Target drug analysis method based on heat stroke data fusion

Abstract

The present application relates to the field of target drug analysis, and particularly relates to a target drug analysis method based on heat stroke data fusion, comprising: obtaining a medicament-related compound and a medicament-related target protein; obtaining an EHS-related target protein; obtaining a common desired target point; obtaining a node number and an edge number of a PPI network after constructing the PPI network and obtaining an action evaluation coefficient; obtaining a preliminary screening target point by using a standard of topological analysis for preliminary screening, and performing secondary filtering to obtain a secondary screening target point when it is determined that the preliminary screening does not meet a preset standard; performing KEGG pathway enrichment analysis; obtaining an intersection of the preliminary screening target point or the secondary screening target point and a KEGG pathway set to preliminarily determine a pre-core target point; removing data that does not meet the preset standard from the pre-core target point; and performing molecular docking to determine a target target point and a corresponding compound. The present application explores a potential action mechanism of a drug by network pharmacology analysis, and provides a scientific basis for preventing and treating heat stroke by using a heat stroke drug.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of target drug analysis, in particular to a target drug analysis method based on heat stroke data fusion. BACKGROUND

[0002] The most serious threat posed by heat stress in a hot environment is the development of heat stroke (HS), a disease characterized by rapid onset, rapid progression, and abnormally high morbidity and mortality. HS can be divided into two types based on its etiology and susceptible population: classic heat stroke (CHS) and exertional heat stroke (EHS). EHS mainly affects healthy young people who are often required to work or engage in physical activity in hot conditions. Therefore, it is crucial to implement targeted preventive measures for this population.

[0003] Traditional Chinese medicine emphasizes the importance of disease prevention, focusing on maintaining balance and harmony within the body, and avoiding disease before it occurs. This approach combines lifestyle adjustments, dietary therapy, and the use of traditional Chinese medicine compounds to strengthen the body's resistance to external and internal factors that can lead to disease. Some Chinese patent medicines or decoctions have the functions of clearing summer-heat, replenishing qi, nourishing yin, and promoting fluid production, but experimental studies and mechanism studies on their protective effects are still lacking. SUMMARY

[0004] To this end, the present application provides a target drug analysis method based on heat stroke data fusion to overcome the high cost and low precision of target drug analysis in the prior art.

[0005] To achieve the above-mentioned purpose, the present application provides a target drug analysis method based on heat stroke data fusion, comprising:

[0006] Step S1: Based on the components of traditional Chinese medicine, set the retrieval keywords to retrieve in each target database to preliminarily obtain medicine-related compounds and medicine-related target proteins;

[0007] Step S2: Set the disease keywords and retrieve from the gene card database, omim, allogeneic library and drug library, then unify the gene and protein names and perform deduplication processing to obtain EHS-related target proteins;

[0008] Step S3: Take the intersection of the medicine-related target proteins and the EHS-related target proteins to obtain the common desired target points;

[0009] Step S4: Based on the medicine-related compounds and the common desired target points, construct a PPI network in the string database, then obtain the number of nodes and edges of the PPI network and calculate the action evaluation coefficient;

[0010] Step S5, the initial screening target is obtained by adopting the standard preliminary screening of topology analysis, when the initial screening proportion and the initial screening target number determine that the preliminary screening does not meet the preset standard, the centrality index is obtained and the mean value of each centrality index is used as a threshold to perform secondary filtering on the initial screening data to obtain the secondary screening target;

[0011] Step S6, KEGG pathway enrichment analysis is performed on the common target and a KEGG pathway set belonging to environmental information processing is obtained;

[0012] Step S7, the intersection of the initial screening target or the secondary screening target and the KEGG pathway set is obtained to preliminarily determine the pre-core target;

[0013] Step S8, the data in the pre-core target that does not meet the preset standard is removed based on the action evaluation coefficient;

[0014] Step S9, molecular docking is performed on each pre-core target remaining after removal to determine the target target and the corresponding compound through binding energy.

