Raemisch, S. | Search bioRxiv

bioRxiv2024

Diverse competitor B cell responses to a germline-targeting priming immunogen in human Ig loci transgenic mice

Broadly neutralizing antibodies (bnAbs) have promise to protect against HIV infection, but induction of bnAbs by immunization is an unsolved vaccine design challenge. Germline-targeting priming immunogens aim to initiate the induction of bnAbs by specifically activating rare bnAb-precursor B cells that can subsequently be matured using suitable heterologous boosting and shepherding immunogens. Several pre-clinical studies, and the IAVI G001 human clinical trial, have demonstrated the ability of a germline-targeting priming immunogen, eOD-GT8 60mer, to induce precursors of the VRC01 class of bnAbs. However, much less is known about B cells induced against other epitopes of the immunogen. Here, we performed unbiased analysis of B cells induced by eOD-GT8 60mers in Intelliselect Transgenic mice (Kymice) that are transgenic for the human Ig loci and produce human-like BCRs. B cells isolated with intact eOD-GT8 60mer nanoparticles showed a large diversity of non-VRC01-class B cells, with 38% unique clonotypes and only 5% of BCRs belonging to public lineages shared among all animals. We found that many competitors recognize epitopes in close proximity to or overlapping with the VRC01 epitope. These results indicate that optimal boosting of VRC01-class bnAb-precursor B cells primed by eOD-GT8 60mer might require a first-boost immunogen that minimizes recognition of competitor B cells, and such competitors isolated from Kymice could serve as valuable reagents for boost development.

immunology↗

bioRxiv2021

Growing Glycans in Rosetta: Accurate de novo glycan modeling, density fitting, and rational sequon design

Carbohydrates and glycoproteins modulate key biological functions. Computational approaches inform function to aid in carbohydrate structure prediction, structure determination, and design. However, experimental structure determination of sugar polymers is notoriously difficult as glycans can sample a wide range of low energy conformations, thus limiting the study of glycan-mediated molecular interactions. In this work, we expanded the RosettaCarbohydrate framework, developed and benchmarked effective tools for glycan modeling and design, and extended the Rosetta software suite to better aid in structural analysis and benchmarking tasks through the SimpleMetrics framework. We developed a glycan-modeling algorithm, GlycanTreeModeler, that computationally builds glycans layer-by-layer, using adaptive kernel density estimates (KDE) of common glycan conformations derived from data in the Protein Data Bank (PDB) and from quantum mechanics (QM) calculations. After a rigorous optimization of kinematic and energetic considerations to improve near-native sampling enrichment and decoy discrimination, GlycanTreeModeler was benchmarked on a test set of diverse glycan structures, or "trees". Structures predicted by GlycanTreeModeler agreed with native structures at high accuracy for both de novo modeling and experimental density-guided building. GlycanTreeModeler algorithms and associated tools were employed to design de novo glycan trees into a protein nanoparticle vaccine that are able to direct the immune response by shielding regions of the scaffold from antibody recognition. This work will inform glycoprotein model prediction, aid in both X-ray and electron microscopy density solutions and refinement, and help lead the way towards a new era of computational glycobiology.

molecular biology↗