1
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Kimanius D, Schwab J. Confronting heterogeneity in cryogenic electron microscopy data: Innovative strategies and future perspectives with data-driven methods. Curr Opin Struct Biol 2024; 86:102815. [PMID: 38657561 DOI: 10.1016/j.sbi.2024.102815] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Revised: 02/26/2024] [Accepted: 03/26/2024] [Indexed: 04/26/2024]
Abstract
The surge in the influx of data from cryogenic electron microscopy (cryo-EM) experiments has intensified the demand for robust algorithms capable of autonomously managing structurally heterogeneous datasets. This presents a wealth of exciting opportunities from a data science viewpoint, inspiring the development of numerous innovative, application-specific methods, many of which leverage contemporary data-driven techniques. However, addressing the challenges posed by heterogeneous datasets remains a paramount yet unresolved issue in the field. Here, we explore the subtleties of this challenge and the array of strategies devised to confront it. We pinpoint the shortcomings of existing methodologies and deliberate on prospective avenues for improvement. Specifically, our discussion focuses on strategies to mitigate model overfitting and manage data noise, as well as the effects of constraints, priors, and invariances on the optimization process.
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Affiliation(s)
- Dari Kimanius
- MRC Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge, CB2 0QH, UK; CZ Imaging Institute, 3400 Bridge Parkway, Redwood City, CA 94065, USA.
| | - Johannes Schwab
- MRC Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge, CB2 0QH, UK
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2
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Yoshidome T. Four-dimensional imaging for cryo-electron microscopy experiments using molecular simulations and manifold learning. J Comput Chem 2024; 45:738-751. [PMID: 38112413 DOI: 10.1002/jcc.27290] [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: 07/30/2023] [Revised: 11/20/2023] [Accepted: 12/01/2023] [Indexed: 12/21/2023]
Abstract
Elucidating protein conformational changes is essential because conformational changes are closely related to the functions of proteins. Cryo-electron microscopy (cryo-EM) experiment can be used to reconstruct protein conformational changes via a method that involves using the experimental data (two-dimensional protein images). In this study, a reconstruction method, referred to as the "four-dimensional imaging," was proposed. In our four-dimensional imaging technique, the protein conformational change was obtained using the two-dimensional protein images (the three-dimensional electron density maps used in previously proposed techniques were not used). The protein conformation for each two-dimensional protein image was obtained using our original protocol with molecular dynamics simulations. Using a manifold-learning technique and two-dimensional protein images, the protein conformations were arranged according to the conformational change of the protein. By arranging the protein conformations according to the arrangement of the protein images, four-dimensional imaging is constructed. A simulation for a cryo-EM experiment demonstrated the validity of our four-dimensional imaging technique.
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Affiliation(s)
- Takashi Yoshidome
- Department of Applied Physics, Graduate School of Engineering, Tohoku University, Sendai, Japan
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3
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Lam JH, Nakano A, Katritch V. Scalable computation of anisotropic vibrations for large macromolecular assemblies. Nat Commun 2024; 15:3479. [PMID: 38658556 PMCID: PMC11043083 DOI: 10.1038/s41467-024-47685-8] [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: 08/31/2023] [Accepted: 04/02/2024] [Indexed: 04/26/2024] Open
Abstract
The Normal Mode Analysis (NMA) is a standard approach to elucidate the anisotropic vibrations of macromolecules at their folded states, where low-frequency collective motions can reveal rearrangements of domains and changes in the exposed surface of macromolecules. Recent advances in structural biology have enabled the resolution of megascale macromolecules with millions of atoms. However, the calculation of their vibrational modes remains elusive due to the prohibitive cost associated with constructing and diagonalizing the underlying eigenproblem and the current approaches to NMA are not readily adaptable for efficient parallel computing on graphic processing unit (GPU). Here, we present eigenproblem construction and diagonalization approach that implements level-structure bandwidth-reducing algorithms to transform the sparse computation in NMA to a globally-sparse-yet-locally-dense computation, allowing batched tensor products to be most efficiently executed on GPU. We map, optimize, and compare several low-complexity Krylov-subspace eigensolvers, supplemented by techniques such as Chebyshev filtering, sum decomposition, external explicit deflation and shift-and-inverse, to allow fast GPU-resident calculations. The method allows accurate calculation of the first 1000 vibrational modes of some largest structures in PDB ( > 2.4 million atoms) at least 250 times faster than existing methods.
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Affiliation(s)
- Jordy Homing Lam
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA
- Bridge Institute and Michelson Center for Convergent Biosciences, University of Southern California, Los Angeles, CA, USA
- Center for New Technologies in Drug Discovery and Development, University of Southern California, Los Angeles, CA, USA
| | - Aiichiro Nakano
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA.
- Department of Physics and Astronomy, University of Southern California, Los Angeles, CA, USA.
- Department of Computer Science, University of Southern California, Los Angeles, CA, USA.
| | - Vsevolod Katritch
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA.
- Bridge Institute and Michelson Center for Convergent Biosciences, University of Southern California, Los Angeles, CA, USA.
- Center for New Technologies in Drug Discovery and Development, University of Southern California, Los Angeles, CA, USA.
- Department of Chemistry, University of Southern California, Los Angeles, CA, USA.
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4
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Vuillemot R, Harastani M, Hamitouche I, Jonic S. MDSPACE and MDTOMO Software for Extracting Continuous Conformational Landscapes from Datasets of Single Particle Images and Subtomograms Based on Molecular Dynamics Simulations: Latest Developments in ContinuousFlex Software Package. Int J Mol Sci 2023; 25:20. [PMID: 38203192 PMCID: PMC10779004 DOI: 10.3390/ijms25010020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Revised: 12/16/2023] [Accepted: 12/17/2023] [Indexed: 01/12/2024] Open
Abstract
Cryo electron microscopy (cryo-EM) instrumentation allows obtaining 3D reconstruction of the structure of biomolecular complexes in vitro (purified complexes studied by single particle analysis) and in situ (complexes studied in cells by cryo electron tomography). Standard cryo-EM approaches allow high-resolution reconstruction of only a few conformational states of a molecular complex, as they rely on data classification into a given number of classes to increase the resolution of the reconstruction from the most populated classes while discarding all other classes. Such discrete classification approaches result in a partial picture of the full conformational variability of the complex, due to continuous conformational transitions with many, uncountable intermediate states. In this article, we present the software with a user-friendly graphical interface for running two recently introduced methods, namely, MDSPACE and MDTOMO, to obtain continuous conformational landscapes of biomolecules by analyzing in vitro and in situ cryo-EM data (single particle images and subtomograms) based on molecular dynamics simulations of an available atomic model of one of the conformations. The MDSPACE and MDTOMO software is part of the open-source ContinuousFlex software package (starting from version 3.4.2 of ContinuousFlex), which can be run as a plugin of the Scipion software package (version 3.1 and later), broadly used in the cryo-EM field.
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Affiliation(s)
| | | | | | - Slavica Jonic
- IMPMC-UMR 7590 CNRS, Sorbonne Université, MNHN, 75005 Paris, France
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5
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Miyashita O, Tama F. Advancing cryo-electron microscopy data analysis through accelerated simulation-based flexible fitting approaches. Curr Opin Struct Biol 2023; 82:102653. [PMID: 37451233 DOI: 10.1016/j.sbi.2023.102653] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2023] [Revised: 05/30/2023] [Accepted: 06/19/2023] [Indexed: 07/18/2023]
Abstract
Flexible fitting based on molecular dynamics simulation is a technique for structure modeling from cryo-EM data. It has been utilized for nearly two decades, and while cryo-EM resolution has improved significantly, it remains a powerful approach that can provide structural and dynamical insights that are not directly accessible from experimental data alone. Molecular dynamics simulations provide a means to extract atomistic details of conformational changes that are encoded in cryo-EM data and can also assist in improving the quality of structural models. Additionally, molecular dynamics simulations enable the characterization of conformational heterogeneity in cryo-EM data. We will summarize the advancements made in these techniques and highlight recent developments in this field.
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Affiliation(s)
- Osamu Miyashita
- RIKEN Center for Computational Science, 6-7-1, Minatojima-minami-machi, Chuo-ku, Kobe, Hyogo 650-0047, Japan.
| | - Florence Tama
- RIKEN Center for Computational Science, 6-7-1, Minatojima-minami-machi, Chuo-ku, Kobe, Hyogo 650-0047, Japan; Department of Physics, Graduate School of Science, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, Aichi 464-8602, Japan; Institute of Transformative Bio-Molecules, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, Aichi 464-8602, Japan.
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6
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Krieger JM, Sorzano COS, Carazo JM. Scipion-EM-ProDy: A Graphical Interface for the ProDy Python Package within the Scipion Workflow Engine Enabling Integration of Databases, Simulations and Cryo-Electron Microscopy Image Processing. Int J Mol Sci 2023; 24:14245. [PMID: 37762547 PMCID: PMC10532346 DOI: 10.3390/ijms241814245] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Revised: 09/10/2023] [Accepted: 09/15/2023] [Indexed: 09/29/2023] Open
Abstract
Macromolecular assemblies, such as protein complexes, undergo continuous structural dynamics, including global reconfigurations critical for their function. Two fast analytical methods are widely used to study these global dynamics, namely elastic network model normal mode analysis and principal component analysis of ensembles of structures. These approaches have found wide use in various computational studies, driving the development of complex pipelines in several software packages. One common theme has been conformational sampling through hybrid simulations incorporating all-atom molecular dynamics and global modes of motion. However, wide functionality is only available for experienced programmers with limited capabilities for other users. We have, therefore, integrated one popular and extensively developed software for such analyses, the ProDy Python application programming interface, into the Scipion workflow engine. This enables a wider range of users to access a complete range of macromolecular dynamics pipelines beyond the core functionalities available in its command-line applications and the normal mode wizard in VMD. The new protocols and pipelines can be further expanded and integrated into larger workflows, together with other software packages for cryo-electron microscopy image analysis and molecular simulations. We present the resulting plugin, Scipion-EM-ProDy, in detail, highlighting the rich functionality made available by its development.
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Affiliation(s)
- James M. Krieger
- Biocomputing Unit, National Centre for Biotechnology (CNB CSIC), Campus Universidad Autónoma de Madrid, Darwin 3, Cantoblanco, 28049 Madrid, Spain
| | | | - Jose Maria Carazo
- Biocomputing Unit, National Centre for Biotechnology (CNB CSIC), Campus Universidad Autónoma de Madrid, Darwin 3, Cantoblanco, 28049 Madrid, Spain
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7
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DiIorio MC, Kulczyk AW. Novel Artificial Intelligence-Based Approaches for Ab Initio Structure Determination and Atomic Model Building for Cryo-Electron Microscopy. MICROMACHINES 2023; 14:1674. [PMID: 37763837 PMCID: PMC10534518 DOI: 10.3390/mi14091674] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 08/21/2023] [Accepted: 08/25/2023] [Indexed: 09/29/2023]
Abstract
Single particle cryo-electron microscopy (cryo-EM) has emerged as the prevailing method for near-atomic structure determination, shedding light on the important molecular mechanisms of biological macromolecules. However, the inherent dynamics and structural variability of biological complexes coupled with the large number of experimental images generated by a cryo-EM experiment make data processing nontrivial. In particular, ab initio reconstruction and atomic model building remain major bottlenecks that demand substantial computational resources and manual intervention. Approaches utilizing recent innovations in artificial intelligence (AI) technology, particularly deep learning, have the potential to overcome the limitations that cannot be adequately addressed by traditional image processing approaches. Here, we review newly proposed AI-based methods for ab initio volume generation, heterogeneous 3D reconstruction, and atomic model building. We highlight the advancements made by the implementation of AI methods, as well as discuss remaining limitations and areas for future development.
