Analysis method based on network pharmacology and molecular docking technology

Abstract

The invention belongs to the technical field of biological computer analysis, and discloses an analysis method based on network pharmacology and a molecular docking technology. The psoriasis treatment of fucoidin is analyzed on the basis of network pharmacology and a molecular docking technology, five core targets, 30 GO annotations and 66 KEGG pathways are screened through the network pharmacology and the molecular docking technology, and the affinity of active ingredients and the core targets is good.

Description

Technical Field

[0001] This invention belongs to the field of biological computer analysis technology, specifically relating to an analytical method based on network pharmacology and molecular docking technology. Background Technology

[0002] Background of Psoriasis: Psoriasis is caused by various factors such as genetics, immune dysregulation, and environmental stress, which can lead to impaired skin barrier function and chronic inflammation. Psoriasis is a common, immune-mediated, chronic, relapsing inflammatory skin disease characterized by well-defined red plaques covered with silvery-white scales. [1] With the accelerated pace of life and changes in environmental factors, the incidence of psoriasis is on the rise. Traditional Chinese medicine refers to psoriasis as "Bai Bi" or "Gan Xuan," believing it is mostly caused by blood heat, blood stasis, blood dryness, or combined with wind-damp-heat pathogens, emotional distress, etc., leading to malnourishment of the skin. Its etiology and pathogenesis are complex and diverse, not only affecting the appearance of the skin but also often accompanied by itching and pain, severely impacting patients' quality of life, social activities, and mental health. [2] Currently, treatments for psoriasis mainly include topical medications (such as corticosteroids and vitamin D3 derivatives), phototherapy, systemic medications (such as methotrexate, cyclosporine, and retinoids), and biologics. While Western medicine treatments have some effect, long-term use of corticosteroids can easily cause side effects such as skin atrophy and telangiectasia. Systemic medications and biologics, on the other hand, have problems such as liver and kidney toxicity, bone marrow suppression, increased risk of infection, and high costs. Traditional Chinese medicine, however, has the advantages and potential for treating psoriasis through multi-target and holistic regulation. In recent years, due to its complex immune-inflammatory network, long course, recurrent nature, and the economic burden of treatment, psoriasis can easily trigger or aggravate psychological problems such as anxiety and depression in patients, and increase the risk of comorbidities such as psoriatic arthritis, metabolic syndrome (such as obesity, diabetes, and hyperlipidemia), and cardiovascular disease, making its treatment a long-standing challenge in the field of dermatology. [3] .

[0003] Fucoidan is a natural sulfated polysaccharide derived from brown algae. As a key active ingredient in seaweed medicine for its effects of "resolving phlegm, softening hard masses, dispersing nodules, promoting diuresis, and reducing swelling," modern research has confirmed its significant immunomodulatory, anti-inflammatory, and antioxidant activities, demonstrating great potential and safety advantages in treating immune-related skin diseases such as psoriasis. Fucoidan is mainly composed of monosaccharides such as fucose containing sulfated ester groups, and its biological activity is closely related to its specific structure. [4]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 of fucoidan in treating psoriasis 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 fucoidan in treating psoriasis, 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 an analytical method based on network pharmacology and molecular docking technology. The method analyzes the therapeutic effect of fucoidan on psoriasis using network pharmacology and molecular docking technology. Five core targets, 30 GO annotations, and 66 KEGG pathways were screened using network pharmacology and molecular docking technology, and the active ingredient showed good affinity with the core targets.

[0005] The above-mentioned objective of this invention is achieved through the following technical solution: an analytical method based on network pharmacology and molecular docking technology, specifically a method for analyzing fucoidan in the treatment of psoriasis, the steps of which are as follows:

[0006] S1. Obtain the SMILES structural formula of fucoidan, and obtain the target sites corresponding to the active ingredients of fucoidan through the structural formula;

[0007] S2. Obtain the gene targets of psoriasis and combine them with the fucoidan target obtained in step S1 to obtain the intersection gene targets;

[0008] S3. Constructing a PPI network of fucoidan targets for psoriasis;

[0009] S4. Enrichment analysis of core target GO and KEGG;

[0010] S5. Molecular docking.

[0011] Furthermore, in step S1, the SMILES structural formula of fucoidan is obtained from the PubChem website, and then imported into the Swiss TargetPridiction platform to obtain the target corresponding to the active ingredient of fucoidan.

