Umami peptides derived from Litopenaeus vannamei and their applications

Abstract

This invention discloses umami peptides derived from Litopenaeus vannamei and their applications, belonging to the field of bioactive peptide technology. Seven umami peptides are described, with amino acid sequences as follows: ASRM, RCNG, DNCY, GAEF, DEGF, EDMQF, and PNRMPY, as shown in SEQ ID NO. 1–7. The invention also discusses the application of these umami peptides as or in the preparation of umami enhancers. This invention utilizes virtual screening and molecular simulation techniques to discover new umami peptides from the protein sequences of Litopenaeus vannamei, enriching the marine umami peptide library. Furthermore, it investigates the key amino acids and mechanisms of action of umami peptide receptor binding, providing a reference for studying the umami presentation mechanisms of peptides with different structures.

Description

Technical Field

[0001] This invention relates to umami peptides derived from Litopenaeus vannamei and their applications, belonging to the field of bioactive peptide technology. Background Technology

[0002] Umami, one of the five basic tastes, enhances the flavor of food, leading to stronger liking and dependence on it. Umami substances mainly include amino acids, organic acids, nucleotides, and polypeptides. Among them, umami peptides have attracted much attention due to their natural and safe origin, natural presence in many foods, high content, excellent taste characteristics, and superior nutritional and functional properties. Enzymatic hydrolysis, stepwise separation, and mass spectrometry are the most commonly used methods for discovering umami peptides. However, the long experimental cycle and high experimental costs hinder the rapid discovery of novel umami peptides. In recent years, with the development of computer technology and the improvement of cheminformatics, quantitative structure-activity relationship (QSAR) models have shown great potential in the stepwise prediction of material properties, especially for the prediction of bioactive peptides. Charoenkwan et al. first developed the iUmami-SCM model in 2020, a umami peptide prediction model based on sequence and machine learning techniques. It was developed using a simple and interpretable scorecard method (SCM) combined with estimated propensity scores for 20 amino acids and 400 dipeptides. However, due to insufficient information features and the use of only a single code and ML classifier, its overall prediction performance was not satisfactory. Subsequently, umami prediction models infused with more complex ML algorithms and more comprehensive feature encodings emerged. The virtual screening strategy for umami peptides is based on machine prediction models. It generates a large number of peptide sequences through virtual enzymatic digestion of proteins, and then screens them using methods such as machine prediction and molecular docking to quickly and accurately discover umami peptides.

[0003] Umami receptors are key to umami perception. T1R1 and T1R3 are the most extensively studied umami receptors, belonging to the G protein-coupled receptor family. Their taste recognition domain, known as the "fly trap" domain (VFTD), has been proven crucial 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 unclear, many researchers have used molecular docking and modeling techniques to study the interactions between umami peptides and their corresponding receptors.

[0004] Litopenaeus vannamei (also known as the whiteleg shrimp) is one of the most important commercial shrimp species in China. Its meat is rich in various amino acids and small peptides, resulting in a strong umami flavor. However, the umami peptides found 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 their applications, belonging to the field of bioactive peptide technology.

[0006] This invention is achieved through the following technical solution:

[0007] There are 7 umami peptides derived from Litopenaeus vannamei, and their amino acid sequences are as follows:

[0008] (1) The amino acid sequence is ASRM, as shown in SEQ ID NO.1.

[0009] (2) The amino acid sequence is RCNG, as shown in SEQ ID NO.2.

[0010] (3) The amino acid sequence is DNCY, as shown in SEQ ID NO.3.

[0011] (4) The amino acid sequence is GAEF, as shown in SEQ ID NO.4.

[0012] (5) The amino acid sequence is DEGF, as shown in SEQ ID NO.5.

[0013] (6) The amino acid sequence is EDMQF, as shown in SEQ ID NO.6.

[0014] (7) The amino acid sequence is PNRMPY, as shown in SEQ ID NO.7.

[0015] The above-mentioned umami peptides are used as or in the preparation of umami agents.

