0.49 H
-ATOM 216 HB2 MET A 263 -1.473 8.320 -0.651 1.00 0.48 H
-ATOM 217 HB3 MET A 263 -2.528 9.467 0.162 1.00 0.53 H
-ATOM 218 HG2 MET A 263 -3.350 7.442 -1.906 1.00 0.61 H
-ATOM 219 HG3 MET A 263 -3.656 7.357 -0.173 1.00 0.65 H
-ATOM 220 HE1 MET A 263 -4.967 10.758 0.280 1.00 1.29 H
-ATOM 221 HE2 MET A 263 -4.619 9.222 1.073 1.00 1.42 H
-ATOM 222 HE3 MET A 263 -6.283 9.655 0.681 1.00 1.38 H
-ATOM 223 N THR A 264 0.234 10.551 -1.467 1.00 0.35 N
-ATOM 224 CA THR A 264 1.325 11.445 -1.055 1.00 0.38 C
-ATOM 225 C THR A 264 1.683 12.475 -2.145 1.00 0.34 C
-ATOM 226 O THR A 264 2.842 12.602 -2.546 1.00 0.40 O
-ATOM 227 CB THR A 264 2.596 10.663 -0.629 1.00 0.46 C
-ATOM 228 OG1 THR A 264 3.620 11.580 -0.224 1.00 0.53 O
-ATOM 229 CG2 THR A 264 3.125 9.777 -1.750 1.00 0.48 C
-ATOM 230 H THR A 264 0.442 9.633 -1.765 1.00 0.35 H
-ATOM 231 HA THR A 264 0.974 11.991 -0.190 1.00 0.45 H
-ATOM 232 HB THR A 264 2.340 10.035 0.214 1.00 0.53 H
-ATOM 233 HG1 THR A 264 3.834 12.165 -0.964 1.00 0.52 H
-ATOM 234 HG21 THR A 264 2.349 9.093 -2.063 1.00 1.03 H
-ATOM 235 HG22 THR A 264 3.976 9.216 -1.394 1.00 0.95 H
-ATOM 236 HG23 THR A 264 3.421 10.392 -2.586 1.00 0.98 H
-ATOM 237 N THR A 265 0.678 13.211 -2.616 1.00 0.32 N
-ATOM 238 CA THR A 265 0.884 14.229 -3.653 1.00 0.34 C
-ATOM 239 C THR A 265 1.861 15.322 -3.208 1.00 0.39 C
-ATOM 240 O THR A 265 2.772 15.680 -3.957 1.00 0.48 O
-ATOM 241 CB THR A 265 -0.445 14.891 -4.097 1.00 0.37 C
-ATOM 242 OG1 THR A 265 -0.180 16.084 -4.855 1.00 0.39 O
-ATOM 243 CG2 THR A 265 -1.326 15.230 -2.902 1.00 0.44 C
-ATOM 244 H THR A 265 -0.227 13.066 -2.254 1.00 0.35 H
-ATOM 245 HA THR A 265 1.306 13.729 -4.514 1.00 0.42 H
-ATOM 246 HB THR A 265 -0.978 14.191 -4.727 1.00 0.46 H
-ATOM 247 HG1 THR A 265 0.117 15.843 -5.751 1.00 0.40 H
-ATOM 248 HG21 THR A 265 -2.083 15.939 -3.201 1.00 1.08 H
-ATOM 249 HG22 THR A 265 -0.719 15.659 -2.118 1.00 1.12 H
-ATOM 250 HG23 THR A 265 -1.799 14.329 -2.537 1.00 1.15 H
-ATOM 251 N GLN A 266 1.666 15.842 -1.987 1.00 0.45 N
-ATOM 252 CA GLN A 266 2.519 16.901 -1.412 1.00 0.59 C
-ATOM 253 C GLN A 266 2.346 18.244 -2.136 1.00 0.59 C
-ATOM 254 O GLN A 266 2.179 19.284 -1.500 1.00 0.71 O
-ATOM 255 CB GLN A 266 3.994 16.483 -1.422 1.00 0.74 C
-ATOM 256 CG GLN A 266 4.281 15.231 -0.606 1.00 0.86 C
-ATOM 257 CD GLN A 266 5.736 14.798 -0.671 1.00 1.12 C
-ATOM 258 OE1 GLN A 266 6.114 13.771 -0.119 1.00 1.33 O
-ATOM 259 NE2 GLN A 266 6.566 15.582 -1.340 1.00 1.25 N
-ATOM 260 H GLN A 266 0.919 15.503 -1.452 1.00 0.46 H
-ATOM 261 HA GLN A 266 2.210 17.034 -0.389 1.00 0.68 H
-ATOM 262 HB2 GLN A 266 4.297 16.298 -2.442 1.00 0.75 H
-ATOM 263 HB3 GLN A 266 4.588 17.291 -1.021 1.00 0.88 H
-ATOM 264 HG2 GLN A 266 4.028 15.424 0.426 1.00 0.95 H
-ATOM 265 HG3 GLN A 266 3.665 14.426 -0.980 1.00 0.82 H
-ATOM 266 HE21 GLN A 266 6.206 16.390 -1.756 1.00 1.19 H
-ATOM 267 HE22 GLN A 266 7.507 15.318 -1.389 1.00 1.48 H
-ATOM 268 N SER A 267 2.381 18.211 -3.465 1.00 0.53 N
-ATOM 269 CA SER A 267 2.223 19.415 -4.282 1.00 0.59 C
-ATOM 270 C SER A 267 0.792 19.953 -4.221 1.00 0.61 C
-ATOM 271 O SER A 267 0.559 21.130 -4.495 1.00 0.74 O
-ATOM 272 CB SER A 267 2.603 19.137 -5.738 1.00 0.62 C
-ATOM 273 OG SER A 267 2.423 20.294 -6.540 1.00 0.75 O
-ATOM 274 H SER A 267 2.515 17.341 -3.912 1.00 0.49 H
-ATOM 275 HA SER A 267 2.889 20.168 -3.888 1.00 0.72 H
-ATOM 276 HB2 SER A 267 3.639 18.838 -5.787 1.00 0.70 H
-ATOM 277 HB3 SER A 267 1.981 18.344 -6.126 1.00 0.58 H
-ATOM 278 HG SER A 267 1.920 20.954 -6.047 1.00 0.76 H
-ATOM 279 N SER A 268 -0.157 19.076 -3.869 1.00 0.58 N
-ATOM 280 CA SER A 268 -1.578 19.439 -3.766 1.00 0.73 C
-ATOM 281 C SER A 268 -2.171 19.815 -5.130 1.00 0.74 C
-ATOM 282 O SER A 268 -2.856 20.831 -5.272 1.00 0.97 O
-ATOM 283 CB SER A 268 -1.780 20.587 -2.764 1.00 0.94 C
-ATOM 284 OG SER A 268 -3.146 20.957 -2.677 1.00 1.15 O
-ATOM 285 H SER A 268 0.109 18.152 -3.673 1.00 0.55 H
-ATOM 286 HA SER A 268 -2.104 18.570 -3.400 1.00 0.76 H
-ATOM 287 HB2 SER A 268 -1.443 20.272 -1.788 1.00 0.99 H
-ATOM 288 HB3 SER A 268 -1.206 21.444 -3.084 1.00 0.98 H
-ATOM 289 HG SER A 268 -3.473 21.168 -3.563 1.00 1.14 H
-ATOM 290 N GLY A 269 -1.905 18.981 -6.130 1.00 0.62 N
-ATOM 291 CA GLY A 269 -2.416 19.235 -7.469 1.00 0.70 C
-ATOM 292 C GLY A 269 -2.719 17.956 -8.235 1.00 0.61 C
-ATOM 293 O GLY A 269 -2.291 17.799 -9.384 1.00 0.59 O
-ATOM 294 H GLY A 269 -1.354 18.187 -5.959 1.00 0.60 H
-ATOM 295 HA2 GLY A 269 -3.322 19.818 -7.391 1.00 0.86 H
-ATOM 296 HA3 GLY A 269 -1.682 19.805 -8.020 1.00 0.73 H
-ATOM 297 N SER A 270 -3.461 17.045 -7.586 1.00 0.63 N
-ATOM 298 CA SER A 270 -3.855 15.744 -8.168 1.00 0.63 C
-ATOM 299 C SER A 270 -2.663 14.791 -8.325 1.00 0.53 C
-ATOM 300 O SER A 270 -2.699 13.656 -7.851 1.00 0.61 O
-ATOM 301 CB SER A 270 -4.556 15.932 -9.522 1.00 0.72 C
-ATOM 302 OG SER A 270 -5.740 16.700 -9.387 1.00 0.91 O
-ATOM 303 H SER A 270 -3.759 17.254 -6.676 1.00 0.69 H
-ATOM 304 HA SER A 270 -4.558 15.291 -7.483 1.00 0.72 H
-ATOM 305 HB2 SER A 270 -3.888 16.441 -10.201 1.00 0.71 H
-ATOM 306 HB3 SER A 270 -4.812 14.964 -9.927 1.00 0.78 H
-ATOM 307 HG SER A 270 -5.942 17.129 -10.223 1.00 1.09 H
-ATOM 308 N ARG A 271 -1.611 15.264 -8.980 1.00 0.48 N
-ATOM 309 CA ARG A 271 -0.404 14.468 -9.187 1.00 0.47 C
-ATOM 310 C ARG A 271 0.541 14.598 -7.995 1.00 0.40 C
-ATOM 311 O ARG A 271 0.549 15.621 -7.308 1.00 0.40 O
-ATOM 312 CB ARG A 271 0.343 14.903 -10.457 1.00 0.52 C
-ATOM 313 CG ARG A 271 -0.417 14.685 -11.761 1.00 0.70 C
-ATOM 314 CD ARG A 271 -1.423 15.798 -12.035 1.00 0.79 C
-ATOM 315 NE ARG A 271 -0.818 17.134 -11.994 1.00 0.73 N
-ATOM 316 CZ ARG A 271 0.090 17.579 -12.849 1.00 0.71 C
-ATOM 317 NH1 ARG A 271 0.512 16.841 -13.840 1.00 0.74 N
-ATOM 318 NH2 ARG A 271 0.579 18.781 -12.721 1.00 0.80 N
-ATOM 319 H ARG A 271 -1.642 16.187 -9.326 1.00 0.54 H
-ATOM 320 HA ARG A 271 -0.701 13.435 -9.289 1.00 0.59 H
-ATOM 321 HB2 ARG A 271 0.571 15.955 -10.379 1.00 0.47 H
-ATOM 322 HB3 ARG A 271 1.271 14.351 -10.515 1.00 0.61 H
-ATOM 323 HG2 ARG A 271 0.291 14.650 -12.575 1.00 0.72 H
-ATOM 324 HG3 ARG A 271 -0.945 13.744 -11.701 1.00 0.85 H
-ATOM 325 HD2 ARG A 271 -1.852 15.641 -13.012 1.00 0.94 H
-ATOM 326 HD3 ARG A 271 -2.205 15.746 -11.291 1.00 0.86 H
-ATOM 327 HE ARG A 271 -1.112 17.734 -11.276 1.00 0.79 H
-ATOM 328 HH11 ARG A 271 0.152 15.924 -13.973 1.00 0.83 H
-ATOM 329 HH12 ARG A 271 1.213 17.215 -14.474 1.00 0.77 H
-ATOM 330 HH21 ARG A 271 0.271 19.374 -11.983 1.00 0.91 H
-ATOM 331 HH22 ARG A 271 1.267 19.105 -13.378 1.00 0.83 H
-ATOM 332 N ALA A 272 1.348 13.568 -7.764 1.00 0.45 N
-ATOM 333 CA ALA A 272 2.318 13.584 -6.671 1.00 0.47 C
-ATOM 334 C ALA A 272 3.512 14.477 -7.036 1.00 0.41 C
-ATOM 335 O ALA A 272 3.344 15.655 -7.355 1.00 0.45 O
-ATOM 336 CB ALA A 272 2.777 12.167 -6.356 1.00 0.61 C
-ATOM 337 H ALA A 272 1.304 12.785 -8.352 1.00 0.54 H
-ATOM 338 HA ALA A 272 1.830 13.989 -5.795 1.00 0.51 H
-ATOM 339 HB1 ALA A 272 1.914 11.544 -6.169 1.00 1.25 H
-ATOM 340 HB2 ALA A 272 3.408 12.180 -5.479 1.00 1.20 H
-ATOM 341 HB3 ALA A 272 3.331 11.774 -7.194 1.00 1.15 H
-ATOM 342 N SER A 273 4.716 13.917 -7.022 1.00 0.44 N
-ATOM 343 CA SER A 273 5.910 14.677 -7.387 1.00 0.46 C
-ATOM 344 C SER A 273 6.065 14.769 -8.891 1.00 0.42 C
-ATOM 345 O SER A 273 7.133 14.513 -9.447 1.00 0.53 O
-ATOM 346 CB SER A 273 7.162 14.074 -6.770 1.00 0.62 C
-ATOM 347 OG SER A 273 7.239 12.680 -7.023 1.00 0.72 O
-ATOM 348 H SER A 273 4.807 12.970 -6.784 1.00 0.52 H
-ATOM 349 HA SER A 273 5.790 15.688 -7.017 1.00 0.46 H
-ATOM 350 HB2 SER A 273 8.028 14.556 -7.201 1.00 0.68 H
-ATOM 351 HB3 SER A 273 7.147 14.242 -5.707 1.00 0.72 H
-ATOM 352 HG SER A 273 7.764 12.529 -7.816 1.00 1.02 H
-ATOM 353 N LEU A 274 4.998 15.201 -9.536 1.00 0.36 N
-ATOM 354 CA LEU A 274 5.004 15.407 -10.966 1.00 0.40 C
-ATOM 355 C LEU A 274 4.471 16.813 -11.293 1.00 0.36 C
-ATOM 356 O LEU A 274 3.582 16.954 -12.133 1.00 0.46 O
-ATOM 357 CB LEU A 274 4.164 14.331 -11.665 1.00 0.53 C
-ATOM 358 CG LEU A 274 4.303 14.279 -13.190 1.00 0.65 C
-ATOM 359 CD1 LEU A 274 5.735 13.949 -13.585 1.00 0.72 C
-ATOM 360 CD2 LEU A 274 3.336 13.263 -13.778 1.00 0.84 C
-ATOM 361 H LEU A 274 4.192 15.432 -9.018 1.00 0.35 H
-ATOM 362 HA LEU A 274 6.026 15.334 -11.307 1.00 0.45 H
-ATOM 363 HB2 LEU A 274 4.447 13.368 -11.265 1.00 0.63 H
-ATOM 364 HB3 LEU A 274 3.125 14.507 -11.428 1.00 0.57 H
-ATOM 365 HG LEU A 274 4.061 15.249 -13.600 1.00 0.67 H
-ATOM 366 HD11 LEU A 274 6.401 14.700 -13.186 1.00 1.33 H
-ATOM 367 HD12 LEU A 274 5.816 13.931 -14.662 1.00 1.24 H
-ATOM 368 HD13 LEU A 274 6.005 12.981 -13.188 1.00 1.23 H
-ATOM 369 HD21 LEU A 274 3.020 12.578 -13.006 1.00 1.45 H
-ATOM 370 HD22 LEU A 274 3.827 12.714 -14.568 1.00 1.38 H
-ATOM 371 HD23 LEU A 274 2.475 13.776 -14.180 1.00 1.24 H
-ATOM 372 N PRO A 275 4.978 17.879 -10.599 1.00 0.34 N
-ATOM 373 CA PRO A 275 4.523 19.280 -10.811 1.00 0.45 C
-ATOM 374 C PRO A 275 4.757 19.828 -12.238 1.00 0.51 C
-ATOM 375 O PRO A 275 5.410 20.857 -12.417 1.00 0.60 O
-ATOM 376 CB PRO A 275 5.363 20.078 -9.806 1.00 0.56 C
-ATOM 377 CG PRO A 275 5.710 19.087 -8.763 1.00 0.52 C
-ATOM 378 CD PRO A 275 5.993 17.824 -9.514 1.00 0.40 C
-ATOM 379 HA PRO A 275 3.481 19.388 -10.554 1.00 0.51 H
-ATOM 380 HB2 PRO A 275 6.245 20.467 -10.296 1.00 0.62 H
-ATOM 381 HB3 PRO A 275 4.777 20.890 -9.403 1.00 0.68 H
-ATOM 382 HG2 PRO A 275 6.580 19.410 -8.211 1.00 0.65 H
-ATOM 383 HG3 PRO A 275 4.866 18.948 -8.106 1.00 0.54 H
-ATOM 384 HD2 PRO A 275 6.998 17.831 -9.906 1.00 0.49 H
-ATOM 385 HD3 PRO A 275 5.840 16.952 -8.871 1.00 0.39 H
-ATOM 386 N LYS A 276 4.216 19.140 -13.240 1.00 0.57 N
-ATOM 387 CA LYS A 276 4.349 19.542 -14.642 1.00 0.68 C
-ATOM 388 C LYS A 276 3.326 18.812 -15.522 1.00 0.80 C
-ATOM 389 O LYS A 276 3.585 18.668 -16.742 1.00 0.94 O
-ATOM 390 CB LYS A 276 5.762 19.252 -15.158 1.00 0.70 C
-ATOM 391 CG LYS A 276 6.176 17.793 -15.024 1.00 0.73 C
-ATOM 392 CD LYS A 276 7.182 17.392 -16.092 1.00 0.87 C
-ATOM 393 CE LYS A 276 6.496 16.938 -17.378 1.00 1.01 C
-ATOM 394 NZ LYS A 276 5.706 18.025 -18.024 1.00 1.07 N
-ATOM 395 OXT LYS A 276 2.283 18.374 -14.981 1.00 0.87 O
-ATOM 396 H LYS A 276 3.702 18.323 -13.028 1.00 0.60 H
-ATOM 397 HA LYS A 276 4.164 20.605 -14.701 1.00 0.75 H
-ATOM 398 HB2 LYS A 276 5.814 19.523 -16.202 1.00 0.79 H
-ATOM 399 HB3 LYS A 276 6.466 19.855 -14.604 1.00 0.71 H
-ATOM 400 HG2 LYS A 276 6.621 17.643 -14.052 1.00 0.74 H
-ATOM 401 HG3 LYS A 276 5.297 17.171 -15.118 1.00 0.79 H
-ATOM 402 HD2 LYS A 276 7.810 18.241 -16.316 1.00 0.91 H
-ATOM 403 HD3 LYS A 276 7.790 16.583 -15.714 1.00 0.95 H
-ATOM 404 HE2 LYS A 276 7.252 16.599 -18.071 1.00 1.14 H
-ATOM 405 HE3 LYS A 276 5.835 16.116 -17.143 1.00 1.10 H
-ATOM 406 HZ1 LYS A 276 6.266 18.899 -18.068 1.00 1.34 H
-ATOM 407 HZ2 LYS A 276 4.827 18.215 -17.474 1.00 1.52 H
-ATOM 408 HZ3 LYS A 276 5.440 17.746 -18.989 1.00 1.34 H
-TER
-ENDMDL
-MODEL 2
-ATOM 1 N ALA A 249 2.985 -12.247 3.088 1.00 3.58 N
-ATOM 2 CA ALA A 249 3.192 -11.415 1.867 1.00 3.44 C
-ATOM 3 C ALA A 249 1.900 -10.700 1.455 1.00 2.78 C
-ATOM 4 O ALA A 249 0.807 -11.156 1.783 1.00 2.62 O
-ATOM 5 CB ALA A 249 3.715 -12.274 0.725 1.00 3.93 C
-ATOM 6 H1 ALA A 249 3.513 -11.846 3.890 1.00 3.69 H
-ATOM 7 H2 ALA A 249 3.321 -13.217 2.919 1.00 4.00 H
-ATOM 8 H3 ALA A 249 1.972 -12.281 3.329 1.00 3.31 H
-ATOM 9 HA ALA A 249 3.942 -10.672 2.096 1.00 3.65 H
-ATOM 10 HB1 ALA A 249 4.795 -12.266 0.739 1.00 4.14 H
-ATOM 11 HB2 ALA A 249 3.366 -11.874 -0.215 1.00 4.21 H
-ATOM 12 HB3 ALA A 249 3.356 -13.283 0.843 1.00 4.24 H
-ATOM 13 N PRO A 250 2.021 -9.559 0.744 1.00 2.62 N
-ATOM 14 CA PRO A 250 0.870 -8.765 0.292 1.00 2.15 C
-ATOM 15 C PRO A 250 0.142 -9.379 -0.923 1.00 1.89 C
-ATOM 16 O PRO A 250 0.069 -10.601 -1.062 1.00 2.59 O
-ATOM 17 CB PRO A 250 1.514 -7.415 -0.054 1.00 2.42 C
-ATOM 18 CG PRO A 250 2.919 -7.721 -0.427 1.00 2.96 C
-ATOM 19 CD PRO A 250 3.302 -8.957 0.333 1.00 3.13 C
-ATOM 20 HA PRO A 250 0.158 -8.622 1.091 1.00 2.16 H
-ATOM 21 HB2 PRO A 250 0.988 -6.960 -0.872 1.00 2.41 H
-ATOM 22 HB3 PRO A 250 1.479 -6.770 0.811 1.00 2.56 H
-ATOM 23 HG2 PRO A 250 2.988 -7.896 -1.489 1.00 3.35 H
-ATOM 24 HG3 PRO A 250 3.560 -6.896 -0.141 1.00 3.12 H
-ATOM 25 HD2 PRO A 250 3.840 -9.629 -0.308 1.00 3.40 H
-ATOM 26 HD3 PRO A 250 3.895 -8.695 1.194 1.00 3.54 H
-ATOM 27 N ALA A 251 -0.403 -8.526 -1.790 1.00 1.31 N
-ATOM 28 CA ALA A 251 -1.132 -8.991 -2.979 1.00 1.53 C
-ATOM 29 C ALA A 251 -1.037 -7.981 -4.124 1.00 1.32 C
-ATOM 30 O ALA A 251 -0.369 -8.220 -5.129 1.00 1.33 O
-ATOM 31 CB ALA A 251 -2.600 -9.242 -2.640 1.00 2.06 C
-ATOM 32 H ALA A 251 -0.321 -7.566 -1.623 1.00 1.26 H
-ATOM 33 HA ALA A 251 -0.697 -9.928 -3.299 1.00 1.86 H
-ATOM 34 HB1 ALA A 251 -2.891 -10.221 -2.990 1.00 2.55 H
-ATOM 35 HB2 ALA A 251 -3.212 -8.493 -3.121 1.00 2.27 H
-ATOM 36 HB3 ALA A 251 -2.738 -9.185 -1.571 1.00 2.39 H
-ATOM 37 N ARG A 252 -1.704 -6.845 -3.966 1.00 1.19 N
-ATOM 38 CA ARG A 252 -1.683 -5.805 -4.988 1.00 1.05 C
-ATOM 39 C ARG A 252 -0.619 -4.759 -4.710 1.00 0.92 C
-ATOM 40 O ARG A 252 -0.918 -3.602 -4.412 1.00 0.85 O
-ATOM 41 CB ARG A 252 -3.054 -5.163 -5.146 1.00 1.10 C
-ATOM 42 CG ARG A 252 -3.997 -6.015 -5.976 1.00 1.27 C
-ATOM 43 CD ARG A 252 -3.261 -6.654 -7.155 1.00 1.27 C
-ATOM 44 NE ARG A 252 -4.128 -7.532 -7.948 1.00 1.46 N
-ATOM 45 CZ ARG A 252 -3.711 -8.266 -8.965 1.00 1.53 C
-ATOM 46 NH1 ARG A 252 -2.458 -8.222 -9.342 1.00 1.47 N
-ATOM 47 NH2 ARG A 252 -4.551 -9.046 -9.604 1.00 1.74 N
-ATOM 48 H ARG A 252 -2.215 -6.700 -3.148 1.00 1.26 H
-ATOM 49 HA ARG A 252 -1.431 -6.290 -5.919 1.00 1.09 H
-ATOM 50 HB2 ARG A 252 -3.488 -5.020 -4.166 1.00 1.15 H
-ATOM 51 HB3 ARG A 252 -2.942 -4.203 -5.629 1.00 1.03 H
-ATOM 52 HG2 ARG A 252 -4.409 -6.793 -5.351 1.00 1.42 H
-ATOM 53 HG3 ARG A 252 -4.793 -5.391 -6.353 1.00 1.32 H
-ATOM 54 HD2 ARG A 252 -2.874 -5.869 -7.789 1.00 1.17 H
-ATOM 55 HD3 ARG A 252 -2.431 -7.234 -6.766 1.00 1.28 H
-ATOM 56 HE ARG A 252 -5.072 -7.580 -7.698 1.00 1.59 H
-ATOM 57 HH11 ARG A 252 -1.815 -7.632 -8.864 1.00 1.37 H
-ATOM 58 HH12 ARG A 252 -2.149 -8.781 -10.108 1.00 1.58 H
-ATOM 59 HH21 ARG A 252 -5.508 -9.086 -9.326 1.00 1.86 H
-ATOM 60 HH22 ARG A 252 -4.232 -9.602 -10.370 1.00 1.83 H
-ATOM 61 N VAL A 253 0.623 -5.192 -4.819 1.00 0.97 N
-ATOM 62 CA VAL A 253 1.778 -4.331 -4.596 1.00 0.96 C
-ATOM 63 C VAL A 253 1.731 -3.087 -5.487 1.00 0.80 C
-ATOM 64 O VAL A 253 1.861 -1.963 -5.005 1.00 0.73 O
-ATOM 65 CB VAL A 253 3.076 -5.099 -4.881 1.00 1.16 C
-ATOM 66 CG1 VAL A 253 4.278 -4.324 -4.377 1.00 1.33 C
-ATOM 67 CG2 VAL A 253 3.014 -6.492 -4.267 1.00 1.34 C
-ATOM 68 H VAL A 253 0.769 -6.132 -5.058 1.00 1.06 H
-ATOM 69 HA VAL A 253 1.780 -4.028 -3.562 1.00 1.00 H
-ATOM 70 HB VAL A 253 3.173 -5.210 -5.950 1.00 1.16 H
-ATOM 71 HG11 VAL A 253 4.092 -3.266 -4.488 1.00 1.70 H
-ATOM 72 HG12 VAL A 253 5.149 -4.600 -4.949 1.00 1.74 H
-ATOM 73 HG13 VAL A 253 4.442 -4.553 -3.334 1.00 1.70 H
-ATOM 74 HG21 VAL A 253 3.630 -6.525 -3.382 1.00 1.76 H
-ATOM 75 HG22 VAL A 253 3.368 -7.218 -4.983 1.00 1.73 H
-ATOM 76 HG23 VAL A 253 1.990 -6.721 -4.001 1.00 1.69 H
-ATOM 77 N GLY A 254 1.527 -3.308 -6.787 1.00 0.81 N
-ATOM 78 CA GLY A 254 1.447 -2.208 -7.738 1.00 0.77 C
-ATOM 79 C GLY A 254 0.328 -1.237 -7.399 1.00 0.64 C
-ATOM 80 O GLY A 254 0.530 -0.020 -7.386 1.00 0.61 O
-ATOM 81 H GLY A 254 1.422 -4.228 -7.098 1.00 0.89 H
-ATOM 82 HA2 GLY A 254 2.386 -1.673 -7.736 1.00 0.86 H
-ATOM 83 HA3 GLY A 254 1.275 -2.610 -8.724 1.00 0.84 H
-ATOM 84 N LEU A 255 -0.849 -1.778 -7.095 1.00 0.64 N
-ATOM 85 CA LEU A 255 -2.001 -0.960 -6.721 1.00 0.64 C
-ATOM 86 C LEU A 255 -1.719 -0.182 -5.442 1.00 0.56 C
-ATOM 87 O LEU A 255 -2.011 1.009 -5.350 1.00 0.58 O
-ATOM 88 CB LEU A 255 -3.235 -1.829 -6.509 1.00 0.79 C
-ATOM 89 CG LEU A 255 -4.223 -1.846 -7.671 1.00 0.97 C
-ATOM 90 CD1 LEU A 255 -5.417 -2.723 -7.333 1.00 1.15 C
-ATOM 91 CD2 LEU A 255 -4.670 -0.430 -8.001 1.00 1.12 C
-ATOM 92 H LEU A 255 -0.940 -2.752 -7.102 1.00 0.71 H
-ATOM 93 HA LEU A 255 -2.200 -0.266 -7.523 1.00 0.71 H
-ATOM 94 HB2 LEU A 255 -2.908 -2.842 -6.326 1.00 0.86 H
-ATOM 95 HB3 LEU A 255 -3.753 -1.471 -5.632 1.00 0.87 H
-ATOM 96 HG LEU A 255 -3.737 -2.259 -8.542 1.00 1.02 H
-ATOM 97 HD11 LEU A 255 -6.117 -2.704 -8.153 1.00 1.51 H
-ATOM 98 HD12 LEU A 255 -5.895 -2.351 -6.439 1.00 1.58 H
-ATOM 99 HD13 LEU A 255 -5.083 -3.737 -7.167 1.00 1.62 H
-ATOM 100 HD21 LEU A 255 -4.707 -0.306 -9.072 1.00 1.29 H
-ATOM 101 HD22 LEU A 255 -3.967 0.275 -7.581 1.00 1.11 H
-ATOM 102 HD23 LEU A 255 -5.648 -0.254 -7.582 1.00 1.28 H
-ATOM 103 N GLY A 256 -1.147 -0.868 -4.457 1.00 0.57 N
-ATOM 104 CA GLY A 256 -0.831 -0.238 -3.197 1.00 0.58 C
-ATOM 105 C GLY A 256 0.171 0.891 -3.339 1.00 0.50 C
-ATOM 106 O GLY A 256 -0.075 1.996 -2.861 1.00 0.51 O
-ATOM 107 H GLY A 256 -0.939 -1.820 -4.590 1.00 0.64 H
-ATOM 108 HA2 GLY A 256 -1.741 0.158 -2.770 1.00 0.64 H
-ATOM 109 HA3 GLY A 256 -0.427 -0.982 -2.532 1.00 0.69 H
-ATOM 110 N ILE A 257 1.296 0.631 -4.010 1.00 0.55 N
-ATOM 111 CA ILE A 257 2.309 1.669 -4.200 1.00 0.62 C
-ATOM 112 C ILE A 257 1.755 2.817 -5.051 1.00 0.58 C
-ATOM 113 O ILE A 257 2.047 3.984 -4.796 1.00 0.66 O
-ATOM 114 CB ILE A 257 3.633 1.124 -4.797 1.00 0.81 C
-ATOM 115 CG1 ILE A 257 3.435 0.517 -6.191 1.00 0.84 C
-ATOM 116 CG2 ILE A 257 4.244 0.105 -3.849 1.00 0.94 C
-ATOM 117 CD1 ILE A 257 3.737 1.478 -7.319 1.00 0.96 C
-ATOM 118 H ILE A 257 1.442 -0.270 -4.384 1.00 0.60 H
-ATOM 119 HA ILE A 257 2.533 2.067 -3.217 1.00 0.66 H
-ATOM 120 HB ILE A 257 4.324 1.948 -4.874 1.00 0.89 H
-ATOM 121 HG12 ILE A 257 4.082 -0.339 -6.306 1.00 0.98 H
-ATOM 122 HG13 ILE A 257 2.408 0.201 -6.296 1.00 0.75 H
-ATOM 123 HG21 ILE A 257 4.423 -0.820 -4.377 1.00 1.44 H
-ATOM 124 HG22 ILE A 257 3.565 -0.075 -3.029 1.00 1.38 H
-ATOM 125 HG23 ILE A 257 5.179 0.487 -3.463 1.00 1.44 H
-ATOM 126 HD11 ILE A 257 3.038 2.303 -7.287 1.00 1.42 H
-ATOM 127 HD12 ILE A 257 3.647 0.962 -8.262 1.00 1.45 H
-ATOM 128 HD13 ILE A 257 4.740 1.854 -7.212 1.00 1.41 H
-ATOM 129 N THR A 258 0.923 2.481 -6.040 1.00 0.56 N
-ATOM 130 CA THR A 258 0.302 3.491 -6.894 1.00 0.68 C
-ATOM 131 C THR A 258 -0.662 4.346 -6.066 1.00 0.67 C
-ATOM 132 O THR A 258 -0.694 5.569 -6.195 1.00 0.78 O
-ATOM 133 CB THR A 258 -0.475 2.859 -8.072 1.00 0.78 C
-ATOM 134 OG1 THR A 258 0.404 2.062 -8.876 1.00 0.84 O
-ATOM 135 CG2 THR A 258 -1.126 3.928 -8.939 1.00 1.01 C
-ATOM 136 H THR A 258 0.704 1.531 -6.182 1.00 0.53 H
-ATOM 137 HA THR A 258 1.084 4.121 -7.294 1.00 0.80 H
-ATOM 138 HB THR A 258 -1.253 2.226 -7.667 1.00 0.74 H
-ATOM 139 HG1 THR A 258 0.519 1.193 -8.464 1.00 0.76 H
-ATOM 140 HG21 THR A 258 -0.876 3.753 -9.974 1.00 1.46 H
-ATOM 141 HG22 THR A 258 -0.765 4.904 -8.640 1.00 1.48 H
-ATOM 142 HG23 THR A 258 -2.198 3.890 -8.817 1.00 1.50 H
-ATOM 143 N THR A 259 -1.428 3.683 -5.191 1.00 0.61 N
-ATOM 144 CA THR A 259 -2.374 4.371 -4.311 1.00 0.72 C
-ATOM 145 C THR A 259 -1.622 5.288 -3.351 1.00 0.69 C
-ATOM 146 O THR A 259 -1.947 6.469 -3.224 1.00 0.82 O
-ATOM 147 CB THR A 259 -3.231 3.376 -3.493 1.00 0.79 C
-ATOM 148 OG1 THR A 259 -3.925 2.474 -4.369 1.00 0.86 O
-ATOM 149 CG2 THR A 259 -4.243 4.108 -2.623 1.00 0.99 C
-ATOM 150 H THR A 259 -1.337 2.705 -5.121 1.00 0.56 H
-ATOM 151 HA THR A 259 -3.033 4.968 -4.927 1.00 0.86 H
-ATOM 152 HB THR A 259 -2.577 2.804 -2.850 1.00 0.70 H
-ATOM 153 HG1 THR A 259 -3.284 1.903 -4.817 1.00 0.78 H
-ATOM 154 HG21 THR A 259 -3.751 4.907 -2.091 1.00 1.43 H
-ATOM 155 HG22 THR A 259 -4.675 3.416 -1.912 1.00 1.42 H
-ATOM 156 HG23 THR A 259 -5.025 4.516 -3.246 1.00 1.51 H
-ATOM 157 N VAL A 260 -0.591 4.743 -2.705 1.00 0.59 N
-ATOM 158 CA VAL A 260 0.233 5.519 -1.781 1.00 0.63 C
-ATOM 159 C VAL A 260 0.882 6.697 -2.507 1.00 0.67 C
-ATOM 160 O VAL A 260 0.800 7.833 -2.043 1.00 0.76 O
-ATOM 161 CB VAL A 260 1.331 4.650 -1.128 1.00 0.67 C
-ATOM 162 CG1 VAL A 260 2.257 5.502 -0.271 1.00 0.81 C
-ATOM 163 CG2 VAL A 260 0.714 3.539 -0.294 1.00 0.76 C
-ATOM 164 H VAL A 260 -0.370 3.795 -2.868 1.00 0.54 H
-ATOM 165 HA VAL A 260 -0.412 5.900 -1.001 1.00 0.74 H
-ATOM 166 HB VAL A 260 1.918 4.200 -1.914 1.00 0.68 H
-ATOM 167 HG11 VAL A 260 1.889 5.524 0.743 1.00 1.31 H
-ATOM 168 HG12 VAL A 260 2.288 6.508 -0.667 1.00 1.35 H
-ATOM 169 HG13 VAL A 260 3.250 5.078 -0.288 1.00 1.33 H
-ATOM 170 HG21 VAL A 260 1.136 2.590 -0.593 1.00 1.28 H
-ATOM 171 HG22 VAL A 260 -0.354 3.522 -0.448 1.00 1.30 H
-ATOM 172 HG23 VAL A 260 0.926 3.712 0.750 1.00 1.29 H
-ATOM 173 N LEU A 261 1.500 6.421 -3.662 1.00 0.68 N
-ATOM 174 CA LEU A 261 2.143 7.461 -4.471 1.00 0.83 C
-ATOM 175 C LEU A 261 1.126 8.550 -4.820 1.00 0.93 C
-ATOM 176 O LEU A 261 1.418 9.741 -4.734 1.00 1.05 O
-ATOM 177 CB LEU A 261 2.718 6.849 -5.757 1.00 0.92 C
-ATOM 178 CG LEU A 261 4.091 7.376 -6.197 1.00 0.89 C
-ATOM 179 CD1 LEU A 261 4.026 8.853 -6.553 1.00 0.97 C
-ATOM 180 CD2 LEU A 261 5.126 7.132 -5.109 1.00 1.30 C
-ATOM 181 H LEU A 261 1.514 5.490 -3.988 1.00 0.64 H
-ATOM 182 HA LEU A 261 2.943 7.895 -3.891 1.00 0.89 H
-ATOM 183 HB2 LEU A 261 2.800 5.782 -5.613 1.00 1.20 H
-ATOM 184 HB3 LEU A 261 2.018 7.032 -6.557 1.00 1.01 H
-ATOM 185 HG LEU A 261 4.403 6.837 -7.079 1.00 1.01 H
-ATOM 186 HD11 LEU A 261 4.627 9.418 -5.856 1.00 1.48 H
-ATOM 187 HD12 LEU A 261 3.002 9.191 -6.502 1.00 1.47 H
-ATOM 188 HD13 LEU A 261 4.404 8.998 -7.554 1.00 1.45 H
-ATOM 189 HD21 LEU A 261 5.403 6.088 -5.105 1.00 1.73 H
-ATOM 190 HD22 LEU A 261 4.708 7.399 -4.150 1.00 1.57 H
-ATOM 191 HD23 LEU A 261 6.000 7.736 -5.303 1.00 1.75 H
-ATOM 192 N THR A 262 -0.079 8.120 -5.187 1.00 0.93 N
-ATOM 193 CA THR A 262 -1.168 9.039 -5.522 1.00 1.10 C
-ATOM 194 C THR A 262 -1.529 9.912 -4.316 1.00 1.13 C
-ATOM 195 O THR A 262 -1.655 11.131 -4.434 1.00 1.25 O
-ATOM 196 CB THR A 262 -2.423 8.266 -5.984 1.00 1.16 C
-ATOM 197 OG1 THR A 262 -2.134 7.528 -7.180 1.00 1.20 O
-ATOM 198 CG2 THR A 262 -3.591 9.206 -6.238 1.00 1.33 C
-ATOM 199 H THR A 262 -0.248 7.150 -5.218 1.00 0.84 H
-ATOM 200 HA THR A 262 -0.836 9.673 -6.332 1.00 1.22 H
-ATOM 201 HB THR A 262 -2.705 7.572 -5.205 1.00 1.10 H
-ATOM 202 HG1 THR A 262 -1.597 6.753 -6.954 1.00 1.06 H
-ATOM 203 HG21 THR A 262 -4.128 9.369 -5.314 1.00 1.71 H
-ATOM 204 HG22 THR A 262 -4.255 8.765 -6.967 1.00 1.72 H
-ATOM 205 HG23 THR A 262 -3.220 10.149 -6.610 1.00 1.69 H
-ATOM 206 N MET A 263 -1.684 9.272 -3.157 1.00 1.04 N
-ATOM 207 CA MET A 263 -2.021 9.974 -1.915 1.00 1.11 C
-ATOM 208 C MET A 263 -0.924 10.969 -1.510 1.00 1.10 C
-ATOM 209 O MET A 263 -1.195 12.152 -1.315 1.00 1.23 O
-ATOM 210 CB MET A 263 -2.246 8.969 -0.784 1.00 1.09 C
-ATOM 211 CG MET A 263 -3.440 8.056 -1.004 1.00 1.19 C
-ATOM 212 SD MET A 263 -3.588 6.786 0.269 1.00 1.30 S
-ATOM 213 CE MET A 263 -3.811 7.794 1.733 1.00 1.51 C
-ATOM 214 H MET A 263 -1.567 8.295 -3.138 1.00 0.96 H
-ATOM 215 HA MET A 263 -2.937 10.519 -2.084 1.00 1.26 H
-ATOM 216 HB2 MET A 263 -1.363 8.354 -0.684 1.00 0.99 H
-ATOM 217 HB3 MET A 263 -2.400 9.512 0.137 1.00 1.20 H
-ATOM 218 HG2 MET A 263 -4.339 8.654 -1.000 1.00 1.36 H
-ATOM 219 HG3 MET A 263 -3.334 7.572 -1.964 1.00 1.15 H
-ATOM 220 HE1 MET A 263 -4.373 8.680 1.477 1.00 1.81 H
-ATOM 221 HE2 MET A 263 -2.845 8.081 2.122 1.00 1.94 H
-ATOM 222 HE3 MET A 263 -4.348 7.230 2.481 1.00 1.84 H
-ATOM 223 N THR A 264 0.318 10.493 -1.397 1.00 0.99 N
-ATOM 224 CA THR A 264 1.439 11.370 -1.027 1.00 1.03 C
-ATOM 225 C THR A 264 2.044 12.055 -2.266 1.00 1.05 C
-ATOM 226 O THR A 264 3.264 12.106 -2.443 1.00 1.11 O
-ATOM 227 CB THR A 264 2.546 10.613 -0.240 1.00 1.05 C
-ATOM 228 OG1 THR A 264 3.602 11.513 0.118 1.00 1.15 O
-ATOM 229 CG2 THR A 264 3.122 9.452 -1.041 1.00 1.00 C
-ATOM 230 H THR A 264 0.488 9.536 -1.576 1.00 0.93 H
-ATOM 231 HA THR A 264 1.042 12.141 -0.379 1.00 1.12 H
-ATOM 232 HB THR A 264 2.106 10.218 0.666 1.00 1.11 H
-ATOM 233 HG1 THR A 264 4.007 11.864 -0.684 1.00 1.16 H
-ATOM 234 HG21 THR A 264 3.116 8.557 -0.436 1.00 1.40 H
-ATOM 235 HG22 THR A 264 4.136 9.683 -1.332 1.00 1.41 H
-ATOM 236 HG23 THR A 264 2.521 9.292 -1.925 1.00 1.45 H
-ATOM 237 N THR A 265 1.171 12.585 -3.122 1.00 1.11 N
-ATOM 238 CA THR A 265 1.586 13.265 -4.356 1.00 1.22 C
-ATOM 239 C THR A 265 2.187 14.660 -4.090 1.00 1.09 C
-ATOM 240 O THR A 265 1.897 15.626 -4.796 1.00 1.22 O
-ATOM 241 CB THR A 265 0.394 13.385 -5.337 1.00 1.39 C
-ATOM 242 OG1 THR A 265 0.808 13.987 -6.573 1.00 1.55 O
-ATOM 243 CG2 THR A 265 -0.748 14.192 -4.726 1.00 1.38 C
-ATOM 244 H THR A 265 0.210 12.511 -2.920 1.00 1.13 H
-ATOM 245 HA THR A 265 2.342 12.652 -4.827 1.00 1.33 H
-ATOM 246 HB THR A 265 0.028 12.389 -5.544 1.00 1.47 H
-ATOM 247 HG1 THR A 265 1.308 14.798 -6.386 1.00 1.53 H
-ATOM 248 HG21 THR A 265 -0.353 15.089 -4.273 1.00 1.68 H
-ATOM 249 HG22 THR A 265 -1.246 13.598 -3.972 1.00 1.70 H
-ATOM 250 HG23 THR A 265 -1.454 14.459 -5.498 1.00 1.78 H
-ATOM 251 N GLN A 266 3.041 14.755 -3.080 1.00 0.93 N
-ATOM 252 CA GLN A 266 3.690 16.019 -2.732 1.00 0.87 C
-ATOM 253 C GLN A 266 5.093 16.112 -3.349 1.00 0.94 C
-ATOM 254 O GLN A 266 6.093 16.152 -2.635 1.00 1.06 O
-ATOM 255 CB GLN A 266 3.767 16.172 -1.206 1.00 0.95 C
-ATOM 256 CG GLN A 266 2.419 16.422 -0.544 1.00 1.03 C
-ATOM 257 CD GLN A 266 1.827 17.771 -0.909 1.00 1.03 C
-ATOM 258 OE1 GLN A 266 2.395 18.815 -0.610 1.00 1.10 O
-ATOM 259 NE2 GLN A 266 0.682 17.758 -1.571 1.00 1.24 N
-ATOM 260 H GLN A 266 3.252 13.946 -2.559 1.00 0.93 H
-ATOM 261 HA GLN A 266 3.085 16.818 -3.130 1.00 0.80 H
-ATOM 262 HB2 GLN A 266 4.185 15.269 -0.786 1.00 1.16 H
-ATOM 263 HB3 GLN A 266 4.417 17.001 -0.971 1.00 0.94 H
-ATOM 264 HG2 GLN A 266 1.730 15.649 -0.851 1.00 1.20 H
-ATOM 265 HG3 GLN A 266 2.546 16.382 0.528 1.00 1.16 H
-ATOM 266 HE21 GLN A 266 0.278 16.894 -1.787 1.00 1.34 H
-ATOM 267 HE22 GLN A 266 0.284 18.620 -1.808 1.00 1.41 H
-ATOM 268 N SER A 267 5.165 16.140 -4.678 1.00 1.19 N
-ATOM 269 CA SER A 267 6.461 16.223 -5.366 1.00 1.43 C
-ATOM 270 C SER A 267 6.314 16.773 -6.785 1.00 1.43 C
-ATOM 271 O SER A 267 5.480 16.308 -7.564 1.00 1.44 O
-ATOM 272 CB SER A 267 7.135 14.844 -5.417 1.00 1.68 C
-ATOM 273 OG SER A 267 8.420 14.924 -6.017 1.00 1.93 O
-ATOM 274 H SER A 267 4.338 16.102 -5.207 1.00 1.35 H
-ATOM 275 HA SER A 267 7.089 16.895 -4.800 1.00 1.52 H
-ATOM 276 HB2 SER A 267 7.244 14.460 -4.415 1.00 1.75 H
-ATOM 277 HB3 SER A 267 6.523 14.168 -5.997 1.00 1.65 H
-ATOM 278 HG SER A 267 9.094 14.709 -5.365 1.00 2.14 H
-ATOM 279 N SER A 268 7.139 17.763 -7.118 1.00 1.60 N
-ATOM 280 CA SER A 268 7.115 18.371 -8.448 1.00 1.69 C
-ATOM 281 C SER A 268 8.107 17.668 -9.380 1.00 1.99 C
-ATOM 282 O SER A 268 9.307 17.631 -9.108 1.00 2.31 O
-ATOM 283 CB SER A 268 7.446 19.869 -8.367 1.00 1.93 C
-ATOM 284 OG SER A 268 6.452 20.582 -7.644 1.00 1.79 O
-ATOM 285 H SER A 268 7.786 18.085 -6.457 1.00 1.77 H
-ATOM 286 HA SER A 268 6.119 18.252 -8.848 1.00 1.45 H
-ATOM 287 HB2 SER A 268 8.393 19.999 -7.866 1.00 2.18 H
-ATOM 288 HB3 SER A 268 7.509 20.276 -9.365 1.00 2.07 H
-ATOM 289 HG SER A 268 6.732 21.495 -7.525 1.00 2.04 H
-ATOM 290 N GLY A 269 7.601 17.110 -10.479 1.00 1.92 N
-ATOM 291 CA GLY A 269 8.464 16.414 -11.425 1.00 2.23 C
-ATOM 292 C GLY A 269 7.693 15.782 -12.574 1.00 2.14 C
-ATOM 293 O GLY A 269 6.829 16.423 -13.170 1.00 2.00 O
-ATOM 294 H GLY A 269 6.637 17.166 -10.646 1.00 1.69 H
-ATOM 295 HA2 GLY A 269 9.175 17.118 -11.831 1.00 2.45 H
-ATOM 296 HA3 GLY A 269 9.002 15.639 -10.899 1.00 2.35 H
-ATOM 297 N SER A 270 8.008 14.523 -12.890 1.00 2.28 N
-ATOM 298 CA SER A 270 7.333 13.809 -13.983 1.00 2.29 C
-ATOM 299 C SER A 270 5.832 13.660 -13.711 1.00 1.96 C
-ATOM 300 O SER A 270 5.012 13.825 -14.612 1.00 1.89 O
-ATOM 301 CB SER A 270 7.965 12.428 -14.199 1.00 2.61 C
-ATOM 302 OG SER A 270 9.359 12.534 -14.439 1.00 2.93 O
-ATOM 303 H SER A 270 8.710 14.065 -12.383 1.00 2.44 H
-ATOM 304 HA SER A 270 7.462 14.394 -14.881 1.00 2.35 H
-ATOM 305 HB2 SER A 270 7.810 11.821 -13.320 1.00 2.58 H
-ATOM 306 HB3 SER A 270 7.501 11.954 -15.051 1.00 2.67 H
-ATOM 307 HG SER A 270 9.557 12.227 -15.328 1.00 3.17 H
-ATOM 308 N ARG A 271 5.475 13.365 -12.462 1.00 1.81 N
-ATOM 309 CA ARG A 271 4.069 13.224 -12.085 1.00 1.54 C
-ATOM 310 C ARG A 271 3.413 14.597 -11.915 1.00 1.20 C
-ATOM 311 O ARG A 271 3.916 15.451 -11.183 1.00 1.14 O
-ATOM 312 CB ARG A 271 3.930 12.428 -10.781 1.00 1.60 C
-ATOM 313 CG ARG A 271 2.496 12.356 -10.269 1.00 1.45 C
-ATOM 314 CD ARG A 271 2.421 11.761 -8.870 1.00 1.56 C
-ATOM 315 NE ARG A 271 1.105 11.948 -8.260 1.00 1.51 N
-ATOM 316 CZ ARG A 271 0.028 11.256 -8.567 1.00 1.63 C
-ATOM 317 NH1 ARG A 271 0.079 10.304 -9.465 1.00 1.80 N
-ATOM 318 NH2 ARG A 271 -1.103 11.523 -7.972 1.00 1.65 N
-ATOM 319 H ARG A 271 6.168 13.257 -11.778 1.00 1.91 H
-ATOM 320 HA ARG A 271 3.565 12.692 -12.876 1.00 1.63 H
-ATOM 321 HB2 ARG A 271 4.282 11.421 -10.946 1.00 1.88 H
-ATOM 322 HB3 ARG A 271 4.539 12.894 -10.020 1.00 1.56 H
-ATOM 323 HG2 ARG A 271 2.084 13.353 -10.244 1.00 1.21 H
-ATOM 324 HG3 ARG A 271 1.916 11.744 -10.943 1.00 1.59 H
-ATOM 325 HD2 ARG A 271 2.631 10.703 -8.932 1.00 1.80 H
-ATOM 326 HD3 ARG A 271 3.166 12.238 -8.250 1.00 1.53 H
-ATOM 327 HE ARG A 271 1.020 12.662 -7.577 1.00 1.44 H
-ATOM 328 HH11 ARG A 271 0.938 10.102 -9.926 1.00 1.86 H
-ATOM 329 HH12 ARG A 271 -0.742 9.777 -9.679 1.00 1.92 H
-ATOM 330 HH21 ARG A 271 -1.143 12.249 -7.290 1.00 1.60 H
-ATOM 331 HH22 ARG A 271 -1.922 11.000 -8.197 1.00 1.77 H
-ATOM 332 N ALA A 272 2.288 14.800 -12.589 1.00 1.09 N
-ATOM 333 CA ALA A 272 1.562 16.063 -12.508 1.00 0.89 C
-ATOM 334 C ALA A 272 0.851 16.210 -11.162 1.00 0.66 C
-ATOM 335 O ALA A 272 -0.326 15.864 -11.030 1.00 0.79 O
-ATOM 336 CB ALA A 272 0.561 16.169 -13.649 1.00 1.02 C
-ATOM 337 H ALA A 272 1.934 14.082 -13.152 1.00 1.24 H
-ATOM 338 HA ALA A 272 2.277 16.866 -12.613 1.00 0.99 H
-ATOM 339 HB1 ALA A 272 -0.351 16.619 -13.287 1.00 1.44 H
-ATOM 340 HB2 ALA A 272 0.348 15.183 -14.033 1.00 1.54 H
-ATOM 341 HB3 ALA A 272 0.977 16.781 -14.436 1.00 1.44 H
-ATOM 342 N SER A 273 1.569 16.719 -10.160 1.00 0.69 N
-ATOM 343 CA SER A 273 0.996 16.907 -8.820 1.00 0.54 C
-ATOM 344 C SER A 273 0.031 18.082 -8.769 1.00 0.77 C
-ATOM 345 O SER A 273 0.212 19.022 -7.995 1.00 1.09 O
-ATOM 346 CB SER A 273 2.078 17.093 -7.758 1.00 0.73 C
-ATOM 347 OG SER A 273 2.882 15.931 -7.624 1.00 1.41 O
-ATOM 348 H SER A 273 2.504 16.970 -10.322 1.00 0.99 H
-ATOM 349 HA SER A 273 0.430 16.018 -8.579 1.00 0.58 H
-ATOM 350 HB2 SER A 273 2.707 17.925 -8.031 1.00 1.13 H
-ATOM 351 HB3 SER A 273 1.603 17.297 -6.808 1.00 0.97 H
-ATOM 352 HG SER A 273 3.821 16.175 -7.677 1.00 1.52 H
-ATOM 353 N LEU A 274 -1.024 17.991 -9.561 1.00 0.89 N
-ATOM 354 CA LEU A 274 -2.056 19.011 -9.566 1.00 1.35 C
-ATOM 355 C LEU A 274 -3.448 18.393 -9.290 1.00 1.63 C
-ATOM 356 O LEU A 274 -4.427 18.760 -9.939 1.00 1.96 O
-ATOM 357 CB LEU A 274 -2.052 19.756 -10.907 1.00 1.52 C
-ATOM 358 CG LEU A 274 -2.865 21.054 -10.936 1.00 2.05 C
-ATOM 359 CD1 LEU A 274 -2.301 22.065 -9.950 1.00 2.31 C
-ATOM 360 CD2 LEU A 274 -2.886 21.636 -12.340 1.00 2.24 C
-ATOM 361 H LEU A 274 -1.129 17.191 -10.128 1.00 0.80 H
-ATOM 362 HA LEU A 274 -1.827 19.713 -8.778 1.00 1.53 H
-ATOM 363 HB2 LEU A 274 -1.029 19.990 -11.163 1.00 1.46 H
-ATOM 364 HB3 LEU A 274 -2.450 19.094 -11.663 1.00 1.44 H
-ATOM 365 HG LEU A 274 -3.883 20.839 -10.647 1.00 2.24 H
-ATOM 366 HD11 LEU A 274 -2.187 23.021 -10.442 1.00 2.56 H
-ATOM 367 HD12 LEU A 274 -1.339 21.725 -9.597 1.00 2.61 H
-ATOM 368 HD13 LEU A 274 -2.977 22.168 -9.114 1.00 2.61 H
-ATOM 369 HD21 LEU A 274 -3.166 22.679 -12.291 1.00 2.49 H
-ATOM 370 HD22 LEU A 274 -3.603 21.098 -12.941 1.00 2.55 H
-ATOM 371 HD23 LEU A 274 -1.905 21.546 -12.782 1.00 2.49 H
-ATOM 372 N PRO A 275 -3.572 17.431 -8.323 1.00 1.64 N
-ATOM 373 CA PRO A 275 -4.869 16.792 -7.999 1.00 2.09 C
-ATOM 374 C PRO A 275 -5.915 17.797 -7.511 1.00 2.53 C
-ATOM 375 O PRO A 275 -7.116 17.622 -7.729 1.00 2.89 O
-ATOM 376 CB PRO A 275 -4.512 15.811 -6.877 1.00 2.10 C
-ATOM 377 CG PRO A 275 -3.074 15.540 -7.097 1.00 1.63 C
-ATOM 378 CD PRO A 275 -2.498 16.870 -7.474 1.00 1.31 C
-ATOM 379 HA PRO A 275 -5.255 16.244 -8.842 1.00 2.17 H
-ATOM 380 HB2 PRO A 275 -4.690 16.272 -5.915 1.00 2.22 H
-ATOM 381 HB3 PRO A 275 -5.104 14.913 -6.971 1.00 2.38 H
-ATOM 382 HG2 PRO A 275 -2.617 15.164 -6.192 1.00 1.63 H
-ATOM 383 HG3 PRO A 275 -2.960 14.839 -7.908 1.00 1.64 H
-ATOM 384 HD2 PRO A 275 -2.329 17.474 -6.597 1.00 1.36 H
-ATOM 385 HD3 PRO A 275 -1.573 16.747 -8.037 1.00 0.97 H
-ATOM 386 N LYS A 276 -5.445 18.853 -6.850 1.00 2.58 N
-ATOM 387 CA LYS A 276 -6.317 19.894 -6.332 1.00 3.06 C
-ATOM 388 C LYS A 276 -5.544 21.198 -6.099 1.00 3.25 C
-ATOM 389 O LYS A 276 -4.312 21.129 -5.893 1.00 3.07 O
-ATOM 390 CB LYS A 276 -6.982 19.438 -5.029 1.00 3.29 C
-ATOM 391 CG LYS A 276 -8.100 20.354 -4.554 1.00 3.81 C
-ATOM 392 CD LYS A 276 -9.332 20.252 -5.444 1.00 4.19 C
-ATOM 393 CE LYS A 276 -9.160 20.974 -6.776 1.00 4.29 C
-ATOM 394 NZ LYS A 276 -8.703 22.380 -6.596 1.00 4.42 N
-ATOM 395 OXT LYS A 276 -6.185 22.279 -6.133 1.00 3.77 O
-ATOM 396 H LYS A 276 -4.481 18.938 -6.714 1.00 2.34 H
-ATOM 397 HA LYS A 276 -7.083 20.076 -7.070 1.00 3.34 H
-ATOM 398 HB2 LYS A 276 -7.398 18.453 -5.180 1.00 3.36 H
-ATOM 399 HB3 LYS A 276 -6.233 19.386 -4.253 1.00 3.19 H
-ATOM 400 HG2 LYS A 276 -8.373 20.079 -3.546 1.00 4.00 H
-ATOM 401 HG3 LYS A 276 -7.743 21.375 -4.564 1.00 3.88 H
-ATOM 402 HD2 LYS A 276 -9.518 19.208 -5.646 1.00 4.20 H
-ATOM 403 HD3 LYS A 276 -10.174 20.675 -4.922 1.00 4.56 H
-ATOM 404 HE2 LYS A 276 -8.428 20.441 -7.366 1.00 4.02 H
-ATOM 405 HE3 LYS A 276 -10.107 20.974 -7.294 1.00 4.71 H
-ATOM 406 HZ1 LYS A 276 -7.661 22.403 -6.431 1.00 4.15 H
-ATOM 407 HZ2 LYS A 276 -9.180 22.809 -5.780 1.00 4.60 H
-ATOM 408 HZ3 LYS A 276 -8.919 22.938 -7.445 1.00 4.75 H
-TER
-ENDMDL
-MODEL 3
-ATOM 1 N ALA A 249 1.538 -11.872 -11.252 1.00 1.98 N
-ATOM 2 CA ALA A 249 2.154 -10.524 -11.138 1.00 1.52 C
-ATOM 3 C ALA A 249 1.771 -9.857 -9.816 1.00 1.10 C
-ATOM 4 O ALA A 249 0.736 -10.173 -9.230 1.00 1.28 O
-ATOM 5 CB ALA A 249 1.749 -9.647 -12.314 1.00 1.84 C
-ATOM 6 H1 ALA A 249 0.866 -12.025 -10.470 1.00 2.21 H
-ATOM 7 H2 ALA A 249 2.274 -12.605 -11.209 1.00 2.24 H
-ATOM 8 H3 ALA A 249 1.029 -11.958 -12.154 1.00 2.27 H
-ATOM 9 HA ALA A 249 3.228 -10.642 -11.165 1.00 1.44 H
-ATOM 10 HB1 ALA A 249 2.065 -8.629 -12.126 1.00 2.26 H
-ATOM 11 HB2 ALA A 249 0.678 -9.675 -12.433 1.00 2.12 H
-ATOM 12 HB3 ALA A 249 2.225 -10.010 -13.213 1.00 2.20 H
-ATOM 13 N PRO A 250 2.611 -8.931 -9.325 1.00 0.77 N
-ATOM 14 CA PRO A 250 2.379 -8.214 -8.064 1.00 0.67 C
-ATOM 15 C PRO A 250 1.294 -7.129 -8.171 1.00 0.54 C
-ATOM 16 O PRO A 250 1.555 -5.946 -7.938 1.00 0.61 O
-ATOM 17 CB PRO A 250 3.751 -7.594 -7.791 1.00 1.01 C
-ATOM 18 CG PRO A 250 4.307 -7.336 -9.138 1.00 1.24 C
-ATOM 19 CD PRO A 250 3.869 -8.504 -9.967 1.00 1.02 C
-ATOM 20 HA PRO A 250 2.126 -8.895 -7.264 1.00 0.90 H
-ATOM 21 HB2 PRO A 250 3.637 -6.683 -7.231 1.00 1.30 H
-ATOM 22 HB3 PRO A 250 4.364 -8.295 -7.246 1.00 1.26 H
-ATOM 23 HG2 PRO A 250 3.902 -6.416 -9.535 1.00 1.68 H
-ATOM 24 HG3 PRO A 250 5.384 -7.289 -9.091 1.00 1.55 H
-ATOM 25 HD2 PRO A 250 3.694 -8.196 -10.985 1.00 1.31 H
-ATOM 26 HD3 PRO A 250 4.604 -9.290 -9.926 1.00 1.11 H
-ATOM 27 N ALA A 251 0.074 -7.539 -8.529 1.00 0.51 N
-ATOM 28 CA ALA A 251 -1.049 -6.606 -8.676 1.00 0.55 C
-ATOM 29 C ALA A 251 -1.336 -5.848 -7.377 1.00 0.48 C
-ATOM 30 O ALA A 251 -1.529 -4.631 -7.388 1.00 0.50 O
-ATOM 31 CB ALA A 251 -2.293 -7.346 -9.145 1.00 0.73 C
-ATOM 32 H ALA A 251 -0.073 -8.497 -8.706 1.00 0.59 H
-ATOM 33 HA ALA A 251 -0.781 -5.890 -9.440 1.00 0.63 H
-ATOM 34 HB1 ALA A 251 -2.397 -8.266 -8.587 1.00 1.40 H
-ATOM 35 HB2 ALA A 251 -2.203 -7.572 -10.197 1.00 1.25 H
-ATOM 36 HB3 ALA A 251 -3.163 -6.727 -8.983 1.00 1.14 H
-ATOM 37 N ARG A 252 -1.347 -6.572 -6.256 1.00 0.52 N
-ATOM 38 CA ARG A 252 -1.594 -5.965 -4.946 1.00 0.59 C
-ATOM 39 C ARG A 252 -0.523 -4.912 -4.644 1.00 0.52 C
-ATOM 40 O ARG A 252 -0.825 -3.806 -4.188 1.00 0.56 O
-ATOM 41 CB ARG A 252 -1.586 -7.047 -3.856 1.00 0.74 C
-ATOM 42 CG ARG A 252 -2.633 -6.846 -2.765 1.00 0.88 C
-ATOM 43 CD ARG A 252 -2.566 -5.453 -2.159 1.00 0.92 C
-ATOM 44 NE ARG A 252 -3.531 -5.272 -1.081 1.00 1.14 N
-ATOM 45 CZ ARG A 252 -3.834 -4.103 -0.560 1.00 1.38 C
-ATOM 46 NH1 ARG A 252 -3.307 -3.006 -1.044 1.00 1.84 N
-ATOM 47 NH2 ARG A 252 -4.677 -4.031 0.434 1.00 1.57 N
-ATOM 48 H ARG A 252 -1.178 -7.534 -6.312 1.00 0.58 H
-ATOM 49 HA ARG A 252 -2.561 -5.490 -4.971 1.00 0.64 H
-ATOM 50 HB2 ARG A 252 -1.762 -8.007 -4.318 1.00 0.86 H
-ATOM 51 HB3 ARG A 252 -0.612 -7.059 -3.388 1.00 0.78 H
-ATOM 52 HG2 ARG A 252 -3.613 -6.993 -3.192 1.00 0.99 H
-ATOM 53 HG3 ARG A 252 -2.466 -7.576 -1.986 1.00 1.01 H
-ATOM 54 HD2 ARG A 252 -1.572 -5.293 -1.769 1.00 0.98 H
-ATOM 55 HD3 ARG A 252 -2.766 -4.729 -2.934 1.00 0.91 H
-ATOM 56 HE ARG A 252 -3.962 -6.071 -0.718 1.00 1.46 H
-ATOM 57 HH11 ARG A 252 -2.672 -3.056 -1.812 1.00 1.81 H
-ATOM 58 HH12 ARG A 252 -3.541 -2.122 -0.647 1.00 2.36 H
-ATOM 59 HH21 ARG A 252 -5.090 -4.861 0.797 1.00 1.60 H
-ATOM 60 HH22 ARG A 252 -4.909 -3.147 0.827 1.00 1.93 H
-ATOM 61 N VAL A 253 0.729 -5.268 -4.923 1.00 0.51 N
-ATOM 62 CA VAL A 253 1.860 -4.370 -4.709 1.00 0.56 C
-ATOM 63 C VAL A 253 1.732 -3.123 -5.581 1.00 0.47 C
-ATOM 64 O VAL A 253 1.851 -1.998 -5.091 1.00 0.52 O
-ATOM 65 CB VAL A 253 3.198 -5.075 -5.018 1.00 0.67 C
-ATOM 66 CG1 VAL A 253 4.378 -4.147 -4.761 1.00 0.86 C
-ATOM 67 CG2 VAL A 253 3.327 -6.351 -4.199 1.00 0.80 C
-ATOM 68 H VAL A 253 0.892 -6.156 -5.297 1.00 0.52 H
-ATOM 69 HA VAL A 253 1.860 -4.075 -3.672 1.00 0.67 H
-ATOM 70 HB VAL A 253 3.203 -5.344 -6.064 1.00 0.65 H
-ATOM 71 HG11 VAL A 253 4.559 -3.546 -5.639 1.00 1.36 H
-ATOM 72 HG12 VAL A 253 5.257 -4.736 -4.540 1.00 1.41 H
-ATOM 73 HG13 VAL A 253 4.154 -3.504 -3.924 1.00 1.35 H
-ATOM 74 HG21 VAL A 253 3.718 -6.113 -3.221 1.00 1.36 H
-ATOM 75 HG22 VAL A 253 3.998 -7.033 -4.700 1.00 1.38 H
-ATOM 76 HG23 VAL A 253 2.355 -6.811 -4.096 1.00 1.22 H
-ATOM 77 N GLY A 254 1.462 -3.338 -6.870 1.00 0.40 N
-ATOM 78 CA GLY A 254 1.294 -2.229 -7.799 1.00 0.40 C
-ATOM 79 C GLY A 254 0.190 -1.281 -7.360 1.00 0.31 C
-ATOM 80 O GLY A 254 0.379 -0.063 -7.325 1.00 0.33 O
-ATOM 81 H GLY A 254 1.363 -4.264 -7.190 1.00 0.43 H
-ATOM 82 HA2 GLY A 254 2.223 -1.681 -7.865 1.00 0.49 H
-ATOM 83 HA3 GLY A 254 1.049 -2.622 -8.775 1.00 0.45 H
-ATOM 84 N LEU A 255 -0.956 -1.845 -6.988 1.00 0.30 N
-ATOM 85 CA LEU A 255 -2.087 -1.052 -6.512 1.00 0.34 C
-ATOM 86 C LEU A 255 -1.722 -0.263 -5.260 1.00 0.35 C
-ATOM 87 O LEU A 255 -2.023 0.926 -5.143 1.00 0.37 O
-ATOM 88 CB LEU A 255 -3.271 -1.953 -6.203 1.00 0.46 C
-ATOM 89 CG LEU A 255 -4.239 -2.139 -7.358 1.00 0.62 C
-ATOM 90 CD1 LEU A 255 -5.358 -3.074 -6.945 1.00 0.78 C
-ATOM 91 CD2 LEU A 255 -4.787 -0.788 -7.789 1.00 0.80 C
-ATOM 92 H LEU A 255 -1.037 -2.827 -7.017 1.00 0.33 H
-ATOM 93 HA LEU A 255 -2.371 -0.365 -7.294 1.00 0.37 H
-ATOM 94 HB2 LEU A 255 -2.895 -2.923 -5.911 1.00 0.51 H
-ATOM 95 HB3 LEU A 255 -3.815 -1.531 -5.373 1.00 0.55 H
-ATOM 96 HG LEU A 255 -3.719 -2.577 -8.196 1.00 0.66 H
-ATOM 97 HD11 LEU A 255 -5.015 -4.097 -7.018 1.00 1.30 H
-ATOM 98 HD12 LEU A 255 -6.206 -2.931 -7.594 1.00 1.36 H
-ATOM 99 HD13 LEU A 255 -5.643 -2.862 -5.924 1.00 1.27 H
-ATOM 100 HD21 LEU A 255 -4.927 -0.164 -6.919 1.00 1.36 H
-ATOM 101 HD22 LEU A 255 -5.730 -0.923 -8.292 1.00 1.34 H
-ATOM 102 HD23 LEU A 255 -4.084 -0.314 -8.457 1.00 1.30 H
-ATOM 103 N GLY A 256 -1.073 -0.940 -4.322 1.00 0.42 N
-ATOM 104 CA GLY A 256 -0.679 -0.299 -3.089 1.00 0.51 C
-ATOM 105 C GLY A 256 0.303 0.844 -3.298 1.00 0.48 C
-ATOM 106 O GLY A 256 0.079 1.951 -2.811 1.00 0.51 O
-ATOM 107 H GLY A 256 -0.862 -1.892 -4.471 1.00 0.44 H
-ATOM 108 HA2 GLY A 256 -1.565 0.088 -2.609 1.00 0.57 H
-ATOM 109 HA3 GLY A 256 -0.229 -1.037 -2.447 1.00 0.61 H
-ATOM 110 N ILE A 257 1.394 0.591 -4.024 1.00 0.49 N
-ATOM 111 CA ILE A 257 2.390 1.638 -4.266 1.00 0.54 C
-ATOM 112 C ILE A 257 1.809 2.806 -5.077 1.00 0.44 C
-ATOM 113 O ILE A 257 2.086 3.967 -4.775 1.00 0.50 O
-ATOM 114 CB ILE A 257 3.678 1.095 -4.941 1.00 0.66 C
-ATOM 115 CG1 ILE A 257 3.388 0.451 -6.300 1.00 0.60 C
-ATOM 116 CG2 ILE A 257 4.370 0.098 -4.024 1.00 0.82 C
-ATOM 117 CD1 ILE A 257 3.751 1.325 -7.481 1.00 0.69 C
-ATOM 118 H ILE A 257 1.531 -0.311 -4.399 1.00 0.49 H
-ATOM 119 HA ILE A 257 2.673 2.025 -3.296 1.00 0.62 H
-ATOM 120 HB ILE A 257 4.350 1.929 -5.087 1.00 0.75 H
-ATOM 121 HG12 ILE A 257 3.951 -0.467 -6.382 1.00 0.69 H
-ATOM 122 HG13 ILE A 257 2.333 0.226 -6.365 1.00 0.54 H
-ATOM 123 HG21 ILE A 257 3.973 0.192 -3.024 1.00 1.36 H
-ATOM 124 HG22 ILE A 257 5.433 0.299 -4.009 1.00 1.36 H
-ATOM 125 HG23 ILE A 257 4.198 -0.904 -4.387 1.00 1.28 H
-ATOM 126 HD11 ILE A 257 4.781 1.148 -7.757 1.00 1.32 H
-ATOM 127 HD12 ILE A 257 3.623 2.364 -7.214 1.00 1.17 H
-ATOM 128 HD13 ILE A 257 3.110 1.086 -8.317 1.00 1.25 H
-ATOM 129 N THR A 258 0.995 2.505 -6.093 1.00 0.35 N
-ATOM 130 CA THR A 258 0.386 3.556 -6.918 1.00 0.33 C
-ATOM 131 C THR A 258 -0.599 4.408 -6.116 1.00 0.28 C
-ATOM 132 O THR A 258 -0.594 5.635 -6.226 1.00 0.33 O
-ATOM 133 CB THR A 258 -0.343 2.994 -8.156 1.00 0.37 C
-ATOM 134 OG1 THR A 258 -1.240 1.943 -7.776 1.00 0.34 O
-ATOM 135 CG2 THR A 258 0.649 2.484 -9.191 1.00 0.52 C
-ATOM 136 H THR A 258 0.796 1.559 -6.290 1.00 0.33 H
-ATOM 137 HA THR A 258 1.184 4.197 -7.265 1.00 0.43 H
-ATOM 138 HB THR A 258 -0.918 3.793 -8.602 1.00 0.45 H
-ATOM 139 HG1 THR A 258 -0.748 1.110 -7.704 1.00 0.40 H
-ATOM 140 HG21 THR A 258 1.028 3.315 -9.767 1.00 0.96 H
-ATOM 141 HG22 THR A 258 0.156 1.784 -9.850 1.00 1.32 H
-ATOM 142 HG23 THR A 258 1.469 1.989 -8.691 1.00 1.09 H
-ATOM 143 N THR A 259 -1.439 3.765 -5.304 1.00 0.28 N
-ATOM 144 CA THR A 259 -2.408 4.500 -4.490 1.00 0.33 C
-ATOM 145 C THR A 259 -1.696 5.354 -3.449 1.00 0.35 C
-ATOM 146 O THR A 259 -1.998 6.539 -3.300 1.00 0.38 O
-ATOM 147 CB THR A 259 -3.420 3.573 -3.786 1.00 0.46 C
-ATOM 148 OG1 THR A 259 -2.737 2.514 -3.104 1.00 0.49 O
-ATOM 149 CG2 THR A 259 -4.408 2.993 -4.786 1.00 0.54 C
-ATOM 150 H THR A 259 -1.399 2.781 -5.243 1.00 0.31 H
-ATOM 151 HA THR A 259 -2.958 5.155 -5.152 1.00 0.36 H
-ATOM 152 HB THR A 259 -3.970 4.157 -3.062 1.00 0.55 H
-ATOM 153 HG1 THR A 259 -2.500 1.822 -3.740 1.00 0.51 H
-ATOM 154 HG21 THR A 259 -3.941 2.180 -5.323 1.00 1.17 H
-ATOM 155 HG22 THR A 259 -4.705 3.761 -5.484 1.00 1.13 H
-ATOM 156 HG23 THR A 259 -5.279 2.626 -4.262 1.00 1.20 H
-ATOM 157 N VAL A 260 -0.729 4.757 -2.750 1.00 0.43 N
-ATOM 158 CA VAL A 260 0.043 5.487 -1.746 1.00 0.54 C
-ATOM 159 C VAL A 260 0.768 6.675 -2.388 1.00 0.51 C
-ATOM 160 O VAL A 260 0.721 7.791 -1.870 1.00 0.54 O
-ATOM 161 CB VAL A 260 1.063 4.569 -1.034 1.00 0.67 C
-ATOM 162 CG1 VAL A 260 1.993 5.375 -0.138 1.00 0.84 C
-ATOM 163 CG2 VAL A 260 0.337 3.506 -0.221 1.00 0.75 C
-ATOM 164 H VAL A 260 -0.519 3.807 -2.925 1.00 0.45 H
-ATOM 165 HA VAL A 260 -0.649 5.862 -1.006 1.00 0.59 H
-ATOM 166 HB VAL A 260 1.661 4.072 -1.783 1.00 0.65 H
-ATOM 167 HG11 VAL A 260 2.959 5.466 -0.613 1.00 1.30 H
-ATOM 168 HG12 VAL A 260 2.104 4.874 0.811 1.00 1.28 H
-ATOM 169 HG13 VAL A 260 1.576 6.358 0.020 1.00 1.40 H
-ATOM 170 HG21 VAL A 260 0.951 3.210 0.616 1.00 1.25 H
-ATOM 171 HG22 VAL A 260 0.142 2.647 -0.846 1.00 1.26 H
-ATOM 172 HG23 VAL A 260 -0.598 3.907 0.141 1.00 1.34 H
-ATOM 173 N LEU A 261 1.408 6.433 -3.536 1.00 0.50 N
-ATOM 174 CA LEU A 261 2.113 7.489 -4.263 1.00 0.56 C
-ATOM 175 C LEU A 261 1.141 8.598 -4.675 1.00 0.47 C
-ATOM 176 O LEU A 261 1.413 9.782 -4.478 1.00 0.56 O
-ATOM 177 CB LEU A 261 2.804 6.921 -5.499 1.00 0.65 C
-ATOM 178 CG LEU A 261 3.759 7.887 -6.194 1.00 0.83 C
-ATOM 179 CD1 LEU A 261 4.810 8.387 -5.212 1.00 0.98 C
-ATOM 180 CD2 LEU A 261 4.416 7.213 -7.385 1.00 1.02 C
-ATOM 181 H LEU A 261 1.390 5.523 -3.913 1.00 0.50 H
-ATOM 182 HA LEU A 261 2.864 7.906 -3.610 1.00 0.66 H
-ATOM 183 HB2 LEU A 261 3.359 6.042 -5.206 1.00 0.72 H
-ATOM 184 HB3 LEU A 261 2.045 6.628 -6.210 1.00 0.63 H
-ATOM 185 HG LEU A 261 3.203 8.740 -6.552 1.00 0.83 H
-ATOM 186 HD11 LEU A 261 5.091 7.584 -4.546 1.00 1.48 H
-ATOM 187 HD12 LEU A 261 4.401 9.204 -4.634 1.00 1.39 H
-ATOM 188 HD13 LEU A 261 5.678 8.729 -5.754 1.00 1.46 H
-ATOM 189 HD21 LEU A 261 4.932 6.323 -7.055 1.00 1.54 H
-ATOM 190 HD22 LEU A 261 5.120 7.893 -7.840 1.00 1.39 H
-ATOM 191 HD23 LEU A 261 3.658 6.943 -8.107 1.00 1.50 H
-ATOM 192 N THR A 262 -0.003 8.202 -5.231 1.00 0.37 N
-ATOM 193 CA THR A 262 -1.031 9.156 -5.654 1.00 0.39 C
-ATOM 194 C THR A 262 -1.520 9.985 -4.465 1.00 0.35 C
-ATOM 195 O THR A 262 -1.576 11.214 -4.521 1.00 0.42 O
-ATOM 196 CB THR A 262 -2.243 8.433 -6.280 1.00 0.45 C
-ATOM 197 OG1 THR A 262 -1.825 7.636 -7.395 1.00 0.59 O
-ATOM 198 CG2 THR A 262 -3.300 9.428 -6.731 1.00 0.56 C
-ATOM 199 H THR A 262 -0.166 7.240 -5.351 1.00 0.36 H
-ATOM 200 HA THR A 262 -0.602 9.815 -6.395 1.00 0.48 H
-ATOM 201 HB THR A 262 -2.679 7.786 -5.531 1.00 0.46 H
-ATOM 202 HG1 THR A 262 -1.396 6.828 -7.071 1.00 0.90 H
-ATOM 203 HG21 THR A 262 -2.991 9.884 -7.659 1.00 1.20 H
-ATOM 204 HG22 THR A 262 -3.419 10.191 -5.975 1.00 1.14 H
-ATOM 205 HG23 THR A 262 -4.238 8.914 -6.875 1.00 1.19 H
-ATOM 206 N MET A 263 -1.865 9.294 -3.388 1.00 0.35 N
-ATOM 207 CA MET A 263 -2.345 9.939 -2.168 1.00 0.42 C
-ATOM 208 C MET A 263 -1.297 10.891 -1.572 1.00 0.43 C
-ATOM 209 O MET A 263 -1.617 12.028 -1.215 1.00 0.47 O
-ATOM 210 CB MET A 263 -2.726 8.870 -1.140 1.00 0.55 C
-ATOM 211 CG MET A 263 -3.402 9.418 0.105 1.00 0.74 C
-ATOM 212 SD MET A 263 -3.776 8.132 1.312 1.00 1.00 S
-ATOM 213 CE MET A 263 -4.830 7.047 0.350 1.00 1.21 C
-ATOM 214 H MET A 263 -1.795 8.311 -3.413 1.00 0.36 H
-ATOM 215 HA MET A 263 -3.227 10.508 -2.422 1.00 0.47 H
-ATOM 216 HB2 MET A 263 -3.399 8.164 -1.605 1.00 0.62 H
-ATOM 217 HB3 MET A 263 -1.830 8.348 -0.836 1.00 0.59 H
-ATOM 218 HG2 MET A 263 -2.746 10.144 0.564 1.00 0.76 H
-ATOM 219 HG3 MET A 263 -4.324 9.900 -0.185 1.00 0.86 H
-ATOM 220 HE1 MET A 263 -4.428 6.045 0.367 1.00 1.50 H
-ATOM 221 HE2 MET A 263 -4.875 7.401 -0.669 1.00 1.59 H
-ATOM 222 HE3 MET A 263 -5.823 7.042 0.775 1.00 1.77 H
-ATOM 223 N THR A 264 -0.048 10.426 -1.454 1.00 0.46 N
-ATOM 224 CA THR A 264 1.015 11.258 -0.879 1.00 0.57 C
-ATOM 225 C THR A 264 1.347 12.479 -1.746 1.00 0.53 C
-ATOM 226 O THR A 264 1.609 13.555 -1.212 1.00 0.64 O
-ATOM 227 CB THR A 264 2.313 10.466 -0.581 1.00 0.72 C
-ATOM 228 OG1 THR A 264 3.244 11.310 0.108 1.00 0.88 O
-ATOM 229 CG2 THR A 264 2.969 9.939 -1.846 1.00 0.74 C
-ATOM 230 H THR A 264 0.157 9.505 -1.745 1.00 0.46 H
-ATOM 231 HA THR A 264 0.642 11.625 0.068 1.00 0.63 H
-ATOM 232 HB THR A 264 2.064 9.627 0.055 1.00 0.79 H
-ATOM 233 HG1 THR A 264 2.949 12.225 0.054 1.00 1.20 H
-ATOM 234 HG21 THR A 264 2.826 8.868 -1.907 1.00 1.32 H
-ATOM 235 HG22 THR A 264 4.025 10.160 -1.823 1.00 1.21 H
-ATOM 236 HG23 THR A 264 2.519 10.410 -2.708 1.00 1.28 H
-ATOM 237 N THR A 265 1.338 12.323 -3.074 1.00 0.49 N
-ATOM 238 CA THR A 265 1.640 13.455 -3.960 1.00 0.57 C
-ATOM 239 C THR A 265 0.481 14.452 -3.988 1.00 0.51 C
-ATOM 240 O THR A 265 0.696 15.658 -4.101 1.00 0.63 O
-ATOM 241 CB THR A 265 1.989 13.025 -5.405 1.00 0.69 C
-ATOM 242 OG1 THR A 265 2.388 14.174 -6.163 1.00 0.85 O
-ATOM 243 CG2 THR A 265 0.816 12.354 -6.102 1.00 0.64 C
-ATOM 244 H THR A 265 1.126 11.442 -3.459 1.00 0.48 H
-ATOM 245 HA THR A 265 2.503 13.959 -3.547 1.00 0.67 H
-ATOM 246 HB THR A 265 2.814 12.328 -5.366 1.00 0.79 H
-ATOM 247 HG1 THR A 265 2.157 14.975 -5.682 1.00 0.99 H
-ATOM 248 HG21 THR A 265 0.703 11.347 -5.727 1.00 1.29 H
-ATOM 249 HG22 THR A 265 0.997 12.325 -7.166 1.00 1.16 H
-ATOM 250 HG23 THR A 265 -0.087 12.915 -5.906 1.00 1.16 H
-ATOM 251 N GLN A 266 -0.746 13.939 -3.868 1.00 0.41 N
-ATOM 252 CA GLN A 266 -1.939 14.783 -3.854 1.00 0.44 C
-ATOM 253 C GLN A 266 -1.978 15.609 -2.566 1.00 0.45 C
-ATOM 254 O GLN A 266 -2.289 16.800 -2.581 1.00 0.57 O
-ATOM 255 CB GLN A 266 -3.197 13.915 -3.963 1.00 0.51 C
-ATOM 256 CG GLN A 266 -4.493 14.707 -3.999 1.00 0.68 C
-ATOM 257 CD GLN A 266 -5.716 13.814 -4.024 1.00 0.84 C
-ATOM 258 OE1 GLN A 266 -5.919 13.040 -4.953 1.00 1.00 O
-ATOM 259 NE2 GLN A 266 -6.541 13.914 -2.998 1.00 0.98 N
-ATOM 260 H GLN A 266 -0.849 12.966 -3.774 1.00 0.40 H
-ATOM 261 HA GLN A 266 -1.889 15.450 -4.701 1.00 0.53 H
-ATOM 262 HB2 GLN A 266 -3.135 13.327 -4.868 1.00 0.57 H
-ATOM 263 HB3 GLN A 266 -3.232 13.248 -3.115 1.00 0.54 H
-ATOM 264 HG2 GLN A 266 -4.541 15.335 -3.121 1.00 0.74 H
-ATOM 265 HG3 GLN A 266 -4.497 15.326 -4.884 1.00 0.81 H
-ATOM 266 HE21 GLN A 266 -6.322 14.547 -2.287 1.00 1.01 H
-ATOM 267 HE22 GLN A 266 -7.341 13.349 -2.997 1.00 1.13 H
-ATOM 268 N SER A 267 -1.640 14.957 -1.457 1.00 0.44 N
-ATOM 269 CA SER A 267 -1.605 15.614 -0.151 1.00 0.54 C
-ATOM 270 C SER A 267 -0.202 16.181 0.120 1.00 0.44 C
-ATOM 271 O SER A 267 0.539 16.499 -0.810 1.00 1.00 O
-ATOM 272 CB SER A 267 -2.005 14.614 0.946 1.00 0.71 C
-ATOM 273 OG SER A 267 -2.047 15.223 2.231 1.00 0.87 O
-ATOM 274 H SER A 267 -1.389 14.009 -1.521 1.00 0.47 H
-ATOM 275 HA SER A 267 -2.315 16.428 -0.167 1.00 0.70 H
-ATOM 276 HB2 SER A 267 -2.984 14.216 0.724 1.00 0.84 H
-ATOM 277 HB3 SER A 267 -1.289 13.805 0.969 1.00 0.71 H
-ATOM 278 HG SER A 267 -2.811 14.896 2.715 1.00 1.03 H
-ATOM 279 N SER A 268 0.167 16.296 1.389 1.00 0.65 N
-ATOM 280 CA SER A 268 1.486 16.810 1.755 1.00 0.66 C
-ATOM 281 C SER A 268 2.567 15.765 1.473 1.00 0.60 C
-ATOM 282 O SER A 268 2.486 14.637 1.965 1.00 0.61 O
-ATOM 283 CB SER A 268 1.529 17.200 3.239 1.00 0.72 C
-ATOM 284 OG SER A 268 1.568 16.050 4.076 1.00 0.72 O
-ATOM 285 H SER A 268 -0.462 16.020 2.099 1.00 1.15 H
-ATOM 286 HA SER A 268 1.681 17.686 1.154 1.00 0.79 H
-ATOM 287 HB2 SER A 268 2.409 17.796 3.428 1.00 0.84 H
-ATOM 288 HB3 SER A 268 0.647 17.773 3.481 1.00 0.83 H
-ATOM 289 HG SER A 268 1.780 15.270 3.538 1.00 0.81 H
-ATOM 290 N GLY A 269 3.576 16.147 0.691 1.00 0.69 N
-ATOM 291 CA GLY A 269 4.667 15.235 0.362 1.00 0.78 C
-ATOM 292 C GLY A 269 5.590 14.968 1.543 1.00 0.70 C
-ATOM 293 O GLY A 269 6.772 15.304 1.509 1.00 0.81 O
-ATOM 294 H GLY A 269 3.583 17.060 0.334 1.00 0.78 H
-ATOM 295 HA2 GLY A 269 4.247 14.297 0.028 1.00 0.84 H
-ATOM 296 HA3 GLY A 269 5.247 15.662 -0.444 1.00 0.94 H
-ATOM 297 N SER A 270 5.036 14.377 2.595 1.00 0.60 N
-ATOM 298 CA SER A 270 5.789 14.073 3.809 1.00 0.60 C
-ATOM 299 C SER A 270 5.014 13.106 4.702 1.00 0.58 C
-ATOM 300 O SER A 270 3.787 13.187 4.793 1.00 0.63 O
-ATOM 301 CB SER A 270 6.090 15.357 4.588 1.00 0.66 C
-ATOM 302 OG SER A 270 6.710 15.069 5.829 1.00 0.86 O
-ATOM 303 H SER A 270 4.077 14.147 2.560 1.00 0.61 H
-ATOM 304 HA SER A 270 6.720 13.611 3.518 1.00 0.70 H
-ATOM 305 HB2 SER A 270 6.753 15.979 4.006 1.00 0.74 H
-ATOM 306 HB3 SER A 270 5.168 15.888 4.774 1.00 0.65 H
-ATOM 307 HG SER A 270 7.261 15.811 6.091 1.00 1.09 H
-ATOM 308 N ARG A 271 5.746 12.201 5.360 1.00 0.66 N
-ATOM 309 CA ARG A 271 5.159 11.197 6.262 1.00 0.74 C
-ATOM 310 C ARG A 271 4.392 10.119 5.491 1.00 0.72 C
-ATOM 311 O ARG A 271 3.295 9.720 5.881 1.00 0.91 O
-ATOM 312 CB ARG A 271 4.242 11.857 7.295 1.00 0.81 C
-ATOM 313 CG ARG A 271 4.947 12.903 8.133 1.00 0.90 C
-ATOM 314 CD ARG A 271 3.966 13.915 8.696 1.00 0.93 C
-ATOM 315 NE ARG A 271 3.205 14.586 7.642 1.00 0.83 N
-ATOM 316 CZ ARG A 271 2.351 15.560 7.866 1.00 0.90 C
-ATOM 317 NH1 ARG A 271 2.145 15.988 9.085 1.00 1.09 N
-ATOM 318 NH2 ARG A 271 1.704 16.111 6.870 1.00 0.87 N
-ATOM 319 H ARG A 271 6.717 12.209 5.234 1.00 0.74 H
-ATOM 320 HA ARG A 271 5.976 10.727 6.786 1.00 0.84 H
-ATOM 321 HB2 ARG A 271 3.419 12.330 6.780 1.00 0.75 H
-ATOM 322 HB3 ARG A 271 3.855 11.096 7.956 1.00 0.94 H
-ATOM 323 HG2 ARG A 271 5.454 12.413 8.953 1.00 1.04 H
-ATOM 324 HG3 ARG A 271 5.669 13.412 7.514 1.00 0.88 H
-ATOM 325 HD2 ARG A 271 3.277 13.404 9.352 1.00 1.02 H
-ATOM 326 HD3 ARG A 271 4.515 14.657 9.257 1.00 1.00 H
-ATOM 327 HE ARG A 271 3.343 14.284 6.718 1.00 0.76 H
-ATOM 328 HH11 ARG A 271 2.638 15.575 9.848 1.00 1.17 H
-ATOM 329 HH12 ARG A 271 1.497 16.727 9.251 1.00 1.20 H
-ATOM 330 HH21 ARG A 271 1.858 15.794 5.928 1.00 0.80 H
-ATOM 331 HH22 ARG A 271 1.058 16.847 7.046 1.00 0.97 H
-ATOM 332 N ALA A 272 4.985 9.644 4.401 1.00 0.91 N
-ATOM 333 CA ALA A 272 4.367 8.605 3.585 1.00 0.96 C
-ATOM 334 C ALA A 272 4.773 7.211 4.065 1.00 0.90 C
-ATOM 335 O ALA A 272 5.956 6.935 4.272 1.00 1.32 O
-ATOM 336 CB ALA A 272 4.740 8.791 2.122 1.00 1.04 C
-ATOM 337 H ALA A 272 5.863 9.993 4.145 1.00 1.20 H
-ATOM 338 HA ALA A 272 3.294 8.706 3.674 1.00 1.03 H
-ATOM 339 HB1 ALA A 272 3.841 8.888 1.531 1.00 1.45 H
-ATOM 340 HB2 ALA A 272 5.305 7.936 1.782 1.00 1.60 H
-ATOM 341 HB3 ALA A 272 5.339 9.683 2.014 1.00 1.31 H
-ATOM 342 N SER A 273 3.790 6.336 4.246 1.00 0.71 N
-ATOM 343 CA SER A 273 4.055 4.972 4.702 1.00 0.73 C
-ATOM 344 C SER A 273 4.327 4.023 3.544 1.00 0.69 C
-ATOM 345 O SER A 273 4.432 4.432 2.388 1.00 0.73 O
-ATOM 346 CB SER A 273 2.897 4.445 5.546 1.00 0.91 C
-ATOM 347 OG SER A 273 1.679 4.457 4.816 1.00 1.06 O
-ATOM 348 H SER A 273 2.866 6.610 4.068 1.00 0.90 H
-ATOM 349 HA SER A 273 4.949 4.994 5.316 1.00 0.70 H
-ATOM 350 HB2 SER A 273 3.113 3.430 5.846 1.00 0.98 H
-ATOM 351 HB3 SER A 273 2.787 5.065 6.423 1.00 0.98 H
-ATOM 352 HG SER A 273 1.081 3.801 5.186 1.00 1.37 H
-ATOM 353 N LEU A 274 4.489 2.752 3.897 1.00 0.74 N
-ATOM 354 CA LEU A 274 4.818 1.693 2.943 1.00 0.78 C
-ATOM 355 C LEU A 274 6.113 2.008 2.151 1.00 0.72 C
-ATOM 356 O LEU A 274 6.173 1.770 0.944 1.00 0.79 O
-ATOM 357 CB LEU A 274 3.645 1.446 1.981 1.00 0.88 C
-ATOM 358 CG LEU A 274 3.752 0.166 1.147 1.00 1.01 C
-ATOM 359 CD1 LEU A 274 3.743 -1.060 2.048 1.00 1.23 C
-ATOM 360 CD2 LEU A 274 2.623 0.091 0.130 1.00 1.14 C
-ATOM 361 H LEU A 274 4.427 2.522 4.846 1.00 0.82 H
-ATOM 362 HA LEU A 274 4.985 0.794 3.516 1.00 0.88 H
-ATOM 363 HB2 LEU A 274 2.735 1.398 2.563 1.00 0.99 H
-ATOM 364 HB3 LEU A 274 3.577 2.285 1.306 1.00 0.86 H
-ATOM 365 HG LEU A 274 4.689 0.175 0.607 1.00 1.01 H
-ATOM 366 HD11 LEU A 274 4.620 -1.048 2.679 1.00 1.61 H
-ATOM 367 HD12 LEU A 274 3.748 -1.953 1.442 1.00 1.72 H
-ATOM 368 HD13 LEU A 274 2.856 -1.047 2.664 1.00 1.63 H
-ATOM 369 HD21 LEU A 274 1.695 0.385 0.599 1.00 1.48 H
-ATOM 370 HD22 LEU A 274 2.537 -0.921 -0.236 1.00 1.60 H
-ATOM 371 HD23 LEU A 274 2.837 0.755 -0.695 1.00 1.50 H
-ATOM 372 N PRO A 275 7.172 2.568 2.807 1.00 0.70 N
-ATOM 373 CA PRO A 275 8.437 2.909 2.121 1.00 0.80 C
-ATOM 374 C PRO A 275 9.054 1.745 1.329 1.00 0.90 C
-ATOM 375 O PRO A 275 9.183 0.628 1.835 1.00 1.02 O
-ATOM 376 CB PRO A 275 9.358 3.302 3.275 1.00 0.92 C
-ATOM 377 CG PRO A 275 8.433 3.839 4.300 1.00 0.81 C
-ATOM 378 CD PRO A 275 7.228 2.950 4.242 1.00 0.69 C
-ATOM 379 HA PRO A 275 8.307 3.758 1.469 1.00 0.85 H
-ATOM 380 HB2 PRO A 275 9.889 2.431 3.632 1.00 1.02 H
-ATOM 381 HB3 PRO A 275 10.060 4.053 2.943 1.00 1.06 H
-ATOM 382 HG2 PRO A 275 8.893 3.799 5.276 1.00 0.93 H
-ATOM 383 HG3 PRO A 275 8.162 4.852 4.046 1.00 0.84 H
-ATOM 384 HD2 PRO A 275 7.356 2.090 4.877 1.00 0.82 H
-ATOM 385 HD3 PRO A 275 6.332 3.505 4.529 1.00 0.65 H
-ATOM 386 N LYS A 276 9.449 2.026 0.086 1.00 1.04 N
-ATOM 387 CA LYS A 276 10.070 1.021 -0.785 1.00 1.16 C
-ATOM 388 C LYS A 276 11.236 1.622 -1.576 1.00 1.79 C
-ATOM 389 O LYS A 276 11.982 0.845 -2.210 1.00 1.93 O
-ATOM 390 CB LYS A 276 9.045 0.432 -1.765 1.00 1.50 C
-ATOM 391 CG LYS A 276 8.097 -0.590 -1.147 1.00 1.66 C
-ATOM 392 CD LYS A 276 8.836 -1.827 -0.655 1.00 1.68 C
-ATOM 393 CE LYS A 276 9.682 -2.457 -1.754 1.00 1.72 C
-ATOM 394 NZ LYS A 276 8.876 -2.801 -2.958 1.00 2.06 N
-ATOM 395 OXT LYS A 276 11.388 2.863 -1.554 1.00 2.43 O
-ATOM 396 H LYS A 276 9.334 2.939 -0.256 1.00 1.16 H
-ATOM 397 HA LYS A 276 10.448 0.229 -0.156 1.00 1.11 H
-ATOM 398 HB2 LYS A 276 8.451 1.237 -2.172 1.00 2.11 H
-ATOM 399 HB3 LYS A 276 9.577 -0.050 -2.572 1.00 1.71 H
-ATOM 400 HG2 LYS A 276 7.590 -0.133 -0.311 1.00 1.97 H
-ATOM 401 HG3 LYS A 276 7.372 -0.887 -1.890 1.00 2.19 H
-ATOM 402 HD2 LYS A 276 9.480 -1.547 0.164 1.00 1.74 H
-ATOM 403 HD3 LYS A 276 8.113 -2.552 -0.313 1.00 2.29 H
-ATOM 404 HE2 LYS A 276 10.455 -1.760 -2.038 1.00 1.70 H
-ATOM 405 HE3 LYS A 276 10.136 -3.357 -1.367 1.00 2.19 H
-ATOM 406 HZ1 LYS A 276 7.994 -3.275 -2.675 1.00 2.32 H
-ATOM 407 HZ2 LYS A 276 9.417 -3.438 -3.578 1.00 2.41 H
-ATOM 408 HZ3 LYS A 276 8.640 -1.937 -3.486 1.00 2.48 H
-TER
-ENDMDL
-MODEL 4
-ATOM 1 N ALA A 249 4.167 -11.688 1.475 1.00 2.29 N
-ATOM 2 CA ALA A 249 4.399 -11.189 0.094 1.00 1.77 C
-ATOM 3 C ALA A 249 3.650 -9.880 -0.137 1.00 1.27 C
-ATOM 4 O ALA A 249 2.649 -9.608 0.527 1.00 1.51 O
-ATOM 5 CB ALA A 249 3.973 -12.231 -0.932 1.00 2.17 C
-ATOM 6 H1 ALA A 249 4.039 -12.719 1.468 1.00 2.60 H
-ATOM 7 H2 ALA A 249 3.312 -11.247 1.874 1.00 2.70 H
-ATOM 8 H3 ALA A 249 4.979 -11.451 2.082 1.00 2.56 H
-ATOM 9 HA ALA A 249 5.457 -11.010 -0.029 1.00 1.68 H
-ATOM 10 HB1 ALA A 249 4.845 -12.598 -1.454 1.00 2.44 H
-ATOM 11 HB2 ALA A 249 3.295 -11.779 -1.641 1.00 2.51 H
-ATOM 12 HB3 ALA A 249 3.481 -13.049 -0.432 1.00 2.55 H
-ATOM 13 N PRO A 250 4.132 -9.050 -1.074 1.00 0.93 N
-ATOM 14 CA PRO A 250 3.520 -7.757 -1.398 1.00 0.88 C
-ATOM 15 C PRO A 250 2.170 -7.899 -2.112 1.00 0.72 C
-ATOM 16 O PRO A 250 2.083 -7.724 -3.330 1.00 0.66 O
-ATOM 17 CB PRO A 250 4.552 -7.103 -2.332 1.00 1.29 C
-ATOM 18 CG PRO A 250 5.794 -7.903 -2.170 1.00 1.40 C
-ATOM 19 CD PRO A 250 5.321 -9.289 -1.897 1.00 1.24 C
-ATOM 20 HA PRO A 250 3.395 -7.147 -0.515 1.00 1.16 H
-ATOM 21 HB2 PRO A 250 4.194 -7.147 -3.347 1.00 1.46 H
-ATOM 22 HB3 PRO A 250 4.707 -6.079 -2.040 1.00 1.62 H
-ATOM 23 HG2 PRO A 250 6.376 -7.873 -3.079 1.00 1.82 H
-ATOM 24 HG3 PRO A 250 6.370 -7.531 -1.336 1.00 1.44 H
-ATOM 25 HD2 PRO A 250 5.058 -9.786 -2.819 1.00 1.52 H
-ATOM 26 HD3 PRO A 250 6.063 -9.847 -1.354 1.00 1.40 H
-ATOM 27 N ALA A 251 1.124 -8.227 -1.351 1.00 0.77 N
-ATOM 28 CA ALA A 251 -0.214 -8.396 -1.913 1.00 0.74 C
-ATOM 29 C ALA A 251 -0.662 -7.143 -2.660 1.00 0.62 C
-ATOM 30 O ALA A 251 -0.781 -6.068 -2.073 1.00 0.65 O
-ATOM 31 CB ALA A 251 -1.210 -8.736 -0.819 1.00 0.94 C
-ATOM 32 H ALA A 251 1.258 -8.363 -0.387 1.00 0.90 H
-ATOM 33 HA ALA A 251 -0.182 -9.223 -2.607 1.00 0.75 H
-ATOM 34 HB1 ALA A 251 -1.946 -9.424 -1.206 1.00 1.50 H
-ATOM 35 HB2 ALA A 251 -1.699 -7.833 -0.487 1.00 1.25 H
-ATOM 36 HB3 ALA A 251 -0.691 -9.191 0.010 1.00 1.40 H
-ATOM 37 N ARG A 252 -0.904 -7.308 -3.958 1.00 0.58 N
-ATOM 38 CA ARG A 252 -1.335 -6.223 -4.832 1.00 0.55 C
-ATOM 39 C ARG A 252 -0.431 -4.986 -4.696 1.00 0.44 C
-ATOM 40 O ARG A 252 -0.898 -3.843 -4.609 1.00 0.47 O
-ATOM 41 CB ARG A 252 -2.789 -5.894 -4.546 1.00 0.69 C
-ATOM 42 CG ARG A 252 -3.453 -5.091 -5.648 1.00 0.73 C
-ATOM 43 CD ARG A 252 -3.147 -5.687 -7.017 1.00 0.78 C
-ATOM 44 NE ARG A 252 -3.981 -5.119 -8.071 1.00 1.02 N
-ATOM 45 CZ ARG A 252 -5.283 -5.284 -8.146 1.00 1.23 C
-ATOM 46 NH1 ARG A 252 -5.910 -6.016 -7.262 1.00 1.31 N
-ATOM 47 NH2 ARG A 252 -5.959 -4.723 -9.114 1.00 1.49 N
-ATOM 48 H ARG A 252 -0.795 -8.193 -4.343 1.00 0.64 H
-ATOM 49 HA ARG A 252 -1.259 -6.582 -5.847 1.00 0.57 H
-ATOM 50 HB2 ARG A 252 -3.330 -6.824 -4.426 1.00 0.80 H
-ATOM 51 HB3 ARG A 252 -2.843 -5.333 -3.628 1.00 0.72 H
-ATOM 52 HG2 ARG A 252 -4.522 -5.097 -5.493 1.00 0.86 H
-ATOM 53 HG3 ARG A 252 -3.084 -4.077 -5.615 1.00 0.70 H
-ATOM 54 HD2 ARG A 252 -2.109 -5.494 -7.251 1.00 0.73 H
-ATOM 55 HD3 ARG A 252 -3.310 -6.754 -6.975 1.00 0.84 H
-ATOM 56 HE ARG A 252 -3.539 -4.581 -8.757 1.00 1.11 H
-ATOM 57 HH11 ARG A 252 -5.399 -6.454 -6.529 1.00 1.21 H
-ATOM 58 HH12 ARG A 252 -6.897 -6.133 -7.321 1.00 1.55 H
-ATOM 59 HH21 ARG A 252 -5.488 -4.169 -9.795 1.00 1.56 H
-ATOM 60 HH22 ARG A 252 -6.946 -4.852 -9.172 1.00 1.69 H
-ATOM 61 N VAL A 253 0.873 -5.250 -4.676 1.00 0.39 N
-ATOM 62 CA VAL A 253 1.899 -4.215 -4.546 1.00 0.36 C
-ATOM 63 C VAL A 253 1.679 -3.032 -5.495 1.00 0.32 C
-ATOM 64 O VAL A 253 1.678 -1.882 -5.067 1.00 0.34 O
-ATOM 65 CB VAL A 253 3.298 -4.820 -4.793 1.00 0.46 C
-ATOM 66 CG1 VAL A 253 3.381 -5.526 -6.142 1.00 0.53 C
-ATOM 67 CG2 VAL A 253 4.377 -3.756 -4.665 1.00 0.60 C
-ATOM 68 H VAL A 253 1.158 -6.188 -4.734 1.00 0.43 H
-ATOM 69 HA VAL A 253 1.870 -3.851 -3.532 1.00 0.40 H
-ATOM 70 HB VAL A 253 3.465 -5.557 -4.039 1.00 0.50 H
-ATOM 71 HG11 VAL A 253 4.134 -6.298 -6.100 1.00 1.16 H
-ATOM 72 HG12 VAL A 253 3.640 -4.810 -6.908 1.00 1.15 H
-ATOM 73 HG13 VAL A 253 2.423 -5.969 -6.375 1.00 1.19 H
-ATOM 74 HG21 VAL A 253 4.836 -3.592 -5.628 1.00 1.16 H
-ATOM 75 HG22 VAL A 253 5.125 -4.086 -3.960 1.00 0.98 H
-ATOM 76 HG23 VAL A 253 3.934 -2.836 -4.315 1.00 1.16 H
-ATOM 77 N GLY A 254 1.506 -3.335 -6.774 1.00 0.36 N
-ATOM 78 CA GLY A 254 1.299 -2.300 -7.789 1.00 0.41 C
-ATOM 79 C GLY A 254 0.207 -1.301 -7.424 1.00 0.40 C
-ATOM 80 O GLY A 254 0.440 -0.087 -7.419 1.00 0.42 O
-ATOM 81 H GLY A 254 1.538 -4.279 -7.032 1.00 0.41 H
-ATOM 82 HA2 GLY A 254 2.226 -1.762 -7.927 1.00 0.44 H
-ATOM 83 HA3 GLY A 254 1.035 -2.778 -8.721 1.00 0.48 H
-ATOM 84 N LEU A 255 -0.979 -1.803 -7.093 1.00 0.43 N
-ATOM 85 CA LEU A 255 -2.084 -0.932 -6.704 1.00 0.48 C
-ATOM 86 C LEU A 255 -1.761 -0.214 -5.398 1.00 0.42 C
-ATOM 87 O LEU A 255 -2.033 0.975 -5.251 1.00 0.45 O
-ATOM 88 CB LEU A 255 -3.384 -1.718 -6.554 1.00 0.60 C
-ATOM 89 CG LEU A 255 -4.495 -1.311 -7.524 1.00 0.78 C
-ATOM 90 CD1 LEU A 255 -5.796 -2.003 -7.160 1.00 0.92 C
-ATOM 91 CD2 LEU A 255 -4.676 0.200 -7.530 1.00 0.89 C
-ATOM 92 H LEU A 255 -1.106 -2.774 -7.092 1.00 0.46 H
-ATOM 93 HA LEU A 255 -2.213 -0.194 -7.482 1.00 0.53 H
-ATOM 94 HB2 LEU A 255 -3.167 -2.766 -6.705 1.00 0.65 H
-ATOM 95 HB3 LEU A 255 -3.749 -1.583 -5.547 1.00 0.66 H
-ATOM 96 HG LEU A 255 -4.220 -1.620 -8.523 1.00 0.87 H
-ATOM 97 HD11 LEU A 255 -6.515 -1.268 -6.833 1.00 1.42 H
-ATOM 98 HD12 LEU A 255 -5.616 -2.711 -6.364 1.00 1.39 H
-ATOM 99 HD13 LEU A 255 -6.180 -2.523 -8.025 1.00 1.40 H
-ATOM 100 HD21 LEU A 255 -3.928 0.655 -6.897 1.00 1.42 H
-ATOM 101 HD22 LEU A 255 -5.660 0.447 -7.161 1.00 1.37 H
-ATOM 102 HD23 LEU A 255 -4.566 0.571 -8.538 1.00 1.35 H
-ATOM 103 N GLY A 256 -1.165 -0.942 -4.459 1.00 0.38 N
-ATOM 104 CA GLY A 256 -0.800 -0.352 -3.185 1.00 0.39 C
-ATOM 105 C GLY A 256 0.183 0.801 -3.332 1.00 0.34 C
-ATOM 106 O GLY A 256 -0.059 1.894 -2.821 1.00 0.38 O
-ATOM 107 H GLY A 256 -0.962 -1.891 -4.637 1.00 0.40 H
-ATOM 108 HA2 GLY A 256 -1.695 0.014 -2.702 1.00 0.47 H
-ATOM 109 HA3 GLY A 256 -0.354 -1.113 -2.562 1.00 0.44 H
-ATOM 110 N ILE A 257 1.292 0.565 -4.038 1.00 0.32 N
-ATOM 111 CA ILE A 257 2.298 1.610 -4.235 1.00 0.35 C
-ATOM 112 C ILE A 257 1.751 2.779 -5.060 1.00 0.34 C
-ATOM 113 O ILE A 257 2.001 3.937 -4.726 1.00 0.39 O
-ATOM 114 CB ILE A 257 3.614 1.071 -4.857 1.00 0.43 C
-ATOM 115 CG1 ILE A 257 3.386 0.428 -6.229 1.00 0.45 C
-ATOM 116 CG2 ILE A 257 4.271 0.077 -3.913 1.00 0.52 C
-ATOM 117 CD1 ILE A 257 3.784 1.314 -7.391 1.00 0.53 C
-ATOM 118 H ILE A 257 1.433 -0.330 -4.433 1.00 0.33 H
-ATOM 119 HA ILE A 257 2.540 1.992 -3.253 1.00 0.39 H
-ATOM 120 HB ILE A 257 4.290 1.907 -4.972 1.00 0.50 H
-ATOM 121 HG12 ILE A 257 3.965 -0.481 -6.294 1.00 0.53 H
-ATOM 122 HG13 ILE A 257 2.338 0.189 -6.337 1.00 0.46 H
-ATOM 123 HG21 ILE A 257 5.298 -0.076 -4.208 1.00 1.16 H
-ATOM 124 HG22 ILE A 257 3.741 -0.863 -3.953 1.00 1.07 H
-ATOM 125 HG23 ILE A 257 4.240 0.464 -2.905 1.00 1.04 H
-ATOM 126 HD11 ILE A 257 4.279 2.198 -7.018 1.00 1.19 H
-ATOM 127 HD12 ILE A 257 2.902 1.602 -7.944 1.00 1.15 H
-ATOM 128 HD13 ILE A 257 4.456 0.774 -8.043 1.00 1.16 H
-ATOM 129 N THR A 258 0.984 2.493 -6.119 1.00 0.34 N
-ATOM 130 CA THR A 258 0.407 3.566 -6.936 1.00 0.41 C
-ATOM 131 C THR A 258 -0.589 4.390 -6.120 1.00 0.40 C
-ATOM 132 O THR A 258 -0.634 5.618 -6.236 1.00 0.43 O
-ATOM 133 CB THR A 258 -0.286 3.045 -8.213 1.00 0.50 C
-ATOM 134 OG1 THR A 258 -1.166 1.960 -7.899 1.00 0.52 O
-ATOM 135 CG2 THR A 258 0.739 2.597 -9.244 1.00 0.58 C
-ATOM 136 H THR A 258 0.792 1.550 -6.344 1.00 0.35 H
-ATOM 137 HA THR A 258 1.220 4.213 -7.237 1.00 0.45 H
-ATOM 138 HB THR A 258 -0.866 3.851 -8.638 1.00 0.56 H
-ATOM 139 HG1 THR A 258 -0.660 1.134 -7.876 1.00 0.59 H
-ATOM 140 HG21 THR A 258 0.919 1.537 -9.136 1.00 1.21 H
-ATOM 141 HG22 THR A 258 1.662 3.136 -9.092 1.00 1.15 H
-ATOM 142 HG23 THR A 258 0.363 2.799 -10.236 1.00 1.20 H
-ATOM 143 N THR A 259 -1.367 3.716 -5.271 1.00 0.42 N
-ATOM 144 CA THR A 259 -2.337 4.399 -4.415 1.00 0.46 C
-ATOM 145 C THR A 259 -1.612 5.292 -3.414 1.00 0.45 C
-ATOM 146 O THR A 259 -1.935 6.471 -3.278 1.00 0.50 O
-ATOM 147 CB THR A 259 -3.236 3.404 -3.646 1.00 0.52 C
-ATOM 148 OG1 THR A 259 -3.933 2.556 -4.566 1.00 0.56 O
-ATOM 149 CG2 THR A 259 -4.246 4.138 -2.775 1.00 0.64 C
-ATOM 150 H THR A 259 -1.272 2.738 -5.206 1.00 0.43 H
-ATOM 151 HA THR A 259 -2.965 5.014 -5.045 1.00 0.51 H
-ATOM 152 HB THR A 259 -2.610 2.795 -3.010 1.00 0.52 H
-ATOM 153 HG1 THR A 259 -3.316 1.900 -4.927 1.00 0.56 H
-ATOM 154 HG21 THR A 259 -4.023 5.194 -2.775 1.00 1.21 H
-ATOM 155 HG22 THR A 259 -4.191 3.759 -1.764 1.00 1.24 H
-ATOM 156 HG23 THR A 259 -5.241 3.980 -3.164 1.00 1.20 H
-ATOM 157 N VAL A 260 -0.608 4.731 -2.735 1.00 0.45 N
-ATOM 158 CA VAL A 260 0.178 5.495 -1.769 1.00 0.48 C
-ATOM 159 C VAL A 260 0.855 6.690 -2.450 1.00 0.44 C
-ATOM 160 O VAL A 260 0.809 7.811 -1.940 1.00 0.49 O
-ATOM 161 CB VAL A 260 1.245 4.616 -1.080 1.00 0.53 C
-ATOM 162 CG1 VAL A 260 2.145 5.452 -0.181 1.00 0.61 C
-ATOM 163 CG2 VAL A 260 0.581 3.508 -0.279 1.00 0.59 C
-ATOM 164 H VAL A 260 -0.381 3.784 -2.900 1.00 0.45 H
-ATOM 165 HA VAL A 260 -0.498 5.865 -1.011 1.00 0.53 H
-ATOM 166 HB VAL A 260 1.859 4.161 -1.844 1.00 0.54 H
-ATOM 167 HG11 VAL A 260 1.699 5.533 0.799 1.00 0.99 H
-ATOM 168 HG12 VAL A 260 2.263 6.438 -0.606 1.00 0.99 H
-ATOM 169 HG13 VAL A 260 3.112 4.978 -0.098 1.00 0.82 H
-ATOM 170 HG21 VAL A 260 0.055 2.845 -0.951 1.00 1.21 H
-ATOM 171 HG22 VAL A 260 -0.121 3.939 0.420 1.00 1.19 H
-ATOM 172 HG23 VAL A 260 1.332 2.951 0.261 1.00 1.17 H
-ATOM 173 N LEU A 261 1.457 6.445 -3.617 1.00 0.40 N
-ATOM 174 CA LEU A 261 2.120 7.501 -4.380 1.00 0.41 C
-ATOM 175 C LEU A 261 1.121 8.612 -4.721 1.00 0.40 C
-ATOM 176 O LEU A 261 1.406 9.796 -4.549 1.00 0.45 O
-ATOM 177 CB LEU A 261 2.730 6.922 -5.664 1.00 0.47 C
-ATOM 178 CG LEU A 261 4.094 7.497 -6.073 1.00 0.79 C
-ATOM 179 CD1 LEU A 261 3.985 8.974 -6.425 1.00 1.35 C
-ATOM 180 CD2 LEU A 261 5.111 7.288 -4.962 1.00 1.30 C
-ATOM 181 H LEU A 261 1.443 5.530 -3.981 1.00 0.40 H
-ATOM 182 HA LEU A 261 2.907 7.913 -3.766 1.00 0.47 H
-ATOM 183 HB2 LEU A 261 2.840 5.856 -5.531 1.00 0.63 H
-ATOM 184 HB3 LEU A 261 2.037 7.093 -6.473 1.00 0.77 H
-ATOM 185 HG LEU A 261 4.448 6.972 -6.950 1.00 0.76 H
-ATOM 186 HD11 LEU A 261 3.578 9.515 -5.584 1.00 1.85 H
-ATOM 187 HD12 LEU A 261 3.335 9.095 -7.279 1.00 1.65 H
-ATOM 188 HD13 LEU A 261 4.965 9.360 -6.661 1.00 1.95 H
-ATOM 189 HD21 LEU A 261 4.899 6.361 -4.449 1.00 1.75 H
-ATOM 190 HD22 LEU A 261 5.051 8.107 -4.260 1.00 1.75 H
-ATOM 191 HD23 LEU A 261 6.104 7.247 -5.383 1.00 1.86 H
-ATOM 192 N THR A 262 -0.063 8.210 -5.178 1.00 0.39 N
-ATOM 193 CA THR A 262 -1.130 9.153 -5.522 1.00 0.44 C
-ATOM 194 C THR A 262 -1.541 9.971 -4.300 1.00 0.45 C
-ATOM 195 O THR A 262 -1.615 11.202 -4.350 1.00 0.50 O
-ATOM 196 CB THR A 262 -2.373 8.413 -6.061 1.00 0.51 C
-ATOM 197 OG1 THR A 262 -2.026 7.621 -7.206 1.00 0.54 O
-ATOM 198 CG2 THR A 262 -3.469 9.397 -6.435 1.00 0.61 C
-ATOM 199 H THR A 262 -0.230 7.247 -5.274 1.00 0.41 H
-ATOM 200 HA THR A 262 -0.763 9.817 -6.288 1.00 0.47 H
-ATOM 201 HB THR A 262 -2.748 7.761 -5.284 1.00 0.53 H
-ATOM 202 HG1 THR A 262 -1.578 6.811 -6.918 1.00 0.52 H
-ATOM 203 HG21 THR A 262 -3.560 10.143 -5.659 1.00 1.18 H
-ATOM 204 HG22 THR A 262 -4.404 8.870 -6.539 1.00 1.22 H
-ATOM 205 HG23 THR A 262 -3.219 9.876 -7.370 1.00 1.20 H
-ATOM 206 N MET A 263 -1.803 9.274 -3.200 1.00 0.47 N
-ATOM 207 CA MET A 263 -2.203 9.915 -1.949 1.00 0.52 C
-ATOM 208 C MET A 263 -1.130 10.881 -1.434 1.00 0.49 C
-ATOM 209 O MET A 263 -1.441 12.004 -1.041 1.00 0.54 O
-ATOM 210 CB MET A 263 -2.503 8.861 -0.879 1.00 0.62 C
-ATOM 211 CG MET A 263 -3.706 7.993 -1.201 1.00 0.72 C
-ATOM 212 SD MET A 263 -5.223 8.952 -1.361 1.00 0.89 S
-ATOM 213 CE MET A 263 -6.389 7.674 -1.819 1.00 1.09 C
-ATOM 214 H MET A 263 -1.725 8.293 -3.232 1.00 0.47 H
-ATOM 215 HA MET A 263 -3.104 10.477 -2.143 1.00 0.56 H
-ATOM 216 HB2 MET A 263 -1.642 8.218 -0.772 1.00 0.62 H
-ATOM 217 HB3 MET A 263 -2.686 9.360 0.061 1.00 0.70 H
-ATOM 218 HG2 MET A 263 -3.524 7.478 -2.133 1.00 0.69 H
-ATOM 219 HG3 MET A 263 -3.836 7.271 -0.410 1.00 0.82 H
-ATOM 220 HE1 MET A 263 -7.090 8.067 -2.542 1.00 1.65 H
-ATOM 221 HE2 MET A 263 -6.925 7.343 -0.942 1.00 1.47 H
-ATOM 222 HE3 MET A 263 -5.857 6.840 -2.251 1.00 1.48 H
-ATOM 223 N THR A 264 0.132 10.446 -1.418 1.00 0.48 N
-ATOM 224 CA THR A 264 1.211 11.304 -0.923 1.00 0.52 C
-ATOM 225 C THR A 264 1.492 12.501 -1.845 1.00 0.49 C
-ATOM 226 O THR A 264 1.739 13.604 -1.358 1.00 0.59 O
-ATOM 227 CB THR A 264 2.528 10.529 -0.666 1.00 0.60 C
-ATOM 228 OG1 THR A 264 3.495 11.403 -0.075 1.00 0.72 O
-ATOM 229 CG2 THR A 264 3.109 9.944 -1.944 1.00 0.58 C
-ATOM 230 H THR A 264 0.338 9.531 -1.731 1.00 0.51 H
-ATOM 231 HA THR A 264 0.881 11.697 0.026 1.00 0.58 H
-ATOM 232 HB THR A 264 2.321 9.720 0.020 1.00 0.67 H
-ATOM 233 HG1 THR A 264 3.251 12.318 -0.249 1.00 1.04 H
-ATOM 234 HG21 THR A 264 2.345 9.922 -2.707 1.00 1.19 H
-ATOM 235 HG22 THR A 264 3.457 8.939 -1.754 1.00 1.14 H
-ATOM 236 HG23 THR A 264 3.933 10.555 -2.277 1.00 1.20 H
-ATOM 237 N THR A 265 1.464 12.294 -3.166 1.00 0.43 N
-ATOM 238 CA THR A 265 1.731 13.392 -4.105 1.00 0.47 C
-ATOM 239 C THR A 265 0.617 14.443 -4.077 1.00 0.45 C
-ATOM 240 O THR A 265 0.884 15.638 -4.206 1.00 0.55 O
-ATOM 241 CB THR A 265 1.952 12.904 -5.561 1.00 0.53 C
-ATOM 242 OG1 THR A 265 2.400 13.994 -6.377 1.00 0.67 O
-ATOM 243 CG2 THR A 265 0.684 12.321 -6.169 1.00 0.52 C
-ATOM 244 H THR A 265 1.269 11.393 -3.515 1.00 0.40 H
-ATOM 245 HA THR A 265 2.645 13.869 -3.778 1.00 0.54 H
-ATOM 246 HB THR A 265 2.714 12.138 -5.552 1.00 0.60 H
-ATOM 247 HG1 THR A 265 2.109 14.827 -5.995 1.00 0.94 H
-ATOM 248 HG21 THR A 265 0.052 11.935 -5.383 1.00 1.15 H
-ATOM 249 HG22 THR A 265 0.946 11.521 -6.846 1.00 1.19 H
-ATOM 250 HG23 THR A 265 0.156 13.091 -6.710 1.00 1.10 H
-ATOM 251 N GLN A 266 -0.626 14.000 -3.905 1.00 0.40 N
-ATOM 252 CA GLN A 266 -1.760 14.919 -3.854 1.00 0.42 C
-ATOM 253 C GLN A 266 -1.898 15.546 -2.461 1.00 0.44 C
-ATOM 254 O GLN A 266 -2.279 16.712 -2.334 1.00 0.50 O
-ATOM 255 CB GLN A 266 -3.057 14.203 -4.244 1.00 0.47 C
-ATOM 256 CG GLN A 266 -4.277 15.113 -4.241 1.00 0.54 C
-ATOM 257 CD GLN A 266 -5.542 14.411 -4.699 1.00 0.68 C
-ATOM 258 OE1 GLN A 266 -5.889 13.346 -4.203 1.00 0.80 O
-ATOM 259 NE2 GLN A 266 -6.254 15.017 -5.631 1.00 0.83 N
-ATOM 260 H GLN A 266 -0.783 13.033 -3.808 1.00 0.41 H
-ATOM 261 HA GLN A 266 -1.571 15.708 -4.565 1.00 0.48 H
-ATOM 262 HB2 GLN A 266 -2.944 13.792 -5.236 1.00 0.56 H
-ATOM 263 HB3 GLN A 266 -3.235 13.397 -3.547 1.00 0.52 H
-ATOM 264 HG2 GLN A 266 -4.435 15.480 -3.237 1.00 0.60 H
-ATOM 265 HG3 GLN A 266 -4.088 15.947 -4.901 1.00 0.62 H
-ATOM 266 HE21 GLN A 266 -5.936 15.889 -5.974 1.00 0.88 H
-ATOM 267 HE22 GLN A 266 -7.068 14.577 -5.941 1.00 0.96 H
-ATOM 268 N SER A 267 -1.595 14.759 -1.423 1.00 0.50 N
-ATOM 269 CA SER A 267 -1.690 15.221 -0.032 1.00 0.60 C
-ATOM 270 C SER A 267 -3.132 15.609 0.325 1.00 0.63 C
-ATOM 271 O SER A 267 -4.055 14.816 0.144 1.00 0.70 O
-ATOM 272 CB SER A 267 -0.731 16.394 0.216 1.00 0.70 C
-ATOM 273 OG SER A 267 0.620 16.006 0.024 1.00 0.78 O
-ATOM 274 H SER A 267 -1.306 13.833 -1.595 1.00 0.52 H
-ATOM 275 HA SER A 267 -1.396 14.395 0.601 1.00 0.69 H
-ATOM 276 HB2 SER A 267 -0.963 17.195 -0.469 1.00 0.76 H
-ATOM 277 HB3 SER A 267 -0.851 16.744 1.231 1.00 0.84 H
-ATOM 278 HG SER A 267 0.666 15.282 -0.613 1.00 0.79 H
-ATOM 279 N SER A 268 -3.331 16.830 0.827 1.00 0.69 N
-ATOM 280 CA SER A 268 -4.671 17.306 1.201 1.00 0.78 C
-ATOM 281 C SER A 268 -5.502 17.716 -0.026 1.00 0.66 C
-ATOM 282 O SER A 268 -6.179 18.747 -0.019 1.00 0.70 O
-ATOM 283 CB SER A 268 -4.556 18.488 2.168 1.00 0.98 C
-ATOM 284 OG SER A 268 -3.736 19.514 1.630 1.00 1.02 O
-ATOM 285 H SER A 268 -2.565 17.429 0.949 1.00 0.74 H
-ATOM 286 HA SER A 268 -5.177 16.496 1.702 1.00 0.88 H
-ATOM 287 HB2 SER A 268 -5.539 18.893 2.357 1.00 1.12 H
-ATOM 288 HB3 SER A 268 -4.122 18.149 3.097 1.00 1.08 H
-ATOM 289 HG SER A 268 -4.076 20.372 1.902 1.00 1.27 H
-ATOM 290 N GLY A 269 -5.452 16.896 -1.076 1.00 0.62 N
-ATOM 291 CA GLY A 269 -6.200 17.177 -2.298 1.00 0.60 C
-ATOM 292 C GLY A 269 -7.688 16.873 -2.185 1.00 0.62 C
-ATOM 293 O GLY A 269 -8.235 16.102 -2.977 1.00 0.77 O
-ATOM 294 H GLY A 269 -4.899 16.082 -1.019 1.00 0.69 H
-ATOM 295 HA2 GLY A 269 -6.081 18.222 -2.543 1.00 0.58 H
-ATOM 296 HA3 GLY A 269 -5.786 16.584 -3.100 1.00 0.73 H
-ATOM 297 N SER A 270 -8.341 17.489 -1.201 1.00 0.60 N
-ATOM 298 CA SER A 270 -9.778 17.300 -0.966 1.00 0.75 C
-ATOM 299 C SER A 270 -10.615 17.645 -2.198 1.00 0.70 C
-ATOM 300 O SER A 270 -10.380 18.663 -2.848 1.00 0.55 O
-ATOM 301 CB SER A 270 -10.236 18.172 0.208 1.00 0.85 C
-ATOM 302 OG SER A 270 -11.653 18.205 0.305 1.00 1.02 O
-ATOM 303 H SER A 270 -7.836 18.097 -0.613 1.00 0.59 H
-ATOM 304 HA SER A 270 -9.939 16.264 -0.714 1.00 0.93 H
-ATOM 305 HB2 SER A 270 -9.836 17.772 1.126 1.00 0.95 H
-ATOM 306 HB3 SER A 270 -9.875 19.180 0.067 1.00 0.80 H
-ATOM 307 HG SER A 270 -11.931 17.720 1.089 1.00 1.20 H
-ATOM 308 N ARG A 271 -11.602 16.793 -2.496 1.00 0.91 N
-ATOM 309 CA ARG A 271 -12.503 17.002 -3.636 1.00 0.96 C
-ATOM 310 C ARG A 271 -11.743 17.083 -4.967 1.00 0.94 C
-ATOM 311 O ARG A 271 -11.962 18.003 -5.755 1.00 0.93 O
-ATOM 312 CB ARG A 271 -13.326 18.276 -3.413 1.00 0.88 C
-ATOM 313 CG ARG A 271 -14.282 18.184 -2.232 1.00 1.01 C
-ATOM 314 CD ARG A 271 -14.613 19.559 -1.672 1.00 0.94 C
-ATOM 315 NE ARG A 271 -13.512 20.109 -0.879 1.00 0.80 N
-ATOM 316 CZ ARG A 271 -13.470 21.350 -0.440 1.00 0.77 C
-ATOM 317 NH1 ARG A 271 -14.424 22.194 -0.721 1.00 0.86 N
-ATOM 318 NH2 ARG A 271 -12.469 21.761 0.283 1.00 0.80 N
-ATOM 319 H ARG A 271 -11.739 16.009 -1.923 1.00 1.06 H
-ATOM 320 HA ARG A 271 -13.177 16.160 -3.676 1.00 1.15 H
-ATOM 321 HB2 ARG A 271 -12.652 19.102 -3.240 1.00 0.72 H
-ATOM 322 HB3 ARG A 271 -13.905 18.477 -4.302 1.00 0.95 H
-ATOM 323 HG2 ARG A 271 -15.196 17.710 -2.558 1.00 1.18 H
-ATOM 324 HG3 ARG A 271 -13.822 17.589 -1.457 1.00 1.07 H
-ATOM 325 HD2 ARG A 271 -14.821 20.229 -2.493 1.00 0.92 H
-ATOM 326 HD3 ARG A 271 -15.489 19.476 -1.044 1.00 1.11 H
-ATOM 327 HE ARG A 271 -12.768 19.506 -0.654 1.00 0.82 H
-ATOM 328 HH11 ARG A 271 -15.199 21.906 -1.274 1.00 0.94 H
-ATOM 329 HH12 ARG A 271 -14.368 23.135 -0.376 1.00 0.91 H
-ATOM 330 HH21 ARG A 271 -11.727 21.138 0.512 1.00 0.83 H
-ATOM 331 HH22 ARG A 271 -12.450 22.727 0.601 1.00 0.87 H
-ATOM 332 N ALA A 272 -10.846 16.116 -5.200 1.00 1.01 N
-ATOM 333 CA ALA A 272 -10.038 16.068 -6.425 1.00 1.06 C
-ATOM 334 C ALA A 272 -9.157 17.316 -6.561 1.00 0.89 C
-ATOM 335 O ALA A 272 -9.132 17.965 -7.608 1.00 1.00 O
-ATOM 336 CB ALA A 272 -10.928 15.895 -7.654 1.00 1.29 C
-ATOM 337 H ALA A 272 -10.720 15.419 -4.523 1.00 1.07 H
-ATOM 338 HA ALA A 272 -9.396 15.202 -6.358 1.00 1.13 H
-ATOM 339 HB1 ALA A 272 -11.503 14.986 -7.557 1.00 1.67 H
-ATOM 340 HB2 ALA A 272 -10.312 15.838 -8.539 1.00 1.72 H
-ATOM 341 HB3 ALA A 272 -11.597 16.738 -7.735 1.00 1.66 H
-ATOM 342 N SER A 273 -8.443 17.638 -5.477 1.00 0.70 N
-ATOM 343 CA SER A 273 -7.551 18.806 -5.423 1.00 0.62 C
-ATOM 344 C SER A 273 -8.314 20.110 -5.564 1.00 0.61 C
-ATOM 345 O SER A 273 -7.938 20.993 -6.335 1.00 0.76 O
-ATOM 346 CB SER A 273 -6.442 18.740 -6.474 1.00 0.81 C
-ATOM 347 OG SER A 273 -5.684 17.538 -6.372 1.00 0.87 O
-ATOM 348 H SER A 273 -8.524 17.070 -4.678 1.00 0.69 H
-ATOM 349 HA SER A 273 -7.091 18.821 -4.441 1.00 0.52 H
-ATOM 350 HB2 SER A 273 -6.883 18.795 -7.454 1.00 0.95 H
-ATOM 351 HB3 SER A 273 -5.778 19.580 -6.329 1.00 0.86 H
-ATOM 352 HG SER A 273 -4.816 17.671 -6.767 1.00 1.23 H
-ATOM 353 N LEU A 274 -9.349 20.248 -4.754 1.00 0.52 N
-ATOM 354 CA LEU A 274 -10.131 21.468 -4.717 1.00 0.55 C
-ATOM 355 C LEU A 274 -10.222 21.984 -3.266 1.00 0.43 C
-ATOM 356 O LEU A 274 -11.317 22.251 -2.774 1.00 0.49 O
-ATOM 357 CB LEU A 274 -11.530 21.216 -5.293 1.00 0.70 C
-ATOM 358 CG LEU A 274 -12.372 22.470 -5.549 1.00 0.82 C
-ATOM 359 CD1 LEU A 274 -11.741 23.327 -6.635 1.00 1.04 C
-ATOM 360 CD2 LEU A 274 -13.793 22.084 -5.928 1.00 1.01 C
-ATOM 361 H LEU A 274 -9.567 19.521 -4.123 1.00 0.50 H
-ATOM 362 HA LEU A 274 -9.625 22.207 -5.322 1.00 0.68 H
-ATOM 363 HB2 LEU A 274 -11.420 20.686 -6.227 1.00 0.86 H
-ATOM 364 HB3 LEU A 274 -12.069 20.584 -4.603 1.00 0.74 H
-ATOM 365 HG LEU A 274 -12.414 23.057 -4.643 1.00 0.83 H
-ATOM 366 HD11 LEU A 274 -10.726 23.000 -6.810 1.00 1.34 H
-ATOM 367 HD12 LEU A 274 -11.736 24.361 -6.321 1.00 1.46 H
-ATOM 368 HD13 LEU A 274 -12.311 23.232 -7.546 1.00 1.68 H
-ATOM 369 HD21 LEU A 274 -14.238 21.514 -5.126 1.00 1.34 H
-ATOM 370 HD22 LEU A 274 -13.776 21.487 -6.827 1.00 1.23 H
-ATOM 371 HD23 LEU A 274 -14.374 22.977 -6.100 1.00 1.55 H
-ATOM 372 N PRO A 275 -9.066 22.084 -2.538 1.00 0.43 N
-ATOM 373 CA PRO A 275 -9.026 22.533 -1.118 1.00 0.55 C
-ATOM 374 C PRO A 275 -9.566 23.957 -0.855 1.00 0.70 C
-ATOM 375 O PRO A 275 -8.844 24.819 -0.354 1.00 0.90 O
-ATOM 376 CB PRO A 275 -7.532 22.483 -0.781 1.00 0.69 C
-ATOM 377 CG PRO A 275 -6.989 21.468 -1.712 1.00 0.57 C
-ATOM 378 CD PRO A 275 -7.703 21.716 -3.003 1.00 0.50 C
-ATOM 379 HA PRO A 275 -9.545 21.835 -0.482 1.00 0.56 H
-ATOM 380 HB2 PRO A 275 -7.088 23.454 -0.946 1.00 0.83 H
-ATOM 381 HB3 PRO A 275 -7.399 22.187 0.249 1.00 0.79 H
-ATOM 382 HG2 PRO A 275 -5.923 21.599 -1.830 1.00 0.71 H
-ATOM 383 HG3 PRO A 275 -7.217 20.481 -1.342 1.00 0.52 H
-ATOM 384 HD2 PRO A 275 -7.237 22.521 -3.548 1.00 0.66 H
-ATOM 385 HD3 PRO A 275 -7.725 20.807 -3.612 1.00 0.46 H
-ATOM 386 N LYS A 276 -10.834 24.193 -1.175 1.00 0.72 N
-ATOM 387 CA LYS A 276 -11.459 25.499 -0.955 1.00 0.89 C
-ATOM 388 C LYS A 276 -12.606 25.406 0.061 1.00 0.98 C
-ATOM 389 O LYS A 276 -12.898 24.276 0.527 1.00 0.92 O
-ATOM 390 CB LYS A 276 -11.986 26.069 -2.274 1.00 0.96 C
-ATOM 391 CG LYS A 276 -10.901 26.328 -3.304 1.00 1.05 C
-ATOM 392 CD LYS A 276 -11.484 26.886 -4.591 1.00 1.21 C
-ATOM 393 CE LYS A 276 -10.398 27.191 -5.607 1.00 1.48 C
-ATOM 394 NZ LYS A 276 -10.962 27.723 -6.878 1.00 1.76 N
-ATOM 395 OXT LYS A 276 -13.206 26.454 0.377 1.00 1.28 O
-ATOM 396 H LYS A 276 -11.371 23.463 -1.567 1.00 0.70 H
-ATOM 397 HA LYS A 276 -10.704 26.165 -0.565 1.00 1.02 H
-ATOM 398 HB2 LYS A 276 -12.695 25.371 -2.697 1.00 0.90 H
-ATOM 399 HB3 LYS A 276 -12.491 27.002 -2.072 1.00 1.13 H
-ATOM 400 HG2 LYS A 276 -10.197 27.040 -2.902 1.00 1.23 H
-ATOM 401 HG3 LYS A 276 -10.394 25.400 -3.522 1.00 1.01 H
-ATOM 402 HD2 LYS A 276 -12.163 26.159 -5.012 1.00 1.17 H
-ATOM 403 HD3 LYS A 276 -12.022 27.796 -4.367 1.00 1.36 H
-ATOM 404 HE2 LYS A 276 -9.724 27.924 -5.187 1.00 1.64 H
-ATOM 405 HE3 LYS A 276 -9.853 26.281 -5.817 1.00 1.46 H
-ATOM 406 HZ1 LYS A 276 -10.471 28.599 -7.148 1.00 2.15 H
-ATOM 407 HZ2 LYS A 276 -11.974 27.928 -6.760 1.00 2.01 H
-ATOM 408 HZ3 LYS A 276 -10.848 27.023 -7.638 1.00 2.10 H
-TER
-ENDMDL
-MODEL 5
-ATOM 1 N ALA A 249 1.664 -11.791 -11.075 1.00 2.74 N
-ATOM 2 CA ALA A 249 2.234 -10.419 -11.017 1.00 2.53 C
-ATOM 3 C ALA A 249 1.865 -9.733 -9.700 1.00 2.36 C
-ATOM 4 O ALA A 249 0.857 -10.074 -9.082 1.00 2.39 O
-ATOM 5 CB ALA A 249 1.752 -9.597 -12.204 1.00 2.50 C
-ATOM 6 H1 ALA A 249 1.157 -11.929 -11.973 1.00 3.03 H
-ATOM 7 H2 ALA A 249 0.997 -11.935 -10.289 1.00 2.82 H
-ATOM 8 H3 ALA A 249 2.424 -12.498 -11.006 1.00 2.82 H
-ATOM 9 HA ALA A 249 3.310 -10.496 -11.078 1.00 2.57 H
-ATOM 10 HB1 ALA A 249 2.601 -9.143 -12.694 1.00 2.71 H
-ATOM 11 HB2 ALA A 249 1.081 -8.824 -11.858 1.00 2.72 H
-ATOM 12 HB3 ALA A 249 1.234 -10.239 -12.900 1.00 2.68 H
-ATOM 13 N PRO A 250 2.671 -8.751 -9.259 1.00 2.21 N
-ATOM 14 CA PRO A 250 2.423 -8.021 -8.022 1.00 2.08 C
-ATOM 15 C PRO A 250 1.305 -6.978 -8.161 1.00 1.90 C
-ATOM 16 O PRO A 250 1.525 -5.788 -7.939 1.00 1.76 O
-ATOM 17 CB PRO A 250 3.760 -7.335 -7.718 1.00 2.07 C
-ATOM 18 CG PRO A 250 4.671 -7.588 -8.871 1.00 2.01 C
-ATOM 19 CD PRO A 250 3.876 -8.271 -9.938 1.00 2.23 C
-ATOM 20 HA PRO A 250 2.174 -8.694 -7.215 1.00 2.18 H
-ATOM 21 HB2 PRO A 250 3.595 -6.280 -7.592 1.00 2.18 H
-ATOM 22 HB3 PRO A 250 4.168 -7.750 -6.812 1.00 2.11 H
-ATOM 23 HG2 PRO A 250 5.050 -6.647 -9.241 1.00 1.74 H
-ATOM 24 HG3 PRO A 250 5.490 -8.217 -8.555 1.00 2.40 H
-ATOM 25 HD2 PRO A 250 3.617 -7.566 -10.710 1.00 2.09 H
-ATOM 26 HD3 PRO A 250 4.437 -9.092 -10.344 1.00 2.65 H
-ATOM 27 N ALA A 251 0.108 -7.432 -8.536 1.00 1.95 N
-ATOM 28 CA ALA A 251 -1.042 -6.538 -8.713 1.00 1.85 C
-ATOM 29 C ALA A 251 -1.365 -5.757 -7.434 1.00 1.71 C
-ATOM 30 O ALA A 251 -1.552 -4.542 -7.473 1.00 1.56 O
-ATOM 31 CB ALA A 251 -2.260 -7.329 -9.170 1.00 2.00 C
-ATOM 32 H ALA A 251 -0.001 -8.396 -8.706 1.00 2.09 H
-ATOM 33 HA ALA A 251 -0.791 -5.834 -9.493 1.00 1.81 H
-ATOM 34 HB1 ALA A 251 -2.832 -7.638 -8.308 1.00 2.26 H
-ATOM 35 HB2 ALA A 251 -1.938 -8.200 -9.721 1.00 2.28 H
-ATOM 36 HB3 ALA A 251 -2.873 -6.707 -9.805 1.00 2.29 H
-ATOM 37 N ARG A 252 -1.418 -6.461 -6.303 1.00 1.81 N
-ATOM 38 CA ARG A 252 -1.708 -5.828 -5.015 1.00 1.75 C
-ATOM 39 C ARG A 252 -0.605 -4.827 -4.654 1.00 1.62 C
-ATOM 40 O ARG A 252 -0.882 -3.726 -4.179 1.00 1.49 O
-ATOM 41 CB ARG A 252 -1.851 -6.896 -3.923 1.00 1.95 C
-ATOM 42 CG ARG A 252 -2.868 -6.551 -2.837 1.00 2.07 C
-ATOM 43 CD ARG A 252 -2.386 -5.429 -1.926 1.00 2.09 C
-ATOM 44 NE ARG A 252 -3.392 -5.053 -0.934 1.00 2.30 N
-ATOM 45 CZ ARG A 252 -4.475 -4.354 -1.207 1.00 2.40 C
-ATOM 46 NH1 ARG A 252 -4.684 -3.903 -2.418 1.00 2.34 N
-ATOM 47 NH2 ARG A 252 -5.345 -4.096 -0.265 1.00 2.65 N
-ATOM 48 H ARG A 252 -1.253 -7.425 -6.335 1.00 1.94 H
-ATOM 49 HA ARG A 252 -2.640 -5.293 -5.109 1.00 1.70 H
-ATOM 50 HB2 ARG A 252 -2.156 -7.824 -4.384 1.00 2.05 H
-ATOM 51 HB3 ARG A 252 -0.891 -7.040 -3.452 1.00 1.99 H
-ATOM 52 HG2 ARG A 252 -3.788 -6.243 -3.308 1.00 2.09 H
-ATOM 53 HG3 ARG A 252 -3.048 -7.434 -2.240 1.00 2.22 H
-ATOM 54 HD2 ARG A 252 -1.494 -5.758 -1.416 1.00 2.15 H
-ATOM 55 HD3 ARG A 252 -2.153 -4.567 -2.535 1.00 1.96 H
-ATOM 56 HE ARG A 252 -3.253 -5.354 -0.013 1.00 2.44 H
-ATOM 57 HH11 ARG A 252 -4.019 -4.087 -3.138 1.00 2.19 H
-ATOM 58 HH12 ARG A 252 -5.509 -3.382 -2.620 1.00 2.50 H
-ATOM 59 HH21 ARG A 252 -5.188 -4.427 0.663 1.00 2.75 H
-ATOM 60 HH22 ARG A 252 -6.162 -3.563 -0.472 1.00 2.79 H
-ATOM 61 N VAL A 253 0.643 -5.215 -4.909 1.00 1.69 N
-ATOM 62 CA VAL A 253 1.792 -4.352 -4.638 1.00 1.66 C
-ATOM 63 C VAL A 253 1.737 -3.103 -5.519 1.00 1.46 C
-ATOM 64 O VAL A 253 1.856 -1.980 -5.026 1.00 1.36 O
-ATOM 65 CB VAL A 253 3.126 -5.094 -4.879 1.00 1.83 C
-ATOM 66 CG1 VAL A 253 4.315 -4.203 -4.548 1.00 1.87 C
-ATOM 67 CG2 VAL A 253 3.175 -6.379 -4.064 1.00 2.06 C
-ATOM 68 H VAL A 253 0.793 -6.095 -5.306 1.00 1.81 H
-ATOM 69 HA VAL A 253 1.748 -4.053 -3.603 1.00 1.69 H
-ATOM 70 HB VAL A 253 3.183 -5.355 -5.925 1.00 1.82 H
-ATOM 71 HG11 VAL A 253 5.051 -4.774 -4.003 1.00 2.14 H
-ATOM 72 HG12 VAL A 253 3.986 -3.371 -3.945 1.00 2.18 H
-ATOM 73 HG13 VAL A 253 4.753 -3.833 -5.463 1.00 2.13 H
-ATOM 74 HG21 VAL A 253 4.131 -6.452 -3.567 1.00 2.35 H
-ATOM 75 HG22 VAL A 253 3.043 -7.225 -4.720 1.00 2.34 H
-ATOM 76 HG23 VAL A 253 2.386 -6.367 -3.328 1.00 2.35 H
-ATOM 77 N GLY A 254 1.526 -3.314 -6.818 1.00 1.45 N
-ATOM 78 CA GLY A 254 1.426 -2.206 -7.757 1.00 1.33 C
-ATOM 79 C GLY A 254 0.298 -1.256 -7.391 1.00 1.15 C
-ATOM 80 O GLY A 254 0.490 -0.038 -7.346 1.00 1.03 O
-ATOM 81 H GLY A 254 1.422 -4.239 -7.141 1.00 1.56 H
-ATOM 82 HA2 GLY A 254 2.359 -1.661 -7.759 1.00 1.34 H
-ATOM 83 HA3 GLY A 254 1.246 -2.599 -8.746 1.00 1.40 H
-ATOM 84 N LEU A 255 -0.873 -1.819 -7.100 1.00 1.18 N
-ATOM 85 CA LEU A 255 -2.032 -1.026 -6.698 1.00 1.08 C
-ATOM 86 C LEU A 255 -1.739 -0.243 -5.425 1.00 0.96 C
-ATOM 87 O LEU A 255 -2.039 0.948 -5.327 1.00 0.84 O
-ATOM 88 CB LEU A 255 -3.241 -1.924 -6.461 1.00 1.22 C
-ATOM 89 CG LEU A 255 -4.186 -2.046 -7.645 1.00 1.37 C
-ATOM 90 CD1 LEU A 255 -5.345 -2.961 -7.296 1.00 1.56 C
-ATOM 91 CD2 LEU A 255 -4.685 -0.669 -8.054 1.00 1.34 C
-ATOM 92 H LEU A 255 -0.956 -2.800 -7.135 1.00 1.31 H
-ATOM 93 HA LEU A 255 -2.263 -0.336 -7.496 1.00 1.05 H
-ATOM 94 HB2 LEU A 255 -2.884 -2.911 -6.206 1.00 1.34 H
-ATOM 95 HB3 LEU A 255 -3.796 -1.532 -5.623 1.00 1.20 H
-ATOM 96 HG LEU A 255 -3.655 -2.475 -8.482 1.00 1.45 H
-ATOM 97 HD11 LEU A 255 -5.025 -3.990 -7.367 1.00 1.90 H
-ATOM 98 HD12 LEU A 255 -6.161 -2.788 -7.981 1.00 1.91 H
-ATOM 99 HD13 LEU A 255 -5.672 -2.755 -6.286 1.00 1.91 H
-ATOM 100 HD21 LEU A 255 -3.951 -0.195 -8.691 1.00 1.71 H
-ATOM 101 HD22 LEU A 255 -4.834 -0.065 -7.171 1.00 1.71 H
-ATOM 102 HD23 LEU A 255 -5.617 -0.765 -8.587 1.00 1.66 H
-ATOM 103 N GLY A 256 -1.151 -0.930 -4.451 1.00 1.08 N
-ATOM 104 CA GLY A 256 -0.823 -0.306 -3.190 1.00 1.09 C
-ATOM 105 C GLY A 256 0.172 0.838 -3.328 1.00 1.01 C
-ATOM 106 O GLY A 256 -0.086 1.942 -2.846 1.00 0.93 O
-ATOM 107 H GLY A 256 -0.942 -1.880 -4.594 1.00 1.20 H
-ATOM 108 HA2 GLY A 256 -1.733 0.076 -2.751 1.00 1.06 H
-ATOM 109 HA3 GLY A 256 -0.408 -1.054 -2.534 1.00 1.25 H
-ATOM 110 N ILE A 257 1.306 0.592 -3.993 1.00 1.09 N
-ATOM 111 CA ILE A 257 2.310 1.645 -4.176 1.00 1.09 C
-ATOM 112 C ILE A 257 1.745 2.806 -5.000 1.00 0.90 C
-ATOM 113 O ILE A 257 2.012 3.970 -4.704 1.00 0.89 O
-ATOM 114 CB ILE A 257 3.633 1.120 -4.802 1.00 1.27 C
-ATOM 115 CG1 ILE A 257 3.410 0.491 -6.182 1.00 1.25 C
-ATOM 116 CG2 ILE A 257 4.298 0.121 -3.868 1.00 1.50 C
-ATOM 117 CD1 ILE A 257 3.720 1.422 -7.338 1.00 1.22 C
-ATOM 118 H ILE A 257 1.467 -0.307 -4.369 1.00 1.19 H
-ATOM 119 HA ILE A 257 2.545 2.025 -3.190 1.00 1.17 H
-ATOM 120 HB ILE A 257 4.301 1.963 -4.907 1.00 1.29 H
-ATOM 121 HG12 ILE A 257 4.045 -0.377 -6.279 1.00 1.40 H
-ATOM 122 HG13 ILE A 257 2.378 0.187 -6.268 1.00 1.19 H
-ATOM 123 HG21 ILE A 257 4.267 0.498 -2.857 1.00 1.86 H
-ATOM 124 HG22 ILE A 257 5.326 -0.022 -4.168 1.00 1.85 H
-ATOM 125 HG23 ILE A 257 3.774 -0.821 -3.917 1.00 1.88 H
-ATOM 126 HD11 ILE A 257 4.119 2.350 -6.956 1.00 1.56 H
-ATOM 127 HD12 ILE A 257 2.815 1.621 -7.893 1.00 1.60 H
-ATOM 128 HD13 ILE A 257 4.446 0.959 -7.989 1.00 1.63 H
-ATOM 129 N THR A 258 0.937 2.485 -6.016 1.00 0.80 N
-ATOM 130 CA THR A 258 0.314 3.509 -6.856 1.00 0.70 C
-ATOM 131 C THR A 258 -0.651 4.356 -6.028 1.00 0.57 C
-ATOM 132 O THR A 258 -0.678 5.582 -6.150 1.00 0.54 O
-ATOM 133 CB THR A 258 -0.454 2.888 -8.046 1.00 0.78 C
-ATOM 134 OG1 THR A 258 0.431 2.089 -8.841 1.00 0.96 O
-ATOM 135 CG2 THR A 258 -1.084 3.966 -8.920 1.00 0.85 C
-ATOM 136 H THR A 258 0.743 1.534 -6.194 1.00 0.87 H
-ATOM 137 HA THR A 258 1.096 4.144 -7.247 1.00 0.75 H
-ATOM 138 HB THR A 258 -1.241 2.258 -7.656 1.00 0.79 H
-ATOM 139 HG1 THR A 258 0.550 1.224 -8.418 1.00 1.03 H
-ATOM 140 HG21 THR A 258 -2.159 3.909 -8.842 1.00 1.33 H
-ATOM 141 HG22 THR A 258 -0.787 3.813 -9.948 1.00 1.35 H
-ATOM 142 HG23 THR A 258 -0.750 4.938 -8.588 1.00 1.35 H
-ATOM 143 N THR A 259 -1.424 3.695 -5.166 1.00 0.57 N
-ATOM 144 CA THR A 259 -2.375 4.386 -4.295 1.00 0.53 C
-ATOM 145 C THR A 259 -1.632 5.309 -3.331 1.00 0.56 C
-ATOM 146 O THR A 259 -1.988 6.479 -3.179 1.00 0.56 O
-ATOM 147 CB THR A 259 -3.235 3.390 -3.480 1.00 0.64 C
-ATOM 148 OG1 THR A 259 -3.904 2.476 -4.361 1.00 0.71 O
-ATOM 149 CG2 THR A 259 -4.268 4.122 -2.634 1.00 0.68 C
-ATOM 150 H THR A 259 -1.340 2.718 -5.103 1.00 0.65 H
-ATOM 151 HA THR A 259 -3.031 4.977 -4.915 1.00 0.50 H
-ATOM 152 HB THR A 259 -2.583 2.831 -2.821 1.00 0.75 H
-ATOM 153 HG1 THR A 259 -3.258 1.860 -4.740 1.00 0.77 H
-ATOM 154 HG21 THR A 259 -3.791 4.526 -1.753 1.00 1.25 H
-ATOM 155 HG22 THR A 259 -5.045 3.432 -2.338 1.00 1.23 H
-ATOM 156 HG23 THR A 259 -4.701 4.925 -3.211 1.00 1.24 H
-ATOM 157 N VAL A 260 -0.582 4.778 -2.702 1.00 0.70 N
-ATOM 158 CA VAL A 260 0.231 5.558 -1.770 1.00 0.84 C
-ATOM 159 C VAL A 260 0.882 6.749 -2.479 1.00 0.83 C
-ATOM 160 O VAL A 260 0.800 7.880 -2.002 1.00 0.89 O
-ATOM 161 CB VAL A 260 1.326 4.690 -1.109 1.00 1.04 C
-ATOM 162 CG1 VAL A 260 2.237 5.539 -0.236 1.00 1.25 C
-ATOM 163 CG2 VAL A 260 0.699 3.570 -0.292 1.00 1.14 C
-ATOM 164 H VAL A 260 -0.340 3.838 -2.881 1.00 0.75 H
-ATOM 165 HA VAL A 260 -0.421 5.931 -0.993 1.00 0.87 H
-ATOM 166 HB VAL A 260 1.925 4.245 -1.892 1.00 1.04 H
-ATOM 167 HG11 VAL A 260 3.224 5.574 -0.672 1.00 1.67 H
-ATOM 168 HG12 VAL A 260 2.295 5.106 0.752 1.00 1.61 H
-ATOM 169 HG13 VAL A 260 1.838 6.540 -0.167 1.00 1.67 H
-ATOM 170 HG21 VAL A 260 0.704 2.657 -0.869 1.00 1.53 H
-ATOM 171 HG22 VAL A 260 -0.319 3.833 -0.046 1.00 1.57 H
-ATOM 172 HG23 VAL A 260 1.264 3.427 0.615 1.00 1.56 H
-ATOM 173 N LEU A 261 1.506 6.485 -3.632 1.00 0.80 N
-ATOM 174 CA LEU A 261 2.154 7.535 -4.424 1.00 0.83 C
-ATOM 175 C LEU A 261 1.137 8.612 -4.815 1.00 0.74 C
-ATOM 176 O LEU A 261 1.416 9.808 -4.739 1.00 0.85 O
-ATOM 177 CB LEU A 261 2.783 6.928 -5.684 1.00 0.86 C
-ATOM 178 CG LEU A 261 4.161 7.476 -6.068 1.00 1.10 C
-ATOM 179 CD1 LEU A 261 4.087 8.954 -6.425 1.00 1.52 C
-ATOM 180 CD2 LEU A 261 5.155 7.248 -4.939 1.00 1.54 C
-ATOM 181 H LEU A 261 1.522 5.558 -3.966 1.00 0.79 H
-ATOM 182 HA LEU A 261 2.928 7.983 -3.819 1.00 0.98 H
-ATOM 183 HB2 LEU A 261 2.874 5.861 -5.535 1.00 1.01 H
-ATOM 184 HB3 LEU A 261 2.111 7.099 -6.511 1.00 0.97 H
-ATOM 185 HG LEU A 261 4.519 6.944 -6.939 1.00 1.09 H
-ATOM 186 HD11 LEU A 261 3.829 9.059 -7.469 1.00 1.91 H
-ATOM 187 HD12 LEU A 261 5.045 9.417 -6.242 1.00 1.94 H
-ATOM 188 HD13 LEU A 261 3.333 9.433 -5.818 1.00 1.97 H
-ATOM 189 HD21 LEU A 261 6.084 7.748 -5.169 1.00 1.98 H
-ATOM 190 HD22 LEU A 261 5.334 6.188 -4.828 1.00 1.94 H
-ATOM 191 HD23 LEU A 261 4.751 7.643 -4.019 1.00 1.98 H
-ATOM 192 N THR A 262 -0.052 8.167 -5.220 1.00 0.62 N
-ATOM 193 CA THR A 262 -1.132 9.074 -5.607 1.00 0.61 C
-ATOM 194 C THR A 262 -1.576 9.936 -4.422 1.00 0.70 C
-ATOM 195 O THR A 262 -1.694 11.156 -4.535 1.00 0.82 O
-ATOM 196 CB THR A 262 -2.355 8.292 -6.136 1.00 0.56 C
-ATOM 197 OG1 THR A 262 -1.988 7.522 -7.288 1.00 0.59 O
-ATOM 198 CG2 THR A 262 -3.497 9.231 -6.492 1.00 0.73 C
-ATOM 199 H THR A 262 -0.208 7.196 -5.250 1.00 0.59 H
-ATOM 200 HA THR A 262 -0.767 9.717 -6.396 1.00 0.68 H
-ATOM 201 HB THR A 262 -2.691 7.621 -5.359 1.00 0.52 H
-ATOM 202 HG1 THR A 262 -1.515 6.724 -7.004 1.00 0.72 H
-ATOM 203 HG21 THR A 262 -3.725 9.139 -7.543 1.00 1.30 H
-ATOM 204 HG22 THR A 262 -3.209 10.248 -6.272 1.00 1.27 H
-ATOM 205 HG23 THR A 262 -4.369 8.971 -5.910 1.00 1.29 H
-ATOM 206 N MET A 263 -1.824 9.284 -3.287 1.00 0.71 N
-ATOM 207 CA MET A 263 -2.258 9.979 -2.074 1.00 0.84 C
-ATOM 208 C MET A 263 -1.184 10.952 -1.568 1.00 0.94 C
-ATOM 209 O MET A 263 -1.460 12.132 -1.347 1.00 1.06 O
-ATOM 210 CB MET A 263 -2.596 8.961 -0.981 1.00 0.90 C
-ATOM 211 CG MET A 263 -3.191 9.584 0.271 1.00 1.08 C
-ATOM 212 SD MET A 263 -4.737 10.449 -0.056 1.00 1.24 S
-ATOM 213 CE MET A 263 -5.162 11.020 1.587 1.00 1.54 C
-ATOM 214 H MET A 263 -1.716 8.305 -3.265 1.00 0.65 H
-ATOM 215 HA MET A 263 -3.148 10.539 -2.316 1.00 0.89 H
-ATOM 216 HB2 MET A 263 -3.307 8.250 -1.375 1.00 0.87 H
-ATOM 217 HB3 MET A 263 -1.694 8.437 -0.702 1.00 0.92 H
-ATOM 218 HG2 MET A 263 -3.378 8.802 0.992 1.00 1.15 H
-ATOM 219 HG3 MET A 263 -2.480 10.286 0.680 1.00 1.16 H
-ATOM 220 HE1 MET A 263 -4.268 11.349 2.096 1.00 1.91 H
-ATOM 221 HE2 MET A 263 -5.615 10.212 2.142 1.00 1.87 H
-ATOM 222 HE3 MET A 263 -5.857 11.843 1.514 1.00 1.93 H
-ATOM 223 N THR A 264 0.037 10.458 -1.380 1.00 0.95 N
-ATOM 224 CA THR A 264 1.126 11.296 -0.900 1.00 1.06 C
-ATOM 225 C THR A 264 2.396 11.118 -1.739 1.00 1.09 C
-ATOM 226 O THR A 264 2.804 10.001 -2.054 1.00 1.11 O
-ATOM 227 CB THR A 264 1.434 11.003 0.581 1.00 1.12 C
-ATOM 228 OG1 THR A 264 2.365 11.968 1.092 1.00 1.21 O
-ATOM 229 CG2 THR A 264 1.995 9.598 0.777 1.00 1.13 C
-ATOM 230 H THR A 264 0.208 9.509 -1.559 1.00 0.92 H
-ATOM 231 HA THR A 264 0.805 12.325 -0.976 1.00 1.11 H
-ATOM 232 HB THR A 264 0.509 11.074 1.127 1.00 1.13 H
-ATOM 233 HG1 THR A 264 2.594 11.749 2.009 1.00 1.27 H
-ATOM 234 HG21 THR A 264 2.403 9.510 1.774 1.00 1.53 H
-ATOM 235 HG22 THR A 264 2.774 9.417 0.051 1.00 1.54 H
-ATOM 236 HG23 THR A 264 1.205 8.873 0.646 1.00 1.49 H
-ATOM 237 N THR A 265 3.020 12.233 -2.101 1.00 1.13 N
-ATOM 238 CA THR A 265 4.243 12.195 -2.901 1.00 1.17 C
-ATOM 239 C THR A 265 5.444 11.735 -2.070 1.00 1.07 C
-ATOM 240 O THR A 265 6.353 11.086 -2.585 1.00 1.15 O
-ATOM 241 CB THR A 265 4.555 13.566 -3.542 1.00 1.22 C
-ATOM 242 OG1 THR A 265 5.824 13.524 -4.211 1.00 1.29 O
-ATOM 243 CG2 THR A 265 4.561 14.677 -2.500 1.00 1.15 C
-ATOM 244 H THR A 265 2.650 13.097 -1.826 1.00 1.15 H
-ATOM 245 HA THR A 265 4.087 11.483 -3.699 1.00 1.31 H
-ATOM 246 HB THR A 265 3.785 13.785 -4.270 1.00 1.34 H
-ATOM 247 HG1 THR A 265 6.171 12.625 -4.189 1.00 1.30 H
-ATOM 248 HG21 THR A 265 4.757 15.623 -2.983 1.00 1.54 H
-ATOM 249 HG22 THR A 265 5.330 14.479 -1.769 1.00 1.48 H
-ATOM 250 HG23 THR A 265 3.599 14.715 -2.010 1.00 1.58 H
-ATOM 251 N GLN A 266 5.429 12.072 -0.782 1.00 0.95 N
-ATOM 252 CA GLN A 266 6.506 11.695 0.131 1.00 0.90 C
-ATOM 253 C GLN A 266 6.049 11.820 1.584 1.00 0.81 C
-ATOM 254 O GLN A 266 5.548 12.864 2.004 1.00 0.74 O
-ATOM 255 CB GLN A 266 7.742 12.570 -0.112 1.00 0.86 C
-ATOM 256 CG GLN A 266 8.882 12.313 0.862 1.00 0.89 C
-ATOM 257 CD GLN A 266 10.134 13.088 0.502 1.00 1.01 C
-ATOM 258 OE1 GLN A 266 10.777 12.818 -0.505 1.00 1.20 O
-ATOM 259 NE2 GLN A 266 10.477 14.075 1.310 1.00 1.00 N
-ATOM 260 H GLN A 266 4.669 12.584 -0.436 1.00 0.94 H
-ATOM 261 HA GLN A 266 6.760 10.665 -0.066 1.00 1.05 H
-ATOM 262 HB2 GLN A 266 8.106 12.386 -1.111 1.00 0.99 H
-ATOM 263 HB3 GLN A 266 7.455 13.608 -0.029 1.00 0.81 H
-ATOM 264 HG2 GLN A 266 8.568 12.606 1.852 1.00 0.84 H
-ATOM 265 HG3 GLN A 266 9.116 11.259 0.855 1.00 1.02 H
-ATOM 266 HE21 GLN A 266 9.901 14.258 2.105 1.00 0.93 H
-ATOM 267 HE22 GLN A 266 11.279 14.586 1.092 1.00 1.14 H
-ATOM 268 N SER A 267 6.226 10.746 2.346 1.00 0.90 N
-ATOM 269 CA SER A 267 5.838 10.717 3.762 1.00 0.88 C
-ATOM 270 C SER A 267 6.506 9.545 4.478 1.00 1.05 C
-ATOM 271 O SER A 267 7.163 9.725 5.502 1.00 1.07 O
-ATOM 272 CB SER A 267 4.314 10.612 3.919 1.00 0.96 C
-ATOM 273 OG SER A 267 3.655 11.774 3.435 1.00 0.89 O
-ATOM 274 H SER A 267 6.631 9.947 1.947 1.00 1.02 H
-ATOM 275 HA SER A 267 6.176 11.638 4.213 1.00 0.76 H
-ATOM 276 HB2 SER A 267 3.956 9.757 3.364 1.00 1.11 H
-ATOM 277 HB3 SER A 267 4.073 10.487 4.965 1.00 1.02 H
-ATOM 278 HG SER A 267 4.304 12.360 3.008 1.00 0.82 H
-ATOM 279 N SER A 268 6.347 8.346 3.924 1.00 1.26 N
-ATOM 280 CA SER A 268 6.951 7.146 4.502 1.00 1.48 C
-ATOM 281 C SER A 268 8.473 7.179 4.366 1.00 1.51 C
-ATOM 282 O SER A 268 9.004 7.528 3.311 1.00 1.58 O
-ATOM 283 CB SER A 268 6.394 5.893 3.822 1.00 1.83 C
-ATOM 284 OG SER A 268 6.327 6.063 2.413 1.00 1.91 O
-ATOM 285 H SER A 268 5.821 8.264 3.100 1.00 1.32 H
-ATOM 286 HA SER A 268 6.696 7.119 5.550 1.00 1.47 H
-ATOM 287 HB2 SER A 268 7.038 5.054 4.040 1.00 2.01 H
-ATOM 288 HB3 SER A 268 5.401 5.693 4.197 1.00 1.91 H
-ATOM 289 HG SER A 268 7.205 5.958 2.034 1.00 1.95 H
-ATOM 290 N GLY A 269 9.172 6.822 5.439 1.00 1.57 N
-ATOM 291 CA GLY A 269 10.628 6.834 5.414 1.00 1.69 C
-ATOM 292 C GLY A 269 11.258 5.776 6.310 1.00 1.94 C
-ATOM 293 O GLY A 269 10.665 5.355 7.303 1.00 2.03 O
-ATOM 294 H GLY A 269 8.696 6.557 6.261 1.00 1.60 H
-ATOM 295 HA2 GLY A 269 10.956 6.668 4.399 1.00 1.80 H
-ATOM 296 HA3 GLY A 269 10.970 7.807 5.734 1.00 1.58 H
-ATOM 297 N SER A 270 12.463 5.339 5.942 1.00 2.17 N
-ATOM 298 CA SER A 270 13.196 4.311 6.698 1.00 2.46 C
-ATOM 299 C SER A 270 13.547 4.774 8.117 1.00 2.44 C
-ATOM 300 O SER A 270 14.028 5.891 8.310 1.00 2.37 O
-ATOM 301 CB SER A 270 14.479 3.941 5.948 1.00 2.77 C
-ATOM 302 OG SER A 270 15.158 2.853 6.559 1.00 3.07 O
-ATOM 303 H SER A 270 12.871 5.715 5.134 1.00 2.23 H
-ATOM 304 HA SER A 270 12.565 3.438 6.761 1.00 2.55 H
-ATOM 305 HB2 SER A 270 14.232 3.665 4.934 1.00 2.85 H
-ATOM 306 HB3 SER A 270 15.139 4.796 5.933 1.00 2.74 H
-ATOM 307 HG SER A 270 15.915 2.613 6.013 1.00 3.46 H
-ATOM 308 N ARG A 271 13.295 3.895 9.101 1.00 2.58 N
-ATOM 309 CA ARG A 271 13.566 4.169 10.532 1.00 2.69 C
-ATOM 310 C ARG A 271 12.624 5.233 11.105 1.00 2.50 C
-ATOM 311 O ARG A 271 11.957 5.008 12.113 1.00 2.61 O
-ATOM 312 CB ARG A 271 15.027 4.590 10.761 1.00 2.87 C
-ATOM 313 CG ARG A 271 16.002 3.429 10.922 1.00 3.17 C
-ATOM 314 CD ARG A 271 16.115 2.601 9.652 1.00 3.18 C
-ATOM 315 NE ARG A 271 17.047 1.488 9.799 1.00 3.50 N
-ATOM 316 CZ ARG A 271 17.269 0.590 8.864 1.00 3.64 C
-ATOM 317 NH1 ARG A 271 16.615 0.638 7.732 1.00 3.52 N
-ATOM 318 NH2 ARG A 271 18.141 -0.363 9.068 1.00 3.97 N
-ATOM 319 H ARG A 271 12.910 3.029 8.858 1.00 2.68 H
-ATOM 320 HA ARG A 271 13.393 3.248 11.070 1.00 2.86 H
-ATOM 321 HB2 ARG A 271 15.350 5.182 9.918 1.00 2.78 H
-ATOM 322 HB3 ARG A 271 15.078 5.199 11.652 1.00 2.93 H
-ATOM 323 HG2 ARG A 271 16.975 3.822 11.168 1.00 3.32 H
-ATOM 324 HG3 ARG A 271 15.657 2.794 11.725 1.00 3.31 H
-ATOM 325 HD2 ARG A 271 15.138 2.209 9.406 1.00 3.10 H
-ATOM 326 HD3 ARG A 271 16.455 3.241 8.851 1.00 3.08 H
-ATOM 327 HE ARG A 271 17.538 1.416 10.641 1.00 3.67 H
-ATOM 328 HH11 ARG A 271 15.941 1.364 7.575 1.00 3.30 H
-ATOM 329 HH12 ARG A 271 16.784 -0.046 7.028 1.00 3.70 H
-ATOM 330 HH21 ARG A 271 18.636 -0.407 9.932 1.00 4.11 H
-ATOM 331 HH22 ARG A 271 18.307 -1.044 8.362 1.00 4.12 H
-ATOM 332 N ALA A 272 12.565 6.378 10.450 1.00 2.37 N
-ATOM 333 CA ALA A 272 11.703 7.469 10.873 1.00 2.22 C
-ATOM 334 C ALA A 272 11.066 8.124 9.653 1.00 1.92 C
-ATOM 335 O ALA A 272 11.762 8.557 8.735 1.00 1.92 O
-ATOM 336 CB ALA A 272 12.492 8.492 11.681 1.00 2.37 C
-ATOM 337 H ALA A 272 13.115 6.492 9.639 1.00 2.48 H
-ATOM 338 HA ALA A 272 10.923 7.062 11.503 1.00 2.28 H
-ATOM 339 HB1 ALA A 272 12.692 9.355 11.066 1.00 2.60 H
-ATOM 340 HB2 ALA A 272 13.429 8.057 12.002 1.00 2.61 H
-ATOM 341 HB3 ALA A 272 11.917 8.791 12.548 1.00 2.67 H
-ATOM 342 N SER A 273 9.742 8.177 9.632 1.00 1.74 N
-ATOM 343 CA SER A 273 9.029 8.769 8.506 1.00 1.48 C
-ATOM 344 C SER A 273 8.778 10.249 8.714 1.00 1.32 C
-ATOM 345 O SER A 273 9.218 10.838 9.701 1.00 1.49 O
-ATOM 346 CB SER A 273 7.715 8.041 8.250 1.00 1.47 C
-ATOM 347 OG SER A 273 7.945 6.671 7.947 1.00 1.74 O
-ATOM 348 H SER A 273 9.235 7.807 10.384 1.00 1.84 H
-ATOM 349 HA SER A 273 9.657 8.669 7.627 1.00 1.49 H
-ATOM 350 HB2 SER A 273 7.093 8.109 9.127 1.00 1.52 H
-ATOM 351 HB3 SER A 273 7.212 8.503 7.411 1.00 1.32 H
-ATOM 352 HG SER A 273 8.773 6.383 8.354 1.00 2.24 H
-ATOM 353 N LEU A 274 8.113 10.846 7.732 1.00 1.09 N
-ATOM 354 CA LEU A 274 7.835 12.273 7.724 1.00 1.01 C
-ATOM 355 C LEU A 274 9.142 13.108 7.777 1.00 1.11 C
-ATOM 356 O LEU A 274 9.176 14.157 8.424 1.00 1.28 O
-ATOM 357 CB LEU A 274 6.905 12.642 8.889 1.00 1.20 C
-ATOM 358 CG LEU A 274 6.247 14.019 8.793 1.00 1.31 C
-ATOM 359 CD1 LEU A 274 5.349 14.097 7.567 1.00 1.24 C
-ATOM 360 CD2 LEU A 274 5.460 14.316 10.060 1.00 1.63 C
-ATOM 361 H LEU A 274 7.830 10.307 6.952 1.00 1.07 H
-ATOM 362 HA LEU A 274 7.330 12.496 6.796 1.00 0.88 H
-ATOM 363 HB2 LEU A 274 6.125 11.897 8.946 1.00 1.22 H
-ATOM 364 HB3 LEU A 274 7.479 12.606 9.803 1.00 1.37 H
-ATOM 365 HG LEU A 274 7.016 14.771 8.692 1.00 1.38 H
-ATOM 366 HD11 LEU A 274 4.598 14.857 7.718 1.00 1.62 H
-ATOM 367 HD12 LEU A 274 4.870 13.142 7.410 1.00 1.73 H
-ATOM 368 HD13 LEU A 274 5.944 14.346 6.701 1.00 1.48 H
-ATOM 369 HD21 LEU A 274 4.525 13.775 10.039 1.00 2.02 H
-ATOM 370 HD22 LEU A 274 5.261 15.376 10.120 1.00 1.96 H
-ATOM 371 HD23 LEU A 274 6.034 14.007 10.921 1.00 1.98 H
-ATOM 372 N PRO A 275 10.255 12.668 7.107 1.00 1.14 N
-ATOM 373 CA PRO A 275 11.527 13.418 7.123 1.00 1.35 C
-ATOM 374 C PRO A 275 11.381 14.830 6.550 1.00 1.28 C
-ATOM 375 O PRO A 275 11.964 15.790 7.058 1.00 1.55 O
-ATOM 376 CB PRO A 275 12.445 12.583 6.226 1.00 1.44 C
-ATOM 377 CG PRO A 275 11.875 11.219 6.309 1.00 1.44 C
-ATOM 378 CD PRO A 275 10.391 11.426 6.310 1.00 1.15 C
-ATOM 379 HA PRO A 275 11.943 13.465 8.115 1.00 1.59 H
-ATOM 380 HB2 PRO A 275 12.413 12.967 5.216 1.00 1.34 H
-ATOM 381 HB3 PRO A 275 13.455 12.615 6.603 1.00 1.70 H
-ATOM 382 HG2 PRO A 275 12.182 10.629 5.458 1.00 1.54 H
-ATOM 383 HG3 PRO A 275 12.186 10.756 7.233 1.00 1.63 H
-ATOM 384 HD2 PRO A 275 10.023 11.557 5.304 1.00 1.05 H
-ATOM 385 HD3 PRO A 275 9.886 10.593 6.800 1.00 1.19 H
-ATOM 386 N LYS A 276 10.592 14.930 5.486 1.00 1.02 N
-ATOM 387 CA LYS A 276 10.330 16.194 4.807 1.00 1.04 C
-ATOM 388 C LYS A 276 9.223 15.994 3.766 1.00 0.93 C
-ATOM 389 O LYS A 276 9.063 14.843 3.298 1.00 0.89 O
-ATOM 390 CB LYS A 276 11.619 16.720 4.147 1.00 1.21 C
-ATOM 391 CG LYS A 276 11.463 18.066 3.455 1.00 1.40 C
-ATOM 392 CD LYS A 276 11.108 17.905 1.984 1.00 1.44 C
-ATOM 393 CE LYS A 276 10.296 19.084 1.479 1.00 1.73 C
-ATOM 394 NZ LYS A 276 8.965 19.158 2.145 1.00 1.74 N
-ATOM 395 OXT LYS A 276 8.521 16.978 3.431 1.00 1.14 O
-ATOM 396 H LYS A 276 10.162 14.119 5.143 1.00 0.90 H
-ATOM 397 HA LYS A 276 9.992 16.906 5.545 1.00 1.23 H
-ATOM 398 HB2 LYS A 276 12.380 16.821 4.906 1.00 1.41 H
-ATOM 399 HB3 LYS A 276 11.952 16.001 3.413 1.00 1.16 H
-ATOM 400 HG2 LYS A 276 10.677 18.621 3.945 1.00 1.51 H
-ATOM 401 HG3 LYS A 276 12.392 18.610 3.535 1.00 1.64 H
-ATOM 402 HD2 LYS A 276 12.018 17.834 1.408 1.00 1.60 H
-ATOM 403 HD3 LYS A 276 10.529 17.001 1.859 1.00 1.32 H
-ATOM 404 HE2 LYS A 276 10.842 19.994 1.680 1.00 1.94 H
-ATOM 405 HE3 LYS A 276 10.152 18.978 0.413 1.00 1.91 H
-ATOM 406 HZ1 LYS A 276 8.952 19.936 2.834 1.00 1.98 H
-ATOM 407 HZ2 LYS A 276 8.761 18.255 2.653 1.00 1.46 H
-ATOM 408 HZ3 LYS A 276 8.220 19.318 1.439 1.00 2.00 H
-TER
-ENDMDL
-MODEL 6
-ATOM 1 N ALA A 249 -2.113 -2.429 -13.441 1.00 1.95 N
-ATOM 2 CA ALA A 249 -1.140 -2.258 -12.328 1.00 1.57 C
-ATOM 3 C ALA A 249 -0.568 -3.605 -11.886 1.00 1.35 C
-ATOM 4 O ALA A 249 -1.211 -4.641 -12.055 1.00 1.49 O
-ATOM 5 CB ALA A 249 -1.793 -1.548 -11.150 1.00 1.56 C
-ATOM 6 H1 ALA A 249 -3.086 -2.410 -13.074 1.00 2.27 H
-ATOM 7 H2 ALA A 249 -1.954 -3.342 -13.914 1.00 2.25 H
-ATOM 8 H3 ALA A 249 -2.001 -1.665 -14.137 1.00 2.22 H
-ATOM 9 HA ALA A 249 -0.329 -1.638 -12.683 1.00 1.55 H
-ATOM 10 HB1 ALA A 249 -2.170 -2.281 -10.454 1.00 1.83 H
-ATOM 11 HB2 ALA A 249 -2.606 -0.934 -11.506 1.00 1.90 H
-ATOM 12 HB3 ALA A 249 -1.061 -0.926 -10.657 1.00 1.90 H
-ATOM 13 N PRO A 250 0.659 -3.602 -11.328 1.00 1.10 N
-ATOM 14 CA PRO A 250 1.355 -4.816 -10.856 1.00 1.02 C
-ATOM 15 C PRO A 250 0.674 -5.500 -9.654 1.00 0.83 C
-ATOM 16 O PRO A 250 1.271 -5.635 -8.585 1.00 0.77 O
-ATOM 17 CB PRO A 250 2.742 -4.288 -10.454 1.00 1.02 C
-ATOM 18 CG PRO A 250 2.867 -2.980 -11.143 1.00 1.10 C
-ATOM 19 CD PRO A 250 1.489 -2.410 -11.123 1.00 1.05 C
-ATOM 20 HA PRO A 250 1.465 -5.536 -11.654 1.00 1.21 H
-ATOM 21 HB2 PRO A 250 2.783 -4.167 -9.382 1.00 0.90 H
-ATOM 22 HB3 PRO A 250 3.501 -4.975 -10.779 1.00 1.20 H
-ATOM 23 HG2 PRO A 250 3.550 -2.341 -10.603 1.00 1.11 H
-ATOM 24 HG3 PRO A 250 3.201 -3.125 -12.159 1.00 1.30 H
-ATOM 25 HD2 PRO A 250 1.283 -1.956 -10.164 1.00 0.95 H
-ATOM 26 HD3 PRO A 250 1.356 -1.705 -11.923 1.00 1.22 H
-ATOM 27 N ALA A 251 -0.570 -5.933 -9.850 1.00 0.88 N
-ATOM 28 CA ALA A 251 -1.352 -6.620 -8.815 1.00 0.86 C
-ATOM 29 C ALA A 251 -1.499 -5.808 -7.517 1.00 0.78 C
-ATOM 30 O ALA A 251 -1.667 -4.587 -7.544 1.00 0.74 O
-ATOM 31 CB ALA A 251 -0.745 -7.990 -8.529 1.00 0.93 C
-ATOM 32 H ALA A 251 -0.980 -5.794 -10.735 1.00 1.01 H
-ATOM 33 HA ALA A 251 -2.340 -6.785 -9.221 1.00 0.99 H
-ATOM 34 HB1 ALA A 251 -1.454 -8.589 -7.976 1.00 1.39 H
-ATOM 35 HB2 ALA A 251 0.156 -7.869 -7.945 1.00 1.41 H
-ATOM 36 HB3 ALA A 251 -0.507 -8.480 -9.460 1.00 1.39 H
-ATOM 37 N ARG A 252 -1.459 -6.512 -6.383 1.00 0.80 N
-ATOM 38 CA ARG A 252 -1.615 -5.899 -5.061 1.00 0.80 C
-ATOM 39 C ARG A 252 -0.515 -4.871 -4.762 1.00 0.70 C
-ATOM 40 O ARG A 252 -0.806 -3.741 -4.365 1.00 0.68 O
-ATOM 41 CB ARG A 252 -1.611 -6.997 -3.989 1.00 0.93 C
-ATOM 42 CG ARG A 252 -2.523 -6.726 -2.796 1.00 1.08 C
-ATOM 43 CD ARG A 252 -2.089 -5.501 -2.004 1.00 1.05 C
-ATOM 44 NE ARG A 252 -2.894 -5.314 -0.800 1.00 1.29 N
-ATOM 45 CZ ARG A 252 -2.775 -4.282 0.007 1.00 1.42 C
-ATOM 46 NH1 ARG A 252 -1.935 -3.319 -0.270 1.00 1.36 N
-ATOM 47 NH2 ARG A 252 -3.508 -4.208 1.087 1.00 1.70 N
-ATOM 48 H ARG A 252 -1.343 -7.483 -6.438 1.00 0.85 H
-ATOM 49 HA ARG A 252 -2.567 -5.398 -5.040 1.00 0.85 H
-ATOM 50 HB2 ARG A 252 -1.925 -7.924 -4.444 1.00 1.02 H
-ATOM 51 HB3 ARG A 252 -0.603 -7.115 -3.621 1.00 0.91 H
-ATOM 52 HG2 ARG A 252 -3.528 -6.566 -3.156 1.00 1.19 H
-ATOM 53 HG3 ARG A 252 -2.507 -7.587 -2.145 1.00 1.21 H
-ATOM 54 HD2 ARG A 252 -1.055 -5.622 -1.719 1.00 1.04 H
-ATOM 55 HD3 ARG A 252 -2.189 -4.629 -2.633 1.00 0.98 H
-ATOM 56 HE ARG A 252 -3.546 -6.008 -0.580 1.00 1.43 H
-ATOM 57 HH11 ARG A 252 -1.381 -3.366 -1.098 1.00 1.19 H
-ATOM 58 HH12 ARG A 252 -1.848 -2.542 0.346 1.00 1.53 H
-ATOM 59 HH21 ARG A 252 -4.157 -4.935 1.300 1.00 1.81 H
-ATOM 60 HH22 ARG A 252 -3.417 -3.426 1.698 1.00 1.83 H
-ATOM 61 N VAL A 253 0.742 -5.270 -4.948 1.00 0.69 N
-ATOM 62 CA VAL A 253 1.876 -4.381 -4.690 1.00 0.69 C
-ATOM 63 C VAL A 253 1.791 -3.125 -5.553 1.00 0.58 C
-ATOM 64 O VAL A 253 1.930 -2.003 -5.060 1.00 0.55 O
-ATOM 65 CB VAL A 253 3.226 -5.107 -4.917 1.00 0.81 C
-ATOM 66 CG1 VAL A 253 3.409 -5.561 -6.359 1.00 0.81 C
-ATOM 67 CG2 VAL A 253 4.393 -4.238 -4.472 1.00 0.97 C
-ATOM 68 H VAL A 253 0.909 -6.181 -5.261 1.00 0.73 H
-ATOM 69 HA VAL A 253 1.826 -4.086 -3.653 1.00 0.76 H
-ATOM 70 HB VAL A 253 3.218 -5.987 -4.310 1.00 0.89 H
-ATOM 71 HG11 VAL A 253 2.623 -6.254 -6.620 1.00 1.31 H
-ATOM 72 HG12 VAL A 253 4.367 -6.047 -6.465 1.00 1.38 H
-ATOM 73 HG13 VAL A 253 3.366 -4.704 -7.014 1.00 1.23 H
-ATOM 74 HG21 VAL A 253 4.022 -3.407 -3.890 1.00 1.43 H
-ATOM 75 HG22 VAL A 253 4.916 -3.865 -5.339 1.00 1.45 H
-ATOM 76 HG23 VAL A 253 5.069 -4.825 -3.868 1.00 1.43 H
-ATOM 77 N GLY A 254 1.536 -3.330 -6.835 1.00 0.56 N
-ATOM 78 CA GLY A 254 1.405 -2.222 -7.765 1.00 0.53 C
-ATOM 79 C GLY A 254 0.285 -1.274 -7.374 1.00 0.46 C
-ATOM 80 O GLY A 254 0.472 -0.056 -7.352 1.00 0.45 O
-ATOM 81 H GLY A 254 1.428 -4.258 -7.148 1.00 0.62 H
-ATOM 82 HA2 GLY A 254 2.335 -1.675 -7.791 1.00 0.56 H
-ATOM 83 HA3 GLY A 254 1.201 -2.614 -8.752 1.00 0.58 H
-ATOM 84 N LEU A 255 -0.873 -1.832 -7.035 1.00 0.49 N
-ATOM 85 CA LEU A 255 -2.015 -1.027 -6.609 1.00 0.51 C
-ATOM 86 C LEU A 255 -1.692 -0.255 -5.337 1.00 0.47 C
-ATOM 87 O LEU A 255 -1.980 0.939 -5.226 1.00 0.49 O
-ATOM 88 CB LEU A 255 -3.232 -1.912 -6.367 1.00 0.64 C
-ATOM 89 CG LEU A 255 -4.222 -1.963 -7.520 1.00 0.83 C
-ATOM 90 CD1 LEU A 255 -5.393 -2.860 -7.163 1.00 1.00 C
-ATOM 91 CD2 LEU A 255 -4.696 -0.558 -7.860 1.00 0.95 C
-ATOM 92 H LEU A 255 -0.957 -2.814 -7.054 1.00 0.53 H
-ATOM 93 HA LEU A 255 -2.246 -0.329 -7.399 1.00 0.54 H
-ATOM 94 HB2 LEU A 255 -2.887 -2.917 -6.168 1.00 0.73 H
-ATOM 95 HB3 LEU A 255 -3.748 -1.546 -5.493 1.00 0.68 H
-ATOM 96 HG LEU A 255 -3.732 -2.374 -8.390 1.00 0.91 H
-ATOM 97 HD11 LEU A 255 -6.296 -2.464 -7.602 1.00 1.41 H
-ATOM 98 HD12 LEU A 255 -5.500 -2.898 -6.089 1.00 1.51 H
-ATOM 99 HD13 LEU A 255 -5.212 -3.853 -7.544 1.00 1.45 H
-ATOM 100 HD21 LEU A 255 -4.036 0.166 -7.400 1.00 1.39 H
-ATOM 101 HD22 LEU A 255 -5.699 -0.416 -7.488 1.00 1.45 H
-ATOM 102 HD23 LEU A 255 -4.686 -0.423 -8.931 1.00 1.44 H
-ATOM 103 N GLY A 256 -1.089 -0.948 -4.378 1.00 0.48 N
-ATOM 104 CA GLY A 256 -0.732 -0.325 -3.122 1.00 0.52 C
-ATOM 105 C GLY A 256 0.228 0.844 -3.291 1.00 0.44 C
-ATOM 106 O GLY A 256 -0.024 1.933 -2.783 1.00 0.45 O
-ATOM 107 H GLY A 256 -0.884 -1.900 -4.527 1.00 0.50 H
-ATOM 108 HA2 GLY A 256 -1.635 0.032 -2.650 1.00 0.60 H
-ATOM 109 HA3 GLY A 256 -0.274 -1.066 -2.486 1.00 0.60 H
-ATOM 110 N ILE A 257 1.323 0.634 -4.021 1.00 0.42 N
-ATOM 111 CA ILE A 257 2.287 1.709 -4.243 1.00 0.40 C
-ATOM 112 C ILE A 257 1.672 2.841 -5.076 1.00 0.33 C
-ATOM 113 O ILE A 257 1.925 4.015 -4.813 1.00 0.35 O
-ATOM 114 CB ILE A 257 3.609 1.207 -4.886 1.00 0.49 C
-ATOM 115 CG1 ILE A 257 3.376 0.545 -6.248 1.00 0.51 C
-ATOM 116 CG2 ILE A 257 4.315 0.240 -3.947 1.00 0.62 C
-ATOM 117 CD1 ILE A 257 3.630 1.461 -7.428 1.00 0.58 C
-ATOM 118 H ILE A 257 1.480 -0.254 -4.422 1.00 0.46 H
-ATOM 119 HA ILE A 257 2.532 2.113 -3.269 1.00 0.46 H
-ATOM 120 HB ILE A 257 4.256 2.062 -5.019 1.00 0.52 H
-ATOM 121 HG12 ILE A 257 4.034 -0.306 -6.344 1.00 0.62 H
-ATOM 122 HG13 ILE A 257 2.351 0.208 -6.305 1.00 0.53 H
-ATOM 123 HG21 ILE A 257 4.814 0.796 -3.166 1.00 1.31 H
-ATOM 124 HG22 ILE A 257 5.041 -0.334 -4.501 1.00 1.21 H
-ATOM 125 HG23 ILE A 257 3.589 -0.427 -3.506 1.00 1.12 H
-ATOM 126 HD11 ILE A 257 2.934 1.231 -8.221 1.00 1.26 H
-ATOM 127 HD12 ILE A 257 4.640 1.319 -7.783 1.00 1.15 H
-ATOM 128 HD13 ILE A 257 3.499 2.488 -7.121 1.00 1.16 H
-ATOM 129 N THR A 258 0.843 2.488 -6.065 1.00 0.33 N
-ATOM 130 CA THR A 258 0.183 3.490 -6.908 1.00 0.35 C
-ATOM 131 C THR A 258 -0.739 4.381 -6.077 1.00 0.34 C
-ATOM 132 O THR A 258 -0.712 5.606 -6.202 1.00 0.37 O
-ATOM 133 CB THR A 258 -0.643 2.837 -8.041 1.00 0.45 C
-ATOM 134 OG1 THR A 258 0.198 2.018 -8.860 1.00 0.52 O
-ATOM 135 CG2 THR A 258 -1.315 3.893 -8.911 1.00 0.58 C
-ATOM 136 H THR A 258 0.662 1.532 -6.223 1.00 0.37 H
-ATOM 137 HA THR A 258 0.951 4.104 -7.357 1.00 0.39 H
-ATOM 138 HB THR A 258 -1.410 2.221 -7.596 1.00 0.45 H
-ATOM 139 HG1 THR A 258 0.362 1.176 -8.411 1.00 0.49 H
-ATOM 140 HG21 THR A 258 -0.997 4.875 -8.595 1.00 1.23 H
-ATOM 141 HG22 THR A 258 -2.387 3.812 -8.810 1.00 1.18 H
-ATOM 142 HG23 THR A 258 -1.037 3.739 -9.943 1.00 1.16 H
-ATOM 143 N THR A 259 -1.544 3.758 -5.219 1.00 0.38 N
-ATOM 144 CA THR A 259 -2.466 4.495 -4.359 1.00 0.46 C
-ATOM 145 C THR A 259 -1.702 5.354 -3.358 1.00 0.44 C
-ATOM 146 O THR A 259 -2.002 6.538 -3.196 1.00 0.48 O
-ATOM 147 CB THR A 259 -3.428 3.554 -3.605 1.00 0.57 C
-ATOM 148 OG1 THR A 259 -2.693 2.559 -2.883 1.00 0.58 O
-ATOM 149 CG2 THR A 259 -4.382 2.876 -4.575 1.00 0.67 C
-ATOM 150 H THR A 259 -1.510 2.778 -5.156 1.00 0.40 H
-ATOM 151 HA THR A 259 -3.055 5.144 -4.991 1.00 0.50 H
-ATOM 152 HB THR A 259 -4.008 4.141 -2.906 1.00 0.64 H
-ATOM 153 HG1 THR A 259 -2.398 1.871 -3.491 1.00 1.02 H
-ATOM 154 HG21 THR A 259 -4.581 1.869 -4.238 1.00 1.33 H
-ATOM 155 HG22 THR A 259 -3.934 2.845 -5.557 1.00 1.23 H
-ATOM 156 HG23 THR A 259 -5.306 3.430 -4.618 1.00 1.00 H
-ATOM 157 N VAL A 260 -0.698 4.763 -2.709 1.00 0.43 N
-ATOM 158 CA VAL A 260 0.120 5.500 -1.749 1.00 0.47 C
-ATOM 159 C VAL A 260 0.818 6.684 -2.430 1.00 0.41 C
-ATOM 160 O VAL A 260 0.730 7.817 -1.955 1.00 0.47 O
-ATOM 161 CB VAL A 260 1.175 4.589 -1.083 1.00 0.53 C
-ATOM 162 CG1 VAL A 260 2.130 5.401 -0.222 1.00 0.66 C
-ATOM 163 CG2 VAL A 260 0.498 3.512 -0.249 1.00 0.64 C
-ATOM 164 H VAL A 260 -0.495 3.813 -2.890 1.00 0.43 H
-ATOM 165 HA VAL A 260 -0.536 5.881 -0.978 1.00 0.56 H
-ATOM 166 HB VAL A 260 1.748 4.106 -1.861 1.00 0.49 H
-ATOM 167 HG11 VAL A 260 1.642 5.669 0.703 1.00 1.21 H
-ATOM 168 HG12 VAL A 260 2.416 6.298 -0.752 1.00 1.19 H
-ATOM 169 HG13 VAL A 260 3.010 4.814 -0.008 1.00 1.29 H
-ATOM 170 HG21 VAL A 260 -0.409 3.907 0.183 1.00 1.32 H
-ATOM 171 HG22 VAL A 260 1.164 3.193 0.538 1.00 1.18 H
-ATOM 172 HG23 VAL A 260 0.257 2.668 -0.880 1.00 1.16 H
-ATOM 173 N LEU A 261 1.487 6.418 -3.559 1.00 0.36 N
-ATOM 174 CA LEU A 261 2.176 7.465 -4.315 1.00 0.36 C
-ATOM 175 C LEU A 261 1.203 8.575 -4.720 1.00 0.36 C
-ATOM 176 O LEU A 261 1.486 9.755 -4.530 1.00 0.42 O
-ATOM 177 CB LEU A 261 2.844 6.881 -5.557 1.00 0.39 C
-ATOM 178 CG LEU A 261 3.782 7.839 -6.285 1.00 0.52 C
-ATOM 179 CD1 LEU A 261 4.855 8.358 -5.335 1.00 0.68 C
-ATOM 180 CD2 LEU A 261 4.414 7.149 -7.479 1.00 0.63 C
-ATOM 181 H LEU A 261 1.505 5.491 -3.900 1.00 0.36 H
-ATOM 182 HA LEU A 261 2.943 7.889 -3.682 1.00 0.41 H
-ATOM 183 HB2 LEU A 261 3.407 6.009 -5.261 1.00 0.45 H
-ATOM 184 HB3 LEU A 261 2.072 6.576 -6.247 1.00 0.42 H
-ATOM 185 HG LEU A 261 3.217 8.685 -6.645 1.00 0.61 H
-ATOM 186 HD11 LEU A 261 5.737 7.741 -5.415 1.00 1.20 H
-ATOM 187 HD12 LEU A 261 4.481 8.323 -4.321 1.00 1.36 H
-ATOM 188 HD13 LEU A 261 5.102 9.378 -5.591 1.00 1.20 H
-ATOM 189 HD21 LEU A 261 3.670 7.012 -8.250 1.00 1.22 H
-ATOM 190 HD22 LEU A 261 4.799 6.186 -7.176 1.00 1.30 H
-ATOM 191 HD23 LEU A 261 5.222 7.756 -7.860 1.00 1.11 H
-ATOM 192 N THR A 262 0.049 8.180 -5.254 1.00 0.34 N
-ATOM 193 CA THR A 262 -0.986 9.137 -5.666 1.00 0.39 C
-ATOM 194 C THR A 262 -1.416 10.017 -4.491 1.00 0.43 C
-ATOM 195 O THR A 262 -1.496 11.244 -4.607 1.00 0.49 O
-ATOM 196 CB THR A 262 -2.229 8.400 -6.216 1.00 0.44 C
-ATOM 197 OG1 THR A 262 -1.877 7.626 -7.369 1.00 0.46 O
-ATOM 198 CG2 THR A 262 -3.337 9.376 -6.577 1.00 0.54 C
-ATOM 199 H THR A 262 -0.121 7.217 -5.362 1.00 0.34 H
-ATOM 200 HA THR A 262 -0.581 9.760 -6.449 1.00 0.43 H
-ATOM 201 HB THR A 262 -2.597 7.733 -5.449 1.00 0.47 H
-ATOM 202 HG1 THR A 262 -1.411 6.823 -7.085 1.00 0.54 H
-ATOM 203 HG21 THR A 262 -3.741 9.808 -5.674 1.00 1.06 H
-ATOM 204 HG22 THR A 262 -4.119 8.853 -7.108 1.00 1.17 H
-ATOM 205 HG23 THR A 262 -2.936 10.160 -7.202 1.00 1.15 H
-ATOM 206 N MET A 263 -1.690 9.376 -3.363 1.00 0.44 N
-ATOM 207 CA MET A 263 -2.115 10.081 -2.155 1.00 0.53 C
-ATOM 208 C MET A 263 -1.005 10.978 -1.577 1.00 0.54 C
-ATOM 209 O MET A 263 -1.259 12.127 -1.209 1.00 0.61 O
-ATOM 210 CB MET A 263 -2.570 9.064 -1.105 1.00 0.60 C
-ATOM 211 CG MET A 263 -3.076 9.687 0.185 1.00 0.74 C
-ATOM 212 SD MET A 263 -3.656 8.449 1.359 1.00 0.93 S
-ATOM 213 CE MET A 263 -4.029 9.475 2.781 1.00 1.14 C
-ATOM 214 H MET A 263 -1.608 8.394 -3.342 1.00 0.43 H
-ATOM 215 HA MET A 263 -2.956 10.702 -2.419 1.00 0.58 H
-ATOM 216 HB2 MET A 263 -3.366 8.465 -1.523 1.00 0.63 H
-ATOM 217 HB3 MET A 263 -1.738 8.419 -0.865 1.00 0.62 H
-ATOM 218 HG2 MET A 263 -2.272 10.247 0.640 1.00 0.78 H
-ATOM 219 HG3 MET A 263 -3.892 10.355 -0.048 1.00 0.84 H
-ATOM 220 HE1 MET A 263 -4.575 8.894 3.510 1.00 1.62 H
-ATOM 221 HE2 MET A 263 -4.631 10.316 2.471 1.00 1.70 H
-ATOM 222 HE3 MET A 263 -3.109 9.831 3.219 1.00 1.28 H
-ATOM 223 N THR A 264 0.220 10.452 -1.476 1.00 0.52 N
-ATOM 224 CA THR A 264 1.339 11.225 -0.912 1.00 0.57 C
-ATOM 225 C THR A 264 1.766 12.406 -1.793 1.00 0.57 C
-ATOM 226 O THR A 264 2.159 13.450 -1.274 1.00 0.64 O
-ATOM 227 CB THR A 264 2.580 10.352 -0.606 1.00 0.61 C
-ATOM 228 OG1 THR A 264 3.571 11.138 0.066 1.00 0.69 O
-ATOM 229 CG2 THR A 264 3.191 9.766 -1.871 1.00 0.59 C
-ATOM 230 H THR A 264 0.372 9.520 -1.769 1.00 0.49 H
-ATOM 231 HA THR A 264 0.993 11.627 0.030 1.00 0.63 H
-ATOM 232 HB THR A 264 2.280 9.539 0.040 1.00 0.67 H
-ATOM 233 HG1 THR A 264 3.470 12.064 -0.180 1.00 0.94 H
-ATOM 234 HG21 THR A 264 2.525 9.936 -2.704 1.00 1.24 H
-ATOM 235 HG22 THR A 264 3.341 8.704 -1.740 1.00 1.15 H
-ATOM 236 HG23 THR A 264 4.140 10.242 -2.065 1.00 1.15 H
-ATOM 237 N THR A 265 1.700 12.251 -3.117 1.00 0.53 N
-ATOM 238 CA THR A 265 2.095 13.334 -4.028 1.00 0.59 C
-ATOM 239 C THR A 265 1.023 14.421 -4.112 1.00 0.55 C
-ATOM 240 O THR A 265 1.196 15.414 -4.823 1.00 0.63 O
-ATOM 241 CB THR A 265 2.387 12.828 -5.461 1.00 0.64 C
-ATOM 242 OG1 THR A 265 2.895 13.901 -6.261 1.00 0.77 O
-ATOM 243 CG2 THR A 265 1.131 12.276 -6.121 1.00 0.59 C
-ATOM 244 H THR A 265 1.388 11.394 -3.488 1.00 0.50 H
-ATOM 245 HA THR A 265 3.003 13.774 -3.638 1.00 0.68 H
-ATOM 246 HB THR A 265 3.126 12.042 -5.410 1.00 0.69 H
-ATOM 247 HG1 THR A 265 2.405 14.711 -6.057 1.00 0.83 H
-ATOM 248 HG21 THR A 265 0.615 11.629 -5.426 1.00 1.17 H
-ATOM 249 HG22 THR A 265 1.404 11.714 -7.002 1.00 1.18 H
-ATOM 250 HG23 THR A 265 0.484 13.093 -6.401 1.00 1.19 H
-ATOM 251 N GLN A 266 -0.088 14.213 -3.397 1.00 0.51 N
-ATOM 252 CA GLN A 266 -1.208 15.155 -3.393 1.00 0.53 C
-ATOM 253 C GLN A 266 -1.810 15.291 -4.794 1.00 0.45 C
-ATOM 254 O GLN A 266 -2.055 16.396 -5.279 1.00 0.53 O
-ATOM 255 CB GLN A 266 -0.784 16.527 -2.852 1.00 0.70 C
-ATOM 256 CG GLN A 266 -0.505 16.535 -1.355 1.00 0.82 C
-ATOM 257 CD GLN A 266 -0.194 17.924 -0.825 1.00 0.99 C
-ATOM 258 OE1 GLN A 266 0.790 18.545 -1.209 1.00 1.08 O
-ATOM 259 NE2 GLN A 266 -1.037 18.425 0.065 1.00 1.12 N
-ATOM 260 H GLN A 266 -0.160 13.391 -2.864 1.00 0.53 H
-ATOM 261 HA GLN A 266 -1.965 14.747 -2.740 1.00 0.56 H
-ATOM 262 HB2 GLN A 266 0.114 16.839 -3.364 1.00 0.74 H
-ATOM 263 HB3 GLN A 266 -1.570 17.238 -3.054 1.00 0.78 H
-ATOM 264 HG2 GLN A 266 -1.372 16.155 -0.837 1.00 0.86 H
-ATOM 265 HG3 GLN A 266 0.342 15.894 -1.157 1.00 0.86 H
-ATOM 266 HE21 GLN A 266 -1.805 17.883 0.332 1.00 1.14 H
-ATOM 267 HE22 GLN A 266 -0.854 19.321 0.416 1.00 1.24 H
-ATOM 268 N SER A 267 -2.048 14.145 -5.428 1.00 0.41 N
-ATOM 269 CA SER A 267 -2.632 14.104 -6.769 1.00 0.42 C
-ATOM 270 C SER A 267 -4.094 14.549 -6.743 1.00 0.44 C
-ATOM 271 O SER A 267 -4.780 14.400 -5.728 1.00 0.56 O
-ATOM 272 CB SER A 267 -2.526 12.694 -7.356 1.00 0.61 C
-ATOM 273 OG SER A 267 -3.117 12.621 -8.643 1.00 0.70 O
-ATOM 274 H SER A 267 -1.831 13.298 -4.978 1.00 0.47 H
-ATOM 275 HA SER A 267 -2.073 14.785 -7.393 1.00 0.52 H
-ATOM 276 HB2 SER A 267 -1.485 12.418 -7.436 1.00 0.77 H
-ATOM 277 HB3 SER A 267 -3.033 11.997 -6.703 1.00 0.70 H
-ATOM 278 HG SER A 267 -3.152 11.703 -8.930 1.00 1.06 H
-ATOM 279 N SER A 268 -4.560 15.101 -7.860 1.00 0.58 N
-ATOM 280 CA SER A 268 -5.940 15.587 -7.985 1.00 0.83 C
-ATOM 281 C SER A 268 -6.967 14.442 -8.006 1.00 0.95 C
-ATOM 282 O SER A 268 -7.758 14.322 -8.941 1.00 1.14 O
-ATOM 283 CB SER A 268 -6.081 16.429 -9.258 1.00 1.03 C
-ATOM 284 OG SER A 268 -5.089 17.442 -9.312 1.00 1.13 O
-ATOM 285 H SER A 268 -3.957 15.193 -8.626 1.00 0.62 H
-ATOM 286 HA SER A 268 -6.144 16.216 -7.132 1.00 0.90 H
-ATOM 287 HB2 SER A 268 -5.973 15.791 -10.122 1.00 0.99 H
-ATOM 288 HB3 SER A 268 -7.054 16.895 -9.273 1.00 1.29 H
-ATOM 289 HG SER A 268 -5.368 18.132 -9.919 1.00 1.55 H
-ATOM 290 N GLY A 269 -6.948 13.612 -6.970 1.00 0.92 N
-ATOM 291 CA GLY A 269 -7.873 12.493 -6.873 1.00 1.15 C
-ATOM 292 C GLY A 269 -7.660 11.681 -5.604 1.00 1.06 C
-ATOM 293 O GLY A 269 -7.390 12.249 -4.545 1.00 0.93 O
-ATOM 294 H GLY A 269 -6.289 13.763 -6.250 1.00 0.79 H
-ATOM 295 HA2 GLY A 269 -8.884 12.874 -6.879 1.00 1.34 H
-ATOM 296 HA3 GLY A 269 -7.735 11.848 -7.729 1.00 1.27 H
-ATOM 297 N SER A 270 -7.770 10.353 -5.712 1.00 1.21 N
-ATOM 298 CA SER A 270 -7.581 9.451 -4.566 1.00 1.24 C
-ATOM 299 C SER A 270 -8.561 9.773 -3.421 1.00 1.27 C
-ATOM 300 O SER A 270 -9.720 10.107 -3.670 1.00 1.33 O
-ATOM 301 CB SER A 270 -6.124 9.520 -4.073 1.00 1.09 C
-ATOM 302 OG SER A 270 -5.841 8.497 -3.128 1.00 1.23 O
-ATOM 303 H SER A 270 -7.983 9.957 -6.600 1.00 1.35 H
-ATOM 304 HA SER A 270 -7.779 8.447 -4.911 1.00 1.44 H
-ATOM 305 HB2 SER A 270 -5.457 9.406 -4.914 1.00 1.16 H
-ATOM 306 HB3 SER A 270 -5.951 10.479 -3.607 1.00 0.93 H
-ATOM 307 HG SER A 270 -5.048 8.020 -3.397 1.00 1.74 H
-ATOM 308 N ARG A 271 -8.076 9.659 -2.176 1.00 1.30 N
-ATOM 309 CA ARG A 271 -8.878 9.922 -0.964 1.00 1.40 C
-ATOM 310 C ARG A 271 -9.918 8.820 -0.712 1.00 1.41 C
-ATOM 311 O ARG A 271 -10.778 8.557 -1.550 1.00 1.20 O
-ATOM 312 CB ARG A 271 -9.568 11.291 -1.048 1.00 1.46 C
-ATOM 313 CG ARG A 271 -8.604 12.467 -0.986 1.00 1.58 C
-ATOM 314 CD ARG A 271 -9.228 13.739 -1.548 1.00 1.68 C
-ATOM 315 NE ARG A 271 -10.450 14.130 -0.844 1.00 1.70 N
-ATOM 316 CZ ARG A 271 -10.485 14.712 0.341 1.00 1.78 C
-ATOM 317 NH1 ARG A 271 -9.381 14.998 0.987 1.00 1.87 N
-ATOM 318 NH2 ARG A 271 -11.635 15.008 0.881 1.00 1.90 N
-ATOM 319 H ARG A 271 -7.139 9.377 -2.067 1.00 1.30 H
-ATOM 320 HA ARG A 271 -8.194 9.939 -0.129 1.00 1.51 H
-ATOM 321 HB2 ARG A 271 -10.114 11.349 -1.979 1.00 1.41 H
-ATOM 322 HB3 ARG A 271 -10.265 11.381 -0.228 1.00 1.55 H
-ATOM 323 HG2 ARG A 271 -8.331 12.639 0.044 1.00 1.66 H
-ATOM 324 HG3 ARG A 271 -7.722 12.223 -1.558 1.00 1.65 H
-ATOM 325 HD2 ARG A 271 -8.508 14.539 -1.466 1.00 1.81 H
-ATOM 326 HD3 ARG A 271 -9.461 13.576 -2.591 1.00 1.73 H
-ATOM 327 HE ARG A 271 -11.299 13.940 -1.288 1.00 1.74 H
-ATOM 328 HH11 ARG A 271 -8.497 14.777 0.581 1.00 1.86 H
-ATOM 329 HH12 ARG A 271 -9.428 15.438 1.881 1.00 2.02 H
-ATOM 330 HH21 ARG A 271 -12.482 14.797 0.398 1.00 1.92 H
-ATOM 331 HH22 ARG A 271 -11.668 15.449 1.775 1.00 2.05 H
-ATOM 332 N ALA A 272 -9.824 8.179 0.459 1.00 1.76 N
-ATOM 333 CA ALA A 272 -10.746 7.098 0.840 1.00 1.90 C
-ATOM 334 C ALA A 272 -10.726 5.962 -0.191 1.00 1.71 C
-ATOM 335 O ALA A 272 -9.671 5.610 -0.716 1.00 1.72 O
-ATOM 336 CB ALA A 272 -12.161 7.640 1.017 1.00 1.93 C
-ATOM 337 H ALA A 272 -9.113 8.435 1.082 1.00 1.96 H
-ATOM 338 HA ALA A 272 -10.417 6.706 1.793 1.00 2.23 H
-ATOM 339 HB1 ALA A 272 -12.133 8.718 1.060 1.00 2.18 H
-ATOM 340 HB2 ALA A 272 -12.583 7.254 1.934 1.00 2.19 H
-ATOM 341 HB3 ALA A 272 -12.772 7.328 0.182 1.00 2.23 H
-ATOM 342 N SER A 273 -11.896 5.398 -0.492 1.00 1.66 N
-ATOM 343 CA SER A 273 -11.986 4.319 -1.476 1.00 1.62 C
-ATOM 344 C SER A 273 -12.127 4.848 -2.885 1.00 1.26 C
-ATOM 345 O SER A 273 -12.958 4.381 -3.664 1.00 1.29 O
-ATOM 346 CB SER A 273 -13.135 3.368 -1.172 1.00 1.88 C
-ATOM 347 OG SER A 273 -14.369 4.062 -1.079 1.00 1.86 O
-ATOM 348 H SER A 273 -12.712 5.720 -0.057 1.00 1.74 H
-ATOM 349 HA SER A 273 -11.059 3.761 -1.442 1.00 1.79 H
-ATOM 350 HB2 SER A 273 -13.203 2.642 -1.969 1.00 1.91 H
-ATOM 351 HB3 SER A 273 -12.940 2.864 -0.240 1.00 2.16 H
-ATOM 352 HG SER A 273 -14.626 4.370 -1.954 1.00 1.94 H
-ATOM 353 N LEU A 274 -11.257 5.776 -3.230 1.00 1.06 N
-ATOM 354 CA LEU A 274 -11.224 6.316 -4.575 1.00 0.86 C
-ATOM 355 C LEU A 274 -9.808 6.210 -5.154 1.00 0.94 C
-ATOM 356 O LEU A 274 -9.257 7.205 -5.613 1.00 0.87 O
-ATOM 357 CB LEU A 274 -11.715 7.765 -4.616 1.00 0.79 C
-ATOM 358 CG LEU A 274 -13.237 7.951 -4.593 1.00 0.99 C
-ATOM 359 CD1 LEU A 274 -13.915 7.008 -5.573 1.00 1.20 C
-ATOM 360 CD2 LEU A 274 -13.798 7.773 -3.190 1.00 1.12 C
-ATOM 361 H LEU A 274 -10.584 6.070 -2.574 1.00 1.18 H
-ATOM 362 HA LEU A 274 -11.880 5.709 -5.181 1.00 0.97 H
-ATOM 363 HB2 LEU A 274 -11.298 8.288 -3.768 1.00 0.86 H
-ATOM 364 HB3 LEU A 274 -11.336 8.224 -5.518 1.00 0.87 H
-ATOM 365 HG LEU A 274 -13.457 8.952 -4.909 1.00 1.17 H
-ATOM 366 HD11 LEU A 274 -14.590 6.357 -5.038 1.00 1.61 H
-ATOM 367 HD12 LEU A 274 -13.168 6.415 -6.079 1.00 1.63 H
-ATOM 368 HD13 LEU A 274 -14.470 7.584 -6.299 1.00 1.63 H
-ATOM 369 HD21 LEU A 274 -14.518 6.967 -3.191 1.00 1.61 H
-ATOM 370 HD22 LEU A 274 -14.283 8.687 -2.881 1.00 1.62 H
-ATOM 371 HD23 LEU A 274 -12.996 7.541 -2.506 1.00 1.44 H
-ATOM 372 N PRO A 275 -9.188 4.993 -5.097 1.00 1.27 N
-ATOM 373 CA PRO A 275 -7.802 4.730 -5.591 1.00 1.52 C
-ATOM 374 C PRO A 275 -7.533 5.085 -7.070 1.00 1.56 C
-ATOM 375 O PRO A 275 -6.994 4.270 -7.820 1.00 2.24 O
-ATOM 376 CB PRO A 275 -7.667 3.215 -5.410 1.00 1.88 C
-ATOM 377 CG PRO A 275 -8.557 2.912 -4.269 1.00 1.87 C
-ATOM 378 CD PRO A 275 -9.765 3.768 -4.490 1.00 1.52 C
-ATOM 379 HA PRO A 275 -7.071 5.220 -4.971 1.00 1.57 H
-ATOM 380 HB2 PRO A 275 -7.989 2.713 -6.310 1.00 1.96 H
-ATOM 381 HB3 PRO A 275 -6.640 2.960 -5.193 1.00 2.10 H
-ATOM 382 HG2 PRO A 275 -8.822 1.866 -4.265 1.00 2.08 H
-ATOM 383 HG3 PRO A 275 -8.068 3.188 -3.347 1.00 1.98 H
-ATOM 384 HD2 PRO A 275 -10.462 3.289 -5.160 1.00 1.55 H
-ATOM 385 HD3 PRO A 275 -10.252 3.998 -3.537 1.00 1.53 H
-ATOM 386 N LYS A 276 -7.897 6.291 -7.485 1.00 1.39 N
-ATOM 387 CA LYS A 276 -7.696 6.740 -8.851 1.00 1.65 C
-ATOM 388 C LYS A 276 -7.734 8.278 -8.921 1.00 1.63 C
-ATOM 389 O LYS A 276 -7.377 8.845 -9.980 1.00 2.33 O
-ATOM 390 CB LYS A 276 -8.782 6.130 -9.744 1.00 2.30 C
-ATOM 391 CG LYS A 276 -8.712 6.558 -11.196 1.00 2.74 C
-ATOM 392 CD LYS A 276 -10.080 6.956 -11.712 1.00 3.55 C
-ATOM 393 CE LYS A 276 -10.608 8.208 -11.024 1.00 3.69 C
-ATOM 394 NZ LYS A 276 -9.659 9.358 -11.131 1.00 3.38 N
-ATOM 395 OXT LYS A 276 -8.140 8.911 -7.919 1.00 1.33 O
-ATOM 396 H LYS A 276 -8.331 6.906 -6.849 1.00 1.59 H
-ATOM 397 HA LYS A 276 -6.728 6.393 -9.178 1.00 1.74 H
-ATOM 398 HB2 LYS A 276 -8.689 5.055 -9.708 1.00 2.56 H
-ATOM 399 HB3 LYS A 276 -9.751 6.409 -9.356 1.00 2.43 H
-ATOM 400 HG2 LYS A 276 -8.046 7.404 -11.284 1.00 2.41 H
-ATOM 401 HG3 LYS A 276 -8.338 5.737 -11.788 1.00 3.02 H
-ATOM 402 HD2 LYS A 276 -10.015 7.140 -12.770 1.00 3.85 H
-ATOM 403 HD3 LYS A 276 -10.767 6.143 -11.526 1.00 3.96 H
-ATOM 404 HE2 LYS A 276 -11.545 8.485 -11.485 1.00 4.35 H
-ATOM 405 HE3 LYS A 276 -10.774 7.985 -9.981 1.00 3.57 H
-ATOM 406 HZ1 LYS A 276 -8.711 9.088 -10.759 1.00 2.89 H
-ATOM 407 HZ2 LYS A 276 -10.010 10.164 -10.579 1.00 3.83 H
-ATOM 408 HZ3 LYS A 276 -9.559 9.649 -12.123 1.00 3.41 H
-TER
-ENDMDL
-MODEL 7
-ATOM 1 N ALA A 249 -1.976 -1.121 -13.700 1.00 2.96 N
-ATOM 2 CA ALA A 249 -1.072 -1.555 -12.605 1.00 2.32 C
-ATOM 3 C ALA A 249 -1.161 -3.067 -12.390 1.00 1.85 C
-ATOM 4 O ALA A 249 -2.142 -3.692 -12.786 1.00 2.08 O
-ATOM 5 CB ALA A 249 -1.399 -0.811 -11.321 1.00 2.41 C
-ATOM 6 H1 ALA A 249 -1.648 -1.504 -14.608 1.00 3.21 H
-ATOM 7 H2 ALA A 249 -1.994 -0.083 -13.759 1.00 3.50 H
-ATOM 8 H3 ALA A 249 -2.942 -1.464 -13.519 1.00 3.05 H
-ATOM 9 HA ALA A 249 -0.059 -1.305 -12.884 1.00 2.27 H
-ATOM 10 HB1 ALA A 249 -0.514 -0.771 -10.700 1.00 2.64 H
-ATOM 11 HB2 ALA A 249 -2.189 -1.327 -10.796 1.00 2.70 H
-ATOM 12 HB3 ALA A 249 -1.718 0.193 -11.557 1.00 2.64 H
-ATOM 13 N PRO A 250 -0.127 -3.672 -11.772 1.00 1.32 N
-ATOM 14 CA PRO A 250 -0.080 -5.120 -11.509 1.00 1.13 C
-ATOM 15 C PRO A 250 -1.019 -5.575 -10.384 1.00 0.86 C
-ATOM 16 O PRO A 250 -2.166 -5.142 -10.298 1.00 0.92 O
-ATOM 17 CB PRO A 250 1.384 -5.328 -11.113 1.00 0.96 C
-ATOM 18 CG PRO A 250 1.771 -4.050 -10.477 1.00 0.75 C
-ATOM 19 CD PRO A 250 1.090 -2.995 -11.285 1.00 1.15 C
-ATOM 20 HA PRO A 250 -0.292 -5.691 -12.396 1.00 1.58 H
-ATOM 21 HB2 PRO A 250 1.464 -6.150 -10.425 1.00 1.09 H
-ATOM 22 HB3 PRO A 250 1.974 -5.518 -11.994 1.00 1.32 H
-ATOM 23 HG2 PRO A 250 1.425 -4.025 -9.455 1.00 0.85 H
-ATOM 24 HG3 PRO A 250 2.843 -3.924 -10.519 1.00 0.80 H
-ATOM 25 HD2 PRO A 250 0.840 -2.152 -10.663 1.00 1.42 H
-ATOM 26 HD3 PRO A 250 1.715 -2.695 -12.109 1.00 1.36 H
-ATOM 27 N ALA A 251 -0.525 -6.469 -9.535 1.00 0.74 N
-ATOM 28 CA ALA A 251 -1.317 -7.006 -8.425 1.00 0.65 C
-ATOM 29 C ALA A 251 -1.454 -6.019 -7.249 1.00 0.55 C
-ATOM 30 O ALA A 251 -1.489 -4.800 -7.432 1.00 0.55 O
-ATOM 31 CB ALA A 251 -0.710 -8.320 -7.948 1.00 0.85 C
-ATOM 32 H ALA A 251 0.390 -6.790 -9.668 1.00 0.89 H
-ATOM 33 HA ALA A 251 -2.304 -7.221 -8.806 1.00 0.76 H
-ATOM 34 HB1 ALA A 251 -1.208 -9.144 -8.435 1.00 1.37 H
-ATOM 35 HB2 ALA A 251 -0.834 -8.406 -6.878 1.00 1.38 H
-ATOM 36 HB3 ALA A 251 0.343 -8.341 -8.191 1.00 1.38 H
-ATOM 37 N ARG A 252 -1.545 -6.572 -6.036 1.00 0.59 N
-ATOM 38 CA ARG A 252 -1.700 -5.779 -4.811 1.00 0.57 C
-ATOM 39 C ARG A 252 -0.536 -4.798 -4.614 1.00 0.43 C
-ATOM 40 O ARG A 252 -0.746 -3.650 -4.224 1.00 0.40 O
-ATOM 41 CB ARG A 252 -1.807 -6.720 -3.600 1.00 0.72 C
-ATOM 42 CG ARG A 252 -2.738 -6.234 -2.493 1.00 0.83 C
-ATOM 43 CD ARG A 252 -2.214 -4.984 -1.803 1.00 0.68 C
-ATOM 44 NE ARG A 252 -3.130 -4.507 -0.770 1.00 0.92 N
-ATOM 45 CZ ARG A 252 -2.949 -3.401 -0.082 1.00 0.96 C
-ATOM 46 NH1 ARG A 252 -1.908 -2.640 -0.303 1.00 0.90 N
-ATOM 47 NH2 ARG A 252 -3.818 -3.052 0.827 1.00 1.26 N
-ATOM 48 H ARG A 252 -1.521 -7.548 -5.964 1.00 0.70 H
-ATOM 49 HA ARG A 252 -2.616 -5.218 -4.897 1.00 0.62 H
-ATOM 50 HB2 ARG A 252 -2.164 -7.680 -3.939 1.00 0.85 H
-ATOM 51 HB3 ARG A 252 -0.821 -6.846 -3.177 1.00 0.69 H
-ATOM 52 HG2 ARG A 252 -3.704 -6.014 -2.921 1.00 0.98 H
-ATOM 53 HG3 ARG A 252 -2.841 -7.019 -1.759 1.00 1.02 H
-ATOM 54 HD2 ARG A 252 -1.263 -5.212 -1.348 1.00 0.68 H
-ATOM 55 HD3 ARG A 252 -2.083 -4.208 -2.542 1.00 0.61 H
-ATOM 56 HE ARG A 252 -3.921 -5.050 -0.583 1.00 1.18 H
-ATOM 57 HH11 ARG A 252 -1.243 -2.898 -0.998 1.00 0.82 H
-ATOM 58 HH12 ARG A 252 -1.781 -1.803 0.223 1.00 1.09 H
-ATOM 59 HH21 ARG A 252 -4.616 -3.625 1.000 1.00 1.48 H
-ATOM 60 HH22 ARG A 252 -3.684 -2.214 1.350 1.00 1.36 H
-ATOM 61 N VAL A 253 0.683 -5.262 -4.881 1.00 0.42 N
-ATOM 62 CA VAL A 253 1.881 -4.431 -4.731 1.00 0.38 C
-ATOM 63 C VAL A 253 1.775 -3.146 -5.557 1.00 0.31 C
-ATOM 64 O VAL A 253 1.915 -2.039 -5.033 1.00 0.33 O
-ATOM 65 CB VAL A 253 3.158 -5.216 -5.127 1.00 0.48 C
-ATOM 66 CG1 VAL A 253 3.066 -5.785 -6.539 1.00 0.54 C
-ATOM 67 CG2 VAL A 253 4.402 -4.347 -4.972 1.00 0.59 C
-ATOM 68 H VAL A 253 0.780 -6.187 -5.185 1.00 0.49 H
-ATOM 69 HA VAL A 253 1.963 -4.161 -3.690 1.00 0.40 H
-ATOM 70 HB VAL A 253 3.245 -6.043 -4.456 1.00 0.55 H
-ATOM 71 HG11 VAL A 253 2.031 -5.984 -6.778 1.00 1.21 H
-ATOM 72 HG12 VAL A 253 3.631 -6.703 -6.594 1.00 0.99 H
-ATOM 73 HG13 VAL A 253 3.468 -5.071 -7.241 1.00 1.14 H
-ATOM 74 HG21 VAL A 253 5.280 -4.976 -4.952 1.00 1.14 H
-ATOM 75 HG22 VAL A 253 4.339 -3.790 -4.049 1.00 1.21 H
-ATOM 76 HG23 VAL A 253 4.469 -3.662 -5.802 1.00 1.24 H
-ATOM 77 N GLY A 254 1.505 -3.315 -6.841 1.00 0.30 N
-ATOM 78 CA GLY A 254 1.358 -2.182 -7.742 1.00 0.32 C
-ATOM 79 C GLY A 254 0.223 -1.265 -7.331 1.00 0.31 C
-ATOM 80 O GLY A 254 0.385 -0.045 -7.290 1.00 0.35 O
-ATOM 81 H GLY A 254 1.398 -4.229 -7.176 1.00 0.33 H
-ATOM 82 HA2 GLY A 254 2.280 -1.618 -7.748 1.00 0.36 H
-ATOM 83 HA3 GLY A 254 1.167 -2.551 -8.739 1.00 0.39 H
-ATOM 84 N LEU A 255 -0.922 -1.857 -7.001 1.00 0.33 N
-ATOM 85 CA LEU A 255 -2.082 -1.087 -6.562 1.00 0.38 C
-ATOM 86 C LEU A 255 -1.758 -0.270 -5.318 1.00 0.34 C
-ATOM 87 O LEU A 255 -2.077 0.917 -5.235 1.00 0.40 O
-ATOM 88 CB LEU A 255 -3.251 -2.014 -6.259 1.00 0.47 C
-ATOM 89 CG LEU A 255 -4.191 -2.246 -7.428 1.00 0.62 C
-ATOM 90 CD1 LEU A 255 -5.306 -3.189 -7.017 1.00 0.73 C
-ATOM 91 CD2 LEU A 255 -4.750 -0.917 -7.913 1.00 0.72 C
-ATOM 92 H LEU A 255 -0.983 -2.838 -7.038 1.00 0.36 H
-ATOM 93 HA LEU A 255 -2.366 -0.422 -7.363 1.00 0.43 H
-ATOM 94 HB2 LEU A 255 -2.856 -2.969 -5.942 1.00 0.47 H
-ATOM 95 HB3 LEU A 255 -3.820 -1.589 -5.446 1.00 0.54 H
-ATOM 96 HG LEU A 255 -3.643 -2.699 -8.240 1.00 0.66 H
-ATOM 97 HD11 LEU A 255 -5.364 -3.226 -5.939 1.00 1.28 H
-ATOM 98 HD12 LEU A 255 -5.099 -4.178 -7.400 1.00 1.29 H
-ATOM 99 HD13 LEU A 255 -6.242 -2.834 -7.417 1.00 1.26 H
-ATOM 100 HD21 LEU A 255 -5.808 -0.872 -7.703 1.00 1.34 H
-ATOM 101 HD22 LEU A 255 -4.588 -0.824 -8.976 1.00 1.29 H
-ATOM 102 HD23 LEU A 255 -4.246 -0.109 -7.401 1.00 1.17 H
-ATOM 103 N GLY A 256 -1.122 -0.918 -4.354 1.00 0.30 N
-ATOM 104 CA GLY A 256 -0.763 -0.254 -3.123 1.00 0.30 C
-ATOM 105 C GLY A 256 0.231 0.882 -3.319 1.00 0.26 C
-ATOM 106 O GLY A 256 -0.006 1.995 -2.852 1.00 0.30 O
-ATOM 107 H GLY A 256 -0.896 -1.870 -4.481 1.00 0.31 H
-ATOM 108 HA2 GLY A 256 -1.663 0.144 -2.678 1.00 0.37 H
-ATOM 109 HA3 GLY A 256 -0.337 -0.983 -2.453 1.00 0.35 H
-ATOM 110 N ILE A 257 1.342 0.615 -4.011 1.00 0.26 N
-ATOM 111 CA ILE A 257 2.347 1.655 -4.242 1.00 0.31 C
-ATOM 112 C ILE A 257 1.780 2.816 -5.068 1.00 0.35 C
-ATOM 113 O ILE A 257 2.038 3.976 -4.760 1.00 0.41 O
-ATOM 114 CB ILE A 257 3.647 1.105 -4.891 1.00 0.39 C
-ATOM 115 CG1 ILE A 257 3.382 0.456 -6.253 1.00 0.42 C
-ATOM 116 CG2 ILE A 257 4.321 0.112 -3.956 1.00 0.47 C
-ATOM 117 CD1 ILE A 257 3.767 1.329 -7.429 1.00 0.54 C
-ATOM 118 H ILE A 257 1.488 -0.292 -4.370 1.00 0.29 H
-ATOM 119 HA ILE A 257 2.614 2.048 -3.269 1.00 0.34 H
-ATOM 120 HB ILE A 257 4.324 1.936 -5.027 1.00 0.47 H
-ATOM 121 HG12 ILE A 257 3.948 -0.462 -6.323 1.00 0.48 H
-ATOM 122 HG13 ILE A 257 2.329 0.229 -6.336 1.00 0.45 H
-ATOM 123 HG21 ILE A 257 4.828 0.648 -3.167 1.00 1.11 H
-ATOM 124 HG22 ILE A 257 5.037 -0.477 -4.510 1.00 1.14 H
-ATOM 125 HG23 ILE A 257 3.575 -0.540 -3.525 1.00 1.16 H
-ATOM 126 HD11 ILE A 257 4.696 1.834 -7.214 1.00 1.14 H
-ATOM 127 HD12 ILE A 257 2.991 2.061 -7.603 1.00 1.19 H
-ATOM 128 HD13 ILE A 257 3.885 0.715 -8.309 1.00 1.18 H
-ATOM 129 N THR A 258 0.985 2.512 -6.099 1.00 0.35 N
-ATOM 130 CA THR A 258 0.381 3.563 -6.923 1.00 0.44 C
-ATOM 131 C THR A 258 -0.627 4.381 -6.115 1.00 0.44 C
-ATOM 132 O THR A 258 -0.670 5.608 -6.225 1.00 0.50 O
-ATOM 133 CB THR A 258 -0.312 3.011 -8.189 1.00 0.51 C
-ATOM 134 OG1 THR A 258 -1.194 1.935 -7.851 1.00 0.49 O
-ATOM 135 CG2 THR A 258 0.713 2.536 -9.208 1.00 0.57 C
-ATOM 136 H THR A 258 0.790 1.568 -6.297 1.00 0.32 H
-ATOM 137 HA THR A 258 1.179 4.221 -7.240 1.00 0.49 H
-ATOM 138 HB THR A 258 -0.890 3.807 -8.634 1.00 0.58 H
-ATOM 139 HG1 THR A 258 -0.682 1.119 -7.741 1.00 0.48 H
-ATOM 140 HG21 THR A 258 1.210 3.390 -9.645 1.00 1.16 H
-ATOM 141 HG22 THR A 258 0.215 1.974 -9.985 1.00 1.16 H
-ATOM 142 HG23 THR A 258 1.443 1.907 -8.719 1.00 1.21 H
-ATOM 143 N THR A 259 -1.422 3.702 -5.282 1.00 0.40 N
-ATOM 144 CA THR A 259 -2.406 4.388 -4.441 1.00 0.43 C
-ATOM 145 C THR A 259 -1.697 5.300 -3.441 1.00 0.39 C
-ATOM 146 O THR A 259 -2.035 6.479 -3.313 1.00 0.45 O
-ATOM 147 CB THR A 259 -3.302 3.388 -3.674 1.00 0.47 C
-ATOM 148 OG1 THR A 259 -3.990 2.534 -4.597 1.00 0.54 O
-ATOM 149 CG2 THR A 259 -4.320 4.116 -2.806 1.00 0.57 C
-ATOM 150 H THR A 259 -1.333 2.723 -5.220 1.00 0.38 H
-ATOM 151 HA THR A 259 -3.034 4.989 -5.082 1.00 0.51 H
-ATOM 152 HB THR A 259 -2.674 2.783 -3.035 1.00 0.44 H
-ATOM 153 HG1 THR A 259 -3.370 1.875 -4.948 1.00 0.53 H
-ATOM 154 HG21 THR A 259 -4.443 3.585 -1.874 1.00 1.19 H
-ATOM 155 HG22 THR A 259 -5.267 4.161 -3.323 1.00 1.18 H
-ATOM 156 HG23 THR A 259 -3.970 5.118 -2.605 1.00 1.18 H
-ATOM 157 N VAL A 260 -0.690 4.753 -2.757 1.00 0.33 N
-ATOM 158 CA VAL A 260 0.091 5.524 -1.789 1.00 0.34 C
-ATOM 159 C VAL A 260 0.782 6.709 -2.466 1.00 0.35 C
-ATOM 160 O VAL A 260 0.694 7.839 -1.982 1.00 0.39 O
-ATOM 161 CB VAL A 260 1.151 4.645 -1.089 1.00 0.39 C
-ATOM 162 CG1 VAL A 260 2.068 5.490 -0.217 1.00 0.50 C
-ATOM 163 CG2 VAL A 260 0.482 3.561 -0.259 1.00 0.47 C
-ATOM 164 H VAL A 260 -0.455 3.810 -2.921 1.00 0.33 H
-ATOM 165 HA VAL A 260 -0.588 5.899 -1.038 1.00 0.38 H
-ATOM 166 HB VAL A 260 1.754 4.166 -1.847 1.00 0.42 H
-ATOM 167 HG11 VAL A 260 3.085 5.400 -0.569 1.00 1.21 H
-ATOM 168 HG12 VAL A 260 2.009 5.148 0.806 1.00 1.14 H
-ATOM 169 HG13 VAL A 260 1.760 6.524 -0.269 1.00 1.10 H
-ATOM 170 HG21 VAL A 260 1.135 2.704 -0.190 1.00 1.12 H
-ATOM 171 HG22 VAL A 260 -0.446 3.269 -0.729 1.00 1.16 H
-ATOM 172 HG23 VAL A 260 0.279 3.939 0.732 1.00 1.12 H
-ATOM 173 N LEU A 261 1.452 6.446 -3.594 1.00 0.38 N
-ATOM 174 CA LEU A 261 2.142 7.498 -4.349 1.00 0.46 C
-ATOM 175 C LEU A 261 1.165 8.609 -4.726 1.00 0.49 C
-ATOM 176 O LEU A 261 1.471 9.794 -4.595 1.00 0.57 O
-ATOM 177 CB LEU A 261 2.776 6.913 -5.613 1.00 0.52 C
-ATOM 178 CG LEU A 261 4.269 7.201 -5.794 1.00 0.69 C
-ATOM 179 CD1 LEU A 261 4.520 8.696 -5.930 1.00 1.07 C
-ATOM 180 CD2 LEU A 261 5.065 6.627 -4.630 1.00 1.15 C
-ATOM 181 H LEU A 261 1.475 5.521 -3.935 1.00 0.37 H
-ATOM 182 HA LEU A 261 2.917 7.909 -3.720 1.00 0.49 H
-ATOM 183 HB2 LEU A 261 2.638 5.842 -5.596 1.00 0.75 H
-ATOM 184 HB3 LEU A 261 2.252 7.312 -6.469 1.00 0.68 H
-ATOM 185 HG LEU A 261 4.608 6.724 -6.699 1.00 0.71 H
-ATOM 186 HD11 LEU A 261 5.121 9.038 -5.101 1.00 1.57 H
-ATOM 187 HD12 LEU A 261 3.576 9.221 -5.932 1.00 1.44 H
-ATOM 188 HD13 LEU A 261 5.041 8.889 -6.855 1.00 1.68 H
-ATOM 189 HD21 LEU A 261 6.044 7.081 -4.605 1.00 1.72 H
-ATOM 190 HD22 LEU A 261 5.167 5.558 -4.757 1.00 1.46 H
-ATOM 191 HD23 LEU A 261 4.548 6.831 -3.704 1.00 1.74 H
-ATOM 192 N THR A 262 -0.020 8.206 -5.180 1.00 0.49 N
-ATOM 193 CA THR A 262 -1.068 9.155 -5.556 1.00 0.56 C
-ATOM 194 C THR A 262 -1.475 10.017 -4.357 1.00 0.55 C
-ATOM 195 O THR A 262 -1.545 11.242 -4.453 1.00 0.64 O
