A method for screening multifunctional peptides for treating diabetes and hypertension simultaneously from perilla albumin

Abstract

The application discloses a method for screening multifunctional peptides of perilla albumin source for treating diabetes and hypertension, which comprises the following steps: obtaining perilla albumin sequence, predicting the potential of perilla albumin as DPP-Ⅳ and ACE inhibitory peptide source, performing computer simulation digestion, and screening DPP-Ⅳ and ACE inhibitory peptides. Through the bioactive peptide database BIOPEP-UWM, 40 perilla albumin sequences are predicted for active fragments, and it is found that perilla albumin is rich in a large number of fragments with DPP-Ⅳ and ACE inhibitory activity. Perilla albumin is subjected to computer simulation digestion, polypeptides capable of resisting gastrointestinal digestion are obtained, and perilla albumin source multifunctional peptides with DPP-Ⅳ and ACE inhibitory activity are screened through related activity prediction and molecular docking. The practicability of the multifunctional peptides is predicted, and two multifunctional peptides without toxicity, allergenicity, bitterness and hemolytic activity, namely CDAF and CCAL, are screened. The two multifunctional peptides inhibit the catalytic activity of DPP-Ⅳ and ACE by competitively combining with the active centers of DPP-Ⅳ and ACE, so as to play the roles of reducing blood sugar and blood pressure.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of biological medicine, and in particular to a method for screening a multifunctional peptide for treating diabetes and hypertension simultaneously, which is based on molecular simulation technology. BACKGROUND

[0002] In recent years, with the improvement of people's living standards and the change of work rhythm, the incidence of hypertension (HTN) and type-Ⅱ diabetes (T2DM) has increased significantly. According to the incomplete statistics of the World Health Organization (WHO) in 2021, the number of T2DM patients worldwide reached 378 million, and the number of HTN patients reached 128 million. HTN and T2DM are closely related, and about 75% of T2DM patients will develop HTN. High blood sugar can promote the formation of arteriosclerosis and thrombosis, thereby aggravating HTN. Elevated blood pressure in turn can lead to abnormal glucose metabolism, resulting in poor blood glucose control in diabetic patients. In addition, sustained high blood pressure and high blood sugar can induce cardiovascular and renal diseases, thereby increasing the mortality of patients.

[0003] With the exploration of the occurrence and mechanism of various difficult diseases, targeted therapy for developing specific therapeutic drugs targeting specific pathogenic molecules has also developed rapidly. Angiotensin converting enzyme (ACE) is a negative regulator of the rennin-angiotensin-aldosterone system (RAAS) and the kallikrein-kinin system (KKS), which is involved in the catalysis of angiotensin I to angiotensin II and the inactivation of bradykinin, thereby leading to elevated blood pressure. Dipeptidyl peptidase-IV (DPP-IV) is a kind of dipeptidase that can degrade glucose-dependent insulinotropic peptide (GIP) and glucagon-like peptide-1 (GLP-1), thereby reducing insulin secretion and leading to increased blood glucose. Therefore, multifunctional peptides with DPP-Ⅳ and ACE inhibitory activity are expected to achieve the treatment of different diseases simultaneously by regulating important targets of T2DM and HTN.

[0004] Among a series of drug treatment methods designed according to target points, peptide-based therapy is attracting more and more attention due to its wide source, small toxicity, good stability and strong intestinal absorption capacity. However, bioactive peptides are inactive in protein sequences and need to be released from proteins by protease enzymolysis. However, the identification of available peptides with specific biological activity from protein hydrolysates involves complex separation, purification, identification and verification steps, which are time-consuming and expensive. In contrast, computer-aided enzymolysis and bioinformatics screening of active peptides can overcome the shortcomings of traditional methods.

