In Silico Epitope-Based Peptide Vaccine Design Against Influenza B Virus: An Immunoinformatics Approach

Background/Objectives: Influenza viruses are highly transmissible and mutable, posing a significant burden on public health. This study aimed to design a recombinant multi-epitope vaccine with broad protective potential. Methods: Immunoinformatic approaches were employed to predict epitopes from over 30,000 protein sequences retrieved from protein databases. Epitopes were filtered using four key indicators: antigenicity, allergenicity, toxicity, and conservancy. Population coverage analysis was conducted to estimate the proportion of the global population that could potentially benefit from the vaccine. Secondary and tertiary structures of the recombinant vaccine were predicted using the PSIPRED server and AlphaFold2. The vaccine efficacy was validated through an immune simulation, molecular docking, and molecular dynamics simulation. Results: A recombinant multi-epitope vaccine demonstrating strong antigenicity, no allergenicity or toxicity, and high conservation across different subtypes was successfully constructed. Population coverage analysis indicated that the vaccine could elicit an immune response in 90.14% of the global population. Both the secondary and tertiary structures of the vaccine were accurately predicted. Molecular dynamics simulations further validated the structural stability and interactions of the vaccine components with TRL4. Molecular docking confirmed the robust binding affinity of T-cell epitopes to MHC molecules. Simulated immunity studies showed that the vaccine induced the proliferation of memory B cells and T cells, enabling rapid antibody production during viral challenges. Conclusions: This study provides a promising basis for the development of a broadly protective influenza vaccine, leveraging cutting-edge immunoinformatics and molecular dynamics simulations to address the global challenge posed by influenza virus variability.