A hypoglycemic peptide derived from dark muscle of skipjack tuna and screening method and application thereof

Abstract

The application discloses a hypoglycemic peptide derived from dark meat of tuna, a screening method and application thereof, and has the characteristics that the amino acid sequence of the hypoglycemic peptide is DMEKIWHHTF, the preparation method is as follows: the hypoglycemic peptide is extracted from the dark meat of tuna, homogenized, enzymolyzed, separated through gel chromatography, and high-activity components are screened; the peptide sequence is identified through LC-MS / MS, combined with biological activity prediction, water solubility and toxicity analysis, three new hypoglycemic peptides with low binding energy to alpha-amylase and alpha-glucosidase are finally screened through molecular docking; the hypoglycemic peptide has the advantages that the hypoglycemic peptide has good water solubility and no toxicity, can be stably combined with the active center of alpha-amylase and alpha-glucosidase, and can improve insulin resistance from three dimensions of sugar metabolism, lipid metabolism and oxidative stress through multi-target synergistic effect, and has a significant hypoglycemic function.

Description

Technical Field

[0001] This invention belongs to the field of biotechnology, specifically relating to a dual-target hypoglycemic peptide derived from the dark flesh of bonito, which has inhibitory activities on α-amylase and α-glucosidase, as well as its virtual screening, preparation method, and application. Background Technology

[0002] In recent years, naturally derived bioactive peptides have gradually become a hot topic in research on hypoglycemic functional factors due to their easy digestibility and absorption, good biocompatibility, and low toxicity. Among them, hypoglycemic peptides can slow down the digestion and absorption of carbohydrates by inhibiting the activity of α-glucosidase and α-amylase, thereby reducing postprandial blood glucose levels. Currently, various hypoglycemic active peptides have been screened from milk protein, plant protein, and aquatic protein, but research on hypoglycemic peptides derived from marine fish by-products is still relatively limited.

[0003] Skipjack tuna (Katsuwonus pelamis) is an important mid-to-upper-level migratory fish. Its processing generates a large amount of byproducts, including the head, skin, viscera, and dark-colored flesh with a strong fishy odor. These byproducts are highly nutritious, but their utilization rate is currently low. Previous studies have shown that enzymatic hydrolysates of skipjack tuna dark flesh possess antioxidant, blood pressure-lowering, antibacterial, and uric acid-lowering bioactivities, but research reports on its hypoglycemic activity are limited. Therefore, developing novel hypoglycemic peptides from skipjack tuna dark flesh could both increase the added value of these byproducts and provide new natural hypoglycemic components for the functional food industry.

[0004] Traditional methods for screening bioactive peptides mainly rely on enzymatic digestion combined with multi-stage chromatographic separation and activity tracking, which are time-consuming, labor-intensive, and require a large workload. In recent years, the application of computer-aided virtual screening technologies (such as the BIOPEP database, PeptideRanker, and molecular docking software) has enabled the rapid prediction of potential active peptides in protein sequences, the simulation of enzymatic release processes, and the assessment of their bioactivity, significantly shortening the research and development cycle and reducing costs. However, no research has yet been reported on the systematic development of novel hypoglycemic peptides from the dark flesh of skipjack tuna by combining virtual screening, enzymatic digestion preparation, structural characterization, and dual-target molecular docking verification. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a hypoglycemic peptide derived from the dark meat of bonito with dual inhibitory activities of α-amylase and α-glucosidase, as well as its screening method and application.

[0006] The technical solution adopted by the present invention to solve the above-mentioned technical problem is: a hypoglycemic peptide derived from the dark meat of bonito, wherein the hypoglycemic peptide is selected from one of the following amino acid sequences: DMEKIWHHTF, IEKPMGIF, or EKIWHHTF; preferably, the amino acid sequence of the hypoglycemic peptide is DMEKIWHHTF.

[0007] The present invention also provides a method for screening the above-mentioned hypoglycemic peptides, comprising the following steps: Step 1: Take the dark-colored meat of bonito, add water at a ratio of 1g:(2-10)mL, then add pepsin for enzymatic hydrolysis, centrifuge after enzyme inactivation, take the supernatant and freeze dry to obtain the enzymatic hydrolysate; Step 2: The enzymatic hydrolysate obtained in Step 1 is separated and purified using a Sephadex G-25 gel chromatography column. Each component is collected and its inhibitory activity against α-amylase and α-glucosidase is measured to obtain the purified component with the highest activity. Step 3: The purified fraction with the highest activity obtained in Step 2 was identified by LC-MS / MS to obtain the peptide sequence; Step 4: The peptide sequences identified in Step 3 are used to predict bioactivity using PeptideRanker to screen peptides with a hypoglycemic activity score >0.5. At the same time, the water solubility and toxicity are predicted using the Innovagen and ToxinPred online platforms to screen for non-toxic, water-soluble peptides that have not been reported (by analyzing the obtained peptides on the BIOPEP website, it can be determined whether the peptides have been reported). Step 5: Perform molecular docking of the peptides screened in Step 4 with α-glucosidase and α-amylase respectively, calculate the binding free energy, and finally screen out hypoglycemic peptides that have low binding energy with both enzymes. The amino acid sequence of the hypoglycemic peptide is at least one of DMEKIWHHTF, IEKPMGIF and EKIWHHTF.

[0008] Furthermore, the enzymatic hydrolysis conditions described in step 1 are as follows: hydrolysis time 2-4 hours, hydrolysis temperature 35-40℃, pH 2-4, and pepsin addition amount 2000-3000U / g fish meat.

[0009] The present invention also provides the use of the above-mentioned hypoglycemic peptide in the preparation of α-amylase and / or α-glucosidase inhibitors.

