Dataset Information

Prediction of enzyme function by combining sequence similarity and protein interactions.

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

ACCESS DATA

Publications

Prediction of enzyme function by combining sequence similarity and protein interactions.

Espadaler Jordi J Eswar Narayanan N Querol Enrique E Avilés Francesc X FX Sali Andrej A Marti-Renom Marc A MA Oliva Baldomero B

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]

PMID: 18505562

Similar Datasets

Project description:BackgroundPredicting protein function from primary sequence is an important open problem in modern biology. Not only are there many thousands of proteins of unknown function, current approaches for predicting function must be improved upon. One problem in particular is overly-specific function predictions which we address here with a new statistical model of the relationship between protein sequence similarity and protein function similarity.MethodologyOur statistical model is based on sets of proteins with experimentally validated functions and numeric measures of function specificity and function similarity derived from the Gene Ontology. The model predicts the similarity of function between two proteins given their amino acid sequence similarity measured by statistics from the BLAST sequence alignment algorithm. A novel aspect of our model is that it predicts the degree of function similarity shared between two proteins over a continuous range of sequence similarity, facilitating prediction of function with an appropriate level of specificity.SignificanceOur model shows nearly exact function similarity for proteins with high sequence similarity (bit score >244.7, e-value >1e(-62), non-redundant NCBI protein database (NRDB)) and only small likelihood of specific function match for proteins with low sequence similarity (bit score <54.6, e-value <1e(-05), NRDB). For sequence similarity ranges in between our annotation model shows an increasing relationship between function similarity and sequence similarity, but with considerable variability. We applied the model to a large set of proteins of unknown function, and predicted functions for thousands of these proteins ranging from general to very specific. We also applied the model to a data set of proteins with previously assigned, specific functions that were electronically based. We show that, on average, these prior function predictions are more specific (quite possibly overly-specific) compared to predictions from our model that is based on proteins with experimentally determined function.

Dataset Information

Prediction of enzyme function by combining sequence similarity and protein interactions.

Background

Results

Conclusion

Publications

Prediction of enzyme function by combining sequence similarity and protein interactions.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets