Coarse-graining molecular dynamics: stochastic models with non-Gaussian force distributions

TL;DR

This paper introduces nonlinear stochastic coarse-grained models for molecular dynamics that accurately incorporate non-Gaussian force distributions, improving multiscale modeling of cellular processes.

Contribution

It presents a novel nonlinear SCG model that captures non-Gaussian forces and remains easy to parametrize using MD simulations.

Findings

Non-Gaussian force distributions are effectively modeled.

Nonlinearities do not hinder model parametrization.

Solutions involve gamma functions.

Abstract

Incorporating atomistic and molecular information into models of cellular behaviour is challenging because of a vast separation of spatial and temporal scales between processes happening at the atomic and cellular levels. Multiscale or multi-resolution methodologies address this difficulty by using molecular dynamics (MD) and coarse-grained models in different parts of the cell. Their applicability depends on the accuracy and properties of the coarse-grained model which approximates the detailed MD description. A family of stochastic coarse-grained (SCG) models, written as relatively low-dimensional systems of nonlinear stochastic differential equations, is presented. The nonlinear SCG model incorporates the non-Gaussian force distribution which is observed in MD simulations and which cannot be described by linear models. It is shown that the nonlinearities can be chosen in such a way that they do not complicate parametrization of the SCG description by detailed MD simulations. The solution of the SCG model is found in terms of gamma functions.