Back

An iteratively curated CRISPR library reveals target-specific biological resistance landscapes across targeted protein degraders

Liu, L.; Voulgaris, O.; Wang, C.; Gannon, D.; Ritchie, M. E.; Feltham, R.; Vervoort, S. J.

2026-08-21 genomics
10.64898/2026.08.14.744749 bioRxiv
Show abstract

Targeted protein degradation (TPD) has emerged as an increasingly powerful approach for therapeutic development and biological discovery. TPD compounds including proteolysis-targeting chimeras (PROTACs), molecular glues, and tag-targeting protein degraders (tTPD) enable rapid, selective and reversible degradation of proteins through recruitment of the ubiquitin-proteasome system (UPS). However, genome-wide CRISPR screens performed with targeted protein degraders are frequently dominated by resistance mechanisms that disrupt degrader activity, including loss of recruited E3 ligase components and broader UPS regulators. The strong selective advantage conferred by these perturbations can obscure less penetrant, biological genetic interactions that operate downstream of target degradation. To overcome this limitation, through iterative genome-wide screening and manual curation, we developed a TPD-compatible CRISPR knockout library that retains near-genome-scale coverage while excluding a focused set of genes recurrently associated with degrader failure. Across multiple degrader screens, this library reduced the dominance of UPS-associated resistance mechanisms and improved the detection and prioritization of genetic interactions linked to target biology. Using the RBM39 molecular glue degrader indisulam as a model, we identified ZMAT2 loss as a resistance mechanism that preserves RBM39 degradation but attenuates the transcriptional and splicing consequences of target depletion. Together, our work establishes a TPD-compatible CRISPR screening framework that improves the biological resolution of degrader resistance screens and facilitates the discovery of genetic dependencies operating downstream of targeted protein degradation.

Matching journals

The top 7 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.