1
|
McGuire KL, Cook BD, Narehood SM, Herzik MA. Tuning ice thickness using the chameleon for high-quality cryoEM data collection. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.01.592094. [PMID: 38746094 PMCID: PMC11092644 DOI: 10.1101/2024.05.01.592094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
Advances in single-particle cryogenic electron microscopy (cryoEM) now allow for routine structure determination of well-behaved biological specimens to high-resolution. Despite advances in the electron microscope, direct electron detectors, and data processing software, the preparation of high-quality grids with thin layers of vitreous ice containing the specimen of interest in random orientations remains a critical bottleneck for many projects. Although numerous efforts have been dedicated to overcoming hurdles frequently encountered during specimen vitrification using traditional blot-and-plunge specimen preparation techniques, the development of blot-free grid preparation devices provide a unique opportunity to carefully tune ice thickness, particle density, and specimen behavior during the vitrification process for improvements in image quality. Here, we describe critical steps of high-quality grid preparation using a SPT Labtech chameleon, evaluation of grid quality/ice thickness using the chameleon software, high-throughput imaging in the electron microscope, and recommend steps for troubleshooting grid preparation when standard parameters fail to yield suitable specimen.
Collapse
Affiliation(s)
- Kelly L. McGuire
- Department of Chemistry and Biochemistry, University of California, San Diego, California, US
| | - Brian D. Cook
- Department of Chemistry and Biochemistry, University of California, San Diego, California, US
| | - Sarah M. Narehood
- Department of Chemistry and Biochemistry, University of California, San Diego, California, US
| | - Mark A. Herzik
- Department of Chemistry and Biochemistry, University of California, San Diego, California, US
| |
Collapse
|
2
|
Khoshouei A, Kempf G, Mykhailiuk V, Griessing JM, Honemann MN, Kater L, Cavadini S, Dietz H. Designing Rigid DNA Origami Templates for Molecular Visualization Using Cryo-EM. NANO LETTERS 2024; 24. [PMID: 38602296 PMCID: PMC11057029 DOI: 10.1021/acs.nanolett.4c00915] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/21/2024] [Revised: 04/07/2024] [Accepted: 04/09/2024] [Indexed: 04/12/2024]
Abstract
DNA origami, a method for constructing nanostructures from DNA, offers potential for diverse scientific and technological applications due to its ability to integrate various molecular functionalities in a programmable manner. In this study, we examined the impact of internal crossover distribution and the compositional uniformity of staple strands on the structure of multilayer DNA origami using cryogenic electron microscopy (cryo-EM) single-particle analysis. A refined DNA object was utilized as an alignment framework in a host-guest model, where we successfully resolved an 8 kDa thrombin binding aptamer (TBA) linked to the host object. Our results broaden the spectrum of DNA in structural applications.
Collapse
Affiliation(s)
- Ali Khoshouei
- Laboratory
for Biomolecular Nanotechnology, Department of Biosciences, School
of Natural Sciences, Technical University
of Munich, Am Coulombwall 4a, 85748 Garching, Germany
- Munich
Institute of Biomedical Engineering, Technical
University of Munich, Boltzmannstraße 11, 85748 Garching, Germany
| | - Georg Kempf
- Friedrich
Miescher Institute for Biomedical Research, Maulbeerstrasse 66, 4058 Basel, Switzerland
| | - Volodymyr Mykhailiuk
- Laboratory
for Biomolecular Nanotechnology, Department of Biosciences, School
of Natural Sciences, Technical University
of Munich, Am Coulombwall 4a, 85748 Garching, Germany
- Munich
Institute of Biomedical Engineering, Technical
University of Munich, Boltzmannstraße 11, 85748 Garching, Germany
| | - Johanna Mariko Griessing
- Laboratory
for Biomolecular Nanotechnology, Department of Biosciences, School
of Natural Sciences, Technical University
of Munich, Am Coulombwall 4a, 85748 Garching, Germany
- Munich
Institute of Biomedical Engineering, Technical
University of Munich, Boltzmannstraße 11, 85748 Garching, Germany
| | - Maximilian Nicolas Honemann
- Laboratory
for Biomolecular Nanotechnology, Department of Biosciences, School
of Natural Sciences, Technical University
of Munich, Am Coulombwall 4a, 85748 Garching, Germany
- Munich
Institute of Biomedical Engineering, Technical
University of Munich, Boltzmannstraße 11, 85748 Garching, Germany
| | - Lukas Kater
- Friedrich
Miescher Institute for Biomedical Research, Maulbeerstrasse 66, 4058 Basel, Switzerland
| | - Simone Cavadini
- Friedrich
Miescher Institute for Biomedical Research, Maulbeerstrasse 66, 4058 Basel, Switzerland
| | - Hendrik Dietz
- Laboratory
for Biomolecular Nanotechnology, Department of Biosciences, School
of Natural Sciences, Technical University
of Munich, Am Coulombwall 4a, 85748 Garching, Germany
- Munich
Institute of Biomedical Engineering, Technical
University of Munich, Boltzmannstraße 11, 85748 Garching, Germany
| |
Collapse
|
3
|
Bialy N, Alber F, Andrews B, Angelo M, Beliveau B, Bintu L, Boettiger A, Boehm U, Brown CM, Maina MB, Chambers JJ, Cimini BA, Eliceiri K, Errington R, Faklaris O, Gaudreault N, Germain RN, Goscinski W, Grunwald D, Halter M, Hanein D, Hickey JW, Lacoste J, Laude A, Lundberg E, Ma J, Malacrida L, Moore J, Nelson G, Neumann EK, Nitschke R, Onami S, Pimentel JA, Plant AL, Radtke AJ, Sabata B, Schapiro D, Schöneberg J, Spraggins JM, Sudar D, Adrien Maria Vierdag WM, Volkmann N, Wählby C, Wang SS, Yaniv Z, Strambio-De-Castillia C. Harmonizing the Generation and Pre-publication Stewardship of FAIR Image data. ARXIV 2024:arXiv:2401.13022v4. [PMID: 38351940 PMCID: PMC10862930] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/19/2024]
Abstract
Together with the molecular knowledge of genes and proteins, biological images promise to significantly enhance the scientific understanding of complex cellular systems and to advance predictive and personalized therapeutic products for human health. For this potential to be realized, quality-assured image data must be shared among labs at a global scale to be compared, pooled, and reanalyzed, thus unleashing untold potential beyond the original purpose for which the data was generated. There are two broad sets of requirements to enable image data sharing in the life sciences. One set of requirements is articulated in the companion White Paper entitled "Enabling Global Image Data Sharing in the Life Sciences," which is published in parallel and addresses the need to build the cyberinfrastructure for sharing the digital array data (arXiv:2401.13023 [q-bio.OT], https://doi.org/10.48550/arXiv.2401.13023). In this White Paper, we detail a broad set of requirements, which involves collecting, managing, presenting, and propagating contextual information essential to assess the quality, understand the content, interpret the scientific implications, and reuse image data in the context of the experimental details. We start by providing an overview of the main lessons learned to date through international community activities, which have recently made considerable progress toward generating community standard practices for imaging Quality Control (QC) and metadata. We then provide a clear set of recommendations for amplifying this work. The driving goal is to address remaining challenges, and democratize access to common practices and tools for a spectrum of biomedical researchers, regardless of their expertise, access to resources, and geographical location.
Collapse
Affiliation(s)
- Nikki Bialy
- Morgridge Institute for Research, Madison, USA
| | | | | | | | | | | | | | | | | | | | | | - Beth A Cimini
- Broad Institute of MIT and Harvard, Imaging Platform, Cambridge, USA
| | - Kevin Eliceiri
- Morgridge Institute for Research, Madison, USA
- University of Wisconsin-Madison, Madison, USA
| | | | | | | | - Ronald N Germain
- National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, USA
| | | | | | - Michael Halter
- National Institute of Standards and Technology, Gaithersburg, USA
| | | | | | | | - Alex Laude
- Newcastle University, Newcastle upon Tyne, UK
| | - Emma Lundberg
- Stanford University, Palo Alto, USA
- SciLifeLab, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Jian Ma
- Carnegie Mellon University, Pittsburgh, USA
| | - Leonel Malacrida
- Institut Pasteur de Montevideo, & Universidad de la República, Montevideo, Uruguay
| | - Josh Moore
- German BioImaging-Gesellschaft für Mikroskopie und Bildanalyse e.V., Constance, Germany
| | - Glyn Nelson
- Newcastle University, Newcastle upon Tyne, UK
| | | | | | - Shuichi Onami
- RIKEN Center for Biosystems Dynamics Research, Kobe, Japan
| | | | - Anne L Plant
- National Institute of Standards and Technology, Gaithersburg, USA
| | - Andrea J Radtke
- National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, USA
| | | | | | | | | | - Damir Sudar
- Quantitative Imaging Systems LLC, Portland, USA
| | | | | | | | | | - Ziv Yaniv
- National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, USA
| | | |
Collapse
|
4
|
Beton JG, Mulvaney T, Cragnolini T, Topf M. Cryo-EM structure and B-factor refinement with ensemble representation. Nat Commun 2024; 15:444. [PMID: 38200043 PMCID: PMC10781738 DOI: 10.1038/s41467-023-44593-1] [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: 07/11/2022] [Accepted: 12/20/2023] [Indexed: 01/12/2024] Open
Abstract
Cryo-EM experiments produce images of macromolecular assemblies that are combined to produce three-dimensional density maps. Typically, atomic models of the constituent molecules are fitted into these maps, followed by a density-guided refinement. We introduce TEMPy-ReFF, a method for atomic structure refinement in cryo-EM density maps. Our method represents atomic positions as components of a Gaussian mixture model, utilising their variances as B-factors, which are used to derive an ensemble description. Extensively tested on a substantial dataset of 229 cryo-EM maps from EMDB ranging in resolution from 2.1-4.9 Å with corresponding PDB and CERES atomic models, our results demonstrate that TEMPy-ReFF ensembles provide a superior representation of cryo-EM maps. On a single-model basis, it performs similarly to the CERES re-refinement protocol, although there are cases where it provides a better fit to the map. Furthermore, our method enables the creation of composite maps free of boundary artefacts. TEMPy-ReFF is useful for better interpretation of flexible structures, such as those involving RNA, DNA or ligands.
Collapse
Affiliation(s)
- Joseph G Beton
- Leibniz Institute of Virology (LIV) and Universitätsklinikum Hamburg Eppendorf (UKE), Centre for Structural Systems Biology (CSSB), 22607, Hamburg, Germany
| | - Thomas Mulvaney
- Leibniz Institute of Virology (LIV) and Universitätsklinikum Hamburg Eppendorf (UKE), Centre for Structural Systems Biology (CSSB), 22607, Hamburg, Germany
| | - Tristan Cragnolini
- Leibniz Institute of Virology (LIV) and Universitätsklinikum Hamburg Eppendorf (UKE), Centre for Structural Systems Biology (CSSB), 22607, Hamburg, Germany
- Institute of Structural and Molecular Biology, Birkbeck, University of London, London, UK
| | - Maya Topf
- Leibniz Institute of Virology (LIV) and Universitätsklinikum Hamburg Eppendorf (UKE), Centre for Structural Systems Biology (CSSB), 22607, Hamburg, Germany.
| |
Collapse
|
5
|
Turner J, Abbott S, Fonseca N, Pye R, Carrijo L, Duraisamy AK, Salih O, Wang Z, Kleywegt GJ, Morris KL, Patwardhan A, Burley SK, Crichlow G, Feng Z, Flatt JW, Ghosh S, Hudson BP, Lawson CL, Liang Y, Peisach E, Persikova I, Sekharan M, Shao C, Young J, Velankar S, Armstrong D, Bage M, Bueno WM, Evans G, Gaborova R, Ganguly S, Gupta D, Harrus D, Tanweer A, Bansal M, Rangannan V, Kurisu G, Cho H, Ikegawa Y, Kengaku Y, Kim JY, Niwa S, Sato J, Takuwa A, Yu J, Hoch JC, Baskaran K, Xu W, Zhang W, Ma X. EMDB-the Electron Microscopy Data Bank. Nucleic Acids Res 2024; 52:D456-D465. [PMID: 37994703 PMCID: PMC10767987 DOI: 10.1093/nar/gkad1019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Revised: 10/18/2023] [Accepted: 10/20/2023] [Indexed: 11/24/2023] Open
Abstract
The Electron Microscopy Data Bank (EMDB) is the global public archive of three-dimensional electron microscopy (3DEM) maps of biological specimens derived from transmission electron microscopy experiments. As of 2021, EMDB is managed by the Worldwide Protein Data Bank consortium (wwPDB; wwpdb.org) as a wwPDB Core Archive, and the EMDB team is a core member of the consortium. Today, EMDB houses over 30 000 entries with maps containing macromolecules, complexes, viruses, organelles and cells. Herein, we provide an overview of the rapidly growing EMDB archive, including its current holdings, recent updates, and future plans.
