Motif extraction and protein classification.
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.
Kunik
Vered
V
School of Computer Science, Tel-Aviv University, Tel-Aviv 69978, Israel. kunikver@tau.ac.il
Solan
Zach
Z
Edelman
Shimon
S
Ruppin
Eytan
E
Horn
David
D
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
D011506:Proteins