-ATOM 196 CB THR A 262 -2.317 8.420 -6.089 1.00 0.60 C
-ATOM 197 OG1 THR A 262 -1.979 7.636 -7.239 1.00 0.65 O
-ATOM 198 CG2 THR A 262 -3.421 9.404 -6.448 1.00 0.72 C
-ATOM 199 H THR A 262 -0.199 7.240 -5.252 1.00 0.47 H
-ATOM 200 HA THR A 262 -0.683 9.791 -6.339 1.00 0.63 H
-ATOM 201 HB THR A 262 -2.684 7.763 -5.313 1.00 0.56 H
-ATOM 202 HG1 THR A 262 -1.551 6.813 -6.953 1.00 0.68 H
-ATOM 203 HG21 THR A 262 -3.491 9.488 -7.522 1.00 1.28 H
-ATOM 204 HG22 THR A 262 -3.192 10.370 -6.024 1.00 1.26 H
-ATOM 205 HG23 THR A 262 -4.360 9.050 -6.050 1.00 1.25 H
-ATOM 206 N MET A 263 -1.742 9.361 -3.229 1.00 0.46 N
-ATOM 207 CA MET A 263 -2.142 10.059 -2.004 1.00 0.46 C
-ATOM 208 C MET A 263 -1.036 10.983 -1.468 1.00 0.44 C
-ATOM 209 O MET A 263 -1.269 12.170 -1.246 1.00 0.48 O
-ATOM 210 CB MET A 263 -2.536 9.049 -0.923 1.00 0.47 C
-ATOM 211 CG MET A 263 -3.767 8.230 -1.275 1.00 0.53 C
-ATOM 212 SD MET A 263 -4.182 7.016 -0.008 1.00 0.64 S
-ATOM 213 CE MET A 263 -4.485 8.079 1.400 1.00 0.78 C
-ATOM 214 H MET A 263 -1.671 8.378 -3.222 1.00 0.43 H
-ATOM 215 HA MET A 263 -3.005 10.663 -2.241 1.00 0.50 H
-ATOM 216 HB2 MET A 263 -1.711 8.371 -0.765 1.00 0.49 H
-ATOM 217 HB3 MET A 263 -2.735 9.582 -0.006 1.00 0.52 H
-ATOM 218 HG2 MET A 263 -4.605 8.899 -1.400 1.00 0.61 H
-ATOM 219 HG3 MET A 263 -3.583 7.710 -2.205 1.00 0.63 H
-ATOM 220 HE1 MET A 263 -5.519 8.390 1.402 1.00 1.23 H
-ATOM 221 HE2 MET A 263 -3.848 8.949 1.339 1.00 1.22 H
-ATOM 222 HE3 MET A 263 -4.270 7.538 2.311 1.00 1.38 H
-ATOM 223 N THR A 264 0.159 10.438 -1.249 1.00 0.42 N
-ATOM 224 CA THR A 264 1.268 11.234 -0.729 1.00 0.42 C
-ATOM 225 C THR A 264 2.516 11.129 -1.618 1.00 0.52 C
-ATOM 226 O THR A 264 2.898 10.043 -2.056 1.00 0.59 O
-ATOM 227 CB THR A 264 1.610 10.816 0.717 1.00 0.36 C
-ATOM 228 OG1 THR A 264 2.550 11.736 1.298 1.00 0.38 O
-ATOM 229 CG2 THR A 264 2.168 9.399 0.777 1.00 0.42 C
-ATOM 230 H THR A 264 0.297 9.482 -1.430 1.00 0.43 H
-ATOM 231 HA THR A 264 0.949 12.265 -0.710 1.00 0.46 H
-ATOM 232 HB THR A 264 0.695 10.840 1.288 1.00 0.35 H
-ATOM 233 HG1 THR A 264 3.396 11.279 1.465 1.00 0.43 H
-ATOM 234 HG21 THR A 264 2.884 9.327 1.582 1.00 1.11 H
-ATOM 235 HG22 THR A 264 2.654 9.164 -0.159 1.00 1.16 H
-ATOM 236 HG23 THR A 264 1.361 8.702 0.948 1.00 1.07 H
-ATOM 237 N THR A 265 3.144 12.272 -1.889 1.00 0.58 N
-ATOM 238 CA THR A 265 4.340 12.310 -2.736 1.00 0.71 C
-ATOM 239 C THR A 265 5.517 11.559 -2.103 1.00 0.71 C
-ATOM 240 O THR A 265 5.982 11.903 -1.013 1.00 0.66 O
-ATOM 241 CB THR A 265 4.767 13.760 -3.060 1.00 0.79 C
-ATOM 242 OG1 THR A 265 6.038 13.763 -3.723 1.00 0.93 O
-ATOM 243 CG2 THR A 265 4.839 14.618 -1.802 1.00 0.73 C
-ATOM 244 H THR A 265 2.790 13.110 -1.520 1.00 0.57 H
-ATOM 245 HA THR A 265 4.090 11.824 -3.668 1.00 0.80 H
-ATOM 246 HB THR A 265 4.030 14.189 -3.724 1.00 0.87 H
-ATOM 247 HG1 THR A 265 6.583 14.474 -3.372 1.00 1.30 H
-ATOM 248 HG21 THR A 265 4.954 13.981 -0.938 1.00 1.27 H
-ATOM 249 HG22 THR A 265 3.929 15.194 -1.707 1.00 1.27 H
-ATOM 250 HG23 THR A 265 5.684 15.289 -1.872 1.00 1.22 H
-ATOM 251 N GLN A 266 5.982 10.519 -2.812 1.00 0.83 N
-ATOM 252 CA GLN A 266 7.101 9.668 -2.373 1.00 0.91 C
-ATOM 253 C GLN A 266 6.747 8.832 -1.133 1.00 0.86 C
-ATOM 254 O GLN A 266 6.752 7.602 -1.182 1.00 0.97 O
-ATOM 255 CB GLN A 266 8.352 10.513 -2.092 1.00 0.97 C
-ATOM 256 CG GLN A 266 9.559 9.691 -1.664 1.00 1.11 C
-ATOM 257 CD GLN A 266 10.738 10.552 -1.257 1.00 1.21 C
-ATOM 258 OE1 GLN A 266 11.235 11.357 -2.036 1.00 1.32 O
-ATOM 259 NE2 GLN A 266 11.200 10.383 -0.031 1.00 1.29 N
-ATOM 260 H GLN A 266 5.550 10.312 -3.667 1.00 0.89 H
-ATOM 261 HA GLN A 266 7.322 8.991 -3.179 1.00 1.03 H
-ATOM 262 HB2 GLN A 266 8.612 11.056 -2.987 1.00 1.04 H
-ATOM 263 HB3 GLN A 266 8.126 11.218 -1.305 1.00 0.91 H
-ATOM 264 HG2 GLN A 266 9.278 9.075 -0.823 1.00 1.11 H
-ATOM 265 HG3 GLN A 266 9.858 9.060 -2.488 1.00 1.26 H
-ATOM 266 HE21 GLN A 266 10.764 9.722 0.541 1.00 1.30 H
-ATOM 267 HE22 GLN A 266 11.961 10.929 0.250 1.00 1.41 H
-ATOM 268 N SER A 267 6.445 9.511 -0.033 1.00 0.73 N
-ATOM 269 CA SER A 267 6.094 8.854 1.227 1.00 0.72 C
-ATOM 270 C SER A 267 5.615 9.890 2.251 1.00 0.61 C
-ATOM 271 O SER A 267 4.915 10.840 1.895 1.00 0.50 O
-ATOM 272 CB SER A 267 7.308 8.082 1.773 1.00 0.89 C
-ATOM 273 OG SER A 267 6.983 7.349 2.946 1.00 0.98 O
-ATOM 274 H SER A 267 6.457 10.496 -0.072 1.00 0.68 H
-ATOM 275 HA SER A 267 5.291 8.159 1.027 1.00 0.72 H
-ATOM 276 HB2 SER A 267 7.657 7.390 1.022 1.00 0.99 H
-ATOM 277 HB3 SER A 267 8.096 8.781 2.010 1.00 0.93 H
-ATOM 278 HG SER A 267 7.051 6.406 2.765 1.00 1.22 H
-ATOM 279 N SER A 268 5.994 9.703 3.516 1.00 0.72 N
-ATOM 280 CA SER A 268 5.611 10.613 4.603 1.00 0.72 C
-ATOM 281 C SER A 268 4.085 10.669 4.773 1.00 0.64 C
-ATOM 282 O SER A 268 3.404 9.650 4.667 1.00 0.77 O
-ATOM 283 CB SER A 268 6.189 12.019 4.350 1.00 0.70 C
-ATOM 284 OG SER A 268 5.916 12.905 5.429 1.00 0.76 O
-ATOM 285 H SER A 268 6.550 8.916 3.726 1.00 0.86 H
-ATOM 286 HA SER A 268 6.040 10.224 5.515 1.00 0.89 H
-ATOM 287 HB2 SER A 268 7.258 11.948 4.226 1.00 0.84 H
-ATOM 288 HB3 SER A 268 5.751 12.426 3.449 1.00 0.58 H
-ATOM 289 HG SER A 268 6.734 13.317 5.720 1.00 0.96 H
-ATOM 290 N GLY A 269 3.556 11.855 5.042 1.00 0.57 N
-ATOM 291 CA GLY A 269 2.124 12.008 5.228 1.00 0.58 C
-ATOM 292 C GLY A 269 1.603 13.348 4.736 1.00 0.49 C
-ATOM 293 O GLY A 269 1.375 14.260 5.528 1.00 0.65 O
-ATOM 294 H GLY A 269 4.154 12.636 5.126 1.00 0.64 H
-ATOM 295 HA2 GLY A 269 1.618 11.219 4.690 1.00 0.55 H
-ATOM 296 HA3 GLY A 269 1.898 11.912 6.280 1.00 0.76 H
-ATOM 297 N SER A 270 1.423 13.464 3.421 1.00 0.38 N
-ATOM 298 CA SER A 270 0.927 14.702 2.810 1.00 0.45 C
-ATOM 299 C SER A 270 -0.463 15.070 3.334 1.00 0.59 C
-ATOM 300 O SER A 270 -1.342 14.214 3.453 1.00 0.59 O
-ATOM 301 CB SER A 270 0.872 14.566 1.286 1.00 0.47 C
-ATOM 302 OG SER A 270 2.148 14.258 0.747 1.00 0.45 O
-ATOM 303 H SER A 270 1.631 12.696 2.843 1.00 0.39 H
-ATOM 304 HA SER A 270 1.615 15.494 3.066 1.00 0.51 H
-ATOM 305 HB2 SER A 270 0.185 13.776 1.022 1.00 0.48 H
-ATOM 306 HB3 SER A 270 0.529 15.497 0.857 1.00 0.62 H
-ATOM 307 HG SER A 270 2.369 13.332 0.951 1.00 0.38 H
-ATOM 308 N ARG A 271 -0.655 16.349 3.647 1.00 0.78 N
-ATOM 309 CA ARG A 271 -1.939 16.834 4.161 1.00 0.97 C
-ATOM 310 C ARG A 271 -3.069 16.634 3.143 1.00 0.95 C
-ATOM 311 O ARG A 271 -4.194 16.296 3.511 1.00 1.02 O
-ATOM 312 CB ARG A 271 -1.838 18.317 4.538 1.00 1.21 C
-ATOM 313 CG ARG A 271 -3.129 18.889 5.109 1.00 1.39 C
-ATOM 314 CD ARG A 271 -3.011 20.378 5.402 1.00 1.62 C
-ATOM 315 NE ARG A 271 -4.261 20.931 5.921 1.00 1.85 N
-ATOM 316 CZ ARG A 271 -4.441 22.200 6.225 1.00 2.08 C
-ATOM 317 NH1 ARG A 271 -3.473 23.065 6.066 1.00 2.17 N
-ATOM 318 NH2 ARG A 271 -5.594 22.602 6.688 1.00 2.28 N
-ATOM 319 H ARG A 271 0.083 16.983 3.533 1.00 0.83 H
-ATOM 320 HA ARG A 271 -2.171 16.265 5.049 1.00 1.00 H
-ATOM 321 HB2 ARG A 271 -1.059 18.437 5.275 1.00 1.27 H
-ATOM 322 HB3 ARG A 271 -1.578 18.884 3.656 1.00 1.26 H
-ATOM 323 HG2 ARG A 271 -3.924 18.738 4.394 1.00 1.38 H
-ATOM 324 HG3 ARG A 271 -3.365 18.370 6.026 1.00 1.43 H
-ATOM 325 HD2 ARG A 271 -2.231 20.529 6.133 1.00 1.67 H
-ATOM 326 HD3 ARG A 271 -2.752 20.893 4.488 1.00 1.65 H
-ATOM 327 HE ARG A 271 -5.007 20.312 6.049 1.00 1.87 H
-ATOM 328 HH11 ARG A 271 -2.592 22.763 5.710 1.00 2.08 H
-ATOM 329 HH12 ARG A 271 -3.617 24.023 6.299 1.00 2.37 H
-ATOM 330 HH21 ARG A 271 -6.337 21.948 6.810 1.00 2.30 H
-ATOM 331 HH22 ARG A 271 -5.730 23.562 6.920 1.00 2.46 H
-ATOM 332 N ALA A 272 -2.770 16.855 1.866 1.00 0.96 N
-ATOM 333 CA ALA A 272 -3.769 16.711 0.806 1.00 1.02 C
-ATOM 334 C ALA A 272 -3.976 15.254 0.366 1.00 0.71 C
-ATOM 335 O ALA A 272 -4.269 14.992 -0.800 1.00 0.82 O
-ATOM 336 CB ALA A 272 -3.378 17.568 -0.388 1.00 1.38 C
-ATOM 337 H ALA A 272 -1.860 17.129 1.630 1.00 1.00 H
-ATOM 338 HA ALA A 272 -4.705 17.090 1.188 1.00 1.15 H
-ATOM 339 HB1 ALA A 272 -2.349 17.879 -0.288 1.00 1.73 H
-ATOM 340 HB2 ALA A 272 -4.015 18.439 -0.429 1.00 1.66 H
-ATOM 341 HB3 ALA A 272 -3.493 16.995 -1.296 1.00 1.97 H
-ATOM 342 N SER A 273 -3.858 14.306 1.296 1.00 0.47 N
-ATOM 343 CA SER A 273 -4.065 12.891 0.970 1.00 0.37 C
-ATOM 344 C SER A 273 -5.544 12.543 0.920 1.00 0.40 C
-ATOM 345 O SER A 273 -6.009 11.622 1.593 1.00 0.60 O
-ATOM 346 CB SER A 273 -3.365 11.968 1.962 1.00 0.42 C
-ATOM 347 OG SER A 273 -1.964 12.184 1.983 1.00 0.43 O
-ATOM 348 H SER A 273 -3.645 14.564 2.219 1.00 0.57 H
-ATOM 349 HA SER A 273 -3.658 12.712 -0.017 1.00 0.57 H
-ATOM 350 HB2 SER A 273 -3.763 12.143 2.947 1.00 0.48 H
-ATOM 351 HB3 SER A 273 -3.554 10.944 1.674 1.00 0.62 H
-ATOM 352 HG SER A 273 -1.756 12.905 2.599 1.00 0.46 H
-ATOM 353 N LEU A 274 -6.269 13.256 0.079 1.00 0.42 N
-ATOM 354 CA LEU A 274 -7.682 12.995 -0.114 1.00 0.55 C
-ATOM 355 C LEU A 274 -8.001 12.823 -1.615 1.00 0.80 C
-ATOM 356 O LEU A 274 -9.000 13.361 -2.105 1.00 0.85 O
-ATOM 357 CB LEU A 274 -8.508 14.138 0.489 1.00 0.51 C
-ATOM 358 CG LEU A 274 -10.003 13.855 0.657 1.00 0.75 C
-ATOM 359 CD1 LEU A 274 -10.226 12.682 1.599 1.00 1.08 C
-ATOM 360 CD2 LEU A 274 -10.721 15.096 1.169 1.00 0.89 C
-ATOM 361 H LEU A 274 -5.829 13.956 -0.457 1.00 0.49 H
-ATOM 362 HA LEU A 274 -7.923 12.077 0.401 1.00 0.75 H
-ATOM 363 HB2 LEU A 274 -8.098 14.375 1.459 1.00 0.61 H
-ATOM 364 HB3 LEU A 274 -8.400 15.004 -0.148 1.00 0.54 H
-ATOM 365 HG LEU A 274 -10.425 13.597 -0.304 1.00 0.82 H
-ATOM 366 HD11 LEU A 274 -9.272 12.325 1.961 1.00 1.55 H
-ATOM 367 HD12 LEU A 274 -10.731 11.887 1.072 1.00 1.59 H
-ATOM 368 HD13 LEU A 274 -10.830 13.001 2.435 1.00 1.51 H
-ATOM 369 HD21 LEU A 274 -10.618 15.154 2.243 1.00 1.36 H
-ATOM 370 HD22 LEU A 274 -11.769 15.037 0.911 1.00 1.57 H
-ATOM 371 HD23 LEU A 274 -10.287 15.975 0.716 1.00 1.21 H
-ATOM 372 N PRO A 275 -7.150 12.085 -2.388 1.00 1.08 N
-ATOM 373 CA PRO A 275 -7.368 11.878 -3.834 1.00 1.45 C
-ATOM 374 C PRO A 275 -8.638 11.083 -4.144 1.00 1.66 C
-ATOM 375 O PRO A 275 -8.653 9.855 -4.049 1.00 2.20 O
-ATOM 376 CB PRO A 275 -6.131 11.086 -4.274 1.00 1.76 C
-ATOM 377 CG PRO A 275 -5.125 11.405 -3.237 1.00 1.50 C
-ATOM 378 CD PRO A 275 -5.901 11.412 -1.960 1.00 1.13 C
-ATOM 379 HA PRO A 275 -7.389 12.819 -4.360 1.00 1.46 H
-ATOM 380 HB2 PRO A 275 -6.362 10.031 -4.296 1.00 1.96 H
-ATOM 381 HB3 PRO A 275 -5.812 11.416 -5.251 1.00 1.99 H
-ATOM 382 HG2 PRO A 275 -4.351 10.651 -3.216 1.00 1.69 H
-ATOM 383 HG3 PRO A 275 -4.711 12.383 -3.427 1.00 1.47 H
-ATOM 384 HD2 PRO A 275 -6.088 10.404 -1.622 1.00 1.29 H
-ATOM 385 HD3 PRO A 275 -5.383 11.986 -1.189 1.00 0.85 H
-ATOM 386 N LYS A 276 -9.695 11.800 -4.524 1.00 1.39 N
-ATOM 387 CA LYS A 276 -10.987 11.192 -4.863 1.00 1.61 C
-ATOM 388 C LYS A 276 -11.937 12.240 -5.451 1.00 1.62 C
-ATOM 389 O LYS A 276 -11.711 13.449 -5.194 1.00 1.47 O
-ATOM 390 CB LYS A 276 -11.620 10.520 -3.628 1.00 1.64 C
-ATOM 391 CG LYS A 276 -11.711 11.412 -2.392 1.00 1.42 C
-ATOM 392 CD LYS A 276 -12.909 12.349 -2.441 1.00 1.50 C
-ATOM 393 CE LYS A 276 -12.538 13.741 -1.959 1.00 1.38 C
-ATOM 394 NZ LYS A 276 -11.462 14.337 -2.799 1.00 1.24 N
-ATOM 395 OXT LYS A 276 -12.893 11.852 -6.151 1.00 1.88 O
-ATOM 396 H LYS A 276 -9.605 12.775 -4.583 1.00 1.19 H
-ATOM 397 HA LYS A 276 -10.803 10.438 -5.614 1.00 1.86 H
-ATOM 398 HB2 LYS A 276 -12.620 10.202 -3.883 1.00 1.83 H
-ATOM 399 HB3 LYS A 276 -11.035 9.649 -3.371 1.00 1.76 H
-ATOM 400 HG2 LYS A 276 -11.796 10.785 -1.517 1.00 1.56 H
-ATOM 401 HG3 LYS A 276 -10.807 12.002 -2.323 1.00 1.20 H
-ATOM 402 HD2 LYS A 276 -13.265 12.413 -3.458 1.00 1.57 H
-ATOM 403 HD3 LYS A 276 -13.689 11.955 -1.805 1.00 1.73 H
-ATOM 404 HE2 LYS A 276 -13.412 14.372 -2.007 1.00 1.57 H
-ATOM 405 HE3 LYS A 276 -12.193 13.677 -0.938 1.00 1.40 H
-ATOM 406 HZ1 LYS A 276 -11.468 15.371 -2.711 1.00 1.37 H
-ATOM 407 HZ2 LYS A 276 -11.611 14.081 -3.811 1.00 1.28 H
-ATOM 408 HZ3 LYS A 276 -10.528 13.973 -2.498 1.00 1.13 H
-TER
-ENDMDL
-MODEL 8
-ATOM 1 N ALA A 249 1.445 -4.274 -13.954 1.00 1.31 N
-ATOM 2 CA ALA A 249 1.757 -3.868 -12.556 1.00 1.10 C
-ATOM 3 C ALA A 249 1.809 -5.083 -11.630 1.00 1.01 C
-ATOM 4 O ALA A 249 1.178 -6.104 -11.904 1.00 1.07 O
-ATOM 5 CB ALA A 249 0.733 -2.862 -12.050 1.00 1.05 C
-ATOM 6 H1 ALA A 249 2.290 -4.175 -14.553 1.00 1.62 H
-ATOM 7 H2 ALA A 249 0.688 -3.675 -14.341 1.00 1.66 H
-ATOM 8 H3 ALA A 249 1.131 -5.267 -13.974 1.00 1.50 H
-ATOM 9 HA ALA A 249 2.726 -3.388 -12.552 1.00 1.18 H
-ATOM 10 HB1 ALA A 249 1.230 -2.128 -11.431 1.00 1.46 H
-ATOM 11 HB2 ALA A 249 -0.016 -3.375 -11.466 1.00 1.48 H
-ATOM 12 HB3 ALA A 249 0.266 -2.371 -12.887 1.00 1.45 H
-ATOM 13 N PRO A 250 2.580 -4.991 -10.530 1.00 0.98 N
-ATOM 14 CA PRO A 250 2.749 -6.080 -9.550 1.00 1.04 C
-ATOM 15 C PRO A 250 1.495 -6.364 -8.703 1.00 0.95 C
-ATOM 16 O PRO A 250 1.578 -6.462 -7.477 1.00 1.02 O
-ATOM 17 CB PRO A 250 3.889 -5.567 -8.653 1.00 1.14 C
-ATOM 18 CG PRO A 250 4.508 -4.445 -9.406 1.00 1.19 C
-ATOM 19 CD PRO A 250 3.379 -3.821 -10.153 1.00 1.04 C
-ATOM 20 HA PRO A 250 3.061 -6.994 -10.033 1.00 1.17 H
-ATOM 21 HB2 PRO A 250 3.481 -5.224 -7.715 1.00 1.08 H
-ATOM 22 HB3 PRO A 250 4.597 -6.357 -8.479 1.00 1.32 H
-ATOM 23 HG2 PRO A 250 4.947 -3.735 -8.720 1.00 1.25 H
-ATOM 24 HG3 PRO A 250 5.251 -4.823 -10.091 1.00 1.36 H
-ATOM 25 HD2 PRO A 250 2.818 -3.162 -9.508 1.00 0.95 H
-ATOM 26 HD3 PRO A 250 3.739 -3.301 -11.023 1.00 1.15 H
-ATOM 27 N ALA A 251 0.345 -6.512 -9.368 1.00 0.91 N
-ATOM 28 CA ALA A 251 -0.933 -6.809 -8.704 1.00 0.96 C
-ATOM 29 C ALA A 251 -1.208 -5.912 -7.481 1.00 0.86 C
-ATOM 30 O ALA A 251 -1.231 -4.683 -7.588 1.00 0.74 O
-ATOM 31 CB ALA A 251 -0.979 -8.284 -8.314 1.00 1.19 C
-ATOM 32 H ALA A 251 0.360 -6.433 -10.350 1.00 0.94 H
-ATOM 33 HA ALA A 251 -1.717 -6.642 -9.427 1.00 1.02 H
-ATOM 34 HB1 ALA A 251 -0.798 -8.382 -7.253 1.00 1.60 H
-ATOM 35 HB2 ALA A 251 -0.221 -8.824 -8.860 1.00 1.52 H
-ATOM 36 HB3 ALA A 251 -1.951 -8.690 -8.552 1.00 1.70 H
-ATOM 37 N ARG A 252 -1.430 -6.549 -6.326 1.00 0.99 N
-ATOM 38 CA ARG A 252 -1.720 -5.845 -5.073 1.00 1.01 C
-ATOM 39 C ARG A 252 -0.608 -4.853 -4.712 1.00 0.88 C
-ATOM 40 O ARG A 252 -0.883 -3.737 -4.266 1.00 0.82 O
-ATOM 41 CB ARG A 252 -1.903 -6.863 -3.941 1.00 1.27 C
-ATOM 42 CG ARG A 252 -3.113 -6.603 -3.051 1.00 1.43 C
-ATOM 43 CD ARG A 252 -2.992 -5.293 -2.288 1.00 1.40 C
-ATOM 44 NE ARG A 252 -4.126 -5.068 -1.395 1.00 1.65 N
-ATOM 45 CZ ARG A 252 -4.265 -3.999 -0.642 1.00 1.79 C
-ATOM 46 NH1 ARG A 252 -3.372 -3.047 -0.676 1.00 1.76 N
-ATOM 47 NH2 ARG A 252 -5.303 -3.878 0.144 1.00 2.07 N
-ATOM 48 H ARG A 252 -1.406 -7.528 -6.318 1.00 1.13 H
-ATOM 49 HA ARG A 252 -2.641 -5.300 -5.206 1.00 1.01 H
-ATOM 50 HB2 ARG A 252 -2.010 -7.846 -4.374 1.00 1.39 H
-ATOM 51 HB3 ARG A 252 -1.020 -6.851 -3.319 1.00 1.32 H
-ATOM 52 HG2 ARG A 252 -3.998 -6.565 -3.669 1.00 1.48 H
-ATOM 53 HG3 ARG A 252 -3.204 -7.414 -2.343 1.00 1.62 H
-ATOM 54 HD2 ARG A 252 -2.084 -5.315 -1.704 1.00 1.44 H
-ATOM 55 HD3 ARG A 252 -2.940 -4.482 -3.000 1.00 1.29 H
-ATOM 56 HE ARG A 252 -4.814 -5.761 -1.355 1.00 1.80 H
-ATOM 57 HH11 ARG A 252 -2.580 -3.130 -1.276 1.00 1.62 H
-ATOM 58 HH12 ARG A 252 -3.481 -2.241 -0.103 1.00 1.96 H
-ATOM 59 HH21 ARG A 252 -5.992 -4.596 0.175 1.00 2.18 H
-ATOM 60 HH22 ARG A 252 -5.405 -3.064 0.712 1.00 2.22 H
-ATOM 61 N VAL A 253 0.642 -5.262 -4.920 1.00 0.91 N
-ATOM 62 CA VAL A 253 1.791 -4.404 -4.629 1.00 0.87 C
-ATOM 63 C VAL A 253 1.755 -3.153 -5.507 1.00 0.66 C
-ATOM 64 O VAL A 253 1.942 -2.034 -5.026 1.00 0.61 O
-ATOM 65 CB VAL A 253 3.126 -5.150 -4.851 1.00 1.03 C
-ATOM 66 CG1 VAL A 253 4.312 -4.275 -4.470 1.00 1.10 C
-ATOM 67 CG2 VAL A 253 3.150 -6.452 -4.065 1.00 1.26 C
-ATOM 68 H VAL A 253 0.793 -6.156 -5.290 1.00 1.01 H
-ATOM 69 HA VAL A 253 1.732 -4.107 -3.594 1.00 0.93 H
-ATOM 70 HB VAL A 253 3.210 -5.390 -5.902 1.00 1.01 H
-ATOM 71 HG11 VAL A 253 5.194 -4.615 -4.993 1.00 1.33 H
-ATOM 72 HG12 VAL A 253 4.479 -4.340 -3.405 1.00 1.53 H
-ATOM 73 HG13 VAL A 253 4.106 -3.250 -4.740 1.00 1.35 H
-ATOM 74 HG21 VAL A 253 4.172 -6.778 -3.939 1.00 1.74 H
-ATOM 75 HG22 VAL A 253 2.595 -7.207 -4.601 1.00 1.62 H
-ATOM 76 HG23 VAL A 253 2.701 -6.296 -3.096 1.00 1.63 H
-ATOM 77 N GLY A 254 1.479 -3.358 -6.794 1.00 0.60 N
-ATOM 78 CA GLY A 254 1.388 -2.248 -7.730 1.00 0.47 C
-ATOM 79 C GLY A 254 0.279 -1.278 -7.359 1.00 0.36 C
-ATOM 80 O GLY A 254 0.489 -0.065 -7.332 1.00 0.36 O
-ATOM 81 H GLY A 254 1.316 -4.274 -7.104 1.00 0.68 H
-ATOM 82 HA2 GLY A 254 2.329 -1.718 -7.739 1.00 0.53 H
-ATOM 83 HA3 GLY A 254 1.195 -2.639 -8.718 1.00 0.51 H
-ATOM 84 N LEU A 255 -0.897 -1.818 -7.045 1.00 0.41 N
-ATOM 85 CA LEU A 255 -2.036 -0.996 -6.644 1.00 0.48 C
-ATOM 86 C LEU A 255 -1.717 -0.240 -5.351 1.00 0.48 C
-ATOM 87 O LEU A 255 -2.039 0.942 -5.208 1.00 0.52 O
-ATOM 88 CB LEU A 255 -3.274 -1.858 -6.454 1.00 0.67 C
-ATOM 89 CG LEU A 255 -4.578 -1.075 -6.494 1.00 0.84 C
-ATOM 90 CD1 LEU A 255 -4.772 -0.446 -7.867 1.00 0.91 C
-ATOM 91 CD2 LEU A 255 -5.751 -1.968 -6.136 1.00 1.05 C
-ATOM 92 H LEU A 255 -0.996 -2.797 -7.069 1.00 0.48 H
-ATOM 93 HA LEU A 255 -2.236 -0.279 -7.426 1.00 0.51 H
-ATOM 94 HB2 LEU A 255 -3.295 -2.606 -7.234 1.00 0.71 H
-ATOM 95 HB3 LEU A 255 -3.204 -2.355 -5.499 1.00 0.73 H
-ATOM 96 HG LEU A 255 -4.524 -0.276 -5.772 1.00 0.84 H
-ATOM 97 HD11 LEU A 255 -4.085 0.380 -7.984 1.00 1.40 H
-ATOM 98 HD12 LEU A 255 -5.785 -0.087 -7.960 1.00 1.36 H
-ATOM 99 HD13 LEU A 255 -4.578 -1.184 -8.631 1.00 1.40 H
-ATOM 100 HD21 LEU A 255 -5.891 -2.704 -6.913 1.00 1.58 H
-ATOM 101 HD22 LEU A 255 -6.642 -1.369 -6.041 1.00 1.44 H
-ATOM 102 HD23 LEU A 255 -5.549 -2.467 -5.200 1.00 1.49 H
-ATOM 103 N GLY A 256 -1.063 -0.932 -4.419 1.00 0.54 N
-ATOM 104 CA GLY A 256 -0.689 -0.321 -3.157 1.00 0.61 C
-ATOM 105 C GLY A 256 0.271 0.845 -3.333 1.00 0.49 C
-ATOM 106 O GLY A 256 0.007 1.944 -2.854 1.00 0.51 O
-ATOM 107 H GLY A 256 -0.823 -1.870 -4.597 1.00 0.58 H
-ATOM 108 HA2 GLY A 256 -1.582 0.034 -2.664 1.00 0.71 H
-ATOM 109 HA3 GLY A 256 -0.219 -1.068 -2.534 1.00 0.72 H
-ATOM 110 N ILE A 257 1.383 0.621 -4.034 1.00 0.45 N
-ATOM 111 CA ILE A 257 2.356 1.692 -4.256 1.00 0.46 C
-ATOM 112 C ILE A 257 1.759 2.824 -5.102 1.00 0.40 C
-ATOM 113 O ILE A 257 2.052 3.996 -4.870 1.00 0.49 O
-ATOM 114 CB ILE A 257 3.680 1.178 -4.881 1.00 0.60 C
-ATOM 115 CG1 ILE A 257 3.454 0.499 -6.238 1.00 0.60 C
-ATOM 116 CG2 ILE A 257 4.373 0.220 -3.925 1.00 0.81 C
-ATOM 117 CD1 ILE A 257 3.687 1.408 -7.427 1.00 0.70 C
-ATOM 118 H ILE A 257 1.551 -0.276 -4.410 1.00 0.50 H
-ATOM 119 HA ILE A 257 2.594 2.101 -3.283 1.00 0.53 H
-ATOM 120 HB ILE A 257 4.330 2.030 -5.022 1.00 0.69 H
-ATOM 121 HG12 ILE A 257 4.126 -0.341 -6.328 1.00 0.75 H
-ATOM 122 HG13 ILE A 257 2.434 0.142 -6.287 1.00 0.58 H
-ATOM 123 HG21 ILE A 257 4.053 0.428 -2.914 1.00 1.33 H
-ATOM 124 HG22 ILE A 257 5.443 0.351 -3.997 1.00 1.34 H
-ATOM 125 HG23 ILE A 257 4.116 -0.796 -4.183 1.00 1.35 H
-ATOM 126 HD11 ILE A 257 3.052 2.277 -7.347 1.00 1.21 H
-ATOM 127 HD12 ILE A 257 3.456 0.876 -8.339 1.00 1.28 H
-ATOM 128 HD13 ILE A 257 4.722 1.718 -7.445 1.00 1.29 H
-ATOM 129 N THR A 258 0.905 2.474 -6.067 1.00 0.39 N
-ATOM 130 CA THR A 258 0.263 3.477 -6.918 1.00 0.52 C
-ATOM 131 C THR A 258 -0.661 4.375 -6.096 1.00 0.59 C
-ATOM 132 O THR A 258 -0.607 5.602 -6.201 1.00 0.71 O
-ATOM 133 CB THR A 258 -0.557 2.829 -8.058 1.00 0.61 C
-ATOM 134 OG1 THR A 258 0.288 2.001 -8.866 1.00 0.62 O
-ATOM 135 CG2 THR A 258 -1.214 3.889 -8.934 1.00 0.86 C
-ATOM 136 H THR A 258 0.695 1.521 -6.202 1.00 0.36 H
-ATOM 137 HA THR A 258 1.039 4.086 -7.360 1.00 0.61 H
-ATOM 138 HB THR A 258 -1.331 2.218 -7.619 1.00 0.61 H
-ATOM 139 HG1 THR A 258 0.437 1.157 -8.414 1.00 0.54 H
-ATOM 140 HG21 THR A 258 -2.274 3.693 -9.001 1.00 1.38 H
-ATOM 141 HG22 THR A 258 -0.779 3.860 -9.922 1.00 1.44 H
-ATOM 142 HG23 THR A 258 -1.057 4.865 -8.499 1.00 1.29 H
-ATOM 143 N THR A 259 -1.498 3.760 -5.264 1.00 0.60 N
-ATOM 144 CA THR A 259 -2.421 4.515 -4.416 1.00 0.74 C
-ATOM 145 C THR A 259 -1.661 5.362 -3.400 1.00 0.70 C
-ATOM 146 O THR A 259 -1.960 6.545 -3.228 1.00 0.84 O
-ATOM 147 CB THR A 259 -3.425 3.600 -3.685 1.00 0.83 C
-ATOM 148 OG1 THR A 259 -2.740 2.525 -3.031 1.00 0.78 O
-ATOM 149 CG2 THR A 259 -4.450 3.038 -4.659 1.00 0.97 C
-ATOM 150 H THR A 259 -1.487 2.777 -5.207 1.00 0.55 H
-ATOM 151 HA THR A 259 -2.981 5.178 -5.061 1.00 0.86 H
-ATOM 152 HB THR A 259 -3.945 4.187 -2.941 1.00 0.93 H
-ATOM 153 HG1 THR A 259 -2.533 1.836 -3.676 1.00 0.96 H
-ATOM 154 HG21 THR A 259 -4.003 2.238 -5.230 1.00 1.41 H
-ATOM 155 HG22 THR A 259 -4.777 3.820 -5.330 1.00 1.45 H
-ATOM 156 HG23 THR A 259 -5.299 2.658 -4.110 1.00 1.45 H
-ATOM 157 N VAL A 260 -0.662 4.764 -2.746 1.00 0.56 N
-ATOM 158 CA VAL A 260 0.151 5.489 -1.768 1.00 0.56 C
-ATOM 159 C VAL A 260 0.843 6.694 -2.419 1.00 0.56 C
-ATOM 160 O VAL A 260 0.779 7.808 -1.895 1.00 0.67 O
-ATOM 161 CB VAL A 260 1.208 4.572 -1.116 1.00 0.56 C
-ATOM 162 CG1 VAL A 260 2.152 5.374 -0.232 1.00 0.69 C
-ATOM 163 CG2 VAL A 260 0.532 3.474 -0.309 1.00 0.72 C
-ATOM 164 H VAL A 260 -0.460 3.815 -2.935 1.00 0.50 H
-ATOM 165 HA VAL A 260 -0.511 5.848 -0.992 1.00 0.68 H
-ATOM 166 HB VAL A 260 1.789 4.109 -1.900 1.00 0.51 H
-ATOM 167 HG11 VAL A 260 2.277 6.363 -0.646 1.00 1.22 H
-ATOM 168 HG12 VAL A 260 3.109 4.878 -0.187 1.00 1.26 H
-ATOM 169 HG13 VAL A 260 1.737 5.448 0.762 1.00 1.28 H
-ATOM 170 HG21 VAL A 260 -0.517 3.705 -0.198 1.00 1.32 H
-ATOM 171 HG22 VAL A 260 0.991 3.406 0.664 1.00 1.20 H
-ATOM 172 HG23 VAL A 260 0.640 2.531 -0.826 1.00 1.19 H
-ATOM 173 N LEU A 261 1.478 6.467 -3.574 1.00 0.53 N
-ATOM 174 CA LEU A 261 2.156 7.540 -4.304 1.00 0.66 C
-ATOM 175 C LEU A 261 1.156 8.614 -4.744 1.00 0.81 C
-ATOM 176 O LEU A 261 1.452 9.808 -4.709 1.00 0.94 O
-ATOM 177 CB LEU A 261 2.887 6.979 -5.522 1.00 0.74 C
-ATOM 178 CG LEU A 261 3.856 7.954 -6.188 1.00 0.96 C
-ATOM 179 CD1 LEU A 261 4.864 8.474 -5.173 1.00 1.08 C
-ATOM 180 CD2 LEU A 261 4.563 7.280 -7.350 1.00 1.09 C
-ATOM 181 H LEU A 261 1.479 5.557 -3.950 1.00 0.48 H
-ATOM 182 HA LEU A 261 2.883 7.989 -3.644 1.00 0.68 H
-ATOM 183 HB2 LEU A 261 3.440 6.103 -5.214 1.00 0.73 H
-ATOM 184 HB3 LEU A 261 2.150 6.681 -6.254 1.00 0.80 H
-ATOM 185 HG LEU A 261 3.301 8.798 -6.573 1.00 1.08 H
-ATOM 186 HD11 LEU A 261 4.339 8.920 -4.340 1.00 1.43 H
-ATOM 187 HD12 LEU A 261 5.496 9.215 -5.638 1.00 1.65 H
-ATOM 188 HD13 LEU A 261 5.472 7.655 -4.818 1.00 1.44 H
-ATOM 189 HD21 LEU A 261 4.356 6.221 -7.330 1.00 1.61 H
-ATOM 190 HD22 LEU A 261 5.627 7.442 -7.265 1.00 1.46 H
-ATOM 191 HD23 LEU A 261 4.207 7.700 -8.278 1.00 1.48 H
-ATOM 192 N THR A 262 -0.034 8.173 -5.151 1.00 0.84 N
-ATOM 193 CA THR A 262 -1.093 9.089 -5.584 1.00 1.05 C
-ATOM 194 C THR A 262 -1.553 9.979 -4.426 1.00 1.10 C
-ATOM 195 O THR A 262 -1.649 11.198 -4.565 1.00 1.25 O
-ATOM 196 CB THR A 262 -2.312 8.317 -6.136 1.00 1.14 C
-ATOM 197 OG1 THR A 262 -1.931 7.542 -7.281 1.00 1.14 O
-ATOM 198 CG2 THR A 262 -3.442 9.263 -6.516 1.00 1.39 C
-ATOM 199 H THR A 262 -0.208 7.204 -5.153 1.00 0.76 H
-ATOM 200 HA THR A 262 -0.696 9.712 -6.373 1.00 1.15 H
-ATOM 201 HB THR A 262 -2.670 7.648 -5.365 1.00 1.07 H
-ATOM 202 HG1 THR A 262 -1.418 6.771 -6.991 1.00 1.00 H
-ATOM 203 HG21 THR A 262 -4.054 9.462 -5.648 1.00 1.84 H
-ATOM 204 HG22 THR A 262 -4.046 8.808 -7.286 1.00 1.73 H
-ATOM 205 HG23 THR A 262 -3.027 10.189 -6.886 1.00 1.75 H
-ATOM 206 N MET A 263 -1.839 9.356 -3.284 1.00 1.01 N
-ATOM 207 CA MET A 263 -2.291 10.089 -2.098 1.00 1.10 C
-ATOM 208 C MET A 263 -1.190 10.995 -1.525 1.00 1.03 C
-ATOM 209 O MET A 263 -1.426 12.174 -1.262 1.00 1.17 O
-ATOM 210 CB MET A 263 -2.771 9.115 -1.019 1.00 1.09 C
-ATOM 211 CG MET A 263 -3.977 8.288 -1.436 1.00 1.22 C
-ATOM 212 SD MET A 263 -4.563 7.203 -0.120 1.00 1.32 S
-ATOM 213 CE MET A 263 -3.126 6.165 0.139 1.00 1.21 C
-ATOM 214 H MET A 263 -1.746 8.376 -3.239 1.00 0.92 H
-ATOM 215 HA MET A 263 -3.122 10.709 -2.397 1.00 1.26 H
-ATOM 216 HB2 MET A 263 -1.965 8.440 -0.776 1.00 0.97 H
-ATOM 217 HB3 MET A 263 -3.037 9.678 -0.137 1.00 1.18 H
-ATOM 218 HG2 MET A 263 -4.776 8.956 -1.716 1.00 1.36 H
-ATOM 219 HG3 MET A 263 -3.702 7.681 -2.287 1.00 1.20 H
-ATOM 220 HE1 MET A 263 -2.236 6.777 0.134 1.00 1.68 H
-ATOM 221 HE2 MET A 263 -3.063 5.433 -0.652 1.00 1.44 H
-ATOM 222 HE3 MET A 263 -3.212 5.662 1.090 1.00 1.60 H
-ATOM 223 N THR A 264 0.006 10.445 -1.314 1.00 0.88 N
-ATOM 224 CA THR A 264 1.110 11.223 -0.758 1.00 0.88 C
-ATOM 225 C THR A 264 2.380 11.120 -1.609 1.00 0.83 C
-ATOM 226 O THR A 264 2.777 10.035 -2.039 1.00 0.79 O
-ATOM 227 CB THR A 264 1.417 10.781 0.688 1.00 0.86 C
-ATOM 228 OG1 THR A 264 2.393 11.649 1.286 1.00 0.97 O
-ATOM 229 CG2 THR A 264 1.918 9.342 0.742 1.00 0.80 C
-ATOM 230 H THR A 264 0.146 9.496 -1.519 1.00 0.81 H
-ATOM 231 HA THR A 264 0.798 12.258 -0.729 1.00 1.00 H
-ATOM 232 HB THR A 264 0.499 10.837 1.250 1.00 0.92 H
-ATOM 233 HG1 THR A 264 1.988 12.135 2.014 1.00 1.14 H
-ATOM 234 HG21 THR A 264 2.995 9.332 0.663 1.00 1.30 H
-ATOM 235 HG22 THR A 264 1.492 8.784 -0.080 1.00 1.30 H
-ATOM 236 HG23 THR A 264 1.620 8.890 1.676 1.00 1.17 H
-ATOM 237 N THR A 265 3.023 12.261 -1.848 1.00 0.92 N
-ATOM 238 CA THR A 265 4.253 12.297 -2.643 1.00 0.99 C
-ATOM 239 C THR A 265 5.468 11.814 -1.834 1.00 0.92 C
-ATOM 240 O THR A 265 6.432 12.555 -1.611 1.00 1.05 O
-ATOM 241 CB THR A 265 4.527 13.704 -3.228 1.00 1.20 C
-ATOM 242 OG1 THR A 265 5.827 13.743 -3.834 1.00 1.31 O
-ATOM 243 CG2 THR A 265 4.420 14.787 -2.159 1.00 1.28 C
-ATOM 244 H THR A 265 2.666 13.095 -1.477 1.00 1.01 H
-ATOM 245 HA THR A 265 4.113 11.618 -3.472 1.00 1.01 H
-ATOM 246 HB THR A 265 3.786 13.903 -3.990 1.00 1.29 H
-ATOM 247 HG1 THR A 265 6.491 13.494 -3.177 1.00 1.49 H
-ATOM 248 HG21 THR A 265 3.698 15.529 -2.469 1.00 1.66 H
-ATOM 249 HG22 THR A 265 5.383 15.257 -2.024 1.00 1.64 H
-ATOM 250 HG23 THR A 265 4.103 14.343 -1.227 1.00 1.67 H
-ATOM 251 N GLN A 266 5.405 10.559 -1.394 1.00 0.83 N
-ATOM 252 CA GLN A 266 6.476 9.943 -0.610 1.00 0.87 C
-ATOM 253 C GLN A 266 7.766 9.810 -1.432 1.00 0.99 C
-ATOM 254 O GLN A 266 7.974 8.815 -2.127 1.00 1.22 O
-ATOM 255 CB GLN A 266 6.027 8.562 -0.118 1.00 0.93 C
-ATOM 256 CG GLN A 266 7.022 7.898 0.821 1.00 1.06 C
-ATOM 257 CD GLN A 266 7.048 8.529 2.200 1.00 0.89 C
-ATOM 258 OE1 GLN A 266 8.061 8.480 2.892 1.00 1.14 O
-ATOM 259 NE2 GLN A 266 5.928 9.099 2.624 1.00 1.29 N
-ATOM 260 H GLN A 266 4.603 10.027 -1.607 1.00 0.82 H
-ATOM 261 HA GLN A 266 6.670 10.571 0.244 1.00 0.89 H
-ATOM 262 HB2 GLN A 266 5.087 8.669 0.405 1.00 0.95 H
-ATOM 263 HB3 GLN A 266 5.882 7.918 -0.972 1.00 0.98 H
-ATOM 264 HG2 GLN A 266 6.758 6.857 0.926 1.00 1.49 H
-ATOM 265 HG3 GLN A 266 8.008 7.976 0.387 1.00 1.51 H
-ATOM 266 HE21 GLN A 266 5.147 9.083 2.033 1.00 1.49 H
-ATOM 267 HE22 GLN A 266 5.927 9.516 3.516 1.00 1.66 H
-ATOM 268 N SER A 267 8.634 10.815 -1.348 1.00 0.98 N
-ATOM 269 CA SER A 267 9.900 10.805 -2.089 1.00 1.15 C
-ATOM 270 C SER A 267 10.953 9.927 -1.407 1.00 1.08 C
-ATOM 271 O SER A 267 12.105 10.340 -1.251 1.00 1.16 O
-ATOM 272 CB SER A 267 10.436 12.231 -2.249 1.00 1.26 C
-ATOM 273 OG SER A 267 10.707 12.822 -0.989 1.00 1.15 O
-ATOM 274 H SER A 267 8.418 11.586 -0.775 1.00 0.95 H
-ATOM 275 HA SER A 267 9.699 10.398 -3.069 1.00 1.40 H
-ATOM 276 HB2 SER A 267 11.349 12.207 -2.825 1.00 1.41 H
-ATOM 277 HB3 SER A 267 9.702 12.832 -2.763 1.00 1.44 H
-ATOM 278 HG SER A 267 11.656 12.949 -0.893 1.00 1.36 H
-ATOM 279 N SER A 268 10.544 8.714 -1.014 1.00 1.29 N
-ATOM 280 CA SER A 268 11.421 7.734 -0.348 1.00 1.54 C
-ATOM 281 C SER A 268 11.855 8.192 1.055 1.00 1.49 C
-ATOM 282 O SER A 268 11.659 7.474 2.036 1.00 1.96 O
-ATOM 283 CB SER A 268 12.655 7.431 -1.211 1.00 1.68 C
-ATOM 284 OG SER A 268 13.508 6.487 -0.583 1.00 1.92 O
-ATOM 285 H SER A 268 9.607 8.463 -1.187 1.00 1.46 H
-ATOM 286 HA SER A 268 10.850 6.822 -0.239 1.00 1.98 H
-ATOM 287 HB2 SER A 268 12.337 7.028 -2.162 1.00 2.05 H
-ATOM 288 HB3 SER A 268 13.208 8.344 -1.376 1.00 1.48 H
-ATOM 289 HG SER A 268 12.986 5.881 -0.051 1.00 1.97 H
-ATOM 290 N GLY A 269 12.440 9.383 1.148 1.00 1.35 N
-ATOM 291 CA GLY A 269 12.881 9.902 2.434 1.00 1.81 C
-ATOM 292 C GLY A 269 12.096 11.128 2.882 1.00 1.48 C
-ATOM 293 O GLY A 269 12.641 12.007 3.552 1.00 1.52 O
-ATOM 294 H GLY A 269 12.572 9.917 0.327 1.00 1.27 H
-ATOM 295 HA2 GLY A 269 12.775 9.127 3.179 1.00 2.34 H
-ATOM 296 HA3 GLY A 269 13.925 10.169 2.357 1.00 2.11 H
-ATOM 297 N SER A 270 10.818 11.190 2.514 1.00 1.27 N
-ATOM 298 CA SER A 270 9.966 12.324 2.885 1.00 0.98 C
-ATOM 299 C SER A 270 9.152 12.037 4.151 1.00 1.04 C
-ATOM 300 O SER A 270 9.023 12.904 5.018 1.00 1.01 O
-ATOM 301 CB SER A 270 9.017 12.685 1.736 1.00 0.75 C
-ATOM 302 OG SER A 270 8.060 11.660 1.512 1.00 0.89 O
-ATOM 303 H SER A 270 10.440 10.460 1.979 1.00 1.38 H
-ATOM 304 HA SER A 270 10.612 13.168 3.078 1.00 1.06 H
-ATOM 305 HB2 SER A 270 8.495 13.600 1.975 1.00 0.69 H
-ATOM 306 HB3 SER A 270 9.591 12.825 0.832 1.00 0.79 H
-ATOM 307 HG SER A 270 7.162 12.038 1.537 1.00 0.94 H
-ATOM 308 N ARG A 271 8.590 10.824 4.231 1.00 1.28 N
-ATOM 309 CA ARG A 271 7.760 10.394 5.365 1.00 1.48 C
-ATOM 310 C ARG A 271 6.436 11.160 5.418 1.00 1.36 C
-ATOM 311 O ARG A 271 5.390 10.624 5.054 1.00 1.57 O
-ATOM 312 CB ARG A 271 8.517 10.540 6.685 1.00 1.66 C
-ATOM 313 CG ARG A 271 9.631 9.522 6.850 1.00 2.01 C
-ATOM 314 CD ARG A 271 10.561 9.896 7.994 1.00 2.18 C
-ATOM 315 NE ARG A 271 11.230 11.177 7.765 1.00 2.07 N
-ATOM 316 CZ ARG A 271 12.155 11.379 6.847 1.00 2.15 C
-ATOM 317 NH1 ARG A 271 12.579 10.391 6.103 1.00 2.31 N
-ATOM 318 NH2 ARG A 271 12.671 12.570 6.689 1.00 2.18 N
-ATOM 319 H ARG A 271 8.723 10.194 3.485 1.00 1.39 H
-ATOM 320 HA ARG A 271 7.539 9.347 5.215 1.00 1.72 H
-ATOM 321 HB2 ARG A 271 8.951 11.528 6.731 1.00 1.53 H
-ATOM 322 HB3 ARG A 271 7.823 10.421 7.503 1.00 1.76 H
-ATOM 323 HG2 ARG A 271 9.194 8.556 7.054 1.00 2.23 H
-ATOM 324 HG3 ARG A 271 10.199 9.477 5.934 1.00 1.99 H
-ATOM 325 HD2 ARG A 271 9.983 9.960 8.905 1.00 2.22 H
-ATOM 326 HD3 ARG A 271 11.309 9.123 8.098 1.00 2.46 H
-ATOM 327 HE ARG A 271 10.957 11.932 8.320 1.00 2.01 H
-ATOM 328 HH11 ARG A 271 12.203 9.477 6.229 1.00 2.38 H
-ATOM 329 HH12 ARG A 271 13.267 10.557 5.401 1.00 2.42 H
-ATOM 330 HH21 ARG A 271 12.366 13.326 7.263 1.00 2.14 H
-ATOM 331 HH22 ARG A 271 13.367 12.722 5.990 1.00 2.31 H
-ATOM 332 N ALA A 272 6.496 12.415 5.848 1.00 1.16 N
-ATOM 333 CA ALA A 272 5.310 13.263 5.928 1.00 1.18 C
-ATOM 334 C ALA A 272 4.915 13.788 4.548 1.00 1.11 C
-ATOM 335 O ALA A 272 3.731 13.880 4.224 1.00 1.41 O
-ATOM 336 CB ALA A 272 5.556 14.420 6.886 1.00 1.15 C
-ATOM 337 H ALA A 272 7.369 12.786 6.105 1.00 1.13 H
-ATOM 338 HA ALA A 272 4.501 12.666 6.321 1.00 1.44 H
-ATOM 339 HB1 ALA A 272 6.566 14.368 7.265 1.00 1.71 H
-ATOM 340 HB2 ALA A 272 4.860 14.363 7.710 1.00 1.54 H
-ATOM 341 HB3 ALA A 272 5.417 15.355 6.364 1.00 1.26 H
-ATOM 342 N SER A 273 5.927 14.131 3.745 1.00 0.84 N
-ATOM 343 CA SER A 273 5.724 14.657 2.385 1.00 0.89 C
-ATOM 344 C SER A 273 5.038 16.010 2.394 1.00 1.10 C
-ATOM 345 O SER A 273 4.002 16.201 1.758 1.00 1.43 O
-ATOM 346 CB SER A 273 4.923 13.697 1.507 1.00 1.18 C
-ATOM 347 OG SER A 273 5.492 12.397 1.487 1.00 1.25 O
-ATOM 348 H SER A 273 6.843 14.025 4.079 1.00 0.75 H
-ATOM 349 HA SER A 273 6.698 14.799 1.937 1.00 0.75 H
-ATOM 350 HB2 SER A 273 3.916 13.631 1.884 1.00 1.38 H
-ATOM 351 HB3 SER A 273 4.902 14.082 0.497 1.00 1.30 H
-ATOM 352 HG SER A 273 4.792 11.751 1.328 1.00 1.72 H
-ATOM 353 N LEU A 274 5.655 16.967 3.073 1.00 1.07 N
-ATOM 354 CA LEU A 274 5.132 18.330 3.107 1.00 1.43 C
-ATOM 355 C LEU A 274 6.160 19.358 2.598 1.00 1.52 C
-ATOM 356 O LEU A 274 6.340 20.414 3.211 1.00 1.74 O
-ATOM 357 CB LEU A 274 4.685 18.701 4.514 1.00 1.61 C
-ATOM 358 CG LEU A 274 3.199 18.489 4.796 1.00 2.05 C
-ATOM 359 CD1 LEU A 274 2.346 19.177 3.739 1.00 2.49 C
-ATOM 360 CD2 LEU A 274 2.855 17.010 4.883 1.00 2.27 C
-ATOM 361 H LEU A 274 6.500 16.765 3.524 1.00 0.90 H
-ATOM 362 HA LEU A 274 4.272 18.359 2.455 1.00 1.63 H
-ATOM 363 HB2 LEU A 274 5.255 18.111 5.218 1.00 1.49 H
-ATOM 364 HB3 LEU A 274 4.913 19.743 4.677 1.00 1.76 H
-ATOM 365 HG LEU A 274 2.971 18.939 5.739 1.00 2.15 H
-ATOM 366 HD11 LEU A 274 2.046 18.455 2.992 1.00 2.79 H
-ATOM 367 HD12 LEU A 274 2.920 19.962 3.270 1.00 2.81 H
-ATOM 368 HD13 LEU A 274 1.468 19.601 4.204 1.00 2.85 H
-ATOM 369 HD21 LEU A 274 1.783 16.888 4.839 1.00 2.56 H
-ATOM 370 HD22 LEU A 274 3.225 16.607 5.813 1.00 2.59 H
-ATOM 371 HD23 LEU A 274 3.311 16.485 4.056 1.00 2.57 H
-ATOM 372 N PRO A 275 6.854 19.074 1.467 1.00 1.45 N
-ATOM 373 CA PRO A 275 7.857 19.995 0.895 1.00 1.67 C
-ATOM 374 C PRO A 275 7.282 21.383 0.590 1.00 2.04 C
-ATOM 375 O PRO A 275 7.905 22.411 0.880 1.00 2.26 O
-ATOM 376 CB PRO A 275 8.264 19.300 -0.407 1.00 1.64 C
-ATOM 377 CG PRO A 275 8.007 17.866 -0.135 1.00 1.31 C
-ATOM 378 CD PRO A 275 6.734 17.847 0.655 1.00 1.25 C
-ATOM 379 HA PRO A 275 8.717 20.087 1.536 1.00 1.67 H
-ATOM 380 HB2 PRO A 275 7.658 19.668 -1.223 1.00 1.81 H
-ATOM 381 HB3 PRO A 275 9.308 19.486 -0.610 1.00 1.76 H
-ATOM 382 HG2 PRO A 275 7.895 17.320 -1.060 1.00 1.35 H
-ATOM 383 HG3 PRO A 275 8.815 17.462 0.455 1.00 1.23 H
-ATOM 384 HD2 PRO A 275 5.873 17.890 0.007 1.00 1.42 H
-ATOM 385 HD3 PRO A 275 6.682 16.963 1.295 1.00 1.04 H
-ATOM 386 N LYS A 276 6.080 21.381 0.014 1.00 2.19 N
-ATOM 387 CA LYS A 276 5.353 22.601 -0.348 1.00 2.58 C
-ATOM 388 C LYS A 276 3.855 22.305 -0.438 1.00 2.73 C
-ATOM 389 O LYS A 276 3.488 21.108 -0.376 1.00 2.57 O
-ATOM 390 CB LYS A 276 5.840 23.163 -1.693 1.00 2.82 C
-ATOM 391 CG LYS A 276 7.191 23.858 -1.626 1.00 2.95 C
-ATOM 392 CD LYS A 276 7.128 25.119 -0.780 1.00 3.20 C
-ATOM 393 CE LYS A 276 8.518 25.661 -0.494 1.00 3.34 C
-ATOM 394 NZ LYS A 276 9.316 24.714 0.335 1.00 3.07 N
-ATOM 395 OXT LYS A 276 3.065 23.263 -0.574 1.00 3.09 O
-ATOM 396 H LYS A 276 5.652 20.517 -0.169 1.00 2.07 H
-ATOM 397 HA LYS A 276 5.521 23.333 0.428 1.00 2.68 H
-ATOM 398 HB2 LYS A 276 5.915 22.351 -2.400 1.00 2.72 H
-ATOM 399 HB3 LYS A 276 5.113 23.874 -2.053 1.00 3.10 H
-ATOM 400 HG2 LYS A 276 7.911 23.182 -1.191 1.00 2.73 H
-ATOM 401 HG3 LYS A 276 7.500 24.121 -2.628 1.00 3.15 H
-ATOM 402 HD2 LYS A 276 6.562 25.871 -1.308 1.00 3.47 H
-ATOM 403 HD3 LYS A 276 6.642 24.890 0.157 1.00 3.08 H
-ATOM 404 HE2 LYS A 276 9.028 25.821 -1.433 1.00 3.49 H
-ATOM 405 HE3 LYS A 276 8.427 26.600 0.031 1.00 3.55 H
-ATOM 406 HZ1 LYS A 276 8.730 23.887 0.601 1.00 2.88 H
-ATOM 407 HZ2 LYS A 276 9.642 25.186 1.202 1.00 3.26 H
-ATOM 408 HZ3 LYS A 276 10.143 24.382 -0.198 1.00 3.15 H
-TER
-ENDMDL
-MODEL 9
-ATOM 1 N ALA A 249 1.028 -12.240 -10.406 1.00 1.89 N
-ATOM 2 CA ALA A 249 1.678 -10.906 -10.514 1.00 1.98 C
-ATOM 3 C ALA A 249 1.362 -10.048 -9.290 1.00 1.58 C
-ATOM 4 O ALA A 249 0.329 -10.236 -8.646 1.00 1.21 O
-ATOM 5 CB ALA A 249 1.240 -10.200 -11.790 1.00 2.17 C
-ATOM 6 H1 ALA A 249 0.503 -12.455 -11.277 1.00 1.90 H
-ATOM 7 H2 ALA A 249 0.365 -12.249 -9.603 1.00 1.79 H
-ATOM 8 H3 ALA A 249 1.747 -12.976 -10.254 1.00 2.21 H
-ATOM 9 HA ALA A 249 2.747 -11.055 -10.566 1.00 2.34 H
-ATOM 10 HB1 ALA A 249 2.050 -10.217 -12.505 1.00 2.46 H
-ATOM 11 HB2 ALA A 249 0.985 -9.174 -11.563 1.00 2.48 H
-ATOM 12 HB3 ALA A 249 0.381 -10.703 -12.205 1.00 2.44 H
-ATOM 13 N PRO A 250 2.256 -9.102 -8.947 1.00 1.75 N
-ATOM 14 CA PRO A 250 2.100 -8.205 -7.789 1.00 1.53 C
-ATOM 15 C PRO A 250 0.998 -7.150 -7.972 1.00 1.04 C
-ATOM 16 O PRO A 250 1.256 -5.949 -7.864 1.00 1.05 O
-ATOM 17 CB PRO A 250 3.473 -7.515 -7.694 1.00 1.99 C
-ATOM 18 CG PRO A 250 4.378 -8.321 -8.551 1.00 2.47 C
-ATOM 19 CD PRO A 250 3.510 -8.827 -9.650 1.00 2.29 C
-ATOM 20 HA PRO A 250 1.913 -8.759 -6.881 1.00 1.57 H
-ATOM 21 HB2 PRO A 250 3.392 -6.505 -8.062 1.00 1.88 H
-ATOM 22 HB3 PRO A 250 3.806 -7.510 -6.672 1.00 2.15 H
-ATOM 23 HG2 PRO A 250 5.167 -7.697 -8.947 1.00 2.77 H
-ATOM 24 HG3 PRO A 250 4.790 -9.144 -7.987 1.00 2.70 H
-ATOM 25 HD2 PRO A 250 3.370 -8.064 -10.400 1.00 2.34 H
-ATOM 26 HD3 PRO A 250 3.918 -9.725 -10.077 1.00 2.59 H
-ATOM 27 N ALA A 251 -0.228 -7.600 -8.250 1.00 0.75 N
-ATOM 28 CA ALA A 251 -1.359 -6.689 -8.448 1.00 0.73 C
-ATOM 29 C ALA A 251 -1.587 -5.808 -7.218 1.00 0.65 C
-ATOM 30 O ALA A 251 -1.741 -4.592 -7.334 1.00 0.72 O
-ATOM 31 CB ALA A 251 -2.622 -7.473 -8.780 1.00 1.05 C
-ATOM 32 H ALA A 251 -0.372 -8.571 -8.326 1.00 0.80 H
-ATOM 33 HA ALA A 251 -1.128 -6.055 -9.292 1.00 1.00 H
-ATOM 34 HB1 ALA A 251 -2.384 -8.523 -8.856 1.00 1.56 H
-ATOM 35 HB2 ALA A 251 -3.023 -7.127 -9.720 1.00 1.59 H
-ATOM 36 HB3 ALA A 251 -3.354 -7.324 -8.001 1.00 1.41 H
-ATOM 37 N ARG A 252 -1.584 -6.425 -6.035 1.00 0.90 N
-ATOM 38 CA ARG A 252 -1.769 -5.688 -4.787 1.00 1.28 C
-ATOM 39 C ARG A 252 -0.606 -4.722 -4.551 1.00 1.12 C
-ATOM 40 O ARG A 252 -0.815 -3.566 -4.191 1.00 1.14 O
-ATOM 41 CB ARG A 252 -1.919 -6.656 -3.598 1.00 1.87 C
-ATOM 42 CG ARG A 252 -1.853 -5.984 -2.226 1.00 2.35 C
-ATOM 43 CD ARG A 252 -2.707 -4.727 -2.170 1.00 2.40 C
-ATOM 44 NE ARG A 252 -2.479 -3.950 -0.954 1.00 2.83 N
-ATOM 45 CZ ARG A 252 -2.880 -2.706 -0.799 1.00 2.95 C
-ATOM 46 NH1 ARG A 252 -3.511 -2.089 -1.764 1.00 2.76 N
-ATOM 47 NH2 ARG A 252 -2.638 -2.072 0.318 1.00 3.35 N
-ATOM 48 H ARG A 252 -1.440 -7.393 -6.005 1.00 1.00 H
-ATOM 49 HA ARG A 252 -2.675 -5.112 -4.883 1.00 1.43 H
-ATOM 50 HB2 ARG A 252 -2.871 -7.159 -3.678 1.00 2.07 H
-ATOM 51 HB3 ARG A 252 -1.130 -7.391 -3.650 1.00 1.89 H
-ATOM 52 HG2 ARG A 252 -2.206 -6.679 -1.479 1.00 2.81 H
-ATOM 53 HG3 ARG A 252 -0.826 -5.720 -2.015 1.00 2.32 H
-ATOM 54 HD2 ARG A 252 -2.464 -4.112 -3.025 1.00 2.01 H
-ATOM 55 HD3 ARG A 252 -3.745 -5.013 -2.217 1.00 2.58 H
-ATOM 56 HE ARG A 252 -2.002 -4.385 -0.220 1.00 3.11 H
-ATOM 57 HH11 ARG A 252 -3.691 -2.562 -2.622 1.00 2.52 H
-ATOM 58 HH12 ARG A 252 -3.813 -1.147 -1.640 1.00 2.95 H
-ATOM 59 HH21 ARG A 252 -2.152 -2.532 1.057 1.00 3.60 H
-ATOM 60 HH22 ARG A 252 -2.936 -1.127 0.428 1.00 3.46 H
-ATOM 61 N VAL A 253 0.611 -5.197 -4.761 1.00 1.19 N
-ATOM 62 CA VAL A 253 1.798 -4.365 -4.572 1.00 1.43 C
-ATOM 63 C VAL A 253 1.765 -3.148 -5.499 1.00 1.07 C
-ATOM 64 O VAL A 253 1.971 -2.016 -5.057 1.00 1.11 O
-ATOM 65 CB VAL A 253 3.088 -5.174 -4.800 1.00 1.89 C
-ATOM 66 CG1 VAL A 253 4.313 -4.342 -4.452 1.00 2.33 C
-ATOM 67 CG2 VAL A 253 3.047 -6.458 -3.982 1.00 2.23 C
-ATOM 68 H VAL A 253 0.717 -6.123 -5.054 1.00 1.24 H
-ATOM 69 HA VAL A 253 1.793 -4.018 -3.551 1.00 1.70 H
-ATOM 70 HB VAL A 253 3.143 -5.440 -5.845 1.00 1.77 H
-ATOM 71 HG11 VAL A 253 5.179 -4.983 -4.395 1.00 2.64 H
-ATOM 72 HG12 VAL A 253 4.158 -3.858 -3.497 1.00 2.64 H
-ATOM 73 HG13 VAL A 253 4.467 -3.593 -5.213 1.00 2.64 H
-ATOM 74 HG21 VAL A 253 3.999 -6.602 -3.491 1.00 2.57 H
-ATOM 75 HG22 VAL A 253 2.847 -7.295 -4.634 1.00 2.51 H
-ATOM 76 HG23 VAL A 253 2.266 -6.386 -3.239 1.00 2.52 H
-ATOM 77 N GLY A 254 1.471 -3.387 -6.777 1.00 0.90 N
-ATOM 78 CA GLY A 254 1.379 -2.299 -7.741 1.00 1.00 C
-ATOM 79 C GLY A 254 0.285 -1.307 -7.373 1.00 0.65 C
-ATOM 80 O GLY A 254 0.519 -0.097 -7.324 1.00 0.78 O
-ATOM 81 H GLY A 254 1.293 -4.313 -7.063 1.00 0.91 H
-ATOM 82 HA2 GLY A 254 2.325 -1.781 -7.778 1.00 1.34 H
-ATOM 83 HA3 GLY A 254 1.164 -2.711 -8.716 1.00 1.20 H
-ATOM 84 N LEU A 255 -0.909 -1.826 -7.081 1.00 0.51 N
-ATOM 85 CA LEU A 255 -2.040 -0.988 -6.682 1.00 0.85 C
-ATOM 86 C LEU A 255 -1.715 -0.231 -5.392 1.00 0.71 C
-ATOM 87 O LEU A 255 -2.054 0.942 -5.238 1.00 0.85 O
-ATOM 88 CB LEU A 255 -3.286 -1.839 -6.485 1.00 1.30 C
-ATOM 89 CG LEU A 255 -4.582 -1.041 -6.478 1.00 1.86 C
-ATOM 90 CD1 LEU A 255 -4.819 -0.401 -7.841 1.00 2.13 C
-ATOM 91 CD2 LEU A 255 -5.750 -1.926 -6.082 1.00 2.34 C
-ATOM 92 H LEU A 255 -1.027 -2.803 -7.117 1.00 0.49 H
-ATOM 93 HA LEU A 255 -2.238 -0.273 -7.466 1.00 1.12 H
-ATOM 94 HB2 LEU A 255 -3.335 -2.569 -7.282 1.00 1.35 H
-ATOM 95 HB3 LEU A 255 -3.203 -2.359 -5.542 1.00 1.32 H
-ATOM 96 HG LEU A 255 -4.494 -0.248 -5.754 1.00 1.85 H
-ATOM 97 HD11 LEU A 255 -4.420 -1.041 -8.614 1.00 2.42 H
-ATOM 98 HD12 LEU A 255 -4.324 0.559 -7.879 1.00 2.45 H
-ATOM 99 HD13 LEU A 255 -5.878 -0.265 -7.995 1.00 2.44 H
-ATOM 100 HD21 LEU A 255 -6.670 -1.368 -6.166 1.00 2.65 H
-ATOM 101 HD22 LEU A 255 -5.618 -2.256 -5.062 1.00 2.67 H
-ATOM 102 HD23 LEU A 255 -5.787 -2.784 -6.736 1.00 2.66 H
-ATOM 103 N GLY A 256 -1.040 -0.917 -4.472 1.00 0.78 N
-ATOM 104 CA GLY A 256 -0.658 -0.313 -3.210 1.00 1.13 C
-ATOM 105 C GLY A 256 0.299 0.853 -3.381 1.00 0.92 C
-ATOM 106 O GLY A 256 0.024 1.951 -2.904 1.00 0.97 O
-ATOM 107 H GLY A 256 -0.790 -1.852 -4.658 1.00 0.77 H
-ATOM 108 HA2 GLY A 256 -1.548 0.036 -2.709 1.00 1.42 H
-ATOM 109 HA3 GLY A 256 -0.185 -1.064 -2.596 1.00 1.45 H
-ATOM 110 N ILE A 257 1.417 0.634 -4.070 1.00 0.91 N
-ATOM 111 CA ILE A 257 2.383 1.712 -4.286 1.00 1.10 C
-ATOM 112 C ILE A 257 1.768 2.845 -5.117 1.00 0.75 C
-ATOM 113 O ILE A 257 2.050 4.018 -4.878 1.00 0.83 O
-ATOM 114 CB ILE A 257 3.706 1.209 -4.927 1.00 1.60 C
-ATOM 115 CG1 ILE A 257 3.476 0.536 -6.286 1.00 1.55 C
-ATOM 116 CG2 ILE A 257 4.413 0.250 -3.982 1.00 2.03 C
-ATOM 117 CD1 ILE A 257 3.698 1.451 -7.472 1.00 1.81 C
-ATOM 118 H ILE A 257 1.593 -0.262 -4.445 1.00 0.97 H
-ATOM 119 HA ILE A 257 2.626 2.113 -3.310 1.00 1.35 H
-ATOM 120 HB ILE A 257 4.352 2.066 -5.067 1.00 1.83 H
-ATOM 121 HG12 ILE A 257 4.153 -0.299 -6.384 1.00 1.85 H
-ATOM 122 HG13 ILE A 257 2.460 0.174 -6.331 1.00 1.18 H
-ATOM 123 HG21 ILE A 257 4.161 -0.766 -4.246 1.00 2.37 H
-ATOM 124 HG22 ILE A 257 4.098 0.446 -2.968 1.00 2.36 H
-ATOM 125 HG23 ILE A 257 5.481 0.388 -4.060 1.00 2.40 H
-ATOM 126 HD11 ILE A 257 3.265 1.007 -8.356 1.00 2.16 H
-ATOM 127 HD12 ILE A 257 4.758 1.594 -7.625 1.00 2.14 H
-ATOM 128 HD13 ILE A 257 3.231 2.407 -7.283 1.00 2.16 H
-ATOM 129 N THR A 258 0.907 2.487 -6.075 1.00 0.60 N
-ATOM 130 CA THR A 258 0.237 3.478 -6.923 1.00 0.89 C
-ATOM 131 C THR A 258 -0.690 4.375 -6.097 1.00 0.77 C
-ATOM 132 O THR A 258 -0.657 5.601 -6.221 1.00 0.94 O
-ATOM 133 CB THR A 258 -0.594 2.805 -8.036 1.00 1.28 C
-ATOM 134 OG1 THR A 258 0.246 1.965 -8.837 1.00 1.50 O
-ATOM 135 CG2 THR A 258 -1.267 3.841 -8.926 1.00 1.79 C
-ATOM 136 H THR A 258 0.711 1.530 -6.208 1.00 0.55 H
-ATOM 137 HA THR A 258 0.995 4.091 -7.387 1.00 1.19 H
-ATOM 138 HB THR A 258 -1.361 2.197 -7.575 1.00 1.16 H
-ATOM 139 HG1 THR A 258 0.431 1.143 -8.357 1.00 1.29 H
-ATOM 140 HG21 THR A 258 -0.535 4.564 -9.254 1.00 2.14 H
-ATOM 141 HG22 THR A 258 -2.044 4.343 -8.369 1.00 2.17 H
-ATOM 142 HG23 THR A 258 -1.700 3.351 -9.786 1.00 2.20 H
-ATOM 143 N THR A 259 -1.508 3.757 -5.247 1.00 0.78 N
-ATOM 144 CA THR A 259 -2.433 4.507 -4.398 1.00 1.16 C
-ATOM 145 C THR A 259 -1.675 5.352 -3.385 1.00 0.97 C
-ATOM 146 O THR A 259 -1.968 6.534 -3.211 1.00 1.13 O
-ATOM 147 CB THR A 259 -3.425 3.582 -3.662 1.00 1.61 C
-ATOM 148 OG1 THR A 259 -2.727 2.521 -2.999 1.00 1.50 O
-ATOM 149 CG2 THR A 259 -4.437 3.003 -4.638 1.00 1.94 C
-ATOM 150 H THR A 259 -1.484 2.777 -5.182 1.00 0.72 H
-ATOM 151 HA THR A 259 -3.002 5.168 -5.037 1.00 1.43 H
-ATOM 152 HB THR A 259 -3.956 4.167 -2.923 1.00 1.93 H
-ATOM 153 HG1 THR A 259 -2.582 1.793 -3.620 1.00 1.30 H
-ATOM 154 HG21 THR A 259 -4.718 3.760 -5.355 1.00 2.08 H
-ATOM 155 HG22 THR A 259 -5.312 2.676 -4.097 1.00 2.36 H
-ATOM 156 HG23 THR A 259 -3.997 2.163 -5.155 1.00 1.89 H
-ATOM 157 N VAL A 260 -0.680 4.749 -2.735 1.00 0.86 N
-ATOM 158 CA VAL A 260 0.135 5.468 -1.762 1.00 1.06 C
-ATOM 159 C VAL A 260 0.839 6.660 -2.422 1.00 0.74 C
-ATOM 160 O VAL A 260 0.785 7.774 -1.903 1.00 0.84 O
-ATOM 161 CB VAL A 260 1.173 4.540 -1.096 1.00 1.44 C
-ATOM 162 CG1 VAL A 260 2.134 5.334 -0.222 1.00 1.85 C
-ATOM 163 CG2 VAL A 260 0.468 3.471 -0.273 1.00 1.82 C
-ATOM 164 H VAL A 260 -0.482 3.803 -2.928 1.00 0.82 H
-ATOM 165 HA VAL A 260 -0.527 5.841 -0.994 1.00 1.38 H
-ATOM 166 HB VAL A 260 1.742 4.050 -1.872 1.00 1.29 H
-ATOM 167 HG11 VAL A 260 2.128 4.930 0.779 1.00 2.14 H
-ATOM 168 HG12 VAL A 260 1.824 6.368 -0.196 1.00 2.20 H
-ATOM 169 HG13 VAL A 260 3.130 5.267 -0.632 1.00 2.25 H
-ATOM 170 HG21 VAL A 260 0.215 2.636 -0.911 1.00 2.16 H
-ATOM 171 HG22 VAL A 260 -0.434 3.883 0.156 1.00 2.17 H
-ATOM 172 HG23 VAL A 260 1.123 3.135 0.516 1.00 2.18 H
-ATOM 173 N LEU A 261 1.466 6.430 -3.584 1.00 0.60 N
-ATOM 174 CA LEU A 261 2.143 7.502 -4.318 1.00 0.82 C
-ATOM 175 C LEU A 261 1.147 8.604 -4.686 1.00 0.72 C
-ATOM 176 O LEU A 261 1.420 9.787 -4.497 1.00 0.83 O
-ATOM 177 CB LEU A 261 2.808 6.959 -5.582 1.00 1.24 C
-ATOM 178 CG LEU A 261 3.684 7.967 -6.327 1.00 1.75 C
-ATOM 179 CD1 LEU A 261 4.724 8.565 -5.388 1.00 1.96 C
-ATOM 180 CD2 LEU A 261 4.359 7.303 -7.514 1.00 2.23 C
-ATOM 181 H LEU A 261 1.456 5.522 -3.965 1.00 0.60 H
-ATOM 182 HA LEU A 261 2.907 7.919 -3.678 1.00 1.06 H
-ATOM 183 HB2 LEU A 261 3.421 6.111 -5.309 1.00 1.38 H
-ATOM 184 HB3 LEU A 261 2.035 6.620 -6.257 1.00 1.20 H
-ATOM 185 HG LEU A 261 3.065 8.770 -6.698 1.00 1.74 H
-ATOM 186 HD11 LEU A 261 4.245 9.272 -4.726 1.00 2.29 H
-ATOM 187 HD12 LEU A 261 5.484 9.068 -5.963 1.00 2.28 H
-ATOM 188 HD13 LEU A 261 5.176 7.776 -4.804 1.00 2.24 H
-ATOM 189 HD21 LEU A 261 4.036 7.781 -8.426 1.00 2.57 H
-ATOM 190 HD22 LEU A 261 4.091 6.257 -7.540 1.00 2.53 H
-ATOM 191 HD23 LEU A 261 5.430 7.397 -7.417 1.00 2.57 H
-ATOM 192 N THR A 262 -0.016 8.199 -5.189 1.00 0.83 N
-ATOM 193 CA THR A 262 -1.074 9.141 -5.565 1.00 1.26 C
-ATOM 194 C THR A 262 -1.539 9.967 -4.360 1.00 1.21 C
-ATOM 195 O THR A 262 -1.693 11.185 -4.441 1.00 1.40 O
-ATOM 196 CB THR A 262 -2.287 8.394 -6.156 1.00 1.65 C
-ATOM 197 OG1 THR A 262 -1.900 7.684 -7.339 1.00 1.80 O
-ATOM 198 CG2 THR A 262 -3.420 9.353 -6.482 1.00 2.17 C
-ATOM 199 H THR A 262 -0.176 7.232 -5.301 1.00 0.77 H
-ATOM 200 HA THR A 262 -0.680 9.806 -6.320 1.00 1.50 H
-ATOM 201 HB THR A 262 -2.641 7.682 -5.424 1.00 1.57 H
-ATOM 202 HG1 THR A 262 -1.432 6.872 -7.090 1.00 1.55 H
-ATOM 203 HG21 THR A 262 -3.113 10.013 -7.279 1.00 2.51 H
-ATOM 204 HG22 THR A 262 -3.658 9.936 -5.604 1.00 2.48 H
-ATOM 205 HG23 THR A 262 -4.289 8.792 -6.789 1.00 2.54 H
-ATOM 206 N MET A 263 -1.765 9.285 -3.248 1.00 1.15 N
-ATOM 207 CA MET A 263 -2.216 9.927 -2.013 1.00 1.42 C
-ATOM 208 C MET A 263 -1.142 10.844 -1.408 1.00 1.19 C
-ATOM 209 O MET A 263 -1.451 11.939 -0.940 1.00 1.40 O
-ATOM 210 CB MET A 263 -2.620 8.854 -0.998 1.00 1.73 C
-ATOM 211 CG MET A 263 -3.049 9.402 0.353 1.00 2.19 C
-ATOM 212 SD MET A 263 -3.368 8.098 1.555 1.00 2.72 S
-ATOM 213 CE MET A 263 -1.752 7.332 1.660 1.00 2.53 C
-ATOM 214 H MET A 263 -1.633 8.312 -3.257 1.00 1.05 H
-ATOM 215 HA MET A 263 -3.084 10.525 -2.250 1.00 1.75 H
-ATOM 216 HB2 MET A 263 -3.444 8.286 -1.403 1.00 1.91 H
-ATOM 217 HB3 MET A 263 -1.783 8.190 -0.842 1.00 1.55 H
-ATOM 218 HG2 MET A 263 -2.262 10.037 0.734 1.00 2.08 H
-ATOM 219 HG3 MET A 263 -3.949 9.982 0.224 1.00 2.43 H
-ATOM 220 HE1 MET A 263 -1.208 7.517 0.745 1.00 2.24 H
-ATOM 221 HE2 MET A 263 -1.866 6.268 1.804 1.00 2.75 H
-ATOM 222 HE3 MET A 263 -1.208 7.753 2.493 1.00 2.74 H
-ATOM 223 N THR A 264 0.112 10.389 -1.393 1.00 0.90 N
-ATOM 224 CA THR A 264 1.201 11.187 -0.810 1.00 0.97 C
-ATOM 225 C THR A 264 1.552 12.421 -1.649 1.00 0.73 C
-ATOM 226 O THR A 264 2.032 13.419 -1.107 1.00 0.87 O
-ATOM 227 CB THR A 264 2.484 10.361 -0.559 1.00 1.28 C
-ATOM 228 OG1 THR A 264 3.445 11.163 0.139 1.00 1.64 O
-ATOM 229 CG2 THR A 264 3.102 9.864 -1.857 1.00 1.20 C
-ATOM 230 H THR A 264 0.310 9.494 -1.760 1.00 0.80 H
-ATOM 231 HA THR A 264 0.849 11.536 0.152 1.00 1.27 H
-ATOM 232 HB THR A 264 2.230 9.506 0.053 1.00 1.52 H
-ATOM 233 HG1 THR A 264 3.276 12.095 -0.041 1.00 1.57 H
-ATOM 234 HG21 THR A 264 2.555 9.002 -2.207 1.00 1.53 H
-ATOM 235 HG22 THR A 264 4.131 9.591 -1.685 1.00 1.60 H
-ATOM 236 HG23 THR A 264 3.056 10.645 -2.599 1.00 1.62 H
-ATOM 237 N THR A 265 1.328 12.362 -2.963 1.00 0.70 N
-ATOM 238 CA THR A 265 1.636 13.506 -3.829 1.00 1.02 C
-ATOM 239 C THR A 265 0.493 14.541 -3.805 1.00 1.18 C
-ATOM 240 O THR A 265 -0.102 14.785 -2.755 1.00 1.14 O
-ATOM 241 CB THR A 265 1.962 13.063 -5.285 1.00 1.45 C
-ATOM 242 OG1 THR A 265 2.358 14.193 -6.078 1.00 1.89 O
-ATOM 243 CG2 THR A 265 0.784 12.373 -5.955 1.00 1.57 C
-ATOM 244 H THR A 265 0.949 11.544 -3.353 1.00 0.70 H
-ATOM 245 HA THR A 265 2.522 13.978 -3.422 1.00 1.09 H
-ATOM 246 HB THR A 265 2.786 12.365 -5.247 1.00 1.49 H
-ATOM 247 HG1 THR A 265 3.132 13.961 -6.601 1.00 2.21 H
-ATOM 248 HG21 THR A 265 -0.138 12.809 -5.601 1.00 1.92 H
-ATOM 249 HG22 THR A 265 0.798 11.319 -5.716 1.00 1.91 H
-ATOM 250 HG23 THR A 265 0.854 12.499 -7.025 1.00 1.93 H
-ATOM 251 N GLN A 266 0.202 15.160 -4.953 1.00 1.61 N
-ATOM 252 CA GLN A 266 -0.855 16.175 -5.057 1.00 1.95 C
-ATOM 253 C GLN A 266 -0.534 17.388 -4.176 1.00 1.88 C
-ATOM 254 O GLN A 266 -1.428 18.048 -3.645 1.00 1.98 O
-ATOM 255 CB GLN A 266 -2.225 15.588 -4.673 1.00 2.08 C
-ATOM 256 CG GLN A 266 -2.644 14.393 -5.517 1.00 2.18 C
-ATOM 257 CD GLN A 266 -3.996 13.829 -5.112 1.00 2.37 C
-ATOM 258 OE1 GLN A 266 -5.014 14.506 -5.192 1.00 2.76 O
-ATOM 259 NE2 GLN A 266 -4.015 12.583 -4.669 1.00 2.18 N
-ATOM 260 H GLN A 266 0.726 14.933 -5.756 1.00 1.79 H
-ATOM 261 HA GLN A 266 -0.892 16.502 -6.086 1.00 2.29 H
-ATOM 262 HB2 GLN A 266 -2.191 15.278 -3.641 1.00 1.86 H
-ATOM 263 HB3 GLN A 266 -2.975 16.360 -4.783 1.00 2.41 H
-ATOM 264 HG2 GLN A 266 -2.696 14.699 -6.551 1.00 2.47 H
-ATOM 265 HG3 GLN A 266 -1.901 13.617 -5.411 1.00 1.90 H
-ATOM 266 HE21 GLN A 266 -3.160 12.092 -4.623 1.00 1.88 H
-ATOM 267 HE22 GLN A 266 -4.876 12.203 -4.404 1.00 2.37 H
-ATOM 268 N SER A 267 0.757 17.676 -4.033 1.00 1.78 N
-ATOM 269 CA SER A 267 1.211 18.805 -3.223 1.00 1.70 C
-ATOM 270 C SER A 267 1.828 19.898 -4.094 1.00 1.36 C
-ATOM 271 O SER A 267 2.623 19.622 -4.992 1.00 1.54 O
-ATOM 272 CB SER A 267 2.228 18.346 -2.173 1.00 2.20 C
-ATOM 273 OG SER A 267 2.684 19.436 -1.383 1.00 2.14 O
-ATOM 274 H SER A 267 1.421 17.116 -4.484 1.00 1.81 H
-ATOM 275 HA SER A 267 0.349 19.214 -2.717 1.00 1.67 H
-ATOM 276 HB2 SER A 267 1.764 17.618 -1.523 1.00 2.52 H
-ATOM 277 HB3 SER A 267 3.075 17.897 -2.668 1.00 2.40 H
-ATOM 278 HG SER A 267 3.215 19.099 -0.654 1.00 2.51 H
-ATOM 279 N SER A 268 1.448 21.142 -3.819 1.00 1.07 N
-ATOM 280 CA SER A 268 1.950 22.298 -4.566 1.00 1.05 C
-ATOM 281 C SER A 268 3.468 22.454 -4.425 1.00 1.50 C
-ATOM 282 O SER A 268 4.164 22.771 -5.390 1.00 1.86 O
-ATOM 283 CB SER A 268 1.245 23.565 -4.082 1.00 0.92 C
-ATOM 284 OG SER A 268 1.783 24.725 -4.688 1.00 1.28 O
-ATOM 285 H SER A 268 0.808 21.289 -3.091 1.00 1.08 H
-ATOM 286 HA SER A 268 1.713 22.145 -5.608 1.00 1.16 H
-ATOM 287 HB2 SER A 268 0.195 23.503 -4.325 1.00 1.01 H
-ATOM 288 HB3 SER A 268 1.360 23.649 -3.011 1.00 0.85 H
-ATOM 289 HG SER A 268 1.559 25.498 -4.144 1.00 1.48 H
-ATOM 290 N GLY A 269 3.978 22.220 -3.219 1.00 1.68 N
-ATOM 291 CA GLY A 269 5.412 22.328 -2.977 1.00 2.22 C
-ATOM 292 C GLY A 269 5.906 23.763 -2.824 1.00 2.26 C
-ATOM 293 O GLY A 269 6.562 24.090 -1.834 1.00 2.51 O
-ATOM 294 H GLY A 269 3.377 21.962 -2.489 1.00 1.57 H
-ATOM 295 HA2 GLY A 269 5.646 21.785 -2.072 1.00 2.43 H
-ATOM 296 HA3 GLY A 269 5.938 21.866 -3.801 1.00 2.49 H
-ATOM 297 N SER A 270 5.592 24.617 -3.799 1.00 2.15 N
-ATOM 298 CA SER A 270 6.011 26.026 -3.760 1.00 2.32 C
-ATOM 299 C SER A 270 5.476 26.718 -2.502 1.00 1.91 C
-ATOM 300 O SER A 270 4.268 26.765 -2.285 1.00 1.48 O
-ATOM 301 CB SER A 270 5.517 26.767 -5.010 1.00 2.52 C
-ATOM 302 OG SER A 270 5.997 26.156 -6.199 1.00 2.95 O
-ATOM 303 H SER A 270 5.067 24.294 -4.566 1.00 2.05 H
-ATOM 304 HA SER A 270 7.089 26.052 -3.738 1.00 2.75 H
-ATOM 305 HB2 SER A 270 4.437 26.760 -5.030 1.00 2.21 H
-ATOM 306 HB3 SER A 270 5.866 27.789 -4.981 1.00 2.74 H
-ATOM 307 HG SER A 270 5.252 25.858 -6.730 1.00 3.37 H
-ATOM 308 N ARG A 271 6.386 27.235 -1.670 1.00 2.11 N
-ATOM 309 CA ARG A 271 6.013 27.903 -0.413 1.00 1.84 C
-ATOM 310 C ARG A 271 5.005 29.036 -0.621 1.00 1.59 C
-ATOM 311 O ARG A 271 4.051 29.164 0.151 1.00 1.28 O
-ATOM 312 CB ARG A 271 7.246 28.443 0.302 1.00 2.30 C
-ATOM 313 CG ARG A 271 6.921 28.978 1.683 1.00 2.15 C
-ATOM 314 CD ARG A 271 6.575 27.856 2.652 1.00 2.19 C
-ATOM 315 NE ARG A 271 6.056 28.358 3.925 1.00 2.27 N
-ATOM 316 CZ ARG A 271 4.864 28.906 4.078 1.00 2.05 C
-ATOM 317 NH1 ARG A 271 4.051 29.039 3.057 1.00 1.71 N
-ATOM 318 NH2 ARG A 271 4.483 29.318 5.260 1.00 2.37 N
-ATOM 319 H ARG A 271 7.335 27.148 -1.896 1.00 2.52 H
-ATOM 320 HA ARG A 271 5.553 27.167 0.225 1.00 1.64 H
-ATOM 321 HB2 ARG A 271 7.973 27.649 0.403 1.00 2.64 H
-ATOM 322 HB3 ARG A 271 7.672 29.243 -0.283 1.00 2.55 H
-ATOM 323 HG2 ARG A 271 7.768 29.529 2.063 1.00 2.48 H
-ATOM 324 HG3 ARG A 271 6.068 29.633 1.597 1.00 1.84 H
-ATOM 325 HD2 ARG A 271 5.826 27.226 2.197 1.00 1.92 H
-ATOM 326 HD3 ARG A 271 7.465 27.275 2.841 1.00 2.60 H
-ATOM 327 HE ARG A 271 6.634 28.278 4.709 1.00 2.62 H
-ATOM 328 HH11 ARG A 271 4.329 28.726 2.149 1.00 1.54 H
-ATOM 329 HH12 ARG A 271 3.155 29.453 3.186 1.00 1.78 H
-ATOM 330 HH21 ARG A 271 5.091 29.219 6.044 1.00 2.68 H
-ATOM 331 HH22 ARG A 271 3.584 29.733 5.375 1.00 2.40 H
-ATOM 332 N ALA A 272 5.216 29.853 -1.651 1.00 1.96 N
-ATOM 333 CA ALA A 272 4.309 30.965 -1.942 1.00 2.09 C
-ATOM 334 C ALA A 272 2.903 30.451 -2.262 1.00 1.91 C
-ATOM 335 O ALA A 272 1.904 30.992 -1.789 1.00 2.05 O
-ATOM 336 CB ALA A 272 4.850 31.801 -3.093 1.00 2.72 C
-ATOM 337 H ALA A 272 5.993 29.703 -2.228 1.00 2.29 H
-ATOM 338 HA ALA A 272 4.261 31.592 -1.064 1.00 2.01 H
-ATOM 339 HB1 ALA A 272 5.581 32.501 -2.717 1.00 3.07 H
-ATOM 340 HB2 ALA A 272 4.038 32.343 -3.557 1.00 3.04 H
-ATOM 341 HB3 ALA A 272 5.314 31.153 -3.822 1.00 3.02 H
-ATOM 342 N SER A 273 2.838 29.390 -3.053 1.00 1.75 N
-ATOM 343 CA SER A 273 1.564 28.787 -3.423 1.00 1.75 C
-ATOM 344 C SER A 273 1.233 27.610 -2.522 1.00 1.15 C
-ATOM 345 O SER A 273 0.900 26.528 -2.998 1.00 1.03 O
-ATOM 346 CB SER A 273 1.584 28.317 -4.871 1.00 2.12 C
-ATOM 347 OG SER A 273 2.128 29.307 -5.731 1.00 2.82 O
-ATOM 348 H SER A 273 3.669 28.992 -3.386 1.00 1.77 H
-ATOM 349 HA SER A 273 0.789 29.533 -3.304 1.00 2.10 H
-ATOM 350 HB2 SER A 273 2.177 27.422 -4.942 1.00 1.92 H
-ATOM 351 HB3 SER A 273 0.569 28.104 -5.184 1.00 2.19 H
-ATOM 352 HG SER A 273 1.829 30.176 -5.450 1.00 3.16 H
-ATOM 353 N LEU A 274 1.285 27.833 -1.216 1.00 1.04 N
-ATOM 354 CA LEU A 274 0.944 26.787 -0.262 1.00 1.14 C
-ATOM 355 C LEU A 274 -0.327 27.113 0.551 1.00 1.86 C
-ATOM 356 O LEU A 274 -0.367 26.813 1.744 1.00 2.22 O
-ATOM 357 CB LEU A 274 2.108 26.536 0.704 1.00 1.20 C
-ATOM 358 CG LEU A 274 3.173 25.553 0.218 1.00 1.22 C
-ATOM 359 CD1 LEU A 274 4.166 25.255 1.329 1.00 1.84 C
-ATOM 360 CD2 LEU A 274 2.531 24.267 -0.280 1.00 1.42 C
-ATOM 361 H LEU A 274 1.533 28.724 -0.891 1.00 1.23 H
-ATOM 362 HA LEU A 274 0.767 25.882 -0.823 1.00 1.04 H
-ATOM 363 HB2 LEU A 274 2.590 27.484 0.898 1.00 1.22 H
-ATOM 364 HB3 LEU A 274 1.702 26.164 1.631 1.00 1.67 H
-ATOM 365 HG LEU A 274 3.717 25.997 -0.603 1.00 1.05 H
-ATOM 366 HD11 LEU A 274 4.298 26.134 1.942 1.00 2.28 H
-ATOM 367 HD12 LEU A 274 5.115 24.970 0.897 1.00 2.23 H
-ATOM 368 HD13 LEU A 274 3.791 24.445 1.940 1.00 2.21 H
-ATOM 369 HD21 LEU A 274 1.523 24.196 0.099 1.00 1.80 H
-ATOM 370 HD22 LEU A 274 3.106 23.420 0.065 1.00 1.77 H
-ATOM 371 HD23 LEU A 274 2.509 24.271 -1.360 1.00 1.81 H
-ATOM 372 N PRO A 275 -1.395 27.724 -0.046 1.00 2.27 N
-ATOM 373 CA PRO A 275 -2.626 28.042 0.710 1.00 2.99 C
-ATOM 374 C PRO A 275 -3.265 26.795 1.333 1.00 3.32 C
-ATOM 375 O PRO A 275 -3.804 26.851 2.439 1.00 3.84 O
-ATOM 376 CB PRO A 275 -3.547 28.654 -0.351 1.00 3.37 C
-ATOM 377 CG PRO A 275 -2.600 29.204 -1.350 1.00 3.02 C
-ATOM 378 CD PRO A 275 -1.525 28.167 -1.455 1.00 2.29 C
-ATOM 379 HA PRO A 275 -2.433 28.771 1.480 1.00 3.15 H
-ATOM 380 HB2 PRO A 275 -4.177 27.887 -0.777 1.00 3.48 H
-ATOM 381 HB3 PRO A 275 -4.153 29.432 0.091 1.00 3.85 H
-ATOM 382 HG2 PRO A 275 -3.093 29.349 -2.300 1.00 3.29 H
-ATOM 383 HG3 PRO A 275 -2.189 30.132 -0.985 1.00 3.11 H
-ATOM 384 HD2 PRO A 275 -1.840 27.358 -2.096 1.00 2.21 H
-ATOM 385 HD3 PRO A 275 -0.596 28.604 -1.822 1.00 2.03 H
-ATOM 386 N LYS A 276 -3.168 25.672 0.609 1.00 3.09 N
-ATOM 387 CA LYS A 276 -3.700 24.375 1.054 1.00 3.47 C
-ATOM 388 C LYS A 276 -5.220 24.427 1.280 1.00 4.14 C
-ATOM 389 O LYS A 276 -5.653 24.511 2.456 1.00 4.77 O
-ATOM 390 CB LYS A 276 -2.969 23.919 2.328 1.00 3.72 C
-ATOM 391 CG LYS A 276 -2.862 22.404 2.482 1.00 4.01 C
-ATOM 392 CD LYS A 276 -4.178 21.754 2.895 1.00 4.67 C
-ATOM 393 CE LYS A 276 -4.510 22.014 4.364 1.00 5.37 C
-ATOM 394 NZ LYS A 276 -4.903 23.431 4.625 1.00 5.46 N
-ATOM 395 OXT LYS A 276 -5.962 24.363 0.276 1.00 4.17 O
-ATOM 396 H LYS A 276 -2.707 25.714 -0.253 1.00 2.71 H
-ATOM 397 HA LYS A 276 -3.501 23.660 0.269 1.00 3.19 H
-ATOM 398 HB2 LYS A 276 -1.971 24.328 2.317 1.00 3.39 H
-ATOM 399 HB3 LYS A 276 -3.497 24.308 3.186 1.00 4.23 H
-ATOM 400 HG2 LYS A 276 -2.547 21.981 1.541 1.00 3.69 H
-ATOM 401 HG3 LYS A 276 -2.118 22.188 3.235 1.00 4.22 H
-ATOM 402 HD2 LYS A 276 -4.972 22.153 2.282 1.00 4.58 H
-ATOM 403 HD3 LYS A 276 -4.104 20.686 2.736 1.00 4.88 H
-ATOM 404 HE2 LYS A 276 -5.322 21.366 4.655 1.00 5.88 H
-ATOM 405 HE3 LYS A 276 -3.639 21.780 4.956 1.00 5.43 H
-ATOM 406 HZ1 LYS A 276 -5.317 23.859 3.752 1.00 5.15 H
-ATOM 407 HZ2 LYS A 276 -4.072 23.989 4.907 1.00 5.41 H
-ATOM 408 HZ3 LYS A 276 -5.611 23.473 5.384 1.00 6.01 H
-TER
-ENDMDL
-MODEL 10
-ATOM 1 N ALA A 249 1.888 -10.410 -12.513 1.00 3.22 N
-ATOM 2 CA ALA A 249 1.926 -8.921 -12.567 1.00 2.63 C
-ATOM 3 C ALA A 249 1.754 -8.312 -11.171 1.00 1.94 C
-ATOM 4 O ALA A 249 1.173 -8.939 -10.286 1.00 2.04 O
-ATOM 5 CB ALA A 249 0.853 -8.399 -13.518 1.00 2.94 C
-ATOM 6 H1 ALA A 249 2.516 -10.812 -13.240 1.00 3.65 H
-ATOM 7 H2 ALA A 249 0.919 -10.752 -12.680 1.00 3.25 H
-ATOM 8 H3 ALA A 249 2.201 -10.744 -11.578 1.00 3.59 H
-ATOM 9 HA ALA A 249 2.888 -8.625 -12.957 1.00 2.64 H
-ATOM 10 HB1 ALA A 249 0.244 -7.669 -13.007 1.00 3.23 H
-ATOM 11 HB2 ALA A 249 0.235 -9.219 -13.848 1.00 3.16 H
-ATOM 12 HB3 ALA A 249 1.325 -7.937 -14.373 1.00 3.22 H
-ATOM 13 N PRO A 250 2.271 -7.085 -10.956 1.00 1.47 N
-ATOM 14 CA PRO A 250 2.190 -6.376 -9.665 1.00 1.05 C
-ATOM 15 C PRO A 250 0.762 -5.933 -9.308 1.00 0.93 C
-ATOM 16 O PRO A 250 0.458 -4.744 -9.312 1.00 1.17 O
-ATOM 17 CB PRO A 250 3.085 -5.140 -9.889 1.00 1.21 C
-ATOM 18 CG PRO A 250 3.889 -5.452 -11.096 1.00 1.50 C
-ATOM 19 CD PRO A 250 2.988 -6.276 -11.949 1.00 1.75 C
-ATOM 20 HA PRO A 250 2.592 -6.972 -8.858 1.00 1.23 H
-ATOM 21 HB2 PRO A 250 2.465 -4.274 -10.053 1.00 1.43 H
-ATOM 22 HB3 PRO A 250 3.715 -4.986 -9.029 1.00 1.35 H
-ATOM 23 HG2 PRO A 250 4.164 -4.539 -11.604 1.00 1.86 H
-ATOM 24 HG3 PRO A 250 4.769 -6.014 -10.821 1.00 1.47 H
-ATOM 25 HD2 PRO A 250 2.305 -5.643 -12.494 1.00 2.00 H
-ATOM 26 HD3 PRO A 250 3.558 -6.897 -12.616 1.00 2.10 H
-ATOM 27 N ALA A 251 -0.114 -6.893 -9.018 1.00 0.77 N
-ATOM 28 CA ALA A 251 -1.508 -6.585 -8.681 1.00 0.88 C
-ATOM 29 C ALA A 251 -1.643 -5.841 -7.345 1.00 0.70 C
-ATOM 30 O ALA A 251 -1.935 -4.645 -7.320 1.00 0.66 O
-ATOM 31 CB ALA A 251 -2.335 -7.864 -8.659 1.00 1.11 C
-ATOM 32 H ALA A 251 0.179 -7.831 -9.045 1.00 0.77 H
-ATOM 33 HA ALA A 251 -1.900 -5.955 -9.467 1.00 1.08 H
-ATOM 34 HB1 ALA A 251 -2.633 -8.083 -7.643 1.00 1.18 H
-ATOM 35 HB2 ALA A 251 -1.745 -8.683 -9.044 1.00 1.14 H
-ATOM 36 HB3 ALA A 251 -3.214 -7.735 -9.272 1.00 1.40 H
-ATOM 37 N ARG A 252 -1.444 -6.553 -6.236 1.00 0.74 N
-ATOM 38 CA ARG A 252 -1.564 -5.954 -4.901 1.00 0.70 C
-ATOM 39 C ARG A 252 -0.476 -4.900 -4.655 1.00 0.54 C
-ATOM 40 O ARG A 252 -0.773 -3.771 -4.260 1.00 0.54 O
-ATOM 41 CB ARG A 252 -1.490 -7.050 -3.827 1.00 0.83 C
-ATOM 42 CG ARG A 252 -2.313 -6.764 -2.572 1.00 0.98 C
-ATOM 43 CD ARG A 252 -1.863 -5.496 -1.861 1.00 0.99 C
-ATOM 44 NE ARG A 252 -2.630 -5.239 -0.645 1.00 1.28 N
-ATOM 45 CZ ARG A 252 -2.569 -4.113 0.030 1.00 1.43 C
-ATOM 46 NH1 ARG A 252 -1.842 -3.116 -0.410 1.00 1.39 N
-ATOM 47 NH2 ARG A 252 -3.250 -3.976 1.140 1.00 1.74 N
-ATOM 48 H ARG A 252 -1.224 -7.505 -6.315 1.00 0.89 H
-ATOM 49 HA ARG A 252 -2.526 -5.474 -4.843 1.00 0.76 H
-ATOM 50 HB2 ARG A 252 -1.842 -7.976 -4.254 1.00 0.93 H
-ATOM 51 HB3 ARG A 252 -0.458 -7.174 -3.531 1.00 0.80 H
-ATOM 52 HG2 ARG A 252 -3.348 -6.655 -2.853 1.00 1.10 H
-ATOM 53 HG3 ARG A 252 -2.211 -7.600 -1.893 1.00 1.09 H
-ATOM 54 HD2 ARG A 252 -0.820 -5.596 -1.601 1.00 0.99 H
-ATOM 55 HD3 ARG A 252 -1.985 -4.660 -2.535 1.00 0.92 H
-ATOM 56 HE ARG A 252 -3.202 -5.956 -0.310 1.00 1.42 H
-ATOM 57 HH11 ARG A 252 -1.331 -3.213 -1.261 1.00 1.23 H
-ATOM 58 HH12 ARG A 252 -1.799 -2.262 0.103 1.00 1.60 H
-ATOM 59 HH21 ARG A 252 -3.816 -4.727 1.474 1.00 1.85 H
-ATOM 60 HH22 ARG A 252 -3.204 -3.122 1.651 1.00 1.90 H
-ATOM 61 N VAL A 253 0.776 -5.280 -4.890 1.00 0.53 N
-ATOM 62 CA VAL A 253 1.908 -4.373 -4.693 1.00 0.51 C
-ATOM 63 C VAL A 253 1.760 -3.113 -5.546 1.00 0.45 C
-ATOM 64 O VAL A 253 1.858 -1.993 -5.044 1.00 0.45 O
-ATOM 65 CB VAL A 253 3.249 -5.077 -5.008 1.00 0.64 C
-ATOM 66 CG1 VAL A 253 3.268 -5.671 -6.410 1.00 0.76 C
-ATOM 67 CG2 VAL A 253 4.423 -4.130 -4.806 1.00 0.77 C
-ATOM 68 H VAL A 253 0.943 -6.191 -5.199 1.00 0.62 H
-ATOM 69 HA VAL A 253 1.918 -4.083 -3.653 1.00 0.57 H
-ATOM 70 HB VAL A 253 3.353 -5.888 -4.320 1.00 0.72 H
-ATOM 71 HG11 VAL A 253 3.103 -4.889 -7.135 1.00 1.37 H
-ATOM 72 HG12 VAL A 253 2.489 -6.414 -6.496 1.00 1.27 H
-ATOM 73 HG13 VAL A 253 4.228 -6.134 -6.592 1.00 1.26 H
-ATOM 74 HG21 VAL A 253 5.323 -4.583 -5.198 1.00 1.30 H
-ATOM 75 HG22 VAL A 253 4.549 -3.932 -3.753 1.00 1.31 H
-ATOM 76 HG23 VAL A 253 4.232 -3.203 -5.326 1.00 1.30 H
-ATOM 77 N GLY A 254 1.499 -3.323 -6.827 1.00 0.51 N
-ATOM 78 CA GLY A 254 1.311 -2.218 -7.758 1.00 0.56 C
-ATOM 79 C GLY A 254 0.192 -1.283 -7.330 1.00 0.49 C
-ATOM 80 O GLY A 254 0.376 -0.066 -7.285 1.00 0.47 O
-ATOM 81 H GLY A 254 1.427 -4.249 -7.140 1.00 0.59 H
-ATOM 82 HA2 GLY A 254 2.231 -1.656 -7.824 1.00 0.60 H
-ATOM 83 HA3 GLY A 254 1.077 -2.620 -8.733 1.00 0.68 H
-ATOM 84 N LEU A 255 -0.963 -1.851 -6.987 1.00 0.52 N
-ATOM 85 CA LEU A 255 -2.102 -1.054 -6.529 1.00 0.54 C
-ATOM 86 C LEU A 255 -1.748 -0.266 -5.274 1.00 0.44 C
-ATOM 87 O LEU A 255 -2.066 0.920 -5.150 1.00 0.46 O
-ATOM 88 CB LEU A 255 -3.300 -1.949 -6.235 1.00 0.68 C
-ATOM 89 CG LEU A 255 -4.315 -2.041 -7.364 1.00 0.86 C
-ATOM 90 CD1 LEU A 255 -5.464 -2.947 -6.961 1.00 1.00 C
-ATOM 91 CD2 LEU A 255 -4.819 -0.651 -7.725 1.00 0.99 C
-ATOM 92 H LEU A 255 -1.048 -2.831 -7.024 1.00 0.57 H
-ATOM 93 HA LEU A 255 -2.367 -0.367 -7.318 1.00 0.61 H
-ATOM 94 HB2 LEU A 255 -2.937 -2.944 -6.019 1.00 0.74 H
-ATOM 95 HB3 LEU A 255 -3.803 -1.568 -5.359 1.00 0.72 H
-ATOM 96 HG LEU A 255 -3.841 -2.465 -8.236 1.00 0.92 H
-ATOM 97 HD11 LEU A 255 -6.124 -2.414 -6.292 1.00 1.58 H
-ATOM 98 HD12 LEU A 255 -5.073 -3.820 -6.459 1.00 1.38 H
-ATOM 99 HD13 LEU A 255 -6.010 -3.251 -7.841 1.00 1.41 H
-ATOM 100 HD21 LEU A 255 -4.233 0.089 -7.198 1.00 1.36 H
-ATOM 101 HD22 LEU A 255 -5.855 -0.558 -7.441 1.00 1.42 H
-ATOM 102 HD23 LEU A 255 -4.720 -0.497 -8.789 1.00 1.58 H
-ATOM 103 N GLY A 256 -1.084 -0.935 -4.344 1.00 0.42 N
-ATOM 104 CA GLY A 256 -0.695 -0.293 -3.111 1.00 0.44 C
-ATOM 105 C GLY A 256 0.280 0.855 -3.321 1.00 0.40 C
-ATOM 106 O GLY A 256 0.041 1.962 -2.847 1.00 0.44 O
-ATOM 107 H GLY A 256 -0.858 -1.881 -4.498 1.00 0.44 H
-ATOM 108 HA2 GLY A 256 -1.584 0.087 -2.629 1.00 0.53 H
-ATOM 109 HA3 GLY A 256 -0.238 -1.029 -2.468 1.00 0.49 H
-ATOM 110 N ILE A 257 1.378 0.607 -4.037 1.00 0.38 N
-ATOM 111 CA ILE A 257 2.367 1.658 -4.283 1.00 0.44 C
-ATOM 112 C ILE A 257 1.783 2.815 -5.104 1.00 0.41 C
-ATOM 113 O ILE A 257 2.065 3.977 -4.823 1.00 0.47 O
-ATOM 114 CB ILE A 257 3.660 1.119 -4.952 1.00 0.54 C
-ATOM 115 CG1 ILE A 257 3.380 0.464 -6.310 1.00 0.55 C
-ATOM 116 CG2 ILE A 257 4.355 0.132 -4.028 1.00 0.62 C
-ATOM 117 CD1 ILE A 257 3.718 1.346 -7.495 1.00 0.68 C
-ATOM 118 H ILE A 257 1.525 -0.297 -4.403 1.00 0.39 H
-ATOM 119 HA ILE A 257 2.645 2.053 -3.316 1.00 0.51 H
-ATOM 120 HB ILE A 257 4.329 1.954 -5.099 1.00 0.62 H
-ATOM 121 HG12 ILE A 257 3.964 -0.439 -6.394 1.00 0.64 H
-ATOM 122 HG13 ILE A 257 2.330 0.216 -6.371 1.00 0.53 H
-ATOM 123 HG21 ILE A 257 5.408 0.366 -3.977 1.00 1.21 H
-ATOM 124 HG22 ILE A 257 4.227 -0.871 -4.409 1.00 1.19 H
-ATOM 125 HG23 ILE A 257 3.923 0.199 -3.040 1.00 1.23 H
-ATOM 126 HD11 ILE A 257 3.257 2.315 -7.366 1.00 1.22 H
-ATOM 127 HD12 ILE A 257 3.347 0.890 -8.401 1.00 1.21 H
-ATOM 128 HD13 ILE A 257 4.789 1.464 -7.562 1.00 1.31 H
-ATOM 129 N THR A 258 0.958 2.501 -6.108 1.00 0.38 N
-ATOM 130 CA THR A 258 0.343 3.541 -6.939 1.00 0.42 C
-ATOM 131 C THR A 258 -0.628 4.404 -6.134 1.00 0.38 C
-ATOM 132 O THR A 258 -0.594 5.632 -6.231 1.00 0.44 O
-ATOM 133 CB THR A 258 -0.399 2.963 -8.163 1.00 0.51 C
-ATOM 134 OG1 THR A 258 -1.270 1.899 -7.766 1.00 0.49 O
-ATOM 135 CG2 THR A 258 0.587 2.463 -9.212 1.00 0.64 C
-ATOM 136 H THR A 258 0.754 1.554 -6.287 1.00 0.39 H
-ATOM 137 HA THR A 258 1.139 4.176 -7.301 1.00 0.48 H
-ATOM 138 HB THR A 258 -0.992 3.751 -8.603 1.00 0.57 H
-ATOM 139 HG1 THR A 258 -0.759 1.080 -7.679 1.00 0.54 H
-ATOM 140 HG21 THR A 258 1.133 3.300 -9.619 1.00 1.29 H
-ATOM 141 HG22 THR A 258 0.048 1.964 -10.002 1.00 1.17 H
-ATOM 142 HG23 THR A 258 1.277 1.770 -8.753 1.00 1.22 H
-ATOM 143 N THR A 259 -1.485 3.773 -5.330 1.00 0.37 N
-ATOM 144 CA THR A 259 -2.442 4.521 -4.511 1.00 0.43 C
-ATOM 145 C THR A 259 -1.713 5.362 -3.467 1.00 0.43 C
-ATOM 146 O THR A 259 -2.014 6.543 -3.297 1.00 0.48 O
-ATOM 147 CB THR A 259 -3.466 3.603 -3.809 1.00 0.54 C
-ATOM 148 OG1 THR A 259 -2.796 2.539 -3.122 1.00 0.58 O
-ATOM 149 CG2 THR A 259 -4.454 3.026 -4.813 1.00 0.65 C
-ATOM 150 H THR A 259 -1.466 2.789 -5.276 1.00 0.38 H
-ATOM 151 HA THR A 259 -2.983 5.187 -5.170 1.00 0.47 H
-ATOM 152 HB THR A 259 -4.015 4.192 -3.088 1.00 0.61 H
-ATOM 153 HG1 THR A 259 -2.599 1.827 -3.749 1.00 0.67 H
-ATOM 154 HG21 THR A 259 -4.681 2.005 -4.548 1.00 1.25 H
-ATOM 155 HG22 THR A 259 -4.020 3.054 -5.801 1.00 1.24 H
-ATOM 156 HG23 THR A 259 -5.362 3.611 -4.802 1.00 1.21 H
-ATOM 157 N VAL A 260 -0.737 4.758 -2.790 1.00 0.43 N
-ATOM 158 CA VAL A 260 0.048 5.473 -1.784 1.00 0.52 C
-ATOM 159 C VAL A 260 0.783 6.659 -2.417 1.00 0.52 C
-ATOM 160 O VAL A 260 0.738 7.772 -1.893 1.00 0.59 O
-ATOM 161 CB VAL A 260 1.060 4.540 -1.081 1.00 0.58 C
-ATOM 162 CG1 VAL A 260 2.012 5.333 -0.199 1.00 0.75 C
-ATOM 163 CG2 VAL A 260 0.328 3.488 -0.259 1.00 0.65 C
-ATOM 164 H VAL A 260 -0.529 3.811 -2.980 1.00 0.42 H
-ATOM 165 HA VAL A 260 -0.639 5.850 -1.039 1.00 0.60 H
-ATOM 166 HB VAL A 260 1.642 4.034 -1.838 1.00 0.56 H
-ATOM 167 HG11 VAL A 260 2.662 5.933 -0.820 1.00 1.24 H
-ATOM 168 HG12 VAL A 260 2.604 4.653 0.393 1.00 1.30 H
-ATOM 169 HG13 VAL A 260 1.442 5.978 0.455 1.00 1.35 H
-ATOM 170 HG21 VAL A 260 -0.289 3.976 0.483 1.00 1.18 H
-ATOM 171 HG22 VAL A 260 1.046 2.849 0.233 1.00 1.29 H
-ATOM 172 HG23 VAL A 260 -0.297 2.893 -0.909 1.00 1.15 H
-ATOM 173 N LEU A 261 1.432 6.416 -3.560 1.00 0.49 N
-ATOM 174 CA LEU A 261 2.152 7.472 -4.276 1.00 0.56 C
-ATOM 175 C LEU A 261 1.181 8.594 -4.657 1.00 0.54 C
-ATOM 176 O LEU A 261 1.461 9.774 -4.452 1.00 0.63 O
-ATOM 177 CB LEU A 261 2.818 6.895 -5.536 1.00 0.60 C
-ATOM 178 CG LEU A 261 4.114 7.584 -5.995 1.00 0.95 C
-ATOM 179 CD1 LEU A 261 3.852 9.000 -6.486 1.00 1.39 C
-ATOM 180 CD2 LEU A 261 5.141 7.594 -4.869 1.00 1.33 C
-ATOM 181 H LEU A 261 1.415 5.506 -3.937 1.00 0.47 H
-ATOM 182 HA LEU A 261 2.912 7.868 -3.620 1.00 0.65 H
-ATOM 183 HB2 LEU A 261 3.040 5.854 -5.349 1.00 0.67 H
-ATOM 184 HB3 LEU A 261 2.106 6.949 -6.346 1.00 0.74 H
-ATOM 185 HG LEU A 261 4.533 7.024 -6.818 1.00 0.96 H
-ATOM 186 HD11 LEU A 261 3.604 8.979 -7.537 1.00 1.76 H
-ATOM 187 HD12 LEU A 261 4.737 9.602 -6.337 1.00 1.74 H
-ATOM 188 HD13 LEU A 261 3.029 9.428 -5.931 1.00 1.63 H
-ATOM 189 HD21 LEU A 261 5.852 8.389 -5.036 1.00 1.75 H
-ATOM 190 HD22 LEU A 261 5.658 6.646 -4.847 1.00 1.81 H
-ATOM 191 HD23 LEU A 261 4.641 7.751 -3.924 1.00 1.77 H
-ATOM 192 N THR A 262 0.026 8.202 -5.190 1.00 0.47 N
-ATOM 193 CA THR A 262 -1.014 9.153 -5.582 1.00 0.49 C
-ATOM 194 C THR A 262 -1.490 9.969 -4.379 1.00 0.51 C
-ATOM 195 O THR A 262 -1.558 11.197 -4.427 1.00 0.58 O
-ATOM 196 CB THR A 262 -2.229 8.421 -6.196 1.00 0.52 C
-ATOM 197 OG1 THR A 262 -1.818 7.639 -7.324 1.00 0.59 O
-ATOM 198 CG2 THR A 262 -3.304 9.408 -6.623 1.00 0.59 C
-ATOM 199 H THR A 262 -0.137 7.240 -5.310 1.00 0.45 H
-ATOM 200 HA THR A 262 -0.603 9.820 -6.326 1.00 0.55 H
-ATOM 201 HB THR A 262 -2.645 7.762 -5.447 1.00 0.54 H
-ATOM 202 HG1 THR A 262 -1.385 6.827 -7.015 1.00 0.90 H
-ATOM 203 HG21 THR A 262 -4.268 9.054 -6.288 1.00 1.20 H
-ATOM 204 HG22 THR A 262 -3.305 9.496 -7.698 1.00 1.26 H
-ATOM 205 HG23 THR A 262 -3.101 10.372 -6.181 1.00 1.09 H
-ATOM 206 N MET A 263 -1.817 9.271 -3.299 1.00 0.50 N
-ATOM 207 CA MET A 263 -2.286 9.914 -2.075 1.00 0.59 C
-ATOM 208 C MET A 263 -1.216 10.831 -1.461 1.00 0.62 C
-ATOM 209 O MET A 263 -1.511 11.966 -1.082 1.00 0.66 O
-ATOM 210 CB MET A 263 -2.704 8.851 -1.058 1.00 0.70 C
-ATOM 211 CG MET A 263 -3.297 9.421 0.221 1.00 0.88 C
-ATOM 212 SD MET A 263 -3.745 8.140 1.407 1.00 1.11 S
-ATOM 213 CE MET A 263 -4.426 9.132 2.734 1.00 1.45 C
-ATOM 214 H MET A 263 -1.746 8.289 -3.326 1.00 0.48 H
-ATOM 215 HA MET A 263 -3.148 10.512 -2.328 1.00 0.62 H
-ATOM 216 HB2 MET A 263 -3.440 8.204 -1.512 1.00 0.73 H
-ATOM 217 HB3 MET A 263 -1.837 8.262 -0.795 1.00 0.75 H
-ATOM 218 HG2 MET A 263 -2.570 10.076 0.678 1.00 0.94 H
-ATOM 219 HG3 MET A 263 -4.182 9.986 -0.029 1.00 0.94 H
-ATOM 220 HE1 MET A 263 -4.139 8.705 3.685 1.00 1.83 H
-ATOM 221 HE2 MET A 263 -5.503 9.145 2.657 1.00 1.83 H
-ATOM 222 HE3 MET A 263 -4.046 10.140 2.662 1.00 1.86 H
-ATOM 223 N THR A 264 0.019 10.335 -1.346 1.00 0.64 N
-ATOM 224 CA THR A 264 1.099 11.129 -0.750 1.00 0.74 C
-ATOM 225 C THR A 264 1.465 12.360 -1.587 1.00 0.72 C
-ATOM 226 O THR A 264 1.788 13.408 -1.028 1.00 0.82 O
-ATOM 227 CB THR A 264 2.380 10.303 -0.471 1.00 0.82 C
-ATOM 228 OG1 THR A 264 3.309 11.092 0.284 1.00 0.98 O
-ATOM 229 CG2 THR A 264 3.055 9.841 -1.755 1.00 0.78 C
-ATOM 230 H THR A 264 0.204 9.412 -1.652 1.00 0.64 H
-ATOM 231 HA THR A 264 0.731 11.481 0.205 1.00 0.80 H
-ATOM 232 HB THR A 264 2.108 9.432 0.109 1.00 0.87 H
-ATOM 233 HG1 THR A 264 3.224 12.018 0.032 1.00 1.37 H
-ATOM 234 HG21 THR A 264 2.489 9.028 -2.188 1.00 1.32 H
-ATOM 235 HG22 THR A 264 4.056 9.503 -1.533 1.00 1.25 H
-ATOM 236 HG23 THR A 264 3.098 10.663 -2.454 1.00 1.28 H
-ATOM 237 N THR A 265 1.426 12.247 -2.918 1.00 0.64 N
-ATOM 238 CA THR A 265 1.768 13.389 -3.770 1.00 0.68 C
-ATOM 239 C THR A 265 0.663 14.448 -3.753 1.00 0.64 C
-ATOM 240 O THR A 265 0.947 15.646 -3.749 1.00 0.71 O
-ATOM 241 CB THR A 265 2.090 12.982 -5.231 1.00 0.74 C
-ATOM 242 OG1 THR A 265 2.596 14.116 -5.946 1.00 0.83 O
-ATOM 243 CG2 THR A 265 0.866 12.437 -5.958 1.00 0.73 C
-ATOM 244 H THR A 265 1.169 11.389 -3.327 1.00 0.61 H
-ATOM 245 HA THR A 265 2.660 13.835 -3.352 1.00 0.75 H
-ATOM 246 HB THR A 265 2.849 12.214 -5.213 1.00 0.81 H
-ATOM 247 HG1 THR A 265 3.232 14.587 -5.395 1.00 1.10 H
-ATOM 248 HG21 THR A 265 0.997 12.559 -7.024 1.00 1.18 H
-ATOM 249 HG22 THR A 265 -0.013 12.977 -5.638 1.00 1.15 H
-ATOM 250 HG23 THR A 265 0.749 11.388 -5.727 1.00 1.08 H
-ATOM 251 N GLN A 266 -0.595 14.003 -3.726 1.00 0.58 N
-ATOM 252 CA GLN A 266 -1.736 14.919 -3.690 1.00 0.57 C
-ATOM 253 C GLN A 266 -1.901 15.541 -2.300 1.00 0.56 C
-ATOM 254 O GLN A 266 -2.218 16.727 -2.177 1.00 0.57 O
-ATOM 255 CB GLN A 266 -3.020 14.190 -4.088 1.00 0.61 C
-ATOM 256 CG GLN A 266 -4.235 15.102 -4.138 1.00 0.74 C
-ATOM 257 CD GLN A 266 -4.120 16.178 -5.200 1.00 0.90 C
-ATOM 258 OE1 GLN A 266 -4.104 15.894 -6.394 1.00 1.06 O
-ATOM 259 NE2 GLN A 266 -4.028 17.426 -4.772 1.00 1.02 N
-ATOM 260 H GLN A 266 -0.760 13.034 -3.721 1.00 0.60 H
-ATOM 261 HA GLN A 266 -1.553 15.707 -4.398 1.00 0.61 H
-ATOM 262 HB2 GLN A 266 -2.885 13.749 -5.064 1.00 0.68 H
-ATOM 263 HB3 GLN A 266 -3.214 13.406 -3.370 1.00 0.61 H
-ATOM 264 HG2 GLN A 266 -5.107 14.505 -4.348 1.00 0.82 H
-ATOM 265 HG3 GLN A 266 -4.347 15.579 -3.176 1.00 0.78 H
-ATOM 266 HE21 GLN A 266 -4.035 17.586 -3.806 1.00 1.04 H
-ATOM 267 HE22 GLN A 266 -3.953 18.135 -5.441 1.00 1.17 H
-ATOM 268 N SER A 267 -1.695 14.726 -1.262 1.00 0.62 N
-ATOM 269 CA SER A 267 -1.827 15.172 0.130 1.00 0.70 C
-ATOM 270 C SER A 267 -3.269 15.609 0.427 1.00 0.68 C
-ATOM 271 O SER A 267 -4.219 14.988 -0.050 1.00 0.74 O
-ATOM 272 CB SER A 267 -0.833 16.312 0.423 1.00 0.77 C
-ATOM 273 OG SER A 267 -0.857 16.694 1.791 1.00 0.93 O
-ATOM 274 H SER A 267 -1.455 13.786 -1.437 1.00 0.68 H
-ATOM 275 HA SER A 267 -1.587 14.331 0.764 1.00 0.83 H
-ATOM 276 HB2 SER A 267 0.165 15.985 0.174 1.00 0.85 H
-ATOM 277 HB3 SER A 267 -1.090 17.170 -0.182 1.00 0.73 H
-ATOM 278 HG SER A 267 0.031 16.631 2.155 1.00 1.14 H
-ATOM 279 N SER A 268 -3.431 16.670 1.212 1.00 0.72 N
-ATOM 280 CA SER A 268 -4.760 17.176 1.560 1.00 0.80 C
-ATOM 281 C SER A 268 -5.401 17.918 0.383 1.00 0.81 C
-ATOM 282 O SER A 268 -6.573 17.712 0.070 1.00 0.95 O
-ATOM 283 CB SER A 268 -4.677 18.101 2.777 1.00 0.93 C
-ATOM 284 OG SER A 268 -3.779 19.175 2.545 1.00 0.95 O
-ATOM 285 H SER A 268 -2.631 17.125 1.570 1.00 0.76 H
-ATOM 286 HA SER A 268 -5.379 16.327 1.810 1.00 0.86 H
-ATOM 287 HB2 SER A 268 -5.656 18.506 2.987 1.00 1.05 H
-ATOM 288 HB3 SER A 268 -4.331 17.538 3.631 1.00 1.04 H
-ATOM 289 HG SER A 268 -3.912 19.854 3.214 1.00 1.10 H
-ATOM 290 N GLY A 269 -4.625 18.782 -0.264 1.00 0.80 N
-ATOM 291 CA GLY A 269 -5.137 19.536 -1.398 1.00 0.91 C
-ATOM 292 C GLY A 269 -4.248 20.711 -1.775 1.00 0.92 C
-ATOM 293 O GLY A 269 -3.022 20.615 -1.706 1.00 0.79 O
-ATOM 294 H GLY A 269 -3.699 18.908 0.029 1.00 0.79 H
-ATOM 295 HA2 GLY A 269 -5.217 18.874 -2.247 1.00 0.92 H
-ATOM 296 HA3 GLY A 269 -6.121 19.908 -1.153 1.00 1.07 H
-ATOM 297 N SER A 270 -4.876 21.822 -2.176 1.00 1.14 N
-ATOM 298 CA SER A 270 -4.155 23.040 -2.573 1.00 1.22 C
-ATOM 299 C SER A 270 -3.148 22.745 -3.702 1.00 1.02 C
-ATOM 300 O SER A 270 -3.501 22.118 -4.701 1.00 1.03 O
-ATOM 301 CB SER A 270 -3.455 23.669 -1.355 1.00 1.31 C
-ATOM 302 OG SER A 270 -2.891 24.933 -1.671 1.00 1.44 O
-ATOM 303 H SER A 270 -5.855 21.824 -2.205 1.00 1.30 H
-ATOM 304 HA SER A 270 -4.888 23.739 -2.949 1.00 1.43 H
-ATOM 305 HB2 SER A 270 -4.174 23.801 -0.561 1.00 1.49 H
-ATOM 306 HB3 SER A 270 -2.667 23.011 -1.018 1.00 1.17 H
-ATOM 307 HG SER A 270 -3.337 25.621 -1.166 1.00 1.64 H
-ATOM 308 N ARG A 271 -1.905 23.193 -3.545 1.00 0.92 N
-ATOM 309 CA ARG A 271 -0.871 22.968 -4.560 1.00 0.77 C
-ATOM 310 C ARG A 271 -0.206 21.586 -4.403 1.00 0.61 C
-ATOM 311 O ARG A 271 0.791 21.290 -5.063 1.00 0.49 O
-ATOM 312 CB ARG A 271 0.186 24.074 -4.482 1.00 0.83 C
-ATOM 313 CG ARG A 271 1.058 24.157 -5.721 1.00 0.75 C
-ATOM 314 CD ARG A 271 0.291 24.712 -6.914 1.00 0.81 C
-ATOM 315 NE ARG A 271 1.008 24.516 -8.175 1.00 0.83 N
-ATOM 316 CZ ARG A 271 2.170 25.066 -8.469 1.00 0.82 C
-ATOM 317 NH1 ARG A 271 2.740 25.919 -7.665 1.00 0.82 N
-ATOM 318 NH2 ARG A 271 2.765 24.781 -9.593 1.00 1.00 N
-ATOM 319 H ARG A 271 -1.679 23.695 -2.729 1.00 1.02 H
-ATOM 320 HA ARG A 271 -1.343 23.012 -5.529 1.00 0.84 H
-ATOM 321 HB2 ARG A 271 -0.310 25.025 -4.351 1.00 1.00 H
-ATOM 322 HB3 ARG A 271 0.824 23.889 -3.630 1.00 0.87 H
-ATOM 323 HG2 ARG A 271 1.903 24.798 -5.518 1.00 0.82 H
-ATOM 324 HG3 ARG A 271 1.405 23.164 -5.963 1.00 0.73 H
-ATOM 325 HD2 ARG A 271 -0.664 24.210 -6.974 1.00 0.93 H
-ATOM 326 HD3 ARG A 271 0.131 25.769 -6.762 1.00 0.91 H
-ATOM 327 HE ARG A 271 0.603 23.915 -8.830 1.00 0.99 H
-ATOM 328 HH11 ARG A 271 2.302 26.178 -6.812 1.00 0.87 H
-ATOM 329 HH12 ARG A 271 3.649 26.303 -7.915 1.00 0.92 H
-ATOM 330 HH21 ARG A 271 2.347 24.150 -10.237 1.00 1.16 H
-ATOM 331 HH22 ARG A 271 3.656 25.203 -9.797 1.00 1.07 H
-ATOM 332 N ALA A 272 -0.769 20.745 -3.527 1.00 0.77 N
-ATOM 333 CA ALA A 272 -0.243 19.397 -3.275 1.00 0.72 C
-ATOM 334 C ALA A 272 1.255 19.429 -2.942 1.00 0.59 C
-ATOM 335 O ALA A 272 1.721 20.321 -2.231 1.00 0.79 O
-ATOM 336 CB ALA A 272 -0.519 18.487 -4.467 1.00 0.78 C
-ATOM 337 H ALA A 272 -1.566 21.041 -3.034 1.00 0.99 H
-ATOM 338 HA ALA A 272 -0.773 18.996 -2.421 1.00 0.91 H
-ATOM 339 HB1 ALA A 272 -1.563 18.206 -4.471 1.00 1.39 H
-ATOM 340 HB2 ALA A 272 0.091 17.599 -4.390 1.00 1.25 H
-ATOM 341 HB3 ALA A 272 -0.282 19.010 -5.382 1.00 1.28 H
-ATOM 342 N SER A 273 2.017 18.466 -3.457 1.00 0.55 N
-ATOM 343 CA SER A 273 3.455 18.432 -3.196 1.00 0.69 C
-ATOM 344 C SER A 273 4.270 18.943 -4.363 1.00 0.77 C
-ATOM 345 O SER A 273 5.422 18.554 -4.552 1.00 1.04 O
-ATOM 346 CB SER A 273 3.928 17.042 -2.818 1.00 0.90 C
-ATOM 347 OG SER A 273 3.524 16.071 -3.777 1.00 0.99 O
-ATOM 348 H SER A 273 1.608 17.775 -4.023 1.00 0.67 H
-ATOM 349 HA SER A 273 3.644 19.097 -2.362 1.00 0.80 H
-ATOM 350 HB2 SER A 273 5.007 17.052 -2.763 1.00 1.13 H
-ATOM 351 HB3 SER A 273 3.519 16.786 -1.856 1.00 0.95 H
-ATOM 352 HG SER A 273 2.572 15.905 -3.687 1.00 1.32 H
-ATOM 353 N LEU A 274 3.706 19.881 -5.095 1.00 0.65 N
-ATOM 354 CA LEU A 274 4.431 20.507 -6.183 1.00 0.85 C
-ATOM 355 C LEU A 274 4.485 22.032 -5.986 1.00 0.87 C
-ATOM 356 O LEU A 274 4.222 22.784 -6.927 1.00 0.87 O
-ATOM 357 CB LEU A 274 3.816 20.154 -7.547 1.00 0.94 C
-ATOM 358 CG LEU A 274 2.297 20.309 -7.657 1.00 0.86 C
-ATOM 359 CD1 LEU A 274 1.914 20.839 -9.030 1.00 1.05 C
-ATOM 360 CD2 LEU A 274 1.605 18.975 -7.404 1.00 1.04 C
-ATOM 361 H LEU A 274 2.806 20.200 -4.862 1.00 0.55 H
-ATOM 362 HA LEU A 274 5.442 20.128 -6.153 1.00 1.10 H
-ATOM 363 HB2 LEU A 274 4.274 20.784 -8.295 1.00 1.10 H
-ATOM 364 HB3 LEU A 274 4.065 19.126 -7.771 1.00 1.14 H
-ATOM 365 HG LEU A 274 1.954 21.014 -6.914 1.00 0.94 H
-ATOM 366 HD11 LEU A 274 1.480 20.042 -9.615 1.00 1.56 H
-ATOM 367 HD12 LEU A 274 2.795 21.214 -9.530 1.00 1.49 H
-ATOM 368 HD13 LEU A 274 1.196 21.637 -8.920 1.00 1.51 H
-ATOM 369 HD21 LEU A 274 2.187 18.180 -7.846 1.00 1.57 H
-ATOM 370 HD22 LEU A 274 0.621 18.991 -7.848 1.00 1.48 H
-ATOM 371 HD23 LEU A 274 1.520 18.811 -6.340 1.00 1.50 H
-ATOM 372 N PRO A 275 4.807 22.526 -4.748 1.00 1.05 N
-ATOM 373 CA PRO A 275 4.871 23.980 -4.450 1.00 1.30 C
-ATOM 374 C PRO A 275 5.974 24.736 -5.219 1.00 1.52 C
-ATOM 375 O PRO A 275 6.880 25.312 -4.615 1.00 2.03 O
-ATOM 376 CB PRO A 275 5.165 24.019 -2.942 1.00 1.64 C
-ATOM 377 CG PRO A 275 4.687 22.704 -2.454 1.00 1.44 C
-ATOM 378 CD PRO A 275 5.106 21.741 -3.520 1.00 1.17 C
-ATOM 379 HA PRO A 275 3.918 24.451 -4.631 1.00 1.21 H
-ATOM 380 HB2 PRO A 275 6.226 24.142 -2.780 1.00 1.89 H
-ATOM 381 HB3 PRO A 275 4.623 24.833 -2.485 1.00 1.82 H
-ATOM 382 HG2 PRO A 275 5.148 22.462 -1.508 1.00 1.68 H
-ATOM 383 HG3 PRO A 275 3.612 22.722 -2.370 1.00 1.35 H
-ATOM 384 HD2 PRO A 275 6.160 21.519 -3.435 1.00 1.40 H
-ATOM 385 HD3 PRO A 275 4.512 20.818 -3.471 1.00 0.95 H
-ATOM 386 N LYS A 276 5.888 24.732 -6.546 1.00 1.24 N
-ATOM 387 CA LYS A 276 6.873 25.413 -7.391 1.00 1.48 C
-ATOM 388 C LYS A 276 6.248 25.926 -8.697 1.00 1.27 C
-ATOM 389 O LYS A 276 6.991 26.098 -9.684 1.00 1.51 O
-ATOM 390 CB LYS A 276 8.063 24.479 -7.688 1.00 1.85 C
-ATOM 391 CG LYS A 276 7.682 23.036 -8.039 1.00 1.88 C
-ATOM 392 CD LYS A 276 7.072 22.903 -9.432 1.00 1.90 C
-ATOM 393 CE LYS A 276 8.051 23.313 -10.526 1.00 2.23 C
-ATOM 394 NZ LYS A 276 9.299 22.497 -10.496 1.00 2.76 N
-ATOM 395 OXT LYS A 276 5.016 26.150 -8.719 1.00 1.15 O
-ATOM 396 H LYS A 276 5.139 24.252 -6.972 1.00 0.97 H
-ATOM 397 HA LYS A 276 7.239 26.264 -6.835 1.00 1.78 H
-ATOM 398 HB2 LYS A 276 8.621 24.887 -8.517 1.00 1.98 H
-ATOM 399 HB3 LYS A 276 8.703 24.454 -6.819 1.00 2.18 H
-ATOM 400 HG2 LYS A 276 8.570 22.423 -7.994 1.00 2.28 H
-ATOM 401 HG3 LYS A 276 6.968 22.681 -7.309 1.00 1.70 H
-ATOM 402 HD2 LYS A 276 6.787 21.873 -9.590 1.00 2.15 H
-ATOM 403 HD3 LYS A 276 6.196 23.533 -9.490 1.00 1.65 H
-ATOM 404 HE2 LYS A 276 7.571 23.183 -11.483 1.00 2.36 H
-ATOM 405 HE3 LYS A 276 8.308 24.353 -10.391 1.00 2.13 H
-ATOM 406 HZ1 LYS A 276 9.265 21.822 -9.707 1.00 3.15 H
-ATOM 407 HZ2 LYS A 276 10.128 23.116 -10.372 1.00 3.01 H
-ATOM 408 HZ3 LYS A 276 9.404 21.971 -11.387 1.00 3.01 H
-TER
-ENDMDL
-MODEL 11
-ATOM 1 N ALA A 249 -1.950 -2.063 -13.217 1.00 1.34 N
-ATOM 2 CA ALA A 249 -0.885 -2.100 -12.178 1.00 1.01 C
-ATOM 3 C ALA A 249 -0.476 -3.535 -11.869 1.00 0.81 C
-ATOM 4 O ALA A 249 -1.262 -4.467 -12.046 1.00 0.96 O
-ATOM 5 CB ALA A 249 -1.333 -1.392 -10.910 1.00 1.10 C
-ATOM 6 H1 ALA A 249 -2.787 -1.563 -12.854 1.00 1.62 H
-ATOM 7 H2 ALA A 249 -2.223 -3.033 -13.480 1.00 1.77 H
-ATOM 8 H3 ALA A 249 -1.605 -1.571 -14.066 1.00 1.78 H
-ATOM 9 HA ALA A 249 -0.022 -1.576 -12.565 1.00 1.00 H
-ATOM 10 HB1 ALA A 249 -0.462 -1.031 -10.378 1.00 1.55 H
-ATOM 11 HB2 ALA A 249 -1.873 -2.084 -10.283 1.00 1.53 H
-ATOM 12 HB3 ALA A 249 -1.968 -0.560 -11.167 1.00 1.50 H
-ATOM 13 N PRO A 250 0.771 -3.726 -11.416 1.00 0.65 N
-ATOM 14 CA PRO A 250 1.315 -5.048 -11.089 1.00 0.66 C
-ATOM 15 C PRO A 250 0.711 -5.664 -9.818 1.00 0.50 C
-ATOM 16 O PRO A 250 1.361 -5.737 -8.775 1.00 0.46 O
-ATOM 17 CB PRO A 250 2.807 -4.764 -10.918 1.00 0.86 C
-ATOM 18 CG PRO A 250 2.874 -3.353 -10.478 1.00 0.84 C
-ATOM 19 CD PRO A 250 1.762 -2.659 -11.199 1.00 0.75 C
-ATOM 20 HA PRO A 250 1.177 -5.738 -11.910 1.00 0.84 H
-ATOM 21 HB2 PRO A 250 3.221 -5.424 -10.179 1.00 1.13 H
-ATOM 22 HB3 PRO A 250 3.307 -4.898 -11.862 1.00 1.12 H
-ATOM 23 HG2 PRO A 250 2.727 -3.292 -9.409 1.00 1.14 H
-ATOM 24 HG3 PRO A 250 3.825 -2.925 -10.755 1.00 1.00 H
-ATOM 25 HD2 PRO A 250 1.348 -1.877 -10.583 1.00 0.78 H
-ATOM 26 HD3 PRO A 250 2.110 -2.265 -12.138 1.00 0.95 H
-ATOM 27 N ALA A 251 -0.541 -6.111 -9.932 1.00 0.64 N
-ATOM 28 CA ALA A 251 -1.273 -6.748 -8.832 1.00 0.70 C
-ATOM 29 C ALA A 251 -1.465 -5.833 -7.611 1.00 0.56 C
-ATOM 30 O ALA A 251 -1.571 -4.611 -7.738 1.00 0.55 O
-ATOM 31 CB ALA A 251 -0.583 -8.046 -8.434 1.00 0.81 C
-ATOM 32 H ALA A 251 -0.989 -6.020 -10.801 1.00 0.82 H
-ATOM 33 HA ALA A 251 -2.251 -7.006 -9.212 1.00 0.91 H
-ATOM 34 HB1 ALA A 251 -1.038 -8.435 -7.537 1.00 1.34 H
-ATOM 35 HB2 ALA A 251 0.464 -7.855 -8.256 1.00 1.32 H
-ATOM 36 HB3 ALA A 251 -0.688 -8.766 -9.233 1.00 1.33 H
-ATOM 37 N ARG A 252 -1.541 -6.447 -6.425 1.00 0.61 N
-ATOM 38 CA ARG A 252 -1.761 -5.713 -5.178 1.00 0.59 C
-ATOM 39 C ARG A 252 -0.602 -4.768 -4.851 1.00 0.50 C
-ATOM 40 O ARG A 252 -0.830 -3.611 -4.503 1.00 0.50 O
-ATOM 41 CB ARG A 252 -1.988 -6.692 -4.017 1.00 0.77 C
-ATOM 42 CG ARG A 252 -2.981 -6.192 -2.969 1.00 0.95 C
-ATOM 43 CD ARG A 252 -2.506 -4.909 -2.298 1.00 0.99 C
-ATOM 44 NE ARG A 252 -3.510 -4.337 -1.403 1.00 1.30 N
-ATOM 45 CZ ARG A 252 -3.853 -4.860 -0.243 1.00 1.59 C
-ATOM 46 NH1 ARG A 252 -3.278 -5.950 0.190 1.00 1.68 N
-ATOM 47 NH2 ARG A 252 -4.766 -4.282 0.489 1.00 1.92 N
-ATOM 48 H ARG A 252 -1.474 -7.423 -6.394 1.00 0.75 H
-ATOM 49 HA ARG A 252 -2.650 -5.120 -5.306 1.00 0.63 H
-ATOM 50 HB2 ARG A 252 -2.362 -7.623 -4.416 1.00 0.85 H
-ATOM 51 HB3 ARG A 252 -1.044 -6.875 -3.527 1.00 0.81 H
-ATOM 52 HG2 ARG A 252 -3.929 -6.003 -3.448 1.00 0.99 H
-ATOM 53 HG3 ARG A 252 -3.104 -6.956 -2.216 1.00 1.12 H
-ATOM 54 HD2 ARG A 252 -1.617 -5.129 -1.726 1.00 1.07 H
-ATOM 55 HD3 ARG A 252 -2.268 -4.187 -3.064 1.00 0.91 H
-ATOM 56 HE ARG A 252 -3.952 -3.515 -1.692 1.00 1.39 H
-ATOM 57 HH11 ARG A 252 -2.575 -6.393 -0.358 1.00 1.52 H
-ATOM 58 HH12 ARG A 252 -3.543 -6.335 1.070 1.00 1.98 H
-ATOM 59 HH21 ARG A 252 -5.204 -3.444 0.174 1.00 1.98 H
-ATOM 60 HH22 ARG A 252 -5.024 -4.680 1.366 1.00 2.16 H
-ATOM 61 N VAL A 253 0.632 -5.256 -4.961 1.00 0.52 N
-ATOM 62 CA VAL A 253 1.805 -4.429 -4.669 1.00 0.55 C
-ATOM 63 C VAL A 253 1.783 -3.159 -5.515 1.00 0.45 C
-ATOM 64 O VAL A 253 1.951 -2.047 -5.006 1.00 0.45 O
-ATOM 65 CB VAL A 253 3.124 -5.210 -4.892 1.00 0.66 C
-ATOM 66 CG1 VAL A 253 3.290 -5.672 -6.333 1.00 0.62 C
-ATOM 67 CG2 VAL A 253 4.323 -4.387 -4.446 1.00 0.82 C
-ATOM 68 H VAL A 253 0.755 -6.183 -5.242 1.00 0.57 H
-ATOM 69 HA VAL A 253 1.753 -4.147 -3.627 1.00 0.63 H
-ATOM 70 HB VAL A 253 3.080 -6.089 -4.283 1.00 0.76 H
-ATOM 71 HG11 VAL A 253 3.526 -4.824 -6.957 1.00 1.20 H
-ATOM 72 HG12 VAL A 253 2.372 -6.127 -6.673 1.00 1.16 H
-ATOM 73 HG13 VAL A 253 4.092 -6.394 -6.389 1.00 1.22 H
-ATOM 74 HG21 VAL A 253 5.233 -4.906 -4.707 1.00 1.28 H
-ATOM 75 HG22 VAL A 253 4.284 -4.245 -3.375 1.00 1.33 H
-ATOM 76 HG23 VAL A 253 4.302 -3.425 -4.937 1.00 1.41 H
-ATOM 77 N GLY A 254 1.531 -3.347 -6.801 1.00 0.43 N
-ATOM 78 CA GLY A 254 1.440 -2.230 -7.720 1.00 0.42 C
-ATOM 79 C GLY A 254 0.320 -1.276 -7.350 1.00 0.35 C
-ATOM 80 O GLY A 254 0.533 -0.068 -7.256 1.00 0.39 O
-ATOM 81 H GLY A 254 1.387 -4.267 -7.119 1.00 0.48 H
-ATOM 82 HA2 GLY A 254 2.377 -1.692 -7.713 1.00 0.49 H
-ATOM 83 HA3 GLY A 254 1.261 -2.610 -8.716 1.00 0.46 H
-ATOM 84 N LEU A 255 -0.873 -1.820 -7.113 1.00 0.34 N
-ATOM 85 CA LEU A 255 -2.019 -1.005 -6.721 1.00 0.37 C
-ATOM 86 C LEU A 255 -1.738 -0.246 -5.431 1.00 0.31 C
-ATOM 87 O LEU A 255 -2.056 0.938 -5.309 1.00 0.33 O
-ATOM 88 CB LEU A 255 -3.255 -1.875 -6.531 1.00 0.50 C
-ATOM 89 CG LEU A 255 -4.195 -1.904 -7.725 1.00 0.70 C
-ATOM 90 CD1 LEU A 255 -5.400 -2.775 -7.421 1.00 0.86 C
-ATOM 91 CD2 LEU A 255 -4.622 -0.488 -8.080 1.00 0.87 C
-ATOM 92 H LEU A 255 -0.979 -2.796 -7.186 1.00 0.38 H
-ATOM 93 HA LEU A 255 -2.212 -0.297 -7.512 1.00 0.43 H
-ATOM 94 HB2 LEU A 255 -2.930 -2.886 -6.325 1.00 0.61 H
-ATOM 95 HB3 LEU A 255 -3.802 -1.508 -5.678 1.00 0.56 H
-ATOM 96 HG LEU A 255 -3.678 -2.324 -8.576 1.00 0.83 H
-ATOM 97 HD11 LEU A 255 -5.381 -3.062 -6.378 1.00 1.33 H
-ATOM 98 HD12 LEU A 255 -5.369 -3.660 -8.038 1.00 1.37 H
-ATOM 99 HD13 LEU A 255 -6.303 -2.223 -7.625 1.00 1.39 H
-ATOM 100 HD21 LEU A 255 -4.754 0.084 -7.172 1.00 1.37 H
-ATOM 101 HD22 LEU A 255 -5.552 -0.515 -8.626 1.00 1.29 H
-ATOM 102 HD23 LEU A 255 -3.859 -0.023 -8.687 1.00 1.39 H
-ATOM 103 N GLY A 256 -1.142 -0.939 -4.472 1.00 0.33 N
-ATOM 104 CA GLY A 256 -0.826 -0.332 -3.200 1.00 0.36 C
-ATOM 105 C GLY A 256 0.149 0.829 -3.323 1.00 0.33 C
-ATOM 106 O GLY A 256 -0.139 1.931 -2.852 1.00 0.35 O
-ATOM 107 H GLY A 256 -0.915 -1.887 -4.632 1.00 0.38 H
-ATOM 108 HA2 GLY A 256 -1.743 0.029 -2.759 1.00 0.40 H
-ATOM 109 HA3 GLY A 256 -0.399 -1.083 -2.556 1.00 0.45 H
-ATOM 110 N ILE A 257 1.300 0.600 -3.966 1.00 0.37 N
-ATOM 111 CA ILE A 257 2.289 1.669 -4.138 1.00 0.43 C
-ATOM 112 C ILE A 257 1.713 2.819 -4.973 1.00 0.38 C
-ATOM 113 O ILE A 257 1.960 3.989 -4.678 1.00 0.43 O
-ATOM 114 CB ILE A 257 3.623 1.165 -4.752 1.00 0.53 C
-ATOM 115 CG1 ILE A 257 3.418 0.524 -6.129 1.00 0.52 C
-ATOM 116 CG2 ILE A 257 4.297 0.182 -3.807 1.00 0.64 C
-ATOM 117 CD1 ILE A 257 3.737 1.448 -7.288 1.00 0.63 C
-ATOM 118 H ILE A 257 1.482 -0.298 -4.336 1.00 0.39 H
-ATOM 119 HA ILE A 257 2.507 2.054 -3.151 1.00 0.48 H
-ATOM 120 HB ILE A 257 4.279 2.016 -4.858 1.00 0.60 H
-ATOM 121 HG12 ILE A 257 4.056 -0.343 -6.214 1.00 0.60 H
-ATOM 122 HG13 ILE A 257 2.386 0.216 -6.224 1.00 0.50 H
-ATOM 123 HG21 ILE A 257 5.067 0.692 -3.248 1.00 1.20 H
-ATOM 124 HG22 ILE A 257 4.738 -0.621 -4.379 1.00 1.25 H
-ATOM 125 HG23 ILE A 257 3.564 -0.222 -3.124 1.00 1.22 H
-ATOM 126 HD11 ILE A 257 3.748 2.471 -6.939 1.00 1.20 H
-ATOM 127 HD12 ILE A 257 2.986 1.337 -8.055 1.00 1.25 H
-ATOM 128 HD13 ILE A 257 4.706 1.195 -7.693 1.00 1.20 H
-ATOM 129 N THR A 258 0.923 2.478 -5.997 1.00 0.35 N
-ATOM 130 CA THR A 258 0.291 3.486 -6.850 1.00 0.38 C
-ATOM 131 C THR A 258 -0.665 4.345 -6.023 1.00 0.35 C
-ATOM 132 O THR A 258 -0.683 5.572 -6.145 1.00 0.40 O
-ATOM 133 CB THR A 258 -0.494 2.848 -8.019 1.00 0.40 C
-ATOM 134 OG1 THR A 258 0.355 1.968 -8.766 1.00 0.47 O
-ATOM 135 CG2 THR A 258 -1.054 3.914 -8.951 1.00 0.54 C
-ATOM 136 H THR A 258 0.750 1.524 -6.173 1.00 0.36 H
-ATOM 137 HA THR A 258 1.068 4.115 -7.261 1.00 0.44 H
-ATOM 138 HB THR A 258 -1.319 2.280 -7.612 1.00 0.38 H
-ATOM 139 HG1 THR A 258 0.465 1.137 -8.278 1.00 0.48 H
-ATOM 140 HG21 THR A 258 -0.697 4.885 -8.641 1.00 1.15 H
-ATOM 141 HG22 THR A 258 -2.133 3.897 -8.910 1.00 1.25 H
-ATOM 142 HG23 THR A 258 -0.728 3.716 -9.961 1.00 1.12 H
-ATOM 143 N THR A 259 -1.442 3.691 -5.159 1.00 0.32 N
-ATOM 144 CA THR A 259 -2.385 4.389 -4.289 1.00 0.32 C
-ATOM 145 C THR A 259 -1.633 5.312 -3.334 1.00 0.32 C
-ATOM 146 O THR A 259 -1.989 6.482 -3.172 1.00 0.37 O
-ATOM 147 CB THR A 259 -3.248 3.403 -3.471 1.00 0.35 C
-ATOM 148 OG1 THR A 259 -3.909 2.479 -4.347 1.00 0.39 O
-ATOM 149 CG2 THR A 259 -4.291 4.145 -2.644 1.00 0.49 C
-ATOM 150 H THR A 259 -1.366 2.711 -5.094 1.00 0.32 H
-ATOM 151 HA THR A 259 -3.039 4.984 -4.911 1.00 0.37 H
-ATOM 152 HB THR A 259 -2.604 2.855 -2.800 1.00 0.38 H
-ATOM 153 HG1 THR A 259 -3.257 1.867 -4.723 1.00 0.51 H
-ATOM 154 HG21 THR A 259 -3.955 5.157 -2.464 1.00 1.14 H
-ATOM 155 HG22 THR A 259 -4.429 3.639 -1.700 1.00 1.15 H
-ATOM 156 HG23 THR A 259 -5.227 4.167 -3.181 1.00 1.17 H
-ATOM 157 N VAL A 260 -0.571 4.784 -2.720 1.00 0.32 N
-ATOM 158 CA VAL A 260 0.247 5.569 -1.800 1.00 0.36 C
-ATOM 159 C VAL A 260 0.880 6.764 -2.518 1.00 0.36 C
-ATOM 160 O VAL A 260 0.773 7.895 -2.048 1.00 0.41 O
-ATOM 161 CB VAL A 260 1.359 4.715 -1.152 1.00 0.43 C
-ATOM 162 CG1 VAL A 260 2.252 5.571 -0.266 1.00 0.55 C
-ATOM 163 CG2 VAL A 260 0.755 3.572 -0.350 1.00 0.49 C
-ATOM 164 H VAL A 260 -0.327 3.847 -2.904 1.00 0.32 H
-ATOM 165 HA VAL A 260 -0.397 5.938 -1.015 1.00 0.39 H
-ATOM 166 HB VAL A 260 1.967 4.291 -1.939 1.00 0.47 H
-ATOM 167 HG11 VAL A 260 1.688 5.915 0.588 1.00 1.21 H
-ATOM 168 HG12 VAL A 260 2.608 6.422 -0.829 1.00 1.19 H
-ATOM 169 HG13 VAL A 260 3.093 4.984 0.070 1.00 1.13 H
-ATOM 170 HG21 VAL A 260 1.543 3.016 0.137 1.00 1.14 H
-ATOM 171 HG22 VAL A 260 0.208 2.918 -1.013 1.00 1.14 H
-ATOM 172 HG23 VAL A 260 0.083 3.972 0.395 1.00 1.16 H
-ATOM 173 N LEU A 261 1.516 6.506 -3.668 1.00 0.36 N
-ATOM 174 CA LEU A 261 2.151 7.562 -4.463 1.00 0.41 C
-ATOM 175 C LEU A 261 1.133 8.648 -4.822 1.00 0.40 C
-ATOM 176 O LEU A 261 1.416 9.843 -4.719 1.00 0.46 O
-ATOM 177 CB LEU A 261 2.758 6.972 -5.741 1.00 0.46 C
-ATOM 178 CG LEU A 261 4.221 7.341 -6.012 1.00 0.67 C
-ATOM 179 CD1 LEU A 261 4.387 8.845 -6.173 1.00 1.18 C
-ATOM 180 CD2 LEU A 261 5.117 6.823 -4.897 1.00 1.24 C
-ATOM 181 H LEU A 261 1.552 5.576 -3.997 1.00 0.37 H
-ATOM 182 HA LEU A 261 2.938 8.002 -3.867 1.00 0.45 H
-ATOM 183 HB2 LEU A 261 2.688 5.895 -5.682 1.00 0.73 H
-ATOM 184 HB3 LEU A 261 2.168 7.308 -6.579 1.00 0.73 H
-ATOM 185 HG LEU A 261 4.533 6.874 -6.934 1.00 0.66 H
-ATOM 186 HD11 LEU A 261 4.148 9.128 -7.187 1.00 1.65 H
-ATOM 187 HD12 LEU A 261 5.408 9.121 -5.953 1.00 1.67 H
-ATOM 188 HD13 LEU A 261 3.724 9.354 -5.490 1.00 1.73 H
-ATOM 189 HD21 LEU A 261 5.533 7.659 -4.352 1.00 1.70 H
-ATOM 190 HD22 LEU A 261 5.917 6.235 -5.321 1.00 1.77 H
-ATOM 191 HD23 LEU A 261 4.538 6.208 -4.225 1.00 1.73 H
-ATOM 192 N THR A 262 -0.057 8.215 -5.223 1.00 0.39 N
-ATOM 193 CA THR A 262 -1.137 9.137 -5.576 1.00 0.42 C
-ATOM 194 C THR A 262 -1.544 9.979 -4.365 1.00 0.42 C
-ATOM 195 O THR A 262 -1.608 11.205 -4.437 1.00 0.46 O
-ATOM 196 CB THR A 262 -2.378 8.375 -6.089 1.00 0.46 C
-ATOM 197 OG1 THR A 262 -2.026 7.550 -7.208 1.00 0.51 O
-ATOM 198 CG2 THR A 262 -3.481 9.340 -6.494 1.00 0.54 C
-ATOM 199 H THR A 262 -0.219 7.245 -5.273 1.00 0.39 H
-ATOM 200 HA THR A 262 -0.782 9.789 -6.360 1.00 0.45 H
-ATOM 201 HB THR A 262 -2.747 7.746 -5.292 1.00 0.44 H
-ATOM 202 HG1 THR A 262 -1.593 6.742 -6.890 1.00 0.57 H
-ATOM 203 HG21 THR A 262 -3.485 10.183 -5.819 1.00 1.21 H
-ATOM 204 HG22 THR A 262 -4.435 8.835 -6.444 1.00 1.10 H
-ATOM 205 HG23 THR A 262 -3.308 9.684 -7.502 1.00 1.20 H
-ATOM 206 N MET A 263 -1.807 9.301 -3.250 1.00 0.42 N
-ATOM 207 CA MET A 263 -2.202 9.961 -2.004 1.00 0.46 C
-ATOM 208 C MET A 263 -1.121 10.930 -1.500 1.00 0.45 C
-ATOM 209 O MET A 263 -1.421 12.060 -1.120 1.00 0.49 O
-ATOM 210 CB MET A 263 -2.492 8.906 -0.932 1.00 0.52 C
-ATOM 211 CG MET A 263 -2.943 9.488 0.401 1.00 0.59 C
-ATOM 212 SD MET A 263 -3.177 8.226 1.669 1.00 0.73 S
-ATOM 213 CE MET A 263 -1.525 7.546 1.775 1.00 0.83 C
-ATOM 214 H MET A 263 -1.733 8.318 -3.266 1.00 0.41 H
-ATOM 215 HA MET A 263 -3.105 10.519 -2.196 1.00 0.50 H
-ATOM 216 HB2 MET A 263 -3.269 8.249 -1.293 1.00 0.59 H
-ATOM 217 HB3 MET A 263 -1.596 8.328 -0.763 1.00 0.54 H
-ATOM 218 HG2 MET A 263 -2.193 10.185 0.744 1.00 0.63 H
-ATOM 219 HG3 MET A 263 -3.877 10.010 0.254 1.00 0.69 H
-ATOM 220 HE1 MET A 263 -1.009 7.713 0.842 1.00 0.90 H
-ATOM 221 HE2 MET A 263 -1.584 6.485 1.969 1.00 1.07 H
-ATOM 222 HE3 MET A 263 -0.986 8.030 2.577 1.00 0.93 H
-ATOM 223 N THR A 264 0.137 10.484 -1.490 1.00 0.43 N
-ATOM 224 CA THR A 264 1.243 11.332 -1.019 1.00 0.45 C
-ATOM 225 C THR A 264 1.631 12.414 -2.037 1.00 0.40 C
-ATOM 226 O THR A 264 2.400 13.318 -1.709 1.00 0.43 O
-ATOM 227 CB THR A 264 2.504 10.511 -0.659 1.00 0.50 C
-ATOM 228 OG1 THR A 264 3.503 11.371 -0.100 1.00 0.60 O
-ATOM 229 CG2 THR A 264 3.079 9.810 -1.880 1.00 0.47 C
-ATOM 230 H THR A 264 0.326 9.562 -1.799 1.00 0.44 H
-ATOM 231 HA THR A 264 0.904 11.824 -0.118 1.00 0.53 H
-ATOM 232 HB THR A 264 2.232 9.764 0.074 1.00 0.59 H
-ATOM 233 HG1 THR A 264 3.429 12.250 -0.496 1.00 0.63 H
-ATOM 234 HG21 THR A 264 2.575 8.866 -2.024 1.00 1.05 H
-ATOM 235 HG22 THR A 264 4.134 9.637 -1.730 1.00 1.08 H
-ATOM 236 HG23 THR A 264 2.937 10.432 -2.751 1.00 1.01 H
-ATOM 237 N THR A 265 1.080 12.309 -3.261 1.00 0.39 N
-ATOM 238 CA THR A 265 1.322 13.252 -4.373 1.00 0.39 C
-ATOM 239 C THR A 265 2.743 13.841 -4.381 1.00 0.38 C
-ATOM 240 O THR A 265 2.943 15.058 -4.321 1.00 0.43 O
-ATOM 241 CB THR A 265 0.250 14.368 -4.419 1.00 0.44 C
-ATOM 242 OG1 THR A 265 0.655 15.431 -5.294 1.00 0.50 O
-ATOM 243 CG2 THR A 265 -0.048 14.918 -3.034 1.00 0.45 C
-ATOM 244 H THR A 265 0.470 11.568 -3.425 1.00 0.45 H
-ATOM 245 HA THR A 265 1.214 12.676 -5.283 1.00 0.44 H
-ATOM 246 HB THR A 265 -0.660 13.934 -4.809 1.00 0.52 H
-ATOM 247 HG1 THR A 265 1.522 15.757 -5.012 1.00 0.64 H
-ATOM 248 HG21 THR A 265 -0.832 15.654 -3.099 1.00 1.02 H
-ATOM 249 HG22 THR A 265 0.845 15.370 -2.629 1.00 1.01 H
-ATOM 250 HG23 THR A 265 -0.364 14.108 -2.391 1.00 1.06 H
-ATOM 251 N GLN A 266 3.730 12.953 -4.452 1.00 0.43 N
-ATOM 252 CA GLN A 266 5.139 13.354 -4.462 1.00 0.50 C
-ATOM 253 C GLN A 266 5.543 14.049 -5.769 1.00 0.49 C
-ATOM 254 O GLN A 266 6.487 14.838 -5.790 1.00 0.57 O
-ATOM 255 CB GLN A 266 6.030 12.136 -4.224 1.00 0.65 C
-ATOM 256 CG GLN A 266 5.936 11.580 -2.813 1.00 0.78 C
-ATOM 257 CD GLN A 266 6.469 12.534 -1.758 1.00 0.82 C
-ATOM 258 OE1 GLN A 266 5.982 13.652 -1.599 1.00 0.84 O
-ATOM 259 NE2 GLN A 266 7.481 12.098 -1.031 1.00 0.96 N
-ATOM 260 H GLN A 266 3.505 11.997 -4.491 1.00 0.47 H
-ATOM 261 HA GLN A 266 5.284 14.049 -3.649 1.00 0.53 H
-ATOM 262 HB2 GLN A 266 5.741 11.357 -4.913 1.00 0.71 H
-ATOM 263 HB3 GLN A 266 7.055 12.413 -4.413 1.00 0.70 H
-ATOM 264 HG2 GLN A 266 4.900 11.371 -2.593 1.00 0.84 H
-ATOM 265 HG3 GLN A 266 6.504 10.662 -2.765 1.00 0.87 H
-ATOM 266 HE21 GLN A 266 7.826 11.199 -1.212 1.00 1.03 H
-ATOM 267 HE22 GLN A 266 7.839 12.688 -0.339 1.00 1.04 H
-ATOM 268 N SER A 267 4.830 13.737 -6.854 1.00 0.53 N
-ATOM 269 CA SER A 267 5.106 14.321 -8.176 1.00 0.59 C
-ATOM 270 C SER A 267 6.491 13.915 -8.695 1.00 0.55 C
-ATOM 271 O SER A 267 7.267 14.755 -9.149 1.00 0.60 O
-ATOM 272 CB SER A 267 4.980 15.852 -8.130 1.00 0.73 C
-ATOM 273 OG SER A 267 3.696 16.247 -7.665 1.00 0.95 O
-ATOM 274 H SER A 267 4.097 13.091 -6.765 1.00 0.60 H
-ATOM 275 HA SER A 267 4.362 13.935 -8.858 1.00 0.69 H
-ATOM 276 HB2 SER A 267 5.728 16.252 -7.463 1.00 0.74 H
-ATOM 277 HB3 SER A 267 5.133 16.253 -9.122 1.00 0.81 H
-ATOM 278 HG SER A 267 3.255 15.485 -7.272 1.00 1.20 H
-ATOM 279 N SER A 268 6.783 12.610 -8.623 1.00 0.61 N
-ATOM 280 CA SER A 268 8.065 12.047 -9.082 1.00 0.72 C
-ATOM 281 C SER A 268 9.260 12.600 -8.288 1.00 0.67 C
-ATOM 282 O SER A 268 10.355 12.780 -8.828 1.00 0.83 O
-ATOM 283 CB SER A 268 8.260 12.300 -10.587 1.00 0.85 C
-ATOM 284 OG SER A 268 9.478 11.741 -11.051 1.00 0.99 O
-ATOM 285 H SER A 268 6.110 12.001 -8.254 1.00 0.69 H
-ATOM 286 HA SER A 268 8.018 10.980 -8.921 1.00 0.83 H
-ATOM 287 HB2 SER A 268 7.444 11.853 -11.133 1.00 0.93 H
-ATOM 288 HB3 SER A 268 8.274 13.365 -10.771 1.00 0.84 H
-ATOM 289 HG SER A 268 10.197 12.014 -10.463 1.00 0.96 H
-ATOM 290 N GLY A 269 9.046 12.855 -7.000 1.00 0.62 N
-ATOM 291 CA GLY A 269 10.109 13.372 -6.152 1.00 0.71 C
-ATOM 292 C GLY A 269 9.685 13.525 -4.699 1.00 0.69 C
-ATOM 293 O GLY A 269 9.842 12.604 -3.899 1.00 0.86 O
-ATOM 294 H GLY A 269 8.158 12.688 -6.620 1.00 0.64 H
-ATOM 295 HA2 GLY A 269 10.951 12.698 -6.197 1.00 0.85 H
-ATOM 296 HA3 GLY A 269 10.415 14.337 -6.528 1.00 0.78 H
-ATOM 297 N SER A 270 9.139 14.692 -4.364 1.00 0.64 N
-ATOM 298 CA SER A 270 8.681 14.978 -2.999 1.00 0.69 C
-ATOM 299 C SER A 270 7.968 16.325 -2.951 1.00 0.62 C
-ATOM 300 O SER A 270 8.434 17.294 -3.550 1.00 0.79 O
-ATOM 301 CB SER A 270 9.860 15.013 -2.023 1.00 0.92 C
-ATOM 302 OG SER A 270 10.648 16.178 -2.216 1.00 1.03 O
-ATOM 303 H SER A 270 9.038 15.382 -5.052 1.00 0.70 H
-ATOM 304 HA SER A 270 7.992 14.202 -2.702 1.00 0.71 H
-ATOM 305 HB2 SER A 270 9.487 15.009 -1.009 1.00 1.00 H
-ATOM 306 HB3 SER A 270 10.480 14.143 -2.182 1.00 1.02 H
-ATOM 307 HG SER A 270 10.155 16.817 -2.745 1.00 1.25 H
-ATOM 308 N ARG A 271 6.850 16.398 -2.240 1.00 0.59 N
-ATOM 309 CA ARG A 271 6.112 17.654 -2.143 1.00 0.58 C
-ATOM 310 C ARG A 271 5.741 17.984 -0.693 1.00 0.70 C
-ATOM 311 O ARG A 271 5.221 17.141 0.035 1.00 0.80 O
-ATOM 312 CB ARG A 271 4.845 17.595 -3.004 1.00 0.51 C
-ATOM 313 CG ARG A 271 4.168 18.947 -3.161 1.00 0.67 C
-ATOM 314 CD ARG A 271 2.785 18.838 -3.786 1.00 0.59 C
-ATOM 315 NE ARG A 271 2.809 18.379 -5.175 1.00 0.44 N
-ATOM 316 CZ ARG A 271 1.873 18.693 -6.050 1.00 0.41 C
-ATOM 317 NH1 ARG A 271 0.932 19.539 -5.727 1.00 0.49 N
-ATOM 318 NH2 ARG A 271 1.896 18.187 -7.253 1.00 0.57 N
-ATOM 319 H ARG A 271 6.513 15.598 -1.774 1.00 0.73 H
-ATOM 320 HA ARG A 271 6.751 18.438 -2.521 1.00 0.66 H
-ATOM 321 HB2 ARG A 271 5.105 17.227 -3.984 1.00 0.52 H
-ATOM 322 HB3 ARG A 271 4.143 16.915 -2.545 1.00 0.56 H
-ATOM 323 HG2 ARG A 271 4.070 19.401 -2.188 1.00 0.86 H
-ATOM 324 HG3 ARG A 271 4.786 19.572 -3.789 1.00 0.80 H
-ATOM 325 HD2 ARG A 271 2.200 18.140 -3.205 1.00 0.68 H
-ATOM 326 HD3 ARG A 271 2.315 19.810 -3.749 1.00 0.77 H
-ATOM 327 HE ARG A 271 3.534 17.785 -5.452 1.00 0.52 H
-ATOM 328 HH11 ARG A 271 0.923 19.959 -4.816 1.00 0.55 H
-ATOM 329 HH12 ARG A 271 0.194 19.762 -6.396 1.00 0.61 H
-ATOM 330 HH21 ARG A 271 2.631 17.548 -7.518 1.00 0.64 H
-ATOM 331 HH22 ARG A 271 1.185 18.437 -7.909 1.00 0.71 H
-ATOM 332 N ALA A 272 6.001 19.226 -0.286 1.00 0.81 N
-ATOM 333 CA ALA A 272 5.681 19.677 1.066 1.00 0.97 C
-ATOM 334 C ALA A 272 4.238 20.203 1.140 1.00 0.79 C
-ATOM 335 O ALA A 272 3.324 19.606 0.569 1.00 0.72 O
-ATOM 336 CB ALA A 272 6.674 20.744 1.505 1.00 1.24 C
-ATOM 337 H ALA A 272 6.409 19.856 -0.915 1.00 0.85 H
-ATOM 338 HA ALA A 272 5.779 18.831 1.731 1.00 1.11 H
-ATOM 339 HB1 ALA A 272 7.613 20.278 1.765 1.00 1.66 H
-ATOM 340 HB2 ALA A 272 6.282 21.270 2.364 1.00 1.65 H
-ATOM 341 HB3 ALA A 272 6.830 21.443 0.697 1.00 1.66 H
-ATOM 342 N SER A 273 4.032 21.328 1.829 1.00 0.81 N
-ATOM 343 CA SER A 273 2.695 21.918 1.948 1.00 0.70 C
-ATOM 344 C SER A 273 2.274 22.638 0.678 1.00 0.60 C
-ATOM 345 O SER A 273 1.957 23.828 0.688 1.00 0.71 O
-ATOM 346 CB SER A 273 2.608 22.867 3.138 1.00 0.87 C
-ATOM 347 OG SER A 273 3.599 23.880 3.071 1.00 1.05 O
-ATOM 348 H SER A 273 4.791 21.776 2.259 1.00 0.94 H
-ATOM 349 HA SER A 273 1.991 21.110 2.101 1.00 0.68 H
-ATOM 350 HB2 SER A 273 1.633 23.334 3.143 1.00 0.90 H
-ATOM 351 HB3 SER A 273 2.741 22.304 4.048 1.00 0.95 H
-ATOM 352 HG SER A 273 3.384 24.487 2.354 1.00 1.08 H
-ATOM 353 N LEU A 274 2.223 21.881 -0.403 1.00 0.48 N
-ATOM 354 CA LEU A 274 1.781 22.402 -1.681 1.00 0.48 C
-ATOM 355 C LEU A 274 0.691 21.487 -2.281 1.00 0.41 C
-ATOM 356 O LEU A 274 0.768 21.111 -3.453 1.00 0.48 O
-ATOM 357 CB LEU A 274 2.973 22.527 -2.638 1.00 0.63 C
-ATOM 358 CG LEU A 274 2.703 23.320 -3.921 1.00 0.80 C
-ATOM 359 CD1 LEU A 274 2.349 24.763 -3.593 1.00 1.02 C
-ATOM 360 CD2 LEU A 274 3.907 23.260 -4.847 1.00 0.99 C
-ATOM 361 H LEU A 274 2.455 20.926 -0.325 1.00 0.49 H
-ATOM 362 HA LEU A 274 1.360 23.383 -1.512 1.00 0.59 H
-ATOM 363 HB2 LEU A 274 3.782 23.008 -2.108 1.00 0.78 H
-ATOM 364 HB3 LEU A 274 3.289 21.533 -2.917 1.00 0.60 H
-ATOM 365 HG LEU A 274 1.861 22.880 -4.436 1.00 0.78 H
-ATOM 366 HD11 LEU A 274 3.099 25.420 -4.006 1.00 1.44 H
-ATOM 367 HD12 LEU A 274 2.312 24.888 -2.521 1.00 1.53 H
-ATOM 368 HD13 LEU A 274 1.385 25.003 -4.016 1.00 1.49 H
-ATOM 369 HD21 LEU A 274 4.384 24.228 -4.880 1.00 1.56 H
-ATOM 370 HD22 LEU A 274 3.585 22.983 -5.840 1.00 1.46 H
-ATOM 371 HD23 LEU A 274 4.610 22.526 -4.479 1.00 1.36 H
-ATOM 372 N PRO A 275 -0.338 21.088 -1.476 1.00 0.46 N
-ATOM 373 CA PRO A 275 -1.423 20.202 -1.952 1.00 0.65 C
-ATOM 374 C PRO A 275 -2.070 20.669 -3.265 1.00 0.75 C
-ATOM 375 O PRO A 275 -2.299 21.865 -3.460 1.00 0.83 O
-ATOM 376 CB PRO A 275 -2.442 20.264 -0.815 1.00 0.83 C
-ATOM 377 CG PRO A 275 -1.614 20.492 0.392 1.00 0.77 C
-ATOM 378 CD PRO A 275 -0.529 21.434 -0.041 1.00 0.53 C
-ATOM 379 HA PRO A 275 -1.076 19.186 -2.062 1.00 0.74 H
-ATOM 380 HB2 PRO A 275 -3.131 21.078 -0.986 1.00 0.88 H
-ATOM 381 HB3 PRO A 275 -2.980 19.330 -0.755 1.00 1.02 H
-ATOM 382 HG2 PRO A 275 -2.211 20.932 1.177 1.00 0.90 H
-ATOM 383 HG3 PRO A 275 -1.185 19.556 0.718 1.00 0.87 H
-ATOM 384 HD2 PRO A 275 -0.846 22.459 0.078 1.00 0.60 H
-ATOM 385 HD3 PRO A 275 0.385 21.255 0.530 1.00 0.51 H
-ATOM 386 N LYS A 276 -2.356 19.701 -4.153 1.00 0.85 N
-ATOM 387 CA LYS A 276 -2.975 19.965 -5.467 1.00 1.02 C
-ATOM 388 C LYS A 276 -2.000 20.665 -6.426 1.00 0.97 C
-ATOM 389 O LYS A 276 -2.064 21.905 -6.546 1.00 1.12 O
-ATOM 390 CB LYS A 276 -4.259 20.796 -5.324 1.00 1.23 C
-ATOM 391 CG LYS A 276 -5.348 20.121 -4.506 1.00 1.37 C
-ATOM 392 CD LYS A 276 -6.562 21.025 -4.356 1.00 1.61 C
-ATOM 393 CE LYS A 276 -7.638 20.383 -3.497 1.00 1.83 C
-ATOM 394 NZ LYS A 276 -8.810 21.285 -3.317 1.00 2.08 N
-ATOM 395 OXT LYS A 276 -1.175 19.957 -7.054 1.00 0.90 O
-ATOM 396 H LYS A 276 -2.139 18.775 -3.919 1.00 0.88 H
-ATOM 397 HA LYS A 276 -3.234 19.007 -5.892 1.00 1.12 H
-ATOM 398 HB2 LYS A 276 -4.014 21.734 -4.848 1.00 1.20 H
-ATOM 399 HB3 LYS A 276 -4.653 20.997 -6.310 1.00 1.36 H
-ATOM 400 HG2 LYS A 276 -5.648 19.211 -5.003 1.00 1.48 H
-ATOM 401 HG3 LYS A 276 -4.958 19.889 -3.525 1.00 1.30 H
-ATOM 402 HD2 LYS A 276 -6.253 21.951 -3.895 1.00 1.61 H
-ATOM 403 HD3 LYS A 276 -6.969 21.228 -5.336 1.00 1.74 H
-ATOM 404 HE2 LYS A 276 -7.967 19.472 -3.975 1.00 1.96 H
-ATOM 405 HE3 LYS A 276 -7.219 20.152 -2.529 1.00 1.80 H
-ATOM 406 HZ1 LYS A 276 -9.489 21.150 -4.093 1.00 2.33 H
-ATOM 407 HZ2 LYS A 276 -8.498 22.277 -3.311 1.00 2.34 H
-ATOM 408 HZ3 LYS A 276 -9.285 21.078 -2.414 1.00 2.36 H
-TER
-ENDMDL
-MODEL 12
-ATOM 1 N ALA A 249 4.884 -8.025 -12.298 1.00 1.90 N
-ATOM 2 CA ALA A 249 4.838 -7.531 -10.896 1.00 1.62 C
-ATOM 3 C ALA A 249 3.487 -7.837 -10.248 1.00 1.21 C
-ATOM 4 O ALA A 249 2.478 -7.971 -10.938 1.00 1.23 O
-ATOM 5 CB ALA A 249 5.124 -6.036 -10.847 1.00 1.91 C
-ATOM 6 H1 ALA A 249 5.824 -7.850 -12.710 1.00 2.23 H
-ATOM 7 H2 ALA A 249 4.169 -7.534 -12.875 1.00 2.06 H
-ATOM 8 H3 ALA A 249 4.689 -9.047 -12.324 1.00 2.31 H
-ATOM 9 HA ALA A 249 5.611 -8.036 -10.336 1.00 1.64 H
-ATOM 10 HB1 ALA A 249 4.196 -5.496 -10.740 1.00 2.25 H
-ATOM 11 HB2 ALA A 249 5.616 -5.734 -11.757 1.00 2.25 H
-ATOM 12 HB3 ALA A 249 5.762 -5.821 -10.000 1.00 2.21 H
-ATOM 13 N PRO A 250 3.454 -7.955 -8.910 1.00 1.02 N
-ATOM 14 CA PRO A 250 2.227 -8.250 -8.158 1.00 0.77 C
-ATOM 15 C PRO A 250 1.179 -7.136 -8.263 1.00 0.63 C
-ATOM 16 O PRO A 250 1.493 -5.953 -8.104 1.00 0.69 O
-ATOM 17 CB PRO A 250 2.728 -8.381 -6.712 1.00 1.05 C
-ATOM 18 CG PRO A 250 4.188 -8.615 -6.830 1.00 1.29 C
-ATOM 19 CD PRO A 250 4.606 -7.812 -8.013 1.00 1.31 C
-ATOM 20 HA PRO A 250 1.786 -9.185 -8.473 1.00 0.78 H
-ATOM 21 HB2 PRO A 250 2.525 -7.465 -6.177 1.00 1.15 H
-ATOM 22 HB3 PRO A 250 2.238 -9.206 -6.229 1.00 1.13 H
-ATOM 23 HG2 PRO A 250 4.693 -8.269 -5.940 1.00 1.59 H
-ATOM 24 HG3 PRO A 250 4.385 -9.662 -6.998 1.00 1.29 H
-ATOM 25 HD2 PRO A 250 4.751 -6.779 -7.733 1.00 1.51 H
-ATOM 26 HD3 PRO A 250 5.496 -8.222 -8.458 1.00 1.48 H
-ATOM 27 N ALA A 251 -0.067 -7.526 -8.536 1.00 0.58 N
-ATOM 28 CA ALA A 251 -1.170 -6.574 -8.677 1.00 0.55 C
-ATOM 29 C ALA A 251 -1.390 -5.748 -7.407 1.00 0.44 C
-ATOM 30 O ALA A 251 -1.483 -4.524 -7.467 1.00 0.45 O
-ATOM 31 CB ALA A 251 -2.446 -7.308 -9.050 1.00 0.68 C
-ATOM 32 H ALA A 251 -0.248 -8.482 -8.656 1.00 0.66 H
-ATOM 33 HA ALA A 251 -0.924 -5.903 -9.488 1.00 0.62 H
-ATOM 34 HB1 ALA A 251 -2.331 -7.759 -10.023 1.00 1.35 H
-ATOM 35 HB2 ALA A 251 -3.270 -6.611 -9.068 1.00 1.30 H
-ATOM 36 HB3 ALA A 251 -2.643 -8.078 -8.317 1.00 1.07 H
-ATOM 37 N ARG A 252 -1.474 -6.424 -6.260 1.00 0.42 N
-ATOM 38 CA ARG A 252 -1.685 -5.740 -4.983 1.00 0.40 C
-ATOM 39 C ARG A 252 -0.567 -4.743 -4.689 1.00 0.35 C
-ATOM 40 O ARG A 252 -0.828 -3.596 -4.333 1.00 0.36 O
-ATOM 41 CB ARG A 252 -1.796 -6.750 -3.832 1.00 0.51 C
-ATOM 42 CG ARG A 252 -1.740 -6.108 -2.445 1.00 0.60 C
-ATOM 43 CD ARG A 252 -2.729 -4.957 -2.321 1.00 0.60 C
-ATOM 44 NE ARG A 252 -2.470 -4.117 -1.153 1.00 0.79 N
-ATOM 45 CZ ARG A 252 -3.073 -2.966 -0.927 1.00 0.81 C
-ATOM 46 NH1 ARG A 252 -3.982 -2.522 -1.751 1.00 0.75 N
-ATOM 47 NH2 ARG A 252 -2.769 -2.255 0.127 1.00 1.09 N
-ATOM 48 H ARG A 252 -1.393 -7.399 -6.274 1.00 0.49 H
-ATOM 49 HA ARG A 252 -2.613 -5.197 -5.058 1.00 0.44 H
-ATOM 50 HB2 ARG A 252 -2.732 -7.282 -3.921 1.00 0.58 H
-ATOM 51 HB3 ARG A 252 -0.983 -7.457 -3.908 1.00 0.60 H
-ATOM 52 HG2 ARG A 252 -1.979 -6.856 -1.703 1.00 0.75 H
-ATOM 53 HG3 ARG A 252 -0.741 -5.734 -2.274 1.00 0.65 H
-ATOM 54 HD2 ARG A 252 -2.653 -4.347 -3.209 1.00 0.53 H
-ATOM 55 HD3 ARG A 252 -3.723 -5.363 -2.248 1.00 0.78 H
-ATOM 56 HE ARG A 252 -1.803 -4.427 -0.510 1.00 1.00 H
-ATOM 57 HH11 ARG A 252 -4.224 -3.046 -2.559 1.00 0.78 H
-ATOM 58 HH12 ARG A 252 -4.455 -1.639 -1.553 1.00 0.82 H
-ATOM 59 HH21 ARG A 252 -2.082 -2.581 0.770 1.00 1.33 H
-ATOM 60 HH22 ARG A 252 -3.217 -1.368 0.277 1.00 1.12 H
-ATOM 61 N VAL A 253 0.670 -5.186 -4.837 1.00 0.40 N
-ATOM 62 CA VAL A 253 1.817 -4.325 -4.578 1.00 0.45 C
-ATOM 63 C VAL A 253 1.817 -3.122 -5.516 1.00 0.39 C
-ATOM 64 O VAL A 253 1.991 -1.988 -5.073 1.00 0.41 O
-ATOM 65 CB VAL A 253 3.140 -5.101 -4.696 1.00 0.57 C
-ATOM 66 CG1 VAL A 253 4.322 -4.204 -4.363 1.00 0.70 C
-ATOM 67 CG2 VAL A 253 3.107 -6.313 -3.777 1.00 0.66 C
-ATOM 68 H VAL A 253 0.814 -6.109 -5.125 1.00 0.45 H
-ATOM 69 HA VAL A 253 1.729 -3.967 -3.565 1.00 0.50 H
-ATOM 70 HB VAL A 253 3.248 -5.447 -5.713 1.00 0.59 H
-ATOM 71 HG11 VAL A 253 4.034 -3.170 -4.488 1.00 1.18 H
-ATOM 72 HG12 VAL A 253 5.144 -4.432 -5.024 1.00 1.20 H
-ATOM 73 HG13 VAL A 253 4.623 -4.372 -3.340 1.00 1.05 H
-ATOM 74 HG21 VAL A 253 4.088 -6.755 -3.727 1.00 1.25 H
-ATOM 75 HG22 VAL A 253 2.403 -7.037 -4.165 1.00 1.20 H
-ATOM 76 HG23 VAL A 253 2.796 -6.005 -2.789 1.00 1.25 H
-ATOM 77 N GLY A 254 1.576 -3.371 -6.802 1.00 0.39 N
-ATOM 78 CA GLY A 254 1.511 -2.286 -7.768 1.00 0.40 C
-ATOM 79 C GLY A 254 0.394 -1.310 -7.434 1.00 0.33 C
-ATOM 80 O GLY A 254 0.607 -0.094 -7.385 1.00 0.33 O
-ATOM 81 H GLY A 254 1.412 -4.297 -7.090 1.00 0.43 H
-ATOM 82 HA2 GLY A 254 2.454 -1.759 -7.768 1.00 0.46 H
-ATOM 83 HA3 GLY A 254 1.337 -2.698 -8.749 1.00 0.45 H
-ATOM 84 N LEU A 255 -0.794 -1.845 -7.169 1.00 0.32 N
-ATOM 85 CA LEU A 255 -1.938 -1.020 -6.804 1.00 0.31 C
-ATOM 86 C LEU A 255 -1.660 -0.238 -5.528 1.00 0.26 C
-ATOM 87 O LEU A 255 -1.866 0.971 -5.481 1.00 0.28 O
-ATOM 88 CB LEU A 255 -3.181 -1.879 -6.611 1.00 0.39 C
-ATOM 89 CG LEU A 255 -4.103 -1.936 -7.820 1.00 0.63 C
-ATOM 90 CD1 LEU A 255 -5.315 -2.796 -7.513 1.00 0.77 C
-ATOM 91 CD2 LEU A 255 -4.522 -0.529 -8.224 1.00 0.81 C
-ATOM 92 H LEU A 255 -0.899 -2.825 -7.203 1.00 0.35 H
-ATOM 93 HA LEU A 255 -2.119 -0.325 -7.611 1.00 0.34 H
-ATOM 94 HB2 LEU A 255 -2.866 -2.885 -6.374 1.00 0.55 H
-ATOM 95 HB3 LEU A 255 -3.742 -1.486 -5.777 1.00 0.45 H
-ATOM 96 HG LEU A 255 -3.573 -2.381 -8.649 1.00 0.78 H
-ATOM 97 HD11 LEU A 255 -5.888 -2.338 -6.721 1.00 1.27 H
-ATOM 98 HD12 LEU A 255 -4.988 -3.777 -7.201 1.00 1.31 H
-ATOM 99 HD13 LEU A 255 -5.925 -2.884 -8.398 1.00 1.32 H
-ATOM 100 HD21 LEU A 255 -5.598 -0.474 -8.277 1.00 1.36 H
-ATOM 101 HD22 LEU A 255 -4.099 -0.290 -9.188 1.00 1.36 H
-ATOM 102 HD23 LEU A 255 -4.161 0.178 -7.489 1.00 1.31 H
-ATOM 103 N GLY A 256 -1.182 -0.941 -4.502 1.00 0.29 N
-ATOM 104 CA GLY A 256 -0.878 -0.309 -3.232 1.00 0.34 C
-ATOM 105 C GLY A 256 0.117 0.834 -3.357 1.00 0.32 C
-ATOM 106 O GLY A 256 -0.143 1.942 -2.875 1.00 0.35 O
-ATOM 107 H GLY A 256 -1.032 -1.908 -4.610 1.00 0.33 H
-ATOM 108 HA2 GLY A 256 -1.798 0.074 -2.813 1.00 0.37 H
-ATOM 109 HA3 GLY A 256 -0.475 -1.054 -2.565 1.00 0.41 H
-ATOM 110 N ILE A 257 1.250 0.588 -4.022 1.00 0.32 N
-ATOM 111 CA ILE A 257 2.248 1.640 -4.209 1.00 0.35 C
-ATOM 112 C ILE A 257 1.660 2.776 -5.043 1.00 0.30 C
-ATOM 113 O ILE A 257 1.971 3.944 -4.814 1.00 0.35 O
-ATOM 114 CB ILE A 257 3.576 1.128 -4.831 1.00 0.42 C
-ATOM 115 CG1 ILE A 257 3.373 0.529 -6.227 1.00 0.42 C
-ATOM 116 CG2 ILE A 257 4.229 0.109 -3.910 1.00 0.53 C
-ATOM 117 CD1 ILE A 257 3.675 1.491 -7.359 1.00 0.50 C
-ATOM 118 H ILE A 257 1.406 -0.307 -4.404 1.00 0.34 H
-ATOM 119 HA ILE A 257 2.476 2.034 -3.226 1.00 0.41 H
-ATOM 120 HB ILE A 257 4.246 1.973 -4.910 1.00 0.48 H
-ATOM 121 HG12 ILE A 257 4.020 -0.327 -6.340 1.00 0.49 H
-ATOM 122 HG13 ILE A 257 2.346 0.213 -6.327 1.00 0.44 H
-ATOM 123 HG21 ILE A 257 5.299 0.118 -4.068 1.00 1.16 H
-ATOM 124 HG22 ILE A 257 3.841 -0.875 -4.129 1.00 1.21 H
-ATOM 125 HG23 ILE A 257 4.015 0.363 -2.883 1.00 1.12 H
-ATOM 126 HD11 ILE A 257 4.308 2.288 -6.995 1.00 1.19 H
-ATOM 127 HD12 ILE A 257 2.752 1.907 -7.735 1.00 1.13 H
-ATOM 128 HD13 ILE A 257 4.182 0.963 -8.153 1.00 1.13 H
-ATOM 129 N THR A 258 0.777 2.434 -5.986 1.00 0.28 N
-ATOM 130 CA THR A 258 0.123 3.444 -6.815 1.00 0.31 C
-ATOM 131 C THR A 258 -0.754 4.360 -5.955 1.00 0.27 C
-ATOM 132 O THR A 258 -0.678 5.583 -6.075 1.00 0.29 O
-ATOM 133 CB THR A 258 -0.747 2.817 -7.930 1.00 0.39 C
-ATOM 134 OG1 THR A 258 0.060 1.995 -8.784 1.00 0.46 O
-ATOM 135 CG2 THR A 258 -1.422 3.894 -8.765 1.00 0.54 C
-ATOM 136 H THR A 258 0.547 1.481 -6.112 1.00 0.31 H
-ATOM 137 HA THR A 258 0.896 4.040 -7.282 1.00 0.36 H
-ATOM 138 HB THR A 258 -1.511 2.206 -7.470 1.00 0.40 H
-ATOM 139 HG1 THR A 258 0.320 1.191 -8.306 1.00 0.44 H
-ATOM 140 HG21 THR A 258 -2.405 3.559 -9.061 1.00 1.02 H
-ATOM 141 HG22 THR A 258 -0.829 4.092 -9.646 1.00 1.16 H
-ATOM 142 HG23 THR A 258 -1.511 4.799 -8.182 1.00 1.08 H
-ATOM 143 N THR A 259 -1.577 3.774 -5.075 1.00 0.25 N
-ATOM 144 CA THR A 259 -2.445 4.571 -4.200 1.00 0.26 C
-ATOM 145 C THR A 259 -1.615 5.425 -3.249 1.00 0.25 C
-ATOM 146 O THR A 259 -1.907 6.605 -3.046 1.00 0.28 O
-ATOM 147 CB THR A 259 -3.413 3.715 -3.356 1.00 0.32 C
-ATOM 148 OG1 THR A 259 -3.349 2.341 -3.736 1.00 0.94 O
-ATOM 149 CG2 THR A 259 -4.839 4.215 -3.506 1.00 1.13 C
-ATOM 150 H THR A 259 -1.596 2.788 -5.006 1.00 0.29 H
-ATOM 151 HA THR A 259 -3.033 5.224 -4.831 1.00 0.28 H
-ATOM 152 HB THR A 259 -3.129 3.802 -2.318 1.00 0.87 H
-ATOM 153 HG1 THR A 259 -3.469 1.780 -2.937 1.00 1.05 H
-ATOM 154 HG21 THR A 259 -5.218 3.940 -4.480 1.00 1.68 H
-ATOM 155 HG22 THR A 259 -4.854 5.289 -3.403 1.00 1.70 H
-ATOM 156 HG23 THR A 259 -5.457 3.771 -2.741 1.00 1.72 H
-ATOM 157 N VAL A 260 -0.569 4.827 -2.677 1.00 0.26 N
-ATOM 158 CA VAL A 260 0.315 5.546 -1.759 1.00 0.31 C
-ATOM 159 C VAL A 260 0.987 6.729 -2.466 1.00 0.32 C
-ATOM 160 O VAL A 260 0.976 7.858 -1.964 1.00 0.36 O
-ATOM 161 CB VAL A 260 1.387 4.605 -1.172 1.00 0.39 C
-ATOM 162 CG1 VAL A 260 2.418 5.386 -0.372 1.00 0.55 C
-ATOM 163 CG2 VAL A 260 0.729 3.543 -0.303 1.00 0.42 C
-ATOM 164 H VAL A 260 -0.382 3.879 -2.886 1.00 0.28 H
-ATOM 165 HA VAL A 260 -0.289 5.923 -0.945 1.00 0.32 H
-ATOM 166 HB VAL A 260 1.893 4.110 -1.987 1.00 0.41 H
-ATOM 167 HG11 VAL A 260 3.305 4.785 -0.243 1.00 1.17 H
-ATOM 168 HG12 VAL A 260 2.008 5.636 0.596 1.00 1.23 H
-ATOM 169 HG13 VAL A 260 2.669 6.293 -0.902 1.00 1.14 H
-ATOM 170 HG21 VAL A 260 0.938 2.565 -0.711 1.00 1.10 H
-ATOM 171 HG22 VAL A 260 -0.339 3.705 -0.286 1.00 1.08 H
-ATOM 172 HG23 VAL A 260 1.119 3.606 0.701 1.00 1.16 H
-ATOM 173 N LEU A 261 1.542 6.474 -3.647 1.00 0.36 N
-ATOM 174 CA LEU A 261 2.183 7.522 -4.434 1.00 0.43 C
-ATOM 175 C LEU A 261 1.169 8.595 -4.821 1.00 0.43 C
-ATOM 176 O LEU A 261 1.458 9.790 -4.775 1.00 0.49 O
-ATOM 177 CB LEU A 261 2.810 6.929 -5.695 1.00 0.50 C
-ATOM 178 CG LEU A 261 4.337 6.916 -5.722 1.00 0.83 C
-ATOM 179 CD1 LEU A 261 4.886 8.334 -5.671 1.00 1.36 C
-ATOM 180 CD2 LEU A 261 4.891 6.091 -4.569 1.00 1.26 C
-ATOM 181 H LEU A 261 1.499 5.557 -4.009 1.00 0.37 H
-ATOM 182 HA LEU A 261 2.956 7.970 -3.829 1.00 0.48 H
-ATOM 183 HB2 LEU A 261 2.460 5.913 -5.797 1.00 0.60 H
-ATOM 184 HB3 LEU A 261 2.463 7.496 -6.546 1.00 0.77 H
-ATOM 185 HG LEU A 261 4.663 6.465 -6.646 1.00 0.83 H
-ATOM 186 HD11 LEU A 261 4.091 9.035 -5.881 1.00 1.81 H
-ATOM 187 HD12 LEU A 261 5.668 8.444 -6.407 1.00 1.81 H
-ATOM 188 HD13 LEU A 261 5.287 8.529 -4.687 1.00 1.87 H
-ATOM 189 HD21 LEU A 261 5.497 6.721 -3.936 1.00 1.66 H
-ATOM 190 HD22 LEU A 261 5.493 5.286 -4.961 1.00 1.75 H
-ATOM 191 HD23 LEU A 261 4.073 5.683 -3.993 1.00 1.78 H
-ATOM 192 N THR A 262 -0.022 8.147 -5.205 1.00 0.39 N
-ATOM 193 CA THR A 262 -1.099 9.052 -5.608 1.00 0.42 C
-ATOM 194 C THR A 262 -1.552 9.958 -4.458 1.00 0.35 C
-ATOM 195 O THR A 262 -1.638 11.174 -4.622 1.00 0.42 O
-ATOM 196 CB THR A 262 -2.330 8.274 -6.131 1.00 0.48 C
-ATOM 197 OG1 THR A 262 -1.966 7.456 -7.251 1.00 0.61 O
-ATOM 198 CG2 THR A 262 -3.444 9.224 -6.541 1.00 0.58 C
-ATOM 199 H THR A 262 -0.176 7.175 -5.226 1.00 0.37 H
-ATOM 200 HA THR A 262 -0.728 9.670 -6.412 1.00 0.53 H
-ATOM 201 HB THR A 262 -2.696 7.639 -5.336 1.00 0.46 H
-ATOM 202 HG1 THR A 262 -1.507 6.663 -6.930 1.00 0.58 H
-ATOM 203 HG21 THR A 262 -3.507 10.034 -5.830 1.00 1.23 H
-ATOM 204 HG22 THR A 262 -4.384 8.691 -6.562 1.00 1.13 H
-ATOM 205 HG23 THR A 262 -3.234 9.623 -7.523 1.00 1.18 H
-ATOM 206 N MET A 263 -1.869 9.370 -3.299 1.00 0.28 N
-ATOM 207 CA MET A 263 -2.338 10.165 -2.168 1.00 0.30 C
-ATOM 208 C MET A 263 -2.190 9.460 -0.805 1.00 0.28 C
-ATOM 209 O MET A 263 -3.168 9.308 -0.070 1.00 0.35 O
-ATOM 210 CB MET A 263 -3.800 10.549 -2.401 1.00 0.46 C
-ATOM 211 CG MET A 263 -4.720 9.369 -2.681 1.00 0.58 C
-ATOM 212 SD MET A 263 -6.426 9.876 -2.967 1.00 0.84 S
-ATOM 213 CE MET A 263 -6.233 10.910 -4.417 1.00 0.95 C
-ATOM 214 H MET A 263 -1.812 8.394 -3.215 1.00 0.29 H
-ATOM 215 HA MET A 263 -1.751 11.069 -2.146 1.00 0.35 H
-ATOM 216 HB2 MET A 263 -4.163 11.059 -1.527 1.00 0.53 H
-ATOM 217 HB3 MET A 263 -3.844 11.218 -3.247 1.00 0.54 H
-ATOM 218 HG2 MET A 263 -4.362 8.851 -3.558 1.00 0.63 H
-ATOM 219 HG3 MET A 263 -4.694 8.701 -1.833 1.00 0.60 H
-ATOM 220 HE1 MET A 263 -5.320 11.480 -4.331 1.00 1.33 H
-ATOM 221 HE2 MET A 263 -7.073 11.585 -4.492 1.00 1.42 H
-ATOM 222 HE3 MET A 263 -6.189 10.289 -5.299 1.00 1.52 H
-ATOM 223 N THR A 264 -0.971 9.047 -0.453 1.00 0.28 N
-ATOM 224 CA THR A 264 -0.729 8.381 0.841 1.00 0.35 C
-ATOM 225 C THR A 264 -1.214 9.231 2.031 1.00 0.32 C
-ATOM 226 O THR A 264 -1.687 8.693 3.034 1.00 0.39 O
-ATOM 227 CB THR A 264 0.767 8.042 1.046 1.00 0.49 C
-ATOM 228 OG1 THR A 264 0.959 7.398 2.314 1.00 0.68 O
-ATOM 229 CG2 THR A 264 1.639 9.290 0.968 1.00 0.52 C
-ATOM 230 H THR A 264 -0.212 9.191 -1.070 1.00 0.31 H
-ATOM 231 HA THR A 264 -1.284 7.453 0.837 1.00 0.44 H
-ATOM 232 HB THR A 264 1.071 7.363 0.262 1.00 0.55 H
-ATOM 233 HG1 THR A 264 1.900 7.350 2.513 1.00 1.11 H
-ATOM 234 HG21 THR A 264 2.189 9.404 1.891 1.00 1.17 H
-ATOM 235 HG22 THR A 264 1.013 10.156 0.814 1.00 1.14 H
-ATOM 236 HG23 THR A 264 2.332 9.195 0.145 1.00 1.16 H
-ATOM 237 N THR A 265 -1.097 10.556 1.906 1.00 0.34 N
-ATOM 238 CA THR A 265 -1.514 11.485 2.965 1.00 0.44 C
-ATOM 239 C THR A 265 -3.041 11.576 3.125 1.00 0.38 C
-ATOM 240 O THR A 265 -3.529 12.241 4.039 1.00 0.53 O
-ATOM 241 CB THR A 265 -0.954 12.905 2.721 1.00 0.63 C
-ATOM 242 OG1 THR A 265 -1.334 13.779 3.793 1.00 0.79 O
-ATOM 243 CG2 THR A 265 -1.454 13.473 1.398 1.00 0.68 C
-ATOM 244 H THR A 265 -0.716 10.919 1.079 1.00 0.37 H
-ATOM 245 HA THR A 265 -1.098 11.124 3.893 1.00 0.56 H
-ATOM 246 HB THR A 265 0.125 12.846 2.683 1.00 0.75 H
-ATOM 247 HG1 THR A 265 -2.199 13.515 4.133 1.00 0.95 H
-ATOM 248 HG21 THR A 265 -1.575 14.543 1.488 1.00 1.32 H
-ATOM 249 HG22 THR A 265 -2.403 13.023 1.149 1.00 1.19 H
-ATOM 250 HG23 THR A 265 -0.737 13.257 0.619 1.00 1.21 H
-ATOM 251 N GLN A 266 -3.792 10.914 2.242 1.00 0.47 N
-ATOM 252 CA GLN A 266 -5.257 10.933 2.313 1.00 0.50 C
-ATOM 253 C GLN A 266 -5.755 10.282 3.608 1.00 0.43 C
-ATOM 254 O GLN A 266 -5.423 9.132 3.906 1.00 0.51 O
-ATOM 255 CB GLN A 266 -5.859 10.202 1.107 1.00 0.68 C
-ATOM 256 CG GLN A 266 -7.369 10.027 1.181 1.00 0.87 C
-ATOM 257 CD GLN A 266 -7.894 9.041 0.153 1.00 1.12 C
-ATOM 258 OE1 GLN A 266 -7.437 7.904 0.081 1.00 1.25 O
-ATOM 259 NE2 GLN A 266 -8.862 9.466 -0.642 1.00 1.42 N
-ATOM 260 H GLN A 266 -3.352 10.398 1.531 1.00 0.64 H
-ATOM 261 HA GLN A 266 -5.576 11.964 2.295 1.00 0.60 H
-ATOM 262 HB2 GLN A 266 -5.627 10.762 0.213 1.00 0.80 H
-ATOM 263 HB3 GLN A 266 -5.408 9.224 1.034 1.00 0.73 H
-ATOM 264 HG2 GLN A 266 -7.629 9.670 2.165 1.00 0.86 H
-ATOM 265 HG3 GLN A 266 -7.837 10.986 1.011 1.00 1.03 H
-ATOM 266 HE21 GLN A 266 -9.188 10.382 -0.529 1.00 1.52 H
-ATOM 267 HE22 GLN A 266 -9.211 8.842 -1.313 1.00 1.62 H
-ATOM 268 N SER A 267 -6.550 11.021 4.370 1.00 0.53 N
-ATOM 269 CA SER A 267 -7.095 10.517 5.629 1.00 0.64 C
-ATOM 270 C SER A 267 -8.337 9.654 5.405 1.00 0.64 C
-ATOM 271 O SER A 267 -9.174 9.949 4.548 1.00 0.81 O
-ATOM 272 CB SER A 267 -7.441 11.676 6.563 1.00 0.92 C
-ATOM 273 OG SER A 267 -8.376 12.556 5.962 1.00 1.06 O
-ATOM 274 H SER A 267 -6.781 11.929 4.081 1.00 0.67 H
-ATOM 275 HA SER A 267 -6.333 9.910 6.096 1.00 0.71 H
-ATOM 276 HB2 SER A 267 -7.867 11.285 7.475 1.00 1.09 H
-ATOM 277 HB3 SER A 267 -6.544 12.230 6.795 1.00 1.00 H
-ATOM 278 HG SER A 267 -9.263 12.194 6.052 1.00 1.33 H
-ATOM 279 N SER A 268 -8.455 8.589 6.190 1.00 0.66 N
-ATOM 280 CA SER A 268 -9.593 7.670 6.104 1.00 0.77 C
-ATOM 281 C SER A 268 -9.713 6.848 7.384 1.00 0.77 C
-ATOM 282 O SER A 268 -8.705 6.470 7.981 1.00 1.35 O
-ATOM 283 CB SER A 268 -9.457 6.741 4.891 1.00 0.96 C
-ATOM 284 OG SER A 268 -9.613 7.463 3.678 1.00 1.05 O
-ATOM 285 H SER A 268 -7.757 8.414 6.856 1.00 0.72 H
-ATOM 286 HA SER A 268 -10.487 8.264 5.990 1.00 0.84 H
-ATOM 287 HB2 SER A 268 -8.478 6.282 4.900 1.00 1.00 H
-ATOM 288 HB3 SER A 268 -10.214 5.973 4.941 1.00 1.13 H
-ATOM 289 HG SER A 268 -9.520 8.414 3.853 1.00 1.19 H
-ATOM 290 N GLY A 269 -10.948 6.595 7.815 1.00 0.57 N
-ATOM 291 CA GLY A 269 -11.167 5.842 9.040 1.00 0.56 C
-ATOM 292 C GLY A 269 -10.783 6.638 10.277 1.00 0.55 C
-ATOM 293 O GLY A 269 -11.164 7.801 10.416 1.00 0.73 O
-ATOM 294 H GLY A 269 -11.714 6.937 7.309 1.00 0.93 H
-ATOM 295 HA2 GLY A 269 -12.211 5.575 9.104 1.00 0.66 H
-ATOM 296 HA3 GLY A 269 -10.575 4.939 9.006 1.00 0.56 H
-ATOM 297 N SER A 270 -10.024 6.017 11.175 1.00 0.47 N
-ATOM 298 CA SER A 270 -9.580 6.682 12.404 1.00 0.56 C
-ATOM 299 C SER A 270 -8.423 5.923 13.043 1.00 0.53 C
-ATOM 300 O SER A 270 -8.490 4.703 13.198 1.00 0.48 O
-ATOM 301 CB SER A 270 -10.727 6.803 13.417 1.00 0.70 C
-ATOM 302 OG SER A 270 -11.718 7.718 12.976 1.00 0.93 O
-ATOM 303 H SER A 270 -9.746 5.090 11.006 1.00 0.48 H
-ATOM 304 HA SER A 270 -9.242 7.673 12.139 1.00 0.65 H
-ATOM 305 HB2 SER A 270 -11.187 5.836 13.552 1.00 0.70 H
-ATOM 306 HB3 SER A 270 -10.333 7.147 14.362 1.00 0.80 H
-ATOM 307 HG SER A 270 -11.608 7.877 12.024 1.00 0.92 H
-ATOM 308 N ARG A 271 -7.364 6.658 13.407 1.00 0.71 N
-ATOM 309 CA ARG A 271 -6.163 6.083 14.041 1.00 0.82 C
-ATOM 310 C ARG A 271 -5.339 5.216 13.070 1.00 0.81 C
-ATOM 311 O ARG A 271 -4.118 5.340 13.002 1.00 1.06 O
-ATOM 312 CB ARG A 271 -6.558 5.269 15.278 1.00 0.85 C
-ATOM 313 CG ARG A 271 -5.382 4.702 16.054 1.00 1.01 C
-ATOM 314 CD ARG A 271 -5.860 3.849 17.216 1.00 1.10 C
-ATOM 315 NE ARG A 271 -6.721 2.758 16.768 1.00 1.02 N
-ATOM 316 CZ ARG A 271 -7.332 1.924 17.574 1.00 1.19 C
-ATOM 317 NH1 ARG A 271 -7.209 2.045 18.868 1.00 1.41 N
-ATOM 318 NH2 ARG A 271 -8.074 0.970 17.084 1.00 1.29 N
-ATOM 319 H ARG A 271 -7.388 7.624 13.245 1.00 0.84 H
-ATOM 320 HA ARG A 271 -5.544 6.909 14.360 1.00 1.03 H
-ATOM 321 HB2 ARG A 271 -7.125 5.903 15.942 1.00 0.98 H
-ATOM 322 HB3 ARG A 271 -7.183 4.445 14.963 1.00 0.75 H
-ATOM 323 HG2 ARG A 271 -4.784 4.092 15.393 1.00 1.00 H
-ATOM 324 HG3 ARG A 271 -4.787 5.517 16.437 1.00 1.19 H
-ATOM 325 HD2 ARG A 271 -5.000 3.433 17.721 1.00 1.25 H
-ATOM 326 HD3 ARG A 271 -6.413 4.472 17.903 1.00 1.23 H
-ATOM 327 HE ARG A 271 -6.841 2.645 15.806 1.00 0.98 H
-ATOM 328 HH11 ARG A 271 -6.645 2.773 19.248 1.00 1.47 H
-ATOM 329 HH12 ARG A 271 -7.680 1.411 19.473 1.00 1.59 H
-ATOM 330 HH21 ARG A 271 -8.178 0.875 16.096 1.00 1.28 H
-ATOM 331 HH22 ARG A 271 -8.535 0.334 17.699 1.00 1.47 H
-ATOM 332 N ALA A 272 -6.011 4.341 12.329 1.00 0.66 N
-ATOM 333 CA ALA A 272 -5.342 3.460 11.376 1.00 0.81 C
-ATOM 334 C ALA A 272 -6.215 3.211 10.140 1.00 0.65 C
-ATOM 335 O ALA A 272 -6.813 4.143 9.602 1.00 0.61 O
-ATOM 336 CB ALA A 272 -4.982 2.144 12.054 1.00 1.12 C
-ATOM 337 H ALA A 272 -6.990 4.287 12.429 1.00 0.58 H
-ATOM 338 HA ALA A 272 -4.425 3.939 11.066 1.00 0.95 H
-ATOM 339 HB1 ALA A 272 -4.000 2.222 12.493 1.00 1.58 H
-ATOM 340 HB2 ALA A 272 -4.988 1.349 11.321 1.00 1.56 H
-ATOM 341 HB3 ALA A 272 -5.707 1.927 12.824 1.00 1.58 H
-ATOM 342 N SER A 273 -6.285 1.946 9.703 1.00 0.81 N
-ATOM 343 CA SER A 273 -7.085 1.544 8.530 1.00 0.85 C
-ATOM 344 C SER A 273 -6.745 2.346 7.281 1.00 0.66 C
-ATOM 345 O SER A 273 -7.607 2.970 6.666 1.00 0.74 O
-ATOM 346 CB SER A 273 -8.595 1.600 8.809 1.00 1.01 C
-ATOM 347 OG SER A 273 -9.022 2.888 9.234 1.00 0.90 O
-ATOM 348 H SER A 273 -5.786 1.256 10.187 1.00 1.01 H
-ATOM 349 HA SER A 273 -6.828 0.515 8.311 1.00 1.05 H
-ATOM 350 HB2 SER A 273 -9.126 1.346 7.903 1.00 1.17 H
-ATOM 351 HB3 SER A 273 -8.837 0.882 9.576 1.00 1.18 H
-ATOM 352 HG SER A 273 -8.277 3.510 9.180 1.00 0.90 H
-ATOM 353 N LEU A 274 -5.484 2.272 6.878 1.00 0.61 N
-ATOM 354 CA LEU A 274 -5.034 2.941 5.666 1.00 0.51 C
-ATOM 355 C LEU A 274 -4.421 1.943 4.656 1.00 0.61 C
-ATOM 356 O LEU A 274 -3.381 2.239 4.068 1.00 0.68 O
-ATOM 357 CB LEU A 274 -4.002 4.022 6.009 1.00 0.67 C
-ATOM 358 CG LEU A 274 -4.505 5.162 6.901 1.00 0.78 C
-ATOM 359 CD1 LEU A 274 -3.353 6.063 7.308 1.00 1.16 C
-ATOM 360 CD2 LEU A 274 -5.577 5.974 6.186 1.00 0.91 C
-ATOM 361 H LEU A 274 -4.851 1.725 7.386 1.00 0.76 H
-ATOM 362 HA LEU A 274 -5.893 3.412 5.211 1.00 0.53 H
-ATOM 363 HB2 LEU A 274 -3.170 3.546 6.506 1.00 0.86 H
-ATOM 364 HB3 LEU A 274 -3.646 4.451 5.084 1.00 0.79 H
-ATOM 365 HG LEU A 274 -4.940 4.746 7.798 1.00 0.78 H
-ATOM 366 HD11 LEU A 274 -3.405 6.988 6.752 1.00 1.68 H
-ATOM 367 HD12 LEU A 274 -2.417 5.568 7.096 1.00 1.36 H
-ATOM 368 HD13 LEU A 274 -3.417 6.274 8.365 1.00 1.68 H
-ATOM 369 HD21 LEU A 274 -5.107 6.655 5.492 1.00 1.43 H
-ATOM 370 HD22 LEU A 274 -6.145 6.537 6.912 1.00 1.40 H
-ATOM 371 HD23 LEU A 274 -6.236 5.309 5.649 1.00 1.41 H
-ATOM 372 N PRO A 275 -5.034 0.742 4.433 1.00 0.81 N
-ATOM 373 CA PRO A 275 -4.490 -0.255 3.483 1.00 1.04 C
-ATOM 374 C PRO A 275 -4.256 0.316 2.080 1.00 0.90 C
-ATOM 375 O PRO A 275 -3.174 0.162 1.510 1.00 0.98 O
-ATOM 376 CB PRO A 275 -5.577 -1.329 3.440 1.00 1.31 C
-ATOM 377 CG PRO A 275 -6.220 -1.232 4.772 1.00 1.34 C
-ATOM 378 CD PRO A 275 -6.272 0.235 5.075 1.00 0.99 C
-ATOM 379 HA PRO A 275 -3.577 -0.689 3.853 1.00 1.21 H
-ATOM 380 HB2 PRO A 275 -6.273 -1.113 2.642 1.00 1.32 H
-ATOM 381 HB3 PRO A 275 -5.128 -2.298 3.287 1.00 1.55 H
-ATOM 382 HG2 PRO A 275 -7.216 -1.651 4.737 1.00 1.52 H
-ATOM 383 HG3 PRO A 275 -5.614 -1.743 5.502 1.00 1.47 H
-ATOM 384 HD2 PRO A 275 -7.154 0.683 4.643 1.00 1.01 H
-ATOM 385 HD3 PRO A 275 -6.252 0.402 6.151 1.00 0.98 H
-ATOM 386 N LYS A 276 -5.281 0.991 1.553 1.00 0.82 N
-ATOM 387 CA LYS A 276 -5.228 1.630 0.225 1.00 0.81 C
-ATOM 388 C LYS A 276 -4.673 0.695 -0.866 1.00 0.89 C
-ATOM 389 O LYS A 276 -3.723 1.093 -1.580 1.00 1.32 O
-ATOM 390 CB LYS A 276 -4.404 2.919 0.304 1.00 0.74 C
-ATOM 391 CG LYS A 276 -4.984 3.943 1.269 1.00 0.78 C
-ATOM 392 CD LYS A 276 -4.162 5.221 1.300 1.00 0.92 C
-ATOM 393 CE LYS A 276 -4.758 6.237 2.259 1.00 1.07 C
-ATOM 394 NZ LYS A 276 -4.046 7.540 2.208 1.00 1.35 N
-ATOM 395 OXT LYS A 276 -5.193 -0.434 -1.002 1.00 0.68 O
-ATOM 396 H LYS A 276 -6.101 1.077 2.082 1.00 0.89 H
-ATOM 397 HA LYS A 276 -6.241 1.891 -0.042 1.00 0.91 H
-ATOM 398 HB2 LYS A 276 -3.402 2.676 0.629 1.00 0.74 H
-ATOM 399 HB3 LYS A 276 -4.357 3.367 -0.677 1.00 0.84 H
-ATOM 400 HG2 LYS A 276 -5.989 4.184 0.959 1.00 0.98 H
-ATOM 401 HG3 LYS A 276 -5.006 3.515 2.262 1.00 0.79 H
-ATOM 402 HD2 LYS A 276 -3.157 4.985 1.620 1.00 0.98 H
-ATOM 403 HD3 LYS A 276 -4.135 5.646 0.308 1.00 1.12 H
-ATOM 404 HE2 LYS A 276 -5.793 6.394 1.998 1.00 1.25 H
-ATOM 405 HE3 LYS A 276 -4.697 5.843 3.263 1.00 1.07 H
-ATOM 406 HZ1 LYS A 276 -3.047 7.418 2.486 1.00 1.65 H
-ATOM 407 HZ2 LYS A 276 -4.496 8.219 2.868 1.00 1.85 H
-ATOM 408 HZ3 LYS A 276 -4.082 7.934 1.243 1.00 1.50 H
-TER
-ENDMDL
-MODEL 13
-ATOM 1 N ALA A 249 1.543 -11.826 -11.005 1.00 3.74 N
-ATOM 2 CA ALA A 249 2.138 -10.463 -10.975 1.00 3.58 C
-ATOM 3 C ALA A 249 1.770 -9.729 -9.686 1.00 3.24 C
-ATOM 4 O ALA A 249 0.738 -10.010 -9.077 1.00 3.10 O
-ATOM 5 CB ALA A 249 1.694 -9.660 -12.189 1.00 3.64 C
-ATOM 6 H1 ALA A 249 1.278 -12.122 -10.041 1.00 3.69 H
-ATOM 7 H2 ALA A 249 2.229 -12.507 -11.389 1.00 4.32 H
-ATOM 8 H3 ALA A 249 0.692 -11.833 -11.604 1.00 3.64 H
-ATOM 9 HA ALA A 249 3.212 -10.565 -11.019 1.00 3.72 H
-ATOM 10 HB1 ALA A 249 0.639 -9.814 -12.357 1.00 3.80 H
-ATOM 11 HB2 ALA A 249 2.250 -9.984 -13.056 1.00 3.78 H
-ATOM 12 HB3 ALA A 249 1.883 -8.610 -12.013 1.00 3.80 H
-ATOM 13 N PRO A 250 2.624 -8.788 -9.248 1.00 3.15 N
-ATOM 14 CA PRO A 250 2.420 -8.000 -8.021 1.00 2.87 C
-ATOM 15 C PRO A 250 1.276 -6.980 -8.133 1.00 2.62 C
-ATOM 16 O PRO A 250 1.488 -5.780 -7.958 1.00 2.49 O
-ATOM 17 CB PRO A 250 3.763 -7.270 -7.847 1.00 3.00 C
-ATOM 18 CG PRO A 250 4.711 -7.964 -8.756 1.00 3.33 C
-ATOM 19 CD PRO A 250 3.877 -8.410 -9.907 1.00 3.39 C
-ATOM 20 HA PRO A 250 2.245 -8.639 -7.168 1.00 2.84 H
-ATOM 21 HB2 PRO A 250 3.647 -6.235 -8.128 1.00 2.92 H
-ATOM 22 HB3 PRO A 250 4.084 -7.339 -6.823 1.00 2.97 H
-ATOM 23 HG2 PRO A 250 5.477 -7.278 -9.087 1.00 3.46 H
-ATOM 24 HG3 PRO A 250 5.150 -8.813 -8.256 1.00 3.44 H
-ATOM 25 HD2 PRO A 250 3.720 -7.594 -10.597 1.00 3.39 H
-ATOM 26 HD3 PRO A 250 4.325 -9.254 -10.400 1.00 3.62 H
-ATOM 27 N ALA A 251 0.068 -7.462 -8.429 1.00 2.59 N
-ATOM 28 CA ALA A 251 -1.103 -6.590 -8.568 1.00 2.42 C
-ATOM 29 C ALA A 251 -1.383 -5.802 -7.286 1.00 2.13 C
-ATOM 30 O ALA A 251 -1.587 -4.589 -7.328 1.00 1.97 O
-ATOM 31 CB ALA A 251 -2.323 -7.406 -8.965 1.00 2.54 C
-ATOM 32 H ALA A 251 -0.038 -8.432 -8.562 1.00 2.73 H
-ATOM 33 HA ALA A 251 -0.897 -5.890 -9.366 1.00 2.47 H
-ATOM 34 HB1 ALA A 251 -3.216 -6.820 -8.801 1.00 2.81 H
-ATOM 35 HB2 ALA A 251 -2.367 -8.305 -8.368 1.00 2.71 H
-ATOM 36 HB3 ALA A 251 -2.255 -7.672 -10.011 1.00 2.79 H
-ATOM 37 N ARG A 252 -1.380 -6.495 -6.147 1.00 2.08 N
-ATOM 38 CA ARG A 252 -1.623 -5.853 -4.852 1.00 1.85 C
-ATOM 39 C ARG A 252 -0.523 -4.828 -4.554 1.00 1.77 C
-ATOM 40 O ARG A 252 -0.797 -3.720 -4.091 1.00 1.55 O
-ATOM 41 CB ARG A 252 -1.680 -6.912 -3.740 1.00 1.96 C
-ATOM 42 CG ARG A 252 -2.671 -6.599 -2.623 1.00 1.86 C
-ATOM 43 CD ARG A 252 -2.314 -5.322 -1.881 1.00 1.70 C
-ATOM 44 NE ARG A 252 -3.238 -5.036 -0.787 1.00 1.83 N
-ATOM 45 CZ ARG A 252 -3.242 -3.905 -0.117 1.00 1.88 C
-ATOM 46 NH1 ARG A 252 -2.432 -2.935 -0.459 1.00 1.83 N
-ATOM 47 NH2 ARG A 252 -4.071 -3.734 0.880 1.00 2.14 N
-ATOM 48 H ARG A 252 -1.203 -7.457 -6.176 1.00 2.24 H
-ATOM 49 HA ARG A 252 -2.571 -5.343 -4.906 1.00 1.75 H
-ATOM 50 HB2 ARG A 252 -1.953 -7.859 -4.177 1.00 2.15 H
-ATOM 51 HB3 ARG A 252 -0.697 -7.001 -3.300 1.00 2.01 H
-ATOM 52 HG2 ARG A 252 -3.655 -6.487 -3.052 1.00 1.92 H
-ATOM 53 HG3 ARG A 252 -2.676 -7.422 -1.922 1.00 1.98 H
-ATOM 54 HD2 ARG A 252 -1.317 -5.425 -1.477 1.00 1.75 H
-ATOM 55 HD3 ARG A 252 -2.333 -4.499 -2.579 1.00 1.58 H
-ATOM 56 HE ARG A 252 -3.871 -5.735 -0.533 1.00 2.01 H
-ATOM 57 HH11 ARG A 252 -1.809 -3.057 -1.228 1.00 1.73 H
-ATOM 58 HH12 ARG A 252 -2.439 -2.077 0.046 1.00 1.99 H
-ATOM 59 HH21 ARG A 252 -4.699 -4.463 1.138 1.00 2.28 H
-ATOM 60 HH22 ARG A 252 -4.071 -2.874 1.386 1.00 2.26 H
-ATOM 61 N VAL A 253 0.721 -5.204 -4.846 1.00 2.00 N
-ATOM 62 CA VAL A 253 1.867 -4.322 -4.632 1.00 2.06 C
-ATOM 63 C VAL A 253 1.767 -3.087 -5.526 1.00 1.95 C
-ATOM 64 O VAL A 253 1.899 -1.956 -5.055 1.00 1.84 O
-ATOM 65 CB VAL A 253 3.199 -5.052 -4.912 1.00 2.40 C
-ATOM 66 CG1 VAL A 253 4.390 -4.145 -4.629 1.00 2.54 C
-ATOM 67 CG2 VAL A 253 3.291 -6.329 -4.090 1.00 2.53 C
-ATOM 68 H VAL A 253 0.868 -6.092 -5.226 1.00 2.18 H
-ATOM 69 HA VAL A 253 1.859 -4.008 -3.601 1.00 1.98 H
-ATOM 70 HB VAL A 253 3.224 -5.322 -5.957 1.00 2.50 H
-ATOM 71 HG11 VAL A 253 4.050 -3.126 -4.518 1.00 2.66 H
-ATOM 72 HG12 VAL A 253 5.090 -4.205 -5.450 1.00 2.77 H
-ATOM 73 HG13 VAL A 253 4.877 -4.464 -3.719 1.00 2.78 H
-ATOM 74 HG21 VAL A 253 3.473 -7.166 -4.746 1.00 2.67 H
-ATOM 75 HG22 VAL A 253 2.362 -6.483 -3.561 1.00 2.84 H
-ATOM 76 HG23 VAL A 253 4.100 -6.243 -3.380 1.00 2.78 H
-ATOM 77 N GLY A 254 1.507 -3.317 -6.813 1.00 2.03 N
-ATOM 78 CA GLY A 254 1.363 -2.222 -7.763 1.00 1.98 C
-ATOM 79 C GLY A 254 0.247 -1.270 -7.368 1.00 1.68 C
-ATOM 80 O GLY A 254 0.441 -0.053 -7.335 1.00 1.58 O
-ATOM 81 H GLY A 254 1.396 -4.248 -7.120 1.00 2.15 H
-ATOM 82 HA2 GLY A 254 2.293 -1.674 -7.808 1.00 2.08 H
-ATOM 83 HA3 GLY A 254 1.147 -2.630 -8.739 1.00 2.10 H
-ATOM 84 N LEU A 255 -0.913 -1.832 -7.032 1.00 1.58 N
-ATOM 85 CA LEU A 255 -2.056 -1.034 -6.598 1.00 1.34 C
-ATOM 86 C LEU A 255 -1.723 -0.250 -5.338 1.00 1.15 C
-ATOM 87 O LEU A 255 -2.044 0.934 -5.215 1.00 1.00 O
-ATOM 88 CB LEU A 255 -3.257 -1.928 -6.325 1.00 1.39 C
-ATOM 89 CG LEU A 255 -4.225 -2.060 -7.489 1.00 1.58 C
-ATOM 90 CD1 LEU A 255 -5.374 -2.977 -7.111 1.00 1.74 C
-ATOM 91 CD2 LEU A 255 -4.736 -0.685 -7.891 1.00 1.53 C
-ATOM 92 H LEU A 255 -0.995 -2.813 -7.057 1.00 1.70 H
-ATOM 93 HA LEU A 255 -2.306 -0.345 -7.391 1.00 1.35 H
-ATOM 94 HB2 LEU A 255 -2.896 -2.914 -6.067 1.00 1.55 H
-ATOM 95 HB3 LEU A 255 -3.796 -1.527 -5.480 1.00 1.26 H
-ATOM 96 HG LEU A 255 -3.709 -2.490 -8.334 1.00 1.77 H
-ATOM 97 HD11 LEU A 255 -5.303 -3.224 -6.062 1.00 2.07 H
-ATOM 98 HD12 LEU A 255 -5.320 -3.882 -7.698 1.00 2.06 H
-ATOM 99 HD13 LEU A 255 -6.311 -2.477 -7.300 1.00 2.05 H
-ATOM 100 HD21 LEU A 255 -3.950 -0.144 -8.398 1.00 1.69 H
-ATOM 101 HD22 LEU A 255 -5.032 -0.139 -7.005 1.00 1.84 H
-ATOM 102 HD23 LEU A 255 -5.583 -0.794 -8.548 1.00 1.97 H
-ATOM 103 N GLY A 256 -1.077 -0.926 -4.398 1.00 1.22 N
-ATOM 104 CA GLY A 256 -0.707 -0.298 -3.153 1.00 1.17 C
-ATOM 105 C GLY A 256 0.267 0.856 -3.335 1.00 1.25 C
-ATOM 106 O GLY A 256 0.015 1.956 -2.852 1.00 1.15 O
-ATOM 107 H GLY A 256 -0.852 -1.872 -4.553 1.00 1.36 H
-ATOM 108 HA2 GLY A 256 -1.604 0.074 -2.681 1.00 1.01 H
-ATOM 109 HA3 GLY A 256 -0.257 -1.041 -2.514 1.00 1.29 H
-ATOM 110 N ILE A 257 1.374 0.625 -4.041 1.00 1.48 N
-ATOM 111 CA ILE A 257 2.351 1.692 -4.262 1.00 1.64 C
-ATOM 112 C ILE A 257 1.747 2.836 -5.086 1.00 1.47 C
-ATOM 113 O ILE A 257 2.021 4.005 -4.822 1.00 1.51 O
-ATOM 114 CB ILE A 257 3.663 1.180 -4.918 1.00 1.96 C
-ATOM 115 CG1 ILE A 257 3.415 0.531 -6.285 1.00 2.01 C
-ATOM 116 CG2 ILE A 257 4.364 0.198 -3.992 1.00 2.15 C
-ATOM 117 CD1 ILE A 257 3.674 1.456 -7.457 1.00 2.09 C
-ATOM 118 H ILE A 257 1.534 -0.272 -4.421 1.00 1.59 H
-ATOM 119 HA ILE A 257 2.607 2.086 -3.288 1.00 1.68 H
-ATOM 120 HB ILE A 257 4.319 2.029 -5.048 1.00 2.08 H
-ATOM 121 HG12 ILE A 257 4.064 -0.325 -6.392 1.00 2.21 H
-ATOM 122 HG13 ILE A 257 2.386 0.206 -6.337 1.00 1.86 H
-ATOM 123 HG21 ILE A 257 3.704 -0.632 -3.781 1.00 2.38 H
-ATOM 124 HG22 ILE A 257 4.625 0.694 -3.069 1.00 2.42 H
-ATOM 125 HG23 ILE A 257 5.261 -0.170 -4.469 1.00 2.47 H
-ATOM 126 HD11 ILE A 257 3.762 0.873 -8.362 1.00 2.18 H
-ATOM 127 HD12 ILE A 257 4.590 2.003 -7.290 1.00 2.25 H
-ATOM 128 HD13 ILE A 257 2.852 2.151 -7.555 1.00 2.12 H
-ATOM 129 N THR A 258 0.912 2.493 -6.069 1.00 1.36 N
-ATOM 130 CA THR A 258 0.261 3.498 -6.914 1.00 1.25 C
-ATOM 131 C THR A 258 -0.687 4.377 -6.096 1.00 0.98 C
-ATOM 132 O THR A 258 -0.654 5.605 -6.196 1.00 0.98 O
-ATOM 133 CB THR A 258 -0.531 2.847 -8.069 1.00 1.30 C
-ATOM 134 OG1 THR A 258 0.338 2.031 -8.864 1.00 1.59 O
-ATOM 135 CG2 THR A 258 -1.184 3.902 -8.950 1.00 1.28 C
-ATOM 136 H THR A 258 0.722 1.539 -6.227 1.00 1.39 H
-ATOM 137 HA THR A 258 1.032 4.123 -7.342 1.00 1.40 H
-ATOM 138 HB THR A 258 -1.306 2.224 -7.645 1.00 1.21 H
-ATOM 139 HG1 THR A 258 0.463 1.175 -8.429 1.00 1.58 H
-ATOM 140 HG21 THR A 258 -1.756 3.417 -9.726 1.00 1.62 H
-ATOM 141 HG22 THR A 258 -0.419 4.518 -9.399 1.00 1.67 H
-ATOM 142 HG23 THR A 258 -1.838 4.518 -8.350 1.00 1.63 H
-ATOM 143 N THR A 259 -1.524 3.744 -5.274 1.00 0.82 N
-ATOM 144 CA THR A 259 -2.467 4.480 -4.433 1.00 0.65 C
-ATOM 145 C THR A 259 -1.726 5.332 -3.410 1.00 0.78 C
-ATOM 146 O THR A 259 -2.046 6.506 -3.225 1.00 0.80 O
-ATOM 147 CB THR A 259 -3.459 3.545 -3.712 1.00 0.60 C
-ATOM 148 OG1 THR A 259 -2.762 2.472 -3.070 1.00 0.70 O
-ATOM 149 CG2 THR A 259 -4.478 2.983 -4.691 1.00 0.70 C
-ATOM 150 H THR A 259 -1.498 2.763 -5.222 1.00 0.88 H
-ATOM 151 HA THR A 259 -3.033 5.137 -5.079 1.00 0.61 H
-ATOM 152 HB THR A 259 -3.986 4.118 -2.961 1.00 0.61 H
-ATOM 153 HG1 THR A 259 -2.579 1.775 -3.716 1.00 0.99 H
-ATOM 154 HG21 THR A 259 -4.099 2.068 -5.121 1.00 1.18 H
-ATOM 155 HG22 THR A 259 -4.657 3.704 -5.475 1.00 1.10 H
-ATOM 156 HG23 THR A 259 -5.403 2.781 -4.172 1.00 1.32 H
-ATOM 157 N VAL A 260 -0.715 4.747 -2.769 1.00 0.98 N
-ATOM 158 CA VAL A 260 0.088 5.472 -1.791 1.00 1.22 C
-ATOM 159 C VAL A 260 0.799 6.655 -2.455 1.00 1.34 C
-ATOM 160 O VAL A 260 0.719 7.782 -1.965 1.00 1.44 O
-ATOM 161 CB VAL A 260 1.127 4.553 -1.115 1.00 1.46 C
-ATOM 162 CG1 VAL A 260 2.100 5.361 -0.269 1.00 1.77 C
-ATOM 163 CG2 VAL A 260 0.432 3.503 -0.262 1.00 1.43 C
-ATOM 164 H VAL A 260 -0.494 3.807 -2.974 1.00 1.00 H
-ATOM 165 HA VAL A 260 -0.580 5.849 -1.029 1.00 1.22 H
-ATOM 166 HB VAL A 260 1.688 4.046 -1.885 1.00 1.51 H
-ATOM 167 HG11 VAL A 260 2.365 4.796 0.612 1.00 2.13 H
-ATOM 168 HG12 VAL A 260 1.634 6.290 0.026 1.00 1.97 H
-ATOM 169 HG13 VAL A 260 2.988 5.571 -0.845 1.00 2.20 H
-ATOM 170 HG21 VAL A 260 0.555 2.531 -0.718 1.00 1.75 H
-ATOM 171 HG22 VAL A 260 -0.622 3.735 -0.194 1.00 1.76 H
-ATOM 172 HG23 VAL A 260 0.865 3.495 0.726 1.00 1.74 H
-ATOM 173 N LEU A 261 1.467 6.397 -3.586 1.00 1.39 N
-ATOM 174 CA LEU A 261 2.169 7.447 -4.331 1.00 1.54 C
-ATOM 175 C LEU A 261 1.203 8.565 -4.726 1.00 1.37 C
-ATOM 176 O LEU A 261 1.521 9.748 -4.604 1.00 1.51 O
-ATOM 177 CB LEU A 261 2.829 6.870 -5.581 1.00 1.66 C
-ATOM 178 CG LEU A 261 3.761 7.832 -6.312 1.00 1.90 C
-ATOM 179 CD1 LEU A 261 4.829 8.361 -5.365 1.00 2.18 C
-ATOM 180 CD2 LEU A 261 4.397 7.144 -7.506 1.00 2.08 C
-ATOM 181 H LEU A 261 1.477 5.473 -3.937 1.00 1.36 H
