Maria Marti-Solano

G protein–coupled receptors (GPCRs) form the largest family of cell-surface receptors and remain prime targets in drug discovery. A central challenge in modern GPCR drug discovery is understanding and exploiting biased agonism: the ability of ligands to favor signaling via therapeutically beneficial pathways while avoiding those that trigger side effects. Therefore, pinpointing the structural determinants of signaling bias is crucial for rational drug design. Biased agonists are particularly compelling for targeting opioid receptors, as in this family, ligands that limit β-arrestin (β-arr) recruitment are believed to preserve analgesia while reducing respiratory depression and addiction liabilities. Here, we use extensive all-atom molecular dynamics (MD) simulations to dissect signaling bias in the δ-opioid receptor (δOR). Focusing on a receptor mutant with a strong β-arr bias, we employed a reverse-engineering approach to reveal the conformational mechanisms that promote β-arr recruitment. Building on these insights, engineer new mutations that reshape the receptor’s signaling profile. Importantly, this approach allowed us to pinpoint signaling bias to motions of a single microswitch and identify how structural receptor motions induced by the mutations and ligand contacts cooperate to promote a specific functional response. In this proof-of-concept study, we not only provide structural insights into δOR pharmacology but also demonstrate how computational methods can be leveraged to probe structural mechanisms of signaling specificity across GPCRs, paving the way for the rational design of tailored receptor variants and novel, safer, and more effective therapeutics.

Obesity causes dyslipidemia and is a major risk factor for cardiovascular disease. However, the mechanisms coupling weight gain and lipid metabolism are poorly understood. Brain melanocortin 4 receptors (MC4Rs) regulate body weight and lipid metabolism in mice, but the relevance of these findings to humans is unclear. Here we investigated lipid levels in men and women with obesity due to MC4R deficiency. Among 7,719 people from the Genetics of Obesity Study cohort, we identified 316 probands and 144 adult family members with loss-of-function (LoF) MC4R mutations. Adults with MC4R deficiency had lower levels of total and low-density lipoprotein (LDL)-cholesterol and triglycerides than 336,728 controls from the UK Biobank, after adjusting for adiposity. Carriers of LoF MC4R variants within the UK Biobank had lower lipid levels and a lower risk of cardiovascular disease, after accounting for body weight, compared to noncarriers. After a high-fat meal, the postprandial rise in triglyceride-rich lipoproteins and metabolomic markers of fatty acid oxidation were reduced in people with MC4R deficiency compared to controls, changes that favor triglyceride storage in adipose tissue. We concluded that central MC4Rs regulate lipid metabolism and cardiovascular disease risk in humans, highlighting potential therapeutic approaches for cardiovascular risk reduction.

G protein–coupled receptors (GPCRs) are transmembrane detectors of extracellular signals that activate heterotrimeric G proteins to regulate intracellular responses. Because there are only 16 Gα proteins that can couple to GPCRs, variation in a single Gα can affect the function of numerous receptors. Here, we investigated two mutant forms of Gα _s (L388R and E392K) that are associated with pseudohypoparathyroidism type Ic (PHPIc), a maternally inherited rare disease. Gα _s is encoded by an imprinted gene, resulting in the mutant form of Gα _s being the only version of the protein present in certain tissues, which leads to tissue-specific disease manifestations. By integrating data from three-dimensional structures, GPCR–G protein coupling specificity, transcriptomics, biophysics, and molecular dynamics with systems pharmacology modeling, we identified GPCRs whose signaling could be altered by Gα _s mutations in the kidney, a tissue involved in the pathophysiology of PHPIc. Analysis of G protein activation by the parathyroid hormone receptor 1 (PTH1R) revealed that L388R impaired Gα _s interaction with the receptor, whereas E392K reduced the receptor-induced activation of heterotrimeric G _s . This indicates that different signal transduction steps can be altered by specific Gα _s mutants associated with the same disease. These findings highlight the importance of investigating mutation-specific perturbations in GPCR signaling to suggest patient-specific treatment strategies. Furthermore, our methods provide a blueprint for interrogating GPCR signaling diversity in different physiological and pathophysiological contexts.

