Current and Future Challenges in GPCR Drug Discovery

A Study on the Robustness and Stability of Explainable Deep Learning in an Imbalanced Setting: The Exploration of the Conformational Space of G Protein-Coupled Receptors

G-protein coupled receptors (GPCRs) are transmembrane proteins that transmit signals from the extracellular environment to the inside of the cells. Their ability to adopt various conformational states, which influence their function, makes them crucial in pharmacoproteomic studies. While many drugs target specific GPCR states to exert their effects-thereby regulating the protein's activity-unraveling the activation pathway remains challenging due to the multitude of intermediate transformations occurring throughout this process, and intrinsically influencing the dynamics of the receptors. In this context, computational modeling, particularly molecular dynamics (MD) simulations, may offer valuable insights into the dynamics and energetics of GPCR transformations, especially when combined with machine learning (ML) methods and techniques for achieving model interpretability for knowledge generation. The current study builds upon previous work in which the layer relevance propagation (LRP) technique was employed to interpret the predictions in a multi-class classification problem concerning the conformational states of the β2-adrenergic (β2AR) receptor from MD simulations. Here, we address the challenges posed by class imbalance and extend previous analyses by evaluating the robustness and stability of deep learning (DL)-based predictions under different imbalance mitigation techniques. By meticulously evaluating explainability and imbalance strategies, we aim to produce reliable and robust insights.

Systematic Evaluation of Protein-Small Molecule Hybrids on the Yeast Surface

Protein-small molecule hybrids are structures that have the potential to combine the inhibitory properties of small molecules and the specificities of binding proteins. However, achieving such synergies is a substantial engineering challenge with fundamental principles yet to be elucidated. Recent work has demonstrated the power of the yeast display-based discovery of hybrids using a combination of fibronectin-binding domains and thiol-mediated conjugations to introduce small-molecule warheads. Here, we systematically study the effects of expanding the chemical diversity of these hybrids on the yeast surface by investigating a combinatorial set of fibronectins, noncanonical amino acid (ncAA) substitutions, and small-molecule pharmacophores. Our results show that previously discovered thiol-fibronectin hybrids are generally tolerant of a range of ncAA substitutions and retain binding functions to carbonic anhydrases following click chemistry-mediated assembly of hybrids with diverse linker structures. Most surprisingly, we identified several cases where replacement of a potent acetazolamide warhead with a substantially weaker benzenesulfonamide warhead still resulted in the assembly of multiple functional hybrids. In addition to these unexpected findings, we expanded the throughput of our system by validating a 96-well plate-based format to produce yeast-displayed hybrid conjugates in parallel. These efficient explorations of hybrid chemical diversity demonstrate that there are abundant opportunities to expand the functions of protein-small molecule hybrids and elucidate principles that dictate their efficient discovery and design.

Is there a common allosteric binding site for G-protein coupled receptors?

Targeting the allosteric sites on G-protein coupled receptors (GPCRs) for drug discovery is attracting increased interest. Given a GPCR target, identifying the allosteric binding sites in it remains a challenge. Previous works from our and other labs suggest the intracellular region below the middle of the transmembrane (TM) domain that spatially overlaps with the G-protein binding site could contain a common allosteric site for all GPCRs. We performed several bioinformatics analyses on this site for more than 100 representative human GPCR structures. Results of the studies confirmed that the proposed region contains an allosteric site that is druggable for 89% of the GPCRs and is not 100% identical between a GPCR and its most similar homolog for 94% of the GPCRs. The physico-chemical properties and amino acid composition of this site vary among and within GPCR classes. Since this proposed region occupies the space existing in all GPCRs of known structure, it could represent a common host of an allosteric site for all GPCRs that can be targeted for structure-based allosteric drug design.