-ATOM 182 HA LEU A 261 2.938 7.858 -3.694 1.00 1.76 H
-ATOM 183 HB2 LEU A 261 3.398 5.996 -5.292 1.00 1.79 H
-ATOM 184 HB3 LEU A 261 2.052 6.563 -6.264 1.00 1.50 H
-ATOM 185 HG LEU A 261 3.186 8.675 -6.674 1.00 1.81 H
-ATOM 186 HD11 LEU A 261 4.452 9.229 -4.844 1.00 2.47 H
-ATOM 187 HD12 LEU A 261 5.709 8.633 -5.928 1.00 2.50 H
-ATOM 188 HD13 LEU A 261 5.083 7.595 -4.646 1.00 2.47 H
-ATOM 189 HD21 LEU A 261 4.278 7.763 -8.382 1.00 2.37 H
-ATOM 190 HD22 LEU A 261 3.913 6.191 -7.668 1.00 2.38 H
-ATOM 191 HD23 LEU A 261 5.446 6.988 -7.313 1.00 2.33 H
-ATOM 192 N THR A 262 0.013 8.174 -5.178 1.00 1.10 N
-ATOM 193 CA THR A 262 -1.021 9.132 -5.568 1.00 0.96 C
-ATOM 194 C THR A 262 -1.436 9.983 -4.367 1.00 1.02 C
-ATOM 195 O THR A 262 -1.488 11.212 -4.442 1.00 1.11 O
-ATOM 196 CB THR A 262 -2.268 8.409 -6.124 1.00 0.79 C
-ATOM 197 OG1 THR A 262 -1.918 7.626 -7.275 1.00 0.89 O
-ATOM 198 CG2 THR A 262 -3.359 9.401 -6.493 1.00 0.80 C
-ATOM 199 H THR A 262 -0.180 7.209 -5.238 1.00 1.03 H
-ATOM 200 HA THR A 262 -0.618 9.773 -6.339 1.00 1.04 H
-ATOM 201 HB THR A 262 -2.650 7.750 -5.359 1.00 0.76 H
-ATOM 202 HG1 THR A 262 -1.443 6.830 -6.993 1.00 0.98 H
-ATOM 203 HG21 THR A 262 -3.115 10.369 -6.081 1.00 1.32 H
-ATOM 204 HG22 THR A 262 -4.302 9.065 -6.088 1.00 1.29 H
-ATOM 205 HG23 THR A 262 -3.432 9.474 -7.567 1.00 1.31 H
-ATOM 206 N MET A 263 -1.717 9.307 -3.255 1.00 1.03 N
-ATOM 207 CA MET A 263 -2.116 9.971 -2.014 1.00 1.19 C
-ATOM 208 C MET A 263 -1.030 10.933 -1.513 1.00 1.40 C
-ATOM 209 O MET A 263 -1.318 12.080 -1.169 1.00 1.50 O
-ATOM 210 CB MET A 263 -2.416 8.918 -0.943 1.00 1.24 C
-ATOM 211 CG MET A 263 -2.851 9.499 0.392 1.00 1.45 C
-ATOM 212 SD MET A 263 -3.166 8.226 1.631 1.00 1.57 S
-ATOM 213 CE MET A 263 -1.560 7.441 1.736 1.00 1.67 C
-ATOM 214 H MET A 263 -1.649 8.324 -3.270 1.00 0.98 H
-ATOM 215 HA MET A 263 -3.016 10.534 -2.213 1.00 1.16 H
-ATOM 216 HB2 MET A 263 -3.203 8.272 -1.302 1.00 1.13 H
-ATOM 217 HB3 MET A 263 -1.526 8.327 -0.778 1.00 1.30 H
-ATOM 218 HG2 MET A 263 -2.072 10.152 0.757 1.00 1.60 H
-ATOM 219 HG3 MET A 263 -3.757 10.069 0.243 1.00 1.47 H
-ATOM 220 HE1 MET A 263 -1.276 7.069 0.762 1.00 1.96 H
-ATOM 221 HE2 MET A 263 -1.606 6.619 2.435 1.00 1.96 H
-ATOM 222 HE3 MET A 263 -0.828 8.161 2.072 1.00 2.06 H
-ATOM 223 N THR A 264 0.220 10.467 -1.476 1.00 1.52 N
-ATOM 224 CA THR A 264 1.331 11.308 -1.012 1.00 1.75 C
-ATOM 225 C THR A 264 1.809 12.292 -2.095 1.00 1.62 C
-ATOM 226 O THR A 264 3.012 12.454 -2.321 1.00 1.66 O
-ATOM 227 CB THR A 264 2.529 10.466 -0.500 1.00 2.02 C
-ATOM 228 OG1 THR A 264 3.558 11.328 0.000 1.00 2.22 O
-ATOM 229 CG2 THR A 264 3.109 9.579 -1.594 1.00 1.96 C
-ATOM 230 H THR A 264 0.399 9.539 -1.766 1.00 1.49 H
-ATOM 231 HA THR A 264 0.960 11.888 -0.177 1.00 1.87 H
-ATOM 232 HB THR A 264 2.181 9.833 0.305 1.00 2.15 H
-ATOM 233 HG1 THR A 264 3.778 11.984 -0.675 1.00 2.13 H
-ATOM 234 HG21 THR A 264 2.421 8.773 -1.808 1.00 2.20 H
-ATOM 235 HG22 THR A 264 4.052 9.169 -1.262 1.00 2.22 H
-ATOM 236 HG23 THR A 264 3.265 10.165 -2.487 1.00 2.21 H
-ATOM 237 N THR A 265 0.861 12.967 -2.748 1.00 1.51 N
-ATOM 238 CA THR A 265 1.187 13.949 -3.790 1.00 1.42 C
-ATOM 239 C THR A 265 2.081 15.066 -3.239 1.00 1.25 C
-ATOM 240 O THR A 265 3.019 15.509 -3.901 1.00 1.20 O
-ATOM 241 CB THR A 265 -0.086 14.563 -4.420 1.00 1.50 C
-ATOM 242 OG1 THR A 265 0.263 15.650 -5.289 1.00 1.46 O
-ATOM 243 CG2 THR A 265 -1.058 15.052 -3.353 1.00 1.52 C
-ATOM 244 H THR A 265 -0.082 12.809 -2.515 1.00 1.52 H
-ATOM 245 HA THR A 265 1.729 13.431 -4.569 1.00 1.48 H
-ATOM 246 HB THR A 265 -0.579 13.798 -5.005 1.00 1.63 H
-ATOM 247 HG1 THR A 265 1.160 15.530 -5.612 1.00 1.55 H
-ATOM 248 HG21 THR A 265 -0.958 14.442 -2.468 1.00 2.08 H
-ATOM 249 HG22 THR A 265 -2.069 14.980 -3.728 1.00 1.64 H
-ATOM 250 HG23 THR A 265 -0.836 16.081 -3.110 1.00 1.75 H
-ATOM 251 N GLN A 266 1.798 15.494 -2.015 1.00 1.26 N
-ATOM 252 CA GLN A 266 2.589 16.528 -1.363 1.00 1.15 C
-ATOM 253 C GLN A 266 3.740 15.896 -0.577 1.00 0.88 C
-ATOM 254 O GLN A 266 3.528 14.968 0.205 1.00 0.93 O
-ATOM 255 CB GLN A 266 1.710 17.359 -0.428 1.00 1.42 C
-ATOM 256 CG GLN A 266 2.468 18.448 0.311 1.00 1.45 C
-ATOM 257 CD GLN A 266 1.574 19.258 1.224 1.00 1.84 C
-ATOM 258 OE1 GLN A 266 0.940 18.725 2.126 1.00 1.97 O
-ATOM 259 NE2 GLN A 266 1.522 20.558 0.997 1.00 2.12 N
-ATOM 260 H GLN A 266 1.051 15.087 -1.528 1.00 1.41 H
-ATOM 261 HA GLN A 266 2.998 17.169 -2.129 1.00 1.22 H
-ATOM 262 HB2 GLN A 266 0.927 17.824 -1.007 1.00 1.67 H
-ATOM 263 HB3 GLN A 266 1.263 16.701 0.304 1.00 1.46 H
-ATOM 264 HG2 GLN A 266 3.245 17.991 0.905 1.00 1.28 H
-ATOM 265 HG3 GLN A 266 2.913 19.112 -0.414 1.00 1.48 H
-ATOM 266 HE21 GLN A 266 2.057 20.920 0.263 1.00 2.09 H
-ATOM 267 HE22 GLN A 266 0.949 21.100 1.574 1.00 2.40 H
-ATOM 268 N SER A 267 4.953 16.397 -0.787 1.00 0.77 N
-ATOM 269 CA SER A 267 6.129 15.868 -0.095 1.00 0.79 C
-ATOM 270 C SER A 267 7.318 16.820 -0.229 1.00 0.79 C
-ATOM 271 O SER A 267 7.357 17.656 -1.132 1.00 0.88 O
-ATOM 272 CB SER A 267 6.503 14.491 -0.654 1.00 1.05 C
-ATOM 273 OG SER A 267 7.527 13.882 0.115 1.00 1.33 O
-ATOM 274 H SER A 267 5.063 17.134 -1.425 1.00 0.84 H
-ATOM 275 HA SER A 267 5.879 15.767 0.950 1.00 0.88 H
-ATOM 276 HB2 SER A 267 5.634 13.851 -0.640 1.00 1.10 H
-ATOM 277 HB3 SER A 267 6.851 14.600 -1.672 1.00 1.17 H
-ATOM 278 HG SER A 267 7.295 12.963 0.279 1.00 1.58 H
-ATOM 279 N SER A 268 8.284 16.690 0.677 1.00 0.86 N
-ATOM 280 CA SER A 268 9.480 17.540 0.662 1.00 0.98 C
-ATOM 281 C SER A 268 10.291 17.360 -0.622 1.00 1.03 C
-ATOM 282 O SER A 268 10.879 18.316 -1.137 1.00 1.18 O
-ATOM 283 CB SER A 268 10.374 17.229 1.866 1.00 1.19 C
-ATOM 284 OG SER A 268 11.604 17.940 1.791 1.00 1.37 O
-ATOM 285 H SER A 268 8.193 16.004 1.372 1.00 0.93 H
-ATOM 286 HA SER A 268 9.155 18.567 0.725 1.00 1.03 H
-ATOM 287 HB2 SER A 268 9.864 17.513 2.775 1.00 1.31 H
-ATOM 288 HB3 SER A 268 10.586 16.170 1.889 1.00 1.21 H
-ATOM 289 HG SER A 268 12.327 17.316 1.622 1.00 1.67 H
-ATOM 290 N GLY A 269 10.343 16.128 -1.122 1.00 1.09 N
-ATOM 291 CA GLY A 269 11.111 15.835 -2.325 1.00 1.29 C
-ATOM 292 C GLY A 269 12.604 15.796 -2.041 1.00 1.44 C
-ATOM 293 O GLY A 269 13.253 14.767 -2.213 1.00 1.69 O
-ATOM 294 H GLY A 269 9.868 15.404 -0.659 1.00 1.14 H
-ATOM 295 HA2 GLY A 269 10.801 14.878 -2.716 1.00 1.44 H
-ATOM 296 HA3 GLY A 269 10.915 16.598 -3.064 1.00 1.34 H
-ATOM 297 N SER A 270 13.136 16.917 -1.568 1.00 1.46 N
-ATOM 298 CA SER A 270 14.549 17.010 -1.218 1.00 1.75 C
-ATOM 299 C SER A 270 14.716 16.988 0.309 1.00 1.72 C
-ATOM 300 O SER A 270 13.882 16.415 1.020 1.00 1.61 O
-ATOM 301 CB SER A 270 15.166 18.285 -1.816 1.00 1.90 C
-ATOM 302 OG SER A 270 16.572 18.339 -1.602 1.00 2.26 O
-ATOM 303 H SER A 270 12.550 17.698 -1.430 1.00 1.41 H
-ATOM 304 HA SER A 270 15.050 16.149 -1.633 1.00 1.97 H
-ATOM 305 HB2 SER A 270 14.978 18.308 -2.879 1.00 1.95 H
-ATOM 306 HB3 SER A 270 14.713 19.150 -1.353 1.00 1.80 H
-ATOM 307 HG SER A 270 17.026 18.340 -2.453 1.00 2.43 H
-ATOM 308 N ARG A 271 15.794 17.610 0.797 1.00 1.92 N
-ATOM 309 CA ARG A 271 16.113 17.679 2.236 1.00 2.03 C
-ATOM 310 C ARG A 271 16.536 16.320 2.807 1.00 2.13 C
-ATOM 311 O ARG A 271 17.567 16.224 3.473 1.00 2.43 O
-ATOM 312 CB ARG A 271 14.940 18.229 3.059 1.00 1.86 C
-ATOM 313 CG ARG A 271 15.014 19.724 3.330 1.00 2.18 C
-ATOM 314 CD ARG A 271 14.806 20.542 2.069 1.00 2.15 C
-ATOM 315 NE ARG A 271 13.488 20.316 1.481 1.00 1.72 N
-ATOM 316 CZ ARG A 271 13.007 21.008 0.476 1.00 1.69 C
-ATOM 317 NH1 ARG A 271 13.711 21.972 -0.059 1.00 2.02 N
-ATOM 318 NH2 ARG A 271 11.824 20.730 0.001 1.00 1.41 N
-ATOM 319 H ARG A 271 16.410 18.041 0.156 1.00 2.08 H
-ATOM 320 HA ARG A 271 16.948 18.357 2.340 1.00 2.31 H
-ATOM 321 HB2 ARG A 271 14.020 18.030 2.528 1.00 1.62 H
-ATOM 322 HB3 ARG A 271 14.912 17.715 4.008 1.00 1.83 H
-ATOM 323 HG2 ARG A 271 14.249 19.986 4.044 1.00 2.17 H
-ATOM 324 HG3 ARG A 271 15.986 19.956 3.743 1.00 2.52 H
-ATOM 325 HD2 ARG A 271 14.907 21.590 2.312 1.00 2.42 H
-ATOM 326 HD3 ARG A 271 15.562 20.267 1.348 1.00 2.28 H
-ATOM 327 HE ARG A 271 12.934 19.600 1.862 1.00 1.52 H
-ATOM 328 HH11 ARG A 271 14.616 22.185 0.302 1.00 2.29 H
-ATOM 329 HH12 ARG A 271 13.342 22.495 -0.822 1.00 2.04 H
-ATOM 330 HH21 ARG A 271 11.286 19.989 0.405 1.00 1.24 H
-ATOM 331 HH22 ARG A 271 11.455 21.255 -0.760 1.00 1.46 H
-ATOM 332 N ALA A 272 15.728 15.284 2.558 1.00 2.02 N
-ATOM 333 CA ALA A 272 16.001 13.932 3.056 1.00 2.27 C
-ATOM 334 C ALA A 272 16.085 13.920 4.584 1.00 2.34 C
-ATOM 335 O ALA A 272 16.960 13.278 5.167 1.00 2.68 O
-ATOM 336 CB ALA A 272 17.282 13.377 2.445 1.00 2.61 C
-ATOM 337 H ALA A 272 14.913 15.440 2.024 1.00 1.87 H
-ATOM 338 HA ALA A 272 15.181 13.296 2.751 1.00 2.28 H
-ATOM 339 HB1 ALA A 272 18.064 13.380 3.189 1.00 2.85 H
-ATOM 340 HB2 ALA A 272 17.578 13.995 1.609 1.00 2.90 H
-ATOM 341 HB3 ALA A 272 17.110 12.367 2.105 1.00 2.89 H
-ATOM 342 N SER A 273 15.169 14.646 5.225 1.00 2.10 N
-ATOM 343 CA SER A 273 15.131 14.739 6.687 1.00 2.19 C
-ATOM 344 C SER A 273 14.602 13.470 7.331 1.00 2.35 C
-ATOM 345 O SER A 273 13.625 13.491 8.079 1.00 2.31 O
-ATOM 346 CB SER A 273 14.289 15.926 7.143 1.00 2.02 C
-ATOM 347 OG SER A 273 14.673 17.116 6.476 1.00 2.11 O
-ATOM 348 H SER A 273 14.507 15.139 4.701 1.00 1.93 H
-ATOM 349 HA SER A 273 16.145 14.882 7.035 1.00 2.42 H
-ATOM 350 HB2 SER A 273 13.252 15.726 6.936 1.00 1.92 H
-ATOM 351 HB3 SER A 273 14.426 16.064 8.206 1.00 2.14 H
-ATOM 352 HG SER A 273 15.426 17.506 6.927 1.00 2.40 H
-ATOM 353 N LEU A 274 15.285 12.374 7.074 1.00 2.68 N
-ATOM 354 CA LEU A 274 14.926 11.099 7.660 1.00 3.01 C
-ATOM 355 C LEU A 274 16.103 10.482 8.446 1.00 3.41 C
-ATOM 356 O LEU A 274 16.326 9.274 8.373 1.00 3.82 O
-ATOM 357 CB LEU A 274 14.432 10.152 6.561 1.00 3.24 C
-ATOM 358 CG LEU A 274 15.219 10.201 5.247 1.00 3.30 C
-ATOM 359 CD1 LEU A 274 16.313 9.149 5.226 1.00 3.97 C
-ATOM 360 CD2 LEU A 274 14.285 10.022 4.061 1.00 3.35 C
-ATOM 361 H LEU A 274 16.080 12.432 6.492 1.00 2.77 H
-ATOM 362 HA LEU A 274 14.114 11.279 8.351 1.00 2.90 H
-ATOM 363 HB2 LEU A 274 14.475 9.144 6.944 1.00 3.63 H
-ATOM 364 HB3 LEU A 274 13.402 10.392 6.345 1.00 3.12 H
-ATOM 365 HG LEU A 274 15.691 11.170 5.155 1.00 3.12 H
-ATOM 366 HD11 LEU A 274 15.917 8.211 5.590 1.00 4.31 H
-ATOM 367 HD12 LEU A 274 17.130 9.465 5.856 1.00 4.24 H
-ATOM 368 HD13 LEU A 274 16.667 9.020 4.215 1.00 4.27 H
-ATOM 369 HD21 LEU A 274 14.138 10.975 3.574 1.00 3.37 H
-ATOM 370 HD22 LEU A 274 13.336 9.644 4.409 1.00 3.59 H
-ATOM 371 HD23 LEU A 274 14.719 9.324 3.362 1.00 3.67 H
-ATOM 372 N PRO A 275 16.883 11.296 9.224 1.00 3.40 N
-ATOM 373 CA PRO A 275 18.020 10.777 10.007 1.00 3.87 C
-ATOM 374 C PRO A 275 17.608 9.683 10.989 1.00 4.12 C
-ATOM 375 O PRO A 275 18.309 8.682 11.141 1.00 4.56 O
-ATOM 376 CB PRO A 275 18.521 12.007 10.768 1.00 3.83 C
-ATOM 377 CG PRO A 275 18.099 13.142 9.915 1.00 3.45 C
-ATOM 378 CD PRO A 275 16.743 12.758 9.419 1.00 3.07 C
-ATOM 379 HA PRO A 275 18.804 10.414 9.363 1.00 4.12 H
-ATOM 380 HB2 PRO A 275 18.056 12.047 11.743 1.00 3.78 H
-ATOM 381 HB3 PRO A 275 19.594 11.966 10.870 1.00 4.18 H
-ATOM 382 HG2 PRO A 275 18.053 14.051 10.496 1.00 3.40 H
-ATOM 383 HG3 PRO A 275 18.783 13.245 9.089 1.00 3.58 H
-ATOM 384 HD2 PRO A 275 15.991 12.988 10.158 1.00 2.92 H
-ATOM 385 HD3 PRO A 275 16.519 13.261 8.480 1.00 2.85 H
-ATOM 386 N LYS A 276 16.448 9.888 11.628 1.00 3.89 N
-ATOM 387 CA LYS A 276 15.884 8.943 12.588 1.00 4.16 C
-ATOM 388 C LYS A 276 16.900 8.507 13.658 1.00 4.54 C
-ATOM 389 O LYS A 276 17.203 9.330 14.549 1.00 4.48 O
-ATOM 390 CB LYS A 276 15.344 7.740 11.832 1.00 4.45 C
-ATOM 391 CG LYS A 276 14.564 6.769 12.686 1.00 4.74 C
-ATOM 392 CD LYS A 276 14.535 5.418 12.018 1.00 5.24 C
-ATOM 393 CE LYS A 276 15.749 4.573 12.399 1.00 5.69 C
-ATOM 394 NZ LYS A 276 17.038 5.269 12.115 1.00 5.61 N
-ATOM 395 OXT LYS A 276 17.377 7.344 13.599 1.00 4.98 O
-ATOM 396 H LYS A 276 15.942 10.692 11.427 1.00 3.57 H
-ATOM 397 HA LYS A 276 15.061 9.434 13.083 1.00 3.94 H
-ATOM 398 HB2 LYS A 276 14.693 8.090 11.044 1.00 4.25 H
-ATOM 399 HB3 LYS A 276 16.173 7.207 11.388 1.00 4.73 H
-ATOM 400 HG2 LYS A 276 15.040 6.681 13.651 1.00 4.84 H
-ATOM 401 HG3 LYS A 276 13.554 7.130 12.802 1.00 4.56 H
-ATOM 402 HD2 LYS A 276 13.636 4.907 12.309 1.00 5.40 H
-ATOM 403 HD3 LYS A 276 14.539 5.572 10.948 1.00 5.20 H
-ATOM 404 HE2 LYS A 276 15.697 4.347 13.453 1.00 5.78 H
-ATOM 405 HE3 LYS A 276 15.717 3.652 11.835 1.00 6.05 H
-ATOM 406 HZ1 LYS A 276 17.836 4.614 12.235 1.00 5.97 H
-ATOM 407 HZ2 LYS A 276 17.167 6.082 12.777 1.00 5.34 H
-ATOM 408 HZ3 LYS A 276 17.042 5.633 11.140 1.00 5.52 H
-TER
-ENDMDL
-MODEL 14
-ATOM 1 N ALA A 249 3.924 -7.852 -12.947 1.00 1.72 N
-ATOM 2 CA ALA A 249 4.121 -7.389 -11.548 1.00 1.46 C
-ATOM 3 C ALA A 249 2.889 -7.692 -10.696 1.00 1.23 C
-ATOM 4 O ALA A 249 1.782 -7.802 -11.219 1.00 1.27 O
-ATOM 5 CB ALA A 249 4.432 -5.901 -11.520 1.00 1.49 C
-ATOM 6 H1 ALA A 249 3.949 -7.041 -13.599 1.00 1.97 H
-ATOM 7 H2 ALA A 249 3.001 -8.326 -13.037 1.00 2.16 H
-ATOM 8 H3 ALA A 249 4.675 -8.522 -13.212 1.00 1.90 H
-ATOM 9 HA ALA A 249 4.968 -7.916 -11.133 1.00 1.52 H
-ATOM 10 HB1 ALA A 249 5.427 -5.749 -11.126 1.00 1.79 H
-ATOM 11 HB2 ALA A 249 3.715 -5.396 -10.888 1.00 1.85 H
-ATOM 12 HB3 ALA A 249 4.376 -5.502 -12.520 1.00 1.82 H
-ATOM 13 N PRO A 250 3.069 -7.840 -9.373 1.00 1.10 N
-ATOM 14 CA PRO A 250 1.971 -8.141 -8.443 1.00 1.00 C
-ATOM 15 C PRO A 250 0.933 -7.014 -8.362 1.00 0.83 C
-ATOM 16 O PRO A 250 1.271 -5.862 -8.081 1.00 0.83 O
-ATOM 17 CB PRO A 250 2.690 -8.303 -7.096 1.00 1.08 C
-ATOM 18 CG PRO A 250 4.110 -8.559 -7.448 1.00 1.21 C
-ATOM 19 CD PRO A 250 4.351 -7.736 -8.667 1.00 1.19 C
-ATOM 20 HA PRO A 250 1.476 -9.065 -8.702 1.00 1.11 H
-ATOM 21 HB2 PRO A 250 2.591 -7.395 -6.524 1.00 1.05 H
-ATOM 22 HB3 PRO A 250 2.267 -9.130 -6.555 1.00 1.20 H
-ATOM 23 HG2 PRO A 250 4.755 -8.242 -6.641 1.00 1.34 H
-ATOM 24 HG3 PRO A 250 4.257 -9.606 -7.665 1.00 1.34 H
-ATOM 25 HD2 PRO A 250 4.558 -6.712 -8.394 1.00 1.22 H
-ATOM 26 HD3 PRO A 250 5.151 -8.150 -9.254 1.00 1.36 H
-ATOM 27 N ALA A 251 -0.330 -7.359 -8.612 1.00 0.79 N
-ATOM 28 CA ALA A 251 -1.426 -6.388 -8.579 1.00 0.73 C
-ATOM 29 C ALA A 251 -1.547 -5.696 -7.219 1.00 0.61 C
-ATOM 30 O ALA A 251 -1.672 -4.476 -7.149 1.00 0.58 O
-ATOM 31 CB ALA A 251 -2.737 -7.069 -8.934 1.00 0.84 C
-ATOM 32 H ALA A 251 -0.531 -8.292 -8.834 1.00 0.88 H
-ATOM 33 HA ALA A 251 -1.228 -5.640 -9.332 1.00 0.80 H
-ATOM 34 HB1 ALA A 251 -2.590 -7.710 -9.790 1.00 1.32 H
-ATOM 35 HB2 ALA A 251 -3.480 -6.322 -9.167 1.00 1.37 H
-ATOM 36 HB3 ALA A 251 -3.074 -7.661 -8.095 1.00 1.34 H
-ATOM 37 N ARG A 252 -1.503 -6.482 -6.144 1.00 0.63 N
-ATOM 38 CA ARG A 252 -1.604 -5.945 -4.783 1.00 0.60 C
-ATOM 39 C ARG A 252 -0.522 -4.898 -4.506 1.00 0.56 C
-ATOM 40 O ARG A 252 -0.802 -3.811 -3.987 1.00 0.53 O
-ATOM 41 CB ARG A 252 -1.494 -7.087 -3.764 1.00 0.71 C
-ATOM 42 CG ARG A 252 -1.301 -6.619 -2.325 1.00 0.78 C
-ATOM 43 CD ARG A 252 -2.449 -5.737 -1.862 1.00 0.78 C
-ATOM 44 NE ARG A 252 -2.249 -5.233 -0.504 1.00 0.90 N
-ATOM 45 CZ ARG A 252 -1.463 -4.219 -0.201 1.00 0.89 C
-ATOM 46 NH1 ARG A 252 -0.864 -3.532 -1.143 1.00 0.78 N
-ATOM 47 NH2 ARG A 252 -1.298 -3.874 1.049 1.00 1.06 N
-ATOM 48 H ARG A 252 -1.400 -7.448 -6.268 1.00 0.71 H
-ATOM 49 HA ARG A 252 -2.570 -5.479 -4.683 1.00 0.60 H
-ATOM 50 HB2 ARG A 252 -2.397 -7.678 -3.805 1.00 0.76 H
-ATOM 51 HB3 ARG A 252 -0.655 -7.711 -4.031 1.00 0.77 H
-ATOM 52 HG2 ARG A 252 -1.242 -7.483 -1.681 1.00 0.91 H
-ATOM 53 HG3 ARG A 252 -0.379 -6.058 -2.262 1.00 0.79 H
-ATOM 54 HD2 ARG A 252 -2.527 -4.897 -2.538 1.00 0.69 H
-ATOM 55 HD3 ARG A 252 -3.360 -6.312 -1.897 1.00 0.86 H
-ATOM 56 HE ARG A 252 -2.714 -5.696 0.220 1.00 1.03 H
-ATOM 57 HH11 ARG A 252 -1.001 -3.776 -2.106 1.00 0.70 H
-ATOM 58 HH12 ARG A 252 -0.275 -2.765 -0.903 1.00 0.82 H
-ATOM 59 HH21 ARG A 252 -1.765 -4.378 1.772 1.00 1.18 H
-ATOM 60 HH22 ARG A 252 -0.702 -3.110 1.278 1.00 1.09 H
-ATOM 61 N VAL A 253 0.709 -5.236 -4.852 1.00 0.64 N
-ATOM 62 CA VAL A 253 1.835 -4.337 -4.642 1.00 0.69 C
-ATOM 63 C VAL A 253 1.739 -3.121 -5.561 1.00 0.62 C
-ATOM 64 O VAL A 253 1.922 -1.987 -5.118 1.00 0.62 O
-ATOM 65 CB VAL A 253 3.176 -5.061 -4.859 1.00 0.85 C
-ATOM 66 CG1 VAL A 253 4.339 -4.153 -4.495 1.00 0.94 C
-ATOM 67 CG2 VAL A 253 3.214 -6.344 -4.041 1.00 0.95 C
-ATOM 68 H VAL A 253 0.863 -6.113 -5.260 1.00 0.71 H
-ATOM 69 HA VAL A 253 1.796 -3.998 -3.619 1.00 0.70 H
-ATOM 70 HB VAL A 253 3.260 -5.320 -5.904 1.00 0.89 H
-ATOM 71 HG11 VAL A 253 3.969 -3.157 -4.299 1.00 1.38 H
-ATOM 72 HG12 VAL A 253 5.041 -4.122 -5.315 1.00 1.54 H
-ATOM 73 HG13 VAL A 253 4.831 -4.534 -3.612 1.00 1.26 H
-ATOM 74 HG21 VAL A 253 3.734 -6.164 -3.113 1.00 1.42 H
-ATOM 75 HG22 VAL A 253 3.726 -7.113 -4.600 1.00 1.40 H
-ATOM 76 HG23 VAL A 253 2.203 -6.666 -3.831 1.00 1.41 H
-ATOM 77 N GLY A 254 1.422 -3.367 -6.832 1.00 0.64 N
-ATOM 78 CA GLY A 254 1.274 -2.281 -7.789 1.00 0.67 C
-ATOM 79 C GLY A 254 0.194 -1.302 -7.362 1.00 0.56 C
-ATOM 80 O GLY A 254 0.417 -0.091 -7.325 1.00 0.56 O
-ATOM 81 H GLY A 254 1.267 -4.297 -7.118 1.00 0.68 H
-ATOM 82 HA2 GLY A 254 2.214 -1.753 -7.872 1.00 0.75 H
-ATOM 83 HA3 GLY A 254 1.014 -2.692 -8.751 1.00 0.75 H
-ATOM 84 N LEU A 255 -0.971 -1.837 -7.000 1.00 0.51 N
-ATOM 85 CA LEU A 255 -2.087 -1.019 -6.533 1.00 0.49 C
-ATOM 86 C LEU A 255 -1.708 -0.256 -5.272 1.00 0.42 C
-ATOM 87 O LEU A 255 -2.011 0.930 -5.129 1.00 0.45 O
-ATOM 88 CB LEU A 255 -3.304 -1.885 -6.242 1.00 0.56 C
-ATOM 89 CG LEU A 255 -4.318 -1.958 -7.371 1.00 0.71 C
-ATOM 90 CD1 LEU A 255 -5.478 -2.850 -6.970 1.00 0.80 C
-ATOM 91 CD2 LEU A 255 -4.802 -0.560 -7.724 1.00 0.89 C
-ATOM 92 H LEU A 255 -1.078 -2.819 -7.028 1.00 0.54 H
-ATOM 93 HA LEU A 255 -2.338 -0.315 -7.312 1.00 0.57 H
-ATOM 94 HB2 LEU A 255 -2.963 -2.888 -6.024 1.00 0.60 H
-ATOM 95 HB3 LEU A 255 -3.800 -1.493 -5.366 1.00 0.60 H
-ATOM 96 HG LEU A 255 -3.849 -2.384 -8.245 1.00 0.76 H
-ATOM 97 HD11 LEU A 255 -5.752 -2.641 -5.947 1.00 1.36 H
-ATOM 98 HD12 LEU A 255 -5.182 -3.885 -7.059 1.00 1.29 H
-ATOM 99 HD13 LEU A 255 -6.320 -2.658 -7.617 1.00 1.27 H
-ATOM 100 HD21 LEU A 255 -3.994 0.144 -7.586 1.00 1.45 H
-ATOM 101 HD22 LEU A 255 -5.626 -0.291 -7.079 1.00 1.31 H
-ATOM 102 HD23 LEU A 255 -5.126 -0.539 -8.753 1.00 1.41 H
-ATOM 103 N GLY A 256 -1.045 -0.948 -4.356 1.00 0.41 N
-ATOM 104 CA GLY A 256 -0.640 -0.322 -3.120 1.00 0.44 C
-ATOM 105 C GLY A 256 0.327 0.837 -3.329 1.00 0.41 C
-ATOM 106 O GLY A 256 0.083 1.942 -2.847 1.00 0.42 O
-ATOM 107 H GLY A 256 -0.834 -1.897 -4.521 1.00 0.43 H
-ATOM 108 HA2 GLY A 256 -1.523 0.047 -2.620 1.00 0.49 H
-ATOM 109 HA3 GLY A 256 -0.170 -1.064 -2.496 1.00 0.52 H
-ATOM 110 N ILE A 257 1.421 0.602 -4.053 1.00 0.46 N
-ATOM 111 CA ILE A 257 2.398 1.665 -4.297 1.00 0.51 C
-ATOM 112 C ILE A 257 1.801 2.815 -5.118 1.00 0.45 C
-ATOM 113 O ILE A 257 2.067 3.979 -4.830 1.00 0.46 O
-ATOM 114 CB ILE A 257 3.702 1.139 -4.957 1.00 0.66 C
-ATOM 115 CG1 ILE A 257 3.436 0.476 -6.313 1.00 0.70 C
-ATOM 116 CG2 ILE A 257 4.406 0.166 -4.025 1.00 0.80 C
-ATOM 117 CD1 ILE A 257 3.757 1.362 -7.499 1.00 0.78 C
-ATOM 118 H ILE A 257 1.574 -0.296 -4.428 1.00 0.50 H
-ATOM 119 HA ILE A 257 2.666 2.062 -3.328 1.00 0.55 H
-ATOM 120 HB ILE A 257 4.359 1.984 -5.105 1.00 0.73 H
-ATOM 121 HG12 ILE A 257 4.040 -0.415 -6.394 1.00 0.80 H
-ATOM 122 HG13 ILE A 257 2.393 0.204 -6.374 1.00 0.65 H
-ATOM 123 HG21 ILE A 257 4.125 -0.845 -4.281 1.00 1.39 H
-ATOM 124 HG22 ILE A 257 4.118 0.372 -3.004 1.00 1.22 H
-ATOM 125 HG23 ILE A 257 5.476 0.280 -4.127 1.00 1.29 H
-ATOM 126 HD11 ILE A 257 3.402 0.892 -8.405 1.00 1.24 H
-ATOM 127 HD12 ILE A 257 4.826 1.506 -7.562 1.00 1.31 H
-ATOM 128 HD13 ILE A 257 3.272 2.319 -7.375 1.00 1.17 H
-ATOM 129 N THR A 258 0.983 2.496 -6.127 1.00 0.45 N
-ATOM 130 CA THR A 258 0.359 3.534 -6.956 1.00 0.50 C
-ATOM 131 C THR A 258 -0.606 4.401 -6.146 1.00 0.45 C
-ATOM 132 O THR A 258 -0.578 5.630 -6.248 1.00 0.48 O
-ATOM 133 CB THR A 258 -0.396 2.946 -8.167 1.00 0.61 C
-ATOM 134 OG1 THR A 258 -1.240 1.864 -7.753 1.00 0.57 O
-ATOM 135 CG2 THR A 258 0.574 2.467 -9.236 1.00 0.77 C
-ATOM 136 H THR A 258 0.790 1.546 -6.313 1.00 0.47 H
-ATOM 137 HA THR A 258 1.151 4.166 -7.332 1.00 0.57 H
-ATOM 138 HB THR A 258 -1.014 3.725 -8.591 1.00 0.70 H
-ATOM 139 HG1 THR A 258 -0.714 1.051 -7.690 1.00 0.75 H
-ATOM 140 HG21 THR A 258 0.073 2.451 -10.194 1.00 1.30 H
-ATOM 141 HG22 THR A 258 0.915 1.472 -8.992 1.00 1.32 H
-ATOM 142 HG23 THR A 258 1.418 3.138 -9.283 1.00 1.29 H
-ATOM 143 N THR A 259 -1.452 3.769 -5.335 1.00 0.42 N
-ATOM 144 CA THR A 259 -2.410 4.510 -4.512 1.00 0.47 C
-ATOM 145 C THR A 259 -1.691 5.348 -3.461 1.00 0.43 C
-ATOM 146 O THR A 259 -1.999 6.526 -3.290 1.00 0.48 O
-ATOM 147 CB THR A 259 -3.431 3.586 -3.816 1.00 0.53 C
-ATOM 148 OG1 THR A 259 -2.761 2.513 -3.144 1.00 0.50 O
-ATOM 149 CG2 THR A 259 -4.426 3.024 -4.821 1.00 0.62 C
-ATOM 150 H THR A 259 -1.428 2.787 -5.282 1.00 0.41 H
-ATOM 151 HA THR A 259 -2.952 5.176 -5.167 1.00 0.54 H
-ATOM 152 HB THR A 259 -3.976 4.168 -3.087 1.00 0.62 H
-ATOM 153 HG1 THR A 259 -2.535 1.822 -3.786 1.00 0.53 H
-ATOM 154 HG21 THR A 259 -4.414 3.629 -5.717 1.00 1.13 H
-ATOM 155 HG22 THR A 259 -5.417 3.035 -4.393 1.00 1.30 H
-ATOM 156 HG23 THR A 259 -4.151 2.009 -5.069 1.00 1.17 H
-ATOM 157 N VAL A 260 -0.718 4.748 -2.776 1.00 0.39 N
-ATOM 158 CA VAL A 260 0.052 5.467 -1.763 1.00 0.44 C
-ATOM 159 C VAL A 260 0.763 6.675 -2.381 1.00 0.39 C
-ATOM 160 O VAL A 260 0.670 7.788 -1.860 1.00 0.45 O
-ATOM 161 CB VAL A 260 1.080 4.546 -1.071 1.00 0.53 C
-ATOM 162 CG1 VAL A 260 2.014 5.345 -0.176 1.00 0.69 C
-ATOM 163 CG2 VAL A 260 0.365 3.471 -0.266 1.00 0.63 C
-ATOM 164 H VAL A 260 -0.503 3.804 -2.966 1.00 0.38 H
-ATOM 165 HA VAL A 260 -0.643 5.821 -1.013 1.00 0.52 H
-ATOM 166 HB VAL A 260 1.672 4.061 -1.833 1.00 0.53 H
-ATOM 167 HG11 VAL A 260 2.775 4.692 0.223 1.00 1.25 H
-ATOM 168 HG12 VAL A 260 1.450 5.779 0.636 1.00 1.22 H
-ATOM 169 HG13 VAL A 260 2.478 6.131 -0.753 1.00 1.28 H
-ATOM 170 HG21 VAL A 260 1.052 3.035 0.444 1.00 0.81 H
-ATOM 171 HG22 VAL A 260 0.003 2.702 -0.934 1.00 0.65 H
-ATOM 172 HG23 VAL A 260 -0.468 3.910 0.262 1.00 0.80 H
-ATOM 173 N LEU A 261 1.444 6.455 -3.508 1.00 0.36 N
-ATOM 174 CA LEU A 261 2.143 7.536 -4.208 1.00 0.40 C
-ATOM 175 C LEU A 261 1.166 8.622 -4.664 1.00 0.40 C
-ATOM 176 O LEU A 261 1.459 9.811 -4.570 1.00 0.45 O
-ATOM 177 CB LEU A 261 2.917 6.991 -5.405 1.00 0.50 C
-ATOM 178 CG LEU A 261 4.165 6.183 -5.047 1.00 0.63 C
-ATOM 179 CD1 LEU A 261 4.888 5.739 -6.305 1.00 0.78 C
-ATOM 180 CD2 LEU A 261 5.087 6.999 -4.152 1.00 0.79 C
-ATOM 181 H LEU A 261 1.465 5.546 -3.889 1.00 0.37 H
-ATOM 182 HA LEU A 261 2.848 7.971 -3.515 1.00 0.45 H
-ATOM 183 HB2 LEU A 261 2.254 6.358 -5.977 1.00 0.54 H
-ATOM 184 HB3 LEU A 261 3.219 7.822 -6.023 1.00 0.58 H
-ATOM 185 HG LEU A 261 3.868 5.298 -4.503 1.00 0.69 H
-ATOM 186 HD11 LEU A 261 4.805 6.510 -7.058 1.00 1.36 H
-ATOM 187 HD12 LEU A 261 4.442 4.827 -6.673 1.00 1.24 H
-ATOM 188 HD13 LEU A 261 5.930 5.566 -6.080 1.00 1.33 H
-ATOM 189 HD21 LEU A 261 6.115 6.769 -4.388 1.00 1.35 H
-ATOM 190 HD22 LEU A 261 4.892 6.758 -3.117 1.00 1.28 H
-ATOM 191 HD23 LEU A 261 4.907 8.052 -4.315 1.00 1.35 H
-ATOM 192 N THR A 262 0.001 8.202 -5.153 1.00 0.42 N
-ATOM 193 CA THR A 262 -1.026 9.140 -5.611 1.00 0.54 C
-ATOM 194 C THR A 262 -1.604 9.949 -4.442 1.00 0.56 C
-ATOM 195 O THR A 262 -1.760 11.168 -4.525 1.00 0.63 O
-ATOM 196 CB THR A 262 -2.180 8.395 -6.314 1.00 0.64 C
-ATOM 197 OG1 THR A 262 -1.685 7.682 -7.453 1.00 0.69 O
-ATOM 198 CG2 THR A 262 -3.273 9.357 -6.746 1.00 0.83 C
-ATOM 199 H THR A 262 -0.176 7.234 -5.202 1.00 0.40 H
-ATOM 200 HA THR A 262 -0.574 9.818 -6.322 1.00 0.59 H
-ATOM 201 HB THR A 262 -2.603 7.685 -5.615 1.00 0.63 H
-ATOM 202 HG1 THR A 262 -1.278 6.851 -7.161 1.00 0.65 H
-ATOM 203 HG21 THR A 262 -3.583 9.950 -5.896 1.00 1.44 H
-ATOM 204 HG22 THR A 262 -4.116 8.798 -7.121 1.00 1.33 H
-ATOM 205 HG23 THR A 262 -2.895 10.007 -7.519 1.00 1.25 H
-ATOM 206 N MET A 263 -1.934 9.246 -3.363 1.00 0.55 N
-ATOM 207 CA MET A 263 -2.514 9.867 -2.170 1.00 0.65 C
-ATOM 208 C MET A 263 -1.542 10.842 -1.489 1.00 0.56 C
-ATOM 209 O MET A 263 -1.941 11.933 -1.077 1.00 0.62 O
-ATOM 210 CB MET A 263 -2.944 8.783 -1.177 1.00 0.77 C
-ATOM 211 CG MET A 263 -3.720 9.316 0.016 1.00 0.97 C
-ATOM 212 SD MET A 263 -5.270 10.105 -0.458 1.00 1.22 S
-ATOM 213 CE MET A 263 -5.905 10.590 1.144 1.00 1.49 C
-ATOM 214 H MET A 263 -1.800 8.271 -3.377 1.00 0.52 H
-ATOM 215 HA MET A 263 -3.389 10.416 -2.481 1.00 0.76 H
-ATOM 216 HB2 MET A 263 -3.568 8.067 -1.693 1.00 0.83 H
-ATOM 217 HB3 MET A 263 -2.063 8.278 -0.810 1.00 0.72 H
-ATOM 218 HG2 MET A 263 -3.941 8.493 0.681 1.00 1.09 H
-ATOM 219 HG3 MET A 263 -3.107 10.039 0.533 1.00 0.98 H
-ATOM 220 HE1 MET A 263 -6.257 11.610 1.099 1.00 1.77 H
-ATOM 221 HE2 MET A 263 -5.120 10.513 1.881 1.00 1.94 H
-ATOM 222 HE3 MET A 263 -6.722 9.939 1.420 1.00 1.85 H
-ATOM 223 N THR A 264 -0.272 10.451 -1.357 1.00 0.47 N
-ATOM 224 CA THR A 264 0.717 11.320 -0.709 1.00 0.45 C
-ATOM 225 C THR A 264 1.065 12.531 -1.584 1.00 0.34 C
-ATOM 226 O THR A 264 1.264 12.412 -2.794 1.00 0.37 O
-ATOM 227 CB THR A 264 2.014 10.566 -0.325 1.00 0.53 C
-ATOM 228 OG1 THR A 264 2.865 11.424 0.445 1.00 0.68 O
-ATOM 229 CG2 THR A 264 2.772 10.078 -1.552 1.00 0.46 C
-ATOM 230 H THR A 264 0.002 9.566 -1.693 1.00 0.47 H
-ATOM 231 HA THR A 264 0.269 11.686 0.203 1.00 0.56 H
-ATOM 232 HB THR A 264 1.746 9.710 0.276 1.00 0.66 H
-ATOM 233 HG1 THR A 264 3.784 11.221 0.255 1.00 0.95 H
-ATOM 234 HG21 THR A 264 2.230 9.264 -2.010 1.00 1.16 H
-ATOM 235 HG22 THR A 264 3.753 9.736 -1.256 1.00 1.11 H
-ATOM 236 HG23 THR A 264 2.871 10.888 -2.260 1.00 1.09 H
-ATOM 237 N THR A 265 1.131 13.701 -0.957 1.00 0.39 N
-ATOM 238 CA THR A 265 1.445 14.940 -1.665 1.00 0.42 C
-ATOM 239 C THR A 265 2.925 15.020 -2.042 1.00 0.40 C
-ATOM 240 O THR A 265 3.797 14.614 -1.268 1.00 0.48 O
-ATOM 241 CB THR A 265 1.062 16.186 -0.830 1.00 0.59 C
-ATOM 242 OG1 THR A 265 1.459 17.384 -1.511 1.00 0.70 O
-ATOM 243 CG2 THR A 265 1.705 16.147 0.551 1.00 0.69 C
-ATOM 244 H THR A 265 0.963 13.732 0.005 1.00 0.51 H
-ATOM 245 HA THR A 265 0.859 14.955 -2.573 1.00 0.46 H
-ATOM 246 HB THR A 265 -0.011 16.195 -0.706 1.00 0.66 H
-ATOM 247 HG1 THR A 265 0.842 18.092 -1.300 1.00 1.00 H
-ATOM 248 HG21 THR A 265 1.108 16.724 1.242 1.00 1.26 H
-ATOM 249 HG22 THR A 265 2.699 16.566 0.497 1.00 1.23 H
-ATOM 250 HG23 THR A 265 1.763 15.125 0.893 1.00 1.27 H
-ATOM 251 N GLN A 266 3.193 15.547 -3.242 1.00 0.45 N
-ATOM 252 CA GLN A 266 4.559 15.709 -3.768 1.00 0.54 C
-ATOM 253 C GLN A 266 5.235 14.375 -4.119 1.00 0.51 C
-ATOM 254 O GLN A 266 5.746 14.212 -5.228 1.00 0.54 O
-ATOM 255 CB GLN A 266 5.428 16.490 -2.777 1.00 0.69 C
-ATOM 256 CG GLN A 266 5.011 17.942 -2.613 1.00 0.84 C
-ATOM 257 CD GLN A 266 5.873 18.684 -1.612 1.00 1.03 C
-ATOM 258 OE1 GLN A 266 7.092 18.723 -1.736 1.00 1.17 O
-ATOM 259 NE2 GLN A 266 5.247 19.281 -0.613 1.00 1.18 N
-ATOM 260 H GLN A 266 2.442 15.845 -3.797 1.00 0.52 H
-ATOM 261 HA GLN A 266 4.481 16.288 -4.674 1.00 0.63 H
-ATOM 262 HB2 GLN A 266 5.367 16.009 -1.810 1.00 0.68 H
-ATOM 263 HB3 GLN A 266 6.452 16.466 -3.116 1.00 0.78 H
-ATOM 264 HG2 GLN A 266 5.091 18.437 -3.569 1.00 0.94 H
-ATOM 265 HG3 GLN A 266 3.985 17.973 -2.275 1.00 0.88 H
-ATOM 266 HE21 GLN A 266 4.272 19.215 -0.570 1.00 1.18 H
-ATOM 267 HE22 GLN A 266 5.790 19.766 0.041 1.00 1.35 H
-ATOM 268 N SER A 267 5.236 13.435 -3.173 1.00 0.54 N
-ATOM 269 CA SER A 267 5.852 12.105 -3.357 1.00 0.63 C
-ATOM 270 C SER A 267 7.328 12.220 -3.770 1.00 0.71 C
-ATOM 271 O SER A 267 7.725 11.758 -4.843 1.00 0.74 O
-ATOM 272 CB SER A 267 5.067 11.252 -4.373 1.00 0.62 C
-ATOM 273 OG SER A 267 5.160 11.766 -5.692 1.00 0.57 O
-ATOM 274 H SER A 267 4.806 13.643 -2.312 1.00 0.58 H
-ATOM 275 HA SER A 267 5.815 11.608 -2.397 1.00 0.74 H
-ATOM 276 HB2 SER A 267 5.460 10.246 -4.369 1.00 0.74 H
-ATOM 277 HB3 SER A 267 4.026 11.227 -4.084 1.00 0.68 H
-ATOM 278 HG SER A 267 5.409 12.706 -5.655 1.00 0.57 H
-ATOM 279 N SER A 268 8.124 12.855 -2.900 1.00 0.83 N
-ATOM 280 CA SER A 268 9.565 13.070 -3.130 1.00 0.98 C
-ATOM 281 C SER A 268 9.809 14.153 -4.190 1.00 0.97 C
-ATOM 282 O SER A 268 10.544 13.943 -5.158 1.00 1.04 O
-ATOM 283 CB SER A 268 10.285 11.768 -3.525 1.00 1.10 C
-ATOM 284 OG SER A 268 10.252 10.817 -2.469 1.00 1.19 O
-ATOM 285 H SER A 268 7.728 13.197 -2.072 1.00 0.85 H
-ATOM 286 HA SER A 268 9.984 13.420 -2.198 1.00 1.08 H
-ATOM 287 HB2 SER A 268 9.801 11.341 -4.390 1.00 1.04 H
-ATOM 288 HB3 SER A 268 11.315 11.988 -3.762 1.00 1.25 H
-ATOM 289 HG SER A 268 10.760 10.041 -2.722 1.00 1.48 H
-ATOM 290 N GLY A 269 9.191 15.317 -3.990 1.00 0.96 N
-ATOM 291 CA GLY A 269 9.350 16.431 -4.918 1.00 1.02 C
-ATOM 292 C GLY A 269 8.454 16.342 -6.148 1.00 0.97 C
-ATOM 293 O GLY A 269 7.740 17.291 -6.469 1.00 1.15 O
-ATOM 294 H GLY A 269 8.626 15.426 -3.199 1.00 0.96 H
-ATOM 295 HA2 GLY A 269 9.126 17.349 -4.396 1.00 1.15 H
-ATOM 296 HA3 GLY A 269 10.380 16.463 -5.243 1.00 1.10 H
-ATOM 297 N SER A 270 8.499 15.210 -6.840 1.00 0.87 N
-ATOM 298 CA SER A 270 7.691 15.012 -8.046 1.00 0.92 C
-ATOM 299 C SER A 270 7.526 13.526 -8.352 1.00 0.76 C
-ATOM 300 O SER A 270 8.381 12.712 -8.000 1.00 0.72 O
-ATOM 301 CB SER A 270 8.336 15.712 -9.245 1.00 1.12 C
-ATOM 302 OG SER A 270 9.611 15.164 -9.538 1.00 1.17 O
-ATOM 303 H SER A 270 9.095 14.489 -6.537 1.00 0.88 H
-ATOM 304 HA SER A 270 6.718 15.442 -7.868 1.00 1.04 H
-ATOM 305 HB2 SER A 270 7.702 15.594 -10.112 1.00 1.25 H
-ATOM 306 HB3 SER A 270 8.452 16.763 -9.026 1.00 1.23 H
-ATOM 307 HG SER A 270 10.292 15.690 -9.107 1.00 1.43 H
-ATOM 308 N ARG A 271 6.424 13.167 -9.010 1.00 0.77 N
-ATOM 309 CA ARG A 271 6.165 11.767 -9.349 1.00 0.68 C
-ATOM 310 C ARG A 271 6.962 11.311 -10.582 1.00 0.73 C
-ATOM 311 O ARG A 271 6.419 10.712 -11.509 1.00 1.15 O
-ATOM 312 CB ARG A 271 4.664 11.524 -9.561 1.00 0.81 C
-ATOM 313 CG ARG A 271 4.272 10.049 -9.499 1.00 0.80 C
-ATOM 314 CD ARG A 271 4.261 9.508 -8.068 1.00 0.75 C
-ATOM 315 NE ARG A 271 5.519 9.750 -7.353 1.00 0.64 N
-ATOM 316 CZ ARG A 271 6.670 9.172 -7.638 1.00 0.66 C
-ATOM 317 NH1 ARG A 271 6.753 8.283 -8.592 1.00 0.76 N
-ATOM 318 NH2 ARG A 271 7.747 9.500 -6.974 1.00 0.70 N
-ATOM 319 H ARG A 271 5.774 13.854 -9.267 1.00 0.90 H
-ATOM 320 HA ARG A 271 6.485 11.171 -8.509 1.00 0.62 H
-ATOM 321 HB2 ARG A 271 4.113 12.055 -8.800 1.00 0.89 H
-ATOM 322 HB3 ARG A 271 4.383 11.907 -10.531 1.00 0.96 H
-ATOM 323 HG2 ARG A 271 3.286 9.931 -9.921 1.00 0.95 H
-ATOM 324 HG3 ARG A 271 4.983 9.480 -10.079 1.00 0.81 H
-ATOM 325 HD2 ARG A 271 3.459 9.986 -7.525 1.00 0.85 H
-ATOM 326 HD3 ARG A 271 4.081 8.443 -8.103 1.00 0.84 H
-ATOM 327 HE ARG A 271 5.499 10.420 -6.625 1.00 0.64 H
-ATOM 328 HH11 ARG A 271 5.940 8.032 -9.110 1.00 0.82 H
-ATOM 329 HH12 ARG A 271 7.643 7.864 -8.813 1.00 0.85 H
-ATOM 330 HH21 ARG A 271 7.700 10.193 -6.251 1.00 0.70 H
-ATOM 331 HH22 ARG A 271 8.618 9.059 -7.188 1.00 0.82 H
-ATOM 332 N ALA A 272 8.260 11.586 -10.584 1.00 0.81 N
-ATOM 333 CA ALA A 272 9.123 11.188 -11.692 1.00 0.96 C
-ATOM 334 C ALA A 272 9.841 9.870 -11.382 1.00 0.89 C
-ATOM 335 O ALA A 272 10.850 9.858 -10.675 1.00 1.09 O
-ATOM 336 CB ALA A 272 10.130 12.287 -11.999 1.00 1.24 C
-ATOM 337 H ALA A 272 8.651 12.060 -9.814 1.00 1.07 H
-ATOM 338 HA ALA A 272 8.500 11.049 -12.566 1.00 1.14 H
-ATOM 339 HB1 ALA A 272 11.096 12.011 -11.601 1.00 1.46 H
-ATOM 340 HB2 ALA A 272 9.804 13.211 -11.544 1.00 1.77 H
-ATOM 341 HB3 ALA A 272 10.204 12.418 -13.068 1.00 1.68 H
-ATOM 342 N SER A 273 9.312 8.762 -11.914 1.00 1.02 N
-ATOM 343 CA SER A 273 9.896 7.429 -11.696 1.00 1.21 C
-ATOM 344 C SER A 273 9.965 7.070 -10.213 1.00 1.07 C
-ATOM 345 O SER A 273 9.135 7.502 -9.411 1.00 0.98 O
-ATOM 346 CB SER A 273 11.292 7.344 -12.327 1.00 1.55 C
-ATOM 347 OG SER A 273 11.235 7.529 -13.731 1.00 1.85 O
-ATOM 348 H SER A 273 8.507 8.841 -12.466 1.00 1.21 H
-ATOM 349 HA SER A 273 9.259 6.696 -12.175 1.00 1.46 H
-ATOM 350 HB2 SER A 273 11.925 8.110 -11.904 1.00 1.51 H
-ATOM 351 HB3 SER A 273 11.719 6.372 -12.122 1.00 1.79 H
-ATOM 352 HG SER A 273 12.100 7.793 -14.056 1.00 2.13 H
-ATOM 353 N LEU A 274 10.946 6.238 -9.874 1.00 1.30 N
-ATOM 354 CA LEU A 274 11.145 5.758 -8.507 1.00 1.40 C
-ATOM 355 C LEU A 274 9.940 4.960 -7.934 1.00 1.31 C
-ATOM 356 O LEU A 274 9.721 5.001 -6.724 1.00 1.41 O
-ATOM 357 CB LEU A 274 11.478 6.935 -7.578 1.00 1.53 C
-ATOM 358 CG LEU A 274 12.742 7.724 -7.938 1.00 1.83 C
-ATOM 359 CD1 LEU A 274 12.925 8.900 -6.992 1.00 2.10 C
-ATOM 360 CD2 LEU A 274 13.968 6.825 -7.903 1.00 2.22 C
-ATOM 361 H LEU A 274 11.550 5.915 -10.573 1.00 1.52 H
-ATOM 362 HA LEU A 274 12.000 5.098 -8.527 1.00 1.66 H
-ATOM 363 HB2 LEU A 274 10.640 7.618 -7.587 1.00 1.39 H
-ATOM 364 HB3 LEU A 274 11.594 6.551 -6.577 1.00 1.73 H
-ATOM 365 HG LEU A 274 12.641 8.114 -8.941 1.00 1.76 H
-ATOM 366 HD11 LEU A 274 12.494 8.660 -6.030 1.00 2.38 H
-ATOM 367 HD12 LEU A 274 12.433 9.771 -7.399 1.00 2.47 H
-ATOM 368 HD13 LEU A 274 13.979 9.105 -6.873 1.00 2.39 H
-ATOM 369 HD21 LEU A 274 13.720 5.863 -8.325 1.00 2.58 H
-ATOM 370 HD22 LEU A 274 14.293 6.700 -6.881 1.00 2.48 H
-ATOM 371 HD23 LEU A 274 14.761 7.278 -8.480 1.00 2.58 H
-ATOM 372 N PRO A 275 9.133 4.214 -8.758 1.00 1.37 N
-ATOM 373 CA PRO A 275 7.988 3.432 -8.232 1.00 1.53 C
-ATOM 374 C PRO A 275 8.386 2.475 -7.100 1.00 1.63 C
-ATOM 375 O PRO A 275 7.615 2.235 -6.170 1.00 1.82 O
-ATOM 376 CB PRO A 275 7.519 2.634 -9.446 1.00 1.78 C
-ATOM 377 CG PRO A 275 7.895 3.489 -10.593 1.00 1.75 C
-ATOM 378 CD PRO A 275 9.233 4.064 -10.231 1.00 1.54 C
-ATOM 379 HA PRO A 275 7.193 4.078 -7.899 1.00 1.56 H
-ATOM 380 HB2 PRO A 275 8.025 1.679 -9.475 1.00 1.90 H
-ATOM 381 HB3 PRO A 275 6.451 2.485 -9.396 1.00 1.97 H
-ATOM 382 HG2 PRO A 275 7.963 2.898 -11.494 1.00 2.00 H
-ATOM 383 HG3 PRO A 275 7.170 4.279 -10.706 1.00 1.76 H
-ATOM 384 HD2 PRO A 275 10.028 3.395 -10.511 1.00 1.72 H
-ATOM 385 HD3 PRO A 275 9.364 5.032 -10.705 1.00 1.48 H
-ATOM 386 N LYS A 276 9.604 1.948 -7.193 1.00 1.67 N
-ATOM 387 CA LYS A 276 10.150 1.033 -6.191 1.00 1.84 C
-ATOM 388 C LYS A 276 11.670 0.956 -6.306 1.00 2.07 C
-ATOM 389 O LYS A 276 12.290 0.251 -5.476 1.00 2.32 O
-ATOM 390 CB LYS A 276 9.552 -0.369 -6.331 1.00 2.03 C
-ATOM 391 CG LYS A 276 8.620 -0.742 -5.190 1.00 2.32 C
-ATOM 392 CD LYS A 276 9.333 -0.758 -3.841 1.00 2.52 C
-ATOM 393 CE LYS A 276 10.203 -1.998 -3.657 1.00 2.62 C
-ATOM 394 NZ LYS A 276 11.495 -1.919 -4.399 1.00 2.58 N
-ATOM 395 OXT LYS A 276 12.225 1.595 -7.226 1.00 2.24 O
-ATOM 396 H LYS A 276 10.169 2.196 -7.955 1.00 1.71 H
-ATOM 397 HA LYS A 276 9.897 1.425 -5.216 1.00 1.90 H
-ATOM 398 HB2 LYS A 276 8.998 -0.423 -7.255 1.00 2.07 H
-ATOM 399 HB3 LYS A 276 10.356 -1.090 -6.359 1.00 2.17 H
-ATOM 400 HG2 LYS A 276 7.820 -0.020 -5.147 1.00 2.43 H
-ATOM 401 HG3 LYS A 276 8.211 -1.723 -5.380 1.00 2.50 H
-ATOM 402 HD2 LYS A 276 9.961 0.116 -3.770 1.00 2.49 H
-ATOM 403 HD3 LYS A 276 8.592 -0.733 -3.058 1.00 2.84 H
-ATOM 404 HE2 LYS A 276 10.413 -2.116 -2.605 1.00 2.91 H
-ATOM 405 HE3 LYS A 276 9.651 -2.858 -4.008 1.00 2.73 H
-ATOM 406 HZ1 LYS A 276 11.665 -0.936 -4.745 1.00 2.40 H
-ATOM 407 HZ2 LYS A 276 11.482 -2.560 -5.216 1.00 2.77 H
-ATOM 408 HZ3 LYS A 276 12.279 -2.189 -3.774 1.00 2.91 H
-TER
-ENDMDL
-MODEL 15
-ATOM 1 N ALA A 249 1.481 -4.140 -13.751 1.00 2.02 N
-ATOM 2 CA ALA A 249 1.920 -3.911 -12.347 1.00 1.70 C
-ATOM 3 C ALA A 249 1.799 -5.191 -11.517 1.00 1.50 C
-ATOM 4 O ALA A 249 1.042 -6.094 -11.874 1.00 1.53 O
-ATOM 5 CB ALA A 249 1.111 -2.790 -11.712 1.00 1.51 C
-ATOM 6 H1 ALA A 249 1.150 -5.122 -13.866 1.00 2.43 H
-ATOM 7 H2 ALA A 249 2.272 -3.974 -14.408 1.00 1.91 H
-ATOM 8 H3 ALA A 249 0.703 -3.494 -13.993 1.00 2.37 H
-ATOM 9 HA ALA A 249 2.958 -3.607 -12.364 1.00 1.82 H
-ATOM 10 HB1 ALA A 249 0.632 -3.157 -10.816 1.00 1.87 H
-ATOM 11 HB2 ALA A 249 0.364 -2.446 -12.408 1.00 1.73 H
-ATOM 12 HB3 ALA A 249 1.771 -1.973 -11.458 1.00 1.77 H
-ATOM 13 N PRO A 250 2.557 -5.291 -10.407 1.00 1.35 N
-ATOM 14 CA PRO A 250 2.548 -6.473 -9.527 1.00 1.22 C
-ATOM 15 C PRO A 250 1.270 -6.615 -8.674 1.00 0.90 C
-ATOM 16 O PRO A 250 1.345 -6.867 -7.470 1.00 0.79 O
-ATOM 17 CB PRO A 250 3.770 -6.232 -8.639 1.00 1.34 C
-ATOM 18 CG PRO A 250 3.888 -4.757 -8.565 1.00 1.34 C
-ATOM 19 CD PRO A 250 3.501 -4.266 -9.924 1.00 1.43 C
-ATOM 20 HA PRO A 250 2.694 -7.381 -10.093 1.00 1.35 H
-ATOM 21 HB2 PRO A 250 3.608 -6.662 -7.668 1.00 1.24 H
-ATOM 22 HB3 PRO A 250 4.643 -6.667 -9.100 1.00 1.57 H
-ATOM 23 HG2 PRO A 250 3.212 -4.370 -7.816 1.00 1.13 H
-ATOM 24 HG3 PRO A 250 4.906 -4.477 -8.339 1.00 1.56 H
-ATOM 25 HD2 PRO A 250 3.017 -3.306 -9.851 1.00 1.34 H
-ATOM 26 HD3 PRO A 250 4.367 -4.213 -10.564 1.00 1.70 H
-ATOM 27 N ALA A 251 0.108 -6.474 -9.320 1.00 0.91 N
-ATOM 28 CA ALA A 251 -1.203 -6.608 -8.661 1.00 0.80 C
-ATOM 29 C ALA A 251 -1.319 -5.794 -7.358 1.00 0.57 C
-ATOM 30 O ALA A 251 -1.276 -4.563 -7.376 1.00 0.57 O
-ATOM 31 CB ALA A 251 -1.505 -8.080 -8.405 1.00 0.86 C
-ATOM 32 H ALA A 251 0.131 -6.293 -10.289 1.00 1.11 H
-ATOM 33 HA ALA A 251 -1.947 -6.242 -9.353 1.00 1.00 H
-ATOM 34 HB1 ALA A 251 -2.419 -8.166 -7.837 1.00 1.34 H
-ATOM 35 HB2 ALA A 251 -0.692 -8.523 -7.849 1.00 1.32 H
-ATOM 36 HB3 ALA A 251 -1.617 -8.594 -9.349 1.00 1.40 H
-ATOM 37 N ARG A 252 -1.485 -6.508 -6.236 1.00 0.52 N
-ATOM 38 CA ARG A 252 -1.631 -5.902 -4.904 1.00 0.61 C
-ATOM 39 C ARG A 252 -0.540 -4.868 -4.609 1.00 0.52 C
-ATOM 40 O ARG A 252 -0.829 -3.753 -4.169 1.00 0.57 O
-ATOM 41 CB ARG A 252 -1.604 -7.015 -3.842 1.00 0.85 C
-ATOM 42 CG ARG A 252 -1.544 -6.531 -2.396 1.00 1.17 C
-ATOM 43 CD ARG A 252 -2.715 -5.626 -2.047 1.00 1.32 C
-ATOM 44 NE ARG A 252 -2.902 -5.478 -0.598 1.00 1.76 N
-ATOM 45 CZ ARG A 252 -3.352 -6.436 0.201 1.00 2.17 C
-ATOM 46 NH1 ARG A 252 -3.695 -7.600 -0.286 1.00 2.22 N
-ATOM 47 NH2 ARG A 252 -3.469 -6.224 1.490 1.00 2.58 N
-ATOM 48 H ARG A 252 -1.522 -7.484 -6.309 1.00 0.57 H
-ATOM 49 HA ARG A 252 -2.589 -5.412 -4.868 1.00 0.77 H
-ATOM 50 HB2 ARG A 252 -2.494 -7.616 -3.953 1.00 0.96 H
-ATOM 51 HB3 ARG A 252 -0.741 -7.639 -4.024 1.00 0.82 H
-ATOM 52 HG2 ARG A 252 -1.561 -7.389 -1.740 1.00 1.41 H
-ATOM 53 HG3 ARG A 252 -0.622 -5.985 -2.251 1.00 1.15 H
-ATOM 54 HD2 ARG A 252 -2.528 -4.651 -2.476 1.00 1.13 H
-ATOM 55 HD3 ARG A 252 -3.611 -6.041 -2.480 1.00 1.45 H
-ATOM 56 HE ARG A 252 -2.674 -4.613 -0.203 1.00 1.79 H
-ATOM 57 HH11 ARG A 252 -3.622 -7.771 -1.264 1.00 1.97 H
-ATOM 58 HH12 ARG A 252 -4.031 -8.315 0.323 1.00 2.56 H
-ATOM 59 HH21 ARG A 252 -3.220 -5.337 1.872 1.00 2.60 H
-ATOM 60 HH22 ARG A 252 -3.808 -6.948 2.086 1.00 2.92 H
-ATOM 61 N VAL A 253 0.706 -5.243 -4.853 1.00 0.58 N
-ATOM 62 CA VAL A 253 1.835 -4.351 -4.609 1.00 0.74 C
-ATOM 63 C VAL A 253 1.775 -3.125 -5.520 1.00 0.61 C
-ATOM 64 O VAL A 253 1.971 -1.996 -5.068 1.00 0.64 O
-ATOM 65 CB VAL A 253 3.177 -5.083 -4.803 1.00 1.02 C
-ATOM 66 CG1 VAL A 253 4.335 -4.208 -4.347 1.00 1.30 C
-ATOM 67 CG2 VAL A 253 3.170 -6.407 -4.050 1.00 1.19 C
-ATOM 68 H VAL A 253 0.871 -6.142 -5.206 1.00 0.61 H
-ATOM 69 HA VAL A 253 1.776 -4.023 -3.584 1.00 0.88 H
-ATOM 70 HB VAL A 253 3.305 -5.292 -5.855 1.00 1.01 H
-ATOM 71 HG11 VAL A 253 5.268 -4.713 -4.546 1.00 1.68 H
-ATOM 72 HG12 VAL A 253 4.246 -4.019 -3.287 1.00 1.72 H
-ATOM 73 HG13 VAL A 253 4.309 -3.271 -4.883 1.00 1.71 H
-ATOM 74 HG21 VAL A 253 4.186 -6.734 -3.887 1.00 1.64 H
-ATOM 75 HG22 VAL A 253 2.642 -7.148 -4.631 1.00 1.60 H
-ATOM 76 HG23 VAL A 253 2.675 -6.278 -3.098 1.00 1.58 H
-ATOM 77 N GLY A 254 1.472 -3.354 -6.795 1.00 0.58 N
-ATOM 78 CA GLY A 254 1.360 -2.259 -7.748 1.00 0.66 C
-ATOM 79 C GLY A 254 0.261 -1.282 -7.366 1.00 0.47 C
-ATOM 80 O GLY A 254 0.481 -0.071 -7.332 1.00 0.49 O
-ATOM 81 H GLY A 254 1.303 -4.274 -7.085 1.00 0.58 H
-ATOM 82 HA2 GLY A 254 2.302 -1.730 -7.787 1.00 0.82 H
-ATOM 83 HA3 GLY A 254 1.145 -2.665 -8.726 1.00 0.80 H
-ATOM 84 N LEU A 255 -0.918 -1.814 -7.050 1.00 0.41 N
-ATOM 85 CA LEU A 255 -2.048 -0.985 -6.638 1.00 0.52 C
-ATOM 86 C LEU A 255 -1.712 -0.223 -5.355 1.00 0.42 C
-ATOM 87 O LEU A 255 -2.011 0.968 -5.227 1.00 0.50 O
-ATOM 88 CB LEU A 255 -3.291 -1.839 -6.426 1.00 0.76 C
-ATOM 89 CG LEU A 255 -4.588 -1.043 -6.431 1.00 1.06 C
-ATOM 90 CD1 LEU A 255 -4.815 -0.410 -7.796 1.00 1.25 C
-ATOM 91 CD2 LEU A 255 -5.759 -1.926 -6.041 1.00 1.33 C
-ATOM 92 H LEU A 255 -1.026 -2.794 -7.078 1.00 0.43 H
-ATOM 93 HA LEU A 255 -2.257 -0.273 -7.421 1.00 0.67 H
-ATOM 94 HB2 LEU A 255 -3.336 -2.581 -7.211 1.00 0.84 H
-ATOM 95 HB3 LEU A 255 -3.204 -2.343 -5.475 1.00 0.75 H
-ATOM 96 HG LEU A 255 -4.508 -0.247 -5.709 1.00 1.04 H
-ATOM 97 HD11 LEU A 255 -5.806 -0.652 -8.148 1.00 1.63 H
-ATOM 98 HD12 LEU A 255 -4.082 -0.789 -8.495 1.00 1.68 H
-ATOM 99 HD13 LEU A 255 -4.712 0.663 -7.716 1.00 1.65 H
-ATOM 100 HD21 LEU A 255 -6.028 -1.734 -5.014 1.00 1.72 H
-ATOM 101 HD22 LEU A 255 -5.480 -2.963 -6.152 1.00 1.72 H
-ATOM 102 HD23 LEU A 255 -6.601 -1.710 -6.681 1.00 1.77 H
-ATOM 103 N GLY A 256 -1.074 -0.916 -4.410 1.00 0.45 N
-ATOM 104 CA GLY A 256 -0.690 -0.294 -3.154 1.00 0.66 C
-ATOM 105 C GLY A 256 0.273 0.869 -3.342 1.00 0.60 C
-ATOM 106 O GLY A 256 0.004 1.976 -2.882 1.00 0.68 O
-ATOM 107 H GLY A 256 -0.855 -1.863 -4.572 1.00 0.43 H
-ATOM 108 HA2 GLY A 256 -1.579 0.066 -2.659 1.00 0.81 H
-ATOM 109 HA3 GLY A 256 -0.219 -1.037 -2.527 1.00 0.81 H
-ATOM 110 N ILE A 257 1.390 0.634 -4.031 1.00 0.59 N
-ATOM 111 CA ILE A 257 2.364 1.703 -4.265 1.00 0.71 C
-ATOM 112 C ILE A 257 1.763 2.830 -5.114 1.00 0.52 C
-ATOM 113 O ILE A 257 2.060 4.000 -4.893 1.00 0.60 O
-ATOM 114 CB ILE A 257 3.684 1.183 -4.901 1.00 0.95 C
-ATOM 115 CG1 ILE A 257 3.447 0.506 -6.256 1.00 0.90 C
-ATOM 116 CG2 ILE A 257 4.382 0.223 -3.951 1.00 1.18 C
-ATOM 117 CD1 ILE A 257 3.677 1.414 -7.448 1.00 1.05 C
-ATOM 118 H ILE A 257 1.559 -0.268 -4.391 1.00 0.59 H
-ATOM 119 HA ILE A 257 2.611 2.116 -3.296 1.00 0.86 H
-ATOM 120 HB ILE A 257 4.336 2.033 -5.044 1.00 1.07 H
-ATOM 121 HG12 ILE A 257 4.116 -0.335 -6.350 1.00 1.06 H
-ATOM 122 HG13 ILE A 257 2.427 0.154 -6.300 1.00 0.71 H
-ATOM 123 HG21 ILE A 257 3.643 -0.298 -3.359 1.00 1.66 H
-ATOM 124 HG22 ILE A 257 5.040 0.777 -3.297 1.00 1.63 H
-ATOM 125 HG23 ILE A 257 4.958 -0.492 -4.520 1.00 1.51 H
-ATOM 126 HD11 ILE A 257 2.749 1.900 -7.715 1.00 1.49 H
-ATOM 127 HD12 ILE A 257 4.030 0.829 -8.283 1.00 1.50 H
-ATOM 128 HD13 ILE A 257 4.414 2.162 -7.194 1.00 1.51 H
-ATOM 129 N THR A 258 0.903 2.474 -6.073 1.00 0.38 N
-ATOM 130 CA THR A 258 0.255 3.470 -6.934 1.00 0.46 C
-ATOM 131 C THR A 258 -0.677 4.374 -6.123 1.00 0.37 C
-ATOM 132 O THR A 258 -0.637 5.601 -6.253 1.00 0.43 O
-ATOM 133 CB THR A 258 -0.556 2.807 -8.071 1.00 0.69 C
-ATOM 134 OG1 THR A 258 0.300 1.980 -8.870 1.00 0.86 O
-ATOM 135 CG2 THR A 258 -1.216 3.852 -8.962 1.00 0.97 C
-ATOM 136 H THR A 258 0.692 1.519 -6.200 1.00 0.36 H
-ATOM 137 HA THR A 258 1.030 4.078 -7.379 1.00 0.64 H
-ATOM 138 HB THR A 258 -1.329 2.194 -7.631 1.00 0.65 H
-ATOM 139 HG1 THR A 258 0.464 1.146 -8.403 1.00 0.75 H
-ATOM 140 HG21 THR A 258 -0.653 4.774 -8.913 1.00 1.45 H
-ATOM 141 HG22 THR A 258 -2.226 4.029 -8.622 1.00 1.44 H
-ATOM 142 HG23 THR A 258 -1.237 3.497 -9.981 1.00 1.48 H
-ATOM 143 N THR A 259 -1.505 3.766 -5.273 1.00 0.42 N
-ATOM 144 CA THR A 259 -2.431 4.526 -4.434 1.00 0.65 C
-ATOM 145 C THR A 259 -1.670 5.363 -3.411 1.00 0.62 C
-ATOM 146 O THR A 259 -1.945 6.552 -3.249 1.00 0.70 O
-ATOM 147 CB THR A 259 -3.444 3.616 -3.709 1.00 0.92 C
-ATOM 148 OG1 THR A 259 -2.764 2.552 -3.034 1.00 0.89 O
-ATOM 149 CG2 THR A 259 -4.455 3.040 -4.691 1.00 1.09 C
-ATOM 150 H THR A 259 -1.483 2.785 -5.199 1.00 0.42 H
-ATOM 151 HA THR A 259 -2.983 5.195 -5.082 1.00 0.76 H
-ATOM 152 HB THR A 259 -3.976 4.210 -2.979 1.00 1.13 H
-ATOM 153 HG1 THR A 259 -2.619 1.820 -3.648 1.00 0.86 H
-ATOM 154 HG21 THR A 259 -4.421 3.602 -5.614 1.00 1.54 H
-ATOM 155 HG22 THR A 259 -5.446 3.104 -4.267 1.00 1.50 H
-ATOM 156 HG23 THR A 259 -4.215 2.006 -4.890 1.00 1.55 H
-ATOM 157 N VAL A 260 -0.694 4.748 -2.741 1.00 0.64 N
-ATOM 158 CA VAL A 260 0.124 5.458 -1.757 1.00 0.82 C
-ATOM 159 C VAL A 260 0.840 6.645 -2.410 1.00 0.69 C
-ATOM 160 O VAL A 260 0.811 7.758 -1.882 1.00 0.78 O
-ATOM 161 CB VAL A 260 1.159 4.523 -1.091 1.00 1.02 C
-ATOM 162 CG1 VAL A 260 2.128 5.310 -0.220 1.00 1.29 C
-ATOM 163 CG2 VAL A 260 0.455 3.456 -0.266 1.00 1.20 C
-ATOM 164 H VAL A 260 -0.509 3.795 -2.924 1.00 0.62 H
-ATOM 165 HA VAL A 260 -0.539 5.833 -0.988 1.00 0.99 H
-ATOM 166 HB VAL A 260 1.725 4.032 -1.868 1.00 0.93 H
-ATOM 167 HG11 VAL A 260 2.988 4.697 0.006 1.00 1.67 H
-ATOM 168 HG12 VAL A 260 1.636 5.593 0.700 1.00 1.72 H
-ATOM 169 HG13 VAL A 260 2.445 6.198 -0.746 1.00 1.69 H
-ATOM 170 HG21 VAL A 260 1.161 3.001 0.413 1.00 1.45 H
-ATOM 171 HG22 VAL A 260 0.053 2.700 -0.924 1.00 1.19 H
-ATOM 172 HG23 VAL A 260 -0.348 3.907 0.298 1.00 1.39 H
-ATOM 173 N LEU A 261 1.453 6.410 -3.576 1.00 0.55 N
-ATOM 174 CA LEU A 261 2.143 7.474 -4.308 1.00 0.59 C
-ATOM 175 C LEU A 261 1.157 8.579 -4.695 1.00 0.44 C
-ATOM 176 O LEU A 261 1.440 9.764 -4.528 1.00 0.53 O
-ATOM 177 CB LEU A 261 2.816 6.919 -5.562 1.00 0.74 C
-ATOM 178 CG LEU A 261 3.733 7.908 -6.282 1.00 1.00 C
-ATOM 179 CD1 LEU A 261 4.788 8.447 -5.326 1.00 1.21 C
-ATOM 180 CD2 LEU A 261 4.389 7.244 -7.480 1.00 1.26 C
-ATOM 181 H LEU A 261 1.424 5.503 -3.961 1.00 0.51 H
-ATOM 182 HA LEU A 261 2.902 7.892 -3.665 1.00 0.78 H
-ATOM 183 HB2 LEU A 261 3.399 6.054 -5.282 1.00 0.87 H
-ATOM 184 HB3 LEU A 261 2.047 6.608 -6.254 1.00 0.67 H
-ATOM 185 HG LEU A 261 3.147 8.742 -6.638 1.00 0.96 H
-ATOM 186 HD11 LEU A 261 5.469 7.653 -5.055 1.00 1.67 H
-ATOM 187 HD12 LEU A 261 4.305 8.824 -4.435 1.00 1.64 H
-ATOM 188 HD13 LEU A 261 5.335 9.245 -5.803 1.00 1.59 H
-ATOM 189 HD21 LEU A 261 3.819 6.371 -7.763 1.00 1.61 H
-ATOM 190 HD22 LEU A 261 5.395 6.950 -7.222 1.00 1.70 H
-ATOM 191 HD23 LEU A 261 4.417 7.939 -8.305 1.00 1.71 H
-ATOM 192 N THR A 262 -0.011 8.175 -5.193 1.00 0.36 N
-ATOM 193 CA THR A 262 -1.058 9.122 -5.585 1.00 0.52 C
-ATOM 194 C THR A 262 -1.513 9.970 -4.393 1.00 0.58 C
-ATOM 195 O THR A 262 -1.612 11.194 -4.483 1.00 0.67 O
-ATOM 196 CB THR A 262 -2.283 8.381 -6.167 1.00 0.76 C
-ATOM 197 OG1 THR A 262 -1.904 7.635 -7.332 1.00 0.84 O
-ATOM 198 CG2 THR A 262 -3.397 9.353 -6.525 1.00 1.07 C
-ATOM 199 H THR A 262 -0.179 7.208 -5.290 1.00 0.33 H
-ATOM 200 HA THR A 262 -0.657 9.771 -6.350 1.00 0.60 H
-ATOM 201 HB THR A 262 -2.655 7.694 -5.420 1.00 0.77 H
-ATOM 202 HG1 THR A 262 -1.439 6.828 -7.062 1.00 0.70 H
-ATOM 203 HG21 THR A 262 -3.102 9.938 -7.381 1.00 1.52 H
-ATOM 204 HG22 THR A 262 -3.583 10.008 -5.686 1.00 1.51 H
-ATOM 205 HG23 THR A 262 -4.295 8.800 -6.756 1.00 1.56 H
-ATOM 206 N MET A 263 -1.788 9.304 -3.275 1.00 0.68 N
-ATOM 207 CA MET A 263 -2.235 9.987 -2.059 1.00 0.93 C
-ATOM 208 C MET A 263 -1.160 10.913 -1.476 1.00 0.88 C
-ATOM 209 O MET A 263 -1.469 12.024 -1.037 1.00 0.99 O
-ATOM 210 CB MET A 263 -2.666 8.968 -1.001 1.00 1.21 C
-ATOM 211 CG MET A 263 -3.931 8.206 -1.366 1.00 1.43 C
-ATOM 212 SD MET A 263 -5.347 9.290 -1.636 1.00 1.85 S
-ATOM 213 CE MET A 263 -5.501 10.074 -0.032 1.00 2.19 C
-ATOM 214 H MET A 263 -1.696 8.322 -3.270 1.00 0.65 H
-ATOM 215 HA MET A 263 -3.092 10.586 -2.324 1.00 1.04 H
-ATOM 216 HB2 MET A 263 -1.868 8.252 -0.862 1.00 1.16 H
-ATOM 217 HB3 MET A 263 -2.840 9.484 -0.068 1.00 1.47 H
-ATOM 218 HG2 MET A 263 -3.747 7.646 -2.271 1.00 1.34 H
-ATOM 219 HG3 MET A 263 -4.167 7.523 -0.564 1.00 1.60 H
-ATOM 220 HE1 MET A 263 -6.303 9.607 0.522 1.00 2.58 H
-ATOM 221 HE2 MET A 263 -5.718 11.124 -0.162 1.00 2.68 H
-ATOM 222 HE3 MET A 263 -4.575 9.964 0.512 1.00 2.16 H
-ATOM 223 N THR A 264 0.097 10.461 -1.445 1.00 0.83 N
-ATOM 224 CA THR A 264 1.177 11.282 -0.882 1.00 0.98 C
-ATOM 225 C THR A 264 1.490 12.521 -1.731 1.00 0.83 C
-ATOM 226 O THR A 264 1.868 13.558 -1.186 1.00 0.99 O
-ATOM 227 CB THR A 264 2.485 10.491 -0.640 1.00 1.14 C
-ATOM 228 OG1 THR A 264 3.440 11.335 0.014 1.00 1.42 O
-ATOM 229 CG2 THR A 264 3.087 9.977 -1.937 1.00 1.00 C
-ATOM 230 H THR A 264 0.299 9.556 -1.789 1.00 0.77 H
-ATOM 231 HA THR A 264 0.830 11.627 0.084 1.00 1.17 H
-ATOM 232 HB THR A 264 2.266 9.647 -0.001 1.00 1.26 H
-ATOM 233 HG1 THR A 264 3.249 12.259 -0.190 1.00 1.66 H
-ATOM 234 HG21 THR A 264 4.157 9.887 -1.826 1.00 1.40 H
-ATOM 235 HG22 THR A 264 2.864 10.668 -2.737 1.00 1.44 H
-ATOM 236 HG23 THR A 264 2.668 9.009 -2.170 1.00 1.43 H
-ATOM 237 N THR A 265 1.343 12.428 -3.055 1.00 0.61 N
-ATOM 238 CA THR A 265 1.629 13.577 -3.928 1.00 0.60 C
-ATOM 239 C THR A 265 0.420 14.522 -4.047 1.00 0.44 C
-ATOM 240 O THR A 265 0.210 15.164 -5.080 1.00 0.52 O
-ATOM 241 CB THR A 265 2.085 13.136 -5.343 1.00 0.74 C
-ATOM 242 OG1 THR A 265 2.445 14.283 -6.123 1.00 0.90 O
-ATOM 243 CG2 THR A 265 0.993 12.362 -6.069 1.00 0.71 C
-ATOM 244 H THR A 265 1.042 11.578 -3.450 1.00 0.55 H
-ATOM 245 HA THR A 265 2.443 14.127 -3.477 1.00 0.77 H
-ATOM 246 HB THR A 265 2.950 12.496 -5.242 1.00 0.90 H
-ATOM 247 HG1 THR A 265 1.714 14.915 -6.113 1.00 0.81 H
-ATOM 248 HG21 THR A 265 0.765 12.851 -7.004 1.00 1.28 H
-ATOM 249 HG22 THR A 265 0.105 12.330 -5.454 1.00 1.22 H
-ATOM 250 HG23 THR A 265 1.332 11.355 -6.263 1.00 1.25 H
-ATOM 251 N GLN A 266 -0.362 14.618 -2.976 1.00 0.47 N
-ATOM 252 CA GLN A 266 -1.541 15.487 -2.949 1.00 0.53 C
-ATOM 253 C GLN A 266 -1.145 16.955 -2.709 1.00 0.50 C
-ATOM 254 O GLN A 266 -1.606 17.594 -1.759 1.00 0.61 O
-ATOM 255 CB GLN A 266 -2.514 15.005 -1.867 1.00 0.79 C
-ATOM 256 CG GLN A 266 -3.894 15.643 -1.938 1.00 0.95 C
-ATOM 257 CD GLN A 266 -4.826 15.159 -0.839 1.00 1.23 C
-ATOM 258 OE1 GLN A 266 -5.956 15.621 -0.722 1.00 1.40 O
-ATOM 259 NE2 GLN A 266 -4.363 14.221 -0.030 1.00 1.35 N
-ATOM 260 H GLN A 266 -0.138 14.093 -2.178 1.00 0.62 H
-ATOM 261 HA GLN A 266 -2.025 15.413 -3.911 1.00 0.59 H
-ATOM 262 HB2 GLN A 266 -2.633 13.936 -1.960 1.00 0.89 H
-ATOM 263 HB3 GLN A 266 -2.090 15.225 -0.897 1.00 0.88 H
-ATOM 264 HG2 GLN A 266 -3.786 16.714 -1.849 1.00 0.91 H
-ATOM 265 HG3 GLN A 266 -4.337 15.406 -2.895 1.00 1.01 H
-ATOM 266 HE21 GLN A 266 -3.455 13.885 -0.179 1.00 1.28 H
-ATOM 267 HE22 GLN A 266 -4.949 13.906 0.685 1.00 1.57 H
-ATOM 268 N SER A 267 -0.282 17.480 -3.574 1.00 0.51 N
-ATOM 269 CA SER A 267 0.191 18.865 -3.468 1.00 0.64 C
-ATOM 270 C SER A 267 -0.880 19.869 -3.906 1.00 0.70 C
-ATOM 271 O SER A 267 -0.653 20.681 -4.802 1.00 0.84 O
-ATOM 272 CB SER A 267 1.455 19.063 -4.312 1.00 0.80 C
-ATOM 273 OG SER A 267 2.488 18.182 -3.905 1.00 0.81 O
-ATOM 274 H SER A 267 0.055 16.915 -4.307 1.00 0.53 H
-ATOM 275 HA SER A 267 0.433 19.051 -2.432 1.00 0.68 H
-ATOM 276 HB2 SER A 267 1.226 18.871 -5.348 1.00 0.84 H
-ATOM 277 HB3 SER A 267 1.801 20.080 -4.203 1.00 0.95 H
-ATOM 278 HG SER A 267 3.342 18.580 -4.097 1.00 1.20 H
-ATOM 279 N SER A 268 -2.046 19.810 -3.271 1.00 0.65 N
-ATOM 280 CA SER A 268 -3.150 20.714 -3.600 1.00 0.74 C
-ATOM 281 C SER A 268 -4.165 20.780 -2.462 1.00 0.72 C
-ATOM 282 O SER A 268 -4.463 19.766 -1.826 1.00 0.72 O
-ATOM 283 CB SER A 268 -3.856 20.264 -4.883 1.00 0.80 C
-ATOM 284 OG SER A 268 -4.449 18.985 -4.723 1.00 0.78 O
-ATOM 285 H SER A 268 -2.168 19.137 -2.560 1.00 0.62 H
-ATOM 286 HA SER A 268 -2.736 21.699 -3.755 1.00 0.83 H
-ATOM 287 HB2 SER A 268 -4.629 20.974 -5.134 1.00 0.91 H
-ATOM 288 HB3 SER A 268 -3.137 20.215 -5.688 1.00 0.86 H
-ATOM 289 HG SER A 268 -4.267 18.654 -3.838 1.00 1.08 H
-ATOM 290 N GLY A 269 -4.693 21.978 -2.215 1.00 0.79 N
-ATOM 291 CA GLY A 269 -5.677 22.173 -1.156 1.00 0.81 C
-ATOM 292 C GLY A 269 -7.045 21.589 -1.487 1.00 0.83 C
-ATOM 293 O GLY A 269 -7.988 22.326 -1.763 1.00 0.97 O
-ATOM 294 H GLY A 269 -4.413 22.744 -2.759 1.00 0.87 H
-ATOM 295 HA2 GLY A 269 -5.312 21.704 -0.253 1.00 0.79 H
-ATOM 296 HA3 GLY A 269 -5.786 23.231 -0.975 1.00 0.88 H
-ATOM 297 N SER A 270 -7.142 20.264 -1.470 1.00 0.82 N
-ATOM 298 CA SER A 270 -8.394 19.566 -1.778 1.00 0.92 C
-ATOM 299 C SER A 270 -9.480 19.847 -0.732 1.00 0.75 C
-ATOM 300 O SER A 270 -10.586 20.266 -1.068 1.00 0.77 O
-ATOM 301 CB SER A 270 -8.141 18.060 -1.853 1.00 1.08 C
-ATOM 302 OG SER A 270 -7.615 17.575 -0.627 1.00 1.02 O
-ATOM 303 H SER A 270 -6.342 19.734 -1.254 1.00 0.84 H
-ATOM 304 HA SER A 270 -8.739 19.912 -2.740 1.00 1.05 H
-ATOM 305 HB2 SER A 270 -9.071 17.550 -2.059 1.00 1.19 H
-ATOM 306 HB3 SER A 270 -7.433 17.853 -2.643 1.00 1.22 H
-ATOM 307 HG SER A 270 -7.018 16.826 -0.798 1.00 1.13 H
-ATOM 308 N ARG A 271 -9.164 19.601 0.536 1.00 0.67 N
-ATOM 309 CA ARG A 271 -10.118 19.814 1.625 1.00 0.54 C
-ATOM 310 C ARG A 271 -10.259 21.305 1.973 1.00 0.55 C
-ATOM 311 O ARG A 271 -9.802 21.757 3.026 1.00 0.74 O
-ATOM 312 CB ARG A 271 -9.702 19.008 2.862 1.00 0.68 C
-ATOM 313 CG ARG A 271 -10.800 18.893 3.909 1.00 0.70 C
-ATOM 314 CD ARG A 271 -10.345 18.106 5.129 1.00 0.91 C
-ATOM 315 NE ARG A 271 -9.282 18.784 5.865 1.00 1.07 N
-ATOM 316 CZ ARG A 271 -8.797 18.351 7.008 1.00 1.33 C
-ATOM 317 NH1 ARG A 271 -9.252 17.252 7.546 1.00 1.44 N
-ATOM 318 NH2 ARG A 271 -7.856 19.016 7.620 1.00 1.59 N
-ATOM 319 H ARG A 271 -8.270 19.252 0.743 1.00 0.75 H
-ATOM 320 HA ARG A 271 -11.079 19.453 1.288 1.00 0.58 H
-ATOM 321 HB2 ARG A 271 -9.424 18.011 2.552 1.00 0.86 H
-ATOM 322 HB3 ARG A 271 -8.846 19.484 3.319 1.00 0.80 H
-ATOM 323 HG2 ARG A 271 -11.087 19.885 4.223 1.00 0.71 H
-ATOM 324 HG3 ARG A 271 -11.651 18.394 3.468 1.00 0.80 H
-ATOM 325 HD2 ARG A 271 -11.191 17.968 5.787 1.00 0.99 H
-ATOM 326 HD3 ARG A 271 -9.986 17.141 4.803 1.00 1.08 H
-ATOM 327 HE ARG A 271 -8.921 19.608 5.484 1.00 1.08 H
-ATOM 328 HH11 ARG A 271 -9.965 16.731 7.089 1.00 1.38 H
-ATOM 329 HH12 ARG A 271 -8.888 16.940 8.446 1.00 1.67 H
-ATOM 330 HH21 ARG A 271 -7.500 19.858 7.225 1.00 1.63 H
-ATOM 331 HH22 ARG A 271 -7.493 18.673 8.487 1.00 1.81 H
-ATOM 332 N ALA A 272 -10.888 22.059 1.073 1.00 0.74 N
-ATOM 333 CA ALA A 272 -11.096 23.500 1.257 1.00 1.03 C
-ATOM 334 C ALA A 272 -11.908 23.823 2.521 1.00 1.05 C
-ATOM 335 O ALA A 272 -11.734 24.881 3.126 1.00 1.30 O
-ATOM 336 CB ALA A 272 -11.781 24.087 0.031 1.00 1.36 C
-ATOM 337 H ALA A 272 -11.216 21.633 0.249 1.00 0.86 H
-ATOM 338 HA ALA A 272 -10.124 23.962 1.344 1.00 1.14 H
-ATOM 339 HB1 ALA A 272 -12.721 23.581 -0.133 1.00 1.78 H
-ATOM 340 HB2 ALA A 272 -11.146 23.958 -0.834 1.00 1.78 H
-ATOM 341 HB3 ALA A 272 -11.962 25.140 0.191 1.00 1.76 H
-ATOM 342 N SER A 273 -12.796 22.917 2.919 1.00 0.91 N
-ATOM 343 CA SER A 273 -13.618 23.128 4.114 1.00 1.00 C
-ATOM 344 C SER A 273 -13.856 21.830 4.868 1.00 0.86 C
-ATOM 345 O SER A 273 -13.267 20.797 4.552 1.00 0.76 O
-ATOM 346 CB SER A 273 -14.953 23.776 3.758 1.00 1.39 C
-ATOM 347 OG SER A 273 -14.759 25.040 3.145 1.00 1.65 O
-ATOM 348 H SER A 273 -12.897 22.088 2.406 1.00 0.89 H
-ATOM 349 HA SER A 273 -13.077 23.793 4.778 1.00 1.02 H
-ATOM 350 HB2 SER A 273 -15.491 23.135 3.079 1.00 1.46 H
-ATOM 351 HB3 SER A 273 -15.531 23.913 4.662 1.00 1.49 H
-ATOM 352 HG SER A 273 -13.817 25.251 3.137 1.00 1.72 H
-ATOM 353 N LEU A 274 -14.685 21.921 5.906 1.00 1.03 N
-ATOM 354 CA LEU A 274 -14.974 20.789 6.786 1.00 1.12 C
-ATOM 355 C LEU A 274 -13.688 20.240 7.458 1.00 0.95 C
-ATOM 356 O LEU A 274 -13.550 19.021 7.605 1.00 1.11 O
-ATOM 357 CB LEU A 274 -15.682 19.667 6.009 1.00 1.32 C
-ATOM 358 CG LEU A 274 -17.047 20.032 5.415 1.00 1.65 C
-ATOM 359 CD1 LEU A 274 -17.606 18.867 4.613 1.00 1.90 C
-ATOM 360 CD2 LEU A 274 -18.022 20.434 6.512 1.00 1.91 C
-ATOM 361 H LEU A 274 -15.081 22.792 6.117 1.00 1.19 H
-ATOM 362 HA LEU A 274 -15.638 21.143 7.561 1.00 1.30 H
-ATOM 363 HB2 LEU A 274 -15.036 19.357 5.201 1.00 1.19 H
-ATOM 364 HB3 LEU A 274 -15.821 18.830 6.677 1.00 1.47 H
-ATOM 365 HG LEU A 274 -16.929 20.872 4.746 1.00 1.65 H
-ATOM 366 HD11 LEU A 274 -18.518 19.172 4.122 1.00 2.28 H
-ATOM 367 HD12 LEU A 274 -17.816 18.041 5.278 1.00 2.17 H
-ATOM 368 HD13 LEU A 274 -16.884 18.559 3.872 1.00 2.18 H
-ATOM 369 HD21 LEU A 274 -18.915 19.833 6.437 1.00 2.19 H
-ATOM 370 HD22 LEU A 274 -18.280 21.478 6.400 1.00 2.26 H
-ATOM 371 HD23 LEU A 274 -17.562 20.280 7.477 1.00 2.21 H
-ATOM 372 N PRO A 275 -12.717 21.119 7.868 1.00 0.81 N
-ATOM 373 CA PRO A 275 -11.451 20.679 8.503 1.00 0.89 C
-ATOM 374 C PRO A 275 -11.634 19.722 9.687 1.00 1.16 C
-ATOM 375 O PRO A 275 -12.459 19.952 10.572 1.00 1.30 O
-ATOM 376 CB PRO A 275 -10.833 21.991 8.987 1.00 1.00 C
-ATOM 377 CG PRO A 275 -11.323 22.990 8.012 1.00 0.94 C
-ATOM 378 CD PRO A 275 -12.744 22.600 7.730 1.00 0.86 C
-ATOM 379 HA PRO A 275 -10.791 20.228 7.779 1.00 0.89 H
-ATOM 380 HB2 PRO A 275 -11.175 22.208 9.989 1.00 1.13 H
-ATOM 381 HB3 PRO A 275 -9.757 21.916 8.971 1.00 1.14 H
-ATOM 382 HG2 PRO A 275 -11.276 23.982 8.438 1.00 1.15 H
-ATOM 383 HG3 PRO A 275 -10.734 22.934 7.108 1.00 0.92 H
-ATOM 384 HD2 PRO A 275 -13.410 23.050 8.446 1.00 1.04 H
-ATOM 385 HD3 PRO A 275 -13.025 22.887 6.716 1.00 0.85 H
-ATOM 386 N LYS A 276 -10.838 18.649 9.688 1.00 1.34 N
-ATOM 387 CA LYS A 276 -10.877 17.635 10.749 1.00 1.67 C
-ATOM 388 C LYS A 276 -9.479 17.053 10.994 1.00 1.92 C
-ATOM 389 O LYS A 276 -9.173 16.709 12.154 1.00 2.26 O
-ATOM 390 CB LYS A 276 -11.837 16.498 10.372 1.00 1.78 C
-ATOM 391 CG LYS A 276 -13.287 16.935 10.236 1.00 1.79 C
-ATOM 392 CD LYS A 276 -14.153 15.827 9.664 1.00 2.02 C
-ATOM 393 CE LYS A 276 -15.582 16.298 9.444 1.00 2.20 C
-ATOM 394 NZ LYS A 276 -15.644 17.497 8.563 1.00 2.05 N
-ATOM 395 OXT LYS A 276 -8.703 16.946 10.010 1.00 1.88 O
-ATOM 396 H LYS A 276 -10.200 18.536 8.954 1.00 1.31 H
-ATOM 397 HA LYS A 276 -11.227 18.108 11.656 1.00 1.77 H
-ATOM 398 HB2 LYS A 276 -11.521 16.077 9.429 1.00 1.71 H
-ATOM 399 HB3 LYS A 276 -11.785 15.733 11.131 1.00 2.07 H
-ATOM 400 HG2 LYS A 276 -13.663 17.205 11.210 1.00 1.98 H
-ATOM 401 HG3 LYS A 276 -13.333 17.792 9.579 1.00 1.60 H
-ATOM 402 HD2 LYS A 276 -13.739 15.510 8.719 1.00 1.95 H
-ATOM 403 HD3 LYS A 276 -14.159 14.997 10.354 1.00 2.26 H
-ATOM 404 HE2 LYS A 276 -16.144 15.498 8.986 1.00 2.41 H
-ATOM 405 HE3 LYS A 276 -16.019 16.543 10.402 1.00 2.38 H
-ATOM 406 HZ1 LYS A 276 -16.127 18.276 9.054 1.00 2.17 H
-ATOM 407 HZ2 LYS A 276 -16.165 17.277 7.692 1.00 2.38 H
-ATOM 408 HZ3 LYS A 276 -14.678 17.809 8.307 1.00 2.23 H
-TER
-ENDMDL
-MODEL 16
-ATOM 1 N ALA A 249 4.871 -8.982 -0.495 1.00 2.03 N
-ATOM 2 CA ALA A 249 3.957 -7.845 -0.781 1.00 1.79 C
-ATOM 3 C ALA A 249 2.499 -8.243 -0.558 1.00 1.42 C
-ATOM 4 O ALA A 249 2.144 -9.415 -0.677 1.00 1.27 O
-ATOM 5 CB ALA A 249 4.154 -7.340 -2.203 1.00 1.71 C
-ATOM 6 H1 ALA A 249 5.399 -8.805 0.384 1.00 2.32 H
-ATOM 7 H2 ALA A 249 5.549 -9.105 -1.274 1.00 2.28 H
-ATOM 8 H3 ALA A 249 4.322 -9.861 -0.386 1.00 1.87 H
-ATOM 9 HA ALA A 249 4.201 -7.038 -0.104 1.00 2.05 H
-ATOM 10 HB1 ALA A 249 4.354 -8.172 -2.858 1.00 1.99 H
-ATOM 11 HB2 ALA A 249 4.985 -6.651 -2.226 1.00 1.95 H
-ATOM 12 HB3 ALA A 249 3.258 -6.832 -2.529 1.00 2.00 H
-ATOM 13 N PRO A 250 1.640 -7.266 -0.219 1.00 1.44 N
-ATOM 14 CA PRO A 250 0.207 -7.481 0.041 1.00 1.36 C
-ATOM 15 C PRO A 250 -0.606 -7.783 -1.227 1.00 1.00 C
-ATOM 16 O PRO A 250 -1.639 -7.152 -1.469 1.00 1.14 O
-ATOM 17 CB PRO A 250 -0.243 -6.140 0.647 1.00 1.73 C
-ATOM 18 CG PRO A 250 1.010 -5.413 0.978 1.00 1.99 C
-ATOM 19 CD PRO A 250 1.990 -5.855 -0.052 1.00 1.76 C
-ATOM 20 HA PRO A 250 0.051 -8.271 0.761 1.00 1.45 H
-ATOM 21 HB2 PRO A 250 -0.823 -5.595 -0.081 1.00 1.66 H
-ATOM 22 HB3 PRO A 250 -0.832 -6.321 1.528 1.00 1.96 H
-ATOM 23 HG2 PRO A 250 0.850 -4.347 0.914 1.00 2.18 H
-ATOM 24 HG3 PRO A 250 1.352 -5.689 1.965 1.00 2.23 H
-ATOM 25 HD2 PRO A 250 1.844 -5.309 -0.972 1.00 1.63 H
-ATOM 26 HD3 PRO A 250 2.999 -5.746 0.308 1.00 2.03 H
-ATOM 27 N ALA A 251 -0.133 -8.755 -2.018 1.00 0.76 N
-ATOM 28 CA ALA A 251 -0.788 -9.178 -3.266 1.00 0.75 C
-ATOM 29 C ALA A 251 -0.831 -8.062 -4.324 1.00 0.62 C
-ATOM 30 O ALA A 251 -0.178 -8.152 -5.363 1.00 0.69 O
-ATOM 31 CB ALA A 251 -2.186 -9.704 -2.976 1.00 1.12 C
-ATOM 32 H ALA A 251 0.691 -9.216 -1.744 1.00 0.85 H
-ATOM 33 HA ALA A 251 -0.208 -9.998 -3.668 1.00 0.88 H
-ATOM 34 HB1 ALA A 251 -2.123 -10.514 -2.264 1.00 1.59 H
-ATOM 35 HB2 ALA A 251 -2.634 -10.061 -3.891 1.00 1.58 H
-ATOM 36 HB3 ALA A 251 -2.790 -8.910 -2.563 1.00 1.62 H
-ATOM 37 N ARG A 252 -1.584 -7.002 -4.052 1.00 0.55 N
-ATOM 38 CA ARG A 252 -1.690 -5.878 -4.978 1.00 0.50 C
-ATOM 39 C ARG A 252 -0.607 -4.832 -4.728 1.00 0.39 C
-ATOM 40 O ARG A 252 -0.898 -3.673 -4.427 1.00 0.37 O
-ATOM 41 CB ARG A 252 -3.079 -5.249 -4.911 1.00 0.62 C
-ATOM 42 CG ARG A 252 -4.127 -6.071 -5.637 1.00 0.78 C
-ATOM 43 CD ARG A 252 -3.664 -6.418 -7.046 1.00 0.84 C
-ATOM 44 NE ARG A 252 -4.639 -7.244 -7.757 1.00 1.04 N
-ATOM 45 CZ ARG A 252 -4.463 -7.718 -8.973 1.00 1.19 C
-ATOM 46 NH1 ARG A 252 -3.362 -7.464 -9.631 1.00 1.25 N
-ATOM 47 NH2 ARG A 252 -5.389 -8.456 -9.529 1.00 1.39 N
-ATOM 48 H ARG A 252 -2.071 -6.970 -3.197 1.00 0.64 H
-ATOM 49 HA ARG A 252 -1.547 -6.274 -5.973 1.00 0.58 H
-ATOM 50 HB2 ARG A 252 -3.374 -5.156 -3.875 1.00 0.66 H
-ATOM 51 HB3 ARG A 252 -3.045 -4.268 -5.359 1.00 0.64 H
-ATOM 52 HG2 ARG A 252 -4.300 -6.985 -5.088 1.00 0.82 H
-ATOM 53 HG3 ARG A 252 -5.043 -5.502 -5.696 1.00 0.89 H
-ATOM 54 HD2 ARG A 252 -3.507 -5.501 -7.597 1.00 0.87 H
-ATOM 55 HD3 ARG A 252 -2.727 -6.958 -6.979 1.00 0.79 H
-ATOM 56 HE ARG A 252 -5.472 -7.456 -7.292 1.00 1.13 H
-ATOM 57 HH11 ARG A 252 -2.649 -6.909 -9.213 1.00 1.16 H
-ATOM 58 HH12 ARG A 252 -3.239 -7.825 -10.553 1.00 1.44 H
-ATOM 59 HH21 ARG A 252 -6.231 -8.659 -9.035 1.00 1.43 H
-ATOM 60 HH22 ARG A 252 -5.253 -8.818 -10.448 1.00 1.53 H
-ATOM 61 N VAL A 253 0.639 -5.262 -4.869 1.00 0.41 N
-ATOM 62 CA VAL A 253 1.802 -4.396 -4.678 1.00 0.46 C
-ATOM 63 C VAL A 253 1.709 -3.122 -5.522 1.00 0.46 C
-ATOM 64 O VAL A 253 1.766 -2.010 -4.996 1.00 0.46 O
-ATOM 65 CB VAL A 253 3.099 -5.162 -5.028 1.00 0.63 C
-ATOM 66 CG1 VAL A 253 2.985 -5.871 -6.373 1.00 0.73 C
-ATOM 67 CG2 VAL A 253 4.302 -4.232 -5.002 1.00 0.79 C
-ATOM 68 H VAL A 253 0.784 -6.201 -5.115 1.00 0.48 H
-ATOM 69 HA VAL A 253 1.843 -4.122 -3.636 1.00 0.46 H
-ATOM 70 HB VAL A 253 3.242 -5.913 -4.281 1.00 0.66 H
-ATOM 71 HG11 VAL A 253 2.878 -6.934 -6.213 1.00 1.26 H
-ATOM 72 HG12 VAL A 253 3.872 -5.681 -6.957 1.00 1.27 H
-ATOM 73 HG13 VAL A 253 2.119 -5.500 -6.903 1.00 1.28 H
-ATOM 74 HG21 VAL A 253 4.276 -3.587 -5.868 1.00 1.23 H
-ATOM 75 HG22 VAL A 253 5.209 -4.818 -5.015 1.00 1.31 H
-ATOM 76 HG23 VAL A 253 4.274 -3.632 -4.105 1.00 1.40 H
-ATOM 77 N GLY A 254 1.559 -3.307 -6.827 1.00 0.55 N
-ATOM 78 CA GLY A 254 1.453 -2.184 -7.754 1.00 0.63 C
-ATOM 79 C GLY A 254 0.316 -1.235 -7.404 1.00 0.54 C
-ATOM 80 O GLY A 254 0.502 -0.018 -7.370 1.00 0.55 O
-ATOM 81 H GLY A 254 1.527 -4.228 -7.162 1.00 0.59 H
-ATOM 82 HA2 GLY A 254 2.381 -1.634 -7.741 1.00 0.70 H
-ATOM 83 HA3 GLY A 254 1.289 -2.570 -8.749 1.00 0.74 H
-ATOM 84 N LEU A 255 -0.856 -1.795 -7.116 1.00 0.50 N
-ATOM 85 CA LEU A 255 -2.017 -0.992 -6.740 1.00 0.49 C
-ATOM 86 C LEU A 255 -1.757 -0.225 -5.451 1.00 0.36 C
-ATOM 87 O LEU A 255 -2.057 0.967 -5.350 1.00 0.38 O
-ATOM 88 CB LEU A 255 -3.246 -1.878 -6.561 1.00 0.58 C
-ATOM 89 CG LEU A 255 -4.195 -1.900 -7.751 1.00 0.78 C
-ATOM 90 CD1 LEU A 255 -5.398 -2.773 -7.445 1.00 0.89 C
-ATOM 91 CD2 LEU A 255 -4.627 -0.483 -8.100 1.00 0.88 C
-ATOM 92 H LEU A 255 -0.935 -2.769 -7.140 1.00 0.53 H
-ATOM 93 HA LEU A 255 -2.209 -0.290 -7.537 1.00 0.59 H
-ATOM 94 HB2 LEU A 255 -2.912 -2.888 -6.371 1.00 0.58 H
-ATOM 95 HB3 LEU A 255 -3.793 -1.528 -5.699 1.00 0.59 H
-ATOM 96 HG LEU A 255 -3.682 -2.316 -8.606 1.00 0.85 H
-ATOM 97 HD11 LEU A 255 -5.063 -3.716 -7.040 1.00 1.41 H
-ATOM 98 HD12 LEU A 255 -5.956 -2.947 -8.352 1.00 1.36 H
-ATOM 99 HD13 LEU A 255 -6.028 -2.274 -6.723 1.00 1.35 H
-ATOM 100 HD21 LEU A 255 -3.819 0.025 -8.605 1.00 1.27 H
-ATOM 101 HD22 LEU A 255 -4.874 0.051 -7.194 1.00 1.37 H
-ATOM 102 HD23 LEU A 255 -5.491 -0.518 -8.744 1.00 1.44 H
-ATOM 103 N GLY A 256 -1.195 -0.918 -4.469 1.00 0.28 N
-ATOM 104 CA GLY A 256 -0.897 -0.299 -3.196 1.00 0.27 C
-ATOM 105 C GLY A 256 0.115 0.827 -3.313 1.00 0.27 C
-ATOM 106 O GLY A 256 -0.134 1.936 -2.838 1.00 0.30 O
-ATOM 107 H GLY A 256 -0.978 -1.869 -4.611 1.00 0.31 H
-ATOM 108 HA2 GLY A 256 -1.815 0.098 -2.786 1.00 0.33 H
-ATOM 109 HA3 GLY A 256 -0.512 -1.051 -2.527 1.00 0.34 H
-ATOM 110 N ILE A 257 1.255 0.559 -3.959 1.00 0.34 N
-ATOM 111 CA ILE A 257 2.282 1.590 -4.129 1.00 0.46 C
-ATOM 112 C ILE A 257 1.755 2.759 -4.966 1.00 0.43 C
-ATOM 113 O ILE A 257 2.052 3.918 -4.676 1.00 0.46 O
-ATOM 114 CB ILE A 257 3.604 1.036 -4.727 1.00 0.64 C
-ATOM 115 CG1 ILE A 257 3.397 0.417 -6.114 1.00 0.69 C
-ATOM 116 CG2 ILE A 257 4.220 0.016 -3.783 1.00 0.73 C
-ATOM 117 CD1 ILE A 257 3.750 1.345 -7.259 1.00 0.82 C
-ATOM 118 H ILE A 257 1.402 -0.343 -4.333 1.00 0.37 H
-ATOM 119 HA ILE A 257 2.507 1.970 -3.140 1.00 0.50 H
-ATOM 120 HB ILE A 257 4.296 1.861 -4.813 1.00 0.75 H
-ATOM 121 HG12 ILE A 257 4.015 -0.464 -6.202 1.00 0.79 H
-ATOM 122 HG13 ILE A 257 2.360 0.136 -6.222 1.00 0.64 H
-ATOM 123 HG21 ILE A 257 3.471 -0.329 -3.085 1.00 1.23 H
-ATOM 124 HG22 ILE A 257 5.035 0.474 -3.240 1.00 1.29 H
-ATOM 125 HG23 ILE A 257 4.594 -0.822 -4.353 1.00 1.29 H
-ATOM 126 HD11 ILE A 257 2.988 1.282 -8.021 1.00 1.38 H
-ATOM 127 HD12 ILE A 257 4.703 1.056 -7.677 1.00 1.34 H
-ATOM 128 HD13 ILE A 257 3.811 2.360 -6.894 1.00 1.28 H
-ATOM 129 N THR A 258 0.944 2.455 -5.985 1.00 0.42 N
-ATOM 130 CA THR A 258 0.357 3.498 -6.828 1.00 0.47 C
-ATOM 131 C THR A 258 -0.593 4.364 -6.003 1.00 0.38 C
-ATOM 132 O THR A 258 -0.593 5.590 -6.118 1.00 0.41 O
-ATOM 133 CB THR A 258 -0.410 2.910 -8.034 1.00 0.56 C
-ATOM 134 OG1 THR A 258 0.465 2.100 -8.825 1.00 0.67 O
-ATOM 135 CG2 THR A 258 -0.999 4.011 -8.904 1.00 0.70 C
-ATOM 136 H THR A 258 0.722 1.511 -6.160 1.00 0.43 H
-ATOM 137 HA THR A 258 1.160 4.118 -7.202 1.00 0.57 H
-ATOM 138 HB THR A 258 -1.217 2.295 -7.661 1.00 0.52 H
-ATOM 139 HG1 THR A 258 0.560 1.228 -8.410 1.00 0.67 H
-ATOM 140 HG21 THR A 258 -0.207 4.655 -9.256 1.00 1.22 H
-ATOM 141 HG22 THR A 258 -1.704 4.590 -8.324 1.00 1.25 H
-ATOM 142 HG23 THR A 258 -1.507 3.570 -9.749 1.00 1.30 H
-ATOM 143 N THR A 259 -1.386 3.716 -5.149 1.00 0.31 N
-ATOM 144 CA THR A 259 -2.325 4.425 -4.280 1.00 0.31 C
-ATOM 145 C THR A 259 -1.563 5.331 -3.314 1.00 0.29 C
-ATOM 146 O THR A 259 -1.901 6.506 -3.153 1.00 0.33 O
-ATOM 147 CB THR A 259 -3.211 3.448 -3.475 1.00 0.38 C
-ATOM 148 OG1 THR A 259 -3.888 2.553 -4.366 1.00 0.46 O
-ATOM 149 CG2 THR A 259 -4.241 4.199 -2.644 1.00 0.52 C
-ATOM 150 H THR A 259 -1.325 2.735 -5.089 1.00 0.31 H
-ATOM 151 HA THR A 259 -2.964 5.035 -4.904 1.00 0.38 H
-ATOM 152 HB THR A 259 -2.580 2.876 -2.810 1.00 0.39 H
-ATOM 153 HG1 THR A 259 -3.257 1.907 -4.719 1.00 0.45 H
-ATOM 154 HG21 THR A 259 -4.091 3.977 -1.598 1.00 1.18 H
-ATOM 155 HG22 THR A 259 -5.234 3.893 -2.938 1.00 1.17 H
-ATOM 156 HG23 THR A 259 -4.129 5.262 -2.805 1.00 1.13 H
-ATOM 157 N VAL A 260 -0.511 4.784 -2.697 1.00 0.31 N
-ATOM 158 CA VAL A 260 0.321 5.552 -1.776 1.00 0.39 C
-ATOM 159 C VAL A 260 0.937 6.751 -2.496 1.00 0.40 C
-ATOM 160 O VAL A 260 0.859 7.878 -2.012 1.00 0.45 O
-ATOM 161 CB VAL A 260 1.441 4.683 -1.160 1.00 0.49 C
-ATOM 162 CG1 VAL A 260 2.361 5.524 -0.286 1.00 0.67 C
-ATOM 163 CG2 VAL A 260 0.847 3.537 -0.355 1.00 0.50 C
-ATOM 164 H VAL A 260 -0.280 3.844 -2.885 1.00 0.32 H
-ATOM 165 HA VAL A 260 -0.311 5.911 -0.975 1.00 0.43 H
-ATOM 166 HB VAL A 260 2.029 4.264 -1.964 1.00 0.51 H
-ATOM 167 HG11 VAL A 260 2.168 6.571 -0.467 1.00 1.24 H
-ATOM 168 HG12 VAL A 260 3.390 5.300 -0.527 1.00 1.26 H
-ATOM 169 HG13 VAL A 260 2.176 5.297 0.754 1.00 1.25 H
-ATOM 170 HG21 VAL A 260 1.121 2.596 -0.811 1.00 1.14 H
-ATOM 171 HG22 VAL A 260 -0.229 3.628 -0.338 1.00 1.17 H
-ATOM 172 HG23 VAL A 260 1.227 3.570 0.655 1.00 1.12 H
-ATOM 173 N LEU A 261 1.516 6.506 -3.673 1.00 0.40 N
-ATOM 174 CA LEU A 261 2.111 7.574 -4.476 1.00 0.47 C
-ATOM 175 C LEU A 261 1.061 8.628 -4.828 1.00 0.42 C
-ATOM 176 O LEU A 261 1.308 9.828 -4.717 1.00 0.49 O
-ATOM 177 CB LEU A 261 2.719 7.002 -5.758 1.00 0.55 C
-ATOM 178 CG LEU A 261 4.248 7.022 -5.826 1.00 0.73 C
-ATOM 179 CD1 LEU A 261 4.767 8.453 -5.796 1.00 1.10 C
-ATOM 180 CD2 LEU A 261 4.853 6.211 -4.689 1.00 1.20 C
-ATOM 181 H LEU A 261 1.525 5.584 -4.021 1.00 0.40 H
-ATOM 182 HA LEU A 261 2.891 8.037 -3.891 1.00 0.55 H
-ATOM 183 HB2 LEU A 261 2.390 5.977 -5.859 1.00 0.81 H
-ATOM 184 HB3 LEU A 261 2.337 7.567 -6.595 1.00 0.73 H
-ATOM 185 HG LEU A 261 4.559 6.577 -6.758 1.00 0.78 H
-ATOM 186 HD11 LEU A 261 5.784 8.474 -6.158 1.00 1.39 H
-ATOM 187 HD12 LEU A 261 4.738 8.825 -4.782 1.00 1.61 H
-ATOM 188 HD13 LEU A 261 4.148 9.074 -6.425 1.00 1.62 H
-ATOM 189 HD21 LEU A 261 5.053 5.207 -5.032 1.00 1.71 H
-ATOM 190 HD22 LEU A 261 4.158 6.178 -3.862 1.00 1.67 H
-ATOM 191 HD23 LEU A 261 5.774 6.671 -4.367 1.00 1.66 H
-ATOM 192 N THR A 262 -0.114 8.160 -5.238 1.00 0.36 N
-ATOM 193 CA THR A 262 -1.225 9.045 -5.596 1.00 0.40 C
-ATOM 194 C THR A 262 -1.620 9.952 -4.425 1.00 0.40 C
-ATOM 195 O THR A 262 -1.758 11.164 -4.583 1.00 0.47 O
-ATOM 196 CB THR A 262 -2.462 8.231 -6.038 1.00 0.43 C
-ATOM 197 OG1 THR A 262 -2.141 7.419 -7.175 1.00 0.49 O
-ATOM 198 CG2 THR A 262 -3.629 9.146 -6.378 1.00 0.55 C
-ATOM 199 H THR A 262 -0.240 7.185 -5.297 1.00 0.35 H
-ATOM 200 HA THR A 262 -0.909 9.659 -6.425 1.00 0.47 H
-ATOM 201 HB THR A 262 -2.758 7.588 -5.222 1.00 0.41 H
-ATOM 202 HG1 THR A 262 -1.583 6.678 -6.894 1.00 0.50 H
-ATOM 203 HG21 THR A 262 -3.520 10.080 -5.846 1.00 1.15 H
-ATOM 204 HG22 THR A 262 -4.555 8.671 -6.084 1.00 1.23 H
-ATOM 205 HG23 THR A 262 -3.641 9.333 -7.441 1.00 1.13 H
-ATOM 206 N MET A 263 -1.810 9.364 -3.249 1.00 0.38 N
-ATOM 207 CA MET A 263 -2.195 10.142 -2.069 1.00 0.45 C
-ATOM 208 C MET A 263 -1.040 10.998 -1.516 1.00 0.47 C
-ATOM 209 O MET A 263 -1.274 12.081 -0.983 1.00 0.55 O
-ATOM 210 CB MET A 263 -2.750 9.228 -0.966 1.00 0.52 C
-ATOM 211 CG MET A 263 -1.752 8.210 -0.435 1.00 0.56 C
-ATOM 212 SD MET A 263 -2.386 7.268 0.964 1.00 0.73 S
-ATOM 213 CE MET A 263 -3.822 6.485 0.237 1.00 0.86 C
-ATOM 214 H MET A 263 -1.698 8.389 -3.175 1.00 0.36 H
-ATOM 215 HA MET A 263 -2.984 10.811 -2.379 1.00 0.50 H
-ATOM 216 HB2 MET A 263 -3.074 9.841 -0.138 1.00 0.60 H
-ATOM 217 HB3 MET A 263 -3.601 8.692 -1.357 1.00 0.59 H
-ATOM 218 HG2 MET A 263 -1.506 7.521 -1.230 1.00 0.59 H
-ATOM 219 HG3 MET A 263 -0.857 8.732 -0.125 1.00 0.62 H
-ATOM 220 HE1 MET A 263 -4.584 7.228 0.054 1.00 1.44 H
-ATOM 221 HE2 MET A 263 -4.206 5.735 0.914 1.00 1.40 H
-ATOM 222 HE3 MET A 263 -3.544 6.017 -0.697 1.00 1.26 H
-ATOM 223 N THR A 264 0.201 10.505 -1.602 1.00 0.45 N
-ATOM 224 CA THR A 264 1.349 11.251 -1.057 1.00 0.52 C
-ATOM 225 C THR A 264 1.862 12.366 -1.976 1.00 0.50 C
-ATOM 226 O THR A 264 2.608 13.232 -1.516 1.00 0.54 O
-ATOM 227 CB THR A 264 2.546 10.342 -0.691 1.00 0.60 C
-ATOM 228 OG1 THR A 264 3.522 11.105 0.033 1.00 0.67 O
-ATOM 229 CG2 THR A 264 3.203 9.752 -1.930 1.00 0.61 C
-ATOM 230 H THR A 264 0.346 9.617 -2.008 1.00 0.44 H
-ATOM 231 HA THR A 264 1.009 11.714 -0.142 1.00 0.57 H
-ATOM 232 HB THR A 264 2.192 9.535 -0.066 1.00 0.67 H
-ATOM 233 HG1 THR A 264 3.461 12.035 -0.235 1.00 0.76 H
-ATOM 234 HG21 THR A 264 3.482 10.550 -2.603 1.00 1.21 H
-ATOM 235 HG22 THR A 264 2.510 9.089 -2.425 1.00 1.23 H
-ATOM 236 HG23 THR A 264 4.085 9.200 -1.640 1.00 1.11 H
-ATOM 237 N THR A 265 1.493 12.354 -3.260 1.00 0.53 N
-ATOM 238 CA THR A 265 1.965 13.391 -4.194 1.00 0.56 C
-ATOM 239 C THR A 265 1.237 14.728 -3.976 1.00 0.50 C
-ATOM 240 O THR A 265 0.741 15.364 -4.910 1.00 0.56 O
-ATOM 241 CB THR A 265 1.855 12.950 -5.677 1.00 0.67 C
-ATOM 242 OG1 THR A 265 2.373 13.973 -6.536 1.00 0.74 O
-ATOM 243 CG2 THR A 265 0.420 12.633 -6.072 1.00 0.71 C
-ATOM 244 H THR A 265 0.907 11.639 -3.584 1.00 0.60 H
-ATOM 245 HA THR A 265 3.013 13.549 -3.979 1.00 0.60 H
-ATOM 246 HB THR A 265 2.449 12.055 -5.808 1.00 0.76 H
-ATOM 247 HG1 THR A 265 1.648 14.533 -6.836 1.00 1.00 H
-ATOM 248 HG21 THR A 265 0.338 12.619 -7.149 1.00 1.09 H
-ATOM 249 HG22 THR A 265 -0.239 13.388 -5.671 1.00 1.39 H
-ATOM 250 HG23 THR A 265 0.141 11.667 -5.678 1.00 1.24 H
-ATOM 251 N GLN A 266 1.200 15.151 -2.720 1.00 0.46 N
-ATOM 252 CA GLN A 266 0.566 16.405 -2.322 1.00 0.50 C
-ATOM 253 C GLN A 266 1.438 17.603 -2.707 1.00 0.51 C
-ATOM 254 O GLN A 266 2.003 18.275 -1.842 1.00 0.53 O
-ATOM 255 CB GLN A 266 0.327 16.401 -0.813 1.00 0.53 C
-ATOM 256 CG GLN A 266 -0.548 15.257 -0.331 1.00 0.57 C
-ATOM 257 CD GLN A 266 -0.666 15.216 1.179 1.00 0.59 C
-ATOM 258 OE1 GLN A 266 0.323 15.051 1.889 1.00 0.66 O
-ATOM 259 NE2 GLN A 266 -1.875 15.374 1.684 1.00 0.73 N
-ATOM 260 H GLN A 266 1.631 14.598 -2.027 1.00 0.46 H
-ATOM 261 HA GLN A 266 -0.382 16.478 -2.832 1.00 0.57 H
-ATOM 262 HB2 GLN A 266 1.281 16.327 -0.312 1.00 0.52 H
-ATOM 263 HB3 GLN A 266 -0.146 17.328 -0.534 1.00 0.62 H
-ATOM 264 HG2 GLN A 266 -1.535 15.374 -0.752 1.00 0.71 H
-ATOM 265 HG3 GLN A 266 -0.119 14.324 -0.670 1.00 0.62 H
-ATOM 266 HE21 GLN A 266 -2.621 15.506 1.067 1.00 0.86 H
-ATOM 267 HE22 GLN A 266 -1.970 15.351 2.657 1.00 0.79 H
-ATOM 268 N SER A 267 1.559 17.845 -4.013 1.00 0.62 N
-ATOM 269 CA SER A 267 2.380 18.943 -4.539 1.00 0.70 C
-ATOM 270 C SER A 267 3.854 18.737 -4.185 1.00 0.57 C
-ATOM 271 O SER A 267 4.548 19.673 -3.788 1.00 0.58 O
-ATOM 272 CB SER A 267 1.896 20.299 -4.010 1.00 0.82 C
-ATOM 273 OG SER A 267 0.552 20.546 -4.386 1.00 1.05 O
-ATOM 274 H SER A 267 1.094 17.252 -4.647 1.00 0.71 H
-ATOM 275 HA SER A 267 2.283 18.935 -5.615 1.00 0.82 H
-ATOM 276 HB2 SER A 267 1.962 20.306 -2.931 1.00 0.74 H
-ATOM 277 HB3 SER A 267 2.519 21.083 -4.413 1.00 0.91 H
-ATOM 278 HG SER A 267 0.108 21.035 -3.688 1.00 1.42 H
-ATOM 279 N SER A 268 4.315 17.493 -4.325 1.00 0.54 N
-ATOM 280 CA SER A 268 5.700 17.129 -4.018 1.00 0.53 C
-ATOM 281 C SER A 268 6.694 17.896 -4.892 1.00 0.66 C
-ATOM 282 O SER A 268 7.737 18.350 -4.409 1.00 0.70 O
-ATOM 283 CB SER A 268 5.901 15.620 -4.198 1.00 0.62 C
-ATOM 284 OG SER A 268 5.012 14.880 -3.372 1.00 0.56 O
-ATOM 285 H SER A 268 3.701 16.794 -4.635 1.00 0.60 H
-ATOM 286 HA SER A 268 5.883 17.383 -2.984 1.00 0.45 H
-ATOM 287 HB2 SER A 268 5.717 15.356 -5.228 1.00 0.76 H
-ATOM 288 HB3 SER A 268 6.916 15.361 -3.935 1.00 0.69 H
-ATOM 289 HG SER A 268 5.140 15.135 -2.445 1.00 0.63 H
-ATOM 290 N GLY A 269 6.360 18.042 -6.175 1.00 0.81 N
-ATOM 291 CA GLY A 269 7.220 18.759 -7.112 1.00 0.97 C
-ATOM 292 C GLY A 269 7.195 20.270 -6.915 1.00 0.97 C
-ATOM 293 O GLY A 269 6.832 21.016 -7.822 1.00 1.45 O
-ATOM 294 H GLY A 269 5.515 17.662 -6.490 1.00 0.84 H
-ATOM 295 HA2 GLY A 269 8.235 18.410 -6.988 1.00 1.02 H
-ATOM 296 HA3 GLY A 269 6.897 18.536 -8.118 1.00 1.09 H
-ATOM 297 N SER A 270 7.579 20.714 -5.725 1.00 0.73 N
-ATOM 298 CA SER A 270 7.603 22.139 -5.394 1.00 0.75 C
-ATOM 299 C SER A 270 8.741 22.449 -4.419 1.00 0.76 C
-ATOM 300 O SER A 270 9.526 23.369 -4.639 1.00 1.12 O
-ATOM 301 CB SER A 270 6.262 22.566 -4.786 1.00 0.71 C
-ATOM 302 OG SER A 270 5.973 21.824 -3.611 1.00 0.59 O
-ATOM 303 H SER A 270 7.852 20.058 -5.044 1.00 0.87 H
-ATOM 304 HA SER A 270 7.766 22.690 -6.309 1.00 0.89 H
-ATOM 305 HB2 SER A 270 6.303 23.615 -4.532 1.00 0.78 H
-ATOM 306 HB3 SER A 270 5.474 22.400 -5.506 1.00 0.80 H
-ATOM 307 HG SER A 270 5.364 21.098 -3.827 1.00 0.64 H
-ATOM 308 N ARG A 271 8.827 21.664 -3.346 1.00 0.60 N
-ATOM 309 CA ARG A 271 9.874 21.842 -2.340 1.00 0.71 C
-ATOM 310 C ARG A 271 10.059 20.553 -1.527 1.00 0.69 C
-ATOM 311 O ARG A 271 10.128 20.584 -0.297 1.00 0.77 O
-ATOM 312 CB ARG A 271 9.539 23.018 -1.411 1.00 0.78 C
-ATOM 313 CG ARG A 271 10.765 23.631 -0.745 1.00 1.16 C
-ATOM 314 CD ARG A 271 10.402 24.810 0.145 1.00 1.32 C
-ATOM 315 NE ARG A 271 9.670 24.402 1.342 1.00 1.09 N
-ATOM 316 CZ ARG A 271 9.345 25.224 2.319 1.00 1.25 C
-ATOM 317 NH1 ARG A 271 9.667 26.491 2.253 1.00 1.60 N
-ATOM 318 NH2 ARG A 271 8.700 24.778 3.365 1.00 1.36 N
-ATOM 319 H ARG A 271 8.169 20.947 -3.229 1.00 0.67 H
-ATOM 320 HA ARG A 271 10.796 22.057 -2.861 1.00 0.82 H
-ATOM 321 HB2 ARG A 271 9.044 23.787 -1.986 1.00 1.14 H
-ATOM 322 HB3 ARG A 271 8.870 22.672 -0.637 1.00 0.80 H
-ATOM 323 HG2 ARG A 271 11.247 22.877 -0.142 1.00 1.34 H
-ATOM 324 HG3 ARG A 271 11.445 23.969 -1.512 1.00 1.53 H
-ATOM 325 HD2 ARG A 271 11.311 25.310 0.446 1.00 1.75 H
-ATOM 326 HD3 ARG A 271 9.789 25.494 -0.424 1.00 1.46 H
-ATOM 327 HE ARG A 271 9.414 23.462 1.415 1.00 1.07 H
-ATOM 328 HH11 ARG A 271 10.157 26.838 1.458 1.00 1.85 H
-ATOM 329 HH12 ARG A 271 9.421 27.109 2.997 1.00 1.75 H
-ATOM 330 HH21 ARG A 271 8.452 23.814 3.424 1.00 1.50 H
-ATOM 331 HH22 ARG A 271 8.460 25.402 4.104 1.00 1.49 H
-ATOM 332 N ALA A 272 10.135 19.421 -2.241 1.00 0.69 N
-ATOM 333 CA ALA A 272 10.312 18.099 -1.625 1.00 0.73 C
-ATOM 334 C ALA A 272 9.155 17.733 -0.685 1.00 0.58 C
-ATOM 335 O ALA A 272 9.371 17.273 0.438 1.00 0.66 O
-ATOM 336 CB ALA A 272 11.646 18.023 -0.889 1.00 0.95 C
-ATOM 337 H ALA A 272 10.071 19.478 -3.217 1.00 0.73 H
-ATOM 338 HA ALA A 272 10.339 17.373 -2.425 1.00 0.78 H
-ATOM 339 HB1 ALA A 272 11.950 19.016 -0.592 1.00 1.36 H
-ATOM 340 HB2 ALA A 272 12.394 17.598 -1.543 1.00 1.45 H
-ATOM 341 HB3 ALA A 272 11.538 17.401 -0.013 1.00 1.47 H
-ATOM 342 N SER A 273 7.925 17.932 -1.152 1.00 0.44 N
-ATOM 343 CA SER A 273 6.737 17.613 -0.349 1.00 0.38 C
-ATOM 344 C SER A 273 6.441 16.121 -0.350 1.00 0.40 C
-ATOM 345 O SER A 273 5.400 15.675 -0.834 1.00 0.49 O
-ATOM 346 CB SER A 273 5.509 18.377 -0.843 1.00 0.40 C
-ATOM 347 OG SER A 273 5.793 19.755 -1.019 1.00 0.55 O
-ATOM 348 H SER A 273 7.810 18.297 -2.054 1.00 0.47 H
-ATOM 349 HA SER A 273 6.936 17.902 0.675 1.00 0.45 H
-ATOM 350 HB2 SER A 273 5.189 17.961 -1.784 1.00 0.48 H
-ATOM 351 HB3 SER A 273 4.717 18.274 -0.114 1.00 0.48 H
-ATOM 352 HG SER A 273 5.299 20.091 -1.777 1.00 0.78 H
-ATOM 353 N LEU A 274 7.350 15.356 0.230 1.00 0.47 N
-ATOM 354 CA LEU A 274 7.172 13.919 0.342 1.00 0.54 C
-ATOM 355 C LEU A 274 7.341 13.454 1.807 1.00 0.59 C
-ATOM 356 O LEU A 274 7.996 12.440 2.060 1.00 0.73 O
-ATOM 357 CB LEU A 274 8.175 13.199 -0.568 1.00 0.75 C
-ATOM 358 CG LEU A 274 7.866 11.725 -0.851 1.00 0.88 C
-ATOM 359 CD1 LEU A 274 6.583 11.591 -1.657 1.00 0.91 C
-ATOM 360 CD2 LEU A 274 9.028 11.070 -1.581 1.00 1.14 C
-ATOM 361 H LEU A 274 8.148 15.777 0.622 1.00 0.54 H
-ATOM 362 HA LEU A 274 6.170 13.684 0.015 1.00 0.53 H
-ATOM 363 HB2 LEU A 274 8.211 13.725 -1.512 1.00 0.79 H
-ATOM 364 HB3 LEU A 274 9.150 13.256 -0.108 1.00 0.89 H
-ATOM 365 HG LEU A 274 7.726 11.208 0.087 1.00 0.94 H
-ATOM 366 HD11 LEU A 274 5.814 12.207 -1.217 1.00 1.41 H
-ATOM 367 HD12 LEU A 274 6.263 10.559 -1.655 1.00 1.36 H
-ATOM 368 HD13 LEU A 274 6.763 11.910 -2.674 1.00 1.38 H
-ATOM 369 HD21 LEU A 274 8.661 10.563 -2.460 1.00 1.55 H
-ATOM 370 HD22 LEU A 274 9.508 10.357 -0.928 1.00 1.59 H
-ATOM 371 HD23 LEU A 274 9.741 11.827 -1.873 1.00 1.60 H
-ATOM 372 N PRO A 275 6.756 14.181 2.806 1.00 0.59 N
-ATOM 373 CA PRO A 275 6.870 13.807 4.234 1.00 0.76 C
-ATOM 374 C PRO A 275 6.459 12.359 4.527 1.00 0.80 C
-ATOM 375 O PRO A 275 5.502 11.841 3.939 1.00 0.75 O
-ATOM 376 CB PRO A 275 5.903 14.771 4.924 1.00 0.87 C
-ATOM 377 CG PRO A 275 5.910 15.965 4.046 1.00 0.79 C
-ATOM 378 CD PRO A 275 5.948 15.420 2.650 1.00 0.55 C
-ATOM 379 HA PRO A 275 7.867 13.982 4.604 1.00 0.88 H
-ATOM 380 HB2 PRO A 275 4.921 14.325 4.984 1.00 0.91 H
-ATOM 381 HB3 PRO A 275 6.264 15.006 5.914 1.00 1.06 H
-ATOM 382 HG2 PRO A 275 5.017 16.551 4.204 1.00 0.89 H
-ATOM 383 HG3 PRO A 275 6.794 16.552 4.241 1.00 0.89 H
-ATOM 384 HD2 PRO A 275 4.952 15.199 2.300 1.00 0.55 H
-ATOM 385 HD3 PRO A 275 6.436 16.123 1.976 1.00 0.51 H
-ATOM 386 N LYS A 276 7.185 11.714 5.439 1.00 0.98 N
-ATOM 387 CA LYS A 276 6.906 10.326 5.825 1.00 1.09 C
-ATOM 388 C LYS A 276 7.118 10.124 7.327 1.00 1.29 C
-ATOM 389 O LYS A 276 7.853 10.936 7.933 1.00 1.38 O
-ATOM 390 CB LYS A 276 7.804 9.354 5.048 1.00 1.15 C
-ATOM 391 CG LYS A 276 7.456 9.245 3.574 1.00 1.05 C
-ATOM 392 CD LYS A 276 6.063 8.670 3.373 1.00 1.01 C
-ATOM 393 CE LYS A 276 5.567 8.908 1.959 1.00 0.93 C
-ATOM 394 NZ LYS A 276 5.482 10.361 1.647 1.00 0.78 N
-ATOM 395 OXT LYS A 276 6.549 9.160 7.881 1.00 1.43 O
-ATOM 396 H LYS A 276 7.929 12.182 5.875 1.00 1.08 H
-ATOM 397 HA LYS A 276 5.873 10.118 5.588 1.00 1.05 H
-ATOM 398 HB2 LYS A 276 8.828 9.686 5.129 1.00 1.23 H
-ATOM 399 HB3 LYS A 276 7.717 8.373 5.489 1.00 1.26 H
-ATOM 400 HG2 LYS A 276 7.495 10.229 3.130 1.00 0.96 H
-ATOM 401 HG3 LYS A 276 8.175 8.600 3.091 1.00 1.18 H
-ATOM 402 HD2 LYS A 276 6.092 7.607 3.560 1.00 1.16 H
-ATOM 403 HD3 LYS A 276 5.385 9.142 4.068 1.00 0.98 H
-ATOM 404 HE2 LYS A 276 6.250 8.438 1.266 1.00 1.06 H
-ATOM 405 HE3 LYS A 276 4.588 8.466 1.854 1.00 1.00 H
-ATOM 406 HZ1 LYS A 276 5.296 10.905 2.523 1.00 1.00 H
-ATOM 407 HZ2 LYS A 276 4.705 10.541 0.968 1.00 0.91 H
-ATOM 408 HZ3 LYS A 276 6.376 10.690 1.233 1.00 1.18 H
-TER
-ENDMDL
-MODEL 17
-ATOM 1 N ALA A 249 1.651 -11.307 -11.781 1.00 2.28 N
-ATOM 2 CA ALA A 249 2.257 -9.991 -11.436 1.00 1.79 C
-ATOM 3 C ALA A 249 1.777 -9.498 -10.069 1.00 1.37 C
-ATOM 4 O ALA A 249 0.681 -9.842 -9.627 1.00 1.54 O
-ATOM 5 CB ALA A 249 1.947 -8.961 -12.512 1.00 2.15 C
-ATOM 6 H1 ALA A 249 2.084 -11.683 -12.650 1.00 2.54 H
-ATOM 7 H2 ALA A 249 0.627 -11.201 -11.935 1.00 2.76 H
-ATOM 8 H3 ALA A 249 1.804 -11.987 -11.010 1.00 2.49 H
-ATOM 9 HA ALA A 249 3.329 -10.118 -11.398 1.00 1.76 H
-ATOM 10 HB1 ALA A 249 2.787 -8.887 -13.190 1.00 2.49 H
-ATOM 11 HB2 ALA A 249 1.776 -8.000 -12.050 1.00 2.49 H
-ATOM 12 HB3 ALA A 249 1.068 -9.262 -13.059 1.00 2.47 H
-ATOM 13 N PRO A 250 2.600 -8.687 -9.381 1.00 1.16 N
-ATOM 14 CA PRO A 250 2.282 -8.136 -8.055 1.00 1.10 C
-ATOM 15 C PRO A 250 1.165 -7.077 -8.090 1.00 0.84 C
-ATOM 16 O PRO A 250 1.410 -5.901 -7.821 1.00 0.92 O
-ATOM 17 CB PRO A 250 3.612 -7.496 -7.615 1.00 1.57 C
-ATOM 18 CG PRO A 250 4.637 -8.043 -8.541 1.00 1.77 C
-ATOM 19 CD PRO A 250 3.918 -8.233 -9.831 1.00 1.51 C
-ATOM 20 HA PRO A 250 2.011 -8.916 -7.359 1.00 1.34 H
-ATOM 21 HB2 PRO A 250 3.543 -6.423 -7.710 1.00 1.66 H
-ATOM 22 HB3 PRO A 250 3.826 -7.763 -6.596 1.00 1.89 H
-ATOM 23 HG2 PRO A 250 5.446 -7.336 -8.658 1.00 2.16 H
-ATOM 24 HG3 PRO A 250 5.006 -8.987 -8.172 1.00 1.91 H
-ATOM 25 HD2 PRO A 250 3.840 -7.293 -10.359 1.00 1.72 H
-ATOM 26 HD3 PRO A 250 4.400 -8.980 -10.433 1.00 1.67 H
-ATOM 27 N ALA A 251 -0.058 -7.502 -8.432 1.00 0.78 N
-ATOM 28 CA ALA A 251 -1.208 -6.589 -8.511 1.00 0.84 C
-ATOM 29 C ALA A 251 -1.422 -5.826 -7.197 1.00 0.70 C
-ATOM 30 O ALA A 251 -1.609 -4.609 -7.203 1.00 0.78 O
-ATOM 31 CB ALA A 251 -2.467 -7.355 -8.888 1.00 1.12 C
-ATOM 32 H ALA A 251 -0.187 -8.454 -8.649 1.00 0.89 H
-ATOM 33 HA ALA A 251 -1.005 -5.874 -9.298 1.00 0.98 H
-ATOM 34 HB1 ALA A 251 -2.587 -8.198 -8.224 1.00 1.22 H
-ATOM 35 HB2 ALA A 251 -2.384 -7.706 -9.907 1.00 1.53 H
-ATOM 36 HB3 ALA A 251 -3.323 -6.703 -8.803 1.00 1.76 H
-ATOM 37 N ARG A 252 -1.375 -6.548 -6.074 1.00 0.65 N
-ATOM 38 CA ARG A 252 -1.544 -5.941 -4.749 1.00 0.69 C
-ATOM 39 C ARG A 252 -0.473 -4.876 -4.497 1.00 0.54 C
-ATOM 40 O ARG A 252 -0.771 -3.770 -4.040 1.00 0.58 O
-ATOM 41 CB ARG A 252 -1.486 -7.034 -3.666 1.00 0.84 C
-ATOM 42 CG ARG A 252 -1.319 -6.523 -2.235 1.00 1.05 C
-ATOM 43 CD ARG A 252 -2.414 -5.540 -1.837 1.00 1.23 C
-ATOM 44 NE ARG A 252 -2.489 -5.344 -0.384 1.00 1.54 N
-ATOM 45 CZ ARG A 252 -2.913 -6.261 0.468 1.00 1.82 C
-ATOM 46 NH1 ARG A 252 -3.339 -7.420 0.034 1.00 1.84 N
-ATOM 47 NH2 ARG A 252 -2.923 -6.014 1.755 1.00 2.16 N
-ATOM 48 H ARG A 252 -1.211 -7.511 -6.140 1.00 0.70 H
-ATOM 49 HA ARG A 252 -2.514 -5.470 -4.720 1.00 0.85 H
-ATOM 50 HB2 ARG A 252 -2.401 -7.606 -3.708 1.00 0.99 H
-ATOM 51 HB3 ARG A 252 -0.657 -7.690 -3.887 1.00 0.80 H
-ATOM 52 HG2 ARG A 252 -1.348 -7.364 -1.559 1.00 1.26 H
-ATOM 53 HG3 ARG A 252 -0.360 -6.030 -2.153 1.00 1.02 H
-ATOM 54 HD2 ARG A 252 -2.207 -4.589 -2.307 1.00 1.16 H
-ATOM 55 HD3 ARG A 252 -3.360 -5.916 -2.190 1.00 1.35 H
-ATOM 56 HE ARG A 252 -2.198 -4.479 -0.032 1.00 1.61 H
-ATOM 57 HH11 ARG A 252 -3.343 -7.610 -0.944 1.00 1.66 H
-ATOM 58 HH12 ARG A 252 -3.658 -8.109 0.680 1.00 2.11 H
-ATOM 59 HH21 ARG A 252 -2.608 -5.129 2.093 1.00 2.21 H
-ATOM 60 HH22 ARG A 252 -3.244 -6.707 2.397 1.00 2.40 H
-ATOM 61 N VAL A 253 0.768 -5.217 -4.814 1.00 0.47 N
-ATOM 62 CA VAL A 253 1.887 -4.298 -4.639 1.00 0.47 C
-ATOM 63 C VAL A 253 1.748 -3.085 -5.558 1.00 0.37 C
-ATOM 64 O VAL A 253 1.868 -1.946 -5.107 1.00 0.38 O
-ATOM 65 CB VAL A 253 3.230 -5.007 -4.900 1.00 0.59 C
-ATOM 66 CG1 VAL A 253 4.399 -4.048 -4.723 1.00 1.48 C
-ATOM 67 CG2 VAL A 253 3.366 -6.206 -3.975 1.00 1.44 C
-ATOM 68 H VAL A 253 0.936 -6.105 -5.183 1.00 0.51 H
-ATOM 69 HA VAL A 253 1.877 -3.959 -3.615 1.00 0.58 H
-ATOM 70 HB VAL A 253 3.234 -5.365 -5.920 1.00 0.83 H
-ATOM 71 HG11 VAL A 253 5.129 -4.488 -4.061 1.00 2.04 H
-ATOM 72 HG12 VAL A 253 4.042 -3.119 -4.301 1.00 2.01 H
-ATOM 73 HG13 VAL A 253 4.855 -3.855 -5.683 1.00 2.03 H
-ATOM 74 HG21 VAL A 253 3.855 -5.900 -3.061 1.00 1.99 H
-ATOM 75 HG22 VAL A 253 3.951 -6.971 -4.458 1.00 1.99 H
-ATOM 76 HG23 VAL A 253 2.383 -6.593 -3.744 1.00 1.98 H
-ATOM 77 N GLY A 254 1.467 -3.340 -6.837 1.00 0.42 N
-ATOM 78 CA GLY A 254 1.287 -2.257 -7.797 1.00 0.52 C
-ATOM 79 C GLY A 254 0.187 -1.298 -7.370 1.00 0.50 C
-ATOM 80 O GLY A 254 0.391 -0.081 -7.332 1.00 0.51 O
-ATOM 81 H GLY A 254 1.364 -4.277 -7.128 1.00 0.47 H
-ATOM 82 HA2 GLY A 254 2.215 -1.710 -7.887 1.00 0.57 H
-ATOM 83 HA3 GLY A 254 1.032 -2.676 -8.759 1.00 0.67 H
-ATOM 84 N LEU A 255 -0.970 -1.853 -7.015 1.00 0.57 N
-ATOM 85 CA LEU A 255 -2.100 -1.047 -6.550 1.00 0.69 C
-ATOM 86 C LEU A 255 -1.731 -0.258 -5.302 1.00 0.60 C
-ATOM 87 O LEU A 255 -2.025 0.933 -5.188 1.00 0.66 O
-ATOM 88 CB LEU A 255 -3.295 -1.938 -6.240 1.00 0.88 C
-ATOM 89 CG LEU A 255 -4.301 -2.071 -7.371 1.00 1.10 C
-ATOM 90 CD1 LEU A 255 -5.437 -2.985 -6.952 1.00 1.29 C
-ATOM 91 CD2 LEU A 255 -4.825 -0.697 -7.764 1.00 1.26 C
-ATOM 92 H LEU A 255 -1.061 -2.834 -7.044 1.00 0.59 H
-ATOM 93 HA LEU A 255 -2.372 -0.363 -7.338 1.00 0.79 H
-ATOM 94 HB2 LEU A 255 -2.929 -2.925 -5.993 1.00 0.92 H
-ATOM 95 HB3 LEU A 255 -3.805 -1.535 -5.378 1.00 0.96 H
-ATOM 96 HG LEU A 255 -3.814 -2.505 -8.230 1.00 1.15 H
-ATOM 97 HD11 LEU A 255 -5.057 -3.984 -6.803 1.00 1.47 H
-ATOM 98 HD12 LEU A 255 -6.192 -2.996 -7.723 1.00 1.50 H
-ATOM 99 HD13 LEU A 255 -5.867 -2.622 -6.029 1.00 1.84 H
-ATOM 100 HD21 LEU A 255 -4.265 -0.325 -8.609 1.00 1.57 H
-ATOM 101 HD22 LEU A 255 -4.710 -0.018 -6.930 1.00 1.74 H
-ATOM 102 HD23 LEU A 255 -5.869 -0.770 -8.025 1.00 1.67 H
-ATOM 103 N GLY A 256 -1.089 -0.936 -4.362 1.00 0.54 N
-ATOM 104 CA GLY A 256 -0.693 -0.297 -3.130 1.00 0.60 C
-ATOM 105 C GLY A 256 0.287 0.850 -3.333 1.00 0.51 C
-ATOM 106 O GLY A 256 0.050 1.958 -2.854 1.00 0.59 O
-ATOM 107 H GLY A 256 -0.886 -1.889 -4.506 1.00 0.53 H
-ATOM 108 HA2 GLY A 256 -1.579 0.084 -2.645 1.00 0.75 H
-ATOM 109 HA3 GLY A 256 -0.238 -1.038 -2.491 1.00 0.65 H
-ATOM 110 N ILE A 257 1.388 0.604 -4.043 1.00 0.41 N
-ATOM 111 CA ILE A 257 2.373 1.660 -4.274 1.00 0.44 C
-ATOM 112 C ILE A 257 1.784 2.815 -5.092 1.00 0.38 C
-ATOM 113 O ILE A 257 2.044 3.978 -4.792 1.00 0.44 O
-ATOM 114 CB ILE A 257 3.679 1.136 -4.929 1.00 0.56 C
-ATOM 115 CG1 ILE A 257 3.422 0.481 -6.291 1.00 0.56 C
-ATOM 116 CG2 ILE A 257 4.378 0.158 -3.998 1.00 0.71 C
-ATOM 117 CD1 ILE A 257 3.762 1.370 -7.469 1.00 0.73 C
-ATOM 118 H ILE A 257 1.539 -0.298 -4.414 1.00 0.40 H
-ATOM 119 HA ILE A 257 2.636 2.052 -3.300 1.00 0.55 H
-ATOM 120 HB ILE A 257 4.338 1.981 -5.068 1.00 0.67 H
-ATOM 121 HG12 ILE A 257 4.020 -0.414 -6.371 1.00 0.66 H
-ATOM 122 HG13 ILE A 257 2.376 0.216 -6.364 1.00 0.52 H
-ATOM 123 HG21 ILE A 257 3.640 -0.435 -3.477 1.00 1.24 H
-ATOM 124 HG22 ILE A 257 4.972 0.705 -3.280 1.00 1.29 H
-ATOM 125 HG23 ILE A 257 5.020 -0.493 -4.574 1.00 1.26 H
-ATOM 126 HD11 ILE A 257 3.317 2.344 -7.325 1.00 1.23 H
-ATOM 127 HD12 ILE A 257 3.376 0.929 -8.377 1.00 1.31 H
-ATOM 128 HD13 ILE A 257 4.834 1.473 -7.546 1.00 1.30 H
-ATOM 129 N THR A 258 0.974 2.501 -6.107 1.00 0.39 N
-ATOM 130 CA THR A 258 0.355 3.546 -6.929 1.00 0.51 C
-ATOM 131 C THR A 258 -0.627 4.394 -6.120 1.00 0.53 C
-ATOM 132 O THR A 258 -0.603 5.624 -6.203 1.00 0.55 O
-ATOM 133 CB THR A 258 -0.374 2.975 -8.165 1.00 0.68 C
-ATOM 134 OG1 THR A 258 -1.249 1.909 -7.781 1.00 0.69 O
-ATOM 135 CG2 THR A 258 0.620 2.481 -9.205 1.00 0.78 C
-ATOM 136 H THR A 258 0.783 1.554 -6.300 1.00 0.41 H
-ATOM 137 HA THR A 258 1.149 4.190 -7.282 1.00 0.55 H
-ATOM 138 HB THR A 258 -0.964 3.766 -8.606 1.00 0.81 H
-ATOM 139 HG1 THR A 258 -0.741 1.087 -7.700 1.00 0.78 H
-ATOM 140 HG21 THR A 258 1.364 1.862 -8.727 1.00 1.21 H
-ATOM 141 HG22 THR A 258 1.102 3.326 -9.673 1.00 1.23 H
-ATOM 142 HG23 THR A 258 0.099 1.903 -9.955 1.00 1.31 H
-ATOM 143 N THR A 259 -1.477 3.745 -5.322 1.00 0.60 N
-ATOM 144 CA THR A 259 -2.442 4.472 -4.497 1.00 0.74 C
-ATOM 145 C THR A 259 -1.727 5.320 -3.453 1.00 0.70 C
-ATOM 146 O THR A 259 -2.063 6.488 -3.262 1.00 0.77 O
-ATOM 147 CB THR A 259 -3.449 3.535 -3.798 1.00 0.90 C
-ATOM 148 OG1 THR A 259 -2.761 2.468 -3.136 1.00 0.84 O
-ATOM 149 CG2 THR A 259 -4.444 2.967 -4.799 1.00 1.04 C
-ATOM 150 H THR A 259 -1.446 2.763 -5.277 1.00 0.60 H
-ATOM 151 HA THR A 259 -2.996 5.131 -5.151 1.00 0.82 H
-ATOM 152 HB THR A 259 -3.994 4.108 -3.061 1.00 1.03 H
-ATOM 153 HG1 THR A 259 -2.535 1.780 -3.780 1.00 0.84 H
-ATOM 154 HG21 THR A 259 -4.423 3.557 -5.703 1.00 1.43 H
-ATOM 155 HG22 THR A 259 -5.437 2.995 -4.375 1.00 1.55 H
-ATOM 156 HG23 THR A 259 -4.180 1.945 -5.029 1.00 1.35 H
-ATOM 157 N VAL A 260 -0.718 4.742 -2.798 1.00 0.63 N
-ATOM 158 CA VAL A 260 0.056 5.475 -1.800 1.00 0.70 C
-ATOM 159 C VAL A 260 0.756 6.679 -2.438 1.00 0.60 C
-ATOM 160 O VAL A 260 0.613 7.806 -1.961 1.00 0.69 O
-ATOM 161 CB VAL A 260 1.094 4.568 -1.107 1.00 0.76 C
-ATOM 162 CG1 VAL A 260 2.037 5.385 -0.236 1.00 0.96 C
-ATOM 163 CG2 VAL A 260 0.393 3.505 -0.276 1.00 0.91 C
-ATOM 164 H VAL A 260 -0.480 3.807 -3.006 1.00 0.59 H
-ATOM 165 HA VAL A 260 -0.635 5.835 -1.049 1.00 0.84 H
-ATOM 166 HB VAL A 260 1.678 4.073 -1.868 1.00 0.68 H
-ATOM 167 HG11 VAL A 260 2.836 5.781 -0.846 1.00 1.25 H
-ATOM 168 HG12 VAL A 260 2.451 4.755 0.537 1.00 1.45 H
-ATOM 169 HG13 VAL A 260 1.492 6.201 0.215 1.00 1.44 H
-ATOM 170 HG21 VAL A 260 -0.488 3.930 0.183 1.00 1.38 H
-ATOM 171 HG22 VAL A 260 1.062 3.147 0.492 1.00 1.04 H
-ATOM 172 HG23 VAL A 260 0.104 2.683 -0.913 1.00 1.27 H
-ATOM 173 N LEU A 261 1.485 6.440 -3.535 1.00 0.48 N
-ATOM 174 CA LEU A 261 2.179 7.517 -4.250 1.00 0.48 C
-ATOM 175 C LEU A 261 1.197 8.601 -4.699 1.00 0.49 C
-ATOM 176 O LEU A 261 1.505 9.788 -4.640 1.00 0.54 O
-ATOM 177 CB LEU A 261 2.937 6.964 -5.455 1.00 0.53 C
-ATOM 178 CG LEU A 261 4.171 6.128 -5.108 1.00 0.68 C
-ATOM 179 CD1 LEU A 261 4.887 5.692 -6.373 1.00 0.84 C
-ATOM 180 CD2 LEU A 261 5.108 6.916 -4.201 1.00 0.86 C
-ATOM 181 H LEU A 261 1.544 5.519 -3.883 1.00 0.46 H
-ATOM 182 HA LEU A 261 2.894 7.954 -3.568 1.00 0.57 H
-ATOM 183 HB2 LEU A 261 2.260 6.349 -6.029 1.00 0.52 H
-ATOM 184 HB3 LEU A 261 3.253 7.794 -6.069 1.00 0.62 H
-ATOM 185 HG LEU A 261 3.859 5.241 -4.579 1.00 0.68 H
-ATOM 186 HD11 LEU A 261 5.119 4.639 -6.310 1.00 1.37 H
-ATOM 187 HD12 LEU A 261 5.801 6.256 -6.482 1.00 1.43 H
-ATOM 188 HD13 LEU A 261 4.250 5.870 -7.227 1.00 1.26 H
-ATOM 189 HD21 LEU A 261 5.783 7.505 -4.803 1.00 1.38 H
-ATOM 190 HD22 LEU A 261 5.675 6.231 -3.588 1.00 1.43 H
-ATOM 191 HD23 LEU A 261 4.527 7.569 -3.566 1.00 1.29 H
-ATOM 192 N THR A 262 0.010 8.182 -5.132 1.00 0.54 N
-ATOM 193 CA THR A 262 -1.029 9.118 -5.568 1.00 0.68 C
-ATOM 194 C THR A 262 -1.514 9.968 -4.393 1.00 0.74 C
-ATOM 195 O THR A 262 -1.593 11.194 -4.482 1.00 0.80 O
-ATOM 196 CB THR A 262 -2.239 8.372 -6.176 1.00 0.83 C
-ATOM 197 OG1 THR A 262 -1.827 7.610 -7.317 1.00 0.84 O
-ATOM 198 CG2 THR A 262 -3.337 9.342 -6.581 1.00 1.03 C
-ATOM 199 H THR A 262 -0.180 7.214 -5.147 1.00 0.54 H
-ATOM 200 HA THR A 262 -0.607 9.765 -6.324 1.00 0.70 H
-ATOM 201 HB THR A 262 -2.635 7.699 -5.429 1.00 0.85 H
-ATOM 202 HG1 THR A 262 -1.322 6.837 -7.020 1.00 0.75 H
-ATOM 203 HG21 THR A 262 -3.061 9.837 -7.500 1.00 1.46 H
-ATOM 204 HG22 THR A 262 -3.472 10.078 -5.801 1.00 1.53 H
-ATOM 205 HG23 THR A 262 -4.260 8.800 -6.726 1.00 1.47 H
-ATOM 206 N MET A 263 -1.831 9.301 -3.286 1.00 0.77 N
-ATOM 207 CA MET A 263 -2.302 9.976 -2.078 1.00 0.90 C
-ATOM 208 C MET A 263 -1.245 10.945 -1.524 1.00 0.84 C
-ATOM 209 O MET A 263 -1.537 12.113 -1.272 1.00 0.97 O
-ATOM 210 CB MET A 263 -2.674 8.940 -1.014 1.00 1.00 C
-ATOM 211 CG MET A 263 -3.269 9.544 0.249 1.00 1.15 C
-ATOM 212 SD MET A 263 -3.684 8.295 1.480 1.00 1.25 S
-ATOM 213 CE MET A 263 -4.309 9.328 2.802 1.00 1.42 C
-ATOM 214 H MET A 263 -1.743 8.319 -3.281 1.00 0.75 H
-ATOM 215 HA MET A 263 -3.184 10.540 -2.340 1.00 1.00 H
-ATOM 216 HB2 MET A 263 -3.397 8.256 -1.433 1.00 1.07 H
-ATOM 217 HB3 MET A 263 -1.788 8.389 -0.741 1.00 0.96 H
-ATOM 218 HG2 MET A 263 -2.552 10.226 0.678 1.00 1.20 H
-ATOM 219 HG3 MET A 263 -4.166 10.082 -0.014 1.00 1.26 H
-ATOM 220 HE1 MET A 263 -3.539 10.018 3.115 1.00 1.87 H
-ATOM 221 HE2 MET A 263 -4.596 8.709 3.639 1.00 1.71 H
-ATOM 222 HE3 MET A 263 -5.168 9.881 2.453 1.00 1.74 H
-ATOM 223 N THR A 264 -0.012 10.467 -1.347 1.00 0.71 N
-ATOM 224 CA THR A 264 1.062 11.326 -0.832 1.00 0.72 C
-ATOM 225 C THR A 264 1.882 11.954 -1.970 1.00 0.56 C
-ATOM 226 O THR A 264 3.108 12.044 -1.899 1.00 0.59 O
-ATOM 227 CB THR A 264 2.004 10.568 0.142 1.00 0.84 C
-ATOM 228 OG1 THR A 264 2.986 11.466 0.687 1.00 0.92 O
-ATOM 229 CG2 THR A 264 2.711 9.402 -0.539 1.00 0.82 C
-ATOM 230 H THR A 264 0.181 9.525 -1.574 1.00 0.67 H
-ATOM 231 HA THR A 264 0.591 12.126 -0.278 1.00 0.85 H
-ATOM 232 HB THR A 264 1.407 10.177 0.954 1.00 1.00 H
-ATOM 233 HG1 THR A 264 3.498 11.861 -0.041 1.00 0.85 H
-ATOM 234 HG21 THR A 264 3.532 9.778 -1.133 1.00 1.31 H
-ATOM 235 HG22 THR A 264 2.012 8.882 -1.177 1.00 1.30 H
-ATOM 236 HG23 THR A 264 3.089 8.724 0.210 1.00 1.33 H
-ATOM 237 N THR A 265 1.189 12.395 -3.019 1.00 0.53 N
-ATOM 238 CA THR A 265 1.840 13.016 -4.182 1.00 0.50 C
-ATOM 239 C THR A 265 2.603 14.296 -3.815 1.00 0.47 C
-ATOM 240 O THR A 265 3.609 14.629 -4.444 1.00 0.54 O
-ATOM 241 CB THR A 265 0.822 13.345 -5.302 1.00 0.62 C
-ATOM 242 OG1 THR A 265 1.468 14.054 -6.369 1.00 0.70 O
-ATOM 243 CG2 THR A 265 -0.340 14.174 -4.768 1.00 0.73 C
-ATOM 244 H THR A 265 0.211 12.295 -3.017 1.00 0.63 H
-ATOM 245 HA THR A 265 2.546 12.300 -4.578 1.00 0.51 H
-ATOM 246 HB THR A 265 0.430 12.414 -5.689 1.00 0.67 H
-ATOM 247 HG1 THR A 265 2.347 14.334 -6.087 1.00 1.20 H
-ATOM 248 HG21 THR A 265 -1.263 13.815 -5.196 1.00 1.26 H
-ATOM 249 HG22 THR A 265 -0.196 15.210 -5.036 1.00 1.21 H
-ATOM 250 HG23 THR A 265 -0.383 14.084 -3.692 1.00 1.34 H
-ATOM 251 N GLN A 266 2.117 15.013 -2.808 1.00 0.46 N
-ATOM 252 CA GLN A 266 2.746 16.259 -2.363 1.00 0.49 C
-ATOM 253 C GLN A 266 3.965 16.003 -1.462 1.00 0.49 C
-ATOM 254 O GLN A 266 4.013 16.477 -0.326 1.00 0.52 O
-ATOM 255 CB GLN A 266 1.721 17.123 -1.622 1.00 0.55 C
-ATOM 256 CG GLN A 266 0.561 17.580 -2.492 1.00 0.62 C
-ATOM 257 CD GLN A 266 0.954 18.650 -3.492 1.00 0.74 C
-ATOM 258 OE1 GLN A 266 1.793 18.434 -4.360 1.00 0.83 O
-ATOM 259 NE2 GLN A 266 0.347 19.817 -3.377 1.00 0.95 N
-ATOM 260 H GLN A 266 1.308 14.699 -2.352 1.00 0.49 H
-ATOM 261 HA GLN A 266 3.071 16.792 -3.244 1.00 0.52 H
-ATOM 262 HB2 GLN A 266 1.321 16.555 -0.796 1.00 0.59 H
-ATOM 263 HB3 GLN A 266 2.219 17.999 -1.236 1.00 0.61 H
-ATOM 264 HG2 GLN A 266 0.180 16.728 -3.036 1.00 0.66 H
-ATOM 265 HG3 GLN A 266 -0.216 17.973 -1.853 1.00 0.71 H
-ATOM 266 HE21 GLN A 266 -0.315 19.928 -2.664 1.00 1.02 H
-ATOM 267 HE22 GLN A 266 0.587 20.523 -4.011 1.00 1.12 H
-ATOM 268 N SER A 267 4.940 15.248 -1.975 1.00 0.54 N
-ATOM 269 CA SER A 267 6.163 14.922 -1.221 1.00 0.60 C
-ATOM 270 C SER A 267 5.841 14.103 0.044 1.00 0.58 C
-ATOM 271 O SER A 267 5.211 13.047 -0.037 1.00 0.74 O
-ATOM 272 CB SER A 267 6.935 16.207 -0.863 1.00 0.68 C
-ATOM 273 OG SER A 267 8.191 15.912 -0.271 1.00 0.81 O
-ATOM 274 H SER A 267 4.836 14.899 -2.890 1.00 0.57 H
-ATOM 275 HA SER A 267 6.784 14.316 -1.865 1.00 0.67 H
-ATOM 276 HB2 SER A 267 7.103 16.783 -1.761 1.00 0.75 H
-ATOM 277 HB3 SER A 267 6.351 16.790 -0.167 1.00 0.69 H
-ATOM 278 HG SER A 267 8.872 16.465 -0.667 1.00 1.10 H
-ATOM 279 N SER A 268 6.284 14.579 1.209 1.00 0.57 N
-ATOM 280 CA SER A 268 6.043 13.878 2.480 1.00 0.61 C
-ATOM 281 C SER A 268 6.175 14.824 3.675 1.00 0.75 C
-ATOM 282 O SER A 268 6.941 15.785 3.637 1.00 1.21 O
-ATOM 283 CB SER A 268 7.014 12.703 2.654 1.00 0.89 C
-ATOM 284 OG SER A 268 6.732 11.659 1.736 1.00 1.04 O
-ATOM 285 H SER A 268 6.795 15.419 1.218 1.00 0.66 H
-ATOM 286 HA SER A 268 5.035 13.495 2.455 1.00 0.56 H
-ATOM 287 HB2 SER A 268 8.024 13.045 2.487 1.00 1.07 H
-ATOM 288 HB3 SER A 268 6.927 12.315 3.659 1.00 0.97 H
-ATOM 289 HG SER A 268 6.270 12.026 0.968 1.00 1.06 H
-ATOM 290 N GLY A 269 5.424 14.540 4.737 1.00 0.77 N
-ATOM 291 CA GLY A 269 5.474 15.372 5.932 1.00 0.97 C
-ATOM 292 C GLY A 269 4.717 14.768 7.108 1.00 0.88 C
-ATOM 293 O GLY A 269 4.846 13.579 7.387 1.00 0.90 O
-ATOM 294 H GLY A 269 4.835 13.758 4.713 1.00 0.95 H
-ATOM 295 HA2 GLY A 269 6.507 15.507 6.217 1.00 1.21 H
-ATOM 296 HA3 GLY A 269 5.048 16.338 5.702 1.00 1.13 H
-ATOM 297 N SER A 270 3.928 15.590 7.798 1.00 0.98 N
-ATOM 298 CA SER A 270 3.145 15.134 8.959 1.00 1.04 C
-ATOM 299 C SER A 270 1.969 14.229 8.562 1.00 0.97 C
-ATOM 300 O SER A 270 1.341 13.617 9.427 1.00 1.12 O
-ATOM 301 CB SER A 270 2.615 16.336 9.748 1.00 1.29 C
-ATOM 302 OG SER A 270 1.794 15.921 10.830 1.00 1.42 O
-ATOM 303 H SER A 270 3.870 16.531 7.526 1.00 1.12 H
-ATOM 304 HA SER A 270 3.810 14.571 9.596 1.00 1.11 H
-ATOM 305 HB2 SER A 270 3.447 16.899 10.144 1.00 1.43 H
-ATOM 306 HB3 SER A 270 2.033 16.967 9.092 1.00 1.39 H
-ATOM 307 HG SER A 270 1.421 15.048 10.639 1.00 1.50 H
-ATOM 308 N ARG A 271 1.665 14.171 7.262 1.00 0.88 N
-ATOM 309 CA ARG A 271 0.550 13.362 6.744 1.00 0.92 C
-ATOM 310 C ARG A 271 -0.798 13.874 7.267 1.00 1.11 C
-ATOM 311 O ARG A 271 -0.991 15.079 7.421 1.00 1.19 O
-ATOM 312 CB ARG A 271 0.718 11.872 7.100 1.00 1.01 C
-ATOM 313 CG ARG A 271 1.525 11.063 6.093 1.00 0.92 C
-ATOM 314 CD ARG A 271 3.001 11.431 6.101 1.00 0.80 C
-ATOM 315 NE ARG A 271 3.754 10.706 5.075 1.00 0.85 N
-ATOM 316 CZ ARG A 271 3.620 10.911 3.778 1.00 0.79 C
-ATOM 317 NH1 ARG A 271 2.840 11.856 3.331 1.00 0.68 N
-ATOM 318 NH2 ARG A 271 4.282 10.185 2.921 1.00 1.01 N
-ATOM 319 H ARG A 271 2.196 14.700 6.634 1.00 0.89 H
-ATOM 320 HA ARG A 271 0.554 13.461 5.669 1.00 0.86 H
-ATOM 321 HB2 ARG A 271 1.213 11.800 8.058 1.00 1.09 H
-ATOM 322 HB3 ARG A 271 -0.262 11.426 7.182 1.00 1.22 H
-ATOM 323 HG2 ARG A 271 1.430 10.015 6.334 1.00 1.17 H
-ATOM 324 HG3 ARG A 271 1.125 11.241 5.104 1.00 0.89 H
-ATOM 325 HD2 ARG A 271 3.095 12.491 5.922 1.00 0.72 H
-ATOM 326 HD3 ARG A 271 3.412 11.193 7.071 1.00 1.00 H
-ATOM 327 HE ARG A 271 4.375 10.014 5.376 1.00 1.06 H
-ATOM 328 HH11 ARG A 271 2.338 12.432 3.966 1.00 0.65 H
-ATOM 329 HH12 ARG A 271 2.747 11.994 2.336 1.00 0.78 H
-ATOM 330 HH21 ARG A 271 4.897 9.469 3.238 1.00 1.20 H
-ATOM 331 HH22 ARG A 271 4.171 10.354 1.939 1.00 1.08 H
-ATOM 332 N ALA A 272 -1.718 12.941 7.535 1.00 1.28 N
-ATOM 333 CA ALA A 272 -3.055 13.266 8.048 1.00 1.53 C
-ATOM 334 C ALA A 272 -3.793 14.283 7.168 1.00 1.42 C
-ATOM 335 O ALA A 272 -4.492 15.164 7.675 1.00 1.62 O
-ATOM 336 CB ALA A 272 -2.960 13.769 9.481 1.00 1.80 C
-ATOM 337 H ALA A 272 -1.487 12.001 7.390 1.00 1.30 H
-ATOM 338 HA ALA A 272 -3.627 12.349 8.061 1.00 1.67 H
-ATOM 339 HB1 ALA A 272 -2.446 14.719 9.496 1.00 2.12 H
-ATOM 340 HB2 ALA A 272 -2.413 13.055 10.079 1.00 2.14 H
-ATOM 341 HB3 ALA A 272 -3.953 13.893 9.887 1.00 2.11 H
-ATOM 342 N SER A 273 -3.641 14.154 5.849 1.00 1.15 N
-ATOM 343 CA SER A 273 -4.299 15.054 4.891 1.00 1.07 C
-ATOM 344 C SER A 273 -4.218 14.503 3.481 1.00 0.85 C
-ATOM 345 O SER A 273 -3.911 13.327 3.276 1.00 0.89 O
-ATOM 346 CB SER A 273 -3.682 16.458 4.917 1.00 1.13 C
-ATOM 347 OG SER A 273 -4.013 17.156 6.105 1.00 1.45 O
-ATOM 348 H SER A 273 -3.078 13.427 5.505 1.00 1.07 H
-ATOM 349 HA SER A 273 -5.348 15.128 5.155 1.00 1.23 H
-ATOM 350 HB2 SER A 273 -2.608 16.377 4.851 1.00 1.09 H
-ATOM 351 HB3 SER A 273 -4.052 17.021 4.071 1.00 1.11 H
-ATOM 352 HG SER A 273 -4.181 16.517 6.817 1.00 1.50 H
-ATOM 353 N LEU A 274 -4.537 15.366 2.521 1.00 0.79 N
-ATOM 354 CA LEU A 274 -4.560 15.004 1.106 1.00 0.74 C
-ATOM 355 C LEU A 274 -5.608 13.909 0.792 1.00 0.76 C
-ATOM 356 O LEU A 274 -5.355 13.059 -0.062 1.00 0.87 O
-ATOM 357 CB LEU A 274 -3.170 14.537 0.648 1.00 0.80 C
-ATOM 358 CG LEU A 274 -2.045 15.563 0.807 1.00 0.96 C
-ATOM 359 CD1 LEU A 274 -0.710 14.950 0.417 1.00 1.11 C
-ATOM 360 CD2 LEU A 274 -2.326 16.801 -0.031 1.00 1.23 C
-ATOM 361 H LEU A 274 -4.799 16.275 2.775 1.00 0.92 H
-ATOM 362 HA LEU A 274 -4.823 15.892 0.551 1.00 0.85 H
-ATOM 363 HB2 LEU A 274 -2.907 13.657 1.216 1.00 0.80 H
-ATOM 364 HB3 LEU A 274 -3.232 14.265 -0.395 1.00 0.94 H
-ATOM 365 HG LEU A 274 -1.984 15.864 1.842 1.00 0.99 H
-ATOM 366 HD11 LEU A 274 -0.008 15.734 0.176 1.00 1.48 H
-ATOM 367 HD12 LEU A 274 -0.845 14.311 -0.443 1.00 1.59 H
-ATOM 368 HD13 LEU A 274 -0.328 14.365 1.242 1.00 1.55 H
-ATOM 369 HD21 LEU A 274 -3.168 16.613 -0.680 1.00 1.53 H
-ATOM 370 HD22 LEU A 274 -1.457 17.035 -0.629 1.00 1.64 H
-ATOM 371 HD23 LEU A 274 -2.550 17.633 0.619 1.00 1.79 H
-ATOM 372 N PRO A 275 -6.801 13.884 1.470 1.00 0.85 N
-ATOM 373 CA PRO A 275 -7.830 12.854 1.207 1.00 1.03 C
-ATOM 374 C PRO A 275 -8.197 12.734 -0.279 1.00 1.04 C
-ATOM 375 O PRO A 275 -8.489 13.732 -0.940 1.00 1.08 O
-ATOM 376 CB PRO A 275 -9.032 13.349 2.012 1.00 1.16 C
-ATOM 377 CG PRO A 275 -8.420 14.088 3.140 1.00 1.17 C
-ATOM 378 CD PRO A 275 -7.245 14.809 2.546 1.00 0.96 C
-ATOM 379 HA PRO A 275 -7.522 11.890 1.583 1.00 1.18 H
-ATOM 380 HB2 PRO A 275 -9.645 13.994 1.398 1.00 1.13 H
-ATOM 381 HB3 PRO A 275 -9.613 12.508 2.359 1.00 1.37 H
-ATOM 382 HG2 PRO A 275 -9.129 14.789 3.557 1.00 1.30 H
-ATOM 383 HG3 PRO A 275 -8.087 13.388 3.891 1.00 1.30 H
-ATOM 384 HD2 PRO A 275 -7.545 15.764 2.148 1.00 1.01 H
-ATOM 385 HD3 PRO A 275 -6.461 14.935 3.291 1.00 1.01 H
-ATOM 386 N LYS A 276 -8.175 11.506 -0.795 1.00 1.25 N
-ATOM 387 CA LYS A 276 -8.498 11.258 -2.200 1.00 1.41 C
-ATOM 388 C LYS A 276 -8.942 9.808 -2.423 1.00 1.82 C
-ATOM 389 O LYS A 276 -9.811 9.583 -3.303 1.00 2.00 O
-ATOM 390 CB LYS A 276 -7.283 11.578 -3.083 1.00 1.55 C
-ATOM 391 CG LYS A 276 -7.509 11.288 -4.558 1.00 1.85 C
-ATOM 392 CD LYS A 276 -8.698 12.061 -5.104 1.00 1.79 C
-ATOM 393 CE LYS A 276 -9.351 11.323 -6.260 1.00 2.03 C
-ATOM 394 NZ LYS A 276 -9.870 9.989 -5.839 1.00 2.16 N
-ATOM 395 OXT LYS A 276 -8.420 8.914 -1.723 1.00 2.09 O
-ATOM 396 H LYS A 276 -7.931 10.748 -0.223 1.00 1.42 H
-ATOM 397 HA LYS A 276 -9.310 11.916 -2.473 1.00 1.36 H
-ATOM 398 HB2 LYS A 276 -7.041 12.625 -2.976 1.00 1.49 H
-ATOM 399 HB3 LYS A 276 -6.443 10.987 -2.747 1.00 1.73 H
-ATOM 400 HG2 LYS A 276 -6.625 11.572 -5.109 1.00 2.09 H
-ATOM 401 HG3 LYS A 276 -7.689 10.231 -4.683 1.00 2.03 H
-ATOM 402 HD2 LYS A 276 -9.424 12.190 -4.316 1.00 1.63 H
-ATOM 403 HD3 LYS A 276 -8.362 13.028 -5.448 1.00 1.92 H
-ATOM 404 HE2 LYS A 276 -10.170 11.917 -6.635 1.00 2.10 H
-ATOM 405 HE3 LYS A 276 -8.619 11.184 -7.041 1.00 2.24 H
-ATOM 406 HZ1 LYS A 276 -9.299 9.233 -6.262 1.00 2.39 H
-ATOM 407 HZ2 LYS A 276 -10.857 9.878 -6.143 1.00 2.31 H
-ATOM 408 HZ3 LYS A 276 -9.828 9.896 -4.787 1.00 2.05 H
-TER
-ENDMDL
-MODEL 18
-ATOM 1 N ALA A 249 5.603 -10.030 0.927 1.00 3.16 N
-ATOM 2 CA ALA A 249 5.263 -8.800 0.161 1.00 2.80 C
-ATOM 3 C ALA A 249 3.761 -8.717 -0.104 1.00 2.34 C
-ATOM 4 O ALA A 249 3.078 -9.737 -0.153 1.00 2.34 O
-ATOM 5 CB ALA A 249 6.037 -8.755 -1.148 1.00 2.94 C
-ATOM 6 H1 ALA A 249 5.184 -10.864 0.465 1.00 3.09 H
-ATOM 7 H2 ALA A 249 5.231 -9.965 1.896 1.00 3.58 H
-ATOM 8 H3 ALA A 249 6.634 -10.153 0.970 1.00 3.46 H
-ATOM 9 HA ALA A 249 5.558 -7.945 0.752 1.00 2.87 H
-ATOM 10 HB1 ALA A 249 5.496 -9.301 -1.905 1.00 3.19 H
-ATOM 11 HB2 ALA A 249 7.011 -9.199 -1.007 1.00 3.14 H
-ATOM 12 HB3 ALA A 249 6.152 -7.726 -1.460 1.00 3.14 H
-ATOM 13 N PRO A 250 3.229 -7.496 -0.270 1.00 2.07 N
-ATOM 14 CA PRO A 250 1.799 -7.272 -0.525 1.00 1.73 C
-ATOM 15 C PRO A 250 1.342 -7.844 -1.870 1.00 1.46 C
-ATOM 16 O PRO A 250 1.983 -7.618 -2.898 1.00 1.46 O
-ATOM 17 CB PRO A 250 1.684 -5.739 -0.532 1.00 1.74 C
-ATOM 18 CG PRO A 250 2.903 -5.259 0.165 1.00 2.10 C
-ATOM 19 CD PRO A 250 3.967 -6.228 -0.222 1.00 2.25 C
-ATOM 20 HA PRO A 250 1.186 -7.678 0.265 1.00 1.84 H
-ATOM 21 HB2 PRO A 250 1.654 -5.385 -1.550 1.00 1.61 H
-ATOM 22 HB3 PRO A 250 0.792 -5.439 -0.010 1.00 1.81 H
-ATOM 23 HG2 PRO A 250 3.155 -4.263 -0.172 1.00 2.22 H
-ATOM 24 HG3 PRO A 250 2.751 -5.272 1.234 1.00 2.27 H
-ATOM 25 HD2 PRO A 250 4.367 -5.979 -1.193 1.00 2.28 H
-ATOM 26 HD3 PRO A 250 4.743 -6.260 0.521 1.00 2.55 H
-ATOM 27 N ALA A 251 0.231 -8.587 -1.858 1.00 1.39 N
-ATOM 28 CA ALA A 251 -0.308 -9.186 -3.081 1.00 1.26 C
-ATOM 29 C ALA A 251 -0.568 -8.119 -4.147 1.00 0.99 C
-ATOM 30 O ALA A 251 -0.144 -8.248 -5.294 1.00 1.00 O
-ATOM 31 CB ALA A 251 -1.584 -9.957 -2.774 1.00 1.42 C
-ATOM 32 H ALA A 251 -0.235 -8.735 -1.008 1.00 1.54 H
-ATOM 33 HA ALA A 251 0.424 -9.886 -3.459 1.00 1.39 H
-ATOM 34 HB1 ALA A 251 -1.392 -11.017 -2.848 1.00 1.78 H
-ATOM 35 HB2 ALA A 251 -2.351 -9.680 -3.482 1.00 1.79 H
-ATOM 36 HB3 ALA A 251 -1.915 -9.721 -1.773 1.00 1.79 H
-ATOM 37 N ARG A 252 -1.245 -7.047 -3.754 1.00 0.89 N
-ATOM 38 CA ARG A 252 -1.528 -5.952 -4.674 1.00 0.71 C
-ATOM 39 C ARG A 252 -0.468 -4.856 -4.578 1.00 0.67 C
-ATOM 40 O ARG A 252 -0.779 -3.688 -4.348 1.00 0.64 O
-ATOM 41 CB ARG A 252 -2.925 -5.384 -4.435 1.00 0.81 C
-ATOM 42 CG ARG A 252 -4.019 -6.271 -5.001 1.00 0.95 C
-ATOM 43 CD ARG A 252 -3.663 -6.731 -6.409 1.00 0.92 C
-ATOM 44 NE ARG A 252 -4.695 -7.585 -6.993 1.00 1.09 N
-ATOM 45 CZ ARG A 252 -4.561 -8.217 -8.140 1.00 1.21 C
-ATOM 46 NH1 ARG A 252 -3.444 -8.131 -8.815 1.00 1.27 N
-ATOM 47 NH2 ARG A 252 -5.541 -8.946 -8.605 1.00 1.38 N
-ATOM 48 H ARG A 252 -1.544 -6.982 -2.823 1.00 1.04 H
-ATOM 49 HA ARG A 252 -1.493 -6.362 -5.673 1.00 0.71 H
-ATOM 50 HB2 ARG A 252 -3.085 -5.280 -3.371 1.00 0.94 H
-ATOM 51 HB3 ARG A 252 -2.996 -4.414 -4.902 1.00 0.81 H
-ATOM 52 HG2 ARG A 252 -4.136 -7.136 -4.365 1.00 1.07 H
-ATOM 53 HG3 ARG A 252 -4.943 -5.714 -5.035 1.00 1.08 H
-ATOM 54 HD2 ARG A 252 -3.530 -5.860 -7.035 1.00 0.92 H
-ATOM 55 HD3 ARG A 252 -2.732 -7.282 -6.367 1.00 0.92 H
-ATOM 56 HE ARG A 252 -5.534 -7.682 -6.502 1.00 1.20 H
-ATOM 57 HH11 ARG A 252 -2.689 -7.585 -8.460 1.00 1.20 H
-ATOM 58 HH12 ARG A 252 -3.348 -8.610 -9.683 1.00 1.46 H
-ATOM 59 HH21 ARG A 252 -6.391 -9.026 -8.091 1.00 1.44 H
-ATOM 60 HH22 ARG A 252 -5.436 -9.425 -9.474 1.00 1.51 H
-ATOM 61 N VAL A 253 0.785 -5.262 -4.761 1.00 0.79 N
-ATOM 62 CA VAL A 253 1.935 -4.358 -4.706 1.00 0.90 C
-ATOM 63 C VAL A 253 1.738 -3.109 -5.573 1.00 0.81 C
-ATOM 64 O VAL A 253 1.808 -1.983 -5.079 1.00 0.75 O
-ATOM 65 CB VAL A 253 3.214 -5.104 -5.148 1.00 1.18 C
-ATOM 66 CG1 VAL A 253 2.991 -5.865 -6.453 1.00 1.24 C
-ATOM 67 CG2 VAL A 253 4.391 -4.150 -5.268 1.00 1.38 C
-ATOM 68 H VAL A 253 0.947 -6.216 -4.937 1.00 0.87 H
-ATOM 69 HA VAL A 253 2.065 -4.051 -3.682 1.00 0.92 H
-ATOM 70 HB VAL A 253 3.444 -5.826 -4.394 1.00 1.29 H
-ATOM 71 HG11 VAL A 253 3.914 -5.902 -7.011 1.00 1.55 H
-ATOM 72 HG12 VAL A 253 2.236 -5.361 -7.037 1.00 1.66 H
-ATOM 73 HG13 VAL A 253 2.662 -6.871 -6.231 1.00 1.64 H
-ATOM 74 HG21 VAL A 253 4.026 -3.152 -5.459 1.00 1.62 H
-ATOM 75 HG22 VAL A 253 5.025 -4.462 -6.084 1.00 1.61 H
-ATOM 76 HG23 VAL A 253 4.956 -4.157 -4.348 1.00 1.99 H
-ATOM 77 N GLY A 254 1.493 -3.329 -6.857 1.00 0.88 N
-ATOM 78 CA GLY A 254 1.284 -2.228 -7.795 1.00 0.95 C
-ATOM 79 C GLY A 254 0.172 -1.288 -7.357 1.00 0.79 C
-ATOM 80 O GLY A 254 0.350 -0.068 -7.328 1.00 0.84 O
-ATOM 81 H GLY A 254 1.460 -4.257 -7.170 1.00 0.93 H
-ATOM 82 HA2 GLY A 254 2.202 -1.665 -7.881 1.00 1.10 H
-ATOM 83 HA3 GLY A 254 1.035 -2.637 -8.762 1.00 1.07 H
-ATOM 84 N LEU A 255 -0.970 -1.859 -6.986 1.00 0.66 N
-ATOM 85 CA LEU A 255 -2.106 -1.073 -6.517 1.00 0.66 C
-ATOM 86 C LEU A 255 -1.743 -0.269 -5.275 1.00 0.52 C
-ATOM 87 O LEU A 255 -2.054 0.918 -5.170 1.00 0.61 O
-ATOM 88 CB LEU A 255 -3.286 -1.982 -6.201 1.00 0.71 C
-ATOM 89 CG LEU A 255 -4.308 -2.107 -7.320 1.00 0.97 C
-ATOM 90 CD1 LEU A 255 -5.443 -3.022 -6.894 1.00 1.09 C
-ATOM 91 CD2 LEU A 255 -4.836 -0.732 -7.699 1.00 1.21 C
-ATOM 92 H LEU A 255 -1.043 -2.835 -7.009 1.00 0.65 H
-ATOM 93 HA LEU A 255 -2.393 -0.394 -7.306 1.00 0.84 H
-ATOM 94 HB2 LEU A 255 -2.905 -2.967 -5.974 1.00 0.66 H
-ATOM 95 HB3 LEU A 255 -3.788 -1.596 -5.326 1.00 0.77 H
-ATOM 96 HG LEU A 255 -3.834 -2.537 -8.189 1.00 1.03 H
-ATOM 97 HD11 LEU A 255 -5.823 -3.549 -7.755 1.00 1.56 H
-ATOM 98 HD12 LEU A 255 -6.232 -2.433 -6.452 1.00 1.46 H
-ATOM 99 HD13 LEU A 255 -5.075 -3.733 -6.168 1.00 1.54 H
-ATOM 100 HD21 LEU A 255 -4.468 -0.461 -8.678 1.00 1.66 H
-ATOM 101 HD22 LEU A 255 -4.493 -0.005 -6.974 1.00 1.64 H
-ATOM 102 HD23 LEU A 255 -5.914 -0.748 -7.710 1.00 1.61 H
-ATOM 103 N GLY A 256 -1.083 -0.927 -4.333 1.00 0.40 N
-ATOM 104 CA GLY A 256 -0.691 -0.267 -3.109 1.00 0.39 C
-ATOM 105 C GLY A 256 0.299 0.867 -3.329 1.00 0.35 C
-ATOM 106 O GLY A 256 0.085 1.977 -2.849 1.00 0.37 O
-ATOM 107 H GLY A 256 -0.863 -1.877 -4.471 1.00 0.44 H
-ATOM 108 HA2 GLY A 256 -1.578 0.132 -2.639 1.00 0.50 H
-ATOM 109 HA3 GLY A 256 -0.249 -0.998 -2.452 1.00 0.50 H
-ATOM 110 N ILE A 257 1.383 0.602 -4.058 1.00 0.50 N
-ATOM 111 CA ILE A 257 2.384 1.640 -4.310 1.00 0.67 C
-ATOM 112 C ILE A 257 1.807 2.809 -5.119 1.00 0.74 C
-ATOM 113 O ILE A 257 2.103 3.966 -4.828 1.00 0.80 O
-ATOM 114 CB ILE A 257 3.666 1.085 -4.989 1.00 0.95 C
-ATOM 115 CG1 ILE A 257 3.367 0.431 -6.341 1.00 1.07 C
-ATOM 116 CG2 ILE A 257 4.357 0.093 -4.067 1.00 1.06 C
-ATOM 117 CD1 ILE A 257 3.704 1.306 -7.530 1.00 1.31 C
-ATOM 118 H ILE A 257 1.512 -0.304 -4.429 1.00 0.58 H
-ATOM 119 HA ILE A 257 2.675 2.026 -3.342 1.00 0.64 H
-ATOM 120 HB ILE A 257 4.341 1.913 -5.143 1.00 1.10 H
-ATOM 121 HG12 ILE A 257 3.944 -0.479 -6.428 1.00 1.19 H
-ATOM 122 HG13 ILE A 257 2.315 0.190 -6.392 1.00 0.96 H
-ATOM 123 HG21 ILE A 257 4.416 -0.869 -4.556 1.00 1.48 H
-ATOM 124 HG22 ILE A 257 3.795 -0.002 -3.151 1.00 1.48 H
-ATOM 125 HG23 ILE A 257 5.353 0.444 -3.844 1.00 1.55 H
-ATOM 126 HD11 ILE A 257 3.489 0.772 -8.443 1.00 1.69 H
-ATOM 127 HD12 ILE A 257 4.751 1.565 -7.502 1.00 1.75 H
-ATOM 128 HD13 ILE A 257 3.108 2.207 -7.493 1.00 1.68 H
-ATOM 129 N THR A 258 0.971 2.515 -6.119 1.00 0.82 N
-ATOM 130 CA THR A 258 0.363 3.575 -6.934 1.00 1.01 C
-ATOM 131 C THR A 258 -0.634 4.408 -6.127 1.00 0.95 C
-ATOM 132 O THR A 258 -0.629 5.639 -6.214 1.00 1.08 O
-ATOM 133 CB THR A 258 -0.348 3.024 -8.187 1.00 1.20 C
-ATOM 134 OG1 THR A 258 -1.240 1.966 -7.828 1.00 1.04 O
-ATOM 135 CG2 THR A 258 0.659 2.526 -9.213 1.00 1.40 C
-ATOM 136 H THR A 258 0.753 1.572 -6.307 1.00 0.78 H
-ATOM 137 HA THR A 258 1.162 4.224 -7.264 1.00 1.14 H
-ATOM 138 HB THR A 258 -0.920 3.825 -8.633 1.00 1.39 H
-ATOM 139 HG1 THR A 258 -0.744 1.136 -7.756 1.00 0.98 H
-ATOM 140 HG21 THR A 258 1.493 3.209 -9.262 1.00 1.78 H
-ATOM 141 HG22 THR A 258 0.186 2.466 -10.183 1.00 1.77 H
-ATOM 142 HG23 THR A 258 1.012 1.545 -8.925 1.00 1.80 H
-ATOM 143 N THR A 259 -1.479 3.746 -5.331 1.00 0.83 N
-ATOM 144 CA THR A 259 -2.459 4.460 -4.508 1.00 0.91 C
-ATOM 145 C THR A 259 -1.757 5.315 -3.461 1.00 0.81 C
-ATOM 146 O THR A 259 -2.099 6.485 -3.278 1.00 0.96 O
-ATOM 147 CB THR A 259 -3.456 3.510 -3.807 1.00 0.93 C
-ATOM 148 OG1 THR A 259 -2.760 2.437 -3.164 1.00 0.74 O
-ATOM 149 CG2 THR A 259 -4.462 2.949 -4.802 1.00 1.10 C
-ATOM 150 H THR A 259 -1.434 2.762 -5.286 1.00 0.74 H
-ATOM 151 HA THR A 259 -3.020 5.111 -5.163 1.00 1.12 H
-ATOM 152 HB THR A 259 -3.995 4.074 -3.058 1.00 1.05 H
-ATOM 153 HG1 THR A 259 -2.554 1.750 -3.817 1.00 0.73 H
-ATOM 154 HG21 THR A 259 -4.294 1.890 -4.927 1.00 1.36 H
-ATOM 155 HG22 THR A 259 -4.343 3.446 -5.754 1.00 1.50 H
-ATOM 156 HG23 THR A 259 -5.464 3.114 -4.434 1.00 1.64 H
-ATOM 157 N VAL A 260 -0.756 4.740 -2.796 1.00 0.59 N
-ATOM 158 CA VAL A 260 0.009 5.473 -1.792 1.00 0.54 C
-ATOM 159 C VAL A 260 0.730 6.667 -2.428 1.00 0.63 C
-ATOM 160 O VAL A 260 0.617 7.791 -1.938 1.00 0.72 O
-ATOM 161 CB VAL A 260 1.031 4.560 -1.080 1.00 0.47 C
-ATOM 162 CG1 VAL A 260 1.971 5.374 -0.205 1.00 0.61 C
-ATOM 163 CG2 VAL A 260 0.309 3.509 -0.250 1.00 0.55 C
-ATOM 164 H VAL A 260 -0.516 3.803 -2.999 1.00 0.52 H
-ATOM 165 HA VAL A 260 -0.687 5.842 -1.053 1.00 0.65 H
-ATOM 166 HB VAL A 260 1.619 4.053 -1.831 1.00 0.48 H
-ATOM 167 HG11 VAL A 260 2.700 4.719 0.246 1.00 1.18 H
-ATOM 168 HG12 VAL A 260 1.403 5.868 0.568 1.00 1.19 H
-ATOM 169 HG13 VAL A 260 2.475 6.114 -0.810 1.00 1.25 H
-ATOM 170 HG21 VAL A 260 0.340 2.560 -0.765 1.00 1.20 H
-ATOM 171 HG22 VAL A 260 -0.720 3.808 -0.109 1.00 1.16 H
-ATOM 172 HG23 VAL A 260 0.792 3.414 0.709 1.00 1.18 H
-ATOM 173 N LEU A 261 1.442 6.421 -3.534 1.00 0.72 N
-ATOM 174 CA LEU A 261 2.158 7.483 -4.250 1.00 0.92 C
-ATOM 175 C LEU A 261 1.201 8.605 -4.648 1.00 1.07 C
-ATOM 176 O LEU A 261 1.497 9.786 -4.462 1.00 1.16 O
-ATOM 177 CB LEU A 261 2.832 6.917 -5.502 1.00 1.09 C
-ATOM 178 CG LEU A 261 4.359 7.016 -5.529 1.00 1.11 C
-ATOM 179 CD1 LEU A 261 4.804 8.471 -5.529 1.00 1.35 C
-ATOM 180 CD2 LEU A 261 4.968 6.273 -4.350 1.00 1.31 C
-ATOM 181 H LEU A 261 1.475 5.502 -3.887 1.00 0.70 H
-ATOM 182 HA LEU A 261 2.913 7.882 -3.590 1.00 0.90 H
-ATOM 183 HB2 LEU A 261 2.560 5.875 -5.590 1.00 1.27 H
-ATOM 184 HB3 LEU A 261 2.445 7.444 -6.361 1.00 1.30 H
-ATOM 185 HG LEU A 261 4.722 6.557 -6.438 1.00 1.26 H
-ATOM 186 HD11 LEU A 261 5.013 8.783 -4.517 1.00 1.87 H
-ATOM 187 HD12 LEU A 261 4.018 9.086 -5.941 1.00 1.63 H
-ATOM 188 HD13 LEU A 261 5.694 8.574 -6.130 1.00 1.81 H
-ATOM 189 HD21 LEU A 261 4.564 5.273 -4.306 1.00 1.78 H
-ATOM 190 HD22 LEU A 261 4.733 6.797 -3.434 1.00 1.69 H
-ATOM 191 HD23 LEU A 261 6.040 6.224 -4.471 1.00 1.74 H
-ATOM 192 N THR A 262 0.044 8.217 -5.177 1.00 1.13 N
-ATOM 193 CA THR A 262 -0.985 9.174 -5.583 1.00 1.34 C
-ATOM 194 C THR A 262 -1.461 9.994 -4.384 1.00 1.32 C
-ATOM 195 O THR A 262 -1.560 11.217 -4.451 1.00 1.46 O
-ATOM 196 CB THR A 262 -2.198 8.450 -6.205 1.00 1.43 C
-ATOM 197 OG1 THR A 262 -1.794 7.724 -7.372 1.00 1.50 O
-ATOM 198 CG2 THR A 262 -3.299 9.434 -6.569 1.00 1.65 C
-ATOM 199 H THR A 262 -0.128 7.254 -5.286 1.00 1.06 H
-ATOM 200 HA THR A 262 -0.561 9.836 -6.324 1.00 1.48 H
-ATOM 201 HB THR A 262 -2.589 7.753 -5.478 1.00 1.35 H
-ATOM 202 HG1 THR A 262 -1.312 6.927 -7.101 1.00 1.38 H
-ATOM 203 HG21 THR A 262 -3.316 10.235 -5.843 1.00 1.95 H
-ATOM 204 HG22 THR A 262 -4.250 8.925 -6.565 1.00 1.99 H
-ATOM 205 HG23 THR A 262 -3.109 9.840 -7.550 1.00 2.00 H
-ATOM 206 N MET A 263 -1.747 9.302 -3.286 1.00 1.18 N
-ATOM 207 CA MET A 263 -2.206 9.948 -2.058 1.00 1.22 C
-ATOM 208 C MET A 263 -1.144 10.902 -1.493 1.00 1.18 C
-ATOM 209 O MET A 263 -1.412 12.084 -1.266 1.00 1.30 O
-ATOM 210 CB MET A 263 -2.555 8.882 -1.018 1.00 1.13 C
-ATOM 211 CG MET A 263 -3.101 9.446 0.283 1.00 1.28 C
-ATOM 212 SD MET A 263 -3.428 8.163 1.505 1.00 1.32 S
-ATOM 213 CE MET A 263 -4.058 9.146 2.863 1.00 1.60 C
-ATOM 214 H MET A 263 -1.647 8.324 -3.303 1.00 1.09 H
-ATOM 215 HA MET A 263 -3.094 10.515 -2.292 1.00 1.41 H
-ATOM 216 HB2 MET A 263 -3.297 8.219 -1.436 1.00 1.20 H
-ATOM 217 HB3 MET A 263 -1.665 8.314 -0.792 1.00 0.98 H
-ATOM 218 HG2 MET A 263 -2.380 10.138 0.692 1.00 1.27 H
-ATOM 219 HG3 MET A 263 -4.023 9.969 0.074 1.00 1.48 H
-ATOM 220 HE1 MET A 263 -3.668 10.150 2.791 1.00 2.10 H
-ATOM 221 HE2 MET A 263 -3.749 8.706 3.799 1.00 1.96 H
-ATOM 222 HE3 MET A 263 -5.137 9.175 2.816 1.00 1.76 H
-ATOM 223 N THR A 264 0.072 10.394 -1.275 1.00 1.05 N
-ATOM 224 CA THR A 264 1.155 11.228 -0.744 1.00 1.04 C
-ATOM 225 C THR A 264 1.905 11.961 -1.865 1.00 1.00 C
-ATOM 226 O THR A 264 3.127 12.121 -1.818 1.00 1.04 O
-ATOM 227 CB THR A 264 2.158 10.414 0.116 1.00 1.11 C
-ATOM 228 OG1 THR A 264 3.157 11.286 0.663 1.00 1.12 O
-ATOM 229 CG2 THR A 264 2.840 9.318 -0.692 1.00 1.15 C
-ATOM 230 H THR A 264 0.243 9.443 -1.483 1.00 0.99 H
-ATOM 231 HA THR A 264 0.700 11.970 -0.103 1.00 1.05 H
-ATOM 232 HB THR A 264 1.615 9.953 0.930 1.00 1.17 H
-ATOM 233 HG1 THR A 264 3.601 11.751 -0.060 1.00 1.14 H
-ATOM 234 HG21 THR A 264 2.337 8.377 -0.519 1.00 1.53 H
-ATOM 235 HG22 THR A 264 3.872 9.234 -0.388 1.00 1.49 H
-ATOM 236 HG23 THR A 264 2.792 9.562 -1.743 1.00 1.63 H
-ATOM 237 N THR A 265 1.158 12.420 -2.869 1.00 0.98 N
-ATOM 238 CA THR A 265 1.741 13.148 -4.000 1.00 0.98 C
-ATOM 239 C THR A 265 2.359 14.474 -3.556 1.00 0.88 C
-ATOM 240 O THR A 265 3.305 14.960 -4.175 1.00 0.92 O
-ATOM 241 CB THR A 265 0.703 13.415 -5.111 1.00 1.06 C
-ATOM 242 OG1 THR A 265 1.303 14.155 -6.183 1.00 1.13 O
-ATOM 243 CG2 THR A 265 -0.498 14.181 -4.573 1.00 1.05 C
-ATOM 244 H THR A 265 0.187 12.267 -2.846 1.00 1.00 H
-ATOM 245 HA THR A 265 2.524 12.528 -4.415 1.00 1.05 H
-ATOM 246 HB THR A 265 0.358 12.464 -5.493 1.00 1.15 H
-ATOM 247 HG1 THR A 265 2.204 14.403 -5.945 1.00 1.10 H
-ATOM 248 HG21 THR A 265 -0.278 15.238 -4.563 1.00 1.49 H
-ATOM 249 HG22 THR A 265 -0.717 13.849 -3.569 1.00 1.45 H
-ATOM 250 HG23 THR A 265 -1.355 13.997 -5.205 1.00 1.48 H
-ATOM 251 N GLN A 266 1.816 15.043 -2.473 1.00 0.84 N
-ATOM 252 CA GLN A 266 2.304 16.310 -1.913 1.00 0.80 C
-ATOM 253 C GLN A 266 2.317 17.440 -2.952 1.00 0.58 C
-ATOM 254 O GLN A 266 1.263 17.949 -3.339 1.00 0.61 O
-ATOM 255 CB GLN A 266 3.698 16.117 -1.303 1.00 0.98 C
-ATOM 256 CG GLN A 266 3.717 15.138 -0.140 1.00 1.22 C
-ATOM 257 CD GLN A 266 5.108 14.912 0.418 1.00 1.45 C
-ATOM 258 OE1 GLN A 266 6.004 14.453 -0.279 1.00 1.58 O
-ATOM 259 NE2 GLN A 266 5.299 15.237 1.683 1.00 1.61 N
-ATOM 260 H GLN A 266 1.070 14.589 -2.031 1.00 0.90 H
-ATOM 261 HA GLN A 266 1.624 16.589 -1.122 1.00 0.86 H
-ATOM 262 HB2 GLN A 266 4.366 15.749 -2.068 1.00 1.03 H
-ATOM 263 HB3 GLN A 266 4.059 17.071 -0.950 1.00 0.98 H
-ATOM 264 HG2 GLN A 266 3.090 15.525 0.649 1.00 1.27 H
-ATOM 265 HG3 GLN A 266 3.323 14.191 -0.478 1.00 1.31 H
-ATOM 266 HE21 GLN A 266 4.543 15.602 2.186 1.00 1.61 H
-ATOM 267 HE22 GLN A 266 6.187 15.090 2.064 1.00 1.77 H
-ATOM 268 N SER A 267 3.509 17.836 -3.390 1.00 0.59 N
-ATOM 269 CA SER A 267 3.657 18.909 -4.375 1.00 0.49 C
-ATOM 270 C SER A 267 5.038 18.873 -5.027 1.00 0.77 C
-ATOM 271 O SER A 267 6.049 18.673 -4.351 1.00 0.94 O
-ATOM 272 CB SER A 267 3.427 20.284 -3.730 1.00 0.40 C
-ATOM 273 OG SER A 267 2.067 20.460 -3.359 1.00 0.46 O
-ATOM 274 H SER A 267 4.314 17.397 -3.041 1.00 0.80 H
-ATOM 275 HA SER A 267 2.910 18.755 -5.141 1.00 0.49 H
-ATOM 276 HB2 SER A 267 4.041 20.369 -2.845 1.00 0.54 H
-ATOM 277 HB3 SER A 267 3.699 21.058 -4.432 1.00 0.51 H
-ATOM 278 HG SER A 267 1.653 19.589 -3.240 1.00 0.52 H
-ATOM 279 N SER A 268 5.075 19.067 -6.343 1.00 0.91 N
-ATOM 280 CA SER A 268 6.331 19.056 -7.102 1.00 1.23 C
-ATOM 281 C SER A 268 7.130 20.356 -6.928 1.00 1.28 C
-ATOM 282 O SER A 268 7.626 20.927 -7.901 1.00 1.49 O
-ATOM 283 CB SER A 268 6.039 18.823 -8.587 1.00 1.42 C
-ATOM 284 OG SER A 268 5.105 19.772 -9.082 1.00 1.35 O
-ATOM 285 H SER A 268 4.228 19.222 -6.828 1.00 0.84 H
-ATOM 286 HA SER A 268 6.927 18.235 -6.735 1.00 1.34 H
-ATOM 287 HB2 SER A 268 6.955 18.914 -9.150 1.00 1.65 H
-ATOM 288 HB3 SER A 268 5.632 17.832 -8.720 1.00 1.46 H
-ATOM 289 HG SER A 268 4.527 19.342 -9.752 1.00 1.45 H
-ATOM 290 N GLY A 269 7.266 20.816 -5.690 1.00 1.15 N
-ATOM 291 CA GLY A 269 8.016 22.035 -5.438 1.00 1.26 C
-ATOM 292 C GLY A 269 7.684 22.678 -4.101 1.00 1.11 C
-ATOM 293 O GLY A 269 7.314 21.997 -3.147 1.00 1.03 O
-ATOM 294 H GLY A 269 6.864 20.319 -4.942 1.00 1.04 H
-ATOM 295 HA2 GLY A 269 9.071 21.805 -5.458 1.00 1.48 H
-ATOM 296 HA3 GLY A 269 7.801 22.742 -6.225 1.00 1.30 H
-ATOM 297 N SER A 270 7.822 23.999 -4.039 1.00 1.17 N
-ATOM 298 CA SER A 270 7.541 24.750 -2.812 1.00 1.15 C
-ATOM 299 C SER A 270 6.093 25.240 -2.767 1.00 1.03 C
-ATOM 300 O SER A 270 5.560 25.517 -1.693 1.00 1.19 O
-ATOM 301 CB SER A 270 8.491 25.945 -2.685 1.00 1.44 C
-ATOM 302 OG SER A 270 8.214 26.703 -1.517 1.00 1.54 O
-ATOM 303 H SER A 270 8.126 24.483 -4.835 1.00 1.29 H
-ATOM 304 HA SER A 270 7.706 24.086 -1.977 1.00 1.14 H
-ATOM 305 HB2 SER A 270 9.510 25.588 -2.634 1.00 1.61 H
-ATOM 306 HB3 SER A 270 8.378 26.584 -3.550 1.00 1.52 H
-ATOM 307 HG SER A 270 7.335 26.484 -1.188 1.00 1.71 H
-ATOM 308 N ARG A 271 5.470 25.361 -3.936 1.00 0.98 N
-ATOM 309 CA ARG A 271 4.087 25.831 -4.032 1.00 0.97 C
-ATOM 310 C ARG A 271 3.102 24.846 -3.397 1.00 0.83 C
-ATOM 311 O ARG A 271 3.130 23.650 -3.682 1.00 0.66 O
-ATOM 312 CB ARG A 271 3.707 26.061 -5.497 1.00 1.06 C
-ATOM 313 CG ARG A 271 2.237 26.406 -5.700 1.00 1.18 C
-ATOM 314 CD ARG A 271 1.895 26.564 -7.173 1.00 1.33 C
-ATOM 315 NE ARG A 271 2.567 27.713 -7.778 1.00 1.50 N
-ATOM 316 CZ ARG A 271 2.249 28.966 -7.535 1.00 1.67 C
-ATOM 317 NH1 ARG A 271 1.238 29.258 -6.757 1.00 1.76 N
-ATOM 318 NH2 ARG A 271 2.935 29.928 -8.087 1.00 1.86 N
-ATOM 319 H ARG A 271 5.952 25.133 -4.756 1.00 1.11 H
-ATOM 320 HA ARG A 271 4.023 26.771 -3.504 1.00 1.14 H
-ATOM 321 HB2 ARG A 271 4.303 26.873 -5.888 1.00 1.26 H
-ATOM 322 HB3 ARG A 271 3.925 25.164 -6.059 1.00 0.99 H
-ATOM 323 HG2 ARG A 271 1.631 25.617 -5.284 1.00 1.13 H
-ATOM 324 HG3 ARG A 271 2.024 27.334 -5.189 1.00 1.37 H
-ATOM 325 HD2 ARG A 271 2.196 25.668 -7.696 1.00 1.30 H
-ATOM 326 HD3 ARG A 271 0.827 26.693 -7.268 1.00 1.47 H
-ATOM 327 HE ARG A 271 3.309 27.530 -8.384 1.00 1.55 H
-ATOM 328 HH11 ARG A 271 0.703 28.525 -6.344 1.00 1.68 H
-ATOM 329 HH12 ARG A 271 1.003 30.210 -6.580 1.00 1.95 H
-ATOM 330 HH21 ARG A 271 3.700 29.711 -8.688 1.00 1.89 H
-ATOM 331 HH22 ARG A 271 2.694 30.878 -7.907 1.00 2.01 H
-ATOM 332 N ALA A 272 2.219 25.369 -2.551 1.00 1.02 N
-ATOM 333 CA ALA A 272 1.207 24.550 -1.893 1.00 1.09 C
-ATOM 334 C ALA A 272 0.162 24.059 -2.898 1.00 1.09 C
-ATOM 335 O ALA A 272 -0.305 24.828 -3.741 1.00 1.23 O
-ATOM 336 CB ALA A 272 0.541 25.338 -0.774 1.00 1.45 C
-ATOM 337 H ALA A 272 2.240 26.331 -2.378 1.00 1.19 H
-ATOM 338 HA ALA A 272 1.702 23.695 -1.454 1.00 0.98 H
-ATOM 339 HB1 ALA A 272 1.242 26.055 -0.372 1.00 1.89 H
-ATOM 340 HB2 ALA A 272 0.229 24.661 0.007 1.00 1.78 H
-ATOM 341 HB3 ALA A 272 -0.321 25.858 -1.165 1.00 1.86 H
-ATOM 342 N SER A 273 -0.185 22.773 -2.804 1.00 0.99 N
-ATOM 343 CA SER A 273 -1.164 22.149 -3.706 1.00 1.06 C
-ATOM 344 C SER A 273 -0.665 22.130 -5.136 1.00 0.93 C
-ATOM 345 O SER A 273 -1.365 22.530 -6.066 1.00 1.11 O
-ATOM 346 CB SER A 273 -2.527 22.836 -3.650 1.00 1.40 C
-ATOM 347 OG SER A 273 -3.038 22.877 -2.326 1.00 1.62 O
-ATOM 348 H SER A 273 0.238 22.221 -2.115 1.00 0.95 H
-ATOM 349 HA SER A 273 -1.285 21.117 -3.402 1.00 1.03 H
-ATOM 350 HB2 SER A 273 -2.430 23.842 -4.022 1.00 1.47 H
-ATOM 351 HB3 SER A 273 -3.219 22.286 -4.273 1.00 1.46 H
-ATOM 352 HG SER A 273 -3.927 23.245 -2.335 1.00 1.96 H
-ATOM 353 N LEU A 274 0.535 21.606 -5.307 1.00 0.69 N
-ATOM 354 CA LEU A 274 1.119 21.466 -6.627 1.00 0.73 C
-ATOM 355 C LEU A 274 1.475 19.991 -6.893 1.00 0.73 C
-ATOM 356 O LEU A 274 2.600 19.686 -7.287 1.00 0.82 O
-ATOM 357 CB LEU A 274 2.365 22.353 -6.752 1.00 0.77 C
-ATOM 358 CG LEU A 274 2.953 22.469 -8.161 1.00 1.05 C
-ATOM 359 CD1 LEU A 274 1.957 23.119 -9.109 1.00 1.28 C
-ATOM 360 CD2 LEU A 274 4.254 23.256 -8.132 1.00 1.20 C
-ATOM 361 H LEU A 274 1.030 21.266 -4.521 1.00 0.58 H
-ATOM 362 HA LEU A 274 0.384 21.784 -7.352 1.00 0.91 H
-ATOM 363 HB2 LEU A 274 2.109 23.345 -6.410 1.00 0.85 H
-ATOM 364 HB3 LEU A 274 3.129 21.954 -6.100 1.00 0.70 H
-ATOM 365 HG LEU A 274 3.169 21.479 -8.535 1.00 1.14 H
-ATOM 366 HD11 LEU A 274 1.414 23.892 -8.585 1.00 1.77 H
-ATOM 367 HD12 LEU A 274 1.263 22.374 -9.471 1.00 1.69 H
-ATOM 368 HD13 LEU A 274 2.485 23.554 -9.944 1.00 1.61 H
-ATOM 369 HD21 LEU A 274 5.067 22.598 -7.865 1.00 1.55 H
-ATOM 370 HD22 LEU A 274 4.179 24.050 -7.402 1.00 1.11 H
-ATOM 371 HD23 LEU A 274 4.440 23.681 -9.108 1.00 1.56 H
-ATOM 372 N PRO A 275 0.526 19.038 -6.660 1.00 0.78 N
-ATOM 373 CA PRO A 275 0.779 17.596 -6.872 1.00 0.90 C
-ATOM 374 C PRO A 275 1.417 17.284 -8.230 1.00 1.12 C
-ATOM 375 O PRO A 275 2.365 16.506 -8.318 1.00 1.27 O
-ATOM 376 CB PRO A 275 -0.622 16.993 -6.794 1.00 1.05 C
-ATOM 377 CG PRO A 275 -1.330 17.875 -5.834 1.00 1.00 C
-ATOM 378 CD PRO A 275 -0.853 19.264 -6.151 1.00 0.89 C
-ATOM 379 HA PRO A 275 1.389 17.187 -6.086 1.00 0.86 H
-ATOM 380 HB2 PRO A 275 -1.083 17.012 -7.771 1.00 1.19 H
-ATOM 381 HB3 PRO A 275 -0.564 15.978 -6.432 1.00 1.12 H
-ATOM 382 HG2 PRO A 275 -2.397 17.797 -5.973 1.00 1.19 H
-ATOM 383 HG3 PRO A 275 -1.054 17.608 -4.825 1.00 0.96 H
-ATOM 384 HD2 PRO A 275 -1.480 19.716 -6.903 1.00 1.03 H
-ATOM 385 HD3 PRO A 275 -0.843 19.884 -5.250 1.00 0.85 H
-ATOM 386 N LYS A 276 0.896 17.914 -9.278 1.00 1.23 N
-ATOM 387 CA LYS A 276 1.419 17.724 -10.630 1.00 1.49 C
-ATOM 388 C LYS A 276 2.404 18.844 -11.004 1.00 1.57 C
-ATOM 389 O LYS A 276 3.621 18.662 -10.790 1.00 1.66 O
-ATOM 390 CB LYS A 276 0.275 17.683 -11.645 1.00 1.68 C
-ATOM 391 CG LYS A 276 0.741 17.399 -13.066 1.00 1.98 C
-ATOM 392 CD LYS A 276 -0.208 17.988 -14.097 1.00 2.16 C
-ATOM 393 CE LYS A 276 -0.317 19.501 -13.953 1.00 2.12 C
-ATOM 394 NZ LYS A 276 1.023 20.153 -13.922 1.00 2.12 N
-ATOM 395 OXT LYS A 276 1.944 19.896 -11.517 1.00 1.66 O
-ATOM 396 H LYS A 276 0.151 18.532 -9.135 1.00 1.19 H
-ATOM 397 HA LYS A 276 1.942 16.779 -10.652 1.00 1.61 H
-ATOM 398 HB2 LYS A 276 -0.422 16.909 -11.355 1.00 1.72 H
-ATOM 399 HB3 LYS A 276 -0.233 18.635 -11.638 1.00 1.67 H
-ATOM 400 HG2 LYS A 276 1.719 17.833 -13.205 1.00 2.02 H
-ATOM 401 HG3 LYS A 276 0.795 16.330 -13.209 1.00 2.11 H
-ATOM 402 HD2 LYS A 276 0.161 17.756 -15.086 1.00 2.37 H
-ATOM 403 HD3 LYS A 276 -1.187 17.551 -13.964 1.00 2.21 H
-ATOM 404 HE2 LYS A 276 -0.877 19.891 -14.790 1.00 2.34 H
-ATOM 405 HE3 LYS A 276 -0.839 19.726 -13.035 1.00 1.98 H
-ATOM 406 HZ1 LYS A 276 1.672 19.669 -14.575 1.00 2.28 H
-ATOM 407 HZ2 LYS A 276 1.426 20.105 -12.946 1.00 1.93 H
-ATOM 408 HZ3 LYS A 276 0.943 21.151 -14.200 1.00 2.23 H
-TER
-ENDMDL
-MODEL 19
-ATOM 1 N ALA A 249 1.328 -11.693 -11.317 1.00 1.62 N
-ATOM 2 CA ALA A 249 2.017 -10.389 -11.129 1.00 1.44 C
-ATOM 3 C ALA A 249 1.612 -9.741 -9.805 1.00 1.14 C
-ATOM 4 O ALA A 249 0.543 -10.032 -9.267 1.00 1.20 O
-ATOM 5 CB ALA A 249 1.719 -9.455 -12.292 1.00 1.70 C
-ATOM 6 H1 ALA A 249 1.986 -12.477 -11.134 1.00 1.82 H
-ATOM 7 H2 ALA A 249 0.973 -11.773 -12.290 1.00 1.94 H
-ATOM 8 H3 ALA A 249 0.524 -11.769 -10.658 1.00 1.98 H
-ATOM 9 HA ALA A 249 3.082 -10.571 -11.112 1.00 1.47 H
-ATOM 10 HB1 ALA A 249 0.652 -9.333 -12.393 1.00 2.02 H
-ATOM 11 HB2 ALA A 249 2.125 -9.874 -13.201 1.00 2.05 H
-ATOM 12 HB3 ALA A 249 2.177 -8.494 -12.105 1.00 2.06 H
-ATOM 13 N PRO A 250 2.468 -8.859 -9.263 1.00 1.00 N
-ATOM 14 CA PRO A 250 2.218 -8.162 -7.993 1.00 0.82 C
-ATOM 15 C PRO A 250 1.150 -7.063 -8.099 1.00 0.76 C
-ATOM 16 O PRO A 250 1.415 -5.896 -7.806 1.00 0.73 O
-ATOM 17 CB PRO A 250 3.588 -7.560 -7.676 1.00 0.97 C
-ATOM 18 CG PRO A 250 4.193 -7.311 -9.003 1.00 1.18 C
-ATOM 19 CD PRO A 250 3.764 -8.468 -9.849 1.00 1.22 C
-ATOM 20 HA PRO A 250 1.941 -8.855 -7.211 1.00 0.90 H
-ATOM 21 HB2 PRO A 250 3.466 -6.647 -7.121 1.00 0.97 H
-ATOM 22 HB3 PRO A 250 4.172 -8.268 -7.110 1.00 1.11 H
-ATOM 23 HG2 PRO A 250 3.819 -6.384 -9.412 1.00 1.21 H
-ATOM 24 HG3 PRO A 250 5.269 -7.284 -8.920 1.00 1.42 H
-ATOM 25 HD2 PRO A 250 3.641 -8.158 -10.873 1.00 1.42 H
-ATOM 26 HD3 PRO A 250 4.475 -9.273 -9.772 1.00 1.34 H
-ATOM 27 N ALA A 251 -0.057 -7.445 -8.523 1.00 0.91 N
-ATOM 28 CA ALA A 251 -1.168 -6.499 -8.677 1.00 1.09 C
-ATOM 29 C ALA A 251 -1.458 -5.739 -7.377 1.00 1.03 C
-ATOM 30 O ALA A 251 -1.625 -4.520 -7.388 1.00 1.05 O
-ATOM 31 CB ALA A 251 -2.416 -7.231 -9.151 1.00 1.42 C
-ATOM 32 H ALA A 251 -0.202 -8.393 -8.747 1.00 1.00 H
-ATOM 33 HA ALA A 251 -0.891 -5.787 -9.441 1.00 1.15 H
-ATOM 34 HB1 ALA A 251 -3.243 -6.993 -8.498 1.00 1.80 H
-ATOM 35 HB2 ALA A 251 -2.236 -8.296 -9.132 1.00 1.82 H
-ATOM 36 HB3 ALA A 251 -2.652 -6.924 -10.159 1.00 1.84 H
-ATOM 37 N ARG A 252 -1.503 -6.464 -6.258 1.00 1.08 N
-ATOM 38 CA ARG A 252 -1.760 -5.853 -4.950 1.00 1.20 C
-ATOM 39 C ARG A 252 -0.648 -4.856 -4.603 1.00 1.00 C
-ATOM 40 O ARG A 252 -0.912 -3.755 -4.116 1.00 1.05 O
-ATOM 41 CB ARG A 252 -1.858 -6.945 -3.874 1.00 1.43 C
-ATOM 42 CG ARG A 252 -2.876 -6.662 -2.770 1.00 1.77 C
-ATOM 43 CD ARG A 252 -2.416 -5.564 -1.818 1.00 1.82 C
-ATOM 44 NE ARG A 252 -3.352 -5.362 -0.706 1.00 2.17 N
-ATOM 45 CZ ARG A 252 -3.563 -6.240 0.264 1.00 2.46 C
-ATOM 46 NH1 ARG A 252 -2.874 -7.351 0.319 1.00 2.44 N
-ATOM 47 NH2 ARG A 252 -4.454 -5.991 1.191 1.00 2.79 N
-ATOM 48 H ARG A 252 -1.354 -7.430 -6.314 1.00 1.10 H
-ATOM 49 HA ARG A 252 -2.698 -5.324 -5.007 1.00 1.37 H
-ATOM 50 HB2 ARG A 252 -2.132 -7.875 -4.350 1.00 1.47 H
-ATOM 51 HB3 ARG A 252 -0.888 -7.064 -3.414 1.00 1.39 H
-ATOM 52 HG2 ARG A 252 -3.805 -6.356 -3.226 1.00 1.86 H
-ATOM 53 HG3 ARG A 252 -3.035 -7.570 -2.205 1.00 1.94 H
-ATOM 54 HD2 ARG A 252 -1.451 -5.834 -1.418 1.00 1.80 H
-ATOM 55 HD3 ARG A 252 -2.327 -4.641 -2.373 1.00 1.66 H
-ATOM 56 HE ARG A 252 -3.862 -4.527 -0.696 1.00 2.23 H
-ATOM 57 HH11 ARG A 252 -2.185 -7.542 -0.375 1.00 2.22 H
-ATOM 58 HH12 ARG A 252 -3.038 -8.005 1.053 1.00 2.68 H
-ATOM 59 HH21 ARG A 252 -4.974 -5.141 1.167 1.00 2.84 H
-ATOM 60 HH22 ARG A 252 -4.612 -6.653 1.921 1.00 3.02 H
-ATOM 61 N VAL A 253 0.595 -5.245 -4.882 1.00 0.83 N
-ATOM 62 CA VAL A 253 1.750 -4.387 -4.625 1.00 0.78 C
-ATOM 63 C VAL A 253 1.699 -3.139 -5.507 1.00 0.58 C
-ATOM 64 O VAL A 253 1.838 -2.016 -5.019 1.00 0.60 O
-ATOM 65 CB VAL A 253 3.076 -5.140 -4.875 1.00 0.88 C
-ATOM 66 CG1 VAL A 253 4.274 -4.259 -4.548 1.00 1.09 C
-ATOM 67 CG2 VAL A 253 3.122 -6.427 -4.065 1.00 1.10 C
-ATOM 68 H VAL A 253 0.735 -6.124 -5.286 1.00 0.83 H
-ATOM 69 HA VAL A 253 1.718 -4.085 -3.590 1.00 0.97 H
-ATOM 70 HB VAL A 253 3.125 -5.398 -5.923 1.00 0.78 H
-ATOM 71 HG11 VAL A 253 5.164 -4.870 -4.488 1.00 1.55 H
-ATOM 72 HG12 VAL A 253 4.111 -3.765 -3.602 1.00 1.58 H
-ATOM 73 HG13 VAL A 253 4.399 -3.518 -5.325 1.00 1.50 H
-ATOM 74 HG21 VAL A 253 2.198 -6.539 -3.517 1.00 1.54 H
-ATOM 75 HG22 VAL A 253 3.950 -6.388 -3.373 1.00 1.56 H
-ATOM 76 HG23 VAL A 253 3.249 -7.266 -4.733 1.00 1.53 H
-ATOM 77 N GLY A 254 1.472 -3.350 -6.804 1.00 0.52 N
-ATOM 78 CA GLY A 254 1.377 -2.241 -7.744 1.00 0.60 C
-ATOM 79 C GLY A 254 0.267 -1.272 -7.376 1.00 0.62 C
-ATOM 80 O GLY A 254 0.478 -0.058 -7.332 1.00 0.60 O
-ATOM 81 H GLY A 254 1.354 -4.275 -7.123 1.00 0.56 H
-ATOM 82 HA2 GLY A 254 2.318 -1.710 -7.756 1.00 0.66 H
-ATOM 83 HA3 GLY A 254 1.184 -2.635 -8.731 1.00 0.79 H
-ATOM 84 N LEU A 255 -0.914 -1.811 -7.081 1.00 0.78 N
-ATOM 85 CA LEU A 255 -2.053 -0.991 -6.682 1.00 0.99 C
-ATOM 86 C LEU A 255 -1.735 -0.239 -5.390 1.00 0.90 C
-ATOM 87 O LEU A 255 -2.032 0.949 -5.258 1.00 0.95 O
-ATOM 88 CB LEU A 255 -3.294 -1.847 -6.491 1.00 1.28 C
-ATOM 89 CG LEU A 255 -4.589 -1.050 -6.473 1.00 1.59 C
-ATOM 90 CD1 LEU A 255 -4.817 -0.383 -7.821 1.00 1.71 C
-ATOM 91 CD2 LEU A 255 -5.758 -1.942 -6.101 1.00 1.91 C
-ATOM 92 H LEU A 255 -1.018 -2.791 -7.115 1.00 0.83 H
-ATOM 93 HA LEU A 255 -2.253 -0.272 -7.461 1.00 1.08 H
-ATOM 94 HB2 LEU A 255 -3.340 -2.569 -7.294 1.00 1.33 H
-ATOM 95 HB3 LEU A 255 -3.206 -2.376 -5.554 1.00 1.30 H
-ATOM 96 HG LEU A 255 -4.504 -0.271 -5.732 1.00 1.56 H
-ATOM 97 HD11 LEU A 255 -5.795 0.072 -7.838 1.00 2.05 H
-ATOM 98 HD12 LEU A 255 -4.748 -1.123 -8.606 1.00 2.01 H
-ATOM 99 HD13 LEU A 255 -4.065 0.377 -7.978 1.00 2.00 H
-ATOM 100 HD21 LEU A 255 -6.564 -1.334 -5.716 1.00 2.18 H
-ATOM 101 HD22 LEU A 255 -5.445 -2.649 -5.348 1.00 2.21 H
-ATOM 102 HD23 LEU A 255 -6.095 -2.472 -6.978 1.00 2.33 H
-ATOM 103 N GLY A 256 -1.115 -0.938 -4.443 1.00 0.88 N
-ATOM 104 CA GLY A 256 -0.752 -0.323 -3.180 1.00 0.97 C
-ATOM 105 C GLY A 256 0.233 0.826 -3.342 1.00 0.78 C
-ATOM 106 O GLY A 256 -0.022 1.930 -2.863 1.00 0.85 O
-ATOM 107 H GLY A 256 -0.894 -1.884 -4.607 1.00 0.87 H
-ATOM 108 HA2 GLY A 256 -1.648 0.051 -2.707 1.00 1.17 H
-ATOM 109 HA3 GLY A 256 -0.309 -1.075 -2.543 1.00 1.08 H
-ATOM 110 N ILE A 257 1.355 0.579 -4.022 1.00 0.61 N
-ATOM 111 CA ILE A 257 2.357 1.629 -4.221 1.00 0.59 C
-ATOM 112 C ILE A 257 1.795 2.800 -5.036 1.00 0.42 C
-ATOM 113 O ILE A 257 2.057 3.957 -4.716 1.00 0.48 O
-ATOM 114 CB ILE A 257 3.669 1.095 -4.859 1.00 0.73 C
-ATOM 115 CG1 ILE A 257 3.433 0.456 -6.230 1.00 0.67 C
-ATOM 116 CG2 ILE A 257 4.338 0.097 -3.926 1.00 0.97 C
-ATOM 117 CD1 ILE A 257 3.785 1.358 -7.393 1.00 0.84 C
-ATOM 118 H ILE A 257 1.512 -0.323 -4.392 1.00 0.59 H
-ATOM 119 HA ILE A 257 2.606 2.006 -3.239 1.00 0.77 H
-ATOM 120 HB ILE A 257 4.342 1.933 -4.975 1.00 0.85 H
-ATOM 121 HG12 ILE A 257 4.036 -0.436 -6.311 1.00 0.81 H
-ATOM 122 HG13 ILE A 257 2.390 0.188 -6.319 1.00 0.55 H
-ATOM 123 HG21 ILE A 257 5.286 0.492 -3.595 1.00 1.43 H
-ATOM 124 HG22 ILE A 257 4.497 -0.833 -4.452 1.00 1.45 H
-ATOM 125 HG23 ILE A 257 3.701 -0.078 -3.070 1.00 1.48 H
-ATOM 126 HD11 ILE A 257 3.229 1.052 -8.267 1.00 1.30 H
-ATOM 127 HD12 ILE A 257 4.843 1.288 -7.597 1.00 1.35 H
-ATOM 128 HD13 ILE A 257 3.533 2.379 -7.145 1.00 1.43 H
-ATOM 129 N THR A 258 0.997 2.506 -6.069 1.00 0.41 N
-ATOM 130 CA THR A 258 0.395 3.568 -6.884 1.00 0.58 C
-ATOM 131 C THR A 258 -0.597 4.391 -6.064 1.00 0.63 C
-ATOM 132 O THR A 258 -0.656 5.615 -6.192 1.00 0.67 O
-ATOM 133 CB THR A 258 -0.314 3.022 -8.141 1.00 0.86 C
-ATOM 134 OG1 THR A 258 -1.178 1.934 -7.798 1.00 0.93 O
-ATOM 135 CG2 THR A 258 0.701 2.570 -9.180 1.00 0.99 C
-ATOM 136 H THR A 258 0.797 1.562 -6.274 1.00 0.44 H
-ATOM 137 HA THR A 258 1.194 4.220 -7.207 1.00 0.63 H
-ATOM 138 HB THR A 258 -0.909 3.817 -8.569 1.00 1.03 H
-ATOM 139 HG1 THR A 258 -0.658 1.117 -7.736 1.00 0.87 H
-ATOM 140 HG21 THR A 258 0.196 2.017 -9.959 1.00 1.50 H
-ATOM 141 HG22 THR A 258 1.439 1.937 -8.710 1.00 1.10 H
-ATOM 142 HG23 THR A 258 1.186 3.433 -9.609 1.00 1.50 H
-ATOM 143 N THR A 259 -1.363 3.716 -5.204 1.00 0.76 N
-ATOM 144 CA THR A 259 -2.333 4.395 -4.343 1.00 0.98 C
-ATOM 145 C THR A 259 -1.607 5.299 -3.354 1.00 0.91 C
-ATOM 146 O THR A 259 -1.956 6.470 -3.195 1.00 0.98 O
-ATOM 147 CB THR A 259 -3.210 3.395 -3.558 1.00 1.22 C
-ATOM 148 OG1 THR A 259 -3.898 2.525 -4.464 1.00 1.33 O
-ATOM 149 CG2 THR A 259 -4.225 4.121 -2.688 1.00 1.49 C
-ATOM 150 H THR A 259 -1.262 2.739 -5.136 1.00 0.75 H
-ATOM 151 HA THR A 259 -2.974 4.998 -4.970 1.00 1.09 H
-ATOM 152 HB THR A 259 -2.569 2.802 -2.921 1.00 1.19 H
-ATOM 153 HG1 THR A 259 -3.274 1.873 -4.822 1.00 1.22 H
-ATOM 154 HG21 THR A 259 -4.340 3.593 -1.752 1.00 1.74 H
-ATOM 155 HG22 THR A 259 -5.176 4.159 -3.199 1.00 1.68 H
-ATOM 156 HG23 THR A 259 -3.880 5.125 -2.495 1.00 1.45 H
-ATOM 157 N VAL A 260 -0.574 4.751 -2.708 1.00 0.85 N
-ATOM 158 CA VAL A 260 0.225 5.515 -1.750 1.00 0.92 C
-ATOM 159 C VAL A 260 0.860 6.728 -2.432 1.00 0.74 C
-ATOM 160 O VAL A 260 0.767 7.849 -1.928 1.00 0.82 O
-ATOM 161 CB VAL A 260 1.329 4.647 -1.107 1.00 1.04 C
-ATOM 162 CG1 VAL A 260 2.239 5.486 -0.219 1.00 1.25 C
-ATOM 163 CG2 VAL A 260 0.711 3.512 -0.305 1.00 1.24 C
-ATOM 164 H VAL A 260 -0.334 3.811 -2.892 1.00 0.81 H
-ATOM 165 HA VAL A 260 -0.435 5.862 -0.968 1.00 1.11 H
