Future-proof vaccine design with a generative model of antibody cross-reactivity

Noor Youssef, Sarah Gurev, Hannah Rivka Pierce-Hoffman, Alexander A Cohen, Luis F Caldera, Pamela J Bjorkman, Debora Susan Marks

Keywords: Future-proof vaccine design, nanoparticle vaccines, viral evolution, antibody, protein fitness models, protein language models, protein design

TL;DR: We develop a future-proof vaccine design method using a generative model of antibody cross-reactivity to focus ellicited antibody immune responses to conserved, neutralizing regions unlikely to mutate.

Abstract: Mosaic nanoparticle vaccines incorporating naturally diverse sarbecovirus receptor binding domains (RBDs) represent a promising approach for pan-coronavirus vaccines. Mosaic nanoparticles elicit broad, cross-reactive immune responses, likely because elicited antibodies utilize avidity effects to preferentially bind conserved regions across neighboring RBDs. However, the diversity in natural RBDs is limited, leading to ‘off-target’ antibodies that bind to conserved regions across the selected RBDs but which are likely to mutate in the future. We therefore develop a novel future-proof vaccine design method, building upon a probabilistic generative model of antibody escape, to computationally design RBDs with further diversity. This approach aims to focus antibody responses to regions that are (1) neutralizing, (2) accessible and (3) unlikely to mutate during future viral evolution. The designs will be assessed by immunizing mice and testing the breadth of neutralizability of the sera compared to a nanoparticle composed of naturally diverse strains.

Submission Number: 36