Collapse
|
6
|
Westphall MS, Lee KW, Salome AZ, Coon JJ, Grant T. Mass spectrometers as cryoEM grid preparation instruments. Curr Opin Struct Biol 2023; 83:102699. [PMID: 37703606 PMCID: PMC11019453 DOI: 10.1016/j.sbi.2023.102699] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2023] [Revised: 07/18/2023] [Accepted: 08/15/2023] [Indexed: 09/15/2023]
Abstract
Structure determination by single-particle cryoEM has matured into a core structural biology technique. Despite many methodological advancements, most cryoEM grids are still prepared using the plunge-freezing method developed ∼40 years ago. Embedding samples in thin films and exposing them to the air-water interface often leads to sample damage and preferential orientation of the particles. Using native mass spectrometry to create cryoEM samples, potentially avoids these problems and allows the use of mass spectrometry sample isolation techniques during EM grid creation. We review the recent publications that have demonstrated protein complexes can be ionized, flown through the mass spectrometer, gently landed onto EM grids, imaged, and reconstructed in 3D. Although many uncertainties and challenges remain, the combination of cryoEM and MS has great potential.
Collapse
Affiliation(s)
- Michael S Westphall
- Department of Biomolecular Chemistry, University of Wisconsin-Madison, Madison, WI 53706, United States
| | - Kenneth W Lee
- Department of Biomolecular Chemistry, University of Wisconsin-Madison, Madison, WI 53706, United States
| | - Austin Z Salome
- Department of Chemistry, University of Wisconsin-Madison, Madison, WI 53706, United States
| | - Joshua J Coon
- Department of Biomolecular Chemistry, University of Wisconsin-Madison, Madison, WI 53706, United States; Department of Chemistry, University of Wisconsin-Madison, Madison, WI 53706, United States; Morgridge Institute for Research, 330 N Orchard Street, Madison, WI 53706, United States.
| | - Timothy Grant
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI 53706, United States; Morgridge Institute for Research, 330 N Orchard Street, Madison, WI 53706, United States.
| |
Collapse
|
7
|
Pintilie G. Diagnosing and treating issues in cryo-EM map-derived models. Structure 2023; 31:759-761. [PMID: 37419099 DOI: 10.1016/j.str.2023.06.009] [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: 06/05/2023] [Revised: 06/05/2023] [Accepted: 06/08/2023] [Indexed: 07/09/2023]
Abstract
In this issue of Structure, Reggiano et al.1 take the evaluation of cryo-EM models to the next level, combining several metrics into one. The new method, MEDIC, evaluates models at the residue level, helping to guide improvements and interpretation of models derived from cryo-EM maps.
Collapse
Affiliation(s)
- Grigore Pintilie
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA.
| |
Collapse
|
8
|
Matsumoto S, Ishida S, Terayama K, Okuno Y. Quantitative analysis of protein dynamics using a deep learning technique combined with experimental cryo-EM density data and MD simulations. Biophys Physicobiol 2023; 20:e200022. [PMID: 38496243 PMCID: PMC10941960 DOI: 10.2142/biophysico.bppb-v20.0022] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Accepted: 05/12/2023] [Indexed: 03/19/2024] Open
Abstract
Protein functions associated with biological activity are precisely regulated by both tertiary structure and dynamic behavior. Thus, elucidating the high-resolution structures and quantitative information on in-solution dynamics is essential for understanding the molecular mechanisms. The main experimental approaches for determining tertiary structures include nuclear magnetic resonance (NMR), X-ray crystallography, and cryogenic electron microscopy (cryo-EM). Among these procedures, recent remarkable advances in the hardware and analytical techniques of cryo-EM have increasingly determined novel atomic structures of macromolecules, especially those with large molecular weights and complex assemblies. In addition to these experimental approaches, deep learning techniques, such as AlphaFold 2, accurately predict structures from amino acid sequences, accelerating structural biology research. Meanwhile, the quantitative analyses of the protein dynamics are conducted using experimental approaches, such as NMR and hydrogen-deuterium mass spectrometry, and computational approaches, such as molecular dynamics (MD) simulations. Although these procedures can quantitatively explore dynamic behavior at high resolution, the fundamental difficulties, such as signal crowding and high computational cost, greatly hinder their application to large and complex biological macromolecules. In recent years, machine learning techniques, especially deep learning techniques, have been actively applied to structural data to identify features that are difficult for humans to recognize from big data. Here, we review our approach to accurately estimate dynamic properties associated with local fluctuations from three-dimensional cryo-EM density data using a deep learning technique combined with MD simulations.
Collapse
Affiliation(s)
| | - Shoichi Ishida
- Graduate School of Medical Life Science, Yokohama City University, Yokohama, Kanagawa 230-0045, Japan
| | - Kei Terayama
- Graduate School of Medical Life Science, Yokohama City University, Yokohama, Kanagawa 230-0045, Japan
- RIKEN Center for Computational Science, Kobe, Hyogo 650-0047, Japan
| | - Yasuhshi Okuno
- Graduate School of Medicine, Kyoto University, Kyoto 606-8507, Japan
- RIKEN Center for Computational Science, Kobe, Hyogo 650-0047, Japan
| |
Collapse
|
9
|
Zeng X, Kahng A, Xue L, Mahamid J, Chang YW, Xu M. High-throughput cryo-ET structural pattern mining by unsupervised deep iterative subtomogram clustering. Proc Natl Acad Sci U S A 2023; 120:e2213149120. [PMID: 37027429 PMCID: PMC10104553 DOI: 10.1073/pnas.2213149120] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Accepted: 02/24/2023] [Indexed: 04/08/2023] Open
Abstract
Cryoelectron tomography directly visualizes heterogeneous macromolecular structures in their native and complex cellular environments. However, existing computer-assisted structure sorting approaches are low throughput or inherently limited due to their dependency on available templates and manual labels. Here, we introduce a high-throughput template-and-label-free deep learning approach, Deep Iterative Subtomogram Clustering Approach (DISCA), that automatically detects subsets of homogeneous structures by learning and modeling 3D structural features and their distributions. Evaluation on five experimental cryo-ET datasets shows that an unsupervised deep learning based method can detect diverse structures with a wide range of molecular sizes. This unsupervised detection paves the way for systematic unbiased recognition of macromolecular complexes in situ.
Collapse
Affiliation(s)
- Xiangrui Zeng
- Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA15213
| | - Anson Kahng
- Computer Science Department, University of Rochester, Rochester, NY14620
| | - Liang Xue
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg69117, Germany
- Faculty of Biosciences, Collaboration for joint PhD degree between European Molecular Biology Laboratory and Heidelberg University, Heidelberg69117, Germany
| | - Julia Mahamid
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg69117, Germany
| | - Yi-Wei Chang
- Department of Biochemistry and Biophysics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Min Xu
- Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA15213
| |
Collapse
|
10
|
Large EE, Chapman MS. Adeno-associated virus receptor complexes and implications for adeno-associated virus immune neutralization. Front Microbiol 2023; 14:1116896. [PMID: 36846761 PMCID: PMC9950413 DOI: 10.3389/fmicb.2023.1116896] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Accepted: 01/20/2023] [Indexed: 02/12/2023] Open
Abstract
Adeno-associated viruses (AAV) are among the foremost vectors for in vivo gene therapy. A number of monoclonal antibodies against several serotypes of AAV have previously been prepared. Many are neutralizing, and the predominant mechanisms have been reported as the inhibition of binding to extracellular glycan receptors or interference with some post-entry step. The identification of a protein receptor and recent structural characterization of its interactions with AAV compel reconsideration of this tenet. AAVs can be divided into two families based on which domain of the receptor is strongly bound. Neighboring domains, unseen in the high-resolution electron microscopy structures have now been located by electron tomography, pointing away from the virus. The epitopes of neutralizing antibodies, previously characterized, are now compared to the distinct protein receptor footprints of the two families of AAV. Comparative structural analysis suggests that antibody interference with protein receptor binding might be the more prevalent mechanism than interference with glycan attachment. Limited competitive binding assays give some support to the hypothesis that inhibition of binding to the protein receptor has been an overlooked mechanism of neutralization. More extensive testing is warranted.
Collapse
Affiliation(s)
| | - Michael S. Chapman
- Department of Biochemistry, University of Missouri, Columbia, MO, United States
| |
Collapse
|
11
|
Rosenberg SC, Shanahan F, Yamazoe S, Kschonsak M, Zeng YJ, Lee J, Plise E, Yen I, Rose CM, Quinn JG, Gazzard LJ, Walters BT, Kirkpatrick DS, Staben ST, Foster SA, Malek S. Ternary complex dissociation kinetics contribute to mutant-selective EGFR degradation. Cell Chem Biol 2023; 30:S2451-9456(23)00030-2. [PMID: 36773603 DOI: 10.1016/j.chembiol.2023.01.007] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2021] [Revised: 09/02/2022] [Accepted: 01/23/2023] [Indexed: 02/12/2023]
Abstract
Targeted degradation of proteins by chimeric heterobifunctional degraders has emerged as a major drug discovery paradigm. Despite the increased interest in this approach, the criteria dictating target protein degradation by a degrader remain poorly understood, and potent target engagement by a degrader does not strongly correlate with target degradation. In this study, we present the biochemical characterization of an epidermal growth factor receptor (EGFR) degrader that potently binds both wild-type and mutant EGFR, but only degrades EGFR mutant variants. Mechanistic studies reveal that ternary complex half-life strongly correlates with processive ubiquitination with purified components and mutant-selective degradation in cells. We present cryoelectron microscopy and hydrogen-deuterium exchange mass spectroscopy data on wild-type and mutant EGFR ternary complexes, which demonstrate that potent target degradation can be achieved in the absence of stable compound-induced protein-protein interactions. These results highlight the importance of considering target conformation during degrader development as well as leveraging heterobifunctional ligand binding kinetics to achieve robust target degradation.
Collapse
Affiliation(s)
- Scott C Rosenberg
- Department of Discovery Oncology, Genentech, Inc., 1 DNA Way, South San Francisco, CA 94080, USA
| | - Frances Shanahan
- Department of Discovery Oncology, Genentech, Inc., 1 DNA Way, South San Francisco, CA 94080, USA
| | - Sayumi Yamazoe
- Department of Discovery Chemistry, Genentech, Inc., 1 DNA Way, South San Francisco, CA 94080, USA
| | - Marc Kschonsak
- Department of Structural Biology, Genentech, Inc., 1 DNA Way, South San Francisco, CA 94080, USA
| | - Yi J Zeng
- Department of Structural Biology, Genentech, Inc., 1 DNA Way, South San Francisco, CA 94080, USA
| | - James Lee
- Department of Discovery Oncology, Genentech, Inc., 1 DNA Way, South San Francisco, CA 94080, USA
| | - Emile Plise
- Department of Drug Metabolism and Pharmacokinetics, Genentech, Inc., 1 DNA Way, South San Francisco, CA 94080, USA
| | - Ivana Yen
- Department of Discovery Oncology, Genentech, Inc., 1 DNA Way, South San Francisco, CA 94080, USA
| | - Christopher M Rose
- Department of Microchemistry, Proteomics and Lipidomics, Genentech, Inc., 1 DNA Way, South San Francisco, CA 94080, USA
| | - John G Quinn
- Department of Biochemical and Cellular Pharmacology, Genentech, Inc., 1 DNA Way, South San Francisco, CA 94080, USA
| | - Lewis J Gazzard
- Department of Discovery Chemistry, Genentech, Inc., 1 DNA Way, South San Francisco, CA 94080, USA
| | - Benjamin T Walters
- Department of Biochemical and Cellular Pharmacology, Genentech, Inc., 1 DNA Way, South San Francisco, CA 94080, USA
| | - Donald S Kirkpatrick
- Department of Microchemistry, Proteomics and Lipidomics, Genentech, Inc., 1 DNA Way, South San Francisco, CA 94080, USA
| | - Steven T Staben
- Department of Discovery Chemistry, Genentech, Inc., 1 DNA Way, South San Francisco, CA 94080, USA
| | - Scott A Foster
- Department of Discovery Oncology, Genentech, Inc., 1 DNA Way, South San Francisco, CA 94080, USA.
| | - Shiva Malek
- Department of Discovery Oncology, Genentech, Inc., 1 DNA Way, South San Francisco, CA 94080, USA.
| |
Collapse
|
12
|
Burton-Smith RN, Murata K. Cryo-electron Microscopy of Protein Cages. Methods Mol Biol 2023; 2671:173-210. [PMID: 37308646 DOI: 10.1007/978-1-0716-3222-2_11] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Protein cages are one of the most widely studied objects in the field of cryogenic electron microscopy-encompassing natural and synthetic constructs, from enzymes assisting protein folding such as chaperonin to virus capsids. Tremendous diversity of morphology and function is demonstrated by the structure and role of proteins, some of which are nearly ubiquitous, while others are present in few organisms. Protein cages are often highly symmetrical, which helps improve the resolution obtained by cryo-electron microscopy (cryo-EM). Cryo-EM is the study of vitrified samples using an electron probe to image the subject. A sample is rapidly frozen in a thin layer on a porous grid, attempting to keep the sample as close to a native state as possible. This grid is kept at cryogenic temperatures throughout imaging in an electron microscope. Once image acquisition is complete, a variety of software packages may be employed to carry out analysis and reconstruction of three-dimensional structures from the two-dimensional micrograph images. Cryo-EM can be used on samples that are too large or too heterogeneous to be amenable to other structural biology techniques like NMR or X-ray crystallography. In recent years, advances in both hardware and software have provided significant improvements to the results obtained using cryo-EM, recently demonstrating true atomic resolution from vitrified aqueous samples. Here, we review these advances in cryo-EM, especially in that of protein cages, and introduce several tips for situations we have experienced.
