1
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Galaz-Montoya JG. The advent of preventive high-resolution structural histopathology by artificial-intelligence-powered cryogenic electron tomography. Front Mol Biosci 2024; 11:1390858. [PMID: 38868297 PMCID: PMC11167099 DOI: 10.3389/fmolb.2024.1390858] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2024] [Accepted: 05/08/2024] [Indexed: 06/14/2024] Open
Abstract
Advances in cryogenic electron microscopy (cryoEM) single particle analysis have revolutionized structural biology by facilitating the in vitro determination of atomic- and near-atomic-resolution structures for fully hydrated macromolecular complexes exhibiting compositional and conformational heterogeneity across a wide range of sizes. Cryogenic electron tomography (cryoET) and subtomogram averaging are rapidly progressing toward delivering similar insights for macromolecular complexes in situ, without requiring tags or harsh biochemical purification. Furthermore, cryoET enables the visualization of cellular and tissue phenotypes directly at molecular, nanometric resolution without chemical fixation or staining artifacts. This forward-looking review covers recent developments in cryoEM/ET and related technologies such as cryogenic focused ion beam milling scanning electron microscopy and correlative light microscopy, increasingly enhanced and supported by artificial intelligence algorithms. Their potential application to emerging concepts is discussed, primarily the prospect of complementing medical histopathology analysis. Machine learning solutions are poised to address current challenges posed by "big data" in cryoET of tissues, cells, and macromolecules, offering the promise of enabling novel, quantitative insights into disease processes, which may translate into the clinic and lead to improved diagnostics and targeted therapeutics.
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Affiliation(s)
- Jesús G. Galaz-Montoya
- Department of Bioengineering, James H. Clark Center, Stanford University, Stanford, CA, United States
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2
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Jia L, Ruben EA, Suarez HJ, Olsen SK, Wasmuth EV. Single particle cryo-electron microscopy with an enhanced 200 kV cryo-TEM configuration achieves near-atomic resolution. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.07.593029. [PMID: 38766263 PMCID: PMC11100677 DOI: 10.1101/2024.05.07.593029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
Single particle cryogenic electron microscopy (cryo-EM) as a structural biology methodology has become increasingly attractive and accessible to investigators in both academia and industry as this ever-advancing technology enables successful structural determination of a wide range of protein and nucleic acid targets. Although data for many high resolution cryo-EM structures are still obtained using a 300 kV cryogenic transmission electron microscope (cryo-TEM), a modern 200 kV cryo-TEM equipped with an advanced direct electron detector and energy filter is a cost-effective choice for most single particle applications, routinely achieving sub 3 angstrom (Å) resolution. Here, we systematically evaluate performance of one such high-end configuration - a 200 kV Glacios microscope coupled with a Falcon 4 direct electron detector and Selectris energy filter (Glacios-F4-S). First, we evaluated data quality on the standard benchmarking sample, rabbit muscle aldolase, using three of the most frequently used cryo-EM data collection software: SerialEM, Leginon and EPU, and found that - despite sample heterogeneity - all final reconstructions yield same overall resolutions of 2.6 Å and map quality when using either of the three software. Furthermore, comparison between Glacios-F4-S and a 300 kV cryo-TEM (Titan Krios with Falcon 4) revealed nominal resolution differences in overall reconstructions of a reconstituted human nucleosome core particle, achieving 2.8 and 2.5 Å, respectively. Finally, we performed comparative data analysis on the human RAD51 paralog complex, BCDX2, a four-protein complex of approximately 150 kilodaltons, and found that a small dataset (≤1,000 micrographs) was sufficient to generate a 3.3 Å reconstruction, with sufficient detail to resolve co-bound ligands, AMP-PNP and Mg +2 . In summary, this study provides evidence that the Glacios-F4-S operates equally well with all standard data collection software, and is sufficient to obtain high resolution structural information of novel macromolecular complexes, readily acquiring single particle data rivaling that of 300 kV cryo-TEMs.
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3
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Berkeley RF, Cook BD, Herzik MA. Machine learning approaches to cryoEM density modification differentially affect biomacromolecule and ligand density quality. Front Mol Biosci 2024; 11:1404885. [PMID: 38698773 PMCID: PMC11063317 DOI: 10.3389/fmolb.2024.1404885] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2024] [Accepted: 04/03/2024] [Indexed: 05/05/2024] Open
Abstract
The application of machine learning to cryogenic electron microscopy (cryoEM) data analysis has added a valuable set of tools to the cryoEM data processing pipeline. As these tools become more accessible and widely available, the implications of their use should be assessed. We noticed that machine learning map modification tools can have differential effects on cryoEM densities. In this perspective, we evaluate these effects to show that machine learning tools generally improve densities for biomacromolecules while generating unpredictable results for ligands. This unpredictable behavior manifests both in quantitative metrics of map quality and in qualitative investigations of modified maps. The results presented here highlight the power and potential of machine learning tools in cryoEM, while also illustrating some of the risks of their unexamined use.
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4
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Yang JE, Larson MR, Sibert BS, Kim JY, Parrell D, Sanchez JC, Pappas V, Kumar A, Cai K, Thompson K, Wright ER. Correlative montage parallel array cryo-tomography for in situ structural cell biology. Nat Methods 2023; 20:1537-1543. [PMID: 37723245 PMCID: PMC10555823 DOI: 10.1038/s41592-023-01999-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Accepted: 08/08/2023] [Indexed: 09/20/2023]
Abstract
Imaging large fields of view while preserving high-resolution structural information remains a challenge in low-dose cryo-electron tomography. Here we present robust tools for montage parallel array cryo-tomography (MPACT) tailored for vitrified specimens. The combination of correlative cryo-fluorescence microscopy, focused-ion-beam milling, substrate micropatterning, and MPACT supports studies that contextually define the three-dimensional architecture of cells. To further extend the flexibility of MPACT, tilt series may be processed in their entirety or as individual tiles suitable for sub-tomogram averaging, enabling efficient data processing and analysis.
