Type-II kinase inhibitors often bind protein kinases in their inactive, DFG-out state in which the aspartate-phenylalanine-glycine (DFG)-motif in the catalytic domain is flipped “out” relative to its conformation in the active state. Limited crystallographic data of protein kinases in the DFG-out conformation hampers the application of rational drug discovery efforts for developing novel type-II inhibitors. Here we present DFGmodel, a computational approach to generate structural models of protein kinases in the DFG-out conformation, and demonstrate its utility for characterizing various protein kinases. In brief, structural models are constructed using MODELLER, based on manually curated structure and sequence- based alignment of all human kinases that corrects for different aspects of protein structure and function (e.g. functionally important residues). DFGmodel relies on multiple templates of experimentally determined structures that sample unique DFG-out conformations. When applicable, the final model is selected based on its ability to discriminate type-II inhibitors from other molecules; thus, the model’s binding site is optimized for protein-type II inhibitor complementarity -- a prerequisite for rationally designing potent type-II kinase inhibitors. The models generated by DFGmodel are accurate, using a variety of assessment measures such as TM-Score. Furthermore, these models’ ability to distinguish known type-II inhibitors from other small molecules with docking is comparable to or exceeds that of the DFG-out crystal structures. Finally, we apply this method to rationalize known kinase-drug interactions, as well as to identify previously unknown, chemically unique kinase inhibitors for a variety of targets in cancer and diabetes.  Our approach provides a framework for developing future novel kinase inhibitors, a highly desirable task for one of the most pharmacologically explored protein families.

Modeling protein kinase structures for the discovery of type-II kinase inhibitors

251st National Meeting (2016)

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