Drug-Targeted Genomes: Mutability of Ion Channels and GPCRs

Mutations of ion channels and G-protein-coupled receptors (GPCRs) are not uncommon and can lead to cardiovascular diseases. Given previously reported multiple factors associated with high mutation rates, we sorted the relative mutability of multiple human genes by (i) proximity to telomeres and/or (ii) high adenine and thymine (A+T) content. We extracted genomic information using the genome data viewer and examined the mutability of 118 ion channel and 143 GPCR genes based on their association with factors (i) and (ii). We then assessed these two factors with 31 genes encoding ion channels or GPCRs that are targeted by the United States Food and Drug Administration (FDA)-approved drugs. Out of the 118 ion channel genes studied, 80 met either factor (i) or (ii), resulting in a 68% match. In contrast, a 78% match was found for the 143 GPCR genes. We also found that the GPCR genes (n = 20) targeted by FDA-approved drugs have a relatively lower mutability than those genes encoding ion channels (n = 11), where targeted genes encoding GPCRs were shorter in length. The result of this study suggests that the use of matching rate analysis on factor-druggable genome is feasible to systematically compare the relative mutability of GPCRs and ion channels. The analysis on chromosomes by two factors identified a unique characteristic of GPCRs, which have a significant relationship between their nucleotide sizes and proximity to telomeres, unlike most genetic loci susceptible to human diseases.

Elucidation of Distinct Modular Assemblies of Smoothened Receptor by Bitopic Ligand Measurement

Class F G protein-coupled receptors are characterized by a large extracellular domain (ECD) in addition to the common transmembrane domain (TMD) with seven α-helixes. For smoothened receptor (SMO), structural studies revealed dissected ECD and TMD, and their integrated assemblies. However, distinct assemblies were reported under different circumstances. Using an unbiased approach based on four series of cross-conjugated bitopic ligands, we explore the relationship between the active status and receptor assembly. Different activity dependency on the linker length for these bitopic ligands corroborates the various occurrences of SMO assembly. These results reveal a rigid "near" assembly for active SMO, which is in contrast to previous results. Conversely, inactive SMO adopts a free ECD, which would be remotely captured at "far" assembly by cholesterol. Altogether, we propose a mechanism of cholesterol flow-caused SMO activation involving an erection of ECD from far to near assembly.

Trends in application of advancing computational approaches in GPCR ligand discovery

G protein-coupled receptors (GPCRs) comprise the most important superfamily of protein targets in current ligand discovery and drug development. GPCRs are integral membrane proteins that play key roles in various cellular signaling processes. Therefore, GPCR signaling pathways are closely associated with numerous diseases, including cancer and several neurological, immunological, and hematological disorders. Computer-aided drug design (CADD) can expedite the process of GPCR drug discovery and potentially reduce the actual cost of research and development. Increasing knowledge of biological structures, as well as improvements on computer power and algorithms, have led to unprecedented use of CADD for the discovery of novel GPCR modulators. Similarly, machine learning approaches are now widely applied in various fields of drug target research. This review briefly summarizes the application of rising CADD methodologies, as well as novel machine learning techniques, in GPCR structural studies and bioligand discovery in the past few years. Recent novel computational strategies and feasible workflows are updated, and representative cases addressing challenging issues on olfactory receptors, biased agonism, and drug-induced cardiotoxic effects are highlighted to provide insights into future GPCR drug discovery.

Orchestration of Intracellular Circuits by G Protein-Coupled Receptor 39 for Hepatitis B Virus Proliferation

