An anti-inflammatory active peptide from dry-cured ham and a preparation method and identification method thereof

Abstract

An anti-inflammatory active peptide derived from dried cured ham, its preparation method, and identification method are disclosed. The preparation method includes: crushing, acid-dissolving, homogenizing, filtering, centrifuging, and ultrafiltration of dried cured ham mince to obtain a crude peptide fraction <3 kDa; purifying the fraction with better anti-inflammatory effect, JHP-P2-P1, using high-flow-rate, high-capacity macroporous resin chromatography and weak anion exchange chromatography; and identifying the peptide sequence with high anti-inflammatory activity using the following methods: identifying the peptides in the fraction using nano-HPLC-MS / MS technology; scoring its solubility, toxicity, penetrantness, and anti-inflammatory activity using in-silico bioinformatics methods; predicting the docking positions of the six top-scoring peptides for potential key targets using molecular docking technology; and finally establishing a cellular inflammation model to verify its anti-inflammatory activity. The oligopeptides screened in this invention exhibit strong anti-inflammatory activity, are highly efficient, have a simple preparation method, and good reproducibility, showing broad prospects in the food and pharmaceutical fields and can be applied to functional foods for alleviating inflammation.

Description

An anti-inflammatory active peptide derived from dry-cured ham, its preparation method and identification method Technical Field

[0001] This invention relates to the field of biotechnology, specifically to an anti-inflammatory active peptide derived from dried cured ham and its preparation and identification methods, particularly to an identification method for the anti-inflammatory active peptide based on rapid preparation and in silico screening. Background Technology

[0002] Inflammation is a defensive response of living tissues to various damaging lesions caused by exogenous and endogenous damaging factors. In recent years, an increasing number of studies have shown that inflammation is closely related to a variety of chronic non-communicable diseases, such as tumors, cardiovascular and cerebrovascular diseases, diabetes, immune system diseases, and inflammatory bowel disease. The incidence of these non-communicable inflammatory diseases is increasing year by year, and the search for effective treatments and methods has become a focus of research in related fields.

[0003] Currently, common anti-inflammatory drugs on the market include steroids, nonsteroidal anti-inflammatory drugs, and antibiotics. Although these drugs have certain clinical efficacy, long-term use may lead to complications such as pancreatitis and various infections. The long-term toxicity or steroid dependence of these drugs limits their use to some patients or forces them to discontinue them. Therefore, there is an urgent need for natural alternative treatments without side effects.

[0004] Lipopolysaccharide (LPS) is a common endotoxin that can activate monocytes, macrophages, endothelial cells, and epithelial cells through the cell signaling system in vivo, synthesizing and releasing various cytokines and inflammatory mediators, thereby triggering a series of responses in the body. Its main mechanism of action has been well understood. When LPS enters the bloodstream, lipopolysaccharide-binding protein (LBP) recognizes LPS, binds to LPS monomers, and transports it to the surface of myeloid-derived cells. mCD14 on the surface of myeloid-derived cells binds to LPS, forming an LPS-LBP-CD14 triplet complex. This complex is then transported to the TLR4-MD2 protein complex. With the help of MD-2, the triplet complex binds to TLR4, activating TLR4 and causing it to dimerize. After TLR4 activation, the intracellular aggregate conformation changes, transmitting signals into the cell and activating intracellular signal transduction pathways. After the signal is transmitted into the cell, downstream pathways are activated successively, such as NF-κB and mitogen-activated protein kinase (MAPK) pathways, and bioactive factors such as NO and inflammatory factors IL-6 and TNF-α are eventually released.

[0005] Cellular inflammation models have long been the preferred choice for researchers in developing anti-inflammatory drugs and studying their mechanisms due to their simplicity, low cost, and short processing time. Currently commonly used cellular inflammation models include mouse macrophage RAW264.7 and mouse microglia BV2 inflammation models, and the common method involves using LPS to induce an inflammatory response in the cells.

[0006] Studies have shown that bioactive peptides derived from food proteins are potential functional agents for regulating metabolism and promoting human health. Bioactive peptides typically consist of 2-20 amino acids and possess anti-inflammatory, hypotensive, hypoglycemic, and antioxidant properties. Compared to proteins, peptides have lower molecular weight, are more easily absorbed, and have higher stability. In 1972, Jennifer et al. first isolated and identified anti-inflammatory peptides from bee venom, whose effects surpassed those of some nonsteroidal anti-inflammatory drugs. Since then, researchers have continuously isolated and identified anti-inflammatory peptides from various sources. Currently, the isolation and identification of anti-inflammatory peptides from food-derived raw materials is widely studied.

