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1.
J Mol Graph Model ; 124: 108535, 2023 Jun 01.
Article in English | MEDLINE | ID: covidwho-20231383

ABSTRACT

The first oral drug for the treatment of COVID-19, Paxlovid, has been authorized; however, nirmatrelvir, a major component of the drug, is reported to be associated with some side effects. Moreover, the appearance of many novel variants raises concerns about drug resistance, and designing new potent inhibitors to prevent viral replication is thus urgent. In this context, using a hybrid approach combining machine learning (ML) and free energy simulations, 6 compounds obtained by modifying nirmatrelvir were proposed to bind strongly to SARS-CoV-2 Mpro. The structural modification of nirmatrelvir significantly enhances the electrostatic interaction free energy between the protein and ligand and slightly decreases the vdW term. However, the vdW term is the most important factor in controlling the ligand-binding affinity. In addition, the modified nirmatrelvir might be less toxic to the human body than the original inhibitor.

2.
RSC advances ; 11(36):22206-22213, 2021.
Article in English | EuropePMC | ID: covidwho-1813065

ABSTRACT

Preventing the biological activity of SARS-CoV-2 main protease using natural compounds is of great interest. In this context, using a combination of AutoDock Vina and fast pulling of ligand simulations, eleven marine fungi compounds were identified that probably play as highly potent inhibitors for preventing viral replication. In particular, four compounds including M15 (3-O-(6-O-α-l-arabinopyranosyl)-β-d-glucopyranosyl-1,4-dimethoxyxanthone), M8 (wailupemycins H), M11 (cottoquinazolines B), and M9 (wailupemycins I) adopted the predicted ligand-binding free energy of −9.87, −9.82, −9.62, and −9.35 kcal mol−1, respectively, whereas the other adopted predicted ligand-binding free energies in the range from −8.54 to −8.94 kcal mol−1. The results were obtained using a combination of Vina and FPL simulations. Notably, although, AutoDock4 adopted higher accurate results in comparison with Vina, Vina is proven to be a more suitable technique for rapidly screening ligand-binding affinity with a large database of compounds since it requires much smaller computing resources. Furthermore, FPL is better than Vina to classify inhibitors upon ROC-AUC analysis. Preventing the biological activity of SARS-CoV-2 main protease using natural compounds is of great interest.

3.
RSC Adv ; 11(36): 22206-22213, 2021 Jun 21.
Article in English | MEDLINE | ID: covidwho-1294511

ABSTRACT

Preventing the biological activity of SARS-CoV-2 main protease using natural compounds is of great interest. In this context, using a combination of AutoDock Vina and fast pulling of ligand simulations, eleven marine fungi compounds were identified that probably play as highly potent inhibitors for preventing viral replication. In particular, four compounds including M15 (3-O-(6-O-α-l-arabinopyranosyl)-ß-d-glucopyranosyl-1,4-dimethoxyxanthone), M8 (wailupemycins H), M11 (cottoquinazolines B), and M9 (wailupemycins I) adopted the predicted ligand-binding free energy of -9.87, -9.82, -9.62, and -9.35 kcal mol-1, respectively, whereas the other adopted predicted ligand-binding free energies in the range from -8.54 to -8.94 kcal mol-1. The results were obtained using a combination of Vina and FPL simulations. Notably, although, AutoDock4 adopted higher accurate results in comparison with Vina, Vina is proven to be a more suitable technique for rapidly screening ligand-binding affinity with a large database of compounds since it requires much smaller computing resources. Furthermore, FPL is better than Vina to classify inhibitors upon ROC-AUC analysis.

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