Hepatitis B virus (HBV), a highly persistent pathogen causing hepatocellular carcinoma (HCC), takes full advantage of host machinery, presenting therapeutic targets. Here we aimed to identify novel druggable host cellular factors using the reporter HBV we have recently generated. In an RNAi screen of G protein-coupled receptors (GPCRs), GPCR39 (GPR39) appeared as the top hit to facilitate HBV proliferation. Lentiviral overexpression of active GPR39 proteins and an agonist enhanced HBV replication and transcriptional activities of viral promoters, inducing the expression of CCAAT/enhancer binding protein (CEBP)-β (CEBPB). Meanwhile, GPR39 was uncovered to activate the heat shock response, upregulating the expression of proviral heat shock proteins (HSPs). In addition, glioma-associated oncogene homologue signaling, a recently reported target of GPR39, was suggested to inhibit HBV replication and eventually suppress expression of CEBPB and HSPs. Thus, GPR39 provirally governed intracellular circuits simultaneously affecting the carcinopathogenetic gene functions. GPR39 and the regulated signaling networks would serve as antiviral targets, and strategies with selective inhibitors of GPR39 functions can develop host-targeted antiviral therapies preventing HCC.

Spinal Inhibition of GABAB Receptors by the Extracellular Matrix Protein Fibulin-2 in Neuropathic Rats

In the central nervous system, the inhibitory GABAB receptor is the archetype of heterodimeric G protein-coupled receptors (GPCRs). Receptor interaction with partner proteins has emerged as a novel mechanism to alter GPCR signaling in pathophysiological conditions. We propose here that GABAB activity is inhibited through the specific binding of fibulin-2, an extracellular matrix protein, to the B1a subunit in a rat model of neuropathic pain. We demonstrate that fibulin-2 hampers GABAB activation, presumably through decreasing agonist-induced conformational changes. Fibulin-2 regulates the GABAB-mediated presynaptic inhibition of neurotransmitter release and weakens the GABAB-mediated inhibitory effect in neuronal cell culture. In the dorsal spinal cord of neuropathic rats, fibulin-2 is overexpressed and colocalized with B1a. Fibulin-2 may thus interact with presynaptic GABAB receptors, including those on nociceptive afferents. By applying anti-fibulin-2 siRNA in vivo, we enhanced the antinociceptive effect of intrathecal baclofen in neuropathic rats, thus demonstrating that fibulin-2 limits the action of GABAB agonists in vivo. Taken together, our data provide an example of an endogenous regulation of GABAB receptor by extracellular matrix proteins and demonstrate its functional impact on pathophysiological processes of pain sensitization.

Understanding Membrane Protein Drug Targets in Computational Perspective

Membrane proteins play crucial physiological roles in vivo and are the major category of drug targets for pharmaceuticals. The research on membrane protein is a significant part in the drug discovery. The biological process is a cycled network, and the membrane protein is a vital hub in the network since most drugs achieve the therapeutic effect via interacting with the membrane protein. In this review, typical membrane protein targets are described, including GPCRs, transporters and ion channels. Also, we conclude network servers and databases that are referring to the drug, drug-target information and their relevant data. Furthermore, we chiefly introduce the development and practice of modern medicines, particularly demonstrating a series of state-of-the-art computational models for the prediction of drug-target interaction containing network-based approach and machine-learningbased approach as well as showing current achievements. Finally, we discuss the prospective orientation of drug repurposing and drug discovery as well as propose some improved framework in bioactivity data, created or improved predicted approaches, alternative understanding approaches of drugs bioactivity and their biological processes.

Allosteric Modulators of Potential Targets Related to Alzheimer's Disease: a Review

Among neurodegenerative disorders, Alzheimer's disease (AD) is the most common type of dementia, and there is an urgent need to discover new and efficacious forms of treatment for it. Pathological patterns of AD include cholinergic dysfunction, increased β-amyloid (Aβ) peptide concentration, the appearance of neurofibrillary tangles, among others, all of which are strongly associated with specific biological targets. Interactions observed between these targets and potential drug candidates in AD most often occur by competitive mechanisms driven by orthosteric ligands that sometimes result in the production of side effects. In this context, the allosteric mechanism represents a key strategy; this can be regarded as the selective modulation of such targets by allosteric modulators in an advantageous manner, as this may decrease the likelihood of side effects. The purpose of this review is to present an overview of compounds that act as allosteric modulators of the main biological targets related to AD.

