ArticlePloS one2012
Prediction and analysis of the protein interactome in Pseudomonas aeruginosa to enable network-based drug target selection.
Article in PloS one, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
18 citing papers in PubMed.
- Mutations in the mucA gene of Pseudomonas aeruginosa promote disease exacerbation.Archives of microbiology · 2026Review
- Combining Network Pharmacology and Experimental Verification to Ascertain the Mechanism of Action of Asparagus officinalis Against the Brain Damage Caused by Fluorosis.Environmental toxicology · 2025Article
- Mechanism of action of Asparagus officinalis extract against multiple myeloma using bioinformatics tools,Frontiers in pharmacology · 2023Article
- Computational Network Inference for Bacterial Interactomics.mSystems · 2022Review
- Exploration in the mechanism of fucosterol for the treatment of non-small cell lung cancer based on network pharmacology and molecular docking.Scientific reports · 2021Article
- Interface-Based Structural Prediction of Novel Host-Pathogen Interactions.Methods in molecular biology (Clifton, N.J.) · 2019Article
- Article
- A Comprehensive In Silico Method to Study the QSTR of the Aconitine Alkaloids for Designing Novel Drugs.Molecules (Basel, Switzerland) · 2018Article
- Protein-protein interactions in bacteria: a promising and challenging avenue towards the discovery of new antibiotics.Beilstein journal of organic chemistry · 2018Review
- Prediction of Host-Pathogen Interactions for Helicobacter pylori by Interface Mimicry and Implications to Gastric Cancer.Journal of molecular biology · 2017Article
- Pseudomonas aeruginosa cells attached to a surface display a typical proteome early as 20 minutes of incubation.PloS one · 2017Article
- Network-assisted investigation of virulence and antibiotic-resistance systems in Pseudomonas aeruginosa.Scientific reports · 2016Article
- Probing the protein interaction network of Pseudomonas aeruginosa cells by chemical cross-linking mass spectrometry.Structure (London, England : 1993) · 2015Article
- Inspecting the potential physiological and biomedical value of 44 conserved uncharacterised proteins of Streptococcus pneumoniae.BMC genomics · 2014Article
- Discovery of an allosteric inhibitor binding site in 3-Oxo-acyl-ACP reductase from Pseudomonas aeruginosa.ACS chemical biology · 2013Article
- Structure and dynamics of molecular networks: a novel paradigm of drug discovery: a comprehensive review.Pharmacology & therapeutics · 2013Review
- SynSysNet: integration of experimental data on synaptic protein-protein interactions with drug-target relations.Nucleic acids research · 2013Article
- Comparative Molecular docking analysis of DNA Gyrase subunit A in Pseudomonas aeruginosaPAO1.Bioinformation · 2013Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Pseudomonas aeruginosa (PA) is a ubiquitous opportunistic pathogen that is capable of causing highly problematic, chronic infections in cystic fibrosis and chronic obstructive pulmonary disease patients. With the increased prevalence of multi-drug resistant PA, the conventional "one gene, one drug, one disease" paradigm is losing effectiveness. Network pharmacology, on the other hand, may hold the promise of discovering new drug targets to treat a variety of PA infections. However, given the urgent need for novel drug target discovery, a PA protein-protein interaction (PPI) network of high accuracy and coverage, has not yet been constructed. In this study, we predicted a genome-scale PPI network of PA by integrating various genomic features of PA proteins/genes by a machine learning-based approach. A total of 54,107 interactions covering 4,181 proteins in PA were predicted. A high-confidence network combining predicted high-confidence interactions, a reference set and verified interactions that consist of 3,343 proteins and 19,416 potential interactions was further assembled and analyzed. The predicted interactome network from this study is the first large-scale PPI network in PA with significant coverage and high accuracy. Subsequent analysis, including validations based on existing small-scale PPI data and the network structure comparison with other model organisms, shows the validity of the predicted PPI network. Potential drug targets were identified and prioritized based on their essentiality and topological importance in the high-confidence network. Host-pathogen protein interactions between human and PA were further extracted and analyzed. In addition, case studies were performed on protein interactions regarding anti-sigma factor MucA, negative periplasmic alginate regulator MucB, and the transcriptional regulator RhlR. A web server to access the predicted PPI dataset is available at http://research.cchmc.org/PPIdatabase/.
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