Prediction of enzyme function by combining sequence similarity and protein interactions

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.

Espadaler, Jordi Eswar, Narayanan Querol, Enrique Aviles, Francesc X Sali, Andrej Marti-Renom, Marc A Oliva, Baldomero GM54762/GM/NIGMS NIH HHS/United States GM71790/GM/NIGMS NIH HHS/United States GM74929/GM/NIGMS NIH HHS/United States GM74945/GM/NIGMS NIH HHS/United States Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov’t England BMC bioinformatics BMC Bioinformatics. 2008 May 27;9:249.