Lessons from (co‐)evolution in the docking of proteins and peptides for CAPRI Rounds 28–35. Issue 3 (24th October 2016)

• Record Type: Journal Article
• Authors: Yu, Jinchao; Andreani, Jessica; Ochsenbein, Françoise; Guerois, Raphaël
• Is Part Of: Proteins. Volume 85:Issue 3(2017)
• Page Start: 378
• Page End: 390
• Publication Date: 2016-10-24
• Subjects: protein–protein docking -- evolutionary information -- coevolution -- CAPRI -- InterEvScore -- InterEvDock -- protein–peptide docking -- protein–protein interaction; Proteins -- Periodicals; Proteins -- Periodicals; 572.6
• DOI: 10.1002/prot.25180
• Languages: English
• ISSNs: 0887-3585

Abstract

ABSTRACT: Computational protein–protein docking is of great importance for understanding protein interactions at the structural level. Critical assessment of prediction of interactions (CAPRI) experiments provide the protein docking community with a unique opportunity to blindly test methods based on real‐life cases and help accelerate methodology development. For CAPRI Rounds 28–35, we used an automatic docking pipeline integrating the coarse‐grained co‐evolution‐based potential InterEvScore. This score was developed to exploit the information contained in the multiple sequence alignments of binding partners and selectively recognize co‐evolved interfaces. Together with Zdock/Frodock for rigid‐body docking, SOAP‐PP for atomic potential and Rosetta applications for structural refinement, this pipeline reached high performance on a majority of targets. For protein–peptide docking and interfacial water position predictions, we also explored different means of taking evolutionary information into account. Overall, our group ranked 1 st by correctly predicting 10 targets, composed of 1 High, 7 Medium and 2 Acceptable predictions. Excellent and Outstanding levels of accuracy were reached for each of the two water prediction targets, respectively. Altogether, in 15 out of 18 targets in total, evolutionary information, either through co‐evolution or conservation analyses, could provide key constraints to guide modeling towards the most likely assemblies. These results open promising perspectives regarding the way evolutionary information can be valuable to improve docking prediction accuracy. Proteins 2017; 85:378–390. © 2016 Wiley Periodicals, Inc.