Drug Design And Repositioning For T2DM Based On GCGR Protein Target

Type 2 diabetes mellitus(T2DM)is a complex disease caused by decreased insulin and/or insulin resistance and excessive glucagon secretion.Abnormal glucagon secretion and dysfunction resulting in hyperglycemia and postprandial glucagon deficiency suppression also play an important role in T2 DM.Therefore,reducing glucagon secretion or inhibiting glucagon action is likely to be a new approach to treating T2 DM.By inhibiting intrahepatic gluconeogenesis and antagonizing the GCGR,glucagon receptor antagonists(GRAs)can reduce blood glucose levels without causing serious consequences such as hypoglycemia or obesity.Consequently,the examination of GCGR and GRAs has advantageous effects on the management of T2 DM.Deep learning techniques were employed to design and optimize ligands that targeted on the active pockets of GCGR,then virtual screening was utilized for the selection of potential compounds in this paper.A combination of molecular docking,molecular dynamics simulations,systems pharmacology,bioinformatics analysis,and cellular experiments to study and analyze the molecular mechanisms of candidate compounds.In addition,a deep neural network prediction model was used to study the repositioning of hypoglycemic agents.This paper’s primary content consists of two components:1.Mol AICal(an artificial intelligence drug design software)was used to perform ligand growth and virtual screening within the GCGR activity pocket,and 8 compounds were ultimately selected for further analysis based on receptor and ligand binding affinity.The eight compounds were tested for druggability using the ADMET prediction tool,and one of the most promising candidates,C5,was selected after a comprehensive bioinformatics drug-target prediction and pathway mechanism analysis.To investigate the interaction between C5 and GCGR,we performed molecular dynamics simulations of GCGR without ligand binding and GCGR complexes bound to C5,respectively.The results showed that the structure of the GCGR complex with C5 was in an unstable state,thus inhibiting the normal binding of glucagon to GCGR,which led to the hypothesis that C5 could act as an inhibitor of glucagon.The antagonistic activity was verified by an IC50 value of 466.5 n M for C5 in the results of the cell activity assay.2.T2 DM shares common pathogenic mechanisms with cancer and neurodegenerative diseases,and the metabolic disorders caused by T2 DM such as hyperglucagonemia,hyperinsulinemia,hyperglycemia,oxidative stress,inflammation and excessive glycosylation accelerate the development of cancer and neurodegenerative diseases.Therefore,T2 DM drug repositioning(drug repositioning)for the treatment of related cancer and neurodegenerative diseases is feasible.Therefore,the study of T2 DM drug repositioning for the treatment of related cancers and neurodegenerative diseases is feasible.Deep learning learns features of various instances from complex multiple networks and predicts interaction relationships.In this paper,a drug-disease relationship(DDI)prediction model was designed for cancer(pancreatic cancer,breast cancer),neurodegenerative diseases(Parkinson’s disease,Alzheimer’s disease)and T2 DM by drawing on the multi-task graph attention(MGA)framework.The results of repositioning prediction of C5 using this classification prediction model showed a corresponding therapeutic relationship between C5 and Alzheimer’s disease,and the target-pathway prediction and bioinformatics analysis of C5 and Alzheimer’s disease were conducted.