Discovery of Potential Inhibitors of Squalene Synthase from Traditional Chinese Medicine Based on Virtual Screening and In Vitro Evaluation of Lipid-Lowering Effect

Abstract

Squalene synthase (SQS), a key downstream enzyme involved in the cholesterol biosynthetic pathway, plays an important role in treating hyperlipidemia. Compared to statins, SQS inhibitors have shown a very significant lipid-lowering effect and do not cause myotoxicity. Thus, the paper aims to discover potential SQS inhibitors from Traditional Chinese Medicine (TCM) by the combination of molecular modeling methods and biological assays. In this study, cynarin was selected as a potential SQS inhibitor candidate compound based on its pharmacophoric properties, molecular docking studies and molecular dynamics (MD) simulations. Cynarin could form hydrophobic interactions with PHE54, LEU211, LEU183 and PRO292, which are regarded as important interactions for the SQS inhibitors. In addition, the lipid-lowering effect of cynarin was tested in sodium oleate-induced HepG2 cells by decreasing the lipidemic parameter triglyceride (TG) level by 22.50%. Finally. cynarin was reversely screened against other anti-hyperlipidemia targets which existed in HepG2 cells and cynarin was unable to map with the pharmacophore of these targets, which indicated that the lipid-lowering effects of cynarin might be due to the inhibition of SQS. This study discovered cynarin is a potential SQS inhibitor from TCM, which could be further clinically explored for the treatment of hyperlipidemia.

Keywords: Traditional Chinese Medicine; drug discovery; hyperlipidemia; molecular modeling; squalene synthase (SQS).

Figure 1

(a) The optimal pharmacophore model Hypo1; Wherein, green features represent hydrogen bond acceptor (A), light blue features represent hydrophobic features (H), orange features represent ring aromatic (R) and gray features represent excluded volumes (Ev); (b) The mapping of the crystallographic ligand with the optimal pharmacophore model Hypo1; (c) mapping of TAK-475 with the Hypo1.

Figure 1

(a) The optimal pharmacophore model Hypo1; Wherein, green features represent hydrogen bond acceptor (A), light blue features represent hydrophobic features (H), orange features represent ring aromatic (R) and gray features represent excluded volumes (Ev); (b) The mapping of the crystallographic ligand with the optimal pharmacophore model Hypo1; (c) mapping of TAK-475 with the Hypo1.

Figure 2

(a) the docking result of the crystallographic ligand with the crystal structure of SQS; (b) the docking result of TAK-475; the pink dash line represented hydrophobic effect; the green dash line represented hydrogen bond donor; the green amino acids represent hydrogen bond interactions; blue amino acids represent hydrophobic interactions.

Figure 3

The frequency of hydrophobic amino acids formed by 22 compounds.

Figure 4

(a) The 2D structures of cynarin; (b) The mapping results of cynarin with Hypo1; (c) the docking result of cynarin with the crystal structure of SQS; the green amino acids represent hydrogen bond interactions; blue amino acids represent hydrophobic interactions.

Figure 5

(a) The trajectory of MD simulations of three complexs: average protein RMSD; Blue, red and green bars represent for the data of D99, TAK-475 and cynarin, respectively; (b) Root mean square fluctuation (RMSF) corresponds to MD trajectory; (c) the analysis of hydrophobic residues implicated in docking.

Figure 5

(a) The trajectory of MD simulations of three complexs: average protein RMSD; Blue, red and green bars represent for the data of D99, TAK-475 and cynarin, respectively; (b) Root mean square fluctuation (RMSF) corresponds to MD trajectory; (c) the analysis of hydrophobic residues implicated in docking.

Figure 6

(a) Cell-viability of different concentration of cynarin on HepG2 cells by the MTT assay; (b) Effect of different concentration of cynarin on the TG content in sodium oleate-induced HepG2 cells (* means p <0.05, ** means p < 0.01 and *** means p < 0.001 compared with the model control group).

Figure 7

Structures, ID and the value of IC₅₀ of 22 compounds in the training set for pharmacophore model generation of SQS.