Identification of Novel Antagonists Targeting Cannabinoid Receptor 2 Using a Multi-Step Virtual Screening Strategy
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ABSTRACT: The endocannabinoid system plays an essential role in the regulation of analgesia and human immunity, and Cannabinoid Receptor 2 (CB2) has been proved to be an ideal target for the treatment of liver diseases and some cancers. In this study, we identified CB2 antagonists using a three-step “deep learning–pharmacophore–molecular docking” virtual screening approach. From the ChemDiv database (1,178,506 compounds), 15 hits were selected and tested by radioligand binding assays and cAMP functional assays. A total of 7 out of the 15 hits were found to exhibit binding affinities in the radioligand binding assays against CB2 receptor, with a pKi of 5.15–6.66, among which five compounds showed antagonistic activities with pIC50 of 5.25–6.93 in the cAMP functional assays. Among these hits, Compound 8 with the 4H-pyrido[1,2-a]pyrimidin-4-one scaffold showed the best binding affinity and antagonistic activity with a pKi of 6.66 and pIC50 of 6.93, respectively. The new scaffold could serve as a lead for further development of CB2 drugs. Additionally, we hope that the model in this study could be further utilized to identify more novel CB2 receptor antagonists, and the developed approach could also be used to design potent ligands for other therapeutic targets.
Project description:Virtual screening was performed against experimentally enabled homology models of the adenosine A(2A) receptor, identifying a diverse range of ligand efficient antagonists (hit rate 9%). By use of ligand docking and Biophysical Mapping (BPM), hits 1 and 5 were optimized to potent and selective lead molecules (11-13 from 5, pK(I) = 7.5-8.5, 13- to >100-fold selective versus adenosine A(1); 14-16 from 1, pK(I) = 7.9-9.0, 19- to 59-fold selective).
Project description:Herein, we described the development of two virtual screens to identify new vitamin D receptor (VDR) antagonists among nuclear receptor (NR) ligands. Therefore, a database of 14330 nuclear receptor ligands and their NR affinities was assembled using the online available "Binding Database". Two different virtual screens were carried out in conjunction with a reported VDR crystal structure applying a stringent and less stringent pharmacophore model to filter docked NR ligand conformations. The pharmacophore models were based on the spatial orientation of the hydroxyl functionalities of VDR's natural ligands 1,25(OH2)D3 and 25(OH2)D3. The first virtual screen identified 32 NR ligands with a calculate free energy of VDR binding of more than -6.0 kJ/mol. All but nordihydroguaiaretic acid (NDGA) are VDR ligands, which inhibited the interaction between VDR and coactivator peptide SRC2-3 with an IC50 value of 15.8 µM. The second screen identified 162 NR ligands with a calculate free energy of VDR binding of more than -6.0 kJ/mol. More than half of these ligands were developed to bind VDR followed by ERα/β ligands (26%), TRα/β ligands (7%) and LxRα/β ligands (7%). The binding between VDR and ERα ligand H6036 as well as TRα/β ligand triiodothyronine and a homoserine analog thereof was confirmed by fluorescence polarization.
Project description:The selective targeting of the cannabinoid receptor 2 (CB2) is crucial for the development of peripheral system-acting cannabinoid analgesics. This work aimed at computer-assisted identification of prospective CB2-selective compounds among the constituents of Cannabis Sativa. The molecular structures and corresponding binding affinities to CB1 and CB2 receptors were collected from ChEMBL. The molecular structures of Cannabis Sativa constituents were collected from a phytochemical database. The collected records were curated and applied for the development of quantitative structure-activity relationship (QSAR) models with a machine learning approach. The validated models predicted the affinities of Cannabis Sativa constituents. Four structures of CB2 were acquired from the Protein Data Bank (PDB) and the discriminatory ability of CB2-selective ligands and two sets of decoys were tested. We succeeded in developing the QSAR model by achieving Q2 5-CV > 0.62. The QSAR models helped to identify three prospective CB2-selective molecules that are dissimilar to already tested compounds. In a complementary structure-based virtual screening study that used available PDB structures of CB2, the agonist-bound, Cryogenic Electron Microscopy structure of CB2 showed the best statistical performance in discriminating between CB2-active and non-active ligands. The same structure also performed best in discriminating between CB2-selective ligands from non-selective ligands.
