RDL receptors are GABA-activated inhibitory Cys-loop receptors found throughout the insect CNS. They are a key target for insecticides. Here, we characterize the GABA binding site in RDL receptors using computational and electrophysiological techniques. A homology model of the extracellular domain of RDL was generated and GABA docked into the binding site. Molecular dynamics simulations predicted critical GABA binding interactions with aromatic residues F206, Y254, and Y109 and hydrophilic residues E204, S176, R111, R166, S176, and T251. These residues were mutated, expressed in Xenopus oocytes, and their functions assessed using electrophysiology. The data support the binding mechanism provided by the simulations, which predict that GABA forms many interactions with binding site residues, the most significant of which are cation-π interactions with F206 and Y254, H-bonds with E204, S205, R111, S176, T251, and ionic interactions with R111 and E204. These findings clarify the roles of a range of residues in binding GABA in the RDL receptor, and also show that molecular dynamics simulations are a useful tool to identify specific interactions in Cys-loop receptors.

Cys-loop receptor binding sites characteristically possess an ‘aromatic box’, where several aromatic amino acid residues surround the bound ligand. A cation-π interaction between one of these residues and the natural agonist is common, although the residue type and location are not conserved. Even in the closely related vertebrate GABAA and GABAC receptors, residues in distinct locations perform this role: in GABAA receptors a Tyr residue in loop A forms a cation-π interaction with GABA, while in GABAC receptors a loop B residue performs this role. GABA-activated Cys-loop receptors also exist in invertebrates, where they have distinct pharmacologies and are the target of a range of pesticides. Here we examine the location of GABA in an insect binding site by incorporating a series of fluorinated Phe derivatives into the receptor binding pocket using unnatural amino acid mutagenesis, and evaluating the resulting receptors when expressed in Xenopus oocytes. A homology model suggests that two aromatic residues (in loops B and C) are positioned such that they could contribute to a cation-π interaction with the primary ammonium of GABA, and the data reveal a clear correlation between the GABA EC50 and the cation-π binding ability both at Phe206 (loop B) and Tyr254 (loop C), demonstrating for the first time the contribution of two aromatic residues to a cation-π interaction in a Cys loop receptor.

Ionotropic GABA receptors are widely distributed throughout the vertebrate and invertebrate central nervous system (CNS) where they mediate inhibitory neurotransmission. One of the most widely studied insect GABA receptors is constructed from RDL (resistance to dieldrin) subunits from Drosophila melanogaster. The aim of this study was to determine critical features of agonists binding to RDL receptors using in silico and experimental data. Partial atomic charges and dipole separation distances of a range of GABA analogues were calculated, and the potency of the analogues was determined using RDL receptors expressed in Xenopus oocytes. These data revealed functional agonists require an ammonium group and an acidic group with an optimum separation distance of ∼5 Å. To determine how the agonists bind to the receptor, a homology model of the extracellular domain was generated and agonists were docked into the binding site. The docking studies support the requirements for functional agonists and also revealed a range of potential interactions with binding site residues, including hydrogen bonds and cation−π interactions. We conclude that the model and docking procedures yield a good model of the insect GABA receptor binding site and the location of agonists within it.

Precision medicine, incorporating personalized medicine, is an emerging medical model that holds great promise for improving the prevention, diagnosis and treatment of many diseases. The future success of precision medicine, however, depends on the establishment of large databases that collate diverse data, including family genealogies, disease histories, drug sensitivities and genomic data. Herein I raise some of the social and ethical challenges that such a system faces, specifically: the enrolment of volunteers into large genetic databases; the need for a change in mindset of clinicians, patients and the wider public; and the need for interdisciplinary ethics considering the emerging issues. Finally I argue that the future potential of 'personalized' medicine crucially depends on 'collective' participation of informed citizens.