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Beck M, Covino R, Hänelt I, Müller-McNicoll M. Understanding the cell: Future views of structural biology. Cell 2024; 187:545-562. [PMID: 38306981 DOI: 10.1016/j.cell.2023.12.017] [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] [Received: 10/04/2023] [Revised: 12/05/2023] [Accepted: 12/11/2023] [Indexed: 02/04/2024]
Abstract
Determining the structure and mechanisms of all individual functional modules of cells at high molecular detail has often been seen as equal to understanding how cells work. Recent technical advances have led to a flush of high-resolution structures of various macromolecular machines, but despite this wealth of detailed information, our understanding of cellular function remains incomplete. Here, we discuss present-day limitations of structural biology and highlight novel technologies that may enable us to analyze molecular functions directly inside cells. We predict that the progression toward structural cell biology will involve a shift toward conceptualizing a 4D virtual reality of cells using digital twins. These will capture cellular segments in a highly enriched molecular detail, include dynamic changes, and facilitate simulations of molecular processes, leading to novel and experimentally testable predictions. Transferring biological questions into algorithms that learn from the existing wealth of data and explore novel solutions may ultimately unveil how cells work.
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Affiliation(s)
- Martin Beck
- Max Planck Institute of Biophysics, Max-von-Laue-Straße 3, 60438 Frankfurt am Main, Germany; Goethe University Frankfurt, Frankfurt, Germany.
| | - Roberto Covino
- Frankfurt Institute for Advanced Studies, Ruth-Moufang-Straße 1, 60438 Frankfurt am Main, Germany.
| | - Inga Hänelt
- Goethe University Frankfurt, Frankfurt, Germany.
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2
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de Teresa-Trueba I, Goetz SK, Mattausch A, Stojanovska F, Zimmerli CE, Toro-Nahuelpan M, Cheng DWC, Tollervey F, Pape C, Beck M, Diz-Muñoz A, Kreshuk A, Mahamid J, Zaugg JB. Convolutional networks for supervised mining of molecular patterns within cellular context. Nat Methods 2023; 20:284-294. [PMID: 36690741 PMCID: PMC9911354 DOI: 10.1038/s41592-022-01746-2] [Citation(s) in RCA: 31] [Impact Index Per Article: 31.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Accepted: 12/02/2022] [Indexed: 01/24/2023]
Abstract
Cryo-electron tomograms capture a wealth of structural information on the molecular constituents of cells and tissues. We present DeePiCt (deep picker in context), an open-source deep-learning framework for supervised segmentation and macromolecular complex localization in cryo-electron tomography. To train and benchmark DeePiCt on experimental data, we comprehensively annotated 20 tomograms of Schizosaccharomyces pombe for ribosomes, fatty acid synthases, membranes, nuclear pore complexes, organelles, and cytosol. By comparing DeePiCt to state-of-the-art approaches on this dataset, we show its unique ability to identify low-abundance and low-density complexes. We use DeePiCt to study compositionally distinct subpopulations of cellular ribosomes, with emphasis on their contextual association with mitochondria and the endoplasmic reticulum. Finally, applying pre-trained networks to a HeLa cell tomogram demonstrates that DeePiCt achieves high-quality predictions in unseen datasets from different biological species in a matter of minutes. The comprehensively annotated experimental data and pre-trained networks are provided for immediate use by the community.
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Affiliation(s)
- Irene de Teresa-Trueba
- grid.4709.a0000 0004 0495 846XStructural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany ,Present Address: Computer Science and Artificial Intelligence Lab, ENGIE Lab Crigen, Stains, France
| | - Sara K. Goetz
- grid.4709.a0000 0004 0495 846XStructural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany ,grid.7700.00000 0001 2190 4373Collaboration for Joint PhD Degree between EMBL and Heidelberg University, Faculty of Biosciences, Heidelberg, Germany
| | - Alexander Mattausch
- grid.4709.a0000 0004 0495 846XStructural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany ,grid.7700.00000 0001 2190 4373Institute of Pharmacy and Molecular Biotechnology, Heidelberg University, Heidelberg, Germany
| | - Frosina Stojanovska
- grid.4709.a0000 0004 0495 846XStructural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany ,grid.7700.00000 0001 2190 4373Collaboration for Joint PhD Degree between EMBL and Heidelberg University, Faculty of Biosciences, Heidelberg, Germany
| | - Christian E. Zimmerli
- grid.4709.a0000 0004 0495 846XStructural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany ,grid.419494.50000 0001 1018 9466Present Address: Department of Molecular Sociology, Max Planck Institute of Biophysics, Frankfurt, Germany
| | - Mauricio Toro-Nahuelpan
- grid.4709.a0000 0004 0495 846XStructural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany ,Present Address: Santiago GmbH & Co. KG, Willich, Germany
| | - Dorothy W. C. Cheng
- grid.7700.00000 0001 2190 4373Collaboration for Joint PhD Degree between EMBL and Heidelberg University, Faculty of Biosciences, Heidelberg, Germany ,grid.4709.a0000 0004 0495 846XCell Biology and Biophysics Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Fergus Tollervey
- grid.4709.a0000 0004 0495 846XStructural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany ,grid.7700.00000 0001 2190 4373Collaboration for Joint PhD Degree between EMBL and Heidelberg University, Faculty of Biosciences, Heidelberg, Germany
| | - Constantin Pape
- grid.4709.a0000 0004 0495 846XCell Biology and Biophysics Unit, European Molecular Biology Laboratory, Heidelberg, Germany ,grid.7450.60000 0001 2364 4210Present Address: Institute for Computer Science, Universität Göttingen, Göttingen, Germany
| | - Martin Beck
- grid.4709.a0000 0004 0495 846XStructural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany ,grid.4709.a0000 0004 0495 846XCell Biology and Biophysics Unit, European Molecular Biology Laboratory, Heidelberg, Germany ,grid.419494.50000 0001 1018 9466Present Address: Department of Molecular Sociology, Max Planck Institute of Biophysics, Frankfurt, Germany
| | - Alba Diz-Muñoz
- grid.4709.a0000 0004 0495 846XCell Biology and Biophysics Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Anna Kreshuk
- grid.4709.a0000 0004 0495 846XCell Biology and Biophysics Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Julia Mahamid
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany. .,Cell Biology and Biophysics Unit, European Molecular Biology Laboratory, Heidelberg, Germany.
