1
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Mendez JH, Chua EYD, Paraan M, Potter CS, Carragher B. Automated pipelines for rapid evaluation during cryoEM data acquisition. Curr Opin Struct Biol 2023; 83:102729. [PMID: 37988815 DOI: 10.1016/j.sbi.2023.102729] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Revised: 10/14/2023] [Accepted: 10/16/2023] [Indexed: 11/23/2023]
Abstract
Cryo-electron microscopy (cryoEM) has become a popular method for determining high-resolution structures of biomolecules. However, data processing can be time-consuming, particularly for new researchers entering the field. To improve data quality and increase data collection efficiency, several software packages have been developed for on-the-fly data processing with various degrees of automation. These software packages allow researchers to perform tasks such as motion correction, CTF estimation, 2D classification, and 3D reconstruction in real-time, with minimal human input. On-the-fly data processing can not only improve data collection efficiency but also increase the productivity of instrumentation in high demand. However, the various software packages available differ in their performance, computational requirements, and levels of automation. In this review, we describe the minimal metrics used to assess data quality during data collection, outline the features of an ideal on-the-fly data processing software systems, and provide results from using three of these systems.
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Affiliation(s)
- Joshua H Mendez
- Simons Electron Microscopy Center, New York Structural Biology Center, New York, NY, USA
| | - Eugene Y D Chua
- Simons Electron Microscopy Center, New York Structural Biology Center, New York, NY, USA
| | - Mohammadreza Paraan
- Simons Electron Microscopy Center, New York Structural Biology Center, New York, NY, USA
| | - Clinton S Potter
- Simons Electron Microscopy Center, New York Structural Biology Center, New York, NY, USA; Department of Biochemistry and Molecular Biophysics, Columbia University, New York, NY, USA
| | - Bridget Carragher
- Simons Electron Microscopy Center, New York Structural Biology Center, New York, NY, USA; Department of Biochemistry and Molecular Biophysics, Columbia University, New York, NY, USA.
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2
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Rice G, Wagner T, Stabrin M, Sitsel O, Prumbaum D, Raunser S. TomoTwin: generalized 3D localization of macromolecules in cryo-electron tomograms with structural data mining. Nat Methods 2023; 20:871-880. [PMID: 37188953 PMCID: PMC10250198 DOI: 10.1038/s41592-023-01878-z] [Citation(s) in RCA: 22] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Accepted: 04/12/2023] [Indexed: 05/17/2023]
Abstract
Cryogenic-electron tomography enables the visualization of cellular environments in extreme detail, however, tools to analyze the full amount of information contained within these densely packed volumes are still needed. Detailed analysis of macromolecules through subtomogram averaging requires particles to first be localized within the tomogram volume, a task complicated by several factors including a low signal to noise ratio and crowding of the cellular space. Available methods for this task suffer either from being error prone or requiring manual annotation of training data. To assist in this crucial particle picking step, we present TomoTwin: an open source general picking model for cryogenic-electron tomograms based on deep metric learning. By embedding tomograms in an information-rich, high-dimensional space that separates macromolecules according to their three-dimensional structure, TomoTwin allows users to identify proteins in tomograms de novo without manually creating training data or retraining the network to locate new proteins.
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Affiliation(s)
- Gavin Rice
- Department of Structural Biochemistry, Max Planck Institute of Molecular Physiology, Dortmund, Germany
| | - Thorsten Wagner
- Department of Structural Biochemistry, Max Planck Institute of Molecular Physiology, Dortmund, Germany
| | - Markus Stabrin
- Department of Structural Biochemistry, Max Planck Institute of Molecular Physiology, Dortmund, Germany
| | - Oleg Sitsel
- Department of Structural Biochemistry, Max Planck Institute of Molecular Physiology, Dortmund, Germany
| | - Daniel Prumbaum
- Department of Structural Biochemistry, Max Planck Institute of Molecular Physiology, Dortmund, Germany
| | - Stefan Raunser
- Department of Structural Biochemistry, Max Planck Institute of Molecular Physiology, Dortmund, Germany.
