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 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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".