1
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Aissaoui N, Mills A, Lai-Kee-Him J, Triomphe N, Cece Q, Doucet C, Bonhoure A, Vidal M, Ke Y, Bellot G. Free-Standing DNA Origami Superlattice to Facilitate Cryo-EM Visualization of Membrane Vesicles. J Am Chem Soc 2024; 146:12925-12932. [PMID: 38691507 DOI: 10.1021/jacs.3c07328] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/03/2024]
Abstract
Technological breakthroughs in cryo-electron microscopy (cryo-EM) methods open new perspectives for highly detailed structural characterizations of extracellular vesicles (EVs) and synthetic liposome-protein assemblies. Structural characterizations of these vesicles in solution under a nearly native hydrated state are of great importance to decipher cell-to-cell communication and to improve EVs' application as markers in diagnosis and as drug carriers in disease therapy. However, difficulties in preparing holey carbon cryo-EM grids with low vesicle heterogeneities, at low concentration and with kinetic control of the chemical reactions or assembly processes, have limited cryo-EM use in the EV study. We report a straightforward membrane vesicle cryo-EM sample preparation method that assists in circumventing these limitations by using a free-standing DNA-affinity superlattice for covering holey carbon cryo-EM grids. Our approach uses DNA origami to self-assemble to a solution-stable and micrometer-sized ordered molecular template in which structure and functional properties can be rationally controlled. We engineered the template with cholesterol-binding sites to specifically trap membrane vesicles. The advantages of this DNA-cholesterol-affinity lattice (DCAL) include (1) local enrichment of artificial and biological vesicles at low concentration and (2) isolation of heterogeneous cell-derived membrane vesicles (exosomes) from a prepurified pellet of cell culture conditioned medium on the grid.
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Affiliation(s)
| | - Allan Mills
- Université de Montpellier, CNRS, INSERM, Centre de Biologie Structurale, F-34000 Montpellier, France
| | - Josephine Lai-Kee-Him
- Université de Montpellier, CNRS, INSERM, Centre de Biologie Structurale, F-34000 Montpellier, France
| | - Nicolas Triomphe
- Université de Montpellier, CNRS, INSERM, Centre de Biologie Structurale, F-34000 Montpellier, France
| | - Quentin Cece
- Université Paris Cité, CNRS, CiTCoM, F-75006 Paris, France
| | - Christine Doucet
- Université de Montpellier, CNRS, INSERM, Centre de Biologie Structurale, F-34000 Montpellier, France
| | - Anne Bonhoure
- Université de Montpellier, CNRS, LPHI, F-34000 Montpellier, France
| | - Michel Vidal
- Université de Montpellier, CNRS, LPHI, F-34000 Montpellier, France
| | - Yonggang Ke
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, 30322 Atlanta, United States
| | - Gaetan Bellot
- Université de Montpellier, CNRS, INSERM, Centre de Biologie Structurale, F-34000 Montpellier, France
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2
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Palukuri MV, Marcotte EM. DeepSLICEM: Clustering CryoEM particles using deep image and similarity graph representations. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.04.578778. [PMID: 38370702 PMCID: PMC10871265 DOI: 10.1101/2024.02.04.578778] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
Finding the 3D structure of proteins and their complexes has several applications, such as developing vaccines that target viral proteins effectively. Methods such as cryogenic electron microscopy (cryo-EM) have improved in their ability to capture high-resolution images, and when applied to a purified sample containing copies of a macromolecule, they can be used to produce a high-quality snapshot of different 2D orientations of the macromolecule, which can be combined to reconstruct its 3D structure. Instead of purifying a sample so that it contains only one macromolecule, a process that can be difficult, time-consuming, and expensive, a cell sample containing multiple particles can be photographed directly and separated into its constituent particles using computational methods. Previous work, SLICEM, has separated 2D projection images of different particles into their respective groups using 2 methods, clustering a graph with edges weighted by pairwise similarities of common lines of the 2D projections. In this work, we develop DeepSLICEM, a pipeline that clusters rich representations of 2D projections, obtained by combining graphical features from a similarity graph based on common lines, with additional image features extracted from a convolutional neural network. DeepSLICEM explores 6 pretrained convolutional neural networks and one supervised Siamese CNN for image representation, 10 pretrained deep graph neural networks for similarity graph node representations, and 4 methods for clustering, along with 8 methods for directly clustering the similarity graph. On 6 synthetic and experimental datasets, the DeepSLICEM pipeline finds 92 method combinations achieving better clustering accuracy than previous methods from SLICEM. Thus, in this paper, we demonstrate that deep neural networks have great potential for accurately separating mixtures of 2D projections of different macromolecules computationally.
