Bone metabolism regulating peptide as well as screening method and application thereof

Abstract

The invention discloses a bone metabolism regulating peptide as well as a screening method and application thereof, and aims to screen potential polypeptides capable of promoting bone formation and inhibiting bone resorption from protein peptides through network pharmacology and a molecular docking technology, and explore the influence of active moringa oleifera leaf protein peptides on specific targets based on molecular dynamics. The DPYLGK peptide is determined to have the effects of promoting bone formation and bone resorption from 23 screened moringa leaf protein peptides, and proliferation and differentiation of osteoclasts and osteoblasts are inhibited mainly by inhibiting SRC gene expression, so that bone resorption and bone formation are inhibited. And a certain theoretical support is provided for subsequent research and development of osteoporosis dual-regulation functional foods and targeted SRC inhibitors.

Description

Technical Field

[0001] The present invention relates to the field of polypeptides, and particularly to a bone metabolism-regulating peptide, a screening method therefor, and an application thereof. Background Art

[0002] The prevention and treatment of osteoporosis mainly involve two approaches: promoting bone formation and inhibiting bone resorption to prevent bone mass loss. A large number of studies have reported the role of osteoblasts or osteoclasts in osteoporosis, but few have reported the dual role of osteoblasts and osteoclasts in osteoporosis. Any disruption during the bone remodeling process or imbalance between bone resorption and formation may lead to bone diseases such as osteoporosis. Currently, there are various drugs used to intervene in osteoporosis, mainly treating osteoporosis by promoting bone formation or inhibiting bone resorption. However, long-term use of these drugs also has many toxic side effects. Therefore, functional foods for preventing osteoporosis that are safe, highly effective, non-toxic, and can be consumed long-term have urgent practical significance.

[0003] In a number of studies, it has been found that collagen bioactive peptides play a positive role in preventing osteoporosis and promoting bone growth. YE ML et al. found that yak bone collagen peptides obtained by enzymatic hydrolysis with neutral protease had obvious improvement effects on bone density, bone microstructure, bone metabolism level, etc. in ovariectomized osteoporosis rats. In another study, LIN XL et al. found a bone-protective peptide that could counteract cadmium-induced osteoporosis in the enzymatic hydrolysate of chicken breast bone cartilage hydrolyzed with alkaline protease. Based on the above research results, the research on the anti-osteoporosis activity of food-derived bioactive peptides has mainly focused on animal-derived bioactive peptides, while the research on plant-derived bioactive peptides in improving osteoporosis is still relatively scarce and there is still a large room for exploration. In addition, there are many types of plant-derived bioactive peptides, and how to screen active peptides for improving osteoporosis is also a major problem. Currently, there is no literature reporting an effective screening method for bone metabolism-regulating peptides. Summary of the Invention

[0004] Aiming at the deficiencies of the prior art, the present invention provides a bone metabolism-regulating peptide, a screening method therefor, and an application thereof. The screening method provided by the present invention has relatively high screening efficiency and accuracy, and can obtain polypeptides that promote bone formation and bone resorption.

[0005] To solve the above technical problems, the technical solution of the present invention is as follows: A screening method for a bone metabolism-regulating peptide, the method comprising the following steps:

[0006] Input multiple protein peptides into the PepSMI database to search for corresponding SMILES codes. Input the SMILES codes into the Swiss TargetPrediction database and the Super-PRED database. Obtain human-related component targets in the Swiss Target Prediction database and component targets in the Super-PRED database. Integrate and remove duplicates of the component targets from the two databases to obtain the total component targets;

[0007] Collect disease targets through the CTD database and the Genecards database. Input "osteoporosis" into the database to obtain disease targets. Obtain disease-related targets in the CTD database and the Genecards database respectively. Through removing duplicates and merging, obtain the total disease targets. Input the component targets of the protein peptides and the osteoporosis disease targets into the VENNY online website to obtain the intersection targets;

[0008] To clarify the key targets of the protein peptides and match related proteins, import the intersection targets into the STRING online website. The comprehensive score is ≥0.9, and the points without interaction relationships are removed to obtain the protein-protein interaction PPI graph. After analysis, obtain the core target protein information of the protein peptides from Moringa oleifera leaves; Use the Cytoscape 3.9.1 software to analyze the topological isomerism of the PPI network, and select the targets above the median values of DegreeCentrality, BetweennessCentrality, and ClosenessCentrality as key targets, and visualize the protein network;

[0009] Import the key targets of the protein peptides from Moringa oleifera leaves into Metascape, select "Homo sapiens" as the species for analysis, and select four parts: GO biological process (BP), GO molecular function (MF), GO cellular component (CC), and KEGG for enrichment analysis; By counting the targets enriched most in the top 10 signaling pathways of the KEGG analysis and the Degree values in the PPI network analysis process, select 3-5 targets as the receptors in molecular docking, and use multiple protein peptides as ligands to obtain the polypeptide with the best binding energy to the representative target protein, which is the peptide for regulating bone metabolism.

[0010] Preferably, the median values of betweenness centrality, closeness centrality, and degree centrality are analyzed using the CytoNCA plugin of Cytoscape software. The median values of betweenness centrality and closeness centrality are used as cut-off values to screen preliminary targets, and then the top 20 - 30 targets are selected as key targets using degree centrality as the final indicator.

[0011] Preferably, to explore the internal influence between the representative target protein and the bone metabolism-regulating peptide, molecular dynamics (MD) analysis was performed on their conjugate, and the root mean square deviation (RMSD); root mean square fluctuation (RMSF) of the molecular dynamics simulation were measured; the data of Cα atoms at their average positions were analyzed to draw a mutual matrix diagram; an image describing the protein movement was obtained by projecting the MD trajectory onto the first principal component PC1 using VMD software; the conformational space of the target protein was analyzed; and the free energy was calculated.

[0012] Preferably, osteoblast and osteoclast models were constructed to verify the ability of the bone metabolism-regulating peptide to promote bone formation and inhibit bone resorption: the effect of the peptide on bone resorption was verified by TRAP staining, F-actin staining, and Q-PCR; the effect of the peptide on bone formation was verified by cell cycle, ALP assay, alizarin S staining, and Q-PCR.

[0013] Preferably, the protein peptide is derived from a natural product peptide; the molecular weight of the natural product peptide is less than 1 kDa.

[0014] Preferably, the natural product peptide is selected from moringa leaf protein peptides.

[0015] Preferably, by statistically analyzing the targets enriched in the top 10 signaling pathways of KEGG analysis and the degree centrality values in the PPI network analysis process, four target proteins, SRC, MAPK1, STAT3, and JUN, are selected as receptors in molecular docking, and moringa leaf protein peptide is used as the ligand. There are many targets for regulating bone metabolism, and there are also many related proteins that 23 kinds of moringa proteins can regulate bone metabolism. The screening of the four target proteins is the intersection of protein predicted targets and disease targets.

