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

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...

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Bibliographic Details
Published inJournal of chemical information and modeling Vol. 60; no. 10; pp. 4436 - 4441
Main Authors Samways, Marley L, Bruce Macdonald, Hannah E, Essex, Jonathan W
Format Journal Article
LanguageEnglish
Published United States American Chemical Society 26.10.2020
Subjects
Bulk density
Chemical potential
Crystallography
Modules
Molecular dynamics
Proteins
Simulation
Trypsin inhibitors
Water chemistry
Water sampling
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Summary: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.
ISSN:1549-9596
1549-960X
DOI:10.1021/acs.jcim.0c00648