[0010] The present invention also provides the use of the above-mentioned hypoglycemic peptide in the preparation of a drug or health food for improving or treating insulin resistance-related conditions, wherein the hypoglycemic peptide is selected from at least one of DMEKIWHHTF, IEKPMGIF, or EKIWHHTF.

[0011] Furthermore, the application is the use of the hypoglycemic peptide in the preparation of reagents or products having at least one of the following functions: (1) Increase glucose consumption in insulin-resistant HepG2 cells; (2) Increase the activity of hexokinase and / or pyruvate kinase in insulin-resistant HepG2 cells; (3) Reduce the levels of total cholesterol and / or triglycerides in insulin-resistant HepG2 cells; (4) Increase the activity of superoxide dismutase in insulin-resistant HepG2 cells; (5) Reduce the level of reactive oxygen species in insulin-resistant HepG2 cells.

[0012] The present invention also provides the application of the above-mentioned hypoglycemic peptide in the preparation of hypoglycemic drugs or functional foods.

[0013] Furthermore, the applications include using the hypoglycemic peptide as an active ingredient to prepare tablets, capsules, oral liquids or powders; or adding it as a functional factor to dairy products, beverages, baked goods or formulated foods.

[0014] The present invention also provides a composition for improving insulin resistance-related conditions, comprising at least one of the above-mentioned hypoglycemic peptides, and an acceptable carrier or excipient.

[0015] Compared with the prior art, the advantages of the present invention are as follows: 1. This invention is the first to identify and screen hypoglycemic peptides with amino acid sequences DMEKIWHHTF, IEKPMGIF, and EKIWHHTF from the dark flesh of bonito. A search of the BIOPEP database revealed no relevant reports for the above peptides, indicating that they belong to novel hypoglycemic active peptides.

[0016] 2. Possesses dual-target inhibitory activity. The hypoglycemic peptide of this invention exhibits significant inhibitory activity against both α-amylase and α-glucosidase, thus constituting a dual-target hypoglycemic peptide. Specifically, DMEKIWHHTF forms 11 and 5 hydrogen bonds with α-glucosidase and α-amylase, respectively, with low binding energy, allowing it to stably bind to the active sites of both enzymes. It exerts its inhibitory effect through hydrogen bonding, hydrophobic interactions, and electrostatic interactions, demonstrating a potential hypoglycemic effect superior to single-target inhibitors.

[0017] 3. The screening method is efficient and accurate. This invention combines traditional enzymatic digestion with computer-aided technologies such as BIOPEP virtual screening, PeptideRanker activity prediction, and molecular docking to construct a systematic screening process of "virtual screening - enzymatic digestion verification - activity tracking - structure identification - molecular docking". This method overcomes the shortcomings of traditional methods, such as being time-consuming, labor-intensive, and prone to errors, and greatly improves the efficiency and accuracy of screening novel bioactive peptides from complex protein sources.

[0018] 4. The hypoglycemic peptides provided by this invention can significantly improve glucose metabolism disorders under insulin resistance. Experiments show that in a glucosamine-induced IR-HepG2 cell model, treatment with the three hypoglycemic peptides Y-1, Y-2, and Y-3 significantly increased glucose consumption in cells; simultaneously, the activities of the key rate-limiting enzymes of glycolysis, hexokinase (HK) and pyruvate kinase (PK), were significantly increased, with HK activity increasing by more than 145% and PK activity increasing by more than 89%, indicating that the above-mentioned hypoglycemic peptides can effectively alleviate glucose metabolism disorders caused by insulin resistance.

[0019] 5. The hypoglycemic peptides provided by this invention can effectively regulate lipid metabolism abnormalities under insulin resistance. Experimental results show that after treatment with the above-mentioned hypoglycemic peptides, the accumulation of total cholesterol (TC) and triglycerides (TG) in IR-HepG2 cells was significantly inhibited, with TC content decreasing by more than 36.5% and TG content decreasing by more than 45.7%, indicating that it has the effect of inhibiting excessive lipid accumulation and improving lipid metabolism disorders, thereby helping to intervene in the occurrence and development of obesity and diabetes from the source.

[0020] 6. The hypoglycemic peptides provided by this invention can significantly enhance the antioxidant capacity of cells and alleviate oxidative stress damage. Experimental results show that after treatment with the above-mentioned hypoglycemic peptides, the activity of superoxide dismutase (SOD) in IR-HepG2 cells significantly increased, with a maximum increase of more than 30%; at the same time, the level of reactive oxygen species (ROS) significantly decreased, with a maximum decrease of more than 17.9%. This indicates that the above-mentioned hypoglycemic peptides can effectively alleviate the oxidative stress state associated with insulin resistance by enhancing the activity of endogenous antioxidant enzymes and clearing excess reactive oxygen species, thus playing a protective role for cells.

[0021] 7. The raw material is a by-product of skipjack tuna processing, realizing high-value utilization of resources. This invention utilizes the dark meat of skipjack tuna, a by-product that is usually discarded or treated as low-value, to develop high-value-added hypoglycemic active peptides, providing a new approach for the comprehensive utilization of aquatic product processing by-products, and has good economic and social benefits.

[0022] 8. The hypoglycemic peptides provided by this invention are derived from natural products or can be artificially synthesized. They are highly safe, have a mild effect, and have good potential for development into auxiliary hypoglycemic health foods or drugs. Cell viability experiments have confirmed that, within the concentration range of 0.05–4 mg / mL, the above-mentioned hypoglycemic peptides have no adverse effects on HepG2 cells, exhibit good biocompatibility, and can be used as active ingredients in functional foods or pharmaceutical preparations.

