Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.
J Comput Chem. 2023 May 30;44(14):1334-1346. doi: 10.1002/jcc.27089. Epub 2023 Feb 21.
Accurate estimation of solvation free energy (SFE) lays the foundation for accurate prediction of binding free energy. The Poisson-Boltzmann (PB) or generalized Born (GB) combined with surface area (SA) continuum solvation method (PBSA and GBSA) have been widely used in SFE calculations because they can achieve good balance between accuracy and efficiency. However, the accuracy of these methods can be affected by several factors such as the charge models, polar and nonpolar SFE calculation methods and the atom radii used in the calculation. In this work, the performance of the ABCG2 (AM1-BCC-GAFF2) charge model as well as other two charge models, that is, RESP (Restrained Electrostatic Potential) and AM1-BCC (Austin Model 1-bond charge corrections), on the SFE prediction of 544 small molecules in water by PBSA/GBSA was evaluated. In order to improve the performance of the PBSA prediction based on the ABCG2 charge, we further explored the influence of atom radii on the prediction accuracy and yielded a set of atom radius parameters for more accurate SFE prediction using PBSA based on the ABCG2/GAFF2 by reproducing the thermodynamic integration (TI) calculation results. The PB radius parameters of carbon, oxygen, sulfur, phosphorus, chloride, bromide and iodine, were adjusted. New atom types, on, oi, hn1, hn2, hn3, were introduced to further improve the fitting performance. Then, we tuned the parameters in the nonpolar SFE model using the experimental SFE data and the PB calculation results. By adopting the new radius parameters and new nonpolar SFE model, the root mean square error (RMSE) of the SFE calculation for the 544 molecules decreased from 2.38 to 1.05 kcal/mol. Finally, the new radius parameters were applied in the prediction of protein-ligand binding free energies using the MM-PBSA method. For the eight systems tested, we could observe higher correlation between the experiment data and calculation results and smaller prediction errors for the absolute binding free energies, demonstrating that our new radius parameters can improve the free energy calculation using the MM-PBSA method.
准确估计溶剂化自由能（SFE）是准确预测结合自由能的基础。泊松-玻尔兹曼（PB）或广义 Born（GB）与表面积（SA）连续体溶剂化方法（PBSA 和 GBSA）已广泛应用于 SFE 计算，因为它们可以在准确性和效率之间取得良好的平衡。然而，这些方法的准确性可能会受到电荷模型、极性和非极性 SFE 计算方法以及计算中使用的原子半径等因素的影响。在这项工作中，评估了 ABCG2（AM1-BCC-GAFF2）电荷模型以及其他两种电荷模型，即 RESP（受约束静电势）和 AM1-BCC（Austin Model 1-键电荷修正），对 544 个小分子在水中的 PBSA/GBSA 溶剂化自由能预测的性能。为了提高基于 ABCG2 电荷的 PBSA 预测的性能，我们进一步探讨了原子半径对预测准确性的影响，并针对基于 ABCG2/GAFF2 的 PBSA 预测，通过重现热力学积分（TI）计算结果，生成了一组更准确的 SFE 预测用原子半径参数。调整了 PB 碳、氧、硫、磷、氯、溴和碘的半径参数。引入了新的原子类型 on、oi、hn1、hn2、hn3，以进一步提高拟合性能。然后，我们使用实验 SFE 数据和 PB 计算结果调整了非极性 SFE 模型中的参数。通过采用新的半径参数和新的非极性 SFE 模型，544 个分子的 SFE 计算的均方根误差（RMSE）从 2.38 降低到 1.05 kcal/mol。最后，新的半径参数应用于使用 MM-PBSA 方法预测蛋白质-配体结合自由能。对于测试的八个系统，我们可以观察到实验数据与计算结果之间的相关性更高，绝对结合自由能的预测误差更小，表明我们的新半径参数可以提高 MM-PBSA 方法的自由能计算。
