GPCR structures in drug design, emerging opportunities with new structures

Computer-aided Drug Discovery Approaches in the Identification of Anticancer Drugs from Natural Products: A Review

Natural plant sources are essential in the development of several anticancer drugs, such as vincristine, vinblastine, vinorelbine, docetaxel, paclitaxel, camptothecin, etoposide, and teniposide. However, various chemotherapies fail due to adverse reactions, drug resistance, and target specificity. Researchers are now focusing on developing drugs that use natural compounds to overcome these issues. These drugs can affect multiple targets, have reduced adverse effects, and are effective against several cancer types. Developing a new drug is a highly complex, expensive, and time-consuming process. Traditional drug discovery methods take up to 15 years for a new medicine to enter the market and cost more than one billion USD. However, recent Computer Aided Drug Discovery (CADD) advancements have changed this situation. This paper aims to comprehensively describe the different CADD approaches in identifying anticancer drugs from natural products. Data from various sources, including Science Direct, Elsevier, NCBI, and Web of Science, are used in this review. In-silico techniques and optimization algorithms can provide versatile solutions in drug discovery ventures. The structure-based drug design technique is widely used to understand chemical constituents' molecular-level interactions and identify hit leads. This review will discuss the concept of CADD, in-silico tools, virtual screening in drug discovery, and the concept of natural products as anticancer therapies. Representative examples of molecules identified will also be provided.

Investigation of Benzimidazole Derivatives in Molecular Targets for Breast Cancer Treatment: A Comprehensive Review

This article provides a basic summary of computational research on benzimidazole and its molecular targets in breast cancer (BC) drug discovery. The drug development process is streamlined, expenses are decreased, and the possibility of finding successful therapies for this difficult illness is increased with the use of computational tools. The utilization of benzimidazole derivatives in medication research and discovery is discussed, along with the results of benzimidazole derivative-related clinical trials conducted against blood cancer during the previous five years. Additionally, it includes analysis of changes in structure and how they affect pharmacology. The structure-based method and other computational tools used in drug development are also covered, as well as the importance of structural information such as stereochemistry, physiological action, and the use of spectroscopic methods like NMR and X-ray crystallography in understanding the interactions between bioactive compounds and receptors. The article highlights the potential of benzimidazoles as bioactive heterocyclic molecules with various biological activities, including antimicrobial and anti-cancer properties.

Guidelines for plasma membrane protein detection by surface biotinylation

Plasma membrane proteins are crucial for signal transduction, trafficking, and cell-cell interactions, all of which are vital for cell survival. These proteins, including G-protein coupled receptors, ion channels, transporters, and receptors, are key drug targets due to their central role in receiving and amplifying cellular signals. However, the isolation and purification of plasma membrane proteins pose significant challenges because of their integration with phospholipid bilayers and the small fraction of these proteins present in the plasma membrane. Biotinylation, in combination with streptavidin beads, provides an effective method for surface protein analysis by specifically labeling surface proteins without penetrating the cell membrane, enabling precise isolation and analysis with minimal contamination. In this study, we describe a 1-step method for analyzing plasma membrane proteins that can be routinely implemented in many laboratories.

Immunomodulatory molecules in colorectal cancer liver metastasis

Colorectal cancer (CRC) ranks as the third most common cancer and the second leading cause of cancer-related deaths. According to clinical diagnosis and treatment, liver metastasis occurs in approximately 50 % of CRC patients, indicating a poor prognosis. The unique immune tolerance of the liver fosters an immunosuppressive tumor microenvironment (TME). In the context of tumors, numerous membrane and secreted proteins have been linked to tumor immune evasion as immunomodulatory molecules, but much remains unknown about how these proteins contribute to immune evasion in colorectal cancer liver metastasis (CRLM). This article reviews recently discovered membrane and secreted proteins with roles as both immunostimulatory and immunosuppressive molecules within the TME that influence immune evasion in CRC primary and metastatic lesions, particularly their mechanisms in promoting CRLM. This article also addresses screening strategies for identifying proteins involved in immune evasion in CRLM and provides insights into potential protein targets for treating CRLM.

Direct Binding Methods to Measure Receptor-Ligand Interactions

G-protein-coupled receptors (GPCRs) contribute to numerous physiological processes via complex network mechanisms. While indirect signaling assays (Ca2+ mobilization, cAMP production, and GTPγS binding) have been useful in identifying and characterizing downstream signaling mechanisms of GPCRs, these methods lack measurements of direct binding affinities, kinetics, binding specificity, and selectivity that are important parameters in GPCR drug discovery. In comparison to existing direct methods that use radio- or fluorescent labels, label-free techniques can closely emulate the native interactions around binding partners. Surface plasmon resonance (SPR) is a label-free technique that utilizes the refractive index (RI) property and is applied widely in quantitative GPCR-ligand binding kinetics measurement including small molecules screening. However, purified GPCRs are further embedded in a synthetic lipid environment which is immobilized through different tags to the SPR sensor surface, resulting in a non-native environment. Here, we introduced a methodology that also uses the RI property to measure binding interactions in a label-free, immobilization-free arrangement. The free-solution technique is successfully applied in quantifying the interaction of bioactive lipids to cognate lipid GPCRs, which is not purified but rather present in near-native conditions, i.e., in milieu of other cytoplasmic lipids and proteins. To further consider the wide applicability of these free-solution approaches in biomolecular interaction research, additional applications on a variety of receptor-ligand pairs are imperative.

Emerging Promise of Computational Techniques in Anti-Cancer Research: At a Glance

Research on the immune system and cancer has led to the development of new medicines that enable the former to attack cancer cells. Drugs that specifically target and destroy cancer cells are on the horizon; there are also drugs that use specific signals to stop cancer cells multiplying. Machine learning algorithms can significantly support and increase the rate of research on complicated diseases to help find new remedies. One area of medical study that could greatly benefit from machine learning algorithms is the exploration of cancer genomes and the discovery of the best treatment protocols for different subtypes of the disease. However, developing a new drug is time-consuming, complicated, dangerous, and costly. Traditional drug production can take up to 15 years, costing over USD 1 billion. Therefore, computer-aided drug design (CADD) has emerged as a powerful and promising technology to develop quicker, cheaper, and more efficient designs. Many new technologies and methods have been introduced to enhance drug development productivity and analytical methodologies, and they have become a crucial part of many drug discovery programs; many scanning programs, for example, use ligand screening and structural virtual screening techniques from hit detection to optimization. In this review, we examined various types of computational methods focusing on anticancer drugs. Machine-based learning in basic and translational cancer research that could reach new levels of personalized medicine marked by speedy and advanced data analysis is still beyond reach. Ending cancer as we know it means ensuring that every patient has access to safe and effective therapies. Recent developments in computational drug discovery technologies have had a large and remarkable impact on the design of anticancer drugs and have also yielded useful insights into the field of cancer therapy. With an emphasis on anticancer medications, we covered the various components of computer-aided drug development in this paper. Transcriptomics, toxicogenomics, functional genomics, and biological networks are only a few examples of the bioinformatics techniques used to forecast anticancer medications and treatment combinations based on multi-omics data. We believe that a general review of the databases that are now available and the computational techniques used today will be beneficial for the creation of new cancer treatment approaches.

