Method for screening and verifying ACE inhibitory peptides of laminaria chloroplast pseudoprotein

Abstract

The application provides a method for screening and verifying ACE inhibitory peptides of laminaria chloroplast pseudoprotein, which comprises two steps: step one, computer-aided enzymatic hydrolysis of laminaria protein, representative protein sequences in the laminaria are selected from an online database, a specific protease is selected to perform virtual enzymatic hydrolysis by using an online enzymatic hydrolysis platform, and a laminaria protein polypeptide classification database is established according to the enzymatic hydrolysis result; and step two, an ACE inhibitory activity peptide QSAR model is established to predict the activity of the ACE inhibitory peptide, thereby, the application provides a method system for developing a functional product rich in ACE inhibitory activity peptides of laminaria based on the combination of bioinformatics and experimental verification of the laminaria.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of mining of active peptides of marine proteins, and particularly relates to a method for screening and verifying ACE inhibitory peptides of laminaria chloroplast pseudoprotein. BACKGROUND

[0002] At present, the processing products of Chinese kelp mainly rely on traditional processing methods. Kelp is one of the important algal economic crops in China. At present, the utilization of kelp in industry mainly relies on traditional processing methods, such as extraction of alginate, sodium alginate, iodine and mannitol. The residual slag after extraction still contains rich brown algal protein, minerals and dietary fiber, which limits the diversification of kelp consumption and value improvement to a certain extent, and the development of deep processing and high value-added products is relatively less. The Food and Agriculture Organization of the United Nations reports that more than 20% of algae are not utilized due to low value discard, storage problems and short shelf life. Foreign development of kelp components has been in a mature stage, i.e. promotion of in-depth processing of high value-added products, and a series of functional foods with kelp as the main raw material have appeared, such as anti-cancer, anti-aging and various health series products. To a great extent, the processing and utilization of kelp are improved, and the economic benefits and people's health consciousness are also rising. The crude protein in kelp contains rich and complete amino acids and polypeptides. However, the research on active peptides of kelp crude protein and enzymatic products is less, which limits the in-depth development and application of functional components of kelp.

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

[0004] However, the traditional bioactive peptide screening method involves multiple steps such as isolation, purification and activity identification, which is time-consuming and high-cost, limiting the discovery of new bioactive peptides. Therefore, high-throughput screening of ACE inhibitory peptides is crucial for the mining and application of active peptides. Quantitative structure-activity relationship (QSAR) can deeply study the mechanism of ACE inhibitors and the relationship between the activity of ACE inhibitors and their blood pressure lowering function. QSAR uses mathematical and statistical methods to study the relationship between the structure or physical and chemical properties of a substance and its biological effects. Such biological effects include the digestion and absorption of small molecules in the human body and their physiological effects on biological molecules. QSAR is developed on the basis of traditional structure-activity relationship, combined with empirical equations in physical chemistry and mathematical methods. The implementation process of QSAR can be divided into the following steps:

[0005] (i) Collecting structure and activity data of compounds; (ii) obtaining structure descriptors for quantitative description of compound structure; (iii) selecting appropriate algorithm to establish mathematical model between structure and activity; (iv) testing fitting ability, robustness and prediction ability of model; (v) explaining model and extracting structure information with greater impact on activity.

[0006] There are few reports on the blood pressure lowering ACE inhibitory activity of seaweed active peptides. Currently, the method for evaluating the activity of ACE inhibitory peptides is expensive and time-consuming, which is not ideal from the time and economic perspectives.

[0007] Therefore, in view of the above-mentioned problems in actual production and implementation, and in the spirit of seeking good and the idea, and with the aid of professional knowledge and experience, as well as after many trials and tests, the present application is created, and a method for screening and verifying ACE inhibitory peptides of seaweed chloroplast pseudoprotein is provided to solve the problems. SUMMARY

[0008] The present application provides a method for screening and verifying ACE inhibitory peptides of seaweed chloroplast pseudoprotein, which is based on the background technology and aims to provide a method for developing functional products rich in ACE inhibitory active peptides based on the combination of bioinformatics and experimental verification of seaweed.

[0009] The present application provides a method for screening and verifying ACE inhibitory peptides of seaweed chloroplast pseudoprotein, which includes the following two steps:

[0010] Step one, computer-aided enzymatic hydrolysis of kelp protein, selecting representative protein sequences in kelp from online database, selecting specific proteases for virtual enzymatic hydroysis using online enzymatic hydrolysis platform, and establishing a kelp protein polypeptide classification database according to the enzymatic hydrolysis results;

[0011] Step two, establishing an ACE inhibitory activity peptide QSAR model to predict the activity of ACE inhibitory peptides, wherein the ACE is angiotensin-I converting enzyme, the QSAR is quantitative structure-activity relationship, the ACE inhibitory peptide QSAR model is composed of a model set of dipeptides, tripeptides, tetrapeptides, pentapeptides, hexapeptides, heptapeptides and octapeptides, the amino acid property parameters of each amino acid in the peptide sequence are characterized by using amino acid descriptor, so as to convert the sequence into a matrix vector of structure descriptor, and all peptides are described in a certain order, the arrangement number of the peptide segment in the analysis result graph of the QSAR model is identified when the peptide segment library is established, and is excluded in the second round of modeling, the data set excluding abnormal values is modeled, and the activity of the peptide segment obtained by online enzymatic hydrolysis in step one is predicted;

[0012] The operation of describing all peptides in a certain order is that five z values are obtained by principal component analysis, which are z1, z2, z3, z4 and z5, the properties of each amino acid are described in turn from N terminal to C terminal by using five parameters z1, z2, z3, z4 and z5, the property descriptor of the first amino acid at the N terminal of the peptide is specified as n1z1, n1z2, n1z3, n1z4 and n1z5, the second amino acid at the N terminal is n2z1, n2z2, n2z3, n2z4 and n2z5, and the same is true for the subsequent description of all peptides in the order.

[0013] According to the method for screening and verifying the kelp chloroplast pseudoprotein ACE inhibitory peptide according to the application, the operation of verifying the ACE inhibitory activity peptide QSAR model is that the correlation between the amino acid descriptor and the ACE inhibitory peptide prediction factor is analyzed by using the partial least squares regression method, and the correlation coefficient and the root mean square error are used to evaluate the fitting ability of the model. The cross-validation coefficient Q 2 The prediction quality of the QSAR model is evaluated.

[0014] According to the method for screening and verifying the kelp chloroplast pseudoprotein ACE inhibitory peptide according to the application, the protein sequence selected in step one is one of Chloroplast light harvesting protein lhcf5, Hypothetical chloroplast protein, Hypothetical chloroplast protein ycf66 and Photosystem I P700 chlorophyll a apoprotein A1.

[0015] According to the method for screening and verifying the ACE inhibitory peptides of the chloroplast pseudoprotein of kelp according to the application, the amino acid property parameters are collected from an amino acid physicochemical property database.

[0016] According to the method for screening and verifying the ACE inhibitory peptides of the chloroplast pseudoprotein of kelp according to the application, in the five z values, the z1-z3 descriptors respectively represent lipophilicity, stereogenicity and electronicity, and the z4 and z5 are related to electronegativity, heat of formation, electrophilicity and hardness.

[0017] In order to verify the accuracy of the ACE inhibitory activity peptide QSAR model in the above summary, a practical experiment can be carried out, and the practical experiment steps include optimizing the enzymolysis conditions of kelp crude protein by a response surface method to improve the degree of hydrolysis, including the following steps:

[0018] (1) salted kelp (removing impurities) → desalting and degumming → drying → crushing → sieving → enzymolysis → alkali liquid stirring extraction → centrifugation → salting out → freeze-drying → kelp crude protein → target enzyme enzymolysis → crude protein enzymolysis liquid.

[0019] (2) On the basis of the single factor experiment, appropriate factor levels are selected, and according to the Box-Benhnken center combination design principle, the enzymolysis time, the enzymolysis temperature and the enzyme addition amount are taken as independent variables, and the degree of hydrolysis is taken as a response value, the response surface is used to optimize the crude protein enzymolysis process, and the optimal enzymolysis process conditions are determined.

[0020] (3) Identification: the freeze-dried sample prepared in (2) is desalted by a C18 desalting column. The sample is analyzed by LC-MS / MS equipped with an online nano-spray ion source.

