A rapid selection strategy for umami peptide screening based on machine learning and molecular docking

文献类型： 外文期刊

第一作者： Li, Chen

作者： Li, Chen;Pan, Daodong;Qi, Lulu;Xiong, Yongzhao;Dang, Yali;Hua, Ying;Xiao, Chaogeng;Lu, Wenjing;Gao, Xinchang;Zhao, Yufen;Zhao, Yufen

作者机构：

关键词： Machine learning; Molecular docking; Umami peptide; Lamb bone

期刊名称：FOOD CHEMISTRY （ 影响因子：8.8； 五年影响因子：8.6 ）

ISSN： 0308-8146

年卷期： 2023 年 404 卷

页码：

收录情况： SCI

摘要： Umami peptides have been the focus of umami studies in recent years because of their high nutritional value and flavor activity. However, the existing screening methods of umami peptides were cumbersome, complex, time-consuming and laborious, and it was difficult to achieve high-throughput screening. In this study, a novel umami peptide rapid screening model was designed and by using lamb bone aqueous extract as raw material, through the step-by-step screening of peptidomics, machine learning methods, and molecular docking technol-ogy. Results showed that six novel peptides about lamb bones were obtained, which verified the feasibility of the model and could be used for high-throughput screening of umami peptides. Results of molecular docking be-tween umami peptide and T1R3 subunit revealed that the main interaction forces were hydrogen bonding and electrostatic interaction, and the key binding sites were GLU277 and SER146. It provides the basis for studying the binding mechanism of umami peptide.

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