A walnut-derived dpp-iv inhibiting peptide, and preparation, identification, screening method and application thereof

Abstract

The application discloses a walnut source DPP-IV inhibiting peptide as well as a preparation, identification and screening method and application thereof, and belongs to the technical field of biological medicines. A series of novel DPP-IV inhibiting peptides, KFPF, IFR, KGFL, KIPF and KLF, are screened and verified from walnut proteins in a high efficiency for the first time. Not only is the rational discovery of high-activity peptides realized through a systematic method of'multi-frequency ultrasonic pretreatment-enzymolysis-ultrafiltration purification-mass spectrometric identification-computer virtual screening-in vitro and in vivo activity verification', but also the clear hypoglycemic effect of the peptides is confirmed in animal experiments. The application provides new candidate molecules for developing natural and safe anti-diabetic functional foods or medicines, and has good industrial value and scientific significance through improving the added value of walnut resources and establishing a replicable active peptide research paradigm.

Description

Technical Field

[0001] This invention belongs to the field of biomedical technology, specifically relating to a walnut-derived DPP-IV inhibitory peptide and its preparation, identification, screening methods and applications. Background Technology

[0002] Currently, diabetes has become one of the fastest-growing diseases globally. Projected data shows that by 2045, the number of people aged 20-79 with diabetes worldwide will reach nearly 800 million. Among these patients, the prevalence of type 2 diabetes mellitus (T2DM) ranges from 89% to 95%. T2DM is widely recognized as one of the most prevalent and harmful chronic diseases globally, contributing significantly to mortality. Dipeptidyl peptidase IV (DPP-IV) is a protease in the human body primarily responsible for degrading incretin hormones, including glucagon-like peptide-1 (GLP-1) and glucose-dependent insulinotropic peptide (GIP). Therefore, inhibiting DPP-IV activity to block the breakdown of endogenous incretins and thus raise blood glucose levels is feasible. Currently, selegiline, saxagliptin, linagliptin, alogliptin, and vildagliptin have been identified as DPP-IV inhibitors. However, these synthetic drugs have been shown to cause a range of adverse reactions, including but not limited to fever, vomiting, urinary tract infections, kidney burden, upper respiratory tract infections, and urticaria. In recent years, bioactive peptides (BPs) derived from food have received increasing attention from the scientific community due to their low toxicity and potential effects such as lowering blood pressure, antioxidation, anticancer, and antidiabetic activity. Numerous studies have focused on synthesizing and investigating DPP-IV inhibitory peptides from various food components, including milk, oats, glutinous rice, and fish.

[0003] Walnut( Juglans regiaWalnut protein (Walnut protein) possesses extremely high medicinal value and is recognized in both traditional medicine and functional foods. It is one of China's most promising green nutritional and health products. Walnut protein (WPs) is renowned for its comprehensive amino acid composition, containing 18 essential amino acids in a balanced ratio, thus classifying it as an important source of high-quality protein. Walnut peptides extracted from walnut protein exhibit various biological activities, including antioxidant, antibacterial, hypoglycemic, and antitumor properties. However, research on the inhibitory effect of walnut peptides on DPP-IV remains limited. Kong et al. were the first to extract and analyze DPP-IV inhibitory peptides from walnut protein hydrolysates (WPHs). Their study found that peptides with lower molecular weights and a higher proportion of basic amino acid residues exhibited potent DPP-IV inhibitory activity. The activity of these peptides remained stable over a wide range of temperature and pH values ​​and retained their inhibitory activity after simulated gastrointestinal digestion. This study obtained walnut DPP-IV inhibitory peptides through general steps in the preparation of bioactive peptides, but did not delve into their potential targets. Mu et al. investigated the effects of different proteolytic enzymes on the inhibition of DPP-IV activity by walnut protein hydrolysates (WPHs), finding that trypsin hydrolysates exhibited the strongest inhibitory effect on DPP-IV. They also proposed that these three identified peptides primarily bind to the DPP-IV active site via hydrogen bonds and van der Waals forces. However, they did not further explore the potential targets and pathways of action of the walnut DPP-IV inhibitory peptides.

[0004] Virtual screening is a technological tool that integrates bioinformatics and computer analysis. By combining enzymatic screening with virtual screening technology, the efficiency and accuracy of DPP-IV inhibitory peptide development can be significantly improved. Guo et al. successfully used virtual screening technology to screen and identify two DPP-IV inhibitory peptides with competitive effects from goat milk. Chen et al. also screened a DPP-IV inhibitory peptide from oysters and verified its high activity in vitro. Molecular docking and network pharmacology analysis techniques have been widely used in the fields of bioscience and drug development. Molecular docking technology provides important insights into the physicochemical properties of bioactive compounds. Network pharmacology is a multidisciplinary field that integrates systems biochemistry, structural biotechnology, and multi-omics strategies. Based on information on active chemical substances, targets, and signaling pathways, it can achieve system-level analysis of bioactive compounds in the pathophysiology of diseases. Through comprehensive network pharmacology analysis, Xin et al. confirmed that the DPP-IV inhibitory peptide from highland barley exhibits multifaceted effects in vivo, can regulate signaling pathways and biological processes, and has the potential to treat diabetes. However, research on this technology in walnut peptides is still very limited. Summary of the Invention

[0005] Based on the aforementioned existing technological status, this invention enhances peptide activity through ultrasound-assisted enzymatic hydrolysis and explores the mechanism of action, potential targets, and metabolic pathways of selected DPP-IV inhibitory peptides using virtual screening, molecular docking, and network pharmacology methods. This aims to provide a scientific basis for the comprehensive development and utilization of walnut protein resources. This invention utilizes ultrasound-assisted enzymatic hydrolysis, ultrafiltration, ion exchange separation, and identification of walnut-derived DPP-IV inhibitory peptides. Through computer-aided multi-condition stepwise screening, novel walnut polypeptides with significant DPP-IV inhibitory activity were provided. The peptide sequences are KFPF, KGFL, KLF, IFR, and KIPF. Furthermore, animal experiments verified that the KFPF peptide has a good in vivo hypoglycemic effect, laying a reliable material and theoretical foundation for the development of walnut-based hypoglycemic functional products.

[0006] To achieve the above-mentioned technical objectives, the present invention adopts the following technical solution: One objective of this invention is to provide a method for preparing a walnut-derived DPP-IV inhibitory peptide, comprising the following steps: (1) Walnut protein was hydrolyzed by alkaline protease to obtain walnut protein hydrolysate; (2) The enzymatic hydrolysate is subjected to ultrafiltration separation to collect the components with a molecular weight of less than 3 kDa; (3) The components with a value less than 3 kDa are separated by cation exchange chromatography to obtain the active peptide components; (4) Mass spectrometry is used to identify the active peptide components to obtain the polypeptide sequence.

[0007] Furthermore, step (1) includes a step of pre-treating walnut protein with multi-frequency ultrasound before enzymatic hydrolysis. The ultrasound conditions are: power 250~350 W, time 20~30 min.

[0008] Furthermore, the ultrafiltration in step (2) is carried out sequentially using ultrafiltration membranes with molecular weight cutoffs of 10 kDa, 5 kDa, and 3 kDa.

[0009] Furthermore, the cation exchange chromatography in step (3) is performed using Sephadex C-25 resin.

[0010] Furthermore, the mass spectrometry identification steps described in step (4) include: ① Dissolve the sample to be tested in NH4HCO3 buffer solution, and add dithiothreitol and iodoacetamide in sequence for reduction and alkylation reactions; ② Desalt and dry the reaction product; ③ The analysis was performed using an Easy-nLC 1200 liquid chromatograph coupled with a Q Exactive mass spectrometer; ④ Use Byonic software to compare mass spectrometry data with the target protein database to identify peptide sequences.

[0011] Furthermore, the preparation method also includes a step of virtual screening of the identified polypeptide sequences, wherein the virtual screening includes at least one of toxicity and bioavailability prediction.

[0012] The second objective of this invention is to provide a walnut-derived DPP-IV inhibitory peptide, which is prepared using the aforementioned preparation method, and whose amino acid sequence is any one of KFPF, IFR, KGFL, KIPF, or KLF.

[0013] A third objective of this invention is to provide a pharmaceutical composition comprising any one or more of the walnut-derived DPP-IV inhibitory peptides, and a pharmaceutically acceptable carrier or excipient.

[0014] The fourth objective of this invention is to provide the application of the walnut-derived DPP-IV inhibitory peptide or the pharmaceutical composition in the preparation of products that inhibit the activity of DPP-IV protease.

[0015] The fifth objective of this invention is to provide the use of the walnut-derived DPP-IV inhibitory peptide or the pharmaceutical composition in the preparation of medicaments for the prevention and / or treatment of diabetes.

[0016] Compared with the prior art, the present invention has the following beneficial effects: This invention marks the first time that a series of novel DPP-IV inhibitory peptides—KFPF, IFR, KGFL, KIPF, and KLF—have been efficiently screened and validated from walnut protein. The systematic approach, employing multi-frequency ultrasonic pretreatment, enzymatic hydrolysis, ultrafiltration purification, mass spectrometry identification, computer-aided virtual screening, and in vitro and in vivo activity verification, not only enabled the rational discovery of highly active peptides but also demonstrated their clear hypoglycemic effects in animal experiments. This invention provides novel candidate molecules for the development of natural and safe anti-diabetic functional foods or drugs, and by enhancing the added value of walnut resources and establishing a replicable paradigm for the research and development of active peptides, it possesses both significant industrial value and scientific significance. Attached Figure Description

[0017] Figure 1 The results of enzymatic hydrolysis and condition optimization in Example 1 of this invention are shown. Specifically: screening of walnut protein hydrolytic enzymes (A), multi-frequency ultrasound power (B), multi-frequency ultrasound time (C), and ultrasound analysis of the amino acid composition of walnut protein hydrolysate (D). Different letters represent statistically significant differences (p < 0.05).

