Engineering and evaluation of amyloid assemblies as a nanovaccine against the Chikungunya virus
Bibliographic record
Abstract
The design of nanoparticles exposing a high density of antigens constitutes a promising strategy to address safety concerns of conventional life-attenuated vaccines as well as to increase the immunogenicity of subunit vaccines. In this study, we developed a fully synthetic nanovaccine based on an amyloid peptide sequence with high self-assembling properties. The immunogenic epitope E2EP3 from the E2 glycoprotein of the Chikungunya virus was used to evaluate the potential of a 10-mer peptide derived from an endogenous amyloidogenic polypeptide as a novel vaccine platform. Chimeric peptides, comprising the peptide antigen attached to the amyloid core by a short flexible linker, were prepared by solid phase synthesis. As observed using atomic force microscopy, these polypeptides self-assembled into linear and unbranched fibrils with a diameter ranging from 6 to 8 nm. A quaternary conformation rich in cross-β-sheets characterized these assemblies, as demonstrated by circular dichroism spectroscopy and thioflavin T fluorescence. ELISA assays and transmission electronic microscopy of immunogold labeled-fibrils revealed a high density of the Chikungunya virus E2 glycoprotein derived epitope exposed on the fibril surface. These amyloid fibrils were cytocompatible and were efficiently uptaken by macrophages. Mice immunization revealed a robust IgG response against the E2EP3 epitope, which was dependent on self-assembly and did not require co-injection of the Alhydrogel adjuvant. These results indicate that cross-β-sheet amyloid assemblies constitute suitable synthetic self-adjuvanted assemblies to anchor antigenic determinants and to increase the immunogenicity of peptide epitopes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".