Identifying Novel Targets by using Drug-binding Site Signature: A Case Study of Kinase Inhibitors

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. We have developed "iDTPnd", 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, 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.