Collapse
Affiliation(s)
- Raymond N Burton-Smith
- Exploratory Research Center on Life and Living Systems (ExCELLS), National Institute for Natural Sciences, Okazaki, Aichi, Japan
- National Institute for Physiological Sciences (NIPS), National Institute for Natural Sciences, Okazaki, Aichi, Japan
| | - Kazuyoshi Murata
- Exploratory Research Center on Life and Living Systems (ExCELLS), National Institute for Natural Sciences, Okazaki, Aichi, Japan.
- National Institute for Physiological Sciences (NIPS), National Institute for Natural Sciences, Okazaki, Aichi, Japan.
- Department of Physiological Sciences, School of Life Science, The Graduate University for Advanced Studies (SOKENDAI), Okazaki, Aichi, Japan.
| |
Collapse
|
13
|
Lugmayr W, Kotov V, Goessweiner-Mohr N, Wald J, DiMaio F, Marlovits TC. StarMap: a user-friendly workflow for Rosetta-driven molecular structure refinement. Nat Protoc 2023; 18:239-264. [PMID: 36323866 DOI: 10.1038/s41596-022-00757-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Accepted: 08/08/2022] [Indexed: 01/13/2023]
Abstract
Cryogenic electron microscopy (cryo-EM) data represent density maps of macromolecular systems at atomic or near-atomic resolution. However, building and refining 3D atomic models by using data from cryo-EM maps is not straightforward and requires significant hands-on experience and manual intervention. We recently developed StarMap, an easy-to-use interface between the popular structural display program ChimeraX and Rosetta, a powerful molecular modeling engine. StarMap offers a general approach for refining structural models of biological macromolecules into cryo-EM density maps by combining Monte Carlo sampling with local density-guided optimization, Rosetta-based all-atom refinement and real-space B-factor calculations in a straightforward workflow. StarMap includes options for structural symmetry, local refinements and independent model validation. The overall quality of the refinement and the structure resolution is then assessed via analytical outputs, such as magnification calibration (pixel size calibration) and Fourier shell correlations. Z-scores reported by StarMap provide an easily interpretable indicator of the goodness of fit for each residue and can be plotted to evaluate structural models and improve local residue refinements, as well as to identify flexible regions and potentially functional sites in large macromolecular complexes. The protocol requires general computer skills, without the need for coding expertise, because most parts of the workflow can be operated by clicking tabs within the ChimeraX graphical user interface. Time requirements for the model refinement depend on the size and quality of the input data; however, this step can typically be completed within 1 d. The analytical parts of the workflow are completed within minutes.
Collapse
Affiliation(s)
- Wolfgang Lugmayr
- University Medical Center Hamburg-Eppendorf (UKE), Institute of Structural and Systems Biology, Hamburg, Germany.,CSSB Centre for Structural Systems Biology, Hamburg, Germany.,Deutsches Elektronen Synchrotron (DESY), Hamburg, Germany.,Research Institute of Molecular Pathology (IMP), Vienna, Austria.,Institute for Molecular Biotechnology (IMBA), Austrian Academy of Sciences, Vienna, Austria
| | - Vadim Kotov
- University Medical Center Hamburg-Eppendorf (UKE), Institute of Structural and Systems Biology, Hamburg, Germany.,CSSB Centre for Structural Systems Biology, Hamburg, Germany.,Deutsches Elektronen Synchrotron (DESY), Hamburg, Germany.,Research Institute of Molecular Pathology (IMP), Vienna, Austria.,Institute for Molecular Biotechnology (IMBA), Austrian Academy of Sciences, Vienna, Austria.,Evotec SE, Hamburg, Germany
| | - Nikolaus Goessweiner-Mohr
- University Medical Center Hamburg-Eppendorf (UKE), Institute of Structural and Systems Biology, Hamburg, Germany.,CSSB Centre for Structural Systems Biology, Hamburg, Germany.,Deutsches Elektronen Synchrotron (DESY), Hamburg, Germany.,Research Institute of Molecular Pathology (IMP), Vienna, Austria.,Institute for Molecular Biotechnology (IMBA), Austrian Academy of Sciences, Vienna, Austria.,Johannes Kepler University, Institute of Biophysics, Linz, Austria
| | - Jiri Wald
- University Medical Center Hamburg-Eppendorf (UKE), Institute of Structural and Systems Biology, Hamburg, Germany.,CSSB Centre for Structural Systems Biology, Hamburg, Germany.,Deutsches Elektronen Synchrotron (DESY), Hamburg, Germany.,Research Institute of Molecular Pathology (IMP), Vienna, Austria.,Institute for Molecular Biotechnology (IMBA), Austrian Academy of Sciences, Vienna, Austria
| | - Frank DiMaio
- University of Washington, Department of Biochemistry, Seattle, WA, USA
| | - Thomas C Marlovits
- University Medical Center Hamburg-Eppendorf (UKE), Institute of Structural and Systems Biology, Hamburg, Germany. .,CSSB Centre for Structural Systems Biology, Hamburg, Germany. .,Deutsches Elektronen Synchrotron (DESY), Hamburg, Germany. .,Research Institute of Molecular Pathology (IMP), Vienna, Austria. .,Institute for Molecular Biotechnology (IMBA), Austrian Academy of Sciences, Vienna, Austria.
| |
Collapse
|
14
|
Terwilliger TC, Poon BK, Afonine PV, Schlicksup CJ, Croll TI, Millán C, Richardson JS, Read RJ, Adams PD. Improved AlphaFold modeling with implicit experimental information. Nat Methods 2022; 19:1376-1382. [PMID: 36266465 PMCID: PMC9636017 DOI: 10.1038/s41592-022-01645-6] [Citation(s) in RCA: 55] [Impact Index Per Article: 27.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Accepted: 09/09/2022] [Indexed: 12/02/2022]
Abstract
Machine-learning prediction algorithms such as AlphaFold and RoseTTAFold can create remarkably accurate protein models, but these models usually have some regions that are predicted with low confidence or poor accuracy. We hypothesized that by implicitly including new experimental information such as a density map, a greater portion of a model could be predicted accurately, and that this might synergistically improve parts of the model that were not fully addressed by either machine learning or experiment alone. An iterative procedure was developed in which AlphaFold models are automatically rebuilt on the basis of experimental density maps and the rebuilt models are used as templates in new AlphaFold predictions. We show that including experimental information improves prediction beyond the improvement obtained with simple rebuilding guided by the experimental data. This procedure for AlphaFold modeling with density has been incorporated into an automated procedure for interpretation of crystallographic and electron cryo-microscopy maps.
Collapse
Affiliation(s)
- Thomas C Terwilliger
- New Mexico Consortium, Los Alamos, NM, USA.
- Los Alamos National Laboratory, Los Alamos, NM, USA.
| | - Billy K Poon
- Molecular Biophysics & Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Pavel V Afonine
- Molecular Biophysics & Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Christopher J Schlicksup
- Molecular Biophysics & Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Tristan I Croll
- Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, UK
| | - Claudia Millán
- Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, UK
| | | | - Randy J Read
- Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, UK
| | - Paul D Adams
- Molecular Biophysics & Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
- Department of Bioengineering, University of California, Berkeley, CA, USA
| |
Collapse
|
15
|
Ranno N, Si D. Neural representations of cryo-EM maps and a graph-based interpretation. BMC Bioinformatics 2022; 23:397. [PMID: 36171544 PMCID: PMC9517980 DOI: 10.1186/s12859-022-04942-1] [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: 09/14/2022] [Accepted: 09/16/2022] [Indexed: 11/10/2022] Open
Abstract
Background Advances in imagery at atomic and near-atomic resolution, such as cryogenic electron microscopy (cryo-EM), have led to an influx of high resolution images of proteins and other macromolecular structures to data banks worldwide. Producing a protein structure from the discrete voxel grid data of cryo-EM maps involves interpolation into the continuous spatial domain. We present a novel data format called the neural cryo-EM map, which is formed from a set of neural networks that accurately parameterize cryo-EM maps and provide native, spatially continuous data for density and gradient. As a case study of this data format, we create graph-based interpretations of high resolution experimental cryo-EM maps. Results Normalized cryo-EM map values interpolated using the non-linear neural cryo-EM format are more accurate, consistently scoring less than 0.01 mean absolute error, than a conventional tri-linear interpolation, which scores up to 0.12 mean absolute error. Our graph-based interpretations of 115 experimental cryo-EM maps from 1.15 to 4.0 Å resolution provide high coverage of the underlying amino acid residue locations, while accuracy of nodes is correlated with resolution. The nodes of graphs created from atomic resolution maps (higher than 1.6 Å) provide greater than 99% residue coverage as well as 85% full atomic coverage with a mean of 0.19 Å root mean squared deviation. Other graphs have a mean 84% residue coverage with less specificity of the nodes due to experimental noise and differences of density context at lower resolutions. Conclusions The fully continuous and differentiable nature of the neural cryo-EM map enables the adaptation of the voxel data to alternative data formats, such as a graph that characterizes the atomic locations of the underlying protein or macromolecular structure. Graphs created from atomic resolution maps are superior in finding atom locations and may serve as input to predictive residue classification and structure segmentation methods. This work may be generalized to transform any 3D grid-based data format into non-linear, continuous, and differentiable format for downstream geometric deep learning applications. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-022-04942-1.
Collapse
Affiliation(s)
- Nathan Ranno
- Department of Computing and Software Systems, University of Washington, Bothell, WA, USA
| | - Dong Si
- Department of Computing and Software Systems, University of Washington, Bothell, WA, USA.
| |
Collapse
|
16
|
Abstract
Adeno-associated virus (AAV) has a single-stranded DNA genome encapsidated in a small icosahedrally symmetric protein shell with 60 subunits. AAV is the leading delivery vector in emerging gene therapy treatments for inherited disorders, so its structure and molecular interactions with human hosts are of intense interest. A wide array of electron microscopic approaches have been used to visualize the virus and its complexes, depending on the scientific question, technology available, and amenability of the sample. Approaches range from subvolume tomographic analyses of complexes with large and flexible host proteins to detailed analysis of atomic interactions within the virus and with small ligands at resolutions as high as 1.6 Å. Analyses have led to the reclassification of glycan receptors as attachment factors, to structures with a new-found receptor protein, to identification of the epitopes of antibodies, and a new understanding of possible neutralization mechanisms. AAV is now well-enough characterized that it has also become a model system for EM methods development. Heralding a new era, cryo-EM is now also being deployed as an analytic tool in the process development and production quality control of high value pharmaceutical biologics, namely AAV vectors.
Collapse
Affiliation(s)
- Scott
M. Stagg
- Department
of Biological Sciences, Florida State University, Tallahassee, Florida 32306, United States
- Institute
of Molecular Biophysics, Florida State University, Tallahassee, Florida 32306, United States
| | - Craig Yoshioka
- Department
of Biomedical Engineering, Oregon Health
& Science University, Portland Oregon 97239, United States
| | - Omar Davulcu
- Environmental
Molecular Sciences Laboratory, Pacific Northwest
National Laboratory, 3335 Innovation Boulevard, Richland, Washington 99354, United States
| | - Michael S. Chapman
- Department
of Biochemistry, University of Missouri, Columbia, Missouri 65211, United States
| |
Collapse
|
17
|
Thangaratnarajah C, Rheinberger J, Paulino C. Cryo-EM studies of membrane proteins at 200 keV. Curr Opin Struct Biol 2022; 76:102440. [PMID: 36029606 DOI: 10.1016/j.sbi.2022.102440] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2022] [Revised: 07/01/2022] [Accepted: 07/06/2022] [Indexed: 11/03/2022]
Abstract
Single-particle cryogenic electron-microscopy (cryo-EM) has emerged as a powerful technique for the structural characterisation of membrane proteins, especially for targets previously thought to be intractable. Taking advantage of the latest hard- and software developments, high-resolution three-dimensional (3D) reconstructions of membrane proteins by cryo-EM has become routine, with 300-kV transmission electron microscopes (TEMs) being the current standard. The use of 200-kV cryo-TEMs is gaining increasingly prominence, showing the capabilities of reaching better than 2 Å resolution for soluble proteins and better than 3 Å resolution for membrane proteins. Here, we highlight the challenges working with membrane proteins and the impact of cryo-EM, and review the technical and practical benefits, achievements and limitations of imaging at lower electron acceleration voltages.
