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

Unraveling novel umami peptides from yeast extract (Saccharomyces cerevisiae) using peptidomics and molecular interaction modeling

Yeast extract is increasingly becoming an attractive source for unraveling novel umami peptides that are healthier and more nutritious than traditional seasonings. In the present study, a strategy for screening novel umami peptides was established using mass spectrometry-based peptidomics combined with molecular interaction modeling, emphasizing on smaller peptides than previously reported. Four representative novel umami peptides of FE, YDQ, FQEY, and SPFSQ from yeast extract (Saccharomyces cerevisiae) were identified and validated by sensory evaluation, with thresholds determined as 0.234 ± 0.045, 0.576 ± 0.175, 0.327 ± 0.057 and 0.456 ± 0.070 mmol/L, respectively. Hydrogen and ionic bonds were the main characteristic interactions between the umami peptides and the well-recognized receptor T1R1/T1R3, in which Asp 110, Thr 112, Arg 114, Arg 240, Lys 342, and Glu 264 were the key sites in ligand-receptor recognition. Our study provides accurate sequences of umami peptides and molecular interaction mechanism for the umami effect.

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

Umami peptides are known for enhancing the taste experience by binding to oral umami T1R1 and T1R3 receptors. Among them, small peptides (composed of 2-4 amino acids) constitute nearly 40% of reported umami peptides. Given the diversity in amino acids and peptide sequences, umami small peptides possess tremendous untapped potential. By investigating 168,400 small peptides, we screened candidates binding to T1R1/T1R3 through molecular docking and molecular dynamics simulations, explored bonding types, amino acid characteristics, preferred binding sites, etc. Utilizing three-dimensional molecular descriptors, bonding information, and a back-propagation neural network, we developed a predictive model with 90.3% accuracy, identifying 24,539 potential umami peptides. Clustering revealed three classes with distinct logP (-2.66 ± 1.02, -3.52 ± 0.93, -2.44 ± 1.23) and asphericity (0.28 ± 0.12, 0.26 ± 0.11, 0.25 ± 0.11), indicating significant differences in shape and hydrophobicity (P < 0.05) among potential umami peptides binding to T1R1/T1R3. Following clustering, nine representative peptides (CQ, DP, NN, CSQ, DMC, TGS, DATE, HANR, and STAN) were synthesized and confirmed to possess umami taste through sensory evaluations and electronic tongue analyses. In summary, this study provides insights into exploring small peptide interactions with umami receptors, advancing umami peptide prediction models.

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

Taste peptides, as an important component of protein-rich foodstuffs, potentiate the nutrition and taste of food. Thereinto, umami- and bitter-taste peptides have been ex tensively reported, while their taste mechanisms remain unclear. Meanwhile, the identification of taste peptides is still a time-consuming and costly task. In this study, 489 peptides with umami/bitter taste from TPDB (http://tastepeptides-meta.com/) were collected and used to train the classification models based on docking analysis, molecular descriptors (MDs), and molecular fingerprints (FPs). A consensus model, taste peptide docking machine (TPDM), was generated based on five learning algorithms (linear regression, random forest, gaussian naive bayes, gradient boosting tree, and stochastic gradient descent) and four molecular representation schemes. Model interpretive analysis showed that MDs (VSA_EState, MinEstateIndex, MolLogP) and FPs (598, 322, 952) had the greatest impact on the umami/bitter prediction of peptides. Based on the consensus docking results, we obtained the key recognition modes of umami/bitter receptors (T1Rs/T2Rs): (1) residues 107S-109S, 148S-154T, 247F-249A mainly form hydrogen bonding contacts and (2) residues 153A-158L, 163L, 181Q, 218D, 247F-249A in T1R1 and 56D, 106P, 107V, 152V-156F, 173K-180F in T2R14 constituted their hydrogen bond pockets. The model is available at http://www.tastepeptides-meta.com/yyds.

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

Umami peptides enhance the umami taste of food and have good food processing properties, nutritional value, and numerous potential applications. Wet testing for the identification of umami peptides is a time-consuming and expensive process. Here, we report the iUmami-DRLF that uses a logistic regression (LR) method solely based on the deep learning pre-trained neural network feature extraction method, unified representation (UniRep based on multiplicative LSTM), for feature extraction from the peptide sequences. The findings demonstrate that deep learning representation learning significantly enhanced the capability of models in identifying umami peptides and predictive precision solely based on peptide sequence information. The newly validated taste sequences were also used to test the iUmami-DRLF and other predictors, and the result indicates that the iUmami-DRLF has better robustness and accuracy and remains valid at higher probability thresholds. The iUmami-DRLF method can aid further studies on enhancing the umami flavor of food for satisfying the need for an umami-flavored diet.