[0021] (4) ACE inhibitory activity determination method:

[0022]

[0023] Among them, [HA]c is the generated concentration of hippuric acid in the control tube; [HA]s is the generated concentration of hippuric acid in the determination tube; [HA]H is the generated concentration of hippuric acid in the blank group. IC 50 : the sample concentration corresponding to 50% ACE inhibitory rate.

[0024] (5) Inhibition type analysis: for the specific sequence of the novel kelp ACE inhibitory peptide screened above, the type of ACE inhibitory effect is explored, and different concentrations of HHL are respectively reacted with different concentrations of kelp peptides. The reciprocal of the rate of HA production is taken as the Y axis, and the reciprocal of the concentration of the substrate HHL is taken as the X axis, and a double-reciprocal Lineweaver-Burk plot is drawn.

[0025] Simulation of the resistance and stability of digestive enzymes before and after simulated gastrointestinal digestion: adjust the pH of the prepared polypeptide sample to acidity with a certain concentration of HCl, then add appropriate pepsin (w / w) to it and mix uniformly, digest in a 37℃ shaking water bath at a speed of 100r / min for a certain time, then take it out to room temperature. Adjust the sample solution to weak alkaline pH with appropriate concentration of NaOH, add appropriate trypsin (w / w) and continue to digest for a period of time, then inactivate the enzyme at 100℃ for 10min, then cool to room temperature, centrifuge and take the supernatant to detect the ACE activity.

[0026] The beneficial effects of the present application are that the established QSAR model combined with computer screening is not only an effective strategy for discovering ACE inhibitory activity peptides, but also an effective method for discovering other beneficial bioactive substances. In the identification and screening of algal bioactive peptides, a prediction model for high-throughput screening and identification of kelp peptides based on bioinformatics and in vitro experimental verification is provided. Specifically, the active peptides are described using amino acid descriptors 5z-scale, the structure-activity relationship system is determined to predict potential ACE inhibitory activity, an ACE inhibitory peptide database is constructed, and high-activity kelp peptides are quickly screened. This provides favorable support for exploring the inhibition type of ACE inhibitors and the stability of active peptides. A series of high-efficiency antihypertensive functional active peptides are screened by QSAR model identification, which are applied to kelp high-value application, and the development of functional ingredient active peptides is based on ACE inhibition. To a great extent, the processing and utilization of kelp are improved. This study not only provides a new idea for the deep development and utilization of kelp, but also provides a reference mode for the development of functional peptides of other marine biological resources. BRIEF DESCRIPTION OF DRAWINGS

[0027] In order to more clearly illustrate the technical solutions of the embodiments of the present application or the prior art, the drawings needed in the embodiment or prior art description will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application, and those skilled in the art can also obtain other drawings according to these drawings without creative labor.

[0028] In the drawings:

[0029] Figure 1 is a part of the analysis result graph of the QSAR model;

[0030] Figure 2 is a statistical graph of the degree of hydrolysis of the enzyme solution obtained by different proteases on kelp (protein) hydrolysis;

[0031] Figure 3Statistical graph of ACE inhibitory activity of target peptides PKKYG (A), VYKNT (B), IKTIQF (C), and GKIPRW (D);

[0032] Figure 4 Lineweaver-Burk analysis of the inhibitory effects of kelp peptides GKIPRW (A), IKTIQF (B), PKKYG (C), and VYKNT (D) on ACE.

[0033] Figure 5 Statistical graph showing the stability analysis of kelp ACE inhibitory peptides and enzymatic hydrolysate before and after gastrointestinal simulation.

[0034] Figure 6 This is a flowchart illustrating the operation of the present invention. Detailed Implementation

[0035] 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.

[0036] The present invention provides a method for screening and verifying ACE inhibitory peptides of kelp chloroplast prosthetic protein, which includes the following two steps:

[0037] Step 1: Computer-aided enzymatic hydrolysis of kelp protein.

[0038] First, the representative protein sequence of kelp, Hypothetical chloroplast protein (J7F9P0), was obtained by searching the Uniport (https: / / www.uniprot.org / ) online database. The obtained protein amino acid sequence was then subjected to combined hydrolysis using papain and chymotrypsin in the "Enzyme action" tool of the BIOPEP-UWM (https: / / biochemia.uwm.edu.pl / biopep / start_biopep.php) program. The resulting peptides were then sorted and classified.

[0039] Step 2: Establish a QSAR (Quantitative Structure-Activity Relationship) model for ACE (angiotensin-I converting enzyme) inhibitory active peptides to predict the activity of ACE inhibitory peptides.

[0040] The model set of the selected peptide segments includes 145 dipeptides, 251 tripeptides, 132 tetrapeptides, 154 pentapeptides, 131 hexapeptides, 77 heptapeptides, and 64 octapeptides. The selected peptide segments are selected according to the characteristics of the peptide segments in the model set and the IC50 values, and the peptide segments with IC50 values less than 18 mM are selected. Each amino acid in the sequence of the selected peptide segments is characterized by using the amino acid descriptor 5z-scale (Table 1). The relationship between the amino acid descriptor (predictor, X) and the log-transformed IC50 value (Y) is analyzed by using partial least squares regression (PLS), and then a two-component PLS model is established to convert the sequence into a matrix vector of structural descriptors.

[0041] The amino acid property parameters are collected from the amino acid physicochemical property database AAindex, and five z values (z1, z2, z3, z4, and z5) are obtained by principal component analysis. The z1-z3 descriptors represent lipophilicity, stericity, and electronicity, respectively, and z4 and z5 are related to other properties such as electronegativity, heat of formation, electrophilicity, and hardness. The properties of each amino acid are described by the five parameters (z1-z5) in sequence from the N-terminal to the C-terminal. The property descriptors of the first amino acid at the N-terminal of the specified peptide are n1z1, n1z2, n1z3, n1z4, and n1z5, and the property descriptors of the second amino acid at the N-terminal are n2z1, n2z2, n2z3, n2z4, and n2z5. Similarly, all the peptides are described in this order. The sequence number of the peptide segment in the library is identified in the figure, and is excluded in the second round of modeling. The data set excluding the outliers is modeled, and the activity of the peptide segments obtained by online enzymolysis in step one is predicted.

[0042] The practical experiment includes optimizing the enzymolysis conditions of kelp crude protein by response surface method to improve the degree of hydrolysis, including the following steps:

[0043] (1) Salted kelp (removing impurities) → desalting and degumming → drying → crushing → sieving → enzymolysis → alkali liquid stirring extraction → centrifugation → salting out → freeze-drying → kelp crude protein → target enzyme enzymolysis → crude protein enzymolysis liquid.

[0044] (2) On the basis of single-factor experiment, appropriate factor levels are selected, and according to the Box-Benhnken central composite design principle, the enzymolysis time, enzymolysis temperature, and enzyme addition amount are taken as independent variables, and the degree of hydrolysis is taken as the response value. The response surface is used to optimize the crude protein enzymolysis process, and the optimal enzymolysis process conditions are determined.

[0045] (3) Identification: the freeze-dried sample prepared in (2) is desalted by C18 desalting column. The sample is analyzed by LC-MS / MS equipped with online nanospray ion source.

[0046] (4) ACE inhibitory activity determination method:

[0047]

[0048] Wherein, [HA]c is the generation concentration of hippuric acid in the control tube; [HA]s is the generation concentration of hippuric acid in the determination tube; [HA]H is the generation concentration of hippuric acid in the blank group. IC 50 : The sample concentration corresponding to 50% ACE inhibition rate.

[0049] Inhibition type analysis: For the novel ACE inhibitory peptides of specific sequences screened above, the type of ACE inhibitory effect was explored, and different concentrations of HHL were reacted with different concentrations of seaweed peptides. The reciprocal of the rate of HA production was taken as the Y axis, and the reciprocal of the concentration of the substrate HHL was taken as the X axis to make a double-reciprocal Lineweaver-Burk plot.

[0050] Simulation of digestive enzyme resistance and stability before and after simulated gastrointestinal digestion: A certain concentration of HCl was used to adjust the pH of the prepared polypeptide sample to acidic, and then appropriate pepsin (w / w) was added and mixed uniformly, and then digested in a 37 ℃ shaking water bath at a speed of 100 r / min for a certain time. After the reaction was completed, it was taken out to room temperature. A suitable concentration of NaOH was used to adjust the sample solution to weak alkaline pH, and appropriate trypsin (w / w) was added for further digestion for a period of time, followed by enzyme inactivation at 100 ℃ for 10 min. After the enzyme inactivation was completed, it was cooled to room temperature, centrifuged, and the supernatant was detected for ACE activity.

