The critical importance of conditions: Reconciling GPCR functionality and biophysical findings.

G-protein-coupled receptor (GPCR) activation relies on conformational sampling, a nuanced but functionally key behavior well suited to elucidation by nuclear magnetic resonance (NMR) spectroscopy. In this issue of Structure, Thakur et al.1 demonstrate that judicious choice of experimental conditions for 19F NMR studies of a GPCR enables rationalization of functional and pharmacological behavior, leading to testable hypotheses correlating structure, dynamics, and function.

Engineering G protein-coupled receptors for stabilization.

G protein-coupled receptors (GPCRs) are one of the most important families of targets for drug discovery. One of the limiting steps in the study of GPCRs has been their stability, with significant and time-consuming protein engineering often used to stabilize GPCRs for structural characterization and drug screening. Unfortunately, computational methods developed using globular soluble proteins have translated poorly to the rational engineering of GPCRs. To fill this gap, we propose GPCR-tm, a novel and personalized structurally driven web-based machine learning tool to study the impacts of mutations on GPCR stability. We show that GPCR-tm performs as well as or better than alternative methods, and that it can accurately rank the stability changes of a wide range of mutations occurring in various types of class A GPCRs. GPCR-tm achieved Pearson's correlation coefficients of 0.74 and 0.46 on 10-fold cross-validation and blind test sets, respectively. We observed that the (structural) graph-based signatures were the most important set of features for predicting destabilizing mutations, which points out that these signatures properly describe the changes in the environment where the mutations occur. More specifically, GPCR-tm was able to accurately rank mutations based on their effect on protein stability, guiding their rational stabilization. GPCR-tm is available through a user-friendly web server at https://biosig.lab.uq.edu.au/gpcr_tm/.

Identification of Gα12-vs-Gα13-coupling determinants and development of a Gα12/13-coupled designer GPCR.

G-protein-coupled receptors (GPCRs) transduce diverse signals into the cell by coupling to one or several Gα subtypes. Of the 16 Gα subtypes in human cells, Gα12 and Gα13 belong to the G12 subfamily and are reported to be functionally different. Notably, certain GPCRs display selective coupling to either Gα12 or Gα13, highlighting their significance in various cellular contexts. However, the structural basis underlying this selectivity remains unclear. Here, using a Gα12-coupled designer receptor exclusively activated by designer drugs (DREADD; G12D) as a model system, we identified residues in the α5 helix and the receptor that collaboratively determine Gα12-vs-Gα13 selectivity. Residue-swapping experiments showed that G12D distinguishes differences between Gα12 and Gα13 in the positions G.H5.09 and G.H5.23 in the α5 helix. Molecular dynamics simulations observed that I378G.H5.23 in Gα12 interacts with N1032.39, S1693.53 and Y17634.53 in G12D, while H364G.H5.09 in Gα12 interact with Q2645.71 in G12D. Screening of mutations at these positions in G12D identified G12D mutants that enhanced coupling with Gα12 and to an even greater extent with Gα13. Combined mutations, most notably the dual Y17634.53H and Q2645.71R mutant, further enhanced Gα12/13 coupling, thereby serving as a potential Gα12/13-DREADD. Such novel Gα12/13-DREADD may be useful in future efforts to develop drugs that target Gα12/13 signaling as well as to identify their therapeutic indications.

Inferring molecular inhibition potency with AlphaFold predicted structures.

Even though in silico drug ligand-based methods have been successful in predicting interactions with known target proteins, they struggle with new, unassessed targets. To address this challenge, we propose an approach that integrates structural data from AlphaFold 2 predicted protein structures into machine learning models. Our method extracts 3D structural protein fingerprints and combines them with ligand structural data to train a single machine learning model. This model captures the relationship between ligand properties and the unique structural features of various target proteins, enabling predictions for never before tested molecules and protein targets. To assess our model, we used a dataset of 144 Human G-protein Coupled Receptors (GPCRs) with over 140,000 measured inhibition constants (Ki) values. Results strongly suggest that our approach performs as well as state-of-the-art ligand-based methods. In a second modeling approach that used 129 targets for training and a separate test set of 15 different protein targets, our model correctly predicted interactions for 73% of targets, with explained variances exceeding 0.50 in 22% of cases. Our findings further verified that the usage of experimentally determined protein structures produced models that were statistically indistinct from the Alphafold synthetic structures. This study presents a proteo-chemometric drug screening approach that uses a simple and scalable method for extracting protein structural information for usage in machine learning models capable of predicting protein-molecule interactions even for orphan targets.

