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Bioinformatics Advance Access published online on December 21, 2004
Bioinformatics, doi:10.1093/bioinformatics/bti242
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Bioinformatics © Oxford University Press 2004; all rights reserved.
Received September 14, 2004
Revised November 18, 2004
Accepted December 16, 2004

Article

Improved prediction of protein-protein binding sites using a support vector machines approach

James R. Bradford 1 and David R. Westhead 1*

1 School Of Biochemistry and Molecular Biology, University of Leeds, Leeds, LS2 9JT, UK

* To whom correspondence should be addressed.
David R. Westhead, E-mail: westhead{at}bmb.leeds.ac.uk


  Abstract

Motivation: Structural genomics projects are beginning to produce protein structures with unknown function therefore accurate, automated predictors of protein function are required if all these structures are to be properly annotated in reasonable time. Identifying the interface between two interacting proteins provides important clues as to the function of a protein, and can reduce the search space required by docking algorithms to predict the structures of complexes.

Results: We have combined a support vector machine (SVM) approach with surface patch analysis to predict protein-protein binding sites. Using a leave-one-out cross validation procedure, we were able to successfully predict the location of the binding site on 76% of our data set made up proteins with both transient and obligate interfaces. With heterogeneous cross-validation, where we trained the SVM on transient complexes to predict on obligate complexes (and vice versa), we still achieved comparable success rates to the leave-one-out cross validation suggesting that sufficient properties are shared between transient and obligate interfaces.

Availability: A web application based on the method can be found at http://www.bioinformatics.leeds.ac.uk/ppi_pred. The data set of 180 proteins used in this study is also available via the same web site.

Supplementary information: Included.


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