[0051] Example 1

[0052] (1) Computer-aided enzymatic digestion of seaweed protein

[0053] The representative protein sequence of seaweed was retrieved on the Uniprot online database (https: / / www.uniprot.org / ): Hypothetical chloroplast protein (J7F9P0).

[0054] The obtained protein amino acid sequence was selected on the "Enzyme action" tool option of the BIOPEP-UWM program (https: / / biochemia.uwm.edu.pl / biopep / start_biopep.php) to perform combined hydrolysis of papain and chymotrypsin on the protein sequence.

[0055] The hydrolyzed polypeptides were sorted and classified, and a database of 45 tripeptides was established.

[0056] ACE inhibitor peptides generated by chymotrypsin and papain Peptide sequence tripeptides ADL, AIN, AIS, APY, ASN, AYL, DDL, DEL, DKI, DNT, DYL, EAL, EDK, EEL, EIL, EPF, EVE, GDF, GKN, GSL, IEE, IEG, IEN, IKT, KER, KKG, KNG, NEG, PEL, QDL, QKF, RPF, RTF, SEG, SET, SEY, SKN, SNL, SSN, SSY, TDY, TIF, VPL, YEY, YYF

[0057] (2) Establishing QSAR model of ACE inhibitory activity peptides

[0058] The peptide segments selected for modeling were based on the characteristics of different peptide segments in the model set and IC 50 values of the peptides. The IC 50 values of the peptides were below 18 mM. Each amino acid in the peptide sequence involved was characterized using the 5z-scale amino acid descriptor (Table 1), and the sequence was converted into a matrix vector of structure descriptors. The SIMCA-P14.1 version software was used to analyze the correlation between the amino acid descriptors and the ACE inhibitory peptide predictor IC 50 values using partial least squares (PLS) regression.

[0059] The variables defining the structural properties of the polypeptides were selected as the X matrix, and the logarithmic values of ACE-I activity IC50 were selected as the Y matrix. The correlation coefficient (R 2 ) and the root mean square error (RMSE) were used to evaluate the fitting ability of the model. In order to ensure the consistency of the data source of the training set, the peptides with a difference between the predicted value and the actual value greater than 1 were identified by the arrangement number of the peptide segment in the graph when establishing the peptide segment library, as shown in Figure 1 (a), and were excluded in the second round of modeling. After modeling the data set excluding the outliers, the correlation coefficient R 2 increased significantly, as shown in Figure 1 (b), R 2 was equal to 0.6124, and RMSE was equal to 0.499.

[0060] To verify the accuracy of the model, the data of the response variable (Y) were randomly shuffled, the independent variable (X) was kept unchanged, and a PLS model was re-established. The cross-validation coefficient Q 2 was used to evaluate the prediction quality of the QSAR model. The number of data permutations was set to 20, and the prediction performance indicators were calculated. The simulated values of the model after permutation (left) were all lower than the actual values (right), and the model established in this example all met the ideal limit value R 2 less than 0.3 and Q 2 less than 0.05. Therefore, the model established in this example was reliable and effective, and the prediction ability of the model was significantly better than random guessing. Q 2 was equal to -0.614.

[0061] Further analysis of the partial least squares regression coefficients to show the relationship between each physical and chemical constant (X variable) and the polypeptide titer (Y), the importance of the defined X to Y is proportional to its distance from zero and corresponds to the partial least squares regression coefficient, the tripeptide model, n1z1, n1z4, n1z5, n2z1, n2z3, n3z1 is positively correlated with the log value, n2z2, n2z4, n2z5, n3z2, n3z3, n3z4 is negatively correlated with the log value. n1z1, n2z3, n2z4, n3z1 have a greater impact on the model, and the lipophilic properties (z1) of the amino acid residues in the amino acid sequence and other properties (z4) have a greater impact on the activity of ACE inhibitory peptides.

[0062] (3) Optimization of enzymatic hydrolysis conditions of kelp crude protein

[0063] After impurity removal, desalination, degumming, drying, crushing and sieving, the salted kelp is subjected to enzymatic hydrolysis.

[0064] After the degummed kelp powder is dissolved in water, the pH is adjusted to 6.0, a certain amount of cellulase is added, and the initial enzymatic hydrolysis is carried out for 50 minutes, then the pH is adjusted to alkaline, and the mixture is stirred thoroughly and extracted.

[0065] Centrifugation at 5000 r / min for 10 min, precipitation of proteins in the supernatant with saturated ammonium sulfate solution, and centrifugation to obtain the precipitate, which is freeze-dried to obtain kelp crude protein.

[0066] 10 g of kelp crude protein is weighed into a 1 L conical flask, 0.5 L of phosphate buffer solution is added, and five kinds of commercial proteases, including cellulase, papain, trypsin, protease and a mixture of papain and trypsin, are selected to determine the hydrolysis degree of kelp crude protein under different protease hydrolysis conditions.

[0067] Under the optimal conditions, the enzyme amount is 0.1 g, and the other hydrolysis conditions are as follows: solid-liquid ratio 1:3, pH 7, temperature 50℃, constant temperature oscillation hydrolysis for 3 h, inactivation after hydrolysis (85℃, 15 min), filtration with 250 mesh filter cloth, centrifugation, and determination of the hydrolysis degree of the supernatant.

[0068] (4) ACE inhibitory activity determination and analysis of inhibition type

[0069] The total volume of the reaction system is 260 μL. 120 μL of polypeptide sample (1 mg / mL) and 40 μL of ACE solution (0.1 U / mL) are mixed, and the mixture is incubated in a 37℃ water bath for 10 min.

[0070] After incubation, add 100 μL of HHL solution (1 mM), react at 37℃ for 60 min, and then add 200 μL of HCl (1 mol / L) to terminate the reaction.

[0071] 120 μL of borate buffer (pH=8.3, 0.3 mol / L NaCl) was used as a control group to replace the sample solution of 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 of the control group.

[0072] 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.

[0073] 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.

[0074] (5) Simulate stability before and after digestion

[0075] 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 h at 100 r / min in a 37 ℃ 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 2 M NaOH, and 2% w / w trypsin was added for further digestion for 2 h. 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 determine its ACE activity.

[0076] The stability of DDIW gastrointestinal simulated digestion remained essentially unchanged before and after digestion: IC before gastrointestinal simulated digestion 50 (RPF) equals 0.048 mM, IC50 after simulated gastrointestinal digestion 50 (RPF) equals 0.049 mM; pre-digestion IC in the gastrointestinal tract 50 (GDF) equals 0.082 mM, IC50 after simulated gastrointestinal digestion 50 (KDIF) equals 0.084M.

[0077] Example 2

[0078] (1) Computer-aided enzymatic hydrolysis of kelp protein

[0079] The representative protein sequence of Undaria pinnatifida was retrieved from Uniprot online database (https: / / www.uniprot.org / ): Hypothetical chloroplast protein (J7F9P0).

[0080] The obtained protein amino acid sequence was selected for combined hydrolysis by papain and chymotrypsin in the "Enzyme action" tool option of the BIOPEP-UWM program (https: / / biochemia.uwm.edu.pl / biopep / start_biopep.php).

[0081] The hydrolyzed polypeptides were sorted and classified, and a database of 29 tetrapeptides was established.

[0082] ACE inhibitor peptides generated by chymotrypsin and papain Peptide sequence tetrapeptides VPKN, AEIN, VSSR, SEEN, KIDR, AKIG, VEPY, KDIF, DDIW, QKDY, DDSF, PESR, IKSR, KPEY, KKIN, KKGF, PKKY, EGSF, TDEL, ITVL, QKDY, GEIL, QQKF, QNIN, NKKG, APYG, MDKI, WNNT, AKEK

[0083] (2) Establishment of ACE-inhibitory activity peptide QSAR model

[0084] The selected peptide segments were based on the characteristics of different peptide segments in the model set and the IC 50 values, and the peptide segments with IC 50 values below 18 mM were selected. The amino acid descriptors 5z-scale (Table 1) were used to characterize each amino acid in the peptide sequence involved, and the sequence was converted into a matrix vector of structural descriptors. The SIMCA-P14.1 version software was used to analyze the correlation between the amino acid descriptors and the ACE-inhibitory peptide predictor IC 50 values using partial least squares (PLS) regression method.