[0023] In summary, this invention provides a hypoglycemic peptide derived from the dark meat of bonito, along with its screening method and applications. It offers three novel hypoglycemic peptides derived from the dark meat of bonito. These peptides are non-toxic, highly water-soluble, and can stably bind to the active sites of α-amylase and α-glucosidase. Through multi-target synergistic effects, they comprehensively improve insulin resistance from three dimensions: glucose metabolism, lipid metabolism, and oxidative stress, exhibiting significant hypoglycemic function and promising prospects for industrial application. Attached Figure Description

[0024] Figure 1 The results of virtual screening of three protein chains are shown, including (a) the degree of hydrolysis of the three protein chains under four proteases; and (b) the Ae values ​​of the three protein chains under four proteases. Figure 2 The effects of preparation conditions on the degree of hydrolysis and α-amylase inhibition rate of bonito dark muscle hydrolysate were investigated, including (a) the effect of hydrolysis time on the degree of hydrolysis; (b) the effect of hydrolysis time on the α-amylase inhibition rate; and (c) the effect of hydrolysate raw materials on the degree of hydrolysis and α-amylase inhibition rate. Different letters in the same index indicate significant differences (p<0.05). Figure 3 The structure of skipjack tuna peptides was characterized, including (a) Fourier transform infrared spectrum, (b) relative content of protein secondary structure (%), (c) endogenous fluorescence spectrum, and (d) molecular weight distribution. Different letters in the same index indicate significant differences (p<0.05). Figure 4 The chromatograms of the purified skipjack tuna peptides are shown below, where (a) is the α-amylase inhibition rate of skipjack tuna peptides before and after dialysis; (b) is the gel filtration chromatogram of skipjack tuna peptides; and (c) is the hypoglycemic inhibitory activity of each isolated component. Different letters in the same index indicate significant differences (p<0.05). Figure 5 This image shows the molecular docking diagrams of the three selected hypoglycemic peptides with α-glucosidase and α-amylase. In the diagrams, ab represents the molecular docking results of peptide DMEKIWHHTF with α-glucosidase and α-amylase; cd represents the molecular docking results of peptide IEKPMGIF with α-glucosidase and α-amylase; and ef represents the molecular docking results of peptide EKIWHHTF with α-glucosidase and α-amylase. The left side represents α-glucosidase, and the right side represents α-amylase. Note: Purple represents the protein chain, yellow represents the docked peptide chain, the yellow dashed line represents the hydrogen bond between the peptide and protein, and green represents the protein chain site corresponding to the hydrogen bond formation. Figure 6To establish the IR-hepG2 cell model, the following were investigated: (a) the effect of glucosamine on IR-hepG2 cell viability; (b) the effect of a tripeptide on IR-hepG2 cell viability; (c) the effect of different concentrations of glucosamine on cellular glucose consumption; (d) the effect of different concentrations of glucosamine on the rate of decrease in glucose consumption; (e) the optimal treatment time for the IR-HepG2 cell model; and (f) the optimal duration of the IR-HepG2 cell model. * indicates P≤0.05, ** indicates P≤0.01, and *** indicates P≤0.001. Figure 7 The effects of the three peptides on glucose metabolism in IR-hepG2 cells were investigated, including (a) the effect of the three peptides on glucose consumption in IR-hepG2 cells; (b) the effect of the three peptides on HK activity in IR-hepG2 cells; and (c) the effect of the three peptides on PK activity in IR-hepG2 cells. * indicates P≤0.05, ** indicates P≤0.01, *** indicates P≤0.001, and ns indicates no significant difference. Figure 8 The effects of the three peptides on lipid metabolism and oxidative stress in IR-hepG2 cells were investigated, including (a) the effect of the three peptides on TC activity in IR-hepG2 cells; (b) the effect of the three peptides on TG activity in IR-hepG2 cells; (c) the effect of the three peptides on ROS levels in IR-hepG2 cells; and (d) the effect of the three peptides on SOD activity in IR-hepG2 cells. * indicates P≤0.05, ** indicates P≤0.01, *** indicates P≤0.001, and ns indicates no significant difference. Detailed Implementation

[0025] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments.

[0026] Example 1: Virtual screening of hypoglycemic peptides from the dark flesh of bonito. This embodiment aims to rapidly screen protein chains, optimal enzymes, and active peptides with potential hypoglycemic activity from bonito dark meat protein using computer virtual screening technology.

[0027] 1. Experimental Methods 1) Evaluation of potential active fragments of proteins The UniProt database was used to search for the amino acid sequence of validated bonito dark meat. Protein chains were screened to create a map of bioactive peptide precursors. The "Potential Bioactivity Map" tool in BIOPEP was used to identify the type and location of bioactive fragments within the protein sequences. Simultaneously, a quantitative parameter was calculated from the peptides released by the enzyme: the frequency of a specific enzyme (AE) releasing a peptide with a given inhibitory activity, using the frequency of peptides with a given activity in the protein as an evaluation parameter (A). A higher AE value indicates the release of more peptides with the given inhibitory activity.

[0028] Calculate according to formula (1): A = a / N (1), where a is the number of polypeptides with a given activity in the protein sequence; N This represents the number of amino acid residues in the protein.

[0029] Calculate according to formula (2): AE = d / N (2), where d is the number of inhibitory peptides released by one or more specific enzymes; N is the number of amino acid residues in the target protein.

[0030] 2) Virtual enzymatic hydrolysis The composition and proportion of myosin and amino acids in skipjack tuna were analyzed, and their theoretical degree of hydrolysis (TDH) was calculated. The optimal protease was then selected based on the degree of hydrolysis.

