Enhanced variant neutralization through glycan masking of SARS-CoV-2 XBB1.5 RBD
Bibliographic record
Abstract
SARS-CoV-2 continues to evolve antigenically under the immune pressure exerted by both natural infection and vaccination. As new variants emerge, we face the recurring challenge of updating vaccines at significant financial cost to maintain their efficacy. To address this, novel strategies are needed to enhance the breadth of protection offered by vaccines or, at a minimum, extend their effectiveness over time. One such strategy is antigen modification. In this study, we introduce a glycosylation site into a binding but non-neutralizing epitope within the SARS-CoV-2 XBB.1.5 receptor binding domain (RBD) to redirect the immune response towards more potent neutralizing epitopes. Immunization of mice with this modified antigen via the mRNA vaccine platform resulted in a dramatic increase in neutralizing antibodies compared to the wild-type XBB.1.5 RBD, showing superior protection against a range of SARS-CoV-2 Omicron variants, from BA.2 to JN.1. Our findings reinforce the power of the glycan masking approach, which in combination with the now well-established mRNA vaccine platform can contribute to broader and better vaccines.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".