Cover image of the article: Quantitative Structure-Activity Relationships and Molecular Docking Simulation of Allicin Compounds as Inhibitors of COVID-19 Protease Enzyme

Image Credit:J Inflamm Dis

Quantitative Structure-Activity Relationships and Molecular Docking Simulation of Allicin Compounds as Inhibitors of COVID-19 Protease Enzyme

Authors

Hossein Piri, Elham Hajialilo1, Sayyed Nima Hashemi Ghermezi2, Mohammad Taghi Goodarzi3, Saeede Salemi-Bazargani4, Anoosh eghdami5,*
1Department of Parasitology and Mycology, School of Medicine, Qazvin University of Medical Sciences, Qazvin, Iran.
2Department of Industrial Engineering, Faculty of Engineering, Saveh Branch, Islamic Azad University, Saveh, Iran.
3Department of Biochemistry, Islamic Azad University, Shahrood Branch, Shahrood, Iran.
4Student Research Committee, School of Medicine, Qazvin University of Medical Sciences, Qazvin, Iran.
5Department of Biochemistry, School of Medicine, Saveh Branch, Islamic Azad University, Saveh, Iran.
*Corresponding Author: Department of Biochemistry, School of Medicine, Saveh Branch, Islamic Azad University, Saveh, Iran. Email: eghdami49@gmail.com

Journal of Inflammatory Diseases:Vol. 25, issue 3; 161-168
Published online:Dec 31, 2021
Article type:Research Article
How to Cite:Piri H, Hajialilo E, Hashemi Ghermezi SN, Goodarzi MT, Salemi-Bazargani S, et al. Quantitative Structure-Activity Relationships and Molecular Docking Simulation of Allicin Compounds as Inhibitors of COVID-19 Protease Enzyme. J Inflamm Dis. 2024;25(3):e156289. doi:

Abstract

Background: Coronavirus (CoV) is a group of viruses that cause disease in humans and animals. These viruses contain crown-shaped spike glycoproteins on their surface. Objective: We conducted a quantitative structure-activity relationship (QSAR) study on a series of 36 compounds of allicin to assess their antiviral activities against the main protease of COVID-19. Methods: In the present descriptive-analytic study, the information on the structure of compounds, the COVID-19 protease enzyme, and the Allicin derivatives was obtained from the databases such as the Research Collaboratory for Structural Bioinformatics’ Protein Data Bank (PDB) and PubChem. The QSAR method, analysis of correlations and multiple linear regressions were carried out. Six molecular descriptors such as constitutional and molecular topology descriptors were selected for the model. Finally, molecular docking was performed in iGEMDOCK 2.1 software. Results: The obtained multi-parametric model reported a correlation coefficient of about 0.89, indicating that the model was able to satisfactory predict the antiviral activity of allicin compounds. Conclusion: The findings obtained can be valuable in designing, synthesizing, and developing novel antiviral agents with allicin-based scaffold.

Highlights

Copyright

© 2024, Journal of Inflammatory Diseases. This open-access article is available under the Creative Commons Attribution-NonCommercial 4.0 (CC BY-NC 4.0) International License (https://creativecommons.org/licenses/by-nc/4.0/), which allows for the copying and redistribution of the material only for noncommercial purposes, provided that the original work is properly cited.

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