Method for screening and verifying kelp chloroplast prosthesis protein ACE (angiotensin converting enzyme) inhibitory peptide

Abstract

The invention provides a method for screening and verifying kelp chloroplast prosthesis protein ACE (angiotensin converting enzyme) inhibitory peptide, which comprises the following two steps of: 1, carrying out computer-assisted enzymolysis on kelp protein, selecting a representative protein sequence in kelp by using an online database, selecting specific protease by using an online enzymolysis platform to carry out virtual enzymolysis, and screening and verifying the ACE inhibitory peptide according to an enzymolysis result; establishing a kelp protein polypeptide classification database; and step 2, establishing a QSAR model of the ACE inhibitory active peptide, and predicting the activity of the ACE inhibitory active peptide, so that the invention provides a research and development method system for a functional product rich in the ACE inhibitory active peptide based on the combination of kelp bioinformatics and experimental verification.

Description

Technical Field

[0001] This invention belongs to the field of marine protein bioactive peptide mining, and specifically relates to a method for screening and verifying ACE inhibitory peptides of kelp chloroplast prosthetic protein. Background Technology

[0002] Currently, the processing of kelp products in China mainly relies on traditional methods. Kelp is one of my country's important algae economic crops, and its industrial utilization primarily depends on traditional processing methods, such as extracting alginate, sodium alginate, iodine, and mannitol. The residue after extraction still contains relatively rich algal protein, minerals, and dietary fiber, which to some extent limits the diversified consumption and value enhancement of kelp. Deep processing and the development of high-value-added products are relatively limited. A report by the Food and Agriculture Organization of the United Nations states that over 20% of algae is not utilized due to low-value discarding, storage problems, and short shelf life. In contrast, overseas development of kelp components is mature, with the promotion of intensive processing and high-value-added products. A series of functional foods using kelp as the main ingredient have emerged, such as various health products with anti-cancer and anti-aging properties. This has significantly improved the processing and utilization of kelp, while also bringing economic benefits and increasing public awareness of health. The crude protein in kelp is rich in amino acids and polypeptides, and the variety is complete. However, there is currently limited research on crude protein and enzymatic hydrolysate bioactive peptides in kelp, which restricts the in-depth development and application of functional components of kelp.

[0003] Hypertension is the most common and serious chronic health disease in the world today, and a high-risk factor associated with cardiovascular diseases such as atherosclerosis and myocardial infarction. In the RAS pathway, angiotensin-I converting enzyme (ACE) cleaves angiotensin-I into angiotensin-II, which has a strong vasoconstrictive effect. In recent years, the mortality rate of cardiovascular diseases has remained high, exceeding that of other diseases. Inhibiting ACE activity is an effective way to treat hypertension, and some ACE inhibitors have also made effective progress in hypertension-related diseases.

[0004] However, traditional methods for screening bioactive peptides involve multiple steps, including separation, purification, and activity identification, which are time-consuming and costly, limiting the discovery of novel bioactive peptides. Therefore, high-throughput screening of ACE inhibitory peptides is crucial for the discovery and application of bioactive peptides. Quantitative structure-activity relationship (QSAR) can delve into the structure of ACE inhibitors and their mechanisms of ACE inhibition, clarifying the relationship between the activity of ACE inhibitors and their blood pressure-lowering function. QSAR utilizes mathematical and statistical methods to study the relationship between the structure or physicochemical properties of substances and their biological effects. These biological effects include the digestion and absorption of small molecules in the human body and their physiological impact on other molecules in the body. QSAR is developed based on traditional structure-activity relationships, combining empirical equations and mathematical methods from physicochemical processes. The implementation process of QSAR can be divided into the following steps:

[0005] (i) Collect structural and activity data of compounds; (ii) Obtain structural descriptors to quantitatively describe the structure of compounds; (iii) Select an appropriate algorithm to establish a mathematical model between structure and activity; (iv) Test the model's fitting ability, robustness, and predictive ability; (v) Interpret the model and extract structural information that has a significant impact on activity.

[0006] There are few reports on the effects of kelp bioactive peptides on lowering blood pressure and inhibiting ACE. Currently, experimental methods for evaluating the activity of ACE-inhibiting peptides are both expensive and time-consuming, which is not ideal from a time and economic perspective.

[0007] Therefore, in view of the shortcomings of the above-mentioned solutions in actual production and implementation, modifications and improvements have been made. At the same time, in the spirit and concept of seeking excellence, and with the assistance of professional knowledge and experience, and after much ingenuity and experimentation, this invention was created to provide a method for screening and verifying ACE inhibitory peptides of kelp chloroplast prosthesis protein, in order to solve the problem. Summary of the Invention

[0008] This invention proposes a method for screening and verifying ACE inhibitory peptides from kelp chloroplast prosthetic proteins. Based on the problems raised in the background technology, it aims to provide a method system for developing functional products rich in kelp ACE inhibitory active peptides by combining kelp bioinformatics and experimental verification.

[0009] This invention provides a method for screening and verifying ACE inhibitory peptides of kelp chloroplast prosthetic proteins, comprising the following two steps:

[0010] Step 1: Computer-aided enzymatic hydrolysis of kelp protein. Representative protein sequences from kelp are selected from an online database. Specific proteases are selected for virtual enzymatic hydrolysis using an online enzymatic hydrolysis platform. Based on the hydrolysis results, a kelp protein polypeptide classification database is established.

[0011] Step 2: Establish a QSAR model for ACE-inhibiting active peptides to predict their activity.

[0012] According to a method for screening and validating ACE inhibitory peptides from kelp chloroplast prosthetic proteins, the QSAR model for validating the ACE inhibitory active peptides is operated by analyzing amino acid descriptors and ACE inhibitory peptide predictor ICs using partial least squares (PLS) regression. 50 The correlation between values ​​is measured using the correlation coefficient (R²). 2 The model's fit is evaluated using the cross-validation coefficient Q and the root mean square error (RMSE). 2 Evaluate the prediction quality of the QSAR model.

[0013] According to a method for screening and verifying ACE inhibitory peptides of kelp chloroplast prosthesis protein of the present invention, the protein sequences selected in step one are: Chloroplast light harvesting protein lhcf5 (B3VI68), Hypothetical chloroplast protein (J7F9P0), Hypothetical chloroplast protein ycf66 (J7F9N5), Photosystem IP700 chlorophyll a apoprotein A1 (B3VI68), preferably Hypothetical chloroplast protein (J7F9P0).

[0014] According to a method for screening and validating ACE inhibitory peptides of kelp chloroplast prosthetic protein of the present invention, in step two, the ACE inhibitory peptide QSAR model is composed of a model set consisting of dipeptides, tripeptides, tetrapeptides, pentapeptides, hexapeptides, heptapeptides, and octapeptides. The amino acid property parameters of each amino acid in the peptide sequence involved are characterized using the amino acid descriptor 5z-scale, thereby converting the sequence into a matrix vector of structural descriptors. All peptides are described in a certain order. When establishing the peptide library, the sequence number of the peptide is marked in the figure and excluded in the second round of modeling. The dataset excluding outliers is modeled, and the activity prediction of the peptides obtained by online enzymatic digestion in step one is performed.

[0015] According to the present invention, a method for screening and verifying ACE inhibitory peptides of kelp chloroplast prosthetic protein is provided, wherein the amino acid property parameters are collected from the amino acid physicochemical property database AAindex.

[0016] According to a method for screening and verifying ACE inhibitory peptides of kelp chloroplast prosthesis protein of the present invention, the operation of describing all peptides in a certain order is as follows: five z-values ​​are obtained through principal component analysis, namely z1, z2, z3, z4 and z5. From the N-terminus to the C-terminus, the properties of each amino acid are described in sequence using the five parameters z1, z2, z3, z4 and z5. The property descriptors of the first amino acid at the N-terminus of the peptide are designated as n1z1, n1z2, n1z3, n1z4 and n1z5, and the second amino acid at the N-terminus is designated as n2z1, n2z2, n2z3, n2z4 and n2z5. Similarly, all peptides are described in this order.

[0017] According to the present invention, a method for screening and verifying ACE inhibitory peptides of kelp chloroplast prosthesis protein, in the five z values, z1-z3 descriptors represent lipophilicity, stereochemistry, and electronic properties, respectively, while z4 and z5 are related to other properties, such as electronegativity, heat of formation, electrophilicity, and hardness.