-ATOM 166 HB VAL A 260 1.929 4.216 -1.895 1.00 0.91 H
-ATOM 167 HG11 VAL A 260 2.649 4.866 0.563 1.00 1.67 H
-ATOM 168 HG12 VAL A 260 1.669 6.291 0.220 1.00 1.70 H
-ATOM 169 HG13 VAL A 260 3.042 5.896 -0.813 1.00 1.61 H
-ATOM 170 HG21 VAL A 260 0.822 2.585 -0.849 1.00 1.62 H
-ATOM 171 HG22 VAL A 260 -0.338 3.713 -0.147 1.00 1.66 H
-ATOM 172 HG23 VAL A 260 1.211 3.430 0.649 1.00 1.66 H
-ATOM 173 N LEU A 261 1.479 6.503 -3.594 1.00 0.54 N
-ATOM 174 CA LEU A 261 2.100 7.587 -4.354 1.00 0.49 C
-ATOM 175 C LEU A 261 1.066 8.650 -4.731 1.00 0.45 C
-ATOM 176 O LEU A 261 1.292 9.838 -4.529 1.00 0.50 O
-ATOM 177 CB LEU A 261 2.781 7.040 -5.607 1.00 0.57 C
-ATOM 178 CG LEU A 261 4.038 6.212 -5.341 1.00 0.78 C
-ATOM 179 CD1 LEU A 261 4.672 5.777 -6.649 1.00 0.96 C
-ATOM 180 CD2 LEU A 261 5.030 7.003 -4.498 1.00 1.03 C
-ATOM 181 H LEU A 261 1.504 5.585 -3.957 1.00 0.49 H
-ATOM 182 HA LEU A 261 2.852 8.039 -3.725 1.00 0.64 H
-ATOM 183 HB2 LEU A 261 2.071 6.422 -6.136 1.00 0.53 H
-ATOM 184 HB3 LEU A 261 3.051 7.873 -6.239 1.00 0.72 H
-ATOM 185 HG LEU A 261 3.766 5.323 -4.791 1.00 0.76 H
-ATOM 186 HD11 LEU A 261 3.904 5.665 -7.400 1.00 1.44 H
-ATOM 187 HD12 LEU A 261 5.177 4.833 -6.507 1.00 1.45 H
-ATOM 188 HD13 LEU A 261 5.383 6.523 -6.970 1.00 1.45 H
-ATOM 189 HD21 LEU A 261 4.491 7.652 -3.823 1.00 1.49 H
-ATOM 190 HD22 LEU A 261 5.658 7.597 -5.144 1.00 1.50 H
-ATOM 191 HD23 LEU A 261 5.643 6.320 -3.927 1.00 1.50 H
-ATOM 192 N THR A 262 -0.079 8.202 -5.253 1.00 0.53 N
-ATOM 193 CA THR A 262 -1.174 9.101 -5.639 1.00 0.73 C
-ATOM 194 C THR A 262 -1.654 9.933 -4.445 1.00 0.78 C
-ATOM 195 O THR A 262 -1.888 11.137 -4.556 1.00 0.83 O
-ATOM 196 CB THR A 262 -2.374 8.300 -6.191 1.00 0.98 C
-ATOM 197 OG1 THR A 262 -1.989 7.567 -7.361 1.00 1.03 O
-ATOM 198 CG2 THR A 262 -3.540 9.216 -6.522 1.00 1.23 C
-ATOM 199 H THR A 262 -0.199 7.232 -5.369 1.00 0.55 H
-ATOM 200 HA THR A 262 -0.815 9.762 -6.414 1.00 0.76 H
-ATOM 201 HB THR A 262 -2.695 7.600 -5.433 1.00 1.01 H
-ATOM 202 HG1 THR A 262 -1.498 6.774 -7.096 1.00 0.92 H
-ATOM 203 HG21 THR A 262 -3.553 9.412 -7.582 1.00 1.66 H
-ATOM 204 HG22 THR A 262 -3.428 10.145 -5.983 1.00 1.63 H
-ATOM 205 HG23 THR A 262 -4.463 8.740 -6.229 1.00 1.65 H
-ATOM 206 N MET A 263 -1.805 9.267 -3.307 1.00 0.88 N
-ATOM 207 CA MET A 263 -2.261 9.917 -2.080 1.00 1.07 C
-ATOM 208 C MET A 263 -1.219 10.907 -1.534 1.00 1.00 C
-ATOM 209 O MET A 263 -1.561 12.031 -1.167 1.00 1.11 O
-ATOM 210 CB MET A 263 -2.580 8.859 -1.019 1.00 1.27 C
-ATOM 211 CG MET A 263 -3.211 9.424 0.243 1.00 1.57 C
-ATOM 212 SD MET A 263 -4.779 10.252 -0.078 1.00 1.77 S
-ATOM 213 CE MET A 263 -5.241 10.754 1.578 1.00 2.12 C
-ATOM 214 H MET A 263 -1.614 8.303 -3.296 1.00 0.87 H
-ATOM 215 HA MET A 263 -3.164 10.460 -2.311 1.00 1.18 H
-ATOM 216 HB2 MET A 263 -3.262 8.137 -1.443 1.00 1.32 H
-ATOM 217 HB3 MET A 263 -1.664 8.357 -0.743 1.00 1.21 H
-ATOM 218 HG2 MET A 263 -3.384 8.615 0.936 1.00 1.74 H
-ATOM 219 HG3 MET A 263 -2.527 10.136 0.684 1.00 1.56 H
-ATOM 220 HE1 MET A 263 -6.143 11.345 1.537 1.00 2.74 H
-ATOM 221 HE2 MET A 263 -4.446 11.344 2.009 1.00 2.23 H
-ATOM 222 HE3 MET A 263 -5.411 9.879 2.184 1.00 2.24 H
-ATOM 223 N THR A 264 0.050 10.487 -1.463 1.00 0.89 N
-ATOM 224 CA THR A 264 1.110 11.362 -0.938 1.00 0.96 C
-ATOM 225 C THR A 264 1.419 12.531 -1.881 1.00 0.81 C
-ATOM 226 O THR A 264 1.666 13.647 -1.426 1.00 0.96 O
-ATOM 227 CB THR A 264 2.419 10.597 -0.615 1.00 1.05 C
-ATOM 228 OG1 THR A 264 3.372 11.486 -0.015 1.00 1.24 O
-ATOM 229 CG2 THR A 264 3.038 9.972 -1.857 1.00 0.90 C
-ATOM 230 H THR A 264 0.275 9.571 -1.758 1.00 0.84 H
-ATOM 231 HA THR A 264 0.740 11.776 -0.011 1.00 1.14 H
-ATOM 232 HB THR A 264 2.190 9.807 0.087 1.00 1.19 H
-ATOM 233 HG1 THR A 264 3.206 12.386 -0.313 1.00 1.34 H
-ATOM 234 HG21 THR A 264 4.114 9.971 -1.761 1.00 1.33 H
-ATOM 235 HG22 THR A 264 2.755 10.546 -2.727 1.00 1.25 H
-ATOM 236 HG23 THR A 264 2.685 8.958 -1.963 1.00 1.07 H
-ATOM 237 N THR A 265 1.421 12.276 -3.188 1.00 0.62 N
-ATOM 238 CA THR A 265 1.709 13.316 -4.165 1.00 0.63 C
-ATOM 239 C THR A 265 0.794 13.201 -5.388 1.00 0.59 C
-ATOM 240 O THR A 265 0.516 12.109 -5.878 1.00 0.66 O
-ATOM 241 CB THR A 265 3.187 13.264 -4.600 1.00 0.80 C
-ATOM 242 OG1 THR A 265 3.536 14.458 -5.312 1.00 0.93 O
-ATOM 243 CG2 THR A 265 3.482 12.049 -5.471 1.00 0.85 C
-ATOM 244 H THR A 265 1.239 11.369 -3.503 1.00 0.57 H
-ATOM 245 HA THR A 265 1.533 14.271 -3.690 1.00 0.68 H
-ATOM 246 HB THR A 265 3.784 13.191 -3.707 1.00 0.90 H
-ATOM 247 HG1 THR A 265 4.224 14.257 -5.952 1.00 1.31 H
-ATOM 248 HG21 THR A 265 4.405 12.209 -6.010 1.00 1.40 H
-ATOM 249 HG22 THR A 265 2.674 11.906 -6.174 1.00 1.28 H
-ATOM 250 HG23 THR A 265 3.576 11.173 -4.847 1.00 1.35 H
-ATOM 251 N GLN A 266 0.317 14.338 -5.875 1.00 0.58 N
-ATOM 252 CA GLN A 266 -0.577 14.352 -7.033 1.00 0.68 C
-ATOM 253 C GLN A 266 -0.662 15.744 -7.647 1.00 0.76 C
-ATOM 254 O GLN A 266 -0.527 15.901 -8.858 1.00 0.93 O
-ATOM 255 CB GLN A 266 -1.975 13.874 -6.624 1.00 0.75 C
-ATOM 256 CG GLN A 266 -2.964 13.781 -7.778 1.00 0.96 C
-ATOM 257 CD GLN A 266 -4.318 13.240 -7.351 1.00 1.12 C
-ATOM 258 OE1 GLN A 266 -5.237 13.134 -8.154 1.00 1.36 O
-ATOM 259 NE2 GLN A 266 -4.451 12.888 -6.082 1.00 1.17 N
-ATOM 260 H GLN A 266 0.565 15.182 -5.446 1.00 0.59 H
-ATOM 261 HA GLN A 266 -0.176 13.673 -7.767 1.00 0.85 H
-ATOM 262 HB2 GLN A 266 -1.891 12.895 -6.176 1.00 0.86 H
-ATOM 263 HB3 GLN A 266 -2.376 14.559 -5.891 1.00 0.73 H
-ATOM 264 HG2 GLN A 266 -3.106 14.769 -8.194 1.00 1.03 H
-ATOM 265 HG3 GLN A 266 -2.555 13.130 -8.535 1.00 1.12 H
-ATOM 266 HE21 GLN A 266 -3.677 12.993 -5.487 1.00 1.09 H
-ATOM 267 HE22 GLN A 266 -5.315 12.531 -5.793 1.00 1.39 H
-ATOM 268 N SER A 267 -0.883 16.749 -6.796 1.00 0.80 N
-ATOM 269 CA SER A 267 -0.991 18.154 -7.229 1.00 1.02 C
-ATOM 270 C SER A 267 -2.010 18.325 -8.363 1.00 1.00 C
-ATOM 271 O SER A 267 -1.717 18.928 -9.396 1.00 1.22 O
-ATOM 272 CB SER A 267 0.378 18.723 -7.646 1.00 1.30 C
-ATOM 273 OG SER A 267 0.965 17.991 -8.711 1.00 1.38 O
-ATOM 274 H SER A 267 -0.978 16.543 -5.845 1.00 0.81 H
-ATOM 275 HA SER A 267 -1.347 18.717 -6.378 1.00 1.15 H
-ATOM 276 HB2 SER A 267 0.253 19.748 -7.964 1.00 1.44 H
-ATOM 277 HB3 SER A 267 1.046 18.694 -6.797 1.00 1.46 H
-ATOM 278 HG SER A 267 0.425 17.206 -8.902 1.00 1.20 H
-ATOM 279 N SER A 268 -3.210 17.787 -8.165 1.00 0.90 N
-ATOM 280 CA SER A 268 -4.273 17.878 -9.169 1.00 1.04 C
-ATOM 281 C SER A 268 -5.646 17.658 -8.534 1.00 1.13 C
-ATOM 282 O SER A 268 -5.830 17.888 -7.339 1.00 1.58 O
-ATOM 283 CB SER A 268 -4.051 16.845 -10.283 1.00 1.08 C
-ATOM 284 OG SER A 268 -2.892 17.142 -11.046 1.00 1.28 O
-ATOM 285 H SER A 268 -3.390 17.316 -7.323 1.00 0.87 H
-ATOM 286 HA SER A 268 -4.241 18.868 -9.597 1.00 1.22 H
-ATOM 287 HB2 SER A 268 -3.931 15.867 -9.843 1.00 0.98 H
-ATOM 288 HB3 SER A 268 -4.909 16.840 -10.940 1.00 1.25 H
-ATOM 289 HG SER A 268 -2.348 17.792 -10.573 1.00 1.33 H
-ATOM 290 N GLY A 269 -6.605 17.206 -9.335 1.00 1.11 N
-ATOM 291 CA GLY A 269 -7.942 16.963 -8.823 1.00 1.33 C
-ATOM 292 C GLY A 269 -8.945 16.649 -9.920 1.00 1.43 C
-ATOM 293 O GLY A 269 -8.666 15.845 -10.812 1.00 1.34 O
-ATOM 294 H GLY A 269 -6.405 17.034 -10.278 1.00 1.27 H
-ATOM 295 HA2 GLY A 269 -7.904 16.129 -8.139 1.00 1.33 H
-ATOM 296 HA3 GLY A 269 -8.275 17.839 -8.287 1.00 1.59 H
-ATOM 297 N SER A 270 -10.116 17.291 -9.841 1.00 1.81 N
-ATOM 298 CA SER A 270 -11.211 17.112 -10.816 1.00 2.06 C
-ATOM 299 C SER A 270 -11.830 15.707 -10.750 1.00 1.91 C
-ATOM 300 O SER A 270 -13.029 15.562 -10.517 1.00 2.18 O
-ATOM 301 CB SER A 270 -10.735 17.406 -12.246 1.00 2.25 C
-ATOM 302 OG SER A 270 -10.258 18.736 -12.367 1.00 2.54 O
-ATOM 303 H SER A 270 -10.254 17.915 -9.099 1.00 2.00 H
-ATOM 304 HA SER A 270 -11.980 17.826 -10.561 1.00 2.39 H
-ATOM 305 HB2 SER A 270 -9.936 16.727 -12.504 1.00 2.03 H
-ATOM 306 HB3 SER A 270 -11.558 17.268 -12.931 1.00 2.51 H
-ATOM 307 HG SER A 270 -9.316 18.721 -12.561 1.00 2.75 H
-ATOM 308 N ARG A 271 -11.008 14.680 -10.950 1.00 1.62 N
-ATOM 309 CA ARG A 271 -11.477 13.297 -10.907 1.00 1.54 C
-ATOM 310 C ARG A 271 -11.280 12.686 -9.513 1.00 1.61 C
-ATOM 311 O ARG A 271 -12.210 12.108 -8.948 1.00 1.89 O
-ATOM 312 CB ARG A 271 -10.750 12.454 -11.953 1.00 1.36 C
-ATOM 313 CG ARG A 271 -11.359 11.076 -12.108 1.00 1.38 C
-ATOM 314 CD ARG A 271 -12.712 11.136 -12.803 1.00 1.78 C
-ATOM 315 NE ARG A 271 -13.579 10.033 -12.396 1.00 1.90 N
-ATOM 316 CZ ARG A 271 -13.368 8.768 -12.694 1.00 1.80 C
-ATOM 317 NH1 ARG A 271 -12.412 8.422 -13.515 1.00 1.66 N
-ATOM 318 NH2 ARG A 271 -14.138 7.847 -12.185 1.00 2.09 N
-ATOM 319 H ARG A 271 -10.055 14.861 -11.128 1.00 1.58 H
-ATOM 320 HA ARG A 271 -12.531 13.292 -11.134 1.00 1.80 H
-ATOM 321 HB2 ARG A 271 -10.793 12.960 -12.907 1.00 1.58 H
-ATOM 322 HB3 ARG A 271 -9.717 12.339 -11.657 1.00 1.22 H
-ATOM 323 HG2 ARG A 271 -10.692 10.455 -12.686 1.00 1.34 H
-ATOM 324 HG3 ARG A 271 -11.494 10.653 -11.126 1.00 1.39 H
-ATOM 325 HD2 ARG A 271 -13.190 12.071 -12.554 1.00 2.06 H
-ATOM 326 HD3 ARG A 271 -12.556 11.085 -13.871 1.00 1.94 H
-ATOM 327 HE ARG A 271 -14.342 10.252 -11.826 1.00 2.20 H
-ATOM 328 HH11 ARG A 271 -11.833 9.115 -13.928 1.00 1.64 H
-ATOM 329 HH12 ARG A 271 -12.258 7.435 -13.727 1.00 1.76 H
-ATOM 330 HH21 ARG A 271 -14.886 8.100 -11.580 1.00 2.38 H
-ATOM 331 HH22 ARG A 271 -13.959 6.863 -12.392 1.00 2.11 H
-ATOM 332 N ALA A 272 -10.059 12.823 -8.978 1.00 1.59 N
-ATOM 333 CA ALA A 272 -9.686 12.301 -7.647 1.00 1.91 C
-ATOM 334 C ALA A 272 -9.648 10.764 -7.583 1.00 1.85 C
-ATOM 335 O ALA A 272 -8.663 10.179 -7.128 1.00 1.98 O
-ATOM 336 CB ALA A 272 -10.608 12.856 -6.566 1.00 2.40 C
-ATOM 337 H ALA A 272 -9.377 13.295 -9.498 1.00 1.49 H
-ATOM 338 HA ALA A 272 -8.692 12.663 -7.438 1.00 1.99 H
-ATOM 339 HB1 ALA A 272 -11.368 12.126 -6.331 1.00 2.66 H
-ATOM 340 HB2 ALA A 272 -11.077 13.761 -6.924 1.00 2.76 H
-ATOM 341 HB3 ALA A 272 -10.032 13.075 -5.678 1.00 2.76 H
-ATOM 342 N SER A 273 -10.716 10.115 -8.027 1.00 1.78 N
-ATOM 343 CA SER A 273 -10.792 8.652 -8.008 1.00 1.83 C
-ATOM 344 C SER A 273 -9.941 8.025 -9.101 1.00 1.49 C
-ATOM 345 O SER A 273 -10.274 8.083 -10.287 1.00 1.28 O
-ATOM 346 CB SER A 273 -12.234 8.172 -8.163 1.00 2.04 C
-ATOM 347 OG SER A 273 -12.813 8.665 -9.359 1.00 1.86 O
-ATOM 348 H SER A 273 -11.478 10.634 -8.375 1.00 1.80 H
-ATOM 349 HA SER A 273 -10.419 8.310 -7.054 1.00 2.14 H
-ATOM 350 HB2 SER A 273 -12.244 7.090 -8.194 1.00 2.15 H
-ATOM 351 HB3 SER A 273 -12.817 8.512 -7.322 1.00 2.39 H
-ATOM 352 HG SER A 273 -12.182 8.563 -10.083 1.00 1.71 H
-ATOM 353 N LEU A 274 -8.856 7.400 -8.676 1.00 1.70 N
-ATOM 354 CA LEU A 274 -7.943 6.720 -9.582 1.00 1.73 C
-ATOM 355 C LEU A 274 -8.362 5.264 -9.941 1.00 1.78 C
-ATOM 356 O LEU A 274 -7.820 4.717 -10.900 1.00 1.92 O
-ATOM 357 CB LEU A 274 -6.517 6.715 -9.009 1.00 2.18 C
-ATOM 358 CG LEU A 274 -6.299 5.890 -7.733 1.00 2.55 C
-ATOM 359 CD1 LEU A 274 -4.826 5.543 -7.587 1.00 3.04 C
-ATOM 360 CD2 LEU A 274 -6.778 6.647 -6.501 1.00 2.72 C
-ATOM 361 H LEU A 274 -8.670 7.383 -7.715 1.00 2.00 H
-ATOM 362 HA LEU A 274 -7.927 7.292 -10.497 1.00 1.64 H
-ATOM 363 HB2 LEU A 274 -5.852 6.334 -9.769 1.00 2.35 H
-ATOM 364 HB3 LEU A 274 -6.239 7.737 -8.796 1.00 2.25 H
-ATOM 365 HG LEU A 274 -6.857 4.968 -7.804 1.00 2.52 H
-ATOM 366 HD11 LEU A 274 -4.661 4.527 -7.917 1.00 3.33 H
-ATOM 367 HD12 LEU A 274 -4.533 5.638 -6.552 1.00 3.33 H
-ATOM 368 HD13 LEU A 274 -4.237 6.217 -8.192 1.00 3.32 H
-ATOM 369 HD21 LEU A 274 -7.635 7.251 -6.761 1.00 2.95 H
-ATOM 370 HD22 LEU A 274 -5.984 7.284 -6.139 1.00 2.95 H
-ATOM 371 HD23 LEU A 274 -7.053 5.941 -5.731 1.00 2.97 H
-ATOM 372 N PRO A 275 -9.297 4.575 -9.206 1.00 1.90 N
-ATOM 373 CA PRO A 275 -9.671 3.182 -9.539 1.00 2.10 C
-ATOM 374 C PRO A 275 -10.232 3.005 -10.954 1.00 2.06 C
-ATOM 375 O PRO A 275 -9.909 2.032 -11.639 1.00 2.34 O
-ATOM 376 CB PRO A 275 -10.739 2.840 -8.496 1.00 2.38 C
-ATOM 377 CG PRO A 275 -10.422 3.739 -7.365 1.00 2.48 C
-ATOM 378 CD PRO A 275 -10.046 5.033 -8.013 1.00 2.10 C
-ATOM 379 HA PRO A 275 -8.833 2.520 -9.407 1.00 2.27 H
-ATOM 380 HB2 PRO A 275 -11.722 3.034 -8.900 1.00 2.38 H
-ATOM 381 HB3 PRO A 275 -10.655 1.801 -8.212 1.00 2.63 H
-ATOM 382 HG2 PRO A 275 -11.287 3.864 -6.729 1.00 2.73 H
-ATOM 383 HG3 PRO A 275 -9.587 3.343 -6.810 1.00 2.69 H
-ATOM 384 HD2 PRO A 275 -10.933 5.587 -8.299 1.00 2.00 H
-ATOM 385 HD3 PRO A 275 -9.425 5.629 -7.362 1.00 2.22 H
-ATOM 386 N LYS A 276 -11.074 3.944 -11.383 1.00 1.90 N
-ATOM 387 CA LYS A 276 -11.685 3.886 -12.716 1.00 2.12 C
-ATOM 388 C LYS A 276 -12.443 5.179 -13.039 1.00 2.01 C
-ATOM 389 O LYS A 276 -11.970 5.952 -13.902 1.00 1.99 O
-ATOM 390 CB LYS A 276 -12.642 2.691 -12.808 1.00 2.58 C
-ATOM 391 CG LYS A 276 -13.163 2.421 -14.213 1.00 2.97 C
-ATOM 392 CD LYS A 276 -12.033 2.153 -15.199 1.00 3.14 C
-ATOM 393 CE LYS A 276 -11.131 1.016 -14.739 1.00 3.34 C
-ATOM 394 NZ LYS A 276 -11.888 -0.247 -14.525 1.00 3.76 N
-ATOM 395 OXT LYS A 276 -13.505 5.415 -12.426 1.00 2.16 O
-ATOM 396 H LYS A 276 -11.291 4.690 -10.788 1.00 1.77 H
-ATOM 397 HA LYS A 276 -10.893 3.758 -13.440 1.00 2.21 H
-ATOM 398 HB2 LYS A 276 -12.126 1.807 -12.465 1.00 2.71 H
-ATOM 399 HB3 LYS A 276 -13.489 2.874 -12.164 1.00 2.66 H
-ATOM 400 HG2 LYS A 276 -13.812 1.559 -14.185 1.00 3.31 H
-ATOM 401 HG3 LYS A 276 -13.722 3.283 -14.547 1.00 2.94 H
-ATOM 402 HD2 LYS A 276 -12.461 1.892 -16.155 1.00 3.51 H
-ATOM 403 HD3 LYS A 276 -11.441 3.051 -15.304 1.00 2.92 H
-ATOM 404 HE2 LYS A 276 -10.375 0.848 -15.491 1.00 3.60 H
-ATOM 405 HE3 LYS A 276 -10.656 1.303 -13.811 1.00 3.07 H
-ATOM 406 HZ1 LYS A 276 -11.375 -1.048 -14.944 1.00 4.01 H
-ATOM 407 HZ2 LYS A 276 -12.826 -0.181 -14.969 1.00 3.99 H
-ATOM 408 HZ3 LYS A 276 -12.010 -0.422 -13.507 1.00 3.98 H
-TER
-ENDMDL
-MODEL 20
-ATOM 1 N ALA A 249 4.989 -11.131 -1.600 1.00 2.74 N
-ATOM 2 CA ALA A 249 4.588 -9.724 -1.871 1.00 2.30 C
-ATOM 3 C ALA A 249 3.234 -9.408 -1.236 1.00 1.71 C
-ATOM 4 O ALA A 249 2.438 -10.311 -0.981 1.00 1.73 O
-ATOM 5 CB ALA A 249 4.544 -9.460 -3.369 1.00 2.46 C
-ATOM 6 H1 ALA A 249 5.334 -11.580 -2.474 1.00 2.96 H
-ATOM 7 H2 ALA A 249 4.170 -11.671 -1.247 1.00 2.77 H
-ATOM 8 H3 ALA A 249 5.745 -11.160 -0.885 1.00 3.04 H
-ATOM 9 HA ALA A 249 5.334 -9.071 -1.439 1.00 2.46 H
-ATOM 10 HB1 ALA A 249 3.892 -10.177 -3.841 1.00 2.75 H
-ATOM 11 HB2 ALA A 249 5.538 -9.548 -3.779 1.00 2.71 H
-ATOM 12 HB3 ALA A 249 4.169 -8.462 -3.544 1.00 2.65 H
-ATOM 13 N PRO A 250 2.956 -8.119 -0.970 1.00 1.47 N
-ATOM 14 CA PRO A 250 1.694 -7.674 -0.357 1.00 1.23 C
-ATOM 15 C PRO A 250 0.507 -7.692 -1.329 1.00 1.00 C
-ATOM 16 O PRO A 250 -0.219 -6.705 -1.451 1.00 1.11 O
-ATOM 17 CB PRO A 250 2.028 -6.244 0.066 1.00 1.62 C
-ATOM 18 CG PRO A 250 3.004 -5.783 -0.949 1.00 1.80 C
-ATOM 19 CD PRO A 250 3.852 -6.981 -1.249 1.00 1.85 C
-ATOM 20 HA PRO A 250 1.451 -8.262 0.514 1.00 1.35 H
-ATOM 21 HB2 PRO A 250 1.137 -5.643 0.058 1.00 1.71 H
-ATOM 22 HB3 PRO A 250 2.466 -6.251 1.052 1.00 1.90 H
-ATOM 23 HG2 PRO A 250 2.484 -5.454 -1.838 1.00 1.66 H
-ATOM 24 HG3 PRO A 250 3.609 -4.984 -0.547 1.00 2.27 H
-ATOM 25 HD2 PRO A 250 4.155 -6.977 -2.282 1.00 1.98 H
-ATOM 26 HD3 PRO A 250 4.710 -7.005 -0.597 1.00 2.21 H
-ATOM 27 N ALA A 251 0.321 -8.828 -2.007 1.00 1.13 N
-ATOM 28 CA ALA A 251 -0.769 -9.008 -2.971 1.00 1.33 C
-ATOM 29 C ALA A 251 -0.737 -7.945 -4.077 1.00 1.06 C
-ATOM 30 O ALA A 251 0.055 -8.036 -5.017 1.00 1.00 O
-ATOM 31 CB ALA A 251 -2.116 -9.016 -2.253 1.00 1.70 C
-ATOM 32 H ALA A 251 0.940 -9.575 -1.851 1.00 1.35 H
-ATOM 33 HA ALA A 251 -0.636 -9.977 -3.430 1.00 1.67 H
-ATOM 34 HB1 ALA A 251 -2.261 -9.973 -1.773 1.00 2.09 H
-ATOM 35 HB2 ALA A 251 -2.906 -8.849 -2.968 1.00 1.87 H
-ATOM 36 HB3 ALA A 251 -2.132 -8.234 -1.509 1.00 1.96 H
-ATOM 37 N ARG A 252 -1.594 -6.938 -3.958 1.00 1.00 N
-ATOM 38 CA ARG A 252 -1.660 -5.863 -4.941 1.00 0.79 C
-ATOM 39 C ARG A 252 -0.581 -4.805 -4.725 1.00 0.69 C
-ATOM 40 O ARG A 252 -0.879 -3.634 -4.490 1.00 0.65 O
-ATOM 41 CB ARG A 252 -3.044 -5.223 -4.939 1.00 0.86 C
-ATOM 42 CG ARG A 252 -4.061 -6.044 -5.701 1.00 0.96 C
-ATOM 43 CD ARG A 252 -3.563 -6.354 -7.107 1.00 0.91 C
-ATOM 44 NE ARG A 252 -4.508 -7.170 -7.868 1.00 1.07 N
-ATOM 45 CZ ARG A 252 -5.694 -6.758 -8.263 1.00 1.22 C
-ATOM 46 NH1 ARG A 252 -6.077 -5.528 -8.039 1.00 1.26 N
-ATOM 47 NH2 ARG A 252 -6.493 -7.577 -8.900 1.00 1.40 N
-ATOM 48 H ARG A 252 -2.189 -6.913 -3.186 1.00 1.16 H
-ATOM 49 HA ARG A 252 -1.497 -6.311 -5.909 1.00 0.74 H
-ATOM 50 HB2 ARG A 252 -3.381 -5.119 -3.919 1.00 1.02 H
-ATOM 51 HB3 ARG A 252 -2.982 -4.247 -5.395 1.00 0.79 H
-ATOM 52 HG2 ARG A 252 -4.232 -6.972 -5.175 1.00 1.07 H
-ATOM 53 HG3 ARG A 252 -4.984 -5.488 -5.769 1.00 1.06 H
-ATOM 54 HD2 ARG A 252 -3.400 -5.423 -7.630 1.00 0.90 H
-ATOM 55 HD3 ARG A 252 -2.623 -6.886 -7.029 1.00 0.93 H
-ATOM 56 HE ARG A 252 -4.241 -8.085 -8.078 1.00 1.13 H
-ATOM 57 HH11 ARG A 252 -5.466 -4.895 -7.570 1.00 1.17 H
-ATOM 58 HH12 ARG A 252 -6.977 -5.223 -8.337 1.00 1.44 H
-ATOM 59 HH21 ARG A 252 -6.203 -8.512 -9.088 1.00 1.43 H
-ATOM 60 HH22 ARG A 252 -7.392 -7.265 -9.198 1.00 1.57 H
-ATOM 61 N VAL A 253 0.669 -5.234 -4.823 1.00 0.72 N
-ATOM 62 CA VAL A 253 1.825 -4.350 -4.657 1.00 0.72 C
-ATOM 63 C VAL A 253 1.718 -3.096 -5.534 1.00 0.58 C
-ATOM 64 O VAL A 253 1.806 -1.972 -5.042 1.00 0.54 O
-ATOM 65 CB VAL A 253 3.133 -5.106 -4.985 1.00 0.85 C
-ATOM 66 CG1 VAL A 253 3.052 -5.809 -6.335 1.00 0.82 C
-ATOM 67 CG2 VAL A 253 4.332 -4.170 -4.930 1.00 1.00 C
-ATOM 68 H VAL A 253 0.819 -6.187 -5.016 1.00 0.80 H
-ATOM 69 HA VAL A 253 1.863 -4.047 -3.623 1.00 0.80 H
-ATOM 70 HB VAL A 253 3.265 -5.860 -4.240 1.00 0.96 H
-ATOM 71 HG11 VAL A 253 3.683 -6.685 -6.324 1.00 1.31 H
-ATOM 72 HG12 VAL A 253 3.381 -5.138 -7.113 1.00 1.31 H
-ATOM 73 HG13 VAL A 253 2.030 -6.105 -6.525 1.00 1.31 H
-ATOM 74 HG21 VAL A 253 4.170 -3.340 -5.601 1.00 1.48 H
-ATOM 75 HG22 VAL A 253 5.221 -4.705 -5.229 1.00 1.48 H
-ATOM 76 HG23 VAL A 253 4.455 -3.801 -3.924 1.00 1.42 H
-ATOM 77 N GLY A 254 1.524 -3.316 -6.828 1.00 0.57 N
-ATOM 78 CA GLY A 254 1.401 -2.216 -7.781 1.00 0.55 C
-ATOM 79 C GLY A 254 0.300 -1.234 -7.408 1.00 0.46 C
-ATOM 80 O GLY A 254 0.527 -0.024 -7.363 1.00 0.46 O
-ATOM 81 H GLY A 254 1.471 -4.244 -7.136 1.00 0.63 H
-ATOM 82 HA2 GLY A 254 2.340 -1.686 -7.824 1.00 0.62 H
-ATOM 83 HA3 GLY A 254 1.186 -2.625 -8.758 1.00 0.62 H
-ATOM 84 N LEU A 255 -0.888 -1.757 -7.113 1.00 0.48 N
-ATOM 85 CA LEU A 255 -2.017 -0.920 -6.715 1.00 0.50 C
-ATOM 86 C LEU A 255 -1.721 -0.201 -5.402 1.00 0.43 C
-ATOM 87 O LEU A 255 -2.024 0.982 -5.248 1.00 0.43 O
-ATOM 88 CB LEU A 255 -3.285 -1.756 -6.567 1.00 0.65 C
-ATOM 89 CG LEU A 255 -4.335 -1.520 -7.649 1.00 0.82 C
-ATOM 90 CD1 LEU A 255 -5.593 -2.315 -7.347 1.00 1.01 C
-ATOM 91 CD2 LEU A 255 -4.652 -0.036 -7.764 1.00 0.92 C
-ATOM 92 H LEU A 255 -1.002 -2.728 -7.147 1.00 0.54 H
-ATOM 93 HA LEU A 255 -2.174 -0.183 -7.488 1.00 0.54 H
-ATOM 94 HB2 LEU A 255 -3.007 -2.801 -6.584 1.00 0.72 H
-ATOM 95 HB3 LEU A 255 -3.730 -1.534 -5.610 1.00 0.71 H
-ATOM 96 HG LEU A 255 -3.948 -1.857 -8.599 1.00 0.89 H
-ATOM 97 HD11 LEU A 255 -5.607 -3.208 -7.954 1.00 1.66 H
-ATOM 98 HD12 LEU A 255 -6.461 -1.714 -7.569 1.00 1.00 H
-ATOM 99 HD13 LEU A 255 -5.602 -2.590 -6.302 1.00 1.60 H
-ATOM 100 HD21 LEU A 255 -4.541 0.431 -6.797 1.00 1.26 H
-ATOM 101 HD22 LEU A 255 -5.666 0.091 -8.110 1.00 1.51 H
-ATOM 102 HD23 LEU A 255 -3.972 0.423 -8.466 1.00 1.43 H
-ATOM 103 N GLY A 256 -1.118 -0.925 -4.462 1.00 0.46 N
-ATOM 104 CA GLY A 256 -0.780 -0.342 -3.179 1.00 0.50 C
-ATOM 105 C GLY A 256 0.193 0.818 -3.303 1.00 0.41 C
-ATOM 106 O GLY A 256 -0.075 1.908 -2.799 1.00 0.40 O
-ATOM 107 H GLY A 256 -0.896 -1.867 -4.645 1.00 0.52 H
-ATOM 108 HA2 GLY A 256 -1.687 0.010 -2.708 1.00 0.56 H
-ATOM 109 HA3 GLY A 256 -0.339 -1.105 -2.556 1.00 0.61 H
-ATOM 110 N ILE A 257 1.318 0.598 -3.991 1.00 0.42 N
-ATOM 111 CA ILE A 257 2.308 1.660 -4.173 1.00 0.46 C
-ATOM 112 C ILE A 257 1.736 2.813 -5.002 1.00 0.41 C
-ATOM 113 O ILE A 257 2.004 3.979 -4.716 1.00 0.46 O
-ATOM 114 CB ILE A 257 3.635 1.143 -4.794 1.00 0.61 C
-ATOM 115 CG1 ILE A 257 3.421 0.507 -6.173 1.00 0.62 C
-ATOM 116 CG2 ILE A 257 4.301 0.146 -3.857 1.00 0.75 C
-ATOM 117 CD1 ILE A 257 3.720 1.438 -7.331 1.00 0.71 C
-ATOM 118 H ILE A 257 1.477 -0.291 -4.388 1.00 0.46 H
-ATOM 119 HA ILE A 257 2.537 2.045 -3.189 1.00 0.51 H
-ATOM 120 HB ILE A 257 4.301 1.987 -4.900 1.00 0.69 H
-ATOM 121 HG12 ILE A 257 4.065 -0.354 -6.269 1.00 0.72 H
-ATOM 122 HG13 ILE A 257 2.391 0.191 -6.259 1.00 0.57 H
-ATOM 123 HG21 ILE A 257 4.219 -0.848 -4.270 1.00 1.33 H
-ATOM 124 HG22 ILE A 257 3.814 0.177 -2.893 1.00 1.29 H
-ATOM 125 HG23 ILE A 257 5.344 0.403 -3.741 1.00 1.14 H
-ATOM 126 HD11 ILE A 257 3.578 0.911 -8.263 1.00 1.27 H
-ATOM 127 HD12 ILE A 257 4.741 1.781 -7.263 1.00 1.25 H
-ATOM 128 HD13 ILE A 257 3.052 2.285 -7.294 1.00 1.28 H
-ATOM 129 N THR A 258 0.925 2.487 -6.014 1.00 0.38 N
-ATOM 130 CA THR A 258 0.303 3.513 -6.855 1.00 0.45 C
-ATOM 131 C THR A 258 -0.668 4.358 -6.031 1.00 0.40 C
-ATOM 132 O THR A 258 -0.708 5.580 -6.165 1.00 0.49 O
-ATOM 133 CB THR A 258 -0.448 2.895 -8.055 1.00 0.55 C
-ATOM 134 OG1 THR A 258 0.453 2.110 -8.846 1.00 0.64 O
-ATOM 135 CG2 THR A 258 -1.075 3.974 -8.929 1.00 0.71 C
-ATOM 136 H THR A 258 0.730 1.536 -6.189 1.00 0.37 H
-ATOM 137 HA THR A 258 1.087 4.152 -7.235 1.00 0.56 H
-ATOM 138 HB THR A 258 -1.234 2.256 -7.679 1.00 0.52 H
-ATOM 139 HG1 THR A 258 0.593 1.253 -8.416 1.00 0.59 H
-ATOM 140 HG21 THR A 258 -1.068 4.915 -8.401 1.00 1.31 H
-ATOM 141 HG22 THR A 258 -2.093 3.701 -9.164 1.00 1.22 H
-ATOM 142 HG23 THR A 258 -0.508 4.071 -9.843 1.00 1.22 H
-ATOM 143 N THR A 259 -1.433 3.701 -5.159 1.00 0.34 N
-ATOM 144 CA THR A 259 -2.384 4.401 -4.295 1.00 0.39 C
-ATOM 145 C THR A 259 -1.636 5.315 -3.328 1.00 0.33 C
-ATOM 146 O THR A 259 -1.961 6.498 -3.199 1.00 0.38 O
-ATOM 147 CB THR A 259 -3.265 3.418 -3.490 1.00 0.50 C
-ATOM 148 OG1 THR A 259 -3.940 2.520 -4.380 1.00 0.61 O
-ATOM 149 CG2 THR A 259 -4.294 4.166 -2.653 1.00 0.63 C
-ATOM 150 H THR A 259 -1.345 2.724 -5.081 1.00 0.34 H
-ATOM 151 HA THR A 259 -3.026 5.004 -4.923 1.00 0.48 H
-ATOM 152 HB THR A 259 -2.630 2.848 -2.828 1.00 0.51 H
-ATOM 153 HG1 THR A 259 -3.309 1.869 -4.720 1.00 0.59 H
-ATOM 154 HG21 THR A 259 -5.238 4.188 -3.177 1.00 1.25 H
-ATOM 155 HG22 THR A 259 -3.955 5.176 -2.481 1.00 1.20 H
-ATOM 156 HG23 THR A 259 -4.420 3.664 -1.705 1.00 1.19 H
-ATOM 157 N VAL A 260 -0.612 4.765 -2.673 1.00 0.31 N
-ATOM 158 CA VAL A 260 0.203 5.542 -1.741 1.00 0.36 C
-ATOM 159 C VAL A 260 0.876 6.715 -2.461 1.00 0.36 C
-ATOM 160 O VAL A 260 0.840 7.850 -1.981 1.00 0.40 O
-ATOM 161 CB VAL A 260 1.280 4.666 -1.061 1.00 0.49 C
-ATOM 162 CG1 VAL A 260 2.178 5.506 -0.166 1.00 0.63 C
-ATOM 163 CG2 VAL A 260 0.629 3.548 -0.260 1.00 0.59 C
-ATOM 164 H VAL A 260 -0.388 3.818 -2.835 1.00 0.33 H
-ATOM 165 HA VAL A 260 -0.452 5.932 -0.974 1.00 0.40 H
-ATOM 166 HB VAL A 260 1.892 4.220 -1.830 1.00 0.53 H
-ATOM 167 HG11 VAL A 260 3.126 5.007 -0.037 1.00 1.21 H
-ATOM 168 HG12 VAL A 260 1.705 5.636 0.796 1.00 1.20 H
-ATOM 169 HG13 VAL A 260 2.338 6.472 -0.621 1.00 1.25 H
-ATOM 170 HG21 VAL A 260 -0.191 3.131 -0.825 1.00 1.17 H
-ATOM 171 HG22 VAL A 260 0.258 3.943 0.674 1.00 1.29 H
-ATOM 172 HG23 VAL A 260 1.357 2.777 -0.060 1.00 1.11 H
-ATOM 173 N LEU A 261 1.469 6.437 -3.627 1.00 0.41 N
-ATOM 174 CA LEU A 261 2.130 7.471 -4.424 1.00 0.55 C
-ATOM 175 C LEU A 261 1.137 8.562 -4.832 1.00 0.57 C
-ATOM 176 O LEU A 261 1.440 9.748 -4.758 1.00 0.66 O
-ATOM 177 CB LEU A 261 2.770 6.860 -5.669 1.00 0.67 C
-ATOM 178 CG LEU A 261 3.716 7.793 -6.421 1.00 0.86 C
-ATOM 179 CD1 LEU A 261 4.808 8.304 -5.490 1.00 0.95 C
-ATOM 180 CD2 LEU A 261 4.320 7.076 -7.614 1.00 0.99 C
-ATOM 181 H LEU A 261 1.452 5.512 -3.965 1.00 0.40 H
-ATOM 182 HA LEU A 261 2.910 7.916 -3.822 1.00 0.62 H
-ATOM 183 HB2 LEU A 261 3.323 5.981 -5.371 1.00 0.67 H
-ATOM 184 HB3 LEU A 261 1.983 6.561 -6.344 1.00 0.66 H
-ATOM 185 HG LEU A 261 3.162 8.645 -6.788 1.00 0.89 H
-ATOM 186 HD11 LEU A 261 4.510 8.139 -4.464 1.00 1.46 H
-ATOM 187 HD12 LEU A 261 4.955 9.360 -5.654 1.00 1.36 H
-ATOM 188 HD13 LEU A 261 5.728 7.776 -5.688 1.00 1.42 H
-ATOM 189 HD21 LEU A 261 3.549 6.880 -8.345 1.00 1.49 H
-ATOM 190 HD22 LEU A 261 4.757 6.142 -7.290 1.00 1.41 H
-ATOM 191 HD23 LEU A 261 5.084 7.698 -8.054 1.00 1.45 H
-ATOM 192 N THR A 262 -0.056 8.146 -5.244 1.00 0.53 N
-ATOM 193 CA THR A 262 -1.107 9.084 -5.643 1.00 0.62 C
-ATOM 194 C THR A 262 -1.542 9.951 -4.458 1.00 0.59 C
-ATOM 195 O THR A 262 -1.669 11.171 -4.572 1.00 0.69 O
-ATOM 196 CB THR A 262 -2.342 8.335 -6.191 1.00 0.65 C
-ATOM 197 OG1 THR A 262 -1.984 7.565 -7.345 1.00 0.71 O
-ATOM 198 CG2 THR A 262 -3.456 9.306 -6.549 1.00 0.81 C
-ATOM 199 H THR A 262 -0.242 7.180 -5.271 1.00 0.49 H
-ATOM 200 HA THR A 262 -0.716 9.718 -6.424 1.00 0.74 H
-ATOM 201 HB THR A 262 -2.704 7.667 -5.422 1.00 0.58 H
-ATOM 202 HG1 THR A 262 -1.534 6.753 -7.063 1.00 0.70 H
-ATOM 203 HG21 THR A 262 -4.408 8.868 -6.288 1.00 0.91 H
-ATOM 204 HG22 THR A 262 -3.429 9.509 -7.607 1.00 0.97 H
-ATOM 205 HG23 THR A 262 -3.320 10.226 -5.999 1.00 0.79 H
-ATOM 206 N MET A 263 -1.773 9.298 -3.322 1.00 0.49 N
-ATOM 207 CA MET A 263 -2.200 9.976 -2.100 1.00 0.52 C
-ATOM 208 C MET A 263 -1.141 10.967 -1.590 1.00 0.53 C
-ATOM 209 O MET A 263 -1.473 12.090 -1.212 1.00 0.58 O
-ATOM 210 CB MET A 263 -2.510 8.944 -1.013 1.00 0.53 C
-ATOM 211 CG MET A 263 -3.156 9.535 0.230 1.00 0.67 C
-ATOM 212 SD MET A 263 -4.776 10.251 -0.105 1.00 0.88 S
-ATOM 213 CE MET A 263 -5.676 8.813 -0.682 1.00 1.05 C
-ATOM 214 H MET A 263 -1.660 8.321 -3.310 1.00 0.44 H
-ATOM 215 HA MET A 263 -3.103 10.524 -2.325 1.00 0.62 H
-ATOM 216 HB2 MET A 263 -3.178 8.200 -1.422 1.00 0.57 H
-ATOM 217 HB3 MET A 263 -1.589 8.462 -0.719 1.00 0.51 H
-ATOM 218 HG2 MET A 263 -3.270 8.753 0.966 1.00 0.76 H
-ATOM 219 HG3 MET A 263 -2.510 10.305 0.623 1.00 0.68 H
-ATOM 220 HE1 MET A 263 -6.042 8.253 0.166 1.00 1.18 H
-ATOM 221 HE2 MET A 263 -5.018 8.189 -1.268 1.00 1.13 H
-ATOM 222 HE3 MET A 263 -6.509 9.129 -1.292 1.00 1.17 H
-ATOM 223 N THR A 264 0.127 10.550 -1.572 1.00 0.52 N
-ATOM 224 CA THR A 264 1.201 11.428 -1.096 1.00 0.60 C
-ATOM 225 C THR A 264 1.434 12.605 -2.048 1.00 0.67 C
-ATOM 226 O THR A 264 1.304 12.478 -3.267 1.00 0.72 O
-ATOM 227 CB THR A 264 2.540 10.684 -0.877 1.00 0.65 C
-ATOM 228 OG1 THR A 264 3.521 11.594 -0.363 1.00 0.77 O
-ATOM 229 CG2 THR A 264 3.062 10.067 -2.163 1.00 0.68 C
-ATOM 230 H THR A 264 0.342 9.638 -1.879 1.00 0.51 H
-ATOM 231 HA THR A 264 0.887 11.828 -0.142 1.00 0.61 H
-ATOM 232 HB THR A 264 2.380 9.895 -0.154 1.00 0.65 H
-ATOM 233 HG1 THR A 264 4.075 11.141 0.279 1.00 0.89 H
-ATOM 234 HG21 THR A 264 3.927 9.457 -1.946 1.00 1.23 H
-ATOM 235 HG22 THR A 264 3.339 10.851 -2.852 1.00 1.26 H
-ATOM 236 HG23 THR A 264 2.293 9.454 -2.607 1.00 1.22 H
-ATOM 237 N THR A 265 1.778 13.758 -1.478 1.00 0.73 N
-ATOM 238 CA THR A 265 2.025 14.971 -2.263 1.00 0.83 C
-ATOM 239 C THR A 265 3.231 14.833 -3.199 1.00 0.92 C
-ATOM 240 O THR A 265 3.411 15.652 -4.100 1.00 1.02 O
-ATOM 241 CB THR A 265 2.232 16.209 -1.364 1.00 0.92 C
-ATOM 242 OG1 THR A 265 2.469 17.370 -2.177 1.00 1.04 O
-ATOM 243 CG2 THR A 265 3.401 16.004 -0.409 1.00 0.99 C
-ATOM 244 H THR A 265 1.862 13.796 -0.503 1.00 0.73 H
-ATOM 245 HA THR A 265 1.146 15.147 -2.867 1.00 0.84 H
-ATOM 246 HB THR A 265 1.337 16.364 -0.781 1.00 0.90 H
-ATOM 247 HG1 THR A 265 3.120 17.154 -2.861 1.00 1.13 H
-ATOM 248 HG21 THR A 265 3.921 16.940 -0.270 1.00 1.47 H
-ATOM 249 HG22 THR A 265 4.081 15.274 -0.823 1.00 1.37 H
-ATOM 250 HG23 THR A 265 3.033 15.654 0.544 1.00 1.48 H
-ATOM 251 N GLN A 266 4.054 13.804 -2.980 1.00 0.92 N
-ATOM 252 CA GLN A 266 5.241 13.572 -3.808 1.00 1.03 C
-ATOM 253 C GLN A 266 4.886 13.423 -5.291 1.00 1.05 C
-ATOM 254 O GLN A 266 5.538 14.014 -6.150 1.00 1.20 O
-ATOM 255 CB GLN A 266 6.001 12.338 -3.321 1.00 1.04 C
-ATOM 256 CG GLN A 266 6.533 12.470 -1.900 1.00 1.11 C
-ATOM 257 CD GLN A 266 7.668 13.476 -1.756 1.00 1.28 C
-ATOM 258 OE1 GLN A 266 8.103 13.775 -0.649 1.00 1.41 O
-ATOM 259 NE2 GLN A 266 8.171 13.997 -2.866 1.00 1.35 N
-ATOM 260 H GLN A 266 3.858 13.186 -2.242 1.00 0.86 H
-ATOM 261 HA GLN A 266 5.880 14.431 -3.696 1.00 1.13 H
-ATOM 262 HB2 GLN A 266 5.341 11.485 -3.358 1.00 0.96 H
-ATOM 263 HB3 GLN A 266 6.839 12.163 -3.980 1.00 1.14 H
-ATOM 264 HG2 GLN A 266 5.721 12.783 -1.262 1.00 1.08 H
-ATOM 265 HG3 GLN A 266 6.887 11.504 -1.577 1.00 1.13 H
-ATOM 266 HE21 GLN A 266 7.796 13.714 -3.724 1.00 1.31 H
-ATOM 267 HE22 GLN A 266 8.901 14.643 -2.778 1.00 1.49 H
-ATOM 268 N SER A 267 3.842 12.656 -5.591 1.00 0.95 N
-ATOM 269 CA SER A 267 3.408 12.467 -6.982 1.00 0.99 C
-ATOM 270 C SER A 267 2.499 13.619 -7.433 1.00 1.02 C
-ATOM 271 O SER A 267 1.420 13.392 -7.987 1.00 1.02 O
-ATOM 272 CB SER A 267 2.680 11.130 -7.145 1.00 0.93 C
-ATOM 273 OG SER A 267 2.292 10.911 -8.492 1.00 1.04 O
-ATOM 274 H SER A 267 3.343 12.223 -4.866 1.00 0.86 H
-ATOM 275 HA SER A 267 4.292 12.463 -7.603 1.00 1.09 H
-ATOM 276 HB2 SER A 267 3.334 10.327 -6.840 1.00 0.92 H
-ATOM 277 HB3 SER A 267 1.796 11.126 -6.523 1.00 0.87 H
-ATOM 278 HG SER A 267 1.667 11.598 -8.759 1.00 1.12 H
-ATOM 279 N SER A 268 2.946 14.852 -7.178 1.00 1.08 N
-ATOM 280 CA SER A 268 2.188 16.060 -7.534 1.00 1.12 C
-ATOM 281 C SER A 268 0.827 16.088 -6.833 1.00 1.06 C
-ATOM 282 O SER A 268 -0.183 16.461 -7.428 1.00 1.12 O
-ATOM 283 CB SER A 268 1.998 16.166 -9.053 1.00 1.22 C
-ATOM 284 OG SER A 268 3.246 16.242 -9.725 1.00 1.31 O
-ATOM 285 H SER A 268 3.814 14.955 -6.727 1.00 1.12 H
-ATOM 286 HA SER A 268 2.762 16.913 -7.197 1.00 1.17 H
-ATOM 287 HB2 SER A 268 1.466 15.297 -9.408 1.00 1.22 H
-ATOM 288 HB3 SER A 268 1.424 17.052 -9.279 1.00 1.26 H
-ATOM 289 HG SER A 268 3.305 15.531 -10.370 1.00 1.63 H
-ATOM 290 N GLY A 269 0.813 15.690 -5.560 1.00 0.99 N
-ATOM 291 CA GLY A 269 -0.424 15.674 -4.791 1.00 0.97 C
-ATOM 292 C GLY A 269 -0.967 17.069 -4.518 1.00 0.99 C
-ATOM 293 O GLY A 269 -2.164 17.318 -4.660 1.00 1.02 O
-ATOM 294 H GLY A 269 1.653 15.404 -5.140 1.00 0.98 H
-ATOM 295 HA2 GLY A 269 -1.167 15.114 -5.340 1.00 0.99 H
-ATOM 296 HA3 GLY A 269 -0.243 15.180 -3.848 1.00 0.94 H
-ATOM 297 N SER A 270 -0.081 17.984 -4.133 1.00 1.01 N
-ATOM 298 CA SER A 270 -0.468 19.371 -3.846 1.00 1.06 C
-ATOM 299 C SER A 270 -0.645 20.181 -5.133 1.00 1.00 C
-ATOM 300 O SER A 270 -0.110 21.284 -5.262 1.00 0.94 O
-ATOM 301 CB SER A 270 0.583 20.044 -2.977 1.00 1.14 C
-ATOM 302 OG SER A 270 0.778 19.340 -1.759 1.00 1.20 O
-ATOM 303 H SER A 270 0.860 17.724 -4.044 1.00 1.01 H
-ATOM 304 HA SER A 270 -1.406 19.353 -3.313 1.00 1.10 H
-ATOM 305 HB2 SER A 270 1.517 20.073 -3.518 1.00 1.14 H
-ATOM 306 HB3 SER A 270 0.263 21.049 -2.755 1.00 1.23 H
-ATOM 307 HG SER A 270 1.427 18.626 -1.894 1.00 1.24 H
-ATOM 308 N ARG A 271 -1.393 19.606 -6.068 1.00 1.10 N
-ATOM 309 CA ARG A 271 -1.680 20.211 -7.380 1.00 1.10 C
-ATOM 310 C ARG A 271 -0.428 20.248 -8.274 1.00 1.07 C
-ATOM 311 O ARG A 271 0.653 20.650 -7.844 1.00 1.02 O
-ATOM 312 CB ARG A 271 -2.264 21.619 -7.217 1.00 1.04 C
-ATOM 313 CG ARG A 271 -2.804 22.211 -8.508 1.00 1.15 C
-ATOM 314 CD ARG A 271 -3.275 23.640 -8.307 1.00 1.00 C
-ATOM 315 NE ARG A 271 -2.214 24.482 -7.770 1.00 0.87 N
-ATOM 316 CZ ARG A 271 -2.287 25.786 -7.673 1.00 0.93 C
-ATOM 317 NH1 ARG A 271 -3.347 26.432 -8.070 1.00 1.13 N
-ATOM 318 NH2 ARG A 271 -1.294 26.457 -7.182 1.00 1.37 N
-ATOM 319 H ARG A 271 -1.767 18.726 -5.865 1.00 1.20 H
-ATOM 320 HA ARG A 271 -2.419 19.588 -7.863 1.00 1.30 H
-ATOM 321 HB2 ARG A 271 -3.068 21.582 -6.498 1.00 1.20 H
-ATOM 322 HB3 ARG A 271 -1.490 22.274 -6.842 1.00 0.90 H
-ATOM 323 HG2 ARG A 271 -2.021 22.203 -9.252 1.00 1.25 H
-ATOM 324 HG3 ARG A 271 -3.635 21.612 -8.848 1.00 1.58 H
-ATOM 325 HD2 ARG A 271 -3.594 24.041 -9.258 1.00 1.19 H
-ATOM 326 HD3 ARG A 271 -4.107 23.641 -7.618 1.00 1.35 H
-ATOM 327 HE ARG A 271 -1.396 24.037 -7.462 1.00 1.21 H
-ATOM 328 HH11 ARG A 271 -4.121 25.939 -8.451 1.00 1.41 H
-ATOM 329 HH12 ARG A 271 -3.365 27.444 -7.996 1.00 1.34 H
-ATOM 330 HH21 ARG A 271 -0.472 25.986 -6.873 1.00 1.76 H
-ATOM 331 HH22 ARG A 271 -1.351 27.459 -7.131 1.00 1.51 H
-ATOM 332 N ALA A 272 -0.579 19.819 -9.529 1.00 1.27 N
-ATOM 333 CA ALA A 272 0.543 19.794 -10.477 1.00 1.37 C
-ATOM 334 C ALA A 272 0.902 21.195 -11.007 1.00 1.11 C
-ATOM 335 O ALA A 272 1.023 21.399 -12.214 1.00 1.35 O
-ATOM 336 CB ALA A 272 0.233 18.847 -11.631 1.00 1.78 C
-ATOM 337 H ALA A 272 -1.458 19.502 -9.823 1.00 1.45 H
-ATOM 338 HA ALA A 272 1.400 19.400 -9.952 1.00 1.48 H
-ATOM 339 HB1 ALA A 272 0.847 19.102 -12.481 1.00 2.15 H
-ATOM 340 HB2 ALA A 272 -0.809 18.934 -11.900 1.00 2.22 H
-ATOM 341 HB3 ALA A 272 0.441 17.832 -11.328 1.00 2.02 H
-ATOM 342 N SER A 273 1.089 22.147 -10.092 1.00 0.87 N
-ATOM 343 CA SER A 273 1.455 23.522 -10.459 1.00 0.90 C
-ATOM 344 C SER A 273 1.825 24.355 -9.242 1.00 0.99 C
-ATOM 345 O SER A 273 0.989 24.667 -8.390 1.00 0.84 O
-ATOM 346 CB SER A 273 0.334 24.223 -11.234 1.00 0.90 C
-ATOM 347 OG SER A 273 -0.877 24.251 -10.499 1.00 0.79 O
-ATOM 348 H SER A 273 0.992 21.916 -9.140 1.00 0.94 H
-ATOM 349 HA SER A 273 2.326 23.478 -11.096 1.00 1.18 H
-ATOM 350 HB2 SER A 273 0.634 25.239 -11.441 1.00 1.10 H
-ATOM 351 HB3 SER A 273 0.169 23.704 -12.166 1.00 1.12 H
-ATOM 352 HG SER A 273 -0.759 24.782 -9.706 1.00 1.33 H
-ATOM 353 N LEU A 274 3.094 24.741 -9.216 1.00 1.45 N
-ATOM 354 CA LEU A 274 3.655 25.581 -8.165 1.00 1.79 C
-ATOM 355 C LEU A 274 3.100 27.028 -8.180 1.00 1.77 C
-ATOM 356 O LEU A 274 3.074 27.665 -7.126 1.00 1.91 O
-ATOM 357 CB LEU A 274 5.179 25.613 -8.306 1.00 2.40 C
-ATOM 358 CG LEU A 274 5.933 26.385 -7.218 1.00 2.87 C
-ATOM 359 CD1 LEU A 274 5.746 25.731 -5.858 1.00 2.94 C
-ATOM 360 CD2 LEU A 274 7.409 26.473 -7.566 1.00 3.47 C
-ATOM 361 H LEU A 274 3.677 24.467 -9.952 1.00 1.63 H
-ATOM 362 HA LEU A 274 3.406 25.128 -7.218 1.00 1.78 H
-ATOM 363 HB2 LEU A 274 5.540 24.595 -8.308 1.00 2.50 H
-ATOM 364 HB3 LEU A 274 5.417 26.060 -9.259 1.00 2.53 H
-ATOM 365 HG LEU A 274 5.542 27.390 -7.161 1.00 2.83 H
-ATOM 366 HD11 LEU A 274 6.244 24.773 -5.848 1.00 3.10 H
-ATOM 367 HD12 LEU A 274 4.694 25.593 -5.665 1.00 3.17 H
-ATOM 368 HD13 LEU A 274 6.172 26.365 -5.093 1.00 3.16 H
-ATOM 369 HD21 LEU A 274 7.950 25.705 -7.034 1.00 3.76 H
-ATOM 370 HD22 LEU A 274 7.788 27.442 -7.282 1.00 3.77 H
-ATOM 371 HD23 LEU A 274 7.537 26.332 -8.629 1.00 3.74 H
-ATOM 372 N PRO A 275 2.672 27.607 -9.350 1.00 1.77 N
-ATOM 373 CA PRO A 275 2.157 28.998 -9.388 1.00 1.99 C
-ATOM 374 C PRO A 275 0.896 29.212 -8.528 1.00 1.69 C
-ATOM 375 O PRO A 275 0.514 28.353 -7.740 1.00 1.66 O
-ATOM 376 CB PRO A 275 1.830 29.217 -10.872 1.00 2.24 C
-ATOM 377 CG PRO A 275 2.692 28.237 -11.576 1.00 2.29 C
-ATOM 378 CD PRO A 275 2.666 27.017 -10.709 1.00 1.79 C
-ATOM 379 HA PRO A 275 2.921 29.698 -9.092 1.00 2.37 H
-ATOM 380 HB2 PRO A 275 0.781 29.024 -11.048 1.00 2.06 H
-ATOM 381 HB3 PRO A 275 2.072 30.230 -11.155 1.00 2.67 H
-ATOM 382 HG2 PRO A 275 2.294 28.023 -12.558 1.00 2.38 H
-ATOM 383 HG3 PRO A 275 3.695 28.625 -11.642 1.00 2.68 H
-ATOM 384 HD2 PRO A 275 1.761 26.441 -10.882 1.00 1.45 H
-ATOM 385 HD3 PRO A 275 3.537 26.398 -10.874 1.00 1.95 H
-ATOM 386 N LYS A 276 0.242 30.363 -8.688 1.00 1.83 N
-ATOM 387 CA LYS A 276 -0.974 30.663 -7.922 1.00 1.89 C
-ATOM 388 C LYS A 276 -2.176 29.836 -8.411 1.00 1.70 C
-ATOM 389 O LYS A 276 -2.764 30.199 -9.460 1.00 2.09 O
-ATOM 390 CB LYS A 276 -1.312 32.150 -8.018 1.00 2.50 C
-ATOM 391 CG LYS A 276 -2.616 32.520 -7.324 1.00 2.85 C
-ATOM 392 CD LYS A 276 -3.260 33.734 -7.969 1.00 3.49 C
-ATOM 393 CE LYS A 276 -3.515 33.499 -9.450 1.00 3.56 C
-ATOM 394 NZ LYS A 276 -4.303 32.258 -9.687 1.00 3.27 N
-ATOM 395 OXT LYS A 276 -2.535 28.847 -7.733 1.00 1.46 O
-ATOM 396 H LYS A 276 0.579 31.020 -9.331 1.00 2.11 H
-ATOM 397 HA LYS A 276 -0.780 30.415 -6.889 1.00 1.95 H
-ATOM 398 HB2 LYS A 276 -0.515 32.719 -7.564 1.00 2.75 H
-ATOM 399 HB3 LYS A 276 -1.393 32.425 -9.060 1.00 2.65 H
-ATOM 400 HG2 LYS A 276 -3.297 31.684 -7.390 1.00 2.59 H
-ATOM 401 HG3 LYS A 276 -2.411 32.739 -6.288 1.00 3.06 H
-ATOM 402 HD2 LYS A 276 -4.202 33.934 -7.480 1.00 3.78 H
-ATOM 403 HD3 LYS A 276 -2.604 34.581 -7.854 1.00 3.77 H
-ATOM 404 HE2 LYS A 276 -4.060 34.342 -9.845 1.00 4.10 H
-ATOM 405 HE3 LYS A 276 -2.564 33.415 -9.956 1.00 3.45 H
-ATOM 406 HZ1 LYS A 276 -3.662 31.418 -9.709 1.00 3.16 H
-ATOM 407 HZ2 LYS A 276 -4.804 32.321 -10.594 1.00 3.53 H
-ATOM 408 HZ3 LYS A 276 -4.998 32.125 -8.927 1.00 3.13 H
-TER
-ENDMDL
diff --git a/Tests/PDB/4zhl.cif b/Tests/PDB/4zhl.cif
new file mode 100644
index 0000000..0249b3b
--- /dev/null
+++ b/Tests/PDB/4zhl.cif
@@ -0,0 +1,3693 @@
+data_4ZHL
+#
+_entry.id 4ZHL
+#
+_audit_conform.dict_name mmcif_pdbx.dic
+_audit_conform.dict_version 4.058
+_audit_conform.dict_location http://mmcif.pdb.org/dictionaries/ascii/mmcif_pdbx.dic
+#
+loop_
+_database_2.database_id
+_database_2.database_code
+PDB 4ZHL
+WWPDB D_1000209290
+#
+loop_
+_database_PDB_rev.num
+_database_PDB_rev.date
+_database_PDB_rev.date_original
+_database_PDB_rev.status
+_database_PDB_rev.replaces
+_database_PDB_rev.mod_type
+1 2015-09-16 2015-04-25 ? 4ZHL 0
+2 2015-10-14 ? ? 4ZHL 1
+#
+_database_PDB_rev_record.rev_num 2
+_database_PDB_rev_record.type JRNL
+_database_PDB_rev_record.details ?
+#
+_pdbx_database_related.db_name PDB
+_pdbx_database_related.details .
+_pdbx_database_related.db_id 4ZHM
+_pdbx_database_related.content_type unspecified
+#
+_pdbx_database_status.status_code REL
+_pdbx_database_status.status_code_sf REL
+_pdbx_database_status.status_code_mr ?
+_pdbx_database_status.entry_id 4ZHL
+_pdbx_database_status.date_begin_release_preparation .
+_pdbx_database_status.SG_entry N
+_pdbx_database_status.deposit_site RCSB
+_pdbx_database_status.process_site RCSB
+_pdbx_database_status.methods_development_category ?
+_pdbx_database_status.pdb_format_compatible Y
+#
+loop_
+_audit_author.address
+_audit_author.name
+_audit_author.pdbx_ordinal
+? 'Jiang, L.' 1
+? 'Andreasen, P.A.' 2
+? 'Huang, M.' 3
+#
+_citation.abstract ?
+_citation.abstract_id_CAS ?
+_citation.book_id_ISBN ?
+_citation.book_publisher ?
+_citation.book_publisher_city ?
+_citation.book_title ?
+_citation.coordinate_linkage ?
+_citation.country UK
+_citation.database_id_Medline ?
+_citation.details ?
+_citation.id primary
+_citation.journal_abbrev J.Mol.Biol.
+_citation.journal_id_ASTM JMOBAK
+_citation.journal_id_CSD ?
+_citation.journal_id_ISSN 1089-8638
+_citation.journal_full ?
+_citation.journal_issue ?
+_citation.journal_volume 427
+_citation.language ?
+_citation.page_first 3110
+_citation.page_last 3122
+_citation.title
+;Selection of High-Affinity Peptidic Serine Protease Inhibitors with Increased Binding Entropy from a Back-Flip Library of Peptide-Protease Fusions.
+;
+_citation.year 2015
+_citation.database_id_CSD ?
+_citation.pdbx_database_id_DOI 10.1016/j.jmb.2015.08.005
+_citation.pdbx_database_id_PubMed 26281711
+_citation.unpublished_flag ?
+#
+loop_
+_citation_author.citation_id
+_citation_author.name
+_citation_author.ordinal
+primary 'Srensen, H.P.' 1
+primary 'Xu, P.' 2
+primary 'Jiang, L.' 3
+primary 'Kromann-Hansen, T.' 4
+primary 'Jensen, K.J.' 5
+primary 'Huang, M.' 6
+primary 'Andreasen, P.A.' 7
+#
+_cell.entry_id 4ZHL
+_cell.length_a 122.057
+_cell.length_b 122.057
+_cell.length_c 42.555
+_cell.angle_alpha 90.00
+_cell.angle_beta 90.00
+_cell.angle_gamma 120.00
+_cell.Z_PDB 9
+_cell.pdbx_unique_axis ?
+#
+_symmetry.entry_id 4ZHL
+_symmetry.space_group_name_H-M 'H 3'
+_symmetry.pdbx_full_space_group_name_H-M ?
+_symmetry.cell_setting ?
+_symmetry.Int_Tables_number 146
+#
+loop_
+_entity.id
+_entity.type
+_entity.src_method
+_entity.pdbx_description
+_entity.formula_weight
+_entity.pdbx_number_of_molecules
+_entity.pdbx_ec
+_entity.pdbx_mutation
+_entity.pdbx_fragment
+_entity.details
+1 polymer man 'Urokinase-type plasminogen activator' 27869.742 1 3.4.21.73 'H99Y, C122A, N145Q' 'UNP RESIDUES 179-425' ?
+2 polymer syn mupain-1-IG 1133.322 1 ? ? ? ?
+3 water nat water 18.015 50 ? ? ? ?
+#
+_entity_name_com.entity_id 1
+_entity_name_com.name uPA
+#
+loop_
+_entity_poly.entity_id
+_entity_poly.type
+_entity_poly.nstd_linkage
+_entity_poly.nstd_monomer
+_entity_poly.pdbx_seq_one_letter_code
+_entity_poly.pdbx_seq_one_letter_code_can
+_entity_poly.pdbx_strand_id
+_entity_poly.pdbx_target_identifier
+1 'polypeptide(L)' no no
+;IIGGEFTTIENQPWFAAIYRRHRGGSVTYVCGGSLISPCWVISATHCFIDYPKKEDYIVYLGRSRLNSNTQGEMKFEVEN
+LILHKDYSADTLAYHNDIALLKIRSKEGRCAQPSRTIQTIALPSMYNDPQFGTSCEITGFGKEQSTDYLYPEQLKMTVVK
+LISHRECQQPHYYGSEVTTKMLCAADPQWKTDSCQGDSGGPLVCSLQGRMTLTGIVSWGRGCALKDKPGVYTRVSHFLPW
+IRSHTKE
+;
+;IIGGEFTTIENQPWFAAIYRRHRGGSVTYVCGGSLISPCWVISATHCFIDYPKKEDYIVYLGRSRLNSNTQGEMKFEVEN
+LILHKDYSADTLAYHNDIALLKIRSKEGRCAQPSRTIQTIALPSMYNDPQFGTSCEITGFGKEQSTDYLYPEQLKMTVVK
+LISHRECQQPHYYGSEVTTKMLCAADPQWKTDSCQGDSGGPLVCSLQGRMTLTGIVSWGRGCALKDKPGVYTRVSHFLPW
+IRSHTKE
+;
+U ?
+2 'polypeptide(L)' no no CPAYSRYIGC CPAYSRYIGC P ?
+#
+loop_
+_entity_poly_seq.entity_id
+_entity_poly_seq.num
+_entity_poly_seq.mon_id
+_entity_poly_seq.hetero
+1 1 ILE n
+1 2 ILE n
+1 3 GLY n
+1 4 GLY n
+1 5 GLU n
+1 6 PHE n
+1 7 THR n
+1 8 THR n
+1 9 ILE n
+1 10 GLU n
+1 11 ASN n
+1 12 GLN n
+1 13 PRO n
+1 14 TRP n
+1 15 PHE n
+1 16 ALA n
+1 17 ALA n
+1 18 ILE n
+1 19 TYR n
+1 20 ARG n
+1 21 ARG n
+1 22 HIS n
+1 23 ARG n
+1 24 GLY n
+1 25 GLY n
+1 26 SER n
+1 27 VAL n
+1 28 THR n
+1 29 TYR n
+1 30 VAL n
+1 31 CYS n
+1 32 GLY n
+1 33 GLY n
+1 34 SER n
+1 35 LEU n
+1 36 ILE n
+1 37 SER n
+1 38 PRO n
+1 39 CYS n
+1 40 TRP n
+1 41 VAL n
+1 42 ILE n
+1 43 SER n
+1 44 ALA n
+1 45 THR n
+1 46 HIS n
+1 47 CYS n
+1 48 PHE n
+1 49 ILE n
+1 50 ASP n
+1 51 TYR n
+1 52 PRO n
+1 53 LYS n
+1 54 LYS n
+1 55 GLU n
+1 56 ASP n
+1 57 TYR n
+1 58 ILE n
+1 59 VAL n
+1 60 TYR n
+1 61 LEU n
+1 62 GLY n
+1 63 ARG n
+1 64 SER n
+1 65 ARG n
+1 66 LEU n
+1 67 ASN n
+1 68 SER n
+1 69 ASN n
+1 70 THR n
+1 71 GLN n
+1 72 GLY n
+1 73 GLU n
+1 74 MET n
+1 75 LYS n
+1 76 PHE n
+1 77 GLU n
+1 78 VAL n
+1 79 GLU n
+1 80 ASN n
+1 81 LEU n
+1 82 ILE n
+1 83 LEU n
+1 84 HIS n
+1 85 LYS n
+1 86 ASP n
+1 87 TYR n
+1 88 SER n
+1 89 ALA n
+1 90 ASP n
+1 91 THR n
+1 92 LEU n
+1 93 ALA n
+1 94 TYR n
+1 95 HIS n
+1 96 ASN n
+1 97 ASP n
+1 98 ILE n
+1 99 ALA n
+1 100 LEU n
+1 101 LEU n
+1 102 LYS n
+1 103 ILE n
+1 104 ARG n
+1 105 SER n
+1 106 LYS n
+1 107 GLU n
+1 108 GLY n
+1 109 ARG n
+1 110 CYS n
+1 111 ALA n
+1 112 GLN n
+1 113 PRO n
+1 114 SER n
+1 115 ARG n
+1 116 THR n
+1 117 ILE n
+1 118 GLN n
+1 119 THR n
+1 120 ILE n
+1 121 ALA n
+1 122 LEU n
+1 123 PRO n
+1 124 SER n
+1 125 MET n
+1 126 TYR n
+1 127 ASN n
+1 128 ASP n
+1 129 PRO n
+1 130 GLN n
+1 131 PHE n
+1 132 GLY n
+1 133 THR n
+1 134 SER n
+1 135 CYS n
+1 136 GLU n
+1 137 ILE n
+1 138 THR n
+1 139 GLY n
+1 140 PHE n
+1 141 GLY n
+1 142 LYS n
+1 143 GLU n
+1 144 GLN n
+1 145 SER n
+1 146 THR n
+1 147 ASP n
+1 148 TYR n
+1 149 LEU n
+1 150 TYR n
+1 151 PRO n
+1 152 GLU n
+1 153 GLN n
+1 154 LEU n
+1 155 LYS n
+1 156 MET n
+1 157 THR n
+1 158 VAL n
+1 159 VAL n
+1 160 LYS n
+1 161 LEU n
+1 162 ILE n
+1 163 SER n
+1 164 HIS n
+1 165 ARG n
+1 166 GLU n
+1 167 CYS n
+1 168 GLN n
+1 169 GLN n
+1 170 PRO n
+1 171 HIS n
+1 172 TYR n
+1 173 TYR n
+1 174 GLY n
+1 175 SER n
+1 176 GLU n
+1 177 VAL n
+1 178 THR n
+1 179 THR n
+1 180 LYS n
+1 181 MET n
+1 182 LEU n
+1 183 CYS n
+1 184 ALA n
+1 185 ALA n
+1 186 ASP n
+1 187 PRO n
+1 188 GLN n
+1 189 TRP n
+1 190 LYS n
+1 191 THR n
+1 192 ASP n
+1 193 SER n
+1 194 CYS n
+1 195 GLN n
+1 196 GLY n
+1 197 ASP n
+1 198 SER n
+1 199 GLY n
+1 200 GLY n
+1 201 PRO n
+1 202 LEU n
+1 203 VAL n
+1 204 CYS n
+1 205 SER n
+1 206 LEU n
+1 207 GLN n
+1 208 GLY n
+1 209 ARG n
+1 210 MET n
+1 211 THR n
+1 212 LEU n
+1 213 THR n
+1 214 GLY n
+1 215 ILE n
+1 216 VAL n
+1 217 SER n
+1 218 TRP n
+1 219 GLY n
+1 220 ARG n
+1 221 GLY n
+1 222 CYS n
+1 223 ALA n
+1 224 LEU n
+1 225 LYS n
+1 226 ASP n
+1 227 LYS n
+1 228 PRO n
+1 229 GLY n
+1 230 VAL n
+1 231 TYR n
+1 232 THR n
+1 233 ARG n
+1 234 VAL n
+1 235 SER n
+1 236 HIS n
+1 237 PHE n
+1 238 LEU n
+1 239 PRO n
+1 240 TRP n
+1 241 ILE n
+1 242 ARG n
+1 243 SER n
+1 244 HIS n
+1 245 THR n
+1 246 LYS n
+1 247 GLU n
+2 1 CYS n
+2 2 PRO n
+2 3 ALA n
+2 4 TYR n
+2 5 SER n
+2 6 ARG n
+2 7 TYR n
+2 8 ILE n
+2 9 GLY n
+2 10 CYS n
+#
+_entity_src_gen.entity_id 1
+_entity_src_gen.pdbx_src_id 1
+_entity_src_gen.pdbx_alt_source_flag sample
+_entity_src_gen.pdbx_seq_type 'Biological sequence'
+_entity_src_gen.pdbx_beg_seq_num 1
+_entity_src_gen.pdbx_end_seq_num 247
+_entity_src_gen.gene_src_common_name Human
+_entity_src_gen.gene_src_genus ?
+_entity_src_gen.pdbx_gene_src_gene PLAU
+_entity_src_gen.gene_src_species ?
+_entity_src_gen.gene_src_strain ?
+_entity_src_gen.gene_src_tissue ?
+_entity_src_gen.gene_src_tissue_fraction ?
+_entity_src_gen.gene_src_details ?
+_entity_src_gen.pdbx_gene_src_fragment ?
+_entity_src_gen.pdbx_gene_src_scientific_name 'Homo sapiens'
+_entity_src_gen.pdbx_gene_src_ncbi_taxonomy_id 9606
+_entity_src_gen.pdbx_gene_src_variant ?
+_entity_src_gen.pdbx_gene_src_cell_line ?
+_entity_src_gen.pdbx_gene_src_atcc ?
+_entity_src_gen.pdbx_gene_src_organ ?
+_entity_src_gen.pdbx_gene_src_organelle ?
+_entity_src_gen.pdbx_gene_src_cell ?
+_entity_src_gen.pdbx_gene_src_cellular_location ?
+_entity_src_gen.host_org_common_name ?
+_entity_src_gen.pdbx_host_org_scientific_name 'Komagataella pastoris'
+_entity_src_gen.pdbx_host_org_ncbi_taxonomy_id 4922
+_entity_src_gen.host_org_genus ?
+_entity_src_gen.pdbx_host_org_gene ?
+_entity_src_gen.pdbx_host_org_organ ?
+_entity_src_gen.host_org_species ?
+_entity_src_gen.pdbx_host_org_tissue ?
+_entity_src_gen.pdbx_host_org_tissue_fraction ?
+_entity_src_gen.pdbx_host_org_strain ?
+_entity_src_gen.pdbx_host_org_variant ?
+_entity_src_gen.pdbx_host_org_cell_line ?
+_entity_src_gen.pdbx_host_org_atcc ?
+_entity_src_gen.pdbx_host_org_culture_collection ?
+_entity_src_gen.pdbx_host_org_cell ?
+_entity_src_gen.pdbx_host_org_organelle ?
+_entity_src_gen.pdbx_host_org_cellular_location ?
+_entity_src_gen.pdbx_host_org_vector_type ?
+_entity_src_gen.pdbx_host_org_vector ?
+_entity_src_gen.host_org_details ?
+_entity_src_gen.expression_system_id ?
+_entity_src_gen.plasmid_name ?
+_entity_src_gen.plasmid_details ?
+_entity_src_gen.pdbx_description ?
+#
+_pdbx_entity_src_syn.entity_id 2
+_pdbx_entity_src_syn.pdbx_src_id 1
+_pdbx_entity_src_syn.pdbx_alt_source_flag sample
+_pdbx_entity_src_syn.pdbx_beg_seq_num 1
+_pdbx_entity_src_syn.pdbx_end_seq_num 10
+_pdbx_entity_src_syn.organism_scientific 'synthetic construct'
+_pdbx_entity_src_syn.organism_common_name ?
+_pdbx_entity_src_syn.ncbi_taxonomy_id 32630
+_pdbx_entity_src_syn.details ?
+#
+loop_
+_struct_ref.biol_id
+_struct_ref.db_code
+_struct_ref.db_name
+_struct_ref.details
+_struct_ref.entity_id
+_struct_ref.id
+_struct_ref.seq_align
+_struct_ref.seq_dif
+_struct_ref.pdbx_db_accession
+_struct_ref.pdbx_db_isoform
+_struct_ref.pdbx_seq_one_letter_code
+_struct_ref.pdbx_align_begin
+_struct_ref.pdbx_align_end
+? UROK_HUMAN UNP ? 1 1 ? ? P00749 ?
+;IIGGEFTTIENQPWFAAIYRRHRGGSVTYVCGGSLISPCWVISATHCFIDYPKKEDYIVYLGRSRLNSNTQGEMKFEVEN
+LILHKDYSADTLAHHNDIALLKIRSKEGRCAQPSRTIQTICLPSMYNDPQFGTSCEITGFGKENSTDYLYPEQLKMTVVK
+LISHRECQQPHYYGSEVTTKMLCAADPQWKTDSCQGDSGGPLVCSLQGRMTLTGIVSWGRGCALKDKPGVYTRVSHFLPW
+IRSHTKE
+;
+179 ?
+? 4ZHL PDB ? 2 2 ? ? 4ZHL ? ? 1 ?
+#
+loop_
+_struct_ref_seq.align_id
+_struct_ref_seq.ref_id
+_struct_ref_seq.pdbx_PDB_id_code
+_struct_ref_seq.pdbx_strand_id
+_struct_ref_seq.seq_align_beg
+_struct_ref_seq.pdbx_seq_align_beg_ins_code
+_struct_ref_seq.seq_align_end
+_struct_ref_seq.pdbx_seq_align_end_ins_code
+_struct_ref_seq.pdbx_db_accession
+_struct_ref_seq.db_align_beg
+_struct_ref_seq.pdbx_db_align_beg_ins_code
+_struct_ref_seq.db_align_end
+_struct_ref_seq.pdbx_db_align_end_ins_code
+_struct_ref_seq.pdbx_auth_seq_align_beg
+_struct_ref_seq.pdbx_auth_seq_align_end
+1 1 4ZHL U 1 ? 247 ? P00749 179 ? 425 ? 16 244
+2 2 4ZHL P 1 ? 10 ? 4ZHL 1 ? 10 ? 1 10
+#
+loop_
+_struct_ref_seq_dif.align_id
+_struct_ref_seq_dif.pdbx_pdb_id_code
+_struct_ref_seq_dif.mon_id
+_struct_ref_seq_dif.pdbx_pdb_strand_id
+_struct_ref_seq_dif.seq_num
+_struct_ref_seq_dif.pdbx_pdb_ins_code
+_struct_ref_seq_dif.pdbx_seq_db_name
+_struct_ref_seq_dif.pdbx_seq_db_accession_code
+_struct_ref_seq_dif.db_mon_id
+_struct_ref_seq_dif.pdbx_seq_db_seq_num
+_struct_ref_seq_dif.details
+_struct_ref_seq_dif.pdbx_auth_seq_num
+_struct_ref_seq_dif.pdbx_ordinal
+1 4ZHL TYR U 94 ? UNP P00749 HIS 272 'engineered mutation' 99 1
+1 4ZHL ALA U 121 ? UNP P00749 CYS 299 'engineered mutation' 122 2
+1 4ZHL GLN U 144 ? UNP P00749 ASN 322 'engineered mutation' 145 3
+#
+loop_
+_chem_comp.id
+_chem_comp.type
+_chem_comp.mon_nstd_flag
+_chem_comp.name
+_chem_comp.pdbx_synonyms
+_chem_comp.formula
+_chem_comp.formula_weight
+ALA 'L-peptide linking' y ALANINE ? 'C3 H7 N O2' 89.093
+ARG 'L-peptide linking' y ARGININE ? 'C6 H15 N4 O2 1' 175.209
+ASN 'L-peptide linking' y ASPARAGINE ? 'C4 H8 N2 O3' 132.118
+ASP 'L-peptide linking' y 'ASPARTIC ACID' ? 'C4 H7 N O4' 133.103
+CYS 'L-peptide linking' y CYSTEINE ? 'C3 H7 N O2 S' 121.158
+GLN 'L-peptide linking' y GLUTAMINE ? 'C5 H10 N2 O3' 146.144
+GLU 'L-peptide linking' y 'GLUTAMIC ACID' ? 'C5 H9 N O4' 147.129
+GLY 'peptide linking' y GLYCINE ? 'C2 H5 N O2' 75.067
+HIS 'L-peptide linking' y HISTIDINE ? 'C6 H10 N3 O2 1' 156.162
+HOH non-polymer . WATER ? 'H2 O' 18.015
+ILE 'L-peptide linking' y ISOLEUCINE ? 'C6 H13 N O2' 131.173
+LEU 'L-peptide linking' y LEUCINE ? 'C6 H13 N O2' 131.173
+LYS 'L-peptide linking' y LYSINE ? 'C6 H15 N2 O2 1' 147.195
+MET 'L-peptide linking' y METHIONINE ? 'C5 H11 N O2 S' 149.211
+PHE 'L-peptide linking' y PHENYLALANINE ? 'C9 H11 N O2' 165.189
+PRO 'L-peptide linking' y PROLINE ? 'C5 H9 N O2' 115.130
+SER 'L-peptide linking' y SERINE ? 'C3 H7 N O3' 105.093
+THR 'L-peptide linking' y THREONINE ? 'C4 H9 N O3' 119.119
+TRP 'L-peptide linking' y TRYPTOPHAN ? 'C11 H12 N2 O2' 204.225
+TYR 'L-peptide linking' y TYROSINE ? 'C9 H11 N O3' 181.189
+VAL 'L-peptide linking' y VALINE ? 'C5 H11 N O2' 117.146
+#
+_exptl.absorpt_coefficient_mu ?
+_exptl.absorpt_correction_T_max ?
+_exptl.absorpt_correction_T_min ?
+_exptl.absorpt_correction_type ?
+_exptl.absorpt_process_details ?
+_exptl.entry_id 4ZHL
+_exptl.crystals_number ?
+_exptl.details ?
+_exptl.method 'X-RAY DIFFRACTION'
+_exptl.method_details ?
+#
+_exptl_crystal.colour ?
+_exptl_crystal.density_diffrn ?
+_exptl_crystal.density_Matthews 2.10
+_exptl_crystal.density_method ?
+_exptl_crystal.density_percent_sol 41.52
+_exptl_crystal.description ?
+_exptl_crystal.F_000 ?
+_exptl_crystal.id 1
+_exptl_crystal.preparation ?
+_exptl_crystal.size_max ?
+_exptl_crystal.size_mid ?
+_exptl_crystal.size_min ?
+_exptl_crystal.size_rad ?
+_exptl_crystal.colour_lustre ?
+_exptl_crystal.colour_modifier ?
+_exptl_crystal.colour_primary ?
+_exptl_crystal.density_meas ?
+_exptl_crystal.density_meas_esd ?
+_exptl_crystal.density_meas_gt ?
+_exptl_crystal.density_meas_lt ?
+_exptl_crystal.density_meas_temp ?
+_exptl_crystal.density_meas_temp_esd ?
+_exptl_crystal.density_meas_temp_gt ?
+_exptl_crystal.density_meas_temp_lt ?
+_exptl_crystal.pdbx_crystal_image_url ?
+_exptl_crystal.pdbx_crystal_image_format ?
+_exptl_crystal.pdbx_mosaicity ?
+_exptl_crystal.pdbx_mosaicity_esd ?
+#
+_exptl_crystal_grow.apparatus ?
+_exptl_crystal_grow.atmosphere ?
+_exptl_crystal_grow.crystal_id 1
+_exptl_crystal_grow.details ?
+_exptl_crystal_grow.method 'VAPOR DIFFUSION, SITTING DROP'
+_exptl_crystal_grow.method_ref ?
+_exptl_crystal_grow.pH 4.6
+_exptl_crystal_grow.pressure ?
+_exptl_crystal_grow.pressure_esd ?
+_exptl_crystal_grow.seeding ?
+_exptl_crystal_grow.seeding_ref ?
+_exptl_crystal_grow.temp 298
+_exptl_crystal_grow.temp_details ?
+_exptl_crystal_grow.temp_esd ?
+_exptl_crystal_grow.time ?
+_exptl_crystal_grow.pdbx_details '2.0M ammonium sulfate, 50mM sodium citrate, pH 4.6, 5% polyethylene glycol (PEG) 400'
+_exptl_crystal_grow.pdbx_pH_range ?
+#
+_diffrn.ambient_environment ?
+_diffrn.ambient_temp 100
+_diffrn.ambient_temp_details ?
+_diffrn.ambient_temp_esd ?
+_diffrn.crystal_id 1
+_diffrn.crystal_support ?
+_diffrn.crystal_treatment ?
+_diffrn.details ?
+_diffrn.id 1
+_diffrn.ambient_pressure ?
+_diffrn.ambient_pressure_esd ?
+_diffrn.ambient_pressure_gt ?
+_diffrn.ambient_pressure_lt ?
+_diffrn.ambient_temp_gt ?
+_diffrn.ambient_temp_lt ?
+#
+_diffrn_detector.details ?
+_diffrn_detector.detector CCD
+_diffrn_detector.diffrn_id 1
+_diffrn_detector.type 'ADSC QUANTUM 315r'
+_diffrn_detector.area_resol_mean ?
+_diffrn_detector.dtime ?
+_diffrn_detector.pdbx_frames_total ?
+_diffrn_detector.pdbx_collection_time_total ?
+_diffrn_detector.pdbx_collection_date 2013-07-10
+#
+_diffrn_radiation.collimation ?
+_diffrn_radiation.diffrn_id 1
+_diffrn_radiation.filter_edge ?
+_diffrn_radiation.inhomogeneity ?
+_diffrn_radiation.monochromator ?
+_diffrn_radiation.polarisn_norm ?
+_diffrn_radiation.polarisn_ratio ?
+_diffrn_radiation.probe ?
+_diffrn_radiation.type ?
+_diffrn_radiation.xray_symbol ?
+_diffrn_radiation.wavelength_id 1
+_diffrn_radiation.pdbx_monochromatic_or_laue_m_l M
+_diffrn_radiation.pdbx_wavelength_list ?
+_diffrn_radiation.pdbx_wavelength ?
+_diffrn_radiation.pdbx_diffrn_protocol 'SINGLE WAVELENGTH'
+_diffrn_radiation.pdbx_analyzer ?
+_diffrn_radiation.pdbx_scattering_type x-ray
+#
+_diffrn_radiation_wavelength.id 1
+_diffrn_radiation_wavelength.wavelength 0.979
+_diffrn_radiation_wavelength.wt 1.0
+#
+_diffrn_source.current ?
+_diffrn_source.details ?
+_diffrn_source.diffrn_id 1
+_diffrn_source.power ?
+_diffrn_source.size ?
+_diffrn_source.source SYNCHROTRON
+_diffrn_source.target ?
+_diffrn_source.type 'SSRF BEAMLINE BL17U'
+_diffrn_source.voltage ?
+_diffrn_source.take-off_angle ?
+_diffrn_source.pdbx_wavelength_list 0.979
+_diffrn_source.pdbx_wavelength ?
+_diffrn_source.pdbx_synchrotron_beamline BL17U
+_diffrn_source.pdbx_synchrotron_site SSRF
+#
+_reflns.B_iso_Wilson_estimate ?
+_reflns.entry_id 4ZHL
+_reflns.data_reduction_details ?
+_reflns.data_reduction_method ?
+_reflns.d_resolution_high 2.05
+_reflns.d_resolution_low 50
+_reflns.details ?
+_reflns.limit_h_max ?
+_reflns.limit_h_min ?
+_reflns.limit_k_max ?
+_reflns.limit_k_min ?
+_reflns.limit_l_max ?
+_reflns.limit_l_min ?
+_reflns.number_all ?
+_reflns.number_obs 14528
+_reflns.observed_criterion ?
+_reflns.observed_criterion_F_max ?
+_reflns.observed_criterion_F_min ?
+_reflns.observed_criterion_I_max ?
+_reflns.observed_criterion_I_min ?
+_reflns.observed_criterion_sigma_F ?
+_reflns.observed_criterion_sigma_I ?
+_reflns.percent_possible_obs 99.1
+_reflns.R_free_details ?
+_reflns.Rmerge_F_all ?
+_reflns.Rmerge_F_obs ?
+_reflns.Friedel_coverage ?
+_reflns.number_gt ?
+_reflns.threshold_expression ?
+_reflns.pdbx_redundancy 3.4
+_reflns.pdbx_Rmerge_I_obs ?
+_reflns.pdbx_Rmerge_I_all ?
+_reflns.pdbx_Rsym_value ?
+_reflns.pdbx_netI_over_av_sigmaI ?
+_reflns.pdbx_netI_over_sigmaI 22.7
+_reflns.pdbx_res_netI_over_av_sigmaI_2 ?
+_reflns.pdbx_res_netI_over_sigmaI_2 ?
+_reflns.pdbx_chi_squared ?
+_reflns.pdbx_scaling_rejects ?
+_reflns.pdbx_d_res_high_opt ?
+_reflns.pdbx_d_res_low_opt ?
+_reflns.pdbx_d_res_opt_method ?
+_reflns.phase_calculation_details ?
+_reflns.pdbx_Rrim_I_all ?
+_reflns.pdbx_Rpim_I_all ?
+_reflns.pdbx_d_opt ?
+_reflns.pdbx_number_measured_all ?
+_reflns.pdbx_diffrn_id 1
+_reflns.pdbx_ordinal 1
+_reflns.pdbx_CC_half ?
+_reflns.pdbx_R_split ?
+#
+_computing.entry_id 4ZHL
+_computing.cell_refinement ?
+_computing.data_collection ?
+_computing.data_reduction ?
+_computing.molecular_graphics ?
+_computing.publication_material ?
+_computing.structure_refinement 'REFMAC 5.7.0029'
+_computing.structure_solution ?
+_computing.pdbx_structure_refinement_method ?
+_computing.pdbx_data_reduction_ii ?
+_computing.pdbx_data_reduction_ds ?
+#
+_refine.pdbx_refine_id 'X-RAY DIFFRACTION'
+_refine.entry_id 4ZHL
+_refine.pdbx_diffrn_id 1
+_refine.pdbx_TLS_residual_ADP_flag ?
+_refine.ls_number_reflns_obs 13804
+_refine.ls_number_reflns_all ?
+_refine.pdbx_ls_sigma_I ?
+_refine.pdbx_ls_sigma_F ?
+_refine.pdbx_data_cutoff_high_absF ?
+_refine.pdbx_data_cutoff_low_absF ?
+_refine.pdbx_data_cutoff_high_rms_absF ?
+_refine.ls_d_res_low 30.53
+_refine.ls_d_res_high 2.06
+_refine.ls_percent_reflns_obs 99.02
+_refine.ls_R_factor_obs 0.22184
+_refine.ls_R_factor_all ?
+_refine.ls_R_factor_R_work 0.21978
+_refine.ls_R_factor_R_free 0.26237
+_refine.ls_R_factor_R_free_error ?
+_refine.ls_R_factor_R_free_error_details ?
+_refine.ls_percent_reflns_R_free 5.0
+_refine.ls_number_reflns_R_free 724
+_refine.ls_number_parameters ?
+_refine.ls_number_restraints ?
+_refine.occupancy_min ?
+_refine.occupancy_max ?
+_refine.correlation_coeff_Fo_to_Fc 0.950
+_refine.correlation_coeff_Fo_to_Fc_free 0.932
+_refine.B_iso_mean 46.977
+_refine.aniso_B[1][1] -0.52
+_refine.aniso_B[2][2] -0.52
+_refine.aniso_B[3][3] 1.69
+_refine.aniso_B[1][2] -0.52
+_refine.aniso_B[1][3] 0.00
+_refine.aniso_B[2][3] -0.00
+_refine.solvent_model_details MASK
+_refine.solvent_model_param_ksol ?
+_refine.solvent_model_param_bsol ?
+_refine.pdbx_solvent_vdw_probe_radii 1.20
+_refine.pdbx_solvent_ion_probe_radii 0.80
+_refine.pdbx_solvent_shrinkage_radii 0.80
+_refine.pdbx_ls_cross_valid_method THROUGHOUT
+_refine.details
+;HYDROGENS HAVE BEEN ADDED IN THE RIDING POSITIONS
+ U VALUES
+;
+_refine.pdbx_starting_model ?
+_refine.pdbx_method_to_determine_struct ?
+_refine.pdbx_isotropic_thermal_model ?
+_refine.pdbx_stereochemistry_target_values 'MAXIMUM LIKELIHOOD'
+_refine.pdbx_stereochem_target_val_spec_case ?
+_refine.pdbx_R_Free_selection_details RANDOM
+_refine.pdbx_overall_ESU_R 0.284
+_refine.pdbx_overall_ESU_R_Free 0.214
+_refine.overall_SU_ML 0.177
+_refine.pdbx_overall_phase_error ?
+_refine.overall_SU_B 6.512
+_refine.overall_SU_R_Cruickshank_DPI ?
+_refine.pdbx_overall_SU_R_free_Cruickshank_DPI ?
+_refine.pdbx_overall_SU_R_Blow_DPI ?
+_refine.pdbx_overall_SU_R_free_Blow_DPI ?
+#
+_refine_hist.pdbx_refine_id 'X-RAY DIFFRACTION'
+_refine_hist.cycle_id LAST
+_refine_hist.pdbx_number_atoms_protein 2030
+_refine_hist.pdbx_number_atoms_nucleic_acid 0
+_refine_hist.pdbx_number_atoms_ligand 0
+_refine_hist.number_atoms_solvent 50
+_refine_hist.number_atoms_total 2080
+_refine_hist.d_res_high 2.06
+_refine_hist.d_res_low 30.53
+#
+loop_
+_refine_ls_restr.type
+_refine_ls_restr.dev_ideal
+_refine_ls_restr.dev_ideal_target
+_refine_ls_restr.weight
+_refine_ls_restr.number
+_refine_ls_restr.pdbx_refine_id
+_refine_ls_restr.pdbx_restraint_function
+r_bond_refined_d 0.008 0.019 ? 2067 'X-RAY DIFFRACTION' ?
+r_bond_other_d 0.001 0.020 ? 1943 'X-RAY DIFFRACTION' ?
+r_angle_refined_deg 1.265 1.946 ? 2767 'X-RAY DIFFRACTION' ?
+r_angle_other_deg 0.719 3.005 ? 4453 'X-RAY DIFFRACTION' ?
+r_dihedral_angle_1_deg 6.549 5.000 ? 235 'X-RAY DIFFRACTION' ?
+r_dihedral_angle_2_deg 33.531 23.077 ? 91 'X-RAY DIFFRACTION' ?
+r_dihedral_angle_3_deg 16.759 15.000 ? 351 'X-RAY DIFFRACTION' ?
+r_dihedral_angle_4_deg 18.334 15.000 ? 14 'X-RAY DIFFRACTION' ?
+r_chiral_restr 0.079 0.200 ? 302 'X-RAY DIFFRACTION' ?
+r_gen_planes_refined 0.005 0.021 ? 2196 'X-RAY DIFFRACTION' ?
+r_gen_planes_other 0.001 0.020 ? 474 'X-RAY DIFFRACTION' ?
+r_nbd_refined ? ? ? ? 'X-RAY DIFFRACTION' ?
+r_nbd_other ? ? ? ? 'X-RAY DIFFRACTION' ?
+r_nbtor_refined ? ? ? ? 'X-RAY DIFFRACTION' ?
+r_nbtor_other ? ? ? ? 'X-RAY DIFFRACTION' ?
+r_xyhbond_nbd_refined ? ? ? ? 'X-RAY DIFFRACTION' ?
+r_xyhbond_nbd_other ? ? ? ? 'X-RAY DIFFRACTION' ?
+r_metal_ion_refined ? ? ? ? 'X-RAY DIFFRACTION' ?
+r_metal_ion_other ? ? ? ? 'X-RAY DIFFRACTION' ?
+r_symmetry_vdw_refined ? ? ? ? 'X-RAY DIFFRACTION' ?
+r_symmetry_vdw_other ? ? ? ? 'X-RAY DIFFRACTION' ?
+r_symmetry_hbond_refined ? ? ? ? 'X-RAY DIFFRACTION' ?
+r_symmetry_hbond_other ? ? ? ? 'X-RAY DIFFRACTION' ?
+r_symmetry_metal_ion_refined ? ? ? ? 'X-RAY DIFFRACTION' ?
+r_symmetry_metal_ion_other ? ? ? ? 'X-RAY DIFFRACTION' ?
+r_mcbond_it ? ? ? ? 'X-RAY DIFFRACTION' ?
+r_mcbond_other ? ? ? ? 'X-RAY DIFFRACTION' ?
+r_mcangle_it ? ? ? ? 'X-RAY DIFFRACTION' ?
+r_mcangle_other ? ? ? ? 'X-RAY DIFFRACTION' ?
+r_scbond_it ? ? ? ? 'X-RAY DIFFRACTION' ?
+r_scbond_other ? ? ? ? 'X-RAY DIFFRACTION' ?
+r_scangle_it ? ? ? ? 'X-RAY DIFFRACTION' ?
+r_scangle_other ? ? ? ? 'X-RAY DIFFRACTION' ?
+r_long_range_B_refined ? ? ? ? 'X-RAY DIFFRACTION' ?
+r_long_range_B_other ? ? ? ? 'X-RAY DIFFRACTION' ?
+r_rigid_bond_restr ? ? ? ? 'X-RAY DIFFRACTION' ?
+r_sphericity_free ? ? ? ? 'X-RAY DIFFRACTION' ?
+r_sphericity_bonded ? ? ? ? 'X-RAY DIFFRACTION' ?
+#
+_refine_ls_shell.pdbx_refine_id 'X-RAY DIFFRACTION'
+_refine_ls_shell.pdbx_total_number_of_bins_used 20
+_refine_ls_shell.d_res_high 2.057
+_refine_ls_shell.d_res_low 2.111
+_refine_ls_shell.number_reflns_R_work 1025
+_refine_ls_shell.R_factor_R_work 0.302
+_refine_ls_shell.percent_reflns_obs 98.35
+_refine_ls_shell.R_factor_R_free 0.307
+_refine_ls_shell.R_factor_R_free_error ?
+_refine_ls_shell.percent_reflns_R_free ?
+_refine_ls_shell.number_reflns_R_free 46
+_refine_ls_shell.number_reflns_all ?
+_refine_ls_shell.R_factor_all ?
+#
+_struct.entry_id 4ZHL
+_struct.title 'The crystal structure of mupain-1-IG in complex with murinised human uPA at pH7.4'
+_struct.pdbx_descriptor 'Urokinase-type plasminogen activator, mupain-1-IG'
+_struct.pdbx_model_details ?
+_struct.pdbx_formula_weight ?
+_struct.pdbx_formula_weight_method ?
+_struct.pdbx_model_type_details ?
+_struct.pdbx_CASP_flag ?
+#
+_struct_keywords.entry_id 4ZHL
+_struct_keywords.text 'Peptides inhibitor, uPA, serine protease, HYDROLASE-HYDROLASE INHIBITOR complex'
+_struct_keywords.pdbx_keywords 'HYDROLASE/HYDROLASE INHIBITOR'
+#
+loop_
+_struct_asym.id
+_struct_asym.pdbx_blank_PDB_chainid_flag
+_struct_asym.pdbx_modified
+_struct_asym.entity_id
+_struct_asym.details
+A N N 1 ?
+B N N 2 ?
+C N N 3 ?
+D N N 3 ?
+#
+loop_
+_struct_conf.conf_type_id
+_struct_conf.id
+_struct_conf.pdbx_PDB_helix_id
+_struct_conf.beg_label_comp_id
+_struct_conf.beg_label_asym_id
+_struct_conf.beg_label_seq_id
+_struct_conf.pdbx_beg_PDB_ins_code
+_struct_conf.end_label_comp_id
+_struct_conf.end_label_asym_id
+_struct_conf.end_label_seq_id
+_struct_conf.pdbx_end_PDB_ins_code
+_struct_conf.beg_auth_comp_id
+_struct_conf.beg_auth_asym_id
+_struct_conf.beg_auth_seq_id
+_struct_conf.end_auth_comp_id
+_struct_conf.end_auth_asym_id
+_struct_conf.end_auth_seq_id
+_struct_conf.pdbx_PDB_helix_class
+_struct_conf.details
+_struct_conf.pdbx_PDB_helix_length
+HELX_P HELX_P1 AA1 THR A 8 ? GLN A 12 ? THR U 23 GLN U 27 5 ? 5
+HELX_P HELX_P2 AA2 ALA A 44 ? PHE A 48 ? ALA U 55 PHE U 59 5 ? 5
+HELX_P HELX_P3 AA3 LYS A 53 ? GLU A 55 A LYS U 61 GLU U 62 5 ? 3
+HELX_P HELX_P4 AA4 SER A 163 ? GLN A 168 ? SER U 164 GLN U 169 1 ? 6
+HELX_P HELX_P5 AA5 TYR A 173 ? VAL A 177 ? TYR U 172 VAL U 176 5 ? 5
+HELX_P HELX_P6 AA6 PHE A 237 ? LYS A 246 ? PHE U 234 LYS U 243 1 ? 10
+#
+_struct_conf_type.id HELX_P
+_struct_conf_type.criteria ?
+_struct_conf_type.reference ?
+#
+loop_
+_struct_conn.id
+_struct_conn.conn_type_id
+_struct_conn.pdbx_leaving_atom_flag
+_struct_conn.pdbx_PDB_id
+_struct_conn.ptnr1_label_asym_id
+_struct_conn.ptnr1_label_comp_id
+_struct_conn.ptnr1_label_seq_id
+_struct_conn.ptnr1_label_atom_id
+_struct_conn.pdbx_ptnr1_label_alt_id
+_struct_conn.pdbx_ptnr1_PDB_ins_code
+_struct_conn.pdbx_ptnr1_standard_comp_id
+_struct_conn.ptnr1_symmetry
+_struct_conn.ptnr2_label_asym_id
+_struct_conn.ptnr2_label_comp_id
+_struct_conn.ptnr2_label_seq_id
+_struct_conn.ptnr2_label_atom_id
+_struct_conn.pdbx_ptnr2_label_alt_id
+_struct_conn.pdbx_ptnr2_PDB_ins_code
+_struct_conn.ptnr1_auth_asym_id
+_struct_conn.ptnr1_auth_comp_id
+_struct_conn.ptnr1_auth_seq_id
+_struct_conn.ptnr2_auth_asym_id
+_struct_conn.ptnr2_auth_comp_id
+_struct_conn.ptnr2_auth_seq_id
+_struct_conn.ptnr2_symmetry
+_struct_conn.pdbx_ptnr3_label_atom_id
+_struct_conn.pdbx_ptnr3_label_seq_id
+_struct_conn.pdbx_ptnr3_label_comp_id
+_struct_conn.pdbx_ptnr3_label_asym_id
+_struct_conn.pdbx_ptnr3_label_alt_id
+_struct_conn.pdbx_ptnr3_PDB_ins_code
+_struct_conn.details
+_struct_conn.pdbx_dist_value
+_struct_conn.pdbx_value_order
+disulf1 disulf ? ? A CYS 31 SG ? ? ? 1_555 A CYS 47 SG ? ? U CYS 42 U CYS 58 1_555 ? ? ? ? ? ? ? 2.027 ?
+disulf2 disulf ? ? A CYS 167 SG ? ? ? 1_555 A CYS 183 SG ? ? U CYS 168 U CYS 182 1_555 ? ? ? ? ? ? ? 1.911 ?
+disulf3 disulf ? ? A CYS 194 SG ? ? ? 1_555 A CYS 222 SG ? ? U CYS 191 U CYS 220 1_555 ? ? ? ? ? ? ? 1.876 ?
+disulf4 disulf ? ? B CYS 1 SG ? ? ? 1_555 B CYS 10 SG ? ? P CYS 1 P CYS 10 1_555 ? ? ? ? ? ? ? 2.053 ?
+#
+_struct_conn_type.id disulf
+_struct_conn_type.criteria ?
+_struct_conn_type.reference ?
+#
+loop_
+_struct_sheet.id
+_struct_sheet.type
+_struct_sheet.number_strands
+_struct_sheet.details
+AA1 ? 8 ?
+AA2 ? 7 ?
+AA3 ? 2 ?
+#
+loop_
+_struct_sheet_order.sheet_id
+_struct_sheet_order.range_id_1
+_struct_sheet_order.range_id_2
+_struct_sheet_order.offset
+_struct_sheet_order.sense
+AA1 1 2 ? anti-parallel
+AA1 2 3 ? anti-parallel
+AA1 3 4 ? anti-parallel
+AA1 4 5 ? anti-parallel
+AA1 5 6 ? anti-parallel
+AA1 6 7 ? anti-parallel
+AA1 7 8 ? anti-parallel
+AA2 1 2 ? anti-parallel
+AA2 2 3 ? anti-parallel
+AA2 3 4 ? anti-parallel
+AA2 4 5 ? anti-parallel
+AA2 5 6 ? anti-parallel
+AA2 6 7 ? anti-parallel
+AA3 1 2 ? anti-parallel
+#
+loop_
+_struct_sheet_range.sheet_id
+_struct_sheet_range.id
+_struct_sheet_range.beg_label_comp_id
+_struct_sheet_range.beg_label_asym_id
+_struct_sheet_range.beg_label_seq_id
+_struct_sheet_range.pdbx_beg_PDB_ins_code
+_struct_sheet_range.end_label_comp_id
+_struct_sheet_range.end_label_asym_id
+_struct_sheet_range.end_label_seq_id
+_struct_sheet_range.pdbx_end_PDB_ins_code
+_struct_sheet_range.beg_auth_comp_id
+_struct_sheet_range.beg_auth_asym_id
+_struct_sheet_range.beg_auth_seq_id
+_struct_sheet_range.end_auth_comp_id
+_struct_sheet_range.end_auth_asym_id
+_struct_sheet_range.end_auth_seq_id
+AA1 1 GLU A 5 ? PHE A 6 ? GLU U 20 PHE U 21
+AA1 2 LYS A 155 ? ILE A 162 ? LYS U 156 ILE U 163
+AA1 3 MET A 181 ? ALA A 185 ? MET U 180 ALA U 184
+AA1 4 GLY A 229 ? ARG A 233 ? GLY U 226 ARG U 230
+AA1 5 ARG A 209 ? TRP A 218 ? ARG U 206 TRP U 215
+AA1 6 PRO A 201 ? LEU A 206 ? PRO U 198 LEU U 203
+AA1 7 SER A 134 ? GLY A 139 ? SER U 135 GLY U 140
+AA1 8 LYS A 155 ? ILE A 162 ? LYS U 156 ILE U 163
+AA2 1 PHE A 15 ? ARG A 21 ? PHE U 30 ARG U 36
+AA2 2 VAL A 27 ? SER A 37 ? VAL U 38 SER U 48
+AA2 3 TRP A 40 ? SER A 43 ? TRP U 51 SER U 54
+AA2 4 ALA A 99 ? ARG A 104 ? ALA U 104 ARG U 109
+AA2 5 MET A 74 ? LEU A 83 ? MET U 81 LEU U 90
+AA2 6 TYR A 57 ? LEU A 61 ? TYR U 64 LEU U 68
+AA2 7 PHE A 15 ? ARG A 21 ? PHE U 30 ARG U 36
+AA3 1 SER A 88 ? ALA A 89 ? SER U 95 ALA U 96
+AA3 2 TYR A 94 ? HIS A 95 ? TYR U 99 HIS U 100
+#
+loop_
+_pdbx_struct_sheet_hbond.sheet_id
+_pdbx_struct_sheet_hbond.range_id_1
+_pdbx_struct_sheet_hbond.range_id_2
+_pdbx_struct_sheet_hbond.range_1_label_atom_id
+_pdbx_struct_sheet_hbond.range_1_label_comp_id
+_pdbx_struct_sheet_hbond.range_1_label_asym_id
+_pdbx_struct_sheet_hbond.range_1_label_seq_id
+_pdbx_struct_sheet_hbond.range_1_PDB_ins_code
+_pdbx_struct_sheet_hbond.range_1_auth_atom_id
+_pdbx_struct_sheet_hbond.range_1_auth_comp_id
+_pdbx_struct_sheet_hbond.range_1_auth_asym_id
+_pdbx_struct_sheet_hbond.range_1_auth_seq_id
+_pdbx_struct_sheet_hbond.range_2_label_atom_id
+_pdbx_struct_sheet_hbond.range_2_label_comp_id
+_pdbx_struct_sheet_hbond.range_2_label_asym_id
+_pdbx_struct_sheet_hbond.range_2_label_seq_id
+_pdbx_struct_sheet_hbond.range_2_PDB_ins_code
+_pdbx_struct_sheet_hbond.range_2_auth_atom_id
+_pdbx_struct_sheet_hbond.range_2_auth_comp_id
+_pdbx_struct_sheet_hbond.range_2_auth_asym_id
+_pdbx_struct_sheet_hbond.range_2_auth_seq_id
+AA1 1 2 N GLU A 5 ? N GLU U 20 O MET A 156 ? O MET U 157
+AA1 2 3 N ILE A 162 ? N ILE U 163 O CYS A 183 ? O CYS U 182
+AA1 3 4 N LEU A 182 ? N LEU U 181 O TYR A 231 ? O TYR U 228
+AA1 4 5 O VAL A 230 ? O VAL U 227 N TRP A 218 ? N TRP U 215
+AA1 5 6 O ARG A 209 ? O ARG U 206 N LEU A 206 ? N LEU U 203
+AA1 6 7 O VAL A 203 ? O VAL U 200 N GLU A 136 ? N GLU U 137
+AA1 7 8 N CYS A 135 ? N CYS U 136 O VAL A 159 ? O VAL U 160
+AA2 1 2 N ILE A 18 ? N ILE U 33 O VAL A 30 ? O VAL U 41
+AA2 2 3 N SER A 34 ? N SER U 45 O ILE A 42 ? O ILE U 53
+AA2 3 4 N VAL A 41 ? N VAL U 52 O LEU A 101 ? O LEU U 106
+AA2 4 5 O LYS A 102 ? O LYS U 107 N GLU A 79 ? N GLU U 86
+AA2 5 6 O PHE A 76 ? O PHE U 83 N VAL A 59 ? N VAL U 66
+AA2 6 7 O ILE A 58 ? O ILE U 65 N TYR A 19 ? N TYR U 34
+AA3 1 2 N SER A 88 ? N SER U 95 O HIS A 95 ? O HIS U 100
+#
+_atom_sites.entry_id 4ZHL
+_atom_sites.fract_transf_matrix[1][1] 0.008193
+_atom_sites.fract_transf_matrix[1][2] 0.004730
+_atom_sites.fract_transf_matrix[1][3] 0.000000
+_atom_sites.fract_transf_matrix[2][1] -0.000000
+_atom_sites.fract_transf_matrix[2][2] 0.009460
+_atom_sites.fract_transf_matrix[2][3] 0.000000
+_atom_sites.fract_transf_matrix[3][1] 0.000000
+_atom_sites.fract_transf_matrix[3][2] -0.000000
+_atom_sites.fract_transf_matrix[3][3] 0.023499
+_atom_sites.fract_transf_vector[1] 0.00000
+_atom_sites.fract_transf_vector[2] 0.00000
+_atom_sites.fract_transf_vector[3] 0.00000
+#
+loop_
+_atom_type.symbol
+C
+N
+O
+S
+#
+loop_
+_atom_site.group_PDB
+_atom_site.id
+_atom_site.type_symbol
+_atom_site.label_atom_id
+_atom_site.label_alt_id
+_atom_site.label_comp_id
+_atom_site.label_asym_id
+_atom_site.label_entity_id
+_atom_site.label_seq_id
+_atom_site.pdbx_PDB_ins_code
+_atom_site.Cartn_x
+_atom_site.Cartn_y
+_atom_site.Cartn_z
+_atom_site.occupancy
+_atom_site.B_iso_or_equiv
+_atom_site.Cartn_x_esd
+_atom_site.Cartn_y_esd
+_atom_site.Cartn_z_esd
+_atom_site.occupancy_esd
+_atom_site.B_iso_or_equiv_esd
+_atom_site.pdbx_formal_charge
+_atom_site.auth_seq_id
+_atom_site.auth_comp_id
+_atom_site.auth_asym_id
+_atom_site.auth_atom_id
+_atom_site.pdbx_PDB_model_num
+ATOM 1 N N . ILE A 1 1 ? -8.205 -34.800 -23.343 1.00 29.09 ? ? ? ? ? ? 16 ILE U N 1
+ATOM 2 C CA . ILE A 1 1 ? -8.506 -36.232 -23.305 1.00 29.78 ? ? ? ? ? ? 16 ILE U CA 1
+ATOM 3 C C . ILE A 1 1 ? -9.212 -36.712 -24.582 1.00 34.14 ? ? ? ? ? ? 16 ILE U C 1
+ATOM 4 O O . ILE A 1 1 ? -10.327 -36.280 -24.894 1.00 32.31 ? ? ? ? ? ? 16 ILE U O 1
+ATOM 5 C CB . ILE A 1 1 ? -9.382 -36.604 -22.100 1.00 30.71 ? ? ? ? ? ? 16 ILE U CB 1
+ATOM 6 C CG1 . ILE A 1 1 ? -8.712 -36.199 -20.760 1.00 31.15 ? ? ? ? ? ? 16 ILE U CG1 1
+ATOM 7 C CG2 . ILE A 1 1 ? -9.684 -38.091 -22.115 1.00 31.03 ? ? ? ? ? ? 16 ILE U CG2 1
+ATOM 8 C CD1 . ILE A 1 1 ? -7.457 -36.945 -20.455 1.00 30.86 ? ? ? ? ? ? 16 ILE U CD1 1
+ATOM 9 N N . ILE A 1 2 ? -8.553 -37.638 -25.278 1.00 35.77 ? ? ? ? ? ? 17 ILE U N 1
+ATOM 10 C CA . ILE A 1 2 ? -9.098 -38.250 -26.487 1.00 39.69 ? ? ? ? ? ? 17 ILE U CA 1
+ATOM 11 C C . ILE A 1 2 ? -9.914 -39.457 -26.064 1.00 37.64 ? ? ? ? ? ? 17 ILE U C 1
+ATOM 12 O O . ILE A 1 2 ? -9.431 -40.304 -25.296 1.00 39.78 ? ? ? ? ? ? 17 ILE U O 1
+ATOM 13 C CB . ILE A 1 2 ? -7.999 -38.756 -27.451 1.00 40.53 ? ? ? ? ? ? 17 ILE U CB 1
+ATOM 14 C CG1 . ILE A 1 2 ? -6.902 -37.721 -27.644 1.00 41.63 ? ? ? ? ? ? 17 ILE U CG1 1
+ATOM 15 C CG2 . ILE A 1 2 ? -8.614 -39.138 -28.799 1.00 43.93 ? ? ? ? ? ? 17 ILE U CG2 1
+ATOM 16 C CD1 . ILE A 1 2 ? -7.364 -36.417 -28.249 1.00 44.17 ? ? ? ? ? ? 17 ILE U CD1 1
+ATOM 17 N N . GLY A 1 3 ? -11.143 -39.537 -26.566 1.00 37.78 ? ? ? ? ? ? 18 GLY U N 1
+ATOM 18 C CA . GLY A 1 3 ? -12.083 -40.559 -26.142 1.00 38.70 ? ? ? ? ? ? 18 GLY U CA 1
+ATOM 19 C C . GLY A 1 3 ? -12.429 -40.358 -24.674 1.00 39.68 ? ? ? ? ? ? 18 GLY U C 1
+ATOM 20 O O . GLY A 1 3 ? -12.441 -39.238 -24.180 1.00 39.31 ? ? ? ? ? ? 18 GLY U O 1
+ATOM 21 N N . GLY A 1 4 ? -12.706 -41.445 -23.978 1.00 40.56 ? ? ? ? ? ? 19 GLY U N 1
+ATOM 22 C CA . GLY A 1 4 ? -13.076 -41.363 -22.583 1.00 41.99 ? ? ? ? ? ? 19 GLY U CA 1
+ATOM 23 C C . GLY A 1 4 ? -14.420 -40.710 -22.374 1.00 40.17 ? ? ? ? ? ? 19 GLY U C 1
+ATOM 24 O O . GLY A 1 4 ? -15.261 -40.624 -23.283 1.00 37.05 ? ? ? ? ? ? 19 GLY U O 1
+ATOM 25 N N . GLU A 1 5 ? -14.634 -40.246 -21.154 1.00 38.74 ? ? ? ? ? ? 20 GLU U N 1
+ATOM 26 C CA . GLU A 1 5 ? -15.945 -39.802 -20.761 1.00 37.20 ? ? ? ? ? ? 20 GLU U CA 1
+ATOM 27 C C . GLU A 1 5 ? -15.888 -38.463 -20.059 1.00 33.98 ? ? ? ? ? ? 20 GLU U C 1
+ATOM 28 O O . GLU A 1 5 ? -14.894 -38.131 -19.409 1.00 32.19 ? ? ? ? ? ? 20 GLU U O 1
+ATOM 29 C CB . GLU A 1 5 ? -16.575 -40.862 -19.873 1.00 43.14 ? ? ? ? ? ? 20 GLU U CB 1
+ATOM 30 C CG . GLU A 1 5 ? -16.972 -42.120 -20.634 1.00 52.22 ? ? ? ? ? ? 20 GLU U CG 1
+ATOM 31 C CD . GLU A 1 5 ? -17.512 -43.221 -19.733 1.00 59.51 ? ? ? ? ? ? 20 GLU U CD 1
+ATOM 32 O OE1 . GLU A 1 5 ? -17.296 -44.413 -20.060 1.00 72.63 ? ? ? ? ? ? 20 GLU U OE1 1
+ATOM 33 O OE2 . GLU A 1 5 ? -18.149 -42.908 -18.697 1.00 63.66 ? ? ? ? ? ? 20 GLU U OE2 1
+ATOM 34 N N . PHE A 1 6 ? -16.936 -37.670 -20.233 1.00 30.74 ? ? ? ? ? ? 21 PHE U N 1
+ATOM 35 C CA . PHE A 1 6 ? -17.120 -36.491 -19.399 1.00 30.35 ? ? ? ? ? ? 21 PHE U CA 1
+ATOM 36 C C . PHE A 1 6 ? -17.371 -36.954 -17.986 1.00 29.47 ? ? ? ? ? ? 21 PHE U C 1
+ATOM 37 O O . PHE A 1 6 ? -17.958 -38.015 -17.749 1.00 28.85 ? ? ? ? ? ? 21 PHE U O 1
+ATOM 38 C CB . PHE A 1 6 ? -18.254 -35.615 -19.907 1.00 30.52 ? ? ? ? ? ? 21 PHE U CB 1
+ATOM 39 C CG . PHE A 1 6 ? -17.897 -34.881 -21.155 1.00 30.79 ? ? ? ? ? ? 21 PHE U CG 1
+ATOM 40 C CD1 . PHE A 1 6 ? -17.028 -33.808 -21.099 1.00 30.64 ? ? ? ? ? ? 21 PHE U CD1 1
+ATOM 41 C CD2 . PHE A 1 6 ? -18.367 -35.297 -22.378 1.00 31.85 ? ? ? ? ? ? 21 PHE U CD2 1
+ATOM 42 C CE1 . PHE A 1 6 ? -16.662 -33.128 -22.230 1.00 30.52 ? ? ? ? ? ? 21 PHE U CE1 1
+ATOM 43 C CE2 . PHE A 1 6 ? -18.002 -34.627 -23.540 1.00 32.74 ? ? ? ? ? ? 21 PHE U CE2 1
+ATOM 44 C CZ . PHE A 1 6 ? -17.150 -33.537 -23.468 1.00 33.01 ? ? ? ? ? ? 21 PHE U CZ 1
+ATOM 45 N N . THR A 1 7 ? -16.842 -36.202 -17.040 1.00 33.35 ? ? ? ? ? ? 22 THR U N 1
+ATOM 46 C CA . THR A 1 7 ? -16.889 -36.639 -15.650 1.00 31.51 ? ? ? ? ? ? 22 THR U CA 1
+ATOM 47 C C . THR A 1 7 ? -17.075 -35.433 -14.775 1.00 32.09 ? ? ? ? ? ? 22 THR U C 1
+ATOM 48 O O . THR A 1 7 ? -17.268 -34.331 -15.286 1.00 29.52 ? ? ? ? ? ? 22 THR U O 1
+ATOM 49 C CB . THR A 1 7 ? -15.634 -37.460 -15.299 1.00 31.39 ? ? ? ? ? ? 22 THR U CB 1
+ATOM 50 O OG1 . THR A 1 7 ? -15.792 -38.030 -13.996 1.00 33.51 ? ? ? ? ? ? 22 THR U OG1 1
+ATOM 51 C CG2 . THR A 1 7 ? -14.332 -36.630 -15.371 1.00 31.85 ? ? ? ? ? ? 22 THR U CG2 1
+ATOM 52 N N . THR A 1 8 ? -17.093 -35.634 -13.456 1.00 32.21 ? ? ? ? ? ? 23 THR U N 1
+ATOM 53 C CA . THR A 1 8 ? -17.122 -34.517 -12.532 1.00 30.91 ? ? ? ? ? ? 23 THR U CA 1
+ATOM 54 C C . THR A 1 8 ? -15.986 -34.686 -11.521 1.00 28.53 ? ? ? ? ? ? 23 THR U C 1
+ATOM 55 O O . THR A 1 8 ? -15.423 -35.772 -11.355 1.00 25.30 ? ? ? ? ? ? 23 THR U O 1
+ATOM 56 C CB . THR A 1 8 ? -18.484 -34.404 -11.780 1.00 33.82 ? ? ? ? ? ? 23 THR U CB 1
+ATOM 57 O OG1 . THR A 1 8 ? -18.682 -35.565 -10.979 1.00 32.46 ? ? ? ? ? ? 23 THR U OG1 1
+ATOM 58 C CG2 . THR A 1 8 ? -19.670 -34.291 -12.760 1.00 35.38 ? ? ? ? ? ? 23 THR U CG2 1
+ATOM 59 N N . ILE A 1 9 ? -15.669 -33.598 -10.837 1.00 27.33 ? ? ? ? ? ? 24 ILE U N 1
+ATOM 60 C CA . ILE A 1 9 ? -14.538 -33.590 -9.911 1.00 29.72 ? ? ? ? ? ? 24 ILE U CA 1
+ATOM 61 C C . ILE A 1 9 ? -14.636 -34.724 -8.867 1.00 28.82 ? ? ? ? ? ? 24 ILE U C 1
+ATOM 62 O O . ILE A 1 9 ? -13.606 -35.309 -8.474 1.00 29.97 ? ? ? ? ? ? 24 ILE U O 1
+ATOM 63 C CB . ILE A 1 9 ? -14.334 -32.172 -9.302 1.00 27.49 ? ? ? ? ? ? 24 ILE U CB 1
+ATOM 64 C CG1 . ILE A 1 9 ? -12.905 -31.987 -8.737 1.00 27.89 ? ? ? ? ? ? 24 ILE U CG1 1
+ATOM 65 C CG2 . ILE A 1 9 ? -15.381 -31.869 -8.247 1.00 30.07 ? ? ? ? ? ? 24 ILE U CG2 1
+ATOM 66 C CD1 . ILE A 1 9 ? -11.796 -32.250 -9.738 1.00 26.36 ? ? ? ? ? ? 24 ILE U CD1 1
+ATOM 67 N N . GLU A 1 10 ? -15.852 -35.120 -8.483 1.00 29.84 ? ? ? ? ? ? 25 GLU U N 1
+ATOM 68 C CA . GLU A 1 10 ? -16.001 -36.125 -7.431 1.00 30.45 ? ? ? ? ? ? 25 GLU U CA 1
+ATOM 69 C C . GLU A 1 10 ? -15.353 -37.456 -7.782 1.00 33.49 ? ? ? ? ? ? 25 GLU U C 1
+ATOM 70 O O . GLU A 1 10 ? -14.967 -38.212 -6.877 1.00 33.52 ? ? ? ? ? ? 25 GLU U O 1
+ATOM 71 C CB . GLU A 1 10 ? -17.482 -36.375 -7.070 1.00 33.92 ? ? ? ? ? ? 25 GLU U CB 1
+ATOM 72 C CG . GLU A 1 10 ? -18.197 -35.187 -6.460 1.00 34.07 ? ? ? ? ? ? 25 GLU U CG 1
+ATOM 73 C CD . GLU A 1 10 ? -18.835 -34.275 -7.494 1.00 33.18 ? ? ? ? ? ? 25 GLU U CD 1
+ATOM 74 O OE1 . GLU A 1 10 ? -18.400 -34.261 -8.650 1.00 32.10 ? ? ? ? ? ? 25 GLU U OE1 1
+ATOM 75 O OE2 . GLU A 1 10 ? -19.774 -33.558 -7.147 1.00 34.67 ? ? ? ? ? ? 25 GLU U OE2 1
+ATOM 76 N N . ASN A 1 11 ? -15.254 -37.773 -9.075 1.00 30.81 ? ? ? ? ? ? 26 ASN U N 1
+ATOM 77 C CA . ASN A 1 11 ? -14.607 -39.006 -9.494 1.00 35.54 ? ? ? ? ? ? 26 ASN U CA 1
+ATOM 78 C C . ASN A 1 11 ? -13.101 -38.902 -9.702 1.00 32.17 ? ? ? ? ? ? 26 ASN U C 1
+ATOM 79 O O . ASN A 1 11 ? -12.475 -39.875 -10.122 1.00 33.53 ? ? ? ? ? ? 26 ASN U O 1
+ATOM 80 C CB . ASN A 1 11 ? -15.269 -39.557 -10.761 1.00 38.17 ? ? ? ? ? ? 26 ASN U CB 1
+ATOM 81 C CG . ASN A 1 11 ? -16.610 -40.197 -10.469 1.00 42.29 ? ? ? ? ? ? 26 ASN U CG 1
+ATOM 82 O OD1 . ASN A 1 11 ? -16.810 -40.772 -9.392 1.00 41.01 ? ? ? ? ? ? 26 ASN U OD1 1
+ATOM 83 N ND2 . ASN A 1 11 ? -17.550 -40.079 -11.413 1.00 42.92 ? ? ? ? ? ? 26 ASN U ND2 1
+ATOM 84 N N . GLN A 1 12 ? -12.548 -37.731 -9.405 1.00 30.68 ? ? ? ? ? ? 27 GLN U N 1
+ATOM 85 C CA . GLN A 1 12 ? -11.117 -37.430 -9.514 1.00 32.45 ? ? ? ? ? ? 27 GLN U CA 1
+ATOM 86 C C . GLN A 1 12 ? -10.752 -36.236 -8.585 1.00 30.76 ? ? ? ? ? ? 27 GLN U C 1
+ATOM 87 O O . GLN A 1 12 ? -10.166 -35.252 -9.034 1.00 28.91 ? ? ? ? ? ? 27 GLN U O 1
+ATOM 88 C CB . GLN A 1 12 ? -10.791 -37.049 -10.962 1.00 35.45 ? ? ? ? ? ? 27 GLN U CB 1
+ATOM 89 C CG . GLN A 1 12 ? -10.952 -38.156 -11.978 1.00 40.44 ? ? ? ? ? ? 27 GLN U CG 1
+ATOM 90 C CD . GLN A 1 12 ? -9.850 -39.198 -11.861 1.00 46.52 ? ? ? ? ? ? 27 GLN U CD 1
+ATOM 91 O OE1 . GLN A 1 12 ? -8.706 -38.858 -11.556 1.00 51.15 ? ? ? ? ? ? 27 GLN U OE1 1
+ATOM 92 N NE2 . GLN A 1 12 ? -10.180 -40.464 -12.118 1.00 51.17 ? ? ? ? ? ? 27 GLN U NE2 1
+ATOM 93 N N . PRO A 1 13 ? -11.073 -36.341 -7.280 1.00 28.71 ? ? ? ? ? ? 28 PRO U N 1
+ATOM 94 C CA . PRO A 1 13 ? -11.068 -35.188 -6.382 1.00 27.48 ? ? ? ? ? ? 28 PRO U CA 1
+ATOM 95 C C . PRO A 1 13 ? -9.698 -34.638 -6.001 1.00 27.82 ? ? ? ? ? ? 28 PRO U C 1
+ATOM 96 O O . PRO A 1 13 ? -9.619 -33.569 -5.391 1.00 29.23 ? ? ? ? ? ? 28 PRO U O 1
+ATOM 97 C CB . PRO A 1 13 ? -11.872 -35.690 -5.169 1.00 28.82 ? ? ? ? ? ? 28 PRO U CB 1
+ATOM 98 C CG . PRO A 1 13 ? -11.627 -37.134 -5.141 1.00 27.55 ? ? ? ? ? ? 28 PRO U CG 1
+ATOM 99 C CD . PRO A 1 13 ? -11.583 -37.553 -6.604 1.00 28.27 ? ? ? ? ? ? 28 PRO U CD 1
+ATOM 100 N N . TRP A 1 14 ? -8.639 -35.332 -6.423 1.00 29.09 ? ? ? ? ? ? 29 TRP U N 1
+ATOM 101 C CA . TRP A 1 14 ? -7.261 -34.859 -6.346 1.00 28.48 ? ? ? ? ? ? 29 TRP U CA 1
+ATOM 102 C C . TRP A 1 14 ? -6.892 -33.930 -7.504 1.00 29.70 ? ? ? ? ? ? 29 TRP U C 1
+ATOM 103 O O . TRP A 1 14 ? -5.856 -33.247 -7.455 1.00 26.67 ? ? ? ? ? ? 29 TRP U O 1
+ATOM 104 C CB . TRP A 1 14 ? -6.281 -36.051 -6.370 1.00 30.34 ? ? ? ? ? ? 29 TRP U CB 1
+ATOM 105 C CG . TRP A 1 14 ? -6.604 -37.022 -7.437 1.00 30.03 ? ? ? ? ? ? 29 TRP U CG 1
+ATOM 106 C CD1 . TRP A 1 14 ? -6.248 -36.951 -8.758 1.00 33.16 ? ? ? ? ? ? 29 TRP U CD1 1
+ATOM 107 C CD2 . TRP A 1 14 ? -7.410 -38.186 -7.305 1.00 33.78 ? ? ? ? ? ? 29 TRP U CD2 1
+ATOM 108 N NE1 . TRP A 1 14 ? -6.783 -38.004 -9.448 1.00 33.59 ? ? ? ? ? ? 29 TRP U NE1 1
+ATOM 109 C CE2 . TRP A 1 14 ? -7.498 -38.782 -8.575 1.00 33.24 ? ? ? ? ? ? 29 TRP U CE2 1
+ATOM 110 C CE3 . TRP A 1 14 ? -8.062 -38.790 -6.229 1.00 35.07 ? ? ? ? ? ? 29 TRP U CE3 1
+ATOM 111 C CZ2 . TRP A 1 14 ? -8.199 -39.963 -8.792 1.00 35.05 ? ? ? ? ? ? 29 TRP U CZ2 1
+ATOM 112 C CZ3 . TRP A 1 14 ? -8.769 -39.946 -6.456 1.00 35.80 ? ? ? ? ? ? 29 TRP U CZ3 1
+ATOM 113 C CH2 . TRP A 1 14 ? -8.827 -40.524 -7.716 1.00 36.29 ? ? ? ? ? ? 29 TRP U CH2 1
+ATOM 114 N N . PHE A 1 15 ? -7.712 -33.903 -8.554 1.00 28.80 ? ? ? ? ? ? 30 PHE U N 1
+ATOM 115 C CA . PHE A 1 15 ? -7.365 -33.090 -9.703 1.00 24.17 ? ? ? ? ? ? 30 PHE U CA 1
+ATOM 116 C C . PHE A 1 15 ? -7.393 -31.581 -9.478 1.00 23.82 ? ? ? ? ? ? 30 PHE U C 1
+ATOM 117 O O . PHE A 1 15 ? -8.403 -30.996 -9.088 1.00 25.39 ? ? ? ? ? ? 30 PHE U O 1
+ATOM 118 C CB . PHE A 1 15 ? -8.187 -33.484 -10.926 1.00 26.92 ? ? ? ? ? ? 30 PHE U CB 1
+ATOM 119 C CG . PHE A 1 15 ? -7.892 -32.625 -12.108 1.00 25.03 ? ? ? ? ? ? 30 PHE U CG 1
+ATOM 120 C CD1 . PHE A 1 15 ? -6.787 -32.872 -12.879 1.00 27.15 ? ? ? ? ? ? 30 PHE U CD1 1
+ATOM 121 C CD2 . PHE A 1 15 ? -8.684 -31.518 -12.391 1.00 27.45 ? ? ? ? ? ? 30 PHE U CD2 1
+ATOM 122 C CE1 . PHE A 1 15 ? -6.491 -32.072 -13.984 1.00 28.31 ? ? ? ? ? ? 30 PHE U CE1 1
+ATOM 123 C CE2 . PHE A 1 15 ? -8.389 -30.705 -13.477 1.00 27.50 ? ? ? ? ? ? 30 PHE U CE2 1
+ATOM 124 C CZ . PHE A 1 15 ? -7.279 -30.986 -14.272 1.00 26.56 ? ? ? ? ? ? 30 PHE U CZ 1
+ATOM 125 N N . ALA A 1 16 ? -6.255 -30.939 -9.744 1.00 23.80 ? ? ? ? ? ? 31 ALA U N 1
+ATOM 126 C CA . ALA A 1 16 ? -6.089 -29.500 -9.595 1.00 24.25 ? ? ? ? ? ? 31 ALA U CA 1
+ATOM 127 C C . ALA A 1 16 ? -6.104 -28.829 -10.965 1.00 25.43 ? ? ? ? ? ? 31 ALA U C 1
+ATOM 128 O O . ALA A 1 16 ? -5.471 -29.354 -11.882 1.00 26.91 ? ? ? ? ? ? 31 ALA U O 1
+ATOM 129 C CB . ALA A 1 16 ? -4.756 -29.210 -8.945 1.00 24.05 ? ? ? ? ? ? 31 ALA U CB 1
+ATOM 130 N N . ALA A 1 17 ? -6.799 -27.685 -11.072 1.00 25.59 ? ? ? ? ? ? 32 ALA U N 1
+ATOM 131 C CA . ALA A 1 17 ? -6.861 -26.860 -12.293 1.00 28.20 ? ? ? ? ? ? 32 ALA U CA 1
+ATOM 132 C C . ALA A 1 17 ? -5.997 -25.607 -12.139 1.00 28.21 ? ? ? ? ? ? 32 ALA U C 1
+ATOM 133 O O . ALA A 1 17 ? -6.269 -24.758 -11.282 1.00 31.80 ? ? ? ? ? ? 32 ALA U O 1
+ATOM 134 C CB . ALA A 1 17 ? -8.308 -26.482 -12.604 1.00 26.88 ? ? ? ? ? ? 32 ALA U CB 1
+ATOM 135 N N . ILE A 1 18 ? -4.946 -25.492 -12.953 1.00 28.34 ? ? ? ? ? ? 33 ILE U N 1
+ATOM 136 C CA . ILE A 1 18 ? -3.972 -24.378 -12.813 1.00 30.06 ? ? ? ? ? ? 33 ILE U CA 1
+ATOM 137 C C . ILE A 1 18 ? -4.073 -23.331 -13.928 1.00 28.30 ? ? ? ? ? ? 33 ILE U C 1
+ATOM 138 O O . ILE A 1 18 ? -3.978 -23.656 -15.120 1.00 27.59 ? ? ? ? ? ? 33 ILE U O 1
+ATOM 139 C CB . ILE A 1 18 ? -2.519 -24.899 -12.750 1.00 30.88 ? ? ? ? ? ? 33 ILE U CB 1
+ATOM 140 C CG1 . ILE A 1 18 ? -2.428 -26.073 -11.769 1.00 30.29 ? ? ? ? ? ? 33 ILE U CG1 1
+ATOM 141 C CG2 . ILE A 1 18 ? -1.564 -23.750 -12.394 1.00 31.62 ? ? ? ? ? ? 33 ILE U CG2 1
+ATOM 142 C CD1 . ILE A 1 18 ? -1.077 -26.746 -11.685 1.00 30.37 ? ? ? ? ? ? 33 ILE U CD1 1
+ATOM 143 N N . TYR A 1 19 ? -4.228 -22.096 -13.528 1.00 28.37 ? ? ? ? ? ? 34 TYR U N 1
+ATOM 144 C CA . TYR A 1 19 ? -4.441 -20.991 -14.420 1.00 27.98 ? ? ? ? ? ? 34 TYR U CA 1
+ATOM 145 C C . TYR A 1 19 ? -3.389 -19.924 -14.199 1.00 32.27 ? ? ? ? ? ? 34 TYR U C 1
+ATOM 146 O O . TYR A 1 19 ? -2.775 -19.850 -13.167 1.00 28.65 ? ? ? ? ? ? 34 TYR U O 1
+ATOM 147 C CB . TYR A 1 19 ? -5.807 -20.358 -14.201 1.00 27.03 ? ? ? ? ? ? 34 TYR U CB 1
+ATOM 148 C CG . TYR A 1 19 ? -7.026 -21.219 -14.421 1.00 26.58 ? ? ? ? ? ? 34 TYR U CG 1
+ATOM 149 C CD1 . TYR A 1 19 ? -7.385 -22.172 -13.505 1.00 26.20 ? ? ? ? ? ? 34 TYR U CD1 1
+ATOM 150 C CD2 . TYR A 1 19 ? -7.812 -21.075 -15.539 1.00 26.30 ? ? ? ? ? ? 34 TYR U CD2 1
+ATOM 151 C CE1 . TYR A 1 19 ? -8.487 -22.955 -13.674 1.00 27.15 ? ? ? ? ? ? 34 TYR U CE1 1
+ATOM 152 C CE2 . TYR A 1 19 ? -8.921 -21.861 -15.728 1.00 27.93 ? ? ? ? ? ? 34 TYR U CE2 1
+ATOM 153 C CZ . TYR A 1 19 ? -9.246 -22.816 -14.781 1.00 28.87 ? ? ? ? ? ? 34 TYR U CZ 1
+ATOM 154 O OH . TYR A 1 19 ? -10.334 -23.589 -14.898 1.00 26.72 ? ? ? ? ? ? 34 TYR U OH 1
+ATOM 155 N N . ARG A 1 20 ? -3.200 -19.095 -15.207 1.00 35.62 ? ? ? ? ? ? 35 ARG U N 1
+ATOM 156 C CA . ARG A 1 20 ? -2.311 -17.943 -15.124 1.00 42.15 ? ? ? ? ? ? 35 ARG U CA 1
+ATOM 157 C C . ARG A 1 20 ? -2.981 -16.588 -15.460 1.00 47.22 ? ? ? ? ? ? 35 ARG U C 1
+ATOM 158 O O . ARG A 1 20 ? -3.666 -16.473 -16.451 1.00 44.84 ? ? ? ? ? ? 35 ARG U O 1
+ATOM 159 C CB . ARG A 1 20 ? -1.090 -18.128 -16.010 1.00 44.92 ? ? ? ? ? ? 35 ARG U CB 1
+ATOM 160 C CG . ARG A 1 20 ? -0.140 -16.971 -15.879 1.00 50.76 ? ? ? ? ? ? 35 ARG U CG 1
+ATOM 161 C CD . ARG A 1 20 ? 1.079 -17.097 -16.741 1.00 53.58 ? ? ? ? ? ? 35 ARG U CD 1
+ATOM 162 N NE . ARG A 1 20 ? 0.744 -16.851 -18.124 1.00 57.38 ? ? ? ? ? ? 35 ARG U NE 1
+ATOM 163 C CZ . ARG A 1 20 ? 0.878 -17.761 -19.060 1.00 58.24 ? ? ? ? ? ? 35 ARG U CZ 1
+ATOM 164 N NH1 . ARG A 1 20 ? 0.532 -17.500 -20.300 1.00 60.63 ? ? ? ? ? ? 35 ARG U NH1 1
+ATOM 165 N NH2 . ARG A 1 20 ? 1.370 -18.932 -18.740 1.00 58.36 ? ? ? ? ? ? 35 ARG U NH2 1
+ATOM 166 N N . ARG A 1 21 ? -2.754 -15.565 -14.651 1.00 48.52 ? ? ? ? ? ? 36 ARG U N 1
+ATOM 167 C CA . ARG A 1 21 ? -3.295 -14.247 -14.967 1.00 53.45 ? ? ? ? ? ? 36 ARG U CA 1
+ATOM 168 C C . ARG A 1 21 ? -2.285 -13.421 -15.726 1.00 55.80 ? ? ? ? ? ? 36 ARG U C 1
+ATOM 169 O O . ARG A 1 21 ? -1.155 -13.317 -15.308 1.00 59.06 ? ? ? ? ? ? 36 ARG U O 1
+ATOM 170 C CB . ARG A 1 21 ? -3.775 -13.482 -13.745 1.00 54.23 ? ? ? ? ? ? 36 ARG U CB 1
+ATOM 171 C CG . ARG A 1 21 ? -3.452 -14.085 -12.411 1.00 56.79 ? ? ? ? ? ? 36 ARG U CG 1
+ATOM 172 C CD . ARG A 1 21 ? -3.833 -13.082 -11.351 1.00 58.90 ? ? ? ? ? ? 36 ARG U CD 1
+ATOM 173 N NE . ARG A 1 21 ? -4.120 -13.672 -10.061 1.00 60.54 ? ? ? ? ? ? 36 ARG U NE 1
+ATOM 174 C CZ . ARG A 1 21 ? -3.208 -14.189 -9.274 1.00 58.86 ? ? ? ? ? ? 36 ARG U CZ 1
+ATOM 175 N NH1 . ARG A 1 21 ? -3.550 -14.698 -8.118 1.00 58.53 ? ? ? ? ? ? 36 ARG U NH1 1
+ATOM 176 N NH2 . ARG A 1 21 ? -1.956 -14.219 -9.667 1.00 60.99 ? ? ? ? ? ? 36 ARG U NH2 1
+ATOM 177 N N . HIS A 1 22 ? -2.709 -12.870 -16.852 1.00 56.48 ? ? ? ? ? ? 37 HIS U N 1
+ATOM 178 C CA . HIS A 1 22 ? -1.880 -12.046 -17.729 1.00 60.64 ? ? ? ? ? ? 37 HIS U CA 1
+ATOM 179 C C . HIS A 1 22 ? -1.860 -10.563 -17.366 1.00 60.91 ? ? ? ? ? ? 37 HIS U C 1
+ATOM 180 O O . HIS A 1 22 ? -2.516 -10.135 -16.437 1.00 62.61 ? ? ? ? ? ? 37 HIS U O 1
+ATOM 181 C CB . HIS A 1 22 ? -2.272 -12.222 -19.174 1.00 61.91 ? ? ? ? ? ? 37 HIS U CB 1
+ATOM 182 C CG . HIS A 1 22 ? -2.435 -13.642 -19.572 1.00 63.77 ? ? ? ? ? ? 37 HIS U CG 1
+ATOM 183 N ND1 . HIS A 1 22 ? -3.357 -14.467 -18.982 1.00 66.38 ? ? ? ? ? ? 37 HIS U ND1 1
+ATOM 184 C CD2 . HIS A 1 22 ? -1.782 -14.399 -20.478 1.00 62.68 ? ? ? ? ? ? 37 HIS U CD2 1
+ATOM 185 C CE1 . HIS A 1 22 ? -3.281 -15.664 -19.518 1.00 64.27 ? ? ? ? ? ? 37 HIS U CE1 1
+ATOM 186 N NE2 . HIS A 1 22 ? -2.333 -15.651 -20.429 1.00 62.36 ? ? ? ? ? ? 37 HIS U NE2 1
+ATOM 187 N N . ARG A 1 23 A -1.085 -9.777 -18.102 1.00 94.28 ? ? ? ? ? ? 37 ARG U N 1
+ATOM 188 C CA . ARG A 1 23 A -0.915 -8.380 -17.724 1.00 95.71 ? ? ? ? ? ? 37 ARG U CA 1
+ATOM 189 C C . ARG A 1 23 A -2.225 -7.635 -17.710 1.00 91.18 ? ? ? ? ? ? 37 ARG U C 1
+ATOM 190 O O . ARG A 1 23 A -2.406 -6.794 -16.868 1.00 85.08 ? ? ? ? ? ? 37 ARG U O 1
+ATOM 191 C CB . ARG A 1 23 A 0.087 -7.636 -18.570 1.00 100.69 ? ? ? ? ? ? 37 ARG U CB 1
+ATOM 192 C CG . ARG A 1 23 A 0.150 -6.141 -18.231 1.00 103.98 ? ? ? ? ? ? 37 ARG U CG 1
+ATOM 193 C CD . ARG A 1 23 A 0.807 -5.878 -16.882 1.00 105.08 ? ? ? ? ? ? 37 ARG U CD 1
+ATOM 194 N NE . ARG A 1 23 A 0.535 -4.550 -16.333 1.00 106.39 ? ? ? ? ? ? 37 ARG U NE 1
+ATOM 195 C CZ . ARG A 1 23 A 0.470 -3.431 -17.045 1.00 105.57 ? ? ? ? ? ? 37 ARG U CZ 1
+ATOM 196 N NH1 . ARG A 1 23 A 0.208 -2.279 -16.447 1.00 104.48 ? ? ? ? ? ? 37 ARG U NH1 1
+ATOM 197 N NH2 . ARG A 1 23 A 0.641 -3.461 -18.356 1.00 105.86 ? ? ? ? ? ? 37 ARG U NH2 1
+ATOM 198 N N . GLY A 1 24 B -3.131 -7.922 -18.630 1.00 94.70 ? ? ? ? ? ? 37 GLY U N 1
+ATOM 199 C CA . GLY A 1 24 B -4.410 -7.233 -18.626 1.00 93.94 ? ? ? ? ? ? 37 GLY U CA 1
+ATOM 200 C C . GLY A 1 24 B -5.286 -7.423 -17.384 1.00 95.41 ? ? ? ? ? ? 37 GLY U C 1
+ATOM 201 O O . GLY A 1 24 B -5.869 -6.496 -16.850 1.00 96.75 ? ? ? ? ? ? 37 GLY U O 1
+ATOM 202 N N . GLY A 1 25 C -5.339 -8.645 -16.903 1.00 62.34 ? ? ? ? ? ? 37 GLY U N 1
+ATOM 203 C CA . GLY A 1 25 C -6.350 -9.082 -15.990 1.00 63.80 ? ? ? ? ? ? 37 GLY U CA 1
+ATOM 204 C C . GLY A 1 25 C -6.971 -10.356 -16.530 1.00 68.37 ? ? ? ? ? ? 37 GLY U C 1
+ATOM 205 O O . GLY A 1 25 C -7.661 -11.041 -15.790 1.00 69.92 ? ? ? ? ? ? 37 GLY U O 1
+ATOM 206 N N . SER A 1 26 D -6.707 -10.692 -17.793 1.00 59.58 ? ? ? ? ? ? 37 SER U N 1
+ATOM 207 C CA . SER A 1 26 D -7.216 -11.938 -18.376 1.00 56.06 ? ? ? ? ? ? 37 SER U CA 1
+ATOM 208 C C . SER A 1 26 D -6.558 -13.222 -17.834 1.00 54.35 ? ? ? ? ? ? 37 SER U C 1
+ATOM 209 O O . SER A 1 26 D -5.372 -13.274 -17.650 1.00 56.12 ? ? ? ? ? ? 37 SER U O 1
+ATOM 210 C CB . SER A 1 26 D -7.243 -11.894 -19.912 1.00 56.50 ? ? ? ? ? ? 37 SER U CB 1
+ATOM 211 O OG . SER A 1 26 D -5.978 -11.918 -20.510 1.00 57.93 ? ? ? ? ? ? 37 SER U OG 1
+ATOM 212 N N . VAL A 1 27 ? -7.359 -14.236 -17.557 1.00 51.14 ? ? ? ? ? ? 38 VAL U N 1
+ATOM 213 C CA . VAL A 1 27 ? -6.881 -15.450 -16.912 1.00 47.47 ? ? ? ? ? ? 38 VAL U CA 1
+ATOM 214 C C . VAL A 1 27 ? -7.122 -16.633 -17.809 1.00 45.26 ? ? ? ? ? ? 38 VAL U C 1
+ATOM 215 O O . VAL A 1 27 ? -8.221 -16.845 -18.227 1.00 42.71 ? ? ? ? ? ? 38 VAL U O 1
+ATOM 216 C CB . VAL A 1 27 ? -7.628 -15.708 -15.602 1.00 46.73 ? ? ? ? ? ? 38 VAL U CB 1
+ATOM 217 C CG1 . VAL A 1 27 ? -7.099 -16.932 -14.894 1.00 45.35 ? ? ? ? ? ? 38 VAL U CG1 1
+ATOM 218 C CG2 . VAL A 1 27 ? -7.564 -14.506 -14.700 1.00 46.89 ? ? ? ? ? ? 38 VAL U CG2 1
+ATOM 219 N N . THR A 1 28 ? -6.090 -17.410 -18.083 1.00 44.54 ? ? ? ? ? ? 39 THR U N 1
+ATOM 220 C CA . THR A 1 28 ? -6.239 -18.582 -18.922 1.00 42.57 ? ? ? ? ? ? 39 THR U CA 1
+ATOM 221 C C . THR A 1 28 ? -5.630 -19.851 -18.348 1.00 37.32 ? ? ? ? ? ? 39 THR U C 1
+ATOM 222 O O . THR A 1 28 ? -4.739 -19.832 -17.570 1.00 37.04 ? ? ? ? ? ? 39 THR U O 1
+ATOM 223 C CB . THR A 1 28 ? -5.620 -18.388 -20.284 1.00 41.48 ? ? ? ? ? ? 39 THR U CB 1
+ATOM 224 O OG1 . THR A 1 28 ? -4.259 -18.104 -20.116 1.00 45.84 ? ? ? ? ? ? 39 THR U OG1 1
+ATOM 225 C CG2 . THR A 1 28 ? -6.243 -17.294 -21.002 1.00 45.50 ? ? ? ? ? ? 39 THR U CG2 1
+ATOM 226 N N . TYR A 1 29 ? -6.152 -20.965 -18.781 1.00 34.13 ? ? ? ? ? ? 40 TYR U N 1
+ATOM 227 C CA . TYR A 1 29 ? -5.766 -22.266 -18.247 1.00 33.03 ? ? ? ? ? ? 40 TYR U CA 1
+ATOM 228 C C . TYR A 1 29 ? -4.364 -22.631 -18.691 1.00 32.95 ? ? ? ? ? ? 40 TYR U C 1
+ATOM 229 O O . TYR A 1 29 ? -3.931 -22.303 -19.797 1.00 36.85 ? ? ? ? ? ? 40 TYR U O 1
+ATOM 230 C CB . TYR A 1 29 ? -6.763 -23.320 -18.667 1.00 31.24 ? ? ? ? ? ? 40 TYR U CB 1
+ATOM 231 C CG . TYR A 1 29 ? -6.584 -24.677 -18.010 1.00 29.04 ? ? ? ? ? ? 40 TYR U CG 1
+ATOM 232 C CD1 . TYR A 1 29 ? -6.975 -24.894 -16.698 1.00 26.35 ? ? ? ? ? ? 40 TYR U CD1 1
+ATOM 233 C CD2 . TYR A 1 29 ? -6.093 -25.749 -18.724 1.00 25.67 ? ? ? ? ? ? 40 TYR U CD2 1
+ATOM 234 C CE1 . TYR A 1 29 ? -6.862 -26.156 -16.098 1.00 25.41 ? ? ? ? ? ? 40 TYR U CE1 1
+ATOM 235 C CE2 . TYR A 1 29 ? -5.967 -27.004 -18.155 1.00 24.71 ? ? ? ? ? ? 40 TYR U CE2 1
+ATOM 236 C CZ . TYR A 1 29 ? -6.339 -27.208 -16.816 1.00 25.61 ? ? ? ? ? ? 40 TYR U CZ 1
+ATOM 237 O OH . TYR A 1 29 ? -6.243 -28.480 -16.250 1.00 21.91 ? ? ? ? ? ? 40 TYR U OH 1
+ATOM 238 N N . VAL A 1 30 ? -3.624 -23.251 -17.794 1.00 32.69 ? ? ? ? ? ? 41 VAL U N 1
+ATOM 239 C CA . VAL A 1 30 ? -2.239 -23.543 -18.046 1.00 34.50 ? ? ? ? ? ? 41 VAL U CA 1
+ATOM 240 C C . VAL A 1 30 ? -2.081 -25.056 -18.149 1.00 34.30 ? ? ? ? ? ? 41 VAL U C 1
+ATOM 241 O O . VAL A 1 30 ? -1.752 -25.582 -19.214 1.00 31.21 ? ? ? ? ? ? 41 VAL U O 1
+ATOM 242 C CB . VAL A 1 30 ? -1.347 -22.936 -16.946 1.00 34.13 ? ? ? ? ? ? 41 VAL U CB 1
+ATOM 243 C CG1 . VAL A 1 30 ? 0.096 -23.361 -17.105 1.00 35.74 ? ? ? ? ? ? 41 VAL U CG1 1
+ATOM 244 C CG2 . VAL A 1 30 ? -1.444 -21.423 -16.983 1.00 36.98 ? ? ? ? ? ? 41 VAL U CG2 1
+ATOM 245 N N . CYS A 1 31 ? -2.336 -25.744 -17.041 1.00 31.48 ? ? ? ? ? ? 42 CYS U N 1
+ATOM 246 C CA . CYS A 1 31 ? -2.109 -27.178 -16.947 1.00 29.70 ? ? ? ? ? ? 42 CYS U CA 1
+ATOM 247 C C . CYS A 1 31 ? -2.929 -27.812 -15.824 1.00 28.02 ? ? ? ? ? ? 42 CYS U C 1
+ATOM 248 O O . CYS A 1 31 ? -3.457 -27.136 -14.943 1.00 23.83 ? ? ? ? ? ? 42 CYS U O 1
+ATOM 249 C CB . CYS A 1 31 ? -0.657 -27.443 -16.558 1.00 34.09 ? ? ? ? ? ? 42 CYS U CB 1
+ATOM 250 S SG . CYS A 1 31 ? 0.612 -27.294 -17.833 1.00 37.77 ? ? ? ? ? ? 42 CYS U SG 1
+ATOM 251 N N . GLY A 1 32 ? -2.961 -29.131 -15.844 1.00 26.79 ? ? ? ? ? ? 43 GLY U N 1
+ATOM 252 C CA . GLY A 1 32 ? -3.463 -29.891 -14.718 1.00 29.22 ? ? ? ? ? ? 43 GLY U CA 1
+ATOM 253 C C . GLY A 1 32 ? -2.403 -30.055 -13.636 1.00 26.88 ? ? ? ? ? ? 43 GLY U C 1
+ATOM 254 O O . GLY A 1 32 ? -1.266 -29.597 -13.760 1.00 25.22 ? ? ? ? ? ? 43 GLY U O 1
+ATOM 255 N N . GLY A 1 33 ? -2.819 -30.695 -12.552 1.00 27.51 ? ? ? ? ? ? 44 GLY U N 1
+ATOM 256 C CA . GLY A 1 33 ? -1.955 -31.049 -11.447 1.00 26.79 ? ? ? ? ? ? 44 GLY U CA 1
+ATOM 257 C C . GLY A 1 33 ? -2.722 -31.962 -10.507 1.00 27.66 ? ? ? ? ? ? 44 GLY U C 1
+ATOM 258 O O . GLY A 1 33 ? -3.878 -32.316 -10.785 1.00 26.70 ? ? ? ? ? ? 44 GLY U O 1
+ATOM 259 N N . SER A 1 34 ? -2.103 -32.306 -9.376 1.00 29.11 ? ? ? ? ? ? 45 SER U N 1
+ATOM 260 C CA . SER A 1 34 ? -2.669 -33.257 -8.429 1.00 27.38 ? ? ? ? ? ? 45 SER U CA 1
+ATOM 261 C C . SER A 1 34 ? -2.337 -32.889 -6.993 1.00 31.28 ? ? ? ? ? ? 45 SER U C 1
+ATOM 262 O O . SER A 1 34 ? -1.183 -32.588 -6.674 1.00 28.94 ? ? ? ? ? ? 45 SER U O 1
+ATOM 263 C CB . SER A 1 34 ? -2.097 -34.642 -8.658 1.00 29.15 ? ? ? ? ? ? 45 SER U CB 1
+ATOM 264 O OG . SER A 1 34 ? -2.387 -35.131 -9.939 1.00 33.87 ? ? ? ? ? ? 45 SER U OG 1
+ATOM 265 N N . LEU A 1 35 ? -3.351 -32.970 -6.139 1.00 30.66 ? ? ? ? ? ? 46 LEU U N 1
+ATOM 266 C CA . LEU A 1 35 ? -3.222 -32.684 -4.735 1.00 34.40 ? ? ? ? ? ? 46 LEU U CA 1
+ATOM 267 C C . LEU A 1 35 ? -2.629 -33.870 -3.971 1.00 36.06 ? ? ? ? ? ? 46 LEU U C 1
+ATOM 268 O O . LEU A 1 35 ? -3.288 -34.878 -3.748 1.00 39.21 ? ? ? ? ? ? 46 LEU U O 1
+ATOM 269 C CB . LEU A 1 35 ? -4.599 -32.356 -4.167 1.00 34.51 ? ? ? ? ? ? 46 LEU U CB 1
+ATOM 270 C CG . LEU A 1 35 ? -4.581 -31.738 -2.775 1.00 32.68 ? ? ? ? ? ? 46 LEU U CG 1
+ATOM 271 C CD1 . LEU A 1 35 ? -3.791 -30.435 -2.815 1.00 31.78 ? ? ? ? ? ? 46 LEU U CD1 1
+ATOM 272 C CD2 . LEU A 1 35 ? -5.998 -31.537 -2.261 1.00 32.49 ? ? ? ? ? ? 46 LEU U CD2 1
+ATOM 273 N N . ILE A 1 36 ? -1.385 -33.741 -3.546 1.00 34.37 ? ? ? ? ? ? 47 ILE U N 1
+ATOM 274 C CA . ILE A 1 36 ? -0.708 -34.869 -2.914 1.00 35.66 ? ? ? ? ? ? 47 ILE U CA 1
+ATOM 275 C C . ILE A 1 36 ? -0.583 -34.726 -1.390 1.00 37.01 ? ? ? ? ? ? 47 ILE U C 1
+ATOM 276 O O . ILE A 1 36 ? -0.149 -35.640 -0.709 1.00 38.42 ? ? ? ? ? ? 47 ILE U O 1
+ATOM 277 C CB . ILE A 1 36 ? 0.664 -35.102 -3.560 1.00 34.41 ? ? ? ? ? ? 47 ILE U CB 1
+ATOM 278 C CG1 . ILE A 1 36 ? 1.597 -33.928 -3.289 1.00 34.48 ? ? ? ? ? ? 47 ILE U CG1 1
+ATOM 279 C CG2 . ILE A 1 36 ? 0.504 -35.294 -5.067 1.00 36.85 ? ? ? ? ? ? 47 ILE U CG2 1
+ATOM 280 C CD1 . ILE A 1 36 ? 3.050 -34.218 -3.610 1.00 35.77 ? ? ? ? ? ? 47 ILE U CD1 1
+ATOM 281 N N . SER A 1 37 ? -0.938 -33.563 -0.876 1.00 39.39 ? ? ? ? ? ? 48 SER U N 1
+ATOM 282 C CA . SER A 1 37 ? -1.088 -33.356 0.552 1.00 40.38 ? ? ? ? ? ? 48 SER U CA 1
+ATOM 283 C C . SER A 1 37 ? -1.840 -32.048 0.619 1.00 39.36 ? ? ? ? ? ? 48 SER U C 1
+ATOM 284 O O . SER A 1 37 ? -1.955 -31.376 -0.409 1.00 37.01 ? ? ? ? ? ? 48 SER U O 1
+ATOM 285 C CB . SER A 1 37 ? 0.272 -33.278 1.259 1.00 42.52 ? ? ? ? ? ? 48 SER U CB 1
+ATOM 286 O OG . SER A 1 37 ? 0.980 -32.107 0.912 1.00 42.03 ? ? ? ? ? ? 48 SER U OG 1
+ATOM 287 N N . PRO A 1 38 ? -2.371 -31.680 1.799 1.00 38.20 ? ? ? ? ? ? 49 PRO U N 1
+ATOM 288 C CA . PRO A 1 38 ? -3.238 -30.507 1.843 1.00 37.61 ? ? ? ? ? ? 49 PRO U CA 1
+ATOM 289 C C . PRO A 1 38 ? -2.636 -29.221 1.302 1.00 37.67 ? ? ? ? ? ? 49 PRO U C 1
+ATOM 290 O O . PRO A 1 38 ? -3.373 -28.420 0.744 1.00 36.64 ? ? ? ? ? ? 49 PRO U O 1
+ATOM 291 C CB . PRO A 1 38 ? -3.562 -30.354 3.334 1.00 38.26 ? ? ? ? ? ? 49 PRO U CB 1
+ATOM 292 C CG . PRO A 1 38 ? -3.426 -31.721 3.880 1.00 38.94 ? ? ? ? ? ? 49 PRO U CG 1
+ATOM 293 C CD . PRO A 1 38 ? -2.288 -32.341 3.117 1.00 41.02 ? ? ? ? ? ? 49 PRO U CD 1
+ATOM 294 N N . CYS A 1 39 ? -1.332 -29.007 1.485 1.00 38.12 ? ? ? ? ? ? 50 CYS U N 1
+ATOM 295 C CA . CYS A 1 39 ? -0.687 -27.760 1.060 1.00 40.13 ? ? ? ? ? ? 50 CYS U CA 1
+ATOM 296 C C . CYS A 1 39 ? 0.029 -27.853 -0.292 1.00 38.70 ? ? ? ? ? ? 50 CYS U C 1
+ATOM 297 O O . CYS A 1 39 ? 0.545 -26.838 -0.786 1.00 36.86 ? ? ? ? ? ? 50 CYS U O 1
+ATOM 298 C CB . CYS A 1 39 ? 0.328 -27.300 2.109 1.00 43.78 ? ? ? ? ? ? 50 CYS U CB 1
+ATOM 299 S SG . CYS A 1 39 ? -0.374 -27.057 3.754 1.00 48.95 ? ? ? ? ? ? 50 CYS U SG 1
+ATOM 300 N N . TRP A 1 40 ? 0.047 -29.046 -0.891 1.00 39.41 ? ? ? ? ? ? 51 TRP U N 1
+ATOM 301 C CA . TRP A 1 40 ? 0.929 -29.327 -2.028 1.00 40.08 ? ? ? ? ? ? 51 TRP U CA 1
+ATOM 302 C C . TRP A 1 40 ? 0.243 -29.937 -3.242 1.00 37.39 ? ? ? ? ? ? 51 TRP U C 1
+ATOM 303 O O . TRP A 1 40 ? -0.401 -30.986 -3.152 1.00 31.90 ? ? ? ? ? ? 51 TRP U O 1
+ATOM 304 C CB . TRP A 1 40 ? 2.049 -30.268 -1.593 1.00 42.82 ? ? ? ? ? ? 51 TRP U CB 1
+ATOM 305 C CG . TRP A 1 40 ? 3.028 -29.617 -0.683 1.00 45.78 ? ? ? ? ? ? 51 TRP U CG 1
+ATOM 306 C CD1 . TRP A 1 40 ? 3.119 -29.764 0.672 1.00 47.19 ? ? ? ? ? ? 51 TRP U CD1 1
+ATOM 307 C CD2 . TRP A 1 40 ? 4.057 -28.697 -1.057 1.00 46.26 ? ? ? ? ? ? 51 TRP U CD2 1
+ATOM 308 N NE1 . TRP A 1 40 ? 4.147 -28.997 1.164 1.00 45.98 ? ? ? ? ? ? 51 TRP U NE1 1
+ATOM 309 C CE2 . TRP A 1 40 ? 4.740 -28.329 0.129 1.00 46.38 ? ? ? ? ? ? 51 TRP U CE2 1
+ATOM 310 C CE3 . TRP A 1 40 ? 4.470 -28.149 -2.271 1.00 44.74 ? ? ? ? ? ? 51 TRP U CE3 1
+ATOM 311 C CZ2 . TRP A 1 40 ? 5.814 -27.437 0.131 1.00 45.63 ? ? ? ? ? ? 51 TRP U CZ2 1
+ATOM 312 C CZ3 . TRP A 1 40 ? 5.534 -27.266 -2.276 1.00 45.72 ? ? ? ? ? ? 51 TRP U CZ3 1
+ATOM 313 C CH2 . TRP A 1 40 ? 6.203 -26.919 -1.078 1.00 47.45 ? ? ? ? ? ? 51 TRP U CH2 1
+ATOM 314 N N . VAL A 1 41 ? 0.459 -29.300 -4.372 1.00 35.53 ? ? ? ? ? ? 52 VAL U N 1
+ATOM 315 C CA . VAL A 1 41 ? -0.013 -29.782 -5.628 1.00 36.42 ? ? ? ? ? ? 52 VAL U CA 1
+ATOM 316 C C . VAL A 1 41 ? 1.192 -30.090 -6.464 1.00 34.94 ? ? ? ? ? ? 52 VAL U C 1
+ATOM 317 O O . VAL A 1 41 ? 2.089 -29.314 -6.542 1.00 36.38 ? ? ? ? ? ? 52 VAL U O 1
+ATOM 318 C CB . VAL A 1 41 ? -0.920 -28.742 -6.314 1.00 36.44 ? ? ? ? ? ? 52 VAL U CB 1
+ATOM 319 C CG1 . VAL A 1 41 ? -1.040 -28.962 -7.816 1.00 36.92 ? ? ? ? ? ? 52 VAL U CG1 1
+ATOM 320 C CG2 . VAL A 1 41 ? -2.283 -28.709 -5.669 1.00 36.14 ? ? ? ? ? ? 52 VAL U CG2 1
+ATOM 321 N N . ILE A 1 42 ? 1.181 -31.223 -7.111 1.00 33.55 ? ? ? ? ? ? 53 ILE U N 1
+ATOM 322 C CA . ILE A 1 42 ? 2.256 -31.591 -7.996 1.00 32.87 ? ? ? ? ? ? 53 ILE U CA 1
+ATOM 323 C C . ILE A 1 42 ? 1.852 -31.547 -9.487 1.00 30.74 ? ? ? ? ? ? 53 ILE U C 1
+ATOM 324 O O . ILE A 1 42 ? 0.790 -31.921 -9.844 1.00 32.30 ? ? ? ? ? ? 53 ILE U O 1
+ATOM 325 C CB . ILE A 1 42 ? 2.812 -32.951 -7.627 1.00 33.61 ? ? ? ? ? ? 53 ILE U CB 1
+ATOM 326 C CG1 . ILE A 1 42 ? 4.145 -33.162 -8.280 1.00 35.73 ? ? ? ? ? ? 53 ILE U CG1 1
+ATOM 327 C CG2 . ILE A 1 42 ? 1.883 -34.041 -8.067 1.00 35.20 ? ? ? ? ? ? 53 ILE U CG2 1
+ATOM 328 C CD1 . ILE A 1 42 ? 4.924 -34.290 -7.689 1.00 36.12 ? ? ? ? ? ? 53 ILE U CD1 1
+ATOM 329 N N . SER A 1 43 ? 2.743 -31.056 -10.320 1.00 29.70 ? ? ? ? ? ? 54 SER U N 1
+ATOM 330 C CA . SER A 1 43 ? 2.542 -30.862 -11.750 1.00 28.75 ? ? ? ? ? ? 54 SER U CA 1
+ATOM 331 C C . SER A 1 43 ? 3.849 -31.035 -12.524 1.00 32.02 ? ? ? ? ? ? 54 SER U C 1
+ATOM 332 O O . SER A 1 43 ? 4.732 -31.711 -12.091 1.00 33.14 ? ? ? ? ? ? 54 SER U O 1
+ATOM 333 C CB . SER A 1 43 ? 1.960 -29.488 -12.050 1.00 28.79 ? ? ? ? ? ? 54 SER U CB 1
+ATOM 334 O OG . SER A 1 43 ? 1.451 -29.379 -13.341 1.00 26.60 ? ? ? ? ? ? 54 SER U OG 1
+ATOM 335 N N . ALA A 1 44 ? 3.902 -30.448 -13.710 1.00 31.06 ? ? ? ? ? ? 55 ALA U N 1
+ATOM 336 C CA . ALA A 1 44 ? 5.008 -30.548 -14.638 1.00 31.99 ? ? ? ? ? ? 55 ALA U CA 1
+ATOM 337 C C . ALA A 1 44 ? 5.739 -29.218 -14.801 1.00 33.55 ? ? ? ? ? ? 55 ALA U C 1
+ATOM 338 O O . ALA A 1 44 ? 5.145 -28.191 -14.863 1.00 31.53 ? ? ? ? ? ? 55 ALA U O 1
+ATOM 339 C CB . ALA A 1 44 ? 4.511 -31.058 -15.974 1.00 30.04 ? ? ? ? ? ? 55 ALA U CB 1
+ATOM 340 N N . THR A 1 45 ? 7.049 -29.256 -14.824 1.00 36.32 ? ? ? ? ? ? 56 THR U N 1
+ATOM 341 C CA . THR A 1 45 ? 7.838 -28.027 -14.851 1.00 36.09 ? ? ? ? ? ? 56 THR U CA 1
+ATOM 342 C C . THR A 1 45 ? 7.602 -27.194 -16.111 1.00 35.59 ? ? ? ? ? ? 56 THR U C 1
+ATOM 343 O O . THR A 1 45 ? 7.688 -25.962 -16.066 1.00 35.76 ? ? ? ? ? ? 56 THR U O 1
+ATOM 344 C CB . THR A 1 45 ? 9.352 -28.325 -14.676 1.00 38.16 ? ? ? ? ? ? 56 THR U CB 1
+ATOM 345 O OG1 . THR A 1 45 ? 9.659 -28.433 -13.281 1.00 38.04 ? ? ? ? ? ? 56 THR U OG1 1
+ATOM 346 C CG2 . THR A 1 45 ? 10.213 -27.226 -15.247 1.00 40.24 ? ? ? ? ? ? 56 THR U CG2 1
+ATOM 347 N N . HIS A 1 46 ? 7.316 -27.851 -17.229 1.00 34.30 ? ? ? ? ? ? 57 HIS U N 1
+ATOM 348 C CA . HIS A 1 46 ? 7.181 -27.165 -18.502 1.00 33.05 ? ? ? ? ? ? 57 HIS U CA 1
+ATOM 349 C C . HIS A 1 46 ? 5.980 -26.221 -18.485 1.00 36.88 ? ? ? ? ? ? 57 HIS U C 1
+ATOM 350 O O . HIS A 1 46 ? 5.918 -25.274 -19.260 1.00 37.70 ? ? ? ? ? ? 57 HIS U O 1
+ATOM 351 C CB . HIS A 1 46 ? 7.069 -28.177 -19.651 1.00 33.66 ? ? ? ? ? ? 57 HIS U CB 1
+ATOM 352 C CG . HIS A 1 46 ? 5.737 -28.844 -19.746 1.00 35.43 ? ? ? ? ? ? 57 HIS U CG 1
+ATOM 353 N ND1 . HIS A 1 46 ? 5.516 -30.144 -19.344 1.00 36.94 ? ? ? ? ? ? 57 HIS U ND1 1
+ATOM 354 C CD2 . HIS A 1 46 ? 4.548 -28.383 -20.195 1.00 38.79 ? ? ? ? ? ? 57 HIS U CD2 1
+ATOM 355 C CE1 . HIS A 1 46 ? 4.249 -30.457 -19.558 1.00 38.76 ? ? ? ? ? ? 57 HIS U CE1 1
+ATOM 356 N NE2 . HIS A 1 46 ? 3.642 -29.405 -20.077 1.00 37.64 ? ? ? ? ? ? 57 HIS U NE2 1
+ATOM 357 N N . CYS A 1 47 ? 5.012 -26.480 -17.609 1.00 36.96 ? ? ? ? ? ? 58 CYS U N 1
+ATOM 358 C CA . CYS A 1 47 ? 3.859 -25.587 -17.464 1.00 35.30 ? ? ? ? ? ? 58 CYS U CA 1
+ATOM 359 C C . CYS A 1 47 ? 4.212 -24.175 -17.049 1.00 36.84 ? ? ? ? ? ? 58 CYS U C 1
+ATOM 360 O O . CYS A 1 47 ? 3.423 -23.259 -17.238 1.00 35.59 ? ? ? ? ? ? 58 CYS U O 1
+ATOM 361 C CB . CYS A 1 47 ? 2.936 -26.131 -16.391 1.00 36.48 ? ? ? ? ? ? 58 CYS U CB 1
+ATOM 362 S SG . CYS A 1 47 ? 2.276 -27.753 -16.770 1.00 35.39 ? ? ? ? ? ? 58 CYS U SG 1
+ATOM 363 N N . PHE A 1 48 ? 5.343 -24.036 -16.394 1.00 39.75 ? ? ? ? ? ? 59 PHE U N 1
+ATOM 364 C CA . PHE A 1 48 ? 5.699 -22.800 -15.746 1.00 38.36 ? ? ? ? ? ? 59 PHE U CA 1
+ATOM 365 C C . PHE A 1 48 ? 7.001 -22.172 -16.224 1.00 40.62 ? ? ? ? ? ? 59 PHE U C 1
+ATOM 366 O O . PHE A 1 48 ? 7.338 -21.077 -15.869 1.00 39.94 ? ? ? ? ? ? 59 PHE U O 1
+ATOM 367 C CB . PHE A 1 48 ? 5.757 -23.008 -14.240 1.00 36.41 ? ? ? ? ? ? 59 PHE U CB 1
+ATOM 368 C CG . PHE A 1 48 ? 4.612 -23.789 -13.705 1.00 35.50 ? ? ? ? ? ? 59 PHE U CG 1
+ATOM 369 C CD1 . PHE A 1 48 ? 3.420 -23.193 -13.444 1.00 33.86 ? ? ? ? ? ? 59 PHE U CD1 1
+ATOM 370 C CD2 . PHE A 1 48 ? 4.732 -25.129 -13.478 1.00 38.22 ? ? ? ? ? ? 59 PHE U CD2 1
+ATOM 371 C CE1 . PHE A 1 48 ? 2.361 -23.910 -12.956 1.00 34.90 ? ? ? ? ? ? 59 PHE U CE1 1
+ATOM 372 C CE2 . PHE A 1 48 ? 3.674 -25.858 -12.980 1.00 37.02 ? ? ? ? ? ? 59 PHE U CE2 1
+ATOM 373 C CZ . PHE A 1 48 ? 2.480 -25.244 -12.721 1.00 34.41 ? ? ? ? ? ? 59 PHE U CZ 1
+ATOM 374 N N . ILE A 1 49 ? 7.729 -22.899 -17.024 1.00 42.84 ? ? ? ? ? ? 60 ILE U N 1
+ATOM 375 C CA . ILE A 1 49 ? 9.076 -22.521 -17.354 1.00 47.48 ? ? ? ? ? ? 60 ILE U CA 1
+ATOM 376 C C . ILE A 1 49 ? 9.106 -21.178 -18.096 1.00 48.16 ? ? ? ? ? ? 60 ILE U C 1
+ATOM 377 O O . ILE A 1 49 ? 9.938 -20.373 -17.802 1.00 48.43 ? ? ? ? ? ? 60 ILE U O 1
+ATOM 378 C CB . ILE A 1 49 ? 9.842 -23.687 -18.024 1.00 47.93 ? ? ? ? ? ? 60 ILE U CB 1
+ATOM 379 C CG1 . ILE A 1 49 ? 11.336 -23.456 -17.992 1.00 52.13 ? ? ? ? ? ? 60 ILE U CG1 1
+ATOM 380 C CG2 . ILE A 1 49 ? 9.360 -23.938 -19.428 1.00 48.22 ? ? ? ? ? ? 60 ILE U CG2 1
+ATOM 381 C CD1 . ILE A 1 49 ? 11.926 -23.294 -16.621 1.00 52.71 ? ? ? ? ? ? 60 ILE U CD1 1
+ATOM 382 N N . ASP A 1 50 A 8.163 -20.932 -18.993 1.00 41.35 ? ? ? ? ? ? 60 ASP U N 1
+ATOM 383 C CA . ASP A 1 50 A 8.058 -19.640 -19.623 1.00 44.13 ? ? ? ? ? ? 60 ASP U CA 1
+ATOM 384 C C . ASP A 1 50 A 7.706 -18.421 -18.760 1.00 44.67 ? ? ? ? ? ? 60 ASP U C 1
+ATOM 385 O O . ASP A 1 50 A 8.102 -17.354 -19.101 1.00 42.34 ? ? ? ? ? ? 60 ASP U O 1
+ATOM 386 C CB . ASP A 1 50 A 7.194 -19.662 -20.860 1.00 45.99 ? ? ? ? ? ? 60 ASP U CB 1
+ATOM 387 C CG . ASP A 1 50 A 7.483 -20.837 -21.731 1.00 49.54 ? ? ? ? ? ? 60 ASP U CG 1
+ATOM 388 O OD1 . ASP A 1 50 A 8.585 -21.365 -21.626 1.00 56.51 ? ? ? ? ? ? 60 ASP U OD1 1
+ATOM 389 O OD2 . ASP A 1 50 A 6.622 -21.229 -22.526 1.00 50.70 ? ? ? ? ? ? 60 ASP U OD2 1
+ATOM 390 N N . TYR A 1 51 B 6.895 -18.579 -17.749 1.00 52.86 ? ? ? ? ? ? 60 TYR U N 1
+ATOM 391 C CA . TYR A 1 51 B 6.498 -17.493 -16.882 1.00 57.14 ? ? ? ? ? ? 60 TYR U CA 1
+ATOM 392 C C . TYR A 1 51 B 6.672 -17.915 -15.403 1.00 54.75 ? ? ? ? ? ? 60 TYR U C 1
+ATOM 393 O O . TYR A 1 51 B 5.698 -18.216 -14.763 1.00 51.14 ? ? ? ? ? ? 60 TYR U O 1
+ATOM 394 C CB . TYR A 1 51 B 5.062 -17.052 -17.183 1.00 61.66 ? ? ? ? ? ? 60 TYR U CB 1
+ATOM 395 C CG . TYR A 1 51 B 4.707 -16.650 -18.634 1.00 65.97 ? ? ? ? ? ? 60 TYR U CG 1
+ATOM 396 C CD1 . TYR A 1 51 B 4.767 -17.543 -19.680 1.00 69.56 ? ? ? ? ? ? 60 TYR U CD1 1
+ATOM 397 C CD2 . TYR A 1 51 B 4.294 -15.366 -18.935 1.00 70.33 ? ? ? ? ? ? 60 TYR U CD2 1
+ATOM 398 C CE1 . TYR A 1 51 B 4.468 -17.153 -20.977 1.00 71.17 ? ? ? ? ? ? 60 TYR U CE1 1
+ATOM 399 C CE2 . TYR A 1 51 B 4.007 -14.973 -20.221 1.00 70.43 ? ? ? ? ? ? 60 TYR U CE2 1
+ATOM 400 C CZ . TYR A 1 51 B 4.089 -15.876 -21.227 1.00 72.28 ? ? ? ? ? ? 60 TYR U CZ 1
+ATOM 401 O OH . TYR A 1 51 B 3.777 -15.493 -22.492 1.00 73.97 ? ? ? ? ? ? 60 TYR U OH 1
+ATOM 402 N N . PRO A 1 52 C 7.979 -17.918 -14.891 1.00 49.51 ? ? ? ? ? ? 60 PRO U N 1
+ATOM 403 C CA . PRO A 1 52 C 8.094 -18.564 -13.558 1.00 47.83 ? ? ? ? ? ? 60 PRO U CA 1
+ATOM 404 C C . PRO A 1 52 C 7.733 -17.811 -12.282 1.00 46.55 ? ? ? ? ? ? 60 PRO U C 1
+ATOM 405 O O . PRO A 1 52 C 8.088 -18.242 -11.214 1.00 47.71 ? ? ? ? ? ? 60 PRO U O 1
+ATOM 406 C CB . PRO A 1 52 C 9.548 -19.009 -13.497 1.00 47.73 ? ? ? ? ? ? 60 PRO U CB 1
+ATOM 407 C CG . PRO A 1 52 C 10.288 -18.047 -14.304 1.00 49.36 ? ? ? ? ? ? 60 PRO U CG 1
+ATOM 408 C CD . PRO A 1 52 C 9.403 -17.636 -15.402 1.00 49.25 ? ? ? ? ? ? 60 PRO U CD 1
+ATOM 409 N N . LYS A 1 53 ? 7.098 -16.672 -12.396 1.00 48.29 ? ? ? ? ? ? 61 LYS U N 1
+ATOM 410 C CA . LYS A 1 53 ? 6.631 -15.968 -11.223 1.00 49.73 ? ? ? ? ? ? 61 LYS U CA 1
+ATOM 411 C C . LYS A 1 53 ? 5.361 -16.583 -10.633 1.00 45.85 ? ? ? ? ? ? 61 LYS U C 1
+ATOM 412 O O . LYS A 1 53 ? 4.347 -16.593 -11.253 1.00 44.19 ? ? ? ? ? ? 61 LYS U O 1
+ATOM 413 C CB . LYS A 1 53 ? 6.360 -14.515 -11.576 1.00 55.64 ? ? ? ? ? ? 61 LYS U CB 1
+ATOM 414 C CG . LYS A 1 53 ? 7.251 -13.948 -12.670 1.00 61.46 ? ? ? ? ? ? 61 LYS U CG 1
+ATOM 415 C CD . LYS A 1 53 ? 8.691 -13.950 -12.224 1.00 64.72 ? ? ? ? ? ? 61 LYS U CD 1
+ATOM 416 C CE . LYS A 1 53 ? 9.396 -12.700 -12.725 1.00 69.14 ? ? ? ? ? ? 61 LYS U CE 1
+ATOM 417 N NZ . LYS A 1 53 ? 10.710 -12.503 -12.074 1.00 71.19 ? ? ? ? ? ? 61 LYS U NZ 1
+ATOM 418 N N . LYS A 1 54 ? 5.483 -17.083 -9.418 1.00 44.97 ? ? ? ? ? ? 62 LYS U N 1
+ATOM 419 C CA . LYS A 1 54 ? 4.431 -17.730 -8.661 1.00 45.76 ? ? ? ? ? ? 62 LYS U CA 1
+ATOM 420 C C . LYS A 1 54 ? 3.191 -16.882 -8.407 1.00 44.59 ? ? ? ? ? ? 62 LYS U C 1
+ATOM 421 O O . LYS A 1 54 ? 2.111 -17.384 -8.189 1.00 41.78 ? ? ? ? ? ? 62 LYS U O 1
+ATOM 422 C CB . LYS A 1 54 ? 4.992 -18.183 -7.338 1.00 46.22 ? ? ? ? ? ? 62 LYS U CB 1
+ATOM 423 C CG . LYS A 1 54 ? 5.476 -17.057 -6.449 1.00 48.63 ? ? ? ? ? ? 62 LYS U CG 1
+ATOM 424 C CD . LYS A 1 54 ? 5.835 -17.577 -5.062 1.00 51.35 ? ? ? ? ? ? 62 LYS U CD 1
+ATOM 425 C CE . LYS A 1 54 ? 6.801 -16.689 -4.305 1.00 54.94 ? ? ? ? ? ? 62 LYS U CE 1
+ATOM 426 N NZ . LYS A 1 54 ? 6.408 -16.489 -2.895 1.00 55.54 ? ? ? ? ? ? 62 LYS U NZ 1
+ATOM 427 N N . GLU A 1 55 A 3.389 -15.580 -8.426 1.00 47.73 ? ? ? ? ? ? 62 GLU U N 1
+ATOM 428 C CA . GLU A 1 55 A 2.333 -14.602 -8.247 1.00 49.15 ? ? ? ? ? ? 62 GLU U CA 1
+ATOM 429 C C . GLU A 1 55 A 1.278 -14.665 -9.343 1.00 44.73 ? ? ? ? ? ? 62 GLU U C 1
+ATOM 430 O O . GLU A 1 55 A 0.139 -14.379 -9.109 1.00 43.01 ? ? ? ? ? ? 62 GLU U O 1
+ATOM 431 C CB . GLU A 1 55 A 2.917 -13.205 -8.206 1.00 51.22 ? ? ? ? ? ? 62 GLU U CB 1
+ATOM 432 C CG . GLU A 1 55 A 3.623 -12.856 -6.938 1.00 53.81 ? ? ? ? ? ? 62 GLU U CG 1
+ATOM 433 C CD . GLU A 1 55 A 5.017 -13.383 -6.891 1.00 57.28 ? ? ? ? ? ? 62 GLU U CD 1
+ATOM 434 O OE1 . GLU A 1 55 A 5.689 -13.491 -7.922 1.00 62.30 ? ? ? ? ? ? 62 GLU U OE1 1
+ATOM 435 O OE2 . GLU A 1 55 A 5.443 -13.682 -5.792 1.00 64.13 ? ? ? ? ? ? 62 GLU U OE2 1
+ATOM 436 N N . ASP A 1 56 ? 1.698 -14.955 -10.551 1.00 43.02 ? ? ? ? ? ? 63 ASP U N 1
+ATOM 437 C CA . ASP A 1 56 ? 0.780 -15.023 -11.656 1.00 45.68 ? ? ? ? ? ? 63 ASP U CA 1
+ATOM 438 C C . ASP A 1 56 ? -0.146 -16.232 -11.734 1.00 44.27 ? ? ? ? ? ? 63 ASP U C 1
+ATOM 439 O O . ASP A 1 56 ? -0.902 -16.337 -12.656 1.00 42.96 ? ? ? ? ? ? 63 ASP U O 1
+ATOM 440 C CB . ASP A 1 56 ? 1.544 -14.899 -12.942 1.00 47.77 ? ? ? ? ? ? 63 ASP U CB 1
+ATOM 441 C CG . ASP A 1 56 ? 2.149 -13.557 -13.101 1.00 50.56 ? ? ? ? ? ? 63 ASP U CG 1
+ATOM 442 O OD1 . ASP A 1 56 ? 1.601 -12.585 -12.567 1.00 54.24 ? ? ? ? ? ? 63 ASP U OD1 1
+ATOM 443 O OD2 . ASP A 1 56 ? 3.168 -13.478 -13.762 1.00 52.86 ? ? ? ? ? ? 63 ASP U OD2 1
+ATOM 444 N N . TYR A 1 57 ? -0.080 -17.129 -10.767 1.00 41.55 ? ? ? ? ? ? 64 TYR U N 1
+ATOM 445 C CA . TYR A 1 57 ? -0.798 -18.390 -10.855 1.00 40.93 ? ? ? ? ? ? 64 TYR U CA 1
+ATOM 446 C C . TYR A 1 57 ? -1.907 -18.527 -9.850 1.00 38.39 ? ? ? ? ? ? 64 TYR U C 1
+ATOM 447 O O . TYR A 1 57 ? -1.848 -17.992 -8.733 1.00 34.78 ? ? ? ? ? ? 64 TYR U O 1
+ATOM 448 C CB . TYR A 1 57 ? 0.172 -19.553 -10.711 1.00 41.79 ? ? ? ? ? ? 64 TYR U CB 1
+ATOM 449 C CG . TYR A 1 57 ? 1.068 -19.633 -11.889 1.00 39.30 ? ? ? ? ? ? 64 TYR U CG 1
+ATOM 450 C CD1 . TYR A 1 57 ? 0.630 -20.217 -13.072 1.00 39.55 ? ? ? ? ? ? 64 TYR U CD1 1
+ATOM 451 C CD2 . TYR A 1 57 ? 2.339 -19.064 -11.862 1.00 39.57 ? ? ? ? ? ? 64 TYR U CD2 1
+ATOM 452 C CE1 . TYR A 1 57 ? 1.444 -20.271 -14.190 1.00 38.25 ? ? ? ? ? ? 64 TYR U CE1 1
+ATOM 453 C CE2 . TYR A 1 57 ? 3.161 -19.118 -12.975 1.00 39.52 ? ? ? ? ? ? 64 TYR U CE2 1
+ATOM 454 C CZ . TYR A 1 57 ? 2.702 -19.719 -14.133 1.00 38.18 ? ? ? ? ? ? 64 TYR U CZ 1
+ATOM 455 O OH . TYR A 1 57 ? 3.494 -19.771 -15.230 1.00 40.37 ? ? ? ? ? ? 64 TYR U OH 1
+ATOM 456 N N . ILE A 1 58 ? -2.934 -19.248 -10.275 1.00 36.40 ? ? ? ? ? ? 65 ILE U N 1
+ATOM 457 C CA . ILE A 1 58 ? -4.035 -19.595 -9.415 1.00 35.67 ? ? ? ? ? ? 65 ILE U CA 1
+ATOM 458 C C . ILE A 1 58 ? -4.324 -21.076 -9.576 1.00 35.69 ? ? ? ? ? ? 65 ILE U C 1
+ATOM 459 O O . ILE A 1 58 ? -4.316 -21.607 -10.704 1.00 31.59 ? ? ? ? ? ? 65 ILE U O 1
+ATOM 460 C CB . ILE A 1 58 ? -5.319 -18.852 -9.791 1.00 39.32 ? ? ? ? ? ? 65 ILE U CB 1
+ATOM 461 C CG1 . ILE A 1 58 ? -5.113 -17.336 -9.807 1.00 44.29 ? ? ? ? ? ? 65 ILE U CG1 1
+ATOM 462 C CG2 . ILE A 1 58 ? -6.425 -19.216 -8.818 1.00 40.54 ? ? ? ? ? ? 65 ILE U CG2 1
+ATOM 463 C CD1 . ILE A 1 58 ? -6.187 -16.613 -10.605 1.00 47.60 ? ? ? ? ? ? 65 ILE U CD1 1
+ATOM 464 N N . VAL A 1 59 ? -4.615 -21.728 -8.456 1.00 33.57 ? ? ? ? ? ? 66 VAL U N 1
+ATOM 465 C CA . VAL A 1 59 ? -4.941 -23.140 -8.451 1.00 31.83 ? ? ? ? ? ? 66 VAL U CA 1
+ATOM 466 C C . VAL A 1 59 ? -6.346 -23.321 -7.923 1.00 33.49 ? ? ? ? ? ? 66 VAL U C 1
+ATOM 467 O O . VAL A 1 59 ? -6.674 -22.819 -6.834 1.00 35.13 ? ? ? ? ? ? 66 VAL U O 1
+ATOM 468 C CB . VAL A 1 59 ? -3.974 -23.955 -7.593 1.00 31.78 ? ? ? ? ? ? 66 VAL U CB 1
+ATOM 469 C CG1 . VAL A 1 59 ? -4.437 -25.412 -7.492 1.00 32.70 ? ? ? ? ? ? 66 VAL U CG1 1
+ATOM 470 C CG2 . VAL A 1 59 ? -2.563 -23.884 -8.157 1.00 34.87 ? ? ? ? ? ? 66 VAL U CG2 1
+ATOM 471 N N . TYR A 1 60 ? -7.178 -24.031 -8.691 1.00 31.33 ? ? ? ? ? ? 67 TYR U N 1
+ATOM 472 C CA . TYR A 1 60 ? -8.505 -24.426 -8.214 1.00 30.41 ? ? ? ? ? ? 67 TYR U CA 1
+ATOM 473 C C . TYR A 1 60 ? -8.530 -25.903 -7.883 1.00 30.74 ? ? ? ? ? ? 67 TYR U C 1
+ATOM 474 O O . TYR A 1 60 ? -7.925 -26.733 -8.584 1.00 30.60 ? ? ? ? ? ? 67 TYR U O 1
+ATOM 475 C CB . TYR A 1 60 ? -9.597 -24.120 -9.233 1.00 31.58 ? ? ? ? ? ? 67 TYR U CB 1
+ATOM 476 C CG . TYR A 1 60 ? -9.948 -22.671 -9.383 1.00 33.34 ? ? ? ? ? ? 67 TYR U CG 1
+ATOM 477 C CD1 . TYR A 1 60 ? -10.940 -22.077 -8.605 1.00 37.08 ? ? ? ? ? ? 67 TYR U CD1 1
+ATOM 478 C CD2 . TYR A 1 60 ? -9.303 -21.885 -10.335 1.00 35.11 ? ? ? ? ? ? 67 TYR U CD2 1
+ATOM 479 C CE1 . TYR A 1 60 ? -11.274 -20.736 -8.777 1.00 36.71 ? ? ? ? ? ? 67 TYR U CE1 1
+ATOM 480 C CE2 . TYR A 1 60 ? -9.606 -20.555 -10.500 1.00 36.06 ? ? ? ? ? ? 67 TYR U CE2 1
+ATOM 481 C CZ . TYR A 1 60 ? -10.599 -19.979 -9.727 1.00 40.30 ? ? ? ? ? ? 67 TYR U CZ 1
+ATOM 482 O OH . TYR A 1 60 ? -10.877 -18.643 -9.933 1.00 42.27 ? ? ? ? ? ? 67 TYR U OH 1
+ATOM 483 N N . LEU A 1 61 ? -9.242 -26.220 -6.806 1.00 27.82 ? ? ? ? ? ? 68 LEU U N 1
+ATOM 484 C CA . LEU A 1 61 ? -9.547 -27.570 -6.441 1.00 27.14 ? ? ? ? ? ? 68 LEU U CA 1
+ATOM 485 C C . LEU A 1 61 ? -11.039 -27.656 -6.403 1.00 24.84 ? ? ? ? ? ? 68 LEU U C 1
+ATOM 486 O O . LEU A 1 61 ? -11.723 -26.659 -6.229 1.00 27.69 ? ? ? ? ? ? 68 LEU U O 1
+ATOM 487 C CB . LEU A 1 61 ? -8.953 -27.915 -5.065 1.00 29.78 ? ? ? ? ? ? 68 LEU U CB 1
+ATOM 488 C CG . LEU A 1 61 ? -7.436 -27.780 -4.959 1.00 29.69 ? ? ? ? ? ? 68 LEU U CG 1
+ATOM 489 C CD1 . LEU A 1 61 ? -7.012 -28.056 -3.516 1.00 32.25 ? ? ? ? ? ? 68 LEU U CD1 1
+ATOM 490 C CD2 . LEU A 1 61 ? -6.660 -28.690 -5.904 1.00 29.78 ? ? ? ? ? ? 68 LEU U CD2 1
+ATOM 491 N N . GLY A 1 62 ? -11.576 -28.832 -6.646 1.00 23.01 ? ? ? ? ? ? 69 GLY U N 1
+ATOM 492 C CA . GLY A 1 62 ? -13.028 -28.980 -6.588 1.00 23.48 ? ? ? ? ? ? 69 GLY U CA 1
+ATOM 493 C C . GLY A 1 62 ? -13.712 -28.482 -7.864 1.00 23.97 ? ? ? ? ? ? 69 GLY U C 1
+ATOM 494 O O . GLY A 1 62 ? -14.902 -28.204 -7.854 1.00 23.73 ? ? ? ? ? ? 69 GLY U O 1
+ATOM 495 N N . ARG A 1 63 ? -12.954 -28.342 -8.951 1.00 25.54 ? ? ? ? ? ? 70 ARG U N 1
+ATOM 496 C CA . ARG A 1 63 ? -13.453 -27.663 -10.174 1.00 28.15 ? ? ? ? ? ? 70 ARG U CA 1
+ATOM 497 C C . ARG A 1 63 ? -13.744 -28.703 -11.266 1.00 29.82 ? ? ? ? ? ? 70 ARG U C 1
+ATOM 498 O O . ARG A 1 63 ? -12.839 -29.478 -11.631 1.00 30.54 ? ? ? ? ? ? 70 ARG U O 1
+ATOM 499 C CB . ARG A 1 63 ? -12.382 -26.680 -10.633 1.00 31.47 ? ? ? ? ? ? 70 ARG U CB 1
+ATOM 500 C CG . ARG A 1 63 ? -12.684 -25.862 -11.866 1.00 33.99 ? ? ? ? ? ? 70 ARG U CG 1
+ATOM 501 C CD . ARG A 1 63 ? -12.419 -24.410 -11.566 1.00 36.45 ? ? ? ? ? ? 70 ARG U CD 1
+ATOM 502 N NE . ARG A 1 63 ? -12.533 -23.569 -12.730 1.00 39.24 ? ? ? ? ? ? 70 ARG U NE 1
+ATOM 503 C CZ . ARG A 1 63 ? -13.021 -22.333 -12.740 1.00 40.19 ? ? ? ? ? ? 70 ARG U CZ 1
+ATOM 504 N NH1 . ARG A 1 63 ? -13.512 -21.750 -11.642 1.00 40.50 ? ? ? ? ? ? 70 ARG U NH1 1
+ATOM 505 N NH2 . ARG A 1 63 ? -13.051 -21.684 -13.881 1.00 40.00 ? ? ? ? ? ? 70 ARG U NH2 1
+ATOM 506 N N . SER A 1 64 ? -14.984 -28.746 -11.769 1.00 29.34 ? ? ? ? ? ? 71 SER U N 1
+ATOM 507 C CA . SER A 1 64 ? -15.365 -29.682 -12.842 1.00 28.23 ? ? ? ? ? ? 71 SER U CA 1
+ATOM 508 C C . SER A 1 64 ? -15.496 -28.967 -14.234 1.00 30.84 ? ? ? ? ? ? 71 SER U C 1
+ATOM 509 O O . SER A 1 64 ? -15.666 -29.632 -15.263 1.00 29.05 ? ? ? ? ? ? 71 SER U O 1
+ATOM 510 C CB . SER A 1 64 ? -16.704 -30.403 -12.559 1.00 29.02 ? ? ? ? ? ? 71 SER U CB 1
+ATOM 511 O OG . SER A 1 64 ? -16.767 -31.131 -11.322 1.00 29.80 ? ? ? ? ? ? 71 SER U OG 1
+ATOM 512 N N . ARG A 1 65 ? -15.483 -27.641 -14.266 1.00 28.77 ? ? ? ? ? ? 72 ARG U N 1
+ATOM 513 C CA . ARG A 1 65 ? -15.612 -26.925 -15.544 1.00 29.41 ? ? ? ? ? ? 72 ARG U CA 1
+ATOM 514 C C . ARG A 1 65 ? -14.456 -25.974 -15.752 1.00 29.62 ? ? ? ? ? ? 72 ARG U C 1
+ATOM 515 O O . ARG A 1 65 ? -13.892 -25.445 -14.784 1.00 28.27 ? ? ? ? ? ? 72 ARG U O 1
+ATOM 516 C CB . ARG A 1 65 ? -16.959 -26.204 -15.620 1.00 31.78 ? ? ? ? ? ? 72 ARG U CB 1
+ATOM 517 C CG . ARG A 1 65 ? -18.123 -27.190 -15.498 1.00 35.75 ? ? ? ? ? ? 72 ARG U CG 1
+ATOM 518 C CD . ARG A 1 65 ? -19.475 -26.644 -15.947 1.00 40.14 ? ? ? ? ? ? 72 ARG U CD 1
+ATOM 519 N NE . ARG A 1 65 ? -19.496 -26.170 -17.345 1.00 44.42 ? ? ? ? ? ? 72 ARG U NE 1
+ATOM 520 C CZ . ARG A 1 65 ? -19.708 -26.926 -18.439 1.00 46.48 ? ? ? ? ? ? 72 ARG U CZ 1
+ATOM 521 N NH1 . ARG A 1 65 ? -19.898 -28.234 -18.373 1.00 45.52 ? ? ? ? ? ? 72 ARG U NH1 1
+ATOM 522 N NH2 . ARG A 1 65 ? -19.729 -26.355 -19.630 1.00 48.76 ? ? ? ? ? ? 72 ARG U NH2 1
+ATOM 523 N N . LEU A 1 66 ? -14.110 -25.761 -17.025 1.00 28.01 ? ? ? ? ? ? 73 LEU U N 1
+ATOM 524 C CA . LEU A 1 66 ? -12.915 -25.032 -17.408 1.00 26.47 ? ? ? ? ? ? 73 LEU U CA 1
+ATOM 525 C C . LEU A 1 66 ? -13.021 -23.524 -17.204 1.00 30.40 ? ? ? ? ? ? 73 LEU U C 1
+ATOM 526 O O . LEU A 1 66 ? -12.101 -22.890 -16.661 1.00 26.60 ? ? ? ? ? ? 73 LEU U O 1
+ATOM 527 C CB . LEU A 1 66 ? -12.599 -25.332 -18.897 1.00 28.67 ? ? ? ? ? ? 73 LEU U CB 1
+ATOM 528 C CG . LEU A 1 66 ? -11.300 -24.799 -19.493 1.00 29.10 ? ? ? ? ? ? 73 LEU U CG 1
+ATOM 529 C CD1 . LEU A 1 66 ? -10.127 -25.385 -18.730 1.00 30.80 ? ? ? ? ? ? 73 LEU U CD1 1
+ATOM 530 C CD2 . LEU A 1 66 ? -11.199 -25.090 -21.002 1.00 29.19 ? ? ? ? ? ? 73 LEU U CD2 1
+ATOM 531 N N . ASN A 1 67 ? -14.128 -22.942 -17.667 1.00 32.31 ? ? ? ? ? ? 74 ASN U N 1
+ATOM 532 C CA . ASN A 1 67 ? -14.293 -21.502 -17.663 1.00 36.04 ? ? ? ? ? ? 74 ASN U CA 1
+ATOM 533 C C . ASN A 1 67 ? -15.437 -21.006 -16.814 1.00 40.05 ? ? ? ? ? ? 74 ASN U C 1
+ATOM 534 O O . ASN A 1 67 ? -15.785 -19.828 -16.883 1.00 40.93 ? ? ? ? ? ? 74 ASN U O 1
+ATOM 535 C CB . ASN A 1 67 ? -14.527 -21.021 -19.079 1.00 39.03 ? ? ? ? ? ? 74 ASN U CB 1
+ATOM 536 C CG . ASN A 1 67 ? -13.363 -21.323 -19.979 1.00 36.50 ? ? ? ? ? ? 74 ASN U CG 1
+ATOM 537 O OD1 . ASN A 1 67 ? -12.267 -20.846 -19.752 1.00 39.34 ? ? ? ? ? ? 74 ASN U OD1 1
+ATOM 538 N ND2 . ASN A 1 67 ? -13.597 -22.123 -21.000 1.00 42.48 ? ? ? ? ? ? 74 ASN U ND2 1
+ATOM 539 N N . SER A 1 68 ? -16.033 -21.889 -16.026 1.00 40.10 ? ? ? ? ? ? 75 SER U N 1
+ATOM 540 C CA . SER A 1 68 ? -17.071 -21.474 -15.081 1.00 44.32 ? ? ? ? ? ? 75 SER U CA 1
+ATOM 541 C C . SER A 1 68 ? -16.848 -22.173 -13.746 1.00 42.02 ? ? ? ? ? ? 75 SER U C 1
+ATOM 542 O O . SER A 1 68 ? -15.977 -23.035 -13.640 1.00 40.38 ? ? ? ? ? ? 75 SER U O 1
+ATOM 543 C CB . SER A 1 68 ? -18.458 -21.770 -15.643 1.00 45.48 ? ? ? ? ? ? 75 SER U CB 1
+ATOM 544 O OG . SER A 1 68 ? -18.550 -23.123 -16.023 1.00 54.53 ? ? ? ? ? ? 75 SER U OG 1
+ATOM 545 N N . ASN A 1 69 ? -17.639 -21.782 -12.749 1.00 42.37 ? ? ? ? ? ? 76 ASN U N 1
+ATOM 546 C CA . ASN A 1 69 ? -17.487 -22.216 -11.364 1.00 42.76 ? ? ? ? ? ? 76 ASN U CA 1
+ATOM 547 C C . ASN A 1 69 ? -18.261 -23.506 -11.064 1.00 38.96 ? ? ? ? ? ? 76 ASN U C 1
+ATOM 548 O O . ASN A 1 69 ? -19.272 -23.781 -11.679 1.00 38.66 ? ? ? ? ? ? 76 ASN U O 1
+ATOM 549 C CB . ASN A 1 69 ? -17.964 -21.105 -10.423 1.00 44.67 ? ? ? ? ? ? 76 ASN U CB 1
+ATOM 550 C CG . ASN A 1 69 ? -17.266 -19.775 -10.676 1.00 50.73 ? ? ? ? ? ? 76 ASN U CG 1
+ATOM 551 O OD1 . ASN A 1 69 ? -17.903 -18.793 -11.070 1.00 50.09 ? ? ? ? ? ? 76 ASN U OD1 1
+ATOM 552 N ND2 . ASN A 1 69 ? -15.950 -19.736 -10.457 1.00 52.98 ? ? ? ? ? ? 76 ASN U ND2 1
+ATOM 553 N N . THR A 1 70 ? -17.748 -24.289 -10.125 1.00 34.70 ? ? ? ? ? ? 77 THR U N 1
+ATOM 554 C CA . THR A 1 70 ? -18.346 -25.547 -9.682 1.00 35.39 ? ? ? ? ? ? 77 THR U CA 1
+ATOM 555 C C . THR A 1 70 ? -18.674 -25.326 -8.213 1.00 37.56 ? ? ? ? ? ? 77 THR U C 1
+ATOM 556 O O . THR A 1 70 ? -17.883 -24.731 -7.497 1.00 35.42 ? ? ? ? ? ? 77 THR U O 1
+ATOM 557 C CB . THR A 1 70 ? -17.319 -26.694 -9.769 1.00 35.44 ? ? ? ? ? ? 77 THR U CB 1
+ATOM 558 O OG1 . THR A 1 70 ? -17.053 -26.998 -11.134 1.00 31.33 ? ? ? ? ? ? 77 THR U OG1 1
+ATOM 559 C CG2 . THR A 1 70 ? -17.794 -27.976 -9.033 1.00 35.31 ? ? ? ? ? ? 77 THR U CG2 1
+ATOM 560 N N . GLN A 1 71 ? -19.826 -25.808 -7.779 1.00 41.15 ? ? ? ? ? ? 78 GLN U N 1
+ATOM 561 C CA . GLN A 1 71 ? -20.211 -25.733 -6.378 1.00 47.23 ? ? ? ? ? ? 78 GLN U CA 1
+ATOM 562 C C . GLN A 1 71 ? -19.244 -26.576 -5.551 1.00 46.61 ? ? ? ? ? ? 78 GLN U C 1
+ATOM 563 O O . GLN A 1 71 ? -19.097 -27.780 -5.780 1.00 46.23 ? ? ? ? ? ? 78 GLN U O 1
+ATOM 564 C CB . GLN A 1 71 ? -21.656 -26.213 -6.180 1.00 51.41 ? ? ? ? ? ? 78 GLN U CB 1
+ATOM 565 C CG . GLN A 1 71 ? -22.492 -25.267 -5.323 1.00 59.61 ? ? ? ? ? ? 78 GLN U CG 1
+ATOM 566 C CD . GLN A 1 71 ? -23.980 -25.284 -5.662 1.00 65.95 ? ? ? ? ? ? 78 GLN U CD 1
+ATOM 567 O OE1 . GLN A 1 71 ? -24.454 -26.111 -6.458 1.00 68.11 ? ? ? ? ? ? 78 GLN U OE1 1
+ATOM 568 N NE2 . GLN A 1 71 ? -24.732 -24.371 -5.041 1.00 67.46 ? ? ? ? ? ? 78 GLN U NE2 1
+ATOM 569 N N . GLY A 1 72 ? -18.580 -25.927 -4.601 1.00 45.85 ? ? ? ? ? ? 79 GLY U N 1
+ATOM 570 C CA . GLY A 1 72 ? -17.627 -26.602 -3.729 1.00 45.06 ? ? ? ? ? ? 79 GLY U CA 1
+ATOM 571 C C . GLY A 1 72 ? -16.171 -26.332 -4.057 1.00 44.29 ? ? ? ? ? ? 79 GLY U C 1
+ATOM 572 O O . GLY A 1 72 ? -15.280 -26.776 -3.320 1.00 45.23 ? ? ? ? ? ? 79 GLY U O 1
+ATOM 573 N N . GLU A 1 73 ? -15.905 -25.607 -5.144 1.00 42.83 ? ? ? ? ? ? 80 GLU U N 1
+ATOM 574 C CA . GLU A 1 73 ? -14.520 -25.355 -5.536 1.00 42.05 ? ? ? ? ? ? 80 GLU U CA 1
+ATOM 575 C C . GLU A 1 73 ? -13.826 -24.366 -4.596 1.00 39.70 ? ? ? ? ? ? 80 GLU U C 1
+ATOM 576 O O . GLU A 1 73 ? -14.473 -23.532 -3.951 1.00 38.30 ? ? ? ? ? ? 80 GLU U O 1
+ATOM 577 C CB . GLU A 1 73 ? -14.429 -24.851 -6.972 1.00 42.73 ? ? ? ? ? ? 80 GLU U CB 1
+ATOM 578 C CG . GLU A 1 73 ? -14.719 -23.368 -7.130 1.00 44.81 ? ? ? ? ? ? 80 GLU U CG 1
+ATOM 579 C CD . GLU A 1 73 ? -14.891 -22.962 -8.579 1.00 45.13 ? ? ? ? ? ? 80 GLU U CD 1
+ATOM 580 O OE1 . GLU A 1 73 ? -14.881 -23.849 -9.465 1.00 44.54 ? ? ? ? ? ? 80 GLU U OE1 1
+ATOM 581 O OE2 . GLU A 1 73 ? -15.030 -21.758 -8.827 1.00 46.26 ? ? ? ? ? ? 80 GLU U OE2 1
+ATOM 582 N N . MET A 1 74 ? -12.502 -24.460 -4.535 1.00 37.32 ? ? ? ? ? ? 81 MET U N 1
+ATOM 583 C CA . MET A 1 74 ? -11.713 -23.562 -3.704 1.00 39.70 ? ? ? ? ? ? 81 MET U CA 1
+ATOM 584 C C . MET A 1 74 ? -10.539 -23.034 -4.516 1.00 38.38 ? ? ? ? ? ? 81 MET U C 1
+ATOM 585 O O . MET A 1 74 ? -9.808 -23.803 -5.168 1.00 34.71 ? ? ? ? ? ? 81 MET U O 1
+ATOM 586 C CB . MET A 1 74 ? -11.218 -24.292 -2.442 1.00 40.37 ? ? ? ? ? ? 81 MET U CB 1
+ATOM 587 C CG . MET A 1 74 ? -12.330 -24.942 -1.613 1.00 45.21 ? ? ? ? ? ? 81 MET U CG 1
+ATOM 588 S SD . MET A 1 74 ? -13.266 -23.733 -0.647 1.00 56.80 ? ? ? ? ? ? 81 MET U SD 1
+ATOM 589 C CE . MET A 1 74 ? -14.974 -24.174 -0.966 1.00 55.38 ? ? ? ? ? ? 81 MET U CE 1
+ATOM 590 N N . LYS A 1 75 ? -10.368 -21.722 -4.447 1.00 37.43 ? ? ? ? ? ? 82 LYS U N 1
+ATOM 591 C CA . LYS A 1 75 ? -9.313 -20.997 -5.141 1.00 39.98 ? ? ? ? ? ? 82 LYS U CA 1
+ATOM 592 C C . LYS A 1 75 ? -8.073 -20.783 -4.245 1.00 38.66 ? ? ? ? ? ? 82 LYS U C 1
+ATOM 593 O O . LYS A 1 75 ? -8.209 -20.418 -3.073 1.00 37.54 ? ? ? ? ? ? 82 LYS U O 1
+ATOM 594 C CB . LYS A 1 75 ? -9.898 -19.655 -5.582 1.00 40.85 ? ? ? ? ? ? 82 LYS U CB 1
+ATOM 595 C CG . LYS A 1 75 ? -8.985 -18.760 -6.407 1.00 45.62 ? ? ? ? ? ? 82 LYS U CG 1
+ATOM 596 C CD . LYS A 1 75 ? -9.735 -17.485 -6.766 1.00 48.39 ? ? ? ? ? ? 82 LYS U CD 1
+ATOM 597 C CE . LYS A 1 75 ? -8.909 -16.541 -7.614 1.00 53.37 ? ? ? ? ? ? 82 LYS U CE 1
+ATOM 598 N NZ . LYS A 1 75 ? -9.736 -15.382 -8.047 1.00 55.13 ? ? ? ? ? ? 82 LYS U NZ 1
+ATOM 599 N N . PHE A 1 76 ? -6.875 -20.995 -4.802 1.00 38.87 ? ? ? ? ? ? 83 PHE U N 1
+ATOM 600 C CA . PHE A 1 76 ? -5.602 -20.795 -4.080 1.00 37.85 ? ? ? ? ? ? 83 PHE U CA 1
+ATOM 601 C C . PHE A 1 76 ? -4.561 -20.000 -4.855 1.00 40.60 ? ? ? ? ? ? 83 PHE U C 1
+ATOM 602 O O . PHE A 1 76 ? -4.489 -20.054 -6.095 1.00 38.88 ? ? ? ? ? ? 83 PHE U O 1
+ATOM 603 C CB . PHE A 1 76 ? -4.973 -22.129 -3.673 1.00 38.85 ? ? ? ? ? ? 83 PHE U CB 1
+ATOM 604 C CG . PHE A 1 76 ? -5.889 -23.012 -2.897 1.00 35.69 ? ? ? ? ? ? 83 PHE U CG 1
+ATOM 605 C CD1 . PHE A 1 76 ? -6.779 -23.843 -3.551 1.00 34.64 ? ? ? ? ? ? 83 PHE U CD1 1
+ATOM 606 C CD2 . PHE A 1 76 ? -5.875 -23.001 -1.508 1.00 37.02 ? ? ? ? ? ? 83 PHE U CD2 1
+ATOM 607 C CE1 . PHE A 1 76 ? -7.641 -24.643 -2.838 1.00 34.68 ? ? ? ? ? ? 83 PHE U CE1 1
+ATOM 608 C CE2 . PHE A 1 76 ? -6.732 -23.810 -0.786 1.00 34.84 ? ? ? ? ? ? 83 PHE U CE2 1
+ATOM 609 C CZ . PHE A 1 76 ? -7.615 -24.629 -1.446 1.00 35.55 ? ? ? ? ? ? 83 PHE U CZ 1
+ATOM 610 N N . GLU A 1 77 ? -3.777 -19.237 -4.095 1.00 41.36 ? ? ? ? ? ? 84 GLU U N 1
+ATOM 611 C CA . GLU A 1 77 ? -2.550 -18.639 -4.572 1.00 43.68 ? ? ? ? ? ? 84 GLU U CA 1
+ATOM 612 C C . GLU A 1 77 ? -1.439 -19.664 -4.450 1.00 40.61 ? ? ? ? ? ? 84 GLU U C 1
+ATOM 613 O O . GLU A 1 77 ? -1.586 -20.677 -3.778 1.00 40.90 ? ? ? ? ? ? 84 GLU U O 1
+ATOM 614 C CB . GLU A 1 77 ? -2.176 -17.402 -3.739 1.00 49.28 ? ? ? ? ? ? 84 GLU U CB 1
+ATOM 615 C CG . GLU A 1 77 ? -3.204 -16.285 -3.744 1.00 54.72 ? ? ? ? ? ? 84 GLU U CG 1
+ATOM 616 C CD . GLU A 1 77 ? -3.625 -15.891 -5.142 1.00 58.53 ? ? ? ? ? ? 84 GLU U CD 1
+ATOM 617 O OE1 . GLU A 1 77 ? -2.749 -15.482 -5.935 1.00 69.44 ? ? ? ? ? ? 84 GLU U OE1 1
+ATOM 618 O OE2 . GLU A 1 77 ? -4.828 -16.003 -5.459 1.00 65.58 ? ? ? ? ? ? 84 GLU U OE2 1
+ATOM 619 N N . VAL A 1 78 ? -0.324 -19.376 -5.100 1.00 42.51 ? ? ? ? ? ? 85 VAL U N 1
+ATOM 620 C CA . VAL A 1 78 ? 0.849 -20.224 -5.060 1.00 43.38 ? ? ? ? ? ? 85 VAL U CA 1
+ATOM 621 C C . VAL A 1 78 ? 1.891 -19.522 -4.206 1.00 44.18 ? ? ? ? ? ? 85 VAL U C 1
+ATOM 622 O O . VAL A 1 78 ? 2.357 -18.455 -4.576 1.00 42.59 ? ? ? ? ? ? 85 VAL U O 1
+ATOM 623 C CB . VAL A 1 78 ? 1.428 -20.436 -6.466 1.00 43.37 ? ? ? ? ? ? 85 VAL U CB 1
+ATOM 624 C CG1 . VAL A 1 78 ? 2.592 -21.426 -6.414 1.00 42.22 ? ? ? ? ? ? 85 VAL U CG1 1
+ATOM 625 C CG2 . VAL A 1 78 ? 0.332 -20.879 -7.429 1.00 43.12 ? ? ? ? ? ? 85 VAL U CG2 1
+ATOM 626 N N . GLU A 1 79 ? 2.233 -20.126 -3.070 1.00 48.61 ? ? ? ? ? ? 86 GLU U N 1
+ATOM 627 C CA . GLU A 1 79 ? 3.183 -19.557 -2.109 1.00 51.82 ? ? ? ? ? ? 86 GLU U CA 1
+ATOM 628 C C . GLU A 1 79 ? 4.624 -19.956 -2.418 1.00 51.48 ? ? ? ? ? ? 86 GLU U C 1
+ATOM 629 O O . GLU A 1 79 ? 5.558 -19.260 -2.025 1.00 52.60 ? ? ? ? ? ? 86 GLU U O 1
+ATOM 630 C CB . GLU A 1 79 ? 2.831 -20.032 -0.699 1.00 53.85 ? ? ? ? ? ? 86 GLU U CB 1
+ATOM 631 C CG . GLU A 1 79 ? 3.588 -19.326 0.420 1.00 58.64 ? ? ? ? ? ? 86 GLU U CG 1
+ATOM 632 C CD . GLU A 1 79 ? 3.504 -20.059 1.744 1.00 58.86 ? ? ? ? ? ? 86 GLU U CD 1
+ATOM 633 O OE1 . GLU A 1 79 ? 2.562 -20.863 1.921 1.00 57.48 ? ? ? ? ? ? 86 GLU U OE1 1
+ATOM 634 O OE2 . GLU A 1 79 ? 4.380 -19.824 2.610 1.00 63.66 ? ? ? ? ? ? 86 GLU U OE2 1
+ATOM 635 N N . ASN A 1 80 ? 4.800 -21.098 -3.075 1.00 50.59 ? ? ? ? ? ? 87 ASN U N 1
+ATOM 636 C CA . ASN A 1 80 ? 6.115 -21.537 -3.553 1.00 50.47 ? ? ? ? ? ? 87 ASN U CA 1
+ATOM 637 C C . ASN A 1 80 ? 5.950 -22.295 -4.861 1.00 45.00 ? ? ? ? ? ? 87 ASN U C 1
+ATOM 638 O O . ASN A 1 80 ? 5.136 -23.200 -4.947 1.00 46.12 ? ? ? ? ? ? 87 ASN U O 1
+ATOM 639 C CB . ASN A 1 80 ? 6.794 -22.470 -2.543 1.00 52.00 ? ? ? ? ? ? 87 ASN U CB 1
+ATOM 640 C CG . ASN A 1 80 ? 7.708 -21.755 -1.567 1.00 59.19 ? ? ? ? ? ? 87 ASN U CG 1
+ATOM 641 O OD1 . ASN A 1 80 ? 8.273 -22.398 -0.682 1.00 65.99 ? ? ? ? ? ? 87 ASN U OD1 1
+ATOM 642 N ND2 . ASN A 1 80 ? 7.872 -20.442 -1.716 1.00 60.48 ? ? ? ? ? ? 87 ASN U ND2 1
+ATOM 643 N N . LEU A 1 81 ? 6.740 -21.935 -5.861 1.00 42.52 ? ? ? ? ? ? 88 LEU U N 1
+ATOM 644 C CA . LEU A 1 81 ? 6.693 -22.592 -7.169 1.00 39.98 ? ? ? ? ? ? 88 LEU U CA 1
+ATOM 645 C C . LEU A 1 81 ? 8.051 -23.220 -7.415 1.00 38.22 ? ? ? ? ? ? 88 LEU U C 1
+ATOM 646 O O . LEU A 1 81 ? 9.015 -22.508 -7.657 1.00 38.94 ? ? ? ? ? ? 88 LEU U O 1
+ATOM 647 C CB . LEU A 1 81 ? 6.376 -21.584 -8.263 1.00 36.16 ? ? ? ? ? ? 88 LEU U CB 1
+ATOM 648 C CG . LEU A 1 81 ? 6.456 -22.079 -9.716 1.00 35.70 ? ? ? ? ? ? 88 LEU U CG 1
+ATOM 649 C CD1 . LEU A 1 81 ? 5.442 -23.187 -9.952 1.00 34.55 ? ? ? ? ? ? 88 LEU U CD1 1
+ATOM 650 C CD2 . LEU A 1 81 ? 6.230 -20.931 -10.685 1.00 34.60 ? ? ? ? ? ? 88 LEU U CD2 1
+ATOM 651 N N . ILE A 1 82 ? 8.114 -24.548 -7.330 1.00 36.57 ? ? ? ? ? ? 89 ILE U N 1
+ATOM 652 C CA . ILE A 1 82 ? 9.382 -25.277 -7.351 1.00 37.65 ? ? ? ? ? ? 89 ILE U CA 1
+ATOM 653 C C . ILE A 1 82 ? 9.522 -26.064 -8.642 1.00 37.70 ? ? ? ? ? ? 89 ILE U C 1
+ATOM 654 O O . ILE A 1 82 ? 8.869 -27.080 -8.839 1.00 36.15 ? ? ? ? ? ? 89 ILE U O 1
+ATOM 655 C CB . ILE A 1 82 ? 9.505 -26.224 -6.145 1.00 38.48 ? ? ? ? ? ? 89 ILE U CB 1
+ATOM 656 C CG1 . ILE A 1 82 ? 9.243 -25.459 -4.847 1.00 38.36 ? ? ? ? ? ? 89 ILE U CG1 1
+ATOM 657 C CG2 . ILE A 1 82 ? 10.892 -26.836 -6.080 1.00 39.35 ? ? ? ? ? ? 89 ILE U CG2 1
+ATOM 658 C CD1 . ILE A 1 82 ? 9.068 -26.348 -3.633 1.00 38.52 ? ? ? ? ? ? 89 ILE U CD1 1
+ATOM 659 N N . LEU A 1 83 ? 10.361 -25.551 -9.530 1.00 38.01 ? ? ? ? ? ? 90 LEU U N 1
+ATOM 660 C CA . LEU A 1 83 ? 10.698 -26.233 -10.758 1.00 40.07 ? ? ? ? ? ? 90 LEU U CA 1
+ATOM 661 C C . LEU A 1 83 ? 11.911 -27.116 -10.515 1.00 44.64 ? ? ? ? ? ? 90 LEU U C 1
+ATOM 662 O O . LEU A 1 83 ? 12.760 -26.820 -9.663 1.00 50.26 ? ? ? ? ? ? 90 LEU U O 1
+ATOM 663 C CB . LEU A 1 83 ? 10.981 -25.216 -11.864 1.00 39.28 ? ? ? ? ? ? 90 LEU U CB 1
+ATOM 664 C CG . LEU A 1 83 ? 9.855 -24.193 -12.047 1.00 39.59 ? ? ? ? ? ? 90 LEU U CG 1
+ATOM 665 C CD1 . LEU A 1 83 ? 10.140 -23.246 -13.198 1.00 40.61 ? ? ? ? ? ? 90 LEU U CD1 1
+ATOM 666 C CD2 . LEU A 1 83 ? 8.518 -24.898 -12.245 1.00 37.47 ? ? ? ? ? ? 90 LEU U CD2 1
+ATOM 667 N N . HIS A 1 84 ? 11.983 -28.206 -11.259 1.00 45.47 ? ? ? ? ? ? 91 HIS U N 1
+ATOM 668 C CA . HIS A 1 84 ? 13.091 -29.131 -11.130 1.00 49.93 ? ? ? ? ? ? 91 HIS U CA 1
+ATOM 669 C C . HIS A 1 84 ? 14.339 -28.563 -11.814 1.00 49.84 ? ? ? ? ? ? 91 HIS U C 1
+ATOM 670 O O . HIS A 1 84 ? 14.305 -28.206 -13.000 1.00 47.04 ? ? ? ? ? ? 91 HIS U O 1
+ATOM 671 C CB . HIS A 1 84 ? 12.726 -30.492 -11.719 1.00 48.77 ? ? ? ? ? ? 91 HIS U CB 1
+ATOM 672 C CG . HIS A 1 84 ? 13.624 -31.591 -11.263 1.00 51.64 ? ? ? ? ? ? 91 HIS U CG 1
+ATOM 673 N ND1 . HIS A 1 84 ? 14.882 -31.785 -11.789 1.00 52.64 ? ? ? ? ? ? 91 HIS U ND1 1
+ATOM 674 C CD2 . HIS A 1 84 ? 13.457 -32.546 -10.320 1.00 53.16 ? ? ? ? ? ? 91 HIS U CD2 1
+ATOM 675 C CE1 . HIS A 1 84 ? 15.451 -32.818 -11.193 1.00 53.82 ? ? ? ? ? ? 91 HIS U CE1 1
+ATOM 676 N NE2 . HIS A 1 84 ? 14.608 -33.294 -10.294 1.00 55.25 ? ? ? ? ? ? 91 HIS U NE2 1
+ATOM 677 N N . LYS A 1 85 ? 15.439 -28.510 -11.064 1.00 53.53 ? ? ? ? ? ? 92 LYS U N 1
+ATOM 678 C CA . LYS A 1 85 ? 16.693 -27.916 -11.546 1.00 58.53 ? ? ? ? ? ? 92 LYS U CA 1
+ATOM 679 C C . LYS A 1 85 ? 17.238 -28.583 -12.814 1.00 56.64 ? ? ? ? ? ? 92 LYS U C 1
+ATOM 680 O O . LYS A 1 85 ? 17.720 -27.908 -13.732 1.00 55.78 ? ? ? ? ? ? 92 LYS U O 1
+ATOM 681 C CB . LYS A 1 85 ? 17.747 -27.932 -10.430 1.00 63.87 ? ? ? ? ? ? 92 LYS U CB 1
+ATOM 682 C CG . LYS A 1 85 ? 17.404 -27.012 -9.259 1.00 68.59 ? ? ? ? ? ? 92 LYS U CG 1
+ATOM 683 C CD . LYS A 1 85 ? 17.457 -25.532 -9.651 1.00 70.32 ? ? ? ? ? ? 92 LYS U CD 1
+ATOM 684 C CE . LYS A 1 85 ? 16.200 -24.759 -9.247 1.00 73.74 ? ? ? ? ? ? 92 LYS U CE 1
+ATOM 685 N NZ . LYS A 1 85 ? 16.128 -24.441 -7.791 1.00 73.77 ? ? ? ? ? ? 92 LYS U NZ 1
+ATOM 686 N N . ASP A 1 86 ? 17.154 -29.904 -12.870 1.00 52.99 ? ? ? ? ? ? 93 ASP U N 1
+ATOM 687 C CA . ASP A 1 86 ? 17.554 -30.642 -14.069 1.00 51.95 ? ? ? ? ? ? 93 ASP U CA 1
+ATOM 688 C C . ASP A 1 86 ? 16.474 -30.757 -15.142 1.00 48.81 ? ? ? ? ? ? 93 ASP U C 1
+ATOM 689 O O . ASP A 1 86 ? 16.613 -31.564 -16.063 1.00 46.91 ? ? ? ? ? ? 93 ASP U O 1
+ATOM 690 C CB . ASP A 1 86 ? 18.079 -32.035 -13.681 1.00 54.81 ? ? ? ? ? ? 93 ASP U CB 1
+ATOM 691 C CG . ASP A 1 86 ? 19.234 -31.964 -12.699 1.00 54.20 ? ? ? ? ? ? 93 ASP U CG 1
+ATOM 692 O OD1 . ASP A 1 86 ? 20.106 -31.089 -12.878 1.00 58.10 ? ? ? ? ? ? 93 ASP U OD1 1
+ATOM 693 O OD2 . ASP A 1 86 ? 19.272 -32.775 -11.753 1.00 57.48 ? ? ? ? ? ? 93 ASP U OD2 1
+ATOM 694 N N . TYR A 1 87 ? 15.412 -29.954 -15.064 1.00 47.88 ? ? ? ? ? ? 94 TYR U N 1
+ATOM 695 C CA . TYR A 1 87 ? 14.448 -29.910 -16.163 1.00 46.86 ? ? ? ? ? ? 94 TYR U CA 1
+ATOM 696 C C . TYR A 1 87 ? 15.151 -29.568 -17.468 1.00 48.08 ? ? ? ? ? ? 94 TYR U C 1
+ATOM 697 O O . TYR A 1 87 ? 16.112 -28.796 -17.487 1.00 50.34 ? ? ? ? ? ? 94 TYR U O 1
+ATOM 698 C CB . TYR A 1 87 ? 13.316 -28.892 -15.918 1.00 46.92 ? ? ? ? ? ? 94 TYR U CB 1
+ATOM 699 C CG . TYR A 1 87 ? 12.475 -28.568 -17.160 1.00 45.16 ? ? ? ? ? ? 94 TYR U CG 1
+ATOM 700 C CD1 . TYR A 1 87 ? 11.441 -29.412 -17.579 1.00 43.57 ? ? ? ? ? ? 94 TYR U CD1 1
+ATOM 701 C CD2 . TYR A 1 87 ? 12.706 -27.402 -17.901 1.00 43.89 ? ? ? ? ? ? 94 TYR U CD2 1
+ATOM 702 C CE1 . TYR A 1 87 ? 10.679 -29.114 -18.700 1.00 41.92 ? ? ? ? ? ? 94 TYR U CE1 1
+ATOM 703 C CE2 . TYR A 1 87 ? 11.955 -27.101 -19.029 1.00 43.74 ? ? ? ? ? ? 94 TYR U CE2 1
+ATOM 704 C CZ . TYR A 1 87 ? 10.940 -27.954 -19.422 1.00 43.80 ? ? ? ? ? ? 94 TYR U CZ 1
+ATOM 705 O OH . TYR A 1 87 ? 10.192 -27.640 -20.533 1.00 41.36 ? ? ? ? ? ? 94 TYR U OH 1
+ATOM 706 N N . SER A 1 88 ? 14.657 -30.136 -18.558 1.00 45.91 ? ? ? ? ? ? 95 SER U N 1
+ATOM 707 C CA . SER A 1 88 ? 15.128 -29.774 -19.868 1.00 49.16 ? ? ? ? ? ? 95 SER U CA 1
+ATOM 708 C C . SER A 1 88 ? 14.104 -30.162 -20.929 1.00 49.81 ? ? ? ? ? ? 95 SER U C 1
+ATOM 709 O O . SER A 1 88 ? 13.450 -31.203 -20.822 1.00 52.32 ? ? ? ? ? ? 95 SER U O 1
+ATOM 710 C CB . SER A 1 88 ? 16.492 -30.431 -20.128 1.00 49.65 ? ? ? ? ? ? 95 SER U CB 1
+ATOM 711 O OG . SER A 1 88 ? 16.605 -30.878 -21.476 1.00 49.70 ? ? ? ? ? ? 95 SER U OG 1
+ATOM 712 N N . ALA A 1 89 ? 13.977 -29.344 -21.954 1.00 51.29 ? ? ? ? ? ? 96 ALA U N 1
+ATOM 713 C CA . ALA A 1 89 ? 13.162 -29.685 -23.092 1.00 53.47 ? ? ? ? ? ? 96 ALA U CA 1
+ATOM 714 C C . ALA A 1 89 ? 14.043 -30.041 -24.304 1.00 59.81 ? ? ? ? ? ? 96 ALA U C 1
+ATOM 715 O O . ALA A 1 89 ? 14.726 -29.205 -24.849 1.00 63.47 ? ? ? ? ? ? 96 ALA U O 1
+ATOM 716 C CB . ALA A 1 89 ? 12.261 -28.521 -23.421 1.00 51.23 ? ? ? ? ? ? 96 ALA U CB 1
+ATOM 717 N N . ASP A 1 90 ? 13.967 -31.272 -24.750 1.00 62.35 ? ? ? ? ? ? 97 ASP U N 1
+ATOM 718 C CA . ASP A 1 90 ? 14.792 -31.776 -25.816 1.00 64.26 ? ? ? ? ? ? 97 ASP U CA 1
+ATOM 719 C C . ASP A 1 90 ? 14.113 -31.368 -27.064 1.00 62.67 ? ? ? ? ? ? 97 ASP U C 1
+ATOM 720 O O . ASP A 1 90 ? 13.238 -30.563 -27.031 1.00 63.69 ? ? ? ? ? ? 97 ASP U O 1
+ATOM 721 C CB . ASP A 1 90 ? 14.800 -33.280 -25.813 1.00 67.23 ? ? ? ? ? ? 97 ASP U CB 1
+ATOM 722 C CG . ASP A 1 90 ? 15.959 -33.857 -25.056 1.00 73.18 ? ? ? ? ? ? 97 ASP U CG 1
+ATOM 723 O OD1 . ASP A 1 90 ? 16.492 -33.125 -24.206 1.00 75.86 ? ? ? ? ? ? 97 ASP U OD1 1
+ATOM 724 O OD2 . ASP A 1 90 ? 16.318 -35.047 -25.304 1.00 72.97 ? ? ? ? ? ? 97 ASP U OD2 1
+ATOM 725 N N . THR A 1 91 A 14.577 -31.894 -28.181 1.00 65.59 ? ? ? ? ? ? 97 THR U N 1
+ATOM 726 C CA . THR A 1 91 A 13.938 -31.721 -29.457 1.00 68.22 ? ? ? ? ? ? 97 THR U CA 1
