Umami peptide from litopenaeus vannamei and application thereof

Abstract

The invention discloses umami peptide derived from litopenaeus vannamei and application of the umami peptide, and belongs to the technical field of biological active peptide. The number of the flavor peptides is seven, and the amino acid sequences of the flavor peptides are ASRM, RCNG, DNCY, GAEF, DEGF, EDMQF and PNRMPY respectively and are shown as SEQ ID NO.1-7 in sequence. The umami peptide can be used as or used for preparing a flavor enhancer. According to the present invention, the new umami peptide is explored from the litopenaeus vannamei meat protein sequence through the virtual screening, the molecular simulation and other technologies, such that the marine umami peptide library is enriched, the key amino acid combined with the umami peptide receptor and the action mechanism are researched, and the reference is provided for the research of the umami presentation mechanism of the peptides with different structures.

Description

Technical Field

[0001] The invention relates to umami peptides derived from Litopenaeus vannamei and applications thereof, belonging to the technical field of bioactive peptides. Background Art

[0002] Umami, as one of the five basic tastes, can enhance the deliciousness of food and make people have a stronger preference and dependence on food. Umami substances mainly include amino acids, organic acids, nucleotides and peptides. Among them, umami peptides have attracted much attention because of their natural and safe sources, natural presence in many foods, high content, excellent taste characteristics, and superior nutrition and functions. Enzymatic hydrolysis, step-by-step separation and mass spectrometry identification are the most commonly used methods for discovering umami peptides. However, the long experimental cycle and high experimental costs have hindered the rapid discovery of new umami peptides. In recent years, with the development of computer technology and the improvement of the level of chemical informatics, quantitative structure-activity relationship (QSAR) models have shown great potential in the step-by-step prediction of material properties, especially for the prediction of biological peptides. Charoenkwan et al. first developed the iUmami-SCM model in 2020, which is an umami peptide prediction model based on sequence and machine learning technology. It was developed using a simple and interpretable score card method (SCM) combined with the estimated propensity scores of 20 amino acids and 400 dipeptides. However, due to the lack of information features and the use of only a single encoding and ML classifier, its overall prediction performance is not satisfactory. After that, umami prediction models infused with more complex ML algorithms and more comprehensive feature encodings have been introduced one after another. The virtual screening strategy of umami peptides is based on machine prediction models. A large number of peptide sequences are generated by virtual enzymatic hydrolysis of proteins, which are screened by machine prediction and molecular docking methods to quickly and accurately mine umami peptides.

[0003] Umami receptors are key to umami perception. T1R1 and T1R3 are the most widely studied umami receptors, belonging to the G protein-coupled receptor family, and their taste recognition domains are called "flytrap" domains (VFTDs), which have been shown to be essential for the umami perception process. However, since the structures of T1R1 / T1R3 have not yet been resolved and the binding sites of umami peptides are not clear, many scholars have used molecular docking and modeling techniques to study the interaction between umami peptides and their corresponding receptors.

[0004] Litopenaeus vannamei (also known as white shrimp) is one of the most important commercial shrimp species in China. Its shrimp meat is rich in various amino acids and small molecule peptides, and has a strong umami flavor. However, the umami peptides in Litopenaeus vannamei have not yet been developed and utilized. Summary of the invention

[0005] In view of the above-mentioned prior art, the present invention provides several umami peptides derived from Litopenaeus vannamei and applications thereof, belonging to the technical field of bioactive peptides.

[0006] The present invention is achieved through the following technical solutions: There are 7 kinds of umami peptides derived from Penaeus vannamei, and their amino acid sequences are as follows: (1) The amino acid sequence is ASRM, as shown in SEQ ID NO.1.

[0007] (2) The amino acid sequence is RCNG, as shown in SEQ ID NO.2.

[0008] (3) The amino acid sequence is DNCY, as shown in SEQ ID NO.3.

[0009] (4) The amino acid sequence is GAEF, as shown in SEQ ID NO.4.

[0010] (5) The amino acid sequence is DEGF, as shown in SEQ ID NO.5.

[0011] (6) The amino acid sequence is EDMQF, as shown in SEQ ID NO.6.

[0012] (7) The amino acid sequence is PNRMPY, as shown in SEQ ID NO.7.

[0013] The use of the above umami peptide as or in the preparation of umami agent.

[0014] The research of the present invention discovered new umami peptides from the protein sequence of the shrimp meat of Penaeus vannamei through virtual screening, molecular simulation and other technologies, enriched the marine umami peptide library, and studied the key amino acids and action mechanisms of umami peptide receptor binding, providing a reference for studying the umami presentation mechanism of peptides with different structures.

