Evidence map›Paper›PMID 36198874›Full record

ArticleJournal of computer-aided molecular design2022

Enhancing sampling of water rehydration upon ligand binding using variants of grand canonical Monte Carlo.

Yunhui Ge, Oliver J Melling, Weiming Dong, Jonathan W Essex, David L Mobley

Open access · greenAbstract read
In one paragraph

Article in Journal of computer-aided molecular design, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
0.8field-weighted citation impact, top 31% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed, 9 citations in OpenAlex.

  1. Article
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  7. Recent PELE Developments and Applications in Drug Discovery Campaigns.International journal of molecular sciences · 2022
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 2 institutions in 2 countries.

Yunhui GeDepartment of Pharmaceutical Sciences, University of California, Irvine, Irvine, CA, 92697, USA.
Oliver J MellingSchool of Chemistry, University of Southampton, Southampton, SO17 1BJ, UK.
Weiming DongDepartment of Pharmaceutical Sciences, University of California, Irvine, Irvine, CA, 92697, USA.
Jonathan W EssexSchool of Chemistry, University of Southampton, Southampton, SO17 1BJ, UK.
David L MobleyDepartment of Pharmaceutical Sciences, University of California, Irvine, Irvine, CA, 92697, USA. dmobley@mobleylab.org.
University of California, Irvine · USUniversity of Southampton · GB

Funding

Mechanisms of Allostery and Molecular Recognition in the Small Multidrug Resistance FamilyR01AI108889 · NIAID · NEW YORK UNIVERSITY · PI Nathaniel J. Traaseth · 2014 to 2026
$5.4M
Optimizing a couples-based mHealth intervention for weight managementR01DK132386 · NIDDK · UNIVERSITY OF CONNECTICUT STORRS · PI Amy A Gorin, Deborah F. Tate · 2022 to 2026
$3.4M
Open data-driven infrastructure for building biomolecular force fields for predictive biophysics and drug designR01GM132386 · NIGMS · UNIVERSITY OF COLORADO · PI SHIRTS, MICHAEL R · 2020 to 2023
$3.1M
Computational alchemy for molecular design and optimizationR01GM108889 · NIGMS · UNIVERSITY OF CALIFORNIA-IRVINE · PI MOBLEY, DAVID LOWELL · 2014 to 2022
$2.5M
NIAID NIH HHS R01 AI108889NIDDK NIH HHS R01 DK132386NIGMS NIH HHS R01 GM108889NIGMS NIH HHS R01 GM132386
6 · The paper itself

Abstract

Water plays an important role in mediating protein-ligand interactions. Water rearrangement upon a ligand binding or modification can be very slow and beyond typical timescales used in molecular dynamics (MD) simulations. Thus, inadequate sampling of slow water motions in MD simulations often impairs the accuracy of the accuracy of ligand binding free energy calculations. Previous studies suggest grand canonical Monte Carlo (GCMC) outperforms normal MD simulations for water sampling, thus GCMC has been applied to help improve the accuracy of ligand binding free energy calculations. However, in prior work we observed protein and/or ligand motions impaired how well GCMC performs at water rehydration, suggesting more work is needed to improve this method to handle water sampling. In this work, we applied GCMC in 21 protein-ligand systems to assess the performance of GCMC for rehydrating buried water sites. While our results show that GCMC can rapidly rehydrate all selected water sites for most systems, it fails in five systems. In most failed systems, we observe protein/ligand motions, which occur in the absence of water, combine to close water sites and block instantaneous GCMC water insertion moves. For these five failed systems, we both extended our GCMC simulations and tested a new technique named grand canonical nonequilibrium candidate Monte Carlo (GCNCMC). GCNCMC combines GCMC with the nonequilibrium candidate Monte Carlo (NCMC) sampling technique to improve the probability of a successful water insertion/deletion. Our results show that GCNCMC and extended GCMC can rehydrate all target water sites for three of the five problematic systems and GCNCMC is more efficient than GCMC in two out of the three systems. In one system, only GCNCMC can rehydrate all target water sites, while GCMC fails. Both GCNCMC and GCMC fail in one system. This work suggests this new GCNCMC method is promising for water rehydration especially when protein/ligand motions may block water insertion/removal.

Indexed as

Molecular Dynamics SimulationWaterFluid TherapyLigandsMonte Carlo MethodProteinsLigandsProteinsWaterElectron density mapEnhanced water samplingGrand canonical Monte CarloNonequilibrium candidate Monte Carlo

Identifiers

PMID36198874
PMCPMC9869699
OpenAlexW4302282149

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.