[0085] The variables defining the structural properties of the polypeptides were selected as the X matrix, and the logarithmic values of ACE-I activity IC 50 were selected as the Y matrix. The correlation coefficient (R 2 ) and the root mean square error (RMSE) were used to evaluate the fitting ability of the model. In order to ensure the consistency of the source of the training set data, the peptides with a difference between the predicted value and the actual value greater than 1 were identified by the arrangement number in the graph when establishing the peptide segment library, and were excluded in the second round of modeling. After modeling the data set excluding the abnormal values, the correlation coefficient R 2 increased significantly, R 2 was equal to 0.5571, and RMSE was equal to 0.4398.

[0086] To verify the accuracy of the model, the data of the response variable (Y) was randomly shuffled, the independent variable (X) was kept unchanged, and the PLS model was re-established. The cross-validation coefficient Q 2The QSAR model prediction quality was evaluated. The data permutation number was set to 20 times, and the prediction performance index was calculated. The simulated values (left) of the model after permutation were lower than the actual values (right), and the model established in this example case met the ideal limit value R of the effective model 2 less than 0.3, Q 2 less than 0.05. Therefore, the model established in this example is reliable and effective, and the prediction ability of the model is significantly better than random guessing. 2 equal to -0.615.

[0087] The partial least squares regression coefficients were further analyzed to show the relationship between each physicochemical constant (X variable) and the polypeptide titer (Y), and the importance of the defined X to Y was proportional to the distance from zero and corresponded to the partial least squares regression coefficient. In the tetrapeptide model, n1z2, n1z5, n2z2, n2z2, n2z4, n2z5, and n3z5 had a greater impact on the tetrapeptide.

[0088] (3) Optimization of the enzymatic hydrolysis conditions of kelp crude protein

[0089] After impurity removal, salt removal, degumming, drying, crushing, and sieving, the salted kelp was subjected to enzymatic hydrolysis.

[0090] After the degummed kelp powder was dissolved in water, the pH was adjusted to 6.0, a certain amount of cellulase was added, and the initial enzymatic hydrolysis was performed for 50 minutes. Then the pH was adjusted to alkaline, and the mixture was thoroughly stirred and extracted.

[0091] Centrifugation was performed at 5000 r / min for 10 min, and the protein in the supernatant was precipitated with saturated ammonium sulfate solution. After centrifugation, the precipitate was obtained and freeze-dried to obtain kelp crude protein.

[0092] 10 g of kelp crude protein was weighed into a 1 L conical flask, 0.5 L of phosphate buffer solution was added, and five kinds of commercial proteases were selected, including cellulose protease, papain, trypsin, protease, and a mixture of papain and trypsin. The hydrolysis degree of kelp crude protein was determined by different proteases.

[0093] Under the optimal conditions, the enzyme amount was 0.1 g, and the other enzymatic hydrolysis conditions were as follows: solid-liquid ratio 1:3, pH 7, temperature 50°C, constant temperature, and enzymatic hydrolysis for 3 h. After the enzymatic hydrolysis was completed, the enzyme was inactivated (85°C, 15 min), filtered with 250 mesh filter cloth, centrifuged, and the supernatant was collected to determine the hydrolysis degree.

[0094] (4) ACE inhibition activity determination and inhibition type analysis

[0095] The total volume of the reaction system was 260 μL. 120 μL of the polypeptide sample (1 mg / mL) and 40 μL of the ACE solution (0.1 U / mL) were mixed, and the mixture was incubated in a 37°C water bath for 10 min after being fully mixed by a vortex oscillator.

[0096] After the incubation, 100 μL of the HHL solution (1 mM) was added, and the reaction was allowed to proceed at 37°C for 60 min. Then, 200 μL of HCl (1 mol / L) was added to terminate the reaction.

[0097] The control group was prepared by using 120 μL of the boric acid buffer (pH=8.3, 0.3 mol / L NaCl) instead of the sample solution of the determination group. The blank control group was prepared by using 20 μL of the boric acid buffer (pH=8.3, 0.3 mol / L NaCl) instead of the ACE solution of the control group.

[0098] A certain amount of the sample solution was taken from the neck of the sample bottle, and the concentration of HA in the reaction system was determined by RP-HPLC.

[0099] The IC 50 (DDIW) was equal to 0.079 mM, and the inhibition was non-competitive. The IC 50 (KDIF) was equal to 0.191 mM, and the inhibition was competitive.

[0100] (5) Stability before and after simulated digestion

[0101] The mass concentration of the polypeptide sample was 1 mg / mL. First, the pH of the prepared polypeptide sample was adjusted to 2 by using 1 mol / L HCl. Then, 2% of pepsin (w / w) was added to the polypeptide sample, and the mixture was uniformly mixed. The polypeptide sample was digested in a 37°C oscillation water bath at a speed of 100 r / min for 1 h. After the reaction, the sample was taken out and cooled to room temperature. The sample solution was adjusted to pH 7.5 by using 2 M NaOH, and 2% of trypsin (w / w) was added to continue the digestion for 2 h. Then, the enzyme was inactivated at 100°C for 10 min. After the inactivation, the sample was cooled to room temperature, and the supernatant was obtained by centrifugation for the determination of ACE activity.

[0102] The stability of DDIW before and after gastrointestinal simulation digestion remained basically unchanged. The IC 50 (DDIW) was equal to 0.081 mM after the gastrointestinal simulation digestion.

[0103] The activity of KDIF was affected before and after the gastrointestinal simulation digestion. The IC 50 (KDIF) was equal to 0.191 mM before the gastrointestinal simulation digestion, and the IC 50 (KDIF) was equal to 0.238 mM after the gastrointestinal simulation digestion.

[0104] Example 3

[0105] (1) Computer-aided enzymatic hydrolysis of seaweed protein

[0106] The representative protein sequence of seaweed was retrieved from Uniprot online database (https: / / www.uniprot.org / ): Hypothetical chloroplast protein (J7F9P0).

[0107] The obtained protein amino acid sequence was selected for combined hydrolysis by papain and chymotrypsin in the "Enzyme action" tool option of the BIOPEP-UWM program (https: / / biochemia.uwm.edu.pl / biopep / start_biopep.php).

[0108] The hydrolyzed polypeptides were sorted and classified, and a pentapeptide database of 22 entries was established.

[0109] ACE inhibitor peptides generated by chymotrypsin and papain Peptide sequence pentapeptides VPSDF, QDKVG, KISKE, EVPDL, IIPIF, IPEEL, EPDER, SESDT, KIKVY, TTPRN, KTIRF, PESRM, DKIQL, DIASN, TETKN, NVSSR, AINDG, AKEKL, IVKST, PKKYG, VYKNT, QNKYT

[0110] (2) Establishment of ACE-inhibitory activity peptide QSAR model

[0111] The selected peptide segments were based on the characteristics of different peptide segments in the model set and the IC 50 values of the peptides. Peptide segments with IC 50 values below 18 mM were selected. The amino acid descriptors 5z-scale (Table 1) were used to characterize each amino acid in the peptide sequence involved, and the sequence was converted into a matrix vector of structure descriptors. The SIMCA-P14.1 version of the software was used to analyze the correlation between the amino acid descriptors and the ACE-inhibitory peptide predictor IC 50 values using partial least squares (PLS) regression.

[0112] The variables defining the structural properties of the polypeptides were selected as the X matrix, and the logarithmic values of ACE-I activity IC 50 were selected as the Y matrix. The correlation coefficient (R 2 ) and the root mean square error (RMSE) were used to evaluate the fitting ability of the model. In order to ensure the consistency of the source of the training set data, the peptides with a difference between the predicted value and the actual value greater than 1 were identified by the arrangement number in the peptide segment library, as shown in Figure 1 (a), and were excluded in the second round of modeling. After modeling the data set excluding the outliers, the correlation coefficient R 2 increased significantly, as shown in Figure 1 (b), with R 2 equal to 0.5802 and RMSE equal to 0.5208.

