Unleashing the Immune Arsenal: Development of Broad-spectrum Multiepitope Bluetongue Vaccine Targeting Conserved T Cell Epitopes of Structural Proteins
Bibliographic record
Abstract
Bluetongue (BT) is a severe arboviral disease affecting sheep, cows, and other wild ruminants, caused by the Bluetongue virus (BTV). The virus has evolved into over 32 serotypes, rendering existing vaccines less effective. While the structural proteins of this virus represent promising targets for vaccine development, they unfortunately exhibit high amino acid polymorphism and are laden with numerous inhibitory epitopes. However, certain structural proteins such as VP1 and VP7 are highly conserved and may contain epitopes capable of triggering cross-reactive cell-mediated immunity (CMI). In this study, we identified highly conserved MHC-I and -II-restricted T cell epitopes within VP1, VP5, and VP7 BTV proteins and designed multiepitope vaccine constructs using an in silico immunoinformatics pipeline for both laboratory mouse and bovine natural systems. The conserved epitopes utilized in the vaccines are highly antigenic, non-allergenic, non-toxic, and predicted to be capable of inducing IFN-𝛾. Both mouse and bovine vaccines were tethered with Toll-like receptor (TLR)-4-agonist adjuvants, beta-defensin 2–50 S ribosomal unit to stimulate innate immunity for the CMI development. Protein-protein docking analysis suggested favorable binding affinities between the vaccine constructs and TLR4, while 100-nanosecond molecular dynamics simulations supported the structural stability of the complexes. Although these computational findings are promising, all results require experimental validation. Future in vitro and in vivo studies are essential to confirm the immunogenicity, safety, and protective efficacy of the proposed vaccine candidates in target species.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".