Method for analyzing treatment of renal fibrosis by formononetin based on network pharmacology and molecular docking technology

Abstract

The invention belongs to the technical field of network pharmacology and molecular docking, and discloses a method for analyzing the treatment of renal fibrosis by formononetin based on the network pharmacology and molecular docking technology. Comprising the following steps: acquiring targets corresponding to active components, collecting renal fibrosis related targets, constructing a formononetin renal fibrosis target PPI network, performing enrichment analysis on core targets GO and KEGG, and performing molecular docking. Based on an integrated pharmacology platform and molecular docking, the mechanism of formononetin for treating renal fibrosis is explored, and the action mechanism of formononetin for treating renal fibrosis and related target genes are found and predicted.

Description

Technical Field

[0001] This invention belongs to the fields of network pharmacology and molecular docking technology, specifically relating to a method for analyzing the treatment of renal fibrosis using gentianin based on network pharmacology and molecular docking technology. Background Technology

[0002] Renal fibrosis is a common and final pathway leading to end-stage renal failure in various chronic kidney diseases. Essentially, it arises from the loss of control over the kidney's repair mechanisms in response to ongoing damage (such as diabetes, hypertension, and glomerulonephritis), resulting in the irreversible replacement of normal nephrons by excessive extracellular matrix (scar tissue). The core mechanisms of this process involve the activation of intrinsic renal cells (such as pericytes) into scar-producing myofibroblasts, epithelial-mesenchymal transition, and a vicious cycle of chronic inflammation driven by factors such as TGF-β1. Pathologically, it results in renal structural destruction, shrinkage and hardening, and functionally, a decreased filtration rate, toxin accumulation, and regulatory imbalance. Current clinical treatment focuses on slowing its progression, including strict control of the primary disease and the use of nephroprotective drugs (such as ACEIs / ARBs and SGLT2 inhibitors). However, established fibrosis is generally considered irreversible; therefore, treatment for renal fibrosis remains primarily remission-oriented, and its underlying mechanisms are not fully understood, leaving patients without a fundamental cure.

[0003] Aspergillus oryzae is a naturally occurring isoflavone compound with significant biological activity, widely distributed in leguminous plants such as Astragalus membranaceus, Pueraria lobata, and red clover. Its core value lies in its "phytoestrogen" properties; due to its molecular structure, it can bind to human estrogen receptors, thus exerting a weak replacement effect when estrogen levels are low (e.g., in postmenopausal women), effectively alleviating menopausal discomforts such as hot flashes and night sweats. Aspergillus oryzae is not only a key ingredient in dietary supplements for improving women's health and bone health, but it is also added to high-end skincare products to utilize its collagen-boosting and anti-aging effects. Although its broad pharmacological effects are well-recognized, the precise pharmacodynamic material basis (specific active fragments), core target network, and multi-pathway synergistic regulatory mechanism for treating renal fibrosis remain unclear. Network pharmacology can elucidate the mechanism of action of complex natural products at the systemic level. This invention employs network pharmacology combined with molecular docking technology to predict the key active domains, core targets, and signaling pathways of aspergillus oryzae in treating renal fibrosis, providing a scientific basis for elucidating its therapeutic mechanism and subsequent in-depth development and application. Summary of the Invention

[0004] To overcome the shortcomings of existing technologies, this invention provides a method for analyzing the treatment of renal fibrosis using gentianin based on network pharmacology and molecular docking technology. This method screens 5 core targets, 30 GO annotations, and 100 KEGG pathways through network pharmacology and molecular docking technology, and the active ingredient has good affinity with the core targets.