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Affiliation(s)
- Jie E Yang
- Department of Biochemistry, University of Wisconsin, Madison, WI, USA
- Cryo-Electron Microscopy Research Center, Department of Biochemistry, University of Wisconsin, Madison, WI, USA
- Midwest Center for Cryo-Electron Tomography, Department of Biochemistry, University of Wisconsin, Madison, WI, USA
| | - Matthew R Larson
- Department of Biochemistry, University of Wisconsin, Madison, WI, USA
- Cryo-Electron Microscopy Research Center, Department of Biochemistry, University of Wisconsin, Madison, WI, USA
- Midwest Center for Cryo-Electron Tomography, Department of Biochemistry, University of Wisconsin, Madison, WI, USA
| | - Bryan S Sibert
- Department of Biochemistry, University of Wisconsin, Madison, WI, USA
- Cryo-Electron Microscopy Research Center, Department of Biochemistry, University of Wisconsin, Madison, WI, USA
- Midwest Center for Cryo-Electron Tomography, Department of Biochemistry, University of Wisconsin, Madison, WI, USA
| | - Joseph Y Kim
- Department of Biochemistry, University of Wisconsin, Madison, WI, USA
- Department of Chemistry, University of Wisconsin, Madison, WI, USA
| | - Daniel Parrell
- Department of Biochemistry, University of Wisconsin, Madison, WI, USA
- DOE Great Lakes Bioenergy Research Center, University of Wisconsin, Madison, WI, USA
| | - Juan C Sanchez
- Department of Biochemistry, University of Wisconsin, Madison, WI, USA
- Biophysics Graduate Program, University of Wisconsin, Madison, WI, USA
| | - Victoria Pappas
- Department of Biochemistry, University of Wisconsin, Madison, WI, USA
- Biophysics Graduate Program, University of Wisconsin, Madison, WI, USA
| | - Anil Kumar
- Department of Biochemistry, University of Wisconsin, Madison, WI, USA
- Cryo-Electron Microscopy Research Center, Department of Biochemistry, University of Wisconsin, Madison, WI, USA
- Midwest Center for Cryo-Electron Tomography, Department of Biochemistry, University of Wisconsin, Madison, WI, USA
| | - Kai Cai
- Department of Biochemistry, University of Wisconsin, Madison, WI, USA
- Cryo-Electron Microscopy Research Center, Department of Biochemistry, University of Wisconsin, Madison, WI, USA
- Midwest Center for Cryo-Electron Tomography, Department of Biochemistry, University of Wisconsin, Madison, WI, USA
| | - Keith Thompson
- Department of Biochemistry, University of Wisconsin, Madison, WI, USA
- Cryo-Electron Microscopy Research Center, Department of Biochemistry, University of Wisconsin, Madison, WI, USA
- Midwest Center for Cryo-Electron Tomography, Department of Biochemistry, University of Wisconsin, Madison, WI, USA
| | - Elizabeth R Wright
- Department of Biochemistry, University of Wisconsin, Madison, WI, USA.
- Cryo-Electron Microscopy Research Center, Department of Biochemistry, University of Wisconsin, Madison, WI, USA.
- Midwest Center for Cryo-Electron Tomography, Department of Biochemistry, University of Wisconsin, Madison, WI, USA.
- DOE Great Lakes Bioenergy Research Center, University of Wisconsin, Madison, WI, USA.
- Morgridge Institute for Research, Madison, WI, USA.
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5
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Poger D, Yen L, Braet F. Big data in contemporary electron microscopy: challenges and opportunities in data transfer, compute and management. Histochem Cell Biol 2023; 160:169-192. [PMID: 37052655 PMCID: PMC10492738 DOI: 10.1007/s00418-023-02191-8] [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] [Accepted: 03/21/2023] [Indexed: 04/14/2023]
Abstract
The second decade of the twenty-first century witnessed a new challenge in the handling of microscopy data. Big data, data deluge, large data, data compliance, data analytics, data integrity, data interoperability, data retention and data lifecycle are terms that have introduced themselves to the electron microscopy sciences. This is largely attributed to the booming development of new microscopy hardware tools. As a result, large digital image files with an average size of one terabyte within one single acquisition session is not uncommon nowadays, especially in the field of cryogenic electron microscopy. This brings along numerous challenges in data transfer, compute and management. In this review, we will discuss in detail the current state of international knowledge on big data in contemporary electron microscopy and how big data can be transferred, computed and managed efficiently and sustainably. Workflows, solutions, approaches and suggestions will be provided, with the example of the latest experiences in Australia. Finally, important principles such as data integrity, data lifetime and the FAIR and CARE principles will be considered.
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Affiliation(s)
- David Poger
- Microscopy Australia, The University of Sydney, Sydney, NSW, 2006, Australia.
| | - Lisa Yen
- Microscopy Australia, The University of Sydney, Sydney, NSW, 2006, Australia
| | - Filip Braet
- Australian Centre for Microscopy and Microanalysis, The University of Sydney, Sydney, NSW, 2006, Australia
- School of Medical Sciences (Molecular and Cellular Biomedicine), The University of Sydney, Sydney, NSW, 2006, Australia
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6
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DiIorio MC, Kulczyk AW. Novel Artificial Intelligence-Based Approaches for Ab Initio Structure Determination and Atomic Model Building for Cryo-Electron Microscopy. MICROMACHINES 2023; 14:1674. [PMID: 37763837 PMCID: PMC10534518 DOI: 10.3390/mi14091674] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 08/21/2023] [Accepted: 08/25/2023] [Indexed: 09/29/2023]
Abstract
Single particle cryo-electron microscopy (cryo-EM) has emerged as the prevailing method for near-atomic structure determination, shedding light on the important molecular mechanisms of biological macromolecules. However, the inherent dynamics and structural variability of biological complexes coupled with the large number of experimental images generated by a cryo-EM experiment make data processing nontrivial. In particular, ab initio reconstruction and atomic model building remain major bottlenecks that demand substantial computational resources and manual intervention. Approaches utilizing recent innovations in artificial intelligence (AI) technology, particularly deep learning, have the potential to overcome the limitations that cannot be adequately addressed by traditional image processing approaches. Here, we review newly proposed AI-based methods for ab initio volume generation, heterogeneous 3D reconstruction, and atomic model building. We highlight the advancements made by the implementation of AI methods, as well as discuss remaining limitations and areas for future development.
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Affiliation(s)
- Megan C. DiIorio
- Institute for Quantitative Biomedicine, Rutgers University, 174 Frelinghuysen Road, Piscataway, NJ 08854, USA
| | - Arkadiusz W. Kulczyk
- Institute for Quantitative Biomedicine, Rutgers University, 174 Frelinghuysen Road, Piscataway, NJ 08854, USA
- Department of Biochemistry & Microbiology, Rutgers University, 76 Lipman Drive, New Brunswick, NJ 08901, USA
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7
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Zane G, Silveria M, Meyer N, White T, Duan R, Zou X, Chapman M. Cryo-EM structure of adeno-associated virus 4 at 2.2 Å resolution. Acta Crystallogr D Struct Biol 2023; 79:140-153. [PMID: 36762860 PMCID: PMC9912921 DOI: 10.1107/s2059798322012190] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Accepted: 12/26/2022] [Indexed: 01/21/2023] Open
Abstract
Adeno-associated virus (AAV) is the vector of choice for several approved gene-therapy treatments and is the basis for many ongoing clinical trials. Various strains of AAV exist (referred to as serotypes), each with their own transfection characteristics. Here, a high-resolution cryo-electron microscopy structure (2.2 Å) of AAV serotype 4 (AAV4) is presented. The receptor responsible for transduction of the AAV4 clade of AAV viruses (including AAV11, AAV12 and AAVrh32.33) is unknown. Other AAVs interact with the same cell receptor, adeno-associated virus receptor (AAVR), in one of two different ways. AAV5-like viruses interact exclusively with the polycystic kidney disease-like 1 (PKD1) domain of AAVR, while most other AAVs interact primarily with the PKD2 domain. A comparison of the present AAV4 structure with prior corresponding structures of AAV5, AAV2 and AAV1 in complex with AAVR provides a foundation for understanding why the AAV4-like clade is unable to interact with either PKD1 or PKD2 of AAVR. The conformation of the AAV4 capsid in variable regions I, III, IV and V on the viral surface appears to be sufficiently different from AAV2 to ablate binding with PKD2. Differences between AAV4 and AAV5 in variable region VII appear to be sufficient to exclude binding with PKD1.
