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Carlero D, Fukuda S, Bocanegra R, Ando T, Martin-Benito J, Ibarra B. Conformational Dynamics of Influenza A Virus Ribonucleoprotein Complexes during RNA Synthesis. ACS NANO 2024; 18. [PMID: 39013014 PMCID: PMC11295199 DOI: 10.1021/acsnano.4c01362] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Revised: 05/13/2024] [Accepted: 05/22/2024] [Indexed: 07/18/2024]
Abstract
Viral ribonucleoproteins (vRNPs) are the cornerstones of viral proliferation, as they form the macromolecular complexes that are responsible for the transcription and replication of most single-stranded RNA viruses. The influenza A virus (IAV) polymerase catalyzes RNA synthesis within the context of vRNPs where genomic viral RNA (vRNA) is packaged by the viral nucleoprotein (NP). We used high-speed atomic force microscopy and electron microscopy to study the conformational dynamics of individual IAV recombinant RNPs (rRNPs) during RNA synthesis. The rRNPs present an annular organization that allows for the real-time tracking of conformational changes in the NP-vRNA template caused by the advancing polymerase. We demonstrate that the rRNPs undergo a well-defined conformational cycle during RNA synthesis, which can be interpreted in light of previous transcription models. We also present initial estimations of the average RNA synthesis rate in the rRNP and its dependence on the nucleotide concentration and stability of the nascent RNA secondary structures. Furthermore, we provide evidence that rRNPs can perform consecutive cycles of RNA synthesis, accounting for their ability to recycle and generate multiple copies of RNA.
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Affiliation(s)
- Diego Carlero
- Centro
Nacional de Biotecnología Campus de Cantoblanco, 28049, Madrid, Spain
| | - Shingo Fukuda
- WPI
Nano Life Science Institute (WPI-NanoLSI), Kanazawa University, Kakuma-machi, Kanazawa 920-1192, Japan
| | - Rebeca Bocanegra
- Instituto
Madrileño de Estudios Avanzados en Nanociencia, IMDEA Nanociencia, 28049, Madrid, Spain
- IMDEA
Nanociencia & CNB-CSIC-IMDEA Nanociencia Associated Unit “Unidad
de Nanobiotecnología”, 28049, Madrid, Spain
| | - Toshio Ando
- WPI
Nano Life Science Institute (WPI-NanoLSI), Kanazawa University, Kakuma-machi, Kanazawa 920-1192, Japan
| | - Jaime Martin-Benito
- IMDEA
Nanociencia & CNB-CSIC-IMDEA Nanociencia Associated Unit “Unidad
de Nanobiotecnología”, 28049, Madrid, Spain
| | - Borja Ibarra
- Instituto
Madrileño de Estudios Avanzados en Nanociencia, IMDEA Nanociencia, 28049, Madrid, Spain
- IMDEA
Nanociencia & CNB-CSIC-IMDEA Nanociencia Associated Unit “Unidad
de Nanobiotecnología”, 28049, Madrid, Spain
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2
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Wan W, Khavnekar S, Wagner J. STOPGAP: an open-source package for template matching, subtomogram alignment and classification. Acta Crystallogr D Struct Biol 2024; 80:336-349. [PMID: 38606666 PMCID: PMC11066880 DOI: 10.1107/s205979832400295x] [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: 02/09/2024] [Accepted: 04/05/2024] [Indexed: 04/13/2024] Open
Abstract
Cryo-electron tomography (cryo-ET) enables molecular-resolution 3D imaging of complex biological specimens such as viral particles, cellular sections and, in some cases, whole cells. This enables the structural characterization of molecules in their near-native environments, without the need for purification or separation, thereby preserving biological information such as conformational states and spatial relationships between different molecular species. Subtomogram averaging is an image-processing workflow that allows users to leverage cryo-ET data to identify and localize target molecules, determine high-resolution structures of repeating molecular species and classify different conformational states. Here, STOPGAP, an open-source package for subtomogram averaging that is designed to provide users with fine control over each of these steps, is described. In providing detailed descriptions of the image-processing algorithms that STOPGAP uses, this manuscript is also intended to serve as a technical resource to users as well as for further community-driven software development.