[0015] Further, in the step S2, the gene and protein name are unified based on the UniProt protein database.

[0016] Further, in the step S5, it is determined that the preliminary screening does not meet the preset standard in response to the initial screening proportion being less than the preset proportion threshold and the initial screening target number being greater than or equal to the preset target number threshold.

[0017] Further, the centrality index includes degree centrality, proximity centrality, intermediate degree centrality, eigenvector, method based on local average connectivity and network based on local average connectivity.

[0018] Further, in the step S6, the common target is imported into the meta-landscape platform, and then visual analysis is performed using bioinformatics tools to complete the KEGG pathway enrichment analysis.

[0019] Further, in the step S8, the action evaluation coefficient is the sum of the ratio of the node number to the preset node number and the ratio of the edge number to the preset edge number.

[0020] Further, it is determined that the pre-core target does not meet the preset standard in response to the action evaluation coefficient being less than the preset action evaluation coefficient.

[0021] Further, the process of molecular docking includes:

[0022] The core ligand structure file is downloaded from the PubChem database;

[0023] The three-dimensional crystal structure of the target protein is retrieved from the RCSB protein database, and then loaded into PyMOL for ligand extraction;

[0024] The ligand and receptor are introduced into AutoDockTools to sequentially complete dehydration, hydrogenation and charge calculation;

[0025] The grid frame containing the entire target protein is created using AutoDockTools, and a txt file is saved;

[0026] The docking analysis is performed using AutoDockVina.

[0027] Further, the visualization of the molecular docking is displayed in PyMOL, and the binding site is the PLIP site.

[0028] Further, the pre-core target point is determined as a target target point in response to the binding energy being greater than a preset binding energy threshold.

[0029] Compared with the prior art, the beneficial effects of the present application are that: the present application objectively and comprehensively analyzes the chemical composition of traditional Chinese medicine, and targets potential pharmacodynamic compounds, and through network construction, core target point screening, functional enrichment analysis and other technical means related to the drug, it starts from the characteristics of multi-target and multi-path of the drug to explore, predicts the main pharmacologically active ingredients of traditional Chinese medicine, and explains the possible pharmacological action mechanism of the pharmacodynamic material basis. The directional guiding effect can save a lot of manpower, financial resources, time and other costs, reduce research and development risks, and can realize more rational use of traditional Chinese medicine resources.

[0030] The present application uses network pharmacology technology to construct the potential mechanism of revealing the therapeutic effect of the target drug on EHS for the first time, and obtains the core target point NFKB1 and the corresponding compound lycopene. BRIEF DESCRIPTION OF DRAWINGS

[0031] Figure 1 The flowchart of the target drug analysis method based on heat stroke data fusion of the embodiments of the present application;

[0032] Figure 2 The flowchart of determining the pre-core target point of the embodiments of the present application;

[0033] Figure 3 The PPI network diagram of the embodiments of the present application;

[0034] Figure 4 The binding mode diagram of lycopene and NFKB1 of the embodiments of the present application. DETAILED DESCRIPTION

[0035] In order to make the objects and advantages of the present application clearer, the present application will be further described below in conjunction with embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and not used to limit the present application.

[0036] The preferred embodiments of the present application will be described below with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are only used to explain the technical principles of the present application and not used to limit the protection scope of the present application.

[0037] Please refer to Figure 1 , Figure 2 , Figure 3 and Figure 4 , which are respectively a flowchart of a target drug analysis method based on heat stroke data fusion according to an embodiment of the present application, a flowchart of determining a pre-core target according to an embodiment of the present application, a PPI network schematic diagram according to an embodiment of the present application, and a binding mode diagram of lycopene and NFKB1 according to an embodiment of the present application.

[0038] The target drug analysis method based on heat stroke data fusion according to an embodiment of the present application comprises the following steps.

