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de la Cruz MJ, Eng ET. Scaling up cryo-EM for biology and chemistry: The journey from niche technology to mainstream method. Structure 2023; 31:1487-1498. [PMID: 37820731 PMCID: PMC10841453 DOI: 10.1016/j.str.2023.09.009] [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: 07/29/2023] [Revised: 08/31/2023] [Accepted: 09/14/2023] [Indexed: 10/13/2023]
Abstract
Cryoelectron microscopy (cryo-EM) methods have made meaningful contributions in a wide variety of scientific research fields. In structural biology, cryo-EM routinely elucidates molecular structure from isolated biological macromolecular complexes or in a cellular context by harnessing the high-resolution power of the electron in order to image samples in a frozen, hydrated environment. For structural chemistry, the cryo-EM method popularly known as microcrystal electron diffraction (MicroED) has facilitated atomic structure generation of peptides and small molecules from their three-dimensional crystal forms. As cryo-EM has grown from an emerging technology, it has undergone modernization to enable multimodal transmission electron microscopy (TEM) techniques becoming more routine, reproducible, and accessible to accelerate research across multiple disciplines. We review recent advances in modern cryo-EM and assess how they are contributing to the future of the field with an eye to the past.
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Affiliation(s)
- M Jason de la Cruz
- Structural Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA.
| | - Edward T Eng
- Simons Electron Microscopy Center, New York Structural Biology Center, New York, NY 10027, USA.
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2
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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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3
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Tang WS, Zhong ED, Hanson SM, Thiede EH, Cossio P. Conformational heterogeneity and probability distributions from single-particle cryo-electron microscopy. Curr Opin Struct Biol 2023; 81:102626. [PMID: 37311334 DOI: 10.1016/j.sbi.2023.102626] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 04/25/2023] [Accepted: 05/16/2023] [Indexed: 06/15/2023]
Abstract
Single-particle cryo-electron microscopy (cryo-EM) is a technique that takes projection images of biomolecules frozen at cryogenic temperatures. A major advantage of this technique is its ability to image single biomolecules in heterogeneous conformations. While this poses a challenge for data analysis, recent algorithmic advances have enabled the recovery of heterogeneous conformations from the noisy imaging data. Here, we review methods for the reconstruction and heterogeneity analysis of cryo-EM images, ranging from linear-transformation-based methods to nonlinear deep generative models. We overview the dimensionality-reduction techniques used in heterogeneous 3D reconstruction methods and specify what information each method can infer from the data. Then, we review the methods that use cryo-EM images to estimate probability distributions over conformations in reduced subspaces or predefined by atomistic simulations. We conclude with the ongoing challenges for the cryo-EM community.
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Affiliation(s)
- Wai Shing Tang
- Center for Computational Mathematics, Flatiron Institute, 162 5th Ave, New York, NY, 10010, United States. https://twitter.com/WaiShingTang
| | - Ellen D Zhong
- Department of Computer Science, Princeton University, 35 Olden St, Princeton, NJ, 08544, United States. https://twitter.com/ZhongingAlong
| | - Sonya M Hanson
- Center for Computational Mathematics, Flatiron Institute, 162 5th Ave, New York, NY, 10010, United States; Center for Computational Biology, Flatiron Institute, 162 5th Ave, New York, NY, 10010, United States. https://twitter.com/sonyahans
| | - Erik H Thiede
- Center for Computational Mathematics, Flatiron Institute, 162 5th Ave, New York, NY, 10010, United States. https://twitter.com/erik_der_elch
| | - Pilar Cossio
- Center for Computational Mathematics, Flatiron Institute, 162 5th Ave, New York, NY, 10010, United States; Center for Computational Biology, Flatiron Institute, 162 5th Ave, New York, NY, 10010, United States.
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Seitz E, Frank J, Schwander P. Beyond ManifoldEM: geometric relationships between manifold embeddings of a continuum of 3D molecular structures and their 2D projections. DIGITAL DISCOVERY 2023; 2:702-717. [PMID: 37312683 PMCID: PMC10259371 DOI: 10.1039/d2dd00128d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Accepted: 03/21/2023] [Indexed: 06/15/2023]
Abstract
ManifoldEM is an established method of geometric machine learning developed to extract information on conformational motions of molecules from their projections obtained by cryogenic electron microscopy (cryo-EM). In a previous work, in-depth analysis of the properties of manifolds obtained for simulated ground-truth data from molecules exhibiting domain motions has led to improvements of this method, as demonstrated in selected applications of single-particle cryo-EM. In the present work this analysis has been extended to investigate the properties of manifolds constructed by embedding data from synthetic models represented by atomic coordinates in motion, or three-dimensional density maps from biophysical experiments other than single-particle cryo-EM, with extensions to cryo-electron tomography and single-particle imaging with a X-ray free-electron laser. Our theoretical analysis revealed interesting relationships between all these manifolds, which can be exploited in future work.
