Precision epitope engineering for Influenza B Hemagglutinin: leveraging AI and structure-guided approaches for a DNA Influenza vaccine 4328

Abstract Description The hemagglutinin (HA) protein of Influenza Virus B (IVB) is a critical target for vaccine design due to its role in viral entry and its antigenic variability. This study investigates the diversity and epitope landscape of HA of IVB over time, employs structural-guided design and advanced computational tools to develop novel immunogens focused on highly conserved epitopes. A comprehensive analysis of HA’s evolutionary dynamics guided us to conserved regions as targets for vaccine design. Our approach leverages state-of-the-art AI and in house developed software, including optimal glycan addition with Cloaking with Glycans tool to shield non protective epitopes from antibody recognition and refocusing the immune response. We also employed de novo protein design to create miniature proteins that mimic the immunogenic and conserved regions of Hemagglutinin. The designs are validated using simulations and AI tools to predict antibody binding, enabling us to select few candidates for fast and resource aware bench testing. Preliminary assessments indicate that our designed immunogens exhibit strong potential for inducing broad and robust antibody responses. In vitro assays, including antibody binding and neutralization tests, are underway to validate our findings and further refine our immunogen designs. This research demonstrates the potential of integrating AI-driven tools with structural biology to innovate vaccine development for Influenza by focusing on conserved epitopes. Topic Categories Vaccines and Immunotherapy (VAC)