Collapse
Affiliation(s)
- Chancievan Thangaratnarajah
- University of Groningen, Faculty of Science and Engineering, Groningen Biomolecular Sciences and Biotechnology, Electron Microscopy and Membrane Enzymology Group, Nijenborgh 4, 9747 AG, Groningen, Netherlands.
| | - Jan Rheinberger
- University of Groningen, Faculty of Science and Engineering, Groningen Biomolecular Sciences and Biotechnology, Electron Microscopy and Membrane Enzymology Group, Nijenborgh 4, 9747 AG, Groningen, Netherlands. https://twitter.com/rheinbergerj
| | - Cristina Paulino
- University of Groningen, Faculty of Science and Engineering, Groningen Biomolecular Sciences and Biotechnology, Electron Microscopy and Membrane Enzymology Group, Nijenborgh 4, 9747 AG, Groningen, Netherlands.
| |
Collapse
|
18
|
Thorn A. Artificial intelligence in the experimental determination and prediction of macromolecular structures. Curr Opin Struct Biol 2022; 74:102368. [DOI: 10.1016/j.sbi.2022.102368] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Revised: 02/22/2022] [Accepted: 03/08/2022] [Indexed: 11/26/2022]
|
19
|
Zhou X, Li Y, Zhang C, Zheng W, Zhang G, Zhang Y. Progressive assembly of multi-domain protein structures from cryo-EM density maps. NATURE COMPUTATIONAL SCIENCE 2022; 2:265-275. [PMID: 35844960 PMCID: PMC9281201 DOI: 10.1038/s43588-022-00232-1] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Accepted: 03/21/2022] [Indexed: 05/20/2023]
Abstract
Progress in cryo-electron microscopy has provided the potential for large-size protein structure determination. However, the success rate for solving multi-domain proteins remains low because of the difficulty in modelling inter-domain orientations. Here we developed domain enhanced modeling using cryo-electron microscopy (DEMO-EM), an automatic method to assemble multi-domain structures from cryo-electron microscopy maps through a progressive structural refinement procedure combining rigid-body domain fitting and flexible assembly simulations with deep-neural-network inter-domain distance profiles. The method was tested on a large-scale benchmark set of proteins containing up to 12 continuous and discontinuous domains with medium- to low-resolution density maps, where DEMO-EM produced models with correct inter-domain orientations (template modeling score (TM-score) >0.5) for 97% of cases and outperformed state-of-the-art methods. DEMO-EM was applied to the severe acute respiratory syndrome coronavirus 2 genome and generated models with average TM-score and root-mean-square deviation of 0.97 and 1.3 Å, respectively, with respect to the deposited structures. These results demonstrate an efficient pipeline that enables automated and reliable large-scale multi-domain protein structure modelling from cryo-electron microscopy maps.
Collapse
Affiliation(s)
- Xiaogen Zhou
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
- College of Information Engineering, Zhejiang University of Technology, Hangzhou, China
| | - Yang Li
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Chengxin Zhang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Wei Zheng
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Guijun Zhang
- College of Information Engineering, Zhejiang University of Technology, Hangzhou, China
| | - Yang Zhang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
- Department of Biological Chemistry, University of Michigan, Ann Arbor, MI, USA
- Correspondence and requests for materials should be addressed to Yang Zhang.
| |
Collapse
|
20
|
Liedtke J, Depelteau JS, Briegel A. How advances in cryo-electron tomography have contributed to our current view of bacterial cell biology. J Struct Biol X 2022; 6:100065. [PMID: 35252838 PMCID: PMC8894267 DOI: 10.1016/j.yjsbx.2022.100065] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Revised: 02/17/2022] [Accepted: 02/23/2022] [Indexed: 12/13/2022] Open
Abstract
Advancements in the field of cryo-electron tomography have greatly contributed to our current understanding of prokaryotic cell organization and revealed intracellular structures with remarkable architecture. In this review, we present some of the prominent advancements in cryo-electron tomography, illustrated by a subset of structural examples to demonstrate the power of the technique. More specifically, we focus on technical advances in automation of data collection and processing, sample thinning approaches, correlative cryo-light and electron microscopy, and sub-tomogram averaging methods. In turn, each of these advances enabled new insights into bacterial cell architecture, cell cycle progression, and the structure and function of molecular machines. Taken together, these significant advances within the cryo-electron tomography workflow have led to a greater understanding of prokaryotic biology. The advances made the technique available to a wider audience and more biological questions and provide the basis for continued advances in the near future.
Collapse
Affiliation(s)
- Janine Liedtke
- Department of Microbial Sciences, Institute of Biology, Leiden University, Sylviusweg 72, 2333 BE Leiden, The Netherlands.,Centre for Microbial Cell Biology, Leiden University, Sylviusweg 72, 2333 BE Leiden, The Netherlands
| | - Jamie S Depelteau
- Department of Microbial Sciences, Institute of Biology, Leiden University, Sylviusweg 72, 2333 BE Leiden, The Netherlands.,Centre for Microbial Cell Biology, Leiden University, Sylviusweg 72, 2333 BE Leiden, The Netherlands
| | - Ariane Briegel
- Department of Microbial Sciences, Institute of Biology, Leiden University, Sylviusweg 72, 2333 BE Leiden, The Netherlands.,Centre for Microbial Cell Biology, Leiden University, Sylviusweg 72, 2333 BE Leiden, The Netherlands
| |
Collapse
|
21
|
High-resolution structure of phosphoketolase from Bifidobacterium longum determined by cryo-EM single-particle analysis. J Struct Biol 2022; 214:107842. [PMID: 35181457 DOI: 10.1016/j.jsb.2022.107842] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Revised: 01/29/2022] [Accepted: 02/11/2022] [Indexed: 11/22/2022]
Abstract
In bifidobacteria, phosphoketolase (PKT) plays a key role in the central hexose fermentation pathway called "bifid shunt." The three-dimensional structure of PKT from Bifidobacterium longum with co-enzyme thiamine diphosphate (ThDpp) was determined at 2.1 Å resolution by cryo-EM single-particle analysis using 196,147 particles to build up the structural model of a PKT octamer related by D4 symmetry. Although the cryo-EM structure of PKT was almost identical to the X-ray crystal structure previously determined at 2.2 Å resolution, several interesting structural features were observed in the cryo-EM structure. Because this structure was solved at relatively high resolution, it was observed that several amino acid residues adopt multiple conformations. Among them, Q546-D547-H548-N549 (the QN-loop) demonstrate the largest structural change, which seems to be related to the enzymatic function of PKT. The QN-loop is at the entrance to the substrate binding pocket. The minor conformer of the QN-loop is similar to the conformation of the QN-loop in the crystal structure. The major conformer is located further from ThDpp than the minor conformer. Interestingly, the major conformer in the cryo-EM structure of PKT resembles the corresponding loop structure of substrate-bound Escherichia coli transketolase. That is, the minor and major conformers may correspond to "closed" and "open" states for substrate access, respectively. Moreover, because of the high-resolution analysis, many water molecules were observed in the cryo-EM structure of PKT. Structural features of the water molecules in the cryo-EM structure are discussed and compared with water molecules observed in the crystal structure.
Collapse
|
22
|
Vargas J, Gómez-Pedrero JA, Quiroga JA, Alonso J. Enhancement of Cryo-EM maps by a multiscale tubular filter. OPTICS EXPRESS 2022; 30:4515-4527. [PMID: 35209686 DOI: 10.1364/oe.444675] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Accepted: 12/17/2021] [Indexed: 06/14/2023]
Abstract
We present an approach to enhance cryo-electron microscopy (cryo-EM) postprocessed maps based on a multiscale tubular filter. The method determines a tubularness measure locally by the analysis of the eigenvalues of the Hessian matrix. This information is used to enhance elongated local structures and to attenuate blob-like and plate-like structures. The approach, thus, introduces a priori information in the reconstructions to improve their interpretability and analysis at high-resolution. The proposed method has been tested with simulated and real cryo-EM maps including recent reconstructions of the SARS-CoV-2. Our results show that our methods can improve obtained reconstructions.
Collapse
|
23
|
Klykov O, Kopylov M, Carragher B, Heck AJ, Noble AJ, Scheltema RA. Label-free visual proteomics: Coupling MS- and EM-based approaches in structural biology. Mol Cell 2022; 82:285-303. [PMID: 35063097 PMCID: PMC8842845 DOI: 10.1016/j.molcel.2021.12.027] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Revised: 12/14/2021] [Accepted: 12/20/2021] [Indexed: 01/22/2023]
Abstract
Combining diverse experimental structural and interactomic methods allows for the construction of comprehensible molecular encyclopedias of biological systems. Typically, this involves merging several independent approaches that provide complementary structural and functional information from multiple perspectives and at different resolution ranges. A particularly potent combination lies in coupling structural information from cryoelectron microscopy or tomography (cryo-EM or cryo-ET) with interactomic and structural information from mass spectrometry (MS)-based structural proteomics. Cryo-EM/ET allows for sub-nanometer visualization of biological specimens in purified and near-native states, while MS provides bioanalytical information for proteins and protein complexes without introducing additional labels. Here we highlight recent achievements in protein structure and interactome determination using cryo-EM/ET that benefit from additional MS analysis. We also give our perspective on how combining cryo-EM/ET and MS will continue bridging gaps between molecular and cellular studies by capturing and describing 3D snapshots of proteomes and interactomes.
Collapse
Affiliation(s)
- Oleg Klykov
- National Center for In-situ Tomographic Ultramicroscopy, Simons Electron Microscopy Center, New York Structural Biology Center, New York, NY, USA,Department of Biochemistry and Molecular Biophysics, Columbia University, New York, NY, USA
| | - Mykhailo Kopylov
- National Center for In-situ Tomographic Ultramicroscopy, Simons Electron Microscopy Center, New York Structural Biology Center, New York, NY, USA
| | - Bridget Carragher
- National Center for In-situ Tomographic Ultramicroscopy, Simons Electron Microscopy Center, New York Structural Biology Center, New York, NY, USA,Department of Biochemistry and Molecular Biophysics, Columbia University, New York, NY, USA
| | - Albert J.R. Heck
- Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, 3584 CH Utrecht, The Netherlands,Netherlands Proteomics Center, 3584 CH Utrecht, The Netherlands
| | - Alex J Noble
- National Center for In-situ Tomographic Ultramicroscopy, Simons Electron Microscopy Center, New York Structural Biology Center, New York, NY, USA,Corresponding author for cryo-EM/ET/FIB-SEM: Alex J. Noble, tel: (+1) 212-939-0660;
| | - Richard A. Scheltema
- Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, 3584 CH Utrecht, The Netherlands,Netherlands Proteomics Center, 3584 CH Utrecht, The Netherlands,Corresponding author for MS: Richard A. Scheltema, tel: (+31) 30 253 6804;
| |
Collapse
|
24
|
Lomize AL, Todd SC, Pogozheva ID. Spatial arrangement of proteins in planar and curved membranes by PPM 3.0. Protein Sci 2022; 31:209-220. [PMID: 34716622 PMCID: PMC8740824 DOI: 10.1002/pro.4219] [Citation(s) in RCA: 79] [Impact Index Per Article: 39.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Revised: 10/26/2021] [Accepted: 10/28/2021] [Indexed: 01/03/2023]
Abstract
Cellular protrusions, invaginations, and many intracellular organelles have strongly curved membrane regions. Transmembrane and peripheral membrane proteins that induce, sense, or stabilize such regions cannot be properly fitted into a single flat bilayer. To treat such proteins, we developed a new method and a web tool, PPM 3.0, for positioning proteins in curved or planar, single or multiple membranes. This method determines the energetically optimal spatial position, the hydrophobic thickness, and the radius of intrinsic curvature of a membrane-deforming protein structure by arranging it in a single or several sphere-shaped or planar membrane sections. In addition, it can define the lipid-embedded regions of a protein that simultaneously spans several membranes or determine the optimal position of a peptide in a spherical micelle. The PPM 3.0 web server operates with 17 types of biological membranes and 4 types of artificial bilayers. It is publicly available at https://opm.phar.umich.edu/ppm_server3. PPM 3.0 was applied to identify and characterize arrangements in membranes of 128 proteins with a significant intrinsic curvature, such as BAR domains, annexins, Piezo, and MscS mechanosensitive channels, cation-chloride cotransporters, as well as mitochondrial ATP synthases, calcium uniporters, and TOM complexes. These proteins form large complexes that are mainly localized in mitochondria, plasma membranes, and endosomes. Structures of bacterial drug efflux pumps, AcrAB-TolC, MexAB-OrpM, and MacAB-TolC, were positioned in both membranes of the bacterial cell envelop, while structures of multimeric gap-junction channels were arranged in two opposed cellular membranes.
