Design and immunological evaluation of two-component protein nanoparticle vaccines for East Coast fever
Bibliographic record
Abstract
Nanoparticle vaccines usually prime stronger immune responses than soluble antigens. Within this class of subunit vaccines, the recent development of computationally designed self-assembling two-component protein nanoparticle scaffolds provides a powerful and versatile platform for displaying multiple copies of one or more antigens. Here we report the generation of three different nanoparticle immunogens displaying 60 copies of p67C, an 80 amino acid polypeptide from a candidate vaccine antigen of Theileria parva, and their immunogenicity in cattle. p67C is a truncation of p67, the major surface protein of the sporozoite stage of T. parva, an apicomplexan parasite that causes an often-fatal bovine disease called East Coast fever (ECF) in sub-Saharan Africa. Compared to I32-19 and I32-28, we found that I53-50 nanoparticle scaffolds displaying p67C had the best biophysical characteristics. p67C-I53-50 also outperformed the other two nanoparticles in stimulating p67C-specific IgG1 and IgG2 antibodies and CD4+ T-cell responses, as well as sporozoite neutralizing capacity. In experimental cattle vaccine trials, p67C-I53-50 induced significant immunity to ECF, suggesting that the I53-50 scaffold is a promising candidate for developing novel nanoparticle vaccines. To our knowledge this is the first application of computationally designed nanoparticles to the development of livestock vaccines.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".