Designing selective kinase inhibitors via deep learning

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PROJECT SUMMARY/ABSTRACT Modern cancer biology leans heavily on kinase inhibitors as a means to probe the consequences of deactivating a particular kinase, but the majority of commonly-used chemical probes are not sufficiently target- selective for robust interpretation of the observed phenotypes. By assembling large panels of kinases (corresponding to much of the human kinome), it has become possible to determine the selectivity for a given probe: however, these experiments are expensive and impractical to perform at high throughput. We have recently developed a new computational approach for rapidly and accurately building 3D structural models of individual inhibitor/kinase complexes. Aim 1 of this project entails applying deep learning to build models for predicting the binding affinity of individual inhibitor/kinase pairs, using 3D structural descriptors derived from the corresponding inhibitor/kinase complexes. Aim 2 of this project will build very large computational libraries of novel chemical matter, enriched in compounds with 3D properties that complement kinase binding sites. We will first use the tools developed in Aim 1 to re-evaluate the selectivity of chemical probes that are widely used by cell biologists, thus informing on which ones are useful tools and which ones should be deprecated. To provide a replacement for the outdated chemical probes, we will computationally screen the libraries of Aim 2 for more selective compounds, focusing first on CDK kinases and several kinases that represent therapeutic vulnerabilities in GIST. We will synthesize the top-scoring computational hits, and characterize them using an escalation of biochemical assays, proteomic kinome profiling, structural biology, and cellular assays. If successful, this project will deliver new chemical probes for several hitherto unaddressed (“orphan”) kinases, to serve as chemical tools and as starting point for drug development. Equally importantly though, completion of this project will provide a robust and validated approach for designing potent and selective kinase inhibitors, to be subsequently applied for developing new high-quality probes against each of the 500 human kinases.