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Affiliation(s)
- Megan C. DiIorio
- Institute for Quantitative Biomedicine, Rutgers University, 174 Frelinghuysen Road, Piscataway, NJ 08854, USA
| | - Arkadiusz W. Kulczyk
- Institute for Quantitative Biomedicine, Rutgers University, 174 Frelinghuysen Road, Piscataway, NJ 08854, USA
- Department of Biochemistry & Microbiology, Rutgers University, 76 Lipman Drive, New Brunswick, NJ 08901, USA
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8
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Tang WS, Zhong ED, Hanson SM, Thiede EH, Cossio P. Conformational heterogeneity and probability distributions from single-particle cryo-electron microscopy. Curr Opin Struct Biol 2023; 81:102626. [PMID: 37311334 DOI: 10.1016/j.sbi.2023.102626] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 04/25/2023] [Accepted: 05/16/2023] [Indexed: 06/15/2023]
Abstract
Single-particle cryo-electron microscopy (cryo-EM) is a technique that takes projection images of biomolecules frozen at cryogenic temperatures. A major advantage of this technique is its ability to image single biomolecules in heterogeneous conformations. While this poses a challenge for data analysis, recent algorithmic advances have enabled the recovery of heterogeneous conformations from the noisy imaging data. Here, we review methods for the reconstruction and heterogeneity analysis of cryo-EM images, ranging from linear-transformation-based methods to nonlinear deep generative models. We overview the dimensionality-reduction techniques used in heterogeneous 3D reconstruction methods and specify what information each method can infer from the data. Then, we review the methods that use cryo-EM images to estimate probability distributions over conformations in reduced subspaces or predefined by atomistic simulations. We conclude with the ongoing challenges for the cryo-EM community.
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Affiliation(s)
- Wai Shing Tang
- Center for Computational Mathematics, Flatiron Institute, 162 5th Ave, New York, NY, 10010, United States. https://twitter.com/WaiShingTang
| | - Ellen D Zhong
- Department of Computer Science, Princeton University, 35 Olden St, Princeton, NJ, 08544, United States. https://twitter.com/ZhongingAlong
| | - Sonya M Hanson
- Center for Computational Mathematics, Flatiron Institute, 162 5th Ave, New York, NY, 10010, United States; Center for Computational Biology, Flatiron Institute, 162 5th Ave, New York, NY, 10010, United States. https://twitter.com/sonyahans
| | - Erik H Thiede
- Center for Computational Mathematics, Flatiron Institute, 162 5th Ave, New York, NY, 10010, United States. https://twitter.com/erik_der_elch
| | - Pilar Cossio
- Center for Computational Mathematics, Flatiron Institute, 162 5th Ave, New York, NY, 10010, United States; Center for Computational Biology, Flatiron Institute, 162 5th Ave, New York, NY, 10010, United States.
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9
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Vuillemot R, Rouiller I, Jonić S. MDTOMO method for continuous conformational variability analysis in cryo electron subtomograms based on molecular dynamics simulations. Sci Rep 2023; 13:10596. [PMID: 37391578 PMCID: PMC10313669 DOI: 10.1038/s41598-023-37037-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Accepted: 06/14/2023] [Indexed: 07/02/2023] Open
Abstract
Cryo electron tomography (cryo-ET) allows observing macromolecular complexes in their native environment. The common routine of subtomogram averaging (STA) allows obtaining the three-dimensional (3D) structure of abundant macromolecular complexes, and can be coupled with discrete classification to reveal conformational heterogeneity of the sample. However, the number of complexes extracted from cryo-ET data is usually small, which restricts the discrete-classification results to a small number of enough populated states and, thus, results in a largely incomplete conformational landscape. Alternative approaches are currently being investigated to explore the continuity of the conformational landscapes that in situ cryo-ET studies could provide. In this article, we present MDTOMO, a method for analyzing continuous conformational variability in cryo-ET subtomograms based on Molecular Dynamics (MD) simulations. MDTOMO allows obtaining an atomic-scale model of conformational variability and the corresponding free-energy landscape, from a given set of cryo-ET subtomograms. The article presents the performance of MDTOMO on a synthetic ABC exporter dataset and an in situ SARS-CoV-2 spike dataset. MDTOMO allows analyzing dynamic properties of molecular complexes to understand their biological functions, which could also be useful for structure-based drug discovery.
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Affiliation(s)
- Rémi Vuillemot
- IMPMC-UMR 7590 CNRS, Sorbonne Université, Muséum National d'Histoire Naturelle, CC 115, 4 Place Jussieu, 75005, Paris, France
- Department of Biochemistry and Pharmacology and Bio21 Molecular Science and Biotechnology Institute, The University of Melbourne, Melbourne, VIC, 3010, Australia
| | - Isabelle Rouiller
- Department of Biochemistry and Pharmacology and Bio21 Molecular Science and Biotechnology Institute, The University of Melbourne, Melbourne, VIC, 3010, Australia
- Australian Research Council Centre for Cryo-Electron Microscopy of Membrane Proteins, Parkville, VIC, 3052, Australia
| | - Slavica Jonić
- IMPMC-UMR 7590 CNRS, Sorbonne Université, Muséum National d'Histoire Naturelle, CC 115, 4 Place Jussieu, 75005, Paris, France.
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10
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Toader B, Sigworth FJ, Lederman RR. Methods for Cryo-EM Single Particle Reconstruction of Macromolecules Having Continuous Heterogeneity. J Mol Biol 2023; 435:168020. [PMID: 36863660 PMCID: PMC10164696 DOI: 10.1016/j.jmb.2023.168020] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 02/15/2023] [Accepted: 02/16/2023] [Indexed: 03/02/2023]
Abstract
Macromolecules change their shape (conformation) in the process of carrying out their functions. The imaging by cryo-electron microscopy of rapidly-frozen, individual copies of macromolecules (single particles) is a powerful and general approach to understanding the motions and energy landscapes of macromolecules. Widely-used computational methods already allow the recovery of a few distinct conformations from heterogeneous single-particle samples, but the treatment of complex forms of heterogeneity such as the continuum of possible transitory states and flexible regions remains largely an open problem. In recent years there has been a surge of new approaches for treating the more general problem of continuous heterogeneity. This paper surveys the current state of the art in this area.
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Affiliation(s)
- Bogdan Toader
- Department of Statistics and Data Science, Yale University, United States.
| | - Fred J Sigworth
- Department of Cellular and Molecular Physiology, Yale University, United States
| | - Roy R Lederman
- Department of Statistics and Data Science, Yale University, United States. https://twitter.com/roylederman
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11
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Herreros D, Kiska J, Ramirez E, Filipovic J, Carazo JM, Sorzano COS. ZART: A novel multiresolution reconstruction algorithm with motion-blur correction for single particle analysis. J Mol Biol 2023; 435:168088. [PMID: 37030648 DOI: 10.1016/j.jmb.2023.168088] [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: 10/26/2022] [Revised: 03/31/2023] [Accepted: 04/01/2023] [Indexed: 04/10/2023]
Abstract
One of the main purposes of CryoEM Single Particle Analysis is to reconstruct the three-dimensional structure of a macromolecule thanks to the acquisition of many particle images representing different poses of the sample. By estimating the orientation of each projected particle, it is possible to recover the underlying 3D volume by multiple 3D reconstruction methods, usually working either in Fourier or in real space. However, the reconstruction from the projected images works under the assumption that all particles in the dataset correspond to the same conformation of the macromolecule. Although this requisite holds for some macromolecules, it is not true for flexible specimens, leading to motion-induced artefacts in the reconstructed CryoEM maps. In this work, we introduce a new Algebraic Reconstruction Technique called ZART, which is able to include continuous flexibility information during the reconstruction process to improve local resolution and reduce motion blurring. The conformational changes are modelled through Zernike3D polynomials. Our implementation allows for a multiresolution description of the macromolecule adapting itself to the local resolution of the reconstructed map. In addition, ZART has also proven to be a useful algorithm in cases where flexibility is not so dominant, as it improves the overall aspect of the reconstructed maps by improving their local and global resolution.
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Affiliation(s)
- D Herreros
- Centro Nacional de Biotecnologia-CSIC, C/ Darwin, 3, 28049, Cantoblanco, Madrid, Spain.
| | - J Kiska
- Institute of Computer Science, Masaryk University, Botanická 68a, 60200 Brno, Czech Republic
| | - E Ramirez
- Centro Nacional de Biotecnologia-CSIC, C/ Darwin, 3, 28049, Cantoblanco, Madrid, Spain
| | - J Filipovic
- Institute of Computer Science, Masaryk University, Botanická 68a, 60200 Brno, Czech Republic
| | - J M Carazo
- Centro Nacional de Biotecnologia-CSIC, C/ Darwin, 3, 28049, Cantoblanco, Madrid, Spain.
| | - C O S Sorzano
- Centro Nacional de Biotecnologia-CSIC, C/ Darwin, 3, 28049, Cantoblanco, Madrid, Spain.
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12
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Herreros D, Lederman RR, Krieger JM, Jiménez-Moreno A, Martínez M, Myška D, Strelak D, Filipovic J, Sorzano COS, Carazo JM. Estimating conformational landscapes from Cryo-EM particles by 3D Zernike polynomials. Nat Commun 2023; 14:154. [PMID: 36631472 PMCID: PMC9832421 DOI: 10.1038/s41467-023-35791-y] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Accepted: 12/29/2022] [Indexed: 01/12/2023] Open
Abstract
The new developments in Cryo-EM Single Particle Analysis are helping us to understand how the macromolecular structure and function meet to drive biological processes. By capturing many states at the particle level, it is possible to address how macromolecules explore different conformations, information that is classically extracted through 3D classification. However, the limitations of classical approaches prevent us from fully understanding the complete conformational landscape due to the reduced number of discrete states accurately reconstructed. To characterize the whole structural spectrum of a macromolecule, we propose an extension of our Zernike3D approach, able to extract per-image continuous flexibility information directly from a particle dataset. Also, our method can be seamlessly applied to images, maps or atomic models, opening integrative possibilities. Furthermore, we introduce the ZART reconstruction algorithm, which considers the Zernike3D deformation fields to revert particle conformational changes during the reconstruction process, thus minimizing the blurring induced by molecular motions.
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Affiliation(s)
- D Herreros
- Centro Nacional de Biotecnologia-CSIC, C/Darwin, 3, 28049, Cantoblanco, Madrid, Spain.
| | - R R Lederman
- The Department of Statistics and Data Science, Yale University, New Haven, CT, USA
| | - J M Krieger
- Centro Nacional de Biotecnologia-CSIC, C/Darwin, 3, 28049, Cantoblanco, Madrid, Spain
| | - A Jiménez-Moreno
- Centro Nacional de Biotecnologia-CSIC, C/Darwin, 3, 28049, Cantoblanco, Madrid, Spain
| | - M Martínez
- Centro Nacional de Biotecnologia-CSIC, C/Darwin, 3, 28049, Cantoblanco, Madrid, Spain
| | - D Myška
- Institute of Computer Science, Masaryk University, Botanická 68a, 60200, Brno, Czech Republic
| | - D Strelak
- Centro Nacional de Biotecnologia-CSIC, C/Darwin, 3, 28049, Cantoblanco, Madrid, Spain.,Faculty of Informatics, Masaryk University, Botanická 68a, 60200, Brno, Czech Republic
| | - J Filipovic
- Institute of Computer Science, Masaryk University, Botanická 68a, 60200, Brno, Czech Republic
| | - C O S Sorzano
- Centro Nacional de Biotecnologia-CSIC, C/Darwin, 3, 28049, Cantoblanco, Madrid, Spain
| | - J M Carazo
- Centro Nacional de Biotecnologia-CSIC, C/Darwin, 3, 28049, Cantoblanco, Madrid, Spain
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13
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Vuillemot R, Mirzaei A, Harastani M, Hamitouche I, Fréchin L, Klaholz BP, Miyashita O, Tama F, Rouiller I, Jonic S. MDSPACE: Extracting Continuous Conformational Landscapes from Cryo-EM Single Particle Datasets Using 3D-to-2D Flexible Fitting based on Molecular Dynamics Simulation. J Mol Biol 2023; 435:167951. [PMID: 36638910 DOI: 10.1016/j.jmb.2023.167951] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2022] [Revised: 12/08/2022] [Accepted: 01/03/2023] [Indexed: 01/12/2023]
Abstract
This article presents an original approach for extracting atomic-resolution landscapes of continuous conformational variability of biomolecular complexes from cryo electron microscopy (cryo-EM) single particle images. This approach is based on a new 3D-to-2D flexible fitting method, which uses molecular dynamics (MD) simulation and is embedded in an iterative conformational-landscape refinement scheme. This new approach is referred to as MDSPACE, which stands for Molecular Dynamics simulation for Single Particle Analysis of Continuous Conformational hEterogeneity. The article describes the MDSPACE approach and shows its performance using synthetic and experimental datasets.