[0015] Various terms and phrases used herein have the general meanings that are well known to those skilled in the art. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 : Structural prediction model of umami taste receptor T1R1 / T1R3.

[0017] Figure 2 : Raman diagram of T1R1 / T1R3 model.

[0018] Figure 3 : Schematic diagram of molecular docking of peptide and T1R3 receptor, including: (a) EDMQF; (b) ASRM; (c) RCNG; (d) DNCY; (e) DEGF; (f) GAEF; (g) PNRMPY.

[0019] Figure 4 : Electronic tongue radar images of synthetic peptides. DETAILED DESCRIPTION

[0020] The present invention will be further described below in conjunction with the embodiments. However, the scope of the present invention is not limited to the following embodiments. It will be appreciated by those skilled in the art that various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention.

[0021] The instruments, reagents, and materials involved in the following examples, unless otherwise specified, are all conventional instruments, reagents, and materials available in the prior art and can be obtained through regular commercial channels. The experimental methods, detection methods, etc. involved in the following examples, unless otherwise specified, are all conventional experimental methods and detection methods available in the prior art.

[0022] Virtual screening and validation of experimental umami peptides 1. Materials and Methods 1.1 Materials and reagents Chemical reagents Potassium chloride (KCl) and sodium chloride (NaCl) were purchased from Sinopharm Chemical Reagent Co., Ltd. All chemical reagents used in the experiment were of analytical grade. Seven peptides with a purity greater than 95% (ASRM, RCNG, DNCY, GAEF, DEGF, EDMQF, PNRMPY, amino acid sequences are shown in SEQ ID NO. 1 to 7, respectively) were synthesized by Shanghai Sangon Biotechnology Co., Ltd., China, and desalted. Monosodium glutamate was purchased from a supermarket with a purity of ≥99%.

[0023] 1.2 Virtual enzymatic hydrolysis of proteins The myosin sequence of Litopenaeus vannamei was retrieved from the NCBI database (NCBI reference sequence: XP_027238952.1). The "ENZYME(S) ACTION" module in the BIOPEP-UWM database was used to simulate the hydrolysis of proteins. 31 enzymes recorded in the database were selected to hydrolyze the proteins and collect theoretical peptide spectra. A python script was used to capture three to eight peptides for the next step of screening.

[0024] 1.3 Virtual screening of potential umami peptides The captured peptides were screened by three umami prediction models, namely iUmami-SCM (https: / / camt.pythonanywhere.com / iUmami-SCM), TastePeptidesDM (http: / / tastepeptides-meta.com / TPDM) and UMPred-FRL (https: / / pmlabstack .pythonanywhere.com / UMPred-FRL), and then further shortlisted as umami candidate peptides (predicted biological activity) (score > 0.5) using the PeptideRanker (https: / / distilldeep.ucd.ie / PeptideRanker / ) tool.

[0025] 1.4 Prediction of toxicity and physicochemical properties of selected peptides Potential umami peptides were further screened using the online tools ToxinPretool (non-toxic) (https: / / crdd.osdd.net / raghava / toxinpred / index.html) and Innovagen (good water solubility) (https: / / www.innovagen.com / proteomics-tools). The screening results were compared with the peptides reported in the BIOPEUWM active peptide library.

[0026] 1.5 Modeling of umami taste receptors Since human taste receptors T1R1 and T1R3 lack crystal structures, computer modeling methods were used to generate their three-dimensional structures. The amino acid sequences of T1R1 (UniProt ID: Q7RTX1) and T1R3 (UniProt ID: Q7RTX0) were obtained from the UniProt database (https: / / www.uniprot.org / ). The prediction model of the T1R1 / T1R3 heterodimer complex was mainly predicted using multimer of alphafold2.3.0. The protein conformation rationality of the Ramachandran graph was further evaluated using the SAVESv6.0 server. After verification, the homology model was used for subsequent molecular docking studies.

[0027] 1.6 Solid-phase synthesis of peptides The purity of the synthesized peptide was greater than 95.0%, and it was synthesized using the Fmoc solid phase method from Shanghai Sangong Biotechnology Co., Ltd., China. The Fmoc solid phase method is a widely recognized method for peptide synthesis.

[0028] 1.7 Sensory evaluation The sensory panel consisted of 4 males and 4 females (23–33 years old) who received sensory training according to the international standard method for training and supervising assessors (ISO 8586-1:2012). The sensory evaluation was conducted at a temperature of 25 ± 2 ◦C and a humidity of 55 ± 5%. All panelists gave their consent to participate in the sensory evaluation of this study. Panelists were strictly instructed to take a sip of the peptide solution and swirl it in their mouth for 10 seconds before spitting it out. To avoid peptide and taste residue and fatigue, panelists were instructed to clean their mouths with 50–60 mL of drinking water between the evaluation of two different samples. The detection threshold was determined by triangulation. The initial peptide solution was diluted stepwise (1, 0.5, 0.25, 0.125 mg / mL) until the panelists could not distinguish the sample from ultrapure water.