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3
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Kehlenbeck DM, Traore DAK, Josts I, Sander S, Moulin M, Haertlein M, Prevost S, Forsyth VT, Tidow H. Cryo-EM structure of MsbA in saposin-lipid nanoparticles (Salipro) provides insights into nucleotide coordination. FEBS J 2022; 289:2959-2970. [PMID: 34921499 DOI: 10.1111/febs.16327] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Revised: 12/05/2021] [Accepted: 12/16/2021] [Indexed: 01/28/2023]
Abstract
The ATP-binding cassette transporter MsbA is a lipid flippase, translocating lipid A, glycolipids, and lipopolysaccharides from the inner to the outer leaflet of the inner membrane of Gram-negative bacteria. It has been used as a model system for time-resolved structural studies as several MsbA structures in different states and reconstitution systems (detergent/nanodiscs/peptidiscs) are available. However, due to the limited resolution of the available structures, detailed structural information on the bound nucleotides has remained elusive. Here, we have reconstituted MsbA in saposin A-lipoprotein nanoparticles (Salipro) and determined the structure of ADP-vanadate-bound MsbA by single-particle cryo-electron microscopy to 3.5 Å resolution. This procedure has resulted in significantly improved resolution and enabled us to model all side chains and visualise detailed ADP-vanadate interactions in the nucleotide-binding domains. The approach may be applicable to other dynamic membrane proteins.
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Affiliation(s)
- Dominique-Maurice Kehlenbeck
- The Hamburg Advanced Research Center for Bioorganic Chemistry (HARBOR), Germany.,Department of Chemistry, Institute for Biochemistry and Molecular Biology, University of Hamburg, Germany.,Life Sciences Group, Institut Laue-Langevin, Grenoble, France.,Partnership for Structural Biology (PSB), Grenoble, France
| | - Daouda A K Traore
- Life Sciences Group, Institut Laue-Langevin, Grenoble, France.,Partnership for Structural Biology (PSB), Grenoble, France.,Faculty of Natural Sciences, Keele University, UK.,Faculté des Sciences et Techniques, Université des Sciences, des Techniques et des Technologies de Bamako (USTTB), Bamako, Mali
| | - Inokentijs Josts
- The Hamburg Advanced Research Center for Bioorganic Chemistry (HARBOR), Germany.,Department of Chemistry, Institute for Biochemistry and Molecular Biology, University of Hamburg, Germany
| | - Simon Sander
- The Hamburg Advanced Research Center for Bioorganic Chemistry (HARBOR), Germany.,Department of Chemistry, Institute for Biochemistry and Molecular Biology, University of Hamburg, Germany
| | - Martine Moulin
- Life Sciences Group, Institut Laue-Langevin, Grenoble, France.,Partnership for Structural Biology (PSB), Grenoble, France
| | - Michael Haertlein
- Life Sciences Group, Institut Laue-Langevin, Grenoble, France.,Partnership for Structural Biology (PSB), Grenoble, France
| | - Sylvain Prevost
- Large Scale Structures Group, Institut Laue-Langevin, Grenoble, France
| | - V Trevor Forsyth
- Life Sciences Group, Institut Laue-Langevin, Grenoble, France.,Partnership for Structural Biology (PSB), Grenoble, France.,Faculty of Natural Sciences, Keele University, UK
| | - Henning Tidow
- The Hamburg Advanced Research Center for Bioorganic Chemistry (HARBOR), Germany
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4
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Advances in Xmipp for Cryo-Electron Microscopy: From Xmipp to Scipion. Molecules 2021; 26:molecules26206224. [PMID: 34684805 PMCID: PMC8537808 DOI: 10.3390/molecules26206224] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Revised: 09/28/2021] [Accepted: 09/29/2021] [Indexed: 11/21/2022] Open
Abstract
Xmipp is an open-source software package consisting of multiple programs for processing data originating from electron microscopy and electron tomography, designed and managed by the Biocomputing Unit of the Spanish National Center for Biotechnology, although with contributions from many other developers over the world. During its 25 years of existence, Xmipp underwent multiple changes and updates. While there were many publications related to new programs and functionality added to Xmipp, there is no single publication on the Xmipp as a package since 2013. In this article, we give an overview of the changes and new work since 2013, describe technologies and techniques used during the development, and take a peek at the future of the package.
