Screening and identification method of anti-hyperuricemia oat active peptide and application of anti-hyperuricemia oat active peptide

Abstract

The invention belongs to the technical field of polypeptiomics and molecular biology, and discloses a multi-omics-based anti-hyperuricemia oat active peptide screening and identification method and application thereof, oat grains are used as a raw material to extract RNA, after high-throughput transcriptome sequencing, oat reference genomes are compared, and the high-throughput transcriptome sequencing is carried out to obtain the high-throughput transcriptome-based anti-hyperuricemia oat active peptide. Quantifying the expression quantity of the protein coding gene to obtain an oat grain protein sequence; the preparation method comprises the following steps: screening oat active peptide components by utilizing high-throughput virtual enzymolysis, Peptide Ranker and ADME / T, and screening an anti-hyperuricemia active polypeptide from an oat protein source by virtue of a network pharmacology technology; a molecular docking and hyperuricemia in-vitro chemical method model is adopted to verify the actual action effect of the oat active peptide in hyperuricemia; a coarse oat active peptide mixture is prepared through enzymolysis of oat protein, and the in-vitro hyperuricemia resisting effect of an oat protein enzymolysis object is evaluated; and identifying the component polypeptide with the strongest uric acid inhibition effect by using ultra-high performance liquid chromatography high-resolution mass spectrometry.

Description

Technical Field

[0001] The present invention belongs to the field of peptide omics and molecular biology technology, and in particular, relates to a multi-omics-based screening and identification method for anti-hyperuricemia oat active peptides and its application. This technology is represented by oat active peptides, and aims to apply this screening and identification method to other cereal active peptides; however, only the screening and identification methods and processes can be expanded, and the specific effects are determined by the characteristics of the cereals themselves, that is, the anti-hyperuricemia effect of oat peptides is obtained by analysis and verification of the present invention. Background Art

[0002] As an important part of human diet, cereals not only provide basic nutrients, but also contain a variety of bioactive substances, which have a positive impact on health. As an important component of cereal bioactive ingredients, cereal active peptides have health effects such as anti-inflammatory, antibacterial, antioxidant and cardiovascular health protection. The biological activities of cereal peptides depend on the amino acid composition, sequence, length and structural characteristics of the peptides. Short peptides in cereal peptides usually have high biological activity. Oligopeptides are small molecule compounds formed by a small number of amino acids connected by peptide bonds. They are absorbed and metabolized quickly in organisms and are the main factors for exerting various biological functions. At present, the development of short peptides with biological activity is still in the exploratory stage. The development progress of cereal short peptides is achieving positive results in many fields, and it is expected to be more widely used in food, feed, biomedicine and biotechnology in the future.

[0003] Oats (Avena.Sativa), as a plant-based food rich in protein, phytochemicals and dietary fiber, are considered to be one of the healthiest grains. Oat protein has attracted much attention due to its high content, high quality and unique amino acid composition. Oat active peptides are a series of small molecule peptides extracted from oat protein through a specific enzymatic hydrolysis process. They are easily absorbed by the human body and have a variety of physiological functions and therapeutic properties, including but not limited to anti-diabetes, antioxidant, anti-fatigue, immunomodulatory, anti-hypertension, anti-thrombosis, hypocholesterolemia and other properties. Oat peptides have shown their potential in the field of chronic disease rehabilitation, and can provide small molecule-level nutrition, explore and expand the application of oat active peptides in anti-hyperuricemia and gout. Hyperuricemia is a metabolic syndrome caused by purine metabolism disorders. When purine metabolism in the body is abnormal or uric acid excretion is reduced, the uric acid level in the blood increases, exceeding the excretion capacity of the kidneys, resulting in uric acid accumulation in the blood and tissues. Hyperuricemia itself is not directly equivalent to gout, but long-term hyperuricemia increases the risk of gout because when uric acid levels are too high, uric acid forms crystals in the joints, causing gout-related symptoms such as gouty arthritis. Currently, drugs widely used to treat hyperuricemia, such as allopurinol and febuxostat, have been found to have major toxic side effects such as allergic reactions, rashes, and kidney disease. Studies have shown that natural active substances are effective in treating hyperuricemia, have diverse sources, and have few side effects, such as peptides extracted from plants, natural protease hydrolysates, and probiotics. Therefore, it is of great significance and market value to develop safe and effective anti-hyperuricemia active ingredients from natural food raw materials.

[0004] Since most non-model organism genome research data are incomplete, transcriptomic research has become an important means to explain the function of specific genes in the genome of the species and to obtain sufficient genetic information to meet the mining of effective components of proteins and peptides. From a multi-omics perspective based on transcriptomes, high-throughput mining of biologically active peptide molecules in the overall oat protein sequence, including proteins and peptides with very low or even no expression abundance, is performed. The conventional method of screening active peptides from natural proteins is to use traditional enzymatic hydrolysis technology combined with a large number of tedious experimental processes to purify, identify and verify. Compared with traditional enzymatic hydrolysis, the integration of bioinformatics technology to simulate enzymatic hydrolysis can significantly shorten the time required to screen potential active peptides and reduce experimental costs, and help discover new effects of known active peptides.

[0005] Through the above analysis, the problems and defects of the prior art are as follows:

[0006] (1) Traditional screening of cereal active peptides usually requires screening a small number of peptides with biological activity from a large number of candidate peptides, which is a time-consuming and costly process. Virtual screening technology provides new opportunities for the discovery of active peptides, but ensuring that the screened peptides are effective in actual biological systems and how to accurately identify peptides with highly active biological functions from a large number of candidate peptides still require further establishment of an effective screening system.

[0007] (2) The traditional purification and identification process of active peptides usually involves complex steps, takes a long time to operate, and often requires the consumption of a large amount of raw materials and reagents, resulting in high costs. This also limits the rapid discovery and preparation of active peptides. Traditional identification methods cannot accurately identify the sequence and structure of active peptides, limiting the in-depth understanding of the function of active peptides. The application of multi-omics technology can perform qualitative and quantitative identification of all component sequences in the massive data generated by mixed peptide samples, solving problems such as difficulty in separation and purification, and low or even no activity of identified peptides.

[0008] (3) In order to obtain pure polypeptides with specific biological activities, they need to be separated by gel electrophoresis, high-performance liquid chromatography (HPLC), ion exchange chromatography and other methods. These steps are not only technically demanding, but also time-consuming and labor-intensive. Determining the sequence of a polypeptide is the key to understanding its structure and function. Traditional N-terminal sequencing methods (such as Edman degradation) have limitations in some cases. For example, when the amount of polypeptide sample is very small, the polypeptide modification is complex, or there are peptide bonds that are difficult to degrade, complete sequence information cannot be obtained. Summary of the invention

[0009] In view of the problems existing in the prior art, the present invention provides a method for screening and identifying anti-hyperuricemia oat peptides based on multi-omics prediction technology and its application, which mines and screens anti-hyperuricemia oat active peptides from a multi-omics perspective based on transcriptome, and studies the mechanism of action and application value of oat active peptides in anti-hyperuricemia.

[0010] The present invention is based on the oat seed transcript sequence, simulates its protein sequence by sequence conversion, comprehensively utilizes high-throughput virtual enzymolysis, Peptide Ranker and ADME / T to screen oat active peptide components, and uses network pharmacology technology to mine anti-hyperuricemia active peptides derived from oat protein, and then verifies the effect of oat active peptides through in vitro chemical method models, and finally identifies the components of effective active peptides through mass spectrometry technology. It includes the following steps:

[0011] Step 1: extract ribonucleotides (RNA) from oat grains as raw materials, compare with the oat reference genome after high-throughput transcriptome sequencing, and quantify the expression of protein-coding genes to obtain oat grain protein sequences;

[0012] Step 2: Use high-throughput virtual enzymatic hydrolysis, Peptide Ranker and ADME / T to screen oat active peptide components, and use network pharmacology technology to screen oat protein-derived anti-hyperuricemia active peptides;

[0013] Step 3: Use molecular docking and in vitro chemical method model of hyperuricemia to verify the actual effect of oat active peptides in hyperuricemia;

[0014] Step 4, preparing a crude oatmeal active peptide mixture by enzymatic hydrolysis of oatmeal protein, and evaluating the anti-hyperuricemia effect of oatmeal enzymatic hydrolyzate in vitro;

[0015] Step 5: Use ultra-performance liquid chromatography-high-resolution mass spectrometry to identify the component peptide with the strongest uric acid inhibition effect.