[0018] To verify the accuracy of the ACE-inhibiting active peptide QSAR model in the above invention, a practical experiment can be conducted. The practical experiment steps include optimizing the enzymatic hydrolysis conditions of kelp crude protein using response surface methodology to improve the degree of hydrolysis, including the following steps:

[0019] (1) Salted kelp (removing impurities) → desalting and degumming → drying → pulverizing → sieving → enzymatic hydrolysis → alkaline extraction → centrifugation → salting out → freeze drying → kelp crude protein → target enzyme hydrolysis → crude protein hydrolysate.

[0020] (2) Based on the single-factor experiment, select appropriate factor levels, and according to the Box-Benhnken central composite design principle, take the three factors of enzymatic hydrolysis time, enzymatic hydrolysis temperature and enzyme addition amount as independent variables and the degree of hydrolysis as the response value, optimize the crude protein hydrolysis process through response surface methodology, and determine the optimal enzymatic hydrolysis process conditions.

[0021] (3) Identification: An appropriate amount of the freeze-dried sample prepared in (2) was desalted using a C18 desalting column. The sample was then analyzed by LC-MS / MS equipped with an online nano-jet ion source.

[0022] (4) Method for determining ACE inhibitory activity:

[0023]

[0024] Wherein, [HA]c is the concentration of hippuric acid produced in the control tube; [HA]s is the concentration of hippuric acid produced in the assay tube; and [HA]H is the concentration of hippuric acid produced in the blank group. IC 50 : Sample concentration corresponding to an ACE inhibition rate of 50%.

[0025] (5) Inhibition Type Analysis: For the novel kelp ACE-inhibiting peptides with specific sequences screened above, the type of ACE inhibition was investigated. Different concentrations of HHL were used to react with different concentrations of kelp peptides. A double reciprocal Lineweaver-Burk plot was plotted with the reciprocal of the HA production rate as the Y-axis and the reciprocal of the substrate HHL concentration as the X-axis.

[0026] Simulating the digestive enzyme resistance and stability before and after gastrointestinal digestion: The pH of the prepared peptide sample was adjusted to acidic using a certain concentration of HCl. Then, an appropriate amount of pepsin (w / w) was added and mixed thoroughly. The mixture was then digested in a 37℃ shaking water bath at 100 rpm for a certain period of time. After the reaction, the sample was removed and allowed to cool to room temperature. The pH of the sample solution was then adjusted to weakly alkaline using an appropriate concentration of NaOH. An appropriate amount of trypsin (w / w) was added and digestion continued for a period of time. Subsequently, the enzyme was inactivated at 100℃ for 10 min. After enzyme inactivation, the sample was removed, cooled to room temperature, centrifuged, and the supernatant was collected to detect its ACE activity.

[0027] The beneficial effects of this invention are that the established QSAR model, combined with computer screening, is not only an effective strategy for discovering ACE-inhibiting active peptides, but also an effective method for discovering other beneficial bioactive substances. In the identification and screening of algal bioactive peptides, this invention provides a predictive model for high-throughput screening and identification of kelp peptides based on a combination of bioinformatics and in vitro experimental verification. Specifically, it utilizes the 5z-scale amino acid descriptor to describe active peptides, determines the structure-activity relationship system, predicts potential ACE-inhibiting activity, constructs an ACE-inhibiting peptide database, and rapidly screens highly active kelp peptides. This provides strong support for exploring the inhibition types of ACE inhibitors and the stability of active peptides. Through the QSAR model, a series of highly effective blood pressure-lowering functional active peptides have been identified and screened for the high-value application of kelp. The development of functional active peptides, with ACE inhibition as an example, has greatly improved the processing and utilization of kelp. This study not only provides new ideas for the in-depth development and utilization of kelp, but also provides a reference model for the development of functional peptides from other marine biological resources. Attached Figure Description

[0028] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0029] In the diagram:

[0030] Figure 1 Figure 1 shows the analysis results of a portion of the QSAR model;

[0031] Figure 2 A statistical graph showing the degree of hydrolysis of kelp (protein) hydrolysates obtained by different proteases.

[0032] Figure 3 Statistical graph of ACE inhibitory activity of target peptides PKKYG (A), VYKNT (B), IKTIQF (C), and GKIPRW (D);

[0033] Figure 4 Lineweaver-Burk analysis of the inhibitory effects of kelp peptides GKIPRW (A), IKTIQF (B), PKKYG (C), and VYKNT (D) on ACE.

[0034] Figure 5 Statistical graph showing the stability analysis of kelp ACE inhibitory peptides and enzymatic hydrolysate before and after gastrointestinal simulation.

[0035] Figure 6 This is a flowchart illustrating the operation of the present invention. Detailed Implementation

[0036] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0037] The present invention provides a method for screening and verifying ACE inhibitory peptides of kelp chloroplast prosthetic protein, which includes the following two steps:

[0038] Step 1: Computer-aided enzymatic hydrolysis of kelp protein.

[0039] First, the representative protein sequence of kelp, Hypothetical chloroplast protein (J7F9P0), was obtained by searching the Uniport online database (https: / / www.uniprot.org / ). The obtained protein amino acid sequence was then subjected to combined hydrolysis using papain and chymotrypsin in the "Enzymeaction" tool of the BIOPEP-UWM program (https: / / biochemia.uwm.edu.pl / biopep / start_biopep.php). The resulting peptides were then sorted and classified.

[0040] Step 2: Establish a QSAR model for ACE-inhibiting active peptides to predict their activity.

[0041] The peptides selected for modeling comprised a model set consisting of 145 dipeptides, 251 tripeptides, 132 tetrapeptides, 154 pentapeptides, 131 hexapeptides, 77 heptapeptides, and 64 octapeptides. Based on the characteristics and IC50 values ​​of different peptides in the model set, peptides with IC50 values ​​below 18 mM were selected for modeling. Each amino acid in the involved peptide sequences was characterized using the 5z-scale amino acid descriptor (Appendix 1). Partial least squares regression (PLS) was used to analyze the relationship between the amino acid descriptor (predictor, X) and the log-transformed IC50 value (Y). A two-component PLS model was then established to convert the sequences into a matrix vector of structural descriptors.

[0042] Amino acid property parameters were collected from the amino acid physicochemical property database AAindex, and five z-values ​​(z1, z2, z3, z4, and z5) were obtained through principal component analysis. The z1-z3 descriptors represent lipophilicity, stereochemistry, and electronic properties, respectively, while z4 and z5 relate to other properties such as electronegativity, heat of formation, electrophilicity, and hardness. From the N-terminus to the C-terminus, the properties of each amino acid were described sequentially using the five parameters (z1–z5). The property descriptors for the first amino acid at the N-terminus of the peptide were designated as n1z1, n1z2, n1z3, n1z4, and n1z5, and the descriptors for the second amino acid at the N-terminus were designated as n2z1, n2z2, n2z3, n2z4, and n2z5; similarly, all peptides were described sequentially in this order. The sequence number of the peptide was marked in the figure when building the peptide library and was excluded during the second round of modeling. Modeling was performed on the dataset excluding outliers, and the activity of the peptides obtained from the online enzymatic digestion in step one was predicted.

[0043] The practical experiment involved optimizing the enzymatic hydrolysis conditions of kelp crude protein using response surface methodology to improve the degree of hydrolysis. The steps included:

[0044] (1) Salted kelp (removing impurities) → desalting and degumming → drying → pulverizing → sieving → enzymatic hydrolysis → alkaline extraction → centrifugation → salting out → freeze drying → kelp crude protein → target enzyme hydrolysis → crude protein hydrolysate.

[0045] (2) Based on the single-factor experiment, select appropriate factor levels, and according to the Box-Benhnken central composite design principle, take the three factors of enzymatic hydrolysis time, enzymatic hydrolysis temperature and enzyme addition amount as independent variables and the degree of hydrolysis as the response value, optimize the crude protein hydrolysis process through response surface methodology, and determine the optimal enzymatic hydrolysis process conditions.

[0046] (3) Identification: An appropriate amount of the freeze-dried sample prepared in (2) was desalted using a C18 desalting column. The sample was then analyzed by LC-MS / MS equipped with an online nano-jet ion source.

[0047] (4) Method for determining ACE inhibitory activity:

[0048]

[0049] Wherein, [HA]c is the concentration of hippuric acid produced in the control tube; [HA]s is the concentration of hippuric acid produced in the assay tube; and [HA]H is the concentration of hippuric acid produced in the blank group. IC 50 : Sample concentration corresponding to an ACE inhibition rate of 50%.

[0050] Inhibition Type Analysis: For the novel kelp ACE-inhibiting peptides with specific sequences screened above, the type of ACE inhibition was investigated. Different concentrations of HHL were reacted with different concentrations of kelp peptides. A double-reciprocal Lineweaver-Burk plot was plotted with the reciprocal of the HA production rate as the Y-axis and the reciprocal of the substrate HHL concentration as the X-axis.

