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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Abstract
Adeno-associated virus (AAV) has a single-stranded DNA genome encapsidated in a small icosahedrally symmetric protein shell with 60 subunits. AAV is the leading delivery vector in emerging gene therapy treatments for inherited disorders, so its structure and molecular interactions with human hosts are of intense interest. A wide array of electron microscopic approaches have been used to visualize the virus and its complexes, depending on the scientific question, technology available, and amenability of the sample. Approaches range from subvolume tomographic analyses of complexes with large and flexible host proteins to detailed analysis of atomic interactions within the virus and with small ligands at resolutions as high as 1.6 Å. Analyses have led to the reclassification of glycan receptors as attachment factors, to structures with a new-found receptor protein, to identification of the epitopes of antibodies, and a new understanding of possible neutralization mechanisms. AAV is now well-enough characterized that it has also become a model system for EM methods development. Heralding a new era, cryo-EM is now also being deployed as an analytic tool in the process development and production quality control of high value pharmaceutical biologics, namely AAV vectors.
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Affiliation(s)
- Scott
M. Stagg
- Department
of Biological Sciences, Florida State University, Tallahassee, Florida 32306, United States
- Institute
of Molecular Biophysics, Florida State University, Tallahassee, Florida 32306, United States
| | - Craig Yoshioka
- Department
of Biomedical Engineering, Oregon Health
& Science University, Portland Oregon 97239, United States
| | - Omar Davulcu
- Environmental
Molecular Sciences Laboratory, Pacific Northwest
National Laboratory, 3335 Innovation Boulevard, Richland, Washington 99354, United States
| | - Michael S. Chapman
- Department
of Biochemistry, University of Missouri, Columbia, Missouri 65211, United States
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4
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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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5
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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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6
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Abstract
Public data archives are the backbone of modern biological research. Biomolecular archives are well established, but bioimaging resources lag behind them. The technology required for imaging archives is now available, thus enabling the creation of the first public bioimage datasets. We present the rationale for the construction of bioimage archives and their associated databases to underpin the next revolution in bioinformatics discovery.
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7
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Affiliation(s)
- Catherine L Lawson
- Institute for Quantitative Biomedicine, Rutgers University, Piscataway, NJ 08854, USA.
| | - Wah Chiu
- Division of Cryo-EM and Bioimaging, SSRL, SLAC National Accelerator Laboratory, Stanford University, Menlo Park, CA 94025, USA; Department of Bioengineering, Department of Microbiology and Immunology, James H. Clark Center, Stanford University, Stanford, CA 94305, USA.
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8
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Terashi G, Kihara D. De novo main-chain modeling with MAINMAST in 2015/2016 EM Model Challenge. J Struct Biol 2018; 204:351-359. [PMID: 30075190 PMCID: PMC6179447 DOI: 10.1016/j.jsb.2018.07.013] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2018] [Revised: 07/13/2018] [Accepted: 07/19/2018] [Indexed: 11/15/2022]
Abstract
Protein tertiary structure modeling is a critical step for the interpretation of three dimensional (3D) election microscopy density. Our group participated the 2015/2016 EM Model Challenge using the MAINMAST software for a de novo main chain modeling. The software generates local dense points using the mean shifting algorithm, and connects them into Cα models by calculating the minimum spanning tree and the longest path. Subsequently, full atom structure models are generated, which are subject to structural refinement. Here, we summarize the qualities of our submitted models and examine successful and unsuccessful models, including 3D models we did not submit to the Challenge. Our protocol using the MAINMAST software was sometimes able to build correct conformations with 3.4–5.1 Å RMSD. Unsuccessful models had failure of chain traces, however, their Cα positions and some local structures were quite correctly built. For evaluate the quality of the models, the MAINMAST software provides a confidence score for each Cα position from the consensus of top 100 scoring models.
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Affiliation(s)
- Genki Terashi
- Department of Biological Sciences, Purdue University, West Lafayette, IN 47907, USA
| | - Daisuke Kihara
- Department of Biological Sciences, Purdue University, West Lafayette, IN 47907, USA; Department of Computer Science, Purdue University, West Lafayette, IN 47907, USA.
