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Rapid screening based on machine learning and molecular docking of umami peptides from porcine bone

文献类型： 外文期刊

作者： Liu, Qing 3 ; Gao, Xinchang 4 ; Pan, Daodong 3 ; Liu, Zhu 5 ; Xiao, Chaogeng 6 ; Du, Lihui 3 ; Cai, Zhendong 3 ; Lu, Wenjing 6 ; Dang, Yali 1 ; Zou, Ying 2 ;

作者机构： 1.Ningbo Univ, Coll Food & Pharmaceut Sci, State Key Lab Managing Biot & Chem Threats Qual &, Ningbo 315211, Zhejiang, Peoples R China

2.Chinese Med Univ, Affiliated Hosp 2, Hangzhou 310005, Zhejiang, Peoples R China

3.Ningbo Univ, Coll Food & Pharmaceut Sci, State Key Lab Managing Biot & Chem Threats Qual &, Ningbo, Peoples R China

4.Tsinghua Univ, Dept Chem, Beijing, Peoples R China

5.Zhejiang Inst Food & Drug Control, Qual & Res Management Dept, Hangzhou, Peoples R China

6.Zhejiang Acad Agr Sci, Inst Food Sci, Hangzhou, Peoples R China

7.Chinese Med Univ, Affiliated Hosp 2, Hangzhou, Zhejiang, Peoples R China

关键词： umami peptides; rapid screening; machine learning; molecular docking; porcine bone

期刊名称：JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE （ 影响因子：4.125； 五年影响因子：4.096 ）

ISSN： 0022-5142

年卷期：

页码：

收录情况： SCI

摘要： BACKGROUNDThe traditional screening method for umami peptide, extracted from porcine bone, was labor-intensive and time-consuming. In this study, the rapid screening method and molecular mechanism of umami peptide was investigated. RESULTSThis article showed that a more precisely rapid screening method with composite machine learning and molecular docking was used to screen the potential umami peptide from porcine bone. As reference, 24 reported umami peptides were predicated by composite machine learning, with the accuracy of 86.7%. In this study, potential umami peptide sequences from porcine bone were screened by UMPred-FRL, Umami-MRNN Demo, and molecular docking was used to provide further screening. Finally, nine peptides were screened and verified as umami peptides by this method: LREY, HEAL, LAKVH, FQKVVA, HVKELE, AEVKKAP, EAVEKPQS, KALSEEL and KKMFETES. The hydrogen bonding was deemed to be the main interaction force with receptor T1R3, and domain binding sites were Ser146, His121 and Glu277. The result demonstrated the feasibility of machine learning assisted T1R1/T1R3 receptor for rapid screening umami peptides. The screening method would not only adapt to screen umami peptides from porcine bone but possibly applied for other sources. It also provided a reference for rapid screening of umami peptides. CONCLUSIONThe manuscript lays a rapid screening method in screening umami peptide, and nine umami peptides from porcine bone were screened and identified. (c) 2022 Society of Chemical Industry.

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