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

GPR35: from enigma to therapeutic target

The orphan G-protein-coupled receptor 35 (GPR35), although poorly characterised, is attracting considerable interest as a therapeutic target. Marked differences in pharmacology between human and rodent orthologues of the receptor and a dearth of antagonists with affinity for mouse and rat GPR35 have previously restricted the use of preclinical disease models. The development of improved ligands, novel transgenic knock-in mouse lines, and detailed analysis of the disease relevance of single-nucleotide polymorphisms (SNPs) have greatly enhanced understanding of the key roles of GPR35 and have stimulated efforts towards disease-targeted proof-of-concept studies. In this opinion article, new information on the biology of the receptor is considered, whilst insight into how GPR35 is currently being assessed for therapeutic utility - in areas ranging from inflammatory bowel diseases to nonalcoholic steatohepatitis and various cancers - is also provided.

What Makes GPCRs from Different Families Bind to the Same Ligand?

G protein-coupled receptors (GPCRs) are the largest class of cell-surface receptor proteins with important functions in signal transduction and often serve as therapeutic drug targets. With the rapidly growing public data on three dimensional (3D) structures of GPCRs and GPCR-ligand interactions, computational prediction of GPCR ligand binding becomes a convincing option to high throughput screening and other experimental approaches during the beginning phases of ligand discovery. In this work, we set out to computationally uncover and understand the binding of a single ligand to GPCRs from several different families. Three-dimensional structural comparisons of the GPCRs that bind to the same ligand revealed local 3D structural similarities and often these regions overlap with locations of binding pockets. These pockets were found to be similar (based on backbone geometry and side-chain orientation using APoc), and they correlate positively with electrostatic properties of the pockets. Moreover, the more similar the pockets, the more likely a ligand binding to the pockets will interact with similar residues, have similar conformations, and produce similar binding affinities across the pockets. These findings can be exploited to improve protein function inference, drug repurposing and drug toxicity prediction, and accelerate the development of new drugs.

Empowering the discovery of novel target-disease associations via machine learning approaches in the open targets platform

Background

The Open Targets (OT) Platform integrates a wide range of data sources on target-disease associations to facilitate identification of potential therapeutic drug targets to treat human diseases. However, due to the complexity that targets are usually functionally pleiotropic and efficacious for multiple indications, challenges in identifying novel target to indication associations remain. Specifically, persistent need exists for new methods for integration of novel target-disease association evidence and biological knowledge bases via advanced computational methods. These offer promise for increasing power for identification of the most promising target-disease pairs for therapeutic development. Here we introduce a novel approach by integrating additional target-disease features with machine learning models to further uncover druggable disease to target indications.

Results

We derived novel target-disease associations as supplemental features to OT platform-based associations using three data sources: (1) target tissue specificity from GTEx expression profiles; (2) target semantic similarities based on gene ontology; and (3) functional interactions among targets by embedding them from protein–protein interaction (PPI) networks. Machine learning models were applied to evaluate feature importance and performance benchmarks for predicting targets with known drug indications. The evaluation results show the newly integrated features demonstrate higher importance than current features in OT. In addition, these also show superior performance over association benchmarks and may support discovery of novel therapeutic indications for highly pursued targets.

Conclusion

Our newly generated features can be used to represent additional underlying biological relatedness among targets and diseases to further empower improved performance for predicting novel indications for drug targets through advanced machine learning models. The proposed methodology enables a powerful new approach for systematic evaluation of drug targets with novel indications.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04753-4.

GPCR oligomerization as a target for antidepressants: Focus on GPR39

At present most of the evidence for the relevance of oligomerization for the pharmacology of depression comes from in vitro studies which identified oligomers, and from neuropsychopharmacological studies of receptors which participate in oligomerization. For example, behavioural and biochemical studies in knockout animals suggest that GPR39 may mediate the antidepressant action of monoaminergic antidepressants. We have recently found long-lasting antidepressant-like effects of GPR39 agonist, thus suggesting GPR39 as a target for the development of novel antidepressant drugs. In vitro studies have shown that GPR39 oligomerizes with other GPCRs. Oligomerization of GPR39 should thus be considered in relation to the development of new antidepressants targeting this receptor as well as antidepressants targeting other receptors that may form complexes with GPR39. Here, we summarize recent data suggestive of the importance of oligomerization for the pharmacology of depression and discuss approaches for validation of this phenomenon.

Structural Characterization of Receptor-Receptor Interactions in the Allosteric Modulation of G Protein-Coupled Receptor (GPCR) Dimers

G protein-coupled receptor (GPCR) oligomerization, while contentious, continues to attract the attention of researchers. Numerous experimental investigations have validated the presence of GPCR dimers, and the relevance of dimerization in the effectuation of physiological functions intensifies the attractiveness of this concept as a potential therapeutic target. GPCRs, as a single entity, have been the main source of scrutiny for drug design objectives for multiple diseases such as cancer, inflammation, cardiac, and respiratory diseases. The existence of dimers broadens the research scope of GPCR functions, revealing new signaling pathways that can be targeted for disease pathogenesis that have not previously been reported when GPCRs were only viewed in their monomeric form. This review will highlight several aspects of GPCR dimerization, which include a summary of the structural elucidation of the allosteric modulation of class C GPCR activation offered through recent solutions to the three-dimensional, full-length structures of metabotropic glutamate receptor and γ-aminobutyric acid B receptor as well as the role of dimerization in the modification of GPCR function and allostery. With the growing influence of computational methods in the study of GPCRs, we will also be reviewing recent computational tools that have been utilized to map protein-protein interactions (PPI).

Opportunities and challenges of the tag-assisted protein purification techniques: Applications in the pharmaceutical industry

Tag-assisted protein purification is a method of choice for both academic researches and large-scale industrial demands. Application of the purification tags in the protein production process can help to save time and cost, but the design and application of tagged fusion proteins are challenging. An appropriate tagging strategy must provide sufficient expression yield and high purity for the final protein products while preserving their native structure and function. Thanks to the recent advances in the bioinformatics and emergence of high-throughput techniques (e.g. SEREX), many new tags are introduced to the market. A variety of interfering and non-interfering tags have currently broadened their application scope beyond the traditional use as a simple purification tool. They can take part in many biochemical and analytical features and act as solubility and protein expression enhancers, probe tracker for online visualization, detectors of post-translational modifications, and carrier-driven tags. Given the variability and growing number of the purification tags, here we reviewed the protein- and peptide-structured purification tags used in the affinity, ion-exchange, reverse phase, and immobilized metal ion affinity chromatographies. We highlighted the demand for purification tags in the pharmaceutical industry and discussed the impact of self-cleavable tags, aggregating tags, and nanotechnology on both the column-based and column-free purification techniques.

A review of antibody-based therapeutics targeting G protein-coupled receptors: an update

INTRODUCTION

G protein-coupled receptors (GPCRs) play key roles in many biological functions and are linked to many diseases across all therapeutic areas. As such, GPCRs represent a significant opportunity for antibody-based therapeutics.

AREAS COVERED

The structure of the major GPCR families is summarized in the context of choice of antigen source employed in the drug discovery process and receptor biology considerations which may impact on targeting strategies. An overview of the therapeutic GPCR-antibody target landscape and the diversity of current therapeutic programs is provided along with summary case studies for marketed antibody drugs or those in advanced clinical studies. Antibodies in early clinical studies and the emergence of next-generation modalities are also highlighted.

EXPERT OPINION

The GPCR-antibody pipeline has progressed significantly with a number of technical developments enabling the successful resolution of some of the challenges previously encountered and this has contributed to the growing interest in antibody-based therapeutics addressing this target class.