[0016] Furthermore, the oat grains in step 1 are quick-frozen in liquid nitrogen and then stored at -80°C.

[0017] Furthermore, in step 1, the oat reference genome is a reference genome corresponding to a specific variety, namely naked oats (Avena.sativa ssp.Nuda) and hulled oats (Avena.sativa), and the source of the oat reference genome related data file is the OatBioDB database.

[0018] Furthermore, in step 1, the protein coding gene expression was quantified by obtaining the counts of the gene (Gene) and the core coding (CDS) region through the Featurecounts analysis of the Subread software, and normalized using the TPM standardization formula to count the TPM of each gene expression, setting the TPM value ≧10 to output the protein sequence of the expressed gene and store it in Fasta format; the TPM calculation formula is: Where R1 is the target gene count value (the number of reads mapped to the target gene), L1 is the length of the target gene, and the target gene count value is corrected by dividing the number of reads mapped to the target transcript by the length of the target transcript; R (total) is the sum of the corrected count values ​​of all genes.

[0019] Furthermore, the screening of oat active peptide components in step 2 specifically includes:

[0020] (1) Perform high-throughput computer virtual enzymatic hydrolysis: select hydrolytic protease, set up enzyme or chemical running script in the improved version of Refine-PeptideCutter for the Fasta format file containing the parent protein sequence, generate all peptides encrypted in the protein sequence and the number of released peptide segments, and obtain a library of potential bioactive peptides produced by a specific enzyme or a combination of multiple enzymes;

[0021] (2) Prediction of potential biological activity: The peptide library after simulated enzymatic hydrolysis was selected for potential biological activity prediction. When the prediction score of peptides screened by Peptide Ranker was greater than 0.5, it was considered to have potential biological activity;

[0022] (3) ADME / T prediction: Active peptides with a Peptide Ranker score of ≥0.9 in the optimal peptide library were selected for ToxinPred toxicity prediction; Swiss ADME was used to screen oat active peptides with a Peptide Ranker score of ≥0.9 and non-toxicity, and the gastrointestinal absorption was “High” by inputting the SMILES formula. Finally, oat active peptides that were absorbed by the gastrointestinal tract and had good drug-like properties were obtained.

[0023] Further, the high-throughput computer virtual enzymolysis in step 2 specifically includes:

[0024] (1) Enzyme digestion with a single enzyme: Read the protein sequence file in Fasta format, extract the name and amino acid sequence of each protein, and save them in the dictionary Protein_dict; traverse each enzyme digestion rule according to the pre-defined enzyme digestion rules Enzyme_rules (regular expression), perform enzyme digestion operations on each protein, and use the Split function to cut the amino acid sequence into peptide segments according to the rules; count the information under each enzyme digestion rule, including the total number of peptide segments, the number of peptide segments composed of 2-9 amino acids, and the proportion of peptide segments composed of 2-9 amino acids to the total number of peptide segments;

[0025] (2) Enzyme digestion with two or three enzyme combinations: In the second step of the single enzyme digestion process, the Combinations function in the Itertools library is added to obtain the digestion rules of all two or three enzyme combinations, and further digestion is performed to obtain peptide fragments. The other processes are the same as single enzyme digestion.

[0026] Furthermore, the step 2 of screening the oat protein-derived anti-hyperuricemia active polypeptide specifically comprises:

[0027] (1) Screening of oat active peptide targets: The spatial conformations of oat active peptides constructed in ChemDraw 20.0 were uploaded to various target prediction network servers for identification and prediction, including SwissTargetPrediction and PharmMapper. SwissTargetPrediction retrieved targets in the "Homo Sapiens" mode and screened with Probability ≥ 0; PharmMapper selected targets with Normalized Fit Score ≥ 0.9 and entered Uniprot for standardization and verification, with species limited to "Homo Sapiens"; the obtained targets were removed from duplicate options to establish an oat active peptide target database;

[0028] (2) Screening of hyperuricemia-related targets: Using "Hyperuricemia" as the keyword, search the CTD, GeneCards, DisGeNet, and OMIM libraries for selection conditions greater than the median to obtain potential targets for hyperuricemia; remove duplicate values ​​from the obtained targets and merge them to establish a potential target database for hyperuricemia;

[0029] (3) Protein interaction (PPI) network construction: oat active peptides and potential targets of hyperuricemia were imported into VENNY 2.1.0 respectively to obtain the intersection targets of oat active peptides in intervening hyperuricemia. The above intersection targets were submitted to STRINGDatabase, "Homo Sapiens" and 0.4 confidence were set, the data were downloaded and imported into Cytoscape 3.9.1 software, and the built-in Network Analyzer was used for topological analysis. The minimum threshold for node connectivity, betweenness and compactness greater than the median was set to screen and extract key targets;

[0030] (4) GO function enrichment and KEGG pathway enrichment analysis: The key targets of oat active peptides for intervention of hyperuricemia were entered into Metascape, and P < 0.01 was set to analyze the main GO and KEGG metabolic pathways. The top 10 GO and top 20 KEGG pathway enrichment results were selected for data visualization on the Microbiological Information Platform;

[0031] (5) Construction of oat peptide component-target-pathway network diagram: The top 20 signal pathways ranked in the KEGG enrichment analysis related to hyperuricemia were selected, and the connection between oat active peptides and intersection targets involved in the relevant pathways was established. The network topology parameters of the effective ingredients and targets were analyzed using the built-in Network Analyzer of CytoScape3.9.1, and a network diagram of the mutual relationship of "oat active peptide-target-pathway" was constructed to determine the core targets and the main active ingredients of oat active peptides that intervene in hyperuricemia.

[0032] Furthermore, the functional verification of the oat active peptide in step 4 specifically includes:

[0033] (1) Molecular docking verification: The top five targets with the highest connectivity values ​​in the network diagram of the main components of oat active peptides and hyperuricemia targets and human xanthine oxidase (XOD) were selected for molecular docking verification;

[0034] a. Preparation of docking ligands. After constructing the two-dimensional chemical structure using ChemDraw 20.0 software, import it into Chem3D20.0 and use the MMFF94 force field to minimize the energy of the constructed oat active peptide. After converting it into pdb format, import it into AutoDock Tools-1.5.6 software and add atomic charges and assign atomic types. Set all flexible bonds to be rotatable by default and save it as a pdbqt file;

[0035] b. Preparation for docking receptor. The crystal structure of the protein was obtained from the protein RCSB database. Pymol2.6 software was used to delete irrelevant small molecules and water molecules. After conversion to pdb format, it was imported into AutoDock Tools-1.5.6 software to delete water molecules, add hydrogen atoms, and set atom types, and saved as a pdbqt file;

[0036] c. Docking pocket settings. Use Autodock vina to perform molecular docking operations on proteins and peptides, and XOD (PDBID: 2E1Q) uses pymol to construct mutants, restore valine (Val 803) to the original glutamic acid (Glu 803), and extract the ligand to create a binding pocket;

[0037] d. Determination of docking results. The binding activity is determined by the binding energy (KJ / mol). When Binging energy < 0KJ / mol, the docking can be performed in the natural state and has binding activity; when Binging energy < -5KJ / mol, the docking state is considered to be good and the binding activity is better.

