Rational Design of Drugs Targeting G-Protein-Coupled Receptors: A Structural Biology Perspective.

G protein-coupled receptors (GPCRs) play a key role in the transduction of extracellular signals to cells and regulation of many biological processes, which makes these membrane proteins one of the most important targets for pharmacological agents. A significant increase in the number of resolved atomic structures of GPCRs has opened the possibility of developing pharmaceuticals targeting these receptors via structure-based drug design (SBDD). SBDD employs information on the structure of receptor-ligand complexes to search for selective ligands without the need for an extensive high-throughput experimental ligand screening and can significantly expand the chemical space for ligand search. In this review, we describe the process of deciphering GPCR structures using X-ray diffraction analysis and cryoelectron microscopy as an important stage in the rational design of drugs targeting this receptor class. Our main goal was to present modern developments and key features of experimental methods used in SBDD of GPCR-targeting agents to a wide range of specialists.

Revolutionizing GPCR-ligand predictions: DeepGPCR with experimental validation for high-precision drug discovery.

G-protein coupled receptors (GPCRs), crucial in various diseases, are targeted of over 40% of approved drugs. However, the reliable acquisition of experimental GPCRs structures is hindered by their lipid-embedded conformations. Traditional protein-ligand interaction models falter in GPCR-drug interactions, caused by limited and low-quality structures. Generalized models, trained on soluble protein-ligand pairs, are also inadequate. To address these issues, we developed two models, DeepGPCR_BC for binary classification and DeepGPCR_RG for affinity prediction. These models use non-structural GPCR-ligand interaction data, leveraging graph convolutional networks and mol2vec techniques to represent binding pockets and ligands as graphs. This approach significantly speeds up predictions while preserving critical physical-chemical and spatial information. In independent tests, DeepGPCR_BC surpassed Autodock Vina and Schrödinger Dock with an area under the curve of 0.72, accuracy of 0.68 and true positive rate of 0.73, whereas DeepGPCR_RG demonstrated a Pearson correlation of 0.39 and root mean squared error of 1.34. We applied these models to screen drug candidates for GPR35 (Q9HC97), yielding promising results with three (F545-1970, K297-0698, S948-0241) out of eight candidates. Furthermore, we also successfully obtained six active inhibitors for GLP-1R. Our GPCR-specific models pave the way for efficient and accurate large-scale virtual screening, potentially revolutionizing drug discovery in the GPCR field.

Impact of secretin receptor homo-dimerization on natural ligand binding.

Class B G protein-coupled receptors can form dimeric complexes important for high potency biological effects. Here, we apply pharmacological, biochemical, and biophysical techniques to cells and membranes expressing the prototypic secretin receptor (SecR) to gain insights into secretin binding to homo-dimeric and monomeric SecR. Spatial proximity between peptide and receptor residues, probed by disulfide bond formation, demonstrates that the secretin N-terminus moves from adjacent to extracellular loop 3 (ECL3) at wild type SecR toward ECL2 in non-dimerizing mutants. Analysis of fluorescent secretin analogs demonstrates stable engagement of the secretin C-terminal region within the receptor extracellular domain (ECD) for both dimeric and monomeric receptors, while the mid-region exhibits lower mobility while docked at the monomer. Moreover, decoupling of G protein interaction reduces mobility of the peptide mid-region at wild type receptor to levels similar to the mutant, whereas it has no further impact on the monomer. These data support a model of peptide engagement whereby the ability of SecR to dimerize promotes higher conformational dynamics of the peptide-bound receptor ECD and ECLs that likely facilitates more efficient G protein recruitment and activation, consistent with the higher observed functional potency of secretin at wild type SecR relative to the monomeric mutant receptor.

Structural basis for selectivity and antagonism in extracellular GPCR-nanobodies.

G protein-coupled receptors (GPCRs) are pivotal therapeutic targets, but their complex structure poses challenges for effective drug design. Nanobodies, or single-domain antibodies, have emerged as a promising therapeutic strategy to target GPCRs, offering advantages over traditional small molecules and antibodies. However, an incomplete understanding of the structural features enabling GPCR-nanobody interactions has limited their development. In this study, we investigate VUN701, a nanobody antagonist targeting the atypical chemokine receptor 3 (ACKR3). We determine that an extended CDR3 loop is required for ACKR3 binding. Uncommon in most nanobodies, an extended CDR3 is prevalent in GPCR-targeting nanobodies. Combining experimental and computational approaches, we map an inhibitory ACKR3-VUN701 interface and define a distinct conformational mechanism for GPCR inactivation. Our results provide insights into class A GPCR-nanobody selectivity and suggest a strategy for the development of these new therapeutic tools.

