Network pharmacology and molecular docking-based characterization of the mechanisms through which Astragali Radix-Atractylodes macrocephala Koidz herb pair can treat membranous nephropathy

Abstract

Background: The Astragali Radix-Atractylodes macrocephala Koidz herb pair (AAHP) is frequently used to treat membranous nephropathy (MN) as it has been found to be efficacious in this therapeutic setting. The mechanistic basis for its beneficial effects, however, remains poorly understood, thereby limiting its application in the clinic and hampering efforts to develop new drugs for MN treatment.

Methods: The Traditional Chinese Medicine System Pharmacology database was utilized to retrieve the bioactive components of Astragali Radix and Atractylodes macrocephala Koidz, after which the SwissTargetPrediction tool was employed to identify targets associated with these components. MN-related genes were obtained from the Online Mendelian Inheritance in Man and GeneCards databases, with the Cytoscape program then being employed to screen for hub MN-related genes. Venn diagrams were used to assess overlapping targets between MN and AAHP, after which gene ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were conducted with the Database for Annotation, Visualization and Integrated Discovery database. Molecular docking (MD) and molecular dynamics simulations of the active ingredients and core proteins of interest were then analyzed using Auto-Dock Vina and gromacs software.

Results: In total, 28 active compounds associated with 574 targets were identified through screening efforts. These bioactive ingredients were further analyzed based on their topological parameters, ultimately leading to the identification of α-amyrin, astragaloside IV, and FA as key active ingredients. Key targets identified through this approach included SRC, PIK3CA, PIK3R1, HSP90AA1, ESR1, AKT1, PLCG1, EGFR, and JAK2. Enrichment analyses suggested that the core components of AAHP may regulate the PI3K-Akt and JAK-STAT signaling pathways via modulating signal transduction, protein phosphorylation, and the negative regulation of apoptotic activity. MD analyses suggested that most of these active ingredients exhibited binding energies <5.6 kcal/mol for these target proteins encoded by core genes, consistent with stable binding interactions. Molecular dynamics simulations revealed that the binding of 2 ligand-receptor complexes, including AKT1-α-amyrin and JAK2-FA, was relatively stable, which was consistent with the results of MD.

Conclusion: AAHP may represent a promising treatment option for MN through its ability to modulate multiple targets and thereby affect several key signaling pathways, including the JAK-STAT and PI3K-Akt pathways.

Keywords: Astragali Radix; Atractylodes macrocephala Koidz; membranous nephropathy; molecular docking; molecular dynamics simulations; network pharmacology.

Figure 1.

A flow chart overview of the network pharmacology-based approach to employing AAHP as a treatment for MN. AAHP = Astragali Radix-Atractylodes macrocephala Koidz, MN = membranous nephropathy.

Figure 2.

PPI network construction and hub gene identification. Larger node sizes indicate higher degree values. Orange and blue coloration respectively corresponds to higher and lower degree values. PPI = protein–protein interaction.

Figure 3.

GO enrichment analyses for the AAHP-mediated treatment of MN (P < .05). AAHP = Astragali Radix-Atractylodes macrocephala Koidz, MN = membranous nephropathy.

Figure 4.

The top enriched KEGG pathways associated with MN-related target genes. KEGG = Kyoto Encyclopedia of Genes and Genomes, MN = membranous nephropathy.

Figure 5.

Molecular docking results for interactions between AAHP-derived bioactive compounds and core proteins. AAHP = Astragali Radix-Atractylodes macrocephala Koidz.

Figure 6.

Identified docking complexes with the lowest binding energies. (A) SRC-Calycosin; (B) PIK3CA-9,10-dimethoxypterocarpan-3-O-β-D-glucoside; (C) PIK3R1-Mairin; (D) HSP90AA1-Formononetin; (E) ESR1-Calycosin; (F) AKT1-α-amyrin; (G) PLCG1-astragaloside IV; (H) EGFR-FA; (I) JAK2-FA; (J) HRAS-FA.

Figure 7.

Molecular dynamics simulations. (A and B) The RMSD plot of AKT1-α-amyrin and JAK2-FA. (C and D) The RMSF plot of AKT1-α-amyrin and JAK2-FA. (E and F) The hydrogen bond numbers of AKT1-α-amyrin and JAK2-FA. (G and H) Rg plots of AKT1-α-amyrin and JAK2-FA. (I and J) SASA plots of AKT1-α-amyrin and JAK2-FA. RMSF = root mean square fluctuation, SASA = Solvent Accessible Surface Area.