Peptide immunoarrays for rationale development of vaccines with enhanced cross-reactivity
Bibliographic record
Abstract
Vaccines of enhanced range of protection could help to control newly emerging infectious diseases while providing greater resilience to any subsequent variants. Such "universal vaccines" are an idealized, but unrealized, goal that may benefit from unbiased, high-throughput approaches that define antibody cross-reactivity to enable rational selection of cross-protective epitopes. The priority of this investigation is to establish a pipeline for the identification and preliminary characterization of epitopes with enhanced cross-reactivity. A peptide immunoarray representing the proteomes of SARS-CoV, SARS-CoV-II, and MERS-CoV was applied to characterize spike glycoprotein-specific antibody populations within convalescence serum of SARS-CoV-II infected ferrets. Through two alternate bioinformatic approaches, twenty candidate epitopes were identified and translated into vaccines. Epitopes inducing antibodies with cross-reactivity across naturally occurring versions of spike glycoprotein, including SARS-CoV-II Delta and Omicron variants, as well as antigenically distant SARS-CoV and MERS-CoV, were identified. Working from the assumption that cross-reactivity is prerequisite for cross-protection, this highlights the opportunity and mechanisms by which immunoarrays, coupled with in vitro screening assays, can enable rational selection of epitopes with enhanced potential for cross-protection.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".