Taste-modifying peptides and uses thereof

Abstract

This invention relates to the field of small molecule peptide technology, specifically a group of flavor peptides and their applications. The amino acid sequences of the flavor peptides are shown in SEQ ID NO:1, SEQ ID NO:2, SEQ ID NO:3, or SEQ ID NO:4. This invention identifies four flavor peptides in tomato samples that can efficiently bind to umami receptors T1R1 / T1R3 and GRM1 through electrostatic interactions. The flavor peptides of this invention can serve as natural and safe flavor enhancers, widely applicable in the food and condiment industries, and are particularly suitable for developing low-sodium health foods.

Description

Technical Field

[0001] This invention relates to the field of small molecule peptide technology, specifically a group of flavor peptides and their applications. Background Technology

[0002] Umami, as the fifth basic taste, is one of the key indicators for evaluating food quality. Currently, monosodium glutamate (MSG), the most commonly used umami enhancer, is facing limitations in its application due to potential health controversies such as "Chinese restaurant syndrome" and its high sodium content. Flavor peptides, which offer prominent umami, are natural flavor substances derived from proteins. They possess advantages such as high safety, rich flavor, and the potential to help reduce sodium consumption, making them an ideal alternative to MSG.

[0003] Currently, identified flavor peptides mainly originate from animal proteins (such as muscle protein and collagen) and microbial fermentation products, while flavor peptide resources from plant proteins are relatively scarce. In existing technologies, research on obtaining flavor peptides from tomato protein through enzymatic hydrolysis is insufficient, and traditional peptide screening methods heavily rely on time-consuming and labor-intensive purification and sensory evaluation, resulting in low efficiency and high costs. Therefore, addressing the problems mentioned in the background, those skilled in the art have screened a group of flavor peptides from tomatoes that exhibit prominent umami flavor and rich taste, which can be applied in the field of condiment preparation. Summary of the Invention

[0004] The purpose of this invention is to provide a group of flavor peptides and their applications to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] A group of flavor peptides, the amino acid sequences of which are shown in SEQ ID NO:1, SEQ ID NO:2, SEQ ID NO:3 or SEQ ID NO:4.

[0007] The above-mentioned flavor peptides are used in the preparation of food flavoring agents.

[0008] Compared with the prior art, the beneficial effects of the present invention are:

[0009] This invention discloses four flavor peptides (AGPNY, PDQGGR, QGDAVW, and DCGSIR). Molecular dynamics studies show that these peptides can bind efficiently to umami receptors T1R1 / T1R3 and GRM1 through electrostatic interactions. The flavor peptides of this invention can be used as natural and safe flavor enhancers with strong umami characteristics. They can be applied in the food and condiment industries, and are especially suitable for developing low-sodium health foods. Attached Figure Description

[0010] Figure 1 A graph showing the taste intensity values ​​of four flavor peptides;

[0011] Figure 2 Principal component analysis (PCA) score plots of electronic tongue data for four flavor peptides;

[0012] Figure 3 Taste profiles of four flavor peptides obtained from the electronic tongue system;

[0013] Figure 4 The RMSD variation curves of the system obtained from molecular dynamics simulation of four flavor peptides and T1R1 receptor;

[0014] Figure 5 The RMSD variation curves of the system obtained from molecular dynamics simulation of four flavor peptides and T1R3 receptor;

[0015] Figure 6 The RMSD variation curves of the system obtained from molecular dynamics simulation of four flavor peptides and GRM1 receptor;

[0016] Figure 7 This diagram illustrates the interactions between the four flavor peptides and the residues with charged side chains in T1R1.

[0017] Figure 8 This diagram illustrates the interactions between four flavor peptides and the charged side chain residues in T1R3.

[0018] Figure 9 This diagram illustrates the interactions between four flavor peptides and residues with charged side chains in GRM1.

[0019] Figure 10 The image shows the flavor profiles obtained from sensory experiments on four flavor peptides. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] Please see Figures 1 to 10 The present invention provides:

[0022] Example 1: Screening method for flavor peptides

[0023] (1) Virtual enzyme digestion library construction: The proteome sequence of the target plant (tomato variety Heinz 1706) was obtained from a public database (such as NCBI). The PeptideCutter tool was used to perform virtual enzyme digestion of three enzymes (trypsin, chymotrypsin, and pepsin). The peptide length was set to no more than 6 amino acids, and a total of 639 peptides were obtained to construct a peptide library.