Project description:The cannabinoid receptor 2 (CB2R) has been linked with the regulation of inflammation, and selective receptor activation has been proposed as a target for the treatment of a range of inflammatory diseases such as atherosclerosis and arthritis. In order to identify selective CB2R agonists with appropriate physicochemical and ADME properties for future evaluation in vivo, we first performed a ligand-based virtual screen. Subsequent medicinal chemistry optimisation studies led to the identification of a new class of selective CB2R agonists. Several examples showed high levels of activity (EC50<200 nM) and binding affinity (Ki<200 nM) for the CB2R, and no detectable activity at the CB1R. The most promising example, DIAS2, also showed favourable in vitro metabolic stability and absorption properties along with a clean selectivity profile when evaluated against a panel of GPCRs and kinases.
Project description:Androgen receptor (AR), a ligand-activated transcription factor, is a master regulator in the development and progress of prostate cancer (PCa). A major challenge for the clinically used AR antagonists is the rapid emergence of resistance induced by the mutations at AR ligand binding domain (LBD), and therefore the discovery of novel anti-AR therapeutics that can combat mutation-induced resistance is quite demanding. Therein, blocking the interaction between AR and DNA represents an innovative strategy. However, the hits confirmed targeting on it so far are all structurally based on a sole chemical scaffold. In this study, an integrated docking-based virtual screening (VS) strategy based on the crystal structure of the DNA binding domain (DBD) of AR was conducted to search for novel AR antagonists with new scaffolds and 2-(2-butyl-1,3-dioxoisoindoline-5-carboxamido)-4,5-dimethoxybenzoicacid (Cpd39) was identified as a potential hit, which was competent to block the binding of AR DBD to DNA and showed decent potency against AR transcriptional activity. Furthermore, Cpd39 was safe and capable of effectively inhibiting the proliferation of PCa cell lines (i.e., LNCaP, PC3, DU145, and 22RV1) and reducing the expression of the genes regulated by not only the full-length AR but also the splice variant AR-V7. The novel AR DBD-ARE blocker Cpd39 could serve as a starting point for the development of new therapeutics for castration-resistant PCa.
Project description:The P2Y(1) receptor (P2Y(1)R) is a G protein-coupled receptor naturally activated by extracellular ADP. Its stimulation is an essential requirement of ADP-induced platelet aggregation, thus making antagonists highly sought compounds for the development of antithrombotic agents. Here, through a virtual screening campaign based on a pharmacophoric representation of the common characteristics of known P2Y(1)R ligands and the putative shape and size of the receptor binding pocket, we have identified novel antagonist hits of ?M affinity derived from a N,N'-bis-arylurea chemotype. Unlike the vast majority of known P2Y(1)R antagonists, these drug-like compounds do not have a nucleotidic scaffold or highly negatively charged phosphate groups. Hence, our compounds may provide a direction for the development of receptor probes with altered physicochemical properties.
Project description:Currently available progesterone (P4) receptor (PR) antagonists, such as mifepristone (RU486), lack specificity and display partial agonist properties, leading to potential drawbacks in their clinical use. Recent x-ray crystallographic studies have identified key contacts involved in the binding of agonists and antagonists with PR opening the way for a new rational strategy for inactivating PR. We report here the synthesis and characterization of a novel class of PR antagonists (APRn) designed from such studies. The lead molecule, the homosteroid APR19, displays in vivo endometrial anti-P4 activity. APR19 inhibits P4-induced PR recruitment and transactivation from synthetic and endogenous gene promoters. Importantly, it exhibits high PR selectivity with respect to other steroid hormone receptors and is devoid of any partial agonist activity on PR target gene transcription. Two-hybrid and immunostaining experiments reveal that APR19-bound PR is unable to interact with either steroid receptor coactivators 1 and 2 (SRC1 and SCR2) or nuclear receptor corepressor (NcoR) and silencing mediator of retinoid acid and thyroid hormone receptor (SMRT), in contrast to RU486-PR complexes. APR19 also inhibits agonist-induced phosphorylation of serine 294 regulating PR transcriptional activity and turnover kinetics. In silico docking studies based on the crystal structure of the PR ligand-binding domain show that, in contrast to P4, APR19 does not establish stabilizing hydrogen bonds with the ligand-binding cavity, resulting in an unstable ligand-receptor complex. Altogether, these properties highly distinguish APR19 from RU486 and likely its derivatives, suggesting that it belongs to a new class of pure antiprogestins that inactivate PR by a passive mechanism. These specific PR antagonists open new perspectives for long-term hormonal therapy.