[0016] In the second aspect of the present invention, a bone metabolism-regulating peptide is provided. The moringa leaf protein peptide is used to obtain the bone metabolism-regulating peptide through the screening method, and its amino acid sequence is DPYLGK.

[0017] In the third aspect of the present invention, the application of the bone metabolism-regulating peptide in the research and development (or preparation) of functional foods or drugs for preventing osteoporosis is provided.

[0018] The features of the present invention are as follows: (1) Predict the component targets based on network pharmacology, construct a protein-protein interaction network, and enrich the most targets according to KEGG and GO analyses.

[0019] (2) Dock the targets analyzed by network pharmacology with protein peptides. Select the polypeptide with the best binding energy as the research object.

[0020] (3) Use molecular dynamics to explore the influence of the polypeptide on the internal dynamics of the target protein.

[0021] (4) Synthesize the optimal polypeptide simulated by the computer, verify the reliability of the computer simulation results at the cellular level, and simultaneously explore the mechanism of action of the polypeptide on bone resorption and bone formation through experiments.

[0022] Compared with the prior art, the present invention has the following beneficial effects: The screening method provided by the present invention has relatively high screening efficiency and accuracy. The DPYLGK polypeptide screened by the present invention can effectively promote bone formation and bone resorption, and is expected to develop functional foods or even drugs for the dual regulation of osteoporosis prevention and targeted SRC inhibitors. Brief Description of the Drawings

[0023] Figure 1 It is the technical roadmap of the present invention;

[0024] Figure 2 Potential target network diagram of 23 Moringa oleifera leaf protein peptides;

[0025] Figure 3 Intersection target analysis;

[0026] Figure 4 PPI network in the String database;

[0027] Figure 5 PPI network in the Cytoscape software;

[0028] Figure 6 Key target interaction diagram;

[0029] Figure 7 GO enrichment analysis diagram;

[0030] Figure 8 Main KEGG signaling pathway enrichment diagram;

[0031] Figure 9 Target enrichment amount;

[0032] Figure 10 Molecular docking data diagram (kcal / mol);

[0033] Figure 11Visualization diagram of DPYLGK peptide docking;

[0034] Figure 12 Root mean square deviation (RMSD);

[0035] Figure 13 Root mean square fluctuation (RMSF);

[0036] Figure 14 Dynamic cross-correlation matrix (DCCM) diagram: (A) Free SRC, (B) DPYLGK-SRC; Note: The amino acid sequence was reordered, so it starts from sequence number 0;

[0037] Figure 15 Motion of the first principal component of the target protein SRC: (A) Free SRC, (B) SRC-DPYLGK;

[0038] Figure 16 Free energy landscape of the target protein SRC: (A) Free SRC, (B) SRC-DPYLGK;

[0039] Figure 17 Interaction between individual residues and DPYLGK peptide;

[0040] Figure 18 Effect of DPYLGK peptide on the proliferation of MC3T3-E1 cells, ns: not significant; *: P<0.05; ***: P<0.001;

[0041] Figure 19 Effect of DPYLGK peptide on the osteoblast cell cycle, ns: not significant; *: P<0.05; **: P<0.01;

[0042] Figure 20 Effect of DPYLGK peptide on ALP activity, ns: not significant; *: P<0.05; **: P<0.01;

[0043] Figure 21 Effect of DPYLGK peptide on mineralization; Different superscript letters indicate significant differences (P<0.05);

[0044] Figure 22 Effect of DPYLGK peptide on the cytotoxicity of RAW264.7 cells, ns: not significant; **: P<0.01;

[0045] Figure 23 DPYLGK peptide inhibits osteoclastogenesis. Different superscript letters indicate significant differences (P<0.05);

[0046] Figure 24 Effect of DPYLGK peptide on F-actin rings;

[0047] Figure 25 Effect of DPYLGK peptide on gene expression; A: Expression of SRC gene in osteoblasts; B: Expression of SRC gene in osteoclasts, ns: not significant; * or #: P < 0.05; **: P < 0.01 Detailed implementation manners

[0048] The technical solutions of the present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments, but the present invention is not limited to the following technical solutions. The methods of cell resuscitation, culture and cryopreservation, measuring DNA content by flow cytometry, analyzing the distribution of MC3T3-E1 cells in the cell cycle stage, alkaline phosphatase (ALP) assay method, alizarin red S assay method, cytotoxicity test method of Moringa oleifera leaf protein peptide on RAW264.7 cells, osteoclast model establishment method, TRAP assay, F-actin ring staining, and total RNA extraction method in Q-PCR involved in the following examples all belong to existing methods. The symbols and abbreviations involved are shown in Table 1, the databases used in the present invention are shown in Table 2, and the main reagents and equipment used are shown in Table 3.

[0049] Table 1 Symbols and abbreviations

[0050]

[0051]

[0052] Table 2 Databases

[0053]

[0054] Table 3 Main reagents and materials

[0055]

[0056]

[0057] In this example, Moringa oleifera leaf protein peptide is used as the raw material to seek Moringa oleifera leaf protein peptide with higher bone formation promotion and bone resorption inhibition through network pharmacology, molecular docking, molecular dynamics simulation and experimental research. The technical roadmap is as Figure 1 shown.

[0058] In the previous experiments of the research group, Moringa oleifera leaf protein hydrolysate (Moringa oleifera leaf protein was hydrolyzed by alkaline protease at 50 °C, pH 8.0, and addition amount of 10000 U / g for 60 min), and the hydrolysate was fractionated according to molecular weight by ultrafiltration cup membrane filtration technology. Among the polypeptides with molecular weight < 1 kDa, 23 kinds with higher activity potential (rich in Glu, Asp, Ser, Lys and His, and with Lys, Arg as the terminal residues, etc.) were screened out. The 23 kinds of Moringa oleifera leaf protein peptides are shown in Table 4.

[0059] Table 4 Information on 23 Moringa oleifera leaf protein peptides (<1KDa)

[0060]

[0061]

[0062]

[0063] Target screening and prediction of action targets

[0064] The corresponding SMILES codes of 23 Moringa oleifera leaf protein peptides were searched in the PepSMI database, as shown in Table 5. The above SMILES codes were input into the SwissTarget Prediction database and the Super-PRED database. 301 component targets related to humans were obtained in the Swiss Target Prediction database, and 348 component targets were obtained in the Super-PRED database. The component targets of the two databases were integrated and duplicates were removed, and a total of 535 component targets were obtained. The prediction results of the component targets of 23 Moringa oleifera leaf protein peptides are as Figure 2 shown, and the size of the circle represents the number of enriched targets.

