Probing the Structural Basis of Innate G Protein Specificity in G Protein-Coupled Receptor Signaling

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Probing the Structural Basis of Innate G Protein Specificity in G Protein-Coupled Receptor Signaling Abstract G protein-coupled receptors (GPCRs) are integral membrane proteins that directly couple with their signaling partners like G proteins, GPCR kinases (GRKs), and arrestins, in response to extracellular signals and can trigger multiple intracellular signaling cascades that control a range of vital biological processes. Cells have evolved GPCR-mediated signaling mechanisms, where ligands and receptors play unique functional roles. Each receptor has evolved to activate mainly one subfamily of G proteins, which triggers a specific set of signaling cascades. A ligand is more powerful as it can activate multiple receptor subtypes, which in turn activate multiple set of signaling cascades that causes far reaching cellular effects than a receptor alone. The common feature of ligand and receptor function is the ligand-induced innate G protein specificity of the receptors. This proposal is aimed at probing the G protein specificity of receptor subtypes activated by the same ligand (using muscarinic acetylcholine receptors and dopamine receptors as examples), by the development of computational biophysical methods that will explore thermodynamic and kinetic factors behind the observed G protein selectivities. The methods will provide a structural basis for G protein specificity in dopamine D1 and D2 receptors, which will be used to design receptor mutants with G protein specificity different from that of the wild-type receptors. The methods will be validated by cell based biochemical assays using these mutant receptors and in part by reproducing previously observed switches in G protein selectivity of muscarinic receptor mutants. This mechanistic understanding will have broader implications for many GPCR subfamilies and will lay the foundation for drugs targeting specific therapeutically beneficial receptor – G protein interactions to reduce on-target and off-target side-effects.