Targeting Aggregating Proteins with Language Model-Designed Degraders

Abstract

Protein aggregation drives several neurological diseases and pediatric cancers, yet current inhibitors fail to directly target aggregating proteins or provide long-term disease modification. Advances in generative artificial intelligence (AI), particularly protein language models (pLMs), have enabled the design of peptide binders for disordered and oncogenic targets. Using these models, we designed peptide binders for mutant GFAP (Alexander Disease) and PAX3::FOXO1 (Alveolar Rhabdomyosarcoma). When fused to E3 ubiquitin ligase domains, these binders selectively degrade their targets, and in the case of GFAP, reducing aggregation. Our results demonstrate that pLM-designed peptide-guided degraders provide a powerful strategy for treating aggregation-driven diseases.