A rapid selection strategy for umami peptide screening based on machine learning and molecular docking
文献类型： 外文期刊
作者： Li, Chen 1 ; Hua, Ying 2 ; Pan, Daodong 1 ; Qi, Lulu 1 ; Xiao, Chaogeng 3 ; Xiong, Yongzhao 1 ; Lu, Wenjing 3 ; Dang, Yali 1 ; Gao, Xinchang 4 ; Zhao, Yufen 4 ;
作者机构： 1.Ningbo Univ, Coll Food & Pharmaceut Sci, Ningbo 315211, Peoples R China
2.Zhejiang Chinese Med Univ, Sch Pharmaceut Sci, Hangzhou 310053, Zhejiang, Peoples R China
3.Zhejiang Acad Agr Sci, Inst Food Sci, Hangzhou 310022, Zhejiang, Peoples R China
4.Tsinghua Univ, Dept Chem, Beijing 100084, Peoples R China
5.Ningbo Univ, Inst Drug Discovery Technol, Ningbo 315211, Peoples R China
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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