[0007] The stepwise purification of anti-inflammatory peptides through laboratory experiments is labor-intensive, expensive, and time-consuming, making it difficult to apply in a high-throughput manner. Furthermore, with the rapid development and widespread application of next-generation sequencing technologies, the demand for fast, inexpensive, and efficient computational methods is increasing. Therefore, combining laboratory validation with the application of databases containing effective anti-inflammatory peptide properties, and using molecular docking to simulate the target sites of anti-inflammatory peptides in key pathways, holds promise for enabling a rapid and large-scale discovery process of anti-inflammatory peptides.

[0008] Dry-cured ham is rich in protein, unsaturated fatty acids, essential vitamins, and minerals, with a balanced composition of various nutrients, making it a traditional Chinese meat product with high nutritional value. The abundant regional water and heat resources provide favorable conditions for the production of Jinhua ham. During the natural fermentation process after curing, the muscle proteins of the ham undergo hydrolysis, generating bioactive peptides with different functional properties and high health benefits. The scraps generated during the trimming process of the ham are suitable as a source for research on extracting anti-inflammatory peptides. Summary of the Invention

[0009] The purpose of this invention is to provide an anti-inflammatory active peptide and its preparation and identification methods.

[0010] To achieve the above and other related objectives, the technical solution provided by this invention is: an anti-inflammatory active peptide, the amino acid sequence from the N-terminus to the C-terminus being:

[0011] Leu-Glu-Leu-Leu-Lys;

[0012] Leu-Leu-Leu-Leu-Ser;

[0013] Ile-Leu-Ile-Leu-Leu-Thr-Ile-Leu-Glu-Phe;

[0014] Gln-Phe-Ala-Glu-Glu-Asn-Gly-Leu-Leu-Phe-Leu-Glu-Ala-Ser-Ala-Lys;

[0015] Ala-Leu-Gln-Lys-Leu-Glu-Glu-Ala-Glu-Lys-Ala-Ala-Asp-Glu-Ser-Glu-Arg;

[0016] Glu-Ala-Glu-Glu-Arg-Ala-Asp-Ile-Ala-Glu-Ser-Gln-Val-Asn-Lys-Leu-Arg.

[0017] The preferred technical solution is to extract crude polypeptides from dry-cured ham and purify the anti-inflammatory components using macroporous resin chromatography and weak anion exchange chromatography.

[0018] The preferred technical solution includes the following steps:

[0019] Step 1: Remove the muscle fat and fascia tissue from the ham scraps, then add them to hydrochloric acid, homogenize on ice, then centrifuge and separate all the supernatant in the centrifuge tube using membrane separation technology, retaining crude extracts with a molecular weight cutoff of 3 kDa and below, and drying to obtain oligopeptide powder.

[0020] Step 2: The oligopeptide powder was reconstituted to prepare a crude peptide solution. After filtration through a 0.45 μm aqueous microporous membrane, the solution was purified using macroporous resin. The sample loading volume was 10 mL, and the elution flow rate was 5-7 mL / min. When eluting the fractions, ultrapure water was used first, and after the baseline stabilized, 50%-90% ethanol was used for elution. Two fractions were collected: JHP-P1 and JHP-P2. JHP-P2 was purified into JHP-P2-P1 and JHP-P2-P2 using ion exchange chromatography.

[0021] Step 3: Select the component that has the best effect in inhibiting the secretion of pro-inflammatory factors by inflammatory cells for identification, wherein the amino acid sequence of the component is as described in claim 1.

[0022] The preferred technical solution includes the following steps: Specifically, the separation and purification of JHP-P2 into JHP-P2-P1 and JHP-P2-P2 using ion exchange chromatography is as follows: JHP-P2 is redissolved in 10 mg / mL ultrapure water, filtered, and then purified using a DEAE Sepharose FF pre-packed column at a flow rate of 5 mL / min and a sample loading volume of 2 mL. The absorbance of each fraction is measured at a wavelength of 214 nm. After collection, JHP-P2-P1 and JHP-P2-P2 are concentrated and dried.

