Design of a Multi-Epitope Vaccine Against Chikungunya Virus: An InSilico Approach

The chikungunya virus (CHIKV) poses a re-emerging worldwide health risk marked by severe joint pain, with no specific antiviral therapies or widely available vaccines currently available. This requires the creation of new preventive strategies. Reverse vaccinology provides a robust and expedited method for the logical development of successful vaccine candidates. This research utilized an extensive computational framework to create a multi-epitope subunit vaccine. The target antigen chosen was the structural polyprotein of CHIKV (UniProt: Q8JUX5). Epitopes for B-cells, helper T-lymphocytes (HTL), and cytotoxic T-lymphocytes (CTL) were predicted and then refined according to elevated scores for antigenicity, immunogenicity, and the absence of allergenicity and toxicity. A concluding chimeric vaccine structure was created, featuring a beta-defensin 3 adjuvant, a universal PADRE T-helper epitope, and specific linkers to guarantee appropriate epitope presentation. The construct underwent thorough in silico validation, which encompassed physicochemical analysis, tertiary structure modeling, molecular docking with Toll-like receptor 4 (TLR4), MHC-I, and MHC-II molecules, molecular dynamics (MD) simulation, and an immune simulation based on agents. The completed vaccine construct, consisting of 291 amino acids, is expected to be a stable protein (Instability Index: 35.42) that is soluble, non-allergenic, and exhibits high antigenicity. Molecular docking simulations indicated a high binding affinity to crucial immune receptors, especially TLR4, showing a HADDOCK score of -42.2 6.2. MD simulations of the vaccine-TLR4 complex verified its dynamic stability over time. In addition, agent-based immune simulations forecasted the development of a strong, enduring, and well- regulated immune response, marked by elevated levels of class-switched immunoglobulins and the formation of substantial T-cell memory populations. The vaccine composed of multiple epitopes, developed and confirmed through this thorough in silico approach, stands as a hopeful and immunologically robust option for preventing CHIKV infection. These computational results provide robust support for advancing it into phases of experimental validation.