Virtual Screening Based on Pharmacophore to Discover Host ER Alpha-Glucosidase II Inhibitor for Dengue Therapy

Abstract

Dengue is one of the crucial diseases in human-caused by dengue virus (DENV) infection. However, the development of DENV antiviral is often facing a problem because no effective drug to treat infection caused by all DENV serotypes. The inhibition of host protein involved in DENV life cycle can be a potential approach in dengue drug discovery, and also avoiding antiviral resistance. Endoplasmic Reticulum (ER) α-glucosidase II is one of the target host protein in DENV endoplasmic reticulum that plays an important role in the maturation process of DENV envelope glycoprotein. Natural products have been known as an essential source of a lead compound for drug discovery due to their therapeutic potency. In this research, pharmacophore-based virtual screening and molecular docking simulations were performed to find ligand that has potential to inhibit α-glucosidase II activity. About 67,609 natural products from InterBioScreen (IBS) database were used in the simulation as ligands with α-glucosidase II as the protein target. After subjected to Lipinski’s Rule of Five, druglikeness, nasty functions, and toxicity screening using DataWarrior software, 17,462 ligands were obtained. The pharmacophore features for molecular docking simulation was obtained from Protein-Ligand Interaction Fingerprint (PLIF) analysis using eight α-glucosidase II protein with different ligands. Based on virtual screening, rigid, and flexible docking simulations using Molecular Operating Environment (MOE) software, 32 ligands have lower Gibbs free binding energy (ΔGbinding) compared to the standards. Two best ligands, namely STOCK1N-85545 and STOCK1N-86400 which belong alkaloid derivatives, showed the exceptional ligand interaction and had the lowest ΔGbinding of-11.204 and-10.276 kcal/mol, respectively. The ligands were identified to have a binding interaction with amino acid Asp564 and Asp640 in α-glucosidase II catalytic site. STOCK1N-85545 and STOCK1N-86400 were also identified to have a good pharmacological properties after subjected to ADME-tox test using Toxtree, SwissADME, admetSAR, and pkCSM software.