Discovery of novel dual adenosine A1/A2A receptor antagonists using deep learning, pharmacophore modeling and molecular docking.
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 mode...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2021-03-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1008821&type=printable |
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| author | Mukuo Wang Shujing Hou Yu Wei Dongmei Li Jianping Lin |
| author_facet | Mukuo Wang Shujing Hou Yu Wei Dongmei Li Jianping Lin |
| author_sort | Mukuo Wang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-ef473c165dcd4f50be7bb52ffec0279f |
| institution | OA Journals |
| issn | 1553-734X 1553-7358 |
| language | English |
| publishDate | 2021-03-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj-art-ef473c165dcd4f50be7bb52ffec0279f2025-08-20T02:00:59ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582021-03-01173e100882110.1371/journal.pcbi.1008821Discovery of novel dual adenosine A1/A2A receptor antagonists using deep learning, pharmacophore modeling and molecular docking.Mukuo WangShujing HouYu WeiDongmei LiJianping LinAdenosine 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.https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1008821&type=printable |
| spellingShingle | Mukuo Wang Shujing Hou Yu Wei Dongmei Li Jianping Lin Discovery of novel dual adenosine A1/A2A receptor antagonists using deep learning, pharmacophore modeling and molecular docking. PLoS Computational Biology |
| title | Discovery of novel dual adenosine A1/A2A receptor antagonists using deep learning, pharmacophore modeling and molecular docking. |
| title_full | Discovery of novel dual adenosine A1/A2A receptor antagonists using deep learning, pharmacophore modeling and molecular docking. |
| title_fullStr | Discovery of novel dual adenosine A1/A2A receptor antagonists using deep learning, pharmacophore modeling and molecular docking. |
| title_full_unstemmed | Discovery of novel dual adenosine A1/A2A receptor antagonists using deep learning, pharmacophore modeling and molecular docking. |
| title_short | Discovery of novel dual adenosine A1/A2A receptor antagonists using deep learning, pharmacophore modeling and molecular docking. |
| title_sort | discovery of novel dual adenosine a1 a2a receptor antagonists using deep learning pharmacophore modeling and molecular docking |
| url | https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1008821&type=printable |
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