Network pharmacology and molecular docking prediction method of traditional Chinese medicine composition

Abstract

The invention relates to the technical field of traditional Chinese medicine research, in particular to a chemical component analysis method and an anti-inflammatory action mechanism research method of Yuhuang cream. According to the application, based on an ultra-high performance liquid chromatography-quadrupole-time-of-flight mass spectrometry (UPLC-Q-TOF-MS), network pharmacology and molecular docking, the material basis and the action mechanism of the Yuhuang paste for improving inflammation are explored. According to the method, a related network diagram for playing an anti-inflammatory effect of the Yuhuang cream is constructed for the first time, core targets of ten related diseases such as IL6, TNF, IL1BSTAT3, AKT1, IL10, EGFR, TLR4, TP53 and CXCL8 which can act on verification are confirmed, the anti-inflammatory function and pharmacological action mechanism of the Yuhuang cream are researched based on the targets, and the method is an effective alternative scheme for treating or relieving inflammation by utilizing a modern traditional Chinese medicine technology and has a wide application prospect. The in-depth research of the traditional Chinese medicine compound is promoted and the development of clinical indications is expanded.

Description

Technical Field

[0001] The invention relates to the technical field of traditional Chinese medicine research, and in particular to a network pharmacology and molecular docking prediction method for a traditional Chinese medicine composition. Background Art

[0002] According to traditional Chinese medicine, ocular inflammation is mostly caused by exogenous wind-heat evil, obstruction of meridian qi, stagnation of fire, and heat toxicity blocking the eyelid skin meridians. It is mostly characterized by redness, swelling, pain, etc., and inflammation mainly caused by the reaction of the vascular system. Conventional treatment of ocular inflammation in Western medicine generally uses local antibiotic eye drops, eye ointment, hot compress, physical therapy, and oral antibiotics, and even more severe cases require surgical drainage, but the effect is not ideal. Traditional Chinese medicine has unique advantages in the prevention and treatment of ocular inflammation. Clinically, it restores the normal function of the local eye by dispelling wind and heat, detoxifying, reducing swelling and relieving pain. At present, the commonly used traditional Chinese medicine prevention and treatment methods in clinical practice mainly include traditional therapies such as Chinese medicine prescriptions and acupuncture. The selection of Chinese medicines with antibacterial, antiviral, and anti-inflammatory effects has positive significance for the treatment of ocular inflammation. Tianjin Eye Hospital, with many years of experience in the use of traditional Chinese medicine, prepared the Chinese medicine agreement prescription "Xi No. 1" from clinical experience.

[0003] Muhuang ointment is a traditional Chinese medicine ointment prepared based on the traditional Chinese medicine prescription "Xi Yi Hao" of Tianjin Eye Hospital. It is an in-hospital preparation that has been included in Tianjin's medical insurance. The prescription is mainly composed of rhubarb, coptis, scutellaria and phellodendron, and is used to treat blepharitis, tarsalgia and various external eye inflammations caused by damp-heat syndrome and excess heat syndrome. In Muhuang ointment, each medicine has different effects. Among them, rhubarb is bitter and cold, enters the stomach, large intestine, liver, and spleen meridians, and has the functions of attacking stagnation, clearing damp heat, purging fire, cooling blood, removing blood stasis, and detoxifying. Therefore, "Huangdi Neijing" says that "rhubarb, bitter and cold in taste, mainly purging blood, breaking up masses, removing blood stasis, cold and heat, breaking up knots... and removing dampness and heat." Modern clinical reports show that rhubarb can be used to treat gastritis and acute bacillary dysentery, enteritis, acute hepatitis, tonsillitis, mumps, mastitis, etc. Coptis chinensis is bitter and cold, and enters the liver, stomach, and large intestine meridians. It has the effects of clearing heat and drying dampness, purging fire and detoxifying. Coptis chinensis juice can be used externally to treat red eyes due to excessive fire. Scutellaria baicalensis is cold in nature and bitter in taste. It enters the lung, gallbladder, spleen, large intestine, and small intestine meridians. It has the effects of clearing heat and drying dampness, purging fire and detoxifying, stopping bleeding, and calming the fetus. Modern studies have shown that it has pharmacological effects such as anti-inflammatory and anti-allergic, antibacterial, antifungal, and antiviral effects. Phellodendron chinense is bitter and cold, and enters the kidney and bladder meridians. It has the effects of clearing heat and drying dampness, purging fire and removing steam, and detoxifying and curing sores. The "Inner Canon of Medicine" says, "Stir-fry with honey soup, take it to stay in the diaphragm but not go down suddenly, and treat the symptoms of five heart upset heat, eye pain, and mouth sores." The above medicines, according to the basic theory of Chinese medicine prescriptions, are cleverly matched and strictly combined to play the role of clearing heat and detoxifying, reducing swelling and removing dampness, thereby achieving the purpose of healing.