[0038] (2) Evaluation of in vitro XOD inhibitory activity: 25 μL of the sample to be tested and 25 μL of XOD (final concentration of 0.05 U / mL) were added to a 96-well plate. After incubation at 37°C for 30 min, 50 μL of xanthine solution (final concentration of 0.2 mM) was added to initiate the reaction. The kinetic changes of the absorbance of the reaction system at 290 nm were recorded every 30 s at 37°C for 3 min. Allopurinol (final concentration of 40 uM), a common XOD inhibitory drug, was used as a positive control, and PBS buffer solution was used as a negative control. All the required solvents were prepared and used immediately. The XOD inhibition rate can be expressed as: Among them, VBlank is the initial reaction rate without adding sample; V Sample is the reaction rate of the added sample; the inhibitory activity of XOD is measured by IC 50 Value representation.

[0039] Furthermore, the evaluation of the anti-hyperuricemia effect of oatmeal protein hydrolysate in vitro in step 5 specifically includes:

[0040] (1) Oat pretreatment: Put the selected clean oat grains into a grinder and grind them into powder. Pass them through a 60-mesh sieve, and grind the unsieved parts again. Repeat 3-4 times. After the oat grains are thoroughly crushed and sieved, the oat powder is bagged and stored at room temperature. Weigh an appropriate amount of oat powder and put it into a beaker. Add n-hexane in a ratio of 1:2 (g / mL) and stir evenly. After sealing the beaker with plastic wrap, place it on a shaker and shake at 100r / min for 8-10 hours for defatting. After defatting, filter and dry in a fume hood. Use n-hexane for secondary water washing during filtration. After the defatted oat powder is completely dry, bag it and store it at 4°C.

[0041] (2) Oat protein extraction: Weigh an appropriate amount of defatted oat powder and put it into a beaker, add ultrapure water at a ratio of 1:18 (g / mL) and stir to mix, use 1 mol / L NaOH to adjust the pH of the solution to 10.0, put it into a water bath, and bathe it at 50°C for 2 hours. After the water bath ends, centrifuge at 4000 r / min for 20 minutes to take the supernatant, and use 1 mol / L HCl to adjust the pH to 4.0. When the protein precipitates, the solution changes from transparent light yellow to milky white. Then, centrifuge at 4000 r / min for 20 minutes to retain the precipitate. Wash the precipitate twice with pure water, adjust the pH of the solution to neutral, and vacuum freeze-dry for 48 hours to obtain oat protein freeze-dried powder;

[0042] (3) Enzymatic hydrolysis of oat protein: 100 mg of oat protein was placed in a 50 mL centrifuge tube, 15 mL of ultrapure water was added to dissolve, the pH of the solution was adjusted to 8.0 using 0.5 mol / L NaOH, proteinase K was added at a ratio of 200 U / g, and the mixture was shaken and mixed, and then placed in a constant temperature water bath for 5 h. After the water bath, the enzyme was inactivated at 95°C for 15 min to obtain oat protein hydrolyzate;

[0043] (4) Evaluation of in vitro anti-hyperuricemia effect: After the enzyme was inactivated in a water bath, the cells were centrifuged at 10,000 rpm for 10 min and the supernatant was collected for XOD inhibition assay.

[0044] Furthermore, the structural identification of the oat active peptide in step 6 specifically includes:

[0045] (1) Preparation of samples to be tested: centrifuge at 10,000 × g for 10 min, collect the supernatant, filter through a 0.22 μm PTFE membrane, and freeze-dry under vacuum. Dissolve the peptide in 1 mL of acetonitrile-water solution (acetonitrile: water = 95:5). After the solution passes through the membrane, transfer it into a sample injection bottle for analysis.

[0046] (2) Chromatographic analysis: Ultra-high performance liquid chromatography-mass spectrometry was used to identify the peptides. The samples were analyzed by ACQUITY I-Class HPLC, using ACQUITY Reverse phase liquid chromatography was performed on an HSS T3 column (2.1×50 mm, 1.8 μm); 0.1% formic acid (A) in water and 0.1% formic acid (B) in acetonitrile were used as mobile phase components, the flow rate was 0.2 mL / min, the sample load was 10 μL, and the following 10-min gradient method was used: 0-2 min, 100% A; 2-5 min, 100-50% A; 5-7 min, 50-30% A; 7-8 min, 30-100% A; 8-10 min, 100% A; the detection wavelength was 214 nm;

[0047] (3) Mass spectrometry analysis: The data generated by reversed phase liquid chromatography analysis were collected and analyzed by ESI mass spectrometer. The ESI mass spectrometer was set to positive ion mode (ESI+), mass / charge 50-1000 m / z; capillary voltage 2.5 kV; drying gas 280°C, 800 L / h; air flow 50 L / h; source temperature 120°C. For tandem mass spectrometry (MS / MS) analysis, collision energies of 5, 15, 25 and 35 V were used; the MS / MS spectra of the peptide sequences were analyzed and identified by the professional software Compass Data Analysis software.

[0048] Another object of the present invention is to provide a method for screening and identifying anti-hyperuricemia oat active peptides based on multi-omics and its application in screening and identifying anti-hyperuricemia oat active peptides based on multi-omics.

[0049] In combination with the above technical solutions and the technical problems solved, the advantages and positive effects of the technical solutions to be protected by the present invention are as follows:

[0050] First, the peptide-based approach can eliminate the process of crude peptide preparation, separation, purification and identification, and achieve rapid and accurate calculation and prediction of peptide properties. Compared with traditional laboratory enzymatic hydrolysis methods, it has the advantages of low cost, short cycle and batch production of accurate data.

[0051] The multi-omics screening method with transcriptomics as the core described in the technical solution can identify and analyze the overall protein sequences in cereal samples such as oats of different varieties and different reference genomes.

[0052] Second, as auxiliary evidence of the inventiveness of the claims of the present invention, it is also reflected in the following important aspects:

[0053] (1) The expected benefits and commercial value of the technical solution of the present invention after transformation are: obtaining oat active short peptides with high potential for anti-hyperuricemia, and identifying the polypeptide component with the strongest anti-hyperuricemia effect in the crude oat active peptide enzymatic hydrolysis mixture, providing an important lead molecule for the development of new anti-hyperuricemia polypeptide drugs. And through the idea of ​​the current technical solution, it can be expanded to the screening of effective active polypeptides of various cereals.

[0054] (2) The technical solution of the present invention fills the technical gap in the industry at home and abroad: it adopts a virtual screening step with multi-omics technology based on transcriptomics combined with network pharmacology and other methods as the core, which has a guiding role in the design and development of cereal polypeptides with disease resistance.

[0055] (3) The technical solution of the present invention overcomes technical bias: the screened polypeptides predict their targets and mechanisms of action for the biological function of combating hyperuricemia, and the binding ability of the polypeptides with the targets is verified by molecular docking. Potential active polypeptides are then synthesized by chemical synthesis methods. The activity detection efficiency of the polypeptides is higher, which can greatly improve the screening efficiency of anti-hyperuricemia active peptides and has good practical application value.

[0056] Third, the present invention provides a method for screening and identifying active peptides based on oat seed transcript sequences. This method effectively screens and identifies oat peptides with anti-hyperuricemia activity by comprehensively using high-throughput sequencing, virtual enzymolysis, network pharmacology, and in vitro experimental verification. The following is a detailed explanation of the steps of the invention, as well as the technical problems solved and the technical progress achieved:

[0057] Step 1: Oat seed transcriptome sequencing and protein sequence acquisition

[0058] Technical issues: Traditional methods for obtaining protein sequences usually rely on gene cloning and expression, which is time-consuming and costly.

[0059] Technological progress: Through high-throughput transcriptome sequencing technology, the transcriptome data of oat kernels can be quickly obtained and compared with the reference genome to quantify the expression of protein-coding genes, thereby efficiently obtaining the protein sequence of oat kernels.

[0060] Step 2: Screening of oat active peptides

[0061] Technical issues: How to screen out biologically active peptides from a large number of protein sequences is a challenge.