| | - Judith B. Zaugg
- grid.4709.a0000 0004 0495 846XStructural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany ,grid.4709.a0000 0004 0495 846XGenome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
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3
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Rodríguez de Francisco B, Bezault A, Xu XP, Hanein D, Volkmann N. MEPSi: A tool for simulating tomograms of membrane-embedded proteins. J Struct Biol 2022; 214:107921. [PMID: 36372192 DOI: 10.1016/j.jsb.2022.107921] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2022] [Revised: 10/27/2022] [Accepted: 11/05/2022] [Indexed: 11/11/2022]
Abstract
The throughput and fidelity of cryogenic cellular electron tomography (cryo-ET) is constantly increasing through advances in cryogenic electron microscope hardware, direct electron detection devices, and powerful image processing algorithms. However, the need for careful optimization of sample preparations and for access to expensive, high-end equipment, make cryo-ET a costly and time-consuming technique. Generally, only after the last step of the cryo-ET workflow, when reconstructed tomograms are available, it becomes clear whether the chosen imaging parameters were suitable for a specific type of sample in order to answer a specific biological question. Tools for a-priory assessment of the feasibility of samples to answer biological questions and how to optimize imaging parameters to do so would be a major advantage. Here we describe MEPSi (Membrane Embedded Protein Simulator), a simulation tool aimed at rapid and convenient evaluation and optimization of cryo-ET data acquisition parameters for studies of transmembrane proteins in their native environment. We demonstrate the utility of MEPSi by showing how to detangle the influence of different data collection parameters and different orientations in respect to tilt axis and electron beam for two examples: (1) simulated plasma membranes with embedded single-pass transmembrane αIIbβ3 integrin receptors and (2) simulated virus membranes with embedded SARS-CoV-2 spike proteins.
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Affiliation(s)
- Borja Rodríguez de Francisco
- Institut Pasteur, Université Paris Cité, CNRS UMR3528, Structural Studies of Macromolecular Machines in Cellulo Unit, Paris, France; Institut Pasteur, Université Paris Cité, CNRS UMR3528, Structural Image Analysis Unit, Paris, France
| | - Armel Bezault
- Institut Pasteur, Université Paris Cité, CNRS UMR3528, Structural Studies of Macromolecular Machines in Cellulo Unit, Paris, France; Institut Pasteur, Université Paris Cité, CNRS UMR3528, Structural Image Analysis Unit, Paris, France
| | | | - Dorit Hanein
- Institut Pasteur, Université Paris Cité, CNRS UMR3528, Structural Studies of Macromolecular Machines in Cellulo Unit, Paris, France; Scintillon Institute, San Diego, CA 92121, USA
| | - Niels Volkmann
- Institut Pasteur, Université Paris Cité, CNRS UMR3528, Structural Image Analysis Unit, Paris, France.
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4
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Digitalizing neuronal synapses with cryo-electron tomography and correlative microscopy. Curr Opin Neurobiol 2022; 76:102595. [DOI: 10.1016/j.conb.2022.102595] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2022] [Revised: 04/22/2022] [Accepted: 05/24/2022] [Indexed: 11/22/2022]
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5
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Computational methods for ultrastructural analysis of synaptic complexes. Curr Opin Neurobiol 2022; 76:102611. [PMID: 35952541 DOI: 10.1016/j.conb.2022.102611] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 06/27/2022] [Accepted: 06/28/2022] [Indexed: 11/21/2022]
Abstract
Electron microscopy (EM) provided fundamental insights about the ultrastructure of neuronal synapses. The large amount of information present in the contemporary EM datasets precludes a thorough assessment by visual inspection alone, thus requiring computational methods for the analysis of the data. Here, I review image processing software methods ranging from membrane tracing in large volume datasets to high resolution structures of synaptic complexes. Particular attention is payed to molecular level analysis provided by recent cryo-electron microscopy and tomography methods.
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Abstract
The three-dimensional organization of biomolecules important for the functioning of all living systems can be determined by cryo-electron tomography imaging under native biological contexts. Cryo-electron tomography is continually expanding and evolving, and the development of new methods that use the latest technology for sample thinning is enabling the visualization of ever larger and more complex biological systems, allowing imaging across scales. Quantitative cryo-electron tomography possesses the capability of visualizing the impact of molecular and environmental perturbations in subcellular structure and function to understand fundamental biological processes. This review provides an overview of current hardware and software developments that allow quantitative cryo-electron tomography studies and their limitations and how overcoming them may allow us to unleash the full power of cryo-electron tomography.
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Affiliation(s)
- Paula P Navarro
- Department of Molecular Biology, Massachusetts General Hospital, Boston, MA, United States.,Department of Genetics, Harvard Medical School, Boston, MA, United States
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7
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Kyrilis FL, Belapure J, Kastritis PL. Detecting Protein Communities in Native Cell Extracts by Machine Learning: A Structural Biologist's Perspective. Front Mol Biosci 2021; 8:660542. [PMID: 33937337 PMCID: PMC8082361 DOI: 10.3389/fmolb.2021.660542] [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: 01/29/2021] [Accepted: 03/18/2021] [Indexed: 11/13/2022] Open
Abstract
Native cell extracts hold great promise for understanding the molecular structure of ordered biological systems at high resolution. This is because higher-order biomolecular interactions, dubbed as protein communities, may be retained in their (near-)native state, in contrast to extensively purifying or artificially overexpressing the proteins of interest. The distinct machine-learning approaches are applied to discover protein-protein interactions within cell extracts, reconstruct dedicated biological networks, and report on protein community members from various organisms. Their validation is also important, e.g., by the cross-linking mass spectrometry or cell biology methods. In addition, the cell extracts are amenable to structural analysis by cryo-electron microscopy (cryo-EM), but due to their inherent complexity, sorting structural signatures of protein communities derived by cryo-EM comprises a formidable task. The application of image-processing workflows inspired by machine-learning techniques would provide improvements in distinguishing structural signatures, correlating proteomic and network data to structural signatures and subsequently reconstructed cryo-EM maps, and, ultimately, characterizing unidentified protein communities at high resolution. In this review article, we summarize recent literature in detecting protein communities from native cell extracts and identify the remaining challenges and opportunities. We argue that the progress in, and the integration of, machine learning, cryo-EM, and complementary structural proteomics approaches would provide the basis for a multi-scale molecular description of protein communities within native cell extracts.