[0018] Figure 2The results of separation, purification, and virtual screening in Example 1 of the invention are shown. Specifically: ultrafiltration separation (A), ion exchange separation (B), activity verification (C), identification (D), F2 component peptide distribution (E), and virtual screening flowchart (F) of the walnut DPP-IV inhibitory peptide. Different letters indicate statistically significant differences (p < 0.05).

[0019] Figure 3 This is the molecular docking result of the five DPP-IV inhibitory peptides binding to DPP-IV in Example 1 of this invention.

[0020] Figure 4 This is the DPP-IV inhibitory peptide inhibition mode in Example 1 of the present invention.

[0021] Figure 5 This presents the prediction and verification results of the dipeptide modules with DPP-IV inhibitory activity in Example 1 of the present invention. Specifically: molecular docking score of each dipeptide module with DPP-IV (A); in vitro DPP-IV inhibitory activity verification of the top 10 dipeptide modules (B).

[0022] Figure 6 The figures show the body weight (A), food intake (B), and water intake (C) of mice in different treatment groups in Example 1 of this invention.

[0023] Figure 7 The values ​​for FBG (A), AUC (B), and liver glycogen content (C) in mice from different treatment groups in Example 1 of this invention are shown. # indicates a significant difference compared to the Control group. P <0.05), ## indicates a significant difference compared to the Control group ( P <0.01); This indicates a significant difference compared to the Model group. P <0.05), This indicates a significant difference compared to the Model group. P <0.01), the same applies below.

[0024] Figure 8 This invention describes the effect of walnut DPP-IV inhibitory peptide in Example 1 on serum insulin levels (A) and insulin resistance index (B) in mice under different treatment groups.

[0025] Figure 9 The effect of walnut DPP-IV inhibitory peptide on organ indices of mice in different treatment groups in Example 1 of this invention is shown in the figures for heart (A), liver (B), spleen (C), and kidney (D).

[0026] Figure 10The effect of walnut DPP-IV inhibitory peptide on blood lipids in mice under different treatment groups in Example 1 of this invention is shown, where: TC (A), TG (B), LDL-C (C) and HDL-C (D).

[0027] Figure 11 This invention relates to the effect of walnut DPP-IV inhibitory peptide in Example 1 on pathological damage to the liver of mice in different treatment groups.

[0028] Figure 12 This invention relates to the effect of walnut DPP-IV inhibitory peptide in Example 1 on pathological damage to the kidneys of mice in different treatment groups.

[0029] Figure 13 The images show the OTU Rank curve (A), OTU dilution curve (B), and petal diagram (C) of ASV / OTU for the sample (group) in Embodiment 1 of the present invention.

[0030] Figure 14 This is a box plot of the Alpha diversity index in Embodiment 1 of the present invention.

[0031] Figure 15 The results of PCoA analysis (A) and NMDS analysis (B) of the six groups of mice in Example 1 of this invention are shown.

[0032] Figure 16 This is a bar chart showing the species distribution of mice in different treatment groups in Example 1 of the present invention. The data is categorized as follows: phylum (A), family (B), and genus (C).

[0033] Figure 17 The evolutionary clade diagram (A) and LDA value distribution bar chart (genus level) of the six groups of mice in Example 1 of the present invention are shown (B). Detailed Implementation

[0034] The following examples are used to illustrate the present invention, but are not intended to limit the scope of the invention. Any modifications or substitutions made to the methods, steps, or conditions of the present invention without departing from the spirit and essence of the invention are within the scope of the invention. The reagents, products, and instruments used in the following examples are all commercially available, and the methods used in the examples, unless otherwise specified, are consistent with conventionally used methods.

[0035] The main experimental process of this invention is as follows: 1. Raw material acquisition and pretreatment: Walnuts were purchased from the agricultural market in Shihezi City, Xinjiang. After shelling, they were dried at around 45℃, and the walnut kernels were peeled. The peeled walnut kernels were crushed using a crusher. The walnuts were defatted with hexane at a ratio of 1:6 (m / v) for 2 hours, then filtered. The process was repeated until the hexane was clear. The residue was collected, evaporated in a ventilated place, and passed through an 80-mesh sieve to obtain defatted walnut powder. This powder was mixed with deionized water at a ratio of 1:20 (w / v). The pH of the mixture was adjusted to 10.5 using NaOH solution, stirred at room temperature for 1 hour, and centrifuged to obtain the supernatant. The pH of the supernatant was then adjusted to 4.5 using HCl solution, stirred at room temperature for 1 hour, and centrifuged to retain the precipitate. The pH of the precipitate was adjusted to 7.0, and after cold drying, it became WP and stored at -20℃.

[0036] 2. Enzymatic hydrolysis: Walnut protein was pretreated using multi-frequency ultrasound for 25 min at 300 W. Alkaline protease was used for hydrolysis. First, the lyophilized WP was dissolved in distilled water (2.23%, w / v) and mixed thoroughly, followed by ultrasonic pretreatment. Then, 9100 U / g alkaline protease (pH 9.0, 55℃) was added to the walnut protein solution for hydrolysis for 180 min. After hydrolysis, the enzyme was inactivated in a boiling water bath for 10 min, followed by cooling in an ice-water bath and centrifugation at 8000 r / min for 20 min. The supernatant was then freeze-dried to obtain walnut protein hydrolysates (WPHs) and stored at -20℃ for later use.

[0037] 3. Separation and Purification: After cooling the obtained walnut protein hydrolysate (WPHs) to room temperature, ultrafiltration tubes with molecular weights of 10 kDa, 5 kDa, and 3 kDa were added sequentially. Ultrafiltration was performed at 4℃, 8000 g, and 20 min, collecting four fractions: U1 (< 3 kDa), U2 (3~5 kDa), U3 (5~10 kDa), and U4 (> 10 kDa). These fractions were freeze-dried and stored at -20℃ for subsequent experiments. Then, the Sephadex C-25 cation exchange resin was equilibrated with deionized water, and peptide solutions with a molecular weight <3 kDa were loaded. The collected fractions were designated F1 and F2, and both fractions were concentrated, freeze-dried, and stored at -20℃ for later use.

[0038] 4. Identification: The sample was dissolved in 50 mmol / L NH4HCO3, and dithiothreitol (DTT) solution was added to the solution to achieve a final concentration of 10 mmol / L. The solution was then incubated in a water bath at 56°C for 1 h. Next, iodoacetamide (IAM) solution was added to achieve a final concentration of 55 mmol / L, and the reaction was carried out for 40 min in the dark. The product obtained after the reaction was desalted, and then placed in a vacuum centrifuge at 45°C to evaporate the solvent to dryness under vacuum.

[0039] The peptide sequence of the sample was analyzed using an Easy-nLC 1200 liquid chromatograph. 5 μL of sample was loaded into an RPLC C18 (Acclaim PepMap, 150 μm × 180 mm, 3 μm). 0.1% formic acid aqueous solution and 20% formic acid aqueous solution-80% acetonitrile were used as solvent B. The flow rate was 600 nL / min. The solvent flow rates were as follows: 4-8% B for 0-2 min, 8-28% B for 2-45 min, 28-40% B for 45-55 min, 40-95% B for 55-56 min, and 95% B for 56-66 min. The hydrolysate was separated by high-performance liquid chromatography (HPLC) and then analyzed by mass spectrometry using a QExactive mass spectrometer (Thermo Fisher Scientific, USA). Spray voltage: 2.2 kV, capillary temperature: 270 °C. MS parameters: resolution: 70,000, 400 m / z, scan range: 100.0–1500.0 m / z. MS / MS parameters: resolution 17,500, 400 m / z, TopN: 20, NCE / stepped NCE: 28. The raw mass spectrometry file was searched using Byonic based on fixed modifications (Carbamidomethyl (C)) and variable modifications (Oxidation (M) and Acetyl (N-term)) in the target protein database.

[0040] 5. Virtual Screening: The purified and identified bioactive peptides underwent computer-based screening for toxicity and bioavailability. First, binding energy calculations were performed using molecular docking to determine the potential DPP-IV inhibitory activity of the identified peptides. Then, the obtained peptide sequences were compared with and removed from the BIOPEP database and literature. PeptideRanker was used to analyze the peptides, calculating their potential biological activity. PepDraw was used to predict the peptides' hydrophobicity, isoelectric point, and net charge. Proteomics was used to predict the peptides' water solubility. ToxinPred and admetSAR@LMMD (https: / / lmmd.ecust.edu.cn / admetsar1) were used to predict the peptides' toxicity. Furthermore, pharmacokinetic studies of the peptides were performed on the SwissADME platform (http: / / www.swissadme.ch / ) and iDrug (https: / / drug.ai.tecent.com / console / cn / admet). On the admetSAR@LMMD website, when Lipinski is shown as "Yes" in Druglikeness, the potential active peptide as a DPP-IV inhibitory peptide can be retained.

