Protein homology detection using string alignment kernels.

Hiroto Saigo; Jean-Philippe Vert; Nobuhisa Ueda; Tatsuya Akutsu

doi:10.1093/bioinformatics/bth141

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Protein homology detection using string alignment kernels.

Hiroto Saigo·Jean-Philippe Vert·Nobuhisa Ueda·Tatsuya Akutsu

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Abstract

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.

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Algorithms Amino Acid Sequence Artificial Intelligence Computing Methodologies Molecular Sequence Data Pattern Recognition, Automated Protein Structure, Tertiary Proteins Sequence Alignment Sequence Analysis, Protein Sequence Homology, Amino Acid Comparative Study Evaluation Study Journal Article Research Support, Non-U.S. Gov't Validation Study

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Hiroto Saigo, Jean-Philippe Vert, Nobuhisa Ueda, Tatsuya Akutsu. 2004-02-26. Protein homology detection using string alignment kernels.. https://doi.org/10.1093/bioinformatics%2Fbth141

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