Which Proteins? The Challenge of Identifying the Protective Antigens for Next-Generation Capripoxvirus Vaccines
Bibliographic record
Abstract
Sheeppox, goatpox, and lumpy skin disease continue to negatively impact the sheep, goat, and cattle industries in countries where these diseases are present and threaten to spread into new regions. Effective vaccines are available for disease control and eradication. However, commercial vaccines are based on live attenuated virus isolates and therefore it is not currently possible to differentiate between infected and vaccinated animals (DIVA), which severely limits the use of these vaccines in countries that are free from disease and at risk of an incursion. The development of next-generation vaccines, including recombinant protein, viral-vectored, and mRNA, has been limited due to the lack of understanding of the protective antigen(s) of capripoxviruses. The complexity of capripoxviruses, with up to 156 open reading frames, makes the identification of protective antigen(s) difficult. This paper identifies the most promising antigens by first considering the membrane-associated proteins and then further selecting proteins based on immunogenicity and their role in immunity by comparing them to known orthopoxvirus homologues. From the 156 potential antigens, 13 have been identified as being the most likely to be protective. Further evaluation of these proteins, as immunogens, would be required to identify the optimal combination of immunodominant antigen(s) for the development of next-generation capripoxvirus vaccines.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".