[0062] Technological progress: High-throughput virtual enzymatic hydrolysis technology is used to simulate the enzymatic hydrolysis process of proteins, combined with Peptide Ranker and ADME / T (absorption, distribution, metabolism, excretion and toxicity) prediction models to screen out peptides with potential biological activity. At the same time, with the help of network pharmacology technology, peptides with anti-hyperuricemia activity are further screened.

[0063] Step 3: In vitro chemical model validation

[0064] Technical question: How to verify the activity of the screened peptides in actual biological environments?

[0065] Technological progress: Molecular docking technology is used to predict the interaction between peptides and uric acid metabolism-related targets, and the actual effect of peptides is verified through an in vitro chemical model of hyperuricemia. This method can simulate the biological environment in vitro and quickly evaluate the activity of peptides.

[0066] Step 4: In vitro experiment of oatmeal protein hydrolysis

[0067] Technical question: How to evaluate the effect of oatmeal protein hydrolysate in anti-hyperuricemia?

[0068] Technological progress: A crude oatmeal active peptide mixture was prepared by enzymatic hydrolysis of oatmeal protein and its anti-hyperuricemia effect was evaluated in an in vitro experiment. This method can evaluate the overall effect of oatmeal protein hydrolysate in a complex biological environment.

[0069] Step 5: Identification of effective peptides by mass spectrometry

[0070] Technical question: How to accurately identify active polypeptide components?

[0071] Technological progress: Ultra-high performance liquid chromatography-high resolution mass spectrometry was used to identify the component peptide with the strongest uric acid inhibition effect. This method has high sensitivity, high resolution and high accuracy, and can accurately identify the components and structures of the peptides.

[0072] The present invention provides an efficient, accurate and economical method for screening and identifying active peptides in oats that have anti-hyperuricemia effects. By combining technologies such as high-throughput sequencing, virtual enzymolysis, network pharmacology and in vitro experimental verification, the present invention not only solves the technical difficulties in traditional methods, but also significantly improves the screening efficiency and accuracy. This provides strong support for the subsequent development and application of disease-resistant cereal peptides. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments of the present invention. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0074] Figure 1 This is a flow chart of a method for screening and identifying oat active peptides against hyperuricemia based on multi-omics provided in an embodiment of the present invention;

[0075] Figure 2 is a molecular docking validation heat map provided by an embodiment of the present invention;

[0076] Figure 3 3. It is a schematic diagram of the in vitro XOD inhibitory activity verification of the potential oat active peptides provided in the embodiments of the present invention; P>0.05, no significant difference, no indication; P<0.05, * indicates significant difference; P<0.01, very significant difference; P<0.001, * indicates extremely significant difference; P<0.0001 indicates extremely significant difference. DETAILED DESCRIPTION

[0077] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0078] The plant varieties involved in the embodiments of the present invention are all varieties currently sold in the market, and have been disclosed in patent documents, papers or websites.

[0079] 1. Technical Background and Overview

[0080] With the acceleration of modern life and the change of dietary structure, people are paying more and more attention to the development and utilization of healthy food. Cereal active peptides have become a research hotspot in the fields of food science and medicine due to their unique biological activity and nutritional value. As a nutritious cereal, oats have been proven to have a variety of health benefits, such as anti-hyperuric acid effects. This technology aims to provide guidance for the research and development of other cereal active peptides through the screening and identification methods of oat active peptides.

[0081] 2. Screening and identification methods of oat active peptides

[0082] 1) Raw material preparation: Select high-quality oats as raw materials, and obtain oat protein through pretreatment steps such as washing, crushing, and defatting.

[0083] 2) Enzymatic treatment: Use appropriate enzymes (such as proteases, peptidases, etc.) to enzymatically hydrolyze oat protein to generate a variety of oat peptides.

[0084] 3) Peptide separation: Use high performance liquid chromatography (HPLC), gel electrophoresis and other technologies to separate the enzymatic hydrolysis products to obtain oat peptides with different molecular weights and charge characteristics.

[0085] 4) Activity screening: Targeting specific biological activities (such as anti-hyperuricemia effects), the isolated oat peptides are screened through in vitro experiments (such as cell experiments, enzyme inhibition experiments, etc.) and in vivo experiments (such as animal experiments, clinical trials, etc.).

[0086] 5) Structural identification: Mass spectrometry (MS), nuclear magnetic resonance (NMR) and other technologies are used to identify the structure of the screened active oat peptides to clarify their amino acid sequence and spatial structure.

[0087] 3. Scalable screening and identification methods and applications

[0088] 1) Scalability of the method: The screening and identification methods in this technology are not only applicable to oat active peptides, but can also be extended to the research of other cereal active peptides. By adjusting parameters such as raw materials, enzymatic hydrolysis conditions, and separation technology, it can meet the screening and identification needs of different cereal active peptides.

[0089] 2) Application prospects: Cereal active peptides have a wide range of biological activities and application values, such as anti-oxidation, anti-inflammatory, anti-tumor, hypoglycemic, hypolipidemic, etc. The screening and identification methods of this technology can provide strong support for the research and development of these active peptides.

[0090] 4. Design and development of grain peptides with disease resistance

[0091] 1) Design ideas: Based on the screened and identified cereal active peptides, combined with modern biotechnology and food engineering technology, design cereal peptides with specific disease resistance properties. This includes modifying cereal protein genes through genetic engineering technology to express peptides with specific biological activity; or directly preparing peptides with specific sequences and structures through chemical synthesis, enzymatic synthesis and other methods.

[0092] 2) Development strategy: During the design process, factors such as the stability, solubility, and bioavailability of the peptide need to be fully considered to ensure its stability and biological activity during food processing and storage. At the same time, the anti-disease effect of the peptide needs to be verified through in vitro and in vivo experiments, and its preparation process and formula need to be optimized to meet the needs of different application scenarios.

[0093] 3) Application prospects: Cereal peptides with disease-resistant properties can be widely used in the fields of food, medicine, and health products. In the field of food, they can be added to various foods as functional ingredients to improve the nutritional value and health benefits of food; in the field of medicine and health products, they can be used as auxiliary drugs or nutritional supplements for the treatment or prevention of diseases to protect people's health.

[0094] The technology of the embodiment of the present invention is represented by oat active peptides, and an effective screening and identification method is established, and the scalability of this method in the research of other cereal active peptides is explored. At the same time, combined with modern biotechnology and food engineering technology, a design and development idea of ​​cereal peptides with disease resistance properties is proposed. These research results not only provide strong support for the research and development of cereal active peptides, but also make positive contributions to the development of food science and medicine.

[0095] Example 1: Screening and verification of the ability of oat active peptides to inhibit hyperuricemia

[0096] This example aims to screen oat protein-derived active peptides by high-throughput technology and verify their inhibitory effects on hyperuricemia in an in vitro model.

[0097] 1. Ribonucleotide (RNA) extraction and transcriptome sequencing:

[0098] RNA was extracted from oat kernels and subjected to high-throughput sequencing.

[0099] Alignment to the oat reference genome was performed to quantify the expression of protein-coding genes.

[0100] 2. Protein sequence simulation and active peptide screening:

[0101] Simulating the protein sequence of oat kernels based on protein-coding gene expression data.

[0102] Virtual enzymatic hydrolysis technology was used to simulate the digestion process, and Peptide Ranker and ADME / T were used to perform activity scoring and bioavailability prediction on the generated peptide fragments.

[0103] 3. Network pharmacology analysis and peptide screening:

[0104] The network pharmacology method was used to analyze the interaction between the screened high-scoring active peptides and hyperuricemia-related targets.

[0105] 4. In vitro model validation:

[0106] The effects of the screened active peptides were tested using an in vitro chemical model of hyperuricemia, including inhibiting uric acid production and promoting uric acid excretion.

[0107] 5. Effect evaluation and peptide identification:

[0108] The peptides showing the best inhibitory effect were structurally identified by ultra-performance liquid chromatography-high-resolution mass spectrometry.

