阅读说明：本技术 一种口炎清作用靶点的筛选方法 (Method for screening stomatitis clearing action target ) 是由 谌攀 李沛波 苏薇薇 姚宏亮 于 2021-06-04 设计创作，主要内容包括：本发明涉及一种口炎清作用靶点的预测方法。该方法包括如下步骤：采用中药系统药理学平台、毒性与基因比较数据库及靶点预测平台检索预测靶点,构建口炎清活性成分靶点库,并通过软件建立成分和对应靶点的可视化网络。进而构建活性成分和口腔溃疡疾病靶点库。最后对所有成分靶点和口腔溃疡疾病靶点进行了蛋白质-蛋白质相互作用,建立可视化网络,并分析网络的拓扑特征,通过网络结点的度、中介中心性和接近中心性3个拓扑参数筛选预测核心靶点。经试验证明：本方法可以解释口炎清发挥药效的科学内涵,也可以用于其他多成分药物的作用机制研究,为口炎清制剂质量控制提供更全面科学的研究方法,且具有耗时少、成本低的优势。(The invention relates to a method for predicting stomatitis clearing action target. The method comprises the following steps: the method comprises the steps of adopting a traditional Chinese medicine system pharmacology platform, a toxicity and gene comparison database and a target prediction platform to search for predicted targets, constructing a stomatitis clearing active ingredient target library, and establishing a visual network of ingredients and corresponding targets through software. And further constructing an active ingredient and oral ulcer disease target library. And finally, performing protein-protein interaction on all component targets and the oral ulcer disease target, establishing a visual network, analyzing topological characteristics of the network, and screening and predicting the core target through 3 topological parameters of the degree, the intermediary centrality and the approach centrality of network nodes. The test proves that: the method can explain the scientific connotation of the drug effect of the Kouyanqing, can also be used for the research of the action mechanism of other multi-component drugs, provides a more comprehensive and scientific research method for the quality control of the Kouyanqing preparation, and has the advantages of less time consumption and low cost.)

1. A method for screening action targets of a Kouyanqing preparation is characterized by comprising the following steps:

(1) constructing a stomatitis clearing active ingredient target library: searching target points by adopting a traditional Chinese medicine system pharmacology platform and a toxicity and gene comparison database, searching by using a target point prediction platform database to obtain target points corresponding to active ingredients, and establishing a visual network of the ingredients and the corresponding target points through software;

(2) constructing an active ingredient and oral ulcer disease target library: searching disease targets in a human Mendelian genetic database, a human gene database, a gene disease association database and a drug research and development database, and integrating and de-duplicating the results of all databases to obtain an oral ulcer disease target library;

(3) predicting stomatitis clearing action targets through network construction and network topology analysis: and performing protein-protein interaction on all component targets and oral ulcer disease targets, establishing a visual network, analyzing topological characteristics of the network, and screening and predicting core targets through 3 topological parameters of network node degree, intermediary centrality and approach centrality.

2. The method of claim 1, wherein: and (3) the degree in the network constructed by the topological parameters in the step (3) is more than 2 times of a median value, and nodes with the intermediary centrality and the approach centrality both being more than the median value are used as predicted core target points.

Technical Field

The invention relates to the field of research of drug targets, in particular to a method for screening stomatitis clearing action targets.

Background

The traditional Chinese medicine compound stomatitis clearing preparation is used for treating yin deficiency and fire excess type stomatitis, is clinically used for decades, has definite curative effect, but has less research on the action mechanism of the stomatitis clearing due to complex components, and has unclear action target. The traditional Chinese medicine compound generally has the characteristics of multiple components and multiple targets, and the single-channel research is difficult to explain the treatment idea of the holistic concept of the traditional Chinese medicine. Canker sores are a complex immune disorder and research into the treatment of canker sores should be done from a multifactorial perspective. Therefore, the research on the action mechanism and target of the stomatitis-clearing medicament for treating the oral ulcer should be started from an overall point of view. The traditional pharmaceutical action mechanism research method mostly starts from cell experiments and animal experiments, has long time consumption and large risk, and is difficult to satisfy the pharmacological mechanism research of a complex system of a traditional Chinese medicine compound.

Therefore, a research method aiming at the action mechanism and the action target of the traditional Chinese medicine which is fast, economical and accurate for the stomatitis treating preparation is needed to be established, and a better technical basis is provided for the quality control of the production of the stomatitis treating preparation.