[0029] 1.8 Electronic tongue analysis of synthetic peptides The peptides and mixtures were prepared at a concentration of 0.3 mg / mL in 10 mmol / L KCl solution. Monosodium glutamate (MSG) and NaCl solutions of the same concentration were used as controls. The taste characteristics of the synthetic peptides were determined using the SA-402B Taste Analysis System. Each sample was measured four times, and the first measurement was automatically discarded.

[0030] 1.9 Molecular docking of peptides and receptors The molecular structure of the peptide was drawn using ChemDraw 20.0. The peptide molecule was optimized by MM2 force field using ChemDraw 3D 20.0 and saved in mol2 format to obtain the three-dimensional structure of the peptide. The umami receptor model constructed by 2.5 was dehydrated and hydrogenated using pymol, and the obtained three-dimensional structure of T1R1 / T1R3 was docked. The active pocket center of T1R1 / T1R3 was predicted using DeepSite. The processed receptor T1R1 / T1R3 was docked using the Autodock Vina algorithm in PyRx to simulate the interaction. The ligand and receptor were docked 8 times in total. The lowest docking Vina score of the 8 times was taken, and the interaction force and binding site between the peptide ligand and the receptor were analyzed using DiscoveryStudio 4.5.

[0031] 1.10 Statistical analysis The data were expressed as mean ± standard deviation. One-way analysis of variance and Duncan multiple comparison analysis were performed using SPSS 26 and Origin 2021. P < 0.05 was considered statistically significant.

[0032] 2. Results and Discussion 2.1 Virtual enzymatic hydrolysis of Penaeus vannamei myosin and prediction of its peptide properties Generally speaking, myofibrillar proteins account for more than half of the total amount of aquatic animal proteins, and myosin is the main component of myofibrillar proteins. Virtual enzymatic hydrolysis of white shrimp myosin is helpful to study the functional properties of its protein. 1990 peptides (non-repetitive) were captured by scripts, and the umami properties of peptides were predicted using UMPred-FRL, iUmami-SCM and TastePeptidesDM. Bioactivity prediction was performed using PeptideRanker, and Ranker scores above 0.5 were considered to have potential bioactivity. Toxicity and water solubility are also two important indicators for evaluating the application value of peptides. ToxinPred can not only predict the toxicity of peptides, but also evaluate the changes in toxicity of peptides when amino acids mutate. Finally, the Innovagen tool was used to predict the water solubility of candidate peptides. Table 1 shows the peptide sequences that meet these screening criteria.

[0033]

[0034] 2.2 Prediction of T1R1-T1R3 umami taste receptor protein model AlphaFold is a state-of-the-art protein structure prediction method developed by Google DeepMind. It is based on deep learning technology and computer vision, and predicts protein structure by simulating physical principles. The three-dimensional structure of the protein of umami taste receptor T1R1-T1R3 was constructed using AlphaFold2. The results are as follows: Figure 1 The predicted dock Q value of the complex is 0.7277, and the binding score is between 0.49 and 0.8, indicating that T1R1-T1R3 has a certain binding ability. Figure 2 The calculated Raman spectrum shows that 100% of the amino acid residues are in the reasonable area (93.9% in the optimal area, 6.0% in the acceptable area, and 0.1% in the generally allowed area), and 0.0% of the amino acid residues are in the unallowed area. Therefore, according to the evaluation principle that the reasonable area is greater than 90%, it shows that the constructed protein structure model has high reliability and can be used as a template for subsequent research.

[0035] 2.3 Molecular docking Molecular docking is a commonly used molecular simulation method to study the interaction between proteins and ligands. For the T1R1 / T1R3 heterodimer, the crystal structure of T1R1 is a closed conformation, while the crystal structure of T1R3 is an open conformation with a large enough receptor binding site to bind long-chain umami peptides. It has been shown that the identification of umami peptide taste is significantly associated with the T1R3-VFTD domain. DeepSite predicted that the center of the active pocket of T1R1-T1R3 is located within the VFTD of T1R3, which is consistent with the existing theory of umami ligand binding domains. The peptides obtained in 2.1 were further screened according to the Vina score, and the results are shown in Table 2. Generally, the lower the binding energy between the ligand and the receptor, the better the binding ability of the two. The docking results showed that there was no necessary relationship between the Vina scores of these potential umami peptides and the length of the peptides.

