Identifying Novel Drug Targets by iDTPnd: A Case Study of Kinase Inhibitors.
KAUST DepartmentComputer Science Program
Computational Bioscience Research Center (CBRC)
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Biological and Environmental Sciences and Engineering (BESE) Division
KAUST Grant NumberFCC/1/1976-25
Permanent link to this recordhttp://hdl.handle.net/10754/668496
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AbstractCurrent 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. We have developed 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 and 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 were able to confirm the interaction of sorafenib, imatinib, dasatinib, sunitinib, and pazopanib with their known targets at a sensitivity and specificity of 52% and 55%, respectively. We have validated 10 predicted novel targets by using in vitro experiments. Our results suggest that proteins other than kinases, such as nuclear receptors, cytochrome P450, or 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 signature is available at https://sfb.kaust.edu.sa/Documents/iDTP.zip.
CitationNaveed, H., Reglin, C., Schubert, T., Gao, X., Arold, S. T., & Maitland, M. L. (2021). Identifying Novel Drug Targets by iDTPnd: A Case Study of Kinase Inhibitors. Genomics, Proteomics & Bioinformatics. doi:10.1016/j.gpb.2020.05.006
SponsorsThe authors thank Dr. Aly Azeem Khan for helpful discussions. HN was supported by the Toyota Technological Institute at Chicago, King Abdullah University of Science and Technology and a grant to establish Precision Medicine Lab under the umbrella of National Center in Big Data & Cloud Computing from the Higher Education of Pakistan. The research by STA reported in this publication was supported by funding from King Abdullah University of Science and Technology (KAUST), Office of Sponsored Research (OSR), under Grant No. FCC/1/1976-25.
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