Izadi Saeed, Aguilar Boris, Onufriev Alexey V
Department of Biomedical Engineering and Mechanics, Department of Computer Science, and Departments of Computer Science and Physics, Virginia Tech , Blacksburg, Virginia 24060, United States.
J Chem Theory Comput. 2015 Sep 8;11(9):4450-9. doi: 10.1021/acs.jctc.5b00483. Epub 2015 Aug 21.
Accurate yet efficient computational models of solvent environment are central for most calculations that rely on atomistic modeling, such as prediction of protein-ligand binding affinities. In this study, we evaluate the accuracy of a recently developed generalized Born implicit solvent model, GBNSR6 (Aguilar et al. J. Chem. Theory Comput. 2010, 6, 3613-3639), in estimating the electrostatic solvation free energies (ΔG(pol)) and binding free energies (ΔΔG(pol)) for small protein-ligand complexes. We also compare estimates based on three different explicit solvent models (TIP3P, TIP4PEw, and OPC). The two main findings are as follows. First, the deviation (RMSD = 7.04 kcal/mol) of GBNSR6 binding affinities from commonly used TIP3P reference values is comparable to the deviations between explicit models themselves, e.g. TIP4PEw vs TIP3P (RMSD = 5.30 kcal/mol). A simple uniform adjustment of the atomic radii by a single scaling factor reduces the RMS deviation of GBNSR6 from TIP3P to within the above "error margin" - differences between ΔΔG(pol) estimated by different common explicit solvent models. The simple radii scaling virtually eliminates the systematic deviation (ΔΔG(pol)) between GBNSR6 and two out of the three explicit water models and significantly reduces the deviation from the third explicit model. Second, the differences between electrostatic binding energy estimates from different explicit models is disturbingly large; for example, the deviation between TIP4PEw and TIP3P estimates of ΔΔG(pol) values can be up to ∼50% or ∼9 kcal/mol, which is significantly larger than the "chemical accuracy" goal of ∼1 kcal/mol. The absolute ΔG(pol) calculated with different explicit models could differ by tens of kcal/mol. These discrepancies point to unacceptably high sensitivity of binding affinity estimates to the choice of common explicit water models. The absence of a clear "gold standard" among these models strengthens the case for the use of accurate implicit solvation models for binding energetics, which may be orders of magnitude faster.
对于大多数依赖原子模型的计算而言，精确且高效的溶剂环境计算模型至关重要，比如蛋白质 - 配体结合亲和力的预测。在本研究中，我们评估了一种最近开发的广义玻恩隐式溶剂模型GBNSR6（阿吉拉尔等人，《化学理论与计算杂志》，2010年，6卷，3613 - 3639页）在估算小蛋白质 - 配体复合物的静电溶剂化自由能（ΔG(pol)）和结合自由能（ΔΔG(pol)）方面的准确性。我们还比较了基于三种不同显式溶剂模型（TIP3P、TIP4PEw和OPC）的估算结果。主要有两个发现如下。首先，GBNSR6结合亲和力与常用的TIP3P参考值之间的偏差（均方根偏差 = 7.04千卡/摩尔）与显式模型自身之间的偏差相当，例如TIP4PEw与TIP3P之间的偏差（均方根偏差 = 5.30千卡/摩尔）。通过单个缩放因子对原子半径进行简单的统一调整，可将GBNSR6与TIP3P之间的均方根偏差减小到上述“误差范围”之内——即不同常见显式溶剂模型估算的ΔΔG(pol)之间的差异。这种简单的半径缩放实际上消除了GBNSR6与三种显式水模型中的两种之间的系统偏差（ΔΔG(pol)），并显著减小了与第三种显式模型的偏差。其次，不同显式模型的静电结合能估算之间的差异大得令人不安；例如，TIP4PEw和TIP3P对ΔΔG(pol)值的估算之间的偏差可达约50%或约9千卡/摩尔，这明显大于约1千卡/摩尔的“化学精度”目标。用不同显式模型计算的绝对ΔG(pol)可能相差数十千卡/摩尔。这些差异表明，结合亲和力估算对常见显式水模型的选择具有不可接受的高敏感性。这些模型中缺乏明确的“金标准”，这进一步说明了使用精确的隐式溶剂化模型来计算结合能的合理性，因为隐式溶剂化模型的计算速度可能快几个数量级。
