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Insights from the protein-protein interaction network analysis of Mycobacterium tuberculosis toxin-antitoxin systems

机译:从结核分枝杆菌毒素-抗毒素系统的蛋白质-蛋白质相互作用网络分析中获得的见解

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摘要

Protein-protein interaction (PPI) network analysis is a powerful strategy to understand M. tuberculosis (Mtb) system level physiology in the identification of hub proteins. In the present study, the PPI network of 79 Mtb toxin-antitoxin (TA) systems comprising of 167 nodes and 234 edges was investigated. The topological properties of PPI network were examined by 'Network analyzer' a cytoscape plugin app and STRING database. The key enriched biological processes and the molecular functions of Mtb TA systems were analyzed by STRING. Manual curation of the PPI data identified four proteins (i.e. Rv2762c, VapB14, VapB42 and VapC42) to possess the highest number of interacting partners. The top 15% hub proteins were identified in the PPI network by employing two statistical measures, i.e. betweenness and radiality by employing cytohubba. Insights gained from the molecular protein models of VapC9 and VapC10 are also documented.
机译:蛋白质-蛋白质相互作用(PPI)网络分析是一种有效的策略,可用于识别结核分枝杆菌(Mtb)系统水平的生理机制,以识别毂蛋白。在本研究中,研究了由167个节点和234个边缘组成的79个Mtb毒素-抗毒素(TA)系统的PPI网络。通过cytoscape插件应用程序“网络分析仪”和STRING数据库检查了PPI网络的拓扑特性。通过STRING分析了关键富集的生物过程和Mtb TA系统的分子功能。手动管理PPI数据可确定四种蛋白质(即Rv2762c,VapB14,VapB42和VapC42)具有最多数量的相互作用伴侣。通过使用两种统计量度(即通过使用cytohubba的介于中间和径向之间)在PPI网络中鉴定出最高的15%的毂蛋白。还记录了从VapC9和VapC10分子蛋白质模型获得的见解。

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