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Affiliation(s)
- Rémi Vuillemot
- IMPMC-UMR 7590 CNRS, Sorbonne Université, Muséum National d'Histoire Naturelle, Paris, France; Department of Biochemistry & Pharmacology and Bio21 Molecular Science and Biotechnology Institute, The University of Melbourne, Victoria, Australia
| | - Alex Mirzaei
- IMPMC-UMR 7590 CNRS, Sorbonne Université, Muséum National d'Histoire Naturelle, Paris, France
| | - Mohamad Harastani
- IMPMC-UMR 7590 CNRS, Sorbonne Université, Muséum National d'Histoire Naturelle, Paris, France
| | - Ilyes Hamitouche
- IMPMC-UMR 7590 CNRS, Sorbonne Université, Muséum National d'Histoire Naturelle, Paris, France
| | - Léo Fréchin
- Centre for Integrative Biology, Department of Integrated Structural Biology, IGBMC-UMR 7104 CNRS, U964 Inserm, Université de Strasbourg, Strasbourg, France
| | - Bruno P Klaholz
- Centre for Integrative Biology, Department of Integrated Structural Biology, IGBMC-UMR 7104 CNRS, U964 Inserm, Université de Strasbourg, Strasbourg, France
| | | | - Florence Tama
- RIKEN Center for Computational Science, Kobe, Japan; Institute of Transformative Biomolecules, Graduate School of Science, Nagoya University, Nagoya, Japan; Department of Physics, Graduate School of Science, Nagoya University, Nagoya, Japan
| | - Isabelle Rouiller
- Department of Biochemistry & Pharmacology and Bio21 Molecular Science and Biotechnology Institute, The University of Melbourne, Victoria, Australia
| | - Slavica Jonic
- IMPMC-UMR 7590 CNRS, Sorbonne Université, Muséum National d'Histoire Naturelle, Paris, France.
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14
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Harastani M, Vuillemot R, Hamitouche I, Moghadam NB, Jonic S. ContinuousFlex: Software package for analyzing continuous conformational variability of macromolecules in cryo electron microscopy and tomography data. J Struct Biol 2022; 214:107906. [PMID: 36244611 DOI: 10.1016/j.jsb.2022.107906] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2022] [Revised: 09/02/2022] [Accepted: 10/07/2022] [Indexed: 11/06/2022]
Abstract
ContinuousFlex is a user-friendly open-source software package for analyzing continuous conformational variability of macromolecules in cryo electron microscopy (cryo-EM) and cryo electron tomography (cryo-ET) data. In 2019, ContinuousFlex became available as a plugin for Scipion, an image processing software package extensively used in the cryo-EM field. Currently, ContinuousFlex contains software for running (1) recently published methods HEMNMA-3D, TomoFlow, and NMMD; (2) earlier published methods HEMNMA and StructMap; and (3) methods for simulating cryo-EM and cryo-ET data with conformational variability and methods for data preprocessing. It also includes external software for molecular dynamics simulation (GENESIS) and normal mode analysis (ElNemo), used in some of the mentioned methods. The HEMNMA software has been presented in the past, but not the software of other methods. Besides, ContinuousFlex currently also offers a deep learning extension of HEMNMA, named DeepHEMNMA. In this article, we review these methods in the context of the ContinuousFlex package, developed to facilitate their use by the community.
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Affiliation(s)
- Mohamad Harastani
- IMPMC-UMR 7590 CNRS, Sorbonne Université, Muséum National d'Histoire Naturelle, Paris, France
| | - Rémi Vuillemot
- IMPMC-UMR 7590 CNRS, Sorbonne Université, Muséum National d'Histoire Naturelle, Paris, France
| | - Ilyes Hamitouche
- IMPMC-UMR 7590 CNRS, Sorbonne Université, Muséum National d'Histoire Naturelle, Paris, France
| | - Nima Barati Moghadam
- IMPMC-UMR 7590 CNRS, Sorbonne Université, Muséum National d'Histoire Naturelle, Paris, France
| | - Slavica Jonic
- IMPMC-UMR 7590 CNRS, Sorbonne Université, Muséum National d'Histoire Naturelle, Paris, France.
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15
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Beton JG, Cragnolini T, Kaleel M, Mulvaney T, Sweeney A, Topf M. Integrating model simulation tools and
cryo‐electron
microscopy. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2022. [DOI: 10.1002/wcms.1642] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Affiliation(s)
- Joseph George Beton
- Centre for Structural Systems Biology (CSSB) Leibniz‐Institut für Virologie (LIV) Hamburg Germany
| | - Tristan Cragnolini
- Institute of Structural and Molecular Biology, Birkbeck and University College London London UK
| | - Manaz Kaleel
- Centre for Structural Systems Biology (CSSB) Leibniz‐Institut für Virologie (LIV) Hamburg Germany
| | - Thomas Mulvaney
- Centre for Structural Systems Biology (CSSB) Leibniz‐Institut für Virologie (LIV) Hamburg Germany
| | - Aaron Sweeney
- Centre for Structural Systems Biology (CSSB) Leibniz‐Institut für Virologie (LIV) Hamburg Germany
| | - Maya Topf
- Centre for Structural Systems Biology (CSSB) Leibniz‐Institut für Virologie (LIV) Hamburg Germany
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16
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Li Z. Editorial: Methods in structural biology: Cryo-electron microscopy. Front Mol Biosci 2022; 9:1041386. [DOI: 10.3389/fmolb.2022.1041386] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2022] [Accepted: 10/03/2022] [Indexed: 11/11/2022] Open
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17
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Abstract
Cryo-electron microscopy (CryoEM) has become a vital technique in structural biology. It is an interdisciplinary field that takes advantage of advances in biochemistry, physics, and image processing, among other disciplines. Innovations in these three basic pillars have contributed to the boosting of CryoEM in the past decade. This work reviews the main contributions in image processing to the current reconstruction workflow of single particle analysis (SPA) by CryoEM. Our review emphasizes the time evolution of the algorithms across the different steps of the workflow differentiating between two groups of approaches: analytical methods and deep learning algorithms. We present an analysis of the current state of the art. Finally, we discuss the emerging problems and challenges still to be addressed in the evolution of CryoEM image processing methods in SPA.
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Affiliation(s)
- Jose Luis Vilas
- Biocomputing Unit, Centro
Nacional de Biotecnologia (CNB-CSIC), Darwin, 3, Campus Universidad Autonoma, 28049 Cantoblanco, Madrid, Spain
| | - Jose Maria Carazo
- Biocomputing Unit, Centro
Nacional de Biotecnologia (CNB-CSIC), Darwin, 3, Campus Universidad Autonoma, 28049 Cantoblanco, Madrid, Spain
| | - Carlos Oscar S. Sorzano
- Biocomputing Unit, Centro
Nacional de Biotecnologia (CNB-CSIC), Darwin, 3, Campus Universidad Autonoma, 28049 Cantoblanco, Madrid, Spain
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18
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Hamitouche I, Jonic S. DeepHEMNMA: ResNet-based hybrid analysis of continuous conformational heterogeneity in cryo-EM single particle images. Front Mol Biosci 2022; 9:965645. [PMID: 36158571 PMCID: PMC9493108 DOI: 10.3389/fmolb.2022.965645] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Accepted: 08/08/2022] [Indexed: 11/13/2022] Open
Abstract
Single-particle cryo-electron microscopy (cryo-EM) is a technique for biomolecular structure reconstruction from vitrified samples containing many copies of a biomolecular complex (known as single particles) at random unknown 3D orientations and positions. Cryo-EM allows reconstructing multiple conformations of the complexes from images of the same sample, which usually requires many rounds of 2D and 3D classifications to disentangle and interpret the combined conformational, orientational, and translational heterogeneity. The elucidation of different conformations is the key to understand molecular mechanisms behind the biological functions of the complexes and the key to novel drug discovery. Continuous conformational heterogeneity, due to gradual conformational transitions giving raise to many intermediate conformational states of the complexes, is both an obstacle for high-resolution 3D reconstruction of the conformational states and an opportunity to obtain information about multiple coexisting conformational states at once. HEMNMA method, specifically developed for analyzing continuous conformational heterogeneity in cryo-EM, determines the conformation, orientation, and position of the complex in each single particle image by image analysis using normal modes (the motion directions simulated for a given atomic structure or EM map), which in turn allows determining the full conformational space of the complex but at the price of high computational cost. In this article, we present a new method, referred to as DeepHEMNMA, which speeds up HEMNMA by combining it with a residual neural network (ResNet) based deep learning approach. The performance of DeepHEMNMA is shown using synthetic and experimental single particle images.
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Affiliation(s)
| | - Slavica Jonic
- IMPMC - UMR 7590 CNRS, Sorbonne Université, MNHN, Paris, France
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19
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Asi H, Dasgupta B, Nagai T, Miyashita O, Tama F. A hybrid approach to study large conformational transitions of biomolecules from single particle XFEL diffraction data. Front Mol Biosci 2022; 9:913860. [PMID: 36660427 PMCID: PMC9846856 DOI: 10.3389/fmolb.2022.913860] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Accepted: 07/04/2022] [Indexed: 01/06/2023] Open
Abstract
X-ray free-electron laser (XFEL) is the latest generation of the X-ray source that could become an invaluable technique in structural biology. XFEL has ultrashort pulse duration, extreme peak brilliance, and high spatial coherence, which could enable the observation of the biological molecules in near nature state at room temperature without crystallization. However, for biological systems, due to their low diffraction power and complexity of sample delivery, experiments and data analysis are not straightforward, making it extremely challenging to reconstruct three-dimensional (3D) structures from single particle XFEL data. Given the current limitations to the amount and resolution of the data from such XFEL experiments, we propose a new hybrid approach for characterizing biomolecular conformational transitions by using a single 2D low-resolution XFEL diffraction pattern in combination with another known conformation. In our method, we represent the molecular structure with a coarse-grained model, the Gaussian mixture model, to describe large conformational transitions from low-resolution XFEL data. We obtain plausible 3D structural models that are consistent with the XFEL diffraction pattern by deforming an initial structural model to maximize the similarity between the target pattern and the simulated diffraction patterns from the candidate models. We tested the proposed algorithm on two biomolecules of different sizes with different complexities of conformational transitions, adenylate kinase, and elongation factor 2, using synthetic XFEL data. The results show that, with the proposed algorithm, we can successfully describe the conformational transitions by flexibly fitting the coarse-grained model of one conformation to become consistent with an XFEL diffraction pattern simulated from another conformation. In addition, we showed that the incident beam orientation has some effect on the accuracy of the 3D structure modeling and discussed the reasons for the inaccuracies for certain orientations. The proposed method could serve as an alternative approach for retrieving information on 3D conformational transitions from the XFEL diffraction patterns to interpret experimental data. Since the molecules are represented by Gaussian kernels and no atomic structure is needed in principle, such a method could also be used as a tool to seek initial models for 3D reconstruction algorithms.
