Antioxidant Properties and Prediction of Bioactive Peptides Produced from Flixweed (sophia, Descurainis sophia L.) and Camelina (Camelina sativa (L.) Crantz) Seed Meal: Integrated In Vitro and In Silico Studies
Bibliographic record
Abstract
Flixweed (sophia) seed meal and camelina, both by-products of oil processing, were employed to generate protein hydrolysates by applying Flavourzyme and Alcalase. This study aimed to integrate in vitro and in silico methods to analyze sophia and camelina protein hydrolysates for releasing potent antioxidative, dipeptidyl peptidase IV (DPP IV) inhibitors and angiotensin-converting enzyme (ACE) inhibitory peptides. In vitro methods were used to investigate the antioxidant potential of sophia/camelina protein hydrolysates. Bioinformatics techniques, including Peptideranker, BIOPEP, Toxinpred, AlgPred, and SwissADME, were employed to obtain the identification of bioactive peptides produced during the hydrolysis process. Protein hydrolysates produced from sophia and camelina seed meal exhibited higher ABTS and DPPH radical scavenging activities Ithan their protein isolates. Among the produced protein hydrolysates, Alcalase-treated samples showed the highest oxygen radical absorbance capacity and hydroxyl radical scavenging activity. In addition, sophia/camelina hydrolysates prevented hydroxyl and peroxyl radical-induced DNA scission and LDL cholesterol oxidation. In silico proteolysis was conducted on Alcalase-treated samples, and resultant peptides showed potential DPP IV and ACE-inhibitory activities. Identified peptides were further assessed for their toxicity and medicinal properties. Results indicate that all digestive-resistant peptides were non-toxic and had desirable drug-like properties. The findings of this study suggest that sophia/camelina protein hydrolysates are promising candidates for functional foods, nutraceuticals, and natural therapeutics.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".