G protein-coupled receptors (GPCRs): advances in structures, mechanisms, and drug discovery.

G protein-coupled receptors (GPCRs), the largest family of human membrane proteins and an important class of drug targets, play a role in maintaining numerous physiological processes. Agonist or antagonist, orthosteric effects or allosteric effects, and biased signaling or balanced signaling, characterize the complexity of GPCR dynamic features. In this study, we first review the structural advancements, activation mechanisms, and functional diversity of GPCRs. We then focus on GPCR drug discovery by revealing the detailed drug-target interactions and the underlying mechanisms of orthosteric drugs approved by the US Food and Drug Administration in the past five years. Particularly, an up-to-date analysis is performed on available GPCR structures complexed with synthetic small-molecule allosteric modulators to elucidate key receptor-ligand interactions and allosteric mechanisms. Finally, we highlight how the widespread GPCR-druggable allosteric sites can guide structure- or mechanism-based drug design and propose prospects of designing bitopic ligands for the future therapeutic potential of targeting this receptor family.

Bitter taste receptor activation by cholesterol and an intracellular tastant.

Bitter taste sensing is mediated by type 2 taste receptors (TAS2Rs (also known as T2Rs)), which represent a distinct class of G-protein-coupled receptors1. Among the 26 members of the TAS2Rs, TAS2R14 is highly expressed in extraoral tissues and mediates the responses to more than 100 structurally diverse tastants2-6, although the molecular mechanisms for recognizing diverse chemicals and initiating cellular signalling are still poorly understood. Here we report two cryo-electron microscopy structures for TAS2R14 complexed with Ggust (also known as gustducin) and Gi1. Both structures have an orthosteric binding pocket occupied by endogenous cholesterol as well as an intracellular allosteric site bound by the bitter tastant cmpd28.1, including a direct interaction with the α5 helix of Ggust and Gi1. Computational and biochemical studies validate both ligand interactions. Our functional analysis identified cholesterol as an orthosteric agonist and the bitter tastant cmpd28.1 as a positive allosteric modulator with direct agonist activity at TAS2R14. Moreover, the orthosteric pocket is connected to the allosteric site via an elongated cavity, which has a hydrophobic core rich in aromatic residues. Our findings provide insights into the ligand recognition of bitter taste receptors and suggest activities of TAS2R14 beyond bitter taste perception via intracellular allosteric tastants.

Predicting FFAR4 agonists using structure-based machine learning approach based on molecular fingerprints.

Free Fatty Acid Receptor 4 (FFAR4), a G-protein-coupled receptor, is responsible for triggering intracellular signaling pathways that regulate various physiological processes. FFAR4 agonists are associated with enhancing insulin release and mitigating the atherogenic, obesogenic, pro-carcinogenic, and pro-diabetogenic effects, normally associated with the free fatty acids bound to FFAR4. In this research, molecular structure-based machine-learning techniques were employed to evaluate compounds as potential agonists for FFAR4. Molecular structures were encoded into bit arrays, serving as molecular fingerprints, which were subsequently analyzed using the Bayesian network algorithm to identify patterns for screening the data. The shortlisted hits obtained via machine learning protocols were further validated by Molecular Docking and via ADME and Toxicity predictions. The shortlisted compounds were then subjected to MD Simulations of the membrane-bound FFAR4-ligand complexes for 100 ns each. Molecular analyses, encompassing binding interactions, RMSD, RMSF, RoG, PCA, and FEL, were conducted to scrutinize the protein-ligand complexes at the inter-atomic level. The analyses revealed significant interactions of the shortlisted compounds with the crucial residues of FFAR4 previously documented. FFAR4 as part of the complexes demonstrated consistent RMSDs, ranging from 3.57 to 3.64, with minimal residue fluctuations 5.27 to 6.03 nm, suggesting stable complexes. The gyration values fluctuated between 22.8 to 23.5 nm, indicating structural compactness and orderliness across the studied systems. Additionally, distinct conformational motions were observed in each complex, with energy contours shifting to broader energy basins throughout the simulation, suggesting thermodynamically stable protein-ligand complexes. The two compounds CHEMBL2012662 and CHEMBL64616 are presented as potential FFAR4 agonists, based on these insights and in-depth analyses. Collectively, these findings advance our comprehension of FFAR4's functions and mechanisms, highlighting these compounds as potential FFAR4 agonists worthy of further exploration as innovative treatments for metabolic and immune-related conditions.

