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This study utilized integrated sensory-guided, machine learning, and bioinformatics strategies identify umami-enhancing peptides from , investigated their mechanism of umami enhancement, and confirmed their umami-enhancing properties through sensory evaluations and electronic tongue. Three umami-enhancing peptides (APDGLPTGQ, SDDGFQ, and GLGDDL) demonstrated synergistic/additive effects by significantly enhancing umami intensity and duration in monosodium glutamate (MSG). Furthermore, molecular docking showed that these umami-enhancing peptides enhanced both the binding affinity and interaction forces between MSG and the T1R1/T1R3 receptor system, thereby enhancing umami perception. Molecular dynamics simulations further indicated that the presence of umami-enhancing peptides stabilized the binding of MSG to T1R1/T1R3, primarily via hydrogen bonding and hydrophobic interactions. These findings clarified the mechanism by which umami-enhancing peptides potentiate umami and suggested new strategies for reducing MSG and sodium intake, while expanding the repertoire of umami peptides derived from edible mushrooms.
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http://dx.doi.org/10.1016/j.fochx.2025.102964 | DOI Listing |
Food Chem X
August 2025
College of Light Industry and Food Engineering, Guangxi University, Nanning, 530004, China.
This study utilized integrated sensory-guided, machine learning, and bioinformatics strategies identify umami-enhancing peptides from , investigated their mechanism of umami enhancement, and confirmed their umami-enhancing properties through sensory evaluations and electronic tongue. Three umami-enhancing peptides (APDGLPTGQ, SDDGFQ, and GLGDDL) demonstrated synergistic/additive effects by significantly enhancing umami intensity and duration in monosodium glutamate (MSG). Furthermore, molecular docking showed that these umami-enhancing peptides enhanced both the binding affinity and interaction forces between MSG and the T1R1/T1R3 receptor system, thereby enhancing umami perception.
View Article and Find Full Text PDFData Brief
October 2025
Faculty of Food Science and Nutrition, Universiti Malaysia Sabah, Jalan UMS, 88400 Kota Kinabalu, Sabah, Malaysia.
With the growing demand for high-throughput analyses that can detect diverse molecules with varying physicochemical properties in shorter times, researchers are increasingly focused on developing or modifying analytical methods. This is particularly relevant in the food, pharmaceutical/nutraceutical, cosmetic, agricultural, and environmental industries. This study aimed to modify, establish, and validate a high-performance liquid chromatography method with ultraviolet detection (HPLC-UV) for the simultaneous determination of disodium guanylate (GMP) and disodium inosinate (IMP) in mushrooms, using as a model.
View Article and Find Full Text PDFJ Agric Food Chem
August 2025
College of Food Science and Technology, Shanghai Ocean University, Shanghai 201306, China.
are edible fungi with a unique aroma and umami flavor. The current study sought to identify novel umami peptides in while investigating their umami-enhancing properties. We conducted a virtual screening of 12 potential umami peptides from the ultrasound-assisted hydrolysate of , and five potential umami peptides were finally obtained.
View Article and Find Full Text PDFFood Chem
November 2025
Hubei Engineering Research Center for Protection and Utilization of Special Biological Resources in the Hanjiang River Basin, College of Life Science, Jianghan University, Wuhan 430056, Hubei, China. Electronic address:
This investigation identified umami-enhancing peptides derived from Pleurotus ostreatus and elucidated their interaction mechanisms via molecular simulations. We identified six peptide candidates with potential umami-enhancing properties through a comprehensive virtual screening process. Sensory evaluations revealed that these umami peptides vary in taste characteristics, with detection thresholds ranging from 0.
View Article and Find Full Text PDFFood Chem
August 2025
School of Information, Beijing Wuzi University, Beijing 101126, China. Electronic address:
This study screened and designed umami peptides using deep learning model and module substitution strategies. The predictive model, which integrates pre-training, enhanced feature, and contrastive learning module, achieved an accuracy of 0.94, outperforming other models by 2-9 %.
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