A method for analyzing the mechanism of relieving asthma disease by ephedra root based on network pharmacology and an application method thereof

Abstract

The application discloses a network pharmacology-based analysis method and application method for the mechanism of ephedra root in relieving asthma, and belongs to the technical field of network pharmacology of traditional Chinese medicines. The method comprises the following steps: obtaining chemical components of ephedra root, screening potential active components, predicting target points of the active components, obtaining asthma-related target points, and obtaining a potential intersection target point set by taking the intersection; constructing a network of the active components-intersection target points, calculating topological parameters of the network, and screening key active components in the network based on the network topological parameters; constructing a protein-protein interaction (PPI) network, screening core target points in the PPI network through topological characteristic indexes; carrying out gene ontology (GO) function enrichment and KEGG signal pathway enrichment analysis, and determining related signal pathways; and verifying the interaction relationship between the key active components and the core target points. The technical scheme can provide complete and reliable theoretical support for the pharmacological interpretation of ephedra root in relieving asthma, in-vivo and in-vitro experimental design and the development of new drugs for relieving asthma.

Description

Technical Field

[0001] This application belongs to the field of traditional Chinese medicine network pharmacology technology, specifically involving a method for analyzing and applying the mechanism of action of Ephedra root in relieving asthma based on network pharmacology. Background Technology

[0002] Asthma is a common chronic inflammatory disease of the airways with a complex pathogenesis, involving multiple inflammatory cells, inflammatory mediators, and signaling pathways in its progression. Ephedra root is a traditional Chinese medicine often used clinically to relieve cough and asthma, but its corresponding modern pharmacological mechanism of action has not yet been fully and comprehensively elucidated.

[0003] Current research on the antiasthmatic effects of ephedra root mostly focuses on in vitro experiments targeting single chemical components. This allows for the observation of the effects of a single component on individual targets, failing to reflect the synergistic effects of multiple components in traditional Chinese medicine. Existing research methods often employ cell and animal models for efficacy testing, which is time-consuming and limits the number of targets that can be screened at one time.

[0004] Existing research methods make it difficult to establish a complete network of relationships between medicinal materials, components, targets, and diseases, which is not conducive to the discovery of the active substances in ephedra root and the development of related antiasthmatic products. Summary of the Invention

[0005] This application provides a method for analyzing and applying the mechanism of action of Ephedra root in relieving asthma based on network pharmacology. The purpose is to solve the technical problems of existing research on Ephedra root for relieving asthma, which only targets single components and lacks a standardized integrated analysis process. Network pharmacology prediction lacks molecular docking verification and cannot fully build a network of association between medicinal materials, components, targets, and diseases. This application provides a complete analytical method to systematically explain the synergistic mechanism of Ephedra root in relieving asthma.

[0006] In a first aspect, embodiments of this application provide a method for analyzing the mechanism of action of Ephedra root in alleviating asthma based on network pharmacology, the method comprising: Step 1: Obtain the chemical components of Ephedra root from the Chinese herbal medicine database, screen potential active ingredients of Ephedra root using ADME parameters, predict active ingredient targets using a target prediction platform, and simultaneously search the disease database to obtain asthma-related targets. Take the intersection of the active ingredient targets and the asthma-related targets to obtain a potential intersection target set. Step 2: Construct a network of active ingredients and intersection targets based on the potential active ingredients and the potential intersection target set, calculate the topology parameters of the network, and screen key active ingredients in the network based on the network topology parameters; Step 3: Construct a protein-protein interaction (PPI) network based on the potential intersection target set, and screen the core targets in the PPI network using topological feature indicators. Step 4: Perform gene ontology GO function enrichment and KEGG signaling pathway enrichment analysis on the potential intersection target set to screen for significantly enriched biological processes, cellular components, molecular functions and disease-related signaling pathways. Step 5: Using the key active ingredient as a ligand and the core target protein as a receptor, obtain the three-dimensional structures of the ligand and receptor and complete the preprocessing. Use molecular docking software to simulate the binding of the ligand and receptor to verify the interaction between the key active ingredient and the core target.

[0007] Furthermore, step 1 includes: All chemical components of Ephedra root were retrieved using the TCMSP and HERB databases. The ADME screening thresholds were set as follows: oral bioavailability ≥30% and drug-likeness ≥0.18, to screen out potential active ingredients of Ephedra root. Potential active ingredient targets were predicted using TCMSP and SwissTargetPrediction, and then standardized to human gene names. Using "Asthma" as the search term, we retrieved asthma-related targets from the GeneCards, OMIM, DrugBank, and PharmGKB databases. The active ingredient targets and the asthma-related targets are imported into the Jvenn platform, and their intersection is taken to output the potential intersection target set.

[0008] Furthermore, the 13 potential active ingredients are: ZINC03860434, β-sitosterol, kaempferol, naringenin, dihydroquercetin, (+)-catechin, sennaol, 4-(2-eicosenoyloxycarbonyl-vinyl)benzoic acid, 4-(2-eicosenoyloxycarbonyl-vinyl)benzoic acid, ephedrine F, ephedrine G, ephedrine J, and quercetin.