[0005] Perilla frutescens (L.) Britt. is a common annual herb of Lamiaceae, mainly distributed in China, Japan, South Korea, Myanmar, Indonesia and Russia. Perilla is both medicinal and edible, and is usually planted as an oil crop. Perilla seed oil is rich in unsaturated fatty acids, with a content of more than 90%. Among them, omega-3 polyunsaturated fatty acids are the main unsaturated fatty acids, which have the effects of anti-inflammatory, improving cognition, reducing cholesterol, reducing the incidence of colon cancer, and preventing cardiovascular and cerebrovascular diseases. At present, as a by-product of perilla oil extraction, perilla seed meal with high protein content is usually discarded as waste or used as animal feed. However, it has been reported that perilla protein hydrolysate has antioxidant, antibacterial, anti-fatigue, anti-inflammatory, anticancer and ACE inhibitory activities, but fewer peptides with specific activities have been identified, and their absorption and utilization in the human body are not yet clear. Albumin is an important component of perilla seed protein, and no anaphylaxis has been found, which has strong potential research value. At present, there is no method for screening perilla albumin-derived multifunctional peptides for treating diabetes and hypertension based on molecular simulation technology. Therefore, it is of great significance to provide a method for screening perilla albumin-derived multifunctional peptides for treating diabetes and hypertension based on molecular simulation technology. SUMMARY

[0006] The present application provides a method for screening perilla albumin-derived multifunctional peptides for treating diabetes and hypertension, which is based on molecular simulation technology. First, the active fragments of 40 perilla albumin sequences are predicted by BIOPEP-UWM, and it is found that perilla albumin sequences are rich in a large number of fragments with DPP-Ⅳ and ACE inhibitory activity. Perilla albumin is digested by computer simulation to obtain polypeptides that can resist gastrointestinal digestion, and multifunctional peptides with DPP-Ⅳ and ACE inhibitory activity are screened by related activity prediction and molecular docking. The practicability of the multifunctional peptides is predicted, and two multifunctional peptides without toxicity, anaphylaxis, bitterness and hemolysis, namely CDAF and CCAL, are further screened. The two multifunctional peptides inhibit the catalytic activity of DPP-Ⅳ and ACE by competitively binding to the active center of DPP-Ⅳ and ACE, thereby playing a role in reducing blood sugar and blood pressure.

[0007] The technical solutions provided by the present application are as follows:

[0008] A method for screening perilla albumin-derived multifunctional peptides for treating diabetes and hypertension, wherein the method comprises the following steps:

[0009] Step S1: obtaining perilla albumin sequence

[0010] Input the keyword "perilla seed storage albumin" in the NCBI database, and obtain the perilla albumin sequence by searching;

[0011] Step S2: predicting the potential of perilla albumin as a source of DPP-IV and ACE inhibitory peptides

[0012] Using the "Calculate" module of "Batch processing" in the bioactive peptide database BIOPEP-UWM, analyze the potential of perilla albumin as a source of DPP-IV and ACE inhibitory peptides;

[0013] Step S3: computer simulation digestion

[0014] Using the "enzyme(s) action" module in the bioactive peptide database BIOPEP-UWM, use proteases to simulate digestion of perilla albumin to obtain perilla albumin-derived polypeptides;

[0015] Step S4: preliminary screening of DPP-IV and ACE inhibitory peptides

[0016] First, score the potential biological activity of the perilla albumin-derived polypeptides in the PeptideRanker prediction website, then perform molecular docking of the perilla albumin-derived polypeptides that meet the scoring requirements with DPP-IV and ACE, and screen the perilla albumin-derived polypeptides that successfully dock with DPP-IV and ACE, i.e., obtain perilla albumin-derived multifunctional peptides, which have the effect of treating diabetes and hypertension.

[0017] Further, the method further comprises:

[0018] Step S5: practicality analysis of perilla albumin-derived multifunctional peptides

[0019] Toxicity of the perilla albumin-derived multifunctional peptides is predicted by ToxinPred, allergenicity of the perilla albumin-derived multifunctional peptides is predicted by Aller TOP V2.0, bitterness of the perilla albumin-derived multifunctional peptides is predicted by iBitter-SCM, hemolytic activity of the perilla albumin-derived multifunctional peptides is predicted by HemoPred, and physicochemical properties of the perilla albumin-derived multifunctional peptides are predicted by ADMETlab 2.0.