[0113] To verify the accuracy of the model, the data of the response variable (Y) was randomly shuffled, the independent variable (X) was kept unchanged, the PLS model was re-established, and the cross-validation coefficient Q 2 The QSAR model prediction quality was evaluated. The data permutation number was set to 20 times, and the prediction performance index was calculated. For example Figure 1 As shown in (c), the simulated values (left) of the model after permutation are all lower than the actual values (right), and the model established in this example meets the ideal limit value R 2 less than 0.3, Q 2 less than 0.05. Therefore, the model established in this example is reliable and effective, and the prediction ability of the model is significantly better than random guessing. Q 2 equals -0.593.

[0114] The partial least squares regression coefficients were further analyzed to show the relationship between each physical and chemical constant (X variable) and the potency of the polypeptide (Y), and the importance of X to Y was defined in direct proportion to the distance from zero and corresponding to the partial least squares regression coefficient, as shown in (d). Figure 1 As shown in (d), n1z1, n1z5, n2z1, n3z1, n4z3, n5z2, n5z4, and n5z5 in the pentapeptide model are important factors determining the activity of the pentapeptide. The importance of the fourth amino acid residue at the C-terminus is much greater than that of the second and third amino acid residues.

[0115] (3) Optimization of the enzymatic hydrolysis conditions of kelp crude protein

[0116] After impurity removal, salt removal, degumming, drying, crushing, and sieving, the salted kelp was subjected to enzymatic hydrolysis.

[0117] After the degummed kelp powder was dissolved in water, the pH was adjusted to 6.0, a certain amount of cellulase was added, and after 50 minutes of preliminary enzymatic hydrolysis, the pH was adjusted to alkaline, and the mixture was thoroughly stirred and extracted.

[0118] Centrifugation at 5000 r / min for 10 min, precipitation of proteins in the supernatant with saturated ammonium sulfate solution, and after centrifugation, the precipitate was obtained and freeze-dried to obtain kelp crude protein.

[0119] 10 g of kelp crude protein was weighed into a 1 L conical flask, 0.5 L of phosphate buffer solution was added, and five kinds of commercial proteases, including cellulase, papain, trypsin, protease, and a mixture of papain and trypsin, were selected to determine the hydrolysis degree of kelp crude protein by different proteases.

[0120] The optimum conditions were integrated, the amount of enzyme was 0.1 g, and other enzymatic hydrolysis conditions were as follows: the ratio of material to liquid was 1:3, the pH value was 7, the temperature was 50°C, the enzymatic hydrolysis was oscillated for 3 h under constant temperature, the enzymatic hydrolysis was inactivated (85°C, 15 min) after the end of the enzymatic hydrolysis, 250-mesh filter cloth was used for filtration, centrifugation was performed, and the supernatant was used for determination of the degree of hydrolysis.

[0121] (4) ACE inhibitory activity determination and analysis of inhibition type

[0122] The total volume of the reaction system was 260 μL. 120 μL of the polypeptide sample (1 mg / mL) and 40 μL of the ACE solution (0.1 U / mL) were mixed, and the mixture was fully reacted and uniformly oscillated by a vortex oscillator, and then was placed in a 37°C water bath incubator for 10 min.

[0123] After the incubation was completed, 100 μL of HHL solution (1 mM) was added, and the reaction was carried out at 37°C for 60 min, and then 200 μL of HCl (1 mol / L) was added to terminate the reaction.

[0124] 120 μL of boric acid buffer (pH=8.3, 0.3 mol / L NaCl) was used as a control group to replace the sample solution of the determination group; 20 μL of boric acid buffer (pH=8.3, 0.3 mol / L NaCl) was used as a blank control group to replace the ACE solution of the control group.

[0125] A certain amount of sample solution was taken from the neck of the sample bottle, and the concentration of HA generated in the reaction system was determined by RP-HPLC.

[0126] As shown in Figure 3 , IC 50 (PKKYG) was equal to 0.74 mM, and it was non-competitive inhibition; IC 50 (VYKNT) was equal to 0.22 mM, and it was competitive inhibition.

[0127] (5) Stability before and after simulated digestion

[0128] The mass concentration of the polypeptide sample was 1 mg / mL. First, 1 mol / L HCl was used to adjust the pH of the prepared polypeptide sample to 2, then 2% pepsin (w / w) was added to the polypeptide sample and mixed uniformly, and the polypeptide sample was digested in a 37°C oscillation water bath at a speed of 100 r / min for 1 h; after the reaction was completed, the polypeptide sample was taken out to room temperature. 2 M NaOH was used to adjust the pH of the sample solution to 7.5, 2% trypsin (w / w) was added for continuous digestion for 2 h, then the enzyme was inactivated at 100°C for 10 min, after the inactivation was completed, the sample was cooled to room temperature, centrifugation was performed, and the supernatant was used for detection of ACE activity.

[0129] As shown in Figure 5As shown, the stability of PKKYG before and after gastrointestinal simulation digestion remains basically unchanged: IC 50 (PKKYG) equals 0.74 mM before gastrointestinal simulation digestion, and IC 50 (PKKYG) equals 0.77 mM after gastrointestinal simulation digestion.

[0130] The activity of VYKNT before and after gastrointestinal simulation digestion is affected: IC 50 (VYKNT) equals 0.22 mM before gastrointestinal simulation digestion, and IC 50 (VYKNT) equals 0.38 mM after gastrointestinal simulation digestion.

[0131] Example 4

[0132] (1) Computer-aided enzymatic digestion of kelp protein

[0133] The representative protein sequence of kelp was retrieved on the Uniprot online database (https: / / www.uniprot.org / ): Hypothetical chloroplast protein (J7F9P0).

[0134] The obtained protein amino acid sequence was subjected to combined hydrolysis by papain and chymotrypsin on the "Enzyme action" tool option of the BIOPEP-UWM program (https: / / biochemia.uwm.edu.pl / biopep / start_biopep.php).

[0135] The hydrolyzed polypeptides were sorted and classified, and a database of 24 hexapeptides was established.

[0136] ACE inhibitor peptides generated by chymotrypsin and papain Peptide sequence hexapeptides EIDKSL, IVDDSG, KEKKEL, AEIEDL, VKIVSM, ISKKPR, QKIEDY, EIKSSF, GKIDRW, IVKSTN, VEEKEL, VAVPIN, RTDADL, EVEAPY, IKTIQF, EEAQDL, WSEENG, NKISKE, VKKKPN, SNSSYG, NVEPYL, ENNIKT, IENSSN, QKDYPL

[0137] (2) Establishment of ACE-inhibitory activity peptide QSAR model

[0138] The peptide segments selected for modeling were based on the characteristics of different peptide segments in the model set and the IC 50 values, and the peptide segments with IC 50 values below 18 mM were selected. The amino acid descriptors 5z-scale (Table 1) were used to characterize each amino acid in the peptide sequence involved, and the sequence was converted into a matrix vector of structure descriptors. The SIMCA-P14.1 version software was used to analyze the correlation between the amino acid descriptors and the ACE-inhibitory peptide predictive factor IC 50 value by partial least squares (PLS) regression method.

[0139] The variables defining the structural properties of the polypeptide were selected as the X matrix, and the ACE-I activity IC 50log values as Y matrix. The correlation coefficient (R 2 ) and root mean square error (RMSE) were used to evaluate the fitting ability of the model. In order to ensure the consistency of the data source of the training set, the peptide segments with a difference greater than 1 between the predicted value and the actual value were identified by the arrangement number in the figure when the peptide segment library was established, as shown in Figure 1 (e), and were excluded in the second round of modeling. After modeling the data set excluding the outliers, the correlation coefficient R 2 was greatly improved, as shown in Figure 1 (f), R 2 was equal to 0.6694, and RMSE was equal to 0.4677.

[0140] To verify the accuracy of the model, the data of the response variable (Y) were randomly shuffled while keeping the independent variable (X) unchanged, and the PLS model was re-established. The cross-validation coefficient Q 2 was used to evaluate the prediction quality of the QSAR model. The number of data permutation was set to 20 times, and the prediction performance index was calculated. As shown in Figure 1 (g), the simulated values (left) of the model after permutation were all lower than the actual values (right), and the model established in this example all met the ideal limit value R 2 less than 0.3 and Q 2 less than 0.05. Therefore, the model established in this example was reliable and effective, and the prediction ability of the model was significantly better than random guessing. Q 2 was equal to -0.63.