[0009] Furthermore, in step 2, a visual active ingredient-intersection target network is constructed using Cytoscape software. The nodes are sorted by degree value as a topology parameter, and molecules with the highest degree values ​​are selected as the key active ingredients. The key active ingredients include quercetin, kaempferol, β-sitosterol, naringenin, and dihydroquercetin. Accordingly, step 3 includes: Import the potential intersection target set into the STRING database, select human (Homo sapiens) as the species, with a confidence threshold ≥0.9, and export the protein interaction TSV file; Use Cytoscape software to parse TSV files and construct a visual PPI network. Score the network nodes based on at least one of the topological features: degree centrality, betweenness centrality, and proximity centrality. Based on the node scoring results, the core targets were identified as MAPK3 and JUN.

[0010] Furthermore, step 4 includes: GO and KEGG enrichment analyses were performed on the potential intersection target set using online tools such as DAVID or Metascape, with a significance screening threshold of P < 0.05. After enrichment, GO bar charts and KEGG bubble charts were generated using the MicroBioinformatics platform to visualize the results.

[0011] Furthermore, step 5 includes: Download the 3D structure of the key active ingredient from the PubChem database as a docking ligand; Download the protein crystal structure of the core target from the RCSB PDB database, remove water molecules and proligands sequentially, and perform hydrogenation and charge calculation preprocessing to obtain the protein receptor. Semi-flexible batch molecular docking was performed using AutoDock Vina software. The lowest binding energy conformation was selected for analysis. A binding energy ≤ -5 kcal / mol was used to determine that there is a stable interaction between the active ingredient and the target. Simultaneously, a binding energy heatmap and a schematic diagram of the molecular binding mode were drawn to complete the verification visualization.

[0012] Furthermore, the signaling pathways include: lipid and atherosclerosis pathway, AGE-RAGE signaling pathway, and TNF signaling pathway.

[0013] Furthermore, the method also includes: Based on the key active ingredients screened in step 2 and the core targets screened in step 3, we designed experimental protocols for in vitro cell and in vivo animal efficacy verification.

[0014] Secondly, this application provides a method for developing and applying products that use ephedra root to relieve asthma. This method uses the network pharmacology-based mechanism analysis method described above to screen the active ingredients of ephedra root for relieving asthma, analyze the targets and regulatory pathways related to asthma, and prepare traditional Chinese medicine active preparations, asthma-relieving health foods, or anti-asthma drugs to relieve asthma.

[0015] The technical solution provided in this application comprehensively constructs an interaction network between ephedra root, active ingredients, targets, and asthma, enabling rapid batch screening of key active ingredients and core targets of medicinal materials. GO and KEGG enrichment clarifies the asthma-relieving regulatory pathways. The accompanying molecular docking verifies the stability of component-target binding at the atomic level, compensating for the insufficient reliability of simple network predictions. The entire process is standardized, has a short cycle, and aligns with the holistic effects of traditional Chinese medicine. It can provide complete and reliable theoretical support for the pharmacological explanation of ephedra root's asthma-relieving properties, in vitro and in vivo experimental design, and the development of new asthma-relieving drugs. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating the method for analyzing the mechanism of action of Ephedra root in relieving asthma based on network pharmacology, as provided in Embodiment 1 of this application. Figure 2 Network diagram of the intersection targets of ephedra root active ingredients and asthma.

[0017] Figure 3 This is a protein-protein interaction network diagram of the intersection targets.

[0018] Figure 4 This is a screening diagram for core targets.

[0019] Figure 5 Bar chart for GO feature enrichment analysis.

[0020] Figure 6 Bubble chart for KEGG pathway enrichment analysis.

[0021] Figure 7 This is a heatmap showing the binding energy of the key active ingredient and the core target molecule.

[0022] Figure 8 This is a schematic diagram illustrating the binding mode between the key active ingredient and the core target molecule. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of this application clearer, specific embodiments of this application will be described in further detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are merely for explaining this application and not for limiting it. It should also be noted that, for ease of description, only the parts relevant to this application are shown in the drawings, not all of them. Before discussing exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although the flowcharts describe operations (or steps) as sequential processes, many of these operations can be performed in parallel, concurrently, or simultaneously. Furthermore, the order of the operations can be rearranged. The process can be terminated when its operation is completed, but may also have additional steps not included in the drawings. The process can correspond to a method, function, procedure, subroutine, subroutine, etc.

[0024] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0025] The terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such terms can be used interchangeably where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class and the number of objects is not limited; for example, a first object can be one or more. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0026] The following, in conjunction with the accompanying drawings, provides a detailed explanation of the mechanism of action of Ephedra root in alleviating asthma based on network pharmacology, through specific embodiments and application scenarios.

[0027] Example 1 Figure 1 This is a flowchart illustrating the method for analyzing the mechanism of action of Ephedra root in alleviating asthma based on network pharmacology, as provided in Embodiment 1 of this application. Figure 1 As shown, the method specifically includes: Step 1: Obtain the chemical components of Ephedra root from the Chinese herbal medicine database, screen potential active ingredients of Ephedra root using ADME parameters, predict active ingredient targets using a target prediction platform, and simultaneously search the disease database to obtain asthma-related targets. Take the intersection of the active ingredient targets and the asthma-related targets to obtain a potential intersection target set. Traditional Chinese medicine databases refer to professional online databases that collect chemical components of Chinese medicinal materials. These can be TCMSP (Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform) or HERB (Traditional Chinese Medicine High-Precision Database). For example, TCMSP can completely collect information on all known compounds in Ephedra root.