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Affiliation(s)
- William Wan
- Department of Biochemistry and Center for Structural Biology, Vanderbilt University, Nashville, TN 37240, USA
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3
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Hou G, Yang Z, Zang D, Fernández JJ, Zhang F, Han R. MarkerDetector: A method for robust fiducial marker detection in electron micrographs using wavelet-based template. J Struct Biol 2024; 216:108044. [PMID: 37967798 DOI: 10.1016/j.jsb.2023.108044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Revised: 10/19/2023] [Accepted: 11/07/2023] [Indexed: 11/17/2023]
Abstract
Fiducial marker detection in electron micrographs becomes an important and challenging task with the development of large-field electron microscopy. The fiducial marker detection plays an important role in several steps during the process of electron micrographs, such as the alignment and parameter calibrations. However, limited by the conditions of low signal-to-noise ratio (SNR) in the electron micrographs, the performance of fiducial marker detection is severely affected. In this work, we propose the MarkerDetector, a novel algorithm for detecting fiducial markers in electron micrographs. The proposed MarkerDetector is built upon the following contributions: Firstly, a wavelet-based template generation algorithm is devised in MarkerDetector. By adopting a shape-based criterion, a high-quality template can be obtained. Secondly, a robust marker determination strategy is devised by utilizing statistic-based filtering, which can guarantee the correctness of the detected fiducial markers. The average running time of our algorithm is 1.67 seconds with promising accuracy, indicating its practical feasibility for applications in electron micrographs.
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Affiliation(s)
- Gaoxin Hou
- Research Center for Mathematics and Interdisciplinary Sciences, Frontiers Science Center for Nonlinear Expectations (Ministry of Education), Shandong University, Qingdao 266237, China
| | - Zhidong Yang
- High Performance Computer Research Center, Institute of Computing Technology, CAS, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Dawei Zang
- High Performance Computer Research Center, Institute of Computing Technology, CAS, Beijing 100190, China
| | - Jose-Jesus Fernández
- Spanish National Research Council (CINN-CSIC), Health Research Institute of Asturias (ISPA), Av. Hospital Universitario s/n, Oviedo 33011, Spain
| | - Fa Zhang
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China.
| | - Renmin Han
- Research Center for Mathematics and Interdisciplinary Sciences, Frontiers Science Center for Nonlinear Expectations (Ministry of Education), Shandong University, Qingdao 266237, China; King Abdullah University of Science and Technology (KAUST), Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, Thuwal 23955, Saudi Arabia.
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4
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Tang H, Wang Y, Ouyang J, Wang J. Simcryocluster: a semantic similarity clustering method of cryo-EM images by adopting contrastive learning. BMC Bioinformatics 2024; 25:77. [PMID: 38378489 PMCID: PMC11264969 DOI: 10.1186/s12859-023-05565-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Accepted: 11/11/2023] [Indexed: 02/22/2024] Open
Abstract
BACKGROUND Cryo-electron microscopy (Cryo-EM) plays an increasingly important role in the determination of the three-dimensional (3D) structure of macromolecules. In order to achieve 3D reconstruction results close to atomic resolution, 2D single-particle image classification is not only conducive to single-particle selection, but also a key step that affects 3D reconstruction. The main task is to cluster and align 2D single-grain images into non-heterogeneous groups to obtain sharper single-grain images by averaging calculations. The main difficulties are that the cryo-EM single-particle image has a low signal-to-noise ratio (SNR), cannot manually label the data, and the projection direction is random and the distribution is unknown. Therefore, in the low SNR scenario, how to obtain the characteristic information of the effective particles, improve the clustering accuracy, and thus improve the reconstruction accuracy, is a key problem in the 2D image analysis of single particles of cryo-EM. RESULTS Aiming at the above problems, we propose a learnable deep clustering method and a fast alignment weighted averaging method based on frequency domain space to effectively improve the class averaging results and improve the reconstruction accuracy. In particular, it is very prominent in the feature extraction and dimensionality reduction module. Compared with the classification method based on Bayesian and great likelihood, a large amount of single particle data is required to estimate the relative angle orientation of macromolecular single particles in the 3D structure, and we propose that the clustering method shows good results. CONCLUSIONS SimcryoCluster can use the contrastive learning method to perform well in the unlabeled high-noise cryo-EM single particle image classification task, making it an important tool for cryo-EM protein structure determination.
