Motif extraction and protein classification.

Vered Kunik; Zach Solan; Shimon Edelman; Eytan Ruppin; David Horn

doi:10.1109/csb.2005.39

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Motif extraction and protein classification.

Vered Kunik·Zach Solan·Shimon Edelman·Eytan Ruppin·David Horn

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Abstract

We present a novel unsupervised method for extracting meaningful motifs from biological sequence data. This de novo motif extraction (MEX) algorithm is data driven, finding motifs that are not necessarily over-represented in the data. Applying MEX to the oxidoreductases class of enzymes, containing approximately 7000 enzyme sequences, a relatively small set of motifs is obtained. This set spans a motif-space that is used for functional classification of the enzymes by an SVM classifier. The classification based on MEX motifs surpasses that of two other SVM based methods: SVMProt, a method based on the analysis of physical-chemical properties of a protein generated from its sequence of amino acids, and SVM applied to a Smith-Waterman distances matrix. Our findings demonstrate that the MEX algorithm extracts relevant motifs, supporting a successful sequence-to-function classification.

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Algorithms Amino Acid Motifs Amino Acid Sequence Artificial Intelligence Molecular Sequence Data Pattern Recognition, Automated Proteins Sequence Alignment Sequence Analysis, Protein Sequence Homology, Amino Acid Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S.

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Vered Kunik, Zach Solan, Shimon Edelman, Eytan Ruppin, David Horn. 2005. Motif extraction and protein classification.. https://doi.org/10.1109/csb.2005.39

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