Potential inhibitory activity of phytoconstituents against black fungus: in silico admet, molecular docking and MD simulation studies

• Main Authors: Hamaamin Hussen, Narmin, Hameed Hasan, Aso, Jamalis, Joazaizulfazli, Shakya, Sonam, Chander, Subhash, Kharkwal, Harsha, Murugesan, Sankaranaryanan, Ajit Bastikar, Virupaksha, Pyarelal Gupta, Pramodkumar
• Format: Article
• Language: English
• Published: Elsevier B.V. 2022
• Subjects: QD Chemistry
• Online Access: http://eprints.utm.my/id/eprint/98785/1/JoazaizulfazliJamalis2022_PotentialInhibitoryActivityofPhytoconstituents-compressed.pdf; http://eprints.utm.my/id/eprint/98785/; http://dx.doi.org/10.1016/j.comtox.2022.100247

Mucormycosis or “black fungus” has been currently observed in India, as a secondary infection in COVID-19 infected patients in the post-COVID-stage. Fungus is an uncommon opportunistic infection that affects people who have a weak immune system. In this study, 158 antifungal phytochemicals were screened using molecular docking against glucoamylase enzyme of Rhizopus oryzae to identify potential inhibitors. The docking scores of the selected phytochemicals were compared with Isomaltotriose as a positive control. Most of the compounds showed lower binding energy values than Isomaltotriose (-6.4 kcal/mol). Computational studies also revealed the strongest binding affinity of the screened phytochemicals was Dioscin (-9.4 kcal/mol). Furthermore, the binding interactions of the top ten potential phytochemicals were elucidated and further analyzed. In-silico ADME and toxicity prediction were also evaluated using SwissADME and admetSAR online servers. Compounds Piscisoflavone C, 8-O-methylaverufin and Punicalagin exhibited positive results with the Lipinski filter and drug-likeness and showed mild to moderate of toxicity. Molecular dynamics (MD) simulation (at 300 K for 100 ns) was also employed to the docked ligand-target complex to explore the stability of ligand-target complex, improve docking results, and analyze the molecular mechanisms of protein-target interactions.