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      Broadly neutralizing antibodies target a haemagglutinin anchor epitope

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      1 , , 2 , 1 , 1 , 3 , 1 , 16 , 3 , 4 , 4 , 5 , 4 , 16 , 4 , 1 , 6 , 7 , 2 , 2 , 4 , 3 , 1 , 1 , 1 , 1 , 4 , 1 , 4 , 8 , 9 , 10 , 11 , 5 , 6 , 7 , 12 , 13 , 4 , 16 , 4 , 8 , 4 , 14 , 15 , 2 , , 1 , 3 , 17 ,
      Nature
      Nature Publishing Group UK
      Antibodies, Immunological memory

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          Abstract

          Broadly neutralizing antibodies that target epitopes of haemagglutinin on the influenza virus have the potential to provide near universal protection against influenza virus infection 1 . However, viral mutants that escape broadly neutralizing antibodies have been reported 2, 3 . The identification of broadly neutralizing antibody classes that can neutralize viral escape mutants is critical for universal influenza virus vaccine design. Here we report a distinct class of broadly neutralizing antibodies that target a discrete membrane-proximal anchor epitope of the haemagglutinin stalk domain. Anchor epitope-targeting antibodies are broadly neutralizing across H1 viruses and can cross-react with H2 and H5 viruses that are a pandemic threat. Antibodies that target this anchor epitope utilize a highly restricted repertoire, which encodes two public binding motifs that make extensive contacts with conserved residues in the fusion peptide. Moreover, anchor epitope-targeting B cells are common in the human memory B cell repertoire and were recalled in humans by an oil-in-water adjuvanted chimeric haemagglutinin vaccine 4, 5 , which is a potential universal influenza virus vaccine. To maximize protection against seasonal and pandemic influenza viruses, vaccines should aim to boost this previously untapped source of broadly neutralizing antibodies that are widespread in the human memory B cell pool.

          Abstract

          A distinct class of broadly neutralizing antibodies to the influenza virus target a membrane-proximal anchor epitope of the haemagglutinin stalk domain.

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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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            MUSCLE: multiple sequence alignment with high accuracy and high throughput.

            We describe MUSCLE, a new computer program for creating multiple alignments of protein sequences. Elements of the algorithm include fast distance estimation using kmer counting, progressive alignment using a new profile function we call the log-expectation score, and refinement using tree-dependent restricted partitioning. The speed and accuracy of MUSCLE are compared with T-Coffee, MAFFT and CLUSTALW on four test sets of reference alignments: BAliBASE, SABmark, SMART and a new benchmark, PREFAB. MUSCLE achieves the highest, or joint highest, rank in accuracy on each of these sets. Without refinement, MUSCLE achieves average accuracy statistically indistinguishable from T-Coffee and MAFFT, and is the fastest of the tested methods for large numbers of sequences, aligning 5000 sequences of average length 350 in 7 min on a current desktop computer. The MUSCLE program, source code and PREFAB test data are freely available at http://www.drive5. com/muscle.
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              GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers

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                Author and article information

                Contributors
                jguthmiller@uchicago.edu
                andrew@scripps.edu
                pcw4001@med.cornell.edu
                Journal
                Nature
                Nature
                Nature
                Nature Publishing Group UK (London )
                0028-0836
                1476-4687
                23 December 2021
                23 December 2021
                2022
                : 602
                : 7896
                : 314-320
                Affiliations
                [1 ]GRID grid.170205.1, ISNI 0000 0004 1936 7822, Department of Medicine, Section of Rheumatology, , University of Chicago, ; Chicago, IL USA
                [2 ]GRID grid.214007.0, ISNI 0000000122199231, Department of Integrative Structural and Computational Biology, , The Scripps Research Institute, ; La Jolla, CA USA
                [3 ]GRID grid.170205.1, ISNI 0000 0004 1936 7822, Committee on Immunology, , University of Chicago, ; Chicago, IL USA
                [4 ]GRID grid.59734.3c, ISNI 0000 0001 0670 2351, Department of Microbiology, , Icahn School of Medicine at Mount Sinai, ; New York, NY USA
                [5 ]GRID grid.5771.4, ISNI 0000 0001 2151 8122, Center for Molecular Biosciences Innsbruck, Department of General, Inorganic and Theoretical Chemistry, , University of Innsbruck, ; Innsbruck, Austria
                [6 ]GRID grid.270240.3, ISNI 0000 0001 2180 1622, Basic Sciences Division, , Fred Hutchinson Cancer Research Center, ; Seattle, WA USA
                [7 ]GRID grid.34477.33, ISNI 0000000122986657, Department of Microbiology, , University of Washington, ; Seattle, WA USA
                [8 ]GRID grid.59734.3c, ISNI 0000 0001 0670 2351, Department of Medicine, Division of Infectious Diseases, , Icahn School of Medicine at Mount Sinai, ; New York, NY USA
                [9 ]GRID grid.59734.3c, ISNI 0000 0001 0670 2351, Global Health and Emerging Pathogens Institute, , Icahn School of Medicine at Mount Sinai, ; New York, NY USA
                [10 ]GRID grid.59734.3c, ISNI 0000 0001 0670 2351, The Tisch Cancer Center, , Icahn School of Medicine at Mount Sinai, ; New York, NY USA
                [11 ]GRID grid.59734.3c, ISNI 0000 0001 0670 2351, Department of Pathology, Molecular and Cell-Based Medicine, , Icahn School of Medicine at Mount Sinai, ; New York, NY USA
                [12 ]GRID grid.34477.33, ISNI 0000000122986657, Department of Genome Sciences, , University of Washington, ; Seattle, WA USA
                [13 ]GRID grid.270240.3, ISNI 0000 0001 2180 1622, Howard Hughes Medical Institute, , Fred Hutchinson Cancer Research Center, ; Seattle, WA USA
                [14 ]GRID grid.411024.2, ISNI 0000 0001 2175 4264, Department of Microbiology and Immunology, , University of Maryland School of Medicine, ; Baltimore, MD USA
                [15 ]GRID grid.411024.2, ISNI 0000 0001 2175 4264, Center for Vaccine Development and Global Health (CVD), , University of Maryland School of Medicine, ; Baltimore, MD USA
                [16 ]GRID grid.479574.c, ISNI 0000 0004 1791 3172, Present Address: Moderna Inc., ; Cambridge, MA USA
                [17 ]GRID grid.5386.8, ISNI 000000041936877X, Present Address: Drukier Institute for Children’s Health, Department of Pediatrics, , Weill Cornell Medicine, ; New York, NY USA
                Author information
                http://orcid.org/0000-0001-7566-3536
                http://orcid.org/0000-0002-3402-2609
                http://orcid.org/0000-0002-8029-8227
                http://orcid.org/0000-0001-6413-855X
                http://orcid.org/0000-0002-1826-4333
                http://orcid.org/0000-0002-6551-1827
                http://orcid.org/0000-0002-0985-2299
                http://orcid.org/0000-0003-1267-3408
                http://orcid.org/0000-0001-5568-5420
                http://orcid.org/0000-0002-0337-5823
                http://orcid.org/0000-0003-4121-776X
                http://orcid.org/0000-0001-9880-6560
                http://orcid.org/0000-0001-7153-3769
                Article
                4356
                10.1038/s41586-021-04356-8
                8828479
                34942633
                9a4f6fbb-7305-4dee-b57d-7653f0dd7be7
                © The Author(s) 2021

                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
                : 6 August 2021
                : 15 December 2021
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                © The Author(s), under exclusive licence to Springer Nature Limited 2022

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                antibodies,immunological memory
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                antibodies, immunological memory

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