Journal
STRUCTURAL CHEMISTRY
Volume 34, Issue 3, Pages 791-823Publisher
SPRINGER/PLENUM PUBLISHERS
DOI: 10.1007/s11224-022-02029-4
Keywords
Benzodiazepine; GABA(A) receptor; Extracellular domain; Transmembrane domain; Chloride channel; TM2 helix
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In this study, computational methods including molecular docking/dynamic simulations and QSAR analysis were used to analyze a data set of [H-3] diazepam derivatives. The main goals were to understand the binding mechanisms of benzodiazepines to GABA(A) receptors and define the structural requirements for improving the response to benzodiazepine drugs. The results identified potential modulators and confirmed the predictive ability of the developed QSAR model.
In this paper, a data set of [H-3] diazepam derivatives was analyzed using various computational methods: molecular docking/dynamic simulations, and QSAR analysis. The main aims of these studies are to understand the binding mechanisms by which benzodiazepines allosterically modulate GABA(A) receptor alpha(1)beta(2)gamma(2) subtypes, from inducing neuronal inhibition at lower doses to the anesthetic effect at higher doses, and also, to define the structural requirements that contribute to improving the response of GABA(A)/alpha(1)beta(2)gamma(2) receptor to benzodiazepine drugs. The results of the molecular docking study allowed selecting Ro12-6377 and proflazepam as the best modulators for the four binding sites simultaneously. Subsequently, the stability of the selected complexes was investigated by performing molecular dynamics simulation. The latter confirmed the features of both modulators to exert direct effects on the chloride-channel lining residues. Pharmacokinetics and drug-likeness profile were assessed through in silico tool. Furthermore, a QSAR analysis was conducted using an improved vemolecular dynamics simulations proposed byrsion of PLS regression. The goodness of fit and the predictive power of the resulting PLS model were estimated according to internal and external validation parameters: R-2 = 0.632, R-adj(2) = 0.584, F = 12.806; p-value = 6.2050e - 07, Q(loo)(2) = 0.639, and Q(F3)(2) = 0.813. Clearly, the obtained results ensure the predictive ability of the developed QSAR model for the design of new high-potency benzodiazepine drugs.
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