Institute of Functional Nano & Soft Materials and Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Soochow University, Suzhou 215123, PR China.
J Chem Inf Model. 2011 Jan 24;51(1):69-82. doi: 10.1021/ci100275a. Epub 2010 Nov 30.
The Molecular Mechanics/Poisson-Boltzmann Surface Area (MM/PBSA) and the Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) methods calculate binding free energies for macromolecules by combining molecular mechanics calculations and continuum solvation models. To systematically evaluate the performance of these methods, we report here an extensive study of 59 ligands interacting with six different proteins. First, we explored the effects of the length of the molecular dynamics (MD) simulation, ranging from 400 to 4800 ps, and the solute dielectric constant (1, 2, or 4) on the binding free energies predicted by MM/PBSA. The following three important conclusions could be observed: (1) MD simulation length has an obvious impact on the predictions, and longer MD simulation is not always necessary to achieve better predictions. (2) The predictions are quite sensitive to the solute dielectric constant, and this parameter should be carefully determined according to the characteristics of the protein/ligand binding interface. (3) Conformational entropy often show large fluctuations in MD trajectories, and a large number of snapshots are necessary to achieve stable predictions. Next, we evaluated the accuracy of the binding free energies calculated by three Generalized Born (GB) models. We found that the GB model developed by Onufriev and Case was the most successful model in ranking the binding affinities of the studied inhibitors. Finally, we evaluated the performance of MM/GBSA and MM/PBSA in predicting binding free energies. Our results showed that MM/PBSA performed better in calculating absolute, but not necessarily relative, binding free energies than MM/GBSA. Considering its computational efficiency, MM/GBSA can serve as a powerful tool in drug design, where correct ranking of inhibitors is often emphasized.
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）方法通过结合分子力学计算和连续溶剂化模型来计算大分子的结合自由能。为了系统地评估这些方法的性能，我们在此报告了对 59 种与 6 种不同蛋白质相互作用的配体的广泛研究。首先，我们探索了分子动力学（MD）模拟长度（400 到 4800 ps）和溶质介电常数（1、2 或 4）对 MM/PBSA 预测的结合自由能的影响。可以观察到以下三个重要结论：（1）MD 模拟长度对预测有明显影响，并非总是需要更长的 MD 模拟才能实现更好的预测。（2）预测对溶质介电常数非常敏感，应根据蛋白质/配体结合界面的特点仔细确定该参数。（3）构象熵在 MD 轨迹中经常显示出较大的波动，需要大量快照才能实现稳定的预测。接下来，我们评估了三种广义 Born（GB）模型计算的结合自由能的准确性。我们发现，Onufriev 和 Case 开发的 GB 模型在对研究抑制剂的结合亲和力进行排序方面是最成功的模型。最后，我们评估了 MM/GBSA 和 MM/PBSA 预测结合自由能的性能。我们的结果表明，MM/PBSA 在计算绝对但不一定是相对结合自由能方面优于 MM/GBSA。考虑到其计算效率，MM/GBSA 可以作为药物设计中的有力工具，其中经常强调抑制剂的正确排序。
