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

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.