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Bioinformatics Advance Access published online on October 4, 2005
Bioinformatics, doi:10.1093/bioinformatics/bti697
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© The Author (2005). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
Received June 27, 2005
Revised September 27, 2005
Accepted September 27, 2005

Article

Discrimination of outer membrane proteins using support vector machines

Keun-Joon Park 1*, M. Michael Gromiha 2, Paul Horton 2, and Makiko Suwa 2

1 Computational Biology Research Center (CBRC), National Institute of Advanced Industrial Science and Technology (AIST), AIST Tokyo Waterfront Bio-IT Research Building, 2-42 Aomi, Koto-ku, Tokyo, 135-0064, Japan; Laboratory of Functional Analysis in silico, Human Genome Center, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokane-dai Minato-ku, Tokyo 108-8639, Japan
2 Computational Biology Research Center (CBRC), National Institute of Advanced Industrial Science and Technology (AIST), AIST Tokyo Waterfront Bio-IT Research Building, 2-42 Aomi, Koto-ku, Tokyo, 135-0064, Japan

* To whom correspondence should be addressed.
Keun-Joon Park, E-mail: park-kj{at}hgc.jp


  Abstract

Motivation: Discriminating outer membrane proteins from other folding types of globular and membrane proteins is an important task both for dissecting outer membrane proteins (OMPs) from genomic sequences and for the successful prediction of their secondary and tertiary structures.

Results: We have developed a method based on support vector machines (SVMs) using amino acid composition and residue pair information. Our approach with amino acid composition has correctly predicted the OMPs with a cross-validated accuracy of 94% in a set of 208 proteins. Further, this method has successfully excluded 633 of 673 globular proteins and 191 of 206 {alpha}-helical membrane proteins. We obtained an overall accuracy of 92% for correctly picking up the OMPs from a dataset of 1087 proteins belonging to all different types of globular and membrane proteins. Furthermore, residue pair information improved the accuracy from 92% to 94%. This accuracy of discriminating OMPs is higher than other methods in the literature, which could be used for dissecting OMPs from genomic sequences.

Availability: Discrimination results are available at http://tmbeta-svm.cbrc.jp.


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