Innovation Institute for Artificial Intelligence in Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China.
College of Computer Science and Technology, Zhejiang University, Hangzhou 310058, China.
J Chem Inf Model. 2021 Jun 28;61(6):2844-2856. doi: 10.1021/acs.jcim.1c00091. Epub 2021 May 20.
The molecular mechanics/generalized Born surface area (MM/GBSA) has been widely used in end-point binding free energy prediction in structure-based drug design (SBDD). However, in practice, it is usually being treated as a disputed method mostly because of its system dependence. Here, combining with machine-learning optimization, we developed a novel version of MM/GBSA, named variable atomic dielectric MM/GBSA (VAD-MM/GBSA), by assigning variable dielectric constants directly to the protein/ligand atoms. The new strategy exhibits markedly improved accuracy in binding affinity calculations for various protein-ligand systems and is promising to be used in the postprocessing of structure-based virtual screening. Moreover, VAD-MM/GBSA outperformed prime MM/GBSA in Schrödinger software and showed remarkable predictive performance for specific protein targets, such as POL polyprotein, human immunodeficiency virus type 1 (HIV-1) protease, etc. Our study showed that the VAD-MM/GBSA method with little extra computational overhead provides a potential replacement of the MM/GBSA in AMBER software. An online web server of VAD-MMGBSA has been developed and is now available at http://cadd.zju.edu.cn/vdgb.
分子力学/广义 Born 表面积（MM/GBSA）已广泛应用于基于结构的药物设计（SBDD）中的终点结合自由能预测。然而，在实践中，由于其系统依赖性，它通常被视为一种有争议的方法。在这里，我们结合机器学习优化，通过直接为蛋白质/配体原子分配可变介电常数，开发了一种新的 MM/GBSA 版本，命名为可变原子介电常数 MM/GBSA（VAD-MM/GBSA）。该新策略在各种蛋白质-配体系统的结合亲和力计算中表现出显著提高的准确性，有望用于基于结构的虚拟筛选的后处理。此外，VAD-MM/GBSA 在 Schrödinger 软件中优于主要的 MM/GBSA，并对特定蛋白质靶标（如 POL 多蛋白、人类免疫缺陷病毒 1（HIV-1）蛋白酶等）表现出显著的预测性能。我们的研究表明，具有较小额外计算开销的 VAD-MM/GBSA 方法为 AMBER 软件中的 MM/GBSA 提供了一种潜在的替代方法。已经开发了一个 VAD-MMGBSA 的在线网络服务器，现在可以在 http://cadd.zju.edu.cn/vdgb 上访问。