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Affiliation(s)
- Fotis L. Kyrilis
- Interdisciplinary Research Center HALOmem, Charles Tanford Protein Center, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany
- Institute of Biochemistry and Biotechnology, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany
| | - Jaydeep Belapure
- Interdisciplinary Research Center HALOmem, Charles Tanford Protein Center, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany
| | - Panagiotis L. Kastritis
- Interdisciplinary Research Center HALOmem, Charles Tanford Protein Center, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany
- Institute of Biochemistry and Biotechnology, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany
- Biozentrum, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany
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8
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Singla J, White KL, Stevens RC, Alber F. Assessment of scoring functions to rank the quality of 3D subtomogram clusters from cryo-electron tomography. J Struct Biol 2021; 213:107727. [PMID: 33753204 DOI: 10.1016/j.jsb.2021.107727] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2020] [Revised: 03/12/2021] [Accepted: 03/17/2021] [Indexed: 11/17/2022]
Abstract
Cryo-electron tomography provides the opportunity for unsupervised discovery of endogenous complexes in situ. This process usually requires particle picking, clustering and alignment of subtomograms to produce an average structure of the complex. When applied to heterogeneous samples, template-free clustering and alignment of subtomograms can potentially lead to the discovery of structures for unknown endogenous complexes. However, such methods require scoring functions to measure and accurately rank the quality of aligned subtomogram clusters, which can be compromised by contaminations from misclassified complexes and alignment errors. Here, we provide the first study to assess the effectiveness of more than 15 scoring functions for evaluating the quality of subtomogram clusters, which differ in the amount of structural misalignments and contaminations due to misclassified complexes. We assessed both experimental and simulated subtomograms as ground truth data sets. Our analysis showed that the robustness of scoring functions varies largely. Most scores were sensitive to the signal-to-noise ratio of subtomograms and often required Gaussian filtering as preprocessing for improved performance. Two scoring functions, Spectral SNR-based Fourier Shell Correlation and Pearson Correlation in the Fourier domain with missing wedge correction, showed a robust ranking of subtomogram clusters without any preprocessing and irrespective of SNR levels of subtomograms. Of these two scoring functions, Spectral SNR-based Fourier Shell Correlation was fastest to compute and is a better choice for handling large numbers of subtomograms. Our results provide a guidance for choosing an accurate scoring function for template-free approaches to detect complexes from heterogeneous samples.
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Affiliation(s)
- Jitin Singla
- Institute for Quantitative and Computational Biosciences, Department of Microbiology, Immunology, and Molecular Genetics, University of California Los Angeles, 520 Boyer Hall, Los Angeles, CA 90095, USA; Quantitative and Computational Biology, Department of Biological Sciences, University of Southern California, 1050 Childs Way, Los Angeles, CA 90089, USA; Department of Biological Sciences, Bridge Institute, Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA 90089, USA
| | - Kate L White
- Department of Biological Sciences, Bridge Institute, Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA 90089, USA
| | - Raymond C Stevens
- Department of Biological Sciences, Bridge Institute, Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA 90089, USA
| | - Frank Alber
- Institute for Quantitative and Computational Biosciences, Department of Microbiology, Immunology, and Molecular Genetics, University of California Los Angeles, 520 Boyer Hall, Los Angeles, CA 90095, USA; Quantitative and Computational Biology, Department of Biological Sciences, University of Southern California, 1050 Childs Way, Los Angeles, CA 90089, USA.
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Turk M, Baumeister W. The promise and the challenges of cryo-electron tomography. FEBS Lett 2020; 594:3243-3261. [PMID: 33020915 DOI: 10.1002/1873-3468.13948] [Citation(s) in RCA: 144] [Impact Index Per Article: 36.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2020] [Revised: 09/28/2020] [Accepted: 09/28/2020] [Indexed: 01/11/2023]
Abstract
Structural biologists have traditionally approached cellular complexity in a reductionist manner in which the cellular molecular components are fractionated and purified before being studied individually. This 'divide and conquer' approach has been highly successful. However, awareness has grown in recent years that biological functions can rarely be attributed to individual macromolecules. Most cellular functions arise from their concerted action, and there is thus a need for methods enabling structural studies performed in situ, ideally in unperturbed cellular environments. Cryo-electron tomography (Cryo-ET) combines the power of 3D molecular-level imaging with the best structural preservation that is physically possible to achieve. Thus, it has a unique potential to reveal the supramolecular architecture or 'molecular sociology' of cells and to discover the unexpected. Here, we review state-of-the-art Cryo-ET workflows, provide examples of biological applications, and discuss what is needed to realize the full potential of Cryo-ET.
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Affiliation(s)
- Martin Turk
- Department of Molecular Structural Biology, Max Planck Institute of Biochemistry, Martinsried, Germany
| | - Wolfgang Baumeister
- Department of Molecular Structural Biology, Max Planck Institute of Biochemistry, Martinsried, Germany
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10
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Template-free detection and classification of membrane-bound complexes in cryo-electron tomograms. Nat Methods 2020; 17:209-216. [PMID: 31907446 DOI: 10.1038/s41592-019-0675-5] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2018] [Accepted: 11/11/2019] [Indexed: 01/12/2023]
Abstract
With faithful sample preservation and direct imaging of fully hydrated biological material, cryo-electron tomography provides an accurate representation of molecular architecture of cells. However, detection and precise localization of macromolecular complexes within cellular environments is aggravated by the presence of many molecular species and molecular crowding. We developed a template-free image processing procedure for accurate tracing of complex networks of densities in cryo-electron tomograms, a comprehensive and automated detection of heterogeneous membrane-bound complexes and an unsupervised classification (PySeg). Applications to intact cells and isolated endoplasmic reticulum (ER) allowed us to detect and classify small protein complexes. This classification provided sufficiently homogeneous particle sets and initial references to allow subsequent de novo subtomogram averaging. Spatial distribution analysis showed that ER complexes have different localization patterns forming nanodomains. Therefore, this procedure allows a comprehensive detection and structural analysis of complexes in situ.
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11
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Lü Y, Zeng X, Zhao X, Li S, Li H, Gao X, Xu M. Fine-grained alignment of cryo-electron subtomograms based on MPI parallel optimization. BMC Bioinformatics 2019; 20:443. [PMID: 31455212 PMCID: PMC6712796 DOI: 10.1186/s12859-019-3003-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2018] [Accepted: 07/19/2019] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Cryo-electron tomography (Cryo-ET) is an imaging technique used to generate three-dimensional structures of cellular macromolecule complexes in their native environment. Due to developing cryo-electron microscopy technology, the image quality of three-dimensional reconstruction of cryo-electron tomography has greatly improved. However, cryo-ET images are characterized by low resolution, partial data loss and low signal-to-noise ratio (SNR). In order to tackle these challenges and improve resolution, a large number of subtomograms containing the same structure needs to be aligned and averaged. Existing methods for refining and aligning subtomograms are still highly time-consuming, requiring many computationally intensive processing steps (i.e. the rotations and translations of subtomograms in three-dimensional space). RESULTS In this article, we propose a Stochastic Average Gradient (SAG) fine-grained alignment method for optimizing the sum of dissimilarity measure in real space. We introduce a Message Passing Interface (MPI) parallel programming model in order to explore further speedup. CONCLUSIONS We compare our stochastic average gradient fine-grained alignment algorithm with two baseline methods, high-precision alignment and fast alignment. Our SAG fine-grained alignment algorithm is much faster than the two baseline methods. Results on simulated data of GroEL from the Protein Data Bank (PDB ID:1KP8) showed that our parallel SAG-based fine-grained alignment method could achieve close-to-optimal rigid transformations with higher precision than both high-precision alignment and fast alignment at a low SNR (SNR=0.003) with tilt angle range ±60∘ or ±40∘. For the experimental subtomograms data structures of GroEL and GroEL/GroES complexes, our parallel SAG-based fine-grained alignment can achieve higher precision and fewer iterations to converge than the two baseline methods.