[0020] Further, in step S2, the frequency A of the fragments having DPP-IV or ACE inhibitory activity in the perilla albumin is characterized according to the following formula:

[0021] A = a / N

[0022] In the formula:

[0023] a - the number of fragments having ACE inhibitory activity or DPP-IV inhibitory activity in the protein;

[0024] N - the number of amino acid residues in the protein.

[0025] Further, in step S3, the proteases are pepsin [3.4.23.1], trypsin [EC 3.4.21.4] and chymotrypsin [EC 3.4.21.1].

[0026] Further, before the perilla albumin-derived polypeptides are scored for potential biological activities in the PeptideRanker prediction website, the following steps are further included:

[0027] The perilla albumin-derived polypeptides are aligned with the reported bioactive peptides in the bioactive peptide database BIOPEP-UWM, and the perilla albumin-derived polypeptides that have not been reported are screened out.

[0028] Further, after the perilla albumin-derived polypeptides are scored for potential biological activities in the PeptideRanker prediction website, the perilla albumin-derived polypeptides that meet the scoring requirements are subjected to molecular docking with DPP-IV and ACE, specifically as follows:

[0029] A score threshold of 0.8 is set in the PeptideRanker prediction website, the perilla albumin-derived polypeptides are scored for potential biological activities in the PeptideRanker prediction website, and the perilla albumin-derived polypeptides with a score greater than 0.8 are screened out.

[0030] The screened perilla albumin-derived polypeptides are subjected to molecular docking with DPP-IV and ACE using the Libdock module of Discovery Studio software.

[0031] Further, in step S4, the molecular crystal structures of the DPP-IV and the ACE come from the PDB database, the PDB ID of the DPP-IV is 1NU6, and the PDB ID of the ACE is 1O8A, before molecular docking, the position of the pocket is defined by the original ligand, and the radius is set to 4.5 angstroms. Further, in step S4, the molecular crystal structures of the DPP-IV and the ACE come from the PDB database, the PDB ID of the DPP-IV is 1NU6, and the PDB ID of the ACE is 1O8A, before molecular docking, the position of the pocket is defined by the original ligand, and the radius is set to 4.5 angstroms.

[0032] Further, after the screened perilla albumin source polypeptide is subjected to molecular docking with the DPP-IV and the ACE, the method further comprises:

[0033] The perilla albumin source polypeptide with a molecular docking score greater than 100 with the DPP-IV and the ACE is screened out.

[0034] Further, the perilla albumin source polypeptide is subjected to practicality analysis, specifically:

[0035] The toxicity of the perilla albumin source polypeptide is predicted by ToxinPred, the allergenicity of the perilla albumin source polypeptide is predicted by Aller TOP V2.0, the bitterness of the perilla albumin source polypeptide is predicted by iBitter-SCM, the hemolyticity of the perilla albumin source polypeptide is predicted by HemoPred, the physical and chemical properties of the perilla albumin source polypeptide are predicted by ADMETlab 2.0, the perilla albumin source polypeptide without toxicity, allergenicity, bitterness and hemolyticity is screened out, and the physical and chemical properties of the perilla albumin source polypeptide are further analyzed.

