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Abstract:

Bacterial cell envelopes often contain a flagellar motor – a reversible rotary nanomachine with an approximate diameter of 45nm – that allows cells to swim. Power is provided by the movement of H+ or Na+ down the electrochemical gradients across the cytoplasmic membrane, often termed the proton motive force or sodium motive force. A helical filament is rotated by each motor at several hundred revolutions per second. In many species, the motor switches direction stochastically; switching rates are controlled by a network of sensory and signaling proteins. The first direct observation, approximately 40 years ago, of the function of a single molecular motor was of the bacterial flagellar motor. Nevertheless, due to the large size and complexity of the motor, much remains to be discovered about this nanomachine, particularly the many structural details of the torque-generating mechanism. This chapter summarizes what has been learned about the structure and function of the motor with a focus on recent observations, particularly those obtained using single molecule techniques.

Abstract:

We report statistical time-series analysis tools providing improvements in the rapid, precision extraction of discrete state dynamics from time traces of experimental observations of molecular machines. By building physical knowledge and statistical innovations into analysis tools, we provide techniques for estimating discrete state transitions buried in highly correlated molecular noise. We demonstrate the effectiveness of our approach on simulated and real examples of steplike rotation of the bacterial flagellar motor and the F1-ATPase enzyme. We show that our method can clearly identify molecular steps, periodicities and cascaded processes that are too weak for existing algorithms to detect, and can do so much faster than existing algorithms. Our techniques represent a step in the direction toward automated analysis of high-sample-rate, molecular-machine dynamics. Modular, open-source software that implements these techniques is provided. © 2011 Biophysical Society.

Abstract:

We report statistical time-series analysis tools providing improvements in the rapid, precision extraction of discrete state dynamics from time traces of experimental observations of molecular machines. By building physical knowledge and statistical innovations into analysis tools, we provide techniques for estimating discrete state transitions buried in highly correlated molecular noise. We demonstrate the effectiveness of our approach on simulated and real examples of steplike rotation of the bacterial flagellar motor and the F1-ATPase enzyme. We show that our method can clearly identify molecular steps, periodicities and cascaded processes that are too weak for existing algorithms to detect, and can do so much faster than existing algorithms. Our techniques represent a step in the direction toward automated analysis of high-sample-rate, molecular-machine dynamics. Modular, open-source software that implements these techniques is provided.

Abstract:

Modern single-molecule biophysical experiments require high numerical aperture oil-immersion objectives in close contact with the sample. We introduce two methods of high numerical aperture temperature control which can be implemented on any microscope: objective temperature control using a ring-shaped Peltier device, and stage temperature control using a fluid flow cooling chip in close thermal contact with the sample. We demonstrate the efficacy of each system by showing the change in speed with temperature of two molecular motors, the bacterial flagellar motor and skeletal muscle myosin.