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使用量子力学/分子力学方法对经典挖掘极小值预测的多构象进行精确的蛋白质-配体结合自由能估计。

Accurate protein-ligand binding free energy estimation using QM/MM on multi-conformers predicted from classical mining minima.

作者信息

Molani Farzad, Cho Art E

机构信息

Department of Bioinformatics, Korea University, Sejong, Korea.

inCerebro Co. Ltd., Gangnam-gu, Seoul, Korea.

出版信息

Commun Chem. 2024 Oct 28;7(1):247. doi: 10.1038/s42004-024-01328-7.

DOI:10.1038/s42004-024-01328-7
PMID:39468282
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11519471/
Abstract

Accurate prediction of binding free energy is crucial for the rational design of drug candidates and understanding protein-ligand interactions. To address this, we have developed four protocols that combine QM/MM calculations and the mining minima (M2) method, tested on 9 targets and 203 ligands. Our protocols carry out free energy processing with or without conformational search on the selected conformers obtained from M2 calculations, where their force field atomic charge parameters are substituted with those obtained from a QM/MM calculation. The method achieved a high Pearson's correlation coefficient (0.81) with experimental binding free energies across diverse targets, demonstrating its generality. Using a differential evolution algorithm with a universal scaling factor of 0.2, we achieved a low mean absolute error of 0.60 kcal mol. This performance surpasses many existing methods and is comparable to popular relative binding free energy techniques but at significantly lower computational cost.

摘要

准确预测结合自由能对于合理设计候选药物以及理解蛋白质 - 配体相互作用至关重要。为解决这一问题,我们开发了四种将量子力学/分子力学(QM/MM)计算与挖掘极小值(M2)方法相结合的方案,并在9个靶点和203种配体上进行了测试。我们的方案对从M2计算中获得的选定构象进行自由能处理,处理过程中可进行或不进行构象搜索,其中其力场原子电荷参数被QM/MM计算获得的参数所取代。该方法在不同靶点上与实验结合自由能取得了较高的皮尔逊相关系数(0.81),证明了其通用性。使用通用缩放因子为0.2的差分进化算法,我们实现了0.60千卡/摩尔的低平均绝对误差。这一性能超越了许多现有方法,与流行的相对结合自由能技术相当,但计算成本显著更低。

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本文引用的文献

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J Chem Theory Comput. 2024 Jul 23;20(14):6328-6340. doi: 10.1021/acs.jctc.4c00407. Epub 2024 Jul 11.
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Combining QM/MM Calculations with Classical Mining Minima to Predict Protein-Ligand Binding Free Energy.将 QM/MM 计算与经典挖掘极小值相结合,以预测蛋白质-配体结合自由能。
J Chem Inf Model. 2023 May 8;63(9):2728-2734. doi: 10.1021/acs.jcim.2c01637. Epub 2023 Apr 20.
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分子力学力场的端到端可微构建
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