A3 adenosine receptor activation mechanisms: molecular dynamics analysis of inactive, active, and fully active states

Computational Workflow for Refining AlphaFold Models in Drug Design Using Kinetic and Thermodynamic Binding Calculations: A Case Study for the Unresolved Inactive Human Adenosine A3 Receptor

A structure-based drug design pipeline that considers both thermodynamic and kinetic binding data of ligands against a receptor will enable the computational design of improved drug molecules. For unresolved GPCR-ligand complexes, a workflow that can apply both thermodynamic and kinetic binding data in combination with alpha-fold (AF)-derived or other homology models and experimentally resolved binding modes of relevant ligands in GPCR-homologs needs to be tested. Here, as test case, we studied a congeneric set of ligands that bind to a structurally unresolved G protein-coupled receptor (GPCR), the inactive human adenosine A3 receptor (hA3R). We tested three available homology models from which two have been generated from experimental structures of hA1R or hA2AR and one model was a multistate alphafold 2 (AF2)-derived model. We applied alchemical calculations with thermodynamic integration coupled with molecular dynamics (TI/MD) simulations to calculate the experimental relative binding free energies and residence time (τ)-random accelerated MD (τ-RAMD) simulations to calculate the relative residence times (RTs) for antagonists. While the TI/MD calculations produced, for the three homology models, good Pearson correlation coefficients, correspondingly, r = 0.74, 0.62, and 0.67 and mean unsigned error (mue) values of 0.94, 1.31, and 0.81 kcal mol-1, the τ-RAMD method showed r = 0.92 and 0.52 for the first two models but failed to produce accurate results for the multistate AF2-derived model. With subsequent optimization of the AF2-derived model by reorientation of the side chain of R1735.34 located in the extracellular loop 2 (EL2) that blocked ligand's unbinding, the computational model showed r = 0.84 for kinetic data and improved performance for thermodynamic data (r = 0.81, mue = 0.56 kcal mol-1). Overall, after refining the multistate AF2 model with physics-based tools, we were able to show a strong correlation between predicted and experimental ligand relative residence times and affinities, achieving a level of accuracy comparable to an experimental structure. The computational workflow used can be applied to other receptors, helping to rank candidate drugs in a congeneric series and enabling the prioritization of leads with stronger binding affinities and longer residence times.

Assay-Dependent Inverse Agonism at the A3 Adenosine Receptor: When Neutral Is Not Neutral

The A3 adenosine receptor (A3AR) is implicated in a variety of (patho)physiological conditions. While most research has focused on agonists and antagonists, inverse agonism at A3AR has been scarcely studied. Therefore, this study aimed at exploring inverse agonism, using two previously engineered cell lines (hA3ARLgBiT-SmBiTβarr2 and hA3ARLgBiT-SmBiTminiGαi), both employing the NanoBiT technology. The previously established inverse agonist PSB-10 showed a decrease in basal signal in the β-arrestin 2 (βarr2) but not the miniGαi recruitment assay, indicative of inverse agonism in the former assay. Control experiments confirmed the specificity and reversibility of this observation. Evaluation of a set of presumed neutral antagonists (MRS7907, MRS7799, XAC, and MRS1220) revealed that all displayed concentration-dependent signal decreases when tested in the A3AR-βarr2 recruitment assay, yielding EC50 and Emax values for inverse agonism. Conversely, in the miniGαi recruitment assay, no signal decreases were observed. To assess whether this observation was caused by the inability of the ligands to induce inverse agonism in the G protein pathway, or rather by a limitation inherent to the employed A3AR-miniGαi recruitment assay, a GloSensor cAMP assay was performed. The outcome of the latter also suggests inverse agonism by the presumed neutral antagonists in this latter assay. These findings emphasize the importance of prior characterization of ligands in the relevant test system. Moreover, it showed the suitability of the NanoBiT βarr2 recruitment and the GloSensor cAMP assays to capture inverse agonism at the A3AR, as opposed to the NanoBiT miniGαi recruitment assay.

Cipargamin could inhibit human adenosine receptor A3 with higher binding affinity than Plasmodium falciparum P-type ATPase 4: An In silico study

Aim: This study aimed to predict the molecular targets of cipargamin in humans and estimate the structural dynamics and binding affinity of their interactions compared to that of Plasmodium falciparum P-type ATPase 4 (PfATP4). Methods: In silico methods were used in this study which include target prediction, structure modeling and dynamics, and molecular docking. Results: The results showed that cipargamin had 100% probability of binding to the human adenosine A3 receptor (ADORA3) and about 15% for other human targets which include tyrosine-protein kinase JAK2, adenosine A2a receptor, phosphodiesterase 5A and cathepsin K. The results of molecular docking showed that binding energy of cipargamin to PfATP4 and hADORA3 were-12.40 kcal/mol-1 and-13.40 kcal/mol-1 respectively. The docking was validated by the binding of enprofylline and fostamatinib to PfATP4 and hADORA3. Overall, the binding of cipargamin was closely similar to that of fostamatinib. This study shows the potential of cipargamin to modulate the activities of PfATP4 of the parasite (P. falciparum) as well as ADORA3 of the host (Homo sapiens). Conclusion: All the previous studies of cirpagamin have not implicated its action on hADORA3, thus this study provides an insight into a possible role of hADORA3 in the mechanism of malarial infection.

