Curli-Mediated Self-Assembly of a Fibrous Protein Scaffold for Hydroxyapatite Mineralization
Bibliographic record
Abstract
Nanostructures formed by self-assembled peptides have been increasingly exploited as functional materials for a wide variety of applications, from biotechnology to energy. However, it is sometimes challenging to assemble free short peptides into functional supramolecular structures, since not all peptides have the ability to self-assemble. Here, we report a self-assembly mechanism for short functional peptides that we derived from a class of fiber-forming amyloid proteins called curli. CsgA, the major subunit of curli fibers, is a self-assembling β-helical subunit composed of five pseudorepeats (R1-R5). We first deleted the internal repeats (R2, R3, R4), known to be less essential for the aggregation of CsgA monomers into fibers, forming a truncated CsgA variant (R1/R5). As a proof-of-concept to introduce functionality in the fibers, we then genetically substituted the internal repeats by a hydroxyapatite (HAP)-binding peptide, resulting in a R1/HAP/R5 construct. Our method thus utilizes the R1/R5-driven self-assembly mechanism to assemble the HAP-binding peptide and form hydrogel-like materials in macroscopic quantities suitable for biomineralization. We confirmed the expression and fibrillar morphology of the truncated and HAP-containing curli-like amyloid fibers. X-ray diffraction and TEM showed the functionality of the HAP-binding peptide for mineralization and formation of nanocrystalline HAP. Overall, we show that fusion to the R1 and R5 repeats of CsgA enables the self-assembly of functional peptides into micron long fibers. Further, the mineral-templating ability that the R1/HAP/R5 fibers possesses opens up broader applications for curli proteins in the tissue engineering and biomaterials fields.
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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".