During eukaryotic ribosome biogenesis, nascent ribosomal RNA (rRNA) forms pre-ribosomal particles containing ribosomal proteins and assembly factors. Subsequently, these immature rRNAs are processed and remodelled. Little is known about the premature assembly states of rRNAs and their structural rearrangement during ribosome biogenesis. Using cryo-EM we characterize a pre-60S particle, where the 5S rRNA and its associated ribosomal proteins L18 and L5 (5S ribonucleoprotein (RNP)) are rotated by almost 180°when compared with the mature subunit. Consequently, neighbouring 25S rRNA helices that protrude from the base of the central protuberance are deformed. This altered topology is stabilized by nearby assembly factors (Rsa4 and Nog1), which were identified by fitting their three-dimensional structures into the cryo-EM density. We suggest that the 5S RNP performs a semicircular movement during 60S biogenesis to adopt its final position, fulfilling a chaperone-like function in guiding the flanking 25S rRNA helices of the central protuberance to their final topology.

Pre-ribosomal particles evolve in the nucleus through transient interaction with biogenesis factors, before export to the cytoplasm. Here, we report the architecture of the late pre-60S particle purified from Saccharomyces cerevisiae through Arx1, a nuclear export factor with structural homology to methionine aminopeptidases, or its binding partner Alb1. Cryo-electron microscopy reconstruction of the Arx1-particle at 11.9 Å resolution reveals regions of extra densities on the pre-60S particle attributed to associated biogenesis factors, confirming the immature state of the nascent subunit. One of these densities could be unambiguously assigned to Arx1. Immuno-electron microscopy and UV cross-linking localize Arx1 close to the ribosomal exit tunnel in direct contact with ES27, a highly dynamic eukaryotic rRNA expansion segment. The binding of Arx1 at the exit tunnel may position this export factor to prevent premature recruitment of ribosome-associated factors active during translation.

Ribosome-associated chaperones act in early folding events during protein synthesis. Structural information is available for prokaryotic chaperones (such as trigger factor), but structural understanding of these processes in eukaryotes lags far behind. Here we present structural analyses of the eukaryotic ribosome-associated complex (RAC) from Saccharomyces cerevisiae and Chaetomium thermophilum, consisting of heat-shock protein 70 (Hsp70) Ssz1 and the Hsp40 Zuo1. RAC is an elongated complex that crouches over the ribosomal tunnel exit and seems to be stabilized in a distinct conformation by expansion segment ES27. A unique α-helical domain in Zuo1 mediates ribosome interaction of RAC near the ribosomal proteins L22e and L31e and ribosomal RNA helix H59. The crystal structure of the Ssz1 ATPase domain bound to ATP-Mg²⁺ explains its catalytic inactivity and suggests that Ssz1 may act before the RAC-associated chaperone Ssb. Our study offers insights into the interplay between RAC, the ER membrane-integrated Hsp40-type protein ERj1 and the signal-recognition particle.

Cryo-electron microscopy (cryo-EM) studies using single particle reconstruction are extensively used to reveal structural information on macromolecular complexes. Aiming at the highest achievable resolution, state of the art electron microscopes automatically acquire thousands of high-quality micrographs. Particles are detected on and boxed out from each micrograph using fully-or semi-automated approaches. However, the obtained particles still require laborious manual post-picking classification, which is one major bottleneck for single particle analysis of large datasets. We introduce MAPPOS, a supervised post-picking strategy for the classification of boxed particle images, as additional strategy adding to the already efficient automated particle picking routines. MAPPOS employs machine learning techniques to train a robust classifier from a small number of characteristic image features. In order to accurately quantify the performance of MAPPOS we used simulated particle and non-particle images. In addition, we verified our 2 method by applying it to an experimental cryo-EM dataset and comparing the results to the manual classification of the same dataset. Comparisons between MAPPOS and manual postpicking classification by several human experts demonstrated that merely a few hundred sample images are sufficient for MAPPOS to classify an entire dataset with a human-like performance.MAPPOS was shown to greatly accelerate the throughput of large datasets by reducing the manual workload by orders of magnitude while maintaining a reliable identification of nonparticle images.