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Affiliation(s)
- Meghana V Palukuri
- Oden Institute for Computational Engineering and Sciences, University of Texas, Austin, TX 78712, USA
- Department of Molecular Biosciences, University of Texas, Austin, TX 78712, USA
| | - Edward M Marcotte
- Oden Institute for Computational Engineering and Sciences, University of Texas, Austin, TX 78712, USA
- Department of Molecular Biosciences, University of Texas, Austin, TX 78712, USA
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3
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Choi W, Wu H, Yserentant K, Huang B, Cheng Y. Efficient tagging of endogenous proteins in human cell lines for structural studies by single-particle cryo-EM. Proc Natl Acad Sci U S A 2023; 120:e2302471120. [PMID: 37487103 PMCID: PMC10401002 DOI: 10.1073/pnas.2302471120] [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: 02/12/2023] [Accepted: 06/21/2023] [Indexed: 07/26/2023] Open
Abstract
CRISPR/Cas9-based genome engineering has revolutionized our ability to manipulate biological systems, particularly in higher organisms. Here, we designed a set of homology-directed repair donor templates that enable efficient tagging of endogenous proteins with affinity tags by transient transfection and selection of genome-edited cells in various human cell lines. Combined with technological advancements in single-particle cryogenic electron microscopy, this strategy allows efficient structural studies of endogenous proteins captured in their native cellular environment and during different cellular processes. We demonstrated this strategy by tagging six different human proteins in both HEK293T and Jurkat cells. Moreover, analysis of endogenous glyceraldehyde 3-phosphate dehydrogenase (GAPDH) in HEK293T cells allowed us to follow its behavior spatially and temporally in response to prolonged oxidative stress, correlating the increased number of oxidation-induced inactive catalytic sites in GAPDH with its translocation from cytosol to nucleus.
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Affiliation(s)
- Wooyoung Choi
- Department of Biochemistry and Biophysics, University of California, San Francisco, CA94143
| | - Hao Wu
- Department of Biochemistry and Biophysics, University of California, San Francisco, CA94143
| | - Klaus Yserentant
- Department of Pharmaceutical Chemistry, University of California, San Francisco, CA94143
| | - Bo Huang
- Department of Pharmaceutical Chemistry, University of California, San Francisco, CA94143
- Chan Zuckerberg Biohub-San Francisco, San Francisco, CA94158
| | - Yifan Cheng
- Department of Biochemistry and Biophysics, University of California, San Francisco, CA94143
- HHMI, University of California, San Francisco, CA94143
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4
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Su CC, Lyu M, Zhang Z, Miyagi M, Huang W, Taylor DJ, Yu EW. High-resolution structural-omics of human liver enzymes. Cell Rep 2023; 42:112609. [PMID: 37289586 PMCID: PMC10592444 DOI: 10.1016/j.celrep.2023.112609] [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: 12/26/2022] [Revised: 03/28/2023] [Accepted: 05/20/2023] [Indexed: 06/10/2023] Open
Abstract
We applied raw human liver microsome lysate to a holey carbon grid and used cryo-electron microscopy (cryo-EM) to define its composition. From this sample we identified and simultaneously determined high-resolution structural information for ten unique human liver enzymes involved in diverse cellular processes. Notably, we determined the structure of the endoplasmic bifunctional protein H6PD, where the N- and C-terminal domains independently possess glucose-6-phosphate dehydrogenase and 6-phosphogluconolactonase enzymatic activity, respectively. We also obtained the structure of heterodimeric human GANAB, an ER glycoprotein quality-control machinery that contains a catalytic α subunit and a noncatalytic β subunit. In addition, we observed a decameric peroxidase, PRDX4, which directly contacts a disulfide isomerase-related protein, ERp46. Structural data suggest that several glycosylations, bound endogenous compounds, and ions associate with these human liver enzymes. These results highlight the importance of cryo-EM in facilitating the elucidation of human organ proteomics at the atomic level.
