Selecting particles from digital micrographs is an essential step in single-particle electron cryomicroscopy (cryo-EM). As manual selection of complete datasets—typically comprising thousands of particles—is a tedious and time-consuming process, numerous automatic particle pickers have been developed. However, non-ideal datasets pose a challenge to particle picking. Here we present the particle picking software crYOLO which is based on the deep-learning object detection system You Only Look Once (YOLO). After training the network with 200–2500 particles per dataset it automatically recognizes particles with high recall and precision while reaching a speed of up to five micrographs per second. Further, we present a general crYOLO network able to pick from previously unseen datasets, allowing for completely automated on-the-fly cryo-EM data preprocessing during data acquisition. crYOLO is available as a standalone program under http://sphire.mpg.de/ and is distributed as part of the image processing workflow in SPHIRE.

This protocol describes the reconstruction of biological molecules from the electron micrographs of single particles. Computation here is performed using the image-processing software SPIDER and can be managed using a graphical user interface, termed the SPIDER Reconstruction Engine. Two approaches are described to obtain an initial reconstruction: random-conical tilt and common lines. Once an existing model is available, reference-based alignment can be used, a procedure that can be iterated. Also described is supervised classification, a method to look for homogeneous subsets when multiple known conformations of the molecule may coexist.

Selecting particles from digital micrographs is an essential step in single particle electron cryomicroscopy (cryo-EM). Since manual selection of complete datasets typically comprising many thousands of particles is a tedious and time-consuming process, many automatic particle pickers have been developed in the past few decades.However, non-ideal datasets pose a challenge to particle picking. Here, we present a novel automated particle picking software called crYOLO, which is based on the deep learning object detection system "You Only Look Once" (YOLO). After training the network with 500 -2,500 particles per dataset, it automatically recognizes particles with high recall and precision reaching a speed of up to five micrographs per second.Importantly, we demonstrate a powerful general network trained on more than 40 datasets to select previously unseen datasets, thus paving the way for completely automated "on-the-fly" cryo-EM data pre-processing during data acquisition. CrYOLO is available as a standalone program under http://sphire.mpg.de/ and will be part of the image processing workflow in SPHIRE.

Secreted and membrane proteins are translocated across/into cell membranes via a proteinconducting channel (PCC). We present a cryo-EM reconstruction of the E. coli PCC, SecYEG, complexed with the ribosome and a signal anchor containing nascent chain, showing mRNA, three tRNAs, the nascent chain, and detailed features of both a translocating PCC and a second, nontranslocating PCC bound to mRNA hairpins. The translocating PCC forms connections with ribosomal RNA hairpins on two sides and ribosomal proteins at the back, leaving a frontal opening. Normal mode-based flexible fitting of the archaeal SecYEβ structure into the PCC EM densities favors a front-to-front arrangement of two SecYEG complexes in the PCC, and supports channel formation by the opening of two linked SecY halves during polypeptide translocation. Based on our observation in the translocating PCC of two segregated pores with different degrees of access to bulk lipid, we propose a model for co-translational protein translocation.

The bacterial flagellum is a motile organelle, and the flagellar hook is a short, highly curved tubular structure that connects the flagellar motor to the long filament acting as a helical propeller. The hook is made of about 120 copies of a single protein, FlgE, and its function as a nano-sized universal joint is essential for dynamic and efficient bacterial motility and taxis. It transmits the motor torque to the helical propeller over a wide range of its orientation for swimming and tumbling. Here we report a partial atomic model of the hook obtained by X-ray crystallography of FlgE31, a major proteolytic fragment of FlgE lacking unfolded terminal regions, and by electron cryomicroscopy and three-dimensional helical image reconstruction of the hook. The model reveals the intricate molecular interactions and a plausible switching mechanism for the hook to be flexible in bending but rigid against twisting for its universal joint function.