Dissecting Kinase Profiling Data to Predict Activity and Understand Cross-Reactivity of Kinase Inhibitors

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Abstract
The development of selective and multitargeted kinase inhibitors has received much attention, because cross-reactivity with unintended targets may cause toxic side effects, while it can also give rise to efficacious multitargeted drugs. Here we describe a deconvolution approach to dissecting kinase profiling data in order to gain knowledge about cross-reactivity of inhibitors from large-scale profiling data. This approach not only enables activity predictions of given compounds on a kinome-wide scale, but also allows to extract residue–fragment pairs that are associated with activity. We demonstrate its effectiveness using a large-scale public chemogenomics data set and also apply our proposed model to a recently published bioactivity data set. We further illustrate that the preference of given compounds for kinases of interest is better understood by residue–fragment pairs, which could provide both biological and chemical insights into cross-reactivity.

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