centrality analysis of protein-protein interaction networks and molecular docking prioritize potential drug-targets in type 1 diabetes

Type 1 diabetes (t1d) occurs as a consequence of an autoimmune attack against pancreatic β- cells. due to a lack of a clear understanding of the t1d pathogenesis, the identification of effective therapies for t1d is the active area in the research. the study purpose was to prioritize potential drugs and targets in t1d via systems biology approach. gene expression data of peripheral blood mononuclear cells (pbmcs) and pancreatic β-cells in t1d were analyzed and differential expressed genes were integrated with protein-protein interactions (ppi) data. multiple topological centrality parameters of extracted query-query ppi (qqppi) networks were calculated and the interaction of more central proteins with drugs was investigated. molecular docking was performed to further predict the interactions between drugs and the binding sites of targets. central proteins were identified by the analysis of pbmc (myc, erbb2, psma1, abl1 and hsp90aa1) and pancreatic β-cells (hsp90ab1, esr1, rela, rac1, nfkb1, nfkb2, ikbke, arrb2 and src) qqppi networks. thirteen drugs which targeted eight central proteins were identified by further analysis of drug-target interactions. some drugs which investigated for diabetes treatment in the experimental models of t1d were prioritized by literature verification, including melatonin, resveratrol, lapatinib, geldanamycin, eugenol and fostaminib. finally, according on molecular docking analysis, lapatinib-erbb2 and eugenol- esr1 exhibited highest and lowest binding energy, respectively. this study presented promising results for the prioritization of potential drug-targets which might facilitate t1d targeted therapy and its drug discovery process more effectively.