[0039] Step S1, based on the components of traditional Chinese medicine, set the retrieval keywords to search in each target database to preliminarily obtain the medicine-related compounds and medicine-related target proteins, and the target database includes the traditional Chinese medicine system pharmacology database and analysis platform (https: / / tcmsp-e.com / tcmsp.php), Batman TCM (http: / / bionet.ncpsb.org.cn / batman-tcm / index.php), ETCM (http: / / www.tcmip.cn / ETCM / ), herbal medicine (http: / / herb.ac.cn / ) and (http: / / www.symmap.org / );

[0040] Step S2, set the disease keywords and search from the gene card database (https: / / www.genecards.org), omim (https: / / www.omim.org / ), heterologous gene library (https: / / www.disgenet.org / ) and drug library (https: / / go.drugbank.com / ) to unify and remove the gene and protein names after retrieval to obtain the EHS-related target proteins;

[0041] Step S3, take the intersection of the medicine-related target proteins and the EHS-related target proteins to obtain the common desired target;

[0042] Step S4, obtaining the number of nodes and edges of the PPI network and calculating the action evaluation coefficient after constructing the PPI network of the drug-related compound and the common desired target in the string database (http: / / string-db.org / );

[0043] Step S5, obtaining the primary screening target by using the standard of topological analysis for preliminary screening, determining that the preliminary screening does not meet the preset standard based on the primary screening proportion and the number of primary screening targets, calculating the centrality index and taking the mean value of each centrality index as a threshold to perform secondary filtering on the primary screening data to obtain the secondary screening target;

[0044] Step S6, performing KEGG pathway enrichment analysis on the common desired target and obtaining a KEGG pathway set belonging to environmental information processing;

[0045] Step S7, obtaining the intersection of the primary screening target or the secondary screening target and the KEGG pathway set to preliminarily determine the pre-core target;

[0046] Step S8, removing data that does not meet the preset standard from the pre-core target based on the action evaluation coefficient;

[0047] Step S9, performing molecular docking on each pre-core target remaining after removal to determine the target target and the corresponding compound by binding energy.

[0048] Specifically, in the step S2, the gene and protein names are unified based on the UniProt protein database (https: / / www.uniprot.org).

[0049] Specifically, it is determined that the preliminary screening does not meet the preset standard in response to the primary screening proportion being less than a preset proportion threshold of 5% and the number of primary screening targets being greater than or equal to a preset target quantity threshold of 15.

[0050] Specifically, in the step S5, it is determined that the preliminary screening meets the preset standard in response to the primary screening proportion being greater than or equal to a preset proportion threshold of 5% or the number of primary screening targets being less than a preset target quantity threshold of 15.

[0051] The primary screening proportion is the percentage of the primary screening target and the common desired target.

[0052] Specifically, the centrality index includes degree centrality, closeness centrality, intermediate degree centrality, eigenvector, a method based on local average connectivity, and a network based on local average connectivity.

[0053] Specifically, in the step S6, after the common desired target point is introduced into the MetaScape platform (https: / / metascape.org / ), a visual analysis is performed using a bioinformatics tool (https: / / www.bioinformatics.com.cn / ) to complete the KEGG pathway enrichment analysis.

[0054] Specifically, the effect evaluation coefficient is the sum of the ratio of the node number to the preset node number 5 and the ratio of the edge number to the preset edge number 12.

[0055] Specifically, in the step S8, it is determined that the pre-core target point does not meet the preset standard in response to the effect evaluation coefficient being less than a preset effect evaluation coefficient 1.45.

[0056] Specifically, the process of molecular docking includes:

[0057] The core ligand structure file is downloaded from the PubChem database (https: / / pubchem.ncbi.nlm.nih.gov / );

[0058] The three-dimensional crystal structure of the target protein is retrieved from the RCSB protein database, and then loaded into PyMOL for ligand extraction;

[0059] The ligand and receptor are introduced into AutoDockTools to sequentially complete dehydration, hydrogenation and charge calculation;

[0060] After creating a grid frame containing the entire target protein using AutoDockTools, a txt file is saved;

[0061] AutoDockVina is used for docking analysis.

