Small amphipathic peptides are responsible for the assembly of cruciferin nanoparticles
Bibliographic record
Abstract
Abstract Amphipathic peptides are versatile building blocks for fabricating well-ordered nanostructures, which have gained much attention owing to their enormous design possibilities and bio-functionalities. However, using amphipathic peptides from natural proteins to create tunable nanostructures is challenging because of their heterogeneity and great tendency to form aggregates. Here we fabricated two well-defined nanoparticles from cruciferin amphipathic peptides by integrating top-down and bottom-up approach. Alkali hydrolysis (pH 12, 120 °C for 30 min) was introduced to break down intact cruciferin into peptides (top–down). The cruciferin peptides and their fractions were then assembled into nanoparticles (bottom–up) in the presence of calcium ions. The permeate fraction from 10 kDa cut-off membrane formed smaller nanoparticles (F1-NPs) (around 82 nm) than that of unfractionated cruciferin peptides (CRU-NPs, around 185 nm); the electrostatic and hydrophobic interactions were the main driving forces for particle formation. LC-MS/MS analysis characterised that the small amphipathic peptides (Xn1Zn2Xn3Zn4, n1–4 = 0~5), composed of alternating hydrophobic (X) and hydrophilic (Z) amino acid with a length of 5–15 and 5–20 residues for F1-NPs and CRU-NPs, respectively, were responsible for particle formation. Our study established the mechanism of particle formation of the cold gelation is through assembly of amphipathic peptides.
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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".