Prediction of enzyme function by combining sequence similarity and protein interactions

BMC Bioinformatics. 2008 May 27;9:249. doi: 10.1186/1471-2105-9-249.


Background: A number of studies have used protein interaction data alone for protein function prediction. Here, we introduce a computational approach for annotation of enzymes, based on the observation that similar protein sequences are more likely to perform the same function if they share similar interacting partners.

Results: The method has been tested against the PSI-BLAST program using a set of 3,890 protein sequences from which interaction data was available. For protein sequences that align with at least 40% sequence identity to a known enzyme, the specificity of our method in predicting the first three EC digits increased from 80% to 90% at 80% coverage when compared to PSI-BLAST.

Conclusion: Our method can also be used in proteins for which homologous sequences with known interacting partners can be detected. Thus, our method could increase 10% the specificity of genome-wide enzyme predictions based on sequence matching by PSI-BLAST alone.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Amino Acid Sequence / physiology
  • Databases, Protein
  • Enzymes / analysis
  • Enzymes / metabolism*
  • Fuzzy Logic
  • Pattern Recognition, Automated
  • Predictive Value of Tests
  • Protein Interaction Mapping
  • Proteins / analysis
  • Proteins / metabolism
  • Sequence Alignment
  • Sequence Analysis, Protein
  • Sequence Homology, Amino Acid*
  • Software*
  • Structure-Activity Relationship
  • Substrate Specificity / genetics


  • Enzymes
  • Proteins