ArticleBioinformation2017
Insights from the protein-protein interaction network analysis of Mycobacterium tuberculosis toxin-antitoxin systems.
Article in Bioinformation, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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Who cites it
7 citing papers in PubMed.
- Antibody-based alternatives to animal testing for toxin detection and antitoxin evaluation.Frontiers in toxicology · 2025Review
- Machine learning based personalized drug response prediction for lung cancer patients.Scientific reports · 2022Article
- Comparative Proteomic Analysis Reveals Metformin Improves the Expression of Biomarkers of Endometrial Receptivity in Infertile Women with Minimal/Mild Endometriosis.Reproductive sciences (Thousand Oaks, Calif.) · 2022Article
- Downregulation of PPARα mediates FABP1 expression, contributing to IgA nephropathy by stimulating ferroptosis in human mesangial cells.International journal of biological sciences · 2022Article
- Protein Integrated Network Analysis to Reveal Potential Drug Targets Against Extended Drug-Resistant Mycobacterium tuberculosis XDR1219.Molecular biotechnology · 2021Article
- Periostin Contributes to Immunoglobulin a Nephropathy by Promoting the Proliferation of Mesangial Cells: A Weighted Gene Correlation Network Analysis.Frontiers in genetics · 2020Article
- Computer aided analysis of disease linked protein networks.Bioinformation · 2019Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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