Disclosure of Invention

Based on the problems, the invention aims to provide a method for screening stomatitis clearing action target, and overcomes the defects of long time consumption and high cost of the existing method.

The technical scheme adopted by the invention comprises the following steps:

1. constructing a stomatitis clearing active ingredient target library: searching target points by adopting a traditional Chinese medicine system pharmacology platform and a toxicity and gene comparison database, searching by using a target point prediction platform database to obtain target points corresponding to active ingredients, and establishing a visual network of the ingredients and the corresponding target points through software;

2. constructing an active ingredient and oral ulcer disease target library: searching disease targets in a human Mendelian genetic database, a human gene database, a gene disease association database and a drug research and development database, and integrating and de-duplicating the results of all databases to obtain an oral ulcer disease target library;

3. predicting stomatitis clearing action targets through network construction and network topology analysis: and performing protein-protein interaction on all component targets and oral ulcer disease targets, establishing a visual network, analyzing topological characteristics of the network, and screening and predicting core targets through 3 topological parameters of network node degree, intermediary centrality and approach centrality.

And 3, the degree in the network constructed by the topological parameters in the step 3 is more than 2 times of a median value, and nodes with the intermediary centrality and the approaching centrality which are both more than the median value are used as predicted core target points.

The verification test proves that: the method for predicting the action target of the Kouyanqing, which is established by the method, can explain the scientific connotation of the Kouyanqing for exerting the drug effect and can also be applied to the research of the action mechanism of other multi-component drugs. And has the following advantages: the method provides an action mechanism which is more in line with the characteristics of a complex system of the traditional Chinese medicine from the integral angle; the method is based on data mining, network topology analysis and molecular virtual docking means, and has the advantages of less time consumption and low cost.

Drawings

FIG. 1 is a diagram of a component-predicted target network;

FIG. 2 is a graph of the distribution of the degree and centrality of medians for predicted targets;

figure 3 is a graphical representation of the results of target validation animal experiments.

Detailed Description

The present invention will be further described with reference to the following examples, but the present invention is not limited thereto.

1. Research method

(1) Active ingredient target library construction

The target point corresponding to the active ingredient is obtained by searching a target point through a traditional Chinese medicine system pharmacology platform (TCMSP, http:// tcmspw.com/tcmsp.php), Toxicogenomics Database (CTD, http:// ctdbase.org /) and a toxicity and gene comparison Database (CTD, https:// ctdbase.org /), and predicting the target point by using a SwissTargetPrediction (STPD, http:// www.swisstargetprediction.ch /) Database. And establishing a visual network of the components and the corresponding target points through Cytoscape 3.7.0 software. The component-predicted target visualized network diagram is shown in figure 1.

(2) Construction of disease-associated target library

Using "Oral Ulcer" or "Mouth Ulcer" as keywords, disease targets were retrieved in human Mendelian genetic database (OMIM, https:// OMIM. org /), human genetic database (Genecards, https:// www.malacards.org /), genetic disease association database (DisGeNET, http:// www.disg enet. org /), and drug development (Integrity) database (https:// Integrity. thomson-pharma. com/Integrity/xmlxsl /). And then, integrating the results of all databases to remove the duplication to obtain a canker sore disease target library.

(3) Protein-protein interaction (PPI) analysis

In order to search the mutual relation between the component target and the disease target and further discover the mutual relation between the component target and the indirect action target, the protein-protein interaction research is carried out on all the component targets and the disease targets. An active ingredient target spot and a related target spot of the oral ulcer are led into STRING (https:// STRING-db. org /), a good species 'Homo spaies' is selected and submitted, and a 'minimum required interaction score' is set to be 0.4, so that an interaction network of the key effective ingredient target spot and the related target spot of the oral ulcer is obtained, and a result is derived in a TSV format. And establishing a visual network by using the derived result through using Cytoscape 3.7.0 software, and analyzing the topological characteristics of the network.

(4) Screening of core targets

And mapping the component target to a disease target network to obtain a target jointly related to the component and the disease. Taking out the target point which is commonly related to the component diseases and the target point which is directly connected with the common target point, using a STRING database to perform PPI analysis, then using Cytoscape 3.7.0 software to perform visualization, and analyzing the topological characteristics of the network. In the topology analysis of the network, nodes with the degree (degree) of network nodes larger than 2 times median value and the intermediate centrality (Betweenness centre) and the close centrality (Closense centre) larger than the median value are taken as core targets. The distribution of the degree and mesocentrality of the target is shown in FIG. 2.