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Affiliation(s)
- Han Asi
- Department of Physics, Nagoya University, Nagoya, Japan
| | - Bhaskar Dasgupta
- Division of Biological Data Science, Research Center for Advanced Science and Technology, The University of Tokyo, Meguro City, Japan
| | - Tetsuro Nagai
- Department of Advanced Materials Science, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Japan
| | - Osamu Miyashita
- RIKEN Center for Computational Science, Kobe, Japan,*Correspondence: Osamu Miyashita, ; Florence Tama,
| | - Florence Tama
- Department of Physics, Nagoya University, Nagoya, Japan,RIKEN Center for Computational Science, Kobe, Japan,Institute of Transformative Bio-Molecules, Nagoya University, Nagoya, Japan,*Correspondence: Osamu Miyashita, ; Florence Tama,
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20
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Jiménez de la Morena J, Conesa P, Fonseca YC, de Isidro-Gómez FP, Herreros D, Fernández-Giménez E, Strelak D, Moebel E, Buchholz TO, Jug F, Martinez-Sanchez A, Harastani M, Jonic S, Conesa JJ, Cuervo A, Losana P, Sánchez I, Iceta M, Del Cano L, Gragera M, Melero R, Sharov G, Castaño-Díez D, Koster A, Piccirillo JG, Vilas JL, Otón J, Marabini R, Sorzano COS, Carazo JM. ScipionTomo: Towards cryo-electron tomography software integration, reproducibility, and validation. J Struct Biol 2022; 214:107872. [PMID: 35660516 PMCID: PMC7613607 DOI: 10.1016/j.jsb.2022.107872] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 05/26/2022] [Accepted: 05/28/2022] [Indexed: 11/25/2022]
Abstract
Image processing in cryogenic electron tomography (cryoET) is currently at a similar state as Single Particle Analysis (SPA) in cryogenic electron microscopy (cryoEM) was a few years ago. Its data processing workflows are far from being well defined and the user experience is still not smooth. Moreover, file formats of different software packages and their associated metadata are not standardized, mainly since different packages are developed by different groups, focusing on different steps of the data processing pipeline. The Scipion framework, originally developed for SPA (de la Rosa-Trevín et al., 2016), has a generic python workflow engine that gives it the versatility to be extended to other fields, as demonstrated for model building (Martínez et al., 2020). In this article, we provide an extension of Scipion based on a set of tomography plugins (referred to as ScipionTomo hereafter), with a similar purpose: to allow users to be focused on the data processing and analysis instead of having to deal with multiple software installation issues and the inconvenience of switching from one to another, converting metadata files, managing possible incompatibilities, scripting (writing a simple program in a language that the computer must convert to machine language each time the program is run), etcetera. Additionally, having all the software available in an integrated platform allows comparing the results of different algorithms trying to solve the same problem. In this way, the commonalities and differences between estimated parameters shed light on which results can be more trusted than others. ScipionTomo is developed by a collaborative multidisciplinary team composed of Scipion team engineers, structural biologists, and in some cases, the developers whose software packages have been integrated. It is open to anyone in the field willing to contribute to this project. The result is a framework extension that combines the acquired knowledge of Scipion developers in close collaboration with third-party developers, and the on-demand design of functionalities requested by beta testers applying this solution to actual biological problems.
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Affiliation(s)
| | - P Conesa
- National Center of Biotechnology (CNB-CSIC), Madrid, Spain
| | - Y C Fonseca
- National Center of Biotechnology (CNB-CSIC), Madrid, Spain
| | | | - D Herreros
- National Center of Biotechnology (CNB-CSIC), Madrid, Spain
| | | | - D Strelak
- National Center of Biotechnology (CNB-CSIC), Madrid, Spain; Masaryk University, Brno, Czech Republic
| | - E Moebel
- Inria Rennes - Bretagne Atlantique, Rennes
| | - T O Buchholz
- Max Planck Institute of Molecular Cell Biology and Genetics (MPI-CBG), Germany; Center for Systems Biology Dresden (CSBD), Germany
| | - F Jug
- Max Planck Institute of Molecular Cell Biology and Genetics (MPI-CBG), Germany; Fondazione Human Technopole, Milan, Italy
| | - A Martinez-Sanchez
- University of Oviedo, Department of Computer Sciences, Oviedo, Spain; Health Research Institute of Asturias (ISPA), Oviedo, Spain
| | - M Harastani
- IMPMC-UMR 7590 CNRS, Sorbonne Université, MNHN, Paris, France
| | - S Jonic
- IMPMC-UMR 7590 CNRS, Sorbonne Université, MNHN, Paris, France
| | - J J Conesa
- National Center of Biotechnology (CNB-CSIC), Madrid, Spain
| | - A Cuervo
- National Center of Biotechnology (CNB-CSIC), Madrid, Spain
| | - P Losana
- National Center of Biotechnology (CNB-CSIC), Madrid, Spain
| | - I Sánchez
- National Center of Biotechnology (CNB-CSIC), Madrid, Spain
| | - M Iceta
- National Center of Biotechnology (CNB-CSIC), Madrid, Spain
| | - L Del Cano
- National Center of Biotechnology (CNB-CSIC), Madrid, Spain
| | - M Gragera
- National Center of Biotechnology (CNB-CSIC), Madrid, Spain
| | - R Melero
- National Center of Biotechnology (CNB-CSIC), Madrid, Spain
| | - G Sharov
- Structural Studies Division, MRC Laboratory of Molecular Biology, Cambridge, United Kingdom
| | - D Castaño-Díez
- BioEM Lab, Biozentrum, University of Basel, Basel, Switzerland
| | - A Koster
- University of Leiden, Ultrastructural and molecular imaging, Leiden, The Netherlands
| | - J G Piccirillo
- National Center of Biotechnology (CNB-CSIC), Madrid, Spain
| | - J L Vilas
- National Center of Biotechnology (CNB-CSIC), Madrid, Spain
| | - J Otón
- Alba Synchrotron - CELLS (ICTS), Barcelona, Spain
| | - R Marabini
- National Center of Biotechnology (CNB-CSIC), Madrid, Spain; Superior Polytechnic School. Univ. Autónoma of Madrid. Madrid, Spain
| | - C O S Sorzano
- National Center of Biotechnology (CNB-CSIC), Madrid, Spain
| | - J M Carazo
- National Center of Biotechnology (CNB-CSIC), Madrid, Spain
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21
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Sorzano COS, Jiménez-Moreno A, Maluenda D, Martínez M, Ramírez-Aportela E, Krieger J, Melero R, Cuervo A, Conesa J, Filipovic J, Conesa P, del Caño L, Fonseca YC, Jiménez-de la Morena J, Losana P, Sánchez-García R, Strelak D, Fernández-Giménez E, de Isidro-Gómez FP, Herreros D, Vilas JL, Marabini R, Carazo JM. On bias, variance, overfitting, gold standard and consensus in single-particle analysis by cryo-electron microscopy. Acta Crystallogr D Struct Biol 2022; 78:410-423. [PMID: 35362465 PMCID: PMC8972802 DOI: 10.1107/s2059798322001978] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Accepted: 02/18/2022] [Indexed: 12/05/2022] Open
Abstract
Cryo-electron microscopy (cryoEM) has become a well established technique to elucidate the 3D structures of biological macromolecules. Projection images from thousands of macromolecules that are assumed to be structurally identical are combined into a single 3D map representing the Coulomb potential of the macromolecule under study. This article discusses possible caveats along the image-processing path and how to avoid them to obtain a reliable 3D structure. Some of these problems are very well known in the community. These may be referred to as sample-related (such as specimen denaturation at interfaces or non-uniform projection geometry leading to underrepresented projection directions). The rest are related to the algorithms used. While some have been discussed in depth in the literature, such as the use of an incorrect initial volume, others have received much less attention. However, they are fundamental in any data-analysis approach. Chiefly among them, instabilities in estimating many of the key parameters that are required for a correct 3D reconstruction that occur all along the processing workflow are referred to, which may significantly affect the reliability of the whole process. In the field, the term overfitting has been coined to refer to some particular kinds of artifacts. It is argued that overfitting is a statistical bias in key parameter-estimation steps in the 3D reconstruction process, including intrinsic algorithmic bias. It is also shown that common tools (Fourier shell correlation) and strategies (gold standard) that are normally used to detect or prevent overfitting do not fully protect against it. Alternatively, it is proposed that detecting the bias that leads to overfitting is much easier when addressed at the level of parameter estimation, rather than detecting it once the particle images have been combined into a 3D map. Comparing the results from multiple algorithms (or at least, independent executions of the same algorithm) can detect parameter bias. These multiple executions could then be averaged to give a lower variance estimate of the underlying parameters.
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Affiliation(s)
- C. O. S. Sorzano
- Biocomputing Unit, Centro Nacional de Biotecnologia (CNB-CSIC), Calle Darwin 3, 28049 Cantoblanco, Madrid, Spain
| | - A. Jiménez-Moreno
- Biocomputing Unit, Centro Nacional de Biotecnologia (CNB-CSIC), Calle Darwin 3, 28049 Cantoblanco, Madrid, Spain
| | - D. Maluenda
- Biocomputing Unit, Centro Nacional de Biotecnologia (CNB-CSIC), Calle Darwin 3, 28049 Cantoblanco, Madrid, Spain
| | - M. Martínez
- Biocomputing Unit, Centro Nacional de Biotecnologia (CNB-CSIC), Calle Darwin 3, 28049 Cantoblanco, Madrid, Spain
| | - E. Ramírez-Aportela
- Biocomputing Unit, Centro Nacional de Biotecnologia (CNB-CSIC), Calle Darwin 3, 28049 Cantoblanco, Madrid, Spain
| | - J. Krieger
- Biocomputing Unit, Centro Nacional de Biotecnologia (CNB-CSIC), Calle Darwin 3, 28049 Cantoblanco, Madrid, Spain
| | - R. Melero
- Biocomputing Unit, Centro Nacional de Biotecnologia (CNB-CSIC), Calle Darwin 3, 28049 Cantoblanco, Madrid, Spain
| | - A. Cuervo
- Biocomputing Unit, Centro Nacional de Biotecnologia (CNB-CSIC), Calle Darwin 3, 28049 Cantoblanco, Madrid, Spain
| | - J. Conesa
- Biocomputing Unit, Centro Nacional de Biotecnologia (CNB-CSIC), Calle Darwin 3, 28049 Cantoblanco, Madrid, Spain
| | | | - P. Conesa
- Biocomputing Unit, Centro Nacional de Biotecnologia (CNB-CSIC), Calle Darwin 3, 28049 Cantoblanco, Madrid, Spain
| | - L. del Caño
- Biocomputing Unit, Centro Nacional de Biotecnologia (CNB-CSIC), Calle Darwin 3, 28049 Cantoblanco, Madrid, Spain
| | - Y. C. Fonseca
- Biocomputing Unit, Centro Nacional de Biotecnologia (CNB-CSIC), Calle Darwin 3, 28049 Cantoblanco, Madrid, Spain
| | - J. Jiménez-de la Morena
- Biocomputing Unit, Centro Nacional de Biotecnologia (CNB-CSIC), Calle Darwin 3, 28049 Cantoblanco, Madrid, Spain
| | - P. Losana
- Biocomputing Unit, Centro Nacional de Biotecnologia (CNB-CSIC), Calle Darwin 3, 28049 Cantoblanco, Madrid, Spain
| | - R. Sánchez-García
- Biocomputing Unit, Centro Nacional de Biotecnologia (CNB-CSIC), Calle Darwin 3, 28049 Cantoblanco, Madrid, Spain
| | - D. Strelak
- Biocomputing Unit, Centro Nacional de Biotecnologia (CNB-CSIC), Calle Darwin 3, 28049 Cantoblanco, Madrid, Spain
- Masaryk University, Brno, Czech Republic
| | - E. Fernández-Giménez
- Biocomputing Unit, Centro Nacional de Biotecnologia (CNB-CSIC), Calle Darwin 3, 28049 Cantoblanco, Madrid, Spain
| | - F. P. de Isidro-Gómez
- Biocomputing Unit, Centro Nacional de Biotecnologia (CNB-CSIC), Calle Darwin 3, 28049 Cantoblanco, Madrid, Spain
| | - D. Herreros
- Biocomputing Unit, Centro Nacional de Biotecnologia (CNB-CSIC), Calle Darwin 3, 28049 Cantoblanco, Madrid, Spain
| | - J. L. Vilas
- School of Engineering and Applied Science, Yale University, New Haven, CT 06520-829, USA
| | - R. Marabini
- Escuela Politecnica Superior, Universidad Autónoma de Madrid, 28049 Cantoblanco, Madrid, Spain
| | - J. M. Carazo
- Biocomputing Unit, Centro Nacional de Biotecnologia (CNB-CSIC), Calle Darwin 3, 28049 Cantoblanco, Madrid, Spain
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22
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Krieger JM, Sorzano COS, Carazo JM, Bahar I. Protein dynamics developments for the large scale and cryoEM: case study of ProDy 2.0. Acta Crystallogr D Struct Biol 2022; 78:399-409. [PMID: 35362464 PMCID: PMC8972803 DOI: 10.1107/s2059798322001966] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 02/18/2022] [Indexed: 11/24/2022] Open
Abstract
Cryo-electron microscopy (cryoEM) has become a well established technique with the potential to produce structures of large and dynamic supramolecular complexes that are not amenable to traditional approaches for studying structure and dynamics. The size and low resolution of such molecular systems often make structural modelling and molecular dynamics simulations challenging and computationally expensive. This, together with the growing wealth of structural data arising from cryoEM and other structural biology methods, has driven a trend in the computational biophysics community towards the development of new pipelines for analysing global dynamics using coarse-grained models and methods. At the centre of this trend has been a return to elastic network models, normal mode analysis (NMA) and ensemble analyses such as principal component analysis, and the growth of hybrid simulation methodologies that make use of them. Here, this field is reviewed with a focus on ProDy, the Python application programming interface for protein dynamics, which has been developed over the last decade. Two key developments in this area are highlighted: (i) ensemble NMA towards extracting and comparing the signature dynamics of homologous structures, aided by the recent SignDy pipeline, and (ii) pseudoatom fitting for more efficient global dynamics analyses of large and low-resolution supramolecular assemblies from cryoEM, revisited in the CryoDy pipeline. It is believed that such a renewal and extension of old models and methods in new pipelines will be critical for driving the field forward into the next cryoEM revolution.