[0051] Simulating the digestive enzyme resistance and stability before and after gastrointestinal digestion: The pH of the prepared peptide sample was adjusted to acidic using a certain concentration of HCl. Then, an appropriate amount of pepsin (w / w) was added and mixed thoroughly. The mixture was then digested in a 37℃ shaking water bath at 100 rpm for a certain period of time. After the reaction, the sample was removed and allowed to cool to room temperature. The pH of the sample solution was then adjusted to weakly alkaline using an appropriate concentration of NaOH. An appropriate amount of trypsin (w / w) was added and digestion continued for a period of time. Subsequently, the enzyme was inactivated at 100℃ for 10 min. After enzyme inactivation, the sample was removed, cooled to room temperature, centrifuged, and the supernatant was collected to detect its ACE activity.

[0052] Example 1

[0053] (1) Computer-aided enzymatic hydrolysis of kelp protein

[0054] The representative protein sequence of kelp was obtained by searching the Uniprot online database (https: / / www.uniprot.org / ): Hypothetical chloroplast protein (J7F9P0).

[0055] The obtained protein amino acid sequence was subjected to co-hydrolysis using papain and chymotrypsin in the "Enzyme action" tool option of the BIOPEP-UWM program (https: / / biochemia.uwm.edu.pl / biopep / start_biopep.php).

[0056] The peptides obtained from hydrolysis were sorted and classified, and a tripeptide database of 45 entries was established.

[0057]

[0058]

[0059] (2) Establishing a QSAR model of ACE-inhibiting active peptides

[0060] The peptides selected for modeling are based on the characteristics and IC of different peptides in the model set. 50 Value, select IC 50 Peptides with values ​​below 18 mM were included. Each amino acid in the peptide sequences was characterized using the 5z-scale amino acid descriptor (Table 1), converting the sequences into a matrix vector of structural descriptors. Partial least squares (PLS) regression analysis was performed using SIMCA-P14.1 software to analyze the amino acid descriptors and the ACE inhibitory peptide predictor IC50. 50 Correlation between values.

[0061] Variables defining the structural properties of the peptide were selected as the X matrix, and the logarithm of the ACE-I activity IC50 was selected as the Y matrix. Correlation coefficients (R²) were used. 2 The model's fitting ability is evaluated using the root mean square error (RMSE). To ensure the consistency of the training set data source, peptides whose predicted values ​​differ from actual values ​​by more than 1 are marked with their sequence numbers in the diagram when building the peptide library, such as... Figure 1 As shown in (a), these outliers were excluded during the second round of modeling. After modeling the dataset excluding outliers, the correlation coefficient R... 2 Significant improvement, such as Figure 1 As shown in (b), R 2 It equals 0.6124, and RMSE equals 0.499.

[0062] To verify the accuracy of the model, the data for the response variable (Y) were randomly shuffled while the independent variable (X) remained constant. The PLS model was then rebuilt, and the cross-validation coefficient Q was used. 2 Evaluate the prediction quality of the QSAR model. Set the data permutation count to 20 and calculate its prediction performance index. The simulated values ​​after permutation (left side) are all lower than the actual values ​​(right side). The models established in this implementation case all meet the ideal limit R for an effective model. 2 Less than 0.3, Q 2 The value is less than 0.05. Therefore, the model established in this example is reliable and effective, and its predictive ability is significantly better than random guessing. 2 It equals -0.614.

[0063] Further analysis of partial least squares regression coefficients was conducted to reveal the relationship between each physicochemical constant (X variable) and peptide titer (Y). The importance of X to Y was defined as being proportional to its distance from zero, corresponding to the partial least squares regression coefficients. In the tripeptide model, n1z1, n1z4, n1z5, n2z1, n2z3, and n3z1 showed a positive correlation with the log value, while n2z2, n2z4, n2z5, n3z2, n3z3, and n3z4 showed a negative correlation. n1z1, n2z3, n2z4, and n3z1 had a significant impact on the model. The lipophilic properties (z1), spatial characteristics, and other properties (z4) of amino acid residues in the amino acid sequence had a significant influence on the activity of the ACE-inhibiting peptide.

[0064] (3) Optimize the enzymatic hydrolysis conditions of kelp crude protein

[0065] After being cleaned of impurities, desalted and degummed, dried, crushed and sieved, salted kelp undergoes enzymatic hydrolysis.

[0066] After dissolving the degummed kelp powder in water, adjust the pH to 6.0, add a certain amount of cellulase, perform preliminary enzymatic hydrolysis for 50 minutes, adjust the pH to alkaline, stir thoroughly, and extract.

[0067] Centrifuge at 5000 r / min for 10 min, precipitate the protein in the supernatant with saturated ammonium sulfate solution, collect the precipitate after centrifugation, and freeze-dry to obtain crude kelp protein.

[0068] Weigh 10g of crude kelp protein into a 1L Erlenmeyer flask, add 0.5L of phosphate buffer solution, and select five commercial proteases, namely cellulase, papain, chymotrypsin, complex protease and a mixture of papain and chymotrypsin, to determine the degree of hydrolysis of crude kelp protein by different proteases.

[0069] Under the optimal conditions, the amount of enzyme added was 0.1g. Other enzymatic hydrolysis conditions were as follows: material-to-liquid ratio of 1:3, pH value of 7, temperature of 50℃, and enzymatic hydrolysis with shaking at constant temperature for 3 hours. After enzymatic hydrolysis, the enzyme was inactivated (85℃, 15min). The solution was filtered through a 250-mesh filter cloth, centrifuged, and the degree of hydrolysis was determined by taking the supernatant.

[0070] (4) ACE inhibitory activity assay and inhibition type analysis

[0071] The total volume of the reaction system was 260 μL. 120 μL of peptide sample (1 mg / mL) and 40 μL of ACE solution (0.1 U / mL) were mixed and vortexed until homogeneous. The mixture was then incubated in a 37°C water bath for 10 min.

[0072] After incubation, add 100 μL of HHL solution (1 mM), react at 37 °C for 60 min, and then add 200 μL of HCl (1 mol / L) to terminate the reaction.

[0073] 120 μL of borate buffer (pH = 8.3, 0.3 mol / L NaCl) was used as a control group to replace the sample solution in the assay group; 20 μL of borate buffer (pH = 8.3, 0.3 mol / L NaCl) was used as a blank control group to replace the ACE solution in the control group.

[0074] Three sets of sample solutions were pipetted into the neck of the injection bottle, and the concentration of HA generated in the reaction system was determined by RP-HPLC.

[0075] like Figure 3 As shown in Figure 4, IC is obtained. 50 (RPF) equals 0.048 mM, non-competitive inhibition; IC 50 (GDF) equals 0.082mM, indicating competitive inhibition.

[0076] (5) Simulate stability before and after digestion

[0077] The peptide sample had a mass concentration of 1 mg / mL. First, the pH of the prepared peptide sample was adjusted to 2 using 1 mol / L HCl. Then, 2% w / w pepsin was added and mixed thoroughly. The mixture was then digested for 1 hour at 100 rpm in a 37°C shaking water bath. After the reaction, the sample was removed and allowed to cool to room temperature. The sample solution was then adjusted to pH 7.5 using 2M NaOH, and 2% w / w trypsin was added to continue digestion for 2 hours. Subsequently, the enzyme was inactivated at 100°C for 10 minutes. After enzyme inactivation, the sample was removed, cooled to room temperature, centrifuged, and the supernatant was collected to determine its ACE activity.

[0078] The stability of DDIW gastrointestinal simulated digestion remained basically unchanged before and after digestion: IC50 (RPF) before digestion was 0.048 mM, and IC50 (RPF) after digestion was 0.049 mM; IC50 (GDF) before digestion was 0.082 mM, and IC50 (KDIF) after digestion was 0.084 mM.

[0079] Example 2

[0080] (1) Computer-aided enzymatic hydrolysis of kelp protein

[0081] The representative protein sequence of kelp was obtained by searching the Uniprot online database (https: / / www.uniprot.org / ): Hypothetical chloroplast protein (J7F9P0).

[0082] The obtained protein amino acid sequence was subjected to co-hydrolysis using papain and chymotrypsin in the "Enzyme action" tool option of the BIOPEP-UWM program (https: / / biochemia.uwm.edu.pl / biopep / start_biopep.php).

[0083] The polypeptides obtained from hydrolysis were sorted and classified, and a tetrapeptide database of 29 entries was established.

