Introduction: The hepatitis C virus (HCV) poses a major global health challenge, with its non-structural proteins being essential for viral replication and pathogenesis. Mutations in these proteins significantly contribute to drug resistance, necessitating innovative therapeutic strategies. This study aims to identify epitope-based therapeutic targets in the non-structural proteins of HCV genotype 1, employing in-depth in silico tools to counteract emerging drug resistance.
Methods: We retrieved approximately 250 sequences of each non-structural protein from the NCBI database, capturing a broad spectrum of variability and sequence alignments, variability analysis and physicochemical property analysis were conducted. We utilized the TEPITOOL server by IEDB to predict cytotoxic T lymphocyte (CTL) epitopes. Following this, we assessed the efficiency of TAP transport and proteasomal cleavage using IEDB's combined predictor tool. The epitopes were selected based on conservancy analysis, immunogenicity, allergenicity, and presence in non-glycosylated regions, ensuring high predictive scores and suitability as vaccine candidates. Epitopes were docked with the HLA-A*02:01 allele and Toll-like receptor-3 using the ClusPro server. The immune response potential of the epitopes was evaluated through in-silico immune stimulation.
Results: The study identified 27 potential CTL epitopes from the non-structural proteins, including NS3, NS4a, NS4b, NS5a, and NS5b. Out of these, three lead epitopes demonstrated high conservation (>90%), strong binding affinities to HLA-A*02:01 and TLR-3, and robust immune response potential. These epitopes also showed favorable characteristics such as being non-allergenic and non-glycosylated.
Conclusion: This comprehensive in-silico analysis provides a promising foundation for developing an epitope-based vaccine targeting HCV non-structural proteins, offering a novel approach to overcoming drug resistance in HCV treatment.