scholarly journals The computation of free energy of TIP4P water using expanded ensemble method

2003 ◽  
Vol 52 (9) ◽  
pp. 2342
Author(s):  
Sheng Zheng-Mao ◽  
Luo Jun-Wei
Keyword(s):  
Free Energy ◽  
Ensemble Method ◽  
Expanded Ensemble

Determination of Free Energy from Chemical Potentials: Application of the Expanded Ensemble Method

1996 ◽  
Vol 18 (1-2) ◽  
pp. 43-58 ◽  
Author(s):  
A. P. Lyubartsev ◽  
A. Laaksonen ◽  
P. N. Vorontsov-velyaminov
Keyword(s):  
Free Energy ◽  
Ensemble Method ◽  
Chemical Potentials ◽  
Expanded Ensemble

New version of Monte Carlo expanded ensemble method for precise calculations of free energy difference

2006 ◽  
Vol 32 (6) ◽  
pp. 437-442 ◽  
Author(s):  
S. V. Burov ◽  
P. N. Vorontsov-Velyaminov ◽  
E. M. Piotrovskaya
Keyword(s):  
Monte Carlo ◽  
Free Energy ◽  
Energy Difference ◽  
Ensemble Method ◽  
Free Energy Difference ◽  
Expanded Ensemble

Free Energy Calculations by Expanded Ensemble Method for Lattice and Continuous Polymers

1996 ◽  
Vol 100 (4) ◽  
pp. 1153-1158 ◽  
Author(s):  
P. N. Vorontsov-Velyaminov ◽  
A. V. Broukhno ◽  
T. V. Kuznetsova ◽  
A. P. Lyubartsev
Keyword(s):  
Free Energy ◽  
Free Energy Calculations ◽  
Ensemble Method ◽  
Energy Calculations ◽  
Expanded Ensemble

scholarly journals Free Energy Analyses of Cell-Penetrating Peptides Using the Weighted Ensemble Method

Membranes ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 974
Author(s):  
Seungho Choe
Keyword(s):  
Drug Delivery ◽  
Free Energy ◽  
Cell Penetrating Peptides ◽  
Model Membrane ◽  
Model Membranes ◽  
Ensemble Method ◽  
Sampling Techniques ◽  
Path Sampling ◽  
Transport Mechanisms ◽  
Cell Penetrating

Cell-penetrating peptides (CPPs) have been widely used for drug-delivery agents; however, it has not been fully understood how they translocate across cell membranes. The Weighted Ensemble (WE) method, one of the most powerful and flexible path sampling techniques, can be helpful to reveal translocation paths and free energy barriers along those paths. Within the WE approach we show how Arg9 (nona-arginine) and Tat interact with a DOPC/DOPG(4:1) model membrane, and we present free energy (or potential mean of forces, PMFs) profiles of penetration, although a translocation across the membrane has not been observed in the current simulations. Two different compositions of lipid molecules were also tried and compared. Our approach can be applied to any CPPs interacting with various model membranes, and it will provide useful information regarding the transport mechanisms of CPPs.

scholarly journals The SAMPL6 SAMPLing challenge: Assessing the reliability and efficiency of binding free energy calculations

2019 ◽  
Author(s):  
Andrea Rizzi ◽  
Travis Jensen ◽  
David R. Slochower ◽  
Matteo Aldeghi ◽  
Vytautas Gapsys ◽  
...  
Keyword(s):  
Free Energy ◽  
Convergence Rates ◽  
Binding Free Energy ◽  
Potential Of Mean Force ◽  
Free Energy Calculations ◽  
Energy Estimates ◽  
Initial Population ◽  
Energy Calculations ◽  
Partial Charges ◽  
Expanded Ensemble

AbstractApproaches for computing small molecule binding free energies based on molecular simulations are now regularly being employed by academic and industry practitioners to study receptor-ligand systems and prioritize the synthesis of small molecules for ligand design. Given the variety of methods and implementations available, it is natural to ask how the convergence rates and final predictions of these methods compare. In this study, we describe the concept and results for the SAMPL6 SAMPLing challenge, the first challenge from the SAMPL series focusing on the assessment of convergence properties and reproducibility of binding free energy methodologies. We provided parameter files, partial charges, and multiple initial geometries for two octa-acid (OA) and one cucurbit[8]uril (CB8) host-guest systems. Participants submitted binding free energy predictions as a function of the number of force and energy evaluations for seven different alchemical and physical-pathway (i.e., potential of mean force and weighted ensemble of trajectories) methodologies implemented with the GROMACS, AMBER, NAMD, or OpenMM simulation engines. To rank the methods, we developed an efficiency statistic based on bias and variance of the free energy estimates. For the two small OA binders, the free energy estimates computed with alchemical and potential of mean force approaches show relatively similar variance and bias as a function of the number of energy/force evaluations, with the attach-pull-release (APR), GROMACS expanded ensemble, and NAMD double decoupling submissions obtaining the greatest efficiency. The differences between the methods increase when analyzing the CB8-quinine system, where both the guest size and correlation times for system dynamics are greater. For this system, nonequilibrium switching (GROMACS/NS-DS/SB) obtained the overall highest efficiency. Surprisingly, the results suggest that specifying force field parameters and partial charges is insufficient to generally ensure reproducibility, and we observe differences between seemingly converged predictions ranging approximately from 0.3 to 1.0 kcal/mol, even with almost identical simulations parameters and system setup (e.g., Lennard-Jones cutoff, ionic composition). Further work will be required to completely identify the exact source of these discrepancies. Among the conclusions emerging from the data, we found that Hamiltonian replica exchange—while displaying very small variance—can be affected by a slowly-decaying bias that depends on the initial population of the replicas, that bidirectional estimators are significantly more efficient than unidirectional estimators for nonequilibrium free energy calculations for systems considered, and that the Berendsen barostat introduces non-negligible artifacts in expanded ensemble simulations.

scholarly journals Enhanced conformational sampling to visualize a free-energy landscape of protein complex formation

2016 ◽  
Vol 473 (12) ◽  
pp. 1651-1662 ◽  
Author(s):  
Shinji Iida ◽  
Haruki Nakamura ◽  
Junichi Higo
Keyword(s):  
Free Energy ◽  
Room Temperature ◽  
Dimensional Space ◽  
Energy Landscape ◽  
Complex Structure ◽  
Complex Form ◽  
Ensemble Methods ◽  
Ensemble Method ◽  
Free Energy Landscape ◽  
Conformational Sampling

We introduce various, recently developed, generalized ensemble methods, which are useful to sample various molecular configurations emerging in the process of protein–protein or protein–ligand binding. The methods introduced here are those that have been or will be applied to biomolecular binding, where the biomolecules are treated as flexible molecules expressed by an all-atom model in an explicit solvent. Sampling produces an ensemble of conformations (snapshots) that are thermodynamically probable at room temperature. Then, projection of those conformations to an abstract low-dimensional space generates a free-energy landscape. As an example, we show a landscape of homo-dimer formation of an endothelin-1-like molecule computed using a generalized ensemble method. The lowest free-energy cluster at room temperature coincided precisely with the experimentally determined complex structure. Two minor clusters were also found in the landscape, which were largely different from the native complex form. Although those clusters were isolated at room temperature, with rising temperature a pathway emerged linking the lowest and second-lowest free-energy clusters, and a further temperature increment connected all the clusters. This exemplifies that the generalized ensemble method is a powerful tool for computing the free-energy landscape, by which one can discuss the thermodynamic stability of clusters and the temperature dependence of the cluster networks.

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