Ayardulabi, R. | Search bioRxiv

bioRxiv2024

Structure-based Design of Chimeric Influenza Hemagglutinins to Elicit Cross-group Immunity

Antigenic variability among influenza virus strains poses a significant challenge to developing broadly protective, long-lasting vaccines. Current annual vaccines target specific strains, requiring accurate prediction for effective neutralization. Despite sequence diversity across phylogenetic groups, the hemagglutinin (HA) head domains structure remains highly conserved. Utilizing this conservation, we designed cross-group chimeric HAs that combine antigenic surfaces from distant strains. By structure-guided transplantation of receptor-binding site (RBS) residues, we displayed an H3 RBS on an H1 HA scaffold. These chimeric immunogens elicit cross-group polyclonal responses capable of neutralizing both base and distal strains. Additionally, the chimeras integrate heterotrimeric immunogens, enhancing modular vaccine design. This approach enables the inclusion of diverse strain segments to generate broad polyclonal responses. In the future, such modular immunogens may serve as tools for evaluating immunodominance and refining immunization strategies, offering potential to bridge and enhance immune responses in individuals with pre-existing immunity. This strategy holds promise for advancing universal influenza vaccine development. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=105 SRC="FIGDIR/small/628867v2_ufig1.gif" ALT="Figure 1"> View larger version (25K): org.highwire.dtl.DTLVardef@18dec5borg.highwire.dtl.DTLVardef@98587forg.highwire.dtl.DTLVardef@1d9fb2dorg.highwire.dtl.DTLVardef@1f97884_HPS_FORMAT_FIGEXP M_FIG Graphical abstract: Overview of cross-group RBS transplantation approachPhylogenetically diverse HA strains can be incorporated into chimeric immunogens by RBS transplantation. The chimera are evaluated for cross-reactivity to subtype-specific antibodies and the ability to elicit neutralizing antibodies to multiple strains. C_FIG

biochemistry↗

bioRxiv2024

Accurate single domain scaffolding of three non-overlapping protein epitopes using deep learning

De novo protein design has seen major success in scaffolding single functional motifs, however, in nature most proteins present multiple functional sites. Here we describe an approach to simultaneously scaffold multiple functional sites in a single domain protein using deep learning. We designed small single domain immunogens, under 130 residues, that simultaneously present three distinct and irregular motifs from respiratory syncytial virus. These motifs together comprise nearly half of the designed proteins, and hence the overall folds are quite unusual with little global similarity to proteins in the PDB. Despite this, X-ray crystal structures confirm the accuracy of presentation of each of the motifs, and the multi-epitope design yields improved cross-reactive titers and neutralizing response compared to a single-epitope immunogen. The successful presentation of three distinct binding surfaces in a small single domain protein highlights the power of generative deep learning methods to solve complex protein design problems.

bioengineering↗