+ATOM 727 C C . THR A 1 91 A 12.538 -32.337 -29.570 1.00 66.38 ? ? ? ? ? ? 97 THR U C 1
+ATOM 728 O O . THR A 1 91 A 11.665 -31.741 -30.183 1.00 64.95 ? ? ? ? ? ? 97 THR U O 1
+ATOM 729 C CB . THR A 1 91 A 14.855 -32.232 -30.591 1.00 70.20 ? ? ? ? ? ? 97 THR U CB 1
+ATOM 730 O OG1 . THR A 1 91 A 16.047 -32.792 -30.035 1.00 66.08 ? ? ? ? ? ? 97 THR U OG1 1
+ATOM 731 C CG2 . THR A 1 91 A 15.238 -31.101 -31.496 1.00 70.18 ? ? ? ? ? ? 97 THR U CG2 1
+ATOM 732 N N . LEU A 1 92 B 12.341 -33.541 -29.022 1.00 60.49 ? ? ? ? ? ? 97 LEU U N 1
+ATOM 733 C CA . LEU A 1 92 B 11.023 -34.184 -28.990 1.00 59.93 ? ? ? ? ? ? 97 LEU U CA 1
+ATOM 734 C C . LEU A 1 92 B 10.319 -34.329 -27.628 1.00 57.24 ? ? ? ? ? ? 97 LEU U C 1
+ATOM 735 O O . LEU A 1 92 B 9.122 -34.345 -27.583 1.00 58.01 ? ? ? ? ? ? 97 LEU U O 1
+ATOM 736 C CB . LEU A 1 92 B 11.098 -35.577 -29.596 1.00 63.22 ? ? ? ? ? ? 97 LEU U CB 1
+ATOM 737 C CG . LEU A 1 92 B 11.110 -35.808 -31.098 1.00 64.15 ? ? ? ? ? ? 97 LEU U CG 1
+ATOM 738 C CD1 . LEU A 1 92 B 12.069 -34.844 -31.749 1.00 64.12 ? ? ? ? ? ? 97 LEU U CD1 1
+ATOM 739 C CD2 . LEU A 1 92 B 11.498 -37.230 -31.394 1.00 63.60 ? ? ? ? ? ? 97 LEU U CD2 1
+ATOM 740 N N . ALA A 1 93 ? 11.066 -34.430 -26.542 1.00 55.59 ? ? ? ? ? ? 98 ALA U N 1
+ATOM 741 C CA . ALA A 1 93 ? 10.526 -34.770 -25.235 1.00 54.33 ? ? ? ? ? ? 98 ALA U CA 1
+ATOM 742 C C . ALA A 1 93 ? 11.067 -33.878 -24.135 1.00 51.73 ? ? ? ? ? ? 98 ALA U C 1
+ATOM 743 O O . ALA A 1 93 ? 11.925 -33.087 -24.383 1.00 51.68 ? ? ? ? ? ? 98 ALA U O 1
+ATOM 744 C CB . ALA A 1 93 ? 10.810 -36.207 -24.908 1.00 54.68 ? ? ? ? ? ? 98 ALA U CB 1
+ATOM 745 N N . TYR A 1 94 ? 10.515 -33.989 -22.938 1.00 45.95 ? ? ? ? ? ? 99 TYR U N 1
+ATOM 746 C CA . TYR A 1 94 ? 10.949 -33.235 -21.790 1.00 43.29 ? ? ? ? ? ? 99 TYR U CA 1
+ATOM 747 C C . TYR A 1 94 ? 11.557 -34.180 -20.793 1.00 43.60 ? ? ? ? ? ? 99 TYR U C 1
+ATOM 748 O O . TYR A 1 94 ? 11.166 -35.298 -20.710 1.00 47.06 ? ? ? ? ? ? 99 TYR U O 1
+ATOM 749 C CB . TYR A 1 94 ? 9.773 -32.598 -21.077 1.00 42.19 ? ? ? ? ? ? 99 TYR U CB 1
+ATOM 750 C CG . TYR A 1 94 ? 8.923 -31.612 -21.801 1.00 39.46 ? ? ? ? ? ? 99 TYR U CG 1
+ATOM 751 C CD1 . TYR A 1 94 ? 9.287 -30.286 -21.880 1.00 40.17 ? ? ? ? ? ? 99 TYR U CD1 1
+ATOM 752 C CD2 . TYR A 1 94 ? 7.718 -31.977 -22.329 1.00 37.96 ? ? ? ? ? ? 99 TYR U CD2 1
+ATOM 753 C CE1 . TYR A 1 94 ? 8.493 -29.374 -22.498 1.00 38.31 ? ? ? ? ? ? 99 TYR U CE1 1
+ATOM 754 C CE2 . TYR A 1 94 ? 6.924 -31.061 -22.945 1.00 37.18 ? ? ? ? ? ? 99 TYR U CE2 1
+ATOM 755 C CZ . TYR A 1 94 ? 7.326 -29.763 -23.029 1.00 35.69 ? ? ? ? ? ? 99 TYR U CZ 1
+ATOM 756 O OH . TYR A 1 94 ? 6.547 -28.861 -23.635 1.00 35.58 ? ? ? ? ? ? 99 TYR U OH 1
+ATOM 757 N N . HIS A 1 95 ? 12.514 -33.720 -20.026 1.00 42.03 ? ? ? ? ? ? 100 HIS U N 1
+ATOM 758 C CA . HIS A 1 95 ? 13.197 -34.522 -19.021 1.00 44.27 ? ? ? ? ? ? 100 HIS U CA 1
+ATOM 759 C C . HIS A 1 95 ? 13.026 -33.868 -17.666 1.00 40.35 ? ? ? ? ? ? 100 HIS U C 1
+ATOM 760 O O . HIS A 1 95 ? 13.025 -32.641 -17.565 1.00 43.35 ? ? ? ? ? ? 100 HIS U O 1
+ATOM 761 C CB . HIS A 1 95 ? 14.690 -34.650 -19.374 1.00 45.01 ? ? ? ? ? ? 100 HIS U CB 1
+ATOM 762 C CG . HIS A 1 95 ? 14.941 -35.508 -20.570 1.00 46.12 ? ? ? ? ? ? 100 HIS U CG 1
+ATOM 763 N ND1 . HIS A 1 95 ? 14.810 -35.042 -21.862 1.00 50.92 ? ? ? ? ? ? 100 HIS U ND1 1
+ATOM 764 C CD2 . HIS A 1 95 ? 15.270 -36.817 -20.672 1.00 46.22 ? ? ? ? ? ? 100 HIS U CD2 1
+ATOM 765 C CE1 . HIS A 1 95 ? 15.051 -36.028 -22.709 1.00 49.73 ? ? ? ? ? ? 100 HIS U CE1 1
+ATOM 766 N NE2 . HIS A 1 95 ? 15.329 -37.116 -22.011 1.00 48.60 ? ? ? ? ? ? 100 HIS U NE2 1
+ATOM 767 N N . ASN A 1 96 ? 12.920 -34.690 -16.632 1.00 38.56 ? ? ? ? ? ? 101 ASN U N 1
+ATOM 768 C CA . ASN A 1 96 ? 12.687 -34.223 -15.257 1.00 41.04 ? ? ? ? ? ? 101 ASN U CA 1
+ATOM 769 C C . ASN A 1 96 ? 11.533 -33.227 -15.208 1.00 38.44 ? ? ? ? ? ? 101 ASN U C 1
+ATOM 770 O O . ASN A 1 96 ? 11.626 -32.158 -14.590 1.00 39.59 ? ? ? ? ? ? 101 ASN U O 1
+ATOM 771 C CB . ASN A 1 96 ? 13.956 -33.632 -14.653 1.00 42.57 ? ? ? ? ? ? 101 ASN U CB 1
+ATOM 772 C CG . ASN A 1 96 ? 15.093 -34.638 -14.616 1.00 46.09 ? ? ? ? ? ? 101 ASN U CG 1
+ATOM 773 O OD1 . ASN A 1 96 ? 15.080 -35.593 -13.824 1.00 44.95 ? ? ? ? ? ? 101 ASN U OD1 1
+ATOM 774 N ND2 . ASN A 1 96 ? 16.073 -34.442 -15.487 1.00 43.89 ? ? ? ? ? ? 101 ASN U ND2 1
+ATOM 775 N N . ASP A 1 97 ? 10.442 -33.624 -15.859 1.00 36.78 ? ? ? ? ? ? 102 ASP U N 1
+ATOM 776 C CA . ASP A 1 97 ? 9.273 -32.777 -16.036 1.00 35.83 ? ? ? ? ? ? 102 ASP U CA 1
+ATOM 777 C C . ASP A 1 97 ? 8.369 -32.879 -14.817 1.00 33.87 ? ? ? ? ? ? 102 ASP U C 1
+ATOM 778 O O . ASP A 1 97 ? 7.360 -33.588 -14.814 1.00 34.77 ? ? ? ? ? ? 102 ASP U O 1
+ATOM 779 C CB . ASP A 1 97 ? 8.551 -33.169 -17.314 1.00 33.31 ? ? ? ? ? ? 102 ASP U CB 1
+ATOM 780 C CG . ASP A 1 97 ? 7.626 -32.075 -17.818 1.00 31.82 ? ? ? ? ? ? 102 ASP U CG 1
+ATOM 781 O OD1 . ASP A 1 97 ? 7.766 -30.907 -17.397 1.00 31.87 ? ? ? ? ? ? 102 ASP U OD1 1
+ATOM 782 O OD2 . ASP A 1 97 ? 6.754 -32.382 -18.651 1.00 32.25 ? ? ? ? ? ? 102 ASP U OD2 1
+ATOM 783 N N . ILE A 1 98 ? 8.774 -32.198 -13.759 1.00 35.25 ? ? ? ? ? ? 103 ILE U N 1
+ATOM 784 C CA . ILE A 1 98 ? 8.109 -32.308 -12.468 1.00 35.32 ? ? ? ? ? ? 103 ILE U CA 1
+ATOM 785 C C . ILE A 1 98 ? 8.231 -30.995 -11.732 1.00 36.54 ? ? ? ? ? ? 103 ILE U C 1
+ATOM 786 O O . ILE A 1 98 ? 9.278 -30.333 -11.793 1.00 37.38 ? ? ? ? ? ? 103 ILE U O 1
+ATOM 787 C CB . ILE A 1 98 ? 8.696 -33.460 -11.623 1.00 38.65 ? ? ? ? ? ? 103 ILE U CB 1
+ATOM 788 C CG1 . ILE A 1 98 ? 7.863 -33.665 -10.355 1.00 39.84 ? ? ? ? ? ? 103 ILE U CG1 1
+ATOM 789 C CG2 . ILE A 1 98 ? 10.177 -33.221 -11.295 1.00 38.32 ? ? ? ? ? ? 103 ILE U CG2 1
+ATOM 790 C CD1 . ILE A 1 98 ? 7.957 -35.071 -9.775 1.00 40.87 ? ? ? ? ? ? 103 ILE U CD1 1
+ATOM 791 N N . ALA A 1 99 ? 7.151 -30.617 -11.056 1.00 32.80 ? ? ? ? ? ? 104 ALA U N 1
+ATOM 792 C CA . ALA A 1 99 ? 7.053 -29.314 -10.418 1.00 34.70 ? ? ? ? ? ? 104 ALA U CA 1
+ATOM 793 C C . ALA A 1 99 ? 6.174 -29.404 -9.189 1.00 33.92 ? ? ? ? ? ? 104 ALA U C 1
+ATOM 794 O O . ALA A 1 99 ? 5.314 -30.289 -9.084 1.00 34.98 ? ? ? ? ? ? 104 ALA U O 1
+ATOM 795 C CB . ALA A 1 99 ? 6.504 -28.279 -11.398 1.00 33.23 ? ? ? ? ? ? 104 ALA U CB 1
+ATOM 796 N N . LEU A 1 100 ? 6.403 -28.500 -8.246 1.00 38.17 ? ? ? ? ? ? 105 LEU U N 1
+ATOM 797 C CA . LEU A 1 100 ? 5.616 -28.459 -7.025 1.00 37.80 ? ? ? ? ? ? 105 LEU U CA 1
+ATOM 798 C C . LEU A 1 100 ? 5.140 -27.052 -6.735 1.00 38.70 ? ? ? ? ? ? 105 LEU U C 1
+ATOM 799 O O . LEU A 1 100 ? 5.868 -26.086 -6.935 1.00 40.69 ? ? ? ? ? ? 105 LEU U O 1
+ATOM 800 C CB . LEU A 1 100 ? 6.439 -28.988 -5.849 1.00 42.51 ? ? ? ? ? ? 105 LEU U CB 1
+ATOM 801 C CG . LEU A 1 100 ? 6.599 -30.504 -5.721 1.00 41.01 ? ? ? ? ? ? 105 LEU U CG 1
+ATOM 802 C CD1 . LEU A 1 100 ? 7.816 -30.848 -4.856 1.00 43.64 ? ? ? ? ? ? 105 LEU U CD1 1
+ATOM 803 C CD2 . LEU A 1 100 ? 5.342 -31.102 -5.119 1.00 42.10 ? ? ? ? ? ? 105 LEU U CD2 1
+ATOM 804 N N . LEU A 1 101 ? 3.891 -26.954 -6.285 1.00 37.26 ? ? ? ? ? ? 106 LEU U N 1
+ATOM 805 C CA . LEU A 1 101 ? 3.260 -25.704 -5.925 1.00 37.27 ? ? ? ? ? ? 106 LEU U CA 1
+ATOM 806 C C . LEU A 1 101 ? 2.765 -25.848 -4.500 1.00 37.35 ? ? ? ? ? ? 106 LEU U C 1
+ATOM 807 O O . LEU A 1 101 ? 2.016 -26.787 -4.200 1.00 37.50 ? ? ? ? ? ? 106 LEU U O 1
+ATOM 808 C CB . LEU A 1 101 ? 2.055 -25.438 -6.825 1.00 39.35 ? ? ? ? ? ? 106 LEU U CB 1
+ATOM 809 C CG . LEU A 1 101 ? 2.334 -25.167 -8.303 1.00 40.80 ? ? ? ? ? ? 106 LEU U CG 1
+ATOM 810 C CD1 . LEU A 1 101 ? 2.681 -26.449 -9.053 1.00 42.83 ? ? ? ? ? ? 106 LEU U CD1 1
+ATOM 811 C CD2 . LEU A 1 101 ? 1.158 -24.446 -8.956 1.00 40.66 ? ? ? ? ? ? 106 LEU U CD2 1
+ATOM 812 N N . LYS A 1 102 ? 3.196 -24.954 -3.615 1.00 39.53 ? ? ? ? ? ? 107 LYS U N 1
+ATOM 813 C CA . LYS A 1 102 ? 2.574 -24.858 -2.298 1.00 39.22 ? ? ? ? ? ? 107 LYS U CA 1
+ATOM 814 C C . LYS A 1 102 ? 1.446 -23.865 -2.381 1.00 34.16 ? ? ? ? ? ? 107 LYS U C 1
+ATOM 815 O O . LYS A 1 102 ? 1.655 -22.710 -2.689 1.00 36.60 ? ? ? ? ? ? 107 LYS U O 1
+ATOM 816 C CB . LYS A 1 102 ? 3.547 -24.455 -1.173 1.00 43.78 ? ? ? ? ? ? 107 LYS U CB 1
+ATOM 817 C CG . LYS A 1 102 ? 2.807 -24.134 0.133 1.00 49.60 ? ? ? ? ? ? 107 LYS U CG 1
+ATOM 818 C CD . LYS A 1 102 ? 3.360 -24.814 1.382 1.00 55.05 ? ? ? ? ? ? 107 LYS U CD 1
+ATOM 819 C CE . LYS A 1 102 ? 4.298 -23.917 2.166 1.00 58.30 ? ? ? ? ? ? 107 LYS U CE 1
+ATOM 820 N NZ . LYS A 1 102 ? 5.565 -23.677 1.415 1.00 61.03 ? ? ? ? ? ? 107 LYS U NZ 1
+ATOM 821 N N . ILE A 1 103 ? 0.257 -24.291 -2.044 1.00 34.15 ? ? ? ? ? ? 108 ILE U N 1
+ATOM 822 C CA . ILE A 1 103 ? -0.923 -23.490 -2.220 1.00 33.50 ? ? ? ? ? ? 108 ILE U CA 1
+ATOM 823 C C . ILE A 1 103 ? -1.353 -22.832 -0.927 1.00 38.14 ? ? ? ? ? ? 108 ILE U C 1
+ATOM 824 O O . ILE A 1 103 ? -1.304 -23.437 0.112 1.00 32.11 ? ? ? ? ? ? 108 ILE U O 1
+ATOM 825 C CB . ILE A 1 103 ? -2.077 -24.346 -2.746 1.00 34.30 ? ? ? ? ? ? 108 ILE U CB 1
+ATOM 826 C CG1 . ILE A 1 103 ? -2.150 -25.657 -1.997 1.00 33.11 ? ? ? ? ? ? 108 ILE U CG1 1
+ATOM 827 C CG2 . ILE A 1 103 ? -1.862 -24.655 -4.202 1.00 34.18 ? ? ? ? ? ? 108 ILE U CG2 1
+ATOM 828 C CD1 . ILE A 1 103 ? -3.354 -26.472 -2.330 1.00 32.69 ? ? ? ? ? ? 108 ILE U CD1 1
+ATOM 829 N N . ARG A 1 104 ? -1.773 -21.583 -1.028 1.00 39.72 ? ? ? ? ? ? 109 ARG U N 1
+ATOM 830 C CA . ARG A 1 104 ? -2.349 -20.873 0.099 1.00 45.65 ? ? ? ? ? ? 109 ARG U CA 1
+ATOM 831 C C . ARG A 1 104 ? -3.528 -20.074 -0.321 1.00 44.91 ? ? ? ? ? ? 109 ARG U C 1
+ATOM 832 O O . ARG A 1 104 ? -3.409 -19.369 -1.338 1.00 44.66 ? ? ? ? ? ? 109 ARG U O 1
+ATOM 833 C CB . ARG A 1 104 ? -1.339 -19.967 0.773 1.00 52.08 ? ? ? ? ? ? 109 ARG U CB 1
+ATOM 834 C CG . ARG A 1 104 ? -1.688 -19.628 2.208 1.00 57.31 ? ? ? ? ? ? 109 ARG U CG 1
+ATOM 835 C CD . ARG A 1 104 ? -0.587 -18.799 2.848 1.00 63.92 ? ? ? ? ? ? 109 ARG U CD 1
+ATOM 836 N NE . ARG A 1 104 ? -0.500 -17.474 2.254 1.00 69.45 ? ? ? ? ? ? 109 ARG U NE 1
+ATOM 837 C CZ . ARG A 1 104 ? -0.767 -16.350 2.907 1.00 72.43 ? ? ? ? ? ? 109 ARG U CZ 1
+ATOM 838 N NH1 . ARG A 1 104 ? -0.671 -15.200 2.289 1.00 73.28 ? ? ? ? ? ? 109 ARG U NH1 1
+ATOM 839 N NH2 . ARG A 1 104 ? -1.127 -16.376 4.179 1.00 75.36 ? ? ? ? ? ? 109 ARG U NH2 1
+ATOM 840 N N . SER A 1 105 ? -4.600 -20.131 0.398 1.00 44.19 ? ? ? ? ? ? 110 SER U N 1
+ATOM 841 C CA . SER A 1 105 ? -5.735 -19.279 0.098 1.00 45.45 ? ? ? ? ? ? 110 SER U CA 1
+ATOM 842 C C . SER A 1 105 ? -5.322 -17.893 0.624 1.00 46.32 ? ? ? ? ? ? 110 SER U C 1
+ATOM 843 O O . SER A 1 105 ? -4.457 -17.654 1.261 1.00 44.74 ? ? ? ? ? ? 110 SER U O 1
+ATOM 844 C CB . SER A 1 105 ? -6.987 -19.805 0.685 1.00 46.82 ? ? ? ? ? ? 110 SER U CB 1
+ATOM 845 O OG . SER A 1 105 ? -7.254 -19.269 1.959 1.00 54.54 ? ? ? ? ? ? 110 SER U OG 1
+ATOM 846 N N . LYS A 1 106 A -6.105 -16.908 0.066 1.00 89.78 ? ? ? ? ? ? 110 LYS U N 1
+ATOM 847 C CA . LYS A 1 106 A -5.841 -15.529 0.418 1.00 92.42 ? ? ? ? ? ? 110 LYS U CA 1
+ATOM 848 C C . LYS A 1 106 A -6.042 -15.382 1.927 1.00 89.21 ? ? ? ? ? ? 110 LYS U C 1
+ATOM 849 O O . LYS A 1 106 A -5.626 -14.419 2.514 1.00 81.99 ? ? ? ? ? ? 110 LYS U O 1
+ATOM 850 C CB . LYS A 1 106 A -6.710 -14.578 -0.390 1.00 97.09 ? ? ? ? ? ? 110 LYS U CB 1
+ATOM 851 C CG . LYS A 1 106 A -6.506 -13.120 -0.036 1.00 102.34 ? ? ? ? ? ? 110 LYS U CG 1
+ATOM 852 C CD . LYS A 1 106 A -7.568 -12.636 0.947 1.00 105.75 ? ? ? ? ? ? 110 LYS U CD 1
+ATOM 853 C CE . LYS A 1 106 A -7.026 -11.669 1.992 1.00 107.37 ? ? ? ? ? ? 110 LYS U CE 1
+ATOM 854 N NZ . LYS A 1 106 A -8.077 -11.132 2.901 1.00 107.46 ? ? ? ? ? ? 110 LYS U NZ 1
+ATOM 855 N N . GLU A 1 107 B -6.722 -16.350 2.525 1.00 76.77 ? ? ? ? ? ? 110 GLU U N 1
+ATOM 856 C CA . GLU A 1 107 B -7.006 -16.386 3.948 1.00 75.88 ? ? ? ? ? ? 110 GLU U CA 1
+ATOM 857 C C . GLU A 1 107 B -6.050 -17.310 4.703 1.00 71.68 ? ? ? ? ? ? 110 GLU U C 1
+ATOM 858 O O . GLU A 1 107 B -6.278 -17.641 5.838 1.00 63.34 ? ? ? ? ? ? 110 GLU U O 1
+ATOM 859 C CB . GLU A 1 107 B -8.446 -16.812 4.174 1.00 78.12 ? ? ? ? ? ? 110 GLU U CB 1
+ATOM 860 C CG . GLU A 1 107 B -9.484 -15.827 3.669 1.00 82.45 ? ? ? ? ? ? 110 GLU U CG 1
+ATOM 861 C CD . GLU A 1 107 B -9.214 -15.301 2.265 1.00 88.20 ? ? ? ? ? ? 110 GLU U CD 1
+ATOM 862 O OE1 . GLU A 1 107 B -8.621 -16.027 1.447 1.00 92.57 ? ? ? ? ? ? 110 GLU U OE1 1
+ATOM 863 O OE2 . GLU A 1 107 B -9.558 -14.148 1.973 1.00 89.97 ? ? ? ? ? ? 110 GLU U OE2 1
+ATOM 864 N N . GLY A 1 108 C -5.005 -17.756 4.030 1.00 54.37 ? ? ? ? ? ? 110 GLY U N 1
+ATOM 865 C CA . GLY A 1 108 C -3.915 -18.483 4.640 1.00 52.53 ? ? ? ? ? ? 110 GLY U CA 1
+ATOM 866 C C . GLY A 1 108 C -4.029 -19.969 4.824 1.00 51.96 ? ? ? ? ? ? 110 GLY U C 1
+ATOM 867 O O . GLY A 1 108 C -3.116 -20.556 5.349 1.00 58.53 ? ? ? ? ? ? 110 GLY U O 1
+ATOM 868 N N . ARG A 1 109 D -5.128 -20.575 4.384 1.00 53.73 ? ? ? ? ? ? 110 ARG U N 1
+ATOM 869 C CA . ARG A 1 109 D -5.346 -22.017 4.488 1.00 53.51 ? ? ? ? ? ? 110 ARG U CA 1
+ATOM 870 C C . ARG A 1 109 D -4.819 -22.900 3.358 1.00 50.04 ? ? ? ? ? ? 110 ARG U C 1
+ATOM 871 O O . ARG A 1 109 D -4.765 -22.484 2.237 1.00 50.74 ? ? ? ? ? ? 110 ARG U O 1
+ATOM 872 C CB . ARG A 1 109 D -6.823 -22.302 4.642 1.00 54.66 ? ? ? ? ? ? 110 ARG U CB 1
+ATOM 873 C CG . ARG A 1 109 D -7.459 -21.576 5.807 1.00 59.75 ? ? ? ? ? ? 110 ARG U CG 1
+ATOM 874 C CD . ARG A 1 109 D -8.787 -22.170 6.189 1.00 61.17 ? ? ? ? ? ? 110 ARG U CD 1
+ATOM 875 N NE . ARG A 1 109 D -9.724 -21.103 6.434 1.00 68.01 ? ? ? ? ? ? 110 ARG U NE 1
+ATOM 876 C CZ . ARG A 1 109 D -10.771 -21.206 7.226 1.00 74.09 ? ? ? ? ? ? 110 ARG U CZ 1
+ATOM 877 N NH1 . ARG A 1 109 D -11.018 -22.348 7.852 1.00 73.66 ? ? ? ? ? ? 110 ARG U NH1 1
+ATOM 878 N NH2 . ARG A 1 109 D -11.571 -20.166 7.393 1.00 75.12 ? ? ? ? ? ? 110 ARG U NH2 1
+ATOM 879 N N . CYS A 1 110 ? -4.439 -24.120 3.702 1.00 48.67 ? ? ? ? ? ? 111 CYS U N 1
+ATOM 880 C CA . CYS A 1 110 ? -4.184 -25.211 2.778 1.00 49.10 ? ? ? ? ? ? 111 CYS U CA 1
+ATOM 881 C C . CYS A 1 110 ? -5.576 -25.824 2.413 1.00 48.69 ? ? ? ? ? ? 111 CYS U C 1
+ATOM 882 O O . CYS A 1 110 ? -6.564 -25.384 2.915 1.00 49.90 ? ? ? ? ? ? 111 CYS U O 1
+ATOM 883 C CB . CYS A 1 110 ? -3.221 -26.246 3.399 1.00 45.21 ? ? ? ? ? ? 111 CYS U CB 1
+ATOM 884 S SG . CYS A 1 110 ? -1.460 -25.845 3.581 1.00 49.12 ? ? ? ? ? ? 111 CYS U SG 1
+ATOM 885 N N . ALA A 1 111 ? -5.648 -26.817 1.546 1.00 47.44 ? ? ? ? ? ? 112 ALA U N 1
+ATOM 886 C CA . ALA A 1 111 ? -6.923 -27.402 1.122 1.00 46.29 ? ? ? ? ? ? 112 ALA U CA 1
+ATOM 887 C C . ALA A 1 111 ? -7.590 -28.190 2.251 1.00 48.29 ? ? ? ? ? ? 112 ALA U C 1
+ATOM 888 O O . ALA A 1 111 ? -6.906 -28.792 3.087 1.00 50.61 ? ? ? ? ? ? 112 ALA U O 1
+ATOM 889 C CB . ALA A 1 111 ? -6.720 -28.294 -0.087 1.00 43.90 ? ? ? ? ? ? 112 ALA U CB 1
+ATOM 890 N N . GLN A 1 112 ? -8.922 -28.186 2.256 1.00 47.86 ? ? ? ? ? ? 113 GLN U N 1
+ATOM 891 C CA . GLN A 1 112 ? -9.702 -28.987 3.184 1.00 49.20 ? ? ? ? ? ? 113 GLN U CA 1
+ATOM 892 C C . GLN A 1 112 ? -10.562 -30.035 2.470 1.00 46.19 ? ? ? ? ? ? 113 GLN U C 1
+ATOM 893 O O . GLN A 1 112 ? -11.475 -29.693 1.721 1.00 47.37 ? ? ? ? ? ? 113 GLN U O 1
+ATOM 894 C CB . GLN A 1 112 ? -10.570 -28.076 4.032 1.00 52.82 ? ? ? ? ? ? 113 GLN U CB 1
+ATOM 895 C CG . GLN A 1 112 ? -9.764 -27.359 5.105 1.00 55.88 ? ? ? ? ? ? 113 GLN U CG 1
+ATOM 896 C CD . GLN A 1 112 ? -10.651 -26.663 6.105 1.00 59.56 ? ? ? ? ? ? 113 GLN U CD 1
+ATOM 897 O OE1 . GLN A 1 112 ? -10.892 -27.177 7.198 1.00 68.83 ? ? ? ? ? ? 113 GLN U OE1 1
+ATOM 898 N NE2 . GLN A 1 112 ? -11.164 -25.499 5.728 1.00 61.37 ? ? ? ? ? ? 113 GLN U NE2 1
+ATOM 899 N N . PRO A 1 113 ? -10.284 -31.323 2.716 1.00 43.15 ? ? ? ? ? ? 114 PRO U N 1
+ATOM 900 C CA . PRO A 1 113 ? -11.062 -32.379 2.080 1.00 42.41 ? ? ? ? ? ? 114 PRO U CA 1
+ATOM 901 C C . PRO A 1 113 ? -12.573 -32.171 2.179 1.00 42.46 ? ? ? ? ? ? 114 PRO U C 1
+ATOM 902 O O . PRO A 1 113 ? -13.075 -31.708 3.203 1.00 42.37 ? ? ? ? ? ? 114 PRO U O 1
+ATOM 903 C CB . PRO A 1 113 ? -10.657 -33.624 2.849 1.00 44.15 ? ? ? ? ? ? 114 PRO U CB 1
+ATOM 904 C CG . PRO A 1 113 ? -9.266 -33.333 3.295 1.00 46.46 ? ? ? ? ? ? 114 PRO U CG 1
+ATOM 905 C CD . PRO A 1 113 ? -9.217 -31.863 3.571 1.00 44.30 ? ? ? ? ? ? 114 PRO U CD 1
+ATOM 906 N N . SER A 1 114 ? -13.267 -32.507 1.100 1.00 37.53 ? ? ? ? ? ? 115 SER U N 1
+ATOM 907 C CA . SER A 1 114 ? -14.705 -32.430 1.012 1.00 35.28 ? ? ? ? ? ? 115 SER U CA 1
+ATOM 908 C C . SER A 1 114 ? -15.146 -33.489 0.014 1.00 34.98 ? ? ? ? ? ? 115 SER U C 1
+ATOM 909 O O . SER A 1 114 ? -14.323 -34.224 -0.533 1.00 37.22 ? ? ? ? ? ? 115 SER U O 1
+ATOM 910 C CB . SER A 1 114 ? -15.131 -31.052 0.519 1.00 38.27 ? ? ? ? ? ? 115 SER U CB 1
+ATOM 911 O OG . SER A 1 114 ? -14.835 -30.871 -0.876 1.00 36.14 ? ? ? ? ? ? 115 SER U OG 1
+ATOM 912 N N . ARG A 1 115 ? -16.443 -33.545 -0.236 1.00 36.90 ? ? ? ? ? ? 116 ARG U N 1
+ATOM 913 C CA . ARG A 1 115 ? -17.000 -34.380 -1.282 1.00 37.14 ? ? ? ? ? ? 116 ARG U CA 1
+ATOM 914 C C . ARG A 1 115 ? -16.252 -34.194 -2.618 1.00 34.90 ? ? ? ? ? ? 116 ARG U C 1
+ATOM 915 O O . ARG A 1 115 ? -16.205 -35.108 -3.429 1.00 36.84 ? ? ? ? ? ? 116 ARG U O 1
+ATOM 916 C CB . ARG A 1 115 ? -18.471 -34.046 -1.474 1.00 40.66 ? ? ? ? ? ? 116 ARG U CB 1
+ATOM 917 C CG . ARG A 1 115 ? -19.251 -35.089 -2.251 1.00 44.49 ? ? ? ? ? ? 116 ARG U CG 1
+ATOM 918 C CD . ARG A 1 115 ? -20.644 -34.609 -2.585 1.00 45.81 ? ? ? ? ? ? 116 ARG U CD 1
+ATOM 919 N NE . ARG A 1 115 ? -20.641 -33.617 -3.664 1.00 49.41 ? ? ? ? ? ? 116 ARG U NE 1
+ATOM 920 C CZ . ARG A 1 115 ? -20.834 -32.307 -3.505 1.00 52.03 ? ? ? ? ? ? 116 ARG U CZ 1
+ATOM 921 N NH1 . ARG A 1 115 ? -21.061 -31.773 -2.296 1.00 52.16 ? ? ? ? ? ? 116 ARG U NH1 1
+ATOM 922 N NH2 . ARG A 1 115 ? -20.805 -31.516 -4.567 1.00 50.43 ? ? ? ? ? ? 116 ARG U NH2 1
+ATOM 923 N N . THR A 1 116 ? -15.681 -33.014 -2.842 1.00 32.67 ? ? ? ? ? ? 117 THR U N 1
+ATOM 924 C CA . THR A 1 116 ? -15.079 -32.699 -4.145 1.00 33.76 ? ? ? ? ? ? 117 THR U CA 1
+ATOM 925 C C . THR A 1 116 ? -13.568 -32.491 -4.093 1.00 33.28 ? ? ? ? ? ? 117 THR U C 1
+ATOM 926 O O . THR A 1 116 ? -12.976 -32.239 -5.119 1.00 30.65 ? ? ? ? ? ? 117 THR U O 1
+ATOM 927 C CB . THR A 1 116 ? -15.712 -31.449 -4.778 1.00 32.59 ? ? ? ? ? ? 117 THR U CB 1
+ATOM 928 O OG1 . THR A 1 116 ? -15.670 -30.358 -3.857 1.00 32.18 ? ? ? ? ? ? 117 THR U OG1 1
+ATOM 929 C CG2 . THR A 1 116 ? -17.100 -31.707 -5.147 1.00 34.11 ? ? ? ? ? ? 117 THR U CG2 1
+ATOM 930 N N . ILE A 1 117 ? -12.948 -32.598 -2.908 1.00 34.66 ? ? ? ? ? ? 118 ILE U N 1
+ATOM 931 C CA . ILE A 1 117 ? -11.502 -32.387 -2.764 1.00 33.34 ? ? ? ? ? ? 118 ILE U CA 1
+ATOM 932 C C . ILE A 1 117 ? -10.874 -33.441 -1.846 1.00 36.94 ? ? ? ? ? ? 118 ILE U C 1
+ATOM 933 O O . ILE A 1 117 ? -11.227 -33.553 -0.662 1.00 35.59 ? ? ? ? ? ? 118 ILE U O 1
+ATOM 934 C CB . ILE A 1 117 ? -11.206 -30.985 -2.221 1.00 33.91 ? ? ? ? ? ? 118 ILE U CB 1
+ATOM 935 C CG1 . ILE A 1 117 ? -11.819 -29.898 -3.137 1.00 34.55 ? ? ? ? ? ? 118 ILE U CG1 1
+ATOM 936 C CG2 . ILE A 1 117 ? -9.707 -30.795 -2.074 1.00 33.91 ? ? ? ? ? ? 118 ILE U CG2 1
+ATOM 937 C CD1 . ILE A 1 117 ? -11.694 -28.499 -2.577 1.00 35.64 ? ? ? ? ? ? 118 ILE U CD1 1
+ATOM 938 N N . GLN A 1 118 ? -9.957 -34.231 -2.393 1.00 36.92 ? ? ? ? ? ? 119 GLN U N 1
+ATOM 939 C CA . GLN A 1 118 ? -9.206 -35.220 -1.597 1.00 35.98 ? ? ? ? ? ? 119 GLN U CA 1
+ATOM 940 C C . GLN A 1 118 ? -7.772 -35.274 -2.117 1.00 36.81 ? ? ? ? ? ? 119 GLN U C 1
+ATOM 941 O O . GLN A 1 118 ? -7.523 -34.927 -3.287 1.00 35.36 ? ? ? ? ? ? 119 GLN U O 1
+ATOM 942 C CB . GLN A 1 118 ? -9.823 -36.604 -1.752 1.00 38.21 ? ? ? ? ? ? 119 GLN U CB 1
+ATOM 943 C CG . GLN A 1 118 ? -11.275 -36.726 -1.306 1.00 41.27 ? ? ? ? ? ? 119 GLN U CG 1
+ATOM 944 C CD . GLN A 1 118 ? -11.405 -36.753 0.209 1.00 43.52 ? ? ? ? ? ? 119 GLN U CD 1
+ATOM 945 O OE1 . GLN A 1 118 ? -10.549 -37.306 0.890 1.00 47.59 ? ? ? ? ? ? 119 GLN U OE1 1
+ATOM 946 N NE2 . GLN A 1 118 ? -12.463 -36.147 0.739 1.00 42.20 ? ? ? ? ? ? 119 GLN U NE2 1
+ATOM 947 N N . THR A 1 119 ? -6.845 -35.759 -1.286 1.00 32.40 ? ? ? ? ? ? 120 THR U N 1
+ATOM 948 C CA . THR A 1 119 ? -5.441 -35.930 -1.689 1.00 31.34 ? ? ? ? ? ? 120 THR U CA 1
+ATOM 949 C C . THR A 1 119 ? -5.283 -37.273 -2.365 1.00 31.53 ? ? ? ? ? ? 120 THR U C 1
+ATOM 950 O O . THR A 1 119 ? -6.145 -38.111 -2.235 1.00 28.81 ? ? ? ? ? ? 120 THR U O 1
+ATOM 951 C CB . THR A 1 119 ? -4.483 -35.857 -0.472 1.00 33.91 ? ? ? ? ? ? 120 THR U CB 1
+ATOM 952 O OG1 . THR A 1 119 ? -4.841 -36.860 0.491 1.00 32.72 ? ? ? ? ? ? 120 THR U OG1 1
+ATOM 953 C CG2 . THR A 1 119 ? -4.560 -34.515 0.178 1.00 34.42 ? ? ? ? ? ? 120 THR U CG2 1
+ATOM 954 N N . ILE A 1 120 ? -4.210 -37.441 -3.146 1.00 33.28 ? ? ? ? ? ? 121 ILE U N 1
+ATOM 955 C CA . ILE A 1 120 ? -3.874 -38.703 -3.793 1.00 34.08 ? ? ? ? ? ? 121 ILE U CA 1
+ATOM 956 C C . ILE A 1 120 ? -2.496 -39.127 -3.254 1.00 36.42 ? ? ? ? ? ? 121 ILE U C 1
+ATOM 957 O O . ILE A 1 120 ? -1.558 -38.320 -3.208 1.00 33.43 ? ? ? ? ? ? 121 ILE U O 1
+ATOM 958 C CB . ILE A 1 120 ? -3.890 -38.594 -5.358 1.00 34.18 ? ? ? ? ? ? 121 ILE U CB 1
+ATOM 959 C CG1 . ILE A 1 120 ? -3.658 -39.941 -6.037 1.00 36.25 ? ? ? ? ? ? 121 ILE U CG1 1
+ATOM 960 C CG2 . ILE A 1 120 ? -2.861 -37.605 -5.887 1.00 34.96 ? ? ? ? ? ? 121 ILE U CG2 1
+ATOM 961 C CD1 . ILE A 1 120 ? -4.834 -40.896 -5.990 1.00 37.76 ? ? ? ? ? ? 121 ILE U CD1 1
+ATOM 962 N N . ALA A 1 121 ? -2.391 -40.383 -2.831 1.00 38.89 ? ? ? ? ? ? 122 ALA U N 1
+ATOM 963 C CA . ALA A 1 121 ? -1.148 -40.898 -2.272 1.00 38.91 ? ? ? ? ? ? 122 ALA U CA 1
+ATOM 964 C C . ALA A 1 121 ? -0.021 -40.991 -3.319 1.00 40.95 ? ? ? ? ? ? 122 ALA U C 1
+ATOM 965 O O . ALA A 1 121 ? -0.244 -41.316 -4.492 1.00 34.23 ? ? ? ? ? ? 122 ALA U O 1
+ATOM 966 C CB . ALA A 1 121 ? -1.391 -42.262 -1.643 1.00 40.29 ? ? ? ? ? ? 122 ALA U CB 1
+ATOM 967 N N . LEU A 1 122 ? 1.195 -40.696 -2.874 1.00 41.47 ? ? ? ? ? ? 123 LEU U N 1
+ATOM 968 C CA . LEU A 1 122 ? 2.397 -40.983 -3.656 1.00 42.77 ? ? ? ? ? ? 123 LEU U CA 1
+ATOM 969 C C . LEU A 1 122 ? 2.752 -42.453 -3.565 1.00 43.82 ? ? ? ? ? ? 123 LEU U C 1
+ATOM 970 O O . LEU A 1 122 ? 2.408 -43.110 -2.574 1.00 41.98 ? ? ? ? ? ? 123 LEU U O 1
+ATOM 971 C CB . LEU A 1 122 ? 3.573 -40.161 -3.140 1.00 43.24 ? ? ? ? ? ? 123 LEU U CB 1
+ATOM 972 C CG . LEU A 1 122 ? 3.431 -38.657 -3.375 1.00 44.31 ? ? ? ? ? ? 123 LEU U CG 1
+ATOM 973 C CD1 . LEU A 1 122 ? 4.494 -37.904 -2.586 1.00 42.49 ? ? ? ? ? ? 123 LEU U CD1 1
+ATOM 974 C CD2 . LEU A 1 122 ? 3.514 -38.368 -4.870 1.00 44.61 ? ? ? ? ? ? 123 LEU U CD2 1
+ATOM 975 N N . PRO A 1 123 ? 3.454 -42.983 -4.587 1.00 45.13 ? ? ? ? ? ? 124 PRO U N 1
+ATOM 976 C CA . PRO A 1 123 ? 3.901 -44.360 -4.487 1.00 45.17 ? ? ? ? ? ? 124 PRO U CA 1
+ATOM 977 C C . PRO A 1 123 ? 5.124 -44.465 -3.589 1.00 45.56 ? ? ? ? ? ? 124 PRO U C 1
+ATOM 978 O O . PRO A 1 123 ? 5.662 -43.441 -3.138 1.00 41.08 ? ? ? ? ? ? 124 PRO U O 1
+ATOM 979 C CB . PRO A 1 123 ? 4.263 -44.721 -5.933 1.00 46.78 ? ? ? ? ? ? 124 PRO U CB 1
+ATOM 980 C CG . PRO A 1 123 ? 4.560 -43.421 -6.609 1.00 46.27 ? ? ? ? ? ? 124 PRO U CG 1
+ATOM 981 C CD . PRO A 1 123 ? 4.068 -42.299 -5.739 1.00 45.71 ? ? ? ? ? ? 124 PRO U CD 1
+ATOM 982 N N . SER A 1 124 ? 5.528 -45.701 -3.309 1.00 47.71 ? ? ? ? ? ? 125 SER U N 1
+ATOM 983 C CA . SER A 1 124 ? 6.776 -45.977 -2.609 1.00 47.99 ? ? ? ? ? ? 125 SER U CA 1
+ATOM 984 C C . SER A 1 124 ? 7.873 -45.813 -3.621 1.00 47.23 ? ? ? ? ? ? 125 SER U C 1
+ATOM 985 O O . SER A 1 124 ? 7.652 -46.039 -4.823 1.00 46.52 ? ? ? ? ? ? 125 SER U O 1
+ATOM 986 C CB . SER A 1 124 ? 6.813 -47.414 -2.084 1.00 51.90 ? ? ? ? ? ? 125 SER U CB 1
+ATOM 987 O OG . SER A 1 124 ? 5.652 -47.711 -1.352 1.00 55.23 ? ? ? ? ? ? 125 SER U OG 1
+ATOM 988 N N . MET A 1 125 ? 9.062 -45.445 -3.154 1.00 49.17 ? ? ? ? ? ? 126 MET U N 1
+ATOM 989 C CA . MET A 1 125 ? 10.140 -45.136 -4.085 1.00 52.50 ? ? ? ? ? ? 126 MET U CA 1
+ATOM 990 C C . MET A 1 125 ? 10.486 -46.343 -4.966 1.00 48.07 ? ? ? ? ? ? 126 MET U C 1
+ATOM 991 O O . MET A 1 125 ? 10.701 -47.459 -4.485 1.00 46.18 ? ? ? ? ? ? 126 MET U O 1
+ATOM 992 C CB . MET A 1 125 ? 11.385 -44.511 -3.405 1.00 59.78 ? ? ? ? ? ? 126 MET U CB 1
+ATOM 993 C CG . MET A 1 125 ? 11.881 -45.124 -2.097 1.00 64.92 ? ? ? ? ? ? 126 MET U CG 1
+ATOM 994 S SD . MET A 1 125 ? 13.181 -44.078 -1.384 1.00 73.02 ? ? ? ? ? ? 126 MET U SD 1
+ATOM 995 C CE . MET A 1 125 ? 13.436 -44.872 0.204 1.00 74.42 ? ? ? ? ? ? 126 MET U CE 1
+ATOM 996 N N . TYR A 1 126 ? 10.469 -46.093 -6.271 1.00 48.96 ? ? ? ? ? ? 127 TYR U N 1
+ATOM 997 C CA . TYR A 1 126 ? 10.782 -47.078 -7.310 1.00 48.36 ? ? ? ? ? ? 127 TYR U CA 1
+ATOM 998 C C . TYR A 1 126 ? 9.753 -48.197 -7.455 1.00 50.29 ? ? ? ? ? ? 127 TYR U C 1
+ATOM 999 O O . TYR A 1 126 ? 9.926 -49.091 -8.284 1.00 49.66 ? ? ? ? ? ? 127 TYR U O 1
+ATOM 1000 C CB . TYR A 1 126 ? 12.219 -47.584 -7.129 1.00 48.62 ? ? ? ? ? ? 127 TYR U CB 1
+ATOM 1001 C CG . TYR A 1 126 ? 13.164 -46.413 -7.226 1.00 48.15 ? ? ? ? ? ? 127 TYR U CG 1
+ATOM 1002 C CD1 . TYR A 1 126 ? 13.590 -45.952 -8.461 1.00 49.24 ? ? ? ? ? ? 127 TYR U CD1 1
+ATOM 1003 C CD2 . TYR A 1 126 ? 13.547 -45.702 -6.097 1.00 47.88 ? ? ? ? ? ? 127 TYR U CD2 1
+ATOM 1004 C CE1 . TYR A 1 126 ? 14.415 -44.849 -8.569 1.00 48.28 ? ? ? ? ? ? 127 TYR U CE1 1
+ATOM 1005 C CE2 . TYR A 1 126 ? 14.371 -44.593 -6.190 1.00 46.74 ? ? ? ? ? ? 127 TYR U CE2 1
+ATOM 1006 C CZ . TYR A 1 126 ? 14.797 -44.169 -7.428 1.00 48.57 ? ? ? ? ? ? 127 TYR U CZ 1
+ATOM 1007 O OH . TYR A 1 126 ? 15.614 -43.076 -7.534 1.00 47.59 ? ? ? ? ? ? 127 TYR U OH 1
+ATOM 1008 N N . ASN A 1 127 ? 8.663 -48.113 -6.689 1.00 51.91 ? ? ? ? ? ? 128 ASN U N 1
+ATOM 1009 C CA . ASN A 1 127 ? 7.573 -49.087 -6.771 1.00 54.32 ? ? ? ? ? ? 128 ASN U CA 1
+ATOM 1010 C C . ASN A 1 127 ? 6.525 -48.643 -7.799 1.00 53.23 ? ? ? ? ? ? 128 ASN U C 1
+ATOM 1011 O O . ASN A 1 127 ? 5.829 -47.651 -7.582 1.00 51.51 ? ? ? ? ? ? 128 ASN U O 1
+ATOM 1012 C CB . ASN A 1 127 ? 6.919 -49.264 -5.392 1.00 54.56 ? ? ? ? ? ? 128 ASN U CB 1
+ATOM 1013 C CG . ASN A 1 127 ? 5.952 -50.438 -5.336 1.00 55.60 ? ? ? ? ? ? 128 ASN U CG 1
+ATOM 1014 O OD1 . ASN A 1 127 ? 5.079 -50.493 -4.464 1.00 56.96 ? ? ? ? ? ? 128 ASN U OD1 1
+ATOM 1015 N ND2 . ASN A 1 127 ? 6.107 -51.386 -6.254 1.00 54.55 ? ? ? ? ? ? 128 ASN U ND2 1
+ATOM 1016 N N . ASP A 1 128 ? 6.452 -49.366 -8.916 1.00 53.13 ? ? ? ? ? ? 129 ASP U N 1
+ATOM 1017 C CA . ASP A 1 128 ? 5.469 -49.123 -9.982 1.00 56.26 ? ? ? ? ? ? 129 ASP U CA 1
+ATOM 1018 C C . ASP A 1 128 ? 4.711 -50.419 -10.285 1.00 58.41 ? ? ? ? ? ? 129 ASP U C 1
+ATOM 1019 O O . ASP A 1 128 ? 5.258 -51.510 -10.107 1.00 58.63 ? ? ? ? ? ? 129 ASP U O 1
+ATOM 1020 C CB . ASP A 1 128 ? 6.160 -48.666 -11.266 1.00 57.76 ? ? ? ? ? ? 129 ASP U CB 1
+ATOM 1021 C CG . ASP A 1 128 ? 6.842 -47.316 -11.123 1.00 61.96 ? ? ? ? ? ? 129 ASP U CG 1
+ATOM 1022 O OD1 . ASP A 1 128 ? 7.707 -47.159 -10.233 1.00 63.92 ? ? ? ? ? ? 129 ASP U OD1 1
+ATOM 1023 O OD2 . ASP A 1 128 ? 6.527 -46.416 -11.926 1.00 59.48 ? ? ? ? ? ? 129 ASP U OD2 1
+ATOM 1024 N N . PRO A 1 129 ? 3.469 -50.310 -10.789 1.00 57.92 ? ? ? ? ? ? 130 PRO U N 1
+ATOM 1025 C CA . PRO A 1 129 ? 2.702 -51.533 -11.011 1.00 57.29 ? ? ? ? ? ? 130 PRO U CA 1
+ATOM 1026 C C . PRO A 1 129 ? 3.104 -52.221 -12.304 1.00 55.31 ? ? ? ? ? ? 130 PRO U C 1
+ATOM 1027 O O . PRO A 1 129 ? 3.913 -51.691 -13.064 1.00 50.38 ? ? ? ? ? ? 130 PRO U O 1
+ATOM 1028 C CB . PRO A 1 129 ? 1.257 -51.033 -11.082 1.00 58.05 ? ? ? ? ? ? 130 PRO U CB 1
+ATOM 1029 C CG . PRO A 1 129 ? 1.364 -49.631 -11.576 1.00 57.70 ? ? ? ? ? ? 130 PRO U CG 1
+ATOM 1030 C CD . PRO A 1 129 ? 2.757 -49.118 -11.288 1.00 56.11 ? ? ? ? ? ? 130 PRO U CD 1
+ATOM 1031 N N . GLN A 1 130 ? 2.529 -53.393 -12.544 1.00 56.32 ? ? ? ? ? ? 131 GLN U N 1
+ATOM 1032 C CA . GLN A 1 130 ? 2.867 -54.194 -13.708 1.00 59.22 ? ? ? ? ? ? 131 GLN U CA 1
+ATOM 1033 C C . GLN A 1 130 ? 2.515 -53.464 -14.971 1.00 56.80 ? ? ? ? ? ? 131 GLN U C 1
+ATOM 1034 O O . GLN A 1 130 ? 1.588 -52.671 -15.005 1.00 55.40 ? ? ? ? ? ? 131 GLN U O 1
+ATOM 1035 C CB . GLN A 1 130 ? 2.107 -55.535 -13.720 1.00 65.76 ? ? ? ? ? ? 131 GLN U CB 1
+ATOM 1036 C CG . GLN A 1 130 ? 2.916 -56.744 -13.269 1.00 71.02 ? ? ? ? ? ? 131 GLN U CG 1
+ATOM 1037 C CD . GLN A 1 130 ? 3.115 -56.821 -11.765 1.00 73.46 ? ? ? ? ? ? 131 GLN U CD 1
+ATOM 1038 O OE1 . GLN A 1 130 ? 3.912 -57.627 -11.283 1.00 77.35 ? ? ? ? ? ? 131 GLN U OE1 1
+ATOM 1039 N NE2 . GLN A 1 130 ? 2.391 -55.996 -11.016 1.00 73.36 ? ? ? ? ? ? 131 GLN U NE2 1
+ATOM 1040 N N . PHE A 1 131 ? 3.241 -53.786 -16.027 1.00 60.06 ? ? ? ? ? ? 132 PHE U N 1
+ATOM 1041 C CA . PHE A 1 131 ? 2.891 -53.347 -17.356 1.00 60.41 ? ? ? ? ? ? 132 PHE U CA 1
+ATOM 1042 C C . PHE A 1 131 ? 1.614 -54.094 -17.746 1.00 58.35 ? ? ? ? ? ? 132 PHE U C 1
+ATOM 1043 O O . PHE A 1 131 ? 1.399 -55.228 -17.308 1.00 54.52 ? ? ? ? ? ? 132 PHE U O 1
+ATOM 1044 C CB . PHE A 1 131 ? 4.056 -53.621 -18.297 1.00 67.26 ? ? ? ? ? ? 132 PHE U CB 1
+ATOM 1045 C CG . PHE A 1 131 ? 5.382 -53.169 -17.738 1.00 73.40 ? ? ? ? ? ? 132 PHE U CG 1
+ATOM 1046 C CD1 . PHE A 1 131 ? 5.692 -51.811 -17.665 1.00 78.42 ? ? ? ? ? ? 132 PHE U CD1 1
+ATOM 1047 C CD2 . PHE A 1 131 ? 6.295 -54.089 -17.233 1.00 76.81 ? ? ? ? ? ? 132 PHE U CD2 1
+ATOM 1048 C CE1 . PHE A 1 131 ? 6.904 -51.385 -17.140 1.00 79.27 ? ? ? ? ? ? 132 PHE U CE1 1
+ATOM 1049 C CE2 . PHE A 1 131 ? 7.507 -53.669 -16.702 1.00 79.43 ? ? ? ? ? ? 132 PHE U CE2 1
+ATOM 1050 C CZ . PHE A 1 131 ? 7.812 -52.316 -16.657 1.00 80.91 ? ? ? ? ? ? 132 PHE U CZ 1
+ATOM 1051 N N . GLY A 1 132 ? 0.751 -53.429 -18.516 1.00 53.51 ? ? ? ? ? ? 133 GLY U N 1
+ATOM 1052 C CA . GLY A 1 132 ? -0.601 -53.924 -18.792 1.00 51.27 ? ? ? ? ? ? 133 GLY U CA 1
+ATOM 1053 C C . GLY A 1 132 ? -1.640 -53.588 -17.718 1.00 48.81 ? ? ? ? ? ? 133 GLY U C 1
+ATOM 1054 O O . GLY A 1 132 ? -2.820 -53.910 -17.885 1.00 50.33 ? ? ? ? ? ? 133 GLY U O 1
+ATOM 1055 N N . THR A 1 133 ? -1.225 -52.959 -16.616 1.00 44.34 ? ? ? ? ? ? 134 THR U N 1
+ATOM 1056 C CA . THR A 1 133 ? -2.180 -52.468 -15.607 1.00 42.75 ? ? ? ? ? ? 134 THR U CA 1
+ATOM 1057 C C . THR A 1 133 ? -2.981 -51.274 -16.153 1.00 40.10 ? ? ? ? ? ? 134 THR U C 1
+ATOM 1058 O O . THR A 1 133 ? -2.464 -50.463 -16.895 1.00 37.40 ? ? ? ? ? ? 134 THR U O 1
+ATOM 1059 C CB . THR A 1 133 ? -1.475 -52.025 -14.320 1.00 41.47 ? ? ? ? ? ? 134 THR U CB 1
+ATOM 1060 O OG1 . THR A 1 133 ? -0.651 -53.085 -13.832 1.00 41.60 ? ? ? ? ? ? 134 THR U OG1 1
+ATOM 1061 C CG2 . THR A 1 133 ? -2.488 -51.624 -13.240 1.00 42.05 ? ? ? ? ? ? 134 THR U CG2 1
+ATOM 1062 N N . SER A 1 134 ? -4.249 -51.192 -15.777 1.00 41.37 ? ? ? ? ? ? 135 SER U N 1
+ATOM 1063 C CA . SER A 1 134 ? -5.136 -50.117 -16.225 1.00 41.39 ? ? ? ? ? ? 135 SER U CA 1
+ATOM 1064 C C . SER A 1 134 ? -5.102 -48.944 -15.251 1.00 40.48 ? ? ? ? ? ? 135 SER U C 1
+ATOM 1065 O O . SER A 1 134 ? -5.324 -49.124 -14.059 1.00 41.40 ? ? ? ? ? ? 135 SER U O 1
+ATOM 1066 C CB . SER A 1 134 ? -6.571 -50.637 -16.347 1.00 40.39 ? ? ? ? ? ? 135 SER U CB 1
+ATOM 1067 O OG . SER A 1 134 ? -6.740 -51.324 -17.558 1.00 41.15 ? ? ? ? ? ? 135 SER U OG 1
+ATOM 1068 N N . CYS A 1 135 ? -4.846 -47.748 -15.774 1.00 39.96 ? ? ? ? ? ? 136 CYS U N 1
+ATOM 1069 C CA . CYS A 1 135 ? -4.759 -46.524 -14.970 1.00 39.34 ? ? ? ? ? ? 136 CYS U CA 1
+ATOM 1070 C C . CYS A 1 135 ? -5.729 -45.459 -15.487 1.00 36.62 ? ? ? ? ? ? 136 CYS U C 1
+ATOM 1071 O O . CYS A 1 135 ? -6.227 -45.565 -16.606 1.00 35.21 ? ? ? ? ? ? 136 CYS U O 1
+ATOM 1072 C CB . CYS A 1 135 ? -3.327 -45.991 -15.025 1.00 43.13 ? ? ? ? ? ? 136 CYS U CB 1
+ATOM 1073 S SG . CYS A 1 135 ? -2.110 -47.225 -14.492 1.00 45.64 ? ? ? ? ? ? 136 CYS U SG 1
+ATOM 1074 N N . GLU A 1 136 ? -5.978 -44.440 -14.669 1.00 34.81 ? ? ? ? ? ? 137 GLU U N 1
+ATOM 1075 C CA . GLU A 1 136 ? -6.893 -43.350 -15.004 1.00 36.61 ? ? ? ? ? ? 137 GLU U CA 1
+ATOM 1076 C C . GLU A 1 136 ? -6.136 -42.057 -15.245 1.00 33.80 ? ? ? ? ? ? 137 GLU U C 1
+ATOM 1077 O O . GLU A 1 136 ? -5.212 -41.732 -14.500 1.00 33.23 ? ? ? ? ? ? 137 GLU U O 1
+ATOM 1078 C CB . GLU A 1 136 ? -7.848 -43.089 -13.839 1.00 43.81 ? ? ? ? ? ? 137 GLU U CB 1
+ATOM 1079 C CG . GLU A 1 136 ? -8.914 -44.150 -13.652 1.00 51.99 ? ? ? ? ? ? 137 GLU U CG 1
+ATOM 1080 C CD . GLU A 1 136 ? -9.692 -43.966 -12.366 1.00 58.74 ? ? ? ? ? ? 137 GLU U CD 1
+ATOM 1081 O OE1 . GLU A 1 136 ? -9.218 -43.209 -11.478 1.00 57.19 ? ? ? ? ? ? 137 GLU U OE1 1
+ATOM 1082 O OE2 . GLU A 1 136 ? -10.785 -44.577 -12.255 1.00 63.33 ? ? ? ? ? ? 137 GLU U OE2 1
+ATOM 1083 N N . ILE A 1 137 ? -6.551 -41.319 -16.270 1.00 30.78 ? ? ? ? ? ? 138 ILE U N 1
+ATOM 1084 C CA . ILE A 1 137 ? -6.050 -39.956 -16.533 1.00 30.68 ? ? ? ? ? ? 138 ILE U CA 1
+ATOM 1085 C C . ILE A 1 137 ? -7.248 -39.028 -16.607 1.00 29.77 ? ? ? ? ? ? 138 ILE U C 1
+ATOM 1086 O O . ILE A 1 137 ? -8.356 -39.479 -16.876 1.00 30.43 ? ? ? ? ? ? 138 ILE U O 1
+ATOM 1087 C CB . ILE A 1 137 ? -5.240 -39.850 -17.847 1.00 32.00 ? ? ? ? ? ? 138 ILE U CB 1
+ATOM 1088 C CG1 . ILE A 1 137 ? -5.977 -40.566 -18.988 1.00 33.11 ? ? ? ? ? ? 138 ILE U CG1 1
+ATOM 1089 C CG2 . ILE A 1 137 ? -3.839 -40.415 -17.634 1.00 33.32 ? ? ? ? ? ? 138 ILE U CG2 1
+ATOM 1090 C CD1 . ILE A 1 137 ? -5.402 -40.353 -20.382 1.00 34.56 ? ? ? ? ? ? 138 ILE U CD1 1
+ATOM 1091 N N . THR A 1 138 ? -7.018 -37.737 -16.371 1.00 29.88 ? ? ? ? ? ? 139 THR U N 1
+ATOM 1092 C CA . THR A 1 138 ? -8.084 -36.767 -16.256 1.00 31.21 ? ? ? ? ? ? 139 THR U CA 1
+ATOM 1093 C C . THR A 1 138 ? -7.570 -35.452 -16.751 1.00 28.96 ? ? ? ? ? ? 139 THR U C 1
+ATOM 1094 O O . THR A 1 138 ? -6.395 -35.145 -16.553 1.00 29.81 ? ? ? ? ? ? 139 THR U O 1
+ATOM 1095 C CB . THR A 1 138 ? -8.500 -36.529 -14.780 1.00 36.17 ? ? ? ? ? ? 139 THR U CB 1
+ATOM 1096 O OG1 . THR A 1 138 ? -8.443 -37.755 -14.048 1.00 44.21 ? ? ? ? ? ? 139 THR U OG1 1
+ATOM 1097 C CG2 . THR A 1 138 ? -9.907 -35.980 -14.701 1.00 35.56 ? ? ? ? ? ? 139 THR U CG2 1
+ATOM 1098 N N . GLY A 1 139 ? -8.446 -34.633 -17.324 1.00 25.83 ? ? ? ? ? ? 140 GLY U N 1
+ATOM 1099 C CA . GLY A 1 139 ? -8.043 -33.269 -17.685 1.00 25.07 ? ? ? ? ? ? 140 GLY U CA 1
+ATOM 1100 C C . GLY A 1 139 ? -9.023 -32.482 -18.542 1.00 25.81 ? ? ? ? ? ? 140 GLY U C 1
+ATOM 1101 O O . GLY A 1 139 ? -10.085 -32.991 -18.916 1.00 26.46 ? ? ? ? ? ? 140 GLY U O 1
+ATOM 1102 N N . PHE A 1 140 ? -8.638 -31.232 -18.838 1.00 25.60 ? ? ? ? ? ? 141 PHE U N 1
+ATOM 1103 C CA . PHE A 1 140 ? -9.413 -30.307 -19.655 1.00 26.36 ? ? ? ? ? ? 141 PHE U CA 1
+ATOM 1104 C C . PHE A 1 140 ? -8.749 -30.174 -21.032 1.00 26.09 ? ? ? ? ? ? 141 PHE U C 1
+ATOM 1105 O O . PHE A 1 140 ? -8.950 -29.189 -21.742 1.00 26.20 ? ? ? ? ? ? 141 PHE U O 1
+ATOM 1106 C CB . PHE A 1 140 ? -9.504 -28.923 -18.973 1.00 27.59 ? ? ? ? ? ? 141 PHE U CB 1
+ATOM 1107 C CG . PHE A 1 140 ? -10.409 -28.865 -17.730 1.00 24.53 ? ? ? ? ? ? 141 PHE U CG 1
+ATOM 1108 C CD1 . PHE A 1 140 ? -11.780 -29.018 -17.842 1.00 25.90 ? ? ? ? ? ? 141 PHE U CD1 1
+ATOM 1109 C CD2 . PHE A 1 140 ? -9.891 -28.551 -16.482 1.00 27.11 ? ? ? ? ? ? 141 PHE U CD2 1
+ATOM 1110 C CE1 . PHE A 1 140 ? -12.613 -28.906 -16.733 1.00 24.91 ? ? ? ? ? ? 141 PHE U CE1 1
+ATOM 1111 C CE2 . PHE A 1 140 ? -10.717 -28.452 -15.356 1.00 25.48 ? ? ? ? ? ? 141 PHE U CE2 1
+ATOM 1112 C CZ . PHE A 1 140 ? -12.078 -28.625 -15.484 1.00 24.44 ? ? ? ? ? ? 141 PHE U CZ 1
+ATOM 1113 N N . GLY A 1 141 ? -7.996 -31.195 -21.423 1.00 27.17 ? ? ? ? ? ? 142 GLY U N 1
+ATOM 1114 C CA . GLY A 1 141 ? -7.219 -31.141 -22.644 1.00 28.31 ? ? ? ? ? ? 142 GLY U CA 1
+ATOM 1115 C C . GLY A 1 141 ? -8.114 -31.366 -23.857 1.00 30.98 ? ? ? ? ? ? 142 GLY U C 1
+ATOM 1116 O O . GLY A 1 141 ? -9.283 -31.751 -23.740 1.00 26.18 ? ? ? ? ? ? 142 GLY U O 1
+ATOM 1117 N N . LYS A 1 142 ? -7.545 -31.163 -25.038 1.00 33.16 ? ? ? ? ? ? 143 LYS U N 1
+ATOM 1118 C CA . LYS A 1 142 ? -8.315 -31.304 -26.269 1.00 32.87 ? ? ? ? ? ? 143 LYS U CA 1
+ATOM 1119 C C . LYS A 1 142 ? -8.930 -32.665 -26.414 1.00 29.68 ? ? ? ? ? ? 143 LYS U C 1
+ATOM 1120 O O . LYS A 1 142 ? -8.353 -33.660 -26.004 1.00 31.54 ? ? ? ? ? ? 143 LYS U O 1
+ATOM 1121 C CB . LYS A 1 142 ? -7.442 -31.000 -27.495 1.00 36.13 ? ? ? ? ? ? 143 LYS U CB 1
+ATOM 1122 C CG . LYS A 1 142 ? -6.960 -29.562 -27.538 1.00 38.57 ? ? ? ? ? ? 143 LYS U CG 1
+ATOM 1123 C CD . LYS A 1 142 ? -6.288 -29.225 -28.869 1.00 40.16 ? ? ? ? ? ? 143 LYS U CD 1
+ATOM 1124 C CE . LYS A 1 142 ? -5.867 -27.765 -28.898 1.00 42.91 ? ? ? ? ? ? 143 LYS U CE 1
+ATOM 1125 N NZ . LYS A 1 142 ? -4.817 -27.493 -29.925 1.00 45.54 ? ? ? ? ? ? 143 LYS U NZ 1
+ATOM 1126 N N . GLU A 1 143 ? -10.112 -32.711 -27.021 1.00 31.87 ? ? ? ? ? ? 144 GLU U N 1
+ATOM 1127 C CA . GLU A 1 143 ? -10.760 -33.980 -27.344 1.00 34.55 ? ? ? ? ? ? 144 GLU U CA 1
+ATOM 1128 C C . GLU A 1 143 ? -10.281 -34.575 -28.683 1.00 35.87 ? ? ? ? ? ? 144 GLU U C 1
+ATOM 1129 O O . GLU A 1 143 ? -10.622 -35.702 -28.988 1.00 36.47 ? ? ? ? ? ? 144 GLU U O 1
+ATOM 1130 C CB . GLU A 1 143 ? -12.277 -33.807 -27.395 1.00 35.10 ? ? ? ? ? ? 144 GLU U CB 1
+ATOM 1131 C CG . GLU A 1 143 ? -12.880 -33.408 -26.059 1.00 35.77 ? ? ? ? ? ? 144 GLU U CG 1
+ATOM 1132 C CD . GLU A 1 143 ? -14.387 -33.415 -26.105 1.00 35.11 ? ? ? ? ? ? 144 GLU U CD 1
+ATOM 1133 O OE1 . GLU A 1 143 ? -14.967 -32.328 -26.238 1.00 34.58 ? ? ? ? ? ? 144 GLU U OE1 1
+ATOM 1134 O OE2 . GLU A 1 143 ? -14.975 -34.510 -26.019 1.00 38.13 ? ? ? ? ? ? 144 GLU U OE2 1
+ATOM 1135 N N . GLN A 1 144 ? -9.551 -33.789 -29.472 1.00 39.16 ? ? ? ? ? ? 145 GLN U N 1
+ATOM 1136 C CA . GLN A 1 144 ? -8.894 -34.240 -30.711 1.00 38.26 ? ? ? ? ? ? 145 GLN U CA 1
+ATOM 1137 C C . GLN A 1 144 ? -7.651 -33.392 -30.947 1.00 35.78 ? ? ? ? ? ? 145 GLN U C 1
+ATOM 1138 O O . GLN A 1 144 ? -7.672 -32.161 -30.774 1.00 33.16 ? ? ? ? ? ? 145 GLN U O 1
+ATOM 1139 C CB . GLN A 1 144 ? -9.828 -34.084 -31.913 1.00 42.28 ? ? ? ? ? ? 145 GLN U CB 1
+ATOM 1140 C CG . GLN A 1 144 ? -11.065 -34.971 -31.872 1.00 45.91 ? ? ? ? ? ? 145 GLN U CG 1
+ATOM 1141 C CD . GLN A 1 144 ? -12.206 -34.458 -32.735 1.00 56.27 ? ? ? ? ? ? 145 GLN U CD 1
+ATOM 1142 O OE1 . GLN A 1 144 ? -12.346 -33.248 -32.962 1.00 62.36 ? ? ? ? ? ? 145 GLN U OE1 1
+ATOM 1143 N NE2 . GLN A 1 144 ? -13.058 -35.377 -33.189 1.00 59.10 ? ? ? ? ? ? 145 GLN U NE2 1
+ATOM 1144 N N . SER A 1 145 ? -6.566 -34.038 -31.359 1.00 38.86 ? ? ? ? ? ? 146 SER U N 1
+ATOM 1145 C CA . SER A 1 145 ? -5.338 -33.323 -31.762 1.00 44.56 ? ? ? ? ? ? 146 SER U CA 1
+ATOM 1146 C C . SER A 1 145 ? -5.593 -32.069 -32.574 1.00 44.02 ? ? ? ? ? ? 146 SER U C 1
+ATOM 1147 O O . SER A 1 145 ? -4.931 -31.046 -32.378 1.00 46.62 ? ? ? ? ? ? 146 SER U O 1
+ATOM 1148 C CB . SER A 1 145 ? -4.428 -34.236 -32.594 1.00 45.08 ? ? ? ? ? ? 146 SER U CB 1
+ATOM 1149 O OG . SER A 1 145 ? -3.396 -34.742 -31.793 1.00 50.15 ? ? ? ? ? ? 146 SER U OG 1
+ATOM 1150 N N . THR A 1 146 ? -6.536 -32.168 -33.505 1.00 43.87 ? ? ? ? ? ? 147 THR U N 1
+ATOM 1151 C CA . THR A 1 146 ? -6.772 -31.121 -34.489 1.00 44.09 ? ? ? ? ? ? 147 THR U CA 1
+ATOM 1152 C C . THR A 1 146 ? -7.676 -29.990 -34.011 1.00 45.34 ? ? ? ? ? ? 147 THR U C 1
+ATOM 1153 O O . THR A 1 146 ? -7.843 -28.993 -34.724 1.00 44.92 ? ? ? ? ? ? 147 THR U O 1
+ATOM 1154 C CB . THR A 1 146 ? -7.387 -31.740 -35.758 1.00 45.09 ? ? ? ? ? ? 147 THR U CB 1
+ATOM 1155 O OG1 . THR A 1 146 ? -8.587 -32.444 -35.420 1.00 41.63 ? ? ? ? ? ? 147 THR U OG1 1
+ATOM 1156 C CG2 . THR A 1 146 ? -6.400 -32.734 -36.381 1.00 46.78 ? ? ? ? ? ? 147 THR U CG2 1
+ATOM 1157 N N . ASP A 1 147 ? -8.264 -30.134 -32.821 1.00 42.74 ? ? ? ? ? ? 148 ASP U N 1
+ATOM 1158 C CA . ASP A 1 147 ? -9.169 -29.118 -32.290 1.00 42.79 ? ? ? ? ? ? 148 ASP U CA 1
+ATOM 1159 C C . ASP A 1 147 ? -8.409 -27.841 -31.951 1.00 41.48 ? ? ? ? ? ? 148 ASP U C 1
+ATOM 1160 O O . ASP A 1 147 ? -7.205 -27.856 -31.708 1.00 38.50 ? ? ? ? ? ? 148 ASP U O 1
+ATOM 1161 C CB . ASP A 1 147 ? -9.913 -29.620 -31.026 1.00 44.26 ? ? ? ? ? ? 148 ASP U CB 1
+ATOM 1162 C CG . ASP A 1 147 ? -10.984 -30.665 -31.332 1.00 43.73 ? ? ? ? ? ? 148 ASP U CG 1
+ATOM 1163 O OD1 . ASP A 1 147 ? -11.376 -30.808 -32.504 1.00 39.98 ? ? ? ? ? ? 148 ASP U OD1 1
+ATOM 1164 O OD2 . ASP A 1 147 ? -11.450 -31.348 -30.390 1.00 41.35 ? ? ? ? ? ? 148 ASP U OD2 1
+ATOM 1165 N N . TYR A 1 148 ? -9.145 -26.741 -31.956 1.00 42.35 ? ? ? ? ? ? 149 TYR U N 1
+ATOM 1166 C CA . TYR A 1 148 ? -8.658 -25.431 -31.555 1.00 48.15 ? ? ? ? ? ? 149 TYR U CA 1
+ATOM 1167 C C . TYR A 1 148 ? -9.212 -25.090 -30.167 1.00 44.82 ? ? ? ? ? ? 149 TYR U C 1
+ATOM 1168 O O . TYR A 1 148 ? -8.550 -24.421 -29.381 1.00 45.92 ? ? ? ? ? ? 149 TYR U O 1
+ATOM 1169 C CB . TYR A 1 148 ? -9.115 -24.393 -32.581 1.00 53.99 ? ? ? ? ? ? 149 TYR U CB 1
+ATOM 1170 C CG . TYR A 1 148 ? -8.594 -22.988 -32.363 1.00 67.30 ? ? ? ? ? ? 149 TYR U CG 1
+ATOM 1171 C CD1 . TYR A 1 148 ? -7.296 -22.632 -32.743 1.00 72.59 ? ? ? ? ? ? 149 TYR U CD1 1
+ATOM 1172 C CD2 . TYR A 1 148 ? -9.411 -21.997 -31.805 1.00 75.78 ? ? ? ? ? ? 149 TYR U CD2 1
+ATOM 1173 C CE1 . TYR A 1 148 ? -6.821 -21.337 -32.553 1.00 78.23 ? ? ? ? ? ? 149 TYR U CE1 1
+ATOM 1174 C CE2 . TYR A 1 148 ? -8.947 -20.698 -31.614 1.00 78.28 ? ? ? ? ? ? 149 TYR U CE2 1
+ATOM 1175 C CZ . TYR A 1 148 ? -7.652 -20.372 -31.987 1.00 78.98 ? ? ? ? ? ? 149 TYR U CZ 1
+ATOM 1176 O OH . TYR A 1 148 ? -7.189 -19.088 -31.797 1.00 79.95 ? ? ? ? ? ? 149 TYR U OH 1
+ATOM 1177 N N . LEU A 1 149 ? -10.422 -25.560 -29.870 1.00 43.17 ? ? ? ? ? ? 150 LEU U N 1
+ATOM 1178 C CA . LEU A 1 149 ? -11.048 -25.318 -28.573 1.00 42.24 ? ? ? ? ? ? 150 LEU U CA 1
+ATOM 1179 C C . LEU A 1 149 ? -10.788 -26.442 -27.563 1.00 40.09 ? ? ? ? ? ? 150 LEU U C 1
+ATOM 1180 O O . LEU A 1 149 ? -10.355 -27.558 -27.922 1.00 34.76 ? ? ? ? ? ? 150 LEU U O 1
+ATOM 1181 C CB . LEU A 1 149 ? -12.546 -25.108 -28.762 1.00 47.45 ? ? ? ? ? ? 150 LEU U CB 1
+ATOM 1182 C CG . LEU A 1 149 ? -12.935 -23.958 -29.703 1.00 49.63 ? ? ? ? ? ? 150 LEU U CG 1
+ATOM 1183 C CD1 . LEU A 1 149 ? -14.432 -23.957 -29.964 1.00 52.12 ? ? ? ? ? ? 150 LEU U CD1 1
+ATOM 1184 C CD2 . LEU A 1 149 ? -12.495 -22.620 -29.124 1.00 52.03 ? ? ? ? ? ? 150 LEU U CD2 1
+ATOM 1185 N N . TYR A 1 150 ? -11.037 -26.130 -26.291 1.00 34.40 ? ? ? ? ? ? 151 TYR U N 1
+ATOM 1186 C CA . TYR A 1 150 ? -10.911 -27.100 -25.211 1.00 35.83 ? ? ? ? ? ? 151 TYR U CA 1
+ATOM 1187 C C . TYR A 1 150 ? -12.288 -27.368 -24.627 1.00 32.78 ? ? ? ? ? ? 151 TYR U C 1
+ATOM 1188 O O . TYR A 1 150 ? -13.167 -26.504 -24.696 1.00 30.24 ? ? ? ? ? ? 151 TYR U O 1
+ATOM 1189 C CB . TYR A 1 150 ? -9.958 -26.586 -24.133 1.00 35.82 ? ? ? ? ? ? 151 TYR U CB 1
+ATOM 1190 C CG . TYR A 1 150 ? -8.550 -26.444 -24.649 1.00 37.87 ? ? ? ? ? ? 151 TYR U CG 1
+ATOM 1191 C CD1 . TYR A 1 150 ? -8.174 -25.324 -25.378 1.00 39.44 ? ? ? ? ? ? 151 TYR U CD1 1
+ATOM 1192 C CD2 . TYR A 1 150 ? -7.592 -27.418 -24.406 1.00 38.18 ? ? ? ? ? ? 151 TYR U CD2 1
+ATOM 1193 C CE1 . TYR A 1 150 ? -6.882 -25.178 -25.859 1.00 40.53 ? ? ? ? ? ? 151 TYR U CE1 1
+ATOM 1194 C CE2 . TYR A 1 150 ? -6.299 -27.287 -24.882 1.00 37.97 ? ? ? ? ? ? 151 TYR U CE2 1
+ATOM 1195 C CZ . TYR A 1 150 ? -5.949 -26.165 -25.609 1.00 41.92 ? ? ? ? ? ? 151 TYR U CZ 1
+ATOM 1196 O OH . TYR A 1 150 ? -4.672 -26.020 -26.095 1.00 40.49 ? ? ? ? ? ? 151 TYR U OH 1
+ATOM 1197 N N . PRO A 1 151 ? -12.501 -28.589 -24.105 1.00 30.49 ? ? ? ? ? ? 152 PRO U N 1
+ATOM 1198 C CA . PRO A 1 151 ? -13.820 -28.907 -23.600 1.00 31.36 ? ? ? ? ? ? 152 PRO U CA 1
+ATOM 1199 C C . PRO A 1 151 ? -14.097 -28.097 -22.340 1.00 30.71 ? ? ? ? ? ? 152 PRO U C 1
+ATOM 1200 O O . PRO A 1 151 ? -13.140 -27.703 -21.644 1.00 28.82 ? ? ? ? ? ? 152 PRO U O 1
+ATOM 1201 C CB . PRO A 1 151 ? -13.747 -30.410 -23.309 1.00 31.11 ? ? ? ? ? ? 152 PRO U CB 1
+ATOM 1202 C CG . PRO A 1 151 ? -12.294 -30.703 -23.147 1.00 32.17 ? ? ? ? ? ? 152 PRO U CG 1
+ATOM 1203 C CD . PRO A 1 151 ? -11.618 -29.763 -24.104 1.00 32.55 ? ? ? ? ? ? 152 PRO U CD 1
+ATOM 1204 N N . GLU A 1 152 ? -15.372 -27.810 -22.086 1.00 29.22 ? ? ? ? ? ? 153 GLU U N 1
+ATOM 1205 C CA . GLU A 1 152 ? -15.755 -27.014 -20.908 1.00 32.47 ? ? ? ? ? ? 153 GLU U CA 1
+ATOM 1206 C C . GLU A 1 152 ? -15.900 -27.901 -19.671 1.00 28.25 ? ? ? ? ? ? 153 GLU U C 1
+ATOM 1207 O O . GLU A 1 152 ? -15.680 -27.454 -18.553 1.00 31.16 ? ? ? ? ? ? 153 GLU U O 1
+ATOM 1208 C CB . GLU A 1 152 ? -17.035 -26.212 -21.172 1.00 35.26 ? ? ? ? ? ? 153 GLU U CB 1
+ATOM 1209 C CG . GLU A 1 152 ? -16.861 -24.702 -20.960 1.00 45.25 ? ? ? ? ? ? 153 GLU U CG 1
+ATOM 1210 C CD . GLU A 1 152 ? -17.149 -24.235 -19.528 1.00 49.45 ? ? ? ? ? ? 153 GLU U CD 1
+ATOM 1211 O OE1 . GLU A 1 152 ? -16.209 -23.983 -18.755 1.00 49.01 ? ? ? ? ? ? 153 GLU U OE1 1
+ATOM 1212 O OE2 . GLU A 1 152 ? -18.337 -24.098 -19.181 1.00 59.35 ? ? ? ? ? ? 153 GLU U OE2 1
+ATOM 1213 N N . GLN A 1 153 ? -16.218 -29.165 -19.889 1.00 28.98 ? ? ? ? ? ? 154 GLN U N 1
+ATOM 1214 C CA . GLN A 1 153 ? -16.411 -30.134 -18.818 1.00 30.62 ? ? ? ? ? ? 154 GLN U CA 1
+ATOM 1215 C C . GLN A 1 153 ? -15.181 -31.040 -18.694 1.00 30.90 ? ? ? ? ? ? 154 GLN U C 1
+ATOM 1216 O O . GLN A 1 153 ? -14.579 -31.455 -19.684 1.00 27.99 ? ? ? ? ? ? 154 GLN U O 1
+ATOM 1217 C CB . GLN A 1 153 ? -17.671 -30.965 -19.112 1.00 28.97 ? ? ? ? ? ? 154 GLN U CB 1
+ATOM 1218 C CG . GLN A 1 153 ? -18.155 -31.875 -17.990 1.00 30.36 ? ? ? ? ? ? 154 GLN U CG 1
+ATOM 1219 C CD . GLN A 1 153 ? -18.622 -31.139 -16.751 1.00 30.09 ? ? ? ? ? ? 154 GLN U CD 1
+ATOM 1220 O OE1 . GLN A 1 153 ? -19.099 -30.004 -16.827 1.00 28.33 ? ? ? ? ? ? 154 GLN U OE1 1
+ATOM 1221 N NE2 . GLN A 1 153 ? -18.465 -31.781 -15.581 1.00 29.02 ? ? ? ? ? ? 154 GLN U NE2 1
+ATOM 1222 N N . LEU A 1 154 ? -14.831 -31.361 -17.460 1.00 29.24 ? ? ? ? ? ? 155 LEU U N 1
+ATOM 1223 C CA . LEU A 1 154 ? -13.744 -32.290 -17.198 1.00 27.12 ? ? ? ? ? ? 155 LEU U CA 1
+ATOM 1224 C C . LEU A 1 154 ? -13.997 -33.645 -17.822 1.00 26.10 ? ? ? ? ? ? 155 LEU U C 1
+ATOM 1225 O O . LEU A 1 154 ? -15.111 -34.165 -17.770 1.00 29.26 ? ? ? ? ? ? 155 LEU U O 1
+ATOM 1226 C CB . LEU A 1 154 ? -13.591 -32.455 -15.682 1.00 27.00 ? ? ? ? ? ? 155 LEU U CB 1
+ATOM 1227 C CG . LEU A 1 154 ? -12.393 -33.237 -15.159 1.00 25.93 ? ? ? ? ? ? 155 LEU U CG 1
+ATOM 1228 C CD1 . LEU A 1 154 ? -11.061 -32.552 -15.495 1.00 24.96 ? ? ? ? ? ? 155 LEU U CD1 1
+ATOM 1229 C CD2 . LEU A 1 154 ? -12.602 -33.370 -13.649 1.00 28.19 ? ? ? ? ? ? 155 LEU U CD2 1
+ATOM 1230 N N . LYS A 1 155 ? -12.944 -34.259 -18.339 1.00 25.91 ? ? ? ? ? ? 156 LYS U N 1
+ATOM 1231 C CA . LYS A 1 155 ? -13.004 -35.638 -18.793 1.00 25.80 ? ? ? ? ? ? 156 LYS U CA 1
+ATOM 1232 C C . LYS A 1 155 ? -11.982 -36.564 -18.131 1.00 24.15 ? ? ? ? ? ? 156 LYS U C 1
+ATOM 1233 O O . LYS A 1 155 ? -10.993 -36.117 -17.571 1.00 24.53 ? ? ? ? ? ? 156 LYS U O 1
+ATOM 1234 C CB . LYS A 1 155 ? -12.719 -35.686 -20.296 1.00 26.48 ? ? ? ? ? ? 156 LYS U CB 1
+ATOM 1235 C CG . LYS A 1 155 ? -13.726 -34.952 -21.174 1.00 28.36 ? ? ? ? ? ? 156 LYS U CG 1
+ATOM 1236 C CD . LYS A 1 155 ? -13.372 -35.098 -22.654 1.00 29.65 ? ? ? ? ? ? 156 LYS U CD 1
+ATOM 1237 C CE . LYS A 1 155 ? -13.901 -36.391 -23.269 1.00 31.11 ? ? ? ? ? ? 156 LYS U CE 1
+ATOM 1238 N NZ . LYS A 1 155 ? -13.273 -36.663 -24.607 1.00 34.73 ? ? ? ? ? ? 156 LYS U NZ 1
+ATOM 1239 N N . MET A 1 156 ? -12.216 -37.858 -18.260 1.00 27.40 ? ? ? ? ? ? 157 MET U N 1
+ATOM 1240 C CA . MET A 1 156 ? -11.259 -38.894 -17.867 1.00 31.33 ? ? ? ? ? ? 157 MET U CA 1
+ATOM 1241 C C . MET A 1 156 ? -11.330 -40.085 -18.813 1.00 31.61 ? ? ? ? ? ? 157 MET U C 1
+ATOM 1242 O O . MET A 1 156 ? -12.314 -40.265 -19.532 1.00 32.01 ? ? ? ? ? ? 157 MET U O 1
+ATOM 1243 C CB . MET A 1 156 ? -11.523 -39.375 -16.438 1.00 35.87 ? ? ? ? ? ? 157 MET U CB 1
+ATOM 1244 C CG . MET A 1 156 ? -12.868 -40.041 -16.216 1.00 44.32 ? ? ? ? ? ? 157 MET U CG 1
+ATOM 1245 S SD . MET A 1 156 ? -13.024 -40.704 -14.528 1.00 60.90 ? ? ? ? ? ? 157 MET U SD 1
+ATOM 1246 C CE . MET A 1 156 ? -11.749 -41.966 -14.561 1.00 56.17 ? ? ? ? ? ? 157 MET U CE 1
+ATOM 1247 N N . THR A 1 157 ? -10.312 -40.930 -18.765 1.00 31.24 ? ? ? ? ? ? 158 THR U N 1
+ATOM 1248 C CA . THR A 1 157 ? -10.303 -42.146 -19.561 1.00 33.50 ? ? ? ? ? ? 158 THR U CA 1
+ATOM 1249 C C . THR A 1 157 ? -9.359 -43.146 -18.917 1.00 35.48 ? ? ? ? ? ? 158 THR U C 1
+ATOM 1250 O O . THR A 1 157 ? -8.731 -42.835 -17.923 1.00 32.95 ? ? ? ? ? ? 158 THR U O 1
+ATOM 1251 C CB . THR A 1 157 ? -9.911 -41.861 -21.032 1.00 34.92 ? ? ? ? ? ? 158 THR U CB 1
+ATOM 1252 O OG1 . THR A 1 157 ? -10.122 -43.034 -21.832 1.00 35.42 ? ? ? ? ? ? 158 THR U OG1 1
+ATOM 1253 C CG2 . THR A 1 157 ? -8.465 -41.384 -21.143 1.00 35.28 ? ? ? ? ? ? 158 THR U CG2 1
+ATOM 1254 N N . VAL A 1 158 ? -9.290 -44.351 -19.477 1.00 36.79 ? ? ? ? ? ? 159 VAL U N 1
+ATOM 1255 C CA . VAL A 1 158 ? -8.449 -45.403 -18.942 1.00 38.37 ? ? ? ? ? ? 159 VAL U CA 1
+ATOM 1256 C C . VAL A 1 158 ? -7.417 -45.802 -20.002 1.00 39.28 ? ? ? ? ? ? 159 VAL U C 1
+ATOM 1257 O O . VAL A 1 158 ? -7.732 -45.920 -21.193 1.00 34.81 ? ? ? ? ? ? 159 VAL U O 1
+ATOM 1258 C CB . VAL A 1 158 ? -9.295 -46.618 -18.446 1.00 39.88 ? ? ? ? ? ? 159 VAL U CB 1
+ATOM 1259 C CG1 . VAL A 1 158 ? -8.405 -47.798 -18.034 1.00 38.15 ? ? ? ? ? ? 159 VAL U CG1 1
+ATOM 1260 C CG2 . VAL A 1 158 ? -10.162 -46.184 -17.269 1.00 39.06 ? ? ? ? ? ? 159 VAL U CG2 1
+ATOM 1261 N N . VAL A 1 159 ? -6.175 -45.953 -19.546 1.00 42.59 ? ? ? ? ? ? 160 VAL U N 1
+ATOM 1262 C CA . VAL A 1 159 ? -5.066 -46.410 -20.378 1.00 41.57 ? ? ? ? ? ? 160 VAL U CA 1
+ATOM 1263 C C . VAL A 1 159 ? -4.286 -47.465 -19.602 1.00 43.83 ? ? ? ? ? ? 160 VAL U C 1
+ATOM 1264 O O . VAL A 1 159 ? -4.388 -47.544 -18.371 1.00 41.98 ? ? ? ? ? ? 160 VAL U O 1
+ATOM 1265 C CB . VAL A 1 159 ? -4.122 -45.259 -20.782 1.00 41.45 ? ? ? ? ? ? 160 VAL U CB 1
+ATOM 1266 C CG1 . VAL A 1 159 ? -4.735 -44.459 -21.924 1.00 39.83 ? ? ? ? ? ? 160 VAL U CG1 1
+ATOM 1267 C CG2 . VAL A 1 159 ? -3.797 -44.373 -19.586 1.00 41.56 ? ? ? ? ? ? 160 VAL U CG2 1
+ATOM 1268 N N . LYS A 1 160 ? -3.519 -48.275 -20.329 1.00 41.48 ? ? ? ? ? ? 161 LYS U N 1
+ATOM 1269 C CA . LYS A 1 160 ? -2.715 -49.308 -19.719 1.00 43.76 ? ? ? ? ? ? 161 LYS U CA 1
+ATOM 1270 C C . LYS A 1 160 ? -1.236 -48.964 -19.824 1.00 44.55 ? ? ? ? ? ? 161 LYS U C 1
+ATOM 1271 O O . LYS A 1 160 ? -0.768 -48.522 -20.881 1.00 44.98 ? ? ? ? ? ? 161 LYS U O 1
+ATOM 1272 C CB . LYS A 1 160 ? -2.991 -50.659 -20.382 1.00 48.39 ? ? ? ? ? ? 161 LYS U CB 1
+ATOM 1273 C CG . LYS A 1 160 ? -4.424 -51.145 -20.186 1.00 50.56 ? ? ? ? ? ? 161 LYS U CG 1
+ATOM 1274 C CD . LYS A 1 160 ? -4.584 -52.622 -20.529 1.00 52.38 ? ? ? ? ? ? 161 LYS U CD 1
+ATOM 1275 C CE . LYS A 1 160 ? -5.994 -53.123 -20.235 1.00 52.81 ? ? ? ? ? ? 161 LYS U CE 1
+ATOM 1276 N NZ . LYS A 1 160 ? -6.096 -54.614 -20.250 1.00 52.79 ? ? ? ? ? ? 161 LYS U NZ 1
+ATOM 1277 N N . LEU A 1 161 ? -0.519 -49.158 -18.719 1.00 40.16 ? ? ? ? ? ? 162 LEU U N 1
+ATOM 1278 C CA . LEU A 1 161 ? 0.927 -49.008 -18.685 1.00 45.27 ? ? ? ? ? ? 162 LEU U CA 1
+ATOM 1279 C C . LEU A 1 161 ? 1.568 -49.926 -19.704 1.00 47.69 ? ? ? ? ? ? 162 LEU U C 1
+ATOM 1280 O O . LEU A 1 161 ? 1.138 -51.077 -19.888 1.00 42.70 ? ? ? ? ? ? 162 LEU U O 1
+ATOM 1281 C CB . LEU A 1 161 ? 1.475 -49.340 -17.292 1.00 46.19 ? ? ? ? ? ? 162 LEU U CB 1
+ATOM 1282 C CG . LEU A 1 161 ? 1.567 -48.207 -16.258 1.00 48.56 ? ? ? ? ? ? 162 LEU U CG 1
+ATOM 1283 C CD1 . LEU A 1 161 ? 0.644 -47.030 -16.548 1.00 50.54 ? ? ? ? ? ? 162 LEU U CD1 1
+ATOM 1284 C CD2 . LEU A 1 161 ? 1.318 -48.749 -14.863 1.00 47.68 ? ? ? ? ? ? 162 LEU U CD2 1
+ATOM 1285 N N . ILE A 1 162 ? 2.592 -49.400 -20.367 1.00 50.90 ? ? ? ? ? ? 163 ILE U N 1
+ATOM 1286 C CA . ILE A 1 162 ? 3.314 -50.127 -21.401 1.00 53.56 ? ? ? ? ? ? 163 ILE U CA 1
+ATOM 1287 C C . ILE A 1 162 ? 4.718 -50.419 -20.880 1.00 53.46 ? ? ? ? ? ? 163 ILE U C 1
+ATOM 1288 O O . ILE A 1 162 ? 5.267 -49.659 -20.071 1.00 54.05 ? ? ? ? ? ? 163 ILE U O 1
+ATOM 1289 C CB . ILE A 1 162 ? 3.349 -49.324 -22.724 1.00 55.10 ? ? ? ? ? ? 163 ILE U CB 1
+ATOM 1290 C CG1 . ILE A 1 162 ? 1.933 -49.203 -23.291 1.00 57.74 ? ? ? ? ? ? 163 ILE U CG1 1
+ATOM 1291 C CG2 . ILE A 1 162 ? 4.247 -50.000 -23.754 1.00 55.65 ? ? ? ? ? ? 163 ILE U CG2 1
+ATOM 1292 C CD1 . ILE A 1 162 ? 1.751 -48.063 -24.272 1.00 59.80 ? ? ? ? ? ? 163 ILE U CD1 1
+ATOM 1293 N N . SER A 1 163 ? 5.283 -51.539 -21.321 1.00 51.93 ? ? ? ? ? ? 164 SER U N 1
+ATOM 1294 C CA . SER A 1 163 ? 6.615 -51.939 -20.881 1.00 54.46 ? ? ? ? ? ? 164 SER U CA 1
+ATOM 1295 C C . SER A 1 163 ? 7.647 -50.967 -21.428 1.00 52.15 ? ? ? ? ? ? 164 SER U C 1
+ATOM 1296 O O . SER A 1 163 ? 7.493 -50.410 -22.518 1.00 54.35 ? ? ? ? ? ? 164 SER U O 1
+ATOM 1297 C CB . SER A 1 163 ? 6.940 -53.368 -21.324 1.00 50.19 ? ? ? ? ? ? 164 SER U CB 1
+ATOM 1298 O OG . SER A 1 163 ? 7.176 -53.418 -22.724 1.00 49.75 ? ? ? ? ? ? 164 SER U OG 1
+ATOM 1299 N N . HIS A 1 164 ? 8.735 -50.793 -20.740 1.00 56.45 ? ? ? ? ? ? 165 HIS U N 1
+ATOM 1300 C CA . HIS A 1 164 ? 9.777 -49.985 -21.314 1.00 67.84 ? ? ? ? ? ? 165 HIS U CA 1
+ATOM 1301 C C . HIS A 1 164 ? 10.307 -50.540 -22.621 1.00 68.26 ? ? ? ? ? ? 165 HIS U C 1
+ATOM 1302 O O . HIS A 1 164 ? 10.509 -49.827 -23.574 1.00 68.60 ? ? ? ? ? ? 165 HIS U O 1
+ATOM 1303 C CB . HIS A 1 164 ? 10.908 -49.848 -20.325 1.00 70.86 ? ? ? ? ? ? 165 HIS U CB 1
+ATOM 1304 C CG . HIS A 1 164 ? 10.794 -48.616 -19.519 1.00 80.16 ? ? ? ? ? ? 165 HIS U CG 1
+ATOM 1305 N ND1 . HIS A 1 164 ? 10.983 -47.367 -20.061 1.00 85.61 ? ? ? ? ? ? 165 HIS U ND1 1
+ATOM 1306 C CD2 . HIS A 1 164 ? 10.415 -48.420 -18.239 1.00 84.15 ? ? ? ? ? ? 165 HIS U CD2 1
+ATOM 1307 C CE1 . HIS A 1 164 ? 10.752 -46.455 -19.137 1.00 88.15 ? ? ? ? ? ? 165 HIS U CE1 1
+ATOM 1308 N NE2 . HIS A 1 164 ? 10.415 -47.067 -18.021 1.00 89.48 ? ? ? ? ? ? 165 HIS U NE2 1
+ATOM 1309 N N . ARG A 1 165 ? 10.468 -51.837 -22.669 1.00 69.35 ? ? ? ? ? ? 166 ARG U N 1
+ATOM 1310 C CA . ARG A 1 165 ? 10.995 -52.476 -23.833 1.00 71.94 ? ? ? ? ? ? 166 ARG U CA 1
+ATOM 1311 C C . ARG A 1 165 ? 10.086 -52.206 -24.980 1.00 69.85 ? ? ? ? ? ? 166 ARG U C 1
+ATOM 1312 O O . ARG A 1 165 ? 10.549 -51.964 -26.081 1.00 71.36 ? ? ? ? ? ? 166 ARG U O 1
+ATOM 1313 C CB . ARG A 1 165 ? 11.059 -53.988 -23.603 1.00 74.16 ? ? ? ? ? ? 166 ARG U CB 1
+ATOM 1314 C CG . ARG A 1 165 ? 11.232 -54.808 -24.858 1.00 78.74 ? ? ? ? ? ? 166 ARG U CG 1
+ATOM 1315 C CD . ARG A 1 165 ? 10.086 -55.770 -25.053 1.00 81.19 ? ? ? ? ? ? 166 ARG U CD 1
+ATOM 1316 N NE . ARG A 1 165 ? 10.458 -57.081 -24.569 1.00 84.43 ? ? ? ? ? ? 166 ARG U NE 1
+ATOM 1317 C CZ . ARG A 1 165 ? 10.372 -58.196 -25.278 1.00 89.36 ? ? ? ? ? ? 166 ARG U CZ 1
+ATOM 1318 N NH1 . ARG A 1 165 ? 9.900 -58.157 -26.513 1.00 91.97 ? ? ? ? ? ? 166 ARG U NH1 1
+ATOM 1319 N NH2 . ARG A 1 165 ? 10.740 -59.351 -24.742 1.00 89.42 ? ? ? ? ? ? 166 ARG U NH2 1
+ATOM 1320 N N . GLU A 1 166 ? 8.801 -52.309 -24.748 1.00 67.25 ? ? ? ? ? ? 167 GLU U N 1
+ATOM 1321 C CA . GLU A 1 166 ? 7.935 -52.032 -25.823 1.00 63.86 ? ? ? ? ? ? 167 GLU U CA 1
+ATOM 1322 C C . GLU A 1 166 ? 8.146 -50.590 -26.153 1.00 63.77 ? ? ? ? ? ? 167 GLU U C 1
+ATOM 1323 O O . GLU A 1 166 ? 8.217 -50.225 -27.292 1.00 64.60 ? ? ? ? ? ? 167 GLU U O 1
+ATOM 1324 C CB . GLU A 1 166 ? 6.496 -52.282 -25.463 1.00 65.00 ? ? ? ? ? ? 167 GLU U CB 1
+ATOM 1325 C CG . GLU A 1 166 ? 5.627 -52.295 -26.701 1.00 67.37 ? ? ? ? ? ? 167 GLU U CG 1
+ATOM 1326 C CD . GLU A 1 166 ? 4.155 -52.382 -26.426 1.00 71.06 ? ? ? ? ? ? 167 GLU U CD 1
+ATOM 1327 O OE1 . GLU A 1 166 ? 3.774 -52.966 -25.403 1.00 71.41 ? ? ? ? ? ? 167 GLU U OE1 1
+ATOM 1328 O OE2 . GLU A 1 166 ? 3.377 -51.860 -27.246 1.00 74.75 ? ? ? ? ? ? 167 GLU U OE2 1
+ATOM 1329 N N . CYS A 1 167 ? 8.234 -49.759 -25.138 1.00 62.45 ? ? ? ? ? ? 168 CYS U N 1
+ATOM 1330 C CA . CYS A 1 167 ? 8.249 -48.335 -25.364 1.00 62.23 ? ? ? ? ? ? 168 CYS U CA 1
+ATOM 1331 C C . CYS A 1 167 ? 9.440 -47.851 -26.140 1.00 61.76 ? ? ? ? ? ? 168 CYS U C 1
+ATOM 1332 O O . CYS A 1 167 ? 9.331 -46.913 -26.882 1.00 57.53 ? ? ? ? ? ? 168 CYS U O 1
+ATOM 1333 C CB . CYS A 1 167 ? 8.050 -47.535 -24.093 1.00 59.94 ? ? ? ? ? ? 168 CYS U CB 1
+ATOM 1334 S SG . CYS A 1 167 ? 7.015 -46.133 -24.463 1.00 62.94 ? ? ? ? ? ? 168 CYS U SG 1
+ATOM 1335 N N . GLN A 1 168 ? 10.564 -48.512 -25.946 1.00 63.74 ? ? ? ? ? ? 169 GLN U N 1
+ATOM 1336 C CA . GLN A 1 168 ? 11.835 -48.156 -26.553 1.00 66.75 ? ? ? ? ? ? 169 GLN U CA 1
+ATOM 1337 C C . GLN A 1 168 ? 11.978 -48.704 -27.962 1.00 69.12 ? ? ? ? ? ? 169 GLN U C 1
+ATOM 1338 O O . GLN A 1 168 ? 13.020 -48.608 -28.551 1.00 66.73 ? ? ? ? ? ? 169 GLN U O 1
+ATOM 1339 C CB . GLN A 1 168 ? 13.000 -48.633 -25.710 1.00 68.82 ? ? ? ? ? ? 169 GLN U CB 1
+ATOM 1340 C CG . GLN A 1 168 ? 13.274 -47.776 -24.498 1.00 74.80 ? ? ? ? ? ? 169 GLN U CG 1
+ATOM 1341 C CD . GLN A 1 168 ? 13.157 -48.537 -23.185 1.00 79.76 ? ? ? ? ? ? 169 GLN U CD 1
+ATOM 1342 O OE1 . GLN A 1 168 ? 12.889 -49.736 -23.160 1.00 81.85 ? ? ? ? ? ? 169 GLN U OE1 1
+ATOM 1343 N NE2 . GLN A 1 168 ? 13.368 -47.835 -22.085 1.00 81.00 ? ? ? ? ? ? 169 GLN U NE2 1
+ATOM 1344 N N . GLN A 1 169 ? 10.941 -49.334 -28.468 1.00 72.71 ? ? ? ? ? ? 170 GLN U N 1
+ATOM 1345 C CA . GLN A 1 169 ? 10.926 -49.774 -29.839 1.00 72.39 ? ? ? ? ? ? 170 GLN U CA 1
+ATOM 1346 C C . GLN A 1 169 ? 10.944 -48.635 -30.826 1.00 72.96 ? ? ? ? ? ? 170 GLN U C 1
+ATOM 1347 O O . GLN A 1 169 ? 10.348 -47.605 -30.616 1.00 72.34 ? ? ? ? ? ? 170 GLN U O 1
+ATOM 1348 C CB . GLN A 1 169 ? 9.694 -50.586 -30.109 1.00 75.02 ? ? ? ? ? ? 170 GLN U CB 1
+ATOM 1349 C CG . GLN A 1 169 ? 9.710 -51.986 -29.560 1.00 76.78 ? ? ? ? ? ? 170 GLN U CG 1
+ATOM 1350 C CD . GLN A 1 169 ? 8.570 -52.791 -30.111 1.00 76.82 ? ? ? ? ? ? 170 GLN U CD 1
+ATOM 1351 O OE1 . GLN A 1 169 ? 7.503 -52.266 -30.384 1.00 79.40 ? ? ? ? ? ? 170 GLN U OE1 1
+ATOM 1352 N NE2 . GLN A 1 169 ? 8.783 -54.073 -30.258 1.00 76.80 ? ? ? ? ? ? 170 GLN U NE2 1
+ATOM 1353 N N . PRO A 1 170 A 11.682 -48.888 -31.988 1.00 93.83 ? ? ? ? ? ? 170 PRO U N 1
+ATOM 1354 C CA . PRO A 1 170 A 11.828 -47.726 -32.870 1.00 97.45 ? ? ? ? ? ? 170 PRO U CA 1
+ATOM 1355 C C . PRO A 1 170 A 10.541 -47.213 -33.469 1.00 97.89 ? ? ? ? ? ? 170 PRO U C 1
+ATOM 1356 O O . PRO A 1 170 A 10.437 -46.039 -33.668 1.00 106.92 ? ? ? ? ? ? 170 PRO U O 1
+ATOM 1357 C CB . PRO A 1 170 A 12.800 -48.208 -33.955 1.00 97.74 ? ? ? ? ? ? 170 PRO U CB 1
+ATOM 1358 C CG . PRO A 1 170 A 13.569 -49.297 -33.326 1.00 96.65 ? ? ? ? ? ? 170 PRO U CG 1
+ATOM 1359 C CD . PRO A 1 170 A 12.466 -50.027 -32.674 1.00 94.64 ? ? ? ? ? ? 170 PRO U CD 1
+ATOM 1360 N N . HIS A 1 171 B 9.612 -48.075 -33.809 1.00 55.07 ? ? ? ? ? ? 170 HIS U N 1
+ATOM 1361 C CA . HIS A 1 171 B 8.349 -47.606 -34.315 1.00 62.48 ? ? ? ? ? ? 170 HIS U CA 1
+ATOM 1362 C C . HIS A 1 171 B 7.492 -46.961 -33.178 1.00 59.71 ? ? ? ? ? ? 170 HIS U C 1
+ATOM 1363 O O . HIS A 1 171 B 6.510 -46.296 -33.442 1.00 55.93 ? ? ? ? ? ? 170 HIS U O 1
+ATOM 1364 C CB . HIS A 1 171 B 7.664 -48.664 -35.174 1.00 62.91 ? ? ? ? ? ? 170 HIS U CB 1
+ATOM 1365 C CG . HIS A 1 171 B 7.301 -49.896 -34.431 1.00 67.40 ? ? ? ? ? ? 170 HIS U CG 1
+ATOM 1366 N ND1 . HIS A 1 171 B 8.127 -50.461 -33.495 1.00 67.21 ? ? ? ? ? ? 170 HIS U ND1 1
+ATOM 1367 C CD2 . HIS A 1 171 B 6.193 -50.663 -34.474 1.00 69.20 ? ? ? ? ? ? 170 HIS U CD2 1
+ATOM 1368 C CE1 . HIS A 1 171 B 7.539 -51.521 -32.983 1.00 69.84 ? ? ? ? ? ? 170 HIS U CE1 1
+ATOM 1369 N NE2 . HIS A 1 171 B 6.368 -51.669 -33.566 1.00 71.65 ? ? ? ? ? ? 170 HIS U NE2 1
+ATOM 1370 N N . TYR A 1 172 ? 7.894 -47.157 -31.928 1.00 57.86 ? ? ? ? ? ? 171 TYR U N 1
+ATOM 1371 C CA . TYR A 1 172 ? 7.379 -46.346 -30.833 1.00 57.93 ? ? ? ? ? ? 171 TYR U CA 1
+ATOM 1372 C C . TYR A 1 172 ? 8.220 -45.097 -30.624 1.00 57.29 ? ? ? ? ? ? 171 TYR U C 1
+ATOM 1373 O O . TYR A 1 172 ? 8.215 -44.222 -31.447 1.00 57.94 ? ? ? ? ? ? 171 TYR U O 1
+ATOM 1374 C CB . TYR A 1 172 ? 7.322 -47.114 -29.543 1.00 57.50 ? ? ? ? ? ? 171 TYR U CB 1
+ATOM 1375 C CG . TYR A 1 172 ? 6.188 -48.080 -29.478 1.00 57.71 ? ? ? ? ? ? 171 TYR U CG 1
+ATOM 1376 C CD1 . TYR A 1 172 ? 5.706 -48.674 -30.612 1.00 60.53 ? ? ? ? ? ? 171 TYR U CD1 1
+ATOM 1377 C CD2 . TYR A 1 172 ? 5.607 -48.400 -28.284 1.00 58.76 ? ? ? ? ? ? 171 TYR U CD2 1
+ATOM 1378 C CE1 . TYR A 1 172 ? 4.667 -49.566 -30.563 1.00 61.80 ? ? ? ? ? ? 171 TYR U CE1 1
+ATOM 1379 C CE2 . TYR A 1 172 ? 4.571 -49.292 -28.213 1.00 60.92 ? ? ? ? ? ? 171 TYR U CE2 1
+ATOM 1380 C CZ . TYR A 1 172 ? 4.101 -49.876 -29.358 1.00 61.96 ? ? ? ? ? ? 171 TYR U CZ 1
+ATOM 1381 O OH . TYR A 1 172 ? 3.068 -50.757 -29.297 1.00 60.68 ? ? ? ? ? ? 171 TYR U OH 1
+ATOM 1382 N N . TYR A 1 173 ? 8.940 -45.009 -29.528 1.00 54.61 ? ? ? ? ? ? 172 TYR U N 1
+ATOM 1383 C CA . TYR A 1 173 ? 9.700 -43.803 -29.285 1.00 57.83 ? ? ? ? ? ? 172 TYR U CA 1
+ATOM 1384 C C . TYR A 1 173 ? 11.201 -43.994 -29.329 1.00 59.42 ? ? ? ? ? ? 172 TYR U C 1
+ATOM 1385 O O . TYR A 1 173 ? 11.952 -43.037 -29.301 1.00 57.54 ? ? ? ? ? ? 172 TYR U O 1
+ATOM 1386 C CB . TYR A 1 173 ? 9.245 -43.095 -27.996 1.00 56.24 ? ? ? ? ? ? 172 TYR U CB 1
+ATOM 1387 C CG . TYR A 1 173 ? 7.881 -42.418 -28.096 1.00 54.14 ? ? ? ? ? ? 172 TYR U CG 1
+ATOM 1388 C CD1 . TYR A 1 173 ? 7.584 -41.577 -29.132 1.00 49.98 ? ? ? ? ? ? 172 TYR U CD1 1
+ATOM 1389 C CD2 . TYR A 1 173 ? 6.894 -42.643 -27.145 1.00 52.48 ? ? ? ? ? ? 172 TYR U CD2 1
+ATOM 1390 C CE1 . TYR A 1 173 ? 6.357 -40.995 -29.241 1.00 49.41 ? ? ? ? ? ? 172 TYR U CE1 1
+ATOM 1391 C CE2 . TYR A 1 173 ? 5.665 -42.047 -27.248 1.00 49.49 ? ? ? ? ? ? 172 TYR U CE2 1
+ATOM 1392 C CZ . TYR A 1 173 ? 5.413 -41.225 -28.298 1.00 48.49 ? ? ? ? ? ? 172 TYR U CZ 1
+ATOM 1393 O OH . TYR A 1 173 ? 4.220 -40.631 -28.411 1.00 46.09 ? ? ? ? ? ? 172 TYR U OH 1
+ATOM 1394 N N . GLY A 1 174 ? 11.627 -45.236 -29.422 1.00 63.31 ? ? ? ? ? ? 173 GLY U N 1
+ATOM 1395 C CA . GLY A 1 174 ? 13.031 -45.563 -29.427 1.00 62.34 ? ? ? ? ? ? 173 GLY U CA 1
+ATOM 1396 C C . GLY A 1 174 ? 13.750 -45.093 -28.193 1.00 64.74 ? ? ? ? ? ? 173 GLY U C 1
+ATOM 1397 O O . GLY A 1 174 ? 13.325 -45.321 -27.086 1.00 68.20 ? ? ? ? ? ? 173 GLY U O 1
+ATOM 1398 N N . SER A 1 175 ? 14.860 -44.419 -28.404 1.00 60.88 ? ? ? ? ? ? 174 SER U N 1
+ATOM 1399 C CA . SER A 1 175 ? 15.762 -43.988 -27.354 1.00 65.81 ? ? ? ? ? ? 174 SER U CA 1
+ATOM 1400 C C . SER A 1 175 ? 15.238 -42.930 -26.393 1.00 65.08 ? ? ? ? ? ? 174 SER U C 1
+ATOM 1401 O O . SER A 1 175 ? 15.772 -42.724 -25.333 1.00 65.60 ? ? ? ? ? ? 174 SER U O 1
+ATOM 1402 C CB . SER A 1 175 ? 17.076 -43.498 -27.972 1.00 65.66 ? ? ? ? ? ? 174 SER U CB 1
+ATOM 1403 O OG . SER A 1 175 ? 16.872 -42.362 -28.799 1.00 65.97 ? ? ? ? ? ? 174 SER U OG 1
+ATOM 1404 N N . GLU A 1 176 ? 14.236 -42.211 -26.836 1.00 69.07 ? ? ? ? ? ? 175 GLU U N 1
+ATOM 1405 C CA . GLU A 1 176 ? 13.773 -40.968 -26.228 1.00 70.69 ? ? ? ? ? ? 175 GLU U CA 1
+ATOM 1406 C C . GLU A 1 176 ? 12.981 -41.186 -24.937 1.00 70.43 ? ? ? ? ? ? 175 GLU U C 1
+ATOM 1407 O O . GLU A 1 176 ? 12.889 -40.274 -24.117 1.00 66.66 ? ? ? ? ? ? 175 GLU U O 1
+ATOM 1408 C CB . GLU A 1 176 ? 12.968 -40.145 -27.238 1.00 73.46 ? ? ? ? ? ? 175 GLU U CB 1
+ATOM 1409 C CG . GLU A 1 176 ? 12.710 -38.695 -26.833 1.00 78.38 ? ? ? ? ? ? 175 GLU U CG 1
+ATOM 1410 C CD . GLU A 1 176 ? 13.970 -37.914 -26.478 1.00 79.70 ? ? ? ? ? ? 175 GLU U CD 1
+ATOM 1411 O OE1 . GLU A 1 176 ? 14.459 -37.163 -27.349 1.00 81.17 ? ? ? ? ? ? 175 GLU U OE1 1
+ATOM 1412 O OE2 . GLU A 1 176 ? 14.460 -38.039 -25.330 1.00 76.34 ? ? ? ? ? ? 175 GLU U OE2 1
+ATOM 1413 N N . VAL A 1 177 ? 12.419 -42.383 -24.761 1.00 69.71 ? ? ? ? ? ? 176 VAL U N 1
+ATOM 1414 C CA . VAL A 1 177 ? 11.818 -42.772 -23.477 1.00 68.88 ? ? ? ? ? ? 176 VAL U CA 1
+ATOM 1415 C C . VAL A 1 177 ? 12.883 -43.342 -22.557 1.00 65.06 ? ? ? ? ? ? 176 VAL U C 1
+ATOM 1416 O O . VAL A 1 177 ? 13.503 -44.358 -22.866 1.00 66.46 ? ? ? ? ? ? 176 VAL U O 1
+ATOM 1417 C CB . VAL A 1 177 ? 10.670 -43.808 -23.607 1.00 73.97 ? ? ? ? ? ? 176 VAL U CB 1
+ATOM 1418 C CG1 . VAL A 1 177 ? 9.360 -43.111 -23.936 1.00 75.83 ? ? ? ? ? ? 176 VAL U CG1 1
+ATOM 1419 C CG2 . VAL A 1 177 ? 10.989 -44.892 -24.632 1.00 77.21 ? ? ? ? ? ? 176 VAL U CG2 1
+ATOM 1420 N N . THR A 1 178 ? 13.084 -42.682 -21.425 1.00 59.45 ? ? ? ? ? ? 177 THR U N 1
+ATOM 1421 C CA . THR A 1 178 ? 14.099 -43.080 -20.458 1.00 56.02 ? ? ? ? ? ? 177 THR U CA 1
+ATOM 1422 C C . THR A 1 178 ? 13.421 -43.722 -19.264 1.00 56.63 ? ? ? ? ? ? 177 THR U C 1
+ATOM 1423 O O . THR A 1 178 ? 12.185 -43.708 -19.163 1.00 53.04 ? ? ? ? ? ? 177 THR U O 1
+ATOM 1424 C CB . THR A 1 178 ? 14.882 -41.857 -19.969 1.00 53.96 ? ? ? ? ? ? 177 THR U CB 1
+ATOM 1425 O OG1 . THR A 1 178 ? 13.996 -41.001 -19.247 1.00 52.42 ? ? ? ? ? ? 177 THR U OG1 1
+ATOM 1426 C CG2 . THR A 1 178 ? 15.466 -41.091 -21.149 1.00 51.27 ? ? ? ? ? ? 177 THR U CG2 1
+ATOM 1427 N N . THR A 1 179 ? 14.221 -44.267 -18.349 1.00 55.06 ? ? ? ? ? ? 178 THR U N 1
+ATOM 1428 C CA . THR A 1 179 ? 13.682 -45.035 -17.223 1.00 55.23 ? ? ? ? ? ? 178 THR U CA 1
+ATOM 1429 C C . THR A 1 179 ? 12.968 -44.162 -16.197 1.00 52.69 ? ? ? ? ? ? 178 THR U C 1
+ATOM 1430 O O . THR A 1 179 ? 12.304 -44.685 -15.311 1.00 53.39 ? ? ? ? ? ? 178 THR U O 1
+ATOM 1431 C CB . THR A 1 179 ? 14.750 -45.883 -16.486 1.00 53.89 ? ? ? ? ? ? 178 THR U CB 1
+ATOM 1432 O OG1 . THR A 1 179 ? 15.524 -45.050 -15.611 1.00 56.93 ? ? ? ? ? ? 178 THR U OG1 1
+ATOM 1433 C CG2 . THR A 1 179 ? 15.658 -46.601 -17.464 1.00 52.89 ? ? ? ? ? ? 178 THR U CG2 1
+ATOM 1434 N N . LYS A 1 180 ? 13.117 -42.843 -16.305 1.00 54.11 ? ? ? ? ? ? 179 LYS U N 1
+ATOM 1435 C CA . LYS A 1 180 ? 12.454 -41.911 -15.389 1.00 53.34 ? ? ? ? ? ? 179 LYS U CA 1
+ATOM 1436 C C . LYS A 1 180 ? 11.087 -41.497 -15.932 1.00 49.26 ? ? ? ? ? ? 179 LYS U C 1
+ATOM 1437 O O . LYS A 1 180 ? 10.417 -40.661 -15.344 1.00 47.30 ? ? ? ? ? ? 179 LYS U O 1
+ATOM 1438 C CB . LYS A 1 180 ? 13.339 -40.689 -15.127 1.00 58.24 ? ? ? ? ? ? 179 LYS U CB 1
+ATOM 1439 C CG . LYS A 1 180 ? 14.779 -41.052 -14.775 1.00 61.76 ? ? ? ? ? ? 179 LYS U CG 1
+ATOM 1440 C CD . LYS A 1 180 ? 15.522 -39.948 -14.036 1.00 64.91 ? ? ? ? ? ? 179 LYS U CD 1
+ATOM 1441 C CE . LYS A 1 180 ? 15.629 -40.242 -12.546 1.00 70.20 ? ? ? ? ? ? 179 LYS U CE 1
+ATOM 1442 N NZ . LYS A 1 180 ? 16.137 -39.070 -11.781 1.00 72.99 ? ? ? ? ? ? 179 LYS U NZ 1
+ATOM 1443 N N . MET A 1 181 ? 10.691 -42.112 -17.044 1.00 43.39 ? ? ? ? ? ? 180 MET U N 1
+ATOM 1444 C CA . MET A 1 181 ? 9.407 -41.898 -17.671 1.00 43.19 ? ? ? ? ? ? 180 MET U CA 1
+ATOM 1445 C C . MET A 1 181 ? 8.599 -43.210 -17.691 1.00 42.59 ? ? ? ? ? ? 180 MET U C 1
+ATOM 1446 O O . MET A 1 181 ? 9.163 -44.292 -17.613 1.00 40.59 ? ? ? ? ? ? 180 MET U O 1
+ATOM 1447 C CB . MET A 1 181 ? 9.631 -41.403 -19.102 1.00 42.10 ? ? ? ? ? ? 180 MET U CB 1
+ATOM 1448 C CG . MET A 1 181 ? 10.667 -40.291 -19.238 1.00 41.20 ? ? ? ? ? ? 180 MET U CG 1
+ATOM 1449 S SD . MET A 1 181 ? 10.990 -39.856 -20.953 1.00 46.55 ? ? ? ? ? ? 180 MET U SD 1
+ATOM 1450 C CE . MET A 1 181 ? 12.109 -38.474 -20.732 1.00 47.02 ? ? ? ? ? ? 180 MET U CE 1
+ATOM 1451 N N . LEU A 1 182 ? 7.278 -43.101 -17.793 1.00 43.69 ? ? ? ? ? ? 181 LEU U N 1
+ATOM 1452 C CA . LEU A 1 182 ? 6.405 -44.252 -18.082 1.00 43.09 ? ? ? ? ? ? 181 LEU U CA 1
+ATOM 1453 C C . LEU A 1 182 ? 5.593 -43.990 -19.331 1.00 39.21 ? ? ? ? ? ? 181 LEU U C 1
+ATOM 1454 O O . LEU A 1 182 ? 5.142 -42.865 -19.547 1.00 37.78 ? ? ? ? ? ? 181 LEU U O 1
+ATOM 1455 C CB . LEU A 1 182 ? 5.412 -44.494 -16.945 1.00 45.14 ? ? ? ? ? ? 181 LEU U CB 1
+ATOM 1456 C CG . LEU A 1 182 ? 5.935 -44.862 -15.559 1.00 49.66 ? ? ? ? ? ? 181 LEU U CG 1
+ATOM 1457 C CD1 . LEU A 1 182 ? 4.756 -44.904 -14.597 1.00 50.05 ? ? ? ? ? ? 181 LEU U CD1 1
+ATOM 1458 C CD2 . LEU A 1 182 ? 6.683 -46.191 -15.552 1.00 51.13 ? ? ? ? ? ? 181 LEU U CD2 1
+ATOM 1459 N N . CYS A 1 183 ? 5.375 -45.026 -20.134 1.00 37.68 ? ? ? ? ? ? 182 CYS U N 1
+ATOM 1460 C CA . CYS A 1 183 ? 4.428 -44.949 -21.237 1.00 39.57 ? ? ? ? ? ? 182 CYS U CA 1
+ATOM 1461 C C . CYS A 1 183 ? 3.118 -45.603 -20.834 1.00 38.53 ? ? ? ? ? ? 182 CYS U C 1
+ATOM 1462 O O . CYS A 1 183 ? 3.094 -46.527 -20.024 1.00 39.35 ? ? ? ? ? ? 182 CYS U O 1
+ATOM 1463 C CB . CYS A 1 183 ? 4.961 -45.629 -22.503 1.00 43.27 ? ? ? ? ? ? 182 CYS U CB 1
+ATOM 1464 S SG . CYS A 1 183 ? 6.421 -44.838 -23.190 1.00 51.84 ? ? ? ? ? ? 182 CYS U SG 1
+ATOM 1465 N N . ALA A 1 184 ? 2.035 -45.106 -21.414 1.00 37.74 ? ? ? ? ? ? 183 ALA U N 1
+ATOM 1466 C CA . ALA A 1 184 ? 0.698 -45.648 -21.189 1.00 38.15 ? ? ? ? ? ? 183 ALA U CA 1
+ATOM 1467 C C . ALA A 1 184 ? -0.122 -45.287 -22.415 1.00 38.02 ? ? ? ? ? ? 183 ALA U C 1
+ATOM 1468 O O . ALA A 1 184 ? 0.034 -44.202 -22.993 1.00 43.62 ? ? ? ? ? ? 183 ALA U O 1
+ATOM 1469 C CB . ALA A 1 184 ? 0.082 -45.080 -19.920 1.00 37.07 ? ? ? ? ? ? 183 ALA U CB 1
+ATOM 1470 N N . ALA A 1 185 ? -0.942 -46.210 -22.861 1.00 38.98 ? ? ? ? ? ? 184 ALA U N 1
+ATOM 1471 C CA . ALA A 1 185 ? -1.736 -46.005 -24.046 1.00 40.30 ? ? ? ? ? ? 184 ALA U CA 1
+ATOM 1472 C C . ALA A 1 185 ? -2.864 -46.960 -24.041 1.00 40.89 ? ? ? ? ? ? 184 ALA U C 1
+ATOM 1473 O O . ALA A 1 185 ? -2.907 -47.850 -23.247 1.00 37.18 ? ? ? ? ? ? 184 ALA U O 1
+ATOM 1474 C CB . ALA A 1 185 ? -0.929 -46.206 -25.296 1.00 40.64 ? ? ? ? ? ? 184 ALA U CB 1
+ATOM 1475 N N . ASP A 1 186 ? -3.782 -46.730 -24.953 1.00 43.12 ? ? ? ? ? ? 185 ASP U N 1
+ATOM 1476 C CA . ASP A 1 186 ? -4.851 -47.652 -25.242 1.00 46.58 ? ? ? ? ? ? 185 ASP U CA 1
+ATOM 1477 C C . ASP A 1 186 ? -4.410 -48.825 -26.098 1.00 47.01 ? ? ? ? ? ? 185 ASP U C 1
+ATOM 1478 O O . ASP A 1 186 ? -3.766 -48.664 -27.111 1.00 48.86 ? ? ? ? ? ? 185 ASP U O 1
+ATOM 1479 C CB . ASP A 1 186 ? -5.982 -46.936 -25.930 1.00 48.86 ? ? ? ? ? ? 185 ASP U CB 1
+ATOM 1480 C CG . ASP A 1 186 ? -7.122 -47.823 -26.197 1.00 51.39 ? ? ? ? ? ? 185 ASP U CG 1
+ATOM 1481 O OD1 . ASP A 1 186 ? -7.570 -48.474 -25.281 1.00 50.11 ? ? ? ? ? ? 185 ASP U OD1 1
+ATOM 1482 O OD2 . ASP A 1 186 ? -7.568 -47.908 -27.325 1.00 56.77 ? ? ? ? ? ? 185 ASP U OD2 1
+ATOM 1483 N N . PRO A 1 187 A -4.870 -50.066 -25.641 1.00 64.68 ? ? ? ? ? ? 185 PRO U N 1
+ATOM 1484 C CA . PRO A 1 187 A -4.486 -51.188 -26.511 1.00 63.75 ? ? ? ? ? ? 185 PRO U CA 1
+ATOM 1485 C C . PRO A 1 187 A -4.944 -51.011 -27.953 1.00 59.62 ? ? ? ? ? ? 185 PRO U C 1
+ATOM 1486 O O . PRO A 1 187 A -4.174 -51.325 -28.792 1.00 57.77 ? ? ? ? ? ? 185 PRO U O 1
+ATOM 1487 C CB . PRO A 1 187 A -5.187 -52.353 -25.884 1.00 64.93 ? ? ? ? ? ? 185 PRO U CB 1
+ATOM 1488 C CG . PRO A 1 187 A -5.052 -52.105 -24.451 1.00 65.30 ? ? ? ? ? ? 185 PRO U CG 1
+ATOM 1489 C CD . PRO A 1 187 A -5.498 -50.698 -24.377 1.00 64.44 ? ? ? ? ? ? 185 PRO U CD 1
+ATOM 1490 N N . GLN A 1 188 B -6.113 -50.475 -28.245 1.00 63.47 ? ? ? ? ? ? 185 GLN U N 1
+ATOM 1491 C CA . GLN A 1 188 B -6.463 -50.169 -29.641 1.00 61.25 ? ? ? ? ? ? 185 GLN U CA 1
+ATOM 1492 C C . GLN A 1 188 B -6.157 -48.747 -30.114 1.00 55.76 ? ? ? ? ? ? 185 GLN U C 1
+ATOM 1493 O O . GLN A 1 188 B -6.580 -48.346 -31.168 1.00 53.02 ? ? ? ? ? ? 185 GLN U O 1
+ATOM 1494 C CB . GLN A 1 188 B -7.904 -50.500 -29.938 1.00 66.74 ? ? ? ? ? ? 185 GLN U CB 1
+ATOM 1495 C CG . GLN A 1 188 B -8.110 -51.814 -30.661 1.00 69.79 ? ? ? ? ? ? 185 GLN U CG 1
+ATOM 1496 C CD . GLN A 1 188 B -9.568 -52.210 -30.706 1.00 74.72 ? ? ? ? ? ? 185 GLN U CD 1
+ATOM 1497 O OE1 . GLN A 1 188 B -10.452 -51.360 -30.693 1.00 76.45 ? ? ? ? ? ? 185 GLN U OE1 1
+ATOM 1498 N NE2 . GLN A 1 188 B -9.823 -53.497 -30.770 1.00 78.62 ? ? ? ? ? ? 185 GLN U NE2 1
+ATOM 1499 N N . TRP A 1 189 ? -5.458 -47.980 -29.300 1.00 52.78 ? ? ? ? ? ? 186 TRP U N 1
+ATOM 1500 C CA . TRP A 1 189 ? -5.049 -46.633 -29.674 1.00 52.58 ? ? ? ? ? ? 186 TRP U CA 1
+ATOM 1501 C C . TRP A 1 189 ? -6.176 -45.625 -29.877 1.00 49.69 ? ? ? ? ? ? 186 TRP U C 1
+ATOM 1502 O O . TRP A 1 189 ? -6.065 -44.702 -30.638 1.00 50.81 ? ? ? ? ? ? 186 TRP U O 1
+ATOM 1503 C CB . TRP A 1 189 ? -4.196 -46.701 -30.917 1.00 58.56 ? ? ? ? ? ? 186 TRP U CB 1
+ATOM 1504 C CG . TRP A 1 189 ? -3.066 -47.578 -30.758 1.00 60.67 ? ? ? ? ? ? 186 TRP U CG 1
+ATOM 1505 C CD1 . TRP A 1 189 ? -2.968 -48.848 -31.172 1.00 63.63 ? ? ? ? ? ? 186 TRP U CD1 1
+ATOM 1506 C CD2 . TRP A 1 189 ? -1.854 -47.267 -30.114 1.00 61.62 ? ? ? ? ? ? 186 TRP U CD2 1
+ATOM 1507 N NE1 . TRP A 1 189 ? -1.764 -49.363 -30.826 1.00 63.38 ? ? ? ? ? ? 186 TRP U NE1 1
+ATOM 1508 C CE2 . TRP A 1 189 ? -1.058 -48.405 -30.165 1.00 63.67 ? ? ? ? ? ? 186 TRP U CE2 1
+ATOM 1509 C CE3 . TRP A 1 189 ? -1.370 -46.139 -29.482 1.00 61.67 ? ? ? ? ? ? 186 TRP U CE3 1
+ATOM 1510 C CZ2 . TRP A 1 189 ? 0.194 -48.450 -29.620 1.00 65.30 ? ? ? ? ? ? 186 TRP U CZ2 1
+ATOM 1511 C CZ3 . TRP A 1 189 ? -0.144 -46.179 -28.945 1.00 64.20 ? ? ? ? ? ? 186 TRP U CZ3 1
+ATOM 1512 C CH2 . TRP A 1 189 ? 0.637 -47.323 -29.008 1.00 67.05 ? ? ? ? ? ? 186 TRP U CH2 1
+ATOM 1513 N N . LYS A 1 190 ? -7.263 -45.811 -29.172 1.00 47.28 ? ? ? ? ? ? 187 LYS U N 1
+ATOM 1514 C CA . LYS A 1 190 ? -8.457 -44.978 -29.327 1.00 50.42 ? ? ? ? ? ? 187 LYS U CA 1
+ATOM 1515 C C . LYS A 1 190 ? -8.585 -43.886 -28.255 1.00 47.92 ? ? ? ? ? ? 187 LYS U C 1
+ATOM 1516 O O . LYS A 1 190 ? -9.296 -42.905 -28.464 1.00 50.43 ? ? ? ? ? ? 187 LYS U O 1
+ATOM 1517 C CB . LYS A 1 190 ? -9.737 -45.839 -29.341 1.00 51.31 ? ? ? ? ? ? 187 LYS U CB 1
+ATOM 1518 C CG . LYS A 1 190 ? -9.758 -46.968 -30.366 1.00 55.05 ? ? ? ? ? ? 187 LYS U CG 1
+ATOM 1519 C CD . LYS A 1 190 ? -9.491 -46.476 -31.786 1.00 56.67 ? ? ? ? ? ? 187 LYS U CD 1
+ATOM 1520 C CE . LYS A 1 190 ? -9.931 -47.501 -32.818 1.00 57.61 ? ? ? ? ? ? 187 LYS U CE 1
+ATOM 1521 N NZ . LYS A 1 190 ? -9.640 -47.075 -34.212 1.00 60.50 ? ? ? ? ? ? 187 LYS U NZ 1
+ATOM 1522 N N . THR A 1 191 ? -7.916 -44.047 -27.119 1.00 44.56 ? ? ? ? ? ? 188 THR U N 1
+ATOM 1523 C CA . THR A 1 191 ? -7.965 -43.026 -26.077 1.00 43.01 ? ? ? ? ? ? 188 THR U CA 1
+ATOM 1524 C C . THR A 1 191 ? -6.565 -42.614 -25.597 1.00 41.31 ? ? ? ? ? ? 188 THR U C 1
+ATOM 1525 O O . THR A 1 191 ? -5.628 -43.398 -25.674 1.00 41.99 ? ? ? ? ? ? 188 THR U O 1
+ATOM 1526 C CB . THR A 1 191 ? -8.870 -43.473 -24.918 1.00 41.89 ? ? ? ? ? ? 188 THR U CB 1
+ATOM 1527 O OG1 . THR A 1 191 ? -9.215 -42.338 -24.113 1.00 43.76 ? ? ? ? ? ? 188 THR U OG1 1
+ATOM 1528 C CG2 . THR A 1 191 ? -8.215 -44.588 -24.077 1.00 42.33 ? ? ? ? ? ? 188 THR U CG2 1
+ATOM 1529 N N . ASP A 1 192 ? -6.442 -41.367 -25.131 1.00 37.61 ? ? ? ? ? ? 189 ASP U N 1
+ATOM 1530 C CA . ASP A 1 192 ? -5.155 -40.773 -24.775 1.00 36.60 ? ? ? ? ? ? 189 ASP U CA 1
+ATOM 1531 C C . ASP A 1 192 ? -5.379 -39.453 -24.016 1.00 37.72 ? ? ? ? ? ? 189 ASP U C 1
+ATOM 1532 O O . ASP A 1 192 ? -6.487 -38.892 -24.002 1.00 38.44 ? ? ? ? ? ? 189 ASP U O 1
+ATOM 1533 C CB . ASP A 1 192 ? -4.331 -40.516 -26.070 1.00 36.16 ? ? ? ? ? ? 189 ASP U CB 1
+ATOM 1534 C CG . ASP A 1 192 ? -2.809 -40.513 -25.854 1.00 36.91 ? ? ? ? ? ? 189 ASP U CG 1
+ATOM 1535 O OD1 . ASP A 1 192 ? -2.322 -40.682 -24.710 1.00 34.76 ? ? ? ? ? ? 189 ASP U OD1 1
+ATOM 1536 O OD2 . ASP A 1 192 ? -2.071 -40.315 -26.868 1.00 37.12 ? ? ? ? ? ? 189 ASP U OD2 1
+ATOM 1537 N N . SER A 1 193 ? -4.317 -38.963 -23.391 1.00 38.86 ? ? ? ? ? ? 190 SER U N 1
+ATOM 1538 C CA . SER A 1 193 ? -4.228 -37.544 -23.023 1.00 38.26 ? ? ? ? ? ? 190 SER U CA 1
+ATOM 1539 C C . SER A 1 193 ? -3.850 -36.756 -24.277 1.00 39.64 ? ? ? ? ? ? 190 SER U C 1
+ATOM 1540 O O . SER A 1 193 ? -3.350 -37.349 -25.236 1.00 40.42 ? ? ? ? ? ? 190 SER U O 1
+ATOM 1541 C CB . SER A 1 193 ? -3.187 -37.333 -21.926 1.00 36.73 ? ? ? ? ? ? 190 SER U CB 1
+ATOM 1542 O OG . SER A 1 193 ? -1.979 -38.034 -22.198 1.00 33.10 ? ? ? ? ? ? 190 SER U OG 1
+ATOM 1543 N N . CYS A 1 194 ? -4.084 -35.442 -24.260 1.00 37.87 ? ? ? ? ? ? 191 CYS U N 1
+ATOM 1544 C CA . CYS A 1 194 ? -3.764 -34.571 -25.391 1.00 38.48 ? ? ? ? ? ? 191 CYS U CA 1
+ATOM 1545 C C . CYS A 1 194 ? -3.278 -33.199 -24.921 1.00 37.99 ? ? ? ? ? ? 191 CYS U C 1
+ATOM 1546 O O . CYS A 1 194 ? -2.972 -33.006 -23.734 1.00 35.65 ? ? ? ? ? ? 191 CYS U O 1
+ATOM 1547 C CB . CYS A 1 194 ? -4.980 -34.427 -26.307 1.00 42.36 ? ? ? ? ? ? 191 CYS U CB 1
+ATOM 1548 S SG . CYS A 1 194 ? -4.577 -33.967 -28.019 1.00 44.62 ? ? ? ? ? ? 191 CYS U SG 1
+ATOM 1549 N N . GLN A 1 195 ? -3.177 -32.249 -25.850 1.00 37.81 ? ? ? ? ? ? 192 GLN U N 1
+ATOM 1550 C CA . GLN A 1 195 ? -2.729 -30.904 -25.517 1.00 35.44 ? ? ? ? ? ? 192 GLN U CA 1
+ATOM 1551 C C . GLN A 1 195 ? -3.666 -30.313 -24.465 1.00 32.10 ? ? ? ? ? ? 192 GLN U C 1
+ATOM 1552 O O . GLN A 1 195 ? -4.870 -30.379 -24.616 1.00 28.26 ? ? ? ? ? ? 192 GLN U O 1
+ATOM 1553 C CB . GLN A 1 195 ? -2.682 -29.996 -26.756 1.00 37.78 ? ? ? ? ? ? 192 GLN U CB 1
+ATOM 1554 C CG . GLN A 1 195 ? -1.965 -28.664 -26.502 1.00 39.00 ? ? ? ? ? ? 192 GLN U CG 1
+ATOM 1555 C CD . GLN A 1 195 ? -1.872 -27.764 -27.732 1.00 40.61 ? ? ? ? ? ? 192 GLN U CD 1
+ATOM 1556 O OE1 . GLN A 1 195 ? -2.552 -27.981 -28.726 1.00 41.94 ? ? ? ? ? ? 192 GLN U OE1 1
+ATOM 1557 N NE2 . GLN A 1 195 ? -1.043 -26.739 -27.651 1.00 42.25 ? ? ? ? ? ? 192 GLN U NE2 1
+ATOM 1558 N N . GLY A 1 196 ? -3.099 -29.750 -23.401 1.00 32.37 ? ? ? ? ? ? 193 GLY U N 1
+ATOM 1559 C CA . GLY A 1 196 ? -3.899 -29.209 -22.309 1.00 31.88 ? ? ? ? ? ? 193 GLY U CA 1
+ATOM 1560 C C . GLY A 1 196 ? -4.193 -30.167 -21.156 1.00 30.98 ? ? ? ? ? ? 193 GLY U C 1
+ATOM 1561 O O . GLY A 1 196 ? -4.797 -29.765 -20.169 1.00 31.47 ? ? ? ? ? ? 193 GLY U O 1
+ATOM 1562 N N . ASP A 1 197 ? -3.783 -31.420 -21.262 1.00 31.38 ? ? ? ? ? ? 194 ASP U N 1
+ATOM 1563 C CA . ASP A 1 197 ? -3.872 -32.359 -20.126 1.00 32.73 ? ? ? ? ? ? 194 ASP U CA 1
+ATOM 1564 C C . ASP A 1 197 ? -2.562 -32.413 -19.325 1.00 33.97 ? ? ? ? ? ? 194 ASP U C 1
+ATOM 1565 O O . ASP A 1 197 ? -2.484 -33.082 -18.263 1.00 31.27 ? ? ? ? ? ? 194 ASP U O 1
+ATOM 1566 C CB . ASP A 1 197 ? -4.245 -33.765 -20.623 1.00 30.57 ? ? ? ? ? ? 194 ASP U CB 1
+ATOM 1567 C CG . ASP A 1 197 ? -5.683 -33.853 -21.118 1.00 32.29 ? ? ? ? ? ? 194 ASP U CG 1
+ATOM 1568 O OD1 . ASP A 1 197 ? -6.603 -33.255 -20.486 1.00 30.97 ? ? ? ? ? ? 194 ASP U OD1 1
+ATOM 1569 O OD2 . ASP A 1 197 ? -5.918 -34.546 -22.139 1.00 31.06 ? ? ? ? ? ? 194 ASP U OD2 1
+ATOM 1570 N N . SER A 1 198 ? -1.545 -31.741 -19.878 1.00 33.35 ? ? ? ? ? ? 195 SER U N 1
+ATOM 1571 C CA . SER A 1 198 ? -0.218 -31.608 -19.313 1.00 33.59 ? ? ? ? ? ? 195 SER U CA 1
+ATOM 1572 C C . SER A 1 198 ? -0.248 -31.274 -17.850 1.00 30.30 ? ? ? ? ? ? 195 SER U C 1
+ATOM 1573 O O . SER A 1 198 ? -1.061 -30.464 -17.438 1.00 25.80 ? ? ? ? ? ? 195 SER U O 1
+ATOM 1574 C CB . SER A 1 198 ? 0.524 -30.444 -19.988 1.00 35.48 ? ? ? ? ? ? 195 SER U CB 1
+ATOM 1575 O OG . SER A 1 198 ? 1.157 -30.886 -21.155 1.00 46.17 ? ? ? ? ? ? 195 SER U OG 1
+ATOM 1576 N N . GLY A 1 199 ? 0.699 -31.838 -17.099 1.00 28.28 ? ? ? ? ? ? 196 GLY U N 1
+ATOM 1577 C CA . GLY A 1 199 ? 0.749 -31.697 -15.635 1.00 28.43 ? ? ? ? ? ? 196 GLY U CA 1
+ATOM 1578 C C . GLY A 1 199 ? -0.239 -32.553 -14.855 1.00 28.94 ? ? ? ? ? ? 196 GLY U C 1
+ATOM 1579 O O . GLY A 1 199 ? -0.094 -32.729 -13.634 1.00 26.93 ? ? ? ? ? ? 196 GLY U O 1
+ATOM 1580 N N . GLY A 1 200 ? -1.227 -33.116 -15.552 1.00 29.36 ? ? ? ? ? ? 197 GLY U N 1
+ATOM 1581 C CA . GLY A 1 200 ? -2.287 -33.906 -14.943 1.00 26.55 ? ? ? ? ? ? 197 GLY U CA 1
+ATOM 1582 C C . GLY A 1 200 ? -1.865 -35.279 -14.419 1.00 28.76 ? ? ? ? ? ? 197 GLY U C 1
+ATOM 1583 O O . GLY A 1 200 ? -0.766 -35.735 -14.706 1.00 29.09 ? ? ? ? ? ? 197 GLY U O 1
+ATOM 1584 N N . PRO A 1 201 ? -2.750 -35.944 -13.629 1.00 26.42 ? ? ? ? ? ? 198 PRO U N 1
+ATOM 1585 C CA . PRO A 1 201 ? -2.457 -37.233 -13.024 1.00 27.70 ? ? ? ? ? ? 198 PRO U CA 1
+ATOM 1586 C C . PRO A 1 201 ? -2.650 -38.463 -13.905 1.00 29.98 ? ? ? ? ? ? 198 PRO U C 1
+ATOM 1587 O O . PRO A 1 201 ? -3.587 -38.549 -14.707 1.00 29.89 ? ? ? ? ? ? 198 PRO U O 1
+ATOM 1588 C CB . PRO A 1 201 ? -3.435 -37.310 -11.840 1.00 28.60 ? ? ? ? ? ? 198 PRO U CB 1
+ATOM 1589 C CG . PRO A 1 201 ? -4.571 -36.415 -12.201 1.00 27.79 ? ? ? ? ? ? 198 PRO U CG 1
+ATOM 1590 C CD . PRO A 1 201 ? -4.031 -35.386 -13.162 1.00 27.37 ? ? ? ? ? ? 198 PRO U CD 1
+ATOM 1591 N N . LEU A 1 202 ? -1.745 -39.413 -13.719 1.00 30.93 ? ? ? ? ? ? 199 LEU U N 1
+ATOM 1592 C CA . LEU A 1 202 ? -1.978 -40.797 -14.083 1.00 33.41 ? ? ? ? ? ? 199 LEU U CA 1
+ATOM 1593 C C . LEU A 1 202 ? -2.093 -41.553 -12.771 1.00 32.35 ? ? ? ? ? ? 199 LEU U C 1
+ATOM 1594 O O . LEU A 1 202 ? -1.135 -41.592 -12.004 1.00 31.95 ? ? ? ? ? ? 199 LEU U O 1
+ATOM 1595 C CB . LEU A 1 202 ? -0.808 -41.338 -14.884 1.00 34.21 ? ? ? ? ? ? 199 LEU U CB 1
+ATOM 1596 C CG . LEU A 1 202 ? -0.803 -42.818 -15.277 1.00 36.81 ? ? ? ? ? ? 199 LEU U CG 1
+ATOM 1597 C CD1 . LEU A 1 202 ? -1.926 -43.130 -16.246 1.00 35.91 ? ? ? ? ? ? 199 LEU U CD1 1
+ATOM 1598 C CD2 . LEU A 1 202 ? 0.538 -43.127 -15.924 1.00 38.99 ? ? ? ? ? ? 199 LEU U CD2 1
+ATOM 1599 N N . VAL A 1 203 ? -3.246 -42.175 -12.542 1.00 31.59 ? ? ? ? ? ? 200 VAL U N 1
+ATOM 1600 C CA . VAL A 1 203 ? -3.545 -42.822 -11.265 1.00 34.91 ? ? ? ? ? ? 200 VAL U CA 1
+ATOM 1601 C C . VAL A 1 203 ? -3.796 -44.306 -11.481 1.00 36.76 ? ? ? ? ? ? 200 VAL U C 1
+ATOM 1602 O O . VAL A 1 203 ? -4.608 -44.686 -12.317 1.00 34.12 ? ? ? ? ? ? 200 VAL U O 1
+ATOM 1603 C CB . VAL A 1 203 ? -4.796 -42.216 -10.606 1.00 36.21 ? ? ? ? ? ? 200 VAL U CB 1
+ATOM 1604 C CG1 . VAL A 1 203 ? -5.182 -42.999 -9.346 1.00 38.54 ? ? ? ? ? ? 200 VAL U CG1 1
+ATOM 1605 C CG2 . VAL A 1 203 ? -4.559 -40.756 -10.280 1.00 34.67 ? ? ? ? ? ? 200 VAL U CG2 1
+ATOM 1606 N N . CYS A 1 204 ? -3.096 -45.106 -10.718 1.00 41.79 ? ? ? ? ? ? 201 CYS U N 1
+ATOM 1607 C CA . CYS A 1 204 ? -3.132 -46.522 -10.871 1.00 44.77 ? ? ? ? ? ? 201 CYS U CA 1
+ATOM 1608 C C . CYS A 1 204 ? -3.378 -47.091 -9.499 1.00 44.92 ? ? ? ? ? ? 201 CYS U C 1
+ATOM 1609 O O . CYS A 1 204 ? -3.065 -46.475 -8.526 1.00 44.47 ? ? ? ? ? ? 201 CYS U O 1
+ATOM 1610 C CB . CYS A 1 204 ? -1.808 -47.000 -11.451 1.00 45.70 ? ? ? ? ? ? 201 CYS U CB 1
+ATOM 1611 S SG . CYS A 1 204 ? -1.498 -46.727 -13.199 1.00 49.59 ? ? ? ? ? ? 201 CYS U SG 1
+ATOM 1612 N N . SER A 1 205 ? -3.971 -48.264 -9.424 1.00 46.61 ? ? ? ? ? ? 202 SER U N 1
+ATOM 1613 C CA . SER A 1 205 ? -4.148 -48.923 -8.152 1.00 50.62 ? ? ? ? ? ? 202 SER U CA 1
+ATOM 1614 C C . SER A 1 205 ? -2.915 -49.743 -7.855 1.00 52.40 ? ? ? ? ? ? 202 SER U C 1
+ATOM 1615 O O . SER A 1 205 ? -2.580 -50.594 -8.616 1.00 51.03 ? ? ? ? ? ? 202 SER U O 1
+ATOM 1616 C CB . SER A 1 205 ? -5.352 -49.835 -8.209 1.00 52.93 ? ? ? ? ? ? 202 SER U CB 1
+ATOM 1617 O OG . SER A 1 205 ? -5.326 -50.750 -7.164 1.00 56.77 ? ? ? ? ? ? 202 SER U OG 1
+ATOM 1618 N N . LEU A 1 206 ? -2.229 -49.464 -6.762 1.00 54.91 ? ? ? ? ? ? 203 LEU U N 1
+ATOM 1619 C CA . LEU A 1 206 ? -0.997 -50.170 -6.468 1.00 61.90 ? ? ? ? ? ? 203 LEU U CA 1
+ATOM 1620 C C . LEU A 1 206 ? -1.038 -50.732 -5.077 1.00 66.73 ? ? ? ? ? ? 203 LEU U C 1
+ATOM 1621 O O . LEU A 1 206 ? -1.319 -50.011 -4.153 1.00 67.42 ? ? ? ? ? ? 203 LEU U O 1
+ATOM 1622 C CB . LEU A 1 206 ? 0.180 -49.229 -6.607 1.00 64.95 ? ? ? ? ? ? 203 LEU U CB 1
+ATOM 1623 C CG . LEU A 1 206 ? 1.587 -49.821 -6.532 1.00 66.70 ? ? ? ? ? ? 203 LEU U CG 1
+ATOM 1624 C CD1 . LEU A 1 206 ? 1.687 -51.081 -7.354 1.00 65.54 ? ? ? ? ? ? 203 LEU U CD1 1
+ATOM 1625 C CD2 . LEU A 1 206 ? 2.612 -48.795 -6.988 1.00 67.60 ? ? ? ? ? ? 203 LEU U CD2 1
+ATOM 1626 N N . GLN A 1 207 ? -0.768 -52.025 -4.906 1.00 71.72 ? ? ? ? ? ? 204 GLN U N 1
+ATOM 1627 C CA . GLN A 1 207 ? -1.258 -52.750 -3.738 1.00 73.13 ? ? ? ? ? ? 204 GLN U CA 1
+ATOM 1628 C C . GLN A 1 207 ? -2.773 -52.596 -3.814 1.00 71.45 ? ? ? ? ? ? 204 GLN U C 1
+ATOM 1629 O O . GLN A 1 207 ? -3.356 -52.876 -4.846 1.00 71.67 ? ? ? ? ? ? 204 GLN U O 1
+ATOM 1630 C CB . GLN A 1 207 ? -0.709 -52.229 -2.403 1.00 75.96 ? ? ? ? ? ? 204 GLN U CB 1
+ATOM 1631 C CG . GLN A 1 207 ? 0.647 -51.540 -2.430 1.00 77.21 ? ? ? ? ? ? 204 GLN U CG 1
+ATOM 1632 C CD . GLN A 1 207 ? 1.819 -52.489 -2.314 1.00 80.39 ? ? ? ? ? ? 204 GLN U CD 1
+ATOM 1633 O OE1 . GLN A 1 207 ? 2.218 -52.887 -1.204 1.00 78.50 ? ? ? ? ? ? 204 GLN U OE1 1
+ATOM 1634 N NE2 . GLN A 1 207 ? 2.391 -52.857 -3.462 1.00 78.97 ? ? ? ? ? ? 204 GLN U NE2 1
+ATOM 1635 N N . GLY A 1 208 ? -3.406 -52.112 -2.756 1.00 71.52 ? ? ? ? ? ? 205 GLY U N 1
+ATOM 1636 C CA . GLY A 1 208 ? -4.802 -51.729 -2.849 1.00 66.27 ? ? ? ? ? ? 205 GLY U CA 1
+ATOM 1637 C C . GLY A 1 208 ? -5.060 -50.256 -3.024 1.00 62.24 ? ? ? ? ? ? 205 GLY U C 1
+ATOM 1638 O O . GLY A 1 208 ? -6.193 -49.826 -3.065 1.00 64.37 ? ? ? ? ? ? 205 GLY U O 1
+ATOM 1639 N N . ARG A 1 209 ? -4.008 -49.469 -3.117 1.00 59.60 ? ? ? ? ? ? 206 ARG U N 1
+ATOM 1640 C CA . ARG A 1 209 ? -4.159 -48.044 -3.011 1.00 60.84 ? ? ? ? ? ? 206 ARG U CA 1
+ATOM 1641 C C . ARG A 1 209 ? -4.182 -47.400 -4.381 1.00 56.25 ? ? ? ? ? ? 206 ARG U C 1
+ATOM 1642 O O . ARG A 1 209 ? -3.475 -47.810 -5.277 1.00 55.48 ? ? ? ? ? ? 206 ARG U O 1
+ATOM 1643 C CB . ARG A 1 209 ? -3.082 -47.447 -2.086 1.00 63.47 ? ? ? ? ? ? 206 ARG U CB 1
+ATOM 1644 C CG . ARG A 1 209 ? -3.437 -46.111 -1.429 1.00 68.29 ? ? ? ? ? ? 206 ARG U CG 1
+ATOM 1645 C CD . ARG A 1 209 ? -4.587 -46.227 -0.425 1.00 73.26 ? ? ? ? ? ? 206 ARG U CD 1
+ATOM 1646 N NE . ARG A 1 209 ? -5.446 -45.038 -0.311 1.00 78.15 ? ? ? ? ? ? 206 ARG U NE 1
+ATOM 1647 C CZ . ARG A 1 209 ? -6.485 -44.913 0.516 1.00 80.70 ? ? ? ? ? ? 206 ARG U CZ 1
+ATOM 1648 N NH1 . ARG A 1 209 ? -6.828 -45.882 1.339 1.00 79.08 ? ? ? ? ? ? 206 ARG U NH1 1
+ATOM 1649 N NH2 . ARG A 1 209 ? -7.181 -43.802 0.532 1.00 80.94 ? ? ? ? ? ? 206 ARG U NH2 1
+ATOM 1650 N N . MET A 1 210 ? -5.039 -46.408 -4.519 1.00 52.64 ? ? ? ? ? ? 207 MET U N 1
+ATOM 1651 C CA . MET A 1 210 ? -5.034 -45.548 -5.670 1.00 52.89 ? ? ? ? ? ? 207 MET U CA 1
+ATOM 1652 C C . MET A 1 210 ? -3.833 -44.680 -5.495 1.00 46.69 ? ? ? ? ? ? 207 MET U C 1
+ATOM 1653 O O . MET A 1 210 ? -3.756 -43.968 -4.560 1.00 44.64 ? ? ? ? ? ? 207 MET U O 1
+ATOM 1654 C CB . MET A 1 210 ? -6.232 -44.616 -5.623 1.00 55.01 ? ? ? ? ? ? 207 MET U CB 1
+ATOM 1655 C CG . MET A 1 210 ? -7.573 -45.250 -5.934 1.00 61.05 ? ? ? ? ? ? 207 MET U CG 1
+ATOM 1656 S SD . MET A 1 210 ? -7.664 -45.756 -7.631 1.00 64.56 ? ? ? ? ? ? 207 MET U SD 1
+ATOM 1657 C CE . MET A 1 210 ? -9.068 -44.822 -8.174 1.00 65.38 ? ? ? ? ? ? 207 MET U CE 1
+ATOM 1658 N N . THR A 1 211 ? -2.933 -44.692 -6.450 1.00 45.14 ? ? ? ? ? ? 208 THR U N 1
+ATOM 1659 C CA . THR A 1 211 ? -1.677 -43.984 -6.309 1.00 43.27 ? ? ? ? ? ? 208 THR U CA 1
+ATOM 1660 C C . THR A 1 211 ? -1.342 -43.115 -7.526 1.00 38.29 ? ? ? ? ? ? 208 THR U C 1
+ATOM 1661 O O . THR A 1 211 ? -1.716 -43.444 -8.607 1.00 36.05 ? ? ? ? ? ? 208 THR U O 1
+ATOM 1662 C CB . THR A 1 211 ? -0.553 -44.987 -6.012 1.00 46.68 ? ? ? ? ? ? 208 THR U CB 1
+ATOM 1663 O OG1 . THR A 1 211 ? -0.787 -45.582 -4.744 1.00 49.73 ? ? ? ? ? ? 208 THR U OG1 1
+ATOM 1664 C CG2 . THR A 1 211 ? 0.758 -44.327 -5.955 1.00 47.15 ? ? ? ? ? ? 208 THR U CG2 1
+ATOM 1665 N N . LEU A 1 212 ? -0.622 -42.018 -7.315 1.00 36.25 ? ? ? ? ? ? 209 LEU U N 1
+ATOM 1666 C CA . LEU A 1 212 ? -0.178 -41.144 -8.415 1.00 33.00 ? ? ? ? ? ? 209 LEU U CA 1
+ATOM 1667 C C . LEU A 1 212 ? 1.089 -41.709 -9.023 1.00 35.05 ? ? ? ? ? ? 209 LEU U C 1
+ATOM 1668 O O . LEU A 1 212 ? 2.193 -41.539 -8.490 1.00 36.32 ? ? ? ? ? ? 209 LEU U O 1
+ATOM 1669 C CB . LEU A 1 212 ? 0.089 -39.728 -7.921 1.00 32.58 ? ? ? ? ? ? 209 LEU U CB 1
+ATOM 1670 C CG . LEU A 1 212 ? 0.559 -38.756 -9.012 1.00 32.68 ? ? ? ? ? ? 209 LEU U CG 1
+ATOM 1671 C CD1 . LEU A 1 212 ? -0.548 -38.483 -10.015 1.00 31.98 ? ? ? ? ? ? 209 LEU U CD1 1
+ATOM 1672 C CD2 . LEU A 1 212 ? 1.053 -37.472 -8.387 1.00 34.41 ? ? ? ? ? ? 209 LEU U CD2 1
+ATOM 1673 N N . THR A 1 213 ? 0.920 -42.384 -10.141 1.00 32.61 ? ? ? ? ? ? 210 THR U N 1
+ATOM 1674 C CA . THR A 1 213 ? 1.993 -43.107 -10.777 1.00 37.14 ? ? ? ? ? ? 210 THR U CA 1
+ATOM 1675 C C . THR A 1 213 ? 2.679 -42.274 -11.871 1.00 37.48 ? ? ? ? ? ? 210 THR U C 1
+ATOM 1676 O O . THR A 1 213 ? 3.867 -42.451 -12.136 1.00 41.48 ? ? ? ? ? ? 210 THR U O 1
+ATOM 1677 C CB . THR A 1 213 ? 1.434 -44.435 -11.335 1.00 36.82 ? ? ? ? ? ? 210 THR U CB 1
+ATOM 1678 O OG1 . THR A 1 213 ? 1.043 -45.261 -10.229 1.00 39.44 ? ? ? ? ? ? 210 THR U OG1 1
+ATOM 1679 C CG2 . THR A 1 213 ? 2.462 -45.170 -12.177 1.00 39.37 ? ? ? ? ? ? 210 THR U CG2 1
+ATOM 1680 N N . GLY A 1 214 ? 1.941 -41.362 -12.490 1.00 36.47 ? ? ? ? ? ? 211 GLY U N 1
+ATOM 1681 C CA . GLY A 1 214 ? 2.501 -40.503 -13.514 1.00 34.57 ? ? ? ? ? ? 211 GLY U CA 1
+ATOM 1682 C C . GLY A 1 214 ? 1.964 -39.087 -13.537 1.00 35.05 ? ? ? ? ? ? 211 GLY U C 1
+ATOM 1683 O O . GLY A 1 214 ? 0.944 -38.777 -12.905 1.00 32.72 ? ? ? ? ? ? 211 GLY U O 1
+ATOM 1684 N N . ILE A 1 215 ? 2.664 -38.249 -14.307 1.00 33.48 ? ? ? ? ? ? 212 ILE U N 1
+ATOM 1685 C CA . ILE A 1 215 ? 2.300 -36.859 -14.562 1.00 31.96 ? ? ? ? ? ? 212 ILE U CA 1
+ATOM 1686 C C . ILE A 1 215 ? 2.384 -36.665 -16.067 1.00 34.00 ? ? ? ? ? ? 212 ILE U C 1
+ATOM 1687 O O . ILE A 1 215 ? 3.418 -36.949 -16.677 1.00 34.87 ? ? ? ? ? ? 212 ILE U O 1
+ATOM 1688 C CB . ILE A 1 215 ? 3.293 -35.887 -13.912 1.00 32.01 ? ? ? ? ? ? 212 ILE U CB 1
+ATOM 1689 C CG1 . ILE A 1 215 ? 3.278 -36.058 -12.403 1.00 30.50 ? ? ? ? ? ? 212 ILE U CG1 1
+ATOM 1690 C CG2 . ILE A 1 215 ? 2.998 -34.443 -14.314 1.00 33.11 ? ? ? ? ? ? 212 ILE U CG2 1
+ATOM 1691 C CD1 . ILE A 1 215 ? 4.270 -35.204 -11.671 1.00 33.53 ? ? ? ? ? ? 212 ILE U CD1 1
+ATOM 1692 N N . VAL A 1 216 ? 1.316 -36.171 -16.671 1.00 31.64 ? ? ? ? ? ? 213 VAL U N 1
+ATOM 1693 C CA . VAL A 1 216 ? 1.283 -36.094 -18.119 1.00 31.03 ? ? ? ? ? ? 213 VAL U CA 1
+ATOM 1694 C C . VAL A 1 216 ? 2.412 -35.199 -18.595 1.00 32.27 ? ? ? ? ? ? 213 VAL U C 1
+ATOM 1695 O O . VAL A 1 216 ? 2.483 -34.032 -18.194 1.00 31.82 ? ? ? ? ? ? 213 VAL U O 1
+ATOM 1696 C CB . VAL A 1 216 ? -0.041 -35.511 -18.646 1.00 30.18 ? ? ? ? ? ? 213 VAL U CB 1
+ATOM 1697 C CG1 . VAL A 1 216 ? -0.008 -35.433 -20.161 1.00 29.51 ? ? ? ? ? ? 213 VAL U CG1 1
+ATOM 1698 C CG2 . VAL A 1 216 ? -1.226 -36.336 -18.191 1.00 29.61 ? ? ? ? ? ? 213 VAL U CG2 1
+ATOM 1699 N N . SER A 1 217 ? 3.272 -35.745 -19.464 1.00 33.07 ? ? ? ? ? ? 214 SER U N 1
+ATOM 1700 C CA . SER A 1 217 ? 4.489 -35.062 -19.924 1.00 34.62 ? ? ? ? ? ? 214 SER U CA 1
+ATOM 1701 C C . SER A 1 217 ? 4.544 -34.792 -21.426 1.00 34.86 ? ? ? ? ? ? 214 SER U C 1
+ATOM 1702 O O . SER A 1 217 ? 4.639 -33.639 -21.827 1.00 36.34 ? ? ? ? ? ? 214 SER U O 1
+ATOM 1703 C CB . SER A 1 217 ? 5.740 -35.823 -19.496 1.00 35.04 ? ? ? ? ? ? 214 SER U CB 1
+ATOM 1704 O OG . SER A 1 217 ? 6.889 -35.004 -19.589 1.00 36.50 ? ? ? ? ? ? 214 SER U OG 1
+ATOM 1705 N N . TRP A 1 218 ? 4.497 -35.830 -22.254 1.00 35.10 ? ? ? ? ? ? 215 TRP U N 1
+ATOM 1706 C CA . TRP A 1 218 ? 4.537 -35.642 -23.714 1.00 37.36 ? ? ? ? ? ? 215 TRP U CA 1
+ATOM 1707 C C . TRP A 1 218 ? 4.014 -36.859 -24.467 1.00 39.07 ? ? ? ? ? ? 215 TRP U C 1
+ATOM 1708 O O . TRP A 1 218 ? 3.683 -37.876 -23.876 1.00 43.28 ? ? ? ? ? ? 215 TRP U O 1
+ATOM 1709 C CB . TRP A 1 218 ? 5.967 -35.280 -24.190 1.00 36.21 ? ? ? ? ? ? 215 TRP U CB 1
+ATOM 1710 C CG . TRP A 1 218 ? 7.010 -36.276 -23.766 1.00 35.22 ? ? ? ? ? ? 215 TRP U CG 1
+ATOM 1711 C CD1 . TRP A 1 218 ? 7.639 -36.322 -22.568 1.00 36.43 ? ? ? ? ? ? 215 TRP U CD1 1
+ATOM 1712 C CD2 . TRP A 1 218 ? 7.515 -37.376 -24.526 1.00 34.94 ? ? ? ? ? ? 215 TRP U CD2 1
+ATOM 1713 N NE1 . TRP A 1 218 ? 8.516 -37.379 -22.527 1.00 37.30 ? ? ? ? ? ? 215 TRP U NE1 1
+ATOM 1714 C CE2 . TRP A 1 218 ? 8.451 -38.049 -23.715 1.00 37.37 ? ? ? ? ? ? 215 TRP U CE2 1
+ATOM 1715 C CE3 . TRP A 1 218 ? 7.262 -37.864 -25.810 1.00 36.87 ? ? ? ? ? ? 215 TRP U CE3 1
+ATOM 1716 C CZ2 . TRP A 1 218 ? 9.150 -39.184 -24.153 1.00 39.28 ? ? ? ? ? ? 215 TRP U CZ2 1
+ATOM 1717 C CZ3 . TRP A 1 218 ? 7.949 -38.989 -26.244 1.00 39.13 ? ? ? ? ? ? 215 TRP U CZ3 1
+ATOM 1718 C CH2 . TRP A 1 218 ? 8.885 -39.634 -25.420 1.00 40.07 ? ? ? ? ? ? 215 TRP U CH2 1
+ATOM 1719 N N . GLY A 1 219 ? 3.696 -36.679 -25.731 1.00 45.63 ? ? ? ? ? ? 216 GLY U N 1
+ATOM 1720 C CA . GLY A 1 219 ? 3.445 -37.747 -26.654 1.00 46.54 ? ? ? ? ? ? 216 GLY U CA 1
+ATOM 1721 C C . GLY A 1 219 ? 3.290 -37.148 -28.022 1.00 49.46 ? ? ? ? ? ? 216 GLY U C 1
+ATOM 1722 O O . GLY A 1 219 ? 2.894 -36.020 -28.108 1.00 50.30 ? ? ? ? ? ? 216 GLY U O 1
+ATOM 1723 N N . ARG A 1 220 ? 3.498 -37.919 -29.077 1.00 51.54 ? ? ? ? ? ? 217 ARG U N 1
+ATOM 1724 C CA . ARG A 1 220 ? 3.329 -37.424 -30.431 1.00 54.70 ? ? ? ? ? ? 217 ARG U CA 1
+ATOM 1725 C C . ARG A 1 220 ? 1.896 -37.587 -30.821 1.00 53.58 ? ? ? ? ? ? 217 ARG U C 1
+ATOM 1726 O O . ARG A 1 220 ? 1.357 -38.661 -30.731 1.00 60.05 ? ? ? ? ? ? 217 ARG U O 1
+ATOM 1727 C CB . ARG A 1 220 ? 4.238 -38.193 -31.391 1.00 57.79 ? ? ? ? ? ? 217 ARG U CB 1
+ATOM 1728 C CG . ARG A 1 220 ? 4.113 -37.823 -32.849 1.00 62.46 ? ? ? ? ? ? 217 ARG U CG 1
+ATOM 1729 C CD . ARG A 1 220 ? 4.793 -38.829 -33.763 1.00 66.41 ? ? ? ? ? ? 217 ARG U CD 1
+ATOM 1730 N NE . ARG A 1 220 ? 5.322 -38.196 -34.958 1.00 72.21 ? ? ? ? ? ? 217 ARG U NE 1
+ATOM 1731 C CZ . ARG A 1 220 ? 6.238 -38.722 -35.773 1.00 72.39 ? ? ? ? ? ? 217 ARG U CZ 1
+ATOM 1732 N NH1 . ARG A 1 220 ? 6.649 -38.039 -36.837 1.00 69.34 ? ? ? ? ? ? 217 ARG U NH1 1
+ATOM 1733 N NH2 . ARG A 1 220 ? 6.749 -39.913 -35.526 1.00 69.94 ? ? ? ? ? ? 217 ARG U NH2 1
+ATOM 1734 N N . GLY A 1 221 ? 1.256 -36.499 -31.194 1.00 48.92 ? ? ? ? ? ? 219 GLY U N 1
+ATOM 1735 C CA . GLY A 1 221 ? -0.142 -36.526 -31.539 1.00 49.62 ? ? ? ? ? ? 219 GLY U CA 1
+ATOM 1736 C C . GLY A 1 221 ? -0.922 -36.891 -30.297 1.00 50.98 ? ? ? ? ? ? 219 GLY U C 1
+ATOM 1737 O O . GLY A 1 221 ? -0.387 -36.877 -29.226 1.00 47.58 ? ? ? ? ? ? 219 GLY U O 1
+ATOM 1738 N N . CYS A 1 222 ? -2.170 -37.283 -30.463 1.00 48.73 ? ? ? ? ? ? 220 CYS U N 1
+ATOM 1739 C CA . CYS A 1 222 ? -2.923 -37.905 -29.401 1.00 47.18 ? ? ? ? ? ? 220 CYS U CA 1
+ATOM 1740 C C . CYS A 1 222 ? -3.684 -39.155 -29.906 1.00 44.32 ? ? ? ? ? ? 220 CYS U C 1
+ATOM 1741 O O . CYS A 1 222 ? -4.292 -39.111 -30.947 1.00 43.12 ? ? ? ? ? ? 220 CYS U O 1
+ATOM 1742 C CB . CYS A 1 222 ? -3.882 -36.885 -28.811 1.00 44.97 ? ? ? ? ? ? 220 CYS U CB 1
+ATOM 1743 S SG . CYS A 1 222 ? -3.268 -35.222 -28.499 1.00 49.80 ? ? ? ? ? ? 220 CYS U SG 1
+ATOM 1744 N N . ALA A 1 223 ? -3.657 -40.246 -29.159 1.00 44.24 ? ? ? ? ? ? 221 ALA U N 1
+ATOM 1745 C CA . ALA A 1 223 ? -4.330 -41.476 -29.565 1.00 45.80 ? ? ? ? ? ? 221 ALA U CA 1
+ATOM 1746 C C . ALA A 1 223 ? -3.866 -41.835 -30.984 1.00 50.44 ? ? ? ? ? ? 221 ALA U C 1
+ATOM 1747 O O . ALA A 1 223 ? -4.676 -42.175 -31.853 1.00 49.11 ? ? ? ? ? ? 221 ALA U O 1
+ATOM 1748 C CB . ALA A 1 223 ? -5.839 -41.299 -29.521 1.00 46.47 ? ? ? ? ? ? 221 ALA U CB 1
+ATOM 1749 N N . LEU A 1 224 ? -2.570 -41.765 -31.213 1.00 49.32 ? ? ? ? ? ? 222 LEU U N 1
+ATOM 1750 C CA . LEU A 1 224 ? -2.002 -42.001 -32.530 1.00 52.04 ? ? ? ? ? ? 222 LEU U CA 1
+ATOM 1751 C C . LEU A 1 224 ? -1.284 -43.303 -32.507 1.00 53.42 ? ? ? ? ? ? 222 LEU U C 1
+ATOM 1752 O O . LEU A 1 224 ? -0.503 -43.550 -31.620 1.00 50.49 ? ? ? ? ? ? 222 LEU U O 1
+ATOM 1753 C CB . LEU A 1 224 ? -1.033 -40.898 -32.866 1.00 51.24 ? ? ? ? ? ? 222 LEU U CB 1
+ATOM 1754 C CG . LEU A 1 224 ? -0.116 -40.939 -34.060 1.00 53.84 ? ? ? ? ? ? 222 LEU U CG 1
+ATOM 1755 C CD1 . LEU A 1 224 ? -0.901 -40.974 -35.341 1.00 51.26 ? ? ? ? ? ? 222 LEU U CD1 1
+ATOM 1756 C CD2 . LEU A 1 224 ? 0.716 -39.694 -34.004 1.00 54.48 ? ? ? ? ? ? 222 LEU U CD2 1
+ATOM 1757 N N . LYS A 1 225 ? -1.520 -44.140 -33.502 1.00 57.73 ? ? ? ? ? ? 223 LYS U N 1
+ATOM 1758 C CA . LYS A 1 225 ? -0.990 -45.489 -33.450 1.00 60.71 ? ? ? ? ? ? 223 LYS U CA 1
+ATOM 1759 C C . LYS A 1 225 ? 0.510 -45.464 -33.242 1.00 55.18 ? ? ? ? ? ? 223 LYS U C 1
+ATOM 1760 O O . LYS A 1 225 ? 1.217 -44.686 -33.851 1.00 51.83 ? ? ? ? ? ? 223 LYS U O 1
+ATOM 1761 C CB . LYS A 1 225 ? -1.328 -46.232 -34.727 1.00 64.23 ? ? ? ? ? ? 223 LYS U CB 1
+ATOM 1762 C CG . LYS A 1 225 ? -0.926 -47.685 -34.752 1.00 67.90 ? ? ? ? ? ? 223 LYS U CG 1
+ATOM 1763 C CD . LYS A 1 225 ? -1.805 -48.460 -35.721 1.00 69.21 ? ? ? ? ? ? 223 LYS U CD 1
+ATOM 1764 C CE . LYS A 1 225 ? -1.316 -49.874 -35.899 1.00 72.31 ? ? ? ? ? ? 223 LYS U CE 1
+ATOM 1765 N NZ . LYS A 1 225 ? -2.415 -50.762 -36.351 1.00 77.12 ? ? ? ? ? ? 223 LYS U NZ 1
+ATOM 1766 N N . ASP A 1 226 A 0.919 -46.288 -32.285 1.00 56.35 ? ? ? ? ? ? 223 ASP U N 1
+ATOM 1767 C CA . ASP A 1 226 A 2.280 -46.618 -31.905 1.00 62.68 ? ? ? ? ? ? 223 ASP U CA 1
+ATOM 1768 C C . ASP A 1 226 A 3.054 -45.554 -31.179 1.00 63.30 ? ? ? ? ? ? 223 ASP U C 1
+ATOM 1769 O O . ASP A 1 226 A 4.232 -45.717 -30.885 1.00 60.77 ? ? ? ? ? ? 223 ASP U O 1
+ATOM 1770 C CB . ASP A 1 226 A 3.078 -47.256 -33.026 1.00 65.15 ? ? ? ? ? ? 223 ASP U CB 1
+ATOM 1771 C CG . ASP A 1 226 A 2.598 -48.652 -33.348 1.00 67.13 ? ? ? ? ? ? 223 ASP U CG 1
+ATOM 1772 O OD1 . ASP A 1 226 A 2.947 -49.597 -32.629 1.00 64.06 ? ? ? ? ? ? 223 ASP U OD1 1
+ATOM 1773 O OD2 . ASP A 1 226 A 1.855 -48.786 -34.323 1.00 69.76 ? ? ? ? ? ? 223 ASP U OD2 1
+ATOM 1774 N N . LYS A 1 227 ? 2.336 -44.510 -30.805 1.00 62.31 ? ? ? ? ? ? 224 LYS U N 1
+ATOM 1775 C CA . LYS A 1 227 ? 2.873 -43.401 -30.059 1.00 57.48 ? ? ? ? ? ? 224 LYS U CA 1
+ATOM 1776 C C . LYS A 1 227 ? 2.133 -43.180 -28.728 1.00 54.69 ? ? ? ? ? ? 224 LYS U C 1
+ATOM 1777 O O . LYS A 1 227 ? 1.234 -42.392 -28.659 1.00 51.72 ? ? ? ? ? ? 224 LYS U O 1
+ATOM 1778 C CB . LYS A 1 227 ? 2.772 -42.161 -30.920 1.00 58.65 ? ? ? ? ? ? 224 LYS U CB 1
+ATOM 1779 C CG . LYS A 1 227 ? 3.322 -42.339 -32.319 1.00 59.63 ? ? ? ? ? ? 224 LYS U CG 1
+ATOM 1780 C CD . LYS A 1 227 ? 4.827 -42.407 -32.260 1.00 61.08 ? ? ? ? ? ? 224 LYS U CD 1
+ATOM 1781 C CE . LYS A 1 227 ? 5.471 -42.839 -33.569 1.00 60.77 ? ? ? ? ? ? 224 LYS U CE 1
+ATOM 1782 N NZ . LYS A 1 227 ? 6.875 -43.215 -33.286 1.00 62.48 ? ? ? ? ? ? 224 LYS U NZ 1
+ATOM 1783 N N . PRO A 1 228 ? 2.540 -43.875 -27.685 1.00 50.53 ? ? ? ? ? ? 225 PRO U N 1
+ATOM 1784 C CA . PRO A 1 228 ? 1.904 -43.742 -26.365 1.00 50.03 ? ? ? ? ? ? 225 PRO U CA 1
+ATOM 1785 C C . PRO A 1 228 ? 2.020 -42.358 -25.721 1.00 48.14 ? ? ? ? ? ? 225 PRO U C 1
+ATOM 1786 O O . PRO A 1 228 ? 2.935 -41.589 -26.022 1.00 46.97 ? ? ? ? ? ? 225 PRO U O 1
+ATOM 1787 C CB . PRO A 1 228 ? 2.679 -44.738 -25.489 1.00 49.76 ? ? ? ? ? ? 225 PRO U CB 1
+ATOM 1788 C CG . PRO A 1 228 ? 3.371 -45.656 -26.429 1.00 51.71 ? ? ? ? ? ? 225 PRO U CG 1
+ATOM 1789 C CD . PRO A 1 228 ? 3.621 -44.877 -27.677 1.00 50.15 ? ? ? ? ? ? 225 PRO U CD 1
+ATOM 1790 N N . GLY A 1 229 ? 1.108 -42.054 -24.806 1.00 43.25 ? ? ? ? ? ? 226 GLY U N 1
+ATOM 1791 C CA . GLY A 1 229 ? 1.327 -40.940 -23.911 1.00 39.06 ? ? ? ? ? ? 226 GLY U CA 1
+ATOM 1792 C C . GLY A 1 229 ? 2.515 -41.255 -23.020 1.00 39.11 ? ? ? ? ? ? 226 GLY U C 1
+ATOM 1793 O O . GLY A 1 229 ? 2.734 -42.411 -22.636 1.00 37.46 ? ? ? ? ? ? 226 GLY U O 1
+ATOM 1794 N N . VAL A 1 230 ? 3.292 -40.231 -22.690 1.00 37.48 ? ? ? ? ? ? 227 VAL U N 1
+ATOM 1795 C CA . VAL A 1 230 ? 4.478 -40.416 -21.862 1.00 36.17 ? ? ? ? ? ? 227 VAL U CA 1
+ATOM 1796 C C . VAL A 1 230 ? 4.338 -39.581 -20.617 1.00 35.81 ? ? ? ? ? ? 227 VAL U C 1
+ATOM 1797 O O . VAL A 1 230 ? 3.863 -38.432 -20.665 1.00 35.45 ? ? ? ? ? ? 227 VAL U O 1
+ATOM 1798 C CB . VAL A 1 230 ? 5.754 -40.005 -22.629 1.00 36.64 ? ? ? ? ? ? 227 VAL U CB 1
+ATOM 1799 C CG1 . VAL A 1 230 ? 6.998 -40.399 -21.849 1.00 33.70 ? ? ? ? ? ? 227 VAL U CG1 1
+ATOM 1800 C CG2 . VAL A 1 230 ? 5.726 -40.612 -24.029 1.00 35.76 ? ? ? ? ? ? 227 VAL U CG2 1
+ATOM 1801 N N . TYR A 1 231 ? 4.755 -40.158 -19.497 1.00 37.81 ? ? ? ? ? ? 228 TYR U N 1
+ATOM 1802 C CA . TYR A 1 231 ? 4.490 -39.580 -18.194 1.00 36.72 ? ? ? ? ? ? 228 TYR U CA 1
+ATOM 1803 C C . TYR A 1 231 ? 5.725 -39.596 -17.337 1.00 37.14 ? ? ? ? ? ? 228 TYR U C 1
+ATOM 1804 O O . TYR A 1 231 ? 6.563 -40.485 -17.457 1.00 38.67 ? ? ? ? ? ? 228 TYR U O 1
+ATOM 1805 C CB . TYR A 1 231 ? 3.393 -40.374 -17.490 1.00 38.41 ? ? ? ? ? ? 228 TYR U CB 1
+ATOM 1806 C CG . TYR A 1 231 ? 2.079 -40.394 -18.235 1.00 37.85 ? ? ? ? ? ? 228 TYR U CG 1
+ATOM 1807 C CD1 . TYR A 1 231 ? 1.918 -41.145 -19.414 1.00 39.30 ? ? ? ? ? ? 228 TYR U CD1 1
+ATOM 1808 C CD2 . TYR A 1 231 ? 0.987 -39.682 -17.760 1.00 35.57 ? ? ? ? ? ? 228 TYR U CD2 1
+ATOM 1809 C CE1 . TYR A 1 231 ? 0.711 -41.156 -20.093 1.00 36.43 ? ? ? ? ? ? 228 TYR U CE1 1
+ATOM 1810 C CE2 . TYR A 1 231 ? -0.214 -39.697 -18.421 1.00 35.14 ? ? ? ? ? ? 228 TYR U CE2 1
+ATOM 1811 C CZ . TYR A 1 231 ? -0.353 -40.418 -19.586 1.00 35.67 ? ? ? ? ? ? 228 TYR U CZ 1
+ATOM 1812 O OH . TYR A 1 231 ? -1.564 -40.394 -20.221 1.00 35.42 ? ? ? ? ? ? 228 TYR U OH 1
+ATOM 1813 N N . THR A 1 232 ? 5.828 -38.609 -16.456 1.00 35.74 ? ? ? ? ? ? 229 THR U N 1
+ATOM 1814 C CA . THR A 1 232 ? 6.923 -38.536 -15.524 1.00 35.58 ? ? ? ? ? ? 229 THR U CA 1
+ATOM 1815 C C . THR A 1 232 ? 6.692 -39.617 -14.450 1.00 38.63 ? ? ? ? ? ? 229 THR U C 1
+ATOM 1816 O O . THR A 1 232 ? 5.630 -39.677 -13.845 1.00 37.06 ? ? ? ? ? ? 229 THR U O 1
+ATOM 1817 C CB . THR A 1 232 ? 7.004 -37.139 -14.900 1.00 34.20 ? ? ? ? ? ? 229 THR U CB 1
+ATOM 1818 O OG1 . THR A 1 232 ? 7.151 -36.150 -15.947 1.00 31.37 ? ? ? ? ? ? 229 THR U OG1 1
+ATOM 1819 C CG2 . THR A 1 232 ? 8.171 -37.060 -13.904 1.00 33.68 ? ? ? ? ? ? 229 THR U CG2 1
+ATOM 1820 N N . ARG A 1 233 ? 7.688 -40.477 -14.252 1.00 40.72 ? ? ? ? ? ? 230 ARG U N 1
+ATOM 1821 C CA . ARG A 1 233 ? 7.579 -41.624 -13.357 1.00 42.16 ? ? ? ? ? ? 230 ARG U CA 1
+ATOM 1822 C C . ARG A 1 233 ? 7.738 -41.170 -11.918 1.00 44.96 ? ? ? ? ? ? 230 ARG U C 1
+ATOM 1823 O O . ARG A 1 233 ? 8.854 -41.074 -11.388 1.00 45.97 ? ? ? ? ? ? 230 ARG U O 1
+ATOM 1824 C CB . ARG A 1 233 ? 8.639 -42.663 -13.741 1.00 45.67 ? ? ? ? ? ? 230 ARG U CB 1
+ATOM 1825 C CG . ARG A 1 233 ? 8.507 -44.023 -13.091 1.00 47.78 ? ? ? ? ? ? 230 ARG U CG 1
+ATOM 1826 C CD . ARG A 1 233 ? 9.439 -45.014 -13.780 1.00 51.35 ? ? ? ? ? ? 230 ARG U CD 1
+ATOM 1827 N NE . ARG A 1 233 ? 9.548 -46.265 -13.033 1.00 55.93 ? ? ? ? ? ? 230 ARG U NE 1
+ATOM 1828 C CZ . ARG A 1 233 ? 10.170 -47.366 -13.457 1.00 60.26 ? ? ? ? ? ? 230 ARG U CZ 1
+ATOM 1829 N NH1 . ARG A 1 233 ? 10.766 -47.404 -14.648 1.00 57.22 ? ? ? ? ? ? 230 ARG U NH1 1
+ATOM 1830 N NH2 . ARG A 1 233 ? 10.198 -48.445 -12.672 1.00 61.39 ? ? ? ? ? ? 230 ARG U NH2 1
+ATOM 1831 N N . VAL A 1 234 ? 6.606 -40.899 -11.277 1.00 45.54 ? ? ? ? ? ? 231 VAL U N 1
+ATOM 1832 C CA . VAL A 1 234 ? 6.594 -40.278 -9.949 1.00 45.01 ? ? ? ? ? ? 231 VAL U CA 1
+ATOM 1833 C C . VAL A 1 234 ? 7.427 -41.016 -8.905 1.00 47.58 ? ? ? ? ? ? 231 VAL U C 1
+ATOM 1834 O O . VAL A 1 234 ? 8.110 -40.379 -8.085 1.00 42.07 ? ? ? ? ? ? 231 VAL U O 1
+ATOM 1835 C CB . VAL A 1 234 ? 5.144 -40.114 -9.427 1.00 45.33 ? ? ? ? ? ? 231 VAL U CB 1
+ATOM 1836 C CG1 . VAL A 1 234 ? 5.133 -39.662 -7.976 1.00 45.23 ? ? ? ? ? ? 231 VAL U CG1 1
+ATOM 1837 C CG2 . VAL A 1 234 ? 4.384 -39.119 -10.304 1.00 44.83 ? ? ? ? ? ? 231 VAL U CG2 1
+ATOM 1838 N N . SER A 1 235 ? 7.349 -42.349 -8.922 1.00 48.57 ? ? ? ? ? ? 232 SER U N 1
+ATOM 1839 C CA . SER A 1 235 ? 8.043 -43.188 -7.929 1.00 48.44 ? ? ? ? ? ? 232 SER U CA 1
+ATOM 1840 C C . SER A 1 235 ? 9.542 -42.916 -7.887 1.00 50.78 ? ? ? ? ? ? 232 SER U C 1
+ATOM 1841 O O . SER A 1 235 ? 10.191 -43.190 -6.878 1.00 57.08 ? ? ? ? ? ? 232 SER U O 1
+ATOM 1842 C CB . SER A 1 235 ? 7.797 -44.683 -8.208 1.00 46.09 ? ? ? ? ? ? 232 SER U CB 1
+ATOM 1843 O OG . SER A 1 235 ? 8.405 -45.089 -9.425 1.00 44.08 ? ? ? ? ? ? 232 SER U OG 1
+ATOM 1844 N N . HIS A 1 236 ? 10.089 -42.388 -8.979 1.00 52.99 ? ? ? ? ? ? 233 HIS U N 1
+ATOM 1845 C CA . HIS A 1 236 ? 11.504 -42.015 -9.041 1.00 56.17 ? ? ? ? ? ? 233 HIS U CA 1
+ATOM 1846 C C . HIS A 1 236 ? 11.853 -40.654 -8.433 1.00 54.08 ? ? ? ? ? ? 233 HIS U C 1
+ATOM 1847 O O . HIS A 1 236 ? 13.029 -40.302 -8.392 1.00 53.58 ? ? ? ? ? ? 233 HIS U O 1
+ATOM 1848 C CB . HIS A 1 236 ? 12.002 -42.048 -10.493 1.00 57.52 ? ? ? ? ? ? 233 HIS U CB 1
+ATOM 1849 C CG . HIS A 1 236 ? 12.248 -43.428 -11.014 1.00 61.06 ? ? ? ? ? ? 233 HIS U CG 1
+ATOM 1850 N ND1 . HIS A 1 236 ? 11.473 -44.511 -10.655 1.00 63.63 ? ? ? ? ? ? 233 HIS U ND1 1
+ATOM 1851 C CD2 . HIS A 1 236 ? 13.178 -43.900 -11.879 1.00 61.75 ? ? ? ? ? ? 233 HIS U CD2 1
+ATOM 1852 C CE1 . HIS A 1 236 ? 11.920 -45.593 -11.270 1.00 64.37 ? ? ? ? ? ? 233 HIS U CE1 1
+ATOM 1853 N NE2 . HIS A 1 236 ? 12.952 -45.249 -12.020 1.00 64.01 ? ? ? ? ? ? 233 HIS U NE2 1
+ATOM 1854 N N . PHE A 1 237 ? 10.867 -39.896 -7.950 1.00 50.30 ? ? ? ? ? ? 234 PHE U N 1
+ATOM 1855 C CA . PHE A 1 237 ? 11.132 -38.524 -7.481 1.00 47.22 ? ? ? ? ? ? 234 PHE U CA 1
+ATOM 1856 C C . PHE A 1 237 ? 10.774 -38.263 -6.017 1.00 48.08 ? ? ? ? ? ? 234 PHE U C 1
+ATOM 1857 O O . PHE A 1 237 ? 10.667 -37.109 -5.588 1.00 46.92 ? ? ? ? ? ? 234 PHE U O 1
+ATOM 1858 C CB . PHE A 1 237 ? 10.444 -37.512 -8.398 1.00 45.49 ? ? ? ? ? ? 234 PHE U CB 1
+ATOM 1859 C CG . PHE A 1 237 ? 11.076 -37.402 -9.757 1.00 44.01 ? ? ? ? ? ? 234 PHE U CG 1
+ATOM 1860 C CD1 . PHE A 1 237 ? 10.712 -38.258 -10.770 1.00 41.82 ? ? ? ? ? ? 234 PHE U CD1 1
+ATOM 1861 C CD2 . PHE A 1 237 ? 12.046 -36.428 -10.022 1.00 44.60 ? ? ? ? ? ? 234 PHE U CD2 1
+ATOM 1862 C CE1 . PHE A 1 237 ? 11.285 -38.160 -12.029 1.00 41.43 ? ? ? ? ? ? 234 PHE U CE1 1
+ATOM 1863 C CE2 . PHE A 1 237 ? 12.621 -36.320 -11.278 1.00 42.69 ? ? ? ? ? ? 234 PHE U CE2 1
+ATOM 1864 C CZ . PHE A 1 237 ? 12.243 -37.194 -12.282 1.00 42.40 ? ? ? ? ? ? 234 PHE U CZ 1
+ATOM 1865 N N . LEU A 1 238 ? 10.639 -39.328 -5.233 1.00 48.13 ? ? ? ? ? ? 235 LEU U N 1
+ATOM 1866 C CA . LEU A 1 238 ? 10.296 -39.175 -3.821 1.00 48.67 ? ? ? ? ? ? 235 LEU U CA 1
+ATOM 1867 C C . LEU A 1 238 ? 11.294 -38.372 -2.992 1.00 51.19 ? ? ? ? ? ? 235 LEU U C 1
+ATOM 1868 O O . LEU A 1 238 ? 10.882 -37.681 -2.050 1.00 52.19 ? ? ? ? ? ? 235 LEU U O 1
+ATOM 1869 C CB . LEU A 1 238 ? 10.067 -40.537 -3.166 1.00 51.02 ? ? ? ? ? ? 235 LEU U CB 1
+ATOM 1870 C CG . LEU A 1 238 ? 8.768 -41.257 -3.542 1.00 52.30 ? ? ? ? ? ? 235 LEU U CG 1
+ATOM 1871 C CD1 . LEU A 1 238 ? 8.558 -42.458 -2.634 1.00 54.66 ? ? ? ? ? ? 235 LEU U CD1 1
+ATOM 1872 C CD2 . LEU A 1 238 ? 7.556 -40.339 -3.453 1.00 52.39 ? ? ? ? ? ? 235 LEU U CD2 1
+ATOM 1873 N N . PRO A 1 239 ? 12.603 -38.468 -3.308 1.00 51.45 ? ? ? ? ? ? 236 PRO U N 1
+ATOM 1874 C CA . PRO A 1 239 ? 13.566 -37.645 -2.563 1.00 50.77 ? ? ? ? ? ? 236 PRO U CA 1
+ATOM 1875 C C . PRO A 1 239 ? 13.427 -36.174 -2.928 1.00 49.84 ? ? ? ? ? ? 236 PRO U C 1
+ATOM 1876 O O . PRO A 1 239 ? 13.297 -35.315 -2.035 1.00 48.94 ? ? ? ? ? ? 236 PRO U O 1
+ATOM 1877 C CB . PRO A 1 239 ? 14.927 -38.196 -3.008 1.00 49.68 ? ? ? ? ? ? 236 PRO U CB 1
+ATOM 1878 C CG . PRO A 1 239 ? 14.634 -39.550 -3.558 1.00 49.22 ? ? ? ? ? ? 236 PRO U CG 1
+ATOM 1879 C CD . PRO A 1 239 ? 13.284 -39.447 -4.173 1.00 47.92 ? ? ? ? ? ? 236 PRO U CD 1
+ATOM 1880 N N . TRP A 1 240 ? 13.439 -35.896 -4.233 1.00 46.84 ? ? ? ? ? ? 237 TRP U N 1
+ATOM 1881 C CA . TRP A 1 240 ? 13.155 -34.555 -4.733 1.00 44.84 ? ? ? ? ? ? 237 TRP U CA 1
+ATOM 1882 C C . TRP A 1 240 ? 11.886 -34.033 -4.064 1.00 43.35 ? ? ? ? ? ? 237 TRP U C 1
+ATOM 1883 O O . TRP A 1 240 ? 11.890 -32.969 -3.442 1.00 44.13 ? ? ? ? ? ? 237 TRP U O 1
+ATOM 1884 C CB . TRP A 1 240 ? 13.038 -34.564 -6.268 1.00 47.28 ? ? ? ? ? ? 237 TRP U CB 1
+ATOM 1885 C CG . TRP A 1 240 ? 12.903 -33.186 -6.849 1.00 48.30 ? ? ? ? ? ? 237 TRP U CG 1
+ATOM 1886 C CD1 . TRP A 1 240 ? 13.913 -32.310 -7.159 1.00 51.54 ? ? ? ? ? ? 237 TRP U CD1 1
+ATOM 1887 C CD2 . TRP A 1 240 ? 11.681 -32.505 -7.140 1.00 47.99 ? ? ? ? ? ? 237 TRP U CD2 1
+ATOM 1888 N NE1 . TRP A 1 240 ? 13.387 -31.130 -7.636 1.00 50.76 ? ? ? ? ? ? 237 TRP U NE1 1
+ATOM 1889 C CE2 . TRP A 1 240 ? 12.019 -31.226 -7.637 1.00 49.18 ? ? ? ? ? ? 237 TRP U CE2 1
+ATOM 1890 C CE3 . TRP A 1 240 ? 10.330 -32.855 -7.035 1.00 45.30 ? ? ? ? ? ? 237 TRP U CE3 1
+ATOM 1891 C CZ2 . TRP A 1 240 ? 11.051 -30.303 -8.044 1.00 49.49 ? ? ? ? ? ? 237 TRP U CZ2 1
+ATOM 1892 C CZ3 . TRP A 1 240 ? 9.372 -31.938 -7.436 1.00 44.24 ? ? ? ? ? ? 237 TRP U CZ3 1
+ATOM 1893 C CH2 . TRP A 1 240 ? 9.737 -30.680 -7.932 1.00 49.14 ? ? ? ? ? ? 237 TRP U CH2 1
+ATOM 1894 N N . ILE A 1 241 ? 10.816 -34.822 -4.107 1.00 46.25 ? ? ? ? ? ? 238 ILE U N 1
+ATOM 1895 C CA . ILE A 1 241 ? 9.541 -34.403 -3.505 1.00 45.62 ? ? ? ? ? ? 238 ILE U CA 1
+ATOM 1896 C C . ILE A 1 241 ? 9.613 -34.198 -1.988 1.00 49.87 ? ? ? ? ? ? 238 ILE U C 1
+ATOM 1897 O O . ILE A 1 241 ? 9.224 -33.138 -1.474 1.00 45.97 ? ? ? ? ? ? 238 ILE U O 1
+ATOM 1898 C CB . ILE A 1 241 ? 8.415 -35.414 -3.809 1.00 43.33 ? ? ? ? ? ? 238 ILE U CB 1
+ATOM 1899 C CG1 . ILE A 1 241 ? 8.057 -35.364 -5.304 1.00 42.43 ? ? ? ? ? ? 238 ILE U CG1 1
+ATOM 1900 C CG2 . ILE A 1 241 ? 7.190 -35.130 -2.944 1.00 41.30 ? ? ? ? ? ? 238 ILE U CG2 1
+ATOM 1901 C CD1 . ILE A 1 241 ? 7.360 -36.600 -5.819 1.00 42.78 ? ? ? ? ? ? 238 ILE U CD1 1
+ATOM 1902 N N . ARG A 1 242 ? 10.081 -35.213 -1.265 1.00 54.45 ? ? ? ? ? ? 239 ARG U N 1
+ATOM 1903 C CA . ARG A 1 242 ? 10.082 -35.132 0.198 1.00 57.40 ? ? ? ? ? ? 239 ARG U CA 1
+ATOM 1904 C C . ARG A 1 242 ? 10.957 -33.987 0.705 1.00 55.46 ? ? ? ? ? ? 239 ARG U C 1
+ATOM 1905 O O . ARG A 1 242 ? 10.628 -33.378 1.720 1.00 56.87 ? ? ? ? ? ? 239 ARG U O 1
+ATOM 1906 C CB . ARG A 1 242 ? 10.462 -36.464 0.854 1.00 60.79 ? ? ? ? ? ? 239 ARG U CB 1
+ATOM 1907 C CG . ARG A 1 242 ? 9.311 -37.467 0.920 1.00 65.69 ? ? ? ? ? ? 239 ARG U CG 1
+ATOM 1908 C CD . ARG A 1 242 ? 9.643 -38.679 1.783 1.00 70.68 ? ? ? ? ? ? 239 ARG U CD 1
+ATOM 1909 N NE . ARG A 1 242 ? 10.797 -39.432 1.268 1.00 78.05 ? ? ? ? ? ? 239 ARG U NE 1
+ATOM 1910 C CZ . ARG A 1 242 ? 10.759 -40.653 0.723 1.00 80.90 ? ? ? ? ? ? 239 ARG U CZ 1
+ATOM 1911 N NH1 . ARG A 1 242 ? 9.615 -41.329 0.607 1.00 82.51 ? ? ? ? ? ? 239 ARG U NH1 1
+ATOM 1912 N NH2 . ARG A 1 242 ? 11.888 -41.213 0.292 1.00 80.69 ? ? ? ? ? ? 239 ARG U NH2 1
+ATOM 1913 N N . SER A 1 243 ? 12.028 -33.657 -0.018 1.00 57.94 ? ? ? ? ? ? 240 SER U N 1
+ATOM 1914 C CA . SER A 1 243 ? 12.914 -32.557 0.391 1.00 61.03 ? ? ? ? ? ? 240 SER U CA 1
+ATOM 1915 C C . SER A 1 243 ? 12.149 -31.244 0.470 1.00 68.10 ? ? ? ? ? ? 240 SER U C 1
+ATOM 1916 O O . SER A 1 243 ? 12.332 -30.462 1.409 1.00 69.05 ? ? ? ? ? ? 240 SER U O 1
+ATOM 1917 C CB . SER A 1 243 ? 14.083 -32.381 -0.577 1.00 63.36 ? ? ? ? ? ? 240 SER U CB 1
+ATOM 1918 O OG . SER A 1 243 ? 13.763 -31.458 -1.613 1.00 62.27 ? ? ? ? ? ? 240 SER U OG 1
+ATOM 1919 N N . HIS A 1 244 ? 11.293 -31.007 -0.523 1.00 68.42 ? ? ? ? ? ? 241 HIS U N 1
+ATOM 1920 C CA . HIS A 1 244 ? 10.561 -29.750 -0.617 1.00 68.66 ? ? ? ? ? ? 241 HIS U CA 1
+ATOM 1921 C C . HIS A 1 244 ? 9.303 -29.761 0.238 1.00 70.80 ? ? ? ? ? ? 241 HIS U C 1
+ATOM 1922 O O . HIS A 1 244 ? 8.932 -28.728 0.794 1.00 69.14 ? ? ? ? ? ? 241 HIS U O 1
+ATOM 1923 C CB . HIS A 1 244 ? 10.214 -29.438 -2.071 1.00 68.22 ? ? ? ? ? ? 241 HIS U CB 1
+ATOM 1924 C CG . HIS A 1 244 ? 11.414 -29.281 -2.954 1.00 67.18 ? ? ? ? ? ? 241 HIS U CG 1
+ATOM 1925 N ND1 . HIS A 1 244 ? 11.873 -30.291 -3.770 1.00 68.96 ? ? ? ? ? ? 241 HIS U ND1 1
+ATOM 1926 C CD2 . HIS A 1 244 ? 12.257 -28.237 -3.137 1.00 68.26 ? ? ? ? ? ? 241 HIS U CD2 1
+ATOM 1927 C CE1 . HIS A 1 244 ? 12.944 -29.877 -4.422 1.00 68.81 ? ? ? ? ? ? 241 HIS U CE1 1
+ATOM 1928 N NE2 . HIS A 1 244 ? 13.196 -28.632 -4.059 1.00 67.66 ? ? ? ? ? ? 241 HIS U NE2 1
+ATOM 1929 N N . THR A 1 245 ? 8.653 -30.920 0.349 1.00 74.54 ? ? ? ? ? ? 242 THR U N 1
+ATOM 1930 C CA . THR A 1 245 ? 7.409 -31.026 1.116 1.00 76.27 ? ? ? ? ? ? 242 THR U CA 1
+ATOM 1931 C C . THR A 1 245 ? 7.657 -30.922 2.624 1.00 80.13 ? ? ? ? ? ? 242 THR U C 1
+ATOM 1932 O O . THR A 1 245 ? 6.841 -30.341 3.339 1.00 77.76 ? ? ? ? ? ? 242 THR U O 1
+ATOM 1933 C CB . THR A 1 245 ? 6.622 -32.311 0.793 1.00 75.34 ? ? ? ? ? ? 242 THR U CB 1
+ATOM 1934 O OG1 . THR A 1 245 ? 7.438 -33.459 1.042 1.00 76.53 ? ? ? ? ? ? 242 THR U OG1 1
+ATOM 1935 C CG2 . THR A 1 245 ? 6.172 -32.307 -0.662 1.00 73.83 ? ? ? ? ? ? 242 THR U CG2 1
+ATOM 1936 N N . LYS A 1 246 ? 8.768 -31.478 3.107 1.00 86.92 ? ? ? ? ? ? 243 LYS U N 1
+ATOM 1937 C CA . LYS A 1 246 ? 9.235 -31.152 4.455 1.00 95.27 ? ? ? ? ? ? 243 LYS U CA 1
+ATOM 1938 C C . LYS A 1 246 ? 9.786 -29.722 4.400 1.00 99.56 ? ? ? ? ? ? 243 LYS U C 1
+ATOM 1939 O O . LYS A 1 246 ? 10.753 -29.448 3.687 1.00 101.05 ? ? ? ? ? ? 243 LYS U O 1
+ATOM 1940 C CB . LYS A 1 246 ? 10.276 -32.164 4.974 1.00 98.66 ? ? ? ? ? ? 243 LYS U CB 1
+ATOM 1941 C CG . LYS A 1 246 ? 11.615 -32.201 4.237 1.00 100.95 ? ? ? ? ? ? 243 LYS U CG 1
+ATOM 1942 C CD . LYS A 1 246 ? 12.713 -31.416 4.945 1.00 100.55 ? ? ? ? ? ? 243 LYS U CD 1
+ATOM 1943 C CE . LYS A 1 246 ? 13.340 -32.222 6.070 1.00 102.22 ? ? ? ? ? ? 243 LYS U CE 1
+ATOM 1944 N NZ . LYS A 1 246 ? 14.226 -31.372 6.911 1.00 103.94 ? ? ? ? ? ? 243 LYS U NZ 1
+ATOM 1945 N N . GLU A 1 247 ? 9.131 -28.808 5.114 1.00 106.90 ? ? ? ? ? ? 244 GLU U N 1
+ATOM 1946 C CA . GLU A 1 247 ? 9.487 -27.380 5.089 1.00 110.20 ? ? ? ? ? ? 244 GLU U CA 1
+ATOM 1947 C C . GLU A 1 247 ? 9.927 -26.901 6.471 1.00 111.64 ? ? ? ? ? ? 244 GLU U C 1
+ATOM 1948 O O . GLU A 1 247 ? 10.291 -25.738 6.651 1.00 112.43 ? ? ? ? ? ? 244 GLU U O 1
+ATOM 1949 C CB . GLU A 1 247 ? 8.299 -26.537 4.604 1.00 108.39 ? ? ? ? ? ? 244 GLU U CB 1
+ATOM 1950 C CG . GLU A 1 247 ? 7.955 -26.707 3.131 1.00 104.51 ? ? ? ? ? ? 244 GLU U CG 1
+ATOM 1951 C CD . GLU A 1 247 ? 8.855 -25.899 2.210 1.00 101.07 ? ? ? ? ? ? 244 GLU U CD 1
+ATOM 1952 O OE1 . GLU A 1 247 ? 8.407 -24.836 1.730 1.00 95.77 ? ? ? ? ? ? 244 GLU U OE1 1
+ATOM 1953 O OE2 . GLU A 1 247 ? 10.008 -26.319 1.965 1.00 97.75 ? ? ? ? ? ? 244 GLU U OE2 1
+ATOM 1954 N N . CYS B 2 1 ? 9.932 -25.495 -26.711 1.00 61.14 ? ? ? ? ? ? 1 CYS P N 1
+ATOM 1955 C CA . CYS B 2 1 ? 8.757 -26.333 -27.107 1.00 61.95 ? ? ? ? ? ? 1 CYS P CA 1
+ATOM 1956 C C . CYS B 2 1 ? 9.165 -27.548 -27.937 1.00 64.13 ? ? ? ? ? ? 1 CYS P C 1
+ATOM 1957 O O . CYS B 2 1 ? 9.308 -27.445 -29.164 1.00 66.79 ? ? ? ? ? ? 1 CYS P O 1
+ATOM 1958 C CB . CYS B 2 1 ? 7.718 -25.518 -27.887 1.00 61.72 ? ? ? ? ? ? 1 CYS P CB 1
+ATOM 1959 S SG . CYS B 2 1 ? 6.591 -24.549 -26.861 1.00 64.20 ? ? ? ? ? ? 1 CYS P SG 1
+ATOM 1960 N N . PRO B 2 2 ? 9.343 -28.707 -27.271 1.00 64.07 ? ? ? ? ? ? 2 PRO P N 1
+ATOM 1961 C CA . PRO B 2 2 ? 9.575 -29.985 -27.948 1.00 61.08 ? ? ? ? ? ? 2 PRO P CA 1
+ATOM 1962 C C . PRO B 2 2 ? 8.482 -30.299 -28.942 1.00 57.86 ? ? ? ? ? ? 2 PRO P C 1
+ATOM 1963 O O . PRO B 2 2 ? 7.348 -29.842 -28.777 1.00 58.73 ? ? ? ? ? ? 2 PRO P O 1
+ATOM 1964 C CB . PRO B 2 2 ? 9.556 -30.998 -26.801 1.00 58.76 ? ? ? ? ? ? 2 PRO P CB 1
+ATOM 1965 C CG . PRO B 2 2 ? 9.989 -30.222 -25.616 1.00 59.79 ? ? ? ? ? ? 2 PRO P CG 1
+ATOM 1966 C CD . PRO B 2 2 ? 9.423 -28.847 -25.805 1.00 61.94 ? ? ? ? ? ? 2 PRO P CD 1
+ATOM 1967 N N . ALA B 2 3 ? 8.811 -31.082 -29.963 1.00 57.28 ? ? ? ? ? ? 3 ALA P N 1
+ATOM 1968 C CA . ALA B 2 3 ? 7.864 -31.357 -31.039 1.00 55.53 ? ? ? ? ? ? 3 ALA P CA 1
+ATOM 1969 C C . ALA B 2 3 ? 6.631 -32.123 -30.551 1.00 52.32 ? ? ? ? ? ? 3 ALA P C 1
+ATOM 1970 O O . ALA B 2 3 ? 5.561 -32.012 -31.156 1.00 49.22 ? ? ? ? ? ? 3 ALA P O 1
+ATOM 1971 C CB . ALA B 2 3 ? 8.543 -32.118 -32.173 1.00 54.04 ? ? ? ? ? ? 3 ALA P CB 1
+ATOM 1972 N N . TYR B 2 4 ? 6.793 -32.899 -29.476 1.00 50.93 ? ? ? ? ? ? 4 TYR P N 1
+ATOM 1973 C CA . TYR B 2 4 ? 5.711 -33.732 -28.926 1.00 52.40 ? ? ? ? ? ? 4 TYR P CA 1
+ATOM 1974 C C . TYR B 2 4 ? 5.147 -33.219 -27.583 1.00 48.85 ? ? ? ? ? ? 4 TYR P C 1
+ATOM 1975 O O . TYR B 2 4 ? 4.501 -33.970 -26.851 1.00 47.07 ? ? ? ? ? ? 4 TYR P O 1
+ATOM 1976 C CB . TYR B 2 4 ? 6.188 -35.179 -28.742 1.00 55.28 ? ? ? ? ? ? 4 TYR P CB 1
+ATOM 1977 C CG . TYR B 2 4 ? 6.721 -35.888 -29.979 1.00 59.55 ? ? ? ? ? ? 4 TYR P CG 1
+ATOM 1978 C CD1 . TYR B 2 4 ? 6.363 -35.487 -31.271 1.00 60.19 ? ? ? ? ? ? 4 TYR P CD1 1
+ATOM 1979 C CD2 . TYR B 2 4 ? 7.539 -37.009 -29.846 1.00 63.16 ? ? ? ? ? ? 4 TYR P CD2 1
+ATOM 1980 C CE1 . TYR B 2 4 ? 6.841 -36.154 -32.387 1.00 63.36 ? ? ? ? ? ? 4 TYR P CE1 1
+ATOM 1981 C CE2 . TYR B 2 4 ? 8.018 -37.687 -30.959 1.00 67.13 ? ? ? ? ? ? 4 TYR P CE2 1
+ATOM 1982 C CZ . TYR B 2 4 ? 7.665 -37.256 -32.228 1.00 66.50 ? ? ? ? ? ? 4 TYR P CZ 1
+ATOM 1983 O OH . TYR B 2 4 ? 8.132 -37.931 -33.337 1.00 70.97 ? ? ? ? ? ? 4 TYR P OH 1
+ATOM 1984 N N . SER B 2 5 ? 5.378 -31.950 -27.262 1.00 48.66 ? ? ? ? ? ? 5 SER P N 1
+ATOM 1985 C CA . SER B 2 5 ? 4.739 -31.325 -26.099 1.00 45.45 ? ? ? ? ? ? 5 SER P CA 1
+ATOM 1986 C C . SER B 2 5 ? 3.224 -31.525 -26.132 1.00 45.97 ? ? ? ? ? ? 5 SER P C 1
+ATOM 1987 O O . SER B 2 5 ? 2.621 -31.604 -27.206 1.00 45.14 ? ? ? ? ? ? 5 SER P O 1
+ATOM 1988 C CB . SER B 2 5 ? 5.039 -29.823 -26.059 1.00 46.03 ? ? ? ? ? ? 5 SER P CB 1
+ATOM 1989 O OG . SER B 2 5 ? 4.527 -29.220 -24.880 1.00 43.06 ? ? ? ? ? ? 5 SER P OG 1
+ATOM 1990 N N . ARG B 2 6 ? 2.614 -31.621 -24.952 1.00 44.10 ? ? ? ? ? ? 6 ARG P N 1
+ATOM 1991 C CA . ARG B 2 6 ? 1.151 -31.611 -24.841 1.00 43.41 ? ? ? ? ? ? 6 ARG P CA 1
+ATOM 1992 C C . ARG B 2 6 ? 0.726 -30.410 -23.993 1.00 42.93 ? ? ? ? ? ? 6 ARG P C 1
+ATOM 1993 O O . ARG B 2 6 ? -0.314 -30.420 -23.330 1.00 36.73 ? ? ? ? ? ? 6 ARG P O 1
+ATOM 1994 C CB . ARG B 2 6 ? 0.657 -32.947 -24.274 1.00 42.63 ? ? ? ? ? ? 6 ARG P CB 1
+ATOM 1995 C CG . ARG B 2 6 ? 0.742 -34.062 -25.308 1.00 43.41 ? ? ? ? ? ? 6 ARG P CG 1
+ATOM 1996 C CD . ARG B 2 6 ? 0.207 -35.400 -24.813 1.00 43.13 ? ? ? ? ? ? 6 ARG P CD 1
+ATOM 1997 N NE . ARG B 2 6 ? 0.206 -36.377 -25.899 1.00 41.21 ? ? ? ? ? ? 6 ARG P NE 1
+ATOM 1998 C CZ . ARG B 2 6 ? -0.245 -37.622 -25.809 1.00 43.13 ? ? ? ? ? ? 6 ARG P CZ 1
+ATOM 1999 N NH1 . ARG B 2 6 ? -0.750 -38.091 -24.669 1.00 41.09 ? ? ? ? ? ? 6 ARG P NH1 1
+ATOM 2000 N NH2 . ARG B 2 6 ? -0.203 -38.406 -26.879 1.00 41.66 ? ? ? ? ? ? 6 ARG P NH2 1
+ATOM 2001 N N . TYR B 2 7 ? 1.559 -29.373 -24.043 1.00 43.50 ? ? ? ? ? ? 7 TYR P N 1
+ATOM 2002 C CA . TYR B 2 7 ? 1.356 -28.150 -23.282 1.00 43.68 ? ? ? ? ? ? 7 TYR P CA 1
+ATOM 2003 C C . TYR B 2 7 ? 0.548 -27.174 -24.105 1.00 46.17 ? ? ? ? ? ? 7 TYR P C 1
+ATOM 2004 O O . TYR B 2 7 ? 0.818 -26.984 -25.307 1.00 38.22 ? ? ? ? ? ? 7 TYR P O 1
+ATOM 2005 C CB . TYR B 2 7 ? 2.710 -27.538 -22.937 1.00 45.68 ? ? ? ? ? ? 7 TYR P CB 1
+ATOM 2006 C CG . TYR B 2 7 ? 2.675 -26.207 -22.189 1.00 46.46 ? ? ? ? ? ? 7 TYR P CG 1
+ATOM 2007 C CD1 . TYR B 2 7 ? 1.857 -26.018 -21.078 1.00 45.08 ? ? ? ? ? ? 7 TYR P CD1 1
+ATOM 2008 C CD2 . TYR B 2 7 ? 3.494 -25.149 -22.588 1.00 46.18 ? ? ? ? ? ? 7 TYR P CD2 1
+ATOM 2009 C CE1 . TYR B 2 7 ? 1.843 -24.809 -20.402 1.00 44.62 ? ? ? ? ? ? 7 TYR P CE1 1
+ATOM 2010 C CE2 . TYR B 2 7 ? 3.497 -23.944 -21.911 1.00 45.96 ? ? ? ? ? ? 7 TYR P CE2 1
+ATOM 2011 C CZ . TYR B 2 7 ? 2.666 -23.772 -20.824 1.00 45.64 ? ? ? ? ? ? 7 TYR P CZ 1
+ATOM 2012 O OH . TYR B 2 7 ? 2.673 -22.560 -20.175 1.00 45.91 ? ? ? ? ? ? 7 TYR P OH 1
+ATOM 2013 N N . ILE B 2 8 ? -0.455 -26.572 -23.459 1.00 45.83 ? ? ? ? ? ? 8 ILE P N 1
+ATOM 2014 C CA . ILE B 2 8 ? -1.235 -25.479 -24.052 1.00 49.63 ? ? ? ? ? ? 8 ILE P CA 1
+ATOM 2015 C C . ILE B 2 8 ? -0.358 -24.407 -24.699 1.00 50.24 ? ? ? ? ? ? 8 ILE P C 1
+ATOM 2016 O O . ILE B 2 8 ? -0.668 -23.921 -25.791 1.00 55.47 ? ? ? ? ? ? 8 ILE P O 1
+ATOM 2017 C CB . ILE B 2 8 ? -2.126 -24.788 -22.994 1.00 50.22 ? ? ? ? ? ? 8 ILE P CB 1
+ATOM 2018 C CG1 . ILE B 2 8 ? -3.401 -25.587 -22.810 1.00 51.23 ? ? ? ? ? ? 8 ILE P CG1 1
+ATOM 2019 C CG2 . ILE B 2 8 ? -2.497 -23.365 -23.414 1.00 50.36 ? ? ? ? ? ? 8 ILE P CG2 1
+ATOM 2020 C CD1 . ILE B 2 8 ? -4.261 -25.116 -21.663 1.00 53.34 ? ? ? ? ? ? 8 ILE P CD1 1
+ATOM 2021 N N . GLY B 2 9 ? 0.720 -24.029 -24.014 1.00 50.02 ? ? ? ? ? ? 9 GLY P N 1
+ATOM 2022 C CA . GLY B 2 9 ? 1.612 -22.976 -24.504 1.00 52.80 ? ? ? ? ? ? 9 GLY P CA 1
+ATOM 2023 C C . GLY B 2 9 ? 2.339 -23.284 -25.806 1.00 51.95 ? ? ? ? ? ? 9 GLY P C 1
+ATOM 2024 O O . GLY B 2 9 ? 2.823 -22.366 -26.468 1.00 54.32 ? ? ? ? ? ? 9 GLY P O 1
+ATOM 2025 N N . CYS B 2 10 ? 2.437 -24.565 -26.160 1.00 50.43 ? ? ? ? ? ? 10 CYS P N 1
+ATOM 2026 C CA . CYS B 2 10 ? 3.038 -24.985 -27.428 1.00 52.19 ? ? ? ? ? ? 10 CYS P CA 1
+ATOM 2027 C C . CYS B 2 10 ? 1.953 -25.392 -28.415 1.00 52.70 ? ? ? ? ? ? 10 CYS P C 1
+ATOM 2028 O O . CYS B 2 10 ? 2.051 -25.095 -29.606 1.00 51.82 ? ? ? ? ? ? 10 CYS P O 1
+ATOM 2029 C CB . CYS B 2 10 ? 4.008 -26.160 -27.221 1.00 54.24 ? ? ? ? ? ? 10 CYS P CB 1
+ATOM 2030 S SG . CYS B 2 10 ? 5.315 -25.901 -25.989 1.00 54.32 ? ? ? ? ? ? 10 CYS P SG 1
+HETATM 2031 O O . HOH C 3 . ? -15.205 -28.509 -1.223 1.00 29.09 ? ? ? ? ? ? 301 HOH U O 1
+HETATM 2032 O O . HOH C 3 . ? -10.582 -31.047 -35.113 1.00 46.52 ? ? ? ? ? ? 302 HOH U O 1
+HETATM 2033 O O . HOH C 3 . ? 7.248 -26.510 -23.512 1.00 44.78 ? ? ? ? ? ? 303 HOH U O 1
+HETATM 2034 O O . HOH C 3 . ? -3.871 -24.043 -27.314 1.00 44.49 ? ? ? ? ? ? 304 HOH U O 1
+HETATM 2035 O O . HOH C 3 . ? -6.024 -30.401 -17.786 1.00 24.98 ? ? ? ? ? ? 305 HOH U O 1
+HETATM 2036 O O . HOH C 3 . ? 3.279 -31.441 -22.381 1.00 38.18 ? ? ? ? ? ? 306 HOH U O 1
+HETATM 2037 O O . HOH C 3 . ? -2.060 -42.028 -22.501 1.00 40.45 ? ? ? ? ? ? 307 HOH U O 1
+HETATM 2038 O O . HOH C 3 . ? -6.449 -39.304 -12.768 1.00 43.65 ? ? ? ? ? ? 308 HOH U O 1
+HETATM 2039 O O . HOH C 3 . ? -11.732 -26.754 -32.343 1.00 47.16 ? ? ? ? ? ? 309 HOH U O 1
+HETATM 2040 O O . HOH C 3 . ? 8.854 -36.746 -19.881 1.00 44.47 ? ? ? ? ? ? 310 HOH U O 1
+HETATM 2041 O O . HOH C 3 . ? -21.614 -29.382 -16.299 1.00 40.58 ? ? ? ? ? ? 311 HOH U O 1
+HETATM 2042 O O . HOH C 3 . ? -13.578 -24.026 -23.861 1.00 39.67 ? ? ? ? ? ? 312 HOH U O 1
+HETATM 2043 O O . HOH C 3 . ? -12.634 -31.149 5.764 1.00 43.05 ? ? ? ? ? ? 313 HOH U O 1
+HETATM 2044 O O . HOH C 3 . ? -4.729 -33.001 -16.829 1.00 27.82 ? ? ? ? ? ? 314 HOH U O 1
+HETATM 2045 O O . HOH C 3 . ? -9.351 -49.347 -35.580 1.00 58.54 ? ? ? ? ? ? 315 HOH U O 1
+HETATM 2046 O O . HOH C 3 . ? -9.715 -31.118 -6.760 1.00 30.03 ? ? ? ? ? ? 316 HOH U O 1
+HETATM 2047 O O . HOH C 3 . ? -10.004 -28.922 -9.641 1.00 27.62 ? ? ? ? ? ? 317 HOH U O 1
+HETATM 2048 O O . HOH C 3 . ? -2.748 -38.564 0.587 1.00 67.64 ? ? ? ? ? ? 318 HOH U O 1
+HETATM 2049 O O . HOH C 3 . ? -1.231 -27.629 -20.903 1.00 30.15 ? ? ? ? ? ? 319 HOH U O 1
+HETATM 2050 O O . HOH C 3 . ? -0.170 -17.083 -6.735 1.00 38.06 ? ? ? ? ? ? 320 HOH U O 1
+HETATM 2051 O O . HOH C 3 . ? -7.957 -36.517 1.153 1.00 39.86 ? ? ? ? ? ? 321 HOH U O 1
+HETATM 2052 O O . HOH C 3 . ? -17.811 -29.994 -2.210 1.00 40.20 ? ? ? ? ? ? 322 HOH U O 1
+HETATM 2053 O O . HOH C 3 . ? 5.563 -42.564 -0.545 1.00 42.63 ? ? ? ? ? ? 323 HOH U O 1
+HETATM 2054 O O . HOH C 3 . ? 0.081 -34.268 -11.373 1.00 25.90 ? ? ? ? ? ? 324 HOH U O 1
+HETATM 2055 O O . HOH C 3 . ? -5.368 -52.995 -14.023 1.00 54.06 ? ? ? ? ? ? 325 HOH U O 1
+HETATM 2056 O O . HOH C 3 . ? -5.478 -54.040 -17.122 1.00 50.78 ? ? ? ? ? ? 326 HOH U O 1
+HETATM 2057 O O . HOH C 3 . ? -10.145 -33.448 -21.719 1.00 22.58 ? ? ? ? ? ? 327 HOH U O 1
+HETATM 2058 O O . HOH C 3 . ? -11.202 -30.221 -27.821 1.00 25.86 ? ? ? ? ? ? 328 HOH U O 1
+HETATM 2059 O O . HOH C 3 . ? -19.287 -38.659 -21.394 1.00 42.56 ? ? ? ? ? ? 329 HOH U O 1
+HETATM 2060 O O . HOH C 3 . ? 6.284 -48.673 1.226 1.00 51.54 ? ? ? ? ? ? 330 HOH U O 1
+HETATM 2061 O O . HOH C 3 . ? 5.772 -43.998 -10.624 1.00 31.93 ? ? ? ? ? ? 331 HOH U O 1
+HETATM 2062 O O . HOH C 3 . ? -13.193 -17.030 -9.501 1.00 49.17 ? ? ? ? ? ? 332 HOH U O 1
+HETATM 2063 O O . HOH C 3 . ? -5.134 -42.308 -2.689 1.00 46.68 ? ? ? ? ? ? 333 HOH U O 1
+HETATM 2064 O O . HOH C 3 . ? -18.403 -32.212 1.364 1.00 48.20 ? ? ? ? ? ? 334 HOH U O 1
+HETATM 2065 O O . HOH C 3 . ? 0.333 -52.903 -21.942 1.00 55.02 ? ? ? ? ? ? 335 HOH U O 1
+HETATM 2066 O O . HOH C 3 . ? 11.524 -22.951 -8.970 1.00 41.19 ? ? ? ? ? ? 336 HOH U O 1
+HETATM 2067 O O . HOH C 3 . ? -15.285 -25.840 -26.512 1.00 41.37 ? ? ? ? ? ? 337 HOH U O 1
+HETATM 2068 O O . HOH C 3 . ? 15.757 -35.672 -9.161 1.00 52.36 ? ? ? ? ? ? 338 HOH U O 1
+HETATM 2069 O O . HOH C 3 . ? -13.785 -29.782 -26.953 1.00 36.62 ? ? ? ? ? ? 339 HOH U O 1
+HETATM 2070 O O . HOH C 3 . ? 17.277 -38.677 -25.037 1.00 61.25 ? ? ? ? ? ? 340 HOH U O 1
+HETATM 2071 O O . HOH C 3 . ? 7.298 -24.742 -21.763 1.00 37.07 ? ? ? ? ? ? 341 HOH U O 1
+HETATM 2072 O O . HOH C 3 . ? 8.838 -20.977 1.814 1.00 53.21 ? ? ? ? ? ? 342 HOH U O 1
+HETATM 2073 O O . HOH C 3 . ? -8.819 -14.105 -10.535 1.00 69.60 ? ? ? ? ? ? 343 HOH U O 1
+HETATM 2074 O O . HOH C 3 . ? -2.071 -20.104 -20.584 1.00 36.73 ? ? ? ? ? ? 344 HOH U O 1
+HETATM 2075 O O . HOH C 3 . ? 8.785 -19.682 -5.908 1.00 43.33 ? ? ? ? ? ? 345 HOH U O 1
+HETATM 2076 O O . HOH C 3 . ? -4.607 -24.422 -29.749 1.00 50.81 ? ? ? ? ? ? 346 HOH U O 1
+HETATM 2077 O O . HOH C 3 . ? 0.634 -19.498 -22.707 1.00 59.12 ? ? ? ? ? ? 347 HOH U O 1
+HETATM 2078 O O . HOH C 3 . ? 14.723 -37.697 -7.396 1.00 54.19 ? ? ? ? ? ? 348 HOH U O 1
+HETATM 2079 O O . HOH D 3 . ? 2.769 -21.902 -31.481 1.00 49.70 ? ? ? ? ? ? 101 HOH P O 1
+HETATM 2080 O O . HOH D 3 . ? 6.454 -23.113 -29.929 1.00 45.25 ? ? ? ? ? ? 102 HOH P O 1
+#
+loop_
+_pdbx_poly_seq_scheme.asym_id
+_pdbx_poly_seq_scheme.entity_id
+_pdbx_poly_seq_scheme.seq_id
+_pdbx_poly_seq_scheme.mon_id
+_pdbx_poly_seq_scheme.ndb_seq_num
+_pdbx_poly_seq_scheme.pdb_seq_num
+_pdbx_poly_seq_scheme.auth_seq_num
+_pdbx_poly_seq_scheme.pdb_mon_id
+_pdbx_poly_seq_scheme.auth_mon_id
+_pdbx_poly_seq_scheme.pdb_strand_id
+_pdbx_poly_seq_scheme.pdb_ins_code
+_pdbx_poly_seq_scheme.hetero
+A 1 1 ILE 1 16 16 ILE ILE U . n
+A 1 2 ILE 2 17 17 ILE ILE U . n
+A 1 3 GLY 3 18 18 GLY GLY U . n
+A 1 4 GLY 4 19 19 GLY GLY U . n
+A 1 5 GLU 5 20 20 GLU GLU U . n
+A 1 6 PHE 6 21 21 PHE PHE U . n
+A 1 7 THR 7 22 22 THR THR U . n
+A 1 8 THR 8 23 23 THR THR U . n
+A 1 9 ILE 9 24 24 ILE ILE U . n
+A 1 10 GLU 10 25 25 GLU GLU U . n
+A 1 11 ASN 11 26 26 ASN ASN U . n
+A 1 12 GLN 12 27 27 GLN GLN U . n
+A 1 13 PRO 13 28 28 PRO PRO U . n
+A 1 14 TRP 14 29 29 TRP TRP U . n
+A 1 15 PHE 15 30 30 PHE PHE U . n
+A 1 16 ALA 16 31 31 ALA ALA U . n
+A 1 17 ALA 17 32 32 ALA ALA U . n
+A 1 18 ILE 18 33 33 ILE ILE U . n
+A 1 19 TYR 19 34 34 TYR TYR U . n
+A 1 20 ARG 20 35 35 ARG ARG U . n
+A 1 21 ARG 21 36 36 ARG ARG U . n
+A 1 22 HIS 22 37 37 HIS HIS U . n
+A 1 23 ARG 23 37 37 ARG ARG U A n
+A 1 24 GLY 24 37 37 GLY GLY U B n
+A 1 25 GLY 25 37 37 GLY GLY U C n
+A 1 26 SER 26 37 37 SER SER U D n
+A 1 27 VAL 27 38 38 VAL VAL U . n
+A 1 28 THR 28 39 39 THR THR U . n
+A 1 29 TYR 29 40 40 TYR TYR U . n
+A 1 30 VAL 30 41 41 VAL VAL U . n
+A 1 31 CYS 31 42 42 CYS CYS U . n
+A 1 32 GLY 32 43 43 GLY GLY U . n
+A 1 33 GLY 33 44 44 GLY GLY U . n
+A 1 34 SER 34 45 45 SER SER U . n
+A 1 35 LEU 35 46 46 LEU LEU U . n
+A 1 36 ILE 36 47 47 ILE ILE U . n
+A 1 37 SER 37 48 48 SER SER U . n
+A 1 38 PRO 38 49 49 PRO PRO U . n
+A 1 39 CYS 39 50 50 CYS CYS U . n
+A 1 40 TRP 40 51 51 TRP TRP U . n
+A 1 41 VAL 41 52 52 VAL VAL U . n
+A 1 42 ILE 42 53 53 ILE ILE U . n
+A 1 43 SER 43 54 54 SER SER U . n
+A 1 44 ALA 44 55 55 ALA ALA U . n
+A 1 45 THR 45 56 56 THR THR U . n
+A 1 46 HIS 46 57 57 HIS HIS U . n
+A 1 47 CYS 47 58 58 CYS CYS U . n
+A 1 48 PHE 48 59 59 PHE PHE U . n
+A 1 49 ILE 49 60 60 ILE ILE U . n
+A 1 50 ASP 50 60 60 ASP ASP U A n
+A 1 51 TYR 51 60 60 TYR TYR U B n
+A 1 52 PRO 52 60 60 PRO PRO U C n
+A 1 53 LYS 53 61 61 LYS LYS U . n
+A 1 54 LYS 54 62 62 LYS LYS U . n
+A 1 55 GLU 55 62 62 GLU GLU U A n
+A 1 56 ASP 56 63 63 ASP ASP U . n
+A 1 57 TYR 57 64 64 TYR TYR U . n
+A 1 58 ILE 58 65 65 ILE ILE U . n
+A 1 59 VAL 59 66 66 VAL VAL U . n
+A 1 60 TYR 60 67 67 TYR TYR U . n
+A 1 61 LEU 61 68 68 LEU LEU U . n
+A 1 62 GLY 62 69 69 GLY GLY U . n
+A 1 63 ARG 63 70 70 ARG ARG U . n
+A 1 64 SER 64 71 71 SER SER U . n
+A 1 65 ARG 65 72 72 ARG ARG U . n
+A 1 66 LEU 66 73 73 LEU LEU U . n
+A 1 67 ASN 67 74 74 ASN ASN U . n
+A 1 68 SER 68 75 75 SER SER U . n
+A 1 69 ASN 69 76 76 ASN ASN U . n
+A 1 70 THR 70 77 77 THR THR U . n
+A 1 71 GLN 71 78 78 GLN GLN U . n
+A 1 72 GLY 72 79 79 GLY GLY U . n
+A 1 73 GLU 73 80 80 GLU GLU U . n
+A 1 74 MET 74 81 81 MET MET U . n
+A 1 75 LYS 75 82 82 LYS LYS U . n
+A 1 76 PHE 76 83 83 PHE PHE U . n
+A 1 77 GLU 77 84 84 GLU GLU U . n
+A 1 78 VAL 78 85 85 VAL VAL U . n
+A 1 79 GLU 79 86 86 GLU GLU U . n
+A 1 80 ASN 80 87 87 ASN ASN U . n
+A 1 81 LEU 81 88 88 LEU LEU U . n
+A 1 82 ILE 82 89 89 ILE ILE U . n
+A 1 83 LEU 83 90 90 LEU LEU U . n
+A 1 84 HIS 84 91 91 HIS HIS U . n
+A 1 85 LYS 85 92 92 LYS LYS U . n
+A 1 86 ASP 86 93 93 ASP ASP U . n
+A 1 87 TYR 87 94 94 TYR TYR U . n
+A 1 88 SER 88 95 95 SER SER U . n
+A 1 89 ALA 89 96 96 ALA ALA U . n
+A 1 90 ASP 90 97 97 ASP ASP U . n
+A 1 91 THR 91 97 97 THR THR U A n
+A 1 92 LEU 92 97 97 LEU LEU U B n
+A 1 93 ALA 93 98 98 ALA ALA U . n
+A 1 94 TYR 94 99 99 TYR TYR U . n
+A 1 95 HIS 95 100 100 HIS HIS U . n
+A 1 96 ASN 96 101 101 ASN ASN U . n
+A 1 97 ASP 97 102 102 ASP ASP U . n
+A 1 98 ILE 98 103 103 ILE ILE U . n
+A 1 99 ALA 99 104 104 ALA ALA U . n
+A 1 100 LEU 100 105 105 LEU LEU U . n
+A 1 101 LEU 101 106 106 LEU LEU U . n
+A 1 102 LYS 102 107 107 LYS LYS U . n
+A 1 103 ILE 103 108 108 ILE ILE U . n
+A 1 104 ARG 104 109 109 ARG ARG U . n
+A 1 105 SER 105 110 110 SER SER U . n
+A 1 106 LYS 106 110 110 LYS LYS U A n
+A 1 107 GLU 107 110 110 GLU GLU U B n
+A 1 108 GLY 108 110 110 GLY GLY U C n
+A 1 109 ARG 109 110 110 ARG ARG U D n
+A 1 110 CYS 110 111 111 CYS CYS U . n
+A 1 111 ALA 111 112 112 ALA ALA U . n
+A 1 112 GLN 112 113 113 GLN GLN U . n
+A 1 113 PRO 113 114 114 PRO PRO U . n
+A 1 114 SER 114 115 115 SER SER U . n
+A 1 115 ARG 115 116 116 ARG ARG U . n
+A 1 116 THR 116 117 117 THR THR U . n
+A 1 117 ILE 117 118 118 ILE ILE U . n
+A 1 118 GLN 118 119 119 GLN GLN U . n
+A 1 119 THR 119 120 120 THR THR U . n
+A 1 120 ILE 120 121 121 ILE ILE U . n
+A 1 121 ALA 121 122 122 ALA ALA U . n
+A 1 122 LEU 122 123 123 LEU LEU U . n
+A 1 123 PRO 123 124 124 PRO PRO U . n
+A 1 124 SER 124 125 125 SER SER U . n
+A 1 125 MET 125 126 126 MET MET U . n
+A 1 126 TYR 126 127 127 TYR TYR U . n
+A 1 127 ASN 127 128 128 ASN ASN U . n
+A 1 128 ASP 128 129 129 ASP ASP U . n
+A 1 129 PRO 129 130 130 PRO PRO U . n
+A 1 130 GLN 130 131 131 GLN GLN U . n
+A 1 131 PHE 131 132 132 PHE PHE U . n
+A 1 132 GLY 132 133 133 GLY GLY U . n
+A 1 133 THR 133 134 134 THR THR U . n
+A 1 134 SER 134 135 135 SER SER U . n
+A 1 135 CYS 135 136 136 CYS CYS U . n
+A 1 136 GLU 136 137 137 GLU GLU U . n
+A 1 137 ILE 137 138 138 ILE ILE U . n
+A 1 138 THR 138 139 139 THR THR U . n
+A 1 139 GLY 139 140 140 GLY GLY U . n
+A 1 140 PHE 140 141 141 PHE PHE U . n
+A 1 141 GLY 141 142 142 GLY GLY U . n
+A 1 142 LYS 142 143 143 LYS LYS U . n
+A 1 143 GLU 143 144 144 GLU GLU U . n
+A 1 144 GLN 144 145 145 GLN GLN U . n
+A 1 145 SER 145 146 146 SER SER U . n
+A 1 146 THR 146 147 147 THR THR U . n
+A 1 147 ASP 147 148 148 ASP ASP U . n
+A 1 148 TYR 148 149 149 TYR TYR U . n
+A 1 149 LEU 149 150 150 LEU LEU U . n
+A 1 150 TYR 150 151 151 TYR TYR U . n
+A 1 151 PRO 151 152 152 PRO PRO U . n
+A 1 152 GLU 152 153 153 GLU GLU U . n
+A 1 153 GLN 153 154 154 GLN GLN U . n
+A 1 154 LEU 154 155 155 LEU LEU U . n
+A 1 155 LYS 155 156 156 LYS LYS U . n
+A 1 156 MET 156 157 157 MET MET U . n
+A 1 157 THR 157 158 158 THR THR U . n
+A 1 158 VAL 158 159 159 VAL VAL U . n
+A 1 159 VAL 159 160 160 VAL VAL U . n
+A 1 160 LYS 160 161 161 LYS LYS U . n
+A 1 161 LEU 161 162 162 LEU LEU U . n
+A 1 162 ILE 162 163 163 ILE ILE U . n
+A 1 163 SER 163 164 164 SER SER U . n
+A 1 164 HIS 164 165 165 HIS HIS U . n
+A 1 165 ARG 165 166 166 ARG ARG U . n
+A 1 166 GLU 166 167 167 GLU GLU U . n
+A 1 167 CYS 167 168 168 CYS CYS U . n
+A 1 168 GLN 168 169 169 GLN GLN U . n
+A 1 169 GLN 169 170 170 GLN GLN U . n
+A 1 170 PRO 170 170 170 PRO PRO U A n
+A 1 171 HIS 171 170 170 HIS HIS U B n
+A 1 172 TYR 172 171 171 TYR TYR U . n
+A 1 173 TYR 173 172 172 TYR TYR U . n
+A 1 174 GLY 174 173 173 GLY GLY U . n
+A 1 175 SER 175 174 174 SER SER U . n
+A 1 176 GLU 176 175 175 GLU GLU U . n
+A 1 177 VAL 177 176 176 VAL VAL U . n
+A 1 178 THR 178 177 177 THR THR U . n
+A 1 179 THR 179 178 178 THR THR U . n
+A 1 180 LYS 180 179 179 LYS LYS U . n
+A 1 181 MET 181 180 180 MET MET U . n
+A 1 182 LEU 182 181 181 LEU LEU U . n
+A 1 183 CYS 183 182 182 CYS CYS U . n
+A 1 184 ALA 184 183 183 ALA ALA U . n
+A 1 185 ALA 185 184 184 ALA ALA U . n
+A 1 186 ASP 186 185 185 ASP ASP U . n
+A 1 187 PRO 187 185 185 PRO PRO U A n
+A 1 188 GLN 188 185 185 GLN GLN U B n
+A 1 189 TRP 189 186 186 TRP TRP U . n
+A 1 190 LYS 190 187 187 LYS LYS U . n
+A 1 191 THR 191 188 188 THR THR U . n
+A 1 192 ASP 192 189 189 ASP ASP U . n
+A 1 193 SER 193 190 190 SER SER U . n
+A 1 194 CYS 194 191 191 CYS CYS U . n
+A 1 195 GLN 195 192 192 GLN GLN U . n
+A 1 196 GLY 196 193 193 GLY GLY U . n
+A 1 197 ASP 197 194 194 ASP ASP U . n
+A 1 198 SER 198 195 195 SER SER U . n
+A 1 199 GLY 199 196 196 GLY GLY U . n
+A 1 200 GLY 200 197 197 GLY GLY U . n
+A 1 201 PRO 201 198 198 PRO PRO U . n
+A 1 202 LEU 202 199 199 LEU LEU U . n
+A 1 203 VAL 203 200 200 VAL VAL U . n
+A 1 204 CYS 204 201 201 CYS CYS U . n
+A 1 205 SER 205 202 202 SER SER U . n
+A 1 206 LEU 206 203 203 LEU LEU U . n
+A 1 207 GLN 207 204 204 GLN GLN U . n
+A 1 208 GLY 208 205 205 GLY GLY U . n
+A 1 209 ARG 209 206 206 ARG ARG U . n
+A 1 210 MET 210 207 207 MET MET U . n
+A 1 211 THR 211 208 208 THR THR U . n
+A 1 212 LEU 212 209 209 LEU LEU U . n
+A 1 213 THR 213 210 210 THR THR U . n
+A 1 214 GLY 214 211 211 GLY GLY U . n
+A 1 215 ILE 215 212 212 ILE ILE U . n
+A 1 216 VAL 216 213 213 VAL VAL U . n
+A 1 217 SER 217 214 214 SER SER U . n
+A 1 218 TRP 218 215 215 TRP TRP U . n
+A 1 219 GLY 219 216 216 GLY GLY U . n
+A 1 220 ARG 220 217 217 ARG ARG U . n
+A 1 221 GLY 221 219 219 GLY GLY U . n
+A 1 222 CYS 222 220 220 CYS CYS U . n
+A 1 223 ALA 223 221 221 ALA ALA U . n
+A 1 224 LEU 224 222 222 LEU LEU U . n
+A 1 225 LYS 225 223 223 LYS LYS U . n
+A 1 226 ASP 226 223 223 ASP ASP U A n
+A 1 227 LYS 227 224 224 LYS LYS U . n
+A 1 228 PRO 228 225 225 PRO PRO U . n
+A 1 229 GLY 229 226 226 GLY GLY U . n
+A 1 230 VAL 230 227 227 VAL VAL U . n
+A 1 231 TYR 231 228 228 TYR TYR U . n
+A 1 232 THR 232 229 229 THR THR U . n
+A 1 233 ARG 233 230 230 ARG ARG U . n
+A 1 234 VAL 234 231 231 VAL VAL U . n
+A 1 235 SER 235 232 232 SER SER U . n
+A 1 236 HIS 236 233 233 HIS HIS U . n
+A 1 237 PHE 237 234 234 PHE PHE U . n
+A 1 238 LEU 238 235 235 LEU LEU U . n
+A 1 239 PRO 239 236 236 PRO PRO U . n
+A 1 240 TRP 240 237 237 TRP TRP U . n
+A 1 241 ILE 241 238 238 ILE ILE U . n
+A 1 242 ARG 242 239 239 ARG ARG U . n
+A 1 243 SER 243 240 240 SER SER U . n
+A 1 244 HIS 244 241 241 HIS HIS U . n
+A 1 245 THR 245 242 242 THR THR U . n
+A 1 246 LYS 246 243 243 LYS LYS U . n
+A 1 247 GLU 247 244 244 GLU GLU U . n
+B 2 1 CYS 1 1 1 CYS CYS P . n
+B 2 2 PRO 2 2 2 PRO PRO P . n
+B 2 3 ALA 3 3 3 ALA ALA P . n
+B 2 4 TYR 4 4 4 TYR TYR P . n
+B 2 5 SER 5 5 5 SER SER P . n
+B 2 6 ARG 6 6 6 ARG ARG P . n
+B 2 7 TYR 7 7 7 TYR TYR P . n
+B 2 8 ILE 8 8 8 ILE ILE P . n
+B 2 9 GLY 9 9 9 GLY GLY P . n
+B 2 10 CYS 10 10 10 CYS CYS P . n
+#
+loop_
+_pdbx_nonpoly_scheme.asym_id
+_pdbx_nonpoly_scheme.entity_id
+_pdbx_nonpoly_scheme.mon_id
+_pdbx_nonpoly_scheme.ndb_seq_num
+_pdbx_nonpoly_scheme.pdb_seq_num
+_pdbx_nonpoly_scheme.auth_seq_num
+_pdbx_nonpoly_scheme.pdb_mon_id
+_pdbx_nonpoly_scheme.auth_mon_id
+_pdbx_nonpoly_scheme.pdb_strand_id
+_pdbx_nonpoly_scheme.pdb_ins_code
+C 3 HOH 1 301 6 HOH HOH U .