[0036]

[0037] 2.4 Analysis of the interaction between umami peptides and T1R3 Perform a visual analysis of the docking results from the previous step. Figure 3 The interaction between T1R3 and the peptide is shown. Figure 3 It can be seen that the peptide ligands can be well embedded in the VFTD of T1R3, and the interaction between the receptor and the peptide is mainly non-covalent, such as hydrogen bonds, van der Waals forces and anionic π bonds. The key binding residues statistics in Table 3 show that HIS145 and GLU45 are the main sites for peptides to bind to the umami receptor.

[0038]

[0039] “+” indicates the degree of binding of the ligand to the amino acid.

[0040] 2.5 Flavor properties of synthetic peptides At present, umami detection technology has its limitations, sensory evaluation is subjective, and electronic tongue technology still needs to be improved in detecting complex tastes. Therefore, combining sensory evaluation with electronic tongue technology can more comprehensively determine the taste properties of umami peptides. Peptides with molecular docking Vina scores less than -7.5 were selected for synthesis, and their sensory properties were confirmed by electronic tongue testing and sensory evaluation. The sensory evaluation results of the synthetic peptides (as shown in Table 4) showed that most peptides showed salty taste, and two peptides, ASRM and RCNG, showed a certain degree of umami. The presence of acidic taste may be due to acetic acid and free amino acid residues during the synthesis process. At the same time, members of the sensory evaluation team analyzed the taste threshold of the peptides, and the taste threshold of peptide DEGF (0.17±0.078 mM) was the lowest.

[0041]

[0042] Compared with traditional sensory evaluation, the electronic tongue uses multi-channel lipid membrane electrodes to identify different tastes, which can distinguish and quantify different tastes more objectively. Figure 4 The results of the electronic tongue test on synthetic umami peptides are shown. The results are basically consistent with the sensory evaluation. The synthetic peptides are mainly salty and sour, with a small amount of umami and bitterness. Although the five peptides DNCY, EDMQF, GAEF, PNRMPY, and DEGF did not show umami in the sensory evaluation, they all had umami in the electronic tongue test. This may be because the sample concentration of the sensory evaluation is low, which is lower than the umami threshold that people can feel and cannot be perceived by sensory members, and does not affect its research value and development potential. Compared with the control group MSG, the synthetic peptides have weaker astringency, and ASRM and RCNG have stronger umami than MSG. It is worth noting that the peptide ASRM has the most prominent salty and umami taste among the seven peptides, and has no sourness and astringency, weak bitterness, good characteristics, and good application value.

[0043] 3. Conclusion Through virtual hydrolysis and virtual screening technology, seven umami peptides in myosin of white shrimp were quickly mined, and their sensory properties were verified. The results showed that the peptide ASRM had a strong umami taste, and its taste threshold was 0.321±0.122 mg / mL. Molecular docking showed that van der Waals force and conventional hydrogen force were the main forces, and HIS145 and GLU45 of umami receptor T1R3 were the main binding sites. This study enriched the marine umami peptide library, improved the value of sequence research on white shrimp, and provided a reference for studying the umami presentation mechanism of umami peptides with different structures.

[0044] The above examples are provided to those skilled in the art to fully disclose and describe how to make and use the claimed embodiments, and are not intended to limit the scope of the disclosure herein. Modifications that are obvious to those skilled in the art will fall within the scope of the appended claims.

Claims

1. An umami peptide derived from Litopenaeus vannamei, characterized in that: The amino acid sequence is ASRM, as shown in SEQ ID NO.

1.

2. An umami peptide derived from Litopenaeus vannamei, characterized in that: The amino acid sequence is RCNG, as shown in SEQ ID NO.

2.

3. An umami peptide derived from Litopenaeus vannamei, characterized in that: The amino acid sequence is DNCY, as shown in SEQ ID NO.

3.

4. An umami peptide derived from Litopenaeus vannamei, characterized in that: The amino acid sequence is GAEF, as shown in SEQ ID NO.

4.

5. An umami peptide derived from Litopenaeus vannamei, characterized in that: The amino acid sequence is DEGF, as shown in SEQ ID NO.

5.

6. An umami peptide derived from Litopenaeus vannamei, characterized in that: The amino acid sequence is EDMQF, as shown in SEQ ID NO.

6.

7. An umami peptide derived from Litopenaeus vannamei, characterized in that: The amino acid sequence is PNRMPY, as shown in SEQ ID NO.

7.

8. Use of the umami peptide according to any one of claims 1 to 7 as or in the preparation of an umami agent.

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