CN113035291B - Method for designing DPP-IV inhibitory peptide by computer-assisted medicine, DPP-IV inhibitory peptide and application thereof - Google Patents

Abstract

The invention provides a method for designing DPP-IV inhibitory peptide by using computer-assisted medicine, DPP-IV inhibitory peptide and application thereof. The method comprises the steps of establishing a database and dividing the database into an internal training set and an external testing set; constructing a structure of a polypeptide in a database; molecular docking of the polypeptide with DPP-IV to select the highest scoring conformation; tripeptide IPI is used as a template molecule and peptide bonds are used as a common skeleton for superposition; manually adjusting the bond angle of each peptide to improve the degree of fitting of the model; the training set is a target model which is built by people, and the testing set is used for testing the forecasting capacity of the model; calculation of pIC of peptides in the test set Using the training set₅₀To calculate the pIC of the peptide₅₀And pIC obtained by experiment₅₀Comparing; then, deriving an equipotential diagram of a training set to guide the design of DPP-IV inhibitory peptide; finally obtaining the novel DPP-IV inhibitory peptide. The novel peptide designed by the invention has high activity of inhibiting DPP-IV enzyme.

Description

Method for designing DPP-IV inhibitory peptide by computer-assisted medicine, DPP-IV inhibitory peptide and application thereof

Technical Field

The invention belongs to the field of bioactive peptides, and particularly relates to a method for designing DPP-IV inhibitory peptide by using computer-assisted medicaments, DPP-IV inhibitory peptide and application thereof.

Background

Diabetes Mellitus (DM) is a common chronic metabolic disorder characterized by hyperglycemia. The chronic hyperglycemia state can cause chronic damage and dysfunction of various tissues and organs, particularly eyes, feet, liver, kidney, heart and the like, and serious complications are generated, thus endangering life and health. The number of patients with type ii diabetes has increased to 5.92 billion by the year 2035 according to prediction. The medicines for treating type II diabetes mainly comprise biguanides, sulfonylureas, glycosidase inhibitors, benzoic acid derivatives and thiazolidinediones. But these approved drugs for the treatment of T2DM are either cost prohibitive or potentially harmful to the body. DPP-IV enzyme is a target for treating diabetes. In the process of regulating blood sugar, glucagon-like peptide-1 (GLP-1) can lower blood sugar by stimulating insulin secretion, promoting proliferation and differentiation of islet beta cells, inhibiting glucagon secretion and the like. DPP-IV enzymatically cleaves GLP-1. Therefore, inhibition of DPP-IV activity and thus reduction of GLP-1 breakdown is one of the important directions for the treatment of diabetes.

Currently available DPP-IV inhibitors include sitagliptin (sitagliptin), vildagliptin (vildagliptin), saxagliptin (saxagliptin), and the like. These are chemically synthesized drugs, and the dosage needs to be increased for long-term use to maintain normal curative effect, which has serious side effect or toxicity. The research on antidiabetic drugs with lower drug resistance and higher safety is a trend of drug development. The DPP-IV inhibitory peptide is a bioactive peptide, can inhibit the activity of DPP-IV enzyme, and has the advantages of high efficiency, no toxicity and low price. At present, many studies on DPP-IV inhibitory peptides are still in the superficial stage. The DPP-IV inhibitory peptide is obtained mainly by enzymolysis of natural product protein, but the preparation of DPP-IV inhibitory peptide by enzymolysis requires a lot of manpower and material resources, and the extracted peptide does not necessarily have good activity.

With the rapid development of computer technology and the rapid updating of equipment, quantitative structure-activity relationship is an important method in computer-aided drug design technology and gradually becomes a research hotspot of researchers, and three-dimensional quantitative structure-activity relationship (3D-QSAR) is the most mature technology developed in quantitative structure-activity relationship at present. A mathematical model between the structure and the biological activity of the compound can be established by using a 3D-QSAR technology, a theoretical basis is provided for the design, screening and modification of DPP-IV inhibitory peptide, the research and development cost of manpower and material resources is low, the efficiency is high, the production cost is low, and the method has important significance for the research and development of novel hypoglycemic drugs with high activity and high safety.

Disclosure of Invention

In order to search DPP-IV inhibitory peptide with lower drug resistance and higher safety and reduce huge material consumption of traditional DPP-IV inhibitory drugs in the research and development process, the invention designs 6 novel DPP-IV inhibitory tripeptides with amino acid sequences of LPI, LPT, LPE, MPL, CPW and WPV by using an efficient method of computer-aided drug design. The experimental study on the in vitro DPP-IV inhibitory activity finds that the 6 peptides have very high inhibitory activity, the activity of the peptides can be ranked in the front of the reported DPP-IV inhibitory peptides, and the peptides have potential application value.

