Epitope Profiling of SARS-CoV-2 Spike Antigen Provides a Novel Strategy for Developing ELISAs Specific for Different Spike Protein Variants in Bivalent Vaccine Formulations
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: An initial COVID-19 candidate vaccine containing a purified ancestral SARS-CoV-2 spike antigen was characterized with an ELISA using recombinant monoclonal antibodies (mAbs) generated against this variant. Upon the emergence of a new Beta (B.1.351) spike variant early in the pandemic, the assessment of a bivalent vaccine containing ancestral and Beta spike antigens began. Due to accelerated project timelines, mAbs generated specifically against the Beta spike antigen were not available at the time to address assay development and vaccine testing requirements. METHODS: Using only the initial mAb panel raised against the ancestral spike antigen, an epitope-blocking ELISA strategy was developed to independently measure Beta spike antigen in bivalent vaccine formulations. To facilitate this, epitope profiling of spike antigens from both ancestral and Beta variants was performed with biolayer interferometry and hydrogen-deuterium exchange mass spectrometry using the original panel of mAbs. RESULTS: The resulting blocking ELISA was precise and specific for the Beta spike antigen and detected the expected amount of this antigen in bivalent vaccine formulations. The specific amount of ancestral spike protein in the bivalent vaccine was also confirmed using the original ELISA developed at the onset of the pandemic. CONCLUSIONS: This epitope-blocking strategy helped to overcome key reagent availability issues and could be applied to other projects involving related proteins.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".