[0024] (2) Preliminary screening of ADMET characteristics: The peptides in the peptide library were screened for bioactivity (using the PeptideRanker tool to screen peptides with a score >0.5), toxicity (using the ToxinPred 3.0 tool to screen non-toxic peptides), water solubility (>60%) and hemolytic activity (<10%), and peptides that did not meet the requirements were removed.

[0025] (3) Umami potential machine learning prediction: Using a machine learning-based flavor peptide prediction platform (such as Mlp4Umami), the umami intensity of peptides that have passed the initial screening is predicted, and candidate peptides with high scores (such as >0.5) are selected.

[0026] (4) Fine screening via molecular docking:

[0027] a. Receptor preparation: Obtain the three-dimensional structures of the umami receptor T1R1 / T1R3 heterodimer and GRM1 homodimer (which can be predicted by homology modeling or AlphaFold, etc.).

[0028] b. Docking calculation: The candidate peptides were molecularly docked with the above receptors (using software such as AutoDock Vina). The binding sites were concentrated in the Venus Flytrap domain of the receptor. Peptides with high binding energy to the receptor (e.g., ≤ -6.5 kcal / mol) were screened. A total of four flavor peptides were screened: AGPNY (SEQ ID NO:1), PDQGGR (SEQ ID NO:2), QGDAVW (SEQ ID NO:3), and DCGSIR (SEQ ID NO:4).

[0029] The full name of AGPNY is: H-Ala-Gly-Pro-Asn-Tyr-OH;

[0030] The full name of PDQGGR is: H-Pro-Asp-Gln-Gly-Gly-Arg-OH;

[0031] The full name of QGDAVW is: H-Gln-Gly-Asp-Ala-Val-Trp-OH;

[0032] The full name of DCGSIR is: H-Asp-Cys-Gly-Ser-Ile-Arg-OH.

[0033] (5) Synthesis and purification of peptides: AGPNY, PDQGGR, QGDAVW and DCGSIR were synthesized by Wuhan Dangang Biotechnology Co., Ltd. using solid phase synthesis. The peptides were purified by HPLC with a purity greater than 95%, and the molecular weight was confirmed by mass spectrometry.

[0034] Example 2: Experimental Verification of Electronic Tongue

[0035] Experimental verification: The candidate peptides finally screened by chemical synthesis were quantitatively verified for umami intensity using an electronic tongue (e-tongue) system. The response value on the AAE sensor was measured. If the response value was higher than the neutral point (0), it was confirmed as a flavor peptide (strong umami). The specific method is as follows:

[0036] Prepare 0.1 mg / mL aqueous solutions of AGPNY, PDQGGR, QGDAVW, and DCGSIR, respectively, and adjust the pH to 6-7 with NaOH or HCl. Measure the results using an Insent SA402B electronic tongue system (Japan), with a 30 mM KCl + 0.3 mM tartaric acid solution as a reference solution.

[0037] Figure 1 The taste intensity values ​​of the four flavor peptides were measured. AGPNY showed a response value of 2.44 ± 0.3 on the AAE sensor (representing umami, 0.56 for monosodium glutamate at 2 mg / ml), significantly higher than the neutral point, with a "Richness" response value of 0.12 ± 0.02. PDQGGR showed a response value of 1.42 ± 0.3 on the AAE sensor, with a "Richness" response value of 0.15 ± 0.02. QGDAVW showed a response value of 0.92 ± 0.3 on the AAE sensor, with a "Richness" response value of 0.11 ± 0.02. DCGSIR showed a response value of 1.25 ± 0.3 on the AAE sensor, with a "Richness" response value of 0.14 ± 0.02. Figure 1 It can be seen that all four flavor peptides have a certain umami intensity, among which AGPNY peptide has the highest umami intensity.

[0038] Regarding the interpretation of the data in the graph, such as the fact that the value of umami is less than that of astringency, that the four flavor peptides can still express umami, and the appearance of negative values ​​on the coordinates, the explanations are as follows:

[0039] The reason why a high astringency value can still indicate a strong umami flavor is that the electronic tongue sensors used to test different flavors are different. They cannot make lateral comparisons and can only output a response intensity. For example, the umami intensity of the four peptides measured by the umami sensor can only be compared relatively within the umami range and cannot be compared with astringency or bitterness.

[0040] Furthermore, since each taste sensor has its own characteristics, their baselines and numerical values ​​are unique and cannot be compared horizontally. The negative values ​​of salty and sour tastes are determined by the characteristics of their respective sensors.