The method designs DPP-IV inhibitory peptide by establishing a 3D-QSAR model, and mainly comprises the following steps: establishing a database and dividing the database into an internal training set and an external testing set; constructing a structure of a polypeptide in a database; molecular docking of the polypeptide with DPP-IV to select the highest scoring conformation; tripeptide IPI is used as a template molecule and peptide bonds are used as a common skeleton for superposition; manually adjusting the bond angle of each peptide to improve the degree of fitting of the model; the training set is a target model which is built by people, and the testing set is used for testing the forecasting capacity of the model; calculation of pIC of peptides in the test set Using the training set₅₀To calculate the pIC of the peptide₅₀And pIC obtained by experiment₅₀Comparing; then, deriving an equipotential diagram of a training set to guide the design of DPP-IV inhibitory peptide; finally obtaining the novel DPP-IV inhibitory peptide.

The invention is realized by the following technical scheme:

a method for computer-aided drug design of DPP-IV inhibitory peptides, comprising the steps of:

the DPP-IV inhibitory peptides are found in the published literature and IC's from the same experimental protocol are screened₅₀Establishing database by several polypeptides of data, and calculating pIC₅₀Value (pIC)₅₀＝-lg IC₅₀) As a data basis for constructing a 3D-QSAR model;

dividing the screened polypeptides into an internal training set and an external testing set;

SYBYL2.1.1 software is used for constructing the structure of the screened polypeptide and performing further energy optimization on the structure;

performing molecular docking with constructed polypeptides by taking DPP-IV as a receptor protein, and selecting a conformation with the highest score of each polypeptide;

tripeptide IPI with the highest activity is taken as a template molecule, and peptide bonds which are common structures of polypeptides are taken as a reference for superposition; establishing a 3D-QSAR model by adopting a partial least square method; the relationship between the structure and the activity is researched by adopting a comparative molecular field analysis method and a comparative molecular similarity index analysis method; cross validation factor Q by manually adjusting the bond angle of the polypeptide²Non-cross validation factor R²F, the inspection value and the standard estimation error all meet the standard of the model;

the method comprises the following steps that a training set is a target model, active peptides are designed and screened through the training set, and the prediction capability of the target model is tested through a test set; calculation of pIC of peptides in the test set Using the training set₅₀For the pIC of the calculated peptide₅₀And pIC obtained by experiment₅₀Calculating difference values, and if the absolute values of the difference values are less than 0.6, showing that the prediction capability of the training set is good;

then, SYBYL-X2.1.1 software is used for deriving an equipotential diagram of a training set to guide the design of DPP-IV inhibitory peptide, finally, novel DPP-IV inhibitory peptide is obtained, activity prediction is carried out on the novel DPPD-IV inhibitory peptide, and peptide with large predicted activity is screened out to carry out in-vitro activity test to verify the real activity of the peptide.

The principle of the invention is as follows: first, it was found in the literature that IC was obtained using the same experimental method₅₀Establishing a database of a plurality of DPP-IV inhibitory peptides of the data; and (4) performing molecular superposition and establishing a corresponding molecular table. The three-dimensional field and the electrostatic field are calculated by using a COMFA analysis method, and two force fields in the COMFA method and related descriptors are linearly regressed with the bioactivity value of the drug by using PLS. The QSAR model is then built using the internal sample set, and the model is further validated and analyzed using the external test set. When the PLS modeling research is carried out, the leave-one-out method is firstly adopted to carry out cross validation analysis to obtain the optimal number of independent variables N, Q_LOO ²And the contribution ratio of the three-dimensional field to the electrostatic field; then, the obtained optimal autovariables are used as a basis, and analysis is carried out through a non-cross verification method to obtain a series of related parameters: s, R²F, and establishing a corresponding 3D-QSAR model。

Then, by using a Stedev coeff method, the contribution of the stereo field and the electrostatic field to the biological activity value of the DPP-IV inhibitory peptide is respectively shown by using an equipotential diagram, so that the favorable information for designing a novel DPP-IV inhibitory peptide analogue can be obtained. And (3) calculating a stereoscopic field, an electrostatic field and a hydrophobic field by adopting a COMSIA analysis method, and analyzing to obtain a three-dimensional equipotential diagram like COMFA (complementary metal ion exchange membrane) for designing a novel DPP-IV (dipeptidyl peptidase-IV) inhibitory peptide.

