Increased Immunogenicity of Full-Length Protein Antigens through Sortase-Mediated Coupling on the PapMV Vaccine Platform
Bibliographic record
Abstract
Background: Flexuous rod-shape nanoparticles—made of the coat protein of papaya mosaic virus (PapMV)—provide a promising vaccine platform for the presentation of viral antigens to immune cells. The PapMV nanoparticles can be combined with viral antigens or covalently linked to them. The coupling to PapMV was shown to improve the immune response triggered against peptide antigens (<39 amino acids) but it remains to be tested if large proteins can be coupled to this platform and if the coupling will lead to an immune response improvement. Methods: Two full-length recombinant viral proteins, the influenza nucleoprotein (NP) and the simian immunodeficiency virus group-specific protein antigen (GAG) were coupled to PapMV nanoparticles using sortase A. Mice were immunized with the nanoparticles coupled to the antigens and the immune response directed to the antigens were analyzed by ELISA and ELISPOT. Results: We showed the feasibility of coupling two different full-length proteins (GAG and NP) to the nanoparticle. We also showed that the coupling to PapMV nanoparticles improved significantly the humoral and the cytotoxic T lymphocyte (CTL) immune response to the antigens. Conclusion: This proof of concept demonstrates the versatility and the efficacy of the PapMV vaccine platform in the design of vaccines against viral diseases.
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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".