ArticleGenes & genomics2018
Identification of drug target candidates of the swine pathogen Actinobacillus pleuropneumoniae by construction of protein-protein interaction network.
Article in Genes & genomics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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Who cites it
4 citing papers in PubMed, 6 citations in OpenAlex.
- Computational Network Inference for Bacterial Interactomics.mSystems · 2022Review
- Protein Integrated Network Analysis to Reveal Potential Drug Targets Against Extended Drug-Resistant Mycobacterium tuberculosis XDR1219.Molecular biotechnology · 2021Article
- Common Genes Involved in Autophagy, Cellular Senescence and the Inflammatory Response in AMD and Drug Discovery Identified via Biomedical Databases.Translational vision science & technology · 2021Article
- Identifying Drug Targets in Pancreatic Ductal Adenocarcinoma Through Machine Learning, Analyzing Biomolecular Networks, and Structural Modeling.Frontiers in pharmacology · 2020Article
Corrections and comments
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Authors and funding
9 authors at 3 institutions in 1 country.
Funding
Abstract
Porcine pleuropneumonia caused by Actinobacillus pleuropneumoniae has led to severe economic losses in the pig industry worldwide. A. pleuropneumoniae displays various levels of antimicrobial resistance, leading to the dire need to identify new drug targets. Protein-protein interaction (PPI) network can aid the identification of drug targets by discovering essential proteins during the life of bacteria. The aim of this study is to identify drug target candidates of A. pleuropneumoniae from essential proteins in PPI network. The homologous protein mapping method (HPM) was utilized to construct A. pleuropneumoniae PPI network. Afterwards, the subnetwork centered with H-NS was selected to verify the PPI network using bacterial two-hybrid assays. Drug target candidates were identified from the hub proteins by analyzing the topology of the network using interaction degree and homologous comparison with the pig proteome. An A. pleuropneumoniae PPI network containing 2737 non-redundant interaction pairs among 533 proteins was constructed. These proteins were distributed in 21 COG functional categories and 28 KEGG metabolic pathways. The A. pleuropneumoniae PPI network was scale free and the similar topological tendencies were found when compared with other bacteria PPI network. Furthermore, 56.3% of the H-NS subnetwork interactions were validated. 57 highly connected proteins (hub proteins) were identified from the A. pleuropneumoniae PPI network. Finally, 9 potential drug targets were identified from the hub proteins, with no homologs in swine. This study provides drug target candidates, which are promising for further investigations to explore lead compounds against A. pleuropneumoniae.
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