Insilico assessment of hesperidin on SARS-CoV-2 main protease and RNA polymerase: Molecular docking and dynamics simulation approach
The present study uses molecular docking and dynamic simulations to evaluate the inhibitory effect of flavonoid glycosides-based compounds on coronavirus Main protease (M<sup>pro</sup>) and RNA polymerase. The Molegro Virtual Docker (MVD) software is utilized to simulate and calculate the binding parameters of compounds with coronavirus. The docking results show that the selected herbal compounds are more effective than those of chemical compounds. It is also revealed that five herbal ligands and two chemical ligands have the best docking scores. Furthermore, a Molecular Dynamics (MD) simulation was conducted for Hesperidin, confirming docking results. Analysis based on different parameters such as Root-mean-square deviation (RMSD), Root mean square fluctuation (RMSF), Radius of gyration (Rg), Solvent accessibility surface area (SASA), and the total number of hydrogen bonds suggests that Hesperidin formed a stable complex with M<sup>pro</sup>. Absorption, Distribution, Metabolism, Excretion, And Toxicity (ADMET) analysis was performed to compare Hesperidin and Grazoprevir as potential antiviral medicines, evaluating both herbal and chemical ligand results. According to the study, herbal compounds could be effective on coronavirus and are admissible candidates for developing potential operative anti-viral medicines. Hesperidin was found to be the most acceptable interaction. Grazoprevir is an encouraging candidate for drug development and clinical trials, with the potential to become a highly effective M<sup>pro</sup> inhibitor. Compared to RNA polymerase, M<sup>pro</sup> showed a greater affinity for bonding with Hesperidin. van der Waals and electrostatic energies dominated, creating a stable Hesperidin-M<sup>pro</sup> and Hesperidin-RNA polymerase complex.
Authors
- Guogang Ren (ORCID: https://orcid.org/0000-0001-8865-1526)
- Ahmad Khosravi (ORCID: https://orcid.org/0000-0002-1106-3782)
- Fereshteh Golestanifar
- Zahra Garakani-Nejad
- Mohammad Reza Aallae
- Mohsen Rezapour (ORCID: https://orcid.org/0000-0002-4008-0100)
- Elaheh Molaakbari
- Mahsa Ghomi
- Rahime Eshaghi Malekshah
Institutions
- Shahid Bahonar University of Kerman (IR)
- University of Hertfordshire (GB)
- Kerman University of Medical Sciences (IR)
- Zabol University of Medical Sciences (IR)
- Tehran University of Medical Sciences (IR)
- The University of Texas Health Science Center at Houston (US)
Publication Details
- Journal
- Biochemistry and Biophysics Reports
- Published
- 2024-08-05
- DOI
- https://doi.org/10.1016/j.bbrep.2024.101804
- Citations
- 5
- Primary Topic
- Computational Drug Discovery Methods
- Type
- article
- Field-Weighted Citation Impact
- 1.81
Funders
- Engineering and Physical Sciences Research Council