[0023] To achieve the above and other related objectives, the technical solution provided by this invention is: a method for identifying anti-inflammatory active peptides, which uses an existing peptide database to identify the anti-inflammatory active peptides described in claim 1, specifically including:

[0024] (1) The anti-inflammatory peptide prediction model AIP-Stack was used to predict the anti-inflammatory potential of 7 peptides and above;

[0025] (2) The Pre-AIP server Prediction of Anti-inflammatory Peptides was used to score and predict whether peptides with 7 or fewer peptides have anti-inflammatory activity;

[0026] (3) Use the cell penetration prediction tool CPPpred to screen for long-chain peptides with good penetration;

[0027] (4) Use the toxicity prediction tool https: / / webs.iiitd.edu.in / raghava / toxinpred and the solubility calculation tool https: / / www.genscript.com / tools / peptide-molecular-weight-calculator to screen for peptides that are non-toxic and have good solubility;

[0028] (5) The novelty of the identified anti-inflammatory active peptides was checked in the BIOPEP database Katedra Biochemii Żywności (uwm.edu.pl); and the eight peptides with the highest comprehensive simulation scores were finally selected.

[0029] Due to the application of the above technical solution, the advantages of this invention compared with the prior art are:

[0030] The oligopeptides screened in this invention have strong anti-inflammatory activity, are highly efficient, have simple preparation methods and good reproducibility, and have broad prospects in the fields of food and medicine. They can be applied to functional foods that relieve inflammation. Attached Figure Description

[0031] Figure 1: A shows two components separated and purified by macroporous resin (DA201-C) for crude polypeptide (JHP) of Jinhua ham: JHP-P1 and JHP-P2; B shows two components separated and purified by weak anion exchange (DAEA FF) of JHP-P2: JHP-P2-P1 and JHP-P2-P2.

[0032] Figure 2: A shows the reduction in NO production of each component after DA201-C separation and purification compared to the LPS-positive control group; B shows the reduction in NO production of each component after DAEAFF separation and purification compared to the LPS-positive control group.

[0033] Figure 3: A shows the reduction in IL-6 production of each component after DAEAFF isolation and purification compared to the LPS-positive group; B shows the reduction in TNF-α production of each component after DAEAFF isolation and purification compared to the LPS-positive group.

[0034] Figure 4. Primary mass spectra of the top six peptides in terms of overall score.

[0035] Figure 5 shows the TLR4-MD2 molecular docking diagram of peptides 1-6.

[0036] Figure 6 shows the effects of LLLLS, ILILLTILEF, and ALQKLEEAEKAADESER on inhibiting LPS-induced NO and TNF-α secretion from RAW264.7. Detailed Implementation

[0037] The following specific embodiments illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in these embodiments.

[0038] Please refer to Figures 1-6. It should be noted that the structures, proportions, sizes, etc., illustrated in the accompanying drawings are merely for illustrative purposes and to aid those skilled in the art in understanding and reading the invention. They are not intended to limit the scope of the invention and therefore have no substantial technical significance. Any modifications to the structure, changes in proportions, or adjustments to size are not permitted. The following embodiments are provided to better understand the invention, but are not intended to limit it. Unless otherwise specified, the experimental methods used in the following embodiments are conventional methods. Unless otherwise specified, the experimental materials used in the following embodiments were purchased from conventional biochemical reagent stores.

[0039] Unless otherwise specified, all reagents or materials described in the following examples are commercially available.

[0040] Example 1: Preparation of different hydrophobic peptides

[0041] Preparation of oligopeptides: Muscle fat and fascia tissue were removed from the minced Jinhua ham. The mixture was then homogenized on ice in 4-6 times its volume of 0.01M HCl at 22000 g for 10 seconds each time, followed by centrifugation at 12000 g and 4℃ for 20 min. The supernatant from the centrifuge tube was separated using membrane separation technology, retaining crude extracts with a molecular weight cutoff of 3 kDa or lower. The crude extract was then evaporated and concentrated, and freeze-dried at -40℃ to 50℃ for 24 h to obtain Jinhua ham-derived oligopeptide powder (JHP). It was stored at -20℃ for further research. Under these conditions, using one-year-old Jinhua ham as an example, the crude extract content (per 100g minced meat) was 2.48 ± 0.35 g / 100g, and the peptide content was 76.58 ± 2.28%.

[0042] 2. Peptide separation: The crude peptide powder was reconstituted to prepare a crude peptide solution of 250 mg / mL. After filtration through a 0.45 μm aqueous microporous membrane, the sample loading volume was 10 mL. The elution flow rate was 5-7 mL / min. When eluting the components, ultrapure water was used first, and 50%-90% ethanol was used for elution after the baseline stabilized.

[0043] Two components were collected: JHP-P1 and JHP-P2. As shown in Figure 1A, cell experiments revealed that JHP-P2 exhibited better anti-inflammatory activity than JHP-P1.

