Design of Peptide-Guided Protein Degraders with Structure-Agnostic Language Models

Abstract

Abstract Here, we integrate fine-tuned protein language models and protein-protein interaction databases to develop a Structure-agnostic Language Transformer & Peptide Prioritization module that efficiently selects peptides from interaction interfaces, without the need for structural information. We experimentally fuse SaLT&PepPr-derived “guide” peptides to E3 ubiquitin ligase domains and reliably identify candidates that induce robust intracellular degradation of clinically-relevant targets, and exhibit high binding affinities, low off-targeting rates, and functional transcriptional effects.