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Affiliation(s)
- Huanrong Tang
- Department of Computing, Xiangtan University, Xiangtan, China
| | - Yaowu Wang
- Department of Computing, Xiangtan University, Xiangtan, China.
| | - Jianquan Ouyang
- Department of Computing, Xiangtan University, Xiangtan, China.
| | - Jinlin Wang
- Department of Computing, Xiangtan University, Xiangtan, China
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5
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Wan W, Khavnekar S, Wagner J. STOPGAP, an open-source package for template matching, subtomogram alignment, and classification. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.20.572665. [PMID: 38187721 PMCID: PMC10769363 DOI: 10.1101/2023.12.20.572665] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
Cryo-electron tomography (cryo-ET) enables molecular-resolution 3D imaging of complex biological specimens such as viral particles, cellular sections, and in some cases, whole cells. This enables the structural characterization of molecules in their near-native environments, without the need for purification or separation, thereby preserving biological information such as conformational states and spatial relationships between different molecular species. Subtomogram averaging is an image processing workflow that allows users to leverage cryo-ET data to identify and localize target molecules, determine high-resolution structures of repeating molecular species, and classifying different conformational states. Here we describe STOPGAP, an open-source package for subtomogram averaging designed to provide users with fine control over each of these steps. In providing detailed descriptions of the image processing algorithms that STOPGAP uses, we intend for this manuscript to also serve as a technical resource to users as well as further community-driven software development.
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Affiliation(s)
- William Wan
- Department of Biochemistry and Center for Structural Biology, Vanderbilt University, Nashville TN, USA
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6
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Zhang D, Yan Y, Huang Y, Liu B, Zheng Q, Zhang J, Xia N. Unsupervised Cryo-EM Images Denoising and Clustering Based on Deep Convolutional Autoencoder and K-Means+. IEEE TRANSACTIONS ON MEDICAL IMAGING 2023; 42:1509-1521. [PMID: 37015394 DOI: 10.1109/tmi.2022.3231626] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Cryo-electron microscopy (cryo-EM) is a widely used structural determination technique. Because of the extremely low signal-to-noise ratio (SNR) of images captured by cryo-EM, clustering single-particle cryo-EM images with high accuracy is challenging. To address this, we proposed an iterative denoising and clustering method based on a deep convolutional variational autoencoder and K-means++. The proposed method contains two modules: a denoising ResNet variational autoencoder (DRVAE), and Balance size K-means++ (BSK-means++). First, the DRVAE is trained in a fully unsupervised manner to initialize the neural network and obtain preliminary denoised images. Second, BSK-means++ is built for clustering denoised images, and images closer to class centers are divided into reliable samples. Third, the training of DRVAE is continued, while the class-average images are used as pseudo supervision of reliable samples to reserve more detailed information of denoised images. Finally, the second and third steps mentioned above can be performed jointly and iteratively until convergence occurs. The experimental results showed that the proposed method can generate reliable class average images and achieve better clustering accuracy and normalized mutual information than current methods. This study confirmed that DRVAE with BSK-means++ could achieve a good denoise performance on single-particle cryo-EM images, which can help researchers obtain information such as symmetry and heterogeneity of the target particles. In addition, the proposed method avoids the extreme imbalance of class size, which improves the reliability of the clustering result.