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Affiliation(s)
- James Michael Krieger
- Biocomputing Unit, Centro Nacional de Biotecnología (CSIC), Calle Darwin 3, 28049 Madrid, Spain
| | - Carlos Oscar S. Sorzano
- Biocomputing Unit, Centro Nacional de Biotecnología (CSIC), Calle Darwin 3, 28049 Madrid, Spain
| | - Jose Maria Carazo
- Biocomputing Unit, Centro Nacional de Biotecnología (CSIC), Calle Darwin 3, 28049 Madrid, Spain
| | - Ivet Bahar
- Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, 800 Murdoch Building, 3420 Forbes Avenue, Pittsburgh, PA 15213, USA
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23
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Wang X, Lu Y, Lin X. Heterogeneous cryo-EM projection image classification using a two-stage spectral clustering based on novel distance measures. Brief Bioinform 2022; 23:6543485. [PMID: 35255494 DOI: 10.1093/bib/bbac032] [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: 12/22/2021] [Revised: 01/17/2022] [Accepted: 01/23/2022] [Indexed: 11/13/2022] Open
Abstract
Single-particle cryo-electron microscopy (cryo-EM) has become one of the mainstream technologies in the field of structural biology to determine the three-dimensional (3D) structures of biological macromolecules. Heterogeneous cryo-EM projection image classification is an effective way to discover conformational heterogeneity of biological macromolecules in different functional states. However, due to the low signal-to-noise ratio of the projection images, the classification of heterogeneous cryo-EM projection images is a very challenging task. In this paper, two novel distance measures between projection images integrating the reliability of common lines, pixel intensity and class averages are designed, and then a two-stage spectral clustering algorithm based on the two distance measures is proposed for heterogeneous cryo-EM projection image classification. In the first stage, the novel distance measure integrating common lines and pixel intensities of projection images is used to obtain preliminary classification results through spectral clustering. In the second stage, another novel distance measure integrating the first novel distance measure and class averages generated from each group of projection images is used to obtain the final classification results through spectral clustering. The proposed two-stage spectral clustering algorithm is applied on a simulated and a real cryo-EM dataset for heterogeneous reconstruction. Results show that the two novel distance measures can be used to improve the classification performance of spectral clustering, and using the proposed two-stage spectral clustering algorithm can achieve higher classification and reconstruction accuracy than using RELION and XMIPP.
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Affiliation(s)
- Xiangwen Wang
- School of Information Science and Engineering, Lanzhou University, 730000, Lanzhou, China.,College of Computer Science and Engineering, Northwest Normal University, 730070, Lanzhou, China
| | - Yonggang Lu
- School of Information Science and Engineering, Lanzhou University, 730000, Lanzhou, China
| | - Xianghong Lin
- College of Computer Science and Engineering, Northwest Normal University, 730070, Lanzhou, China
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24
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Harastani M, Eltsov M, Leforestier A, Jonic S. TomoFlow: Analysis of Continuous Conformational Variability of Macromolecules in Cryogenic Subtomograms based on 3D Dense Optical Flow. J Mol Biol 2021; 434:167381. [PMID: 34848215 DOI: 10.1016/j.jmb.2021.167381] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 11/21/2021] [Accepted: 11/22/2021] [Indexed: 01/14/2023]
Abstract
Cryogenic Electron Tomography (cryo-ET) allows structural and dynamics studies of macromolecules in situ. Averaging different copies of imaged macromolecules is commonly used to obtain their structure at higher resolution and discrete classification to analyze their dynamics. Instrumental and data processing developments are progressively equipping cryo-ET studies with the ability to escape the trap of classification into a complete continuous conformational variability analysis. In this work, we propose TomoFlow, a method for analyzing macromolecular continuous conformational variability in cryo-ET subtomograms based on a three-dimensional dense optical flow (OF) approach. The resultant lower-dimensional conformational space allows generating movies of macromolecular motion and obtaining subtomogram averages by grouping conformationally similar subtomograms. The animations and the subtomogram group averages reveal accurate trajectories of macromolecular motion based on a novel mathematical model that makes use of OF properties. This paper describes TomoFlow with tests on simulated datasets generated using different techniques, namely Normal Mode Analysis and Molecular Dynamics Simulation. It also shows an application of TomoFlow on a dataset of nucleosomes in situ, which provided promising results coherent with previous findings using the same dataset but without imposing any prior knowledge on the analysis of the conformational variability. The method is discussed with its potential uses and limitations.
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Affiliation(s)
- Mohamad Harastani
- IMPMC - UMR 7590 CNRS, Sorbonne Université, Muséum National d'Histoire Naturelle, Paris, France; Laboratoire de Physique des Solides (LPS), UMR 8502 CNRS, Université Paris-Saclay, Orsay, France. https://twitter.com/moh_harastani
| | - Mikhail Eltsov
- Department of Integrated Structural Biology, Institute of Genetics and Molecular and Cellular Biology, Illkirch, France. https://twitter.com/EltsovMikhail
| | - Amélie Leforestier
- Laboratoire de Physique des Solides (LPS), UMR 8502 CNRS, Université Paris-Saclay, Orsay, France
| | - Slavica Jonic
- IMPMC - UMR 7590 CNRS, Sorbonne Université, Muséum National d'Histoire Naturelle, Paris, France.
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25
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Advances in Xmipp for Cryo-Electron Microscopy: From Xmipp to Scipion. Molecules 2021; 26:molecules26206224. [PMID: 34684805 PMCID: PMC8537808 DOI: 10.3390/molecules26206224] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Revised: 09/28/2021] [Accepted: 09/29/2021] [Indexed: 11/21/2022] Open
Abstract
Xmipp is an open-source software package consisting of multiple programs for processing data originating from electron microscopy and electron tomography, designed and managed by the Biocomputing Unit of the Spanish National Center for Biotechnology, although with contributions from many other developers over the world. During its 25 years of existence, Xmipp underwent multiple changes and updates. While there were many publications related to new programs and functionality added to Xmipp, there is no single publication on the Xmipp as a package since 2013. In this article, we give an overview of the changes and new work since 2013, describe technologies and techniques used during the development, and take a peek at the future of the package.
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26
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Giraldo-Barreto J, Ortiz S, Thiede EH, Palacio-Rodriguez K, Carpenter B, Barnett AH, Cossio P. A Bayesian approach to extracting free-energy profiles from cryo-electron microscopy experiments. Sci Rep 2021; 11:13657. [PMID: 34211017 PMCID: PMC8249403 DOI: 10.1038/s41598-021-92621-1] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Accepted: 06/01/2021] [Indexed: 11/08/2022] Open
Abstract
Cryo-electron microscopy (cryo-EM) extracts single-particle density projections of individual biomolecules. Although cryo-EM is widely used for 3D reconstruction, due to its single-particle nature it has the potential to provide information about a biomolecule's conformational variability and underlying free-energy landscape. However, treating cryo-EM as a single-molecule technique is challenging because of the low signal-to-noise ratio (SNR) in individual particles. In this work, we propose the cryo-BIFE method (cryo-EM Bayesian Inference of Free-Energy profiles), which uses a path collective variable to extract free-energy profiles and their uncertainties from cryo-EM images. We test the framework on several synthetic systems where the imaging parameters and conditions were controlled. We found that for realistic cryo-EM environments and relevant biomolecular systems, it is possible to recover the underlying free energy, with the pose accuracy and SNR as crucial determinants. We then use the method to study the conformational transitions of a calcium-activated channel with real cryo-EM particles. Interestingly, we recover not only the most probable conformation (used to generate a high-resolution reconstruction of the calcium-bound state) but also a metastable state that corresponds to the calcium-unbound conformation. As expected for turnover transitions within the same sample, the activation barriers are on the order of [Formula: see text]. We expect our tool for extracting free-energy profiles from cryo-EM images to enable more complete characterization of the thermodynamic ensemble of biomolecules.
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Affiliation(s)
- Julian Giraldo-Barreto
- Biophysics of Tropical Diseases Max Planck Tandem Group, University of Antioquia UdeA, Calle 70 No. 52-21, Medellín, Colombia
- Magnetism and Simulation Group, University of Antioquia UdeA, Calle 70 No. 52-21, Medellín, Colombia
| | - Sebastian Ortiz
- Biophysics of Tropical Diseases Max Planck Tandem Group, University of Antioquia UdeA, Calle 70 No. 52-21, Medellín, Colombia
| | - Erik H Thiede
- Center for Computational Mathematics, Flatiron Institute, New York City, USA
| | - Karen Palacio-Rodriguez
- Institut de Minéralogie, de Physique des Matériaux et de Cosmochimie, Sorbonne Université, Paris, France
| | - Bob Carpenter
- Center for Computational Mathematics, Flatiron Institute, New York City, USA
| | - Alex H Barnett
- Center for Computational Mathematics, Flatiron Institute, New York City, USA
| | - Pilar Cossio
- Biophysics of Tropical Diseases Max Planck Tandem Group, University of Antioquia UdeA, Calle 70 No. 52-21, Medellín, Colombia.
- Department of Theoretical Biophysics, Max Planck Institute of Biophysics, 60438, Frankfurt am Main, Germany.
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27
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Vakili N, Habeck M. Bayesian Random Tomography of Particle Systems. Front Mol Biosci 2021; 8:658269. [PMID: 34095220 PMCID: PMC8177743 DOI: 10.3389/fmolb.2021.658269] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Accepted: 04/26/2021] [Indexed: 11/13/2022] Open
Abstract
Random tomography is a common problem in imaging science and refers to the task of reconstructing a three-dimensional volume from two-dimensional projection images acquired in unknown random directions. We present a Bayesian approach to random tomography. At the center of our approach is a meshless representation of the unknown volume as a mixture of spherical Gaussians. Each Gaussian can be interpreted as a particle such that the unknown volume is represented by a particle cloud. The particle representation allows us to speed up the computation of projection images and to represent a large variety of structures accurately and efficiently. We develop Markov chain Monte Carlo algorithms to infer the particle positions as well as the unknown orientations. Posterior sampling is challenging due to the high dimensionality and multimodality of the posterior distribution. We tackle these challenges by using Hamiltonian Monte Carlo and a global rotational sampling strategy. We test the approach on various simulated and real datasets.