[0005] The above-mentioned objective of this invention is achieved through the following technical solution: a method for analyzing the treatment of renal fibrosis using gentianin based on network pharmacology and molecular docking technology, comprising the following steps: S1. Obtain the SMILES structural formula of strychnosine, and then obtain the target corresponding to the active ingredient of strychnosine. S2. Collection of kidney fibrosis-related targets: Search and export kidney fibrosis results data, screen disease genes to obtain gene targets for kidney fibrosis, and draw Venn diagrams between the targets and disease targets to obtain the intersection gene targets. S3. Construction of PPI network for basidiosin renal fibrosis target: PPI data of intersection target points were obtained, a basidiosin-renal fibrosis target PPI network diagram was drawn, the degree value, betweenness value and closeness value of target points were analyzed, and core target points were screened out; S4. Enrichment analysis of core target GO and KEGG: The GO function enrichment and KEGG pathway enrichment analysis of the intersection genes obtained in step S2 were performed to obtain the GO and KEGG pathways of the core target. S5. Molecular docking: Process the 2D structure of the active ingredient of strychnosine, process the core target obtained in step S3, perform molecular docking between the processed core ingredient and the core target, and finally visualize the docking between the ingredient and the target.

[0006] Furthermore, step S1 involves obtaining the SMILES structural formula of strychnosine from the PubChem website, importing it into the Swiss Target Pridiction platform, and obtaining the target corresponding to the active ingredient of strychnosine.

[0007] Furthermore, step S2 involves using GeneCards and DisGeNET platforms to retrieve and export renal fibrosis result data, screening for disease genes to obtain gene targets for renal fibrosis, and drawing a Venn diagram between the targets and disease targets on the Venny platform to obtain the intersection gene targets.

[0008] Furthermore, step S3 involves importing the intersection target points into the STRING platform to obtain the PPI data of the intersection target points, then importing it into Cytoscape 3.10.1 software to draw a PPI network diagram of strychnosine-renal fibrosis target points, and using the NetworkAnalyzer tool to analyze the Degree value, Betweenness value, and Closeness value of the target points, and then screening out the core target points.

[0009] Furthermore, step S4 involves using the DAVID database to perform GO functional enrichment and KEGG pathway enrichment analysis on the intersection genes obtained in step S2 to obtain the GO and KEGG pathways of the core target.

[0010] Furthermore, step S5 involves processing the 2D structure of the active ingredient of strychnos nuciferin using ChemBio3D software, processing the core target obtained in step S3 using Uniprot database, PDB database, UCSF Chimera, and AutoDock-Vina software, performing molecular docking between the processed core ingredient and the core target using AutoDockVina software, and finally visualizing the docking between the ingredient and the target using UCSF Chimera software.

[0011] The advantages of this invention compared to existing technologies are as follows: This invention focuses on exploring the mechanism of action of gentianin in treating renal fibrosis using network pharmacology and molecular docking technology. Based on an integrated pharmacology platform and molecular docking, this study explores the mechanism of action of gentianin in treating renal fibrosis, discovering and predicting its mechanism of action and related target genes. Gentianin plays an important role in anti-renal fibrosis by influencing potential targets such as SRC, HSP90AA1, MAPK1, CHEK1, and PIK3CD. GO functional enrichment analysis suggests that gentianin chemical components—potential targets of renal fibrosis—are mainly involved in a series of biological processes such as inflammatory response regulation, cellular response to hormone stimulation, and epithelial cell differentiation; and KEGG pathway signal enrichment analysis suggests that the mechanism of action of gentianin on renal fibrosis is closely related to endocrine resistance, inflammation, and oxidative stress. This provides a theoretical basis for the clinical exploration of gentianin in treating renal fibrosis and provides research ideas and directions for further in-depth research on its mechanism of action and the development of novel therapies. Attached Figure Description

[0012] The present invention will be further described below with reference to the accompanying drawings and specific embodiments. Figure 1 This is a VENNY diagram of styracifoliin-renal fibrosis target; Figure 2 This is a protein interaction network diagram of the key target of stylosin-renal fibrosis crossover; Figure 3 It is the GO of the pedunculin-renal fibrosis intersection gene. Figure 1 ; Figure 4 It is the GO of the pedunculin-renal fibrosis intersection gene. Figure 2 ; Figure 5 It is the GO of the pedunculin-renal fibrosis intersection gene. Figure 3 ; Figure 6 This is a KEGG diagram of the intersection genes of styracifolium and renal fibrosis; Figure 7 This is a diagram of the core target of the stylosin-renal fibrosis gene. Figure 8 This is a docking diagram of the key target molecules of strychnosine and renal fibrosis. Detailed Implementation

[0013] The present invention is described in detail below through specific embodiments, but this does not limit the scope of protection of the present invention. Unless otherwise specified, the experimental methods used in the present invention are all conventional methods, and the experimental equipment, materials, reagents, etc. used can all be obtained commercially.