[0004] The present invention takes Muhuang paste as a research object, takes network pharmacology and molecular docking mechanism as entry points, explores the effective compound components of Muhuang paste that exert anti-inflammatory effects and their possible action mechanisms from a molecular level, and analyzes the effective components of Muhuang paste based on high performance liquid chromatography tandem mass spectrometry (UPLC-MS / MS); retrieves the action targets of corresponding compounds in Pubchem database and Swiss target database, retrieves disease targets in GeneCard data and OMIM database, and uniformly converts the protein names of the targets into gene names to obtain the common gene action sites of Muhuang paste and diseases; establishes a network diagram based on active ingredients and intersection target data; constructs a protein-protein interaction PPI network, screens core genes, and performs GO function and KEGG pathway enrichment analysis; takes the top 6 active ingredients with the highest degree values ​​in the network diagram and the top 6 target genes with the highest degree values ​​in the PPI network for molecular docking verification; Muhuang paste may regulate TNF signaling pathway, Toll-like receptor signaling pathway, NF-kappa B signaling pathway, IL-17 signaling pathway, and PI3K-Akt signaling pathway expression, improving the body's inflammation; it provides a theoretical basis for the anti-inflammatory effect of Muhuang ointment, and is of great significance for explaining the clinical application of Muhuang ointment. Summary of the invention

[0005] The purpose of the present invention is to provide a network pharmacology and molecular docking prediction method for a traditional Chinese medicine composition.

[0006] Another purpose of the present invention is to predict the potential quality markers of the traditional Chinese medicine composition Muhuang Gao by combining fingerprint and network pharmacology, and to predict the relevant targets of Muhuang Gao in improving inflammation by using network pharmacology. Finally, the network pharmacology prediction results are verified by molecular docking.

[0007] In order to achieve the above-mentioned object of the invention, the present invention provides the following technical solutions:

[0008] The network pharmacology and molecular docking prediction method of a traditional Chinese medicine composition of the present invention uses the network pharmacology and molecular docking methods to construct a network diagram and analyze the multiple components, multiple targets and multiple pathways of Muhuang Paste, including the following steps:

[0009] S1: Analysis of active ingredients in traditional Chinese medicine compositions based on high performance liquid chromatography tandem mass spectrometry:

[0010] The samples were tested by classical high performance liquid chromatography tandem mass spectrometry, and mass spectrometry information was collected.

[0011] ThermomzValut local database and ThermomzCloud online database identify and analyze active ingredients based on secondary mass spectrometry information;

[0012] S2: Obtaining the gene action sites of the Chinese medicine composition:

[0013] Searching the database for the action targets of the corresponding compounds of the active ingredients of the traditional Chinese medicine composition, and uniformly converting the protein names of the targets into gene names to obtain the action sites of the component genes;

[0014] S3: Obtain the gene action sites of the target disease:

[0015] Search in the database and convert the protein names of the obtained targets into gene names to obtain the target disease targets;

[0016] S4: Construction of network graph:

[0017] The targets obtained from S2 and S3 were plotted with the R language ggvenn package to draw a Venn diagram of intersection targets, potential effective targets were obtained, and a Venn diagram was drawn; the active ingredients and intersection target data were imported into the visualization software to construct a visualization network topology diagram, and the betweenness centrality, closeness centrality and degree of each node were analyzed; the degree value was used to evaluate the importance of each node in the network, and the top compounds were obtained through topological analysis and used as active ingredients with important pharmacological effects;

