论文标题

量子机学习纠正经典力场:在显式溶剂中拉伸DNA碱基对

Quantum Machine Learning Corrects Classical Force Fields: Stretching DNA Base Pairs in Explicit Solvent

论文作者

Berryman, Joshua T., Taghavi, Amirhossein, Mazur, Florian, Tkatchenko, Alexandre

论文摘要

为了提高分子动力学模拟的准确性,经典力场补充了基于核心机械片段能量训练的基于内核的机器学习方法。作为示例应用,考虑到明确的溶剂化和远程电子交换 - 相关效应,将潜在能量表面推广到小型DNA双链体中。对校正势能与扩展的研究表明,领先的经典DNA模型相对于拉伸具有过高的刚度。发现这种差异在多个力场上很常见。量子校正是针对较大DNA双螺旋的实验热力学的定性一致性,为单分子拉伸实验和DNA伸展的经典计算之间的一般和长期差异提供了候选解释。量子计算的新数据集和相关的内核改性分子动力学(KMMD)方法在生物分子模拟中应具有一般效用。 KMMD作为Amber22仿真软件的一部分提供。

In order to improve the accuracy of molecular dynamics simulations, classical force fields are supplemented with a kernel-based machine learning method trained on quantum-mechanical fragment energies. As an example application, a potential-energy surface is generalised for a small DNA duplex, taking into account explicit solvation and long-range electron exchange--correlation effects. Study of the corrected potential energy versus extension shows that leading classical DNA models have excessive stiffness with respect to stretching. This discrepancy is found to be common across multiple forcefields. The quantum correction is in qualitative agreement to the experimental thermodynamics for larger DNA double helices, providing a candidate explanation for the general and long-standing discrepancy between single molecule stretching experiments and classical calculations of DNA stretching. The new dataset of quantum calculations and the associated Kernel Modified Molecular Dynamics (KMMD) method should be of general utility in biomolecular simulations. KMMD is made available as part of the AMBER22 simulation software.

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