[0109] Determine the accurate molecular structure of active peptides and provide a basis for further functional research and application development.

[0110] The results of this embodiment are helpful for developing natural foods or supplements based on cereals such as oats for managing or treating hyperuricemia, such as gout.

[0111] Example 2: Development of an anti-hyperuricemia food supplement based on oat active peptides

[0112] Using the active polypeptide identified in Example 1, a novel oat-based food supplement is developed for preventing or treating hyperuricemia.

[0113] 1. Large-scale production of active peptides:

[0114] Based on the identified active peptide structure, the enzymatic hydrolysis conditions of oat protein are optimized to achieve large-scale production of active peptides.

[0115] Ensure the stability of active ingredients and consistency of biological activity during the production process.

[0116] 2. Formulation development of food supplements:

[0117] Active peptides are formulated with other auxiliary ingredients (such as vitamins, minerals, etc.) into easy-to-ingest food supplement forms such as tablets, powders or liquids.

[0118] 3. Safety and efficacy testing:

[0119] Preclinical trials, including in vitro and animal model tests, were conducted to evaluate the safety and anti-hyperuricemic efficacy of the food supplement.

[0120] 4. Regulatory approval and marketing:

[0121] Complete necessary clinical trials, submit for approval by regulatory agencies, and obtain market access.

[0122] Develop marketing plans, including target consumer education, sales channel construction and brand building.

[0123] The food supplement can be marketed as a natural health product to help patients with hyperuricemia manage blood uric acid levels and reduce the risk of gout attacks.

[0124] These two examples demonstrate the complete process from screening and validating active peptides from oats to developing them into market-viable products, highlighting the product development strategy based on scientific research.

[0125] like Figure 1 As shown, the embodiment of the present invention provides a method for screening and identifying oat active peptides against hyperuricemia based on multi-omics, comprising the following steps:

[0126] Step 1: RNA is extracted from oat grains as raw materials, and after high-throughput transcriptome sequencing, the oat reference genome is aligned and the expression of protein-coding genes is quantified to obtain the oat grain protein sequence;

[0127] Step 2: Use high-throughput virtual enzymatic hydrolysis, Peptide Ranker and ADME / T to screen oat active peptide components, and use network pharmacology technology to screen oat protein-derived anti-hyperuricemia active peptides;

[0128] Step 3: Use molecular docking and in vitro chemical method model of hyperuricemia to verify the actual effect of oat active peptides in hyperuricemia;

[0129] Step 4, preparing a crude oatmeal active peptide mixture by enzymatic hydrolysis of oatmeal protein, and evaluating the anti-hyperuricemia effect of oatmeal enzymatic hydrolyzate in vitro;

[0130] Step 5: Use ultra-performance liquid chromatography-high-resolution mass spectrometry to identify the component peptide with the strongest uric acid inhibition effect.

[0131] In the embodiment of the present invention, step 1: oat seed transcriptome sequencing and protein sequence acquisition

[0132] 1) RNA extraction: Extract ribonucleotides (RNA), which are the information carriers of gene expression, from oat grains.

[0133] 2) High-throughput transcriptome sequencing: Use high-throughput sequencing technology (such as RNA-Seq) to sequence the extracted RNA to obtain a large number of short sequence reads.

[0134] 3) Reference genome sequence alignment: Align the sequenced reads with the oat reference genome to determine the position of each read on the genome.

[0135] 4) Quantification of protein coding gene expression: Based on the comparison results, the expression of protein coding genes is quantified, that is, the amount of RNA produced by the transcription of each gene is calculated.

[0136] 5) Obtain protein sequence: Based on the known genome annotation information and transcriptome data, infer and obtain the protein sequence of oat kernels.

[0137] In the present embodiment, step 2: screening of oat active peptides

[0138] 1) High-throughput virtual enzymatic hydrolysis: simulate the enzymatic hydrolysis process of proteases in vivo and cut protein sequences into peptides of different lengths.

[0139] 2) Peptide Ranker screening: Peptide Ranker and other algorithms are used to screen peptides based on their physicochemical properties (such as charge, hydrophobicity, etc.) to retain peptides with potential biological activity.

[0140] 3) ADME / T prediction: Use the ADME / T (absorption, distribution, metabolism, excretion and toxicity) prediction model to evaluate the bioavailability and potential toxicity of peptides and further screen active peptides.

[0141] 4) Network pharmacology screening: Based on network pharmacology technology, the interaction between peptides and uric acid metabolism-related targets is analyzed to screen out peptides with anti-hyperuricemia activity.

[0142] In the present embodiment, step 3: in vitro chemical method model verification

[0143] 1) Molecular docking: Molecular docking software is used to simulate the interaction between peptides and targets related to uric acid metabolism and predict their binding ability and affinity.

[0144] 2) In vitro chemical model of hyperuricemia: Construct a hyperuricemia model in vitro, such as using a hyperuricemia cell line or enzymes related to uric acid metabolism, adding candidate oat active peptides, and observing their effects on uric acid production or metabolism.

[0145] In the present embodiment, step 4: evaluation of oat protein hydrolysate

[0146] 1) Enzymatic hydrolysis of oat protein: oat protein is hydrolyzed using a specific protease to prepare a crude oat active peptide mixture.

[0147] 2) Evaluation of the anti-hyperuricemia effect in vitro: In an in vitro hyperuricemia model, a mixture of oat active peptides obtained by enzymatic hydrolysis was added to evaluate its effect on uric acid metabolism, such as uric acid production and uricase activity.

[0148] In the embodiment of the present invention, step 5: mass spectrometry identification

[0149] 1) Sample preparation: A mixture of oat active peptides with significant anti-hyperuricemia effects was screened out from in vitro experiments and sample preparation was performed.

[0150] 2) Ultra-high performance liquid chromatography-high resolution mass spectrometry analysis: The samples are analyzed using ultra-high performance liquid chromatography-high resolution mass spectrometry technology, and the peptides are separated and identified based on their molecular weight, charge, sequence and other information, ultimately determining the component peptide with the strongest uric acid inhibition effect.

[0151] Through the above steps, the present invention can efficiently and accurately screen out oat polypeptides with anti-hyperuricemia activity and provide a scientific basis for its subsequent application development.

[0152] The following is a detailed description of the development process of the embodiment of the present invention.

[0153] 1. Preparation of RNA sample library and transcriptome sequencing of oats

[0154] 1.1 Preparation of oat grain samples. Ten oat grains were equally collected from naked oats “Bayou No. 1” and hulled oats “Baiyan No. 7” when they were in the grain formation stage and grain filling stage, respectively, and then quickly frozen in liquid nitrogen and stored at -80°C.

[0155] 1.2 Oat seed transcriptome sequencing. Beijing Novogene Technology Co., Ltd. was commissioned to extract RNA from samples and perform transcriptome sequencing, with a total of 4 sets of oat seed transcriptome data.

[0156] 1.3 Oat reference genome. The reference genome, annotation files, and reference protein sequence files of naked oat “Bayou No. 1” (Avena sativa ssp. Nuda) and hulled oat “Baiyan No. 7” (Avena sativa) are all from OatBioDB.

[0157] 2. Transcriptome data analysis and acquisition of corresponding protein sequences

[0158] 2.1 Sequencing data quality control. FastQC was used to assess the quality of the raw sequencing data, and Trimmomatic (v0.39) software was used to remove adapters and preprocess low-quality bases to obtain valid data. The Phred quality score was set to 33, and the ILLUMINACLIP parameter specified the file containing the sequencing adapter sequence, where the maximum number of mismatches SeedMismatches was 2, Palindrome clip threshold was 30, Simple clip threshold was 10, MinAdapterLength was 1, and KeepBothReads was true. Other parameters were set to LEADING 25 and TRAILING 25. Here, SLIDINGWINDOW was set to 4:20 to perform sliding window quality control, where the window size was 4, the average quality requirement was 20, and finally MINLEN was 40 to ensure that the minimum length of the output sequence was 40 bases.