[0036] Compared with the prior art, the method has the beneficial effects that:

[0037] This invention provides a method for screening perilla albumin-derived multifunctional peptides for treating diabetes and hypertension. The method involves predicting the active fragments of 40 perilla albumin sequences using the BIOPEP-UWM bioactive peptide database, revealing that the corresponding perilla albumin sequences are rich in fragments with DPP-IV and ACE inhibitory activities. Computer simulation digestion of perilla albumin was used to obtain peptides resistant to gastrointestinal digestion. Through related activity prediction and molecular docking, multifunctional peptides derived from perilla albumin with both DPP-IV and ACE inhibitory activities were screened. Their practicality was predicted, and two non-toxic, non-allergenic, non-bitter, and non-hemolytic multifunctional peptides, namely CDAF and CCAL, were further screened. These two perilla albumin-derived multifunctional peptides competitively bind to the active sites of DPP-IV and ACE, inhibiting their catalytic activity, thereby exerting hypoglycemic and hypotensive effects. Physicochemical property analysis showed that most parameters of the two multifunctional peptides were within the expected range. CDAF and CCAL have strong water solubility, meaning that the two peptides are easily soluble in body fluids, increasing their contact area with absorption sites, thereby increasing absorption and bioavailability. Attached Figure Description

[0038] Figure 1 This is a flowchart of the method for screening perilla albumin-derived multifunctional peptides for treating different diseases and hypertension in an embodiment of the present invention;

[0039] Figure 2 These are 3D (left) and 2D (right) schematic diagrams of the interaction between CDAF and DPP-Ⅳ in an embodiment of the present invention;

[0040] Figure 3 These are 3D (left) and 2D (right) schematic diagrams of the interaction between CDAF and ACE in an embodiment of the present invention.

[0041] Figure 4 These are 3D (left) and 2D (right) schematic diagrams of the interaction between CCAL and DPP-Ⅳ in an embodiment of the present invention.

[0042] Figure 5 These are 3D (left) and 2D (right) schematic diagrams of the interaction between CCAL and ACE in an embodiment of the present invention.

[0043] Figure 6 This is a diagram showing the physicochemical properties of CDAF in an embodiment of the present invention;

[0044] Figure 7 This is a physicochemical property diagram of CCAL in an embodiment of the present invention;

[0045] Figure 8 These are the 20 amino acids and their abbreviations in the embodiments of this invention. Detailed Implementation

[0046] In order to make the purposes, technical solutions, and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the embodiments described below are only a part of the embodiments of the present application, rather than all the embodiments of the present application. The components of the embodiments of the present application described and shown in the drawings herein can be arranged and designed in various different configurations.

[0047] Therefore, the detailed description of the embodiments of the present application provided below in conjunction with the drawings is intended to represent only selected embodiments of the present application, and not to limit the scope of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without making creative efforts fall within the scope of the present application.

[0048] As is known, the minimum unit constituting a protein is an amino acid, and the peptide is between the protein and the amino acid. The peptide is a structural fragment and the smallest functional unit of the protein, and is an active group part of the protein to play a role; 20 kinds of amino acids form different kinds of peptides through specific arrangement and combination, so that the protein has tens of thousands of physiological functions. The names and abbreviations of the 20 kinds of amino acids are shown in Table 1. Figure 8 .

[0049] Referring to Figure 1 The present application provides a method for screening perilla albumin-derived multifunctional peptides for treating diabetes and hypertension, which comprises the following steps:

[0050] Step 101: obtaining perilla albumin sequences

[0051] The keyword "perilla seed storage albumin" is input into the NCBI database, and 40 perilla albumin sequences are obtained by searching.

[0052] Step 102: predicting the potential of perilla albumin as a source of DPP-IV and ACE inhibitory peptides

[0053] The "Calculate" module of "Batch processing" in the bioactive peptide database BIOPEP-UWM is used to analyze the potential of the 40 perilla albumin sequences as a source of DPP-IV and ACE inhibitory peptides.

[0054] Among them, the appearance frequency A of the DPP-IV or ACE inhibitory active fragment in the 40 perilla albumin sequences is characterized according to the following formula:

[0055] A=a / N

[0056] In the formula,

[0057] a - the number of fragments in the protein having ACE inhibitory activity or BPP-Ⅳ inhibitory activity;

[0058] N - the number of amino acid residues of the protein.

