A versatile papaya mosaic virus (PapMV) vaccine platform based on sortase-mediated antigen coupling
Bibliographic record
Abstract
BACKGROUND: Flexuous rod-shaped nanoparticles made of the coat protein (CP) of papaya mosaic virus (PapMV) have been shown to trigger innate immunity through engagement of toll-like receptor 7 (TLR7). PapMV nanoparticles can also serve as a vaccine platform as they can increase the immune response to fused peptide antigens. Although this approach shows great potential, fusion of antigens directly to the CP open reading frame (ORF) is challenging because the fused peptides can alter the structure of the CP and its capacity to self assemble into nanoparticles-a property essential for triggering an efficient immune response to the peptide. This represents a serious limitation to the utility of this approach as fusion of small peptides only is tolerated. RESULTS: We have developed a novel approach in which peptides are fused directly to pre-formed PapMV nanoparticles. This approach is based on the use of a bacterial transpeptidase (sortase A; SrtA) that can attach the peptide directly to the nanoparticle. An engineered PapMV CP harbouring the SrtA recognition motif allows efficient coupling. To refine our engineering, and to predict the efficacy of coupling with SrtA, we modeled the PapMV structure based on the known structure of PapMV CP and on recent reports revealing the structure of two closely related potexviruses: pepino mosaic virus (PepMV) and bamboo mosaic virus (BaMV). We show that SrtA can allow the attachment of long peptides [Influenza M2e peptide (26 amino acids) and the HIV-1 T20 peptide (39 amino acids)] to PapMV nanoparticles. Consistent with our PapMV structural model, we show that around 30% of PapMV CP subunits in each nanoparticle can be fused to the peptide antigen. As predicted, engineered nanoparticles were capable of inducing a strong antibody response to the fused antigen. Finally, in a challenge study with influenza virus, we show that mice vaccinated with PapMV-M2e are protected from infection. CONCLUSIONS: This technology will allow the development of vaccines harbouring long peptides containing several B and/or T cell epitopes that can contribute to a broad and robust protection from infection. The design can be fast, versatile and can be adapted to the development of vaccines for many infectious diseases as well as cancer 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 | 0.000 | 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".