The Sec61/SecY channel allows the translocation of many proteins across the eukaryotic endoplasmic reticulum membrane or the prokaryotic plasma membrane. In bacteria, most secretory proteins are transported post-translationally through the SecY channel by the SecA ATPase. How a polypeptide is moved through the SecA-SecY complex is poorly understood, as structural information is lacking. Here, we report an electron cryo-microscopy (cryo-EM) structure of a translocating SecA-SecY complex in a lipid environment. The translocating polypeptide chain can be traced through both SecA and SecY. In the captured transition state of ATP hydrolysis, SecA’s two-helix finger is close to the polypeptide, while SecA’s clamp interacts with the polypeptide in a sequence-independent manner by inducing a short β-strand. Taking into account previous biochemical and biophysical data, our structure is consistent with a model in which the two-helix finger and clamp cooperate during the ATPase cycle to move a polypeptide through the channel.

SecA belongs to the large class of ATPases that use the energy of ATP hydrolysis to perform mechanical work resulting in protein translocation across membranes, protein degradation, and unfolding. SecA translocates polypeptides through the SecY membrane channel during protein secretion in bacteria, but how it achieves directed peptide movement is unclear. Here, we use single‐molecule FRET to derive a model that couples ATP hydrolysis‐dependent conformational changes of SecA with protein translocation. Upon ATP binding, the two‐helix finger of SecA moves toward the SecY channel, pushing a segment of the polypeptide into the channel. The finger retracts during ATP hydrolysis, while the clamp domain of SecA tightens around the polypeptide, preserving progress of translocation. The clamp opens after phosphate release and allows passive sliding of the polypeptide chain through the SecA‐SecY complex until the next ATP binding event. This power‐stroke mechanism may be used by other ATPases that move polypeptides.

Brownian motion experiments have become a staple of the undergraduate advanced laboratory, yet quantification of these experiments is difficult, typically producing errors of 10%–15% or more. Here, we discuss the individual sources of error in the experiment: sampling error, uncertainty in the diffusion coefficient, tracking error, vibration, and microscope drift. We model each source of error using theoretical and computational methods and compare the model to our experimental data. Finally, we describe various ways to reduce each source of error to less than 1%, improving the quantification of Brownian motion.