A Mammalian High-Throughput Assay to Screen AI-Designed Protein Degraders

Lin Zhao, Aastha Pal, Tong Chen, Pranam Chatterjee

Track: Biology: datasets and/or experimental results

Nature Biotechnology: Yes

Keywords: Peptide design, high-throughput screening

TL;DR: We develop a fluorescence-based screen where a library of AI-designed protein binders are fused to E3 ligase domains, enabling screening via target protein degradation.

Abstract: The development of specific protein binders is crucial for biologics and targeted protein degradation (TPD) therapies. However, existing screening methods are low-throughput, labor-intensive, and often rely on non-human display systems such as phage, yeast, or mRNA display, limiting their translational relevance. To address this, we developed a high-throughput, human cell-based binder screening platform that enables the functional evaluation of artificial intelligence (AI)-designed peptide binders in a mammalian context. Our approach utilizes genetically-encodable, doxycycline-inducible ubiquibodies (uAbs), where a library of computationally designed ``guide'' peptides is fused to an E3 ubiquitin ligase domain, enabling modular, CRISPR-like TPD. By monitoring degradation through an mCherry-fused target protein, we screen and validate AI-generated binders in a physiologically relevant setting. We successfully apply this platform to identify functional binders for EWS::FLI1 and $\beta$-catenin, two highly challenging oncogenic targets. Overall, our approach facilitates the discovery of AI-designed binders for diverse therapeutic applications.

Anonymization: This submission has been anonymized for double-blind review via the removal of identifying information such as names, affiliations, and identifying URLs.

Presenter: ~Lin_Zhao17

Format: No, the presenting author is unable to, or unlikely to be able to, attend in person.

Funding: No, the presenting author of this submission does *not* fall under ICLR’s funding aims, or has sufficient alternate funding.

Submission Number: 28