Computational Design and In Silico Evaluation of a Multi-Epitope Vaccine Targeting Conserved Structural Polyproteins of <i>Chikungunya</i> Virus

Chikungunya virus (CHIKV) is an emerging arbovirus for which no licensed vaccine is currently available. In this study, we employed an immunoinformatics-based reverse vaccinology approach to design a novel multi-epitope vaccine (MEV) targeting conserved regions of CHIKV structural polyproteins. Conserved sequences were identified through multiple sequence alignment and used to predict cytotoxic T-lymphocyte (CTL), helper T-lymphocyte (HTL), and B-cell epitopes with high antigenicity, non-allergenicity, and broad HLA coverage. The final MEV construct comprised ten CTL, ten HTL, and three B-cell epitopes linked with suitable linkers and an immunogenic adjuvant. Structural modeling and validation confirmed the stability and quality of the vaccine construct. Molecular docking and 200-ns molecular dynamics simulations demonstrated stable and favorable interactions with immune receptors TLR7 and CCR5. In silico immune simulations predicted robust humoral and cellular immune responses with long-term immunological memory. Collectively, these results highlight the novelty and potential of the proposed MEV as a promising CHIKV vaccine candidate, warranting further experimental validation through in vitro and in vivo studies.