[0059] Step 103, computer simulation digestion

[0060] Using the "enzyme(s) action" module in the bioactive peptide database BIOPEP-UWM, 40 perilla albumin peptides were simulated by protease digestion to obtain perilla albumin-derived polypeptides.

[0061] Among them, the proteases used are pepsin [3.4.23.1], trypsin [EC 3.4.21.4] and chymotrypsin [EC 3.4.21.1].

[0062] Step 104, preliminary screening of DPP-Ⅳ inhibitory peptides and ACE inhibitory peptides

[0063] First, the perilla albumin-derived polypeptides are scored for potential biological activity in the PeptideRanker prediction website, and then the perilla albumin-derived polypeptides that meet the scoring requirements are docked with DPP-Ⅳ and ACE, and the perilla albumin-derived polypeptides that are successfully docked with DPP-Ⅳ and ACE are screened out, i.e. perilla albumin-derived multifunctional peptides are obtained.

[0064] Among them, before step 104, the perilla albumin-derived polypeptides are compared with the bioactive peptides reported in the bioactive peptide database BIOPEP-UWM, and the perilla albumin-derived polypeptides that have not been reported are screened out, which are named as new peptides for easy understanding.

[0065] In step 104, the score threshold is set to 0.8 in the PeptideRanker prediction website, the new peptides are scored for potential biological activity in the PeptideRanker prediction website, the new peptides with a score greater than 0.8 are screened out, and then the Libdock module of Discovery Studio software is used to dock the new peptides with a score greater than 0.8 with DPP-Ⅳ and ACE, and the new peptides with DPP-Ⅳ and ACE inhibitory activity are screened out, i.e. perilla albumin-derived multifunctional peptides are obtained.

[0066] In step 104, the molecular crystal structure of DPP-Ⅳ and ACE comes from the PDB database, the PDB ID of DPP-Ⅳ is 1NU6, and the PDB ID of ACE is 1O8A. Before molecular docking, the position of the pocket is defined by the original ligand, and the radius is set to 4.5 A. Then remove the water molecules and original ligands in the crystal structure and add hydrogen.

[0067] Step 105, perform perilla albumin source multifunctional peptide practicability analysis

[0068] The toxicity of the perilla albumin source multifunctional peptide is predicted by ToxinPred, the allergenicity of the perilla albumin source multifunctional peptide is predicted by Aller TOP V2.0, the bitterness of the perilla albumin source multifunctional peptide is predicted by iBitter-SCM, the hemolyticity of the perilla albumin source multifunctional peptide is predicted by HemoPred, and the physicochemical properties of the perilla albumin source multifunctional peptide are predicted by ADMETlab 2.0.

[0069] Through step 105, the perilla albumin source multifunctional peptide without toxicity, allergenicity, bitterness and hemolyticity is screened out, and the physicochemical properties of the perilla albumin source multifunctional peptide are further analyzed.

[0070] The following is further analyzed through specific examples.

[0071] Example 1

[0072] The application provides a method for screening perilla albumin source multifunctional peptides for treating diabetes and hypertension, which comprises the following steps:

[0073] Step 201, obtaining perilla albumin sequence

[0074] Enter the keyword "perilla seed storage albumin" in the NCBI database, and search to obtain 40 perilla albumin sequences.

[0075] Step 202, perform potential prediction of perilla albumin as DPP-Ⅳ and ACE inhibitory peptide source

[0076] The "Calculate" module of "Batch processing" in the bioactive peptide database BIOPEP-UWM is used to analyze the potential of perilla albumin as DPP-Ⅳ and ACE inhibitory peptide source corresponding to all 40 perilla albumin sequences.

[0077] Through step 202, the frequency of active fragments in perilla albumin corresponding to 40 perilla albumin sequences is evaluated, and the active types include DPP-Ⅳ inhibitory activity, ACE inhibitory activity and other activities predicted in addition to the first two activities. The results show that the number of fragments with DPP-Ⅳ inhibitory activity in the perilla albumin sequence is the most, followed by the fragments with ACE inhibitory activity, and the fragments with other activities account for the least. The specific results are shown in Table 1.

