Fractionation of β-Lactoglobulin Tryptic Peptides by Ampholyte-Free Isoelectric Focusing
Bibliographic record
Abstract
Solutions of tryptic hydrolysate of bovine beta-lactoglobulin were fractionated by liquid-phase IEF in a preparative Rotofor cell at constant power for 2 h without ampholytes in order to identify interactions between peptides. The 20 peptide fractions collected were analyzed by capillary electrophoresis and SDS-PAGE under native, denaturing, and reducing conditions. The hydrolysate was shown to be composed mainly of acidic peptides (pI 2-5, 62%) of molecular mass below 6 kDa, and numerous disulfide bonds were detected. Purified peptides (beta-LG 15-20, 71-75, 76-82, and 84-91) were also focused individually and in mixtures and matched to components of the IEF fractions obtained from the tryptic hydrolysate of beta-LG. The separation of acidic (beta-LG 84-91) and basic (beta-LG 76-82) peptides was achieved by IEF, whereas uncharged peptides (beta-LG 15-20 and 71-75) were poorly separated due to their low electrophoretic mobility. Because no peptide-peptide interaction could be identified by IEF fractionation, it is suggested that electrical fields may decrease electrostatic interactions between charged peptides.
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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".