[0031] The calculation method is shown in formula (3): TDH = d / D × 100% (3), where: TDH is the theoretical degree of hydrolysis, %; D is the total number of peptide bonds in the protein; d is the number of peptide bonds that are broken.

[0032] 2. Experimental Analysis First, validated amino acid sequences of skipjack tuna dark meat protein were retrieved and downloaded from the UniProt database. After screening, three myoglobin chains with high scores were selected: Q9DGJ0 (147 amino acids), A0A146F0J0 (390 amino acids), and Q9DGI8 (146 amino acids), as shown in Table 1.

[0033] Table 1. Protein chains in the dark flesh of bonito.

[0034] The frequency (A value) of α-glucosidase inhibitory peptides and DPP-Ⅳ inhibitory peptides in the three protein chains was calculated using the "Potential Bioactivity Map" tool in the BIOPEP database. The results are shown in Table 2. All three protein chains exhibited high potential hypoglycemic activity, with the A value for the α-glucosidase inhibitory peptide of Q9DGI8 being 0.0616 and the A value for the DPP-Ⅳ inhibitory peptide being 0.6438.

[0035] Table 2. Predicted potential hypoglycemic activity of bonito dark meat protein

[0036] Subsequently, the three protein chains were subjected to virtual enzymatic hydrolysis using the BIOPEP tool. The proteases selected included pepsin (pH 1.3), trypsin, chymotrypsin, and papain. The theoretical degree of hydrolysis (TDH) and the frequency of release of hypoglycemic inhibitory peptides (AE value) for each protease were calculated.

[0037] The results are as follows Figure 1 As shown in (a) and (b), for the Q9DGI8 protein chain, pepsin exhibits the highest degree of hydrolysis (77.852%) and the highest AE value (0.120). Based on comprehensive comparison, Q9DGI8 is determined to be the optimal target protein chain, and pepsin is the best enzyme for hydrolysis.

[0038] Finally, the selected Q9DGI8 protein sequences were used to predict their bioactivity using the online software PeptideRanker. With a threshold of 0.5, peptides with a hypoglycemic activity score >0.5 were selected. As shown in Table 3, three peptide sequences had scores above 0.5: IP, PL, and RL. In particular, peptide PL had a score of 0.811, indicating that it has high potential hypoglycemic activity.

[0039] Table 3. PeptideRanker scores of some potentially active peptides in the Q9DGI8 protein chain.

[0040] Example 2: Preparation and Optimization of Hypoglycemic Peptides from Skipjack Tuna Dark Flesh 1. Experimental Methods 1) Determination of α-amylase inhibition rate Take 30 μL of a 20 mg / mL sample solution, add 30 μL of α-amylase solution (0.035 mg / mL), and incubate in a 37℃ water bath for 10 min. Then add 60 μL of starch solution (0.08%), and continue the reaction in a 37℃ water bath for 15 min. Finally, add 60 μL of HCl to terminate the reaction, obtaining the reaction solution. Add 20 μL of iodine solution (0.01 mol / L) to the reaction solution and mix for color development. Simultaneously, replace the α-amylase solution with 30 μL of PBS buffer as a blank. Measure the absorbance of the sample at 630 nm. Use acarbose (0.8 mg / mL) instead of the sample as a positive control.

[0041] α-Amylase inhibition rate (%) = (4) Where: Asb - sample blank absorbance; Asc - sample control absorbance; Ab - negative blank absorbance; Ac - negative control absorbance.

[0042] 2) Determination of α-glucosidase inhibition rate Add 50 μL of 0.5 mol / L phosphate buffer (pH 6.7), 20 μL of sample solution, acarbose solution, 50 μL of α-glucosidase solution, and 50 μL of substrate PNPG solution sequentially to a 96-well microplate. Mix well and incubate at 37°C for 10 minutes. Then, add 50 μL of Na₂CO₃ solution (0.67 mol / L) to terminate the reaction. Finally, measure the absorbance at 405 nm using a microplate reader. Each sample was repeated three times. The blank group used deionized water instead of the sample solution, and the background group used 50 μL of 0.5 mol / L phosphate buffer (pH 6.7) instead of 50 μL of α-glucosidase solution and 50 μL of substrate PNPG solution, respectively. The calculation formula is as follows: Inhibition rate (%) = × 100% (5) Where A i The absorbance value of the blank group; A B The absorbance values ​​of the sample group; A C The absorbance value of the background group.

[0043] 2. Assay for hypoglycemic activity Thaw frozen bonito dark meat and add ultrapure water at a material-to-liquid ratio of 1:5 (w / v). Homogenize for 1 min. Adjust pH to 3.0 with HCl, add food-grade pepsin (2500 U / g fish meat), and hydrolyze for 1, 2, 3, 4, 5, and 6 h in a 37℃ water bath. After hydrolysis, inactivate the enzyme at 95℃ for 10 min, cool to room temperature, adjust pH to 7.0 with NaOH, centrifuge at 10000 rpm for 15 min at 4℃, and freeze-dry the supernatant to obtain bonito dark meat hydrolysate. The degree of hydrolysis and α-amylase inhibition rate of the hydrolysate at different time points were measured.

[0044] The results are as follows Figure 2 As shown in (a) and (b), the α-amylase inhibition rate and degree of hydrolysis both showed a trend of first increasing and then decreasing with the extension of enzymatic hydrolysis time. At 3 h of enzymatic hydrolysis, the α-amylase inhibition rate reached its highest value of 54.208%, and the degree of hydrolysis also reached a relatively high level of 75.999%. Therefore, the optimal enzymatic hydrolysis time was determined to be 3 h.

