BEMM-GEN: A Toolkit for Generating a Biomolecular Environment-Mimicking Model for Molecular Dynamics Simulation

Area of Science:

• Biophysics
• Computational Biology
• Structural Biology

Background:

• Understanding protein function requires knowledge of the cellular environment's influence on protein conformation.
• Current models using uniform hydrophobic environments (e.g., carbon nanotubes, cages) may oversimplify complex biomolecular interiors like ribosome tunnels and chaperones.
• Accurate simulation of biomolecular environments is essential for predicting protein behavior.

Purpose of the Study:

• To develop a computational tool, BEMM-GEN, for generating diverse and chemically accurate biomolecular environment models.
• To enable the integration of arbitrary protein conformations into these generated models for molecular dynamics (MD) simulations.
• To provide ready-to-use input files for popular MD simulation packages (AMBER, GROMACS).

Main Methods:

• Development of the Biomolecular Environment-Mimicking Model Generator (BEMM-GEN) software.
• Generation of spherical and cylindrical models with user-defined chemical properties.
• Integration of protein structures into the generated environment models.
• Provision of parameter files compatible with AMBER and GROMACS for immediate MD simulation.

Main Results:

• BEMM-GEN successfully generates spherical and cylindrical models mimicking diverse cellular environments.
• The tool allows seamless integration of protein conformations into these models.
• Generated model and protein complex structures are accompanied by ready-to-use MD simulation parameter files.
• The software is accessible via pip and includes source code and a user manual on GitHub.

Conclusions:

• BEMM-GEN offers a significant advancement in simulating protein behavior within realistic cellular environments.
• The tool facilitates more accurate molecular dynamics simulations by accounting for specific biomolecular characteristics.
• This approach enhances the study of protein conformations and functions in biologically relevant contexts.