Steric Effects Governing Disulfide Bond Interchange during Thermal Aggregation in Solutions of β-Lactoglobulin B and α-Lactalbumin
Bibliographic record
Abstract
Intermolecular disulfide bond formation in pure beta-lactoglobulin (beta-Lg) B and in its 1:1 mixture with alpha-lactalbumin (alpha-La), heated at 85 degrees C for 10 min in solutions of low and high (100 mM NaCl) ionic strength and pH 6.0, was studied by reverse-phase HPLC and MALDI-TOF mass spectrometry. Disulfide bonding between beta-Lg monomers was more extensive than reported in the literature for a temperature of 68.5 degrees C, including formation of trimers connected by two of the three adjacent cysteines, C106/C119/C121. The participation of the different thiol groups in disulfide bonds appeared to depend on their location in the native structure, with surface-located cysteines more involved than internally located ones. This also applied to alpha-La-beta-Lg interactions, where the predominant participants were the surface-located alphaC111, alphaC120, alphaC61, and alphaC6. The least active participant was alphaC28, suggesting that it becomes sterically inaccessible during unfolding of the protein. High ionic strength apparently promoted disulfide bonding. The order of cysteine participation at the high ionic strength was similar to that at low ionic strength, with fewer native-location bonds observed and a lower activity of some groups, such as beta-C106/C119/C121 and alphaC61.
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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 | 0.001 |
| 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) | 0.001 | 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".