Method for efficiently preparing bovine colostrum source immunomodulatory active peptide based on model screening
By optimizing protease screening through virtual enzyme digestion and QSAR model, the problem of low screening efficiency in traditional methods was solved, and bovine colostrum protein peptides with immunomodulatory activity were prepared, realizing the efficient utilization of bovine colostrum resources and the development of functional food peptides.
Patent Information
- Application Number
- CN202511085848.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-08-04
- Publication Date
- 2025-11-14
AI Technical Summary
Traditional protease screening methods are time-consuming and inefficient, which limits the development of bovine colostrum protease hydrolysis systems. Furthermore, existing technologies have failed to effectively utilize the specific enzymatic hydrolysis system and structure-activity relationship of bovine colostrum resources.
Virtual enzyme digestion and quantitative structure-activity relationship (QSAR) models were combined with experimental verification to select the optimal enzyme to enzymatically digest peptides from bovine colostrum solution. Components with a molecular weight <3kDa were obtained by ultrafiltration. The protease screening process was optimized using a multi-stage computer-aided screening platform, and bovine colostrum protein bioactive peptides with immunomodulatory activity, such as KTTMPLW, RDMPIQAF, and SRYPSYGL, were prepared.
It significantly improved the efficiency of protease screening, expanded the raw material sources of functional food peptides, realized the high-value utilization of bovine colostrum resources, and provided a new technical path for the development of safe and efficient natural immunomodulators.
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Figure CN120943888A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of immunomodulatory peptide technology, specifically to a method for efficiently preparing bovine colostrum-derived immunomodulatory active peptides based on model screening. Background Technology
[0002] Immunity is the body's "protective umbrella" for health, and its importance has become even more prominent in the post-pandemic era. Chronic diseases, pathogen infections, and long-term unhealthy lifestyle habits are just some of the factors that can impair an individual's immune function, leading to a variety of illnesses. Currently used immunomodulatory drugs (such as glucocorticoids and cyclosporine) generally suffer from low bioavailability and significant toxic side effects, severely limiting their long-term use. In contrast, food-derived immunomodulatory peptides, due to their good biocompatibility, high absorption efficiency, and low toxicity, represent a highly promising alternative.
[0003] Bovine colostrum is rich in immune factors, making it an ideal raw material for producing immunomodulatory peptides. Enzymatic hydrolysis is a conventional method for producing immunomodulatory peptides, with the core lies in the selection of proteases and process optimization. However, traditional protease screening methods are time-consuming and inefficient. The development of computer-aided screening technologies (such as quantitative structure-activity relationship models and molecular docking) has provided new ideas for protease selection. By combining virtual screening with experimental verification, the preparation efficiency of bioactive peptides can be significantly improved. Summary of the Invention
[0004] Technical challenges:
[0005] The selection of proteases is a crucial step in the enzymatic hydrolysis preparation of immunomodulatory peptides. However, traditional protease screening methods are time-consuming and inefficient, severely limiting the large-scale development of highly active peptides. Meanwhile, although bovine colostrum protein is widely recognized as an ideal source of immunomodulatory peptides due to its rich content of immune factors, current technologies lack sufficient research on its specific enzymatic hydrolysis system and the structure-activity relationship of active peptides, resulting in the ineffective utilization of this high-value resource.
[0006] Technical solution:
[0007] This invention is based on virtual enzyme digestion and quantitative structure-activity relationship (QSAR) model, combined with hydrolysis experiments to select the optimal enzyme, and extracts peptides from bovine colostrum solution by enzymatic digestion. The peptides are then obtained by ultrafiltration to obtain components with a molecular weight <3kDa, and finally three bovine colostrum protein bioactive peptides with potential immunomodulatory activity are obtained. The specific sequences of the bovine colostrum protein bioactive peptides are: KTTMPLW, RDMPIQAF, and SRYPSYGL.
[0008] The specific steps are as follows:
[0009] (1) 236 peptide sequences with immunomodulatory activity and an equal number of peptide sequences without immunomodulatory activity were collected for modeling. 75% of the total dataset was used as the training set to build the model, and 25% of the data was used as the test set to test and evaluate the accuracy of the model;
[0010] (2) The peptide sequences were converted into SMILES strings, and three types of descriptors were generated using Chemopy, Mordred, and MOE. The first two types of descriptors were calculated using Python software, while the MOE descriptors were generated by MOE software. Descriptors were optimized through step-by-step screening to improve model performance;
[0011] (3) Based on RStudio, we constructed nine classification models using random forest (RF), support vector machine (SVM) and linear discriminant analysis (LDA) combined with Chemopy, Mordred and MOE descriptors, and evaluated their performance using receiver operating characteristic (ROC) curves and box plots to select the optimal model combination.