Project description:The EphA2 receptor and its ephrin-A1 ligand form a key cell communication system, which has been found overexpressed in many cancer types and involved in tumor growth. Recent medicinal chemistry efforts have identified bile acid derivatives as low micromolar binders of the EphA2 receptor. However, these compounds suffer from poor physicochemical properties, hampering their use in vivo. The identification of compounds able to disrupt the EphA2-ephrin-A1 complex lacking the bile acid scaffold may lead to new pharmacological tools suitable for in vivo studies. To identify the most promising virtual screening (VS) protocol aimed at finding novel EphA2 antagonists, we investigated the ability of both ligand-based and structure-based approaches to retrieve known EphA2 antagonists from libraries of decoys with similar molecular properties. While ligand-based VSs were conducted using UniPR129 and ephrin-A1 ligand as reference structures, structure-based VSs were performed with Glide, using the X-ray structure of the EphA2 receptor/ephrin-A1 complex. A comparison of enrichment factors showed that ligand-based approaches outperformed the structure-based ones, suggesting ligand-based methods using the G-H loop of ephrin-A1 ligand as template as the most promising protocols to search for novel EphA2 antagonists.
Project description:BACKGROUND:Development of a fast and accurate scoring function in virtual screening remains a hot issue in current computer-aided drug research. Different scoring functions focus on diverse aspects of ligand binding, and no single scoring can satisfy the peculiarities of each target system. Therefore, the idea of a consensus score strategy was put forward. Integrating several scoring functions, consensus score re-assesses the docked conformations using a primary scoring function. However, it is not really robust and efficient from the perspective of optimization. Furthermore, to date, the majority of available methods are still based on single objective optimization design. RESULTS:In this paper, two multi-objective optimization methods, called MOSFOM, were developed for virtual screening, which simultaneously consider both the energy score and the contact score. Results suggest that MOSFOM can effectively enhance enrichment and performance compared with a single score. For three different kinds of binding sites, MOSFOM displays an excellent ability to differentiate active compounds through energy and shape complementarity. EFMOGA performed particularly well in the top 2% of database for all three cases, whereas MOEA_Nrg and MOEA_Cnt performed better than the corresponding individual scoring functions if the appropriate type of binding site was selected. CONCLUSION:The multi-objective optimization method was successfully applied in virtual screening with two different scoring functions that can yield reasonable binding poses and can furthermore, be ranked with the potentially compromised conformations of each compound, abandoning those conformations that can not satisfy overall objective functions.
Project description:Glioblastoma (GBM) is a deadly and the most common primary brain tumor in adults. Due to their regulation of a high number ofmRNA transcripts, microRNAs (miRNAs) are key molecules in the control of biological processes and are thereby promisingtherapeutic targets for GBM patients. In this regard, we recently reported miRNAs as strong modulators of GBM aggressiveness.Here, using an integrative and comprehensive analysis of the TCGA database and the transcriptome of GBM biopsies, we identifiedthree critical and clinically relevant miRNAs for GBM, miR-17-3p, miR-222, and miR-340. In addition, we showed that thecombinatorial modulation of three of these miRNAs efficiently inhibited several biological processes in patient-derived GBM cells ofall these three GBM subtypes (Mesenchymal, Proneural, Classical), induced cell death, and delayed tumor growth in a mouse tumormodel. Finally, in a doxycycline-inducible model, we observed a significant inhibition of GBM stem cell viability and a significantdelay of orthotopic tumor growth. Collectively, our results reveal, for the first time, the potential of miR-17-3p, miR-222 and miR-340multi-targeting as a promising therapeutic strategy for GBM patients.Cell Death and Disease (2023) 14:630 ; https://doi.org/10.1038/s41419-023-06117-z