henhaobio
Sign in

bioRxivSynthetic BiologyPreprint

A Mammalian High-Throughput Screen for AI-Designed Peptide-Guided Protein Degraders

Lin ZhaoiD, Anuoluwapo A Mattix, Aastha PaliD, Tong CheniD, Sophia VincoffiD, Lauren HongiD, Diana Renteria, Sunetra Sase, Adeline L. VanderveriD, Daniel Matson, Pranam ChatterjeeiD

PDFDOI

Abstract

Targeted protein degradation (TPD) offers a route to eliminate disease-driving proteins that remain inaccessible to conventional inhibitors. However, degrader discovery remains low-throughput, labor-intensive, and dependent on randomized libraries or non-human display systems, limiting functional selection in mammalian cells. Here, we present a high-throughput, human cell-based platform for screening peptide-guided ubiquibodies (uAbs). These genetically encodable, doxycycline-inducible degraders fuse peptide guides generated by protein language models to the CHIP{Delta}TPR E3 ligase domain, creating a modular, CRISPR-like system for programmable TPD. For each target, we introduce a pooled uAb library into the corresponding fluorescent reporter cell line, isolate cells with reduced target abundance by FACS, and recover enriched peptide guides by sequencing. For {beta}-catenin, enriched uAbs reduced endogenous {beta}-catenin abundance and Wnt signaling in DLD1 cells. GFAP-directed uAbs reduced endogenous GFAP abundance and cell viability in U251 glioblastoma cells, while EWS::FLI1-directed uAbs reduced fusion oncoprotein abundance, suppressed EWSAT1 expression, and increased apoptosis in Ewing sarcoma models. Finally, a screen using endogenously tagged GATA2 further identified uAbs that reduced GATA2 under native genomic regulation. Overall, our platform connects generative peptide design to functional mammalian selection and establishes a scalable strategy for CRISPR-like proteome perturbation.

Citations
0

Reproducibility ledger

Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.

Sign in to file a reproducibility report.

Similarity Map
SelectedSame fieldCross-field
THISYe '26Palepu '22Liu '26Lambrus '17Ascanelli '26Islam '25
67% semantic matchBioengineering
Tianzheng Ye, Azmain Alamgir, Cara M. Robertus+14 more·Science Advances·Aug 28, 2026· 5 citations