Identification of an Optimized Receptor-Binding Domain Subunit Vaccine against SARS-CoV-2
Bibliographic record
Abstract
Current vaccine efforts to combat SARS-CoV-2 are focused on the whole spike protein administered as mRNA, viral vector, or protein subunit. However, the SARS-CoV-2 receptor-binding domain (RBD) is the immunodominant portion of the spike protein, accounting for 90% of serum neutralizing activity. In this study, we constructed several versions of RBD and together with aluminum hydroxide or DDA (dimethyldioctadecylammonium bromide)/TDB (d-(+)-trehalose 6,6'-dibehenate) adjuvant evaluated immunogenicity in mice. We generated human angiotensin-converting enzyme 2 knock-in mice to evaluate vaccine efficacy in vivo following viral challenge. We found that 1) subdomain (SD)1 was essential for the RBD to elicit maximal immunogenicity; 2) RBDSD1 produced in mammalian HEK cells elicited better immunogenicity than did protein produced in insect or yeast cells; 3) RBDSD1 combined with the CD4 Th1 adjuvant DDA/TDB produced higher neutralizing Ab responses and stronger CD4 T cell responses than did aluminum hydroxide; 4) addition of monomeric human Fc receptor to RBDSD1 (RBDSD1Fc) significantly enhanced immunogenicity and neutralizing Ab titers; 5) the Beta version of RBDSD1Fc provided a broad range of cross-neutralization to multiple antigenic variants of concern, including Omicron; and 6) the Beta version of RBDSD1Fc with DDA/TDB provided complete protection against virus challenge in the knock-in mouse model. Thus, we have identified an optimized RBD-based subunit vaccine suitable for clinical trials.
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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".