The proposed work unites complementary innovations to transform traditional vaccine approaches. First, we aim to employ generative Artificial Intelligence-based protein design tools to create self-assembling, stabilized immunogens that direct B cells toward protective lineages, such as VH1-69, which is linked to the recognition of flexible epitopes and viral clearance. By computationally screening trillions of designs, we will rapidly identify candidates most likely to elicit broadly neutralizing antibodies. Second, we aim to overcome the limitations of traditional murine models by using humanized mice expressing human immunoglobulin genes, providing an accurate prediction of human antibody responses in preclinical settings. Third, my group has recently developed epitope knockout probes that would allow us to map, with precision, the specificities of vaccine-induced B cells and antibodies, moving beyond crude measures such as neutralizing titers. Fourth, we will connect epitope specificities of vaccine-induced B cells with B-cell receptor sequences and antibody structures, creating a feedback loop to refine vaccine design. And finally, we will work towards developing a roadmap for vaccine development focused on challenging targets that display high conformational flexibility, informing therapeutic interventions for other infectious diseases, autoimmune processes, and cancer