[0012] (4) Ten proteases were selected to perform virtual enzymatic digestion on the main protein sequences in bovine colostrum. Immunomodulatory peptides have relatively small molecular weights, usually consisting of 2-10 amino acid residues. Therefore, the six proteases with the highest proportion of 2-10 peptides after digestion were selected. The proportion of their potential immunomodulatory peptides was predicted by the optimal QSAR model constructed in step (3). Finally, the first four proteases (trypsin, chymotrypsin, alkaline protease, and papain) were determined for subsequent hydrolysis experiments.
[0013] (5) Bovine colostrum powder was prepared into a 5% (w / v) solution and hydrolyzed using four proteases (trypsin, chymotrypsin, alkaline protease, and papain) at their optimal pH and temperature (enzyme addition 2000 U / g, 4 h). After the reaction was terminated (boiling water bath for 10 min), the supernatant was collected by centrifugation (7000 g, 20 min) and stored at -20℃ for subsequent analysis.
[0014] (6) Dilute the sample obtained in step (5) by 400 times and determine the degree of hydrolysis;
[0015] (7) Dilute the sample obtained in step (5) by 20 times and determine the DPPH free radical scavenging rate;
[0016] (8) Select the optimal protease based on the results of steps (6) and (7);
[0017] (9) Bovine colostrum protein hydrolysate was prepared under the following conditions: hydrolysis time 4.59 h, substrate concentration 5.01%, enzyme addition amount 2355.46 U / g, pH 8.5, and temperature 55℃. After centrifugation at 7000g for 20 min, the supernatant was collected and stored at -20℃ for subsequent experiments.
[0018] (10) Ultrafilter and separate the supernatant obtained in step (9) to obtain three components with MW > 10 kDa, 10 kDa < MW < 3 kDa, and MW < 3 kDa. Freeze-dry the bovine colostrum protein hydrolysates with different molecular weights and then determine the immunomodulatory activities of each component.
[0019] (11) Perform mass spectrometry identification on the component with the best immunomodulatory activity, and obtain potential bovine colostrum protein peptides with immunomodulatory activity through bioinformatics screening and molecular docking analysis.
[0020] (12) Through the evaluations in steps (6) and (7), alkaline protease was selected as the best protease.
[0021] (13) The experimental conditions for step (7) are as follows: Prepare a 0.026 mg / mL DPPH ethanol solution. After diluting the sample 20 times, take 100 μL and react it with an equal volume of DPPH solution (blank group: sample + ethanol; control group: DPPH + ethanol). After reacting in the dark for 30 min,
[0022] Measure the absorbance value at 517 nm. The DPPH radical scavenging rate is calculated according to the following formula:
[0023]
[0024] where A test represents the experimental group, A control represents the control group, and A blank represents the blank group;
[0025] (14) The ultrafiltration conditions for step (10) are as follows: Put the hydrolysate into ultrafiltration centrifugal tubes with a molecular weight cut-off of 10 kDa and 3 kDa, and centrifuge at 5000 × g for 40 min to obtain three components: MW > 10 kDa, 10 kDa < MW < 3 kDa, and MW < 3 kDa. Collect all components and freeze-dry them, and determine the immunomodulatory activities of each component. The results are shown in Figure 5 .
[0026] (15) The mass spectrometry identification in step (11) was performed by LC-MS / MS. The peptide was desalted for mass spectrometry detection. Solution A used in the liquid chromatography was a 0.1% formic acid aqueous solution, and solution B was a 0.1% formic acid-acetonitrile aqueous solution (acetonitrile content 84%). The liquid chromatography column (0.15 mm * 150 mm, RP-C18, Column Technology Inc.) was equilibrated with 95% solution A. The sample was loaded into a Zorbax 300SB-C18 peptide traps (Agilent Technologies, Wilmington, DE) via an autosampler and then separated by the column. The relevant liquid chromatography gradient settings were as follows: 0 min - 50 min, linear gradient of solution B from 4% to 50%; 50 min - 54 min, linear gradient of solution B from 50% to 100%; 54 min - 60 min, solution B maintained at 100%. After separation by capillary high-performance liquid chromatography, the product was analyzed by mass spectrometry using a Q Exactive HF-X mass spectrometer (Thermo Fisher) for 60 min. Detection mode: positive ion. Ten fragment spectra were acquired after each full scan (MS2 scan) to obtain the mass-charge ratio of the peptide and peptide fragments.