Collapse
Affiliation(s)
- Andrei L. Lomize
- College of Pharmacy, Department of Medicinal ChemistryUniversity of MichiganAnn ArborMichiganUSA
| | - Spencer C. Todd
- Department of Electrical Engineering and Computer Science, College of EngineeringUniversity of MichiganAnn ArborMichiganUSA
| | - Irina D. Pogozheva
- College of Pharmacy, Department of Medicinal ChemistryUniversity of MichiganAnn ArborMichiganUSA
| |
Collapse
|
25
|
Pospich S, Sweeney HL, Houdusse A, Raunser S. High-resolution structures of the actomyosin-V complex in three nucleotide states provide insights into the force generation mechanism. eLife 2021; 10:e73724. [PMID: 34812732 PMCID: PMC8735999 DOI: 10.7554/elife.73724] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Accepted: 11/22/2021] [Indexed: 11/13/2022] Open
Abstract
The molecular motor myosin undergoes a series of major structural transitions during its force-producing motor cycle. The underlying mechanism and its coupling to ATP hydrolysis and actin binding are only partially understood, mostly due to sparse structural data on actin-bound states of myosin. Here, we report 26 high-resolution cryo-EM structures of the actomyosin-V complex in the strong-ADP, rigor, and a previously unseen post-rigor transition state that binds the ATP analog AppNHp. The structures reveal a high flexibility of myosin in each state and provide valuable insights into the structural transitions of myosin-V upon ADP release and binding of AppNHp, as well as the actomyosin interface. In addition, they show how myosin is able to specifically alter the structure of F-actin.
Collapse
Affiliation(s)
- Sabrina Pospich
- Department of Structural Biochemistry, Max Planck Institute of Molecular PhysiologyDortmundGermany
| | - H Lee Sweeney
- Department of Pharmacology and Therapeutics and the Myology Institute, University of FloridaGainesvilleUnited States
| | - Anne Houdusse
- Structural Motility, Institut Curie, Centre National de la Recherche ScientifiqueParisFrance
| | - Stefan Raunser
- Department of Structural Biochemistry, Max Planck Institute of Molecular PhysiologyDortmundGermany
| |
Collapse
|
26
|
Britt HM, Cragnolini T, Thalassinos K. Integration of Mass Spectrometry Data for Structural Biology. Chem Rev 2021; 122:7952-7986. [PMID: 34506113 DOI: 10.1021/acs.chemrev.1c00356] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
Mass spectrometry (MS) is increasingly being used to probe the structure and dynamics of proteins and the complexes they form with other macromolecules. There are now several specialized MS methods, each with unique sample preparation, data acquisition, and data processing protocols. Collectively, these methods are referred to as structural MS and include cross-linking, hydrogen-deuterium exchange, hydroxyl radical footprinting, native, ion mobility, and top-down MS. Each of these provides a unique type of structural information, ranging from composition and stoichiometry through to residue level proximity and solvent accessibility. Structural MS has proved particularly beneficial in studying protein classes for which analysis by classic structural biology techniques proves challenging such as glycosylated or intrinsically disordered proteins. To capture the structural details for a particular system, especially larger multiprotein complexes, more than one structural MS method with other structural and biophysical techniques is often required. Key to integrating these diverse data are computational strategies and software solutions to facilitate this process. We provide a background to the structural MS methods and briefly summarize other structural methods and how these are combined with MS. We then describe current state of the art approaches for the integration of structural MS data for structural biology. We quantify how often these methods are used together and provide examples where such combinations have been fruitful. To illustrate the power of integrative approaches, we discuss progress in solving the structures of the proteasome and the nuclear pore complex. We also discuss how information from structural MS, particularly pertaining to protein dynamics, is not currently utilized in integrative workflows and how such information can provide a more accurate picture of the systems studied. We conclude by discussing new developments in the MS and computational fields that will further enable in-cell structural studies.
Collapse
Affiliation(s)
- Hannah M Britt
- Institute of Structural and Molecular Biology, Division of Biosciences, University College London, London WC1E 6BT, United Kingdom
| | - Tristan Cragnolini
- Institute of Structural and Molecular Biology, Division of Biosciences, University College London, London WC1E 6BT, United Kingdom.,Institute of Structural and Molecular Biology, Birkbeck College, University of London, London WC1E 7HX, United Kingdom
| | - Konstantinos Thalassinos
- Institute of Structural and Molecular Biology, Division of Biosciences, University College London, London WC1E 6BT, United Kingdom.,Institute of Structural and Molecular Biology, Birkbeck College, University of London, London WC1E 7HX, United Kingdom
| |
Collapse
|
27
|
Pintilie G, Chiu W. Validation, analysis and annotation of cryo-EM structures. Acta Crystallogr D Struct Biol 2021; 77:1142-1152. [PMID: 34473085 PMCID: PMC8411978 DOI: 10.1107/s2059798321006069] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2020] [Accepted: 06/09/2021] [Indexed: 11/08/2023] Open
Abstract
The process of turning 2D micrographs into 3D atomic models of the imaged macromolecules has been under rapid development and scrutiny in the field of cryo-EM. Here, some important methods for validation at several stages in this process are described. Firstly, how Fourier shell correlation of two independent maps and phase randomization beyond a certain frequency address the assessment of map resolution is reviewed. Techniques for local resolution estimation and map sharpening are also touched upon. The topic of validating models which are either built de novo or based on a known atomic structure fitted into a cryo-EM map is then approached. Map-model comparison using Q-scores and Fourier shell correlation plots is used to assure the agreement of the model with the observed map density. The importance of annotating the model with B factors to account for the resolvability of individual atoms in the map is illustrated. Finally, the timely topic of detecting and validating water molecules and metal ions in maps that have surpassed ∼2 Å resolution is described.
Collapse
Affiliation(s)
- Grigore Pintilie
- Department of Bioengineering, James H. Clark Center, Stanford University, Stanford, CA 94305, USA
| | - Wah Chiu
- Department of Bioengineering, James H. Clark Center, Stanford University, Stanford, CA 94305, USA
- Division of Cryo-EM and Bioimaging, SSRL, SLAC National Accelerator Laboratory, Stanford University, Menlo Park, CA 94025, USA
| |
Collapse
|
28
|
Ronen R, Attias Y, Schechner YY, Jaffe JS, Orenstein E. Plankton reconstruction through robust statistical optical tomography. JOURNAL OF THE OPTICAL SOCIETY OF AMERICA. A, OPTICS, IMAGE SCIENCE, AND VISION 2021; 38:1320-1331. [PMID: 34613139 DOI: 10.1364/josaa.423037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Accepted: 07/07/2021] [Indexed: 06/13/2023]
Abstract
Plankton interact with the environment according to their size and three-dimensional (3D) structure. To study them outdoors, these translucent specimens are imaged in situ. Light projects through a specimen in each image. The specimen has a random scale, drawn from the population's size distribution and random unknown pose. The specimen appears only once before drifting away. We achieve 3D tomography using such a random ensemble to statistically estimate an average volumetric distribution of the plankton type and specimen size. To counter errors due to non-rigid deformations, we weight the data, drawing from advanced models developed for cryo-electron microscopy. The weights convey the confidence in the quality of each datum. This confidence relies on a statistical error model. We demonstrate the approach on live plankton using an underwater field microscope.
Collapse
|
29
|
Tiwari SP, Tama F, Miyashita O. Protocol for Retrieving Three-Dimensional Biological Shapes for a Few XFEL Single-Particle Diffraction Patterns. J Chem Inf Model 2021; 61:4108-4119. [PMID: 34357759 DOI: 10.1021/acs.jcim.1c00602] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
X-ray free-electron laser (XFEL) scattering promises to probe single biomolecular complexes without crystallization, enabling the study of biomolecular structures under near-physiological conditions at room temperature. However, such structural determination of biomolecules is extremely challenging thus far. In addition to the large numbers of diffraction patterns required, the orientation of each diffraction pattern needs to be accurately estimated and the missing phase information needs to be recovered for three-dimensional (3D) structure reconstruction. Given the current limitations to the amount and resolution of the data available from single-particle XFEL scattering experiments, we propose an alternative approach to find plausible 3D biological shapes from a limited number of diffraction patterns to serve as a starting point for further analyses. In our proposed strategy, small sets of input (e.g., five) XFEL diffraction patterns were matched against a library of diffraction patterns simulated from 1628 electron microscopy (EM) models to find potential matching 3D models that are consistent with the input diffraction patterns. This approach was tested for three example cases: EMD-3457 (Thermoplasma acidophilum 20S proteasome), EMD-5141 (Escherichia coli 70S ribosome complex), and EMD-5152 (budding yeast Nup84 complex). We observed that choosing the best strategy to define matching regions on the diffraction patterns is critical for identifying correctly matching diffraction patterns. While increasing the number of input diffraction patterns improved the matches in some cases, we found that the resulting matches are more dependent on the uniqueness or complexity of the shape as captured in the individual input diffraction patterns and the availability of a similar 3D biological shape in the search library. The protocol could be useful for finding candidate models for a limited amount of low-resolution data, even when insufficient for reconstruction, performing a quick exploration of new data upon collection, and the analysis of the conformational heterogeneity of the particle of interest as captured within the diffraction patterns.
Collapse
Affiliation(s)
- Sandhya P Tiwari
- RIKEN Center for Computational Science, Kobe, Hyogo 650-0047, Japan.,Graduate School of Integrated Sciences for Life, Hiroshima University, Higashi-Hiroshima, Hiroshima 739-8521, Japan
| | - Florence Tama
- RIKEN Center for Computational Science, Kobe, Hyogo 650-0047, Japan.,Graduate School of Science, Department of Physics, Nagoya University, Nagoya, Aichi 464-8601, Japan.,Institute of Transformative Bio-Molecules (WPI-ITbM), Nagoya University, Nagoya, Aichi 464-8601, Japan
| | - Osamu Miyashita
- RIKEN Center for Computational Science, Kobe, Hyogo 650-0047, Japan
| |
Collapse
|
30
|
Wilson SL, Way GP, Bittremieux W, Armache JP, Haendel MA, Hoffman MM. Sharing biological data: why, when, and how. FEBS Lett 2021; 595:847-863. [PMID: 33843054 PMCID: PMC10390076 DOI: 10.1002/1873-3468.14067] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Samantha L Wilson
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
| | - Gregory P Way
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Wout Bittremieux
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA.,Department of Computer Science, University of Antwerp, Antwerpen, Belgium
| | - Jean-Paul Armache
- Department of Biochemistry & Molecular Biology, The Huck Institutes of Life Sciences, Pennsylvania State University, University Park, PA, USA
| | | | - Michael M Hoffman
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada.,Department of Medical Biophysics, Department of Computer Science, University of Toronto, Toronto, ON, Canada.,Vector Institute, Toronto, ON, Canada
| |
Collapse
|
31
|
Harrison PW, Lopez R, Rahman N, Allen SG, Aslam R, Buso N, Cummins C, Fathy Y, Felix E, Glont M, Jayathilaka S, Kadam S, Kumar M, Lauer KB, Malhotra G, Mosaku A, Edbali O, Park YM, Parton A, Pearce M, Estrada Pena JF, Rossetto J, Russell C, Selvakumar S, Sitjà XP, Sokolov A, Thorne R, Ventouratou M, Walter P, Yordanova G, Zadissa A, Cochrane G, Blomberg N, Apweiler R. The COVID-19 Data Portal: accelerating SARS-CoV-2 and COVID-19 research through rapid open access data sharing. Nucleic Acids Res 2021; 49:W619-W623. [PMID: 34048576 PMCID: PMC8218199 DOI: 10.1093/nar/gkab417] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Revised: 04/20/2021] [Accepted: 05/01/2021] [Indexed: 01/07/2023] Open
Abstract
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic will be remembered as one of the defining events of the 21st century. The rapid global outbreak has had significant impacts on human society and is already responsible for millions of deaths. Understanding and tackling the impact of the virus has required a worldwide mobilisation and coordination of scientific research. The COVID-19 Data Portal (https://www.covid19dataportal.org/) was first released as part of the European COVID-19 Data Platform, on April 20th 2020 to facilitate rapid and open data sharing and analysis, to accelerate global SARS-CoV-2 and COVID-19 research. The COVID-19 Data Portal has fortnightly feature releases to continue to add new data types, search options, visualisations and improvements based on user feedback and research. The open datasets and intuitive suite of search, identification and download services, represent a truly FAIR (Findable, Accessible, Interoperable and Reusable) resource that enables researchers to easily identify and quickly obtain the key datasets needed for their COVID-19 research.