[0084]

[0085] (2) Establishing a QSAR model of ACE-inhibiting active peptides

[0086] The peptides selected for modeling are based on the characteristics and IC of different peptides in the model set. 50 Value, select IC 50 Peptides with values ​​below 18 mM were included. Each amino acid in the peptide sequences was characterized using the 5z-scale amino acid descriptor (Table 1), converting the sequences into a matrix vector of structural descriptors. Partial least squares (PLS) regression analysis was performed using SIMCA-P14.1 software to analyze the amino acid descriptors and the ACE inhibitory peptide predictor IC50. 50 Correlation between values.

[0087] Variables defining the structural properties of the peptide were selected as the X matrix, and the logarithm of the ACE-I activity IC50 was selected as the Y matrix. Correlation coefficients (R²) were used. 2The model's fitting ability is evaluated using the root mean square error (RMSE). To ensure the consistency of the training set data source, peptides whose predicted values ​​differ from actual values ​​by more than 1 are marked with their sequence numbers in the graph when building the peptide library and are excluded in the second round of modeling. After modeling the dataset excluding outliers, the correlation coefficient R0 is... 2 Significant improvement, R 2 It equals 0.5571, and RMSE equals 0.4398.

[0088] To verify the accuracy of the model, the data for the response variable (Y) were randomly shuffled while the independent variable (X) remained constant. The PLS model was then rebuilt, and the cross-validation coefficient Q was used. 2 Evaluate the prediction quality of the QSAR model. Set the data permutation count to 20 and calculate its prediction performance index. The simulated values ​​after permutation (left side) are all lower than the actual values ​​(right side). The models established in this implementation case all meet the ideal limit R for an effective model. 2 Less than 0.3, Q 2 The value is less than 0.05. Therefore, the model established in this example is reliable and effective, and its predictive ability is significantly better than random guessing. 2 It equals -0.615.

[0089] Further analysis of partial least squares regression coefficients was conducted to show the relationship between each physicochemical constant (X variable) and peptide potency (Y). The importance of X to Y was defined as being proportional to its distance from zero and corresponding to the partial least squares regression coefficients. In the tetrapeptide model, n1z2, n1z5, n2z2, n2z2, n2z4, n2z5, and n3z5 had a significant impact on the tetrapeptide.

[0090] (3) Optimize the enzymatic hydrolysis conditions of kelp crude protein

[0091] After being cleaned of impurities, desalted and degummed, dried, crushed and sieved, salted kelp undergoes enzymatic hydrolysis.

[0092] After dissolving the degummed kelp powder in water, adjust the pH to 6.0, add a certain amount of cellulase, perform preliminary enzymatic hydrolysis for 50 minutes, adjust the pH to alkaline, stir thoroughly, and extract.

[0093] Centrifuge at 5000 r / min for 10 min, precipitate the protein in the supernatant with saturated ammonium sulfate solution, collect the precipitate after centrifugation, and freeze-dry to obtain crude kelp protein.

[0094] Weigh 10g of crude kelp protein into a 1L Erlenmeyer flask, add 0.5L of phosphate buffer solution, and select five commercial proteases, namely cellulase, papain, chymotrypsin, complex protease and a mixture of papain and chymotrypsin, to determine the degree of hydrolysis of crude kelp protein by different proteases.

[0095] Under the optimal conditions, the amount of enzyme added was 0.1g. Other enzymatic hydrolysis conditions were as follows: material-to-liquid ratio of 1:3, pH value of 7, temperature of 50℃, and enzymatic hydrolysis with shaking at constant temperature for 3 hours. After enzymatic hydrolysis, the enzyme was inactivated (85℃, 15min). The solution was filtered through a 250-mesh filter cloth, centrifuged, and the degree of hydrolysis was determined by taking the supernatant.

[0096] (4) ACE inhibitory activity assay and inhibition type analysis

[0097] The total volume of the reaction system was 260 μL. 120 μL of peptide sample (1 mg / mL) and 40 μL of ACE solution (0.1 U / mL) were mixed and vortexed until homogeneous. The mixture was then incubated in a 37°C water bath for 10 min.

[0098] After incubation, add 100 μL of HHL solution (1 mM), react at 37 °C for 60 min, and then add 200 μL of HCl (1 mol / L) to terminate the reaction.

[0099] 120 μL of borate buffer (pH = 8.3, 0.3 mol / L NaCl) was used as a control group to replace the sample solution in the assay group; 20 μL of borate buffer (pH = 8.3, 0.3 mol / L NaCl) was used as a blank control group to replace the ACE solution in the control group.

[0100] Three sets of sample solutions were pipetted into the neck of the injection bottle, and the concentration of HA generated in the reaction system was determined by RP-HPLC.

[0101] Obtain IC 50 (DDIW) equals 0.079mM, non-competitive inhibition; IC 50 (KDIF) equals 0.191mM, indicating competitive inhibition.

[0102] (5) Simulate stability before and after digestion

[0103] The peptide sample had a mass concentration of 1 mg / mL. First, the pH of the prepared peptide sample was adjusted to 2 using 1 mol / L HCl. Then, 2% w / w pepsin was added and mixed thoroughly. The mixture was then digested for 1 hour at 100 rpm in a 37°C shaking water bath. After the reaction, the sample was removed and allowed to cool to room temperature. The sample solution was then adjusted to pH 7.5 using 2M NaOH, and 2% w / w trypsin was added to continue digestion for 2 hours. Subsequently, the enzyme was inactivated at 100°C for 10 minutes. After enzyme inactivation, the sample was removed, cooled to room temperature, centrifuged, and the supernatant was collected to determine its ACE activity.

[0104] The stability of DDIW before and after simulated gastrointestinal digestion remained essentially unchanged: the IC50 (DDIW) before simulated gastrointestinal digestion was 0.079 mM, and the IC50 (DDIW) after simulated gastrointestinal digestion was 0.081 mM.

[0105] The activity of KDIF was affected before and after simulated gastrointestinal digestion: the IC50 (KDIF) before simulated gastrointestinal digestion was 0.191 mM, and the IC50 (KDIF) after simulated gastrointestinal digestion was 0.238 mM.

[0106] Example 3

[0107] (1) Computer-aided enzymatic hydrolysis of kelp protein

[0108] The representative protein sequence of kelp was obtained by searching the Uniprot online database (https: / / www.uniprot.org / ): Hypothetical chloroplast protein (J7F9P0).

[0109] The obtained protein amino acid sequence was subjected to co-hydrolysis using papain and chymotrypsin in the "Enzyme action" tool option of the BIOPEP-UWM program (https: / / biochemia.uwm.edu.pl / biopep / start_biopep.php).

[0110] The polypeptides obtained from hydrolysis were sorted and classified, and a pentapeptide database of 22 entries was established.

[0111]

[0112] (2) Establishing a QSAR model of ACE-inhibiting active peptides

[0113] The peptides selected for modeling are based on the characteristics and IC of different peptides in the model set. 50 Value, select IC 50 Peptides with values ​​below 18 mM were included. Each amino acid in the peptide sequences was characterized using the 5z-scale amino acid descriptor (Table 1), converting the sequences into a matrix vector of structural descriptors. Partial least squares (PLS) regression analysis was performed using SIMCA-P14.1 software to analyze the amino acid descriptors and the ACE inhibitory peptide predictor IC50. 50 Correlation between values.

[0114] The variables defining the structural properties of the peptide are selected as the X matrix, and the ACE-I activity IC50 is calculated. 50 The logarithm of the value is used as the Y matrix. The correlation coefficient (R²) is used. 2The model's fitting ability is evaluated using the root mean square error (RMSE). To ensure the consistency of the training set data source, peptides whose predicted values ​​differ from actual values ​​by more than 1 are marked with their sequence numbers in the diagram when building the peptide library, such as... Figure 1 As shown in (a), these outliers were excluded during the second round of modeling. After modeling the dataset excluding outliers, the correlation coefficient R... 2 Significant improvement, such as Figure 1 As shown in (b), R 2 It equals 0.5802, and RMSE equals 0.5208.

[0115] To verify the accuracy of the model, the data for the response variable (Y) were randomly shuffled while the independent variable (X) remained constant. The PLS model was then rebuilt, and the cross-validation coefficient Q was used. 2 Evaluate the prediction quality of the QSAR model. Set the number of data permutations to 20 and calculate its prediction performance index. For example... Figure 1 As shown in (c), the simulated values ​​(left side) after model permutation are all lower than the actual values ​​(right side). The models established in this implementation case all meet the ideal limit R of an effective model. 2 Less than 0.3, Q 2 The value is less than 0.05. Therefore, the model established in this example is reliable and effective, and its predictive ability is significantly better than random guessing. 2 It equals -0.593.