[0027] (16) The virtual screening step in step (11) is as follows: First, the bioactivity of peptides is predicted using the PeptideRanker online website (http: / / distilldeep.ucd), and peptides with a score greater than 0.5 are defined as peptides with potential bioactivity; then, the AlgPred2.0 online website (https: / / webs.iiitd.edu.in / raghava / algpred2 / batch.html) is used to analyze the sensitization of peptides, and the ToxinPred tool (https: / / webs.iiitd.edu.in / raghava / toxinpred / ) is used to analyze the toxicity and hydrophobicity of peptides;
[0028] (17) The molecular docking step in step (11) is as follows: The crystal structures of TLR2 (PDB ID: 1FYW) and TLR4 / MD-2 (PDB ID: 5IJD) are obtained from the RCSB protein database (https: / / www.rcsb.org / ). Molecular docking analysis is performed using MOE software, and the results are visualized using PyMOL software. The results are shown in […]. Figure 7 .
[0029] Compared with the prior art, the advantages of this invention are:
[0030] This invention integrates virtual enzyme digestion, QSAR prediction model and molecular docking to construct a multi-level computer-aided screening platform, which significantly improves the efficiency of protease screening compared with the traditional hydrolysis screening method.
[0031] This invention uses bovine colostrum as raw material and prepares bioactive peptides with immunomodulatory activity through enzymatic hydrolysis. This not only expands the raw material sources of functional food peptides and realizes the high-value utilization of bovine colostrum resources, but also provides a new technical path for the development of safe and efficient natural immunomodulators. Attached Figure Description
[0032] Figure 1 This is a process flow diagram of the present invention;
[0033] Figure 2 This is a diagram illustrating the process of establishing and selecting QSAR models;
[0034] Figure 3 This is a graph showing the proportion of 2-10 peptides of the main bovine colostrum protein after virtual enzymatic digestion and the percentage of peptides with potential immunomodulatory activity predicted by the QSAR model in each enzymatic digestion product.
[0035] Figure 4 The graph shows the degree of hydrolysis and DPPH free radical scavenging rate of the hydrolysates after hydrolysis by four enzymes.
[0036] Figure 5 This is a graph showing the immunomodulatory activity of each component after ultrafiltration;
[0037] Figure 6 This is a secondary mass spectrum of a partial peptide;
[0038] Figure 7 This is a molecular docking diagram of peptides KTTMPLW, RDMPIQAF, and SRYPSYGL with TLR2 and TLR4 / MD-2. Detailed Implementation
[0039] The specific embodiments are further described below with reference to the accompanying drawings.
[0040] A method for preparing bovine colostrum-derived immunomodulatory peptides, the method comprising the following steps:
[0041] Example 1
[0042] (1) A dataset (75% training set, 25% test set) was constructed by collecting 236 immunomodulatory active peptide sequences and an equal amount of inactive peptide sequences. After converting the peptide sequences into SMILES strings, three types of molecular descriptors were generated using Chemopy, Mordred, and MOE. Nine classification models were constructed by integrating random forest (RF), support vector machine (SVM), and linear discriminant analysis (LDA) algorithms on the RStudio platform. Finally, the optimal model combination was evaluated and selected using ROC curves and box plots. Figure 2 As shown, the optimal model combination obtained through screening is MOE-RF;
[0043] (2) Ten proteases were selected to perform virtual enzymatic digestion on the main protein sequences in bovine colostrum. The six proteases with the highest proportion of peptides 2-10 after digestion were selected, and their potential immunomodulatory peptide proportions were predicted by the optimal QSAR model constructed in step (1). Finally, the first four proteases (trypsin, chymotrypsin, alkaline protease, and papain) were determined for subsequent hydrolysis experiments.