Structural Variability in the RLR-MAVS Pathway and Sensitive Detection of Viral RNAs

Cells need high-sensitivity detection of non-self molecules in order to fight against pathogens. These cellular sensors are thus of significant importance to medicinal purposes, especially for treating novel emerging pathogens. RIG-I-like receptors (RLRs) are intracellular sensors for viral RNAs (vRNAs). Their active forms activate mitochondrial antiviral signaling protein (MAVS) and trigger downstream immune responses against viral infection. Functional and structural studies of the RLR-MAVS signaling pathway have revealed significant supramolecular variability in the past few years, which revealed different aspects of the functional signaling pathway. Here I will discuss the molecular events of RLR-MAVS pathway from the angle of detecting single copy or a very low copy number of vRNAs in the presence of non-specific competition from cytosolic RNAs, and review key structural variability in the RLR / vRNA complexes, the MAVS helical polymers, and the adapter-mediated interactions between the active RLR / vRNA complex and the inactive MAVS in triggering the initiation of the MAVS filaments. These structural variations may not be exclusive to each other, but instead may reflect the adaptation of the signaling pathways to different conditions or reach different levels of sensitivity in its response to exogenous vRNAs.

Orphan Receptor GPR88 as an Emerging Neurotherapeutic Target

Although G protein-coupled receptors (GPCRs) are recognized as pivotal drug targets involved in multiple physiological and pathological processes, the majority of GPCRs including orphan GPCRs (oGPCRs) are unexploited. GPR88, a brain-specific oGPCR with particularly robust expression in the striatum, regulates diverse brain and behavioral functions, including cognition, mood, movement control, and reward-based learning, and is thus emerging as a novel drug target for central nervous system disorders including schizophrenia, Parkinson's disease, anxiety, and addiction. Nevertheless, no effective GPR88 synthetic ligands have yet entered into clinical trials, and GPR88 endogenous ligands remain unknown. Despite the recent discovery and early stage study of several GPR88 agonists, such as 2-PCCA, RTI-13951-33, and phenylglycinol derivatives, further research into GPR88 pharmacology, medicinal chemistry, and chemical biology is urgently needed to yield structurally diversified GPR88-specific ligands. Drug-like pharmacological tool function and relevant signaling elucidation will also accelerate the evaluation of this receptor as a viable neurotherapeutic target.

Uncovering new disease indications for G-protein coupled receptors and their endogenous ligands

Background

The Open Targets Platform integrates different data sources in order to facilitate identification of potential therapeutic drug targets to treat human diseases. It currently provides evidence for nearly 2.6 million potential target-disease pairs. G-protein coupled receptors are a drug target class of high interest because of the number of successful drugs being developed against them over many years. Here we describe a systematic approach utilizing the Open Targets Platform data to uncover and prioritize potential new disease indications for the G-protein coupled receptors and their ligands.

Results

Utilizing the data available in the Open Targets platform, potential G-protein coupled receptor and endogenous ligand disease association pairs were systematically identified. Intriguing examples such as GPR35 for inflammatory bowel disease and CXCR4 for viral infection are used as illustrations of how a systematic approach can aid in the prioritization of interesting drug discovery hypotheses. Combining evidences for G-protein coupled receptors and their corresponding endogenous peptidergic ligands increases confidence and provides supportive evidence for potential new target-disease hypotheses. Comparing such hypotheses to the global pharma drug discovery pipeline to validate the approach showed that more than 93% of G-protein coupled receptor-disease pairs with a high overall Open Targets score involved receptors with an existing drug discovery program.

Conclusions

The Open Targets gene-disease score can be used to prioritize potential G-protein coupled receptors-indication hypotheses. In addition, availability of multiple different evidence types markedly increases confidence as does combining evidence from known receptor-ligand pairs. Comparing the top-ranked hypotheses to the current global pharma pipeline serves validation of our approach and identifies and prioritizes new therapeutic opportunities.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2392-y) contains supplementary material, which is available to authorized users.