[0041] 6. Molecular docking: DPP-IV (PDB ID: 5J3J) was used as the receptor protein for molecular docking, and KFPF, IFR, KGFL, KIPF, KLF, and IPI were used as ligand molecules for molecular docking. The corresponding standard gene name was searched in the Protein Crystal Structure Database (PDB) to find the corresponding receptor protein. The species was selected as "homo sapiens". The PDB format file of the receptor protein was downloaded and processed using PyMOL software to remove water, ligands, and ions. Using the PubChem database, the 3D model of the ligand molecule IPI was searched and downloaded as an SDF format file. At the same time, all ligand molecules in the SDF format files were imported into Chem3D software for energy minimization (mm2) and converted to PDB format files. The PDB format files of the receptor protein and ligand molecules were imported into AutoDockVina for molecular docking preprocessing (converted to PDBQT). Then, semi-flexible (molecular conformation can be twisted, protein remains unchanged) molecular docking was carried out. The docking grid was defined according to the position of the co-crystallized ligands: the grid box size is x = 90.0, y = 80.0, z = 126.0, and the grid box center is x = 28.879, y = -2.763, z = 72.412. The molecular docking results were saved and visualized using PyMOL (3D plotting) and Ligplus (2D plotting) software.

[0042] 7. In vivo activity verification of walnut DPP-IV inhibitory peptides: The synthesized DPP-IV inhibitory peptides (KFPF, KERF) were subjected to animal experiments to analyze their in vivo hypoglycemic effect on type 2 diabetic mice. KERF was a peptide designed in the previous stage through molecular docking and in vitro activity verification.

[0043] The technical solution of the present invention will be further described in detail below with reference to the embodiments.

[0044] Example 1 1. Preparation, enzymatic hydrolysis and optimization of WP 1.1 Preparation of WP After shelling the walnuts and drying them at around 45℃, remove the skin from the walnut kernels. Crush the shelled walnut kernels using a crusher. Degrease the walnuts with n-hexane at a ratio of 1:6 (m / v) for 2 hours. Then filter and repeat the process until the n-hexane is clear. Collect the residue and evaporate it in a ventilated place. Pass it through an 80-mesh sieve to obtain defatted walnut powder. Mix it with deionized water at a ratio of 1:20 (w / v). Adjust the pH of the mixture to 10.5 using NaOH solution, stir at room temperature for 1 hour, and centrifuge to obtain the supernatant. Then adjust the pH of the supernatant to 4.5 using HCl solution, stir at room temperature for 1 hour, centrifuge, and retain the precipitate. Adjust the pH of the precipitate to 7.0, and after cold drying, obtain WP, which is stored at -20℃.

[0045] 1.2 Enzymatic hydrolysis of WP Based on our previous experimental results, we used multi-frequency ultrasound to pretreat walnut protein for 25 minutes at a power of 300W, using alkaline protease as the hydrolytic enzyme. First, the lyophilized WP was dissolved in distilled water (1:25, w / v) and mixed thoroughly, then subjected to ultrasonic pretreatment. Next, alkaline protease (pH 9.0, 55℃, Shanghai Yuanye Biotechnology Co., Ltd., biological reagent) was added to the walnut protein solution for hydrolysis for 180 minutes. After hydrolysis, the enzyme was inactivated in a boiling water bath for 10 minutes, followed by cooling in an ice-water bath and centrifugation at 8000 r / min for 20 minutes. The supernatant was then freeze-dried to obtain walnut protein hydrolysates (WPHs), which were stored at -20℃ for later use.

[0046] 1.3 Determination of degree of hydrolysis Before enzymatic hydrolysis, adjust the pH of the solution to the optimal pH for the enzyme. During hydrolysis, continuously add 0.1 mol / L NaOH solution to maintain pH stability. Record the amount of alkali solution used after the reaction is complete. The calculation formula is as follows: In the formula: c is the concentration of NaOH (mol / L); V is the amount of NaOH consumed (mL); α refers to the degree of dissociation of α-NH2, and 1 / α is a calibration factor, of which alkaline protease is 1.01; neutral protease is 2.27; complex protease is 1.40; trypsin is 3.60; and flavor protease is 1.52. m is the walnut protein mass (g); htot is the number of peptide bonds in walnut protein (mmol / g), 7.35 mmol / g.

[0047] 1.4 DPP-IV inhibitory activity, IC50 50 Determination of value and inhibition mode The sample was dissolved in Tris-HCl buffer (0.1 M, pH 8.0) to a final concentration of 2 mg / mL. 50 μL of DPP-IV (15 U / L) and 25 μL of the sample were incubated in a 96-well plate at 37°C for 10 min. Then, 25 μL of Lly-Pro-pNA (1.6 mM) substrate solution was added, and the plate was incubated at 37°C for 60 min. The reaction was then terminated by adding 100 μL of sodium acetate buffer (1 M, pH 4.0). The absorbance was measured at 405 nm using a 96-well microplate. The DPP-IV inhibition rate was calculated using the following formula: In the formula: Fcontrol refers to the absorbance value of the control group at 405 nm; Fsample refers to the absorbance value of the sample group at 405 nm; Fblank refers to the absorbance value of the blank group at 405 nm.

[0048] At different sample concentrations, the inhibitory activity of fractions of different molecular weights and pure peptides in the purification steps was determined, and the IC50 was calculated from the s-shaped dose-response plot of hydrolysis product or peptide concentration (mg / mL) versus inhibitory activity (%) using GraphPad Prism 10.4.1. 50 value.

[0049] The inhibition type of KFPF was analyzed using the Lineweaver-Buck method. Sample concentrations were 0, 0.26, and 0.52 mg / mL, and substrate concentrations ranged from 1 to 5 μmol / L. Samples were incubated with Gly-Pro-pNA at 37°C for 30 min, and absorbance at 405 nm was measured using a microplate reader.

[0050] 2. Isolation, purification and identification of walnut DPP-IV inhibitory peptide 2.1 Ultrafiltration After cooling the obtained walnut protein hydrolysate (WPHs) to room temperature, ultrafiltration tubes of 10 kDa, 5 kDa, and 3 kDa were added sequentially. Ultrafiltration was performed at 4℃, 8000 g, and 20 min, and four components, U1 (< 3 kDa), U2 (3~5 kDa), U3 (5~10 kDa), and U4 (> 10 kDa), were collected. After freeze-drying, they were stored at -20℃ for subsequent experiments.

[0051] 2.2 Ion Exchange Separation Sephadex C-25 cation exchange resin was equilibrated with deionized water, and then peptide solutions with a relative molecular mass <3kDa were loaded onto the resin. The collected fractions were designated F1 and F2, and both fractions were concentrated, freeze-dried, and stored at -20°C for later use.

[0052] 2.3 Peptide sequence identification The sample was dissolved in 50 mmol / L NH4HCO3, and dithiothreitol (DTT) solution was added to the solution to achieve a final concentration of 10 mmol / L. The solution was then incubated in a water bath at 56 °C for 1 h. Next, iodoacetamide (IAM) solution was added to achieve a final concentration of 55 mmol / L, and the reaction was carried out under light-protected conditions for 40 min. The resulting product was desalted, and then placed in a vacuum centrifuge at 45 °C to evaporate the solvent to dryness under vacuum.

[0053] The peptide sequence of the sample was analyzed using an Easy-nLC 1200 liquid chromatograph. 5 μL of sample was loaded into an RPLC C18 (Acclaim PepMap, 150 μm × 180 mm, 3 μm). 0.1% formic acid aqueous solution and 20% formic acid aqueous solution-80% acetonitrile were used as solvent B. The flow rate was 600 nL / min. The solvent flow rates were as follows: 4-8% B for 0-2 min, 8-28% B for 2-45 min, 28-40% B for 45-55 min, 40-95% B for 55-56 min, and 95% B for 56-66 min. The hydrolysate was separated by high-performance liquid chromatography (HPLC) and then analyzed by mass spectrometry using a QExactive mass spectrometer (Thermo Fisher Scientific, USA). Spray voltage: 2.2 kV, capillary temperature: 270 °C. MS parameters: resolution: 70,000, 400 m / z, scan range: 100.0–1500.0 m / z. MS / MS parameters: resolution 17,500, 400 m / z, TopN: 20, NCE / stepped NCE: 28. The raw mass spectrometry file was searched using Byonic based on fixed modifications (Carbamidomethyl (C)) and variable modifications (Oxidation (M) and Acetyl (N-term)) in the target protein database.

[0054] 3. Screening of DPP-IV inhibitory peptides The purified and identified bioactive peptides underwent computer-based screening for toxicity and bioavailability. First, binding energy calculations were performed using molecular docking to determine the potential DPP-IV inhibitory activity of the identified peptides. Then, the obtained peptide sequences were compared with and removed from the BIOPEP database and literature. PeptideRanker was used to analyze the peptides, calculating their potential biological activity. PepDraw was used to predict the peptides' hydrophobicity, isoelectric point, and net charge. Proteomics was used to predict the peptides' water solubility, and ToxinPred and admetSAR@LMMD (https: / / lmmd.ecust.edu.cn / admetsar1) were used to predict the peptides' toxicity. Furthermore, pharmacokinetic studies of the peptides were performed on the SwissADME platform (http: / / www.swissadme.ch / ) and iDrug (https: / / drug.ai.tecent.com / console / cn / admet). On the admetSAR@LMMD website, when Lipinski is shown as "Yes" in Druglikeness, the potential active peptide as a DPP-IV inhibitory peptide can be retained.

[0055] 4. Peptide synthesis The screened peptides were synthesized artificially using a solid-phase method (purity ≥98%). The molecular weight and purity of the peptides were determined by LC-MS and HPLC (commissioned by Shanghai Taopu Biotechnology Co., Ltd.). First, the resin was swollen, then the first amino acid was inoculated. After deprotection, detection, washing, and condensation, the peptide was cut from the resin, dried, washed, and purified by HPLC. Finally, the purified solution was freeze-dried to obtain powdered peptides, which were then sealed and stored at -20℃ for later use.