The following predictive results were obtained in conjunction with the COMFA and COMSIA analyses: the DPP-IV inhibits the N end of tripeptide and introduces a large group, and the amino acid with negative charge is beneficial to improving the activity; a small group is introduced at the second-position amino acid, and the amino acid with negative charge and hydrophilicity is beneficial to improving the activity; the C end is a large group, which is beneficial to improving the activity. A series of DPP-IV inhibitory peptides are designed according to the prediction result, and finally, the activity of the newly designed peptide is predicted by using Sybyl software, and the peptide with high predicted activity is screened out.

In vitro experiments prove that the novel peptide designed by the method has high activity of inhibiting DPP-IV enzyme. The peptide has the advantages of low drug resistance and high safety. Therefore, the DPP-IV inhibitory peptide can be also applied to the preparation of medicines for treating diabetes or health-care foods for improving symptoms of diabetic patients.

Drawings

FIG. 1 is a three-dimensional equipotential diagram of the COMFA (SE) model of the present invention;

FIG. 2 is a three-dimensional equipotential diagram of the COMSIA (SHE) model of the present invention;

FIG. 3 is a double reciprocal plot of MPL, LPT, LPE;

FIG. 4 is a graph of WPV, CPW, LPI double inverses.

Detailed Description

In order to make the technical problems, technical solutions and advantages to be solved by the present invention clearer, the following detailed description is given with reference to specific embodiments.

A method for computer-aided drug design of DPP-IV inhibitory peptides, comprising the steps of:

the DPP-IV inhibitory peptides are found in the published literature and screened out in the same experimentIC obtained by the method₅₀25 tripeptides and 25 dipeptides of the data, pIC₅₀Value (pIC)₅₀＝-lgIC₅₀) As a data basis for constructing a 3D-QSAR model, according to the requirement that the number of polypeptides in an internal training set is about 3 times that of polypeptides in an external test set, 50 polypeptide molecules are divided into an internal training set (37 active peptide molecules) and an external test set (13 active peptide molecules):

training set: EK. GL, SL, AL, WRP, WRI, WRQ, WRA, WRM, YP, WS, WRY, WRR, WRT, WRW, WRS, WT, WRF, WRK, WRN, WRD, WRE, WY, WM, LPL, VA, WN, HL, WI, WA, WV, VPF, WRG, WL, WK, WR, IPI.

And (3) test set: WRL, VR, LP, WRH, WW, WC, FL, YPY, IP, WQ, WP, YPI, VPV.

The method comprises the steps of constructing structures of 50 polypeptides by using a Biopolymer module of SYBYL2.1.1 software, adding Gasetiger-Hcukle charges, further optimizing the energy of molecules by adopting a Powell conjugate gradient method and a Tripos force field, setting the maximum iteration coefficient to be 1000, limiting the energy convergence to be 0.005kJ/mol, calculating the related force field of the polypeptide drug by using a + 1-valent carbon atom as a probe, and setting other parameters to be default values.

Performing molecular docking with the constructed polypeptide sequence by taking DPP-IV as a receptor protein, and selecting a conformation with the highest score of no-species polypeptides;

the tripeptide IPI with the highest activity is taken as a template molecule and is superposed by taking the peptide bond which is the common structure of the 50 peptides as a reference. Establishing a 3D-QSAR model by adopting a Partial Least Squares (PLS); the relationship between structure and activity was investigated using comparative molecular field analysis (COMFA) and comparative molecular similarity index analysis (COMSIA). Cross validation factor Q by manually adjusting the bond angle of the polypeptide²Non-cross validation factor R²The F-test value and the standard estimation error all meet the standard of the model. The details of the model are shown in table 1.

TABLE 1 details of COMFA and COMSIA modes

Parameter(s)	COMFA(SE field)	COMSIA(SHE field)
			Q2	0.837	0.726
R2	0.991	0.990
			N	6	6
SEE	0.059	0.062
			F value	574.088	518.800
Stereo field contribution value	0.605	0.246
			Contribution of electrostatic field	0.395	0.343
Contribution of hydrophobic field	-	0.411

Wherein N is the optimal principal component number, and SEE is the standard estimation error.

Q²And R²Calculated by the following equation:

wherein Y is_obs、Y_CVpre、Y_predAnd Y_meanRespectively observed, cross-validated, predicted and average activity values for the database.

