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

Abstract

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.

Conserved Sites and Recognition Mechanisms of T1R1 and T2R14 Receptors Revealed by Ensemble Docking and Molecular Descriptors and Fingerprints Combined with Machine Learning

“… a Note: index 0–9 is derived from Liang’s work, index 10–12 is derived from Richter’s work, index 13–21 is derived from Liu’s work, index 22–29 is derived from Shan’s work, index 30–38 is derived from Zhang’s work, index 39–47 is derived from Liang’s work, and index 48–53 is derived from Cui’s work …”

Exploring the Relationship between Small Peptides and the T1R1/T1R3 Umami Taste Receptor for Umami Peptide Prediction: A Combined Approach

“…These findings align with results from earlier molecular docking studies involving umami peptides with T1R1 and T1R3. 5,44,45 Hydrophilic/Hydrophobic Combinations of Peptides in T1R1/T1R3 Pockets. Our hypothesis posits that the bonding patterns of peptides are influenced by the hydrophobicity of each amino acid position in the peptide.…”

“…Notably, the occurrence of pi-cation interactions and pi-pi stacking is minimal in both the T1R1 and T1R3 pockets, suggesting that these interactions may not be the primary driving forces in the binding of umami peptides to these pockets. These findings align with results from earlier molecular docking studies involving umami peptides with T1R1 and T1R3. ,, …”

A Machine Learning Method to Identify Umami Peptide Sequences by Using Multiplicative LSTM Embedded Features

“…To further verify the effectiveness and robustness of the model, we collected 91 wet-experiment verified umami peptide sequences reported in the latest literature [ 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 ]. These empirical umami peptide sequences constituted the dataset UMP-VERIFIED, which was then used to test state-of-the-art methods, including UMPred-FRL [ 11 ] and iUP-BERT [ 12 ] for comparison to iUmami-DRLF.…”

“…The following supporting information can be downloaded at: , Figures S1–S3: iUmami-DRLF web server interface; Table S1: Results of 10-fold cross-validation and independent testing about SMOTE; Table S2: Prediction-probability results of the three models and 91 wet-experiment validated umami peptide sequences [ 5 , 6 , 11 , 56 , 59 , 60 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 ].…”