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Tunable microsecond dynamics of an allosteric switch regulate the activity of a AAA+ disaggregation machine

Author

Listed:
  • Hisham Mazal

    (Weizmann Institute of Science)

  • Marija Iljina

    (Weizmann Institute of Science)

  • Yoav Barak

    (Weizmann Institute of Science)

  • Nadav Elad

    (Weizmann Institute of Science)

  • Rina Rosenzweig

    (Weizmann Institute of Science)

  • Pierre Goloubinoff

    (University of Lausanne)

  • Inbal Riven

    (Weizmann Institute of Science)

  • Gilad Haran

    (Weizmann Institute of Science)

Abstract

Large protein machines are tightly regulated through allosteric communication channels. Here we demonstrate the involvement of ultrafast conformational dynamics in allosteric regulation of ClpB, a hexameric AAA+ machine that rescues aggregated proteins. Each subunit of ClpB contains a unique coiled-coil structure, the middle domain (M domain), proposed as a control element that binds the co-chaperone DnaK. Using single-molecule FRET spectroscopy, we probe the M domain during the chaperone cycle and find it to jump on the microsecond time scale between two states, whose structures are determined. The M-domain jumps are much faster than the overall activity of ClpB, making it an effectively continuous, tunable switch. Indeed, a series of allosteric interactions are found to modulate the dynamics, including binding of nucleotides, DnaK and protein substrates. This mode of dynamic control enables fast cellular adaptation and may be a general mechanism for the regulation of cellular machineries.

Suggested Citation

  • Hisham Mazal & Marija Iljina & Yoav Barak & Nadav Elad & Rina Rosenzweig & Pierre Goloubinoff & Inbal Riven & Gilad Haran, 2019. "Tunable microsecond dynamics of an allosteric switch regulate the activity of a AAA+ disaggregation machine," Nature Communications, Nature, vol. 10(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-09474-6
    DOI: 10.1038/s41467-019-09474-6
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    Cited by:

    1. Claudio Mirabello & Björn Wallner & Björn Nystedt & Stavros Azinas & Marta Carroni, 2024. "Unmasking AlphaFold to integrate experiments and predictions in multimeric complexes," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    2. Paul David Harris & Alessandra Narducci & Christian Gebhardt & Thorben Cordes & Shimon Weiss & Eitan Lerner, 2022. "Multi-parameter photon-by-photon hidden Markov modeling," Nature Communications, Nature, vol. 13(1), pages 1-12, December.

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