Harnessing the Activation of Toll-Like Receptor 2/6 by Self-Assembled Cross-β Fibrils to Design Adjuvanted Nanovaccines
Bibliographic record
Abstract
Protein fibrils characterized with a cross-β-sheet quaternary structure have gained interest as nanomaterials in biomedicine, including in the design of subunit vaccines. Recent studies have shown that by conjugating an antigenic determinant to a self-assembling β-peptide, the resulting supramolecular assemblies act as an antigen delivery system that potentiates the epitope-specific immune response. In this study, we used a ten-mer self-assembling sequence (I10) derived from an amyloidogenic peptide to biophysically and immunologically characterize a nanofibril-based vaccine against the influenza virus. The highly conserved epitope from the ectodomain of the matrix protein 2 (M2e) was elongated at the N-terminus of I10 by solid phase peptide synthesis. The chimeric M2e-I10 peptide readily self-assembled into unbranched, long, and twisted fibrils with a diameter between five and eight nm. These cross-β nanoassemblies were cytocompatible and activated the heterodimeric Toll-like receptor (TLR) 2/6. Upon mice subcutaneous immunization, M2e-fibrils triggered a robust anti-M2e specific immune response, which was dependent on self-assembly and did not require the use of an adjuvant. Overall, this study describes the efficacy of cross-β fibrils to activate the TLR 2/6 and to stimulate the epitope-specific immune response, supporting usage of these proteinaceous assemblies as a self-adjuvanted delivery system for antigens.
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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) | 0.000 | 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".