Computational assessment of the spike protein antigenicity reveals diversity in B cell epitopes but stability in T cell epitopes across SARS-CoV-2 variants
Bibliographic record
Abstract
Abstract Since its emergence into the human population at the end of 2019, SARS-CoV-2 has caused significant morbidity and mortality worldwide. Efforts to develop a protective vaccine against COVID-19 have yielded several vaccine platforms currently in distribution targeting the original SARS-CoV-2 spike protein sequence from the first cases of infection. In recent months, variants of SARS-CoV-2 have raised concerns that viral mutation may undermine vaccination efforts through viral escape of host immune memory acquired from infection or vaccination. We therefore used a computational approach to predict changes in spike protein antigenicity with respect to host B cell and CD8+ T cell immunity across six SARS-CoV-2 variants (D614G, B.1.1.7, B.1.351, P.1, B.1.429, and mink-related). Our epitope analysis using DiscoTope suggests possible changes in B cell epitopes in the S1 region of the spike protein across variants, in particular the B.1.1.7 and B.1.351 lineages, which may influence immunodominance. Additionally, we show that high-affinity MHC-I-binding peptides and glycosylation sites on the spike protein appear consistent between variants with the exception of an extra glycosylation site in the P.1 variant. Together, these analyses suggests T cell vaccine strategies have the most longevity before reformulation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".