Machine learning-based exploration of Umami peptides in Pixian douban: Insights from virtual screening, molecular docking, and post-translational modifications.

Food Chem

Food Microbiology Key Laboratory of Sichuan Province, Chongqing Key Laboratory of Speciality Food Co-Built by Sichuan and Chongqing, Xihua University, No.999 Guangchang Road, Chengdu 610039, China. Electronic address:

Published: March 2025

Pixian Doubanjiang (PXDB)'s distinctive umami profile is primarily attributed to its unique peptides; however, their structural characteristics, sensory mechanisms, and biosynthetic pathways during aging remain poorly understood. This study employed a machine learning-based approach to investigate umami peptides in 1-2 year aged PXDB. We identified 117 peptides, predicting 69 with umami potential. Sensory analysis confirmed VEGGLR's remarkably low umami threshold (0.22 mmol/L). Molecular docking further elucidated VEGGLR's interaction with T1R1/T1R3 receptors via salt bridges and hydrogen bonds, enhancing umami perception. Observed post-translational modifications, including phosphorylation and acetylation on protein N6U2M1/N6UWT4, suggest a potential regulatory role in umami peptide biosynthesis. These findings offer key molecular insights into PXDB umami development, enhancing our understanding of its flavor chemistry.

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http://dx.doi.org/10.1016/j.foodchem.2025.143672DOI Listing

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