In Silico Screening of Natural Flavonoids against 3-Chymotrypsin-like Protease of SARS-CoV-2 Using Machine Learning and Molecular Modeling
The “Long-COVID syndrome” has posed significant challenges due to a lack of validated therapeutic options. We developed a novel multi-step virtual screening strategy to reliably identify inhibitors against 3-chymotrypsin-like protease of SARS-CoV-2 from abundant flavonoids, which represents a promis...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-12-01
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| Series: | Molecules |
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| Online Access: | https://www.mdpi.com/1420-3049/28/24/8034 |
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| author | Lianjin Cai Fengyang Han Beihong Ji Xibing He Luxuan Wang Taoyu Niu Jingchen Zhai Junmei Wang |
| author_facet | Lianjin Cai Fengyang Han Beihong Ji Xibing He Luxuan Wang Taoyu Niu Jingchen Zhai Junmei Wang |
| author_sort | Lianjin Cai |
| collection | DOAJ |
| description | The “Long-COVID syndrome” has posed significant challenges due to a lack of validated therapeutic options. We developed a novel multi-step virtual screening strategy to reliably identify inhibitors against 3-chymotrypsin-like protease of SARS-CoV-2 from abundant flavonoids, which represents a promising source of antiviral and immune-boosting nutrients. We identified 57 interacting residues as contributors to the protein-ligand binding pocket. Their energy interaction profiles constituted the input features for Machine Learning (ML) models. The consensus of 25 classifiers trained using various ML algorithms attained 93.9% accuracy and a 6.4% false-positive-rate. The consensus of 10 regression models for binding energy prediction also achieved a low root-mean-square error of 1.18 kcal/mol. We screened out 120 flavonoid hits first and retained 50 drug-like hits after predefined ADMET filtering to ensure bioavailability and safety profiles. Furthermore, molecular dynamics simulations prioritized nine bioactive flavonoids as promising anti-SARS-CoV-2 agents exhibiting both high structural stability (root-mean-square deviation < 5 Å for 218 ns) and low MM/PBSA binding free energy (<−6 kcal/mol). Among them, KB-2 (PubChem-CID, 14630497) and 9-<i>O</i>-Methylglyceofuran (PubChem-CID, 44257401) displayed excellent binding affinity and desirable pharmacokinetic capabilities. These compounds have great potential to serve as oral nutraceuticals with therapeutic and prophylactic properties as care strategies for patients with long-COVID syndrome. |
| first_indexed | 2024-03-08T20:30:30Z |
| format | Article |
| id | doaj.art-f9b8f5faa34b4b96b8147e429bd0ebf5 |
| institution | Directory Open Access Journal |
| issn | 1420-3049 |
| language | English |
| last_indexed | 2024-03-08T20:30:30Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Molecules |
| spelling | doaj.art-f9b8f5faa34b4b96b8147e429bd0ebf52023-12-22T14:27:28ZengMDPI AGMolecules1420-30492023-12-012824803410.3390/molecules28248034In Silico Screening of Natural Flavonoids against 3-Chymotrypsin-like Protease of SARS-CoV-2 Using Machine Learning and Molecular ModelingLianjin Cai0Fengyang Han1Beihong Ji2Xibing He3Luxuan Wang4Taoyu Niu5Jingchen Zhai6Junmei Wang7School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USASchool of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USASchool of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USASchool of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USASchool of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USASchool of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USASchool of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USASchool of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USAThe “Long-COVID syndrome” has posed significant challenges due to a lack of validated therapeutic options. We developed a novel multi-step virtual screening strategy to reliably identify inhibitors against 3-chymotrypsin-like protease of SARS-CoV-2 from abundant flavonoids, which represents a promising source of antiviral and immune-boosting nutrients. We identified 57 interacting residues as contributors to the protein-ligand binding pocket. Their energy interaction profiles constituted the input features for Machine Learning (ML) models. The consensus of 25 classifiers trained using various ML algorithms attained 93.9% accuracy and a 6.4% false-positive-rate. The consensus of 10 regression models for binding energy prediction also achieved a low root-mean-square error of 1.18 kcal/mol. We screened out 120 flavonoid hits first and retained 50 drug-like hits after predefined ADMET filtering to ensure bioavailability and safety profiles. Furthermore, molecular dynamics simulations prioritized nine bioactive flavonoids as promising anti-SARS-CoV-2 agents exhibiting both high structural stability (root-mean-square deviation < 5 Å for 218 ns) and low MM/PBSA binding free energy (<−6 kcal/mol). Among them, KB-2 (PubChem-CID, 14630497) and 9-<i>O</i>-Methylglyceofuran (PubChem-CID, 44257401) displayed excellent binding affinity and desirable pharmacokinetic capabilities. These compounds have great potential to serve as oral nutraceuticals with therapeutic and prophylactic properties as care strategies for patients with long-COVID syndrome.https://www.mdpi.com/1420-3049/28/24/80343-chyomotrypsin-like protease (3CL-pro)main protease (M-pro)SARS-CoV-2long-COVIDflavonoidsmolecular modeling |
| spellingShingle | Lianjin Cai Fengyang Han Beihong Ji Xibing He Luxuan Wang Taoyu Niu Jingchen Zhai Junmei Wang In Silico Screening of Natural Flavonoids against 3-Chymotrypsin-like Protease of SARS-CoV-2 Using Machine Learning and Molecular Modeling Molecules 3-chyomotrypsin-like protease (3CL-pro) main protease (M-pro) SARS-CoV-2 long-COVID flavonoids molecular modeling |
| title | In Silico Screening of Natural Flavonoids against 3-Chymotrypsin-like Protease of SARS-CoV-2 Using Machine Learning and Molecular Modeling |
| title_full | In Silico Screening of Natural Flavonoids against 3-Chymotrypsin-like Protease of SARS-CoV-2 Using Machine Learning and Molecular Modeling |
| title_fullStr | In Silico Screening of Natural Flavonoids against 3-Chymotrypsin-like Protease of SARS-CoV-2 Using Machine Learning and Molecular Modeling |
| title_full_unstemmed | In Silico Screening of Natural Flavonoids against 3-Chymotrypsin-like Protease of SARS-CoV-2 Using Machine Learning and Molecular Modeling |
| title_short | In Silico Screening of Natural Flavonoids against 3-Chymotrypsin-like Protease of SARS-CoV-2 Using Machine Learning and Molecular Modeling |
| title_sort | in silico screening of natural flavonoids against 3 chymotrypsin like protease of sars cov 2 using machine learning and molecular modeling |
| topic | 3-chyomotrypsin-like protease (3CL-pro) main protease (M-pro) SARS-CoV-2 long-COVID flavonoids molecular modeling |
| url | https://www.mdpi.com/1420-3049/28/24/8034 |
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