From sequence to scaffold: computational design of protein nanoparticle vaccines from AlphaFold2-predicted building blocks

Abstract

Self-assembling protein nanoparticles are being increasingly utilized in the design of next-generation vaccines due to their ability to induce antibody responses of superior magnitude, breadth, and durability. Computational protein design offers a route to novel nanoparticle scaffolds with structural and biochemical features tailored to specific vaccine applications. Although strategies for designing new self-assembling proteins have been established, the recent development of powerful machine learning-based tools for protein structure prediction and design provides an opportunity to overcome several of their limitations. Here, we leveraged these tools to develop a generalizable method for designing novel self-assembling proteins starting from AlphaFold2 predictions of oligomeric protein building blocks. We used the method to generate six new 60-subunit protein nanoparticles with icosahedral symmetry, and single-particle cryo-electron microscopy reconstructions of three of them revealed that they were designed with atomic-level accuracy. To transform one of these nanoparticles into a functional immunogen, we reoriented its termini through circular permutation, added a genetically encoded oligomannose-type glycan, and displayed a stabilized trimeric variant of the influenza hemagglutinin receptor binding domain through a rigid de novo linker. The resultant immunogen elicited potent receptor-blocking and neutralizing antibody responses in mice. Our results demonstrate the practical utility of machine learning-based protein modeling tools in the design of nanoparticle vaccines. More broadly, by eliminating the requirement for experimentally determined structures of protein building blocks, our method dramatically expands the number of starting points available for designing new self-assembling proteins. Significance StatementSelf-assembling protein nanoparticle vaccines have steadily gained traction in both academic and industry vaccine development over the last decade. Recent work has shown that computationally designing new self-assembling proteins allows the structural and functional features of nanoparticle vaccines to be precisely tailored, and that this can significantly affect the vaccine-elicited immune response. To date, designing such nanoparticle vaccines has required the use of known crystal structures as starting points. Here we show how new machine learning tools can be leveraged to design new self-assembling protein nanoparticle immunogens in the absence of experimentally determined structures of the building blocks that elicit strong immune responses in mice.