A multi-state dynamic process confers mechano-adaptation to a biological nanomachine

Navish Wadhwa (), Alberto Sassi, Howard C. Berg and Yuhai Tu ()
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Navish Wadhwa: Arizona State University
Alberto Sassi: IBM T. J. Watson Research Center
Howard C. Berg: Harvard University
Yuhai Tu: IBM T. J. Watson Research Center

Nature Communications, 2022, vol. 13, issue 1, 1-9

Abstract: Abstract Adaptation is a defining feature of living systems. The bacterial flagellar motor adapts to changes in the external mechanical load by adding or removing torque-generating (stator) units. But the molecular mechanism behind this mechano-adaptation remains unclear. Here, we combine single motor eletrorotation experiments and theoretical modeling to show that mechano-adaptation of the flagellar motor is enabled by multiple mechanosensitive internal states. Dwell time statistics from experiments suggest the existence of at least two bound states with a high and a low unbinding rate, respectively. A first-passage-time analysis of a four-state model quantitatively explains the experimental data and determines the transition rates among all four states. The torque generated by bound stator units controls their effective unbinding rate by modulating the transition between the bound states, possibly via a catch bond mechanism. Similar force-mediated feedback enabled by multiple internal states may apply to adaptation in other macromolecular complexes.

Date: 2022
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DOI: 10.1038/s41467-022-33075-5

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