[0014] Example 1 Analysis of methods for treating renal fibrosis using strychnine based on network pharmacology and molecular docking technology: Obtaining drug structural formulas 1. Collection and screening of active ingredients: The Pubchem database was used to search for the SMILES structures of drugs.

[0015] 2. Drug target prediction and target acquisition: The targets were imported into the Swiss Target Pridiction platform, and a total of 28 drug targets were obtained corresponding to the active ingredient of gentianin.

[0016] 3. Screening for disease-related genes: Data on disease targets was searched and downloaded using databases such as GeneCard (https: / / www.genecards.org / ) and OMIM (https: / / www.omim.org / ), and then organized and summarized. The search keyword was: Renal Fibrosis. A total of 8770 disease target genes were obtained from databases such as GeneCard.

[0017] 4. Drug-Disease Target Screening: Drug target genes were converted into target genes. The target genes and disease genes were then processed using Venn diagrams from Microbioinformatics to generate corresponding Venn diagrams, yielding drug-disease correspondence data. Through drug target screening, 81 common targets for both diseases and drugs were identified.

[0018] 5. Data Visualization and PPI Network Construction: Disease-drug interacting proteins were imported into the String PPi protein interaction network (PPI) analysis database. A human database was selected, and after adjusting the score (confidence level) to >0.4, free nodes were hidden, generating a PPI protein interaction network node graph. Based on existing data, 158 protein-protein interaction connections were generated from the PPI database. Subsequently, Cytoscape software (with the CytoNca plugin) was used to select core targets. The top 5 targets based on degree value were SRC, HSP90AA1, MAPK1, CHEK1, and PIK3CD.

[0019] 6. GO Function Enrichment: Microbioinformatics was used to transform data on disease drug targets using intersecting genes, and GO gene functions were enriched, producing bar charts and scatter plots. GO function enrichment analysis in R language yielded 1889 GO entries (P < 0.05).

[0020] 7. KEGG Pathway Enrichment: The data obtained in the previous steps were organized, and data on disease drug targets were transformed using intersecting genes with MicroBio. 100 signaling pathways were obtained through KEGG pathway enrichment screening (P < 0.05).

[0021] 8. Molecular docking: The 2D structure of the active ingredient of gentianin was processed using ChemBio3D software. The core target obtained in step S3 was processed using Uniprot database, PDB database, UCSF Chimera, and AutoDock-Vina software. The processed core ingredient and core target were then molecularly docked using AutoDockVina software. Finally, the docking of the ingredient and target was visualized using UCSF Chimera software.

[0022] The 2D structures of the core components were converted to MO12 format after energy minimization using ChemBio3D software. The full names of the selected core targets were searched in the Uniprot database using "verified, human" as the filtering criteria. The target target numbers were then imported into the PDB database, and the 3D crystal structures of the target proteins were downloaded. The water molecules and ligands were removed using UCSF Chimera and saved in PDB format. After oxygenation, charge calculation, and determination of rigidity properties using UCSF Chimera software, the results were saved as PDBQT files. Molecular docking was performed using Autodock-Vina. Finally, the docking between the components and targets was visualized using UCSF Chimera software. The results are shown in [Figure / Reference]. Figure 8Generally, a binding energy less than 0 kcal / mol indicates spontaneous binding between the ligand and receptor, while less than -5.0 kcal / mol indicates good and strong binding activity. The docking results show that the binding free energy (kcal / mol) of strychnoin to SRC is less than -5, indicating strong binding activity between strychnoin and the predicted target. The binding energy between SRC and strychnoin reaches -10.43 kcal / mol; their interaction relationship is detailed in [link to relevant documentation]. Figure 8 .