[0159] 2.2 Reference genome alignment. The reference genomes of naked oats (Avena sativa ssp. Nuda) and hulled oats (Avena sativa) were imported into the Hisat2 (v2.0.4) software to build indexes for the two reference genomes, and the paired-end data were compared with the default parameters of the valid data after quality control. The Samtools tool converted the Sam format file into the Bam format, and the Samtools Sort tool was used to sort the Bam file.

[0160] 2.3 Quantification of protein coding gene expression. The ordered Bam files and genome annotation files were imported into the Featurecounts tool of the Subread software for quantitative analysis to obtain the Counts of the gene (Gene) and core coding (CDS) regions, and then normalized using the TPM standardization formula to count the TPM of each gene expression. Set the TPM value ≧10, match the expression gene list of different samples with the reference protein sequence file, output the protein sequence of the expressed gene, and store it in Fasta format. The TPM calculation formula is: Where R1 is the target gene count value (the number of reads mapped to the target gene), L1 is the length of the target gene, and the target gene count value is corrected by dividing the number of reads mapped to the target transcript by the length of the target transcript; R (total) is the sum of the corrected count values ​​of all genes.

[0161] 2.4 Protein repeat sequence screening The oat protein sequence file in Fasta format was input using the CD-HIT program, and these protein sequences were screened to remove sequences with a repeatability higher than 90%.

[0162] An example of the partial sequence of the protein encoding gene expression in naked oats "Bayou No. 1-grain formation period"

[0163] >A.satnudSFS1A01G006970.1

[0164] MATKQSLFAAAAAAACILVLLPSASSLTTSFEYCDKGKEYPVKVSAVKMLPDLVIGGYPNFFTIYASTDKTIITEGKLFSAITCETFHVHSEAEDICTLTSCPATADFQVSHQLDMPSTDPNGPYTIRFVMLGEKDEELSCISLSYNLTGGIARALDLTSLFTA

[0165] >A.satnudSFS1A01G006962.1

[0166] MAAAATAPVEVPVRTVKVTNVSLSATVQDIKEFFSFSGDIEHVDMQSGDEWSQVAYVTFKDSQGAETALLLSGATIVDLSVMIAPAPEYQPPPTASAPLMSGTRVPVGGQNVIHKAEDVVSTMLARGFTLGKDAVGRAKSFDETHGFTSTASAKVASIDRKIGLSEKFTMGTTVVNEKVKEMDQKFQVSDKTKSALAAAEQTVSSAGSAIMKNRYVFTSASWVTSAFGKVAKAATDVGTMTKEKMSSEEQQQQRGSGSGSGGNSYTPIR

[0167] >A.satnudSFS1A01G006960.1

[0168]

[0169] 3. Functional annotation of transcripts (differentially expressed genes and functional enrichment analysis)

[0170] 3.1 Differential gene analysis. The edgeR package was used to perform differential gene analysis of different varieties (naked oats and hulled oats) at the same period; the same variety at different periods (grain stage and filling stage). The original expression data were used to filter the CPM value of the same gene in at least one sample greater than 10. The data were normalized according to the default parameters, and the multiple hypothesis test correction method was used for differential analysis. The threshold of differential genes was selected according to the p value less than 0.01 and log2FC greater than 2 to obtain differential genes.

[0171] 3.2 Functional enrichment analysis. The functional pathways of the differentially expressed genes obtained above were annotated, and functional enrichment analysis was performed using the clusterProfiler package. The enriched KEGG function was used to identify significantly enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. The gseKEGG function was used to perform gene set enrichment analysis on the log2FC-ranked gene table obtained by integrating the repeat matrix analysis. The p value < 0.05 was considered significant.

[0172] 4. High-throughput protein virtual enzymatic digestion

[0173] 4.1 High-throughput computer virtual enzymatic hydrolysis design.

[0174] 4.1.1 Selection of protease hydrolysis There are 52 protease hydrolases provided by the public ExPASy Peptide Cutter database (see Table 1). Enzymes with the same cleavage sites were combined to screen out 46 unique cleavage patterns.

[0175] Table 1 Names of 52 proteolytic enzymes

[0176]

[0177]

[0178] 4.1.2 Simulated enzymatic hydrolysis. The fasta format file containing the parent protein sequence is used to set up the enzyme or chemical running script in the improved version of Refine-PeptideCutter to generate all the peptides encrypted in the protein sequence and the number of released peptide segments, and obtain a library of potential bioactive peptides produced by a specific enzyme or a combination of multiple enzymes.

[0179] (1) Single enzyme digestion

[0180] Read protein sequence files in fasta format: extract the name and amino acid sequence of each protein and save them in the dictionary protein_dict. According to the pre-defined enzyme cutting rules enzyme_rules (regular expression), traverse each enzyme cutting rule, perform enzyme cutting operations on each protein, and use the split function to cut the amino acid sequence into peptides according to the rules. Count the information under each enzyme cutting rule, including the total number of peptides, the number of peptides composed of 2-9 amino acids, and the proportion of peptides composed of 2-9 amino acids to the total number of peptides.

[0181] (2) Enzyme digestion with two or three enzyme combinations: In the second step of the single enzyme digestion process, the combinations function in the itertools library is added to obtain the digestion rules of all two or three enzyme combinations, and further digestion is performed to obtain peptide fragments. The other processes are the same as the single enzyme digestion.

[0182] 4.2 Prediction of potential biological activity. Four peptide libraries after proteinase K simulation enzymatic hydrolysis were selected for potential biological activity prediction. When the prediction score of peptides screened by Peptide Ranker was greater than 0.5, they were considered to have potential biological activity.

[0183] 4.3 ADME / T prediction. The peptide library of "Bayou No. 1-Grain Filling Period" after proteinase K simulation enzymatic hydrolysis with a Peptide Ranker score ≧0.9 was selected for ToxinPred prediction of active peptide toxicity. The oat active peptides with a Peptide Ranker score ≧0.9 and non-toxicity in the peptide library of "Bayou No. 1-Grain Filling Period" after Swiss ADME proteinase K simulation enzymatic hydrolysis were used to input the SMILES formula to screen for gastrointestinal absorption as "High", and finally 42 oat active peptides that were absorbed by the gastrointestinal tract and had good drug-like properties were screened.

[0184] 5. Network pharmacology technology to screen the main components of anti-hyperuricemia oat active peptides and predict their mechanism of action

[0185] 5.1 Screening of oat active peptide targets. The spatial conformations of 42 oat active peptides constructed in ChemDraw 20.0 were uploaded to various target prediction network servers for identification and prediction, including SwissTargetPrediction and PharmMapper. SwissTargetPrediction retrieved targets in the "Homo Sapiens" mode and screened with Probability ≥ 0; PharmMapper selected targets with Normalized Fit Score ≥ 0.9, entered Uniprot for standardization and verification, and the species was limited to "Homo Sapiens". The obtained targets were removed from duplicate options to establish an oat active peptide target database.

[0186] 5.2 Screening of hyperuricemia-related targets. Using "Hyperuricemia" as the keyword, search CTD, GeneCards, DisGeNet, and OMIM libraries for selection conditions greater than the median to obtain potential targets for hyperuricemia. The obtained targets were deleted and merged to establish a potential target database for hyperuricemia.

[0187] 5.3 Protein interaction (PPI) network construction. The oat active peptides and potential targets of hyperuricemia were imported into VENNY 2.1.0 respectively to obtain the intersection targets of oat active peptides in intervening hyperuricemia. The above intersection targets were submitted to STRINGDatabase, "Homo Sapiens" and 0.4 confidence were set, the data were downloaded and imported into Cytoscape 3.9.1 software, and the built-in Network Analyzer was used for topological analysis. The connectivity, betweenness and compactness of each node were set to be greater than the median as the minimum threshold to screen and extract key targets.