[0056] 5. Molecular docking Using DPP-IV (PDB ID: 5J3J) as the receptor protein for molecular docking, and KFPF, IFR, KGFL, KIPF, KLF, IPI as the ligand molecules for molecular docking. Search for the corresponding standard gene name in the Protein Data Bank (PDB), query the corresponding receptor protein, select the species as "homo sapiens", download the pdb format file of the receptor protein, and use pymol software to remove water, delete ligands and ions; with the help of the PubChem database, by querying the 3D model diagram of the ligand molecule IPI, download it as an SDF format file, and at the same time import all the ligand molecules in the SDF format files into Chem3D software for energy minimization (mm2) and convert them into pdb format files. Import the pdb format files of the receptor protein and ligand molecules into AutoDockVina for pre-processing before molecular docking (converted to pdbqt), and then carry out semi-flexible (the molecular conformation can be twisted and the protein remains unchanged) molecular docking. Define the docking grid according to the position of the co-crystallized ligand: the size of the grid box is x = 90.0, the size y = 80.0, the size z = 126.0, and the center of the grid box is x = 28.879, y = -2.763, z = 72.412. Save the result data of the molecular docking and perform visualization processing in PyMOL software (3D drawing) and Ligplus software (2D drawing).

[0057] 6. In vivo activity verification of walnut DPP-IV inhibitory peptide 6.1 Experimental animals SPF-grade male C57 / BL mice (4 weeks old, 20±10 g), SPF-grade normal feed and high-fat feed were all purchased from Beijing Huafukang Biotechnology Co., Ltd. The license number for the experimental unit to use is: SYXK (Guangdong) 2019-0204, license number: SYXK (Guangdong) 2020-0051. A total of 36 male mice were randomly divided into 6 groups, with 6 mice in each group. They were adaptively fed in a standard animal house for 1 week, and the feeding environment conditions were: temperature 23±2°C, humidity 55±10%, 12 h light-dark cycle, and food and water were not restricted. The entire experimental process strictly followed the relevant regulations of the National Animal Ethics Committee and the Experimental Animal Ethics Committee of Guangdong Ocean University.

[0058] 6.2 Experimental design and treatment Thirty-six mice were randomly divided into a normal control group (n=6) and a diabetic group. Throughout the experiment, the normal control group was fed a normal diet, while the diabetic group was fed a high-fat diet for one month. After one month, mice were fasted overnight but allowed free water. The diabetic mice were intraperitoneally injected three times with streptozotocin (STZ) solution (40 mg / kg STZ dissolved in 0.1 M citrate buffer, pH 4.5). One week after injection, a fasting blood glucose level ≥11.1 mM was considered a successful induction of type 2 diabetes. Mice that did not meet the standard were continued to be injected with STZ solution until the model was successfully established.

[0059] Mice in the normal control group were designated as the control group. Mice in the type 2 diabetes group were divided into 5 groups (n=6): Model group, low-dose KFPF group (KFPF-L), high-dose KFPF group (KFPF-H), KERF group (KERF), and sitagliptin group (Sit) (Table 1). During gavage, mice in the normal control group continued to be fed a normal diet, while mice in the type 2 diabetes group continued to be fed a high-fat diet. Animal weight, food intake, and water intake were recorded weekly. Each group was administered the medication once daily for 28 consecutive days. The control and model groups received an equal volume of physiological saline. After 4 weeks of treatment, animals were euthanized by cervical dislocation after nighttime fasting. Blood, liver tissue, and kidney tissue were collected and stored at -80°C for biochemical studies.

[0060] Table 1. Grouping and dosage of type 2 diabetic mice under intervention

[0061] 6.3 Measurement of fasting blood glucose After the administration of the drug, fasting blood glucose was measured every 7 days. Before the measurement, the mice were fasted for 12 hours (but not water). Blood was collected by cutting off the tail, and the second drop of blood was placed on a blood glucose test strip. The fasting blood glucose of the mice was measured using a blood glucose meter.

[0062] 6.4 Determination of Oral Glucose Tolerance (OGTT) On day 24 of drug administration, the oral glucose tolerance of mice was determined. Before the experiment, mice were fasted overnight (but allowed water) for 12 hours, and their fasting body weight and fasting blood glucose were measured. After the mice had recovered for 30 minutes, they were administered 20% glucose solution by gavage at a dose of 0.1 ml / 10 g. Blood samples were collected from the tail vein of the mice at 30, 60, 90, and 120 minutes after gavage, and blood glucose levels were measured using a glucometer and blood glucose test strips.

[0063] 6.5 General observation and organ indices Mice were kept in different cages with free access to food and water, and administered the drug daily for 4 weeks. During the test, the weight of each mouse was measured weekly, the total food intake per cage was monitored regularly, and the average for each mouse was calculated. Fasting blood glucose (FBG) was measured weekly after a 6-hour fast. Baseline blood glucose levels were measured using a portable blood glucose meter through the tail notch, and the final blood glucose concentration was measured after the last administration.

[0064] After the experiment, the mice were fasted overnight (12 h). Immediately after cervical dislocation, the heart, liver, spleen, and kidneys were dissected and removed. The organs were rinsed with cold saline, excess water was absorbed with filter paper, and the tissues were collected and weighed. A portion of the liver and kidneys was retained for subsequent histopathological evaluation. The organ index, calculated as the ratio of wet weight to body weight, is shown in Formula 3.1. Among them, "body weight" refers to the weight of the organs after sacrifice, and "mouse weight" refers to the final weight of the mouse before sacrifice.

[0065] 6.6 Animal handling After week 4, mice were fasted for 8 hours, then euthanized by cervical dislocation after blood collection via enucleation. Blood was allowed to stand at room temperature for 20 minutes before centrifugation (3000 rpm, 20 minutes) to obtain serum. Muscle, pancreas, small intestine, liver, and kidney tissues were collected from the dissection, weighed, and aliquoted into cryovials. These were then flash-frozen in liquid nitrogen and transferred to a -80°C freezer for later use. Liver and kidney samples were preserved and immersed in 10% paraformaldehyde fixative for hematoxylin-eosin (H&E) staining.

[0066] 6.7 Homeostasis Model Assessment of Insulin Resistance (HOMA-IR) Mice were euthanized due to spinal cord dislocation. After enucleation, blood samples were collected and placed in 1.5 mL centrifuge tubes. The blood samples were incubated at 25±2℃ for 20 min, then centrifuged at 3000 rpm for 15 min, and the supernatant (serum) was collected. Fasting blood glucose (FBG) was measured using a blood glucose meter, and fasting insulin (FINS) was measured using an ELISA kit. The evaluation criteria were calculated using the following formula: HOMA-IR = FBG × FINS 22.5 6.8 Determination of biochemical indicators in serum Thaw the serum in advance, shake well, and store on ice. Serum glucose, TG, TC, LDL-c, HDL-c, NEAF, and GSP levels were detected using a Nanjing Jiancheng ELISA kit. INS, DPP-IV, and GLP-1 levels in mouse serum were detected using a commercial ELISA kit. All parameters were measured according to the kit instructions.

[0067] 6.9 Determination of glycogen in the liver After adding physiological saline according to the kit instructions, the liver was homogenized on ice and the supernatant was obtained using a low-temperature centrifuge (15 min, 4 ℃, 8000 rpm). The liver glycogen content was then determined using this kit. All test methods were performed in accordance with the operating procedures outlined in the kit instructions.

[0068] 6.10 Animal histopathological observation Mouse livers and kidneys were removed from formaldehyde fixative. The samples were processed to a thickness of less than 0.5 cm and then dehydrated with alcohol, with the alcohol concentration gradually increasing to remove water from the tissue blocks. The dehydrated tissue became transparent. The tissue was immediately immersed in melted paraffin wax until it completely saturated the tissue, embedding it. After hardening, it was sectioned. The sectioned tissue was flattened in hot water, transferred to glass slides, and dried in a 45 °C incubator. After dewaxing, the cell nuclei and cytoplasm were stained with hematoxylin and eosin, and the sections were observed under an optical microscope.

[0069] 6.11 Collection of intestinal contents samples After euthanizing the mice, they were quickly placed on a clean bench, dissected, and the cecum was removed. The contents of the cecum were taken out using a sterile sampling spoon and placed in a sterile cryovial. The contents were then rapidly frozen with liquid nitrogen and stored at -80°C for later use.

[0070] 6.11.1 Sequencing of 16S rRNA amplicon from mouse gut microbiota DNA was extracted, quantified, and its quality was assessed from mouse cecal contents samples. Selected variable regions were subjected to fluorescence quantification, and samples were mixed in appropriate proportions based on the results. Libraries were prepared, quantified after quality control, diluted, and then sequenced.

[0071] 6.11.2 Extraction and amplification of genomic DNA from mouse intestinal flora For microbiome samples from various sources, the most suitable total DNA extraction method was selected based on past project experience. Nanodrop was used for DNA quantification, and DNA extraction quality was assessed by 1.2% agarose gel electrophoresis. Target sequences reflecting microbial community composition and diversity, such as microbial ribosomal RNA or specific gene fragments, were typically used. Primers were designed based on conserved regions within the sequences, and sample-specific barcode sequences were added. PCR amplification was then performed on variable regions (single or multiple consecutive regions) of rRNA genes or specific gene fragments. PCR amplification used Pfu high-fidelity DNA polymerase from TransGen Biotech, with strict control over the number of amplification cycles to ensure consistency in amplification conditions for samples from the same batch. A negative control was also included to detect microbial contamination from the environment and reagents. Any sample population showing bands in the negative control was not used for subsequent experiments.