[0041] The taste value output by the electronic tongue is a relative value, not an absolute value. It measures the potential difference between the sample solution and the reference solution inside the sensor. Specifically:

[0042] In addition, the reference solution serves as another benchmark: before measurement, the instrument calibrates and "zeroes" the sensor using a reference solution (a standard solution with a fixed composition). The potential at this reference point is set as a baseline value.

[0043] The numerical value represents the difference: When the sensor is placed in the sample to be tested, the taste substances in the sample (such as hydrogen ions, sodium ions, etc.) will cause a change in the sensor membrane potential. The final displayed taste value (such as "-1.5") represents the difference between the sample potential and the reference solution potential.

[0044] Negative values ​​indicate that, under the current instrument calibration conditions, the intensity of the sour or salty taste in the sample is lower than the "reference level" represented by the reference solution. Negative values ​​for sourness and saltiness are usually not due to sensor malfunction, but rather determined by its unique "relative measurement" principle and preset taste baselines (tastelessness points). The reference solution for the INSENT electronic tongue is composed of potassium chloride (KCl) and tartaric acid. This means that this reference solution itself already has a slight salty and sour taste. Therefore, the instrument sets the sour and salty response values ​​under this condition as the "tastelessness point," i.e., sourness -13 and saltiness -6. For other tastes such as bitterness and sweetness, the reference solution is tasteless, so their tastelessness points are set to 0.

[0045] Figure 2 Principal component analysis (PCA) score maps of electronic tongue data from different samples were generated. Through PCA, eight complex taste sensory indicators were successfully reduced to two principal dimensions (PC1 and PC2). A planar plot was used to clearly show the distribution and differences of four flavor peptides (AGPNY, DCGSIR, PDQGGR, and QGDAVW) in the "taste space", indicating the position of the four flavor peptides on the new "taste map" composed of PC1 and PC2.

[0046] X-axis (PC1, contribution rate 47.37%): This is the most important taste dimension that distinguishes samples; AGPNY (red) is located alone on the left (negative value area), while the other three taste peptides are concentrated on the right (positive value area), indicating that the taste characteristics of AGPNY are different from the other three.

[0047] Y-axis (PC2, contribution rate 19.11%): This is the second most important taste dimension; QGDAVW (yellow) is mainly distributed in the upper part, while DCGSIR (green) and PDQGGR (blue) are distributed in the lower part, indicating that there are secondary differences between them.

[0048] Figure 2 DCGSIR (green) and PDQGGR (blue) are very similar in taste; AGPNY (red) is far removed from all other groups and is a unique category.

[0049] Table 1: Eigenvectors of Principal Components

[0050] PC1 PC2 PC3 PC4 PC5 sour 0.44 0.34 0.04 0.21 0.42 bitterness 0.13 -0.59 0.31 0.52 -0.03 astringent 0.47 0.07 0.24 -0.22 0.10 Bitter aftertaste -0.11 0.37 -0.62 0.46 -0.17 Astringent aftertaste -0.35 0.18 0.34 0.55 0.39 Umami -0.47 -0.29 -0.02 -0.09 -0.10 Richness -0.13 0.50 0.58 0.03 -0.58 Salty -0.44 0.17 0.13 -0.34 0.53

[0051] Table 2: Eigenvalues ​​and Contribution Rates of Principal Components

[0052] principal component Eigenvalues Contribution rate (%) PC1 3.79 47.37 PC2 1.53 19.11 PC3 1.38 17.19 PC4 0.96 12.06 PC5 0.24 3.05

[0053] Table 1-2 shows the principal component eigenvalues ​​and contribution rates, and the eigenvector table. The cumulative contribution rate of PC1 and PC2 is 47.37% + 19.11% = 66.48%. This means that the first two principal components (i.e., the figure above) capture about 2 / 3 of the variation information in the original 8 taste indicators. This two-dimensional figure is quite representative.

[0054] PC1's core driving metrics:

[0055] In the diagram, the X-axis represents PC1, and the Umami (umami) feature vector (-0.47) indicates that the direction of umami is opposite to that of PC1. In other words, on the PC1 ruler, the further to the left (negative direction) you go, the stronger the umami sensation of the flavor peptides becomes.

[0056] Positive correlation (higher value on the right): Astringency (0.47), Sourness (0.44). This means that the more right a flavor peptide is in the graph, the stronger the perception of astringency and sourness.