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9
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Abstract
CryoEM in structural biology is currently served by three public archives-EMDB for 3DEM reconstructions, PDB for models built from 3DEM reconstructions, and EMPIAR for the raw 2D image data used to obtain the 3DEM reconstructions. These archives play a vital role for both the structural community and the wider biological community in making the data accessible so that results may be reused, reassessed, and integrated with other structural and bioinformatics resources. The important role of the archives is underpinned by the fact that many journals mandate the deposition of data to PDB and EMDB on publication. The field is currently undergoing transformative changes where on the one hand high-resolution structures are becoming a routine occurrence while on the other hand electron tomography is enabling the study of macromolecules in the cellular context. Concomitantly the archives are evolving to best serve their stakeholder communities. In this chapter, we describe the current state of the archives, resources available for depositing, accessing, searching, visualizing and validating data, on-going community-wide initiatives and opportunities, and challenges for the future.
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Affiliation(s)
- A Patwardhan
- Protein Data Bank in Europe, European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, United Kingdom.
| | - C L Lawson
- Research Collaboratory for Structural Bioinformatics, Center for Integrative Proteomics Research, Rutgers, The State University of New Jersey, Piscataway, NJ, United States.
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10
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Segura J, Sanchez-Garcia R, Tabas-Madrid D, Cuenca-Alba J, Sorzano COS, Carazo JM. 3DIANA: 3D Domain Interaction Analysis: A Toolbox for Quaternary Structure Modeling. Biophys J 2016; 110:766-75. [PMID: 26772592 PMCID: PMC4775853 DOI: 10.1016/j.bpj.2015.11.3519] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2015] [Revised: 11/27/2015] [Accepted: 11/30/2015] [Indexed: 11/19/2022] Open
Abstract
Electron microscopy (EM) is experiencing a revolution with the advent of a new generation of Direct Electron Detectors, enabling a broad range of large and flexible structures to be resolved well below 1 nm resolution. Although EM techniques are evolving to the point of directly obtaining structural data at near-atomic resolution, for many molecules the attainable resolution might not be enough to propose high-resolution structural models. However, accessing information on atomic coordinates is a necessary step toward a deeper understanding of the molecular mechanisms that allow proteins to perform specific tasks. For that reason, methods for the integration of EM three-dimensional maps with x-ray and NMR structural data are being developed, a modeling task that is normally referred to as fitting, resulting in the so called hybrid models. In this work, we present a novel application—3DIANA—specially targeted to those cases in which the EM map resolution is medium or low and additional experimental structural information is scarce or even lacking. In this way, 3DIANA statistically evaluates proposed/potential contacts between protein domains, presents a complete catalog of both structurally resolved and predicted interacting regions involving these domains and, finally, suggests structural templates to model the interaction between them. The evaluation of the proposed interactions is computed with DIMERO, a new method that scores physical binding sites based on the topology of protein interaction networks, which has recently shown the capability to increase by 200% the number of domain-domain interactions predicted in interactomes as compared to previous approaches. The new application displays the information at a sequence and structural level and is accessible through a web browser or as a Chimera plugin at http://3diana.cnb.csic.es.
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Affiliation(s)
- Joan Segura
- GN7, Spanish National Institute for Bioinformatics (INB) and Biocomputing Unit, National Center of Biotechnology (CSIC)/Instruct Image Processing Center, Madrid, Spain.
