Pan-cancer functional analysis of somatic mutations in G protein-coupled receptors

G Protein-coupled receptors (GPCRs) are the most frequently exploited drug target family, moreover they are often found mutated in cancer. Here we used a dataset of mutations found in patient samples derived from the Genomic Data Commons and compared it to the natural human variance as exemplified by data from the 1000 genomes project. We explored cancer-related mutation patterns in all GPCR classes combined and individually. While the location of the mutations across the protein domains did not differ significantly in the two datasets, a mutation enrichment in cancer patients was observed among class-specific conserved motifs in GPCRs such as the Class A "DRY" motif. A Two-Entropy Analysis confirmed the correlation between residue conservation and cancer-related mutation frequency. We subsequently created a ranking of high scoring GPCRs, using a multi-objective approach (Pareto Front Ranking). Our approach was confirmed by re-discovery of established cancer targets such as the LPA and mGlu receptor families, but also discovered novel GPCRs which had not been linked to cancer before such as the P2Y Receptor 10 (P2RY10). Overall, this study presents a list of GPCRs that are amenable to experimental follow up to elucidate their role in cancer.Medicinal Chemistr

Structural Mapping of Adenosine Receptor Mutations: Ligand Binding and Signaling Mechanisms

TrendsRecent technological advances in membrane protein crystallization have resulted in a nearly exponential increase of available receptor structures. The AR family is an important example in this respect. Crystal structures of antagonist- and agonist-bound adenosine A2A receptor have recently been supplemented by a fully activated conformation in complex with a G-protein mimic, and by antagonist bound structures of the A1 receptor.SDM experiments have been essential to identify residues involved in molecular interactions between ARs and their ligands. Leveraging on recent crystal structures, this vast amount of data can now be systematically classified and interconnected with chemical and structural information of ligands and receptors.The mapping of mutational data onto crystal structures provides new understanding of molecular interactions involved in ligand recognition. Together with computational modeling, this can be used as a roadmap to create novel hypotheses and assist in the design of more systematic mutagenesis studies to answer remaining structural and functional questions.The four adenosine receptors (ARs), A1, A2A, A2B, and A3, constitute a subfamily of G protein-coupled receptors (GPCRs) with exceptional foundations for structure-based ligand design. The vast amount of mutagenesis data, accumulated in the literature since the 1990s, has been recently supplemented with structural information, currently consisting of several inactive and active structures of the A2A and inactive conformations of the A1 ARs. We provide the first integrated view of the pharmacological, biochemical, and structural data available for this receptor family, by mapping onto the relevant crystal structures all site-directed mutagenesis data, curated and deposited at the GPCR database (available through http://www.gpcrdb.org). This analysis provides novel insights into ligand binding, allosteric modulation, and signaling of the AR family.Keywords: G protein-coupled receptor, adenosine receptor, mutagenesis, chemical modulationMedicinal Chemistr

Potent inhibition of nicotinamide N-Methyltransferase by alkene-linked bisubstrate mimics bearing electron deficient aromatics

Nicotinamide N-methyltransferase (NNMT) methylates nicotinamide (vitamin B3) to generate 1-methylnicotinamide (MNA). NNMT overexpression has been linked to a variety of diseases, most prominently human cancers, indicating its potential as a therapeutic target. The development of small-molecule NNMT inhibitors has gained interest in recent years, with the most potent inhibitors sharing structural features based on elements of the nicotinamide substrate and the S-adenosyl-l-methionine (SAM) cofactor. We here report the development of new bisubstrate inhibitors that include electron-deficient aromatic groups to mimic the nicotinamide moiety. In addition, a trans-alkene linker was found to be optimal for connecting the substrate and cofactor mimics in these inhibitors. The most potent NNMT inhibitor identified exhibits an IC50 value of 3.7 nM, placing it among the most active NNMT inhibitors reported to date. Complementary analytical techniques, modeling studies, and cell-based assays provide insights into the binding mode, affinity, and selectivity of these inhibitors.Medicinal Chemistr

Ligand-, Structure-and Pharmacophore-based Molecular Fingerprints: A Case Study on Adenosine A1, A2A, A2B, and A3 Receptor Antagonists

FLAP fingerprints are applied in the ligand-, structure- and pharmacophore-based mode in a case study on antagonists of all four adenosine receptor (AR) subtypes. Structurally diverse antagonist collections with respect to the different ARs were constructed by including binding data to human species only. FLAP models well discriminate “active” (=highly potent) from “inactive” (=weakly potent) AR antagonists, as indicated by enrichment curves, numbers of false positives, and AUC values. For all FLAP modes, model predictivity slightly decreases as follows: A2BR > A2AR > A3R > A1R antagonists. General performance of FLAP modes in this study is: ligand- > structure- > pharmacophore- based mode. We also compared the FLAP performance with other common ligand- and structure-based fingerprints. Concerning the ligand-based mode, FLAP model performance is superior to ECFP4 and ROCS for all AR subtypes. Although focusing on the early first part of the A2A, A2B and A3 enrichment curves, ECFP4 and ROCS still retain a satisfactory retrieval of actives. FLAP is also superior when comparing the structure-based mode with PLANTS and GOLD. In this study we applied for the first time the novel FLAPPharm tool for pharmacophore generation. Pharmacophore hypotheses, generated with this tool, convincingly match with formerly published data. Finally, we could demonstrate the capability of FLAP models to uncover selectivity aspects although single AR subtype models were not trained for this purpose