[0141] The partial least squares regression coefficients were further analyzed to show the relationship between each physicochemical constant (X variable) and the potency of the polypeptide (Y), and the importance of X to Y was defined in direct proportion to the distance from zero and corresponded to the partial least squares regression coefficient, as shown in Figure 1 (h). For the hexapeptide n4z1, it was a significant factor affecting the hexapeptide. When the C-terminal amino acid was a hydrophobic or aromatic amino acid, it had a significant positive promoting effect on the ACE inhibitory effect.

[0142] (3) Optimization of enzymatic hydrolysis conditions of kelp crude protein

[0143] After impurity removal, desalting, degumming, drying, crushing, and sieving, the salted kelp was subjected to enzymatic hydrolysis.

[0144] After the degummed kelp powder was dissolved in water, the pH was adjusted to 6.0, a certain amount of cellulase was added, and after preliminary enzymatic hydrolysis for 50 minutes, the pH was adjusted to alkaline, and the mixture was thoroughly stirred and extracted.

[0145] Centrifugation at 5000 r / min for 10 min, the protein in the supernatant was precipitated with saturated ammonium sulfate solution, and the precipitate was obtained after centrifugation and freeze-dried to obtain kelp crude protein.

[0146] Take 10 g of kelp crude protein in a 1 L conical flask, add 0.5 L of phosphate buffer solution, select cellulase, papain, chymotrypsin, complex protease and papain and chymotrypsin mixed enzyme as five kinds of commercial protease, and determine the hydrolysis degree of different protease hydrolysis of kelp crude protein.

[0147] Under the optimum conditions, the enzyme amount is 0.1 g, and other enzymolysis conditions are: solid-liquid ratio is 1:3, pH value is 7, temperature is 50℃, constant temperature oscillation enzymolysis for 3h, inactivation after enzymolysis (85℃, 15min), use 250 mesh filter cloth to filter, centrifuge, take supernatant to determine the degree of hydrolysis.

[0148] (4) ACE inhibition activity determination and inhibition type analysis

[0149] The total volume of the reaction system is 260 μL. Take 120 μL of polypeptide sample (1 mg / mL) and 40 μL of ACE solution (0.1 U / mL) and mix them well in a vortex oscillator, then incubate them in a 37℃ water bath for 10 min.

[0150] After incubation, add 100 μL of HHL solution (1 mM) and react at 37℃ for 60 min, then add 200 μL of HCl (1 mol / L) to terminate the reaction.

[0151] Use 120 μL of boric acid buffer (pH=8.3, 0.3 mol / L NaCl) as the control group instead of the sample solution in the determination group; use 20 μL of boric acid buffer (pH=8.3, 0.3 mol / L NaCl) as the blank control group instead of the ACE solution in the control group.

[0152] Take a certain amount of sample solution from the three groups to the neck of the sample bottle, and determine the concentration of HA generated in the reaction system by RP-HPLC method.

[0153] As shown in Figure 3 , the IC 50； of IKTIQF is 0.53 mM, and the mixed type of inhibition; the IC 50 of GKIPRW is 0.78 mM, and the competitive inhibition.

[0154] (5) Stability before and after simulated digestion

[0155] The mass concentration of the polypeptide sample was 1 mg / mL. First, the pH of the prepared polypeptide sample was adjusted to 2 with 1 mol / L HCl, then 2% pepsin (w / w) was added and mixed uniformly, and then digested in a 37°C shaking water bath at a speed of 100 r / min for 1 h; after the reaction was completed, it was taken out to room temperature. The sample solution was adjusted to pH 7.5 with 2 M NaOH, 2% trypsin (w / w) was added for further digestion for 2 h, and then the enzyme was inactivated at 100°C for 10 min. After the inactivation was completed, it was cooled to room temperature, centrifuged, and the supernatant was detected for ACE activity.

[0156] As shown in Figure 5 , the stability of GKIPRW before and after gastrointestinal simulation digestion remained basically unchanged: the IC 50 (GKIPRW) of the polypeptide sample before gastrointestinal simulation digestion was equal to 0.78 mM, and the IC 50 (GKIPRW) of the polypeptide sample after gastrointestinal simulation digestion was equal to 0.81 mM.

[0157] The activity of IKTIQF was affected before and after gastrointestinal simulation digestion: the IC 50 (IKTIQF) of the polypeptide sample before gastrointestinal simulation digestion was equal to 0.53 mM, and the IC 50 (IKTIQF) of the polypeptide sample after gastrointestinal simulation digestion was equal to 0.67 mM.

[0158] Example 5

[0159] (1) Computer-aided enzymatic digestion of kelp protein

[0160] The representative protein sequence of kelp was obtained by searching the Uniprot online database (https: / / www.uniprot.org / ): Hypothetical chloroplast protein (J7F9P0).

[0161] The obtained protein amino acid sequence was selected for combined hydrolysis by papain and chymotrypsin on the "Enzyme action" tool option of the BIOPEP-UWM program (https: / / biochemia.uwm.edu.pl / biopep / start_biopep.php).

[0162] The hydrolyzed polypeptides were sorted and classified, and a heptapeptide database of 9 peptides was established.

[0163] ACE inhibitor peptides generated by chymotrypsin and papain Peptide sequence heptapeptides DSKVDVN, IPSSKSL, IEGRPGN, SESDTVL, NKIKVYF, NVEEKEL, AVPINNF, SEYEYDY, KKINVPL

[0164] (2) Establishment of ACE inhibitory activity peptide QSAR model

[0165] The selected peptide segments were based on the characteristics of different peptide segments in the model set and the IC 50 values of the peptides. Peptide segments with IC 50 values below 18 mM were selected. Each amino acid in the peptide sequence involved was characterized using the amino acid descriptor 5z-scale (Table 1), and the sequence was converted into a matrix vector of structure descriptors. The correlation between the amino acid descriptors and the ACE-inhibitory peptide predictor IC 50 was analyzed using the software version 14.1 of SIMCA-P1 in partial least squares (PLS) regression.

[0166] The variables defining the structural properties of the polypeptides were selected as the X matrix, and the logarithmic values of the ACE-I activity IC 50 were selected as the Y matrix. The correlation coefficient (R 2 ) and the root mean square error (RMSE) were used to evaluate the fitting ability of the model. In order to ensure the consistency of the data source of the training set, the peptide segments with a difference of more than 1 between the predicted value and the actual value were identified by their arrangement numbers in the graph when the peptide segment library was established, and were excluded in the second round of modeling. After modeling the data set excluding the outliers, the correlation coefficient R 2 increased significantly, R 2 was equal to 0.8108, and RMSE was equal to 0.4441.

[0167] In order to verify the accuracy of the model, the data of the response variable (Y) were randomly shuffled, the independent variable (X) was kept unchanged, and a PLS model was re-established. The cross-validation coefficient Q 2 was used to evaluate the prediction quality of the QSAR model. The number of data permutations was set to 20, and the prediction performance indicators were calculated. The simulated values (left) of the model after permutation were all lower than the actual values (right), and the models established in this example all met the ideal limit value R 2 less than 0.3 and Q 2 less than 0.05. Therefore, the model established in this example was reliable and effective, and the prediction ability of the model was significantly better than random guessing. Q 2 was equal to -0.629.

[0168] The partial least squares regression coefficients were further analyzed to show the relationship between each physicochemical constant (X variable) and the potency of the polypeptide (Y), and the importance of X to Y was proportional to the distance from zero and corresponded to the partial least squares regression coefficient. For heptapeptides, c-terminal z1, z3 and z4 played an important role in ACE inhibition.

[0169] (3) Optimization of the enzymatic hydrolysis conditions of kelp crude protein

[0170] After impurity removal, desalting and degumming, drying, crushing and sieving, the salted kelp was subjected to enzymatic hydrolysis.

[0171] The degummed seaweed powder was dissolved in water, the pH was adjusted to 6.0, a certain amount of cellulase was added, and the initial enzymatic hydrolysis was carried out for 50 minutes, then the pH was adjusted to alkaline, and the extraction was carried out after sufficient stirring.

[0172] Centrifugation was carried out at 5000 r / min for 10 min, and the protein in the supernatant was precipitated with saturated ammonium sulfate solution. After centrifugation, the precipitate was obtained and freeze-dried to obtain seaweed crude protein.