[0018] S5: Construction of protein-protein interaction network (PPI) and screening of core genes:

[0019] The intersection targets were imported into the STRING11.0 database for PPI network analysis, with the species limited to "homosapiens" and the minimum interaction score set to highest confidence ≥ 0.9; the tsv table was downloaded and imported into Cytoscape for visualization and topological analysis to obtain the degree value of each target; the targets with the highest degree values ​​were taken as core targets;

[0020] S6: Enrichment analysis of GO functions and KEGG pathways:

[0021] The intersection targets were subjected to Gene Ontology (GO) functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment pathway analysis using the clusterProfiler package in R language;

[0022] S7: Molecular docking:

[0023] Active ingredients with high S4 network degree values ​​and target genes with high PPI network degree values ​​were selected for molecular docking verification.

[0024] Preferably, the detection conditions of the high performance liquid chromatography tandem mass spectrometry in S1 are:

[0025] Instrument analysis platform: Shimadzu Nexera UPLC LC-20A liquid phase system, the chromatographic column is Waters Endeavorsil C18-A chromatographic column (2.1mm×100mm, 1.8μm), the column temperature is 30°C; the flow rate is 0.4mL / min; mobile phase: A is 0.1% formic acid aqueous solution, B is pure acetonitrile, gradient elution is carried out (0 - 9.38min, 90% - 70% A; 9.38 - 14.59min, 70% - 40% A; 14.59 - 16.67min, 40% - 10% A; 16.67 - 23.00min, 10% - 20% A; 23.00 - 30.00min, 20% - 90% A); the injection volume is 2μL, and the temperature of the autosampler is 4°C;

[0026] Mass spectrometry conditions: Electrospray ionization source ESI, positive and negative ion scanning; capillary voltage 2.5kV, cone voltage 40V; ion source temperature 100°C, desolvation gas temperature 350°C; cone gas flow rate 50L / Hr, desolvation gas flow rate 800L / Hr; mass scanning range 100 - 1000, signal acquisition frequency 10s.

[0027] Preferably, the database in S2 is Pubchem database and Swisstargetprediction database.

[0028] Preferably, in S3, disease targets are retrieved in GeneCard data and OMIM database with "inflammation" as the keyword. The screening condition for the GeneCard database is Relevance score ≥ 2 times the median (4.3), and a total of 1001 related genes are screened out.

[0029] Preferably, the active ingredients with important pharmacological effects in S4 are quercetin, Proanthocyanidins, Heriguard, Rheic acid, Baicalin and palmidin B.

[0030] Preferably, the core targets in S5 are IL6, TNF, IL1B, STAT3, AKT1 and IL10.

[0031] Preferably, the GO functions in S6 include biological process BP, cellular component CC, and molecular function MF.

[0032] Preferably, in S7, the molecular docking is to download the 3D structure of the key target protein from the PDB database, download the 3D.sdf file of the main active ingredient from the Pubchem platform, then use the PyMOL software to dehydrate and hydrogenate the key target protein, perform molecular docking through AutoDockTools 1.5.6 and AutoDockVina software, record the binding energy, and finally use Pymol 2.5.0 to visualize the compound with a smaller molecular docking energy and a stable conformation.

[0033] Preferably, the traditional Chinese medicine composition is composed of 1-3 parts of Coptis chinensis, 1-3 parts of Scutellaria baicalensis, 1-3 parts of Phellodendron amurense, and 1-4 parts of Rheum officinale.