| | - Ruben Sanchez-Garcia
- GN7, Spanish National Institute for Bioinformatics (INB) and Biocomputing Unit, National Center of Biotechnology (CSIC)/Instruct Image Processing Center, Madrid, Spain
| | - Daniel Tabas-Madrid
- GN7, Spanish National Institute for Bioinformatics (INB) and Biocomputing Unit, National Center of Biotechnology (CSIC)/Instruct Image Processing Center, Madrid, Spain
| | - Jesus Cuenca-Alba
- GN7, Spanish National Institute for Bioinformatics (INB) and Biocomputing Unit, National Center of Biotechnology (CSIC)/Instruct Image Processing Center, Madrid, Spain
| | - Carlos Oscar S Sorzano
- GN7, Spanish National Institute for Bioinformatics (INB) and Biocomputing Unit, National Center of Biotechnology (CSIC)/Instruct Image Processing Center, Madrid, Spain
| | - Jose Maria Carazo
- GN7, Spanish National Institute for Bioinformatics (INB) and Biocomputing Unit, National Center of Biotechnology (CSIC)/Instruct Image Processing Center, Madrid, Spain
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11
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Lawson CL, Patwardhan A, Baker ML, Hryc C, Garcia ES, Hudson BP, Lagerstedt I, Ludtke SJ, Pintilie G, Sala R, Westbrook JD, Berman HM, Kleywegt GJ, Chiu W. EMDataBank unified data resource for 3DEM. Nucleic Acids Res 2015; 44:D396-403. [PMID: 26578576 PMCID: PMC4702818 DOI: 10.1093/nar/gkv1126] [Citation(s) in RCA: 177] [Impact Index Per Article: 19.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2015] [Accepted: 10/15/2015] [Indexed: 01/10/2023] Open
Abstract
Three-dimensional Electron Microscopy (3DEM) has become a key experimental method in structural biology for a broad spectrum of biological specimens from molecules to cells. The EMDataBank project provides a unified portal for deposition, retrieval and analysis of 3DEM density maps, atomic models and associated metadata (emdatabank.org). We provide here an overview of the rapidly growing 3DEM structural data archives, which include maps in EM Data Bank and map-derived models in the Protein Data Bank. In addition, we describe progress and approaches toward development of validation protocols and methods, working with the scientific community, in order to create a validation pipeline for 3DEM data.
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Affiliation(s)
- Catherine L Lawson
- Department of Chemistry and Chemical Biology and Research Collaboratory for Structural Bioinformatics, Rutgers, The State University of New Jersey, 610 Taylor Road Piscataway, NJ 08854, USA
| | - Ardan Patwardhan
- Protein Data Bank in Europe, European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Matthew L Baker
- Verna and Marrs McLean Department of Biochemistry & Molecular Biology, National Center for Macromolecular Imaging, Baylor College of Medicine, 1 Baylor Plaza, Houston, TX 70030, USA
| | - Corey Hryc
- Verna and Marrs McLean Department of Biochemistry & Molecular Biology, National Center for Macromolecular Imaging, Baylor College of Medicine, 1 Baylor Plaza, Houston, TX 70030, USA
| | - Eduardo Sanz Garcia
- Protein Data Bank in Europe, European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Brian P Hudson
- Department of Chemistry and Chemical Biology and Research Collaboratory for Structural Bioinformatics, Rutgers, The State University of New Jersey, 610 Taylor Road Piscataway, NJ 08854, USA
| | - Ingvar Lagerstedt
- Protein Data Bank in Europe, European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Steven J Ludtke
- Verna and Marrs McLean Department of Biochemistry & Molecular Biology, National Center for Macromolecular Imaging, Baylor College of Medicine, 1 Baylor Plaza, Houston, TX 70030, USA
| | - Grigore Pintilie
- Verna and Marrs McLean Department of Biochemistry & Molecular Biology, National Center for Macromolecular Imaging, Baylor College of Medicine, 1 Baylor Plaza, Houston, TX 70030, USA
| | - Raul Sala
- Department of Chemistry and Chemical Biology and Research Collaboratory for Structural Bioinformatics, Rutgers, The State University of New Jersey, 610 Taylor Road Piscataway, NJ 08854, USA
| | - John D Westbrook
- Department of Chemistry and Chemical Biology and Research Collaboratory for Structural Bioinformatics, Rutgers, The State University of New Jersey, 610 Taylor Road Piscataway, NJ 08854, USA
| | - Helen M Berman
- Department of Chemistry and Chemical Biology and Research Collaboratory for Structural Bioinformatics, Rutgers, The State University of New Jersey, 610 Taylor Road Piscataway, NJ 08854, USA
| | - Gerard J Kleywegt
- Protein Data Bank in Europe, European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Wah Chiu