[0065] Table 5 SMILES codes and numbers of Moringa oleifera leaf protein peptides

[0066]

[0067]

[0068]

[0069] Disease targets were collected through the CTD database and the Genecards database. Entering "osteoporosis" in the database can obtain disease targets. 30,514 disease-related targets were obtained in the CTD database, and 4,736 targets were screened out in the CTD database. Through deduplication and merging, a total of 31,051 disease targets were obtained. The component targets of 23 Moringa oleifera leaf protein peptides and osteoporosis disease targets were input into the VENNY online website to obtain intersection targets. As Figure 3 shown, there are a total of 534 intersection targets for component and disease targets.

[0070] Construction of the core target network (PPI)

[0071] To identify the key targets of 23 Moringa oleifera leaf protein peptides and match the related proteins, the intersection targets were imported into the STRING online website. The selected species was HOMO sapiens, with a comprehensive score ≥ 0.9. Points without interaction relationships were excluded to obtain the protein-protein interaction (PPI) map of the protein relationships, as shown in Figure 4 As shown, there were a total of 533 interacting protein targets and 1905 interaction lines. After analysis, the core target protein information of the Moringa oleifera leaf protein peptides was obtained.

[0072] The PPI network was further analyzed and scored using Cytoscape software. Figure 5 It shows the situation of the PPI network after integration in Cytoscape software.

[0073] The median values of betweenness centrality, closeness centrality, and degree centrality were analyzed using the CytoNCA plugin of Cytoscape software. The median value of the analyzed betweenness centrality was 818.2513661202205, the median value of closeness centrality was 0.000872093672680319, and the median value of degree centrality was 10.300546448087431. Using the median values of betweenness centrality and closeness centrality as the cut-off values, 71 targets were initially screened. Table 6 shows the relevant information of the 71 targets.

[0074] Table 6 Information of 71 targets

[0075]

[0076]

[0077]

[0078] Since genes with more connecting proteins play a more crucial role in the network, play a more significant role in the component-disease correlation, and are more likely to be effective components acting on diseases. Therefore, among the 71 targets, the top 25 proteins with the highest degree centrality were selected as the key targets, Figure 6 showing the interactions of the 25 key targets.

[0079] GO and KEGG analysis

[0080] Metascape is a tool with numerous gene function annotation and analysis functions. Using Metascape to perform biological function analysis on key targets can apply bioinformatics analysis in the fields of genes and proteins. Import the core targets of Moringa oleifera leaf protein peptides into Metascape. Select "Homo sapiens" as the species for analysis, and select four parts: GO Biological Processes (BP), GO Molecular Function (MF), GO Cellular Components (CC), and KEGG for enrichment analysis. To further systematically explore the multiple mechanisms of Moringa oleifera leaf protein peptides on bone resorption and bone formation, we performed GO enrichment analysis and KEGG analysis. The results of the GO enrichment analysis are as Figure 7 shown. 25 key targets can affect 42 GO biological processes in the body. In the biological process (BP), the 25 key targets mainly regulate responses to hormones, transmembrane receptor protein tyrosine kinase signaling pathways, hematopoiesis or lymphoid organ development, etc.

[0081] 143 signaling pathways were enriched by KEGG signaling pathway enrichment analysis. The top 10 signaling pathways are as Figure 8 shown. These 10 signaling pathways are the cancer pathway, prolactin signaling pathway, osteoclast differentiation, Th17 cell differentiation, adipokine signaling pathway, gonadotropin-releasing hormone (GNRH) signaling pathway, necroptosis, alcoholic liver disease, adherens junction, and cell cycle. These signaling pathways affect bone resorption and bone formation in different ways. For example, the Hippo signaling pathway in the cancer pathway affects bone formation by regulating cell proliferation and differentiation processes. The prolactin signaling pathway mainly regulates the proliferation, apoptosis, and differentiation of osteoblasts and osteoclasts. The adipokine signaling pathway can regulate osteoclastogenesis by expressing RANKL and promote bone resorption. Gonadotropin-releasing hormone promotes osteoblast differentiation and bone formation, and inhibits osteoclast formation and bone resorption. Therefore, combined with existing research, it shows that KEGG enrichment analysis can effectively predict the potential targets of Moringa oleifera leaf protein peptides on bone resorption and bone formation.

[0082] Figure 9 shows the proportion of key targets in the top 10 KEGG enrichment pathways. These 10 pathways are mainly enriched in targets such as JUN, MAPK1, SRC, STAT3, AKT1, JAK2, NFκB1, STAT1, and CCND1.

[0083] Molecular docking analysis

[0084] By Figure 9As is known, JUN and MAPK1 are the targets with the most enrichments in the top 10 KEGG signaling pathways and are most likely to represent the effects of Moringa oleifera leaf protein peptides on bone resorption and bone formation. Therefore, these two protein targets were used as ligands for molecular docking. At the same time, SRC, STAT3, AKT1, JAK2, NFκB1, STAT1, and CCND1 are also potentially representative. Therefore, we selected SRC and STAT3 protein targets as ligands for molecular docking according to the Dergee values in Table 7. The structures of the selected 4 targets were downloaded from the PDB database. The relevant information is shown in Table 7. Twenty-three Moringa oleifera leaf protein peptides were drawn using Python. Docking was performed using AutoDockvina, and the docking results were visualized using Python and Discovery Studio 4.5.

[0085] Table 7 Target protein information

[0086]

[0087] The molecular docking results are as Figure 10 shown. Compared with the targets JUN, MAPK1, and STAT3, the target SRC has a better binding energy with 23 Moringa oleifera leaf protein peptides, which means that Moringa oleifera leaf protein peptides may affect bone formation and bone resorption by regulating the target SRC. The binding energies of the DPYLGK peptide to the targets JUN, MAPK1, SRC, and STAT3 among the 23 Moringa oleifera leaf protein peptides are -6.9 kcal / mol, -7.3 kcal / mol, -8.7 kcal / mol, and -6.6 kcal / mol respectively, and its total binding energy can reach -29.5 kcal / mol, which is the peptide with the highest binding energy among the 23 Moringa oleifera leaf protein peptides. This result indicates that Moringa oleifera leaf protein peptides are potential functional components for preventing and regulating osteoporosis. At the same time, the DPYLGK peptide is somewhat representative. In addition, the SRC protein is the target with the most potential regulatory mechanism among the four proteins.