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7
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Zhang H, Li H, Zhang F, Zhu P. A strategy combining denoising and cryo-EM single particle analysis. Brief Bioinform 2023; 24:7140293. [PMID: 37096633 DOI: 10.1093/bib/bbad148] [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: 01/12/2023] [Revised: 02/21/2023] [Accepted: 03/28/2023] [Indexed: 04/26/2023] Open
Abstract
In cryogenic electron microscopy (cryo-EM) single particle analysis (SPA), high-resolution three-dimensional structures of biological macromolecules are determined by iteratively aligning and averaging a large number of two-dimensional projections of molecules. Since the correlation measures are sensitive to the signal-to-noise ratio, various parameter estimation steps in SPA will be disturbed by the high-intensity noise in cryo-EM. However, denoising algorithms tend to damage high frequencies and suppress mid- and high-frequency contrast of micrographs, which exactly the precise parameter estimation relies on, therefore, limiting their application in SPA. In this study, we suggest combining a cryo-EM image processing pipeline with denoising and maximizing the signal's contribution in various parameter estimation steps. To solve the inherent flaws of denoising algorithms, we design an algorithm named MScale to correct the amplitude distortion caused by denoising and propose a new orientation determination strategy to compensate for the high-frequency loss. In the experiments on several real datasets, the denoised particles are successfully applied in the class assignment estimation and orientation determination tasks, ultimately enhancing the quality of biomacromolecule reconstruction. The case study on classification indicates that our strategy not only improves the resolution of difficult classes (up to 5 Å) but also resolves an additional class. In the case study on orientation determination, our strategy improves the resolution of the final reconstructed density map by 0.34 Å compared with conventional strategy. The code is available at https://github.com/zhanghui186/Mscale.
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Affiliation(s)
- Hui Zhang
- National Laboratory of Biomacromolecules, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Hongjia Li
- University of Chinese Academy of Sciences, Beijing 100049, China
- High Performance Computer Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
| | - Fa Zhang
- School of Medical Technology, Beijing Institute of Technology, Beijing, 100081, China
| | - Ping Zhu
- National Laboratory of Biomacromolecules, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
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8
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Sharon G, Shkolnisky Y, Bendory T. Signal enhancement for two-dimensional cryo-EM data processing. BIOLOGICAL IMAGING 2023; 3:e7. [PMID: 38510167 PMCID: PMC10951933 DOI: 10.1017/s2633903x23000065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Revised: 01/27/2023] [Accepted: 02/20/2023] [Indexed: 03/22/2024]
Abstract
Different tasks in the computational pipeline of single-particle cryo-electron microscopy (cryo-EM) require enhancing the quality of the highly noisy raw images. To this end, we develop an efficient algorithm for signal enhancement of cryo-EM images. The enhanced images can be used for a variety of downstream tasks, such as two-dimensional classification, removing uninformative images, constructing ab initio models, generating templates for particle picking, providing a quick assessment of the data set, dimensionality reduction, and symmetry detection. The algorithm includes built-in quality measures to assess its performance and alleviate the risk of model bias. We demonstrate the effectiveness of the proposed algorithm on several experimental data sets. In particular, we show that the quality of the resulting images is high enough to produce ab initio models of Å resolution. The algorithm is accompanied by a publicly available, documented, and easy-to-use code.
