1
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Kleywegt GJ, Adams PD, Butcher SJ, Lawson CL, Rohou A, Rosenthal PB, Subramaniam S, Topf M, Abbott S, Baldwin PR, Berrisford JM, Bricogne G, Choudhary P, Croll TI, Danev R, Ganesan SJ, Grant T, Gutmanas A, Henderson R, Heymann JB, Huiskonen JT, Istrate A, Kato T, Lander GC, Lok SM, Ludtke SJ, Murshudov GN, Pye R, Pintilie GD, Richardson JS, Sachse C, Salih O, Scheres SHW, Schroeder GF, Sorzano COS, Stagg SM, Wang Z, Warshamanage R, Westbrook JD, Winn MD, Young JY, Burley SK, Hoch JC, Kurisu G, Morris K, Patwardhan A, Velankar S. Community recommendations on cryoEM data archiving and validation. IUCRJ 2024; 11:140-151. [PMID: 38358351 PMCID: PMC10916293 DOI: 10.1107/s2052252524001246] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Accepted: 02/06/2024] [Indexed: 02/16/2024]
Abstract
In January 2020, a workshop was held at EMBL-EBI (Hinxton, UK) to discuss data requirements for the deposition and validation of cryoEM structures, with a focus on single-particle analysis. The meeting was attended by 47 experts in data processing, model building and refinement, validation, and archiving of such structures. This report describes the workshop's motivation and history, the topics discussed, and the resulting consensus recommendations. Some challenges for future methods-development efforts in this area are also highlighted, as is the implementation to date of some of the recommendations.
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Affiliation(s)
| | - Paul D. Adams
- Lawrence Berkeley National Laboratory, Berkeley, CA, USA
- University of California, Berkeley, CA, USA
| | | | | | | | | | | | - Maya Topf
- Birkbeck, University of London, London, United Kingdom
| | | | | | | | | | | | | | | | - Sai J. Ganesan
- University of California at San Francisco, San Francisco, CA, USA
| | | | | | | | | | | | | | | | | | | | | | | | - Ryan Pye
- EMBL-EBI, Cambridge, United Kingdom
| | | | | | | | | | | | | | | | | | - Zhe Wang
- EMBL-EBI, Cambridge, United Kingdom
| | | | | | - Martyn D. Winn
- Science and Technology Facilities Council, Research Complex at Harwell, Oxon, United Kingdom
| | - Jasmine Y. Young
- RCSB Protein Data Bank, The State University of New Jersey, NJ, USA
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2
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Corum MR, Venkannagari H, Hryc CF, Baker ML. Predictive modeling and cryo-EM: A synergistic approach to modeling macromolecular structure. Biophys J 2024; 123:435-450. [PMID: 38268190 PMCID: PMC10912932 DOI: 10.1016/j.bpj.2024.01.021] [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: 10/19/2023] [Revised: 01/09/2024] [Accepted: 01/18/2024] [Indexed: 01/26/2024] Open
Abstract
Over the last 15 years, structural biology has seen unprecedented development and improvement in two areas: electron cryo-microscopy (cryo-EM) and predictive modeling. Once relegated to low resolutions, single-particle cryo-EM is now capable of achieving near-atomic resolutions of a wide variety of macromolecular complexes. Ushered in by AlphaFold, machine learning has powered the current generation of predictive modeling tools, which can accurately and reliably predict models for proteins and some complexes directly from the sequence alone. Although they offer new opportunities individually, there is an inherent synergy between these techniques, allowing for the construction of large, complex macromolecular models. Here, we give a brief overview of these approaches in addition to illustrating works that combine these techniques for model building. These examples provide insight into model building, assessment, and limitations when integrating predictive modeling with cryo-EM density maps. Together, these approaches offer the potential to greatly accelerate the generation of macromolecular structural insights, particularly when coupled with experimental data.
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Affiliation(s)
- Michael R Corum
- Department of Biochemistry and Molecular Biology, McGovern Medical School at the University of Texas Health Science Center, Houston, Texas
| | - Harikanth Venkannagari
- Department of Biochemistry and Molecular Biology, McGovern Medical School at the University of Texas Health Science Center, Houston, Texas
| | - Corey F Hryc
- Department of Biochemistry and Molecular Biology, McGovern Medical School at the University of Texas Health Science Center, Houston, Texas
| | - Matthew L Baker
- Department of Biochemistry and Molecular Biology, McGovern Medical School at the University of Texas Health Science Center, Houston, Texas.
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3
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Kleywegt GJ, Adams PD, Butcher SJ, Lawson CL, Rohou A, Rosenthal PB, Subramaniam S, Topf M, Abbott S, Baldwin PR, Berrisford JM, Bricogne G, Choudhary P, Croll TI, Danev R, Ganesan SJ, Grant T, Gutmanas A, Henderson R, Heymann JB, Huiskonen JT, Istrate A, Kato T, Lander GC, Lok SM, Ludtke SJ, Murshudov GN, Pye R, Pintilie GD, Richardson JS, Sachse C, Salih O, Scheres SHW, Schroeder GF, Sorzano COS, Stagg SM, Wang Z, Warshamanage R, Westbrook JD, Winn MD, Young JY, Burley SK, Hoch JC, Kurisu G, Morris K, Patwardhan A, Velankar S. Community recommendations on cryoEM data archiving and validation: Outcomes of a wwPDB/EMDB workshop on cryoEM data management, deposition and validation. ARXIV 2024:arXiv:2311.17640v3. [PMID: 38076521 PMCID: PMC10705588] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 12/21/2023]
Abstract
In January 2020, a workshop was held at EMBL-EBI (Hinxton, UK) to discuss data requirements for deposition and validation of cryoEM structures, with a focus on single-particle analysis. The meeting was attended by 47 experts in data processing, model building and refinement, validation, and archiving of such structures. This report describes the workshop's motivation and history, the topics discussed, and consensus recommendations resulting from the workshop. Some challenges for future methods-development efforts in this area are also highlighted, as is the implementation to date of some of the recommendations.
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Affiliation(s)
| | - Paul D Adams
- Lawrence Berkeley Laboratory, Berkeley, CA, USA and University of California, Berkeley, CA, USA
| | | | - Catherine L Lawson
- RCSB Protein Data Bank, Rutgers, The State University of New Jersey, USA
| | | | | | | | - Maya Topf
- Birkbeck, University of London, London, UK
| | | | | | | | | | | | | | | | - Sai J Ganesan
- University of California at San Francisco, San Francisco, CA, USA
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - John D Westbrook
- RCSB Protein Data Bank, Rutgers, The State University of New Jersey, USA
| | - Martyn D Winn
- Science and Technology Facilities Council, Research Complex at Harwell, Oxon, UK
| | - Jasmine Y Young
- RCSB Protein Data Bank, Rutgers, The State University of New Jersey, USA
| | - Stephen K Burley
- RCSB Protein Data Bank, Rutgers, The State University of New Jersey, USA
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4
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Lawson CL, Kryshtafovych A, Pintilie GD, Burley SK, Černý J, Chen VB, Emsley P, Gobbi A, Joachimiak A, Noreng S, Prisant M, Read RJ, Richardson JS, Rohou AL, Schneider B, Sellers BD, Shao C, Sourial E, Williams CI, Williams CJ, Yang Y, Abbaraju V, Afonine PV, Baker ML, Bond PS, Blundell TL, Burnley T, Campbell A, Cao R, Cheng J, Chojnowski G, Cowtan KD, DiMaio F, Esmaeeli R, Giri N, Grubmüller H, Hoh SW, Hou J, Hryc CF, Hunte C, Igaev M, Joseph AP, Kao WC, Kihara D, Kumar D, Lang L, Lin S, Maddhuri Venkata Subramaniya SR, Mittal S, Mondal A, Moriarty NW, Muenks A, Murshudov GN, Nicholls RA, Olek M, Palmer CM, Perez A, Pohjolainen E, Pothula KR, Rowley CN, Sarkar D, Schäfer LU, Schlicksup CJ, Schröder GF, Shekhar M, Si D, Singharoy A, Sobolev OV, Terashi G, Vaiana AC, Vedithi SC, Verburgt J, Wang X, Warshamanage R, Winn MD, Weyand S, Yamashita K, Zhao M, Schmid MF, Berman HM, Chiu W. Outcomes of the EMDataResource Cryo-EM Ligand Modeling Challenge. RESEARCH SQUARE 2024:rs.3.rs-3864137. [PMID: 38343795 PMCID: PMC10854310 DOI: 10.21203/rs.3.rs-3864137/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/18/2024]
Abstract
The EMDataResource Ligand Model Challenge aimed to assess the reliability and reproducibility of modeling ligands bound to protein and protein/nucleic-acid complexes in cryogenic electron microscopy (cryo-EM) maps determined at near-atomic (1.9-2.5 Å) resolution. Three published maps were selected as targets: E. coli beta-galactosidase with inhibitor, SARS-CoV-2 RNA-dependent RNA polymerase with covalently bound nucleotide analog, and SARS-CoV-2 ion channel ORF3a with bound lipid. Sixty-one models were submitted from 17 independent research groups, each with supporting workflow details. We found that (1) the quality of submitted ligand models and surrounding atoms varied, as judged by visual inspection and quantification of local map quality, model-to-map fit, geometry, energetics, and contact scores, and (2) a composite rather than a single score was needed to assess macromolecule+ligand model quality. These observations lead us to recommend best practices for assessing cryo-EM structures of liganded macromolecules reported at near-atomic resolution.
