Method for analyzing sleep promoting effect of radix curcumae based on network pharmacology and molecular docking technology

Abstract

The invention discloses a method for analyzing the sleep promoting effect of radix curcumae based on network pharmacology and a molecular docking technology, which comprises the following steps: constructing a radix curcumae-active ingredient-target network diagram and a radix curcumae ingredient-insomnia target PPI network by discussing the network pharmacology and the molecular docking technology, selecting 14 active ingredients, 6 core targets, 30 GO annotations and 20 KEGG pathways are provided, and the affinity between the active ingredients and the core targets is good. Molecular docking results show that the docking scores of the core active component and the potential action target of the curcuma aromatica are both less than or equal to-5.0 kcal. Mol <-1 >, so that the curcuma aromatica has very strong binding activity with the predicted action target, and can act on the insomnia core target to play a good role in treating insomnia.

Description

Technical Field

[0001] The invention belongs to the technical field of network pharmacology of traditional Chinese medicine and molecular docking, and particularly relates to a method for analyzing the sleep-promoting effect of turmeric based on network pharmacology and molecular docking technology. Background Art

[0002] Due to various factors such as work or life pressure, sleep disorders and even insomnia can occur. Insomnia refers to the state of difficulty falling asleep, poor sleep quality, insufficient sleep time, etc. It is a kind of sleep disorder and a common mental illness. With the increase of life and work pressure, the insomnia rate of young people is gradually higher than that of middle-aged and elderly people. Traditional Chinese medicine calls insomnia "insomnia". Most patients suffer from insomnia due to emotions, work-rest imbalance, dietary problems and physical weakness. The causes of insomnia are varied and affect people's life, work and physical and mental health. At present, the main methods for treating insomnia are Western medicine, Chinese medicine and acupuncture and massage. Western medicine, such as sedatives and sleeping pills, not only have many adverse reactions, but are also very easy to produce dose dependence. Traditional Chinese medicine has great potential and advantages in treating insomnia. In recent years, insomnia caused by various reasons has complex causes, long treatment cycles, high treatment costs, and worsening insomnia can easily lead to mental illnesses such as anxiety and depression. It also disrupts the body's biological clock and hormone levels in the body, which may further induce various diseases in insomnia patients or aggravate existing diseases, such as cardiovascular and cerebrovascular diseases, hypertension, diabetes, digestive system diseases, etc., especially for the elderly, making them susceptible to a series of problems such as neurasthenia and Alzheimer's disease. Its treatment has always been a difficult problem to be solved in today's medical field.

[0003] Curcumae Radix is ​​a dried root of the genus Curcuma of the ginger family. It is pungent, bitter and cold in nature. It can not only promote blood circulation and remove blood stasis to relieve pain, but also soothe the liver and promote qi circulation to relieve depression. It is good at treating the symptoms of qi stagnation and blood stasis. It has a long history of medicinal use. It is rich in a large number of volatile oils, curcumin and polysaccharides, and has good pharmacological effects such as anti-cancer, liver protection and gallbladder promotion, and central nervous system inhibition. "Compendium of Materia Medica" records that Curcuma: "promotes qi, relieves depression; relieves blood, and breaks blood stasis". Insomnia patients generally have insufficient yin and blood, and internal fire. Curcuma is cold in nature and enters the heart meridian, which can clear heart heat, clear the heart and calm the mind. However, its pharmacological material basis-action target and mechanism are still unclear and need further study. Network pharmacology can explain the drug action mechanism from the overall level and reveal the multi-level relationship between drug-ingredient point-disease. The present invention uses network pharmacology combined with molecular docking technology to predict the active ingredients of Curcuma for the treatment of insomnia and its key points and action pathway mechanism, providing theoretical support for subsequent experiments and clinical applications. Summary of the invention

[0004] The purpose of the present invention is to provide a method for analyzing the sleep-promoting effect of Curcuma based on network pharmacology and molecular docking technology. The method screens 14 active ingredients, 6 core targets, 30 GO annotations and 20 KEGG pathways through network pharmacology and molecular docking technology, and the active ingredients have good affinity with the core targets.

