Stochastic Dynamics of Bionanosystems: Multiscale Analysis and Specialized Ensembles

TL;DR

This paper introduces a multiscale, all-atom simulation approach for bionanosystems like viruses and ribosomes, emphasizing cross-scale interactions and stochastic modeling of structural features without calibration.

Contribution

It develops a multiscale framework with automated order parameter construction and stochastic equations for simulating large bionanosystems efficiently.

Findings

Automated construction of order parameters for nanoscale features

Derivation of stochastic dynamics equations for order parameters

Integration of experimental data into nanoscale modeling

Abstract

An approach for simulating bionanosystems, such as viruses and ribosomes, is presented. This calibration-free approach is based on an all-atom description for bionanosystems, a universal interatomic force field, and a multiscale perspective. The supramillion-atom nature of these bionanosystems prohibits the use of a direct molecular dynamics approach for phenomena like viral structural transitions or self-assembly that develop over milliseconds or longer. A key element of these multiscale systems is the cross-talk between, and consequent strong coupling of, processes over many scales in space and time. We elucidate the role of interscale cross-talk and overcome bionanosystem simulation difficulties with automated construction of order parameters (OPs) describing supra-nanometer scale structural features, construction of OP dependent ensembles describing the statistical properties of atomistic variables that ultimately contribute to the entropies driving the dynamics of the OPs, and the derivation of a rigorous equation for the stochastic dynamics of the OPs. Since the atomic scale features of the system are treated statistically, several ensembles are constructed that reflect various experimental conditions. The theory provides a basis for a practical, quantitative bionanosystem modeling approach that preserves the cross-talk between the atomic and nanoscale features. A method for integrating information from nanotechnical experimental data in the derivation of equations of stochastic OP dynamics is also introduced.