PepRePs: Peptide-Retargeted Phosphatases via Generative Language Models

Abstract

Phosphorylation is a fast, reversible layer of post-translational control that directly governs protein activity, signaling flux, and cell state. Kinases and phosphatases collectively establish these phosphorylation states, but their dysregulation drives cancer, neurodegenerative diseases, and developmental disorders. Existing tools lack the ability to selectively rewrite phosphorylation states of individual, often disordered, proteins in cells. Here, we introduce Peptide-Retargeted Phosphatases (PepRePs), a genetically encoded system that fuses a peptide binder to a serine/threonine phosphatase domain to localize phosphatase activity to a target protein. PepRePs dephosphorylated endogenous and exogenous targets, including tau, Rab8A, STAT3, and PTEN, reducing phosphorylation and producing downstream functional effects on cellular behavior. Peptide binders for each target were designed from protein sequence without structural inputs using protein language models, extending PepRePs to disordered substrates that lack a defined structure for conventional binder design. By coupling programmable peptide binders with modular phosphatase subunits, PepRePs provide a mechanism-aware approach to perturbing signaling networks at the level of post-translational control. This work establishes targeted dephosphorylation as a strategy for probing and reprogramming phosphorylation-driven cell states, with potential applications in systems biology and therapeutic intervention in diseases dominated by signaling dysregulation.