[0044] (1) Cell Culture

[0045] RAW264.7 mouse macrophages were cultured in high-glucose DMEM medium (containing 10% fetal bovine serum and 1% penicillin-streptomycin), and then placed in a 37°C incubator containing 5% CO2. The cells were passaged every 24 hours, and all experiments used cells between passage 12 and 25.

[0046] (3) Determination of NO generation

[0047] RAW264.7 cells (5 × 10⁻⁶) 5 (Number of peptides per well) were seeded into 24-well plates, and cultured for 24 days and 1 night. The old culture medium was then discarded, and peptides were added to each well. A blank control group and an LPS-induced control group were also set up, as detailed below:

[0048] Control group: Normal culture for 2h + 24h;

[0049] Induction group: Normal culture for 2 hours + LPS (1 μg / mL) stimulation for 24 hours;

[0050] Experimental group: Add sample (1 mg / mL) and culture normally for 2 h + stimulate with LPS (1 μg / mL) for 24 h;

[0051] Three replicates were set up for each well. After the culture was completed, the supernatant was collected.

[0052] The Griess method was used to determine the NO concentration in the supernatant. The principle is that NO is oxidized to nitrite in water, and the NO content is determined by measuring the nitrite concentration. The specific steps are as follows: 50 μL of cell supernatant was mixed with 50 μL of Griess I reagent and 50 μL of Gries II reagent. After 3 minutes, the absorbance was measured at 540 nm. The NO content in the cell slurry of each sample was calculated based on the plotted nitrite standard curve.

[0053] The component JHP-P2, which best inhibits NO secretion from inflammatory cells, was selected and further purified by ion exchange (DAEA FF).

[0054] JHP-P2 was redissolved in 10 mg / mL ultrapure water, filtered (0.45 µm), and then purified using a DEAE Sepharose FF pre-packed column. The flow rate was 5 mL / min, and the absorbance of each fraction was measured at 214 nm. After collection, JHP-P2-P1 and JHP-P2-P2 were concentrated and dried, and their anti-inflammatory activity was determined, as detailed below.

[0055] (4) Measurement of cytokines NO, IL-6 and TNF-α

[0056] RAW264.7 cells (5 × 10⁻⁶) 4 (3) The cells were seeded into 24-well plates, with 3 replicates per well. The experimental group, blank group, and induction group were set up in the same way as (3) the determination of NO production. After the culture was completed, the supernatant was collected. The effects of 1 mg / mL JHP-P2, JHP-P2-P1 and JHP-P2-P2 on the production of inflammatory factors (NO, IL-6, TNF-α) in cells after LPS induction were determined by ELISA kit.

[0057] The peptide sequence of JHP-P2-P1, the component with the best effect in inhibiting the secretion of pro-inflammatory factors by inflammatory cells, was identified by nano-HPLC-MS / MS.

[0058] (5) Identification of peptide sequences using Nano-HPLC-MS / MS

[0059] The sample was analyzed by LC-MS / MS equipped with an online nano-spray ion source. The entire system was an Orbitrap fusion Lumos mass spectrometer (Thermo Fisher Scientific, MA, USA) with EASY-nanoLC 1200 in series. A total of 3 μL of sample was loaded (analytical column: Acclaim PepMap C18, 75 μm x 25 cm), and the sample was separated by a gradient over 75 min. The column flow rate was controlled at 300 nL / min, the column temperature at 40°C, and the electrospray voltage at 2 kV. The gradient started from 2% B phase, increased nonlinearly to 35% over 60 min, increased to 100% within 1 min, and was maintained for 14 min.

[0060] The mass spectrometer operates in data-dependent acquisition mode, automatically switching between MS and MS / MS acquisition. The mass spectrometry parameters are set as follows: (1) MS: Scan range (m / z): 200-1500; Resolution: 120,000; AGC target: 4e5; Maximum injection time: 50 ms; (2) HCD-MS / MS: Resolution: 15,000; AGC target: 5e4; Maximum injection time: 50 ms; Collision energy: 30%; Dynamic exclusion time: 30 s.

[0061] After processing the mass spectrometry data, peptides with an abundance of 0 and those without a source in the database were removed, and effective peptide sequences were selected for further analysis.

[0062] (6) In-silico analysis of the identified peptide sequences was performed using existing peptide databases.

[0063] The anti-inflammatory peptide prediction model AIP-Stack was used to predict the anti-inflammatory potential of 7 peptides and above.

