Prediction, molecular docking and identification of novel umami hexapeptides derived from Atlantic cod (<i>Gadus morhua</i>)

Zhu, Wenhui; He, Wei; Wang, Fei; Bu, Ying; Li, Xuepeng; Lǐ, Jiànróng

doi:10.1111/ijfs.14655

Cited by 27 publications

(37 citation statements)

References 28 publications

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“…Zhu et al. (2021) selected five hexapeptides to dock with a T1R1/T1R3 homologous model established by the Swiss model, and evaluated the three peptides with the lowest docking energy using electronic tongue and organoleptic evaluation. The results showed that all three peptides were umami, and the umami threshold of peptide INKPEL was 0.25 mg/ml.…”

Section: Screening Of Umami Peptidesmentioning

confidence: 99%

“…and umami peptide. Zhu et al (2021) digested the myosin of Gadus morhua with a virtual enzyme and predicted the potential biological activities of 48 hex-peptides. Yu et al (2021) obtained 747 peptides after virtual enzymolysis of myosin by pepsin, trypsin, and chymotrypsin.…”

Section: Peptides By Computer Simulation Digestionmentioning

confidence: 99%

“…Zhu et al. (2021) digested the myosin of Gadus morhua with a virtual enzyme and predicted the potential biological activities of 48 hex‐peptides. Yu et al.…”

Section: Identification Of the Peptidesmentioning

confidence: 99%

“…The classical method of using column chromatography to separate and purify umami peptides, however, is usually time‐consuming and expensive (Sun et al., 2017), which has limited the development of industrialization of umami peptides. Recently, computer technology, such as molecular docking (Yu et al., 2021; Zhao et al., 2021; Zhu et al., 2021) and machine learning (Charoenkwan, Yana, Nantasenamat, et al., 2020), has been applied to the screening and identification of umami peptides to promote their development.…”

Section: Introductionmentioning

confidence: 99%

“…In a study by, however, only the umami dipeptide could enter the VFTD domain of T1R1, and it was difficult for other umami peptides to enter this domain. This result may have been caused by different homology modeling Zhu et al (2021). selected five hexapeptides to dock with a T1R1/T1R3 homologous model established by the Swiss model, and evaluated the three peptides with the lowest docking energy using electronic tongue and organoleptic evaluation.…”

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confidence: 99%

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Research progress in the screening and evaluation of umami peptides

Gao

Pan

et al. 2022

Comp Rev Food Sci Food Safe

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Umami is an important element affecting food taste, and the development of umami peptides is a topic of interest in food‐flavoring research. The existing technology used for traditional screening of umami peptides is time‐consuming and labor‐intensive, making it difficult to meet the requirements of high‐throughput screening, which limits the rapid development of umami peptides. The difficulty in performing a standard measurement of umami intensity is another problem that restricts the development of umami peptides. The existing methods are not sensitive and specific, making it difficult to achieve a standard evaluation of umami taste. This review summarizes the umami receptors and umami peptides, focusing on the problems restricting the development of umami peptides, high‐throughput screening, and establishment of evaluation standards. The rapid screening of umami peptides was realized based on molecular docking technology and a machine learning method, and the standard evaluation of umami could be realized with a bionic taste sensor. The progress of rapid screening and evaluation methods significantly promotes the study of umami peptides and increases its application in the seasoning industry.

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Section: Screening Of Umami Peptidesmentioning

confidence: 99%

Section: Peptides By Computer Simulation Digestionmentioning

confidence: 99%

“…Zhu et al. (2021) digested the myosin of Gadus morhua with a virtual enzyme and predicted the potential biological activities of 48 hex‐peptides. Yu et al.…”

Section: Identification Of the Peptidesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Research progress in the screening and evaluation of umami peptides

Gao

Pan

et al. 2022

Comp Rev Food Sci Food Safe

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

et al. 2022

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BACKGROUND: 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.RESULTS: 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. CONCLUSION: The manuscript lays a rapid screening method in screening umami peptide, and nine umami peptides from porcine bone were screened and identified.

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