[0173] 10 g of seaweed crude protein was weighed in a 1 L conical flask, 0.5 L of phosphate buffer solution was added, and five kinds of commercial proteases, cellulase, papain, chymotrypsin, protease and papain and chymotrypsin mixed enzyme were selected to determine the hydrolysis degree of different proteases in the enzymatic hydrolysis of seaweed crude protein.

[0174] Under the optimal conditions, the enzyme amount was 0.1 g, and other enzymatic hydrolysis conditions were as follows: the solid-liquid ratio was 1:3, the pH value was 7, the temperature was 50℃, the enzymatic hydrolysis was carried out under constant temperature for 3 h, and then the enzyme was inactivated (85℃, 15 min). After the end of the enzymatic hydrolysis, the hydrolysis degree was determined by using 250 mesh filter cloth to filter, centrifugation and taking the supernatant.

[0175] (4) ACE inhibition activity determination and inhibition type analysis

[0176] The total volume of the reaction system was 260 μL. 120 μL of polypeptide sample (1 mg / mL) and 40 μL of ACE solution (0.1 U / mL) were mixed, and then the mixture was placed in a 37℃ water bath for 10 min.

[0177] After incubation, 100 μL of HHL solution (1 mM) was added, and the reaction was carried out at 37℃ for 60 min. Then 200 μL of HCl (1 mol / L) was added to terminate the reaction.

[0178] 120 μL of boric acid buffer (pH=8.3, 0.3 mol / L NaCl) was used as a control group to replace the sample solution in the determination group; 20 μL of boric acid 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.

[0179] A certain amount of sample solution was taken from the neck of the sample bottle, and the concentration of HA generated in the reaction system was determined by RP-HPLC method.

[0180] The IC 50 of (NKIKVYF) is equal to 0.049 mM, which is non-competitive inhibition; the IC 50 of (DSKVDVN) is equal to 0.057 mM, which is non-competitive inhibition.

[0181] (5) Stability before and after simulated digestion

[0182] The polypeptide sample with a mass concentration of 1 mg / mL was first prepared by adjusting the pH of the polypeptide sample to 2 with 1 mol / L HCl, then 2% pepsin (w / w) was added and mixed uniformly, and then digested in a 37 ℃ shaking water bath at a speed of 100 r / min for 1 h; after the reaction was completed, it was taken out to room temperature. The sample solution was adjusted to pH 7.5 with 2 M NaOH, 2% trypsin (w / w) was added for further digestion for 2 h, and then the enzyme was inactivated at 100 ℃ for 10 min. After the inactivation was completed, it was cooled to room temperature, centrifuged, and the supernatant was detected for ACE activity.

[0183] The stability of NKIKVYF before and after gastrointestinal simulation digestion remained basically unchanged: IC 50 (NKIKVYF) was equal to 0.049 mM before gastrointestinal simulation digestion, and IC 50 (NKIKVYF) was equal to 0.05 mM after gastrointestinal simulation digestion;

[0184] The activity of DSKVDVN was affected before and after gastrointestinal simulation digestion: IC 50 (DSKVDVN) was equal to 0.057 mM before gastrointestinal simulation digestion, and IC (DSKVDVN) was equal to 0.127 mM after gastrointestinal simulation digestion.

[0185] Example 6

[0186] (1) Computer-aided enzymatic digestion of kelp protein

[0187] The representative protein sequence of kelp was obtained by searching the Uniprot online database (https: / / www.uniprot.org / ): Hypothetical chloroplast protein (J7F9P0).

[0188] The obtained protein amino acid sequence was selected for combined hydrolysis by papain and chymotrypsin on the "Enzyme action" tool option of the BIOPEP-UWM program (https: / / biochemia.uwm.edu.pl / biopep / start_biopep.php).

[0189] The hydrolyzed polypeptides were sorted and classified, and an octapeptide database of 2 was established.

[0190] ACE inhibitor peptides generated by chymotrypsin and papain Peptide sequence octapeptides QEEDESKL, SKNIIPIF

[0191] (2) Establishment of ACE inhibitory activity peptide QSAR model

[0192] The selected peptide segments were based on the characteristics of different peptide segments in the model set and the IC 50 values of the peptides. Peptide segments with IC 50 values below 18 mM were selected. Each amino acid in the peptide sequence involved was characterized using the amino acid descriptor 5z-scale (Table 1), and the sequence was converted into a matrix vector of structure descriptors. The correlation between the amino acid descriptors and the ACE-inhibitory peptide predictor IC 50 was analyzed using the software version 14.1 of SIMCA-P1 in partial least squares (PLS) regression.

[0193] The variables defining the structural properties of the polypeptides were selected as the X matrix, and the logarithmic values of the ACE-I activity IC 50 were selected as the Y matrix. The correlation coefficient (R 2 ) and the root mean square error (RMSE) were used to evaluate the fitting ability of the model. In order to ensure the consistency of the data source of the training set, the peptide segments with a difference of more than 1 between the predicted value and the actual value were identified by the arrangement number in the graph when establishing the peptide segment library, and were excluded in the second round of modeling. After modeling the data set excluding the outliers, the correlation coefficient R 2 increased significantly, R 2 was equal to 0.711, and RMSE was equal to 0.487.

[0194] In order to verify the accuracy of the model, the data of the response variable (Y) were randomly shuffled, the independent variable (X) was kept unchanged, and a PLS model was re-established. The cross-validation coefficient Q 2 was used to evaluate the prediction quality of the QSAR model. The number of data permutations was set to 20, and the prediction performance indicators were calculated. The simulated values (left) of the model after permutation were all lower than the actual values (right), and the models established in this example all met the ideal limit value R 2 less than 0.3 and Q 2 less than 0.05. Therefore, the model established in this example was reliable and effective, and the prediction ability of the model was significantly better than random guessing. Q 2 was equal to -0.546.

[0195] The partial least squares regression coefficients were further analyzed to show the relationship between each physicochemical constant (X variable) and the potency of the polypeptide (Y), and the importance of X to Y was proportional to the distance from zero and corresponded to the partial least squares regression coefficient. For octapeptides, c-terminal z1, z3 and z4 played an important role in ACE inhibition.

[0196] (3) Optimization of the enzymatic hydrolysis conditions of kelp crude protein

[0197] After impurity removal, desalting and degumming, drying, crushing and sieving, the salted kelp was subjected to enzymatic hydrolysis.

[0198] The degummed seaweed powder was dissolved in water, the pH was adjusted to 6.0, a certain amount of cellulase was added, and the initial enzymatic hydrolysis was carried out for 50 minutes, then the pH was adjusted to alkaline, and the extraction was carried out after sufficient stirring.

[0199] Centrifugation was carried out at 5000 r / min for 10 min, and the protein in the supernatant was precipitated with saturated ammonium sulfate solution. After centrifugation, the precipitate was obtained and freeze-dried to obtain seaweed crude protein.

[0200] 10 g of seaweed crude protein was weighed in a 1 L conical flask, 0.5 L of phosphate buffer solution was added, and five kinds of commercial proteases, cellulase, papain, chymotrypsin, protease and papain and chymotrypsin mixed enzyme were selected to determine the hydrolysis degree of different proteases in the enzymatic hydrolysis of seaweed crude protein.

[0201] Under the optimal conditions, the enzyme amount was 0.1 g, and other enzymatic hydrolysis conditions were as follows: the solid-liquid ratio was 1:3, the pH value was 7, the temperature was 50℃, the enzymatic hydrolysis was carried out under constant temperature for 3 h, and then the enzyme was inactivated (85℃, 15 min). After the end of the enzymatic hydrolysis, the hydrolysis degree was determined by using 250 mesh filter cloth to filter, centrifugation and taking the supernatant.

[0202] (4) ACE inhibition activity determination and inhibition type analysis

[0203] The total volume of the reaction system was 260 μL. 120 μL of polypeptide sample (1 mg / mL) and 40 μL of ACE solution (0.1 U / mL) were mixed, and then the mixture was placed in a 37℃ water bath for 10 min.

[0204] After incubation, 100 μL of HHL solution (1 mM) was added, and the reaction was carried out at 37℃ for 60 min, and then 200 μL of HCl (1 mol / L) was added to terminate the reaction.