G protein-coupled receptors (GPCRs) constitute a functionally diverse protein family and are targets for a broad spectrum of pharmaceuticals. Technological progress in X-ray crystallography and cryogenic electron microscopy has enabled extensive, high-resolution structural characterisation of GPCRs in different conformational states. However, as highly dynamic events underlie GPCR signalling, a complete understanding of GPCR functionality requires insights into their conformational dynamics. Here, we present a large dataset of molecular dynamics simulations covering 60% of currently available GPCR structures. Our analysis reveals extensive local “breathing” motions of the receptor on a nano- to microsecond timescale and provides access to numerous previously unexplored receptor conformational states. Furthermore, we reveal that receptor flexibility impacts the shape of allosteric drug binding sites, which frequently adopt partially or completely closed states in the absence of a molecular modulator. We demonstrate that exploring membrane lipid dynamics and their interaction with GPCRs is an efficient approach to expose such hidden allosteric sites and even lateral ligand entrance gateways. The obtained insights and generated dataset on conformations, allosteric sites and lateral entrance gates in GPCRs allows us to better understand the functionality of these receptors and opens new therapeutic avenues for drug-targeting strategies.

G protein-coupled receptors (GPCRs) are transmembrane proteins capable of detecting signals as diverse as odours, neurotransmitters, and hormones. Upon activation, receptor signalling converges onto four Gα protein subtypes to regulate intracellular responses. Therefore, variation in a single Gα protein gene can potentially impact the function of numerous receptors. In this work, we have performed a multidimensional study of rare disease mutations in Gαs, a prototypical Gα protein. By integrating data from 3D structures, GPCR / G protein functional pairings, transcriptomics, biophysics, and molecular dynamics with systems pharmacology modelling, our results reveal why mutations impairing receptor / Gαs coupling result in highly specific context-based signalling defects. Furthermore, we show that mutations leading to the same rare disease can alter different signal transduction steps, highlighting the importance of patient-specific treatment strategies. By closely dissecting G protein coupling, our study provides a blueprint to interrogate GPCR pathway signalling diversity in different (patho)physiological contexts.

Heterotrimeric guanine nucleotide–binding protein (G protein)–coupled receptors (GPCRs) bind to extracellular ligands and drugs and modulate intracellular responses through conformational changes. Despite their importance as drug targets, the molecular origins of pharmacological properties such as efficacy (maximum signaling response) and potency (the ligand concentration at half-maximal response) remain poorly understood for any ligand-receptor-signaling system. We used the prototypical adrenaline–β2 adrenergic receptor–G protein system to reveal how specific receptor residues decode and translate the information encoded in a ligand to mediate a signaling response. We present a data science framework to integrate pharmacological and structural data to uncover structural changes and allosteric networks relevant for ligand pharmacology. These methods can be tailored to study any ligand-receptor-signaling system, and the principles open possibilities for designing orthosteric and allosteric compounds with defined signaling properties.

G protein-coupled receptor (GPCR) family members can sense an extraordinary variety of biomolecules to activate intracellular signalling cascades that modulate key aspects of cell physiology. Apart from their crucial role in maintaining cell homeostasis, these critical sensory and modulatory properties have made GPCRs the most successful drug target class to date. However, establishing direct links between receptor activation of specific intracellular partners and individual physiological outcomes is still an ongoing challenge. By studying this receptor signalling complexity at increasing resolution through the development of novel biosensors and high-throughput techniques, a growing number of studies are revealing how receptor function can be diversified in a spatial, temporal or cell-specific manner. This mini-review will introduce recent examples of this context-dependent receptor signalling and discuss how it can impact our understanding of receptor function in health and disease, and contribute to the search of more selective, efficacious and safer GPCR drug candidates.

