Title

Identification of novel GPR17-agonists by structural bioinformatics and signaling activation.

Document Type

Article

Publication Date

1-1-2018

Keywords

Computational Biology; HEK293 Cells; Humans; Ligands; Molecular Docking Simulation; Molecular Dynamics Simulation; Protein Binding; Protein Conformation; Receptors, G-Protein-Coupled; Signal Transduction; AC1MLNKK; GPR17; Ligand-docking; MDL29951; T0510.3657; cAMP

Abstract

G Protein-coupled Receptor 17 (GPR17) is phylogenetically related to the purinergic receptors emerged as a potential drug target for multiple sclerosis, Parkinson disease, Alzheimer disease and cancer. Unfortunately, the crystal structure of GPR17 is unresolved. With the interest in structure-based ligand discovery, we modeled the structure of GPR17. The model allowed us to identify two novel agonists, AC1MLNKK and T0510.3657 that selectively activate GPR17 which exhibit better interaction properties than previously known ligand, MDL29951. We report detailed protein-ligand interactions and the dynamics of GPR17-ligand interaction by molecular docking and molecular dynamics experiments. Ex vivo validation of GPR17-ligand interaction provides evidence that ligand T0510-3657 and AC1MLNKK inhibit the cAMP levels in GPR17-HEK293T cells, with a pEC

Department

Institute for Systems Biology

Share

COinS