Collapse
Affiliation(s)
- Peter W Harrison
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Rodrigo Lopez
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Nadim Rahman
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Stefan Gutnick Allen
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Raheela Aslam
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Nicola Buso
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Carla Cummins
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Yasmin Fathy
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Eloy Felix
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Mihai Glont
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Suran Jayathilaka
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Sandeep Kadam
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Manish Kumar
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | | | - Geetika Malhotra
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Abayomi Mosaku
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Ossama Edbali
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Young Mi Park
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Andrew Parton
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Matt Pearce
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Jose Francisco Estrada Pena
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Joseph Rossetto
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Craig Russell
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Sandeep Selvakumar
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | | | - Alexey Sokolov
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Ross Thorne
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Marianna Ventouratou
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Peter Walter
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Galabina Yordanova
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Amonida Zadissa
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Guy Cochrane
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Niklas Blomberg
- ELIXIR, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Rolf Apweiler
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| |
Collapse
|
32
|
Croll TI, Diederichs K, Fischer F, Fyfe CD, Gao Y, Horrell S, Joseph AP, Kandler L, Kippes O, Kirsten F, Müller K, Nolte K, Payne AM, Reeves M, Richardson JS, Santoni G, Stäb S, Tronrud DE, von Soosten LC, Williams CJ, Thorn A. Making the invisible enemy visible. Nat Struct Mol Biol 2021; 28:404-408. [PMID: 33972785 DOI: 10.1038/s41594-021-00593-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Affiliation(s)
| | | | - Florens Fischer
- Institut für Nanostruktur und Festkörperphysik, Universität Hamburg, Hamburg, Germany.,Rudolf-Virchow-Zentrum, Julius-Maximilians-Universität Würzburg, Würzburg, Germany
| | | | - Yunyun Gao
- Institut für Nanostruktur und Festkörperphysik, Universität Hamburg, Hamburg, Germany.,Rudolf-Virchow-Zentrum, Julius-Maximilians-Universität Würzburg, Würzburg, Germany
| | | | | | - Luise Kandler
- Institut für Nanostruktur und Festkörperphysik, Universität Hamburg, Hamburg, Germany.,Rudolf-Virchow-Zentrum, Julius-Maximilians-Universität Würzburg, Würzburg, Germany
| | - Oliver Kippes
- Institut für Nanostruktur und Festkörperphysik, Universität Hamburg, Hamburg, Germany.,Rudolf-Virchow-Zentrum, Julius-Maximilians-Universität Würzburg, Würzburg, Germany
| | - Ferdinand Kirsten
- Institut für Nanostruktur und Festkörperphysik, Universität Hamburg, Hamburg, Germany.,Rudolf-Virchow-Zentrum, Julius-Maximilians-Universität Würzburg, Würzburg, Germany
| | - Konstantin Müller
- Rudolf-Virchow-Zentrum, Julius-Maximilians-Universität Würzburg, Würzburg, Germany
| | - Kristopher Nolte
- Institut für Nanostruktur und Festkörperphysik, Universität Hamburg, Hamburg, Germany.,Rudolf-Virchow-Zentrum, Julius-Maximilians-Universität Würzburg, Würzburg, Germany
| | | | - Matthew Reeves
- Rudolf-Virchow-Zentrum, Julius-Maximilians-Universität Würzburg, Würzburg, Germany
| | | | | | - Sabrina Stäb
- Institut für Nanostruktur und Festkörperphysik, Universität Hamburg, Hamburg, Germany.,Rudolf-Virchow-Zentrum, Julius-Maximilians-Universität Würzburg, Würzburg, Germany
| | | | - Lea C von Soosten
- Institut für Nanostruktur und Festkörperphysik, Universität Hamburg, Hamburg, Germany.,Rudolf-Virchow-Zentrum, Julius-Maximilians-Universität Würzburg, Würzburg, Germany
| | | | - Andrea Thorn
- Institut für Nanostruktur und Festkörperphysik, Universität Hamburg, Hamburg, Germany. .,Rudolf-Virchow-Zentrum, Julius-Maximilians-Universität Würzburg, Würzburg, Germany.
| |
Collapse
|
33
|
Kyrilis FL, Belapure J, Kastritis PL. Detecting Protein Communities in Native Cell Extracts by Machine Learning: A Structural Biologist's Perspective. Front Mol Biosci 2021; 8:660542. [PMID: 33937337 PMCID: PMC8082361 DOI: 10.3389/fmolb.2021.660542] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Accepted: 03/18/2021] [Indexed: 11/13/2022] Open
Abstract
Native cell extracts hold great promise for understanding the molecular structure of ordered biological systems at high resolution. This is because higher-order biomolecular interactions, dubbed as protein communities, may be retained in their (near-)native state, in contrast to extensively purifying or artificially overexpressing the proteins of interest. The distinct machine-learning approaches are applied to discover protein-protein interactions within cell extracts, reconstruct dedicated biological networks, and report on protein community members from various organisms. Their validation is also important, e.g., by the cross-linking mass spectrometry or cell biology methods. In addition, the cell extracts are amenable to structural analysis by cryo-electron microscopy (cryo-EM), but due to their inherent complexity, sorting structural signatures of protein communities derived by cryo-EM comprises a formidable task. The application of image-processing workflows inspired by machine-learning techniques would provide improvements in distinguishing structural signatures, correlating proteomic and network data to structural signatures and subsequently reconstructed cryo-EM maps, and, ultimately, characterizing unidentified protein communities at high resolution. In this review article, we summarize recent literature in detecting protein communities from native cell extracts and identify the remaining challenges and opportunities. We argue that the progress in, and the integration of, machine learning, cryo-EM, and complementary structural proteomics approaches would provide the basis for a multi-scale molecular description of protein communities within native cell extracts.
Collapse
Affiliation(s)
- Fotis L. Kyrilis
- Interdisciplinary Research Center HALOmem, Charles Tanford Protein Center, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany
- Institute of Biochemistry and Biotechnology, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany
| | - Jaydeep Belapure
- Interdisciplinary Research Center HALOmem, Charles Tanford Protein Center, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany
| | - Panagiotis L. Kastritis
- Interdisciplinary Research Center HALOmem, Charles Tanford Protein Center, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany
- Institute of Biochemistry and Biotechnology, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany
- Biozentrum, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany
| |
Collapse
|
34
|
Terwilliger TC, Sobolev OV, Afonine PV, Adams PD, Ho CM, Li X, Zhou ZH. Protein identification from electron cryomicroscopy maps by automated model building and side-chain matching. Acta Crystallogr D Struct Biol 2021; 77:457-462. [PMID: 33825706 PMCID: PMC8025881 DOI: 10.1107/s2059798321001765] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Accepted: 02/12/2021] [Indexed: 11/10/2022] Open
Abstract
Using single-particle electron cryo-microscopy (cryo-EM), it is possible to obtain multiple reconstructions showing the 3D structures of proteins imaged as a mixture. Here, it is shown that automatic map interpretation based on such reconstructions can be used to create atomic models of proteins as well as to match the proteins to the correct sequences and thereby to identify them. This procedure was tested using two proteins previously identified from a mixture at resolutions of 3.2 Å, as well as using 91 deposited maps with resolutions between 2 and 4.5 Å. The approach is found to be highly effective for maps obtained at resolutions of 3.5 Å and better, and to have some utility at resolutions as low as 4 Å.
Collapse
Affiliation(s)
- Thomas C. Terwilliger
- New Mexico Consortium, Los Alamos, NM 87544, USA
- Bioscience Division, Los Alamos National Laboratory, Mail Stop M888, Los Alamos, NM 87545, USA
| | - Oleg V. Sobolev
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Pavel V. Afonine
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Paul D. Adams
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
- Department of Bioengineering, University of California Berkeley, Berkeley, California, USA
| | - Chi-Min Ho
- The Molecular Biology Institute, University of California, Los Angeles, CA 90095, USA
- Department of Microbiology, Immunology and Molecular Genetics, University of California, Los Angeles, CA 90095, USA
- California NanoSystems Institute, University of California, Los Angeles, CA 90095, USA
- Department of Microbiology and Immunology, Vagelos College of Physicians and Surgeons, Columbia University, New York, USA
| | - Xiaorun Li
- California NanoSystems Institute, University of California, Los Angeles, CA 90095, USA
- Hefei National Laboratory for Physical Sciences at Microscale, University of Science and Technology of China, Hefei, Anhui 230026, People’s Republic of China
| | - Z. Hong Zhou
- The Molecular Biology Institute, University of California, Los Angeles, CA 90095, USA
- Department of Microbiology, Immunology and Molecular Genetics, University of California, Los Angeles, CA 90095, USA
- California NanoSystems Institute, University of California, Los Angeles, CA 90095, USA
| |
Collapse
|
35
|
Abstract
Abstract
Deep learning is transforming most areas of science and technology, including electron microscopy. This review paper offers a practical perspective aimed at developers with limited familiarity. For context, we review popular applications of deep learning in electron microscopy. Following, we discuss hardware and software needed to get started with deep learning and interface with electron microscopes. We then review neural network components, popular architectures, and their optimization. Finally, we discuss future directions of deep learning in electron microscopy.
Collapse
|
36
|
Chiu W, Schmid MF, Pintilie GD, Lawson CL. Evolution of standardization and dissemination of cryo-EM structures and data jointly by the community, PDB, and EMDB. J Biol Chem 2021; 296:100560. [PMID: 33744287 PMCID: PMC8050867 DOI: 10.1016/j.jbc.2021.100560] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2021] [Revised: 02/08/2021] [Accepted: 03/16/2021] [Indexed: 01/04/2023] Open
Abstract
Cryogenic electron microscopy (cryo-EM) methods began to be used in the mid-1970s to study thin and periodic arrays of proteins. Following a half-century of development in cryo-specimen preparation, instrumentation, data collection, data processing, and modeling software, cryo-EM has become a routine method for solving structures from large biological assemblies to small biomolecules at near to true atomic resolution. This review explores the critical roles played by the Protein Data Bank (PDB) and Electron Microscopy Data Bank (EMDB) in partnership with the community to develop the necessary infrastructure to archive cryo-EM maps and associated models. Public access to cryo-EM structure data has in turn facilitated better understanding of structure–function relationships and advancement of image processing and modeling tool development. The partnership between the global cryo-EM community and PDB and EMDB leadership has synergistically shaped the standards for metadata, one-stop deposition of maps and models, and validation metrics to assess the quality of cryo-EM structures. The advent of cryo-electron tomography (cryo-ET) for in situ molecular cell structures at a broad resolution range and their correlations with other imaging data introduce new data archival challenges in terms of data size and complexity in the years to come.
Collapse
Affiliation(s)
- Wah Chiu
- Department of Bioengineering, Stanford University, Stanford, California, USA; Division of CryoEM and Bioimaging, SLAC National Accelerator Laboratory, Stanford University, Menlo Park, California, USA.
| | - Michael F Schmid
- Division of CryoEM and Bioimaging, SLAC National Accelerator Laboratory, Stanford University, Menlo Park, California, USA
| | - Grigore D Pintilie
- Department of Bioengineering, Stanford University, Stanford, California, USA
| | - Catherine L Lawson
- Institute for Quantitative Biomedicine and Research Collaboratory for Structural Bioinformatics, Rutgers, The State University of New Jersey, Piscataway, New Jersey, USA
| |
Collapse
|
37
|
Croll TI, Williams CJ, Chen VB, Richardson DC, Richardson JS. Improving SARS-CoV-2 structures: Peer review by early coordinate release. Biophys J 2021; 120:1085-1096. [PMID: 33460600 PMCID: PMC7834719 DOI: 10.1016/j.bpj.2020.12.029] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Revised: 12/17/2020] [Accepted: 12/22/2020] [Indexed: 01/18/2023] Open
Abstract
This work builds upon the record-breaking speed and generous immediate release of new experimental three-dimensional structures of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) proteins and complexes, which are crucial to downstream vaccine and drug development. We have surveyed those structures to catch the occasional errors that could be significant for those important uses and for which we were able to provide demonstrably higher-accuracy corrections. This process relied on new validation and correction methods such as CaBLAM and ISOLDE, which are not yet in routine use. We found such important and correctable problems in seven early SARS-CoV-2 structures. Two of the structures were soon superseded by new higher-resolution data, confirming our proposed changes. For the other five, we emailed the depositors a documented and illustrated report and encouraged them to make the model corrections themselves and use the new option at the worldwide Protein Data Bank for depositors to re-version their coordinates without changing the Protein Data Bank code. This quickly and easily makes the better-accuracy coordinates available to anyone who examines or downloads their structure, even before formal publication. The changes have involved sequence misalignments, incorrect RNA conformations near a bound inhibitor, incorrect metal ligands, and cis-trans or peptide flips that prevent good contact at interaction sites. These improvements have propagated into nearly all related structures done afterward. This process constitutes a new form of highly rigorous peer review, which is actually faster and more strict than standard publication review because it has access to coordinates and maps; journal peer review would also be strengthened by such access.
