Abstract
BackgroundComputational approaches to protein-protein docking typically include scoring aimed at improving the rank of the near-native structure relative to the false-positive matches. Knowledge-based potentials improve modeling of protein complexes by taking advantage of the rapidly increasing amount of experimentally derived information on protein-protein association. An essential element of knowledge-based potentials is defining the reference state for an optimal description of the residue-residue (or atom-atom) pairs in the non-interaction state.ResultsThe study presents a new Distance- and Environment-dependent, Coarse-grained, Knowledge-based (DECK) potential for scoring of protein-protein docking predictions. Training sets of protein-protein matches were generated based on bound and unbound forms of proteins taken from the DOCKGROUND resource. Each residue was represented by a pseudo-atom in the geometric center of the side chain. To capture the long-range and the multi-body interactions, residues in different secondary structure elements at protein-protein interfaces were considered as different residue types. Five reference states for the potentials were defined and tested. The optimal reference state was selected and the cutoff effect on the distance-dependent potentials investigated. The potentials were validated on the docking decoys sets, showing better performance than the existing potentials used in scoring of protein-protein docking results.ConclusionsA novel residue-based statistical potential for protein-protein docking was developed and validated on docking decoy sets. The results show that the scoring function DECK can successfully identify near-native protein-protein matches and thus is useful in protein docking. In addition to the practical application of the potentials, the study provides insights into the relative utility of the reference states, the scope of the distance dependence, and the coarse-graining of the potentials.
Highlights
Computational approaches to protein-protein docking typically include scoring aimed at improving the rank of the near-native structure relative to the false-positive matches
The potentials were trained on sets of unbound and bound protein-protein complexes
To select the optimal reference state, the scoring functions were tested on GRAMM-X decoy set [22]
Summary
Computational approaches to protein-protein docking typically include scoring aimed at improving the rank of the near-native structure relative to the false-positive matches. Better understanding of these interactions, coupled with our ability to model them, is essential for the fundamental knowledge of their biology and the multitude of biomedical applications. Computational approaches to structural determination of protein-protein complexes (protein-protein docking) typically involve two steps: the global, often low-resolution, search within a computationally feasible timeframe to detect a set of matches that includes at least one near-. Knowledge-based potentials [1,2], physics-based potentials [3], and the hybrid potentials [4,5,6] have been shown to perform successfully in protein-protein docking benchmark tests. It has been shown that knowledgebased pairwise atomic potentials perform better than the physics-based potentials in the near-native structure refinement [11]
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