Identifying stable-against-mutations viral epitopes in SARS-CoV-2
Bibliographic record
Abstract
Abstract We have developed a computational method “Multi-Stable Epitope Sequencer” to predict mutation-resistant regions with stability against future viral variability. At the beginning of the pandemic, this approach allowed us to identify a set of eight SARS-CoV-2 spike protein sequences that had the potential to be mutationally stable. We have tested this methodology on the SARS-CoV-2 viral linages that occurred throughout the COVID-19 pandemic. These eight peptide sequences (epitopes) have been preserved in 97% of all SARS-CoV-2 lineages reported in the CoV-GLUE dataset during the pandemic period. Likewise, more than 90% of these peptides remained invariable across the 49 predominant viral variants circulating throughout the pandemic (ECDC-WHO). In addition, the eight selected peptides were preserved in 94.1% of all 28 variants considered of most interest in the CoV-GLUE project. Our analyses confirm the predicted mutational stability of the eight selected short viral peptides over the entire COVID-19 pandemic.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".