Prediction of protein secondary structure by combining nearest-neighbor algorithms and multiple sequence alignments.
Recently Yi & Lander used a neural network and nearest-neighbor method with a scoring system that combined a sequence-similarity matrix with the local structural environment scoring scheme described by Bowie and co-workers for predicting protein secondary structure. We have improved their scoring system by taking into consideration N and C-terminal positions of alpha-helices and beta-strands and also beta-turns as distinctive types of secondary structure. Another improvement, which also decreases the time of computation, is performed by restricting a data base with a smaller subset of proteins that are similar with a query sequence. Using multiple sequence alignments rather than single sequences and a simple jury decision procedure our method reaches a sustained overall three-state accuracy of 72.2%, which is better than that observed for the most accurate multilayered neural-network approach, tested on the same data set of 126 non-homologous protein chains.
Salamov
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Department of Cell Biology, Baylor College of Medicine, Houston, TX 77030.
Solovyev
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D000465:Algorithms; D005075:Biological Evolution; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry*; D016415:Sequence Alignment; D017386:Sequence Homology, Amino Acid; D012984:Software
D011506:Proteins