br Experimental procedures br Introduction
Introduction Protein kinases represent one of the largest enzyme STA-21 calculator in the human genome and act as signaling mediators in a variety of cellular processes (Manning et al., 2002). Because many diseases are associated with aberrant protein kinase activity, targeted kinase inhibitors are clinically highly successful, such as imatinib in cancer therapy (Druker et al., 2006, Fabbro, 2015, Wu et al., 2016). ATP-competitive kinase inhibitors can be classified by the conformation of the highly conserved Asp-Phe-Gly (DFG) motif of the kinase upon inhibitor binding (Zhao et al., 2014). Type I inhibitors bind to the active, DFG-Asp-in, conformation. Type II inhibitors bind an inactive conformation in which the aspartate of the DFG motif faces away from the active site into the bulk solvent (DFG-Asp-out) (Wodicka et al., 2010). While both type I and type II kinase inhibitors have been clinically successful, specific kinase inhibition remains challenging due to the high conservation of the ATP binding pocket (Zhang et al., 2009). For example, the type I inhibitor dasatinib and the type II inhibitor imatinib bind 86 and 19 kinases out of 317, respectively (Karaman et al., 2008). While low inhibitor selectivity seems to be clinically tolerable for treating certain types of leukemia, inhibition of off-target kinases often limits the application of kinase inhibitors against solid tumors (Cohen et al., 2017, Eckstein et al., 2014, Lee and Wang, 2009). For this reason, the specificity relationship between inhibitor and kinase is typically viewed from the perspective of the inhibitor (i.e., which kinases does a single inhibitor target?). Instead, examining this relationship from the perspective of the kinase (i.e., which inhibitors does an individual kinase bind?) can lead to a kinome-wide understanding of inhibitor binding behavior (Anastassiadis et al., 2011, Huang et al., 2010). To identify relationships among kinases as determined by the similarity of their inhibition phenotype—the profile of kinase inhibitors for which they have measurable affinity—we perform hierarchical clustering on a previously published dataset assessing the inhibition of 406 kinase constructs by 645 inhibitors (Drewry et al., 2017). We identify two groups of kinases with strikingly different promiscuity toward kinase inhibitors. The group of promiscuous kinases consists of eight Ser/Thr and Tyr kinases including established clinical targets (PDGFRA/B, KIT, and CSF1R), as well as kinases that are not prominent clinical targets (DDR1, DDR2, YSK4, and MEK5) (Wu et al., 2015). Importantly, kinases that are the target of many drug development programs such as EGFR, Abl, BRAF, and IGF1R are not part of this promiscuous group. To determine the structural basis for promiscuity toward kinase inhibitors, we solved the co-crystal structure of DDR1 in complex with two type I inhibitors: the Aurora kinase inhibitor, VX-680, and the pan-tyrosine kinase inhibitor, dasatinib (Harrington et al., 2004, Lombardo et al., 2004). DDR1 is a receptor tyrosine kinase that binds to the extracellular matrix and is characterized by low kinase activity and slow activation kinetics. Surprisingly, our structures show that DDR1 binds both type I inhibitors in the DFG-Asp-out conformation, which is the binding conformation typically reserved for type II inhibitors. This suggests that DDR1 is stable in the DFG-Asp-out inactive conformation. This in itself is unusual since the first structures of kinases in the DFG-Asp-out conformation were considered to be induced by the high-affinity type II inhibitors (Nagar et al., 2002, Schindler et al., 2000). Here, we show that the DFG-Asp-out conformation is not only stable in DDR1 but facilitates promiscuous inhibitor binding. Moreover, we find within the subset of promiscuous kinases a conserved salt bridge that stabilizes the DFG-Asp-out conformation. Disruption of this salt bridge shifts the population of DDR1 toward the active DFG-Asp-in conformation and increases the specific kinase activity 10-fold. The study provides an example of how large functional datasets can be used to group proteins by functional phenotype instead of sequence homology to elucidate a common mechanism. While here we define a single phenotypically different subgroup of kinases and its underlying mechanism, we expect that further analyses of this type will reveal additional phenotype-based kinase subgroups and mechanisms.