FakET: Simulating Cryo-Electron Tomograms with Neural Style Transfer

Particle localization and -classification constitute two of the most fundamental problems in computational microscopy. In recent years, deep learning based approaches have been introduced for these tasks with great success. A key shortcoming of these supervised learning methods is their need for large training data sets, typically generated from particle models in conjunction with complex numerical forward models simulating the physics of transmission electron microscopes. Computer implementations of such forward models are computationally extremely demanding and limit the scope of their applicability. In this paper we propose a method for simulating the forward operator of an electron microscope based on additive noise and Neural Style Transfer techniques. We evaluate the method on localization and classification tasks using one of the established state-of-the-art architectures showing performance on par with the benchmark. In contrast to previous approaches, our method accelerates the data generation process by a factor of 750 while using 33 times less memory and scales well to typical transmission electron microscope detector sizes. It utilizes GPU acceleration and parallel processing. It can be used to adapt a synthetic training data set according to reference data from any transmission electron microscope. The source code is available at https://gitlab.com/deepet/faket.Comment: 18 pages, 1 table, 16 figures. Included fine-tuning, ablation, and noiseless experiment

Structural basis for inhibition of the AAA-ATPase Drg1 by diazaborine

The AAA-ATPase Drg1 is a key factor in eukaryotic ribosome biogenesis that initiates cytoplasmic maturation of the large subunit. Here the authors report the structure of Drg1 in complex with its specific inhibitor diazaborine and provide insight into the mechanism of inhibition and specificity of this class of inhibitors

Visualizing maturation factor extraction from the nascent ribosome by the AAA-ATPase Drg1.

The AAA-ATPase Drg1 is a key factor in eukaryotic ribosome biogenesis that initiates cytoplasmic maturation of the large ribosomal subunit. Drg1 releases the shuttling maturation factor Rlp24 from pre-60S particles shortly after nuclear export, a strict requirement for downstream maturation. The molecular mechanism of release remained elusive. Here, we report a series of cryo-EM structures that captured the extraction of Rlp24 from pre-60S particles by Saccharomyces cerevisiae Drg1. These structures reveal that Arx1 and the eukaryote-specific rRNA expansion segment ES27 form a joint docking platform that positions Drg1 for efficient extraction of Rlp24 from the pre-ribosome. The tips of the Drg1 N domains thereby guide the Rlp24 C terminus into the central pore of the Drg1 hexamer, enabling extraction by a hand-over-hand translocation mechanism. Our results uncover substrate recognition and processing by Drg1 step by step and provide a comprehensive mechanistic picture of the conserved modus operandi of AAA-ATPases

Lysosomal enzyme trafficking factor LYSET enables nutritional usage of extracellular proteins

Mammalian cells can generate amino acids through macropinocytosis and lysosomal breakdown of extracellular proteins, which is exploited by cancer cells to grow in nutrient-poor tumors. Through genetic screens in defined nutrient conditions, we characterized LYSET, a transmembrane protein (TMEM251) selectively required when cells consume extracellular proteins. LYSET was found to associate in the Golgi with GlcNAc-1-phosphotransferase, which targets catabolic enzymes to lysosomes through mannose-6-phosphate modification. Without LYSET, GlcNAc-1-phosphotransferase was unstable because of a hydrophilic transmembrane domain. Consequently, LYSET-deficient cells were depleted of lysosomal enzymes and impaired in turnover of macropinocytic and autophagic cargoes. Thus, LYSET represents a core component of the lysosomal enzyme trafficking pathway, underlies the pathomechanism for hereditary lysosomal storage disorders, and may represent a target to suppress metabolic adaptations in cancer