Comparison of the binding energies of approved mpox drugs and phytochemicals through molecular docking, molecular dynamics simulation, and ADMET studies: An in silico approach
| dc.contributor.author | Kumar, M | |
| dc.contributor.author | Ansari, A | |
| dc.date.accessioned | 2024-10-14T06:53:58Z | |
| dc.date.available | 2024-10-14T06:53:58Z | |
| dc.date.issued | 2023-09 | |
| dc.description.abstract | The mpox (previously monkeypox) outbreak in more than 100 non-endemic countries in 2022 posed a serious global health concern. Mpox is emerging as a global public health threat from a seemingly neglected disease. A42R profilin-like protein from mpox virus (PDB ID: 4QWO) could be a preferred target lead. The binding affinity of commonly used drugs/mAbs (tecovirimat, brincidofovir, cidofovir) for A42R profilin-like protein was examined in silico through molecular docking. Further, the results were com pared with those of the phytochemicals curcumin, rutin, and theaflavin. Tecovirimat ( 7.31 kcal/mol, IC50 = 4.39 lM) and theaflavin ( 6.99 kcal/mol, IC50 = 7.54 lM) had the highest affinities. Molecular dynamics simulation of the theaflavin–4QWO complex was performed to ascertain the stability of ligand–protein interactions in natural charge, molecular electrostatic potential, and frontier molecular orbital analyses. The predicted QSAR and pharmacokinetic properties of all compounds were evaluated to find a suitable candidate for designing and developing new drugs. The evaluated log P values for brin cidofovir and tecovirimat were higher than those of the other drugs in the QSAR study. Theaflavin had an impressive log P of 4.77, which hints at its high biological activity. The findings recommend further in vitro experimental validation to develop potential low-cost mpox therapies. | en_US |
| dc.identifier.uri | http://hdl.handle.net/123456789/1653 | |
| dc.title | Comparison of the binding energies of approved mpox drugs and phytochemicals through molecular docking, molecular dynamics simulation, and ADMET studies: An in silico approach | en_US |
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