- Verna and Marrs McLean Department of Biochemistry & Molecular Biology, National Center for Macromolecular Imaging, Baylor College of Medicine, 1 Baylor Plaza, Houston, TX 70030, USA
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12
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Farabella I, Vasishtan D, Joseph AP, Pandurangan AP, Sahota H, Topf M. TEMPy: a Python library for assessment of three-dimensional electron microscopy density fits. J Appl Crystallogr 2015; 48:1314-1323. [PMID: 26306092 PMCID: PMC4520291 DOI: 10.1107/s1600576715010092] [Citation(s) in RCA: 59] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2014] [Accepted: 05/24/2015] [Indexed: 12/21/2022] Open
Abstract
TEMPy is an object-oriented Python library that provides the means to validate density fits in electron microscopy reconstructions. This article highlights several features of particular interest for this purpose and includes some customized examples. Three-dimensional electron microscopy is currently one of the most promising techniques used to study macromolecular assemblies. Rigid and flexible fitting of atomic models into density maps is often essential to gain further insights into the assemblies they represent. Currently, tools that facilitate the assessment of fitted atomic models and maps are needed. TEMPy (template and electron microscopy comparison using Python) is a toolkit designed for this purpose. The library includes a set of methods to assess density fits in intermediate-to-low resolution maps, both globally and locally. It also provides procedures for single-fit assessment, ensemble generation of fits, clustering, and multiple and consensus scoring, as well as plots and output files for visualization purposes to help the user in analysing rigid and flexible fits. The modular nature of TEMPy helps the integration of scoring and assessment of fits into large pipelines, making it a tool suitable for both novice and expert structural biologists.
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Affiliation(s)
- Irene Farabella
- Institute of Structural and Molecular Biology, Department of Biological Sciences, Birkbeck, University of London , Malet street, London WC1E 7HX, UK
| | - Daven Vasishtan
- Oxford Particle Imaging Centre, Division of Structural Biology, Wellcome Trust Centre for Human Genetics, University of Oxford , Oxford OX3 7BN, UK
| | - Agnel Praveen Joseph
- Scientific Computing Department, Science and Technology Facilities Council, Research Complex at Harwell , Didcot, Oxon OX11 0QX, UK
| | - Arun Prasad Pandurangan
- Institute of Structural and Molecular Biology, Department of Biological Sciences, Birkbeck, University of London , Malet street, London WC1E 7HX, UK
| | - Harpal Sahota
- Institute of Structural and Molecular Biology, Department of Biological Sciences, Birkbeck, University of London , Malet street, London WC1E 7HX, UK
| | - Maya Topf
- Institute of Structural and Molecular Biology, Department of Biological Sciences, Birkbeck, University of London , Malet street, London WC1E 7HX, UK
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13
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Marabini R, Carragher B, Chen S, Chen J, Cheng A, Downing KH, Frank J, Grassucci RA, Bernard Heymann J, Jiang W, Jonic S, Liao HY, Ludtke SJ, Patwari S, Piotrowski AL, Quintana A, Sorzano COS, Stahlberg H, Vargas J, Voss NR, Chiu W, Carazo JM. CTF Challenge: Result summary. J Struct Biol 2015; 190:348-59. [PMID: 25913484 DOI: 10.1016/j.jsb.2015.04.003] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2014] [Revised: 03/26/2015] [Accepted: 04/07/2015] [Indexed: 10/23/2022]
Abstract
Image formation in bright field electron microscopy can be described with the help of the contrast transfer function (CTF). In this work the authors describe the "CTF Estimation Challenge", called by the Madrid Instruct Image Processing Center (I2PC) in collaboration with the National Center for Macromolecular Imaging (NCMI) at Houston. Correcting for the effects of the CTF requires accurate knowledge of the CTF parameters, but these have often been difficult to determine. In this challenge, researchers have had the opportunity to test their ability in estimating some of the key parameters of the electron microscope CTF on a large micrograph data set produced by well-known laboratories on a wide set of experimental conditions. This work presents the first analysis of the results of the CTF Estimation Challenge, including an assessment of the performance of the different software packages under different conditions, so as to identify those areas of research where further developments would be desirable in order to achieve high-resolution structural information.
