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Toward Automated Free Energy Calculation with Accelerated Enveloping Distribution Sampling (A-EDS)
Author(s) -
Jan Walther Perthold,
Dražen Petrov,
Chris Oostenbrink
Publication year - 2020
Publication title -
journal of chemical information and modeling
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.24
H-Index - 160
eISSN - 1549-960X
pISSN - 1549-9596
DOI - 10.1021/acs.jcim.0c00456
Subject(s) - free energy perturbation , parametrization (atmospheric modeling) , phase space , computer science , statistical physics , sampling (signal processing) , energy (signal processing) , transformation (genetics) , distribution (mathematics) , reaction coordinate , thermodynamic integration , algorithm , computational chemistry , physics , chemistry , mathematics , molecular dynamics , quantum mechanics , mathematical analysis , filter (signal processing) , biochemistry , computer vision , gene , radiative transfer
Free-energy perturbation (FEP) methods are commonly used in drug design to calculate relative binding free energies of different ligands to a common host protein. Alchemical ligand transformations are usually performed in multiple steps which need to be chosen carefully to ensure sufficient phase-space overlap between neighboring states. With one-step or single-step FEP techniques, a single reference state is designed that samples phase-space not only representative of a full transformation but also ideally resembles multiple ligand end states and hence allows for efficient multistate perturbations. Enveloping distribution sampling (EDS) is one example for such a method in which the reference state is created by a mathematical combination of the different ligand end states based on solid statistical mechanics. We have recently proposed a novel approach to EDS which enables efficient barrier crossing between the different end states, termed accelerated EDS (A-EDS). In this work, we further simplify the parametrization of the A-EDS reference state and demonstrate the automated calculation of multiple free-energy differences between different ligands from a single simulation in three different well-described drug design model systems.

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