Simulation of Coarse-Grained Protein-Protein Interactions with Graphics Processing Units.

Area of Science:

• Computational Biology
• Biophysics
• High-Performance Computing

Background:

• Simulating large protein assemblies is computationally intensive.
• Current methods struggle with the length and time scales required for complex biological systems.

Purpose of the Study:

• To develop and validate a hybrid parallel central and graphics processing units (CPU-GPU) implementation for coarse-grained replica exchange Monte Carlo (REMC) simulations.
• To significantly enhance the speed and scale of protein assembly simulations.

Main Methods:

• Hybrid CPU-GPU parallelization strategy tailored for GPU hardware.
• Coarse-grained modeling for protein assemblies.
• Replica Exchange Monte Carlo (REMC) sampling.

Main Results:

• Achieved significant speedups compared to single-core CPU simulations, up to 1400x for large systems.
• Demonstrated the ability to simulate larger systems and longer timescales.
• Successfully identified the correct binding structure for human hepatitis B virus capsid fragments.

Conclusions:

• The hybrid CPU-GPU approach vastly increases accessible length and time scales for protein simulations.
• This method facilitates the study of complex interacting protein systems and viral capsid assembly.
• The parallel solution is generalizable to other macromolecules and N-body problems.