The bond angles of the peptides were manually adjusted in order to obtain the best COMFA and COMSIA models:

if Q is calculated²When the ratio is less than 0.6, the bond angle of the peptide is manually adjusted, and Q is calculated²And R². Then repeatedly overlapping, adjusting key angle, and calculating Q²，R²These three steps up to Q²Greater than 0.7, R²Until it is greater than 0.9. (Q of model)²Greater than 0.5, R²If the prediction performance of the model is higher than 0.9, the model is qualified, and in order to improve the prediction performance of the model, the COMFA and COMSIA are adjusted to Q²Greater than 0.7, R²Greater than 0.9).

The training set is the target model, active peptides are designed and screened through the training set, and the prediction capability of the target model is tested through the testing set. Calculation of pIC of peptides in the test set Using the training set₅₀For the pIC of the calculated peptide₅₀And pIC obtained by experiment₅₀And calculating the difference values, and referring to table 2, if the difference values are all less than 0.6, the prediction capability of the training set is good.

TABLE 2 pIC of the CoMFA/CoMSIA model of inhibitory peptide molecules₅₀Residual value table between predicted value and experimental measured value

Then, an equipotential diagram of a training set is derived by using SYBYL-X2.1.1 software to guide the design of DPP-IV inhibitory peptide, the activity of the designed DPP-IV inhibitory peptide is predicted, and 6 novel DPP-IV inhibitory tripeptides with the highest predicted activity are screened, wherein the amino acid sequences are respectively LPI (Leu-Pro-Ile), LPT (Leu-Pro-Thr), LPE (Leu-Pro-Glu), MPL (Met-Pro-Leu), CPW (Cys-Pro-Trp) and WPV (Trp-Pro-Val).

In the CoMFA/CoMSIA model, the relationship between the relevant regions of molecular structure and biological activity can be visualized by three-dimensional equipotential lines. The invention takes a template molecule IPI as a reference molecule to carry out the analysis of a CoMFA/CoMSIA equipotential diagram. The different color blocks in fig. 1 represent different meanings. Green (G) indicates that introduction of large groups is advantageous for improving activity, and yellow (Y) indicates that introduction of small groups is advantageous for improving activity; red (R) indicates that increasing negatively charged groups is beneficial for increasing activity, and blue (B) indicates that positively charged groups is beneficial for increasing activity. Patches that are spatially distant from the molecule do not provide effective prediction information.

As shown in fig. 1 (a), the following prediction results were obtained from the model. A large green color patch (G) was present at the first amino acid residue (N-terminus), indicating that the introduction of a large group at the first residue advantageously increases activity. A large yellow patch (Y) was present at the second amino acid residue, indicating that the introduction of a small group at the second residue advantageously increased activity. As shown in panel b of FIG. 1, there was a medium-sized red (R) color block at the second amino acid residue, indicating that the introduction of a negatively charged amino acid at the second residue also advantageously increased activity. There was a large green color patch at the third amino acid residue (C-terminus). This indicates that the introduction of a bulky group at the third residue is advantageous for improving the activity.

In FIG. 1, panel (B), it is shown that in the electrostatic field of CoMFA, the blue (B) squares indicate that the addition of positively charged amino acids is beneficial for increasing the activity of the peptide, and the red (R) squares indicate that the introduction of negatively charged amino acids is beneficial for increasing the activity of the peptide. There was a medium-sized red (R) color block at the second amino acid residue, indicating that the introduction of a negatively charged amino acid at the second amino acid residue was beneficial for enhancing activity.

In the three-dimensional equipotential diagram of fig. 2, (a) represents the S-field of COMSIA, which gives results similar to those given by the COMFA model. (b) The figure is COMSIA representing the E field. A small red (R) color patch was present at the first amino acid residue (N-terminus), indicating that the introduction of a negatively charged amino acid at the first residue advantageously increases activity. The presence of a medium-sized red (R) color block at the second amino acid residue indicates that the introduction of a negatively charged amino acid at the second residue advantageously increases activity. (c) The graph is the hydrophobic field (H field) of COMSIA, yellow (Y) indicates that increasing hydrophobic groups is beneficial for increasing activity, white (W) indicates that increasing hydrophilic groups is beneficial for increasing activity. As can be seen from the figure, a large white color block is present at the second amino acid residue, indicating that the introduction of a hydrophilic amino acid at the second residue advantageously increases the activity.

TABLE 3 percentage inhibition of DDP-IV enzyme by control peptide (IPI) and 6 novel peptides at different concentrations IC of these 7 peptides was calculated using probit analysis calculation₅₀The value is obtained.