[0072] 6.11.3 Purification of PCR Products PCR products were detected using a 2% agarose gel. Qualified PCR products were purified using magnetic beads, quantified using enzyme-linked immunosorbent assay (ELISA), and mixed in equal volumes according to concentration. After mixing, the PCR products were detected by electrophoresis, and the target band was recovered.

[0073] 6.11.4 Library Construction and Sequencing The library construction kit used was the TruSeq® DNA PCR-Free Sample Preparation Kit. The constructed library was quantified using Qubit and Q-PCR. After the library was identified as qualified, it was sequenced using a NovaSeq 6000.

[0074] 6.11.5 Bioinformatics Analysis The raw data from sequencing were assembled and filtered. Based on the clean data, clustering and species classification analyses of Operational Taxonomic Units (OTUs) were performed. OTU abundance, alpha diversity calculations, Venn diagrams, and petal diagrams were analyzed to obtain information on species richness and evenness within the samples, as well as shared and unique OTUs among different samples or groups. Principal Coordinates Analysis (PCoA), Principal Component Analysis (PCA), and Non-Metric Multi-Dimensional Scaling (NMDS) were used to analyze differences in community structure among different groups. Statistical analysis methods such as T-test, Simper, MetaStat, LEfSe, Anosim, and MRPP were used to test the significance of differences in species composition and community structure among grouped samples. PICRUST, Tax4Fun, FAPROTAX, and BugBase software were used to perform functional prediction analysis on the microbial community in the samples.

[0075] 7. Statistical Analysis Using SPSS 27.0.1 software P One-way ANOVA was performed on the data at a significance level of <0.05 using GraphPad Prism 10.4.1. 50 Value analysis. All experiments were repeated three times, and data are presented as standard deviation ± mean. Statistical analysis and graphing of animal experimental data were performed using R 4.5.1 software. Experimental data are expressed as mean ± standard deviation (SD). Comparisons between two groups were performed using t-tests followed by Tukey post-hoc tests, and comparisons among multiple groups were performed using one-way ANOVA. P A value <0.05 is considered statistically significant.

[0076] 8. Experimental Results and Result Analysis 8.1 Results of Optimization of Enzymatic Hydrolysis Conditions Figure 1A shows the effect of different proteases hydrolyzing WP on the inhibitory activities of DH and DPP-IV. At the same hydrolysis time, alkaline protease showed the highest DH (21.68 ± 0.83%). Furthermore, the DPP-IV inhibitory activities of five walnut protein hydrolysates (WPHs) were determined. The results showed that alkaline protease had the best DPP-IV inhibitory activity, at 58.14 ± 1.13%. This result may be due to the broad range of sites that alkaline protease can bind well to both hydrophilic and hydrophobic amino acids. Therefore, we chose alkaline protease to hydrolyze walnut protein to prepare the DPP-IV inhibitory peptide. The effect of ultrasonic power on the DPP-IV inhibition rate is shown in Figure [Figure number missing]. Figure 1 As shown in Figure B, the inhibitory activity of alkaline protease hydrolysates on DPP-IV initially increased and then decreased with increasing ultrasonic power. The highest DPP-IV inhibitory activity was observed at 300 W (64.47 ± 1.42%). This is because increasing ultrasonic power enhances the mechanical vibration and cavitation effects, leading to protein chain breakage and thus increasing the enzymatic hydrolysis rate. Studies have shown that high-power ultrasound can promote the breaking of intermolecular chemical bonds, thereby accelerating enzyme-substrate binding. However, when the ultrasonic power exceeds 300 W, cavitation tends to saturate, the enzymatic hydrolysis rate decreases, and the DPP-IV inhibition rate declines. This may be due to protein denaturation caused by excessive ultrasonic power, which affects the DPP-IV inhibitory activity. Studies have shown that excessively high ultrasonic levels can damage the enzyme structure and render it ineffective. The effect of ultrasonic time on the DPP-IV inhibition rate is shown in Figure B. Figure 1 As shown in Figure C, the inhibitory activity of WPH against DPP-IV initially increased and then decreased with increasing sonication time. Its DPP-IV inhibitory activity was highest at 25 min (66.83±1.60%), which may be due to the cavitation effect of ultrasound, allowing walnut protein to fully bind with the enzyme, promoting the hydrolysis reaction and increasing the DPP-IV inhibition rate. However, the pressure shock waves and high temperature environment generated by prolonged ultrasound are detrimental to proteolytic enzymes, leading to a decrease in the inhibitory activity of the hydrolysate DPP-IV.

[0077] 8.2 Results of Separation, Purification, Identification, and Virtual Screening The WPH prepared with optimal parameters was subjected to ultrafiltration to obtain four components: U1 (<3 kDa), U2 (3-5 kDa), U3 (5-10 kDa), and U4 (>10 kDa). Figure 2 A shows the DPP-IV inhibition rate and IC50 of each component. 50 Value. Ultrafiltration results showed that the DPP-IV inhibitory activity of the <3 kDa fraction was significantly better than that of the other groups, with an IC50 value. 50The value was 2.81 ± 0.1 mg / mL. The results indicate that the relatively low molecular weight DPP-IV inhibitory peptide exhibited the highest bioactivity. After separating U1 using SP Sephadex C-25, two fractions (F1 and F2) were obtained. The ion exchange separation results are as follows: Figure 2 As shown in B, its inhibition rate and IC 50 Values ​​such as Figure 2 As shown in C. IC50 of component F2. 50 The value (1.47±0.23 mg / mL) was significantly better than that of the F1 component (IC50). 50 The concentration of F2 (2.01 ± 0.31 mg / mL) was significantly better than that of fraction U1, indicating that more active peptide samples were obtained by gel chromatography elution. Therefore, fraction F2 was selected for peptide sequence characterization. The amino acid residue sequence of fraction F2 was identified by LC-MS / MS. Figure 2 D is the total ion chromatogram of the F2 component. A total of 6557 peptides were identified using the database. Figure 2 E shows the length distribution of the F2 component peptides, which are mainly composed of tetrapeptides (31.31%) and pentapeptides (31.66%). Studies have found that the structure of hypoglycemic peptides typically consists of 2–25 protein residues.

[0078] The peptide screening process is as follows: Figure 2 As shown in Figure E, the purified and identified peptide sequences were first subjected to binding energy calculations using molecular docking to identify peptides with potential DPP-IV inhibitory activity. 1401 peptides with potentially high DPP-IV inhibitory activity were screened. Then, using the BIO-UWM database and relevant literature, 1379 new, unreported peptides were identified from these 1401 peptides for further screening. Subsequently, the bioactivity and hydrophobicity of the 1379 peptides were predicted using PeptideRanker and PepDraw. The highest known bioactivity score was 1.0, indicating that highly water-soluble peptides are more readily absorbed. 539 peptides with bioactivity greater than 0.8 were screened, and their water solubility was then predicted, resulting in 78 peptides with good water solubility and significant bioactivity. When predicting peptide toxicity, toxic peptides were removed. After comparing the levels of human intestinal absorption (HIA), oral bioavailability (HOB), Caco-2 cell permeability, plasma protein binding (PPB), and blood-brain barrier permeability (BBBP) of the remaining peptides, five peptides with potentially good biological activity were finally selected. Table 2 shows the specific information corresponding to the peptides.

[0079] Table 2 Physicochemical properties, digestive resistance, transport, absorption, and toxicity of walnut protein DPP-IV inhibitory peptides

[0080] The five DPP-IV inhibitory peptides identified through screening exhibit good water solubility, indicating rapid absorption by the intestine. The sequence lengths of these five DPP-IV inhibitory peptides are all within the range of 3-5 amino acids. The study found that shorter-chain peptides are more stable in vivo, more easily absorbed, and may exhibit better DPP-IV inhibitory activity. Furthermore, all compounds showed beneficial effects on the human gut. This suggests that the five screened peptides may have greater potential for application in drug development and utilization.

[0081] 8.3 Molecular docking results Molecular docking was used to analyze the interaction mechanism between the DPP-IV repressor peptide and DPP-IV, and also to verify the reliability of the peptide screening results. Molecular docking visualization can be found here. Figure 3 In the leftmost image, the three-dimensional protein structure of dipeptidyl peptidase IV (DPP-IV) is shown in pink, while the small molecular structure of the polypeptide is presented in green. The middle image represents the binding of DPP-IV to the polypeptide via amino acid residues connected by yellow lines and hydrogen bonds represented by blue lines. The rightmost image shows the names of the amino acid residue sites where the polypeptide binds to DPP-IV through hydrogen bonds and hydrophobic interactions; the green dashed lines and green amino acid residue names represent hydrogen bonds, while the red eyelash diagram and its black amino acid residue names represent hydrophobic interactions. Studies have shown that hydrogen bond interactions significantly contribute to the DPP-IV inhibitory activity of peptides; the more bonds present, the more effective the binding of DPP-IV to the peptide, and the more significant the inhibitory effect.

