Occurrence of Peptide-Peptide Interactions during the Purification of Self-Assembling Peptide f1-8 from a β-Lactoglobulin Tryptic Hydrolysate
Bibliographic record
Abstract
Self-assembling peptides have gained attention because of their nanotechnological applications. Previous work demonstrated that the self-assembling peptide f1-8 (Pf1-8) that is generated from the tryptic hydrolysis of β-lactoglobulin can form a hydrogel after several purification steps, including membrane filtration and consecutive washes. This study evaluates the impact of each processing step on peptide profile, purity, and gelation capacity of each fraction to understand the purification process of Pf1-8 and the peptide-peptide interactions involved. We showed that peptide-peptide interactions mainly occurred through electrostatic and hydrophobic interactions, influencing the fraction compositions. Indeed, the purity of Pf1-8 did not correlate with the number of wash steps. In addition to Pf1-8, two other hydrophobic peptides were identified, peptide f15-20, and peptide f41-60. The gelation observed could be induced either through peptide-peptide interactions or through self-assembling, both being driven by non-covalent bond and more specifically hydrophobic interactions.
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.
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".