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

Abstract

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,

Identifying Umami Peptides Specific to the T1R1/T1R3 Receptor via Phage Display

“…As shown in Table , 14 valid amino acid sequences were obtained, and the toxicity of peptides was further predicted by the support vector machine (SVM) model for ToxinPred (an online bioinformatics tools, ), indicating that those peptides were non-toxic . Meanwhile, the umami taste of the 14 peptides was predicted using a consensus model, the taste peptide docking machine (TPDM) () . The probability section was between 0 and 1, and the higher value of the confidence probability (≥0.5) indicated the potent umami taste of peptides.…”

“…The FASTA sequence of the human T1R1/T1R3 receptor was searched based on the Uniprot Protein Database () (entry numbers are Q7RTX1 and Q7RTX0), and the three-dimensional (3D) structure of T1R1/T1R3-VFT was built by trRosetta . Next, the semi-flexible docking was conducted to evaluate the possible binding models, which were sorted by the docking energy.…”

“…The model of T1R1/T1R3 VFT (Uniprot ID: Q7RTX1 and Q7RTX0) was constructed based on Cui et al’s work for molecular docking with the screened potential umami peptides . The models of T1R1-VFT and T1R3-VFT were established by trRosetta (), and their template modeling scores (TM-scores) were above 0.7, which were 0.769 and 0.852, respectively .…”

“…The expression and refolding method of T1R1-VFT protein were adopted based on previous studies. 25,31 The identification of umami peptides was through a complete screening procedure (Figure 1). The first part of the experiment involved two rounds of negative screening to eliminate non-specific adsorption of the phages.…”

“…21−23 Our group has also applied T1R1-VFT to systematically analyze the binding characteristics with typical umami substances and explore its potential applications in taste biosensors. 24,25 Therefore, whether this phage display technology can be applied to the field of umami peptides screening has always brought our attention.…”