Valeanu, A. | Search bioRxiv

bioRxiv2024

CellEKT: A robust chemical proteomics workflow to profile cellular target engagement of kinase inhibitors

The human genome encodes 518 protein kinases that are pivotal for drug discovery in various therapeutic areas such as cancer and autoimmune disorders. The majority of kinase inhibitors target the conserved ATP-binding pocket, making it difficult to develop selective inhibitors. To characterize and prioritize kinase-inhibiting drug candidates, efficient methods are desired to determine target engagement across the cellular kinome. In this study, we present CellEKT (Cellular Endogenous Kinase Targeting), an optimized and robust chemical proteomics platform for investigating cellular target engagement of endogenously expressed kinases using the sulfonyl fluoride-based probe XO44 and two new probes ALX005 and ALX011. The optimized workflow enabled the determination of the kinome interaction landscape of covalent and non-covalent drugs across over 300 kinases, expressed as half maximum inhibitory concentration (IC50), which were validated using distinct platforms like phosphoproteomics and NanoBRET. With CellEKT, target engagement profiles were linked to their substrate space. CellEKT has the ability to decrypt drug actions and to guide the discovery and development of drugs. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=188 SRC="FIGDIR/small/616061v1_ufig1.gif" ALT="Figure 1"> View larger version (39K): org.highwire.dtl.DTLVardef@5dd891org.highwire.dtl.DTLVardef@1353379org.highwire.dtl.DTLVardef@1c67382org.highwire.dtl.DTLVardef@1c964eb_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2023

Kinex infers causal kinases from phosphoproteomics data

MotivationPhosphoproteomics data are essential for characterising signalling pathways, identifying drug targets, and evaluating efficacy and safety profiles of drug candidates. Emerging resources, including a substrate-specificity atlas and drug-induced phosphoproteomics profiles, may transform the inference of causal kinases. However, there is currently no open-source software that leverages insights derived from these resources. ResultsWe introduce Kinex, a workflow implemented in the same-name Python package, which infers causal serine/threonine kinases from phosphoproteomics data. Kinex allows users to score kinase-substrate interactions, perform enrichment analysis, visualise candidates of causal regulators, and query similar profiles in a database of drug-induced kinase activities. Analysing seven published studies and one newly generated dataset, we demonstrate that analysis with Kinex recovers causal effects of perturbations and reveals novel biological insights. We foresee that Kinex will become an indispensable tool for basic and translational research including drug discovery. AvailabilityKinex is released with the GNU General Public License and available at https://github.com/bedapub/kinex.

bioinformatics↗