Assessing the performance of the MM/PBSA and MM/GBSA methods. 6. Capability to predict protein-protein binding free energies and re-rank binding poses generated by protein-protein docking
Assessing the performance of the MM/PBSA and MM/GBSA methods. 6. Capability to predict protein-protein binding free energies and re-rank binding poses generated by protein-protein docking
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评估 MM/PBSA 和 MM/GBSA 方法的性能。
DOI：
10.1039/c6cp03670h
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发表时间：
2016-08-28
影响因子：
3.3
通讯作者：
Hou, Tingjun
中科院分区：
文献类型：
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作者：
Chen, Fu;Liu, Hui;Hou, Tingjun
了解蛋白质-蛋白质相互作用（PPIs）对于阐明关键的生物学过程，甚至设计具有药物意义的干扰PPIs的化合物非常重要。蛋白质-蛋白质对接可以提供蛋白质-蛋白质复合物的原子结构细节，但蛋白质-蛋白质系统的三维结构的准确预测仍然是众所周知的困难，部分原因是缺乏一个理想的评分函数的蛋白质-蛋白质对接。与大多数用于蛋白质-蛋白质对接的评分函数相比，分子力学/广义玻恩表面积（MM/GBSA）和分子力学/泊松-玻尔兹曼表面积（MM/PBSA）方法在理论上更为严谨，但它们在预测蛋白质-蛋白质体系结合亲和力和结合位姿方面的整体性能尚未得到系统评价.在这项研究中，我们首先评估了MM/PBSA和MM/GBSA的性能，以预测46种蛋白质-蛋白质复合物的结合亲和力。总体上，不同的力场、溶剂化模型和内部介电常数对MM/GBSA和MM/PBSA的预测精度有明显的影响。MM/GBSA计算基于ff 02力场，由Onufriev等人开发的GB模型和低内部介电常数（r（in）= 1）在预测的结合亲和力和实验数据之间产生最佳相关性（r（p）= -0.647），其优于MM/PBSA（r（p）= -0.523）和用于蛋白质-蛋白质对接的许多经验评分函数（r（p）= -0.141至-0.529）。然后，我们研究了MM/GBSA从ZDOCK产生的43个蛋白质-蛋白质系统的诱饵中识别可能的近天然结合结构的能力。结果表明，MM/GBSA重新评分比ZDOCK评分具有更好的区分正确结合结构和诱饵的能力。此外，通过分析蛋白质-蛋白质结合界面的特性，可以确定用于重新排序对接位姿的MM/GBSA的最佳内部介电常数。考虑到相对较高的预测精度和较低的计算成本，MM/GBSA可能是预测蛋白质-蛋白质系统的结合亲和力和识别正确结合结构的良好选择。
Understanding protein-protein interactions (PPIs) is quite important to elucidate crucial biological processes and even design compounds that interfere with PPIs with pharmaceutical significance. Protein-protein docking can afford the atomic structural details of protein-protein complexes, but the accurate prediction of the three-dimensional structures for protein-protein systems is still notoriously difficult due in part to the lack of an ideal scoring function for protein-protein docking. Compared with most scoring functions used in protein-protein docking, the Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) and Molecular Mechanics/Poisson Boltzmann Surface Area (MM/PBSA) methodologies are more theoretically rigorous, but their overall performance for the predictions of binding affinities and binding poses for protein-protein systems has not been systematically evaluated. In this study, we first evaluated the performance of MM/PBSA and MM/GBSA to predict the binding affinities for 46 protein-protein complexes. On the whole, different force fields, solvation models, and interior dielectric constants have obvious impacts on the prediction accuracy of MM/GBSA and MM/PBSA. The MM/GBSA calculations based on the ff02 force field, the GB model developed by Onufriev et al. and a low interior dielectric constant (epsilon(in) = 1) yield the best correlation between the predicted binding affinities and the experimental data (r(p) = -0.647), which is better than MM/PBSA (r(p) = -0.523) and a number of empirical scoring functions used in protein-protein docking (r(p) = -0.141 to -0.529). Then, we examined the capability of MM/GBSA to identify the possible near-native binding structures from the decoys generated by ZDOCK for 43 protein-protein systems. The results illustrate that the MM/GBSA rescoring has better capability to distinguish the correct binding structures from the decoys than the ZDOCK scoring. Besides, the optimal interior dielectric constant of MM/GBSA for re-ranking docking poses may be determined by analyzing the characteristics of protein-protein binding interfaces. Considering the relatively high prediction accuracy and low computational cost, MM/GBSA may be a good choice for predicting the binding affinities and identifying correct binding structures for protein-protein systems.