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Affiliation(s)
- Nima Vakili
- Microscopic Image Analysis Group, Jena University Hospital, Jena, Germany
| | - Michael Habeck
- Microscopic Image Analysis Group, Jena University Hospital, Jena, Germany
- Statistical Inverse Problems in Biophysics, Max Planck Institute for Biophysical Chemistry, Göttingen, Germany
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28
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Harastani M, Eltsov M, Leforestier A, Jonic S. HEMNMA-3D: Cryo Electron Tomography Method Based on Normal Mode Analysis to Study Continuous Conformational Variability of Macromolecular Complexes. Front Mol Biosci 2021; 8:663121. [PMID: 34095222 PMCID: PMC8170028 DOI: 10.3389/fmolb.2021.663121] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Accepted: 04/09/2021] [Indexed: 12/28/2022] Open
Abstract
Cryogenic electron tomography (cryo-ET) allows structural determination of biomolecules in their native environment (in situ). Its potential of providing information on the dynamics of macromolecular complexes in cells is still largely unexploited, due to the challenges of the data analysis. The crowded cell environment and continuous conformational changes of complexes make difficult disentangling the data heterogeneity. We present HEMNMA-3D, which is, to the best of our knowledge, the first method for analyzing cryo electron subtomograms in terms of continuous conformational changes of complexes. HEMNMA-3D uses a combination of elastic and rigid-body 3D-to-3D iterative alignments of a flexible 3D reference (atomic structure or electron microscopy density map) to match the conformation, orientation, and position of the complex in each subtomogram. The elastic matching combines molecular mechanics simulation (Normal Mode Analysis of the 3D reference) and experimental, subtomogram data analysis. The rigid-body alignment includes compensation for the missing wedge, due to the limited tilt angle of cryo-ET. The conformational parameters (amplitudes of normal modes) of the complexes in subtomograms obtained through the alignment are processed to visualize the distribution of conformations in a space of lower dimension (typically, 2D or 3D) referred to as space of conformations. This allows a visually interpretable insight into the dynamics of the complexes, by calculating 3D averages of subtomograms with similar conformations from selected (densest) regions and by recording movies of the 3D reference's displacement along selected trajectories through the densest regions. We describe HEMNMA-3D and show its validation using synthetic datasets. We apply HEMNMA-3D to an experimental dataset describing in situ nucleosome conformational variability. HEMNMA-3D software is available freely (open-source) as part of ContinuousFlex plugin of Scipion V3.0 (http://scipion.i2pc.es).
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Affiliation(s)
- Mohamad Harastani
- IMPMC-UMR 7590 CNRS, Sorbonne Université, Muséum National d'Histoire Naturelle, Paris, France
| | - Mikhail Eltsov
- Department of Integrated Structural Biology, Institute of Genetics and Molecular and Cellular Biology, Illkirch, France
| | - Amélie Leforestier
- Laboratoire de Physique des Solides, UMR 8502 CNRS, Université Paris-Saclay, Paris, France
| | - Slavica Jonic
- IMPMC-UMR 7590 CNRS, Sorbonne Université, Muséum National d'Histoire Naturelle, Paris, France
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29
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A set of common movements within GPCR-G-protein complexes from variability analysis of cryo-EM datasets. J Struct Biol 2021; 213:107699. [PMID: 33545352 DOI: 10.1016/j.jsb.2021.107699] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Revised: 01/05/2021] [Accepted: 01/22/2021] [Indexed: 12/11/2022]
Abstract
G-protein coupled receptors (GPCRs) are among the most versatile signal transducers in the cell. Once activated, GPCRs sample a large conformational space and couple to G-proteins to initiate distinct signaling pathways. The dynamical behavior of GPCR-G-protein complexes is difficult characterize structurally, and it might hinder obtaining routine high-resolution density maps in single-particle reconstructions. Here, we used variability analysis on the rhodopsin-Gi-Fab16 complex cryo-EM dataset, and the results provide insights into the dynamic nature of the receptor-complex interaction. We compare the outcome of this analysis with recent results obtained on the cannabinoid-Gi- and secretin-Gs-receptor complexes. Despite differences related to the biochemical compositions of the three samples, a set of consensus movements emerges. We anticipate that systematic variability analysis on GPCR-G-protein complexes may provide useful information not only at the biological level, but also for improving the preparation of more stable samples for cryo-EM single-particle analysis.
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30
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Abstract
Single-particle electron cryomicroscopy (cryo-EM) is an increasingly popular technique for elucidating the three-dimensional structure of proteins and other biologically significant complexes at near-atomic resolution. It is an imaging method that does not require crystallization and can capture molecules in their native states. In single-particle cryo-EM, the three-dimensional molecular structure needs to be determined from many noisy two-dimensional tomographic projections of individual molecules, whose orientations and positions are unknown. The high level of noise and the unknown pose parameters are two key elements that make reconstruction a challenging computational problem. Even more challenging is the inference of structural variability and flexible motions when the individual molecules being imaged are in different conformational states. This review discusses computational methods for structure determination by single-particle cryo-EM and their guiding principles from statistical inference, machine learning, and signal processing that also play a significant role in many other data science applications.
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Affiliation(s)
- Amit Singer
- Department of Mathematics and Program in Applied and Computational Mathematics, Princeton University, Princeton, NJ 08544, USA
| | - Fred J Sigworth
- Departments of Cellular and Molecular Physiology, Biomedical Engineering, and Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
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31
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Shape-preserving elastic solid models of macromolecules. PLoS Comput Biol 2020; 16:e1007855. [PMID: 32407309 PMCID: PMC7297265 DOI: 10.1371/journal.pcbi.1007855] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2019] [Revised: 06/09/2020] [Accepted: 04/07/2020] [Indexed: 02/07/2023] Open
Abstract
Mass-spring models have been a standard approach in molecular modeling for the last few decades, such as elastic network models (ENMs) that are widely used for normal mode analysis. In this work, we present a vastly different elastic solid model (ESM) of macromolecules that shares the same simplicity and efficiency as ENMs in producing the equilibrium dynamics and moreover, offers some significant new features that may greatly benefit the research community. ESM is different from ENM in that it treats macromolecules as elastic solids. Our particular version of ESM presented in this work, named αESM, captures the shape of a given biomolecule most economically using alpha shape, a well-established technique from the computational geometry community. Consequently, it can produce most economical coarse-grained models while faithfully preserving the shape and thus makes normal mode computations and visualization of extremely large complexes more manageable. Secondly, as a solid model, ESM’s close link to finite element analysis renders it ideally suited for studying mechanical responses of macromolecules under external force. Lastly, we show that ESM can be applied also to structures without atomic coordinates such as those from cryo-electron microscopy. The complete MATLAB code of αESM is provided. Mass-spring models have been a standard approach in classical molecular modeling where atoms are modeled as spheres with a mass and their interactions modeled as springs. The models have been extremely successful. Thinking ahead, however, as molecular systems of our interest grow more quickly in size or dimension than what our computation resources can keep up with, some adjustments in methodology are timely. This work presents a vastly different elastic solid model (ESM) of macromolecules that shares the same simplicity and efficiency as mass-spring models in producing the equilibrium dynamics and moreover, offers some unique features that make it suitable for much larger systems. ESM is different from ENMs in that it treats macromolecules as elastic solids. Our particular version of ESM model presented in this work, named αESM, captures the shape of a given biomolecule most economically using alpha shape, a well-established technique from the computational geometry community. Consequently, it can produce most economical coarse-grained models while faithfully preserving the shape. ESM can be applied also to structures without atomic coordinates such as those from cryo-electron microscopy.
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32
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Srivastava A, Tiwari SP, Miyashita O, Tama F. Integrative/Hybrid Modeling Approaches for Studying Biomolecules. J Mol Biol 2020; 432:2846-2860. [DOI: 10.1016/j.jmb.2020.01.039] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2019] [Revised: 01/20/2020] [Accepted: 01/24/2020] [Indexed: 12/12/2022]
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33
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Lederman RR, Andén J, Singer A. Hyper-Molecules: on the Representation and Recovery of Dynamical Structures for Applications in Flexible Macro-Molecules in Cryo-EM. INVERSE PROBLEMS 2020; 36:044005. [PMID: 38304203 PMCID: PMC10831863 DOI: 10.1088/1361-6420/ab5ede] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/03/2024]
Abstract
Cryo-electron microscopy (cryo-EM), the subject of the 2017 Nobel Prize in Chemistry, is a technology for obtaining 3-D reconstructions of macromolecules from many noisy 2-D projections of instances of these macromolecules, whose orientations and positions are unknown. These molecules are not rigid objects, but flexible objects involved in dynamical processes. The different conformations are exhibited by different instances of the macromolecule observed in a cryo-EM experiment, each of which is recorded as a particle image. The range of conformations and the conformation of each particle are not known a priori; one of the great promises of cryo-EM is to map this conformation space. Remarkable progress has been made in reconstructing rigid molecules based on homogeneous samples of molecules in spite of the unknown orientation of each particle image and significant progress has been made in recovering a few distinct states from mixtures of rather distinct conformations, but more complex heterogeneous samples remain a major challenge. We introduce the "hyper-molecule" theoretical framework for modeling structures across different states of heterogeneous molecules, including continuums of states. The key idea behind this framework is representing heterogeneous macromolecules as high-dimensional objects, with the additional dimensions representing the conformation space. This idea is then refined to model properties such as localized heterogeneity. In addition, we introduce an algorithmic framework for reconstructing such heterogeneous objects from experimental data using a Bayesian formulation of the problem and Markov chain Monte Carlo (MCMC) algorithms to address the computational challenges in recovering these high dimensional hyper-molecules. We demonstrate these ideas in a preliminary prototype implementation, applied to synthetic data.
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Affiliation(s)
- Roy R Lederman
- The Department of Statistics and Data Science, Yale University, New Haven, CT
| | - Joakim Andén
- Center for Computational Mathematics, Flatiron Institute, New York, NY
| | - Amit Singer
- Department of Mathematics and Program in Applied and Computational Mathematics, Princeton University, Princeton, NJ
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34
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Abstract
Cross-validation is used to determine the validity of a model on unseen data by assessing if the model is overfitted to noise. It is widely used in many fields, from artificial intelligence to structural biology in X-ray crystallography and nuclear magnetic resonance. Although there are concerns of map overfitting in cryo-electron microscopy (cryo-EM), cross-validation is rarely used. The problem is that establishing a performance metric of the maps over unseen data (given by 2D-projection images) is difficult due to the low signal-to-noise ratios in the individual particles. Here, I present recent advances for cryo-EM map reconstruction. I highlight that the gold-standard procedure can fail to detect map overfitting in certain cases, showing the necessity of assessing the map quality on unbiased data. Finally, I describe the challenges and advantages of developing a robust cross-validation methodology for cryo-EM.
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Affiliation(s)
- Pilar Cossio
- Biophysics of Tropical Diseases, Max Planck Tandem Group, University of Antioquia UdeA, Calle 70 No. 52-21, Medellin, Colombia.,Department of Theoretical Biophysics, Max Planck Institute of Biophysics, 60438 Frankfurt am Main, Germany
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35
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Moscovich A, Halevi A, Andén J, Singer A. Cryo-EM reconstruction of continuous heterogeneity by Laplacian spectral volumes. INVERSE PROBLEMS 2020; 36:024003. [PMID: 32394996 PMCID: PMC7213598 DOI: 10.1088/1361-6420/ab4f55] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
Single-particle electron cryomicroscopy is an essential tool for high-resolution 3D reconstruction of proteins and other biological macromolecules. An important challenge in cryo-EM is the reconstruction of non-rigid molecules with parts that move and deform. Traditional reconstruction methods fail in these cases, resulting in smeared reconstructions of the moving parts. This poses a major obstacle for structural biologists, who need high-resolution reconstructions of entire macromolecules, moving parts included. To address this challenge, we present a new method for the reconstruction of macromolecules exhibiting continuous heterogeneity. The proposed method uses projection images from multiple viewing directions to construct a graph Laplacian through which the manifold of three-dimensional conformations is analyzed. The 3D molecular structures are then expanded in a basis of Laplacian eigenvectors, using a novel generalized tomographic reconstruction algorithm to compute the expansion coefficients. These coefficients, which we name spectral volumes, provide a high-resolution visualization of the molecular dynamics. We provide a theoretical analysis and evaluate the method empirically on several simulated data sets.