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Affiliation(s)
- Chih-Chia Su
- Department of Pharmacology, Case Western Reserve University School of Medicine, Cleveland, OH 44106, USA
| | - Meinan Lyu
- Department of Pharmacology, Case Western Reserve University School of Medicine, Cleveland, OH 44106, USA
| | - Zhemin Zhang
- Department of Pharmacology, Case Western Reserve University School of Medicine, Cleveland, OH 44106, USA
| | - Masaru Miyagi
- Department of Pharmacology, Case Western Reserve University School of Medicine, Cleveland, OH 44106, USA
| | - Wei Huang
- Department of Pharmacology, Case Western Reserve University School of Medicine, Cleveland, OH 44106, USA
| | - Derek J Taylor
- Department of Pharmacology, Case Western Reserve University School of Medicine, Cleveland, OH 44106, USA
| | - Edward W Yu
- Department of Pharmacology, Case Western Reserve University School of Medicine, Cleveland, OH 44106, USA.
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5
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Wang X, Lu Y, Lin X, Li J, Zhang Z. An Unsupervised Classification Algorithm for Heterogeneous Cryo-EM Projection Images Based on Autoencoders. Int J Mol Sci 2023; 24:ijms24098380. [PMID: 37176089 PMCID: PMC10179202 DOI: 10.3390/ijms24098380] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 04/29/2023] [Accepted: 04/30/2023] [Indexed: 05/15/2023] Open
Abstract
Heterogeneous three-dimensional (3D) reconstruction in single-particle cryo-electron microscopy (cryo-EM) is an important but very challenging technique for recovering the conformational heterogeneity of flexible biological macromolecules such as proteins in different functional states. Heterogeneous projection image classification is a feasible solution to solve the structural heterogeneity problem in single-particle cryo-EM. The majority of heterogeneous projection image classification methods are developed using supervised learning technology or require a large amount of a priori knowledge, such as the orientations or common lines of the projection images, which leads to certain limitations in their practical applications. In this paper, an unsupervised heterogeneous cryo-EM projection image classification algorithm based on autoencoders is proposed, which only needs to know the number of heterogeneous 3D structures in the dataset and does not require any labeling information of the projection images or other a priori knowledge. A simple autoencoder with multi-layer perceptrons trained in iterative mode and a complex autoencoder with residual networks trained in one-pass learning mode are implemented to convert heterogeneous projection images into latent variables. The extracted high-dimensional features are reduced to two dimensions using the uniform manifold approximation and projection dimensionality reduction algorithm, and then clustered using the spectral clustering algorithm. The proposed algorithm is applied to two heterogeneous cryo-EM datasets for heterogeneous 3D reconstruction. Experimental results show that the proposed algorithm can effectively extract category features of heterogeneous projection images and achieve high classification and reconstruction accuracy, indicating that the proposed algorithm is effective for heterogeneous 3D reconstruction in single-particle cryo-EM.