Discovery of a Novel Chemo-Type for TAAR1 Agonism via Molecular Modeling.

The search for novel effective TAAR1 ligands continues to draw great attention due to the wide range of pharmacological applications related to TAAR1 targeting. Herein, molecular docking studies of known TAAR1 ligands, characterized by an oxazoline core, have been performed in order to identify novel promising chemo-types for the discovery of more active TAAR1 agonists. In particular, the oxazoline-based compound S18616 has been taken as a reference compound for the computational study, leading to the development of quite flat and conformationally locked ligands. The choice of a "Y-shape" conformation was suggested for the design of TAAR1 ligands, interacting with the protein cavity delimited by ASP103 and aromatic residues such as PHE186, PHE195, PHE268, and PHE267. The obtained results allowed us to preliminary in silico screen an in-house series of pyrimidinone-benzimidazoles (1a-10a) as a novel scaffold to target TAAR1. Combined ligand-based (LBCM) and structure based (SBCM) computational methods suggested the biological evaluation of compounds 1a-10a, leading to the identification of derivatives 1a-3a (hTAAR1 EC50 = 526.3-657.4 nM) as promising novel TAAR1 agonists.

Exploring GPCR conformational dynamics using single-molecule fluorescence.

G protein-coupled receptors (GPCRs) are membrane proteins that transmit specific external stimuli into cells by changing their conformation. This conformational change allows them to couple and activate G-proteins to initiate signal transduction. A critical challenge in studying and inferring these structural dynamics arises from the complexity of the cellular environment, including the presence of various endogenous factors. Due to the recent advances in cell-expression systems, membrane-protein purification techniques, and labeling approaches, it is now possible to study the structural dynamics of GPCRs at a single-molecule level both in vitro and in live cells. In this review, we discuss state-of-the-art techniques and strategies for expressing, purifying, and labeling GPCRs in the context of single-molecule research. We also highlight four recent studies that demonstrate the applications of single-molecule microscopy in revealing the dynamics of GPCRs. These techniques are also useful as complementary methods to verify the results obtained from other structural biology tools like cryo-electron microscopy and x-ray crystallography.

In Silico Analyses of Vertebrate G-Protein-Coupled Receptor Fusions United With or Without an Additional Transmembrane Sequence Indicate Classification into Three Groups of Linkers.