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Affiliation(s)
- Grant Zane
- Department of Biochemistry, University of Missouri, Columbia, MO 65211, USA
| | - Mark Silveria
- Department of Biochemistry, University of Missouri, Columbia, MO 65211, USA
| | - Nancy Meyer
- Center for Spatial Systems Biomedicine, Oregon Health Sciences University, Portland, Oregon, USA
| | - Tommi White
- Department of Biochemistry, University of Missouri, Columbia, MO 65211, USA
- Bayer Crop Science, Bayer (United States), Chesterfield, MO 63017, USA
- Electron Microscopy Core, University of Missouri, Columbia, MO 65211, USA
| | - Rui Duan
- Dalton Cardiovascular Center, University of Missouri, Columbia, MO 65211, USA
| | - Xiaoqin Zou
- Department of Biochemistry, University of Missouri, Columbia, MO 65211, USA
- Dalton Cardiovascular Center, University of Missouri, Columbia, MO 65211, USA
- Department of Physics, University of Missouri, Columbia, MO 65211, USA
- Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65211, USA
| | - Michael Chapman
- Department of Biochemistry, University of Missouri, Columbia, MO 65211, USA
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8
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DiIorio MC, Kulczyk AW. Exploring the Structural Variability of Dynamic Biological Complexes by Single-Particle Cryo-Electron Microscopy. MICROMACHINES 2022; 14:mi14010118. [PMID: 36677177 PMCID: PMC9866264 DOI: 10.3390/mi14010118] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Revised: 12/27/2022] [Accepted: 12/30/2022] [Indexed: 05/15/2023]
Abstract
Biological macromolecules and assemblies precisely rearrange their atomic 3D structures to execute cellular functions. Understanding the mechanisms by which these molecular machines operate requires insight into the ensemble of structural states they occupy during the functional cycle. Single-particle cryo-electron microscopy (cryo-EM) has become the preferred method to provide near-atomic resolution, structural information about dynamic biological macromolecules elusive to other structure determination methods. Recent advances in cryo-EM methodology have allowed structural biologists not only to probe the structural intermediates of biochemical reactions, but also to resolve different compositional and conformational states present within the same dataset. This article reviews newly developed sample preparation and single-particle analysis (SPA) techniques for high-resolution structure determination of intrinsically dynamic and heterogeneous samples, shedding light upon the intricate mechanisms employed by molecular machines and helping to guide drug discovery efforts.
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Affiliation(s)
- Megan C. DiIorio
- Institute for Quantitative Biomedicine, Rutgers University, 174 Frelinghuysen Road, Piscataway, NJ 08854, USA
| | - Arkadiusz W. Kulczyk
- Institute for Quantitative Biomedicine, Rutgers University, 174 Frelinghuysen Road, Piscataway, NJ 08854, USA
- Department of Biochemistry and Microbiology, Rutgers University, 75 Lipman Drive, New Brunswick, NJ 08901, USA
- Correspondence:
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9
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Levy A, Poitevin F, Martel J, Nashed Y, Peck A, Miolane N, Ratner D, Dunne M, Wetzstein G. CryoAI: Amortized Inference of Poses for Ab Initio Reconstruction of 3D Molecular Volumes from Real Cryo-EM Images. COMPUTER VISION - ECCV ... : ... EUROPEAN CONFERENCE ON COMPUTER VISION : PROCEEDINGS. EUROPEAN CONFERENCE ON COMPUTER VISION 2022; 13681:540-557. [PMID: 36745134 PMCID: PMC9897229 DOI: 10.1007/978-3-031-19803-8_32] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Cryo-electron microscopy (cryo-EM) has become a tool of fundamental importance in structural biology, helping us understand the basic building blocks of life. The algorithmic challenge of cryo-EM is to jointly estimate the unknown 3D poses and the 3D electron scattering potential of a biomolecule from millions of extremely noisy 2D images. Existing reconstruction algorithms, however, cannot easily keep pace with the rapidly growing size of cryo-EM datasets due to their high computational and memory cost. We introduce cryoAI, an ab initio reconstruction algorithm for homogeneous conformations that uses direct gradient-based optimization of particle poses and the electron scattering potential from single-particle cryo-EM data. CryoAI combines a learned encoder that predicts the poses of each particle image with a physics-based decoder to aggregate each particle image into an implicit representation of the scattering potential volume. This volume is stored in the Fourier domain for computational efficiency and leverages a modern coordinate network architecture for memory efficiency. Combined with a symmetrized loss function, this framework achieves results of a quality on par with state-of-the-art cryo-EM solvers for both simulated and experimental data, one order of magnitude faster for large datasets and with significantly lower memory requirements than existing methods.
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Affiliation(s)
- Axel Levy
- LCLS, SLAC National Accelerator Laboratory, Menlo Park, CA, USA
- Stanford University, Department of Electrical Engineering, Stanford, CA, USA
| | | | - Julien Martel
- Stanford University, Department of Electrical Engineering, Stanford, CA, USA
| | - Youssef Nashed
- ML Initiative, SLAC National Accelerator Laboratory, Menlo Park, CA, USA
| | - Ariana Peck
- LCLS, SLAC National Accelerator Laboratory, Menlo Park, CA, USA
| | - Nina Miolane
- University of California Santa Barbara, Department of Electrical and Computer Engineering, Santa Barbara, CA, USA
| | - Daniel Ratner
- ML Initiative, SLAC National Accelerator Laboratory, Menlo Park, CA, USA
| | - Mike Dunne
- LCLS, SLAC National Accelerator Laboratory, Menlo Park, CA, USA
| | - Gordon Wetzstein
- Stanford University, Department of Electrical Engineering, Stanford, CA, USA
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10
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Abstract
Adeno-associated virus (AAV) has a single-stranded DNA genome encapsidated in a small icosahedrally symmetric protein shell with 60 subunits. AAV is the leading delivery vector in emerging gene therapy treatments for inherited disorders, so its structure and molecular interactions with human hosts are of intense interest. A wide array of electron microscopic approaches have been used to visualize the virus and its complexes, depending on the scientific question, technology available, and amenability of the sample. Approaches range from subvolume tomographic analyses of complexes with large and flexible host proteins to detailed analysis of atomic interactions within the virus and with small ligands at resolutions as high as 1.6 Å. Analyses have led to the reclassification of glycan receptors as attachment factors, to structures with a new-found receptor protein, to identification of the epitopes of antibodies, and a new understanding of possible neutralization mechanisms. AAV is now well-enough characterized that it has also become a model system for EM methods development. Heralding a new era, cryo-EM is now also being deployed as an analytic tool in the process development and production quality control of high value pharmaceutical biologics, namely AAV vectors.