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5
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Sorzano COS, Jiménez-Moreno A, Maluenda D, Ramírez-Aportela E, Martínez M, Cuervo A, Melero R, Conesa JJ, Sánchez-García R, Strelak D, Filipovic J, Fernández-Giménez E, de Isidro-Gómez F, Herreros D, Conesa P, Del Caño L, Fonseca Y, de la Morena JJ, Macías JR, Losana P, Marabini R, Carazo JM. Image Processing in Cryo-Electron Microscopy of Single Particles: The Power of Combining Methods. Methods Mol Biol 2021; 2305:257-289. [PMID: 33950394 DOI: 10.1007/978-1-0716-1406-8_13] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Cryo-electron microscopy has established as a mature structural biology technique to elucidate the three-dimensional structure of biological macromolecules. The Coulomb potential of the sample is imaged by an electron beam, and fast semi-conductor detectors produce movies of the sample under study. These movies have to be further processed by a whole pipeline of image-processing algorithms that produce the final structure of the macromolecule. In this chapter, we illustrate this whole processing pipeline putting in value the strength of "meta algorithms," which are the combination of several algorithms, each one with different mathematical rationale, in order to distinguish correctly from incorrectly estimated parameters. We show how this strategy leads to superior performance of the whole pipeline as well as more confident assessments about the reconstructed structures. The "meta algorithms" strategy is common to many fields and, in particular, it has provided excellent results in bioinformatics. We illustrate this combination using the workflow engine, Scipion.
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Affiliation(s)
| | | | | | | | | | - Ana Cuervo
- National Centre for Biotechnology (CSIC), Madrid, Spain
| | - Robert Melero
- National Centre for Biotechnology (CSIC), Madrid, Spain
| | | | | | - David Strelak
- National Centre for Biotechnology (CSIC), Madrid, Spain
| | | | | | | | | | - Pablo Conesa
- National Centre for Biotechnology (CSIC), Madrid, Spain
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6
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Reboul CF, Heo J, Machello C, Kiesewetter S, Kim BH, Kim S, Elmlund D, Ercius P, Park J, Elmlund H. SINGLE: Atomic-resolution structure identification of nanocrystals by graphene liquid cell EM. SCIENCE ADVANCES 2021; 7:7/5/eabe6679. [PMID: 33514557 PMCID: PMC7846166 DOI: 10.1126/sciadv.abe6679] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/06/2020] [Accepted: 12/10/2020] [Indexed: 06/12/2023]
Abstract
Analysis of the three-dimensional (3D) structures of nanocrystals with solution-phase transmission electron microscopy is beginning to reveal their unique physiochemical properties. We developed a "one-particle Brownian 3D reconstruction method" based on imaging of ensembles of colloidal nanocrystals using graphene liquid cell electron microscopy. Projection images of differently rotated nanocrystals are acquired using a direct electron detector with high temporal (<2.5 ms) resolution and analyzed to obtain an ensemble of 3D reconstructions. Here, we introduce computational methods required for successful atomic-resolution 3D reconstruction: (i) tracking of the individual particles throughout the time series, (ii) subtraction of the interfering background of the graphene liquid cell, (iii) identification and rejection of low-quality images, and (iv) tailored strategies for 2D/3D alignment and averaging that differ from those used in biological cryo-electron microscopy. Our developments are made available through the open-source software package SINGLE.