[0088] Figure 11 shows the molecular docking visualization results of the DPYLGK peptide with the targets JUN, MAPK1, SRC, and STAT3. The results of the molecular docking of the DPYLGK peptide with the target JUN show ( Figure 11 A), the HIS (B:63) residue and ARG (B:40) residue on the target JUN form conventional hydrogen bonds with the DPYLGK peptide, and at the same time, the Arg (B:40) residue forms a carbon-hydrogen bond with H. In addition, the PHE (B:41) residue and the N on the peptide +Form π-cation interactions. The LEU (B106) residue, VAL (B:67) residue, PHE (B:45) residue, and HIS (B:63) residue form alkyl and π-alkyl interaction forces with the polypeptide. The docking results of the DPYLGK peptide with the target MAPK1 molecule show ( Figure 11 B), the ASP (A:165) residue, GLU (A:31) residue, and TYR (A:191) residue on the target MAPK1 form conventional hydrogen bonds with the polypeptide, and the GLY (A:32) residue forms a carbon-hydrogen bond with the polypeptide. The LYS (A:149) residue forms a Pi-Cation interaction with the polypeptide, and the LYS (A:52) residue forms a salt bridge with the polypeptide. The ALA (A:33) residue forms a π-σ bond with the polypeptide, the TYR (A:111) residue forms an amide-π stacking interaction force with the polypeptide, the LEU (A:154) residue and the TRP (A:190) residue form Pi-Alkyl interaction forces with the polypeptide, and the LYS (A:112) residue forms an alkyl interaction force with the polypeptide. In addition, the ARG (A:65) residue and the LYS (A:149) residue form unfavorable interaction forces with the polypeptide. The docking results of the DPYLGK peptide with the target SRC molecule show ( Figure 11C), the GLU (A:342), ASP (A:407), LYS (A:298), GLY (A:282), and PHE (A:281) residues on the target SRC form hydrogen bond interactions with the polypeptide, and the MET (A:344) and SER (A:348) residues form carbon-hydrogen bonds with the polypeptide. The TYR (A:343), GLY (A:347), ALA (A:296), ASP (A:351), GLY (A:277), GLN (A:278), THR (A:341), VAL (A:326), ARG (A:391), ASN (A:394), GLY (A:279), CYS (A:280), GLU (A:283), LEU (A:300), ILE (A:414), and LEU (A:410) residues form van der Waals forces with the polypeptide. The LYS (A:298) residue forms a Pi-Cation interaction with the polypeptide, and the LEU (A:396) residue forms a π-σ bond with the polypeptide. The LEU (A:276), THR (A:341), LEU (A:396), ALA (A:393), and LEU (A:350) residues form alkyl interactions with the polypeptide, and at the same time, the VAL (A:284) and ALA (A:406) residues form Pi-Alkyl interaction forces with the polypeptide. The results of the docking of the DPYLGK peptide with the target STAT3 molecule show ( Figure 11D), the SER(A:319), THR(A:456), THR(A:236) and ASP(A:237) residues on the target STAT3 formed hydrogen bonds with the polypeptide, while the ASN(A:315) and ASP(A:237) residues formed carbon-hydrogen bonds with the polypeptide. LYS(A:244) formed a salt bridge interaction with the polypeptide, HIS(A:457) and LYS(A:488) residues formed an attractive charge interaction with the polypeptide, and ASP(A:237) residue formed a Pi-Cation interaction with the polypeptide. In addition, LEU(A:240), ALA(A:241) and LYS(A:318) residues formed Pi-Alkyl interactions with the polypeptide, PRO(A:487) residue formed an Alkyl interaction with the polypeptide, and VAL(A:322), PHE(A:321), GLU(A:455), GLN(A:232), LEU(A:316), LYS(A:233) residues formed van der Waals forces with the polypeptide. The research results showed that there were only favorable interactions between the DPYLGK peptide and the targets JUN and STAT3, which could effectively improve the stability of the conjugate, but there were fewer favorable interactions, resulting in a lower binding energy. Although there were certain unfavorable interactions between the DPYLGK peptide and the target SRC, there were more van der Waals forces, electrostatic interactions, hydrophobic interactions and hydrogen bond interactions, which could effectively reduce the impact of the unfavorable forces on the stability, resulting in a higher binding energy. The DPYLGK peptide had two different unfavorable interactions with the target MAPK1, but its favorable interactions were higher, so its binding energy was higher than that of the targets JUN and STAT3, but lower than that of the target SRC, which benefited from the fact that the target SRC had more favorable interactions with the DPYLGK peptide. The molecular docking results showed that the DPYLGK peptide had good binding energies with the targets JUN, MAPK1, STAT3 and SRC, among which the binding energy of the target SRC was the highest, indicating that the SRC protein was a representative potential target for the regulation of bone resorption and bone formation by the polypeptide of Moringa oleifera leaves.

[0089] Molecular dynamics

[0090] Molecular dynamics simulation parameter settings

[0091] The initial structure of the target protein molecule was obtained from the PDB database. The polypeptide was drawn using Python software. In the CGenFF server, the molecular force field parameters were generated. At the same time, the molecular force field parameters were converted into GROMACS format using a Python script (cgenff_charmm2gmx_py2.py). The GROMACS 2020.5 software package (with the charmm36 - jul2020 force field) was used for molecular dynamics simulations. The simulations were carried out under periodic boundary conditions. In the cubic box range, the TIP3P water model was used to solvate the target protein and the Moringa oleifera leaf protein peptide. The steepest descent method was used to minimize the energy of all initial structures for 50,000 steps. At the same time, using the NPT ensemble, the Leap - Frog method was used to integrate the equations of motion. The long - range electrostatic interaction used the PME method, and the cutoff distances for the van der Waals interaction and the Coulomb interaction were and updated every 10 steps. The parameters lincs_iter = 1 and lincs_order = 4 were used to constrain all bond lengths. The V - rescale thermostat was used to heat the system temperature from 0K to 333K. The system pressure constant was maintained at 1 bar, and the pressure was kept isotropic. All simulations randomly assigned initial velocities through Maxwell - Boltzmann statistics, with a total of 250,000,000 steps simulated, a time step of 1 fs, and the coordinates were recorded every 10,000 steps. The total simulation time was 250 ns, generating 25,000 conformations.

