Design of Germline-Targeting Immunogens with Deep Learning for Induction of Broadly Neutralizing Influenza Antibodies

Most vaccines protect by inducing antibodies, but highly variable pathogens such as HIV and influenza virus have remained challenging for traditional vaccine strategies.

The identification of broadly neutralizing antibodies (bNAbs) that bind to conserved regions on viral surface proteins have stimulated the development of vaccine strategies to induce bNAbs.

The development of bNAbs requires B cells to acquire multiple affinity-enhancing mutations from their precursor/germline state.

The design of germline targeting immunogens aims to induce bNAbs by expanding the pool of B cell precursors that can later be shepherded through affinity maturation with sequential boosting immunogens.

A key rationale for this strategy is that bNAb precursor B cells usually have no detectable binding affinity for native immunogens. Thus, immunogens need to be engineered to have high affinity for bNAb precursors to initiate this response.

The advances in computational protein structure prediction and design offer exciting opportunities to further optimize the design approach to germline targeting immunogens.

In this proposal, influenza bNAb-specific immunogens will be computationally designed and experimentally tested for targeted B cell activation.

Importantly, the selectivity of B cell activation and the active guiding of critical affinity-enhancing mutations will be a particular focus of the refinements sought in this proposal.