Unveiling dual-functional umami peptides with antioxidant effects from eggshell membrane byproducts: A machine learning-guided approach integrating molecular simulation and quantum chemistry
Bibliographic record
Abstract
This study developed an efficient strategy for exploring novel dual-functional umami peptides with antioxidant activity from eggshell membrane (ESM), a protein-rich byproduct of the food industry, by integrating machine learning, molecular docking, molecular dynamics simulations, quantum chemistry and in vitro validations. Three novel peptides (CDDDF, WHDCHR, and CWDVYR) were identified from in silico ESM hydrolysates. The synthesized peptides exhibited pronounced umami taste with low recognition thresholds (0.04–0.10 mM), significant umami-enhancing effects, and potent free radical scavenging and reducing capacities. Quantum chemical analyses indicated that Cys, Asp, and Trp residues served as key active sites for both umami recognition and antioxidant activities. Molecular docking and dynamics simulations revealed that these peptides could stably bind to key amino acids in T1R1/T1R3 through hydrogen-bonding and hydrophobic interactions. This work provides robust guidance for discovering functional umami peptides from sustainable protein sources and offers novel insights into their structure-activity relationships and taster mechanisms. • Three dual-functional umami peptides were identified by an in silico- guided strategy. • The ESM peptides exhibited umami taste, umami enhancement and antioxidant effects. • The molecular mechanism was explored by molecular simulation and quantum chemistry. • The residues C, D, W were key active sites for both umami and antioxidant function. • Hydrogen bonding and hydrophobic interactions play key roles in umami perception.
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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 | 0.001 |
| 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 | 0.001 |
| 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".