+C 3 HOH 2 302 19 HOH HOH U .
+C 3 HOH 3 303 4 HOH HOH U .
+C 3 HOH 4 304 33 HOH HOH U .
+C 3 HOH 5 305 9 HOH HOH U .
+C 3 HOH 6 306 50 HOH HOH U .
+C 3 HOH 7 307 46 HOH HOH U .
+C 3 HOH 8 308 35 HOH HOH U .
+C 3 HOH 9 309 12 HOH HOH U .
+C 3 HOH 10 310 40 HOH HOH U .
+C 3 HOH 11 311 44 HOH HOH U .
+C 3 HOH 12 312 45 HOH HOH U .
+C 3 HOH 13 313 39 HOH HOH U .
+C 3 HOH 14 314 5 HOH HOH U .
+C 3 HOH 15 315 43 HOH HOH U .
+C 3 HOH 16 316 21 HOH HOH U .
+C 3 HOH 17 317 7 HOH HOH U .
+C 3 HOH 18 318 8 HOH HOH U .
+C 3 HOH 19 319 30 HOH HOH U .
+C 3 HOH 20 320 1 HOH HOH U .
+C 3 HOH 21 321 22 HOH HOH U .
+C 3 HOH 22 322 23 HOH HOH U .
+C 3 HOH 23 323 17 HOH HOH U .
+C 3 HOH 24 324 11 HOH HOH U .
+C 3 HOH 25 325 47 HOH HOH U .
+C 3 HOH 26 326 31 HOH HOH U .
+C 3 HOH 27 327 18 HOH HOH U .
+C 3 HOH 28 328 10 HOH HOH U .
+C 3 HOH 29 329 34 HOH HOH U .
+C 3 HOH 30 330 16 HOH HOH U .
+C 3 HOH 31 331 3 HOH HOH U .
+C 3 HOH 32 332 41 HOH HOH U .
+C 3 HOH 33 333 24 HOH HOH U .
+C 3 HOH 34 334 13 HOH HOH U .
+C 3 HOH 35 335 36 HOH HOH U .
+C 3 HOH 36 336 20 HOH HOH U .
+C 3 HOH 37 337 2 HOH HOH U .
+C 3 HOH 38 338 25 HOH HOH U .
+C 3 HOH 39 339 29 HOH HOH U .
+C 3 HOH 40 340 28 HOH HOH U .
+C 3 HOH 41 341 42 HOH HOH U .
+C 3 HOH 42 342 37 HOH HOH U .
+C 3 HOH 43 343 26 HOH HOH U .
+C 3 HOH 44 344 14 HOH HOH U .
+C 3 HOH 45 345 32 HOH HOH U .
+C 3 HOH 46 346 15 HOH HOH U .
+C 3 HOH 47 347 38 HOH HOH U .
+C 3 HOH 48 348 27 HOH HOH U .
+D 3 HOH 1 101 48 HOH HOH P .
+D 3 HOH 2 102 49 HOH HOH P .
+#
+_pdbx_struct_assembly.id 1
+_pdbx_struct_assembly.details author_and_software_defined_assembly
+_pdbx_struct_assembly.method_details PISA
+_pdbx_struct_assembly.oligomeric_details dimeric
+_pdbx_struct_assembly.oligomeric_count 2
+#
+_pdbx_struct_assembly_gen.assembly_id 1
+_pdbx_struct_assembly_gen.oper_expression 1
+_pdbx_struct_assembly_gen.asym_id_list A,B,C,D
+#
+loop_
+_pdbx_struct_assembly_prop.biol_id
+_pdbx_struct_assembly_prop.type
+_pdbx_struct_assembly_prop.value
+_pdbx_struct_assembly_prop.details
+1 'ABSA (A^2)' 1460 ?
+1 MORE -7 ?
+1 'SSA (A^2)' 11300 ?
+#
+_pdbx_struct_oper_list.id 1
+_pdbx_struct_oper_list.type 'identity operation'
+_pdbx_struct_oper_list.name 1_555
+_pdbx_struct_oper_list.symmetry_operation ?
+_pdbx_struct_oper_list.matrix[1][1] 1.0000000000
+_pdbx_struct_oper_list.matrix[1][2] 0.0000000000
+_pdbx_struct_oper_list.matrix[1][3] 0.0000000000
+_pdbx_struct_oper_list.vector[1] 0.0000000000
+_pdbx_struct_oper_list.matrix[2][1] 0.0000000000
+_pdbx_struct_oper_list.matrix[2][2] 1.0000000000
+_pdbx_struct_oper_list.matrix[2][3] 0.0000000000
+_pdbx_struct_oper_list.vector[2] 0.0000000000
+_pdbx_struct_oper_list.matrix[3][1] 0.0000000000
+_pdbx_struct_oper_list.matrix[3][2] 0.0000000000
+_pdbx_struct_oper_list.matrix[3][3] 1.0000000000
+_pdbx_struct_oper_list.vector[3] 0.0000000000
+#
+loop_
+_pdbx_version.entry_id
+_pdbx_version.revision_date
+_pdbx_version.major_version
+_pdbx_version.minor_version
+_pdbx_version.revision_type
+_pdbx_version.details
+4ZHL 2015-09-16 4 0000 'Initial release' 'Initial release'
+4ZHL 2015-10-14 4 0001 Citation Citation
+#
+loop_
+_software.citation_id
+_software.classification
+_software.compiler_name
+_software.compiler_version
+_software.contact_author
+_software.contact_author_email
+_software.date
+_software.description
+_software.dependencies
+_software.hardware
+_software.language
+_software.location
+_software.mods
+_software.name
+_software.os
+_software.os_version
+_software.type
+_software.version
+_software.pdbx_ordinal
+? refinement ? ? ? ? ? ? ? ? ? ? ? REFMAC ? ? ? 5.7.0029 1
+? 'model building' ? ? ? ? ? ? ? ? ? ? ? Coot ? ? ? . 2
+? 'data scaling' ? ? ? ? ? ? ? ? ? ? ? HKL-2000 ? ? ? . 3
+? 'data processing' ? ? ? ? ? ? ? ? ? ? ? HKL-2000 ? ? ? . 4
+#
+loop_
+_pdbx_validate_close_contact.id
+_pdbx_validate_close_contact.PDB_model_num
+_pdbx_validate_close_contact.auth_atom_id_1
+_pdbx_validate_close_contact.auth_asym_id_1
+_pdbx_validate_close_contact.auth_comp_id_1
+_pdbx_validate_close_contact.auth_seq_id_1
+_pdbx_validate_close_contact.PDB_ins_code_1
+_pdbx_validate_close_contact.label_alt_id_1
+_pdbx_validate_close_contact.auth_atom_id_2
+_pdbx_validate_close_contact.auth_asym_id_2
+_pdbx_validate_close_contact.auth_comp_id_2
+_pdbx_validate_close_contact.auth_seq_id_2
+_pdbx_validate_close_contact.PDB_ins_code_2
+_pdbx_validate_close_contact.label_alt_id_2
+_pdbx_validate_close_contact.dist
+1 1 SG U CYS 136 ? ? SG U CYS 201 ? ? 1.51
+2 1 SG U CYS 50 ? ? SG U CYS 111 ? ? 1.64
+3 1 NH2 U ARG 36 ? ? O U GLU 62 A ? 2.17
+#
+_pdbx_validate_symm_contact.id 1
+_pdbx_validate_symm_contact.PDB_model_num 1
+_pdbx_validate_symm_contact.auth_atom_id_1 OD1
+_pdbx_validate_symm_contact.auth_asym_id_1 U
+_pdbx_validate_symm_contact.auth_comp_id_1 ASN
+_pdbx_validate_symm_contact.auth_seq_id_1 76
+_pdbx_validate_symm_contact.PDB_ins_code_1 ?
+_pdbx_validate_symm_contact.label_alt_id_1 ?
+_pdbx_validate_symm_contact.site_symmetry_1 1_555
+_pdbx_validate_symm_contact.auth_atom_id_2 NH1
+_pdbx_validate_symm_contact.auth_asym_id_2 U
+_pdbx_validate_symm_contact.auth_comp_id_2 ARG
+_pdbx_validate_symm_contact.auth_seq_id_2 206
+_pdbx_validate_symm_contact.PDB_ins_code_2 ?
+_pdbx_validate_symm_contact.label_alt_id_2 ?
+_pdbx_validate_symm_contact.site_symmetry_2 8_444
+_pdbx_validate_symm_contact.dist 1.87
+#
+_pdbx_validate_rmsd_bond.id 1
+_pdbx_validate_rmsd_bond.PDB_model_num 1
+_pdbx_validate_rmsd_bond.auth_atom_id_1 C
+_pdbx_validate_rmsd_bond.auth_asym_id_1 U
+_pdbx_validate_rmsd_bond.auth_comp_id_1 SER
+_pdbx_validate_rmsd_bond.auth_seq_id_1 110
+_pdbx_validate_rmsd_bond.PDB_ins_code_1 ?
+_pdbx_validate_rmsd_bond.label_alt_id_1 ?
+_pdbx_validate_rmsd_bond.auth_atom_id_2 O
+_pdbx_validate_rmsd_bond.auth_asym_id_2 U
+_pdbx_validate_rmsd_bond.auth_comp_id_2 SER
+_pdbx_validate_rmsd_bond.auth_seq_id_2 110
+_pdbx_validate_rmsd_bond.PDB_ins_code_2 ?
+_pdbx_validate_rmsd_bond.label_alt_id_2 ?
+_pdbx_validate_rmsd_bond.bond_value 1.101
+_pdbx_validate_rmsd_bond.bond_target_value 1.229
+_pdbx_validate_rmsd_bond.bond_deviation -0.128
+_pdbx_validate_rmsd_bond.bond_standard_deviation 0.019
+_pdbx_validate_rmsd_bond.linker_flag N
+#
+loop_
+_pdbx_validate_torsion.id
+_pdbx_validate_torsion.PDB_model_num
+_pdbx_validate_torsion.auth_comp_id
+_pdbx_validate_torsion.auth_asym_id
+_pdbx_validate_torsion.auth_seq_id
+_pdbx_validate_torsion.PDB_ins_code
+_pdbx_validate_torsion.label_alt_id
+_pdbx_validate_torsion.phi
+_pdbx_validate_torsion.psi
+1 1 GLN U 27 ? ? -157.33 54.96
+2 1 SER U 54 ? ? -148.41 -157.39
+3 1 TYR U 171 ? ? -89.01 -111.29
+4 1 GLN U 204 ? ? 58.66 -126.06
+#
+_pdbx_entity_nonpoly.entity_id 3
+_pdbx_entity_nonpoly.name water
+_pdbx_entity_nonpoly.comp_id HOH
+#
diff --git a/Tests/PDB/a_structure.pdb b/Tests/PDB/a_structure.pdb
index ca1e3eb..3845eb8 100644
--- a/Tests/PDB/a_structure.pdb
+++ b/Tests/PDB/a_structure.pdb
@@ -1,4 +1,4 @@
-REMARK INTRODUCED BUGS IN RESIDUES 2,3,4 AND WAT 67
+REMARK INTRODUCED BUGS IN RESIDUES 2,3,4 AND WAT 67
REMARK 2 DUPLICATE N
REMARK 3 DISORDERED ATOM WITH BLANC ALTLOC
REMARK 4 CHANGE IN RESNAME
@@ -6,826 +6,826 @@ REMARK WAT 67 IS PRESENT TWICE
REMARK RESIDUE 0 SHOULD GENERATE A POLYPEPTIDE WARNING (DUPLICATED C ATOM)
REMARK MISSING ELEMENTS IN THE FIRST RESIDUE
MODEL
-HETATM 1 N PCA A 1 0.525 2.690 13.317 1.00 20.26
-HETATM 2 CA PCA A 1 -0.993 2.924 13.160 1.00 21.23
-HETATM 3 CB PCA A 1 -1.783 1.879 14.043 1.00 24.73 C
-HETATM 4 CG PCA A 1 -0.609 1.208 14.621 1.00 22.31 C
-HETATM 5 CD PCA A 1 0.869 1.560 14.229 1.00 23.27 C
-HETATM 6 OE PCA A 1 2.053 1.323 14.293 1.00 26.24 O
-HETATM 7 C PCA A 1 -1.375 4.270 13.719 1.00 19.21 C
-HETATM 8 O PCA A 1 -0.389 5.021 14.230 1.00 18.05 O
-HETATM 8 CA PCA A 1 -0.189 5.321 14.250 1.00 18.06
+HETATM 1 N PCA A 1 0.525 2.690 13.317 1.00 20.26 A
+HETATM 2 CA PCA A 1 -0.993 2.924 13.160 1.00 21.23 A
+HETATM 3 CB PCA A 1 -1.783 1.879 14.043 1.00 24.73 C
+HETATM 4 CG PCA A 1 -0.609 1.208 14.621 1.00 22.31 C
+HETATM 5 CD PCA A 1 0.869 1.560 14.229 1.00 23.27 C
+HETATM 6 OE PCA A 1 2.053 1.323 14.293 1.00 26.24 O
+HETATM 7 C PCA A 1 -1.375 4.270 13.719 1.00 19.21 C
+HETATM 8 O PCA A 1 -0.389 5.021 14.230 1.00 18.05 O
+HETATM 8 CA PCA A 1 -0.189 5.321 14.250 1.00 18.06
ENDMDL
-ATOM 22 C CYS A 0 -3.637 9.026 17.672 1.00 16.22 C
-ATOM 9 N ARG A 2 -2.607 4.673 13.504 1.00 20.57 N
-ATOM 9 N ARG A 2 -2.607 4.673 13.504 1.00 20.57 N
-ATOM 10 CA ARG A 2 -3.091 6.010 13.918 1.00 20.82 C
-ATOM 11 C ARG A 2 -3.361 6.101 15.403 1.00 19.55 C
-ATOM 12 O ARG A 2 -3.742 5.121 16.050 1.00 19.99 O
-ATOM 13 CB ARG A 2 -4.348 6.395 13.124 1.00 25.30 C
-ATOM 14 CG ARG A 2 -4.110 6.337 11.619 1.00 30.98 C
-ATOM 15 CD ARG A 2 -4.277 7.652 10.942 1.00 31.79 C
-ATOM 16 NE ARG A 2 -3.441 8.730 11.457 1.00 31.91 N
-ATOM 17 CZ ARG A 2 -3.550 9.979 10.980 1.00 31.95 C
-ATOM 18 NH1 ARG A 2 -4.444 10.212 10.022 1.00 33.34 N
-ATOM 19 NH2 ARG A 2 -2.804 10.949 11.444 1.00 29.59 N
-ATOM 20 N CYS A 3 -3.248 7.317 15.948 1.00 15.71 N
-ATOM 20 N ACYS A 3 -3.248 7.317 15.948 1.00 15.71 N
-ATOM 21 CA CYS A 3 -3.419 7.555 17.361 1.00 14.13 C
-ATOM 22 C CYS A 3 -3.637 9.026 17.672 1.00 16.22 C
-ATOM 23 O CYS A 3 -3.406 9.901 16.828 1.00 15.46 O
-ATOM 24 CB CYS A 3 -2.127 7.069 18.110 1.00 13.74 C
-ATOM 25 SG CYS A 3 -0.659 7.917 17.525 1.00 11.51 S
-ATOM 26 N GLY A 4 -4.122 9.328 18.863 1.00 15.45 N
-ATOM 27 CA GLY A 4 -4.129 10.656 19.402 1.00 17.05 C
-ATOM 28 C GLY A 4 -5.029 11.674 18.735 1.00 16.11 C
-ATOM 29 O GLY A 4 -6.039 11.345 18.125 1.00 17.88 O
-ATOM 29 O SER A 4 -6.039 11.345 18.125 1.00 17.88 O
-ATOM 30 N SER A 5 -4.651 12.958 18.899 1.00 17.09 N
-ATOM 31 CA SER A 5 -5.559 14.034 18.479 1.00 19.08 C
-ATOM 32 C SER A 5 -5.752 14.104 16.987 1.00 21.30 C
-ATOM 33 O SER A 5 -6.858 14.468 16.528 1.00 22.84 O
-ATOM 34 CB SER A 5 -5.107 15.371 19.047 1.00 19.54 C
-ATOM 35 OG SER A 5 -3.926 15.835 18.448 1.00 21.28 O
-ATOM 36 N GLN A 6 -4.747 13.742 16.200 1.00 20.14 N
-ATOM 37 CA GLN A 6 -4.860 13.800 14.746 1.00 22.55 C
-ATOM 38 C GLN A 6 -5.298 12.480 14.152 1.00 23.94 C
-ATOM 39 O GLN A 6 -5.600 12.407 12.952 1.00 26.03 O
-ATOM 40 CB GLN A 6 -3.493 14.198 14.155 1.00 22.92 C
-ATOM 41 CG GLN A 6 -3.158 15.672 14.364 1.00 26.86 C
-ATOM 42 CD GLN A 6 -4.060 16.549 13.492 1.00 30.27 C
-ATOM 43 OE1 GLN A 6 -4.888 17.280 14.006 1.00 32.91 O
-ATOM 44 NE2 GLN A 6 -3.896 16.416 12.182 1.00 32.97 N
-ATOM 45 N GLY A 7 -5.311 11.413 14.956 1.00 21.65 N
-ATOM 46 CA GLY A 7 -5.574 10.092 14.428 1.00 23.37 C
-ATOM 47 C GLY A 7 -6.707 9.337 15.036 1.00 24.14 C
-ATOM 48 O GLY A 7 -6.704 8.084 15.015 1.00 27.63 O
-ATOM 49 N GLY A 8 -7.727 10.003 15.582 1.00 24.63 N
-ATOM 50 CA GLY A 8 -8.913 9.333 16.063 1.00 25.88 C
-ATOM 51 C GLY A 8 -8.994 9.133 17.546 1.00 26.42 C
-ATOM 52 O GLY A 8 -10.027 8.635 18.054 1.00 27.64 O
-ATOM 53 N GLY A 9 -7.952 9.460 18.305 1.00 24.34 N
-ATOM 54 CA GLY A 9 -7.970 9.377 19.738 1.00 25.05 C
-ATOM 55 C GLY A 9 -7.440 8.111 20.351 1.00 24.44 C
-ATOM 56 O GLY A 9 -7.431 8.002 21.594 1.00 26.91 O
-ATOM 57 N AGLY A 10 -6.973 7.149 19.576 0.67 23.87 N
-ATOM 58 CA AGLY A 10 -6.498 5.882 20.072 0.67 23.51 C
-ATOM 59 C AGLY A 10 -5.157 5.939 20.783 0.67 20.73 C
-ATOM 60 O AGLY A 10 -4.407 6.920 20.735 0.67 18.63 O
-ATOM 61 N BSER A 10 -6.973 7.149 19.576 0.33 23.87 N
-ATOM 62 CA BSER A 10 -6.498 5.882 20.072 0.33 23.51 C
-ATOM 63 C BSER A 10 -5.157 5.939 20.783 0.33 20.73 C
-ATOM 64 O BSER A 10 -4.407 6.920 20.735 0.33 18.63 O
-ATOM 65 CB BSER A 10 -6.423 4.843 18.935 0.33 25.06 C
-ATOM 66 OG BSER A 10 -5.085 4.685 18.488 0.33 26.99 O
-ATOM 67 N THR A 11 -4.827 4.849 21.451 1.00 18.73 N
-ATOM 68 CA THR A 11 -3.558 4.608 22.100 1.00 18.41 C
-ATOM 69 C THR A 11 -2.789 3.579 21.270 1.00 17.02 C
-ATOM 70 O THR A 11 -3.445 2.660 20.722 1.00 19.87 O
-ATOM 71 CB THR A 11 -3.766 4.031 23.526 1.00 21.54 C
-ATOM 72 OG1 THR A 11 -4.454 5.039 24.302 1.00 24.31 O
-ATOM 73 CG2 THR A 11 -2.424 3.745 24.174 1.00 22.15 C
-ATOM 74 N CYS A 12 -1.513 3.704 21.104 1.00 14.16 N
-ATOM 75 CA CYS A 12 -0.739 2.807 20.240 1.00 13.37 C
-ATOM 76 C CYS A 12 -0.634 1.400 20.784 1.00 14.05 C
-ATOM 77 O CYS A 12 -0.393 1.199 21.977 1.00 14.75 O
-ATOM 78 CB CYS A 12 0.693 3.394 20.115 1.00 11.29 C
-ATOM 79 SG CYS A 12 0.658 5.001 19.231 1.00 10.97 S
-ATOM 80 N PRO A 13 -0.523 0.411 19.889 1.00 13.45 N
-ATOM 81 CA PRO A 13 -0.169 -0.942 20.296 1.00 14.93 C
-ATOM 82 C PRO A 13 1.156 -0.911 21.027 1.00 14.45 C
-ATOM 83 O PRO A 13 2.082 -0.150 20.677 1.00 14.74 O
-ATOM 84 CB PRO A 13 -0.029 -1.675 18.958 1.00 15.07 C
-ATOM 85 CG PRO A 13 -0.978 -0.948 18.064 1.00 15.93 C
-ATOM 86 CD PRO A 13 -0.772 0.535 18.443 1.00 14.96 C
-ATOM 87 N AALA A 14 1.268 -1.692 22.089 0.56 14.65 N
-ATOM 88 CA AALA A 14 2.445 -1.814 22.901 0.56 16.97 C
-ATOM 89 C AALA A 14 2.754 -0.531 23.694 0.56 14.77 C
-ATOM 90 O AALA A 14 3.849 -0.405 24.231 0.56 17.39 O
-ATOM 91 CB AALA A 14 3.645 -2.269 22.108 0.56 18.86 C
-ATOM 92 N BGLY A 14 1.268 -1.692 22.089 0.44 14.65 N
-ATOM 93 CA BGLY A 14 2.445 -1.814 22.901 0.44 16.97 C
-ATOM 94 C BGLY A 14 2.754 -0.531 23.694 0.44 14.77 C
-ATOM 95 O BGLY A 14 3.849 -0.405 24.231 0.44 17.39 O
-ATOM 96 N LEU A 15 1.754 0.327 23.778 1.00 13.72 N
-ATOM 97 CA LEU A 15 1.873 1.601 24.487 1.00 13.52 C
-ATOM 98 C LEU A 15 3.033 2.427 23.993 1.00 13.54 C
-ATOM 99 O LEU A 15 3.727 3.109 24.761 1.00 13.53 O
-ATOM 100 CB LEU A 15 1.831 1.406 25.994 1.00 16.99 C
-ATOM 101 CG ALEU A 15 0.485 1.257 26.672 0.50 18.92 C
-ATOM 102 CG BLEU A 15 0.561 0.693 26.511 0.50 18.16 C
-ATOM 103 CD1ALEU A 15 -0.272 2.579 26.709 0.50 19.64 C
-ATOM 104 CD1BLEU A 15 0.675 0.390 27.988 0.50 20.69 C
-ATOM 105 CD2ALEU A 15 -0.364 0.182 26.012 0.50 21.59 C
-ATOM 106 CD2BLEU A 15 -0.677 1.532 26.219 0.50 20.18 C
-ATOM 107 N ATRP A 16 3.298 2.388 22.684 0.56 11.29 N
-ATOM 108 CA ATRP A 16 4.273 3.211 22.015 0.56 11.25 C
-ATOM 109 C ATRP A 16 3.838 4.674 21.911 0.56 9.81 C
-ATOM 110 O ATRP A 16 2.731 5.041 22.272 0.56 10.80 O
-ATOM 111 CB ATRP A 16 4.530 2.673 20.587 0.56 15.06 C
-ATOM 112 CG ATRP A 16 5.500 1.531 20.589 0.56 22.22 C
-ATOM 113 CD1ATRP A 16 5.344 0.300 20.028 0.56 22.99 C
-ATOM 114 CD2ATRP A 16 6.804 1.549 21.180 0.56 23.43 C
-ATOM 115 NE1ATRP A 16 6.469 -0.459 20.244 0.56 25.66 N
-ATOM 116 CE2ATRP A 16 7.380 0.282 20.950 0.56 25.18 C
-ATOM 117 CE3ATRP A 16 7.532 2.508 21.894 0.56 26.28 C
-ATOM 118 CZ2ATRP A 16 8.652 -0.045 21.409 0.56 25.91 C
-ATOM 119 CZ3ATRP A 16 8.793 2.178 22.349 0.56 26.49 C
-ATOM 120 CH2ATRP A 16 9.341 0.908 22.104 0.56 25.88 C
-ATOM 121 N BARG A 16 3.259 2.392 22.674 0.44 13.28 N
-ATOM 122 CA BARG A 16 4.280 3.162 22.014 0.44 13.26 C
-ATOM 123 C BARG A 16 3.896 4.656 21.956 0.44 11.99 C
-ATOM 124 O BARG A 16 2.800 5.040 22.308 0.44 12.53 O
-ATOM 125 CB BARG A 16 4.656 2.661 20.638 0.22 13.35 C
-ATOM 126 CB CARG A 16 4.437 2.680 20.555 0.22 16.20 C
-ATOM 127 CG BARG A 16 5.385 1.331 20.601 0.22 17.64 C
-ATOM 128 CG CARG A 16 5.639 1.870 20.229 0.22 21.14 C
-ATOM 129 CD BARG A 16 6.160 1.040 21.834 0.22 19.84 C
-ATOM 130 CD CARG A 16 5.796 0.630 21.081 0.22 21.56 C
-ATOM 131 NE BARG A 16 7.593 1.203 21.768 0.22 21.21 N
-ATOM 132 NE CARG A 16 7.173 0.375 21.465 0.22 24.22 N
-ATOM 133 CZ BARG A 16 8.455 0.224 21.501 0.22 22.06 C
-ATOM 134 CZ CARG A 16 8.046 1.259 21.909 0.22 25.11 C
-ATOM 135 NH1BARG A 16 8.004 -0.997 21.239 0.22 23.25 N
-ATOM 136 NH1CARG A 16 7.750 2.548 22.045 0.22 26.05 N
-ATOM 137 NH2BARG A 16 9.757 0.463 21.492 0.22 23.17 N
-ATOM 138 NH2CARG A 16 9.277 0.859 22.235 0.22 24.63 N
-ATOM 139 N CYS A 17 4.860 5.440 21.477 1.00 11.64 N
-ATOM 140 CA CYS A 17 4.659 6.900 21.426 1.00 10.21 C
-ATOM 141 C CYS A 17 3.731 7.303 20.294 1.00 10.66 C
-ATOM 142 O CYS A 17 3.858 6.790 19.169 1.00 12.03 O
-ATOM 143 CB CYS A 17 5.991 7.617 21.171 1.00 10.96 C
-ATOM 144 SG CYS A 17 7.304 7.032 22.268 1.00 11.64 S
-ATOM 145 N CYS A 18 2.863 8.268 20.538 1.00 10.28 N
-ATOM 146 CA CYS A 18 2.045 8.868 19.490 1.00 11.27 C
-ATOM 147 C CYS A 18 2.592 10.233 19.140 1.00 10.85 C
-ATOM 148 O CYS A 18 2.716 11.073 20.064 1.00 11.54 O
-ATOM 149 CB CYS A 18 0.606 9.001 20.013 1.00 11.58 C
-ATOM 150 SG CYS A 18 -0.516 9.593 18.698 1.00 11.18 S
-ATOM 151 N SER A 19 2.983 10.469 17.906 1.00 10.63 N
-ATOM 152 CA SER A 19 3.522 11.773 17.506 1.00 10.33 C
-ATOM 153 C SER A 19 2.395 12.812 17.355 1.00 10.06 C
-ATOM 154 O SER A 19 1.239 12.483 17.289 1.00 10.50 O
-ATOM 155 CB SER A 19 4.213 11.607 16.137 1.00 12.53 C
-ATOM 156 OG SER A 19 3.218 11.521 15.119 1.00 11.20 O
-ATOM 157 N ILE A 20 2.843 14.085 17.220 1.00 10.99 N
-ATOM 158 CA ILE A 20 1.854 15.159 17.009 1.00 11.86 C
-ATOM 159 C ILE A 20 1.162 15.020 15.673 1.00 13.07 C
-ATOM 160 O ILE A 20 0.135 15.665 15.448 1.00 13.75 O
-ATOM 161 CB ILE A 20 2.505 16.546 17.133 1.00 12.91 C
-ATOM 162 CG1 ILE A 20 3.608 16.733 16.099 1.00 13.68 C
-ATOM 163 CG2 ILE A 20 3.006 16.773 18.552 1.00 14.60 C
-ATOM 164 CD1 ILE A 20 4.127 18.178 16.031 1.00 15.84 C
-ATOM 165 N TRP A 21 1.662 14.176 14.766 1.00 11.46 N
-ATOM 166 CA TRP A 21 1.006 13.921 13.502 1.00 11.50 C
-ATOM 167 C TRP A 21 0.012 12.789 13.564 1.00 12.13 C
-ATOM 168 O TRP A 21 -0.698 12.510 12.602 1.00 14.34 O
-ATOM 169 CB TRP A 21 2.031 13.690 12.369 1.00 12.06 C
-ATOM 170 CG TRP A 21 2.940 14.872 12.185 1.00 13.41 C
-ATOM 171 CD1 TRP A 21 2.610 16.023 11.471 1.00 14.87 C
-ATOM 172 CD2 TRP A 21 4.240 15.070 12.680 1.00 13.19 C
-ATOM 173 NE1 TRP A 21 3.663 16.890 11.537 1.00 14.69 N
-ATOM 174 CE2 TRP A 21 4.693 16.334 12.246 1.00 14.96 C
-ATOM 175 CE3 TRP A 21 5.105 14.273 13.462 1.00 14.98 C
-ATOM 176 CZ2 TRP A 21 5.949 16.830 12.589 1.00 16.73 C
-ATOM 177 CZ3 TRP A 21 6.354 14.760 13.773 1.00 17.06 C
-ATOM 178 CH2 TRP A 21 6.760 16.040 13.348 1.00 18.02 C
-ATOM 179 N GLY A 22 -0.131 12.116 14.717 1.00 11.06 N
-ATOM 180 CA GLY A 22 -1.145 11.092 14.893 1.00 11.02 C
-ATOM 181 C GLY A 22 -0.683 9.708 14.431 1.00 9.47 C
-ATOM 182 O GLY A 22 -1.567 8.923 14.000 1.00 11.91 O
-ATOM 183 N TRP A 23 0.567 9.408 14.566 1.00 10.13 N
-ATOM 184 CA TRP A 23 1.085 8.080 14.169 1.00 11.05 C
-ATOM 185 C TRP A 23 1.906 7.506 15.308 1.00 11.89 C
-ATOM 186 O TRP A 23 2.573 8.227 16.048 1.00 11.18 O
-ATOM 187 CB TRP A 23 1.938 8.195 12.906 1.00 12.91 C
-ATOM 188 CG TRP A 23 1.141 8.597 11.694 1.00 14.32 C
-ATOM 189 CD1 TRP A 23 1.043 9.842 11.148 1.00 15.32 C
-ATOM 190 CD2 TRP A 23 0.335 7.731 10.891 1.00 13.59 C
-ATOM 191 NE1 TRP A 23 0.231 9.802 10.044 1.00 16.07 N
-ATOM 192 CE2 TRP A 23 -0.257 8.535 9.883 1.00 15.22 C
-ATOM 193 CE3 TRP A 23 0.013 6.374 10.946 1.00 15.09 C
-ATOM 194 CZ2 TRP A 23 -1.085 7.991 8.898 1.00 17.75 C
-ATOM 195 CZ3 TRP A 23 -0.823 5.844 9.974 1.00 19.21 C
-ATOM 196 CH2 TRP A 23 -1.358 6.660 8.971 1.00 20.75 C
-ATOM 197 N CYS A 24 1.986 6.179 15.351 1.00 11.52 N
-ATOM 198 CA CYS A 24 2.689 5.457 16.386 1.00 12.25 C
-ATOM 199 C CYS A 24 4.094 5.076 16.014 1.00 11.84 C
-ATOM 200 O CYS A 24 4.313 4.643 14.856 1.00 13.73 O
-ATOM 201 CB CYS A 24 1.937 4.110 16.664 1.00 12.40 C
-ATOM 202 SG CYS A 24 0.275 4.401 17.331 1.00 11.68 S
-ATOM 203 N GLY A 25 5.037 5.149 16.934 1.00 12.17 N
-ATOM 204 CA GLY A 25 6.397 4.709 16.654 1.00 12.62 C
-ATOM 205 C GLY A 25 7.268 4.734 17.898 1.00 13.04 C
-ATOM 206 O GLY A 25 6.806 4.993 19.009 1.00 13.30 O
-ATOM 207 N ASP A 26 8.546 4.411 17.713 1.00 15.66 N
-ATOM 208 CA ASP A 26 9.453 4.213 18.836 1.00 16.94 C
-ATOM 209 C ASP A 26 10.742 4.984 18.704 1.00 19.41 C
-ATOM 210 O ASP A 26 11.679 4.757 19.495 1.00 22.27 O
-ATOM 211 CB ASP A 26 9.733 2.705 19.003 1.00 22.37 C
-ATOM 212 CG ASP A 26 10.242 2.048 17.750 1.00 27.17 C
-ATOM 213 OD1 ASP A 26 10.821 2.716 16.876 1.00 30.74 O
-ATOM 214 OD2 ASP A 26 10.050 0.806 17.615 1.00 33.27 O
-ATOM 215 N SER A 27 10.836 5.914 17.772 1.00 16.57 N
-ATOM 216 CA SER A 27 12.025 6.714 17.541 1.00 18.12 C
-ATOM 217 C SER A 27 11.784 8.177 17.845 1.00 16.83 C
-ATOM 218 O SER A 27 10.654 8.569 18.171 1.00 15.78 O
-ATOM 219 CB SER A 27 12.504 6.526 16.094 1.00 19.54 C
-ATOM 220 OG SER A 27 11.449 6.922 15.205 1.00 21.34 O
-ATOM 221 N GLU A 28 12.795 9.020 17.760 1.00 17.82 N
-ATOM 222 CA GLU A 28 12.747 10.415 18.151 1.00 18.18 C
-ATOM 223 C GLU A 28 11.580 11.219 17.642 1.00 16.44 C
-ATOM 224 O GLU A 28 10.943 11.965 18.410 1.00 15.84 O
-ATOM 225 CB AGLU A 28 14.087 11.070 17.738 0.50 20.93 C
-ATOM 226 CB BGLU A 28 14.080 11.099 17.807 0.50 21.15 C
-ATOM 227 CG AGLU A 28 14.061 12.574 17.631 0.50 22.90 C
-ATOM 228 CG BGLU A 28 14.458 12.231 18.733 0.50 23.48 C
-ATOM 229 CD AGLU A 28 13.788 13.255 18.959 0.50 23.94 C
-ATOM 230 CD BGLU A 28 13.705 13.513 18.493 0.50 25.49 C
-ATOM 231 OE1AGLU A 28 14.121 12.673 20.008 0.50 25.78 O
-ATOM 232 OE1BGLU A 28 13.293 13.783 17.345 0.50 26.68 O
-ATOM 233 OE2AGLU A 28 13.210 14.367 18.948 0.50 25.81 O
-ATOM 234 OE2BGLU A 28 13.513 14.286 19.469 0.50 28.16 O
-ATOM 235 N PRO A 29 11.257 11.149 16.353 1.00 16.44 N
-ATOM 236 CA PRO A 29 10.144 11.917 15.806 1.00 17.18 C
-ATOM 237 C PRO A 29 8.823 11.616 16.471 1.00 14.68 C
-ATOM 238 O PRO A 29 7.892 12.446 16.505 1.00 15.00 O
-ATOM 239 CB PRO A 29 10.136 11.541 14.327 1.00 19.03 C
-ATOM 240 CG PRO A 29 11.501 11.035 14.037 1.00 20.48 C
-ATOM 241 CD PRO A 29 12.006 10.414 15.326 1.00 18.12 C
-ATOM 242 N TYR A 30 8.646 10.397 16.996 1.00 13.61 N
-ATOM 243 CA TYR A 30 7.443 9.994 17.663 1.00 12.47 C
-ATOM 244 C TYR A 30 7.479 10.211 19.186 1.00 12.14 C
-ATOM 245 O TYR A 30 6.458 10.498 19.778 1.00 12.08 O
-ATOM 246 CB TYR A 30 7.181 8.480 17.444 1.00 12.23 C
-ATOM 247 CG TYR A 30 7.057 8.081 15.992 1.00 12.06 C
-ATOM 248 CD1 TYR A 30 8.213 7.750 15.277 1.00 13.86 C
-ATOM 249 CD2 TYR A 30 5.842 8.011 15.344 1.00 11.48 C
-ATOM 250 CE1 TYR A 30 8.133 7.383 13.943 1.00 14.64 C
-ATOM 251 CE2 TYR A 30 5.756 7.627 14.005 1.00 12.08 C
-ATOM 252 CZ TYR A 30 6.908 7.328 13.334 1.00 13.37 C
-ATOM 253 OH TYR A 30 6.830 6.950 11.998 1.00 15.25 O
-ATOM 254 N CYS A 31 8.670 10.046 19.759 1.00 13.67 N
-ATOM 255 CA CYS A 31 8.831 9.940 21.199 1.00 12.61 C
-ATOM 256 C CYS A 31 9.529 11.123 21.850 1.00 14.04 C
-ATOM 257 O CYS A 31 9.506 11.227 23.089 1.00 14.90 O
-ATOM 258 CB CYS A 31 9.719 8.692 21.503 1.00 13.30 C
-ATOM 259 SG CYS A 31 8.953 7.136 21.032 1.00 12.81 S
-ATOM 260 N GLY A 32 10.191 11.953 21.069 1.00 14.92 N
-ATOM 261 CA GLY A 32 10.970 13.072 21.570 1.00 16.76 C
-ATOM 262 C GLY A 32 10.183 14.376 21.576 1.00 16.37 C
-ATOM 263 O GLY A 32 9.089 14.508 22.067 1.00 17.28 O
-ATOM 264 N ARG A 33 10.805 15.400 20.943 1.00 18.68 N
-ATOM 265 CA ARG A 33 10.253 16.736 20.909 1.00 20.22 C
-ATOM 266 C ARG A 33 8.893 16.835 20.258 1.00 19.35 C
-ATOM 267 O ARG A 33 8.053 17.653 20.668 1.00 21.59 O
-ATOM 268 CB ARG A 33 11.250 17.687 20.210 1.00 23.28 C
-ATOM 269 CG ARG A 33 12.707 17.055 20.301 0.00 20.00 C
-ATOM 270 CD ARG A 33 13.726 18.003 19.669 0.00 20.00 C
-ATOM 271 NE ARG A 33 13.518 18.180 18.226 0.00 20.00 N
-ATOM 272 CZ ARG A 33 12.493 18.858 17.694 0.00 20.00 C
-ATOM 273 NH1 ARG A 33 11.568 19.434 18.472 0.00 20.00 N
-ATOM 274 NH2 ARG A 33 12.305 19.018 16.376 0.00 20.00 N
-ATOM 275 N THR A 34 8.629 15.996 19.260 1.00 17.05 N
-ATOM 276 CA THR A 34 7.367 16.075 18.514 1.00 15.55 C
-ATOM 277 C THR A 34 6.416 14.947 18.911 1.00 13.76 C
-ATOM 278 O THR A 34 5.571 14.521 18.145 1.00 14.63 O
-ATOM 279 CB THR A 34 7.620 16.068 17.006 1.00 15.68 C
-ATOM 280 OG1 THR A 34 8.589 15.063 16.678 1.00 18.28 O
-ATOM 281 CG2 THR A 34 8.192 17.420 16.539 1.00 17.63 C
-ATOM 282 N CYS A 35 6.527 14.537 20.184 1.00 12.85 N
-ATOM 283 CA CYS A 35 5.619 13.529 20.733 1.00 11.99 C
-ATOM 284 C CYS A 35 4.388 14.118 21.364 1.00 12.75 C
-ATOM 285 O CYS A 35 4.491 15.127 22.102 1.00 15.67 O
-ATOM 286 CB CYS A 35 6.432 12.713 21.770 1.00 11.83 C
-ATOM 287 SG CYS A 35 5.470 11.406 22.561 1.00 10.89 S
-ATOM 288 N GLU A 36 3.235 13.515 21.205 1.00 10.58 N
-ATOM 289 CA GLU A 36 2.004 13.930 21.842 1.00 12.03 C
-ATOM 290 C GLU A 36 1.683 13.107 23.086 1.00 13.55 C
-ATOM 291 O GLU A 36 1.129 13.669 24.043 1.00 13.26 O
-ATOM 292 CB GLU A 36 0.819 13.833 20.876 1.00 12.59 C
-ATOM 293 CG GLU A 36 -0.497 14.343 21.457 1.00 16.20 C
-ATOM 294 CD GLU A 36 -1.609 14.316 20.436 1.00 18.54 C
-ATOM 295 OE1 GLU A 36 -2.248 13.270 20.239 1.00 18.98 O
-ATOM 296 OE2 GLU A 36 -1.858 15.380 19.821 1.00 23.44 O
-ATOM 297 N ASN A 37 1.897 11.793 23.068 1.00 12.95 N
-ATOM 298 CA ASN A 37 1.497 10.964 24.209 1.00 12.65 C
-ATOM 299 C ASN A 37 2.446 9.775 24.337 1.00 12.21 C
-ATOM 300 O ASN A 37 3.003 9.296 23.346 1.00 12.29 O
-ATOM 301 CB ASN A 37 0.071 10.481 24.067 1.00 15.42 C
-ATOM 302 CG ASN A 37 -0.529 9.956 25.368 1.00 18.91 C
-ATOM 303 OD1 ASN A 37 -0.166 10.434 26.429 1.00 20.42 O
-ATOM 304 ND2 ASN A 37 -1.426 8.985 25.231 1.00 21.73 N
-ATOM 305 N LYS A 38 2.547 9.285 25.569 1.00 12.38 N
-ATOM 306 CA LYS A 38 3.418 8.157 25.928 1.00 11.24 C
-ATOM 307 C LYS A 38 4.814 8.422 25.439 1.00 11.57 C
-ATOM 308 O LYS A 38 5.475 7.649 24.737 1.00 11.73 O
-ATOM 309 CB LYS A 38 2.872 6.835 25.390 1.00 12.50 C
-ATOM 310 CG LYS A 38 1.463 6.489 25.778 1.00 13.84 C
-ATOM 311 CD LYS A 38 1.233 6.470 27.289 1.00 16.31 C
-ATOM 312 CE LYS A 38 -0.159 5.917 27.584 1.00 17.80 C
-ATOM 313 NZ LYS A 38 -0.465 5.932 29.040 1.00 20.45 N
-ATOM 314 N CYS A 39 5.378 9.556 25.880 1.00 12.02 N
-ATOM 315 CA CYS A 39 6.632 10.077 25.433 1.00 11.58 C
-ATOM 316 C CYS A 39 7.809 9.718 26.328 1.00 12.51 C
-ATOM 317 O CYS A 39 7.574 9.420 27.513 1.00 13.84 O
-ATOM 318 CB CYS A 39 6.512 11.641 25.373 1.00 13.49 C
-ATOM 319 SG CYS A 39 5.076 12.190 24.393 1.00 12.58 S
-ATOM 320 N TRP A 40 9.011 9.764 25.795 1.00 11.50 N
-ATOM 321 CA TRP A 40 10.193 9.395 26.592 1.00 14.12 C
-ATOM 322 C TRP A 40 10.259 10.234 27.861 1.00 15.88 C
-ATOM 323 O TRP A 40 10.656 9.728 28.912 1.00 17.55 O
-ATOM 324 CB TRP A 40 11.457 9.577 25.773 1.00 14.30 C
-ATOM 325 CG TRP A 40 11.683 8.533 24.724 1.00 15.08 C
-ATOM 326 CD1 TRP A 40 11.189 7.256 24.703 1.00 14.60 C
-ATOM 327 CD2 TRP A 40 12.438 8.680 23.518 1.00 15.20 C
-ATOM 328 NE1 TRP A 40 11.642 6.583 23.594 1.00 16.14 N
-ATOM 329 CE2 TRP A 40 12.406 7.451 22.843 1.00 15.90 C
-ATOM 330 CE3 TRP A 40 13.169 9.739 22.959 1.00 18.61 C
-ATOM 331 CZ2 TRP A 40 13.047 7.240 21.623 1.00 17.72 C
-ATOM 332 CZ3 TRP A 40 13.814 9.530 21.756 1.00 18.97 C
-ATOM 333 CH2 TRP A 40 13.746 8.296 21.099 1.00 19.39 C
-ATOM 334 N SER A 41 9.951 11.518 27.737 1.00 15.53 N
-ATOM 335 CA SER A 41 10.078 12.431 28.878 1.00 17.72 C
-ATOM 336 C SER A 41 9.043 12.192 29.935 1.00 16.70 C
-ATOM 337 O SER A 41 9.104 12.813 31.031 1.00 20.51 O
-ATOM 338 CB SER A 41 9.984 13.886 28.348 1.00 19.25 C
-ATOM 339 OG SER A 41 8.682 14.140 27.877 1.00 25.31 O
-ATOM 340 N GLY A 42 8.063 11.327 29.727 1.00 14.48 N
-ATOM 341 CA GLY A 42 7.007 11.031 30.616 1.00 14.53 C
-ATOM 342 C GLY A 42 7.286 9.912 31.616 1.00 13.87 C
-ATOM 343 O GLY A 42 6.373 9.593 32.395 1.00 16.81 O
-ATOM 344 N GLU A 43 8.465 9.322 31.606 1.00 13.36 N
-ATOM 345 CA GLU A 43 8.767 8.271 32.589 1.00 13.28 C
-ATOM 346 C GLU A 43 8.673 8.877 33.995 1.00 13.80 C
-ATOM 347 O GLU A 43 9.223 9.950 34.249 1.00 15.48 O
-ATOM 348 CB GLU A 43 10.134 7.656 32.361 1.00 11.95 C
-ATOM 349 CG GLU A 43 10.372 6.481 33.343 1.00 13.25 C
-ATOM 350 CD GLU A 43 11.587 5.683 33.100 1.00 13.51 C
-ATOM 351 OE1 GLU A 43 12.044 5.460 31.959 1.00 13.57 O
-ATOM 352 OE2 GLU A 43 12.195 5.150 34.093 1.00 15.32 O
-ATOM 353 N ARG A 44 8.071 8.145 34.910 1.00 13.63 N
-ATOM 354 CA ARG A 44 7.983 8.564 36.308 1.00 14.52 C
-ATOM 355 C ARG A 44 9.362 8.626 36.944 1.00 14.63 C
-ATOM 356 O ARG A 44 10.261 7.845 36.633 1.00 13.65 O
-ATOM 357 CB ARG A 44 7.130 7.565 37.097 1.00 14.38 C
-ATOM 358 CG ARG A 44 5.662 7.552 36.738 1.00 16.88 C
-ATOM 359 CD ARG A 44 4.918 6.451 37.452 1.00 18.14 C
-ATOM 360 NE ARG A 44 5.070 6.493 38.896 1.00 18.73 N
-ATOM 361 CZ ARG A 44 4.153 6.921 39.751 1.00 23.67 C
-ATOM 362 NH1 ARG A 44 2.982 7.369 39.322 1.00 26.62 N
-ATOM 363 NH2 ARG A 44 4.406 6.879 41.057 1.00 24.53 N
-ATOM 364 N SER A 45 9.537 9.569 37.886 1.00 16.58 N
-ATOM 365 CA SER A 45 10.814 9.666 38.587 1.00 17.75 C
-ATOM 366 C SER A 45 11.163 8.400 39.353 1.00 16.53 C
-ATOM 367 O SER A 45 12.346 8.077 39.486 1.00 16.92 O
-ATOM 368 CB SER A 45 10.784 10.870 39.555 1.00 19.53 C
-ATOM 369 OG ASER A 45 9.839 10.598 40.594 0.50 22.46 O
-ATOM 370 OG BSER A 45 10.680 12.068 38.784 0.50 23.41 O
-ATOM 371 N ASP A 46 10.154 7.656 39.787 1.00 16.16 N
-ATOM 372 CA ASP A 46 10.357 6.414 40.526 1.00 15.58 C
-ATOM 373 C ASP A 46 10.486 5.209 39.608 1.00 16.25 C
-ATOM 374 O ASP A 46 10.715 4.082 40.082 1.00 17.85 O
-ATOM 375 CB ASP A 46 9.273 6.208 41.568 1.00 18.42 C
-ATOM 376 CG ASP A 46 7.890 5.991 41.051 1.00 18.67 C
-ATOM 377 OD1 ASP A 46 7.678 5.851 39.823 1.00 17.94 O
-ATOM 378 OD2 ASP A 46 6.922 5.928 41.861 1.00 23.44 O
-ATOM 379 N HIS A 47 10.390 5.412 38.301 1.00 13.79 N
-ATOM 380 CA HIS A 47 10.535 4.366 37.316 1.00 13.07 C
-ATOM 381 C HIS A 47 9.438 3.321 37.347 1.00 13.66 C
-ATOM 382 O HIS A 47 9.591 2.308 36.646 1.00 14.37 O
-ATOM 383 CB HIS A 47 11.904 3.663 37.393 1.00 13.90 C
-ATOM 384 CG HIS A 47 13.049 4.620 37.468 1.00 14.22 C
-ATOM 385 ND1 HIS A 47 13.258 5.596 36.524 1.00 15.45 N
-ATOM 386 CD2 HIS A 47 14.039 4.749 38.378 1.00 14.58 C
-ATOM 387 CE1 HIS A 47 14.336 6.300 36.845 1.00 16.14 C
-ATOM 388 NE2 HIS A 47 14.821 5.798 37.978 1.00 16.13 N
-ATOM 389 N ARG A 48 8.356 3.510 38.057 1.00 13.49 N
-ATOM 390 CA ARG A 48 7.271 2.527 38.068 1.00 14.17 C
-ATOM 391 C ARG A 48 6.513 2.568 36.750 1.00 15.86 C
-ATOM 392 O ARG A 48 6.416 3.632 36.116 1.00 15.15 O
-ATOM 393 CB ARG A 48 6.310 2.789 39.231 1.00 16.19 C
-ATOM 394 CG ARG A 48 6.953 2.481 40.594 1.00 21.74 C
-ATOM 395 CD ARG A 48 5.897 2.677 41.696 1.00 27.15 C
-ATOM 396 NE ARG A 48 4.979 1.552 41.722 1.00 33.45 N
-ATOM 397 CZ ARG A 48 3.811 1.502 42.336 1.00 35.53 C
-ATOM 398 NH1 ARG A 48 3.350 2.536 43.022 1.00 37.75 N
-ATOM 399 NH2 ARG A 48 3.078 0.387 42.267 1.00 37.26 N
-ATOM 400 N CYS A 49 5.942 1.451 36.352 1.00 14.67 N
-ATOM 401 CA CYS A 49 5.096 1.418 35.149 1.00 14.02 C
-ATOM 402 C CYS A 49 4.011 0.378 35.343 1.00 16.59 C
-ATOM 403 O CYS A 49 3.937 -0.224 36.444 1.00 17.70 O
-ATOM 404 CB CYS A 49 5.922 1.172 33.902 1.00 14.60 C
-ATOM 405 SG CYS A 49 6.790 -0.418 33.889 1.00 13.58 S
-ATOM 406 N GLY A 50 3.193 0.151 34.344 1.00 16.32 N
-ATOM 407 CA GLY A 50 2.143 -0.866 34.446 1.00 18.12 C
-ATOM 408 C GLY A 50 0.822 -0.238 34.870 1.00 20.85 C
-ATOM 409 O GLY A 50 0.703 0.947 35.143 1.00 21.47 O
-ATOM 410 N ALA A 51 -0.178 -1.110 35.040 1.00 23.34 N
-ATOM 411 CA ALA A 51 -1.543 -0.713 35.271 1.00 26.04 C
-ATOM 412 C ALA A 51 -1.795 0.012 36.564 1.00 27.16 C
-ATOM 413 O ALA A 51 -2.665 0.906 36.617 1.00 29.51 O
-ATOM 414 CB ALA A 51 -2.454 -1.955 35.167 1.00 27.39 C
-ATOM 415 N ALA A 52 -1.074 -0.305 37.627 1.00 29.21 N
-ATOM 416 CA ALA A 52 -1.299 0.301 38.927 1.00 30.20 C
-ATOM 417 C ALA A 52 -0.994 1.784 38.962 1.00 30.84 C
-ATOM 418 O ALA A 52 -1.494 2.501 39.844 1.00 32.25 O
-ATOM 419 CB ALA A 52 -0.515 -0.431 40.004 1.00 33.09 C
-ATOM 420 N VAL A 53 -0.141 2.261 38.056 1.00 28.71 N
-ATOM 421 CA VAL A 53 0.194 3.685 38.018 1.00 26.18 C
-ATOM 422 C VAL A 53 -0.261 4.318 36.712 1.00 25.00 C
-ATOM 423 O VAL A 53 0.267 5.337 36.264 1.00 24.56 O
-ATOM 424 CB VAL A 53 1.673 3.942 38.268 1.00 25.15 C
-ATOM 425 CG1 VAL A 53 2.041 3.676 39.733 1.00 26.52 C
-ATOM 426 CG2 VAL A 53 2.533 3.055 37.362 1.00 22.44 C
-ATOM 427 N GLY A 54 -1.239 3.695 36.058 1.00 23.89 N
-ATOM 428 CA GLY A 54 -1.868 4.224 34.880 1.00 23.95 C
-ATOM 429 C GLY A 54 -1.116 4.076 33.585 1.00 23.40 C
-ATOM 430 O GLY A 54 -1.266 4.908 32.665 1.00 23.79 O
-ATOM 431 N ASN A 55 -0.333 3.021 33.429 1.00 21.36 N
-ATOM 432 CA ASN A 55 0.391 2.709 32.223 1.00 19.27 C
-ATOM 433 C ASN A 55 1.196 3.856 31.645 1.00 18.15 C
-ATOM 434 O ASN A 55 1.105 4.166 30.442 1.00 18.32 O
-ATOM 435 CB ASN A 55 -0.582 2.178 31.147 1.00 21.82 C
-ATOM 436 CG ASN A 55 -1.151 0.821 31.511 1.00 26.16 C
-ATOM 437 OD1 ASN A 55 -0.501 -0.011 32.131 1.00 26.45 O
-ATOM 438 ND2 ASN A 55 -2.397 0.598 31.091 1.00 29.80 N
-ATOM 439 N PRO A 56 2.101 4.416 32.416 1.00 14.99 N
-ATOM 440 CA PRO A 56 2.969 5.491 31.927 1.00 13.73 C
-ATOM 441 C PRO A 56 4.009 4.955 30.985 1.00 14.07 C
-ATOM 442 O PRO A 56 4.346 3.755 30.986 1.00 15.11 O
-ATOM 443 CB PRO A 56 3.585 6.035 33.205 1.00 14.47 C
-ATOM 444 CG PRO A 56 3.701 4.793 34.077 1.00 15.83 C
-ATOM 445 CD PRO A 56 2.407 4.054 33.822 1.00 15.95 C
-ATOM 446 N PRO A 57 4.649 5.806 30.194 1.00 11.98 N
-ATOM 447 CA PRO A 57 5.722 5.426 29.322 1.00 12.65 C
-ATOM 448 C PRO A 57 7.036 5.165 30.051 1.00 12.11 C
-ATOM 449 O PRO A 57 7.157 5.472 31.250 1.00 13.60 O
-ATOM 450 CB PRO A 57 5.895 6.659 28.401 1.00 12.88 C
-ATOM 451 CG PRO A 57 5.466 7.798 29.280 1.00 12.77 C
-ATOM 452 CD PRO A 57 4.319 7.262 30.094 1.00 14.56 C
-ATOM 453 N CYS A 58 8.020 4.682 29.324 1.00 11.80 N
-ATOM 454 CA CYS A 58 9.395 4.596 29.803 1.00 12.36 C
-ATOM 455 C CYS A 58 10.282 5.443 28.876 1.00 13.05 C
-ATOM 456 O CYS A 58 9.824 5.791 27.764 1.00 12.47 O
-ATOM 457 CB CYS A 58 9.923 3.175 29.845 1.00 12.38 C
-ATOM 458 SG CYS A 58 8.999 2.008 30.881 1.00 12.44 S
-ATOM 459 N GLY A 59 11.462 5.793 29.318 1.00 13.40 N
-ATOM 460 CA GLY A 59 12.370 6.599 28.530 1.00 13.37 C
-ATOM 461 C GLY A 59 12.983 5.848 27.371 1.00 15.29 C
-ATOM 462 O GLY A 59 12.636 4.695 27.085 1.00 15.41 O
-ATOM 463 N GLN A 60 13.926 6.483 26.680 1.00 15.49 N
-ATOM 464 CA GLN A 60 14.578 5.933 25.521 1.00 16.65 C
-ATOM 465 C GLN A 60 15.322 4.640 25.808 1.00 17.24 C
-ATOM 466 O GLN A 60 16.084 4.583 26.784 1.00 17.93 O
-ATOM 467 CB GLN A 60 15.560 6.976 24.932 1.00 21.03 C
-ATOM 468 CG GLN A 60 16.137 6.596 23.600 1.00 24.12 C
-ATOM 469 CD GLN A 60 16.930 7.708 22.933 1.00 26.24 C
-ATOM 470 OE1 GLN A 60 17.102 8.793 23.476 1.00 28.53 O
-ATOM 471 NE2 GLN A 60 17.396 7.430 21.717 1.00 30.08 N
-ATOM 472 N ASP A 61 15.074 3.617 25.016 1.00 17.93 N
-ATOM 473 CA ASP A 61 15.745 2.336 25.132 1.00 20.04 C
-ATOM 474 C ASP A 61 15.467 1.648 26.455 1.00 20.73 C
-ATOM 475 O ASP A 61 16.257 0.866 26.990 1.00 21.72 O
-ATOM 476 CB ASP A 61 17.234 2.449 24.844 1.00 24.31 C
-ATOM 477 CG ASP A 61 17.506 2.718 23.360 1.00 27.72 C
-ATOM 478 OD1 ASP A 61 16.939 1.999 22.517 1.00 33.16 O
-ATOM 479 OD2 ASP A 61 18.293 3.639 23.088 1.00 32.19 O
-ATOM 480 N ARG A 62 14.303 1.961 27.014 1.00 18.60 N
-ATOM 481 CA ARG A 62 13.834 1.325 28.244 1.00 16.85 C
-ATOM 482 C ARG A 62 12.481 0.710 28.021 1.00 15.70 C
-ATOM 483 O ARG A 62 11.700 1.178 27.159 1.00 17.28 O
-ATOM 484 CB ARG A 62 13.808 2.372 29.372 1.00 15.52 C
-ATOM 485 CG ARG A 62 15.215 2.900 29.694 1.00 16.78 C
-ATOM 486 CD ARG A 62 15.199 4.169 30.493 1.00 17.30 C
-ATOM 487 NE ARG A 62 14.592 4.088 31.791 1.00 15.85 N
-ATOM 488 CZ ARG A 62 15.169 3.631 32.910 1.00 16.22 C
-ATOM 489 NH1 ARG A 62 16.419 3.181 32.862 1.00 19.33 N
-ATOM 490 NH2 ARG A 62 14.505 3.658 34.049 1.00 17.20 N
-ATOM 491 N CYS A 63 12.133 -0.348 28.724 1.00 14.67 N
-ATOM 492 CA CYS A 63 10.895 -1.061 28.575 1.00 14.75 C
-ATOM 493 C CYS A 63 10.220 -1.258 29.921 1.00 12.67 C
-ATOM 494 O CYS A 63 10.923 -1.181 30.961 1.00 13.29 O
-ATOM 495 CB CYS A 63 11.141 -2.476 27.981 1.00 16.49 C
-ATOM 496 SG CYS A 63 12.260 -2.460 26.567 1.00 16.96 S
-ATOM 497 N CYS A 64 8.953 -1.528 29.925 1.00 12.88 N
-ATOM 498 CA CYS A 64 8.206 -1.780 31.148 1.00 12.83 C
-ATOM 499 C CYS A 64 8.146 -3.285 31.429 1.00 15.34 C
-ATOM 500 O CYS A 64 7.524 -4.027 30.660 1.00 15.86 O
-ATOM 501 CB CYS A 64 6.781 -1.219 30.990 1.00 14.85 C
-ATOM 502 SG CYS A 64 5.806 -1.504 32.499 1.00 13.42 S
-ATOM 503 N SER A 65 8.826 -3.717 32.477 1.00 14.67 N
-ATOM 504 CA SER A 65 8.872 -5.161 32.798 1.00 16.00 C
-ATOM 505 C SER A 65 7.551 -5.636 33.353 1.00 16.63 C
-ATOM 506 O SER A 65 6.710 -4.894 33.815 1.00 15.54 O
-ATOM 507 CB SER A 65 9.998 -5.411 33.827 1.00 16.86 C
-ATOM 508 OG SER A 65 9.484 -5.147 35.124 1.00 17.06 O
-ATOM 509 N VAL A 66 7.392 -6.999 33.348 1.00 18.72 N
-ATOM 510 CA VAL A 66 6.204 -7.591 33.922 1.00 19.62 C
-ATOM 511 C VAL A 66 6.103 -7.376 35.428 1.00 19.10 C
-ATOM 512 O VAL A 66 5.023 -7.531 35.993 1.00 22.45 O
-ATOM 513 CB VAL A 66 6.081 -9.099 33.636 1.00 18.76 C
-ATOM 514 CG1 VAL A 66 5.849 -9.334 32.154 1.00 20.22 C
-ATOM 515 CG2 VAL A 66 7.308 -9.852 34.123 1.00 20.88 C
-ATOM 516 N HIS A 67 7.205 -6.932 36.043 1.00 17.59 N
-ATOM 517 CA HIS A 67 7.183 -6.665 37.480 1.00 19.57 C
-ATOM 518 C HIS A 67 6.787 -5.235 37.796 1.00 20.01 C
-ATOM 519 O HIS A 67 6.668 -4.859 38.965 1.00 21.59 O
-ATOM 520 CB HIS A 67 8.525 -7.022 38.123 1.00 19.55 C
-ATOM 521 CG HIS A 67 8.951 -8.426 37.769 1.00 19.66 C
-ATOM 522 ND1 HIS A 67 8.317 -9.542 38.278 1.00 24.79 N
-ATOM 523 CD2 HIS A 67 9.897 -8.863 36.924 1.00 20.78 C
-ATOM 524 CE1 HIS A 67 8.889 -10.621 37.766 1.00 21.81 C
-ATOM 525 NE2 HIS A 67 9.854 -10.250 36.958 1.00 23.01 N
-ATOM 526 N GLY A 68 6.635 -4.403 36.763 1.00 18.21 N
-ATOM 527 CA GLY A 68 6.124 -3.053 36.937 1.00 17.97 C
-ATOM 528 C GLY A 68 7.194 -1.980 37.055 1.00 13.37 C
-ATOM 529 O GLY A 68 6.922 -0.965 37.742 1.00 15.56 O
-ATOM 530 N TRP A 69 8.337 -2.157 36.449 1.00 12.34 N
-ATOM 531 CA TRP A 69 9.382 -1.130 36.486 1.00 12.62 C
-ATOM 532 C TRP A 69 9.962 -0.917 35.099 1.00 13.75 C
-ATOM 533 O TRP A 69 10.077 -1.829 34.287 1.00 13.58 O
-ATOM 534 CB TRP A 69 10.515 -1.554 37.436 1.00 16.11 C
-ATOM 535 CG TRP A 69 10.061 -1.845 38.837 1.00 18.96 C
-ATOM 536 CD1 TRP A 69 9.648 -3.068 39.323 1.00 18.72 C
-ATOM 537 CD2 TRP A 69 9.952 -0.930 39.923 1.00 20.84 C
-ATOM 538 NE1 TRP A 69 9.303 -2.949 40.636 1.00 21.95 N
-ATOM 539 CE2 TRP A 69 9.480 -1.643 41.038 1.00 19.50 C
-ATOM 540 CE3 TRP A 69 10.212 0.438 40.056 1.00 20.51 C
-ATOM 541 CZ2 TRP A 69 9.255 -1.048 42.273 1.00 22.79 C
-ATOM 542 CZ3 TRP A 69 9.979 1.038 41.281 1.00 22.68 C
-ATOM 543 CH2 TRP A 69 9.512 0.293 42.378 1.00 23.67 C
-ATOM 544 N CYS A 70 10.307 0.345 34.795 1.00 13.10 N
-ATOM 545 CA CYS A 70 11.002 0.669 33.563 1.00 13.78 C
-ATOM 546 C CYS A 70 12.499 0.383 33.729 1.00 13.92 C
-ATOM 547 O CYS A 70 13.047 0.691 34.814 1.00 15.28 O
-ATOM 548 CB CYS A 70 10.857 2.178 33.254 1.00 14.26 C
-ATOM 549 SG CYS A 70 9.243 2.751 32.767 1.00 12.08 S
-ATOM 550 N GLY A 71 13.177 -0.024 32.685 1.00 14.37 N
-ATOM 551 CA GLY A 71 14.628 -0.187 32.729 1.00 15.37 C
-ATOM 552 C GLY A 71 15.130 -0.683 31.374 1.00 18.20 C
-ATOM 553 O GLY A 71 14.340 -1.016 30.505 1.00 16.44 O
-ATOM 554 N GLY A 72 16.455 -0.770 31.259 1.00 19.67 N
-ATOM 555 CA GLY A 72 17.033 -1.225 29.985 1.00 22.41 C
-ATOM 556 C GLY A 72 17.590 -2.635 30.117 1.00 24.30 C
-ATOM 557 O GLY A 72 17.886 -3.101 31.216 1.00 26.21 O
-ATOM 558 N GLY A 73 17.694 -3.324 28.982 1.00 25.36 N
-ATOM 559 CA GLY A 73 18.291 -4.645 28.942 1.00 26.44 C
-ATOM 560 C GLY A 73 17.283 -5.775 28.973 1.00 27.24 C
-ATOM 561 O GLY A 73 16.086 -5.595 29.157 1.00 26.15 O
-ATOM 562 N ASN A 74 17.798 -7.004 28.869 1.00 27.53 N
-ATOM 563 CA ASN A 74 17.013 -8.206 28.788 1.00 27.66 C
-ATOM 564 C ASN A 74 16.001 -8.395 29.892 1.00 25.55 C
-ATOM 565 O ASN A 74 14.915 -8.955 29.662 1.00 26.18 O
-ATOM 566 CB ASN A 74 17.981 -9.421 28.763 1.00 32.27 C
-ATOM 567 CG AASN A 74 17.279 -10.749 28.825 0.50 33.40 C
-ATOM 568 CG BASN A 74 18.917 -9.356 27.574 0.50 34.22 C
-ATOM 569 OD1AASN A 74 17.230 -11.395 29.878 0.50 35.56 O
-ATOM 570 OD1BASN A 74 18.479 -9.218 26.432 0.50 36.46 O
-ATOM 571 ND2AASN A 74 16.736 -11.203 27.699 0.50 34.86 N
-ATOM 572 ND2BASN A 74 20.215 -9.465 27.831 0.50 36.27 N
-ATOM 573 N ASP A 75 16.355 -8.036 31.129 1.00 25.87 N
-ATOM 574 CA ASP A 75 15.472 -8.252 32.256 1.00 25.73 C
-ATOM 575 C ASP A 75 14.200 -7.431 32.203 1.00 23.25 C
-ATOM 576 O ASP A 75 13.194 -7.782 32.826 1.00 22.95 O
-ATOM 577 CB ASP A 75 16.209 -8.005 33.580 1.00 31.03 C
-ATOM 578 CG ASP A 75 16.090 -9.241 34.475 1.00 37.03 C
-ATOM 579 OD1 ASP A 75 16.882 -10.182 34.258 1.00 40.33 O
-ATOM 580 OD2 ASP A 75 15.200 -9.254 35.339 1.00 39.88 O
-ATOM 581 N TYR A 76 14.205 -6.354 31.409 1.00 21.83 N
-ATOM 582 CA TYR A 76 13.025 -5.520 31.259 1.00 20.29 C
-ATOM 583 C TYR A 76 12.385 -5.662 29.875 1.00 20.48 C
-ATOM 584 O TYR A 76 11.191 -5.458 29.724 1.00 20.48 O
-ATOM 585 CB TYR A 76 13.423 -4.033 31.431 1.00 18.44 C
-ATOM 586 CG TYR A 76 13.911 -3.669 32.810 1.00 15.85 C
-ATOM 587 CD1 TYR A 76 15.235 -3.876 33.168 1.00 16.59 C
-ATOM 588 CD2 TYR A 76 13.050 -3.117 33.750 1.00 14.97 C
-ATOM 589 CE1 TYR A 76 15.689 -3.555 34.435 1.00 16.24 C
-ATOM 590 CE2 TYR A 76 13.502 -2.778 35.017 1.00 14.15 C
-ATOM 591 CZ TYR A 76 14.809 -3.032 35.355 1.00 15.60 C
-ATOM 592 OH TYR A 76 15.277 -2.679 36.604 1.00 17.09 O
-ATOM 593 N CYS A 77 13.208 -5.961 28.876 1.00 22.81 N
-ATOM 594 CA CYS A 77 12.814 -5.846 27.491 1.00 23.23 C
-ATOM 595 C CYS A 77 12.573 -7.119 26.727 1.00 27.50 C
-ATOM 596 O CYS A 77 11.961 -7.054 25.632 1.00 27.57 O
-ATOM 597 CB CYS A 77 13.893 -5.011 26.744 1.00 21.70 C
-ATOM 598 SG CYS A 77 13.970 -3.285 27.311 1.00 19.69 S
-ATOM 599 N SER A 78 13.001 -8.275 27.211 1.00 29.47 N
-ATOM 600 CA SER A 78 12.928 -9.511 26.448 1.00 32.50 C
-ATOM 601 C SER A 78 11.665 -10.314 26.666 1.00 33.17 C
-ATOM 602 O SER A 78 11.178 -10.437 27.814 1.00 35.05 O
-ATOM 603 CB SER A 78 14.183 -10.362 26.725 1.00 35.08 C
-ATOM 604 OG SER A 78 15.357 -9.643 26.373 1.00 38.49 O
-ATOM 605 N AGLY A 79 11.026 -10.753 25.586 0.56 32.73 N
-ATOM 606 N BGLY A 79 11.268 -11.086 25.671 0.44 33.24 N
-ATOM 607 CA AGLY A 79 9.821 -11.505 25.585 0.56 32.16 C
-ATOM 608 CA BGLY A 79 10.123 -11.952 25.726 0.44 32.77 C
-ATOM 609 C AGLY A 79 9.052 -11.575 26.887 0.56 32.04 C
-ATOM 610 C BGLY A 79 9.866 -12.542 27.101 0.44 32.18 C
-ATOM 611 O AGLY A 79 8.312 -10.654 27.235 0.56 32.01 O
-ATOM 612 O BGLY A 79 10.734 -13.176 27.699 0.44 32.81 O
-ATOM 613 N ASER A 80 9.205 -12.682 27.605 0.56 31.46 N
-ATOM 614 CA ASER A 80 8.473 -12.954 28.816 0.56 31.14 C
-ATOM 615 C ASER A 80 8.880 -12.084 29.993 0.56 30.28 C
-ATOM 616 O ASER A 80 8.341 -12.253 31.098 0.56 30.92 O
-ATOM 617 CB ASER A 80 8.629 -14.441 29.205 0.56 32.24 C
-ATOM 618 OG ASER A 80 9.999 -14.810 29.194 0.56 33.96 O
-ATOM 619 N BGLY A 80 8.644 -12.355 27.599 0.44 31.90 N
-ATOM 620 CA BGLY A 80 8.263 -12.856 28.907 0.44 30.90 C
-ATOM 621 C BGLY A 80 8.635 -11.879 30.017 0.44 29.74 C
-ATOM 622 O BGLY A 80 7.877 -11.725 30.978 0.44 31.09 O
-ATOM 623 N ALYS A 81 9.846 -11.202 29.794 0.56 29.23 N
-ATOM 624 CA ALYS A 81 10.291 -10.308 30.869 0.56 28.16 C
-ATOM 625 C ALYS A 81 9.575 -8.959 30.759 0.56 26.65 C
-ATOM 626 O ALYS A 81 9.248 -8.335 31.767 0.56 24.90 O
-ATOM 627 CB ALYS A 81 11.795 -10.109 30.820 0.56 29.85 C
-ATOM 628 CG ALYS A 81 12.626 -11.336 31.168 0.56 32.07 C
-ATOM 629 CD ALYS A 81 14.097 -11.094 30.837 0.56 33.72 C
-ATOM 630 CE ALYS A 81 14.890 -12.388 30.924 0.56 35.00 C
-ATOM 631 NZ ALYS A 81 16.264 -12.237 30.375 0.56 35.39 N
-ATOM 632 N BASN A 81 9.796 -11.250 29.900 0.44 28.30 N
-ATOM 633 CA BASN A 81 10.254 -10.303 30.910 0.44 26.39 C
-ATOM 634 C BASN A 81 9.566 -8.946 30.756 0.44 24.18 C
-ATOM 635 O BASN A 81 9.603 -8.128 31.673 0.44 23.07 O
-ATOM 636 CB BASN A 81 11.773 -10.095 30.784 0.44 27.87 C
-ATOM 637 CG BASN A 81 12.573 -11.218 31.412 0.44 29.21 C
-ATOM 638 OD1BASN A 81 12.064 -11.941 32.265 0.44 28.74 O
-ATOM 639 ND2BASN A 81 13.822 -11.353 30.992 0.44 30.49 N
-ATOM 640 N CYS A 82 8.950 -8.719 29.607 1.00 22.45 N
-ATOM 641 CA CYS A 82 8.458 -7.382 29.254 1.00 21.45 C
-ATOM 642 C CYS A 82 6.972 -7.304 29.058 1.00 22.09 C
-ATOM 643 O CYS A 82 6.378 -8.160 28.372 1.00 22.87 O
-ATOM 644 CB CYS A 82 9.172 -6.970 27.942 1.00 18.44 C
-ATOM 645 SG CYS A 82 8.735 -5.286 27.433 1.00 17.39 S
-ATOM 646 N GLN A 83 6.311 -6.265 29.586 1.00 20.84 N
-ATOM 647 CA GLN A 83 4.892 -6.092 29.419 1.00 20.12 C
-ATOM 648 C GLN A 83 4.524 -5.110 28.319 1.00 20.04 C
-ATOM 649 O GLN A 83 3.521 -5.334 27.618 1.00 21.43 O
-ATOM 650 CB GLN A 83 4.123 -5.836 30.680 1.00 22.18 C
-ATOM 651 CG GLN A 83 4.345 -4.549 31.422 1.00 23.67 C
-ATOM 652 CD GLN A 83 3.365 -4.361 32.567 1.00 22.91 C
-ATOM 653 OE1 GLN A 83 2.153 -4.246 32.353 1.00 26.53 O
-ATOM 654 NE2 GLN A 83 3.857 -4.338 33.797 1.00 21.79 N
-ATOM 655 N TYR A 84 5.264 -4.025 28.158 1.00 17.66 N
-ATOM 656 CA TYR A 84 5.002 -3.076 27.074 1.00 16.39 C
-ATOM 657 C TYR A 84 6.245 -2.265 26.742 1.00 15.08 C
-ATOM 658 O TYR A 84 7.234 -2.242 27.458 1.00 15.01 O
-ATOM 659 CB TYR A 84 3.797 -2.233 27.255 1.00 15.78 C
-ATOM 660 CG TYR A 84 3.715 -1.293 28.430 1.00 14.92 C
-ATOM 661 CD1 TYR A 84 4.476 -0.110 28.447 1.00 15.62 C
-ATOM 662 CD2 TYR A 84 2.839 -1.516 29.471 1.00 16.69 C
-ATOM 663 CE1 TYR A 84 4.346 0.776 29.503 1.00 15.02 C
-ATOM 664 CE2 TYR A 84 2.695 -0.634 30.519 1.00 15.77 C
-ATOM 665 CZ TYR A 84 3.480 0.519 30.526 1.00 14.45 C
-ATOM 666 OH TYR A 84 3.339 1.400 31.579 1.00 14.64 O
-ATOM 667 N ARG A 85 6.205 -1.609 25.564 1.00 15.20 N
-ATOM 668 CA ARG A 85 7.334 -0.893 25.027 1.00 14.16 C
-ATOM 669 C ARG A 85 8.578 -1.769 24.953 1.00 15.58 C
-ATOM 670 O ARG A 85 9.673 -1.417 25.337 1.00 18.52 O
-ATOM 671 CB ARG A 85 7.617 0.425 25.764 1.00 15.31 C
-ATOM 672 CG ARG A 85 6.496 1.448 25.540 1.00 12.89 C
-ATOM 673 CD ARG A 85 6.532 2.564 26.562 1.00 13.54 C
-ATOM 674 NE ARG A 85 7.452 3.638 26.250 1.00 12.05 N
-ATOM 675 CZ ARG A 85 7.087 4.731 25.517 1.00 10.84 C
-ATOM 676 NH1 ARG A 85 5.893 4.849 25.020 1.00 12.54 N
-ATOM 677 NH2 ARG A 85 8.007 5.678 25.397 1.00 12.10 N
-ATOM 678 N CYS A 86 8.355 -2.983 24.413 1.00 19.00 N
-ATOM 679 CA CYS A 86 9.369 -4.015 24.374 1.00 21.08 C
-ATOM 680 C CYS A 86 10.260 -3.991 23.160 1.00 23.22 C
-ATOM 681 O CYS A 86 9.846 -3.464 22.105 1.00 25.66 O
-ATOM 682 CB CYS A 86 8.644 -5.399 24.430 1.00 21.37 C
-ATOM 683 SG CYS A 86 7.527 -5.528 25.832 1.00 19.98 S
-TER 684 CYS A 86
-HETATM 685 C1 NAG B 1 2.845 5.730 8.410 1.00 13.00 C
-HETATM 686 C2 NAG B 1 2.980 4.600 7.346 1.00 14.42 C
-HETATM 687 C3 NAG B 1 2.175 3.411 7.793 1.00 14.65 C
-HETATM 688 C4 NAG B 1 2.774 2.928 9.166 1.00 14.34 C
-HETATM 689 C5 NAG B 1 2.670 4.122 10.167 1.00 14.77 C
-HETATM 690 C6 NAG B 1 3.374 3.715 11.471 1.00 14.26 C
-HETATM 691 C7 NAG B 1 3.348 5.467 5.049 1.00 14.97 C
-HETATM 692 C8 NAG B 1 2.707 6.219 3.930 1.00 15.87 C
-HETATM 693 N2 NAG B 1 2.477 5.116 6.044 1.00 15.79 N
-HETATM 694 O3 NAG B 1 2.404 2.342 6.849 1.00 16.72 O
-HETATM 695 O4 NAG B 1 1.906 1.888 9.675 1.00 16.83 O
-HETATM 696 O5 NAG B 1 3.422 5.196 9.636 1.00 14.49 O
-HETATM 697 O6 NAG B 1 2.814 4.521 12.515 1.00 17.10 O
-HETATM 698 O7 NAG B 1 4.476 5.236 5.105 1.00 15.81 O
-HETATM 699 C1 NAG B 2 4.454 10.706 8.957 1.00 15.08 C
-HETATM 700 C2 NAG B 2 4.460 9.780 10.201 1.00 13.08 C
-HETATM 701 C3 NAG B 2 4.585 8.348 9.693 1.00 14.61 C
-HETATM 702 C4 NAG B 2 3.438 8.059 8.721 1.00 14.25 C
-HETATM 703 C5 NAG B 2 3.377 9.108 7.581 1.00 15.30 C
-HETATM 704 C6 NAG B 2 2.128 8.919 6.764 1.00 17.09 C
-HETATM 705 C7 NAG B 2 5.438 10.602 12.343 1.00 12.64 C
-HETATM 706 C8 NAG B 2 6.705 10.853 13.053 1.00 14.46 C
-HETATM 707 N2 NAG B 2 5.562 10.111 11.086 1.00 14.12 N
-HETATM 708 O3 NAG B 2 4.506 7.454 10.798 1.00 14.67 O
-HETATM 709 O4 NAG B 2 3.695 6.769 8.064 1.00 13.33 O
-HETATM 710 O5 NAG B 2 3.294 10.396 8.215 1.00 16.15 O
-HETATM 711 O6 NAG B 2 2.067 9.786 5.669 1.00 21.57 O
-HETATM 712 O7 NAG B 2 4.383 10.784 12.792 1.00 13.02 O
-HETATM 713 C1 NAG B 3 4.558 15.870 8.029 1.00 22.68 C
-HETATM 714 C2 NAG B 3 3.781 14.939 7.134 1.00 23.40 C
-HETATM 715 C3 NAG B 3 3.385 13.670 7.891 1.00 21.15 C
-HETATM 716 C4 NAG B 3 4.638 13.082 8.560 1.00 19.08 C
-HETATM 717 C5 NAG B 3 5.387 14.135 9.404 1.00 20.09 C
-HETATM 718 C6 NAG B 3 6.662 13.594 9.979 1.00 20.74 C
-HETATM 719 C7 NAG B 3 2.333 15.941 5.366 1.00 30.04 C
-HETATM 720 C8 NAG B 3 0.935 16.346 5.056 1.00 30.86 C
-HETATM 721 N2 NAG B 3 2.536 15.585 6.659 1.00 26.09 N
-HETATM 722 O3 NAG B 3 2.831 12.779 6.934 1.00 22.73 O
-HETATM 723 O4 NAG B 3 4.253 12.032 9.493 1.00 16.48 O
-HETATM 724 O5 NAG B 3 5.732 15.223 8.531 1.00 21.12 O
-HETATM 725 O6 NAG B 3 7.565 13.140 9.015 1.00 23.92 O
-HETATM 726 O7 NAG B 3 3.172 15.935 4.593 1.00 31.92 O
-HETATM 727 C1 NAG B 4 6.424 20.761 7.837 1.00 35.33 C
-HETATM 728 C2 NAG B 4 7.333 19.637 8.339 1.00 35.66 C
-HETATM 729 C3 NAG B 4 6.907 18.331 7.686 1.00 35.20 C
-HETATM 730 C4 NAG B 4 5.422 18.072 8.014 1.00 33.37 C
-HETATM 731 C5 NAG B 4 4.527 19.299 7.696 1.00 33.27 C
-HETATM 732 C6 NAG B 4 3.173 19.102 8.406 1.00 32.57 C
-HETATM 733 C7 NAG B 4 9.448 20.235 9.622 0.00 40.22 C
-HETATM 734 C8 NAG B 4 10.921 20.272 9.570 0.00 40.07 C
-HETATM 735 N2 NAG B 4 8.744 19.920 8.013 1.00 37.41 N
-HETATM 736 O3 NAG B 4 7.711 17.283 8.235 1.00 36.78 O
-HETATM 737 O4 NAG B 4 4.923 16.987 7.202 1.00 29.35 O
-HETATM 738 O5 NAG B 4 5.078 20.464 8.245 1.00 34.32 O
-HETATM 739 O6 NAG B 4 3.314 19.212 9.787 1.00 31.85 O
-HETATM 740 O7 NAG B 4 8.849 20.404 10.603 0.00 43.75 O
-HETATM 741 O HOH 1 6.997 5.537 34.149 1.00 15.09 O
-HETATM 742 O HOH 2 10.295 3.302 26.095 1.00 16.76 O
-HETATM 743 O HOH 3 10.019 12.949 25.133 1.00 20.11 O
-HETATM 744 O HOH 4 -0.006 5.891 22.509 1.00 18.64 O
-HETATM 745 O HOH 5 3.935 11.236 27.997 1.00 20.18 O
-HETATM 746 O HOH 6 -0.274 -4.153 23.054 1.00 19.37 O
-HETATM 747 O HOH 7 13.817 8.788 41.782 1.00 19.15 O
-HETATM 748 O HOH 8 -1.891 13.459 17.137 1.00 19.03 O
-HETATM 749 O HOH 9 -2.083 8.214 21.987 1.00 20.14 O
-HETATM 750 O HOH 10 2.797 0.928 18.102 1.00 27.68 O
-HETATM 751 O HOH 11 12.691 9.974 30.666 1.00 27.47 O
-HETATM 752 O HOH 12 9.666 4.203 14.797 1.00 27.80 O
-HETATM 753 O HOH 13 1.809 -0.509 7.352 1.00 30.54 O
-HETATM 754 O HOH 14 -1.004 17.744 16.794 1.00 27.36 O
-HETATM 755 O HOH 15 8.061 9.329 9.741 1.00 31.81 O
-HETATM 756 O HOH 16 7.423 9.060 40.549 1.00 26.40 O
-HETATM 757 O HOH 17 -0.420 5.798 5.808 1.00 27.21 O
-HETATM 758 O HOH 18 2.796 -0.282 11.074 1.00 26.18 O
-HETATM 759 O HOH 19 6.627 3.510 13.365 1.00 29.55 O
-HETATM 760 O HOH 20 7.716 11.785 37.937 1.00 33.90 O
-HETATM 761 O HOH 21 13.017 2.743 41.126 1.00 29.39 O
-HETATM 762 O HOH 22 12.586 8.818 35.183 1.00 31.81 O
-HETATM 763 O HOH 23 -0.672 17.945 19.813 1.00 29.17 O
-HETATM 764 O HOH 24 15.414 7.885 17.622 1.00 34.99 O
-HETATM 765 O HOH 25 15.120 1.027 36.513 1.00 34.01 O
-HETATM 766 O HOH 26 12.255 0.609 24.383 1.00 37.64 O
-HETATM 767 O HOH 27 15.910 10.470 25.375 1.00 39.63 O
-HETATM 768 O HOH 28 2.672 -4.881 24.854 1.00 38.54 O
-HETATM 769 O HOH 29 5.651 -3.894 23.305 1.00 37.18 O
-HETATM 770 O HOH 30 1.757 7.661 36.606 1.00 29.03 O
-HETATM 771 O HOH 31 0.331 -3.984 34.349 1.00 30.94 O
-HETATM 772 O HOH 32 14.751 9.106 27.605 1.00 26.56 O
-HETATM 773 O HOH 33 -1.326 14.783 10.991 1.00 28.85 O
-HETATM 774 O HOH 34 14.132 7.665 31.734 1.00 39.44 O
-HETATM 775 O HOH 35 8.520 15.382 25.290 1.00 37.93 O
-HETATM 776 O HOH 36 4.900 1.282 16.585 1.00 39.95 O
-HETATM 777 O HOH 37 16.453 3.015 36.120 1.00 36.31 O
-HETATM 778 O HOH 38 18.443 2.687 30.904 1.00 37.22 O
-HETATM 779 O HOH 39 18.669 1.609 28.224 1.00 39.90 O
-HETATM 780 O HOH 40 9.694 13.193 10.908 1.00 64.04 O
-HETATM 781 O HOH 41 17.631 6.215 28.193 1.00 37.41 O
-HETATM 782 O HOH 42 0.923 7.940 41.082 1.00 47.91 O
-HETATM 783 O HOH 43 -3.992 5.719 31.647 1.00 53.05 O
-HETATM 784 O HOH 44 -0.321 12.255 8.483 1.00 35.86 O
-HETATM 785 O HOH 45 7.615 4.350 11.184 1.00 31.62 O
-HETATM 786 O HOH 46 -3.066 4.753 29.076 1.00 40.05 O
-HETATM 787 O HOH 47 -0.945 1.949 9.712 1.00 36.88 O
-HETATM 788 O HOH 48 -7.059 6.398 16.836 1.00 36.00 O
-HETATM 789 O HOH 49 17.449 -1.699 26.400 1.00 44.33 O
-HETATM 790 O HOH 50 -6.832 2.828 21.276 1.00 40.92 O
-HETATM 791 O HOH 51 12.986 3.279 23.159 1.00 32.76 O
-HETATM 792 O HOH 52 17.990 -0.372 33.679 1.00 38.43 O
-HETATM 793 O HOH 53 2.479 -4.119 36.286 1.00 36.51 O
-HETATM 794 O HOH 54 -2.996 7.951 27.405 1.00 35.06 O
-HETATM 795 O HOH 55 -4.774 1.179 18.535 1.00 46.84 O
-HETATM 796 O HOH 56 8.407 14.447 7.084 1.00 49.80 O
-HETATM 797 O HOH 57 -8.668 12.634 15.670 1.00 40.31 O
-HETATM 798 O HOH 58 6.835 -3.272 20.532 1.00 54.97 O
-HETATM 799 O HOH 59 17.790 -12.129 35.546 1.00 46.47 O
-HETATM 800 O HOH 60 2.036 -1.933 37.909 1.00 45.42 O
-HETATM 801 O HOH 61 10.515 4.141 23.049 1.00 39.36 O
-HETATM 802 O HOH 62 13.853 11.829 27.829 1.00 48.06 O
-HETATM 803 O HOH 63 6.585 -6.548 41.143 1.00 45.67 O
-HETATM 804 O HOH 64 4.117 17.977 21.677 1.00 40.12 O
-HETATM 805 O HOH 65 4.885 -10.712 28.859 1.00 54.15 O
-HETATM 806 O HOH 66 -4.389 3.044 11.770 1.00 43.85 O
-HETATM 807 O HOH 67 12.708 -9.701 34.933 1.00 34.56 O
-HETATM 807 O HOH 67 12.708 -9.701 34.933 1.00 34.56 O
-HETATM 808 O HOH 68 12.853 13.538 25.505 1.00 43.43 O
-HETATM 809 O HOH 69 6.223 15.935 24.195 1.00 56.63 O
-HETATM 810 O HOH 70 -6.184 3.543 26.723 1.00 45.90 O
-HETATM 811 O HOH 71 5.679 -0.794 40.337 1.00 42.73 O
-HETATM 812 O HOH 72 9.327 16.970 10.364 1.00 44.27 O
-HETATM 813 O HOH 73 8.994 19.338 12.497 1.00 57.40 O
-HETATM 814 O HOH 74 0.017 8.087 30.620 1.00 43.92 O
-HETATM 815 O HOH 75 -0.903 8.986 28.883 1.00 45.39 O
-HETATM 816 O HOH 77 0.315 -4.363 30.096 1.00 43.95 O
+ATOM 22 C CYS A 0 -3.637 9.026 17.672 1.00 16.22 C
+ATOM 9 N ARG A 2 -2.607 4.673 13.504 1.00 20.57 N
+ATOM 9 N ARG A 2 -2.607 4.673 13.504 1.00 20.57 N
+ATOM 10 CA ARG A 2 -3.091 6.010 13.918 1.00 20.82 C
+ATOM 11 C ARG A 2 -3.361 6.101 15.403 1.00 19.55 C
+ATOM 12 O ARG A 2 -3.742 5.121 16.050 1.00 19.99 O
+ATOM 13 CB ARG A 2 -4.348 6.395 13.124 1.00 25.30 C
+ATOM 14 CG ARG A 2 -4.110 6.337 11.619 1.00 30.98 C
+ATOM 15 CD ARG A 2 -4.277 7.652 10.942 1.00 31.79 C
+ATOM 16 NE ARG A 2 -3.441 8.730 11.457 1.00 31.91 N
+ATOM 17 CZ ARG A 2 -3.550 9.979 10.980 1.00 31.95 C
+ATOM 18 NH1 ARG A 2 -4.444 10.212 10.022 1.00 33.34 N
+ATOM 19 NH2 ARG A 2 -2.804 10.949 11.444 1.00 29.59 N
+ATOM 20 N CYS A 3 -3.248 7.317 15.948 1.00 15.71 N
+ATOM 20 N ACYS A 3 -3.248 7.317 15.948 1.00 15.71 N
+ATOM 21 CA CYS A 3 -3.419 7.555 17.361 1.00 14.13 C
+ATOM 22 C CYS A 3 -3.637 9.026 17.672 1.00 16.22 C
+ATOM 23 O CYS A 3 -3.406 9.901 16.828 1.00 15.46 O
+ATOM 24 CB CYS A 3 -2.127 7.069 18.110 1.00 13.74 C
+ATOM 25 SG CYS A 3 -0.659 7.917 17.525 1.00 11.51 S
+ATOM 26 N GLY A 4 -4.122 9.328 18.863 1.00 15.45 N
+ATOM 27 CA GLY A 4 -4.129 10.656 19.402 1.00 17.05 C
+ATOM 28 C GLY A 4 -5.029 11.674 18.735 1.00 16.11 C
+ATOM 29 O GLY A 4 -6.039 11.345 18.125 1.00 17.88 O
+ATOM 29 O SER A 4 -6.039 11.345 18.125 1.00 17.88 O
+ATOM 30 N SER A 5 -4.651 12.958 18.899 1.00 17.09 N
+ATOM 31 CA SER A 5 -5.559 14.034 18.479 1.00 19.08 C
+ATOM 32 C SER A 5 -5.752 14.104 16.987 1.00 21.30 C
+ATOM 33 O SER A 5 -6.858 14.468 16.528 1.00 22.84 O
+ATOM 34 CB SER A 5 -5.107 15.371 19.047 1.00 19.54 C
+ATOM 35 OG SER A 5 -3.926 15.835 18.448 1.00 21.28 O
+ATOM 36 N GLN A 6 -4.747 13.742 16.200 1.00 20.14 N
+ATOM 37 CA GLN A 6 -4.860 13.800 14.746 1.00 22.55 C
+ATOM 38 C GLN A 6 -5.298 12.480 14.152 1.00 23.94 C
+ATOM 39 O GLN A 6 -5.600 12.407 12.952 1.00 26.03 O
+ATOM 40 CB GLN A 6 -3.493 14.198 14.155 1.00 22.92 C
+ATOM 41 CG GLN A 6 -3.158 15.672 14.364 1.00 26.86 C
+ATOM 42 CD GLN A 6 -4.060 16.549 13.492 1.00 30.27 C
+ATOM 43 OE1 GLN A 6 -4.888 17.280 14.006 1.00 32.91 O
+ATOM 44 NE2 GLN A 6 -3.896 16.416 12.182 1.00 32.97 N
+ATOM 45 N GLY A 7 -5.311 11.413 14.956 1.00 21.65 N
+ATOM 46 CA GLY A 7 -5.574 10.092 14.428 1.00 23.37 C
+ATOM 47 C GLY A 7 -6.707 9.337 15.036 1.00 24.14 C
+ATOM 48 O GLY A 7 -6.704 8.084 15.015 1.00 27.63 O
+ATOM 49 N GLY A 8 -7.727 10.003 15.582 1.00 24.63 N
+ATOM 50 CA GLY A 8 -8.913 9.333 16.063 1.00 25.88 C
+ATOM 51 C GLY A 8 -8.994 9.133 17.546 1.00 26.42 C
+ATOM 52 O GLY A 8 -10.027 8.635 18.054 1.00 27.64 O
+ATOM 53 N GLY A 9 -7.952 9.460 18.305 1.00 24.34 N
+ATOM 54 CA GLY A 9 -7.970 9.377 19.738 1.00 25.05 C
+ATOM 55 C GLY A 9 -7.440 8.111 20.351 1.00 24.44 C
+ATOM 56 O GLY A 9 -7.431 8.002 21.594 1.00 26.91 O
+ATOM 57 N AGLY A 10 -6.973 7.149 19.576 0.67 23.87 N
+ATOM 58 CA AGLY A 10 -6.498 5.882 20.072 0.67 23.51 C
+ATOM 59 C AGLY A 10 -5.157 5.939 20.783 0.67 20.73 C
+ATOM 60 O AGLY A 10 -4.407 6.920 20.735 0.67 18.63 O
+ATOM 61 N BSER A 10 -6.973 7.149 19.576 0.33 23.87 N
+ATOM 62 CA BSER A 10 -6.498 5.882 20.072 0.33 23.51 C
+ATOM 63 C BSER A 10 -5.157 5.939 20.783 0.33 20.73 C
+ATOM 64 O BSER A 10 -4.407 6.920 20.735 0.33 18.63 O
+ATOM 65 CB BSER A 10 -6.423 4.843 18.935 0.33 25.06 C
+ATOM 66 OG BSER A 10 -5.085 4.685 18.488 0.33 26.99 O
+ATOM 67 N THR A 11 -4.827 4.849 21.451 1.00 18.73 N
+ATOM 68 CA THR A 11 -3.558 4.608 22.100 1.00 18.41 C
+ATOM 69 C THR A 11 -2.789 3.579 21.270 1.00 17.02 C
+ATOM 70 O THR A 11 -3.445 2.660 20.722 1.00 19.87 O
+ATOM 71 CB THR A 11 -3.766 4.031 23.526 1.00 21.54 C
+ATOM 72 OG1 THR A 11 -4.454 5.039 24.302 1.00 24.31 O
+ATOM 73 CG2 THR A 11 -2.424 3.745 24.174 1.00 22.15 C
+ATOM 74 N CYS A 12 -1.513 3.704 21.104 1.00 14.16 N
+ATOM 75 CA CYS A 12 -0.739 2.807 20.240 1.00 13.37 C
+ATOM 76 C CYS A 12 -0.634 1.400 20.784 1.00 14.05 C
+ATOM 77 O CYS A 12 -0.393 1.199 21.977 1.00 14.75 O
+ATOM 78 CB CYS A 12 0.693 3.394 20.115 1.00 11.29 C
+ATOM 79 SG CYS A 12 0.658 5.001 19.231 1.00 10.97 S
+ATOM 80 N PRO A 13 -0.523 0.411 19.889 1.00 13.45 N
+ATOM 81 CA PRO A 13 -0.169 -0.942 20.296 1.00 14.93 C
+ATOM 82 C PRO A 13 1.156 -0.911 21.027 1.00 14.45 C
+ATOM 83 O PRO A 13 2.082 -0.150 20.677 1.00 14.74 O
+ATOM 84 CB PRO A 13 -0.029 -1.675 18.958 1.00 15.07 C
+ATOM 85 CG PRO A 13 -0.978 -0.948 18.064 1.00 15.93 C
+ATOM 86 CD PRO A 13 -0.772 0.535 18.443 1.00 14.96 C
+ATOM 87 N AALA A 14 1.268 -1.692 22.089 0.56 14.65 N
+ATOM 88 CA AALA A 14 2.445 -1.814 22.901 0.56 16.97 C
+ATOM 89 C AALA A 14 2.754 -0.531 23.694 0.56 14.77 C
+ATOM 90 O AALA A 14 3.849 -0.405 24.231 0.56 17.39 O
+ATOM 91 CB AALA A 14 3.645 -2.269 22.108 0.56 18.86 C
+ATOM 92 N BGLY A 14 1.268 -1.692 22.089 0.44 14.65 N
+ATOM 93 CA BGLY A 14 2.445 -1.814 22.901 0.44 16.97 C
+ATOM 94 C BGLY A 14 2.754 -0.531 23.694 0.44 14.77 C
+ATOM 95 O BGLY A 14 3.849 -0.405 24.231 0.44 17.39 O
+ATOM 96 N LEU A 15 1.754 0.327 23.778 1.00 13.72 N
+ATOM 97 CA LEU A 15 1.873 1.601 24.487 1.00 13.52 C
+ATOM 98 C LEU A 15 3.033 2.427 23.993 1.00 13.54 C
+ATOM 99 O LEU A 15 3.727 3.109 24.761 1.00 13.53 O
+ATOM 100 CB LEU A 15 1.831 1.406 25.994 1.00 16.99 C
+ATOM 101 CG ALEU A 15 0.485 1.257 26.672 0.50 18.92 C
+ATOM 102 CG BLEU A 15 0.561 0.693 26.511 0.50 18.16 C
+ATOM 103 CD1ALEU A 15 -0.272 2.579 26.709 0.50 19.64 C
+ATOM 104 CD1BLEU A 15 0.675 0.390 27.988 0.50 20.69 C
+ATOM 105 CD2ALEU A 15 -0.364 0.182 26.012 0.50 21.59 C
+ATOM 106 CD2BLEU A 15 -0.677 1.532 26.219 0.50 20.18 C
+ATOM 107 N ATRP A 16 3.298 2.388 22.684 0.56 11.29 N
+ATOM 108 CA ATRP A 16 4.273 3.211 22.015 0.56 11.25 C
+ATOM 109 C ATRP A 16 3.838 4.674 21.911 0.56 9.81 C
+ATOM 110 O ATRP A 16 2.731 5.041 22.272 0.56 10.80 O
+ATOM 111 CB ATRP A 16 4.530 2.673 20.587 0.56 15.06 C
+ATOM 112 CG ATRP A 16 5.500 1.531 20.589 0.56 22.22 C
+ATOM 113 CD1ATRP A 16 5.344 0.300 20.028 0.56 22.99 C
+ATOM 114 CD2ATRP A 16 6.804 1.549 21.180 0.56 23.43 C
+ATOM 115 NE1ATRP A 16 6.469 -0.459 20.244 0.56 25.66 N
+ATOM 116 CE2ATRP A 16 7.380 0.282 20.950 0.56 25.18 C
+ATOM 117 CE3ATRP A 16 7.532 2.508 21.894 0.56 26.28 C
+ATOM 118 CZ2ATRP A 16 8.652 -0.045 21.409 0.56 25.91 C
+ATOM 119 CZ3ATRP A 16 8.793 2.178 22.349 0.56 26.49 C
+ATOM 120 CH2ATRP A 16 9.341 0.908 22.104 0.56 25.88 C
+ATOM 121 N BARG A 16 3.259 2.392 22.674 0.44 13.28 N
+ATOM 122 CA BARG A 16 4.280 3.162 22.014 0.44 13.26 C
+ATOM 123 C BARG A 16 3.896 4.656 21.956 0.44 11.99 C
+ATOM 124 O BARG A 16 2.800 5.040 22.308 0.44 12.53 O
+ATOM 125 CB BARG A 16 4.656 2.661 20.638 0.22 13.35 C
+ATOM 126 CB CARG A 16 4.437 2.680 20.555 0.22 16.20 C
+ATOM 127 CG BARG A 16 5.385 1.331 20.601 0.22 17.64 C
+ATOM 128 CG CARG A 16 5.639 1.870 20.229 0.22 21.14 C
+ATOM 129 CD BARG A 16 6.160 1.040 21.834 0.22 19.84 C
+ATOM 130 CD CARG A 16 5.796 0.630 21.081 0.22 21.56 C
+ATOM 131 NE BARG A 16 7.593 1.203 21.768 0.22 21.21 N
+ATOM 132 NE CARG A 16 7.173 0.375 21.465 0.22 24.22 N
+ATOM 133 CZ BARG A 16 8.455 0.224 21.501 0.22 22.06 C
+ATOM 134 CZ CARG A 16 8.046 1.259 21.909 0.22 25.11 C
+ATOM 135 NH1BARG A 16 8.004 -0.997 21.239 0.22 23.25 N
+ATOM 136 NH1CARG A 16 7.750 2.548 22.045 0.22 26.05 N
+ATOM 137 NH2BARG A 16 9.757 0.463 21.492 0.22 23.17 N
+ATOM 138 NH2CARG A 16 9.277 0.859 22.235 0.22 24.63 N
+ATOM 139 N CYS A 17 4.860 5.440 21.477 1.00 11.64 N
+ATOM 140 CA CYS A 17 4.659 6.900 21.426 1.00 10.21 C
+ATOM 141 C CYS A 17 3.731 7.303 20.294 1.00 10.66 C
+ATOM 142 O CYS A 17 3.858 6.790 19.169 1.00 12.03 O
+ATOM 143 CB CYS A 17 5.991 7.617 21.171 1.00 10.96 C
+ATOM 144 SG CYS A 17 7.304 7.032 22.268 1.00 11.64 S
+ATOM 145 N CYS A 18 2.863 8.268 20.538 1.00 10.28 N
+ATOM 146 CA CYS A 18 2.045 8.868 19.490 1.00 11.27 C
+ATOM 147 C CYS A 18 2.592 10.233 19.140 1.00 10.85 C
+ATOM 148 O CYS A 18 2.716 11.073 20.064 1.00 11.54 O
+ATOM 149 CB CYS A 18 0.606 9.001 20.013 1.00 11.58 C
+ATOM 150 SG CYS A 18 -0.516 9.593 18.698 1.00 11.18 S
+ATOM 151 N SER A 19 2.983 10.469 17.906 1.00 10.63 N
+ATOM 152 CA SER A 19 3.522 11.773 17.506 1.00 10.33 C
+ATOM 153 C SER A 19 2.395 12.812 17.355 1.00 10.06 C
+ATOM 154 O SER A 19 1.239 12.483 17.289 1.00 10.50 O
+ATOM 155 CB SER A 19 4.213 11.607 16.137 1.00 12.53 C
+ATOM 156 OG SER A 19 3.218 11.521 15.119 1.00 11.20 O
+ATOM 157 N ILE A 20 2.843 14.085 17.220 1.00 10.99 N
+ATOM 158 CA ILE A 20 1.854 15.159 17.009 1.00 11.86 C
+ATOM 159 C ILE A 20 1.162 15.020 15.673 1.00 13.07 C
+ATOM 160 O ILE A 20 0.135 15.665 15.448 1.00 13.75 O
+ATOM 161 CB ILE A 20 2.505 16.546 17.133 1.00 12.91 C
+ATOM 162 CG1 ILE A 20 3.608 16.733 16.099 1.00 13.68 C
+ATOM 163 CG2 ILE A 20 3.006 16.773 18.552 1.00 14.60 C
+ATOM 164 CD1 ILE A 20 4.127 18.178 16.031 1.00 15.84 C
+ATOM 165 N TRP A 21 1.662 14.176 14.766 1.00 11.46 N
+ATOM 166 CA TRP A 21 1.006 13.921 13.502 1.00 11.50 C
+ATOM 167 C TRP A 21 0.012 12.789 13.564 1.00 12.13 C
+ATOM 168 O TRP A 21 -0.698 12.510 12.602 1.00 14.34 O
+ATOM 169 CB TRP A 21 2.031 13.690 12.369 1.00 12.06 C
+ATOM 170 CG TRP A 21 2.940 14.872 12.185 1.00 13.41 C
+ATOM 171 CD1 TRP A 21 2.610 16.023 11.471 1.00 14.87 C
+ATOM 172 CD2 TRP A 21 4.240 15.070 12.680 1.00 13.19 C
+ATOM 173 NE1 TRP A 21 3.663 16.890 11.537 1.00 14.69 N
+ATOM 174 CE2 TRP A 21 4.693 16.334 12.246 1.00 14.96 C
+ATOM 175 CE3 TRP A 21 5.105 14.273 13.462 1.00 14.98 C
+ATOM 176 CZ2 TRP A 21 5.949 16.830 12.589 1.00 16.73 C
+ATOM 177 CZ3 TRP A 21 6.354 14.760 13.773 1.00 17.06 C
+ATOM 178 CH2 TRP A 21 6.760 16.040 13.348 1.00 18.02 C
+ATOM 179 N GLY A 22 -0.131 12.116 14.717 1.00 11.06 N
+ATOM 180 CA GLY A 22 -1.145 11.092 14.893 1.00 11.02 C
+ATOM 181 C GLY A 22 -0.683 9.708 14.431 1.00 9.47 C
+ATOM 182 O GLY A 22 -1.567 8.923 14.000 1.00 11.91 O
+ATOM 183 N TRP A 23 0.567 9.408 14.566 1.00 10.13 N
+ATOM 184 CA TRP A 23 1.085 8.080 14.169 1.00 11.05 C
+ATOM 185 C TRP A 23 1.906 7.506 15.308 1.00 11.89 C
+ATOM 186 O TRP A 23 2.573 8.227 16.048 1.00 11.18 O
+ATOM 187 CB TRP A 23 1.938 8.195 12.906 1.00 12.91 C
+ATOM 188 CG TRP A 23 1.141 8.597 11.694 1.00 14.32 C
+ATOM 189 CD1 TRP A 23 1.043 9.842 11.148 1.00 15.32 C
+ATOM 190 CD2 TRP A 23 0.335 7.731 10.891 1.00 13.59 C
+ATOM 191 NE1 TRP A 23 0.231 9.802 10.044 1.00 16.07 N
+ATOM 192 CE2 TRP A 23 -0.257 8.535 9.883 1.00 15.22 C
+ATOM 193 CE3 TRP A 23 0.013 6.374 10.946 1.00 15.09 C
+ATOM 194 CZ2 TRP A 23 -1.085 7.991 8.898 1.00 17.75 C
+ATOM 195 CZ3 TRP A 23 -0.823 5.844 9.974 1.00 19.21 C
+ATOM 196 CH2 TRP A 23 -1.358 6.660 8.971 1.00 20.75 C
+ATOM 197 N CYS A 24 1.986 6.179 15.351 1.00 11.52 N
+ATOM 198 CA CYS A 24 2.689 5.457 16.386 1.00 12.25 C
+ATOM 199 C CYS A 24 4.094 5.076 16.014 1.00 11.84 C
+ATOM 200 O CYS A 24 4.313 4.643 14.856 1.00 13.73 O
+ATOM 201 CB CYS A 24 1.937 4.110 16.664 1.00 12.40 C
+ATOM 202 SG CYS A 24 0.275 4.401 17.331 1.00 11.68 S
+ATOM 203 N GLY A 25 5.037 5.149 16.934 1.00 12.17 N
+ATOM 204 CA GLY A 25 6.397 4.709 16.654 1.00 12.62 C
+ATOM 205 C GLY A 25 7.268 4.734 17.898 1.00 13.04 C
+ATOM 206 O GLY A 25 6.806 4.993 19.009 1.00 13.30 O
+ATOM 207 N ASP A 26 8.546 4.411 17.713 1.00 15.66 N
+ATOM 208 CA ASP A 26 9.453 4.213 18.836 1.00 16.94 C
+ATOM 209 C ASP A 26 10.742 4.984 18.704 1.00 19.41 C
+ATOM 210 O ASP A 26 11.679 4.757 19.495 1.00 22.27 O
+ATOM 211 CB ASP A 26 9.733 2.705 19.003 1.00 22.37 C
+ATOM 212 CG ASP A 26 10.242 2.048 17.750 1.00 27.17 C
+ATOM 213 OD1 ASP A 26 10.821 2.716 16.876 1.00 30.74 O
+ATOM 214 OD2 ASP A 26 10.050 0.806 17.615 1.00 33.27 O
+ATOM 215 N SER A 27 10.836 5.914 17.772 1.00 16.57 N
+ATOM 216 CA SER A 27 12.025 6.714 17.541 1.00 18.12 C
+ATOM 217 C SER A 27 11.784 8.177 17.845 1.00 16.83 C
+ATOM 218 O SER A 27 10.654 8.569 18.171 1.00 15.78 O
+ATOM 219 CB SER A 27 12.504 6.526 16.094 1.00 19.54 C
+ATOM 220 OG SER A 27 11.449 6.922 15.205 1.00 21.34 O
+ATOM 221 N GLU A 28 12.795 9.020 17.760 1.00 17.82 N
+ATOM 222 CA GLU A 28 12.747 10.415 18.151 1.00 18.18 C
+ATOM 223 C GLU A 28 11.580 11.219 17.642 1.00 16.44 C
+ATOM 224 O GLU A 28 10.943 11.965 18.410 1.00 15.84 O
+ATOM 225 CB AGLU A 28 14.087 11.070 17.738 0.50 20.93 C
+ATOM 226 CB BGLU A 28 14.080 11.099 17.807 0.50 21.15 C
+ATOM 227 CG AGLU A 28 14.061 12.574 17.631 0.50 22.90 C
+ATOM 228 CG BGLU A 28 14.458 12.231 18.733 0.50 23.48 C
+ATOM 229 CD AGLU A 28 13.788 13.255 18.959 0.50 23.94 C
+ATOM 230 CD BGLU A 28 13.705 13.513 18.493 0.50 25.49 C
+ATOM 231 OE1AGLU A 28 14.121 12.673 20.008 0.50 25.78 O
+ATOM 232 OE1BGLU A 28 13.293 13.783 17.345 0.50 26.68 O
+ATOM 233 OE2AGLU A 28 13.210 14.367 18.948 0.50 25.81 O
+ATOM 234 OE2BGLU A 28 13.513 14.286 19.469 0.50 28.16 O
+ATOM 235 N PRO A 29 11.257 11.149 16.353 1.00 16.44 N
+ATOM 236 CA PRO A 29 10.144 11.917 15.806 1.00 17.18 C
+ATOM 237 C PRO A 29 8.823 11.616 16.471 1.00 14.68 C
+ATOM 238 O PRO A 29 7.892 12.446 16.505 1.00 15.00 O
+ATOM 239 CB PRO A 29 10.136 11.541 14.327 1.00 19.03 C
+ATOM 240 CG PRO A 29 11.501 11.035 14.037 1.00 20.48 C
+ATOM 241 CD PRO A 29 12.006 10.414 15.326 1.00 18.12 C
+ATOM 242 N TYR A 30 8.646 10.397 16.996 1.00 13.61 N
+ATOM 243 CA TYR A 30 7.443 9.994 17.663 1.00 12.47 C
+ATOM 244 C TYR A 30 7.479 10.211 19.186 1.00 12.14 C
+ATOM 245 O TYR A 30 6.458 10.498 19.778 1.00 12.08 O
+ATOM 246 CB TYR A 30 7.181 8.480 17.444 1.00 12.23 C
+ATOM 247 CG TYR A 30 7.057 8.081 15.992 1.00 12.06 C
+ATOM 248 CD1 TYR A 30 8.213 7.750 15.277 1.00 13.86 C
+ATOM 249 CD2 TYR A 30 5.842 8.011 15.344 1.00 11.48 C
+ATOM 250 CE1 TYR A 30 8.133 7.383 13.943 1.00 14.64 C
+ATOM 251 CE2 TYR A 30 5.756 7.627 14.005 1.00 12.08 C
+ATOM 252 CZ TYR A 30 6.908 7.328 13.334 1.00 13.37 C
+ATOM 253 OH TYR A 30 6.830 6.950 11.998 1.00 15.25 O
+ATOM 254 N CYS A 31 8.670 10.046 19.759 1.00 13.67 N
+ATOM 255 CA CYS A 31 8.831 9.940 21.199 1.00 12.61 C
+ATOM 256 C CYS A 31 9.529 11.123 21.850 1.00 14.04 C
+ATOM 257 O CYS A 31 9.506 11.227 23.089 1.00 14.90 O
+ATOM 258 CB CYS A 31 9.719 8.692 21.503 1.00 13.30 C
+ATOM 259 SG CYS A 31 8.953 7.136 21.032 1.00 12.81 S
+ATOM 260 N GLY A 32 10.191 11.953 21.069 1.00 14.92 N
+ATOM 261 CA GLY A 32 10.970 13.072 21.570 1.00 16.76 C
+ATOM 262 C GLY A 32 10.183 14.376 21.576 1.00 16.37 C
+ATOM 263 O GLY A 32 9.089 14.508 22.067 1.00 17.28 O
+ATOM 264 N ARG A 33 10.805 15.400 20.943 1.00 18.68 N
+ATOM 265 CA ARG A 33 10.253 16.736 20.909 1.00 20.22 C
+ATOM 266 C ARG A 33 8.893 16.835 20.258 1.00 19.35 C
+ATOM 267 O ARG A 33 8.053 17.653 20.668 1.00 21.59 O
+ATOM 268 CB ARG A 33 11.250 17.687 20.210 1.00 23.28 C
+ATOM 269 CG ARG A 33 12.707 17.055 20.301 0.00 20.00 C
+ATOM 270 CD ARG A 33 13.726 18.003 19.669 0.00 20.00 C
+ATOM 271 NE ARG A 33 13.518 18.180 18.226 0.00 20.00 N
+ATOM 272 CZ ARG A 33 12.493 18.858 17.694 0.00 20.00 C
+ATOM 273 NH1 ARG A 33 11.568 19.434 18.472 0.00 20.00 N
+ATOM 274 NH2 ARG A 33 12.305 19.018 16.376 0.00 20.00 N
+ATOM 275 N THR A 34 8.629 15.996 19.260 1.00 17.05 N
+ATOM 276 CA THR A 34 7.367 16.075 18.514 1.00 15.55 C
+ATOM 277 C THR A 34 6.416 14.947 18.911 1.00 13.76 C
+ATOM 278 O THR A 34 5.571 14.521 18.145 1.00 14.63 O
+ATOM 279 CB THR A 34 7.620 16.068 17.006 1.00 15.68 C
+ATOM 280 OG1 THR A 34 8.589 15.063 16.678 1.00 18.28 O
+ATOM 281 CG2 THR A 34 8.192 17.420 16.539 1.00 17.63 C
+ATOM 282 N CYS A 35 6.527 14.537 20.184 1.00 12.85 N
+ATOM 283 CA CYS A 35 5.619 13.529 20.733 1.00 11.99 C
+ATOM 284 C CYS A 35 4.388 14.118 21.364 1.00 12.75 C
+ATOM 285 O CYS A 35 4.491 15.127 22.102 1.00 15.67 O
+ATOM 286 CB CYS A 35 6.432 12.713 21.770 1.00 11.83 C
+ATOM 287 SG CYS A 35 5.470 11.406 22.561 1.00 10.89 S
+ATOM 288 N GLU A 36 3.235 13.515 21.205 1.00 10.58 N
+ATOM 289 CA GLU A 36 2.004 13.930 21.842 1.00 12.03 C
+ATOM 290 C GLU A 36 1.683 13.107 23.086 1.00 13.55 C
+ATOM 291 O GLU A 36 1.129 13.669 24.043 1.00 13.26 O
+ATOM 292 CB GLU A 36 0.819 13.833 20.876 1.00 12.59 C
+ATOM 293 CG GLU A 36 -0.497 14.343 21.457 1.00 16.20 C
+ATOM 294 CD GLU A 36 -1.609 14.316 20.436 1.00 18.54 C
+ATOM 295 OE1 GLU A 36 -2.248 13.270 20.239 1.00 18.98 O
+ATOM 296 OE2 GLU A 36 -1.858 15.380 19.821 1.00 23.44 O
+ATOM 297 N ASN A 37 1.897 11.793 23.068 1.00 12.95 N
+ATOM 298 CA ASN A 37 1.497 10.964 24.209 1.00 12.65 C
+ATOM 299 C ASN A 37 2.446 9.775 24.337 1.00 12.21 C
+ATOM 300 O ASN A 37 3.003 9.296 23.346 1.00 12.29 O
+ATOM 301 CB ASN A 37 0.071 10.481 24.067 1.00 15.42 C
+ATOM 302 CG ASN A 37 -0.529 9.956 25.368 1.00 18.91 C
+ATOM 303 OD1 ASN A 37 -0.166 10.434 26.429 1.00 20.42 O
+ATOM 304 ND2 ASN A 37 -1.426 8.985 25.231 1.00 21.73 N
+ATOM 305 N LYS A 38 2.547 9.285 25.569 1.00 12.38 N
+ATOM 306 CA LYS A 38 3.418 8.157 25.928 1.00 11.24 C
+ATOM 307 C LYS A 38 4.814 8.422 25.439 1.00 11.57 C
+ATOM 308 O LYS A 38 5.475 7.649 24.737 1.00 11.73 O
+ATOM 309 CB LYS A 38 2.872 6.835 25.390 1.00 12.50 C
+ATOM 310 CG LYS A 38 1.463 6.489 25.778 1.00 13.84 C
+ATOM 311 CD LYS A 38 1.233 6.470 27.289 1.00 16.31 C
+ATOM 312 CE LYS A 38 -0.159 5.917 27.584 1.00 17.80 C
+ATOM 313 NZ LYS A 38 -0.465 5.932 29.040 1.00 20.45 N
+ATOM 314 N CYS A 39 5.378 9.556 25.880 1.00 12.02 N
+ATOM 315 CA CYS A 39 6.632 10.077 25.433 1.00 11.58 C
+ATOM 316 C CYS A 39 7.809 9.718 26.328 1.00 12.51 C
+ATOM 317 O CYS A 39 7.574 9.420 27.513 1.00 13.84 O
+ATOM 318 CB CYS A 39 6.512 11.641 25.373 1.00 13.49 C
+ATOM 319 SG CYS A 39 5.076 12.190 24.393 1.00 12.58 S
+ATOM 320 N TRP A 40 9.011 9.764 25.795 1.00 11.50 N
+ATOM 321 CA TRP A 40 10.193 9.395 26.592 1.00 14.12 C
+ATOM 322 C TRP A 40 10.259 10.234 27.861 1.00 15.88 C
+ATOM 323 O TRP A 40 10.656 9.728 28.912 1.00 17.55 O
+ATOM 324 CB TRP A 40 11.457 9.577 25.773 1.00 14.30 C
+ATOM 325 CG TRP A 40 11.683 8.533 24.724 1.00 15.08 C
+ATOM 326 CD1 TRP A 40 11.189 7.256 24.703 1.00 14.60 C
+ATOM 327 CD2 TRP A 40 12.438 8.680 23.518 1.00 15.20 C
+ATOM 328 NE1 TRP A 40 11.642 6.583 23.594 1.00 16.14 N
+ATOM 329 CE2 TRP A 40 12.406 7.451 22.843 1.00 15.90 C
+ATOM 330 CE3 TRP A 40 13.169 9.739 22.959 1.00 18.61 C
+ATOM 331 CZ2 TRP A 40 13.047 7.240 21.623 1.00 17.72 C
+ATOM 332 CZ3 TRP A 40 13.814 9.530 21.756 1.00 18.97 C
+ATOM 333 CH2 TRP A 40 13.746 8.296 21.099 1.00 19.39 C
+ATOM 334 N SER A 41 9.951 11.518 27.737 1.00 15.53 N
+ATOM 335 CA SER A 41 10.078 12.431 28.878 1.00 17.72 C
+ATOM 336 C SER A 41 9.043 12.192 29.935 1.00 16.70 C
+ATOM 337 O SER A 41 9.104 12.813 31.031 1.00 20.51 O
+ATOM 338 CB SER A 41 9.984 13.886 28.348 1.00 19.25 C
+ATOM 339 OG SER A 41 8.682 14.140 27.877 1.00 25.31 O
+ATOM 340 N GLY A 42 8.063 11.327 29.727 1.00 14.48 N
+ATOM 341 CA GLY A 42 7.007 11.031 30.616 1.00 14.53 C
+ATOM 342 C GLY A 42 7.286 9.912 31.616 1.00 13.87 C
+ATOM 343 O GLY A 42 6.373 9.593 32.395 1.00 16.81 O
+ATOM 344 N GLU A 43 8.465 9.322 31.606 1.00 13.36 N
+ATOM 345 CA GLU A 43 8.767 8.271 32.589 1.00 13.28 C
+ATOM 346 C GLU A 43 8.673 8.877 33.995 1.00 13.80 C
+ATOM 347 O GLU A 43 9.223 9.950 34.249 1.00 15.48 O
+ATOM 348 CB GLU A 43 10.134 7.656 32.361 1.00 11.95 C
+ATOM 349 CG GLU A 43 10.372 6.481 33.343 1.00 13.25 C
+ATOM 350 CD GLU A 43 11.587 5.683 33.100 1.00 13.51 C
+ATOM 351 OE1 GLU A 43 12.044 5.460 31.959 1.00 13.57 O
+ATOM 352 OE2 GLU A 43 12.195 5.150 34.093 1.00 15.32 O
+ATOM 353 N ARG A 44 8.071 8.145 34.910 1.00 13.63 N
+ATOM 354 CA ARG A 44 7.983 8.564 36.308 1.00 14.52 C
+ATOM 355 C ARG A 44 9.362 8.626 36.944 1.00 14.63 C
+ATOM 356 O ARG A 44 10.261 7.845 36.633 1.00 13.65 O
+ATOM 357 CB ARG A 44 7.130 7.565 37.097 1.00 14.38 C
+ATOM 358 CG ARG A 44 5.662 7.552 36.738 1.00 16.88 C
+ATOM 359 CD ARG A 44 4.918 6.451 37.452 1.00 18.14 C
+ATOM 360 NE ARG A 44 5.070 6.493 38.896 1.00 18.73 N
+ATOM 361 CZ ARG A 44 4.153 6.921 39.751 1.00 23.67 C
+ATOM 362 NH1 ARG A 44 2.982 7.369 39.322 1.00 26.62 N
+ATOM 363 NH2 ARG A 44 4.406 6.879 41.057 1.00 24.53 N
+ATOM 364 N SER A 45 9.537 9.569 37.886 1.00 16.58 N
+ATOM 365 CA SER A 45 10.814 9.666 38.587 1.00 17.75 C
+ATOM 366 C SER A 45 11.163 8.400 39.353 1.00 16.53 C
+ATOM 367 O SER A 45 12.346 8.077 39.486 1.00 16.92 O
+ATOM 368 CB SER A 45 10.784 10.870 39.555 1.00 19.53 C
+ATOM 369 OG ASER A 45 9.839 10.598 40.594 0.50 22.46 O
+ATOM 370 OG BSER A 45 10.680 12.068 38.784 0.50 23.41 O
+ATOM 371 N ASP A 46 10.154 7.656 39.787 1.00 16.16 N
+ATOM 372 CA ASP A 46 10.357 6.414 40.526 1.00 15.58 C
+ATOM 373 C ASP A 46 10.486 5.209 39.608 1.00 16.25 C
+ATOM 374 O ASP A 46 10.715 4.082 40.082 1.00 17.85 O
+ATOM 375 CB ASP A 46 9.273 6.208 41.568 1.00 18.42 C
+ATOM 376 CG ASP A 46 7.890 5.991 41.051 1.00 18.67 C
+ATOM 377 OD1 ASP A 46 7.678 5.851 39.823 1.00 17.94 O
+ATOM 378 OD2 ASP A 46 6.922 5.928 41.861 1.00 23.44 O
+ATOM 379 N HIS A 47 10.390 5.412 38.301 1.00 13.79 N
+ATOM 380 CA HIS A 47 10.535 4.366 37.316 1.00 13.07 C
+ATOM 381 C HIS A 47 9.438 3.321 37.347 1.00 13.66 C
+ATOM 382 O HIS A 47 9.591 2.308 36.646 1.00 14.37 O
+ATOM 383 CB HIS A 47 11.904 3.663 37.393 1.00 13.90 C
+ATOM 384 CG HIS A 47 13.049 4.620 37.468 1.00 14.22 C
+ATOM 385 ND1 HIS A 47 13.258 5.596 36.524 1.00 15.45 N
+ATOM 386 CD2 HIS A 47 14.039 4.749 38.378 1.00 14.58 C
+ATOM 387 CE1 HIS A 47 14.336 6.300 36.845 1.00 16.14 C
+ATOM 388 NE2 HIS A 47 14.821 5.798 37.978 1.00 16.13 N
+ATOM 389 N ARG A 48 8.356 3.510 38.057 1.00 13.49 N
+ATOM 390 CA ARG A 48 7.271 2.527 38.068 1.00 14.17 C
+ATOM 391 C ARG A 48 6.513 2.568 36.750 1.00 15.86 C
+ATOM 392 O ARG A 48 6.416 3.632 36.116 1.00 15.15 O
+ATOM 393 CB ARG A 48 6.310 2.789 39.231 1.00 16.19 C
+ATOM 394 CG ARG A 48 6.953 2.481 40.594 1.00 21.74 C
+ATOM 395 CD ARG A 48 5.897 2.677 41.696 1.00 27.15 C
+ATOM 396 NE ARG A 48 4.979 1.552 41.722 1.00 33.45 N
+ATOM 397 CZ ARG A 48 3.811 1.502 42.336 1.00 35.53 C
+ATOM 398 NH1 ARG A 48 3.350 2.536 43.022 1.00 37.75 N
+ATOM 399 NH2 ARG A 48 3.078 0.387 42.267 1.00 37.26 N
+ATOM 400 N CYS A 49 5.942 1.451 36.352 1.00 14.67 N
+ATOM 401 CA CYS A 49 5.096 1.418 35.149 1.00 14.02 C
+ATOM 402 C CYS A 49 4.011 0.378 35.343 1.00 16.59 C
+ATOM 403 O CYS A 49 3.937 -0.224 36.444 1.00 17.70 O
+ATOM 404 CB CYS A 49 5.922 1.172 33.902 1.00 14.60 C
+ATOM 405 SG CYS A 49 6.790 -0.418 33.889 1.00 13.58 S
+ATOM 406 N GLY A 50 3.193 0.151 34.344 1.00 16.32 N
+ATOM 407 CA GLY A 50 2.143 -0.866 34.446 1.00 18.12 C
+ATOM 408 C GLY A 50 0.822 -0.238 34.870 1.00 20.85 C
+ATOM 409 O GLY A 50 0.703 0.947 35.143 1.00 21.47 O
+ATOM 410 N ALA A 51 -0.178 -1.110 35.040 1.00 23.34 N
+ATOM 411 CA ALA A 51 -1.543 -0.713 35.271 1.00 26.04 C
+ATOM 412 C ALA A 51 -1.795 0.012 36.564 1.00 27.16 C
+ATOM 413 O ALA A 51 -2.665 0.906 36.617 1.00 29.51 O
+ATOM 414 CB ALA A 51 -2.454 -1.955 35.167 1.00 27.39 C
+ATOM 415 N ALA A 52 -1.074 -0.305 37.627 1.00 29.21 N
+ATOM 416 CA ALA A 52 -1.299 0.301 38.927 1.00 30.20 C
+ATOM 417 C ALA A 52 -0.994 1.784 38.962 1.00 30.84 C
+ATOM 418 O ALA A 52 -1.494 2.501 39.844 1.00 32.25 O
+ATOM 419 CB ALA A 52 -0.515 -0.431 40.004 1.00 33.09 C
+ATOM 420 N VAL A 53 -0.141 2.261 38.056 1.00 28.71 N
+ATOM 421 CA VAL A 53 0.194 3.685 38.018 1.00 26.18 C
+ATOM 422 C VAL A 53 -0.261 4.318 36.712 1.00 25.00 C
+ATOM 423 O VAL A 53 0.267 5.337 36.264 1.00 24.56 O
+ATOM 424 CB VAL A 53 1.673 3.942 38.268 1.00 25.15 C
+ATOM 425 CG1 VAL A 53 2.041 3.676 39.733 1.00 26.52 C
+ATOM 426 CG2 VAL A 53 2.533 3.055 37.362 1.00 22.44 C
+ATOM 427 N GLY A 54 -1.239 3.695 36.058 1.00 23.89 N
+ATOM 428 CA GLY A 54 -1.868 4.224 34.880 1.00 23.95 C
+ATOM 429 C GLY A 54 -1.116 4.076 33.585 1.00 23.40 C
+ATOM 430 O GLY A 54 -1.266 4.908 32.665 1.00 23.79 O
+ATOM 431 N ASN A 55 -0.333 3.021 33.429 1.00 21.36 N
+ATOM 432 CA ASN A 55 0.391 2.709 32.223 1.00 19.27 C
+ATOM 433 C ASN A 55 1.196 3.856 31.645 1.00 18.15 C
+ATOM 434 O ASN A 55 1.105 4.166 30.442 1.00 18.32 O
+ATOM 435 CB ASN A 55 -0.582 2.178 31.147 1.00 21.82 C
+ATOM 436 CG ASN A 55 -1.151 0.821 31.511 1.00 26.16 C
+ATOM 437 OD1 ASN A 55 -0.501 -0.011 32.131 1.00 26.45 O
+ATOM 438 ND2 ASN A 55 -2.397 0.598 31.091 1.00 29.80 N
+ATOM 439 N PRO A 56 2.101 4.416 32.416 1.00 14.99 N
+ATOM 440 CA PRO A 56 2.969 5.491 31.927 1.00 13.73 C
+ATOM 441 C PRO A 56 4.009 4.955 30.985 1.00 14.07 C
+ATOM 442 O PRO A 56 4.346 3.755 30.986 1.00 15.11 O
+ATOM 443 CB PRO A 56 3.585 6.035 33.205 1.00 14.47 C
+ATOM 444 CG PRO A 56 3.701 4.793 34.077 1.00 15.83 C
+ATOM 445 CD PRO A 56 2.407 4.054 33.822 1.00 15.95 C
+ATOM 446 N PRO A 57 4.649 5.806 30.194 1.00 11.98 N
+ATOM 447 CA PRO A 57 5.722 5.426 29.322 1.00 12.65 C
+ATOM 448 C PRO A 57 7.036 5.165 30.051 1.00 12.11 C
+ATOM 449 O PRO A 57 7.157 5.472 31.250 1.00 13.60 O
+ATOM 450 CB PRO A 57 5.895 6.659 28.401 1.00 12.88 C
+ATOM 451 CG PRO A 57 5.466 7.798 29.280 1.00 12.77 C
+ATOM 452 CD PRO A 57 4.319 7.262 30.094 1.00 14.56 C
+ATOM 453 N CYS A 58 8.020 4.682 29.324 1.00 11.80 N
+ATOM 454 CA CYS A 58 9.395 4.596 29.803 1.00 12.36 C
+ATOM 455 C CYS A 58 10.282 5.443 28.876 1.00 13.05 C
+ATOM 456 O CYS A 58 9.824 5.791 27.764 1.00 12.47 O
+ATOM 457 CB CYS A 58 9.923 3.175 29.845 1.00 12.38 C
+ATOM 458 SG CYS A 58 8.999 2.008 30.881 1.00 12.44 S
+ATOM 459 N GLY A 59 11.462 5.793 29.318 1.00 13.40 N
+ATOM 460 CA GLY A 59 12.370 6.599 28.530 1.00 13.37 C
+ATOM 461 C GLY A 59 12.983 5.848 27.371 1.00 15.29 C
+ATOM 462 O GLY A 59 12.636 4.695 27.085 1.00 15.41 O
+ATOM 463 N GLN A 60 13.926 6.483 26.680 1.00 15.49 N
+ATOM 464 CA GLN A 60 14.578 5.933 25.521 1.00 16.65 C
+ATOM 465 C GLN A 60 15.322 4.640 25.808 1.00 17.24 C
+ATOM 466 O GLN A 60 16.084 4.583 26.784 1.00 17.93 O
+ATOM 467 CB GLN A 60 15.560 6.976 24.932 1.00 21.03 C
+ATOM 468 CG GLN A 60 16.137 6.596 23.600 1.00 24.12 C
+ATOM 469 CD GLN A 60 16.930 7.708 22.933 1.00 26.24 C
+ATOM 470 OE1 GLN A 60 17.102 8.793 23.476 1.00 28.53 O
+ATOM 471 NE2 GLN A 60 17.396 7.430 21.717 1.00 30.08 N
+ATOM 472 N ASP A 61 15.074 3.617 25.016 1.00 17.93 N
+ATOM 473 CA ASP A 61 15.745 2.336 25.132 1.00 20.04 C
+ATOM 474 C ASP A 61 15.467 1.648 26.455 1.00 20.73 C
+ATOM 475 O ASP A 61 16.257 0.866 26.990 1.00 21.72 O
+ATOM 476 CB ASP A 61 17.234 2.449 24.844 1.00 24.31 C
+ATOM 477 CG ASP A 61 17.506 2.718 23.360 1.00 27.72 C
+ATOM 478 OD1 ASP A 61 16.939 1.999 22.517 1.00 33.16 O
+ATOM 479 OD2 ASP A 61 18.293 3.639 23.088 1.00 32.19 O
+ATOM 480 N ARG A 62 14.303 1.961 27.014 1.00 18.60 N
+ATOM 481 CA ARG A 62 13.834 1.325 28.244 1.00 16.85 C
+ATOM 482 C ARG A 62 12.481 0.710 28.021 1.00 15.70 C
+ATOM 483 O ARG A 62 11.700 1.178 27.159 1.00 17.28 O
+ATOM 484 CB ARG A 62 13.808 2.372 29.372 1.00 15.52 C
+ATOM 485 CG ARG A 62 15.215 2.900 29.694 1.00 16.78 C
+ATOM 486 CD ARG A 62 15.199 4.169 30.493 1.00 17.30 C
+ATOM 487 NE ARG A 62 14.592 4.088 31.791 1.00 15.85 N
+ATOM 488 CZ ARG A 62 15.169 3.631 32.910 1.00 16.22 C
+ATOM 489 NH1 ARG A 62 16.419 3.181 32.862 1.00 19.33 N
+ATOM 490 NH2 ARG A 62 14.505 3.658 34.049 1.00 17.20 N
+ATOM 491 N CYS A 63 12.133 -0.348 28.724 1.00 14.67 N
+ATOM 492 CA CYS A 63 10.895 -1.061 28.575 1.00 14.75 C
+ATOM 493 C CYS A 63 10.220 -1.258 29.921 1.00 12.67 C
+ATOM 494 O CYS A 63 10.923 -1.181 30.961 1.00 13.29 O
+ATOM 495 CB CYS A 63 11.141 -2.476 27.981 1.00 16.49 C
+ATOM 496 SG CYS A 63 12.260 -2.460 26.567 1.00 16.96 S
+ATOM 497 N CYS A 64 8.953 -1.528 29.925 1.00 12.88 N
+ATOM 498 CA CYS A 64 8.206 -1.780 31.148 1.00 12.83 C
+ATOM 499 C CYS A 64 8.146 -3.285 31.429 1.00 15.34 C
+ATOM 500 O CYS A 64 7.524 -4.027 30.660 1.00 15.86 O
+ATOM 501 CB CYS A 64 6.781 -1.219 30.990 1.00 14.85 C
+ATOM 502 SG CYS A 64 5.806 -1.504 32.499 1.00 13.42 S
+ATOM 503 N SER A 65 8.826 -3.717 32.477 1.00 14.67 N
+ATOM 504 CA SER A 65 8.872 -5.161 32.798 1.00 16.00 C
+ATOM 505 C SER A 65 7.551 -5.636 33.353 1.00 16.63 C
+ATOM 506 O SER A 65 6.710 -4.894 33.815 1.00 15.54 O
+ATOM 507 CB SER A 65 9.998 -5.411 33.827 1.00 16.86 C
+ATOM 508 OG SER A 65 9.484 -5.147 35.124 1.00 17.06 O
+ATOM 509 N VAL A 66 7.392 -6.999 33.348 1.00 18.72 N
+ATOM 510 CA VAL A 66 6.204 -7.591 33.922 1.00 19.62 C
+ATOM 511 C VAL A 66 6.103 -7.376 35.428 1.00 19.10 C
+ATOM 512 O VAL A 66 5.023 -7.531 35.993 1.00 22.45 O
+ATOM 513 CB VAL A 66 6.081 -9.099 33.636 1.00 18.76 C
+ATOM 514 CG1 VAL A 66 5.849 -9.334 32.154 1.00 20.22 C
+ATOM 515 CG2 VAL A 66 7.308 -9.852 34.123 1.00 20.88 C
+ATOM 516 N HIS A 67 7.205 -6.932 36.043 1.00 17.59 N
+ATOM 517 CA HIS A 67 7.183 -6.665 37.480 1.00 19.57 C
+ATOM 518 C HIS A 67 6.787 -5.235 37.796 1.00 20.01 C
+ATOM 519 O HIS A 67 6.668 -4.859 38.965 1.00 21.59 O
+ATOM 520 CB HIS A 67 8.525 -7.022 38.123 1.00 19.55 C
+ATOM 521 CG HIS A 67 8.951 -8.426 37.769 1.00 19.66 C
+ATOM 522 ND1 HIS A 67 8.317 -9.542 38.278 1.00 24.79 N
+ATOM 523 CD2 HIS A 67 9.897 -8.863 36.924 1.00 20.78 C
+ATOM 524 CE1 HIS A 67 8.889 -10.621 37.766 1.00 21.81 C
+ATOM 525 NE2 HIS A 67 9.854 -10.250 36.958 1.00 23.01 N
+ATOM 526 N GLY A 68 6.635 -4.403 36.763 1.00 18.21 N
+ATOM 527 CA GLY A 68 6.124 -3.053 36.937 1.00 17.97 C
+ATOM 528 C GLY A 68 7.194 -1.980 37.055 1.00 13.37 C
+ATOM 529 O GLY A 68 6.922 -0.965 37.742 1.00 15.56 O
+ATOM 530 N TRP A 69 8.337 -2.157 36.449 1.00 12.34 N
+ATOM 531 CA TRP A 69 9.382 -1.130 36.486 1.00 12.62 C
+ATOM 532 C TRP A 69 9.962 -0.917 35.099 1.00 13.75 C
+ATOM 533 O TRP A 69 10.077 -1.829 34.287 1.00 13.58 O
+ATOM 534 CB TRP A 69 10.515 -1.554 37.436 1.00 16.11 C
+ATOM 535 CG TRP A 69 10.061 -1.845 38.837 1.00 18.96 C
+ATOM 536 CD1 TRP A 69 9.648 -3.068 39.323 1.00 18.72 C
+ATOM 537 CD2 TRP A 69 9.952 -0.930 39.923 1.00 20.84 C
+ATOM 538 NE1 TRP A 69 9.303 -2.949 40.636 1.00 21.95 N
+ATOM 539 CE2 TRP A 69 9.480 -1.643 41.038 1.00 19.50 C
+ATOM 540 CE3 TRP A 69 10.212 0.438 40.056 1.00 20.51 C
+ATOM 541 CZ2 TRP A 69 9.255 -1.048 42.273 1.00 22.79 C
+ATOM 542 CZ3 TRP A 69 9.979 1.038 41.281 1.00 22.68 C
+ATOM 543 CH2 TRP A 69 9.512 0.293 42.378 1.00 23.67 C
+ATOM 544 N CYS A 70 10.307 0.345 34.795 1.00 13.10 N
+ATOM 545 CA CYS A 70 11.002 0.669 33.563 1.00 13.78 C
+ATOM 546 C CYS A 70 12.499 0.383 33.729 1.00 13.92 C
+ATOM 547 O CYS A 70 13.047 0.691 34.814 1.00 15.28 O
+ATOM 548 CB CYS A 70 10.857 2.178 33.254 1.00 14.26 C
+ATOM 549 SG CYS A 70 9.243 2.751 32.767 1.00 12.08 S
+ATOM 550 N GLY A 71 13.177 -0.024 32.685 1.00 14.37 N
+ATOM 551 CA GLY A 71 14.628 -0.187 32.729 1.00 15.37 C
+ATOM 552 C GLY A 71 15.130 -0.683 31.374 1.00 18.20 C
+ATOM 553 O GLY A 71 14.340 -1.016 30.505 1.00 16.44 O
+ATOM 554 N GLY A 72 16.455 -0.770 31.259 1.00 19.67 N
+ATOM 555 CA GLY A 72 17.033 -1.225 29.985 1.00 22.41 C
+ATOM 556 C GLY A 72 17.590 -2.635 30.117 1.00 24.30 C
+ATOM 557 O GLY A 72 17.886 -3.101 31.216 1.00 26.21 O
+ATOM 558 N GLY A 73 17.694 -3.324 28.982 1.00 25.36 N
+ATOM 559 CA GLY A 73 18.291 -4.645 28.942 1.00 26.44 C
+ATOM 560 C GLY A 73 17.283 -5.775 28.973 1.00 27.24 C
+ATOM 561 O GLY A 73 16.086 -5.595 29.157 1.00 26.15 O
+ATOM 562 N ASN A 74 17.798 -7.004 28.869 1.00 27.53 N
+ATOM 563 CA ASN A 74 17.013 -8.206 28.788 1.00 27.66 C
+ATOM 564 C ASN A 74 16.001 -8.395 29.892 1.00 25.55 C
+ATOM 565 O ASN A 74 14.915 -8.955 29.662 1.00 26.18 O
+ATOM 566 CB ASN A 74 17.981 -9.421 28.763 1.00 32.27 C
+ATOM 567 CG AASN A 74 17.279 -10.749 28.825 0.50 33.40 C
+ATOM 568 CG BASN A 74 18.917 -9.356 27.574 0.50 34.22 C
+ATOM 569 OD1AASN A 74 17.230 -11.395 29.878 0.50 35.56 O
+ATOM 570 OD1BASN A 74 18.479 -9.218 26.432 0.50 36.46 O
+ATOM 571 ND2AASN A 74 16.736 -11.203 27.699 0.50 34.86 N
+ATOM 572 ND2BASN A 74 20.215 -9.465 27.831 0.50 36.27 N
+ATOM 573 N ASP A 75 16.355 -8.036 31.129 1.00 25.87 N
+ATOM 574 CA ASP A 75 15.472 -8.252 32.256 1.00 25.73 C
+ATOM 575 C ASP A 75 14.200 -7.431 32.203 1.00 23.25 C
+ATOM 576 O ASP A 75 13.194 -7.782 32.826 1.00 22.95 O
+ATOM 577 CB ASP A 75 16.209 -8.005 33.580 1.00 31.03 C
+ATOM 578 CG ASP A 75 16.090 -9.241 34.475 1.00 37.03 C
+ATOM 579 OD1 ASP A 75 16.882 -10.182 34.258 1.00 40.33 O
+ATOM 580 OD2 ASP A 75 15.200 -9.254 35.339 1.00 39.88 O
+ATOM 581 N TYR A 76 14.205 -6.354 31.409 1.00 21.83 N
+ATOM 582 CA TYR A 76 13.025 -5.520 31.259 1.00 20.29 C
+ATOM 583 C TYR A 76 12.385 -5.662 29.875 1.00 20.48 C
+ATOM 584 O TYR A 76 11.191 -5.458 29.724 1.00 20.48 O
+ATOM 585 CB TYR A 76 13.423 -4.033 31.431 1.00 18.44 C
+ATOM 586 CG TYR A 76 13.911 -3.669 32.810 1.00 15.85 C
+ATOM 587 CD1 TYR A 76 15.235 -3.876 33.168 1.00 16.59 C
+ATOM 588 CD2 TYR A 76 13.050 -3.117 33.750 1.00 14.97 C
+ATOM 589 CE1 TYR A 76 15.689 -3.555 34.435 1.00 16.24 C
+ATOM 590 CE2 TYR A 76 13.502 -2.778 35.017 1.00 14.15 C
+ATOM 591 CZ TYR A 76 14.809 -3.032 35.355 1.00 15.60 C
+ATOM 592 OH TYR A 76 15.277 -2.679 36.604 1.00 17.09 O
+ATOM 593 N CYS A 77 13.208 -5.961 28.876 1.00 22.81 N
+ATOM 594 CA CYS A 77 12.814 -5.846 27.491 1.00 23.23 C
+ATOM 595 C CYS A 77 12.573 -7.119 26.727 1.00 27.50 C
+ATOM 596 O CYS A 77 11.961 -7.054 25.632 1.00 27.57 O
+ATOM 597 CB CYS A 77 13.893 -5.011 26.744 1.00 21.70 C
+ATOM 598 SG CYS A 77 13.970 -3.285 27.311 1.00 19.69 S
+ATOM 599 N SER A 78 13.001 -8.275 27.211 1.00 29.47 N
+ATOM 600 CA SER A 78 12.928 -9.511 26.448 1.00 32.50 C
+ATOM 601 C SER A 78 11.665 -10.314 26.666 1.00 33.17 C
+ATOM 602 O SER A 78 11.178 -10.437 27.814 1.00 35.05 O
+ATOM 603 CB SER A 78 14.183 -10.362 26.725 1.00 35.08 C
+ATOM 604 OG SER A 78 15.357 -9.643 26.373 1.00 38.49 O
+ATOM 605 N AGLY A 79 11.026 -10.753 25.586 0.56 32.73 N
+ATOM 606 N BGLY A 79 11.268 -11.086 25.671 0.44 33.24 N
+ATOM 607 CA AGLY A 79 9.821 -11.505 25.585 0.56 32.16 C
+ATOM 608 CA BGLY A 79 10.123 -11.952 25.726 0.44 32.77 C
+ATOM 609 C AGLY A 79 9.052 -11.575 26.887 0.56 32.04 C
+ATOM 610 C BGLY A 79 9.866 -12.542 27.101 0.44 32.18 C
+ATOM 611 O AGLY A 79 8.312 -10.654 27.235 0.56 32.01 O
+ATOM 612 O BGLY A 79 10.734 -13.176 27.699 0.44 32.81 O
+ATOM 613 N ASER A 80 9.205 -12.682 27.605 0.56 31.46 N
+ATOM 614 CA ASER A 80 8.473 -12.954 28.816 0.56 31.14 C
+ATOM 615 C ASER A 80 8.880 -12.084 29.993 0.56 30.28 C
+ATOM 616 O ASER A 80 8.341 -12.253 31.098 0.56 30.92 O
+ATOM 617 CB ASER A 80 8.629 -14.441 29.205 0.56 32.24 C
+ATOM 618 OG ASER A 80 9.999 -14.810 29.194 0.56 33.96 O
+ATOM 619 N BGLY A 80 8.644 -12.355 27.599 0.44 31.90 N
+ATOM 620 CA BGLY A 80 8.263 -12.856 28.907 0.44 30.90 C
+ATOM 621 C BGLY A 80 8.635 -11.879 30.017 0.44 29.74 C
+ATOM 622 O BGLY A 80 7.877 -11.725 30.978 0.44 31.09 O
+ATOM 623 N ALYS A 81 9.846 -11.202 29.794 0.56 29.23 N
+ATOM 624 CA ALYS A 81 10.291 -10.308 30.869 0.56 28.16 C
+ATOM 625 C ALYS A 81 9.575 -8.959 30.759 0.56 26.65 C
+ATOM 626 O ALYS A 81 9.248 -8.335 31.767 0.56 24.90 O
+ATOM 627 CB ALYS A 81 11.795 -10.109 30.820 0.56 29.85 C
+ATOM 628 CG ALYS A 81 12.626 -11.336 31.168 0.56 32.07 C
+ATOM 629 CD ALYS A 81 14.097 -11.094 30.837 0.56 33.72 C
+ATOM 630 CE ALYS A 81 14.890 -12.388 30.924 0.56 35.00 C
+ATOM 631 NZ ALYS A 81 16.264 -12.237 30.375 0.56 35.39 N
+ATOM 632 N BASN A 81 9.796 -11.250 29.900 0.44 28.30 N
+ATOM 633 CA BASN A 81 10.254 -10.303 30.910 0.44 26.39 C
+ATOM 634 C BASN A 81 9.566 -8.946 30.756 0.44 24.18 C
+ATOM 635 O BASN A 81 9.603 -8.128 31.673 0.44 23.07 O
+ATOM 636 CB BASN A 81 11.773 -10.095 30.784 0.44 27.87 C
+ATOM 637 CG BASN A 81 12.573 -11.218 31.412 0.44 29.21 C
+ATOM 638 OD1BASN A 81 12.064 -11.941 32.265 0.44 28.74 O
+ATOM 639 ND2BASN A 81 13.822 -11.353 30.992 0.44 30.49 N
+ATOM 640 N CYS A 82 8.950 -8.719 29.607 1.00 22.45 N
+ATOM 641 CA CYS A 82 8.458 -7.382 29.254 1.00 21.45 C
+ATOM 642 C CYS A 82 6.972 -7.304 29.058 1.00 22.09 C
+ATOM 643 O CYS A 82 6.378 -8.160 28.372 1.00 22.87 O
+ATOM 644 CB CYS A 82 9.172 -6.970 27.942 1.00 18.44 C
+ATOM 645 SG CYS A 82 8.735 -5.286 27.433 1.00 17.39 S
+ATOM 646 N GLN A 83 6.311 -6.265 29.586 1.00 20.84 N
+ATOM 647 CA GLN A 83 4.892 -6.092 29.419 1.00 20.12 C
+ATOM 648 C GLN A 83 4.524 -5.110 28.319 1.00 20.04 C
+ATOM 649 O GLN A 83 3.521 -5.334 27.618 1.00 21.43 O
+ATOM 650 CB GLN A 83 4.123 -5.836 30.680 1.00 22.18 C
+ATOM 651 CG GLN A 83 4.345 -4.549 31.422 1.00 23.67 C
+ATOM 652 CD GLN A 83 3.365 -4.361 32.567 1.00 22.91 C
+ATOM 653 OE1 GLN A 83 2.153 -4.246 32.353 1.00 26.53 O
+ATOM 654 NE2 GLN A 83 3.857 -4.338 33.797 1.00 21.79 N
+ATOM 655 N TYR A 84 5.264 -4.025 28.158 1.00 17.66 N
+ATOM 656 CA TYR A 84 5.002 -3.076 27.074 1.00 16.39 C
+ATOM 657 C TYR A 84 6.245 -2.265 26.742 1.00 15.08 C
+ATOM 658 O TYR A 84 7.234 -2.242 27.458 1.00 15.01 O
+ATOM 659 CB TYR A 84 3.797 -2.233 27.255 1.00 15.78 C
+ATOM 660 CG TYR A 84 3.715 -1.293 28.430 1.00 14.92 C
+ATOM 661 CD1 TYR A 84 4.476 -0.110 28.447 1.00 15.62 C
+ATOM 662 CD2 TYR A 84 2.839 -1.516 29.471 1.00 16.69 C
+ATOM 663 CE1 TYR A 84 4.346 0.776 29.503 1.00 15.02 C
+ATOM 664 CE2 TYR A 84 2.695 -0.634 30.519 1.00 15.77 C
+ATOM 665 CZ TYR A 84 3.480 0.519 30.526 1.00 14.45 C
+ATOM 666 OH TYR A 84 3.339 1.400 31.579 1.00 14.64 O
+ATOM 667 N ARG A 85 6.205 -1.609 25.564 1.00 15.20 N
+ATOM 668 CA ARG A 85 7.334 -0.893 25.027 1.00 14.16 C
+ATOM 669 C ARG A 85 8.578 -1.769 24.953 1.00 15.58 C
+ATOM 670 O ARG A 85 9.673 -1.417 25.337 1.00 18.52 O
+ATOM 671 CB ARG A 85 7.617 0.425 25.764 1.00 15.31 C
+ATOM 672 CG ARG A 85 6.496 1.448 25.540 1.00 12.89 C
+ATOM 673 CD ARG A 85 6.532 2.564 26.562 1.00 13.54 C
+ATOM 674 NE ARG A 85 7.452 3.638 26.250 1.00 12.05 N
+ATOM 675 CZ ARG A 85 7.087 4.731 25.517 1.00 10.84 C
+ATOM 676 NH1 ARG A 85 5.893 4.849 25.020 1.00 12.54 N
+ATOM 677 NH2 ARG A 85 8.007 5.678 25.397 1.00 12.10 N
+ATOM 678 N CYS A 86 8.355 -2.983 24.413 1.00 19.00 N
+ATOM 679 CA CYS A 86 9.369 -4.015 24.374 1.00 21.08 C
+ATOM 680 C CYS A 86 10.260 -3.991 23.160 1.00 23.22 C
+ATOM 681 O CYS A 86 9.846 -3.464 22.105 1.00 25.66 O
+ATOM 682 CB CYS A 86 8.644 -5.399 24.430 1.00 21.37 C
+ATOM 683 SG CYS A 86 7.527 -5.528 25.832 1.00 19.98 S
+TER 684 CYS A 86
+HETATM 685 C1 NAG B 1 2.845 5.730 8.410 1.00 13.00 C
+HETATM 686 C2 NAG B 1 2.980 4.600 7.346 1.00 14.42 C
+HETATM 687 C3 NAG B 1 2.175 3.411 7.793 1.00 14.65 C
+HETATM 688 C4 NAG B 1 2.774 2.928 9.166 1.00 14.34 C
+HETATM 689 C5 NAG B 1 2.670 4.122 10.167 1.00 14.77 C
+HETATM 690 C6 NAG B 1 3.374 3.715 11.471 1.00 14.26 C
+HETATM 691 C7 NAG B 1 3.348 5.467 5.049 1.00 14.97 C
+HETATM 692 C8 NAG B 1 2.707 6.219 3.930 1.00 15.87 C
+HETATM 693 N2 NAG B 1 2.477 5.116 6.044 1.00 15.79 N
+HETATM 694 O3 NAG B 1 2.404 2.342 6.849 1.00 16.72 O
+HETATM 695 O4 NAG B 1 1.906 1.888 9.675 1.00 16.83 O
+HETATM 696 O5 NAG B 1 3.422 5.196 9.636 1.00 14.49 O
+HETATM 697 O6 NAG B 1 2.814 4.521 12.515 1.00 17.10 O
+HETATM 698 O7 NAG B 1 4.476 5.236 5.105 1.00 15.81 O
+HETATM 699 C1 NAG B 2 4.454 10.706 8.957 1.00 15.08 C
+HETATM 700 C2 NAG B 2 4.460 9.780 10.201 1.00 13.08 C
+HETATM 701 C3 NAG B 2 4.585 8.348 9.693 1.00 14.61 C
+HETATM 702 C4 NAG B 2 3.438 8.059 8.721 1.00 14.25 C
+HETATM 703 C5 NAG B 2 3.377 9.108 7.581 1.00 15.30 C
+HETATM 704 C6 NAG B 2 2.128 8.919 6.764 1.00 17.09 C
+HETATM 705 C7 NAG B 2 5.438 10.602 12.343 1.00 12.64 C
+HETATM 706 C8 NAG B 2 6.705 10.853 13.053 1.00 14.46 C
+HETATM 707 N2 NAG B 2 5.562 10.111 11.086 1.00 14.12 N
+HETATM 708 O3 NAG B 2 4.506 7.454 10.798 1.00 14.67 O
+HETATM 709 O4 NAG B 2 3.695 6.769 8.064 1.00 13.33 O
+HETATM 710 O5 NAG B 2 3.294 10.396 8.215 1.00 16.15 O
+HETATM 711 O6 NAG B 2 2.067 9.786 5.669 1.00 21.57 O
+HETATM 712 O7 NAG B 2 4.383 10.784 12.792 1.00 13.02 O
+HETATM 713 C1 NAG B 3 4.558 15.870 8.029 1.00 22.68 C
+HETATM 714 C2 NAG B 3 3.781 14.939 7.134 1.00 23.40 C
+HETATM 715 C3 NAG B 3 3.385 13.670 7.891 1.00 21.15 C
+HETATM 716 C4 NAG B 3 4.638 13.082 8.560 1.00 19.08 C
+HETATM 717 C5 NAG B 3 5.387 14.135 9.404 1.00 20.09 C
+HETATM 718 C6 NAG B 3 6.662 13.594 9.979 1.00 20.74 C
+HETATM 719 C7 NAG B 3 2.333 15.941 5.366 1.00 30.04 C
+HETATM 720 C8 NAG B 3 0.935 16.346 5.056 1.00 30.86 C
+HETATM 721 N2 NAG B 3 2.536 15.585 6.659 1.00 26.09 N
+HETATM 722 O3 NAG B 3 2.831 12.779 6.934 1.00 22.73 O
+HETATM 723 O4 NAG B 3 4.253 12.032 9.493 1.00 16.48 O
+HETATM 724 O5 NAG B 3 5.732 15.223 8.531 1.00 21.12 O
+HETATM 725 O6 NAG B 3 7.565 13.140 9.015 1.00 23.92 O
+HETATM 726 O7 NAG B 3 3.172 15.935 4.593 1.00 31.92 O
+HETATM 727 C1 NAG B 4 6.424 20.761 7.837 1.00 35.33 C
+HETATM 728 C2 NAG B 4 7.333 19.637 8.339 1.00 35.66 C
+HETATM 729 C3 NAG B 4 6.907 18.331 7.686 1.00 35.20 C
+HETATM 730 C4 NAG B 4 5.422 18.072 8.014 1.00 33.37 C
+HETATM 731 C5 NAG B 4 4.527 19.299 7.696 1.00 33.27 C
+HETATM 732 C6 NAG B 4 3.173 19.102 8.406 1.00 32.57 C
+HETATM 733 C7 NAG B 4 9.448 20.235 9.622 0.00 40.22 C
+HETATM 734 C8 NAG B 4 10.921 20.272 9.570 0.00 40.07 C
+HETATM 735 N2 NAG B 4 8.744 19.920 8.013 1.00 37.41 N
+HETATM 736 O3 NAG B 4 7.711 17.283 8.235 1.00 36.78 O
+HETATM 737 O4 NAG B 4 4.923 16.987 7.202 1.00 29.35 O
+HETATM 738 O5 NAG B 4 5.078 20.464 8.245 1.00 34.32 O
+HETATM 739 O6 NAG B 4 3.314 19.212 9.787 1.00 31.85 O
+HETATM 740 O7 NAG B 4 8.849 20.404 10.603 0.00 43.75 O
+HETATM 741 O HOH 1 6.997 5.537 34.149 1.00 15.09 O
+HETATM 742 O HOH 2 10.295 3.302 26.095 1.00 16.76 O
+HETATM 743 O HOH 3 10.019 12.949 25.133 1.00 20.11 O
+HETATM 744 O HOH 4 -0.006 5.891 22.509 1.00 18.64 O
+HETATM 745 O HOH 5 3.935 11.236 27.997 1.00 20.18 O
+HETATM 746 O HOH 6 -0.274 -4.153 23.054 1.00 19.37 O
+HETATM 747 O HOH 7 13.817 8.788 41.782 1.00 19.15 O
+HETATM 748 O HOH 8 -1.891 13.459 17.137 1.00 19.03 O
+HETATM 749 O HOH 9 -2.083 8.214 21.987 1.00 20.14 O
+HETATM 750 O HOH 10 2.797 0.928 18.102 1.00 27.68 O
+HETATM 751 O HOH 11 12.691 9.974 30.666 1.00 27.47 O
+HETATM 752 O HOH 12 9.666 4.203 14.797 1.00 27.80 O
+HETATM 753 O HOH 13 1.809 -0.509 7.352 1.00 30.54 O
+HETATM 754 O HOH 14 -1.004 17.744 16.794 1.00 27.36 O
+HETATM 755 O HOH 15 8.061 9.329 9.741 1.00 31.81 O
+HETATM 756 O HOH 16 7.423 9.060 40.549 1.00 26.40 O
+HETATM 757 O HOH 17 -0.420 5.798 5.808 1.00 27.21 O
+HETATM 758 O HOH 18 2.796 -0.282 11.074 1.00 26.18 O
+HETATM 759 O HOH 19 6.627 3.510 13.365 1.00 29.55 O
+HETATM 760 O HOH 20 7.716 11.785 37.937 1.00 33.90 O
+HETATM 761 O HOH 21 13.017 2.743 41.126 1.00 29.39 O
+HETATM 762 O HOH 22 12.586 8.818 35.183 1.00 31.81 O
+HETATM 763 O HOH 23 -0.672 17.945 19.813 1.00 29.17 O
+HETATM 764 O HOH 24 15.414 7.885 17.622 1.00 34.99 O
+HETATM 765 O HOH 25 15.120 1.027 36.513 1.00 34.01 O
+HETATM 766 O HOH 26 12.255 0.609 24.383 1.00 37.64 O
+HETATM 767 O HOH 27 15.910 10.470 25.375 1.00 39.63 O
+HETATM 768 O HOH 28 2.672 -4.881 24.854 1.00 38.54 O
+HETATM 769 O HOH 29 5.651 -3.894 23.305 1.00 37.18 O
+HETATM 770 O HOH 30 1.757 7.661 36.606 1.00 29.03 O
+HETATM 771 O HOH 31 0.331 -3.984 34.349 1.00 30.94 O
+HETATM 772 O HOH 32 14.751 9.106 27.605 1.00 26.56 O
+HETATM 773 O HOH 33 -1.326 14.783 10.991 1.00 28.85 O
+HETATM 774 O HOH 34 14.132 7.665 31.734 1.00 39.44 O
+HETATM 775 O HOH 35 8.520 15.382 25.290 1.00 37.93 O
+HETATM 776 O HOH 36 4.900 1.282 16.585 1.00 39.95 O
+HETATM 777 O HOH 37 16.453 3.015 36.120 1.00 36.31 O
+HETATM 778 O HOH 38 18.443 2.687 30.904 1.00 37.22 O
+HETATM 779 O HOH 39 18.669 1.609 28.224 1.00 39.90 O
+HETATM 780 O HOH 40 9.694 13.193 10.908 1.00 64.04 O
+HETATM 781 O HOH 41 17.631 6.215 28.193 1.00 37.41 O
+HETATM 782 O HOH 42 0.923 7.940 41.082 1.00 47.91 O
+HETATM 783 O HOH 43 -3.992 5.719 31.647 1.00 53.05 O
+HETATM 784 O HOH 44 -0.321 12.255 8.483 1.00 35.86 O
+HETATM 785 O HOH 45 7.615 4.350 11.184 1.00 31.62 O
+HETATM 786 O HOH 46 -3.066 4.753 29.076 1.00 40.05 O
+HETATM 787 O HOH 47 -0.945 1.949 9.712 1.00 36.88 O
+HETATM 788 O HOH 48 -7.059 6.398 16.836 1.00 36.00 O
+HETATM 789 O HOH 49 17.449 -1.699 26.400 1.00 44.33 O
+HETATM 790 O HOH 50 -6.832 2.828 21.276 1.00 40.92 O
+HETATM 791 O HOH 51 12.986 3.279 23.159 1.00 32.76 O
+HETATM 792 O HOH 52 17.990 -0.372 33.679 1.00 38.43 O
+HETATM 793 O HOH 53 2.479 -4.119 36.286 1.00 36.51 O
+HETATM 794 O HOH 54 -2.996 7.951 27.405 1.00 35.06 O
+HETATM 795 O HOH 55 -4.774 1.179 18.535 1.00 46.84 O
+HETATM 796 O HOH 56 8.407 14.447 7.084 1.00 49.80 O
+HETATM 797 O HOH 57 -8.668 12.634 15.670 1.00 40.31 O
+HETATM 798 O HOH 58 6.835 -3.272 20.532 1.00 54.97 O
+HETATM 799 O HOH 59 17.790 -12.129 35.546 1.00 46.47 O
+HETATM 800 O HOH 60 2.036 -1.933 37.909 1.00 45.42 O
+HETATM 801 O HOH 61 10.515 4.141 23.049 1.00 39.36 O
+HETATM 802 O HOH 62 13.853 11.829 27.829 1.00 48.06 O
+HETATM 803 O HOH 63 6.585 -6.548 41.143 1.00 45.67 O
+HETATM 804 O HOH 64 4.117 17.977 21.677 1.00 40.12 O
+HETATM 805 O HOH 65 4.885 -10.712 28.859 1.00 54.15 O
+HETATM 806 O HOH 66 -4.389 3.044 11.770 1.00 43.85 O
+HETATM 807 O HOH 67 12.708 -9.701 34.933 1.00 34.56 O
+HETATM 807 O HOH 67 12.708 -9.701 34.933 1.00 34.56 O
+HETATM 808 O HOH 68 12.853 13.538 25.505 1.00 43.43 O
+HETATM 809 O HOH 69 6.223 15.935 24.195 1.00 56.63 O
+HETATM 810 O HOH 70 -6.184 3.543 26.723 1.00 45.90 O
+HETATM 811 O HOH 71 5.679 -0.794 40.337 1.00 42.73 O
+HETATM 812 O HOH 72 9.327 16.970 10.364 1.00 44.27 O
+HETATM 813 O HOH 73 8.994 19.338 12.497 1.00 57.40 O
+HETATM 814 O HOH 74 0.017 8.087 30.620 1.00 43.92 O
+HETATM 815 O HOH 75 -0.903 8.986 28.883 1.00 45.39 O
+HETATM 816 O HOH 77 0.315 -4.363 30.096 1.00 43.95 O
diff --git a/Tests/biosql.ini.sample b/Tests/biosql.ini.sample
new file mode 100644
index 0000000..3f25c0e
--- /dev/null
+++ b/Tests/biosql.ini.sample
@@ -0,0 +1,25 @@
+# We provide biosql.ini.sample as an example
+# (preconfigured to work on the virtual machines
+# used for automated TravisCI testing)
+#
+# Please copy this to biosql.ini and edit it
+# match your local system if you want to run
+# the Biopython BioSQL tests locally yourself.
+#
+# To disable testing against any specific back-end,
+# set dbuser to an empty string. There is no
+# configuration entry needed for sqlite3.
+
+[mysql]
+# Covers DBDRIVER="MySQLdb" and "mysql.connector" etc
+dbhost=localhost
+dbuser=root
+dbpasswd=
+testdb=biosql_test
+
+[pg]
+# Covers DBDRIVER="psycopg2" etc
+dbhost=localhost
+dbuser=postgres
+dbpasswd=
+testdb=biosql_test
diff --git a/Tests/common_BioSQL.py b/Tests/common_BioSQL.py
index baeec63..5cc00eb 100644
--- a/Tests/common_BioSQL.py
+++ b/Tests/common_BioSQL.py
@@ -10,6 +10,10 @@ import platform
import unittest
import tempfile
import time
+try:
+ import configparser # Python 3
+except ImportError:
+ import ConfigParser as configparser # Python 2
from Bio._py3k import StringIO
from Bio._py3k import zip
@@ -36,13 +40,30 @@ from seq_tests_common import compare_record, compare_records
if __name__ == "__main__":
raise RuntimeError("Call this via test_BioSQL_*.py not directly")
-global DBDRIVER, DBTYPE, DBHOST, DBUSER, DBPASSWD, TESTDB, DBSCHEMA, SQL_FILE
-global SYSTEM
+# Exporting these to the test_BioSQL_XXX.py files which import this file:
+# DBDRIVER, DBTYPE, DBHOST, DBUSER, DBPASSWD, TESTDB, DBSCHEMA, SQL_FILE, SYSTEM
SYSTEM = platform.system()
+def load_biosql_ini(DBTYPE):
+ """Load the database settings from INI file."""
+ if not os.path.isfile("biosql.ini"):
+ raise MissingExternalDependencyError("BioSQL test configuration"
+ " file biosql.ini missing"
+ " (see biosql.ini.sample)")
+
+ config = configparser.ConfigParser()
+ config.read("biosql.ini")
+ DBHOST = config.get(DBTYPE, "dbhost")
+ DBUSER = config.get(DBTYPE, "dbuser")
+ DBPASSWD = config.get(DBTYPE, "dbpasswd")
+ TESTDB = config.get(DBTYPE, "testdb")
+ return DBHOST, DBUSER, DBPASSWD, TESTDB
+
+
def temp_db_filename():
+ """Generate a temporary filename for SQLite database."""
# In memory SQLite does not work with current test structure since the tests
# expect databases to be retained between individual tests.
# TESTDB = ':memory:'
@@ -57,6 +78,7 @@ def temp_db_filename():
def check_config(dbdriver, dbtype, dbhost, dbuser, dbpasswd, testdb):
+ """Verify the database settings work for connecting."""
global DBDRIVER, DBTYPE, DBHOST, DBUSER, DBPASSWD, TESTDB, DBSCHEMA
global SYSTEM, SQL_FILE
DBDRIVER = dbdriver
@@ -66,6 +88,10 @@ def check_config(dbdriver, dbtype, dbhost, dbuser, dbpasswd, testdb):
DBPASSWD = dbpasswd
TESTDB = testdb
+ if not DBDRIVER or not DBTYPE or not DBUSER:
+ # No point going any further...
+ raise MissingExternalDependencyError("Incomplete BioSQL test settings")
+
# Check the database driver is installed:
if SYSTEM == "Java":
try:
@@ -148,7 +174,7 @@ def _do_db_create():
def create_database():
- """Delete any existing BioSQL test database, then (re)create an empty BioSQL database."""
+ """Delete any existing BioSQL test DB, then (re)create an empty BioSQL DB."""
if DBDRIVER in ["sqlite3"]:
global TESTDB
if os.path.exists(TESTDB):
@@ -158,7 +184,7 @@ def create_database():
time.sleep(1)
try:
os.remove(TESTDB)
- except:
+ except Exception:
# Seen this with PyPy 2.1 (and older) on Windows -
# which suggests an open handle still exists?
print("Could not remove %r" % TESTDB)
@@ -213,6 +239,115 @@ def load_database(gb_filename_or_handle):
return count
+def load_multi_database(gb_filename_or_handle, gb_filename_or_handle2):
+ """Load two GenBank files into a new BioSQL database as different subdatabases.
+
+ This is useful for running tests against a newly created database.
+ """
+
+ create_database()
+ # now open a connection to load the database
+ db_name = "biosql-test"
+ db_name2 = "biosql-test2"
+ server = BioSeqDatabase.open_database(driver=DBDRIVER,
+ user=DBUSER, passwd=DBPASSWD,
+ host=DBHOST, db=TESTDB)
+ db = server.new_database(db_name)
+
+ # get the GenBank file we are going to put into it
+ iterator = SeqIO.parse(gb_filename_or_handle, "gb")
+ count = db.load(iterator)
+
+ db = server.new_database(db_name2)
+
+ # get the GenBank file we are going to put into it
+ iterator = SeqIO.parse(gb_filename_or_handle2, "gb")
+ # finally put it in the database
+ count2 = db.load(iterator)
+ server.commit()
+
+ server.close()
+ return count + count2
+
+
+class MultiReadTest(unittest.TestCase):
+ """Test reading a database with multiple namespaces."""
+
+ loaded_db = 0
+
+ def setUp(self):
+ """Connect to and load up the database.
+ """
+ load_multi_database("GenBank/cor6_6.gb", "GenBank/NC_000932.gb")
+
+ self.server = BioSeqDatabase.open_database(driver=DBDRIVER,
+ user=DBUSER,
+ passwd=DBPASSWD,
+ host=DBHOST,
+ db=TESTDB)
+
+ self.db = self.server["biosql-test"]
+ self.db2 = self.server['biosql-test2']
+
+ def tearDown(self):
+ self.server.close()
+ destroy_database()
+ del self.db
+ del self.db2
+ del self.server
+
+ def test_server(self):
+ """Check BioSeqDatabase methods"""
+ server = self.server
+ self.assertTrue("biosql-test" in server)
+ self.assertTrue("biosql-test2" in server)
+ self.assertEqual(2, len(server))
+ self.assertEqual(["biosql-test", 'biosql-test2'], list(server.keys()))
+ # Check we can delete the namespace...
+ del server["biosql-test"]
+ del server["biosql-test2"]
+ self.assertEqual(0, len(server))
+ try:
+ del server["non-existant-name"]
+ assert False, "Should have raised KeyError"
+ except KeyError:
+ pass
+
+ def test_get_db_items(self):
+ """Check list, keys, length etc"""
+ db = self.db
+ items = list(db.values())
+ keys = list(db)
+ l = len(items)
+ self.assertEqual(l, len(db))
+ self.assertEqual(l, len(list(db.items())))
+ self.assertEqual(l, len(list(db)))
+ self.assertEqual(l, len(list(db.values())))
+ for (k1, r1), (k2, r2) in zip(zip(keys, items), db.items()):
+ self.assertEqual(k1, k2)
+ self.assertEqual(r1.id, r2.id)
+ for k in keys:
+ del db[k]
+ self.assertEqual(0, len(db))
+ try:
+ del db["non-existant-name"]
+ assert False, "Should have raised KeyError"
+ except KeyError:
+ pass
+
+ def test_cross_retrieval_of_items(self):
+ """Test that valid ids can't be retrieved between namespaces.
+ """
+ db = self.db
+ db2 = self.db2
+ for db2_id in db2.keys():
+ try:
+ rec = db[db2_id]
+ assert False, "Should have raised KeyError"
+ except KeyError:
+ pass
+
+
class ReadTest(unittest.TestCase):
"""Test reading a database from an already built database."""
@@ -475,6 +610,120 @@ class LoaderTest(unittest.TestCase):
'M81224.1', 'X55053.1', 'X62281.1'])
+class TaxonomyTest(unittest.TestCase):
+ """Test proper insertion and retrieval of taxonomy data
+ """
+ def setUp(self):
+ from Bio import Entrez
+ Entrez.email = "biopython-dev at biopython.org"
+ # create TESTDB
+ create_database()
+
+ # load the database
+ db_name = "biosql-test"
+ self.server = BioSeqDatabase.open_database(driver=DBDRIVER,
+ user=DBUSER, passwd=DBPASSWD,
+ host=DBHOST, db=TESTDB)
+
+ # remove the database if it already exists
+ try:
+ self.server[db_name]
+ self.server.remove_database(db_name)
+ except KeyError:
+ pass
+
+ self.db = self.server.new_database(db_name)
+
+ # get the GenBank file we are going to put into it
+ self.iterator = SeqIO.parse("GenBank/cor6_6.gb", "gb")
+
+ def tearDown(self):
+ self.server.close()
+ destroy_database()
+ del self.db
+ del self.server
+
+ def test_taxon_left_right_values(self):
+ self.db.load(self.iterator, True)
+ sql = """SELECT DISTINCT include.ncbi_taxon_id FROM taxon
+ INNER JOIN taxon AS include ON
+ (include.left_value BETWEEN taxon.left_value
+ AND taxon.right_value)
+ WHERE taxon.taxon_id IN
+ (SELECT taxon_id FROM taxon_name
+ WHERE name = 'Brassicales')
+ AND include.right_value - include.left_value = 1"""
+
+ rows = self.db.adaptor.execute_and_fetchall(sql)
+ self.assertEqual(4, len(rows))
+ values = set()
+ for row in rows:
+ values.add(row[0])
+ self.assertEqual(set([3704, 3711, 3708, 3702]), set(values))
+
+
+class DeleteTest(unittest.TestCase):
+ """Test proper deletion of entries from a database."""
+
+ loaded_db = 0
+
+ def setUp(self):
+ """Connect to and load up the database.
+ """
+ load_database("GenBank/cor6_6.gb")
+
+ self.server = BioSeqDatabase.open_database(driver=DBDRIVER,
+ user=DBUSER,
+ passwd=DBPASSWD,
+ host=DBHOST,
+ db=TESTDB)
+
+ self.db = self.server["biosql-test"]
+
+ def tearDown(self):
+ self.server.close()
+ destroy_database()
+ del self.db
+ del self.server
+
+ def test_server(self):
+ """Check BioSeqDatabase methods"""
+ server = self.server
+ self.assertTrue("biosql-test" in server)
+ self.assertEqual(1, len(server))
+ self.assertEqual(["biosql-test"], list(server.keys()))
+ # Check we can delete the namespace...
+ del server["biosql-test"]
+ self.assertEqual(0, len(server))
+ try:
+ del server["non-existant-name"]
+ assert False, "Should have raised KeyError"
+ except KeyError:
+ pass
+
+ def test_del_db_items(self):
+ """Check all associated data is deleted from an item"""
+ db = self.db
+ items = list(db.values())
+ keys = list(db)
+ l = len(items)
+
+ for seq_id in self.db.keys():
+ sql = "SELECT seqfeature_id from seqfeature where bioentry_id = '%s'"
+ # get the original number of seqfeatures associated with the bioentry
+ seqfeatures = self.db.adaptor.execute_and_fetchall(sql % (seq_id))
+
+ del db[seq_id]
+ # check to see that the entry in the bioentry table is removed
+ self.assertEqual(seq_id in db, False)
+
+ # no need to check seqfeature presence if it had none to begin with
+ if len(seqfeatures):
+ rows_d = self.db.adaptor.execute_and_fetchall(sql % (seq_id))
+ # check to see that associated data is removed
+ self.assertEqual(len(rows_d), 0)
+
+
class DupLoadTest(unittest.TestCase):
"""Check a few duplicate conditions fail."""
@@ -553,7 +802,10 @@ class ClosedLoopTest(unittest.TestCase):
def test_NC_005816(self):
"""GenBank file to BioSQL and back to a GenBank file, NC_005816."""
- self.loop("GenBank/NC_005816.gb", "gb")
+ with warnings.catch_warnings():
+ # BiopythonWarning: order location operators are not fully supported
+ warnings.simplefilter('ignore', BiopythonWarning)
+ self.loop("GenBank/NC_005816.gb", "gb")
def test_NC_000932(self):
"""GenBank file to BioSQL and back to a GenBank file, NC_000932."""
@@ -565,7 +817,10 @@ class ClosedLoopTest(unittest.TestCase):
def test_protein_refseq2(self):
"""GenBank file to BioSQL and back to a GenBank file, protein_refseq2."""
- self.loop("GenBank/protein_refseq2.gb", "gb")
+ with warnings.catch_warnings():
+ # BiopythonWarning: order location operators are not fully supported
+ warnings.simplefilter('ignore', BiopythonWarning)
+ self.loop("GenBank/protein_refseq2.gb", "gb")
def test_no_ref(self):
"""GenBank file to BioSQL and back to a GenBank file, noref."""
@@ -629,7 +884,10 @@ class TransferTest(unittest.TestCase):
def test_NC_005816(self):
"""GenBank file to BioSQL, then again to a new namespace, NC_005816."""
- self.trans("GenBank/NC_005816.gb", "gb")
+ with warnings.catch_warnings():
+ # BiopythonWarning: order location operators are not fully supported
+ warnings.simplefilter('ignore', BiopythonWarning)
+ self.trans("GenBank/NC_005816.gb", "gb")
def test_NC_000932(self):
"""GenBank file to BioSQL, then again to a new namespace, NC_000932."""
@@ -641,7 +899,10 @@ class TransferTest(unittest.TestCase):
def test_protein_refseq2(self):
"""GenBank file to BioSQL, then again to a new namespace, protein_refseq2."""
- self.trans("GenBank/protein_refseq2.gb", "gb")
+ with warnings.catch_warnings():
+ # BiopythonWarning: order location operators are not fully supported
+ warnings.simplefilter('ignore', BiopythonWarning)
+ self.trans("GenBank/protein_refseq2.gb", "gb")
def test_no_ref(self):
"""GenBank file to BioSQL, then again to a new namespace, noref."""
@@ -935,7 +1196,9 @@ class AutoSeqIOTests(unittest.TestCase):
# BiopythonWarning: bond location operators are not fully supported
warnings.simplefilter("ignore", BiopythonWarning)
self.check('genbank', 'GenBank/dbsource_wrap.gb')
- self.check('genbank', 'GenBank/NC_005816.gb')
+ # BiopythonWarning: order location operators are not fully
+ # supported
+ self.check('genbank', 'GenBank/NC_005816.gb')
self.check('genbank', 'GenBank/gbvrl1_start.seq', 3)
self.check('genbank', 'GFF/NC_001422.gbk')
self.check('embl', 'EMBL/TRBG361.embl')
diff --git a/Tests/output/test_KEGG b/Tests/output/test_KEGG
index 2f372e6..fc1cc98 100644
--- a/Tests/output/test_KEGG
+++ b/Tests/output/test_KEGG
@@ -507,6 +507,100 @@ DBLINKS ExplorEnz - The Enzyme Database: 6.2.1.25
BRENDA, the Enzyme Database: 6.2.1.25
CAS: 95329-17-2
///
+ENTRY EC 6.2.1.25
+NAME benzoate---CoA ligase
+ benzoate---coenzyme A ligase
+ benzoyl-coenzyme A synthetase
+ benzoyl CoA synthetase (AMP forming)
+CLASS Ligases;
+ Forming carbon-sulfur bonds;
+ Acid-thiol ligases
+SYSNAME benzoate:CoA ligase (AMP-forming)
+REACTION ATP + benzoate + CoA = AMP + diphosphate + benzoyl-CoA [RN:R01422]
+SUBSTRATE ATP [CPD:C00002]
+ benzoate [CPD:C00180]
+ CoA [CPD:C00010]
+PRODUCT AMP [CPD:C00020]
+ diphosphate [CPD:C00013]
+ benzoyl-CoA [CPD:C00512]
+COMMENT Also acts on 2-, 3- and 4-fluorobenzoate, but only very slowly on
+ the corresponding chlorobenzoates.
+PATHWAY PATH: ec00362 Benzoate degradation
+ PATH: ec00627 Aminobenzoate degradation
+ PATH: ec01100 Metabolic pathways
+ PATH: ec01120 Microbial metabolism in diverse environments
+GENES REU: Reut_A1327
+ REH: H16_A1412 H16_B1918
+ RME: Rmet_1224
+ CTI: RALTA_A1325 RALTA_B1617
+ CNC: CNE_1c14400 CNE_2c18780
+ BXE: Bxe_A1419 Bxe_C0896
+ BPH: Bphy_1543
+ BPY: Bphyt_2700
+ BGL: bglu_2g11460
+ BUG: BC1001_2395
+ BGE: BC1002_1980
+ BGF: BC1003_1063
+ BRH: RBRH_00494
+ BPT: Bpet3574
+ AXY: AXYL_05035
+ AKA: TKWG_07570
+ RFR: Rfer_0216
+ POL: Bpro_1624 Bpro_2983
+ PNA: Pnap_2948
+ VEI: Veis_0730
+ DAC: Daci_0076
+ DEL: DelCs14_0077
+ VAP: Vapar_0089
+ VPE: Varpa_0102
+ CTT: CtCNB1_0097
+ RTA: Rta_22340
+ LCH: Lcho_3655
+ EBA: ebA2757 ebA5301
+ AZO: azo3052
+ TMZ: Tmz1t_3136
+ GME: Gmet_2143
+ GLO: Glov_2397
+ GEO: Geob_0200
+ GEM: GM21_2825
+ DMA: DMR_19390
+ DAS: Daes_3238
+ DPR: Despr_3172
+ DTI: Desti_5323
+ BJA: blr1077
+ BRA: BRADO6791
+ BBT: BBta_0747 BBta_6637
+ RPA: RPA0661 RPA0669
+ RPB: RPB_4656
+ RPC: RPC_1016 RPC_1025
+ RPD: RPD_1526 RPD_1534
+ RPE: RPE_0592 RPE_0604
+ RPT: Rpal_0728 Rpal_0736
+ RPX: Rpdx1_4117 Rpdx1_4125
+ RVA: Rvan_0044
+ SIL: SPO3697
+ JAN: Jann_0669
+ SWI: Swit_0829
+ RPM: RSPPHO_01133
+ MAG: amb2869
+ GYC: GYMC61_2828
+ GYA: GYMC52_1957
+ GCT: GC56T3_1519
+ SCB: SCAB_8391
+ SCT: SCAT_5491
+ SCY: SCATT_54890
+ BSD: BLASA_2555
+ AMD: AMED_8609
+ AMN: RAM_35745 RAM_44185
+ AMM: AMES_6861 AMES_8478
+ PDX: Psed_1491
+DBLINKS ExplorEnz - The Enzyme Database: 6.2.1.25
+ IUBMB Enzyme Nomenclature: 6.2.1.25
+ ExPASy - ENZYME nomenclature database: 6.2.1.25
+ UM-BBD (Biocatalysis/Biodegradation Database): 6.2.1.25
+ BRENDA, the Enzyme Database: 6.2.1.25
+ CAS: 95329-17-2
+///
Testing Bio.KEGG.Compound on compound.sample
diff --git a/Tests/output/test_SeqIO b/Tests/output/test_SeqIO
index eb1ddaa..55b6136 100644
--- a/Tests/output/test_SeqIO
+++ b/Tests/output/test_SeqIO
@@ -2309,6 +2309,22 @@ Testing reading genbank format file GenBank/1MRR_A.gp
Checking can write/read as 'tab' format
Checking can write/read as 'nexus' format
Checking can write/read as 'phylip-sequential' format
+Testing reading genbank format file GenBank/DS830848.gb
+ ID = 'DS830848.1', Name='DS830848',
+ Seq='NNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNN...NNNNNNN', length=1311
+ Checking can write/read as 'embl' format
+ Checking can write/read as 'genbank' format
+ Checking can write/read as 'imgt' format
+ Checking can write/read as 'qual' format
+ Failed: No suitable quality scores found in letter_annotations of SeqRecord (id=DS830848.1).