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Affiliation(s)
- Xiangwen Wang
- College of Computer Science and Engineering, Northwest Normal University, Lanzhou 730070, China
| | - Yonggang Lu
- School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China
| | - Xianghong Lin
- College of Computer Science and Engineering, Northwest Normal University, Lanzhou 730070, China
| | - Jianwei Li
- School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China
| | - Zequn Zhang
- College of Computer Science and Engineering, Northwest Normal University, Lanzhou 730070, China
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6
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Nakamura A, Meng H, Zhao M, Wang F, Hou J, Cao R, Si D. Fast and automated protein-DNA/RNA macromolecular complex modeling from cryo-EM maps. Brief Bioinform 2023; 24:bbac632. [PMID: 36682003 PMCID: PMC10399284 DOI: 10.1093/bib/bbac632] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 12/15/2022] [Accepted: 12/29/2022] [Indexed: 01/23/2023] Open
Abstract
Cryo-electron microscopy (cryo-EM) allows a macromolecular structure such as protein-DNA/RNA complexes to be reconstructed in a three-dimensional coulomb potential map. The structural information of these macromolecular complexes forms the foundation for understanding the molecular mechanism including many human diseases. However, the model building of large macromolecular complexes is often difficult and time-consuming. We recently developed DeepTracer-2.0, an artificial-intelligence-based pipeline that can build amino acid and nucleic acid backbones from a single cryo-EM map, and even predict the best-fitting residues according to the density of side chains. The experiments showed improved accuracy and efficiency when benchmarking the performance on independent experimental maps of protein-DNA/RNA complexes and demonstrated the promising future of macromolecular modeling from cryo-EM maps. Our method and pipeline could benefit researchers worldwide who work in molecular biomedicine and drug discovery, and substantially increase the throughput of the cryo-EM model building. The pipeline has been integrated into the web portal https://deeptracer.uw.edu/.
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Affiliation(s)
- Andrew Nakamura
- Division of Computing and Software Systems, University of Washington Bothell, Bothell, WA 98011, USA
| | - Hanze Meng
- Department of Computer Science, Duke University, Durham, NC 27708, USA
| | - Minglei Zhao
- Department of Biochemistry and Molecular Biology, The University of Chicago, Chicago, IL 60637, USA
| | - Fengbin Wang
- Department of Biochemistry and Molecular Genetics, University of Alabama Birmingham, Heersink School of Medicine, Birmingham, AL 35233, USA
| | - Jie Hou
- Department of Computer Science, Saint Louis University, Saint Louis, MO 63103, USA
| | - Renzhi Cao
- Department of Computer Science, Pacific Lutheran University, Tacoma, WA 98447, USA
| | - Dong Si
- Corresponding author: Dong Si, Division of Computing and Software Systems, University of Washington Bothell, Bothell, WA 98011, USA. E-mail:
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7
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Zhou Y, Moscovich A, Bartesaghi A. Data-driven determination of number of discrete conformations in single-particle cryo-EM. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 221:106892. [PMID: 35597206 PMCID: PMC10131080 DOI: 10.1016/j.cmpb.2022.106892] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Revised: 05/11/2022] [Accepted: 05/12/2022] [Indexed: 05/20/2023]
Abstract
BACKGROUND AND OBJECTIVE One of the strengths of single-particle cryo-EM compared to other structural determination techniques is its ability to image heterogeneous samples containing multiple molecular species, different oligomeric states or distinct conformations. This is achieved using routines for in-silico 3D classification that are now well established in the field and have successfully been used to characterize the structural heterogeneity of important biomolecules. These techniques, however, rely on expert-user knowledge and trial-and-error experimentation to determine the correct number of conformations, making it a labor intensive, subjective, and difficult to reproduce procedure. METHODS We propose an approach to address the problem of automatically determining the number of discrete conformations present in heterogeneous single-particle cryo-EM datasets. We do this by systematically evaluating all possible partitions of the data and selecting the result that maximizes the average variance of similarities measured between particle images and the corresponding 3D reconstructions. RESULTS Using this strategy, we successfully analyzed datasets of heterogeneous protein complexes, including: 1) in-silico mixtures obtained by combining closely related antibody-bound HIV-1 Env trimers and other important membrane channels, and 2) naturally occurring mixtures from diverse and dynamic protein complexes representing varying degrees of structural heterogeneity and conformational plasticity. CONCLUSIONS The availability of unsupervised strategies for 3D classification combined with existing approaches for fully automatic pre-processing and 3D refinement, represents an important step towards converting single-particle cryo-EM into a high-throughput technique.
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Affiliation(s)
- Ye Zhou
- Department of Computer Science, Duke University, Durham, NC 27708, USA
| | - Amit Moscovich
- Department of Statistics and Operations Research, Tel Aviv University, Tel Aviv, Israel
| | - Alberto Bartesaghi
- Department of Computer Science, Duke University, Durham, NC 27708, USA; Department of Biochemistry, Duke University School of Medicine, Durham, NC 27708, USA; Department of Electrical and Computer Engineering, Duke University, Durham, NC 27708, USA.