[0064] The Pre-AIP server Prediction of Anti-inflammatory Peptides (kyutech.ac.jp) was used to score and predict whether peptides with 7 or fewer peptides have anti-inflammatory activity.

[0065] Use the cell penetration prediction tool CPPpred (ucd.ie) to screen for long-chain peptides (10 peptides and above) with good penetration.

[0066] Peptides with good non-toxicity and solubility were screened using a toxicity prediction tool (https: / / webs.iiitd.edu.in / raghava / toxinpred) and a solubility calculation tool (https: / / www.genscript.com / tools / peptide-molecular-weight-calculator);

[0067] The novelty of the identified anti-inflammatory peptides was examined in the BIOPEP database (Katedra Biochemii Żywności (uwm.edu.pl)); the eight peptides with the highest comprehensive simulation scores were finally selected. See Table 1.

[0068] (7) Molecular docking

[0069] ①Receptor processing

[0070] The X-ray crystal structure of TLR4-MD2 (PDB ID: 2Z64) was downloaded from the RCSB protein database. Autodocktools 1.5.6 software was used to perform hydrogenation and water molecule removal on the receptor TLR4-MD2. ② Receptor preparation

[0071] The structures of the identified small molecule peptides were plotted using ChemDraw.

[0072] ③ Docking process

[0073] Using MOE as the docking software, peptides were docked with the receptor protein TLR4-MD2. The fit between different ligand conformations and the crystal structure of TLR4-MD2 was calculated and scored. Docking energy is generally negative; a smaller value indicates a higher docking score, meaning a tighter binding and stronger interaction between the ligand and receptor protein, and a higher likelihood of anti-inflammatory activity. Finally, the interactions between individual small molecules and ligands were visualized and analyzed.

[0074] The present invention will be further explained and illustrated below with specific data.

[0075] The high anti-inflammatory active components selected during the separation and purification process are based on

[0076] Effects of different hydrophobic peptides on cellular NO secretion

[0077] Two peptide components, JHP-P1 and JHP-P2, with different hydrophobicities were purified using DA201-C. We compared their effects with unpurified JHP in inhibiting LPS-induced NO production in RAW264.7 cells. The results, shown in Figure 2A, revealed that JHP-P2 significantly reduced NO production by 28.48 ± 0.84% ​​compared to the LPS-induced inflammation model group. Therefore, we further purified the second component, JHP-P2, using ion exchange to obtain JHP-P2-P1 and JHP-P2-P2, to further explore components with better anti-inflammatory activity.

[0078] Effects of JHP-P2 and its anion exchange-separated components on the secretion of NO, IL-6, and TNF-α

[0079] As shown in Figure 2B, after LPS stimulation, RAW264.7 cells produced 26.86±1.19 μM NO. The JHP-P2 comparison inflammation model group produced 18.47±0.37 μM NO at 1 mg / mL, the JHP-P2-P1 group produced 22.04±0.84 μM NO, and the JHP-P2-P2 group produced 19.13±0.82 μM NO.

[0080] As shown in Figure 3A, after LPS stimulation, RAW264.7 cells produced 707.80±132.35 pg / mL IL-6. In contrast, the control LPS group, JHP-P2-P1 cells produced even less IL-6: 107.28±12.69 pg / mL, a significant decrease of 85.01%. As shown in Figure 3B, after LPS stimulation, RAW264.7 cells produced 2199.89±178.45 pg / mL TNF-α. In contrast, the control LPS group, JHP-P2-P1 cells produced even less TNF-α: 1858.68±68.96 pg / mL, a significant decrease of 15.51%.

[0081] Based on the combined purification results from the two studies and the cell inflammation model experiment, it can be concluded that the component with better anti-inflammatory effect is JHP-P2-P1. This component can be rapidly and extensively purified using a high-flow-rate macroporous resin purification column and a high-capacity weak anion exchange column.

[0082] Nano-HPLC-MS / MS for peptide sequence identification

[0083] Tandem mass spectra were analyzed using PEAKS Studio version 10.6 (Bioinformatics Solutions Inc., Waterloo, Canada). PEAKS DB was used to search the uniprot-Sus scrofa (version 2022, 22164 entries) database with enzymatic digestion set to none. Search parameters included a fragment ion mass tolerance of 0.02 Da, a precursor ion mass tolerance of 7 ppm, a protein card value of 1% FDR (containing at least one unique peptide), and a peptide card value of 1% FDR. A total of 3657 peptides were identified, of which 50.1% had a molecular weight less than 1 kDa. Studies have shown that smaller peptides often exhibit greater biological activity; therefore, combining existing peptide databases with bioinformatics analysis can better identify potential anti-inflammatory peptide sequences.