[0005] The present invention adopts the following technical scheme to achieve the above-mentioned purpose: a method for analyzing the sleep-promoting effect of Curcuma based on network pharmacology and molecular docking technology, and the specific steps are: Step S1, collection of ingredients and targets: Using the TCMSP database (http: / / tcmspw.com / tcmsp.php), the compound ingredients were searched with "Yu Jin" as the keyword, and based on the pharmacokinetic principle, oral bioavailability (OB) ≥ 30% and drug-likeness (DL) ≥ 0.18 were used as screening conditions to screen out ingredients that met the conditions as active ingredients, set the screening conditions of the Swiss ADME database, and import the screened main active ingredients of Yu Jin into the Swiss Target Pridiction platform in turn to obtain the targets corresponding to the active ingredients of Yu Jin: Step S2, collection of insomnia-related targets: using GeneCards and DisGeNET platforms to retrieve and export insomnia result data, screen disease genes, and obtain gene targets for insomnia diseases; drawing a Venn diagram between component points and disease targets on the Venny platform to obtain intersection gene targets; Step S3, construction of a network diagram of Curcuma-active ingredients-targets: the targets corresponding to the active ingredients of Curcuma obtained by screening in step S1 and the intersection gene targets obtained in step S2 were intersected on the Venny platform, and the intersection was imported into Cytoscape3.9.1 software to draw a network diagram of Curcuma-active ingredients-targets, and the Degree value of each node was calculated using the Network Analyzer tool; Step S4, constructing a PPI network of Curcuma components and insomnia targets: importing the intersection targets into the STRING platform to obtain the PPI data of the intersection targets, and then importing them into the Cytoscape3.7.1 software to draw a Curcuma components-insomnia target PPI network diagram, using the Network Analyzer tool to analyze the Degree value, Betweenness value and Closeness value of the targets, and screen out the core targets; Step S5, GO and KEGG enrichment analysis of core targets: the core targets obtained in step S4 were subjected to GO functional enrichment and KEGG pathway enrichment analysis using the DAVID database to obtain the GO and KEGG pathways of the core targets; Step S6, molecular docking: The 2D structure of the main active ingredient with a higher degree value screened in step S1 is processed using ChemBio3D software, and the core target obtained in step S4 is processed using Uniprot database, PDB database, Pymol software, and AutoDockVina software. The processed core components and core targets are then molecularly docked using AutoDockVina software, and finally the docking of the components and targets is visualized using Pymol software.

[0006] It is further defined that in step S1, Probability > 0 is set in the Swiss Target Pridiction platform to screen the active ingredient targets.

[0007] It is further defined that the keyword searched by GeneCards and DisGeNET platforms in step S2 is Isomnia.

[0008] It is further defined that in step S4, the STRING platform sets the conditions as follows: selecting human as the analysis species and setting the confidence level to 0.40.

[0009] It is further defined that the core targets in step S4 are IL1B, CASP3, ESR1, PPARG, BCL2, and GSK3B.

[0010] It is further defined that in step S5, the DAVID database sets the conditions as follows: setting P<0.05 and selecting the species as human.

[0011] It is further defined that in step S6, the Uniprot database sets the condition: verified and human as the screening condition.

[0012] Compared with the prior art, the present invention has the following advantages and beneficial effects: The present invention collects the possible chemical components of the medicinal material Curcuma, constructs a medicinal material-component-target network diagram, explores the possible components of its sleep-promoting efficacy and the insomnia mechanism, and verifies it through molecular docking. The present invention selects 14 active ingredients, 6 core targets, 30 GO annotations and 20 KEGG pathways through network pharmacology and molecular docking technology, and the active ingredients have good affinity with the core targets. The results show that Curcuma can combine well with the relevant core targets through the core active ingredients, and play a good anti-insomnia effect.

[0013] Among the 14 main active ingredients screened, β-sitosterol (Mol000358), curcumolactone C (Mol004253), (E)-1,7-diphenyl-3-hydroxy-1-heptene-5-one (Mol004260), (E)-5-hydroxy-7-(4-hydroxyphenyl)-1-phenyl-1-heptene (Mol004263), ethylbenzene-acetic acid-(2S,5E)-6-phenylhex-5-en-2-yl ester (MOL004316), naringenin (MOL004328) were confirmed to be important potential active compounds in Curcuma. These core ingredients have large degree values ​​and more matching targets. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 This is the VENNY diagram of Curcuma components and disease targets.