[0044] (3) Prepare a 5% (w / v) aqueous solution of bovine colostrum powder. Under conditions of pH 8.0 and 37℃, add trypsin at an enzyme addition rate of 2000 U / g for 4 hours for enzymatic hydrolysis. After the reaction is completed, terminate the reaction by inactivating the enzyme by treating with a boiling water bath for 10 minutes. Subsequently, centrifuge the hydrolysate at 7000g for 20 minutes, collect the supernatant and store it at -20℃ for later use;
[0045] (4) Dilute the sample prepared in step (3) by 400 times and then determine the degree of hydrolysis;
[0046] (5) Dilute the sample prepared in step (3) 20 times and determine the DPPH free radical scavenging rate, such as Figure 4 As shown, the degree of hydrolysis of bovine colostrum solution by trypsin was 4.20%, and the DPPH free radical scavenging rate was 50.12%.
[0047] Example 2
[0048] (1) A dataset (75% training set, 25% test set) was constructed by collecting 236 immunomodulatory active peptide sequences and an equal amount of inactive peptide sequences. After converting the peptide sequences into SMILES strings, three types of molecular descriptors were generated using Chemopy, Mordred, and MOE. Nine classification models were constructed by integrating random forest (RF), support vector machine (SVM), and linear discriminant analysis (LDA) algorithms on the RStudio platform. Finally, the optimal model combination was evaluated and selected using ROC curves and box plots. Figure 2 As shown, the optimal model combination obtained through screening is MOE-RF;
[0049] (2) Ten proteases were selected to perform virtual enzymatic digestion on the main protein sequences in bovine colostrum. The six proteases with the highest proportion of peptides 2-10 after digestion were selected, and their potential immunomodulatory peptide proportions were predicted by the optimal QSAR model constructed in step (1). Finally, the first four proteases (trypsin, chymotrypsin, alkaline protease, and papain) were determined for subsequent hydrolysis experiments.
[0050] (3) Prepare a 5% (w / v) aqueous solution of bovine colostrum powder. Under conditions of pH 8.0 and 37℃, add chymotrypsin at an enzyme addition rate of 2000 U / g and carry out enzymatic hydrolysis for 4 hours. After the reaction is completed, terminate the reaction by inactivating the enzyme by treating with a boiling water bath for 10 minutes. Subsequently, centrifuge the hydrolysate at 7000g for 20 minutes, collect the supernatant and store it at -20℃ for later use;
[0051] (4) Dilute the sample prepared in step (3) by 400 times and then determine the degree of hydrolysis;
[0052] (5) Dilute the sample prepared in step (3) 20 times and determine the DPPH free radical scavenging rate, such as Figure 4 As shown, the degree of hydrolysis of bovine colostrum solution by chymotrypsin was 2.93%, and the DPPH free radical scavenging rate was 39.52%.
[0053] Example 3
[0054] (1) A dataset (75% training set, 25% test set) was constructed by collecting 236 immunomodulatory active peptide sequences and an equal amount of inactive peptide sequences. After converting the peptide sequences into SMILES strings, three types of molecular descriptors were generated using Chemopy, Mordred, and MOE. Nine classification models were constructed by integrating random forest (RF), support vector machine (SVM), and linear discriminant analysis (LDA) algorithms on the RStudio platform. Finally, the optimal model combination was evaluated and selected using ROC curves and box plots. Figure 2 As shown, the optimal model combination obtained through screening is MOE-RF;
[0055] (2) Ten proteases were selected to perform virtual enzymatic digestion on the main protein sequences in bovine colostrum. The six proteases with the highest proportion of peptides 2-10 after digestion were selected, and their potential immunomodulatory peptide proportions were predicted by the optimal QSAR model constructed in step (1). Finally, the first four proteases (trypsin, chymotrypsin, alkaline protease, and papain) were determined for subsequent hydrolysis experiments.
[0056] (3) Prepare a 5% (w / v) aqueous solution of bovine colostrum powder. Under conditions of pH 8.0 and 55℃, add papain at an enzyme addition rate of 2000 U / g for 4 hours for enzymatic hydrolysis. After the reaction is completed, terminate the reaction by inactivating the enzyme by boiling in a water bath for 10 minutes. Subsequently, centrifuge the hydrolysate at 7000g for 20 minutes, collect the supernatant and store it at -20℃ for later use;
[0057] (4) Dilute the sample prepared in step (3) by 400 times and then determine the degree of hydrolysis;
[0058] (5) Dilute the sample prepared in step (3) 20 times and determine the DPPH free radical scavenging rate, such as Figure 4 As shown, the degree of hydrolysis of bovine colostrum solution by papain was 2.48%, and the DPPH free radical scavenging rate was 54.04%.