G protein-coupled receptors (GPCRs) are the largest family of cell surface receptors and are a common drug target. They can be stabilised in different conformational states by ligands to activate multiple transducers and effectors leading to a variety of cellular responses. The potential of agonists to activate select pathways has important implications for drug discovery. Thus, there is a clear need to profile the initial GPCR signal transduction event, activation of G proteins, to enhance understanding of receptor coupling and guide drug design. The BRET-based biosensor suite, TRUPATH, was recently developed to enable quantification of the activation profiles of all non-visual G proteins (excluding G _olf and G ₁₄ ) and has since been utilised in numerous studies. However, it fails to detect G _q/11 activation for a number of GPCRs previously reported to display promiscuous secondary coupling to G _q/11 . Here we report modifications to the Gα _q and Gα ₁₁ biosensors in the switch I region that prevent intrinsic GTPase activity (R183C/Q). Except for the PAC1R, substitution with cancer-associated mutations, Cys or Gln, significantly increased sensitivity to allow detection of robust, reliable, and representative G _q/11 responses to Class B1 GPCRs. We also demonstrate the utility of these modified biosensors for promiscuously coupled class A GPCR that have primary G _s -coupling. Thus, we propose that modification to Gα _q/11 may also be necessary in other biosensor systems to enable detection of G _q/11 activation.

G protein-coupled receptors (GPCRs) translate binding of extracellular ligands into intracellular responses through conformational changes. Ligand properties are described by the maximum response (efficacy) and the agonist concentration at half-maximal response (potency). Integrating structural changes with pharmacological properties remains challenging and has not yet been performed at the resolution of individual amino acids. We use epinephrine and β2-adrenergic receptor as a model to integrate residue-level pharmacology data with intramolecular residue contact data describing receptor activation. This unveils the allosteric networks driving ligand efficacy and potency. We provide detailed insights into how structural rearrangements are linked to fundamental pharmacological properties at single-residue level in a receptor-ligand system. Our approach can be used to determine such pharmacological networks for any receptor-ligand complex.

In this work, Minikel et al. exploit predicted loss-of-function (pLoF) data from the Genome Aggregation Database (gnomAD), including information from 141,456 individuals, to assess how genetic variation can inform drug target selection. Essential genes are likely to accumulate less loss of function variants (i.e. be more constrained) in the human population compared to non-essential genes. The authors first assess to what degree current drug targets are affected by this constraint. They infer this by computing a measure of essentiality that is a ratio between the pLoF variants found in a gene and the expected number of variants based on DNA mutation rates. Although they observe that current drug targets are significantly more constrained (i.e. have lower observed vs. expected ratios) than the overall protein-coding genes, they also see a wide degree of variability in constraint values among drug targets. If they consider drug indications, they observe that drugs used in cancer treatment show a high degree of constraint compared to other drugs. This would be expected, as genes coding for such drug targets are usually essential genes targeted by cytotoxic drugs. This interpretation is more complicated for other indications as in the case of highly constrained targets for drugs treating neurological diseases. In this case, according to the authors, these targets exemplify how a gene may essentiality be restricted to particular time points in the lifespan of an individual (e.g. during embryological development), and inhibition may still be well tolerated in adults. Overall, ~20% of the analyzed drug targets have lower observed vs. expected values than the average found for genes known to cause severe diseases of haploinsufficiency. Therefore, these results speak against considering constraint as a hard filter for target druggability. Furthermore, the authors also assess the likelihood of detecting ‘human knockouts’ for particular genes of interest in their dataset as these knockouts could reveal the lifelong biological impact of the inhibition of potential drug targets. According to their calculations, although pLoF heterozygosity already exists in around 80% of genes in gnomAD, detecting total knockout individuals in outbred populations would require a 1000-fold larger sample size and, in some cases, would never be attained. However, sequencing currently under-sampled populations, including more data on bottlenecked populations, and especially focusing on consanguineous individuals could enhance the possibilities of detecting tolerated complete knockouts of interest. In summary, this analysis calls for a careful assessment of gene variation in candidate drug targets. It also provides useful estimates on the work that still needs to be done to ensure data on genomic variability can be used to obtain a deeper understanding of gene function and to select new effective drug targets and drug indications.