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Affiliation(s)
- Cyril F Reboul
- Department of Biochemistry and Molecular Biology, Biomedicine Discovery Institute, Monash University, Melbourne, Victoria, Australia
- Australian Research Council Centre of Excellence in Advanced Molecular Imaging, Monash University, Melbourne, Victoria, Australia
| | - Junyoung Heo
- Center for Nanoparticle Research, Institute for Basic Science (IBS), Seoul 08826, South Korea
- School of Chemical and Biological Engineering, Institute of Chemical Process, Seoul National University, Seoul 08826, South Korea
| | - Chiara Machello
- Department of Biochemistry and Molecular Biology, Biomedicine Discovery Institute, Monash University, Melbourne, Victoria, Australia
- Australian Research Council Centre of Excellence in Advanced Molecular Imaging, Monash University, Melbourne, Victoria, Australia
| | - Simon Kiesewetter
- Department of Biochemistry and Molecular Biology, Biomedicine Discovery Institute, Monash University, Melbourne, Victoria, Australia
- Australian Research Council Centre of Excellence in Advanced Molecular Imaging, Monash University, Melbourne, Victoria, Australia
| | - Byung Hyo Kim
- Center for Nanoparticle Research, Institute for Basic Science (IBS), Seoul 08826, South Korea
- School of Chemical and Biological Engineering, Institute of Chemical Process, Seoul National University, Seoul 08826, South Korea
- Department of Organic Materials and Fiber Engineering, Soongsil University, Seoul 06978, South Korea
| | - Sungin Kim
- Center for Nanoparticle Research, Institute for Basic Science (IBS), Seoul 08826, South Korea
- School of Chemical and Biological Engineering, Institute of Chemical Process, Seoul National University, Seoul 08826, South Korea
| | - Dominika Elmlund
- Department of Biochemistry and Molecular Biology, Biomedicine Discovery Institute, Monash University, Melbourne, Victoria, Australia
- Australian Research Council Centre of Excellence in Advanced Molecular Imaging, Monash University, Melbourne, Victoria, Australia
| | - Peter Ercius
- National Center for Electron Microscopy, Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Jungwon Park
- Center for Nanoparticle Research, Institute for Basic Science (IBS), Seoul 08826, South Korea.
- School of Chemical and Biological Engineering, Institute of Chemical Process, Seoul National University, Seoul 08826, South Korea
| | - Hans Elmlund
- Department of Biochemistry and Molecular Biology, Biomedicine Discovery Institute, Monash University, Melbourne, Victoria, Australia.
- Australian Research Council Centre of Excellence in Advanced Molecular Imaging, Monash University, Melbourne, Victoria, Australia
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7
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Caesar J, Reboul CF, Machello C, Kiesewetter S, Tang ML, Deme JC, Johnson S, Elmlund D, Lea SM, Elmlund H. WITHDRAWN: SIMPLE 3.0. Stream single-particle cryo-EM analysis in real time. J Struct Biol 2020; 212:107635. [PMID: 33022362 DOI: 10.1016/j.jsb.2020.107635] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2020] [Revised: 09/26/2020] [Accepted: 09/28/2020] [Indexed: 11/29/2022]
Affiliation(s)
- Joseph Caesar
- Sir William Dunn School of Pathology, University of Oxford, Oxford, UK; Central Oxford Structural Microscopy and Imaging Centre, University of Oxford, Oxford UK
| | - Cyril F Reboul
- Department of Biochemistry and Molecular Biology, Biomedicine Discovery Institute, Monash University, Melbourne, Victoria, Australia; Australian Research Council Centre of Excellence in Advanced Molecular Imaging, Monash University, Melbourne, Victoria, Australia
| | - Chiara Machello
- Department of Biochemistry and Molecular Biology, Biomedicine Discovery Institute, Monash University, Melbourne, Victoria, Australia; Australian Research Council Centre of Excellence in Advanced Molecular Imaging, Monash University, Melbourne, Victoria, Australia
| | - Simon Kiesewetter
