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Centrality Analysis of Protein-Protein Interaction Networks and Molecular Docking Prioritize Potential Drug-Targets
Asma Soofi1, Mohammad Taghizadeh2, Seyyed Mohammad Tabatabaei3
1Department of Physical Chemistry, School of Chemistry, College of Sciences, University of Tehran, Tehran, Iran.
Abstract:
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.
Insights
This study used systems biology to identify potential drug targets for type 1 diabetes (T1D). Researchers prioritized thirteen drugs, including melatonin and resveratrol, for T1D targeted therapy and drug discovery.
Area of Science:
- Immunology
- Endocrinology
- Computational Biology
Background:
- Type 1 diabetes (T1D) results from autoimmune destruction of pancreatic beta cells.
- Understanding T1D pathogenesis is crucial for developing effective therapies.
- Current research actively seeks novel therapeutic strategies for T1D.
Purpose of the Study:
- To prioritize potential therapeutic drugs and molecular targets for T1D using a systems biology approach.
- To identify key proteins involved in T1D pathogenesis through network analysis.
- To evaluate drug-target interactions and binding affinities for potential T1D treatments.
Main Methods:
- Analysis of gene expression data from peripheral blood mononuclear cells (PBMCs) and pancreatic beta cells in T1D patients.
- Integration of differential gene expression data with protein-protein interaction (PPI) networks.
- Calculation of topological centrality parameters for query-query PPI (QQPPI) networks and molecular docking simulations.
Main Results:
- Identification of central proteins in PBMC (MYC, ERBB2, PSMA1, ABL1, HSP90AA1) and pancreatic beta cells (HSP90AB1, ESR1, RELA, RAC1, NFKB1, NFKB2, IKBKE, ARRB2, SRC) QQPPI networks.
- Prioritization of thirteen drugs targeting eight central proteins, with melatonin, resveratrol, lapatinib, geldanamycin, eugenol, and fostaminib noted.
- Molecular docking revealed lapatinib-ERBB2 with the highest binding energy and eugenol-ESR1 with the lowest.
Conclusions:
- The study successfully prioritized potential drug-target candidates for T1D.
- Findings offer a promising foundation for advancing T1D targeted therapy and drug discovery.
- The systems biology approach provides a framework for identifying novel therapeutic interventions for T1D.
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