来自量子化学碎片方法的蛋白质配体相互作用能:利用多体贡献升级 MFCC-Scheme。

IF 2.8 2区 化学 Q3 CHEMISTRY, PHYSICAL The Journal of Physical Chemistry B Pub Date : 2024-11-17 DOI:10.1021/acs.jpcb.4c05645
Johannes R Vornweg, Christoph R Jacob
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引用次数: 0

摘要

量子化学碎裂方法为准确计算蛋白质-配体相互作用能量提供了一种极具吸引力的方法。虽然带共轭帽的分子分馏(MFCC)方案为此提供了一种相当直接的方法,但其准确性往往不够。在这里,我们通过加入多体贡献,升级了用于计算蛋白质-配体相互作用的 MFCC 方案。由此产生的碎裂方案是我们之前开发的 MFCC-MBE(2) 方案的扩展[《计算化学》2023 年第 44 期,1634-1644]。对于蛋白质-配体复合物的各种测试集,我们证明,通过使用多体贡献对 MFCC 方案进行升级,蛋白质-配体相互作用能量的误差可以显著降低,误差一般低于 20 kJ/mol。我们的方案通过加入高阶多体贡献,可以系统地减少这些误差。由于我们的方案将单个氨基酸片段的使用与高精度相结合,因此为蛋白质和蛋白质-配体相互作用的精确机器学习势能参数化提供了一个理想的起点。
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Protein-Ligand Interaction Energies from Quantum-Chemical Fragmentation Methods: Upgrading the MFCC-Scheme with Many-Body Contributions.

Quantum-chemical fragmentation methods offer an attractive approach for the accurate calculation of protein-ligand interaction energies. While the molecular fractionation with conjugate caps (MFCC) scheme offers a rather straightforward approach for this purpose, its accuracy is often not sufficient. Here, we upgrade the MFCC scheme for the calculation of protein-ligand interactions by including many-body contributions. The resulting fragmentation scheme is an extension of our previously developed MFCC-MBE(2) scheme [J. Comput. Chem. 2023, 44, 1634-1644]. For a diverse test set of protein-ligand complexes, we demonstrate that by upgrading the MFCC scheme with many-body contributions, the error in protein-ligand interaction energies can be reduced significantly, and one generally achieves errors below 20 kJ/mol. Our scheme allows for systematically reducing these errors by including higher-order many-body contributions. As it combines the use of single amino acid fragments with high accuracy, our scheme provides an ideal starting point for the parametrization of accurate machine learning potentials for proteins and protein-ligand interactions.

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来源期刊
CiteScore
5.80
自引率
9.10%
发文量
965
审稿时长
1.6 months
期刊介绍: An essential criterion for acceptance of research articles in the journal is that they provide new physical insight. Please refer to the New Physical Insights virtual issue on what constitutes new physical insight. Manuscripts that are essentially reporting data or applications of data are, in general, not suitable for publication in JPC B.
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