Artificial intelligence-assisted cryoEM structure of Bfr2-Lcp5 complex observed in the yeast small subunit processome

H/ACA snR30 snoRNP guides independent 18S rRNA subdomain formation

Ribosome biogenesis follows a cascade of pre-rRNA folding and processing steps, coordinated with ribosomal protein incorporation. Nucleolar 90S pre-ribosomes are well-described stable intermediates, composed of pre-18S rRNA, ribosomal S-proteins, U3 snoRNA, and ~70 assembly factors. However, how numerous snoRNAs control pre-rRNA modification and folding during early maturation events remains unclear. We identify snR30 (human U17), the only essential H/ACA snoRNA in yeast, which binds with Cbf5-Gar1-Nop10-Nhp2 to a pre-18S rRNA subdomain containing platform helices and ES6 of the 40S central domain. Integration into the 90S is blocked by RNA hybridization with snR30. The snoRNP complex coordinates the recruitment of early assembly factors Krr1-Utp23-Kri1 and ribosomal proteins uS11-uS15, enabling isolated subdomain assembly. Krr1-dependent release of snR30 culminates in integration of the platform into the 90S. Our study reveals the essential role of snR30 in chaperoning central domain formation as a discrete assembly unit externalized from the pre-ribosomal core.

A UTP3-dependent nucleolar translocation pathway facilitates pre-rRNA 5'ETS processing

The ribosome small subunit (SSU) is assembled by the SSU processome which contains approximately 70 non-ribosomal protein factors. Whilst the biochemical mechanisms of the SSU processome in 18S rRNA processing and maturation have been extensively studied, how SSU processome components enter the nucleolus has yet to be systematically investigated. Here, in examining the nucleolar localization of 50 human SSU processome components, we found that UTP3, together with another 24 proteins, enter the nucleolus autonomously. For the remaining 25 proteins we found that UTP3/SAS10 assists the nucleolar localization of five proteins (MPP10, UTP25, EMG1 and the two UTP-B components UTP12 and UTP13), likely through its interaction with nuclear importin α. This 'ferrying' function of UTP3 was then confirmed as conserved in the zebrafish. We also found that knockdown of human UTP3 impairs cleavage at the A0-site while loss-of-function of either utp3/sas10 or utp13/tbl3 in zebrafish causes the accumulation of aberrantly processed 5'ETS products, which highlights the crucial role of UTP3 in mediating 5'ETS processing. Mechanistically, we found that UTP3 facilitates the degradation of processed 5'ETS by recruiting the RNA exosome component EXOSC10 to the nucleolus. These findings lay the groundwork for studying the mechanism of cytoplasm-to-nucleolus trafficking of SSU processome components.

AlphaFold two years on: Validation and impact

Two years on from the initial release of AlphaFold, we have seen its widespread adoption as a structure prediction tool. Here, we discuss some of the latest work based on AlphaFold, with a particular focus on its use within the structural biology community. This encompasses use cases like speeding up structure determination itself, enabling new computational studies, and building new tools and workflows. We also look at the ongoing validation of AlphaFold, as its predictions continue to be compared against large numbers of experimental structures to further delineate the model's capabilities and limitations.

Molecular Basis for RNA Cytidine Acetylation by NAT10

Human NAT10 acetylates the N4 position of cytidine in RNA, predominantly on rRNA and tRNA, to facilitate ribosome biogenesis and protein translation. NAT10 has been proposed as a therapeutic target in cancers as well as aging-associated pathologies such as Hutchinson-Gilford Progeria Syndrome (HGPS). The ∼120 kDa NAT10 protein uses its acetyl-CoA-dependent acetyltransferase, ATP-dependent helicase, and RNA binding domains in concert to mediate RNA-specific N4-cytidine acetylation. While the biochemical activity of NAT10 is well known, the molecular basis for catalysis of eukaryotic RNA acetylation remains relatively undefined. To provide molecular insights into the RNA-specific acetylation by NAT10, we determined the single particle cryo-EM structures of Chaetomium thermophilum NAT10 ( Ct NAT10) bound to a bisubstrate cytidine-CoA probe with and without ADP. The structures reveal that NAT10 forms a symmetrical heart-shaped dimer with conserved functional domains surrounding the acetyltransferase active sites harboring the cytidine-CoA probe. Structure-based mutagenesis with analysis of mutants in vitro supports the catalytic role of two conserved active site residues (His548 and Tyr549 in Ct NAT10), and two basic patches, both proximal and distal to the active site for RNA-specific acetylation. Yeast complementation analyses and senescence assays in human cells also implicates NAT10 catalytic activity in yeast thermoadaptation and cellular senescence. Comparison of the NAT10 structure to protein lysine and N-terminal acetyltransferase enzymes reveals an unusually open active site suggesting that these enzymes have been evolutionarily tailored for RNA recognition and cytidine-specific acetylation.

Molecular Dynamics and Other HPC Simulations for Drug Discovery

High performance computing (HPC) is taking an increasingly important place in drug discovery. It makes possible the simulation of complex biochemical systems with high precision in a short time, thanks to the use of sophisticated algorithms. It promotes the advancement of knowledge in fields that are inaccessible or difficult to access through experimentation and it contributes to accelerating the discovery of drugs for unmet medical needs while reducing costs. Herein, we report how computational performance has evolved over the past years, and then we detail three domains where HPC is essential. Molecular dynamics (MD) is commonly used to explore the flexibility of proteins, thus generating a better understanding of different possible approaches to modulate their activity. Modeling and simulation of biopolymer complexes enables the study of protein-protein interactions (PPI) in healthy and disease states, thus helping the identification of targets of pharmacological interest. Virtual screening (VS) also benefits from HPC to predict in a short time, among millions or billions of virtual chemical compounds, the best potential ligands that will be tested in relevant assays to start a rational drug design process.