Iterative immunogen optimization to focus elicited responses on a conserved, subdominant influenza hemagglutinin epitope

Abstract

Designing effective vaccination strategies against genetically diverse viruses, such as HIV or influenza, is hindered by the ability of these pathogens to mutate and readily evade immune responses. While these viruses contain conserved regions that cannot easily mutate without affecting viral fitness, these sites are subdominant, meaning that they are not the primary target of antibodies elicited by natural infection or current vaccines. Here, we integrated recent advances in machine learning methods for protein structure prediction and design to engineer protein immunogens that focus immune responses on a conserved, subdominant HA epitope targeted by broadly cross-reactive influenza antibodies. Iterating between computation-guided optimization and in vivo analyses generated immunogens that faithfully displayed the target epitope and redirected humoral responses toward it upon vaccination. These results provide a blueprint for applying recent protein engineering approaches to immunogen design and may inform the development of broadly protective influenza vaccines.