Immunoinformatics-Based Multi-Epitope Vaccine Design Against Influenza A (H1N1) Hemagglutinin

Abstract

Background: Influenza A (H1N1) remains a significant global health threat due to its high mutation rate and antigenic variability, which limit the long-term efficacy of conventional strain-specific vaccines. This study employed an immunoinformatics approach to design a broadly protective multi-epitope vaccine targeting the hemagglutinin (HA) protein. Methods: The HA protein sequence was analyzed for physicochemical properties, antigenicity, and epitope prediction. Promising B-cell and T-cell epitopes were selected and assembled into a multi-epitope vaccine construct. Structural modeling, molecular docking with Toll-like receptor 3 (TLR3), molecular dynamics simulation, population coverage analysis, codon optimization, and in silico cloning were performed to evaluate the vaccine candidate. Results: The HA protein exhibited favorable physicochemical characteristics and strong antigenicity. The final vaccine construct was predicted to be highly antigenic, non-allergenic, and non-toxic, with a global population coverage of 81.68%. Structural validation confirmed model quality, while docking and molecular dynamics analyses demonstrated stable interactions with TLR3, indicating its potential to induce robust immune responses. Codon optimization and in silico cloning suggested efficient expression in the host system. Conclusion: The designed HA-based multi-epitope vaccine demonstrated promising immunogenicity, safety, structural stability, and broad population coverage in silico. These findings support its potential as a vaccine candidate against Influenza A (H1N1), warranting further experimental validation through in vitro and in vivo studies.