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Affiliation(s)
- Catherine L. Lawson
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
| | | | - Grigore D. Pintilie
- Departments of Bioengineering and of Microbiology and Immunology, Stanford University, Stanford, CA, USA
| | - Stephen K. Burley
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
- Rutgers Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, NJ USA
- San Diego Supercomputer Center, University of California San Diego, La Jolla, CA USA
| | - Jiří Černý
- Institute of Biotechnology, Czech Academy of Sciences, Vestec, CZ
| | | | - Paul Emsley
- MRC Laboratory of Molecular Biology, Cambridge, UK
| | - Alberto Gobbi
- Discovery Chemistry, Genentech Inc, South San Francisco, USA
| | - Andrzej Joachimiak
- Structural Biology Center, X-ray Science Division, Argonne National Laboratory, Argonne, IL, USA
| | - Sigrid Noreng
- Structural Biology, Genentech Inc, South San Francisco, USA
| | | | - Randy J. Read
- Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, UK
| | | | | | - Bohdan Schneider
- Institute of Biotechnology, Czech Academy of Sciences, Vestec, CZ
| | | | - Chenghua Shao
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
| | | | | | | | - Ying Yang
- Structural Biology, Genentech Inc, South San Francisco, USA
| | - Venkat Abbaraju
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
| | - Pavel V. Afonine
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Matthew L. Baker
- Department of Biochemistry and Molecular Biology, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Paul S. Bond
- York Structural Biology Laboratory, Department of Chemistry, University of York, York, UK
| | - Tom L. Blundell
- Department of Biochemistry, University of Cambridge, Cambridge, UK
| | - Tom Burnley
- Scientific Computing Department, UKRI Science and Technology Facilities Council, Research Complex at Harwell, Didcot, UK
| | - Arthur Campbell
- Center for Development of Therapeutics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Renzhi Cao
- Department of Computer Science, Pacific Lutheran University, Tacoma, WA, USA
| | - Jianlin Cheng
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, USA
| | | | - Kevin D. Cowtan
- York Structural Biology Laboratory, Department of Chemistry, University of York, York, UK
| | - Frank DiMaio
- Department of Biochemistry and Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Reza Esmaeeli
- Department of Chemistry and Quantum Theory Project, University of Florida, Gainesville, FL, USA
| | - Nabin Giri
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, USA
| | - Helmut Grubmüller
- Theoretical and Computational Biophysics Department, Max Planck Institute for Multidisciplinary Sciences, Göttingen, Germany
| | - Soon Wen Hoh
- York Structural Biology Laboratory, Department of Chemistry, University of York, York, UK
| | - Jie Hou
- Department of Computer Science, Saint Louis University, St. Louis, MO, USA
| | - Corey F. Hryc
- Department of Biochemistry and Molecular Biology, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Carola Hunte
- Institute of Biochemistry and Molecular Biology, ZBMZ, Faculty of Medicine and CIBSS - Centre for Integrative Biological Signalling Studies, University of Freiburg, 79104 Freiburg, Germany
| | - Maxim Igaev
- Theoretical and Computational Biophysics Department, Max Planck Institute for Multidisciplinary Sciences, Göttingen, Germany
| | - Agnel P. Joseph
- Scientific Computing Department, UKRI Science and Technology Facilities Council, Research Complex at Harwell, Didcot, UK
| | - Wei-Chun Kao
- Institute of Biochemistry and Molecular Biology, ZBMZ, Faculty of Medicine and CIBSS - Centre for Integrative Biological Signalling Studies, University of Freiburg, 79104 Freiburg, Germany
| | - Daisuke Kihara
- Department of Biological Sciences, Purdue University, West Lafayette, IN, USA
- Department of Computer Science, Purdue University, West Lafayette, IN, USA
| | - Dilip Kumar
- Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, TX, USA
| | - Lijun Lang
- Department of Chemistry and Quantum Theory Project, University of Florida, Gainesville, FL, USA
| | - Sean Lin
- Division of Computing & Software Systems, University of Washington, Bothell, WA, USA
| | | | - Sumit Mittal
- Biodesign Institute, Arizona State University, Tempe, AZ, USA
- School of Advanced Sciences and Languages, VIT Bhopal University, Bhopal, India
| | - Arup Mondal
- Department of Chemistry and Quantum Theory Project, University of Florida, Gainesville, FL, USA
| | - Nigel W. Moriarty
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Andrew Muenks
- Department of Biochemistry and Institute for Protein Design, University of Washington, Seattle, WA, USA
| | | | | | - Mateusz Olek
- York Structural Biology Laboratory, Department of Chemistry, University of York, York, UK
- Electron Bio-Imaging Centre, Diamond Light Source, Harwell Science and Innovation Campus, Didcot, UK
| | - Colin M. Palmer
- Scientific Computing Department, UKRI Science and Technology Facilities Council, Research Complex at Harwell, Didcot, UK
| | - Alberto Perez
- Department of Chemistry and Quantum Theory Project, University of Florida, Gainesville, FL, USA
| | - Emmi Pohjolainen
- Theoretical and Computational Biophysics Department, Max Planck Institute for Multidisciplinary Sciences, Göttingen, Germany
| | - Karunakar R. Pothula
- Institute of Biological Information Processing (IBI-7: Structural Biochemistry) and Jülich Centre for Structural Biology (JuStruct), Forschungszentrum Jülich, Jülich, Germany
| | | | - Daipayan Sarkar
- Department of Biological Sciences, Purdue University, West Lafayette, IN, USA
- Biodesign Institute, Arizona State University, Tempe, AZ, USA
| | - Luisa U. Schäfer
- Institute of Biological Information Processing (IBI-7: Structural Biochemistry) and Jülich Centre for Structural Biology (JuStruct), Forschungszentrum Jülich, Jülich, Germany
| | - Christopher J. Schlicksup
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Gunnar F. Schröder
- Institute of Biological Information Processing (IBI-7: Structural Biochemistry) and Jülich Centre for Structural Biology (JuStruct), Forschungszentrum Jülich, Jülich, Germany
- Physics Department, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Mrinal Shekhar
- Center for Development of Therapeutics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Biodesign Institute, Arizona State University, Tempe, AZ, USA
| | - Dong Si
- Division of Computing & Software Systems, University of Washington, Bothell, WA, USA
| | | | - Oleg V. Sobolev
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Genki Terashi
- Department of Biological Sciences, Purdue University, West Lafayette, IN, USA
| | - Andrea C. Vaiana
- Theoretical and Computational Biophysics Department, Max Planck Institute for Multidisciplinary Sciences, Göttingen, Germany
- Nature’s Toolbox (NTx), Rio Rancho, NM, USA
| | | | - Jacob Verburgt
- Department of Biological Sciences, Purdue University, West Lafayette, IN, USA
| | - Xiao Wang
- Department of Biological Sciences, Purdue University, West Lafayette, IN, USA
| | | | - Martyn D. Winn
- Scientific Computing Department, UKRI Science and Technology Facilities Council, Research Complex at Harwell, Didcot, UK
| | - Simone Weyand
- Department of Biochemistry, University of Cambridge, Cambridge, UK
| | | | - Minglei Zhao
- Department of Biochemistry and Molecular Biology, University of Chicago, Chicago, IL, USA
| | - Michael F. Schmid
- Division of Cryo-EM and Bioimaging, SSRL, SLAC National Accelerator Laboratory, Menlo Park, CA, USA
| | - Helen M. Berman
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA 90089, USA
| | - Wah Chiu
- Departments of Bioengineering and of Microbiology and Immunology, Stanford University, Stanford, CA, USA
- Division of Cryo-EM and Bioimaging, SSRL, SLAC National Accelerator Laboratory, Menlo Park, CA, USA
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5
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Burley SK, Berman HM, Chiu W, Dai W, Flatt JW, Hudson BP, Kaelber JT, Khare SD, Kulczyk AW, Lawson CL, Pintilie GD, Sali A, Vallat B, Westbrook JD, Young JY, Zardecki C. Electron microscopy holdings of the Protein Data Bank: the impact of the resolution revolution, new validation tools, and implications for the future. Biophys Rev 2022; 14:1281-1301. [PMID: 36474933 PMCID: PMC9715422 DOI: 10.1007/s12551-022-01013-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Accepted: 11/06/2022] [Indexed: 12/04/2022] Open
Abstract
As a discipline, structural biology has been transformed by the three-dimensional electron microscopy (3DEM) "Resolution Revolution" made possible by convergence of robust cryo-preservation of vitrified biological materials, sample handling systems, and measurement stages operating a liquid nitrogen temperature, improvements in electron optics that preserve phase information at the atomic level, direct electron detectors (DEDs), high-speed computing with graphics processing units, and rapid advances in data acquisition and processing software. 3DEM structure information (atomic coordinates and related metadata) are archived in the open-access Protein Data Bank (PDB), which currently holds more than 11,000 3DEM structures of proteins and nucleic acids, and their complexes with one another and small-molecule ligands (~ 6% of the archive). Underlying experimental data (3DEM density maps and related metadata) are stored in the Electron Microscopy Data Bank (EMDB), which currently holds more than 21,000 3DEM density maps. After describing the history of the PDB and the Worldwide Protein Data Bank (wwPDB) partnership, which jointly manages both the PDB and EMDB archives, this review examines the origins of the resolution revolution and analyzes its impact on structural biology viewed through the lens of PDB holdings. Six areas of focus exemplifying the impact of 3DEM across the biosciences are discussed in detail (icosahedral viruses, ribosomes, integral membrane proteins, SARS-CoV-2 spike proteins, cryogenic electron tomography, and integrative structure determination combining 3DEM with complementary biophysical measurement techniques), followed by a review of 3DEM structure validation by the wwPDB that underscores the importance of community engagement.
