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      Cryo-electron microscopy of the f1 filamentous phage reveals insights into viral infection and assembly

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          Abstract

          Phages are viruses that infect bacteria and dominate every ecosystem on our planet. As well as impacting microbial ecology, physiology and evolution, phages are exploited as tools in molecular biology and biotechnology. This is particularly true for the Ff (f1, fd or M13) phages, which represent a widely distributed group of filamentous viruses. Over nearly five decades, Ffs have seen an extraordinary range of applications, yet the complete structure of the phage capsid and consequently the mechanisms of infection and assembly remain largely mysterious. In this work, we use cryo-electron microscopy and a highly efficient system for production of short Ff-derived nanorods to determine a structure of a filamentous virus including the tips. We show that structure combined with mutagenesis can identify phage domains that are important in bacterial attack and for release of new progeny, allowing new models to be proposed for the phage lifecycle.

          Abstract

          In this work, the authors report a system for production of short versions of a filamentous phage enables the structure to be determined by cryo-electron microscopy. Structure combined with mutagenesis allows the identification of phage domains that are important in bacterial attack and for release of new viral progeny.

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          Most cited references71

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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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            NIH Image to ImageJ: 25 years of image analysis

            For the past twenty five years the NIH family of imaging software, NIH Image and ImageJ have been pioneers as open tools for scientific image analysis. We discuss the origins, challenges and solutions of these two programs, and how their history can serve to advise and inform other software projects.
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              UCSF Chimera--a visualization system for exploratory research and analysis.

              The design, implementation, and capabilities of an extensible visualization system, UCSF Chimera, are discussed. Chimera is segmented into a core that provides basic services and visualization, and extensions that provide most higher level functionality. This architecture ensures that the extension mechanism satisfies the demands of outside developers who wish to incorporate new features. Two unusual extensions are presented: Multiscale, which adds the ability to visualize large-scale molecular assemblies such as viral coats, and Collaboratory, which allows researchers to share a Chimera session interactively despite being at separate locales. Other extensions include Multalign Viewer, for showing multiple sequence alignments and associated structures; ViewDock, for screening docked ligand orientations; Movie, for replaying molecular dynamics trajectories; and Volume Viewer, for display and analysis of volumetric data. A discussion of the usage of Chimera in real-world situations is given, along with anticipated future directions. Chimera includes full user documentation, is free to academic and nonprofit users, and is available for Microsoft Windows, Linux, Apple Mac OS X, SGI IRIX, and HP Tru64 Unix from http://www.cgl.ucsf.edu/chimera/. Copyright 2004 Wiley Periodicals, Inc.
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                Author and article information

                Contributors
                j.rakonjac@massey.ac.nz
                v.a.m.gold@exeter.ac.uk
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                11 May 2023
                11 May 2023
                2023
                : 14
                : 2724
                Affiliations
                [1 ]GRID grid.8391.3, ISNI 0000 0004 1936 8024, Living Systems Institute, , University of Exeter, ; Stocker Road, Exeter, EX4 4QD UK
                [2 ]GRID grid.8391.3, ISNI 0000 0004 1936 8024, Faculty of Health and Life Sciences, , University of Exeter, ; Exeter, EX4 4QD UK
                [3 ]GRID grid.148374.d, ISNI 0000 0001 0696 9806, School of Natural Sciences, , Massey University, ; Palmerston North, New Zealand
                [4 ]Nanophage Technologies, Palmerston North, New Zealand
                Author information
                http://orcid.org/0000-0002-8653-1771
                http://orcid.org/0000-0001-5348-4035
                http://orcid.org/0000-0003-2634-2618
                http://orcid.org/0000-0002-3767-264X
                http://orcid.org/0000-0002-6756-4682
                http://orcid.org/0000-0002-6908-0745
                Article
                37915
                10.1038/s41467-023-37915-w
                10175506
                37169795
                e0cf9611-c490-43d9-92c1-52a0d5cdc05a
                © The Author(s) 2023

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 21 December 2022
                : 4 April 2023
                Funding
                Funded by: FundRef https://doi.org/10.13039/100004440, Wellcome Trust (Wellcome);
                Award ID: 210363/Z/18/Z
                Award ID: 202904/Z/16/Z
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/501100000268, RCUK | Biotechnology and Biological Sciences Research Council (BBSRC);
                Award ID: BB/R008639/1
                Award ID: BB/R000484/1
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100008354, Callaghan Innovation;
                Award ID: BNANO2101
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100012500, Palmerston North Medical Research Foundation (PNMRF);
                Funded by: FundRef https://doi.org/10.13039/100010663, EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Excellent Science | H2020 European Research Council (H2020 Excellent Science - European Research Council);
                Award ID: 803894
                Award Recipient :
                Categories
                Article
                Custom metadata
                © Springer Nature Limited 2023

                Uncategorized
                cryoelectron microscopy,phage biology
                Uncategorized
                cryoelectron microscopy, phage biology

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