PyCGTOOL: Automated Generation of Coarse-Grained Molecular Dynamics Models from Atomistic Trajectories

Area of Science:

• Computational Chemistry
• Materials Science
• Biophysics

Background:

• Developing coarse-grained (CG) molecular dynamics models is typically time-consuming and relies on approximations.
• Existing methods often use generic parameters, which may not accurately represent specific molecular systems.

Purpose of the Study:

• To automate the construction of CG molecular dynamics models.
• To improve the accuracy of CG models by deriving parameters directly from atomistic simulations.
• To facilitate the rapid investigation of different CG model configurations.

Main Methods:

• PyCGTOOL calculates equilibrium values and force constants of internal coordinates from atomistic molecular dynamics trajectories.
• The software automates the parametrization process, reducing manual effort.
• It supports various atom-to-bead mappings and topologies.

Main Results:

• Bespoke CG model parameters derived from atomistic simulations enhance model quality compared to generic parameters.
• Automation significantly decreases the time required for CG model development.
• PyCGTOOL's flexible configuration allows for efficient exploration of diverse model setups.

Conclusions:

• PyCGTOOL offers an automated and accurate approach to CG model development.
• The software improves the efficiency and reliability of creating CG models for molecular dynamics simulations.
• It is compatible with standard trajectory formats and can be used with different molecular dynamics engines like GROMACS.