Collapse
Affiliation(s)
| | | | - Vincent B Chen
- Department of Biochemistry, Duke University, Durham, North Carolina
| | | | - Jane S Richardson
- Department of Biochemistry, Duke University, Durham, North Carolina.
| |
Collapse
|
38
|
Kaur S, Gomez-Blanco J, Khalifa AAZ, Adinarayanan S, Sanchez-Garcia R, Wrapp D, McLellan JS, Bui KH, Vargas J. Local computational methods to improve the interpretability and analysis of cryo-EM maps. Nat Commun 2021; 12:1240. [PMID: 33623015 PMCID: PMC7902670 DOI: 10.1038/s41467-021-21509-5] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Accepted: 01/29/2021] [Indexed: 12/13/2022] Open
Abstract
Cryo-electron microscopy (cryo-EM) maps usually show heterogeneous distributions of B-factors and electron density occupancies and are typically B-factor sharpened to improve their contrast and interpretability at high-resolutions. However, 'over-sharpening' due to the application of a single global B-factor can distort processed maps causing connected densities to appear broken and disconnected. This issue limits the interpretability of cryo-EM maps, i.e. ab initio modelling. In this work, we propose 1) approaches to enhance high-resolution features of cryo-EM maps, while preventing map distortions and 2) methods to obtain local B-factors and electron density occupancy maps. These algorithms have as common link the use of the spiral phase transformation and are called LocSpiral, LocBSharpen, LocBFactor and LocOccupancy. Our results, which include improved maps of recent SARS-CoV-2 structures, show that our methods can improve the interpretability and analysis of obtained reconstructions.
Collapse
Affiliation(s)
- Satinder Kaur
- Departament of Anatomy and Cell Biology, McGill University 3640 Rue University, Montréal, QC, Canada
| | - Josue Gomez-Blanco
- Departament of Anatomy and Cell Biology, McGill University 3640 Rue University, Montréal, QC, Canada
| | - Ahmad A Z Khalifa
- Departament of Anatomy and Cell Biology, McGill University 3640 Rue University, Montréal, QC, Canada
| | - Swathi Adinarayanan
- Departament of Anatomy and Cell Biology, McGill University 3640 Rue University, Montréal, QC, Canada
| | - Ruben Sanchez-Garcia
- Biocomputing Unit, Centro Nacional de Biotecnología-CSIC C/Darwin 3, Cantoblanco, Madrid, Spain
| | - Daniel Wrapp
- Department of Molecular Biosciences, The University of Texas at Austin, Austin, TX, USA
| | - Jason S McLellan
- Department of Molecular Biosciences, The University of Texas at Austin, Austin, TX, USA
| | - Khanh Huy Bui
- Departament of Anatomy and Cell Biology, McGill University 3640 Rue University, Montréal, QC, Canada
| | - Javier Vargas
- Departmento de Óptica, Universidad Complutense de Madrid, Madrid, Spain.
| |
Collapse
|
39
|
|
40
|
Modeling the
Influenza A
NP-vRNA-Polymerase Complex in Atomic Detail. Biomolecules 2021; 11:biom11010124. [PMID: 33477938 PMCID: PMC7833383 DOI: 10.3390/biom11010124] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Revised: 01/07/2021] [Accepted: 01/13/2021] [Indexed: 11/17/2022] Open
Abstract
Seasonal flu is an acute respiratory disease that exacts a massive toll on human populations, healthcare systems and economies. The disease is caused by an enveloped Influenza virus containing eight ribonucleoprotein (RNP) complexes. Each RNP incorporates multiple copies of nucleoprotein (NP), a fragment of the viral genome (vRNA), and a viral RNA-dependent RNA polymerase (POL), and is responsible for packaging the viral genome and performing critical functions including replication and transcription. A complete model of an Influenza RNP in atomic detail can elucidate the structural basis for viral genome functions, and identify potential targets for viral therapeutics. In this work we construct a model of a complete Influenza A RNP complex in atomic detail using multiple sources of structural and sequence information and a series of homology-modeling techniques, including a motif-matching fragment assembly method. Our final model provides a rationale for experimentally-observed changes to viral polymerase activity in numerous mutational assays. Further, our model reveals specific interactions between the three primary structural components of the RNP, including potential targets for blocking POL-binding to the NP-vRNA complex. The methods developed in this work open the possibility of elucidating other functionally-relevant atomic-scale interactions in additional RNP structures and other biomolecular complexes.
Collapse
|
41
|
Zhao T, Chen YM, Li Y, Wang J, Chen S, Gao N, Qian W. Disome-seq reveals widespread ribosome collisions that promote cotranslational protein folding. Genome Biol 2021; 22:16. [PMID: 33402206 PMCID: PMC7784341 DOI: 10.1186/s13059-020-02256-0] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2020] [Accepted: 12/20/2020] [Indexed: 12/16/2022] Open
Abstract
BACKGROUND The folding of proteins is challenging in the highly crowded and sticky environment of a cell. Regulation of translation elongation may play a crucial role in ensuring the correct folding of proteins. Much of our knowledge regarding translation elongation comes from the sequencing of mRNA fragments protected by single ribosomes by ribo-seq. However, larger protected mRNA fragments have been observed, suggesting the existence of an alternative and previously hidden layer of regulation. RESULTS In this study, we performed disome-seq to sequence mRNA fragments protected by two stacked ribosomes, a product of translational pauses during which the 5'-elongating ribosome collides with the 3'-paused one. We detected widespread ribosome collisions that are related to slow ribosome release when stop codons are at the A-site, slow peptide bond formation from proline, glycine, asparagine, and cysteine when they are at the P-site, and slow leaving of polylysine from the exit tunnel of ribosomes. The structure of disomes obtained by cryo-electron microscopy suggests a different conformation from the substrate of the ribosome-associated protein quality control pathway. Collisions occurred more frequently in the gap regions between α-helices, where a translational pause can prevent the folding interference from the downstream peptides. Paused or collided ribosomes are associated with specific chaperones, which can aid in the cotranslational folding of the nascent peptides. CONCLUSIONS Therefore, cells use regulated ribosome collisions to ensure protein homeostasis.
Collapse
Affiliation(s)
- Taolan Zhao
- State Key Laboratory of Plant Genomics, Institute of Genetics and Developmental Biology, Innovation Academy for Seed Design, Chinese Academy of Sciences, Beijing, 100101, China. .,Key Laboratory of Genetic Network Biology, Institute of Genetics and Developmental Biology, Innovation Academy for Seed Design, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Yan-Ming Chen
- State Key Laboratory of Plant Genomics, Institute of Genetics and Developmental Biology, Innovation Academy for Seed Design, Chinese Academy of Sciences, Beijing, 100101, China.,Key Laboratory of Genetic Network Biology, Institute of Genetics and Developmental Biology, Innovation Academy for Seed Design, Chinese Academy of Sciences, Beijing, 100101, China.,University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Yu Li
- Peking University-Tsinghua University-National Institute of Biological Sciences Joint Graduate Program, School of Life Science, Tsinghua University, Beijing, 100084, China.,State Key Laboratory of Membrane Biology, Peking-Tsinghua Center for Life Sciences, School of Life Sciences, Peking University, Beijing, 100871, China
| | - Jia Wang
- State Key Laboratory of Plant Genomics, Institute of Genetics and Developmental Biology, Innovation Academy for Seed Design, Chinese Academy of Sciences, Beijing, 100101, China.,Key Laboratory of Genetic Network Biology, Institute of Genetics and Developmental Biology, Innovation Academy for Seed Design, Chinese Academy of Sciences, Beijing, 100101, China.,University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Siyu Chen
- State Key Laboratory of Plant Genomics, Institute of Genetics and Developmental Biology, Innovation Academy for Seed Design, Chinese Academy of Sciences, Beijing, 100101, China.,Key Laboratory of Genetic Network Biology, Institute of Genetics and Developmental Biology, Innovation Academy for Seed Design, Chinese Academy of Sciences, Beijing, 100101, China.,University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Ning Gao
- State Key Laboratory of Membrane Biology, Peking-Tsinghua Center for Life Sciences, School of Life Sciences, Peking University, Beijing, 100871, China.
| | - Wenfeng Qian
- State Key Laboratory of Plant Genomics, Institute of Genetics and Developmental Biology, Innovation Academy for Seed Design, Chinese Academy of Sciences, Beijing, 100101, China. .,Key Laboratory of Genetic Network Biology, Institute of Genetics and Developmental Biology, Innovation Academy for Seed Design, Chinese Academy of Sciences, Beijing, 100101, China. .,University of Chinese Academy of Sciences, Beijing, 100049, China.
| |
Collapse
|
42
|
Liebschner D, Afonine PV, Moriarty NW, Poon BK, Chen VB, Adams PD. CERES: a cryo-EM re-refinement system for continuous improvement of deposited models. Acta Crystallogr D Struct Biol 2021; 77:48-61. [PMID: 33404525 PMCID: PMC7787109 DOI: 10.1107/s2059798320015879] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2020] [Accepted: 12/03/2020] [Indexed: 11/10/2022] Open
Abstract
The field of electron cryomicroscopy (cryo-EM) has advanced quickly in recent years as the result of numerous technological and methodological developments. This has led to an increase in the number of atomic structures determined using this method. Recently, several tools for the analysis of cryo-EM data and models have been developed within the Phenix software package, such as phenix.real_space_refine for the refinement of atomic models against real-space maps. Also, new validation metrics have been developed for low-resolution cryo-EM models. To understand the quality of deposited cryo-EM structures and how they might be improved, models deposited in the Protein Data Bank that have map resolutions of better than 5 Å were automatically re-refined using current versions of Phenix tools. The results are available on a publicly accessible web page (https://cci.lbl.gov/ceres). The implementation of a Cryo-EM Re-refinement System (CERES) for the improvement of models deposited in the wwPDB, and the results of the re-refinements, are described. Based on these results, contents are proposed for a `cryo-EM Table 1', which summarizes experimental details and validation metrics in a similar way to `Table 1' in crystallography. The consistent use of robust metrics for the evaluation of cryo-EM models and data should accompany every structure deposition and be reported in scientific publications.
Collapse
Affiliation(s)
- Dorothee Liebschner
- Molecular Biosciences and Integrated Bioimaging, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720, USA
| | - Pavel V. Afonine
- Molecular Biosciences and Integrated Bioimaging, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720, USA
| | - Nigel W. Moriarty
- Molecular Biosciences and Integrated Bioimaging, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720, USA
| | - Billy K. Poon
- Molecular Biosciences and Integrated Bioimaging, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720, USA
| | - Vincent B. Chen
- Department of Biochemistry, Duke University, Durham, NC 27710, USA
| | - Paul D. Adams
- Molecular Biosciences and Integrated Bioimaging, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720, USA
- Department of Bioengineering, University of California Berkeley, Berkeley, CA 94720, USA
| |
Collapse
|
43
|
Croll T, Diederichs K, Fischer F, Fyfe C, Gao Y, Horrell S, Joseph AP, Kandler L, Kippes O, Kirsten F, Müller K, Nolte K, Payne A, Reeves MG, Richardson J, Santoni G, Stäb S, Tronrud D, von Soosten L, Williams C, Thorn A. Making the invisible enemy visible. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2020:2020.10.07.307546. [PMID: 33052340 PMCID: PMC7553165 DOI: 10.1101/2020.10.07.307546] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
During the COVID-19 pandemic, structural biologists rushed to solve the structures of the 28 proteins encoded by the SARS-CoV-2 genome in order to understand the viral life cycle and enable structure-based drug design. In addition to the 204 previously solved structures from SARS-CoV-1, 548 structures covering 16 of the SARS-CoV-2 viral proteins have been released in a span of only 6 months. These structural models serve as the basis for research to understand how the virus hijacks human cells, for structure-based drug design, and to aid in the development of vaccines. However, errors often occur in even the most careful structure determination - and may be even more common among these structures, which were solved quickly and under immense pressure. The Coronavirus Structural Task Force has responded to this challenge by rapidly categorizing, evaluating and reviewing all of these experimental protein structures in order to help downstream users and original authors. In addition, the Task Force provided improved models for key structures online, which have been used by Folding@Home, OpenPandemics, the EU JEDI COVID-19 challenge and others.