[0057] Negative correlation (higher values ​​on the left): Umami (-0.47), Saltiness (-0.44). This means that the more leftward a flavor peptide is in the graph, the stronger the perception of umami and saltiness.

[0058] Conclusion: PC1 is essentially a "sour and astringent vs. umami and salty" dimension. AGPNY is located on the left, indicating that its umami and salty characteristics are prominent; while other flavor peptides are on the right, indicating that their sour and astringent characteristics are more obvious.

[0059] Similarly, the Y-axis represents PC2.

[0060] PC2's core driving metrics:

[0061] Positive correlation (high upper value): Richness (0.50).

[0062] Negative correlation (high value at the bottom): Bitterness (-0.59).

[0063] Conclusion: PC2 is essentially a "richness vs. bitterness" dimension. QGDAVW is located at the top, indicating a higher richness; while DCGSIR and PDQGGR are located at the bottom, indicating a stronger bitterness.

[0064] Figure 3 Taste characteristics of four flavor peptides obtained from the electronic tongue system; radar plots show the intensity of eight taste attributes (sourness, bitterness, astringency, aftertaste B, aftertaste A, umami, richness, and saltiness) for the flavor peptides AGPNY, DCGSIR, PDQGGR, and QGDAVW. Each axis represents a specific taste attribute, showing different taste characteristic patterns across the four samples.

[0065] Example 3: Molecular Dynamics Simulation

[0066] Figure 4-6 The RMSD variation curves of the system obtained from molecular dynamics simulations of different samples reflect the structural stability of the peptide-receptor complex during the 200 ns simulation process. If the curve fluctuations are stable, it indicates that the protein remains stable during the simulation process and can be used for subsequent studies on the interaction between peptide chains and proteins.

[0067] The specific process is as follows:

[0068] Molecular dynamics simulations were performed using the Amber 22 package with the docked complex as the initial conformation. For the flavor peptide ligand, the generalized amber force field (GAFF) was applied. Atomic charges were determined using the Restrained ElectroStatic Potential (RESP) method, and electrostatic potential data were calculated using gaussian16 software at the B3LYP / 6-31G* theoretical level. The generation and integration of force field parameters (including bonding and non-bonding parameters) were performed using the antechamber program. The protein was set as the receptor, and the ff14SB force field was applied. An octahedral box was created around the system, filled with a TIP3P water model, and the gap between the box and the solute surface was set to 1.0 nm. Periodic conditions were used to mitigate edge effects. To ensure the system was electroneutrally neutral, sodium or chloride ions were added to the box to balance the charge based on charge calculations.

[0069] Dynamical simulations of the three complex systems were performed using the pmemd.cuda module in AMBER 22. First, two energy minimization operations were performed. The first energy minimization involved 10,000 steps of steepest descent algorithm followed by 10,000 steps of conjugate gradient algorithm. The second energy minimization involved 20,000 steps of steepest descent algorithm followed by 20,000 steps of conjugate gradient algorithm. Furthermore, a 150 ps NPT ensemble equilibrium was established to ensure the system remained stable. Finally, a 200 ns molecular dynamics simulation was performed, using the SHAKE algorithm to constrain all hydrogen-containing bonds and setting the cutoff distance for non-bonded interactions to 1 nm.

[0070] After trajectories were simulated, the gmx_MMPBSA tool in the Gromacs 2023.4 software package was used. This module can calculate the binding free energy using the molecular mechanics / generalized Born surface area (MM / GBSA) method by setting parameters, and estimate the contribution of each residue to the decomposition of the free energy. A 20 ns interval with relatively stable root mean square deviation (RMSD) was selected from the trajectory, and a conformation frame was taken every 20 ps, ​​for a total of 1000 frames used for calculation. The resulting ΔGvdw represents the van der Waals interaction in the binding free energy; ΔGelec is the electrostatic interaction; ΔEGB is the polar solvation free energy; ΔESURF is the nonpolar solvation free energy; ΔGgas is the molecular mechanics term; ΔGsolv is the solvation energy; and ΔGtotal is the total binding free energy.