[0205] 120 μL of boric acid buffer (pH=8.3, 0.3 mol / L NaCl) was used as a control group to replace the sample solution in the determination group; 20 μL of boric acid 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.

[0206] A certain amount of sample solution was taken into the sample bottle neck, and the concentration of HA generated in the reaction system was determined by RP-HPLC method.

[0207] The IC 50 of (QEEDESKL) is 0.091 mM, which is non-competitive inhibition; the IC 50 of (DSKVDVN) is 0.079 mM, which is non-competitive inhibition.

[0208] (5) Stability before and after simulated digestion

[0209] 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 h at 100 r / min in a 37 ℃ 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 2 M NaOH, and 2% w / w trypsin was added for further digestion for 2 h. 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 determine its ACE activity.

[0210] 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;

[0211] 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 0.114mM.

[0212] Table 1. Descriptive symbols for amino acid characterization

[0213] Abbrev Name z1 z2 z3 z4 z5 Ala A 0.24 -2.32 0.60 -0.14 1.30 Arg R 3.52 2.50 -3.50 1.99 -0.17 Asn N 3.05 1.62 1.04 -1.15 1.61 Asp D 3.98 0.93 1.93 -2.46 0.75 Cys C 0.84 -1.67 3.71 0.18 -2.65 Gln Q 1.75 0.50 -1.44 -1.34 0.66 Glu E 3.11 0.26 -0.11 -3.04 -0.25 Gly G 2.05 -4.06 0.36 -0.82 -0.38 His H 2.47 1.95 0.26 3.90 0.09 Ile I -3.89 -1.73 -1.71 -0.84 0.26 Leu L -4.28 -1.30 -1.49 -0.72 0.84 Lys K 2.29 0.89 -2.49 1.49 0.31 Met M -2.85 -0.22 0.47 1.94 -0.98 Phe F -4.22 1.94 1.06 0.54 -0.62 Pro P -1.66 0.27 1.84 0.70 2.00 Ser S 2.39 -1.07 1.15 -1.39 0.67 Thr T 0.75 -2.18 -1.12 -1.46 -0.40 Trp W -4.36 3.94 0.59 3.44 -1.59 Tyr Y -2.54 2.44 0.43 0.04 -1.47 Val V -2.59 -2.64 -1.54 -0.85 -0.02

[0214] 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:

[0215] (1) A polypeptide dataset was obtained by computer simulation of BIOPEP online enzymatic hydrolysis of kelp chloroplast protein, including 58 dipeptides, 45 tripeptides, 29 tetrapeptides, 22 pentapeptides, 2 hexapeptides, 9 heptapeptides, and 2 octapeptides.

[0216] (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, the QSAR models for 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.

[0217] (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 hydrolysis effect of crude kelp 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:

[0218] Y=13.62-0.47A-0.56B-0.78C+0.052AB+0.49AC-0.32BC-3.24A 2- 2.69B 2 -2.74C 2

[0219] 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.

[0220] (4) Two potential ACE-inhibitory hexapeptides, GKIPRW and IKTIQF, and two ACE-inhibitory pentapeptides, PKKYG and VYKNT, were screened by the combination of QSAR model and computer screening. VYKNT showed the highest ACE-inhibitory activity with IC50 value of 0.22 ± 7.59 mM in vitro, and the inhibitory mode was a combination of non-competitive and competitive mechanisms. PKKYG and GKIPRW showed strong resistance to digestive enzymes and stability, and the activity of the crude protease solution slightly decreased after gastrointestinal digestion, which indicated that they had the potential to be used as a formula for reducing blood pressure food.

[0221] The combination of the QSAR model and computer screening is not only an effective strategy for discovering ACE-inhibitory peptides, but also an effective method for discovering other beneficial bioactive substances. In the identification and screening of algal bioactive peptides, a prediction model for high-throughput screening and identification of kelp peptides based on bioinformatics and in vitro experimental verification is provided. Specifically, the 5z-scale descriptor is used to describe the active peptides, the structure-activity relationship system is determined to predict potential ACE-inhibitory activity, the ACE-inhibitory peptide database is constructed, and the high-activity kelp peptides are quickly screened. This provides favorable support for exploring the inhibitory type of ACE inhibitors and the stability of active peptides. A series of high-efficiency blood pressure-lowering functional active peptides are screened by the QSAR model, which are applied to the high-value application of kelp, and 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 a new idea for the deep development and utilization of kelp, but also provides a reference model for the development of functional peptides from other marine biological resources.

[0222] According to the characteristics of kelp genes and protein sequences, a polypeptide dataset was constructed by computer BIOPEP online virtual enzymolysis of kelp hypothetical chloroplast protein, and a QSAR model based on amino acid 5z-scale characteristic descriptor was established to high-throughput screen ACE-inhibitory peptides and predict their activity. The results of substitution verification showed that the six different QSAR models of peptide segments all had high correlation coefficients, and the R 2 and Q 2All are lower than the true value; compared with the reported model, the correlation coefficient and model quality of each model constructed are significantly improved (p<0.05), the screening prediction ability is strong, and the model is simple and effective. At the same time, according to the enzyme cutting site predicted and recommended by the above model, the best preparation process is obtained by selecting commercial protease for offline mixed directional enzymolysis experiment and optimizing; then the digestion enzyme resistance and stability of the ACE inhibitory peptide obtained by simulation screening verification are analyzed and the inhibition type is determined. The patent adopts an innovative integration method, which combines quantitative structure-activity relationship (QSAR) model, computer-aided screening and experimental verification to high-throughput screen potential ACE inhibitory peptides in kelp. Compared with the traditional active peptide screening method, this method not only improves the efficiency and convenience, but also significantly reduces the experimental cost in the separation and purification process, and provides an effective strategy for discovering ACE inhibitory peptides.

[0223] The above is only a preferred embodiment of the present application, and is not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for screening and validating ACE inhibitory peptides from kelp chloroplast prosthetic proteins, characterized in that, It includes the following two steps: 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. Step 2: Establish a QSAR model for ACE inhibitory active peptides to predict the activity of ACE inhibitory peptides. Here, ACE refers to angiotensin-I converting enzyme, and QSAR is quantitative structure-activity relationship. The ACE inhibitory peptide QSAR model consists of a model set composed of dipeptides, tripeptides, tetrapeptides, pentapeptides, hexapeptides, heptapeptides, and octapeptides. Amino acid descriptive symbols are used to characterize the amino acid properties of each amino acid in the peptide sequences involved, thereby converting the sequences into a matrix vector of structural descriptors. All peptides are described sequentially in a certain order. When building the peptide library, the sequence number of the peptide is marked in the QSAR model analysis results graph and excluded during the second round of modeling. Modeling is performed on the dataset excluding outliers, and the activity of the peptides obtained from online enzymatic digestion in Step 1 is predicted. 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 property descriptors of the second amino acid at the N-terminus are designated as n2z1, n2z2, n2z3, n2z4, and n2z5. Similarly, all peptides are described in this order.

2. The method for screening and verifying ACE inhibitory peptides of kelp chloroplast prosthetic protein according to claim 1, characterized in that, The QSAR model for ACE inhibitory peptides was validated by analyzing the correlation between amino acid descriptors and ACE inhibitory peptide predictors using partial least squares regression. The correlation coefficient and root mean square error were used to evaluate the model's fitting ability, and the cross-validation coefficient Q was employed. 2 Evaluate the prediction quality of the QSAR model.

3. The method for screening and verifying ACE inhibitory peptides of kelp chloroplast prosthetic protein according to claim 1, characterized in that, The protein sequence selected in step one is one of the following: Chloroplast light harvesting protein lhcf5, Hypothetical chloroplast protein, Hypothetical chloroplast protein ycf66, Photosystem I P700 chlorophyll a apoprotein A1.

4. The method for screening and verifying ACE inhibitory peptides of kelp chloroplast prosthetic protein according to claim 1, characterized in that, The amino acid property parameters were collected from an amino acid physicochemical property database.

5. The method for screening and verifying ACE inhibitory peptides of kelp chloroplast prosthetic protein according to claim 1, characterized in that, Of the five z values, z1-z3 descriptors represent lipophilicity, stereochemistry, and electronic properties, respectively, while z4 and z5 are related to electronegativity, heat of formation, electrophilicity, and hardness.

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