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

Autor:	Thakur Z; Centre for Biotechnology, Maharshi Dayanand University (MDU), Rohtak-124001 (Haryana), India., Dharra R; Centre for Biotechnology, Maharshi Dayanand University (MDU), Rohtak-124001 (Haryana), India., Saini V; Toxicology & Computational Biology Group, Centre for Bioinformatics, Maharshi Dayanand University (MDU), Rohtak-124001 (Haryana), India., Kumar A; Toxicology & Computational Biology Group, Centre for Bioinformatics, Maharshi Dayanand University (MDU), Rohtak-124001 (Haryana), India., Mehta PK; Centre for Biotechnology, Maharshi Dayanand University (MDU), Rohtak-124001 (Haryana), India.
Jazyk:	angličtina
Zdroj:	Bioinformation [Bioinformation] 2017 Nov 30; Vol. 13 (11), pp. 380-387. Date of Electronic Publication: 2017 Nov 30 (Print Publication: 2017).
DOI:	10.6026/97320630013380
Abstrakt:	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.
Databáze:	MEDLINE
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