Multiscale approaches to protein-mediated interactions between membranes - Relating microscopic and macroscopic dynamics in radially growing adhesions

TL;DR

This paper introduces a Monte Carlo simulation method that effectively models membrane adhesion by integrating membrane effects into local molecular recognition rates, enabling large-scale, accurate simulations of adhesion domain growth.

Contribution

The authors develop a novel Monte Carlo scheme that captures multiscale membrane-protein interactions, bridging microscopic events and macroscopic adhesion dynamics efficiently.

Findings

The Monte Carlo approach accurately reproduces Langevin simulation results.

The method reveals microscopic mechanisms behind radial adhesion domain growth.

Simulation results align with experimental observations of micron-sized adhesion domains.

Abstract

Macromolecular complexation leading to coupling of two or more cellular membranes is a crucial step in a number of biological functions of the cell. While other mechanisms may also play a role, adhesion always involves the fluctuations of deformable membranes, the diffusion of proteins and the molecular binding and unbinding. Because these stochastic processes couple over a multitude of time and length scales, theoretical modeling of membrane adhesion has been a major challenge. Here we present an effective Monte Carlo scheme within which the effects of the membrane are integrated into local rates for molecular recognition. The latter step in the Monte Carlo approach enables us to simulate the nucleation and growth of adhesion domains within a system of the size of a cell for tens of seconds without loss of accuracy, as shown by comparison to $10^6$ times more expensive Langevin simulations. To perform this validation, the Langevin approach was augmented to simulate diffusion of proteins explicitly, together with reaction kinetics and membrane dynamics. We use the Monte Carlo scheme to gain deeper insight to the experimentally observed radial growth of micron sized adhesion domains, and connect the effective rate with which the domain is growing to the underlying microscopic events. We thus demonstrate that our technique yields detailed information about protein transport and complexation in membranes, which is a fundamental step toward understanding even more complex membrane interactions in the cellular context.