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8
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Wang X, Lu Y, Lin X. Heterogeneous cryo-EM projection image classification using a two-stage spectral clustering based on novel distance measures. Brief Bioinform 2022; 23:6543485. [PMID: 35255494 DOI: 10.1093/bib/bbac032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 01/17/2022] [Accepted: 01/23/2022] [Indexed: 11/13/2022] Open
Abstract
Single-particle cryo-electron microscopy (cryo-EM) has become one of the mainstream technologies in the field of structural biology to determine the three-dimensional (3D) structures of biological macromolecules. Heterogeneous cryo-EM projection image classification is an effective way to discover conformational heterogeneity of biological macromolecules in different functional states. However, due to the low signal-to-noise ratio of the projection images, the classification of heterogeneous cryo-EM projection images is a very challenging task. In this paper, two novel distance measures between projection images integrating the reliability of common lines, pixel intensity and class averages are designed, and then a two-stage spectral clustering algorithm based on the two distance measures is proposed for heterogeneous cryo-EM projection image classification. In the first stage, the novel distance measure integrating common lines and pixel intensities of projection images is used to obtain preliminary classification results through spectral clustering. In the second stage, another novel distance measure integrating the first novel distance measure and class averages generated from each group of projection images is used to obtain the final classification results through spectral clustering. The proposed two-stage spectral clustering algorithm is applied on a simulated and a real cryo-EM dataset for heterogeneous reconstruction. Results show that the two novel distance measures can be used to improve the classification performance of spectral clustering, and using the proposed two-stage spectral clustering algorithm can achieve higher classification and reconstruction accuracy than using RELION and XMIPP.
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Affiliation(s)
- Xiangwen Wang
- School of Information Science and Engineering, Lanzhou University, 730000, Lanzhou, China.,College of Computer Science and Engineering, Northwest Normal University, 730070, Lanzhou, China
| | - Yonggang Lu
- School of Information Science and Engineering, Lanzhou University, 730000, Lanzhou, China
| | - Xianghong Lin
- College of Computer Science and Engineering, Northwest Normal University, 730070, Lanzhou, China
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9
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Arimura Y, Shih RM, Froom R, Funabiki H. Structural features of nucleosomes in interphase and metaphase chromosomes. Mol Cell 2021; 81:4377-4397.e12. [PMID: 34478647 PMCID: PMC8571072 DOI: 10.1016/j.molcel.2021.08.010] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Revised: 08/05/2021] [Accepted: 08/06/2021] [Indexed: 12/17/2022]
Abstract
Structural heterogeneity of nucleosomes in functional chromosomes is unknown. Here, we devise the template-, reference- and selection-free (TRSF) cryo-EM pipeline to simultaneously reconstruct cryo-EM structures of protein complexes from interphase or metaphase chromosomes. The reconstructed interphase and metaphase nucleosome structures are on average indistinguishable from canonical nucleosome structures, despite DNA sequence heterogeneity, cell-cycle-specific posttranslational modifications, and interacting proteins. Nucleosome structures determined by a decoy-classifying method and structure variability analyses reveal the nucleosome structural variations in linker DNA, histone tails, and nucleosome core particle configurations, suggesting that the opening of linker DNA, which is correlated with H2A C-terminal tail positioning, is suppressed in chromosomes. High-resolution (3.4-3.5 Å) nucleosome structures indicate DNA-sequence-independent stabilization of superhelical locations ±0-1 and ±3.5-4.5. The linker histone H1.8 preferentially binds to metaphase chromatin, from which chromatosome cryo-EM structures with H1.8 at the on-dyad position are reconstituted. This study presents the structural characteristics of nucleosomes in chromosomes.
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Affiliation(s)
- Yasuhiro Arimura
- Laboratory of Chromosome and Cell Biology, The Rockefeller University, New York, NY 10065, USA.
| | - Rochelle M Shih
- Laboratory of Chromosome and Cell Biology, The Rockefeller University, New York, NY 10065, USA
| | - Ruby Froom
- Laboratory of Molecular Biophysics, The Rockefeller University, New York, NY 10065, USA
| | - Hironori Funabiki
- Laboratory of Chromosome and Cell Biology, The Rockefeller University, New York, NY 10065, USA.