[0188] 5.4GO functional enrichment and KEGG pathway enrichment analysis. The key targets of oat active peptides for intervention of hyperuricemia were entered into Metascape, and P < 0.01 was set to analyze the main GO and KEGG metabolic pathways. The top 10 GO and top 20 KEGG pathway enrichment results were selected for data visualization on the Microbiological Information Platform.

[0189] 5.5 Construction of oat peptide component-target-pathway network diagram. The top 20 signal pathways ranked in the KEGG enrichment analysis related to hyperuricemia were selected, and the connection between oat active peptides and intersection targets involved in the relevant pathways was established. The network topology parameters of the effective ingredients and targets were analyzed using the built-in Network Analyzer of CytoScape3.9.1, and the interrelationship network diagram of "oat active peptide-target-pathway" was constructed to determine the core targets and the main active ingredients of oat active peptides that intervene in hyperuricemia.

[0190] 6. Functional verification of oat active peptides

[0191] 6.1 Molecular docking verification. The top five targets with the highest connectivity values ​​in the network diagram of the main component of oat active peptides-hyperuricemia targets and human xanthine oxidase (XOD) were selected for molecular docking verification.

[0192] 6.1.1 Preparation of docking ligands. After constructing the two-dimensional chemical structure using ChemDraw 20.0 software, import it into Chem3D 20.0 and use the MMFF94 force field to minimize the energy of the constructed oat active peptide. After converting it into pdb format, import it into AutoDock Tools-1.5.6 software and add atomic charges and assign atomic types. Set all flexible bonds to be rotatable by default and save it as a pdbqt file.

[0193] 6.1.2 Preparation of Docking Receptor The crystal structure of the protein was obtained from the protein RCSB database, and irrelevant small molecules and water molecules were deleted using Pymol2.6 software. After conversion to pdb format, it was imported into AutoDock Tools-1.5.6 software to delete water molecules, add hydrogen atoms, and set atom types, and saved as a pdbqt file.

[0194] 6.1.3 Docking pocket settings. Molecular docking operations were performed on proteins and peptides using Autodock vina, and XOD (PDB ID: 2E1Q) was used to construct mutants using pymol, and valine (Val 803) was restored to the original glutamic acid (Glu 803). The ligand was extracted to create a binding pocket, (i) center_x:y:z = 30:11:189, and (ii) center_x:y:z = 35:14:152. Protein ALB (PDB ID: 1N5U) center_x:y:z=31.82:7.54:32.66; IL1B (PDB ID: 1HIB) center_x:y:z=20.97:3.31:73.73; SRC (PDB ID:1KSW)center_x:y:z=-0.38:50.27:2.00; CASPS(PDB ID:1NMS)center_x:y:z=-9.1:-4.0:24.2; STATS(PDB ID: 6NJS) center_x:y:z=13:56.64:0.25, the pocket sizes are size_x:y:z=22.5:22.5:22.5.

[0195] 6.1.4 Docking result judgment. The binding activity is judged by the binding energy (KJ / mol). When the Binging energy < 0KJ / mol, the docking can be carried out in the natural state and has binding activity; when the Binging energy < -5KJ / mol, the docking state is considered to be good and the binding activity is good.

[0196] 6.2 Evaluation of XOD inhibitory activity in vitro. Add 25 μL of the sample to be tested and 25 μL of XOD (final concentration of 0.05 U / mL) to each well of a 96-well plate. After incubation at 37°C for 30 min, add 50 μL of xanthine solution (final concentration of 0.2 mM) to initiate the reaction. Record the kinetic changes in the absorbance of the reaction system at 290 nm every 30 s at 37°C for 3 min. The common XOD inhibitory drug allopurinol (final concentration of 40 uM) was used as a positive control, and PBS buffer solution was used as a negative control. All the required solvents were prepared and used immediately. The XOD inhibitory activity was quantified by determining the rate of uric acid production in the enzyme-catalyzed reaction. The inhibition rate of XOD can be expressed as: Among them, V Blank is the initial reaction rate without adding sample; V Sample is the reaction rate of the added sample; the inhibitory activity of XOD is measured by IC 50 Value representation.

[0197] 7. Evaluation of the anti-hyperuricemia effect of oatmeal protein hydrolysate in vitro

[0198] 7.1 Pretreatment of oats. Put the selected clean oat grains into a grinder and grind them into powder. Pass them through a 60-mesh sieve, and grind the unscreened parts again. Repeat 3-4 times. After the oat grains are thoroughly crushed and sieved, the oat powder is bagged and stored at room temperature. Weigh an appropriate amount of oat powder and put it into a beaker. Add n-hexane in a ratio of 1:2 (g / mL) and stir evenly. After sealing the beaker with plastic wrap, place it on a shaker and vibrate at 100r / min for 8-10h for defatting. After defatting, filter and dry it in a fume hood. Use n-hexane for secondary water washing during filtration. After the defatted oat powder is completely dry, bag it and store it at 4℃.

[0199] 7.2 Extraction of oat protein. Weigh an appropriate amount of defatted oat powder and put it into a beaker. Add ultrapure water at a ratio of 1:18 (g / mL) and stir to mix. Use 1mol / L NaOH to adjust the pH of the solution to 10.0. Put it into a water bath and bathe it at 50℃ for 2h. After the water bath, centrifuge at 4000r / min for 20min to take the supernatant. Use 1mol / L HCl to adjust the pH to 4.0. When the protein precipitates, the solution changes from transparent light yellow to milky white. Then centrifuge at 4000r / min for 20min to retain the precipitate. Wash the precipitate twice with pure water, adjust the pH of the solution to neutral, and vacuum freeze-dry for 48h to obtain oat protein freeze-dried powder.

[0200] 7.3 Enzymatic hydrolysis of oat protein. Take 100 mg of oat protein in a 50 mL centrifuge tube and add 15 mL of ultrapure water to dissolve it. Use 0.5 mol / L NaOH to adjust the pH of the solution to 8.0, add proteinase K at a ratio of 200 U / g, shake and mix well, put it into a constant temperature water bath, and bathe it for 5 hours. After the water bath ends, inactivate the enzyme at 95 ° C for 15 minutes to obtain oat protein hydrolyzate.

[0201] 7.4 Evaluation of in vitro anti-hyperuricemia effect After the enzyme was inactivated in a water bath, centrifuge at 10,000 r / min for 10 min and take the supernatant for XOD inhibition assay (same as 6.2)

[0202] 8. Structural identification of oat active peptides

[0203] 8.1 Preparation of samples to be tested: Centrifuge at 10000×g for 10 min, take the supernatant, filter through a 0.22 μm PTFE membrane, vacuum freeze-dry, dissolve the polypeptide in 1 mL acetonitrile-water solution (acetonitrile: water = 95:5), transfer the solution to a sample injection bottle after passing through the membrane, and wait for analysis.

[0204] 8.2 Chromatographic analysis. Ultra-high performance liquid chromatography high-resolution mass spectrometry was used to identify the peptides. I-Class HPLC, using ACQUITY Reverse phase liquid chromatography was performed on an HSS T3 column (2.1×50 mm, 1.8 μm). The mobile phase components were 0.1% formic acid (A) in water and 0.1% formic acid (B) in acetonitrile, the flow rate was 0.2 mL / min, the sample load was 10 μL, and the following 10-minute gradient method was used: 0-2 min, 100% A; 2-5 min, 100-50% A; 5-7 min, 50-30% A; 7-8 min, 30-100% A; 8-10 min, 100% A. The detection wavelength was 214 nm.

[0205] 8.3 Mass spectrometry analysis. The data generated by reverse phase liquid chromatography analysis were collected and analyzed by ESI mass spectrometer. The ESI mass spectrometer was set to positive ion mode, mass / charge 50-1000m / z; capillary voltage 2.5kV; drying gas 280℃, 800L / h; air flow 50L / h; source temperature 120℃. For tandem mass spectrometry analysis, collision energies of 5, 15, 25 and 35v were used. The MS / MS spectra of the peptide sequence were analyzed and identified by the professional software Compass Data Analysis software.

