Abstract
BackgroundProtein-protein docking is a valuable computational approach for investigating protein-protein interactions. Shape complementarity is the most basic component of a scoring function and plays an important role in protein-protein docking. Despite significant progresses, shape representation remains an open question in the development of protein-protein docking algorithms, especially for grid-based docking approaches.ResultsWe have proposed a new pairwise shape-based scoring function (LSC) for protein-protein docking which adopts an exponential form to take into account long-range interactions between protein atoms. The LSC scoring function was incorporated into our FFT-based docking program and evaluated for both bound and unbound docking on the protein docking benchmark 4.0. It was shown that our LSC achieved a significantly better performance than four other similar docking methods, ZDOCK 2.1, MolFit/G, GRAMM, and FTDock/G, in both success rate and number of hits. When considering the top 10 predictions, LSC obtained a success rate of 51.71% and 6.82% for bound and unbound docking, respectively, compared to 42.61% and 4.55% for the second-best program ZDOCK 2.1. LSC also yielded an average of 8.38 and 3.94 hits per complex in the top 1000 predictions for bound and unbound docking, respectively, followed by 6.38 and 2.96 hits for the second-best ZDOCK 2.1.ConclusionsThe present LSC method will not only provide an initial-stage docking approach for post-docking processes but also have a general implementation for accurate representation of other energy terms on grids in protein-protein docking. The software has been implemented in our HDOCK web server at http://hdock.phys.hust.edu.cn/.
Highlights
Protein-protein docking is a valuable computational approach for investigating protein-protein interactions
A grid-based shape complementarity (GSC) scoring function is used in GRAMM and FTDock/G, while a pairwise shape complementarity (PSC) function is adopted by ZDOCK 2.1 in docking
For post-docking purposes, if the computing resource is limited, it is suggested that the top 100 predictions are kept for enzyme-inhibitor cases (EI) complexes while the top 1000 predictions are retained for antigen cases (AA) and other types (OT) complexes, which correspond to a success rate of about 50% in the present long-range pairwise shape-based scoring function (LSC)-based docking
Summary
Protein-protein docking is a valuable computational approach for investigating protein-protein interactions. Shape representation remains an open question in the development of protein-protein docking algorithms, especially for grid-based docking approaches. FFT-based docking is a grid-based algorithm and was first proposed by Katchalski-Katzir et al [40], in which the search for binding modes is accelerated by an FFT algorithm in three-dimensional translational space and the computational time was reduced from O(N6) to O(N3 log(N3)) in global sampling [40,41,42,43,44,45,46,47,48,49,50,51]. Due to its fast global search, many FFT-based proteinprotein docking algorithms have been developed in the past decade and achieved considerable successes in the community-wide CAPRI (Critical Assessment of Prediction of Interactions) experiments (http://capri.ebi.ac.uk/) [52,53,54,55,56,57]. As the biological information and protein flexibility can be conveniently incorporated in a small number of docking solutions, post-docking refinement/filtering has been become a common procedure during protein-protein docking processes and received significant successes in the field [26, 52,53,54,55,56, 65]
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