Finding correct protein-protein docking models using ProQDock
2016 (English)In: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811, Vol. 32, no 12, 262-270 p.Article in journal (Refereed) PublishedText
Motivation: Protein-protein interactions are a key in virtually all biological processes. For a detailed understanding of the biological processes, the structure of the protein complex is essential. Given the current experimental techniques for structure determination, the vast majority of all protein complexes will never be solved by experimental techniques. In lack of experimental data, computational docking methods can be used to predict the structure of the protein complex. A common strategy is to generate many alternative docking solutions (atomic models) and then use a scoring function to select the best. The success of the computational docking technique is, to a large degree, dependent on the ability of the scoring function to accurately rank and score the many alternative docking models. Results: Here, we present ProQDock, a scoring function that predicts the absolute quality of docking model measured by a novel protein docking quality score (DockQ). ProQDock uses support vector machines trained to predict the quality of protein docking models using features that can be calculated from the docking model itself. By combining different types of features describing both the protein-protein interface and the overall physical chemistry, it was possible to improve the correlation with DockQ from 0.25 for the best individual feature (electrostatic complementarity) to 0.49 for the final version of ProQDock. ProQDock performed better than the state-of-the-art methods ZRANK and ZRANK2 in terms of correlations, ranking and finding correct models on an independent test set. Finally, we also demonstrate that it is possible to combine ProQDock with ZRANK and ZRANK2 to improve performance even further.
Place, publisher, year, edition, pages
OXFORD UNIV PRESS , 2016. Vol. 32, no 12, 262-270 p.
Bioinformatics (Computational Biology)
IdentifiersURN: urn:nbn:se:liu:diva-130431DOI: 10.1093/bioinformatics/btw257ISI: 000379734300030PubMedID: 27307625OAI: oai:DiVA.org:liu-130431DiVA: diva2:951180
24th Annual Conference on Intelligent Systems for Molecular Biology (ISMB)