[0062] Specifically, the visual display of the molecular docking is in PyMOL, and the binding site is the PLIP site.

[0063] Specifically, in response to the binding energy being greater than a preset binding energy threshold -5 kJmol -1 The pre-core target point is determined as the target target point.

[0064] Example 1: Step S1, set the retrieval keywords "Panax quinquefolius, Dendrobium, Ophiopogon japonicus, Coptis chinensis, bamboo leaves, lotus seeds, Astragalus membranaceus, licorice, rice, watermelon peel" based on the components of traditional Chinese medicine "Qingshu Yiqi Decoction" to retrieve in each target database to preliminarily obtain medicine-related compounds and medicine-related target proteins, the target databases include Traditional Chinese Medicine System Pharmacology Database and Analysis Platform (https: / / tcmsp-e.com / tcmsp.php), Batman-TCM (http: / / bionet.ncpsb.org.cn / batman-tcm / index.php), ETCM (http: / / www.tcmip.cn / ETCM / ), Herbal (http: / / herb.ac.cn / ) and SYMMap (http: / / www.symmap.org / ), after retrieval, 460 medicine-related compounds and 675 medicine-related target proteins were obtained;

[0065] Step S2, set the disease keyword "heat stroke" and retrieve the target from GeneCards database (https: / / www.genecards.org), omim (https: / / www.omim.org / ), Disgenet (https: / / www.disgenet.org / ) and DrugBank (https: / / go.drugbank.com / ), after unifying the gene and protein names, 3526, 54, 3 and 2 EHS-related target proteins were obtained respectively, after deduplication processing, 3553 EHS-related target proteins were obtained;

[0066] Step S3, taking the intersection of the medicine-related target proteins and the EHS-related target proteins, 406 common target points were obtained;

[0067] Step S4, based on 460 medicine-related compounds and 406 common target points, the PPI network was constructed in the STRING database (http: / / string-db.org / ), the number of nodes and edges of the PPI network was obtained, and the action evaluation coefficient was calculated;

[0068] Step S5, 27 primary screening target points were obtained by adopting the standard of topological analysis for preliminary screening, based on the primary screening ratio of 6.65% and the number of primary screening target points 27, it was determined whether the preliminary screening met the preset standard, after determination, it was determined that the preliminary screening did not meet the preset standard, the centrality index was calculated, and based on the mean value of each centrality index as the threshold, the secondary filtering of the primary screening data was carried out, 16 secondary screening target points were obtained, including NFKB1;

[0069] Step S6, KEGG pathway enrichment analysis was performed on the 406 common target points, and the KEGG pathway set belonging to environmental information processing was obtained, among the top 10 KEGG pathways, the NF-kappa B signaling pathway was significantly represented;

[0070] Step S7, obtaining the intersection of the primary screening target or the secondary screening target and the KEGG pathway set to preliminarily determine the pre-core target point;

[0071] Step S8, removing data in the pre-core target point that does not meet the preset standard based on the action evaluation coefficient;

[0072] Step S9, performing molecular docking on each pre-core target point remaining after removal, determining the target target point and the corresponding compound through the binding energy, wherein, in response to the binding energy being greater than the preset binding energy threshold-5 kJmol -1 determining the pre-core target point as the target target point, wherein the target target point is NFKB1 and the corresponding compound is lycopene, and performing molecular docking on them to evaluate the potential of protein and ligand binding, and the result shows that the binding energy of the receptor-ligand pair is-6.2 kJmol -1 , indicating that the target point has good binding affinity with the component. The interaction and binding mode diagram of lycopene and NFKB1 are observed by using PyMOL.

[0073] So far, the technical solutions of the present application have been described in combination with the preferred embodiments shown in the accompanying drawings, but it is easy for those skilled in the art to understand that the protection scope of the present application is obviously not limited to these specific embodiments. Those skilled in the art can make equivalent changes or replacements to the related technical features without departing from the principles of the present application, and the technical solutions after the changes or replacements will fall within the protection scope of the present application.

