Back

Multi-Step Hsp70 Recruitment by Class B J-Domain Proteins Drives Efficient Protein Disaggregation

Wyszkowski, H.; Konieczna, A.; Pokornowska, M.; Sztangierska, W.; Kus, F.; Malolepszy, M.; Kalinowska, K.; Purzycki, D.; Tomiczek, B.; Liberek, K.; Klosowska, A. A.

2026-02-09 biochemistry
10.64898/2026.02.06.704373 bioRxiv
Show abstract

Protein disaggregation by the Hsp70 system is essential for maintaining cellular proteostasis, especially under stress and proteotoxic diseases. J-Domain Proteins (JDPs) assist and regulate Hsp70 by stimulating substrate binding and ATP hydrolysis through their signature J-domain. In Class B JDPs, which play a key role in disaggregation of amyloids and amorphous aggregates, the J-domain is autoinhibited and requires activation via an auxiliary interaction with the EEVD motif of Hsp70. We reconstituted Hsp70 system from yeast and human to examine how the distinctive properties of Class B JDPs--autoinhibition and EEVD binding--promote the accumulation of Hsp70 molecules at the aggregate surface, facilitating aggregate disassembly. We revise the Hsp70 functional cycle to account for EEVD binding, showing that it enables the formation of a JDP-Hsp70-aggregate complex. This implies a model of multi-step Hsp70 clustering on aggregates, in which Class B JDPs in complex with aggregate-bound Hsp70 recruit additional Hsp70 molecules through an EEVD-independent mechanism. Experiments with the Hsp70 variant lacking EEVD validate this model but also reveal negative consequences of the stable Hsp70-JDP interaction that make the J-domain autoinhibition indispensable. Collectively, our results highlight EEVD binding and JDP autoinhibition as a coupled regulatory strategy leading to enhanced Hsp70-mediated disaggregation.

Matching journals

The top 5 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.