G protein-coupled receptors (GPCRs) in rotifers and cladocerans: Potential applications in ecotoxicology, ecophysiology, comparative endocrinology, and pharmacology

The G protein-coupled receptor (GPCR) superfamily plays a fundamental role in both sensory functions and the regulation of homeostasis, and is highly conserved across the eukaryote taxa. Its functional diversity is related to a conserved seven-transmembrane core and invariant set of intracellular signaling mechanisms. The interplay between these properties is key to the evolutionary success of GPCR. As this superfamily originated from a common ancestor, GPCR genes have evolved via lineage-specific duplications through the process of adaptation. Here we summarized information on GPCR gene families in rotifers and cladocerans based on their evolutionary position in aquatic invertebrates and their potential application in ecotoxicology, ecophysiology, comparative endocrinology, and pharmacology. Phylogenetic analyses were conducted to examine the evolutionary significance of GPCR gene families and to provide structural insight on their role in aquatic invertebrates. In particular, most GPCR gene families have undergone sporadic evolutionary processes, but some GPCRs are highly conserved across species despite the dynamics of GPCR evolution. Overall, this review provides a better understanding of GPCR evolution in aquatic invertebrates and expand our knowledge of the potential application of these receptors in various fields.

Probing the local secondary structure of bacteriophage S21 pinholin membrane protein using electron spin echo envelope modulation spectroscopy

There have recently been advances in methods for detecting local secondary structures of membrane protein using electron paramagnetic resonance (EPR). A three pulsed electron spin echo envelope modulation (ESEEM) approach was used to determine the local helical secondary structure of the small hole forming membrane protein, S²¹ pinholin. This ESEEM approach uses a combination of site-directed spin labeling and ²H-labeled side chains. Pinholin S²¹ is responsible for the permeabilization of the inner cytosolic membrane of double stranded DNA bacteriophage host cells. In this study, we report on the overall global helical structure using circular dichroism (CD) spectroscopy for the active form and the negative-dominant inactive mutant form of S²¹ pinholin. The local helical secondary structure was confirmed for both transmembrane domains (TMDs) for the active and inactive S²¹ pinholin using the ESEEM spectroscopic technique. Comparison of the ESEEM normalized frequency domain intensity for each transmembrane domain gives an insight into the α-helical folding nature of these domains as opposed to a π or 3₁₀-helix which have been observed in other channel forming proteins.

Research progress in the screening and evaluation of umami peptides

Umami is an important element affecting food taste, and the development of umami peptides is a topic of interest in food-flavoring research. The existing technology used for traditional screening of umami peptides is time-consuming and labor-intensive, making it difficult to meet the requirements of high-throughput screening, which limits the rapid development of umami peptides. The difficulty in performing a standard measurement of umami intensity is another problem that restricts the development of umami peptides. The existing methods are not sensitive and specific, making it difficult to achieve a standard evaluation of umami taste. This review summarizes the umami receptors and umami peptides, focusing on the problems restricting the development of umami peptides, high-throughput screening, and establishment of evaluation standards. The rapid screening of umami peptides was realized based on molecular docking technology and a machine learning method, and the standard evaluation of umami could be realized with a bionic taste sensor. The progress of rapid screening and evaluation methods significantly promotes the study of umami peptides and increases its application in the seasoning industry.

Structure-activity relationship of ipglycermide binding to phosphoglycerate mutases

Catalysis of human phosphoglycerate mutase is dependent on a 2,3-bisphosphoglycerate cofactor (dPGM), whereas the nonhomologous isozyme in many parasitic species is cofactor independent (iPGM). This mechanistic and phylogenetic diversity offers an opportunity for selective pharmacologic targeting of glycolysis in disease-causing organisms. We previously discovered ipglycermide, a potent inhibitor of iPGM, from a large combinatorial cyclic peptide library. To fully delineate the ipglycermide pharmacophore, herein we construct a detailed structure–activity relationship using 280 substituted ipglycermide analogs. Binding affinities of these analogs to immobilized Caenorhabditis elegans iPGM, measured as fold enrichment relative to the index residue by deep sequencing of an mRNA display library, illuminated the significance of each amino acid to the pharmacophore. Using cocrystal structures and binding kinetics, we show that the high affinity of ipglycermide for iPGM orthologs, from Brugia malayi, Onchocerca volvulus, Dirofilaria immitis, and Escherichia coli, is achieved by a codependence between (1) the off-rate mediated by the macrocycle Cys14 thiolate coordination to an active-site Zn²⁺ in the iPGM phosphatase domain and (2) shape complementarity surrounding the macrocyclic core at the phosphotransferase–phosphatase domain interface. Our results show that the high-affinity binding of ipglycermide to iPGMs freezes these structurally dynamic enzymes into an inactive, stable complex.

Guiding Medicinal Chemistry with Fragment Molecular Orbital (FMO) Method

The understanding of binding interactions between a protein and a small molecule plays a key role in the rationalization of potency and selectivity and in design of new ideas. However, even when a target of interest is structurally enabled, visual inspection and force field-based molecular mechanics calculations cannot always explain the full complexity of the molecular interactions that are critical in drug design. Quantum mechanical methods have the potential to address this shortcoming, but traditionally, computational expense has made the application of these calculations impractical. The fragment molecular orbital (FMO) method offers a solution that combines accuracy, speed, and the ability to characterize important interactions (i.e. its strength in kcal/mol and chemical nature: hydrophobic, electrostatic, etc) that would otherwise be hard to detect. In this chapter, we describe the FMO method and illustrate its application in the discovery of the benzothiazole (BZT) series as novel tyrosine kinase ITK inhibitors for treatment of allergic asthma.

