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      An RNA-Binding Complex Involved in Ribosome Biogenesis Contains a Protein with Homology to tRNA CCA-Adding Enzyme

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          Abstract

          The structure of a complex of two ribosome synthesis factors and identification of their ribosomal binding sites provides insights into early stages of ribosome biogenesis.

          Abstract

          A multitude of proteins and small nucleolar RNAs transiently associate with eukaryotic ribosomal RNAs to direct their modification and processing and the assembly of ribosomal proteins. Utp22 and Rrp7, two interacting proteins with no recognizable domain, are components of the 90S preribosome or the small subunit processome that conducts early processing of 18S rRNA. Here, we determine the cocrystal structure of Utp22 and Rrp7 complex at 1.97 Å resolution and the NMR structure of a C-terminal fragment of Rrp7, which is not visible in the crystal structure. The structure reveals that Utp22 surprisingly resembles a dimeric class I tRNA CCA-adding enzyme yet with degenerate active sites, raising an interesting evolutionary connection between tRNA and rRNA processing machineries. Rrp7 binds extensively to Utp22 using a deviant RNA recognition motif and an extended linker. Functional sites on the two proteins were identified by structure-based mutagenesis in yeast. We show that Rrp7 contains a flexible RNA-binding C-terminal tail that is essential for association with preribosomes. RNA–protein crosslinking shows that Rrp7 binds at the central domain of 18S rRNA and shares a neighborhood with two processing H/ACA snoRNAs snR30 and snR10. Depletion of snR30 prevents the stable assembly of Rrp7 into preribosomes. Our results provide insight into the evolutionary origin and functional context of Utp22 and Rrp7.

          Author Summary

          Ribosomes are large RNA–protein complexes that manufacture proteins in all living organisms. Synthesis of large and small ribosomal subunits is a fundamental and enormous task that requires activities of approximately 200 assembly factors in eukaryotic cells. These factors transiently associate with the ribosome, forming a series of pre-ribosomal particles. We currently have a poor understanding of the structure and assembly of ribosome precursors. Utp22 and Rrp7 are two interacting proteins present in early precursors of the small ribosomal subunit. In this study, we determined the structure of the Utp22 and Rrp7 complex by X-ray crystallography and NMR and dissected their functional domains by mutagenesis. The structure of Utp22 reveals an unexpected structural similarity to the tRNA CCA-adding enzyme, providing insight into the evolutionary origin of Utp22. Utp22 apparently lacks any enzymatic activity and functions instead as a structural building block. Rrp7 associates extensively with Utp22 and appears to be anchored to pre-ribosomes via a flexible RNA-binding tail. We used RNA–protein crosslinking to identify the binding site and neighboring factor of Rrp7 on pre-ribosomes. Our study provides a detailed insight into the structure of small ribosomal subunit precursors.

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          Epitope tagging of yeast genes using a PCR-based strategy: more tags and improved practical routines.

          Epitope tagging of proteins as a strategy for the analysis of function, interactions and the subcellular distribution of proteins has become widely used. In the yeast Saccharomyces cerevisiae, molecular biological techniques have been developed that use a simple PCR-based strategy to introduce epitope tags to chromosomal loci (Wach et al., 1994). To further employ the power of this strategy, a variety of novel tags was constructed. These tags were combined with different selectable marker genes, resulting in PCR amplificable modules. Only one set of primers is required for the amplification of any module. Furthermore, convenient laboratory techniques are described that facilitate the genetic manipulations of yeast strains, as well as the analysis of the epitope-tagged proteins. Copyright 1999 John Wiley & Sons, Ltd.
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            Protein NMR structure determination with automated NOE assignment using the new software CANDID and the torsion angle dynamics algorithm DYANA.