Exploring the Relationship between Small Peptides and the T1R1/T1R3 Umami Taste Receptor for Umami Peptide Prediction: A Combined Approach.

Umami peptides are known for enhancing the taste experience by binding to oral umami T1R1 and T1R3 receptors. Among them, small peptides (composed of 2-4 amino acids) constitute nearly 40% of reported umami peptides. Given the diversity in amino acids and peptide sequences, umami small peptides possess tremendous untapped potential. By investigating 168,400 small peptides, we screened candidates binding to T1R1/T1R3 through molecular docking and molecular dynamics simulations, explored bonding types, amino acid characteristics, preferred binding sites, etc. Utilizing three-dimensional molecular descriptors, bonding information, and a back-propagation neural network, we developed a predictive model with 90.3% accuracy, identifying 24,539 potential umami peptides. Clustering revealed three classes with distinct logP (-2.66 ± 1.02, -3.52 ± 0.93, -2.44 ± 1.23) and asphericity (0.28 ± 0.12, 0.26 ± 0.11, 0.25 ± 0.11), indicating significant differences in shape and hydrophobicity (P < 0.05) among potential umami peptides binding to T1R1/T1R3. Following clustering, nine representative peptides (CQ, DP, NN, CSQ, DMC, TGS, DATE, HANR, and STAN) were synthesized and confirmed to possess umami taste through sensory evaluations and electronic tongue analyses. In summary, this study provides insights into exploring small peptide interactions with umami receptors, advancing umami peptide prediction models.

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.

Integrating Computational and Experimental Methods to Identify Novel Sweet Peptides from Egg and Soy Proteins.

Sweetness in food delivers a delightful sensory experience, underscoring the crucial role of sweeteners in the food industry. However, the widespread use of sweeteners has sparked health concerns. This underscores the importance of developing and screening natural, health-conscious sweeteners. Our study represents a groundbreaking venture into the discovery of such sweeteners derived from egg and soy proteins. Employing virtual hydrolysis as a novel technique, our research entailed a comprehensive screening process that evaluated biological activity, solubility, and toxicity of the derived compounds. We harnessed cutting-edge machine learning methodologies, specifically the latest graph neural network models, for predicting the sweetness of molecules. Subsequent refinements were made through molecular docking screenings and molecular dynamics simulations. This meticulous research approach culminated in the identification of three promising sweet peptides: DCY(Asp-Cys-Tyr), GGR(Gly-Gly-Arg), and IGR(Ile-Gly-Arg). Their binding affinity with T1R2/T1R3 was lower than -15 kcal/mol. Using an electronic tongue, we verified the taste profiles of these peptides, with IGR emerging as the most favorable in terms of taste with a sweetness value of 19.29 and bitterness value of 1.71. This study not only reveals the potential of these natural peptides as healthier alternatives to traditional sweeteners in food applications but also demonstrates the successful synergy of computational predictions and experimental validations in the realm of flavor science.

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.

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.

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.

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.

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.

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.

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.

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.

ADGRG1, an adhesion G protein-coupled receptor, forms oligomers.

G protein-coupled receptor (GPCR) oligomerization is a highly debated topic in the field. While initially believed to function as monomers, current literature increasingly suggests that these cell surface receptors, spanning almost all GPCR families, function as homo- or hetero-oligomers. Yet, the functional consequences of these oligomeric complexes remain largely unknown. Adhesion GPCRs (aGPCRs) present an intriguing family of receptors characterized by their large and multi-domain N-terminal fragments (NTFs), intricate activation mechanisms, and the prevalence of numerous splice variants in almost all family members. In the present study, bioluminescence energy transfer (BRET) and Förster resonance energy transfer (FRET) were used to study the homo-oligomerization of adhesion G protein-coupled receptor G1 (ADGRG1; also known as GPR56) and to assess the involvement of NTFs in these receptor complexes. Based on the results presented herein, we propose that ADGRG1 forms 7-transmembrane-driven homo-oligomers on the plasma membrane. Additionally, Stachel motif interactions appear to influence the conformation of these receptor complexes.