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

@article{citeulike:2838456, 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 using interaction data about 3,890 protein sequences and averaging the results within protein families to account for over- and under-representation. 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 be applied not only to proteins for which we know interacting partners but also to their homologs. The method can be used for large-scale enzymatic functional annotation of protein sequences to refine predictions based on sequence matching alone, increasing the specificity of 10\% of the predictions made by PSI-BLAST alone.}, author = {Espadaler, Jordi and Eswar, Narayanan and Querol, Enric and Aviles, Francesc and Sali, Andrej and Renom, Marc M. and Oliva, Baldomero}, citeulike-article-id = {2838456}, citeulike-linkout-0 = {http://dx.doi.org/10.1186/1471-2105-9-249}, citeulike-linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/18505562}, citeulike-linkout-2 = {http://www.hubmed.org/display.cgi?uids=18505562}, doi = {10.1186/1471-2105-9-249}, journal = {BMC Bioinformatics}, keywords = {enzyme\_prediction}, number = {1}, posted-at = {2008-10-07 15:01:37}, priority = {5}, title = {Prediction of enzyme function by combining sequence similarity and protein interactions}, url = {http://dx.doi.org/10.1186/1471-2105-9-249}, volume = {9}, year = {2008} }

TY - JOUR ID - citeulike:2838456 L3 - citeulike-article-id:2838456 N2 - 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 using interaction data about 3,890 protein sequences and averaging the results within protein families to account for over- and under-representation. 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 be applied not only to proteins for which we know interacting partners but also to their homologs. The method can be used for large-scale enzymatic functional annotation of protein sequences to refine predictions based on sequence matching alone, increasing the specificity of 10% of the predictions made by PSI-BLAST alone. IS - 1 JF - BMC Bioinformatics TI - Prediction of enzyme function by combining sequence similarity and protein interactions VL - 9 KW - enzyme_prediction AU - Espadaler, Jordi AU - Eswar, Narayanan AU - Querol, Enric AU - Aviles, Francesc AU - Sali, Andrej AU - Renom, Marc AU - Oliva, Baldomero PY - 2008/// UR - http://dx.doi.org/10.1186/1471-2105-9-249 ER -