[0028] This solution allows users to input the name of the medicinal herb, retrieve database entries, and export the raw data of all compounds in Ephedra root, thereby completing the collection of chemical composition information.

[0029] The chemical components of ephedra root refer to various compounds isolated and identified from ephedra root medicinal materials. These can be flavonoids, sterols, and organic acids, such as quercetin, ephedrine F, and β-sitosterol.

[0030] ADME (Absorption, Distribution, Metabolism, Excretion) parameters refer to indicators that evaluate the potential of compounds to be absorbed, distributed, metabolized, and excreted in vivo to become drugs. They include two core indicators: oral bioavailability and drug-likeness.

[0031] This method can filter all chemical components by setting a fixed numerical threshold, eliminating compounds with poor in vivo drug efficacy, and retaining only substances that meet the standards.

[0032] Potential active ingredients refer to ephedra root compounds that have the potential to relieve asthma in vivo after being screened using ADME parameters, such as kaempferol and naringenin.

[0033] A target prediction platform is an online tool that uses molecular similarity algorithms to match compounds with binding proteins. It can be TCMSP or SwissTargetPrediction (an online target prediction platform).

[0034] The target prediction platform reads the molecular structure of compounds, matches them with human proteins that they can bind to, and outputs a set of candidate target genes.

[0035] Active ingredient targets refer to human protein genes that potential active ingredients can specifically target, such as MAPK3 and JUN.

[0036] Disease databases are professional databases that contain disease-causing genes and inflammation-related proteins, such as GeneCards, OMIM, DrugBank, and PharmGKB.

[0037] Specifically, you can search the disease database using "Asthma" as a keyword to extract the complete set of targets related to airway inflammation in asthma.

[0038] Asthma-related targets refer to pathogenic proteins involved in asthma immune disorders and airway inflammatory responses, such as inflammatory regulatory gene proteins.

[0039] The potential intersection target set refers to the set of all common protein targets that can be regulated by the active ingredients of Ephedra root and directly participate in the pathogenesis of asthma.

[0040] Step 2: Construct a network of active ingredients and intersection targets based on the potential active ingredients and the potential intersection target set, calculate the topology parameters of the network, and screen key active ingredients in the network based on the network topology parameters; This solution can input the correspondence between active ingredients and targets into visualization software to generate a node interaction network diagram.

[0041] The network of active ingredients and their overlapping targets is a visual map composed of active ingredient nodes, target nodes, and interaction lines, which can intuitively show the regulatory correspondence between ingredients and targets.

[0042] Topology parameters are numerical indicators that quantify the importance of network nodes. They can be node degree values, which represent the number of other nodes connected to a single node.

[0043] Specifically, the built-in algorithm of the visualization software can automatically count the number of connections for each node, output the topology parameter values ​​for all nodes, sort them from high to low according to the topology parameter values, and select the top-ranked compounds as the core active ingredients.

[0044] Key active ingredients refer to core bronchodilator substances with high network intensity that can simultaneously regulate multiple asthma targets, such as quercetin and kaempferol.

[0045] Step 3: Construct a protein-protein interaction (PPI) network based on the potential intersection target set, and screen the core targets in the PPI network using topological feature indicators. This approach allows you to import target genes into a protein interaction database to obtain protein associations, and then import them into visualization software to generate an interaction map.

[0046] A PPI (Protein-Protein Interaction) network is a visual map that shows the binding and regulatory relationships between target proteins.

[0047] Topological characteristic indicators are quantitative indicators that evaluate the regulatory role of proteins in disease pathways, and can include degree centrality, betweenness centrality, and proximity centrality.

[0048] Core targets refer to key pivotal proteins that run through multiple asthma inflammatory pathways, such as MAPK3 and JUN.

[0049] Step 4: Perform gene ontology GO function enrichment and KEGG signaling pathway enrichment analysis on the potential intersection target set to screen for significantly enriched biological processes, cellular components, molecular functions and disease-related signaling pathways. GO (Gene Ontology) functional enrichment refers to annotating the physiological functions of target genes from three dimensions: biological processes, cellular components, and molecular functions.

[0050] KEGG (Kyoto Encyclopedia of Genes and Genomes) signaling pathway enrichment analysis refers to matching target genes involved in human metabolic and inflammatory regulatory pathways.

[0051] Specifically, using P less than 0.05 as the significance threshold, annotation entries with strong enrichment correlation and high enrichment degree are retained.

[0052] Biological processes refer to cellular physiological activities involving target proteins, which can include responses to exogenous biological stimuli and positive regulation of gene expression.

[0053] Cellular components refer to the distribution location of target proteins within cells, which can be extracellular spaces or extracellular regions.