[0012] Furthermore, step S2 involves using the GeneCards and DisGeNET platforms to retrieve and export psoriasis result data, screening for disease genes to obtain psoriasis gene targets, and drawing a Venn diagram on the Venny platform to obtain intersection gene targets by combining the fucoidan target and the disease target.

[0013] Furthermore, step S3 specifically involves: importing the intersection gene target points obtained in step S2 into the STRING platform to obtain the PPI data of the intersection target points, then importing them into Cytoscape 3.10.1 software to draw a fucoidan-psoriasis target PPI network diagram, using the Network Analyzer tool to analyze the Degree value, Betweenness value, and Closeness value of the target points, and screening out the core target points.

[0014] Furthermore, step S4 specifically 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.

[0015] Furthermore, step S5 specifically involves: processing the 2D structure of the active component of fucoidan 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 component and the core target using AutoDockVina software; and finally visualizing the docking between the component and the target using UCSF Chimera software.

[0016] The advantages of this invention compared to existing technologies are as follows: This invention focuses on exploring the mechanism of action of fucoidan in treating psoriasis using network pharmacology and molecular docking technology. This study, based on an integrated pharmacology platform and molecular docking, explores the mechanism of action of fucoidan in treating psoriasis, discovering and predicting its mechanism of action and related target genes. Fucoidan plays an important role in anti-psoriasis by influencing potential targets such as AKT1, MMP2, ACE, FGF2, and CASP3. GO functional enrichment analysis suggests that the chemical components of fucoidan—potential targets for psoriasis—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 by which fucoidan acts on psoriasis is closely related to the role of proteoglycans in cancer, the AGE-RAGE signaling pathway, the NF-κB signaling pathway, the Rap1 signaling pathway, and psoriasis itself. This provides a theoretical basis for the clinical exploration and application of fucoidan in the treatment of psoriasis, and provides research ideas and directions for further in-depth research on its mechanism of action and the development of new therapies. Attached Figure Description

[0017] The present invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0018] Figure 1 This is a VENNY diagram of fucoidan-psoriasis target;

[0019] Figure 2 This is a protein interaction network diagram of fucoidan-psoriasis cross-key targets;

[0020] Figure 3 This is a GO diagram of the intersection genes between fucoidan and psoriasis;

[0021] Figure 4 This is a biofunctional MF diagram of the intersection genes between fucoidan and psoriasis.

[0022] Figure 5 This is a KEGG diagram of the intersection genes between fucoidan and psoriasis.

[0023] Figure 6 This is a diagram of the core target of the fucoidan-psoriasis gene;

[0024] Figure 7 This is a diagram of the key target molecules of fucoidan-AKT1. Detailed Implementation

[0025] 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.

[0026] Example 1

[0027] Obtaining drug structural formulas

[0028] 1. Collection and screening of active ingredients: The Pubchem database was used to search for the SMILES structures of drugs.

[0029] 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 ingredients of fucoidan.

[0030] 3. Screening and use of disease-related genes

[0031] GeneCard database (https: / / www.genecards.org / ), OMIM database (https: / / www.omim.org / )

[0032] We searched for and downloaded data on disease targets, then organized and summarized the information using the keyword "psoriasis". A total of 4943 disease target genes were obtained from databases such as Genecard.

[0033] 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, 26 common targets for both diseases and drugs were identified.

[0034] 5. Data Visualization and PPI Network Construction: Disease-drug interaction proteins were imported into the String PPi protein interaction network (PPI) analysis database. The human database was selected, and after adjustments, a score (confidence level) greater than 0.4 was set. Free nodes were hidden, and a PPI protein interaction network node graph was generated. Based on existing data, 53 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 BC values ​​were AKT1, MMP2, ACE, FGF2, and CASP3.

[0035] 6. GO Function Enrichment: Using Microbioinformatics, data on disease drug targets from overlapping genes were transformed to enrich GO gene functions, generating bar charts and scatter plots. GO function enrichment analysis in R language yielded 1885 GO entries (P < 0.05).

[0036] 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. KEGG pathway enrichment screening yielded 66 signaling pathways (P < 0.05).

[0037] 8. Molecular docking: The 2D structure of the active component of fucoidan 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 component and core target were then molecularly docked using AutoDockVina software. Finally, the docking of the component and target was visualized using UCSF Chimera software.

