Data Mining of Enzymes

July 22, 2009
DOI: 10.4016/12044.01
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Conference:
ISMB/ECCB 2009
Abstract:
Predicting the function of a protein from its sequence is a long-standing challenge, typically addressed using either sequence-similarity or sequence-motifs. We employ the novel motif method of Kunik et al, introducing Specific Peptides (SPs) that are unique to specific branches of the Enzyme Commission (EC) functional classification. We devise the methodology that allows for searching SPs on arbitrary proteins, determining from its sequence whether a protein is an enzyme and what the enzyme’s EC classification is. We show that the predictive power of SPs, both for true-positives (enzymes) and true-negatives (non-enzymes), depends on the coverage length L of all SP matches (the number of amino-acids matched on the protein sequence). In our analysis, L?7 leads to highly accurate results.
Key findings:
Accurate functional prediction of enzymes.
Description:
Protein functional prediction