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Evaluation of Predicted Protein–Protein Complexes by Binding Free Energy Simulations

热力学积分 力场(虚构) 溶剂模型 对接(动物) 自由能微扰 计算机科学 能量最小化 分子动力学 寻找对接的构象空间 化学 蛋白质结构 统计物理学 溶剂化 计算化学 物理 分子 人工智能 医学 生物化学 护理部 有机化学
作者
Till Siebenmorgen,Martin Zacharias
出处
期刊:Journal of Chemical Theory and Computation [American Chemical Society]
卷期号:15 (3): 2071-2086 被引量:55
标识
DOI:10.1021/acs.jctc.8b01022
摘要

The accurate prediction of protein-protein complex geometries is of major importance to ultimately model the complete interactome of interacting proteins in a cell. A major bottleneck is the realistic free energy evaluation of predicted docked structures. Typically, simple scoring functions applied to single-complex structures are employed that neglect conformational entropy and often solvent effects completely. The binding free energy of a predicted protein-protein complex can, however, be calculated using umbrella sampling (US) along a predefined dissociation/association coordinate of a complex. We employed atomistic US-molecular dynamics simulations including appropriate conformational and axial restraints and an implicit generalized Born solvent model to calculate binding free energies of a large set of docked decoys for 20 different complexes. Free energies associated with the restraints were calculated separately. In principle, the approach includes all energetic and entropic contributions to the binding process. The evaluation of docked complexes based on binding free energy calculation was in better agreement with experiment compared to a simple scoring based on energy minimization or MD refinement using exactly the same force field description. Even calculated absolute binding free energies of structures close to the native binding geometry showed a reasonable correlation to experiment. However, still for a number of complexes docked decoys of lower free energy than near-native geometries were found indicating inaccuracies in the force field or the implicit solvent model. Although time consuming the approach may open up a new route for realistic ranking of predicted geometries based on calculated free energy of binding.
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