[0054] Molecular function refers to the biochemical activity possessed by the target protein, which can be enzyme binding activity or protein homodimerization activity.

[0055] Disease-related signaling pathways refer to inflammatory and metabolic regulatory pathways that are significantly enriched in terms of target concentration and directly involved in the pathogenesis of asthma, such as the AGE-RAGE signaling pathway and the TNF signaling pathway.

[0056] Step 5: Using the key active ingredient as a ligand and the core target protein as a receptor, obtain the three-dimensional structures of the ligand and receptor and complete the preprocessing. Use molecular docking software to simulate the binding of the ligand and receptor to verify the interaction between the key active ingredient and the core target.

[0057] A ligand is a three-dimensional structural file of the active ingredient that binds small molecules in molecular docking; it can be the quercetin 3D structure downloaded from PubChem.

[0058] A receptor is a three-dimensional macromolecular structure of a target protein that has been preprocessed by software and is used to bind to ligand molecules.

[0059] Specifically, ligand 3D structures can be downloaded from the PubChem database, and protein raw crystal structures can be downloaded from the RCSB PDB database.

[0060] Preprocessing refers to the process of removing water molecules and proligands from protein crystals using software, performing hydrogenation and charge calculations, and eliminating interference from irrelevant structures.

[0061] Molecular docking software refers to computational software that simulates the spatial binding of small molecules to proteins; it can be AutoDock Vina.

[0062] Simulation refers to software automatically traversing multiple spatial conformations of ligands and receptors and calculating the binding energy value corresponding to each conformation.

[0063] Verification refers to comparing the minimum binding energy values ​​to determine whether there is a stable binding effect between the active ingredient and the target protein.

[0064] Interaction relationships refer to the hydrogen bonds, hydrophobic binding, and other forces formed between small molecule ligands and protein amino acid residues.

[0065] The technical solution provided in this embodiment establishes a complete analytical system from the mining of medicinal components, network analysis, pathway annotation to molecular docking verification. It is in line with the overall characteristics of the synergistic effects of multiple components, multiple targets, and multiple pathways in traditional Chinese medicine, and systematically and completely analyzes the molecular mechanism of action of ephedra root in relieving asthma.

[0066] In one embodiment, step 1 may optionally include: All chemical components of Ephedra root were retrieved using the TCMSP and HERB databases. The ADME screening thresholds were set as follows: oral bioavailability ≥30% and drug-likeness ≥0.18, to screen out potential active ingredients of Ephedra root. Potential active ingredient targets were predicted using TCMSP and SwissTargetPrediction, and then standardized to human gene names. Using "Asthma" as the search term, we retrieved asthma-related targets from the GeneCards, OMIM, DrugBank, and PharmGKB databases. The active ingredient targets and the asthma-related targets are imported into the Jvenn platform, and their intersection is taken to output the potential intersection target set.

[0067] Among them, TCMSP (Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform) and HERB (Traditional Chinese Medicine High-Precision Database) refer to traditional Chinese medicine databases used to retrieve all original compounds of Ephedra root.

[0068] ADME screening thresholds refer to the criteria for determining the drug potential of a compound, which include two indicators: oral bioavailability ≥30% and drug-likeness ≥0.18.

[0069] SwissTargetPrediction is an online tool used to predict active ingredient targets in batches and standardize them with human gene names.

[0070] Asthma is an English keyword used to search for targets of asthma.

[0071] GeneCards, OMIM, DrugBank, and PharmGKB are disease databases that contain pathogenic targets for asthma.

[0072] The Jvenn platform is an online analysis platform used for intersection calculations of two sets of target points and generating Venn diagram visualization results.

[0073] This technical solution provides a standardized database, screening thresholds, and operating platform to support its implementation, forming a repeatable and standardized component target mining process and reducing data bias caused by manual operation.

[0074] In one embodiment, optionally, the potential active ingredients comprise 13 types, namely: ZINC03860434, β-sitosterol, kaempferol, naringenin, dihydroquercetin, (+)-catechin, sennaol, 4-(2-eicosicooxycarbonyl-vinyl)benzoic acid, 4-(2-eicosicooxycarbonyl-vinyl)benzoic acid, ephedrine F, ephedrine G, ephedrine J, and quercetin.

[0075] Among them, the potential active ingredients refer to the 13 medicinal components of Ephedra root obtained after ADME threshold screening, namely ZINC03860434, β-sitosterol, kaempferol, naringenin, dihydroquercetin, (+)-catechin, sennaol, 4-(2-eicosicooxycarbonyl-vinyl)benzoic acid, 4-(2-eicosicooxycarbonyl-vinyl)benzoic acid, ephedrine F, ephedrine G, ephedrine J and quercetin.

[0076] This technical solution provides a complete list of candidate pharmacodynamic substances from Ephedra root, identifies the research targets for network analysis and molecular docking, and defines a clear component range for the study of the pharmacodynamic material basis of Ephedra root in relieving asthma.