[0023] In summary, this invention focuses on exploring the mechanism of action of basidiostanoside in treating renal fibrosis using network pharmacology and molecular docking technology. This study, based on an integrated pharmacology platform and molecular docking, explored the mechanism of action of basidiostanoside in treating renal fibrosis, discovering and predicting its mechanism of action and related target genes. Basilostanoside plays an important role in anti-renal fibrosis by influencing potential targets such as SRC, HSP90AA1, MAPK1, CHEK1, and PIK3CD. GO functional enrichment analysis suggests that the chemical components of basidiostanoside—potential targets of renal fibrosis—are mainly involved in a series of biological processes, including the regulation of inflammatory responses, cellular responses to hormone stimulation, and epithelial cell differentiation. Furthermore, KEGG pathway signal enrichment analysis indicates that the mechanism of action of basidiostanoside on renal fibrosis is closely related to endocrine resistance, inflammation, and oxidative stress. This provides a theoretical basis for the clinical exploration of basidiostanoside in treating renal fibrosis and offers research ideas and directions for further in-depth research on its mechanism of action and the development of novel therapies.

[0024] The embodiments described above are merely preferred embodiments of the present invention, and not all feasible embodiments of the present invention. Any obvious modifications made by those skilled in the art without departing from the principles and spirit of the present invention should be considered to be included within the scope of protection of the claims of the present invention.

Claims

1.A method for analyzing the treatment of renal fibrosis by formononetin based on network pharmacology and molecular docking technology, characterized in that, The steps are: S1. Obtain the SMILES structure of formononetin, and then obtain the target corresponding to the active ingredient of formononetin; S2. Collection of renal fibrosis related targets: retrieve the renal fibrosis result data to screen disease genes to obtain the gene targets of renal fibrosis, and draw a Venn diagram of the targets and disease targets to obtain the intersection gene targets; S3. Construction of formononetin renal fibrosis target PPI network: obtain PPI data of the intersection targets, draw a formononetin-renal fibrosis target PPI network diagram, analyze the Degree value, Betweenness value and Closeness value of the targets, and screen out the core targets; S4. GO and KEGG enrichment analysis of core targets: GO function enrichment and KEGG pathway enrichment analysis of the intersection genes obtained in step S2 to obtain the GO and KEGG pathway of the core targets; S5. Molecular docking: process the 2D structure of the active ingredient of formononetin, process the core targets obtained in step S3, and then perform molecular docking of the processed core ingredients and core targets, and finally visualize the ingredient and target docking. 2.The method for analyzing the treatment of renal fibrosis by formononetin based on network pharmacology and molecular docking technology according to claim 1, characterized in that, The step S1 is to obtain the SMILES structure of formononetin on the pubchem website, import it into the Swiss Target Pridiction platform, and obtain the target corresponding to the active ingredient of formononetin. 3.The method for analyzing the treatment of renal fibrosis by formononetin based on network pharmacology and molecular docking technology according to claim 1, characterized in that, The step S2 is to use GeneCards and DisGeNET platforms to retrieve renal fibrosis result data to screen disease genes to obtain the gene targets of renal fibrosis, and draw a Venn diagram of the targets and disease targets on the Venny platform to obtain the intersection gene targets. 4.The method for analyzing the treatment of renal fibrosis by formononetin based on network pharmacology and molecular docking technology according to claim 1, characterized in that, The step S3 is to import the intersection targets into the STRING platform to obtain the PPI data of the intersection targets, then import them into the Cytoscape3.10.1 software, draw a formononetin-renal fibrosis target PPI network diagram, and use the Network Analyzer tool to analyze the Degree value, Betweenness value and Closeness value of the targets, and screen out the core targets. 5.The method for analyzing the treatment of renal fibrosis by formononetin based on network pharmacology and molecular docking technology according to claim 1, characterized in that, The step S4 is to use the DAVID database to perform GO function enrichment and KEGG pathway enrichment analysis on the intersection genes obtained in step S2 to obtain the GO and KEGG pathway of the core targets. 6.The method for analyzing the treatment of renal fibrosis by formononetin based on network pharmacology and molecular docking technology according to claim 1, characterized in that, The step S5 is to process the 2D structure of the active ingredient of formononetin with ChemBio3D software, process the core targets obtained in step S3 with Uniprot database, PDB database, UCSF Chimera and AutoDock-Vina software, then perform molecular docking of the processed core ingredients and core targets with AutoDockVina software, and finally visualize the ingredient and target docking with UCSF Chimera software.

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