Name
Abstract | ||
|---|---|---|
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 mu s simulation collected on a GPU over a similar timespan. The folding and unfolded rates were of comparable accuracy. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1021/ci500321g | JOURNAL OF CHEMICAL INFORMATION AND MODELING |
DocType | Volume | Issue |
Journal | 54 | 10 |
ISSN | Citations | PageRank |
1549-9596 | 0 | 0.34 |
References | Authors | |
0 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Badi Abdul-Wahid | 1 | 1 | 0.69 |
| Haoyun Feng | 2 | 4 | 1.41 |
| Dinesh Rajan | 3 | 191 | 33.05 |
| Ronan Costaouec | 4 | 0 | 0.34 |
| Eric Darve | 5 | 440 | 44.79 |
| Douglas Thain | 6 | 1530 | 127.00 |
| Jesús A. Izaguirre | 7 | 129 | 15.40 |