Detection of Selective Glucose Transporter Activators for Type 2 Diabetes by Artificial Intelligence Modules

Abstract

Type 2 diabetes (T2D),a global pandemic disease, is characterized with high blood glucose levels and dysregulation in glucose metabolism. These conditions are attributed to insulin resistance which leads to the failure in the translocation of glucose transporter protein (GLUT4). During the dearth of insulin, GLUT4 is confined to storage vesicles within the cells and less than 5% of the transporter is present on the cell membranes. In this context, we intent to detect the effect of seagrass metabolites on the regulation of GLUT4 transport protein which is an hallmark of type 2 diabetes. In order to detect the novel selective activators of GLUT4 protein, artificial intelligence model was adopted  to dock natural ligands with the transporter protein. Due to non availability of GLUT4 crystal structure, a homology model was constructed based on the experimental data available on GLUT1. The homology model represented glucose transport channel along with the substrate interacting residues. Among the six metabolites of seagrass extract, Cis-9 Oleic acid, hexadecanoic acid, beta-sitosterol and ?-1,4 dicarboxylic acid were seem to be potential activators with high docking energy value and H-bond interactions. Thus, the in-silico data supplements the natural activators of GLUT4 in designing the anti-diabetic gents.