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MELD在行动:利用数据加速分子动力学

MELD in Action: Harnessing Data to Accelerate Molecular Dynamics.

作者信息

Gaza Jokent, Brini Emiliano, MacCallum Justin L, Dill Ken A, Perez Alberto

机构信息

Department of Chemistry, University of Florida, Gainesville, Florida 32611, United States.

Quantum Theory Project, University of Florida, Gainesville, Florida 32611, United States.

出版信息

J Chem Inf Model. 2025 Feb 24;65(4):1685-1693. doi: 10.1021/acs.jcim.4c02108. Epub 2025 Feb 2.

Abstract

We review MELD, an accelerator of Molecular Dynamics simulations of biomolecules. MELD (Modeling Employing Limited Data) integrates molecular dynamics (MD) with a variety of types of structural information through Bayesian inference, generating ensembles of protein and DNA structures having proper Boltzmann populations. MELD minimizes the computational sampling of irrelevant regions of phase space by applying energetic penalties to areas that conflict with the available data. MELD is effective in refining protein structures using NMR or cryo-EM data or predicting protein-ligand binding poses. As a plugin for OpenMM, MELD is interoperable with other enhanced sampling methods, offering a versatile tool for structural determination in computational chemistry and biophysics.

摘要

我们回顾了MELD,一种生物分子分子动力学模拟的加速器。MELD(利用有限数据建模)通过贝叶斯推理将分子动力学(MD)与各种类型的结构信息相结合,生成具有适当玻尔兹曼分布的蛋白质和DNA结构集合。MELD通过对与现有数据冲突的区域施加能量惩罚,将相空间中无关区域的计算采样降至最低。MELD在使用NMR或冷冻电镜数据优化蛋白质结构或预测蛋白质-配体结合姿势方面很有效。作为OpenMM的插件,MELD可与其他增强采样方法互操作,为计算化学和生物物理学中的结构测定提供了一个通用工具。

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本文引用的文献

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RNA-Puzzles Round V: blind predictions of 23 RNA structures.
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3
Outcomes of the EMDataResource cryo-EM Ligand Modeling Challenge.
Nat Methods. 2024 Jul;21(7):1340-1348. doi: 10.1038/s41592-024-02321-7. Epub 2024 Jun 25.
5
A Computational Pipeline for Accurate Prioritization of Protein-Protein Binding Candidates in High-Throughput Protein Libraries.
Angew Chem Int Ed Engl. 2024 Jun 10;63(24):e202405767. doi: 10.1002/anie.202405767. Epub 2024 May 8.
6
When MELD Meets GaMD: Accelerating Biomolecular Landscape Exploration.
J Chem Theory Comput. 2023 Dec 12;19(23):8743-8750. doi: 10.1021/acs.jctc.3c01019. Epub 2023 Dec 1.
7
Computing Free Energies of Fold-Switching Proteins Using MELD x MD.
J Chem Theory Comput. 2023 Oct 10;19(19):6839-6847. doi: 10.1021/acs.jctc.3c00679. Epub 2023 Sep 19.
8
De novo design of protein structure and function with RFdiffusion.
Nature. 2023 Aug;620(7976):1089-1100. doi: 10.1038/s41586-023-06415-8. Epub 2023 Jul 11.
9
Improving de novo protein binder design with deep learning.
Nat Commun. 2023 May 6;14(1):2625. doi: 10.1038/s41467-023-38328-5.
10
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