Suppr超能文献

在 OpenMM 中实现 Martini 粗粒化力场。

An implementation of the Martini coarse-grained force field in OpenMM.

机构信息

Department of Chemistry, University of Calgary, Calgary, Alberta, Canada; Centre for Molecular Simulation, University of Calgary, Calgary, Alberta, Canada.

Department of Biological Sciences, University of Calgary, Calgary, Alberta, Canada; Centre for Molecular Simulation, University of Calgary, Calgary, Alberta, Canada.

出版信息

Biophys J. 2023 Jul 25;122(14):2864-2870. doi: 10.1016/j.bpj.2023.04.007. Epub 2023 Apr 11.

Abstract

We describe a complete implementation of Martini 2 and Martini 3 in the OpenMM molecular dynamics software package. Martini is a widely used coarse-grained force field with applications in biomolecular simulation, materials, and broader areas of chemistry. It is implemented as a force field but makes extensive use of facilities unique to the GROMACS software, including virtual sites and bonded terms that are not commonly used in standard atomistic force fields. OpenMM is a flexible molecular dynamics package widely used for methods development and is competitive in speed on GPUs with other commonly used packages. OpenMM has facilities to easily implement new force field terms, external forces and fields, and other nonstandard features, which we use to implement all force field terms used in Martini 2 and Martini 3. This allows Martini simulations, starting with GROMACS topology files that are processed by custom scripts, with all the added flexibility of OpenMM. We provide a GitHub repository with test cases, compare accuracy and performance between GROMACS and OpenMM, and discuss the limitations of our implementation in terms of direct comparison with GROMACS. We describe a use case that implements the Modeling Employing Limited Data method to apply experimental constraints in a Martini simulation to efficiently determine the structure of a protein complex. We also discuss issues and a potential solution with the Martini 2 topology for cholesterol.

摘要

我们在 OpenMM 分子动力学软件包中完整实现了 Martini 2 和 Martini 3。Martini 是一种广泛应用于生物分子模拟、材料和更广泛化学领域的粗粒化力场。它被实现为一种力场,但广泛利用了 GROMACS 软件特有的设施,包括虚拟站点和键合项,这些在标准原子力场中并不常用。OpenMM 是一个灵活的分子动力学包,广泛用于方法开发,在 GPU 上的速度与其他常用包相当。OpenMM 具有易于实现新力场项、外力和场以及其他非标准特性的设施,我们使用这些设施来实现 Martini 2 和 Martini 3 中使用的所有力场项。这允许从经过自定义脚本处理的 GROMACS 拓扑文件开始进行 Martini 模拟,并具有 OpenMM 的所有附加灵活性。我们提供了一个带有测试用例的 GitHub 存储库,比较了 GROMACS 和 OpenMM 的准确性和性能,并讨论了我们在与 GROMACS 直接比较方面的实现的局限性。我们描述了一个用例,该用例实现了有限数据建模方法,以在 Martini 模拟中应用实验约束,从而有效地确定蛋白质复合物的结构。我们还讨论了 Martini 2 拓扑结构中胆固醇的问题和潜在解决方案。

相似文献

1
An implementation of the Martini coarse-grained force field in OpenMM.在 OpenMM 中实现 Martini 粗粒化力场。
Biophys J. 2023 Jul 25;122(14):2864-2870. doi: 10.1016/j.bpj.2023.04.007. Epub 2023 Apr 11.
4
CHARMM-GUI 10 years for biomolecular modeling and simulation.CHARMM-GUI 10 年用于生物分子建模与模拟。
J Comput Chem. 2017 Jun 5;38(15):1114-1124. doi: 10.1002/jcc.24660. Epub 2016 Nov 14.
7
Coarse-grained force fields for molecular simulations.用于分子模拟的粗粒度力场。
Methods Mol Biol. 2015;1215:125-49. doi: 10.1007/978-1-4939-1465-4_7.

引用本文的文献

2
MELD in Action: Harnessing Data to Accelerate Molecular Dynamics.MELD在行动:利用数据加速分子动力学
J Chem Inf Model. 2025 Feb 24;65(4):1685-1693. doi: 10.1021/acs.jcim.4c02108. Epub 2025 Feb 2.
6
[Research progress of coarse-grained molecular dynamics in drug carrier materials].[粗粒度分子动力学在药物载体材料中的研究进展]
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2023 Aug 25;40(4):799-804. doi: 10.7507/1001-5515.202303008.
7
Biophysics at the dawn of exascale computers.百亿亿次计算机时代初期的生物物理学。
Biophys J. 2023 Jul 25;122(14):E1-E2. doi: 10.1016/j.bpj.2023.06.017. Epub 2023 Jul 6.

本文引用的文献

1
Optimal Bond Constraint Topology for Molecular Dynamics Simulations of Cholesterol.胆固醇分子动力学模拟的最优键约束拓扑结构。
J Chem Theory Comput. 2023 Mar 14;19(5):1592-1601. doi: 10.1021/acs.jctc.2c01032. Epub 2023 Feb 17.
3
LiPyphilic: A Python Toolkit for the Analysis of Lipid Membrane Simulations.LiPyphilic:用于脂质膜模拟分析的Python工具包。
J Chem Theory Comput. 2021 Sep 14;17(9):5907-5919. doi: 10.1021/acs.jctc.1c00447. Epub 2021 Aug 27.
5
Highly accurate protein structure prediction with AlphaFold.利用 AlphaFold 进行高精度蛋白质结构预测。
Nature. 2021 Aug;596(7873):583-589. doi: 10.1038/s41586-021-03819-2. Epub 2021 Jul 15.

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验