Dataset Information
Prediction of enzyme function by combining sequence similarity and protein interactions.
Ontology highlight
ABSTRACT: 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.
SUBMITTER: Espadaler J
PROVIDER: S-EPMC2430716 | biostudies-literature | 2008 May
REPOSITORIES: biostudies-literature
Publications
Prediction of enzyme function by combining sequence similarity and protein interactions.
BMC bioinformatics 20080527
<h4>Background</h4>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.<h4>Results</h4>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 seque ...[more]