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This article is part of the supplement: The Second Automated Function Prediction Meeting

Open Access Proceedings

SVM-Fold: a tool for discriminative multi-class protein fold and superfamily recognition

Iain Melvin12, Eugene Ie3, Rui Kuang4, Jason Weston1, William Stafford Noble5 and Christina Leslie26*

Author Affiliations

1 NEC Laboratories of America, Princeton, NJ, USA

2 Center for Computational Learning Systems, Columbia University, New York, NY, USA

3 Department of Computer Science, UCSD, San Diego, CA, USA

4 Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN, USA

5 Department of Genome Sciences, University of Washington, Seattle, WA, USA

6 Center for Computational Biology and Bioinformatics, Columbia University, New York, NY, USA

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BMC Bioinformatics 2007, 8(Suppl 4):S2  doi:10.1186/1471-2105-8-S4-S2

Published: 22 May 2007

Abstract

Background

Predicting a protein's structural class from its amino acid sequence is a fundamental problem in computational biology. Much recent work has focused on developing new representations for protein sequences, called string kernels, for use with support vector machine (SVM) classifiers. However, while some of these approaches exhibit state-of-the-art performance at the binary protein classification problem, i.e. discriminating between a particular protein class and all other classes, few of these studies have addressed the real problem of multi-class superfamily or fold recognition. Moreover, there are only limited software tools and systems for SVM-based protein classification available to the bioinformatics community.

Results

We present a new multi-class SVM-based protein fold and superfamily recognition system and web server called SVM-Fold, which can be found at http://svm-fold.c2b2.columbia.edu webcite. Our system uses an efficient implementation of a state-of-the-art string kernel for sequence profiles, called the profile kernel, where the underlying feature representation is a histogram of inexact matching k-mer frequencies. We also employ a novel machine learning approach to solve the difficult multi-class problem of classifying a sequence of amino acids into one of many known protein structural classes. Binary one-vs-the-rest SVM classifiers that are trained to recognize individual structural classes yield prediction scores that are not comparable, so that standard "one-vs-all" classification fails to perform well. Moreover, SVMs for classes at different levels of the protein structural hierarchy may make useful predictions, but one-vs-all does not try to combine these multiple predictions. To deal with these problems, our method learns relative weights between one-vs-the-rest classifiers and encodes information about the protein structural hierarchy for multi-class prediction. In large-scale benchmark results based on the SCOP database, our code weighting approach significantly improves on the standard one-vs-all method for both the superfamily and fold prediction in the remote homology setting and on the fold recognition problem. Moreover, our code weight learning algorithm strongly outperforms nearest-neighbor methods based on PSI-BLAST in terms of prediction accuracy on every structure classification problem we consider.

Conclusion

By combining state-of-the-art SVM kernel methods with a novel multi-class algorithm, the SVM-Fold system delivers efficient and accurate protein fold and superfamily recognition.