New insights into the GABA(A) receptor structure and orthosteric ligand binding: receptor modeling guided by experimental data.

TLDR: An exhaustive approach for creating a high quality model of the α1β2γ2 subtype of the GABAAR ligand binding domain is presented, and its usefulness in understanding details of orthosteric ligandbinding is demonstrated.

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 with that of the 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.