[0082] like Figure 3 As shown in Figure A, KFPF forms six hydrogen bonds with the amino acid residues Phe695 (bond length 2.3 Å), Asp729 (bond length 2.4 Å), and Gln731 (bond lengths 2.1 Å and 2.4 Å) of DPP-IV. It also forms hydrophobic interactions with DPP-IV through ten amino acid residues, including Glu699, His757, and Gln761. IFR ( Figure 3 B) binds to the amino acid residues Gln761 (bond length 2.8 Å), Lys696 (bond lengths 2.2, 2.3 Å), and Phe695 (bond lengths 2.0, 2.5 Å) of DPP-IV, forming 8 hydrogen bonds and 6 hydrophobic interactions. KGFL ( Figure 3 C) binds to the amino acid residues His740 (bond length 3.2 Å), Tyr547 (bond length 2.4 Å), Asn710 (bond length 2.8 Å), and Arg125 (bond length 3.2 Å) of DPP-IV, forming 10 hydrogen bonds and 10 hydrophobic interactions. KIPF ( Figure 3D) binds to the amino acid residues His754 (bond length 2.4 Å), Gln761 (bond length 2.8 Å), and Gln731 (bond length 3.0 Å) of DPP-IV, forming 8 hydrogen bonds and 8 hydrophobic interactions. KLF ( Figure 3 E) The amino acid residues Glu205 (bond length 2.1 Å), Arg669 (bond lengths 3.0 and 3.3 Å), and Arg358 (bond length 2.9 Å) of DPP-IV bind to DPP-IV, forming 5 hydrogen bonds and 9 hydrophobic interactions. The docking results are shown in Table 3. Generally, a binding energy < -4 kcal / mol indicates good binding. As shown in the table, the binding energies of the five peptides are all negative (-6.6 ~ -7.3 kcal / mol), indicating that they can all bind tightly to DPP-IV. Among them, the binding energies of KFPF, IFR, KGFL, and KIPF after docking with DPP-IV are all ≤ -7 kcal / mol, indicating strong docking, good activity, and conformational stability. These results indicate that the five peptides screened by computer have good potential DPP-IV inhibitory activity.

[0083] Table 3 Molecular docking results

[0084] 8.4 In vitro activity of DPP-IV inhibitory peptide In vitro activity assays (Table 4) showed that KFPF exhibited the highest inhibitory activity, with an IC50 value of [missing value]. 50 The value was 0.26 mg / mL, which was significantly better than GPFPIIV (IC50), the DPP-IV inhibitory peptide currently identified from milk. 50 = 0.30 mg / mL) and DPP-IV inhibitory peptide (IC50) obtained from sorghum 50 = 0.48 mg / mL). Next is KIPF (IC50). 50 = 0.49 mg / mL), KGFL (IC 50 = 1.46 mg / mL), IFR (IC 50 = 4.74 mg / mL), KLF (IC 50= 9.35 mg / mL). This is inconsistent with the predicted result based on the molecular docking binding energy score. The reason for this phenomenon may be that the software automatically optimized the spatial configuration of the repressive peptide. Studies have found no correlation between the molecular docking score of DPP-IV repressive peptides from bovine milk proteins and the actual activity of the peptides. Most peptide repressive sequences of DPP-IV contain one or more hydrophobic amino acids. This is because hydrophobic amino acids can bind more tightly to the relevant active pockets of DPP-IV during binding, thus exhibiting better inhibitory activity. Studies have also found that peptides with Pro or Ala residues in their sequences can act as strong DPP-IV inhibitors. Among the five peptides screened in this invention, KFPF and KIPF both contain three hydrophobic amino acids and both contain proline, which may be the reason for the high inhibitory activity of KFPF and KIPF.

[0085] Table 4. In vitro activity and IC50 of peptides 50 value

[0086] Note: Letters in the table indicate the significance of differences within the same column. Letters are not interchangeable across different columns. Different lowercase letters indicate significant differences between groups (P<0.05), while the same lowercase letter indicates no significant differences between groups (P>0.05).

[0087] 8.5 Inhibition patterns of the five peptides The inhibition mode of KFPF was determined using the Lineweaver-Buck mapping method. Figure 4 As shown, the slope gradually increases with increasing KFPF concentration, but the position of the intersection point with the vertical axis remains unchanged. This indicates that as the inhibitor concentration increases, the Km value increases while Vmax remains constant, belonging to the typical competitive inhibition type such as IPI (a classic DPP-IV inhibitor). This phenomenon suggests that KFPF inhibits the reaction between DPP-IV and its substrate by interacting with the active site of DPP-IV. Studies have also found that the peptide YPR isolated from pea protein exhibits high DPP-IV inhibitory activity through competitive inhibition. Furthermore, the highly active DPP-IV inhibitory peptide SPPEFLR found in goat milk whey protein also exhibits competitive inhibition of DPP-IV.

[0088] 8.6 Determination of the Highly Active Dipeptide Module for Lowering Blood Glucose Fragment substitution provides an effective strategy for structural modification of food-derived bioactive peptides. These fragments are relatively simple in structure and can be obtained using solid-phase synthesis or expression in food-grade hosts. Previous studies have confirmed that this method has successfully enhanced insulin resistance activity in egg white oocyte transferrin peptides. However, this approach typically limits substitution to single amino acid residues. A further strategy based on functional module substitution based on activity contribution has been proposed, replacing fragments with lower activity contributions in natural peptides with structural units of higher activity, thereby achieving synergistic effects. This study used a combination of molecular docking and functional validation to screen for low- and high-contribution modules suitable for substitution. Applying this strategy to the natural peptide GPAGPR, its in vitro xanthine oxidase inhibitory activity increased by 26.4%, and its uric acid regulation capacity was enhanced by 28.0%. Therefore, to improve the discovery efficiency of food-derived DPP-IV inhibitory peptides, this invention uses the above modification method to modify walnut DPP-IV peptides and verifies their activity through subsequent in vivo experiments, aiming to provide a theoretical basis for related research.

[0089] Molecular docking quantified the interaction energies between DPP-IV and various dipeptides, predicting the potential DPP-IV inhibitory activity of 400 dipeptide modules. Figure 5 A). Subsequently, ten dipeptides with docking scores ranging from a minimum of 33.46 to a maximum of 90.04 were selected for in vitro DPP-IV inhibitory activity assays. The in vitro DPP-IV inhibitory activities are as follows: Figure 5 As shown in Figure B, dipeptides EK (64.47%), KE (70.97%), EE (65.23%), and ER (77.97%) exhibited the highest DPP-IV inhibition rates, while QE and ED showed almost no DPP-IV inhibitory activity. Molecular docking results indicated that the predicted activity of dipeptide IP was low. Therefore, the KIPF peptide obtained from the walnut DPP-IV inhibitory peptide was replaced with KERF. Simultaneously, virtual screening of KERF was performed to detect its toxicity and potential activity, showing good performance. Further animal experiments will be conducted to investigate its in vivo activity.

[0090] 8.7 Animal Experiment Results 8.7.1 Effects of walnut DPP-IV inhibitory peptide on water intake, food consumption, and body weight in type 2 diabetic mice like Figure 6 As shown in Figure A, 28 days of weight monitoring revealed that, compared to the Control group, the Model group showed a decreasing weight trend over time. This indicates that successful modeling led to weight loss. P<0.05); Compared with the Model group, the intervention groups such as KFPF-L group, KFPF-H group, KERF group and Sit group improved the trend of weight loss in T2DM mice, and their weight increased to a certain extent, suggesting the potential regulatory effect of walnut DPP-IV inhibitory peptide on weight change.

[0091] like Figure 6 As shown in B, compared with the Control group, the food intake in the Model group showed a significant increasing trend over time, and the difference in food intake at the last test was statistically significant. P <0.05, fully demonstrating the impact of the T2DM model on the dietary behavior of the experimental subjects. Further analysis revealed that, compared with the Model group, the increase in food intake was reduced in the KFPF-L group, KFPF-H group, KERF group, and Sit group ( P The value was <0.05, suggesting that intervention with walnut DPP-IV inhibitory peptides can improve the abnormal increase in food intake caused by the T2DM model and has a regulatory effect on eating behavior.

[0092] like Figure 6 As shown in Figure C, compared with the Control group, the water intake of the Model group showed a significant increasing trend over time. P <0.05), this result further verifies the successful establishment of the T2DM model. Compared with the Model group, the water intake of the KFPF-L group, KFPF-H group, KERF group, and Sit group was reduced ( P The value <0.05 indicates that intervention with walnut DPP-IV inhibitory peptides has a significant regulatory effect on abnormal water intake caused by the T2DM model.

[0093] 8.7.2 Regulatory effect of walnut DPP-IV inhibitory peptide on oral glucose tolerance like Figure 7 As shown in Figure A, after establishing the T2DM model, compared with the Control group, the fasting blood glucose levels of mice in each intervention group (Model group, KFPF-L group, KFPF-H group, KERF group, and Sit group) were significantly increased. P <0.05). Compared with the Model group, after 4 weeks of gavage, the fasting blood glucose levels of mice in the intervention groups (KFPF-L group, KFPF-H group, KERF group and Sit group) showed a decreasing trend, decreasing by 32.41%, 46.97%, 33.73% and 51.78%, respectively, indicating that walnut DPP-IV inhibitory peptide can improve the fasting blood glucose level of mice.

[0094] The area under the blood glucose-time curve (AUC) in mice was measured to assess the ability of pancreatic β cells to regulate glucose levels. Figure 7As shown in B, after establishing the T2DM model, the AUC of mice in each group (Model group, KFPF-L group, KFPF-H group, KERF group, and Sit group) was significantly increased. P <0.05), indicating that the establishment of the T2DM model inhibited the ability of mice to regulate glucose levels. After 4 weeks of gavage, compared with the Model group, the AUC values ​​of mice in the intervention groups (KFPF-L group, KFPF-H group, FERF group, and Sit group) decreased by 9.69%, 18.04%, 10.67%, and 22.78%, respectively. P <0.01). This indicates that the walnut DPP-IV inhibitory peptide can significantly alleviate the glucose regulation ability of diseased mice. Similarly, studies have observed that pumpkin seed protein peptides LPGFF, LPGF, and MPLPA reduced the AUC by 17.86%, 18.65%, and 14.98%, respectively.

