Thermal stability of β-lactoglobulins A and B: effect of SDS, urea, cysteine and N-ethylmaleimide
Bibliographic record
Abstract
Fourier transform infrared spectroscopy (FTIR) and differential scanning calorimetry (DSC) were used to monitor changes in the secondary structure and thermal stability of beta-lactoglobulin A and B in the presence of sodium dodecyl sulphate (SDS), N-ethylmaleimide (NEM), urea and cysteine. An increase in the thermal stabilities of both proteins was noted in the presence of 10 mM-SDS. In the presence of 50 mM-SDS, there was extensive denaturation of both variants. In general, the beta-strand/beta-sheet regions in the secondary structure of both variants were very susceptible to denaturation by SDS and cysteine, suggesting that these regions may be held by hydrophobic and disulphide bonds. At ambient temperature and physiological pH, a notable difference was observed in the 1636 and 1627 cm(-1) regions of the FTIR spectra of the two beta-lg variants. The results suggest possible differences in the nature of the beta-sheet/beta-strand distribution/ content of the two proteins. Urea and NEM at a concentration of 50 mM, had little effect on the secondary structure and denaturation of both variants. New findings are presented which further indicate that although the beta-lg B variant showed greater thermal stability than the A variant in all the cases studied, its denaturation temperature and secondary structure were affected to a greater extent by the protein perturbants than beta-lg A.
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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 | 0.000 |
| 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) | 0.001 | 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".