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

被引:0
|
作者
Yunhui Ge
Oliver J. Melling
Weiming Dong
Jonathan W. Essex
David L. Mobley
机构
[1] University of California,Department of Pharmaceutical Sciences
[2] Irvine,School of Chemistry
[3] University of Southampton,Department of Chemistry
[4] University of California,undefined
[5] Irvine,undefined
关键词
Nonequilibrium candidate Monte Carlo; Enhanced water sampling; Electron density map; Grand canonical Monte Carlo;
D O I
暂无
中图分类号
学科分类号
摘要
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.
引用
收藏
页码:767 / 779
页数:12
相关论文
共 50 条
  • [1] Enhancing sampling of water rehydration upon ligand binding using variants of grand canonical Monte Carlo
    Ge, Yunhui
    Melling, Oliver J.
    Dong, Weiming
    Essex, Jonathan W.
    Mobley, David L.
    JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN, 2022, 36 (10) : 767 - 779
  • [2] Enhancing Water Sampling in Free Energy Calculations with Grand Canonical Monte Carlo
    Ross, Gregory A.
    Russell, Ellery
    Deng, Yuqing
    Lu, Chao
    Harder, Edward D.
    Abel, Robert
    Wang, Lingle
    JOURNAL OF CHEMICAL THEORY AND COMPUTATION, 2020, 16 (10) : 6061 - 6076
  • [3] Predicting water networks and ligand binding free energies in proteins using grand canonical Monte Carlo
    Macdonald, Hannah Bruce
    Cave-Ayland, Christopher
    Essex, Jonathan
    ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY, 2018, 255
  • [4] Grand canonical Monte Carlo simulation of ligand-protein binding
    Clark, M
    Guarnieri, F
    Shkurko, I
    Wiseman, J
    JOURNAL OF CHEMICAL INFORMATION AND MODELING, 2006, 46 (01) : 231 - 242
  • [5] Enhancing Sampling of Water Rehydration on Ligand Binding: A Comparison of Techniques
    Ge, Yunhui
    Wych, David C.
    Samways, Marley L.
    Wall, Michael E.
    Essex, Jonathan W.
    Mobley, David L.
    JOURNAL OF CHEMICAL THEORY AND COMPUTATION, 2022, 18 (03) : 1359 - 1381
  • [6] Enhanced Grand Canonical Sampling of Occluded Water Sites Using Nonequilibrium Candidate Monte Carlo
    Melling, Oliver J.
    Samways, Marley L.
    Ge, Yunhui
    Mobley, David L.
    Essex, Jonathan W.
    JOURNAL OF CHEMICAL THEORY AND COMPUTATION, 2023, 19 (03) : 1050 - 1062
  • [7] Enhancing Ligand and Protein Sampling Using Sequential Monte Carlo
    Suruzhon, Miroslav
    Bodnarchuk, Michael S.
    Ciancetta, Antonella
    Wall, Ian D.
    Essex, Jonathan W.
    JOURNAL OF CHEMICAL THEORY AND COMPUTATION, 2022, 18 (06) : 3894 - 3910
  • [8] Enhancing water sampling of buried binding sites using nonequilibrium candidate Monte Carlo
    Bergazin, Teresa Danielle
    Ben-Shalom, Ido Y.
    Lim, Nathan M.
    Gill, Sam C.
    Gilson, Michael K.
    Mobley, David L.
    JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN, 2021, 35 (02) : 167 - 177
  • [9] Enhancing water sampling of buried binding sites using nonequilibrium candidate Monte Carlo
    Teresa Danielle Bergazin
    Ido Y. Ben-Shalom
    Nathan M. Lim
    Sam C. Gill
    Michael K. Gilson
    David L. Mobley
    Journal of Computer-Aided Molecular Design, 2021, 35 : 167 - 177
  • [10] ON THE SAMPLING METHOD FOR GRAND-CANONICAL MONTE-CARLO SIMULATIONS
    CRACKNELL, RF
    MOLECULAR SIMULATION, 1994, 13 (03) : 235 - 240