ArticlebioRxiv : the preprint server for biology2026
A designed overlapping variant immunogen pool elicits broad sarbecovirus neutralization.
Article in bioRxiv : the preprint server for biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Authors and funding
15 authors.
Funding
Abstract
A central problem in achieving vaccine-based protection against viral infections is eliciting antibodies that are resilient to viral variation. Successive waves of SARS-CoV-2 infection during the COVID19 pandemic were driven by variants that acquired resistance to neutralizing antibodies elicited by prior SARS-CoV-2 variants. To the extent that serum neutralization breadth occurs in individuals with multiple exposures to SARS-CoV-2 antigens, we and others find that it is largely comprised of antibodies that target the variable receptor binding domain (RBD), rather than more conserved spike protein domains. By designing synthetic dimeric RBD immunogens we show that limiting divergence in heterodimeric components favors the generation of cross-reactive B cells and antibodies. We thus devised a vaccine approach based on a two-dose immunization with a pool of five overlapping heterodimeric synthetic RBD variants. Collectively, the RBD heterodimer pool was designed to cover 10% sequence variation and elicited greater antibody cross-reactivity and neutralization breadth than homodimers or heterodimers with highly divergent components. Using an unconventional 'prospective' challenge model in mice, we demonstrate the effectiveness of the RBD heterodimer pool in inducing antibody responses that attenuate infection by future SARS-CoV-2 variants, as well as protection in a challenge model based on a chimeric vesicular stomatitis virus bearing a spike protein from SARS-CoV-1.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.