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      Liquid–liquid phase separation underpins the formation of replication factories in rotaviruses

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          Abstract

          RNA viruses induce the formation of subcellular organelles that provide microenvironments conducive to their replication. Here we show that replication factories of rotaviruses represent protein‐RNA condensates that are formed via liquid–liquid phase separation of the viroplasm‐forming proteins NSP5 and rotavirus RNA chaperone NSP2. Upon mixing, these proteins readily form condensates at physiologically relevant low micromolar concentrations achieved in the cytoplasm of virus‐infected cells. Early infection stage condensates could be reversibly dissolved by 1,6‐hexanediol, as well as propylene glycol that released rotavirus transcripts from these condensates. During the early stages of infection, propylene glycol treatments reduced viral replication and phosphorylation of the condensate‐forming protein NSP5. During late infection, these condensates exhibited altered material properties and became resistant to propylene glycol, coinciding with hyperphosphorylation of NSP5. Some aspects of the assembly of cytoplasmic rotavirus replication factories mirror the formation of other ribonucleoprotein granules. Such viral RNA‐rich condensates that support replication of multi‐segmented genomes represent an attractive target for developing novel therapeutic approaches.

          Abstract

          Viroplasms represent protein/RNA‐rich condensates formed by phase‐separation of NSP2 and NSP5 proteins, whose disruption by chemical compounds reduces rotavirus replication.

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          Fiji: an open-source platform for biological-image analysis.

          Fiji is a distribution of the popular open-source software ImageJ focused on biological-image analysis. Fiji uses modern software engineering practices to combine powerful software libraries with a broad range of scripting languages to enable rapid prototyping of image-processing algorithms. Fiji facilitates the transformation of new algorithms into ImageJ plugins that can be shared with end users through an integrated update system. We propose Fiji as a platform for productive collaboration between computer science and biology research communities.
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            Highly accurate protein structure prediction with AlphaFold

            Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort 1 – 4 , the structures of around 100,000 unique proteins have been determined 5 , but this represents a small fraction of the billions of known protein sequences 6 , 7 . Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence—the structure prediction component of the ‘protein folding problem’ 8 —has been an important open research problem for more than 50 years 9 . Despite recent progress 10 – 14 , existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14) 15 , demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm. AlphaFold predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture.
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              Biomolecular condensates: organizers of cellular biochemistry

              In addition to membrane-bound organelles, eukaryotic cells feature various membraneless compartments, including the centrosome, the nucleolus and various granules. Many of these compartments form through liquid–liquid phase separation, and the principles, mechanisms and regulation of their assembly as well as their cellular functions are now beginning to emerge.
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                Author and article information

                Contributors
                ab2677@cam.ac.uk
                Journal
                EMBO J
                EMBO J
                10.1002/(ISSN)1460-2075
                EMBJ
                embojnl
                The EMBO Journal
                John Wiley and Sons Inc. (Hoboken )
                0261-4189
                1460-2075
                15 September 2021
                02 November 2021
                15 September 2021
                : 40
                : 21 ( doiID: 10.1002/embj.v40.21 )
                : e107711
                Affiliations
                [ 1 ] Department of Chemistry Ludwig‐Maximilians‐Universität München Munich Germany
                [ 2 ] Department of Biochemistry University of Cambridge Cambridge UK
                [ 3 ] International Center for Genetic Engineering and Biotechnology Trieste Italy
                [ 4 ] Department of Chemistry University of Cambridge Cambridge UK
                [ 5 ] Department of Physics and Center for Nanoscience Max Planck Institute of Biochemistry Munich Germany
                [ 6 ] Institute of Pharmaceutical Sciences Karl‐Franzens‐Universität Graz Graz Austria
                [ 7 ]Present address: Medical Research Council Laboratory of Molecular Biology (MRC LMB) Cambridge UK
                [ 8 ]Present address: Department of Molecular Biosciences University of Texas at Austin Austin TX USA
                Author notes
                [*] [* ] Corresponding author. Tel: +44 1223 766058; E‐mail: ab2677@ 123456cam.ac.uk

                [ † ]

                These authors contributed equally to this work as first authors

                [ ‡ ]

                These authors contributed equally to this work as second authors

                Author information
                https://orcid.org/0000-0002-6422-6514
                https://orcid.org/0000-0002-5215-0014
                https://orcid.org/0000-0002-3615-1885
                https://orcid.org/0000-0003-4607-3312
                https://orcid.org/0000-0001-9529-9436
                https://orcid.org/0000-0002-5729-2687
                Article
                EMBJ2021107711
                10.15252/embj.2021107711
                8561643
                34524703
                9a8e8eaf-c938-4e9e-a50f-90c4d19ee2d0
                © 2021 The Authors. Published under the terms of the CC BY 4.0 license

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 23 August 2021
                : 12 January 2021
                : 27 August 2021
                Page count
                Figures: 15, Tables: 0, Pages: 24, Words: 17782
                Funding
                Funded by: Wellcome Trust
                Award ID: 213437/Z/18/Z
                Funded by: Deutsche Forschungsgemeinschaft (DFG) , doi 10.13039/501100001659;
                Categories
                Article
                Articles
                Custom metadata
                2.0
                02 November 2021
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.0.8 mode:remove_FC converted:02.11.2021

                Molecular biology
                biomolecular condensates,microfluidics,rnp granules,viral genome assembly,microbiology, virology & host pathogen interaction,structural biology

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