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Affiliation(s)
- Scott
M. Stagg
- Department
of Biological Sciences, Florida State University, Tallahassee, Florida 32306, United States
- Institute
of Molecular Biophysics, Florida State University, Tallahassee, Florida 32306, United States
| | - Craig Yoshioka
- Department
of Biomedical Engineering, Oregon Health
& Science University, Portland Oregon 97239, United States
| | - Omar Davulcu
- Environmental
Molecular Sciences Laboratory, Pacific Northwest
National Laboratory, 3335 Innovation Boulevard, Richland, Washington 99354, United States
| | - Michael S. Chapman
- Department
of Biochemistry, University of Missouri, Columbia, Missouri 65211, United States
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11
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Aplin C, Milano SK, Zielinski KA, Pollack L, Cerione RA. Evolving Experimental Techniques for Structure-Based Drug Design. J Phys Chem B 2022; 126:6599-6607. [PMID: 36029222 PMCID: PMC10161966 DOI: 10.1021/acs.jpcb.2c04344] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Structure-based drug design (SBDD) is a prominent method in rational drug development and has traditionally benefitted from the atomic models of protein targets obtained using X-ray crystallography at cryogenic temperatures. In this perspective, we highlight recent advances in the development of structural techniques that are capable of probing dynamic information about protein targets. First, we discuss advances in the field of X-ray crystallography including serial room-temperature crystallography as a method for obtaining high-resolution conformational dynamics of protein-inhibitor complexes. Next, we look at cryogenic electron microscopy (cryoEM), another high-resolution technique that has recently been used to study proteins and protein complexes that are too difficult to crystallize. Finally, we present small-angle X-ray scattering (SAXS) as a potential high-throughput screening tool to identify inhibitors that target protein complexes and protein oligomerization.
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Affiliation(s)
- Cody Aplin
- Department of Chemistry and Chemical Biology, Cornell University, Ithaca, New York 14853, United States
| | - Shawn K Milano
- Department of Chemistry and Chemical Biology, Cornell University, Ithaca, New York 14853, United States
| | - Kara A Zielinski
- School of Applied and Engineering Physics, Cornell University, Ithaca, New York 14853, United States
| | - Lois Pollack
- School of Applied and Engineering Physics, Cornell University, Ithaca, New York 14853, United States
| | - Richard A Cerione
- Department of Chemistry and Chemical Biology, Cornell University, Ithaca, New York 14853, United States.,Department of Molecular Medicine, Cornell University, Ithaca, New York 14853, United States
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12
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Vanslembrouck B, Chen JH, Larabell C, van Hengel J. Microscopic Visualization of Cell-Cell Adhesion Complexes at Micro and Nanoscale. Front Cell Dev Biol 2022; 10:819534. [PMID: 35517500 PMCID: PMC9065677 DOI: 10.3389/fcell.2022.819534] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2021] [Accepted: 03/21/2022] [Indexed: 12/25/2022] Open
Abstract
Considerable progress has been made in our knowledge of the morphological and functional varieties of anchoring junctions. Cell-cell adhesion contacts consist of discrete junctional structures responsible for the mechanical coupling of cytoskeletons and allow the transmission of mechanical signals across the cell collective. The three main adhesion complexes are adherens junctions, tight junctions, and desmosomes. Microscopy has played a fundamental role in understanding these adhesion complexes on different levels in both physiological and pathological conditions. In this review, we discuss the main light and electron microscopy techniques used to unravel the structure and composition of the three cell-cell contacts in epithelial and endothelial cells. It functions as a guide to pick the appropriate imaging technique(s) for the adhesion complexes of interest. We also point out the latest techniques that have emerged. At the end, we discuss the problems investigators encounter during their cell-cell adhesion research using microscopic techniques.
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Affiliation(s)
- Bieke Vanslembrouck
- Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, CA, United States
- Department of Anatomy, University of San Francisco, San Francisco, CA, United States
- *Correspondence: Bieke Vanslembrouck, ; Jolanda van Hengel,
| | - Jian-hua Chen
- Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, CA, United States
- Department of Anatomy, University of San Francisco, San Francisco, CA, United States
| | - Carolyn Larabell
- Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, CA, United States
- Department of Anatomy, University of San Francisco, San Francisco, CA, United States
| | - Jolanda van Hengel
- Medical Cell Biology Research Group, Department of Human Structure and Repair, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium
- *Correspondence: Bieke Vanslembrouck, ; Jolanda van Hengel,
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13
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Molza AE, Westermaier Y, Moutte M, Ducrot P, Danilowicz C, Godoy-Carter V, Prentiss M, Robert CH, Baaden M, Prévost C. Building Biological Relevance Into Integrative Modelling of Macromolecular Assemblies. Front Mol Biosci 2022; 9:826136. [PMID: 35480882 PMCID: PMC9035671 DOI: 10.3389/fmolb.2022.826136] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 02/21/2022] [Indexed: 01/25/2023] Open
Abstract
Recent advances in structural biophysics and integrative modelling methods now allow us to decipher the structures of large macromolecular assemblies. Understanding the dynamics and mechanisms involved in their biological function requires rigorous integration of all available data. We have developed a complete modelling pipeline that includes analyses to extract biologically significant information by consistently combining automated and interactive human-guided steps. We illustrate this idea with two examples. First, we describe the ryanodine receptor, an ion channel that controls ion flux across the cell membrane through transitions between open and closed states. The conformational changes associated with the transitions are small compared to the considerable system size of the receptor; it is challenging to consistently track these states with the available cryo-EM structures. The second example involves homologous recombination, in which long filaments of a recombinase protein and DNA catalyse the exchange of homologous DNA strands to reliably repair DNA double-strand breaks. The nucleoprotein filament reaction intermediates in this process are short-lived and heterogeneous, making their structures particularly elusive. The pipeline we describe, which incorporates experimental and theoretical knowledge combined with state-of-the-art interactive and immersive modelling tools, can help overcome these challenges. In both examples, we point to new insights into biological processes that arise from such interdisciplinary approaches.