[0116] Further analysis of the partial least squares regression coefficients was conducted to show the relationship between each physicochemical constant (X variable) and peptide titer (Y). The importance of X to Y was defined as proportional to its distance from zero, corresponding to the partial least squares regression coefficients, such as... Figure 1 As shown in (d), n1z1, n1z5, n2z1, n3z1, n4z3, n5z2, n5z4, and n5z5 are important factors determining the activity of the pentapeptide in the pentapeptide model. The fourth amino acid residue at the C-terminus is far more important than the second and third amino acid residues.

[0117] (3) Optimize the enzymatic hydrolysis conditions of kelp crude protein

[0118] After being cleaned of impurities, desalted and degummed, dried, crushed and sieved, salted kelp undergoes enzymatic hydrolysis.

[0119] After dissolving the degummed kelp powder in water, adjust the pH to 6.0, add a certain amount of cellulase, perform preliminary enzymatic hydrolysis for 50 minutes, adjust the pH to alkaline, stir thoroughly, and extract.

[0120] Centrifuge at 5000 r / min for 10 min, precipitate the protein in the supernatant with saturated ammonium sulfate solution, collect the precipitate after centrifugation, and freeze-dry to obtain crude kelp protein.

[0121] Weigh 10g of crude kelp protein into a 1L Erlenmeyer flask, add 0.5L of phosphate buffer solution, and select five commercial proteases, namely cellulase, papain, chymotrypsin, complex protease and a mixture of papain and chymotrypsin, to determine the degree of hydrolysis of crude kelp protein by different proteases.

[0122] Under the optimal conditions, the amount of enzyme added was 0.1g. Other enzymatic hydrolysis conditions were as follows: material-to-liquid ratio of 1:3, pH value of 7, temperature of 50℃, and enzymatic hydrolysis with shaking at constant temperature for 3 hours. After enzymatic hydrolysis, the enzyme was inactivated (85℃, 15min). The solution was filtered through a 250-mesh filter cloth, centrifuged, and the degree of hydrolysis was determined by taking the supernatant.

[0123] (4) ACE inhibitory activity assay and inhibition type analysis

[0124] The total volume of the reaction system was 260 μL. 120 μL of peptide sample (1 mg / mL) and 40 μL of ACE solution (0.1 U / mL) were mixed and vortexed until homogeneous. The mixture was then incubated in a 37°C water bath for 10 min.

[0125] After incubation, add 100 μL of HHL solution (1 mM), react at 37 °C for 60 min, and then add 200 μL of HCl (1 mol / L) to terminate the reaction.

[0126] 120 μL of borate buffer (pH = 8.3, 0.3 mol / L NaCl) was used as a control group to replace the sample solution in the assay group; 20 μL of borate buffer (pH = 8.3, 0.3 mol / L NaCl) was used as a blank control group to replace the ACE solution in the control group.

[0127] Three sets of sample solutions were pipetted into the neck of the injection bottle, and the concentration of HA generated in the reaction system was determined by RP-HPLC.

[0128] like Figure 3 As shown in Figure 4, IC is obtained. 50 (PKKYG) equals 0.74 mM, indicating non-competitive inhibition; IC 50 (VYKNT) equals 0.22mM, indicating competitive inhibition.

[0129] (5) Simulate stability before and after digestion

[0130] The peptide sample had a mass concentration of 1 mg / mL. First, the pH of the prepared peptide sample was adjusted to 2 using 1 mol / L HCl. Then, 2% w / w pepsin was added and mixed thoroughly. The mixture was then digested for 1 hour at 100 rpm in a 37°C shaking water bath. After the reaction, the sample was removed and allowed to cool to room temperature. The sample solution was then adjusted to pH 7.5 using 2M NaOH, and 2% w / w trypsin was added to continue digestion for 2 hours. Subsequently, the enzyme was inactivated at 100°C for 10 minutes. After enzyme inactivation, the sample was removed, cooled to room temperature, centrifuged, and the supernatant was collected to determine its ACE activity.

[0131] like Figure 5 As shown, the stability of PKKYG before and after simulated gastrointestinal digestion remained essentially unchanged: IC50 before simulated gastrointestinal digestion 50 (PKKYG) equals 0.74 mM, IC50 after simulated gastrointestinal digestion 50 (PKKYG) equals 0.77mM;

[0132] VYKNT gastrointestinal simulated digestion activity was affected before and after digestion: pre-gastrointestinal simulated digestion IC 50 (VYKNT) equals 0.22 mM, IC50 after simulated gastrointestinal digestion 50 (VYKNT) equals 0.38mM.

[0133] Example 4

[0134] (1) Computer-aided enzymatic hydrolysis of kelp protein

[0135] The representative protein sequence of kelp was obtained by searching the Uniprot online database (https: / / www.uniprot.org / ): Hypothetical chloroplast protein (J7F9P0).

[0136] The obtained protein amino acid sequence was subjected to co-hydrolysis using papain and chymotrypsin in the "Enzyme action" tool option of the BIOPEP-UWM program (https: / / biochemia.uwm.edu.pl / biopep / start_biopep.php).

[0137] The polypeptides obtained from hydrolysis were sorted and classified, and a hexapeptide database of 24 entries was established.

[0138]

[0139] (2) Establishing a QSAR model of ACE-inhibiting active peptides

[0140] The peptides selected for modeling are based on the characteristics and IC of different peptides in the model set. 50Value, select IC 50 Peptides with values ​​below 18 mM were included. Each amino acid in the peptide sequences was characterized using the 5z-scale amino acid descriptor (Table 1), converting the sequences into a matrix vector of structural descriptors. Partial least squares (PLS) regression analysis was performed using SIMCA-P14.1 software to analyze the amino acid descriptors and the ACE inhibitory peptide predictor IC50. 50 Correlation between values.

[0141] The variables defining the structural properties of the peptide are selected as the X matrix, and the ACE-I activity IC50 is calculated. 50 The logarithm of the value is used as the Y matrix. The correlation coefficient (R²) is used. 2 The model's fitting ability is evaluated using the root mean square error (RMSE). To ensure the consistency of the training set data source, peptides whose predicted values ​​differ from actual values ​​by more than 1 are marked with their sequence numbers in the diagram when building the peptide library, such as... Figure 1 As shown in (e), these outliers were excluded during the second round of modeling. After modeling the dataset excluding outliers, the correlation coefficient R... 2 Significant improvement, such as Figure 1 As shown in (f), R 2 It equals 0.6694, and RMSE equals 0.4677.

[0142] To verify the accuracy of the model, the data for the response variable (Y) were randomly shuffled while the independent variable (X) remained constant. The PLS model was then rebuilt, and the cross-validation coefficient Q was used. 2 Evaluate the prediction quality of the QSAR model. Set the number of data permutations to 20 and calculate its prediction performance index. For example... Figure 1 As shown in (g), the simulated values ​​(left side) after model permutation are all lower than the actual values ​​(right side). The models established in this implementation case all meet the ideal limit R of an effective model. 2 Less than 0.3, Q 2 The value is less than 0.05. Therefore, the model established in this example is reliable and effective, and its predictive ability is significantly better than random guessing. 2 It equals -0.63.

[0143] Further analysis of the partial least squares regression coefficients was conducted to show the relationship between each physicochemical constant (X variable) and peptide titer (Y). The importance of X to Y was defined as proportional to its distance from zero, corresponding to the partial least squares regression coefficients, such as... Figure 1 As shown in (h), n4z1 is a significant factor affecting the hexapeptide. When the C-terminal amino acid is hydrophobic or aromatic, it has a significant positive promoting effect on ACE inhibition.

[0144] (3) Optimize the enzymatic hydrolysis conditions of kelp crude protein

[0145] After being cleaned of impurities, desalted and degummed, dried, crushed and sieved, salted kelp undergoes enzymatic hydrolysis.

[0146] After dissolving the degummed kelp powder in water, adjust the pH to 6.0, add a certain amount of cellulase, perform preliminary enzymatic hydrolysis for 50 minutes, adjust the pH to alkaline, stir thoroughly, and extract.

[0147] Centrifuge at 5000 r / min for 10 min, precipitate the protein in the supernatant with saturated ammonium sulfate solution, collect the precipitate after centrifugation, and freeze-dry to obtain crude kelp protein.

[0148] Weigh 10g of crude kelp protein into a 1L Erlenmeyer flask, add 0.5L of phosphate buffer solution, and select five commercial proteases, namely cellulase, papain, chymotrypsin, complex protease and a mixture of papain and chymotrypsin, to determine the degree of hydrolysis of crude kelp protein by different proteases.

