期刊
GENOMICS PROTEOMICS & BIOINFORMATICS
卷 19, 期 6, 页码 986-997出版社
ELSEVIER
DOI: 10.1016/j.gpb.2020.05.006
关键词
Protein-drug interaction; iDTPnd; Kinase inhibitor; Drug-binding site signature; Drug repurposing
资金
- Toyota Technological Institute at Chicago, King Abdullah University of Science and Technology
- Higher Education of Pakistan
- King Abdullah University of Science and Technology, Office of Sponsored Research Grant [FCC/1/1976-25]
The study introduces iDTPnd, a computational approach for large-scale discovery of novel drug targets. The method provides a docking-based interaction score for evaluating unintended targets and successfully validated interactions of several drugs with known targets, as well as predicting and validating new potential targets.
Current FDA-approved kinase inhibitors cause diverse adverse effects, some of which are due to the mechanism-independent effects of these drugs. Identifying these mechanism-independent interactions could improve drug safety and support drug repurposing. Here, we develop iDTPnd (integrated Drug Target Predictor with negative dataset), a computational approach for large-scale discovery of novel targets for known drugs. For a given drug, we construct a positive structural signature as well as a negative structural signature that captures the weakly conserved structural features of drug-binding sites. To facilitate assessment of unintended targets, iDTPnd also provides a docking-based interaction score and its statistical significance. We confirm the interactions of sorafenib, imatinib, dasatinib, sunitinib, and pazopanib with their known targets at a sensitivity of 52% and a specificity of 55%. We also validate 10 predicted novel targets by using in vitro experiments. Our results suggest that proteins other than kinases, such as nuclear receptors, cytochrome P450, and MHC class I molecules, can also be physiologically relevant targets of kinase inhibitors. Our method is general and broadly applicable for the identification of protein-small molecule interactions, when sufficient drug-target 3D data are available. The code for constructing the structural signatures is available at https://sfb.kaust.edu.sa/Documents/iDTP.zip.
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