Alchemical Free Energy Perturbation Predicts Relative Binding Affinities of Propofol Analogs and Etomidate Stereoisomers at the GABAAR
Abstract General anesthetics such as propofol and etomidate exert their clinical effects primarily through positive allosteric modulation of γ-aminobutyric acid type A receptors (GABAAR). Despite decades of research, the quantitative structure–activity relationships governing anesthetic binding remain incompletely characterized at the atomic level. Here, we employ rigorous alchemical free energy perturbation (FEP) calculations using a CHARMm GPU-accelerated protocol to compute relative binding free energies for a series of 13 propofol analogs at the wild-type GABAAR, as well as for the stereoisomeric pair of etomidate (R-etomidate vs S-etomidate). For propofol analogs at the wild-type receptor, computed relative binding free energies (ΔΔG) correlate with experimentally measured GABA EC50 potentiation values, with propofol (2,6-diisopropylphenol) and disec-butylphenol predicted as the most potent analogs, consistent with experimental data (Pearson r = 0.85, 95% CI 0.50–0.96, p = 0.001; mean of three independent runs). For etomidate, FEP calculations correctly predict that R-etomidate binds approximately 1.8 kcal/mol more favorably than S-etomidate, corresponding to a ∼10-fold difference in potency consistent with the known stereoselective pharmacology of this agent. These results demonstrate that alchemical FEP calculations, implemented through a standardized computational protocol, can quantitatively rank-order anesthetic binding affinities and discriminate stereoisomeric preferences at Cys-loop receptor binding sites, providing a framework for rational design of next-generation anesthetic agents that is more quantitatively robust than simple molecular docking methodologies.
Authors
- Pritam Kumar Panda (ORCID: https://orcid.org/0000-0003-4879-2302)
- Edward Bertaccini (ORCID: https://orcid.org/0000-0002-9062-0566)
Institutions
- VA Palo Alto Health Care System (US)
- Stanford University (US)
Publication Details
- Journal
- ACS Omega
- Published
- 2026-09-09
- DOI
- https://doi.org/10.1021/acsomega.6c08745
- Primary Topic
- Computational Drug Discovery Methods
- Type
- article
- Field-Weighted Citation Impact
- 0.00