[0016] This invention utilizes virtual screening, molecular simulation, and other techniques to discover novel umami peptides from the protein sequence of Litopenaeus vannamei, enriching the marine umami peptide library. It also investigates the key amino acids and mechanisms of action of umami peptide receptor binding, providing a reference for studying the umami presentation mechanisms of peptides with different structures.

[0017] The various terms and phrases used in this invention have their general meanings known to those skilled in the art. Attached Figure Description

[0018] Figure 1 : Predictive model of the structure of umami receptor T1R1 / T1R3.

[0019] Figure 2 Raman diagram of the T1R1 / T1R3 model.

[0020] Figure 3 : Schematic diagram of molecular docking between peptide and T1R3 receptor, where (a) EDMQF; (b) ASRM; (c) RCNG; (d) DNCY; (e) DEGF; (f) GAEF; (g) PNRMPY.

[0021] Figure 4Radar image of an electronic tongue for synthetic peptides. Detailed Implementation

[0022] The present invention will be further described below with reference to embodiments. However, the scope of the present invention is not limited to the following embodiments. Those skilled in the art will understand that various changes and modifications can be made to the present invention without departing from the spirit and scope thereof.

[0023] Unless otherwise specified, the instruments, reagents, and materials used in the following embodiments are all conventional instruments, reagents, and materials already available in the prior art and can be obtained through legitimate commercial channels. Unless otherwise specified, the experimental methods and detection methods used in the following embodiments are all conventional experimental methods and detection methods already available in the prior art.

[0024] Virtual screening and validation of experimental umami peptides

[0025] 1. Materials and Methods

[0026] 1.1 Materials and Reagents

[0027] Potassium chloride (KCl) and sodium chloride (NaCl) were purchased from Sinopharm Chemical Reagent Co., Ltd. All chemical reagents used in the experiment were analytical grade. Seven polypeptides (ASRM, RCNG, DNCY, GAEF, DEGF, EDMQF, PNRMPY, amino acid sequences shown in SEQ ID NO. 1–7) with a purity greater than 95% were synthesized by Shanghai Sangon Biotech Co., Ltd., and then desalted. Monosodium glutamate (MSG) was purchased from a supermarket with a purity ≥99%.

[0028] 1.2 Virtual enzymatic digestion of proteins

[0029] The myosin sequence of Litopenaeus vannamei was obtained from the NCBI database (NCBI reference sequence: XP_027238952.1). Protein hydrolysis was simulated using the "ENZYME(S) ACTION" module in the BIOPEP-UWM database. Thirty-one enzymes recorded in the database were selected to digest the protein, and theoretical peptide profiles were collected. A Python script was used to extract tripeptides to octapeptides for further screening.

[0030] 1.3 Virtual screening of potential umami peptides

[0031] After the captured peptides were screened by three umami prediction models—iUmami-SCM (https: / / camt.pythonanywhere.com / iUmami-SCM), TastePeptidesDM (http: / / tastepeptides-meta.com / TPDM), and UMPed-FRL (https: / / pmlabstack.pythonanywhere.com / UMPred-FRL)—they were further shortlisted as umami candidate peptides (predicted bioactivity) using the PeptideRanker tool (https: / / distilldeep.ucd.ie / PeptideRanker / ) (score > 0.5).

[0032] 1.4 Prediction of the toxicity and physicochemical properties of the selected peptides

[0033] 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 peptides reported in the BIOPEUWM bioactive peptide library.

[0034] 1.5 Modeling of umami receptors

[0035] Since the human taste receptors T1R1 and T1R3 lack crystal structures, their three-dimensional structures were generated using computer modeling. 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 T1R1 / T1R3 heterodimeric complex prediction model primarily utilized the multimer of alphafold 2.3.0 for prediction. The protein conformational rationality of the Ramachandran diagram was further evaluated using the SAVESv6.0 server. After validation, the homology model was used for subsequent molecular docking studies.

[0036] Solid-phase synthesis of 1,6-peptides

[0037] The synthesized peptides had a purity greater than 95.0% and were synthesized using the Fmoc solid-phase method developed by Shanghai Sango Biotechnology Co., Ltd., China. The Fmoc solid-phase method is a widely accepted peptide synthesis method.

