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      Baited reconstruction with 2D template matching for high-resolution structure determination in vitro and in vivo without template bias

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      1 , 2 , 3 , , 1 , 4 , 1 , 4 ,
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      eLife
      eLife Sciences Publications, Ltd
      electron microscopy, image analysis, ribosome, cryo-EM, 3D structure, in situ, E. coli, S. cerevisiae

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          Abstract

          Previously we showed that 2D template matching (2DTM) can be used to localize macromolecular complexes in images recorded by cryogenic electron microscopy (cryo-EM) with high precision, even in the presence of noise and cellular background (Lucas et al., 2021; Lucas et al., 2022). Here, we show that once localized, these particles may be averaged together to generate high-resolution 3D reconstructions. However, regions included in the template may suffer from template bias, leading to inflated resolution estimates and making the interpretation of high-resolution features unreliable. We evaluate conditions that minimize template bias while retaining the benefits of high-precision localization, and we show that molecular features not present in the template can be reconstructed at high resolution from targets found by 2DTM, extending prior work at low-resolution. Moreover, we present a quantitative metric for template bias to aid the interpretation of 3D reconstructions calculated with particles localized using high-resolution templates and fine angular sampling.

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          Most cited references38

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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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            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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              UCSF ChimeraX : Structure visualization for researchers, educators, and developers

              UCSF ChimeraX is the next-generation interactive visualization program from the Resource for Biocomputing, Visualization, and Informatics (RBVI), following UCSF Chimera. ChimeraX brings (a) significant performance and graphics enhancements; (b) new implementations of Chimera's most highly used tools, many with further improvements; (c) several entirely new analysis features; (d) support for new areas such as virtual reality, light-sheet microscopy, and medical imaging data; (e) major ease-of-use advances, including toolbars with icons to perform actions with a single click, basic "undo" capabilities, and more logical and consistent commands; and (f) an app store for researchers to contribute new tools. ChimeraX includes full user documentation and is free for noncommercial use, with downloads available for Windows, Linux, and macOS from https://www.rbvi.ucsf.edu/chimerax.
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                Author and article information

                Contributors
                Role: Reviewing Editor
                Role: Senior Editor
                Journal
                eLife
                Elife
                eLife
                eLife
                eLife Sciences Publications, Ltd
                2050-084X
                27 November 2023
                2023
                : 12
                : RP90486
                Affiliations
                [1 ] RNA Therapeutics Institute, University of Massachusetts Chan Medical School ( https://ror.org/0464eyp60) Worcester United States
                [2 ] Department of Molecular and Cell Biology, University of California Berkeley ( https://ror.org/01an7q238) Berkeley United States
                [3 ] Center for Computational Biology, University of California Berkeley ( https://ror.org/01an7q238) Berkeley United States
                [4 ] Howard Hughes Medical Institute ( https://ror.org/006w34k90) Chevy Chase United States
                MRC Laboratory of Molecular Biology ( https://ror.org/00tw3jy02) United Kingdom
                Stanford University School of Medicine ( https://ror.org/00f54p054) United States
                MRC Laboratory of Molecular Biology United Kingdom
                University of California, Berkeley Berkeley United States
                Stochastic Analytics Smithfield United States
                University of Massachusetts Chan Medical School Worcester United States
                Author information
                https://orcid.org/0000-0001-9162-0421
                https://orcid.org/0000-0001-7777-0298
                https://orcid.org/0000-0002-1506-909X
                Article
                90486
                10.7554/eLife.90486
                10681363
                38010355
                b3217bdf-3525-4b69-8122-25da55b776a0
                © 2023, Lucas et al

                This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

                History
                : 30 June 2023
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000011, Howard Hughes Medical Institute;
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100014989, Chan Zuckerberg Initiative;
                Award ID: 2021-234617 (5022)
                Award Recipient :
                The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
                Categories
                Research Advance
                Cell Biology
                Structural Biology and Molecular Biophysics
                Custom metadata
                2D template matching enables a streamlined single-particle cryogenic electron microscopy workflow that can be used to discover new high-resolution structural features in molecules and complexes, such as bound ligands and drugs.
                prc

                Life sciences
                electron microscopy,image analysis,ribosome,cryo-em,3d structure,in situ,e. coli,s. cerevisiae
                Life sciences
                electron microscopy, image analysis, ribosome, cryo-em, 3d structure, in situ, e. coli, s. cerevisiae

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