[0077] In one embodiment, optionally, step 2 uses Cytoscape software to build a visual active ingredient-intersection target network, sorts the nodes by degree value as a topology parameter, and selects molecules with the highest degree values ​​as the key active ingredients. The key active ingredients include quercetin, kaempferol, β-sitosterol, naringenin, and dihydroquercetin. Accordingly, step 3 includes: Import the potential intersection target set into the STRING database, select human (Homo sapiens) as the species, with a confidence threshold ≥0.9, and export the protein interaction TSV file; Use Cytoscape software to parse TSV files and construct a visual PPI network. Score the network nodes based on at least one of the topological features: degree centrality, betweenness centrality, and proximity centrality. Based on the node scoring results, the core targets were identified as MAPK3 and JUN.

[0078] Cytoscape software refers to bioinformatics visualization and analysis software used to construct and visualize component-target networks and PPI networks.

[0079] Node degree is a topology parameter used to measure the number of target points that a single component node can regulate.

[0080] The STRING database is a professional database that records the interactions between proteins in the human body.

[0081] Homo sapiens refers to human-derived species and is the species classification selected when searching for protein interactions in the STRING database.

[0082] The confidence threshold is the criterion for judging reliable protein-protein interactions in the STRING database. This scheme sets it to ≥0.9.

[0083] TSV files are text-based files that store protein-protein interaction relationships and can be directly imported into Cytoscape software for parsing.

[0084] Degree centrality, betweenness centrality, and proximity centrality are three types of PPI network topology characteristics used to screen core targets.

[0085] MAPK3 and JUN refer to the core target proteins that regulate asthma inflammation, obtained through topological scoring screening.

[0086] This technical solution clarifies the software, parameters, and judgment criteria for constructing a two-layer network and screening core targets, accurately identifying 5 key active ingredients and 2 core targets, and significantly narrowing the research scope of subsequent molecular docking and physical experiments.

[0087] In one embodiment, step 4 may optionally include: GO and KEGG enrichment analyses were performed on the potential intersection target set using online tools such as DAVID or Metascape, with a significance screening threshold of P < 0.05. After enrichment, GO bar charts and KEGG bubble charts were generated using the MicroBioinformatics platform to visualize the results.

[0088] DAVID and Metascape are online bioinformatics tools used for batch GO and KEGG enrichment annotation.

[0089] P refers to the statistical value of the enrichment result. In this scheme, P < 0.05 is set as the significant enrichment screening threshold.

[0090] MicroBioinformatics platform refers to an online plotting tool used for visualizing enrichment results.

[0091] A GO bar chart is a bar-shaped visual graph used to intuitively display the enrichment scores of GO feature enrichment entries.

[0092] A KEGG bubble chart is a bubble visualization used to show the quantity and significance of KEGG pathway enrichment.

[0093] This technical solution limits the enrichment analysis tools and visualization methods, making the enrichment results intuitive and readable, and facilitating the rapid identification of the core biological functions and regulatory pathways corresponding to the bronchodilator effect of ephedra root.

[0094] In one embodiment, step 5 may optionally include: Download the 3D structure of the key active ingredient from the PubChem database as a docking ligand; Download the protein crystal structure of the core target from the RCSB PDB database, remove water molecules and proligands sequentially, and perform hydrogenation and charge calculation preprocessing to obtain the protein receptor. Semi-flexible batch molecular docking was performed using AutoDock Vina software. The lowest binding energy conformation was selected for analysis. A binding energy ≤ -5 kcal / mol was used to determine that there is a stable interaction between the active ingredient and the target. Simultaneously, a binding energy heatmap and a schematic diagram of the molecular binding mode were drawn to complete the verification visualization.

[0095] PubChem is a public chemical database that contains the 3D structures of various small molecule compounds.

[0096] The RCSB PDB database is a public protein database that contains the three-dimensional structures of various protein crystals.

[0097] Proligands are small, naturally occurring binding molecules that are present in the protein crystal structure and need to be removed during the pretreatment stage.

[0098] This method can use software to add hydrogen atoms to the amino acid residues of proteins, restoring their true charged state in vivo.

[0099] Charge calculation refers to the software's calculation of the charge values ​​of each atom in protein and ligand molecules to ensure the accuracy of docking simulation.

[0100] AutoDock Vina software refers to professional computational software used for semi-flexible molecular docking simulation.

[0101] Semi-flexible batch molecular docking refers to fixing the protein backbone and allowing only the amino acid side chains and ligand molecules to rotate freely, thus completing multiple sets of ligand-receptor docking in batches.

[0102] Binding energy is a numerical value that quantifies the stability of ligand binding to protein. The lower the value, the more stable the binding. Binding energy ≤ -5 kcal / mol is the critical threshold for determining that there is a stable interaction between the active ingredient and the target. Binding energy heatmap is a thermal graph that visualizes the binding energy values ​​of multiple components-target docking.

[0103] A schematic diagram of molecular binding modes refers to a magnified view of a local structure showing the hydrogen bonds and hydrophobic interactions between ligands and protein amino acid residues.

[0104] This technical solution clarifies the complete standardized operating procedures and quantitative judgment thresholds for molecular docking, enabling automated batch verification of multiple components and providing evidence for the reliability of network pharmacology prediction results at the molecular and atomic level.

[0105] In one embodiment, optionally, the signaling pathway includes: the lipid and atherosclerosis pathway, the AGE-RAGE signaling pathway, and the TNF signaling pathway.