Natural G-protein-coupled receptors (GPCRs) rarely have an additional transmembrane (TM) helix, such as an artificial TM-linker that can unite two class A GPCRs in tandem as a single-polypeptide chain (sc). Here, we report that three groups of TM-linkers exist in the intervening regions of natural GPCR fusions from vertebrates: (1) the original consensus (i.e., consensus 1) and consensus 2~4 (related to GPCR itself or its receptor-interacting proteins); (2) the consensus but GPCR-unrelated ones, 1~7; and (3) the inability to apply 1/2 that show no similarity to any other proteins. In silico analyses indicated that all natural GPCR fusions from Amphibia lack a TM-linker, and reptiles have no GPCR fusions; moreover, in either the GPCR-GPCR fusion or fusion protein of (GPCR monomer) and non-GPCR proteins from vertebrates, excluding tetrapods, i.e., so-called fishes, TM-linkers differ from previously reported mammalian and are avian sequences and are classified as Groups 2 and 3. Thus, previously reported TM-linkers were arranged: Consensus 1 is [T(I/A/P)(A/S)-(L/N)(I/W/L)(I/A/V)GL(L/G)(A/T)(S/L/G)(I/L)] first identified in invertebrate sea anemone Exaiptasia diaphana (LOC110241027) and (330-SPSFLCI-L-SLL-340) identified in a tropical bird Opisthocomus hoazin protein LOC104327099 (XP_009930279.1); GPCR-related consensus 2~4 are, respectively, (371-prlilyavfc fgtatg-386) in the desert woodrat Neotoma lepida A6R68_19462 (OBS78147.1), (363-lsipfcll yiaallgnfi llfvi-385) in Gavia stellate (red-throated loon) LOC104264164 (XP_009819412.1), and (479-ti vvvymivcvi glvgnflvmy viir-504) in a snailfish GPCR (TNN80062.1); In Mammals Neotoma lepida, Aves Erythrura gouldiae, and fishes protein (respectively, OBS83645.1, RLW13346.1 and KPP79779.1), the TM-linkers are Group 2. Here, we categorized, for the first time, natural TM-linkers as rare evolutionary events among all vertebrates.

The Molecular Theory of Sweet Taste: Revisit, Update, and Beyond.

The molecular origin of the sweet taste is still elusive. Herein, the canonical AH-B-X theory of sweet taste is extended by resurveying various sweeteners, which provides deeper insights into an analogous intramolecular connectivity pattern of both glucophores in sweeteners and their interaction counterparts in sweet taste receptor TAS1R2/TAS1R3: electrostatic complementarity and topochemical compatibility. Furthermore, their complementary interaction is elaborately illustrated, accounting for the common molecular feature of eliciting sweetness. Moreover, it highlights that multiple glucophores in a topological system synergistically mediate the elicitation and performance of sweetness. This perspective presents a meaningful framework for the structure-activity relationship-based molecular design and modification of sweeteners and sheds light on the mechanism of molecular evolution of TAS1R2s/TAS1R3s. The link between palatability of sweeteners and harmony relationships between their structural components via stereochemistry and network has significant implications to illuminate the underlying mechanisms by which nature designs chemical reactions to elicit the most important taste.

GPCR-IPL score: multilevel featurization of GPCR-ligand interaction patterns and prediction of ligand functions from selectivity to biased activation.

G-protein-coupled receptors (GPCRs) mediate diverse cell signaling cascades after recognizing extracellular ligands. Despite the successful history of known GPCR drugs, a lack of mechanistic insight into GPCR challenges both the deorphanization of some GPCRs and optimization of the structure-activity relationship of their ligands. Notably, replacing a small substituent on a GPCR ligand can significantly alter extracellular GPCR-ligand interaction patterns and motion of transmembrane helices in turn to occur post-binding events of the ligand. In this study, we designed 3D multilevel features to describe the extracellular interaction patterns. Subsequently, these 3D features were utilized to predict the post-binding events that result from conformational dynamics from the extracellular to intracellular areas. To understand the adaptability of GPCR ligands, we collected the conformational information of flexible residues during binding and performed molecular featurization on a broad range of GPCR-ligand complexes. As a result, we developed GPCR-ligand interaction patterns, binding pockets, and ligand features as score (GPCR-IPL score) for predicting the functional selectivity of GPCR ligands (agonism versus antagonism), using the multilevel features of (1) zoomed-out 'residue level' (for flexible transmembrane helices of GPCRs), (2) zoomed-in 'pocket level' (for sophisticated mode of action) and (3) 'atom level' (for the conformational adaptability of GPCR ligands). GPCR-IPL score demonstrated reliable performance, achieving area under the receiver operating characteristic of 0.938 and area under the precision-recall curve of 0.907 (available in gpcr-ipl-score.onrender.com). Furthermore, we used the molecular features to predict the biased activation of downstream signaling (Gi/o, Gq/11, Gs and ß-arrestin) as well as the functional selectivity. The resulting models are interpreted and applied to out-of-set validation with three scenarios including the identification of a new MRGPRX antagonist.

The molecular basis of sugar detection by an insect taste receptor.

