Energy Decomposition Analysis of Protein-Ligand Interactions Using Molecules-in-Molecules Fragmentation-Based Method.,Journal of Chemical Information and Modeling

当前位置： X-MOL 学术 › J. Chem. Inf. Model. › 论文详情

Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)

Energy Decomposition Analysis of Protein-Ligand Interactions Using Molecules-in-Molecules Fragmentation-Based Method.
Journal of Chemical Information and Modeling ( IF 5.6 ) Pub Date : 2019-08-12 , DOI: 10.1021/acs.jcim.9b00432
Bishnu Thapa ₁ , Krishnan Raghavachari ₁

Affiliation

Accurate prediction of protein-ligand binding affinities and their quantitative decomposition into residue-specific contributions represent challenging problems in drug discovery. While quantum mechanical (QM) methods can provide an accurate description of such interactions, the associated computational cost is normally prohibitive for broad-based applications. Recently, we have shown that QM-based protein-ligand interaction energies in the gas phase can be determined accurately using our multilayer molecules-in-molecules (MIM) fragmentation-based method at a significantly lower computational cost. In this paper, we present a new approach for calculating protein-ligand interactions using our three-layer model (MIM3) that allows us to decompose the total binding affinity into quantitative contributions from individual residues (or backbone and side chain), crystal water molecules, solvation energy, and entropy. In our approach, the desolvation energy and entropy changes during protein-ligand binding are modeled using simple and inexpensive empirical models while intermolecular interactions are computed using an accurate QM method. The performance of our approach has been assessed on a congeneric series of 22 thrombin inhibitors, all with experimentally known binding affinities, using a binding pocket cutout of 120 residues with more than 1550 atoms. Comparison of our MIM3-calculated binding affinities calculated at the B97-D3BJ/6-311++G(2d,2p) level with experiment shows a good correlation with an R2 range of 0.81-0.88 and a Spearman rank correlation coefficient (ρ) range of 0.84-0.89 while providing a quantitative description of residue-specific interactions. We show that such residue-specific interaction energies can be employed to identify and rationalize both obvious (e.g., hydrogen bonds, π···π) and nonobvious (e.g., CH···π) interactions that play a critical role in protein-ligand binding. We suggest that such quantitative information can be used to identify the key residues that determine the comparative binding affinities of different ligands in order to improve and optimize the effectiveness of computational drug design.

中文翻译：

使用分子中基于分子碎片的方法对蛋白质-配体相互作用进行能量分解分析。

蛋白质-配体结合亲和力的准确预测及其定量分解为残基特异性的贡献代表了药物发现中的难题。虽然量子力学（QM）方法可以提供此类相互作用的准确描述，但相关的计算成本通常对于基础广泛的应用而言是令人望而却步的。最近，我们已经表明，使用我们的多层分子中分子（MIM）片段化方法可以准确地确定气相中基于QM的蛋白质-配体相互作用能，而计算成本却低得多。在本文中，我们提供了一种使用三层模型（MIM3）计算蛋白质-配体相互作用的新方法，该模型使我们能够将总结合亲和力分解成来自各个残基（或主链和侧链），结晶水分子，溶剂化能，和熵。在我们的方法中，使用简单和廉价的经验模型对蛋白质-配体结合过程中的去溶剂化能量和熵变化进行建模，同时使用精确的QM方法计算分子间的相互作用。我们对22种凝血酶抑制剂的同类系列进行了评估，这些抑制剂均具有实验已知的结合亲和力，使用了120个残基超过1550个原子的结合袋切口。在B97-D3BJ / 6-311 ++ G（2d，2p）水平的实验显示，R2范围为0.81-0.88，Spearman等级相关系数（ρ）范围为0.84-0.89，具有良好的相关性，同时提供了残基特异性相互作用的定量描述。我们表明，此类残基特异性相互作用能可用于识别和合理化在蛋白质-蛋白质中起关键作用的明显（例如氢键，π··π）和非明显（例如CH··π）相互作用。配体结合。我们建议，此类定量信息可用于识别确定不同配体的相对结合亲和力的关键残基，以改善和优化计算药物设计的有效性。89提供了残基特异性相互作用的定量描述。我们表明，此类残基特异性相互作用能可用于识别和合理化在蛋白质-蛋白质中起关键作用的明显（例如氢键，π··π）和非明显（例如CH··π）相互作用。配体结合。我们建议，此类定量信息可用于识别确定不同配体的相对结合亲和力的关键残基，以改善和优化计算药物设计的有效性。89，同时提供了残基特异性相互作用的定量描述。我们表明，此类残基特异性相互作用能可用于识别和合理化在蛋白质-蛋白质中起关键作用的明显（例如氢键，π··π）和非明显（例如CH··π）相互作用。配体结合。我们建议，此类定量信息可用于识别确定不同配体的相对结合亲和力的关键残基，以改善和优化计算药物设计的有效性。

更新日期：2019-07-29

点击分享查看原文

点击收藏

阅读更多本刊新发论文本刊介绍/投稿指南