Programmable protein stabilization with language model-derived peptide guides

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Chapter 1

nature communications

Programmable protein stabilization with language model-derived peptide guides

Received: 21 July 2024

Accepted: 2 April 2025

Published online: 15 April 2025

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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 dysregu- lation, 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.

The ubiquitin-proteasomal pathway regulates critical processes, including protein folding, DNA repair, and cell differentiation, thus helping to maintain proteostasis1. Dysregulation of this pathway-such as improper degradation of tumor suppressors or mutant, misfolded proteins-can lead to severe pathogenic phenotypes, such as cancer, neurodegenerative disease, cystic fibrosis, and diabetes2-5. Therefore, there is a need for proteome editing tools that are capable of cor- recting this dysregulation by selectively removing ubiquitin from tar- get proteins. While the controllable installation of ubiquitin has been extensively exploited in the form of targeted protein degradation (TPD) strategies such as PROTACs and molecular glues1, only recently has the reverse process, targeted protein stabilization (TPS), gained attention6. The current state-of-the-art TPS modality, termed deubiquitinase-targeting chimeras or DUBTACs, is analogous to https://doi.org/10.1038/s41467-025-58872-6

PROTACs: they recruit endogenous deubiquitinases (DUBs), but still rely on the arduous design of chemical linkers and existence of small- molecule warheads, which do not exist for classically "undruggable" proteins due to their conformational disorder and lack of putative or cryptic binding site accessibility6. Due to the labor-intensive and time- consuming process of designing de novo binders-whether small molecules or biologics-for target proteins7, achieving a truly pro- grammable TPS system currently remains unrealized.

In recent years, our team has described a unique TPD strategy that involves genetically fusing target-specific short "guide" peptides, designed via sequence-based algorithms, to the ubiquitin conjugation domain of the human E3 ubiquitin ligase, CHIPS-12. Without the requirement of a stable target structure, this programmable design process results in chimeric proteins called "ubiquibodies" (uAbs) for

TPD which can target a conformationally varied array of target proteins8-12. Here, we design the analogous platform for TPS, termed deubiquibodies (duAbs), by fusing computationally-designed peptide guides to the catalytic domain of the potent OTUB1 deubiquitinase. Utilizing pre-existing binders, our first-generation fusion duAb archi- tecture effectively stabilizes exogenous and endogenous proteins in a DUB-dependent manner following ectopic expression in human cells. We showcase the inherent programmability of duAbs by swapping in target-binding peptides designed via recent generative protein lan- guage models (pLMs), SaLT&PepPr, PepPrCLIP, and PepMLM10-12. These peptide-guided duAbs stabilize their intended target substrates, including the transcription factors ß-catenin and FOXP3, the tumor suppressors WEE1 and p53, and a disordered fusion oncoprotein PAX3 :: FOXO1. As a final step toward in vivo translation, we deliver p53- targeting duAbs as mRNA in lipid nanoparticles (LNPs), and demon- strate effective intracellular delivery, p53 stabilization, and apoptosis induction.

Results Peptide-deubiquitinase catalytic domain fusions stabilize target proteins

Peptide-generating language models enable programmable target stabilization

duAbs delivered via lipid nanoparticles induce functional p53 stabilization

Discussion

Methods

Plasmid generation

Cell culture

Lentiviral production

Target-mCherry reporter monoclonal cell line generation

Proteomics

Functional assays

Preparation of lipid nanoparticles encapsulating p53- stabilizing duAbs

Cell fractionation and immunoblotting

Structure prediction

Statistical analysis and reproducibility

Reporting summary

Data availability

References

Acknowledgements

Author contributions

Competing interests

Additional information

Overview

The study introduces a novel approach to protein stabilization using deubiquibodies that fuse peptide guides to the OTUB1 deubiquitinase domain, demonstrating their ability to stabilize various important proteins within human cells through a programmable method leveraging protein language models.

Key Points

1Deubiquibodies (duAbs) can stabilize exogenous and endogenous proteins effectively in a DUB-dependent manner
2The use of protein language models aids in designing target-binding peptides for difficult-to-drug proteins
3Intracellular delivery of mRNA-encoded duAbs shows promising results in protein stabilization and apoptosis activation
4The study highlights the need for advanced proteome editing tools to correct dysregulation in critical cellular pathways
5The approach could lead to new therapeutic strategies for diseases involving protein misfolding and degradation.

Details

Authors: Lauren Hong, Tianzheng Ye, Tian Z. Wang, Divya Srijay, Howard Liu, Lin Zhao, Rio Watson, Sophia Vincoff, Tianlai Chen, Kseniia Kholina, Shrey Goel, Matthew P. DeLisa, Pranam Chatterjee
Category: Biology and Natural Sciences

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