Unleashing the Immune Arsenal: Development of Broad-spectrum Multiepitope Bluetongue Vaccine Targeting Conserved T Cell Epitopes of Structural Proteins 4777
Bibliographic record
Abstract
Abstract Description Bluetongue (BT) is a severe arboviral disease of ruminants caused by Bluetongue virus (BTV), which has over 32 serotypes, complicating vaccine efficacy. The virus’s structural proteins are promising vaccine targets but exhibit significant amino acid polymorphism and inhibitory epitopes. This study focused on identifying highly conserved T cell epitopes within VP1, VP5, and VP7 proteins to design a broad-spectrum multiepitope vaccine for bovine and mouse models. Using immuno-informatics, we selected MHC-I and -II-restricted epitopes that are highly antigenic, non-allergenic, and non-toxic. CD4+ T cell epitopes also induced IFN-γ responses. The vaccine design incorporated TLR4-agonist adjuvants, beta-defensin 2, and the 50S ribosomal unit to enhance innate and cell-mediated immunity. Protein-protein docking revealed strong binding affinities, and 100-nanosecond molecular dynamics simulations confirmed stable interactions between the vaccine and TLR4. In silico vaccination studies demonstrated robust proinflammatory responses, supporting the vaccine’s potential to induce cross-reactive immunity. These results lay the foundation for further wet lab validation to assess immunogenicity, safety, and practical applicability in livestock. Funding Sources Tier 2 Canada Research Chair, CIHR 2021-00215 Topic Categories Veterinary and Comparative Immunology (VET)
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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 | 0.000 |
| 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 | 0.000 | 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".