EquilibraTor streamlines molecular dynamics simulations in a single execution.

Area of Science:

• Computational chemistry and structural biology
• Bioinformatics and computational drug discovery

Background:

• Molecular dynamics (MD) simulations are crucial for studying biomolecular systems but often involve complex, time-consuming manual setup.
• Existing computational tools for MD simulations can be resource-limited or fragmented, hindering accessibility for non-expert users.

Purpose of the Study:

• To develop a streamlined, integrated workflow for the preparation, execution, and analysis of molecular dynamics simulations.
• To provide a user-friendly command-line tool that automates key steps in the MD simulation process.

Main Methods:

• Development of EquilibraTor, a Python-based command-line tool.
• Automation of file preprocessing, topology generation, simulation execution, and convergence diagnostics.
• Generation of simulation metric plots and equilibrated representative PDB structures.

Main Results:

• EquilibraTor successfully automates the entire MD simulation workflow, from setup to analysis.
• The tool produces essential outputs like simulation metric plots and stable, equilibrated PDB structures.
• Demonstrated utility through simulation of the KALB_4073-preQ0 enzyme-substrate complex.

Conclusions:

• EquilibraTor significantly simplifies and accelerates the process of performing MD simulations.
• The tool empowers researchers, including those with limited computational expertise, to integrate MD into their pipelines.
• Facilitates structural biology and drug discovery research by providing accessible and robust MD simulation capabilities.