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Discovery of novel dual adenosine A1/A2A receptor antagonists using deep learning, pharmacophore modeling and molecular docking.

ABSTRACT: Adenosine receptors (ARs) have been demonstrated to be potential therapeutic targets against Parkinson's disease (PD). In the present study, we describe a multistage virtual screening approach that identifies dual adenosine A1 and A2A receptor antagonists using deep learning, pharmacophore models, and molecular docking methods. Nineteen hits from the ChemDiv library containing 1,178,506 compounds were selected and further tested by in vitro assays (cAMP functional assay and radioligand binding assay); of these hits, two compounds (C8 and C9) with 1,2,4-triazole scaffolds possessing the most potent binding affinity and antagonistic activity for A1/A2A ARs at the nanomolar level (pKi of 7.16-7.49 and pIC50 of 6.31-6.78) were identified. Further molecular dynamics (MD) simulations suggested similarly strong binding interactions of the complexes between the A1/A2A ARs and two compounds (C8 and C9). Notably, the 1,2,4-triazole derivatives (compounds C8 and C9) were identified as the most potent dual A1/A2A AR antagonists in our study and could serve as a basis for further development. The effective multistage screening approach developed in this study can be utilized to identify potent ligands for other drug targets.

SUBMITTER: Wang M

PROVIDER: S-EPMC7978378 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature

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Discovery of novel dual adenosine A1/A2A receptor antagonists using deep learning, pharmacophore modeling and molecular docking.

Wang Mukuo M Hou Shujing S Wei Yu Y Li Dongmei D Lin Jianping J

PLoS computational biology 20210319 3

Adenosine receptors (ARs) have been demonstrated to be potential therapeutic targets against Parkinson's disease (PD). In the present study, we describe a multistage virtual screening approach that identifies dual adenosine A1 and A2A receptor antagonists using deep learning, pharmacophore models, and molecular docking methods. Nineteen hits from the ChemDiv library containing 1,178,506 compounds were selected and further tested by in vitro assays (cAMP functional assay and radioligand binding a ...[more]

PMID: 33739970

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Project description:A three-dimensional model of the human A(2A) adenosine receptor (AR) and its docked ligands was built by homology to rhodopsin and validated with site-directed mutagenesis and the synthesis of chemically complementary agonists. Different binding modes of A(2A)AR antagonists and agonists were compared by using the FlexiDock automated docking procedure, with manual adjustment. Putative binding regions for the 9H-purine ring in agonist NECA 3 and the 1H-[1,2,4]triazolo[1,5-c]quinazoline ring in antagonist CGS15943 1 overlapped, and the exocyclic amino groups of each were H-bonded to the side chain of N(6.55). For bound agonist, H-bonds formed between the ribose 3'- and 5'-substituents and the hydrophilic amino acids T(3.36), S(7.42), and H(7.43), and the terminal methyl group of the 5'-uronamide interacted with the hydrophobic side chain of F(6.44). Formation of the agonist complex destabilized the ground-state structure of the A(2A)AR, which was stabilized through a network of H-bonding and hydrophobic interactions in the transmembrane helical domain (TM) regions, facilitating a conformational change upon activation. Both flexibility of the ribose moiety, required for the movement of TM6, and its H-bonding to the receptor were important for agonism. Two sets of interhelical H-bonds involved residues conserved among ARs but not in rhodopsin: (1) E13(1.39) and H278(7.43) and (2) D52(2.50), with the highly conserved amino acids N280(7.45) and S281(7.46), and N284(7.49) with S91(3.39). Most of the amino acid residues lining the putative binding site(s) were conserved among the four AR subtypes. The A(2A)AR/3 complex showed a preference for an intermediate conformation about the glycosidic bond, unlike in the A(3)AR/3 complex, which featured an anti-conformation. Hydrophilic amino acids of TMs 3 and 7 (ribose-binding region) were replaced with anionic residues for enhanced binding to amine-derivatized agonists. We identified new neoceptor (T88D)-neoligand pairs that were consistent with the model.

Dataset Information

Discovery of novel dual adenosine A1/A2A receptor antagonists using deep learning, pharmacophore modeling and molecular docking.

Publications

Discovery of novel dual adenosine A1/A2A receptor antagonists using deep learning, pharmacophore modeling and molecular docking.

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