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Affiliation(s)
- Anne-Elisabeth Molza
- CNRS, Université Paris-Cité, UPR 9080, Laboratoire de Biochimie Théorique, Paris, France
- Institut de Biologie Physico-Chimique-Fondation Edmond de Rothschild, PSL Research University, Paris, France
| | - Yvonne Westermaier
- Biophysics and Modelling Department/In Vitro Pharmacology Unit–IDRS (Servier Research Institute), Croissy-sur-Seine, France
| | | | - Pierre Ducrot
- Biophysics and Modelling Department/In Vitro Pharmacology Unit–IDRS (Servier Research Institute), Croissy-sur-Seine, France
| | | | | | - Mara Prentiss
- Department of Physics, Harvard University, Cambridge, MA, United States
| | - Charles H. Robert
- CNRS, Université Paris-Cité, UPR 9080, Laboratoire de Biochimie Théorique, Paris, France
- Institut de Biologie Physico-Chimique-Fondation Edmond de Rothschild, PSL Research University, Paris, France
| | - Marc Baaden
- CNRS, Université Paris-Cité, UPR 9080, Laboratoire de Biochimie Théorique, Paris, France
- Institut de Biologie Physico-Chimique-Fondation Edmond de Rothschild, PSL Research University, Paris, France
| | - Chantal Prévost
- CNRS, Université Paris-Cité, UPR 9080, Laboratoire de Biochimie Théorique, Paris, France
- Institut de Biologie Physico-Chimique-Fondation Edmond de Rothschild, PSL Research University, Paris, France
- *Correspondence: Chantal Prévost ,
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14
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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: 7] [Impact Index Per Article: 3.5] [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.
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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
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15
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Weissenberger G, Henderikx RJM, Peters PJ. Understanding the invisible hands of sample preparation for cryo-EM. Nat Methods 2021; 18:463-471. [PMID: 33963356 DOI: 10.1038/s41592-021-01130-6] [Citation(s) in RCA: 64] [Impact Index Per Article: 21.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2020] [Accepted: 03/30/2021] [Indexed: 02/03/2023]
Abstract
Cryo-electron microscopy (cryo-EM) is rapidly becoming an attractive method in the field of structural biology. With the exploding popularity of cryo-EM, sample preparation must evolve to prevent congestion in the workflow. The dire need for improved microscopy samples has led to a diversification of methods. This Review aims to categorize and explain the principles behind various techniques in the preparation of vitrified samples for the electron microscope. Various aspects and challenges in the workflow are discussed, from sample optimization and carriers to deposition and vitrification. Reliable and versatile specimen preparation remains a challenge, and we hope to give guidelines and posit future directions for improvement.
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Affiliation(s)
- Giulia Weissenberger
- CryoSol-World, Maastricht, the Netherlands.,Maastricht Multimodal Molecular Imaging Institute (M4i), Division of Nanoscopy, Maastricht University, Maastricht, the Netherlands
| | - Rene J M Henderikx
- CryoSol-World, Maastricht, the Netherlands.,Maastricht Multimodal Molecular Imaging Institute (M4i), Division of Nanoscopy, Maastricht University, Maastricht, the Netherlands
| | - Peter J Peters
- Maastricht Multimodal Molecular Imaging Institute (M4i), Division of Nanoscopy, Maastricht University, Maastricht, the Netherlands.
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16
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Lévy D, Di Cicco A, Bertin A, Dezi M. [Cryo-electron microcopy for a new vision of the cell and its components]. Med Sci (Paris) 2021; 37:379-385. [PMID: 33908856 DOI: 10.1051/medsci/2021034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
Abstract
Cryo-electron microscopy (cryo-EM) is a technique for imaging biological samples that plays a central role in structural biology, with high impact on research fields such as cell and developmental biology, bioinformatics, cell physics and applied mathematics. It allows the determination of structures of purified proteins within cells. This review describes the main recent advances in cryo-EM, illustrated by examples of proteins of biomedical interest, and the avenues for future development.
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Affiliation(s)
- Daniel Lévy
- Institut Curie, Université PSL, Sorbonne Université, CNRS UMR 168, Laboratoire Physico- Chimie Curie, 11 rue Pierre et Marie Curie, 75005 Paris, France
| | - Aurélie Di Cicco
- Institut Curie, Université PSL, Sorbonne Université, CNRS UMR 168, Laboratoire Physico- Chimie Curie, 11 rue Pierre et Marie Curie, 75005 Paris, France
| | - Aurélie Bertin
- Institut Curie, Université PSL, Sorbonne Université, CNRS UMR 168, Laboratoire Physico- Chimie Curie, 11 rue Pierre et Marie Curie, 75005 Paris, France
| | - Manuela Dezi
- Institut Curie, Université PSL, Sorbonne Université, CNRS UMR 168, Laboratoire Physico- Chimie Curie, 11 rue Pierre et Marie Curie, 75005 Paris, France
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17
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Impens F, Dussurget O. Three decades of listeriology through the prism of technological advances. Cell Microbiol 2021; 22:e13183. [PMID: 32185895 DOI: 10.1111/cmi.13183] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2019] [Revised: 01/28/2020] [Accepted: 01/29/2020] [Indexed: 12/15/2022]
Abstract
Decades of breakthroughs resulting from cross feeding of microbiological research and technological innovation have promoted Listeria monocytogenes to the rank of model microorganism to study host-pathogen interactions. The extraordinary capacity of this bacterium to interfere with a vast array of host cellular processes uncovered new concepts in microbiology, cell biology and infection biology. Here, we review technological advances that revealed how bacteria and host interact in space and time at the molecular, cellular, tissue and whole body scales, ultimately revolutionising our understanding of Listeria pathogenesis. With the current bloom of multidisciplinary integrative approaches, Listeria entered a new microbiology era.
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Affiliation(s)
- Francis Impens
- Center for Medical Biotechnology, VIB, Ghent, Belgium.,Department for Biomedical Medicine, Ghent University, Ghent, Belgium.,VIB Proteomics Core, VIB, Ghent, Belgium
| | - Olivier Dussurget
- Institut Pasteur, Unité de Recherche Yersinia, Paris, France.,Université de Paris, Sorbonne Paris Cité, Paris, France
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18
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Spurgeon SR, Ophus C, Jones L, Petford-Long A, Kalinin SV, Olszta MJ, Dunin-Borkowski RE, Salmon N, Hattar K, Yang WCD, Sharma R, Du Y, Chiaramonti A, Zheng H, Buck EC, Kovarik L, Penn RL, Li D, Zhang X, Murayama M, Taheri ML. Towards data-driven next-generation transmission electron microscopy. NATURE MATERIALS 2021; 20:274-279. [PMID: 33106651 PMCID: PMC8485843 DOI: 10.1038/s41563-020-00833-z] [Citation(s) in RCA: 56] [Impact Index Per Article: 18.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
Electron microscopy touches on nearly every aspect of modern life, underpinning materials development for quantum computing, energy and medicine. We discuss the open, highly integrated and data-driven microscopy architecture needed to realize transformative discoveries in the coming decade.
