Extensive benchmark of rDock as a peptide-protein docking tool

Abstract

Peptide-protein interactions are ubiquitous in living cells and essential to a wide range of biological processes, as well as pathologies such as cancer or cardiovascular disease. Yet, obtaining reliable binding mode predictions in peptide-protein docking remains a great challenge for most computational docking programs. The main goal of this study was to assess the performance of the small molecule docking program rDock in comparison to other widely used small molecule docking programs, using 100 peptide-protein systems with peptides ranging from 2 to 12 residues. As we used two large independent benchmark sets previously published for other small-molecule docking programs (AutoDockVina benchmark and LEADSPEP), the performance of rDock could directly be compared to the performances of AutoDockVina, Surflex, GOLD, and Glide, as well as to the peptide docking protocol PIPER-FlexPepDock and the webserver HPepDock. Our benchmark reveals that rDock can dock the 100 peptides with an overall backbone RMSD below 2.5 Å in 58.5% of the cases (76% for the 47 systems of the AutoDockVina benchmark set and 43% for the 53 systems of the LEADSPEP benchmark set). More specifically, rDock docks up to 11-residue peptides with a backbone RMSD below 2.5 Å in 60.75% of the cases. rDock displays higher accuracy than most available small molecule docking programs for 6-10-residue peptides and can sometimes perform similarly to the peptide docking tool, especially at a high level of exhaustiveness (100 or 150 runs). Its performance, as is the case for many other unguided small molecule docking tools, is compromised when the peptides adopt secondary structures upon binding. However, our analyses suggest that rDock could be used for predicting how medium-sized biologically relevant peptides bind to their respective protein targets when the latter bind in an extended mode.

Keywords: Benchmark; Docking; Peptide; Protein; rDock.