FOM Institute AMOLF, Science Park 104, 1098 XG Amsterdam, The Netherlands.
Department of Bioengineering, Imperial College London, South Kensington Campus, London SW7 2AZ, United Kingdom.
J Chem Phys. 2017 Mar 21;146(11):114106. doi: 10.1063/1.4977515.
The modeling of complex reaction-diffusion processes in, for instance, cellular biochemical networks or self-assembling soft matter can be tremendously sped up by employing a multiscale algorithm which combines the mesoscopic Green's Function Reaction Dynamics (GFRD) method with explicit stochastic Brownian, Langevin, or deterministic molecular dynamics to treat reactants at the microscopic scale [A. Vijaykumar, P. G. Bolhuis, and P. R. ten Wolde, J. Chem. Phys. 143, 214102 (2015)]. Here we extend this multiscale MD-GFRD approach to include the orientational dynamics that is crucial to describe the anisotropic interactions often prevalent in biomolecular systems. We present the novel algorithm focusing on Brownian dynamics only, although the methodology is generic. We illustrate the novel algorithm using a simple patchy particle model. After validation of the algorithm, we discuss its performance. The rotational Brownian dynamics MD-GFRD multiscale method will open up the possibility for large scale simulations of protein signalling networks.
通过采用多尺度算法，可以极大地加快复杂反应扩散过程的建模，该算法将介观格林函数反应动力学（GFRD）方法与显式随机布朗运动、朗之万或确定性分子动力学相结合，以在微观尺度上处理反应物[ A. Vijaykumar、P. G. Bolhuis 和 P. R. ten Wolde，J. Chem. Phys. 143, 214102（2015）]。在这里，我们将这种多尺度 MD-GFRD 方法扩展到包括对各向异性相互作用至关重要的取向动力学，这种相互作用在生物分子系统中很常见。我们专注于布朗动力学提出了新颖的算法，尽管该方法是通用的。我们使用简单的镶嵌粒子模型说明了新算法。在验证算法之后，我们讨论了其性能。旋转布朗动力学 MD-GFRD 多尺度方法将为蛋白质信号网络的大规模模拟开辟可能性。