Analyzing GPCR-Ligand Interactions with the Fragment Molecular Orbital (FMO) Method

G-protein-coupled receptors (GPCRs) have enormous physiological and biomedical importance, and therefore it is not surprising that they are the targets of many prescribed drugs. Further progress in GPCR drug discovery is highly dependent on the availability of protein structural information. However, the ability of X-ray crystallography to guide the drug discovery process for GPCR targets is limited by the availability of accurate tools to explore receptor-ligand interactions. Visual inspection and molecular mechanics approaches cannot explain the full complexity of molecular interactions. Quantum mechanics (QM) approaches are often too computationally expensive to be of practical use in time-sensitive situations, but the fragment molecular orbital (FMO) method offers an excellent solution that combines accuracy, speed, and the ability to reveal key interactions that would otherwise be hard to detect. Integration of GPCR crystallography or homology modelling with FMO reveals atomistic details of the individual contributions of each residue and water molecule toward ligand binding, including an analysis of their chemical nature. Such information is essential for an efficient structure-based drug design (SBDD) process. In this chapter, we describe how to use FMO in the characterization of GPCR-ligand interactions.

Improving the Identification and Coverage of Plant Transmembrane Proteins in Medicago Using Bottom-Up Proteomics

Plant transmembrane proteins (TMPs) are essential for normal cellular homeostasis, nutrient exchange, and responses to environmental cues. Commonly used bottom-up proteomic approaches fail to identify a broad coverage of peptide fragments derived from TMPs. Here, we used mass spectrometry (MS) to compare the effectiveness of two solubilization and protein cleavage methods to identify shoot-derived TMPs from the legume Medicago. We compared a urea solubilization, trypsin Lys-C (UR-TLC) cleavage method to a formic acid solubilization, cyanogen bromide and trypsin Lys-C (FA-CTLC) cleavage method. We assessed the effectiveness of these methods by (i) comparing total protein identifications, (ii) determining how many TMPs were identified, and (iii) defining how many peptides incorporate all, or part, of transmembrane domains (TMD) sequences. The results show that the FA-CTLC method identified nine-fold more TMDs, and enriched more hydrophobic TMPs than the UR-TLC method. FA-CTLC identified more TMPs, particularly transporters, whereas UR-TLC preferentially identified TMPs with one TMD, particularly signaling proteins. The results suggest that combining plant membrane purification techniques with both the FA-CTLC and UR-TLC methods will achieve a more complete identification and coverage of TMPs.

G-Protein coupled receptors: structure and function in drug discovery

The G-protein coupled receptors (GPCRs) superfamily comprise similar proteins arranged into families or classes thus making it one of the largest in the mammalian genome. GPCRs take part in many vital physiological functions making them targets for numerous novel drugs. GPCRs share some distinctive features, such as the seven transmembrane domains, they also differ in the number of conserved residues in their transmembrane domain. Here we provide an introductory and accessible review detailing the computational advances in GPCR pharmacology and drug discovery. An overview is provided on family A-C GPCRs; their structural differences, GPCR signalling, allosteric binding and cooperativity. The dielectric constant (relative permittivity) of proteins is also discussed in the context of site-specific environmental effects.

Discovering Anti-Cancer Drugs via Computational Methods

New drug discovery has been acknowledged as a complicated, expensive, time-consuming, and challenging project. It has been estimated that around 12 years and 2.7 billion USD, on average, are demanded for a new drug discovery via traditional drug development pipeline. How to reduce the research cost and speed up the development process of new drug discovery has become a challenging, urgent question for the pharmaceutical industry. Computer-aided drug discovery (CADD) has emerged as a powerful, and promising technology for faster, cheaper, and more effective drug design. Recently, the rapid growth of computational tools for drug discovery, including anticancer therapies, has exhibited a significant and outstanding impact on anticancer drug design, and has also provided fruitful insights into the area of cancer therapy. In this work, we discussed the different subareas of the computer-aided drug discovery process with a focus on anticancer drugs.

Characterizing Interhelical Interactions of G-Protein Coupled Receptors with the Fragment Molecular Orbital Method

G-protein coupled receptors (GPCRs) are the largest superfamily of membrane proteins, regulating almost every aspect of cellular activity and serving as key targets for drug discovery. We have identified an accurate and reliable computational method to characterize the strength and chemical nature of the interhelical interactions between the residues of transmembrane (TM) domains during different receptor activation states, something that cannot be characterized solely by visual inspection of structural information. Using the fragment molecular orbital (FMO) quantum mechanics method to analyze 35 crystal structures representing different branches of the class A GPCR family, we have identified 69 topologically equivalent TM residues that form a consensus network of 51 inter-TM interactions, providing novel results that are consistent with and help to rationalize experimental data. This discovery establishes a comprehensive picture of how defined molecular forces govern specific interhelical interactions which, in turn, support the structural stability, ligand binding, and activation of GPCRs.

Homodimerization of Drosophila Class A neuropeptide GPCRs: Evidence for conservation of GPCR dimerization throughout metazoan evolution

While many instances of GPCR dimerization have been reported for vertebrate receptors, invertebrate GPCR dimerization remains poorly investigated, with few invertebrate GPCRs having been shown to assemble as dimers. To date, no Drosophila GPCRs have been shown to assemble as dimers. To explore the evolutionary conservation of GPCR dimerization, we employed an acceptor-photobleaching FRET methodology to evaluate whether multiple subclasses of Drosophila GPCRs assembled as homodimers when heterologously expressed in HEK-293 T cells. We C-terminally tagged multiple Drosophila neuropeptide GPCRs that exhibited structural homology with a vertebrate GPCR family member previously shown to assemble as a dimer with CFP and YFP fluorophores and visualized these receptors through confocal microscopy. FRET responses were determined based on the increase in CFP emission intensity following YFP photobleaching for each receptor pair tested. A significant FRET response was observed for each receptor expressed as a homodimer pair, while non-significant FRET responses were displayed by both cytosolic CFP and YFP expressed alone, and a heterodimeric pair of receptors from unrelated families. These findings suggest that receptors exhibiting positive FRET responses assemble as homodimers at the plasma membrane and are the first to suggest that Drosophila GPCRs assemble as homodimeric complexes. We propose that GPCR dimerization arose early in metazoan evolution and likely plays an important and underappreciated role in the cellular signaling of all animals.

