Back

OptoChaperone - A biohybrid tool for regulating protein condensates in cells and in vitro

Tuan, D. T.; Matsusaki, M.; Ota, H.; Kawagoe, S.; Isozumi, N.; Kumeta, H.; Ishimori, K.; Mori, E.; Saio, T.

2025-07-16 biochemistry
10.1101/2025.07.15.665009 bioRxiv
Show abstract

Protein condensates formed via liquid-liquid phase separation (LLPS) are increasingly recognized as key players in diverse cellular processes, including those associated with disease. Despite extensive efforts to characterize their formation and function, tools that enable precise, reversible, and spatiotemporal control of LLPS remain limited. Here, we report OptoChaperone, a light-activatable molecular system designed to manipulate protein condensates both in vitro and in living cells. This biohybrid system leverages photoresponsive switching to control chaperone activity: blue light triggers the suppressive function, leading to the dissolution of protein condensates, whereas UV light deactivates the system, allowing condensate formation. We demonstrate the efficacy of OptoChaperone in regulating several disease-related protein condensates, such as fused in sarcoma, TAR DNA-binding protein 43, and heat shock factor 1. Importantly, the system exhibits reversible and robust control over droplet dynamics without requiring chemical additives or genetic modifications of the client proteins. Given the reversibility and efficiency of OptoChaperone in the manipulation of protein condensates, this tool offers a powerful platform for dissecting the roles of protein condensation in cellular physiology and pathology. This strategy also holds potential for broader applications in synthetic biology, biomolecular engineering, and therapeutic modulation of aberrant phase separation.

Published in Journal of the American Chemical Society (predicted rank #3) · training set

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.