Animals crave sugars because of their energy potential and the pleasurable sensation of tasting sweetness. Yet all sugars are not metabolically equivalent, requiring mechanisms to detect and differentiate between chemically similar sweet substances. Insects use a family of ionotropic gustatory receptors to discriminate sugars1, each of which is selectively activated by specific sweet molecules2-6. Here, to gain insight into the molecular basis of sugar selectivity, we determined structures of Gr9, a gustatory receptor from the silkworm Bombyx mori (BmGr9), in the absence and presence of its sole activating ligand, D-fructose. These structures, along with structure-guided mutagenesis and functional assays, illustrate how D-fructose is enveloped by a ligand-binding pocket that precisely matches the overall shape and pattern of chemical groups in D-fructose. However, our computational docking and experimental binding assays revealed that other sugars also bind BmGr9, yet they are unable to activate the receptor. We determined the structure of BmGr9 in complex with one such non-activating sugar, L-sorbose. Although both sugars bind a similar position, only D-fructose is capable of engaging a bridge of two conserved aromatic residues that connects the pocket to the pore helix, inducing a conformational change that allows the ion-conducting pore to open. Thus, chemical specificity does not depend solely on the selectivity of the ligand-binding pocket, but it is an emergent property arising from a combination of receptor-ligand interactions and allosteric coupling. Our results support a model whereby coarse receptor tuning is derived from the size and chemical characteristics of the pocket, whereas fine-tuning of receptor activation is achieved through the selective engagement of an allosteric pathway that regulates ion conduction.

Probing Activation and Conformational Dynamics of the Vesicle-Reconstituted ß2 Adrenergic Receptor at the Single-Molecule Level.

G-protein-coupled receptors (GPCRs) are structurally flexible membrane proteins that mediate a host of physiological responses to extracellular ligands like hormones and neurotransmitters. Fine features of their dynamic structural behavior are hypothesized to encode the functional plasticity seen in GPCR activity, where ligands with different efficacies can direct the same receptor toward different signaling phenotypes. Although the number of GPCR crystal structures is increasing, the receptors are characterized by complex and poorly understood conformational landscapes. Therefore, we employed a fluorescence microscopy assay to monitor conformational dynamics of single ß2 adrenergic receptors (ß2ARs). To increase the biological relevance of our findings, we decided not to reconstitute the receptor in detergent micelles but rather lipid membranes as proteoliposomes. The conformational dynamics were monitored by changes in the intensity of an environmentally sensitive boron-dipyrromethene (BODIPY 493/503) fluorophore conjugated to an endogenous cysteine (located at the cytoplasmic end of the sixth transmembrane helix of the receptor). Using total internal reflection fluorescence microscopy (TIRFM) and a single small unilamellar liposome assay that we previously developed, we followed the real-time dynamic properties of hundreds of single ß2ARs reconstituted in a native-like environment─lipid membranes. Our results showed that ß2AR-BODIPY fluctuates between several states of different intensity on a time scale of seconds, compared to BODIPY-lipid conjugates that show almost entirely stable fluorescence emission in the absence and presence of the full agonist BI-167107. Agonist stimulation changes the ß2AR dynamics, increasing the population of states with higher intensities and prolonging their durations, consistent with bulk experiments. The transition density plot demonstrates that ß2AR-BODIPY, in the absence of the full agonist, interconverts between states of low and moderate intensity, while the full agonist renders transitions between moderate and high-intensity states more probable. This redistribution is consistent with a mechanism of conformational selection and is a promising first step toward characterizing the conformational dynamics of GPCRs embedded in a lipid bilayer.

Heterotrimeric G protein signaling without GPCRs: The Gα-binding-and-activating (GBA) motif.

