Porcine Reproductive and Respiratory Syndrome Virus (PRRSV) and Infectious Bronchitis Virus (IBV) pose significant economic threats to the global swine and poultry industries, respectively, driven by their high mutation rates and resulting vaccine challenges. Since its emergence in 1987, PRRSV alone has inflicted economic damages exceeding half a billion dollars annually in the USA. Likewise, IBV is a widespread avian coronavirus responsible for severe economic losses through high morbidity rates, ranking just below avian influenza in its economic impact. To tackle these viral pathogens effectively, we developed an integrative immunoinformatics and artificial intelligence-driven approach to vaccine design. For PRRSV, 63 critical protein-protein interactions necessary for infection and 75 essential epitope regions were identified and an epitope atlas was created. In parallel, analysis of 56 IBV genomes revealed 466 highly conserved epitopes, from which 258 epitopes with optimal antigenicity, non-toxicity, and non-allergenicity were selected. Leveraging advanced machine learning structural diffusion models, we engineered multi-epitope vaccine (MEV) candidates, preserving native structural motifs crucial for robust immunogenicity. Biophysical characterizations, including molecular docking and molecular dynamics simulations, confirmed the strong binding affinities and structural stability of these MEVs with respective host immune receptors. Our results underscore the effectiveness of combining immunoinformatics and AI-driven protein engineering to create stable, potent, and broadly protective vaccine candidates. This provides a versatile and rapid platform adaptable to combat diverse highly mutable viral pathogens affecting animal health and agricultural productivity.
