Amyloid self‐assembling peptides: Potential applications in nanovaccine engineering and biosensing
Bibliographic record
Abstract
Abstract Living organisms are an inestimable source of inspiration for the design of biomaterials and nanostructures for medical and technological applications. Amyloids, which were historically associated with diseases, have recently been recognized as a biological structure that performs vital physiological functions in host organisms, highlighting their potential as life‐inspired assemblies. Amyloids are highly organized proteinaceous assemblies characterized by a cross‐β‐sheet quaternary conformation. The mechanical, physical, and biological properties of amyloids suggest that these nanostructures hold great potential as soft materials, nanoparticles, and biomatrices. This potential is associated with many characteristics, including the spontaneous self‐assembly of many polypeptide sequences, high mechanical resistance, biocompatibility, biodegradability as well as thermal, chemical, and enzymatic stability. Moreover, peptide‐based amyloid assemblies can efficiently be obtained by standard solid phase peptide synthesis and orthogonally functionalized with a wide range of biomolecules, such as large proteins and DNA. In this review, after briefly introducing the amyloid structure and the mechanisms of self‐assembly, we describe approaches to identify and design short self‐assembling amyloidogenic peptides. Afterward, we introduce strategies used to functionalize amyloid materials and we highlight some relevant examples in the development of nanovaccines and biosensors.
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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".