Heterotrimeric G proteins (Gαßγ) are molecular switches that relay signals from 7-transmembrane receptors located at the cell surface to the cytoplasm. The function of these receptors is so intimately linked to heterotrimeric G proteins that they are named G protein-coupled receptors (GPCRs), showcasing the interdependent nature of this archetypical receptor-transducer axis of transmembrane signaling in eukaryotes. It is generally assumed that activation of heterotrimeric G protein signaling occurs exclusively by the action of GPCRs, but this idea has been challenged by the discovery of alternative mechanisms by which G proteins can propagate signals in the cell. This review will focus on a general principle of G protein signaling that operates without the direct involvement of GPCRs. The mechanism of G protein signaling reviewed here is mediated by a class of G protein regulators defined by containing an evolutionarily conserved sequence named the Gα-binding-and-activating (GBA) motif. Using the best characterized proteins with a GBA motif as examples, Gα-interacting vesicle-associated protein (GIV)/Girdin and dishevelled-associating protein with a high frequency of leucine residues (DAPLE), this review will cover (i) the mechanisms by which extracellular cues not relayed by GPCRs promote the coupling of GBA motif-containing regulators with G proteins, (ii) the structural and molecular basis for how GBA motifs interact with Gα subunits to facilitate signaling, (iii) the relevance of this mechanism in different cellular and pathological processes, including cancer and birth defects, and (iv) strategies to manipulate GBA-G protein coupling for experimental therapeutics purposes, including the development of rationally engineered proteins and chemical probes.

Scaling up Functional Analyses of the G Protein-Coupled Receptor Rhodopsin.

Eukaryotic cells use G protein-coupled receptors (GPCRs) to convert external stimuli into internal signals to elicit cellular responses. However, how mutations in GPCR-coding genes affect GPCR activation and downstream signaling pathways remain poorly understood. Approaches such as deep mutational scanning show promise in investigations of GPCRs, but a high-throughput method to measure rhodopsin activation has yet to be achieved. Here, we scale up a fluorescent reporter assay in budding yeast that we engineered to study rhodopsin's light-activated signal transduction. Using this approach, we measured the mutational effects of over 1200 individual human rhodopsin mutants, generated by low-frequency random mutagenesis of the GPCR rhodopsin (RHO) gene. Analysis of the data in the context of rhodopsin's three-dimensional structure reveals that transmembrane helices are generally less tolerant to mutations compared to flanking helices that face the lipid bilayer, which suggest that mutational tolerance is contingent on both the local environment surrounding specific residues and the specific position of these residues in the protein structure. Comparison of functional scores from our screen to clinically identified rhodopsin disease variants found many pathogenic mutants to be loss of function. Lastly, functional scores from our assay were consistent with a complex counterion mechanism involved in ligand-binding and rhodopsin activation. Our results demonstrate that deep mutational scanning is possible for rhodopsin activation and can be an effective method for revealing properties of mutational tolerance that may be generalizable to other transmembrane proteins.

The repertoire and structure of adhesion GPCR transcript variants assembled from publicly available deep-sequenced human samples.

Alternative splicing and multiple transcription start and termination sites can produce a diverse repertoire of mRNA transcript variants from a given gene. While the full picture of the human transcriptome is still incomplete, publicly available RNA datasets have enabled the assembly of transcripts. Using publicly available deep sequencing data from 927 human samples across 48 tissues, we quantified known and new transcript variants, provide an interactive, browser-based application Splice-O-Mat and demonstrate its relevance using adhesion G protein-coupled receptors (aGPCRs) as an example. On average, 24 different transcript variants were detected for each of the 33 human aGPCR genes, and several dominant transcript variants were not yet annotated. Variable transcription starts and complex exon-intron structures encode a flexible protein domain architecture of the N- and C termini and the seven-transmembrane helix domain (7TMD). Notably, we discovered the first GPCR (ADGRG7/GPR128) with eight transmembrane helices. Both the N- and C terminus of this aGPCR were intracellularly oriented, anchoring the N terminus in the plasma membrane. Moreover, the assessment of tissue-specific transcript variants, also for other gene classes, in our application may change the evaluation of disease-causing mutations, as their position in different transcript variants may explain tissue-specific phenotypes.

GPCR-BERT: Interpreting Sequential Design of G Protein-Coupled Receptors Using Protein Language Models.

