Lineage‐specific epitope profiles for <scp>HPAI</scp> H5 pre‐pandemic vaccine selection and evaluation
Bibliographic record
Abstract
BACKGROUND: Multiple highly pathogenic avian influenza (HPAI) H5 viruses continue to co-circulate. This has complicated pandemic preparedness and confounded effective vaccine candidate selection and evaluation. OBJECTIVES: In this study, we aimed to predict and map the diversity of CD8+ T-cell epitopes among H5 hemagglutinin (HA) gene lineages to estimate CD8+ T-cell immunity in humans induced by vaccine candidates. METHODS: A dataset consisting of 1125 H5 HA sequences collected between 1996 and 2017 from avian and humans was assembled for phylogenetic and lineage-specific epitope analyses. Conserved epitopes were predicted from WHO-endorsed vaccine candidates and representative clade-defining strains by pairwise comparison with Immune Epitope Database (IEDB). The distribution of predicted epitopes was mapped to each HPAI H5 lineage. We assume that high similarity and conservancy of predicted epitopes from vaccine candidates among all circulating HPAI H5 lineages is correlated with high immunity. RESULTS: A total of 49 conserved CD8+ T-cell epitopes were predicted at 28 different amino acid positions of the HA protein. Mapping these epitopes to the phylogenetic tree allowed us to develop epitope profiles, or "fingerprints," for each HPAI H5 lineage. Vaccine epitope percentage analyses showed some epitope profiles were highly conserved for all H5 isolates and may be valuable for universal vaccine design. However, the positions with low coverage may explain why the vaccine candidates do not always function well. CONCLUSIONS: These findings demonstrate that our analytical approach to evaluate conserved CD8+ T-cell epitope prediction in a phylogenetic framework may provide important insights for computational design of vaccine selection and future epitope-based design.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 | 0.000 |
| 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".