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

2020 ◽  
Vol 16 (10) ◽  
pp. 6061-6076
Author(s):  
Gregory A. Ross ◽  
Ellery Russell ◽  
Yuqing Deng ◽  
Chao Lu ◽  
Edward D. Harder ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Free Energy ◽  
Free Energy Calculations ◽  
Water Sampling ◽  
Grand Canonical Monte Carlo ◽  
Energy Calculations ◽  
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>

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>

Monte Carlo free energy calculations using electronic structure methods

2008 ◽  
Vol 128 (15) ◽  
pp. 154110 ◽  
Author(s):  
Daniel R. Matusek ◽  
Sébastien Osborne ◽  
Alain St-Amant
Keyword(s):  
Monte Carlo ◽  
Free Energy ◽  
Electronic Structure ◽  
Free Energy Calculations ◽  
Energy Calculations ◽  
Electronic Structure Methods
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