Elucidating the binding mechanisms of GABA and Muscimol as an avenue to discover novel GABA-mimetic small molecules

Abstract

Gamma-aminobutyric acid (GABA) signaling is the principal inhibitory pathway in the central nervous system. It is critical in neuronal cell proliferation and fate determination. Any aberration in GABA inhibition results in psychiatric and neurological diseases. Thus, modulating GABAergic neurotransmission has become the basis of drug therapy for psychiatric and several neurological diseases. Though GABA and muscimol are classical inhibitors of GABA receptors, the search for novel inhibitors continues unabated. In this study, the binding mechanism of GABA and muscimol was elucidated and applied in the search for small molecule GABAergic inhibitors using comprehensive computational techniques. It was revealed that a high-affinity binding of GABA and muscimol was mediated by a water molecule involving α₁Thr129 and then stabilized by strong interactions including salt bridges with β₂Glu155 and α₁Arg66 amidst hydrogen bonds, π-π stacking, and π -cation interactions with other residues. The binding of GABA and muscimol was also characterized by stability and deeper penetration into the hydrophobic core of the protein which resulted in conformational changes of the binding pocket and domain, by inducing correlated motions of the residues. Thermodynamics analysis showed GABA and muscimol exhibited total binding free energies of −19.85 ± 8.83 Kcal/mol and −26.55 ± 3.42 Kcal/mol, respectively. A pharmacophore model search, based on the energy contributions of implicating binding residues, resulted in the identification of ZINC68604167, ZINC19735138, ZINC04202466, ZINC00901626, and ZINC01532854 as potential GABA-mimetic compounds from metabolites and natural products libraries. This study has elucidated the binding mechanisms of GABA and muscimol and successfully applied in the identification of GABA-mimetic compounds.

Communicated by Ramaswamy H. Sarma

Keywords:

• GABA
• Muscimol
• GABA_A-R
• molecular dynamics simulation
• pharmacophore modelling

Acknowledgements

The authors acknowledge the Centre of High-Performance Computing (CHPC, www.chpc.ac.za), Cape Town, South Africa for making computational resources available.

Disclosure statement

Authors declare no financial and intellectual conflict of interests.

Data availability statement

All the data is available within the manuscript.

Additional information

Funding

This study received no funding.