[0038] 1.7 Sensory Evaluation

[0039] The sensory panel consisted of four men and four women (aged 23–33 years) who received sensory training according to the international standard method for training and supervising evaluators (ISO 8586-1:2012). Sensory evaluations were conducted at 25 ± 2 °C and 55 ± 5% humidity. All panel members agreed to participate in the sensory evaluations of this study. Panel members were strictly instructed to take a small sip of the peptide solution, swirl it in their mouths for 10 seconds, and then spit it out. To avoid peptide and taste residue and fatigue, panel members were instructed to clean their mouths with 50–60 mL of drinking water between evaluations of two different samples. The detection threshold was determined using the triangulation test. The initial peptide solution was progressively diluted (1, 0.5, 0.25, 0.125 mg / mL) until panel members could no longer distinguish the sample from ultrapure water.

[0040] 1.8 Electronic tongue analysis of synthetic peptides

[0041] 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 an SA-402B taste analysis system. Each sample was measured four times, with the first measurement automatically discarded.

[0042] Molecular docking of 1.9 peptide with receptor

[0043] The molecular structure of the peptide was drawn using ChemDraw 20.0. After MM2 force field optimization using ChemDraw 3D 20.0, the peptide molecule was saved in mol2 format, yielding the three-dimensional structure. The umami receptor model constructed using PyMOL 2.5 was dehydrated and hydrogenated, and the resulting T1R1 / T1R3 three-dimensional structure 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 interactions. The ligand and receptor were docked a total of 8 times. The lowest Vina score from the 8 dockings was taken, and DiscoveryStudio 4.5 was used to analyze the interaction forces and binding sites between the peptide ligand and receptor.

[0044] 1.10 Statistical Analysis

[0045] Data are expressed as mean ± standard deviation. One-way ANOVA and Duncan's multiple comparison analysis were performed using SPSS 26 and Origin 2021. P < 0.05 was considered statistically significant.

[0046] 2. Results and Discussion

[0047] 2.1 Virtual enzymatic hydrolysis of myosin from Litopenaeus vannamei and prediction of its polypeptide properties

[0048] Generally, myofibrillar proteins account for more than half of the total protein composition in aquatic animals, and myosin is the main component of myofibrillar proteins. Virtual enzymatic digestion of myosin from Litopenaeus vannamei helps in studying its functional properties. 1990 peptides (non-repeating) were obtained using a script, and their umami properties were predicted using UMPred-FRL, iUmami-SCM, and TastePeptidesDM. Bioactivity was predicted using PeptideRanker; a Ranker score higher than 0.5 was considered to indicate potential bioactivity. Toxicity and water solubility are also two important indicators for evaluating the application value of peptides. ToxinPred can not only predict peptide toxicity but also assess changes in peptide toxicity due to amino acid mutations. Finally, the water solubility of candidate peptides was predicted using the Innovagen tool. Table 1 shows the peptide sequences that meet these screening criteria.

[0049]

[0050] 2.2 T1R1-T1R3 umami receptor protein model prediction

[0051] AlphaFold is a state-of-the-art protein structure prediction method from Google DeepMind. Its principle is based on deep learning technology and computer vision, predicting protein structure by simulating physical principles. The three-dimensional structures of the umami receptors T1R1-T1R3 were constructed using AlphaFold2, and the results are as follows: Figure 1 As shown in the figure, the predicted dock Q value of the complex is 0.7277, and the binding fraction is between 0.49 and 0.8, indicating that T1R1-T1R3 has a certain binding capacity. Figure 2 The calculated Raman spectrum shows that 100% of the amino acid residues are in the reasonable region (93.9% in the optimal region, 6.0% in the acceptable region, and 0.1% in the generally permissible region), while 0.0% are in the unacceptable region. Therefore, based on the evaluation principle that the reasonable region is greater than 90%, the constructed protein structure model has high reliability and can be used as a template for subsequent research.

