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

Chen Li1, Ying Hua2, Daodong Pan1

  • 1College of Food and Pharmaceutical Sciences, Ningbo University, Ningbo 315211, China.

Food Chemistry
|October 16, 2022
PubMed
Summary

A new rapid screening model for umami peptides was developed using peptidomics, machine learning, and molecular docking. This method successfully identified six novel umami peptides from lamb bone extract, enabling high-throughput screening.

Keywords:
Lamb boneMachine learningMolecular dockingUmami peptide

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Area of Science:

  • Food Science
  • Biotechnology
  • Computational Chemistry

Background:

  • Umami peptides are recognized for their nutritional value and flavor.
  • Current methods for screening umami peptides are inefficient and not high-throughput.

Purpose of the Study:

  • To design and validate a novel rapid screening model for umami peptides.
  • To identify novel umami peptides from lamb bone extract using the developed model.

Main Methods:

  • Utilized peptidomics for initial peptide identification.
  • Applied machine learning algorithms for rapid screening.
  • Employed molecular docking technology to analyze peptide-target interactions.

Main Results:

  • Successfully developed a novel rapid screening model for umami peptides.
  • Identified six novel umami peptides from lamb bone aqueous extract.
  • Molecular docking revealed hydrogen bonding and electrostatic interactions with the T1R3 subunit at GLU277 and SER146.

Conclusions:

  • The developed model is feasible and effective for high-throughput screening of umami peptides.
  • The findings provide a basis for understanding the binding mechanisms of umami peptides.
  • This approach accelerates the discovery of new umami peptides with potential applications.