Effect of Physicochemical Conditions on Peptide−Peptide Interactions in a Tryptic Hydrolysate of β-Lactoglobulin and Identification of Aggregating Peptides
Bibliographic record
Abstract
The objective of this study was to characterize the changes in peptide solubility resulting from changing some physicochemical conditions in a tryptic hydrolysate of beta-lactoglobulin (beta-LG). The turbidity (500 nm) of a 1% solution of tryptic peptides was measured at pH 3-10, at 5, 25, and 50 degrees C, in the presence of different salt concentrations (0, 0.5, and 1 M NaCl), in the presence of denaturing and reducing agents (6 M urea, 5% SDS, or 5% beta-mercaptoethanol), and under an electric field (isoelectric focusing). The results reveal an increase in turbidity of the peptide solution at pH 4, but a slight increase in turbidity was also observed at pH 8, which is attributable to peptides linked by disulfide bridges. The effect of temperature and ionic strength on the turbidity occurring at pH 4 indicates that mainly hydrophobic interactions are involved in the aggregation process. The material in the precipitate at pH 4 was identified as the peptides beta-LG 1-8, 15-20, and 41-60 and non-hydrolyzed alpha-lactalbumin. These results suggest that a limited number of peptides are involved in the aggregation process observed at pH 4, some of which having bioactive (beta-LG 15-20, ACE inhibitor, and opioid) or emulsifying properties (beta-LG 41-60). Aggregation of these peptides at acidic pH indicates that a simple acidification step could represent an easy process for isolating peptidic fractions enriched in bioactive or functional peptides.
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 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".