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10
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Steroid receptor-coregulator transcriptional complexes: new insights from CryoEM. Essays Biochem 2021; 65:857-866. [PMID: 34061186 DOI: 10.1042/ebc20210019] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Revised: 05/04/2021] [Accepted: 05/06/2021] [Indexed: 01/14/2023]
Abstract
Steroid receptors activate gene transcription through recruitment of a number of coregulators to facilitate histone modification, chromatin remodeling, and general transcription machinery stabilization. Understanding the structures of full-length steroid receptor and coregulatory complexes has been difficult due to their large molecular sizes and dynamic structural conformations. Recent developments in cryo-electron microscopy (cryoEM) technology and proteomics have advanced the structural studies of steroid receptor complexes. Here, we will review the insights we learned from cryoEM studies of the estrogen and androgen receptor transcriptional complexes. Despite similar domain organizations, the two receptors have different coregulator interaction modes. The cryoEM structures now have revealed the fundamental differences between the two receptors and their functional mechanisms.
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11
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Kyrilis FL, Belapure J, Kastritis PL. Detecting Protein Communities in Native Cell Extracts by Machine Learning: A Structural Biologist's Perspective. Front Mol Biosci 2021; 8:660542. [PMID: 33937337 PMCID: PMC8082361 DOI: 10.3389/fmolb.2021.660542] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Accepted: 03/18/2021] [Indexed: 11/13/2022] Open
Abstract
Native cell extracts hold great promise for understanding the molecular structure of ordered biological systems at high resolution. This is because higher-order biomolecular interactions, dubbed as protein communities, may be retained in their (near-)native state, in contrast to extensively purifying or artificially overexpressing the proteins of interest. The distinct machine-learning approaches are applied to discover protein-protein interactions within cell extracts, reconstruct dedicated biological networks, and report on protein community members from various organisms. Their validation is also important, e.g., by the cross-linking mass spectrometry or cell biology methods. In addition, the cell extracts are amenable to structural analysis by cryo-electron microscopy (cryo-EM), but due to their inherent complexity, sorting structural signatures of protein communities derived by cryo-EM comprises a formidable task. The application of image-processing workflows inspired by machine-learning techniques would provide improvements in distinguishing structural signatures, correlating proteomic and network data to structural signatures and subsequently reconstructed cryo-EM maps, and, ultimately, characterizing unidentified protein communities at high resolution. In this review article, we summarize recent literature in detecting protein communities from native cell extracts and identify the remaining challenges and opportunities. We argue that the progress in, and the integration of, machine learning, cryo-EM, and complementary structural proteomics approaches would provide the basis for a multi-scale molecular description of protein communities within native cell extracts.
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Affiliation(s)
- Fotis L. Kyrilis
- Interdisciplinary Research Center HALOmem, Charles Tanford Protein Center, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany
- Institute of Biochemistry and Biotechnology, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany
| | - Jaydeep Belapure
- Interdisciplinary Research Center HALOmem, Charles Tanford Protein Center, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany
| | - Panagiotis L. Kastritis
- Interdisciplinary Research Center HALOmem, Charles Tanford Protein Center, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany
- Institute of Biochemistry and Biotechnology, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany
- Biozentrum, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany
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12
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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.
