Structure and Function of Mung Bean Protein-Derived Iron-Binding Antioxidant Peptides
Bibliographic record
Abstract
An iron-binding mung bean protein hydrolysate (MBPH) was prepared using a continuous enzymatic membrane reactor followed by peptide separation on anion-exchange (AEC) and reverse-phase HPLC (RP-HPLC) columns. Amino acid sequences of peptides present in the RP-HPLC fraction with the strongest iron-binding capacity were identified using mass spectrometry, and ten peptides of 5–8 amino acids synthesized for antioxidant characterization. Five fractions (AF1– AF5) with higher iron-binding capacity (88.86 ± 6.43 to 153.59 ± 2.18 mg/g peptide) when compared to the MBPH (36.81 ± 0.93 mg/g peptide) were obtained from AEC. PAIDL had the significantly (p < 0.05) highest iron-binding capacity, but LLLLG and LLGIL showed the strongest metal chelating activity. However, PAIDL (46.63%) and LLGIL (81.27%) had significantly (p < 0.05) better DPPH radical scavenging activity than the other peptides. PAIDL and LLGIL were also the most effective (p < 0.05) hydroxyl radical neutralizers with an effective concentration that scavenged 50% (EC50) values of 0.09 and 0.37 mM, respectively. PAIDL and AIVIL showed the lowest EC50 values of 0.07 mM each for superoxide radical scavenging activity. We conclude that short chain length in combination with leucine as the C-terminal amino acid residue contributed to the strong antioxidant properties of peptides in this study.
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How this classification was reachedexpand
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 | 0.000 |
| 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 teacher head, 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".