title: Predicting active site residue annotations in the Pfam database.
abstract: BACKGROUND
  Approximately 5% of Pfam families are enzymatic, but only a small fraction of the sequences within these families (<0.5%) have had the residues responsible for catalysis determined. To increase the active site annotations in the Pfam database, we have developed a strict set of rules, chosen to reduce the rate of false positives, which enable the transfer of experimentally determined active site residue data to other sequences within the same Pfam family.
  
  
  METHODS
  We have created a large database of predicted active site residues. On comparing our active site predictions to those found in UniProtKB, Catalytic Site Atlas, PROSITE and MEROPS we find that we make many novel predictions. On investigating the small subset of predictions made by these databases that are not predicted by us, we found these sequences did not meet our strict criteria for prediction. We assessed the sensitivity and specificity of our methodology and estimate that only 3% of our predicted sequences are false positives.
  
  
  CONCLUSIONS
  We have predicted 606110 active site residues, of which 94% are not found in UniProtKB, and have increased the active site annotations in Pfam by more than 200 fold. Although implemented for Pfam, the tool we have developed for transferring the data can be applied to any alignment with associated experimental active site data and is available for download. Our active site predictions are re-calculated at each Pfam release to ensure they are comprehensive and up to date. They provide one of the largest available databases of active site annotation.
authors: Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK. jm14@sanger.ac.uk
authors: Jaina
authors: J
authors: Mistry
authors: Alex
authors: A
authors: Bateman
authors: Robert D
authors: RD
authors: Finn
mesh_terms: D000595:Amino Acid Sequence; D001665:Binding Sites; D030562:Databases, Protein / Q000639:trends; D008969:Molecular Sequence Data; D011237:Predictive Value of Tests; D016415:Sequence Alignment / Q000379:methods / Q000639:trends; D017386:Sequence Homology, Amino Acid; D012985:Software Design
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