Role of cholesterol-mediated effects in GPCR heterodimers

G protein-coupled receptors (GPCRs) are transmembrane receptors that mediate a large number of cellular responses. The organization of GPCRs into dimers and higher-order oligomers is known to allow a larger repertoire of downstream signaling events. In this context, a crosstalk between the adenosine and dopamine receptors has been reported, indicating the presence of heterodimers that are functionally relevant. In this paper, we explored the effect of membrane cholesterol on the adenosine_2A (A_2A) and dopamine D₃ (D₃) receptors using coarse-grain molecular dynamics simulations. We analyzed cholesterol interaction sites on the A_2A receptor and were able to reproduce the sites indicated by crystallography and previous atomistic simulations. We predict novel cholesterol interaction sites on the D₃ receptor that could be important in the reported cholesterol sensitivity in receptor function. Further, we analyzed the formation of heterodimers between the two receptors. Our results suggest that membrane cholesterol modulates the relative population of several co-existing heterodimer conformations. Both direct receptor-cholesterol interaction and indirect membrane effects contribute toward the modulation of heterodimer conformations. These results constitute one of the first examples of modulation of GPCR hetero-dimerization by membrane cholesterol, and could prove to be useful in designing better therapeutic strategies.

Small-Scale Panel Comprising Diverse Gene Family Targets To Evaluate Compound Promiscuity

Despite the recent advances in the life sciences and the remarkable investment in drug discovery research, the success rate of small-molecule drug development remains low. Safety is the second most influential factor of drug attrition in clinical studies; thus, the selection of compounds with fewer toxicity concerns is crucial to increase the success rate of drug discovery. Compounds that promiscuously bind to multiple targets are likely to cause unexpected pharmacological activity that may lead to adverse effects. Therefore, avoiding such compounds during early research stages would contribute to identifying compounds with a higher chance of success in the clinic. To evaluate the interaction profile against a wide variety of targets, we constructed a small-scale promiscuity panel (PP) consisting of eight targets (ROCK1, PDE4D2, GR, PPARγ, 5-HT_2B, adenosine A₃, M1, and GABA_A) that were selected from diverse gene families. The validity of this panel was confirmed by comparison with the promiscuity index evaluated from larger-scale panels. Analysis of data from the PP revealed that both lipophilicity and basicity are likely to increase promiscuity, while the molecular weight does not significantly contribute. Additionally, the promiscuity assessed using our PP correlated with the occurrence of both in vitro cytotoxicity and in vivo toxicity, suggesting that the PP is useful to identify compounds with fewer toxicity concerns. In summary, this small-scale and cost-effective PP can contribute to the identification of safer compounds that would lead to a reduction in drug attrition due to safety issues.

WDL-RF: predicting bioactivities of ligand molecules acting with G protein-coupled receptors by combining weighted deep learning and random forest

Motivation

Precise assessment of ligand bioactivities (including IC50, EC50, Ki, Kd, etc.) is essential for virtual screening and lead compound identification. However, not all ligands have experimentally determined activities. In particular, many G protein-coupled receptors (GPCRs), which are the largest integral membrane protein family and represent targets of nearly 40% drugs on the market, lack published experimental data about ligand interactions. Computational methods with the ability to accurately predict the bioactivity of ligands can help efficiently address this problem.

Results

We proposed a new method, WDL-RF, using weighted deep learning and random forest, to model the bioactivity of GPCR-associated ligand molecules. The pipeline of our algorithm consists of two consecutive stages: (i) molecular fingerprint generation through a new weighted deep learning method, and (ii) bioactivity calculations with a random forest model; where one uniqueness of the approach is that the model allows end-to-end learning of prediction pipelines with input ligands being of arbitrary size. The method was tested on a set of twenty-six non-redundant GPCRs that have a high number of active ligands, each with 200-4000 ligand associations. The results from our benchmark show that WDL-RF can generate bioactivity predictions with an average root-mean square error 1.33 and correlation coefficient (r2) 0.80 compared to the experimental measurements, which are significantly more accurate than the control predictors with different molecular fingerprints and descriptors. In particular, data-driven molecular fingerprint features, as extracted from the weighted deep learning models, can help solve deficiencies stemming from the use of traditional hand-crafted features and significantly increase the efficiency of short molecular fingerprints in virtual screening.

Availability and implementation

The WDL-RF web server, as well as source codes and datasets of WDL-RF, is freely available at https://zhanglab.ccmb.med.umich.edu/WDL-RF/ for academic purposes.

Supplementary information

Supplementary data are available at Bioinformatics online.

Influence of the Structural Accuracy of Homology Models on Their Applicability to Docking-Based Virtual Screening: The β2 Adrenergic Receptor as a Case Study

How accurate do structures of the β₂ adrenergic receptor (β₂AR) need to be to effectively serve as platforms for docking-based virtual screening campaigns? To answer this research question, here, we targeted through controlled virtual screening experiments 23 homology models of the β₂AR endowed with different levels of structural accuracy. Subsequently, we studied the correlation between virtual screening performance and structural accuracy of the targeted models. Moreover, we studied the correlation between virtual screening performance and template/target receptor sequence identity. Our study demonstrates that docking-based virtual screening campaigns targeting homology models of the β₂AR, in the majority of the cases, yielded results that exceeded random expectations in terms of area under the receiver operating characteristic curve (ROC AUC). Moreover, with the most effective scoring method, over one-third and one-quarter of the models yielded results that exceeded random expectation also in terms of enrichment factors (EF1, EF5, and EF10) and BEDROC (α = 160.9), respectively. Not surprisingly, we found a detectable linear correlation between virtual screening performance and structural accuracy of the ligand-binding cavity. We also found a detectable linear correlation between virtual screening performance and structural accuracy of the second extracellular loop (EL2). Finally, our data indicate that, although there is no detectable linear correlation between virtual screening performance and template/β₂AR sequence identity, models built on the basis of templates that show high sequence identity with the β₂AR, especially within the ligand-biding cavity, performed consistently well. Conversely, models with lower sequence identity displayed performance levels that ranged from very good to random, with no apparent correlation with the sequence identity itself.

Beyond the Flavour: The Potential Druggability of Chemosensory G Protein-Coupled Receptors

G protein-coupled receptors (GPCRs) belong to the largest class of drug targets. Approximately half of the members of the human GPCR superfamily are chemosensory receptors, including odorant receptors (ORs), trace amine-associated receptors (TAARs), bitter taste receptors (TAS2Rs), sweet and umami taste receptors (TAS1Rs). Interestingly, these chemosensory GPCRs (csGPCRs) are expressed in several tissues of the body where they are supposed to play a role in biological functions other than chemosensation. Despite their abundance and physiological/pathological relevance, the druggability of csGPCRs has been suggested but not fully characterized. Here, we aim to explore the potential of targeting csGPCRs to treat diseases by reviewing the current knowledge of csGPCRs expressed throughout the body and by analysing the chemical space and the drug-likeness of flavour molecules.