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Affiliation(s)
- Yongchun Lü
- University of Chinese Academy of Sciences, Beijing, China
- Institute of Computing Technology of the Chinese Academy of Sciences, Beijing, China
- Key Laboratory of Intelligent Information Processing, CAS, Beijing, China
| | - Xiangrui Zeng
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, USA
| | - Xiaofang Zhao
- University of Chinese Academy of Sciences, Beijing, China
- Institute of Computing Technology of the Chinese Academy of Sciences, Beijing, China
| | - Shirui Li
- Institute of Computing Technology of the Chinese Academy of Sciences, Beijing, China
- Key Laboratory of Intelligent Information Processing, CAS, Beijing, China
| | - Hua Li
- University of Chinese Academy of Sciences, Beijing, China
- Institute of Computing Technology of the Chinese Academy of Sciences, Beijing, China
- Key Laboratory of Intelligent Information Processing, CAS, Beijing, China
| | - Xin Gao
- King Abdullah University of Science and Technology (KAUST), Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, Thuwal, Saudi Arabia
| | - Min Xu
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, USA
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12
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Xu M, Singla J, Tocheva EI, Chang YW, Stevens RC, Jensen GJ, Alber F. De Novo Structural Pattern Mining in Cellular Electron Cryotomograms. Structure 2019; 27:679-691.e14. [PMID: 30744995 DOI: 10.1016/j.str.2019.01.005] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2018] [Revised: 07/27/2018] [Accepted: 01/14/2019] [Indexed: 11/16/2022]
Abstract
Electron cryotomography enables 3D visualization of cells in a near-native state at molecular resolution. The produced cellular tomograms contain detailed information about a plethora of macromolecular complexes, their structures, abundances, and specific spatial locations in the cell. However, extracting this information in a systematic way is very challenging, and current methods usually rely on individual templates of known structures. Here, we propose a framework called "Multi-Pattern Pursuit" for de novo discovery of different complexes from highly heterogeneous sets of particles extracted from entire cellular tomograms without using information of known structures. These initially detected structures can then serve as input for more targeted refinement efforts. Our tests on simulated and experimental tomograms show that our automated method is a promising tool for supporting large-scale template-free visual proteomics analysis.
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Affiliation(s)
- Min Xu
- Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
| | - Jitin Singla
- Institute for Quantitative and Computational Biosciences, Department of Microbiology, Immunology, and Molecular Genetics, University of California, Los Angeles, CA 90095, USA; Quantitative and Computational Biology, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA
| | - Elitza I Tocheva
- Department of Microbiology and Immunology, Life Sciences Institute, The University of British Columbia, Vancouver, BC V6T 1Z3, Canada
| | - Yi-Wei Chang
- Department of Biochemistry and Biophysics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Raymond C Stevens
- Department of Biological Sciences and Department of Chemistry, Bridge Institute, University of Southern California, Los Angeles, CA 90089, USA
| | - Grant J Jensen
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA; Howard Hughes Medical Institute, Pasadena, CA 91125, USA
| | - Frank Alber
- Institute for Quantitative and Computational Biosciences, Department of Microbiology, Immunology, and Molecular Genetics, University of California, Los Angeles, CA 90095, USA; Quantitative and Computational Biology, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA.
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13
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Che C, Lin R, Zeng X, Elmaaroufi K, Galeotti J, Xu M. Improved deep learning-based macromolecules structure classification from electron cryo-tomograms. MACHINE VISION AND APPLICATIONS 2018; 29:1227-1236. [PMID: 31511756 PMCID: PMC6738941 DOI: 10.1007/s00138-018-0949-4] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/14/2017] [Revised: 01/16/2018] [Accepted: 05/18/2018] [Indexed: 05/30/2023]
Abstract
Cellular processes are governed by macromolecular complexes inside the cell. Study of the native structures of macromolecular complexes has been extremely difficult due to lack of data. With recent breakthroughs in Cellular Electron Cryo-Tomography (CECT) 3D imaging technology, it is now possible for researchers to gain accesses to fully study and understand the macro-molecular structures single cells. However, systematic recovery of macromolecular structures from CECT is very difficult due to high degree of structural complexity and practical imaging limitations. Specifically, we proposed a deep learning-based image classification approach for large-scale systematic macromolecular structure separation from CECT data. However, our previous work was only a very initial step toward exploration of the full potential of deep learning-based macromolecule separation. In this paper, we focus on improving classification performance by proposing three newly designed individual CNN models: an extended version of (Deep Small Receptive Field) DSRF3D, donated as DSRF3D-v2, a 3D residual block-based neural network, named as RB3D, and a convolutional 3D (C3D)-based model, CB3D. We compare them with our previously developed model (DSRF3D) on 12 datasets with different SNRs and tilt angle ranges. The experiments show that our new models achieved significantly higher classification accuracies. The accuracies are not only higher than 0.9 on normal datasets, but also demonstrate potentials to operate on datasets with high levels of noises and missing wedge effects presented.
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Affiliation(s)
- Chengqian Che
- The Robotics Institute, Carnegie Mellon University,Pittsburgh, USA
| | - Ruogu Lin
- Department of Automation, Tsinghua University, Beijing, China
| | - Xiangrui Zeng
- Computational Biology Department, Carnegie Mellon University, Pittsburgh, USA
| | - Karim Elmaaroufi
- Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, USA
| | - John Galeotti
- The Robotics Institute, Carnegie Mellon University,Pittsburgh, USA
| | - Min Xu
- Computational Biology Department, Carnegie Mellon University, Pittsburgh, USA
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14
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Goodsell DS, Franzen MA, Herman T. From Atoms to Cells: Using Mesoscale Landscapes to Construct Visual Narratives. J Mol Biol 2018; 430:3954-3968. [PMID: 29885327 PMCID: PMC6186495 DOI: 10.1016/j.jmb.2018.06.009] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2018] [Revised: 05/31/2018] [Accepted: 06/01/2018] [Indexed: 10/14/2022]
Abstract
Modeling and visualization of the cellular mesoscale, bridging the nanometer scale of molecules to the micrometer scale of cells, is being studied by an integrative approach. Data from structural biology, proteomics, and microscopy are combined to simulate the molecular structure of living cells. These cellular landscapes are used as research tools for hypothesis generation and testing, and to present visual narratives of the cellular context of molecular biology for dissemination, education, and outreach.