[0078] Table 1 Frequency (A) distribution of active fragments in perilla albumin

[0079]

[0080]

[0081] Step 203, computer simulation of digestion and preliminary screening: 40 perilla albumin peptides were simulated to be digested by pepsin [3.4.23.1], trypsin [EC 3.4.21.4], chymotrypsin [EC 3.4.21.1], and screened by the bioactive peptide database BIOPEP-UWM to screen out new peptides that have not been reported. Then the bioactive peptide scoring prediction was performed by the PeptideRanker prediction website, and the bioactive peptides with a score > 0.8 were screened out. In the Discovery Studio software, molecular docking was performed with DPP-Ⅳ and ACE, and perilla albumin-derived multifunctional peptides with DPP-Ⅳ and ACE inhibitory activity were screened out, i.e. perilla albumin-derived multifunctional peptides were obtained. The detailed results are shown in Table 2.

[0082] Table 2 Perilla albumin-derived multifunctional peptides with DPP-Ⅳ and ACE inhibitory activity

[0083]

[0084]

[0085] Example 2

[0086] Based on toxicity, allergenicity, hemolyticity, and bitterness analysis, perilla albumin-derived multifunctional peptides with DPP-Ⅳ and ACE inhibitory activity were rescreened.

[0087] The toxicity, allergenicity, and hemolyticity of 22 perilla albumin-derived multifunctional peptides obtained by Example 1 were analyzed as shown in Table 3. Except for ECCPL, all perilla albumin-derived multifunctional peptides were predicted to be non-toxic. Peptides predicted to be non-allergenic, non-hemolytic, and non-bitter accounted for 54.55%, 68.18%, and 36.36%, respectively. However, only CDAF and CCAL were predicted to be non-toxic, non-allergenic, non-hemolytic, and non-bitter. Figures 2 to 5 3D and 2D mode diagrams of CDAF and CCAL docking with DPP-Ⅳ and ACE.

[0088] Through Figure 2It can be seen that CDAF forms conventional hydrogen bond with ARG125, SER630, GLU205 of DPP-IV, carbon hydrogen bond with GLU741, HIS740, Pi-Cation with ARG125, Pi-Anion with ASP709, Pi-Sigma, Pi-Alkyl and Unfavorable-Donor-Donor with TYR665, van der waals with TRP629, VAL711, TRP659, TYR547, VAL656, TYR631, PRO550, GLY549, GLU206, TYR662, TRP201, ASN710, TRP124, LYS122, ASP739; through Figure 3 It can be seen that CDAF forms conventional hydrogen bond with GLU411, TYR523, TYR394, ARG402 of ACE, carbon hydrogen bond with TYR523, HIS353, HIS410, Pi-Sulfur with TYR394, Pi-Pi Stacked with TYR523, van der waals with GLU403, GLY404, TYR360, ASP358, HIS383, PHE291, GLU384, PHE457, GLN281, TYR520, LYS511, HIS513, ALA354, PHE512, VAL518, SER355, ALA356, HIS387, ARG524, PRO407; through Figure 4 It can be seen that CCAL forms conventional hydrogen bond with TYR547, ASN710, TYR662, GLY741, HIS740, ARG125, LYS122 of DPP-IV, carbon hydrogen bond with TYR662, TRP629, Unfavorable Donor-Donor with ASN710, ARG125, electrostatic attraction with GLU205, GLU206, ARG125, LYS122, Pi-Sulfur with TYR631, Alkyl and Pi-Alkyl with TRP201, van der waals with VAL711, TYR666, VAL656, SER630, ASP709, TRP124, ASP739; through Figure 5It can be seen that CCAL forms hydrogen bonds with GLN281, TYR520, ALA354, ALA356 of ACE, forms carbon hydrogen bonds with HIS513, GLU384, ALA354, GLU411, forms metal receptor action with zinc ion (ZN701), forms salt bridge with GLN411, forms electrostatic attraction interaction (Attractive Charge) with LYS511, forms π-cation interaction with HIS387, forms Pi-Sulfur interaction with PHE353, HIS353, forms π-alkyl interaction with PHE527, HIS383, TYR523, and forms van der Waals force with PHE457, VAL380, VAL518, ARG522, HIS410, PHE391, SER526, ASP415, LYS454.