[0149] Under the optimal conditions, the amount of enzyme added was 0.1g. Other enzymatic hydrolysis conditions were as follows: material-to-liquid ratio of 1:3, pH value of 7, temperature of 50℃, and enzymatic hydrolysis with shaking at constant temperature for 3 hours. After enzymatic hydrolysis, the enzyme was inactivated (85℃, 15min). The solution was filtered through a 250-mesh filter cloth, centrifuged, and the degree of hydrolysis was determined by taking the supernatant.

[0150] (4) ACE inhibitory activity assay and inhibition type analysis

[0151] The total volume of the reaction system was 260 μL. 120 μL of peptide sample (1 mg / mL) and 40 μL of ACE solution (0.1 U / mL) were mixed and vortexed until homogeneous. The mixture was then incubated in a 37°C water bath for 10 min.

[0152] After incubation, add 100 μL of HHL solution (1 mM), react at 37 °C for 60 min, and then add 200 μL of HCl (1 mol / L) to terminate the reaction.

[0153] 120 μL of borate buffer (pH = 8.3, 0.3 mol / L NaCl) was used as a control group to replace the sample solution in the assay group; 20 μL of borate buffer (pH = 8.3, 0.3 mol / L NaCl) was used as a blank control group to replace the ACE solution in the control group.

[0154] Three sets of sample solutions were pipetted into the neck of the injection bottle, and the concentration of HA generated in the reaction system was determined by RP-HPLC.

[0155] like Figure 3 As shown in Figure 4, IC is obtained. 50； (IKTIQF) equals 0.53 mM, mixed inhibition type; IC50 (GKIPRW) equals 0.78mM, indicating competitive inhibition.

[0156] (5) Simulate stability before and after digestion

[0157] The peptide sample had a mass concentration of 1 mg / mL. First, the pH of the prepared peptide sample was adjusted to 2 using 1 mol / L HCl. Then, 2% w / w pepsin was added and mixed thoroughly. The mixture was then digested for 1 hour at 100 rpm in a 37°C shaking water bath. After the reaction, the sample was removed and allowed to cool to room temperature. The sample solution was then adjusted to pH 7.5 using 2M NaOH, and 2% w / w trypsin was added to continue digestion for 2 hours. Subsequently, the enzyme was inactivated at 100°C for 10 minutes. After enzyme inactivation, the sample was removed, cooled to room temperature, centrifuged, and the supernatant was collected to determine its ACE activity.

[0158] like Figure 5 As shown, the stability of the GKIPRW gastrointestinal simulated digestion remained essentially unchanged before and after digestion: IC50 before gastrointestinal simulated digestion 50 (GKIPRW) equals 0.78 mM, IC50 after simulated gastrointestinal digestion 50 (GKIPRW) equals 0.81mM;

[0159] IKTIQF gastrointestinal simulated digestion activity was affected before and after digestion: IC50 before gastrointestinal simulated digestion 50 (IKTIQF) equals 0.53 mM, IC50 after simulated gastrointestinal digestion 50 (IKTIQF) equals 0.67mM.

[0160] Example 5

[0161] (1) Computer-aided enzymatic hydrolysis of kelp protein

[0162] The representative protein sequence of kelp was obtained by searching the Uniprot online database (https: / / www.uniprot.org / ): Hypothetical chloroplast protein (J7F9P0).

[0163] The obtained protein amino acid sequence was subjected to co-hydrolysis using papain and chymotrypsin in the "Enzyme action" tool option of the BIOPEP-UWM program (https: / / biochemia.uwm.edu.pl / biopep / start_biopep.php).

[0164] The polypeptides obtained from hydrolysis were sorted and classified, and a database of 9 heptapeptides was established.

[0165]

[0166] (2) Establishing a QSAR model of ACE-inhibiting active peptides

[0167] The peptides selected for modeling are based on the characteristics and IC of different peptides in the model set. 50 Value, select IC 50 Peptides with values ​​below 18 mM were included. Each amino acid in the peptide sequences was characterized using the 5z-scale amino acid descriptor (Table 1), converting the sequences into a matrix vector of structural descriptors. Partial least squares (PLS) regression analysis was performed using SIMCA-P14.1 software to analyze the amino acid descriptors and the ACE inhibitory peptide predictor IC50. 50 Correlation between values.

[0168] Variables defining the structural properties of the peptide were selected as the X matrix, and the logarithm of the ACE-I activity IC50 was selected as the Y matrix. Correlation coefficients (R²) were used. 2 The model's fitting ability is evaluated using the root mean square error (RMSE). To ensure the consistency of the training set data source, peptides whose predicted values ​​differ from actual values ​​by more than 1 are marked with their sequence numbers in the graph when building the peptide library and are excluded in the second round of modeling. After modeling the dataset excluding outliers, the correlation coefficient R0 is... 2 Significant improvement, R 2 It equals 0.8108, and RMSE equals 0.4441.

[0169] To verify the accuracy of the model, the data for the response variable (Y) were randomly shuffled while the independent variable (X) remained constant. The PLS model was then rebuilt, and the cross-validation coefficient Q was used. 2 Evaluate the prediction quality of the QSAR model. Set the data permutation count to 20 and calculate its prediction performance index. The simulated values ​​after permutation (left side) are all lower than the actual values ​​(right side). The models established in this implementation case all meet the ideal limit R for an effective model. 2 Less than 0.3, Q 2 The value is less than 0.05. Therefore, the model established in this example is reliable and effective, and its predictive ability is significantly better than random guessing. 2 It equals -0.629.

[0170] Further analysis of partial least squares regression coefficients was conducted to show the relationship between each physicochemical constant (X variable) and peptide titer (Y). The importance of X to Y was defined as being proportional to its distance from zero and corresponding to the partial least squares regression coefficients. For heptapeptides, z1, z3, and z4 at the C-terminus play important roles in ACE inhibition.

[0171] (3) Optimize the enzymatic hydrolysis conditions of kelp crude protein

[0172] After being cleaned of impurities, desalted and degummed, dried, crushed and sieved, salted kelp undergoes enzymatic hydrolysis.

[0173] After dissolving the degummed kelp powder in water, adjust the pH to 6.0, add a certain amount of cellulase, perform preliminary enzymatic hydrolysis for 50 minutes, adjust the pH to alkaline, stir thoroughly, and extract.

[0174] Centrifuge at 5000 r / min for 10 min, precipitate the protein in the supernatant with saturated ammonium sulfate solution, collect the precipitate after centrifugation, and freeze-dry to obtain crude kelp protein.

[0175] Weigh 10g of crude kelp protein into a 1L Erlenmeyer flask, add 0.5L of phosphate buffer solution, and select five commercial proteases, namely cellulase, papain, chymotrypsin, complex protease and a mixture of papain and chymotrypsin, to determine the degree of hydrolysis of crude kelp protein by different proteases.

[0176] Under the optimal conditions, the amount of enzyme added was 0.1g. Other enzymatic hydrolysis conditions were as follows: material-to-liquid ratio of 1:3, pH value of 7, temperature of 50℃, and enzymatic hydrolysis with shaking at constant temperature for 3 hours. After enzymatic hydrolysis, the enzyme was inactivated (85℃, 15min). The solution was filtered through a 250-mesh filter cloth, centrifuged, and the degree of hydrolysis was determined by taking the supernatant.

[0177] (4) ACE inhibitory activity assay and inhibition type analysis

[0178] The total volume of the reaction system was 260 μL. 120 μL of peptide sample (1 mg / mL) and 40 μL of ACE solution (0.1 U / mL) were mixed and vortexed until homogeneous. The mixture was then incubated in a 37°C water bath for 10 min.

[0179] After incubation, add 100 μL of HHL solution (1 mM), react at 37 °C for 60 min, and then add 200 μL of HCl (1 mol / L) to terminate the reaction.

[0180] 120 μL of borate buffer (pH = 8.3, 0.3 mol / L NaCl) was used as a control group to replace the sample solution in the assay group; 20 μL of borate buffer (pH = 8.3, 0.3 mol / L NaCl) was used as a blank control group to replace the ACE solution in the control group.

[0181] Three sets of sample solutions were pipetted into the neck of the injection bottle, and the concentration of HA generated in the reaction system was determined by RP-HPLC.

[0182] Obtain IC 50 (NKIKVYF) equals 0.049 mM, indicating non-competitive inhibition; IC 50(DSKVDVN) equals 0.057mM, indicating non-competitive inhibition.

[0183] (5) Simulate stability before and after digestion

[0184] The peptide sample had a mass concentration of 1 mg / mL. First, the pH of the prepared peptide sample was adjusted to 2 using 1 mol / L HCl. Then, 2% w / w pepsin was added and mixed thoroughly. The mixture was then digested for 1 hour at 100 rpm in a 37°C shaking water bath. After the reaction, the sample was removed and allowed to cool to room temperature. The sample solution was then adjusted to pH 7.5 using 2M NaOH, and 2% w / w trypsin was added to continue digestion for 2 hours. Subsequently, the enzyme was inactivated at 100°C for 10 minutes. After enzyme inactivation, the sample was removed, cooled to room temperature, centrifuged, and the supernatant was collected to determine its ACE activity.

