Amit Kumar Singh Sharda University
The recent COVID-19 pandemic caused by SARS-CoV-2 has recorded a high number of infected people across the globe. The notorious nature of the virus makes it necessary for us to identify promising therapeutic agents in a time-sensitive manner. The current study utilises an in silico based drug repurposing approach to identify potential drug candidates targeting non-structural protein 15 (NSP15), i.e. a uridylate specific endoribonuclease of SARS-CoV-2
which plays an indispensable role in RNA processing and viral immune evasion from the host immune system. NSP15 was screened against an in-house library of 123 antiviral drugs obtained from the DrugBank database from which three promising drug candidates were identified based on their estimated free energy of binding (ΔG), estimated inhibition constant (Ki), the orientation of drug molecules in the active site and the key interacting residues of
NSP15. The MD simulations were performed for the selected NSP15-drug complexes along with free protein to mimic on their physiological state. The binding free energies of the selected NSP15-drug complexes were also calculated using the trajectories of MD simulations of NSP15-drug complexes through MM/PBSA (Molecular Mechanics with Poisson-Boltzmann and surface area solvation) approach where NSP15-Simeprevir (-242.559 kJ/mol) and NSP15-Paritaprevir (-149.557 kJ/mol) exhibited the strongest binding affinities. Together with the results of molecular docking, global dynamics, essential dynamics and binding free energy analysis, we propose that Simeprevir and Paritaprevir are promising drug candidates for the inhibition of NSP15 and could act as potential therapeutic agents against SARS-CoV-2.
This is version one of the current manuscript.
download asset Main_Text.pdf 1 MB [opens in a new tab] cloud_download
pdf : 1 MB
download asset Supplementary Data.pdf 0.22 MB [opens in a new tab] cloud_download
pdf : 0.22 MB
download asset Main_Text.docx 10 MB [opens in a new tab] cloud_download
docx : 10 MB
download asset Supplementary Data.docx 0.02 MB [opens in a new tab] cloud_download
docx : 0.02 MB