Programmable Protein Stabilization with Language Model-Derived Peptide Guides

利用语言模型衍生的肽指南进行可编程蛋白质稳定

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作者:Lauren Hong, Tianzheng Ye, Tian Zi Wang, Divya Srijay, Lin Zhao, Rio Watson, Sophia Vincoff, Tianlai Chen, Kseniia Kholina, Shrey Goel, Matthew P DeLisa, Pranam Chatterjee

Abstract

Dysregulated protein degradation via the ubiquitin-proteasomal pathway can induce numerous disease phenotypes, including cancer, neurodegeneration, and diabetes. Stabilizing improperly ubiquitinated proteins via target-specific deubiquitination is thus a critical therapeutic goal. Building off the major advances in targeted protein degradation (TPD) using bifunctional small-molecule degraders, targeted protein stabilization (TPS) modalities have been described recently. However, these rely on a limited set of chemical linkers and warheads, which are difficult to generate de novo for new targets and do not exist for classically "undruggable" targets. To address the limited reach of small molecule-based degraders, we previously engineered ubiquibodies (uAbs) by fusing computationally-designed "guide" peptides to E3 ubiquitin ligase domains for modular, CRISPR-analogous TPD. Here, we expand the TPS target space by engineering "deubiquibodies" (duAbs) via fusion of computationally-designed guides to the catalytic domain of the potent OTUB1 deubiquitinase. In human cells, duAbs effectively stabilize exogenous and endogenous proteins in a DUB-dependent manner. To demonstrate duAb modularity, we swap in new target-binding peptides designed via our generative language models to stabilize diverse target proteins, including key tumor suppressor proteins such as p53 and WEE1, as well as heavily-disordered fusion oncoproteins, such as PAX3::FOXO1. In total, our duAb system represents a simple, programmable, CRISPR-analogous strategy for TPS.

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