Automated Training of ReaxFF Reactive Force Fields for Energetics of Enzymatic Reactions,Journal of Chemical Theory and Computation

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Automated Training of ReaxFF Reactive Force Fields for Energetics of Enzymatic Reactions
Journal of Chemical Theory and Computation ( IF 5.7 ) Pub Date : 2017-12-06 00:00:00 , DOI: 10.1021/acs.jctc.7b00870
Tomáš Trnka , Igor Tvaroška ₁ , Jaroslav Koča

Affiliation

Computational studies of the reaction mechanisms of various enzymes are nowadays based almost exclusively on hybrid QM/MM models. Unfortunately, the success of this approach strongly depends on the selection of the QM region, and computational cost is a crucial limiting factor. An interesting alternative is offered by empirical reactive molecular force fields, especially the ReaxFF potential developed by van Duin and co-workers. However, even though an initial parametrization of ReaxFF for biomolecules already exists, it does not provide the desired level of accuracy. We have conducted a thorough refitting of the ReaxFF force field to improve the description of reaction energetics. To minimize the human effort required, we propose a fully automated approach to generate an extensive training set comprised of thousands of different geometries and molecular fragments starting from a few model molecules. Electrostatic parameters were optimized with QM electrostatic potentials as the main target quantity, avoiding excessive dependence on the choice of reference atomic charges and improving robustness and transferability. The remaining force field parameters were optimized using the VD-CMA-ES variant of the CMA-ES optimization algorithm. This method is able to optimize hundreds of parameters simultaneously with unprecedented speed and reliability. The resulting force field was validated on a real enzymatic system, ppGalNAcT2 glycosyltransferase. The new force field offers excellent qualitative agreement with the reference QM/MM reaction energy profile, matches the relative energies of intermediate and product minima almost exactly, and reduces the overestimation of transition state energies by 27–48% compared with the previous parametrization.

中文翻译：

ReaxFF反应力场对酶反应能量的自动训练

如今，各种酶的反应机理的计算研究几乎完全基于混合QM / MM模型。不幸的是，这种方法的成功很大程度上取决于QM区域的选择，并且计算成本是一个关键的限制因素。经验反应性分子力场提供了一个有趣的替代方法，尤其是van Duin及其同事开发的ReaxFF势。但是，即使已经存在针对生物分子的ReaxFF的初始参数化，也无法提供所需的准确性。我们已经对ReaxFF力场进行了彻底的改造，以改进对反应能的描述。为了最大程度地减少所需的人力，我们提出了一种完全自动化的方法来生成广泛的训练集，该训练集由数千个不同的几何形状和从几个模型分子开始的分子片段组成。以QM静电势作为主要目标量对静电参数进行了优化，避免了对参考原子电荷选择的过度依赖，并提高了鲁棒性和可转移性。使用CMA-ES优化算法的VD-CMA-ES变体优化了其余力场参数。这种方法能够以前所未有的速度和可靠性同时优化数百个参数。在真实的酶促系统ppGalNAcT2糖基转移酶上验证了产生的力场。新的力场与参考QM / MM反应能曲线具有极好的定性一致性，

更新日期：2017-12-06

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