[0015] Figure 2 This is the network diagram of Curcuma-active ingredients-targets.

[0016] Figure 3 This is the PPI network diagram of Curcuma-insomnia target.

[0017] Figure 4 , Figure 5 GO and KEGG enrichment analysis of core targets respectively.

[0018] Figure 6 Docking heat map analysis for molecular docking binding energy.

[0019] Figure 7 Figure 2 is the relationship between the components and the target sites. A is β-sitosterol (Mol000358)-CASP3, B is β-sitosterol (Mol000358)-GSK3B, C is naringenin (MOL004328)-ESR1, D is β-sitosterol (Mol000358)-PPARG, E is naringenin (MOL004328)-IL1B, and F is curcuminol C (Mol004253)-ESR1. DETAILED DESCRIPTION

[0020] The above contents of the present invention are further described in detail below through examples, but this should not be understood as the scope of the above subject matter of the present invention being limited to the following examples, and all technologies implemented based on the above contents of the present invention belong to the scope of the present invention. Example

[0021] 1. Collection of components and targets 222 structures of components related to Curcuma were collected. After screening, 14 main active ingredients that met the criteria were obtained. The screened active ingredients of Curcuma were successively imported into the Swiss Target Pridiction platform, and Probability > 0 was set. The potential targets of the active ingredients of Curcuma were collected, and 623 potential predicted targets were obtained after merging and removing duplicates.

[0022] 2. Collection of insomnia-related targets Using "Isomnia" as the keyword, we searched and exported the result data on GeneCards, DisGeNET and other platforms, screened the disease genes by "Relevance score", removed duplicates and obtained the genes of insomnia disease, merged 6584 duplicates, intersected the component targets with the disease gene targets and drew a Venn diagram on the Venny platform, and obtained 121 intersection genes, see Figure 1 .

[0023] 3. Construction of Curcuma-Active Ingredient-Target Network Diagram The targets corresponding to the active ingredients of Curcuma aromatica obtained by screening were intersected with the intersection gene targets on the Venny platform and imported into the Cytoscape3.9.1 software to draw a Curcuma aromatica-active ingredient-target network diagram, obtaining 128 nodes and 218 edges.

[0024] See Figure 2 , the Network Analyzer tool is used to calculate the degree value of each node. The more edges connected to the node, the higher the degree value of the node. Curcuma drug (Drug) is represented by diamonds, the effective active ingredients of Curcuma are represented by triangles, and the predicted targets are represented by circles. The larger the degree value, the larger the shape and the darker the color. Among them, MOL004260 has a degree value of 36.0, MOL004328 has a degree value of 33.0, MOL004263 has a degree value of 33.0, MOL004253 has a degree value of 25.0, MOL000358 has a degree value of 22.0, and MOL004316 has a degree value of 21.0. In addition, the degree values ​​of the active ingredients are also high. The results show that these ingredients may be the key ingredients of Curcuma for the treatment of insomnia.

[0025] 4. Construction of Curcuma Ingredient-Insomnia Target PPI Network The intersection targets were imported into the STRING platform, and the "human" analysis species was selected. The confidence level was set to 0.40 to obtain the PPI data of the intersection targets and store them in stv format. The file was imported into Cytoscape 3.9.1 software to draw the PPI network diagram of Curcuma component-insomnia target, see Figure 3The Network Analyzer tool was used to analyze the target, and the Betweenness, Closeness, and degree values ​​were 138.8205128, 0.004029311, and 16.82051282, respectively. The node size was set from small to large, the node color was changed from dark to bright, and the connection line was set from thin to thick according to the combined score value to process the network diagram. By setting the screening conditions: Betweenness ≥ 138.8205128, Closeness ≥ 0.004029311, Degree ≥ 16.82051282 to screen the core gene targets, and the degree values ​​were used to screen the disease nodes in the network diagram from large to small, it was found that the top 6 targets were IL1B, CASP3, ESR1, PPARG, BCL2, GSK3B, etc., which had high node degree values ​​and might be the core targets of the Curcuma herb pair, playing a hub role in the entire network.