[0084] The identified peptide sequences were selected and in-silico analysis was performed using existing peptide databases.

[0085] The high-throughput anti-inflammatory peptide prediction models AIP-Stack and Pre-AIP were used to score and predict whether peptides (7 or more amino acid sequences and less than 7 amino acid sequences) have anti-inflammatory activity.

[0086] Use cell penetration prediction tools to screen for long-chain peptides (10 peptides and above) with good penetration.

[0087] Use toxicity prediction tools and solubility calculation tools to screen for peptides that are non-toxic and have good solubility;

[0088] The novelty of the identified anti-inflammatory active peptides was checked in the BIOPEP database; the six peptides with the highest comprehensive simulation scores were finally selected.

[0089] The specific scores of the six peptides are shown in Table 1. As can be seen from the table, for peptides with 7 or fewer peptides, the first two peptides scored close to 0.6 in the Pre-AIP server (the scores of the other peptides did not exceed this); for peptides with more than 7 peptides, the scores of the last four peptides were close to 0.9 (the scores of the other peptides did not exceed this). All six peptides are non-toxic, have good penetrability as long peptides, and their solubility includes both water solubility and lipid solubility. They can be further investigated as potential anti-inflammatory peptides to see if they can dock with key proteins in the inflammatory pathway.

[0090] Molecular docking technology

[0091] Using MOE as the docking software, peptides were docked with the receptor protein TLR4-MD2. The fit between different ligand conformations and the crystal structure of TLR4-MD2 was calculated and scored. Generally, if the binding energy between the ligand and the target protein is less than 0, it indicates that the ligand and receptor protein can bind spontaneously; if the binding energy is less than -5, it indicates that the binding between the ligand and the receptor protein is stable. The docking energy is generally negative; the smaller the value, the higher the docking score, indicating a tighter binding and stronger interaction between the ligand and the receptor protein, and a greater likelihood of anti-inflammatory activity.

[0092] The binding energies of TLR4-MD2 protein to peptide chain 1 are -6.5 kcal / mol, to peptide chain 2 are -6.0 kcal / mol, to peptide chain 3 are -9.7 kcal / mol, to peptide chain 4 are -10.0 kcal / mol, to peptide chain 5 are -11.7 kcal / mol, and to peptide chain 6 are -12.2 kcal / mol.

[0093] Cell experiments

[0094] Repeating the cell experiments in (1), the results showed that LLLLS had the best effect in inhibiting NO production, producing 4.61±0.22μM, compared with 5.96±0.89μM produced by the LPS group, effectively reducing it by 22.65%; in the TNF-α inhibition experiment, ILILLTILEF had the best effect, reducing NO from 1999.88±143.45 pg / mL to 1558.68±48.96 pg / mL.

[0095] In summary, the oligomeric ham anti-inflammatory peptides mainly bind to the TLR4-MD2 receptor through hydrogen bonds and hydrophobic interactions, and this binding mode may be the reason for the antagonistic effect of lipopolysaccharide. Therefore, the peptides with high anti-inflammatory activity identified in this invention endow them with anti-inflammatory activity by blocking the binding of LPS to the TLR4 / MD-2 complex.

[0096] Both wet experimental data and computer verification demonstrate that the peptide components and their sequences prepared, identified, and screened using this invention possess high anti-inflammatory activity. Specifically, the mechanism of action involves competitively binding to TLR4-MD2, which antagonizes LPS and induces inflammation, thereby inhibiting the inflammatory response.

[0097] Our method is more efficient and cost-effective than single-stage chromatographic separation and purification, which can only identify two or three anti-inflammatory peptides at a time. It achieves the goal of rapid and high-throughput identification of anti-inflammatory peptides.

[0098] Table 1

[0099]

[0100] The above description is merely a preferred embodiment for explaining the present invention and is not intended to limit the present invention in any way. Therefore, any modifications or changes made to the present invention under the same inventive spirit should still be included within the scope of protection intended by the present invention.

Claims

1. An anti-inflammatory active peptide, characterized in that: The amino acid sequence from the N-terminus to the C-terminus is: Ala-Leu-Gln-Lys-Leu-Glu-Glu-Ala-Glu-Lys-Ala-Ala-Asp-Glu-Ser-Glu-Arg.

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