[0092] Molecular Dynamics Analysis

[0093] RMSD and RMDF Analysis

[0094] To deeply explore the internal influence between the representative target SRC and the DPYLGK peptide, we performed molecular dynamics (MD) analysis on the SRC - DPYLGK peptide. The root - mean - square deviation (RMSD) of molecular dynamics simulations can reflect the motion of the complex, which is the average deviation of the protein structure from the original conformation at a specific time and is an important indicator to evaluate the stability of the simulation system. Small - range RMSD fluctuations can be judged that the molecular structure has reached convergence and stability. As Figure 12As shown, the changes in the two systems are very small after 50 ns, and the deviation of all RMSD from the average position is less than 0.02 nm. The two systems have basically reached a stable steady state. The average RMSD values in the stable state (50 ns - 250 ns) were statistically analyzed to evaluate the conformational changes of the systems. In the stable state, the average RMSD values of SRC and SRC-DPYLGK were 0.183 nm and 0.215 nm, respectively. This conformational change is due to the certain rigidity of the SRC protein backbone, which makes the molecule have no large position changes and structural deformations during the molecular dynamics simulation. Therefore, its RMSD value is small (0.183 nm). The RMSD value of SRC-DPYLGK is larger than that of free SRC because the DPYLGK peptide has greater flexibility, and its position changes and structural deformations in the cavity increase the RMSD value of the complex. Secondly, it is because adverse interaction forces are generated during the binding of the target protein SRC and the DPYLGK peptide, which have a certain degree of influence on the conformations of both, thus increasing the RMSD value.

[0095] The root mean square fluctuation (RMSF) can more detailedly evaluate the fluctuations of SRC to reveal the fluctuations of individual residues of the protein during the molecular dynamics simulation. As Figure 13 shown, free SRC and SRC-DPYLGK have similar fluctuations. Among them, free SRC fluctuates in the range of RMSF 0.041 - 0.613 nm, and SRC-DPYLGK fluctuates in the range of RMSF 0.040 - 0.554 nm. The binding of the DPYLGK peptide to the target protein SRC increases the fluctuations of residues such as GLU264, ARG271, MET305, GLU308, GLU334, ARG463, and GLU492 of the target protein SRC, but also decreases the fluctuations of residues such as THR292, LYS318, ARG321, GLU323, GLU327, TYR343, GLU356, ARG362, GLN372, VAL386, GLU418, ARG422, LYS445, ASP476, GLU489, ARG503, and GLN531. The RMSF results show that the addition of the DPYLGK peptide causes different fluctuations of individual residues of the target protein SRC and leads to conformational changes.

[0096] Internal dynamics changes of the target protein SRC

[0097] To further study the effect of the binding of the DPYLGK peptide on the internal dynamics of the target protein SRC, the data of the Cα atoms at their average positions were analyzed, and a cross-correlation matrix map was drawn ( Figure 14)。The blue area is the positive value area, which represents the positive correlated motion between residues; the pink area represents the negative value area, representing the negative correlated motion between residues; the off-diagonal area characterizes the relative motion between different residues; the diagonal area represents the relative motion of the residue itself. As shown in the figure, the two motion modes have certain differences, indicating that the binding of the DPYLGK peptide has different effects on the internal dynamics of the target protein SRC. For the dynamic cross-correlation matrix of the free target protein SRC ( Figure 14 A), the overall area is mainly negative correlated motion. Through comparison, it is found that the binding of the DPYLGK peptide to the protein has a great impact on the correlated motion of SRC ( Figure 14 B). The binding of the DPYLGK peptide to the protein enhances the correlated motion of the overall area of the residues of the target protein SRC. The presence of the DPYLGK peptide significantly enhances the negative correlated motion of the target protein SRC, and at the same time enhances the corresponding positive correlated motion. The above analysis shows that the binding of the DPYLGK peptide to the target protein SRC has an obvious impact on the motion mode of SRC, and the difference in internal dynamics reflects the change in the relative position between the key residues caused by the binding of the DPYLGK peptide.

[0098] Dynamic analysis and conformational space change

[0099] To understand in more detail the effect of DPYLGK peptide binding on the conformational change of SRC, an image describing the protein motion was obtained by applying VMD software to the projection of the MD trajectory on the first principal component PC1 ( Figure 15 ). The direction of the arrow is the direction of the collective motion, and the length of the arrow is the intensity of the motion. As Figure 15 shown, the free target protein SRC shows strong motion in the regions of residues 260 - 261, residues 415 - 424, and residues 532 - 536. In contrast, the presence of the DPYLGK peptide has different effects on the motion of SRC ( Figure 15 B). The addition of the DPYLGK peptide changes the region of residues 260 - 261 to 260 - 262, and at the same time changes the motion direction from the upper oblique motion to the lower oblique motion. Although the motion region increases, the motion intensity decreases. The direction and motion intensity in the region of residues 415 - 424 are less affected, but it reduces the motion region (from residues 415 - 424 to residues 416 - 422). In addition, compared with the 532 - 536 region in the free state of SRC, the SRC-DPYLGK peptide no longer has strong motion intensity. The above results show that the addition of the DPYLGK peptide makes the target protein SRC more stable. Figure 15The red region shows the movement of the polypeptide. It can be seen that during the molecular dynamics simulation, the polypeptide shows a tendency to move outwards. It is precisely because of the outward movement of the DPYLGK peptide that the target protein SRC undergoes movements in different directions, an expansion of the residue movement region, and a change in the movement intensity, thereby stably binding the DPYLGK peptide to SRC.

[0100] To gain a deeper understanding of the conformational changes of SRC, the conformational space of SRC was analyzed ( Figure 16 ). The free SRC gave rise to two energy basins, mainly distributed in two different conformational subspaces. The binding of the DPYLGK peptide to SRC led to a redistribution of the SRC conformation, causing the conformation of SRC to concentrate on three subspaces. The results indicate that the binding of the DPYLGK peptide has an important impact on the conformation of the target protein SRC.

[0101] Binding free energy calculation

[0102] To better evaluate the binding ability of the DPYLGK peptide to SRC, its binding free energy was calculated. As can be seen from Table 8, the van der Waals interaction between DPYLGK and the protein is -48.34 ± 1.13 kcal / mol, and the electrostatic interaction with SRC is -67.28 ± 9.58 kcal / mol, which is extremely beneficial for the binding of the polypeptide to the protein; the polar solvation free energy between the DPYLGK peptide and the protein SRC is 100.58 ± 7.04 kcal / mol, completely offsetting the favorable electrostatic interaction. By comparing the binding free energies of the three systems, it was found that DPYLGK has a strong binding ability to SRC.