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Affiliation(s)
- Stephen K. Burley
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854 USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854 USA
- Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, NJ 08901 USA
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California San Diego, La Jolla, CA 92093 USA
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, 174 Frelinghuysen Road, Piscataway, NJ 08854 USA
| | - Helen M. Berman
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854 USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854 USA
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, 174 Frelinghuysen Road, Piscataway, NJ 08854 USA
| | - Wah Chiu
- Department of Bioengineering, Stanford University, Stanford, CA USA
- Division of CryoEM and Bioimaging, SSRL, SLAC National Accelerator Laboratory, Stanford University, Menlo Park, CA USA
| | - Wei Dai
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854 USA
- Department of Cell Biology and Neuroscience, Rutgers, The State University of New Jersey, Piscataway, NJ 08854 USA
| | - Justin W. Flatt
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854 USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854 USA
| | - Brian P. Hudson
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854 USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854 USA
| | - Jason T. Kaelber
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854 USA
| | - Sagar D. Khare
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854 USA
- Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, NJ 08901 USA
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, 174 Frelinghuysen Road, Piscataway, NJ 08854 USA
| | - Arkadiusz W. Kulczyk
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854 USA
- Department of Biochemistry and Microbiology, Rutgers, The State University of New Jersey, Piscataway, NJ 08901 USA
| | - Catherine L. Lawson
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854 USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854 USA
| | | | - Andrej Sali
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, Quantitative Biosciences Institute, University of California San Francisco, San Francisco, CA 94158 USA
| | - Brinda Vallat
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854 USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854 USA
- Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, NJ 08901 USA
| | - John D. Westbrook
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854 USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854 USA
- Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, NJ 08901 USA
| | - Jasmine Y. Young
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854 USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854 USA
| | - Christine Zardecki
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854 USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854 USA
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6
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Sánchez Rodríguez F, Chojnowski G, Keegan RM, Rigden DJ. Using deep-learning predictions of inter-residue distances for model validation. Acta Crystallogr D Struct Biol 2022; 78:1412-1427. [PMID: 36458613 PMCID: PMC9716559 DOI: 10.1107/s2059798322010415] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Accepted: 10/28/2022] [Indexed: 11/27/2022] Open
Abstract
Determination of protein structures typically entails building a model that satisfies the collected experimental observations and its deposition in the Protein Data Bank. Experimental limitations can lead to unavoidable uncertainties during the process of model building, which result in the introduction of errors into the deposited model. Many metrics are available for model validation, but most are limited to consideration of the physico-chemical aspects of the model or its match to the experimental data. The latest advances in the field of deep learning have enabled the increasingly accurate prediction of inter-residue distances, an advance which has played a pivotal role in the recent improvements observed in the field of protein ab initio modelling. Here, new validation methods are presented based on the use of these precise inter-residue distance predictions, which are compared with the distances observed in the protein model. Sequence-register errors are particularly clearly detected and the register shifts required for their correction can be reliably determined. The method is available in the ConKit package (https://www.conkit.org).
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Affiliation(s)
- Filomeno Sánchez Rodríguez
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool L69 7ZB, United Kingdom,Life Science, Diamond Light Source, Harwell Science and Innovation Campus, Didcot OX11 0DE, United Kingdom
| | - Grzegorz Chojnowski
- European Molecular Biology Laboratory, Hamburg Unit, Notkestrasse 85, 22607 Hamburg, Germany
| | - Ronan M. Keegan
- UKRI–STFC, Rutherford Appleton Laboratory, Research Complex at Harwell, Didcot OX11 0FA, United Kingdom
| | - Daniel J. Rigden
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool L69 7ZB, United Kingdom,Correspondence e-mail:
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7
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Joseph AP, Malhotra S, Burnley T, Winn MD. Overview and applications of map and model validation tools in the CCP-EM software suite. Faraday Discuss 2022; 240:196-209. [PMID: 35916020 PMCID: PMC9642004 DOI: 10.1039/d2fd00103a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Cryogenic electron microscopy (cryo-EM) has recently been established as a powerful technique for solving macromolecular structures. Although the best resolutions achievable are improving, a significant majority of data are still resolved at resolutions worse than 3 Å, where it is non-trivial to build or fit atomic models. The map reconstructions and atomic models derived from the maps are also prone to errors accumulated through the different stages of data processing. Here, we highlight the need to evaluate both model geometry and fit to data at different resolutions. Assessment of cryo-EM structures from SARS-CoV-2 highlights a bias towards optimising the model geometry to agree with the most common conformations, compared to the agreement with data. We present the CoVal web service which provides multiple validation metrics to reflect the quality of atomic models derived from cryo-EM data of structures from SARS-CoV-2. We demonstrate that further refinement can lead to improvement of the agreement with data without the loss of geometric quality. We also discuss the recent CCP-EM developments aimed at addressing some of the current shortcomings.
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Affiliation(s)
- Agnel Praveen Joseph
- Scientific Computing Department, Science and Technology Facilities CouncilDidcot OX11 0FAUK
| | - Sony Malhotra
- Scientific Computing Department, Science and Technology Facilities CouncilDidcot OX11 0FAUK
| | - Tom Burnley
- Scientific Computing Department, Science and Technology Facilities CouncilDidcot OX11 0FAUK
| | - Martyn D. Winn
- Scientific Computing Department, Science and Technology Facilities CouncilDidcot OX11 0FAUK
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8
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Artificial Intelligence in Cryo-Electron Microscopy. LIFE (BASEL, SWITZERLAND) 2022; 12:life12081267. [PMID: 36013446 PMCID: PMC9410485 DOI: 10.3390/life12081267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 08/15/2022] [Accepted: 08/18/2022] [Indexed: 11/17/2022]
Abstract
Cryo-electron microscopy (cryo-EM) has become an unrivaled tool for determining the structure of macromolecular complexes. The biological function of macromolecular complexes is inextricably tied to the flexibility of these complexes. Single particle cryo-EM can reveal the conformational heterogeneity of a biochemically pure sample, leading to well-founded mechanistic hypotheses about the roles these complexes play in biology. However, the processing of increasingly large, complex datasets using traditional data processing strategies is exceedingly expensive in both user time and computational resources. Current innovations in data processing capitalize on artificial intelligence (AI) to improve the efficiency of data analysis and validation. Here, we review new tools that use AI to automate the data analysis steps of particle picking, 3D map reconstruction, and local resolution determination. We discuss how the application of AI moves the field forward, and what obstacles remain. We also introduce potential future applications of AI to use cryo-EM in understanding protein communities in cells.
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9
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Joseph AP, Olek M, Malhotra S, Zhang P, Cowtan K, Burnley T, Winn MD. Atomic model validation using the CCP-EM software suite. Acta Crystallogr D Struct Biol 2022; 78:152-161. [PMID: 35102881 PMCID: PMC8805302 DOI: 10.1107/s205979832101278x] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Accepted: 12/01/2021] [Indexed: 12/02/2022] Open
Abstract
Recently, there has been a dramatic improvement in the quality and quantity of data derived using cryogenic electron microscopy (cryo-EM). This is also associated with a large increase in the number of atomic models built. Although the best resolutions that are achievable are improving, often the local resolution is variable, and a significant majority of data are still resolved at resolutions worse than 3 Å. Model building and refinement is often challenging at these resolutions, and hence atomic model validation becomes even more crucial to identify less reliable regions of the model. Here, a graphical user interface for atomic model validation, implemented in the CCP-EM software suite, is presented. It is aimed to develop this into a platform where users can access multiple complementary validation metrics that work across a range of resolutions and obtain a summary of evaluations. Based on the validation estimates from atomic models associated with cryo-EM structures from SARS-CoV-2, it was observed that models typically favor adopting the most common conformations over fitting the observations when compared with the model agreement with data. At low resolutions, the stereochemical quality may be favored over data fit, but care should be taken to ensure that the model agrees with the data in terms of resolvable features. It is demonstrated that further re-refinement can lead to improvement of the agreement with data without the loss of geometric quality. This also highlights the need for improved resolution-dependent weight optimization in model refinement and an effective test for overfitting that would help to guide the refinement process.
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Affiliation(s)
- Agnel Praveen Joseph
- Scientific Computing Department, Science and Technology Facilities Council, Didcot, United Kingdom
| | - Mateusz Olek
- Department of Chemistry, University of York, York, United Kingdom
- Electron BioImaging Center, Diamond Light Source, Rutherford Appleton Laboratory, Didcot, United Kingdom
| | - Sony Malhotra
- Scientific Computing Department, Science and Technology Facilities Council, Didcot, United Kingdom
| | - Peijun Zhang
- Electron BioImaging Center, Diamond Light Source, Rutherford Appleton Laboratory, Didcot, United Kingdom
| | - Kevin Cowtan
- Department of Chemistry, University of York, York, United Kingdom
| | - Tom Burnley
- Scientific Computing Department, Science and Technology Facilities Council, Didcot, United Kingdom
| | - Martyn D. Winn
- Scientific Computing Department, Science and Technology Facilities Council, Didcot, United Kingdom
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10
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Waman VP, Orengo C, Kleywegt GJ, Lesk AM. Three-dimensional Structure Databases of Biological Macromolecules. Methods Mol Biol 2022; 2449:43-91. [PMID: 35507259 DOI: 10.1007/978-1-0716-2095-3_3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Databases of three-dimensional structures of proteins (and their associated molecules) provide: (a) Curated repositories of coordinates of experimentally determined structures, including extensive metadata; for instance information about provenance, details about data collection and interpretation, and validation of results. (b) Information-retrieval tools to allow searching to identify entries of interest and provide access to them. (c) Links among databases, especially to databases of amino-acid and genetic sequences, and of protein function; and links to software for analysis of amino-acid sequence and protein structure, and for structure prediction. (d) Collections of predicted three-dimensional structures of proteins. These will become more and more important after the breakthrough in structure prediction achieved by AlphaFold2. The single global archive of experimentally determined biomacromolecular structures is the Protein Data Bank (PDB). It is managed by wwPDB, a consortium of five partner institutions: the Protein Data Bank in Europe (PDBe), the Research Collaboratory for Structural Bioinformatics (RCSB), the Protein Data Bank Japan (PDBj), the BioMagResBank (BMRB), and the Electron Microscopy Data Bank (EMDB). In addition to jointly managing the PDB repository, the individual wwPDB partners offer many tools for analysis of protein and nucleic acid structures and their complexes, including providing computer-graphic representations. Their collective and individual websites serve as hubs of the community of structural biologists, offering newsletters, reports from Task Forces, training courses, and "helpdesks," as well as links to external software.Many specialized projects are based on the information contained in the PDB. Especially important are SCOP, CATH, and ECOD, which present classifications of protein domains.
