title: Data mining of enzymes using specific peptides.
abstract: BACKGROUND
  Predicting the function of a protein from its sequence is a long-standing challenge of bioinformatic research, typically addressed using either sequence-similarity or sequence-motifs. We employ the novel motif method that consists of Specific Peptides (SPs) that are unique to specific branches of the Enzyme Commission (EC) functional classification. We devise the Data Mining of Enzymes (DME) 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.
  
  
  RESULTS
  We extract novel SP sets from Swiss-Prot enzyme data. Using a training set of July 2006, and test sets of July 2008, we find that the predictive power of SPs, both for true-positives (enzymes) and true-negatives (non-enzymes), depends on the coverage length of all SP matches (the number of amino-acids matched on the protein sequence). DME is quite different from BLAST. Comparing the two on an enzyme test set of July 2008, we find that DME has lower recall. On the other hand, DME can provide predictions for proteins regarded by BLAST as having low homologies with known enzymes, thus supplying complementary information. We test our method on a set of proteins belonging to 10 bacteria, dated July 2008, establishing the usefulness of the coverage-length cutoff to determine true-negatives. Moreover, sifting through our predictions we find that some of them have been substantiated by Swiss-Prot annotations by July 2009. Finally we extract, for production purposes, a novel SP set trained on all Swiss-Prot enzymes as of July 2009. This new set increases considerably the recall of DME. The new SP set is being applied to three metagenomes: Sargasso Sea with over 1,000,000 proteins, producing predictions of over 220,000 enzymes, and two human gut metagenomes. The outcome of these analyses can be characterized by the enzymatic profile of the metagenomes, describing the relative numbers of enzymes observed for different EC categories.
  
  
  CONCLUSIONS
  Employing SPs for predicting enzymatic activity of proteins works well once one utilizes coverage-length criteria. In our analysis, L >or= 7 has led to highly accurate results.
authors.affiliation: School of Physics and Astronomy, Tel Aviv University, Tel Aviv 69978, Israel. uriweing@tau.ac.il
authors.forename: Uri
authors.initials: U
authors.lastname: Weingart
authors.forename: Yair
authors.initials: Y
authors.lastname: Lavi
authors.forename: David
authors.initials: D
authors.lastname: Horn
mesh_terms: D000595:Amino Acid Sequence; D057225:Data Mining / Q000379:methods*; D010455:Peptides / Q000737:chemistry*; D011506:Proteins / Q000737:chemistry* / Q000145:classification; D016415:Sequence Alignment / Q000379:methods; D020539:Sequence Analysis, Protein / Q000379:methods*
chemical_list: D010455:Peptides; D011506:Proteins