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The architecture of the spliceosomal U4/U6.U5 tri-snRNP

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      Abstract

      U4/U6.U5 tri-snRNP is a 1.5 MDa pre-assembled spliceosomal complex comprising U5 snRNA, extensively base-paired U4/U6 snRNAs and >30 proteins, including the key components Prp8, Brr2 and Snu114. The tri-snRNP combines with a pre-mRNA substrate bound to U1 and U2 snRNPs and transforms into a catalytically active spliceosome following extensive compositional and conformational changes triggered by unwinding of the U4/U6 snRNAs. CryoEM single-particle reconstruction of yeast tri-snRNP at 5.9Å resolution reveals the essentially complete organization of its RNA and protein components. The single-stranded region of U4 snRNA between its 3′-stem-loop and the U4/U6 snRNA stem I is loaded into the Brr2 helicase active site ready for unwinding. Snu114 and the N-terminal domain of Prp8 position U5 snRNA to insert its Loop I, which aligns the exons for splicing, into the Prp8 active site cavity. The structure provides crucial insights into the activation process and the active site of the spliceosome.

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      Most cited references 75

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      I-TASSER server for protein 3D structure prediction

       Yang Zhang (2008)
      Background Prediction of 3-dimensional protein structures from amino acid sequences represents one of the most important problems in computational structural biology. The community-wide Critical Assessment of Structure Prediction (CASP) experiments have been designed to obtain an objective assessment of the state-of-the-art of the field, where I-TASSER was ranked as the best method in the server section of the recent 7th CASP experiment. Our laboratory has since then received numerous requests about the public availability of the I-TASSER algorithm and the usage of the I-TASSER predictions. Results An on-line version of I-TASSER is developed at the KU Center for Bioinformatics which has generated protein structure predictions for thousands of modeling requests from more than 35 countries. A scoring function (C-score) based on the relative clustering structural density and the consensus significance score of multiple threading templates is introduced to estimate the accuracy of the I-TASSER predictions. A large-scale benchmark test demonstrates a strong correlation between the C-score and the TM-score (a structural similarity measurement with values in [0, 1]) of the first models with a correlation coefficient of 0.91. Using a C-score cutoff > -1.5 for the models of correct topology, both false positive and false negative rates are below 0.1. Combining C-score and protein length, the accuracy of the I-TASSER models can be predicted with an average error of 0.08 for TM-score and 2 Å for RMSD. Conclusion The I-TASSER server has been developed to generate automated full-length 3D protein structural predictions where the benchmarked scoring system helps users to obtain quantitative assessments of the I-TASSER models. The output of the I-TASSER server for each query includes up to five full-length models, the confidence score, the estimated TM-score and RMSD, and the standard deviation of the estimations. The I-TASSER server is freely available to the academic community at .
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        EMAN2: an extensible image processing suite for electron microscopy.

        EMAN is a scientific image processing package with a particular focus on single particle reconstruction from transmission electron microscopy (TEM) images. It was first released in 1999, and new versions have been released typically 2-3 times each year since that time. EMAN2 has been under development for the last two years, with a completely refactored image processing library, and a wide range of features to make it much more flexible and extensible than EMAN1. The user-level programs are better documented, more straightforward to use, and written in the Python scripting language, so advanced users can modify the programs' behavior without any recompilation. A completely rewritten 3D transformation class simplifies translation between Euler angle standards and symmetry conventions. The core C++ library has over 500 functions for image processing and associated tasks, and it is modular with introspection capabilities, so programmers can add new algorithms with minimal effort and programs can incorporate new capabilities automatically. Finally, a flexible new parallelism system has been designed to address the shortcomings in the rigid system in EMAN1.
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          Electron counting and beam-induced motion correction enable near atomic resolution single particle cryoEM

          In recent work with large high symmetry viruses, single particle electron cryomicroscopy (cryoEM) has reached the milestone of determining near atomic resolution structures by allowing direct fitting of atomic models into experimental density maps. However, achieving this goal with smaller particles of lower symmetry remains extraordinarily challenging. Using a newly developed single electron counting detector, we confirm that electron beam induced motion significantly degrades resolution and, importantly, show how the combination of rapid readout and nearly noiseless electron counting allow image blurring to be corrected to subpixel accuracy. Thus, intrinsic image information can be restored to high resolution (Thon rings visible to ~3 Å). Using this approach we determined a 3.3 Å resolution structure of a ~700 kDa protein with D7 symmetry showing clear side chain density. Our method greatly enhances image quality and data acquisition efficiency - key bottlenecks in applying near atomic resolution cryoEM to a broad range of protein samples.
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            Author and article information

            Affiliations
            MRC Laboratory of Molecular Biology Francis Crick Avenue Cambridge CB2 0QH UK
            Author notes
            Correspondence and requests for materials should be addressed to T.H.D.N. ( knguyen@ 123456mrc-lmb.cam.ac.uk ) and K.N. ( kn@ 123456mrc-lmb.cam.ac.uk )

            Author Contributions

            T.H.D.N. developed the purification procedure for yeast tri-snRNP, prepared EM grids, collected all EM images, processed data, calculated the maps and built and fitted most of the components into the map. W.P.G built most of the unknown components and made essential contributions to sequence analysis, homology modelling, and model fitting. X.-C.B. helped T.H.D.N. with image processing and map calculation. C.G.S. guided T.H.D.N. with EM sample preparation and data collection. A.J.N. produced the Brr2 TAPS-tagged strain and contributed to the project through his knowledge of yeast spliceosome. T.H.D.N. and W.P.G prepared all illustrations. T.H.D.N prepared the video. S.H.W.S. carried out multi-body refinement and oversaw the EM analysis. K.N. initiated and orchestrated the project. T.H.D.N., W.P.G., A.J.N. and K.N. interpreted the results and wrote the paper with crucial contribution from all other authors.

            Journal
            0410462
            6011
            Nature
            Nature
            Nature
            0028-0836
            1476-4687
            8 May 2015
            24 June 2015
            2 July 2015
            02 January 2016
            : 523
            : 7558
            : 47-52
            26106855 4536768 10.1038/nature14548 EMS63361

            Reprints and permissions information is available at www.nature.com/reprints.

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