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Related Experiment Videos

Predicting active site residue annotations in the Pfam database.

Jaina Mistry1, Alex Bateman, Robert D Finn

  • 1Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK. jm14@sanger.ac.uk

BMC Bioinformatics
|August 11, 2007
PubMed
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This study developed a method to predict enzyme active sites, significantly increasing annotations in the Pfam database by over 200-fold. The new predictions cover millions of residues, greatly expanding available catalytic site data.

Area of Science:

  • Biochemistry
  • Bioinformatics
  • Structural Biology

Background:

  • Enzymatic proteins constitute a small portion of protein families, with limited experimentally determined active site data.
  • Existing active site annotations in databases like Pfam are sparse, hindering functional understanding and research.

Purpose of the Study:

  • To develop a robust method for predicting active site residues in enzymatic proteins.
  • To significantly enhance the active site annotations within the Pfam database.

Main Methods:

  • Established strict criteria for transferring experimentally determined active site data to homologous sequences within Pfam families.
  • Developed a computational approach to predict active site residues across a large protein sequence dataset.

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Main Results:

  • Successfully predicted over 600,000 active site residues, with 94% being novel annotations not present in UniProtKB.
  • Achieved a high level of accuracy, with an estimated false positive rate of only 3%.
  • Increased Pfam's active site annotations by over 200-fold.

Conclusions:

  • The developed methodology provides a scalable solution for annotating enzyme active sites, significantly expanding the available data.
  • The prediction tool is versatile and applicable to any protein alignment with experimental active site data.
  • The predictions are regularly updated with Pfam releases, ensuring comprehensive and current active site annotation resources.