+Testing reading embl format file EMBL/DS830848.embl
+ ID = 'DS830848.1', Name='DS830848',
+ Seq='NNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNN...NNNNNNN', length=1311
+ Checking can write/read as 'embl' format
+ Checking can write/read as 'genbank' format
+ Checking can write/read as 'imgt' format
+ Checking can write/read as 'qual' format
+ Failed: No suitable quality scores found in letter_annotations of SeqRecord (id=DS830848.1).
Testing reading embl format file EMBL/epo_prt_selection.embl
ID = 'A00022.1', Name='A00022',
Seq='CLARIIRYFYNAKA', length=14
diff --git a/Tests/phenotype/BadPlate.json b/Tests/phenotype/BadPlate.json
new file mode 100644
index 0000000..0a21823
--- /dev/null
+++ b/Tests/phenotype/BadPlate.json
@@ -0,0 +1 @@
+{"aggr_settings": {"software": "opm", "options": {"Dummy": "Dummy"}, "method": "opm"}, "measurements": {"H09": [13.0, 11.0, 11.0, 11.0, 11.0, 11.0, 11.0, 11.0, 11.0, 11.0, 11.0, 12.0, 11.0, 11.0, 11.0, 11.0, 11.0, 14.0, 14.0, 14.0, 14.0, 17.0, 20.0, 19.0, 19.0, 20.0, 20.0, 21.0, 22.0, 24.0, 23.0, 25.0, 20.0, 22.0, 20.0, 20.0, 19.0, 25.0, 26.0, 22.0, 26.0, 22.0, 23.0, 27.0, 27.0, 26.0, 24.0, 23.0, 35.0, 27.0, 28.0, 26.0, 28.0, 27.0, 30.0, 33.0, 31.0, 32.0, 32.0, 33.0, 38.0, 39.0, 38.0, 35 [...]
\ No newline at end of file
diff --git a/Tests/phenotype/Plate.json b/Tests/phenotype/Plate.json
new file mode 100644
index 0000000..9a52387
--- /dev/null
+++ b/Tests/phenotype/Plate.json
@@ -0,0 +1 @@
+{"aggr_settings": {"options": {"Dummy": "Dummy"}, "method": "opm", "software": "opm"}, "aggregated": {"H09": {"A": 69.0, "lambda CI95 low": ".na.real", "AUC": 4989.75, "lambda CI95 high": ".na.real", "A CI95 high": ".na.real", "AUC CI95 high": ".na.real", "A CI95 low": ".na.real", "mu": ".na.real", "mu CI95 high": ".na.real", "AUC CI95 low": ".na.real", "mu CI95 low": ".na.real", "lambda": ".na.real"}, "H08": {"A": 282.0, "lambda CI95 low": ".na.real", "AUC": 20859.88, "lambda CI95 high" [...]
\ No newline at end of file
diff --git a/Tests/phenotype/Plate_2.json b/Tests/phenotype/Plate_2.json
new file mode 100644
index 0000000..e7add44
--- /dev/null
+++ b/Tests/phenotype/Plate_2.json
@@ -0,0 +1 @@
+{"metadata": [], "measurements": {"H09": [1.0, 1.0, 1.0, 5.0, 14.0, 14.0, 14.0, 14.0, 14.0, 14.0, 14.0, 14.0, 14.0, 14.0, 16.0, 16.0, 16.0, 16.0, 16.0, 16.0, 16.0, 16.0, 16.0, 16.0, 16.0, 16.0, 16.0, 16.0, 16.0, 16.0, 17.0, 17.0, 17.0, 17.0, 17.0, 17.0, 17.0, 17.0, 17.0, 17.0, 17.0, 17.0, 17.0, 17.0, 17.0, 17.0, 17.0, 17.0, 17.0, 17.0, 17.0, 17.0, 17.0, 17.0, 18.0, 18.0, 18.0, 18.0, 18.0, 18.0, 18.0, 18.0, 18.0, 18.0, 18.0, 18.0, 18.0, 18.0, 18.0, 18.0, 18.0, 18.0, 18.0, 18.0, 18.0, 18.0 [...]
\ No newline at end of file
diff --git a/Tests/phenotype/Plates.csv b/Tests/phenotype/Plates.csv
new file mode 100644
index 0000000..be0dd29
--- /dev/null
+++ b/Tests/phenotype/Plates.csv
@@ -0,0 +1,602 @@
+"Data File "," C:\Documents and Settings\Standard\Desktop\esperimenti e lavori in corso\Phenotype MicroArray\dati PM\2007\10\smel_Rm1021___1_30_IDS_207_Octx01x2007_A_25A.oka",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Set up Time ","Oct 01 2007 3:12 PM",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Position ","25-A",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Plate Type ","PM 1-",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Strain Type ","NOT APPLICABLE",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Sample Number","Rm1021",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Strain Name ","Rm1021",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Strain Number","Rm1021",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Other ",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Hour"," A01"," A02"," A03"," A04"," A05"," A06"," A07"," A08"," A09"," A10"," A11"," A12"," B01"," B02"," B03"," B04"," B05"," B06"," B07"," B08"," B09"," B10"," B11"," B12"," C01"," C02"," C03"," C04"," C05"," C06"," C07"," C08"," C09"," C10"," C11"," C12"," D01"," D02"," D03"," D04"," D05"," D06"," D07"," D08"," D09"," D10"," D11"," D12"," E01"," E02"," E03"," E04"," E05"," E06"," E07"," E08"," E09"," E10"," E11"," E12"," F0 [...]
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+,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Data File "," C:\Documents and Settings\Standard\Desktop\esperimenti e lavori in corso\Phenotype MicroArray\dati PM\2007\10\smel_Rm1021___9_30_IDS_207_Octx01x2007_A_23A.oka",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Set up Time ","Oct 01 2007 3:12 PM",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Position ","23-A",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Plate Type ","PM 9-",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Strain Type ","NOT APPLICABLE",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Sample Number","Rm1021",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Strain Name ","Rm1021",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Strain Number","Rm1021",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Other ",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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+,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
diff --git a/Tests/phenotype/SmallPlate.json b/Tests/phenotype/SmallPlate.json
new file mode 100644
index 0000000..d332398
--- /dev/null
+++ b/Tests/phenotype/SmallPlate.json
@@ -0,0 +1 @@
+{"aggr_settings": {"software": "opm", "options": {"Dummy": "Dummy"}, "method": "opm"}, "measurements": {"Hour": [0.0, 0.25, 0.5, 0.75, 1.0, 1.25, 1.5, 1.75, 2.0, 2.25, 2.5, 2.75, 3.0, 3.25, 3.5, 3.75, 4.0, 4.25, 4.5, 4.75, 5.0, 5.25, 5.5, 5.75, 6.0, 6.25, 6.5, 6.75, 7.0, 7.25, 7.5, 7.75, 8.0, 8.25, 8.5, 8.75, 9.0, 9.25, 9.5, 9.75, 10.0, 10.25, 10.5, 10.75, 11.0, 11.25, 11.5, 11.75, 12.0, 12.25, 12.5, 12.75, 13.0, 13.25, 13.5, 13.75, 14.0, 14.25, 14.5, 14.75, 15.0, 15.25, 15.5, 15.75, 16. [...]
\ No newline at end of file
diff --git a/Tests/phenotype/SmallPlate_2.json b/Tests/phenotype/SmallPlate_2.json
new file mode 100644
index 0000000..8c1d1f6
--- /dev/null
+++ b/Tests/phenotype/SmallPlate_2.json
@@ -0,0 +1 @@
+{"measurements": {"Hour": [0.0, 0.25, 0.5, 0.75, 1.0, 1.25, 1.5, 1.75, 2.0, 2.25, 2.5, 2.75, 3.0, 3.25, 3.5, 3.75, 4.0, 4.25, 4.5, 4.75, 5.0, 5.25, 5.5, 5.75, 6.0, 6.25, 6.5, 6.75, 7.0, 7.25, 7.5, 7.75, 8.0, 8.25, 8.5, 8.75, 9.0, 9.25, 9.5, 9.75, 10.0, 10.25, 10.5, 10.75, 11.0, 11.25, 11.5, 11.75, 12.0, 12.25, 12.5, 12.75, 13.0, 13.25, 13.5, 13.75, 14.0, 14.25, 14.5, 14.75, 15.0, 15.25, 15.5, 15.75, 16.0, 16.25, 16.5, 16.75, 17.0, 17.25, 17.5, 17.75, 18.0, 18.25, 18.5, 18.75, 19.0, 19.25 [...]
\ No newline at end of file
diff --git a/Tests/phenotype/SmallPlates.csv b/Tests/phenotype/SmallPlates.csv
new file mode 100644
index 0000000..240f29e
--- /dev/null
+++ b/Tests/phenotype/SmallPlates.csv
@@ -0,0 +1,600 @@
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+Set up Time ,Oct 01 2007 3:12 PM,,
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+Set up Time ,Oct 01 2007 3:12 PM,,
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+Sample Number,Rm1021,,
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+25,15,180,10
+25.25,15,183,10
+25.5,15,187,10
+25.75,15,190,10
+26,15,194,10
+26.25,15,194,10
+26.5,15,195,10
+26.75,15,198,10
+27,15,201,10
+27.25,15,203,10
+27.5,15,205,10
+27.75,15,209,10
+28,15,209,10
+28.25,15,212,10
+28.5,15,214,10
+28.75,15,219,10
+29,15,219,10
+29.25,16,221,10
+29.5,20,222,10
+29.75,23,225,10
+30,24,225,10
+30.25,28,230,10
+30.5,30,232,10
+30.75,31,234,10
+31,36,236,10
+31.25,38,236,10
+31.5,41,237,10
+31.75,44,241,10
+32,46,241,10
+32.25,50,243,10
+32.5,52,247,10
+32.75,54,247,10
+33,59,251,10
+33.25,61,252,10
+33.5,63,252,10
+33.75,65,252,10
+34,69,255,10
+34.25,70,255,10
+34.5,73,258,10
+34.75,74,263,10
+35,76,263,10
+35.25,78,266,10
+35.5,82,266,10
+35.75,84,268,10
+36,85,270,10
+36.25,88,271,10
+36.5,91,271,10
+36.75,91,273,10
+37,95,273,10
+37.25,96,275,10
+37.5,98,277,10
+37.75,101,277,10
+38,104,282,10
+38.25,104,283,10
+38.5,108,283,10
+38.75,110,285,10
+39,112,286,10
+39.25,114,288,10
+39.5,115,289,10
+39.75,119,293,10
+40,120,293,10
+40.25,124,295,10
+40.5,124,295,10
+40.75,129,295,10
+41,129,295,10
+41.25,132,297,10
+41.5,133,298,10
+41.75,138,301,10
+42,139,305,10
+42.25,141,305,10
+42.5,142,305,10
+42.75,148,306,10
+43,148,308,10
+43.25,149,308,10
+43.5,154,310,10
+43.75,157,311,10
+44,158,312,10
+44.25,158,313,10
+44.5,165,315,10
+44.75,165,317,10
+45,166,317,10
+45.25,168,317,10
+45.5,173,319,10
+45.75,173,320,10
+46,179,321,10
+46.25,179,321,10
+46.5,180,324,10
+46.75,183,326,10
+47,188,326,10
+47.25,192,326,10
+47.5,195,329,10
+47.75,195,329,10
+48,197,331,10
+48.25,197,331,10
+48.5,203,331,10
+48.75,203,332,10
+49,206,333,10
+49.25,206,333,10
+49.5,206,335,10
+49.75,212,336,10
+50,212,336,10
+50.25,218,336,10
+50.5,218,338,10
+50.75,225,339,10
+51,226,340,10
+51.25,231,340,10
+51.5,234,340,10
+51.75,234,340,10
+52,234,340,10
+52.25,238,340,10
+52.5,239,340,10
+52.75,243,344,10
+53,243,344,10
+53.25,244,344,10
+53.5,250,344,10
+53.75,253,344,10
+54,255,344,10
+54.25,259,344,10
+54.5,265,345,10
+54.75,265,345,10
+55,266,345,10
+55.25,267,345,10
+55.5,273,345,10
+55.75,273,345,10
+56,285,346,10
+56.25,286,348,10
+56.5,290,348,10
+56.75,290,348,10
+57,296,348,10
+57.25,296,348,10
+57.5,303,348,10
+57.75,305,349,10
+58,305,350,10
+58.25,305,351,10
+58.5,307,351,10
+58.75,307,351,10
+59,310,351,10
+59.25,313,351,10
+59.5,313,351,10
+59.75,327,353,10
+60,328,353,10
+60.25,328,353,10
+60.5,331,353,10
+60.75,331,353,10
+61,332,353,10
+61.25,332,353,10
+61.5,345,354,10
+61.75,349,354,10
+62,349,354,10
+62.25,349,354,10
+62.5,349,354,10
+62.75,353,354,10
+63,353,355,10
+63.25,357,358,10
+63.5,357,358,10
+63.75,363,358,10
+64,366,358,10
+64.25,366,358,10
+64.5,369,358,10
+64.75,369,358,10
+65,370,358,10
+65.25,379,358,10
+65.5,382,358,10
+65.75,382,358,10
+66,382,358,10
+66.25,382,358,10
+66.5,382,358,10
+66.75,382,358,10
+67,382,361,10
+67.25,384,361,10
+67.5,390,361,10
+67.75,393,362,10
+68,393,362,10
+68.25,393,362,10
+68.5,393,362,10
+68.75,398,362,10
+69,398,362,10
+69.25,399,362,10
+69.5,401,363,10
+69.75,401,364,10
+70,404,364,10
+70.25,404,364,10
+70.5,404,364,10
+70.75,404,364,10
+71,405,364,10
+71.25,410,364,10
+71.5,410,364,10
+71.75,415,364,10
diff --git a/Tests/run_tests.py b/Tests/run_tests.py
index 12c126a..31382ed 100644
--- a/Tests/run_tests.py
+++ b/Tests/run_tests.py
@@ -20,14 +20,12 @@ Command line options:
<test_name> -- supply the name of one (or more) tests to be run.
The .py file extension is optional.
doctest -- run the docstring tests.
+
By default, all tests are run.
"""
-# The default verbosity (not verbose)
from __future__ import print_function
-VERBOSITY = 0
-
# standard modules
import sys
import os
@@ -71,6 +69,9 @@ def is_numpy():
except ImportError:
return False
+# The default verbosity (not verbose)
+VERBOSITY = 0
+
# This is the list of modules containing docstring tests.
# If you develop docstring tests for other modules, please add
# those modules here. Please sort names alphabetically.
@@ -79,6 +80,7 @@ DOCTEST_MODULES = [
"Bio.Align.Generic",
"Bio.Align.Applications._Clustalw",
"Bio.Align.Applications._ClustalOmega",
+ "Bio.Align.Applications._Dialign",
"Bio.Align.Applications._MSAProbs",
"Bio.Align.Applications._Mafft",
"Bio.Align.Applications._Muscle",
@@ -91,18 +93,21 @@ DOCTEST_MODULES = [
"Bio.Application",
"Bio.bgzf",
"Bio.codonalign",
+ "Bio.codonalign.codonalignment",
+ "Bio.codonalign.codonalphabet",
+ "Bio.codonalign.codonseq",
"Bio.Blast.Applications",
"Bio.Emboss.Applications",
"Bio.GenBank",
"Bio.KEGG.Compound",
"Bio.KEGG.Enzyme",
"Bio.NMR.xpktools",
- "Bio.Motif",
"Bio.motifs",
- "Bio.motifs.applications._alignace",
"Bio.motifs.applications._xxmotif",
"Bio.pairwise2",
"Bio.Phylo.Applications._Raxml",
+ "Bio.Phylo.Consensus",
+ "Bio.Phylo.BaseTree",
"Bio.SearchIO",
"Bio.SearchIO._model",
"Bio.SearchIO._model.query",
@@ -115,14 +120,19 @@ DOCTEST_MODULES = [
"Bio.SearchIO.ExonerateIO",
"Bio.Seq",
"Bio.SeqIO",
- "Bio.SeqIO.FastaIO",
"Bio.SeqIO.AceIO",
+ "Bio.SeqIO.FastaIO",
+ "Bio.SeqIO.IgIO",
+ "Bio.SeqIO.InsdcIO",
"Bio.SeqIO.PhdIO",
+ "Bio.SeqIO.PirIO",
"Bio.SeqIO.QualityIO",
"Bio.SeqIO.SffIO",
+ "Bio.SeqIO.TabIO",
"Bio.SeqFeature",
"Bio.SeqRecord",
"Bio.SeqUtils",
+ "Bio.SeqUtils.CheckSum",
"Bio.SeqUtils.MeltingTemp",
"Bio.Sequencing.Applications._Novoalign",
"Bio.Sequencing.Applications._bwa",
@@ -135,7 +145,8 @@ if is_numpy():
DOCTEST_MODULES.extend(["Bio.Affy.CelFile",
"Bio.Statistics.lowess",
"Bio.PDB.Polypeptide",
- "Bio.PDB.Selection"
+ "Bio.PDB.Selection",
+ "Bio.SeqIO.PdbIO",
])
@@ -367,9 +378,9 @@ class TestRunner(unittest.TextTestRunner):
file = sys.argv[0]
else:
file = __file__
- testdir = os.path.dirname(file) or os.curdir
+ testdir = os.path.abspath(os.path.dirname(file) or os.curdir)
- def __init__(self, tests=[], verbosity=0):
+ def __init__(self, tests=(), verbosity=0):
# if no tests were specified to run, we run them all
# including the doctests
self.tests = tests
@@ -396,8 +407,9 @@ class TestRunner(unittest.TextTestRunner):
# test changed this, e.g. to help with detecting command line tools)
global system_lang
os.environ['LANG'] = system_lang
- # Note the current directory:
- cur_dir = os.path.abspath(".")
+ # Always run tests from the Tests/ folder where run_tests.py
+ # should be located (as we assume this with relative paths etc)
+ os.chdir(self.testdir)
try:
stdout = sys.stdout
sys.stdout = output
@@ -430,21 +442,20 @@ class TestRunner(unittest.TextTestRunner):
else:
# It's a doc test
sys.stderr.write("%s docstring test ... " % name)
- # Can't use fromlist=name.split(".") until python 2.5+
- module = __import__(name, None, None, name.split("."))
+ module = __import__(name, fromlist=name.split("."))
suite = doctest.DocTestSuite(module,
optionflags=doctest.ELLIPSIS)
del module
suite.run(result)
- if cur_dir != os.path.abspath("."):
+ if self.testdir != os.path.abspath("."):
sys.stderr.write("FAIL\n")
result.stream.write(result.separator1 + "\n")
result.stream.write("ERROR: %s\n" % name)
result.stream.write(result.separator2 + "\n")
result.stream.write("Current directory changed\n")
- result.stream.write("Was: %s\n" % cur_dir)
+ result.stream.write("Was: %s\n" % self.testdir)
result.stream.write("Now: %s\n" % os.path.abspath("."))
- os.chdir(cur_dir)
+ os.chdir(self.testdir)
if not result.wasSuccessful():
result.printErrors()
return False
diff --git a/Tests/search_tests_common.py b/Tests/search_tests_common.py
index fb671b8..9e9bbe1 100644
--- a/Tests/search_tests_common.py
+++ b/Tests/search_tests_common.py
@@ -1,5 +1,5 @@
# Copyright 2012 by Wibowo Arindrarto. All rights reserved.
-# Revisions Copyright 2012 by Peter Cock. All rights reserved.
+# Revisions Copyright 2012-2015 by Peter Cock. All rights reserved.
# This code is part of the Biopython distribution and governed by its
# license. Please see the LICENSE file that should have been included
# as part of this package.
@@ -35,15 +35,21 @@ class CheckRaw(unittest.TestCase):
raw = _as_bytes(raw)
# Anticipate cases where the raw string and/or file uses different
# newline characters ~ we set everything to \n.
+ new = idx.get_raw(id)
+ self.assertTrue(isinstance(new, bytes),
+ "Didn't get bytes from %s get_raw" % self.fmt)
self.assertEqual(raw.replace(b'\r\n', b'\n'),
- idx.get_raw(id).replace(b'\r\n', b'\n'))
+ new.replace(b'\r\n', b'\n'))
idx.close()
# Now again, but using SQLite backend
if sqlite3:
idx = SearchIO.index_db(":memory:", filename, self.fmt, **kwargs)
+ new = idx.get_raw(id)
+ self.assertTrue(isinstance(new, bytes),
+ "Didn't get bytes from %s get_raw" % self.fmt)
self.assertEqual(raw.replace(b'\r\n', b'\n'),
- idx.get_raw(id).replace(b'\r\n', b'\n'))
+ new.replace(b'\r\n', b'\n'))
idx.close()
if os.path.isfile(filename + ".bgz"):
diff --git a/Tests/seq_tests_common.py b/Tests/seq_tests_common.py
index f51d556..79276b7 100644
--- a/Tests/seq_tests_common.py
+++ b/Tests/seq_tests_common.py
@@ -188,7 +188,7 @@ def compare_feature(old_f, new_f):
% (repr(old_f.qualifiers[key]),
repr(new_f.qualifiers[key]))
else:
- assert False, "Problem with feature's '%s' qualifier" & key
+ assert False, "Problem with feature's '%s' qualifier" % key
else:
# Should both be lists of strings...
assert old_f.qualifiers[key] == new_f.qualifiers[key], \
@@ -291,12 +291,13 @@ def compare_record(old, new):
# TODO - address these, see Bug 2681?
new_keys = set(new.annotations).difference(old.annotations)
new_keys = new_keys.difference(['cross_references', 'date',
- 'data_file_division', 'ncbi_taxid', 'gi'])
+ 'data_file_division', 'ncbi_taxid',
+ 'gi'])
assert not new_keys, "Unexpected new annotation keys: %s" \
% ", ".join(new_keys)
missing_keys = set(old.annotations).difference(new.annotations)
missing_keys = missing_keys.difference(['ncbi_taxid', # Can't store chimeras
- ])
+ 'structured_comment'])
assert not missing_keys, "Unexpectedly missing annotation keys: %s" \
% ", ".join(missing_keys)
diff --git a/Tests/test_AlignIO.py b/Tests/test_AlignIO.py
index fe983e0..9eade9a 100644
--- a/Tests/test_AlignIO.py
+++ b/Tests/test_AlignIO.py
@@ -91,14 +91,14 @@ def alignment_summary(alignment, index=" ", vertical_threshold=5):
else:
# Show each sequence row vertically
for i in range(min(5, alignment_len)):
- answer.append(index + str_summary(alignment[:, i])
- + " alignment column %i" % i)
+ answer.append(index + str_summary(alignment[:, i]) +
+ " alignment column %i" % i)
if alignment_len > 5:
i = alignment_len - 1
- answer.append(index + str_summary("|" * rec_count)
- + " ...")
- answer.append(index + str_summary(alignment[:, i])
- + " alignment column %i" % i)
+ answer.append(index + str_summary("|" * rec_count) +
+ " ...")
+ answer.append(index + str_summary(alignment[:, i]) +
+ " alignment column %i" % i)
return "\n".join(answer)
diff --git a/Tests/test_AlignInfo.py b/Tests/test_AlignInfo.py
new file mode 100644
index 0000000..2934828
--- /dev/null
+++ b/Tests/test_AlignInfo.py
@@ -0,0 +1,119 @@
+# Copyright 2016 by Peter Cock. All rights reserved.
+# This code is part of the Biopython distribution and governed by its
+# license. Please see the LICENSE file that should have been included
+# as part of this package.
+
+"""Bio.Align.AlignInfo related tests."""
+import unittest
+
+from Bio.Alphabet import DNAAlphabet, generic_protein
+from Bio.Alphabet import HasStopCodon, Gapped
+from Bio.Alphabet.IUPAC import unambiguous_dna
+from Bio.Align import MultipleSeqAlignment
+from Bio.Seq import Seq
+from Bio.SeqRecord import SeqRecord
+from Bio import AlignIO
+from Bio.SubsMat.FreqTable import FreqTable, FREQ
+from Bio.Align.AlignInfo import SummaryInfo
+
+
+class AlignInfoTests(unittest.TestCase):
+ """Test basic usage."""
+
+ def test_nucleotides(self):
+ filename = "GFF/multi.fna"
+ format = "fasta"
+ alignment = AlignIO.read(filename, format, alphabet=unambiguous_dna)
+ summary = SummaryInfo(alignment)
+
+ c = summary.dumb_consensus(ambiguous="N")
+ self.assertEqual(str(c), 'NNNNNNNN')
+ self.assertNotEqual(c.alphabet, unambiguous_dna)
+ self.assertTrue(isinstance(c.alphabet, DNAAlphabet))
+
+ c = summary.gap_consensus(ambiguous="N")
+ self.assertEqual(str(c), 'NNNNNNNN')
+ self.assertNotEqual(c.alphabet, unambiguous_dna)
+ self.assertTrue(isinstance(c.alphabet, DNAAlphabet))
+
+ expected = FreqTable({"A": 0.25, "G": 0.25, "T": 0.25, "C": 0.25},
+ FREQ, unambiguous_dna)
+
+ m = summary.pos_specific_score_matrix(chars_to_ignore=['-'],
+ axis_seq=c)
+ self.assertEqual(str(m), """ A C G T
+N 2.0 0.0 1.0 0.0
+N 1.0 1.0 1.0 0.0
+N 1.0 0.0 2.0 0.0
+N 0.0 1.0 1.0 1.0
+N 1.0 2.0 0.0 0.0
+N 0.0 2.0 1.0 0.0
+N 1.0 2.0 0.0 0.0
+N 0.0 2.0 1.0 0.0
+""")
+
+ # Have a generic alphabet, without a declared gap char, so must tell
+ # provide the frequencies and chars to ignore explicitly.
+ ic = summary.information_content(e_freq_table=expected,
+ chars_to_ignore=['-'])
+ self.assertAlmostEqual(ic, 7.32029999423075, places=6)
+
+ def test_proteins(self):
+ alpha = HasStopCodon(Gapped(generic_protein, "-"), "*")
+ a = MultipleSeqAlignment([
+ SeqRecord(Seq("MHQAIFIYQIGYP*LKSGYIQSIRSPEYDNW-", alpha), id="ID001"),
+ SeqRecord(Seq("MH--IFIYQIGYAYLKSGYIQSIRSPEY-NW*", alpha), id="ID002"),
+ SeqRecord(Seq("MHQAIFIYQIGYPYLKSGYIQSIRSPEYDNW*", alpha), id="ID003")])
+ self.assertEqual(32, a.get_alignment_length())
+
+ s = SummaryInfo(a)
+
+ c = s.dumb_consensus(ambiguous="X")
+ self.assertEqual(str(c), "MHQAIFIYQIGYXXLKSGYIQSIRSPEYDNW*")
+
+ c = s.gap_consensus(ambiguous="X")
+ self.assertEqual(str(c), "MHXXIFIYQIGYXXLKSGYIQSIRSPEYXNWX")
+
+ m = s.pos_specific_score_matrix(chars_to_ignore=['-', '*'], axis_seq=c)
+ self.assertEqual(str(m), """ A D E F G H I K L M N P Q R S W Y
+M 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
+H 0.0 0.0 0.0 0.0 0.0 3.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
+X 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 0.0 0.0 0.0 0.0
+X 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
+I 0.0 0.0 0.0 0.0 0.0 0.0 3.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
+F 0.0 0.0 0.0 3.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
+I 0.0 0.0 0.0 0.0 0.0 0.0 3.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
+Y 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3.0
+Q 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3.0 0.0 0.0 0.0 0.0
+I 0.0 0.0 0.0 0.0 0.0 0.0 3.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
+G 0.0 0.0 0.0 0.0 3.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
+Y 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3.0
+X 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0
+X 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0
+L 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
+K 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
+S 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3.0 0.0 0.0
+G 0.0 0.0 0.0 0.0 3.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
+Y 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3.0
+I 0.0 0.0 0.0 0.0 0.0 0.0 3.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
+Q 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3.0 0.0 0.0 0.0 0.0
+S 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3.0 0.0 0.0
+I 0.0 0.0 0.0 0.0 0.0 0.0 3.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
+R 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3.0 0.0 0.0 0.0
+S 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3.0 0.0 0.0
+P 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3.0 0.0 0.0 0.0 0.0 0.0
+E 0.0 0.0 3.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
+Y 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3.0
+X 0.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
+N 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3.0 0.0 0.0 0.0 0.0 0.0 0.0
+W 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3.0 0.0
+X 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
+""")
+
+ ic = s.information_content(chars_to_ignore=['-', '*'])
+ self.assertAlmostEqual(ic, 133.061475107, places=6)
+
+
+if __name__ == "__main__":
+ runner = unittest.TextTestRunner(verbosity=2)
+ unittest.main(testRunner=runner)
diff --git a/Tests/test_BioSQL_MySQLdb.py b/Tests/test_BioSQL_MySQLdb.py
index 5e21d1c..2996e0d 100644
--- a/Tests/test_BioSQL_MySQLdb.py
+++ b/Tests/test_BioSQL_MySQLdb.py
@@ -9,23 +9,11 @@ from BioSQL import BioSeqDatabase
from common_BioSQL import *
-##################################
-# Start of user-editable section #
-##################################
-
-# Constants for the database driver
-DBHOST = 'localhost'
-DBUSER = 'root'
-DBPASSWD = ''
-TESTDB = 'biosql_test'
-
-################################
-# End of user-editable section #
-################################
-
DBDRIVER = 'MySQLdb'
DBTYPE = 'mysql'
+DBHOST, DBUSER, DBPASSWD, TESTDB = load_biosql_ini(DBTYPE)
+
# This will abort if driver not installed etc:
check_config(DBDRIVER, DBTYPE, DBHOST, DBUSER, DBPASSWD, TESTDB)
diff --git a/Tests/test_BioSQL_mysql_connector.py b/Tests/test_BioSQL_mysql_connector.py
index 5a43e28..fa1185d 100644
--- a/Tests/test_BioSQL_mysql_connector.py
+++ b/Tests/test_BioSQL_mysql_connector.py
@@ -9,23 +9,11 @@ from BioSQL import BioSeqDatabase
from common_BioSQL import *
-##################################
-# Start of user-editable section #
-##################################
-
-# Constants for the database driver
-DBHOST = 'localhost'
-DBUSER = 'root'
-DBPASSWD = ''
-TESTDB = 'biosql_test'
-
-################################
-# End of user-editable section #
-################################
-
DBDRIVER = 'mysql.connector'
DBTYPE = 'mysql'
+DBHOST, DBUSER, DBPASSWD, TESTDB = load_biosql_ini(DBTYPE)
+
# This will abort if driver not installed etc:
check_config(DBDRIVER, DBTYPE, DBHOST, DBUSER, DBPASSWD, TESTDB)
diff --git a/Tests/test_BioSQL_psycopg2.py b/Tests/test_BioSQL_psycopg2.py
index 51908b4..bc14248 100644
--- a/Tests/test_BioSQL_psycopg2.py
+++ b/Tests/test_BioSQL_psycopg2.py
@@ -9,22 +9,9 @@ from BioSQL import BioSeqDatabase
from common_BioSQL import *
-##################################
-# Start of user-editable section #
-##################################
-
-# Constants for the database driver
-DBHOST = 'localhost'
-DBUSER = 'postgres'
-DBPASSWD = ''
-TESTDB = 'biosql_test'
-
-################################
-# End of user-editable section #
-################################
-
DBDRIVER = 'psycopg2'
DBTYPE = 'pg'
+DBHOST, DBUSER, DBPASSWD, TESTDB = load_biosql_ini(DBTYPE)
# This will abort if driver not installed etc:
check_config(DBDRIVER, DBTYPE, DBHOST, DBUSER, DBPASSWD, TESTDB)
diff --git a/Tests/test_BioSQL_sqlite3.py b/Tests/test_BioSQL_sqlite3.py
index 4be7831..0b03c91 100644
--- a/Tests/test_BioSQL_sqlite3.py
+++ b/Tests/test_BioSQL_sqlite3.py
@@ -13,16 +13,14 @@ from BioSQL import BioSeqDatabase
from common_BioSQL import *
# Constants for the database driver
-DBHOST = 'localhost'
-DBUSER = 'root'
-DBPASSWD = ''
-
DBDRIVER = 'sqlite3'
DBTYPE = 'sqlite'
+DBHOST = None
+DBUSER = 'root'
+DBPASSWD = None
TESTDB = temp_db_filename()
-
# This will abort if driver not installed etc:
check_config(DBDRIVER, DBTYPE, DBHOST, DBUSER, DBPASSWD, TESTDB)
@@ -66,6 +64,7 @@ class BackwardsCompatibilityTest(unittest.TestCase):
for rec in original_records]
# And check they agree
self.assertTrue(compare_records(original_records, biosql_records))
+ server.close()
if __name__ == "__main__":
# Run the test cases
diff --git a/Tests/test_Chi2.py b/Tests/test_Chi2.py
index 6eb9dee..ef3a28f 100644
--- a/Tests/test_Chi2.py
+++ b/Tests/test_Chi2.py
@@ -9,18 +9,18 @@ from Bio.Phylo.PAML import chi2
class ModTest(unittest.TestCase):
- def testCdfChi2(self):
+ def test_cdf_chi2(self):
self.assertRaises(ValueError, chi2.cdf_chi2, df=0, stat=3.84)
self.assertRaises(ValueError, chi2.cdf_chi2, df=1, stat=-3.84)
self.assertRaises(TypeError, chi2.cdf_chi2, df="d", stat="stat")
self.assertAlmostEqual(chi2.cdf_chi2(2, 3.84), 0.1466070, places=5)
- def testLnGamma(self):
+ def test_ln_gamma(self):
self.assertRaises(ValueError, chi2._ln_gamma_function, -1)
self.assertAlmostEqual(chi2._ln_gamma_function(10), 12.80183,
places=5)
- def testIncompleteGamma(self):
+ def test_incomplete_gamma(self):
self.assertRaises(ValueError, chi2._incomplete_gamma, x=0.5,
alpha=-1)
self.assertAlmostEqual(chi2._incomplete_gamma(0.5, 0.5), 0.6826895,
diff --git a/Tests/test_Cluster.py b/Tests/test_Cluster.py
index ae923c0..235ac7c 100644
--- a/Tests/test_Cluster.py
+++ b/Tests/test_Cluster.py
@@ -101,12 +101,12 @@ class TestCluster(unittest.TestCase):
try:
treecluster(data1)
- except:
+ except Exception: # TODO - Which exceptions?
self.fail("treecluster failed to accept matrix data1")
try:
treecluster(data2)
- except:
+ except Exception: # TODO - Which exceptions?
self.fail("treecluster failed to accept matrix data2")
self.assertRaises(TypeError, lambda: treecluster(data3))
diff --git a/Tests/test_Crystal.py b/Tests/test_Crystal.py
index 1d0b079..2bddc75 100644
--- a/Tests/test_Crystal.py
+++ b/Tests/test_Crystal.py
@@ -463,10 +463,6 @@ class CrystalTestCase(unittest.TestCase):
self.assertEqual(list(self.crystal.items()),
list(self.crystal.data.items()))
- def testKeys(self):
- self.assertEqual(list(self.crystal.keys()),
- list(self.crystal.data.keys()))
-
def testHasKey(self):
self.assertTrue('b' in self.crystal)
self.assertTrue('c' in self.crystal)
diff --git a/Tests/test_Emboss.py b/Tests/test_Emboss.py
index 03020d6..3c6a7c7 100644
--- a/Tests/test_Emboss.py
+++ b/Tests/test_Emboss.py
@@ -225,7 +225,7 @@ def compare_records(old_list, new_list):
if old.features and new.features \
and len(old.features) != len(new.features):
raise ValueError("%i vs %i features"
- % (len(old.features, len(new.features))))
+ % (len(old.features), len(new.features)))
# TODO - check annotation
return True
@@ -249,7 +249,7 @@ class SeqRetSeqIOTests(unittest.TestCase):
def tearDown(self):
clean_up()
- def check_SeqIO_to_EMBOSS(self, in_filename, in_format, skip_formats=[],
+ def check_SeqIO_to_EMBOSS(self, in_filename, in_format, skip_formats=(),
alphabet=None):
"""Can Bio.SeqIO write files seqret can read back?"""
if alphabet:
@@ -267,7 +267,7 @@ class SeqRetSeqIOTests(unittest.TestCase):
% (in_format, in_filename, temp_format, err))
def check_EMBOSS_to_SeqIO(self, filename, old_format,
- skip_formats=[]):
+ skip_formats=()):
"""Can Bio.SeqIO read seqret's conversion of the file?"""
# TODO: Why can't we read EMBOSS's swiss output?
self.assertTrue(os.path.isfile(filename))
@@ -284,7 +284,7 @@ class SeqRetSeqIOTests(unittest.TestCase):
raise ValueError("Disagree on %s file %s in %s format: %s"
% (old_format, filename, new_format, err))
- def check_SeqIO_with_EMBOSS(self, filename, old_format, skip_formats=[],
+ def check_SeqIO_with_EMBOSS(self, filename, old_format, skip_formats=(),
alphabet=None):
# Check EMBOSS can read Bio.SeqIO output...
self.check_SeqIO_to_EMBOSS(filename, old_format, skip_formats,
@@ -361,7 +361,7 @@ class SeqRetAlignIOTests(unittest.TestCase):
clean_up()
def check_EMBOSS_to_AlignIO(self, filename, old_format,
- skip_formats=[]):
+ skip_formats=()):
"""Can AlignIO read seqret's conversion of the file?"""
self.assertTrue(os.path.isfile(filename), filename)
old_aligns = list(AlignIO.parse(filename, old_format))
@@ -374,7 +374,7 @@ class SeqRetAlignIOTests(unittest.TestCase):
handle = emboss_convert(filename, old_format, new_format)
try:
new_aligns = list(AlignIO.parse(handle, new_format))
- except:
+ except Exception: # TODO - Which exceptions?
handle.close()
raise ValueError("Can't parse %s file %s in %s format."
% (old_format, filename, new_format))
@@ -385,7 +385,7 @@ class SeqRetAlignIOTests(unittest.TestCase):
raise ValueError("Disagree on %s file %s in %s format: %s"
% (old_format, filename, new_format, err))
- def check_AlignIO_to_EMBOSS(self, in_filename, in_format, skip_formats=[],
+ def check_AlignIO_to_EMBOSS(self, in_filename, in_format, skip_formats=(),
alphabet=None):
"""Can Bio.AlignIO write files seqret can read back?"""
if alphabet:
@@ -415,11 +415,11 @@ class SeqRetAlignIOTests(unittest.TestCase):
raise ValueError("Disagree on file %s %s in %s format: %s"
% (in_format, in_filename, temp_format, err))
- def check_AlignIO_with_EMBOSS(self, filename, old_format, skip_formats=[],
+ def check_AlignIO_with_EMBOSS(self, filename, old_format, skip_formats=(),
alphabet=None):
# Check EMBOSS can read Bio.AlignIO output...
self.check_AlignIO_to_EMBOSS(filename, old_format, skip_formats,
- alphabet)
+ alphabet)
# Check Bio.AlignIO can read EMBOSS seqret output...
self.check_EMBOSS_to_AlignIO(filename, old_format, skip_formats)
@@ -520,10 +520,10 @@ class PairwiseAlignmentTests(unittest.TestCase):
gapextend=0.5,
auto=True, filter=True)
self.assertEqual(str(cline),
- exes["water"] + " -auto -filter"
- + " -asequence=asis:ACCCGGGCGCGGT"
- + " -bsequence=asis:ACCCGAGCGCGGT"
- + " -gapopen=10 -gapextend=0.5")
+ exes["water"] + " -auto -filter" +
+ " -asequence=asis:ACCCGGGCGCGGT" +
+ " -bsequence=asis:ACCCGAGCGCGGT" +
+ " -gapopen=10 -gapextend=0.5")
# Run the tool,
child = subprocess.Popen(str(cline),
stdin=subprocess.PIPE,
@@ -580,10 +580,10 @@ class PairwiseAlignmentTests(unittest.TestCase):
gapextend=0.5,
auto=True, filter=True)
self.assertEqual(str(cline),
- exes["needle"] + " -auto -filter"
- + " -asequence=asis:ACCCGGGCGCGGT"
- + " -bsequence=asis:ACCCGAGCGCGGT"
- + " -gapopen=10 -gapextend=0.5")
+ exes["needle"] + " -auto -filter" +
+ " -asequence=asis:ACCCGGGCGCGGT" +
+ " -bsequence=asis:ACCCGAGCGCGGT" +
+ " -gapopen=10 -gapextend=0.5")
# Run the tool,
child = subprocess.Popen(str(cline),
stdin=subprocess.PIPE,
@@ -750,9 +750,9 @@ class PairwiseAlignmentTests(unittest.TestCase):
aformat="pair", wordsize=9,
auto=True, stdout=True)
self.assertEqual(str(cline),
- exes["seqmatchall"] + " -auto -stdout"
- + " -sequence=Fasta/f002"
- + " -wordsize=9 -aformat=pair")
+ exes["seqmatchall"] + " -auto -stdout" +
+ " -sequence=Fasta/f002" +
+ " -wordsize=9 -aformat=pair")
# Run the tool,
child = subprocess.Popen(str(cline),
stdin=subprocess.PIPE,
@@ -933,7 +933,7 @@ def clean_up():
if filename.startswith("temp_"):
try:
os.remove(filename)
- except:
+ except Exception: # TODO - Which exceptions?
pass
if __name__ == "__main__":
diff --git a/Tests/test_Entrez.py b/Tests/test_Entrez.py
index 0a28f6a..6025420 100644
--- a/Tests/test_Entrez.py
+++ b/Tests/test_Entrez.py
@@ -1,5 +1,5 @@
# Copyright 2008-2010 by Michiel de Hoon. All rights reserved.
-# Revisions copyright 2009-2013 by Peter Cock. All rights reserved.
+# Revisions copyright 2009-2016 by Peter Cock. All rights reserved.
# This code is part of the Biopython distribution and governed by its
# license. Please see the LICENSE file that should have been included
# as part of this package.
@@ -7,7 +7,7 @@
'''
import unittest
-
+import sys
import os
if os.name == 'java':
try:
@@ -18,7 +18,8 @@ if os.name == 'java':
raise MissingPythonDependencyError("The Bio.Entrez XML parser fails on "
"Jython, see http://bugs.jython.org/issue1447")
-
+from io import BytesIO
+from Bio._py3k import StringIO
from Bio import Entrez
@@ -31,6 +32,36 @@ class GeneralTests(unittest.TestCase):
handle.close()
self.assertRaises(IOError, Entrez.read, handle)
+ def test_bytes_handle(self):
+ """Test parsing a handle opened in binary mode."""
+ with open("Entrez/einfo1.xml", "rb") as handle:
+ record = Entrez.read(handle)
+ self.assertTrue("DbList" in record)
+
+ def test_text_handle(self):
+ """Test parsing a handle opened in text mode."""
+ with open("Entrez/einfo1.xml", "rt") as handle:
+ record = Entrez.read(handle)
+ self.assertTrue("DbList" in record)
+
+ def test_BytesIO(self):
+ """Test parsing a BytesIO handle (bytes not unicode)."""
+ with open("Entrez/einfo1.xml", "rb") as in_handle:
+ data = in_handle.read()
+ handle = BytesIO(data)
+ record = Entrez.read(handle)
+ self.assertTrue("DbList" in record)
+ handle.close()
+
+ def test_StringIO(self):
+ """Test parsing a StringIO handle (unicode not bytes)."""
+ with open("Entrez/einfo1.xml", "rt") as in_handle:
+ data = in_handle.read()
+ handle = StringIO(data)
+ record = Entrez.read(handle)
+ self.assertTrue("DbList" in record)
+ handle.close()
+
class EInfoTest(unittest.TestCase):
'''Tests for parsing XML output returned by EInfo
@@ -40,9 +71,8 @@ class EInfoTest(unittest.TestCase):
'''
# To create the XML file, use
# >>> Bio.Entrez.einfo()
- handle = open('Entrez/einfo1.xml', "rb")
- record = Entrez.read(handle)
- handle.close()
+ with open('Entrez/einfo1.xml', "rb") as handle:
+ record = Entrez.read(handle)
self.assertEqual(record["DbList"], ['pubmed',
'protein',
'nucleotide',
@@ -87,9 +117,8 @@ class EInfoTest(unittest.TestCase):
'''
# To create the XML file, use
# >>> Bio.Entrez.einfo(db="pubmed")
- handle = open('Entrez/einfo2.xml', "rb")
- record = Entrez.read(handle)
- handle.close()
+ with open('Entrez/einfo2.xml', "rb") as handle:
+ record = Entrez.read(handle)
self.assertEqual(record["DbInfo"]['DbName'], 'pubmed')
self.assertEqual(record["DbInfo"]['MenuName'], 'PubMed')
self.assertEqual(record["DbInfo"]['Description'], 'PubMed bibliographic record')
@@ -123,9 +152,8 @@ class EInfoTest(unittest.TestCase):
# Starting some time in 2010, the results returned by Bio.Entrez
# included some tags that are not part of the corresponding DTD.
from Bio.Entrez import Parser
- handle = open('Entrez/einfo3.xml', "rb")
- self.assertRaises(Parser.ValidationError, Entrez.read, handle)
- handle.close()
+ with open('Entrez/einfo3.xml', "rb") as handle:
+ self.assertRaises(Parser.ValidationError, Entrez.read, handle)
def test_pubmed3(self):
'''Test non-validating parser on XML with an inconsistent DTD
@@ -134,9 +162,8 @@ class EInfoTest(unittest.TestCase):
# >>> Bio.Entrez.einfo(db="pubmed")
# Starting some time in 2010, the results returned by Bio.Entrez
# included some tags that are not part of the corresponding DTD.
- handle = open('Entrez/einfo3.xml', "rb")
- record = Entrez.read(handle, validate=False)
- handle.close()
+ with open('Entrez/einfo3.xml', "rb") as handle:
+ record = Entrez.read(handle, validate=False)
self.assertEqual(record["DbInfo"]['DbName'], 'pubmed')
self.assertEqual(record["DbInfo"]['MenuName'], 'PubMed')
self.assertEqual(record["DbInfo"]['Description'], 'PubMed bibliographic record')
@@ -786,9 +813,8 @@ class EInfoTest(unittest.TestCase):
# >>> Bio.Entrez.einfo()
# and manually delete the last couple of lines
from Bio.Entrez import Parser
- handle = open('Entrez/einfo4.xml', "rb")
- self.assertRaises(Parser.CorruptedXMLError, Entrez.read, handle)
- handle.close()
+ with open('Entrez/einfo4.xml', "rb") as handle:
+ self.assertRaises(Parser.CorruptedXMLError, Entrez.read, handle)
class ESearchTest(unittest.TestCase):
@@ -799,9 +825,8 @@ class ESearchTest(unittest.TestCase):
'''
# To create the XML file, use
# >>> Bio.Entrez.esearch(db="pubmed", term="biopython")
- handle = open('Entrez/esearch1.xml', "rb")
- record = Entrez.read(handle)
- handle.close()
+ with open('Entrez/esearch1.xml', "rb") as handle:
+ record = Entrez.read(handle)
self.assertEqual(record['Count'], '5')
self.assertEqual(record['RetMax'], '5')
self.assertEqual(record['RetStart'], '0')
@@ -829,9 +854,8 @@ class ESearchTest(unittest.TestCase):
# To create the XML file, use
# >>> Bio.Entrez.esearch(db="pubmed", term="cancer", reldate=60,
# datetype="edat", retmax=100, usehistory="y")
- handle = open('Entrez/esearch2.xml', "rb")
- record = Entrez.read(handle)
- handle.close()
+ with open('Entrez/esearch2.xml', "rb") as handle:
+ record = Entrez.read(handle)
self.assertEqual(record['Count'], "10238")
self.assertEqual(record['RetMax'], "100")
self.assertEqual(record['RetStart'], "0")
@@ -983,9 +1007,8 @@ class ESearchTest(unittest.TestCase):
# To create the XML file, use
# >>> Bio.Entrez.esearch(db="pubmed", term="PNAS[ta] AND 97[vi]",
# retstart=6, retmax=6)
- handle = open('Entrez/esearch3.xml', "rb")
- record = Entrez.read(handle)
- handle.close()
+ with open('Entrez/esearch3.xml', "rb") as handle:
+ record = Entrez.read(handle)
self.assertEqual(record['Count'], '2652')
self.assertEqual(record['RetMax'], '6')
self.assertEqual(record['RetStart'], '6')
@@ -1017,9 +1040,8 @@ class ESearchTest(unittest.TestCase):
# Search in Journals for the term obstetrics.
# To create the XML file, use
# >>> Bio.Entrez.esearch(db="journals", term="obstetrics")
- handle = open('Entrez/esearch4.xml', "rb")
- record = Entrez.read(handle)
- handle.close()
+ with open('Entrez/esearch4.xml', "rb") as handle:
+ record = Entrez.read(handle)
self.assertEqual(record['Count'], '177')
self.assertEqual(record['RetMax'], '20')
self.assertEqual(record['RetStart'], '0')
@@ -1062,9 +1084,8 @@ class ESearchTest(unittest.TestCase):
# To create the XML file, use
# >>> Bio.Entrez.esearch(db="pmc",
# term="stem cells AND free fulltext[filter]")
- handle = open('Entrez/esearch5.xml', "rb")
- record = Entrez.read(handle)
- handle.close()
+ with open('Entrez/esearch5.xml', "rb") as handle:
+ record = Entrez.read(handle)
self.assertEqual(record['Count'], '23492')
self.assertEqual(record['RetMax'], '20')
self.assertEqual(record['RetStart'], '0')
@@ -1141,9 +1162,8 @@ class ESearchTest(unittest.TestCase):
# Search in Nucleotide for a property of the sequence,
# To create the XML file, use
# >>> Bio.Entrez.esearch(db="nucleotide", term="biomol trna[prop]")
- handle = open('Entrez/esearch6.xml', "rb")
- record = Entrez.read(handle)
- handle.close()
+ with open('Entrez/esearch6.xml', "rb") as handle:
+ record = Entrez.read(handle)
self.assertEqual(record['Count'], "699")
self.assertEqual(record['RetMax'], "20")
self.assertEqual(record['RetStart'], "0")
@@ -1177,9 +1197,8 @@ class ESearchTest(unittest.TestCase):
# Search in Protein for a molecular weight
# To create the XML file, use
# >>> Bio.Entrez.esearch(db="protein", term="200020[molecular weight]")
- handle = open('Entrez/esearch7.xml', "rb")
- record = Entrez.read(handle)
- handle.close()
+ with open('Entrez/esearch7.xml', "rb") as handle:
+ record = Entrez.read(handle)
self.assertEqual(record['Count'], '3')
self.assertEqual(record['RetMax'], '3')
self.assertEqual(record['RetStart'], '0')
@@ -1201,33 +1220,8 @@ class ESearchTest(unittest.TestCase):
'''
# To create the XML file, use
# >>> Bio.Entrez.esearch(db="protein", term="abcXYZ")
- handle = open('Entrez/esearch8.xml')
- record = Entrez.read(handle)
- handle.close()
- self.assertEqual(record['Count'], '3')
- self.assertEqual(record['RetMax'], '3')
- self.assertEqual(record['RetStart'], '0')
- self.assertEqual(len(record['IdList']), 3)
- self.assertEqual(record['IdList'][0], '16766766')
- self.assertEqual(record['IdList'][1], '16422035')
- self.assertEqual(record['IdList'][2], '4104812')
- self.assertEqual(len(record['TranslationSet']), 0)
- self.assertEqual(len(record['TranslationStack']), 2)
- self.assertEqual(record['TranslationStack'][0]['Term'], '000200020[molecular weight]')
- self.assertEqual(record['TranslationStack'][0]['Field'], 'molecular weight')
- self.assertEqual(record['TranslationStack'][0]['Count'], '3')
- self.assertEqual(record['TranslationStack'][0]['Explode'], 'Y')
- self.assertEqual(record['TranslationStack'][1], 'GROUP')
- self.assertEqual(record['QueryTranslation'], '000200020[molecular weight]')
-
- def test_notfound(self):
- '''Test parsing XML returned by ESearch when no items were found
- '''
- # To create the XML file, use
- # >>> Bio.Entrez.esearch(db="protein", term="abcXYZ")
- handle = open('Entrez/esearch8.xml', "rb")
- record = Entrez.read(handle)
- handle.close()
+ with open('Entrez/esearch8.xml', "rb") as handle:
+ record = Entrez.read(handle)
self.assertEqual(record['Count'], "0")
self.assertEqual(record['RetMax'], "0")
self.assertEqual(record['RetStart'], "0")
@@ -1260,9 +1254,8 @@ class EPostTest(unittest.TestCase):
'''
# To create the XML file, use
# >>> Bio.Entrez.epost(db="pubmed", id="11237011")
- handle = open('Entrez/epost1.xml', "rb")
- record = Entrez.read(handle)
- handle.close()
+ with open('Entrez/epost1.xml', "rb") as handle:
+ record = Entrez.read(handle)
self.assertEqual(record["QueryKey"], '1')
self.assertEqual(record["WebEnv"], '0zYsuLk3zG_lRMkblPBEqnT8nIENUGw4HAy8xXChTnoVm7GEnWY71jv3nz at 1FC077F3806DE010_0042SID')
@@ -1271,18 +1264,16 @@ class EPostTest(unittest.TestCase):
'''
# To create the XML file, use
# >>> Bio.Entrez.epost(db="nothing")
- handle = open('Entrez/epost2.xml', "rb")
- self.assertRaises(RuntimeError, Entrez.read, handle)
- handle.close()
+ with open('Entrez/epost2.xml', "rb") as handle:
+ self.assertRaises(RuntimeError, Entrez.read, handle)
def test_invalid(self):
'''Test parsing XML returned by EPost with an invalid id (overflow tag)
'''
# To create the XML file, use
# >>> Bio.Entrez.epost(db="pubmed", id=99999999999999999999999999999999)
- handle = open('Entrez/epost3.xml', "rb")
- record = Entrez.read(handle)
- handle.close()
+ with open('Entrez/epost3.xml', "rb") as handle:
+ record = Entrez.read(handle)
self.assertEqual(record["InvalidIdList"], ["-1"])
self.assertEqual(record["QueryKey"], "1")
self.assertEqual(record["WebEnv"], "08AIUeBsfIk6BfdzKnd3GM2RtCudczC9jm5aeb4US0o7azCTQCeCsr-xg0 at 1EDE54E680D03C40_0011SID")
@@ -1303,9 +1294,8 @@ class ESummaryTest(unittest.TestCase):
# To create the XML file, use
# >>> Bio.Entrez.esummary(db="pubmed", id=["11850928","11482001"],
# retmode="xml")
- handle = open('Entrez/esummary1.xml', "rb")
- record = Entrez.read(handle)
- handle.close()
+ with open('Entrez/esummary1.xml', "rb") as handle:
+ record = Entrez.read(handle)
self.assertEqual(record[0]["Id"], "11850928")
self.assertEqual(record[0]["PubDate"], "1965 Aug")
self.assertEqual(record[0]["EPubDate"], "")
@@ -1381,9 +1371,8 @@ class ESummaryTest(unittest.TestCase):
# In Journals display records for journal IDs 27731,439,735,905
# To create the XML file, use
# >>> Bio.Entrez.esummary(db="journals", id="27731,439,735,905")
- handle = open('Entrez/esummary2.xml', "rb")
- record = Entrez.read(handle)
- handle.close()
+ with open('Entrez/esummary2.xml', "rb") as handle:
+ record = Entrez.read(handle)
self.assertEqual(record[0]["Id"], "27731")
self.assertEqual(record[0]["Title"], "The American journal of obstetrics and diseases of women and children")
self.assertEqual(record[0]["MedAbbr"], "Am J Obstet Dis Women Child")
@@ -1456,9 +1445,8 @@ class ESummaryTest(unittest.TestCase):
# In Protein display records for GIs 28800982 and 28628843 in xml retrieval mode
# To create the XML file, use
# >>> Bio.Entrez.esummary(db="protein", id="28800982,28628843", retmode="xml")
- handle = open('Entrez/esummary3.xml', "rb")
- record = Entrez.read(handle)
- handle.close()
+ with open('Entrez/esummary3.xml', "rb") as handle:
+ record = Entrez.read(handle)
self.assertEqual(record[0]["Id"], "28800982")
self.assertEqual(record[0]["Caption"], "AAO47091")
self.assertEqual(record[0]["Title"], "hemochromatosis [Homo sapiens]")
@@ -1495,9 +1483,8 @@ class ESummaryTest(unittest.TestCase):
# To create the XML file, use
# >>> Bio.Entrez.esummary(db="nucleotide", id="28864546,28800981",
# retmode="xml")
- handle = open('Entrez/esummary4.xml', "rb")
- record = Entrez.read(handle)
- handle.close()
+ with open('Entrez/esummary4.xml', "rb") as handle:
+ record = Entrez.read(handle)
self.assertEqual(record[0]["Id"], "28864546")
self.assertEqual(record[0]["Caption"], "AY207443")
self.assertEqual(record[0]["Title"], "Homo sapiens alpha hemoglobin (HBZP) pseudogene 3' UTR/AluJo repeat breakpoint junction")
@@ -1534,9 +1521,8 @@ class ESummaryTest(unittest.TestCase):
# To create the XML file, use
# >>> Bio.Entrez.esummary(db="structure", id=["19923","12120"],
# retmode="xml")
- handle = open('Entrez/esummary5.xml', "rb")
- record = Entrez.read(handle)
- handle.close()
+ with open('Entrez/esummary5.xml', "rb") as handle:
+ record = Entrez.read(handle)
self.assertEqual(record[0]["Id"], "19923")
self.assertEqual(record[0]["PdbAcc"], "1L5J")
self.assertEqual(record[0]["PdbDescr"], "Crystal Structure Of E. Coli Aconitase B")
@@ -1585,9 +1571,8 @@ class ESummaryTest(unittest.TestCase):
# To create the XML file, use
# >>> Bio.Entrez.esummary(db="taxonomy", id=["9913","30521"],
# retmode="xml")
- handle = open('Entrez/esummary6.xml', "rb")
- record = Entrez.read(handle)
- handle.close()
+ with open('Entrez/esummary6.xml', "rb") as handle:
+ record = Entrez.read(handle)
self.assertEqual(record[0]["Id"], "9913")
self.assertEqual(record[0]["Rank"], "species")
self.assertEqual(record[0]["Division"], "even-toed ungulates")
@@ -1626,9 +1611,8 @@ class ESummaryTest(unittest.TestCase):
# To create the XML file, use
# >>> Bio.Entrez.esummary(db="unists", id=["254085","254086"],
# retmode="xml")
- handle = open('Entrez/esummary7.xml', "rb")
- record = Entrez.read(handle)
- handle.close()
+ with open('Entrez/esummary7.xml', "rb") as handle:
+ record = Entrez.read(handle)
self.assertEqual(record[0]["Id"], "254085")
self.assertEqual(record[0]["Marker_Name"], "SE234324")
self.assertEqual(len(record[0]["Map_Gene_Summary_List"]), 1)
@@ -1652,9 +1636,8 @@ class ESummaryTest(unittest.TestCase):
'''
# To create the XML file, use
# >>> Bio.Entrez.esummary()
- handle = open('Entrez/esummary8.xml', "rb")
- self.assertRaises(RuntimeError, Entrez.read, handle)
- handle.close()
+ with open('Entrez/esummary8.xml', "rb") as handle:
+ self.assertRaises(RuntimeError, Entrez.read, handle)
class ELinkTest(unittest.TestCase):
@@ -1666,9 +1649,8 @@ class ELinkTest(unittest.TestCase):
# Retrieve IDs from PubMed for PMID 9298984 to the PubMed database
# To create the XML file, use
# >>> Bio.Entrez.elink(dbfrom="pubmed", id="9298984", cmd="neighbor")
- handle = open('Entrez/elink1.xml', "rb")
- record = Entrez.read(handle)
- handle.close()
+ with open('Entrez/elink1.xml', "rb") as handle:
+ record = Entrez.read(handle)
self.assertEqual(len(record), 1)
self.assertEqual(len(record[0]), 5)
self.assertEqual(record[0]["DbFrom"], "pubmed")
@@ -1877,9 +1859,8 @@ class ELinkTest(unittest.TestCase):
# To create the XML file, use
# >>> Bio.Entrez.elink(dbfrom="nucleotide", db="protein",
# id="48819,7140345")
- handle = open('Entrez/elink2.xml', "rb")
- record = Entrez.read(handle)
- handle.close()
+ with open('Entrez/elink2.xml', "rb") as handle:
+ record = Entrez.read(handle)
self.assertEqual(len(record), 1)
self.assertEqual(len(record[0]), 5)
self.assertEqual(record[0]["DbFrom"], "nuccore")
@@ -1918,9 +1899,8 @@ class ELinkTest(unittest.TestCase):
# To create the XML file, use
# >>> Bio.Entrez.elink(dbfrom="pubmed", id="11812492,11774222",
# db="pubmed", mindate="1995", datetype="pdat")
- handle = open('Entrez/elink3.xml', "rb")
- record = Entrez.read(handle)
- handle.close()
+ with open('Entrez/elink3.xml', "rb") as handle:
+ record = Entrez.read(handle)
self.assertEqual(len(record), 1)
self.assertEqual(record[0]["DbFrom"], "pubmed")
self.assertEqual(len(record[0]['IdList']), 2)
@@ -2298,9 +2278,8 @@ class ELinkTest(unittest.TestCase):
# To create the XML file, use
# >>> Bio.Entrez.elink(dbfrom="pubmed", id="12242737", db="pubmed",
# term="medline[sb]")
- handle = open('Entrez/elink4.xml', "rb")
- record = Entrez.read(handle)
- handle.close()
+ with open('Entrez/elink4.xml', "rb") as handle:
+ record = Entrez.read(handle)
self.assertEqual(len(record), 1)
self.assertEqual(record[0]["DbFrom"], "pubmed")
self.assertEqual(record[0]["IdList"], ["12242737"])
@@ -2532,9 +2511,8 @@ class ELinkTest(unittest.TestCase):
# To create the XML file, use
# >>> Bio.Entrez.elink(dbfrom="pubmed", id="10611131", cmd="prlinks")
- handle = open('Entrez/elink5.xml', "rb")
- record = Entrez.read(handle)
- handle.close()
+ with open('Entrez/elink5.xml', "rb") as handle:
+ record = Entrez.read(handle)
self.assertEqual(len(record), 1)
self.assertEqual(len(record[0]), 5)
self.assertEqual(record[0]["DbFrom"], "pubmed")
@@ -2566,9 +2544,8 @@ class ELinkTest(unittest.TestCase):
# PMIDs 12085856 and 12085853
# To create the XML file, use
# >>> Bio.Entrez.elink(dbfrom="pubmed", id="12085856,12085853", cmd="llinks")
- handle = open('Entrez/elink6.xml', "rb")
- record = Entrez.read(handle)
- handle.close()
+ with open('Entrez/elink6.xml', "rb") as handle:
+ record = Entrez.read(handle)
self.assertEqual(record[0]["DbFrom"], "pubmed")
self.assertEqual(len(record[0]["IdUrlList"]), 2)
self.assertEqual(record[0]["IdUrlList"]["IdUrlSet"][0]["Id"], "12085856")
@@ -2633,9 +2610,8 @@ class ELinkTest(unittest.TestCase):
# To create the XML file, use
# >>> Bio.Entrez.elink(dbfrom="pubmed", id="12169658,11748140",
# cmd="acheck")
- handle = open('Entrez/elink7.xml', "rb")
- record = Entrez.read(handle)
- handle.close()
+ with open('Entrez/elink7.xml', "rb") as handle:
+ record = Entrez.read(handle)
self.assertEqual(len(record), 1)
self.assertEqual(record[0]["DbFrom"], "pubmed")
self.assertEqual(len(record[0]["IdCheckList"]), 2)
@@ -2856,9 +2832,8 @@ class ELinkTest(unittest.TestCase):
# To create the XML file, use
# >>> Bio.Entrez.elink(dbfrom="pubmed", id="12068369", cmd="ncheck")
- handle = open('Entrez/elink8.xml', "rb")
- record = Entrez.read(handle)
- handle.close()
+ with open('Entrez/elink8.xml', "rb") as handle:
+ record = Entrez.read(handle)
self.assertEqual(len(record), 1)
self.assertEqual(record[0]["DbFrom"], "pubmed")
self.assertEqual(len(record[0]["IdCheckList"]), 2)
@@ -2878,11 +2853,9 @@ class EGQueryTest(unittest.TestCase):
# Display counts in XML for stem cells in each Entrez database
# To create the XML file, use
# >>> Bio.Entrez.egquery(term="stem cells")
- handle = open('Entrez/egquery1.xml', "rb")
- record = Entrez.read(handle)
- handle.close()
+ with open('Entrez/egquery1.xml', "rb") as handle:
+ record = Entrez.read(handle)
self.assertEqual(record["Term"], "stem cells")
-
self.assertEqual(record["eGQueryResult"][0]["DbName"], "pubmed")
self.assertEqual(record["eGQueryResult"][0]["MenuName"], "PubMed")
self.assertEqual(record["eGQueryResult"][0]["Count"], "392")
@@ -3030,11 +3003,9 @@ class EGQueryTest(unittest.TestCase):
# Display counts in XML for brca1 or brca2 for each Entrez database
# To create the XML file, use
# >>> Bio.Entrez.egquery(term="brca1 OR brca2")
- handle = open('Entrez/egquery2.xml', "rb")
- record = Entrez.read(handle)
- handle.close()
+ with open('Entrez/egquery2.xml', "rb") as handle:
+ record = Entrez.read(handle)
self.assertEqual(record["Term"], "brca1 OR brca2")
-
self.assertEqual(record["eGQueryResult"][0]["DbName"], "pubmed")
self.assertEqual(record["eGQueryResult"][0]["MenuName"], "PubMed")
self.assertEqual(record["eGQueryResult"][0]["Count"], "0")
@@ -3186,9 +3157,8 @@ class ESpellTest(unittest.TestCase):
# Request suggestions for the PubMed search biopythooon
# To create the XML file, use
# >>> Bio.Entrez.espell(db="pubmed", term="biopythooon")
- handle = open('Entrez/espell.xml', "rb")
- record = Entrez.read(handle)
- handle.close()
+ with open('Entrez/espell.xml', "rb") as handle:
+ record = Entrez.read(handle)
self.assertEqual(record["Database"], "pubmed")
self.assertEqual(record["Query"], "biopythooon")
self.assertEqual(record["CorrectedQuery"], "biopython")
@@ -3208,9 +3178,8 @@ class EFetchTest(unittest.TestCase):
# To create the XML file, use
# >>> Bio.Entrez.efetch(db='pubmed', id='12091962,9997',
# retmode='xml', rettype='abstract')
- handle = open('Entrez/pubmed1.xml', "rb")
- record = Entrez.read(handle)
- handle.close()
+ with open('Entrez/pubmed1.xml', "rb") as handle:
+ record = Entrez.read(handle)
self.assertEqual(record[0]["MedlineCitation"].attributes["Owner"], "KIE")
self.assertEqual(record[0]["MedlineCitation"].attributes["Status"], "MEDLINE")
self.assertEqual(record[0]["MedlineCitation"]["PMID"], "12091962")
@@ -3410,9 +3379,8 @@ class EFetchTest(unittest.TestCase):
# To create the XML file, use
# >>> Bio.Entrez.efetch(db='pubmed', id="11748933,11700088",
# retmode="xml")
- handle = open('Entrez/pubmed2.xml', "rb")
- record = Entrez.read(handle)
- handle.close()
+ with open('Entrez/pubmed2.xml', "rb") as handle:
+ record = Entrez.read(handle)
self.assertEqual(record[0]["MedlineCitation"].attributes["Owner"], "NLM")
self.assertEqual(record[0]["MedlineCitation"].attributes["Status"], "MEDLINE")
self.assertEqual(record[0]["MedlineCitation"]["PMID"], "11748933")
@@ -3624,9 +3592,8 @@ class EFetchTest(unittest.TestCase):
# To create the XML file, use
# >>> Bio.Entrez.efetch(db="omim", id="601100", retmode='xml',
# rettype='full')
- handle = open('Entrez/ncbi_mim.xml', "rb")
- record = Entrez.read(handle)
- handle.close()
+ with open('Entrez/ncbi_mim.xml', "rb") as handle:
+ record = Entrez.read(handle)
self.assertEqual(len(record), 1)
self.assertEqual(record[0]["Mim-entry_mimNumber"], "601100")
self.assertEqual(record[0]["Mim-entry_mimType"], "1")
@@ -3801,9 +3768,8 @@ class EFetchTest(unittest.TestCase):
# Access the Taxonomy database using efetch.
# To create the XML file, use
# >>> Bio.Entrez.efetch(db="taxonomy", id="9685", retmode="xml")
- handle = open('Entrez/taxonomy.xml', "rb")
- record = Entrez.read(handle)
- handle.close()
+ with open('Entrez/taxonomy.xml', "rb") as handle:
+ record = Entrez.read(handle)
self.assertEqual(len(record), 1)
self.assertEqual(record[0]["TaxId"], "9685")
self.assertEqual(record[0]["ScientificName"], "Felis catus")
@@ -3910,9 +3876,8 @@ class EFetchTest(unittest.TestCase):
# Access the nucleotide database using efetch.
# To create the XML file, use
# >>> Bio.Entrez.efetch(db='nucleotide', id=5, retmode='xml')
- handle = open('Entrez/nucleotide1.xml', "rb")
- record = Entrez.read(handle)
- handle.close()
+ with open('Entrez/nucleotide1.xml', "rb") as handle:
+ record = Entrez.read(handle)
self.assertEqual(record[0]["GBSeq_locus"], "X60065")
self.assertEqual(record[0]["GBSeq_length"], "1136")
self.assertEqual(record[0]["GBSeq_strandedness"], "single")
@@ -4034,9 +3999,8 @@ class EFetchTest(unittest.TestCase):
# To create the XML file, use
# >>> Bio.Entrez.efetch(db='nucleotide', id=5,
# rettype='fasta', complexity=0, retmode='xml')
- handle = open('Entrez/nucleotide2.xml', "rb")
- record = Entrez.read(handle)
- handle.close()
+ with open('Entrez/nucleotide2.xml', "rb") as handle:
+ record = Entrez.read(handle)
self.assertEqual(record[0]["TSeq_seqtype"], "")
self.assertEqual(record[0]["TSeq_seqtype"].attributes["value"], "nucleotide")
self.assertEqual(record[0]["TSeq_gi"], "5")
@@ -4056,15 +4020,14 @@ class EFetchTest(unittest.TestCase):
self.assertEqual(record[1]["TSeq_length"], "342")
self.assertEqual(record[1]["TSeq_sequence"], "PALVLLLGFLCHVAIAGRTCPKPDELPFSTVVPLKRTYEPGEQIVFSCQPGYVSRGGIRRFTCPLTGLWPINTLKCMPRVCPFAGILENGTVRYTTFEYPNTISFSCHTGFYLKGASSAKCTEEGKWSPDLPVCAPITCPPPPIPKFASLSVYKPLAGNNSFYGSKAVFKCLPHHAMFGNDTVTCTEHGNWTQLPECREVRCPFPSRPDNGFVNHPANPVLYYKDTATFGCHETYSLDGPEEVECSKFGNWSAQPSCKASCKLSIKRATVIYEGERVAIQNKFKNGMLHGQKVSFFCKHKEKKCSYTEDAQCIDGTIEIPKCFKEHSSLAFWKTDASDVKPC")
- def test_nucleotide2(self):
+ def test_protein(self):
'''Test parsing XML returned by EFetch, Protein database
'''
# Access the protein database using efetch.
# To create the XML file, use
# >>> Bio.Entrez.efetch(db='protein', id=8, rettype='gp', retmode='xml')
- handle = open('Entrez/protein.xml', "rb")
- record = Entrez.read(handle)
- handle.close()
+ with open('Entrez/protein.xml', "rb") as handle:
+ record = Entrez.read(handle)
self.assertEqual(record[0]["GBSeq_locus"], "CAA35997")
self.assertEqual(record[0]["GBSeq_length"], "100")
self.assertEqual(record[0]["GBSeq_moltype"], "AA")
@@ -4156,25 +4119,21 @@ class EFetchTest(unittest.TestCase):
# To create the GenBank file, use
# >>> Bio.Entrez.efetch(db='nucleotide', id='NT_019265', rettype='gb')
from Bio.Entrez import Parser
- handle = open('GenBank/NT_019265.gb', "rb")
- self.assertRaises(Parser.NotXMLError, Entrez.read, handle)
- handle.close()
- handle = open('GenBank/NT_019265.gb', "rb")
- iterator = Entrez.parse(handle)
- self.assertRaises(Parser.NotXMLError, next, iterator)
- handle.close()
+ with open('GenBank/NT_019265.gb', "rb") as handle:
+ self.assertRaises(Parser.NotXMLError, Entrez.read, handle)
+ with open('GenBank/NT_019265.gb', "rb") as handle:
+ iterator = Entrez.parse(handle)
+ self.assertRaises(Parser.NotXMLError, next, iterator)
def test_fasta(self):
'''Test error handling when presented with Fasta non-XML data
'''
from Bio.Entrez import Parser
- handle = open('Fasta/wisteria.nu', "rb")
- self.assertRaises(Parser.NotXMLError, Entrez.read, handle)
- handle.close()
- handle = open('Fasta/wisteria.nu', "rb")
- iterator = Entrez.parse(handle)
- self.assertRaises(Parser.NotXMLError, next, iterator)
- handle.close()
+ with open('Fasta/wisteria.nu', "rb") as handle:
+ self.assertRaises(Parser.NotXMLError, Entrez.read, handle)
+ with open('Fasta/wisteria.nu', "rb") as handle:
+ iterator = Entrez.parse(handle)
+ self.assertRaises(Parser.NotXMLError, next, iterator)
def test_pubmed_html(self):
'''Test error handling when presented with HTML (so XML-like) data
@@ -4182,14 +4141,12 @@ class EFetchTest(unittest.TestCase):
# To create the HTML file, use
# >>> Bio.Entrez.efetch(db="pubmed", id="19304878")
from Bio.Entrez import Parser
- handle = open('Entrez/pubmed3.html', "rb")
- self.assertRaises(Parser.NotXMLError, Entrez.read, handle)
- handle.close()
+ with open('Entrez/pubmed3.html', "rb") as handle:
+ self.assertRaises(Parser.NotXMLError, Entrez.read, handle)
# Test if the error is also raised with Entrez.parse
- handle = open('Entrez/pubmed3.html', "rb")
- records = Entrez.parse(handle)
- self.assertRaises(Parser.NotXMLError, next, records)
- handle.close()
+ with open('Entrez/pubmed3.html', "rb") as handle:
+ records = Entrez.parse(handle)
+ self.assertRaises(Parser.NotXMLError, next, records)
def test_xml_without_declaration(self):
'''Test error handling for a missing XML declaration
@@ -4197,14 +4154,12 @@ class EFetchTest(unittest.TestCase):
# To create the XML file, use
# >>> Bio.Entrez.efetch(db="journals",id="2830,6011,7473",retmode='xml')
from Bio.Entrez import Parser
- handle = open('Entrez/journals.xml', "rb")
- self.assertRaises(Parser.NotXMLError, Entrez.read, handle)
- handle.close()
+ with open('Entrez/journals.xml', "rb") as handle:
+ self.assertRaises(Parser.NotXMLError, Entrez.read, handle)
# Test if the error is also raised with Entrez.parse
- handle = open('Entrez/journals.xml', "rb")
- records = Entrez.parse(handle)
- self.assertRaises(Parser.NotXMLError, next, records)
- handle.close()
+ with open('Entrez/journals.xml', "rb") as handle:
+ records = Entrez.parse(handle)
+ self.assertRaises(Parser.NotXMLError, next, records)
def test_truncated_xml(self):
"""Test error handling for a truncated XML declaration"""
diff --git a/Tests/test_Entrez_online.py b/Tests/test_Entrez_online.py
index 307d4c5..b4bbdca 100644
--- a/Tests/test_Entrez_online.py
+++ b/Tests/test_Entrez_online.py
@@ -131,6 +131,15 @@ class EntrezOnlineCase(unittest.TestCase):
self.assertEqual(len(records), 1)
self.assertEqual(records[0]['System_sysid']['Sys-id']['Sys-id_bsid'], '1134002')
+ def test_efetch_taxonomy_xml(self):
+ """Test Entrez using a integer id - like a taxon id
+ """
+ handle = Entrez.efetch( db="taxonomy", id=3702, retmode="XML")
+ taxon_record = Entrez.read(handle)
+ self.assertTrue(1, len(taxon_record))
+ self.assertTrue('TaxId' in taxon_record[0])
+ self.assertTrue('3702', taxon_record[0]['TaxId'])
+
def test_elink(self):
# Commas: Link from protein to gene
handle = Entrez.elink(db="gene", dbfrom="protein",
diff --git a/Tests/test_GAOrganism.py b/Tests/test_GAOrganism.py
index abb416c..9ab9d28 100644
--- a/Tests/test_GAOrganism.py
+++ b/Tests/test_GAOrganism.py
@@ -85,13 +85,13 @@ class CreatePopulationTest(unittest.TestCase):
def test_random_population_types(self):
"""Creating a random population with different types of alphabets.
"""
- class DoubleAlphabet:
+ class DoubleAlphabet(object):
letters = [1.0, 2.0]
- class CharacterAlphabet:
+ class CharacterAlphabet(object):
letters = ["a", "b"]
- class IntegerAlphabet:
+ class IntegerAlphabet(object):
letters = [1, 2]
def test_fitness(genome):
diff --git a/Tests/test_GenBank_unittest.py b/Tests/test_GenBank_unittest.py
index 9344402..d23f4d9 100644
--- a/Tests/test_GenBank_unittest.py
+++ b/Tests/test_GenBank_unittest.py
@@ -1,4 +1,5 @@
# Copyright 2013 by Kai Blin.
+# Revisions copyright 2015 by Peter Cock.
# This code is part of the Biopython distribution and governed by its
# license. Please see the LICENSE file that should have been included
# as part of this package.
@@ -84,6 +85,65 @@ class GenBankTests(unittest.TestCase):
self.assertEqual(rec.annotations["organism"], ".")
self.assertEqual(rec.annotations["taxonomy"], [])
+ def test_dblink(self):
+ """GenBank record with old DBLINK project entry."""
+ record = SeqIO.read("GenBank/NC_005816.gb", "gb")
+ self.assertEqual(record.dbxrefs, ["Project:58037"])
+ embl = record.format("embl")
+ self.assertTrue("XX\nPR Project:58037;\nXX\n" in embl, embl)
+
+ def test_dblink_two(self):
+ """GenBank record with old and new DBLINK project entries."""
+ record = SeqIO.read("GenBank/NP_416719.gbwithparts", "gb")
+ self.assertEqual(record.dbxrefs,
+ ["Project:57779", "BioProject:PRJNA57779"])
+ embl = record.format("embl")
+ self.assertTrue("XX\nPR Project:PRJNA57779;\nXX\n" in embl, embl)
+
+ def test_dbline_gb_embl(self):
+ """GenBank / EMBL paired records with PR project entry: GenBank"""
+ record = SeqIO.read("GenBank/DS830848.gb", "gb")
+ self.assertTrue("BioProject:PRJNA16232" in record.dbxrefs, record.dbxrefs)
+ gb = record.format("gb")
+ self.assertTrue("\nDBLINK BioProject:PRJNA16232\n" in gb, gb)
+ # Also check EMBL output
+ embl = record.format("embl")
+ self.assertTrue("XX\nPR Project:PRJNA16232;\nXX\n" in embl, embl)
+
+ def test_dbline_embl_gb(self):
+ """GenBank / EMBL paired records with PR project entry: EMBL"""
+ record = SeqIO.read("EMBL/DS830848.embl", "embl")
+ # TODO: Should we map this to BioProject:PRJNA16232
+ self.assertTrue("Project:PRJNA16232" in record.dbxrefs, record.dbxrefs)
+ gb = record.format("gb")
+ self.assertTrue("\nDBLINK Project:PRJNA16232\n" in gb, gb)
+ embl = record.format("embl")
+ self.assertTrue("XX\nPR Project:PRJNA16232;\nXX\n" in embl, embl)
+
+ def test_structured_comment_parsing(self):
+ # GISAID_EpiFlu(TM)Data, HM138502.gbk has both 'comment' and 'structured_comment'
+ record = SeqIO.read(path.join('GenBank', 'HM138502.gbk'), 'genbank')
+ self.assertEqual(record.annotations['comment'],
+ "Swine influenza A (H1N1) virus isolated during human swine flu\noutbreak of 2009.")
+ self.assertEqual(record.annotations['structured_comment']['GISAID_EpiFlu(TM)Data']['Lineage'], 'swl')
+ self.assertEqual(len(record.annotations['structured_comment']['GISAID_EpiFlu(TM)Data']), 3)
+ # FluData structured comment
+ record = SeqIO.read(path.join('GenBank', 'EU851978.gbk'), 'genbank')
+ self.assertEqual(record.annotations['structured_comment']['FluData']['LabID'], '2008704957')
+ self.assertEqual(len(record.annotations['structured_comment']['FluData']), 5)
+ # Assembly-Data structured comment
+ record = SeqIO.read(path.join('GenBank', 'KF527485.gbk'), 'genbank')
+ self.assertEqual(record.annotations['structured_comment']['Assembly-Data']['Assembly Method'], 'Lasergene v. 10')
+ self.assertEqual(len(record.annotations['structured_comment']['Assembly-Data']), 2)
+ # No structured comment in NC_000932.gb, just a regular comment
+ record = SeqIO.read(path.join('GenBank', 'NC_000932.gb'), 'genbank')
+ self.assertFalse("structured_comment" in record.annotations)
+ self.assertEqual(record.annotations['comment'],
+ 'REVIEWED REFSEQ: This record has been curated by NCBI staff. The\n'
+ 'reference sequence was derived from AP000423.\n'
+ 'COMPLETENESS: full length.')
+
+
if __name__ == "__main__":
runner = unittest.TextTestRunner(verbosity=2)
unittest.main(testRunner=runner)
diff --git a/Tests/test_GenomeDiagram.py b/Tests/test_GenomeDiagram.py
index 4a3a089..e8bf35e 100755
--- a/Tests/test_GenomeDiagram.py
+++ b/Tests/test_GenomeDiagram.py
@@ -24,7 +24,7 @@ try:
from reportlab.lib.units import cm
except ImportError:
raise MissingPythonDependencyError(
- "Install reportlab if you want to use Bio.Graphics.")
+ "Install reportlab if you want to use Bio.Graphics.")
try:
# The preferred PIL import has changed over time...
@@ -39,17 +39,11 @@ except ImportError:
# bitmap format is attempted.
renderPM = None
-# Biopython core
from Bio import SeqIO
from Bio.SeqFeature import SeqFeature, FeatureLocation
-from Bio import SeqUtils
-# Bio.Graphics.GenomeDiagram
from Bio.Graphics.GenomeDiagram import FeatureSet, GraphSet, Track, Diagram
from Bio.Graphics.GenomeDiagram import CrossLink
-# from Bio.Graphics.GenomeDiagram.Utilities import *
-
-# Currently private, but we test them here:
from Bio.Graphics.GenomeDiagram._Graph import GraphData
from Bio.Graphics.GenomeDiagram._Colors import ColorTranslator
@@ -70,21 +64,20 @@ def fill_and_border(base_color, alpha=0.5):
def apply_to_window(sequence, window_size, function, step=None):
- """ apply_to_window(sequence, window_size, function) -> [(int, float),(int, float),...]
-
- o sequence Bio.Seq.Seq object
+ """Returns a list of (position, value) tuples for fragments of the passed
+ sequence of length window_size (stepped by step), calculated by the passed
+ function. Returned positions are the midpoint of each window.
- o window_size Int describing the length of sequence to consider
+ Arguments:
+ - sequence - Bio.Seq.Seq object.
+ - window_size - an integer describing the length of sequence to consider.
+ - step - an integer describing the step to take between windows
+ (default = window_size//2).
- o step Int describing the step to take between windows
- (default = window_size//2)
+ - function - Method or function that accepts a Bio.Seq.Seq object
+ as its sole argument and returns a single value.
- o function Method or function that accepts a Bio.Seq.Seq object
- as its sole argument and returns a single value
-
- Returns a list of (position, value) tuples for fragments of the passed
- sequence of length window_size (stepped by step), calculated by the
- passed function. Returned positions are the midpoint of each window.
+ apply_to_window(sequence, window_size, function) -> [(int, float),(int, float),...]
"""
seqlen = len(sequence) # Total length of sequence to be used
if step is None: # No step specified, so use half window-width or 1 if larger
@@ -118,18 +111,16 @@ def apply_to_window(sequence, window_size, function, step=None):
value = function(fragment)
results.append((middle, value)) # Add results to list
- # Check on last sequence
- # print(fragment)
- # print(seq[-100:])
return results # Return the list of (position, value) results
def calc_gc_content(sequence):
- """ calc_gc_content(sequence)
+ """Returns the % G+C content in a passed sequence.
- o sequence A Bio.Seq.Seq object
+ Arguments:
+ - sequence - a Bio.Seq.Seq object.
- Returns the % G+C content in a passed sequence
+ calc_gc_content(sequence)
"""
d = {}
for nt in ['A', 'T', 'G', 'C']:
@@ -143,11 +134,12 @@ def calc_gc_content(sequence):
def calc_at_content(sequence):
- """ calc_at_content(sequence)
+ """Returns the % A+T content in a passed sequence.
- o sequence A Bio.Seq.Seq object
+ Arguments:
+ - sequence - a Bio.Seq.Seq object.
- Returns the % A+T content in a passed sequence
+ calc_at_content(sequence)
"""
d = {}
for nt in ['A', 'T', 'G', 'C']:
@@ -160,11 +152,12 @@ def calc_at_content(sequence):
def calc_gc_skew(sequence):
- """ calc_gc_skew(sequence)
+ """Returns the (G-C)/(G+C) GC skew in a passed sequence.
- o sequence A Bio.Seq.Seq object
+ Arguments:
+ - sequence - a Bio.Seq.Seq object.
- Returns the (G-C)/(G+C) GC skew in a passed sequence
+ calc_gc_skew(sequence)
"""
g = sequence.count('G') + sequence.count('g')
c = sequence.count('C') + sequence.count('c')
@@ -175,11 +168,12 @@ def calc_gc_skew(sequence):
def calc_at_skew(sequence):
- """ calc_at_skew(sequence)
+ """Returns the (A-T)/(A+T) AT skew in a passed sequence.
- o sequence A Bio.Seq.Seq object
+ Arguments:
+ - sequence - a Bio.Seq.Seq object.
- Returns the (A-T)/(A+T) AT skew in a passed sequence
+ calc_at_skew(sequence)
"""
a = sequence.count('A') + sequence.count('a')
t = sequence.count('T') + sequence.count('t')
@@ -274,7 +268,7 @@ class GraphTest(unittest.TestCase):
gd.add_point((10, 20))
assert gd[4:16] == [(5, 15), (10, 20)], \
- "Unable to insert and retrieve points correctly"
+ "Unable to insert and retrieve points correctly"
class LabelTest(unittest.TestCase):
@@ -287,7 +281,7 @@ class LabelTest(unittest.TestCase):
def finish(self, name, circular=True):
# And draw it...
tracks = len(self.gdd.tracks)
- # Work arround the page orientation code being too clever
+ # Work around the page orientation code being too clever
# and flipping the h & w round:
if tracks <= 3:
orient = "landscape"
@@ -307,7 +301,8 @@ class LabelTest(unittest.TestCase):
except renderPM.RenderPMError:
# Probably a font problem, e.g.
# RenderPMError: Can't setFont(Times-Roman) missing the T1 files?
- # Originally <type 'exceptions.TypeError'>: makeT1Font() argument 2 must be string, not None
+ # Originally <type 'exceptions.TypeError'>: makeT1Font() argument 2
+ # must be string, not None
renderPM = None
except IOError:
# Probably a library problem, e.g.
@@ -378,7 +373,7 @@ class SigilsTest(unittest.TestCase):
def finish(self, name, circular=True):
# And draw it...
tracks = len(self.gdd.tracks)
- # Work arround the page orientation code being too clever
+ # Work around the page orientation code being too clever
# and flipping the h & w round:
if tracks <= 3:
orient = "landscape"
@@ -556,8 +551,8 @@ class SigilsTest(unittest.TestCase):
"""Feature ARROW sigil heads within bounding box."""
self.long_sigils("ARROW")
- def test_long_arrow_heads(self):
- """Feature ARROW sigil heads within bounding box."""
+ def test_long_bigarrow_heads(self):
+ """Feature BIGARROW sigil heads within bounding box."""
self.long_sigils("BIGARROW")
def test_long_octo_heads(self):
@@ -577,6 +572,66 @@ class DiagramTest(unittest.TestCase):
self.record = SeqIO.read(handle, "genbank")
handle.close()
+ self.gdd = Diagram('Test Diagram')
+ # Add a track of features,
+ self.gdd.new_track(1, greytrack=True, name="CDS Features",
+ greytrack_labels=0, height=0.5)
+
+ def tearDown(self):
+ del self.gdd
+
+ def test_str(self):
+ """Test diagram's info as string."""
+ expected = "\n<<class 'Bio.Graphics.GenomeDiagram._Diagram.Diagram'>: Test Diagram>" \
+ "\n1 tracks" \
+ "\nTrack 1: " \
+ "\n<<class 'Bio.Graphics.GenomeDiagram._Track.Track'>: CDS Features>" \
+ "\n0 sets" \
+ "\n"
+ self.assertEqual(expected, str(self.gdd))
+
+ def test_add_track(self):
+ track = Track(name="Annotated Features")
+ self.gdd.add_track(track, 2)
+ self.assertEqual(2, len(self.gdd.get_tracks()))
+
+ def test_add_track_to_occupied_level(self):
+ new_track = self.gdd.get_tracks()[0]
+ self.gdd.add_track(new_track, 1)
+ self.assertEqual(2, len(self.gdd.get_tracks()))
+
+ def test_add_track_error(self):
+ """Test adding unspecified track."""
+ self.assertRaises(ValueError, self.gdd.add_track, None, 1)
+
+ def test_del_tracks(self):
+ self.gdd.del_track(1)
+ self.assertEqual(0, len(self.gdd.get_tracks()))
+
+ def test_get_tracks(self):
+ self.assertEqual(1, len(self.gdd.get_tracks()))
+
+ def test_move_track(self):
+ self.gdd.move_track(1, 2)
+ expected = "\n<<class 'Bio.Graphics.GenomeDiagram._Diagram.Diagram'>: Test Diagram>" \
+ "\n1 tracks" \
+ "\nTrack 2: " \
+ "\n<<class 'Bio.Graphics.GenomeDiagram._Track.Track'>: CDS Features>" \
+ "\n0 sets" \
+ "\n"
+ self.assertEqual(expected, str(self.gdd))
+
+ def test_renumber(self):
+ """Test renumbering tracks."""
+ self.gdd.renumber_tracks(0)
+ expected = "\n<<class 'Bio.Graphics.GenomeDiagram._Diagram.Diagram'>: Test Diagram>" \
+ "\n1 tracks" \
+ "\nTrack 0: " \
+ "\n<<class 'Bio.Graphics.GenomeDiagram._Track.Track'>: CDS Features>" \
+ "\n0 sets" \
+ "\n"
+ self.assertEqual(expected, str(self.gdd))
+
def test_write_arguments(self):
"""Check how the write methods respond to output format arguments."""
gdd = Diagram('Test Diagram')
@@ -587,15 +642,15 @@ class DiagramTest(unittest.TestCase):
try:
gdd.write(filename, output)
assert False, \
- "Should have rejected %s as an output format" % output
- except ValueError as e:
+ "Should have rejected %s as an output format" % output
+ except ValueError:
# Good!
pass
try:
gdd.write_to_string(output)
assert False, \
- "Should have rejected %s as an output format" % output
- except ValueError as e:
+ "Should have rejected %s as an output format" % output
+ except ValueError:
# Good!
pass
@@ -697,13 +752,13 @@ class DiagramTest(unittest.TestCase):
else:
color = "lightblue"
gds_features.add_feature(feature, color=color,
- # label_position="middle",
- # label_position="end",
- label_position="start",
- label_size=11,
- # label_angle=90,
- sigil="ARROW",
- label=True)
+ # label_position="middle",
+ # label_position="end",
+ label_position="start",
+ label_size=11,
+ # label_angle=90,
+ sigil="ARROW",
+ label=True)
# I want to include some strandless features, so for an example
# will use EcoRI recognition sites etc.
@@ -745,17 +800,17 @@ class DiagramTest(unittest.TestCase):
step = len(genbank_entry) // 200
gds_at_gc.new_graph(apply_to_window(genbank_entry.seq, step, calc_gc_content, step),
- 'GC content', style='line',
- color=colors.lightgreen,
- altcolor=colors.darkseagreen)
+ 'GC content', style='line',
+ color=colors.lightgreen,
+ altcolor=colors.darkseagreen)
gds_at_gc.new_graph(apply_to_window(genbank_entry.seq, step, calc_at_content, step),
- 'AT content', style='line',
- color=colors.orange,
- altcolor=colors.red)
+ 'AT content', style='line',
+ color=colors.orange,
+ altcolor=colors.red)
# Finally draw it in both formats,
- gdd.draw(format='linear', orientation='landscape',
- tracklines=0, pagesize='A4', fragments=3)
+ gdd.draw(format='linear', orientation='landscape', tracklines=0,
+ pagesize='A4', fragments=3)
output_filename = os.path.join('Graphics', 'GD_by_meth_linear.pdf')
gdd.write(output_filename, 'PDF')
@@ -775,8 +830,7 @@ class DiagramTest(unittest.TestCase):
greytrack_labels=10,
greytrack_font_color="red",
scale_format="SInt")
- gdt2 = Track('gene features', greytrack=1,
- scale_largetick_interval=1e4)
+ gdt2 = Track('gene features', greytrack=1, scale_largetick_interval=1e4)
# First add some feature sets:
gdfsA = FeatureSet(name='CDS backgrounds')
@@ -800,7 +854,7 @@ class DiagramTest(unittest.TestCase):
dark, light = colors.burlywood, colors.bisque
# Background for CDS,
a = gdfsA.add_feature(SeqFeature(FeatureLocation(feature.location.start, feature.location.end, strand=0)),
- color=dark)
+ color=dark)
# Background for gene,
b = gdfsB.add_feature(SeqFeature(FeatureLocation(prev_gene.location.start, prev_gene.location.end, strand=0)),
color=dark)
@@ -894,7 +948,7 @@ class DiagramTest(unittest.TestCase):
gdt2.add_set(gdfs2)
gdt3 = Track('misc features and repeats', greytrack=1,
- scale_largetick_interval=1e4)
+ scale_largetick_interval=1e4)
gdt3.add_set(gdfs3)
gdt3.add_set(gdfs4)
@@ -907,30 +961,23 @@ class DiagramTest(unittest.TestCase):
graphdata1 = apply_to_window(genbank_entry.seq, step, calc_gc_skew, step)
gdgs1.new_graph(graphdata1, 'GC Skew', style='bar',
- color=colors.violet,
- altcolor=colors.purple)
+ color=colors.violet, altcolor=colors.purple)
- gdt4 = Track(
- 'GC Skew (bar)',
- height=1.94, greytrack=1,
- scale_largetick_interval=1e4)
+ gdt4 = Track('GC Skew (bar)', height=1.94, greytrack=1,
+ scale_largetick_interval=1e4)
gdt4.add_set(gdgs1)
gdgs2 = GraphSet('GC and AT Content')
gdgs2.new_graph(apply_to_window(genbank_entry.seq, step, calc_gc_content, step),
- 'GC content', style='line',
- color=colors.lightgreen,
+ 'GC content', style='line', color=colors.lightgreen,
altcolor=colors.darkseagreen)
gdgs2.new_graph(apply_to_window(genbank_entry.seq, step, calc_at_content, step),
- 'AT content', style='line',
- color=colors.orange,
+ 'AT content', style='line', color=colors.orange,
altcolor=colors.red)
- gdt5 = Track(
- 'GC Content(green line), AT Content(red line)',
- height=1.94, greytrack=1,
- scale_largetick_interval=1e4)
+ gdt5 = Track('GC Content(green line), AT Content(red line)',
+ height=1.94, greytrack=1, scale_largetick_interval=1e4)
gdt5.add_set(gdgs2)
gdgs3 = GraphSet('Di-nucleotide count')
@@ -952,25 +999,25 @@ class DiagramTest(unittest.TestCase):
# Finally draw it in both formats, and full view and partial
gdd.draw(format='circular', orientation='landscape',
- tracklines=0, pagesize='A0')
+ tracklines=0, pagesize='A0')
output_filename = os.path.join('Graphics', 'GD_by_obj_circular.pdf')
gdd.write(output_filename, 'PDF')
gdd.circular = False
gdd.draw(format='circular', orientation='landscape',
- tracklines=0, pagesize='A0', start=3000, end=6300)
+ tracklines=0, pagesize='A0', start=3000, end=6300)
output_filename = os.path.join('Graphics', 'GD_by_obj_frag_circular.pdf')
gdd.write(output_filename, 'PDF')
gdd.draw(format='linear', orientation='landscape',
- tracklines=0, pagesize='A0', fragments=3)
+ tracklines=0, pagesize='A0', fragments=3)
output_filename = os.path.join('Graphics', 'GD_by_obj_linear.pdf')
gdd.write(output_filename, 'PDF')
gdd.set_all_tracks("greytrack_labels", 2)
gdd.draw(format='linear', orientation='landscape',
- tracklines=0, pagesize=(30 * cm, 10 * cm), fragments=1,
- start=3000, end=6300)
+ tracklines=0, pagesize=(30 * cm, 10 * cm), fragments=1,
+ start=3000, end=6300)
output_filename = os.path.join('Graphics', 'GD_by_obj_frag_linear.pdf')
gdd.write(output_filename, 'PDF')
diff --git a/Tests/test_KEGG.py b/Tests/test_KEGG.py
index 12e877f..9426b11 100644
--- a/Tests/test_KEGG.py
+++ b/Tests/test_KEGG.py
@@ -27,8 +27,19 @@ def t_KEGG_Enzyme(testfiles):
fh = open(os.path.join("KEGG", file))
print("Testing Bio.KEGG.Enzyme on " + file + "\n\n")
records = Enzyme.parse(fh)
- for record in records:
+ for i, record in enumerate(records):
print(record)
+
+ fh.seek(0)
+ if i == 0:
+ print(Enzyme.read(fh))
+ else:
+ try:
+ print(Enzyme.read(fh))
+ assert False
+ except ValueError as e:
+ assert str(e) == 'More than one record found in handle'
+
print("\n")
fh.close()
diff --git a/Tests/test_KEGG_online.py b/Tests/test_KEGG_online.py
index da135e0..65b115b 100644
--- a/Tests/test_KEGG_online.py
+++ b/Tests/test_KEGG_online.py
@@ -15,11 +15,11 @@ import tempfile
import requires_internet
requires_internet.check()
-from Bio._py3k import _as_string
-
from Bio.KEGG.KGML import KGML_parser
from Bio.KEGG.REST import *
+from Bio import SeqIO
+
# TODO - revert to using with statements once we drop
# Python 2.6 and 2.7, see http://bugs.python.org/issue12487
@@ -120,25 +120,29 @@ class KEGGTests(unittest.TestCase):
def test_find_compound_C7H10O5_formula(self):
h = kegg_find("compound", "C7H10O5", "formula")
h.read()
- self.assertEqual(h.url, "http://rest.kegg.jp/find/compound/C7H10O5/formula")
+ self.assertEqual(h.url,
+ "http://rest.kegg.jp/find/compound/C7H10O5/formula")
h.close()
def test_find_compound_O5C7_formula(self):
h = kegg_find("compound", "O5C7", "formula")
h.read()
- self.assertEqual(h.url, "http://rest.kegg.jp/find/compound/O5C7/formula")
+ self.assertEqual(h.url,
+ "http://rest.kegg.jp/find/compound/O5C7/formula")
h.close()
def test_find_compound_exact_mass(self):
h = kegg_find("compound", "174.05", "exact_mass")
h.read()
- self.assertEqual(h.url, "http://rest.kegg.jp/find/compound/174.05/exact_mass")
+ self.assertEqual(h.url,
+ "http://rest.kegg.jp/find/compound/174.05/exact_mass")
h.close()
def test_find_compound_weight(self):
h = kegg_find("compound", "300-310", "mol_weight")
h.read()
- self.assertEqual(h.url, "http://rest.kegg.jp/find/compound/300-310/mol_weight")
+ self.assertEqual(h.url,
+ "http://rest.kegg.jp/find/compound/300-310/mol_weight")
h.close()
def test_get_cpd_C01290_plus_gl_G00092(self):
@@ -179,15 +183,34 @@ class KEGGTests(unittest.TestCase):
def test_get_hsa_10458_plus_ece_Z5100_as_aaseq(self):
h = kegg_get("hsa:10458+ece:Z5100", "aaseq")
- h.read()
- self.assertEqual(h.url, "http://rest.kegg.jp/get/hsa:10458+ece:Z5100/aaseq")
+ self.assertEqual(h.url,
+ "http://rest.kegg.jp/get/hsa:10458+ece:Z5100/aaseq")
+ data = SeqIO.parse(h, 'fasta')
+ self.assertEqual(len(list(data)), 2)
h.close()
def test_get_hsa_10458_list_ece_Z5100_as_aaseq(self):
h = kegg_get(["hsa:10458", "ece:Z5100"], "aaseq")
- data = _as_string(h.read())
- self.assertEqual(data.count(">"), 2)
- self.assertEqual(h.url, "http://rest.kegg.jp/get/hsa:10458+ece:Z5100/aaseq")
+ self.assertEqual(h.url,
+ "http://rest.kegg.jp/get/hsa:10458+ece:Z5100/aaseq")
+ data = SeqIO.parse(h, 'fasta')
+ self.assertEqual(len(list(data)), 2)
+ h.close()
+
+ def test_get_hsa_10458_plus_ece_Z5100_as_ntseq(self):
+ h = kegg_get("hsa:10458+ece:Z5100", "ntseq")
+ self.assertEqual(h.url,
+ "http://rest.kegg.jp/get/hsa:10458+ece:Z5100/ntseq")
+ data = SeqIO.parse(h, 'fasta')
+ self.assertEqual(len(list(data)), 2)
+ h.close()
+
+ def test_get_hsa_10458_list_ece_Z5100_as_ntseq(self):
+ h = kegg_get(["hsa:10458", "ece:Z5100"], "ntseq")
+ self.assertEqual(h.url,
+ "http://rest.kegg.jp/get/hsa:10458+ece:Z5100/ntseq")
+ data = SeqIO.parse(h, 'fasta')
+ self.assertEqual(len(list(data)), 2)
h.close()
def test_get_hsa05130_image(self):
@@ -212,13 +235,15 @@ class KEGGTests(unittest.TestCase):
def test_conv_ncbi_gi_hsa_10458_plus_ece_Z5100(self):
h = kegg_conv("ncbi-gi", "hsa:10458+ece:Z5100")
h.read()
- self.assertEqual(h.url, "http://rest.kegg.jp/conv/ncbi-gi/hsa:10458+ece:Z5100")
+ self.assertEqual(h.url,
+ "http://rest.kegg.jp/conv/ncbi-gi/hsa:10458+ece:Z5100")
h.close()
def test_conv_ncbi_gi_hsa_10458_list_ece_Z5100(self):
h = kegg_conv("ncbi-gi", ["hsa:10458", "ece:Z5100"])
h.read()
- self.assertEqual(h.url, "http://rest.kegg.jp/conv/ncbi-gi/hsa:10458+ece:Z5100")
+ self.assertEqual(h.url,
+ "http://rest.kegg.jp/conv/ncbi-gi/hsa:10458+ece:Z5100")
h.close()
def test_link_pathway_hsa(self):
@@ -236,13 +261,15 @@ class KEGGTests(unittest.TestCase):
def test_pathway_hsa_10458_plus_ece_Z5100(self):
h = kegg_link("pathway", "hsa:10458+ece:Z5100")
h.read()
- self.assertEqual(h.url, "http://rest.kegg.jp/link/pathway/hsa:10458+ece:Z5100")
+ self.assertEqual(h.url,
+ "http://rest.kegg.jp/link/pathway/hsa:10458+ece:Z5100")
h.close()
def test_pathway_hsa_10458_list_ece_Z5100(self):
h = kegg_link("pathway", ["hsa:10458", "ece:Z5100"])
h.read()
- self.assertEqual(h.url, "http://rest.kegg.jp/link/pathway/hsa:10458+ece:Z5100")
+ self.assertEqual(h.url,
+ "http://rest.kegg.jp/link/pathway/hsa:10458+ece:Z5100")
h.close()
@@ -250,8 +277,7 @@ class KGMLPathwayTests(unittest.TestCase):
"""Tests with metabolic maps."""
def test_parse_remote_pathway(self):
- """Download a KEGG pathway from the KEGG server and write KGML."""
- # Download the KEGG ko03070 pathway as a filehandle
+ """Download a KEGG pathway from the KEGG server and parse KGML."""
h = kegg_get("ko03070", "kgml")
pathway = KGML_parser.read(h)
self.assertEqual(pathway.name, "path:ko03070")
diff --git a/Tests/test_KGML_graphics.py b/Tests/test_KGML_graphics.py
index f894387..50b68d7 100644
--- a/Tests/test_KGML_graphics.py
+++ b/Tests/test_KGML_graphics.py
@@ -18,6 +18,7 @@ from Bio.Graphics.ColorSpiral import ColorSpiral
# Do we have ReportLab? Raise error if not present.
from Bio import MissingExternalDependencyError
try:
+ # Not actually using these imports directly:
from reportlab.pdfgen.canvas import Canvas
from reportlab.lib.pagesizes import A4
from reportlab.lib.colors import HexColor
@@ -26,7 +27,7 @@ except ImportError:
"Install reportlab if you want to use Bio.Graphics.")
try:
- c = HexColor('#8080F780')
+ c = HexColor('#8080F780')
except TypeError:
# Known to fail under ReportLab 2.6 with:
# unsupported operand type(s) for &: 'int' and 'float'
@@ -66,8 +67,8 @@ class PathwayData(object):
class KGMLPathwayTest(unittest.TestCase):
"""Import XML file and write KGML
- Import the ko01100 metabolic map from a local .xml KGML file,
- and write valid KGML output for each
+ Import the ko01100 metabolic map from a local .xml KGML file,
+ and write valid KGML output for each.
"""
def setUp(self):
# Does our output directory exist? If not, create it
@@ -182,7 +183,6 @@ class KGMLPathwayTest(unittest.TestCase):
kgml_map.draw(p[1].output_stem + '_transparency.pdf')
-
if __name__ == '__main__':
runner = unittest.TextTestRunner(verbosity=2)
unittest.main(testRunner=runner)
diff --git a/Tests/test_MMCIF.py b/Tests/test_MMCIF.py
index f447acf..a062be9 100644
--- a/Tests/test_MMCIF.py
+++ b/Tests/test_MMCIF.py
@@ -10,6 +10,7 @@
"""Unit tests for the MMCIF portion of the Bio.PDB module."""
import unittest
+import warnings
try:
import numpy
@@ -27,41 +28,67 @@ from Bio.Alphabet import generic_protein
from Bio.PDB.PDBExceptions import PDBConstructionException, PDBConstructionWarning
from Bio.PDB import PPBuilder, CaPPBuilder
-from Bio.PDB.MMCIFParser import MMCIFParser
+from Bio.PDB.MMCIFParser import MMCIFParser, FastMMCIFParser
class ParseReal(unittest.TestCase):
"""Testing with real CIF file(s)."""
- def test_parser(self):
+ def test_parsers(self):
"""Extract polypeptides from 1A80."""
+
parser = MMCIFParser()
+ fast_parser = FastMMCIFParser()
+
structure = parser.get_structure("example", "PDB/1A8O.cif")
+ f_structure = fast_parser.get_structure("example", "PDB/1A8O.cif")
+
self.assertEqual(len(structure), 1)
+ self.assertEqual(len(f_structure), 1)
+
for ppbuild in [PPBuilder(), CaPPBuilder()]:
# ==========================================================
# Check that serial_num (model column) is stored properly
self.assertEqual(structure[0].serial_num, 1)
+ self.assertEqual(f_structure[0].serial_num, structure[0].serial_num)
+
# First try allowing non-standard amino acids,
polypeptides = ppbuild.build_peptides(structure[0], False)
+ f_polypeptides = ppbuild.build_peptides(f_structure[0], False)
+
self.assertEqual(len(polypeptides), 1)
+ self.assertEqual(len(f_polypeptides), 1)
+
pp = polypeptides[0]
+ f_pp = f_polypeptides[0]
+
# Check the start and end positions
self.assertEqual(pp[0].get_id()[1], 151)
self.assertEqual(pp[-1].get_id()[1], 220)
+
+ self.assertEqual(f_pp[0].get_id()[1], 151)
+ self.assertEqual(f_pp[-1].get_id()[1], 220)
+
# Check the sequence
s = pp.get_sequence()
+ f_s = f_pp.get_sequence()
+
+ self.assertEqual(s, f_s) # enough to test this
+
self.assertTrue(isinstance(s, Seq))
self.assertEqual(s.alphabet, generic_protein)
+
# Here non-standard MSE are shown as M
self.assertEqual("MDIRQGPKEPFRDYVDRFYKTLRAEQASQEVKNWMTETLLVQ"
"NANPDCKTILKALGPGATLEEMMTACQG", str(s))
+
# ==========================================================
# Now try strict version with only standard amino acids
# Should ignore MSE 151 at start, and then break the chain
# at MSE 185, and MSE 214,215
polypeptides = ppbuild.build_peptides(structure[0], True)
self.assertEqual(len(polypeptides), 3)
+
# First fragment
pp = polypeptides[0]
self.assertEqual(pp[0].get_id()[1], 152)
@@ -70,6 +97,7 @@ class ParseReal(unittest.TestCase):
self.assertTrue(isinstance(s, Seq))
self.assertEqual(s.alphabet, generic_protein)
self.assertEqual("DIRQGPKEPFRDYVDRFYKTLRAEQASQEVKNW", str(s))
+
# Second fragment
pp = polypeptides[1]
self.assertEqual(pp[0].get_id()[1], 186)
@@ -78,6 +106,7 @@ class ParseReal(unittest.TestCase):
self.assertTrue(isinstance(s, Seq))
self.assertEqual(s.alphabet, generic_protein)
self.assertEqual("TETLLVQNANPDCKTILKALGPGATLEE", str(s))
+
# Third fragment
pp = polypeptides[2]
self.assertEqual(pp[0].get_id()[1], 216)
@@ -89,50 +118,82 @@ class ParseReal(unittest.TestCase):
def testModels(self):
"""Test file with multiple models"""
- parser = MMCIFParser()
- structure = parser.get_structure("example", "PDB/1LCD.cif")
+
+ parser = MMCIFParser(QUIET=1)
+ f_parser = FastMMCIFParser(QUIET=1)
+ with warnings.catch_warnings():
+ warnings.simplefilter('ignore', PDBConstructionWarning)
+ structure = parser.get_structure("example", "PDB/1LCD.cif")
+ f_structure = f_parser.get_structure("example", "PDB/1LCD.cif")
+
self.assertEqual(len(structure), 3)
+ self.assertEqual(len(f_structure), 3)
+
for ppbuild in [PPBuilder(), CaPPBuilder()]:
- # ==========================================================
- # Check that serial_num (model column) is stored properly
- self.assertEqual(structure[0].serial_num, 1)
- self.assertEqual(structure[1].serial_num, 2)
- self.assertEqual(structure[2].serial_num, 3)
- # First try allowing non-standard amino acids,
- polypeptides = ppbuild.build_peptides(structure[0], False)
- self.assertEqual(len(polypeptides), 1)
- pp = polypeptides[0]
- # Check the start and end positions
- self.assertEqual(pp[0].get_id()[1], 1)
- self.assertEqual(pp[-1].get_id()[1], 51)
- # Check the sequence
- s = pp.get_sequence()
- self.assertTrue(isinstance(s, Seq))
- self.assertEqual(s.alphabet, generic_protein)
- # Here non-standard MSE are shown as M
- self.assertEqual("MKPVTLYDVAEYAGVSYQTVSRVVNQASHVSAKTREKVEAAMAELNYIPNR",
- str(s))
- # ==========================================================
- # Now try strict version with only standard amino acids
- polypeptides = ppbuild.build_peptides(structure[0], True)
- self.assertEqual(len(polypeptides), 1)
- pp = polypeptides[0]
- # Check the start and end positions
- self.assertEqual(pp[0].get_id()[1], 1)
- self.assertEqual(pp[-1].get_id()[1], 51)
- # Check the sequence
- s = pp.get_sequence()
- self.assertTrue(isinstance(s, Seq))
- self.assertEqual(s.alphabet, generic_protein)
- self.assertEqual("MKPVTLYDVAEYAGVSYQTVSRVVNQASHVSAKTREKVEAAMAELNYIPNR",
- str(s))
+ # ==========================================================
+ # Check that serial_num (model column) is stored properly
+ self.assertEqual(structure[0].serial_num, 1)
+ self.assertEqual(structure[1].serial_num, 2)
+ self.assertEqual(structure[2].serial_num, 3)
+ # First try allowing non-standard amino acids,
+ polypeptides = ppbuild.build_peptides(structure[0], False)
+ self.assertEqual(len(polypeptides), 1)
+ pp = polypeptides[0]
+ # Check the start and end positions
+ self.assertEqual(pp[0].get_id()[1], 1)
+ self.assertEqual(pp[-1].get_id()[1], 51)
+ # Check the sequence
+ s = pp.get_sequence()
+ self.assertTrue(isinstance(s, Seq))
+ self.assertEqual(s.alphabet, generic_protein)
+ # Here non-standard MSE are shown as M
+ self.assertEqual("MKPVTLYDVAEYAGVSYQTVSRVVNQASHVSAKTREKVEAAMAELNYIPNR",
+ str(s))
+ # ==========================================================
+ # Now try strict version with only standard amino acids
+ polypeptides = ppbuild.build_peptides(structure[0], True)
+ self.assertEqual(len(polypeptides), 1)
+ pp = polypeptides[0]
+ # Check the start and end positions
+ self.assertEqual(pp[0].get_id()[1], 1)
+ self.assertEqual(pp[-1].get_id()[1], 51)
+ # Check the sequence
+ s = pp.get_sequence()
+ self.assertTrue(isinstance(s, Seq))
+ self.assertEqual(s.alphabet, generic_protein)
+ self.assertEqual("MKPVTLYDVAEYAGVSYQTVSRVVNQASHVSAKTREKVEAAMAELNYIPNR",
+ str(s))
- parser = MMCIFParser()
# This structure contains several models with multiple lengths.
# The tests were failing.
structure = parser.get_structure("example", "PDB/2OFG.cif")
self.assertEqual(len(structure), 3)
+ def test_insertions(self):
+ """Test file with residue insertion codes"""
+ parser = MMCIFParser(QUIET=1)
+ with warnings.catch_warnings():
+ warnings.simplefilter('ignore', PDBConstructionWarning)
+ structure = parser.get_structure("example", "PDB/4zhl.cif")
+ for ppbuild in [PPBuilder(), CaPPBuilder()]:
+ # First try allowing non-standard amino acids,
+ polypeptides = ppbuild.build_peptides(structure[0], False)
+ self.assertEqual(len(polypeptides), 2)
+ pp = polypeptides[0]
+ # Check the start and end positions (first segment only)
+ self.assertEqual(pp[0].get_id()[1], 16)
+ self.assertEqual(pp[-1].get_id()[1], 244)
+ # Check the sequence
+ refseq = "IIGGEFTTIENQPWFAAIYRRHRGGSVTYVCGGSLISPCWVISATHCFIDYPKKEDYIVYLGR" \
+ "SRLNSNTQGEMKFEVENLILHKDYSADTLAYHNDIALLKIRSKEGRCAQPSRTIQTIALPSMY" \
+ "NDPQFGTSCEITGFGKEQSTDYLYPEQLKMTVVKLISHRECQQPHYYGSEVTTKMLCAADPQW" \
+ "KTDSCQGDSGGPLVCSLQGRMTLTGIVSWGRGCALKDKPGVYTRVSHFLPWIRSHTKE"
+
+ s = pp.get_sequence()
+ self.assertTrue(isinstance(s, Seq))
+ self.assertEqual(s.alphabet, generic_protein)
+ self.assertEqual(refseq, str(s))
+
def test_filehandle(self):
"""Test if the parser can handle file handle as well as filename"""
parser = MMCIFParser()
diff --git a/Tests/test_Mafft_tool.py b/Tests/test_Mafft_tool.py
index 251c70d..a591ffd 100644
--- a/Tests/test_Mafft_tool.py
+++ b/Tests/test_Mafft_tool.py
@@ -163,11 +163,11 @@ class MafftApplication(unittest.TestCase):
cmdline.set_parameter("--treeout", True)
cmdline.set_parameter("nuc", True)
self.assertEqual(str(eval(repr(cmdline))), str(cmdline))
- self.assertEqual(str(cmdline), mafft_exe
- + " --localpair --weighti 4.2 --retree 5 "
- + "--maxiterate 200 --nofft --op 2.04 --ep 0.51"
- + " --lop 0.233 --lep 0.2 --reorder --treeout"
- + " --nuc Fasta/f002")
+ self.assertEqual(str(cmdline), mafft_exe +
+ " --localpair --weighti 4.2 --retree 5 " +
+ "--maxiterate 200 --nofft --op 2.04 --ep 0.51" +
+ " --lop 0.233 --lep 0.2 --reorder --treeout" +
+ " --nuc Fasta/f002")
stdoutdata, stderrdata = cmdline()
self.assertTrue(stdoutdata.startswith(">gi|1348912|gb|G26680|G26680"))
self.assertTrue("$#=0" not in stderrdata)
diff --git a/Tests/test_Motif.py b/Tests/test_Motif.py
deleted file mode 100644
index cb532c4..0000000
--- a/Tests/test_Motif.py
+++ /dev/null
@@ -1,1536 +0,0 @@
-# Copyright 2008 by Bartek Wilczynski. All rights reserved.
-# Adapted from test_Mymodule.py by Jeff Chang
-# This code is part of the Biopython distribution and governed by its
-# license. Please see the LICENSE file that should have been included
-# as part of this package.
-
-import os
-import unittest
-import warnings
-
-from Bio import BiopythonDeprecationWarning
-with warnings.catch_warnings():
- warnings.simplefilter("ignore", BiopythonDeprecationWarning)
- from Bio import Motif
-
-from Bio.Alphabet import IUPAC
-from Bio.Seq import Seq
-
-
-class MotifTestsBasic(unittest.TestCase):
- def setUp(self):
- self.PFMin = open("Motif/SRF.pfm")
- self.SITESin = open("Motif/Arnt.sites")
- self.TFout = "Motif/tf.out"
- self.FAout = "Motif/fa.out"
- self.PFMout = "Motif/fa.out"
- self.m = Motif.Motif()
- self.m.add_instance(Seq("ATATA", self.m.alphabet))
-
- def tearDown(self):
- self.PFMin.close()
- self.SITESin.close()
- if os.path.exists(self.TFout):
- os.remove(self.TFout)
- if os.path.exists(self.FAout):
- os.remove(self.FAout)
-
- def test_alignace_parsing(self):
- """Test if Motif can parse AlignAce output files.
- """
- from Bio.Motif.Parsers import AlignAce
- handle = open("Motif/alignace.out")
- record = AlignAce.read(handle)
- handle.close()
- self.assertEqual(record.ver, "AlignACE 4.0 05/13/04\n")
- self.assertEqual(record.cmd_line, "./AlignACE -i test.fa \n")
- self.assertEqual(len(record.param_dict), 7)
- self.assertEqual(record.param_dict['expect'], "10")
- self.assertEqual(record.param_dict['gcback'], "0.38")
- self.assertEqual(record.param_dict['minpass'], "200")
- self.assertEqual(record.param_dict['seed'], "1227623309")
- self.assertEqual(record.param_dict['numcols'], "10")
- self.assertEqual(record.param_dict['undersample'], "1")
- self.assertEqual(record.param_dict['oversample'], "1")
- self.assertEqual(len(record.seq_dict), 10)
- self.assertEqual(record.seq_dict[0], "SEQ1; M: CTCAATCGTAGA at 52\n")
- self.assertEqual(record.seq_dict[1], "SEQ2; M: CTCAATCGTAGA at 172\n")
- self.assertEqual(record.seq_dict[2], "SEQ3; M: CTCAATCGTAGA at 112\n")
- self.assertEqual(record.seq_dict[3], "SEQ4; M: CTCAATCGTAGA at 173\n")
- self.assertEqual(record.seq_dict[4], "SEQ5; M: CTCAATCGTAGA at 185\n")
- self.assertEqual(record.seq_dict[5], "SEQ6; M: CTCAATCGTAGA at 105\n")
- self.assertEqual(record.seq_dict[6], "SEQ7; M: CTCAATCGTAGA at 177\n")
- self.assertEqual(record.seq_dict[7], "SEQ8; M: CTCAATCGTAGA at 172\n")
- self.assertEqual(record.seq_dict[8], "SEQ9; M: CTCAATCGTAGA at 93\n")
- self.assertEqual(record.seq_dict[9], "SEQ10; M: CTCAATCGTAGA at 3\n")
- self.assertEqual(len(record.motifs), 16)
- self.assertEqual(record.motifs[0].alphabet, IUPAC.unambiguous_dna)
- self.assertEqual(len(record.motifs[0].instances), 11)
- self.assertEqual(str(record.motifs[0].instances[0]), "TCTACGATTGAG")
- self.assertEqual(str(record.motifs[0].instances[1]), "TCTACGATTGAG")
- self.assertEqual(str(record.motifs[0].instances[2]), "TCTACGATTGAG")
- self.assertEqual(str(record.motifs[0].instances[3]), "TCTACGATTGAG")
- self.assertEqual(str(record.motifs[0].instances[4]), "TCTACGATTGAG")
- self.assertEqual(str(record.motifs[0].instances[5]), "TCTACGATTGAG")
- self.assertEqual(str(record.motifs[0].instances[6]), "TCTACGATTGAG")
- self.assertEqual(str(record.motifs[0].instances[7]), "TCTACGATTGAG")
- self.assertEqual(str(record.motifs[0].instances[8]), "TCTACGATTGAG")
- self.assertEqual(str(record.motifs[0].instances[9]), "TCAAAGATAGAG")
- self.assertEqual(str(record.motifs[0].instances[10]), "TCTACGATTGAG")
- self.assertEqual(record.motifs[0].mask, [1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1])
- self.assertAlmostEqual(record.motifs[0].score, 57.9079)
- self.assertEqual(record.motifs[1].alphabet, IUPAC.unambiguous_dna)
- self.assertEqual(len(record.motifs[1].instances), 22)
- self.assertEqual(str(record.motifs[1].instances[0]), "GCGAAGGAAGCAGCGCGTGTG")
- self.assertEqual(str(record.motifs[1].instances[1]), "GGCACCGCCTCTACGATTGAG")
- self.assertEqual(str(record.motifs[1].instances[2]), "CAGAGCTTAGCATTGAACGCG")
- self.assertEqual(str(record.motifs[1].instances[3]), "CTAATGAAAGCAATGAGAGTG")
- self.assertEqual(str(record.motifs[1].instances[4]), "CTTGTGCCCTCTAAGCGTCCG")
- self.assertEqual(str(record.motifs[1].instances[5]), "GAGCACGACGCTTTGTACCTG")
- self.assertEqual(str(record.motifs[1].instances[6]), "CGGCACTTAGCAGCGTATCGT")
- self.assertEqual(str(record.motifs[1].instances[7]), "CTGGTTTCATCTACGATTGAG")
- self.assertEqual(str(record.motifs[1].instances[8]), "GGGCCAATAGCGGCGCCGGAG")
- self.assertEqual(str(record.motifs[1].instances[9]), "GTGGAGTTATCTTAGTGCGCG")
- self.assertEqual(str(record.motifs[1].instances[10]), "GAGAGGTTATCTACGATTGAG")
- self.assertEqual(str(record.motifs[1].instances[11]), "CTGCTCCCCGCATACAGCGCG")
- self.assertEqual(str(record.motifs[1].instances[12]), "CAGAACCGAGGTCCGGTACGG")
- self.assertEqual(str(record.motifs[1].instances[13]), "GTGCCCCAAGCTTACCCAGGG")
- self.assertEqual(str(record.motifs[1].instances[14]), "CGCCTCTGATCTACGATTGAG")
- self.assertEqual(str(record.motifs[1].instances[15]), "GTGCTCATAGGGACGTCGCGG")
- self.assertEqual(str(record.motifs[1].instances[16]), "CTGCCCCCCGCATAGTAGGGG")
- self.assertEqual(str(record.motifs[1].instances[17]), "GTAAAGAAATCGATGTGCCAG")
- self.assertEqual(str(record.motifs[1].instances[18]), "CACCTGCAATTGCTGGCAGCG")
- self.assertEqual(str(record.motifs[1].instances[19]), "GGCGGGCCATCCCTGTATGAA")
- self.assertEqual(str(record.motifs[1].instances[20]), "CTCCAGGTCGCATGGAGAGAG")
- self.assertEqual(str(record.motifs[1].instances[21]), "CCTCGGATCGCTTGGGAAGAG")
- self.assertEqual(record.motifs[1].mask, [1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1])
- self.assertAlmostEqual(record.motifs[1].score, 19.6235)
-
- self.assertEqual(record.motifs[2].alphabet, IUPAC.unambiguous_dna)
- self.assertEqual(len(record.motifs[2].instances), 18)
- self.assertEqual(str(record.motifs[2].instances[0]), "GTGCGCGAAGGAAGCAGCGCG")
- self.assertEqual(str(record.motifs[2].instances[1]), "CAGAGCTTAGCATTGAACGCG")
- self.assertEqual(str(record.motifs[2].instances[2]), "GTGCCCGATGACCACCCGTCG")
- self.assertEqual(str(record.motifs[2].instances[3]), "GCCCTCTAAGCGTCCGCGGAT")
- self.assertEqual(str(record.motifs[2].instances[4]), "GAGCACGACGCTTTGTACCTG")
- self.assertEqual(str(record.motifs[2].instances[5]), "CGGCACTTAGCAGCGTATCGT")
- self.assertEqual(str(record.motifs[2].instances[6]), "GGGCCAATAGCGGCGCCGGAG")
- self.assertEqual(str(record.motifs[2].instances[7]), "GCGCACTAAGATAACTCCACG")
- self.assertEqual(str(record.motifs[2].instances[8]), "CGGCCCGTTGTCCAGCAGACG")
- self.assertEqual(str(record.motifs[2].instances[9]), "CTGCTCCCCGCATACAGCGCG")
- self.assertEqual(str(record.motifs[2].instances[10]), "GTGCCCCAAGCTTACCCAGGG")
- self.assertEqual(str(record.motifs[2].instances[11]), "GTGCTCATAGGGACGTCGCGG")
- self.assertEqual(str(record.motifs[2].instances[12]), "CTGCCCCCCGCATAGTAGGGG")
- self.assertEqual(str(record.motifs[2].instances[13]), "CGCCGCCATGCGACGCAGAGG")
- self.assertEqual(str(record.motifs[2].instances[14]), "AACCTCTAAGCATACTCTACG")
- self.assertEqual(str(record.motifs[2].instances[15]), "GACCTGGAGGCTTAGACTTGG")
- self.assertEqual(str(record.motifs[2].instances[16]), "GCGCTCTTCCCAAGCGATCCG")
- self.assertEqual(str(record.motifs[2].instances[17]), "GGGCCGTCAGCTCTCAAGTCT")
- self.assertEqual(record.motifs[2].mask, [1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1])
- self.assertAlmostEqual(record.motifs[2].score, 19.1804)
-
- self.assertEqual(record.motifs[3].alphabet, IUPAC.unambiguous_dna)
- self.assertEqual(len(record.motifs[3].instances), 16)
- self.assertEqual(str(record.motifs[3].instances[0]), "GCCCCAAGCTTACCCAGGGAC")
- self.assertEqual(str(record.motifs[3].instances[1]), "GCCGTCTGCTGGACAACGGGC")
- self.assertEqual(str(record.motifs[3].instances[2]), "GCCGACGGGTGGTCATCGGGC")
- self.assertEqual(str(record.motifs[3].instances[3]), "GCCAATAGCGGCGCCGGAGTC")
- self.assertEqual(str(record.motifs[3].instances[4]), "GCCCCCCGCATAGTAGGGGGA")
- self.assertEqual(str(record.motifs[3].instances[5]), "GCCCGTACCGGACCTCGGTTC")
- self.assertEqual(str(record.motifs[3].instances[6]), "GCCTCATGTACCGGAAGGGAC")
- self.assertEqual(str(record.motifs[3].instances[7]), "GACACGCGCCTGGGAGGGTTC")
- self.assertEqual(str(record.motifs[3].instances[8]), "GCCTTTGGCCTTGGATGAGAA")
- self.assertEqual(str(record.motifs[3].instances[9]), "GGCCCTCGGATCGCTTGGGAA")
- self.assertEqual(str(record.motifs[3].instances[10]), "GCATGTTGGGAATCCGCGGAC")
- self.assertEqual(str(record.motifs[3].instances[11]), "GACACGCGCTGTATGCGGGGA")
- self.assertEqual(str(record.motifs[3].instances[12]), "GCCAGGTACAAAGCGTCGTGC")
- self.assertEqual(str(record.motifs[3].instances[13]), "GCGATCAGCTTGTGGGCGTGC")
- self.assertEqual(str(record.motifs[3].instances[14]), "GACAAATCGGATACTGGGGCA")
- self.assertEqual(str(record.motifs[3].instances[15]), "GCACTTAGCAGCGTATCGTTA")
- self.assertEqual(record.motifs[3].mask, [1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1])
- self.assertAlmostEqual(record.motifs[3].score, 18.0097)
- self.assertEqual(record.motifs[4].alphabet, IUPAC.unambiguous_dna)
- self.assertEqual(len(record.motifs[4].instances), 15)
- self.assertEqual(str(record.motifs[4].instances[0]), "CGGCACAGAGCTT")
- self.assertEqual(str(record.motifs[4].instances[1]), "ATCCGCGGACGCT")
- self.assertEqual(str(record.motifs[4].instances[2]), "CGCCTGGGAGGGT")
- self.assertEqual(str(record.motifs[4].instances[3]), "CGGAAGGGACGTT")
- self.assertEqual(str(record.motifs[4].instances[4]), "ACACACAGACGGT")
- self.assertEqual(str(record.motifs[4].instances[5]), "TGCCAGAGAGGTT")
- self.assertEqual(str(record.motifs[4].instances[6]), "AGACTGAGACGTT")
- self.assertEqual(str(record.motifs[4].instances[7]), "AATCGTAGAGGAT")
- self.assertEqual(str(record.motifs[4].instances[8]), "CGTCTCGTAGGGT")
- self.assertEqual(str(record.motifs[4].instances[9]), "CGTCGCGGAGGAT")
- self.assertEqual(str(record.motifs[4].instances[10]), "CTTCTTAGACGCT")
- self.assertEqual(str(record.motifs[4].instances[11]), "CGACGCAGAGGAT")
- self.assertEqual(str(record.motifs[4].instances[12]), "ATGCTTAGAGGTT")
- self.assertEqual(str(record.motifs[4].instances[13]), "AGACTTGGGCGAT")
- self.assertEqual(str(record.motifs[4].instances[14]), "CGACCTGGAGGCT")
- self.assertEqual(record.motifs[4].mask, [1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1])
- self.assertAlmostEqual(record.motifs[4].score, 16.8287)
- self.assertEqual(record.motifs[5].alphabet, IUPAC.unambiguous_dna)
- self.assertEqual(len(record.motifs[5].instances), 18)
- self.assertEqual(str(record.motifs[5].instances[0]), "GTGCGCGAAGGAAGCAGCGCGTG")
- self.assertEqual(str(record.motifs[5].instances[1]), "TTGAGCCGAGTAAAGGGCTGGTG")
- self.assertEqual(str(record.motifs[5].instances[2]), "CAATGCTAAGCTCTGTGCCGACG")
- self.assertEqual(str(record.motifs[5].instances[3]), "CAACTCTCTATGTAGTGCCCGAG")
- self.assertEqual(str(record.motifs[5].instances[4]), "CGACGCTTTGTACCTGGCTTGCG")
- self.assertEqual(str(record.motifs[5].instances[5]), "CGAGTCAATGACACGCGCCTGGG")
- self.assertEqual(str(record.motifs[5].instances[6]), "CGATACGCTGCTAAGTGCCGTCC")
- self.assertEqual(str(record.motifs[5].instances[7]), "CCGGGCCAATAGCGGCGCCGGAG")
- self.assertEqual(str(record.motifs[5].instances[8]), "CCACGCTTCGACACGTGGTATAG")
- self.assertEqual(str(record.motifs[5].instances[9]), "CCGAGCCTCATGTACCGGAAGGG")
- self.assertEqual(str(record.motifs[5].instances[10]), "CTGCTCCCCGCATACAGCGCGTG")
- self.assertEqual(str(record.motifs[5].instances[11]), "CCGAGGTCCGGTACGGGCAAGCC")
- self.assertEqual(str(record.motifs[5].instances[12]), "GTGCTCATAGGGACGTCGCGGAG")
- self.assertEqual(str(record.motifs[5].instances[13]), "CCCTACTATGCGGGGGGCAGGTC")
- self.assertEqual(str(record.motifs[5].instances[14]), "GCCAGCAATTGCAGGTGGTCGTG")
- self.assertEqual(str(record.motifs[5].instances[15]), "CTCTGCGTCGCATGGCGGCGTGG")
- self.assertEqual(str(record.motifs[5].instances[16]), "GGAGGCTTAGACTTGGGCGATAC")
- self.assertEqual(str(record.motifs[5].instances[17]), "GCATGGAGAGAGATCCGGAGGAG")
- self.assertEqual(record.motifs[5].mask, [1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1])
- self.assertAlmostEqual(record.motifs[5].score, 15.0441)
- self.assertEqual(record.motifs[6].alphabet, IUPAC.unambiguous_dna)
- self.assertEqual(len(record.motifs[6].instances), 20)
- self.assertEqual(str(record.motifs[6].instances[0]), "GCGCGTGTGTGTAAC")
- self.assertEqual(str(record.motifs[6].instances[1]), "GCACAGAGCTTAGCA")
- self.assertEqual(str(record.motifs[6].instances[2]), "GGTGGTCATCGGGCA")
- self.assertEqual(str(record.motifs[6].instances[3]), "GCGCGTGTCATTGAC")
- self.assertEqual(str(record.motifs[6].instances[4]), "GGACGGCACTTAGCA")
- self.assertEqual(str(record.motifs[6].instances[5]), "GCGCGTCCCGGGCCA")
- self.assertEqual(str(record.motifs[6].instances[6]), "GCTCGGCCCGTTGTC")
- self.assertEqual(str(record.motifs[6].instances[7]), "GCGCGTGTCCTTTAA")
- self.assertEqual(str(record.motifs[6].instances[8]), "GCTGATCGCTGCTCC")
- self.assertEqual(str(record.motifs[6].instances[9]), "GCCCGTACCGGACCT")
- self.assertEqual(str(record.motifs[6].instances[10]), "GGACGTCGCGGAGGA")
- self.assertEqual(str(record.motifs[6].instances[11]), "GCGGGGGGCAGGTCA")
- self.assertEqual(str(record.motifs[6].instances[12]), "GGACGTACTGGCACA")
- self.assertEqual(str(record.motifs[6].instances[13]), "GCAGGTGGTCGTGCA")
- self.assertEqual(str(record.motifs[6].instances[14]), "GCGCATACCTTAACA")
- self.assertEqual(str(record.motifs[6].instances[15]), "GCACGGGACTTCAAC")
- self.assertEqual(str(record.motifs[6].instances[16]), "GCACGTAGCTGGTAA")
- self.assertEqual(str(record.motifs[6].instances[17]), "GCTCGTCTATGGTCA")
- self.assertEqual(str(record.motifs[6].instances[18]), "GCGCATGCTGGATCC")
- self.assertEqual(str(record.motifs[6].instances[19]), "GGCCGTCAGCTCTCA")
- self.assertEqual(record.motifs[6].mask, [1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1])
- self.assertAlmostEqual(record.motifs[6].score, 13.3145)
- self.assertEqual(record.motifs[7].alphabet, IUPAC.unambiguous_dna)
- self.assertEqual(len(record.motifs[7].instances), 20)
- self.assertEqual(str(record.motifs[7].instances[0]), "GAACCGAGGTCCGGTACGGGC")
- self.assertEqual(str(record.motifs[7].instances[1]), "GCCCCCCGCATAGTAGGGGGA")
- self.assertEqual(str(record.motifs[7].instances[2]), "GTCCCTGGGTAAGCTTGGGGC")
- self.assertEqual(str(record.motifs[7].instances[3]), "ACTCCACGCTTCGACACGTGG")
- self.assertEqual(str(record.motifs[7].instances[4]), "ATCCTCTGCGTCGCATGGCGG")
- self.assertEqual(str(record.motifs[7].instances[5]), "GTTCAATGCTAAGCTCTGTGC")
- self.assertEqual(str(record.motifs[7].instances[6]), "GCTCATAGGGACGTCGCGGAG")
- self.assertEqual(str(record.motifs[7].instances[7]), "GTCCCGGGCCAATAGCGGCGC")
- self.assertEqual(str(record.motifs[7].instances[8]), "GCACTTAGCAGCGTATCGTTA")
- self.assertEqual(str(record.motifs[7].instances[9]), "GGCCCTCGGATCGCTTGGGAA")
- self.assertEqual(str(record.motifs[7].instances[10]), "CTGCTGGACAACGGGCCGAGC")
- self.assertEqual(str(record.motifs[7].instances[11]), "GGGCACTACATAGAGAGTTGC")
- self.assertEqual(str(record.motifs[7].instances[12]), "AGCCTCCAGGTCGCATGGAGA")
- self.assertEqual(str(record.motifs[7].instances[13]), "AATCGTAGATCAGAGGCGAGA")
- self.assertEqual(str(record.motifs[7].instances[14]), "GAACTCCACTAAGACTTGAGA")
- self.assertEqual(str(record.motifs[7].instances[15]), "GAGCAGCGATCAGCTTGTGGG")
- self.assertEqual(str(record.motifs[7].instances[16]), "GCCAGGTACAAAGCGTCGTGC")
- self.assertEqual(str(record.motifs[7].instances[17]), "AGTCAATGACACGCGCCTGGG")
- self.assertEqual(str(record.motifs[7].instances[18]), "GGTCATGGAATCTTATGTAGC")
- self.assertEqual(str(record.motifs[7].instances[19]), "GTAGATAACAGAGGTCGGGGG")
- self.assertEqual(record.motifs[7].mask, [1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1])
- self.assertAlmostEqual(record.motifs[7].score, 11.6098)
- self.assertEqual(record.motifs[8].alphabet, IUPAC.unambiguous_dna)
- self.assertEqual(len(record.motifs[8].instances), 14)
- self.assertEqual(str(record.motifs[8].instances[0]), "CCGAGTAAAGGGCTG")
- self.assertEqual(str(record.motifs[8].instances[1]), "GTGGTCATCGGGCAC")
- self.assertEqual(str(record.motifs[8].instances[2]), "GATAACAGAGGTCGG")
- self.assertEqual(str(record.motifs[8].instances[3]), "CGGCGCCGGAGTCTG")
- self.assertEqual(str(record.motifs[8].instances[4]), "GCGCGTCCCGGGCCA")
- self.assertEqual(str(record.motifs[8].instances[5]), "CTGGACAACGGGCCG")
- self.assertEqual(str(record.motifs[8].instances[6]), "CGGATACTGGGGCAG")
- self.assertEqual(str(record.motifs[8].instances[7]), "GGGAGCAGCGATCAG")
- self.assertEqual(str(record.motifs[8].instances[8]), "CAGAACCGAGGTCCG")
- self.assertEqual(str(record.motifs[8].instances[9]), "GGGTCCCTGGGTAAG")
- self.assertEqual(str(record.motifs[8].instances[10]), "GTGCTCATAGGGACG")
- self.assertEqual(str(record.motifs[8].instances[11]), "GAGATCCGGAGGAGG")
- self.assertEqual(str(record.motifs[8].instances[12]), "GCGATCCGAGGGCCG")
- self.assertEqual(str(record.motifs[8].instances[13]), "GAGTTCACATGGCTG")
- self.assertEqual(record.motifs[8].mask, [1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1])
- self.assertAlmostEqual(record.motifs[8].score, 11.2943)
- self.assertEqual(record.motifs[9].alphabet, IUPAC.unambiguous_dna)
- self.assertEqual(len(record.motifs[9].instances), 18)
- self.assertEqual(str(record.motifs[9].instances[0]), "TAGAGGCGGTG")
- self.assertEqual(str(record.motifs[9].instances[1]), "GCTAAGCTCTG")
- self.assertEqual(str(record.motifs[9].instances[2]), "TGGAAGCAGTG")
- self.assertEqual(str(record.motifs[9].instances[3]), "GCGAGGCTGTG")
- self.assertEqual(str(record.motifs[9].instances[4]), "ACGACGCTTTG")
- self.assertEqual(str(record.motifs[9].instances[5]), "GGGACGCGCAC")
- self.assertEqual(str(record.motifs[9].instances[6]), "TCGAAGCGTGG")
- self.assertEqual(str(record.motifs[9].instances[7]), "TGTATGCGGGG")
- self.assertEqual(str(record.motifs[9].instances[8]), "GGTAAGCTTGG")
- self.assertEqual(str(record.motifs[9].instances[9]), "TGTACGCTGGG")
- self.assertEqual(str(record.motifs[9].instances[10]), "ACTATGCGGGG")
- self.assertEqual(str(record.motifs[9].instances[11]), "GGTATGCGCTG")
- self.assertEqual(str(record.motifs[9].instances[12]), "GGTACCCGGAG")
- self.assertEqual(str(record.motifs[9].instances[13]), "GCGACGCAGAG")
- self.assertEqual(str(record.motifs[9].instances[14]), "TGGCGGCGTGG")
- self.assertEqual(str(record.motifs[9].instances[15]), "TCTAGGCGGGC")
- self.assertEqual(str(record.motifs[9].instances[16]), "AGTATGCTTAG")
- self.assertEqual(str(record.motifs[9].instances[17]), "TGGAGGCTTAG")
- self.assertEqual(record.motifs[9].mask, [1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1])
- self.assertAlmostEqual(record.motifs[9].score, 9.7924)
- self.assertEqual(record.motifs[10].alphabet, IUPAC.unambiguous_dna)
- self.assertEqual(len(record.motifs[10].instances), 13)
- self.assertEqual(str(record.motifs[10].instances[0]), "GCACAGAGCTTAGCATTGAAC")
- self.assertEqual(str(record.motifs[10].instances[1]), "GTCCGCGGATTCCCAACATGC")
- self.assertEqual(str(record.motifs[10].instances[2]), "ATACACAGCCTCGCAAGCCAG")
- self.assertEqual(str(record.motifs[10].instances[3]), "GGCCCGGGACGCGCACTAAGA")
- self.assertEqual(str(record.motifs[10].instances[4]), "GCCCGTTGTCCAGCAGACGGC")
- self.assertEqual(str(record.motifs[10].instances[5]), "GAGCAGCGATCAGCTTGTGGG")
- self.assertEqual(str(record.motifs[10].instances[6]), "GAACCGAGGTCCGGTACGGGC")
- self.assertEqual(str(record.motifs[10].instances[7]), "GTCCCTGGGTAAGCTTGGGGC")
- self.assertEqual(str(record.motifs[10].instances[8]), "GACCTGCCCCCCGCATAGTAG")
- self.assertEqual(str(record.motifs[10].instances[9]), "AACCAGCGCATACCTTAACAG")
- self.assertEqual(str(record.motifs[10].instances[10]), "ATCCTCTGCGTCGCATGGCGG")
- self.assertEqual(str(record.motifs[10].instances[11]), "GACCATAGACGAGCATCAAAG")
- self.assertEqual(str(record.motifs[10].instances[12]), "GGCCCTCGGATCGCTTGGGAA")
- self.assertEqual(record.motifs[10].mask, [1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1])
- self.assertAlmostEqual(record.motifs[10].score, 9.01393)
- self.assertEqual(record.motifs[11].alphabet, IUPAC.unambiguous_dna)
- self.assertEqual(len(record.motifs[11].instances), 16)
- self.assertEqual(str(record.motifs[11].instances[0]), "GCCGTCCGTC")
- self.assertEqual(str(record.motifs[11].instances[1]), "GGCGTGCGCG")
- self.assertEqual(str(record.motifs[11].instances[2]), "GGCGCGTGTC")
- self.assertEqual(str(record.motifs[11].instances[3]), "AGCGCGTGTG")
- self.assertEqual(str(record.motifs[11].instances[4]), "GCGGTGCGTG")
- self.assertEqual(str(record.motifs[11].instances[5]), "AGCGCGTGTC")
- self.assertEqual(str(record.motifs[11].instances[6]), "AGCGTCCGCG")
- self.assertEqual(str(record.motifs[11].instances[7]), "ACCGTCTGTG")
- self.assertEqual(str(record.motifs[11].instances[8]), "GCCATGCGAC")
- self.assertEqual(str(record.motifs[11].instances[9]), "ACCACCCGTC")
- self.assertEqual(str(record.motifs[11].instances[10]), "GGCGCCGGAG")
- self.assertEqual(str(record.motifs[11].instances[11]), "ACCACGTGTC")
- self.assertEqual(str(record.motifs[11].instances[12]), "GGCTTGCGAG")
- self.assertEqual(str(record.motifs[11].instances[13]), "GCGATCCGAG")
- self.assertEqual(str(record.motifs[11].instances[14]), "AGTGCGCGTC")
- self.assertEqual(str(record.motifs[11].instances[15]), "AGTGCCCGAG")
- self.assertEqual(record.motifs[11].mask, [1, 1, 1, 1, 1, 1, 1, 1, 1, 1])
- self.assertAlmostEqual(record.motifs[11].score, 7.51121)
- self.assertEqual(record.motifs[12].alphabet, IUPAC.unambiguous_dna)
- self.assertEqual(len(record.motifs[12].instances), 16)
- self.assertEqual(str(record.motifs[12].instances[0]), "GCCGACGGGTGGTCATCGGG")
- self.assertEqual(str(record.motifs[12].instances[1]), "GCACGACGCTTTGTACCTGG")
- self.assertEqual(str(record.motifs[12].instances[2]), "CCTGGGAGGGTTCAATAACG")
- self.assertEqual(str(record.motifs[12].instances[3]), "GCGCGTCCCGGGCCAATAGC")
- self.assertEqual(str(record.motifs[12].instances[4]), "GCCGTCTGCTGGACAACGGG")
- self.assertEqual(str(record.motifs[12].instances[5]), "GTCCCTTCCGGTACATGAGG")
- self.assertEqual(str(record.motifs[12].instances[6]), "GCTGCTCCCCGCATACAGCG")
- self.assertEqual(str(record.motifs[12].instances[7]), "GCCCCAAGCTTACCCAGGGA")
- self.assertEqual(str(record.motifs[12].instances[8]), "ACCGGCTGACGCTAATACGG")
- self.assertEqual(str(record.motifs[12].instances[9]), "GCGGGGGGCAGGTCATTACA")
- self.assertEqual(str(record.motifs[12].instances[10]), "GCTGGCAGCGTCTAAGAAGG")
- self.assertEqual(str(record.motifs[12].instances[11]), "GCAGGTGGTCGTGCAATACG")
- self.assertEqual(str(record.motifs[12].instances[12]), "GCTGGTTGAAGTCCCGTGCG")
- self.assertEqual(str(record.motifs[12].instances[13]), "GCACGTAGCTGGTAAATAGG")
- self.assertEqual(str(record.motifs[12].instances[14]), "GCGGCGTGGATTTCATACAG")
- self.assertEqual(str(record.motifs[12].instances[15]), "CCTGGAGGCTTAGACTTGGG")
- self.assertEqual(record.motifs[12].mask, [1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1])
- self.assertAlmostEqual(record.motifs[12].score, 5.63667)
- self.assertEqual(record.motifs[13].alphabet, IUPAC.unambiguous_dna)
- self.assertEqual(len(record.motifs[13].instances), 15)
- self.assertEqual(str(record.motifs[13].instances[0]), "GCCGACGGGTGGTCATCGGG")
- self.assertEqual(str(record.motifs[13].instances[1]), "ATCCGCGGACGCTTAGAGGG")
- self.assertEqual(str(record.motifs[13].instances[2]), "ACGCTTTGTACCTGGCTTGC")
- self.assertEqual(str(record.motifs[13].instances[3]), "ACGGACGGCACTTAGCAGCG")
- self.assertEqual(str(record.motifs[13].instances[4]), "GCCGTCTGCTGGACAACGGG")
- self.assertEqual(str(record.motifs[13].instances[5]), "ACACACAGACGGTTGAAAGG")
- self.assertEqual(str(record.motifs[13].instances[6]), "GCCGATAGTGCTTAAGTTCG")
- self.assertEqual(str(record.motifs[13].instances[7]), "CTTGCCCGTACCGGACCTCG")
- self.assertEqual(str(record.motifs[13].instances[8]), "ACCGGCTGACGCTAATACGG")
- self.assertEqual(str(record.motifs[13].instances[9]), "GCCCCCCGCATAGTAGGGGG")
- self.assertEqual(str(record.motifs[13].instances[10]), "GCTGGCAGCGTCTAAGAAGG")
- self.assertEqual(str(record.motifs[13].instances[11]), "GCAGGTGGTCGTGCAATACG")
- self.assertEqual(str(record.motifs[13].instances[12]), "ACGCACGGGACTTCAACCAG")
- self.assertEqual(str(record.motifs[13].instances[13]), "GCACGTAGCTGGTAAATAGG")
- self.assertEqual(str(record.motifs[13].instances[14]), "ATCCTCTGCGTCGCATGGCG")
- self.assertEqual(record.motifs[13].mask, [1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1])
- self.assertAlmostEqual(record.motifs[13].score, 3.89842)
- self.assertEqual(record.motifs[14].alphabet, IUPAC.unambiguous_dna)
- self.assertEqual(len(record.motifs[14].instances), 14)
- self.assertEqual(str(record.motifs[14].instances[0]), "GAGGCTGTGTAT")
- self.assertEqual(str(record.motifs[14].instances[1]), "GAGGTCGGGGGT")
- self.assertEqual(str(record.motifs[14].instances[2]), "GACGGACGGCAC")
- self.assertEqual(str(record.motifs[14].instances[3]), "TTGGCCCGGGAC")
- self.assertEqual(str(record.motifs[14].instances[4]), "GAGGCTCGGCCC")
- self.assertEqual(str(record.motifs[14].instances[5]), "CACGCGCTGTAT")
- self.assertEqual(str(record.motifs[14].instances[6]), "TAGGCCAGGTAT")
- self.assertEqual(str(record.motifs[14].instances[7]), "GAGGTCCGGTAC")
- self.assertEqual(str(record.motifs[14].instances[8]), "TACGCTGGGGAT")
- self.assertEqual(str(record.motifs[14].instances[9]), "GTCGCGGAGGAT")
- self.assertEqual(str(record.motifs[14].instances[10]), "TACGCACGGGAC")
- self.assertEqual(str(record.motifs[14].instances[11]), "TACTCCGGGTAC")
- self.assertEqual(str(record.motifs[14].instances[12]), "GACGCAGAGGAT")
- self.assertEqual(str(record.motifs[14].instances[13]), "TAGGCGGGCCAT")
- self.assertEqual(record.motifs[14].mask, [1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1])
- self.assertAlmostEqual(record.motifs[14].score, 3.33444)
- self.assertEqual(record.motifs[15].alphabet, IUPAC.unambiguous_dna)
- self.assertEqual(len(record.motifs[15].instances), 21)
- self.assertEqual(str(record.motifs[15].instances[0]), "CGGCTCAATCGTAGAGGC")
- self.assertEqual(str(record.motifs[15].instances[1]), "CGACGGGTGGTCATCGGG")
- self.assertEqual(str(record.motifs[15].instances[2]), "CGCTTAGAGGGCACAAGC")
- self.assertEqual(str(record.motifs[15].instances[3]), "TGACACGCGCCTGGGAGG")
- self.assertEqual(str(record.motifs[15].instances[4]), "CGATACGCTGCTAAGTGC")
- self.assertEqual(str(record.motifs[15].instances[5]), "CGTCCCGGGCCAATAGCG")
- self.assertEqual(str(record.motifs[15].instances[6]), "CCACGCTTCGACACGTGG")
- self.assertEqual(str(record.motifs[15].instances[7]), "CGTCTGCTGGACAACGGG")
- self.assertEqual(str(record.motifs[15].instances[8]), "ACACAGACGGTTGAAAGG")
- self.assertEqual(str(record.motifs[15].instances[9]), "TGCTCCCCGCATACAGCG")
- self.assertEqual(str(record.motifs[15].instances[10]), "TGAGGCTTGCCCGTACCG")
- self.assertEqual(str(record.motifs[15].instances[11]), "TGCCCCAAGCTTACCCAG")
- self.assertEqual(str(record.motifs[15].instances[12]), "CGGCTGACGCTAATACGG")
- self.assertEqual(str(record.motifs[15].instances[13]), "CGCGACGTCCCTATGAGC")
- self.assertEqual(str(record.motifs[15].instances[14]), "TGCCCCCCGCATAGTAGG")
- self.assertEqual(str(record.motifs[15].instances[15]), "CGTTGCCTTCTTAGACGC")
- self.assertEqual(str(record.motifs[15].instances[16]), "TGACTCAATCGTAGACCC")
- self.assertEqual(str(record.motifs[15].instances[17]), "AGTCCCGTGCGTATGTGG")
- self.assertEqual(str(record.motifs[15].instances[18]), "AGGCTCGCACGTAGCTGG")
- self.assertEqual(str(record.motifs[15].instances[19]), "CCACGCCGCCATGCGACG")
- self.assertEqual(str(record.motifs[15].instances[20]), "AGCCTCCAGGTCGCATGG")
- self.assertEqual(record.motifs[15].mask, [1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1])
- self.assertAlmostEqual(record.motifs[15].score, 1.0395)
-
- def test_pfm_parsing(self):
- """Test to be sure that Motif can parse pfm files.
- """
- motif = Motif.read(self.PFMin, "jaspar-pfm")
- self.assertEqual(motif.length, 12)
-
- def test_sites_parsing(self):
- """Test to be sure that Motif can parse sites files.
- """
- motif = Motif.read(self.SITESin, "jaspar-sites")
- self.assertEqual(motif.length, 6)
-
- def test_FAoutput(self):
- """Ensure that we can write proper FASTA output files.
- """
- output_handle = open(self.FAout, "w")
- output_handle.write(self.m.format("fasta"))
- output_handle.close()
-
- def test_TFoutput(self):
- """Ensure that we can write proper TransFac output files.
- """
- output_handle = open(self.TFout, "w")
- output_handle.write(self.m.format("transfac"))
- output_handle.close()
-
- def test_pfm_output(self):
- """Ensure that we can write proper pfm output files.
- """
- output_handle = open(self.PFMout, "w")
- output_handle.write(self.m.format("jaspar-pfm"))
- output_handle.close()
-
-
-class TestMEME(unittest.TestCase):
-
- def test_meme_parser_1(self):
- """Test if Motif can parse MEME output files (first test)
- """
- from Bio.Motif.Parsers import MEME
- handle = open("Motif/meme.out")
- record = MEME.read(handle)
- self.assertEqual(record.version, '3.5.7')
- self.assertEqual(record.datafile, 'test.fa')
- self.assertEqual(record.alphabet, IUPAC.unambiguous_dna)
- self.assertEqual(len(record.sequence_names), 10)
- self.assertEqual(record.sequence_names[0], 'SEQ1;')
- self.assertEqual(record.sequence_names[1], 'SEQ2;')
- self.assertEqual(record.sequence_names[2], 'SEQ3;')
- self.assertEqual(record.sequence_names[3], 'SEQ4;')
- self.assertEqual(record.sequence_names[4], 'SEQ5;')
- self.assertEqual(record.sequence_names[5], 'SEQ6;')
- self.assertEqual(record.sequence_names[6], 'SEQ7;')
- self.assertEqual(record.sequence_names[7], 'SEQ8;')
- self.assertEqual(record.sequence_names[8], 'SEQ9;')
- self.assertEqual(record.sequence_names[9], 'SEQ10;')
- self.assertEqual(record.command, 'meme test.fa -dna -w 10 -dir /home/bartek/MetaMotif/meme')
- self.assertEqual(len(record.motifs), 1)
- motif = record.motifs[0]
- self.assertEqual(motif.num_occurrences, 10)
- self.assertAlmostEqual(motif.evalue, 1.1e-22)
- self.assertEqual(motif.alphabet, IUPAC.unambiguous_dna)
- self.assertEqual(motif.name, "Motif 1")
- self.assertEqual(len(motif.instances), 10)
- self.assertAlmostEqual(motif.instances[0].pvalue, 8.71e-07)
- self.assertAlmostEqual(motif.instances[1].pvalue, 8.71e-07)
- self.assertAlmostEqual(motif.instances[2].pvalue, 8.71e-07)
- self.assertAlmostEqual(motif.instances[3].pvalue, 8.71e-07)
- self.assertAlmostEqual(motif.instances[4].pvalue, 8.71e-07)
- self.assertAlmostEqual(motif.instances[5].pvalue, 8.71e-07)
- self.assertAlmostEqual(motif.instances[6].pvalue, 8.71e-07)
- self.assertAlmostEqual(motif.instances[7].pvalue, 8.71e-07)
- self.assertAlmostEqual(motif.instances[8].pvalue, 8.71e-07)
- self.assertAlmostEqual(motif.instances[9].pvalue, 8.71e-07)
- self.assertEqual(motif.instances[0].sequence_name, 'SEQ10;')
- self.assertEqual(motif.instances[1].sequence_name, 'SEQ9;')
- self.assertEqual(motif.instances[2].sequence_name, 'SEQ8;')
- self.assertEqual(motif.instances[3].sequence_name, 'SEQ7;')
- self.assertEqual(motif.instances[4].sequence_name, 'SEQ6;')
- self.assertEqual(motif.instances[5].sequence_name, 'SEQ5;')
- self.assertEqual(motif.instances[6].sequence_name, 'SEQ4;')
- self.assertEqual(motif.instances[7].sequence_name, 'SEQ3;')
- self.assertEqual(motif.instances[8].sequence_name, 'SEQ2;')
- self.assertEqual(motif.instances[9].sequence_name, 'SEQ1;')
- self.assertEqual(motif.instances[0].start, 3)
- self.assertEqual(motif.instances[1].start, 93)
- self.assertEqual(motif.instances[2].start, 172)
- self.assertEqual(motif.instances[3].start, 177)
- self.assertEqual(motif.instances[4].start, 105)
- self.assertEqual(motif.instances[5].start, 185)
- self.assertEqual(motif.instances[6].start, 173)
- self.assertEqual(motif.instances[7].start, 112)
- self.assertEqual(motif.instances[8].start, 172)
- self.assertEqual(motif.instances[9].start, 52)
- self.assertEqual(motif.instances[0].strand, '+')
- self.assertEqual(motif.instances[1].strand, '+')
- self.assertEqual(motif.instances[2].strand, '+')
- self.assertEqual(motif.instances[3].strand, '+')
- self.assertEqual(motif.instances[4].strand, '+')
- self.assertEqual(motif.instances[5].strand, '+')
- self.assertEqual(motif.instances[6].strand, '+')
- self.assertEqual(motif.instances[7].strand, '+')
- self.assertEqual(motif.instances[8].strand, '+')
- self.assertEqual(motif.instances[9].strand, '+')
- self.assertEqual(motif.instances[0].length, 10)
- self.assertEqual(motif.instances[1].length, 10)
- self.assertEqual(motif.instances[2].length, 10)
- self.assertEqual(motif.instances[3].length, 10)
- self.assertEqual(motif.instances[4].length, 10)
- self.assertEqual(motif.instances[5].length, 10)
- self.assertEqual(motif.instances[6].length, 10)
- self.assertEqual(motif.instances[7].length, 10)
- self.assertEqual(motif.instances[8].length, 10)
- self.assertEqual(motif.instances[9].length, 10)
- self.assertEqual(motif.instances[0].motif_name, 'Motif 1')
- self.assertEqual(motif.instances[1].motif_name, 'Motif 1')
- self.assertEqual(motif.instances[2].motif_name, 'Motif 1')
- self.assertEqual(motif.instances[3].motif_name, 'Motif 1')
- self.assertEqual(motif.instances[4].motif_name, 'Motif 1')
- self.assertEqual(motif.instances[5].motif_name, 'Motif 1')
- self.assertEqual(motif.instances[6].motif_name, 'Motif 1')
- self.assertEqual(motif.instances[7].motif_name, 'Motif 1')
- self.assertEqual(motif.instances[8].motif_name, 'Motif 1')
- self.assertEqual(motif.instances[9].motif_name, 'Motif 1')
- self.assertEqual(motif.instances[0].alphabet, IUPAC.unambiguous_dna)
- self.assertEqual(motif.instances[1].alphabet, IUPAC.unambiguous_dna)
- self.assertEqual(motif.instances[2].alphabet, IUPAC.unambiguous_dna)
- self.assertEqual(motif.instances[3].alphabet, IUPAC.unambiguous_dna)
- self.assertEqual(motif.instances[4].alphabet, IUPAC.unambiguous_dna)
- self.assertEqual(motif.instances[5].alphabet, IUPAC.unambiguous_dna)
- self.assertEqual(motif.instances[6].alphabet, IUPAC.unambiguous_dna)
- self.assertEqual(motif.instances[7].alphabet, IUPAC.unambiguous_dna)
- self.assertEqual(motif.instances[8].alphabet, IUPAC.unambiguous_dna)
- self.assertEqual(motif.instances[9].alphabet, IUPAC.unambiguous_dna)
- self.assertEqual(str(motif.instances[0]), "CTCAATCGTA")
- self.assertEqual(str(motif.instances[1]), "CTCAATCGTA")
- self.assertEqual(str(motif.instances[2]), "CTCAATCGTA")
- self.assertEqual(str(motif.instances[3]), "CTCAATCGTA")
- self.assertEqual(str(motif.instances[4]), "CTCAATCGTA")
- self.assertEqual(str(motif.instances[5]), "CTCAATCGTA")
- self.assertEqual(str(motif.instances[6]), "CTCAATCGTA")
- self.assertEqual(str(motif.instances[7]), "CTCAATCGTA")
- self.assertEqual(str(motif.instances[8]), "CTCAATCGTA")
- self.assertEqual(str(motif.instances[9]), "CTCAATCGTA")
- handle.close()
-
- def test_meme_parser_2(self):
- """Test if Motif can parse MEME output files (second test)
- """
- from Bio.Motif.Parsers import MEME
- handle = open("Motif/meme.dna.oops.txt")
- record = MEME.read(handle)
- self.assertEqual(record.version, '3.0')
- self.assertEqual(record.datafile, 'INO_up800.s')
- self.assertEqual(record.alphabet, IUPAC.unambiguous_dna)
- self.assertEqual(len(record.sequence_names), 7)
- self.assertEqual(record.sequence_names[0], 'CHO1')
- self.assertEqual(record.sequence_names[1], 'CHO2')
- self.assertEqual(record.sequence_names[2], 'FAS1')
- self.assertEqual(record.sequence_names[3], 'FAS2')
- self.assertEqual(record.sequence_names[4], 'ACC1')
- self.assertEqual(record.sequence_names[5], 'INO1')
- self.assertEqual(record.sequence_names[6], 'OPI3')
- self.assertEqual(record.command, 'meme -mod oops -dna -revcomp -nmotifs 2 -bfile yeast.nc.6.freq INO_up800.s')
- self.assertEqual(len(record.motifs), 2)
- motif = record.motifs[0]
- self.assertEqual(motif.num_occurrences, 7)
- self.assertAlmostEqual(motif.evalue, 0.2)
- self.assertEqual(motif.alphabet, IUPAC.unambiguous_dna)
- self.assertEqual(motif.name, "Motif 1")
- self.assertEqual(len(motif.instances), 7)
- self.assertAlmostEqual(motif.instances[0].pvalue, 1.85e-08)
- self.assertAlmostEqual(motif.instances[1].pvalue, 1.85e-08)
- self.assertAlmostEqual(motif.instances[2].pvalue, 1.52e-07)
- self.assertAlmostEqual(motif.instances[3].pvalue, 2.52e-07)
- self.assertAlmostEqual(motif.instances[4].pvalue, 4.23e-07)
- self.assertAlmostEqual(motif.instances[5].pvalue, 9.43e-07)
- self.assertAlmostEqual(motif.instances[6].pvalue, 3.32e-06)
- self.assertEqual(motif.instances[0].sequence_name, 'INO1')
- self.assertEqual(motif.instances[1].sequence_name, 'FAS1')
- self.assertEqual(motif.instances[2].sequence_name, 'ACC1')
- self.assertEqual(motif.instances[3].sequence_name, 'CHO2')
- self.assertEqual(motif.instances[4].sequence_name, 'CHO1')
- self.assertEqual(motif.instances[5].sequence_name, 'FAS2')
- self.assertEqual(motif.instances[6].sequence_name, 'OPI3')
- self.assertEqual(motif.instances[0].strand, '-')
- self.assertEqual(motif.instances[1].strand, '+')
- self.assertEqual(motif.instances[2].strand, '+')
- self.assertEqual(motif.instances[3].strand, '+')
- self.assertEqual(motif.instances[4].strand, '+')
- self.assertEqual(motif.instances[5].strand, '+')
- self.assertEqual(motif.instances[6].strand, '+')
- self.assertEqual(motif.instances[0].length, 12)
- self.assertEqual(motif.instances[1].length, 12)
- self.assertEqual(motif.instances[2].length, 12)
- self.assertEqual(motif.instances[3].length, 12)
- self.assertEqual(motif.instances[4].length, 12)
- self.assertEqual(motif.instances[5].length, 12)
- self.assertEqual(motif.instances[6].length, 12)
- self.assertEqual(motif.instances[0].start, 620)
- self.assertEqual(motif.instances[1].start, 95)
- self.assertEqual(motif.instances[2].start, 83)
- self.assertEqual(motif.instances[3].start, 354)
- self.assertEqual(motif.instances[4].start, 611)
- self.assertEqual(motif.instances[5].start, 567)
- self.assertEqual(motif.instances[6].start, 340)
- self.assertEqual(str(motif.instances[0]), "TTCACATGCCGC")
- self.assertEqual(str(motif.instances[1]), "TTCACATGCCGC")
- self.assertEqual(str(motif.instances[2]), "TTCACATGGCCC")
- self.assertEqual(str(motif.instances[3]), "TTCTCATGCCGC")
- self.assertEqual(str(motif.instances[4]), "TTCACACGGCAC")
- self.assertEqual(str(motif.instances[5]), "TTCACATGCTAC")
- self.assertEqual(str(motif.instances[6]), "TTCAGATCGCTC")
- motif = record.motifs[1]
- self.assertEqual(motif.num_occurrences, 7)
- self.assertAlmostEqual(motif.evalue, 110)
- self.assertEqual(motif.alphabet, IUPAC.unambiguous_dna)
- self.assertEqual(motif.name, "Motif 2")
- self.assertEqual(len(motif.instances), 7)
- self.assertAlmostEqual(motif.instances[0].pvalue, 3.24e-07)
- self.assertAlmostEqual(motif.instances[1].pvalue, 3.24e-07)
- self.assertAlmostEqual(motif.instances[2].pvalue, 3.24e-07)
- self.assertAlmostEqual(motif.instances[3].pvalue, 5.29e-06)
- self.assertAlmostEqual(motif.instances[4].pvalue, 6.25e-06)
- self.assertAlmostEqual(motif.instances[5].pvalue, 8.48e-06)
- self.assertAlmostEqual(motif.instances[6].pvalue, 8.48e-06)
- self.assertEqual(motif.instances[0].sequence_name, 'OPI3')
- self.assertEqual(motif.instances[1].sequence_name, 'ACC1')
- self.assertEqual(motif.instances[2].sequence_name, 'CHO1')
- self.assertEqual(motif.instances[3].sequence_name, 'INO1')
- self.assertEqual(motif.instances[4].sequence_name, 'FAS1')
- self.assertEqual(motif.instances[5].sequence_name, 'FAS2')
- self.assertEqual(motif.instances[6].sequence_name, 'CHO2')
- self.assertEqual(motif.instances[0].strand, '-')
- self.assertEqual(motif.instances[1].strand, '+')
- self.assertEqual(motif.instances[2].strand, '-')
- self.assertEqual(motif.instances[3].strand, '-')
- self.assertEqual(motif.instances[4].strand, '+')
- self.assertEqual(motif.instances[5].strand, '-')
- self.assertEqual(motif.instances[6].strand, '-')
- self.assertEqual(motif.instances[0].length, 10)
- self.assertEqual(motif.instances[1].length, 10)
- self.assertEqual(motif.instances[2].length, 10)
- self.assertEqual(motif.instances[3].length, 10)
- self.assertEqual(motif.instances[4].length, 10)
- self.assertEqual(motif.instances[5].length, 10)
- self.assertEqual(motif.instances[6].length, 10)
- self.assertEqual(motif.instances[0].start, 186)
- self.assertEqual(motif.instances[1].start, 232)
- self.assertEqual(motif.instances[2].start, 559)
- self.assertEqual(motif.instances[3].start, 283)
- self.assertEqual(motif.instances[4].start, 44)
- self.assertEqual(motif.instances[5].start, 185)
- self.assertEqual(motif.instances[6].start, 413)
- self.assertEqual(str(motif.instances[0]), "TCTGGCACAG")
- self.assertEqual(str(motif.instances[1]), "TCTGGCACAG")
- self.assertEqual(str(motif.instances[2]), "TCTGGCACAG")
- self.assertEqual(str(motif.instances[3]), "GCGGGCGCAG")
- self.assertEqual(str(motif.instances[4]), "GCAGGCACGG")
- self.assertEqual(str(motif.instances[5]), "TCTGGCACTC")
- self.assertEqual(str(motif.instances[6]), "TCTGGCATCG")
- handle.close()
-
- def test_meme_parser_3(self):
- """Test if Motif can parse MEME output files (third test)
- """
- from Bio.Motif.Parsers import MEME
- handle = open("Motif/meme.protein.oops.txt")
- record = MEME.read(handle)
- self.assertEqual(record.version, '3.0')
- self.assertEqual(record.datafile, 'adh.s')
- self.assertEqual(record.alphabet, IUPAC.protein)
- self.assertEqual(len(record.sequence_names), 33)
- self.assertEqual(record.sequence_names[0], "2BHD_STREX")
- self.assertEqual(record.sequence_names[1], "3BHD_COMTE")
- self.assertEqual(record.sequence_names[2], "ADH_DROME")
- self.assertEqual(record.sequence_names[3], "AP27_MOUSE")
- self.assertEqual(record.sequence_names[4], "BA72_EUBSP")
- self.assertEqual(record.sequence_names[5], "BDH_HUMAN")
- self.assertEqual(record.sequence_names[6], "BPHB_PSEPS")
- self.assertEqual(record.sequence_names[7], "BUDC_KLETE")
- self.assertEqual(record.sequence_names[8], "DHES_HUMAN")
- self.assertEqual(record.sequence_names[9], "DHGB_BACME")
- self.assertEqual(record.sequence_names[10], "DHII_HUMAN")
- self.assertEqual(record.sequence_names[11], "DHMA_FLAS1")
- self.assertEqual(record.sequence_names[12], "ENTA_ECOLI")
- self.assertEqual(record.sequence_names[13], "FIXR_BRAJA")
- self.assertEqual(record.sequence_names[14], "GUTD_ECOLI")
- self.assertEqual(record.sequence_names[15], "HDE_CANTR")
- self.assertEqual(record.sequence_names[16], "HDHA_ECOLI")
- self.assertEqual(record.sequence_names[17], "LIGD_PSEPA")
- self.assertEqual(record.sequence_names[18], "NODG_RHIME")
- self.assertEqual(record.sequence_names[19], "RIDH_KLEAE")
- self.assertEqual(record.sequence_names[20], "YINL_LISMO")
- self.assertEqual(record.sequence_names[21], "YRTP_BACSU")
- self.assertEqual(record.sequence_names[22], "CSGA_MYXXA")
- self.assertEqual(record.sequence_names[23], "DHB2_HUMAN")
- self.assertEqual(record.sequence_names[24], "DHB3_HUMAN")
- self.assertEqual(record.sequence_names[25], "DHCA_HUMAN")
- self.assertEqual(record.sequence_names[26], "FABI_ECOLI")
- self.assertEqual(record.sequence_names[27], "FVT1_HUMAN")
- self.assertEqual(record.sequence_names[28], "HMTR_LEIMA")
- self.assertEqual(record.sequence_names[29], "MAS1_AGRRA")
- self.assertEqual(record.sequence_names[30], "PCR_PEA")
- self.assertEqual(record.sequence_names[31], "RFBB_NEIGO")
- self.assertEqual(record.sequence_names[32], "YURA_MYXXA")
- self.assertEqual(record.command, 'meme adh.s -mod oops -protein -nmotifs 2')
- self.assertEqual(len(record.motifs), 2)
- motif = record.motifs[0]
- self.assertEqual(motif.num_occurrences, 33)
- self.assertAlmostEqual(motif.evalue, 3.6e-165)
- self.assertEqual(motif.alphabet, IUPAC.protein)
- self.assertEqual(motif.name, "Motif 1")
- self.assertEqual(len(motif.instances), 33)
- self.assertAlmostEqual(motif.instances[0].pvalue, 1.64e-22)
- self.assertAlmostEqual(motif.instances[1].pvalue, 6.32e-22)
- self.assertAlmostEqual(motif.instances[2].pvalue, 1.13e-21)
- self.assertAlmostEqual(motif.instances[3].pvalue, 4.04e-21)
- self.assertAlmostEqual(motif.instances[4].pvalue, 6.12e-21)
- self.assertAlmostEqual(motif.instances[5].pvalue, 7.52e-20)
- self.assertAlmostEqual(motif.instances[6].pvalue, 3.35e-19)
- self.assertAlmostEqual(motif.instances[7].pvalue, 4.82e-19)
- self.assertAlmostEqual(motif.instances[8].pvalue, 4.82e-19)
- self.assertAlmostEqual(motif.instances[9].pvalue, 1.11e-18)
- self.assertAlmostEqual(motif.instances[10].pvalue, 1.25e-18)
- self.assertAlmostEqual(motif.instances[11].pvalue, 2.23e-18)
- self.assertAlmostEqual(motif.instances[12].pvalue, 5.53e-18)
- self.assertAlmostEqual(motif.instances[13].pvalue, 9.65e-18)
- self.assertAlmostEqual(motif.instances[14].pvalue, 2.86e-17)
- self.assertAlmostEqual(motif.instances[15].pvalue, 8.20e-17)
- self.assertAlmostEqual(motif.instances[16].pvalue, 9.09e-17)
- self.assertAlmostEqual(motif.instances[17].pvalue, 1.37e-16)
- self.assertAlmostEqual(motif.instances[18].pvalue, 2.52e-16)
- self.assertAlmostEqual(motif.instances[19].pvalue, 1.21e-15)
- self.assertAlmostEqual(motif.instances[20].pvalue, 1.61e-15)
- self.assertAlmostEqual(motif.instances[21].pvalue, 1.77e-15)
- self.assertAlmostEqual(motif.instances[22].pvalue, 7.81e-15)
- self.assertAlmostEqual(motif.instances[23].pvalue, 8.55e-15)
- self.assertAlmostEqual(motif.instances[24].pvalue, 1.47e-14)
- self.assertAlmostEqual(motif.instances[25].pvalue, 3.24e-14)
- self.assertAlmostEqual(motif.instances[26].pvalue, 1.80e-12)
- self.assertAlmostEqual(motif.instances[27].pvalue, 2.10e-12)
- self.assertAlmostEqual(motif.instances[28].pvalue, 4.15e-12)
- self.assertAlmostEqual(motif.instances[29].pvalue, 5.20e-12)
- self.assertAlmostEqual(motif.instances[30].pvalue, 4.80e-10)
- self.assertAlmostEqual(motif.instances[31].pvalue, 2.77e-08)
- self.assertAlmostEqual(motif.instances[32].pvalue, 5.72e-08)
- self.assertEqual(motif.instances[0].sequence_name, 'YRTP_BACSU')
- self.assertEqual(motif.instances[1].sequence_name, 'AP27_MOUSE')
- self.assertEqual(motif.instances[2].sequence_name, 'NODG_RHIME')
- self.assertEqual(motif.instances[3].sequence_name, 'BUDC_KLETE')
- self.assertEqual(motif.instances[4].sequence_name, 'FIXR_BRAJA')
- self.assertEqual(motif.instances[5].sequence_name, 'DHGB_BACME')
- self.assertEqual(motif.instances[6].sequence_name, 'HMTR_LEIMA')
- self.assertEqual(motif.instances[7].sequence_name, 'YURA_MYXXA')
- self.assertEqual(motif.instances[8].sequence_name, 'GUTD_ECOLI')
- self.assertEqual(motif.instances[9].sequence_name, '2BHD_STREX')
- self.assertEqual(motif.instances[10].sequence_name, 'HDHA_ECOLI')
- self.assertEqual(motif.instances[11].sequence_name, 'DHB2_HUMAN')
- self.assertEqual(motif.instances[12].sequence_name, 'DHMA_FLAS1')
- self.assertEqual(motif.instances[13].sequence_name, 'HDE_CANTR')
- self.assertEqual(motif.instances[14].sequence_name, 'FVT1_HUMAN')
- self.assertEqual(motif.instances[15].sequence_name, 'BDH_HUMAN')
- self.assertEqual(motif.instances[16].sequence_name, 'RIDH_KLEAE')
- self.assertEqual(motif.instances[17].sequence_name, 'DHES_HUMAN')
- self.assertEqual(motif.instances[18].sequence_name, 'BA72_EUBSP')
- self.assertEqual(motif.instances[19].sequence_name, 'LIGD_PSEPA')
- self.assertEqual(motif.instances[20].sequence_name, 'DHII_HUMAN')
- self.assertEqual(motif.instances[21].sequence_name, 'ENTA_ECOLI')
- self.assertEqual(motif.instances[22].sequence_name, '3BHD_COMTE')
- self.assertEqual(motif.instances[23].sequence_name, 'DHB3_HUMAN')
- self.assertEqual(motif.instances[24].sequence_name, 'RFBB_NEIGO')
- self.assertEqual(motif.instances[25].sequence_name, 'YINL_LISMO')
- self.assertEqual(motif.instances[26].sequence_name, 'BPHB_PSEPS')
- self.assertEqual(motif.instances[27].sequence_name, 'CSGA_MYXXA')
- self.assertEqual(motif.instances[28].sequence_name, 'FABI_ECOLI')
- self.assertEqual(motif.instances[29].sequence_name, 'ADH_DROME')
- self.assertEqual(motif.instances[30].sequence_name, 'DHCA_HUMAN')
- self.assertEqual(motif.instances[31].sequence_name, 'PCR_PEA')
- self.assertEqual(motif.instances[32].sequence_name, 'MAS1_AGRRA')
- self.assertEqual(motif.instances[0].strand, '+')
- self.assertEqual(motif.instances[1].strand, '+')
- self.assertEqual(motif.instances[2].strand, '+')
- self.assertEqual(motif.instances[3].strand, '+')
- self.assertEqual(motif.instances[4].strand, '+')
- self.assertEqual(motif.instances[5].strand, '+')
- self.assertEqual(motif.instances[6].strand, '+')
- self.assertEqual(motif.instances[7].strand, '+')
- self.assertEqual(motif.instances[8].strand, '+')
- self.assertEqual(motif.instances[9].strand, '+')
- self.assertEqual(motif.instances[10].strand, '+')
- self.assertEqual(motif.instances[11].strand, '+')
- self.assertEqual(motif.instances[12].strand, '+')
- self.assertEqual(motif.instances[13].strand, '+')
- self.assertEqual(motif.instances[14].strand, '+')
- self.assertEqual(motif.instances[15].strand, '+')
- self.assertEqual(motif.instances[16].strand, '+')
- self.assertEqual(motif.instances[17].strand, '+')
- self.assertEqual(motif.instances[18].strand, '+')
- self.assertEqual(motif.instances[19].strand, '+')
- self.assertEqual(motif.instances[20].strand, '+')
- self.assertEqual(motif.instances[21].strand, '+')
- self.assertEqual(motif.instances[22].strand, '+')
- self.assertEqual(motif.instances[23].strand, '+')
- self.assertEqual(motif.instances[24].strand, '+')
- self.assertEqual(motif.instances[25].strand, '+')
- self.assertEqual(motif.instances[26].strand, '+')
- self.assertEqual(motif.instances[27].strand, '+')
- self.assertEqual(motif.instances[28].strand, '+')
- self.assertEqual(motif.instances[29].strand, '+')
- self.assertEqual(motif.instances[30].strand, '+')
- self.assertEqual(motif.instances[31].strand, '+')
- self.assertEqual(motif.instances[32].strand, '+')
- self.assertEqual(motif.instances[0].length, 29)
- self.assertEqual(motif.instances[1].length, 29)
- self.assertEqual(motif.instances[2].length, 29)
- self.assertEqual(motif.instances[3].length, 29)
- self.assertEqual(motif.instances[4].length, 29)
- self.assertEqual(motif.instances[5].length, 29)
- self.assertEqual(motif.instances[6].length, 29)
- self.assertEqual(motif.instances[7].length, 29)
- self.assertEqual(motif.instances[8].length, 29)
- self.assertEqual(motif.instances[9].length, 29)
- self.assertEqual(motif.instances[10].length, 29)
- self.assertEqual(motif.instances[11].length, 29)
- self.assertEqual(motif.instances[12].length, 29)
- self.assertEqual(motif.instances[13].length, 29)
- self.assertEqual(motif.instances[14].length, 29)
- self.assertEqual(motif.instances[15].length, 29)
- self.assertEqual(motif.instances[16].length, 29)
- self.assertEqual(motif.instances[17].length, 29)
- self.assertEqual(motif.instances[18].length, 29)
- self.assertEqual(motif.instances[19].length, 29)
- self.assertEqual(motif.instances[20].length, 29)
- self.assertEqual(motif.instances[21].length, 29)
- self.assertEqual(motif.instances[22].length, 29)
- self.assertEqual(motif.instances[23].length, 29)
- self.assertEqual(motif.instances[24].length, 29)
- self.assertEqual(motif.instances[25].length, 29)
- self.assertEqual(motif.instances[26].length, 29)
- self.assertEqual(motif.instances[27].length, 29)
- self.assertEqual(motif.instances[28].length, 29)
- self.assertEqual(motif.instances[29].length, 29)
- self.assertEqual(motif.instances[30].length, 29)
- self.assertEqual(motif.instances[31].length, 29)
- self.assertEqual(motif.instances[32].length, 29)
- self.assertEqual(motif.instances[0].start, 155)
- self.assertEqual(motif.instances[1].start, 149)
- self.assertEqual(motif.instances[2].start, 152)
- self.assertEqual(motif.instances[3].start, 152)
- self.assertEqual(motif.instances[4].start, 189)
- self.assertEqual(motif.instances[5].start, 160)
- self.assertEqual(motif.instances[6].start, 193)
- self.assertEqual(motif.instances[7].start, 160)
- self.assertEqual(motif.instances[8].start, 154)
- self.assertEqual(motif.instances[9].start, 152)
- self.assertEqual(motif.instances[10].start, 159)
- self.assertEqual(motif.instances[11].start, 232)
- self.assertEqual(motif.instances[12].start, 165)
- self.assertEqual(motif.instances[13].start, 467)
- self.assertEqual(motif.instances[14].start, 186)
- self.assertEqual(motif.instances[15].start, 208)
- self.assertEqual(motif.instances[16].start, 160)
- self.assertEqual(motif.instances[17].start, 155)
- self.assertEqual(motif.instances[18].start, 157)
- self.assertEqual(motif.instances[19].start, 157)
- self.assertEqual(motif.instances[20].start, 183)
- self.assertEqual(motif.instances[21].start, 144)
- self.assertEqual(motif.instances[22].start, 151)
- self.assertEqual(motif.instances[23].start, 198)
- self.assertEqual(motif.instances[24].start, 165)
- self.assertEqual(motif.instances[25].start, 154)
- self.assertEqual(motif.instances[26].start, 153)
- self.assertEqual(motif.instances[27].start, 88)
- self.assertEqual(motif.instances[28].start, 159)
- self.assertEqual(motif.instances[29].start, 152)
- self.assertEqual(motif.instances[30].start, 193)
- self.assertEqual(motif.instances[31].start, 26)
- self.assertEqual(motif.instances[32].start, 349)
- self.assertEqual(str(motif.instances[0]), "YSASKFAVLGLTESLMQEVRKHNIRVSAL")
- self.assertEqual(str(motif.instances[1]), "YSSTKGAMTMLTKAMAMELGPHKIRVNSV")
- self.assertEqual(str(motif.instances[2]), "YCASKAGMIGFSKSLAQEIATRNITVNCV")
- self.assertEqual(str(motif.instances[3]), "YSSSKFAVRGLTQTAARDLAPLGITVNGF")
- self.assertEqual(str(motif.instances[4]), "YATSKAALASLTRELAHDYAPHGIRVNAI")
- self.assertEqual(str(motif.instances[5]), "YAASKGGMKLMTETLALEYAPKGIRVNNI")
- self.assertEqual(str(motif.instances[6]), "YTMAKGALEGLTRSAALELAPLQIRVNGV")
- self.assertEqual(str(motif.instances[7]), "YSASKAFLSTFMESLRVDLRGTGVRVTCI")
- self.assertEqual(str(motif.instances[8]), "YSAAKFGGVGLTQSLALDLAEYGITVHSL")
- self.assertEqual(str(motif.instances[9]), "YGASKWGVRGLSKLAAVELGTDRIRVNSV")
- self.assertEqual(str(motif.instances[10]), "YASSKAAASHLVRNMAFDLGEKNIRVNGI")
- self.assertEqual(str(motif.instances[11]), "YGSSKAAVTMFSSVMRLELSKWGIKVASI")
- self.assertEqual(str(motif.instances[12]), "YVAAKGGVAMLTRAMAVDLARHGILVNMI")
- self.assertEqual(str(motif.instances[13]), "YSSSKAGILGLSKTMAIEGAKNNIKVNIV")
- self.assertEqual(str(motif.instances[14]), "YSASKFAIRGLAEALQMEVKPYNVYITVA")
- self.assertEqual(str(motif.instances[15]), "YCITKFGVEAFSDCLRYEMYPLGVKVSVV")
- self.assertEqual(str(motif.instances[16]), "YTASKFAVQAFVHTTRRQVAQYGVRVGAV")
- self.assertEqual(str(motif.instances[17]), "YCASKFALEGLCESLAVLLLPFGVHLSLI")
- self.assertEqual(str(motif.instances[18]), "YPASKASVIGLTHGLGREIIRKNIRVVGV")
- self.assertEqual(str(motif.instances[19]), "YSAAKAASINLMEGYRQGLEKYGIGVSVC")
- self.assertEqual(str(motif.instances[20]), "YSASKFALDGFFSSIRKEYSVSRVNVSIT")
- self.assertEqual(str(motif.instances[21]), "YGASKAALKSLALSVGLELAGSGVRCNVV")
- self.assertEqual(str(motif.instances[22]), "YSASKAAVSALTRAAALSCRKQGYAIRVN")
- self.assertEqual(str(motif.instances[23]), "YSASKAFVCAFSKALQEEYKAKEVIIQVL")
- self.assertEqual(str(motif.instances[24]), "YSASKAAADHLVRAWQRTYRLPSIVSNCS")
- self.assertEqual(str(motif.instances[25]), "YGATKWAVRDLMEVLRMESAQEGTNIRTA")
- self.assertEqual(str(motif.instances[26]), "YTAAKQAIVGLVRELAFELAPYVRVNGVG")
- self.assertEqual(str(motif.instances[27]), "YRMSKAALNMAVRSMSTDLRPEGFVTVLL")
- self.assertEqual(str(motif.instances[28]), "MGLAKASLEANVRYMANAMGPEGVRVNAI")
- self.assertEqual(str(motif.instances[29]), "YSGTKAAVVNFTSSLAKLAPITGVTAYTV")
- self.assertEqual(str(motif.instances[30]), "YGVTKIGVTVLSRIHARKLSEQRKGDKIL")
- self.assertEqual(str(motif.instances[31]), "KDSTLFGVSSLSDSLKGDFTSSALRCKEL")
- self.assertEqual(str(motif.instances[32]), "YINCVAPLRMTELCLPHLYETGSGRIVNI")
- motif = record.motifs[1]
- self.assertEqual(motif.num_occurrences, 33)
- self.assertAlmostEqual(motif.evalue, 2.3e-159)
- self.assertEqual(motif.alphabet, IUPAC.protein)
- self.assertEqual(motif.name, "Motif 2")
- self.assertEqual(len(motif.instances), 33)
- self.assertAlmostEqual(motif.instances[0].pvalue, 2.44e-23)
- self.assertAlmostEqual(motif.instances[1].pvalue, 5.50e-23)
- self.assertAlmostEqual(motif.instances[2].pvalue, 5.38e-22)
- self.assertAlmostEqual(motif.instances[3].pvalue, 5.65e-20)
- self.assertAlmostEqual(motif.instances[4].pvalue, 1.17e-19)
- self.assertAlmostEqual(motif.instances[5].pvalue, 1.17e-19)
- self.assertAlmostEqual(motif.instances[6].pvalue, 4.74e-19)
- self.assertAlmostEqual(motif.instances[7].pvalue, 9.31e-19)
- self.assertAlmostEqual(motif.instances[8].pvalue, 2.50e-18)
- self.assertAlmostEqual(motif.instances[9].pvalue, 3.45e-18)
- self.assertAlmostEqual(motif.instances[10].pvalue, 5.86e-18)
- self.assertAlmostEqual(motif.instances[11].pvalue, 9.86e-18)
- self.assertAlmostEqual(motif.instances[12].pvalue, 2.47e-17)
- self.assertAlmostEqual(motif.instances[13].pvalue, 3.01e-17)
- self.assertAlmostEqual(motif.instances[14].pvalue, 3.33e-17)
- self.assertAlmostEqual(motif.instances[15].pvalue, 4.06e-17)
- self.assertAlmostEqual(motif.instances[16].pvalue, 4.06e-17)
- self.assertAlmostEqual(motif.instances[17].pvalue, 8.05e-17)
- self.assertAlmostEqual(motif.instances[18].pvalue, 1.90e-16)
- self.assertAlmostEqual(motif.instances[19].pvalue, 2.77e-16)
- self.assertAlmostEqual(motif.instances[20].pvalue, 3.65e-16)
- self.assertAlmostEqual(motif.instances[21].pvalue, 8.31e-16)
- self.assertAlmostEqual(motif.instances[22].pvalue, 4.05e-15)
- self.assertAlmostEqual(motif.instances[23].pvalue, 5.24e-15)
- self.assertAlmostEqual(motif.instances[24].pvalue, 3.00e-14)
- self.assertAlmostEqual(motif.instances[25].pvalue, 8.47e-14)
- self.assertAlmostEqual(motif.instances[26].pvalue, 1.46e-13)
- self.assertAlmostEqual(motif.instances[27].pvalue, 1.46e-13)
- self.assertAlmostEqual(motif.instances[28].pvalue, 1.59e-12)
- self.assertAlmostEqual(motif.instances[29].pvalue, 6.97e-10)
- self.assertAlmostEqual(motif.instances[30].pvalue, 3.15e-09)
- self.assertAlmostEqual(motif.instances[31].pvalue, 2.77e-07)
- self.assertAlmostEqual(motif.instances[32].pvalue, 4.24e-07)
- self.assertEqual(motif.instances[0].sequence_name, 'HDE_CANTR')
- self.assertEqual(motif.instances[1].sequence_name, 'DHII_HUMAN')
- self.assertEqual(motif.instances[2].sequence_name, 'YINL_LISMO')
- self.assertEqual(motif.instances[3].sequence_name, 'HDHA_ECOLI')
- self.assertEqual(motif.instances[4].sequence_name, 'RIDH_KLEAE')
- self.assertEqual(motif.instances[5].sequence_name, 'BUDC_KLETE')
- self.assertEqual(motif.instances[6].sequence_name, 'ENTA_ECOLI')
- self.assertEqual(motif.instances[7].sequence_name, 'AP27_MOUSE')
- self.assertEqual(motif.instances[8].sequence_name, 'DHMA_FLAS1')
- self.assertEqual(motif.instances[9].sequence_name, 'YRTP_BACSU')
- self.assertEqual(motif.instances[10].sequence_name, 'DHGB_BACME')
- self.assertEqual(motif.instances[11].sequence_name, 'DHB3_HUMAN')
- self.assertEqual(motif.instances[12].sequence_name, 'PCR_PEA')
- self.assertEqual(motif.instances[13].sequence_name, 'BDH_HUMAN')
- self.assertEqual(motif.instances[14].sequence_name, 'BA72_EUBSP')
- self.assertEqual(motif.instances[15].sequence_name, 'FIXR_BRAJA')
- self.assertEqual(motif.instances[16].sequence_name, '3BHD_COMTE')
- self.assertEqual(motif.instances[17].sequence_name, '2BHD_STREX')
- self.assertEqual(motif.instances[18].sequence_name, 'HMTR_LEIMA')
- self.assertEqual(motif.instances[19].sequence_name, 'FVT1_HUMAN')
- self.assertEqual(motif.instances[20].sequence_name, 'DHB2_HUMAN')
- self.assertEqual(motif.instances[21].sequence_name, 'LIGD_PSEPA')
- self.assertEqual(motif.instances[22].sequence_name, 'NODG_RHIME')
- self.assertEqual(motif.instances[23].sequence_name, 'DHCA_HUMAN')
- self.assertEqual(motif.instances[24].sequence_name, 'MAS1_AGRRA')
- self.assertEqual(motif.instances[25].sequence_name, 'BPHB_PSEPS')
- self.assertEqual(motif.instances[26].sequence_name, 'GUTD_ECOLI')
- self.assertEqual(motif.instances[27].sequence_name, 'DHES_HUMAN')
- self.assertEqual(motif.instances[28].sequence_name, 'RFBB_NEIGO')
- self.assertEqual(motif.instances[29].sequence_name, 'ADH_DROME')
- self.assertEqual(motif.instances[30].sequence_name, 'FABI_ECOLI')
- self.assertEqual(motif.instances[31].sequence_name, 'YURA_MYXXA')
- self.assertEqual(motif.instances[32].sequence_name, 'CSGA_MYXXA')
- self.assertEqual(motif.instances[0].start, 323)
- self.assertEqual(motif.instances[1].start, 35)
- self.assertEqual(motif.instances[2].start, 6)
- self.assertEqual(motif.instances[3].start, 12)
- self.assertEqual(motif.instances[4].start, 15)
- self.assertEqual(motif.instances[5].start, 3)
- self.assertEqual(motif.instances[6].start, 6)
- self.assertEqual(motif.instances[7].start, 8)
- self.assertEqual(motif.instances[8].start, 15)
- self.assertEqual(motif.instances[9].start, 7)
- self.assertEqual(motif.instances[10].start, 8)
- self.assertEqual(motif.instances[11].start, 49)
- self.assertEqual(motif.instances[12].start, 87)
- self.assertEqual(motif.instances[13].start, 56)
- self.assertEqual(motif.instances[14].start, 7)
- self.assertEqual(motif.instances[15].start, 37)
- self.assertEqual(motif.instances[16].start, 7)
- self.assertEqual(motif.instances[17].start, 7)
- self.assertEqual(motif.instances[18].start, 7)
- self.assertEqual(motif.instances[19].start, 33)
- self.assertEqual(motif.instances[20].start, 83)
- self.assertEqual(motif.instances[21].start, 7)
- self.assertEqual(motif.instances[22].start, 7)
- self.assertEqual(motif.instances[23].start, 5)
- self.assertEqual(motif.instances[24].start, 246)
- self.assertEqual(motif.instances[25].start, 6)
- self.assertEqual(motif.instances[26].start, 3)
- self.assertEqual(motif.instances[27].start, 3)
- self.assertEqual(motif.instances[28].start, 7)
- self.assertEqual(motif.instances[29].start, 7)
- self.assertEqual(motif.instances[30].start, 7)
- self.assertEqual(motif.instances[31].start, 117)
- self.assertEqual(motif.instances[32].start, 52)
- self.assertEqual(str(motif.instances[0]), 'KVVLITGAGAGLGKEYAKWFAKYGAKVVV')
- self.assertEqual(str(motif.instances[1]), 'KKVIVTGASKGIGREMAYHLAKMGAHVVV')
- self.assertEqual(str(motif.instances[2]), 'KVIIITGASSGIGKATALLLAEKGAKLVL')
- self.assertEqual(str(motif.instances[3]), 'KCAIITGAGAGIGKEIAITFATAGASVVV')
- self.assertEqual(str(motif.instances[4]), 'KVAAITGAASGIGLECARTLLGAGAKVVL')
- self.assertEqual(str(motif.instances[5]), 'KVALVTGAGQGIGKAIALRLVKDGFAVAI')
- self.assertEqual(str(motif.instances[6]), 'KNVWVTGAGKGIGYATALAFVEAGAKVTG')
- self.assertEqual(str(motif.instances[7]), 'LRALVTGAGKGIGRDTVKALHASGAKVVA')
- self.assertEqual(str(motif.instances[8]), 'KAAIVTGAAGGIGRATVEAYLREGASVVA')
- self.assertEqual(str(motif.instances[9]), 'KTALITGGGRGIGRATALALAKEGVNIGL')
- self.assertEqual(str(motif.instances[10]), 'KVVVITGSSTGLGKSMAIRFATEKAKVVV')
- self.assertEqual(str(motif.instances[11]), 'QWAVITGAGDGIGKAYSFELAKRGLNVVL')
- self.assertEqual(str(motif.instances[12]), 'GNVVITGASSGLGLATAKALAESGKWHVI')
- self.assertEqual(str(motif.instances[13]), 'KAVLVTGCDSGFGFSLAKHLHSKGFLVFA')
- self.assertEqual(str(motif.instances[14]), 'KVTIITGGTRGIGFAAAKIFIDNGAKVSI')
- self.assertEqual(str(motif.instances[15]), 'KVMLLTGASRGIGHATAKLFSEAGWRIIS')
- self.assertEqual(str(motif.instances[16]), 'KVALVTGGASGVGLEVVKLLLGEGAKVAF')
- self.assertEqual(str(motif.instances[17]), 'KTVIITGGARGLGAEAARQAVAAGARVVL')
- self.assertEqual(str(motif.instances[18]), 'PVALVTGAAKRLGRSIAEGLHAEGYAVCL')
- self.assertEqual(str(motif.instances[19]), 'AHVVVTGGSSGIGKCIAIECYKQGAFITL')
- self.assertEqual(str(motif.instances[20]), 'KAVLVTGGDCGLGHALCKYLDELGFTVFA')
- self.assertEqual(str(motif.instances[21]), 'QVAFITGGASGAGFGQAKVFGQAGAKIVV')
- self.assertEqual(str(motif.instances[22]), 'RKALVTGASGAIGGAIARVLHAQGAIVGL')
- self.assertEqual(str(motif.instances[23]), 'HVALVTGGNKGIGLAIVRDLCRLFSGDVV')
- self.assertEqual(str(motif.instances[24]), 'PVILVSGSNRGVGKAIAEDLIAHGYRLSL')
- self.assertEqual(str(motif.instances[25]), 'EAVLITGGASGLGRALVDRFVAEAKVAVL')
- self.assertEqual(str(motif.instances[26]), 'QVAVVIGGGQTLGAFLCHGLAAEGYRVAV')
- self.assertEqual(str(motif.instances[27]), 'TVVLITGCSSGIGLHLAVRLASDPSQSFK')
- self.assertEqual(str(motif.instances[28]), 'KNILVTGGAGFIGSAVVRHIIQNTRDSVV')
- self.assertEqual(str(motif.instances[29]), 'KNVIFVAGLGGIGLDTSKELLKRDLKNLV')
- self.assertEqual(str(motif.instances[30]), 'KRILVTGVASKLSIAYGIAQAMHREGAEL')
- self.assertEqual(str(motif.instances[31]), 'IDTNVTGAAATLSAVLPQMVERKRGHLVG')
- self.assertEqual(str(motif.instances[32]), 'TSAMLPGLRQGALRRVAHVTSRMGSLAAN')
- handle.close()
-
- def test_meme_parser_4(self):
- """Test if Motif can parse MEME output files (fourth test)
- """
- from Bio.Motif.Parsers import MEME
- handle = open("Motif/meme.protein.tcm.txt")
- record = MEME.read(handle)
- self.assertEqual(record.version, '3.0')
- self.assertEqual(record.datafile, 'farntrans5.s')
- self.assertEqual(record.alphabet, IUPAC.protein)
- self.assertEqual(len(record.sequence_names), 5)
- self.assertEqual(record.sequence_names[0], "RAM1_YEAST")
- self.assertEqual(record.sequence_names[1], "PFTB_RAT")
- self.assertEqual(record.sequence_names[2], "BET2_YEAST")
- self.assertEqual(record.sequence_names[3], "RATRABGERB")
- self.assertEqual(record.sequence_names[4], "CAL1_YEAST")
- self.assertEqual(record.command, 'meme farntrans5.s -mod tcm -protein -nmotifs 2')
- self.assertEqual(len(record.motifs), 2)
- motif = record.motifs[0]
- self.assertEqual(motif.num_occurrences, 24)
- self.assertAlmostEqual(motif.evalue, 2.2e-94)
- self.assertEqual(motif.alphabet, IUPAC.protein)
- self.assertEqual(motif.name, "Motif 1")
- self.assertEqual(len(motif.instances), 24)
- self.assertAlmostEqual(motif.instances[0].pvalue, 7.28e-22)
- self.assertAlmostEqual(motif.instances[1].pvalue, 6.18e-21)
- self.assertAlmostEqual(motif.instances[2].pvalue, 9.17e-20)
- self.assertAlmostEqual(motif.instances[3].pvalue, 1.15e-19)
- self.assertAlmostEqual(motif.instances[4].pvalue, 4.30e-19)
- self.assertAlmostEqual(motif.instances[5].pvalue, 7.36e-19)
- self.assertAlmostEqual(motif.instances[6].pvalue, 8.19e-19)
- self.assertAlmostEqual(motif.instances[7].pvalue, 2.10e-18)
- self.assertAlmostEqual(motif.instances[8].pvalue, 1.43e-17)
- self.assertAlmostEqual(motif.instances[9].pvalue, 3.41e-17)
- self.assertAlmostEqual(motif.instances[10].pvalue, 5.00e-17)
- self.assertAlmostEqual(motif.instances[11].pvalue, 6.64e-17)
- self.assertAlmostEqual(motif.instances[12].pvalue, 1.27e-16)
- self.assertAlmostEqual(motif.instances[13].pvalue, 3.17e-16)
- self.assertAlmostEqual(motif.instances[14].pvalue, 3.47e-16)
- self.assertAlmostEqual(motif.instances[15].pvalue, 4.30e-15)
- self.assertAlmostEqual(motif.instances[16].pvalue, 2.40e-14)
- self.assertAlmostEqual(motif.instances[17].pvalue, 2.81e-14)
- self.assertAlmostEqual(motif.instances[18].pvalue, 7.78e-14)
- self.assertAlmostEqual(motif.instances[19].pvalue, 1.14e-13)
- self.assertAlmostEqual(motif.instances[20].pvalue, 1.33e-13)
- self.assertAlmostEqual(motif.instances[21].pvalue, 3.52e-13)
- self.assertAlmostEqual(motif.instances[22].pvalue, 5.47e-13)
- self.assertAlmostEqual(motif.instances[23].pvalue, 3.11e-10)
- self.assertEqual(motif.instances[0].sequence_name, "BET2_YEAST")
- self.assertEqual(motif.instances[1].sequence_name, "RATRABGERB")
- self.assertEqual(motif.instances[2].sequence_name, "CAL1_YEAST")
- self.assertEqual(motif.instances[3].sequence_name, "PFTB_RAT")
- self.assertEqual(motif.instances[4].sequence_name, "PFTB_RAT")
- self.assertEqual(motif.instances[5].sequence_name, "RATRABGERB")
- self.assertEqual(motif.instances[6].sequence_name, "RATRABGERB")
- self.assertEqual(motif.instances[7].sequence_name, "BET2_YEAST")
- self.assertEqual(motif.instances[8].sequence_name, "RATRABGERB")
- self.assertEqual(motif.instances[9].sequence_name, "BET2_YEAST")
- self.assertEqual(motif.instances[10].sequence_name, "RAM1_YEAST")
- self.assertEqual(motif.instances[11].sequence_name, "BET2_YEAST")
- self.assertEqual(motif.instances[12].sequence_name, "RAM1_YEAST")
- self.assertEqual(motif.instances[13].sequence_name, "PFTB_RAT")
- self.assertEqual(motif.instances[14].sequence_name, "RAM1_YEAST")
- self.assertEqual(motif.instances[15].sequence_name, "PFTB_RAT")
- self.assertEqual(motif.instances[16].sequence_name, "RATRABGERB")
- self.assertEqual(motif.instances[17].sequence_name, "PFTB_RAT")
- self.assertEqual(motif.instances[18].sequence_name, "BET2_YEAST")
- self.assertEqual(motif.instances[19].sequence_name, "CAL1_YEAST")
- self.assertEqual(motif.instances[20].sequence_name, "RAM1_YEAST")
- self.assertEqual(motif.instances[21].sequence_name, "RAM1_YEAST")
- self.assertEqual(motif.instances[22].sequence_name, "CAL1_YEAST")
- self.assertEqual(motif.instances[23].sequence_name, "BET2_YEAST")
- self.assertEqual(motif.instances[0].strand, '+')
- self.assertEqual(motif.instances[1].strand, '+')
- self.assertEqual(motif.instances[2].strand, '+')
- self.assertEqual(motif.instances[3].strand, '+')
- self.assertEqual(motif.instances[4].strand, '+')
- self.assertEqual(motif.instances[5].strand, '+')
- self.assertEqual(motif.instances[6].strand, '+')
- self.assertEqual(motif.instances[7].strand, '+')
- self.assertEqual(motif.instances[8].strand, '+')
- self.assertEqual(motif.instances[9].strand, '+')
- self.assertEqual(motif.instances[10].strand, '+')
- self.assertEqual(motif.instances[11].strand, '+')
- self.assertEqual(motif.instances[12].strand, '+')
- self.assertEqual(motif.instances[13].strand, '+')
- self.assertEqual(motif.instances[14].strand, '+')
- self.assertEqual(motif.instances[15].strand, '+')
- self.assertEqual(motif.instances[16].strand, '+')
- self.assertEqual(motif.instances[17].strand, '+')
- self.assertEqual(motif.instances[18].strand, '+')
- self.assertEqual(motif.instances[19].strand, '+')
- self.assertEqual(motif.instances[20].strand, '+')
- self.assertEqual(motif.instances[21].strand, '+')
- self.assertEqual(motif.instances[22].strand, '+')
- self.assertEqual(motif.instances[23].strand, '+')
- self.assertEqual(motif.instances[0].length, 30)
- self.assertEqual(motif.instances[1].length, 30)
- self.assertEqual(motif.instances[2].length, 30)
- self.assertEqual(motif.instances[3].length, 30)
- self.assertEqual(motif.instances[4].length, 30)
- self.assertEqual(motif.instances[5].length, 30)
- self.assertEqual(motif.instances[6].length, 30)
- self.assertEqual(motif.instances[7].length, 30)
- self.assertEqual(motif.instances[8].length, 30)
- self.assertEqual(motif.instances[9].length, 30)
- self.assertEqual(motif.instances[10].length, 30)
- self.assertEqual(motif.instances[11].length, 30)
- self.assertEqual(motif.instances[12].length, 30)
- self.assertEqual(motif.instances[13].length, 30)
- self.assertEqual(motif.instances[14].length, 30)
- self.assertEqual(motif.instances[15].length, 30)
- self.assertEqual(motif.instances[16].length, 30)
- self.assertEqual(motif.instances[17].length, 30)
- self.assertEqual(motif.instances[18].length, 30)
- self.assertEqual(motif.instances[19].length, 30)
- self.assertEqual(motif.instances[20].length, 30)
- self.assertEqual(motif.instances[21].length, 30)
- self.assertEqual(motif.instances[22].length, 30)
- self.assertEqual(motif.instances[23].length, 30)
- self.assertEqual(motif.instances[0].start, 223)
- self.assertEqual(motif.instances[1].start, 227)
- self.assertEqual(motif.instances[2].start, 275)
- self.assertEqual(motif.instances[3].start, 237)
- self.assertEqual(motif.instances[4].start, 138)
- self.assertEqual(motif.instances[5].start, 179)
- self.assertEqual(motif.instances[6].start, 131)
- self.assertEqual(motif.instances[7].start, 172)
- self.assertEqual(motif.instances[8].start, 276)
- self.assertEqual(motif.instances[9].start, 124)
- self.assertEqual(motif.instances[10].start, 247)
- self.assertEqual(motif.instances[11].start, 272)
- self.assertEqual(motif.instances[12].start, 145)
- self.assertEqual(motif.instances[13].start, 286)
- self.assertEqual(motif.instances[14].start, 296)
- self.assertEqual(motif.instances[15].start, 348)
- self.assertEqual(motif.instances[16].start, 83)
- self.assertEqual(motif.instances[17].start, 189)
- self.assertEqual(motif.instances[18].start, 73)
- self.assertEqual(motif.instances[19].start, 205)
- self.assertEqual(motif.instances[20].start, 198)
- self.assertEqual(motif.instances[21].start, 349)
- self.assertEqual(motif.instances[22].start, 327)
- self.assertEqual(motif.instances[23].start, 24)
- self.assertEqual(str(motif.instances[0]), "GGLNGRPSKLPDVCYSWWVLSSLAIIGRLD")
- self.assertEqual(str(motif.instances[1]), "GGLNGRPEKLPDVCYSWWVLASLKIIGRLH")
- self.assertEqual(str(motif.instances[2]), "GGFQGRENKFADTCYAFWCLNSLHLLTKDW")
- self.assertEqual(str(motif.instances[3]), "GGIGGVPGMEAHGGYTFCGLAALVILKKER")
- self.assertEqual(str(motif.instances[4]), "GGFGGGPGQYPHLAPTYAAVNALCIIGTEE")
- self.assertEqual(str(motif.instances[5]), "GGFGCRPGSESHAGQIYCCTGFLAITSQLH")
- self.assertEqual(str(motif.instances[6]), "GSFAGDIWGEIDTRFSFCAVATLALLGKLD")
- self.assertEqual(str(motif.instances[7]), "GGFGLCPNAESHAAQAFTCLGALAIANKLD")
- self.assertEqual(str(motif.instances[8]), "GGFADRPGDMVDPFHTLFGIAGLSLLGEEQ")
- self.assertEqual(str(motif.instances[9]), "GSFQGDRFGEVDTRFVYTALSALSILGELT")
- self.assertEqual(str(motif.instances[10]), "GFGSCPHVDEAHGGYTFCATASLAILRSMD")
- self.assertEqual(str(motif.instances[11]), "GGISDRPENEVDVFHTVFGVAGLSLMGYDN")
- self.assertEqual(str(motif.instances[12]), "GPFGGGPGQLSHLASTYAAINALSLCDNID")
- self.assertEqual(str(motif.instances[13]), "GGFQGRCNKLVDGCYSFWQAGLLPLLHRAL")
- self.assertEqual(str(motif.instances[14]), "RGFCGRSNKLVDGCYSFWVGGSAAILEAFG")
- self.assertEqual(str(motif.instances[15]), "GGLLDKPGKSRDFYHTCYCLSGLSIAQHFG")
- self.assertEqual(str(motif.instances[16]), "GGVSASIGHDPHLLYTLSAVQILTLYDSIH")
- self.assertEqual(str(motif.instances[17]), "GSFLMHVGGEVDVRSAYCAASVASLTNIIT")
- self.assertEqual(str(motif.instances[18]), "GAFAPFPRHDAHLLTTLSAVQILATYDALD")
- self.assertEqual(str(motif.instances[19]), "YNGAFGAHNEPHSGYTSCALSTLALLSSLE")
- self.assertEqual(str(motif.instances[20]), "GFKTCLEVGEVDTRGIYCALSIATLLNILT")
- self.assertEqual(str(motif.instances[21]), "PGLRDKPGAHSDFYHTNYCLLGLAVAESSY")
- self.assertEqual(str(motif.instances[22]), "GGFSKNDEEDADLYHSCLGSAALALIEGKF")
- self.assertEqual(str(motif.instances[23]), "HNFEYWLTEHLRLNGIYWGLTALCVLDSPE")
- motif = record.motifs[1]
- self.assertEqual(motif.num_occurrences, 21)
- self.assertAlmostEqual(motif.evalue, 3.1e-19)
- self.assertEqual(motif.alphabet, IUPAC.protein)
- self.assertEqual(motif.name, "Motif 2")
- self.assertEqual(len(motif.instances), 21)
- self.assertAlmostEqual(motif.instances[0].pvalue, 2.24e-13)
- self.assertAlmostEqual(motif.instances[1].pvalue, 1.30e-12)
- self.assertAlmostEqual(motif.instances[2].pvalue, 4.20e-12)
- self.assertAlmostEqual(motif.instances[3].pvalue, 9.60e-12)
- self.assertAlmostEqual(motif.instances[4].pvalue, 5.08e-11)
- self.assertAlmostEqual(motif.instances[5].pvalue, 5.01e-10)
- self.assertAlmostEqual(motif.instances[6].pvalue, 6.90e-10)
- self.assertAlmostEqual(motif.instances[7].pvalue, 1.57e-09)
- self.assertAlmostEqual(motif.instances[8].pvalue, 2.34e-09)
- self.assertAlmostEqual(motif.instances[9].pvalue, 4.59e-09)
- self.assertAlmostEqual(motif.instances[10].pvalue, 1.65e-08)
- self.assertAlmostEqual(motif.instances[11].pvalue, 1.65e-08)
- self.assertAlmostEqual(motif.instances[12].pvalue, 1.65e-08)
- self.assertAlmostEqual(motif.instances[13].pvalue, 2.54e-08)
- self.assertAlmostEqual(motif.instances[14].pvalue, 4.58e-08)
- self.assertAlmostEqual(motif.instances[15].pvalue, 5.86e-08)
- self.assertAlmostEqual(motif.instances[16].pvalue, 1.52e-07)
- self.assertAlmostEqual(motif.instances[17].pvalue, 1.91e-07)
- self.assertAlmostEqual(motif.instances[18].pvalue, 4.34e-07)
- self.assertAlmostEqual(motif.instances[19].pvalue, 5.01e-07)
- self.assertAlmostEqual(motif.instances[20].pvalue, 5.78e-07)
- self.assertEqual(motif.instances[0].sequence_name, "BET2_YEAST")
- self.assertEqual(motif.instances[1].sequence_name, "RATRABGERB")
- self.assertEqual(motif.instances[2].sequence_name, "RATRABGERB")
- self.assertEqual(motif.instances[3].sequence_name, "RATRABGERB")
- self.assertEqual(motif.instances[4].sequence_name, "RAM1_YEAST")
- self.assertEqual(motif.instances[5].sequence_name, "CAL1_YEAST")
- self.assertEqual(motif.instances[6].sequence_name, "BET2_YEAST")
- self.assertEqual(motif.instances[7].sequence_name, "RATRABGERB")
- self.assertEqual(motif.instances[8].sequence_name, "PFTB_RAT")
- self.assertEqual(motif.instances[9].sequence_name, "RAM1_YEAST")
- self.assertEqual(motif.instances[10].sequence_name, "CAL1_YEAST")
- self.assertEqual(motif.instances[11].sequence_name, "PFTB_RAT")
- self.assertEqual(motif.instances[12].sequence_name, "PFTB_RAT")
- self.assertEqual(motif.instances[13].sequence_name, "RAM1_YEAST")
- self.assertEqual(motif.instances[14].sequence_name, "PFTB_RAT")
- self.assertEqual(motif.instances[15].sequence_name, "CAL1_YEAST")
- self.assertEqual(motif.instances[16].sequence_name, "PFTB_RAT")
- self.assertEqual(motif.instances[17].sequence_name, "CAL1_YEAST")
- self.assertEqual(motif.instances[18].sequence_name, "BET2_YEAST")
- self.assertEqual(motif.instances[19].sequence_name, "BET2_YEAST")
- self.assertEqual(motif.instances[20].sequence_name, "RAM1_YEAST")
- self.assertEqual(motif.instances[0].strand, '+')
- self.assertEqual(motif.instances[1].strand, '+')
- self.assertEqual(motif.instances[2].strand, '+')
- self.assertEqual(motif.instances[3].strand, '+')
- self.assertEqual(motif.instances[4].strand, '+')
- self.assertEqual(motif.instances[5].strand, '+')
- self.assertEqual(motif.instances[6].strand, '+')
- self.assertEqual(motif.instances[7].strand, '+')
- self.assertEqual(motif.instances[8].strand, '+')
- self.assertEqual(motif.instances[9].strand, '+')
- self.assertEqual(motif.instances[10].strand, '+')
- self.assertEqual(motif.instances[11].strand, '+')
- self.assertEqual(motif.instances[12].strand, '+')
- self.assertEqual(motif.instances[13].strand, '+')
- self.assertEqual(motif.instances[14].strand, '+')
- self.assertEqual(motif.instances[15].strand, '+')
- self.assertEqual(motif.instances[16].strand, '+')
- self.assertEqual(motif.instances[17].strand, '+')
- self.assertEqual(motif.instances[18].strand, '+')
- self.assertEqual(motif.instances[19].strand, '+')
- self.assertEqual(motif.instances[20].strand, '+')
- self.assertEqual(motif.instances[0].length, 14)
- self.assertEqual(motif.instances[1].length, 14)
- self.assertEqual(motif.instances[2].length, 14)
- self.assertEqual(motif.instances[3].length, 14)
- self.assertEqual(motif.instances[4].length, 14)
- self.assertEqual(motif.instances[5].length, 14)
- self.assertEqual(motif.instances[6].length, 14)
- self.assertEqual(motif.instances[7].length, 14)
- self.assertEqual(motif.instances[8].length, 14)
- self.assertEqual(motif.instances[9].length, 14)
- self.assertEqual(motif.instances[10].length, 14)
- self.assertEqual(motif.instances[11].length, 14)
- self.assertEqual(motif.instances[12].length, 14)
- self.assertEqual(motif.instances[13].length, 14)
- self.assertEqual(motif.instances[14].length, 14)
- self.assertEqual(motif.instances[15].length, 14)
- self.assertEqual(motif.instances[16].length, 14)
- self.assertEqual(motif.instances[17].length, 14)
- self.assertEqual(motif.instances[18].length, 14)
- self.assertEqual(motif.instances[19].length, 14)
- self.assertEqual(motif.instances[20].length, 14)
- self.assertEqual(motif.instances[0].start, 254)
- self.assertEqual(motif.instances[1].start, 258)
- self.assertEqual(motif.instances[2].start, 162)
- self.assertEqual(motif.instances[3].start, 66)
- self.assertEqual(motif.instances[4].start, 278)
- self.assertEqual(motif.instances[5].start, 190)
- self.assertEqual(motif.instances[6].start, 55)
- self.assertEqual(motif.instances[7].start, 114)
- self.assertEqual(motif.instances[8].start, 172)
- self.assertEqual(motif.instances[9].start, 330)
- self.assertEqual(motif.instances[10].start, 126)
- self.assertEqual(motif.instances[11].start, 268)
- self.assertEqual(motif.instances[12].start, 220)
- self.assertEqual(motif.instances[13].start, 229)
- self.assertEqual(motif.instances[14].start, 330)
- self.assertEqual(motif.instances[15].start, 239)
- self.assertEqual(motif.instances[16].start, 121)
- self.assertEqual(motif.instances[17].start, 362)
- self.assertEqual(motif.instances[18].start, 107)
- self.assertEqual(motif.instances[19].start, 155)
- self.assertEqual(motif.instances[20].start, 180)
- self.assertEqual(str(motif.instances[0]), "INYEKLTEFILKCQ")
- self.assertEqual(str(motif.instances[1]), "IDREKLRSFILACQ")
- self.assertEqual(str(motif.instances[2]), "INVEKAIEFVLSCM")
- self.assertEqual(str(motif.instances[3]), "MNKEEILVFIKSCQ")
- self.assertEqual(str(motif.instances[4]), "INVEKLLEWSSARQ")
- self.assertEqual(str(motif.instances[5]), "IDTEKLLGYIMSQQ")
- self.assertEqual(str(motif.instances[6]), "FVKEEVISFVLSCW")
- self.assertEqual(str(motif.instances[7]), "INVDKVVAYVQSLQ")
- self.assertEqual(str(motif.instances[8]), "INREKLLQYLYSLK")
- self.assertEqual(str(motif.instances[9]), "FNKHALRDYILYCC")
- self.assertEqual(str(motif.instances[10]), "LDKRSLARFVSKCQ")
- self.assertEqual(str(motif.instances[11]), "LNLKSLLQWVTSRQ")
- self.assertEqual(str(motif.instances[12]), "DLFEGTAEWIARCQ")
- self.assertEqual(str(motif.instances[13]), "ELTEGVLNYLKNCQ")
- self.assertEqual(str(motif.instances[14]), "FHQQALQEYILMCC")
- self.assertEqual(str(motif.instances[15]), "KFKEDTITWLLHRQ")
- self.assertEqual(str(motif.instances[16]), "IVATDVCQFLELCQ")
- self.assertEqual(str(motif.instances[17]), "IPQEIFNDFSKRCC")
- self.assertEqual(str(motif.instances[18]), "DRKVRLISFIRGNQ")
- self.assertEqual(str(motif.instances[19]), "EVVDPAVDFVLKCY")
- self.assertEqual(str(motif.instances[20]), "IDRKGIYQWLISLK")
- handle.close()
-
-
-class TestMAST(unittest.TestCase):
-
- def test_mast_parser_1(self):
- """Test if Motif can parse MAST output files (first test)
- """
- from Bio.Motif.Parsers import MAST
- handle = open("Motif/mast.dna.oops.txt")
- record = MAST.read(handle)
- self.assertEqual(record.version, "3.0")
- self.assertEqual(record.database, "INO_up800.s")
- self.assertEqual(record.alphabet, IUPAC.unambiguous_dna)
- self.assertEqual(len(record.motifs), 2)
- self.assertEqual(record.motifs[0].alphabet, IUPAC.unambiguous_dna)
- self.assertEqual(record.motifs[0].length, 12)
- self.assertEqual(record.motifs[0].name, "1")
- self.assertEqual(record.motifs[1].alphabet, IUPAC.unambiguous_dna)
- self.assertEqual(record.motifs[1].length, 10)
- self.assertEqual(record.motifs[1].name, "2")
- self.assertEqual(len(record.sequences), 7)
- self.assertEqual(record.sequences[0], "ACC1")
- self.assertEqual(record.sequences[1], "CHO1")
- self.assertEqual(record.sequences[2], "INO1")
- self.assertEqual(record.sequences[3], "FAS1")
- self.assertEqual(record.sequences[4], "OPI3")
- self.assertEqual(record.sequences[5], "CHO2")
- self.assertEqual(record.sequences[6], "FAS2")
- self.assertEqual(record.diagrams["ACC1"], "82_[+1]_137_[+2]_559")
- self.assertEqual(record.diagrams["CHO1"], "152_[+2]_396_[-2]_42_[+1]_17_[+1]_149")
- self.assertEqual(record.diagrams["INO1"], "282_[-2]_327_[-1]_55_[+1]_102")
- self.assertEqual(record.diagrams["FAS1"], "43_[+2]_41_[+1]_694")
- self.assertEqual(record.diagrams["OPI3"], "185_[-2]_144_[+1]_449")
- self.assertEqual(record.diagrams["CHO2"], "353_[+1]_47_[-2]_378")
- self.assertEqual(record.diagrams["FAS2"], "184_[-2]_372_[+1]_222")
- handle.close()
-
- def test_mast_parser_2(self):
- """Test if Motif can parse MAST output files (second test)
- """
- from Bio.Motif.Parsers import MAST
- handle = open("Motif/mast.protein.oops.txt")
- record = MAST.read(handle)
- self.assertEqual(record.version, "3.0")
- self.assertEqual(record.database, "adh.s")
- self.assertEqual(record.alphabet, IUPAC.protein)
- self.assertEqual(len(record.motifs), 2)
- self.assertEqual(record.motifs[0].alphabet, IUPAC.protein)
- self.assertEqual(record.motifs[0].length, 29)
- self.assertEqual(record.motifs[0].name, "1")
- self.assertEqual(record.motifs[1].alphabet, IUPAC.protein)
- self.assertEqual(record.motifs[1].length, 29)
- self.assertEqual(record.motifs[1].name, "2")
- self.assertEqual(len(record.sequences), 33)
- self.assertEqual(record.sequences[0], "BUDC_KLETE")
- self.assertEqual(record.sequences[1], "YRTP_BACSU")
- self.assertEqual(record.sequences[2], "AP27_MOUSE")
- self.assertEqual(record.sequences[3], "HDE_CANTR")
- self.assertEqual(record.sequences[4], "HDHA_ECOLI")
- self.assertEqual(record.sequences[5], "DHII_HUMAN")
- self.assertEqual(record.sequences[6], "FIXR_BRAJA")
- self.assertEqual(record.sequences[7], "DHGB_BACME")
- self.assertEqual(record.sequences[8], "NODG_RHIME")
- self.assertEqual(record.sequences[9], "RIDH_KLEAE")
- self.assertEqual(record.sequences[10], "YINL_LISMO")
- self.assertEqual(record.sequences[11], "DHMA_FLAS1")
- self.assertEqual(record.sequences[12], "HMTR_LEIMA")
- self.assertEqual(record.sequences[13], "2BHD_STREX")
- self.assertEqual(record.sequences[14], "ENTA_ECOLI")
- self.assertEqual(record.sequences[15], "DHB2_HUMAN")
- self.assertEqual(record.sequences[16], "BDH_HUMAN")
- self.assertEqual(record.sequences[17], "BA72_EUBSP")
- self.assertEqual(record.sequences[18], "FVT1_HUMAN")
- self.assertEqual(record.sequences[19], "GUTD_ECOLI")
- self.assertEqual(record.sequences[20], "DHB3_HUMAN")
- self.assertEqual(record.sequences[21], "3BHD_COMTE")
- self.assertEqual(record.sequences[22], "LIGD_PSEPA")
- self.assertEqual(record.sequences[23], "DHES_HUMAN")
- self.assertEqual(record.sequences[24], "RFBB_NEIGO")
- self.assertEqual(record.sequences[25], "BPHB_PSEPS")
- self.assertEqual(record.sequences[26], "YURA_MYXXA")
- self.assertEqual(record.sequences[27], "PCR_PEA")
- self.assertEqual(record.sequences[28], "DHCA_HUMAN")
- self.assertEqual(record.sequences[29], "ADH_DROME")
- self.assertEqual(record.sequences[30], "MAS1_AGRRA")
- self.assertEqual(record.sequences[31], "FABI_ECOLI")
- self.assertEqual(record.sequences[32], "CSGA_MYXXA")
- self.assertEqual(record.diagrams["BUDC_KLETE"], "2_[2]_120_[1]_61")
- self.assertEqual(record.diagrams["YRTP_BACSU"], "6_[2]_119_[1]_55")
- self.assertEqual(record.diagrams["AP27_MOUSE"], "7_[2]_112_[1]_67")
- self.assertEqual(record.diagrams["HDE_CANTR"], "8_[2]_125_[1]_131_[2]_115_[1]_411")
- self.assertEqual(record.diagrams["HDHA_ECOLI"], "11_[2]_74_[1]_15_[1]_68")
- self.assertEqual(record.diagrams["DHII_HUMAN"], "34_[2]_119_[1]_81")
- self.assertEqual(record.diagrams["FIXR_BRAJA"], "36_[2]_123_[1]_61")
- self.assertEqual(record.diagrams["DHGB_BACME"], "7_[2]_123_[1]_74")
- self.assertEqual(record.diagrams["NODG_RHIME"], "6_[2]_116_[1]_65")
- self.assertEqual(record.diagrams["RIDH_KLEAE"], "14_[2]_116_[1]_61")
- self.assertEqual(record.diagrams["YINL_LISMO"], "5_[2]_75_[2]_15_[1]_66")
- self.assertEqual(record.diagrams["DHMA_FLAS1"], "14_[2]_121_[1]_77")
- self.assertEqual(record.diagrams["HMTR_LEIMA"], "6_[2]_157_[1]_66")
- self.assertEqual(record.diagrams["2BHD_STREX"], "6_[2]_116_[1]_75")
- self.assertEqual(record.diagrams["ENTA_ECOLI"], "5_[2]_109_[1]_76")
- self.assertEqual(record.diagrams["DHB2_HUMAN"], "82_[2]_120_[1]_127")
- self.assertEqual(record.diagrams["BDH_HUMAN"], "55_[2]_123_[1]_107")
- self.assertEqual(record.diagrams["BA72_EUBSP"], "6_[2]_121_[1]_64")
- self.assertEqual(record.diagrams["FVT1_HUMAN"], "32_[2]_124_[1]_118")
- self.assertEqual(record.diagrams["GUTD_ECOLI"], "2_[2]_122_[1]_77")
- self.assertEqual(record.diagrams["DHB3_HUMAN"], "48_[2]_120_[1]_84")
- self.assertEqual(record.diagrams["3BHD_COMTE"], "6_[2]_115_[1]_74")
- self.assertEqual(record.diagrams["LIGD_PSEPA"], "6_[2]_121_[1]_120")
- self.assertEqual(record.diagrams["DHES_HUMAN"], "2_[2]_50_[2]_44_[1]_144")
- self.assertEqual(record.diagrams["RFBB_NEIGO"], "6_[2]_129_[1]_153")
- self.assertEqual(record.diagrams["BPHB_PSEPS"], "5_[2]_118_[1]_94")
- self.assertEqual(record.diagrams["YURA_MYXXA"], "65_[2]_22_[2]_14_[1]_70")
- self.assertEqual(record.diagrams["PCR_PEA"], "25_[1]_32_[2]_284")
- self.assertEqual(record.diagrams["DHCA_HUMAN"], "4_[2]_159_[1]_55")
- self.assertEqual(record.diagrams["ADH_DROME"], "6_[2]_116_[1]_75")
- self.assertEqual(record.diagrams["MAS1_AGRRA"], "245_[2]_74_[1]_14_[1]_56")
- self.assertEqual(record.diagrams["FABI_ECOLI"], "6_[2]_123_[1]_75")
- self.assertEqual(record.diagrams["CSGA_MYXXA"], "51_[2]_7_[1]_50")
- handle.close()
-
- def test_mast_parser_3(self):
- """Test if Motif can parse MAST output files (third test)
- """
- from Bio.Motif.Parsers import MAST
- handle = open("Motif/mast.protein.tcm.txt")
- record = MAST.read(handle)
- self.assertEqual(record.version, "3.0")
- self.assertEqual(record.database, "farntrans5.s")
- self.assertEqual(record.alphabet, IUPAC.protein)
- self.assertEqual(len(record.motifs), 2)
- self.assertEqual(record.motifs[0].alphabet, IUPAC.protein)
- self.assertEqual(record.motifs[0].length, 30)
- self.assertEqual(record.motifs[0].name, "1")
- self.assertEqual(record.motifs[1].alphabet, IUPAC.protein)
- self.assertEqual(record.motifs[1].length, 14)
- self.assertEqual(record.motifs[1].name, "2")
- self.assertEqual(len(record.sequences), 5)
- self.assertEqual(record.sequences[0], "BET2_YEAST")
- self.assertEqual(record.sequences[1], "RATRABGERB")
- self.assertEqual(record.sequences[2], "CAL1_YEAST")
- self.assertEqual(record.sequences[3], "PFTB_RAT")
- self.assertEqual(record.sequences[4], "RAM1_YEAST")
- self.assertEqual(record.diagrams["BET2_YEAST"], "6_[2]_3_[1]_1_[2]_4_[1]_4_[2]_3_[1]_1_[2]_3_[1]_21_[1]_1_[2]_4_[1]_24")
- self.assertEqual(record.diagrams["RATRABGERB"], "65_[2]_3_[1]_1_[2]_3_[1]_1_[2]_3_[1]_18_[1]_1_[2]_4_[1]_26")
- self.assertEqual(record.diagrams["CAL1_YEAST"], "125_[2]_50_[2]_1_[1]_4_[2]_22_[1]_22_[1]_5_[2]_1")
- self.assertEqual(record.diagrams["PFTB_RAT"], "120_[2]_3_[1]_4_[2]_3_[1]_1_[2]_3_[1]_1_[2]_4_[1]_14_[2]_4_[1]_60")
- self.assertEqual(record.diagrams["RAM1_YEAST"], "144_[1]_5_[2]_4_[1]_1_[2]_4_[1]_1_[2]_4_[1]_4_[2]_5_[1]_35_[2]_4")
- handle.close()
-
-
-class MotifTestPWM(unittest.TestCase):
- def setUp(self):
- handle = open("Motif/SRF.pfm")
- self.m = Motif.read(handle, "jaspar-pfm")
- handle.close()
- self.s = Seq("ACGTGTGCGTAGTGCGT", self.m.alphabet)
-
- def test_simple(self):
- """Test if Motif PWM scoring works."""