[0185] The stability of NKIKVYF remained essentially unchanged before and after simulated gastrointestinal digestion: the IC50 (NKIKVYF) before simulated gastrointestinal digestion was 0.049 mM, and the IC50 (NKIKVYF) after simulated gastrointestinal digestion was 0.05 mM.

[0186] The activity of DSKVDVN was affected before and after simulated gastrointestinal digestion: the IC50 (DSKVDVN) before simulated gastrointestinal digestion was 0.057 mM, and the IC50 (KDIF) after simulated gastrointestinal digestion was 0.127 mM.

[0187] Example 6

[0188] (1) Computer-aided enzymatic hydrolysis of kelp protein

[0189] The representative protein sequence of kelp was obtained by searching the Uniprot online database (https: / / www.uniprot.org / ): Hypothetical chloroplast protein (J7F9P0).

[0190] The obtained protein amino acid sequence was subjected to co-hydrolysis using papain and chymotrypsin in the "Enzyme action" tool option of the BIOPEP-UWM program (https: / / biochemia.uwm.edu.pl / biopep / start_biopep.php).

[0191] The polypeptides obtained from hydrolysis were sorted and classified, and two entries in the octapeptide database were established.

[0192]

[0193] (2) Establishing a QSAR model of ACE-inhibiting active peptides

[0194] The peptides selected for modeling are based on the characteristics and IC of different peptides in the model set. 50 Value, select IC 50 Peptides with values ​​below 18 mM were included. Each amino acid in the peptide sequences was characterized using the 5z-scale amino acid descriptor (Table 1), converting the sequences into a matrix vector of structural descriptors. Partial least squares (PLS) regression analysis was performed using SIMCA-P14.1 software to analyze the amino acid descriptors and the ACE inhibitory peptide predictor IC50. 50 Correlation between values.

[0195] Variables defining the structural properties of the peptide were selected as the X matrix, and the logarithm of the ACE-I activity IC50 was selected as the Y matrix. Correlation coefficients (R²) were used. 2 The model's fitting ability is evaluated using the root mean square error (RMSE). To ensure the consistency of the training set data source, peptides whose predicted values ​​differ from actual values ​​by more than 1 are marked with their sequence numbers in the graph when building the peptide library and are excluded in the second round of modeling. After modeling the dataset excluding outliers, the correlation coefficient R0 is... 2 Significant improvement, R 2 It equals 0.711, and RMSE equals 0.487.

[0196] To verify the accuracy of the model, the data for the response variable (Y) were randomly shuffled while the independent variable (X) remained constant. The PLS model was then rebuilt, and the cross-validation coefficient Q was used. 2 Evaluate the prediction quality of the QSAR model. Set the data permutation count to 20 and calculate its prediction performance index. The simulated values ​​after permutation (left side) are all lower than the actual values ​​(right side). The models established in this implementation case all meet the ideal limit R for an effective model. 2 Less than 0.3, Q 2 The value is less than 0.05. Therefore, the model established in this example is reliable and effective, and its predictive ability is significantly better than random guessing. 2 It equals -0.546.

[0197] Further analysis of partial least squares regression coefficients was conducted to show the relationship between each physicochemical constant (X variable) and peptide titer (Y). The importance of X to Y was defined as being proportional to its distance from zero and corresponding to the partial least squares regression coefficients. For the octapeptide, z1, z3 and z4 at the C-terminus play important roles in ACE inhibition.

[0198] (3) Optimize the enzymatic hydrolysis conditions of kelp crude protein

[0199] After being cleaned of impurities, desalted and degummed, dried, crushed and sieved, salted kelp undergoes enzymatic hydrolysis.

[0200] After dissolving the degummed kelp powder in water, adjust the pH to 6.0, add a certain amount of cellulase, perform preliminary enzymatic hydrolysis for 50 minutes, adjust the pH to alkaline, stir thoroughly, and extract.

[0201] Centrifuge at 5000 r / min for 10 min, precipitate the protein in the supernatant with saturated ammonium sulfate solution, collect the precipitate after centrifugation, and freeze-dry to obtain crude kelp protein.

[0202] Weigh 10g of crude kelp protein into a 1L Erlenmeyer flask, add 0.5L of phosphate buffer solution, and select five commercial proteases, namely cellulase, papain, chymotrypsin, complex protease and a mixture of papain and chymotrypsin, to determine the degree of hydrolysis of crude kelp protein by different proteases.

[0203] Under the optimal conditions, the amount of enzyme added was 0.1g. Other enzymatic hydrolysis conditions were as follows: material-to-liquid ratio of 1:3, pH value of 7, temperature of 50℃, and enzymatic hydrolysis with shaking at constant temperature for 3 hours. After enzymatic hydrolysis, the enzyme was inactivated (85℃, 15min). The solution was filtered through a 250-mesh filter cloth, centrifuged, and the degree of hydrolysis was determined by taking the supernatant.

[0204] (4) ACE inhibitory activity assay and inhibition type analysis

[0205] The total volume of the reaction system was 260 μL. 120 μL of peptide sample (1 mg / mL) and 40 μL of ACE solution (0.1 U / mL) were mixed and vortexed until homogeneous. The mixture was then incubated in a 37°C water bath for 10 min.

[0206] After incubation, add 100 μL of HHL solution (1 mM), react at 37 °C for 60 min, and then add 200 μL of HCl (1 mol / L) to terminate the reaction.

[0207] 120 μL of borate buffer (pH = 8.3, 0.3 mol / L NaCl) was used as a control group to replace the sample solution in the assay group; 20 μL of borate buffer (pH = 8.3, 0.3 mol / L NaCl) was used as a blank control group to replace the ACE solution in the control group.

[0208] Three sets of sample solutions were pipetted into the neck of the injection bottle, and the concentration of HA generated in the reaction system was determined by RP-HPLC.

[0209] Obtain IC 50 (QEEDESKL) equals 0.091 mM, non-competitive inhibition; IC 50 (DSKVDVN) equals 0.079mM, indicating non-competitive inhibition.

[0210] (5) Simulate stability before and after digestion

[0211] The peptide sample had a mass concentration of 1 mg / mL. First, the pH of the prepared peptide sample was adjusted to 2 using 1 mol / L HCl. Then, 2% w / w pepsin was added and mixed thoroughly. The mixture was then digested for 1 hour at 100 rpm in a 37°C shaking water bath. After the reaction, the sample was removed and allowed to cool to room temperature. The sample solution was then adjusted to pH 7.5 using 2M NaOH, and 2% w / w trypsin was added to continue digestion for 2 hours. Subsequently, the enzyme was inactivated at 100°C for 10 minutes. After enzyme inactivation, the sample was removed, cooled to room temperature, centrifuged, and the supernatant was collected to determine its ACE activity.

[0212] QEEDESKL's stability remained essentially unchanged before and after simulated gastrointestinal digestion: IC50 before simulated gastrointestinal digestion 50 (QEEDESKL) equals 0.091 mM, IC50 after simulated gastrointestinal digestion 50 (QEEDESKL) equals 0.093mM;

[0213] DSKVDVN's activity before and after simulated gastrointestinal digestion is affected: IC50 before simulated gastrointestinal digestion 50 (DSKVDVN) equals 0.079 mM, IC50 after simulated gastrointestinal digestion 50 (KDIF) equals 0.114 mM.

[0214] Table 1. Descriptive symbols for amino acid characterization

[0215]

[0216]

[0217] In summary, this invention provides a method for screening and verifying ACE-inhibiting peptides of kelp chloroplast prosthetic proteins. This method, based on bioinformatics, combines computer-simulated BIOPEP online enzymatic digestion and QSAR model with experimental verification to screen and prepare kelp ACE-inhibiting active peptides. Furthermore, it analyzes their digestive enzyme resistance and stability and clarifies their inhibition mode. The method includes the following steps:

[0218] (1) A polypeptide dataset was obtained by computer simulation of online enzymatic hydrolysis of kelp chloroplast protein using BIOPEP, including 58 dipeptides, 45 tripeptides, 29 tetrapeptides, 22 pentapeptides, 2 hexapeptides, 9 heptapeptides, and 2 octapeptides.

