ArticleBMC bioinformatics2009
Assessing the druggability of protein-protein interactions by a supervised machine-learning method.
Article in BMC bioinformatics, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Integrated Computational Analysis of Genes Associated with Human Hereditary Insensitivity to Pain. A Drug Repurposing Perspective.Frontiers in molecular neuroscience · 2017Article
- 2P2I HUNTER: a tool for filtering orthosteric protein-protein interaction modulators via a dedicated support vector machine.Journal of the Royal Society, Interface · 2014Article
- Druggable orthosteric and allosteric hot spots to target protein-protein interactions.Current pharmaceutical design · 2014Review
- Using a fragment-based approach to target protein-protein interactions.Chembiochem : a European journal of chemical biology · 2013Article
- The evolutionary rate of antibacterial drug targets.BMC bioinformatics · 2013Article
- Insights into an original pocket-ligand pair classification: a promising tool for ligand profile prediction.PloS one · 2013Article
- Article
- Scaling the druggability landscape of human bromodomains, a new class of drug targets.Journal of medicinal chemistry · 2012Article
- Data mining approaches for genome-wide association of mood disorders.Psychiatric genetics · 2012Article
- Dr. PIAS 2.0: an update of a database of predicted druggable protein-protein interactions.Database : the journal of biological databases and curation · 2012Article
- UniDrug-target: a computational tool to identify unique drug targets in pathogenic bacteria.PloS one · 2012Article
- Targeting oncogenic protein-protein interactions by diversity oriented synthesis and combinatorial chemistry approaches.Molecules (Basel, Switzerland) · 2011Review
- Dr. PIAS: an integrative system for assessing the druggability of protein-protein interactions.BMC bioinformatics · 2011Article
- A machine learning approach for genome-wide prediction of morbid and druggable human genes based on systems-level data.BMC genomics · 2010Article
- Article
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundProtein-protein interactions (PPIs) are challenging but attractive targets of small molecule drugs for therapeutic interventions of human diseases. In this era of rapid accumulation of PPI data, there is great need for a methodology that can efficiently select drug target PPIs by holistically assessing the druggability of PPIs. To address this need, we propose here a novel approach based on a supervised machine-learning method, support vector machine (SVM).
resultsTo assess the druggability of the PPIs, 69 attributes were selected to cover a wide range of structural, drug and chemical, and functional information on the PPIs. These attributes were used as feature vectors in the SVM-based method. Thirty PPIs known to be druggable were carefully selected from previous studies; these were used as positive instances. Our approach was applied to 1,295 human PPIs with tertiary structures of their protein complexes already solved. The best SVM model constructed discriminated the already-known target PPIs from others at an accuracy of 81% (sensitivity, 82%; specificity, 79%) in cross-validation. Among the attributes, the two with the greatest discriminative power in the best SVM model were the number of interacting proteins and the number of pathways.
conclusionUsing the model, we predicted several promising candidates for druggable PPIs, such as SMAD4/SKI. As more PPI data are accumulated in the near future, our method will have increased ability to accelerate the discovery of druggable PPIs.
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