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Affiliation(s)
- Vaishali P Waman
- Institute of Structural and Molecular Biology, University College London, London, UK
| | - Christine Orengo
- Institute of Structural and Molecular Biology, University College London, London, UK
| | - Gerard J Kleywegt
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK
| | - Arthur M Lesk
- Department of Biochemistry and Molecular Biology and Center for Computational Biology and Bioinformatics, The Pennsylvania State University, University Park, PA, USA.
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11
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Zielinski M, Röder C, Schröder GF. Challenges in sample preparation and structure determination of amyloids by cryo-EM. J Biol Chem 2021; 297:100938. [PMID: 34224730 PMCID: PMC8335658 DOI: 10.1016/j.jbc.2021.100938] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Revised: 06/28/2021] [Accepted: 07/01/2021] [Indexed: 01/12/2023] Open
Abstract
Amyloids share a common architecture but play disparate biological roles in processes ranging from bacterial defense mechanisms to protein misfolding diseases. Their structures are highly polymorphic, which makes them difficult to study by X-ray diffraction or NMR spectroscopy. Our understanding of amyloid structures is due in large part to recent advances in the field of cryo-EM, which allows for determining the polymorphs separately. In this review, we highlight the main stepping stones leading to the substantial number of high-resolution amyloid fibril structures known today as well as recent developments regarding automation and software in cryo-EM. We discuss that sample preparation should move closer to physiological conditions to understand how amyloid aggregation and disease are linked. We further highlight new approaches to address heterogeneity and polymorphism of amyloid fibrils in EM image processing and give an outlook to the upcoming challenges in researching the structural biology of amyloids.
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Affiliation(s)
- Mara Zielinski
- Institute of Biological Information Processing, Structural Biochemistry (IBI-7) and JuStruct, Jülich Center for Structural Biology, Forschungszentrum Jülich, Jülich, Germany
| | - Christine Röder
- Institute of Biological Information Processing, Structural Biochemistry (IBI-7) and JuStruct, Jülich Center for Structural Biology, Forschungszentrum Jülich, Jülich, Germany; Institut für Physikalische Biologie, Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Germany
| | - Gunnar F Schröder
- Institute of Biological Information Processing, Structural Biochemistry (IBI-7) and JuStruct, Jülich Center for Structural Biology, Forschungszentrum Jülich, Jülich, Germany; Physics Department, Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Germany.
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12
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van Belkum A, Almeida C, Bardiaux B, Barrass SV, Butcher SJ, Çaykara T, Chowdhury S, Datar R, Eastwood I, Goldman A, Goyal M, Happonen L, Izadi-Pruneyre N, Jacobsen T, Johnson PH, Kempf VAJ, Kiessling A, Bueno JL, Malik A, Malmström J, Meuskens I, Milner PA, Nilges M, Pamme N, Peyman SA, Rodrigues LR, Rodriguez-Mateos P, Sande MG, Silva CJ, Stasiak AC, Stehle T, Thibau A, Vaca DJ, Linke D. Host-Pathogen Adhesion as the Basis of Innovative Diagnostics for Emerging Pathogens. Diagnostics (Basel) 2021; 11:diagnostics11071259. [PMID: 34359341 PMCID: PMC8305138 DOI: 10.3390/diagnostics11071259] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Revised: 06/19/2021] [Accepted: 06/21/2021] [Indexed: 12/18/2022] Open
Abstract
Infectious diseases are an existential health threat, potentiated by emerging and re-emerging viruses and increasing bacterial antibiotic resistance. Targeted treatment of infectious diseases requires precision diagnostics, especially in cases where broad-range therapeutics such as antibiotics fail. There is thus an increasing need for new approaches to develop sensitive and specific in vitro diagnostic (IVD) tests. Basic science and translational research are needed to identify key microbial molecules as diagnostic targets, to identify relevant host counterparts, and to use this knowledge in developing or improving IVD. In this regard, an overlooked feature is the capacity of pathogens to adhere specifically to host cells and tissues. The molecular entities relevant for pathogen–surface interaction are the so-called adhesins. Adhesins vary from protein compounds to (poly-)saccharides or lipid structures that interact with eukaryotic host cell matrix molecules and receptors. Such interactions co-define the specificity and sensitivity of a diagnostic test. Currently, adhesin-receptor binding is typically used in the pre-analytical phase of IVD tests, focusing on pathogen enrichment. Further exploration of adhesin–ligand interaction, supported by present high-throughput “omics” technologies, might stimulate a new generation of broadly applicable pathogen detection and characterization tools. This review describes recent results of novel structure-defining technologies allowing for detailed molecular analysis of adhesins, their receptors and complexes. Since the host ligands evolve slowly, the corresponding adhesin interaction is under selective pressure to maintain a constant receptor binding domain. IVD should exploit such conserved binding sites and, in particular, use the human ligand to enrich the pathogen. We provide an inventory of methods based on adhesion factors and pathogen attachment mechanisms, which can also be of relevance to currently emerging pathogens, including SARS-CoV-2, the causative agent of COVID-19.
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Affiliation(s)
- Alex van Belkum
- BioMérieux, Open Innovation & Partnerships, 38390 La Balme Les Grottes, France;
- Correspondence: (A.v.B.); (D.L.)
| | | | - Benjamin Bardiaux
- Institut Pasteur, Structural Biology and Chemistry, 75724 Paris, France; (B.B.); (N.I.-P.); (T.J.); (M.N.)
| | - Sarah V. Barrass
- Department of Biological Sciences, University of Helsinki, 00014 Helsinki, Finland; (S.V.B.); (S.J.B.); (A.G.)
| | - Sarah J. Butcher
- Department of Biological Sciences, University of Helsinki, 00014 Helsinki, Finland; (S.V.B.); (S.J.B.); (A.G.)
| | - Tuğçe Çaykara
- Centre for Nanotechnology and Smart Materials, 4760-034 Vila Nova de Famalicão, Portugal; (T.Ç.); (C.J.S.)
| | - Sounak Chowdhury
- Division of Infection Medicine, Department of Clinical Sciences, Lund University, 22242 Lund, Sweden; (S.C.); (L.H.); (J.M.)
| | - Rucha Datar
- BioMérieux, Microbiology R&D, 38390 La Balme Les Grottes, France;
| | | | - Adrian Goldman
- Department of Biological Sciences, University of Helsinki, 00014 Helsinki, Finland; (S.V.B.); (S.J.B.); (A.G.)
- School of Biomedical Sciences, University of Leeds, Leeds LS2 9JT, UK; (P.H.J.); (A.K.); (J.L.B.); (A.M.); (P.A.M.); (S.A.P.)
| | - Manisha Goyal
- BioMérieux, Open Innovation & Partnerships, 38390 La Balme Les Grottes, France;
| | - Lotta Happonen
- Division of Infection Medicine, Department of Clinical Sciences, Lund University, 22242 Lund, Sweden; (S.C.); (L.H.); (J.M.)
| | - Nadia Izadi-Pruneyre
- Institut Pasteur, Structural Biology and Chemistry, 75724 Paris, France; (B.B.); (N.I.-P.); (T.J.); (M.N.)
| | - Theis Jacobsen
- Institut Pasteur, Structural Biology and Chemistry, 75724 Paris, France; (B.B.); (N.I.-P.); (T.J.); (M.N.)
| | - Pirjo H. Johnson
- School of Biomedical Sciences, University of Leeds, Leeds LS2 9JT, UK; (P.H.J.); (A.K.); (J.L.B.); (A.M.); (P.A.M.); (S.A.P.)
| | - Volkhard A. J. Kempf
- Institute for Medical Microbiology and Infection Control, University Hospital, Goethe-University, 60596 Frankfurt am Main, Germany; (V.A.J.K.); (A.T.); (D.J.V.)
| | - Andreas Kiessling
- School of Biomedical Sciences, University of Leeds, Leeds LS2 9JT, UK; (P.H.J.); (A.K.); (J.L.B.); (A.M.); (P.A.M.); (S.A.P.)
| | - Juan Leva Bueno
- School of Biomedical Sciences, University of Leeds, Leeds LS2 9JT, UK; (P.H.J.); (A.K.); (J.L.B.); (A.M.); (P.A.M.); (S.A.P.)
| | - Anchal Malik
- School of Biomedical Sciences, University of Leeds, Leeds LS2 9JT, UK; (P.H.J.); (A.K.); (J.L.B.); (A.M.); (P.A.M.); (S.A.P.)
| | - Johan Malmström
- Division of Infection Medicine, Department of Clinical Sciences, Lund University, 22242 Lund, Sweden; (S.C.); (L.H.); (J.M.)
| | - Ina Meuskens
- Department of Biosciences, University of Oslo, 0316 Oslo, Norway;
| | - Paul A. Milner
- School of Biomedical Sciences, University of Leeds, Leeds LS2 9JT, UK; (P.H.J.); (A.K.); (J.L.B.); (A.M.); (P.A.M.); (S.A.P.)
| | - Michael Nilges
- Institut Pasteur, Structural Biology and Chemistry, 75724 Paris, France; (B.B.); (N.I.-P.); (T.J.); (M.N.)
| | - Nicole Pamme
- School of Mathematics and Physical Sciences, University of Hull, Hull HU6 7RX, UK; (N.P.); (P.R.-M.)
| | - Sally A. Peyman
- School of Biomedical Sciences, University of Leeds, Leeds LS2 9JT, UK; (P.H.J.); (A.K.); (J.L.B.); (A.M.); (P.A.M.); (S.A.P.)
| | - Ligia R. Rodrigues
- CEB—Centre of Biological Engineering, University of Minho, 4710-057 Braga, Portugal; (L.R.R.); (M.G.S.)
| | - Pablo Rodriguez-Mateos
- School of Mathematics and Physical Sciences, University of Hull, Hull HU6 7RX, UK; (N.P.); (P.R.-M.)
| | - Maria G. Sande
- CEB—Centre of Biological Engineering, University of Minho, 4710-057 Braga, Portugal; (L.R.R.); (M.G.S.)
| | - Carla Joana Silva
- Centre for Nanotechnology and Smart Materials, 4760-034 Vila Nova de Famalicão, Portugal; (T.Ç.); (C.J.S.)
| | - Aleksandra Cecylia Stasiak
- Interfaculty Institute of Biochemistry, University of Tübingen, 72076 Tübingen, Germany; (A.C.S.); (T.S.)
| | - Thilo Stehle
- Interfaculty Institute of Biochemistry, University of Tübingen, 72076 Tübingen, Germany; (A.C.S.); (T.S.)
| | - Arno Thibau
- Institute for Medical Microbiology and Infection Control, University Hospital, Goethe-University, 60596 Frankfurt am Main, Germany; (V.A.J.K.); (A.T.); (D.J.V.)
| | - Diana J. Vaca
- Institute for Medical Microbiology and Infection Control, University Hospital, Goethe-University, 60596 Frankfurt am Main, Germany; (V.A.J.K.); (A.T.); (D.J.V.)
| | - Dirk Linke
- Department of Biosciences, University of Oslo, 0316 Oslo, Norway;
- Correspondence: (A.v.B.); (D.L.)