Function Prediction for G Protein-Coupled Receptors through Text Mining and Induction Matrix Completion

G protein-coupled receptors (GPCRs) constitute the key component of cellular signal transduction. Accurately annotating the biological functions of GPCR proteins is vital to the understanding of the physiological processes they involve in. With the rapid development of text mining technologies and the exponential growth of biomedical literature, it becomes urgent to explore biological functional information from various literature for systematically and reliably annotating these known GPCRs. We design a novel three-stage approach, TM-IMC, using text mining and inductive matrix completion, for automated prediction of the gene ontology (GO) terms of the GPCR proteins. Large-scale benchmark tests show that inductive matrix completion models contribute to GPCR-GO association prediction for both molecular function and biological process aspects. Moreover, our detailed data analysis shows that information extracted from GPCR-associated literature indeed contributes to the prediction of GPCR-GO associations. The study demonstrated a new avenue to enhance the accuracy of GPCR function annotation through the combination of text mining and induction matrix completion over baseline methods in critical assessment of protein function annotation algorithms and literature-based GO annotation methods. Source codes of TM-IMC and the involved datasets can be freely downloaded from https://zhanglab.ccmb.med.umich.edu/TM-IMC for academic purposes.

Inhibition of B cell-dependent lymphoid follicle formation prevents lymphocytic bronchiolitis after lung transplantation

Lung transplantation (LTx) is the only therapeutic option for many patients with chronic lung disease. However, long-term survival after LTx is severely compromised by chronic rejection (chronic lung allograft dysfunction [CLAD]), which affects 50% of recipients after 5 years. The underlying mechanisms for CLAD are poorly understood, largely due to a lack of clinically relevant animal models, but lymphocytic bronchiolitis is an early sign of CLAD. Here, we report that lymphocytic bronchiolitis occurs early in a long-term murine orthotopic LTx model, based on a single mismatch (grafts from HLA-A2:B6-knockin donors transplanted into B6 recipients). Lymphocytic bronchiolitis is followed by formation of B cell-dependent lymphoid follicles that induce adjacent bronchial epithelial cell dysfunction in a spatiotemporal fashion. B cell deficiency using recipient μMT-/- mice prevented intrapulmonary lymphoid follicle formation and lymphocytic bronchiolitis. Importantly, selective inhibition of the follicle-organizing receptor EBI2, using genetic deletion or pharmacologic inhibition, prevented functional and histological deterioration of mismatched lung grafts. In sum, we provided what we believe to be a mouse model of chronic rejection and lymphocytic bronchiolitis after LTx and identified intrapulmonary lymphoid follicle formation as a target for pharmacological intervention of long-term allograft dysfunction after LTx.

New Binding Sites, New Opportunities for GPCR Drug Discovery

Many central biological events rely on protein-ligand interactions. The identification and characterization of protein-binding sites for ligands are crucial for the understanding of functions of both endogenous ligands and synthetic drug molecules. G protein-coupled receptors (GPCRs) typically detect extracellular signal molecules on the cell surface and transfer these chemical signals across the membrane, inducing downstream cellular responses via G proteins or β-arrestin. GPCRs mediate many central physiological processes, making them important targets for modern drug discovery. Here, we focus on the most recent breakthroughs in finding new binding sites and binding modes of GPCRs and their potentials for the development of new medicines.

GPCR drug discovery: integrating solution NMR data with crystal and cryo-EM structures

The 826 G protein-coupled receptors (GPCRs) in the human proteome regulate key physiological processes and thus have long been attractive drug targets. With the crystal structures of more than 50 different human GPCRs determined over the past decade, an initial platform for structure-based rational design has been established for drugs that target GPCRs, which is currently being augmented with cryo-electron microscopy (cryo-EM) structures of higher-order GPCR complexes. Nuclear magnetic resonance (NMR) spectroscopy in solution is one of the key approaches for expanding this platform with dynamic features, which can be accessed at physiological temperature and with minimal modification of the wild-type GPCR covalent structures. Here, we review strategies for the use of advanced biochemistry and NMR techniques with GPCRs, survey projects in which crystal or cryo-EM structures have been complemented with NMR investigations and discuss the impact of this integrative approach on GPCR biology and drug discovery.

Could the presence of sodium ion influence the accuracy and precision of the ligand-posing in the human A2A adenosine receptor orthosteric binding site using a molecular docking approach? Insights from Dockbench

The allosteric modulation of G protein-coupled receptors (GPCRs) by sodium ions has received considerable attention as crystal structures of several receptors, in their inactive conformation, show a Na⁺ ion bound to specific residues which, in the human A_2A adenosine receptor (hA_2A AR), are Ser91^3.39, Trp246^6.48, Asn280^7.45, and Asn284^7.49. A cluster of water molecules completes the coordination of the sodium ion in the putative allosteric site. It is absolutely consolidated that the progress made in the field of GPCRs structural determination has increased the adoption of docking-driven approaches for the identification or the optimization of novel potent and selective ligands. Despite the extensive use of docking protocols in virtual screening approaches, to date, almost any of these studies have been carried out without taking into account the presence of the sodium cation and its first solvation shell in the putative allosteric binding site. In this study, we have focused our attention on determining how the presence of sodium ion binding and additionally its first hydration sphere, in hA_2AAR could influence the ligand positioning accuracy during molecular docking simulations for most of the available resting and activated hA_2A AR crystal structures, using DockBench as a comparative benchmarking tool and implementing a new correlation coefficient (EM). This work provides indications on the evidence that the posing performance (accuracy and/or precision) of the docking protocols in reproducing the crystallographic poses of different hA_2A AR antagonists is generally increased in the presence of the sodium cation and its first solvation shell, in agreement with experimental observations. Consequently, the inclusion of sodium ion and its first solvation shell should be considered in order to facilitate the selection of new potential ligands in all molecular docking-based virtual screening protocols that aim to find novel GPCRs antagonists and inverse agonists.