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Affiliation(s)
- Roberto Marabini
- Escuela Politécnica Superior, Universidad Autónoma de Madrid, 28049 Cantoblanco, Madrid, Spain.
| | - Bridget Carragher
- The National Resource for Automated Molecular Microscopy, The Scripps Research Institute, La Jolla, CA 92037, USA
| | | | - James Chen
- Massachusetts Institute of Technology, USA
| | - Anchi Cheng
- The National Resource for Automated Molecular Microscopy, The Scripps Research Institute, La Jolla, CA 92037, USA
| | | | - Joachim Frank
- Howard Hughes Medical Institute, Columbia University, NY 10032, USA
| | | | - J Bernard Heymann
- Laboratory of Structural Biology Research, National Institute of Arthritis, Musculoskeletal and Skin Diseases, National Institutes of Health, Bethesda, MD 20892, USA
| | - Wen Jiang
- Purdue University, Biological Sciences, IN 47907-2054, USA
| | - Slavica Jonic
- IMPMC, Sorbonne Universités - CNRS UMR 7590, UPMC Univ Paris 6, MNHN, IRD UMR 206, 75005 Paris, France
| | - Hstau Y Liao
- Howard Hughes Medical Institute, Columbia University, NY 10032, USA
| | | | - Shail Patwari
- Roosevelt University, Department of Biological, Chemical, and Physical Sciences, 1400 N. Roosevelt Blvd., Schaumburg, IL 60173, USA
| | - Angela L Piotrowski
- Roosevelt University, Department of Biological, Chemical, and Physical Sciences, 1400 N. Roosevelt Blvd., Schaumburg, IL 60173, USA
| | - Adrian Quintana
- Biocomputing Unit, National Center for Biotechnology (CSIC), C/Darwin, 3, Campus Universidad Autónoma, 28049 Cantoblanco, Madrid, Spain
| | - Carlos O S Sorzano
- Biocomputing Unit, National Center for Biotechnology (CSIC), C/Darwin, 3, Campus Universidad Autónoma, 28049 Cantoblanco, Madrid, Spain
| | | | - Javier Vargas
- Biocomputing Unit, National Center for Biotechnology (CSIC), C/Darwin, 3, Campus Universidad Autónoma, 28049 Cantoblanco, Madrid, Spain
| | - Neil R Voss
- Roosevelt University, Department of Biological, Chemical, and Physical Sciences, 1400 N. Roosevelt Blvd., Schaumburg, IL 60173, USA
| | - Wah Chiu
- Baylor College of Medicine, Houston, TX 77030, USA
| | - Jose M Carazo
- Biocomputing Unit, National Center for Biotechnology (CSIC), C/Darwin, 3, Campus Universidad Autónoma, 28049 Cantoblanco, Madrid, Spain
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14
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Vashisth H, Skiniotis G, Brooks CL. Collective variable approaches for single molecule flexible fitting and enhanced sampling. Chem Rev 2014; 114:3353-65. [PMID: 24446720 PMCID: PMC3983124 DOI: 10.1021/cr4005988] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2013] [Indexed: 12/12/2022]
Affiliation(s)
- Harish Vashisth
- Department
of Chemical Engineering, University of New
Hampshire, Durham, New Hampshire 03824, United States
| | - Georgios Skiniotis
- Life Sciences Institute, Department
of Biological Chemistry, and
Biophysics Program, and Department of Chemistry and Biophysics Program, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Charles Lee Brooks
- Life Sciences Institute, Department
of Biological Chemistry, and
Biophysics Program, and Department of Chemistry and Biophysics Program, University of Michigan, Ann Arbor, Michigan 48109, United States
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15
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iMODFIT: efficient and robust flexible fitting based on vibrational analysis in internal coordinates. J Struct Biol 2013; 184:261-70. [PMID: 23999189 DOI: 10.1016/j.jsb.2013.08.010] [Citation(s) in RCA: 127] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2013] [Revised: 08/20/2013] [Accepted: 08/22/2013] [Indexed: 12/31/2022]
Abstract
Here, we employed the collective motions extracted from Normal Mode Analysis (NMA) in internal coordinates (torsional space) for the flexible fitting of atomic-resolution structures into electron microscopy (EM) density maps. The proposed methodology was validated using a benchmark of simulated cases, highlighting its robustness over the full range of EM resolutions and even over coarse-grained representations. A systematic comparison with other methods further showcased the advantages of this proposed methodology, especially at medium to lower resolutions. Using this method, computational costs and potential overfitting problems are naturally reduced by constraining the search in low-frequency NMA space, where covalent geometry is implicitly maintained. This method also effectively captures the macromolecular changes of a representative set of experimental test cases. We believe that this novel approach will extend the currently available EM hybrid methods to the atomic-level interpretation of large conformational changes and their functional implications.
