From Molecular Dynamics to Taste Sensory Perception: A Comprehensive Study on the Interaction of Umami Peptides with the T1R1/T1R3-VFT Receptor.

The research on the umami receptor-ligand interaction is crucial for understanding umami perception. This study integrated molecular simulations, sensory evaluation, and biosensor technology to analyze the interaction between umami peptides and the umami receptor T1R1/T1R3-VFT. Molecular dynamics simulations were used to investigate the dissociation process of seven umami peptides with the umami receptor T1R1/T1R3-VFT, and by calculating the potential mean force curve using the Jarzynski equation, it was found that the binding free energy of umami peptide is between -58.80 and -12.17 kcal/mol, which had a strong correlation with the umami intensity obtained by time intensity sensory evaluation. Through correlation analysis, the dissociation rate constants (0.0126-0.394 1/s) of umami peptides were found to have a great impact on umami perception. The faster the dissociation rate of umami peptides from receptors, the stronger the perceived intensity of the umami taste. This research aims to elucidate the relationship between the umami peptide-receptor interaction and umami perception, providing theoretical support for the exploration of umami perception mechanisms.

Noteworthy Consensus Effects of D/E Residues in Umami Peptides Used for Designing the Novel Umami Peptides.

Aspartic acid (D) and glutamic acid (E) play vital roles in the umami peptides. To understand their exact mechanism of action, umami peptides were collected and cut into 1/2/3/4 fragments. Connecting D/E to the N/C-termini of the fragments formed D/E consensus effect groups (DEEGs), and all fragments containing DEEG were summarized according to the ratio and ranking obtained in the above four situations. The interaction patterns between peptides in DEEG and T1R1/T1R3-VFD were compared by statistical analysis and molecular docking, and the most conservative contacts were found to be HdB_277_ARG and HdB_148_SER. The molecular docking score of the effector peptides significantly dropped compared to that of their original peptides (-1.076 ± 0.658 kcal/mol, p value < 0.05). Six types of consensus fingerprints were set according to the Top7 contacts. The exponential of relative umami was linearly correlated with ΔGbind (R2 = 0.961). Under the D/E consensus effect, the electrostatic effect of the umami peptide was improved, and the energy gap between the highest occupied molecular orbital-the least unoccupied molecular orbital (HOMO-LUMO) was decreased. The shortest path map showed that the peptides had similar T1R1-T1R3 recognition pathways. This study helps to reveal umami perception rules and provides support for the efficient screening of umami peptides based on the material richness in D/E sequences.

Bimetallic bionic taste sensor for perception of the synergistic effect of umami substances.

Synergistic effect is one of the main properties of umami substances, elucidating the synergistic effect of umami is of great significance in the food industry. In this study, a bimetallic bionic taste sensor was developed to evaluate the synergistic effect of umami substances based on the perceptual mechanism of the human taste system. The Venus flytrap domain of T1R1 which is in charge of recognizing umami ligands was employed as the sensing element and self-assembled on the bimetallic nanomaterial (MoS2-PtPd) by Au-S bonding, the binding of receptors and ligands is characterized by changes of electrical signals. The sensor had good linearity (R2 > 0.99) and wide detection range in the detection of different kinds of umami substances (amino acids, nucleotides, organic acids, umami peptides) with detection limits as low as 0.03 pM. Comparing with electronic tongues, the sensor owned multiple characteristics of human taste system and could recognize the presence of synergistic effect of umami substances in a variety of real samples. Moreover, the differences in synergistic effect at different concentrations and ratios were also explored, the findings showed that the synergistic effect was more obvious at lower concentrations and balanced ratios of multiple umami substances added. The strategy would afford a promising platform for in-depth research on the mechanism of synergistic effect and multifunctional industrial applications.

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 TastePeptides-Meta system including an umami/bitter classification model Umami_YYDS, a TastePeptidesDB database and an open-source package Auto_Taste_ML.

Taste peptides with umami/bitterness play a role in food attributes. However, the taste mechanisms of peptides are not fully understood, and the identification of these peptides is time-consuming. Here, we created a taste peptide database by collecting the reported taste peptide information. Eight key molecular descriptors from di/tri-peptides were selected and obtained by modeling screening. A gradient boosting decision tree model named Umami_YYDS (89.6% accuracy) was established by data enhancement, comparison algorithm and model optimization. Our model showed a great prediction performance compared to other models, and its outstanding ability was verified by sensory experiments. To provide a convenient approach, we deployed a prediction website based on Umami_YYDS and uploaded the Auto_Taste_ML machine learning package. In summary, we established the system TastePeptides-Meta, containing a taste peptide database TastePeptidesDB an umami/bitter taste prediction model Umami_YYDS and an open-source machine learning package Auto_Taste_ML, which were helpful for rapid screening of umami peptides.

In Vivo Detection of Tetrodotoxin in Takifugu obscurus Based on Solid-Phase Microextraction Coupled with Ultrahigh-Performance Liquid Chromatography-Tandem Mass Spectrometry.

Pufferfish is nutritious and delicious, but the tetrodotoxin (TTX) that may exist in its body poses a serious safety hazard. It is important to use scientific and effective methods to detect the TTX in pufferfish, but most of the existing methods require complex pre-treatment steps and have sample lethality. The solid-phase microextraction (SPME) technology can be used for in vivo detection due to its advantages such as no solvent demand, simple operation, and fast detection speed. In this study, the GO-PAN@PNE SPME fibers were made via a dipping method, and their extraction effect was verified in the TTX aqueous and spiked fish. The established method has good reproducibility, and the limit of detection of TTX in pufferfish was 32 ng·g-1, and the limit of quantitation was 150 ng·g-1, which can meet the detection needs of pufferfish for safe consumption. This method was used to in vivo detect the Takifugu obscurus exposed to the TTX, to determine the content of TTX in the pufferfish muscle. The detection method established in this study can relatively quickly and easily realize the in vivo detection of TTX in the pufferfish, which can provide theoretical support for improvement in the food safety level of the pufferfish.