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Affiliation(s)
- David S Goodsell
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA; RCSB Protein Data Bank & Center for Integrative Proteomics Research, Rutgers State University, Piscataway, NJ 08854, USA.
| | - Margaret A Franzen
- Center for BioMolecular Modeling, Milwaukee School of Engineering, Milwaukee, WI 53202, USA
| | - Tim Herman
- Center for BioMolecular Modeling, Milwaukee School of Engineering, Milwaukee, WI 53202, USA
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15
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Zeng X, Leung MR, Zeev-Ben-Mordehai T, Xu M. A convolutional autoencoder approach for mining features in cellular electron cryo-tomograms and weakly supervised coarse segmentation. J Struct Biol 2018; 202:150-160. [PMID: 29289599 PMCID: PMC6661905 DOI: 10.1016/j.jsb.2017.12.015] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2017] [Revised: 12/24/2017] [Accepted: 12/27/2017] [Indexed: 01/08/2023]
Abstract
Cellular electron cryo-tomography enables the 3D visualization of cellular organization in the near-native state and at submolecular resolution. However, the contents of cellular tomograms are often complex, making it difficult to automatically isolate different in situ cellular components. In this paper, we propose a convolutional autoencoder-based unsupervised approach to provide a coarse grouping of 3D small subvolumes extracted from tomograms. We demonstrate that the autoencoder can be used for efficient and coarse characterization of features of macromolecular complexes and surfaces, such as membranes. In addition, the autoencoder can be used to detect non-cellular features related to sample preparation and data collection, such as carbon edges from the grid and tomogram boundaries. The autoencoder is also able to detect patterns that may indicate spatial interactions between cellular components. Furthermore, we demonstrate that our autoencoder can be used for weakly supervised semantic segmentation of cellular components, requiring a very small amount of manual annotation.
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Affiliation(s)
- Xiangrui Zeng
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh 15213, USA
| | - Miguel Ricardo Leung
- Division of Structural Biology, Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK; Cryo-electron Microscopy, Bijvoet Center for Biomolecular Research, Utrecht University, Utrecht, Netherlands
| | - Tzviya Zeev-Ben-Mordehai
- Division of Structural Biology, Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK; Cryo-electron Microscopy, Bijvoet Center for Biomolecular Research, Utrecht University, Utrecht, Netherlands
| | - Min Xu
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh 15213, USA.
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16
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Xu M, Chai X, Muthakana H, Liang X, Yang G, Zeev-Ben-Mordehai T, Xing EP. Deep learning-based subdivision approach for large scale macromolecules structure recovery from electron cryo tomograms. Bioinformatics 2018; 33:i13-i22. [PMID: 28881965 PMCID: PMC5946875 DOI: 10.1093/bioinformatics/btx230] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
Motivation Cellular Electron CryoTomography (CECT) enables 3D visualization of cellular organization at near-native state and in sub-molecular resolution, making it a powerful tool for analyzing structures of macromolecular complexes and their spatial organizations inside single cells. However, high degree of structural complexity together with practical imaging limitations makes the systematic de novo discovery of structures within cells challenging. It would likely require averaging and classifying millions of subtomograms potentially containing hundreds of highly heterogeneous structural classes. Although it is no longer difficult to acquire CECT data containing such amount of subtomograms due to advances in data acquisition automation, existing computational approaches have very limited scalability or discrimination ability, making them incapable of processing such amount of data. Results To complement existing approaches, in this article we propose a new approach for subdividing subtomograms into smaller but relatively homogeneous subsets. The structures in these subsets can then be separately recovered using existing computation intensive methods. Our approach is based on supervised structural feature extraction using deep learning, in combination with unsupervised clustering and reference-free classification. Our experiments show that, compared with existing unsupervised rotation invariant feature and pose-normalization based approaches, our new approach achieves significant improvements in both discrimination ability and scalability. More importantly, our new approach is able to discover new structural classes and recover structures that do not exist in training data. Availability and Implementation Source code freely available at http://www.cs.cmu.edu/∼mxu1/software. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Min Xu
- Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Xiaoqi Chai
- Biomedical Engineering Department, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Hariank Muthakana
- Computer Science Department, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Xiaodan Liang
- Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Ge Yang
- Biomedical Engineering Department, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Tzviya Zeev-Ben-Mordehai
- Division of Structural Biology, Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Eric P Xing
- Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA, USA
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17
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Singla J, McClary KM, White KL, Alber F, Sali A, Stevens RC. Opportunities and Challenges in Building a Spatiotemporal Multi-scale Model of the Human Pancreatic β Cell. Cell 2018; 173:11-19. [PMID: 29570991 PMCID: PMC6014618 DOI: 10.1016/j.cell.2018.03.014] [Citation(s) in RCA: 45] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2017] [Revised: 11/25/2017] [Accepted: 03/06/2018] [Indexed: 12/25/2022]
Abstract
The construction of a predictive model of an entire eukaryotic cell that describes its dynamic structure from atomic to cellular scales is a grand challenge at the intersection of biology, chemistry, physics, and computer science. Having such a model will open new dimensions in biological research and accelerate healthcare advancements. Developing the necessary experimental and modeling methods presents abundant opportunities for a community effort to realize this goal. Here, we present a vision for creation of a spatiotemporal multi-scale model of the pancreatic β-cell, a relevant target for understanding and modulating the pathogenesis of diabetes.
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Affiliation(s)
- Jitin Singla
- Molecular and Computational Biology, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA; Department of Biological Sciences, Bridge Institute, Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA 90089, USA
| | - Kyle M McClary
- Department of Chemistry, Bridge Institute, Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA 90089, USA
| | - Kate L White
- Department of Biological Sciences, Bridge Institute, Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA 90089, USA; Department of Chemistry, Bridge Institute, Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA 90089, USA
| | - Frank Alber
- Molecular and Computational Biology, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA; Department of Biological Sciences, Bridge Institute, Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA 90089, USA.
| | - Andrej Sali
- California Institute for Quantitative Biosciences, Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94158, USA.
| | - Raymond C Stevens
- Department of Biological Sciences, Bridge Institute, Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA 90089, USA; Department of Chemistry, Bridge Institute, Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA 90089, USA.
