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In silico discovery of food-derived phytochemicals against asialoglycoprotein receptor 1 for treatment of hypercholesterolemia: Pharmacophore modeling, molecular docking and molecular dynamics simulation approach

Gao, Shengyun, Wang, Lei, Bai, Feng, Xu, Shaohua·2023·Journal of Molecular Graphics and Modelling
DOI 10.1016/j.jmgm.2023.108614
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Abstract

Hypercholesterolemia is a significant risk factor for atherosclerotic cardiovascular disease (ASCVD). Successful management of cholesterol metabolism disorders can prevent these ASCVD effectively. Asialoglycoprotein receptor 1 (ASGR1) is the main subtype of sialoglycoprotein receptor, which is specifically expressed in the liver and mediates the endocytosis of blood asialoglycoprotein to lysosome degradation. Recently, ASGR1 has been reported as a new therapeutic target for the treatment of hypercholesterolemia. In this study, the main aim was to identify natural ASGR1 inhibitors from plant food chemicals library through pharmacophore and docking based virtual screening. Total 14 phytochemicals of potential ASGR1 inhibitors were identified, which presented docking affinity higher than control compound through docking based virtual screening. The docking pose showed the top three hits interacted residues were located at active pocket of ASGR1 with hydrogen bonds, hydrophobic interactions and electrostatic interactions. The top three hits (ZINC85664954, ZINC169372863, and ZINC195764535) were then subjected to 200 ns molecular dynamics simulation to evaluate the stability of docked complexes. These results showed that selected phytochemicals bound to ASGR1 with higher stability than control compound. Binding free energy of each docked complex was calculated by the Molecular Mechanics Poisson-Boltzmann Surface Area (MM/PBSA) method. The binding free energy of ZINC85664954, ZINC16…

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Metadata

Authors
Gao, S., Wang, L., Bai, F., & Xu, S.
ORCID
Xu, Shaohua
Venue
Journal of Molecular Graphics and Modelling
Publisher
Elsevier BV
Published
2023-12
Volume
125
Pages
108614
ISSN
1093-3263
DOI
10.1016/j.jmgm.2023.108614
Cited by
12
References
3
Language
en
Licence
Funders
Science and Technology Plan Project of Wenzhou Municipality (WW2101130)

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