Programmable protein stabilization with language model-derived peptide guides.

利用语言模型衍生的肽段引导实现可编程蛋白质稳定化

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作者:Hong Lauren, Ye Tianzheng, Wang Tian Z, Srijay Divya, Liu Howard, Zhao Lin, Watson Rio, Vincoff Sophia, Chen Tianlai, Kholina Kseniia, Goel Shrey, DeLisa Matthew P, Chatterjee Pranam
Dysregulated protein degradation via the ubiquitin-proteasomal pathway can induce numerous disease phenotypes, including cancer, neurodegeneration, and diabetes. While small molecule-based targeted protein degradation (TPD) and targeted protein stabilization (TPS) platforms can address this dysregulation, they rely on structured and stable binding pockets, which do not exist to classically "undruggable" targets. Here, we expand the TPS target space by engineering "deubiquibodies" (duAbs) via fusion of computationally-designed peptide binders to the catalytic domain of the potent OTUB1 deubiquitinase. In human cells, duAbs effectively stabilize exogenous and endogenous proteins in a DUB-dependent manner. Using protein language models to generate target-binding peptides, we engineer duAbs to conformationally diverse target proteins, including key tumor suppressor proteins p53 and WEE1, and heavily-disordered fusion oncoproteins, such as PAX3::FOXO1. We further encapsulate p53-targeting duAbs as mRNA in lipid nanoparticles and demonstrate effective intracellular delivery, p53 stabilization, and apoptosis activation, motivating further in vivo translation.

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