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Affiliation(s)
- Steven R Spurgeon
- Energy and Environment Directorate, Pacific Northwest National Laboratory, Richland, WA, USA.
| | - Colin Ophus
- National Center for Electron Microscopy, Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Lewys Jones
- Advanced Microscopy Laboratory, Centre for Research on Adaptive Nanostructures and Nanodevices (CRANN), Dublin, Ireland
- School of Physics, Trinity College Dublin, The University of Dublin, Dublin, Ireland
| | | | - Sergei V Kalinin
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Matthew J Olszta
- Energy and Environment Directorate, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Rafal E Dunin-Borkowski
- Ernst Ruska-Centre for Microscopy and Spectroscopy with Electrons and Peter Grünberg Institute, Forschungszentrum Jülich, Jülich, Germany
| | | | - Khalid Hattar
- Center for Integrated Nanotechnologies, Sandia National Laboratories, Albuquerque, NM, USA
| | - Wei-Chang D Yang
- Physical Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD, USA
| | - Renu Sharma
- Physical Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD, USA
| | - Yingge Du
- Physical and Computational Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Ann Chiaramonti
- Applied Chemicals and Materials Division, Material Measurement Laboratory, National Institute of Standards and Technology, Boulder, CO, USA
| | - Haimei Zheng
- Materials Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Edgar C Buck
- Energy and Environment Directorate, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Libor Kovarik
- Physical and Computational Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, USA
| | - R Lee Penn
- Department of Chemistry, University of Minnesota, Minneapolis, MN, USA
| | - Dongsheng Li
- Physical and Computational Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Xin Zhang
- Physical and Computational Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Mitsuhiro Murayama
- Department of Materials Science and Engineering, Virginia Tech, Blacksburg, VA, USA
| | - Mitra L Taheri
- Department of Materials Science and Engineering, Johns Hopkins University, Baltimore, MD, USA.
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19
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OUP accepted manuscript. Microscopy (Oxf) 2021; 71:i3-i14. [DOI: 10.1093/jmicro/dfab049] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Revised: 11/19/2021] [Accepted: 12/21/2021] [Indexed: 11/13/2022] Open
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20
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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.
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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
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21
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Li Y, Cash JN, Tesmer JJG, Cianfrocco MA. High-Throughput Cryo-EM Enabled by User-Free Preprocessing Routines. Structure 2020; 28:858-869.e3. [PMID: 32294468 PMCID: PMC7347462 DOI: 10.1016/j.str.2020.03.008] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2020] [Revised: 03/02/2020] [Accepted: 03/17/2020] [Indexed: 12/18/2022]
Abstract
Single-particle cryoelectron microscopy (cryo-EM) continues to grow into a mainstream structural biology technique. Recent developments in data collection strategies alongside new sample preparation devices herald a future where users will collect multiple datasets per microscope session. To make cryo-EM data processing more automatic and user-friendly, we have developed an automatic pipeline for cryo-EM data preprocessing and assessment using a combination of deep-learning and image-analysis tools. We have verified the performance of this pipeline on a number of datasets and extended its scope to include sample screening by the user-free assessment of the qualities of a series of datasets under different conditions. We propose that our workflow provides a decision-free solution for cryo-EM, making data preprocessing more generalized and robust in the high-throughput era as well as more convenient for users from a range of backgrounds.
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Affiliation(s)
- Yilai Li
- Life Sciences Institute, Department of Biological Chemistry, University of Michigan, Ann Arbor, MI, USA
| | - Jennifer N Cash
- Life Sciences Institute, Department of Biological Chemistry, University of Michigan, Ann Arbor, MI, USA
| | - John J G Tesmer
- Departments of Biological Sciences and of Medicinal Chemistry and Molecular Pharmacology, Purdue University, West Lafayette, IN, USA
| | - Michael A Cianfrocco
- Life Sciences Institute, Department of Biological Chemistry, University of Michigan, Ann Arbor, MI, USA.
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22
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Thompson MC, Yeates TO, Rodriguez JA. Advances in methods for atomic resolution macromolecular structure determination. F1000Res 2020; 9:F1000 Faculty Rev-667. [PMID: 32676184 PMCID: PMC7333361 DOI: 10.12688/f1000research.25097.1] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 06/25/2020] [Indexed: 12/13/2022] Open
Abstract
Recent technical advances have dramatically increased the power and scope of structural biology. New developments in high-resolution cryo-electron microscopy, serial X-ray crystallography, and electron diffraction have been especially transformative. Here we highlight some of the latest advances and current challenges at the frontiers of atomic resolution methods for elucidating the structures and dynamical properties of macromolecules and their complexes.
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Affiliation(s)
- Michael C. Thompson
- Department of Chemistry and Chemical Biology, University of California, Merced, CA, USA
| | - Todd O. Yeates
- Department of Chemistry and Biochemistry, University of California Los Angeles, Los Angeles, CA, USA
- UCLA-DOE Institute for Genomics and Proteomics, Los Angeles, CA, USA
| | - Jose A. Rodriguez
- Department of Chemistry and Biochemistry, University of California Los Angeles, Los Angeles, CA, USA
- UCLA-DOE Institute for Genomics and Proteomics, Los Angeles, CA, USA
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23
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Zeng X, Xu M. Gum-Net: Unsupervised Geometric Matching for Fast and Accurate 3D Subtomogram Image Alignment and Averaging. PROCEEDINGS. IEEE COMPUTER SOCIETY CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION 2020; 2020:4072-4082. [PMID: 33716478 PMCID: PMC7955792 DOI: 10.1109/cvpr42600.2020.00413] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
We propose a Geometric unsupervised matching Network (Gum-Net) for finding the geometric correspondence between two images with application to 3D subtomogram alignment and averaging. Subtomogram alignment is the most important task in cryo-electron tomography (cryo-ET), a revolutionary 3D imaging technique for visualizing the molecular organization of unperturbed cellular landscapes in single cells. However, subtomogram alignment and averaging are very challenging due to severe imaging limits such as noise and missing wedge effects. We introduce an end-to-end trainable architecture with three novel modules specifically designed for preserving feature spatial information and propagating feature matching information. The training is performed in a fully unsupervised fashion to optimize a matching metric. No ground truth transformation information nor category-level or instance-level matching supervision information is needed. After systematic assessments on six real and nine simulated datasets, we demonstrate that Gum-Net reduced the alignment error by 40 to 50% and improved the averaging resolution by 10%. Gum-Net also achieved 70 to 110 times speedup in practice with GPU acceleration compared to state-of-the-art subtomogram alignment methods. Our work is the first 3D unsupervised geometric matching method for images of strong transformation variation and high noise level. The training code, trained model, and datasets are available in our open-source software AITom.