[0052] 2.3 Molecular docking

[0053] Molecular docking is a commonly used molecular simulation method for studying the interactions between proteins and ligands. For the T1R1 / T1R3 heterodimer, T1R1 exhibits a closed crystal structure, while T1R3 has an open crystal structure with a sufficiently large receptor binding site to bind long-chain umami peptides. It has been demonstrated that the identification of umami peptide flavor is significantly correlated with the T1R3-VFTD domain. DeepSite prediction showed that the active pocket center of T1R1-T1R3 is located within the VFTD of T1R3, consistent with existing theories of umami ligand binding domains. Peptides obtained in section 2.1 were further screened based on Vina scores, and the results are shown in Table 2. Generally, the lower the binding energy between the ligand and receptor, the better the binding affinity. The docking results showed no necessary relationship between the Vina scores of these potential umami peptides and peptide length.

[0054]

[0055] 2.4 Interaction analysis between umami peptides and T1R3

[0056] Perform a visual analysis of the docking results from the previous step. Figure 3 The interaction forces between T1R3 and the peptide were demonstrated. (By...) Figure 3 It is evident that peptide ligands can be well intercalated into the VFTD of T1R3, and the interaction forces between the receptor and the peptide are mainly non-covalent, such as hydrogen bonds, van der Waals forces, and anionic π bonds. Table 3 shows that HIS145 and GLU45 are the main binding residue sites for peptides and umami receptors.

[0057]

[0058] "+" indicates the degree of binding between the ligand and the amino acid.

[0059] 2.5 Flavor Properties of Synthetic Peptides

[0060] Currently, umami detection technology has its limitations, sensory evaluation is subjective, and electronic tongue technology still needs improvement in detecting complex tastes. Therefore, combining sensory evaluation with electronic tongue technology can more comprehensively determine the taste characteristics of umami peptides. Peptides with a molecular docking Vina score less than -7.5 were selected for synthesis, and their sensory characteristics were confirmed through electronic tongue testing and sensory evaluation. The sensory evaluation results of the synthesized peptides (as shown in Table 4) indicate that most peptides exhibited a salty taste, while ASRM and RCNG peptides 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. Simultaneously, the sensory evaluation team analyzed the taste thresholds of the peptides, with peptide DEGF showing the lowest taste threshold (0.17 ± 0.078 mM).

[0061]

[0062] Compared to traditional sensory evaluation, the electronic tongue uses multi-channel lipid membrane electrodes to identify different tastes, enabling it to more objectively distinguish and quantify different flavors. Figure 4 The results of the electronic tongue assay for synthetic umami peptides were presented. The results were largely consistent with the sensory evaluation. The synthetic peptides were primarily salty and sour, with minor amounts of umami and bitterness. Although five peptides—DNCY, EDMQF, GAEF, PNRMPY, and DEGF—did not exhibit umami in the sensory evaluation, they all showed umami in the electronic tongue assay. This may be because the sample concentrations in the sensory evaluation were low, below the human threshold for umami, and therefore imperceptible to sensory participants; this does not affect the research value or development potential. Compared to the control group (MSG), the synthetic peptides showed weaker astringency, while ASRM and RCNG exhibited stronger umami. Notably, among the seven peptides, ASRM showed the most prominent salty and umami flavors, with no sourness or astringency, and a weak bitterness, demonstrating good characteristics and significant application value.

[0063] 3. Conclusion

[0064] Seven umami peptides from the myosin of Litopenaeus vannamei were rapidly identified using virtual hydrolysis and virtual screening techniques, and their sensory properties were verified. Results showed that peptide ASRM possesses a strong umami flavor, with a flavor threshold of 0.321 ± 0.122 mg / mL. Molecular docking revealed that van der Waals forces and conventional hydrogen forces are the main interactions, and HIS145 and GLU45 of the umami receptor T1R3 are the main binding sites. This study enriches the marine-derived umami peptide library, enhances the value of sequence studies of Litopenaeus vannamei, and provides a reference for investigating the umami presentation mechanisms of umami peptides with different structures.

[0065] The above embodiments are provided to those skilled in the art to fully disclose and describe how the claimed implementations can be carried out and used, and are not intended to limit the scope of the disclosure herein. Modifications that will be obvious to those skilled in the art will be within the scope of the appended claims.

Claims

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

1.

2. The use of the umami peptide according to claim 1 as or in the preparation of umami agents.

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