title: Motif extraction and protein classification.
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.
authors.affiliation: School of Computer Science, Tel-Aviv University, Tel-Aviv 69978, Israel. kunikver@tau.ac.il
authors.forename: Vered
authors.initials: V
authors.lastname: Kunik
authors.forename: Zach
authors.initials: Z
authors.lastname: Solan
authors.forename: Shimon
authors.initials: S
authors.lastname: Edelman
authors.forename: Eytan
authors.initials: E
authors.lastname: Ruppin
authors.forename: David
authors.initials: D
authors.lastname: Horn
mesh_terms: D000465:Algorithms; D020816:Amino Acid Motifs; D000595:Amino Acid Sequence; D001185:Artificial Intelligence; D008969:Molecular Sequence Data; D010363:Pattern Recognition, Automated / Q000379:methods*; D011506:Proteins / Q000737:chemistry* / Q000145:classification; D016415:Sequence Alignment / Q000379:methods*; D020539:Sequence Analysis, Protein / Q000379:methods*; D017386:Sequence Homology, Amino Acid
chemical_list: D011506:Proteins