Abstract
Background
Hepatitis C virus (HCV) is a global medical condition that causes several life-threatening chronic diseases in the liver. The conventional interferon-free treatment regimens are currently in use by a blend of direct-acting antiviral agents (DAAs) aiming at the viral NS3 protease. However, major concerns may be the issue of DAA-resistant HCV strains and the limited availability to the DAAs due to their high price. Due to this crisis, the developments of a new molecule with high potency as an NS3/4A protease inhibitor of the hepatitis-C virus remain a high priority for medical research. This study aimed to use in-silico methods to identify high potent molecule as an NS3/4A protease inhibitor and investigating the binding energy of the identified molecule in comparison with approved direct-acting antiviral agents (Telaprevir, Simeprevir, and Voxilaprevir) through molecular docking.
Results
The model obtained by in-silico method have the following statistical records, coefficient of determination (r2) of 0.7704, cross-validation (q2LOO = 0.6914); external test set (r2(pred) = 0.7049) and Y-randomization assessment (cR2p = 0.7025). The results from the model were used to identify 12 new potential human HCV NS3/4A protease inhibitors, and it was observed that the identified molecule is well-fixed when docked with the receptor and was found to have the lowest binding energy of − 10.7, compared to approved direct-acting antiviral agents (Telaprevir, Simeprevir, and Voxilaprevir) with − 9.5, − 10.0, − 10.5 binding energy, respectively.
Conclusion
The binding affinity (− 10.7) of the newly identified molecule docked with 3D structures of HCV NS3/4a protease/helicase (PDB ID: 4A92) was found to be better than that of Telaprevir, Simeprevir, and Voxilaprevir (approved direct-acting antiviral agents) which are − 9.5, − 10.0, and − 10.5, respectively. Hence, a novel molecule was identified showing high potency as HCV NS3/4a protease inhibitors.
Identification of Novel Hepatitis C Virus NS3-4A Protease Inhibitors by Virtual Screening Approach
