Computer-aided identification of a series of novel ligands showing high potency as hepatitis C virus NS3/4A protease inhibitors

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.

Computational insight to design new potential hepatitis C virus NS5B polymerase inhibitors with drug-likeness and pharmacokinetic ADMET parameters predictions

“…The result of the y-randomization test demonstrates that Fig. 2 The model applicable domain the model's random R 2 (cR 2 p = 0.6570) is significantly greater than the recommended value of 0.50, indicating that the model is not the result of pure chance [12]. In this study, no Y outlier was detected and one X outlier was detected (molecule 51) accounting for 1% of the entire dataset (see Fig.…”

“…The selected QSAR model is statistically sound because it met the conditions listed in Table 2 as a threshold, and hence, has an appropriate predictive capability. Model 2 met Tropsha's and the Organization for Economic Cooperation and Development's (OECD) requirements [12,17] as it explains 71% and predicts 70% of the variances of the HCV NS5B polymerase inhibitors with their bioactivity as presented in Table 2. It indicates that the models precisely regressed the data and that it can predict the fitting training set for the model, as it predicted around 70% of the data and so met the minimum criteria of 60% [11].…”

“…GFA is a heuristic search approach for locating exact or approximate answers to optimization and search issues. It has the advantage of constructing many models, use the lack of fit function to avoid overfitting, and allowing the user to regulate the length of the equation [11,12].…”