PaCS-Q: Python Toolkits for Path Sampling in MD and QM/MM MD Simulation.

Area of Science:

• Computational Chemistry
• Molecular Dynamics
• Quantum Mechanics

Background:

• Molecular dynamics (MD) and QM/MM simulations are crucial for studying complex chemical processes.
• Exploring reaction pathways and molecular interactions often requires sophisticated computational tools.
• Current methods can be complex, demanding significant expertise and setup time.

Purpose of the Study:

• To introduce PaCS-Q, an open-source Python toolkit designed to simplify QM/MM MD and MD simulations.
• To enhance the accessibility and user-friendliness of complex pathway sampling techniques.
• To automate the process of exploring reaction pathways without the need for predefined reaction coordinates.

Main Methods:

• PaCS-Q integrates seamlessly with the AMBER MD suite.
• It automates QM/MM MD simulations using the parallel cascade selection (PaCS) algorithm.
• Supports RMSD- and distance-based sampling for covalent reactions and ligand binding.
• Automatically generates QM input files for Gaussian and ORCA from representative structures.

Main Results:

• PaCS-Q streamlines the workflow from MD to quantum calculations.
• Built-in tools facilitate structure analysis and energy profiling.
• The toolkit minimizes setup complexity and enhances the reproducibility of simulations.
• Enables efficient exploration of reaction pathways and molecular interactions.

Conclusions:

• PaCS-Q provides a practical and versatile solution for computational chemists and drug discovery researchers.
• It makes advanced simulations more accessible, accurate, and efficient.
• The toolkit facilitates rapid analysis of molecular dynamics and quantum mechanics calculations.
• Publicly available on GitHub, promoting open-source collaboration.