New insights into the GABA_A receptor structure and orthosteric ligand binding: Receptor modeling guided by experimental data

Abstract

GABA A receptors (GABA A Rs) are ligand gated chloride ion channels that mediate overall inhibitory signaling in the CNS. A detailed understanding of their structure is important to gain insights in e.g. ligand binding and functional properties of this pharmaceutically important target. Homology modeling is a necessary tool in this regard because experimentally determined structures are lacking. Here we present an exhaustive approach for creating a high quality model of the α 1 β 2 γ 2 subtype of the GABA A R ligand binding domain, and we demonstrate its usefulness in understanding details of orthosteric ligand binding.The model was constructed by using multiple templates and by incorporation of knowledge from biochemical/pharmacological experiments. It was validated on the basis of objective energy functions, its ability to account for available residue specific information, and its stability in molecular dynamics (MD) compared to that of two homologous crystal structures. We then combined the model with extensive structure-activity relationships available from two homologous series of orthosteric GABA A R antagonists to create a detailed hypothesis for their binding modes. Excellent agreement with key experimental data was found, including the ability of the model to accommodate and explain a previously developed pharmacophore model. A coupling to agonist binding was thereby established and discussed in relation to activation mechanisms.Our results highlight the importance of critical evaluation and optimization of each step in the homology modeling process. The approach taken here can greatly aid in increasing the understanding of GABA A Rs and related receptors where structural insight is limited and reliable models are difficult to obtain.

A novel GABAA alpha 5 receptor inhibitor with therapeutic potential

“…Taken together with the present data, it is attractive to speculate that the tricyclic oxazolo-2,3-benzodiazepine (OBDZ) scaffold of the compounds reported here has the same role as the PIOL scaffold i.e. anchor the compound in the GABA binding-pocket (Sander et al, 2011). The stringent requirement of the regiochemistry of the oxazolone ring is in line with this notion.…”

“…Previously, Frølund and co-workers have reported a series of GABA A orthosteric antagonists based on the 5-(4-piperidyl)-3-isoxazolol (PIOL) scaffold (Frølund et al, 2005;Frølund et al, 2007;Frølund et al, 2002). These studies indicated that the PIOL moiety interacts with amino acid residues also known to be required for the binding to GABA at the  subunit interface (Sander et al, 2011). Phenyl-PIOL compounds behaved as competitive antagonists of GABA, moreover, bulky hydrocarbon ring substitutions of PIOL were also tolerated and in some cases increased the affinity of the compounds (Frølund et al, 2005;Frølund et al, 2007;Frølund et al, 2002).…”

Intersubunit Bridge Formation Governs Agonist Efficacy at Nicotinic Acetylcholine α4β2 Receptors

“…These studies also indicate that such a strong inter-interface interaction can be greatly aided through halogen bonding. The compound-mediated contacts between the two subunits in the binding interface has also been suggested to be important for the efficacy on GABA A receptors (69), and it is, therefore, likely to be a general feature for the Cys-loop receptors.…”

From Laptop to Benchtop to Bedside: Structure-based Drug Design on Protein Targets

“…This “ligand-centric” approach has been widely employed in identification of novel inhibitors against GPCR targets including MCH-R1, mGluR and CCR2 [29, 34, 37–39]. Other applications include protein structural analysis and function prediction [40–45], understanding of protein-ligand interactions [46–53] and compound optimization [54–56]. Successful stories for drug design starting from homology models are listed in Table 4.…”

“…Some applications include protein structural analysis and function prediction [40–45], understanding of protein-ligand interactions [46–53] and compound optimization [54–56]. In order to compensate from the low-resolution models, additional target-specific constraints and/or more accurate scoring function are often applied.…”