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

• Published in: Journal of chemical theory and computation Vol. 18; no. 8; pp. 4983 - 4994
• Main Authors: Barhaghi, Mohammad Soroush, Crawford, Brad, Schwing, Gregory, Hardy, David J., Stone, John E., Schwiebert, Loren, Potoff, Jeffrey, Tajkhorshid, Emad
• Format: Journal Article
• Language: English
• Published: United States American Chemical Society 09.08.2022
• Subjects: Algorithms; Animals; Biomolecular Systems; Cattle; Communications systems; Computational efficiency; Computer simulation; Computing costs; Computing time; Crystallography; Efficiency; Graphene; Molecular dynamics; Molecular Dynamics Simulation; Monte Carlo Method; Simulation; Software; Thermodynamic properties; Thermodynamics; Trajectory analysis; Trypsin inhibitors; Water - chemistry; Workflow
• ISSN: 1549-9618; 1549-9626; 1549-9626
• DOI: 10.1021/acs.jctc.1c00911

py-MCMD, an open-source Python software, provides a robust workflow layer that manages communication of relevant system information between the simulation engines NAMD and GOMC and generates coherent thermodynamic properties and trajectories for analysis. To validate the workflow and highlight its capabilities, hybrid Monte Carlo/molecular dynamics (MC/MD) simulations are performed for SPC/E water in the isobaric–isothermal (NPT) and grand canonical (GC) ensembles as well as with Gibbs ensemble Monte Carlo (GEMC). The hybrid MC/MD approach shows close agreement with reference MC simulations and has a computational efficiency that is 2 to 136 times greater than traditional Monte Carlo simulations. MC/MD simulations performed for water in a graphene slit pore illustrate significant gains in sampling efficiency when the coupled–decoupled configurational-bias MC (CD–CBMC) algorithm is used compared with simulations using a single unbiased random trial position. Simulations using CD–CBMC reach equilibrium with 25 times fewer cycles than simulations using a single unbiased random trial position, with a small increase in computational cost. In a more challenging application, hybrid grand canonical Monte Carlo/molecular dynamics (GCMC/MD) simulations are used to hydrate a buried binding pocket in bovine pancreatic trypsin inhibitor. Water occupancies produced by GCMC/MD simulations are in close agreement with crystallographically identified positions, and GCMC/MD simulations have a computational efficiency that is 5 times better than MD simulations. py-MCMD is available on GitHub at https://github.com/GOMC-WSU/py-MCMD.