[0089] In summary, CDAF and CCAL mainly interact with the active sites of DPP-IV and ACE through traditional hydrogen bonds, van der Waals forces, electrostatic attraction forces and other interaction forces, occupy the catalytic center of the enzyme, thereby inhibit the activity, and play the role of lowering blood pressure and reducing blood sugar.

[0090] The physicochemical properties of CDAF and CCAL were predicted using ADMETlab2.0, and the results are shown in Figure 6 、 Figure 7 Among the various physicochemical parameter indicators of the two, in addition to water solubility (LogS), oil-water partition coefficient (LogP, LogD), topological polar surface area (TPSA) and number of rotatable bonds (nRot), the rest of the indicators (nHA: number of hydrogen bond acceptors; nHD: number of hydrogen bond donors; nRing: number of rings; MaxRing: number of atoms in the largest ring; nHet: number of heteroatoms; fChar: formal charge; nRig: number of non-rotatable bonds; MW: molecular weight) are within the expected range. The LogP value and the LogD value of CDAF are lower, the LogS value of CDAF is higher, and the LogD value is higher, indicating that the two perilla albumin-derived multifunctional peptides have poor fat solubility, but relatively high water solubility. The TPSA value and nRot value of CDAF and CCAL are higher, but the gap from the highest limit is smaller.

[0091] Table 3 Toxicity, allergenicity, hemolyticity and bitterness analysis of multifunctional peptides

[0092]

[0093]

[0094] In summary, the application provides a method for screening perilla albumin-derived multifunctional peptides for treating diabetes and hypertension based on molecular simulation technology. The active fragment prediction of 40 perilla albumin sequences is performed by using the bioactive peptide database BIOPEP-UWM, and it is found that the corresponding perilla albumin is rich in a large number of fragments with DPP-IV and ACE inhibitory activity. The perilla albumin is digested by computer simulation to obtain polypeptides that can resist gastrointestinal digestion, and the perilla albumin-derived multifunctional peptides with DPP-IV and ACE inhibitory activity are screened by related activity prediction and molecular docking. The practicability of the perilla albumin-derived multifunctional peptides is predicted, and two perilla albumin-derived multifunctional peptides without toxicity, allergenicity, bitterness and hemolytic activity, namely CDAF and CCAL, are further screened. The two perilla albumin-derived multifunctional peptides inhibit the catalytic activity of DPP-IV and ACE by competitively binding to the active center of DPP-IV and ACE, thereby playing a role in reducing blood sugar and blood pressure. The physical and chemical property analysis shows that most of the parameter indexes of the two multifunctional peptides are within the expected range. CDAF and CCAL have strong water solubility, which means that the two polypeptides are easy to dissolve in body fluids, increasing the contact area with the absorption site, thereby increasing the absorption and bioavailability.

[0095] The above merely describes the most optimal specific embodiments of the application, but the protection scope of the application is not limited thereto, and any changes or replacements within the technical scope disclosed in the application should be covered in the protection scope of the application. Therefore, the protection scope of the application should be subject to the protection scope of the claims.

