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Article details
Title
Molecule-Centered Method for Accelerating the Calculation of Hydrodynamic Interactions in Brownian Dynamics Simulations Containing Many Flexible Biomolecules
Published in
Journal of Chemical Theory and Computation, June 2013
DOI 10.1021/ct400240w
Pubmed ID
Authors
Abstract

Inclusion of hydrodynamic interactions (HIs) is essential in simulations of biological macromolecules that treat the solvent implicitly if the macromolecules are to exhibit correct translational and rotational diffusion. The present work describes the development and testing of a simple approach aimed at allowing more rapid computation of HIs in coarse-grained Brownian dynamics simulations of systems that contain large numbers of flexible macromolecules. The method combines a complete treatment of intramolecular HIs with an approximate treatment of the intermolecular HIs which assumes that the molecules are effectively spherical; all of the HIs are calculated at the Rotne-Prager-Yamakawa level of theory. When combined with Fixman's Chebyshev polynomial method for calculating correlated random displacements, the proposed method provides an approach that is simple to program but sufficiently fast that it makes it computationally viable to include HIs in large-scale simulations. Test calculations performed on very coarse-grained models of the pyruvate dehydrogenase (PDH) E2 complex and on oligomers of ParM (ranging in size from 1 to 20 monomers) indicate that the method reproduces the translational diffusion behavior seen in more complete HI simulations surprisingly well; the method performs less well at capturing rotational diffusion but its discrepancies diminish with increasing size of the simulated assembly. Simulations of residue-level models of two tetrameric protein models demonstrate that the method also works well when more structurally detailed models are used in the simulations. Finally, test simulations of systems containing up to 1024 coarse-grained PDH molecules indicate that the proposed method rapidly becomes more efficient than the conventional BD approach in which correlated random displacements are obtained via a Cholesky decomposition of the complete diffusion tensor.

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Mendeley demographics

Mendeley demographics

The data shown below were compiled from readership statistics for 36 Mendeley readers of this research output. Click here to see the associated Mendeley record.
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Geographical breakdown

Geographical breakdown
Country Count As %
United States 3 8%
Taiwan 1 3%
Russia 1 3%
Norway 1 3%
Italy 1 3%
Germany 1 3%
Unknown 28 78%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Student > Ph. D. Student 12 33%
Researcher 12 33%
Student > Master 5 14%
Student > Doctoral Student 2 6%
Professor > Associate Professor 2 6%
Other 1 3%
Unknown 2 6%
Readers by discipline
Readers by discipline Count As %
Chemistry 9 25%
Agricultural and Biological Sciences 8 22%
Biochemistry, Genetics and Molecular Biology 4 11%
Physics and Astronomy 4 11%
Engineering 3 8%
Other 6 17%
Unknown 2 6%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 12 June 2013.
All research outputs
#20,194,368
of 22,711,645 outputs
Outputs from Journal of Chemical Theory and Computation
#5,984
of 6,703 outputs
Outputs of similar age
#172,414
of 197,310 outputs
Outputs of similar age from Journal of Chemical Theory and Computation
#43
of 121 outputs
Altmetric has tracked 22,711,645 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 6,703 research outputs from this source. They receive a mean Attention Score of 3.9. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 197,310 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 121 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.