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

Samways, Marley L.; Macdonald, Hannah E. Bruce; Essex, Jonathan W.

doi:10.1021/acs.jcim.0c00648

Cited by 34 publications

(74 citation statements)

References 47 publications

(106 reference statements)

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“…This reinforces the need for methods such as GCMC that can place water molecules during a simulation. 82,83 However, when water molecules are included in the starting structure, the accuracy is typically better then 2.0 kcal/mol and the R 2 values are higher than 0.6. Absolute binding free energies may also benefit from the use of enhanced sampling techniques such as solute tempering 84 or temperature replica exchange 85 but have not been explored here.…”

Section: Discussionmentioning

confidence: 99%

Comparing the performance of different AMBER protein forcefields, partial charge assignments, and water models for absolute binding free energy calculations

Huggins

2022

Preprint

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Identifying chemical starting points is a vital first step in small molecule drug discovery and can take significant time and money. For this reason, computational approaches to virtual screening are of great interest as they can lower cost and shorten timeframes. However, simple approaches such as molecular docking and pharmacophore screening are of limited accuracy and provide a low probability of success. Alchemical binding free energies represent a promising approach for virtual screening as they naturally incorporate the key effects of water molecules, protein flexibility, and binding entropy. However, the calculations are technically very challenging, with performance depending on the specific forcefield used. For this reason, it is important that the community has access to benchmark test sets to assess prediction accuracy. In this paper we present an approach to alchemical binding free energies using OpenMM. We identify effective simulation parameters using an existing BRD4(1) test set and present two new benchmark sets (cMET and PDE2A) that can be used in the community for validation purposes. Our findings also highlight the effectiveness of some AMBER forcefields, in particular AMBER ff15ipq.

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Section: Discussionmentioning

confidence: 99%

Comparing the performance of different AMBER protein forcefields, partial charge assignments, and water models for absolute binding free energy calculations

Huggins

2022

Preprint

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“…In this work, the grand package 63 was used to perform GCMC moves with MD sampling using OpenMM simulation engine. 42 We used OpenMM so that all of simulation techniques (MD, NCMC, GCMC) studied in this work used the same engine (OpenMM) which provides an opportunity to conduct a relatively fair comparison between these techniques, avoiding scenarios where implementation differences in different engines might bias the results.…”

Section: Methodsmentioning

confidence: 99%

“…The details of these calculations can be found in prior work 62 and the calculated results at different temperatures used in this work can be found in Table S1.…”

Section: Grand Simulationsmentioning

confidence: 99%

Enhancing Sampling of Water Rehydration on Ligand Binding: A Comparison of Techniques

et al. 2021

Preprint

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Water often plays a key role in protein structure, molecular recognition, and mediating protein-ligand interactions. Thus, free energy calculations must adequately sample water motions, which often proves challenging in typical MD simulation timescales. Thus, the accuracy of methods relying on MD simulations ends up limited by slow water sampling. Particularly, as a ligand is removed or modified, bulk water may not have time to fill or rearrange in the binding site. In this work, we focus on several molecular dynamics (MD) simulation-based methods attempting to help address water motions and occupancies: BLUES, using nonequilibrium candidate Monte Carlo (NCMC); grand, using grand canonical Monte Carlo (GCMC); and normal MD. We assess the accuracy and efficiency of these methods in sampling water motions. We selected a range of systems with varying numbers of waters in the binding site, as well as those where water occupancy is coupled to the identity or binding mode of the ligand. We analyzed water motions and occupancies using both clustering of trajectories and direct analysis of electron density maps. Our results suggest both BLUES and grand enhance water sampling relative to normal MD and grand is more robust than BLUES, but also that water sampling remains a major challenge for all of the methods tested. The lessons we learned for these methods and systems are discussed.

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“…Pioneering approaches to speed the equilibration of water molecules between bulk solvent and the protein interior have interleaved grand canonical Monte Carlo (GCMC) water moves 8 with standard MD steps, 3,[9][10][11][12] and recent work 13 has made this technology available in the OpenMM simulation toolkit. 14 A GCMC move starts with an attempt ("try") to insert/remove a water molecule to/from a random point in the simulated system.…”

Section: Introductionmentioning

confidence: 99%

Fast Equilibration of Water between Buried Sites and Bulk by MD with Parallel Monte Carlo Water Moves on GPUs

Ben-Shalom

Lin

Radak

et al. 2021

Preprint

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Molecular dynamics (MD) simulations of proteins are commonly used to sample from the Boltzmann distribution of conformational states, with wide-ranging applications spanning chemistry, biophysics, and drug discovery. However, MD can be inefficient at equilibrating water occupancy for buried cavities in proteins that are inaccessible to the surrounding solvent. Indeed, the time needed for water molecules to equilibrate between bulk solvent and the binding site can be well beyond what is practical with standard MD, which typically ranges to hundreds of nanoseconds to low microseconds. We recently introduced a hybrid Monte Carlo/MD (MC/MD) method, which speeds up the equilibration of water between buried cavities and the surrounding solvent, while sampling from the thermodynamically correct distribution of states. While the initial implementation of the MC functionality led to considerable slowing of the overall simulations, here we address this problem with a parallel MC algorithm implemented on graphical processing units (GPUs). This results in speedups of 10-fold to 1000-fold over the original MC/MD algorithm, depending on the system and simulation parameters. The present method is available for use in the AMBER simulation software.

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

Cited by 34 publications

References 47 publications

Comparing the performance of different AMBER protein forcefields, partial charge assignments, and water models for absolute binding free energy calculations

Comparing the performance of different AMBER protein forcefields, partial charge assignments, and water models for absolute binding free energy calculations

Enhancing Sampling of Water Rehydration on Ligand Binding: A Comparison of Techniques

Fast Equilibration of Water between Buried Sites and Bulk by MD with Parallel Monte Carlo Water Moves on GPUs

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