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Affiliation(s)
- Evan Seitz
- Department of Biochemistry and Molecular Biophysics, Columbia University Medical Center New York NY 10032 USA
- Department of Biological Sciences, Columbia University New York NY 10027 USA
| | - Joachim Frank
- Department of Biochemistry and Molecular Biophysics, Columbia University Medical Center New York NY 10032 USA
- Department of Biological Sciences, Columbia University New York NY 10027 USA
| | - Peter Schwander
- Department of Physics, University of Wisconsin-Milwaukee Milwaukee WI 53211 USA
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5
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Chen M, Toader B, Lederman R. Integrating Molecular Models Into CryoEM Heterogeneity Analysis Using Scalable High-resolution Deep Gaussian Mixture Models. J Mol Biol 2023; 435:168014. [PMID: 36806476 PMCID: PMC10164680 DOI: 10.1016/j.jmb.2023.168014] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 02/07/2023] [Accepted: 02/08/2023] [Indexed: 02/17/2023]
Abstract
Resolving the structural variability of proteins is often key to understanding the structure-function relationship of those macromolecular machines. Single particle analysis using Cryogenic electron microscopy (CryoEM), combined with machine learning algorithms, provides a way to reveal the dynamics within the protein system from noisy micrographs. Here, we introduce an improved computational method that uses Gaussian mixture models for protein structure representation and deep neural networks for conformation space embedding. By integrating information from molecular models into the heterogeneity analysis, we can analyze continuous protein conformational changes using structural information at the frequency of 1/3 Å-1, and present the results in a more interpretable form.
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Affiliation(s)
- Muyuan Chen
- Division of CryoEM and Bioimaging, SSRL, SLAC National Accelerator Laboratory, Stanford University, Menlo Park, CA, USA
| | - Bogdan Toader
- Department of Statistics and Data Science, Yale University, New Haven, CT, USA
| | - Roy Lederman
- Department of Statistics and Data Science, Yale University, New Haven, CT, USA
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Toader B, Sigworth FJ, Lederman RR. Methods for Cryo-EM Single Particle Reconstruction of Macromolecules Having Continuous Heterogeneity. J Mol Biol 2023; 435:168020. [PMID: 36863660 PMCID: PMC10164696 DOI: 10.1016/j.jmb.2023.168020] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 02/15/2023] [Accepted: 02/16/2023] [Indexed: 03/02/2023]
Abstract
Macromolecules change their shape (conformation) in the process of carrying out their functions. The imaging by cryo-electron microscopy of rapidly-frozen, individual copies of macromolecules (single particles) is a powerful and general approach to understanding the motions and energy landscapes of macromolecules. Widely-used computational methods already allow the recovery of a few distinct conformations from heterogeneous single-particle samples, but the treatment of complex forms of heterogeneity such as the continuum of possible transitory states and flexible regions remains largely an open problem. In recent years there has been a surge of new approaches for treating the more general problem of continuous heterogeneity. This paper surveys the current state of the art in this area.
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Affiliation(s)
- Bogdan Toader
- Department of Statistics and Data Science, Yale University, United States.
| | - Fred J Sigworth
- Department of Cellular and Molecular Physiology, Yale University, United States
| | - Roy R Lederman
- Department of Statistics and Data Science, Yale University, United States. https://twitter.com/roylederman
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Dsouza R, Mashayekhi G, Etemadpour R, Schwander P, Ourmazd A. Energy landscapes from cryo-EM snapshots: a benchmarking study. Sci Rep 2023; 13:1372. [PMID: 36697500 PMCID: PMC9876912 DOI: 10.1038/s41598-023-28401-w] [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: 08/05/2022] [Accepted: 01/18/2023] [Indexed: 01/27/2023] Open
Abstract
Biomolecules undergo continuous conformational motions, a subset of which are functionally relevant. Understanding, and ultimately controlling biomolecular function are predicated on the ability to map continuous conformational motions, and identify the functionally relevant conformational trajectories. For equilibrium and near-equilibrium processes, function proceeds along minimum-energy pathways on one or more energy landscapes, because higher-energy conformations are only weakly occupied. With the growing interest in identifying functional trajectories, the need for reliable mapping of energy landscapes has become paramount. In response, various data-analytical tools for determining structural variability are emerging. A key question concerns the veracity with which each data-analytical tool can extract functionally relevant conformational trajectories from a collection of single-particle cryo-EM snapshots. Using synthetic data as an independently known ground truth, we benchmark the ability of four leading algorithms to determine biomolecular energy landscapes and identify the functionally relevant conformational paths on these landscapes. Such benchmarking is essential for systematic progress toward atomic-level movies of continuous biomolecular function.
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Affiliation(s)
- Raison Dsouza
- University of Wisconsin Milwaukee, 3135 N. Maryland Ave, Milwaukee, WI, 53211, USA
| | - Ghoncheh Mashayekhi
- University of Wisconsin Milwaukee, 3135 N. Maryland Ave, Milwaukee, WI, 53211, USA
| | - Roshanak Etemadpour
- University of Wisconsin Milwaukee, 3135 N. Maryland Ave, Milwaukee, WI, 53211, USA
| | - Peter Schwander
- University of Wisconsin Milwaukee, 3135 N. Maryland Ave, Milwaukee, WI, 53211, USA
| | - Abbas Ourmazd
- University of Wisconsin Milwaukee, 3135 N. Maryland Ave, Milwaukee, WI, 53211, USA.
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DiIorio MC, Kulczyk AW. Exploring the Structural Variability of Dynamic Biological Complexes by Single-Particle Cryo-Electron Microscopy. MICROMACHINES 2022; 14:118. [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
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