Cargando…

Gaussian Accelerated Molecular Dynamics in NAMD

[Image: see text] 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...

Descripción completa

Detalles Bibliográficos
Autores principales: Pang, Yui Tik, Miao, Yinglong, Wang, Yi, McCammon, J. Andrew
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2016
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5743237/
https://www.ncbi.nlm.nih.gov/pubmed/28034310
http://dx.doi.org/10.1021/acs.jctc.6b00931
_version_ 1783288526224228352
author Pang, Yui Tik
Miao, Yinglong
Wang, Yi
McCammon, J. Andrew
author_facet Pang, Yui Tik
Miao, Yinglong
Wang, Yi
McCammon, J. Andrew
author_sort Pang, Yui Tik
collection PubMed
description [Image: see text] 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(3) 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(3) 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.
format Online
Article
Text
id pubmed-5743237
institution National Center for Biotechnology Information
language English
publishDate 2016
publisher American Chemical Society
record_format MEDLINE/PubMed
spelling pubmed-57432372017-12-27 Gaussian Accelerated Molecular Dynamics in NAMD Pang, Yui Tik Miao, Yinglong Wang, Yi McCammon, J. Andrew J Chem Theory Comput [Image: see text] 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(3) 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(3) 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. American Chemical Society 2016-12-07 2017-01-10 /pmc/articles/PMC5743237/ /pubmed/28034310 http://dx.doi.org/10.1021/acs.jctc.6b00931 Text en Copyright © 2016 American Chemical Society This is an open access article published under an ACS AuthorChoice License (http://pubs.acs.org/page/policy/authorchoice_termsofuse.html) , which permits copying and redistribution of the article or any adaptations for non-commercial purposes.
spellingShingle Pang, Yui Tik
Miao, Yinglong
Wang, Yi
McCammon, J. Andrew
Gaussian Accelerated Molecular Dynamics in NAMD
title Gaussian Accelerated Molecular Dynamics in NAMD
title_full Gaussian Accelerated Molecular Dynamics in NAMD
title_fullStr Gaussian Accelerated Molecular Dynamics in NAMD
title_full_unstemmed Gaussian Accelerated Molecular Dynamics in NAMD
title_short Gaussian Accelerated Molecular Dynamics in NAMD
title_sort gaussian accelerated molecular dynamics in namd
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5743237/
https://www.ncbi.nlm.nih.gov/pubmed/28034310
http://dx.doi.org/10.1021/acs.jctc.6b00931
work_keys_str_mv AT pangyuitik gaussianacceleratedmoleculardynamicsinnamd
AT miaoyinglong gaussianacceleratedmoleculardynamicsinnamd
AT wangyi gaussianacceleratedmoleculardynamicsinnamd
AT mccammonjandrew gaussianacceleratedmoleculardynamicsinnamd