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Affiliation(s)
- Amit Moscovich
- Program in Applied & Computational Mathematics, Princeton University, Princeton, NJ
| | - Amit Halevi
- Program in Applied & Computational Mathematics, Princeton University, Princeton, NJ
| | - Joakim Andén
- Center for Computational Mathematics, Flatiron Institute, New York, NY
| | - Amit Singer
- Program in Applied & Computational Mathematics, Princeton University, Princeton, NJ
- Department of Mathematics, Princeton University, Princeton, NJ
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36
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Harastani M, Sorzano COS, Jonić S. Hybrid Electron Microscopy Normal Mode Analysis with Scipion. Protein Sci 2019; 29:223-236. [PMID: 31693263 DOI: 10.1002/pro.3772] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2019] [Revised: 11/03/2019] [Accepted: 11/04/2019] [Indexed: 12/12/2022]
Abstract
Hybrid Electron Microscopy Normal Mode Analysis (HEMNMA) method was introduced in 2014. HEMNMA computes normal modes of a reference model (an atomic structure or an electron microscopy map) of a molecular complex and uses this model and its normal modes to analyze single-particle images of the complex to obtain information on its continuous conformational changes, by determining the full distribution of conformational variability from the images. An advantage of HEMNMA is a simultaneous determination of all parameters of each image (particle conformation, orientation, and shift) through their iterative optimization, which allows applications of HEMNMA even when the effects of conformational changes dominate those of orientational changes. HEMNMA was first implemented in Xmipp and was using MATLAB for statistical analysis of obtained conformational distributions and for fitting of underlying trajectories of conformational changes. A HEMNMA implementation independent of MATLAB is now available as part of a plugin of Scipion V2.0 (http://scipion.i2pc.es). This plugin, named ContinuousFlex, can be installed by following the instructions at https://pypi.org/project/scipion-em-continuousflex. In this article, we present this new HEMNMA software, which is user-friendly, totally free, and open-source. STATEMENT FOR A BROADER AUDIENCE: This article presents Hybrid Electron Microscopy Normal Mode Analysis (HEMNMA) software that allows analyzing single-particle images of a complex to obtain information on continuous conformational changes of the complex, by determining the full distribution of conformational variability from the images. The HEMNMA software is user-friendly, totally free, open-source, and available as part of ContinuousFlex plugin (https://pypi.org/project/scipion-em-continuousflex) of Scipion V2.0 (http://scipion.i2pc.es).
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Affiliation(s)
- Mohamad Harastani
- Sorbonne Université, UMR CNRS 7590, Muséum National d'Histoire Naturelle, IRD, Institut de Minéralogie, de Physique des Matériaux et de Cosmochimie, IMPMC, Paris, France
| | | | - Slavica Jonić
- Sorbonne Université, UMR CNRS 7590, Muséum National d'Histoire Naturelle, IRD, Institut de Minéralogie, de Physique des Matériaux et de Cosmochimie, IMPMC, Paris, France
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37
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Xu N, Doerschuk PC. Reconstruction of Stochastic 3D Signals With Symmetric Statistics From 2D Projection Images Motivated by Cryo-Electron Microscopy. IEEE TRANSACTIONS ON IMAGE PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 2019; 28:5479-5494. [PMID: 31095482 DOI: 10.1109/tip.2019.2915631] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Cryo-electron microscopy provides 2D projection images of the 3D electron scattering intensity of many instances of the particle under study (e.g., a virus). Both symmetry (rotational point groups) and heterogeneity are important aspects of biological particles and both aspects can be combined by describing the electron scattering intensity of the particle as a stochastic process with a symmetric probability law and, therefore, symmetric moments. A maximum likelihood estimator implemented by an expectation-maximization algorithm is described, which estimates the unknown statistics of the electron scattering intensity stochastic process from the images of instances of the particle. The algorithm is demonstrated on the bacteriophage HK97 and the virus [Formula: see text]. The results are contrasted with the existing algorithms, which assume that each instance of the particle has the symmetry rather than the less restrictive assumption that the probability law has the symmetry.
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38
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Malhotra S, Träger S, Dal Peraro M, Topf M. Modelling structures in cryo-EM maps. Curr Opin Struct Biol 2019; 58:105-114. [PMID: 31394387 DOI: 10.1016/j.sbi.2019.05.024] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2019] [Revised: 05/23/2019] [Accepted: 05/25/2019] [Indexed: 12/20/2022]
Abstract
Recent advances in structure determination of sub-cellular structures using cryo-electron microscopy and tomography have enabled us to understand their architecture in a more detailed manner and gain insight into their function. The choice of approach to use for atomic model building, fitting, refinement and validation in the 3D map resulting from these experiments depends primarily on the resolution of the map and the prior information on the corresponding model. Here, we survey some of such methods and approaches and highlight their uses in specific recent examples.
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Affiliation(s)
- Sony Malhotra
- Institute of Structural and Molecular Biology, Department of Biological Sciences, Birkbeck College, University of London, Malet Street, London WC1E 7HX, United Kingdom
| | - Sylvain Träger
- Institute of Bioengineering, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, Lausanne 1015, Switzerland
| | - Matteo Dal Peraro
- Institute of Bioengineering, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, Lausanne 1015, Switzerland
| | - Maya Topf
- Institute of Structural and Molecular Biology, Department of Biological Sciences, Birkbeck College, University of London, Malet Street, London WC1E 7HX, United Kingdom.
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Dasgupta B, Miyashita O, Tama F. Reconstruction of low-resolution molecular structures from simulated atomic force microscopy images. Biochim Biophys Acta Gen Subj 2019; 1864:129420. [PMID: 31472175 DOI: 10.1016/j.bbagen.2019.129420] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2019] [Revised: 08/22/2019] [Accepted: 08/26/2019] [Indexed: 12/16/2022]
Abstract
BACKGROUND Atomic Force Microscopy (AFM) is an experimental technique to study structure-function relationship of biomolecules. AFM provides images of biomolecules at nanometer resolution. High-speed AFM experiments produce a series of images following dynamics of biomolecules. To further understand biomolecular functions, information on three-dimensional (3D) structures is beneficial. METHOD We aim to recover 3D information from an AFM image by computational modeling. The AFM image includes only low-resolution representation of a molecule; therefore we represent the structures by a coarse grained model (Gaussian mixture model). Using Monte-Carlo sampling, candidate models are generated to increase similarity between AFM images simulated from the models and target AFM image. RESULTS The algorithm was tested on two proteins to model their conformational transitions. Using a simulated AFM image as reference, the algorithm can produce a low-resolution 3D model of the target molecule. Effect of molecular orientations captured in AFM images on the 3D modeling performance was also examined and it is shown that similar accuracy can be obtained for many orientations. CONCLUSIONS The proposed algorithm can generate 3D low-resolution protein models, from which conformational transitions observed in AFM images can be interpreted in more detail. GENERAL SIGNIFICANCE High-speed AFM experiments allow us to directly observe biomolecules in action, which provides insights on biomolecular function through dynamics. However, as only partial structural information can be obtained from AFM data, this new AFM based hybrid modeling method would be useful to retrieve 3D information of the entire biomolecule.
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Affiliation(s)
- Bhaskar Dasgupta
- Center for Computational Science, RIKEN, Kobe, Hyogo, 650-0047, Japan.
| | - Osamu Miyashita
- Center for Computational Science, RIKEN, Kobe, Hyogo, 650-0047, Japan.
| | - Florence Tama
- Center for Computational Science, RIKEN, Kobe, Hyogo, 650-0047, Japan; Department of Physics, Graduate School of Science, Nagoya University, Aichi, 464-8602, Japan; Institute of Transformative Bio-Molecules (WPI-ITbM), Nagoya University, Aichi, 464-8601, Japan.
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40
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Eltsov M, Grewe D, Lemercier N, Frangakis A, Livolant F, Leforestier A. Nucleosome conformational variability in solution and in interphase nuclei evidenced by cryo-electron microscopy of vitreous sections. Nucleic Acids Res 2019; 46:9189-9200. [PMID: 30053160 PMCID: PMC6158616 DOI: 10.1093/nar/gky670] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2018] [Accepted: 07/13/2018] [Indexed: 01/04/2023] Open
Abstract
In Eukaryotes, DNA is wound around the histone octamer forming the basic chromatin unit, the nucleosome. Atomic structures have been obtained from crystallography and single particle cryo-electron microscopy (cryoEM) of identical engineered particles. But native nucleosomes are dynamical entities with diverse DNA sequence and histone content, and little is known about their conformational variability, especially in the cellular context. Using cryoEM and tomography of vitreous sections we analyse native nucleosomes, both in vitro, using purified particles solubilized at physiologically relevant concentrations (25–50%), and in situ, within interphase nuclei. We visualize individual nucleosomes at a level of detail that allows us to measure the distance between the DNA gyres wrapped around. In concentrated solutions, we demonstrate a salt-dependent transition, with a high salt compact conformation resembling the canonical nucleosome and an open low salt one, closer to nuclear nucleosomes. Although further particle characterization and cartography are needed to understand the relationship between this conformational variability and chromatin functional states, this work opens a route to chromatin exploration in situ.
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Affiliation(s)
- Mikhail Eltsov
- Buchmann Institute for Molecular Life Sciences, Goethe University, 60438 Frankfurt am Main, Germany
| | - Diana Grewe
- Buchmann Institute for Molecular Life Sciences, Goethe University, 60438 Frankfurt am Main, Germany
| | - Nicolas Lemercier
- Laboratoire de Physique des Solides, UMR 8502 CNRS, Université Paris-Sud, Université Paris-Saclay, Bat 510, 91405 Orsay Cedex, France
| | - Achilleas Frangakis
- Buchmann Institute for Molecular Life Sciences, Goethe University, 60438 Frankfurt am Main, Germany
| | - Françoise Livolant
- Laboratoire de Physique des Solides, UMR 8502 CNRS, Université Paris-Sud, Université Paris-Saclay, Bat 510, 91405 Orsay Cedex, France
| | - Amélie Leforestier
- Laboratoire de Physique des Solides, UMR 8502 CNRS, Université Paris-Sud, Université Paris-Saclay, Bat 510, 91405 Orsay Cedex, France
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41
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Affiliation(s)
- Giulia Palermo
- Department of Bioengineering , University of California Riverside , Riverside , California 92521 , United States
| | - Yuji Sugita
- Theoretical Molecular Science Laboratory , RIKEN Cluster for Pioneering Research , 2-1 Hirosawa , Wako , Saitama 351-0198 , Japan.,Computational Biophysics Research Team , RIKEN Center for Computational Science , 7-1-26 Minatojima-Minamimachi , Chuo-ku, Kobe , Hyogo 650-0047 , Japan.,Laboratory for Biomolecular Function Simulation , RIKEN Center for Biosystems Dynamics Research , 1-6-5 Minatojima-Minamimachi , Chuo-ku, Kobe , Hyogo 650-0047 , Japan
| | - Willy Wriggers
- Department of Mechanical and Aerospace Engineering , Old Dominion University , Norfolk , Virginia 23529 , United States
| | - Rommie E Amaro
- Department of Chemistry and Biochemistry , University of California San Diego , San Diego , California 92093-0340 , United States
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42
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Abstract
Cryogenic electron microscopy (cryo-EM) enables structure determination of macromolecular objects and their assemblies. Although the techniques have been developing for nearly four decades, they have gained widespread attention in recent years due to technical advances on numerous fronts, enabling traditional microscopists to break into the world of molecular structural biology. Many samples can now be routinely analyzed at near-atomic resolution using standard imaging and image analysis techniques. However, numerous challenges to conventional workflows remain, and continued technical advances open entirely novel opportunities for discovery and exploration. Here, I will review some of the main methods surrounding cryo-EM with an emphasis specifically on single-particle analysis, and I will highlight challenges, open questions, and opportunities for methodology development.