[0219] (2) A QSAR model based on amino acid 5z-scale feature descriptors was established to screen and predict ACE-inhibiting peptides. The results showed that the model had high accuracy in predicting whether active peptides possessed ACE-inhibiting activity. In the QSAR model, all six peptides showed high correlation coefficients, with the tripeptide R... 2 =0.6124, tetrapeptide R 2 =0.5571, Pentapeptide R 2 =0.5802, hexapeptide R 2 =0.6694, heptapeptide R 2 =0.8108, Octapeptide R 2 =0.711; Compared with previous models, the correlation coefficients and model quality of each model have been significantly improved, and the R-values ​​of all simulation values ​​are higher than those of previous models. 2 and Q 2 Both are lower than the true value and R. 2 If the value is less than 0.3, Q 2 The four models established in this study are valid if the value is less than 0.05. The activity of ACE-inhibiting tripeptides generally shows that z1 and z4 have a greater influence on the model; that is, the lipophilicity, spatial characteristics, and other properties of amino acid residues in the amino acid sequence have a significant impact on the activity of ACE-inhibiting peptides. For tetrapeptides and pentapeptides, the fourth amino acid residue at the C-terminus is far more important than the second and third amino acid residues. For hexapeptides, n4z1 is a significant factor affecting the hexapeptide's activity. For heptapeptides and octapeptides, z1, z3, and z4 corresponding to the C-terminus play important roles in ACE inhibition.

[0220] (3) A mixed protease was selected to enzymatically hydrolyze kelp. The effects of three factors (hydrolysis time, hydrolysis temperature, and enzyme addition amount) on the crude kelp hydrolysis effect were studied through single-factor experiments. Based on the results of the single-factor experiments, the extraction process was optimized by response surface methodology, and a linear regression equation with the degree of hydrolysis as the indicator was obtained:

[0221] Y=13.62-0.47A-0.56B-0.78C+0.052AB+0.49AC-0.32BC-3.24A 2- 2.69B 2 -2.74C 2

[0222] The optimal enzymatic hydrolysis conditions were obtained through response surface methodology validation: hydrolysis temperature 58℃, hydrolysis time 7.5h, and enzyme dosage 1‰. Under these conditions, the degree of hydrolysis of crude kelp protein was 21.64%, which is close to the predicted value, proving the effectiveness of the model. This also saved some economic costs.

[0223] (4) Using a combination of QSAR model and computer screening, two potential ACE-inhibiting hexapeptides, GKIPRW and IKTIQF, and two ACE-inhibiting pentapeptides, PKKYG and VYKNT, were screened. VYKNT showed the highest ACE-inhibiting activity, with an in vitro IC50 value of 0.22±7.59 mM, and the inhibition mode was both non-competitive and competitive binding. PKKYG and GKIPRW showed strong resistance and stability to digestive enzymes; their activity decreased slightly after gastrointestinal digestion of the crude protein hydrolysate, indicating their potential as a food formulation for lowering blood pressure.

[0224] The QSAR model established in this invention, combined with computer screening, is not only an effective strategy for discovering ACE-inhibiting active peptides but also an effective method for discovering other beneficial bioactive substances. In the identification and screening of algal bioactive peptides, this invention provides a predictive model for high-throughput screening and identification of kelp peptides based on a combination of bioinformatics and in vitro experimental verification. Specifically, it utilizes the 5z-scale amino acid descriptor to describe active peptides, determines the structure-activity relationship system, predicts potential ACE-inhibiting activity, constructs an ACE-inhibiting peptide database, and rapidly screens highly active kelp peptides. This provides strong support for exploring the inhibition types of ACE inhibitors and the stability of active peptides. The QSAR model identifies and screens a series of highly effective blood pressure-lowering functional active peptides for the high-value application of kelp. The development of functional active peptides, with ACE inhibition as an example, greatly improves the processing and utilization of kelp. This study not only provides new ideas for the in-depth development and utilization of kelp but also provides a reference model for the development of functional peptides from other marine biological resources.

[0225] Based on the genetic and protein sequence characteristics of kelp, a peptide dataset was constructed using virtual enzymatic digestion of kelp hypothetical chloroplast protein via the BIOPEP computer. A QSAR model based on amino acid 5z-scale feature descriptors was then established for high-throughput screening of ACE-inhibiting peptides and prediction of their activity. Permutation validation results showed that the six constructed QSAR models for different peptides all had high correlation coefficients, and the R-values ​​for all simulations were high. 2 and Q 2All values ​​were lower than the true values. Compared with reported models, the correlation coefficients and model quality of the constructed models were significantly improved (p<0.05), demonstrating strong screening and prediction capabilities, and the models were simple and effective. Simultaneously, based on the enzyme cleavage sites predicted and recommended by the above models, commercial proteases were selected for offline mixed-directed enzymatic hydrolysis experiments on kelp, and the optimal preparation process was optimized. Furthermore, the digestive enzyme resistance and stability of the ACE-inhibiting peptides obtained from the simulated screening were analyzed, and their inhibitory activity types were clarified. This patent employs an innovative integrated method, combining quantitative structure-activity relationship (QSAR) models, computer-aided screening, and experimental verification to perform high-throughput screening of potential ACE-inhibiting peptides in kelp. Compared with traditional active peptide screening methods, this method not only improves efficiency and convenience but also significantly reduces experimental costs in the separation and purification process, providing an effective strategy for discovering ACE-inhibiting peptides.

[0226] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for screening and verifying ACE inhibitory peptides of kelp chloroplast prosthetic proteins, characterized in that: It includes the following two steps: Step 1: Computer-assisted enzymatic hydrolysis of kelp protein, select representative protein sequences in kelp from the online database, select specific proteases for virtual enzymatic hydrolysis using the online enzymatic hydrolysis platform, and establish a kelp protein polypeptide classification database based on the enzymatic hydrolysis results; Step 2: Establish a QSAR model for ACE inhibitory peptides to predict the activity of ACE inhibitory peptides.

2. The method for screening and verifying ACE inhibitory peptides of kelp chloroplast prosthesis proteins according to claim 1, characterized in that: The ACE inhibitory peptide QSAR model was validated by analyzing the amino acid descriptors and ACE inhibitory peptide prediction factor IC using partial least squares (PLS) regression method. 50 The correlation between the values ​​is calculated using the correlation coefficient (R 2 ) and root mean square error (RMSE) were used to evaluate the model's fitting ability. The cross-validation coefficient Q 2 Evaluate the prediction quality of QSAR models.

3. The method for screening and verifying ACE inhibitory peptides of kelp chloroplast prosthesis protein according to claim 1, characterized in that: The protein sequence selected in the step 1 is one of: Chloroplast light harvesting protein lhcf5 (B3VI68), Hypothetical chloroplast protein (J7F9P0), Hypothetical chloroplastprotein ycf66 (J7F9N5), and Photosystem IP700 chlorophyll aapoprotein A1 (B3VI68).

4. The method for screening and verifying ACE inhibitory peptides of kelp chloroplast prosthesis protein according to claim 1, characterized in that: In the step 2, the ACE inhibitory peptide QSAR model is composed of a model set consisting of a dipeptide, a tripeptide, a tetrapeptide, a pentapeptide, a hexapeptide, a heptapeptide and an octapeptide, respectively. The amino acid property parameters of each amino acid in the peptide sequence involved are characterized by the amino acid description symbol 5z-scale, so that the sequence is converted into a matrix vector of a structural descriptor, and all peptides are described in sequence in a certain order. When establishing a peptide library, the arrangement number of the peptide segment is marked in the figure and excluded in the second round of modeling. The data set excluding outliers is modeled, and the activity of the peptide segment obtained by online enzymatic hydrolysis in step 1 is predicted.

5. The method for screening and verifying ACE inhibitory peptides of kelp chloroplast prosthetic proteins according to claim 4, characterized in that: The amino acid property parameters are collected from the amino acid physicochemical property database AAindex.

6. The method for screening and verifying ACE inhibitory peptides of kelp chloroplast pseudoprotein according to claim 4, characterized in that: The operation of describing all peptides in a certain order is to obtain five z values, namely z1, z2, z3, z4 and z5, through principal component analysis. From the N-terminus to the C-terminus, the five parameters z1, z2, z3, z4 and z5 are used to describe the properties of each amino acid in turn, and the first amino acid property descriptor of the N-terminus of the peptide is specified as n1z1, n1z2, n1z3, n1z4 and n1z5, and the second amino acid at the N-terminus of the amino acid is n2z1, n2z2, n2z3, n2z4 and n2z5; similarly, all peptides are described in this order.

7. The method for screening and verifying ACE inhibitory peptides of kelp chloroplast prosthetic proteins according to claim 6, characterized in that: Among the five z values, the descriptors z1-z3 represent lipophilicity, stericity, and electronicity, respectively.

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