Suppr超能文献

使用 NAMD 在 CPU 和 GPU 架构上进行可扩展的分子动力学。

Scalable molecular dynamics on CPU and GPU architectures with NAMD.

机构信息

NIH Center for Macromolecular Modeling and Bioinformatics, Theoretical and Computational Biophysics Group, Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA.

National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, Maryland 20814, USA.

出版信息

J Chem Phys. 2020 Jul 28;153(4):044130. doi: 10.1063/5.0014475.

Abstract

NAMDis a molecular dynamics program designed for high-performance simulations of very large biological objects on CPU- and GPU-based architectures. NAMD offers scalable performance on petascale parallel supercomputers consisting of hundreds of thousands of cores, as well as on inexpensive commodity clusters commonly found in academic environments. It is written in C++ and leans on Charm++ parallel objects for optimal performance on low-latency architectures. NAMD is a versatile, multipurpose code that gathers state-of-the-art algorithms to carry out simulations in apt thermodynamic ensembles, using the widely popular CHARMM, AMBER, OPLS, and GROMOS biomolecular force fields. Here, we review the main features of NAMD that allow both equilibrium and enhanced-sampling molecular dynamics simulations with numerical efficiency. We describe the underlying concepts utilized by NAMD and their implementation, most notably for handling long-range electrostatics; controlling the temperature, pressure, and pH; applying external potentials on tailored grids; leveraging massively parallel resources in multiple-copy simulations; and hybrid quantum-mechanical/molecular-mechanical descriptions. We detail the variety of options offered by NAMD for enhanced-sampling simulations aimed at determining free-energy differences of either alchemical or geometrical transformations and outline their applicability to specific problems. Last, we discuss the roadmap for the development of NAMD and our current efforts toward achieving optimal performance on GPU-based architectures, for pushing back the limitations that have prevented biologically realistic billion-atom objects to be fruitfully simulated, and for making large-scale simulations less expensive and easier to set up, run, and analyze. NAMD is distributed free of charge with its source code at www.ks.uiuc.edu.

摘要

NAMDis 是一个分子动力学程序,专为在基于 CPU 和 GPU 的架构上对非常大的生物对象进行高性能模拟而设计。NAMD 提供了可扩展的性能,可在由数十万核组成的千万亿级并行超级计算机上运行,也可在学术环境中常见的廉价商用集群上运行。它是用 C++编写的,并依赖 Charm++并行对象在低延迟架构上实现最佳性能。NAMD 是一种通用的、多用途的代码,它汇集了最先进的算法,以在适当的热力学系综中进行模拟,使用广泛流行的 CHARMM、AMBER、OPLS 和 GROMOS 生物分子力场。在这里,我们回顾了 NAMD 的主要特点,这些特点允许进行平衡和增强采样分子动力学模拟,并具有数值效率。我们描述了 NAMD 所使用的基本概念及其实现,特别是用于处理长程静电的概念;控制温度、压力和 pH 值;在定制的网格上施加外部势;在多副本模拟中利用大规模并行资源;以及混合量子力学/分子力学描述。我们详细介绍了 NAMD 为增强采样模拟提供的各种选项,这些选项旨在确定化学或几何变换的自由能差异,并概述了它们在特定问题中的适用性。最后,我们讨论了 NAMD 的发展路线图以及我们目前在基于 GPU 的架构上实现最佳性能、克服阻止对生物上逼真的十亿原子对象进行有效模拟的限制以及使大规模模拟更便宜、更容易设置、运行和分析的努力。NAMD 可以免费从 www.ks.uiuc.edu 获得其源代码。