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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
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13
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Kyrilis FL, Semchonok DA, Skalidis I, Tüting C, Hamdi F, O'Reilly FJ, Rappsilber J, Kastritis PL. Integrative structure of a 10-megadalton eukaryotic pyruvate dehydrogenase complex from native cell extracts. Cell Rep 2021; 34:108727. [PMID: 33567276 DOI: 10.1016/j.celrep.2021.108727] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2020] [Revised: 12/02/2020] [Accepted: 01/14/2021] [Indexed: 12/29/2022] Open
Abstract
The pyruvate dehydrogenase complex (PDHc) is a giant enzymatic assembly involved in pyruvate oxidation. PDHc components have been characterized in isolation, but the complex's quaternary structure has remained elusive due to sheer size, heterogeneity, and plasticity. Here, we identify fully assembled Chaetomium thermophilum α-keto acid dehydrogenase complexes in native cell extracts and characterize their domain arrangements utilizing mass spectrometry, activity assays, crosslinking, electron microscopy (EM), and computational modeling. We report the cryo-EM structure of the PDHc core and observe unique features of the previously unknown native state. The asymmetric reconstruction of the 10-MDa PDHc resolves spatial proximity of its components, agrees with stoichiometric data (60 E2p:12 E3BP:∼20 E1p: ≤ 12 E3), and proposes a minimum reaction path among component enzymes. The PDHc shows the presence of a dynamic pyruvate oxidation compartment, organized by core and peripheral protein species. Our data provide a framework for further understanding PDHc and α-keto acid dehydrogenase complex structure and function.
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Affiliation(s)
- Fotis L Kyrilis
- Interdisciplinary Research Center HALOmem, Charles Tanford Protein Center, Martin Luther University Halle-Wittenberg, Kurt-Mothes-Straße 3a, Halle/Saale, Germany; Institute of Biochemistry and Biotechnology, Martin Luther University Halle-Wittenberg, Kurt-Mothes-Straße 3, Halle/Saale, Germany
| | - Dmitry A Semchonok
- Interdisciplinary Research Center HALOmem, Charles Tanford Protein Center, Martin Luther University Halle-Wittenberg, Kurt-Mothes-Straße 3a, Halle/Saale, Germany
| | - Ioannis Skalidis
- Interdisciplinary Research Center HALOmem, Charles Tanford Protein Center, Martin Luther University Halle-Wittenberg, Kurt-Mothes-Straße 3a, Halle/Saale, Germany; Institute of Biochemistry and Biotechnology, Martin Luther University Halle-Wittenberg, Kurt-Mothes-Straße 3, Halle/Saale, Germany
| | - Christian Tüting
- Interdisciplinary Research Center HALOmem, Charles Tanford Protein Center, Martin Luther University Halle-Wittenberg, Kurt-Mothes-Straße 3a, Halle/Saale, Germany
| | - Farzad Hamdi
- Interdisciplinary Research Center HALOmem, Charles Tanford Protein Center, Martin Luther University Halle-Wittenberg, Kurt-Mothes-Straße 3a, Halle/Saale, Germany
| | - Francis J O'Reilly
- Bioanalytics, Institute of Biotechnology, Technische Universität Berlin, 13355 Berlin, Germany
| | - Juri Rappsilber
- Bioanalytics, Institute of Biotechnology, Technische Universität Berlin, 13355 Berlin, Germany; Wellcome Centre for Cell Biology, School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3BF, Scotland, United Kingdom
| | - Panagiotis L Kastritis
- Interdisciplinary Research Center HALOmem, Charles Tanford Protein Center, Martin Luther University Halle-Wittenberg, Kurt-Mothes-Straße 3a, Halle/Saale, Germany; Institute of Biochemistry and Biotechnology, Martin Luther University Halle-Wittenberg, Kurt-Mothes-Straße 3, Halle/Saale, Germany; Biozentrum, Martin Luther University Halle-Wittenberg, Weinbergweg 22, Halle/Saale, Germany.
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14
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Kirykowicz AM, Woodward JD. Shotgun EM of mycobacterial protein complexes during stationary phase stress. Curr Res Struct Biol 2020; 2:204-212. [PMID: 34235480 PMCID: PMC8244302 DOI: 10.1016/j.crstbi.2020.09.002] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2020] [Revised: 09/08/2020] [Accepted: 09/11/2020] [Indexed: 10/27/2022] Open
Abstract
There is little structural information about the protein complexes conferring resistance in Mycobacterium tuberculosis (Mtb) to anti-microbial oxygen and nitrogen radicals in the phagolysosome. Here, we expose the model Mycobacterium, Mycobacterium smegmatis, to simulated oxidative-stress conditions and apply a shotgun EM method for the structural detection of the resulting protein assemblies. We identified: glutamine synthetase I, essential for Mtb virulence; bacterioferritin A, critical for Mtb iron regulation; aspartyl aminopeptidase M18, a protease; and encapsulin, which produces a cage-like structure to enclose cargo proteins. After further investigation, we found that encapsulin carries dye-decolourising peroxidase, a protein antioxidant, as its primary cargo under the conditions tested.