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16
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Gutmanas A, Oldfield TJ, Patwardhan A, Sen S, Velankar S, Kleywegt GJ. The role of structural bioinformatics resources in the era of integrative structural biology. ACTA CRYSTALLOGRAPHICA. SECTION D, BIOLOGICAL CRYSTALLOGRAPHY 2013; 69:710-21. [PMID: 23633580 PMCID: PMC3640467 DOI: 10.1107/s0907444913001157] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/06/2012] [Accepted: 01/11/2013] [Indexed: 11/10/2022]
Abstract
The history and the current state of the PDB and EMDB archives is briefly described, as well as some of the challenges that they face. It seems natural that the role of structural biology archives will change from being a pure repository of historic data into becoming an indispensable resource for the wider biomedical community. As part of this transformation, it will be necessary to validate the biomacromolecular structure data and ensure the highest possible quality for the archive holdings, to combine structural data from different spatial scales into a unified resource and to integrate structural data with functional, genetic and taxonomic data as well as other information available in bioinformatics resources. Some recent developments and plans to address these challenges at PDBe are presented.
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Affiliation(s)
- Aleksandras Gutmanas
- Protein Data Bank in Europe, EMBL–EBI, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, England
| | - Thomas J. Oldfield
- Protein Data Bank in Europe, EMBL–EBI, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, England
| | - Ardan Patwardhan
- Protein Data Bank in Europe, EMBL–EBI, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, England
| | - Sanchayita Sen
- Protein Data Bank in Europe, EMBL–EBI, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, England
| | - Sameer Velankar
- Protein Data Bank in Europe, EMBL–EBI, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, England
| | - Gerard J. Kleywegt
- Protein Data Bank in Europe, EMBL–EBI, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, England
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17
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Consensus among multiple approaches as a reliability measure for flexible fitting into cryo-EM data. J Struct Biol 2013; 182:67-77. [PMID: 23416197 DOI: 10.1016/j.jsb.2013.02.002] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2012] [Revised: 01/29/2013] [Accepted: 02/01/2013] [Indexed: 12/14/2022]
Abstract
Cryo-electron microscopy (cryo-EM) can provide low-resolution density maps of large macromolecular assemblies. As the number of structures deposited in the Protein Data Bank by fitting a high-resolution structure into a low-resolution cryo-EM map is increasing, there is a need to revise the protocols and improve the measures for fitting. A recent study suggested using a combination of multiple automated flexible fitting approaches to improve the interpretation of cryo-EM data. The current work further explores the use of multiple approaches by validating this "consensus" fitting approach and deriving a local reliability measure. Here four different flexible fitting approaches are applied for fitting an initial structure into a simulated density map of known target structure from a dataset of proteins. It is found that the models produced from different approaches often have a consensus in conformation and are also near to the target structure, whereas cases not showing consensus are away from the target. A high correlation is also observed between the RMSF profiles calculated with respect to the average and the target structures, which indicates that the relation between consensus and accuracy can also be extended to a per-residue level. Therefore, the RMSF among the fitted models is proposed as a local reliability measure, which can be used to assess the reliability of the fit at specific regions. Hence, we encourage the community to use consensus flexible fitting with different methods to report on local reliability of the resulting models and improve the interpretation of cryo-EM data.
