Computational Network Pharmacology, Molecular Docking, and Molecular Dynamics to Decipher Natural Compounds of Alchornea laxiflora for Liver Cancer Chemotherapy

Abstract

Background:Alchornea laxiflora (Benth.) Pax & K. Hoffm. (A. laxiflora) is utilized as a traditional herb for treating several diseases. Objective: Our study aims to identify the active phytochemical candidates from A. laxiflora and analyses to predict their anticancer activity mechanism by employing network pharmacology, molecular docking, and molecular dynamics (MD). Methods: The phytoconstituents of A. laxiflora were retrieved from the literature, and phytoconstituent-related targets implicated in hepatocellular carcinoma (HCC) were collected from respective databases. Computational methods were employed to recognize essential compounds, hub gene targets, and signaling pathways. Results: Our study has identified 12 potentially bioactive compounds, 150 potential anti-HCC targets, and 15 hub gene targets for A. laxiflora. Molecular docking results recognized the better binding energy values of below -5.6 kcal/mol. Further, MD simulations of the three of the top-scoring protein-ligand complexes (MAPK-3-acetylursolic acid, AKT1-quercetin, and AKT1-3-acetylursolic acid) allowed us to validate the docking results, evaluate the stability of the complexes, and associated conformational changes. Conclusions: Our research claims that phytoconstituents of A. laxiflora are crucial for treating liver cancer, and the recognized protein targets can serve as biomarkers, respectively.

Keywords: Alchornea laxiflora; liver cancer; molecular docking; molecular dynamics simulation; network pharmacology.

Figure 1

(A) Targets of HCC and (B) common targets between A. laxiflora and HCC.

Figure 2

BA-TAR network with 161 nodes and 239 edges linking screened bioactive compounds with HCC targets. Blue-colored nodes indicate 11 bioactives and pink triangles depict 150 HCC targets.

Figure 3

PPI network of potential anti-HCC targets. (A) STRING database PPI network (confidence score ≥ 0.70) and (B) Cytoscape v3.9.1-mapped PPI network. Nodes depicting the target and edges represent the interaction between protein targets. The darker and larger nodes correspond to the higher degree and greater therapeutic importance.

Figure 4

Hub gene screening.

Figure 5

GO annotation chart.

Figure 6

KEGG pathway analysis.

Figure 7

BA-TAR-PATH network.

Figure 8

Cluster analysis of the intersecting protein targets within the PPI network.

Figure 9

Clustering heatmap of binding energies (kcal/mol) from molecular docking. The darker the color, the higher the free energy for the phytocompounds to bind to the hub targets.

Figure 10

The 100 ns MD simulation analysis of MAPK—3-acetylursolic acid complex: (A) protein—ligand RMSD; (B) protein RMSF; (C) ligand RMSF; and (D–F) protein–ligand interaction diagrams.

Figure 11

The 100 ns MD simulation analysis of AKT1—quercetin complex: (A) protein–ligand RMSD; (B) protein RMSF; (C) ligand RMSF; and (D–F) protein–ligand interaction diagrams.

Figure 12

The 100 ns MD simulation analysis of AKT1—3-acetylursolic acid complex: (A) protein–ligand RMSD; (B) protein RMSF; (C) ligand RMSF; and (D–F) protein–ligand interaction diagrams.

Figure 13

KM plotter analysis of the relationship between hub target gene expression and HCC patient survival.

Figure 14

The methodological layout of the present study.