[0071] Table 3: MM-GBSA calculation of the binding free energy (kJ·mol) of four flavor peptides with T1R1 -1 )

[0072] Energy contribution T1R1-AGPNY T1R1-PDQGGR T1R1-DCGSIR T1R1-QGDAVW ΔGvdw -119.99 -186.73 -228.82 -137.69 ΔGelec -721.07 -942.02 -548.10 -120.79 ΔEGB 782.86 1004.57 704.37 251.20 ΔESURF -18.91 -30.96 -31.71 -20.92 ΔGgas -841.06 -1128.75 -776.92 -258.52 ΔGsolv 763.91 973.57 672.66 230.28 GTOTAL -77.15 -155.14 -104.26 -28.24

[0073] Table 4: MM-GBSA calculation of the binding free energy (kJ·mol) of four flavor peptides with T1R3 -1 )

[0074] Energy contribution T1R3-AGPNY T1R3-PDQGGR T1R3-DCGSIR T1R3-QGDAVW ΔGvdw -256.18 -216.39 -161.25 -284.80 ΔGelec -390.32 -686.09 -810.81 -814.66 ΔEGB 522.62 818.47 900.77 1054.70 ΔESURF -34.14 -32.17 -25.77 -36.65 ΔGgas -646.55 -902.53 -972.06 -1099.51 ΔGsolv 488.44 786.29 874.99 1018.00 GTOTAL -158.07 -116.23 -97.06 -81.46

[0075] Table 5: MM-GBSA calculation of the binding free energy (kJ·mol) of four flavor peptides to GRM1 -1 )

[0076] Energy contribution GRM1-AGPNY GRM1-PDQGGR GRM1-DCGSIR GRM1-QGDAVW ΔGvdw -156.9 -151.50 -200.12 -124.51 ΔGelec -579.94 -1526.32 -803.11 -123.51 ΔEGB 609.60 1580.33 938.42 232.46 ΔESURF -26.56 -23.47 -31.54 -16.15 ΔGgas -736.84 -1677.82 -1003.23 -248.06 ΔGsolv 582.99 1556.82 906.88 216.31 ΔGTOTAL -153.84 -121.00 -96.35 -31.71

[0077] As shown in Table 3-5, the total binding free energy of the protein as a whole and the umami peptides reveals that the electrostatic interaction between the umami peptides and the umami receptors is significantly greater than that of van der Waals interactions. Considering that both ends of the short peptides are charged, and some short peptides also contain other residues with charged side chains, this strong electrostatic interaction is easily understood. Based on these findings, it is preliminarily speculated that the localization of short peptides in the protein, or the strength of their binding affinity, is related to this charge-charge interaction.

[0078] After completing a 200 ns molecular dynamics simulation, the conformations of each complex were extracted from the simulated trajectory, and the interaction between the peptide chain and the umami receptor was analyzed. The results are as follows:

[0079] Figure 7 This diagram illustrates the interactions between four flavor peptides and the charged side chains of T1R1. A represents flavor peptide AGPNY, which primarily forms stable charge-charge interactions with ASP147 and ARG277 residues within its binding site. B represents flavor peptide PDQGGR, which binds to Asp108 and Asp218 residues surrounding its substrate binding site, forming a strong charge-charge interaction. C represents flavor peptide DCGSIR, whose carboxyl terminus can be positioned between Arg151 and Arg277 in the positively charged region of the protein shell, forming a strong electrostatic interaction that prevents the peptide chain from penetrating the binding site. D represents flavor peptide QGDAVW, whose negatively charged amino terminus is close to the charged residue aggregation region within the substrate binding site, potentially indicating a strong charge-dipole interaction.

[0080] Figure 8This diagram illustrates the interactions between four flavor peptides and the charged side chains of T1R3. A represents flavor peptide AGPNY, whose carboxyl terminus is relatively open, forming a hydrogen bond with the outer Asn68. In this binding mode, the flavor peptide is very close to the His145-Glu148 region, allowing for significant interactions with these residues. B represents flavor peptide PDQGGR, which exhibits strong charge-charge interactions with Arg54, Glu45, and Asp307. C represents flavor peptide DCGSIR, where the positive charge on the carboxyl terminus is relatively open, inserting into the negatively charged pocket on the Asp190 and Glu301 side, and exhibiting strong charge-charge interactions with these two residues. D represents flavor peptide QGDAVW, whose amino terminus inserts into the P2 pocket of T1R3 (containing amino acid residues Glu105, His145, Glu148, and Ser147), but is simultaneously repelled by the positively charged Arg64 residue near the P2 pocket.