[0034] The beneficial effects of the present invention are as follows:

[0035] 1. The present invention uses network pharmacology technology and molecular methods to analyze the effective active ingredients, potential targets and pathways of Muhuang Ointment in improving inflammation, analyzes its "multi-component - multi-target - multi-pathway" mechanism of action, uses the Pubchem database and Swiss target database to obtain active ingredients and their potential action targets, obtains the targets of diabetes complicated with urinary tract infection diseases based on GeneCard data and OMIM database, and uniformly converts the protein names of the targets into gene names, and establishes a "Muhuang Ointment - ingredient - target" network based on the active ingredient and intersection target data; constructs a protein - protein interaction PPI network, screens core genes, and performs GO function and KEGG pathway enrichment analysis; Results: The active ingredients closely related to the target genes mainly include Quercetin, Proanthocyanidins, Heriguard, etc.; the key target genes mainly include IL6, TNF, IL1B, STAT3, etc.; the binding energy between the main active ingredients and their target proteins is less than -5 kJ / mol, indicating that the two can bind stably. Therefore, the method of the present invention provides a theoretical basis for the anti-inflammatory effect of Muhuang Ointment and is of great significance for explaining the clinical application of Muhuang Ointment.

[0036] 2. The method of the present invention is established for the first time, and the possible mechanism of Muhuang Ointment in exerting anti-inflammatory effects is deeply analyzed from multiple angles based on network pharmacology and molecular docking research; Results: Muhuang Ointment can intervene in inflammation by regulating TNF signaling pathway, Toll-like receptor signaling pathway, NF-kappa B signaling pathway, IL-17 signaling pathway, PI3K-Akt signaling pathway, etc.; Overall, Muhuang Ointment regulates the body's immune function through different pathways, accelerates the clearance of inflammation, and reduces tissue damage. Description of the Drawings

[0037] To more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the accompanying drawings required for the description of the embodiments. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other accompanying drawings can also be obtained based on these drawings without exceeding the scope of protection required by the present invention.

[0038] Figure 1 It is the fingerprint spectrum of M Huang Gao;

[0039] Figure 2 It is the Venn diagram of M Huang Gao - inflammation; the left circle is M Huang Gao, the right circle is inflammation, and the middle is the intersection;

[0040] Figure 3 It is the PPI network of M Huang Gao - inflammation;

[0041] Figure 4 It is the GO analysis of the intersection targets for M Huang Gao to improve inflammation;

[0042] Figure 5 It is the KEGG analysis of the intersection targets for M Huang Gao to improve inflammation;

[0043] Figure 6 It is the drug - ingredient - target network diagram for M Huang Gao to improve inflammation;

[0044] Figure 7 It is the heat map of 6 compounds with better activities and 6 core target protein molecules;

[0045] Figure 8 It is the visualization diagram of the 9 optimal docking structures in the docking results. Specific embodiments

[0046] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of them. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0047] Unless otherwise stated, all technical and scientific terms and abbreviations used herein have the meanings commonly understood by those of ordinary skill in the technical field of the present invention or the field where the term is applied. Although any methods, conditions, substances, or materials similar to or equivalent to those disclosed herein can be used in the implementation of the present invention, the preferred methods, conditions, substances, or materials are described herein.

[0048] The present invention is intended to encompass all alternatives, variations and equivalents that may be included in the present invention as defined by the claims. Those skilled in the art will recognize many methods and materials similar or equivalent to those described herein that can be used in the practice of the present invention. The present invention is in no way limited to the methods and materials described.

[0049] Please also read Figures 1-8 This example is used to illustrate a network pharmacology and molecular docking prediction method for a traditional Chinese medicine composition, comprising the following steps:

[0050] S1: Analysis of active ingredients in traditional Chinese medicine compositions based on high performance liquid chromatography tandem mass spectrometry:

[0051] The samples were tested by classical high performance liquid chromatography tandem mass spectrometry, and mass spectrometry information was collected.

[0052] ThermomzValut local database and ThermomzCloud online database identify and analyze active ingredients based on secondary mass spectrometry information;

[0053] S2: Obtaining the gene action sites of the Chinese medicine composition:

[0054] Searching the database for the action targets of the corresponding compounds of the active ingredients of the traditional Chinese medicine composition, and uniformly converting the protein names of the targets into gene names to obtain the action sites of the component genes;

[0055] S3: Obtain the gene action sites of the target disease:

[0056] Search in the database and convert the protein names of the obtained targets into gene names to obtain the target disease targets;

[0057] S4: Construction of network graph:

[0058] The targets obtained from S2 and S3 were plotted with the R language ggvenn package to draw a Venn diagram of intersection targets, potential effective targets were obtained, and a Venn diagram was drawn; the active ingredients and intersection target data were imported into the visualization software to construct a visualization network topology diagram, and the betweenness centrality, closeness centrality and degree of each node were analyzed; the degree value was used to evaluate the importance of each node in the network, and the top compounds were obtained through topological analysis and used as active ingredients with important pharmacological effects;

[0059] S5: Construction of protein-protein interaction network (PPI) and screening of core genes:

[0060] The intersection targets were imported into the STRING 11.0 database for PPI network analysis. The species was restricted to "homo sapiens", and the minimum interaction score was set to highest confidence ≥ 0.9. The tsv table was downloaded and imported into Cytoscape for visualization and topological analysis to obtain the degree values of each target. The targets with the top degree values were used as the core targets.

[0061] S6: Enrichment analysis of GO functions and KEGG pathways:

[0062] The intersection targets were subjected to gene ontology (GO) function enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment pathway analysis using the R language clusterProfiler package.

[0063] S7: Molecular docking:

[0064] The active ingredients with the top network degree values in S4 and the target genes with the top PPI network degree values were used for molecular docking verification.

[0065] As a preferred embodiment of this application, S1 specifically is:

[0066] Four herbs, namely Rheum palmatum, Coptis chinensis, Phellodendron amurense, and Scutellaria baicalensis, were extracted with water twice. The decoctions were combined, filtered, and concentrated. Take 2 g of the extract of M Huang Gao, add 20 ml of ethyl acetate - methanol (3:1), sonicate, filter, evaporate the filtrate to dryness, and dissolve the residue in methanol for detection.

[0067] Chromatographic conditions for detecting the components of M Huang Gao by UPLC-Q-Tof-MS method: Shimadzu Nexera UPLC LC-20A liquid phase system, the chromatographic column was Waters Endeavorsil C18-A chromatographic column (2.1 mm × 100 mm, 1.8 μm), the column temperature was 30°C; the flow rate was 0.4 mL / min; the mobile phase: A was 0.1% formic acid aqueous solution, B was pure acetonitrile, and gradient elution was carried out (0 - 9.38 min, 90% - 70% A; 9.38 - 14.59 min, 70% - 40% A; 14.59 - 16.67 min, 40% - 10% A; 16.67 - 23.00 min, 10% - 20% A; 23.00 - 30.00 min, 20% - 90% A); the injection volume was 2 μL, and the temperature of the autosampler was 4°C; mass spectrometry conditions: electrospray ionization source ESI, positive and negative ion scanning; capillary voltage 2.5 kV, cone voltage 40 V; ion source temperature 100°C, desolvation gas temperature 350°C; cone gas flow rate 50 L / Hr, desolvation gas flow rate 800 L / Hr; mass scanning range 100 - 1000, signal acquisition frequency 10 s, to obtain Figure 1 .

[0068] The raw data obtained by the above method is subjected to baseline filtering, peak identification, integration, retention time correction, peak alignment, and normalization using processing software. The identification of compounds is based on exact mass numbers, secondary fragments, and isotope distributions, as well as the qualitative scores of secondary fragments, which are matched with the theoretical fragments in the database to obtain the composition information of Mhuang Ointment (Table 1).

[0069] Table 1 Composition information of Mhuang Ointment

[0070]

[0071]

[0072]

[0073]

[0074] As a preferred embodiment of the present application, S2 is specifically as follows:

[0075] Retrieve the action targets of the corresponding compounds of the active ingredients of the traditional Chinese medicine composition in the Pubchem database and the Swisstargetprediction database, and uniformly convert the protein names of the targets into gene names to obtain 5235 component target sites of the composition;

[0076] As a preferred embodiment of the present application, S3 is specifically as follows:

[0077] Retrieve disease targets in the GeneCard data and OMIM database using "inflammation" as the keyword. The screening condition for the GeneCard database is Relevance score ≥ 2 times the median (4.3), and uniformly convert the protein names of the obtained targets into gene names to obtain 1001 target disease targets;

