Amyloid self‐assembling peptides: Potential applications in nanovaccine engineering and biosensing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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 it