Evaluating free energy, enthalpy, and entropy of protonated water clusters by a grand canonical Monte Carlo simulation

2000 ◽  
Vol 113 (22) ◽  
pp. 10100-10104 ◽  
Author(s):  
Isamu Kusaka ◽  
David W. Oxtoby
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Free Energy ◽  
Water Clusters ◽  
Grand Canonical Monte Carlo ◽  
Enthalpy And Entropy ◽  
Grand Canonical

scholarly journals Adsorption of Thiophene in MCM-22 Zeolite by Grand Canonical Monte Carlo Simulation

2013 ◽  
Vol 25 (2) ◽  
pp. 1011-1014 ◽  
Author(s):  
Li-Chun Xuan ◽  
Ming-Xia Li ◽  
Qing-Jiang Pan ◽  
Guo Zhang
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Grand Canonical Monte Carlo ◽  
Grand Canonical

scholarly journals Enhancing Water Sampling in Free Energy Calculations with Grand Canonical Monte Carlo

2020 ◽  
Author(s):  
Gregory Ross ◽  
Ellery Russell ◽  
Yuqing Deng ◽  
Chao Lu ◽  
Edward Harder ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Free Energy ◽  
Drug Discovery ◽  
Free Energy Calculations ◽  
Data Sets ◽  
Grand Canonical Monte Carlo ◽  
Enhanced Sampling ◽  
Computational Performance ◽  
Energy Perturbation ◽  
Grand Canonical

<div>The prediction of protein-ligand binding affinities using free energy perturbation (FEP) is becoming increasingly routine in structure-based drug discovery. Most FEP packages use molecular dynamics (MD) to sample the configurations of proteins and ligands, as MD is well-suited to capturing coupled motion. However, MD can be prohibitively inefficient at sampling water molecules that are buried within binding sites, which has severely limited the domain of applicability of FEP and its prospective usage in drug discovery. In this paper, we present an advancement of FEP that augments MD with grand canonical Monte Carlo (GCMC), an enhanced sampling method, to overcome the problem of sampling water. We accomplished this without degrading computational performance. On both old and newly assembled data sets of proteinligand complexes, we show that the use of GCMC in FEP is essential for accurate and robust predictions for ligand perturbations that disrupt buried water. <br></div>

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