Enzymatic generation of peptides from potato proteins by selected proteases and characterization of their structural properties
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Bibliographic record
Abstract
The use of low grade starting material for the generation of peptides with bioactivity properties is of interest. The proteins from the potato starch industry byproduct is a promising source, as several health benefits may be associated with their hydrolysates. The efficiency of selected proteases (Novo Pro-D, Alcalase, Flavourzyme, and Papain), exhibiting different substrate specificities and cleavage action modes, to hydrolyze potato protein isolate (patatin and protease inhibitors) was investigated. Novo Pro-D resulted in the lowest degree of hydrolysis, whereas Alcalase, Flavourzyme, and Papain exhibited a high catalytic efficiency for the hydrolysis of potato proteins. The degree of hydrolysis behaved in a concentration dependent manner. However, the end-product profile (peptides and free amino acids) was dependent not only on the protease specificity, its cleavage action mode (endo/exo) and the availability of the intermediate substrates but also on the contribution of the protease inhibitors to the reaction kinetics through their inhibitory effects. Indeed, the dependence of the exo-activity on the catalytic efficiency of the endo-action of protease was shown to be significant. Papain generated more unique peptide sequences with homology assessment matching several potato proteins when compared with Flavourzyme. This can be attributed to the high exo-peptidase activity of Flavourzyme resulting in the generation of shorter peptides which were difficult to match. Flavourzyme produced more peptides originated from patatin fraction, whereas Papain resulted in the release of more peptides corresponding to the protease inhibitor fractions. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:420-429, 2016.
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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 | 0.001 |
| 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 it