School of Chemistry and Chemical Engineering, Key Laboratory of Mesoscopic Chemistry of Ministry of Education, Institute of Theoretical and Computational Chemistry, Nanjing University, Nanjing, 210023, P. R. China.
Phys Chem Chem Phys. 2022 Aug 10;24(31):18559-18567. doi: 10.1039/d2cp02192g.
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.
我们开发了一种基于片段的机器学习（ML）力场和分子力学（MM）力场的组合，用于模拟溶液中大分子的结构，然后使用基于广义能量分解（GEBF）方法的密度泛函理论（DFT）计算其 NMR 化学位移。在这项工作中，我们首先基于大分子的 GEBF 子系统构建了基于高斯逼近的势能，用于 MD 模拟，然后构建了基于 GEBF 的神经网络（GEBF-NN）和用于研究大分子的深势能模型。然后，我们通过将溶质分子的 GEBF-NN 力场和溶剂分子的 ff14SB 力场相结合，开发了一种用于溶液中大分子的 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，然后使用 GEBF 方法在 DFT 水平上进行 NMR 计算，以计算溶质分子的 NMR 化学位移。以氯仿溶液中的七聚体寡吡啶二羧酸酰胺为例，我们的结果表明，与参考 DFT 结果相比，GEBF-NN 力场对该七聚体非常准确。对于氯仿溶液中的这种七聚体，GEBF-NN/MM 和经典 MD 模拟都可以从相同的初始扩展结构导致螺旋结构。GEBF-DFT NMR 结果表明，与实验 NMR 结果相比，GEBF-NN/MM 力场可以导致氢原子的 NMR 化学位移更准确。因此，与经典力场相比，GEBF-NN/MM 力场可用于预测复杂体系在溶液中的更准确的动力学行为。
