MM/GBSA 方法在 FGFR 抑制剂结合构象预测中的应用。
The application of the MM/GBSA method in the binding pose prediction of FGFR inhibitors.
机构信息
Key Laboratory of Drug-Targeting and Drug Delivery System of the Education Ministry, Sichuan Engineering Laboratory for Plant-Sourced Drug and Sichuan Research Centre for Drug Precision Industrial Technology, West China School of Pharmacy, Sichuan University, Chengdu 610041, China.
School of Health Sciences and Sports, Macao Polytechnic Institute, Rua de Luís Gonzaga Gomes, Macao, China.
出版信息
Phys Chem Chem Phys. 2020 May 6;22(17):9656-9663. doi: 10.1039/d0cp00831a.
The success of a structure-based drug is highly dependent on a known binding pose of the protein-ligand system. However, this is not always available. In this study, we set out to explore the applicability of the popular and easy-to-use MD-based MM/GBSA method to determine the binding poses of known FGFR inhibitors. It was found that MM/GBSA combined with 100 ns of MD simulation significantly improved the success rate of docking methods from 30-40% to 70%. This work demonstrates a way that the MM/GBSA method can be more accurate than it is in ligand ranking, filling a gap in structure-based drug discovery when the binding pose is unknown.
基于结构的药物的成功在很大程度上取决于已知的蛋白质-配体系统的结合构象。然而，这并不总是可用的。在这项研究中，我们着手探索流行且易于使用的基于 MD 的 MM/GBSA 方法在确定已知 FGFR 抑制剂的结合构象方面的适用性。结果发现，将 MM/GBSA 与 100 ns 的 MD 模拟相结合，可将对接方法的成功率从 30-40%显著提高到 70%。这项工作展示了一种方法，即 MM/GBSA 方法在配体排序方面可以比现在更准确，填补了结构为基础的药物发现中在未知结合构象时的一个空白。
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