Epitope Mapping of Bovine Viral Diarrhea Virus Antigens E1, E2, Erns, and NS3 using Phage Display and Peptide Scanning
Bibliographic record
Abstract
Bovine viral diarrhea virus is a cattle pathogen with global distribution, and substantial economic impact. Control of viral transmission is challenging by the lifelong viral shedding by persistently infected (PI) animals, and the significant diversity of the virus. While surveillance and removal of PI animals is the primary focus of control programs, insight into the antigenicity of the virus could be valuable for developing broadly protective vaccines to reduce the spread of the virus within farms and in this way the incidence of persistently infected animals (PI’s). This study aims to characterise the viral proteins of BVDV which elicit specific antibody responses. These proteins are the surface glycoproteins E1, E2 and Erns, as well as the non-structural protein NS3. This work employs the techniques of phage display, and peptide scanning to identify epitope structures on these four antigens and puts them into the context of existing structural models. Linear epitopes of potential interest for future vaccine development were identified on each of the four antigens. While attempts at characterizing conformational epitopes highlighted shortcomings in the computational workflow for such efforts, valuable insight into the limitations and future directions are drawn.
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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".