[0103] Table 8 Binding free energy of the DPYLGK peptide to the SRC protein calculated based on the MMPBSA method a

[0104]

[0105] Note: a. All values are in kcal / mol Note: a. All values are in kcal / mol b. ΔG solv = ΔG nonpolar + ΔG polar

[0106] c. ΔG gas = ΔG vdw + ΔG ele d. ΔG total = ΔG solv + ΔG gas

[0107] Residue free energy decomposition

[0108] Residue-based free energy decomposition analysis can clarify the role of individual residues in binding. To better clarify the binding mechanism between the DPYLGK peptide and the target protein SRC, the residue-based free energy decomposition method was used to calculate the contribution of individual residues to binding, as Figure 17 shown. Seven residues interacted with the DPYLGK peptide stronger than 1 kcal / mol, and these residues were Leu276, Cys280, Val284, Lys298, Gly347, Ser348, and Leu396, respectively. Structurally, residue Leu276 formed an alkyl interaction ( Figure 11 C) with the DPYLGK peptide, and this interaction provided -2.54 kcal / mol of energy for the binding of SRC and the DPYLGK peptide. Residue Val284 formed an alkyl interaction and a π-alkyl interaction ( Figure 11 C) with the DPYLGK peptide, and these two interactions provided -1.14 kcal / mol of energy for the binding of SRC and the DPYLGK peptide. In addition, residue Leu396 formed an alkyl interaction and a π-σ bond interaction with the DPYLGK peptide, and these two interactions provided -1.58 kcal / mol for the binding. Residues Lys298 and Ser348 formed hydrogen bond forces with the polypeptide, providing -1.00 kcal / mol and -1.48 kcal / mol of energy for the binding of SRC and the DPYLGK peptide, respectively. At the same time, residues Cys280 and Gly347 formed van der Waals interactions, providing -1.18 kcal / mol and -1.52 kcal / mol of energy for the protein-polypeptide binding, respectively. In addition, the bond lengths and bond angles of the hydrogen bond forces of Lys298 and Ser348 were analyzed using DS software, as shown in Table 9. The HZ2 of residue Lys298 formed a hydrogen bond with the O on the Asp amino acid of the polypeptide, and its bond length was and the bond angle was 129.69°. The CB of residue Ser348 formed a carbon-hydrogen bond with the O on the Asp amino acid of the polypeptide, and its bond length was and the bond angle was 106.97°.

[0109] The above research results show that based on the above analysis, the interaction of the residues at the active site with the DPYLGK peptide makes a significant contribution to the binding of the target protein SRC and the DPYLGK peptide. Hydrophobic interaction, hydrogen bond interaction, and van der Waals interaction are the main forces driving the DPYLGK-SRC binding. Therefore, optimizing the hydrophobic interaction, hydrogen bond interaction, and van der Waals interaction between the DPYLGK peptide and the target protein SRC plays an important role in the process of designing drugs for the treatment of osteoporosis.

[0110] Table 9 Hydrogen bond interactions between DPYLGK peptide and major residues in SRC

[0111]

[0112] Effect of DPYLGK peptide on bone formation

[0113] Effect of DPYLGK peptide on the proliferation of MC3T3-E1 cells

[0114] In this experiment, the CCK-8 method was used to evaluate the promoting effect of DPYLGK peptide at different concentrations on the proliferation of MC3T3-E1 cells. The results are as Figure 18 shown. Low concentrations of DPYLGK peptide had no significant promoting effect on the proliferation of MC3T3-E1 cells, and high concentrations of DPYLGK peptide had a certain toxic effect on MC3T3-E1 cells, resulting in no promoting effect of high concentrations of DPYLGK peptide on cell proliferation. Therefore, in subsequent experiments, DPYLGK peptide in the concentration range of 60 μg / mL, 100 μg / mL, and 200 μg / mL was used to further study bone formation. Effect of DPYLGK peptide on the cell cycle of MC3T3-E1 cells

[0115] The cell cycle process is considered to be one of the important mechanisms affecting osteocyte activity, and the transformation of cells from G0 / G1 to G2 / M is considered to be a key process regulating cell proliferation. Therefore, cell cycle analysis was performed to detect the effect of DPYLGK peptide on the cell cycle regulation of MC3T3-E1 cells. As Figure 19 (A-D) shown, the addition of DPYLGK peptide promoted the cell cycle of MC3T3-E1 cells. Figure 19E shows the quantitative analysis of different groups. At the G0 / G1 phase, the proportion of cells in the control group was 71.93 ± 1.63%, which decreased to varying degrees with the addition of DPYLGK peptide. The DPYLGK peptide at a concentration of 60 μg / mL decreased the proportion of cells in the G0 / G1 phase to 68.23 ± 0.68%; 100 μg / mL decreased it to 67.33 ± 1.89%. However, the effects of DPYLGK peptides at concentrations of 60 μg / mL and 100 μg / mL on the proportion of cells in the G0 / G1 phase were not statistically significant. The DPYLGK peptide at a concentration of 200 μg / mL significantly decreased (P<0.01) the proportion of cells in the G0 / G1 phase to 62.57 ± 1.73%. The above research results indicate that DPYLGK peptide can promote the transformation of cells from the G0 / G1 phase to the S phase and reduce the cell arrest in the G0 / G1 phase. At the S phase, compared with the control group, there was no obvious trend for DPYLGK peptide. At the G2 / M phase, the proportion of cells in the control group was 12.63 ± 0.66%, and the proportion of cells with 60 μg / mL of DPYLGK peptide was 16.87 ± 0.76%, but there was no statistical significance (P>0.05). However, DPYLGK peptides at concentrations of 100 μg / mL and 200 μg / mL could significantly promote the proportion of cells in the G2 / M phase, and their proportions of cells were 17.97 ± 2.00% and 21.37 ± 2.30% respectively. Compared with the control group, the proportions of cells in the G2 / M phase increased by 5.34% and 8.74% respectively. The above results indicate that DPYLGK peptide promotes the transformation of cells from the G0 / G1 phase to the G2 / M phase, thus promoting the proliferation of cells.

[0116] Effect of DPYLGK Peptide on Alkaline Phosphatase (ALP) of Osteoblasts

[0117] In addition to osteoblast proliferation, cell differentiation is also an important process of bone formation. Alkaline phosphatase is a marker in the early stage of osteogenesis. Therefore, this study measured the activity of alkaline phosphatase to evaluate the effect of DPYLGK peptide on cell differentiation. Figure 20 It shows the effect of DPYLGK peptide at different concentrations on the alkaline phosphatase of osteoblasts. The DPYLGK peptide at 60 μg / mL had no significant effect on osteoblast differentiation, but the DPYLGK peptides at 100 μg / mL and 200 μg / mL had a promoting effect on the activity of alkaline phosphatase. Compared with the untreated group, the activities of alkaline phosphatase of DPYLGK peptides at 100 μg / mL and 200 μg / mL increased by 27.16% and 31.60% respectively. The increase in the activity of alkaline phosphatase indicates that DPYLGK peptide can promote the differentiation of pre-osteoblasts, indicating that DPYLGK peptide has potential osteogenic function.

