Anti-Diabetic Properties of Hydrolysates from Egg White Proteins Using Immobilized Enzymes Followed by in vitro Gastrointestinal Digestion
Bibliographic record
Abstract
Background and Objective: Enzyme-assisted hydrolysis of the proteins from food sources is an effective way to generate peptides with various bioactive properties. Furthermore, enzyme immobilization is a way to recycle enzymes for the future uses. The objective of this study was to investigate the effects of neutrase and thermolysin immobilization on the enzyme properties and in vitro anti-diabetic properties of intestinal digests achieved by the simulated digestion of egg white protein hydrolysates. Material and Methods: Neutrase and thermolysin were immobilized on cellulose-coated magnetite nanoparticles. Then, enzyme activity, thermal resistance, reusability and optimum conditions of the egg white protein hydrolysis were assessed. Egg white protein hydrolysates were then digested in vitro and inhibitory activities of the intestinal digests against dipeptidyl peptidase IV (DPP-IV) and α-glucosidase were investigated. Results and Conclusion: Enzymes immobilization resulted in increases in the thermal stabilities of them. Optimum temperatures for the egg white protein hydrolysis increased by 4.0 and 3.2 °C for neutrase and thermolysin, respectively. Digests from the hydrolysates of free neutrase effectively inhibited DPP-IV and α-glucosidase by 17.9 and 29.7%, respectively. These values for the hydrolysates released by the free thermolysin were higher (37.2 and 35.1%, respectively). The enzyme immobilization resulted in a 4.4% decrease in DPP-IV inhibitory activities of the digests for the hydrolysates from neutrase and a 28.6% decrease for those from thermolysin. Decreases in α-glucosidase inhibition due to the immobilization included 9.8% for neutrase and 12.2% for thermolysin for the digests from the hydrolysates. Based on the results from the current study, hydrolysates from the egg white proteins achieved by the free and immobilized neutrase and thermolysin can be used in formulations of the functional foods and nutraceuticals with multifunctional properties.
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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) | 0.001 | 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 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".