With the rise of transformers and large language models (LLMs) in chemistry and biology, new avenues for the design and understanding of therapeutics have been opened up to the scientific community. Protein sequences can be modeled as language and can take advantage of recent advances in LLMs, specifically with the abundance of our access to the protein sequence data sets. In this letter, we developed the GPCR-BERT model for understanding the sequential design of G protein-coupled receptors (GPCRs). GPCRs are the target of over one-third of Food and Drug Administration-approved pharmaceuticals. However, there is a lack of comprehensive understanding regarding the relationship among amino acid sequence, ligand selectivity, and conformational motifs (such as NPxxY, CWxP, and E/DRY). By utilizing the pretrained protein model (Prot-Bert) and fine-tuning with prediction tasks of variations in the motifs, we were able to shed light on several relationships between residues in the binding pocket and some of the conserved motifs. To achieve this, we took advantage of attention weights and hidden states of the model that are interpreted to extract the extent of contributions of amino acids in dictating the type of masked ones. The fine-tuned models demonstrated high accuracy in predicting hidden residues within the motifs. In addition, the analysis of embedding was performed over 3D structures to elucidate the higher-order interactions within the conformations of the receptors.

Cryo-electron microscopy for GPCR research and drug discovery in endocrinology and metabolism.

G protein-coupled receptors (GPCRs) are the largest family of cell surface receptors, with many GPCRs having crucial roles in endocrinology and metabolism. Cryogenic electron microscopy (cryo-EM) has revolutionized the field of structural biology, particularly regarding GPCRs, over the past decade. Since the first pair of GPCR structures resolved by cryo-EM were published in 2017, the number of GPCR structures resolved by cryo-EM has surpassed the number resolved by X-ray crystallography by 30%, reaching >650, and the number has doubled every ~0.63 years for the past 6 years. At this pace, it is predicted that the structure of 90% of all human GPCRs will be completed within the next 5-7 years. This Review highlights the general structural features and principles that guide GPCR ligand recognition, receptor activation, G protein coupling, arrestin recruitment and regulation by GPCR kinases. The Review also highlights the diversity of GPCR allosteric binding sites and how allosteric ligands could dictate biased signalling that is selective for a G protein pathway or an arrestin pathway. Finally, the authors use the examples of glycoprotein hormone receptors and glucagon-like peptide 1 receptor to illustrate the effect of cryo-EM on understanding GPCR biology in endocrinology and metabolism, as well as on GPCR-related endocrine diseases and drug discovery.

Biased Signaling in Mutated Variants of ß2-Adrenergic Receptor: Insights from Molecular Dynamics Simulations.

The molecular basis of receptor bias in G protein-coupled receptors (GPCRs) caused by mutations that preferentially activate specific intracellular transducers over others remains poorly understood. Two experimentally identified biased variants of ß2-adrenergic receptors (ß2AR), a prototypical GPCR, are a triple mutant (T68F, Y132A, and Y219A) and a single mutant (Y219A); the former bias the receptor toward the ß-arrestin pathway by disfavoring G protein engagement, while the latter induces G protein signaling explicitly due to selection against GPCR kinases (GRKs) that phosphorylate the receptor as a prerequisite of ß-arrestin binding. Though rigorous characterizations have revealed functional implications of these mutations, the atomistic origin of the observed transducer selectivity is not clear. In this study, we investigated the allosteric mechanism of receptor bias in ß2AR using microseconds of all-atom Gaussian accelerated molecular dynamics (GaMD) simulations. Our observations reveal distinct rearrangements in transmembrane helices, intracellular loop 3, and critical residues R1313.50 and Y3267.53 in the conserved motifs D(E)RY and NPxxY for the mutant receptors, leading to their specific transducer interactions. Moreover, partial dissociation of G protein from the receptor core is observed in the simulations of the triple mutant in contrast to the single mutant and wild-type receptor. The reorganization of allosteric communications from the extracellular agonist BI-167107 to the intracellular receptor-transducer interfaces drives the conformational rearrangements responsible for receptor bias in the single and triple mutants. The molecular insights into receptor bias of ß2AR presented here could improve the understanding of biased signaling in GPCRs, potentially opening new avenues for designing novel therapeutics with fewer side-effects and superior efficacy.