Remote homology detection: a motif based approach

A method for detecting remote homology that is based on the presence of discrete sequence motifs that performs significantly better than an SVM method that uses BLAST or Smith-Waterman similarity scores as features.

Abstract

Abstract 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: [email protected] Keywords: remote homology, discrete sequence motifs, sequence similarity, Support Vector Machines, kernel methods *To whom correspondence should be addressed.