Therapeutic modalities targeting pathogenic proteins are the gold standard of treatment for multiple disease indications. Unfortunately, a significant portion of these proteins are considered "undruggable" by standard small molecule-based approaches, largely due to their disordered nature and instability. Designing functional peptides to undruggable targets, either as standalone binders or fusions to effector domains, thus presents a unique opportunity for therapeutic intervention. In this work, we adapt recent models for contrastive language-image pre-training (CLIP) to devise a unified, sequence-based framework to design target-specific peptides. Furthermore, by leveraging known experimental binding proteins as scaffolds, we create a streamlined inference pipeline, termed Cut&CLIP, that efficiently selects peptides for downstream screening. Finally, we experimentally fuse candidate peptides to E3 ubiquitin ligase domains and demonstrate robust intracellular degradation of pathogenic protein targets in human cells, motivating further development of our technology for future clinical translation.

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.