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13
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Olek M, Joseph AP. Cryo-EM Map-Based Model Validation Using the False Discovery Rate Approach. Front Mol Biosci 2021; 8:652530. [PMID: 34084774 PMCID: PMC8167059 DOI: 10.3389/fmolb.2021.652530] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Accepted: 04/26/2021] [Indexed: 01/18/2023] Open
Abstract
Significant technological developments and increasing scientific interest in cryogenic electron microscopy (cryo-EM) has resulted in a rapid increase in the amount of data generated by these experiments and the derived atomic models. Robust measures for the validation of 3D reconstructions and atomic models are essential for appropriate interpretation of the data. The resolution of data and availability of software tools that work across a range of resolutions often limit the quality of derived models. Hence, the final atomic model is often incomplete or contains regions where atomic positions are less reliable or incorrectly built. Extensive manual pruning and local adjustments or rebuilding are usually required to address these issues. The presented research introduces a software tool for the validation of the backbone trace of atomic models built in the cryo-EM density maps. In this study, we use the false discovery rate analysis, which can be used to segregate molecular signals from the background. Each atomic position in the model can be associated with an FDR backbone validation score, which can be used to identify potential mistraced residues. We demonstrate that the proposed validation score is complementary to existing validation metrics and is useful especially in cases where the model is built in the maps having varying local resolution. We also discuss the application of the score for automated pruning of atomic models built ab-initio during the iterative model building process in Buccaneer. We have implemented this score in the CCP-EM software suite.
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Affiliation(s)
- Mateusz Olek
- Department of Chemistry, University of York, York, United Kingdom.,Electron BioImaging Center, Rutherford Appleton Laboratory, Didcot, United Kingdom
| | - Agnel Praveen Joseph
- Scientific Computing Department, Science and Technology Facilities Council, Research Complex at Harwell, Didcot, United Kingdom
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14
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Chiu W, Schmid MF, Pintilie GD, Lawson CL. Evolution of standardization and dissemination of cryo-EM structures and data jointly by the community, PDB, and EMDB. J Biol Chem 2021; 296:100560. [PMID: 33744287 PMCID: PMC8050867 DOI: 10.1016/j.jbc.2021.100560] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2021] [Revised: 02/08/2021] [Accepted: 03/16/2021] [Indexed: 01/04/2023] Open
Abstract
Cryogenic electron microscopy (cryo-EM) methods began to be used in the mid-1970s to study thin and periodic arrays of proteins. Following a half-century of development in cryo-specimen preparation, instrumentation, data collection, data processing, and modeling software, cryo-EM has become a routine method for solving structures from large biological assemblies to small biomolecules at near to true atomic resolution. This review explores the critical roles played by the Protein Data Bank (PDB) and Electron Microscopy Data Bank (EMDB) in partnership with the community to develop the necessary infrastructure to archive cryo-EM maps and associated models. Public access to cryo-EM structure data has in turn facilitated better understanding of structure–function relationships and advancement of image processing and modeling tool development. The partnership between the global cryo-EM community and PDB and EMDB leadership has synergistically shaped the standards for metadata, one-stop deposition of maps and models, and validation metrics to assess the quality of cryo-EM structures. The advent of cryo-electron tomography (cryo-ET) for in situ molecular cell structures at a broad resolution range and their correlations with other imaging data introduce new data archival challenges in terms of data size and complexity in the years to come.
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Affiliation(s)
- Wah Chiu
- Department of Bioengineering, Stanford University, Stanford, California, USA; Division of CryoEM and Bioimaging, SLAC National Accelerator Laboratory, Stanford University, Menlo Park, California, USA.
| | - Michael F Schmid
- Division of CryoEM and Bioimaging, SLAC National Accelerator Laboratory, Stanford University, Menlo Park, California, USA
| | - Grigore D Pintilie
- Department of Bioengineering, Stanford University, Stanford, California, USA
| | - Catherine L Lawson
- Institute for Quantitative Biomedicine and Research Collaboratory for Structural Bioinformatics, Rutgers, The State University of New Jersey, Piscataway, New Jersey, USA
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15
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Beckers M, Mann D, Sachse C. Structural interpretation of cryo-EM image reconstructions. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2021; 160:26-36. [PMID: 32735944 DOI: 10.1016/j.pbiomolbio.2020.07.004] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 07/03/2020] [Accepted: 07/13/2020] [Indexed: 06/11/2023]
Abstract
The productivity of single-particle cryo-EM as a structure determination method has rapidly increased as many novel biological structures are being elucidated. The ultimate result of the cryo-EM experiment is an atomic model that should faithfully represent the computed image reconstruction. Although the principal approach of atomic model building and refinement from maps resembles that of the X-ray crystallographic methods, there are important differences due to the unique properties resulting from the 3D image reconstructions. In this review, we discuss the practiced work-flow from the cryo-EM image reconstruction to the atomic model. We give an overview of (i) resolution determination methods in cryo-EM including local and directional resolution variation, (ii) cryo-EM map contrast optimization including complementary map types that can help in identifying ambiguous density features, (iii) atomic model building and (iv) refinement in various resolution regimes including (v) their validation and (vi) discuss differences between X-ray and cryo-EM maps. Based on the methods originally developed for X-ray crystallography, the path from 3D image reconstruction to atomic coordinates has become an integral and important part of the cryo-EM structure determination work-flow that routinely delivers atomic models.
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Affiliation(s)
- Maximilian Beckers
- European Molecular Biology Laboratory (EMBL), Structural and Computational Biology Unit, Meyerhofstraße 1, 69117, Heidelberg, Germany; Candidate for Joint PhD Degree from EMBL and Heidelberg University, Faculty of Biosciences, Germany; Ernst-Ruska Centre for Microscopy and Spectroscopy with Electrons (ER-C-3/Structural Biology), Forschungszentrum Jülich, 52425, Jülich, Germany; JuStruct: Jülich Center for Structural Biology, Forschungszentrum Jülich, 52425, Jülich, Germany
| | - Daniel Mann
- Ernst-Ruska Centre for Microscopy and Spectroscopy with Electrons (ER-C-3/Structural Biology), Forschungszentrum Jülich, 52425, Jülich, Germany; JuStruct: Jülich Center for Structural Biology, Forschungszentrum Jülich, 52425, Jülich, Germany
| | - Carsten Sachse
- Ernst-Ruska Centre for Microscopy and Spectroscopy with Electrons (ER-C-3/Structural Biology), Forschungszentrum Jülich, 52425, Jülich, Germany; JuStruct: Jülich Center for Structural Biology, Forschungszentrum Jülich, 52425, Jülich, Germany; Chemistry Department, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.
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16
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Abstract
Every protein has a story-how it folds, what it binds, its biological actions, and how it misbehaves in aging or disease. Stories are often inferred from a protein's shape (i.e., its structure). But increasingly, stories are told using computational molecular physics (CMP). CMP is rooted in the principled physics of driving forces and reveals granular detail of conformational populations in space and time. Recent advances are accessing longer time scales, larger actions, and blind testing, enabling more of biology's stories to be told in the language of atomistic physics.
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Affiliation(s)
- Emiliano Brini
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794, USA
| | - Carlos Simmerling
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794, USA.,Department of Chemistry, Stony Brook University, Stony Brook, NY 11794, USA
| | - Ken Dill
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794, USA. .,Department of Chemistry, Stony Brook University, Stony Brook, NY 11794, USA.,Department of Physics and Astronomy, Stony Brook University, Stony Brook, New NY 11794, USA
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17
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Lawson CL, Kryshtafovych A, Adams PD, Afonine PV, Baker ML, Barad BA, Bond P, Burnley T, Cao R, Cheng J, Chojnowski G, Cowtan K, Dill KA, DiMaio F, Farrell DP, Fraser JS, Herzik MA, Hoh SW, Hou J, Hung LW, Igaev M, Joseph AP, Kihara D, Kumar D, Mittal S, Monastyrskyy B, Olek M, Palmer CM, Patwardhan A, Perez A, Pfab J, Pintilie GD, Richardson JS, Rosenthal PB, Sarkar D, Schäfer LU, Schmid MF, Schröder GF, Shekhar M, Si D, Singharoy A, Terashi G, Terwilliger TC, Vaiana A, Wang L, Wang Z, Wankowicz SA, Williams CJ, Winn M, Wu T, Yu X, Zhang K, Berman HM, Chiu W. Cryo-EM model validation recommendations based on outcomes of the 2019 EMDataResource challenge. Nat Methods 2021; 18:156-164. [PMID: 33542514 PMCID: PMC7864804 DOI: 10.1038/s41592-020-01051-w] [Citation(s) in RCA: 57] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Accepted: 12/21/2020] [Indexed: 01/30/2023]
Abstract
This paper describes outcomes of the 2019 Cryo-EM Model Challenge. The goals were to (1) assess the quality of models that can be produced from cryogenic electron microscopy (cryo-EM) maps using current modeling software, (2) evaluate reproducibility of modeling results from different software developers and users and (3) compare performance of current metrics used for model evaluation, particularly Fit-to-Map metrics, with focus on near-atomic resolution. Our findings demonstrate the relatively high accuracy and reproducibility of cryo-EM models derived by 13 participating teams from four benchmark maps, including three forming a resolution series (1.8 to 3.1 Å). The results permit specific recommendations to be made about validating near-atomic cryo-EM structures both in the context of individual experiments and structure data archives such as the Protein Data Bank. We recommend the adoption of multiple scoring parameters to provide full and objective annotation and assessment of the model, reflective of the observed cryo-EM map density.
