Multiscale molecular kinetics by coupling Markov state models and reaction-diffusion dynamics

Abstract

A novel approach to simulate simple protein-ligand systems at large time- and length-scales is to couple Markov state models (MSMs) of molecular kinetics with particle-based reaction-diffusion (RD) simulations, MSM/RD. Currently, MSM/RD lacks a mathematical framework to derive coupling schemes; is limited to isotropic ligands in a single conformational state, and is lacking a multi-particle extensions. In this work, we address these needs by developing a general MSM/RD framework by coarse-graining molecular dynamics into hybrid switching diffusion processes. Given enough data to parametrize the model, it is capable of modeling protein-protein interactions over large time- and length-scales, and it can be extended to handle multiple molecules. We derive the MSM/RD framework, and we implement and verify it for two protein-protein benchmark systems and one multiparticle implementation to model the formation of pentameric ring molecules. To enable reproducibility, we have published our code in the MSM/RD software package.

References in corpus (3)

• Dynamics of protein-protein encounter: a Langevin equation approach with reaction patches
• Studying protein assembly with reversible Brownian dynamics of patchy particles
• Fluorescence Correlation Spectroscopy and Nonlinear Stochastic Reaction-Diffusion

Cited by in corpus (7)

• A probabilistic framework for particle-based reaction-diffusion dynamics using classical Fock space representations
• Entropy production limits all fluctuation oscillations
• Chemical diffusion master equation: formulations of reaction--diffusion processes on the molecular level
• Data-driven dynamical coarse-graining for condensed matter systems
• Field theories and quantum methods for stochastic reaction-diffusion systems
• Dynamics of systems with varying number of particles: from Liouville equations to general master equations for open systems
• Open reaction-diffusion systems: bridging probabilistic theory and simulations across scales