- Department of Biochemistry and Molecular Biology, Biomedicine Discovery Institute, Monash University, Melbourne, Victoria, Australia; Australian Research Council Centre of Excellence in Advanced Molecular Imaging, Monash University, Melbourne, Victoria, Australia
| | - Molly L Tang
- Sir William Dunn School of Pathology, University of Oxford, Oxford, UK; Central Oxford Structural Microscopy and Imaging Centre, University of Oxford, Oxford UK
| | - Justin C Deme
- Sir William Dunn School of Pathology, University of Oxford, Oxford, UK; Central Oxford Structural Microscopy and Imaging Centre, University of Oxford, Oxford UK
| | - Steven Johnson
- Sir William Dunn School of Pathology, University of Oxford, Oxford, UK
| | - Dominika Elmlund
- Department of Biochemistry and Molecular Biology, Biomedicine Discovery Institute, Monash University, Melbourne, Victoria, Australia; Australian Research Council Centre of Excellence in Advanced Molecular Imaging, Monash University, Melbourne, Victoria, Australia
| | - Susan M Lea
- Sir William Dunn School of Pathology, University of Oxford, Oxford, UK; Central Oxford Structural Microscopy and Imaging Centre, University of Oxford, Oxford UK.
| | - Hans Elmlund
- Department of Biochemistry and Molecular Biology, Biomedicine Discovery Institute, Monash University, Melbourne, Victoria, Australia; Australian Research Council Centre of Excellence in Advanced Molecular Imaging, Monash University, Melbourne, Victoria, Australia.
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8
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Stabrin M, Schoenfeld F, Wagner T, Pospich S, Gatsogiannis C, Raunser S. TranSPHIRE: automated and feedback-optimized on-the-fly processing for cryo-EM. Nat Commun 2020; 11:5716. [PMID: 33177513 PMCID: PMC7658977 DOI: 10.1038/s41467-020-19513-2] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Accepted: 10/15/2020] [Indexed: 12/17/2022] Open
Abstract
Single particle cryo-EM requires full automation to allow high-throughput structure determination. Although software packages exist where parts of the cryo-EM pipeline are automated, a complete solution that offers reliable on-the-fly processing, resulting in high-resolution structures, does not exist. Here we present TranSPHIRE: A software package for fully-automated processing of cryo-EM datasets during data acquisition. TranSPHIRE transfers data from the microscope, automatically applies the common pre-processing steps, picks particles, performs 2D clustering, and 3D refinement parallel to image recording. Importantly, TranSPHIRE introduces a machine learning-based feedback loop to re-train its picking model to adapt to any given data set live during processing. This elegant approach enables TranSPHIRE to process data more effectively, producing high-quality particle stacks. TranSPHIRE collects and displays all metrics and microscope settings to allow users to quickly evaluate data during acquisition. TranSPHIRE can run on a single work station and also includes the automated processing of filaments.
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Affiliation(s)
- Markus Stabrin
- Department of Structural Biochemistry, Max Planck Institute of Molecular Physiology, Otto-Hahn-Straße 11, 44227, Dortmund, Germany
| | - Fabian Schoenfeld
- Department of Structural Biochemistry, Max Planck Institute of Molecular Physiology, Otto-Hahn-Straße 11, 44227, Dortmund, Germany
| | - Thorsten Wagner
- Department of Structural Biochemistry, Max Planck Institute of Molecular Physiology, Otto-Hahn-Straße 11, 44227, Dortmund, Germany
| | - Sabrina Pospich
- Department of Structural Biochemistry, Max Planck Institute of Molecular Physiology, Otto-Hahn-Straße 11, 44227, Dortmund, Germany
| | - Christos Gatsogiannis
- Department of Structural Biochemistry, Max Planck Institute of Molecular Physiology, Otto-Hahn-Straße 11, 44227, Dortmund, Germany
| | - Stefan Raunser
- Department of Structural Biochemistry, Max Planck Institute of Molecular Physiology, Otto-Hahn-Straße 11, 44227, Dortmund, Germany.