[0206] The present invention verifies the anti-hyperuricemia activity of oat active peptides through molecular docking and in vitro chemical models, and selects the top five targets ALB, IL1B, SRC, CASP3 and STAT3 in the network diagram of hyperuricemia targets with the main components of oat active peptides and molecular docking with human xanthine oxidase XOD. The results show that the binding energy is less than -5kJ / mol, accounting for 82.86% of the whole. The binding energy of oat active peptides PPF, PPPL, MPF, MPL and PPPF with the main targets (ALB, SRC, CASP3) and human XOD is relatively high, indicating that the strong binding activity means that the main active components of oat polypeptides have good binding activity with most targets. The present invention constructs an in vitro chemical method model of hyperuricemia to verify the XOD activity of synthetic oat polypeptides. The results show that the optimal oat synthetic polypeptides PPF and PPPF show good XOD inhibition effect (IC 50 =6.132mM and 15.360mM), indicating that oat polypeptide has a significant effect in lowering uric acid.

[0207] A high-throughput transcriptome sequencing-based analysis of naked oat and braised oat sequences was implemented, and the expression levels of protein-coding genes were represented by calculating the TPM value. It was found that the naked oat "Bao No. 1" (both are existing varieties on the market) produced 11,920 and 6,269 protein sequences in the grain formation and filling stages, respectively, and the braised oat "Bai Yan No. 7" (both are existing varieties on the market) produced 9,689 and 8,595 protein sequences in the grain formation and filling stages, respectively.

[0208] In the second implementation, a high-throughput virtual enzymatic hydrolysis technology was developed to obtain a complete protein simulated enzymatic hydrolysis peptide library, 46 unique enzymatic cleavage methods were screened, a single enzyme or two / three enzymes were selected, and the script was run to generate all the peptides encrypted in the protein sequence and the number of released peptide segments, to obtain a potential bioactive peptide library produced by a specific enzyme or a combination of multiple enzymes.

[0209] Three computer-assisted drug analyses were implemented to screen oat active peptides. Proteins from naked oats "Bayou No. 1" during the filling period were selected and the peptide library obtained by virtual enzymatic hydrolysis with proteinase K was used to predict 1532 oat peptides with good potential activity. After ADME / T evaluation, 1446 non-cytotoxic oat active peptides were obtained, and 42 oat active peptides with high safety, gastrointestinal absorption and good drug-like properties were screened.

[0210] Implementation 4 uses network pharmacology to mine specific anti-hyperuricemia oat active peptide sequences and related mechanisms. The preliminary screening of the main anti-hyperuricemia oat active peptide sequences is PPF, PPPL, MPF, MPL and PPPF. The key targets that occupy the central position in the treatment of hyperuricemia by oat active peptides are ALB, IL1B, SRC, CASP3 and STAT3, acting on signaling pathways such as cancer, lipids and atherosclerosis and chemical carcinogen-receptor activation to intervene in hyperuricemia.

[0211] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any modifications, equivalent substitutions and improvements made by any technician familiar with the technical field within the technical scope disclosed by the present invention and within the spirit and principle of the present invention should be covered by the protection scope of the present invention.

Claims

1. A multi-omics-based screening and identification method for anti-hyperuricemia oat active peptides. It is characterized in that The following steps are involved: Step 1: RNA is extracted from oat grains, and after high-throughput transcriptome sequencing, the oat reference genome is aligned, and the expression of protein-coding genes is quantified to obtain the oat grain protein sequence; Step 2: Use high-throughput virtual enzymatic hydrolysis, Peptide Ranker and ADME / T to screen oat active peptide components, and use network pharmacology technology to screen oat protein-derived anti-hyperuricemia active peptides; Step 3: Use molecular docking and in vitro chemical method model of hyperuricemia to verify the actual effect of oat active peptides in hyperuricemia; Step 4, preparing a crude oatmeal active peptide mixture by enzymatic hydrolysis of oatmeal protein, and evaluating the anti-hyperuricemia effect of oatmeal enzymatic hydrolyzate in vitro; Step 5: Use ultra-performance liquid chromatography-high-resolution mass spectrometry to structurally identify the component peptide with the strongest uric acid inhibitory effect.

2. The multi-omics-based screening and identification method for anti-hyperuricemia oat active peptides according to claim 1, It is characterized in that The oat grains in step 1 are oat grains of naked oats "Bayou No. 1" and hulled oats "Baiyan No. 7" in the grain formation stage and filling stage, respectively, which are quick-frozen in liquid nitrogen and stored at -80°C; the transcriptomic detection and analysis includes transcriptome sequencing and alignment with a reference genome, and quantification of protein-coding gene expression, and sequence conversion simulates its protein sequence to obtain the protein sequence expressed by the entire oat.

3. The multi-omics-based screening and identification method for anti-hyperuricemia oat active peptides according to claim 1, It is characterized in that The high-throughput virtual enzymatic hydrolysis technology described in step 2 is to screen out unique enzymatic cleavage methods from the proteolytic enzymes provided by the public ExPASy Peptide Cutter database, and simulate enzymatic hydrolysis with reference to their specific enzymatic cleavage sites, set up enzyme or chemical running scripts, and automatically generate all peptide sequences encrypted in the protein sequence and the number of released peptide segments to obtain a potential bioactive peptide library produced by a specific enzyme or a combination of multiple enzymes.

4. The multi-omics-based screening and identification method for anti-hyperuricemia oat active peptides according to claim 1, It is characterized in that The polypeptide biological activity prediction website in step 2 is Peptide Ranker; the polypeptide ADME / T property prediction website is SwissADME and ToxinPred; the spatial conformation of the oat active peptide sequence screened in step 2 is constructed by ChemDraw 20.0 and then uploaded to different target prediction network servers for identification and prediction.

5. The multi-omics-based screening and identification method for anti-hyperuricemia oat active peptides according to claim 1, It is characterized in that The disease targets predicted in the step 2 are related targets of hyperuricemia screened through CTD, GeneCards, DisGeNet and OMIM databases; the anti-hyperuricemia oat active peptides in the step 2 are the top five active peptides screened by topological analysis using the NetworkAnalyzer built into the Cytoscape 3.9.1 software.

6. The multi-omics-based screening and identification method for anti-hyperuricemia oat active peptides according to claim 1, It is characterized in that The receptors verified by molecular docking in step three are the top 5 targets with the highest connectivity value in the main component of oat active peptides - hyperuricemia target network diagram and human xanthine oxidase XOD, and the ligands are the top 5 peptide sequences with the highest correlation with oat active peptides.

7. The multi-omics-based screening and identification method for anti-hyperuricemia oat active peptides according to claim 1, It is characterized in that The hyperuricemia activity verification experiment in step 3 is an in vitro chemical XOD inhibition activity test.

8. The multi-omics-based screening and identification method for anti-hyperuricemia oat active peptides according to claim 1, It is characterized in that The oat raw materials described in step 4 are oat grains of naked oats "Bayou No. 1" and hulled oats "Baiyan No. 7", which are ground, defatted, and dried for pretreatment, and oat protein is extracted using an alkali extraction and acid precipitation method under certain conditions.

9. The multi-omics-based screening and identification method for anti-hyperuricemia oat active peptides according to claim 1, It is characterized in that In the step 4, proteinase K is used for enzymatic hydrolysis, and enzymatic hydrolysis is performed under certain conditions to obtain a polypeptide mixture; in the step 5, the polypeptide identification method uses ultra-high performance liquid chromatography high-resolution mass spectrometry to analyze and identify the structural sequence of the polypeptide.

10. Use of the multi-omics-based anti-hyperuricemia oat active peptide screening and identification method as claimed in any one of claims 1 to 9 in screening and identifying multi-omics-based anti-hyperuricemia oat active peptides.

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