Purinergic GPCR transmembrane residues involved in ligand recognition and dimerization

We compare the GPCR-ligand interactions and highlight important residues for recognition in purinergic receptors-from both X-ray crystallographic and cryo-EM structures. These include A1 and A2A adenosine receptors, and P2Y1 and P2Y12 receptors that respond to ADP and other nucleotides. These receptors are important drug discovery targets for immune, metabolic and nervous system disorders. In most cases, orthosteric ligands are represented, except for one allosteric P2Y1 antagonist. This review catalogs the residues and regions that engage in contacts with ligands or with other GPCR protomers in dimeric forms. Residues that are in proximity to bound ligands within purinergic GPCR families are correlated. There is extensive conservation of recognition motifs between adenosine receptors, but the P2Y1 and P2Y12 receptors are each structurally distinct in their ligand recognition. Identifying common interaction features for ligand recognition within a receptor class that has multiple structures available can aid in the drug discovery process.

Purinergic Signaling: Impact of GPCR Structures on Rational Drug Design

The purinergic signaling system includes membrane-bound receptors for extracellular purines and pyrimidines, and enzymes/transporters that regulate receptor activation by endogenous agonists. Receptors include: adenosine (A1 , A2A , A2B, and A3 ) and P2Y (P2Y1 , P2Y2 , P2Y4 , P2Y6 , P2Y11 , P2Y12 , P2Y13 , and P2Y14 ) receptors (all GPCRs), as well as P2X receptors (ion channels). Receptor activation, especially accompanying physiological stress or damage, creates a temporal sequence of signaling to counteract this stress and either mobilize (P2Rs) or suppress (ARs) immune responses. Thus, modulation of this large signaling family has broad potential for treating chronic diseases. Experimentally determined structures represent each of the three receptor families. We focus on selective purinergic agonists (A1 , A3 ), antagonists (A3 , P2Y14 ), and allosteric modulators (P2Y1 , A3 ). Examples of applying structure-based design, including the rational modification of known ligands, are presented for antithrombotic P2Y1 R antagonists and anti-inflammatory P2Y14 R antagonists and A3 AR agonists. A3 AR agonists are a potential, nonaddictive treatment for chronic neuropathic pain.

In Silico Drug Design for Purinergic GPCRs: Overview on Molecular Dynamics Applied to Adenosine and P2Y Receptors

Molecular modeling has contributed to drug discovery for purinergic GPCRs, including adenosine receptors (ARs) and P2Y receptors (P2YRs). Experimental structures and homology modeling have proven to be useful in understanding and predicting structure activity relationships (SAR) of agonists and antagonists. This review provides an excursus on molecular dynamics (MD) simulations applied to ARs and P2YRs. The binding modes of newly synthesized A1AR- and A3AR-selective nucleoside derivatives, potentially of use against depression and inflammation, respectively, have been predicted to recapitulate their SAR and the species dependence of A3AR affinity. P2Y12R and P2Y1R crystallographic structures, respectively, have provided a detailed understanding of the recognition of anti-inflammatory P2Y14R antagonists and a large group of allosteric and orthosteric antagonists of P2Y1R, an antithrombotic and neuroprotective target. MD of A2AAR (an anticancer and neuroprotective target), A3AR, and P2Y1R has identified microswitches that are putatively involved in receptor activation. The approach pathways of different ligands toward A2AAR and P2Y1R binding sites have also been explored. A1AR, A2AAR, and A3AR were utilizes to study allosteric phenomena, but locating the binding site of structurally diverse allosteric modulators, such as an A3AR enhancer LUF6000, is challenging. Ligand residence time, a predictor of in vivo efficacy, and the structural role of water were investigated through A2AAR MD simulations. Thus, new MD and other modeling algorithms have contributed to purinergic GPCR drug discovery.

Computational Investigations on the Binding Mode of Ligands for the Cannabinoid-Activated G Protein-Coupled Receptor GPR18

GPR18 is an orphan G protein-coupled receptor (GPCR) expressed in cells of the immune system. It is activated by the cannabinoid receptor (CB) agonist ∆⁹-tetrahydrocannabinol (THC). Several further lipids have been proposed to act as GPR18 agonists, but these results still require unambiguous confirmation. In the present study, we constructed a homology model of the human GPR18 based on an ensemble of three GPCR crystal structures to investigate the binding modes of the agonist THC and the recently reported antagonists which feature an imidazothiazinone core to which a (substituted) phenyl ring is connected via a lipophilic linker. Docking and molecular dynamics simulation studies were performed. As a result, a hydrophobic binding pocket is predicted to accommodate the imidazothiazinone core, while the terminal phenyl ring projects towards an aromatic pocket. Hydrophobic interaction of Cys251 with substituents on the phenyl ring could explain the high potency of the most potent derivatives. Molecular dynamics simulation studies suggest that the binding of imidazothiazinone antagonists stabilizes transmembrane regions TM1, TM6 and TM7 of the receptor through a salt bridge between Asp118 and Lys133. The agonist THC is presumed to bind differently to GPR18 than to the distantly related CB receptors. This study provides insights into the binding mode of GPR18 agonists and antagonists which will facilitate future drug design for this promising potential drug target.