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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. © 2022 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

• A novel rapid screening model for umami peptides was proposed and validated. • Six new lamb bone umami peptides were screened through the rapid screening model. 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 technology. 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 between 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. [ABSTRACT FROM AUTHOR]