            Combined automated NOE assignment and structure determination module (CANDID) is a new software for efficient NMR structure determination of proteins by automated assignment of the NOESY spectra. CANDID uses an iterative approach with multiple cycles of NOE cross-peak assignment and protein structure calculation using the fast DYANA torsion angle dynamics algorithm, so that the result from each CANDID cycle consists of exhaustive, possibly ambiguous NOE cross-peak assignments in all available spectra and a three-dimensional protein structure represented by a bundle of conformers. The input for the first CANDID cycle consists of the amino acid sequence, the chemical shift list from the sequence-specific resonance assignment, and listings of the cross-peak positions and volumes in one or several two, three or four-dimensional NOESY spectra. The input for the second and subsequent CANDID cycles contains the three-dimensional protein structure from the previous cycle, in addition to the complete input used for the first cycle. CANDID includes two new elements that make it robust with respect to the presence of artifacts in the input data, i.e. network-anchoring and constraint-combination, which have a key role in de novo protein structure determinations for the successful generation of the correct polypeptide fold by the first CANDID cycle. Network-anchoring makes use of the fact that any network of correct NOE cross-peak assignments forms a self-consistent set; the initial, chemical shift-based assignments for each individual NOE cross-peak are therefore weighted by the extent to which they can be embedded into the network formed by all other NOE cross-peak assignments. Constraint-combination reduces the deleterious impact of artifact NOE upper distance constraints in the input for a protein structure calculation by combining the assignments for two or several peaks into a single upper limit distance constraint, which lowers the probability that the presence of an artifact peak will influence the outcome of the structure calculation. CANDID test calculations were performed with NMR data sets of four proteins for which high-quality structures had previously been solved by interactive protocols, and they yielded comparable results to these reference structure determinations with regard to both the residual constraint violations, and the precision and accuracy of the atomic coordinates. The CANDID approach has further been validated by de novo NMR structure determinations of four additional proteins. The experience gained in these calculations shows that once nearly complete sequence-specific resonance assignments are available, the automated CANDID approach results in greatly enhanced efficiency of the NOESY spectral analysis. The fact that the correct fold is obtained in cycle 1 of a de novo structure calculation is the single most important advance achieved with CANDID, when compared with previously proposed automated NOESY assignment methods that do not use network-anchoring and constraint-combination.
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              Automated structure solution with autoSHARP.

              We present here the automated structure solution pipeline "autoSHARP." It is built around the heavy-atom refinement and phasing program SHARP, the density modification program SOLOMON, and the ARP/wARP package for automated model building and refinement (using REFMAC). It allows fully automated structure solution, from merged reflection data to an initial model, without any user intervention. We describe and discuss the preparation of the user input, the data flow through the pipeline, and the various results obtained throughout the procedure.
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                Author and article information

                Contributors
                Role: Academic Editor
                Journal
                PLoS Biol
                PLoS Biol
                plos
                plosbiol
                PLoS Biology
                Public Library of Science (San Francisco, USA )
                1544-9173
                1545-7885
                October 2013
                October 2013
                1 October 2013
                : 11
                : 10
                : e1001669
                Affiliations
                [1 ]National Institute of Biological Sciences, Beijing, China
                [2 ]Graduate School of Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
                [3 ]Shandong Provincial Key Laboratory of Energy Genetics, Qingdao Institute of BioEnergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, Shangdong, China
                Brandeis University, United States of America
                Author notes

                The authors have declared that no competing interests exist.

                The author(s) have made the following declarations about their contributions: Conceived and designed the experiments: JZL JL KY. Performed the experiments: JZL JL YF MS KY. Analyzed the data: JZL JL YF KY. Wrote the paper: KY.

                Article
                PBIOLOGY-D-13-01266
                10.1371/journal.pbio.1001669
                3794860
                24130456
                3f31e6e4-a6d0-4373-b6f3-4f0abd0aee91
                Copyright @ 2013

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 30 March 2013
                : 15 August 2013
                Page count
                Pages: 16
                Funding
                This research was supported by the National Basic Research Program of China (973 Program) (2010CB835402, 2012CB910900) and by the Beijing Municipal Government. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article

                Life sciences
                Life sciences

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