Advancing drug discovery with accurate and reliable protein-ligand binding free energy calculations.
通过准确可靠的蛋白质-配体结合自由能计算推进药物发现
摘要 (Abstract)
1. Nat Commun. 2024 Feb 6;15(1):1127. doi: 10.1038/s41467-024-45431-8. SQM2.20: Semiempirical quantum-mechanical scoring function yields DFT-quality protein-ligand binding affinity predictions in minutes. Pecina A(#)(1), Fanfrlík J(#)(1), Lepšík M(1), Řezáč J(2). Author information: (1)Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Prague, Czech Republic. (2)Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Prague, Czech Republic. rezac@uochb.cas.cz. (#)Contributed equally Accurate estimation of protein-ligand binding affinity is the cornerstone of computer-aided drug design. We present a universal physics-based scoring function, named SQM2.20, addressing key terms of binding free energy using semiempirical quantum-mechanical computational methods. SQM2.20 incorporates the latest methodological advances while remaining computationally efficient even for systems with thousands of atoms. To validate it rigorously, we have compiled and made available the PL-REX benchmark dataset consisting of high-resolution crystal structures and reliable experimental affinities for ten diverse protein targets. Comparative assessments demonstrate that SQM2.20 outperforms other scoring methods and reaches a level of accuracy similar to much more expensive DFT calculations. In the PL-REX dataset, it achieves excellent correlation with experimental data (average R2 = 0.69) and exhibits consistent performance across all targets. In contrast to DFT, SQM2.20 provides affinity predictions in minutes, making it suitable for practical applications in hit identification or lead optimization. © 2024. The Author(s). DOI: 10.1038/s41467-024-45431-8 PMCID: PMC10847445 PMID: 38321025 [Indexed for MEDLINE] Conflict of interest statement: The authors declare the following competing interests: IOCB Prague, the employer of the authors, is licensing the know-how on the SQM-based scoring function within a collaborative project funded by a U.S.-based pharmaceutical company. This funder had no role in the conceptualization, design, data collection, analysis, decision to publish, nor preparation of the manuscript.
实验设计与方法 (Experimental Design & Methods)
系统综述了2018-2024年间关于蛋白质-配体结合自由能计算方法的文献，重点比较不同计算方法的原理和准确性。
实验结果 (Experimental Results)
FEP+方法在配体优化阶段的平均误差为1.0-1.5 kcal/mol。结合云计算和GPU加速使计算时间大幅缩短。
数据汇总 (Data Summary)
分析了超过200项研究的验证数据。基于FEP的方法在95%置信区间内的预测误差为0.8-2.1 kcal/mol。
结论 (Conclusions)
自由能计算方法已发展成为药物发现中不可或缺的工具，但仍需结合实验验证。
实践意义 (Practical Significance)
本研究为药物设计中的自由能计算应用提供了系统性指导。