Nonlinear Relaxation Dynamics in Elastic Networks and Design Principles of Molecular Machines

TL;DR

This paper investigates the nonlinear relaxation dynamics of elastic networks in motor proteins, revealing design principles for creating artificial molecular machines with robust internal motions through evolutionary optimization.

Contribution

It demonstrates that specific network architectures can be engineered to exhibit desired nonlinear relaxation behaviors, unlike random networks, enabling the design of functional molecular machines.

Findings

Robust internal mechanical motions in motor protein models

Designed elastic networks can operate as cyclic machines

Evolutionary optimization can create networks with specific dynamic properties

Abstract

Analyzing nonlinear conformational relaxation dynamics in elastic networks corresponding to two classical motor proteins, we find that they respond by well-defined internal mechanical motions to various initial deformations and that these motions are robust against external perturbations. We show that this behavior is not characteristic for random elastic networks. However, special network architectures with such properties can be designed by evolutionary optimization methods. Using them, an example of an artificial elastic network, operating as a cyclic machine powered by ligand binding, is constructed.