Review

Structural elements of Gα subunits that interact with Gβγ, receptors, and effectors

As an important drug target, β2 adrenergic receptor (B2AR) regulates many physiological processes, including cardiac function, airway tone and metabolic functions. The selective coupling between B2AR and specific transducers is critical for the physiological action of the receptor. However, the molecular mechanism by which B2AR recognizes different transducers remains elusive. Here, molecular dynamics simulations of B2AR binding to three functionally important transducers (Gs, Gi and β-arrestin 1) unveiled distinct binding modes of the receptor. Involving transmembrane helices TMs 2–7 and intracellular loops ICLs 2–3, different binding interfaces for Gs and β-arrestin 1 were identified in the simulation models and further validated by various assays. The distinct recognition mode of B2AR for Gi was computationally characterized. Insights into receptor-transducer communication not only enhance our understanding of signaling bias, but also offer hints for rational drug design targeting specific signaling pathways of G-protein coupled receptors (GPCRs).

Some G protein alpha subunits contain a mitogen-activated protein kinase (MAPK) docking motif (D-motif) near the amino terminus that can impact cellular responses to external signals. The Dictyostelium Gα2 G protein subunit is required for chemotaxis to cAMP during the onset of multicellular development and this subunit contains a putative D-motif near the amino terminus. The Gα2 subunit D-motif was altered to examine its potential role in chemotaxis and multicellular development. In gα2⁻ cells the expression of the D-motif mutant (Gα2^D-) or wild-type subunit from high copy number vectors rescued cell aggregation but blocked the transition of mounds into slugs. This phenotype was also observed in parental strains with a wild-type gα2 locus indicating that the heterologous Gα2 subunit expression interferes with multicellular morphogenesis. Expression of the Gα2^D- subunit from a low copy number vectors in gα2⁻ cells did not rescue aggregation whereas the wild-type Gα2 subunit rescued aggregation efficiently and allowed wild-type morphological development. The Gα2^D- and Gα2 subunit were both capable of restoring comparable levels of cAMP stimulated motility and the ability to co-aggregate with wild-type cells implying that the aggregation defect of Gα2^D- expressing cells is due to insufficient intercellular signaling. Expression of the Gα2 subunit but not the Gα2^D- subunit fully restored the ability of cAMP to stimulate the translocation of the GtaC transcription factor suggesting the D-motif is important for transcription factor regulation. These results suggest that the D-motif of Gα2 plays a role in aggregation and other developmental responses involved with cAMP signaling.

Aging is a highly complex molecular process, affecting nearly all tissue systems in humans and is the highest risk factor in developing neurodegenerative disorders such as Alzheimer’s and Parkinson’s disease, cardiovascular disease and Type 2 diabetes mellitus. The intense complexity of the aging process creates an incentive to develop more specific drugs that attenuate or even reverse some of the features of premature aging. As our current pharmacopeia is dominated by therapeutics that target members of the G protein-coupled receptor (GPCR) superfamily it may be prudent to search for effective anti-aging therapeutics in this fertile domain. Since the first demonstration of GPCR-based β-arrestin signaling, it has become clear that an enhanced appreciation of GPCR signaling diversity may facilitate the creation of therapeutics with selective signaling activities. Such ‘biased’ ligand signaling profiles can be effectively investigated using both standard molecular biological techniques as well as high-dimensionality data analyses. Through a more nuanced appreciation of the quantitative nature across the multiple dimensions of signaling bias that drugs possess, researchers may be able to further refine the efficacy of GPCR modulators to impact the complex aberrations that constitute the aging process. Identifying novel effector profiles could expand the effective pharmacopeia and assist in the design of precision medicines. This review discusses potential non-G protein effectors, and specifically their potential therapeutic suitability in aging and age-related disorders.

Cardiac disease remains the leading cause of morbidity and mortality worldwide. The β₁-adrenergic receptor (β₁-AR) is a major regulator of cardiac functions and is downregulated in the majority of heart failure cases. A key physiological process is the activation of heterotrimeric G-protein Gs by β₁-ARs, leading to increased heart rate and contractility. Here, we use cryo-electron microscopy and functional studies to investigate the molecular mechanism by which β₁-AR activates Gs. We find that the tilting of α5-helix breaks a hydrogen bond between the sidechain of His373 in the C-terminal α5-helix and the backbone carbonyl of Arg38 in the N-terminal αN-helix of Gα_s. Together with the disruption of another interacting network involving Gln59 in the α1-helix, Ala352 in the β6-α5 loop, and Thr355 in the α5-helix, these conformational changes might lead to the deformation of the GDP-binding pocket. Our data provide molecular insights into the activation of G-proteins by G-protein-coupled receptors.

Although the importance of the C terminus of the α subunit of the heterotrimeric G protein in G protein-coupled receptor (GPCR)-G protein pairing is well established, the structural basis of selective interactions remains unknown. Here, we combine live cell FRET-based measurements and molecular dynamics simulations of the interaction between the GPCR and a peptide derived from the C terminus of the Gα subunit (Gα peptide) to dissect the molecular mechanisms of G protein selectivity. We observe a direct link between Gα peptide binding and stabilization of the GPCR conformational ensemble. We find that cognate and non-cognate Gα peptides show deep and shallow binding, respectively, and in distinct orientations within the GPCR. Binding of the cognate Gα peptide stabilizes the agonist-bound GPCR conformational ensemble resulting in favorable binding energy and lower flexibility of the agonist-GPCR pair. We identify three hot spot residues (Gα_s/Gα_q-Gln-384/Leu-349, Gln-390/Glu-355, and Glu-392/Asn-357) that contribute to selective interactions between the β2-adrenergic receptor (β2-AR)-Gα_s and V_1A receptor (V_1AR)-Gα_q. The Gα_s and Gα_q peptides adopt different orientations in β2-AR and V_1AR, respectively. The β2-AR/Gα_s peptide interface is dominated by electrostatic interactions, whereas the V_1AR/Gα_q peptide interactions are predominantly hydrophobic. Interestingly, our study reveals a role for both favorable and unfavorable interactions in G protein selection. Residue Glu-355 in Gα_q prevents this peptide from interacting strongly with β2-AR. Mutagenesis to the Gα_s counterpart (E355Q) imparts a cognate-like interaction. Overall, our study highlights the synergy in molecular dynamics and FRET-based approaches to dissect the structural basis of selective G protein interactions.

Naturally selected amino-acid sequences or experimentally derived ones are often the basis for understanding how protein three-dimensional conformation and function are determined by primary structure. Such sequences for a protein family comprise only a small fraction of all possible variants, however, representing the fitness landscape with limited scope. Explicitly sampling and characterizing alternative, unexplored protein sequences would directly identify fundamental reasons for sequence robustness (or variability), and we demonstrate that computational methods offer an efficient mechanism toward this end, on a large scale. The dead-end elimination and A^∗ search algorithms were used here to find all low-energy single mutant variants, and corresponding structures of a G-protein heterotrimer, to measure changes in structural stability and binding interactions to define a protein fitness landscape. We established consistency between these algorithms with known biophysical and evolutionary trends for amino-acid substitutions, and could thus recapitulate known protein side-chain interactions and predict novel ones.