Liu Qing, Gao Xinchang, Pan Daodong, Liu Zhu, Xiao Chaogeng, Du Lihui, Cai Zhendong, Lu Wenjing, Dang Yali, Zou Ying
State Key Laboratory for Managing Biotic and Chemical Threats to the Quality and Safety of Agro-products, College of Food and Pharmaceutical Sciences, Ningbo University, Ningbo, China.
Department of Chemistry, Tsinghua University, Beijing, China.
J Sci Food Agric. 2023 Jun;103(8):3915-3925. doi: 10.1002/jsfa.12319. Epub 2022 Nov 26.
The 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.
This 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.
The manuscript lays a rapid screening method in screening umami peptide, and nine umami peptides from porcine bone were screened and identified. © 2022 Society of Chemical Industry.
从猪骨中提取鲜味肽的传统筛选方法 labor-intensive 且耗时。本研究对鲜味肽的快速筛选方法及分子机制进行了研究。
本文表明，采用复合机器学习和分子对接的更精确快速筛选方法从猪骨中筛选潜在鲜味肽。作为参考，通过复合机器学习预测了24种已报道的鲜味肽，准确率为86.7%。在本研究中，通过UMPred-FRL、Umami-MRNN Demo筛选猪骨中潜在的鲜味肽序列，并使用分子对接进行进一步筛选。最终，通过该方法筛选并验证了9种肽为鲜味肽：LREY、HEAL、LAKVH、FQKVVA、HVKELE、AEVKKAP、EAVEKPQS、KALSEEL和KKMFETES。氢键被认为是与受体T1R3的主要相互作用力，结构域结合位点为Ser146、His121和Glu277。结果证明了机器学习辅助T1R1/T1R3受体快速筛选鲜味肽的可行性。该筛选方法不仅适用于从猪骨中筛选鲜味肽，还可能应用于其他来源。它也为鲜味肽的快速筛选提供了参考。
该手稿提出了一种筛选鲜味肽的快速筛选方法，并筛选鉴定了9种来自猪骨的鲜味肽。© 2022化学工业协会。
