• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

NAMD中的高斯加速分子动力学

Gaussian Accelerated Molecular Dynamics in NAMD.

作者信息

Pang Yui Tik, Miao Yinglong, Wang Yi, McCammon J Andrew

机构信息

Department of Physics, The Chinese University of Hong Kong , Shatin, New Territories, Hong Kong.

出版信息

J Chem Theory Comput. 2017 Jan 10;13(1):9-19. doi: 10.1021/acs.jctc.6b00931. Epub 2016 Dec 30.

DOI:10.1021/acs.jctc.6b00931
PMID:28034310
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5743237/
Abstract

Gaussian accelerated molecular dynamics (GaMD) is a recently developed enhanced sampling technique that provides efficient free energy calculations of biomolecules. Like the previous accelerated molecular dynamics (aMD), GaMD allows for "unconstrained" enhanced sampling without the need to set predefined collective variables and so is useful for studying complex biomolecular conformational changes such as protein folding and ligand binding. Furthermore, because the boost potential is constructed using a harmonic function that follows Gaussian distribution in GaMD, cumulant expansion to the second order can be applied to recover the original free energy profiles of proteins and other large biomolecules, which solves a long-standing energetic reweighting problem of the previous aMD method. Taken together, GaMD offers major advantages for both unconstrained enhanced sampling and free energy calculations of large biomolecules. Here, we have implemented GaMD in the NAMD package on top of the existing aMD feature and validated it on three model systems: alanine dipeptide, the chignolin fast-folding protein, and the M muscarinic G protein-coupled receptor (GPCR). For alanine dipeptide, while conventional molecular dynamics (cMD) simulations performed for 30 ns are poorly converged, GaMD simulations of the same length yield free energy profiles that agree quantitatively with those of 1000 ns cMD simulation. Further GaMD simulations have captured folding of the chignolin and binding of the acetylcholine (ACh) endogenous agonist to the M muscarinic receptor. The reweighted free energy profiles are used to characterize the protein folding and ligand binding pathways quantitatively. GaMD implemented in the scalable NAMD is widely applicable to enhanced sampling and free energy calculations of large biomolecules.

摘要

高斯加速分子动力学(GaMD)是一种最近开发的增强采样技术,可对生物分子进行高效的自由能计算。与之前的加速分子动力学(aMD)一样,GaMD允许进行“无约束”的增强采样,无需设置预定义的集体变量,因此对于研究复杂的生物分子构象变化(如蛋白质折叠和配体结合)很有用。此外,由于在GaMD中使用遵循高斯分布的谐波函数构建增强势,因此可以应用二阶累积量展开来恢复蛋白质和其他大型生物分子的原始自由能分布,这解决了先前aMD方法长期存在的能量重加权问题。综上所述,GaMD在大型生物分子的无约束增强采样和自由能计算方面都具有主要优势。在这里,我们在现有aMD功能的基础上,在NAMD软件包中实现了GaMD,并在三个模型系统上进行了验证:丙氨酸二肽、奇果菌素快速折叠蛋白和M型毒蕈碱G蛋白偶联受体(GPCR)。对于丙氨酸二肽,虽然进行30 ns的传统分子动力学(cMD)模拟收敛性很差,但相同长度的GaMD模拟产生的自由能分布与1000 ns cMD模拟的自由能分布在数量上一致。进一步的GaMD模拟捕捉到了奇果菌素的折叠以及内源性激动剂乙酰胆碱(ACh)与M型毒蕈碱受体的结合。重新加权的自由能分布用于定量表征蛋白质折叠和配体结合途径。在可扩展的NAMD中实现的GaMD广泛适用于大型生物分子的增强采样和自由能计算。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/601d/5743237/89283592e5ab/ct-2016-00931t_0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/601d/5743237/1da41f3b4846/ct-2016-00931t_0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/601d/5743237/4e7ae4258331/ct-2016-00931t_0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/601d/5743237/89283592e5ab/ct-2016-00931t_0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/601d/5743237/1da41f3b4846/ct-2016-00931t_0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/601d/5743237/4e7ae4258331/ct-2016-00931t_0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/601d/5743237/89283592e5ab/ct-2016-00931t_0003.jpg

