Prediction of enzyme function by combining sequence similarity and protein interactions

Jordi Espadaler, Narayanan Eswar, Enrique Querol, Francesc X. Avilés, Andrej Sali, Marc A. Marti-Renom, Baldomero Oliva

Research output: Contribution to journalArticleResearchpeer-review

20 Citations (Scopus)


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. © 2008 Espadaler et al; licensee BioMed Central Ltd.
Original languageEnglish
Article number249
JournalBMC Bioinformatics
Publication statusPublished - 27 May 2008


Dive into the research topics of 'Prediction of enzyme function by combining sequence similarity and protein interactions'. Together they form a unique fingerprint.

Cite this