%0 Journal Article %J BMC Bioinformatics %D 2008 %T Prediction of enzyme function by combining sequence similarity and protein interactions %A Espadaler, J. %A Eswar, N. %A Querol, E. %A Aviles, F. X. %A Sali, A. %A M. A. Marti-Renom %A Oliva, B. %K Amino Acid *Software Structure-Activity Relationship Substrate Specificity/genetics %K Amino Acid Sequence/physiology Databases %K Automated Predictive Value of Tests Protein Interaction Mapping Proteins/analysis/metabolism Sequence Alignment Sequence Analysis %K Protein *Sequence Homology %K Protein Enzymes/analysis/*metabolism Fuzzy Logic Pattern Recognition %X 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. %B BMC Bioinformatics %V 9 %P 249 %G eng %U http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=18505562