In silico studies on Epicoccum spp. Secondary metabolites as potential drugs for mucormycosis

Abstract

Mucormycosis is a rare and severe form of fungal infection caused by some fungal species belonged to mucormycetes that mainly infects skin but also eyes and other organs of the human body. The COVID-19 pandemic has demonstrated the rise in mucormycosis cases due to usage of industrial grade oxygen and overuse of steroids particularly in developing nations, where the pandemic has stressed the medical system to its limit. The major aim of this study was to screen potential antifungal compounds produced by Epicoccum spp. against target enzyme lanosterol 1,4-alpha demethylase of Rhizopus arrhizus var. delemar (strain RA 99–880 / ATCC MYA-4621 / FGSC 9543 / NRRL 43880), to evaluate potential drug candidates using in silico approach, compounds that may be explored further for the treatment of mucormycosis. Sequence of enzyme was retrieved from the UniProtKB database and ligands from PubChem database. 3D structure was obtained using protein homology modelling (SWISS-MODEL server). The target enzyme was prepared along with ligands using ADT software and then subjected to molecular docking using Autodock Vina. The results and protein-target interactions were studied using the BIOVIA Discovery studio. After virtual screening of secondary metabolites produced by Epicoccum spp. and after comparing them with the results of control drugs, the compound curvularin specifically showed potential interactions with amino acid residues of target enzymes. It is also exclusively binding to the iron atom of the hemoglobin (HEME) group with binding affinity of −12.3 kcal/mol, where the hemoglobin is the main target of current existing drugs used for treating patients. The stability of molecular complexes can be measured through molecular dynamic simulation and root mean square fluctuation (RMSF) values. In this study, stable complexes were formed with curvularin and rostratin A having the highest RMSF values of 4.3 Å and 4.5 Å, respectively. Lower RMSF values indicate stability, while higher values indicate flexibility. The ADMET score gave an outlook about the absorption, distribution, metabolism, excretion and toxicity of the ligands. All the drug candidates used in this study passed the basic Lipinski rule of five except orevactaene. Curvularin and rostratin A were predicted as enzyme inhibitors by a bioactivity prediction tool (Molinspiration). In vitro and in vivo studies will be a way to go further.