ArticleFrontiers in chemistry2021
Automatically Constructed Neural Network Potentials for Molecular Dynamics Simulation of Zinc Proteins.
Article in Frontiers in chemistry, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Molecular dynamics simulations of proteins: an in-depth review of computational strategies, structural insights, and their role in medicinal chemistry and drug development.Biological cybernetics · 2025Review
- A Computational Perspective to Intermolecular Interactions and the Role of the Solvent on Regulating Protein Properties.Chemical reviews · 2025Review
- Structure-Based Virtual Screening for Methyltransferase Inhibitors of SARS-CoV-2 nsp14 and nsp16.Molecules (Basel, Switzerland) · 2024Article
- Implementation and Validation of an OpenMM Plugin for the Deep Potential Representation of Potential Energy.International journal of molecular sciences · 2024Article
- NNP/MM: Accelerating Molecular Dynamics Simulations with Machine Learning Potentials and Molecular Mechanics.Journal of chemical information and modeling · 2023Article
- Drug Design in the Exascale Era: A Perspective from Massively Parallel QM/MM Simulations.Journal of chemical information and modeling · 2023Review
- Metal3D: a general deep learning framework for accurate metal ion location prediction in proteins.Nature communications · 2023Article
- Machine Learning Approaches for Metalloproteins.Molecules (Basel, Switzerland) · 2022Review
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3 authors.
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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.
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