AWE-WQ: Fast-Forwarding Molecular Dynamics Using the Accelerated Weighted Ensemble

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 eme...

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Bibliographic Details
Published in:Journal of chemical information and modeling 2014-10, Vol.54 (10), p.3033-3043
Main Authors: Abdul-Wahid, Badi’, Feng, Haoyun, Rajan, Dinesh, Costaouec, Ronan, Darve, Eric, Thain, Douglas, Izaguirre, Jesús A
Format: Article
Language:English
Subjects:
Algorithms
Comparative analysis
Computer simulation
Computer Systems
Dynamical systems
Dynamics
Mathematical models
Molecular dynamics
Molecular Dynamics Simulation
Molecules
Protein Folding
Protein Structure, Tertiary
Protein Unfolding
Proteins
Proteins - chemistry
Samples
Simulation
Statistical analysis
Statistical methods
Thermodynamics
Tryptophan - chemistry
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Summary: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.
ISSN:1549-9596
1549-960X
DOI:10.1021/ci500321g