[0045] Furthermore, the effectiveness of raw dark meat and cooked dark meat (pre-cooked) as raw materials for enzymatic hydrolysis was compared. The results are as follows: Figure 2As shown in (c), the degree of hydrolysis (75.999%) and α-amylase inhibition rate (54.208%) of raw dark-colored meat after 3 hours of enzymatic hydrolysis were significantly higher than those of cooked dark-colored meat. Therefore, raw bonito dark-colored meat was determined to be the optimal raw material for enzymatic hydrolysis.

[0046] Example 3: Structural characterization of hypoglycemic peptides from the dark flesh of bonito. The lyophilized samples from Example 2 with different enzymatic hydrolysis times (1-6 h) were subjected to structural characterization.

[0047] 1. Fourier Transform Infrared Spectroscopy (FTIR) Analysis Take 1 mg of lyophilized powder of bonito dark flesh enzymatic hydrolysate, mix it with dried KBr, compress it into tablets, and microwave at a wavenumber of 400-4000 cm⁻¹. -1 Range, resolution 4 cm -1 Infrared spectroscopy was performed under the conditions of 32 scans and a scan speed of 0.2 cm / s (FTIR-4700, JASCO Corporation, Tokyo, Japan). The secondary structure of the protein was calculated and analyzed using OMNIC™ software and Peakfit software.

[0048] Fourier transform infrared spectroscopy (FTIR) analysis results are as follows Figure 3 As shown in (a), as the enzymatic hydrolysis time increased from 1 h to 6 h, the amide I band (approximately 1650 cm⁻¹) increased. -1 The peak position gradually shifts to lower wavenumbers, and the peak shape gradually broadens from sharp to wide. Meanwhile, at 3300 cm⁻¹... -1 A slight blue shift in the position of the nearby absorption peaks indicates that the ordered structure of the peptide chain was disrupted during enzymatic hydrolysis, the hydrogen bond network was reconstructed, and the conformation changed from ordered to disordered. For example... Figure 3 As shown in (b), the secondary structure content calculations indicate that after 3 hours of enzymatic hydrolysis, the β-sheet content significantly increased to its highest value, while the random coil content decreased to its lowest value. This suggests that the peptide chain conformation tends to be more ordered and compact. This ordered conformation is conducive to the binding of peptide segments to the α-amylase active site through hydrogen bonds and hydrophobic interactions, thereby enhancing inhibitory activity. With further extension of the hydrolysis time to 6 hours, the peptide chain was largely cleaved into short peptides, the β-sheet and β-turn contents significantly decreased, and the random coil content significantly increased. The peptide chain conformation tended to be more loose and disordered. The disruption of the ordered structure may lead to a decrease in the binding ability of the peptide to the enzyme, resulting in reduced inhibitory activity. This indicates that the secondary structure composition of the peptide at 3 hours of enzymatic hydrolysis is most conducive to exerting α-amylase inhibitory effects.

[0049] 2. Intrinsic fluorescence spectroscopy analysis Fluorescence spectroscopy analysis was performed using a fluorescence spectrophotometer (F-7100, Hitachi, Japan). Samples were diluted to a protein concentration of 2.0 mg / mL. Intrinsic fluorescence data were collected at an excitation wavelength of 280 nm, with emission spectra ranging from 300 to 450 nm. The slit widths for both excitation and emission were set to 5 nm.

[0050] The results of the intrinsic fluorescence spectroscopy analysis are as follows: Figure 3 As shown in (c), all samples exhibited a characteristic fluorescence emission peak at approximately 291 nm. With increasing digestion time from 1 h to 4 h, the fluorescence intensity significantly increased, and the peak position shifted blue, indicating that the tyrosine residues were encapsulated in a more hydrophobic microenvironment, resulting in a compact conformation. Subsequently, the fluorescence intensity decreased, and the peak position shifted red, indicating a looser conformation. This result is consistent with the FTIR results, confirming that the peptide conformation at 3 h of digestion is most favorable for activity.

[0051] 3. Determination of molecular weight distribution Molecular weight distribution results are as follows Figure 3 As shown in (d), in the products of pepsin hydrolysis for 3 hours, peptides with a molecular weight below 3000 Da accounted for 100%, of which 74.367% of the peptides were concentrated in the 800-1600 Da range (i.e., 7-15 peptides). This indicates that pepsin hydrolysis mainly produces small molecule oligopeptides, which are beneficial for absorption and activity.

[0052] Example 4: Isolation and purification of hypoglycemic active components The enzymatic hydrolysate prepared under the optimal conditions in Example 2 (enzymatic hydrolysis for 3 hours) was dialyzed with a 100 Da dialysis bag for 24 hours to remove salt. Figure 4 As shown in (a), the α-amylase inhibition rate of skipjack tuna peptides after dialysis was higher than that before dialysis, reaching its maximum value at 3 hours, further verifying that 3 hours is the optimal enzymatic hydrolysis time.

[0053] The dialysis-desalted sample was dissolved in pure water to prepare a 10 mg / mL solution. After filtration through a 0.45 μm filter membrane, the solution was loaded onto a Sephadex G-25 gel chromatography column (2 cm × 60 cm). The loading volume was 3 mL. After loading, the sample was allowed to stand for 30 min, and then eluted with ultrapure water at a flow rate of 0.5 mL / min. 3 mL samples were collected per tube using an automated collector. The absorbance was measured at 230 nm, and the separation peak was plotted. Components at the same peak were mixed, lyophilized, and stored at -20℃. The inhibitory activities of α-amylase and α-glucosidase were measured. The results are as follows: Figure 4As shown in (b), the enzymatic hydrolysate was separated into three main components: F1, F2, and F3. According to the principle of dextran gel column separation, the components eluted first have larger molecular weights, with the molecular weights of F1, F2, and F3 decreasing sequentially (F1>F2>F3). The hypoglycemic inhibitory activity increases with decreasing molecular weight. The smaller molecular weight of component F3 results in less steric hindrance, allowing it to more easily enter the active site and exhibiting higher inhibitory activity against α-amylase and α-glucosidase.

