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

Ian Tunbridge1, Robert B Best1, James Gain1

  • 1Department of Computer Science,University of Cape Town, Cape Town, South Africa and Department of Chemistry, Cambridge University, Cambridge, United Kingdom.

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Summary

We developed a hybrid CPU-GPU simulation method for protein assemblies. This approach significantly accelerates large-scale simulations, enabling the study of complex biological systems like viral capsids.

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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.