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Affiliation(s)
- Xiangrui Zeng
- Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA 15213
| | - Min Xu
- Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA 15213
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24
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He B, Zhang Y, Liu X, Chen L. In‐situ Transmission Electron Microscope Techniques for Heterogeneous Catalysis. ChemCatChem 2020. [DOI: 10.1002/cctc.201902285] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Affiliation(s)
- Bowen He
- In-situ Center for Physical Sciences School of Chemistry and Chemical EngineeringShanghai Jiao Tong University Shanghai 200240 P.R. China
| | - Yixiao Zhang
- In-situ Center for Physical Sciences School of Chemistry and Chemical EngineeringShanghai Jiao Tong University Shanghai 200240 P.R. China
| | - Xi Liu
- In-situ Center for Physical Sciences School of Chemistry and Chemical EngineeringShanghai Jiao Tong University Shanghai 200240 P.R. China
- SynCat@BeijingSynfuels China Technology Co.Ltd Beijing 101407 P.R. China
- State Key Laboratory of Coal Conversion Institute of Coal ChemistryChinese Academy of Sciences Taiyuan 030001 P.R. China
| | - Liwei Chen
- In-situ Center for Physical Sciences School of Chemistry and Chemical EngineeringShanghai Jiao Tong University Shanghai 200240 P.R. China
- i-Lab, CAS Center for Excellence in Nanoscience Suzhou Institute of Nano-Tech and Nano-Bionics (SINANO)Chinese Academy of Sciences Suzhou 215123 P.R. China
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25
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Data-driven challenges and opportunities in crystallography. Emerg Top Life Sci 2019; 3:423-432. [PMID: 33523208 PMCID: PMC7289006 DOI: 10.1042/etls20180177] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2019] [Revised: 06/13/2019] [Accepted: 06/24/2019] [Indexed: 11/17/2022]
Abstract
Abstract
Structural biology is in the midst of a revolution fueled by faster and more powerful instruments capable of delivering orders of magnitude more data than their predecessors. This increased pace in data gathering introduces new experimental and computational challenges, frustrating real-time processing and interpretation of data and requiring long-term solutions for data archival and retrieval. This combination of challenges and opportunities is driving the exploration of new areas of structural biology, including studies of macromolecular dynamics and the investigation of molecular ensembles in search of a better understanding of conformational landscapes. The next generation of instruments promises to yield even greater data rates, requiring a concerted effort by institutions, centers and individuals to extract meaning from every bit and make data accessible to the community at large, facilitating data mining efforts by individuals or groups as analysis tools improve.
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26
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Abstract
For automated acquisition of tilt series for electron tomography, software needs to handle complications such as movements of the sample in x/y and z, increased projected thickness at high tilt, specimen drift, etc. In addition, many applications require special functionality such as low dose acquisition, automated sequential (batch) tomography, or montage tomography. After reviewing how these difficulties can be addressed and a closer look at what advanced acquisition strategies are employed in biosciences, this chapter introduces acquisition software both developed in academia as well as by hardware vendors. It covers the hardware requirements and compatibility, the functional principle and workflow implemented, as well as what advanced functions are supported by the individual programs.
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Affiliation(s)
- Guenter P Resch
- Nexperion e.U.-Solutions for Electron Microscopy, Vienna, Austria.
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27
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Gan L, Ng CT, Chen C, Cai S. A collection of yeast cellular electron cryotomography data. Gigascience 2019; 8:giz077. [PMID: 31247098 PMCID: PMC6596884 DOI: 10.1093/gigascience/giz077] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2019] [Revised: 05/09/2019] [Accepted: 06/10/2019] [Indexed: 11/21/2022] Open
Abstract
BACKGROUND Cells are powered by a large set of macromolecular complexes, which work together in a crowded environment. The in situ mechanisms of these complexes are unclear because their 3D distribution, organization, and interactions are largely unknown. Electron cryotomography (cryo-ET) can address these knowledge gaps because it produces cryotomograms-3D images that reveal biological structure at ∼4-nm resolution. Cryo-ET uses no fixation, dehydration, staining, or plastic embedment, so cellular features are visualized in a life-like, frozen-hydrated state. To study chromatin and mitotic machinery in situ, we subjected yeast cells to genetic and chemical perturbations, cryosectioned them, and then imaged the cells by cryo-ET. FINDINGS Here we share >1,000 cryo-ET raw datasets of cryosectioned budding yeast Saccharomyces cerevisiaecollected as part of previously published studies. These data will be valuable to cell biologists who are interested in the nanoscale organization of yeasts and of eukaryotic cells in general. All the unpublished tilt series and a subset of corresponding cryotomograms have been deposited in the EMPIAR resource for the community to use freely. To improve tilt series discoverability, we have uploaded metadata and preliminary notes to publicly accessible Google Sheets, EMPIAR, and GigaDB. CONCLUSIONS Cellular cryo-ET data can be mined to obtain new cell-biological, structural, and 3D statistical insights in situ. These data contain structures not visible in traditional electron-microscopy data. Template matching and subtomogram averaging of known macromolecular complexes can reveal their 3D distributions and low-resolution structures. Furthermore, these data can serve as testbeds for high-throughput image-analysis pipelines, as training sets for feature-recognition software, for feasibility analysis when planning new structural-cell-biology projects, and as practice data for students.
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Affiliation(s)
- Lu Gan
- Department of Biological Sciences and Centre for BioImaging Sciences, National University of Singapore, 14 Science Drive 4, Singapore 117543
| | - Cai Tong Ng
- Department of Biological Sciences and Centre for BioImaging Sciences, National University of Singapore, 14 Science Drive 4, Singapore 117543
| | - Chen Chen
- Department of Biological Sciences and Centre for BioImaging Sciences, National University of Singapore, 14 Science Drive 4, Singapore 117543
| | - Shujun Cai
- Department of Biological Sciences and Centre for BioImaging Sciences, National University of Singapore, 14 Science Drive 4, Singapore 117543
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28
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Eng ET, Kopylov M, Negro CJ, Dallaykan S, Rice WJ, Jordan KD, Kelley K, Carragher B, Potter CS. Reducing cryoEM file storage using lossy image formats. J Struct Biol 2019; 207:49-55. [PMID: 31121317 DOI: 10.1016/j.jsb.2019.04.013] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2018] [Revised: 03/11/2019] [Accepted: 04/16/2019] [Indexed: 10/26/2022]
Abstract
Recent advances in instrumentation and software for cryoEM have increased the applicability and utility of this method. High levels of automation and faster data acquisition rates require hard decisions to be made regarding data retention. Here we investigate the efficacy of data compression applied to aligned summed movie files. Surprisingly, these images can be compressed using a standard lossy method that reduces file storage by 90-95% and yet can still be processed to provide sub-2 Å reconstructed maps. We do not advocate this as an archival method, but it may provide a useful means for retaining images as an historical record, especially at large facilities.
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Affiliation(s)
- Edward T Eng
- Simons Electron Microscopy Center, New York Structural Biology Center, 89 Convent Ave, New York, NY 10027, USA
| | - Mykhailo Kopylov
- Simons Electron Microscopy Center, New York Structural Biology Center, 89 Convent Ave, New York, NY 10027, USA
| | - Carl J Negro
- Simons Electron Microscopy Center, New York Structural Biology Center, 89 Convent Ave, New York, NY 10027, USA
| | - Sarkis Dallaykan
- Simons Electron Microscopy Center, New York Structural Biology Center, 89 Convent Ave, New York, NY 10027, USA
| | - William J Rice
- Simons Electron Microscopy Center, New York Structural Biology Center, 89 Convent Ave, New York, NY 10027, USA
| | - Kelsey D Jordan
- Simons Electron Microscopy Center, New York Structural Biology Center, 89 Convent Ave, New York, NY 10027, USA
| | - Kotaro Kelley
- Simons Electron Microscopy Center, New York Structural Biology Center, 89 Convent Ave, New York, NY 10027, USA
| | - Bridget Carragher
- Simons Electron Microscopy Center, New York Structural Biology Center, 89 Convent Ave, New York, NY 10027, USA; Department of Biochemistry and Molecular Biophysics, Columbia University, New York, NY 10032, USA
| | - Clinton S Potter
- Simons Electron Microscopy Center, New York Structural Biology Center, 89 Convent Ave, New York, NY 10027, USA; Department of Biochemistry and Molecular Biophysics, Columbia University, New York, NY 10032, USA.
