Bioinformatics Vol. 19 Suppl. 1 2003
Pages i26-i33
© 2003 Oxford University Press
Remote homology detection: a motif based approach
Department of Biochemistry, B400 Beckman Center, Stanford University, CA 94305-5307, USA
Received on January 6, 2003
; accepted on February 20, 2003
Motivation: Remote homology detection is the problem of detecting homology in cases of low sequence similarity. It is a hard computational problem with no approach that works well in all cases.
Results: We present a method for detecting remote homology that is based on the presence of discrete sequence motifs. The motif content of a pair of sequences is used to define a similarity that is used as a kernel for a Support Vector Machine (SVM) classifier. We test the method on two remote homology detection tasks: prediction of a previously unseen SCOP family and prediction of an enzyme class given other enzymes that have a similar function on other substrates. We find that it performs significantly better than an SVM method that uses BLAST or Smith-Waterman similarity scores as features.
Availability: The software is available from the authors upon request.
Contact: asa.benhur{at}stanford.edu
Keywords: remote homology, discrete sequence motifs, sequence similarity, Support Vector Machines, kernel methods
* To whom correspondence should be addressed.
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