C. Geourjon et G. Deleage, SOPMA - SIGNIFICANT IMPROVEMENTS IN PROTEIN SECONDARY STRUCTURE PREDICTION BY CONSENSUS PREDICTION FROM MULTIPLE ALIGNMENTS, Computer applications in the biosciences, 11(6), 1995, pp. 681-684
Recently a new method called the self-optimized prediction method (SOP
M) has been described to improve the success rate in the prediction of
the secondary structure of proteins. rn this paper we report improvem
ents brought about by predicting all the sequences of a set of aligned
proteins belonging to the same family. This improved SOPM method (SOP
MA) correctly predicts 69.5% of amino acids for a three-state descript
ion of the secondary structure (alpha-helix, beta-sheet and coil) in a
whole database containing 126 chains of non-homologous (less than 25%
identity) proteins. Joint prediction with SOPMA and a neural networks
method (PHD) correctly predicts 82.2% of residues for 74% of co-predi
cted amino acids. Predictions are available by Email to deleage@ibcp.f
r or on a Web page (http://www.ibcp,fl/predict.html).