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18
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Atomic modeling of cryo-electron microscopy reconstructions--joint refinement of model and imaging parameters. J Struct Biol 2013; 182:10-21. [PMID: 23376441 DOI: 10.1016/j.jsb.2013.01.003] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2012] [Revised: 12/20/2012] [Accepted: 01/11/2013] [Indexed: 11/22/2022]
Abstract
When refining the fit of component atomic structures into electron microscopic reconstructions, use of a resolution-dependent atomic density function makes it possible to jointly optimize the atomic model and imaging parameters of the microscope. Atomic density is calculated by one-dimensional Fourier transform of atomic form factors convoluted with a microscope envelope correction and a low-pass filter, allowing refinement of imaging parameters such as resolution, by optimizing the agreement of calculated and experimental maps. A similar approach allows refinement of atomic displacement parameters, providing indications of molecular flexibility even at low resolution. A modest improvement in atomic coordinates is possible following optimization of these additional parameters. Methods have been implemented in a Python program that can be used in stand-alone mode for rigid-group refinement, or embedded in other optimizers for flexible refinement with stereochemical restraints. The approach is demonstrated with refinements of virus and chaperonin structures at resolutions of 9 through 4.5 Å, representing regimes where rigid-group and fully flexible parameterizations are appropriate. Through comparisons to known crystal structures, flexible fitting by RSRef is shown to be an improvement relative to other methods and to generate models with all-atom rms accuracies of 1.5-2.5 Å at resolutions of 4.5-6 Å.
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19
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Milne JLS, Borgnia MJ, Bartesaghi A, Tran EEH, Earl LA, Schauder DM, Lengyel J, Pierson J, Patwardhan A, Subramaniam S. Cryo-electron microscopy--a primer for the non-microscopist. FEBS J 2012. [PMID: 23181775 DOI: 10.1111/febs.12078] [Citation(s) in RCA: 142] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Cryo-electron microscopy (cryo-EM) is increasingly becoming a mainstream technology for studying the architecture of cells, viruses and protein assemblies at molecular resolution. Recent developments in microscope design and imaging hardware, paired with enhanced image processing and automation capabilities, are poised to further advance the effectiveness of cryo-EM methods. These developments promise to increase the speed and extent of automation, and to improve the resolutions that may be achieved, making this technology useful to determine a wide variety of biological structures. Additionally, established modalities for structure determination, such as X-ray crystallography and nuclear magnetic resonance spectroscopy, are being routinely integrated with cryo-EM density maps to achieve atomic-resolution models of complex, dynamic molecular assemblies. In this review, which is directed towards readers who are not experts in cryo-EM methodology, we provide an overview of emerging themes in the application of this technology to investigate diverse questions in biology and medicine. We discuss the ways in which these methods are being used to study structures of macromolecular assemblies that range in size from whole cells to small proteins. Finally, we include a description of how the structural information obtained by cryo-EM is deposited and archived in a publicly accessible database.
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Affiliation(s)
- Jacqueline L S Milne
- Laboratory of Cell Biology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
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20
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Lander GC, Saibil HR, Nogales E. Go hybrid: EM, crystallography, and beyond. Curr Opin Struct Biol 2012; 22:627-35. [PMID: 22835744 DOI: 10.1016/j.sbi.2012.07.006] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2012] [Revised: 06/21/2012] [Accepted: 07/09/2012] [Indexed: 01/30/2023]
Abstract
A mechanistic understanding of the molecular transactions that govern cellular function requires knowledge of the dynamic organization of the macromolecular machines involved in these processes. Structural biologists employ a variety of biophysical methods to study large macromolecular complexes, but no single technique is likely to provide a complete description of the structure-function relationship of all the constituent components. Since structural studies generally only provide snapshots of these dynamic machines as they accomplish their molecular functions, combining data from many methodologies is crucial to our understanding of molecular function.
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Affiliation(s)
- Gabriel C Lander
- Life Science Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
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