MapInDegraders Closing the gap: studying non-obvious ligand induced degradation events

Summary

Inhibitor-induced target destabilisation has been reported sporadically in recent years. Importantly, despite never actually having been put in place on purpose – or by design – these effects often play a significant role towards their therapeutic effect. To date, no systematic approach to map or quantify these serendipitous destabilising events has, however, been performed, representing a huge underexplored space with large translational potential. I here propose to study ligand-induced destabilisation by utilising the plethora of available protein kinase inhibitors to systematically map their kinase degrading abilities. First, an innovative in vivo assay will be developed to monitor kinase stability at scale. Next a protein kinase inhibitor library will be screened and inhibitor-destabilised kinase pairs will be identified using automated high-throughput microscopy, coupled to fluorescent activated cell sorting and next generation sequencing. Pharmacokinetic mode-of-action binning and machine-learning based computational data mining will chart the map of ligand-induced destabilisation and delineate crucial protein, chemical and/or biophysical properties. Finally, based on the principal mode-of-action identification, measured effect size and translational potential, in-depth mechanism of action studies will be conducted for one selected inhibitor-kinase pair. To that end, a multi-omics approach consisting of genome-wide CRISPR screens and orthogonal genomics and proteomics strategies will be conducted. The project will delineate the underlying concepts of ligand-induced destabilisation using protein kinase inhibitors as a chemically well explored compound space and thus open new avenues to tap into the translationally intriguing area of designing targeted protein degraders for future medical applications.

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Web resources:	https://cordis.europa.eu/project/id/101029199
Start date:	01-04-2021
End date:	31-03-2023
Total budget - Public funding:	174 167,04 Euro - 174 167,00 Euro