Contrast-Matching Detergent in Small-Angle Neutron Scattering Experiments for Membrane Protein Structural Analysis and Ab Initio Modeling

The biological small-angle neutron scattering instrument at the High-Flux Isotope Reactor of Oak Ridge National Laboratory is dedicated to the investigation of biological materials, biofuel processing, and bio-inspired materials covering nanometer to micrometer length scales. The methods presented here for investigating physical properties (i.e., size and shape) of membrane proteins (here, MmIAP, an intramembrane aspartyl protease from Methanoculleus marisnigri) in solutions of micelle-forming detergents are well-suited for this small-angle neutron scattering instrument, among others. Other biophysical characterization techniques are hindered by their inability to address the detergent contributions in a protein-detergent complex structure. Additionally, access to the Bio-Deuteration Lab provides unique capabilities for preparing large-scale cultivations and expressing deuterium-labeled proteins for enhanced scattering signal from the protein. While this technique does not provide structural details at high-resolution, the structural knowledge gap for membrane proteins contains many addressable areas of research without requiring near-atomic resolution. For example, these areas include determination of oligomeric states, complex formation, conformational changes during perturbation, and folding/unfolding events. These investigations can be readily accomplished through applications of this method.

US28: HCMV's Swiss Army Knife

US28 is one of four G protein coupled receptors (GPCRs) encoded by human cytomegalovirus (HCMV). The US28 protein (pUS28) is a potent signaling molecule that alters a variety of cellular pathways that ultimately alter the host cell environment. This viral GPCR is expressed not only in the context of lytic replication but also during viral latency, highlighting its multifunctional properties. pUS28 is a functional GPCR, and its manipulation of multiple signaling pathways likely impacts HCMV pathogenesis. Herein, we will discuss the impact of pUS28 on both lytic and latent infection, pUS28-mediated signaling and its downstream consequences, and the influence this viral GPCR may have on disease states, including cardiovascular disease and cancer. We will also discuss the potential for and progress towards exploiting pUS28 as a novel therapeutic to combat HCMV.

Truly Target-Focused Pharmacophore Modeling: A Novel Tool for Mapping Intermolecular Surfaces

Pharmacophore models are an accurate and minimal tridimensional abstraction of intermolecular interactions between chemical structures, usually derived from a group of molecules or from a ligand-target complex. Only a limited amount of solutions exists to model comprehensive pharmacophores using the information of a particular target structure without knowledge of any binding ligand. In this work, an automated and customable tool for truly target-focused (T²F) pharmacophore modeling is introduced. Key molecular interaction fields of a macromolecular structure are calculated using the AutoGRID energy functions. The most relevant points are selected by a newly developed filtering cascade and clustered to pharmacophore features with a density-based algorithm. Using five different protein classes, the ability of this method to identify essential pharmacophore features was compared to structure-based pharmacophores derived from ligand-target interactions. This method represents an extremely valuable instrument for drug design in a situation of scarce ligand information available, but also in the case of underexplored therapeutic targets, as well as to investigate protein allosteric pockets and protein-protein interactions.

Breakthrough in GPCR Crystallography and Its Impact on Computer-Aided Drug Design

Recent crystallographic structures of G protein-coupled receptors (GPCRs) have greatly advanced our understanding of the recognition of their diverse agonist and antagonist ligands. We illustrate here how this applies to A_2A adenosine receptors (ARs) and to P2Y₁ and P2Y₁₂ receptors (P2YRs) for ADP. These X-ray structures have impacted the medicinal chemistry aimed at discovering new ligands for these two receptor families, including receptors that have not yet been crystallized but are closely related to the known structures. In this Chapter, we discuss recent structure-based drug design projects that led to the discovery of: (a) novel A₃AR agonists based on a highly rigidified (N)-methanocarba scaffold for the treatment of chronic neuropathic pain and other conditions, (b) fluorescent probes of the ARs and P2Y₁₄R, as chemical tools for structural probing of these GPCRs and for improving assay capabilities, and (c) new more drug-like antagonists of the inflammation-related P2Y₁₄R. We also describe the computationally enabled molecular recognition of positive (for A₃AR) and negative (P2Y₁R) allosteric modulators that in some cases are shown to be consistent with structure-activity relationship (SAR) data. Thus, computational modeling has become an essential tool for the design of purine receptor ligands.

Allosteric Regulation of the Follicle-Stimulating Hormone Receptor

Follicle-stimulating hormone receptor (FSHR) belongs to the leucine-rich repeat family of the G protein-coupled receptor (LGR), which includes the glycoprotein hormone receptors luteinizing hormone receptor, thyrotropin receptor, and other LGRs 4, 5, 6, and 7. FSH is the key regulator of folliculogenesis in females and spermatogenesis in males. FSH elicits its physiological response through its cognate receptor on the cell surface. Binding of the hormone FSH to its receptor FSHR brings about conformational changes in the receptor that are transduced through the transmembrane domain to the intracellular region, where the downstream effector interaction takes place, leading to activation of the downstream signaling cascade. Identification of small molecules that could activate or antagonize FSHR provided interesting tools to study the signal transduction mechanism of the receptor. However, because of the nature of the ligand-receptor interaction of FSH-FSHR, which contains multiple sites in the extracellular binding domain, most of the small-molecule modulators of FSHR are unable to bind to the orthosteric site of the receptors. Rather they modulate receptor activation through allosteric sites in the transmembrane region. This review will discuss allosteric modulation of FSHR primarily through the discovery of small-molecule modulators, focusing on current data on the status of development and the utility of these as tools to better understand signaling mechanisms.

G-protein-coupled receptor signaling and neural tube closure defects

Disruption of the normal mechanisms that mediate neural tube closure can result in neural tube defects (NTDs) with devastating consequences in affected patients. With the advent of next-generation sequencing, we are increasingly detecting mutations in multiple genes in NTD cases. However, our ability to determine which of these genes contribute to the malformation is limited by our understanding of the pathways controlling neural tube closure. G-protein-coupled receptors (GPCRs) comprise the largest family of transmembrane receptors in humans and have been historically favored as drug targets. Recent studies implicate several GPCRs and downstream signaling pathways in neural tube development and closure. In this review, we will discuss our current understanding of GPCR signaling pathways in pathogenesis of NTDs. Notable examples include the orphan primary cilia-localized GPCR, Gpr161 that regulates the basal suppression machinery of sonic hedgehog pathway by means of activation of cAMP-protein kinase A signaling in the neural tube, and protease-activated receptors that are activated by a local network of membrane-tethered proteases during neural tube closure involving the surface ectoderm. Understanding the role of these GPCR-regulated pathways in neural tube development and closure is essential toward identification of underlying genetic causes to prevent NTDs. Birth Defects Research 109:129-139, 2017. © 2016 Wiley Periodicals, Inc.