相似文献

1
Gaussian Accelerated Molecular Dynamics in NAMD.NAMD中的高斯加速分子动力学
J Chem Theory Comput. 2017 Jan 10;13(1):9-19. doi: 10.1021/acs.jctc.6b00931. Epub 2016 Dec 30.
2
Gaussian Accelerated Molecular Dynamics: Unconstrained Enhanced Sampling and Free Energy Calculation.高斯加速分子动力学:无约束增强采样与自由能计算
J Chem Theory Comput. 2015 Aug 11;11(8):3584-3595. doi: 10.1021/acs.jctc.5b00436. Epub 2015 Jul 14.
3
Gaussian Accelerated Molecular Dynamics: Theory, Implementation, and Applications.高斯加速分子动力学:理论、实现与应用
Annu Rep Comput Chem. 2017;13:231-278. doi: 10.1016/bs.arcc.2017.06.005. Epub 2017 Aug 10.
4
Gaussian accelerated molecular dynamics (GaMD): principles and applications.高斯加速分子动力学(GaMD):原理与应用
Wiley Interdiscip Rev Comput Mol Sci. 2021 Sep-Oct;11(5). doi: 10.1002/wcms.1521. Epub 2021 Mar 1.
5
Gaussian Accelerated Molecular Dynamics in OpenMM.OpenMM 中的高斯加速分子动力学。
J Phys Chem B. 2022 Aug 11;126(31):5810-5820. doi: 10.1021/acs.jpcb.2c03765. Epub 2022 Jul 27.
6
Replica Exchange Gaussian Accelerated Molecular Dynamics: Improved Enhanced Sampling and Free Energy Calculation.复制交换高斯加速分子动力学:改进的增强采样和自由能计算。
J Chem Theory Comput. 2018 Apr 10;14(4):1853-1864. doi: 10.1021/acs.jctc.7b01226. Epub 2018 Mar 12.
7
Replica-Exchange Umbrella Sampling Combined with Gaussian Accelerated Molecular Dynamics for Free-Energy Calculation of Biomolecules.复制交换伞状采样与高斯加速分子动力学在生物分子自由能计算中的应用。
J Chem Theory Comput. 2019 Oct 8;15(10):5199-5208. doi: 10.1021/acs.jctc.9b00761. Epub 2019 Sep 27.
8
Acceleration of biomolecular kinetics in Gaussian accelerated molecular dynamics.在高斯加速分子动力学中生物分子动力学的加速。
J Chem Phys. 2018 Aug 21;149(7):072308. doi: 10.1063/1.5024217.
9
Multiple Parameter Replica Exchange Gaussian Accelerated Molecular Dynamics for Enhanced Sampling and Free Energy Calculation of Biomolecular Systems.多参数副本交换高斯加速分子动力学用于增强生物分子体系的采样和自由能计算。
J Chem Theory Comput. 2024 Aug 13;20(15):6485-6499. doi: 10.1021/acs.jctc.4c00501. Epub 2024 Jul 31.
10
Peptide Gaussian accelerated molecular dynamics (Pep-GaMD): Enhanced sampling and free energy and kinetics calculations of peptide binding.肽高斯加速分子动力学(Pep-GaMD):增强肽结合的采样能力以及自由能和动力学计算。
J Chem Phys. 2020 Oct 21;153(15):154109. doi: 10.1063/5.0021399.

引用本文的文献

1
Running Gaussian-accelerated Molecular Dynamics Simulations in NAMD [Article v1.0].在NAMD中运行高斯加速分子动力学模拟[文章版本1.0]
Living J Comput Mol Sci. 2025;6(1). doi: 10.33011/livecoms.6.1.3815. Epub 2025 Jul 12.
2
Advanced modeling of salt-inducible kinase (SIK) inhibitors incorporating protein flexibility through molecular dynamics and cross-docking.通过分子动力学和交叉对接纳入蛋白质柔性的盐诱导激酶(SIK)抑制剂的高级建模。
Sci Rep. 2025 May 29;15(1):18868. doi: 10.1038/s41598-025-03699-w.
3
Molecular docking analysis of breast cancer target RAC1B with ligands.