[0106] The lipid and atherosclerosis pathway, the AGE-RAGE (advanced glycation end products-advanced glycation end products receptor) signaling pathway, and the TNF (tumor necrosis factor) signaling pathway refer to the three core inflammatory regulatory pathways of Ephedra root intervention in asthma, which were identified through enrichment analysis.

[0107] This technical solution clarifies the core regulatory pathway system of ephedra root in relieving asthma and fully reveals the multi-level pathway mechanism of ephedra root in inhibiting airway inflammation, providing pathway target basis for the development of new targeted drugs for relieving asthma.

[0108] In one embodiment, optionally, the method further includes: Based on the key active ingredients screened in step 2 and the core targets screened in step 3, we designed experimental protocols for in vitro cell and in vivo animal efficacy verification.

[0109] In vitro cell efficacy validation experimental protocol refers to the experimental design for detecting the efficacy of active ingredients using an airway inflammation cell model.

[0110] An in vivo animal efficacy verification experimental protocol refers to an experimental design that uses asthma model animals to conduct overall efficacy evaluation.

[0111] This technical solution bridges the gap between bioinformatics prediction and in vivo and in vitro pharmacodynamic experiments, providing a clear and feasible design for subsequent experiments to verify the efficacy of ephedra root in relieving asthma, and enhancing the practical application value of the entire analytical method.

[0112] To enable those skilled in the art to better understand this solution, this application also provides a preferred embodiment.

[0113] This embodiment illustrates a method for analyzing the mechanism of action of Ephedra root in alleviating asthma based on network pharmacology and molecular docking. The specific steps of the method are as follows: (1) The screening of potential active ingredients and prediction of their targets for action of Ephedra root were carried out in the following steps: First, the chemical components of Ephedra root were retrieved from the Traditional Chinese Medicine Systems Pharmacology Database (TCMSP, website: https: / / tcmsp-e.com / tcmspsearch.php). Screening conditions were set according to ADME (absorption, distribution, metabolism, excretion) parameters, namely, oral bioavailability of not less than 30% and drug-likeness of not less than 0.18, so as to obtain candidate active ingredients that meet the standards. Subsequently, the targets of action of these screened active ingredients were predicted using the SwissTargetPrediction online platform.

[0114] (2) To systematically reveal the synergistic mechanism of "multi-component-multi-target", this invention constructs a composite network to visualize and analyze the interaction relationships between various elements. Specifically, the data of ephedra root, screened active ingredients and their corresponding disease targets are imported into Cytoscape 3.7.2 software to draw a "medicinal material-component-target" network diagram.

[0115] (3) In this invention, the asthma-related disease targets were obtained from the GeneCards, OMIM, DrugBank, and PharmGKB (https: / / www.clinpgx.org / ) databases using "Asthma" as the keyword. To clarify the potential targets of Ephedra root for asthma, the above-mentioned disease targets and the targets of Ephedra root active ingredients were then imported into the Jvenn platform (https: / / jvenn.toulouse.inrae.fr / app / example.html) for intersection analysis, and the results were visualized and screened using Venn diagrams.

[0116] (4) The screening of core targets was completed based on protein-protein interaction network analysis. Specifically, common targets were submitted to the STRING database (species: "Homo sapiens"; confidence score: ≥0.9), and TSV files were exported after obtaining the interaction relationships. Then, Cytoscape 3.7.2 software was used to parse the files and construct the network. By calculating indicators such as node centrality, the core targets in the PPI network were finally identified.

[0117] (5) To elucidate the mechanism from the functional and pathway levels, the above-mentioned overlapping targets were submitted to the DAVID platform for GO function and KEGG pathway enrichment analysis. After obtaining the annotation information, the data was then visualized using the Bioinformatics platform (https: / / www.bioinformatics.com.cn / ).

[0118] (6) This section verifies the binding relationships predicted by network analysis through molecular docking simulation. The main operations include: 1) Receptor preparation: obtaining the target protein structure from the PDB database; 2) Docking simulation: using AutoDock Vina, performing batch docking of 5 core active ingredients with 2 core targets; 3) Result judgment: using binding energy as the main evaluation criterion, a binding energy ≤ -5 kcal / mol means that there is a stable and good interaction between the component and the target.

[0119] Based on the criteria of oral bioavailability (OB) ≥30% and drug-likeness (DL) ≥0.18%, a total of 13 active ingredients from Ephedra root were obtained.

[0120] Table 1. Main active ingredients in Ephedra root for asthma.

[0121] Data on ephedra root, screened active ingredients, and their corresponding disease targets were imported into Cytoscape 3.7.2 software to create a "medicinal material-ingredient-target" network diagram, as shown below. Figure 2 As shown.

