Assessment of Free Energies From Electrostatic Embedding Density Functional Tight Binding-Based/Molecular Mechanics in Periodic Boundary Conditions

IF 3.4 3区 化学 Q2 CHEMISTRY, MULTIDISCIPLINARY
Simone Bonfrate, Woojin Park, Dulce Trejo-Zamora, Nicolas Ferré, Cheol Ho Choi, Miquel Huix-Rotllant
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Abstract

Electrostatic embedding quantum mechanics/molecular mechanics (QM/MM) methods in periodic boundary conditions (PBC) can successfully describe the condensed phase reactivity of a fragment treated at the QM level with an atomistic description of an electrostatic environment treated at the MM level. The computational cost of ab initio QM methods limits the phase space sampling, thus affecting statistical quantities like free energies. Here, we describe the implementation of a PBC-adapted QM/MM model based on the semi-empirical density-functional based tight-binding (DFTB) method within the GAMESS-US quantum package interfaced with Tinker. Further, we take advantage of the free energy methods provided by a newly developed interface with the PLUMED plugin. The versatility of the implementation is illustrated by the prediction of the free energy profile for three different families of reactions in solution. Overall, using the DFTB/MM, it has been possible to obtain results that are at least in a qualitatively agreement with respect to the experimental data or high-level ab initio simulations.

Abstract Image

周期边界条件下静电包埋密度泛函紧密结合/分子力学自由能的评估
周期边界条件(PBC)中的静电嵌入量子力学/分子力学(QM/MM)方法可以成功地描述在量子力学水平上处理的片段与在分子力学水平上处理的静电环境的原子论描述之间的凝聚相反应性。由于计算成本的原因,ab initio QM 方法限制了相空间采样,从而影响了自由能等统计量。在此,我们介绍了基于半经验密度函数紧密结合(DFTB)方法的 PBC 适配 QM/MM 模型在 GAMESS-US 量子软件包与 Tinker 接口中的实施情况。此外,我们还利用了新开发的 PLUMED 插件接口提供的自由能方法。通过预测溶液中三个不同反应系列的自由能曲线,我们了解到了该实现方法的多功能性。总之,使用 DFTB/MM 可以获得与实验数据或高级原子弹模拟至少在质量上一致的结果。
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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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