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Affiliation(s)
- Catherine L. Lawson
- grid.430387.b0000 0004 1936 8796Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ USA
| | - Andriy Kryshtafovych
- grid.27860.3b0000 0004 1936 9684Genome Center, University of California, Davis, CA USA
| | - Paul D. Adams
- grid.184769.50000 0001 2231 4551Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA USA ,grid.47840.3f0000 0001 2181 7878Department of Bioengineering, University of California Berkeley, Berkeley, CA USA
| | - Pavel V. Afonine
- grid.184769.50000 0001 2231 4551Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA USA
| | - Matthew L. Baker
- grid.267308.80000 0000 9206 2401Department of Biochemistry and Molecular Biology, The University of Texas Health Science Center at Houston, Houston, TX USA
| | - Benjamin A. Barad
- grid.214007.00000000122199231Department of Integrated Computational Structural Biology, The Scripps Research Institute, La Jolla, CA USA
| | - Paul Bond
- grid.5685.e0000 0004 1936 9668York Structural Biology Laboratory, Department of Chemistry, University of York, York, UK
| | - Tom Burnley
- grid.465239.fScientific Computing Department, UKRI Science and Technology Facilities Council, Research Complex at Harwell, Didcot, UK
| | - Renzhi Cao
- grid.261584.c0000 0001 0492 9915Department of Computer Science, Pacific Lutheran University, Tacoma, WA USA
| | - Jianlin Cheng
- grid.134936.a0000 0001 2162 3504Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO USA
| | - Grzegorz Chojnowski
- grid.475756.20000 0004 0444 5410European Molecular Biology Laboratory, c/o DESY, Hamburg, Germany
| | - Kevin Cowtan
- grid.5685.e0000 0004 1936 9668York Structural Biology Laboratory, Department of Chemistry, University of York, York, UK
| | - Ken A. Dill
- grid.36425.360000 0001 2216 9681Laufer Center, Stony Brook University, Stony Brook, NY USA
| | - Frank DiMaio
- grid.34477.330000000122986657Department of Biochemistry and Institute for Protein Design, University of Washington, Seattle, WA USA
| | - Daniel P. Farrell
- grid.34477.330000000122986657Department of Biochemistry and Institute for Protein Design, University of Washington, Seattle, WA USA
| | - James S. Fraser
- grid.266102.10000 0001 2297 6811Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA USA
| | - Mark A. Herzik
- grid.266100.30000 0001 2107 4242Department of Chemistry and Biochemistry, University of California, San Diego, La Jolla, CA USA
| | - Soon Wen Hoh
- grid.5685.e0000 0004 1936 9668York Structural Biology Laboratory, Department of Chemistry, University of York, York, UK
| | - Jie Hou
- grid.262962.b0000 0004 1936 9342Department of Computer Science, Saint Louis University, St. Louis, MO USA
| | - Li-Wei Hung
- grid.148313.c0000 0004 0428 3079Los Alamos National Laboratory, Los Alamos, NM USA
| | - Maxim Igaev
- grid.418140.80000 0001 2104 4211Theoretical and Computational Biophysics, Max Planck Institute for Biophysical Chemistry, Göttingen, Germany
| | - Agnel P. Joseph
- grid.465239.fScientific Computing Department, UKRI Science and Technology Facilities Council, Research Complex at Harwell, Didcot, UK
| | - Daisuke Kihara
- grid.169077.e0000 0004 1937 2197Department of Biological Sciences, Purdue University, West Lafayette, IN USA ,grid.169077.e0000 0004 1937 2197Department of Computer Science, Purdue University, West Lafayette, IN USA
| | - Dilip Kumar
- grid.39382.330000 0001 2160 926XVerna and Marrs McLean Department of Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, TX USA
| | - Sumit Mittal
- grid.215654.10000 0001 2151 2636Biodesign Institute, Arizona State University, Tempe, AZ USA ,grid.411530.20000 0001 0694 3745School of Advanced Sciences and Languages, VIT Bhopal University, Bhopal, India
| | - Bohdan Monastyrskyy
- grid.27860.3b0000 0004 1936 9684Genome Center, University of California, Davis, CA USA
| | - Mateusz Olek
- grid.5685.e0000 0004 1936 9668York Structural Biology Laboratory, Department of Chemistry, University of York, York, UK
| | - Colin M. Palmer
- grid.465239.fScientific Computing Department, UKRI Science and Technology Facilities Council, Research Complex at Harwell, Didcot, UK
| | - Ardan Patwardhan
- grid.225360.00000 0000 9709 7726The European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, UK
| | - Alberto Perez
- grid.15276.370000 0004 1936 8091Department of Chemistry, University of Florida, Gainesville, FL USA
| | - Jonas Pfab
- grid.462982.30000 0000 8883 2602Division of Computing & Software Systems, University of Washington, Bothell, WA USA
| | - Grigore D. Pintilie
- grid.168010.e0000000419368956Department of Bioengineering, Stanford University, Stanford, CA USA
| | - Jane S. Richardson
- grid.26009.3d0000 0004 1936 7961Department of Biochemistry, Duke University, Durham, NC USA
| | - Peter B. Rosenthal
- grid.451388.30000 0004 1795 1830Structural Biology of Cells and Viruses Laboratory, Francis Crick Institute, London, UK
| | - Daipayan Sarkar
- grid.169077.e0000 0004 1937 2197Department of Biological Sciences, Purdue University, West Lafayette, IN USA ,grid.215654.10000 0001 2151 2636Biodesign Institute, Arizona State University, Tempe, AZ USA
| | - Luisa U. Schäfer
- grid.8385.60000 0001 2297 375XInstitute of Biological Information Processing (IBI-7: Structural Biochemistry) and Jülich Centre for Structural Biology (JuStruct), Forschungszentrum Jülich, Jülich, Germany
| | - Michael F. Schmid
- grid.168010.e0000000419368956Division of CryoEM and Biomaging, SSRL, SLAC National Accelerator Laboratory, Stanford University, Menlo Park, CA USA
| | - Gunnar F. Schröder
- grid.8385.60000 0001 2297 375XInstitute of Biological Information Processing (IBI-7: Structural Biochemistry) and Jülich Centre for Structural Biology (JuStruct), Forschungszentrum Jülich, Jülich, Germany ,grid.411327.20000 0001 2176 9917Physics Department, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Mrinal Shekhar
- grid.215654.10000 0001 2151 2636Biodesign Institute, Arizona State University, Tempe, AZ USA ,grid.66859.34Center for Development of Therapeutics, Broad Institute of MIT and Harvard, Cambridge, MA USA
| | - Dong Si
- grid.462982.30000 0000 8883 2602Division of Computing & Software Systems, University of Washington, Bothell, WA USA
| | - Abishek Singharoy
- grid.215654.10000 0001 2151 2636Biodesign Institute, Arizona State University, Tempe, AZ USA
| | - Genki Terashi
- grid.418140.80000 0001 2104 4211Theoretical and Computational Biophysics, Max Planck Institute for Biophysical Chemistry, Göttingen, Germany
| | | | - Andrea Vaiana
- grid.418140.80000 0001 2104 4211Theoretical and Computational Biophysics, Max Planck Institute for Biophysical Chemistry, Göttingen, Germany
| | - Liguo Wang
- grid.34477.330000000122986657Department of Biological Structure, University of Washington, Seattle, WA USA
| | - Zhe Wang
- grid.225360.00000 0000 9709 7726The European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, UK
| | - Stephanie A. Wankowicz
- grid.266102.10000 0001 2297 6811Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA USA ,grid.266102.10000 0001 2297 6811Biophysics Graduate Program, University of California, San Francisco, CA USA
| | | | - Martyn Winn
- grid.465239.fScientific Computing Department, UKRI Science and Technology Facilities Council, Research Complex at Harwell, Didcot, UK
| | - Tianqi Wu
- grid.134936.a0000 0001 2162 3504Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO USA
| | - Xiaodi Yu
- grid.497530.c0000 0004 0389 4927SMPS, Janssen Research and Development, Spring House, PA USA
| | - Kaiming Zhang
- grid.168010.e0000000419368956Department of Bioengineering, Stanford University, Stanford, CA USA
| | - Helen M. Berman
- grid.430387.b0000 0004 1936 8796Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ USA ,grid.42505.360000 0001 2156 6853Department of Biological Sciences and Bridge Institute, University of Southern California, Los Angeles, CA USA
| | - Wah Chiu
- grid.168010.e0000000419368956Department of Bioengineering, Stanford University, Stanford, CA USA ,grid.168010.e0000000419368956Division of CryoEM and Biomaging, SSRL, SLAC National Accelerator Laboratory, Stanford University, Menlo Park, CA USA
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18
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Li Y, Cash JN, Tesmer JJG, Cianfrocco MA. High-Throughput Cryo-EM Enabled by User-Free Preprocessing Routines. Structure 2020; 28:858-869.e3. [PMID: 32294468 PMCID: PMC7347462 DOI: 10.1016/j.str.2020.03.008] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2020] [Revised: 03/02/2020] [Accepted: 03/17/2020] [Indexed: 12/18/2022]
Abstract
Single-particle cryoelectron microscopy (cryo-EM) continues to grow into a mainstream structural biology technique. Recent developments in data collection strategies alongside new sample preparation devices herald a future where users will collect multiple datasets per microscope session. To make cryo-EM data processing more automatic and user-friendly, we have developed an automatic pipeline for cryo-EM data preprocessing and assessment using a combination of deep-learning and image-analysis tools. We have verified the performance of this pipeline on a number of datasets and extended its scope to include sample screening by the user-free assessment of the qualities of a series of datasets under different conditions. We propose that our workflow provides a decision-free solution for cryo-EM, making data preprocessing more generalized and robust in the high-throughput era as well as more convenient for users from a range of backgrounds.
