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Barhaghi MS, Crawford B, Schwing G, Hardy DJ, Stone JE, Schwiebert L, Potoff J, Tajkhorshid E. py-MCMD: Python Software for Performing Hybrid Monte Carlo/Molecular Dynamics Simulations with GOMC and NAMD. J Chem Theory Comput 2022; 18:4983-4994. [PMID: 35621307 PMCID: PMC9760104 DOI: 10.1021/acs.jctc.1c00911] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
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.
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Affiliation(s)
- Mohammad Soroush Barhaghi
- Theoretical 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
| | - Brad Crawford
- Department of Chemical Engineering and Materials Science, Wayne State University, Detroit, Michigan 48202, United States
| | - Gregory Schwing
- Department of Computer Science, Wayne State University, Detroit, Michigan 48202, United States
| | - David J Hardy
- Theoretical 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
| | - John E Stone
- Theoretical 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
| | - Loren Schwiebert
- Department of Computer Science, Wayne State University, Detroit, Michigan 48202, United States
| | - Jeffrey Potoff
- Department of Chemical Engineering and Materials Science, Wayne State University, Detroit, Michigan 48202, United States
| | - Emad Tajkhorshid
- Theoretical 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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Samways ML, Bruce Macdonald HE, Essex JW. grand: A Python Module for Grand Canonical Water Sampling in OpenMM. J Chem Inf Model 2020; 60:4436-4441. [DOI: 10.1021/acs.jcim.0c00648] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Affiliation(s)
- Marley L. Samways
- School of Chemistry, University of Southampton, Southampton, SO17 1BJ, United Kingdom
| | - Hannah E. Bruce Macdonald
- Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, New York 10065, United States
| | - Jonathan W. Essex
- School of Chemistry, University of Southampton, Southampton, SO17 1BJ, United Kingdom
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