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18
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Frazier Z, Xu M, Alber F. TomoMiner and TomoMinerCloud: A Software Platform for Large-Scale Subtomogram Structural Analysis. Structure 2017; 25:951-961.e2. [PMID: 28552576 DOI: 10.1016/j.str.2017.04.016] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2015] [Revised: 12/17/2016] [Accepted: 04/28/2017] [Indexed: 11/19/2022]
Abstract
Cryo-electron tomography (cryo-ET) captures the 3D electron density distribution of macromolecular complexes in close to native state. With the rapid advance of cryo-ET acquisition technologies, it is possible to generate large numbers (>100,000) of subtomograms, each containing a macromolecular complex. Often, these subtomograms represent a heterogeneous sample due to variations in the structure and composition of a complex in situ form or because particles are a mixture of different complexes. In this case subtomograms must be classified. However, classification of large numbers of subtomograms is a time-intensive task and often a limiting bottleneck. This paper introduces an open source software platform, TomoMiner, for large-scale subtomogram classification, template matching, subtomogram averaging, and alignment. Its scalable and robust parallel processing allows efficient classification of tens to hundreds of thousands of subtomograms. In addition, TomoMiner provides a pre-configured TomoMinerCloud computing service permitting users without sufficient computing resources instant access to TomoMiners high-performance features.
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Affiliation(s)
- Zachary Frazier
- Molecular and Computational Biology, Department of Biological Sciences, University of Southern California, 1050 Childs Way, Los Angeles, CA 90089, USA
| | - Min Xu
- Computational Biology Department, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA.
| | - Frank Alber
- Molecular and Computational Biology, Department of Biological Sciences, University of Southern California, 1050 Childs Way, Los Angeles, CA 90089, USA.
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19
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Pei L, Xu M, Frazier Z, Alber F. Simulating cryo electron tomograms of crowded cell cytoplasm for assessment of automated particle picking. BMC Bioinformatics 2016; 17:405. [PMID: 27716029 PMCID: PMC5050594 DOI: 10.1186/s12859-016-1283-3] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2016] [Accepted: 09/27/2016] [Indexed: 11/11/2022] Open
Abstract
BACKGROUND Cryo-electron tomography is an important tool to study structures of macromolecular complexes in close to native states. A whole cell cryo electron tomogram contains structural information of all its macromolecular complexes. However, extracting this information remains challenging, and relies on sophisticated image processing, in particular for template-free particle extraction, classification and averaging. To develop these methods it is crucial to realistically simulate tomograms of crowded cellular environments, which can then serve as ground truth models for assessing and optimizing methods for detection of complexes in cell tomograms. RESULTS We present a framework to generate crowded mixtures of macromolecular complexes for realistically simulating cryo electron tomograms including noise and image distortions due to the missing-wedge effects. Simulated tomograms are then used for assessing the template-free Difference-of-Gaussian (DoG) particle-picking method to detect complexes of different shapes and sizes under various crowding and noise levels. We identified DoG parameter settings that maximize precision and recall for detecting particles over a wide range of sizes and shapes. We observed that medium sized DoG scaling factors showed the overall best performance. To further improve performance, we propose a combination strategy for integrating results from multiple parameter settings. With increasing macromolecular crowding levels, the precision of particle picking remained relatively high, while the recall was dramatically reduced, which limits the detection of sufficient copy numbers of complexes in a crowded environment. Over a wide range of increasing noise levels, the DoG particle picking performance remained stable, but dramatically reduced beyond a specific noise threshold. CONCLUSIONS Automatic and reference-free particle picking is an important first step in a visual proteomics analysis of cell tomograms. However, cell cytoplasm is highly crowded, which makes particle detection challenging. It is therefore important to test particle-picking methods in a realistic crowded setting. Here, we present a framework for simulating tomograms of cellular environments at high crowding levels and assess the DoG particle picking method. We determined optimal parameter settings to maximize the performance of the DoG particle-picking method.
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Affiliation(s)
- Long Pei
- Molecular and Computational Biology, Department of Biological Sciences, University of Southern California, 1050 Childs Way, Los Angeles, CA 90089 USA
| | - Min Xu
- Molecular and Computational Biology, Department of Biological Sciences, University of Southern California, 1050 Childs Way, Los Angeles, CA 90089 USA
| | - Zachary Frazier
- Molecular and Computational Biology, Department of Biological Sciences, University of Southern California, 1050 Childs Way, Los Angeles, CA 90089 USA
| | - Frank Alber
- Molecular and Computational Biology, Department of Biological Sciences, University of Southern California, 1050 Childs Way, Los Angeles, CA 90089 USA
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20
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Developments in cryo-electron tomography for in situ structural analysis. Arch Biochem Biophys 2015; 581:78-85. [DOI: 10.1016/j.abb.2015.04.006] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2015] [Revised: 04/14/2015] [Accepted: 04/19/2015] [Indexed: 12/31/2022]
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21
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Esquivel-Rodríguez J, Xiong Y, Han X, Guang S, Christoffer C, Kihara D. Navigating 3D electron microscopy maps with EM-SURFER. BMC Bioinformatics 2015; 16:181. [PMID: 26025554 PMCID: PMC4448178 DOI: 10.1186/s12859-015-0580-6] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2014] [Accepted: 04/20/2015] [Indexed: 03/18/2023] Open
Abstract
BACKGROUND The Electron Microscopy DataBank (EMDB) is growing rapidly, accumulating biological structural data obtained mainly by electron microscopy and tomography, which are emerging techniques for determining large biomolecular complex and subcellular structures. Together with the Protein Data Bank (PDB), EMDB is becoming a fundamental resource of the tertiary structures of biological macromolecules. To take full advantage of this indispensable resource, the ability to search the database by structural similarity is essential. However, unlike high-resolution structures stored in PDB, methods for comparing low-resolution electron microscopy (EM) density maps in EMDB are not well established. RESULTS We developed a computational method for efficiently searching low-resolution EM maps. The method uses a compact fingerprint representation of EM maps based on the 3D Zernike descriptor, which is derived from a mathematical series expansion for EM maps that are considered as 3D functions. The method is implemented in a web server named EM-SURFER, which allows users to search against the entire EMDB in real-time. EM-SURFER compares the global shapes of EM maps. Examples of search results from different types of query structures are discussed. CONCLUSIONS We developed EM-SURFER, which retrieves structurally relevant matches for query EM maps from EMDB within seconds. The unique capability of EM-SURFER to detect 3D shape similarity of low-resolution EM maps should prove invaluable in structural biology.
