ArticleJournal of computer-aided molecular design2022
Enhancing sampling of water rehydration upon ligand binding using variants of grand canonical Monte Carlo.
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.
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Who cites it
7 citing papers in PubMed, 9 citations in OpenAlex.
- Large-Scale Collaborative Assessment of Binding Free Energy Calculations for Drug Discovery Using OpenFE.Journal of chemical information and modeling · 2026Article
- Accelerating fragment-based drug discovery using grand canonical nonequilibrium candidate Monte Carlo.Nature communications · 2025Article
- Enhanced Sampling of Buried Charges in Free Energy Calculations Using Replica Exchange with Charge Tempering.Journal of chemical theory and computation · 2024Article
- GPU-specific algorithms for improved solute sampling in grand canonical Monte Carlo simulations.Journal of computational chemistry · 2023Article
- Heterogeneous and Allosteric Role of Surface Hydration for Protein-Ligand Binding.Journal of chemical theory and computation · 2023Article
- Enhanced Grand Canonical Sampling of Occluded Water Sites Using Nonequilibrium Candidate Monte Carlo.Journal of chemical theory and computation · 2023Article
- Recent PELE Developments and Applications in Drug Discovery Campaigns.International journal of molecular sciences · 2022Review
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Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
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.
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