Systematic hierarchical coarse-graining with the inverse Monte Carlo method.

Area of Science:

• Computational chemistry
• Soft matter physics
• Molecular modeling

Background:

• Bridging micro- and mesoscales in soft matter and biological systems is computationally challenging.
• Atomistic simulations provide detailed insights but are limited in scale.
• Coarse-graining (CG) methods offer a way to simulate larger systems by reducing complexity.

Purpose of the Study:

• To develop and apply a coarse-graining strategy for linking molecular dynamics (MD) simulation scales.
• To create effective pairwise interaction potentials for CG simulations.
• To model DNA-protein interactions at a CG level.

Main Methods:

• Utilized effective pairwise interaction potentials derived from detailed atomistic MD simulations.
• Employed the inverse Monte Carlo (IMC) method on radial distribution functions to obtain potentials.
• Developed in-house software package MagiC for generating effective potentials.
• Applied the method to model bacterial LiaR regulator bound to DNA at physiological salt concentration.

Main Results:

• Successfully computed effective potentials for coarse-grained DNA-protein interactions.
• The computed potentials were fitted to a functional form (five Gaussians and a repulsive wall).
• Simulations showed stable association between DNA and the model protein.
• Observed similar position fluctuation profiles compared to more detailed models.

Conclusions:

• The developed coarse-graining strategy effectively links micro- and mesoscales for soft matter and biological systems.
• The method provides a computationally efficient way to model complex molecular interactions like DNA-protein binding.
• The fitted potentials offer a robust representation for large-scale simulations of such systems.