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Affiliation(s)
- Guy Sharon
- School of Electrical Engineering, Tel Aviv University, Tel Aviv, Israel
| | - Yoel Shkolnisky
- School of Mathematical Sciences, Tel Aviv University, Tel Aviv, Israel
| | - Tamir Bendory
- School of Electrical Engineering, Tel Aviv University, Tel Aviv, Israel
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9
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Chen YX, Feng D, Shen HB. Cryo-EM image alignment: From pair-wise to joint with deep unsupervised difference learning. J Struct Biol 2023; 215:107940. [PMID: 36709787 DOI: 10.1016/j.jsb.2023.107940] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2022] [Revised: 12/22/2022] [Accepted: 01/22/2023] [Indexed: 01/27/2023]
Abstract
Cryo-electron microscopy (cryo-EM) single-particle analysis is a revolutionary imaging technique to resolve and visualize biomacromolecules. Image alignment in cryo-EM is an important and basic step to improve the precision of the image distance calculation. However, it is a very challenging task due to high noise and low signal-to-noise ratio. Therefore, we propose a new deep unsupervised difference learning (UDL) strategy with novel pseudo-label guided learning network architecture and apply it to pair-wise image alignment in cryo-EM. The training framework is fully unsupervised. Furthermore, a variant of UDL called joint UDL (JUDL), is also proposed, which is capable of utilizing the similarity information of the whole dataset and thus further increase the alignment precision. Assessments on both real-world and synthetic cryo-EM single-particle image datasets suggest the new unsupervised joint alignment method can achieve more accurate alignment results. Our method is highly efficient by taking advantages of GPU devices. The source code of our methods is publicly available at "http://www.csbio.sjtu.edu.cn/bioinf/JointUDL/" for academic use.
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Affiliation(s)
- Yu-Xuan Chen
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China
| | - Dagan Feng
- School of Computer Science, University of Sydney, Australia
| | - Hong-Bin Shen
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China.
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10
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Troman L, Alvira S, Daum B, Gold VAM, Collinson I. Interaction of the periplasmic chaperone SurA with the inner membrane protein secretion (SEC) machinery. Biochem J 2023; 480:283-296. [PMID: 36701201 PMCID: PMC9987972 DOI: 10.1042/bcj20220480] [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: 09/15/2022] [Revised: 01/11/2023] [Accepted: 01/25/2023] [Indexed: 01/27/2023]
Abstract
Gram-negative bacteria are surrounded by two protein-rich membranes with a peptidoglycan layer sandwiched between them. Together they form the envelope (or cell wall), crucial for energy production, lipid biosynthesis, structural integrity, and for protection against physical and chemical environmental challenges. To achieve envelope biogenesis, periplasmic and outer-membrane proteins (OMPs) must be transported from the cytosol and through the inner-membrane, via the ubiquitous SecYEG protein-channel. Emergent proteins either fold in the periplasm or cross the peptidoglycan (PG) layer towards the outer-membrane for insertion through the β-barrel assembly machinery (BAM). Trafficking of hydrophobic proteins through the periplasm is particularly treacherous given the high protein density and the absence of energy (ATP or chemiosmotic potential). Numerous molecular chaperones assist in the prevention and recovery from aggregation, and of these SurA is known to interact with BAM, facilitating delivery to the outer-membrane. However, it is unclear how proteins emerging from the Sec-machinery are received and protected from aggregation and proteolysis prior to an interaction with SurA. Through biochemical analysis and electron microscopy we demonstrate the binding capabilities of the unoccupied and substrate-engaged SurA to the inner-membrane translocation machinery complex of SecYEG-SecDF-YidC - aka the holo-translocon (HTL). Supported by AlphaFold predictions, we suggest a role for periplasmic domains of SecDF in chaperone recruitment to the protein translocation exit site in SecYEG. We propose that this immediate interaction with the enlisted chaperone helps to prevent aggregation and degradation of nascent envelope proteins, facilitating their safe passage to the periplasm and outer-membrane.
