Functional reconstruction of a eukaryotic-like E1/E2/(RING) E3 ubiquitylation cascade from an uncultured archaeon.

The covalent modification of protein substrates by ubiquitin regulates a diverse range of critical biological functions. Although it has been established that ubiquitin-like modifiers evolved from prokaryotic sulphur transfer proteins it is less clear how complex eukaryotic ubiquitylation system arose and diversified from these prokaryotic antecedents. The discovery of ubiquitin, E1-like, E2-like and small-RING finger (srfp) protein components in the Aigarchaeota and the Asgard archaea superphyla has provided a substantive step toward addressing this evolutionary question. Encoded in operons, these components are likely representative of the progenitor apparatus that founded the modern eukaryotic ubiquitin modification systems. Here we report that these proteins from the archaeon Candidatus 'Caldiarchaeum subterraneum' operate together as a bona fide ubiquitin modification system, mediating a sequential ubiquitylation cascade reminiscent of the eukaryotic process. Our observations support the hypothesis that complex eukaryotic ubiquitylation signalling pathways have developed from compact systems originally inherited from an archaeal ancestor

PickYOLO: Fast deep learning particle detector for annotation of cryo electron tomograms

Particle localization (picking) in digital tomograms is a laborious and time-intensive step in cryogenic electron tomography (cryoET) analysis often requiring considerable user involvement, thus becoming a bottleneck for automated cryoET subtomogram averaging (STA) pipelines. In this paper, we introduce a deep learning framework called PickYOLO to tackle this problem. PickYOLO is a super-fast, universal particle detector based on the deep-learning real-time object recognition system YOLO (You Only Look Once), and tested on single particles, filamentous structures, and membrane-embedded particles. After training with the centre coordinates of a few hundred representative particles, the network automatically detects additional particles with high yield and reliability at a rate of 0.24–3.75 s per tomogram. PickYOLO can automatically detect number of particles comparable to those manually selected by experienced microscopists. This makes PickYOLO a valuable tool to substantially reduce the time and manual effort needed to analyse cryoET data for STA, greatly aiding in high-resolution cryoET structure determination