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SVM-based detection of distant protein structural relationships using pairwise probabilistic suffix trees

Paper ID Volume ID Publish Year Pages File Format Full-Text
15565 1430 2006 8 PDF Available
Title
SVM-based detection of distant protein structural relationships using pairwise probabilistic suffix trees
Abstract

A new method based on probabilistic suffix trees (PSTs) is defined for pairwise comparison of distantly related protein sequences. The new definition is adopted in a discriminative framework for protein classification using pairwise sequence similarity scores in feature encoding. The framework uses support vector machines (SVMs) to separate structurally similar and dissimilar examples. The new discriminative system, which we call as SVM–PST, has been tested for SCOP family classification task, and compared with existing discriminative methods SVM–BLAST and SVM–Pairwise, which use BLAST similarity scores and dynamic-programming-based alignment scores, respectively. Results have shown that SVM–PST is more accurate than SVM–BLAST and competitive with SVM–Pairwise. In terms of computational efficiency, PST-based comparison is much better than dynamic-programming-based alignment. We also compared our results with the original family-based PST approach from which we were inspired. The present method provides a significantly better solution for protein classification in comparison with the family-based PST model.

Keywords
Family classification; Probabilistic suffix tree; Sequence similarity; Support vector machine
First Page Preview
SVM-based detection of distant protein structural relationships using pairwise probabilistic suffix trees
Publisher
Database: Elsevier - ScienceDirect
Journal: Computational Biology and Chemistry - Volume 30, Issue 4, August 2006, Pages 292–299
Authors
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Subjects
Physical Sciences and Engineering Chemical Engineering Bioengineering