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AWE-WQ: Fast-Forwarding Molecular Dynamics Using the Accelerated Weighted Ensemble
[Image: see text] A limitation of traditional molecular dynamics (MD) is that reaction rates are difficult to compute. This is due to the rarity of observing transitions between metastable states since high energy barriers trap the system in these states. Recently the weighted ensemble (WE) family o...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Chemical
Society
2014
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4210180/ https://www.ncbi.nlm.nih.gov/pubmed/25207854 http://dx.doi.org/10.1021/ci500321g |
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author | Abdul-Wahid, Badi’ Feng, Haoyun Rajan, Dinesh Costaouec, Ronan Darve, Eric Thain, Douglas Izaguirre, Jesús A. |
author_facet | Abdul-Wahid, Badi’ Feng, Haoyun Rajan, Dinesh Costaouec, Ronan Darve, Eric Thain, Douglas Izaguirre, Jesús A. |
author_sort | Abdul-Wahid, Badi’ |
collection | PubMed |
description | [Image: see text] A limitation of traditional molecular dynamics (MD) is that reaction rates are difficult to compute. This is due to the rarity of observing transitions between metastable states since high energy barriers trap the system in these states. Recently the weighted ensemble (WE) family of methods have emerged which can flexibly and efficiently sample conformational space without being trapped and allow calculation of unbiased rates. However, while WE can sample correctly and efficiently, a scalable implementation applicable to interesting biomolecular systems is not available. We provide here a GPLv2 implementation called AWE-WQ of a WE algorithm using the master/worker distributed computing WorkQueue (WQ) framework. AWE-WQ is scalable to thousands of nodes and supports dynamic allocation of computer resources, heterogeneous resource usage (such as central processing units (CPU) and graphical processing units (GPUs) concurrently), seamless heterogeneous cluster usage (i.e., campus grids and cloud providers), and support for arbitrary MD codes such as GROMACS, while ensuring that all statistics are unbiased. We applied AWE-WQ to a 34 residue protein which simulated 1.5 ms over 8 months with peak aggregate performance of 1000 ns/h. Comparison was done with a 200 μs simulation collected on a GPU over a similar timespan. The folding and unfolded rates were of comparable accuracy. |
format | Online Article Text |
id | pubmed-4210180 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | American Chemical
Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-42101802015-09-10 AWE-WQ: Fast-Forwarding Molecular Dynamics Using the Accelerated Weighted Ensemble Abdul-Wahid, Badi’ Feng, Haoyun Rajan, Dinesh Costaouec, Ronan Darve, Eric Thain, Douglas Izaguirre, Jesús A. J Chem Inf Model [Image: see text] A limitation of traditional molecular dynamics (MD) is that reaction rates are difficult to compute. This is due to the rarity of observing transitions between metastable states since high energy barriers trap the system in these states. Recently the weighted ensemble (WE) family of methods have emerged which can flexibly and efficiently sample conformational space without being trapped and allow calculation of unbiased rates. However, while WE can sample correctly and efficiently, a scalable implementation applicable to interesting biomolecular systems is not available. We provide here a GPLv2 implementation called AWE-WQ of a WE algorithm using the master/worker distributed computing WorkQueue (WQ) framework. AWE-WQ is scalable to thousands of nodes and supports dynamic allocation of computer resources, heterogeneous resource usage (such as central processing units (CPU) and graphical processing units (GPUs) concurrently), seamless heterogeneous cluster usage (i.e., campus grids and cloud providers), and support for arbitrary MD codes such as GROMACS, while ensuring that all statistics are unbiased. We applied AWE-WQ to a 34 residue protein which simulated 1.5 ms over 8 months with peak aggregate performance of 1000 ns/h. Comparison was done with a 200 μs simulation collected on a GPU over a similar timespan. The folding and unfolded rates were of comparable accuracy. American Chemical Society 2014-09-10 2014-10-27 /pmc/articles/PMC4210180/ /pubmed/25207854 http://dx.doi.org/10.1021/ci500321g Text en Copyright © 2014 American Chemical Society Terms of Use (http://pubs.acs.org/page/policy/authorchoice_termsofuse.html) |
spellingShingle | Abdul-Wahid, Badi’ Feng, Haoyun Rajan, Dinesh Costaouec, Ronan Darve, Eric Thain, Douglas Izaguirre, Jesús A. AWE-WQ: Fast-Forwarding Molecular Dynamics Using the Accelerated Weighted Ensemble |
title | AWE-WQ:
Fast-Forwarding Molecular Dynamics Using the
Accelerated Weighted Ensemble |
title_full | AWE-WQ:
Fast-Forwarding Molecular Dynamics Using the
Accelerated Weighted Ensemble |
title_fullStr | AWE-WQ:
Fast-Forwarding Molecular Dynamics Using the
Accelerated Weighted Ensemble |
title_full_unstemmed | AWE-WQ:
Fast-Forwarding Molecular Dynamics Using the
Accelerated Weighted Ensemble |
title_short | AWE-WQ:
Fast-Forwarding Molecular Dynamics Using the
Accelerated Weighted Ensemble |
title_sort | awe-wq:
fast-forwarding molecular dynamics using the
accelerated weighted ensemble |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4210180/ https://www.ncbi.nlm.nih.gov/pubmed/25207854 http://dx.doi.org/10.1021/ci500321g |
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