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Affiliation(s)
- Dmitry Lyumkis
- From the Laboratory of Genetics and Helmsley Center for Genomic Medicine, The Salk Institute for Biological Studies, La Jolla, California 92037
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43
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Sorzano COS, Jiménez A, Mota J, Vilas JL, Maluenda D, Martínez M, Ramírez-Aportela E, Majtner T, Segura J, Sánchez-García R, Rancel Y, del Caño L, Conesa P, Melero R, Jonic S, Vargas J, Cazals F, Freyberg Z, Krieger J, Bahar I, Marabini R, Carazo JM. Survey of the analysis of continuous conformational variability of biological macromolecules by electron microscopy. Acta Crystallogr F Struct Biol Commun 2019; 75:19-32. [PMID: 30605122 PMCID: PMC6317454 DOI: 10.1107/s2053230x18015108] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2018] [Accepted: 10/26/2018] [Indexed: 11/10/2022] Open
Abstract
Single-particle analysis by electron microscopy is a well established technique for analyzing the three-dimensional structures of biological macromolecules. Besides its ability to produce high-resolution structures, it also provides insights into the dynamic behavior of the structures by elucidating their conformational variability. Here, the different image-processing methods currently available to study continuous conformational changes are reviewed.
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Affiliation(s)
| | - A. Jiménez
- National Center of Biotechnology (CSIC), Spain
| | - J. Mota
- National Center of Biotechnology (CSIC), Spain
| | - J. L. Vilas
- National Center of Biotechnology (CSIC), Spain
| | - D. Maluenda
- National Center of Biotechnology (CSIC), Spain
| | - M. Martínez
- National Center of Biotechnology (CSIC), Spain
| | | | - T. Majtner
- National Center of Biotechnology (CSIC), Spain
| | - J. Segura
- National Center of Biotechnology (CSIC), Spain
| | | | - Y. Rancel
- National Center of Biotechnology (CSIC), Spain
| | - L. del Caño
- National Center of Biotechnology (CSIC), Spain
| | - P. Conesa
- National Center of Biotechnology (CSIC), Spain
| | - R. Melero
- National Center of Biotechnology (CSIC), Spain
| | - S. Jonic
- Sorbonne Université, UMR CNRS 7590, Muséum National d’Histoire Naturelle, IRD, Institut de Minéralogie, de Physique des Matériaux et de Cosmochimie, IMPMC, Paris, France
| | | | - F. Cazals
- Inria Sophia Antipolis – Méditerranée, France
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44
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Advances in image processing for single-particle analysis by electron cryomicroscopy and challenges ahead. Curr Opin Struct Biol 2018; 52:127-145. [PMID: 30509756 DOI: 10.1016/j.sbi.2018.11.004] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2018] [Revised: 10/26/2018] [Accepted: 11/17/2018] [Indexed: 12/20/2022]
Abstract
Electron cryomicroscopy (cryoEM) is essential for the study and functional understanding of non-crystalline macromolecules such as proteins. These molecules cannot be imaged using X-ray crystallography or other popular methods. CryoEM has been successfully used to visualize macromolecular complexes such as ribosomes, viruses, and ion channels. Determination of structural models of these at various conformational states leads to insight on how these molecules function. Recent advances in imaging technology have given cryoEM a scientific rebirth. As a result of these technological advances image processing and analysis have yielded molecular structures at atomic resolution. Nevertheless there continue to be challenges in image processing, and in this article we will touch on the most essential in order to derive an accurate three-dimensional model from noisy projection images. Traditional approaches, such as k-means clustering for class averaging, will be provided as background. We will then highlight new approaches for each image processing subproblem, including a 3D reconstruction method for asymmetric molecules using just two projection images and deep learning algorithms for automated particle picking.
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45
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Jonic S, Miyashita O, Callebaut I. Editorial: Hybrid Biomolecular Modeling. Front Mol Biosci 2018; 5:98. [PMID: 30474030 PMCID: PMC6238118 DOI: 10.3389/fmolb.2018.00098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2018] [Accepted: 10/23/2018] [Indexed: 12/03/2022] Open
Affiliation(s)
- Slavica Jonic
- Sorbonne Université, UMR CNRS 7590, Muséum National d'Histoire Naturelle, IRD, Institut de Minéralogie, de Physique des Matériaux et de Cosmochimie, IMPMC, Paris, France
| | | | - Isabelle Callebaut
- Sorbonne Université, UMR CNRS 7590, Muséum National d'Histoire Naturelle, IRD, Institut de Minéralogie, de Physique des Matériaux et de Cosmochimie, IMPMC, Paris, France
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46
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A methodology using Gaussian-based density map approximation to assess sets of cryo-electron microscopy density maps. J Struct Biol 2018; 204:344-350. [PMID: 30036578 DOI: 10.1016/j.jsb.2018.07.014] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2018] [Revised: 07/16/2018] [Accepted: 07/19/2018] [Indexed: 12/24/2022]
Abstract
This article presents a methodology to assess a set of density maps, as used in the Blind Assessment Phase of the 2015/2016 Map Challenge (EMDataBank Validation Challenges). The synthetic and experimental cryo-electron microscopy (cryo-EM) density maps obtained by different single particle analysis protocols and by different participants, submitted in the Challenge Phase for assessment, were analyzed with this methodology and the obtained results are presented and discussed here. The goal of using such a methodology was to blindly identify the density maps with globally similar structural information, meaning the maps with the structural information mostly "reproduced" by different protocols. To this end, the density maps are "coarsened" using Gaussian-based approximations, with the same input approximation parameters for all maps of the target biological complex. The approximated maps are then represented in a common reduced-dimension (here, 3D) space of their correlation-coefficient-based distances, in which close maps mean similar maps. The distance analysis allows identifying maps with the most "reproduced" structural information by different protocols. The obtained results are also discussed taking into account the detailed information about the protocols that has been released after the end of the Blind Assessment Phase.
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47
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Nakane T, Kimanius D, Lindahl E, Scheres SH. Characterisation of molecular motions in cryo-EM single-particle data by multi-body refinement in RELION. eLife 2018; 7:36861. [PMID: 29856314 PMCID: PMC6005684 DOI: 10.7554/elife.36861] [Citation(s) in RCA: 309] [Impact Index Per Article: 51.5] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2018] [Accepted: 05/31/2018] [Indexed: 12/18/2022] Open
Abstract
Macromolecular complexes that exhibit continuous forms of structural flexibility pose a challenge for many existing tools in cryo-EM single-particle analysis. We describe a new tool, called multi-body refinement, which models flexible complexes as a user-defined number of rigid bodies that move independently from each other. Using separate focused refinements with iteratively improved partial signal subtraction, the new tool generates improved reconstructions for each of the defined bodies in a fully automated manner. Moreover, using principal component analysis on the relative orientations of the bodies over all particle images in the data set, we generate movies that describe the most important motions in the data. Our results on two test cases, a cytoplasmic ribosome from Plasmodium falciparum, and the spliceosomal B-complex from yeast, illustrate how multi-body refinement can be useful to gain unique insights into the structure and dynamics of large and flexible macromolecular complexes.
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Affiliation(s)
- Takanori Nakane
- MRC Laboratory of Molecular Biology, Cambridge, United Kingdom
| | - Dari Kimanius
- Department of Biochemistry and Biophysics, Science for Life Laboratory, Stockholm University, Stockholm, Sweden
| | - Erik Lindahl
- Department of Biochemistry and Biophysics, Science for Life Laboratory, Stockholm University, Stockholm, Sweden.,Swedish e-Science Research Center, KTH Royal Institute of Technology, Stockholm, Sweden
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48
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Andén J, Singer A. Structural Variability from Noisy Tomographic Projections. SIAM JOURNAL ON IMAGING SCIENCES 2018; 11:1441-1492. [PMID: 30555617 PMCID: PMC6294454 DOI: 10.1137/17m1153509] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2023]
Abstract
In cryo-electron microscopy, the three-dimensional (3D) electric potentials of an ensemble of molecules are projected along arbitrary viewing directions to yield noisy two-dimensional images. The volume maps representing these potentials typically exhibit a great deal of structural variability, which is described by their 3D covariance matrix. Typically, this covariance matrix is approximately low rank and can be used to cluster the volumes or estimate the intrinsic geometry of the conformation space. We formulate the estimation of this covariance matrix as a linear inverse problem, yielding a consistent least-squares estimator. For n images of size N-by-N pixels, we propose an algorithm for calculating this covariance estimator with computational complexity O ( n N 4 + κ N 6 log N ) , where the condition number κ is empirically in the range 10-200. Its efficiency relies on the observation that the normal equations are equivalent to a deconvolution problem in six dimensions. This is then solved by the conjugate gradient method with an appropriate circulant preconditioner. The result is the first computationally efficient algorithm for consistent estimation of the 3D covariance from noisy projections. It also compares favorably in runtime with respect to previously proposed nonconsistent estimators. Motivated by the recent success of eigenvalue shrinkage procedures for high-dimensional covariance matrix estimation, we incorporate a shrinkage procedure that improves accuracy at lower signal-to-noise ratios. We evaluate our methods on simulated datasets and achieve classification results comparable to state-of-the-art methods in shorter running time. We also present results on clustering volumes in an experimental dataset, illustrating the power of the proposed algorithm for practical determination of structural variability.
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Affiliation(s)
- Joakim Andén
- Center for Computational Biology, Flatiron Institute, New York, NY 10100
| | - Amit Singer
- Department of Mathematics and Program in Applied and Computational Mathematics, Princeton University, Princeton, NJ 08544
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49
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Image processing for cryogenic transmission electron microscopy of symmetry-mismatched complexes. Biosci Rep 2018; 38:BSR20170203. [PMID: 29439140 PMCID: PMC5857907 DOI: 10.1042/bsr20170203] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2017] [Revised: 02/05/2018] [Accepted: 02/05/2018] [Indexed: 12/23/2022] Open
Abstract
Cryogenic transmission electron microscopy (cryo-TEM) is a high-resolution biological imaging method, whereby biological samples, such as purified proteins, macromolecular complexes, viral particles, organelles and cells, are embedded in vitreous ice preserving their native structures. Due to sensitivity of biological materials to the electron beam of the microscope, only relatively low electron doses can be applied during imaging. As a result, the signal arising from the structure of interest is overpowered by noise in the images. To increase the signal-to-noise ratio, different image processing-based strategies that aim at coherent averaging of signal have been devised. In such strategies, images are generally assumed to arise from multiple identical copies of the structure. Prior to averaging, the images must be grouped according to the view of the structure they represent and images representing the same view must be simultaneously aligned relatively to each other. For computational reconstruction of the 3D structure, images must contain different views of the original structure. Structures with multiple symmetry-related substructures are advantageous in averaging approaches because each image provides multiple views of the substructures. However, the symmetry assumption may be valid for only parts of the structure, leading to incoherent averaging of the other parts. Several image processing approaches have been adapted to tackle symmetry-mismatched substructures with increasing success. Such structures are ubiquitous in nature and further computational method development is needed to understanding their biological functions.
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50
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Cossio P, Hummer G. Likelihood-based structural analysis of electron microscopy images. Curr Opin Struct Biol 2018; 49:162-168. [PMID: 29579548 DOI: 10.1016/j.sbi.2018.03.004] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2017] [Revised: 01/24/2018] [Accepted: 03/06/2018] [Indexed: 10/17/2022]
Abstract
Likelihood-based analysis of single-particle electron microscopy images has contributed much to the recent improvements in resolution. By treating particle orientations and classes probabilistically, uncertainties in the reconstruction process are explicitly accounted for, and the risk of bias towards the initial model is diminished. As a result, the quality and reliability of the reconstructions have greatly improved at manageable computational cost. Likelihood-based analysis of electron microscopy images also offers a route to direct coordinate refinement for dynamic systems, as an alternative to 3D density reconstruction. Here, we review recent developments in the algorithms used for reconstructions of high-resolution maps, and in the integrative framework of combining likelihood methods with simulations to address conformational variability in cryo-electron microscopy.
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Affiliation(s)
- Pilar Cossio
- Biophysics of Tropical Diseases, Max Planck Tandem Group, University of Antioquia, Medellín, Colombia; Department of Theoretical Biophysics, Max Planck Institute of Biophysics, 60438 Frankfurt am Main, Germany.
| | - Gerhard Hummer
- Department of Theoretical Biophysics, Max Planck Institute of Biophysics, 60438 Frankfurt am Main, Germany; Institute of Biophysics, Goethe University Frankfurt, 60438 Frankfurt am Main, Germany
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