PathMolD-AB: Spatiotemporal pathways of protein folding using parallel molecular dynamics with a coarse-grained...

Area of Science:

• Computational Biophysics
• Protein Folding Dynamics

Background:

• The protein folding problem (PFP) remains a significant challenge in computational biophysics.
• Molecular dynamics (MD) simulations are crucial for studying protein folding pathways but are computationally intensive.

Purpose of the Study:

• To introduce the PathMolD-AB framework for efficient, massively parallel MD simulations of protein folding.
• To analyze and visualize protein folding pathways using a minimalist AB-model.
• To compare simulation results with experimentally determined protein structures.

Main Methods:

• Development and application of the PathMolD-AB software package for massively parallel MD simulations.
• Utilized a coarse-grained minimalist AB-model for protein representation.
• Simulated folding pathways for four proteins (13FIBO, 2GB1, 1PLC, 5ANZ) generating extensive datasets.

Main Results:

• The PathMolD-AB framework demonstrated a logarithmic speedup with increasing protein length, making it suitable for diverse protein sizes.
• Simulations generated large datasets covering 3500 folding pathways and 35 million states, including spatial positions, free energies, and radii of gyration.
• Predicted protein structures showed high similarity to re-scaled biological structures from the Protein Data Bank (PDB).

Conclusions:

• The PathMolD-AB framework offers a promising and efficient computational tool for tackling the protein folding problem.
• The approach's scalability and accuracy suggest its utility for studying a wide range of proteins.
• Further application of this framework can advance our understanding of protein structure and function.