[0078] As a preferred embodiment of the present application, S4 is specifically as follows:

[0079] Use the ggvenn package of R language to draw a Venn diagram of the intersection targets for the targets obtained in S2 and the targets obtained in S3 to obtain potential effective targets, and draw a Venn diagram ( Figure 2) Import the active ingredients and intersection target data into the Cytoscape software to construct a visualized network topology relationship graph, and analyze the betweenness centrality, closeness centrality, and degree value of each node; among them, the degree value is used to evaluate the importance of each node in the network, and quercetin, Proanthocyanidins, Heriguard, Rheic acid, Baicalin, and palmidinB are obtained as active ingredients with important pharmacological effects through topological analysis (Table 2, Figure 3 );

[0080] Table 2 Information of top 10 compounds

[0081]

[0082] As a preferred embodiment of the present application, the S5 is specifically:

[0083] Import the intersection targets into the STRING 11.0 database for PPI network analysis, limit the species to "homo sapiens", and set the minimum interaction score to highest confidence≥0.9; download the tsv table and import it into Cytoscape for visualization and topological analysis to obtain the degree values of each target; take IL6, TNF, IL1B, STAT3, AKT1, and IL10 with the top degree values as core targets ( Figure 4 );

[0084] As a preferred embodiment of the present application, the S6 is specifically:

[0085] Use the R language clusterProfiler package to perform gene ontology (GO) function enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment pathway analysis on the intersection targets. For GO analysis, in the biological process (BP) analysis, the targets are mainly involved in processes such as positive regulation of gene expression and signal transduction of inflammatory responses; in the cellular component (CC) analysis, the targets are mainly distributed in the cytoplasm and nucleoplasm and participate in the formation of receptor complexes; in the molecular function (MF) analysis, the targets are mainly associated with ATP binding and have functions such as kinase activity, see Figure 5 . For KEGG pathway enrichment analysis, the targets are mainly concentrated in the TNF signaling pathway, Toll-like receptor signaling pathway, NF-kappaB signaling pathway, IL-17 signaling pathway, and PI3K-Akt signaling pathway, see Figure 6 .

[0086] As a preferred embodiment of the present application, the S7 is specifically:

[0087] Select the active ingredients with the top S4 network degree values and the target genes with the top PPI network degree values for molecular docking verification. Download the 3D structures of key target proteins from the PDB database, download the 3D.sdf files of the main active ingredients from the Pubchem platform, then use the PyMOL software to perform dehydration and hydrogenation operations on the key target proteins, conduct molecular docking through the AutoDockTools1.5.6 and AutoDockVina software, record the binding energy (Table 3), and finally use Pymol2.5.0 to visualize the compounds with smaller molecular docking energy and stable conformations( Figure 7 ), and draw a visual score heat map( Figure 8 ).

[0088] Table 3 Molecular docking binding energy

[0089]

Claims

1. A network pharmacology and molecular docking prediction method for a traditional Chinese medicine composition, characterized in that: The steps include: S1: Analysis of active ingredients in traditional Chinese medicine compositions based on high performance liquid chromatography tandem mass spectrometry: The samples were tested using classical high performance liquid chromatography tandem mass spectrometry to collect mass spectrometry information, and the active ingredients were identified and analyzed based on the secondary mass spectrometry information using the ThermomzValut local database and the ThermomzCloud online database; S2: Obtaining the gene action sites of the Chinese medicine composition: Searching the database for the action targets of the corresponding compounds of the active ingredients of the traditional Chinese medicine composition, and uniformly converting the protein names of the targets into gene names to obtain the action sites of the component genes; S3: Obtain the gene action sites of the target disease: Search in the database and convert the protein names of the obtained targets into gene names to obtain the target disease targets; S4: Construction of network graph: The targets obtained from S2 and S3 were plotted with the R language ggvenn package to draw a Venn diagram of intersection targets, potential effective targets were obtained, and a Venn diagram was drawn; the active ingredients and intersection target data were imported into the visualization software to construct a visualization network topology diagram, and the betweenness centrality, closeness centrality and degree of each node were analyzed; the degree value was used to evaluate the importance of each node in the network, and the top compounds were obtained through topological analysis and used as active ingredients with important pharmacological effects; S5: Construction of protein-protein interaction network (PPI) and screening of core genes: The intersection targets were imported into the STRING11.0 database for PPI network analysis, with the species limited to "homo sapiens" and the minimum interaction score set to highest confidence ≥ 0.9; the tsv table was downloaded and imported into Cytoscape for visualization and topological analysis to obtain the degree value of each target; the targets with the highest degree values ​​were taken as core targets; S6: Enrichment analysis of GO functions and KEGG pathways: The intersection targets were subjected to Gene Ontology (GO) functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment pathway analysis using the clusterProfiler package in R language; S7: Molecular docking: Active ingredients with high S4 network degree values ​​and target genes with high PPI network degree values ​​were selected for molecular docking verification.