TABLE 3

As can be seen from the table, the IC of DPP-IV is inhibited by the control peptide (IPI)₅₀3.66uM, consistent with the values reported in many documents. IC for inhibiting DPP-IV by LPI, MPL and LPT₅₀7.51, 20.73 and 22.88uM respectively. The activity of these three peptides is highest among hundreds of DPP-IV inhibitory peptides that have been reported. LPI is the most active of the three polypeptides, the IC of which₅₀The level reached 1 bit. Of the hundreds of DPP-IV inhibitory peptides reported, it ranks third.

The molecular formula of LPI is C₁₇H₃₁N₃O₄The chemical structure is simply shown as:

the molecular formula of LPT is C₁₅H₂₇N₃O₅The chemical structure is simply shown as:

the molecular formula of LPE is C₁₆H₂₇N₃O₆The chemical structure is simply shown as:

the molecular formula of MPL is C₁₆H₂₉N₃O₄S, the chemical structure is simply shown as:

the molecular formula of CPW is C₂₁H₂₇N₄O₄The chemical structure is simply shown as:

the molecular formula of WPV is C₁₉H₂₃N₄O₄S, the chemical structure is simply shown as:

according to the designed sequence, the 6 peptides were synthesized by professional institutions with a purity of 99% for in vitro experimental validation.

Experimental Material

DDP-IV inhibitory peptides LPI, LPT, LPE, MPL, CPW, WPV (purity > 99%), synthesized by Shanghai Qianyao Biotech, Inc.; DPP-IV/CD26, Human Recombinanty, supplied by Biovision; glycyl-prolyl-p-nitroaniline (Gly-Pro-pNA) and Diprotin A (IPI) were supplied by Cayman corporation; Tris-HCl buffer (Tris-HCl buffer) was supplied by Shanghai leaf Biotech Co., Ltd.

DPP-IV inhibition experiment

The DPP-IV inhibition assay procedure is as follows: the test sample peptides were dissolved in Tris-HCl buffer (100mM, pH 8.0) at a concentration of 10 to 500 uM. The test sample (50. mu.L) was added to a 96-well microplate, and the reaction substrate Gly-Pro-pNA (final concentration 0.2mM) was added thereto. The temperature of the incubator of the microplate reader is adjusted to 37 ℃, and the mixture is shaken in the microplate reader for 10 minutes and mixed evenly. The reaction was then initiated by the addition of DPP-IV (final concentration 0.01U/mL). All reagents and samples were diluted in Tris-HCl buffer (100mM, pH 8.0). Diprotin A was used as a positive control. Tris-HCl buffer (50. mu.L) was used as a negative control instead of the test sample solution. Three replicates of each sample were run. The plate was incubated at 37 ℃ for 15 minutes and the absorbance of the released pNA was measured at 405 nm. IC of DPP-IV₅₀The value (the concentration of peptide required to reduce DPP-IV activity by half) was determined by plotting the percent inhibition as a function of the concentration of test compound. The inhibition patterns of these six peptides were studied using the double reciprocal mapping method (Lineweaver-Burk mapping method).

According to the Lineweaver-Burk mapping method, a double reciprocal diagram is drawn for MPL, LPT, LPE, WPV, CPW and LPI, and a diagram of FIG. 3 and FIG. 4 is obtained. The principle is to judge competitive and non-competitive inhibitors at a range of substrate concentrations (0.2 to 1mM) with and without the effect of inhibitor addition on the enzymatic reaction. Wherein the concentration of the inhibitor is its IC₅₀And (4) concentration. The line for inhibitory peptides MPL, LPT, LPE, CPW, LPI intersects the line for non-inhibitory peptides on the Y-axis. Indicating that MPL, LPT, LPE, CPW, LPI are competitive inhibitors of DPP-IV. And the line inhibiting peptide WPV intersects the line without inhibitor on the X axis, indicating that WPV is a non-competitive inhibitor of DPP-IV.

The polypeptide designed by the model of the invention has high inhibitory activity on DPP-IV, and the predicted pIC is shown in Table 4₅₀Value and experimental result baseThe method is consistent. This shows that our modeling method is very accurate. And the established model has strong prediction capability and high reliability.

TABLE 4 pIC of 6 novel DPP-IV peptides of the invention₅₀

While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention as defined in the appended claims.

