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

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    conSSert: Consensus SVM Model for Accurate Prediction of Ordered Secondary Structure
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    † ‡ †Artie McFerrin Department of Chemical Engineering and ‡Texas A&M Energy Institute, Texas A&M University, College Station, Texas 77843, United States
    § Department of Chemical and Biological Engineering, Princeton University, Princeton, New Jersey 08544, United States
    *E-mail: [email protected]
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    Journal of Chemical Information and Modeling

    Cite this: J. Chem. Inf. Model. 2016, 56, 3, 455–461
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    https://doi.org/10.1021/acs.jcim.5b00566
    Published February 29, 2016
    Copyright © 2016 American Chemical Society
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    Abstract

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    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/.

    Copyright © 2016 American Chemical Society

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    The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.jcim.5b00566.

    • The Supporting Information file includes tables describing the compositions of training and testing sets, as well as the full cross validation results from which means and standard deviations were derived. (PDF)

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    Cite this: J. Chem. Inf. Model. 2016, 56, 3, 455–461
    Click to copy citationCitation copied!
    https://doi.org/10.1021/acs.jcim.5b00566
    Published February 29, 2016
    Copyright © 2016 American Chemical Society
    Request reuse permissions

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