- result = self.m.scanPWM(self.s)
- self.assertEqual(6, len(result))
- # The fast C-code in Bio/Motif/_pwm.c stores all results as 32-bit
- # floats; the slower Python code in Bio/Motif/_Motif.py uses 64-bit
- # doubles. The C-code and Python code results will therefore not be
- # exactly equal. Test the first 5 decimal places only to avoid either
- # the C-code or the Python code to inadvertently fail this test.
- self.assertAlmostEqual(result[0], -29.18363571, places=5)
- self.assertAlmostEqual(result[1], -38.3365097, places=5)
- self.assertAlmostEqual(result[2], -29.17756271, places=5)
- self.assertAlmostEqual(result[3], -38.04542542, places=5)
- self.assertAlmostEqual(result[4], -20.3014183, places=5)
- self.assertAlmostEqual(result[5], -25.18009186, places=5)
-
- def test_with_alt_alphabet(self):
- """Test motif search using alternative instance of alphabet."""
- self.s = Seq(str(self.s), IUPAC.IUPACUnambiguousDNA())
- self.test_simple()
-
-
-if __name__ == "__main__":
- runner = unittest.TextTestRunner(verbosity=2)
- unittest.main(testRunner=runner)
diff --git a/Tests/test_Muscle_tool.py b/Tests/test_Muscle_tool.py
index 80c2ce1..23548ce 100644
--- a/Tests/test_Muscle_tool.py
+++ b/Tests/test_Muscle_tool.py
@@ -89,8 +89,8 @@ class MuscleApplication(unittest.TestCase):
cmdline = MuscleCommandline(muscle_exe,
input=self.infile1,
out=self.outfile1)
- self.assertEqual(str(cmdline), _escape_filename(muscle_exe)
- + ' -in Fasta/f002 -out "Fasta/temp align out1.fa"')
+ self.assertEqual(str(cmdline), _escape_filename(muscle_exe) +
+ ' -in Fasta/f002 -out "Fasta/temp align out1.fa"')
self.assertEqual(str(eval(repr(cmdline))), str(cmdline))
output, error = cmdline()
self.assertEqual(output, "")
diff --git a/Tests/test_NCBI_BLAST_tools.py b/Tests/test_NCBI_BLAST_tools.py
index 369bf95..511908a 100644
--- a/Tests/test_NCBI_BLAST_tools.py
+++ b/Tests/test_NCBI_BLAST_tools.py
@@ -39,7 +39,8 @@ else:
for folder in likely_dirs:
if not os.path.isdir(folder):
continue
- for name in wanted:
+ # Loop over copy as will remove entries from wanted:
+ for name in wanted[:]:
if sys.platform == "win32":
exe_name = os.path.join(folder, name + ".exe")
else:
@@ -61,6 +62,7 @@ for folder in likely_dirs:
if name == "blast_formatter" and " -archive " not in output:
continue
exe_names[name] = exe_name
+ wanted.remove(name) # can stop search for this now
# else:
# print("Rejecting %r" % exe_name)
del exe_name, name
@@ -88,9 +90,9 @@ class Pairwise(unittest.TestCase):
query="Fasta/rose.pro",
subject="GenBank/NC_005816.faa",
evalue=1)
- self.assertEqual(str(cline), _escape_filename(exe_names["blastp"])
- + " -query Fasta/rose.pro -evalue 1"
- + " -subject GenBank/NC_005816.faa")
+ self.assertEqual(str(cline), _escape_filename(exe_names["blastp"]) +
+ " -query Fasta/rose.pro -evalue 1" +
+ " -subject GenBank/NC_005816.faa")
child = subprocess.Popen(str(cline),
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
@@ -100,12 +102,18 @@ class Pairwise(unittest.TestCase):
return_code = child.returncode
self.assertEqual(return_code, 0, "Got error code %i back from:\n%s"
% (return_code, cline))
- self.assertEqual(10, stdoutdata.count("Query= "))
- if stdoutdata.count("***** No hits found *****") == 7:
- # This happens with BLAST 2.2.26+ which is potentially a bug
- pass
+ # Used to get 10 matches from 10 pairwise searches,
+ # as of NCBI BLAST+ 2.3.0 only get 1 Query= line:
+ if stdoutdata.count("Query= ") == 10:
+ if stdoutdata.count("***** No hits found *****") == 7:
+ # This happens with BLAST 2.2.26+ which is potentially a bug
+ pass
+ else:
+ self.assertEqual(9, stdoutdata.count("***** No hits found *****"))
else:
- self.assertEqual(9, stdoutdata.count("***** No hits found *****"))
+ # Assume this is NCBI BLAST+ 2.3.0 or later,
+ self.assertEqual(1, stdoutdata.count("Query= "))
+ self.assertEqual(0, stdoutdata.count("***** No hits found *****"))
# TODO - Parse it? I think we'd need to update this obsole code :(
# records = list(NCBIStandalone.Iterator(StringIO(stdoutdata),
@@ -118,9 +126,9 @@ class Pairwise(unittest.TestCase):
query="GenBank/NC_005816.ffn",
subject="GenBank/NC_005816.fna",
evalue="0.000001")
- self.assertEqual(str(cline), _escape_filename(exe_names["blastn"])
- + " -query GenBank/NC_005816.ffn -evalue 0.000001"
- + " -subject GenBank/NC_005816.fna")
+ self.assertEqual(str(cline), _escape_filename(exe_names["blastn"]) +
+ " -query GenBank/NC_005816.ffn -evalue 0.000001" +
+ " -subject GenBank/NC_005816.fna")
child = subprocess.Popen(str(cline),
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
@@ -141,9 +149,9 @@ class Pairwise(unittest.TestCase):
query="GenBank/NC_005816.faa",
subject="GenBank/NC_005816.fna",
evalue="1e-6")
- self.assertEqual(str(cline), _escape_filename(exe_names["tblastn"])
- + " -query GenBank/NC_005816.faa -evalue 1e-6"
- + " -subject GenBank/NC_005816.fna")
+ self.assertEqual(str(cline), _escape_filename(exe_names["tblastn"]) +
+ " -query GenBank/NC_005816.faa -evalue 1e-6" +
+ " -subject GenBank/NC_005816.fna")
child = subprocess.Popen(str(cline),
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
@@ -269,6 +277,9 @@ class CheckCompleteArgList(unittest.TestCase):
"rpstblastn", "rpsblast", "tblastn", "tblastx"]:
# New in BLAST+ 2.2.30 so will look like extra args on BLAST+ 2.2.29 etc
extra = extra.difference(["-line_length", "-qcov_hsp_perc", "-sum_stats"])
+ if exe_name in ["deltablast", "psiblast"]:
+ # New in BLAST+ 2.3.0 so will look like extra args on older verions
+ extra = extra.difference(["-save_each_pssm", "-save_pssm_after_last_round"])
if extra or missing:
import warnings
diff --git a/Tests/test_NCBI_qblast.py b/Tests/test_NCBI_qblast.py
index 76a5fb1..35ff12b 100644
--- a/Tests/test_NCBI_qblast.py
+++ b/Tests/test_NCBI_qblast.py
@@ -1,4 +1,4 @@
-# Copyright 2008 by Peter Cock. All rights reserved.
+# Copyright 2008-2016 by Peter Cock. All rights reserved.
# This code is part of the Biopython distribution and governed by its
# license. Please see the LICENSE file that should have been included
# as part of this package.
@@ -18,14 +18,14 @@ import unittest
from Bio._py3k import HTTPError
from Bio._py3k import StringIO
-import requires_internet
-requires_internet.check()
from Bio import MissingExternalDependencyError
# We want to test these:
from Bio.Blast import NCBIWWW
from Bio.Blast import NCBIXML
+import requires_internet
+requires_internet.check()
#####################################################################
@@ -51,7 +51,10 @@ class TestQblast(unittest.TestCase):
def test_pcr_primers(self):
# This next example finds PCR primer matches in Chimpanzees, e.g. BRCA1:
self.run_qblast("blastn", "nr", "GTACCTTGATTTCGTATTC" + ("N" * 30) + "GACTCTACTACCTTTACCC",
- 10, "pan [ORGN]", ["37953274", "51104367", "51104367", "51104367"])
+ 10, "pan [ORGN]", ["XM_009432096.2", "XM_009432102.2", "XM_009432101.2",
+ "XM_016930487.1", "XM_009432104.2", "XM_009432099.2",
+ "XR_001710553.1", "XM_016930485.1", "XM_009432089.2",
+ "XM_016930484.1"])
def test_orchid_est(self):
# Try an orchid EST (nucleotide) sequence against NR using BLASTX
@@ -95,9 +98,13 @@ class TestQblast(unittest.TestCase):
# We used a FASTA record as the query
expected = query[1:].split("\n", 1)[0]
self.assertEqual(expected, record.query)
+ elif record.query_id.startswith("Query_") and len(query) == record.query_letters:
+ # We used a sequence as the entry and it was given a placeholder name
+ pass
else:
# We used an identifier as the query
- self.assertTrue(query in record.query_id.split("|"))
+ self.assertTrue(query in record.query_id.split("|"),
+ "Expected %r within query_id %r" % (query, record.query_id))
# Check the recorded input parameters agree with those requested
self.assertEqual(float(record.expect), e_value)
diff --git a/Tests/test_NNGene.py b/Tests/test_NNGene.py
index 60eac54..bc9b3b4 100644
--- a/Tests/test_NNGene.py
+++ b/Tests/test_NNGene.py
@@ -52,14 +52,12 @@ class PatternIOTest(unittest.TestCase):
"""
# write to a file
motifs = ["GAC", "AAA", "TTT", "GGG"]
- output_handle = open(self.test_file, "w")
- self.pattern_io.write(motifs, output_handle)
- output_handle.close()
+ with open(self.test_file, "w") as output_handle:
+ self.pattern_io.write(motifs, output_handle)
# read 'em back
- input_handle = open(self.test_file, "r")
- read_motifs = self.pattern_io.read(input_handle)
- input_handle.close()
+ with open(self.test_file, "r") as input_handle:
+ read_motifs = self.pattern_io.read(input_handle)
self.assertEqual(read_motifs, motifs,
"Failed to get back expected motifs %s, got %s"
% (motifs, read_motifs))
@@ -68,14 +66,12 @@ class PatternIOTest(unittest.TestCase):
seq_motifs = []
for motif in motifs:
seq_motifs.append(Seq(motif, self.alphabet))
- output_handle = open(self.test_file, "w")
- self.pattern_io.write_seq(seq_motifs, output_handle)
- output_handle.close()
+ with open(self.test_file, "w") as output_handle:
+ self.pattern_io.write_seq(seq_motifs, output_handle)
# read the seqs back
- input_handle = open(self.test_file, "r")
- read_motifs = self.pattern_io.read(input_handle)
- input_handle.close()
+ with open(self.test_file, "r") as input_handle:
+ read_motifs = self.pattern_io.read(input_handle)
self.assertEqual(read_motifs, motifs,
"Failed to get back expected motifs %s from seqs, got %s"
% (motifs, read_motifs))
@@ -85,23 +81,20 @@ class PatternIOTest(unittest.TestCase):
"""
schemas = ["GTR", "GAC"]
# write out the schemas
- output_handle = open(self.test_file, "w")
- self.pattern_io.write(schemas, output_handle)
- output_handle.close()
+ with open(self.test_file, "w") as output_handle:
+ self.pattern_io.write(schemas, output_handle)
# read back the schemas
- input_handle = open(self.test_file, "r")
- read_schemas = self.pattern_io.read(input_handle)
- input_handle.close()
+ with open(self.test_file, "r") as input_handle:
+ read_schemas = self.pattern_io.read(input_handle)
self.assertEqual(schemas, read_schemas,
- "Read incorrect schemas %s, expected %s."
- % (read_schemas, schemas))
+ "Read incorrect schemas %s, expected %s."
+ % (read_schemas, schemas))
# --- make sure inappropriate alphabets are reported
schemas = ["GTR", "G*C"] # '*' not in the unambigous alphabet
- output_handle = open(self.test_file, "w")
- self.pattern_io.write(schemas, output_handle)
- output_handle.close()
+ with open(self.test_file, "w") as output_handle:
+ self.pattern_io.write(schemas, output_handle)
input_handle = open(self.test_file, "r")
try:
@@ -111,16 +104,14 @@ class PatternIOTest(unittest.TestCase):
pass # expected behavior
except:
raise AssertionError("Got unexpected error while reading.")
-
input_handle.close()
def test_signature(self):
"""Reading and writing signatures to a file.
"""
signatures = [("GAC", "GAC"), ("AAA", "TTT")]
- output_handle = open(self.test_file, "w")
- self.pattern_io.write(signatures, output_handle)
- output_handle.close()
+ with open(self.test_file, "w") as output_handle:
+ self.pattern_io.write(signatures, output_handle)
input_handle = open(self.test_file, "r")
read_sigs = self.pattern_io.read(input_handle)
@@ -481,8 +472,8 @@ class SchemaCoderTest(unittest.TestCase):
class SchemaMatchingTest(unittest.TestCase):
- """Matching schema to strings works correctly.
- """
+ """Matching schema to strings works correctly."""
+
def shortDescription(self):
return "%s:%s" % (self.__class__.__name__, self.__doc__)
diff --git a/Tests/test_NaiveBayes.py b/Tests/test_NaiveBayes.py
new file mode 100644
index 0000000..3835100
--- /dev/null
+++ b/Tests/test_NaiveBayes.py
@@ -0,0 +1,116 @@
+# coding=utf-8
+import copy
+import unittest
+
+try:
+ import numpy
+except ImportError:
+ from Bio import MissingPythonDependencyError
+ raise MissingPythonDependencyError(
+ "Install NumPy if you want to use Bio.NaiveBayes.")
+
+from Bio import NaiveBayes
+
+
+class CarTest(unittest.TestCase):
+ def test_car_data(self):
+ """Simple example using car data."""
+ # Car data from example 'Naive Bayes Classifier example'
+ # by Eric Meisner November 22, 2003
+ # http://www.inf.u-szeged.hu/~ormandi/teaching/mi2/02-naiveBayes-example.pdf
+ xcar = [
+ ['Red', 'Sports', 'Domestic'],
+ ['Red', 'Sports', 'Domestic'],
+ ['Red', 'Sports', 'Domestic'],
+ ['Yellow', 'Sports', 'Domestic'],
+ ['Yellow', 'Sports', 'Imported'],
+ ['Yellow', 'SUV', 'Imported'],
+ ['Yellow', 'SUV', 'Imported'],
+ ['Yellow', 'SUV', 'Domestic'],
+ ['Red', 'SUV', 'Imported'],
+ ['Red', 'Sports', 'Imported'],
+ ]
+
+ ycar = [
+ 'Yes',
+ 'No',
+ 'Yes',
+ 'No',
+ 'Yes',
+ 'No',
+ 'Yes',
+ 'No',
+ 'No',
+ 'Yes',
+ ]
+
+ carmodel = NaiveBayes.train(xcar, ycar)
+ self.assertEqual("Yes", NaiveBayes.classify(carmodel, ['Red', 'Sports', 'Domestic']))
+ self.assertEqual("No", NaiveBayes.classify(carmodel, ['Red', 'SUV', 'Domestic']))
+
+
+class NaiveBayesTest(unittest.TestCase):
+ def setUp(self):
+ # Using example from https://en.wikipedia.org/wiki/Naive_Bayes_classifier
+ # height (feet), weight (lbs), foot size (inches)
+ self.xs = [
+ [6, 180, 12],
+ [5.92, 190, 11],
+ [5.58, 170, 12],
+ [5.92, 165, 10],
+ [5, 100, 6],
+ [5.5, 150, 8],
+ [5.42, 130, 7],
+ [5.75, 150, 9],
+ ]
+ self.ys = [
+ 'male',
+ 'male',
+ 'male',
+ 'male',
+ 'female',
+ 'female',
+ 'female',
+ 'female',
+ ]
+ self.model = NaiveBayes.train(self.xs, self.ys)
+ self.test = [6, 130, 8]
+
+ def test_train_function_no_training_set(self):
+ self.assertRaises(ValueError, NaiveBayes.train, [], self.ys)
+
+ def test_train_function_input_lengths(self):
+ ys = copy.copy(self.ys)
+ ys.pop()
+ self.assertRaises(ValueError, NaiveBayes.train, self.xs, ys)
+
+ def test_train_function_uneven_dimension_of_training_set(self):
+ xs = copy.copy(self.xs)
+ xs[0] = [1]
+ self.assertRaises(ValueError, NaiveBayes.train, xs, self.ys)
+
+ def test_train_function_with_priors(self):
+ model = NaiveBayes.train(self.xs, self.ys, priors={'male': 0.1, 'female': 0.9})
+ result = NaiveBayes.calculate(model, self.test, scale=True)
+ expected = -692.0
+ self.assertEqual(expected, round(result['male']))
+
+ def test_classify_function(self):
+ expected = "female"
+ result = NaiveBayes.classify(self.model, self.test)
+ self.assertEqual(expected, result)
+
+ def test_calculate_function_wrong_dimensionality(self):
+ xs = self.xs[0]
+ xs.append(100)
+ self.assertRaises(ValueError, NaiveBayes.calculate, self.model, xs)
+
+ def test_calculate_function_with_scale(self):
+ result = NaiveBayes.calculate(self.model, self.test, scale=True)
+ expected = -689.0
+ self.assertEqual(expected, round(result['male']))
+
+
+if __name__ == "__main__":
+ runner = unittest.TextTestRunner(verbosity=2)
+ unittest.main(testRunner=runner)
diff --git a/Tests/test_Nexus.py b/Tests/test_Nexus.py
index 5fcb94b..e8cc7ec 100755
--- a/Tests/test_Nexus.py
+++ b/Tests/test_Nexus.py
@@ -13,7 +13,11 @@ import sys
from Bio._py3k import StringIO
from Bio._py3k import range
+from Bio.SeqRecord import SeqRecord
from Bio.Nexus import Nexus, Trees
+from Bio.Seq import Seq
+from Bio.Alphabet.IUPAC import ambiguous_dna
+from Bio import SeqIO
class NexusTest1(unittest.TestCase):
@@ -35,6 +39,12 @@ class NexusTest1(unittest.TestCase):
self.assertTrue(os.path.isfile(filename))
os.remove(filename)
+ def test_write_with_dups(self):
+ # see issue: biopython/Bio/Nexus/Nexus.py _unique_label() eval error #633
+ records = [SeqRecord(Seq("ATGCTGCTGAT", alphabet=ambiguous_dna), id="foo") for _ in range(4)]
+ out_file = StringIO()
+ self.assertEqual(4, SeqIO.write(records, out_file, "nexus"))
+
def test_NexusTest1(self):
"""Test Nexus module"""
# check data of main nexus file
@@ -67,68 +77,68 @@ class NexusTest1(unittest.TestCase):
29: "1,2,3 can't decide for a name?!",
47: "final"})
self.assertEqual(n.charsets, {
- "big": [0, 2, 4, 6],
- "bigchunk": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46],
- "byname": [0, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29],
- "c1": [0, 1, 2, 3, 4, 5, 6, 7],
- "c2": [8, 9, 10, 11, 12, 13, 14, 15],
- "c3": [16, 17, 18, 19, 20, 21, 22, 23],
- "firsthalf": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23],
- "mix": [0, 1, 4, 7, 10, 13, 16, 19, 22, 25, 28, 31, 34, 37, 40, 43, 46],
- "mux": [0, 1, 4, 7, 8, 10, 13, 16, 17, 18, 19, 20, 21, 22, 23, 25, 28, 31, 34, 37, 40, 43, 46],
- "pos1": [0, 3, 6, 9, 12, 15, 18, 21, 24, 27, 30, 33, 36, 39, 42, 45],
- "pos2": [1, 4, 7, 10, 13, 16, 19, 22, 25, 28, 31, 34, 37, 40, 43, 46],
- "pos3": [2, 5, 8, 11, 14, 17, 20, 23, 26, 29, 32, 35, 38, 41, 44, 47],
- "secondhalf": [24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47],
- })
+ "big": [0, 2, 4, 6],
+ "bigchunk": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46],
+ "byname": [0, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29],
+ "c1": [0, 1, 2, 3, 4, 5, 6, 7],
+ "c2": [8, 9, 10, 11, 12, 13, 14, 15],
+ "c3": [16, 17, 18, 19, 20, 21, 22, 23],
+ "firsthalf": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23],
+ "mix": [0, 1, 4, 7, 10, 13, 16, 19, 22, 25, 28, 31, 34, 37, 40, 43, 46],
+ "mux": [0, 1, 4, 7, 8, 10, 13, 16, 17, 18, 19, 20, 21, 22, 23, 25, 28, 31, 34, 37, 40, 43, 46],
+ "pos1": [0, 3, 6, 9, 12, 15, 18, 21, 24, 27, 30, 33, 36, 39, 42, 45],
+ "pos2": [1, 4, 7, 10, 13, 16, 19, 22, 25, 28, 31, 34, 37, 40, 43, 46],
+ "pos3": [2, 5, 8, 11, 14, 17, 20, 23, 26, 29, 32, 35, 38, 41, 44, 47],
+ "secondhalf": [24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47],
+ })
self.assertEqual(n.taxsets, {
- "normal": ["isn'that [a] strange name?",
- 'one should be punished, for (that)!',
- 't1',
- 't5',
- 't6',
- 't8'],
- "reference": ["isn'that [a] strange name?",
- 'one should be punished, for (that)!',
- 't1',
- 't2 the name',
- 't5',
- 't6'],
- "tbyname1": ["isn'that [a] strange name?",
- 'one should be punished, for (that)!',
- 't1',
- 't2 the name',
- 't5',
- 't6'],
- "tbyname2": ["isn'that [a] strange name?",
- 'one should be punished, for (that)!',
- 't2 the name',
- 't5',
- 't6',
- 't7'],
- "tbyname3": ['t1',
- 't2 the name'],
- })
+ "normal": ["isn'that [a] strange name?",
+ 'one should be punished, for (that)!',
+ 't1',
+ 't5',
+ 't6',
+ 't8'],
+ "reference": ["isn'that [a] strange name?",
+ 'one should be punished, for (that)!',
+ 't1',
+ 't2 the name',
+ 't5',
+ 't6'],
+ "tbyname1": ["isn'that [a] strange name?",
+ 'one should be punished, for (that)!',
+ 't1',
+ 't2 the name',
+ 't5',
+ 't6'],
+ "tbyname2": ["isn'that [a] strange name?",
+ 'one should be punished, for (that)!',
+ 't2 the name',
+ 't5',
+ 't6',
+ 't7'],
+ "tbyname3": ['t1',
+ 't2 the name'],
+ })
self.assertEqual(len(n.charpartitions), 2)
self.assertTrue('codons' in n.charpartitions)
self.assertTrue('part' in n.charpartitions)
self.assertEqual(n.charpartitions['codons'],
- {'a': [0, 3, 6, 9, 12, 15, 18, 21, 24, 27, 30, 33, 36, 39, 42, 45],
- 'b': [1, 4, 7, 10, 13, 16, 19, 22, 25, 28, 31, 34, 37, 40, 43, 46],
- 'c': [2, 5, 8, 11, 14, 17, 20, 23, 26, 29, 32, 35, 38, 41, 44, 47],
- })
+ {'a': [0, 3, 6, 9, 12, 15, 18, 21, 24, 27, 30, 33, 36, 39, 42, 45],
+ 'b': [1, 4, 7, 10, 13, 16, 19, 22, 25, 28, 31, 34, 37, 40, 43, 46],
+ 'c': [2, 5, 8, 11, 14, 17, 20, 23, 26, 29, 32, 35, 38, 41, 44, 47],
+ })
self.assertEqual(n.charpartitions['part'],
- {"one": [0, 1, 2, 3, 4, 5, 6, 7],
- "three": [16, 17, 18, 19, 20, 21, 22, 23],
- "two": [8, 9, 10, 11, 12, 13, 14, 15],
- })
+ {"one": [0, 1, 2, 3, 4, 5, 6, 7],
+ "three": [16, 17, 18, 19, 20, 21, 22, 23],
+ "two": [8, 9, 10, 11, 12, 13, 14, 15],
+ })
self.assertEqual(list(n.taxpartitions), ['taxpart'])
self.assertEqual(n.taxpartitions['taxpart'],
- {"badnames": ["isn'that [a] strange name?",
- 'one should be punished, for (that)!',
- 't2 the name'],
- "goodnames": ['t1', 't5', 't6', 't7', 't8', 't9'],
- })
+ {"badnames": ["isn'that [a] strange name?",
+ 'one should be punished, for (that)!',
+ 't2 the name'],
+ "goodnames": ['t1', 't5', 't6', 't7', 't8', 't9'],
+ })
# now we check excluding characters, deleting taxa,
# and exporting adjusted sets
@@ -154,40 +164,40 @@ class NexusTest1(unittest.TestCase):
"t8",
"t9"])
self.assertEqual(nf1.charlabels, {0: 'a', 1: 'c', 2: 'f'})
- self.assertEqual(nf1.charsets, {'big': [0, 1, 2, 3],
- 'bigchunk': [1, 2, 3],
- 'byname': [0, 2, 3],
- 'c1': [0, 1, 2, 3],
+ self.assertEqual(nf1.charsets, {'big': [0, 1, 2, 3],
+ 'bigchunk': [1, 2, 3],
+ 'byname': [0, 2, 3],
+ 'c1': [0, 1, 2, 3],
'firsthalf': [0, 1, 2, 3],
- 'mix': [0, 2],
- 'mux': [0, 2],
- 'pos1': [0, 3],
- 'pos2': [2],
- 'pos3': [1],
- })
+ 'mix': [0, 2],
+ 'mux': [0, 2],
+ 'pos1': [0, 3],
+ 'pos2': [2],
+ 'pos3': [1],
+ })
self.assertEqual(nf1.taxsets, {
- 'normal': ["isn'that [a] strange name?",
- 'one should be punished, for (that)!',
- 't5',
- 't6',
- 't8'],
- 'reference': ["isn'that [a] strange name?",
- 'one should be punished, for (that)!',
- 't2 the name',
- 't5',
- 't6'],
- 'tbyname1': ["isn'that [a] strange name?",
- 'one should be punished, for (that)!',
- 't2 the name',
- 't5',
- 't6'],
- 'tbyname2': ["isn'that [a] strange name?",
- 'one should be punished, for (that)!',
- 't2 the name',
- 't5',
- 't6'],
- 'tbyname3': ['t2 the name'],
- })
+ 'normal': ["isn'that [a] strange name?",
+ 'one should be punished, for (that)!',
+ 't5',
+ 't6',
+ 't8'],
+ 'reference': ["isn'that [a] strange name?",
+ 'one should be punished, for (that)!',
+ 't2 the name',
+ 't5',
+ 't6'],
+ 'tbyname1': ["isn'that [a] strange name?",
+ 'one should be punished, for (that)!',
+ 't2 the name',
+ 't5',
+ 't6'],
+ 'tbyname2': ["isn'that [a] strange name?",
+ 'one should be punished, for (that)!',
+ 't2 the name',
+ 't5',
+ 't6'],
+ 'tbyname3': ['t2 the name'],
+ })
self.assertEqual(len(nf1.charpartitions), 2)
self.assertTrue('codons' in nf1.charpartitions)
self.assertTrue('part' in nf1.charpartitions)
@@ -198,11 +208,11 @@ class NexusTest1(unittest.TestCase):
self.assertEqual(list(nf1.taxpartitions), ['taxpart'])
self.assertEqual(nf1.taxpartitions['taxpart'],
- {"badnames": ["isn'that [a] strange name?",
- 'one should be punished, for (that)!',
- 't2 the name'],
- "goodnames": ['t5', 't6', 't8', 't9'],
- })
+ {"badnames": ["isn'that [a] strange name?",
+ 'one should be punished, for (that)!',
+ 't2 the name'],
+ "goodnames": ['t5', 't6', 't8', 't9'],
+ })
f2 = tempfile.NamedTemporaryFile("w+")
n.write_nexus_data(f2,
@@ -237,68 +247,68 @@ class NexusTest1(unittest.TestCase):
22: "1,2,3 can't decide for a name?!",
37: "final"})
self.assertEqual(nf2.charsets,
- {"big": [0, 2, 3, 5],
- "bigchunk": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36],
- "byname": [0, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22],
- "c1": [0, 1, 2, 3, 4, 5],
- "c2": [6, 7, 8, 9, 10, 11],
- "c3": [12, 13, 14, 15, 16, 17],
- "firsthalf": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17],
- "mix": [0, 1, 3, 8, 10, 12, 17, 19, 21, 26, 28, 30, 33, 36],
- "mux": [0, 1, 3, 6, 8, 10, 12, 13, 14, 15, 16, 17, 19, 21, 26, 28, 30, 33, 36],
- "pos1": [0, 5, 7, 9, 14, 16, 18, 23, 25, 27, 32, 35],
- "pos2": [1, 3, 8, 10, 12, 17, 19, 21, 26, 28, 30, 33, 36],
- "pos3": [2, 4, 6, 11, 13, 15, 20, 22, 24, 29, 31, 34, 37],
- "secondhalf": [18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37],
- })
+ {"big": [0, 2, 3, 5],
+ "bigchunk": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36],
+ "byname": [0, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22],
+ "c1": [0, 1, 2, 3, 4, 5],
+ "c2": [6, 7, 8, 9, 10, 11],
+ "c3": [12, 13, 14, 15, 16, 17],
+ "firsthalf": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17],
+ "mix": [0, 1, 3, 8, 10, 12, 17, 19, 21, 26, 28, 30, 33, 36],
+ "mux": [0, 1, 3, 6, 8, 10, 12, 13, 14, 15, 16, 17, 19, 21, 26, 28, 30, 33, 36],
+ "pos1": [0, 5, 7, 9, 14, 16, 18, 23, 25, 27, 32, 35],
+ "pos2": [1, 3, 8, 10, 12, 17, 19, 21, 26, 28, 30, 33, 36],
+ "pos3": [2, 4, 6, 11, 13, 15, 20, 22, 24, 29, 31, 34, 37],
+ "secondhalf": [18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37],
+ })
self.assertEqual(nf2.taxsets,
- {"normal": ["isn'that [a] strange name?",
- 'one should be punished, for (that)!',
- 't1',
- 't5',
- 't6',
- 't8'],
- "reference": ["isn'that [a] strange name?",
- 'one should be punished, for (that)!',
- 't1',
- 't2 the name',
- 't5',
- 't6'],
- "tbyname1": ["isn'that [a] strange name?",
- 'one should be punished, for (that)!',
- 't1',
- 't2 the name',
- 't5',
- 't6'],
- "tbyname2": ["isn'that [a] strange name?",
- 'one should be punished, for (that)!',
- 't2 the name',
- 't5',
- 't6',
- 't7'],
- "tbyname3": ['t1',
- 't2 the name']})
+ {"normal": ["isn'that [a] strange name?",
+ 'one should be punished, for (that)!',
+ 't1',
+ 't5',
+ 't6',
+ 't8'],
+ "reference": ["isn'that [a] strange name?",
+ 'one should be punished, for (that)!',
+ 't1',
+ 't2 the name',
+ 't5',
+ 't6'],
+ "tbyname1": ["isn'that [a] strange name?",
+ 'one should be punished, for (that)!',
+ 't1',
+ 't2 the name',
+ 't5',
+ 't6'],
+ "tbyname2": ["isn'that [a] strange name?",
+ 'one should be punished, for (that)!',
+ 't2 the name',
+ 't5',
+ 't6',
+ 't7'],
+ "tbyname3": ['t1',
+ 't2 the name']})
self.assertEqual(len(nf2.charpartitions), 2)
self.assertTrue('codons' in nf2.charpartitions)
self.assertTrue('part' in nf2.charpartitions)
self.assertEqual(nf2.charpartitions['codons'],
- {"a": [0, 5, 7, 9, 14, 16, 18, 23, 25, 27, 32, 35],
- "b": [1, 3, 8, 10, 12, 17, 19, 21, 26, 28, 30, 33, 36],
- "c": [2, 4, 6, 11, 13, 15, 20, 22, 24, 29, 31, 34, 37],
- })
+ {"a": [0, 5, 7, 9, 14, 16, 18, 23, 25, 27, 32, 35],
+ "b": [1, 3, 8, 10, 12, 17, 19, 21, 26, 28, 30, 33, 36],
+ "c": [2, 4, 6, 11, 13, 15, 20, 22, 24, 29, 31, 34, 37],
+ })
self.assertEqual(nf2.charpartitions['part'],
- {"one": [0, 1, 2, 3, 4, 5],
- "three": [12, 13, 14, 15, 16, 17],
- "two": [6, 7, 8, 9, 10, 11],
- })
+ {"one": [0, 1, 2, 3, 4, 5],
+ "three": [12, 13, 14, 15, 16, 17],
+ "two": [6, 7, 8, 9, 10, 11],
+ })
self.assertEqual(list(nf2.taxpartitions), ['taxpart'])
self.assertEqual(nf2.taxpartitions['taxpart'],
- {"badnames": ["isn'that [a] strange name?",
- 'one should be punished, for (that)!',
- 't2 the name'],
- "goodnames": ['t1', 't5', 't6', 't7', 't8', 't9'],
- })
+ {"badnames": ["isn'that [a] strange name?",
+ 'one should be punished, for (that)!',
+ 't2 the name'],
+ "goodnames": ['t1', 't5', 't6', 't7', 't8', 't9'],
+ })
# check the stepmatrix
self.assertEqual(n.weighted_stepmatrix(name='matrix_test'),
"""\
@@ -385,10 +395,9 @@ Root: 16
"t1:0.130208)F:0.0318288)D:0.0273876);"
tree = Trees.Tree(ts2)
- large_ex_handle = open(os.path.join(self.testfile_dir,
- "int_node_labels.nwk"))
- tree = Trees.Tree(large_ex_handle.read())
- large_ex_handle.close()
+ def test_large_newick(self):
+ with open(os.path.join(self.testfile_dir, "int_node_labels.nwk")) as large_ex_handle:
+ tree = Trees.Tree(large_ex_handle.read())
def _get_flat_nodes(self, tree):
cur_nodes = [tree.node(tree.root)]
diff --git a/Tests/test_PAML_baseml.py b/Tests/test_PAML_baseml.py
index 0ff7283..7519b98 100644
--- a/Tests/test_PAML_baseml.py
+++ b/Tests/test_PAML_baseml.py
@@ -29,8 +29,8 @@ class ModTest(unittest.TestCase):
def tearDown(self):
"""Just in case BASEML creates some junk files, do a clean-up."""
del_files = [self.out_file, "2base.t",
- "in.basemlg", "baseml.ctl", "lnf", "rates", "rst", "rst1",
- "rub"]
+ "in.basemlg", "baseml.ctl", "lnf", "rates", "rst", "rst1",
+ "rub"]
for filename in del_files:
if os.path.exists(filename):
os.remove(filename)
@@ -45,39 +45,39 @@ class ModTest(unittest.TestCase):
def testAlignmentFileIsValid(self):
self.assertRaises((AttributeError, TypeError, OSError),
- baseml.Baseml, alignment=list())
+ baseml.Baseml, alignment=list())
self.bml.alignment = list()
self.bml.tree = self.tree_file
self.bml.out_file = self.out_file
self.assertRaises((AttributeError, TypeError, OSError),
- self.bml.run)
+ self.bml.run)
def testAlignmentExists(self):
self.assertRaises((EnvironmentError, IOError), baseml.Baseml,
- alignment="nonexistent")
+ alignment="nonexistent")
self.bml.alignment = "nonexistent"
self.bml.tree = self.tree_file
self.bml.out_file = self.out_file
self.assertRaises((EnvironmentError, IOError),
- self.bml.run)
+ self.bml.run)
def testTreeFileValid(self):
self.assertRaises((AttributeError, TypeError, OSError),
- baseml.Baseml, tree=list())
+ baseml.Baseml, tree=list())
self.bml.alignment = self.align_file
self.bml.tree = list()
self.bml.out_file = self.out_file
self.assertRaises((AttributeError, TypeError, OSError),
- self.bml.run)
+ self.bml.run)
def testTreeExists(self):
self.assertRaises((EnvironmentError, IOError), baseml.Baseml,
- tree="nonexistent")
+ tree="nonexistent")
self.bml.alignment = self.align_file
self.bml.tree = "nonexistent"
self.bml.out_file = self.out_file
self.assertRaises((EnvironmentError, IOError),
- self.bml.run)
+ self.bml.run)
def testWorkingDirValid(self):
self.bml.tree = self.tree_file
@@ -85,14 +85,14 @@ class ModTest(unittest.TestCase):
self.bml.out_file = self.out_file
self.bml.working_dir = list()
self.assertRaises((AttributeError, TypeError, OSError),
- self.bml.run)
+ self.bml.run)
def testOutputFileValid(self):
self.bml.tree = self.tree_file
self.bml.alignment = self.align_file
self.bml.out_file = list()
self.assertRaises((AttributeError, ValueError, OSError),
- self.bml.run)
+ self.bml.run)
def testOptionExists(self):
self.assertRaises((AttributeError, KeyError),
@@ -104,73 +104,73 @@ class ModTest(unittest.TestCase):
self.bml.tree = self.tree_file
self.bml.out_file = self.out_file
self.assertRaises((AttributeError, ValueError),
- self.bml.run)
+ self.bml.run)
def testTreeSpecified(self):
self.bml.alignment = self.align_file
self.bml.out_file = self.out_file
self.assertRaises((AttributeError, ValueError),
- self.bml.run)
+ self.bml.run)
def testOutputFileSpecified(self):
self.bml.alignment = self.align_file
self.bml.tree = self.tree_file
self.assertRaises((AttributeError, ValueError),
- self.bml.run)
+ self.bml.run)
def testPamlErrorsCaught(self):
self.bml.alignment = self.align_file
self.bml.tree = self.bad_tree_file
self.bml.out_file = self.out_file
self.assertRaises((EnvironmentError, PamlError),
- self.bml.run)
+ self.bml.run)
def testCtlFileValidOnRun(self):
self.bml.alignment = self.align_file
self.bml.tree = self.tree_file
self.bml.out_file = self.out_file
self.assertRaises((AttributeError, TypeError, OSError),
- self.bml.run, ctl_file=list())
+ self.bml.run, ctl_file=list())
def testCtlFileExistsOnRun(self):
self.bml.alignment = self.align_file
self.bml.tree = self.tree_file
self.bml.out_file = self.out_file
self.assertRaises((EnvironmentError, IOError),
- self.bml.run, ctl_file="nonexistent")
+ self.bml.run, ctl_file="nonexistent")
def testCtlFileValidOnRead(self):
self.assertRaises((AttributeError, TypeError, OSError),
- self.bml.read_ctl_file, list())
+ self.bml.read_ctl_file, list())
self.assertRaises((AttributeError, KeyError),
- self.bml.read_ctl_file, self.bad_ctl_file1)
+ self.bml.read_ctl_file, self.bad_ctl_file1)
self.assertRaises(AttributeError,
- self.bml.read_ctl_file, self.bad_ctl_file2)
+ self.bml.read_ctl_file, self.bad_ctl_file2)
target_options = {"noisy": 0,
- "verbose": 0,
- "runmode": 0,
- "model": 6,
- "model_options": None,
- "Mgene": 1,
- "ndata": None,
- "clock": 0,
- "fix_kappa": 0,
- "kappa": 5,
- "fix_alpha": 0,
- "alpha": 0.5,
- "Malpha": 0,
- "ncatG": 5,
- "fix_rho": 1,
- "rho": 0,
- "nparK": 0,
- "nhomo": 0,
- "getSE": 0,
- "RateAncestor": 0,
- "Small_Diff": 7e-6,
- "cleandata": 1,
- "icode": None,
- "fix_blength": None,
- "method": 0}
+ "verbose": 0,
+ "runmode": 0,
+ "model": 6,
+ "model_options": None,
+ "Mgene": 1,
+ "ndata": None,
+ "clock": 0,
+ "fix_kappa": 0,
+ "kappa": 5,
+ "fix_alpha": 0,
+ "alpha": 0.5,
+ "Malpha": 0,
+ "ncatG": 5,
+ "fix_rho": 1,
+ "rho": 0,
+ "nparK": 0,
+ "nhomo": 0,
+ "getSE": 0,
+ "RateAncestor": 0,
+ "Small_Diff": 7e-6,
+ "cleandata": 1,
+ "icode": None,
+ "fix_blength": None,
+ "method": 0}
self.bml.read_ctl_file(self.ctl_file)
# Compare the dictionary keys:
self.assertEqual(sorted(self.bml._options), sorted(target_options))
@@ -181,11 +181,11 @@ class ModTest(unittest.TestCase):
def testCtlFileExistsOnRead(self):
self.assertRaises(IOError,
- self.bml.read_ctl_file, ctl_file="nonexistent")
+ self.bml.read_ctl_file, ctl_file="nonexistent")
def testResultsValid(self):
self.assertRaises((AttributeError, TypeError, OSError),
- baseml.read, list())
+ baseml.read, list())
def testResultsExist(self):
self.assertRaises(IOError, baseml.read, "nonexistent")
@@ -208,7 +208,7 @@ class ModTest(unittest.TestCase):
version = results_file.split('-')[1].split('.')[0]
model = results_file[5]
version_msg = "Improper parsing for model %s version %s" \
- % (model, version.replace('_', '.'))
+ % (model, version.replace('_', '.'))
results_path = os.path.join(res_dir, results_file)
results = baseml.read(results_path)
# There are 6 top-levels: parameters, tree, lnL, version,
@@ -242,7 +242,7 @@ class ModTest(unittest.TestCase):
version = results_file.split('-')[1].split('.')[0]
model = results_file[5]
version_msg = "Improper parsing for model %s version %s" \
- % (model, version.replace('_', '.'))
+ % (model, version.replace('_', '.'))
results_path = os.path.join(res_dir, results_file)
results = baseml.read(results_path)
# There are 6 top-levels: parameters, tree, lnL, version,
@@ -262,7 +262,7 @@ class ModTest(unittest.TestCase):
version = results_file.split('-')[1].split('.')[0]
n = results_file[5]
version_msg = "Improper parsing for nhomo %s version %s" \
- % (n, version.replace('_', '.'))
+ % (n, version.replace('_', '.'))
results_path = os.path.join(res_dir, results_file)
results = baseml.read(results_path)
# There are 6 top-levels: parameters, tree, lnL, version,
@@ -283,7 +283,7 @@ class ModTest(unittest.TestCase):
for results_file in os.listdir(res_dir):
version = results_file.split('-')[1].split('.')[0]
version_msg = "Improper parsing for version %s" \
- % version.replace('_', '.')
+ % version.replace('_', '.')
results_path = os.path.join(res_dir, results_file)
results = baseml.read(results_path)
# There are 6 top-levels: parameters, tree, lnL, version,
diff --git a/Tests/test_PAML_codeml.py b/Tests/test_PAML_codeml.py
index 601b48d..cca7964 100644
--- a/Tests/test_PAML_codeml.py
+++ b/Tests/test_PAML_codeml.py
@@ -1,4 +1,4 @@
-# Copyright (C) 2011, 2015 by Brandon Invergo (b.invergo at gmail.com)
+# Copyright (C) 2011, 2015, 2016 by Brandon Invergo (b.invergo at gmail.com)
# This code is part of the Biopython distribution and governed by its
# license. Please see the LICENSE file that should have been included
# as part of this package.
@@ -13,10 +13,10 @@ from Bio.Phylo.PAML._paml import PamlError
# Some constants to assist with testing:
# This is the number of parameters that should be parsed for each
# NSsites site class model
-SITECLASS_PARAMS = {0: 6, 1: 4, 2: 4, 3: 4, 7: 5, 8: 8}
+SITECLASS_PARAMS = {0: 6, 1: 4, 2: 4, 3: 4, 7: 5, 8: 8, 22: 4}
# This is the default number of site classes per NSsites site
# class model
-SITECLASSES = {0: None, 1: 2, 2: 3, 3: 3, 7: 10, 8: 11}
+SITECLASSES = {0: None, 1: 2, 2: 3, 3: 3, 7: 10, 8: 11, 22: 3}
class ModTest(unittest.TestCase):
@@ -40,7 +40,7 @@ class ModTest(unittest.TestCase):
def tearDown(self):
"""Just in case CODEML creates some junk files, do a clean-up."""
del_files = [self.out_file, "2NG.dN", "2NG.dS", "2NG.t", "codeml.ctl",
- "lnf", "rst", "rst1", "rub"]
+ "lnf", "rst", "rst1", "rub"]
for filename in del_files:
if os.path.exists(filename):
os.remove(filename)
@@ -55,16 +55,16 @@ class ModTest(unittest.TestCase):
def testAlignmentFileIsValid(self):
self.assertRaises((AttributeError, TypeError, OSError),
- codeml.Codeml, alignment=list())
+ codeml.Codeml, alignment=list())
self.cml.alignment = list()
self.cml.tree = self.tree_file
self.cml.out_file = self.out_file
self.assertRaises((AttributeError, TypeError, OSError),
- self.cml.run)
+ self.cml.run)
def testAlignmentExists(self):
self.assertRaises((EnvironmentError, IOError), codeml.Codeml,
- alignment="nonexistent")
+ alignment="nonexistent")
self.cml.alignment = "nonexistent"
self.cml.tree = self.tree_file
self.cml.out_file = self.out_file
@@ -72,16 +72,16 @@ class ModTest(unittest.TestCase):
def testTreeFileValid(self):
self.assertRaises((AttributeError, TypeError, OSError),
- codeml.Codeml, tree=list())
+ codeml.Codeml, tree=list())
self.cml.alignment = self.align_file
self.cml.tree = list()
self.cml.out_file = self.out_file
self.assertRaises((AttributeError, TypeError, OSError),
- self.cml.run)
+ self.cml.run)
def testTreeExists(self):
self.assertRaises((EnvironmentError, IOError), codeml.Codeml,
- tree="nonexistent")
+ tree="nonexistent")
self.cml.alignment = self.align_file
self.cml.tree = "nonexistent"
self.cml.out_file = self.out_file
@@ -93,14 +93,14 @@ class ModTest(unittest.TestCase):
self.cml.out_file = self.out_file
self.cml.working_dir = list()
self.assertRaises((AttributeError, TypeError, OSError),
- self.cml.run)
+ self.cml.run)
def testOutputFileValid(self):
self.cml.tree = self.tree_file
self.cml.alignment = self.align_file
self.cml.out_file = list()
self.assertRaises((AttributeError, ValueError, OSError),
- self.cml.run)
+ self.cml.run)
def testOptionExists(self):
self.assertRaises((AttributeError, KeyError),
@@ -112,26 +112,26 @@ class ModTest(unittest.TestCase):
self.cml.tree = self.tree_file
self.cml.out_file = self.out_file
self.assertRaises((AttributeError, ValueError),
- self.cml.run)
+ self.cml.run)
def testTreeSpecified(self):
self.cml.alignment = self.align_file
self.cml.out_file = self.out_file
self.assertRaises((AttributeError, ValueError),
- self.cml.run)
+ self.cml.run)
def testOutputFileSpecified(self):
self.cml.alignment = self.align_file
self.cml.tree = self.tree_file
self.assertRaises((AttributeError, ValueError),
- self.cml.run)
+ self.cml.run)
def testPamlErrorsCaught(self):
self.cml.alignment = self.align_file
self.cml.tree = self.bad_tree_file
self.cml.out_file = self.out_file
self.assertRaises((EnvironmentError, PamlError),
- self.cml.run)
+ self.cml.run)
def testCtlFileValidOnRun(self):
self.cml.alignment = self.align_file
@@ -524,6 +524,24 @@ class ModTest(unittest.TestCase):
self.assertTrue(m0["tree"] is not None)
self.assertNotEqual(len(m0["tree"]), 0)
+ def testParseM2arel(self):
+ res_dir = os.path.join(self.results_dir, "codeml", "m2a_rel")
+ for results_file in os.listdir(res_dir):
+ version = results_file.split('-')[1].split('.')[0]
+ version_msg = "Improper parsing for version %s" \
+ % version.replace('_', '.')
+ results_path = os.path.join(res_dir, results_file)
+ results = codeml.read(results_path)
+ self.assertTrue("NSsites" in results)
+ self.assertTrue(22 in results["NSsites"])
+ model = results["NSsites"][22]
+ self.assertEqual(len(model), 5, version_msg)
+ params = model["parameters"]
+ self.assertEqual(len(params), SITECLASS_PARAMS[22],
+ version_msg)
+
+
+
if __name__ == "__main__":
runner = unittest.TextTestRunner(verbosity=2)
unittest.main(testRunner=runner)
diff --git a/Tests/test_PDB.py b/Tests/test_PDB.py
index 0d7bb3e..aa3f64c 100644
--- a/Tests/test_PDB.py
+++ b/Tests/test_PDB.py
@@ -48,47 +48,33 @@ class A_ExceptionTest(unittest.TestCase):
works -- a warning is only logged the first time it is encountered.
"""
def test_1_warnings(self):
- """Check warnings: Parse a flawed PDB file in permissive mode.
-
- NB: The try/finally block is adapted from the warnings.catch_warnings
- context manager in the Python 2.6 standard library.
-
- TODO: Now we require Python 2.6, switch to using warnings.catch_warnings
- """
- warnings.simplefilter('always', PDBConstructionWarning)
- try:
- # Equivalent to warnings.catch_warnings -- hackmagic
- orig_showwarning = warnings.showwarning
- all_warns = []
-
- def showwarning(*args, **kwargs):
- all_warns.append(args[0])
+ """Check warnings: Parse a flawed PDB file in permissive mode."""
+ with warnings.catch_warnings(record=True) as w:
+ warnings.simplefilter('always', PDBConstructionWarning)
- warnings.showwarning = showwarning
# Trigger warnings
p = PDBParser(PERMISSIVE=True)
p.get_structure("example", "PDB/a_structure.pdb")
- self.assertEqual(len(all_warns), 14)
- for wrn, msg in zip(all_warns, [
- # Expected warning messages:
- "Used element 'N' for Atom (name=N) with given element ''",
- "Used element 'C' for Atom (name=CA) with given element ''",
- "Atom names ' CA ' and 'CA ' differ only in spaces at line 17.",
- "Used element 'CA' for Atom (name=CA ) with given element ''",
- 'Atom N defined twice in residue <Residue ARG het= resseq=2 icode= > at line 21.',
- 'disordered atom found with blank altloc before line 33.',
- "Residue (' ', 4, ' ') redefined at line 43.",
- "Blank altlocs in duplicate residue SER (' ', 4, ' ') at line 43.",
- "Residue (' ', 10, ' ') redefined at line 75.",
- "Residue (' ', 14, ' ') redefined at line 106.",
- "Residue (' ', 16, ' ') redefined at line 135.",
- "Residue (' ', 80, ' ') redefined at line 633.",
- "Residue (' ', 81, ' ') redefined at line 646.",
- 'Atom O defined twice in residue <Residue HOH het=W resseq=67 icode= > at line 822.'
- ]):
+
+ self.assertEqual(len(w), 14)
+ for wrn, msg in zip(w, [
+ # Expected warning messages:
+ "Used element 'N' for Atom (name=N) with given element ''",
+ "Used element 'C' for Atom (name=CA) with given element ''",
+ "Atom names ' CA ' and 'CA ' differ only in spaces at line 17.",
+ "Used element 'CA' for Atom (name=CA ) with given element ''",
+ 'Atom N defined twice in residue <Residue ARG het= resseq=2 icode= > at line 21.',
+ 'disordered atom found with blank altloc before line 33.',
+ "Residue (' ', 4, ' ') redefined at line 43.",
+ "Blank altlocs in duplicate residue SER (' ', 4, ' ') at line 43.",
+ "Residue (' ', 10, ' ') redefined at line 75.",
+ "Residue (' ', 14, ' ') redefined at line 106.",
+ "Residue (' ', 16, ' ') redefined at line 135.",
+ "Residue (' ', 80, ' ') redefined at line 633.",
+ "Residue (' ', 81, ' ') redefined at line 646.",
+ 'Atom O defined twice in residue <Residue HOH het=W resseq=67 icode= > at line 822.'
+ ]):
self.assertTrue(msg in str(wrn), str(wrn))
- finally:
- warnings.showwarning = orig_showwarning
def test_2_strict(self):
"""Check error: Parse a flawed PDB file in strict mode."""
@@ -519,6 +505,24 @@ class ParseTest(unittest.TestCase):
"C C O C S N C C O C C C O N N C C O C C C C C C C O "
"N C C O C C C N C N N N C C O C S")
+ def test_pdbio_write_truncated(self):
+ """Test parsing of truncated lines"""
+ io = PDBIO()
+ struct = self.structure
+ # Write to temp file
+ io.set_structure(struct)
+ filenumber, filename = tempfile.mkstemp()
+ os.close(filenumber)
+ try:
+ io.save(filename)
+ # Check if there are lines besides 'ATOM', 'TER' and 'END'
+ with open(filename, 'rU') as handle:
+ record_set = set([l[0:6] for l in handle])
+ record_set -= set(('ATOM ', 'HETATM', 'MODEL ', 'ENDMDL', 'TER\n', 'END\n'))
+ self.assertEqual(record_set, set())
+ finally:
+ os.remove(filename)
+
class ParseReal(unittest.TestCase):
"""Testing with real PDB files."""
@@ -684,13 +688,25 @@ class ParseReal(unittest.TestCase):
def test_model_numbering(self):
"""Preserve model serial numbers during I/O."""
def confirm_numbering(struct):
- self.assertEqual(len(struct), 20)
+ self.assertEqual(len(struct), 3)
for idx, model in enumerate(struct):
- self.assertTrue(model.serial_num, idx + 1)
- self.assertTrue(model.serial_num, model.id + 1)
- parser = PDBParser()
- struct1 = parser.get_structure("1mot", "PDB/1MOT.pdb")
+ self.assertEqual(model.serial_num, idx + 1)
+ self.assertEqual(model.serial_num, model.id + 1)
+
+ def confirm_single_end(fname):
+ """Ensure there is only one END statement in multi-model files"""
+ with open(fname) as handle:
+ end_stment = []
+ for iline, line in enumerate(handle):
+ if line.strip() == 'END':
+ end_stment.append((line, iline))
+ self.assertEqual(len(end_stment), 1) # Only one?
+ self.assertEqual(end_stment[0][1], iline) # Last line of the file?
+
+ parser = PDBParser(QUIET=1)
+ struct1 = parser.get_structure("1lcd", "PDB/1LCD.pdb")
confirm_numbering(struct1)
+
# Round trip: serialize and parse again
io = PDBIO()
io.set_structure(struct1)
@@ -698,8 +714,9 @@ class ParseReal(unittest.TestCase):
os.close(filenumber)
try:
io.save(filename)
- struct2 = parser.get_structure("1mot", filename)
+ struct2 = parser.get_structure("1lcd", filename)
confirm_numbering(struct2)
+ confirm_single_end(filename)
finally:
os.remove(filename)
@@ -956,7 +973,9 @@ class Atom_Element(unittest.TestCase):
class IterationTests(unittest.TestCase):
def setUp(self):
- self.struc = PDBParser(PERMISSIVE=True).get_structure('X', "PDB/a_structure.pdb")
+ with warnings.catch_warnings():
+ warnings.simplefilter("ignore", PDBConstructionWarning)
+ self.struc = PDBParser(PERMISSIVE=True).get_structure('X', "PDB/a_structure.pdb")
def test_get_chains(self):
"""Yields chains from different models separately."""
@@ -993,8 +1012,10 @@ class IterationTests(unittest.TestCase):
class TransformTests(unittest.TestCase):
def setUp(self):
- self.s = PDBParser(PERMISSIVE=True).get_structure(
- 'X', "PDB/a_structure.pdb")
+ with warnings.catch_warnings():
+ warnings.simplefilter("ignore", PDBConstructionWarning)
+ self.s = PDBParser(PERMISSIVE=True).get_structure(
+ 'X', "PDB/a_structure.pdb")
self.m = self.s.get_list()[0]
self.c = self.m.get_list()[0]
self.r = self.c.get_list()[0]
@@ -1038,8 +1059,10 @@ class TransformTests(unittest.TestCase):
class CopyTests(unittest.TestCase):
def setUp(self):
- self.s = PDBParser(PERMISSIVE=True).get_structure(
- 'X', "PDB/a_structure.pdb")
+ with warnings.catch_warnings():
+ warnings.simplefilter("ignore", PDBConstructionWarning)
+ self.s = PDBParser(PERMISSIVE=True).get_structure(
+ 'X', "PDB/a_structure.pdb")
self.m = self.s.get_list()[0]
self.c = self.m.get_list()[0]
self.r = self.c.get_list()[0]
diff --git a/Tests/test_PDB_KDTree.py b/Tests/test_PDB_KDTree.py
index edcf70c..70d5dd8 100644
--- a/Tests/test_PDB_KDTree.py
+++ b/Tests/test_PDB_KDTree.py
@@ -35,7 +35,7 @@ class NeighborTest(unittest.TestCase):
Based on the self test in Bio.PDB.NeighborSearch.
"""
- class RandomAtom:
+ class RandomAtom(object):
def __init__(self):
self.coord = 100 * random(3)
diff --git a/Tests/test_Pathway.py b/Tests/test_Pathway.py
index 7059602..b3c49ff 100644
--- a/Tests/test_Pathway.py
+++ b/Tests/test_Pathway.py
@@ -14,7 +14,7 @@ from Bio.Pathway.Rep.MultiGraph import MultiGraph
class GraphTestCase(unittest.TestCase):
- def testEquals(self):
+ def test_Equals(self):
a = Graph(['a', 'b', 'c'])
a.add_edge('a', 'b', 'label1')
a.add_edge('b', 'c', 'label1')
@@ -34,7 +34,7 @@ class GraphTestCase(unittest.TestCase):
self.assertNotEqual(c, Graph(), "equal to empty graph")
self.assertEqual(Graph(), Graph(), "empty graph not equal to self")
- def testNodes(self):
+ def test_Nodes(self):
a = Graph()
self.assertEqual(a.nodes(), [], "default graph not empty")
a.add_node('a')
@@ -45,7 +45,7 @@ class GraphTestCase(unittest.TestCase):
l = sorted(a.nodes())
self.assertEqual(l, ['a', 'b'], "second node not added")
- def testEdges(self):
+ def test_Edges(self):
a = Graph(['a', 'b', 'c', 'd'])
a.add_edge('a', 'b', 'label1')
self.assertEqual(a.child_edges('a'), [('b', 'label1')]) # , "incorrect child edges")
@@ -58,7 +58,7 @@ class GraphTestCase(unittest.TestCase):
self.assertEqual(a.children('d'), [], "incorrect children for singleton")
self.assertEqual(a.parents('a'), ['b'], "incorrect parents")
- def testRemoveNode(self):
+ def test_RemoveNode(self):
a = Graph(['a', 'b', 'c', 'd', 'e'])
a.add_edge('a', 'e', 'label1')
a.add_edge('b', 'e', 'label1')
@@ -74,7 +74,7 @@ class GraphTestCase(unittest.TestCase):
class MultiGraphTestCase(unittest.TestCase):
- def testEquals(self):
+ def test_Equals(self):
a = MultiGraph(['a', 'b', 'c'])
a.add_edge('a', 'b', 'label1')
a.add_edge('b', 'c', 'label1')
@@ -94,7 +94,7 @@ class MultiGraphTestCase(unittest.TestCase):
self.assertNotEqual(c, MultiGraph(), "equal to empty graph")
self.assertEqual(MultiGraph(), MultiGraph(), "empty graph not equal to self")
- def testNodes(self):
+ def test_Nodes(self):
a = MultiGraph()
self.assertEqual(a.nodes(), [], "default graph not empty")
a.add_node('a')
@@ -105,7 +105,7 @@ class MultiGraphTestCase(unittest.TestCase):
l = sorted(a.nodes())
self.assertEqual(l, ['a', 'b'], "second node not added")
- def testEdges(self):
+ def test_Edges(self):
a = MultiGraph(['a', 'b', 'c', 'd'])
a.add_edge('a', 'b', 'label1')
self.assertEqual(a.child_edges('a'), [('b', 'label1')]) # , "incorrect child edges")
@@ -122,7 +122,7 @@ class MultiGraphTestCase(unittest.TestCase):
self.assertEqual(a.children('d'), [], "incorrect children for singleton")
self.assertEqual(a.parents('a'), ['b'], "incorrect parents")
- def testRemoveNode(self):
+ def test_RemoveNode(self):
a = MultiGraph(['a', 'b', 'c', 'd', 'e'])
a.add_edge('a', 'e', 'label1')
self.assertEqual(repr(a), "<MultiGraph: ('a': ('e', 'label1'))('b': )('c': )('d': )('e': )>")
@@ -153,11 +153,11 @@ class ReactionTestCase(unittest.TestCase):
self.r_3 = Reaction({"a": -1, "d": 2})
self.r_4 = Reaction({"c": -1, "d": -1, "a": 1, "e": 2})
- def testEq(self):
+ def test_eq(self):
self.assertEqual(self.r_1, self.r_1i) # , "not equal to similar")
self.assertNotEqual(self.r_3, self.r_4) # , "equal to different")
- def testRev(self):
+ def test_rev(self):
self.assertEqual(self.r_empty.reverse(), self.r_empty, "empty reversed not empty")
self.assertEqual(self.r_prod.reverse(), self.r_dest,
"reversed reaction not equal to similar")
diff --git a/Tests/test_Phylo.py b/Tests/test_Phylo.py
index 6d6251d..3ce25df 100644
--- a/Tests/test_Phylo.py
+++ b/Tests/test_Phylo.py
@@ -93,6 +93,34 @@ class IOTests(unittest.TestCase):
self.assertEqual(clade_a.name, 'foo')
self.assertAlmostEqual(clade_a.branch_length, 0.1)
+
+ """Additional tests to check correct parsing"""
+ tree = Phylo.read(StringIO("(A:1, B:-2, (C:3, D:4):-2)"),'newick')
+ self.assertEqual(tree.distance('A'),1)
+ self.assertEqual(tree.distance('B'),-2)
+ self.assertEqual(tree.distance('C'),1)
+ self.assertEqual(tree.distance('D'),2)
+
+ tree = Phylo.read(StringIO("((A:1, B:-2):-5, (C:3, D:4):-2)"),'newick')
+ self.assertEqual(tree.distance('A'),-4)
+ self.assertEqual(tree.distance('B'),-7)
+ self.assertEqual(tree.distance('C'),1)
+ self.assertEqual(tree.distance('D'),2)
+
+ tree = Phylo.read(StringIO("((:1, B:-2):-5, (C:3, D:4):-2)"),'newick')
+ distances = {-4.0:1,-7.0:1,1:1,2:1}
+ for x in tree.get_terminals():
+ entry = int(tree.distance(x))
+ distances[entry] -= distances[entry]
+ self.assertEqual(distances[entry],0)
+
+ tree = Phylo.read(StringIO("((:\n1\n,\n B:-2):-5, (C:3, D:4):-2);"),'newick')
+ distances = {-4.0:1,-7.0:1,1:1,2:1}
+ for x in tree.get_terminals():
+ entry = int(tree.distance(x))
+ distances[entry] -= distances[entry]
+ self.assertEqual(distances[entry],0)
+
def test_format_branch_length(self):
"""Custom format string for Newick branch length serialization."""
tree = Phylo.read(StringIO('A:0.1;'), 'newick')
diff --git a/Tests/test_PhyloXML.py b/Tests/test_PhyloXML.py
index 0e1c5bd..31ff16b 100644
--- a/Tests/test_PhyloXML.py
+++ b/Tests/test_PhyloXML.py
@@ -202,7 +202,6 @@ class TreeTests(unittest.TestCase):
'a pylogeny of some monitor lizards')
self.assertEqual(trees[9].rooted, True)
# Network (unrooted)
- tree6 = trees[6]
self.assertEqual(trees[6].name,
'network, node B is connected to TWO nodes: AB and C')
self.assertEqual(trees[6].rooted, False)
@@ -233,11 +232,8 @@ class TreeTests(unittest.TestCase):
def test_BinaryCharacters(self):
"""Instantiation of BinaryCharacters objects."""
- # Because we short circult interation, must close handle explicitly
- # to avoid a ResourceWarning
- handle = open(EX_DOLLO)
- tree = next(PhyloXMLIO.parse(handle))
- handle.close()
+ with open(EX_DOLLO) as handle:
+ tree = next(PhyloXMLIO.parse(handle))
bchars = tree.clade[0, 0].binary_characters
self.assertTrue(isinstance(bchars, PX.BinaryCharacters))
self.assertEqual(bchars.type, 'parsimony inferred')
@@ -344,9 +340,9 @@ class TreeTests(unittest.TestCase):
(6, 109, 605, 647, 689, 733, 872, 993, 1075, 1117, 1168),
(90, 414, 643, 685, 729, 771, 910, 1031, 1113, 1155, 1204),
(7.0e-26, 7.2e-117, 2.4e-6, 1.1e-12, 2.4e-7, 4.7e-14, 2.5e-8,
- 4.6e-6, 6.3e-7, 1.4e-7, 0.3),
+ 4.6e-6, 6.3e-7, 1.4e-7, 0.3),
('CARD', 'NB-ARC', 'WD40', 'WD40', 'WD40', 'WD40', 'WD40',
- 'WD40', 'WD40', 'WD40', 'WD40')):
+ 'WD40', 'WD40', 'WD40', 'WD40')):
self.assertTrue(isinstance(domain, PX.ProteinDomain))
self.assertEqual(domain.start + 1, start)
self.assertEqual(domain.end, end)
diff --git a/Tests/test_Phylo_NeXML.py b/Tests/test_Phylo_NeXML.py
index 87c6110..2597bfb 100644
--- a/Tests/test_Phylo_NeXML.py
+++ b/Tests/test_Phylo_NeXML.py
@@ -11,7 +11,7 @@ import tempfile
import unittest
import Bio.Phylo as bp
-from Bio.Phylo import NeXML, NeXMLIO
+from Bio.Phylo import NeXMLIO
# Example NeXML files
nexml_files = (
@@ -74,10 +74,6 @@ def _test_write_factory(source):
tests on files (re)generated by NeXMLIO's own writer.
"""
filename = os.path.join('NeXML/', source)
- if source in tree_counts:
- count = tree_counts[source]
- else:
- count = 1
def test_write(self):
"""Parse, rewrite and retest an example file."""
diff --git a/Tests/test_Phylo_depend.py b/Tests/test_Phylo_depend.py
index a2f03a0..d7d741b 100644
--- a/Tests/test_Phylo_depend.py
+++ b/Tests/test_Phylo_depend.py
@@ -64,6 +64,36 @@ class UtilTests(unittest.TestCase):
Phylo.draw(apaf, do_show=False, branch_labels={apaf.root: 'Root'})
Phylo.draw(apaf, do_show=False, branch_labels=lambda c: c.branch_length)
+ def test_draw_with_label_colors_dict(self):
+ """Run the tree layout algorithm with a label_colors argument passed in
+ as a dictionary. Don't display tree."""
+ pyplot.ioff() # Turn off interactive display
+ dollo = Phylo.read(EX_DOLLO, 'phyloxml')
+ apaf = Phylo.read(EX_APAF, 'phyloxml')
+ label_colors_dollo = {
+ 'f_50': 'red',
+ 'f_34': 'blue',
+ }
+ label_colors_apaf = {
+ '22_MOUSE': 'red',
+ '18_NEMVE': 'blue',
+ }
+ Phylo.draw(dollo, label_colors=label_colors_dollo, do_show=False)
+ Phylo.draw(apaf, label_colors=label_colors_apaf, do_show=False)
+
+ def test_draw_with_label_colors_callable(self):
+ """Run the tree layout algorithm with a label_colors argument passed in
+ as a callable. Don't display tree."""
+ pyplot.ioff() # Turn off interactive display
+ dollo = Phylo.read(EX_DOLLO, 'phyloxml')
+ apaf = Phylo.read(EX_APAF, 'phyloxml')
+
+ label_colors_dollo = lambda label: 'r' if label == 'f_50' else 'k'
+ label_colors_apaf = lambda label: 'r'
+
+ Phylo.draw(dollo, label_colors=label_colors_dollo, do_show=False)
+ Phylo.draw(apaf, label_colors=label_colors_apaf, do_show=False)
+
def test_draw_ascii(self):
"""Tree to Graph conversion."""
handle = StringIO()
diff --git a/Tests/test_PopGen_GenePop_nodepend.py b/Tests/test_PopGen_GenePop_nodepend.py
index 2f2a5e2..1c57530 100644
--- a/Tests/test_PopGen_GenePop_nodepend.py
+++ b/Tests/test_PopGen_GenePop_nodepend.py
@@ -64,13 +64,12 @@ class ParserTest(unittest.TestCase):
def test_wrong_file_parser(self):
"""Testing the ability to deal with wrongly formatted files
"""
- f = open(os.path.join("PopGen", "fdist1"))
- try:
- rec = GenePop.read(f)
- raise Error("Should have raised exception")
- except ValueError:
- pass
- f.close()
+ with open(os.path.join("PopGen", "fdist1")) as f:
+ try:
+ rec = GenePop.read(f)
+ raise Exception("Should have raised exception")
+ except ValueError:
+ pass
class FileParserTest(unittest.TestCase):
@@ -98,9 +97,9 @@ class FileParserTest(unittest.TestCase):
assert len(rec.loci_list) == self.num_loci[index]
for skip in range(self.pops_indivs[index][0]):
if rec.skip_population() is False:
- raise Error("Not enough populations")
+ raise Exception("Not enough populations")
if rec.skip_population() is True:
- raise Error("Too much populations")
+ raise Exception("Too much populations")
for i in range(self.pops_indivs[index][0]):
continue
assert len(rec.populations[i]) == \
@@ -110,13 +109,12 @@ class FileParserTest(unittest.TestCase):
def test_wrong_file_parser(self):
"""Testing the ability to deal with wrongly formatted files
"""
- f = open(os.path.join("PopGen", "fdist1"))
- try:
- rec = GenePop.read(f)
- raise Error("Should have raised exception")
- except ValueError:
- pass
- f.close()
+ with open(os.path.join("PopGen", "fdist1")) as f:
+ try:
+ rec = GenePop.read(f)
+ raise Exception("Should have raised exception")
+ except ValueError:
+ pass
class UtilsTest(unittest.TestCase):
diff --git a/Tests/test_PopGen_SimCoal_nodepend.py b/Tests/test_PopGen_SimCoal_nodepend.py
index cdf06ed..ad5e1e5 100644
--- a/Tests/test_PopGen_SimCoal_nodepend.py
+++ b/Tests/test_PopGen_SimCoal_nodepend.py
@@ -7,7 +7,6 @@ from Bio._py3k import _universal_read_mode
import os
import unittest
-from Bio.PopGen import SimCoal
from Bio.PopGen.SimCoal.Template import generate_simcoal_from_template
# Tests simcoal related code. Note: this case doesn't require simcoal
@@ -25,12 +24,10 @@ class TemplateTest(unittest.TestCase):
'PopGen')
# Confirm the files match (ignoring any switch of line endings
# possible if the input file used a different OS convention)
- handle = open(os.path.join('PopGen', 'simple.par'), _universal_read_mode)
- old = handle.readlines()
- handle.close()
- handle = open(os.path.join('PopGen', 'simple_100_30.par'))
- new = handle.readlines()
- handle.close()
+ with open(os.path.join('PopGen', 'simple.par'), _universal_read_mode) as handle:
+ old = handle.readlines()
+ with open(os.path.join('PopGen', 'simple_100_30.par')) as handle:
+ new = handle.readlines()
assert old == new, "Error - Old:\n%s\n\nNew:\n%s\n" % (old, new)
# assert(os.stat('PopGen' + os.sep + 'simple.par').st_size ==
# os.stat('PopGen' + os.sep + 'simple_100_30.par').st_size)
diff --git a/Tests/test_Prank_tool.py b/Tests/test_Prank_tool.py
index f271fb7..952e9f3 100644
--- a/Tests/test_Prank_tool.py
+++ b/Tests/test_Prank_tool.py
@@ -161,11 +161,11 @@ class PrankConversion(unittest.TestCase):
cmdline = PrankCommandline(prank_exe, d=self.input,
convert=True, f=prank_number,
o='"%s"' % self.output)
- self.assertEqual(str(cmdline), _escape_filename(prank_exe)
- + ' -d=%s' % self.input
- + ' -o="%s"' % self.output
- + ' -f=%i' % prank_number
- + ' -convert')
+ self.assertEqual(str(cmdline), _escape_filename(prank_exe) +
+ ' -d=%s' % self.input +
+ ' -o="%s"' % self.output +
+ ' -f=%i' % prank_number +
+ ' -convert')
self.assertEqual(str(eval(repr(cmdline))), str(cmdline))
message, error = cmdline()
self.assertTrue("PRANK" in message, message)
diff --git a/Tests/test_QCPSuperimposer.py b/Tests/test_QCPSuperimposer.py
index 1724c3d..9e2d43a 100644
--- a/Tests/test_QCPSuperimposer.py
+++ b/Tests/test_QCPSuperimposer.py
@@ -2,10 +2,11 @@
# license. Please see the LICENSE file that should have been included
# as part of this package.
-# TODO - Don't use "from XXX import *"
from __future__ import print_function
+import warnings
try:
+ # TODO - Don't use "from XXX import *"
from numpy import *
from numpy import dot # missing in old PyPy's micronumpy
except ImportError:
@@ -13,7 +14,10 @@ except ImportError:
raise MissingPythonDependencyError(
"Install NumPy if you want to use Bio.QCPSuperimposer.")
-from Bio.PDB.QCPSuperimposer import QCPSuperimposer
+from Bio import BiopythonExperimentalWarning
+with warnings.catch_warnings():
+ warnings.simplefilter('ignore', BiopythonExperimentalWarning)
+ from Bio.PDB.QCPSuperimposer import QCPSuperimposer
# start with two coordinate sets (Nx3 arrays - Float0)
diff --git a/Tests/test_RCSBFormats.py b/Tests/test_RCSBFormats.py
new file mode 100644
index 0000000..5e0ad2e
--- /dev/null
+++ b/Tests/test_RCSBFormats.py
@@ -0,0 +1,69 @@
+# Copyright 2016 Joao P. Rodrigues (j.p.g.l.m.rodrigues at gmail.com).
+# All rights reserved.
+#
+# This code is part of the Biopython distribution and governed by its
+# license. Please see the LICENSE file that should have been included
+# as part of this package.
+
+"""Unit tests to compare objects parsed by different structure parsers."""
+
+import os
+import unittest
+import warnings
+
+try:
+ import numpy
+ from numpy import dot # Missing on old PyPy's micronumpy
+ del dot
+ from numpy.linalg import svd, det # Missing in PyPy 2.0 numpypy
+except ImportError:
+ from Bio import MissingPythonDependencyError
+ raise MissingPythonDependencyError(
+ "Install NumPy if you want to use Bio.PDB.")
+
+from Bio.PDB.MMCIFParser import MMCIFParser
+from Bio.PDB import PDBParser
+from Bio.PDB.PDBExceptions import PDBConstructionException, PDBConstructionWarning
+
+class CompareStructures(unittest.TestCase):
+ """Tests for comparing the same structure parsed by PDB and MMCIF parsers"""
+
+ def setUp(self):
+
+ # Silence!
+ warnings.simplefilter('ignore', PDBConstructionWarning)
+
+ pdbparser = PDBParser(QUIET=1)
+ cifparser = MMCIFParser(QUIET=1)
+
+ modpath = os.path.abspath(os.path.dirname(__file__))
+
+ pdb_file = os.path.join(modpath, "PDB", "1LCD.pdb")
+ cif_file = os.path.join(modpath, "PDB", "1LCD.cif")
+
+ self.pdbo = pdbparser.get_structure('pdb', pdb_file)
+ self.cifo = cifparser.get_structure('pdb', cif_file)
+
+ def test_compare_models(self):
+ """Compared parsed models"""
+
+ cif_models = [(m.id, len(m.child_list)) for m in self.cifo.get_models()]
+ pdb_models = [(m.id, len(m.child_list)) for m in self.pdbo.get_models()]
+
+ self.assertEqual( len(cif_models), len(pdb_models) )
+ self.assertEqual( [i[0] for i in cif_models], [i[0] for i in pdb_models] )
+ self.assertEqual( [i[1] for i in cif_models], [i[1] for i in pdb_models] )
+
+ def test_compare_chains(self):
+ """Compare parsed chains"""
+
+ cif_chains = [(c.id, len(c.child_list)) for c in self.cifo.get_chains()]
+ pdb_chains = [(c.id, len(c.child_list)) for c in self.pdbo.get_chains()]
+
+ self.assertEqual( len(cif_chains), len(pdb_chains) )
+ self.assertEqual( [i[0] for i in cif_chains], [i[0] for i in pdb_chains] )
+ self.assertEqual( [i[1] for i in cif_chains], [i[1] for i in pdb_chains] )
+
+if __name__ == '__main__':
+ runner = unittest.TextTestRunner(verbosity=2)
+ unittest.main(testRunner=runner)
diff --git a/Tests/test_SCOP_Raf.py b/Tests/test_SCOP_Raf.py
index 7f8a6d4..e9d8bbd 100644
--- a/Tests/test_SCOP_Raf.py
+++ b/Tests/test_SCOP_Raf.py
@@ -9,6 +9,7 @@
import unittest
from Bio.SCOP import Raf
+from Bio._py3k import StringIO
class RafTests(unittest.TestCase):
@@ -60,6 +61,14 @@ class RafTests(unittest.TestCase):
r = r[r.index("124"): r.index("135") + 1]
self.assertEqual(len(r.res), 12)
+ def test_SeqMap_getAtoms_err(self):
+ r = Raf.SeqMap(self.rafLine)
+ # There is no overlap with this PDB file...
+ with open("PDB/1A8O.pdb") as pdb_handle:
+ out_handle = StringIO()
+ self.assertRaises(RuntimeError, r.getAtoms,
+ *(pdb_handle, out_handle))
+
def testSeqMapIndex(self):
filename = ("./SCOP/raftest.txt")
diff --git a/Tests/test_SCOP_Scop.py b/Tests/test_SCOP_Scop.py
index f3f8730..269ae68 100644
--- a/Tests/test_SCOP_Scop.py
+++ b/Tests/test_SCOP_Scop.py
@@ -13,7 +13,7 @@ import unittest
from Bio._py3k import StringIO
from Bio._py3k import zip
-from Bio.SCOP import *
+from Bio.SCOP import Scop, cmp_sccs, parse_domain
class ScopTests(unittest.TestCase):
diff --git a/Tests/test_SVDSuperimposer.py b/Tests/test_SVDSuperimposer.py
index 5b5cc2f..9dac0a4 100644
--- a/Tests/test_SVDSuperimposer.py
+++ b/Tests/test_SVDSuperimposer.py
@@ -2,11 +2,10 @@
# license. Please see the LICENSE file that should have been included
# as part of this package.
-# TODO - Don't use "from XXX import *"
from __future__ import print_function
try:
- from numpy import *
+ from numpy import array
from numpy import dot # missing in old PyPy's micronumpy
from numpy.linalg import svd, det # Missing in PyPy 2.0 numpypy
except ImportError:
@@ -19,14 +18,14 @@ from Bio.SVDSuperimposer import SVDSuperimposer
# start with two coordinate sets (Nx3 arrays - Float0)
x = array([[51.65, -1.90, 50.07],
- [50.40, -1.23, 50.65],
- [50.68, -0.04, 51.54],
- [50.22, -0.02, 52.85]], 'f')
+ [50.40, -1.23, 50.65],
+ [50.68, -0.04, 51.54],
+ [50.22, -0.02, 52.85]], 'f')
y = array([[51.30, -2.99, 46.54],
- [51.09, -1.88, 47.58],
- [52.36, -1.20, 48.03],
- [52.71, -1.18, 49.38]], 'f')
+ [51.09, -1.88, 47.58],
+ [52.36, -1.20, 48.03],
+ [52.71, -1.18, 49.38]], 'f')
sup = SVDSuperimposer()
diff --git a/Tests/test_SearchIO_model.py b/Tests/test_SearchIO_model.py
index 8e846b1..937d320 100644
--- a/Tests/test_SearchIO_model.py
+++ b/Tests/test_SearchIO_model.py
@@ -484,7 +484,7 @@ class QueryResultCases(unittest.TestCase):
filtered = self.qresult.hit_filter(filter_func)
self.assertEqual([hit11, hit31], list(filtered.hits))
# make sure all remaining hits return True for the filter function
- self.assertTrue(all([filter_func(hit) for hit in filtered]))
+ self.assertTrue(all(filter_func(hit) for hit in filtered))
self.assertEqual(1102, filtered.seq_len)
self.assertEqual('refseq_rna', filtered.target)
@@ -555,11 +555,11 @@ class QueryResultCases(unittest.TestCase):
self.assertTrue('hit2' not in filtered)
self.assertTrue('hit3' in filtered)
# test hsps in hit11
- self.assertTrue(all([hsp in filtered['hit1'] for hsp in
- [hsp111, hsp112, hsp114]]))
+ self.assertTrue(all(hsp in filtered['hit1'] for hsp in
+ [hsp111, hsp112, hsp114]))
# test hsps in hit31
- self.assertTrue(all([hsp in filtered['hit3'] for hsp in
- [hsp311, hsp312]]))
+ self.assertTrue(all(hsp in filtered['hit3'] for hsp in
+ [hsp311, hsp312]))
def test_hsp_filter_no_func(self):
"""Test QueryResult.hsp_filter, no arguments"""
@@ -944,7 +944,7 @@ class HitCases(unittest.TestCase):
filtered = self.hit.filter(filter_func)
self.assertEqual([hsp111, hsp113], filtered.hsps)
# make sure all remaining hits return True for the filter function
- self.assertTrue(all([filter_func(hit) for hit in filtered]))
+ self.assertTrue(all(filter_func(hit) for hit in filtered))
self.assertEqual(5e-10, filtered.evalue)
self.assertEqual('test', filtered.name)
diff --git a/Tests/test_SeqFeature.py b/Tests/test_SeqFeature.py
new file mode 100644
index 0000000..7f04128
--- /dev/null
+++ b/Tests/test_SeqFeature.py
@@ -0,0 +1,28 @@
+# Copyright 2015 by Kai Blin. All rights reserved.
+# This code is part of the Biopython distribution and governed by its
+# license. Please see the LICENSE file that should have been included
+# as part of this package.
+
+"""Tests Bio.SeqFeature.
+"""
+import unittest
+from os import path
+from Bio import SeqIO
+
+
+class TestReference(unittest.TestCase):
+ """Tests for the SeqFeature.Reference class"""
+
+ def test_eq_identical(self):
+ """Test two identical references eq() to True"""
+ testfile = path.join('GenBank', 'origin_line.gb')
+ rec1 = SeqIO.read(testfile, 'genbank')
+ rec2 = SeqIO.read(testfile, 'genbank')
+
+ self.assertEqual(rec1.annotations['references'][0], rec1.annotations['references'][0])
+ cmp1, cmp2 = rec1.annotations['references'][0], rec2.annotations['references'][0]
+ self.assertEqual(cmp1, cmp2)
+ self.assertNotEqual(rec1.annotations['references'][0], rec1.annotations['references'][1])
+ self.assertNotEqual(rec1.annotations['references'][0], rec2.annotations['references'][1])
+ self.assertEqual(rec1.annotations['references'][1], rec1.annotations['references'][1])
+ self.assertEqual(rec1.annotations['references'][1], rec2.annotations['references'][1])
diff --git a/Tests/test_SeqIO.py b/Tests/test_SeqIO.py
index 6d1c52f..e3b1152 100644
--- a/Tests/test_SeqIO.py
+++ b/Tests/test_SeqIO.py
@@ -1,4 +1,4 @@
-# Copyright 2007-2015 by Peter Cock. All rights reserved.
+# Copyright 2007-2016 by Peter Cock. All rights reserved.
# This code is part of the Biopython distribution and governed by its
# license. Please see the LICENSE file that should have been included
# as part of this package.
@@ -159,6 +159,8 @@ test_files = [
("genbank", False, 'GenBank/NP_416719.gbwithparts', 1),
# GenPept file with nasty bond locations,
("genbank", False, 'GenBank/1MRR_A.gp', 1),
+ # These are a pair, and should be roughly equivalent:
+ ("genbank", False, 'GenBank/DS830848.gb', 1), ("embl", False, 'EMBL/DS830848.embl', 1),
# Following files are currently only used here or in test_SeqIO_index.py:
("embl", False, 'EMBL/epo_prt_selection.embl', 9), # proteins
("embl", False, 'EMBL/patents.embl', 4), # more proteins, but no seq
@@ -321,14 +323,14 @@ def alignment_summary(alignment, index=" "):
alignment_len = alignment.get_alignment_length()
rec_count = len(alignment)
for i in range(min(5, alignment_len)):
- answer.append(index + col_summary(alignment[:, i])
- + " alignment column %i" % i)
+ answer.append(index + col_summary(alignment[:, i]) +
+ " alignment column %i" % i)
if alignment_len > 5:
i = alignment_len - 1
- answer.append(index + col_summary("|" * rec_count)
- + " ...")
- answer.append(index + col_summary(alignment[:, i])
- + " alignment column %i" % i)
+ answer.append(index + col_summary("|" * rec_count) +
+ " ...")
+ answer.append(index + col_summary(alignment[:, i]) +
+ " alignment column %i" % i)
return "\n".join(answer)
@@ -438,8 +440,10 @@ def check_simple_write_read(records, indent=" "):
handle = BytesIO()
else:
handle = StringIO()
- SeqIO.write(records[0], handle, format)
- assert handle.getvalue() == records[0].format(format)
+ with warnings.catch_warnings():
+ warnings.simplefilter("ignore", BiopythonWarning)
+ SeqIO.write(records[0], handle, format)
+ assert handle.getvalue() == records[0].format(format)
# Check parsers can cope with an empty file
@@ -528,135 +532,135 @@ for (t_format, t_alignment, t_filename, t_count) in test_files:
assert len(records5) == t_count
h.close()
- for i in range(t_count):
- record = records[i]
+ for i in range(t_count):
+ record = records[i]
- # Check returned expected object type
- assert isinstance(record, SeqRecord)
- if t_format in possible_unknown_seq_formats:
- assert isinstance(record.seq, Seq) or \
- isinstance(record.seq, UnknownSeq)
- else:
- assert isinstance(record.seq, Seq)
- assert isinstance(record.id, basestring)
- assert isinstance(record.name, basestring)
- assert isinstance(record.description, basestring)
- assert record.id != ""
-
- if "accessions" in record.annotations:
- accs = record.annotations["accessions"]
- # Check for blanks, or entries with leading/trailing spaces
- for acc in accs:
- assert acc and acc == acc.strip(), \
- "Bad accession in annotations: %s" % repr(acc)
- assert len(set(accs)) == len(accs), \
- "Repeated accession in annotations: %s" % repr(accs)
- for ref in record.dbxrefs:
- assert ref and ref == ref.strip(), \
- "Bad cross reference in dbxrefs: %s" % repr(ref)
- assert len(record.dbxrefs) == len(record.dbxrefs), \
- "Repeated cross reference in dbxrefs: %s" % repr(record.dbxrefs)
-
- # Check the lists obtained by the different methods agree
- assert compare_record(record, records2[i])
- assert compare_record(record, records3[i])
- assert compare_record(record, records4[i])
- assert compare_record(record, records5[i])
-
- if i < 3:
- print(record_summary(record))
- # Only printed the only first three records: 0,1,2
- if t_count > 4:
- print(" ...")
- if t_count > 3:
- print(record_summary(records[-1]))
-
- # Check Bio.SeqIO.read(...)
- if t_count == 1:
- record = SeqIO.read(t_filename, format=t_format)
- assert isinstance(record, SeqRecord)
- else:
- try:
- record = SeqIO.read(t_filename, t_format)
- assert False, "Bio.SeqIO.read(...) should have failed"
- except ValueError:
- # Expected to fail
- pass
-
- # Check alphabets
- for record in records:
- base_alpha = Alphabet._get_base_alphabet(record.seq.alphabet)
- if isinstance(base_alpha, Alphabet.SingleLetterAlphabet):
- if t_format in no_alpha_formats:
- # Too harsh?
- assert base_alpha == Alphabet.single_letter_alphabet
+ # Check returned expected object type
+ assert isinstance(record, SeqRecord)
+ if t_format in possible_unknown_seq_formats:
+ assert isinstance(record.seq, Seq) or \
+ isinstance(record.seq, UnknownSeq)
+ else:
+ assert isinstance(record.seq, Seq)
+ assert isinstance(record.id, basestring)
+ assert isinstance(record.name, basestring)
+ assert isinstance(record.description, basestring)
+ assert record.id != ""
+
+ if "accessions" in record.annotations:
+ accs = record.annotations["accessions"]
+ # Check for blanks, or entries with leading/trailing spaces
+ for acc in accs:
+ assert acc and acc == acc.strip(), \
+ "Bad accession in annotations: %s" % repr(acc)
+ assert len(set(accs)) == len(accs), \
+ "Repeated accession in annotations: %s" % repr(accs)
+ for ref in record.dbxrefs:
+ assert ref and ref == ref.strip(), \
+ "Bad cross reference in dbxrefs: %s" % repr(ref)
+ assert len(record.dbxrefs) == len(record.dbxrefs), \
+ "Repeated cross reference in dbxrefs: %s" % repr(record.dbxrefs)
+
+ # Check the lists obtained by the different methods agree
+ assert compare_record(record, records2[i])
+ assert compare_record(record, records3[i])
+ assert compare_record(record, records4[i])
+ assert compare_record(record, records5[i])
+
+ if i < 3:
+ print(record_summary(record))
+ # Only printed the only first three records: 0,1,2
+ if t_count > 4:
+ print(" ...")
+ if t_count > 3:
+ print(record_summary(records[-1]))
+
+ # Check Bio.SeqIO.read(...)
+ if t_count == 1:
+ record = SeqIO.read(t_filename, format=t_format)
+ assert isinstance(record, SeqRecord)
else:
- base_alpha = None
- if base_alpha is None:
- good = []
- bad = []
- given_alpha = None
- elif isinstance(base_alpha, Alphabet.ProteinAlphabet):
- good = protein_alphas
- bad = dna_alphas + rna_alphas + nucleotide_alphas
- elif isinstance(base_alpha, Alphabet.RNAAlphabet):
- good = nucleotide_alphas + rna_alphas
- bad = protein_alphas + dna_alphas
- elif isinstance(base_alpha, Alphabet.DNAAlphabet):
- good = nucleotide_alphas + dna_alphas
- bad = protein_alphas + rna_alphas
- elif isinstance(base_alpha, Alphabet.NucleotideAlphabet):
- good = nucleotide_alphas
- bad = protein_alphas
- else:
- assert t_format in no_alpha_formats, "Got %s from %s file" \
- % (repr(base_alpha), t_format)
- good = protein_alphas + dna_alphas + rna_alphas + nucleotide_alphas
- bad = []
- for given_alpha in good:
- # These should all work...
- given_base = Alphabet._get_base_alphabet(given_alpha)
- for record in SeqIO.parse(t_filename, t_format, given_alpha):
+ try:
+ record = SeqIO.read(t_filename, t_format)
+ assert False, "Bio.SeqIO.read(...) should have failed"
+ except ValueError:
+ # Expected to fail
+ pass
+
+ # Check alphabets
+ for record in records:
base_alpha = Alphabet._get_base_alphabet(record.seq.alphabet)
- assert isinstance(base_alpha, given_base.__class__)
- assert base_alpha == given_base
- if t_count == 1:
+ if isinstance(base_alpha, Alphabet.SingleLetterAlphabet):
+ if t_format in no_alpha_formats:
+ # Too harsh?
+ assert base_alpha == Alphabet.single_letter_alphabet
+ else:
+ base_alpha = None
+ if base_alpha is None:
+ good = []
+ bad = []
+ given_alpha = None
+ elif isinstance(base_alpha, Alphabet.ProteinAlphabet):
+ good = protein_alphas
+ bad = dna_alphas + rna_alphas + nucleotide_alphas
+ elif isinstance(base_alpha, Alphabet.RNAAlphabet):
+ good = nucleotide_alphas + rna_alphas
+ bad = protein_alphas + dna_alphas
+ elif isinstance(base_alpha, Alphabet.DNAAlphabet):
+ good = nucleotide_alphas + dna_alphas
+ bad = protein_alphas + rna_alphas
+ elif isinstance(base_alpha, Alphabet.NucleotideAlphabet):
+ good = nucleotide_alphas
+ bad = protein_alphas
+ else:
+ assert t_format in no_alpha_formats, "Got %s from %s file" \
+ % (repr(base_alpha), t_format)
+ good = protein_alphas + dna_alphas + rna_alphas + nucleotide_alphas
+ bad = []
+ for given_alpha in good:
+ # These should all work...
+ given_base = Alphabet._get_base_alphabet(given_alpha)
+ for record in SeqIO.parse(t_filename, t_format, given_alpha):
+ base_alpha = Alphabet._get_base_alphabet(record.seq.alphabet)
+ assert isinstance(base_alpha, given_base.__class__)
+ assert base_alpha == given_base
+ if t_count == 1:
+ h = open(t_filename, mode)
+ record = SeqIO.read(h, t_format, given_alpha)
+ h.close()
+ assert isinstance(base_alpha, given_base.__class__)
+ assert base_alpha == given_base
+ for given_alpha in bad:
+ # These should all fail...
h = open(t_filename, mode)
- record = SeqIO.read(h, t_format, given_alpha)
- h.close()
- assert isinstance(base_alpha, given_base.__class__)
- assert base_alpha == given_base
- for given_alpha in bad:
- # These should all fail...
- h = open(t_filename, mode)
- try:
- print(next(SeqIO.parse(h, t_format, given_alpha)))
+ try:
+ print(next(SeqIO.parse(h, t_format, given_alpha)))
+ h.close()
+ assert False, "Forcing wrong alphabet, %s, should fail (%s)" \
+ % (repr(given_alpha), t_filename)
+ except ValueError:
+ # Good - should fail
+ pass
h.close()
- assert False, "Forcing wrong alphabet, %s, should fail (%s)" \
- % (repr(given_alpha), t_filename)
- except ValueError:
- # Good - should fail
- pass
- h.close()
- del good, bad, given_alpha, base_alpha
+ del good, bad, given_alpha, base_alpha
- if t_alignment:
- print("Testing reading %s format file %s as an alignment"
- % (t_format, t_filename))
+ if t_alignment:
+ print("Testing reading %s format file %s as an alignment"
+ % (t_format, t_filename))
- alignment = MultipleSeqAlignment(SeqIO.parse(
- handle=t_filename, format=t_format))
- assert len(alignment) == t_count
+ alignment = MultipleSeqAlignment(SeqIO.parse(
+ handle=t_filename, format=t_format))
+ assert len(alignment) == t_count
- alignment_len = alignment.get_alignment_length()
+ alignment_len = alignment.get_alignment_length()
- # Check the record order agrees, and double check the
- # sequence lengths all agree too.
- for i in range(t_count):
- assert compare_record(records[i], alignment[i])
- assert len(records[i].seq) == alignment_len
+ # Check the record order agrees, and double check the
+ # sequence lengths all agree too.
+ for i in range(t_count):
+ assert compare_record(records[i], alignment[i])
+ assert len(records[i].seq) == alignment_len
- print(alignment_summary(alignment))
+ print(alignment_summary(alignment))
# Some alignment file formats have magic characters which mean
# use the letter in this position in the first sequence.
diff --git a/Tests/test_SeqIO_FastaIO.py b/Tests/test_SeqIO_FastaIO.py
index 20389b9..e7f1868 100644
--- a/Tests/test_SeqIO_FastaIO.py
+++ b/Tests/test_SeqIO_FastaIO.py
@@ -44,29 +44,27 @@ def title_to_ids(title):
def read_single_with_titles(filename, alphabet):
global title_to_ids
- handle = open(filename)
- iterator = FastaIterator(handle, alphabet, title_to_ids)
- record = next(iterator)
- try:
- second = next(iterator)
- except StopIteration:
- second = None
- handle.close()
+ with open(filename) as handle:
+ iterator = FastaIterator(handle, alphabet, title_to_ids)
+ record = next(iterator)
+ try:
+ second = next(iterator)
+ except StopIteration:
+ second = None
assert record is not None and second is None
return record
def read_title_and_seq(filename):
"""Crude parser that gets the first record from a FASTA file."""
- handle = open(filename)
- title = handle.readline().rstrip()
- assert title.startswith(">")
- seq = ""
- for line in handle:
- if line.startswith(">"):
- break
- seq += line.strip()
- handle.close()
+ with open(filename) as handle:
+ title = handle.readline().rstrip()
+ assert title.startswith(">")
+ seq = ""
+ for line in handle:
+ if line.startswith(">"):
+ break
+ seq += line.strip()
return title[1:], seq
diff --git a/Tests/test_SeqIO_PdbIO.py b/Tests/test_SeqIO_PdbIO.py
index 913a51f..7b67348 100644
--- a/Tests/test_SeqIO_PdbIO.py
+++ b/Tests/test_SeqIO_PdbIO.py
@@ -51,7 +51,7 @@ class TestPdbSeqres(unittest.TestCase):
def test_seqres_missing(self):
"""Parse a PDB with no SEQRES entries."""
- chains = list(SeqIO.parse('PDB/1MOT.pdb', 'pdb-seqres'))
+ chains = list(SeqIO.parse('PDB/a_structure.pdb', 'pdb-seqres'))
self.assertEqual(len(chains), 0)
@@ -102,10 +102,14 @@ class TestPdbAtom(unittest.TestCase):
def test_atom_noheader(self):
"""Parse a PDB with no HEADER line."""
- warnings.simplefilter('ignore', UserWarning)
- chains = list(SeqIO.parse('PDB/1MOT.pdb', 'pdb-atom'))
+
+ with warnings.catch_warnings():
+ warnings.simplefilter('ignore', PDBConstructionWarning)
+ warnings.simplefilter('ignore', UserWarning)
+ chains = list(SeqIO.parse('PDB/1LCD.pdb', 'pdb-atom'))
+
self.assertEqual(len(chains), 1)
- self.assertEqual(str(chains[0].seq), 'APARVGLGITTVLTMTTQSSGSRASLPK')
+ self.assertEqual(str(chains[0].seq), 'MKPVTLYDVAEYAGVSYQTVSRVVNQASHVSAKTREKVEAAMAELNYIPNR')
if __name__ == "__main__":
diff --git a/Tests/test_SeqIO_QualityIO.py b/Tests/test_SeqIO_QualityIO.py
index 011862f..5273278 100644
--- a/Tests/test_SeqIO_QualityIO.py
+++ b/Tests/test_SeqIO_QualityIO.py
@@ -289,7 +289,6 @@ class TestReferenceFastqConversions(unittest.TestCase):
if out_variant != "sanger":
# Ignore data loss warnings from max qualities
warnings.simplefilter("ignore", BiopythonWarning)
- warnings.simplefilter("ignore", UserWarning)
# Check matches using convert...
handle = StringIO()
SeqIO.convert(in_filename, "fastq-" + in_variant,
@@ -500,8 +499,11 @@ class TestWriteRead(unittest.TestCase):
"""Write and read back sanger_93.fastq"""
self.check(os.path.join("Quality", "sanger_93.fastq"), "fastq",
["fastq", "fastq-sanger", "fasta", "qual", "phd"])
- self.check(os.path.join("Quality", "sanger_93.fastq"), "fastq",
- ["fastq-solexa", "fastq-illumina"])
+ with warnings.catch_warnings():
+ # TODO - Have a Biopython defined "DataLossWarning?"
+ warnings.simplefilter('ignore', BiopythonWarning)
+ self.check(os.path.join("Quality", "sanger_93.fastq"), "fastq",
+ ["fastq-solexa", "fastq-illumina"])
def test_sanger_faked(self):
"""Write and read back sanger_faked.fastq"""
diff --git a/Tests/test_SeqIO_convert.py b/Tests/test_SeqIO_convert.py
index 58e1362..db3f4bf 100644
--- a/Tests/test_SeqIO_convert.py
+++ b/Tests/test_SeqIO_convert.py
@@ -8,6 +8,7 @@ from __future__ import print_function
import unittest
import warnings
+from Bio import BiopythonWarning
from Bio.Seq import UnknownSeq
from Bio import SeqIO
from Bio.SeqIO import QualityIO
@@ -34,7 +35,7 @@ def check_convert(in_filename, in_format, out_format, alphabet=None):
qual_truncate = truncation_expected(out_format)
with warnings.catch_warnings():
if qual_truncate:
- warnings.simplefilter('ignore', UserWarning)
+ warnings.simplefilter('ignore', BiopythonWarning)
SeqIO.write(records, handle, out_format)
handle.seek(0)
# Now load it back and check it agrees,
@@ -44,7 +45,7 @@ def check_convert(in_filename, in_format, out_format, alphabet=None):
handle2 = StringIO()
with warnings.catch_warnings():
if qual_truncate:
- warnings.simplefilter('ignore', UserWarning)
+ warnings.simplefilter('ignore', BiopythonWarning)
SeqIO.convert(in_filename, in_format, handle2, out_format, alphabet)
# We could re-parse this, but it is simpler and stricter:
assert handle.getvalue() == handle2.getvalue()
@@ -59,7 +60,7 @@ def check_convert_fails(in_filename, in_format, out_format, alphabet=None):
handle = StringIO()
with warnings.catch_warnings():
if qual_truncate:
- warnings.simplefilter('ignore', UserWarning)
+ warnings.simplefilter('ignore', BiopythonWarning)
SeqIO.write(records, handle, out_format)
handle.seek(0)
assert False, "Parse or write should have failed!"
@@ -70,7 +71,7 @@ def check_convert_fails(in_filename, in_format, out_format, alphabet=None):
handle2 = StringIO()
with warnings.catch_warnings():
if qual_truncate:
- warnings.simplefilter('ignore', UserWarning)
+ warnings.simplefilter('ignore', BiopythonWarning)
SeqIO.convert(in_filename, in_format, handle2, out_format, alphabet)
assert False, "Convert should have failed!"
except ValueError as err2:
diff --git a/Tests/test_SeqIO_features.py b/Tests/test_SeqIO_features.py
index 8b3820a..f0381f3 100644
--- a/Tests/test_SeqIO_features.py
+++ b/Tests/test_SeqIO_features.py
@@ -21,8 +21,8 @@ from Bio.Data.CodonTable import TranslationError
from Bio.Seq import Seq, UnknownSeq, MutableSeq, reverse_complement
from Bio.SeqRecord import SeqRecord
from Bio.SeqFeature import SeqFeature, FeatureLocation, CompoundLocation
-from Bio.SeqFeature import ExactPosition, BeforePosition, AfterPosition, \
- OneOfPosition, WithinPosition
+from Bio.SeqFeature import ExactPosition, BeforePosition, AfterPosition
+from Bio.SeqFeature import OneOfPosition, WithinPosition, UnknownPosition
from Bio.SeqIO.InsdcIO import _insdc_location_string
from Bio.SeqIO.InsdcIO import _insdc_feature_location_string
@@ -37,7 +37,7 @@ def _get_location_string(feature, record_length):
# Top level function as this makes it easier to use for debugging:
-def write_read(filename, in_format="gb", out_formats=["gb", "embl", "imgt"]):
+def write_read(filename, in_format="gb", out_formats=("gb", "embl", "imgt")):
for out_format in out_formats:
gb_records = list(SeqIO.parse(filename, in_format))
# Write it out...
@@ -421,7 +421,7 @@ class SeqFeatureExtractionWritingReading(unittest.TestCase):
f = make_join_feature([f1, f2])
self.check(s, f, "YWSMKVDN", "join(6..10,13..15)")
- def test_simple_dna_join(self):
+ def test_simple_dna_join_strand_minus(self):
"""Feature on DNA (join, strand -1)"""
s = Seq("AAAAACCCCCTTTTTGGGGG", generic_dna)
f1 = SeqFeature(FeatureLocation(5, 10), strand=-1)
@@ -430,7 +430,7 @@ class SeqFeatureExtractionWritingReading(unittest.TestCase):
self.check(s, f, reverse_complement("CCCCC" + "TTT"),
"complement(join(6..10,13..15))")
- def test_simple_dna_join(self):
+ def test_simple_dna_join_before(self):
"""Feature on DNA (join, strand -1, before position)"""
s = Seq("AAAAACCCCCTTTTTGGGGG", generic_dna)
f1 = SeqFeature(FeatureLocation(BeforePosition(5), 10), strand=-1)
@@ -550,7 +550,7 @@ class FeatureWriting(unittest.TestCase):
record2 = SeqIO.read(handle, format)
compare_record(self.record, record2)
- def write_read_checks(self, formats=["gb", "embl", "imgt"]):
+ def write_read_checks(self, formats=("gb", "embl", "imgt")):
for f in formats:
self.write_read_check(f)
@@ -626,6 +626,34 @@ class FeatureWriting(unittest.TestCase):
self.record.features.append(f)
self.write_read_checks()
+ def test_unknown(self):
+ """GenBank/EMBL write/read with unknown end points."""
+ f = SeqFeature(FeatureLocation(10, 15), strand=+1, type="region")
+ self.assertEqual(_get_location_string(f, 100),
+ "11..15")
+ self.record.features.append(f)
+ f = SeqFeature(FeatureLocation(10, UnknownPosition()), strand=+1, type="region")
+ self.assertEqual(_get_location_string(f, 100),
+ "11..>11")
+ self.record.features.append(f)
+ f = SeqFeature(FeatureLocation(UnknownPosition(), 15), strand=+1, type="region")
+ self.assertEqual(_get_location_string(f, 100),
+ "<15..15")
+ self.record.features.append(f)
+ f = SeqFeature(FeatureLocation(10, 15), strand=-1, type="region")
+ self.assertEqual(_get_location_string(f, 100),
+ "complement(11..15)")
+ f = SeqFeature(FeatureLocation(10, UnknownPosition()), strand=-1, type="region")
+ self.assertEqual(_get_location_string(f, 100),
+ "complement(11..>11)")
+ self.record.features.append(f)
+ f = SeqFeature(FeatureLocation(UnknownPosition(), 15), strand=-1, type="region")
+ self.assertEqual(_get_location_string(f, 100),
+ "complement(<15..15)")
+ self.record.features.append(f)
+ # This doesn't round trip
+ # self.write_read_checks()
+
def test_join(self):
"""GenBank/EMBL write/read simple join locations."""
f1 = SeqFeature(FeatureLocation(10, 20), strand=+1)
diff --git a/Tests/test_SeqIO_index.py b/Tests/test_SeqIO_index.py
index fd4b1e6..add9717 100644
--- a/Tests/test_SeqIO_index.py
+++ b/Tests/test_SeqIO_index.py
@@ -1,4 +1,4 @@
-# Copyright 2009-2012 by Peter Cock. All rights reserved.
+# Copyright 2009-2015 by Peter Cock. All rights reserved.
# This code is part of the Biopython distribution and governed by its
# license. Please see the LICENSE file that should have been included
# as part of this package.
@@ -314,62 +314,69 @@ class IndexDictTests(unittest.TestCase):
else:
id_list = [rec.id for rec in SeqIO.parse(filename, format, alphabet)]
- rec_dict = SeqIO.index(filename, format, alphabet)
- self.check_dict_methods(rec_dict, id_list, id_list)
- rec_dict.close()
- del rec_dict
+ with warnings.catch_warnings():
+ if "_alt_index_" in filename:
+ # BiopythonParserWarning: Could not parse the SFF index:
+ # Unknown magic number b'.diy' in SFF index header:
+ # b'.diy1.00'
+ warnings.simplefilter('ignore', BiopythonParserWarning)
- if not sqlite3:
- return
+ rec_dict = SeqIO.index(filename, format, alphabet)
+ self.check_dict_methods(rec_dict, id_list, id_list)
+ rec_dict.close()
+ del rec_dict
- # In memory,
- # note here give filenames as list of strings
- rec_dict = SeqIO.index_db(":memory:", [filename], format,
- alphabet)
- self.check_dict_methods(rec_dict, id_list, id_list)
- rec_dict.close()
- del rec_dict
+ if not sqlite3:
+ return
- # check error conditions
- self.assertRaises(ValueError, SeqIO.index_db,
- ":memory:", format="dummy")
- self.assertRaises(ValueError, SeqIO.index_db,
- ":memory:", filenames=["dummy"])
+ # In memory,
+ # note here give filenames as list of strings
+ rec_dict = SeqIO.index_db(":memory:", [filename], format,
+ alphabet)
+ self.check_dict_methods(rec_dict, id_list, id_list)
+ rec_dict.close()
+ del rec_dict
- # Saving to file...
- index_tmp = self.index_tmp
- if os.path.isfile(index_tmp):
- os.remove(index_tmp)
-
- # To disk,
- # note here we give the filename as a single string
- # to confirm that works too (convience feature).
- rec_dict = SeqIO.index_db(index_tmp, filename, format,
- alphabet)
- self.check_dict_methods(rec_dict, id_list, id_list)
- rec_dict.close()
- rec_dict._con.close() # hack for PyPy
- del rec_dict
-
- # Now reload it...
- rec_dict = SeqIO.index_db(index_tmp, [filename], format,
- alphabet)
- self.check_dict_methods(rec_dict, id_list, id_list)
- rec_dict.close()
- rec_dict._con.close() # hack for PyPy
- del rec_dict
-
- # Now reload without passing filenames and format
- # and switch directory to check paths still work
- index_tmp = os.path.abspath(index_tmp)
- os.chdir(os.path.dirname(filename))
- rec_dict = SeqIO.index_db(index_tmp, alphabet=alphabet)
- self.check_dict_methods(rec_dict, id_list, id_list)
- rec_dict.close()
- rec_dict._con.close() # hack for PyPy
- del rec_dict
+ # check error conditions
+ self.assertRaises(ValueError, SeqIO.index_db,
+ ":memory:", format="dummy")
+ self.assertRaises(ValueError, SeqIO.index_db,
+ ":memory:", filenames=["dummy"])
+
+ # Saving to file...
+ index_tmp = self.index_tmp
+ if os.path.isfile(index_tmp):
+ os.remove(index_tmp)
+
+ # To disk,
+ # note here we give the filename as a single string
+ # to confirm that works too (convience feature).
+ rec_dict = SeqIO.index_db(index_tmp, filename, format,
+ alphabet)
+ self.check_dict_methods(rec_dict, id_list, id_list)
+ rec_dict.close()
+ rec_dict._con.close() # hack for PyPy
+ del rec_dict
+
+ # Now reload it...
+ rec_dict = SeqIO.index_db(index_tmp, [filename], format,
+ alphabet)
+ self.check_dict_methods(rec_dict, id_list, id_list)
+ rec_dict.close()
+ rec_dict._con.close() # hack for PyPy
+ del rec_dict
+
+ # Now reload without passing filenames and format
+ # and switch directory to check paths still work
+ index_tmp = os.path.abspath(index_tmp)
+ os.chdir(os.path.dirname(filename))
+ rec_dict = SeqIO.index_db(index_tmp, alphabet=alphabet)
+ self.check_dict_methods(rec_dict, id_list, id_list)
+ rec_dict.close()
+ rec_dict._con.close() # hack for PyPy
+ del rec_dict
- os.remove(index_tmp)
+ os.remove(index_tmp)
def key_check(self, filename, format, alphabet, comp):
"""Check indexing with a key function."""
@@ -381,56 +388,64 @@ class IndexDictTests(unittest.TestCase):
id_list = [rec.id for rec in SeqIO.parse(filename, format, alphabet)]
key_list = [add_prefix(id) for id in id_list]
- rec_dict = SeqIO.index(filename, format, alphabet, add_prefix)
- self.check_dict_methods(rec_dict, key_list, id_list)
- rec_dict.close()
- del rec_dict
- if not sqlite3:
- return
-
- # In memory,
- rec_dict = SeqIO.index_db(":memory:", [filename], format, alphabet,
- add_prefix)
- self.check_dict_methods(rec_dict, key_list, id_list)
- # check error conditions
- self.assertRaises(ValueError, SeqIO.index_db,
- ":memory:", format="dummy",
- key_function=add_prefix)
- self.assertRaises(ValueError, SeqIO.index_db,
- ":memory:", filenames=["dummy"],
- key_function=add_prefix)
- rec_dict.close()
- del rec_dict
+ with warnings.catch_warnings():
+ if "_alt_index_" in filename:
+ # BiopythonParserWarning: Could not parse the SFF index:
+ # Unknown magic number b'.diy' in SFF index header:
+ # b'.diy1.00'
+ warnings.simplefilter('ignore', BiopythonParserWarning)
- # Saving to file...
- index_tmp = filename + ".key.idx"
- if os.path.isfile(index_tmp):
- os.remove(index_tmp)
- rec_dict = SeqIO.index_db(index_tmp, [filename], format, alphabet,
- add_prefix)
- self.check_dict_methods(rec_dict, key_list, id_list)
- rec_dict.close()
- rec_dict._con.close() # hack for PyPy
- del rec_dict
+ rec_dict = SeqIO.index(filename, format, alphabet, add_prefix)
+ self.check_dict_methods(rec_dict, key_list, id_list)
+ rec_dict.close()
+ del rec_dict
- # Now reload it...
- rec_dict = SeqIO.index_db(index_tmp, [filename], format, alphabet,
- add_prefix)
- self.check_dict_methods(rec_dict, key_list, id_list)
- rec_dict.close()
- rec_dict._con.close() # hack for PyPy
- del rec_dict
+ if not sqlite3:
+ return
- # Now reload without passing filenames and format
- rec_dict = SeqIO.index_db(index_tmp, alphabet=alphabet,
- key_function=add_prefix)
- self.check_dict_methods(rec_dict, key_list, id_list)
- rec_dict.close()
- rec_dict._con.close() # hack for PyPy
- del rec_dict
- os.remove(index_tmp)
- # Done
+ # In memory,
+ rec_dict = SeqIO.index_db(":memory:", [filename], format, alphabet,
+ add_prefix)
+ self.check_dict_methods(rec_dict, key_list, id_list)
+ # check error conditions
+ self.assertRaises(ValueError, SeqIO.index_db,
+ ":memory:", format="dummy",
+ key_function=add_prefix)
+ self.assertRaises(ValueError, SeqIO.index_db,
+ ":memory:", filenames=["dummy"],
+ key_function=add_prefix)
+ rec_dict.close()
+ del rec_dict
+
+ # Saving to file...
+ index_tmp = filename + ".key.idx"
+ if os.path.isfile(index_tmp):
+ os.remove(index_tmp)
+ rec_dict = SeqIO.index_db(index_tmp, [filename], format, alphabet,
+ add_prefix)
+ self.check_dict_methods(rec_dict, key_list, id_list)
+ rec_dict.close()
+ rec_dict._con.close() # hack for PyPy
+ del rec_dict
+
+ # Now reload it...
+ rec_dict = SeqIO.index_db(index_tmp, [filename], format, alphabet,
+ add_prefix)
+ self.check_dict_methods(rec_dict, key_list, id_list)
+ rec_dict.close()
+ rec_dict._con.close() # hack for PyPy
+ del rec_dict
+
+ # Now reload without passing filenames and format
+ rec_dict = SeqIO.index_db(index_tmp, alphabet=alphabet,
+ key_function=add_prefix)
+ self.check_dict_methods(rec_dict, key_list, id_list)
+ rec_dict.close()
+ rec_dict._con.close() # hack for PyPy
+ del rec_dict
+ os.remove(index_tmp)
+ # Done
def check_dict_methods(self, rec_dict, keys, ids):
self.assertEqual(set(keys), set(rec_dict))
@@ -510,6 +525,8 @@ class IndexDictTests(unittest.TestCase):
self.assertEqual(key, rec_dict[key].id.lower())
self.assertEqual(key, rec_dict.get(key).id.lower())
raw = rec_dict.get_raw(key)
+ self.assertTrue(isinstance(raw, bytes),
+ "Didn't get bytes from %s get_raw" % format)
self.assertTrue(raw.strip())
self.assertTrue(raw in raw_file)
rec1 = rec_dict[key]
@@ -627,20 +644,20 @@ tests = [
("Roche/paired.sff", "sff", None),
("Roche/paired.sff", "sff-trim", None),
]
-for filename, format, alphabet in tests:
+for filename1, format, alphabet in tests:
assert format in _FormatToRandomAccess
- tasks = [(filename, None)]
- if do_bgzf and os.path.isfile(filename + ".bgz"):
- tasks.append((filename + ".bgz", "bgzf"))
- for filename, comp in tasks:
+ tasks = [(filename1, None)]
+ if do_bgzf and os.path.isfile(filename1 + ".bgz"):
+ tasks.append((filename1 + ".bgz", "bgzf"))
+ for filename2, comp in tasks:
def funct(fn, fmt, alpha, c):
f = lambda x: x.simple_check(fn, fmt, alpha, c)
f.__doc__ = "Index %s file %s defaults" % (fmt, fn)
return f
setattr(IndexDictTests, "test_%s_%s_simple"
- % (format, filename.replace("/", "_").replace(".", "_")),
- funct(filename, format, alphabet, comp))
+ % (format, filename2.replace("/", "_").replace(".", "_")),
+ funct(filename2, format, alphabet, comp))
del funct
def funct(fn, fmt, alpha, c):
@@ -648,8 +665,8 @@ for filename, format, alphabet in tests:
f.__doc__ = "Index %s file %s with key function" % (fmt, fn)
return f
setattr(IndexDictTests, "test_%s_%s_keyf"
- % (format, filename.replace("/", "_").replace(".", "_")),
- funct(filename, format, alphabet, comp))
+ % (format, filename2.replace("/", "_").replace(".", "_")),
+ funct(filename2, format, alphabet, comp))
del funct
def funct(fn, fmt, alpha, c):
@@ -657,8 +674,8 @@ for filename, format, alphabet in tests:
f.__doc__ = "Index %s file %s get_raw" % (fmt, fn)
return f
setattr(IndexDictTests, "test_%s_%s_get_raw"
- % (format, filename.replace("/", "_").replace(".", "_")),
- funct(filename, format, alphabet, comp))
+ % (format, filename2.replace("/", "_").replace(".", "_")),
+ funct(filename2, format, alphabet, comp))
del funct
if __name__ == "__main__":
diff --git a/Tests/test_SeqIO_online.py b/Tests/test_SeqIO_online.py
index d5f0507..0300581 100644
--- a/Tests/test_SeqIO_online.py
+++ b/Tests/test_SeqIO_online.py
@@ -60,8 +60,8 @@ class EntrezTests(unittest.TestCase):
handle.close()
self.assertTrue((entry in record.name) or
(entry in record.id) or
- ("gi" in record.annotations
- and record.annotations["gi"] == entry),
+ ("gi" in record.annotations and
+ record.annotations["gi"] == entry),
"%s got %s, %s" % (entry, record.name, record.id))
self.assertEqual(len(record), length)
self.assertEqual(seguid(record.seq), checksum)
diff --git a/Tests/test_SeqIO_write.py b/Tests/test_SeqIO_write.py
index b2f745b..7a65f76 100644
--- a/Tests/test_SeqIO_write.py
+++ b/Tests/test_SeqIO_write.py
@@ -62,11 +62,11 @@ test_records = [
# Meddle with the annotation too:
assert test_records[4][1] == "3 DNA seq alignment with CR/LF in name/descr"
# Add a list of strings,
-test_records[4][0][2].annotations["note"] = ["Note%salso" % os.linesep
- + "\r\nhas\n evil line\rbreaks!", "Wow"]
+test_records[4][0][2].annotations["note"] = ["Note%salso" % os.linesep +
+ "\r\nhas\n evil line\rbreaks!", "Wow"]
# Add a simple string
-test_records[4][0][2].annotations["comment"] = "More%sof" % os.linesep \
- + "\r\nthese\n evil line\rbreaks!"
+test_records[4][0][2].annotations["comment"] = ("More%sof" % os.linesep +
+ "\r\nthese\n evil line\rbreaks!")
# Add a float too:
test_records[4][0][2].annotations["weight"] = 2.5
diff --git a/Tests/test_SeqRecord.py b/Tests/test_SeqRecord.py
index 71cb7a4..23393c2 100644
--- a/Tests/test_SeqRecord.py
+++ b/Tests/test_SeqRecord.py
@@ -338,6 +338,36 @@ Seq('ABCDEFGHIJKLMNOPQRSTUVWZYX', ProteinAlphabet())"""
s = SeqRecord(MutableSeq("ACTG"))
self.assertEqual("CAGT", str(s.reverse_complement().seq))
+ def test_gt_exception(self):
+ def lt():
+ SeqRecord(Seq("A")) < SeqRecord(Seq("A"))
+ self.assertRaises(NotImplementedError, lt)
+
+ def test_le_exception(self):
+ def le():
+ SeqRecord(Seq("A")) <= SeqRecord(Seq("A"))
+ self.assertRaises(NotImplementedError, le)
+
+ def test_eq_exception(self):
+ def equality():
+ SeqRecord(Seq("A")) == SeqRecord(Seq("A"))
+ self.assertRaises(NotImplementedError, equality)
+
+ def test_ne_exception(self):
+ def notequality():
+ SeqRecord(Seq("A")) != SeqRecord(Seq("A"))
+ self.assertRaises(NotImplementedError, notequality)
+
+ def test_gt_exception(self):
+ def gt():
+ SeqRecord(Seq("A")) > SeqRecord(Seq("A"))
+ self.assertRaises(NotImplementedError, gt)
+
+ def test_ge_exception(self):
+ def ge():
+ SeqRecord(Seq("A")) >= SeqRecord(Seq("A"))
+ self.assertRaises(NotImplementedError, ge)
+
if __name__ == "__main__":
runner = unittest.TextTestRunner(verbosity=2)
diff --git a/Tests/test_Seq_objs.py b/Tests/test_Seq_objs.py
index e4ca35d..c42613b 100644
--- a/Tests/test_Seq_objs.py
+++ b/Tests/test_Seq_objs.py
@@ -500,6 +500,7 @@ class StringMethodTests(unittest.TestCase):
self.assertEqual("***RR", str(nuc.translate(table=11)))
self.assertEqual("***RR", str(nuc.translate(table='11')))
self.assertEqual("***RR", str(nuc.translate(table='Bacterial')))
+ self.assertEqual("**GRR", str(nuc.translate(table=25)))
self.assertEqual("", str(nuc.translate(to_stop=True)))
self.assertEqual("O*ORR", str(nuc.translate(table=special_table)))
self.assertEqual("*QWRR", str(nuc.translate(table=Chilodonella_uncinata_table)))
diff --git a/Tests/test_SffIO.py b/Tests/test_SffIO.py
index 7e6912e..6edde57 100644
--- a/Tests/test_SffIO.py
+++ b/Tests/test_SffIO.py
@@ -1,12 +1,17 @@
# Copyright 2012 by Jeff Hussmann. All rights reserved.
-# Revisions copyright 2013 by Peter Cock. All rights reserved.
+# Revisions copyright 2013-2016 by Peter Cock. All rights reserved.
# This code is part of the Biopython distribution and governed by its
# license. Please see the LICENSE file that should have been included
# as part of this package.
+import sys
import re
import unittest
+from io import BytesIO
+from Bio.SeqIO.SffIO import _sff_find_roche_index, _sff_read_roche_index
+from Bio.SeqIO.SffIO import _sff_do_slow_index
+from Bio.SeqIO.SffIO import SffIterator, SffWriter, ReadRocheXmlManifest
from Bio import SeqIO
# sffinfo E3MFGYR02_random_10_reads.sff | sed -n '/>\|Run Prefix\|Region\|XY/p'
@@ -85,6 +90,224 @@ class TestUAN(unittest.TestCase):
self.assertEqual(record.annotations["coords"], self.test_annotations[record.name]["coords"])
+class TestErrors(unittest.TestCase):
+ with open("Roche/E3MFGYR02_random_10_reads.sff", "rb") as handle:
+ good = handle.read()
+
+ def test_empty(self):
+ fh = BytesIO()
+ try:
+ records = list(SeqIO.parse(fh, "sff"))
+ except ValueError as err:
+ self.assertEqual(str(err), "Empty file.")
+ else:
+ self.assertTrue(False, "Empty file did not raise exception")
+
+ def check_bad_header(self, header, msg):
+ try:
+ records = list(SeqIO.parse(BytesIO(header), "sff"))
+ except ValueError as err:
+ if isinstance(msg, (tuple, list)):
+ self.assertTrue(str(err) in msg, "Unexpected error: %s" % err)
+ else:
+ self.assertEqual(str(err), msg)
+ else:
+ self.assertTrue(False, "Test SFF header only did not raise exception")
+
+ def test_30bytes(self):
+ self.check_bad_header(b"x" * 30,
+ "File too small to hold a valid SFF header.")
+
+ def test_31bytes(self):
+ self.check_bad_header(b"x" * 31,
+ ("SFF file did not start '.sff', but 'xxxx'",
+ "SFF file did not start '.sff', but b'xxxx'"))
+
+ def test_31bytes_index_header(self):
+ self.check_bad_header(b".srt" + b"x" * 27,
+ "Handle seems to be at SFF index block, not start")
+
+ def test_31bytes_bad_ver(self):
+ self.check_bad_header(b".sff1.00" + b"x" * 23,
+ "Unsupported SFF version in header, 49.46.48.48")
+
+ def test_31bytes_bad_flowgram(self):
+ self.check_bad_header(b".sff\x00\x00\x00\x01" + b"x" * 23,
+ "Flowgram format code 120 not supported")
+
+ def test_bad_index_offset(self):
+ bad = self.good[:12] + b"\x00\x00\x00\x00" + self.good[16:]
+ self.check_bad_header(bad,
+ "Index offset 0 but index length 764")
+
+ def test_bad_index_length(self):
+ bad = self.good[:16] + b"\x00\x00\x00\x00" + self.good[20:]
+ self.check_bad_header(bad,
+ "Index offset 16824 but index length 0")
+
+ def test_bad_index_eof(self):
+ # Semi-random edit to the index offset value,
+ bad = self.good[:13] + b"\x01" + self.good[14:]
+ self.check_bad_header(bad,
+ "Gap of 65536 bytes after final record end 16824, "
+ "before 82360 where index starts?")
+
+ def test_no_index(self):
+ # Does a lot of work to create a no-index SFF file
+ # (in the process checking this bit of SffWriter works)
+ records = list(SeqIO.parse(BytesIO(self.good), "sff"))
+ with BytesIO() as handle:
+ writer = SffWriter(handle, index=False)
+ count = writer.write_file(records)
+ self.assertEqual(count, len(records))
+ handle.seek(0)
+ new = list(SeqIO.parse(handle, "sff"))
+ self.assertEqual(len(records), len(new))
+ for a, b in zip(records, new):
+ self.assertEqual(a.id, b.id)
+ handle.seek(0)
+ try:
+ values = _sff_find_roche_index(handle)
+ except ValueError as err:
+ self.assertEqual(str(err), "No index present in this SFF file")
+ else:
+ self.assertTrue(False, "Test _sff_find_roche_index did not raise exception")
+
+ def test_unknown_index(self):
+ # TODO - Add SFF file with no index,
+ # self.assertEqual(str(err), "No index present in this SFF file")
+ with open("Roche/E3MFGYR02_alt_index_in_middle.sff", "rb") as handle:
+ try:
+ values = _sff_find_roche_index(handle)
+ except ValueError as err:
+ self.assertTrue(str(err) in ("Unknown magic number '.diy' in SFF index header:\n'.diy1.00'",
+ "Unknown magic number b'.diy' in SFF index header:\nb'.diy1.00'"))
+ else:
+ self.assertTrue(False, "Test _sff_find_roche_index did not raise exception")
+
+ def check_sff_read_roche_index(self, data, msg):
+ handle = BytesIO(data)
+ try:
+ index = list(_sff_read_roche_index(handle))
+ except ValueError as err:
+ self.assertEqual(str(err), msg)
+ else:
+ self.assertTrue(False, "_sff_read_roche_index did not raise exception")
+
+ def test_premature_end_of_index(self):
+ self.check_sff_read_roche_index(self.good[:-50],
+ "Premature end of file!")
+
+ def test_index_name_no_null(self):
+ self.assertEqual(self.good[17502:17503], b"\x00")
+ self.check_sff_read_roche_index(self.good[:17502] + b"x" + self.good[17503:],
+ "Expected a null terminator to the read name.")
+
+ def test_index_mft_version(self):
+ self.assertEqual(self.good[16824:16832], b".mft1.00")
+ self.check_sff_read_roche_index(self.good[:16828] + b"\x01\x02\x03\x04" + self.good[16832:],
+ "Unsupported version in .mft index header, 1.2.3.4")
+
+ def test_index_mft_data_size(self):
+ self.assertEqual(self.good[16824:16832], b".mft1.00")
+ self.check_sff_read_roche_index(self.good[:16836] + b"\x00\x00\x00\x00" + self.good[16840:],
+ "Problem understanding .mft index header, 764 != 8 + 8 + 548 + 0")
+
+ def test_index_lengths(self):
+ # Reduce the number of reads from 10 to 9 so index loading fails...
+ self.assertEqual(self.good[20:24], b"\x00\x00\x00\x0A")
+ self.check_sff_read_roche_index(self.good[:20] + b"\x00\x00\x00\x09" + self.good[24:],
+ "Problem with index length? 17568 vs 17588")
+
+ def test_no_manifest_xml(self):
+ with open("Roche/E3MFGYR02_no_manifest.sff", "rb") as handle:
+ try:
+ xml = ReadRocheXmlManifest(handle)
+ except ValueError as err:
+ self.assertEqual(str(err), "No XML manifest found")
+ else:
+ self.assertTrue(False, "ReadRocheXmlManifest did not raise exception")
+
+
+class TestIndex(unittest.TestCase):
+
+ def test_manifest(self):
+ filename = "Roche/E3MFGYR02_random_10_reads.sff"
+ with open(filename, "rb") as handle:
+ metadata = ReadRocheXmlManifest(handle)
+
+ def test_both_ways(self):
+ filename = "Roche/E3MFGYR02_random_10_reads.sff"
+ with open(filename, "rb") as handle:
+ index1 = sorted(_sff_read_roche_index(handle))
+ with open(filename, "rb") as handle:
+ index2 = sorted(_sff_do_slow_index(handle))
+ self.assertEqual(index1, index2)
+ with open(filename, "rb") as handle:
+ self.assertEqual(len(index1), len(list(SffIterator(handle))))
+ with open(filename, "rb") as handle:
+ self.assertEqual(len(index1), len(list(SffIterator(BytesIO(handle.read())))))
+
+ if sys.platform != "win32" and sys.version_info[0] < 3:
+ # Can be lazy and treat as binary...
+ with open(filename, "r") as handle:
+ self.assertEqual(len(index1), len(list(SffIterator(handle))))
+ with open(filename) as handle:
+ index2 = sorted(_sff_read_roche_index(handle))
+ self.assertEqual(index1, index2)
+ with open(filename, "r") as handle:
+ index2 = sorted(_sff_do_slow_index(handle))
+ self.assertEqual(index1, index2)
+ with open(filename, "r") as handle:
+ self.assertEqual(len(index1), len(list(SffIterator(handle))))
+ with open(filename, "r") as handle:
+ self.assertEqual(len(index1), len(list(SffIterator(BytesIO(handle.read())))))
+
+
+class TestAlternativeIndexes(unittest.TestCase):
+ filename = "Roche/E3MFGYR02_random_10_reads.sff"
+ with open(filename, "rb") as handle:
+ sff = list(SffIterator(handle))
+
+ def check_same(self, new_sff):
+ self.assertEqual(len(self.sff), len(new_sff))
+ for old, new in zip(self.sff, new_sff):
+ self.assertEqual(old.id, new.id)
+ self.assertEqual(str(old.seq), str(new.seq))
+
+ def test_alt_index_at_end(self):
+ with open("Roche/E3MFGYR02_alt_index_at_end.sff", "rb") as handle:
+ sff2 = list(SffIterator(handle))
+ self.check_same(sff2)
+
+ def test_alt_index_at_start(self):
+ with open("Roche/E3MFGYR02_alt_index_at_start.sff", "rb") as handle:
+ sff2 = list(SffIterator(handle))
+ self.check_same(sff2)
+
+ def test_alt_index_in_middle(self):
+ with open("Roche/E3MFGYR02_alt_index_in_middle.sff", "rb") as handle:
+ sff2 = list(SffIterator(handle))
+ self.check_same(sff2)
+
+ def test_index_at_start(self):
+ with open("Roche/E3MFGYR02_index_at_start.sff", "rb") as handle:
+ sff2 = list(SffIterator(handle))
+ self.check_same(sff2)
+
+ def test_index_in_middle(self):
+ with open("Roche/E3MFGYR02_index_in_middle.sff", "rb") as handle:
+ sff2 = list(SffIterator(handle))
+ self.check_same(sff2)
+
+ def test_trim(self):
+ with open(self.filename, "rb") as handle:
+ sff_trim = list(SffIterator(handle, trim=True))
+ self.assertEqual(len(self.sff), len(sff_trim))
+ for old, new in zip(self.sff, sff_trim):
+ self.assertEqual(old.id, new.id)
+
+
class TestConcatenated(unittest.TestCase):
def test_parses_gzipped_stream(self):
import gzip
diff --git a/Tests/test_SwissProt.py b/Tests/test_SwissProt.py
index e9febdf..15f1435 100644
--- a/Tests/test_SwissProt.py
+++ b/Tests/test_SwissProt.py
@@ -23,9 +23,8 @@ class TestSwissProt(unittest.TestCase):
datafile = os.path.join('SwissProt', filename)
- test_handle = open(datafile)
- seq_record = SeqIO.read(test_handle, "swiss")
- test_handle.close()
+ with open(datafile) as test_handle:
+ seq_record = SeqIO.read(test_handle, "swiss")
self.assertTrue(isinstance(seq_record, SeqRecord))
@@ -34,9 +33,8 @@ class TestSwissProt(unittest.TestCase):
self.assertEqual(seq_record.description, "N33 PROTEIN.")
self.assertEqual(repr(seq_record.seq), "Seq('MGARGAPSRRRQAGRRLRYLPTGSFPFLLLLLLLCIQLGGGQKKKENLLAEKVE...DFE', ProteinAlphabet())")
- test_handle = open(datafile)
- record = SwissProt.read(test_handle)
- test_handle.close()
+ with open(datafile) as test_handle:
+ record = SwissProt.read(test_handle)
# test a couple of things on the record -- this is not exhaustive
self.assertEqual(record.entry_name, "N33_HUMAN")
@@ -68,9 +66,8 @@ class TestSwissProt(unittest.TestCase):
# test cases have only one record.
# With the SequenceParser
- test_handle = open(datafile)
- records = list(SeqIO.parse(test_handle, "swiss"))
- test_handle.close()
+ with open(datafile) as test_handle:
+ records = list(SeqIO.parse(test_handle, "swiss"))
self.assertEqual(len(records), 1)
self.assertTrue(isinstance(records[0], SeqRecord))
@@ -82,9 +79,8 @@ class TestSwissProt(unittest.TestCase):
self.assertEqual(records[0].id, seq_record.id)
# With the RecordParser
- test_handle = open(datafile)
- records = list(SwissProt.parse(test_handle))
- test_handle.close()
+ with open(datafile) as test_handle:
+ records = list(SwissProt.parse(test_handle))
self.assertEqual(len(records), 1)
self.assertTrue(isinstance(records[0], SwissProt.Record))
@@ -103,9 +99,8 @@ class TestSwissProt(unittest.TestCase):
datafile = os.path.join('SwissProt', filename)
- test_handle = open(datafile)
- seq_record = SeqIO.read(test_handle, "swiss")
- test_handle.close()
+ with open(datafile) as test_handle:
+ seq_record = SeqIO.read(test_handle, "swiss")
self.assertTrue(isinstance(seq_record, SeqRecord))
@@ -114,9 +109,8 @@ class TestSwissProt(unittest.TestCase):
self.assertEqual(seq_record.description, "CYSTEINE STRING PROTEIN (CSP).")
self.assertEqual(repr(seq_record.seq), "Seq('MADQRQRSLSTSGESLYHVLGLDKNATSDDIKKSYRKLALKYHPDKNPDNPEAA...GFN', ProteinAlphabet())")
- test_handle = open(datafile)
- record = SwissProt.read(test_handle)
- test_handle.close()
+ with open(datafile) as test_handle:
+ record = SwissProt.read(test_handle)
# test a couple of things on the record -- this is not exhaustive
self.assertEqual(record.entry_name, "CSP_MOUSE")
@@ -151,9 +145,8 @@ class TestSwissProt(unittest.TestCase):
# test cases have only one record.
# With the SequenceParser
- test_handle = open(datafile)
- records = list(SeqIO.parse(test_handle, "swiss"))
- test_handle.close()
+ with open(datafile) as test_handle:
+ records = list(SeqIO.parse(test_handle, "swiss"))
self.assertEqual(len(records), 1)
self.assertTrue(isinstance(records[0], SeqRecord))
@@ -165,9 +158,8 @@ class TestSwissProt(unittest.TestCase):
self.assertEqual(records[0].id, seq_record.id)
# With the RecordParser
- test_handle = open(datafile)
- records = list(SwissProt.parse(test_handle))
- test_handle.close()
+ with open(datafile) as test_handle:
+ records = list(SwissProt.parse(test_handle))
self.assertEqual(len(records), 1)
self.assertTrue(isinstance(records[0], SwissProt.Record))
@@ -186,9 +178,8 @@ class TestSwissProt(unittest.TestCase):
datafile = os.path.join('SwissProt', filename)
- test_handle = open(datafile)
- seq_record = SeqIO.read(test_handle, "swiss")
- test_handle.close()
+ with open(datafile) as test_handle:
+ seq_record = SeqIO.read(test_handle, "swiss")
self.assertTrue(isinstance(seq_record, SeqRecord))
@@ -197,9 +188,8 @@ class TestSwissProt(unittest.TestCase):
self.assertEqual(seq_record.description, "14-3-3 PROTEIN EPSILON (MITOCHONDRIAL IMPORT STIMULATION FACTOR L SUBUNIT) (PROTEIN KINASE C INHIBITOR PROTEIN-1) (KCIP-1) (14-3-3E).")
self.assertEqual(repr(seq_record.seq), "Seq('MDDREDLVYQAKLAEQAERYDEMVESMKKVAGMDVELTVEERNLLSVAYKNVIG...ENQ', ProteinAlphabet())")
- test_handle = open(datafile)
- record = SwissProt.read(test_handle)
- test_handle.close()
+ with open(datafile) as test_handle:
+ record = SwissProt.read(test_handle)
# test a couple of things on the record -- this is not exhaustive
self.assertEqual(record.entry_name, "143E_HUMAN")
@@ -274,9 +264,8 @@ class TestSwissProt(unittest.TestCase):
# test cases have only one record.
# With the SequenceParser
- test_handle = open(datafile)
- records = list(SeqIO.parse(test_handle, "swiss"))
- test_handle.close()
+ with open(datafile) as test_handle:
+ records = list(SeqIO.parse(test_handle, "swiss"))
self.assertEqual(len(records), 1)
self.assertTrue(isinstance(records[0], SeqRecord))
@@ -288,9 +277,8 @@ class TestSwissProt(unittest.TestCase):
self.assertEqual(records[0].id, seq_record.id)
# With the RecordParser
- test_handle = open(datafile)
- records = list(SwissProt.parse(test_handle))
- test_handle.close()
+ with open(datafile) as test_handle:
+ records = list(SwissProt.parse(test_handle))
self.assertEqual(len(records), 1)
self.assertTrue(isinstance(records[0], SwissProt.Record))
@@ -309,9 +297,8 @@ class TestSwissProt(unittest.TestCase):
datafile = os.path.join('SwissProt', filename)
- test_handle = open(datafile)
- seq_record = SeqIO.read(test_handle, "swiss")
- test_handle.close()
+ with open(datafile) as test_handle:
+ seq_record = SeqIO.read(test_handle, "swiss")
self.assertTrue(isinstance(seq_record, SeqRecord))
@@ -320,9 +307,8 @@ class TestSwissProt(unittest.TestCase):
self.assertEqual(seq_record.description, "NAPHTHALENE 1,2-DIOXYGENASE SYSTEM FERREDOXIN COMPONENT.")
self.assertEqual(repr(seq_record.seq), "Seq('TVKWIEAVALSDILEGDVLGVTVEGKELALYEVEGEIYATDNLCTHGSARMSDG...DLS', ProteinAlphabet())")
- test_handle = open(datafile)
- record = SwissProt.read(test_handle)
- test_handle.close()
+ with open(datafile) as test_handle:
+ record = SwissProt.read(test_handle)
# test a couple of things on the record -- this is not exhaustive
self.assertEqual(record.entry_name, "NDOA_PSEPU")
@@ -371,9 +357,8 @@ class TestSwissProt(unittest.TestCase):
# test cases have only one record.
# With the SequenceParser
- test_handle = open(datafile)
- records = list(SeqIO.parse(test_handle, "swiss"))
- test_handle.close()
+ with open(datafile) as test_handle:
+ records = list(SeqIO.parse(test_handle, "swiss"))
self.assertEqual(len(records), 1)
self.assertTrue(isinstance(records[0], SeqRecord))
@@ -385,9 +370,8 @@ class TestSwissProt(unittest.TestCase):
self.assertEqual(records[0].id, seq_record.id)
# With the RecordParser
- test_handle = open(datafile)
- records = list(SwissProt.parse(test_handle))
- test_handle.close()
+ with open(datafile) as test_handle:
+ records = list(SwissProt.parse(test_handle))
self.assertEqual(len(records), 1)
self.assertTrue(isinstance(records[0], SwissProt.Record))
@@ -406,9 +390,8 @@ class TestSwissProt(unittest.TestCase):
datafile = os.path.join('SwissProt', filename)
- test_handle = open(datafile)
- seq_record = SeqIO.read(test_handle, "swiss")
- test_handle.close()
+ with open(datafile) as test_handle:
+ seq_record = SeqIO.read(test_handle, "swiss")
self.assertTrue(isinstance(seq_record, SeqRecord))
@@ -417,9 +400,8 @@ class TestSwissProt(unittest.TestCase):
self.assertEqual(seq_record.description, "NADH-UBIQUINONE OXIDOREDUCTASE CHAIN 3 (EC 1.6.5.3).")
self.assertEqual(repr(seq_record.seq), "Seq('MNLLLTLLTNTTLALLLVFIAFWLPQLNVYAEKTSPYECGFDPMGSARLPFSMK...WAE', ProteinAlphabet())")
- test_handle = open(datafile)
- record = SwissProt.read(test_handle)
- test_handle.close()
+ with open(datafile) as test_handle:
+ record = SwissProt.read(test_handle)
# test a couple of things on the record -- this is not exhaustive
self.assertEqual(record.entry_name, "NU3M_BALPH")
@@ -449,9 +431,8 @@ class TestSwissProt(unittest.TestCase):
# test cases have only one record.
# With the SequenceParser
- test_handle = open(datafile)
- records = list(SeqIO.parse(test_handle, "swiss"))
- test_handle.close()
+ with open(datafile) as test_handle:
+ records = list(SeqIO.parse(test_handle, "swiss"))
self.assertEqual(len(records), 1)
self.assertTrue(isinstance(records[0], SeqRecord))
@@ -463,9 +444,8 @@ class TestSwissProt(unittest.TestCase):
self.assertEqual(records[0].id, seq_record.id)
# With the RecordParser
- test_handle = open(datafile)
- records = list(SwissProt.parse(test_handle))
- test_handle.close()
+ with open(datafile) as test_handle:
+ records = list(SwissProt.parse(test_handle))
self.assertEqual(len(records), 1)
self.assertTrue(isinstance(records[0], SwissProt.Record))
@@ -484,9 +464,8 @@ class TestSwissProt(unittest.TestCase):
datafile = os.path.join('SwissProt', filename)
- test_handle = open(datafile)
- seq_record = SeqIO.read(test_handle, "swiss")
- test_handle.close()
+ with open(datafile) as test_handle:
+ seq_record = SeqIO.read(test_handle, "swiss")
self.assertTrue(isinstance(seq_record, SeqRecord))
@@ -495,9 +474,8 @@ class TestSwissProt(unittest.TestCase):
self.assertEqual(seq_record.description, "TETRACENOMYCIN POLYKETIDE SYNTHESIS 8-O-METHYL TRANSFERASE TCMO (EC 2.1.1.-).")
self.assertEqual(repr(seq_record.seq), "Seq('MTPHTHVRGPGDILQLTMAFYGSRALISAVELDLFTLLAGKPLPLGELCERAGI...KPR', ProteinAlphabet())")
- test_handle = open(datafile)
- record = SwissProt.read(test_handle)
- test_handle.close()
+ with open(datafile) as test_handle:
+ record = SwissProt.read(test_handle)
# test a couple of things on the record -- this is not exhaustive
self.assertEqual(record.entry_name, "TCMO_STRGA")
@@ -523,9 +501,8 @@ class TestSwissProt(unittest.TestCase):
# test cases have only one record.
# With the SequenceParser
- test_handle = open(datafile)
- records = list(SeqIO.parse(test_handle, "swiss"))
- test_handle.close()
+ with open(datafile) as test_handle:
+ records = list(SeqIO.parse(test_handle, "swiss"))
self.assertEqual(len(records), 1)
self.assertTrue(isinstance(records[0], SeqRecord))
@@ -537,9 +514,8 @@ class TestSwissProt(unittest.TestCase):
self.assertEqual(records[0].id, seq_record.id)
# With the RecordParser
- test_handle = open(datafile)
- records = list(SwissProt.parse(test_handle))
- test_handle.close()
+ with open(datafile) as test_handle:
+ records = list(SwissProt.parse(test_handle))
self.assertEqual(len(records), 1)
self.assertTrue(isinstance(records[0], SwissProt.Record))
@@ -558,9 +534,8 @@ class TestSwissProt(unittest.TestCase):
datafile = os.path.join('SwissProt', filename)
- test_handle = open(datafile)
- seq_record = SeqIO.read(test_handle, "swiss")
- test_handle.close()
+ with open(datafile) as test_handle:
+ seq_record = SeqIO.read(test_handle, "swiss")
self.assertTrue(isinstance(seq_record, SeqRecord))
@@ -569,9 +544,8 @@ class TestSwissProt(unittest.TestCase):
self.assertEqual(seq_record.description, "CLAUDIN-1 (SENESCENCE-ASSOCIATED EPITHELIAL MEMBRANE PROTEIN).")
self.assertEqual(repr(seq_record.seq), "Seq('MANAGLQLLGFILAFLGWIGAIVSTALPQWRIYSYAGDNIVTAQAMYEGLWMSC...DYV', ProteinAlphabet())")
- test_handle = open(datafile)
- record = SwissProt.read(test_handle)
- test_handle.close()
+ with open(datafile) as test_handle:
+ record = SwissProt.read(test_handle)
# test a couple of things on the record -- this is not exhaustive
self.assertEqual(record.entry_name, "CLD1_HUMAN")
@@ -606,9 +580,8 @@ class TestSwissProt(unittest.TestCase):
# test cases have only one record.
# With the SequenceParser
- test_handle = open(datafile)
- records = list(SeqIO.parse(test_handle, "swiss"))
- test_handle.close()
+ with open(datafile) as test_handle:
+ records = list(SeqIO.parse(test_handle, "swiss"))
self.assertEqual(len(records), 1)
self.assertTrue(isinstance(records[0], SeqRecord))
@@ -620,9 +593,8 @@ class TestSwissProt(unittest.TestCase):
self.assertEqual(records[0].id, seq_record.id)
# With the RecordParser
- test_handle = open(datafile)
- records = list(SwissProt.parse(test_handle))
- test_handle.close()
+ with open(datafile) as test_handle:
+ records = list(SwissProt.parse(test_handle))
self.assertEqual(len(records), 1)
self.assertTrue(isinstance(records[0], SwissProt.Record))
@@ -641,9 +613,8 @@ class TestSwissProt(unittest.TestCase):
datafile = os.path.join('SwissProt', filename)
- test_handle = open(datafile)
- seq_record = SeqIO.read(test_handle, "swiss")
- test_handle.close()
+ with open(datafile) as test_handle:
+ seq_record = SeqIO.read(test_handle, "swiss")
self.assertTrue(isinstance(seq_record, SeqRecord))
@@ -652,9 +623,8 @@ class TestSwissProt(unittest.TestCase):
self.assertEqual(seq_record.description, "HLA CLASS I HISTOCOMPATIBILITY ANTIGEN, A-2 ALPHA CHAIN PRECURSOR.")
self.assertEqual(repr(seq_record.seq), "Seq('MAVMAPRTLVLLLSGALALTQTWAGSHSMRYFFTSVSRPGRGEPRFIAVGYVDD...CKV', ProteinAlphabet())")
- test_handle = open(datafile)
- record = SwissProt.read(test_handle)
- test_handle.close()
+ with open(datafile) as test_handle:
+ record = SwissProt.read(test_handle)
# test a couple of things on the record -- this is not exhaustive
self.assertEqual(record.entry_name, "1A02_HUMAN")
@@ -852,9 +822,8 @@ class TestSwissProt(unittest.TestCase):
# test cases have only one record.
# With the SequenceParser
- test_handle = open(datafile)
- records = list(SeqIO.parse(test_handle, "swiss"))
- test_handle.close()
+ with open(datafile) as test_handle:
+ records = list(SeqIO.parse(test_handle, "swiss"))
self.assertEqual(len(records), 1)
self.assertTrue(isinstance(records[0], SeqRecord))
@@ -866,9 +835,8 @@ class TestSwissProt(unittest.TestCase):
self.assertEqual(records[0].id, seq_record.id)
# With the RecordParser
- test_handle = open(datafile)
- records = list(SwissProt.parse(test_handle))
- test_handle.close()
+ with open(datafile) as test_handle:
+ records = list(SwissProt.parse(test_handle))
self.assertEqual(len(records), 1)
self.assertTrue(isinstance(records[0], SwissProt.Record))
@@ -887,9 +855,8 @@ class TestSwissProt(unittest.TestCase):
datafile = os.path.join('SwissProt', filename)
- test_handle = open(datafile)
- seq_record = SeqIO.read(test_handle, "swiss")
- test_handle.close()
+ with open(datafile) as test_handle:
+ seq_record = SeqIO.read(test_handle, "swiss")
self.assertTrue(isinstance(seq_record, SeqRecord))
@@ -898,9 +865,8 @@ class TestSwissProt(unittest.TestCase):
self.assertEqual(seq_record.description, "CHALCONE SYNTHASE 3 (EC 2.3.1.74) (NARINGENIN-CHALCONE SYNTHASE 3).")
self.assertEqual(repr(seq_record.seq), "Seq('MAPAMEEIRQAQRAEGPAAVLAIGTSTPPNALYQADYPDYYFRITKSEHLTELK...GAE', ProteinAlphabet())")
- test_handle = open(datafile)
- record = SwissProt.read(test_handle)
- test_handle.close()
+ with open(datafile) as test_handle:
+ record = SwissProt.read(test_handle)
# test a couple of things on the record -- this is not exhaustive
self.assertEqual(record.entry_name, "CHS3_BROFI")
@@ -926,9 +892,8 @@ class TestSwissProt(unittest.TestCase):
# test cases have only one record.
# With the SequenceParser
- test_handle = open(datafile)
- records = list(SeqIO.parse(test_handle, "swiss"))
- test_handle.close()
+ with open(datafile) as test_handle:
+ records = list(SeqIO.parse(test_handle, "swiss"))
self.assertEqual(len(records), 1)
self.assertTrue(isinstance(records[0], SeqRecord))
@@ -940,9 +905,8 @@ class TestSwissProt(unittest.TestCase):
self.assertEqual(records[0].id, seq_record.id)
# With the RecordParser
- test_handle = open(datafile)
- records = list(SwissProt.parse(test_handle))
- test_handle.close()
+ with open(datafile) as test_handle:
+ records = list(SwissProt.parse(test_handle))
self.assertEqual(len(records), 1)
self.assertTrue(isinstance(records[0], SwissProt.Record))
@@ -961,9 +925,8 @@ class TestSwissProt(unittest.TestCase):
datafile = os.path.join('SwissProt', filename)
- test_handle = open(datafile)
- seq_record = SeqIO.read(test_handle, "swiss")
- test_handle.close()
+ with open(datafile) as test_handle:
+ seq_record = SeqIO.read(test_handle, "swiss")
self.assertTrue(isinstance(seq_record, SeqRecord))
@@ -972,9 +935,8 @@ class TestSwissProt(unittest.TestCase):
self.assertEqual(seq_record.description, "5-HYDROXYTRYPTAMINE 4 RECEPTOR (5-HT-4) (SEROTONIN RECEPTOR) (5-HT4).")
self.assertEqual(repr(seq_record.seq), "Seq('MDKLDANVSSEEGFGSVEKVVLLTFLSTVILMAILGNLLVMVAVCWDRQLRKIK...SDT', ProteinAlphabet())")
- test_handle = open(datafile)
- record = SwissProt.read(test_handle)
- test_handle.close()
+ with open(datafile) as test_handle:
+ record = SwissProt.read(test_handle)
# test a couple of things on the record -- this is not exhaustive
self.assertEqual(record.entry_name, "5H4_HUMAN")
@@ -1042,9 +1004,7 @@ class TestSwissProt(unittest.TestCase):
# test cases have only one record.
# With the SequenceParser
- test_handle = open(datafile)
- records = list(SeqIO.parse(test_handle, "swiss"))
- test_handle.close()
+ records = list(SeqIO.parse(datafile, "swiss"))
self.assertEqual(len(records), 1)
self.assertTrue(isinstance(records[0], SeqRecord))
@@ -1056,9 +1016,8 @@ class TestSwissProt(unittest.TestCase):
self.assertEqual(records[0].id, seq_record.id)
# With the RecordParser
- test_handle = open(datafile)
- records = list(SwissProt.parse(test_handle))
- test_handle.close()
+ with open(datafile) as test_handle:
+ records = list(SwissProt.parse(test_handle))
self.assertEqual(len(records), 1)
self.assertTrue(isinstance(records[0], SwissProt.Record))
@@ -1076,10 +1035,7 @@ class TestSwissProt(unittest.TestCase):
# test the record parser
datafile = os.path.join('SwissProt', filename)
-
- test_handle = open(datafile)
- seq_record = SeqIO.read(test_handle, "swiss")
- test_handle.close()
+ seq_record = SeqIO.read(datafile, "swiss")
self.assertTrue(isinstance(seq_record, SeqRecord))
@@ -1088,9 +1044,8 @@ class TestSwissProt(unittest.TestCase):
self.assertEqual(seq_record.description, "LUTROPIN-CHORIOGONADOTROPIC HORMONE RECEPTOR PRECURSOR (LH/CG-R) (LSH-R) (LUTEINIZING HORMONE RECEPTOR).")
self.assertEqual(repr(seq_record.seq), "Seq('MGRRVPALRQLLVLAVLLLKPSQLQSRELSGSRCPEPCDCAPDGALRCPGPRAG...LTH', ProteinAlphabet())")
- test_handle = open(datafile)
- record = SwissProt.read(test_handle)
- test_handle.close()
+ with open(datafile) as test_handle:
+ record = SwissProt.read(test_handle)
# test a couple of things on the record -- this is not exhaustive
self.assertEqual(record.entry_name, "LSHR_RAT")
@@ -1204,9 +1159,7 @@ class TestSwissProt(unittest.TestCase):
# test cases have only one record.
# With the SequenceParser
- test_handle = open(datafile)
- records = list(SeqIO.parse(test_handle, "swiss"))
- test_handle.close()
+ records = list(SeqIO.parse(datafile, "swiss"))
self.assertEqual(len(records), 1)
self.assertTrue(isinstance(records[0], SeqRecord))
@@ -1218,9 +1171,8 @@ class TestSwissProt(unittest.TestCase):
self.assertEqual(records[0].id, seq_record.id)
# With the RecordParser
- test_handle = open(datafile)
- records = list(SwissProt.parse(test_handle))
- test_handle.close()
+ with open(datafile) as test_handle:
+ records = list(SwissProt.parse(test_handle))
self.assertEqual(len(records), 1)
self.assertTrue(isinstance(records[0], SwissProt.Record))
@@ -1238,10 +1190,7 @@ class TestSwissProt(unittest.TestCase):
# test the record parser
datafile = os.path.join('SwissProt', filename)
-
- test_handle = open(datafile)
- seq_record = SeqIO.read(test_handle, "swiss")
- test_handle.close()
+ seq_record = SeqIO.read(datafile, "swiss")
self.assertTrue(isinstance(seq_record, SeqRecord))
@@ -1250,9 +1199,8 @@ class TestSwissProt(unittest.TestCase):
self.assertEqual(seq_record.description, "UBIQUITIN.")
self.assertEqual(repr(seq_record.seq), "Seq('MQIFVKTLTGKTITLEVESSDTIDNVKTKIQDKEGIPPDQQRLIFAGKQLEDGR...GGN', ProteinAlphabet())")
- test_handle = open(datafile)
- record = SwissProt.read(test_handle)
- test_handle.close()
+ with open(datafile) as test_handle:
+ record = SwissProt.read(test_handle)
# test a couple of things on the record -- this is not exhaustive
self.assertEqual(record.entry_name, "Q9Y736")
@@ -1280,9 +1228,7 @@ class TestSwissProt(unittest.TestCase):
# test cases have only one record.
# With the SequenceParser
- test_handle = open(datafile)
- records = list(SeqIO.parse(test_handle, "swiss"))
- test_handle.close()
+ records = list(SeqIO.parse(datafile, "swiss"))
self.assertEqual(len(records), 1)
self.assertTrue(isinstance(records[0], SeqRecord))
@@ -1294,9 +1240,8 @@ class TestSwissProt(unittest.TestCase):
self.assertEqual(records[0].id, seq_record.id)
# With the RecordParser
- test_handle = open(datafile)
- records = list(SwissProt.parse(test_handle))
- test_handle.close()
+ with open(datafile) as test_handle:
+ records = list(SwissProt.parse(test_handle))
self.assertEqual(len(records), 1)
self.assertTrue(isinstance(records[0], SwissProt.Record))
@@ -1314,10 +1259,7 @@ class TestSwissProt(unittest.TestCase):
# test the record parser
datafile = os.path.join('SwissProt', filename)
-
- test_handle = open(datafile)
- seq_record = SeqIO.read(test_handle, "swiss")
- test_handle.close()
+ seq_record = SeqIO.read(datafile, "swiss")
self.assertTrue(isinstance(seq_record, SeqRecord))
@@ -1326,9 +1268,8 @@ class TestSwissProt(unittest.TestCase):
self.assertEqual(seq_record.description, "MITOCHONDRIAL 28S RIBOSOMAL PROTEIN S36 (MRP-S36).")
self.assertEqual(repr(seq_record.seq), "Seq('MGSKMASASRVVQVVKPHTPLIRFPDRRDNPKPNVSEALRSAGLPSHSSVISQH...GPE', ProteinAlphabet())")
- test_handle = open(datafile)
- record = SwissProt.read(test_handle)
- test_handle.close()
+ with open(datafile) as test_handle:
+ record = SwissProt.read(test_handle)
# test a couple of things on the record -- this is not exhaustive
self.assertEqual(record.entry_name, "P82909")
@@ -1356,9 +1297,7 @@ class TestSwissProt(unittest.TestCase):
# test cases have only one record.
# With the SequenceParser
- test_handle = open(datafile)
- records = list(SeqIO.parse(test_handle, "swiss"))
- test_handle.close()
+ records = list(SeqIO.parse(datafile, "swiss"))
self.assertEqual(len(records), 1)
self.assertTrue(isinstance(records[0], SeqRecord))
@@ -1370,9 +1309,8 @@ class TestSwissProt(unittest.TestCase):
self.assertEqual(records[0].id, seq_record.id)
# With the RecordParser
- test_handle = open(datafile)
- records = list(SwissProt.parse(test_handle))
- test_handle.close()
+ with open(datafile) as test_handle:
+ records = list(SwissProt.parse(test_handle))
self.assertEqual(len(records), 1)
self.assertTrue(isinstance(records[0], SwissProt.Record))
@@ -1390,10 +1328,7 @@ class TestSwissProt(unittest.TestCase):
# test the record parser
datafile = os.path.join('SwissProt', filename)
-
- test_handle = open(datafile)
- seq_record = SeqIO.read(test_handle, "swiss")
- test_handle.close()
+ seq_record = SeqIO.read(datafile, "swiss")
self.assertTrue(isinstance(seq_record, SeqRecord))
@@ -1402,9 +1337,8 @@ class TestSwissProt(unittest.TestCase):
self.assertEqual(seq_record.description, "PHOTOSYSTEM II REACTION CENTER L PROTEIN (PSII 5 KDA PROTEIN).")
self.assertEqual(repr(seq_record.seq), "Seq('TQSNPNEQNVELNRTSLYWGLLLIFVLAVLFSNYFFN', ProteinAlphabet())")
- test_handle = open(datafile)
- record = SwissProt.read(test_handle)
- test_handle.close()
+ with open(datafile) as test_handle:
+ record = SwissProt.read(test_handle)
# test a couple of things on the record -- this is not exhaustive
self.assertEqual(record.entry_name, "PSBL_ORYSA")
@@ -1523,9 +1457,7 @@ class TestSwissProt(unittest.TestCase):
# test cases have only one record.
# With the SequenceParser
- test_handle = open(datafile)
- records = list(SeqIO.parse(test_handle, "swiss"))
- test_handle.close()
+ records = list(SeqIO.parse(datafile, "swiss"))
self.assertEqual(len(records), 1)
self.assertTrue(isinstance(records[0], SeqRecord))
@@ -1537,9 +1469,8 @@ class TestSwissProt(unittest.TestCase):
self.assertEqual(records[0].id, seq_record.id)
# With the RecordParser
- test_handle = open(datafile)
- records = list(SwissProt.parse(test_handle))
- test_handle.close()
+ with open(datafile) as test_handle:
+ records = list(SwissProt.parse(test_handle))
self.assertEqual(len(records), 1)
self.assertTrue(isinstance(records[0], SwissProt.Record))
@@ -1558,9 +1489,8 @@ class TestSwissProt(unittest.TestCase):
datafile = os.path.join('SwissProt', filename)
- test_handle = open(datafile)
- seq_record = SeqIO.read(test_handle, "swiss")
- test_handle.close()
+ with open(datafile) as test_handle:
+ seq_record = SeqIO.read(test_handle, "swiss")
self.assertTrue(isinstance(seq_record, SeqRecord))
@@ -1569,9 +1499,8 @@ class TestSwissProt(unittest.TestCase):
self.assertEqual(seq_record.description, "")
self.assertEqual(repr(seq_record.seq), "Seq('MSFQAPRRLLELAGQSLLRDQALAISVLDELPRELFPRLFVEAFTSRRCEVLKV...TPC', ProteinAlphabet())")
- test_handle = open(datafile)
- record = SwissProt.read(test_handle)
- test_handle.close()
+ with open(datafile) as test_handle:
+ record = SwissProt.read(test_handle)
# test a couple of things on the record -- this is not exhaustive
self.assertEqual(record.entry_name, "IPI00383150.2")
@@ -1592,9 +1521,8 @@ class TestSwissProt(unittest.TestCase):
# test cases have only one record.
# With the SequenceParser
- test_handle = open(datafile)
- records = list(SeqIO.parse(test_handle, "swiss"))
- test_handle.close()
+ with open(datafile) as test_handle:
+ records = list(SeqIO.parse(test_handle, "swiss"))
self.assertEqual(len(records), 1)
self.assertTrue(isinstance(records[0], SeqRecord))
@@ -1606,9 +1534,8 @@ class TestSwissProt(unittest.TestCase):
self.assertEqual(records[0].id, seq_record.id)
# With the RecordParser
- test_handle = open(datafile)
- records = list(SwissProt.parse(test_handle))
- test_handle.close()
+ with open(datafile) as test_handle:
+ records = list(SwissProt.parse(test_handle))
self.assertEqual(len(records), 1)
self.assertTrue(isinstance(records[0], SwissProt.Record))
diff --git a/Tests/test_TogoWS.py b/Tests/test_TogoWS.py
index 1b7453d..52316ae 100644
--- a/Tests/test_TogoWS.py
+++ b/Tests/test_TogoWS.py
@@ -254,7 +254,7 @@ class TogoEntry(unittest.TestCase):
handle.close()
self.assertEqual(data, "X52960")
- def test_nucleotide_genbank_accession(self):
+ def test_nucleotide_genbank_version(self):
"""Bio.TogoWS.entry("nucleotide", "X52960", field="version")"""
handle = TogoWS.entry("nucleotide", "X52960", field="version")
data = handle.read().strip() # ignore trailing \n
@@ -457,7 +457,7 @@ class TogoSearch(unittest.TestCase):
"""
self.check("uniprot", "terminal+lung+cancer", limit=150)
- def check(self, database, search_term, expected_matches=[], limit=None):
+ def check(self, database, search_term, expected_matches=(), limit=None):
if expected_matches and limit:
raise ValueError("Bad test - TogoWS makes no promises about order")
search_count = TogoWS.search_count(database, search_term)
diff --git a/Tests/test_Tutorial.py b/Tests/test_Tutorial.py
index f0acf4b..97963b5 100644
--- a/Tests/test_Tutorial.py
+++ b/Tests/test_Tutorial.py
@@ -1,4 +1,4 @@
-# Copyright 2011-2013 by Peter Cock. All rights reserved.
+# Copyright 2011-2016 by Peter Cock. All rights reserved.
# This code is part of the Biopython distribution and governed by its
# license. Please see the LICENSE file that should have been included
# as part of this package.
@@ -27,18 +27,30 @@ if sys.version_info[0] >= 3:
'Two plus two is 4\n', "example2") == \
'>>> print("Two plus two is", 2+2)\nTwo plus two is 4\n'
+# Cache this to restore the cwd at the end of the tests
+original_path = os.path.abspath(".")
-tutorial = os.path.join(os.path.dirname(sys.argv[0]), "../Doc/Tutorial.tex")
+if os.path.basename(sys.argv[0]) == "test_Tutorial.py":
+ # sys.argv[0] will be (relative) path to test_Turorial.py - use this to allow, e.g.
+ # [base]$ python Tests/test_Tutorial.py
+ # [Tests/]$ python test_Tutorial.py
+ tutorial_base = os.path.abspath(os.path.join(os.path.dirname(sys.argv[0]), "../Doc/"))
+ tutorial = os.path.join(tutorial_base, "Tutorial.tex")
+else:
+ # Probably called via run_tests.py so current directory should (now) be Tests/
+ # but may have been changed by run_tests.py so can't infer from sys.argv[0] with e.g.
+ # [base]$ python Tests/run_tests.py test_Tutorial
+ tutorial_base = os.path.abspath("../Doc/")
+ tutorial = os.path.join(tutorial_base, "Tutorial.tex")
if not os.path.isfile(tutorial):
from Bio import MissingExternalDependencyError
raise MissingExternalDependencyError("Could not find ../Doc/Tutorial.tex file")
+
+# Build a list of all the Tutorial LaTeX files:
files = [tutorial]
-for latex in os.listdir("../Doc/Tutorial/"):
+for latex in os.listdir(os.path.join(tutorial_base, "Tutorial/")):
if latex.startswith("chapter_") and latex.endswith(".tex"):
- files.append(os.path.join(os.path.dirname(sys.argv[0]), "../Doc/Tutorial", latex))
-
-tutorial_base = os.path.abspath(os.path.join(os.path.dirname(sys.argv[0]), "../Doc/"))
-original_path = os.path.abspath(".")
+ files.append(os.path.join(tutorial_base, "Tutorial", latex))
def _extract(handle):
@@ -67,38 +79,37 @@ def extract_doctests(latex_filename):
This is a generator, yielding one tuple per doctest.
"""
base_name = os.path.splitext(os.path.basename(latex_filename))[0]
- handle = open(latex_filename, _universal_read_mode)
- line_number = 0
- in_test = False
- lines = []
- name = None
- while True:
- line = handle.readline()
- line_number += 1
- if not line:
- # End of file
- break
- elif line.startswith("%cont-doctest"):
- x = _extract(handle)
- lines.extend(x)
- line_number += len(x) + 2
- elif line.startswith("%doctest"):
- if lines:
- if not lines[0].startswith(">>> "):
- raise ValueError("Should start '>>> ' not %r" % lines[0])
- yield name, "".join(lines), folder, deps
- lines = []
- deps = [x.strip() for x in line.split()[1:]]
- if deps:
- folder = deps[0]
- deps = deps[1:]
- else:
- folder = ""
- name = "test_%s_line_%05i" % (base_name, line_number)
- x = _extract(handle)
- lines.extend(x)
- line_number += len(x) + 2
- handle.close()
+ with open(latex_filename, _universal_read_mode) as handle:
+ line_number = 0
+ in_test = False
+ lines = []
+ name = None
+ while True:
+ line = handle.readline()
+ line_number += 1
+ if not line:
+ # End of file
+ break
+ elif line.startswith("%cont-doctest"):
+ x = _extract(handle)
+ lines.extend(x)
+ line_number += len(x) + 2
+ elif line.startswith("%doctest"):
+ if lines:
+ if not lines[0].startswith(">>> "):
+ raise ValueError("Should start '>>> ' not %r" % lines[0])
+ yield name, "".join(lines), folder, deps
+ lines = []
+ deps = [x.strip() for x in line.split()[1:]]
+ if deps:
+ folder = deps[0]
+ deps = deps[1:]
+ else:
+ folder = ""
+ name = "test_%s_line_%05i" % (base_name, line_number)
+ x = _extract(handle)
+ lines.extend(x)
+ line_number += len(x) + 2
if lines:
if not lines[0].startswith(">>> "):
raise ValueError("Should start '>>> ' not %r" % lines[0])
@@ -188,7 +199,6 @@ if __name__ == "__main__":
print("Running Tutorial doctests...")
import doctest
tests = doctest.testmod()
- if tests[0]:
- # Note on Python 2.5+ can use tests.failed rather than tests[0]
+ if tests.failed:
raise RuntimeError("%i/%i tests failed" % tests)
print("Tests done")
diff --git a/Tests/test_XXmotif_tool.py b/Tests/test_XXmotif_tool.py
index 50ae2e7..521498d 100644
--- a/Tests/test_XXmotif_tool.py
+++ b/Tests/test_XXmotif_tool.py
@@ -12,7 +12,7 @@ import unittest
from Bio import MissingExternalDependencyError
from Bio import SeqIO
from Bio.Application import ApplicationError
-from Bio.Motif.Applications import XXmotifCommandline
+from Bio.motifs.applications import XXmotifCommandline
# Try to avoid problems when the OS is in another language
diff --git a/Tests/test_codonalign.py b/Tests/test_codonalign.py
index 51cf54b..ebdf06a 100644
--- a/Tests/test_codonalign.py
+++ b/Tests/test_codonalign.py
@@ -5,13 +5,11 @@
"""Unit tests for the Bio.codonalign modules.
"""
-import sys
import warnings
import tempfile
-import platform
import unittest
-from Bio import BiopythonExperimentalWarning
+from Bio import BiopythonWarning, BiopythonExperimentalWarning
from Bio import SeqIO
from Bio import AlignIO
from Bio.Seq import Seq
@@ -163,7 +161,9 @@ class Test_dn_ds(unittest.TestCase):
prot = AlignIO.read(TEST_ALIGN_FILE6[0][1], 'clustal', alphabet=IUPAC.protein)
with open(TEST_ALIGN_FILE6[0][2]) as handle:
id_corr = dict((i.split()[0], i.split()[1]) for i in handle)
- aln = codonalign.build(prot, nucl, corr_dict=id_corr, alphabet=codonalign.default_codon_alphabet)
+ with warnings.catch_warnings():
+ warnings.simplefilter('ignore', BiopythonWarning)
+ aln = codonalign.build(prot, nucl, corr_dict=id_corr, alphabet=codonalign.default_codon_alphabet)
self.aln = aln
def test_dn_ds(self):
diff --git a/Tests/test_motifs.py b/Tests/test_motifs.py
index da78b62..f8e49ed 100644
--- a/Tests/test_motifs.py
+++ b/Tests/test_motifs.py
@@ -6,10 +6,12 @@
import os
import unittest
+import math
+from Bio.Alphabet import generic_dna
+from Bio.Alphabet import Gapped
from Bio.Alphabet import IUPAC
from Bio import motifs
-from Bio.motifs.matrix import _isnan
from Bio.Seq import Seq
@@ -1686,7 +1688,20 @@ class MotifTestPWM(unittest.TestCase):
self.assertAlmostEqual(result[3], -38.04542542, places=5)
self.assertAlmostEqual(result[4], -20.3014183, places=5)
self.assertAlmostEqual(result[5], -25.18009186, places=5)
- self.assertTrue(_isnan(result[6]), "Expected nan, not %r" % result[6])
+ self.assertTrue(math.isnan(result[6]), "Expected nan, not %r" % result[6])
+
+ def test_mixed_alphabets(self):
+ """Test creating motif with mixed alphabets."""
+ # TODO - Can we support this?
+ seqs = [Seq("TACAA", IUPAC.unambiguous_dna),
+ Seq("TACGC", IUPAC.ambiguous_dna),
+ Seq("TACAC", IUPAC.extended_dna),
+ Seq("TACCC", Gapped(IUPAC.unambiguous_dna)),
+ Seq("AACCC", IUPAC.unambiguous_dna),
+ Seq("AATGC", IUPAC.unambiguous_dna),
+ Seq("AATGC", generic_dna)]
+ # ValueError: Alphabets are inconsistent
+ self.assertRaises(ValueError, motifs.create, seqs)
if __name__ == "__main__":
diff --git a/Tests/test_motifs_online.py b/Tests/test_motifs_online.py
index 9541fb8..0ed259a 100644
--- a/Tests/test_motifs_online.py
+++ b/Tests/test_motifs_online.py
@@ -8,24 +8,46 @@
import os
import unittest
-import requires_internet
-requires_internet.check()
-
# We want to test these:
from Bio import motifs
+from Bio.Alphabet.IUPAC import extended_dna, unambiguous_rna
+from Bio.Alphabet.IUPAC import extended_protein
# In order to check any sequences returned
from Bio.Seq import Seq
+import requires_internet
+requires_internet.check()
-class TestMotifWeblogo(unittest.TestCase):
- def setUp(self):
- self.m = motifs.create([
- Seq("TACAA"), Seq("TACGC"), Seq("TACAC"), Seq("TACCC"),
- Seq("AACCC"), Seq("AATGC"), Seq("AATGC")])
- def test_weblogo(self):
- self.m.weblogo(os.devnull)
+class TestotifWeblogo(unittest.TestCase):
+ """Tests Bio.motifs online code."""
+
+ def check(self, seqs_as_strs, alpha):
+ # Using Seq objects and passing exactly the same alphabet:
+ m = motifs.create([Seq(s, alpha) for s in seqs_as_strs], alpha)
+ m.weblogo(os.devnull)
+ # Using Seq objects but not passing alphabet:
+ m = motifs.create([Seq(s, alpha) for s in seqs_as_strs])
+ m.weblogo(os.devnull)
+ # Using strings and passing alphabet:
+ m = motifs.create(seqs_as_strs, alpha)
+ m.weblogo(os.devnull)
+
+ def test_dna(self):
+ """Test Bio.Motif.weblogo with a DNA sequence."""
+ self.check(["TACAA", "TACGC", "TACAC", "TACCC",
+ "AACCC", "AATGC", "AATGC"], extended_dna)
+
+ def test_rna(self):
+ """Test Bio.Motif.weblogo with an RNA sequence."""
+ self.check(["UACAA", "UACGC", "UACAC", "UACCC",
+ "AACCC", "AAUGC", "AAUGC"], unambiguous_rna)
+
+ def test_protein(self):
+ """Test Bio.Motif.weblogo with a protein sequence."""
+ self.check(["ACDEG", "AYCRN", "HYLID", "AYHEL",
+ "ACDEH", "AYYRN", "HYIID"], extended_protein)
if __name__ == "__main__":
diff --git a/Tests/test_phenotype.py b/Tests/test_phenotype.py
new file mode 100644
index 0000000..50ba87c
--- /dev/null
+++ b/Tests/test_phenotype.py
@@ -0,0 +1,304 @@
+# Copyright 2014-2016 Marco Galardini. All rights reserved.
+# Adapted from test_Mymodule.py by Jeff Chang
+# This code is part of the Biopython distribution and governed by its
+# license. Please see the LICENSE file that should have been included
+# as part of this package.
+
+try:
+ import numpy
+except ImportError:
+ from Bio import MissingExternalDependencyError
+ raise MissingExternalDependencyError(
+ "Install NumPy if you want to use Bio.phenotype.")
+
+import json
+import unittest
+
+from Bio._py3k import StringIO
+
+from Bio import BiopythonExperimentalWarning
+
+import warnings
+with warnings.catch_warnings():
+ warnings.simplefilter('ignore', BiopythonExperimentalWarning)
+ from Bio import phenotype
+
+# Example plate files
+SMALL_JSON_PLATE = 'phenotype/SmallPlate.json'
+SMALL_JSON_PLATE_2 = 'phenotype/SmallPlate_2.json'
+JSON_PLATE = 'phenotype/Plate.json'
+JSON_PLATE_2 = 'phenotype/Plate_2.json'
+JSON_PLATE_3 = 'phenotype/Plate_3.json'
+JSON_BAD = 'phenotype/BadPlate.json'
+
+SMALL_CSV_PLATES = 'phenotype/SmallPlates.csv'
+CSV_PLATES = 'phenotype/Plates.csv'
+
+
+class TestPhenoMicro(unittest.TestCase):
+
+ def test_phenotype_IO_errors(self):
+ '''Test bad arguments to phenotype IO methods'''
+ self.assertRaises(ValueError, phenotype.read, CSV_PLATES, 'pm-csv')
+ self.assertRaises(ValueError, phenotype.read, CSV_PLATES, 'pm-json')
+ self.assertRaises(
+ ValueError,
+ phenotype.read,
+ CSV_PLATES,
+ 'pm-noformat')
+ self.assertRaises(ValueError, phenotype.read, CSV_PLATES, 'PM-CSV')
+ self.assertRaises(TypeError, phenotype.read, CSV_PLATES, 1)
+ self.assertRaises(KeyError, phenotype.read, JSON_BAD, 'pm-json')
+
+ def test_phenotype_IO(self):
+ '''Test basic functionalities of phenotype IO methods'''
+ p1 = phenotype.read(SMALL_JSON_PLATE, 'pm-json')
+ p2 = next(phenotype.parse(SMALL_CSV_PLATES, 'pm-csv'))
+
+ handle = StringIO()
+
+ c = phenotype.write([p1, p2], handle, 'pm-json')
+ self.assertEqual(c, 2)
+
+ handle.flush()
+ handle.seek(0)
+ # Now ready to read back from the handle...
+ try:
+ records = list(phenotype.parse(handle, 'pm-json'))
+ except ValueError as e:
+ # This is BAD. We can't read our own output.
+ # I want to see the output when called from the test harness,
+ # run_tests.py (which can be funny about new lines on Windows)
+ handle.seek(0)
+ raise ValueError("%s\n\n%s\n\n%s"
+ % (str(e), repr(handle.read()), repr(records)))
+
+ self.assertEqual(p1, records[0])
+
+ handle.close()
+ handle = StringIO()
+ self.assertRaises(TypeError, phenotype.write, p1, handle, 1)
+ self.assertRaises(ValueError, phenotype.write, p1, handle, 'PM-JSON')
+ self.assertRaises(ValueError, phenotype.write, p1, handle, 'pm-csv')
+ handle.close()
+
+ def test_PlateRecord_errors(self):
+ '''Test bad arguments with PlateRecord objects'''
+ self.assertRaises(ValueError,
+ phenotype.phen_micro.PlateRecord, 'test', [1, 2, 3])
+ self.assertRaises(TypeError,
+ phenotype.phen_micro.PlateRecord, 'test', 1)
+
+ def test_PlateRecord(self):
+ '''Test basic functionalities of PlateRecord objects'''
+ with open(SMALL_JSON_PLATE) as handle:
+ j = json.load(handle)
+
+ p = phenotype.phen_micro.PlateRecord(j['csv_data']['Plate Type'])
+
+ times = j['measurements']['Hour']
+ for k in j['measurements']:
+ if k == 'Hour':
+ continue
+ p[k] = phenotype.phen_micro.WellRecord(k,
+ signals=dict([(times[i], j['measurements'][k][i])
+ for i in range(len(times))]))
+
+ del j['measurements']
+ p.qualifiers = j
+
+ self.assertEqual(p.id, 'PM01')
+ self.assertEqual(len(p), 24)
+ self.assertEqual(p.qualifiers, j)
+ self.assertRaises(ValueError, p._is_well, 'a')
+ self.assertEqual(p['A01'].id, 'A01')
+ self.assertRaises(KeyError, p.__getitem__, 'test')
+ self.assertEqual(len(p[1]), 12)
+ self.assertEqual(len(p[1:2:2]), 12)
+ self.assertEqual(p[1, 2], p['B03'])
+ self.assertEqual(len(p[:, 1]), 2)
+ self.assertEqual(len(p[:, 1:4:2]), 4)
+ self.assertRaises(TypeError, p.__getitem__, 1, 2, 3)
+ self.assertRaises(IndexError, p.__getitem__, 13)
+ self.assertRaises(ValueError, p.__setitem__, 'A02', p['A01'])
+ self.assertRaises(ValueError, p.__setitem__, 'A02', 'a')
+ p['A02'] = p['A02']
+ for w in p:
+ pass
+ self.assertEqual('A01' in p, True)
+ self.assertEqual('test' in p, False)
+ self.assertRaises(ValueError, next, p.get_row('test'))
+ self.assertEqual(next(p.get_row('A')), p['A01'])
+ self.assertRaises(ValueError, next, p.get_column('test'))
+ self.assertEqual(next(p.get_column('12')), p['A12'])
+ self.assertEqual(next(p.get_column('1')), p['A01'])
+ self.assertRaises(ValueError, p.subtract_control, 'A121')
+ self.assertRaises(ValueError, p.subtract_control, wells=['A121'])
+ p2 = p.subtract_control()
+ self.assertEqual(p2.id, p.id)
+ self.assertEqual(p2['A02'], p['A02'] - p['A01'])
+ self.assertEqual(repr(p), "PlateRecord('WellRecord['A01'], WellRecord" +
+ "['A02'], WellRecord['A03'], ..., WellRecord['B12']')")
+ self.assertEqual(str(p), "Plate ID: PM01\nWell: 24\nRows: 2\nColumns: " +
+ "12\nPlateRecord('WellRecord['A01'], WellRecord['A02'], WellRecord" +
+ "['A03'], ..., WellRecord['B12']')")
+
+ with open(SMALL_JSON_PLATE_2) as handle:
+ j = json.load(handle)
+
+ p1 = phenotype.phen_micro.PlateRecord(j['csv_data']['Plate Type'])
+
+ times = j['measurements']['Hour']
+ for k in j['measurements']:
+ if k == 'Hour':
+ continue
+ p1[k] = phenotype.phen_micro.WellRecord(k,
+ signals=dict([(times[i], j['measurements'][k][i])
+ for i in range(len(times))]))
+
+ del j['measurements']
+ p1.qualifiers = j
+
+ self.assertRaises(TypeError, p.__add__, 'a')
+ self.assertRaises(TypeError, p.__sub__, 'a')
+
+ p3 = p + p1
+ self.assertEqual(p3['A02'], p['A02'] + p1['A02'])
+
+ p3 = p - p1
+ self.assertEqual(p3['A02'], p['A02'] - p1['A02'])
+
+ del p['A02']
+ self.assertRaises(ValueError, p.__add__, p1)
+ self.assertRaises(ValueError, p.__sub__, p1)
+
+ def test_bad_fit_args(self):
+ """Test error handling of the fit method."""
+ with open(JSON_PLATE) as handle:
+ p = json.load(handle)
+
+ times = p['measurements']['Hour']
+ w = phenotype.phen_micro.WellRecord('A10',
+ signals=dict([(times[i], p['measurements']['A10'][i])
+ for i in range(len(times))]))
+
+ self.assertRaises(ValueError, w.fit, "wibble")
+ self.assertRaises(ValueError, w.fit, ["wibble"])
+ self.assertRaises(ValueError, w.fit, ("logistic", "wibble"))
+ self.assertRaises(ValueError, w.fit, ("wibble", "logistic"))
+ self.assertRaises(ValueError, w.fit, "logistic") # should be a list/tuple!
+
+ def test_WellRecord(self):
+ '''Test basic functionalities of WellRecord objects'''
+ with open(JSON_PLATE) as handle:
+ p = json.load(handle)
+
+ times = p['measurements']['Hour']
+ w = phenotype.phen_micro.WellRecord('A10',
+ signals=dict([(times[i], p['measurements']['A10'][i])
+ for i in range(len(times))]))
+
+ w1 = phenotype.phen_micro.WellRecord('H12',
+ signals=dict([(times[i], p['measurements']['H12'][i])
+ for i in range(len(times))]))
+
+ # self.assertIsInstance(w.plate,
+ # phenotype.phen_micro.PlateRecord)
+ self.assertTrue(isinstance(w.plate, phenotype.phen_micro.PlateRecord))
+ self.assertEqual(w.id, 'A10')
+ self.assertEqual(len(w), len(times))
+ self.assertEqual(len(w), 384)
+ self.assertEqual(max(w), (95.75, 217.0))
+ self.assertEqual(min(w), (0.0, 37.0))
+ self.assertEqual(max(w, key=lambda x: x[1]),
+ (16.75, 313.0))
+ self.assertEqual(min(w, key=lambda x: x[1]),
+ (0.25, 29.0))
+ self.assertEqual(len(w[:]), 96)
+ self.assertEqual(w[1], 29.)
+ self.assertEqual(w[12], 272.)
+ self.assertEqual(w[1:5], [29., 35., 39., 43.])
+ self.assertRaises(ValueError, w.__getitem__, 'a')
+ self.assertAlmostEqual(w[1:2:.25][0], 29.)
+ self.assertAlmostEqual(w[1.3567], 33.7196)
+ self.assertEqual(w.get_raw()[0], (0.0, 37.0))
+ self.assertEqual(w.get_raw()[-1], (95.75, 217.0))
+ self.assertEqual(w.get_times()[0], 0.0)
+ self.assertEqual(w.get_times()[-1], 95.75)
+ self.assertEqual(w.get_signals()[0], 37.0)
+ self.assertEqual(w.get_signals()[-1], 217.0)
+ self.assertEqual(repr(w),
+ "WellRecord('(0.0, 37.0), (0.25, 29.0), (0.5, 32.0)," +
+ " (0.75, 30.0), (1.0, 29.0), ..., (95.75, 217.0)')")
+ self.assertEqual(str(w),
+ "Well ID: A10\nTime points: 384\nMinum signal 0.25 at " +
+ "time 29.00\nMaximum signal 16.75 at time " +
+ "313.00\nWellRecord('(0.0, 37.0), (0.25, 29.0), " +
+ "(0.5, 32.0), (0.75, 30.0), " +
+ "(1.0, 29.0), ..., (95.75, 217.0)')")
+
+ w.fit(None)
+ self.assertEqual(w.area, None)
+ self.assertEqual(w.model, None)
+ self.assertEqual(w.lag, None)
+ self.assertEqual(w.plateau, None)
+ self.assertEqual(w.slope, None)
+ self.assertEqual(w.v, None)
+ self.assertEqual(w.y0, None)
+ self.assertEqual(w.max, 313.0)
+ self.assertEqual(w.min, 29.0)
+ self.assertEqual(w.average_height, 217.82552083333334)
+
+ self.assertRaises(TypeError, w.__add__, 'a')
+
+ w2 = w + w1
+ self.assertEqual(w2.id, 'A10')
+ self.assertEqual(len(w2), len(times))
+ self.assertEqual(len(w2), 384)
+ self.assertEqual(max(w2), (95.75, 327.0))
+ self.assertEqual(min(w2), (0.0, 63.0))
+ self.assertEqual(max(w2, key=lambda x: x[1]),
+ (18.25, 357.0))
+ self.assertEqual(min(w2, key=lambda x: x[1]),
+ (0.25, 55.0))
+ self.assertEqual(w2[1], 71.)
+ self.assertEqual(w2[12], 316.)
+ self.assertEqual(w2[1:5], [71.0, 88.0, 94.0, 94.0])
+ self.assertAlmostEqual(w2[1:2:.25][0], 71.0)
+ self.assertAlmostEqual(w2[1.3567], 77.7196)
+ self.assertEqual(w2.get_raw()[0], (0.0, 63.0))
+ self.assertEqual(w2.get_raw()[-1], (95.75, 327.0))
+ self.assertEqual(w2.get_times()[0], 0.0)
+ self.assertEqual(w2.get_times()[-1], 95.75)
+ self.assertEqual(w2.get_signals()[0], 63.0)
+ self.assertEqual(w2.get_signals()[-1], 327.0)
+
+ self.assertRaises(TypeError, w.__sub__, 'a')
+
+ w2 = w - w1
+ self.assertEqual(w2.id, 'A10')
+ self.assertEqual(len(w2), len(times))
+ self.assertEqual(len(w2), 384)
+ self.assertEqual(max(w2), (95.75, 107.0))
+ self.assertEqual(min(w2), (0.0, 11.0))
+ self.assertEqual(max(w2, key=lambda x: x[1]),
+ (15.75, 274.0))
+ self.assertEqual(min(w2, key=lambda x: x[1]),
+ (3.25, -20.0))
+ self.assertEqual(w2[1], -13.)
+ self.assertEqual(w2[12], 228.)
+ self.assertEqual(w2[1:5], [-13.0, -18.0, -16.0, -8.0])
+ self.assertAlmostEqual(w2[1:2:.25][0], -13.0)
+ self.assertAlmostEqual(w2[1.3567], -10.2804)
+ self.assertEqual(w2.get_raw()[0], (0.0, 11.0))
+ self.assertEqual(w2.get_raw()[-1], (95.75, 107.0))
+ self.assertEqual(w2.get_times()[0], 0.0)
+ self.assertEqual(w2.get_times()[-1], 95.75)
+ self.assertEqual(w2.get_signals()[0], 11.0)
+ self.assertEqual(w2.get_signals()[-1], 107.0)
+
+ w[1] = 1
+
+if __name__ == "__main__":
+ runner = unittest.TextTestRunner(verbosity=2)
+ unittest.main(testRunner=runner)
diff --git a/Tests/test_phenotype_fit.py b/Tests/test_phenotype_fit.py
new file mode 100644
index 0000000..a010976
--- /dev/null
+++ b/Tests/test_phenotype_fit.py
@@ -0,0 +1,63 @@
+# Copyright 2014-2016 Marco Galardini. All rights reserved.
+# Adapted from test_Mymodule.py by Jeff Chang
+# This code is part of the Biopython distribution and governed by its
+# license. Please see the LICENSE file that should have been included
+# as part of this package.
+
+try:
+ import numpy
+except ImportError:
+ from Bio import MissingExternalDependencyError
+ raise MissingExternalDependencyError(
+ "Install NumPy if you want to use Bio.phenotype.")
+try:
+ import scipy
+ from scipy.optimize import OptimizeWarning
+except ImportError:
+ from Bio import MissingExternalDependencyError
+ raise MissingExternalDependencyError(
+ "Install SciPy if you want to use Bio.phenotype fit functionality.")
+
+import json
+import unittest
+
+from Bio import BiopythonExperimentalWarning
+
+import warnings
+with warnings.catch_warnings():
+ warnings.simplefilter('ignore', BiopythonExperimentalWarning)
+ from Bio import phenotype
+
+# Example plate files
+JSON_PLATE = 'phenotype/Plate.json'
+
+
+class TestPhenoMicro(unittest.TestCase):
+
+ def test_WellRecord(self):
+ '''Test basic functionalities of WellRecord objects'''
+ with open(JSON_PLATE) as handle:
+ p = json.load(handle)
+
+ times = p['measurements']['Hour']
+ w = phenotype.phen_micro.WellRecord('A10',
+ signals=dict([(times[i], p['measurements']['A10'][i])
+ for i in range(len(times))]))
+
+ with warnings.catch_warnings():
+ warnings.simplefilter('ignore', OptimizeWarning)
+ w.fit()
+ self.assertAlmostEqual(w.area, 20879.5)
+ self.assertEqual(w.model, 'gompertz')
+ self.assertAlmostEqual(w.lag, 6.0425868725090357, places=5)
+ self.assertAlmostEqual(w.plateau, 188.51404344898586, places=5)
+ self.assertAlmostEqual(w.slope, 48.190618284831132, places=5)
+ self.assertAlmostEqual(w.v, 0.10000000000000001, places=5)
+ self.assertAlmostEqual(w.y0, 45.879770069807989, places=5)
+ self.assertEqual(w.max, 313.0)
+ self.assertEqual(w.min, 29.0)
+ self.assertEqual(w.average_height, 217.82552083333334)
+
+if __name__ == "__main__":
+ runner = unittest.TextTestRunner(verbosity=2)
+ unittest.main(testRunner=runner)
diff --git a/Tests/test_phyml_tool.py b/Tests/test_phyml_tool.py
index 940a64b..8894697 100644
--- a/Tests/test_phyml_tool.py
+++ b/Tests/test_phyml_tool.py
@@ -17,14 +17,19 @@ from Bio import MissingExternalDependencyError
os.environ['LANG'] = 'C'
phyml_exe = None
-if sys.platform == "win32":
- raise MissingExternalDependencyError(
- "Testing PhyML on Windows not supported yet")
-else:
- from Bio._py3k import getoutput
- output = getoutput("phyml --version")
+exe_name = "PhyML-3.1_win32.exe" if sys.platform == "win32" else "phyml"
+from Bio._py3k import getoutput
+try:
+ output = getoutput(exe_name + " --version")
if "not found" not in output and "20" in output:
- phyml_exe = "phyml"
+ phyml_exe = exe_name
+except OSError:
+ # TODO: Use FileNotFoundError once we drop Python 2
+ # Python 2.6 or 2.7 on Windows XP:
+ # WindowsError: [Error 2] The system cannot find the file specified
+ # Python 3.3 or 3.4 on Windows XP:
+ # FileNotFoundError: [WinError 2] The system cannot find the file specified
+ pass
if not phyml_exe:
raise MissingExternalDependencyError(
diff --git a/Tests/test_prodoc.py b/Tests/test_prodoc.py
index 7d3dbcb..5985b51 100644
--- a/Tests/test_prodoc.py
+++ b/Tests/test_prodoc.py
@@ -14,9 +14,8 @@ class TestProdocRead(unittest.TestCase):
def test_read_pdoc00100(self):
"""Reading Prodoc record PDOC00100"""
filename = os.path.join('Prosite', 'Doc', 'pdoc00100.txt')
- handle = open(filename)
- record = Prodoc.read(handle)
- handle.close()
+ with open(filename) as handle:
+ record = Prodoc.read(handle)
self.assertEqual(record.accession, "PDOC00100")
self.assertEqual(len(record.prosite_refs), 4)
@@ -173,9 +172,8 @@ PubMed=2165531""")
def test_read_pdoc00113(self):
"""Reading Prodoc record PDOC00113"""
filename = os.path.join('Prosite', 'Doc', 'pdoc00113.txt')
- handle = open(filename)
- record = Prodoc.read(handle)
- handle.close()
+ with open(filename) as handle:
+ record = Prodoc.read(handle)
self.assertEqual(record.accession, "PDOC00113")
self.assertEqual(len(record.prosite_refs), 1)
@@ -244,9 +242,8 @@ PubMed=1654502""")
def test_read_pdoc00144(self):
"""Reading Prodoc record PDOC00144"""
filename = os.path.join('Prosite', 'Doc', 'pdoc00144.txt')
- handle = open(filename)
- record = Prodoc.read(handle)
- handle.close()
+ with open(filename) as handle:
+ record = Prodoc.read(handle)
self.assertEqual(record.accession, "PDOC00144")
self.assertEqual(len(record.prosite_refs), 2)
@@ -304,9 +301,8 @@ PubMed=3136164""")
def test_read_pdoc00149(self):
"Reading Prodoc record PDOC00149"
filename = os.path.join('Prosite', 'Doc', 'pdoc00149.txt')
- handle = open(filename)
- record = Prodoc.read(handle)
- handle.close()
+ with open(filename) as handle:
+ record = Prodoc.read(handle)
self.assertEqual(record.accession, "PDOC00149")
self.assertEqual(len(record.prosite_refs), 1)
@@ -384,9 +380,8 @@ PubMed=8236444""")
def test_read_pdoc00340(self):
"Reading Prodoc record PDOC00340"
filename = os.path.join('Prosite', 'Doc', 'pdoc00340.txt')
- handle = open(filename)
- record = Prodoc.read(handle)
- handle.close()
+ with open(filename) as handle:
+ record = Prodoc.read(handle)
self.assertEqual(record.accession, "PDOC00340")
self.assertEqual(len(record.prosite_refs), 3)
@@ -487,9 +482,8 @@ PubMed=2203335""")
def test_read_pdoc00424(self):
"""Reading Prodoc record PDOC00424"""
filename = os.path.join('Prosite', 'Doc', 'pdoc00424.txt',)
- handle = open(filename)
- record = Prodoc.read(handle)
- handle.close()
+ with open(filename) as handle:
+ record = Prodoc.read(handle)
self.assertEqual(record.accession, "PDOC00424")
self.assertEqual(len(record.prosite_refs), 1)
@@ -568,9 +562,8 @@ PubMed=7961661""")
def test_read_pdoc00472(self):
"""Reading Prodoc record PDOC00472"""
filename = os.path.join('Prosite', 'Doc', 'pdoc00472.txt')
- handle = open(filename)
- record = Prodoc.read(handle)
- handle.close()
+ with open(filename) as handle:
+ record = Prodoc.read(handle)
self.assertEqual(record.accession, "PDOC00472")
self.assertEqual(len(record.prosite_refs), 1)
@@ -662,9 +655,8 @@ PubMed=1584806""")
def test_read_pdoc00640(self):
"""Reading Prodoc record PDOC00640"""
filename = os.path.join('Prosite', 'Doc', 'pdoc00640.txt',)
- handle = open(filename)
- record = Prodoc.read(handle)
- handle.close()
+ with open(filename) as handle:
+ record = Prodoc.read(handle)
self.assertEqual(record.accession, "PDOC00640")
self.assertEqual(len(record.prosite_refs), 1)
@@ -757,9 +749,8 @@ PubMed=8805535""")
def test_read_pdoc00787(self):
"""Reading Prodoc record PDOC00787"""
filename = os.path.join('Prosite', 'Doc', 'pdoc00787.txt')
- handle = open(filename)
- record = Prodoc.read(handle)
- handle.close()
+ with open(filename) as handle:
+ record = Prodoc.read(handle)
self.assertEqual(record.accession, "PDOC00787")
self.assertEqual(len(record.prosite_refs), 1)
@@ -820,9 +811,8 @@ PubMed=7624375""")
def test_read_pdoc0933(self):
"""Reading Prodoc record PDOC00933"""
filename = os.path.join('Prosite', 'Doc', 'pdoc00933.txt')
- handle = open(filename)
- record = Prodoc.read(handle)
- handle.close()
+ with open(filename) as handle:
+ record = Prodoc.read(handle)
self.assertEqual(record.accession, "PDOC00933")
self.assertEqual(len(record.prosite_refs), 1)
@@ -892,14 +882,14 @@ class TestProdocParse(unittest.TestCase):
"""Parsing an excerpt of prosite.doc"""
filename = os.path.join('Prosite', 'Doc',
'prosite.excerpt.doc')
- handle = open(filename)
- records = Prodoc.parse(handle)
-
- # Testing the first parsed record
- record = next(records)
- self.assertEqual(record.accession, "PDOC00000")
- self.assertEqual(len(record.prosite_refs), 0)
- self.assertEqual(record.text, """\
+ with open(filename) as handle:
+ records = Prodoc.parse(handle)
+
+ # Testing the first parsed record
+ record = next(records)
+ self.assertEqual(record.accession, "PDOC00000")
+ self.assertEqual(len(record.prosite_refs), 0)
+ self.assertEqual(record.text, """\
**********************************
*** PROSITE documentation file ***
**********************************
@@ -957,13 +947,13 @@ Acknowledgements:
""")
- # Testing the second parsed record
- record = next(records)
- self.assertEqual(record.accession, "PDOC00001")
- self.assertEqual(len(record.prosite_refs), 1)
- self.assertEqual(record.prosite_refs[0],
- ("PS00001", "ASN_GLYCOSYLATION"))
- self.assertEqual(record.text, """\
+ # Testing the second parsed record
+ record = next(records)
+ self.assertEqual(record.accession, "PDOC00001")
+ self.assertEqual(len(record.prosite_refs), 1)
+ self.assertEqual(record.prosite_refs[0],
+ ("PS00001", "ASN_GLYCOSYLATION"))
+ self.assertEqual(record.text, """\
************************
* N-glycosylation site *
************************
@@ -987,52 +977,52 @@ of such a non-standard site is found in the plasma protein C [5].
-Last update: May 1991 / Text revised.
""")
- self.assertEqual(record.references[0].number, "1")
- self.assertEqual(record.references[0].authors,
- "Marshall R.D.")
- self.assertEqual(record.references[0].citation, """\
+ self.assertEqual(record.references[0].number, "1")
+ self.assertEqual(record.references[0].authors,
+ "Marshall R.D.")
+ self.assertEqual(record.references[0].citation, """\
"Glycoproteins."
Annu. Rev. Biochem. 41:673-702(1972).
PubMed=4563441; DOI=10.1146/annurev.bi.41.070172.003325""")
- self.assertEqual(record.references[1].number, "2")
- self.assertEqual(record.references[1].authors,
- "Pless D.D., Lennarz W.J.")
- self.assertEqual(record.references[1].citation, """\
+ self.assertEqual(record.references[1].number, "2")
+ self.assertEqual(record.references[1].authors,
+ "Pless D.D., Lennarz W.J.")
+ self.assertEqual(record.references[1].citation, """\
"Enzymatic conversion of proteins to glycoproteins."
Proc. Natl. Acad. Sci. U.S.A. 74:134-138(1977).
PubMed=264667""")
- self.assertEqual(record.references[2].number, "3")
- self.assertEqual(record.references[2].authors, "Bause E.")
- self.assertEqual(record.references[2].citation, """\
+ self.assertEqual(record.references[2].number, "3")
+ self.assertEqual(record.references[2].authors, "Bause E.")
+ self.assertEqual(record.references[2].citation, """\
"Structural requirements of N-glycosylation of proteins. Studies with
proline peptides as conformational probes."
Biochem. J. 209:331-336(1983).
PubMed=6847620""")
- self.assertEqual(record.references[3].number, "4")
- self.assertEqual(record.references[3].authors,
- "Gavel Y., von Heijne G.")
- self.assertEqual(record.references[3].citation, """\
+ self.assertEqual(record.references[3].number, "4")
+ self.assertEqual(record.references[3].authors,
+ "Gavel Y., von Heijne G.")
+ self.assertEqual(record.references[3].citation, """\
"Sequence differences between glycosylated and non-glycosylated
Asn-X-Thr/Ser acceptor sites: implications for protein engineering."
Protein Eng. 3:433-442(1990).
PubMed=2349213""")
- self.assertEqual(record.references[4].number, "5")
- self.assertEqual(record.references[4].authors,
- "Miletich J.P., Broze G.J. Jr.")
- self.assertEqual(record.references[4].citation, """\
+ self.assertEqual(record.references[4].number, "5")
+ self.assertEqual(record.references[4].authors,
+ "Miletich J.P., Broze G.J. Jr.")
+ self.assertEqual(record.references[4].citation, """\
"Beta protein C is not glycosylated at asparagine 329. The rate of
translation may influence the frequency of usage at
asparagine-X-cysteine sites."
J. Biol. Chem. 265:11397-11404(1990).
PubMed=1694179""")
- # Testing the third parsed record
- record = next(records)
- self.assertEqual(record.accession, "PDOC00004")
- self.assertEqual(len(record.prosite_refs), 1)
- self.assertEqual(record.prosite_refs[0],
- ("PS00004", "CAMP_PHOSPHO_SITE"))
- self.assertEqual(record.text, """\
+ # Testing the third parsed record
+ record = next(records)
+ self.assertEqual(record.accession, "PDOC00004")
+ self.assertEqual(len(record.prosite_refs), 1)
+ self.assertEqual(record.prosite_refs[0],
+ ("PS00004", "CAMP_PHOSPHO_SITE"))
+ self.assertEqual(record.text, """\
****************************************************************
* cAMP- and cGMP-dependent protein kinase phosphorylation site *
****************************************************************
@@ -1049,36 +1039,36 @@ to note that there are quite a number of exceptions to this rule.
""")
- self.assertEqual(record.references[0].number, "1")
- self.assertEqual(record.references[0].authors,
- "Fremisco J.R., Glass D.B., Krebs E.G.")
- self.assertEqual(record.references[0].citation, """\
+ self.assertEqual(record.references[0].number, "1")
+ self.assertEqual(record.references[0].authors,
+ "Fremisco J.R., Glass D.B., Krebs E.G.")
+ self.assertEqual(record.references[0].citation, """\
J. Biol. Chem. 255:4240-4245(1980).""")
- self.assertEqual(record.references[1].number, "2")
- self.assertEqual(record.references[1].authors,
- "Glass D.B., Smith S.B.")
- self.assertEqual(record.references[1].citation, """\
+ self.assertEqual(record.references[1].number, "2")
+ self.assertEqual(record.references[1].authors,
+ "Glass D.B., Smith S.B.")
+ self.assertEqual(record.references[1].citation, """\
"Phosphorylation by cyclic GMP-dependent protein kinase of a synthetic
peptide corresponding to the autophosphorylation site in the enzyme."
J. Biol. Chem. 258:14797-14803(1983).
PubMed=6317673""")
- self.assertEqual(record.references[2].number, "3")
- self.assertEqual(record.references[2].authors,
- "Glass D.B., el-Maghrabi M.R., Pilkis S.J.")
- self.assertEqual(record.references[2].citation, """\
+ self.assertEqual(record.references[2].number, "3")
+ self.assertEqual(record.references[2].authors,
+ "Glass D.B., el-Maghrabi M.R., Pilkis S.J.")
+ self.assertEqual(record.references[2].citation, """\
"Synthetic peptides corresponding to the site phosphorylated in
6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase as substrates of
cyclic nucleotide-dependent protein kinases."
J. Biol. Chem. 261:2987-2993(1986).
PubMed=3005275""")
- # Testing the fourth parsed record
- record = next(records)
- self.assertEqual(record.accession, "PDOC60030")
- self.assertEqual(len(record.prosite_refs), 1)
- self.assertEqual(record.prosite_refs[0],
- ("PS60030", "BACTERIOCIN_IIA"))
- self.assertEqual(record.text, """\
+ # Testing the fourth parsed record
+ record = next(records)
+ self.assertEqual(record.accession, "PDOC60030")
+ self.assertEqual(len(record.prosite_refs), 1)
+ self.assertEqual(record.prosite_refs[0],
+ ("PS60030", "BACTERIOCIN_IIA"))
+ self.assertEqual(record.text, """\
******************************************
* Bacteriocin class IIa family signature *
******************************************
@@ -1133,26 +1123,26 @@ The pattern we developed for the class IIa bacteriocin family covers the
""")
- self.assertEqual(record.references[0].number, "1")
- self.assertEqual(record.references[0].authors,
- "Ennahar S., Sonomoto K., Ishizaki A.")
- self.assertEqual(record.references[0].citation, """\
+ self.assertEqual(record.references[0].number, "1")
+ self.assertEqual(record.references[0].authors,
+ "Ennahar S., Sonomoto K., Ishizaki A.")
+ self.assertEqual(record.references[0].citation, """\
"Class IIa bacteriocins from lactic acid bacteria: antibacterial
activity and food preservation."
J. Biosci. Bioeng. 87:705-716(1999).
PubMed=16232543""")
- self.assertEqual(record.references[1].number, "2")
- self.assertEqual(record.references[1].authors,
- "Johnsen L., Fimland G., Nissen-Meyer J.")
- self.assertEqual(record.references[1].citation, """\
+ self.assertEqual(record.references[1].number, "2")
+ self.assertEqual(record.references[1].authors,
+ "Johnsen L., Fimland G., Nissen-Meyer J.")
+ self.assertEqual(record.references[1].citation, """\
"The C-terminal domain of pediocin-like antimicrobial peptides (class
IIa bacteriocins) is involved in specific recognition of the
C-terminal part of cognate immunity proteins and in determining the
antimicrobial spectrum."
J. Biol. Chem. 280:9243-9250(2005).
PubMed=15611086; DOI=10.1074/jbc.M412712200""")
- self.assertEqual(record.references[2].number, "3")
- self.assertEqual(record.references[2].authors, "Fimland G., Johnsen L., Dalhus B., Nissen-Meyer J.")
+ self.assertEqual(record.references[2].number, "3")
+ self.assertEqual(record.references[2].authors, "Fimland G., Johnsen L., Dalhus B., Nissen-Meyer J.")
self.assertEqual(record.references[2].citation, """\
"Pediocin-like antimicrobial peptides (class IIa bacteriocins) and
their immunity proteins: biosynthesis, structure, and mode of
@@ -1160,8 +1150,6 @@ action."
J. Pept. Sci. 11:688-696(2005).
PubMed=16059970; DOI=10.1002/psc.699""")
- handle.close()
-
if __name__ == "__main__":
runner = unittest.TextTestRunner(verbosity=2)
diff --git a/Tests/test_prosite1.py b/Tests/test_prosite1.py
index c5665ab..78ea784 100644
--- a/Tests/test_prosite1.py
+++ b/Tests/test_prosite1.py
@@ -14,11 +14,10 @@ from Bio.ExPASy import Prosite
class TestPrositeRead(unittest.TestCase):
def test_read1(self):
- "Parsing Prosite record ps00107.txt"
+ """Parsing Prosite record ps00107.txt"""
filename = os.path.join('Prosite', 'ps00107.txt')
- handle = open(filename)
- record = Prosite.read(handle)
- handle.close()
+ with open(filename) as handle:
+ record = Prosite.read(handle)
self.assertEqual(record.name, "PROTEIN_KINASE_ATP")
self.assertEqual(record.type, "PATTERN")
self.assertEqual(record.accession, "PS00107")
@@ -38,6 +37,7 @@ class TestPrositeRead(unittest.TestCase):
self.read1_positive1(record)
self.read1_positive2(record)
self.read1_positive3(record)
+ self.read1_positive4(record)
self.read1_false_neg(record)
self.read1_false_pos(record)
self.read1_potential(record)
@@ -1551,7 +1551,7 @@ class TestPrositeRead(unittest.TestCase):
self.assertEqual(record.dr_positive[1498], ('Q62270', 'SRMS_MOUSE'))
self.assertEqual(record.dr_positive[1499], ('Q96SB4', 'SRPK1_HUMAN'))
- def read1_positive3(self, record):
+ def read1_positive4(self, record):
self.assertEqual(len(record.dr_positive), 1689)
self.assertEqual(record.dr_positive[1500], ('O70551', 'SRPK1_MOUSE'))
self.assertEqual(record.dr_positive[1501], ('Q5RD27', 'SRPK1_PONPY'))
@@ -2523,11 +2523,10 @@ class TestPrositeRead(unittest.TestCase):
self.assertEqual(record.pdb_structs[330], '4ERK')
def test_read2(self):
- "Parsing Prosite record ps00159.txt"
+ """Parsing Prosite record ps00159.txt"""
filename = os.path.join('Prosite', 'ps00159.txt')
- handle = open(filename)
- record = Prosite.read(handle)
- handle.close()
+ with open(filename) as handle:
+ record = Prosite.read(handle)
self.assertEqual(record.name, "ALDOLASE_KDPG_KHG_1")
self.assertEqual(record.type, "PATTERN")
self.assertEqual(record.accession, "PS00159")
@@ -2572,11 +2571,10 @@ class TestPrositeRead(unittest.TestCase):
self.assertEqual(record.pdb_structs[5], "1VHC")
def test_read3(self):
- "Parsing Prosite record ps00165.txt"
+ """Parsing Prosite record ps00165.txt"""
filename = os.path.join('Prosite', 'ps00165.txt')
- handle = open(filename)
- record = Prosite.read(handle)
- handle.close()
+ with open(filename) as handle:
+ record = Prosite.read(handle)
self.assertEqual(record.name, "DEHYDRATASE_SER_THR")
self.assertEqual(record.type, "PATTERN")
self.assertEqual(record.accession, "PS00165")
diff --git a/Tests/test_prosite2.py b/Tests/test_prosite2.py
index e4b2cd7..53be98d 100644
--- a/Tests/test_prosite2.py
+++ b/Tests/test_prosite2.py
@@ -14,11 +14,10 @@ from Bio.ExPASy import Prosite
class TestPrositeRead(unittest.TestCase):
def test_read4(self):
- "Parsing Prosite record ps00432.txt"
+ """Parsing Prosite record ps00432.txt"""
filename = os.path.join('Prosite', 'ps00432.txt')
- handle = open(filename)
- record = Prosite.read(handle)
- handle.close()
+ with open(filename) as handle:
+ record = Prosite.read(handle)
self.assertEqual(record.name, "ACTINS_2")
self.assertEqual(record.type, "PATTERN")
self.assertEqual(record.accession, "PS00432")
@@ -453,11 +452,10 @@ class TestPrositeRead(unittest.TestCase):
self.assertEqual(record.pdb_structs[59], '2BTF')
def test_read5(self):
- "Parsing Prosite record ps00488.txt"
+ """Parsing Prosite record ps00488.txt"""
filename = os.path.join('Prosite', 'ps00488.txt')
- handle = open(filename)
- record = Prosite.read(handle)
- handle.close()
+ with open(filename) as handle:
+ record = Prosite.read(handle)
self.assertEqual(record.name, "PAL_HISTIDASE")
self.assertEqual(record.type, "PATTERN")
self.assertEqual(record.accession, "PS00488")
@@ -621,11 +619,10 @@ class TestPrositeRead(unittest.TestCase):
self.assertEqual(record.pdb_structs[5], "1Y2M")
def test_read6(self):
- "Parsing Prosite record ps00546.txt"
+ """Parsing Prosite record ps00546.txt"""
filename = os.path.join('Prosite', 'ps00546.txt')
- handle = open(filename)
- record = Prosite.read(handle)
- handle.close()
+ with open(filename) as handle:
+ record = Prosite.read(handle)
self.assertEqual(record.name, "CYSTEINE_SWITCH")
self.assertEqual(record.type, "PATTERN")
self.assertEqual(record.accession, "PS00546")
diff --git a/Tests/test_raxml_tool.py b/Tests/test_raxml_tool.py
index fc0e82b..44c15fd 100644
--- a/Tests/test_raxml_tool.py
+++ b/Tests/test_raxml_tool.py
@@ -14,14 +14,15 @@ from Bio.Phylo.Applications import RaxmlCommandline
from Bio import MissingExternalDependencyError
raxml_exe = None
-if sys.platform == "win32":
- raise MissingExternalDependencyError(
- "Testing RAxML on Windows not supported yet")
-else:
+try:
from Bio._py3k import getoutput
output = getoutput("raxmlHPC -v")
if "not found" not in output and "This is RAxML" in output:
raxml_exe = "raxmlHPC"
+except OSError:
+ # TODO: Use FileNotFoundError once we drop Python 2
+ pass
+
if not raxml_exe:
raise MissingExternalDependencyError(
"Install RAxML (binary raxmlHPC) if you want to test the Bio.Phylo.Applications wrapper.")
diff --git a/Tests/test_samtools_tool.py b/Tests/test_samtools_tool.py
index f7059d4..c16a17e 100644
--- a/Tests/test_samtools_tool.py
+++ b/Tests/test_samtools_tool.py
@@ -11,6 +11,8 @@ from Bio import MissingExternalDependencyError
import sys
import os
import unittest
+
+from Bio.Application import ApplicationError
from Bio.Sequencing.Applications import SamtoolsViewCommandline
from Bio.Sequencing.Applications import SamtoolsCalmdCommandline
from Bio.Sequencing.Applications import SamtoolsCatCommandline
@@ -180,7 +182,14 @@ class SamtoolsTestCase(unittest.TestCase):
cmdline = SamtoolsSortCommandline(samtools_exe)
cmdline.set_parameter("input_bam", self.bamfile1)
cmdline.set_parameter("out_prefix", "SamBam/out")
- stdout, stderr = cmdline()
+ try:
+ stdout, stderr = cmdline()
+ except ApplicationError as err:
+ if "[bam_sort] Use -T PREFIX / -o FILE to specify temporary and final output files" in str(err):
+ # TODO: The samtools sort API changed...
+ return
+ else:
+ raise
self.assertFalse(stderr,
"Samtools sort failed:\n%s\nStderr:%s"
% (cmdline, stderr))
@@ -209,7 +218,9 @@ class SamtoolsTestCase(unittest.TestCase):
cmdline.set_parameter("out_bam", self.outbamfile)
cmdline.set_parameter("f", True) # Overwrite out.bam if it exists
stdout, stderr = cmdline()
- self.assertFalse(stderr,
+ # Worked up to v1.2, then there was a regression failing with message
+ # but as of v1.3 expect a warning: [W::bam_merge_core2] No @HD tag found.
+ self.assertTrue(not stderr or stderr.strip() == "[W::bam_merge_core2] No @HD tag found.",
"Samtools merge failed:\n%s\nStderr:%s"
% (cmdline, stderr))
self.assertTrue(os.path.exists(self.outbamfile))
diff --git a/Tests/test_seq.py b/Tests/test_seq.py
index 5f1fc19..f3a3360 100644
--- a/Tests/test_seq.py
+++ b/Tests/test_seq.py
@@ -20,7 +20,7 @@ else:
from Bio import Alphabet
from Bio import Seq
-from Bio.Alphabet import IUPAC
+from Bio.Alphabet import IUPAC, Gapped
from Bio.Data.IUPACData import ambiguous_dna_complement, ambiguous_rna_complement
from Bio.Data.IUPACData import ambiguous_dna_values, ambiguous_rna_values
from Bio.Data.CodonTable import TranslationError
@@ -950,6 +950,105 @@ class TestTranslating(unittest.TestCase):
expected = Seq.translate(nucleotide_seq)
self.assertEqual(repr(expected), repr(nucleotide_seq.translate()))
+ def test_alphabets_of_translated_seqs(self):
+
+ def triple_pad(s):
+ """Add N to ensure length is a multiple of three (whole codons)."""
+ while len(s) % 3:
+ s += "N"
+ return s
+
+ self.assertEqual("IUPACProtein()", repr(self.test_seqs[0].translate().alphabet))
+ self.assertEqual("ExtendedIUPACProtein()", repr(self.test_seqs[1].translate().alphabet))
+ self.assertEqual("ExtendedIUPACProtein()", repr(self.test_seqs[2].translate().alphabet))
+ self.assertEqual("ExtendedIUPACProtein()", repr(self.test_seqs[3].translate().alphabet))
+ self.assertEqual("ExtendedIUPACProtein()", repr(self.test_seqs[10].translate().alphabet))
+ self.assertEqual("ExtendedIUPACProtein()", repr(self.test_seqs[11].translate().alphabet))
+ self.assertEqual("IUPACProtein()", repr(self.test_seqs[12].translate().alphabet))
+ self.assertEqual("ExtendedIUPACProtein()",
+ repr(triple_pad(self.test_seqs[13]).translate().alphabet))
+ self.assertEqual("ExtendedIUPACProtein()", repr(self.test_seqs[14].translate().alphabet))
+ self.assertEqual("IUPACProtein()", repr(self.test_seqs[15].translate().alphabet))
+ self.assertEqual("ExtendedIUPACProtein()",
+ repr(triple_pad(self.test_seqs[16]).translate().alphabet))
+ self.assertEqual("ExtendedIUPACProtein()",
+ repr(triple_pad(self.test_seqs[17]).translate().alphabet))
+
+ def test_translation_of_gapped_seq_with_gap_char_given(self):
+ seq = Seq.Seq("ATG---AAACTG")
+ self.assertEqual("M-KL", seq.translate(gap="-"))
+ self.assertRaises(TranslationError, seq.translate, gap="~")
+
+ def test_translation_of_gapped_seq_with_stop_codon_and_gap_char_given(self):
+ seq = Seq.Seq("GTG---GCCATTGTAATGGGCCGCTGAAAGGGTGCCCGATAG")
+ self.assertEqual("V-AIVMGR*KGAR*", seq.translate(gap="-"))
+ self.assertRaises(TranslationError, seq.translate)
+
+ def test_translation_of_gapped_seq_with_gap_char_given_and_inferred_from_alphabet(self):
+ seq = Seq.Seq("ATG---AAACTG", Gapped(IUPAC.unambiguous_dna))
+ self.assertEqual("M-KL", seq.translate(gap="-"))
+ self.assertRaises(ValueError, seq.translate, gap="~")
+
+ seq = Seq.Seq("ATG~~~AAACTG", Gapped(IUPAC.unambiguous_dna))
+ self.assertRaises(ValueError, seq.translate, gap="~")
+ self.assertRaises(TranslationError, seq.translate, gap="-")
+
+ def test_translation_of_gapped_seq_with_gap_char_given_and_inferred_from_alphabet2(self):
+ """Test using stop codon in sequence"""
+ seq = Seq.Seq("ATG---AAACTGTAG", Gapped(IUPAC.unambiguous_dna))
+ self.assertEqual("M-KL*", seq.translate(gap="-"))
+ self.assertRaises(ValueError, seq.translate, gap="~")
+
+ seq = Seq.Seq("ATG---AAACTGTAG", Gapped(IUPAC.unambiguous_dna))
+ self.assertEqual("M-KL@", seq.translate(gap="-", stop_symbol="@"))
+ self.assertRaises(ValueError, seq.translate, gap="~")
+
+ seq = Seq.Seq("ATG~~~AAACTGTAG", Gapped(IUPAC.unambiguous_dna))
+ self.assertRaises(ValueError, seq.translate, gap="~")
+ self.assertRaises(TranslationError, seq.translate, gap="-")
+
+ def test_translation_of_gapped_seq_no_gap_char_given(self):
+ seq = Seq.Seq("ATG---AAACTG")
+ self.assertRaises(TranslationError, seq.translate)
+
+ def test_translation_of_gapped_seq_no_gap_char_given_and_inferred_from_alphabet(self):
+ seq = Seq.Seq("ATG---AAACTG", Gapped(IUPAC.unambiguous_dna))
+ self.assertEqual("M-KL", seq.translate())
+
+ seq = Seq.Seq("ATG~~~AAACTG", Gapped(IUPAC.unambiguous_dna))
+ self.assertRaises(TranslationError, seq.translate)
+
+ seq = Seq.Seq("ATG~~~AAACTG", Gapped(IUPAC.unambiguous_dna, "~"))
+ self.assertEqual("M~KL", seq.translate())
+
+ def test_alphabet_of_translated_gapped_seq(self):
+ seq = Seq.Seq("ATG---AAACTG", Gapped(IUPAC.unambiguous_dna))
+ self.assertEqual("Gapped(ExtendedIUPACProtein(), '-')", repr(seq.translate().alphabet))
+
+ seq = Seq.Seq("ATG---AAACTG", Gapped(IUPAC.unambiguous_dna, "-"))
+ self.assertEqual("Gapped(ExtendedIUPACProtein(), '-')", repr(seq.translate().alphabet))
+
+ seq = Seq.Seq("ATG~~~AAACTG", Gapped(IUPAC.unambiguous_dna, "~"))
+ self.assertEqual("Gapped(ExtendedIUPACProtein(), '~')", repr(seq.translate().alphabet))
+
+ seq = Seq.Seq("ATG---AAACTG")
+ self.assertEqual("Gapped(ExtendedIUPACProtein(), '-')", repr(seq.translate(gap="-").alphabet))
+
+ seq = Seq.Seq("ATG~~~AAACTG")
+ self.assertEqual("Gapped(ExtendedIUPACProtein(), '~')", repr(seq.translate(gap="~").alphabet))
+
+ seq = Seq.Seq("ATG~~~AAACTGTAG")
+ self.assertEqual("HasStopCodon(Gapped(ExtendedIUPACProtein(), '~'), '*')",
+ repr(seq.translate(gap="~").alphabet))
+
+ seq = Seq.Seq("ATG---AAACTGTGA")
+ self.assertEqual("HasStopCodon(Gapped(ExtendedIUPACProtein(), '-'), '*')",
+ repr(seq.translate(gap="-").alphabet))
+
+ seq = Seq.Seq("ATG---AAACTGTGA")
+ self.assertEqual("HasStopCodon(Gapped(ExtendedIUPACProtein(), '-'), '@')",
+ repr(seq.translate(gap="-", stop_symbol="@").alphabet))
+
def test_translation_wrong_type(self):
"""Test translation table cannot be CodonTable"""
seq = Seq.Seq("ATCGTA")
@@ -960,6 +1059,17 @@ class TestTranslating(unittest.TestCase):
seq = "GTGGCCATTGTAATGGGCCGC"
self.assertEqual("VAIVMGR", Seq.translate(seq))
+ def test_translation_of_gapped_string_with_gap_char_given(self):
+ seq = "GTG---GCCATTGTAATGGGCCGC"
+ expected = "V-AIVMGR"
+ self.assertEqual(expected, Seq.translate(seq, gap="-"))
+ self.assertRaises(TypeError, Seq.translate, seq, gap=[])
+ self.assertRaises(ValueError, Seq.translate, seq, gap="-*")
+
+ def test_translation_of_gapped_string_no_gap_char_given(self):
+ seq = "GTG---GCCATTGTAATGGGCCGC"
+ self.assertRaises(TranslationError, Seq.translate, seq)
+
def test_translation_to_stop(self):
for nucleotide_seq in self.test_seqs:
nucleotide_seq = nucleotide_seq[:3 * (len(nucleotide_seq) // 3)]
diff --git a/setup.py b/setup.py
index 54d4f87..8b94436 100644
--- a/setup.py
+++ b/setup.py
@@ -136,6 +136,8 @@ elif sys.version_info[0] == 3 and sys.version_info[:2] < (3, 3):
print("Biopython requires Python 3.3 or later (or Python 2.6 or 2.7). "
"Python %d.%d detected" % sys.version_info[:2])
sys.exit(1)
+elif sys.version_info[:2] == (3, 3):
+ print("WARNING: Biopython support for Python 3.3 is now deprecated.")
def check_dependencies_once():
@@ -339,9 +341,6 @@ PACKAGES = [
'Bio.KEGG.Map',
'Bio.KEGG.KGML',
'Bio.Medline',
- 'Bio.Motif',
- 'Bio.Motif.Parsers',
- 'Bio.Motif.Applications',
'Bio.motifs',
'Bio.motifs.applications',
'Bio.motifs.jaspar',
@@ -391,6 +390,7 @@ NUMPY_PACKAGES = [
'Bio.Affy',
'Bio.Cluster',
'Bio.KDTree',
+ 'Bio.phenotype',
]
if os.name == 'java':
@@ -431,11 +431,6 @@ if is_Numpy_installed():
include_dirs=[numpy_include_dir],
))
EXTENSIONS.append(
- Extension('Bio.Motif._pwm',
- ["Bio/Motif/_pwm.c"],
- include_dirs=[numpy_include_dir],
- ))
- EXTENSIONS.append(
Extension('Bio.motifs._pwm',
["Bio/motifs/_pwm.c"],
include_dirs=[numpy_include_dir],
--
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