[med-svn] r7960 - trunk/packages/rostlab/profphd/trunk/debian

[Laszlo Kajan]

Author: lkajan-guest
Date: 2011-09-27 15:59:57 +0000 (Tue, 27 Sep 2011)
New Revision: 7960

Modified:
   trunk/packages/rostlab/profphd/trunk/debian/control
Log:
more concise long description

Modified: trunk/packages/rostlab/profphd/trunk/debian/control
===================================================================
--- trunk/packages/rostlab/profphd/trunk/debian/control	2011-09-27 15:41:44 UTC (rev 7959)
+++ trunk/packages/rostlab/profphd/trunk/debian/control	2011-09-27 15:59:57 UTC (rev 7960)
@@ -16,33 +16,11 @@
 Replaces: profphd-data, profphd-perl
 Conflicts: profphd-data, profphd-perl
 Description: secondary structure and solvent accessibility predictor
- Solvent accessibility is predicted by a neural network method rating at a
- correlation coefficient (correlation between experimentally observed and
- predicted relative solvent accessibility) of 0.54 cross-validated on a set of
- 238 globular proteins (Rost & Sander, Proteins, 1994, 20, 216-226;
- evaluation of accuracy). The output of the neural network codes for 10 states
- of relative accessibility. Expressed in units of the difference between
- prediction by homology modelling (best method) and prediction at random
- (worst method), PROFacc is some 26 percentage points superior to a comparable
- neural network using three output states (buried, intermediate, exposed) and
- using no information from multiple alignments.
+ profphd predicts protein:
  .
- Transmembrane helices
- in integral membrane proteins are predicted by a system of neural networks.
- The shortcoming of the network system is that often too long helices are
- predicted. These are cut by an empirical filter. The final prediction
- (Rost et al., Protein Science, 1995, 4, 521-533; evaluation of accuracy)
- has an expected per-residue accuracy of about 95%. The number of false
- positives, i.e., transmembrane helices predicted in globular proteins, is
- about 2%.
+ * secondary structure
+ * solvent accessibility
+ * transmembrane helices
  .
- The neural network prediction of transmembrane helices
- (PHDhtm) is refined by a dynamic programming-like algorithm. This method
- resulted in correct predictions of all transmembrane helices for 89% of the
- 131 proteins used in a cross-validation test; more than 98% of the
- transmembrane helices were correctly predicted. The output of this method
- is used to predict topology, i.e., the orientation of the N-term with respect
- to the membrane. The expected accuracy of the topology prediction is > 86%.
- Prediction accuracy is higher than average for eukaryotic proteins and lower
- than average for prokaryotes. PHDtopology is more accurate than all other
- methods tested on identical data sets.
+ Prediction is either done from protein sequence alone or from an alignment -
+ the latter should be used for optimal performance.