Protein-protein interactions (PPIs) play important roles in a variety of biological
processes, and many PPIs have been regarded as biologically compelling targets
for drug discovery. Extensive efforts have been made to develop feasible proteinprotein
docking approaches to study PPIs in silico. Most of these approaches are
composed of two stages: sampling and scoring. Sampling is used to generate a number
of plausible protein-protein binding conformations and scoring can rank all those
conformations. Due to large and flexible binding interface of PPI, determination of
the near native structures is computationally expensive, and therefore computational
efficiency is the most challenging issue in protein-protein docking. Here, we have reviewed
the basic concepts and implementations of the sampling, scoring and acceleration
algorithms in some established docking programs, and the limitations of these algorithms have been
discussed. Then, some suggestions to the future directions for sampling, scoring and acceleration algorithms
have been proposed. This review is expected to provide a better understanding of protein-protein
docking and give some clues for the optimization and improvement of available approaches.
Keywords: Acceleration, GPU, machine learning, protein-protein docking, ranking, scoring function.
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