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Bioinformatics Advance Access originally published online on January 18, 2007
Bioinformatics 2007 23(9):1090-1098; 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

Jian Qiu 1, Martial Hue 3,*, Asa Ben-Hur 4, Jean-Philippe Vert 3 and William Stafford Noble 1,2

1Department of Genome Sciences, 2Department of Computer Science and Engineering, University of Washington, Seattle, WA, USA, 3Center for Computational Biology, Ecole des Mines de Paris, Fontainebleau, France and 4Department of Computer Science, Colorado State University, Ft. Collins, CO, USA

*The first two authors contributed equally to this work.


  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 out-performs 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

Contact: noble{at}gs.washington.edu

Associate Editor: Anna Tramontano

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

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