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Affiliation(s)
- Yilai Li
- Life Sciences Institute, Department of Biological Chemistry, University of Michigan, Ann Arbor, MI, USA
| | - Jennifer N Cash
- Life Sciences Institute, Department of Biological Chemistry, University of Michigan, Ann Arbor, MI, USA
| | - John J G Tesmer
- Departments of Biological Sciences and of Medicinal Chemistry and Molecular Pharmacology, Purdue University, West Lafayette, IN, USA
| | - Michael A Cianfrocco
- Life Sciences Institute, Department of Biological Chemistry, University of Michigan, Ann Arbor, MI, USA.
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19
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Berman HM, Vallat B, Lawson CL. The data universe of structural biology. IUCRJ 2020; 7:630-638. [PMID: 32695409 PMCID: PMC7340255 DOI: 10.1107/s205225252000562x] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/20/2020] [Accepted: 04/21/2020] [Indexed: 05/05/2023]
Abstract
The Protein Data Bank (PDB) has grown from a small data resource for crystallographers to a worldwide resource serving structural biology. The history of the growth of the PDB and the role that the community has played in developing standards and policies are described. This article also illustrates how other biophysics communities are collaborating with the worldwide PDB to create a network of interoperating data resources. This network will expand the capabilities of structural biology and enable the determination and archiving of increasingly complex structures.
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Affiliation(s)
- Helen M. Berman
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Department of Biological Sciences and Bridge Institute, University of Southern California, Los Angeles, CA 90089, USA
| | - Brinda Vallat
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Catherine L. Lawson
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
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20
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Vant JW, Lahey SLJ, Jana K, Shekhar M, Sarkar D, Munk BH, Kleinekathöfer U, Mittal S, Rowley C, Singharoy A. Flexible Fitting of Small Molecules into Electron Microscopy Maps Using Molecular Dynamics Simulations with Neural Network Potentials. J Chem Inf Model 2020; 60:2591-2604. [PMID: 32207947 PMCID: PMC7311632 DOI: 10.1021/acs.jcim.9b01167] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
Despite significant advances in resolution, the potential for cryo-electron microscopy (EM) to be used in determining the structures of protein-drug complexes remains unrealized. Determination of accurate structures and coordination of bound ligands necessitates simultaneous fitting of the models into the density envelopes, exhaustive sampling of the ligand geometries, and, most importantly, concomitant rearrangements in the side chains to optimize the binding energy changes. In this article, we present a flexible-fitting pipeline where molecular dynamics flexible fitting (MDFF) is used to refine structures of protein-ligand complexes from 3 to 5 Å electron density data. Enhanced sampling is employed to explore the binding pocket rearrangements. To provide a model that can accurately describe the conformational dynamics of the chemically diverse set of small-molecule drugs inside MDFF, we use QM/MM and neural-network potential (NNP)/MM models of protein-ligand complexes, where the ligand is represented using the QM or NNP model, and the protein is represented using established molecular mechanical force fields (e.g., CHARMM). This pipeline offers structures commensurate to or better than recently submitted high-resolution cryo-EM or X-ray models, even when given medium to low-resolution data as input. The use of the NNPs makes the algorithm more robust to the choice of search models, offering a radius of convergence of 6.5 Å for ligand structure determination. The quality of the predicted structures was also judged by density functional theory calculations of ligand strain energy. This strain potential energy is found to systematically decrease with better fitting to density and improved ligand coordination, indicating correct binding interactions. A computationally inexpensive protocol for computing strain energy is reported as part of the model analysis protocol that monitors both the ligand fit as well as model quality.
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Affiliation(s)
- John W. Vant
- School of Molecular Sciences, Arizona State University, Tempe, USA
| | - Shae-Lynn J. Lahey
- Department of Chemistry, Memorial University of Newfoundland, St. John’s, NL, Canada
| | - Kalyanashis Jana
- Department of Physics and Earth Sciences, Jacobs University Bremen, 28759 Bremen, Germany
| | - Mrinal Shekhar
- School of Molecular Sciences, Arizona State University, Tempe, USA
- Center for Development of Therapeutics, Broad Institute of MIT and Harvard, Cambridge, USA
| | - Daipayan Sarkar
- School of Molecular Sciences, Arizona State University, Tempe, USA
| | - Barbara H. Munk
- School of Molecular Sciences, Arizona State University, Tempe, USA
| | - Ulrich Kleinekathöfer
- Department of Physics and Earth Sciences, Jacobs University Bremen, 28759 Bremen, Germany
| | - Sumit Mittal
- School of Molecular Sciences, Arizona State University, Tempe, USA
- School of Advanced Sciences and Languages, VIT Bhopal University, Bhopal, India
| | - Christopher Rowley
- Department of Chemistry, Memorial University of Newfoundland, St. John’s, NL, Canada
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21
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Joseph AP, Lagerstedt I, Jakobi A, Burnley T, Patwardhan A, Topf M, Winn M. Comparing Cryo-EM Reconstructions and Validating Atomic Model Fit Using Difference Maps. J Chem Inf Model 2020; 60:2552-2560. [PMID: 32043355 PMCID: PMC7254831 DOI: 10.1021/acs.jcim.9b01103] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Cryogenic electron microscopy (cryo-EM) is a powerful technique for determining structures of multiple conformational or compositional states of macromolecular assemblies involved in cellular processes. Recent technological developments have led to a leap in the resolution of many cryo-EM data sets, making atomic model building more common for data interpretation. We present a method for calculating differences between two cryo-EM maps or a map and a fitted atomic model. The proposed approach works by scaling the maps using amplitude matching in resolution shells. To account for variability in local resolution of cryo-EM data, we include a procedure for local amplitude scaling that enables appropriate scaling of local map contrast. The approach is implemented as a user-friendly tool in the CCP-EM software package. To obtain clean and interpretable differences, we propose a protocol involving steps to process the input maps and output differences. We demonstrate the utility of the method for identifying conformational and compositional differences including ligands. We also highlight the use of difference maps for evaluating atomic model fit in cryo-EM maps.
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Affiliation(s)
- Agnel Praveen Joseph
- Scientific Computing Department, Science and Technology Facilities Council, Research Complex at Harwell, Didcot OX11 0FA, United Kingdom
| | - Ingvar Lagerstedt
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom
| | - Arjen Jakobi
- Kavli Institute of Nanoscience Delft (KIND), Department of Bionanoscienes, Delft University of Technology, Van der Maasweg 9, 2629 HZ Delft, The Netherlands
| | - Tom Burnley
- Scientific Computing Department, Science and Technology Facilities Council, Research Complex at Harwell, Didcot OX11 0FA, United Kingdom
| | - Ardan Patwardhan
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom
| | - Maya Topf
- Institute of Structural and Molecular Biology, Department of Biological Sciences, Birkbeck College, University of London, Malet Street, London WC1E 7HX, United Kingdom
| | - Martyn Winn
- Scientific Computing Department, Science and Technology Facilities Council, Research Complex at Harwell, Didcot OX11 0FA, United Kingdom
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22
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Koukos P, Bonvin A. Integrative Modelling of Biomolecular Complexes. J Mol Biol 2020; 432:2861-2881. [DOI: 10.1016/j.jmb.2019.11.009] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2019] [Revised: 11/12/2019] [Accepted: 11/13/2019] [Indexed: 12/31/2022]
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23
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Lawson CL, Berman HM, Chiu W. Evolving data standards for cryo-EM structures. STRUCTURAL DYNAMICS (MELVILLE, N.Y.) 2020; 7:014701. [PMID: 32002441 PMCID: PMC6980868 DOI: 10.1063/1.5138589] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/14/2019] [Accepted: 01/07/2020] [Indexed: 05/04/2023]
Abstract
Electron cryo-microscopy (cryo-EM) is increasingly being used to determine 3D structures of a broad spectrum of biological specimens from molecules to cells. Anticipating this progress in the early 2000s, an international collaboration of scientists with expertise in both cryo-EM and structure data archiving was established (EMDataResource, previously known as EMDataBank). The major goals of the collaboration have been twofold: to develop the necessary infrastructure for archiving cryo-EM-derived density maps and models, and to promote development of cryo-EM structure validation standards. We describe how cryo-EM data archiving and validation have been developed and jointly coordinated for the Electron Microscopy Data Bank and Protein Data Bank archives over the past two decades, as well as the impact of evolving technology on data standards. Just as for X-ray crystallography and nuclear magnetic resonance, engaging the scientific community via workshops and challenging activities has played a central role in developing recommendations and requirements for the cryo-EM structure data archives.
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Affiliation(s)
- Catherine L. Lawson
- Institute for Quantitative Biomedicine and Research Collaboratory for Structural Bioinformatics, Rutgers, The State University of New Jersey, Piscataway, New Jersey 08854, USA
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24
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Kryshtafovych A, Malhotra S, Monastyrskyy B, Cragnolini T, Joseph AP, Chiu W, Topf M. Cryo-electron microscopy targets in CASP13: Overview and evaluation of results. Proteins 2019; 87:1128-1140. [PMID: 31576602 PMCID: PMC7197460 DOI: 10.1002/prot.25817] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2019] [Revised: 08/30/2019] [Accepted: 09/13/2019] [Indexed: 11/07/2022]
Abstract
Structures of seven CASP13 targets were determined using cryo-electron microscopy (cryo-EM) technique with resolution between 3.0 and 4.0 Å. We provide an overview of the experimentally derived structures and describe results of the numerical evaluation of the submitted models. The evaluation is carried out by comparing coordinates of models to those of reference structures (CASP-style evaluation), as well as checking goodness-of-fit of modeled structures to the cryo-EM density maps. The performance of contributing research groups in the CASP-style evaluation is measured in terms of backbone accuracy, all-atom local geometry and similarity of inter-subunit interfaces. The results on the cryo-EM targets are compared with those on the whole set of eighty CASP13 targets. A posteriori refinement of the best models in their corresponding cryo-EM density maps resulted in structures that are very close to the reference structure, including some regions with better fit to the density.
