Molecular Docking of Anthocyanin Compound as Anti-Hyperlipidemia Against PPARα, HMG-CoA Reductase and ACAT Proteins

Abstract

Hyperlipidemia can increase the risk of cardiovascular diseases, such as coronary heart disease (CHD), the number one cause of death worldwide each year. Therefore, needed efforts to reduce the prevalence of cardiovascular disease, one of which is parijoto fruit (Medinilla speciosa), which has a high anthocyanin content. Previous research stated that parijoto extract reduced total cholesterol, LDL, triglycerides (TG), and increased HDL levels in the blood of hyperlipidemic rats. However, the molecular mechanism of anthocyanin in reducing lipid levels is still unknown, so an in silico study using molecular docking techniques is necessary. Molecular docking provides molecular modeling of receptor proteins and their interactions with ligands. This study aims to determine the potential of anthocyanin as an anti-hyperlipidemia compound-CoA reductase, and ACAT in silico with molecular docking techniques. Several stages in this research are data collection of receptor proteins and ligand, download of receptor proteins and ligand, molecular docking, and data analysis. Molecular docking includes protein separation with accompanying components, receptor and ligand preparation, running molecular docking using PyRx and AutoDock Vina, docking result validation, and visualization of docking results using Biovia Discovery Studio 2020. The molecular docking results were analyzed by looking at the affinity energy value (kcal/mol) and the complex conformation between the receptor-ligand. The results showed that the anthocyanin compound had a docking score of-8.8 kcal/mol;-6.0 kcal/mol; and-7.6 kcal/mol for PPAR-A reductase, and ACAT proteins, respectively. This value is similar to the docking score produced by Simvastatin as a positive control which is-7.8 kcal/mol,-6.4 kcal/mol, and-7.7 kcal/mol. Anthocyanin compounds have the potential to inhibit PPAR-A reductase, and ACAT and act as anti-hyperlipidemic in silico.