Identification of Three Novel Umami Peptides from Metagenomics of Traditional Fermented Fish, Suanyu, and Receptor Binding Mechanism via the Graph Neural Network-Based Model and Molecular Dynamics Simulation

Fermented fish products are vital sources of umami peptides. In this study, a hierarchical graph attention network-based model was developed to identify candidate umami peptides. Via an integrated approach combining metagenomics, molecular docking, attention weight analysis, molecular dynamics simulations, and experimental validation, three novel umami peptides (GYSSYK, LYSDSK, and TRTKASY) were identified from the Suanyu system, a traditional fermented fish product. It was revealed that T1R1 and T1R3 could form stable complexes with these peptides involving critical residues: GLU301, ARG277, LYS328, SER384, ASP147, GLN278, and HIS71. In sensory evaluation, candidate peptides showed high umami properties with umami threshold values of 0.28 (±0.14) mg/mL. Overall, this study presents a hierarchical graph attention network-based screening methodology for the rapid screening and in-depth study of umami peptides.