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

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