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Affiliation(s)
- Andriy Kryshtafovych
- Genome Center, University of California, Davis, 451 Health Sciences Drive, Davis, CA 95616, USA
| | - Sony Malhotra
- Institute of Structural and Molecular Biology, Birkbeck, University College London, Malet Street, London WC1E 7HX, UK
| | - Bohdan Monastyrskyy
- Genome Center, University of California, Davis, 451 Health Sciences Drive, Davis, CA 95616, USA
| | - Tristan Cragnolini
- Institute of Structural and Molecular Biology, Birkbeck, University College London, Malet Street, London WC1E 7HX, UK
| | - Agnel-Praveen Joseph
- Institute of Structural and Molecular Biology, Birkbeck, University College London, Malet Street, London WC1E 7HX, UK
| | - Wah Chiu
- Department of Bioengineering, Microbiology and Immunology and Photon Science, Stanford University, James H. Clark Center, MC5447, 318 Campus Drive, Stanford, CA 94305, USA
| | - Maya Topf
- Institute of Structural and Molecular Biology, Birkbeck, University College London, Malet Street, London WC1E 7HX, UK
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25
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Berman HM, Adams PD, Bonvin AA, Burley SK, Carragher B, Chiu W, DiMaio F, Ferrin TE, Gabanyi MJ, Goddard TD, Griffin PR, Haas J, Hanke CA, Hoch JC, Hummer G, Kurisu G, Lawson CL, Leitner A, Markley JL, Meiler J, Montelione GT, Phillips GN, Prisner T, Rappsilber J, Schriemer DC, Schwede T, Seidel CAM, Strutzenberg TS, Svergun DI, Tajkhorshid E, Trewhella J, Vallat B, Velankar S, Vuister GW, Webb B, Westbrook JD, White KL, Sali A. Federating Structural Models and Data: Outcomes from A Workshop on Archiving Integrative Structures. Structure 2019; 27:1745-1759. [PMID: 31780431 DOI: 10.1016/j.str.2019.11.002] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2019] [Revised: 10/31/2019] [Accepted: 11/06/2019] [Indexed: 12/23/2022]
Abstract
Structures of biomolecular systems are increasingly computed by integrative modeling. In this approach, a structural model is constructed by combining information from multiple sources, including varied experimental methods and prior models. In 2019, a Workshop was held as a Biophysical Society Satellite Meeting to assess progress and discuss further requirements for archiving integrative structures. The primary goal of the Workshop was to build consensus for addressing the challenges involved in creating common data standards, building methods for federated data exchange, and developing mechanisms for validating integrative structures. The summary of the Workshop and the recommendations that emerged are presented here.
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Affiliation(s)
- Helen M Berman
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA; Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA; Bridge Institute, Michelson Center, University of Southern California, Los Angeles, CA 90089, USA.
| | - Paul D Adams
- Physical Biosciences Division, Lawrence Berkeley Laboratory, Berkeley, CA 94720-8235, USA; Department of Bioengineering, University of California-Berkeley, Berkeley, CA 94720, USA
| | - Alexandre A Bonvin
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, 3584 CH Utrecht, the Netherlands
| | - Stephen K Burley
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, The State University of New Jersey, Piscataway, NJ 08854, USA; Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA; Skaggs School of Pharmacy and Pharmaceutical Sciences and San Diego Supercomputer Center, University of California, San Diego, La Jolla, CA 92093, USA; Rutgers Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, NJ 08903, USA
| | - Bridget Carragher
- Simons Electron Microscopy Center, New York Structural Biology Center, New York, NY 10027, USA; Department of Biochemistry and Molecular Biophysics, Columbia University, New York, NY 10032, USA
| | - Wah Chiu
- Department of Bioengineering, Department of Microbiology and Immunology, Stanford University, Stanford, CA 94305-5447, USA; SLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA
| | - Frank DiMaio
- Department of Biochemistry and Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
| | - Thomas E Ferrin
- Department of Pharmaceutical Chemistry, University of California, San Francisco, CA 94158, USA
| | - Margaret J Gabanyi
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Thomas D Goddard
- Department of Pharmaceutical Chemistry, University of California, San Francisco, CA 94158, USA
| | | | - Juergen Haas
- Swiss Institute of Bioinformatics and Biozentrum, University of Basel, 4056 Basel, Switzerland
| | - Christian A Hanke
- Molecular Physical Chemistry, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany
| | - Jeffrey C Hoch
- Department of Molecular Biology and Biophysics, UConn Health, Farmington, CT 06030, USA
| | - Gerhard Hummer
- Department of Theoretical Biophysics, Max Planck Institute of Biophysics, 60438 Frankfurt am Main, Germany; Institute for Biophysics, Goethe University Frankfurt, 60438 Frankfurt am Main, Germany
| | - Genji Kurisu
- Protein Data Bank Japan (PDBj), Institute for Protein Research, Osaka University, Osaka 565-0871, Japan
| | - Catherine L Lawson
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Alexander Leitner
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, 8093 Zurich, Switzerland
| | - John L Markley
- BioMagResBank (BMRB), Biochemistry Department, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Jens Meiler
- Center for Structural Biology, Vanderbilt University, 465 21st Avenue South, Nashville, TN 37221, USA
| | - Gaetano T Montelione
- Center for Advanced Biotechnology and Medicine, Department of Molecular Biology and Biochemistry, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA; Department of Biochemistry, Robert Wood Johnson Medical School, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA; Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytech Institute, Troy, NY 12180, USA
| | - George N Phillips
- BioSciences at Rice and Department of Chemistry, Rice University, Houston, TX 77251, USA
| | - Thomas Prisner
- Institute of Physical and Theoretical Chemistry and Center of Biomolecular Magnetic Resonance, Goethe University Frankfurt, 60438 Frankfurt am Main, Germany
| | - Juri Rappsilber
- Wellcome Trust Centre for Cell Biology, Edinburgh EH9 3JR, Scotland
| | - David C Schriemer
- Department of Biochemistry & Molecular Biology, Robson DNA Science Centre, University of Calgary, Calgary, AB T2N 4N1, Canada
| | - Torsten Schwede
- Swiss Institute of Bioinformatics and Biozentrum, University of Basel, 4056 Basel, Switzerland
| | - Claus A M Seidel
- Molecular Physical Chemistry, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany
| | | | - Dmitri I Svergun
- European Molecular Biology Laboratory (EMBL), Hamburg Outstation, Notkestrasse 85, 22607 Hamburg, Germany
| | - Emad Tajkhorshid
- Department of Biochemistry, NIH Center for Macromolecular Modeling and Bioinformatics, Center for Biophysics and Quantitative Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA; Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Jill Trewhella
- School of Life and Environmental Sciences, The University of Sydney, Sydney, NSW 2006, Australia; Department of Chemistry, University of Utah, Salt Lake City, UT 84112, USA
| | - Brinda Vallat
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Sameer Velankar
- Protein Data Bank in Europe (PDBe), European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire CB10 1SD, UK
| | - Geerten W Vuister
- Department of Molecular and Cell Biology, Leicester Institute of Structural and Chemical Biology, University of Leicester, Leicester LE1 9HN, UK
| | - Benjamin Webb
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - John D Westbrook
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, The State University of New Jersey, Piscataway, NJ 08854, USA; Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Kate L White
- Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA; Bridge Institute, Michelson Center, University of Southern California, Los Angeles, CA 90089, USA
| | - Andrej Sali
- Department of Pharmaceutical Chemistry, University of California, San Francisco, CA 94158, USA; Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA 94158, USA; California Institute for Quantitative Biosciences, University of California, San Francisco, San Francisco, CA 94158, USA.
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Khatib F, Desfosses A, Koepnick B, Flatten J, Popović Z, Baker D, Cooper S, Gutsche I, Horowitz S. Building de novo cryo-electron microscopy structures collaboratively with citizen scientists. PLoS Biol 2019; 17:e3000472. [PMID: 31714936 PMCID: PMC6850521 DOI: 10.1371/journal.pbio.3000472] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Abstract
With the rapid improvement of cryo-electron microscopy (cryo-EM) resolution, new computational tools are needed to assist and improve upon atomic model building and refinement options. This communication demonstrates that microscopists can now collaborate with the players of the computer game Foldit to generate high-quality de novo structural models. This development could greatly speed the generation of excellent cryo-EM structures when used in addition to current methods.
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Affiliation(s)
- Firas Khatib
- Department of Computer and Information Science, University of Massachusetts Dartmouth, Dartmouth, Massachusetts, United States of America
- * E-mail: (FB); (SH)
| | - Ambroise Desfosses
- Institut de Biologie Structurale, University Grenoble Alpes, CEA, CNRS, Grenoble, France
| | | | - Brian Koepnick
- Department of Biochemistry, University of Washington, Seattle, Washington, United States of America
| | - Jeff Flatten
- Center for Game Science, Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, Washington, United States of America
| | - Zoran Popović
- Center for Game Science, Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, Washington, United States of America
| | - David Baker
- Department of Biochemistry, University of Washington, Seattle, Washington, United States of America
| | - Seth Cooper
- Khoury College of Computer Sciences, Northeastern University, Boston, Massachusetts, United States of America
| | - Irina Gutsche
- Institut de Biologie Structurale, University Grenoble Alpes, CEA, CNRS, Grenoble, France
| | - Scott Horowitz
- Department of Chemistry and Biochemistry and the Knoebel Institute for Healthy Aging, University of Denver, Denver, Colorado, United States of America
- * E-mail: (FB); (SH)
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