Claims

1. A method for screening multifunctional peptides for treating diabetes and hypertension from perilla albumin, characterized in that, Comprising the following steps: Step S1: input the keyword "perilla seed storage albumin" in the NCBI database, and obtain the perilla albumin sequence by searching; Step S2: using the "Batch processing" "Calculate" module in the bioactive peptide database BIOPEP-UWM, analyze the potential of perilla albumin corresponding to the perilla albumin sequence as a source of DPP-Ⅳ and ACE inhibitory peptides; Step S3: using the "enzyme action" module in the bioactive peptide database BIOPEP-UWM, using protease to simulate the digestion of perilla albumin, and obtain perilla albumin-derived polypeptides; Step S4: first, score the potential biological activity of the perilla albumin-derived polypeptides in the PeptideRanker prediction website, then molecularly dock the perilla albumin-derived polypeptides that meet the scoring requirements with DPP-Ⅳ and ACE, and screen out the perilla albumin-derived polypeptides that are successfully docked with DPP-Ⅳ and ACE, i.e. perilla albumin-derived multifunctional peptides, which have the effect of reducing blood sugar and blood pressure; Step S5: predict the toxicity of the perilla albumin-derived multifunctional peptides by ToxinPred, predict the allergenicity of the perilla albumin-derived multifunctional peptides by Aller TOP V2.0, predict the bitterness of the perilla albumin-derived multifunctional peptides by iBitter-SCM, predict the hemolyticity of the perilla albumin-derived multifunctional peptides by HemoPred, and predict the physical and chemical properties of the perilla albumin-derived multifunctional peptides by ADMETlab 2.0, screen out the perilla albumin-derived multifunctional peptides that are non-toxic, non-allergenic, non-bitter, and non-hemolytic, and further analyze the physical and chemical properties of the perilla albumin-derived multifunctional peptides; In step S2, the frequency A of the DPP-Ⅳ or ACE inhibitory active fragment in the perilla albumin is characterized according to the following formula: A = a / N wherein: a— the number of fragments with ACE inhibitory activity or DPP-Ⅳ inhibitory activity in the protein; N— the number of amino acid residues in the protein; In step S3, the protease is pepsin [3.4.23.1], trypsin [EC 3.4.21.4], and chymotrypsin [EC 3.4.21.1].

2. The method of claim 1, wherein the perilla albumin-derived polypeptide is subjected to a potential bioactivity score in a PeptideRanker prediction website prior to the screening of the perilla albumin-derived polypeptide for diabetes hypertension co-morbidity treating multifunctional peptides, and Also comprising: Aligning the perilla albumin-derived polypeptides with the bioactive peptides reported in the bioactive peptide database BIOPEP-UWM, and screening out the perilla albumin-derived polypeptides that have not been reported.

3. The method of claim 1, wherein the perilla albumin-derived polypeptides are first scored for potential bioactivity in a PeptideRanker prediction website, and then the perilla albumin-derived polypeptides that meet the scoring requirements are subjected to molecular docking with DPP-IV and ACE. Specifically: Setting the score threshold to 0.8 in the PeptideRanker prediction website, scoring the potential biological activity of the perilla albumin-derived polypeptides in the PeptideRanker prediction website, and screening out the perilla albumin-derived polypeptides with a score greater than 0.8; The screened perilla albumin-derived polypeptide is subjected to molecular docking with DPP-Ⅳ and ACE by using the Libdock module of Discovery Studio software.

4. The method according to claim 3, wherein the perilla albumin-derived multifunctional peptide for treating diabetes and hypertension is screened. In step S4, the molecular crystal structure of DPP-Ⅳ and ACE is obtained from the PDB database, the PDB ID of DPP-Ⅳ is 1NU6, and the PDB ID of ACE is 1O8A. Before molecular docking, the position of the pocket is defined by the original ligand, the radius is set to 10 angstroms, and the water molecules and original ligands in the crystal structure are deleted and hydrogenated.

5. The method for screening perilla albumin-derived multifunctional peptides for treating diabetes and hypertension according to claim 4, wherein the perilla albumin-derived peptides screened are subjected to molecular docking with DPP-IV and ACE. Further comprising: The perilla albumin-derived polypeptide with a molecular docking score greater than 100 with DPP-Ⅳ and ACE is screened.

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