Previous kinase inhibitor profiling studies have revealed an unexpected number of off-target kinase interactions, even for highly characterized kinase inhibitors1, 2
. These findings have emphasized the importance of broad kinase profiling of these compounds and are supported by our data. Quantitative assessment of inhibitor selectivity is increasingly important as ever larger kinase profiling datasets are reported. While strong kinase selectivity may not be essential for efficacy of therapeutic agents27
, it is critical for tool compounds used to elucidate kinase biology. We therefore applied the Gini coefficient as a measure of kinase inhibitor selectivity14
, thus avoiding the necessity for arbitrary hit thresholds used by previous methods2
. Comparison of Gini scores across multiple inhibitors targeting a specific kinase of interest should provide a powerful basis for choosing the most selective inhibitor for investigating kinase function. For example, the compound collection contains four well-established inhibitors of the AGC subfamily kinase ROCK (Rho associated kinase): Rockout, glycyl-H-1152 (Rho Kinase Inhibitor IV), Y-27632 and the clinical agent Fasudil (HA-1077)28, 29
. Gini score analysis revealed greatest selectivity for glycyl-H-1152 (0.738) and, indeed, this compound inhibited both ROCK I and II significantly more potently than any other kinase (not shown). By contrast Fasudil showed more potent inhibition of PRKX and KHS than ROCK. Strikingly, hierarchical clustering based on target spectrum clustered Rockout, Inhibitor IV and Y27632 together (Supplementary Fig. 2
), despite no clear structural similarity in the compounds. In fact, the secondary targets shared by these compounds are almost all other members of the AGC kinase subfamily, demonstrating that a variety of distinct chemotypes can be employed to selectively inhibit AGC kinases, perhaps due to greater sequence divergence of this subfamily from other subfamilies. These findings illustrate the utility of the present dataset in guiding both tool compound selection and the development of new inhibitors selective for particular kinase subfamilies.
We also introduce the concept of uni-specificity as a way of quantitatively assessing the differential activity of an inhibitor toward its most sensitive and its next most sensitive kinase targets. Compounds exhibiting the greatest degree of uni-specificity are expected to provide the widest dosing window within which only a single kinase target is inhibited. We used this metric to prioritize the characterization of new inhibitor targets. Six uni-specific compounds were found that inhibit other kinases more potently than their intended targets. In all cases, these compounds represent previously unknown targets for these compounds.
While the high-throughput assay used here to systematically measure kinase activity is economical, rapid, and robust, extrapolation of these in vitro
results to predict cellular efficacy must be made with caution. First our screen was carried out in the presence of 10 μM ATP regardless of the affinity of individual kinases for ATP. Potency of ATP-competitive kinase inhibitors in the cellular context is dictated not only by the intrinsic affinity of the inhibitor for the kinase, but also by the Michaelis-Menten constant for ATP binding30
and the cellular concentration of ATP. Thus, the relative rank order of inhibited kinases determined here may differ in the cellular context. Second, many kinases in the panel are represented by truncated constructs whose interactions with compound could differ in the context of the full-length kinase or in the cellular milieu. In addition, many kinases can adopt multiple conformational states and only one such state was assayed for each kinase. Third, though the kinase panel tested here is among the largest available for biochemical measurements of kinase catalytic activity, a minority of kinases are not included in the panel. Thus, additional off-target activities against untested kinases can be reasonably expected. Nevertheless, the data presented here provide a rich resource of information concerning kinase-inhibitor interactions, and biochemical analysis of kinase-inhibitor interactions generally correlates with cellular efficacy30
Protein kinase research has been predominantly focused on a small subset of the kinome31
. The identification of selective inhibitors targeting poorly understood kinases would greatly facilitate elucidation of their function. Our identification of a uni-specific inhibitor of Haspin provides one example of how large-scale kinase profiling can identify new tool compounds to stimulate new research. Crystallographic studies may also benefit from the present study. Protein kinases exhibit significant conformational plasticity that can make it difficult to obtain diffracting crystals of unliganded kinases32
. ATP-competitive kinase inhibitors can be used to stabilize kinases for crystallographic structure determination3
. The dataset presented here provides a library of candidates, on average nine per kinase, to support such studies. In addition, we illustrate how the present dataset can be mined to reveal new opportunities for multi-targeted kinase inhibition (). Indeed, new statistical methods have been recently developed15
to facilitate analysis of potential drug polypharmacology using robust kinase-inhibitor interaction maps such as this. Finally, we expect that the inhibitor collection characterized here, with activity against the majority of human protein kinases, will be a powerful tool to elucidate kinase functions in cell models.