[0026] 5. GO and KEGG enrichment analysis of core keywords The core targets were imported into the DAVID database to set the conditions; P < 0.05 was set, and the species was selected as human; GO functional enrichment and KEGG pathway enrichment analysis were performed to obtain a total of 30 GOs, see Figure 4 and 20 KEGG pathways, see Figure 5 , among which GO involved 10 biological processes (BP), BP mainly involved response to exogenous stimuli, positive regulation of extracellular regulated protein kinase ERK1 and ERK2 cascade, positive regulation of mitogen-activated protein kinase, protein phosphorylation, hypoxia response, positive regulation of protein kinase b signal transduction, positive regulation of cytoplasmic calcium ion concentration, phospholipase C activated G protein coupled receptor signaling pathway and other processes. There were 10 molecular functions (MF), MF mainly involved protein kinase activity, protein serine / threonine / tyrosine kinase activity, RNA polymerase II transcription factor activity, homologous protein binding and other enrichments were more significant, 10 cellular components (CC), mainly enriched in plasma membrane, plasma membrane components, presynaptic membrane, presynaptic membrane components, dendrites, presynaptic membrane components and other enrichments were more significant. A total of 106 KEGG pathways were screened through the DAVID database (P<0.05), mainly involving neuroactive receptor ligand interactions, tumor pathways, AGE-RAGE signaling pathways in diabetic complications, prostate cancer, EGFR tyrosine kinase inhibitor resistance, and calcium signaling pathways.

[0027] 6. Molecular Docking The 2D structure of the core component was converted into mo12 format after energy minimization by ChemBio3D software. The full name of the screened core target was found in the Uniprot database with "verified, human" as the screening condition, and the target number was imported into the PDB database. The three-dimensional crystal structure of the target protein was downloaded, and the water molecule and ligand were removed by Pymol software and saved in pdb format. AutoDockTools software was used to add oxygen to the point, calculate the charge, determine its rigid properties, and store them in pdbqt format files respectively. Then, the "Grid" and "Docking" of AutoDock Tools software were used, and the Autodock Vina function was used for molecular docking. Finally, the docking of the component and the target was visualized by Pymol software. The results are shown in the figure. Figure 6 . It is generally believed that a binding energy less than 0 kcal / mol indicates that the ligand and receptor can bind spontaneously, and less than -5.0 kcal / mol indicates that the ligand and receptor have good and strong binding activity. From the heat map analysis of the docking results, it is known that the binding free energy (kcal / mol) of Curcuma to each protein is less than -5, which indicates that Curcuma has a strong binding activity with the predicted target. The value in each small square represents the binding energy of the active ingredient docking with the core target molecule. The lower the binding energy, the closer it is to blue, and the higher the binding energy, the closer it is to yellow. The binding energy of β-sitosterol to CASP3 is -9.60 kcal / mol, the binding energy to GSK3B is -9.10 kcal / mol, the binding energy of naringenin to ESR1 is -8.7 kcal / mol, the binding energy of β-sitosterol to PPARG is -8.6 kcal / mol, the binding energy of naringenin to IL1B is -8.3 kcal / mol, and the binding energy of curcumin C (Mol004253) to ESR1 is -8.2 kcal / mol. The interaction relationships are shown in Figure 7 .

[0028] The present invention focuses on the use of network pharmacology and molecular docking technology to explore the mechanism of action of turmeric in treating insomnia. Among them, 14 main active ingredients with anti-insomnia effects were screened, and β-sitosterol, curcumolactone C, (E)-1,7-diphenyl-3-hydroxy-1-heptene-5-one, (E)-5-hydroxy-7-(4-hydroxyphenyl)-1-phenyl-1-heptene, ethylbenzene-acetic acid-(2S,5E)-6-phenylhex-5-ene-2-yl ester, naringenin, etc. were confirmed to be important potential active compounds in turmeric. These active ingredients have large degree values ​​and more matching targets. The molecular docking results show that these ingredients may be the key ingredients of turmeric in treating insomnia.