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Affiliation(s)
- Angela M. Kirykowicz
- Department of Biochemistry, University of Cambridge, Sanger Building, Tennis Court Road, Cambridge, CB2 1GA, UK
- Division of Medical Biochemistry and Structural Biology, Department of Integrative Biomedical Sciences, University of Cape Town, Anzio Road, Observatory, 7925, Cape Town, South Africa
| | - Jeremy D. Woodward
- Division of Medical Biochemistry and Structural Biology, Department of Integrative Biomedical Sciences, University of Cape Town, Anzio Road, Observatory, 7925, Cape Town, South Africa
- Structural Biology Research Unit, University of Cape Town, South Africa
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15
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McCafferty CL, Verbeke EJ, Marcotte EM, Taylor DW. Structural Biology in the Multi-Omics Era. J Chem Inf Model 2020; 60:2424-2429. [PMID: 32129623 PMCID: PMC7254829 DOI: 10.1021/acs.jcim.9b01164] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2019] [Indexed: 12/12/2022]
Abstract
Rapid developments in cryogenic electron microscopy have opened new avenues to probe the structures of protein assemblies in their near native states. Recent studies have begun applying single -particle analysis to heterogeneous mixtures, revealing the potential of structural-omics approaches that combine the power of mass spectrometry and electron microscopy. Here we highlight advances and challenges in sample preparation, data processing, and molecular modeling for handling increasingly complex mixtures. Such advances will help structural-omics methods extend to cellular-level models of structural biology.
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Affiliation(s)
- Caitlyn L. McCafferty
- Department
of Molecular Biosciences, University of
Texas at Austin, Austin, Texas 78712, United States
| | - Eric J. Verbeke
- Department
of Molecular Biosciences, University of
Texas at Austin, Austin, Texas 78712, United States
| | - Edward M. Marcotte
- Department
of Molecular Biosciences, University of
Texas at Austin, Austin, Texas 78712, United States
- Institute
for Cellular and Molecular Biology, University
of Texas at Austin, Austin, Texas 78712, United States
- Center
for Systems and Synthetic Biology, University
of Texas at Austin, Austin, Texas 78712, United States
| | - David W. Taylor
- Department
of Molecular Biosciences, University of
Texas at Austin, Austin, Texas 78712, United States
- Institute
for Cellular and Molecular Biology, University
of Texas at Austin, Austin, Texas 78712, United States
- Center
for Systems and Synthetic Biology, University
of Texas at Austin, Austin, Texas 78712, United States
- LIVESTRONG
Cancer Institutes, Dell Medical School, Austin, Texas 78712, United States
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16
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Cianfrocco MA, Kellogg EH. What Could Go Wrong? A Practical Guide to Single-Particle Cryo-EM: From Biochemistry to Atomic Models. J Chem Inf Model 2020; 60:2458-2469. [PMID: 32078321 DOI: 10.1021/acs.jcim.9b01178] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Cryo-electron microscopy (cryo-EM) has enjoyed explosive recent growth due to revolutionary advances in hardware and software, resulting in a steady stream of long-awaited, high-resolution structures with unprecedented atomic detail. With this comes an increased number of microscopes, cryo-EM facilities, and scientists eager to leverage the ability to determine protein structures without crystallization. However, numerous pitfalls and considerations beset the path toward high-resolution structures and are not necessarily obvious from literature surveys. Here, we detail the most common misconceptions when initiating a cryo-EM project and common technical hurdles, as well as their solutions, and we conclude with a vision for the future of this exciting field.
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Affiliation(s)
- Michael A Cianfrocco
- Life Sciences Institute and Department of Biological Chemistry, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Elizabeth H Kellogg
- Department of Molecular Biology and Genetics,Cornell University, Ithaca, New York 14850, United States
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