Non-equilibrium Markov state modeling of periodically driven biomolecules

Area of Science:

• Computational Biology
• Biophysics
• Statistical Mechanics

Background:

• Molecular dynamics (MD) simulations offer high-resolution insights into biomolecular structure and dynamics.
• Many biological processes occur over timescales too long for atomistic MD, necessitating coarse-grained multiscale models.
• Existing methods for constructing these models often assume systems remain in equilibrium (obey detailed balance).

Purpose of the Study:

• To develop a method for creating Markov state models (MSMs) for systems driven out of equilibrium.
• To address the challenge of modeling biomolecules functioning in a non-equilibrium cellular environment.
• To provide a computational framework for systems influenced by time-dependent external parameters.

Main Methods:

• Introduction of a novel method to construct MSMs for periodically driven systems.
• Application of the method to alanine dipeptide, a standard benchmark molecule.
• Simulation of alanine dipeptide under a time-dependent electric field to break detailed balance.

Main Results:

• Successfully constructed a Markov state model for a non-equilibrium system.
• Demonstrated the feasibility of modeling biomolecular dynamics under external perturbations.
• Provided a computational approach applicable to systems deviating from detailed balance.

Conclusions:

• The developed method enables the creation of MSMs for biomolecules operating out of equilibrium.
• This approach is crucial for understanding biological functions that rely on non-equilibrium dynamics.
• The study offers a valuable tool for computational biophysics and drug discovery.