Sodium Ion Binding Pocket Mutations and Adenosine A2A Receptor Function

Pharmacolog

Deciphering conformational selectivity in the A(2A) adenosine G protein-coupled receptor by free energy simulations

Transmembranal G Protein-Coupled Receptors (GPCRs) transduce extracellular chemical signals to the cell, via conformational change from a resting (inactive) to an active (canonically bound to a G-protein) conformation. Receptor activation is normally modulated by extracellular ligand binding, but mutations in the receptor can also shift this equilibrium by stabilizing different conformational states. In this work, we built structure-energetic relationships of receptor activation based on original thermodynamic cycles that represent the conformational equilibrium of the prototypical A(2A) adenosine receptor (AR). These cycles were solved with efficient free energy perturbation (FEP) protocols, allowing to distinguish the pharmacological profile of different series of A(2A)AR agonists with different efficacies. The modulatory effects of point mutations on the basal activity of the receptor or on ligand efficacies could also be detected. This methodology can guide GPCR ligand design with tailored pharmacological properties, or allow the identification of mutations that modulate receptor activation with potential clinical implications

Pan-cancer functional analysis of somatic mutations in G protein-coupled receptors

G Protein-coupled receptors (GPCRs) are the most frequently exploited drug target family, moreover they are often found mutated in cancer. Here we used a dataset of mutations found in patient samples derived from the Genomic Data Commons and compared it to the natural human variance as exemplified by data from the 1000 genomes project. We explored cancer-related mutation patterns in all GPCR classes combined and individually. While the location of the mutations across the protein domains did not differ significantly in the two datasets, a mutation enrichment in cancer patients was observed among class-specific conserved motifs in GPCRs such as the Class A "DRY" motif. A Two-Entropy Analysis confirmed the correlation between residue conservation and cancer-related mutation frequency. We subsequently created a ranking of high scoring GPCRs, using a multi-objective approach (Pareto Front Ranking). Our approach was confirmed by re-discovery of established cancer targets such as the LPA and mGlu receptor families, but also discovered novel GPCRs which had not been linked to cancer before such as the P2Y Receptor 10 (P2RY10). Overall, this study presents a list of GPCRs that are amenable to experimental follow up to elucidate their role in cancer.</p

Characterization of cancer-related somatic mutations in the adenosine A2B receptor

In cancer, G protein-coupled receptors (GPCRs) are involved in tumor progression and metastasis. In this study we particularly examined one GPCR, the adenosine A2B receptor. This receptor is activated by high concentrations of its endogenous ligand adenosine, which suppresses the immune response to fight tumor progression. A series of adenosine A2B receptor mutations were retrieved from the Cancer Genome Atlas harboring data from patient samples with different cancer types. The main goal of this work was to investigate the pharmacology of these mutant receptors using a ‘single-GPCR-one-G protein’ yeast assay technology. Concentration-growth curves were obtained with the full agonist NECA for the wild-type receptor and 15 mutants. Compared to wild-type receptor, the constitutive activity levels in mutant receptors F141L4.61, Y202C5.58 and L310P8.63 were high, while the potency and efficacy of NECA and BAY 60–6583 on Y202C5.58 was lower. A 33- and 26-fold higher constitutive activity on F141L4.61 and L310P8.63 was reduced to wild-type levels in response to the inverse agonist ZM241385. These constitutively active mutants may thus be tumor promoting. Mutant receptors F259S6.60 and Y113F34.53 showed a more than one log-unit decrease in potency. A complete loss of activation was observed in mutant receptors C29R1.54, W130C4.50 and P249L6.50. All mutations were characterized at the structural level, generating hypotheses of their roles on modulating the receptor conformational equilibrium. Taken together, this study is the first to investigate the nature of adenosine A2B receptor cancer mutations and may thus provide insights in mutant receptor function in cancer.Toxicolog

Identification of V6.51L as a selectivity hotspot in stereoselective A(2B) adenosine receptor antagonist recognition

The four adenosine receptors (ARs) A(1)AR, A(2A)AR, A(2B)AR(,) and A(3)AR are G protein-coupled receptors (GPCRs) for which an exceptional amount of experimental and structural data is available. Still, limited success has been achieved in getting new chemical modulators on the market. As such, there is a clear interest in the design of novel selective chemical entities for this family of receptors. In this work, we investigate the selective recognition of ISAM-140, a recently reported A(2B)AR reference antagonist. A combination of semipreparative chiral HPLC, circular dichroism and X-ray crystallography was used to separate and unequivocally assign the configuration of each enantiomer. Subsequently affinity evaluation for both A(2A) and A(2B) receptors demonstrate the stereospecific and selective recognition of (S)-ISAM140 to the A(2B)AR. The molecular modeling suggested that the structural determinants of this selectivity profile would be residue V250(6.51) in A(2B)AR, which is a leucine in all other ARs including the closely related A(2A)AR. This was herein confirmed by radioligand binding assays and rigorous free energy perturbation (FEP) calculations performed on the L249V(6.51) mutant A(2A)AR receptor. Taken together, this study provides further insights in the binding mode of these A(2B)AR antagonists, paving the way for future ligand optimization