Dataset Information

SOP-GPU: influence of solvent-induced hydrodynamic interactions on dynamic structural transitions in protein assemblies.

ABSTRACT: Hydrodynamic interactions (HI) are incorporated into Langevin dynamics of the C? -based protein model using the Truncated Expansion approximation (TEA) to the Rotne-Prager-Yamakawa diffusion tensor. Computational performance of the obtained GPU realization demonstrates the model's capability for describing protein systems of varying complexity (10(2) -10(5) residues), including biological particles (filaments, virus shells). Comparison of numerical accuracy of the TEA versus exact description of HI reveals similar results for the kinetics and thermodynamics of protein unfolding. The HI speed up and couple biomolecular transitions through cross-communication among protein domains, which result in more collective displacements of structure elements governed by more deterministic (less variable) dynamics. The force-extension/deformation spectra from nanomanipulations in silico exhibit sharper force signals that match well the experimental profiles. Hence, biomolecular simulations without HI overestimate the role of tension/stress fluctuations. Our findings establish the importance of incorporating implicit water-mediated many-body effects into theoretical modeling of dynamic processes involving biomolecules. © 2016 Wiley Periodicals, Inc.

SUBMITTER: Alekseenko A

PROVIDER: S-EPMC5021127 | biostudies-literature | 2016 Jun

REPOSITORIES: biostudies-literature

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SOP-GPU: influence of solvent-induced hydrodynamic interactions on dynamic structural transitions in protein assemblies.

Alekseenko Andrey A Kononova Olga O Kholodov Yaroslav Y Marx Kenneth A KA Barsegov Valeri V

Journal of computational chemistry 20160326 17

Hydrodynamic interactions (HI) are incorporated into Langevin dynamics of the Cα -based protein model using the Truncated Expansion approximation (TEA) to the Rotne-Prager-Yamakawa diffusion tensor. Computational performance of the obtained GPU realization demonstrates the model's capability for describing protein systems of varying complexity (10(2) -10(5) residues), including biological particles (filaments, virus shells). Comparison of numerical accuracy of the TEA versus exact description of ...[more]

PMID: 27015749

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Project description:Hydrodynamic interactions exert a critical effect on the dynamics of macromolecules. As the concentration of macromolecules increases, by analogy to the behavior of semidilute polymer solutions or the flow in porous media, one might expect hydrodynamic screening to occur. Hydrodynamic screening would have implications both for the understanding of macromolecular dynamics as well as practical implications for the simulation of concentrated macromolecular solutions, e.g., in cells. Stokesian dynamics (SD) is one of the most accurate methods for simulating the motions of N particles suspended in a viscous fluid at low Reynolds number, in that it considers both far-field and near-field hydrodynamic interactions. This algorithm traditionally involves an O(N(3)) operation to compute Brownian forces at each time step, although asymptotically faster but more complex SD methods are now available. Motivated by the idea of hydrodynamic screening, the far-field part of the hydrodynamic matrix in SD may be approximated by a diagonal matrix, which is equivalent to assuming that long range hydrodynamic interactions are completely screened. This approximation allows sparse matrix methods to be used, which can reduce the apparent computational scaling to O(N). Previously there were several simulation studies using this approximation for monodisperse suspensions. Here, we employ newly designed preconditioned iterative methods for both the computation of Brownian forces and the solution of linear systems, and consider the validity of this approximation in polydisperse suspensions. We evaluate the accuracy of the diagonal approximation method using an intracellular-like suspension. The diffusivities of particles obtained with this approximation are close to those with the original method. However, this approximation underestimates intermolecular correlated motions, which is a trade-off between accuracy and computing efficiency. The new method makes it possible to perform large-scale and long-time simulation with an approximate accounting of hydrodynamic interactions.

Dataset Information

SOP-GPU: influence of solvent-induced hydrodynamic interactions on dynamic structural transitions in protein assemblies.

Publications

SOP-GPU: influence of solvent-induced hydrodynamic interactions on dynamic structural transitions in protein assemblies.

Similar Datasets

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