Bioinformatics Advance Access originally published online on February 26, 2004
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Bioinformatics 20(11) © Oxford University Press 2004; all rights reserved.
Protein homology detection using string alignment kernels
1 Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, 611-0011, Japan and 2 Centre de Géostatistique, Ecole des Mines de Paris, 35 rue Saint-Honoré, Fontainebleau, 77300, France
Received on April 30, 2003; revised on December 9, 2003; accepted on January 8, 2004
Advance Access Publication February 26, 2004
Motivation: Remote homology detection between protein sequences is a central problem in computational biology. Discriminative methods involving support vector machines (SVMs) are currently the most effective methods for the problem of superfamily recognition in the Structural Classification Of Proteins (SCOP) database. The performance of SVMs depends critically on the kernel function used to quantify the similarity between sequences.
Results: We propose new kernels for strings adapted to biological sequences, which we call local alignment kernels. These kernels measure the similarity between two sequences by summing up scores obtained from local alignments with gaps of the sequences. When tested in combination with SVM on their ability to recognize SCOP superfamilies on a benchmark dataset, the new kernels outperform state-of-the-art methods for remote homology detection.
Availability: Software and data available upon request.
Contact: Jean-Philippe.Vert{at}mines.org
* To whom correspondence should be addressed.
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