par Hou, Qingzhen 
;De Geest, Paul P.F.G.;Griffioen, Christian C.J.;Abeln, Sanne;Heringa, Jaap;Feenstra, Anton K.A.
Référence Bioinformatics, 35, 22, page (4794-4796)
Publication Publié, 2019-11
;De Geest, Paul P.F.G.;Griffioen, Christian C.J.;Abeln, Sanne;Heringa, Jaap;Feenstra, Anton K.A.Référence Bioinformatics, 35, 22, page (4794-4796)
Publication Publié, 2019-11
Article révisé par les pairs
| Résumé : | MOTIVATION: Interpretation of ubiquitous protein sequence data has become a bottleneck in biomolecular research, due to a lack of structural and other experimental annotation data for these proteins. Prediction of protein interaction sites from sequence may be a viable substitute. We therefore recently developed a sequence-based random forest method for protein-protein interface prediction, which yielded a significantly increased performance than other methods on both homomeric and heteromeric protein-protein interactions. Here, we present a webserver that implements this method efficiently. RESULTS: With the aim of accelerating our previous approach, we obtained sequence conservation profiles by re-mastering the alignment of homologous sequences found by PSI-BLAST. This yielded a more than 10-fold speedup and at least the same accuracy, as reported previously for our method; these results allowed us to offer the method as a webserver. The web-server interface is targeted to the non-expert user. The input is simply a sequence of the protein of interest, and the output a table with scores indicating the likelihood of having an interaction interface at a certain position. As the method is sequence-based and not sensitive to the type of protein interaction, we expect this webserver to be of interest to many biological researchers in academia and in industry. AVAILABILITY AND IMPLEMENTATION: Webserver, source code and datasets are available at www.ibi.vu.nl/programs/serendipwww/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. |
