Ying Liu, Shibo Xu, Yang Li, Cong Shen, Kunlun Liu
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Discovering of novel umami-enhancing peptides from Flammulina filiformis: Combining virtual screening, machine learning, molecular dynamics simulations, and sensory evaluation
This research employed integrated machine learning and bioinformatics approaches to identify umami-enhancing peptides from Flammulina filiformis, elucidate their mechanisms of umami augmentation, and validate their efficacy through sensory evaluations. Three peptides, APDNRF, FSGLDPR, and ADELF, showed synergistic or additive effects by notably enhancing the intensity by 50.87 % to 77.37 % and prolonging the duration by 60 % to 100 % of umami perception in monosodium glutamate (MSG). Moreover, molecular docking analysis revealed that these peptides strengthened both the binding affinity and interaction forces between MSG and the T1R1/T1R3 receptor complex, thereby enhancing umami perception. Further molecular dynamics simulations indicated that these peptides stabilize MSG binding to T1R1/T1R3 primarily via hydrogen bonding and hydrophobic interactions. Overall, this study offers novel insights into the rapid, efficient identification of novel umami peptides. It broadens the scope of umami peptides originating from edible mushrooms and clarifies the mechanism by which umami-enhancing peptides enhance umami perception. The findings deliver a theoretical foundation for developing mushroom-based seasoning products.
期刊介绍:
Food Chemistry publishes original research papers dealing with the advancement of the chemistry and biochemistry of foods or the analytical methods/ approach used. All papers should focus on the novelty of the research carried out.