[0118] Effect of DPYLGK Peptide on Mineralization of Osteoblasts

[0119] Mineralization is the final step of osteoblast differentiation and can visually reflect the degree of bone formation. The complexation reaction between calcium ions and alizarin red S in mineralized nodules shows a dark red color. Therefore, to further investigate the effect of DPYLGK peptide on bone mineralization, alizarin red S staining was used for mineralization assay. Osteogenic differentiation was evaluated by the formation of mineralized calcium nodules. As Figure 21 shown in A, mineralization occurred on the 7th day of induction, and the DPYLGK peptide promoted the formation of osteoblast mineralization in a concentration-dependent manner. Compared with the 7th day, mineralization was more obvious on the 14th day, and obvious mineral nodules also appeared in the control group. Under the action of 200 μg / mL DPYLGK peptide, an obvious mineralization state could be observed. On the 21st day, osteoblast mineralization could be clearly observed in all groups, and the drug treatment group promoted the formation of mineralization in a dose-dependent manner. The results of mineralization quantitative analysis ( Figure 21 B) showed that the DPYLGK peptide could significantly promote the formation of mineral nodules on the 21st day. The mineral nodules of the 200 μg / mL DPYLGK peptide group were 1.98 ± 0.067 times that of the control group. The above research results indicate that the DPYLGK peptide can effectively promote the differentiation of osteoblasts.

[0120] Effect of DPYLGK peptide on bone resorption

[0121] Cytotoxicity test of DPYLGK peptide on RAW264.7 cells

[0122] Osteoclasts are derived from hematopoietic progenitor cells in the bone and are crucial for bone growth and remodeling, maintaining bone structure throughout the life cycle and calcium metabolism during bone homeostasis. However, osteoclasts are terminally differentiated cells with the characteristics of non-subculture and short survival time. The in vitro culture of osteoclasts is mainly the differentiation of mononuclear macrophages into multinucleated giant cells with bone resorption function, and the current mainstream method is to induce the fusion of mouse mononuclear macrophages (RAW264.7) into osteoclasts by RANKL. Before exploring the effect of DPYLGK peptide on osteoclast differentiation, we used the CCK-8 method to detect the cytotoxicity of DPYLGK peptide on RAW264.7 cells to determine the safe dose of DPYLGK peptide. As Figure 22As shown, in the concentration range of 20 - 100 μg / mL and 400 - 600 μg / mL, the DPYLGK peptide had no significant effect on cell viability. However, at 200 μg / mL and 300 μg / mL, it could significantly promote the growth of RAW264.7 cells, and the cell viabilities were 1.39 ± 0.24 and 1.38 ± 0.23 respectively (P < 0.01). The above results indicate that in the concentration range of 0 - 600 μg / mL, the DPYLGK peptide has no toxic or side effects on RAW264.7 cells. Therefore, the concentrations of 20 μg / mL, 100 μg / mL and 200 μg / mL were finally selected to further explore the effect of the DPYLGK peptide on bone resorption.

[0123] Effect of DPYLGK peptide on the TRAP activity of osteoclasts

[0124] RAW264.7 cells were induced to differentiate into osteoclasts by RANKL to establish a bone resorption model. Through this model, it was explored whether the DPYLGK peptide had inhibitory activity on the osteoclast differentiation of RANKL-induced RAW264.7 cells. Normal RAW264.7 cells were induced by 50 ng / mL of RANKL. Generally, after 3 days of induction, osteoclasts were generated, and after 5 days of induction, a large number of osteoclasts were produced. Tartrate-resistant acid phosphatase (TRAP) is a marker for osteoclast differentiation and activation. Osteoclasts stained wine red or light purple can be visualized by TRAP staining to observe the generation of osteoclasts. As Figure 23 shown in A, large multinucleated TRAP-positive osteoclasts appeared in the model group with only RANKL added. Compared with the model group, at the concentrations of 20 μg / mL, 100 μg / mL and 200 μg / mL of the DPYLGK peptide, it played an inhibitory role in the generation of RANKL-induced osteoclasts and showed a dose-dependent manner. In addition, we also obtained the same conclusion by counting the TRAP-positive osteoclasts. As Figure 23 shown in B, the number of TRAP-positive osteoclasts in the model group, 20 μg / mL, 100 μg / mL and 200 μg / mL DPYLGK peptide groups was significantly higher than that in the uninduced group (P < 0.05). The number of TRAP-positive osteoclasts was 9.14 ± 0.22 times, 7.95 ± 0.23 times, 6.05 ± 0.17 times and 3.55 ± 0.11 times that of the uninduced group respectively. Compared with the model group, the drug groups at the concentrations of 20 μg / mL, 100 μg / mL and 200 μg / mL significantly inhibited the differentiation of osteoclasts (P < 0.05), and the inhibition rates were 12.94 ± 2.54%, 33.83 ± 1.86% and 61.19 ± 1.22% respectively. The above results indicate that the DPYLGK peptide effectively inhibits the number and size of RANKL-induced osteoclasts in a dose-dependent manner.

[0125] Effect of DPYLGK Peptide on F-actin Ring of Osteoclasts

[0126] The bone resorption function of osteoclasts depends on the dynamic regulation of the actin cytoskeleton, and the actin ring structure is a cytoskeletal feature of osteoclasts. In mature osteoclasts, F-actin arranges in a ring structure at the periphery of osteoclasts. This ring structure secretes various matrix metalloproteinases and acids, creating an environment for bone degradation. Therefore, we detected the F-actin ring structure of mature osteoclasts affected by different concentrations of DPYLGK peptide (20 μg / mL, 100 μg / mL, 200 μg / mL). As Figure 24 shown, no F-actin ring appeared in the control group, but after induction with 50 ng / mL RANKL, a large number of osteoclasts with large F-actin ring structures appeared, indicating the successful establishment of our model. Compared with the model group, the 20 μg / mL DPYLGK peptide relatively reduced the number of F-actin rings. With the continuous increase in the concentration of DPYLGK peptide, the 100 μg / mL DPYLGK peptide significantly reduced the number and size of F-actin rings. When the drug concentration reached 200 μg / mL, almost no F-actin ring structure was visible. The above research results show that with the increase in the concentration of DPYLGK peptide, the number of intact actin rings gradually decreases, and DPYLGK peptide significantly inhibits osteoclast differentiation in a dose-dependent manner, thereby inhibiting bone resorption.

[0127] Q-PCR Analysis

[0128] The above network pharmacology and molecular docking studies showed that the target protein SRC is a potential target for the regulation of bone resorption and bone formation by Moringa leaf protein peptide. Therefore, Q-PCR was used to explore the expression of SRC gene in osteoclasts and osteoblasts. The amplification experimental conditions of quantitative PCR are shown in Tables 10 - 12.

