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py-MCMD: Python Software for Performing Hybrid Monte Carlo/Molecular Dynamics Simulations with GOMC and NAMD.

Mohammad Soroush Barhaghi1, Brad Crawford2, Gregory Schwing3

  • 1Theoretical and Computational Biophysics Group, NIH Center for Macromolecular Modeling and Bioinformatics, Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.

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py-MCMD software integrates NAMD and GOMC for efficient hybrid Monte Carlo/molecular dynamics (MC/MD) simulations. This workflow significantly enhances computational efficiency and sampling for complex systems like water in pores and protein hydration.

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Area of Science:

  • Computational chemistry and biophysics
  • Molecular simulation and modeling
  • Software development for scientific computing

Background:

  • Molecular dynamics (MD) and Monte Carlo (MC) simulations are powerful tools for studying molecular systems.
  • Integrating different simulation methods can overcome limitations and improve efficiency.
  • Efficient workflows are crucial for managing complex simulation data and analysis.

Purpose of the Study:

  • To introduce py-MCMD, an open-source Python software for hybrid MC/MD simulations.
  • To validate the py-MCMD workflow by performing simulations for water and protein hydration.
  • To demonstrate the computational efficiency and sampling advantages of the py-MCMD approach.

Main Methods:

  • Developed py-MCMD as a workflow layer managing communication between NAMD and GOMC.
  • Performed hybrid MC/MD simulations for SPC/E water in NPT and GC ensembles, and GEMC.
  • Applied hybrid GCMC/MD simulations to hydrate a binding pocket in bovine pancreatic trypsin inhibitor.

Main Results:

  • Hybrid MC/MD simulations showed close agreement with reference MC simulations, with 2-136x greater computational efficiency.
  • Coupled-decoupled configurational-bias MC (CD-CBMC) algorithm in MC/MD simulations reached equilibrium 25 times faster for water in graphene pores.
  • GCMC/MD simulations for protein hydration yielded water occupancies matching crystallographic data, with 5x better efficiency than MD.

Conclusions:

  • py-MCMD provides a robust and efficient workflow for hybrid MC/MD simulations.
  • The software enables significant gains in computational efficiency and sampling for various molecular systems.
  • py-MCMD facilitates accurate modeling of complex phenomena, including protein hydration and confinement effects.