相似文献

1
Scalable molecular dynamics on CPU and GPU architectures with NAMD.
J Chem Phys. 2020 Jul 28;153(4):044130. doi: 10.1063/5.0014475.
2
Scalable molecular dynamics with NAMD.
J Comput Chem. 2005 Dec;26(16):1781-802. doi: 10.1002/jcc.20289.
3
Boosting Free-Energy Perturbation Calculations with GPU-Accelerated NAMD.
J Chem Inf Model. 2020 Nov 23;60(11):5301-5307. doi: 10.1021/acs.jcim.0c00745. Epub 2020 Sep 1.
4
Scalable Molecular Dynamics with NAMD on the Summit System.
IBM J Res Dev. 2018 Nov-Dec;62(6):1-9. doi: 10.1147/jrd.2018.2888986. Epub 2018 Dec 21.
6
Generalized Scalable Multiple Copy Algorithms for Molecular Dynamics Simulations in NAMD.
Comput Phys Commun. 2014 Mar;185(3):908-916. doi: 10.1016/j.cpc.2013.12.014.
7
Pouring SIRAH on NAMD.
J Phys Chem B. 2024 Dec 5;128(48):11971-11980. doi: 10.1021/acs.jpcb.4c03278. Epub 2024 Sep 25.
8
AMBERff at Scale: Multimillion-Atom Simulations with AMBER Force Fields in NAMD.
J Chem Inf Model. 2024 Jan 22;64(2):543-554. doi: 10.1021/acs.jcim.3c01648. Epub 2024 Jan 4.
9
Predicting Kinetics and Dynamics of Spin-Dependent Processes.
Acc Chem Res. 2023 Apr 4;56(7):856-866. doi: 10.1021/acs.accounts.2c00843. Epub 2023 Mar 16.
10
CHARMM-GUI Free Energy Calculator for Practical Ligand Binding Free Energy Simulations with AMBER.
J Chem Inf Model. 2021 Sep 27;61(9):4145-4151. doi: 10.1021/acs.jcim.1c00747. Epub 2021 Sep 15.

引用本文的文献

2
Ultrastrong adhesion to human skin: Calcium as a key regulator of noncovalent interactions.
Sci Adv. 2025 Sep 5;11(36):eadu7457. doi: 10.1126/sciadv.adu7457. Epub 2025 Sep 3.
3
The J-shape of β2GPI reveals a cryptic discontinuous epitope across domains I and II.
J Struct Biol X. 2025 Aug 20;12:100135. doi: 10.1016/j.yjsbx.2025.100135. eCollection 2025 Dec.
5
Peptide codes for organ-selective mRNA delivery.
Nat Mater. 2025 Sep 1. doi: 10.1038/s41563-025-02331-6.
6
Structural basis for HIV-1 capsid adaption to a deficiency in IP6 packaging.
Nat Commun. 2025 Sep 1;16(1):8152. doi: 10.1038/s41467-025-63363-9.
9
Hybrid nucleobase-heterocycle-2-oxindole scaffolds as innovative cell cycle modulators with potential anticancer activity.
RSC Adv. 2025 Aug 22;15(36):29753-29776. doi: 10.1039/d5ra04997k. eCollection 2025 Aug 18.
10
Multi-state catch bond formed in the Izumo1:Juno complex that initiates human fertilization.
Nat Commun. 2025 Aug 26;16(1):7952. doi: 10.1038/s41467-025-62427-0.

本文引用的文献

1
Further Optimization and Validation of the Classical Drude Polarizable Protein Force Field.
J Chem Theory Comput. 2020 May 12;16(5):3221-3239. doi: 10.1021/acs.jctc.0c00057. Epub 2020 Apr 27.
2
Scalable Molecular Dynamics with NAMD on the Summit System.
IBM J Res Dev. 2018 Nov-Dec;62(6):1-9. doi: 10.1147/jrd.2018.2888986. Epub 2018 Dec 21.
3
Mesoscale All-Atom Influenza Virus Simulations Suggest New Substrate Binding Mechanism.
ACS Cent Sci. 2020 Feb 26;6(2):189-196. doi: 10.1021/acscentsci.9b01071. Epub 2020 Feb 19.
4
The SAMPL6 SAMPLing challenge: assessing the reliability and efficiency of binding free energy calculations.
J Comput Aided Mol Des. 2020 May;34(5):601-633. doi: 10.1007/s10822-020-00290-5. Epub 2020 Jan 27.
5
Atoms to Phenotypes: Molecular Design Principles of Cellular Energy Metabolism.
Cell. 2019 Nov 14;179(5):1098-1111.e23. doi: 10.1016/j.cell.2019.10.021.
6
Taming Rugged Free Energy Landscapes Using an Average Force.
Acc Chem Res. 2019 Nov 19;52(11):3254-3264. doi: 10.1021/acs.accounts.9b00473. Epub 2019 Nov 4.
8
10
Accelerating Membrane Simulations with Hydrogen Mass Repartitioning.
J Chem Theory Comput. 2019 Aug 13;15(8):4673-4686. doi: 10.1021/acs.jctc.9b00160. Epub 2019 Jul 2.

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