Screening for Potential Antiviral Compounds from Cyanobacterial Secondary Metabolites Using Machine Learning
The secondary metabolites of seawater and freshwater blue-green algae are a rich natural product pool containing diverse compounds with various functions, including antiviral compounds; however, high-efficiency methods to screen such compounds are lacking. Advanced virtual screening techniques can s...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-11-01
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| Series: | Marine Drugs |
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| Online Access: | https://www.mdpi.com/1660-3397/22/11/501 |
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| author | Tingrui Zhang Geyao Sun Xueyu Cheng Cheng Cao Zhonghua Cai Jin Zhou |
| author_facet | Tingrui Zhang Geyao Sun Xueyu Cheng Cheng Cao Zhonghua Cai Jin Zhou |
| author_sort | Tingrui Zhang |
| collection | DOAJ |
| description | The secondary metabolites of seawater and freshwater blue-green algae are a rich natural product pool containing diverse compounds with various functions, including antiviral compounds; however, high-efficiency methods to screen such compounds are lacking. Advanced virtual screening techniques can significantly reduce the time and cost of novel antiviral drug identification. In this study, we used a cyanobacterial secondary metabolite library as an example and trained three models to identify compounds with potential antiviral activity using a machine learning method based on message-passing neural networks. Using this method, 364 potential antiviral compounds were screened from >2000 cyanobacterial secondary metabolites, with amides predominating (area under the receiver operating characteristic curve value: 0.98). To verify the actual effectiveness of the candidate antiviral compounds, HIV virus reverse transcriptase (HIV-1 RT) was selected as a target to evaluate their antiviral potential. Molecular docking experiments demonstrated that candidate compounds, including kororamide, mollamide E, nostopeptolide A3, anachelin-H, and kasumigamide, produced relatively robust non-covalent bonding interactions with the RNase H active site on HIV-1 RT, supporting the effectiveness of the proposed screening model. Our data demonstrate that artificial intelligence-based screening methods are effective tools for mining potential antiviral compounds, which can facilitate the exploration of various natural product libraries. |
| format | Article |
| id | doaj-art-4df7376bcb0f4f00af2c7d0bdd410d17 |
| institution | OA Journals |
| issn | 1660-3397 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Marine Drugs |
| spelling | doaj-art-4df7376bcb0f4f00af2c7d0bdd410d172025-08-20T02:04:58ZengMDPI AGMarine Drugs1660-33972024-11-01221150110.3390/md22110501Screening for Potential Antiviral Compounds from Cyanobacterial Secondary Metabolites Using Machine LearningTingrui Zhang0Geyao Sun1Xueyu Cheng2Cheng Cao3Zhonghua Cai4Jin Zhou5Marine Ecology and Human Factors Assessment Technical Innovation Center of Natural Resources Ministry, Tsinghua Shenzhen International Graduate School, Shenzhen 518055, ChinaInstitute for Ocean Engineering, Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, ChinaMarine Ecology and Human Factors Assessment Technical Innovation Center of Natural Resources Ministry, Tsinghua Shenzhen International Graduate School, Shenzhen 518055, ChinaMarine Ecology and Human Factors Assessment Technical Innovation Center of Natural Resources Ministry, Tsinghua Shenzhen International Graduate School, Shenzhen 518055, ChinaMarine Ecology and Human Factors Assessment Technical Innovation Center of Natural Resources Ministry, Tsinghua Shenzhen International Graduate School, Shenzhen 518055, ChinaMarine Ecology and Human Factors Assessment Technical Innovation Center of Natural Resources Ministry, Tsinghua Shenzhen International Graduate School, Shenzhen 518055, ChinaThe secondary metabolites of seawater and freshwater blue-green algae are a rich natural product pool containing diverse compounds with various functions, including antiviral compounds; however, high-efficiency methods to screen such compounds are lacking. Advanced virtual screening techniques can significantly reduce the time and cost of novel antiviral drug identification. In this study, we used a cyanobacterial secondary metabolite library as an example and trained three models to identify compounds with potential antiviral activity using a machine learning method based on message-passing neural networks. Using this method, 364 potential antiviral compounds were screened from >2000 cyanobacterial secondary metabolites, with amides predominating (area under the receiver operating characteristic curve value: 0.98). To verify the actual effectiveness of the candidate antiviral compounds, HIV virus reverse transcriptase (HIV-1 RT) was selected as a target to evaluate their antiviral potential. Molecular docking experiments demonstrated that candidate compounds, including kororamide, mollamide E, nostopeptolide A3, anachelin-H, and kasumigamide, produced relatively robust non-covalent bonding interactions with the RNase H active site on HIV-1 RT, supporting the effectiveness of the proposed screening model. Our data demonstrate that artificial intelligence-based screening methods are effective tools for mining potential antiviral compounds, which can facilitate the exploration of various natural product libraries.https://www.mdpi.com/1660-3397/22/11/501cyanobacterial secondary metabolitesanti-viral compoundsmachine learningmolecular dockingapplication potential |
| spellingShingle | Tingrui Zhang Geyao Sun Xueyu Cheng Cheng Cao Zhonghua Cai Jin Zhou Screening for Potential Antiviral Compounds from Cyanobacterial Secondary Metabolites Using Machine Learning Marine Drugs cyanobacterial secondary metabolites anti-viral compounds machine learning molecular docking application potential |
| title | Screening for Potential Antiviral Compounds from Cyanobacterial Secondary Metabolites Using Machine Learning |
| title_full | Screening for Potential Antiviral Compounds from Cyanobacterial Secondary Metabolites Using Machine Learning |
| title_fullStr | Screening for Potential Antiviral Compounds from Cyanobacterial Secondary Metabolites Using Machine Learning |
| title_full_unstemmed | Screening for Potential Antiviral Compounds from Cyanobacterial Secondary Metabolites Using Machine Learning |
| title_short | Screening for Potential Antiviral Compounds from Cyanobacterial Secondary Metabolites Using Machine Learning |
| title_sort | screening for potential antiviral compounds from cyanobacterial secondary metabolites using machine learning |
| topic | cyanobacterial secondary metabolites anti-viral compounds machine learning molecular docking application potential |
| url | https://www.mdpi.com/1660-3397/22/11/501 |
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