Computational targeting of iron uptake proteins in Covid-19 induced mucormycosis to identify inhibitors via molecular dynamics, molecular mechanics and density function theory studies.

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Mucormycosis is a concerning invasive fungal infection with difficult diagnosis, high mortality rates, and limited treatment options. Iron availability is crucial for fungal growth that causes this disease. This study aimed to computationally target iron uptake proteins in and to identify inhibitors, thereby halting fungal growth and intervening in mucormycosis pathogenesis. Seven important iron uptake proteins were identified, modeled, and validated using Ramachandran plots. An in-house antifungal library of ~ 15,401 compounds was screened in molecular docking studies with these proteins. The best small molecule-protein complexes were simulated at 100 ns using Maestro, Schrodinger. Toxicity predictions suggested all six molecules, identified as the best binding compounds to seven proteins, belonged to lower toxicity levels per GHS classification. A molecular mechanics GBSA study for all seven complexes indicated low standard deviations after calculating free binding energies every 10 ns of the 100 ns trajectory. Density functional theory via quantum mechanics approaches highlighted the HOMO, LUMO, and other properties of the six best-bound molecules, revealing their binding capabilities and behaviour. This study sheds light on the molecular mechanisms and protein-ligand interactions, providing a multi-dimensional view towards the use of FDBD01920, FDBD01923, and FDBD01848 as stable antifungal ligands.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s40203-024-00264-7.