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Affiliation(s)
| | - Yi Xiong
- Department of Biological Sciences, Purdue University, West Lafayette, IN, 47907, USA.
| | - Xusi Han
- Department of Biological Sciences, Purdue University, West Lafayette, IN, 47907, USA.
| | - Shuomeng Guang
- Department of Biological Sciences, Purdue University, West Lafayette, IN, 47907, USA.
| | - Charles Christoffer
- Department of Computer Science, Purdue University, West Lafayette, IN, 47907, USA.
- Department of Mathematics, Purdue University, West Lafayette, IN, 47907, USA.
| | - Daisuke Kihara
- Department of Computer Science, Purdue University, West Lafayette, IN, 47907, USA.
- Department of Biological Sciences, Purdue University, West Lafayette, IN, 47907, USA.
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22
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Turoňová B, Marsalek L, Davidovič T, Slusallek P. Progressive Stochastic Reconstruction Technique (PSRT) for cryo electron tomography. J Struct Biol 2015; 189:195-206. [PMID: 25659894 DOI: 10.1016/j.jsb.2015.01.011] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2014] [Revised: 12/22/2014] [Accepted: 01/20/2015] [Indexed: 10/24/2022]
Abstract
Cryo Electron Tomography (cryoET) plays an essential role in Structural Biology, as it is the only technique that allows to study the structure of large macromolecular complexes in their close to native environment in situ. The reconstruction methods currently in use, such as Weighted Back Projection (WBP) or Simultaneous Iterative Reconstruction Technique (SIRT), deliver noisy and low-contrast reconstructions, which complicates the application of high-resolution protocols, such as Subtomogram Averaging (SA). We propose a Progressive Stochastic Reconstruction Technique (PSRT) - a novel iterative approach to tomographic reconstruction in cryoET based on Monte Carlo random walks guided by Metropolis-Hastings sampling strategy. We design a progressive reconstruction scheme to suit the conditions present in cryoET and apply it successfully to reconstructions of macromolecular complexes from both synthetic and experimental datasets. We show how to integrate PSRT into SA, where it provides an elegant solution to the region-of-interest problem and delivers high-contrast reconstructions that significantly improve template-based localization without any loss of high-resolution structural information. Furthermore, the locality of SA is exploited to design an importance sampling scheme which significantly speeds up the otherwise slow Monte Carlo approach. Finally, we design a new memory efficient solution for the specimen-level interior problem of cryoET, removing all associated artifacts.
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Affiliation(s)
- Beata Turoňová
- Saarland University, Campus E 1.1, 66123 Saarbrücken, Germany; IMPRS-CS, Max-Planck Institute for Informatics, Campus E 1.4, 66123 Saarbrücken, Germany.
| | - Lukas Marsalek
- Saarland University, Campus E 1.1, 66123 Saarbrücken, Germany; Agents and Simulated Reality Group, DFKI GmbH, Campus E 3.4, 66123 Saarbrücken, Germany; Eyen SE, Na Nivách 1043/16, 14100 Prague, Czech Republic
| | - Tomáš Davidovič
- Saarland University, Campus E 1.1, 66123 Saarbrücken, Germany; Intel VCI, Campus E 1.1, 66123 Saarbrücken, Germany
| | - Philipp Slusallek
- Saarland University, Campus E 1.1, 66123 Saarbrücken, Germany; Agents and Simulated Reality Group, DFKI GmbH, Campus E 3.4, 66123 Saarbrücken, Germany; Intel VCI, Campus E 1.1, 66123 Saarbrücken, Germany
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23
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Xu M, Alber F. Automated target segmentation and real space fast alignment methods for high-throughput classification and averaging of crowded cryo-electron subtomograms. Bioinformatics 2013; 29:i274-82. [PMID: 23812994 PMCID: PMC3694676 DOI: 10.1093/bioinformatics/btt225] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION Cryo-electron tomography allows the imaging of macromolecular complexes in near living conditions. To enhance the nominal resolution of a structure it is necessary to align and average individual subtomograms each containing identical complexes. However, if the sample of complexes is heterogeneous, it is necessary to first classify subtomograms into groups of identical complexes. This task becomes challenging when tomograms contain mixtures of unknown complexes extracted from a crowded environment. Two main challenges must be overcomed: First, classification of subtomograms must be performed without knowledge of template structures. However, most alignment methods are too slow to perform reference-free classification of a large number of (e.g. tens of thousands) of subtomograms. Second, subtomograms extracted from crowded cellular environments, contain often fragments of other structures besides the target complex. However, alignment methods generally assume that each subtomogram only contains one complex. Automatic methods are needed to identify the target complexes in a subtomogram even when its shape is unknown. RESULTS In this article, we propose an automatic and systematic method for the isolation and masking of target complexes in subtomograms extracted from crowded environments. Moreover, we also propose a fast alignment method using fast rotational matching in real space. Our experiments show that, compared with our previously proposed fast alignment method in reciprocal space, our new method significantly improves the alignment accuracy for highly distorted and especially crowded subtomograms. Such improvements are important for achieving successful and unbiased high-throughput reference-free structural classification of complexes inside whole-cell tomograms. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Min Xu
- University of Southern California, 1050 Childs Way, Los Angeles, CA 90089, USA
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24
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Fernandez JJ. Computational methods for electron tomography. Micron 2012; 43:1010-30. [DOI: 10.1016/j.micron.2012.05.003] [Citation(s) in RCA: 86] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2012] [Revised: 05/08/2012] [Accepted: 05/08/2012] [Indexed: 01/13/2023]
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25
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Xu M, Alber F. High precision alignment of cryo-electron subtomograms through gradient-based parallel optimization. BMC SYSTEMS BIOLOGY 2012; 6 Suppl 1:S18. [PMID: 23046491 PMCID: PMC3403359 DOI: 10.1186/1752-0509-6-s1-s18] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
BACKGROUND Cryo-electron tomography emerges as an important component for structural system biology. It not only allows the structural characterization of macromolecular complexes, but also the detection of their cellular localizations in near living conditions. However, the method is hampered by low resolution, missing data and low signal-to-noise ratio (SNR). To overcome some of these difficulties and enhance the nominal resolution one can align and average a large set of subtomograms. Existing methods for obtaining the optimal alignments are mostly based on an exhaustive scanning of all but discrete relative rigid transformations (i.e. rotations and translations) of one subtomogram with respect to the other. RESULTS In this paper, we propose gradient-guided alignment methods based on two popular subtomogram similarity measures, a real space as well as a Fourier-space constrained score. We also propose a stochastic parallel refinement method that increases significantly the efficiency for the simultaneous refinement of a set of alignment candidates. We estimate that our stochastic parallel refinement is on average about 20 to 40 fold faster in comparison to the standard independent refinement approach. Results on simulated data of model complexes and experimental structures of protein complexes show that even for highly distorted subtomograms and with only a small number of very sparsely distributed initial alignment seeds, our combined methods can accurately recover true transformations with a substantially higher precision than the scanning based alignment methods. CONCLUSIONS Our methods increase significantly the efficiency and accuracy for subtomogram alignments, which is a key factor for the systematic classification of macromolecular complexes in cryo-electron tomograms of whole cells.
