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. 2021 Sep 23:2021:6015702.
doi: 10.1155/2021/6015702. eCollection 2021.

Exploring the Potential Mechanism of Shennao Fuyuan Tang for Ischemic Stroke Based on Network Pharmacology and Molecular Docking

Jia Min Li  1 , Zhen Ni Mu  2 , Tian Tian Zhang  2 , Xin Li  2 , Yan Shang  2 , Guo Heng Hu  1
Affiliations

Exploring the Potential Mechanism of Shennao Fuyuan Tang for Ischemic Stroke Based on Network Pharmacology and Molecular Docking

Jia Min Li et al. Evid Based Complement Alternat Med. .

Abstract

Methods: Screen the biologically active components and potential targets of SNFYT through Traditional Chinese Medicine Systems Pharmacology (TCMSP), Traditional Chinese Medicines Integrated Database (TCMID), and related literature. In addition, DrugBank, OMIM, DisGeNET, and the Therapeutic Target Database were searched to explore the therapeutic targets of IS. The cross-targets of SNFYT potential targets and IS treatment targets were taken as candidate gene targets, and GO and KEGG enrichment analyses were performed on the candidate targets. On this basis, the SNFYT-component-target network and protein-protein interaction (PPI) network were constructed using Cytoscape 3.7.2. Finally, AutoDock was used to verify the molecular docking of core components and core targets.

Results: We screened out 95 potentially active components and 143 candidate targets. SNFYT-component-target network, PPI network, and Cytoscape analysis identified four core active ingredients and 14 core targets. GO enrichment analyzed 2333 biological processes, 79 cell components, and 149 molecular functions. There are 170 KEGG-related signal pathways (P < 0.05), including the IL-17 signal pathway, TNF signal pathway, and HIF-1 signal pathway. The molecular docking results of the core components and the core targets showed good binding power.

Conclusions: SNFYT may achieve the effect of treating ischemic stroke through its anti-inflammatory effect through a signal pathway with core targets as the core.

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Conflict of interest statement

All authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Flow chart of network pharmacology study on the mechanism of action of SNFYT in the treatment of IS.
Figure 2
Figure 2
Intersection targets' venn diagram of SNFYT and IS.
Figure 3
Figure 3
Component-target network. Green is the TCM formula, purple is an herb, blue is the component, and red is the gene target. The size of component nodes is proportional to the number of degrees.
Figure 4
Figure 4
Component-target core network. Green is the TCM formula, purple is an herb, blue is the component, and red is the gene target. The size of component nodes is proportional to the number of degrees.
Figure 5
Figure 5
PPI network of candidate targets of SNFYT for IS: (a) construction of PPI network in SNFYT in treating IS using STRING database. (b) Use cytoscape to visualize the PPI network further. The node size and color represent the importance of the node in the network. (c) Topological analysis of potential targets in SNFYT in treating IS by using network analyzer. (d) The top 25 core genes visualization was obtained using R software according to the relevant number of nodes.
Figure 6
Figure 6
Enrichment analysis of candidate targets. (a) Barplot: biological process (GO enrichment analysis). (b) Barplot: molecular function (GO enrichment analysis). (c) Barplot: cell component (GO enrichment analysis). (d) Barplot: KEGG enrichment analysis.
Figure 7
Figure 7
Target-pathway network. The orange nodes represent the target nodes. The brick-red nodes represent the corresponding pathways.
Figure 8
Figure 8
Molecular docking heat map of 4 core components and 14 core targets. The color indicates an affinity score. Red represents the lowest affinity score, the highest affinity between receptor and ligand, blue represents the highest affinity score and the lowest affinity between receptor and ligand.
Figure 9
Figure 9
The docking mode of PTGS1 (a) and PTGS2 (b) with stigmasterol (A), quercetin (B), kaempferol (C), and beta-sitosterol (D), respectively.
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