3D-QSAR and molecular docking studies on designing inhibitors of the hepatitis C virus NS5B polymerase

Viral hepatitis C infection is one of the main causes of the hepatitis after blood transfusion and hepatitis C virus (HCV) infection is a global health threat. The HCV NS5B polymerase, an RNA dependent RNA polymerase (RdRp) and an essential role in the replication of the virus, has no functional equivalent in mammalian cells. So the research and development of efficient NS5B polymerase inhibitors provides a great strategy for antiviral therapy against HCV. A combined three-dimensional quantitative structure activity relationship (QSAR) modeling was accomplished to profoundly understand the structure activity correlation of a train of indole-based inhibitors of the HCV NS5B polymerase to against HCV. A comparative molecular similarity indices analysis (COMSIA) model as the foundation of the maximum common substructure alignment was developed. The optimum model exhibited statistically significant results: the cross-validated correlation coefficient q(2) was 0.627 and non-cross-validated r2 value was 0.943. In addition, the results of internal validations of bootstrapping and Y-randomization confirmed the rationality and good predictive ability of the model, as well as external validation (the external predictive correlation coefficient Lixt = 0.629). The information obtained from the COMSIA contour maps enables the interpretation of their structure activity relationship. Furthermore, the molecular docking study of the compounds for 3TYV as the protein target revealed important interactions between active compounds and amino acids, and several new potential inhibitors with higher activity predicted were designed basis on our analyses and supported by the simulation of molecular docking. Meanwhile, the OSIRIS Property Explorer was introduced to help select more satisfactory compounds. The satisfactory results from this study may lay a reliable theoretical base for drug development of hepatitis C virus NS5B polymerase inhibitors. (C) 2016 Elsevier B.V. All rights reserved.