Virtual Screening of <i>C. Sativa</i> Constituents for the Identification of Selective Ligands for Cannabinoid Receptor 2
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 <i>Cannabis Sativa</i>. The...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-07-01
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| Series: | International Journal of Molecular Sciences |
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| Online Access: | https://www.mdpi.com/1422-0067/21/15/5308 |
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| author | Mikołaj Mizera Dorota Latek Judyta Cielecka-Piontek |
| author_facet | Mikołaj Mizera Dorota Latek Judyta Cielecka-Piontek |
| author_sort | Mikołaj Mizera |
| collection | DOAJ |
| 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 <i>Cannabis Sativa</i>. The molecular structures and corresponding binding affinities to CB1 and CB2 receptors were collected from ChEMBL. The molecular structures of <i>Cannabis Sativa</i> 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 <i>Cannabis Sativa</i> 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 Q<sup>2</sup> 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. |
| first_indexed | 2024-03-10T18:12:10Z |
| format | Article |
| id | doaj.art-87a665d20b414b97a1ab143e93b8611a |
| institution | Directory Open Access Journal |
| issn | 1661-6596 1422-0067 |
| language | English |
| last_indexed | 2024-03-10T18:12:10Z |
| publishDate | 2020-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | International Journal of Molecular Sciences |
| spelling | doaj.art-87a665d20b414b97a1ab143e93b8611a2023-11-20T08:01:23ZengMDPI AGInternational Journal of Molecular Sciences1661-65961422-00672020-07-012115530810.3390/ijms21155308Virtual Screening of <i>C. Sativa</i> Constituents for the Identification of Selective Ligands for Cannabinoid Receptor 2Mikołaj Mizera0Dorota Latek1Judyta Cielecka-Piontek2Department of Pharmacognosy, Poznan University of Medical Sciences, 60-781 Poznań, PolandFaculty of Chemistry, University of Warsaw, 02-093 Warsaw, PolandDepartment of Pharmacognosy, Poznan University of Medical Sciences, 60-781 Poznań, PolandThe 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 <i>Cannabis Sativa</i>. The molecular structures and corresponding binding affinities to CB1 and CB2 receptors were collected from ChEMBL. The molecular structures of <i>Cannabis Sativa</i> 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 <i>Cannabis Sativa</i> 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 Q<sup>2</sup> 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.https://www.mdpi.com/1422-0067/21/15/5308QSARendocannabinoid system<i>Cannabis Sativa</i> |
| spellingShingle | Mikołaj Mizera Dorota Latek Judyta Cielecka-Piontek Virtual Screening of <i>C. Sativa</i> Constituents for the Identification of Selective Ligands for Cannabinoid Receptor 2 International Journal of Molecular Sciences QSAR endocannabinoid system <i>Cannabis Sativa</i> |
| title | Virtual Screening of <i>C. Sativa</i> Constituents for the Identification of Selective Ligands for Cannabinoid Receptor 2 |
| title_full | Virtual Screening of <i>C. Sativa</i> Constituents for the Identification of Selective Ligands for Cannabinoid Receptor 2 |
| title_fullStr | Virtual Screening of <i>C. Sativa</i> Constituents for the Identification of Selective Ligands for Cannabinoid Receptor 2 |
| title_full_unstemmed | Virtual Screening of <i>C. Sativa</i> Constituents for the Identification of Selective Ligands for Cannabinoid Receptor 2 |
| title_short | Virtual Screening of <i>C. Sativa</i> Constituents for the Identification of Selective Ligands for Cannabinoid Receptor 2 |
| title_sort | virtual screening of i c sativa i constituents for the identification of selective ligands for cannabinoid receptor 2 |
| topic | QSAR endocannabinoid system <i>Cannabis Sativa</i> |
| url | https://www.mdpi.com/1422-0067/21/15/5308 |
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