Non-Covalent Molecular Interaction Rules to Define Internal Dimer Coordinates for Quantum Mechanical Potential Energy Scans

IF 3.4 3区 化学 Q2 CHEMISTRY, MULTIDISCIPLINARY
Suliman Sharif, Anmol Kumar, Alexander D. MacKerell Jr.
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Abstract

Non-covalent interactions (NCI) dominate the properties of condensed phase systems. Towards a detailed understanding of NCI, quantum mechanical (QM) methods allow for accurate estimates of interaction energies and geometries, allowing for the contributions of different types of NCI to condensed phase properties to be understood. In addition, such information can be used for the optimization of empirical force fields, including the specific contribution of electrostatic versus van der Waals interactions. However, to date, the relative orientation of monomers defining molecular interactions of dimers is often based on full geometry optimizations of all degrees of freedom or extracted from known experimental structures of biological molecules. In such cases, the spatial relationship of the monomers often leads to multiple atoms in each monomer making significant contributions to the interactions occurring in the dimer, confounding understanding of the contributions of specific atoms or functional groups. To overcome this, a workflow is presented that allows for systematic control of the interaction orientation between monomers to be performed through the use of molecular interaction rules (MIR) in an extendable tool that can be applied to a broad range of chemical space. Using the “MIR workflow” allows a user to perform automation of the determination of well-defined monomer interaction orientations in dimers using Z-matrices, allowing for potential energy scans (PES) to be performed on combinatorial pairs of the monomers. In addition, compiled monomer and dimer geometries and PES data are stored in an extendable database. Illustration of the utility of the workflow is performed based on a collection of 89 monomers encompassing a variety of functional group classes from which 10,616 interaction dimers can be automatically generated. PES between all dimers were calculated at the QM HF/6-31G*, MP2/6-31G*, and ωb97x-d3/6-31G* model chemistries. In addition, analysis of the benzene dimer in three interaction orientations, a hydrogen bond interaction between azetidinone and N-methylacetamide, and the interaction of pyridine with acetone in the Burgi–Dunitz orientation are presented including results with the aug-cc-pVDZ basis set. Results show the impact of different QM model chemistries on minimum interaction energies and distances over a large ensemble of intermolecular interactions with emphasis on the contributions of dispersion.

Abstract Image

定义量子力学势能扫描内部二聚体坐标的非共价分子相互作用规则
非共价相互作用(NCI)主导了凝聚相体系的性质。为了详细了解NCI,量子力学(QM)方法允许对相互作用能量和几何形状进行准确估计,从而允许理解不同类型的NCI对凝聚相性质的贡献。此外,这些信息可用于经验力场的优化,包括静电与范德华相互作用的具体贡献。然而,迄今为止,定义二聚体分子相互作用的单体的相对取向通常是基于所有自由度的完整几何优化或从已知的生物分子实验结构中提取的。在这种情况下,单体的空间关系往往导致每个单体中的多个原子对二聚体中发生的相互作用做出重大贡献,混淆了对特定原子或官能团贡献的理解。为了克服这一点,提出了一个工作流,允许系统地控制单体之间的相互作用方向,通过使用分子相互作用规则(MIR)在一个可扩展的工具中执行,该工具可以应用于广泛的化学空间。使用“MIR工作流”,用户可以使用z矩阵自动确定二聚体中定义良好的单体相互作用方向,允许对单体的组合对进行势能扫描(PES)。此外,编译的单体和二聚体几何形状和PES数据存储在可扩展的数据库中。工作流实用程序的说明是基于包含各种功能基团类的89个单体的集合执行的,从中可以自动生成10,616个相互作用二聚体。在QM HF/6-31G*、MP2/6-31G*和ωb97x-d3/6-31G*模型化学中计算了所有二聚体之间的PES。此外,还分析了苯二聚体在3个相互作用取向上的反应,氮杂二酮与n -甲基乙酰胺之间的氢键相互作用,以及吡啶与丙酮在Burgi-Dunitz取向上的相互作用,包括基于8 -cc- pvdz基集的结果。结果显示了不同的QM模型化学对分子间相互作用的最小相互作用能和距离的影响,并强调了色散的贡献。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
6.60
自引率
3.30%
发文量
247
审稿时长
1.7 months
期刊介绍: This distinguished journal publishes articles concerned with all aspects of computational chemistry: analytical, biological, inorganic, organic, physical, and materials. The Journal of Computational Chemistry presents original research, contemporary developments in theory and methodology, and state-of-the-art applications. Computational areas that are featured in the journal include ab initio and semiempirical quantum mechanics, density functional theory, molecular mechanics, molecular dynamics, statistical mechanics, cheminformatics, biomolecular structure prediction, molecular design, and bioinformatics.
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