(5) Component-core target network construction and analysis

The core target point is led into a STRING database for PPI analysis, the TSV format file result is led in, the result and key pharmacodynamic component information are led into Cytoscape 3.7.0 software, an active component-core target point network is constructed by utilizing the visualization function of the result and the key pharmacodynamic component information, and the topological characteristic of the network is analyzed.

(6) GO and KEGG pathway enrichment analysis

To speculate that core targets may be involved in Biological processes and signaling pathways, the present study performed GO (gene connectivity) annotation analysis of core targets, including Biological processes, Cell components, and Molecular functions, using the David database (https:// David. ncifcrf. gov.) (V6.8). At the same time, a KEGG signal pathway enrichment analysis was performed on this data. And taking the item before GO analysis 10 and the item before signal path 15 according to the P-Value (P is less than or equal to 0.05). Meanwhile, the Cytoscape 3.7.0 software was used to construct a component-target-pathway network map.

(7) Molecular docking study

The SDF files of 16 stomatitis clearing active ingredients are downloaded from Pubchem, introduced into software Discovery Studio 2016, subjected to Prepare Ligand treatment, subjected to energy optimization treatment on the conformation of small molecules to generate a three-dimensional structure, and hydrogenated to generate isomers. Treatment with 16 compounds yielded 41 ligand molecules for docking. PDB protein files for PTGS2, MMP9, TNF, TP53, ALB, IL6, CASP3, BCL2, JUN, and EGF were downloaded from the RCSD protein database (https:// www.rcsb.org /). Firstly, deleting water molecules and protoligand molecules in a PDB structure, and utilizing a Clean Protein function to complete incomplete residues, delete redundant Protein conformations, hydrogenate, distribute related charges and the like to obtain a PDB file for docking. The active pocket of the dock is defined by the original ligand molecule. After setting the docking parameters, we re-dock the original ligand molecules in the crystal structure to the pre-defined active pockets. The pro-ligands of each target protein are docked into their corresponding protein receptors in the same manner as described above. The strength of interaction between the active ingredient and the target was evaluated by comparing the results of docking scores (LibDockScore) of the active ingredient and the original ligand.

(8) Target point verification

Male Sprague-Dawley rats (8 weeks old) were randomized into 5 groups: normal group (Control), Model group (Model), and Low (KYQG-Low), medium (KYQG-Mid), High dose group (KYQG-High) stomatitis-clearing. The animals in each group were raised in 12h day and night with free supply of drinking water and food. The low, medium and high dose groups of stomatitis treating are respectively irrigated with 0.522, 1.57 and 4.70g/kg/d from day 1 to day 9, and the other groups are irrigated with distilled water. On day 4, buccal mucosa of model group and low, medium and high dose group rats was chemically injured with phenol, and an almost uniform circular ulcer was formed in buccal mucosa region of oral cavity on day 6. Then, the rats in the model group and the low, medium and high dose groups of stomatitis were subjected to sleep deprivation for 72 h. After sleep deprivation was complete, the animals were anesthetized with isoflurane and the abdominal aorta was bled. The blood was centrifuged at 5000rpm for 20min at 4 ℃ to obtain serum, which was then stored at-80 ℃. Serum levels of cyclooxygenase-2 (COX-2), matrix metalloproteinase 9(MMP-9), and tumor necrosis factor-alpha (TNF-alpha) were determined by enzyme-linked immunosorbent assay (ELISA) kits.