3D-e-Chem: Structural Cheminformatics Workflows for Computer-Aided Drug Discovery

eScience technologies are needed to process the information available in many heterogeneous types of protein-ligand interaction data and to capture these data into models that enable the design of efficacious and safe medicines. Here we present scientific KNIME tools and workflows that enable the integration of chemical, pharmacological, and structural information for: i) structure-based bioactivity data mapping, ii) structure-based identification of scaffold replacement strategies for ligand design, iii) ligand-based target prediction, iv) protein sequence-based binding site identification and ligand repurposing, and v) structure-based pharmacophore comparison for ligand repurposing across protein families. The modular setup of the workflows and the use of well-established standards allows the re-use of these protocols and facilitates the design of customized computer-aided drug discovery workflows.

Better agonist for the opioid receptors

This commentary highlights the recent work published in journal Nature on the structural based discovery of novel analgesic compounds for opioid receptors with minimal effects. Manglik et al. selectively targeted the Gi based μOR pathway instead of the β-arrestin pathway of the opioids. The computational screening of millions of compounds showed a list of several competent ligands. From these ligands they synthesized the compounds with the best docking score, which were further optimized by adding side residues for better interaction with the μOR. A promising compound, PZM21, was a selective agonist of μOR. It has better analgesic properties with minimal side effects of respiratory depression and constipation. This work is a step towards better drug designing and synthesizing in terms of efficacy, specificity with least side effects of targeted GPCR proteins present in the human proteome.

A Structural Framework for GPCR Chemogenomics: What's In a Residue Number?

The recent surge of crystal structures of G protein-coupled receptors (GPCRs), as well as comprehensive collections of sequence, structural, ligand bioactivity, and mutation data, has enabled the development of integrated chemogenomics workflows for this important target family. This chapter will focus on cross-family and cross-class studies of GPCRs that have pinpointed the need for, and the implementation of, a generic numbering scheme for referring to specific structural elements of GPCRs. Sequence- and structure-based numbering schemes for different receptor classes will be introduced and the remaining caveats will be discussed. The use of these numbering schemes has facilitated many chemogenomics studies such as consensus binding site definition, binding site comparison, ligand repurposing (e.g. for orphan receptors), sequence-based pharmacophore generation for homology modeling or virtual screening, and class-wide chemogenomics studies of GPCRs.

Computational Methods Used in Hit-to-Lead and Lead Optimization Stages of Structure-Based Drug Discovery

GPCR modeling approaches are widely used in the hit-to-lead (H2L) and lead optimization (LO) stages of drug discovery. The aims of these modeling approaches are to predict the 3D structures of the receptor-ligand complexes, to explore the key interactions between the receptor and the ligand and to utilize these insights in the design of new molecules with improved binding, selectivity or other pharmacological properties. In this book chapter, we present a brief survey of key computational approaches integrated with hierarchical GPCR modeling protocol (HGMP) used in hit-to-lead (H2L) and in lead optimization (LO) stages of structure-based drug discovery (SBDD). We outline the differences in modeling strategies used in H2L and LO of SBDD and illustrate how these tools have been applied in three drug discovery projects.

Exploring GPCR-Ligand Interactions with the Fragment Molecular Orbital (FMO) Method

The understanding of binding interactions between any protein and a small molecule plays a key role in the rationalization of affinity and selectivity. It is essential for an efficient structure-based drug design (SBDD) process. FMO enables ab initio approaches to be applied to systems that conventional quantum-mechanical (QM) methods would find challenging. The key advantage of the Fragment Molecular Orbital Method (FMO) is that it can reveal atomistic details about the individual contributions and chemical nature of each residue and water molecule toward ligand binding which would otherwise be difficult to detect without using QM methods. In this chapter, we demonstrate the typical use of FMO to analyze 19 crystal structures of β1 and β2 adrenergic receptors with their corresponding agonists and antagonists.

Modeling and Deorphanization of Orphan GPCRs

Despite tremendous efforts, approximately 120 GPCRs remain orphan. Their physiological functions and their potential roles in diseases are poorly understood. Orphan GPCRs are extremely important because they may provide novel therapeutic targets for unmet medical needs. As a complement to experimental approaches, molecular modeling and virtual screening are efficient techniques to discover synthetic surrogate ligands which can help to elucidate the role of oGPCRs. Constitutively activated mutants and recently published active structures of GPCRs provide stimulating opportunities for building active molecular models for oGPCRs and identifying activators using virtual screening of compound libraries. We describe the molecular modeling and virtual screening process we have applied in the discovery of surrogate ligands, and provide examples for CCKA, a simulated oGPCR, and for two oGPCRs, GPR52 and GPR34.

Agonist Binding to Chemosensory Receptors: A Systematic Bioinformatics Analysis

Human G-protein coupled receptors (hGPCRs) constitute a large and highly pharmaceutically relevant membrane receptor superfamily. About half of the hGPCRs' family members are chemosensory receptors, involved in bitter taste and olfaction, along with a variety of other physiological processes. Hence these receptors constitute promising targets for pharmaceutical intervention. Molecular modeling has been so far the most important tool to get insights on agonist binding and receptor activation. Here we investigate both aspects by bioinformatics-based predictions across all bitter taste and odorant receptors for which site-directed mutagenesis data are available. First, we observe that state-of-the-art homology modeling combined with previously used docking procedures turned out to reproduce only a limited fraction of ligand/receptor interactions inferred by experiments. This is most probably caused by the low sequence identity with available structural templates, which limits the accuracy of the protein model and in particular of the side-chains' orientations. Methods which transcend the limited sampling of the conformational space of docking may improve the predictions. As an example corroborating this, we review here multi-scale simulations from our lab and show that, for the three complexes studied so far, they significantly enhance the predictive power of the computational approach. Second, our bioinformatics analysis provides support to previous claims that several residues, including those at positions 1.50, 2.50, and 7.52, are involved in receptor activation.

