Structural bioinformatics approaches for predicting novel drug targets in hepatitis C virus proteins: a comprehensive analysis

Abstract

Abstract This study employs structural bioinformatics approaches to identify and evaluate potential drug targets within the Hepatitis C virus (HCV) proteome. Through integration of homology modeling, molecular docking, and molecular dynamics simulations, we analyzed the structural features and druggability of key HCV proteins. The research focused on predicting binding sites, evaluating protein-ligand interactions, and assessing the therapeutic potential of identified targets. Our findings revealed promising drug targets including the NS3 protease, NS5B polymerase, core protein, and NS5A, with detailed characterization of their binding pockets and interaction patterns. The study provides structural insights for rational drug design against HCV and demonstrates the utility of computational approaches in antiviral drug discovery. While experimental validation is needed, these results contribute to the development of novel anti-HCV therapeutics and highlight potential strategies for targeted intervention.