2. Results of the study

(1) Construction and analysis of drug effect component-core target network

97 disease targets related to the dental ulcer are obtained after the duplication of each database is integrated and removed, 12 targets related to the components and the diseases are obtained by mapping the component targets to a disease target network, PPI analysis is carried out on the targets related to the component diseases and the targets directly connected with the common targets, then a visualized network is established by utilizing Cytoscape software, and the topological characteristics of the network are analyzed. In the topology analysis of the network, 47 nodes with the degree (degree) of network nodes larger than 2 times median and with the Betweenness center and the Closeness center larger than the median are used as core targets. A component-core target network diagram (see fig. 1) is built by using Cytoscape, and 63 nodes are formed in total, 970 edges are formed. In this study, the importance of nodes in the network was evaluated by the degree of computation and the centrality of the intermediaries. Degrees represent the number of edges connected to the node. The intermediary centrality reflects the number of times a node connects the shortest path between two other nodes as a bridge. Thus, some targets with higher values and/or higher values of mesocentrality are considered as targets that play a critical role. The trends in target density and mesocentrality are highly correlated (figure 2). The peaks of the mesocentrality lines represent nodes that still possess high mesocentrality values at relatively low values, such as MMP2, RELA, NOS3, and NOS 2. These nodes connect some influential targets and are also relatively important targets. Of the 47 core targets, top 10 ranked targets were PTGS2, MMP9, TNF, TP53, ALB, IL6, CASP3, BCL2, JUN, and EGF. Wherein the Degree ═ 15, 9, 8, 6 and B are higher.

(2) GO analysis and KEGG pathway analysis

GO enrichment analysis was performed on 47 core targets, listing top 10 biological processes, cellular components and molecular functions according to P-Value values. The results show that the Kouyanqing granules can regulate biological processes such as drug reaction, lipopolysaccharide regulation signal path, apoptosis and the like in cell components such as extracellular regions, fovea and cytoplasm nucleus through molecular functions such as enzyme binding, heme binding and receptor binding, and further play a therapeutic role. The core target was analyzed by KEGG and the top 15 pathways were listed according to the P-Value. The HIF-1 signaling pathway and TNF signaling pathway, both pathways, were retrieved from the CTD database to be associated with canker sore disease. It can be seen that these key components have associated targets involved in both pathways. Therefore, the HIF-1 signal pathway and the TNF signal pathway are probably key pathways involved in regulation and control when the stomatitis clearing granules exert efficacy.

(3) Results of molecular docking studies

Targets with proprotein ligand inhibitors in the HIF-1 signaling pathway and TNF signaling pathway were selected for molecular docking. We have found that the different components exhibit different binding activities to the selected target protein. It can be seen from the heat map of the interaction between the active ingredient and the target protein that isochlorogenic acid B and isochlorogenic acid A, isoquercitrin and harpagoside have strong interaction with HMOX1, NOS2, EGFR and NOS 3; moderate strength interactions with MMP9, MAPK3, MAPK1, CASP3, TNF, and BCL 2; the docking with PTGS2 was not successful. Harpagide, chlorogenic acid, luteoloside, neochlorogenic acid, cryptochlorogenic acid and isoquercitrin have moderate-strength interactions with HMOX1, NOS2, EGFR, NOS3, PTGS2 and MMP 9. Tyrosine, lysine, chelidonic acid, gamma-aminobutyric acid and cinnamic acid almost have only weak effect or no interaction with each target point. According to the docking result, angoroside has stronger binding specificity to NOS2, MAPK3, CASP3, TNF and BCL 2. Targets such as BCL2, EGFR, HMOX1, MAPK1, MAPK3, NOS2 and NOS3 in a HIF-1 signal path and PTGS2, CASP3, TNF, MMP9, MAPK1, MAPK3 and the like in a TNF signal path have stronger interaction with at least one drug effect component.

(4) Target point verification

The action mechanism and the target point of the Kouyanqing granules for treating the oral ulcer are preliminarily deduced by a network pharmacological method, but further experimental verification is needed. Therefore, animal experiments and verification are carried out on a rat model of oral ulcer with fire excess from yin deficiency. In the component-key target network, PTGS2, also known as cyclooxygenase 2(COX2), MMP9, and TNF- α are top three ranked targets, and may be important targets for the efficacy of the stomatitis-cleaning granule. The results show that, as shown in figure 3, stomatitis clearing (4.7g/kg/d) can remarkably inhibit the increase of COX2, MMP9 and TNF-alpha in serum. It can be seen that the therapeutic effect of stomatitis is related to the inhibition of the increase of COX2, MMP9 and TNF- α, which is consistent with the results of the study of cybepharmacology. The results of animal experiments prove the reliability of the target point predicted by the method.

In conclusion, the method discovers that the Kouyanqing has the action characteristics of multiple components and multiple targets. The stomatitis clearing agent is based on 47 key targets such as COX2, MMP9, TNF-alpha and the like, and plays a role in treating oral ulcer by inhibiting inflammation, regulating immune response and inhibiting oxidative stress. Among other things, the TNF signaling pathway and the HIF-1 signaling pathway may play a key role in the treatment of oral ulcers by stomatitis particles.