[0081] Appendix Figure 9 This diagram illustrates the interactions between four flavor peptides and the charged side chains of GRM1. In A, flavor peptide AGPNY exhibits charge-charge interactions only with Arg71 and Arg323 at its carboxyl terminus, while its amino terminus, located near Glu233, forms a stable interaction. In B, flavor peptide PDQGGR shows charge-charge interactions between its positively charged side chain at the carboxyl terminus and Asp208, Glu292, and Asp318 surrounding the binding site, while its amino terminus, located near Asp191, forms a strong interaction. In C, flavor peptide DCGSIR shows the side chain charge of its carboxyl terminus interacting with the negatively charged Asp208 and Asp318 in the outer region of the binding pocket. The two terminal charges of the flavor peptide QGDAVW are close to each other and can form a strong interaction. The amino terminus is located near the negatively charged Glu233 and Glu292 and forms a charge-charge interaction. However, it is far away from these two residues, so the electrostatic attraction is also weaker. D represents the two terminal charges of the flavor peptide QGDAVW are close to each other and can form a charge-charge interaction. The overall molecule is ring-shaped, which makes it difficult for it to penetrate into the positively charged pocket of GRM1. However, it will be located between Arg71 in the positively charged region of the protein and Glu292 in the negatively charged region, so that the terminal charge forms an ion-ion interaction with these two residues respectively.

[0082] After generating the 200 ns Amber trajectory for each system, the analysis of the binding free energy of different systems and the specific interaction between the flavor peptides and umami receptors showed that all four flavor peptides have a certain binding strength with the three receptors.

[0083] Example 4: Sensory Experiment Verification

[0084] Ten participants were selected to conduct a food sensory evaluation experiment. The ten participants consisted of five men and five women, aged 25-45, with no history of smoking or alcohol abuse. Before the formal experiment, participants were required to evaluate the intensity of five basic taste standard solutions: i) sourness – citric acid solution (0.8 mg / mL); ii) umami – monosodium glutamate solution (3.5 mg / mL); iii) bitterness – L-isoleucine solution (2.5 mg / mL); iv) saltiness – sodium chloride solution (3.5 mg / mL); v) sweetness – sucrose solution (10 mg / mL). Through repeated practice, participants became familiar with the sensory characteristics of each solution and were able to describe their sensory experiences in a standardized manner to ensure consistency in the experimental evaluation. Those who passed the selection process were eligible to participate in the formal experiment.

[0085] In the formal sensory evaluation experiment, a double-blind testing method was adopted. The ambient temperature was controlled at (22.5±2.5)℃, and natural light conditions were used. All synthetic peptide samples were prepared into solutions with a concentration of 5 mg / mL, and the pH was maintained between 6 and 7 (adjusted using 1M citric acid or 1M sodium hydroxide). An intensity scale of 0-5 was used for evaluation, where "no perception" was rated as 0 points and "strong perception" as 5 points. The standard solution was set as the baseline score of 2.5 points. The experimental flavor peptides were assigned four-digit random codes. Participants were required to gargle with the sample for 10 seconds and then spit it out. After each evaluation, participants rinsed their mouths twice with 50 mL of ultrapure water and rested for 2 minutes to prevent taste fatigue. Each flavor peptide was evaluated three times. All experimental procedures followed the ethical guidelines for human trials outlined in the Declaration of Helsinki of the World Medical Association.

[0086] result:

[0087] As shown in the figure, after the double-blind experiment was completed, the umami intensity was obtained based on the collected data. The order of umami intensity was: AGPNY > PDQGGR > DCGSIR > QGDAVW. This result is highly consistent with the electronic tongue. Among them, according to the subjects' feedback, AGPNY has a relatively stronger umami flavor, and the sour and bitter flavors are not prominent, and the aftertaste lasts for a short time.

[0088] Electronic tongue sensors detect umami-related electrochemical signals based on electrode response values, which makes it difficult to simulate the dynamic regulation of peptide flavor characteristics by salivary enzymatic hydrolysis. Furthermore, the allosteric activation mechanism of human taste receptors T1R1 / T1R3 is specifically regulated by peptide sequences, leading to mechanistic differences between instrument detection and sensory perception.

[0089] Therefore, based on comprehensive sensory evaluation, electronic tongue and molecular docking data, AGPNY, PDQGGR, QGDAVW and DCGSIR all have strong umami flavor and have the unique advantage of enhancing flavor in food systems without masking the original flavor of the matrix. They are expected to be used as core flavor peptides for functional verification and structure-activity relationship studies.

[0090] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A group of flavor peptides, characterized in that, The amino acid sequence of the flavor peptide is shown in SEQ ID NO:1, SEQ ID NO:2, SEQ ID NO:3 or SEQ ID NO:

4.

2. The application of the flavor peptide as described in claim 1 in the preparation of food flavoring agents.

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