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Bioinformatics Advance Access published online on January 18, 2007
Bioinformatics, doi:10.1093/bioinformatics/btl642
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© The Author (2007). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

A structural alignment kernel for protein structures A structural alignment kernel for protein structures

Jian Qiu a, Martial Hue c,*, Asa Ben-Hur d, Jean-Philippe Vert c and William Stafford Noble a,b

aDepartment of Genome Sciences, bDepartment of Computer Science and Engineering, University of Washington, Seattle, WA, USA, cCenter for Computational Biology, Ecole des Mines de Paris, Fontainebleau, France, dDepartment of Computer Science, Colorado State University, Ft. Collins, CO, USA

To whom correspondence should be addressed. , E-mail: noble{at}gs.washington.edu


  Abstract

Motivation: This work aims to develop computational methods to annotate protein structures in an automated fashion. We employ a support vector machine (SVM) classifier to map from a given class of structures to their corresponding structural (SCOP) or functional (Gene Ontology) annotation. In particular, we build upon recent work describing various kernels for protein structures, where a kernel is a similarity function that the classifier uses to compare pairs of structures.

Results: We describe a kernel that is derived in a straightforward fashion from an existing structural alignment program, MAMMOTH. We find in our benchmark experiments that this kernel significantly outperforms a variety of other kernels, including several previously described kernels. Furthermore, in both benchmarks, classifying structures using MAMMOTH alone does not work as well as using an SVM with the MAMMOTH kernel.

Availability: http://noble.gs.washington.edu/proj/3dkernel

* The first two authors contributed equally to this work

Associate Editor: Anna Tramontano

Received on August 17, 2006; revised on November 22, 2006; accepted on December 15, 2006

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