Collapse
|
44
|
Kube M, Kohler F, Feigl E, Nagel-Yüksel B, Willner EM, Funke JJ, Gerling T, Stömmer P, Honemann MN, Martin TG, Scheres SHW, Dietz H. Revealing the structures of megadalton-scale DNA complexes with nucleotide resolution. Nat Commun 2020; 11:6229. [PMID: 33277481 PMCID: PMC7718922 DOI: 10.1038/s41467-020-20020-7] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2020] [Accepted: 11/11/2020] [Indexed: 11/25/2022] Open
Abstract
The methods of DNA nanotechnology enable the rational design of custom shapes that self-assemble in solution from sets of DNA molecules. DNA origami, in which a long template DNA single strand is folded by many short DNA oligonucleotides, can be employed to make objects comprising hundreds of unique DNA strands and thousands of base pairs, thus in principle providing many degrees of freedom for modelling complex objects of defined 3D shapes and sizes. Here, we address the problem of accurate structural validation of DNA objects in solution with cryo-EM based methodologies. By taking into account structural fluctuations, we can determine structures with improved detail compared to previous work. To interpret the experimental cryo-EM maps, we present molecular-dynamics-based methods for building pseudo-atomic models in a semi-automated fashion. Among other features, our data allows discerning details such as helical grooves, single-strand versus double-strand crossovers, backbone phosphate positions, and single-strand breaks. Obtaining this higher level of detail is a step forward that now allows designers to inspect and refine their designs with base-pair level interventions. Precision design of DNA origami needs precision validation. Here, the authors developed cryo-EM methods for obtaining high resolution structural data and for constructing pseudo-atomic models in a semi-automated fashion, allowing for iterative nanodevice inspection and refinement.
Collapse
Affiliation(s)
- Massimo Kube
- Physik Department, Technische Universität München, Garching, Germany
| | - Fabian Kohler
- Physik Department, Technische Universität München, Garching, Germany
| | - Elija Feigl
- Physik Department, Technische Universität München, Garching, Germany
| | - Baki Nagel-Yüksel
- Physik Department, Technische Universität München, Garching, Germany
| | - Elena M Willner
- Physik Department, Technische Universität München, Garching, Germany
| | - Jonas J Funke
- Physik Department, Technische Universität München, Garching, Germany
| | - Thomas Gerling
- Physik Department, Technische Universität München, Garching, Germany
| | - Pierre Stömmer
- Physik Department, Technische Universität München, Garching, Germany
| | | | | | | | - Hendrik Dietz
- Physik Department, Technische Universität München, Garching, Germany.
| |
Collapse
|
45
|
Wang L, Kruse H, Sobolev OV, Moriarty NW, Waller MP, Afonine PV, Biczysko M. Real-space quantum-based refinement for cryo-EM: Q|R#3. ACTA CRYSTALLOGRAPHICA SECTION D-STRUCTURAL BIOLOGY 2020; 76:1184-1191. [PMID: 33263324 DOI: 10.1107/s2059798320013194] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/25/2020] [Accepted: 09/29/2020] [Indexed: 11/10/2022]
Abstract
Electron cryo-microscopy (cryo-EM) is rapidly becoming a major competitor to X-ray crystallography, especially for large structures that are difficult or impossible to crystallize. While recent spectacular technological improvements have led to significantly higher resolution three-dimensional reconstructions, the average quality of cryo-EM maps is still at the low-resolution end of the range compared with crystallography. A long-standing challenge for atomic model refinement has been the production of stereochemically meaningful models for this resolution regime. Here, it is demonstrated that including accurate model geometry restraints derived from ab initio quantum-chemical calculations (HF-D3/6-31G) can improve the refinement of an example structure (chain A of PDB entry 3j63). The robustness of the procedure is tested for additional structures with up to 7000 atoms (PDB entry 3a5x and chain C of PDB entry 5fn5) using the less expensive semi-empirical (GFN1-xTB) model. The necessary algorithms enabling real-space quantum refinement have been implemented in the latest version of qr.refine and are described here.
Collapse
Affiliation(s)
- Lum Wang
- International Center for Quantum and Molecular Structures, Shanghai University, Shanghai 200444, People's Republic of China
| | - Holger Kruse
- Institute of Biophysics of the Czech Academy of Sciences, Brno, Czech Republic
| | - Oleg V Sobolev
- Molecular Biosciences and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Nigel W Moriarty
- Molecular Biosciences and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Mark P Waller
- Pending AI Pty Ltd, iAccelerat, Innovation Campus, North Wollongong, NSW 2500, Australia
| | - Pavel V Afonine
- Molecular Biosciences and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Malgorzata Biczysko
- International Center for Quantum and Molecular Structures, Shanghai University, Shanghai 200444, People's Republic of China
| |
Collapse
|
46
|
Zhou X, Li Y, Zhang C, Zheng W, Zhang G, Zhang Y. Progressive and accurate assembly of multi-domain protein structures from cryo-EM density maps. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2020:2020.10.15.340455. [PMID: 33083802 PMCID: PMC7574260 DOI: 10.1101/2020.10.15.340455] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
Progress in cryo-electron microscopy (cryo-EM) has provided the potential for large-size protein structure determination. However, the solution rate for multi-domain proteins remains low due to the difficulty in modeling inter-domain orientations. We developed DEMO-EM, an automatic method to assemble multi-domain structures from cryo-EM maps through a progressive structural refinement procedure combining rigid-body domain fitting and flexible assembly simulations with deep neural network inter-domain distance profiles. The method was tested on a large-scale benchmark set of proteins containing up to twelve continuous and discontinuous domains with medium-to-low-resolution density maps, where DEMO-EM produced models with correct inter-domain orientations (TM-score >0.5) for 98% of cases and significantly outperformed the state-of-the-art methods. DEMO-EM was applied to SARS-Cov-2 coronavirus genome and generated models with average TM-score/RMSD of 0.97/1.4Å to the deposited structures. These results demonstrated an efficient pipeline that enables automated and reliable large-scale multi-domain protein structure modeling with atomic-level accuracy from cryo-EM maps.
Collapse
Affiliation(s)
- Xiaogen Zhou
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Yang Li
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Chengxin Zhang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Wei Zheng
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Guijun Zhang
- College of Information Engineering, Zhejiang University of Technology, HangZhou 310023, China
| | - Yang Zhang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
- Department of Biological Chemistry, University of Michigan, Ann Arbor, MI 48109, USA
| |
Collapse
|
47
|
Toward Increased Reliability, Transparency, and Accessibility in Cross-linking Mass Spectrometry. Structure 2020; 28:1259-1268. [PMID: 33065067 DOI: 10.1016/j.str.2020.09.011] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2020] [Revised: 09/02/2020] [Accepted: 09/24/2020] [Indexed: 01/09/2023]
Abstract
Cross-linking mass spectrometry (MS) has substantially matured as a method over the past 2 decades through parallel development in multiple labs, demonstrating its applicability to protein structure determination, conformation analysis, and mapping protein interactions in complex mixtures. Cross-linking MS has become a much-appreciated and routinely applied tool, especially in structural biology. Therefore, it is timely that the community commits to the development of methodological and reporting standards. This white paper builds on an open process comprising a number of events at community conferences since 2015 and identifies aspects of Cross-linking MS for which guidelines should be developed as part of a Cross-linking MS standards initiative.
Collapse
|
48
|
Terwilliger TC, Sobolev OV, Afonine PV, Adams PD, Read RJ. Density modification of cryo-EM maps. Acta Crystallogr D Struct Biol 2020; 76:912-925. [PMID: 33021493 PMCID: PMC7543659 DOI: 10.1107/s205979832001061x] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Accepted: 08/02/2020] [Indexed: 11/25/2022] Open
Abstract
Density modification uses expectations about features of a map such as a flat solvent and expected distributions of density in the region of the macromolecule to improve individual Fourier terms representing the map. This process transfers information from one part of a map to another and can improve the accuracy of a map. Here, the assumptions behind density modification for maps from electron cryomicroscopy are examined and a procedure is presented that allows the incorporation of model-based information. Density modification works best in cases where unfiltered, unmasked maps with clear boundaries between the macromolecule and solvent are visible, and where there is substantial noise in the map, both in the region of the macromolecule and the solvent. It also is most effective if the characteristics of the map are relatively constant within regions of the macromolecule and the solvent. Model-based information can be used to improve density modification, but model bias can in principle occur. Here, model bias is reduced by using ensemble models that allow an estimation of model uncertainty. A test of model bias is presented that suggests that even if the expected density in a region of a map is specified incorrectly by using an incorrect model, the incorrect expectations do not strongly affect the final map.
Collapse
Affiliation(s)
- Thomas C. Terwilliger
- New Mexico Consortium, Los Alamos, NM 87544, USA
- Bioscience Division, Los Alamos National Laboratory, Mail Stop M888, Los Alamos, NM 87545, USA
| | - Oleg V. Sobolev
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Pavel V. Afonine
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Paul D. Adams
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
- Department of Bioengineering, University of California Berkeley, Berkeley, California, USA
| | - Randy J. Read
- Department of Haematology, University of Cambridge, Cambridge Institute for Medical Research, The Keith Peters Building, Hills Road, Cambridge CB2 0XY, United Kingdom
| |
Collapse
|
49
|
Melero R, Sorzano COS, Foster B, Vilas JL, Martínez M, Marabini R, Ramírez-Aportela E, Sanchez-Garcia R, Herreros D, del Caño L, Losana P, Fonseca-Reyna YC, Conesa P, Wrapp D, Chacon P, McLellan JS, Tagare HD, Carazo JM. Continuous flexibility analysis of SARS-CoV-2 spike prefusion structures. IUCRJ 2020; 7:S2052252520012725. [PMID: 33063791 PMCID: PMC7553147 DOI: 10.1107/s2052252520012725] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/23/2020] [Accepted: 09/18/2020] [Indexed: 05/09/2023]
Abstract
Using a new consensus-based image-processing approach together with principal component analysis, the flexibility and conformational dynamics of the SARS-CoV-2 spike in the prefusion state have been analysed. These studies revealed concerted motions involving the receptor-binding domain (RBD), N-terminal domain, and subdomains 1 and 2 around the previously characterized 1-RBD-up state, which have been modeled as elastic deformations. It is shown that in this data set there are not well defined, stable spike conformations, but virtually a continuum of states. An ensemble map was obtained with minimum bias, from which the extremes of the change along the direction of maximal variance were modeled by flexible fitting. The results provide a warning of the potential image-processing classification instability of these complicated data sets, which has a direct impact on the interpretability of the results.
Collapse
Affiliation(s)
- Roberto Melero
- Centro Nacional de Biotecnologia–CSIC, Calle Darwin 3, 28049 Cantoblanco, Madrid, Spain
| | | | - Brent Foster
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT 06520, USA
| | - José-Luis Vilas
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT 06520, USA
| | - Marta Martínez
- Centro Nacional de Biotecnologia–CSIC, Calle Darwin 3, 28049 Cantoblanco, Madrid, Spain
| | - Roberto Marabini
- Centro Nacional de Biotecnologia–CSIC, Calle Darwin 3, 28049 Cantoblanco, Madrid, Spain
- Universidad Autónoma de Madrid, Calle Francisco Tomás y Valiente 11, 28049 Cantoblanco, Madrid, Spain
| | | | - Ruben Sanchez-Garcia
- Centro Nacional de Biotecnologia–CSIC, Calle Darwin 3, 28049 Cantoblanco, Madrid, Spain
| | - David Herreros
- Centro Nacional de Biotecnologia–CSIC, Calle Darwin 3, 28049 Cantoblanco, Madrid, Spain
| | - Laura del Caño
- Centro Nacional de Biotecnologia–CSIC, Calle Darwin 3, 28049 Cantoblanco, Madrid, Spain
| | - Patricia Losana
- Centro Nacional de Biotecnologia–CSIC, Calle Darwin 3, 28049 Cantoblanco, Madrid, Spain
| | | | - Pablo Conesa
- Centro Nacional de Biotecnologia–CSIC, Calle Darwin 3, 28049 Cantoblanco, Madrid, Spain
| | - Daniel Wrapp
- Department of Molecular Biosciences, The University of Texas at Austin, Austin, TX 78712, USA
| | - Pablo Chacon
- Department of Biological Physical Chemistry, Instituto Rocasolano–CSIC, Calle de Serrano 119, 28006 Madrid, Spain
| | - Jason S. McLellan
- Department of Molecular Biosciences, The University of Texas at Austin, Austin, TX 78712, USA
| | - Hemant D. Tagare
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT 06520, USA
| | - Jose-Maria Carazo
- Centro Nacional de Biotecnologia–CSIC, Calle Darwin 3, 28049 Cantoblanco, Madrid, Spain
| |
Collapse
|
50
|
Abstract
A density-modification procedure for improving maps from single-particle electron cryogenic microscopy (cryo-EM) is presented. The theoretical basis of the method is identical to that of maximum-likelihood density modification, previously used to improve maps from macromolecular X-ray crystallography. Key differences from applications in crystallography are that the errors in Fourier coefficients are largely in the phases in crystallography but in both phases and amplitudes in cryo-EM, and that half-maps with independent errors are available in cryo-EM. These differences lead to a distinct approach for combination of information from starting maps with information obtained in the density-modification process. The density-modification procedure was applied to a set of 104 datasets and improved map-model correlation and increased the visibility of details in many of the maps. The procedure requires two unmasked half-maps and a sequence file or other source of information on the volume of the macromolecule that has been imaged.
Collapse
|