Mining and Validation of Novel Umami Peptides in Non-Alcoholic Beer by Integrating Machine Learning Prediction,...

This study aimed to identify umami peptides in non-alcoholic beer and clarify their potential contribution to taste reconstruction and aftertaste improvement. Peptides were profiled by RPLC-Q-TOF-MS and screened using machine learning prediction, molecular docking, MM-GBSA analysis, and sensory validation. Under the criteria of -10logP ≥ 15 and ALC ≥ 90.00%, 2081 peptides were identified. Among them, 122 potential umami peptides were predicted, and 117 peptides were successfully docked with the T1R1/T1R3 umami receptor. The docked peptides were mainly short to medium oligopeptides, especially tetrapeptides and pentapeptides, which accounted for 40.17% and 35.90%, respectively. Based on docking score, structural diversity, and peptide length distribution, CTGAA, IDQILG, KDTHP, QRQ, and EITGR were selected as representative candidates. These peptides showed favorable receptor binding, mainly supported by hydrogen bonding, electrostatic interactions, and local hydrophobic contacts. Sensory validation further showed that the 5 peptides improved umami and aftertaste cleanliness to different degrees. Umami intensity increased by 7.58% to 22.73%, while aftertaste cleanliness increased by 5.80% to 17.39%. Among them, CTGAA showed the strongest umami enhancement, and QRQ produced the greatest improvement in aftertaste cleanliness. These results suggest that selected umami peptides may contribute to flavor reconstruction in non-alcoholic beer by enhancing umami perception and improving aftertaste quality.