Unveiling dual-functional umami peptides with antioxidant effects from eggshell membrane byproducts: A machine learning-guided approach integrating molecular simulation and quantum chemistry

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 <i>in vitro</i> validations. Three novel peptides (CDDDF, WHDCHR, and CWDVYR) were identified from <i>in silico</i> 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.