[0095] like Figure 7 As shown in C, compared with the control group, the liver glycogen levels in the model group mice decreased by 68.36% ( P <0.01). Compared with the Model group, the intervention groups (KFPF-L group, KFPF-H group, FERF group and Sit group) all alleviated the decrease in liver glycogen in mice, increasing it by 17.70%, 90.04%, 46.50% and 120.08%, respectively, indicating that gavage administration of walnut DPP-IV inhibitory peptide and sitagliptin effectively improved liver glycogen storage.

[0096] 8.7.3 Effects of walnut peptides on insulin resistance in type 2 diabetes mellitus (T2DM) mice The pancreas is a major target organ damaged by diabetes. Insulin resistance (IR) is a key characteristic of type 2 diabetes mellitus (T2DM) and is significantly associated with obesity, hypertension, cancer, and autoimmune diseases. Serum insulin levels were measured, and the homeostasis model assessment (HOMA-IR) index in mice was analyzed. HOMA-IR was analyzed to assess the body's utilization of insulin. Figure 8 As shown in Figure A, after modeling, compared with the Control group, the serum insulin level in the Model group increased by 32.95% ( P <0.01), indicating that the high-fat diet and STZ-induced hyperinsulinemia in mice was in a state of compensatory hyperinsulinemia. Compared with the Model group, the intervention groups (KFPF-L group, KFPF-H group, FERF group, and Sit group) all showed reduced serum insulin levels, decreasing by 18.80%, 10.25%, 15.02%, and 8.24%, respectively. Figure 8 As shown in B, after modeling, the HOMA-IR of the Model group was significantly higher than that of the Control group. P<0.01 indicates that a high-fat diet and STZ induced increased insulin resistance in mice, reducing insulin efficiency. Compared with the Model group, the intervention groups (KFPF-L group, KFPF-H group, KERF group, and Sit group) significantly reduced serum insulin resistance in mice, decreasing by 45.14%, 52.16%, 44.11%, and 55.75%, respectively, thus improving insulin efficiency in mice. P <0.01).

[0097] 8.7.4 Effects of walnut peptides on organ indices in T2DM mice Mouse organ indices such as Figure 9 As shown, the organ indices (heart, liver, spleen, and kidney) of mice in the Control group were all lower. The results showed that, compared with the Control group, the cardiac organ indices of mice in the intervention groups (KFPF-L group, KFPF-H group, KERF group, and Sit group) were all increased to some extent. Figure 9 A). After the onset of the disease, compared with the control group, the liver and spleen organ indices of mice in the model group increased by 38.13% and 38.29%, respectively. P <0.01), the kidney index increased by 8.46%. Compared with the Model group, the liver index of mice in the intervention groups (KFPF-L group, KFPF-H group, KERF group, and Sit group) decreased by 10.74%, 1.75%, 2.26%, and 3.01%, respectively. Figure 9 B), the spleen index decreased by 16.94%, 25.43%, 27.99%, and 21.58%, respectively. Figure 9 C), the kidney index decreased by 18.60%, 11.79%, 17.89%, and 6.84%, respectively. Figure 9 D).

[0098] 8.7.5 Effects of walnut peptides on blood lipids in type 2 diabetes mellitus (T2DM) mice The effects of walnut DPP-IV inhibitory peptide on lipid metabolism in type 2 diabetes mellitus (T2DM) model mice were investigated by measuring the serum levels of total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C) in each group of mice. Results are shown in [Table missing]. Figure 10 .like Figure 10 As shown in Figure A, the TC in the Model group was significantly higher than that in the Control group. P <0.01); the TC levels in the KFPF-H group, KERF group, and Sit group decreased by 25.66%, 20.12%, and 29.38% respectively compared to the Model. P <0.01), the KFPF-L group decreased by 11.25%, indicating that the intervention of walnut DPP-IV inhibitory peptide has a regulatory effect on TC abnormalities. Figure 10 As shown in B, the TG in the Model group was significantly higher than that in the Control group. P <0.01 indicates successful modeling and induction of dyslipidemia; compared with the Model, the KFPF-H group, KERF group, and Sit group showed decreases of 36.78%, 21.50%, and 52.07%, respectively. P <0.05), the KFPF-L group decreased by 14.65%. For example... Figure 10 As shown in C, the LDL-C of the Model group was significantly higher than that of the Control group. P <0.01); compared with the Model group, the LDL-C of the KFPF-L group, KFPF-H group, and Sit group decreased by 21.94%, 26.21%, and 33.05%, respectively. P The concentration of LDL-C in the KERF group decreased by 15.67% (<0.01), indicating that the walnut DPP-IV inhibitory peptide has a certain effect on improving LDL-C in mouse serum. This suggests an effect on HDL-C supplementation. Figure 10 As shown in D, the HDL-C of the Model group is significantly lower than that of the Control group ( P <0.01); HDL-C levels in the KFPF-L group, KFPF-H group, KERF group, and Sit group were significantly higher than those in the Model group. P The value <0.01 indicates that intervention with walnut DPP-IV inhibitory peptide can increase HDL-C levels in T2DM model mice and improve lipid metabolism.

[0099] 8.7.6 Effects of walnut peptides on kidney pathological damage in mice The liver is a core organ for metabolism, and its functional state has a decisive impact on metabolic homeostasis in patients with type 2 diabetes mellitus (T2DM). To investigate the regulatory effect of walnut protein DPP-IV inhibitory peptide on liver function, liver tissues from mice in different treatment groups were collected for pathological examination. The pathological changes in liver tissues from each group of mice are shown below. Figure 11As shown, stem cells in the Control group were intact, exhibiting normal tissue structure, no cell rupture, uniform distribution, and neatly arranged hepatic cords with normal morphology and no lipid droplets. In contrast, the Model group showed numerous diffuse lipid droplets and vacuoles in the cytoplasm of hepatocytes, indicating significant fatty degeneration. Compared to the Model group, the intervention groups (KFPF-L, KFPF-H, FERF, and Sit groups) showed varying degrees of improvement and repair effects on liver damage in T2DM mice. Specifically, the KFPF-L, KFPF-H, and KERF intervention groups showed partial atrophy of hepatocyte nuclei, reduced inflammatory cell infiltration, and a significant reduction in lipid droplets. The Sit group showed normal hepatocyte arrangement and structure, with no significant inflammatory cell infiltration or cytoplasmic vacuolation, and a significant reduction in lipid droplets. These results suggest that intervention with walnut DPP-IV inhibitory peptides can significantly improve the fat accumulation in the liver of T2DM mice. Previous studies have found that, compared to the model group, mice in the mulberry leaf protein peptide MLPHL group had a denser liver tissue structure and fewer hepatocyte vacuoles.

[0100] 8.7.7 Effects of walnut peptides on kidney pathological damage in mice HE staining of mouse kidney tissue as shown Figure 12 As shown, compared with the Control group, the Model group exhibited abnormal overall kidney tissue structure, with glomerular atrophy, vacuolation, thickening of the glomerular basement membrane, and widening of the lumen. The tissue structural damage in each treatment group (KFPF-L group, KFPF-H group, KERF group, and Sit group) was less severe than that in the Model group, indicating that the walnut DPP-IV inhibitory peptide has a certain protective effect on the kidneys of T2DM model mice.

[0101] 8.7.8 Effects of walnut peptides on gut microbiota in T2DM mice Type 2 diabetes mellitus (T2DM) is a complex metabolic disease characterized by hyperglycemia, dyslipidemia, and insulin resistance. Its occurrence is often associated with a high-sugar, high-fat diet, and these factors collectively lead to gut microbiota imbalance. In recent years, increasing research has confirmed that imbalances in glucose homeostasis are closely related to changes in gut microbiota structure, significantly influencing the occurrence and progression of T2DM and its complications. Therefore, in-depth exploration of the causal relationship between the gut microbiota and host metabolic risk will provide important theoretical basis for identifying susceptible populations and implementing early targeted interventions.

[0102] 8.7.8.1 Evaluation of Microbial 16S rDNA Gene Sequencing and Analysis of Overall Microbial Diversity OTUs in Samples Rank-abundance curves reflect both species abundance and species evenness. Species abundance is represented by the length of the curve on the horizontal axis; the larger the range of the curve on the horizontal axis, the higher the species abundance. Species evenness is represented by the shape (smoothness) of the curve; the flatter the curve, the higher the species evenness. For example... Figure 13 As shown in Figure A, the transverse undulations of each group of samples are relatively wide and eventually tend to flatten, indicating that the sample composition is uniform and abundant. Figure 13 B shows that the number of OTUs gradually increases with increasing sequencing depth, eventually leveling off, indicating a reasonable data volume. To investigate the species composition of each sample, OTUs were clustered across all samples, and species annotations were performed, resulting in a petal diagram. The results are as follows: Figure 13 As shown in C, the Control, Model, KFPF-L, KFPF-H, KERF, and Sit groups share 108 OTUs, the Control group has 2699 unique OTUs, the Model group has 478 unique OTUs, and the KFPF-L, KFPF-H, KERF, and Sit groups have 362, 268, 313, and 269 unique OTUs, respectively. This suggests that T2DM causes a change in the number of OTUs compared to the normal mice.