本文引用的文献

1
Graded activation and free energy landscapes of a muscarinic G-protein-coupled receptor.毒蕈碱型 G 蛋白偶联受体的分级激活与自由能景观
Proc Natl Acad Sci U S A. 2016 Oct 25;113(43):12162-12167. doi: 10.1073/pnas.1614538113. Epub 2016 Oct 10.
2
Routine Microsecond Molecular Dynamics Simulations with AMBER on GPUs. 2. Explicit Solvent Particle Mesh Ewald.使用AMBER在GPU上进行常规微秒级分子动力学模拟。2. 显式溶剂粒子网格埃瓦尔德方法
J Chem Theory Comput. 2013 Sep 10;9(9):3878-88. doi: 10.1021/ct400314y. Epub 2013 Aug 20.
3
PTRAJ and CPPTRAJ: Software for Processing and Analysis of Molecular Dynamics Trajectory Data.
乳腺癌靶点RAC1B与配体的分子对接分析
Bioinformation. 2024 Nov 5;20(11):1467-1478. doi: 10.6026/9732063002001467. eCollection 2024.
4
DeepPath: Overcoming data scarcity for protein transition pathway prediction using physics-based deep learning.深度路径:利用基于物理学的深度学习克服蛋白质转变途径预测中的数据稀缺问题。
bioRxiv. 2025 Mar 2:2025.02.27.640693. doi: 10.1101/2025.02.27.640693.
5
Mechanisms of Peptide Agonist Dissociation and Deactivation of Adhesion G-Protein-Coupled Receptors.肽类激动剂与黏附G蛋白偶联受体解离及失活的机制
Biochemistry. 2025 Feb 18;64(4):871-878. doi: 10.1021/acs.biochem.4c00531. Epub 2025 Feb 4.
6
Molecular simulations reveal intricate coupling between agonist-bound β-adrenergic receptors and G protein.分子模拟揭示了激动剂结合的β-肾上腺素能受体与G蛋白之间的复杂耦合。
iScience. 2025 Jan 2;28(2):111741. doi: 10.1016/j.isci.2024.111741. eCollection 2025 Feb 21.
7
Computational Methods for Modeling Lipid-Mediated Active Pharmaceutical Ingredient Delivery.脂质介导的活性药物成分递送建模的计算方法
Mol Pharm. 2025 Mar 3;22(3):1110-1141. doi: 10.1021/acs.molpharmaceut.4c00744. Epub 2025 Jan 29.
8
Recent advances from computer-aided drug design to artificial intelligence drug design.从计算机辅助药物设计到人工智能药物设计的最新进展。
RSC Med Chem. 2024 Oct 11;15(12):3978-4000. doi: 10.1039/d4md00522h.
9
Activation of polycystin-1 signaling by binding of stalk-derived peptide agonists.通过与茎衍生肽激动剂结合激活多囊蛋白-1信号通路。
Elife. 2024 Oct 7;13:RP95992. doi: 10.7554/eLife.95992.
10
Mechanisms of peptide agonist dissociation and deactivation of adhesion G-protein-coupled receptors.肽类激动剂与黏附性G蛋白偶联受体解离及失活的机制
bioRxiv. 2024 Sep 14:2024.09.07.611823. doi: 10.1101/2024.09.07.611823.
PTRAJ和CPPTRAJ:用于处理和分析分子动力学轨迹数据的软件。
J Chem Theory Comput. 2013 Jul 9;9(7):3084-95. doi: 10.1021/ct400341p. Epub 2013 Jun 25.
4
Accelerated molecular dynamics simulations of ligand binding to a muscarinic G-protein-coupled receptor.配体与毒蕈碱型G蛋白偶联受体结合的加速分子动力学模拟
Q Rev Biophys. 2015 Nov;48(4):479-87. doi: 10.1017/S0033583515000153.
5
Gaussian Accelerated Molecular Dynamics: Unconstrained Enhanced Sampling and Free Energy Calculation.高斯加速分子动力学:无约束增强采样与自由能计算
J Chem Theory Comput. 2015 Aug 11;11(8):3584-3595. doi: 10.1021/acs.jctc.5b00436. Epub 2015 Jul 14.
6
Achieving Rigorous Accelerated Conformational Sampling in Explicit Solvent.在显式溶剂中实现严格的加速构象采样
J Phys Chem Lett. 2014 Apr 3;5(7):1217-24. doi: 10.1021/jz500179a. Epub 2014 Mar 24.
7
Accelerated molecular dynamics simulations of protein folding.蛋白质折叠的加速分子动力学模拟
J Comput Chem. 2015 Jul 30;36(20):1536-49. doi: 10.1002/jcc.23964. Epub 2015 Jun 12.
8
Allosteric effects of sodium ion binding on activation of the m3 muscarinic g-protein-coupled receptor.钠离子结合对 m3 毒蕈碱型 G 蛋白偶联受体激活的变构效应。
Biophys J. 2015 Apr 7;108(7):1796-1806. doi: 10.1016/j.bpj.2015.03.003.
9
Improved Reweighting of Accelerated Molecular Dynamics Simulations for Free Energy Calculation.用于自由能计算的加速分子动力学模拟的改进重加权法
J Chem Theory Comput. 2014 Jul 8;10(7):2677-2689. doi: 10.1021/ct500090q. Epub 2014 May 1.
10
Free energy landscape of G-protein coupled receptors, explored by accelerated molecular dynamics.G 蛋白偶联受体的自由能景观,通过加速分子动力学进行探索。
Phys Chem Chem Phys. 2014 Apr 14;16(14):6398-406. doi: 10.1039/c3cp53962h. Epub 2014 Jan 21.