卵殻膜副産物由来の抗酸化作用を有するデュアル機能性うま味ペプチドの解明：分子シミュレーションと量子化学を統合した機械学習誘導アプローチ

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.