Tanvir R. Shaikh

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.

In translation, elongation factor Tu (EF-Tu) molecules deliver aminoacyl-tRNAs to the mRNA-programmed ribosome. The GTPase activity of EF-Tu is triggered by ribosome-induced conformational changes of the factor that play a pivotal role in the selection of the cognate aminoacyl-tRNAs. We present a 6.7-A cryo-electron microscopy map of the aminoacyl-tRNA x EF-Tu x GDP x kirromycin-bound Escherichia coli ribosome, together with an atomic model of the complex obtained through molecular dynamics flexible fitting. The model reveals the conformational changes in the conserved GTPase switch regions of EF-Tu that trigger hydrolysis of GTP, along with key interactions, including those between the sarcin-ricin loop and the P loop of EF-Tu, and between the effector loop of EF-Tu and a conserved region of the 16S rRNA. Our data suggest that GTP hydrolysis on EF-Tu is controlled through a hydrophobic gate mechanism.