Effects of molecular charge and hydrophobicity on the antioxidative properties of pea (Pisum sativum L.) protein hydrolysate fractions
Bibliographic record
Abstract
Reactive oxygen species are implicated to be the basis for a variety of disease conditions, including cardiovascular disease.When produced in excess, reactive oxygen species can have deleterious effects in the biological system; therefore, compounds that can scavenge free radicals could be useful therapeutic agents.We studied the antioxidant activities of peptides derived from pea protein hydrolysate using seven in vitro antioxidant evaluation systems that included the chelation of metal ions and the ability to scavenge reactive oxygen species.Pea protein hydrolysate was separated based on net hydrophobic properties and net cationic charges using reverse phase high performance liquid chromatography and cation exchange chromatography, respectively.Five fractions with arange of net hydrophobic properlies were obtained and screened for antioxidant activities.The fractions with the highest net hydrophobic properties exhibited the strongest scavenging and lnetal chelating activities except hydrophobic properties did not play a role in the reducing power.The fraction with the most hydrophobic properties also contained the highest concentration of hydrophobic amino acids and the lowest concentration of charged amino acids.Separation of pea protein hydrolysate based on net cationic charge yielded five fractions with a range of net cationic charges.The fractions with the least net cationic charge displayed the strongest scavenging activities such as superoxide, hydrogen peroxide and 1,1-diphenyl-2-picrylhydrazyl scavenging with the exception of the reducing power.Peptides separated by net cationic charge did not display scavenging activity against the hydroxyl radical and had zero metal chelating activity.
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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".