Claims (8)

1. A method for computer-aided drug design of DPP-IV inhibitory peptides, comprising the steps of:

finding out DPP-IV inhibitory peptide in a published document, screening a plurality of polypeptides of IC50 data obtained by the same experimental method to establish a database, and calculating pIC50 value as a data base for constructing a 3D-QSAR model;

dividing the screened polypeptides into an internal training set and an external testing set;

SYBYL2.1.1 software is used for constructing the structure of the screened polypeptide and performing further energy optimization on the structure;

taking DPP-IV as a receptor protein to perform molecular docking with the constructed polypeptides respectively, and selecting the conformation with the highest score of each polypeptide;

tripeptide IPI with the highest activity is taken as a template molecule, and peptide bonds which are common structures of polypeptides are taken as a reference for superposition; establishing a 3D-QSAR model by adopting a partial least square method; the relationship between the structure and the activity is researched by adopting a comparative molecular field analysis method and a comparative molecular similarity index analysis method; the bond angle of the polypeptide is manually adjusted to ensure that the cross validation coefficient Q2, the non-cross validation coefficient R2, the F check value and the standard estimation error all meet the standard of the model;

designing and screening active peptides through a training set, and testing the prediction capability of a target model through a testing set; calculating pIC50 of the polypeptide in the test set by using the training set, and calculating a difference value by using the pIC50 and the pIC50 obtained by the experiment; if the absolute values of the differences are less than 0.6, the prediction capability of the training set is good;

then, an equipotential diagram of a training set is derived by SYBYL-X2.1.1 software to guide the design of the DPP-IV inhibitory peptide, and finally, the novel DPP-IV inhibitory peptide is obtained;

and performing activity prediction on the obtained novel DPP-IV inhibitory peptide, and screening six tripeptides with high predicted activity, wherein the amino acid sequences of the six tripeptides are LPI, LPT, LPE, MPL, CPW and WPV respectively.

2. The method of computer-assisted drug design for DPP-IV inhibitory peptides according to claim 1,

the polypeptide database screened for modeling included 25 tripeptides and 25 dipeptides: EK. GL, SL, AL, WRP, WRI, WRQ, WRA, WRM, YP, WS, WRY, WRR, WRT, WRW, WRS, WT, WRF, WRK, WRN, WRD, WRE, WY, WM, LPL, VA, WN, HL, WI, WA, WV, VPF, WRG, WL, WK, WR, IPI, WRL, VR, LP, WRH, WW, WC, FL, YPY, IP, WQ, WP, YPI, VPV.

3. The method of computer-aided drug design for DPP-IV inhibitory peptides according to claim 2,

the training set had 37 polypeptides: EK. GL, SL, AL, WRP, WRI, WRQ, WRA, WRM, YP, WS, WRY, WRR, WRT, WRW, WRS, WT, WRF, WRK, WRN, WRD, WRE, WY, WM, LPL, VA, WN, HL, WI, WA, WV, VPF, WRG, WL, WK, WR, IPI;

the test set had 13 polypeptides: WRL, VR, LP, WRH, WW, WC, FL, YPY, IP, WQ, WP, YPI, VPV.

4. The method of computer-assisted drug design for DPP-IV inhibitory peptides according to claim 1,

in energy optimization, the maximum iteration coefficient is set to be 1000, energy convergence is limited to be 0.005kJ/mol, a + 1-valent carbon atom is used as a probe to calculate a related force field of the polypeptide drug, and the rest parameter settings adopt default values.

5. The method of computer-assisted drug design for DPP-IV inhibitory peptides according to claim 1,

after folding, the procedure for manual adjustment of the peptide bond angles was also included in order to obtain the best COMFA and COMSIA models:

if Q2 is less than 0.6, then manually adjusting the bond angle of the peptide, and calculating Q2 and R2; and then repeating the three steps of folding, key angle adjustment, Q2 calculation and R2 calculation until Q2 is greater than 0.7 and R2 is greater than 0.9.

6. The DPP-IV inhibitory peptide obtained by the method for designing DPP-IV inhibitory peptide by computer-assisted medicine according to any one of claims 1 to 5, comprising LPI, LPT, LPE, MPL, CPW and WPV.

7. The application of the DPP-IV inhibitory peptide obtained by the method for designing the DPP-IV inhibitory peptide by the computer-assisted medicine according to any one of claims 1 to 5 in preparing the medicine for treating diabetes.

8. The application of the DPP-IV inhibitory peptide obtained by the method for designing the DPP-IV inhibitory peptide by the computer-assisted medicine according to any one of claims 1 to 5 in preparing food for improving symptoms of diabetic patients.

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