School of Physics, Xi'an Jiaotong University, Xi'an 710049, China.
Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States.
J Chem Inf Model. 2020 Dec 28;60(12):6624-6633. doi: 10.1021/acs.jcim.0c00934. Epub 2020 Nov 19.
With continually increased computer power, molecular mechanics force field-based approaches, such as the endpoint methods of molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) and molecular mechanics generalized Born surface area (MM-GBSA), have been routinely applied in both drug lead identification and optimization. However, the MM-PB/GBSA method is not as accurate as the pathway-based alchemical free energy methods, such as thermodynamic integration (TI) or free energy perturbation (FEP). Although the pathway-based methods are more rigorous in theory, they suffer from slow convergence and computational cost. Moreover, choosing adequate perturbation routes is also crucial for the pathway-based methods. Recently, we proposed a new method, coined extended linear interaction energy (ELIE) method, to overcome some disadvantages of the MM-PB/GBSA method to improve the accuracy of binding free energy calculation. In this work, we have systematically assessed this approach using in total 229 protein-ligand complexes for eight protein targets. Our results showed that ELIE performed much better than the molecular docking and MM-PBSA method in terms of root-mean-square error (RMSE), correlation coefficient (), predictive index (PI), and Kendall's τ. The mean values of PI, , and τ are 0.62, 0.58, and 0.44 for ELIE calculations. We also explored the impact of the length of simulation, ranging from 1 to 100 ns, on the performance of binding free energy calculation. In general, extending simulation length up to 25 ns could significantly improve the performance of ELIE, while longer molecular dynamics (MD) simulation does not always perform better than short MD simulation. Considering both the computational efficiency and achieved accuracy, ELIE is adequate in filling the gap between the efficient docking methods and computationally demanding alchemical free energy methods. Therefore, ELIE provides a practical solution for the routine ranking of compounds in lead optimization.
随着计算机能力的不断提高，基于分子力学力场的方法，如分子力学泊松-玻尔兹曼表面区域（MM-PBSA）和分子力学广义 Born 表面区域（MM-GBSA）的端点方法，已在药物先导物识别和优化中得到了常规应用。然而，MM-PB/GBSA 方法不如基于路径的热力学自由能方法（如热力学积分（TI）或自由能微扰（FEP））准确。尽管基于路径的方法在理论上更为严格，但它们存在收敛缓慢和计算成本高的问题。此外，选择合适的微扰路径对于基于路径的方法也是至关重要的。最近，我们提出了一种新方法，称为扩展线性相互作用能（ELIE）方法，以克服 MM-PB/GBSA 方法的一些缺点，提高结合自由能计算的准确性。在这项工作中，我们总共使用了 229 个蛋白质-配体复合物，对 8 个蛋白质靶标进行了系统评估。结果表明，ELIE 在均方根误差（RMSE）、相关系数（）、预测指数（PI）和肯德尔τ方面的表现明显优于分子对接和 MM-PBSA 方法。ELIE 计算的 PI、和τ的平均值分别为 0.62、0.58 和 0.44。我们还探讨了模拟长度（从 1 到 100 ns）对结合自由能计算性能的影响。一般来说，将模拟长度延长至 25 ns 可以显著提高 ELIE 的性能，而较长的分子动力学（MD）模拟并不总是比短 MD 模拟表现更好。考虑到计算效率和实现的准确性，ELIE 在高效对接方法和计算密集型热力学自由能方法之间的差距方面是足够的。因此，ELIE 为化合物在先导优化中的常规排序提供了一种实用的解决方案。
