A Master equation approach to modeling an artificial protein motor

TL;DR

This paper introduces a Master equation-based stochastic modeling approach, combined with Langevin and molecular dynamics simulations, to analyze the complex multi-scale processes of an artificial protein motor called Tumbleweed.

Contribution

It presents a novel integrated modeling framework for artificial molecular motors, capturing multi-scale dynamics and interdependencies of motor processes.

Findings

Successfully modeled Tumbleweed's operation across different time scales

Revealed key process interdependencies affecting motor function

Provided insights into design principles for artificial protein motors

Abstract

Linear bio-molecular motors move unidirectionally along a track by coordinating several different processes, such as fuel (ATP) capture, hydrolysis, conformational changes, binding and unbinding from a track, and center-of-mass diffusion. A better understanding of the interdependencies between these processes, which take place over a wide range of different time scales, would help elucidate the general operational principles of molecular motors. Artificial molecular motors present a unique opportunity for such a study because motor structure and function are a priori known. Here we describe use of a Master equation approach, integrated with input from Langevin and molecular dynamics modeling, to stochastically model a molecular motor across many time scales. We apply this approach to a specific concept for an artificial protein motor, the Tumbleweed.