2. The network pharmacology and molecular docking prediction method for a traditional Chinese medicine composition according to claim 1, characterized in that: The detection conditions of the high performance liquid chromatography tandem mass spectrometry in step S1 are: Instrumental analysis platform: Shimadzu Nexera UPLC LC-20A liquid phase system, chromatographic column is Waters Endeavorsil C18-A chromatographic column (2.1 mm × 100 mm, 1.8 μm), column temperature is 30 ° C; flow rate is 0.4 mL / min; mobile phase: A is 0.1% formic acid aqueous solution, B is pure acetonitrile, gradient elution is performed (0-9.38 min, 90%-70% A; 9.38-14.59 min, 70%-40% A; 14.59-16.67 min, 40%-10% A; 16.67-23.00 min, 10%-20% A; 23.00-30.00 min, 20%-90% A); injection volume is 2 μL, automatic sampler temperature is 4 ° C; Mass spectrometry conditions: electrospray ion source ESI, positive and negative ion scanning; capillary voltage 2.5 kV, cone voltage 40 V; ion source temperature 100 ° C, desolvation gas temperature 350 ° C; cone gas flow rate 50 L / Hr, desolvation gas flow rate 800 L / Hr; mass scanning range 100-1000, signal acquisition frequency 10s.

3. The network pharmacology and molecular docking prediction method for a traditional Chinese medicine composition according to claim 1, characterized in that: The databases in step S2 are Pubchem database and Swisstargetprediction database.

4. The network pharmacology and molecular docking prediction method for a traditional Chinese medicine composition according to claim 1, characterized in that: In step S3, the disease targets are searched in the GeneCard data and OMIM database using "inflammation" as the keyword, wherein the screening condition of the GeneCard database is Relevance score ≥ 2 times the median (4.3).

5. The network pharmacology and molecular docking prediction method for a traditional Chinese medicine composition according to claim 1, characterized in that: The active ingredients with important pharmacological effects in step S4 are quercetin, proanthocyanidins, heriguard, rheic acid, baicalin and palmidin B.

6. The network pharmacology and molecular docking prediction method for a traditional Chinese medicine composition according to claim 1, characterized in that: The core targets in step S5 are IL6, TNF, IL1B, STAT3, AKT1 and IL10.

7. The network pharmacology and molecular docking prediction method for a traditional Chinese medicine composition according to claim 1, characterized in that: The GO functions in step S6 include biological process BP, cell component CC, and molecular function MF.

8. The network pharmacology and molecular docking prediction method for a traditional Chinese medicine composition according to claim 1, characterized in that: The molecular docking described in step S7 is to download the 3D structure of the key target protein from the PDB database, download the main active ingredient 3D.sdf file from the Pubchem platform, and then use PyMOL software to dehydrate and hydrogenate the key target protein, perform molecular docking through AutoDockTools1.5.6 and AutoDockVina software, record the binding energy, and finally use Pymol2.5.0 to visualize the compounds with small molecular docking energy and stable conformation.

9. The network pharmacology and molecular docking prediction method for a traditional Chinese medicine composition according to claim 1, characterized in that: The traditional Chinese medicine composition consists of 1-3 parts of coptis root, 1-3 parts of scutellaria root, 1-3 parts of phellodendron amurense and 1-4 parts of rhubarb.

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