[0059] Example 4
[0060] (1) A dataset (75% training set, 25% test set) was constructed by collecting 236 immunomodulatory active peptide sequences and an equal amount of inactive peptide sequences. After converting the peptide sequences into SMILES strings, three types of molecular descriptors were generated using Chemopy, Mordred, and MOE. Nine classification models were constructed by integrating random forest (RF), support vector machine (SVM), and linear discriminant analysis (LDA) algorithms on the RStudio platform. Finally, the optimal model combination was evaluated and selected using ROC curves and box plots. Figure 2 As shown, the optimal model combination obtained through screening is MOE-RF;
[0061] (2) Ten proteases were selected to perform virtual enzymatic digestion on the main protein sequences in bovine colostrum. The six proteases with the highest proportion of peptides 2-10 after digestion were selected, and their potential immunomodulatory peptide proportions were predicted by the optimal QSAR model constructed in step (1). Finally, the first four proteases (trypsin, chymotrypsin, alkaline protease, and papain) were determined for subsequent hydrolysis experiments.
[0062] (3) Prepare a 5% (w / v) aqueous solution of bovine colostrum powder. Under conditions of pH 8.5 and 55℃, add alkaline protease at an enzyme addition rate of 2000 U / g for 4 hours for enzymatic hydrolysis. After the reaction is completed, terminate the reaction by inactivating the enzyme by treating with a boiling water bath for 10 minutes. Subsequently, centrifuge the hydrolysate at 7000g for 20 minutes, collect the supernatant and store it at -20℃ for later use;
[0063] (4) Dilute the sample prepared in step (3) by 400 times and then determine the degree of hydrolysis;
[0064] (5) Dilute the sample prepared in step (3) 20 times and determine the DPPH free radical scavenging rate, such as Figure 4 As shown, the degree of hydrolysis of bovine colostrum solution by alkaline protease was 20.29%, and the DPPH free radical scavenging rate was 61.40%.
[0065] (6) The components with MW < 3 kDa after ultrafiltration separation of the hydrolysate were identified by mass spectrometry. The identified peptides were screened using computer virtual screening and molecular docking. The information of the screened peptides is shown in Table 1:
[0066] Table 1
[0067] After screening, KTTMPLW, RDMPIQAF, and SRYPSYGL were finally identified as potential immunomodulatory peptides.
[0068] Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Anyone skilled in the art can make various modifications and alterations without departing from the spirit and scope of the present invention. Therefore, the scope of protection of the present invention should be determined by the claims.
Claims
1. A method for efficiently preparing bovine colostrum-derived immunomodulatory active peptides based on model screening, characterized in that, The sequence of the colostrum-derived immunomodulatory peptide is: KTTMPLW, RDMPIQAF, SRYPSYGL.
2. The method for preparing bovine colostrum-derived immunomodulatory active peptides according to claim 1, characterized in that: It includes the following steps: (1) Generate three types of molecular descriptors using Chemopy, Mordred, and MOE, and construct 9 QSAR classification models based on the RStudio platform integrating algorithms such as random forest (RF), support vector machine (SVM), and linear discriminant analysis (LDA). Evaluate and screen the optimal model combination through the ROC curve and box plot; (2) Select 10 proteases to perform virtual digestion on the main protein sequences in colostrum, select 6 proteases with the highest proportion of 2-10 peptides after digestion, and predict the proportion of their potential immunomodulatory peptides through the optimal QSAR model constructed in step (1). Finally, determine the top 4 proteases (trypsin, chymotrypsin, alkaline protease, papain) for subsequent hydrolysis experiments; (3) Prepare a 5% (w / v) solution of colostrum powder, and hydrolyze it with 4 proteases (trypsin, chymotrypsin, alkaline protease, papain) at the optimal pH and temperature of each enzyme (enzyme addition amount 2000U / g, 4h). After the reaction is terminated (boiling water bath for 10 min), centrifuge (7000r / min, 20 min) to take the supernatant, and store it at -20°C for subsequent analysis; (4) Measure the degree of hydrolysis and DPPH free radical scavenging rate of the hydrolyzate in (3) to select the best protease; (5) Prepare colostrum protein hydrolyzate under the conditions of enzyme hydrolysis time of 4.59 h, substrate concentration of 5.01%, enzyme addition amount of 2355.46 U / g, pH 8.5, and temperature of 55°C. After centrifugation at 7000g for 20 min, take the supernatant and store it at -20°C for subsequent experiments; (6) Ultrafilter and separate the supernatant in (5) to obtain three components with MW>10 kDa, 10 kDa<MW<3 kDa, and MW<3 kDa, and measure their immunomodulatory activities; (7) Perform mass spectrometry identification on the component with MW<3 kDa in (6). After virtual screening and molecular docking analysis, obtain 3 potential immunomodulatory peptides with amino acid sequences: KTTMPLW, RDMPIQAF, SRYPSYGL.
