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Prediction and analysis of the protein interactome in Pseudomonas aeruginosa to enable network-based drug target selection.

Zhang M, Su S, Bhatnagar RK, Hassett DJ, Lu LJ - PLoS ONE (2012)

Bottom Line: With the increased prevalence of multi-drug resistant PA, the conventional "one gene, one drug, one disease" paradigm is losing effectiveness.The predicted interactome network from this study is the first large-scale PPI network in PA with significant coverage and high accuracy.In addition, case studies were performed on protein interactions regarding anti-sigma factor MucA, negative periplasmic alginate regulator MucB, and the transcriptional regulator RhlR.

View Article: PubMed Central - PubMed

Affiliation: Division of Biomedical Informatics, Cincinnati Children's Hospital Research Foundation, Cincinnati, Ohio, United States of America.

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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Related in: MedlinePlus

Receiver operator characteristic (ROC) curves of testing by 10-fold cross-validation.The ROC curve indicate the performance of 10-fold cross-validation by using eight features. The area under curve (AUC) is 0.865.
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pone-0041202-g001: Receiver operator characteristic (ROC) curves of testing by 10-fold cross-validation.The ROC curve indicate the performance of 10-fold cross-validation by using eight features. The area under curve (AUC) is 0.865.

Mentions: Based on the reference datasets and all eight features, we have trained and tested a random forest classifier that outperforms various other classification models including support vector machines, Bayesian networks, logistic regression, and artificial neural networks, in this study [Table S3]. 10-fold cross validation using all eight features yields an area under receiver operator characteristic curve (AUC) score of 0.865 [Table 2; Figure 1; Figure S1]. The excellent AUC score indicates both the effectiveness of the classification method and the high quality of the reference dataset. The precision and recall scores for the positive class are 0.659 and 0.414, respectively, with a false positive rate of 0.003. This implies strong performance, albeit with an influence by the dominance of negative interactions, or sparseness in PPI networks.


Prediction and analysis of the protein interactome in Pseudomonas aeruginosa to enable network-based drug target selection.

Zhang M, Su S, Bhatnagar RK, Hassett DJ, Lu LJ - PLoS ONE (2012)

Receiver operator characteristic (ROC) curves of testing by 10-fold cross-validation.The ROC curve indicate the performance of 10-fold cross-validation by using eight features. The area under curve (AUC) is 0.865.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3404098&req=5

pone-0041202-g001: Receiver operator characteristic (ROC) curves of testing by 10-fold cross-validation.The ROC curve indicate the performance of 10-fold cross-validation by using eight features. The area under curve (AUC) is 0.865.
Mentions: Based on the reference datasets and all eight features, we have trained and tested a random forest classifier that outperforms various other classification models including support vector machines, Bayesian networks, logistic regression, and artificial neural networks, in this study [Table S3]. 10-fold cross validation using all eight features yields an area under receiver operator characteristic curve (AUC) score of 0.865 [Table 2; Figure 1; Figure S1]. The excellent AUC score indicates both the effectiveness of the classification method and the high quality of the reference dataset. The precision and recall scores for the positive class are 0.659 and 0.414, respectively, with a false positive rate of 0.003. This implies strong performance, albeit with an influence by the dominance of negative interactions, or sparseness in PPI networks.

Bottom Line: With the increased prevalence of multi-drug resistant PA, the conventional "one gene, one drug, one disease" paradigm is losing effectiveness.The predicted interactome network from this study is the first large-scale PPI network in PA with significant coverage and high accuracy.In addition, case studies were performed on protein interactions regarding anti-sigma factor MucA, negative periplasmic alginate regulator MucB, and the transcriptional regulator RhlR.

View Article: PubMed Central - PubMed

Affiliation: Division of Biomedical Informatics, Cincinnati Children's Hospital Research Foundation, Cincinnati, Ohio, United States of America.

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/.

Show MeSH
Related in: MedlinePlus