Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China.
Phys Chem Chem Phys. 2019 Sep 21;21(35):18958-18969. doi: 10.1039/c9cp04096j. Epub 2019 Aug 27.
Enhanced sampling has been extensively used to capture the conformational transitions in protein folding, but it attracts much less attention in the studies of protein-protein recognition. In this study, we evaluated the impact of enhanced sampling methods and solute dielectric constants on the overall accuracy of the molecular mechanics/Poisson-Boltzmann surface area (MM/PBSA) and molecular mechanics/generalized Born surface area (MM/GBSA) approaches for the protein-protein binding free energy calculations. Here, two widely used enhanced sampling methods, including aMD and GaMD, and conventional molecular dynamics (cMD) simulations with two AMBER force fields (ff03 and ff14SB) were used to sample the conformations for 21 protein-protein complexes. The MM/PBSA and MM/GBSA calculation results illustrate that the standard MM/GBSA based on the cMD simulations yields the best Pearson correlation (r = -0.523) between the predicted binding affinities and the experimental data, which is much higher than that given by MM/PBSA (r = -0.212). Two enhanced sampling methods (aMD and GaMD) are indeed more efficient for conformational sampling, but they did not improve the binding affinity predictions for protein-protein systems, suggesting that the aMD or GaMD sampling (at least in short timescale simulations) may not be a good choice for the MM/PBSA and MM/GBSA predictions of protein-protein complexes. The solute dielectric constant of 1.0 is recommended to MM/GBSA, but a higher solute dielectric constant is recommended to MM/PBSA, especially for the systems with higher polarity on the protein-protein binding interfaces. Then, a preliminary assessment of the MM/GBSA calculations based on a variable dielectric generalized Born (VDGB) model was conducted. The results highlight the potential power of VDGB in the free energy predictions for protein-protein systems, but more thorough studies should be done in the future.
增强采样技术被广泛应用于捕捉蛋白质折叠中的构象转变，但在蛋白质-蛋白质识别研究中却受到较少关注。在本研究中，我们评估了增强采样方法和溶剂介电常数对分子力学/泊松-玻尔兹曼表面积（MM/PBSA）和分子力学/广义 Born 表面积（MM/GBSA）方法计算蛋白质-蛋白质结合自由能的整体准确性的影响。在这里，我们使用了两种广泛使用的增强采样方法，包括 aMD 和 GaMD，以及两种 AMBER 力场（ff03 和 ff14SB）的传统分子动力学（cMD）模拟，用于采样 21 个蛋白质-蛋白质复合物的构象。MM/PBSA 和 MM/GBSA 计算结果表明，基于 cMD 模拟的标准 MM/GBSA 与实验数据之间的预测结合亲和力具有最佳的皮尔逊相关性（r = -0.523），明显高于 MM/PBSA 的相关性（r = -0.212）。两种增强采样方法（aMD 和 GaMD）确实更有效地进行构象采样，但它们并没有提高蛋白质-蛋白质体系的结合亲和力预测，这表明 aMD 或 GaMD 采样（至少在短时间尺度模拟中）可能不是 MM/PBSA 和 MM/GBSA 预测蛋白质-蛋白质复合物的好选择。建议将溶剂介电常数设为 1.0 用于 MM/GBSA，但建议将更高的溶剂介电常数用于 MM/PBSA，特别是对于蛋白质-蛋白质结合界面极性较高的体系。然后，我们对基于可变介电广义 Born（VDGB）模型的 MM/GBSA 计算进行了初步评估。结果突出了 VDGB 在蛋白质-蛋白质体系自由能预测中的潜在能力，但未来还需要进行更深入的研究。
