Next: Introduction
Application of multiple sequence alignment
profiles to improve protein secondary structure
prediction.
James A. Cuff 1,2 and G. J. Barton 2
1. Laboratory of Molecular Biophysics, Rex Richards Building, South
Parks Road, Oxford, OX1 3QU, UK
2. European Molecular Biology Laboratory - European Bioinformatics
Institute. Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10
1SD, UK
Corresponding Author: G. J. Barton, EMBL-European
Bioinformatics Institute,
Wellcome Trust Genome Campus, Hinxton,
Cambridge, CB10 1SD, UK.
Keywords: protein, secondary structure prediction, multiple sequence alignment, profiles, PSSM.
Abstract:
The effect of training a neural network secondary structure prediction
algorithm with different types of multiple sequence alignment profiles
derived from the same sequences, is shown to provide a range of
accuracy from 70.5% to 76.4%. The best accuracy of 76.4% (standard
deviation 8.4%), is 3.1% (
Q3) and 4.4% (SOV2) better than the
PHD algorithm run on the same set of 406 sequence non-redundant
proteins that were not used to train either method. Residues
predicted by the new method with a confidence value of 5 or greater,
have an average
Q3 accuracy of 84%, and cover 68% of the
residues. Relative solvent accessibility based on a two state model,
for 25, 5 and 0% accessibility are predicted at 76.2, 79.8 and 86.6%
accuracy respectively. The source of the improvements obtained from
training with different representations of the same alignment data are
described in detail. The new Jnet prediction method resulting from
this study is available in the Jpred secondary structure prediction
server, and as a stand-alone computer program from:
http://barton.ebi.ac.uk/
Next: Introduction
James Cuff
2001-06-29