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9
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Caesar J, Reboul CF, Machello C, Kiesewetter S, Tang ML, Deme JC, Johnson S, Elmlund D, Lea SM, Elmlund H. SIMPLE 3.0. Stream single-particle cryo-EM analysis in real time. JOURNAL OF STRUCTURAL BIOLOGY-X 2020; 4:100040. [PMID: 33294840 PMCID: PMC7695977 DOI: 10.1016/j.yjsbx.2020.100040] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
We here introduce the third major release of the SIMPLE (Single-particle IMage Processing Linux Engine) open-source software package for analysis of cryogenic transmission electron microscopy (cryo-EM) movies of single-particles (Single-Particle Analysis, SPA). Development of SIMPLE 3.0 has been focused on real-time data processing using minimal CPU computing resources to allow easy and cost-efficient scaling of processing as data rates escalate. Our stream SPA tool implements the steps of anisotropic motion correction and CTF estimation, rapid template-based particle identification and 2D clustering with automatic class rejection. SIMPLE 3.0 additionally features an easy-to-use web-based graphical user interface (GUI) that can be run on any device (workstation, laptop, tablet or phone) and supports a remote multi-user environment over the network. The new project-based execution model automatically records the executed workflow and represents it as a flow diagram in the GUI. This facilitates meta-data handling and greatly simplifies usage. Using SIMPLE 3.0, it is possible to automatically obtain a clean SP data set amenable to high-resolution 3D reconstruction directly upon completion of the data acquisition, without the need for extensive image processing post collection. Only minimal standard CPU computing resources are required to keep up with a rate of ∼300 Gatan K3 direct electron detector movies per hour. SIMPLE 3.0 is available for download from simplecryoem.com.
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Affiliation(s)
- Joseph Caesar
- Sir William Dunn School of Pathology, University of Oxford, Oxford, UK.,Central Oxford Structural Microscopy and Imaging Centre, University of Oxford, Oxford UK
| | - Cyril F Reboul
- Department of Biochemistry and Molecular Biology, Biomedicine Discovery Institute, Monash University, Melbourne, Victoria, Australia.,Australian Research Council Centre of Excellence in Advanced Molecular Imaging, Monash University, Melbourne, Victoria, Australia
| | - Chiara Machello
- Department of Biochemistry and Molecular Biology, Biomedicine Discovery Institute, Monash University, Melbourne, Victoria, Australia.,Australian Research Council Centre of Excellence in Advanced Molecular Imaging, Monash University, Melbourne, Victoria, Australia
| | - Simon Kiesewetter
- Department of Biochemistry and Molecular Biology, Biomedicine Discovery Institute, Monash University, Melbourne, Victoria, Australia.,Australian Research Council Centre of Excellence in Advanced Molecular Imaging, Monash University, Melbourne, Victoria, Australia
| | - Molly L Tang
- Sir William Dunn School of Pathology, University of Oxford, Oxford, UK.,Central Oxford Structural Microscopy and Imaging Centre, University of Oxford, Oxford UK
| | - Justin C Deme
- Sir William Dunn School of Pathology, University of Oxford, Oxford, UK.,Central Oxford Structural Microscopy and Imaging Centre, University of Oxford, Oxford UK
| | - Steven Johnson
- Sir William Dunn School of Pathology, University of Oxford, Oxford, UK
| | - Dominika Elmlund
- Department of Biochemistry and Molecular Biology, Biomedicine Discovery Institute, Monash University, Melbourne, Victoria, Australia.,Australian Research Council Centre of Excellence in Advanced Molecular Imaging, Monash University, Melbourne, Victoria, Australia
| | - Susan M Lea
- Sir William Dunn School of Pathology, University of Oxford, Oxford, UK.,Central Oxford Structural Microscopy and Imaging Centre, University of Oxford, Oxford UK