Evolutionary Cell Biology of Proteins from Protists to Humans and Plants

During evolution, the cell as a fine-tuned machine had to undergo permanent adjustments to match changes in its environment, while "closed for repair work" was not possible. Evolution from protists (protozoa and unicellular algae) to multicellular organisms may have occurred in basically two lineages, Unikonta and Bikonta, culminating in mammals and angiosperms (flowering plants), respectively. Unicellular models for unikont evolution are myxamoebae (Dictyostelium) and increasingly also choanoflagellates, whereas for bikonts, ciliates are preferred models. Information accumulating from combined molecular database search and experimental verification allows new insights into evolutionary diversification and maintenance of genes/proteins from protozoa on, eventually with orthologs in bacteria. However, proteins have rarely been followed up systematically for maintenance or change of function or intracellular localization, acquirement of new domains, partial deletion (e.g. of subunits), and refunctionalization, etc. These aspects are discussed in this review, envisaging "evolutionary cell biology." Protozoan heritage is found for most important cellular structures and functions up to humans and flowering plants. Examples discussed include refunctionalization of voltage-dependent Ca²⁺ channels in cilia and replacement by other types during evolution. Altogether components serving Ca²⁺ signaling are very flexible throughout evolution, calmodulin being a most conservative example, in contrast to calcineurin whose catalytic subunit is lost in plants, whereas both subunits are maintained up to mammals for complex functions (immune defense and learning). Domain structure of R-type SNAREs differs in mono- and bikonta, as do Ca²⁺ -dependent protein kinases. Unprecedented selective expansion of the subunit a which connects multimeric base piece and head parts (V0, V1) of H⁺ -ATPase/pump may well reflect the intriguing vesicle trafficking system in ciliates, specifically in Paramecium. One of the most flexible proteins is centrin when its intracellular localization and function throughout evolution is traced. There are many more examples documenting evolutionary flexibility of translation products depending on requirements and potential for implantation within the actual cellular context at different levels of evolution. From estimates of gene and protein numbers per organism, it appears that much of the basic inventory of protozoan precursors could be transmitted to highest eukaryotic levels, with some losses and also with important additional "inventions."

Determination of the endothelin-1 recognition sites of endothelin receptor type A by the directed-degeneration method

G-protein coupled receptors (GPCRs) play indispensable physiological roles in cell proliferation, differentiation, and migration; therefore, identifying the mechanisms by which ligands bind to GPCRs is crucial for developing GPCR-targeting pharmaceutics and for understanding critical biological functions. Although some structural information is available regarding the interactions between GPCRs and their small molecule ligands, knowledge of how GPCRs interact with their corresponding macromolecule ligands, such as peptides and proteins, remains elusive. In this study, we have developed a novel strategy to investigate the precise ligand recognition mechanisms involved in the interaction of endothelin receptor type A (ET_A) with its ligand, endothelin-1 (ET-1); we call this method “directed degeneration” method. Through flow cytometric screening of a randomized ET_A library, statistical analysis of the identified sequences, and biochemical studies, the ligand interaction map was successfully obtained.

Compound Ranking Based on Fuzzy Three-Dimensional Similarity Improves the Performance of Docking into Homology Models of G-Protein-Coupled Receptors

Ligand docking into homology models of G-protein-coupled receptors (GPCRs) is a widely used approach in computational compound screening. The generation of "double-hypothetical" models of ligand-target complexes has intrinsic accuracy limitations that further complicate compound ranking and selection compared to those of X-ray structures. Given these uncertainties, we have explored "fuzzy 3D similarity" between hypothetical binding modes of known ligands in homology models and docking poses of database compounds as an alternative to conventional scoring schemes. Therefore, GPCR homology models at varying accuracy levels were generated and used for docking. Increases in recall performance were observed for fuzzy 3D similarity ranking using single or multiple ligand poses compared to that of conventional scoring functions and interaction fingerprints. Fuzzy similarity ranking was also successfully applied to docking into an external model of a GPCR for which no experimental structure is currently available. Taken together, our results indicate that the use of putative ligand poses, albeit approximate at best, increases the odds of identifying active compounds in docking screens of GPCR homology models.

Nanodiscs for INPHARMA NMR Characterization of GPCRs: Ligand Binding to the Human A2A Adenosine Receptor

G-protein-coupled-receptors (GPCRs) are of fundamental importance for signal transduction through cell membranes. This makes them important drug targets, but structure-based drug design (SBDD) is still hampered by the limitations for structure determination of unmodified GPCRs. We show that the interligand NOEs for pharmacophore mapping (INPHARMA) method can provide valuable information on ligand poses inside the binding site of the unmodified human A_2A adenosine receptor reconstituted in nanodiscs. By comparing experimental INPHARMA spectra with back-calculated spectra based on ligand poses obtained from molecular dynamics simulations, a complex structure for A_2A R with the low-affinity ligand 3-pyrrolidin-1-ylquinoxalin-2-amine was determined based on the X-ray structure of ligand ZM-241,358 in complex with a modified A_2A R.

Pharmacological property optimization for allosteric ligands: A medicinal chemistry perspective

New strategies to potentially improve drug safety and efficacy emerge with allosteric programs. Biased allosteric modulators can be designed with high subtype selectivity and defined receptor signaling endpoints, however, selecting the most meaningful parameters for optimization can be perplexing. Historically, "potency hunting" at the expense of physicochemical and pharmacokinetic optimization has led to numerous tool compounds with excellent pharmacological properties but no path to drug development. Conversely, extensive physicochemical and pharmacokinetic screening with only post hoc bias and allosteric characterization has led to inefficacious compounds or compounds with on-target toxicities. This field is rapidly evolving with new mechanistic understanding, changes in terminology, and novel opportunities. The intent of this digest is to summarize current understanding and debates within the field. We aim to discuss, from a medicinal chemistry perspective, the parameter choices available to drive SAR.

An update on the physiological and therapeutic relevance of GPCR oligomers

The traditional view on GPCRs held that they function as single monomeric units composed of identical subunits. This notion was overturned by the discovery that GPCRs can form homo- and hetero-oligomers, some of which are obligatory, and can further assemble into receptor mosaics consisting of three or more protomers. Oligomerisation exerts significant impacts on receptor function and physiology, offering a platform for the diversification of receptor signalling, pharmacology, regulation, crosstalk, internalization and trafficking. Given their involvement in the modulation of crucial physiological processes, heteromers could constitute important therapeutic targets for a wide range of diseases, including schizophrenia, Parkinson's disease, substance abuse or obesity. This review aims at depicting the current developments in GPCR oligomerisation research, documenting various class A, B and C GPCR heteromers detected in vitro and in vivo using biochemical and biophysical approaches, as well as recently identified higher-order oligomeric complexes. It explores the current understanding of dimerization dynamics and the possible interaction interfaces that drive oligomerisation. Most importantly, it provides an inventory of the wide range of physiological processes and pathophysiological conditions to which GPCR oligomers contribute, surveying some of the oligomers that constitute potential drug targets. Finally, it delineates the efforts to develop novel classes of ligands that specifically target and tether to receptor oligomers instead of a single monomeric entity, thus ameliorating their ability to modulate GPCR function.