Simulating a chemically fueled molecular motor with nonequilibrium molecular dynamics.

Area of Science:

• Computational chemistry and molecular dynamics
• Chemical physics and nonequilibrium thermodynamics

Background:

• Traditional molecular dynamics simulations often assume equilibrium conditions, limiting their applicability to systems driven by chemical reactions.
• Molecular motors and catalysts function dynamically, driven by thermodynamic forces that create nonequilibrium steady states.
• Simulations require specialized methods, such as chemostats, to maintain chemical concentrations in these nonequilibrium states.

Purpose of the Study:

• To develop a dynamic simulation scheme with chemostats for preserving chemical concentrations in nonequilibrium steady states.
• To observe the operational cycles of a particle-based classical model of a catenane-like molecular motor.
• To investigate how inter-particle interactions influence the rates and function of molecular motors.

Main Methods:

• Development of a novel dynamic simulation scheme incorporating chemostats to maintain chemical nonequilibrium.
• Particle-based classical modeling of a catenane-like molecular motor.
• Coarse-graining microscopic dynamics to construct detailed-balance-breaking Markov models and extract reaction rates.

Main Results:

• Successfully simulated the cycles of a molecular motor under nonequilibrium conditions.
• Identified specific inter-particle interactions that effectively tune the motor's operational rates.
• Demonstrated the ability to extract Markov model rates from microscopic simulation data.

Conclusions:

• The developed chemostat scheme is effective for simulating molecular motors driven by chemical thermodynamics.
• Inter-particle interactions play a crucial role in determining the functionality and efficiency of molecular motors.
• This computational approach provides a valuable tool for studying directional bias, current generation, and coupling in molecular ratchets.