Formation and Stability of Nanofibers from a Milk-Derived Peptide
Bibliographic record
Abstract
The objective of the present work was to investigate the physicochemical conditions that trigger the self-assembly of peptide β-lg f1-8 and therefore lead to nanofibers and hydrogel formation. Nanostructures formed by self-assembly of peptide β-lg f1-8 in the pH range of 2.0-11.0 were studied by transmission electron microscopy (TEM). Hydrogel formation was studied as a function of pH and resulted in evidence of a link between hydrogel formation and the charge distribution carried by the peptide structure. Finally, circular dichroism (CD) spectroscopy was used to characterize the effects of peptide concentration (0.4-2.0 mg/mL), ionic strength (0-1 M NaCl), and temperature (20-80 °C) on the secondary structure of peptide β-lg f1-8. Hydrogels were obtained at peptide concentrations above 2.5 mg/mL. Peptide concentration and pH adjustment were shown to trigger self-assembly of β-lg f1-8, but increasing ionic strength had no effect. Heating to 80 °C induced a stronger CD signal intensity due to an increase in solubility of the peptide, whereas only slight changes in CD pattern were found upon cooling to 20 °C. Overall, results emphasize the role of particular molecular interactions in β-sheet self-assembly of peptide β-lg f1-8 and pH-dependent electrostatic interactions occurring between β-lg f1-8 units, which can explain its propensity to self-assembly.
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How this classification was reachedexpand
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 | 0.000 | 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 teacher head, 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".