Computational Prediction of HCV RNA Polymerase Inhibitors from Alkaloid Library

Abstract

Hepatitis C virus (HCV) is a global challenge and the leading cause of chronic liver disease. Approximately 30% of a patient infected with chronic HCV results into liver cirrhosis. WHO reports that 3% of the world’s population has been infected with HCV, which signifies that about 170 million people are at risk of developing chronic liver diseases globally. Research has shown that HCV NS5B polymerase is one of the six non-structural proteins encoded in the approximately 9600 nucleotide genome of HCV, which plays a vital role in the replication and infection of HCV virus; therefore, it serves as a target enzyme for antiviral therapy against HCV. In this study, Alkaloids, a group of vital secondary metabolites in plants, with cyclic structures containing nitrogen in a negative oxidation state which is limitedly distributed in a living organism, were mined from an online database and screened computationally using a molecular docking approach to predict its inhibitory potential against the replication of HCV’s viral RNA. 259 Alkaloids was retrieved and docked, and it resulted that 3-(4-methoxyphenyl)-3-[[2-(4-methoxyphenyl)-1-oxoethyl]amino]propanoic acid, 14-norpseurotin A, and (+)-aplysinilin are predicted to be suitable inhibitors against NS5B through their binding pose and interactions with the amino acid residues at the binding site of NS5B. Additionally, hit compounds from the docking result were further subjected to ADME/Tox screening to predict their drug-likeness characteristics, and 3-(4-methoxyphenyl)-3-[[2-(4-methoxyphenyl)-1-oxoethyl]amino]propanoic acid stands out by showing more drug-like characteristics.