[0129] Table 10 Configuration of PCR Reaction System

[0130]

[0131] Table 11 Amplification Step Program

[0132]

[0133] Table 12 Primer Information

[0134]

[0135] Figure 25A shows the expression of SRC gene in osteoblasts. As can be seen from the figure, compared with the control group, DPYLGK can significantly reduce the expression of SRC gene to promote the generation of osteoblasts, and the gene expression level decreased by 23.04 ± 2.13%. Figure 25 B shows the expression of SRC gene in osteoclasts. Compared with the model group, the addition of DPYLGK significantly inhibited the expression of SRC gene, and the gene expression level decreased by 32.38 ± 8.63%. The research shows that DPYLGK promotes bone formation and inhibits bone resorption by inhibiting the SRC gene.

[0136] In summary, the present invention clarifies the pathway and target of Moringa oleifera leaf protein peptide. Moringa oleifera leaf protein peptide mainly regulates bone formation and bone resorption through JUN, MAPK1, SRC and STAT3 targets. Among them, DPYLGK peptide may be a potential functional component to promote bone formation and inhibit bone resorption, and the target SRC may be the optimal target for Moringa oleifera leaf protein peptide to target and regulate osteoporosis. The kinetic relationship between DPYLGK peptide and SRC target protein is clarified. Compared with the free state of SRC, DPYLGK peptide reduces the stability of DPYLGK - SRC complex by affecting the non - critical residues of target protein SRC. The residues Leu276, Cys280, Val284, Lys298, Gly347, Ser348 and Leu396 of target protein SRC make significant contributions to the binding with DPYLGK peptide, and the binding of these key residues with DPYLGK peptide involves hydrophobic interaction, hydrogen bond interaction and van der Waals interaction. Cell experiments clarify the effects of DPYLGK peptide on bone formation and bone resorption. DPYLGK peptide promotes bone formation and inhibits bone resorption in a dose - dependent manner. During bone resorption, DPYLGK peptide inhibits bone resorption by inhibiting the expression of SRC gene; during bone formation, DPYLGK peptide promotes bone formation by inhibiting the expression of SRC gene and promoting the expression of MAPK1, JUN and STAT3 genes.

[0137] The DPYLGK peptide screened from Moringa oleifera leaf protein peptide has the ability to significantly promote bone formation and inhibit bone resorption. It is a potential functional food raw material for preventing osteoporosis and may also be a potential inhibitor targeting SRC.

[0138] The above - mentioned is only the preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution and inventive concept of the present invention, making equivalent substitutions or changes should be covered within the protection scope of the present invention.

Claims

1. A screening method for regulating bone metabolism peptides, characterized in that, The method comprises the following steps: Input multiple protein peptides into the PepSMI database to search for corresponding SMILES codes, input SMILES codes into the Swiss TargetPrediction database and the Super-PRED database, obtain human-related component targets in the Swiss Target Prediction database, obtain component targets in the Super-PRED database, integrate and remove duplicate component targets from the two databases, and obtain the total component targets; Disease targets were collected through the CTD database and Genecards database. Disease targets were obtained by entering "osteoporosis" in the database. Disease-related targets were obtained in the CTD database and Genecards database respectively. The total disease targets were obtained by removing overlaps and merging. The component targets of protein peptides and osteoporosis disease targets were entered into the VENNY online website to obtain intersection targets. In order to identify the key targets of protein peptides and match related proteins, the intersection targets were imported into the STRING online website, with a comprehensive score of ≥0.9, and the points without interaction relationships were eliminated to obtain the protein-protein relationship PPI diagram. After analysis, the core target protein information of Moringa leaf protein peptides was obtained; the topological isomerism of the PPI network was analyzed using Cytoscape3.9.1 software, and the targets above the median values ​​of DegreeCentrality, BetweennessCentrality and ClosenessCentrality were selected as key targets, and the protein network was visualized; The key targets of Moringa leaf protein peptides were imported into Metascape, and "Homo sapiens" was selected as the species for analysis. The four parts of GO biological process, GO molecular function, GO cellular component and KEGG were selected for enrichment analysis. By counting the most enriched targets in the top 10 signal pathways analyzed by KEGG and the Degree values ​​in the PPI network analysis process, 3-5 targets were selected as receptors in molecular docking, and a variety of protein peptides were used as ligands to obtain the peptides with the best binding energy with the representative target proteins, which were peptides for regulating bone metabolism.

2. The screening method for regulating bone metabolism peptides according to claim 1, characterized in that, The CytoNCA plug-in of Cytoscape software was used to analyze the medians of Betweenness Centrality, Closeness Centrality and DegreeCentrality. The median values ​​of Betweenness Centrality and Closeness Centrality were used as card values ​​to screen preliminary targets. Then, DegreeCentrality was used as the final indicator to screen the top 20-30 targets as key targets.

3. The screening method for regulating bone metabolism peptides according to claim 1, characterized in that, To explore the internal influence between representative target proteins and bone metabolism-regulating peptides, molecular dynamics analysis was performed on their conjugates, and the root mean square deviation and root mean square fluctuation of the molecular dynamics simulation were measured; the data of Cα atoms at their average positions were analyzed to draw a mutual matrix diagram; an image describing protein motion was obtained by projecting the MD trajectory onto the first principal component PC1 using VMD software; the conformational space of the target protein was analyzed; and the free energy was calculated.

4. The screening method for regulating bone metabolism peptides according to claim 1, characterized in that, Osteoblast and osteoclast models were constructed to verify the ability of the bone metabolism-regulating peptide to promote bone formation and inhibit bone resorption: the effect of the peptide on bone resorption was verified by TRAP staining, F-actin staining, and Q-PCR; the effect of the peptide on bone formation was verified by cell cycle, ALP assay, alizarin S staining, and Q-PCR.

5. The screening method for regulating bone metabolism peptides according to claim 1, characterized in that, The protein peptide is derived from a natural product peptide; the molecular weight of the natural product peptide is less than 1 kDa.

6. The screening method for regulating bone metabolism peptides according to claim 5, characterized in that, The natural product peptide is selected from moringa leaf protein peptides.

7. The screening method for regulating bone metabolism peptides according to claim 6, characterized in that, By statistically analyzing the top 10 signaling pathways enriched with the most targets in the KEGG analysis and the Degree Centrality values in the PPI network analysis, four target proteins, SRC, MAPK1, STAT3, and JUN, were selected as receptors in the molecular docking, and moringa leaf protein peptide was used as the ligand.

8. A regulating bone metabolism peptide, characterized in that, The moringa leaf protein peptide is obtained by the screening method described in any one of claims 1-7 to obtain a bone metabolism-regulating peptide, and its amino acid sequence is DPYLGK.

9. Use of the regulating bone metabolism peptide according to claim 8 in the research and development of functional foods or drugs for preventing osteoporosis.

Images