Remote homology detection: a motif based approach |
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Authors: | Ben-Hur Asa Brutlag Douglas |
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Affiliation: | Department of Biochemistry, B400 Beckman Center, Stanford University, CA 94305-5307, USA. asa.benhur@stanford.edu |
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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. |
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