[0054] The inhibitory activities of the three components on α-amylase and α-glucosidase were measured separately. The results are as follows: Figure 4 As shown in (c), the inhibitory activity of fraction F3 was significantly higher than that of F1 and F2 (p<0.05). Fraction F3 exhibited the highest inhibition rates against α-amylase and α-glucosidase, respectively. Therefore, fraction F3 was collected, lyophilized, and used for subsequent mass spectrometry identification.

[0055] Example 5: Amino Acid Sequence Identification and Bioinformatics Analysis The most active component F3 obtained in Example 4 was identified by LC-MS / MS.

[0056] 1. Peptide extraction Add an appropriate amount of 0.25% acetic acid to the sample, shake thoroughly to mix, and centrifuge at high speed. Collect the supernatant peptide extract, freeze-dry, reconstitute with an appropriate amount of 0.1% TFA, filter through a 10K ultrafiltration tube, wash twice with 0.1% TFA, desalt the resulting filtrate using a C18 StageTip filter, and vacuum dry. Reconstitute the dried peptides with 0.1% TFA, and measure the OD... 260 Peptide concentrations were determined for LC-MS analysis.

[0057] 2. LC-MS / MS analysis For each sample, an appropriate amount of peptide was taken and chromatographically separated using a Nanoliter flow rate Easy nLC1200 chromatography system (ThermoScientific). Buffer solutions: Solution A was a 0.1% formic acid aqueous solution, and Solution B was a mixture of 0.1% formic acid, acetonitrile, and water (acetonitrile comprising 80%). The column was equilibrated with 100% Solution A. After the sample was injected into a Trap Column (100µm × 20mm, 5µm, C18, Dr. Maisch GmbH), it underwent gradient separation using an analytical column (75µm × 150mm, 3µm, C18, Dr. Maisch GmbH) at a flow rate of 300 nL / min. The liquid phase separation gradients were as follows: 0-2 minutes, linear gradient of solution B from 3% to 5%; 2-42 minutes, linear gradient of solution B from 5% to 25%; 42-52 minutes, linear gradient of solution B from 25% to 45%; 52-55 minutes, linear gradient of solution B from 45% to 90%; 55-70 minutes, solution B maintained at 90%. After peptide separation, DDA (data-dependent acquisition) mass spectrometry analysis was performed using a Q-Exactive Plus mass spectrometer (Thermo Scientific). The analysis time was 70 min, detection mode: positive ion, precursor ion scan range: 300-1800 m / z, primary mass resolution: 70,000 @m / z 200, AGC target: 3e6, primary maximum IT: 30 ms. Secondary mass spectrometry (MS2) analysis of peptides was performed using the following method: MS2 scans of the 20 highest-intensity precursor ions were acquired after each full scan. MS2 resolution: 17,500 m / z 200; AGC target: 2e5; Maximum IT: 60 ms; MS2 activation type: HCD; isolation window: 1.6 m / z; normalized collision energy: 30. Database searches identified a total of 179 peptides.

[0058] PeptideRanker assesses the probability of bioactivity of peptides based on their amino acid composition and sequence characteristics, with a score range of 0 to 1. A score closer to 1 indicates a higher likelihood of bioactivity. In this study, a screening threshold of 0.5 was used, and Table 4 shows that 15 potential bioactive peptides were identified. Water solubility and toxicity were predicted using the Innovagen and ToxinPred online platforms. All candidate peptides were found to be non-toxic and exhibited good water solubility. A search of the BIOPEP database revealed no prior reports for these 15 peptides, indicating they are novel peptides, as shown in Table 4.

[0059] Table 4. Computer simulations predict the properties of α-glucosidase and α-amylase inhibitory peptides.

[0060] Example 6: Molecular docking verification and identification of dominant hypoglycemic peptides To further screen peptides with dual-target inhibitory potential, the 15 peptides in Table 4 were molecularly docked with α-glucosidase (PDB ID: 2QMJ) and α-amylase (PDB ID: 1PIF). 3D structures of the peptides were constructed using ChemDraw3D as ligands. The crystal structures of the target enzymes were downloaded from the PDB database, and water molecules and the original ligands were removed using Chimera software. After hydrogenation, the structures served as acceptors. Molecular docking was performed using AutoDock Vina (4.2.6), and the binding free energy was calculated.

[0061] The docking results are shown in Table 4. All 15 peptides showed low binding energies to both target enzymes. Considering activity score, toxicity, water solubility, novelty, binding energy to both enzymes, and peak intensity ratio, three superior hypoglycemic peptides were finally screened: DMEKIWHHTF, IEKPMGIF, and EKIWHHTF.

[0062] like Figure 5 As shown, molecular docking results indicate that all three peptides can stably bind to the active pockets of α-glucosidase and α-amylase, forming hydrogen bonds, hydrophobic interactions, and electrostatic interactions with key amino acid residues. Figure 5 The ab-values ​​show that DMEKIWHHTF forms 11 and 5 hydrogen bonds with α-glucosidase and α-amylase, respectively. Figure 5 The CD shows that IEKPMGIF forms 9 and 8 hydrogen bonds respectively; by Figure 5 The results showed that EKIWHHTF formed 10 and 3 hydrogen bonds, respectively. The results indicate that DMEKIWHHTF and IEKPMGIF exhibit strong binding stability to both target enzymes, while EKIWHHTF may have a stronger binding affinity to α-glucosidase. Further analysis revealed that all three peptides can interact with key residues such as Asp, Arg, Glu, His, and Tyr, suggesting that they may exert their inhibitory effects by occupying the enzyme's active site, hindering substrate binding, or interfering with catalytic conformation. These results indicate that all three peptides are novel hypoglycemic active peptides with dual-target inhibitory potential.