[0029] The basic principles, main features and advantages of the present invention are shown and described above. Without departing from the spirit and scope of the present invention, the present invention may also be subject to various changes and improvements, which all fall within the scope of the present invention to be protected.

Claims

1. A method for analyzing the sleep-promoting effect of Curcuma aromatica based on network pharmacology and molecular docking technology, characterized in that The specific steps are: Step S1, collection of ingredients and targets: Using the TCMSP database, search for compound ingredients with "curcuma" as the keyword, based on the pharmacokinetic principle, with oral bioavailability ≥ 30% and drug-like property ≥ 0.18 as screening conditions, screen out ingredients that meet the conditions as active ingredients, set the screening conditions of the SwissADME database, and import the screened main active ingredients of turmeric into the Swiss Target Pridiction platform in turn to obtain the targets corresponding to the active ingredients of turmeric: Step S2, collection of insomnia-related targets: using GeneCards and DisGeNET platforms to retrieve and export insomnia result data, screen disease genes, and obtain gene targets of insomnia diseases; drawing a Venn diagram between component targets and disease targets on the Venny platform to obtain intersection gene targets; Step S3, construction of a network diagram of Curcuma-active ingredients-targets: the targets corresponding to the active ingredients of Curcuma obtained by screening in step S1 and the intersection gene targets obtained in step S2 were intersected on the Venny platform, and the intersection was imported into Cytoscape3.9.1 software to draw a network diagram of Curcuma-active ingredients-targets, and the Degree value of each node was calculated using the Network Analyzer tool; Step S4, constructing a PPI network of Curcuma components and insomnia targets: importing the intersection targets into the STRING platform to obtain the PPI data of the intersection targets, and then importing the Cytoscape3.9.1 software to draw a Curcuma components-insomnia target PPI network diagram, using the Network Analyzer tool to analyze the Degree value, Betweenness value and Closeness value of the targets, and screen out the core targets; Step S5, GO and KEGG enrichment analysis of core targets: the core targets obtained in step S4 were subjected to GO functional enrichment and KEGG pathway enrichment analysis using the DAVID database to obtain the GO and KEGG pathways of the core targets; Step S6, molecular docking: The 2D structure of the main active ingredient with a higher degree value screened in step S1 is processed using ChemBio3D software, and the core target obtained in step S4 is processed using Uniprot database, PDB database, Pymol software, and AutoDockVina software. The processed core components and core targets are then molecularly docked using AutoDockVina software, and finally the docking of the components and targets is visualized using Pymol software.

2. The method for analyzing the sleep-promoting effect of Curcuma aromatica based on network pharmacology and molecular docking technology according to claim 1, characterized in that: In step S1, Probability > 0 was set in the Swiss Target Pridiction platform to screen active ingredient targets.

3. The method for analyzing the sleep-promoting effect of Curcuma aromatica based on network pharmacology and molecular docking technology according to claim 1, characterized in that: In step S2, the keyword searched by GeneCards and DisGeNET platforms is Isomnia.

4. The method for analyzing the sleep-promoting effect of Curcuma aromatica based on network pharmacology and molecular docking technology according to claim 1, characterized in that: In step S4, the STRING platform set conditions: select human as the analysis species and set the confidence level to 0.

40.

5. The method for analyzing the sleep-promoting effect of Curcuma aromatica based on network pharmacology and molecular docking technology according to claim 1, characterized in that: The core targets in step S4 are IL1B, CASP3, ESR1, PPARG, BCL2, and GSK3B.

6. The method for analyzing the sleep-promoting effect of Curcuma aromatica based on network pharmacology and molecular docking technology according to claim 1, characterized in that: In step S5, the DAVID database setting conditions are as follows: P<0.05 is set and the species is selected as human.

7. The method for analyzing the sleep-promoting effect of Curcuma aromatica based on network pharmacology and molecular docking technology according to claim 1, characterized in that: In step S6, the Uniprot database sets the conditions: verified and human as the screening conditions.

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