title: SOPMA: significant improvements in protein secondary structure prediction by consensus prediction from multiple alignments.
abstract: Recently a new method called the self-optimized prediction method (SOPM) has been described to improve the success rate in the prediction of the secondary structure of proteins. In this paper we report improvements brought about by predicting all the sequences of a set of aligned proteins belonging to the same family. This improved SOPM method (SOPMA) correctly predicts 69.5% of amino acids for a three-state description 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-predicted amino acids. Predictions are available by Email to deleage@ibcp.fr or on a Web page (http:@www.ibcp.fr/predict.html).
authors.affiliation: Institut de Biologie et de Chimie des Protéines, UPR 412-CNRS, Lyon, France.
authors.forename: C
authors.initials: C
authors.lastname: Geourjon
authors.forename: G
authors.initials: G
authors.lastname: Deléage
mesh_terms: D016208:Databases, Factual; D016571:Neural Networks, Computer; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry*; D016415:Sequence Alignment / Q000379:methods* / Q000706:statistics & numerical data; D017386:Sequence Homology, Amino Acid; D012984:Software
chemical_list: D011506:Proteins