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29
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Ortega DR, Oikonomou CM, Ding HJ, Rees-Lee P, Jensen GJ. ETDB-Caltech: A blockchain-based distributed public database for electron tomography. PLoS One 2019; 14:e0215531. [PMID: 30986271 PMCID: PMC6464211 DOI: 10.1371/journal.pone.0215531] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2018] [Accepted: 04/03/2019] [Indexed: 01/12/2023] Open
Abstract
Three-dimensional electron microscopy techniques like electron tomography provide valuable insights into cellular structures, and present significant challenges for data storage and dissemination. Here we explored a novel method to publicly release more than 11,000 such datasets, more than 30 TB in total, collected by our group. Our method, based on a peer-to-peer file sharing network built around a blockchain ledger, offers a distributed solution to data storage. In addition, we offer a user-friendly browser-based interface, https://etdb.caltech.edu, for anyone interested to explore and download our data. We discuss the relative advantages and disadvantages of this system and provide tools for other groups to mine our data and/or use the same approach to share their own imaging datasets.
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Affiliation(s)
- Davi R. Ortega
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California, United States of America
| | - Catherine M. Oikonomou
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California, United States of America
| | - H. Jane Ding
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California, United States of America
| | - Prudence Rees-Lee
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California, United States of America
| | | | - Grant J. Jensen
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California, United States of America
- Howard Hughes Medical Institute, Pasadena, California, United States of America
- * E-mail:
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30
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Heimowitz A, Andén J, Singer A. APPLE picker: Automatic particle picking, a low-effort cryo-EM framework. J Struct Biol 2018; 204:215-227. [PMID: 30134153 PMCID: PMC6183064 DOI: 10.1016/j.jsb.2018.08.012] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2018] [Revised: 08/10/2018] [Accepted: 08/16/2018] [Indexed: 10/28/2022]
Abstract
Particle picking is a crucial first step in the computational pipeline of single-particle cryo-electron microscopy (cryo-EM). Selecting particles from the micrographs is difficult especially for small particles with low contrast. As high-resolution reconstruction typically requires hundreds of thousands of particles, manually picking that many particles is often too time-consuming. While template-based particle picking is currently a popular approach, it may suffer from introducing manual bias into the selection process. In addition, this approach is still somewhat time-consuming. This paper presents the APPLE (Automatic Particle Picking with Low user Effort) picker, a simple and novel approach for fast, accurate, and template-free particle picking. This approach is evaluated on publicly available datasets containing micrographs of β-galactosidase, T20S proteasome, 70S ribosome and keyhole limpet hemocyanin projections.
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Affiliation(s)
- Ayelet Heimowitz
- The Program in Applied and Computational Mathematics, Princeton University, Princeton, NJ, United States.
| | - Joakim Andén
- Center for Computational Biology, Flatiron Institute, New York, NY, United States.
| | - Amit Singer
- The Program in Applied and Computational Mathematics, Princeton University, Princeton, NJ, United States; Department of Mathematics, Princeton University, Princeton, NJ, United States.
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31
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Mura C, Draizen EJ, Bourne PE. Structural biology meets data science: does anything change? Curr Opin Struct Biol 2018; 52:95-102. [PMID: 30267935 DOI: 10.1016/j.sbi.2018.09.003] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2018] [Revised: 08/31/2018] [Accepted: 09/07/2018] [Indexed: 01/22/2023]
Abstract
Data science has emerged from the proliferation of digital data, coupled with advances in algorithms, software and hardware (e.g., GPU computing). Innovations in structural biology have been driven by similar factors, spurring us to ask: can these two fields impact one another in deep and hitherto unforeseen ways? We posit that the answer is yes. New biological knowledge lies in the relationships between sequence, structure, function and disease, all of which play out on the stage of evolution, and data science enables us to elucidate these relationships at scale. Here, we consider the above question from the five key pillars of data science: acquisition, engineering, analytics, visualization and policy, with an emphasis on machine learning as the premier analytics approach.
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Affiliation(s)
- Cameron Mura
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA
| | - Eli J Draizen
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA
| | - Philip E Bourne
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA; Data Science Institute, University of Virginia, Charlottesville, VA 22904, USA.
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32
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Terwilliger TC, Sobolev OV, Afonine PV, Adams PD. Automated map sharpening by maximization of detail and connectivity. Acta Crystallogr D Struct Biol 2018; 74:545-559. [PMID: 29872005 PMCID: PMC6096490 DOI: 10.1107/s2059798318004655] [Citation(s) in RCA: 167] [Impact Index Per Article: 27.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2017] [Accepted: 03/21/2018] [Indexed: 01/18/2023] Open
Abstract
An algorithm for automatic map sharpening is presented that is based on optimization of the detail and connectivity of the sharpened map. The detail in the map is reflected in the surface area of an iso-contour surface that contains a fixed fraction of the volume of the map, where a map with high level of detail has a high surface area. The connectivity of the sharpened map is reflected in the number of connected regions defined by the same iso-contour surfaces, where a map with high connectivity has a small number of connected regions. By combining these two measures in a metric termed the `adjusted surface area', map quality can be evaluated in an automated fashion. This metric was used to choose optimal map-sharpening parameters without reference to a model or other interpretations of the map. Map sharpening by optimization of the adjusted surface area can be carried out for a map as a whole or it can be carried out locally, yielding a locally sharpened map. To evaluate the performance of various approaches, a simple metric based on map-model correlation that can reproduce visual choices of optimally sharpened maps was used. The map-model correlation is calculated using a model with B factors (atomic displacement factors; ADPs) set to zero. This model-based metric was used to evaluate map sharpening and to evaluate map-sharpening approaches, and it was found that optimization of the adjusted surface area can be an effective tool for map sharpening.
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Affiliation(s)
- Thomas C. Terwilliger
- Bioscience Division, Los Alamos National Laboratory, Mail Stop M888, Los Alamos, NM 87545, USA
- New Mexico Consortium, Los Alamos, NM 87544, 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
- Department of Bioengineering, University of California Berkeley, Berkeley, California, USA
| | - Paul D. Adams
- Department of Bioengineering, University of California Berkeley, Berkeley, California, USA
- Department of Physics and International Centre for Quantum and Molecular Structures, Shanghai University, Shanghai, 200444, People’s Republic of China
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