[0103] 8.7.8.2 Analysis of Microbial Community Diversity in Samples Alpha diversity analysis was used to assess the diversity and richness of gut bacteria in mice from the Control, Model, KFPF-L, KFPF-H, KERF, and Sit groups. Simpson and Shannon indices were calculated for all samples to assess species diversity, Chao1 and Observed species indices were used to characterize richness, and Faith's PD, Pielou's sevenness, and Good's coverage were used to characterize evolution-based diversity, evenness, and coverage, respectively. Figure 14 As shown, the Simpson and Shannon indices of the Control group, Model group, KFPF-L group, KFPF-H group, KERF group, and Sit group all showed significant differences. P <0.05 indicates that modeling and drug administration significantly altered species diversity. Compared to the Control group, the Model group showed a significant decrease in Chao1, Simpson, Shannon, and Observed species indices. P<0.05 indicates a decrease in the number of gut microbiota species in T2DM mice, with a trend towards reduced diversity and evenness of distribution. Compared with the Model group, the Shannon and Simpson indices were slightly increased in the KFPF-L, KFPF-H, KERF, and Sit groups, while the Observed species and Chao1 indices were decreased, although the statistical significance was not significant. P (>0.05), but it still suggests that the richness of the intestinal flora in mice decreased but the evenness increased after feeding with walnut DPP-IV inhibitory peptides, indicating a trend of flora optimization.

[0104] 8.7.8.3 Comparison of multiple samples of mouse gut microbiota Beta diversity is an indicator that measures the differences in species diversity between different microbial communities. It reveals differences in microbial community structure between samples by comparing the species composition of the communities. To visually represent these differences, principal coordinate analysis (PCoA) and non-metric multidimensional scaling (NMDS) can be used to reflect the differences between samples. In the PCoA(A) and NMDS(B) plots, the distance between samples reflects their similarity in composition and abundance. Specifically, the smaller the distance between samples, the more similar their microbial community composition and species abundance. Figure 15 As shown in A and B, the composition and structure of the microbial community were completely separated between the Control group and the Model group. The T2DM group was well separated from the KFPF-L group, KFPF-H group, KERF group, and Sit group, indicating that T2DM alters the structure of the mouse gut microbiota. Walnut DPP-IV inhibitory peptide and sitagliptin can regulate the composition and diversity of the gut microbiota in T2DM mice, and have a preventive and protective effect against gut microbiota dysbiosis in T2DM mice.

[0105] 8.7.8.4 Effects of walnut peptide gavage on the relative abundance of gut microbiota species in T2DM mice Based on the species annotation results, the top 20 species with the highest abundance at the phylum level in each group were selected, and a relative abundance histogram of species was generated. For example... Figure 16As shown in Figure A, the dominant bacterial phyla in the mouse gut were Firmicutes and Bacteroidota. Compared with the Control group, the relative abundance of Firmicutes was increased and the relative abundance of Bacteroidota was decreased in the Model, KFPF-L, KFPF-H, KERF, and Sit groups, suggesting a disordered gut microbiota structure consistent with the course of T2DM. Compared with the Model group, after gavage administration of walnut DPP-IV inhibitory peptide (KFPF-L, KFPF-H, and KERF groups) and sitagliptin, the relative abundance of Firmicutes and Actinobacteriota in the mouse gut increased, while the relative abundance of Bacteroidota, Verrucomicrobiota, and Proteobacteria decreased. Studies have shown that an increased relative abundance of Actinobacteria is beneficial for maintaining glucose homeostasis and has anti-inflammatory effects. Meanwhile, the relative abundance of Proteobacteria decreased. This indicates that gavage administration of walnut DPP-IV inhibitory peptide (KFPF-L group, KFPF-H group and KERF group) and sitagliptin to T2DM mice optimized the structure of the gut microbiota to some extent.

[0106] Based on the species annotation results, the top 20 species with the highest abundance at the family level in each group were selected, and a relative abundance histogram of species was generated. For example... Figure 16As shown in Figure B, compared to the Control group, the relative abundance of Muribacterium and Lachnospiraceae was decreased in the T2DM group. Compared to the Model group, after gavage with walnut DPP-IV inhibitory peptide and sitagliptin, the relative abundance of Peptostreptococcaceae was increased in the KFPF-L, KFPF-H, KERF, and Sit groups. Previous studies have shown that Peptostreptococcaceae has the function of improving host metabolic health and intestinal homeostasis. The relative abundance of Erysipelotrichaceae decreased in the KFPF-L group, and previous studies have found that a decrease in the relative abundance of Erysipelotrichaceae is beneficial to the development of T2DM. The relative abundance of Lachnospiraceae increased in the KFPF-L and KERF groups, and the relative abundance of Lactobacillaceae increased in the KFPF-H group. Previous studies have also shown that the proliferation of Lachnospiraceae and Lactobacillaceae is associated with improved postprandial blood glucose levels. Therefore, these results all indicate that gavage administration of walnut DPP-IV inhibitory peptides is beneficial for the enrichment of beneficial bacteria in the gut of T2DM mice, which may be one of the key factors in the symptom relief of T2DM mice by walnut DPP-IV inhibitory peptides.

[0107] Select the top 20 species with the highest abundance at the genus level for each group and generate a histogram of relative species abundance. For example... Figure 16 As shown in Figure C, at the genus level, compared with the Control group, the relative abundance of beneficial bacteria such as *Muribaculum*, *Ligilactobacillus*, and *Adlercreutzia* was decreased in the Model group. Following gavage administration of walnut protein DPP-IV inhibitory peptide and sitagliptin, compared with the Model group, the relative abundance of *Romboutsia* and *Dubosiella* was increased in the KFPF-L, KFPF-H, KERF, and Sit groups. Recent studies have shown that *Dubosiella* is considered a highly promising probiotic with positive effects on intestinal mucus secretion and intestinal mucosal barrier repair, while also improving chronic inflammation and metabolic disorders. *Romboutsia*, on the other hand, has a restorative effect on antibiotic-disrupted gut microbiota homeostasis and on the intestinal barrier in diet-induced T2DM mice.

[0108] 8.7.8.6 LEfSe (Line Discriminant Analysis Effect Size) analysis LEfSe (Line Discriminant Analysis Effect Size) analysis was performed to assess the magnitude of the effect of each species abundance on the differential effect and to screen out the differential species. Figure 17 Figure A displays species information from phylum to genus in the sample community, arranged from the inside out. Node size, hollow nodes, and color variations represent the average abundance of that taxonomic unit, respectively. Taxonomic units with insignificant intergroup differences and those with significant intergroup differences are shown. Differential bacterial comparisons were performed at the genus level with an LDA score threshold of 3. Figure 17 As shown in B, the length and color of the bars represent the magnitude of the influence of different species and the species in different groups, respectively. Compared to the Control group, Duncanella abundance decreased in the Model group. Compared to the Model group, after gavage with walnut DPP-IV inhibitory peptide and sitagliptin, Adlercreutzia abundance was higher in the KFPF-L group, Lactobacillus and Bifidobacterium abundance was higher in the KFPF-H group, and Dubosiella abundance was higher in the Sit group, consistent with the trend in species composition analysis.

[0109] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made by those skilled in the art to the technical solutions of the present invention without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. A method for preparing a walnut-derived DPP-IV inhibitory peptide, characterized in that, Includes the following steps: (1) Walnut protein was hydrolyzed by alkaline protease to obtain walnut protein hydrolysate; (2) The enzymatic hydrolysate is subjected to ultrafiltration separation to collect the components with a molecular weight of less than 3 kDa; (3) The components with a value less than 3 kDa are separated by cation exchange chromatography to obtain the active peptide components; (4) Mass spectrometry is used to identify the active peptide components to obtain the polypeptide sequence.

2. The preparation method according to claim 1, characterized in that, Step (1) includes a step of pre-treating walnut protein with multi-frequency ultrasound before enzymatic hydrolysis. The ultrasound conditions are: power 250~350 W, time 20~30 min.

3. The preparation method according to claim 1, characterized in that, The ultrafiltration in step (2) is carried out sequentially using ultrafiltration membranes with molecular weight cutoffs of 10 kDa, 5 kDa and 3 kDa.

4. The preparation method according to claim 1, characterized in that, The cation exchange chromatography in step (3) is performed using Sephadex C-25 resin.

5. The preparation method according to claim 1, characterized in that, The mass spectrometry identification steps described in step (4) include: ① Dissolve the sample to be tested in NH4HCO3 buffer solution, and add dithiothreitol and iodoacetamide in sequence for reduction and alkylation reactions; ② Desalt and dry the reaction product; ③ The analysis was performed using an Easy-nLC 1200 liquid chromatograph coupled with a Q Exactive mass spectrometer; ④ Use Byonic software to compare mass spectrometry data with the target protein database to identify polypeptide sequences.

6. The preparation method according to claim 1, characterized in that, The preparation method further includes a step of virtual screening of the identified polypeptide sequences, wherein the virtual screening includes at least one of toxicity and bioavailability prediction.

7. A walnut-derived DPP-IV inhibitory peptide, characterized in that, It is prepared using any one of the preparation methods described in claims 1 to 6, and its amino acid sequence is any one of KFPF, IFR, KGFL, KIPF or KLF.

8. A pharmaceutical composition, characterized in that, It includes any one or more of the walnut-derived DPP-IV inhibitory peptides of claim 7, and pharmaceutically acceptable carriers or excipients.

9. The use of the walnut-derived DPP-IV inhibitory peptide of claim 7 or the pharmaceutical composition of claim 8 in the preparation of a product that inhibits DPP-IV protease activity.

10. The use of the walnut-derived DPP-IV inhibitory peptide of claim 7 or the pharmaceutical composition of claim 8 in the preparation of a medicament for the prevention and / or treatment of diabetes.

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