Computational prediction of highly conserved CD8+ and CD4+ T cell epitopes in bluetongue virus: A promising data for developing broad-spectrum bluetongue vaccine
Bibliographic record
Abstract
Abstract Bluetongue (BT) is an economically important arboviral disease of sheep, cattle, goats, and wild ruminants, particularly in America and Europe. However, it has remained uncontrolled due to the evolution of >32 serologically distinct BT virus (BTV) serotypes and the lack of broad-spectrum vaccines. While outer VP2 and VP5 proteins, involved in cell penetration, are less conserved, certain core BTV proteins NS1, NS2, NS3, and VP7 have higher amino acid conservancy. Here, using in silico epitope mapping and multiple sequence alignment, we analyzed all the antigenic BTV proteins for the presence of conserved T cell epitopes recognized by different mammals. We find that mouse Major Histocompatibility Complex-I (MHC-I) conserved epitopes are present in NS1, NS3, and VP7, and MHC-II-conserved epitopes in NS1, NS2, NS3, and VP7; and in bovines, Bovine Leukocyte antigen class-I and II-(BoLA-I & II)-conserved epitopes present in NS1, NS2, NS3, and VP7 proteins. The presence of these conserved epitopes from NS1, NS2, NS3, and VP7 proteins of BTV in the selected mammals, including bovine that is closely related to sheep, we believe they are also likely to be presented by MHCs of the primary natural host sheep. Thus, harnessing this knowledge in developing a pan-BTV vaccine would confer broad-spectrum protection against all the BTV serotypes in sheep and bovines. We are grateful for start-up funding by the Department of Microbiology and Immunology, Faculty of Medicine, Dalhousie University. PPCM wishes to acknowledge support from EU H20:20 grant ‘PALE-Blu’ (project number 727393-2).
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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".