| | - Hans Elmlund
- Department of Biochemistry and Molecular Biology, Biomedicine Discovery Institute, Monash University, Melbourne, Victoria, Australia.,Australian Research Council Centre of Excellence in Advanced Molecular Imaging, Monash University, Melbourne, Victoria, Australia
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10
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Martínez M, Jiménez-Moreno A, Maluenda D, Ramírez-Aportela E, Melero R, Cuervo A, Conesa P, Del Caño L, Fonseca YC, Sánchez-García R, Strelak D, Conesa JJ, Fernández-Giménez E, de Isidro F, Sorzano COS, Carazo JM, Marabini R. Integration of Cryo-EM Model Building Software in Scipion. J Chem Inf Model 2020; 60:2533-2540. [PMID: 31994878 DOI: 10.1021/acs.jcim.9b01032] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
Advances in cryo-electron microscopy (cryo-EM) have made it possible to obtain structures of large biological macromolecules at near-atomic resolution. This "resolution revolution" has encouraged the use and development of modeling tools able to produce high-quality atomic models from cryo-EM density maps. Unfortunately, many practical problems appear when combining different packages in the same processing workflow, which make difficult the use of these tools by non-experts and, therefore, reduce their utility. We present here a major extension of the image processing framework Scipion that provides inter-package integration in the model building area and full tracking of the complete workflow, from image processing to structure validation.
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Affiliation(s)
- M Martínez
- CNB-CSIC, C/Darwin 3, 28049 Madrid, Spain
| | | | - D Maluenda
- CNB-CSIC, C/Darwin 3, 28049 Madrid, Spain
| | | | - R Melero
- CNB-CSIC, C/Darwin 3, 28049 Madrid, Spain
| | - A Cuervo
- CNB-CSIC, C/Darwin 3, 28049 Madrid, Spain
| | - P Conesa
- CNB-CSIC, C/Darwin 3, 28049 Madrid, Spain
| | - L Del Caño
- CNB-CSIC, C/Darwin 3, 28049 Madrid, Spain
| | | | | | - D Strelak
- CNB-CSIC, C/Darwin 3, 28049 Madrid, Spain.,Institute of Computer Science, Masaryk University, Botanická 68a, 60200 Brno, Czech Republic
| | - J J Conesa
- CNB-CSIC, C/Darwin 3, 28049 Madrid, Spain
| | | | | | | | - J M Carazo
- CNB-CSIC, C/Darwin 3, 28049 Madrid, Spain
| | - R Marabini
- Escuela Politécnica, Universidad Autónoma de Madrid, C/Francisco Tomás y Valiente 11, 28049 Madrid, Spain
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11
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The evolution of SPHIRE-crYOLO particle picking and its application in automated cryo-EM processing workflows. Commun Biol 2020; 3:61. [PMID: 32047248 PMCID: PMC7012881 DOI: 10.1038/s42003-020-0790-y] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2020] [Accepted: 01/24/2020] [Indexed: 02/06/2023] Open
Abstract
Particle selection is a crucial step when processing electron cryo microscopy data. Several automated particle picking procedures were developed in the past but most struggle with non-ideal data sets. In our recent Communications Biology article, we presented crYOLO, a deep learning based particle picking program. It enables fast, automated particle picking at human levels of accuracy with low effort. A general model allows the use of crYOLO for selecting particles in previously unseen data sets without further training. Here we describe how crYOLO has evolved since its initial release. We have introduced filament picking, a new denoising technique, and a new graphical user interface. Moreover, we outline its usage in automated processing pipelines, which is an important advancement on the horizon of the field. Wagner and Raunser recently presented a deep learning based particle picking program for Cryo-EM, crYOLO. Here they discuss recent improvements to the program, a graphical user interface and share their thoughts on desired future developments.
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