Immuno-informatics Study Identifies Conserved T Cell Epitopes in Non-structural Proteins of Bluetongue Virus Serotypes: Formulation of Computationally Optimized Next-Generation Broad-spectrum Multiepitope Vaccine
Bibliographic record
Abstract
Abstract Bluetongue (BT) is a significant arboviral disease affecting sheep, cattle, goats, and wild ruminants, posing serious economic challenges to livestock industry. Control efforts have been hampered by the existence of over 32 distinct BT virus (BTV) serotypes and the absence of broad-spectrum vaccines. Some key non-structural proteins of BTV, including NS1, NS2, and NS3, exhibit notable amino acid sequence conservation. Our findings reveal that mouse MHC class I (MHC-I) CD8+ T cell epitopes are highly conserved in NS1 and NS3, while MHC-II epitopes are prevalent in all the three non-structural NS 1-3 proteins. Similarly, both class I and II Bovine Leukocyte antigen-restricted CD8+ and CD4+ T cell epitopes are conserved within NS1, NS2, and NS3 proteins. To construct in silico broad-spectrum vaccine, we subsequently screened these conserved epitopes based on antigenicity, allergenicity, toxicity, and solubility. Modeling and Refinement of the 3D structure models of vaccine constructs were achieved using protein modeling web servers. Our analysis revealed promising epitopes that exhibit strong binding affinities with low energies against two TLR receptors (TLR3 and TLR4). To ensure atomic-level stability, we evaluated the docking complexes of epitopes and receptors through all-atom molecular dynamics simulations (MDS). Encouragingly, our 100 nanoseconds MDS showed stable complexes with minimal RMSF values. Our study offers valuable insights into these conserved T cell epitopes as promising candidates for a broad-spectrum BT vaccine. We therefore encourage for their evaluation in animal models and natural hosts to assess their immunogenicity, safety, and efficacy for field use in the livestock.
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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 | 0.001 |
| 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".