A priori inference of cross reactivity for drug-targeted kinases

Kinases are central targets for drug-based treatment of diseases such as cancer, diabetes, and arthritis. Progress in drug development faces challenges due to undesirable cross reactivity and difficulties in modulating selectivity, both consequences of fold conservation. Here we present a structure-based predictor of cross reactivity and validate it against affinity fingerprinting of the kinases and our own drug redesign geared at sharpening the inhibitory impact. The predictor assesses protein environments of binding pockets, compares patterns of packing defects, and introduces a packing distance in kinase space. This metric is conclusively shown to be equivalent to pharmacological distance generated by comparing affinity fingerprintings. Our packing distance metric is further extended to infer cross reactivity over all human tyrosine kinases. This tool should prove useful to target clinically relevant regions of the pharmacokinome, as our experimental assays reveal.