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.

JE was supported by predoctoral fellowship from the Generalitat de Catalunya and CERBA (Spain). EQ acknowledges grants from the Spanish Ministerio de Educación y Ciencia (BIO2007-67904-C02-01). FXA acknowledges grants from the Spanish Ministerio de Educación y Ciencia (BIO2007-68046). AS acknowledges the financial support by the Sandler Family Supporting Foundation, IBM, HP, Netapps, and Intel for hardware gifts, and NIH grants GM74945, GM74929, GM71790, and GM54762. MAM-R acknowledges support from the Spanish Ministerio de Educación y Ciencia (BIO2007-66670). BO acknowledges grants from Generalitat de Catalunya (CIDEM), Spanish Ministerio de Educación y Ciencia (MEC BIO2005-00533 and PROFIT PSE-010000-2007-1) and by European Union INFOBIOMED-NoE (IST-507585) and @NEURIST (IST-2004-027703)