Discovery of Novel c-Jun N-Terminal Kinase 1 Inhibitors from Natural Products: Integrating Artificial Intelligence with Structure-Based Virtual Screening and Biological Evaluation
c-Jun N-terminal kinase 1 (JNK1) is currently considered a critical therapeutic target for type-2 diabetes. In recent years, there has been a great interest in naturopathic molecules, and the discovery of active ingredients from natural products for specific targets has received increasing attention...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-09-01
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| Series: | Molecules |
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| Online Access: | https://www.mdpi.com/1420-3049/27/19/6249 |
| _version_ | 1797478021278466048 |
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| author | Ruoqi Yang Guiping Zhao Bin Yan |
| author_facet | Ruoqi Yang Guiping Zhao Bin Yan |
| author_sort | Ruoqi Yang |
| collection | DOAJ |
| description | c-Jun N-terminal kinase 1 (JNK1) is currently considered a critical therapeutic target for type-2 diabetes. In recent years, there has been a great interest in naturopathic molecules, and the discovery of active ingredients from natural products for specific targets has received increasing attention. Based on the above background, this research aims to combine emerging Artificial Intelligence technologies with traditional Computer-Aided Drug Design methods to find natural products with JNK1 inhibitory activity. First, we constructed three machine learning models (Support Vector Machine, Random Forest, and Artificial Neural Network) and performed model fusion based on Voting and Stacking strategies. The integrated models with better performance (AUC of 0.906 and 0.908, respectively) were then employed for the virtual screening of 4112 natural products in the ZINC database. After further drug-likeness filtering, we calculated the binding free energy of 22 screened compounds using molecular docking and performed a consensus analysis of the two methodologies. Subsequently, we identified the three most promising candidates (Lariciresinol, Tricin, and 4′-Demethylepipodophyllotoxin) according to the obtained probability values and relevant reports, while their binding characteristics were preliminarily explored by molecular dynamics simulations. Finally, we performed in vitro biological validation of these three compounds, and the results showed that Tricin exhibited an acceptable inhibitory activity against JNK1 (IC<sub>50</sub> = 17.68 μM). This natural product can be used as a template molecule for the design of novel JNK1 inhibitors. |
| first_indexed | 2024-03-09T21:27:01Z |
| format | Article |
| id | doaj.art-db50f0e45275471d8553d1026a3acc7f |
| institution | Directory Open Access Journal |
| issn | 1420-3049 |
| language | English |
| last_indexed | 2024-03-09T21:27:01Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Molecules |
| spelling | doaj.art-db50f0e45275471d8553d1026a3acc7f2023-11-23T21:08:15ZengMDPI AGMolecules1420-30492022-09-012719624910.3390/molecules27196249Discovery of Novel c-Jun N-Terminal Kinase 1 Inhibitors from Natural Products: Integrating Artificial Intelligence with Structure-Based Virtual Screening and Biological EvaluationRuoqi Yang0Guiping Zhao1Bin Yan2College of Pharmacy, Shandong University of Traditional Chinese Medicine, Jinan 250355, ChinaInstitute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, ChinaCollege of Pharmacy, Shandong University of Traditional Chinese Medicine, Jinan 250355, Chinac-Jun N-terminal kinase 1 (JNK1) is currently considered a critical therapeutic target for type-2 diabetes. In recent years, there has been a great interest in naturopathic molecules, and the discovery of active ingredients from natural products for specific targets has received increasing attention. Based on the above background, this research aims to combine emerging Artificial Intelligence technologies with traditional Computer-Aided Drug Design methods to find natural products with JNK1 inhibitory activity. First, we constructed three machine learning models (Support Vector Machine, Random Forest, and Artificial Neural Network) and performed model fusion based on Voting and Stacking strategies. The integrated models with better performance (AUC of 0.906 and 0.908, respectively) were then employed for the virtual screening of 4112 natural products in the ZINC database. After further drug-likeness filtering, we calculated the binding free energy of 22 screened compounds using molecular docking and performed a consensus analysis of the two methodologies. Subsequently, we identified the three most promising candidates (Lariciresinol, Tricin, and 4′-Demethylepipodophyllotoxin) according to the obtained probability values and relevant reports, while their binding characteristics were preliminarily explored by molecular dynamics simulations. Finally, we performed in vitro biological validation of these three compounds, and the results showed that Tricin exhibited an acceptable inhibitory activity against JNK1 (IC<sub>50</sub> = 17.68 μM). This natural product can be used as a template molecule for the design of novel JNK1 inhibitors.https://www.mdpi.com/1420-3049/27/19/6249JNK1natural productsvirtual screeningartificial intelligencecomputer-aided drug design |
| spellingShingle | Ruoqi Yang Guiping Zhao Bin Yan Discovery of Novel c-Jun N-Terminal Kinase 1 Inhibitors from Natural Products: Integrating Artificial Intelligence with Structure-Based Virtual Screening and Biological Evaluation Molecules JNK1 natural products virtual screening artificial intelligence computer-aided drug design |
| title | Discovery of Novel c-Jun N-Terminal Kinase 1 Inhibitors from Natural Products: Integrating Artificial Intelligence with Structure-Based Virtual Screening and Biological Evaluation |
| title_full | Discovery of Novel c-Jun N-Terminal Kinase 1 Inhibitors from Natural Products: Integrating Artificial Intelligence with Structure-Based Virtual Screening and Biological Evaluation |
| title_fullStr | Discovery of Novel c-Jun N-Terminal Kinase 1 Inhibitors from Natural Products: Integrating Artificial Intelligence with Structure-Based Virtual Screening and Biological Evaluation |
| title_full_unstemmed | Discovery of Novel c-Jun N-Terminal Kinase 1 Inhibitors from Natural Products: Integrating Artificial Intelligence with Structure-Based Virtual Screening and Biological Evaluation |
| title_short | Discovery of Novel c-Jun N-Terminal Kinase 1 Inhibitors from Natural Products: Integrating Artificial Intelligence with Structure-Based Virtual Screening and Biological Evaluation |
| title_sort | discovery of novel c jun n terminal kinase 1 inhibitors from natural products integrating artificial intelligence with structure based virtual screening and biological evaluation |
| topic | JNK1 natural products virtual screening artificial intelligence computer-aided drug design |
| url | https://www.mdpi.com/1420-3049/27/19/6249 |
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