[0063] Cellular experiments of the three hypoglycemic peptides obtained in Examples 7 and 6 The synthetic peptide was used in the IR-HepG2 cell model to measure its glucose metabolism, lipid metabolism, and oxidative stress indicators, which verified its good hypoglycemic function.

[0064] 1. Establishment of the IR-hepG2 cell model This experiment used glucosamine to construct an IR-HepG2 insulin-resistant cell model. Glucosamine can activate the hexosamine synthesis pathway, inhibit the activation of the insulin PI3K / Akt signaling pathway, reduce the insulin sensitivity of hepatocytes, inhibit cellular glucose uptake and glycogen synthesis, disrupt glucose and lipid metabolism balance, and induce oxidative stress damage, thus effectively simulating the pathological state of insulin resistance in the liver of type 2 diabetes. It is suitable for in vitro evaluation of the hypoglycemic and metabolic regulatory effects of hypoglycemic peptides. The effect of substances on HepG2 cell viability is the basis for in vitro cell experiments with synthetic peptides; the effect may vary for different concentrations. This experiment used the CCK-8 assay to determine the effects of synthetic peptides and glucosamine treatment on HepG2 cell viability. Figure 6 As can be seen from (a) and (b), the concentration of synthetic peptides in the range of 0.05-4 mg / mL and the concentration of glucosamine in the range of 9-18 mmol / L have no adverse effect on cell viability and can be used for subsequent experiments.

[0065] The glucose content in HepG2 cell culture medium under different concentrations of glucosamine was determined by the glucose oxidase-peroxidase method, and the glucose consumption was calculated. Figure 6 As shown in (c) and (d), as the glucosamine concentration increased from 9 mmol / L to 15 mmol / L, cellular glucose consumption gradually decreased, reaching its lowest point at 15 mmol / L. Compared to the control group, HepG2 cells consumed 64.685% less glucose from the culture medium. When the glucosamine concentration increased from 15 mmol / L to 18 mmol / L, the model group still exhibited significant insulin resistance compared to the control group, but glucose consumption tended to increase. This may be because prolonged exposure to high concentrations of glucosamine led to the recovery of some insulin pathways in the cells, thus disrupting the insulin resistance state. Therefore, the optimal concentration for the high-concentration glucosamine-stimulated HepG2 cell insulin resistance model is 15 mmol / L.

[0066] like Figure 6As shown in (e), when HepG2 cells were treated with glucosamine at a concentration of 15 mmol / L for 12, 18, and 24 h, the decrease rates of glucose consumption were 39.152%, 56.473%, and 35.569%, respectively. The results indicate that the glucose consumption was significantly different from the normal group at an insulin induction time of 18 h (P < 0.01), and the decrease rate of glucose consumption was also the highest. Therefore, 18 h was selected as the optimal induction time for constructing the IR-HepG2 cell model. To verify the stability of the insulin resistance model, the IR-HepG2 cell model was constructed according to the above optimal induction time. The original culture medium was discarded, and high-glucose DMEM complete culture medium was added for further culture for 12 and 24 h. Figure 6 As shown in Figure (f), the difference in glucose consumption between the 24-h model group and the 15 mmol / L glucosamine group was extremely significant (P < 0.01), and the glucose consumption reduction rate was the highest. The treatment time for IR-HepG2 cells with the hypoglycemic peptide was 24 h.

[0067] 2. Measurement of glucose metabolism in the IR-hepG2 cell model like Figure 7 As shown in (a), after 18 h of glucosamine treatment and 24 h of drug administration, the glucose consumption of HepG2 cells in the Model group was significantly lower than that in the Control group, indicating that the model was successfully established. Compared with the Model group, the glucose consumption of both the metformin-positive group and the sample group increased, and the glucose consumption of the three peptide sample groups generally showed a trend of first increasing and then decreasing. In conclusion, low, medium, and high doses of Y-1 (DMEKIWHHTF), Y-2 (IEKPMGIF), and Y-3 (EKIWHHTF) can all increase the glucose consumption of IR-HepG2 cells and have a certain effect on improving cellular insulin resistance.

[0068] Glycolysis is crucial for regulating glucose utilization and maintaining glucose homeostasis. Hib and pharmacokinetic (PK) enzymes are key rate-limiting enzymes in the first and last steps of the glycolytic pathway, respectively. Decreased activity of both HK and PK can lead to glucose metabolism disorders. This study assessed the impact of glycemic-lowering peptides on glucose metabolism disorders by detecting changes in HK and PK activity after treating IR-HepG2 cells with these peptides. Figure 7As shown in (b) and (c), compared with the normal group, the activities of HK (reduced by 65.086%) and PK (reduced by 52.424%) in the model group were significantly decreased (P<0.01), indicating that the IR-HepG2 cell model causes cellular glucose metabolism disorder. Compared with the model group, the positive control group showed a significant increase in HK and PK activities (P<0.01), increasing by 145.942% and 89.713%, respectively. After treatment with a certain concentration of hypoglycemic peptides, the intracellular HK and PK activities first increased and then decreased, both higher than those in the model group (P<0.01). The results indicate that the three hypoglycemic peptides can increase HK and PK activities, thereby alleviating glucose metabolism disorder in IR-HepG2 cells, suggesting that these three hypoglycemic peptides have a good protective effect against glucose metabolism disorder in IR-HepG2 cells and are potential resources for health foods or drugs with hypoglycemic functions.

