Combined fragment-based machine learning force field with classical force field and its application in the NMR calculations of macromolecules in solutions
Combined fragment-based machine learning force field with classical force field and its application in the NMR calculations of macromolecules in solutions
复制标题
基于片段的机器学习力场与经典力场的结合及其在溶液中大分子核磁共振计算中的应用
DOI：
10.1039/d2cp02192g
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发表时间：
2022-07-22
影响因子：
3.3
通讯作者：
Li, Shuhua
中科院分区：
文献类型：
--
作者：
Liao, Kang;Dong, Shiyu;Li, Shuhua
我们开发了一个基于碎片的机器学习（ML）力场和分子力学（MM）力场相结合的方法来模拟溶液中大分子的结构，然后在密度泛函理论（DFT）水平上用广义能量碎片化（GEBF）方法计算其NMR化学位移.在这项工作中，我们首先构造高斯近似势的基础上的GEBF子系统的大分子的MD模拟，然后基于GEBF神经网络（GEBF-NN）与所研究的大分子的深势模型。然后，我们通过结合溶质分子的GEBF-NN力场和溶剂分子的ff14 SB力场，建立了溶液中大分子的GEBF-NN/MM力场。使用GEBF-NN/MM MD模拟生成溶质/溶剂团簇的快照结构，然后在DFT水平上使用GEBF方法进行NMR计算以计算溶质分子的NMR化学位移。以低聚吡啶-二甲酰胺的氯仿溶液为例，通过与DFT结果的比较，我们的结果表明GEBF-NN力场对于该七聚体是相当精确的.对于这个七聚体在氯仿溶液中，无论是GEBF-NN/MM和经典的MD模拟可以导致从相同的初始扩展结构的螺旋结构。与实验结果相比，GEBF-DFT NMR结果表明，GEBF-NN/MM力场能使氢原子的NMR化学位移更精确.因此，GEBF-NN/MM力场可以用于预测更准确的动力学行为比经典力场的复杂系统的解决方案。
We have developed a combined fragment-based machine learning (ML) force field and molecular mechanics (MM) force field for simulating the structures of macromolecules in solutions, and then compute its NMR chemical shifts with the generalized energy-based fragmentation (GEBF) approach at the level of density functional theory (DFT). In this work, we first construct Gaussian approximation potential based on GEBF subsystems of macromolecules for MD simulations and then a GEBF-based neural network (GEBF-NN) with deep potential model for the studied macromolecule. Then, we develop a GEBF-NN/MM force field for macromolecules in solutions by combining the GEBF-NN force field for the solute molecule and ff14SB force field for solvent molecules. Using the GEBF-NN/MM MD simulation to generate snapshot structures of solute/solvent clusters, we then perform the NMR calculations with the GEBF approach at the DFT level to calculate NMR chemical shifts of the solute molecule. Taking a heptamer of oligopyridine-dicarboxamides in chloroform solution as an example, our results show that the GEBF-NN force field is quite accurate for this heptamer by comparing with the reference DFT results. For this heptamer in chloroform solution, both the GEBF-NN/MM and classical MD simulations could lead to helical structures from the same initial extended structure. The GEBF-DFT NMR results indicate that the GEBF-NN/MM force field could lead to more accurate NMR chemical shifts on hydrogen atoms by comparing with the experimental NMR results. Therefore, the GEBF-NN/MM force field could be employed for predicting more accurate dynamical behaviors than the classical force field for complex systems in solutions.