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Affiliation(s)
- Min Xu
- Program in Molecular and Computational Biology, University of Southern California, Los Angeles, CA 90089, USA
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26
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High-throughput subtomogram alignment and classification by Fourier space constrained fast volumetric matching. J Struct Biol 2012; 178:152-64. [PMID: 22420977 DOI: 10.1016/j.jsb.2012.02.014] [Citation(s) in RCA: 47] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2011] [Revised: 12/22/2011] [Accepted: 02/27/2012] [Indexed: 11/21/2022]
Abstract
Cryo-electron tomography allows the visualization of macromolecular complexes in their cellular environments in close-to-live conditions. The nominal resolution of subtomograms can be significantly increased when individual subtomograms of the same kind are aligned and averaged. A vital step for such a procedure are algorithms that speedup subtomogram alignment and improve its accuracy to allow reference-free subtomogram classifications. Such methods will facilitate automation of tomography analysis and overall high throughput in the data processing. Building on previous work, here we propose a fast rotational alignment method that uses the Fourier equivalent form of a popular constrained correlation measure that considers missing wedge corrections and density variances in the subtomograms. The fast rotational search is based on 3D volumetric matching, which improves the rotational alignment accuracy in particular for highly distorted subtomograms with low SNR and tilt angle ranges in comparison to fast rotational matching of projected 2D spherical images. We further integrate our fast rotational alignment method in a reference-free iterative subtomogram classification scheme, and propose a local feature enhancement strategy in the classification process. As a proof of principle, we can demonstrate that the automatic method can successfully classify a large number of experimental subtomograms without the need of a reference structure.
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27
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Hrabe T, Chen Y, Pfeffer S, Cuellar LK, Mangold AV, Förster F. PyTom: a python-based toolbox for localization of macromolecules in cryo-electron tomograms and subtomogram analysis. J Struct Biol 2011; 178:177-88. [PMID: 22193517 DOI: 10.1016/j.jsb.2011.12.003] [Citation(s) in RCA: 155] [Impact Index Per Article: 11.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2011] [Revised: 11/25/2011] [Accepted: 12/05/2011] [Indexed: 10/14/2022]
Abstract
Cryo-electron tomography (CET) is a three-dimensional imaging technique for structural studies of macromolecules under close-to-native conditions. In-depth analysis of macromolecule populations depicted in tomograms requires identification of subtomograms corresponding to putative particles, averaging of subtomograms to enhance their signal, and classification to capture the structural variations among them. Here, we introduce the open-source platform PyTom that unifies standard tomogram processing steps in a python toolbox. For subtomogram averaging, we implemented an adaptive adjustment of scoring and sampling that clearly improves the resolution of averages compared to static strategies. Furthermore, we present a novel stochastic classification method that yields significantly more accurate classification results than two deterministic approaches in simulations. We demonstrate that the PyTom workflow yields faithful results for alignment and classification of simulated and experimental subtomograms of ribosomes and GroEL(14)/GroEL(14)GroES(7), respectively, as well as for the analysis of ribosomal 60S subunits in yeast cell lysate. PyTom enables parallelized processing of large numbers of tomograms, but also provides a convenient, sustainable environment for algorithmic development.
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Affiliation(s)
- Thomas Hrabe
- Max-Planck Institute of Biochemistry, Am Klopferspitz 18, D-82152 Martinsried, Germany
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28
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Volkmann N. Putting structure into context: fitting of atomic models into electron microscopic and electron tomographic reconstructions. Curr Opin Cell Biol 2011; 24:141-7. [PMID: 22152946 DOI: 10.1016/j.ceb.2011.11.002] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2011] [Accepted: 11/02/2011] [Indexed: 11/29/2022]
Abstract
A complete understanding of complex dynamic cellular processes such as cell migration or cell adhesion requires the integration of atomic level structural information into the larger cellular context. While direct atomic-level information at the cellular level remains inaccessible, electron microscopy, electron tomography and their associated computational image processing approaches have now matured to a point where sub-cellular structures can be imaged in three dimensions at the nanometer scale. Atomic-resolution information obtained by other means can be combined with this data to obtain three-dimensional models of large macromolecular assemblies in their cellular context. This article summarizes some recent advances in this field.
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Affiliation(s)
- Niels Volkmann
- Sanford-Burnham Medical Research Institute, 10901 N Torrey Pines Road, La Jolla, CA 92037, USA.
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29
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Xu M, Alber F. Gradient-based high precision alignment of cryo-electron subtomograms. IEEE INTERNATIONAL CONFERENCE ON SYSTEMS BIOLOGY : [PROCEEDINGS]. IEEE INTERNATIONAL CONFERENCE ON SYSTEMS BIOLOGY 2011:279-284. [PMID: 25068871 DOI: 10.1109/isb.2011.6033166] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
Whole cell cryo-electron tomography emerges as an important component for structural system biology approaches. It allows the localization and structural characterization of macromolecular complexes in near living conditions. However, the method is hampered by low resolution, missing data and low signal-to-noise ratio (SNR). To overcome some of these difficulties one can align and average a large set of subtomograms. Existing alignment methods are mostly based on an exhaustive scanning and sampling of all but discrete relative rotations and translations of one subtomogram with respect to the other. In this paper, we propose a gradient-guided alignment method based on two subtomogram similarity measures. We also propose a stochastic parallel optimization that increases significantly the efficiency for the simultaneous refinement of a set of alignment candidates. Results on simulated data of model complexes and experimental structures of protein complexes show that even for highly distorted subtomograms and with only a small number of very sparsely distributed initial alignment seeds, our method can accurately recover true transformations with a significantly higher precision than scanning based alignment methods.
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Affiliation(s)
- Min Xu
- Program in Molecular and Computational Biology University of Southern California, Los Angeles, CA 90089, USA
| | - Frank Alber
- Program in Molecular and Computational Biology University of Southern California, Los Angeles, CA 90089, USA
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