Many-particle Brownian and Langevin Dynamics Simulations with the Brownmove package

Tihamér Geyer

doi:10.1186/2046-1682-4-7

Abstract

BackgroundBrownian Dynamics (BD) is a coarse-grained implicit-solvent simulation method that is routinely used to investigate binary protein association dynamics, but due to its efficiency in handling large simulation volumes and particle numbers it is well suited to also describe many-protein scenarios as they often occur in biological cells.ResultsHere we introduce our "brownmove" simulation package which was designed to handle many-particle problems with varying particle numbers and allows for a very flexible definition of rigid and flexible protein and polymer models. Both a Brownian and a Langevin dynamics (LD) propagation scheme can be used and hydrodynamic interactions are treated efficiently with our recently introduced TEA-HI ansatz [Geyer, Winter, JCP 130 (2009) 114905]. With simulations of constrained polymers and flexible models of spherical proteins we demonstrate that it is crucial to include hydrodynamics when multi-bead models are used in BD or LD simulations. Only then both the translational and the rotational diffusion coefficients and the timescales of the internal dynamics can be reproduced correctly. In the third example project we show how constant density boundary conditions [Geyer et al, JCP 120 (2004) 4573] can be used to set up a non-equilibrium simulation of diffusional transport across an array of fixed obstacles. Finally, we demonstrate how the agglomeration dynamics of multiple particles with attractive patches can be analysed conveniently with the help of a dynamic interaction network.ConclusionsCombining BD and LD propagation, fast hydrodynamics, a flexible protein model, and interfaces for "open" simulation settings, our freely available "brownmove" simulation package constitutes a new platform for coarse-grained many-particle simulations of biologically relevant diffusion and transport processes.

Highlights

Brownian Dynamics (BD) is a coarse-grained implicit-solvent simulation method that is routinely used to investigate binary protein association dynamics, but due to its efficiency in handling large simulation volumes and particle numbers it is well suited to describe many-protein scenarios as they often occur in biological cells
From the vast number of possible settings we chose two examples that highlight the importance of hydrodynamic interactions in coarse-grained simulations of flexible protein models and two manyparticle scenarios
In the first example we investigated how the stiffness of a bead-spring polymer which was controlled by additional springs between neighbours, affects the translational and rotational diffusion coefficients

Summary

Introduction

Brownian Dynamics (BD) is a coarse-grained implicit-solvent simulation method that is routinely used to investigate binary protein association dynamics, but due to its efficiency in handling large simulation volumes and particle numbers it is well suited to describe many-protein scenarios as they often occur in biological cells. Some of these information are microscopic detailed spatial pictures like crystal structures while others come from macroscopically measured data about turnovers or global reaction thermal motion of the water molecules which made the pollen grains move He found that one does not have to know their individual trajectories, but that a heat bath and a Stokesian friction term are enough to describe how they push the large pollen particles around. With its continuous solvent only the trajectories of the larger particles of interest are evaluated, which allows for large simulation volumes with many particles and long simulation times This method has been applied successfully to study, for example, bimolecular association reactions [3,4,5,6], the dynamics of colloidal suspensions and polymers [7,8], or, recently, the dynamics inside the crowded cytoplasm of a cell [9]. Other solvent mediated effects are the short ranged hydrophobic and hydrophilic interactions between proteins and the so-called hydrodynamic interactions which stem from the displacement of the solvent by the moving proteins, giving rise to many-particle velocity correlations

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Conclusion