Automatically Constructed Neural Network Potentials for Molecular Dynamics Simulation of Zinc Proteins

Mingyuan Xu; Tong Zhu; John Z H Zhang

doi:10.3389/fchem.2021.692200

Automatically Constructed Neural Network Potentials for Molecular Dynamics Simulation of Zinc Proteins

Front Chem. 2021 Jun 18:9:692200. doi: 10.3389/fchem.2021.692200. eCollection 2021.

Authors

Mingyuan Xu¹, Tong Zhu^{1

2}, John Z H Zhang^{1

2

3

4}

Affiliations

¹ Shanghai Engineering Research Center of Molecular Therapeutics and New Drug Development, Shanghai Key Laboratory of Green Chemistry and Chemical Process, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai, China.
² NYU-ECNU Center for Computational Chemistry at NYU Shanghai, Shanghai, China.
³ Department of Chemistry, New York University, New York, NY, United States.
⁴ Collaborative Innovation Center of Extreme Optics, Shanxi University, Taiyuan, China.

Abstract

The development of accurate and efficient potential energy functions for the molecular dynamics simulation of metalloproteins has long been a great challenge for the theoretical chemistry community. An artificial neural network provides the possibility to develop potential energy functions with both the efficiency of the classical force fields and the accuracy of the quantum chemical methods. In this work, neural network potentials were automatically constructed by using the ESOINN-DP method for typical zinc proteins. For the four most common zinc coordination modes in proteins, the potential energy, atomic forces, and atomic charges predicted by neural network models show great agreement with quantum mechanics calculations and the neural network potential can maintain the coordination geometry correctly. In addition, MD simulation and energy optimization with the neural network potential can be readily used for structural refinement. The neural network potential is not limited by the function form and complex parameterization process, and important quantum effects such as polarization and charge transfer can be accurately considered. The algorithm proposed in this work can also be directly applied to proteins containing other metal ions.

Keywords: force field; metalloproteins; molecular dynamic simulation; neural network; zinc protein.