3. The protease selection method according to claim 1, characterized in that, In (2): The immunomodulatory peptide usually consists of a short peptide chain composed of 2-10 amino acid residues, and its amino acid composition has significant characteristics: It mainly contains hydrophobic amino acids (such as tryptophan, proline, and phenylalanine), branched-chain amino acids (such as valine, leucine, and isoleucine), and basic amino acids (such as lysine, arginine, and histidine).
4. The protease selection method according to claim 1, characterized in that, In (2): the main proteins in the bovine colostrum include α s1 -Casein, α s2 - Casein, κ-casein, β-casein, α-lactalbumin, β-lactoglobulin, and immunoglobulin G; the alkaline protease can specifically cleave the carboxyl-terminal peptide bonds of aromatic amino acids and hydrophobic amino acids in the main proteins of bovine colostrum, and can produce a large number of 2-10 peptides, with a high proportion of peptides containing the characteristic amino acids of claim 1.
5. The protease selection method according to claim 2, characterized in that... In (2), (3), and (4): The enzyme is alkaline protease.
6. The method for preparing bovine colostrum-derived immunomodulatory active peptides according to claim 2, characterized in that... In (4), the method for measuring the DPPH free radical scavenging rate is to prepare a 0.026 mg / mL DPPH ethanol solution. After diluting the sample 20 times, take 1×10⁻⁴ L and react with an equal volume of DPPH solution (blank group: sample + ethanol; Control group: DPPH + ethanol. After reacting in the dark for 30 min, the absorbance was measured at 517 nm. The DPPH free radical scavenging rate was calculated according to the following formula: Where A test Representing the experimental group, A blank Representing the blank group, A control This represents the control group.
7. The method for preparing bovine colostrum-derived immunomodulatory active peptides according to claim 2, characterized in that... In (6) mentioned above, the method for determining the phagocytic activity of RAW264.7 cells in the immunomodulatory activity is to use cells in the logarithmic phase at 5 × 10⁻⁶ cells / day. 4 Macrophages were seeded at a density of [number] cells / well in 96-well plates. After adhesion, they were co-incubated with different concentrations of test samples (10 μg / mL LPS was used as a positive control) for 24 h. The supernatant was discarded, and 100 μL of 0.075% neutral red solution was added to each well. The plates were incubated at 37°C for 1 h. After washing three times with PBS, 100 μL of cell lysis buffer (acetic acid:ethanol = 1:1, v / v) was added, and the plates were incubated at room temperature for 2 h. The absorbance was measured at 540 nm, and the macrophage phagocytic activity was calculated using the following formula: Among them, A test Representing the experimental group, A blank Representing the blank group, A control This represents the control group.
8. The method for preparing bovine colostrum-derived immunomodulatory active peptides according to claim 2, characterized in that... In (6) mentioned above, the method for determining the level of reactive oxygen species (ROS) in immunomodulatory activity is to use logarithmic growth phase cells at 5 × 10⁻⁶ cells / day. 4 Seeds were planted at a density of 10 samples per well on a 96-well transparent black plate. After 24 h of adhesion, samples of varying concentrations or LPS (10 μg / mL) were added and treated for 24 h. The samples were then incubated with a 10 μM MDCFH-DA probe at 37 °C in the dark for 30 min. After washing with PBS, the fluorescence intensity at 485 / 525 nm was measured, and the relative fluorescence intensity was used to characterize the ROS level. Among them, A test Representing the experimental group, A blank This represents the blank group.
9. The method for preparing bovine colostrum-derived immunomodulatory active peptides according to claim 2, characterized in that... In (6) and (7): the bovine colostrum protein hydrolysate obtained by alkaline protease hydrolysis is ultrafiltered to obtain components with MW < 3kDa; and potential bovine colostrum-derived bioactive peptides with immunomodulatory activity are obtained through computer virtual screening and molecular docking analysis.