Data mining of enzymes using specific peptides

Abstract

BackgroundPredicting 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.ResultsWe 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.ConclusionsEmploying SPs for predicting enzymatic activity of proteins works well once one utilizes coverage-length criteria. In our analysis, L ≥ 7 has led to highly accurate results.

Discovery of Klotho peptide antagonists against Wnt3 and Wnt3a target proteins using combination of protein engineering, protein–protein docking, peptide docking and molecular dynamics simulations

“…Accumulatively, eight strings of residues (Peptide 4–11) of KL1 domain of Klotho were marked as making good interactions with Wnt3 and Wnt3a. Furthermore, three new peptides (Peptide 1–3) were also predicted having glycosidase activity using DME 32 . Thus, in total 11 peptides were taken for docking simulations with important domains of Wnt3 and Wnt3a ( Table 2 ).…”

“…The resulting docked complexes from both docking programs were analyzed to understand interactions of Klotho amino acid residues with Wnt proteins. Furthermore, three new peptides in Klotho’s KL1 domain were predicted by Data Mining for Enzymes Search Utility (DME) which were expected having glycosidase activity 32 .…”

Computational Approaches for Automated Classification of Enzyme Sequences

“…The SPs are strings of amino acids, derived from enzyme sequences using MEX and showed that the coverage of the SPs is better than that of PROSITE motifs in finding the function of enzyme families. Further, Weingart et al [39] have demonstrated how SPs can be employed on Data Mining of Enzymes (DME) on any given set of protein sequences. They use a peptide length of greater than six for the protein sequences that carried the same EC assignment for better accuracy.…”

The use of evolutionary patterns in protein annotation

“…lower false positives, by recognizing functionally inconsistent differences among key residues. Several sequence motif-based algorithms were designed specifically for this task, including Confunc 28 , DME 29 , and EFICAz2 30 . All rely on discovering discriminatory sequence fragments shared by proteins with identical function and not others.…”