Molecular exchange Monte Carlo: A generalized method for identity exchanges in grand canonical Monte Carlo simulations

Barhaghi, Mohammad Soroush; Torabi, Korosh; Nejahi, Younes; Schwiebert, Loren; Potoff, Jeffrey J.

doi:10.1063/1.5025184

Cited by 9 publications

(5 citation statements)

References 69 publications

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“…It should be noted that this section describes a basic implementation of GCMC moves, for which the acceptance rate is typically low in condensed systems. Enhanced sampling techniques, such as the configurational 30 and cavity bias 12 methods, as well as continuous fractional component 31 and molecular exchange approaches, 32 can be used to increase the acceptance rate observed, 33 albeit at an increased computational cost and often the introduction of additional userdefined parameters. These methods to boost the efficiency will be the subject of future work, but version 1.0.0 of grand does not support this.…”

Section: ■ Theorymentioning

confidence: 99%

grand: A Python Module for Grand Canonical Water Sampling in OpenMM

Samways

Macdonald

Essex

2020

J. Chem. Inf. Model.

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Networks of water molecules can play a critical role at the protein-ligand interface, and can directly influence drug-target interactions. Grand canonical methods aid in the sampling of these water molecules, where conventional molecular dynamics equilibration times are often long, by allowing waters to be inserted and deleted from the system, according to the chemical potential. Here, we present our open source Python module, grand (https://github.com/essex-lab/grand), which allows molecular dynamics simulations to be performed in conjunction with grand canonical Monte Carlo sampling, using the OpenMM simulation engine. We demonstrate the accuracy of this module by reproducing the density of bulk water observed from constant pressure simulations.Application of this code to the bovine pancreatic trypsin inhibitor protein reproduces three buried crystallographic water sites that are poorly sampled using conventional molecular dynamics.

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Section: ■ Theorymentioning

confidence: 99%

grand: A Python Module for Grand Canonical Water Sampling in OpenMM

Samways

Macdonald

Essex

2020

J. Chem. Inf. Model.

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“…The use of GCMC sampling has been found to significantly improve the accuracy of ligand binding free energy calculations, where displaced waters that are not expelled sufficiently quickly from the binding site can have a serious impact on the free energy results, when using conventional sampling methods. ,,, However, the acceptance rates for unbiased and instantaneous particle insertions and deletions in condensed phases are typically very low, with around 1 in every 10,000 moves attempting to insert/delete water molecules to/from a bulk water system being accepted . A number of enhanced sampling techniques have been developed to improve the acceptance rates of GCMC, including cavity biasing, continuous fractional component Monte Carlo, configurational biasing, and molecular exchange approaches . Here, we investigate the use of nonequilibrium switching to enhance the acceptance rates and, in turn, the efficiency of GCMC moves.…”

Section: Introductionmentioning

confidence: 99%

“… 33 A number of enhanced sampling techniques have been developed to improve the acceptance rates of GCMC, including cavity biasing, 25 continuous fractional component Monte Carlo, 34 configurational biasing, 35 and molecular exchange approaches. 36 Here, we investigate the use of nonequilibrium switching to enhance the acceptance rates and, in turn, the efficiency of GCMC moves.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Grand Canonical Sampling of Occluded Water Sites Using Nonequilibrium Candidate Monte Carlo

Melling

Samways

et al. 2023

J. Chem. Theory Comput.

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Water molecules play a key role in many biomolecular systems, particularly when bound at protein–ligand interfaces. However, molecular simulation studies on such systems are hampered by the relatively long time scales over which water exchange between a protein and solvent takes place. Grand canonical Monte Carlo (GCMC) is a simulation technique that avoids this issue by attempting the insertion and deletion of water molecules within a given structure. The approach is constrained by low acceptance probabilities for insertions in congested systems, however. To address this issue, here, we combine GCMC with nonequilibium candidate Monte Carlo (NCMC) to yield a method that we refer to as grand canonical nonequilibrium candidate Monte Carlo (GCNCMC), in which the water insertions and deletions are carried out in a gradual, nonequilibrium fashion. We validate this new approach by comparing GCNCMC and GCMC simulations of bulk water and three protein binding sites. We find that not only is the efficiency of the water sampling improved by GCNCMC but that it also results in increased sampling of ligand conformations in a protein binding site, revealing new water-mediated ligand-binding geometries that are not observed using alternative enhanced sampling techniques.

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“…As an MC engine, GOMC includes a number of advanced configurational-bias algorithms that support the insertion of molecules having more complex topologies with acceptance rates that are 1–2 orders of magnitude greater than those of naive approaches. − While the examples provided here focused on water, it is straightforward to perform similar calculations with larger, more complex molecules without modification of the software. In addition to the applications provided here, py-MCMD could be used to simulate other phenomena, such as diffusion or gas adsorption in polymers …”

Section: Discussionmentioning

confidence: 99%

“…These codes are linked with py-MCMD, an open-source Python program that oversees information transfer and the execution of each code . By linking of GOMC with NAMD, it is possible to perform hybrid GCMC/MD simulations that utilize the full suite of advanced configurational-bias sampling algorithms that are available in GOMC. − With py-MCMD, it is also possible to integrate MD sampling of configurational space into MC simulations, such as Gibbs ensemble (GE), leading to enhanced sampling efficiency over standard Gibbs ensemble Monte Carlo (GEMC) simulations …”

Section: Introductionmentioning

confidence: 99%

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

Barhaghi

Crawford

Schwing

et al. 2022

J. Chem. Theory Comput.

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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 .

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Molecular exchange Monte Carlo: A generalized method for identity exchanges in grand canonical Monte Carlo simulations

Cited by 9 publications

References 69 publications

grand: A Python Module for Grand Canonical Water Sampling in OpenMM

grand: A Python Module for Grand Canonical Water Sampling in OpenMM

Enhanced Grand Canonical Sampling of Occluded Water Sites Using Nonequilibrium Candidate Monte Carlo

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

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