[0122] Asthma-related disease targets were obtained from the GeneCards, OMIM, DrugBank, and PharmGKB (https: / / www.clinpgx.org / ) databases using "Asthma" as the keyword. To clarify the potential targets of Ephedra root for asthma, the aforementioned disease targets and the active ingredient targets of Ephedra root were imported into the Jvenn platform (https: / / jvenn.toulouse.inrae.fr / app / example.html) for intersection analysis. The results were visualized using Venn diagrams to identify common targets of Ephedra root and asthma. These common targets were then submitted to the STRING database (species: "Homo sapiens"; confidence score: ≥0.9) to obtain interaction relationships and export TSV files. Subsequently, Cytoscape 3.7.2 software was used to parse the files and construct networks. By calculating indicators such as node centrality, the core targets in the PPI network were finally identified, such as... Figure 3 and Figure 4 As shown. Using the above methods, two core targets for asthma were identified: MAPK3 and JUN.

[0123] The aforementioned overlapping target sites were submitted to the DAVID platform for GO function and KEGG pathway enrichment analysis. After obtaining the annotation information, visualization was then performed using the MicroBioInformatics platform (https: / / www.bioinformatics.com.cn / ), as shown below. Figure 5 and Figure 6 As shown. From Figure 5 and Figure 6 It can be seen that responses to xenobiotic stimuli and positive regulation of gene expression are significantly correlated. Cellular components are significantly correlated with extracellular space and extracellular region. Molecular functions are significantly correlated with enzyme binding and protein homodimerization activity. The targets of Ephedra root in alleviating asthma are mainly concentrated on pathways such as lipids and atherosclerosis, and the AGE-RAGE signaling pathway in diabetic complications.

[0124] Target protein structures were obtained from the PDB database. Using AutoDock Vina, batch docking was performed between five core active ingredients and two core targets. Binding energy was used as the primary evaluation criterion; a binding energy ≤ -5 kcal / mol indicated a stable and good interaction between the ingredient and the target. Molecular docking binding energy diagrams and visualization analysis figures are shown below. Figure 7 and Figure 8 As shown in the figure, the binding energies of the molecular docking results are all less than -5 kcal / mol, indicating that Ephedra root has an effect in relieving asthma.

[0125] The beneficial effects of this invention are: 1. Systematicity: This invention is the first to combine network pharmacology prediction with precise molecular docking verification, constructing a complete analytical chain from "component-target-pathway-disease", systematically elucidating the synergistic effect network of multiple components, multiple targets, and multiple pathways of Ephedra root in relieving asthma, overcoming the limitations of traditional single research models.

[0126] 2. High efficiency and predictability: This method can quickly screen key active ingredients, core targets and key signaling pathways from massive amounts of data, which greatly shortens the cycle of initial mechanism exploration and provides clear predictive directions and hypotheses for further experimental research.

[0127] 3. Verifiability and reliability: Through molecular docking technology, computer simulations have verified that there is indeed a stable possibility of binding between the core target predicted by the network and the active ingredient, which enhances the reliability of the prediction results and lays a solid theoretical foundation for subsequent in vitro and in vivo experimental verification.

[0128] 4. Universality: The methodological framework established in this invention is not only applicable to the study of ephedra root and asthma, but can also be extended to the study of the mechanism of action of other Chinese herbal compound or single herb in the treatment of complex diseases, and has broad application prospects.

[0129] 5. Promoting the modernization of traditional Chinese medicine: This invention provides modern scientific evidence for elucidating the traditional efficacy of traditional Chinese medicine, and helps to promote the elucidation of the mechanism of action of traditional Chinese medicine, the research and development of new drugs, and the precise clinical use of drugs.

[0130] Example 2 This application provides a method for developing and applying products that use ephedra root to relieve asthma. The method employs the network pharmacology-based mechanism analysis method described above to screen the active ingredients of ephedra root for relieving asthma, analyze asthma-related targets and regulatory pathways, and prepare traditional Chinese medicine active preparations, asthma-relieving health foods, or anti-asthma drugs to relieve asthma.

[0131] The method for developing and applying ephedra root to relieve asthma provided in this embodiment has the same execution process and beneficial effects as the above-described method embodiments. To avoid repetition, it will not be described again here.

[0132] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element. Furthermore, it should be noted that the scope of the methods and systems in the embodiments of this application is not limited to performing functions in the order shown or discussed, but may also include performing functions substantially simultaneously or in the reverse order, depending on the functions involved. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

[0133] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a computer software product. This computer software product is stored in a storage medium (such as ROM / RAM, disk, optical disk) and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0134] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms fall within the scope of protection of this application.

[0135] The above description is merely a preferred embodiment and the technical principles employed in this application. This application is not limited to the specific embodiments described herein. Various obvious changes, readjustments, and substitutions that can be made by those skilled in the art will not depart from the scope of protection of this application. Therefore, although this application has been described in detail through the above embodiments, this application is not limited to the above embodiments. Many other equivalent embodiments may be included without departing from the concept of this application, and the scope of this application is determined by the scope of the claims.