[0074] The above only describes the preferred embodiments of the present application and is not intended to limit the present application; for those skilled in the art, the present application can have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A target drug analysis method based on heatstroke data fusion, characterized in that, Step S1: Based on the components of traditional Chinese medicine preparations, search keywords are set to search in various target databases to initially obtain drug-related compounds and drug-related target proteins; Step S2: Set disease keywords and search from gene card database, omim, allogeneic database and drug database, unify gene and protein names and perform deduplication to obtain EHS-related target proteins; Step S3: Obtain the common desired target by taking the intersection of the drug-related target protein and the EHS-related target protein; Step S4: After constructing a PPI network in the string database based on the drug-related compounds and the common desired target, obtain the number of nodes and edges of the PPI network and calculate the effect evaluation coefficient; Step S5: Use topology analysis as the standard for initial screening to obtain initial screening targets. Based on the initial screening ratio and the number of initial screening targets, determine when the initial screening does not meet the preset standard. Calculate the centrality index and use the mean of each centrality index as the threshold to perform secondary filtering on the initial screening data to obtain secondary screening targets. Step S6: Perform KEGG pathway enrichment analysis on the common desired target and obtain a set of KEGG pathways belonging to environmental information processing. Step S7: Obtain the intersection of the initial screening target or the second screening target with the KEGG pathway set to preliminarily determine the pre-core target. Step S8: Based on the effect evaluation coefficient, remove data from the pre-core target that do not meet the preset standard; Step S9 involves performing molecular docking on the remaining pre-core targets after removal, and determining the target targets and corresponding compounds by binding energy.

2. The target drug analysis method based on heatstroke data fusion according to claim 1, characterized in that, In step S2, the gene and protein names are standardized based on the UniProt protein database.

3. The target drug analysis method based on heatstroke data fusion according to claim 2, characterized in that, In step S5, in response to the initial screening ratio being less than a preset ratio threshold and the number of initial screening targets being greater than or equal to a preset target number threshold, it is determined that the initial screening does not meet the preset standard.

4. The target drug analysis method based on heatstroke data fusion according to claim 1, characterized in that, The centrality metrics include degree centrality, proximity centrality, intermediate degree centrality, eigenvectors, methods based on local average connectivity, and networks based on local average connectivity.

5. The target drug analysis method based on heatstroke data fusion according to claim 1, characterized in that, In step S6, after the common target is imported into the meta-landscape platform, bioinformatics tools are used for visualization analysis to complete the KEGG pathway enrichment analysis.

6. The target drug analysis method based on heatstroke data fusion according to claim 1, characterized in that, The performance evaluation coefficient is the sum of the ratio of the number of nodes to the preset number of nodes and the ratio of the number of edges to the preset number of edges.

7. The target drug analysis method based on heatstroke data fusion according to claim 1, characterized in that, In step S8, in response to the effect evaluation coefficient being less than the preset effect evaluation coefficient, it is determined that the pre-core target does not meet the preset standard.

8. The target drug analysis method based on heatstroke data fusion according to claim 1, characterized in that, The molecular docking process includes: Download the core ligand structure file from the PubChem database; The three-dimensional crystal structure of the target protein was retrieved from the RCSB protein database and then loaded into PyMOL for ligand extraction. The ligand and acceptor are imported into AutoDockTools to perform dehydration, hydrogenation, and charge calculations in sequence; After creating a grid containing the entire target protein using AutoDockTools, save it as a txt file; Use AutoDockVina for docking analysis.

9. The target drug analysis method based on heatstroke data fusion according to claim 8, characterized in that, The molecular docking visualization is displayed in PyMOL, and the binding site is the PLIP site.

10. The target drug analysis method based on heatstroke data fusion according to claim 1, characterized in that, In response to the binding energy being greater than a preset binding energy threshold, the pre-core target point is determined as the target target point.

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