Molecular modelling and simulation techniques to investigate the effects of fungal metabolites on the SARS-CoV-2 RdRp protein inhibition

Abstract

Various protein/receptor targets have been discovered through in-silico research. They are expanding rapidly due to their extensive advantage of delivering new drug candidates more quickly, efficiently, and at a lower cost. The automation of organic synthesis and biochemical screening will lead to a revolution in the entire research arena in drug discovery. In this research article, a few fungal metabolites were examined through an in-silico approach which involves major steps such as (a) Molecular Docking Analysis, (b) Drug likeness and ADMET studies, and (c) Molecular Dynamics Simulation. Fungal metabolites were taken from Antibiotic Database which showed antiviral effects on severe viral diseases such as HIV. Docking, Lipinski's, and ADMET analyses investigated the binding affinity and toxicity of five metabolites: Chromophilone I, iso; F13459; Stachyflin, acetyl; A-108836; Integracide A (A-108835). Chromophilone I, iso was subjected to additional analysis, including a 50 ns MD simulation of the protein to assess the occurring alterations. This molecule's docking data shows that it had the highest binding affinity. ADMET research revealed that the ligand might be employed as an oral medication. MD simulation revealed that the ligand–protein interaction was stable. Finally, this ligand can be exploited to develop SARS-CoV-2 therapeutic options. Fungal metabolites that have been studied could be a potential source for future lead candidates. Further study of these molecules may result in creating an antiviral drug to battle the SARS-CoV-2 virus.

Keywords

• SARS-CoV-2
• RdRp
• Insilico molecular docking analysis
• Molecular dynamics simulation
• ADMET analysis
• Fungal metabolites