Claims

1. A method for analyzing the mechanism of action of Ephedra root in alleviating asthma based on network pharmacology, characterized in that, Includes the following steps: Step 1: Obtain the chemical components of Ephedra root from the Chinese herbal medicine database, screen potential active ingredients of Ephedra root using ADME parameters, predict active ingredient targets using a target prediction platform, and simultaneously search the disease database to obtain asthma-related targets. Take the intersection of the active ingredient targets and the asthma-related targets to obtain a potential intersection target set. Step 2: Construct a network of active ingredients and intersection targets based on the potential active ingredients and the potential intersection target set, calculate the topology parameters of the network, and screen key active ingredients in the network based on the network topology parameters; Step 3: Construct a protein-protein interaction (PPI) network based on the potential intersection target set, and screen the core targets in the PPI network using topological feature indicators. Step 4: Perform gene ontology GO function enrichment and KEGG signaling pathway enrichment analysis on the potential intersection target set to screen for significantly enriched biological processes, cellular components, molecular functions and disease-related signaling pathways. Step 5: Using the key active ingredient as a ligand and the core target protein as a receptor, obtain the three-dimensional structures of the ligand and receptor and complete the preprocessing. Use molecular docking software to simulate the binding of the ligand and receptor to verify the interaction between the key active ingredient and the core target.

2. The method for analyzing the mechanism of action of Ephedra root in relieving asthma based on network pharmacology according to claim 1, characterized in that, Step 1 includes: All chemical components of Ephedra root were retrieved using the TCMSP and HERB databases. The ADME screening thresholds were set as follows: oral bioavailability ≥30% and drug-likeness ≥0.18, to screen out potential active ingredients of Ephedra root. Potential active ingredient targets were predicted using TCMSP and SwissTargetPrediction, and then standardized to human gene names. Using "Asthma" as the search term, we retrieved asthma-related targets from the GeneCards, OMIM, DrugBank, and PharmGKB databases. The active ingredient targets and the asthma-related targets are imported into the Jvenn platform, their intersection is taken, and the potential intersection target set is output.

3. The method for analyzing the mechanism of action of Ephedra root in relieving asthma based on network pharmacology according to claim 2, characterized in that, The 13 potential active ingredients are: ZINC03860434, β-sitosterol, kaempferol, naringenin, dihydroquercetin, (+)-catechin, sennaol, 4-(2-eicosenoyloxycarbonyl-vinyl)benzoic acid, 4-(2-eicosenoyloxycarbonyl-vinyl)benzoic acid, ephedrine F, ephedrine G, ephedrine J, and quercetin.

4. The method for analyzing the mechanism of action of Ephedra root in relieving asthma based on network pharmacology according to claim 1, characterized in that, Step 2 uses Cytoscape software to build a visual active ingredient-intersection target network, sorts the nodes by degree value as a topology parameter, and selects molecules with the highest degree values ​​as the key active ingredients. The key active ingredients include quercetin, kaempferol, β-sitosterol, naringenin, and dihydroquercetin. Accordingly, step 3 includes: Import the potential intersection target set into the STRING database, select human (Homo sapiens) as the species, with a confidence threshold ≥0.9, and export the protein interaction TSV file; Use Cytoscape software to parse TSV files and construct a visual PPI network. Score the network nodes based on at least one of the topological features: degree centrality, betweenness centrality, and proximity centrality. Based on the node scoring results, the core targets were identified as MAPK3 and JUN.

5. The method for analyzing the mechanism of action of Ephedra root in relieving asthma based on network pharmacology according to claim 1, characterized in that, Step 4 includes: GO and KEGG enrichment analyses were performed on the potential intersection target set using online tools such as DAVID or Metascape, with a significance screening threshold of P < 0.

05. After enrichment, GO bar charts and KEGG bubble charts were generated using the MicroBioinformatics platform to visualize the results.

6. The method for analyzing the mechanism of action of Ephedra root in relieving asthma based on network pharmacology according to claim 1, characterized in that, Step 5 includes: Download the 3D structure of the key active ingredient from the PubChem database as a docking ligand; Download the protein crystal structure of the core target from the RCSB PDB database, remove water molecules and proligands sequentially, and perform hydrogenation and charge calculation preprocessing to obtain the protein receptor. Semi-flexible batch molecular docking was performed using AutoDock Vina software. The lowest binding energy conformation was selected for analysis. A binding energy ≤ -5 kcal / mol was used to determine that there is a stable interaction between the active ingredient and the target. Simultaneously, a binding energy heatmap and a schematic diagram of the molecular binding mode were drawn to complete the verification visualization.

7. The method for analyzing the mechanism of action of Ephedra root in relieving asthma based on network pharmacology according to claim 1, characterized in that, The signaling pathways include: lipid and atherosclerosis pathway, AGE-RAGE signaling pathway, and TNF signaling pathway.

8. The method for analyzing the mechanism of action of Ephedra root in relieving asthma based on network pharmacology according to claim 1, characterized in that, The method further includes: Based on the key active ingredients screened in step 2 and the core targets screened in step 3, we designed experimental protocols for in vitro cell and in vivo animal efficacy verification.

9. A method for developing and applying a product containing ephedra root to relieve asthma, characterized in that, Using the network pharmacology-based mechanism analysis method for relieving asthma by Ephedra root as described in any of claims 1-8, we can screen the active ingredients of Ephedra root for relieving asthma, analyze the asthma-related targets and regulatory pathways, and use them to prepare traditional Chinese medicine active preparations, asthma-relieving health foods, or anti-asthma drugs to relieve asthma.

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