[0069] 3. Measurement of lipid metabolism in the IR-hepG2 cell model Excessive lipid accumulation is a prerequisite for obesity and diabetes. Figure 8 As shown in (a) and (b), the TC and TG levels in the model group were significantly increased compared to the normal group (P<0.01), increasing by 75.884% and 107.260%, respectively, indicating successful modeling. Compared to the model group, the positive control group showed a significant decrease in TC (36.503%) and TG (45.716%) levels (P<0.01). After treatment with the hypoglycemic peptide, the intracellular TC and TG levels were lower than those in the model group. The results indicate that the active peptide treatment group can inhibit the accumulation of TG and TC in IR-HepG2 cells and regulate cellular lipid metabolism.

[0070] 4. Measurement of oxidative stress in R-hepG2 cell model Oxidative stress is a significant cause of insulin resistance (IR), and inhibiting oxidative stress can serve as an adjunct therapy for lowering blood glucose and alleviating diabetic complications. Reactive oxygen species (ROS) can activate multiple oxidative stress pathways, while superoxide dismutase (SOD) can reduce the toxicity of free radicals. This experiment assessed the effects of hypoglycemic peptides on oxidative stress in IR-HepG2 cells by detecting intracellular SOD activity and ROS levels. Figure 8As shown in (c) and (d), compared with the normal group, the model group showed a significant decrease in SOD activity (10.022% decrease, P<0.01) and a significant increase in ROS level (20.803% increase, P<0.01), indicating that insulin resistance leads to increased oxidative stress and decreased antioxidant capacity. Compared with the model group, the positive control group showed a significant increase in SOD activity (30.390% increase, P<0.01) and a significant decrease in ROS level (17.933% decrease, P<0.01). After treatment with the hypoglycemic peptide, compared with the model group, SOD activity in IR-HepG2 cells increased and ROS level decreased. The results indicate that high glucose and high insulin levels induce oxidative stress in HepG2 cells, and the action of the active peptide can enhance the antioxidant capacity of IR-HepG2 cells, showing a potential protective effect against cellular oxidative stress.

[0071] The data are expressed as mean ± standard deviation (mean ± SD), and one-way ANOVA was performed using SPSS 26.0 software. A p-value < 0.05 was considered statistically significant. Graphs were generated using Origin 2024 (Origin-Lab, Northampton, USA).

[0072] The foregoing description is not intended to limit the invention, nor is the invention limited to the examples given. Any changes, modifications, additions, or substitutions made by those skilled in the art within the scope of the invention should also be considered within the protection scope of the invention.

Claims

1. A hypoglycemic peptide derived from the dark flesh of bonito, characterized in that: The amino acid sequence of the hypoglycemic peptide is DMEKIWHHTF.

2. A method for screening hypoglycemic peptides according to claim 1, characterized in that... Includes the following steps: Step 1: Take the dark-colored meat of bonito, add water at a ratio of 1g:2-10mL, then add pepsin for enzymatic hydrolysis, centrifuge after enzyme inactivation, take the supernatant and freeze-dry to obtain the enzymatic hydrolysate; Step 2: The enzymatic hydrolysate obtained in Step 1 is separated and purified using a Sephadex G-25 gel chromatography column. Each component is collected and its inhibitory activity against α-amylase and α-glucosidase is measured to obtain the purified component with the highest activity. Step 3: The purified fraction with the highest activity obtained in Step 2 was identified by LC-MS / MS to obtain the peptide sequence; Step 4: The peptide sequences identified in Step 3 were used to predict bioactivity using PeptideRanker to screen peptides with a hypoglycemic activity score >0.

5. At the same time, the water solubility and toxicity were predicted using the Innovagen and ToxinPred online platforms to screen out non-toxic, water-soluble peptides that have not been reported before. Step 5: Perform molecular docking of the peptides screened in Step 4 with α-glucosidase and α-amylase respectively, calculate the binding free energy, and finally screen out the hypoglycemic peptides that have low binding energy with both enzymes. The amino acid sequence of the hypoglycemic peptide is DMEKIWHHTF.

3. The screening method according to claim 2, characterized in that: The enzymatic hydrolysis conditions described in step 1 are as follows: hydrolysis time 2-4 hours, hydrolysis temperature 35-40℃, pH 2-4, and pepsin addition amount 2000-3000U / g fish meat.

4. The use of the hypoglycemic peptide according to claim 1 in the preparation of α-amylase and / or α-glucosidase inhibitors.

5. The use of the hypoglycemic peptide of claim 1 in the preparation of a medicament for improving or treating insulin resistance-related conditions, characterized in that, The blood sugar-lowering peptide was selected from DMEKIWHHTF.

6. The application according to claim 5, characterized in that... The application is the use of the glycemic lowering peptide in the preparation of reagents or products having at least one of the following functions: (1) Increase glucose consumption in insulin-resistant HepG2 cells; (2) Increase the activity of hexokinase and / or pyruvate kinase in insulin-resistant HepG2 cells; (3) Reduce the levels of total cholesterol and / or triglycerides in insulin-resistant HepG2 cells; (4) Increase the activity of superoxide dismutase in insulin-resistant HepG2 cells; (5) Reduce the level of reactive oxygen species in insulin-resistant HepG2 cells.

7. The use of the hypoglycemic peptide according to claim 1 in the preparation of hypoglycemic drugs.

8. The application according to claim 7, characterized in that, The applications include using the hypoglycemic peptide as an active ingredient to prepare tablets, capsules, oral liquids, or powders.

9. A composition for improving insulin resistance-related conditions, characterized in that, It comprises the hypoglycemic peptide as described in claim 1, and an acceptable carrier or excipient.

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