conSSert: Consensus SVM Model for Accurate Prediction of Ordered Secondary Structure

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• 作者：Chris A. Kieslich ; James Smadbeck ; George A. Khoury ; Christodoulos A. Floudas
• 刊名：Journal of Chemical Information and Modeling
• 出版年：2016
• 出版时间：March 28, 2016
• 年：2016
• 卷：56
• 期：3
• 页码：455-461
• 全文大小：323K
• ISSN：1549-960X

文摘

Accurate prediction of protein secondary structure remains a crucial step in most approaches to the protein-folding problem, yet the prediction of ordered secondary structure, specifically beta-strands, remains a challenge. We developed a consensus secondary structure prediction method, conSSert, which is based on support vector machines (SVM) and provides exceptional accuracy for the prediction of beta-strands with QE accuracy of over 0.82 and a Q2-EH of 0.86. conSSert uses as input probabilities for the three types of secondary structure (helix, strand, and coil) that are predicted by four top performing methods: PSSpred, PSIPRED, SPINE-X, and RAPTOR. conSSert was trained/tested using 4261 protein chains from PDBSelect25, and 8632 chains from PISCES. Further validation was performed using targets from CASP9, CASP10, and CASP11. Our data suggest that poor performance in strand prediction is likely a result of training bias and not solely due to the nonlocal nature of beta-sheet contacts. conSSert is freely available for noncommercial use as a webservice: http://ares.tamu.edu/conSSert/.