[0038] 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 7 Generally, a binding energy less than 0 kcal / mol indicates spontaneous binding between the ligand and acceptor, while less than -5.0 kcal / mol indicates good and strong binding activity. The docking results show that the binding free energy (kcal / mol) between fucoidan and AKT1 is less than -5, indicating strong binding activity between fucoidan and the predicted target. Specifically, the binding energy between AKT1 and fucoidan reaches -5.16 kcal / mol.

[0039] Their interaction relationship is shown in Figure 7

[0040] In summary, this invention focuses on exploring the mechanism of action of fucoidan in treating psoriasis using network pharmacology and molecular docking technology. This study, based on an integrated pharmacology platform and molecular docking, explored the mechanism of fucoidan in treating psoriasis, discovering and predicting its therapeutic mechanism and related target genes. Fucoidan plays an important role in its anti-psoriatic effect by influencing potential targets such as AKT1, MMP2, ACE, FGF2, and CASP3. GO functional enrichment analysis suggests that the chemical components of fucoidan—potential targets for psoriasis—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 fucoidan's action on psoriasis is closely related to the role of proteoglycans in cancer, the AGE-RAGE signaling pathway, the NF-κB signaling pathway, the Rap1 signaling pathway, and psoriasis itself. This provides a theoretical basis for the clinical exploration of fucoidan in treating psoriasis and offers research ideas and directions for further in-depth research on its mechanism of action and the development of novel therapies.

[0041] 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.

[0042] References:

[0043] [1] Griffiths CEM, Armstrong AW, Gudjonsson JE, Barker JNWN. Psoriasis. Lancet. 2021;397(10281):1301 - 1315. doi:10.1016 / S0140 - 6736(20)32549 - 6

[0044] [2] Griffiths CE, Barker JN. Pathogenesis and clinical features of psoriasis. Lancet. 2007;370(9583):263 - 271. doi:10.1016 / S0140 - 6736(07)61128 - 3

[0045] [3] Lowes MA, Suárez - M, Krueger JG. Immunology of psoriasis. Annu Rev Immunol. 2014;32:227 - 255. doi:10.1146 / annurev - immunol - 032713 - 120225

[0046] [4] Abbas MF, Karim DK, Kareem HR, et al. Fucoidan and its derivatives: From extraction to cutting - edge biomedical applications. Carbohydr Polym. 2025;357:123468. doi:10.1016 / j.carbpol.2025.123468。

Claims

1. An analytical method based on network pharmacology and molecular docking technology, characterized in that, Specifically, the method of using fucoidan to treat psoriasis is analyzed, and the steps are as follows: S1. Obtain the SMILES structural formula of fucoidan, and obtain the target sites corresponding to the active ingredients of fucoidan through the structural formula; S2. Obtain the gene targets of psoriasis and combine them with the fucoidan target obtained in step S1 to obtain the intersection gene targets; S3. Constructing a PPI network of fucoidan targets for psoriasis; S4. Enrichment analysis of core target GO and KEGG; S5. Molecular docking.

2. The analytical method based on network pharmacology and molecular docking technology according to claim 1, characterized in that, In step S1, the SMILES structural formula of fucoidan is obtained from the PubChem website and then imported into the SwissTarget Pridiction platform to obtain the target corresponding to the active ingredient of fucoidan.

3. The analytical method based on network pharmacology and molecular docking technology according to claim 1, characterized in that, Step S2 involves using GeneCards and DisGeNET platforms to retrieve and export psoriasis result data, screening for disease genes to obtain psoriasis gene targets, and drawing a Venn diagram on the Venny platform to obtain intersection gene targets by combining fucoidan targets and disease targets.

4. The analytical method based on network pharmacology and molecular docking technology according to claim 1, characterized in that, Step S3 specifically involves: importing the intersection gene target points obtained in step S2 into the STRING platform to obtain the PPI data of the intersection target points, then importing them into Cytoscape 3.10.1 software to draw a fucoidan-psoriasis target PPI network diagram, using the NetworkAnalyzer tool to analyze the Degree value, Betweenness value, and Closeness value of the target points, and screening out the core target points.

5. The analytical method based on network pharmacology and molecular docking technology according to claim 1, characterized in that, Step S4 specifically 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.

6. The analytical method based on network pharmacology and molecular docking technology according to claim 4, characterized in that, Step S5 specifically involves: processing the 2D structure of the active component of fucoidan 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 component and the core target using AutoDockVina software; and finally visualizing the docking between the component and the target using UCSF Chimera software.

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