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Affiliation(s)
- Lucy Troman
- School of Biochemistry, University of Bristol, Bristol BS8 1TD, U.K
| | - Sara Alvira
- School of Biochemistry, University of Bristol, Bristol BS8 1TD, U.K
| | - Bertram Daum
- Living Systems Institute, University of Exeter, Exeter, U.K
- College of Life and Environmental Sciences, Geoffrey Pope, University of Exeter, Exeter, U.K
| | - Vicki A. M. Gold
- Living Systems Institute, University of Exeter, Exeter, U.K
- College of Life and Environmental Sciences, Geoffrey Pope, University of Exeter, Exeter, U.K
| | - Ian Collinson
- School of Biochemistry, University of Bristol, Bristol BS8 1TD, U.K
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11
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Ramírez-Aportela E, Carazo JM, Sorzano COS. Higher resolution in cryo-EM by the combination of macromolecular prior knowledge and image-processing tools. IUCRJ 2022; 9:632-638. [PMID: 36071808 PMCID: PMC9438491 DOI: 10.1107/s2052252522006959] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Accepted: 07/07/2022] [Indexed: 06/15/2023]
Abstract
Single-particle cryo-electron microscopy has become a powerful technique for the 3D structure determination of biological molecules. The last decade has seen an astonishing development of both hardware and software, and an exponential growth of new structures obtained at medium-high resolution. However, the knowledge accumulated in this field over the years has hardly been utilized as feedback in the reconstruction of new structures. In this context, this article explores the use of the deep-learning approach deepEMhancer as a regularizer in the RELION refinement process. deepEMhancer introduces prior information derived from macromolecular structures, and contributes to noise reduction and signal enhancement, as well as a higher degree of isotropy. These features have a direct effect on image alignment and reduction of overfitting during iterative refinement. The advantages of this combination are demonstrated for several membrane proteins, for which it is especially useful because of their high disorder and flexibility.
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Affiliation(s)
- Erney Ramírez-Aportela
- Biocomputing Unit, National Centre for Biotechnology (CNB CSIC), Darwin 3, Campus Universidad Autónoma de Madrid, Cantoblanco, Madrid 28049, Spain
| | - Jose M. Carazo
- Biocomputing Unit, National Centre for Biotechnology (CNB CSIC), Darwin 3, Campus Universidad Autónoma de Madrid, Cantoblanco, Madrid 28049, Spain
| | - Carlos Oscar S. Sorzano
- Biocomputing Unit, National Centre for Biotechnology (CNB CSIC), Darwin 3, Campus Universidad Autónoma de Madrid, Cantoblanco, Madrid 28049, Spain
- Universidad CEU San Pablo, Campus Urb. Montepríncipe, Boadilla del Monte, Madrid 28668, Spain
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12
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Vilas JL, Carazo JM, Sorzano COS. Emerging Themes in CryoEM─Single Particle Analysis Image Processing. Chem Rev 2022; 122:13915-13951. [PMID: 35785962 PMCID: PMC9479088 DOI: 10.1021/acs.chemrev.1c00850] [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] [Indexed: 11/29/2022]
Abstract
Cryo-electron microscopy (CryoEM) has become a vital technique in structural biology. It is an interdisciplinary field that takes advantage of advances in biochemistry, physics, and image processing, among other disciplines. Innovations in these three basic pillars have contributed to the boosting of CryoEM in the past decade. This work reviews the main contributions in image processing to the current reconstruction workflow of single particle analysis (SPA) by CryoEM. Our review emphasizes the time evolution of the algorithms across the different steps of the workflow differentiating between two groups of approaches: analytical methods and deep learning algorithms. We present an analysis of the current state of the art. Finally, we discuss the emerging problems and challenges still to be addressed in the evolution of CryoEM image processing methods in SPA.
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Affiliation(s)
- Jose Luis Vilas
- Biocomputing Unit, Centro Nacional de Biotecnologia (CNB-CSIC), Darwin, 3, Campus Universidad Autonoma, 28049 Cantoblanco, Madrid, Spain
| | - Jose Maria Carazo
- Biocomputing Unit, Centro Nacional de Biotecnologia (CNB-CSIC), Darwin, 3, Campus Universidad Autonoma, 28049 Cantoblanco, Madrid, Spain
| | - Carlos Oscar S Sorzano
- Biocomputing Unit, Centro Nacional de Biotecnologia (CNB-CSIC), Darwin, 3, Campus Universidad Autonoma, 28049 Cantoblanco, Madrid, Spain
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13
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Profiling of hMPV F-specific antibodies isolated from human memory B cells. Nat Commun 2022; 13:2546. [PMID: 35538099 PMCID: PMC9091222 DOI: 10.1038/s41467-022-30205-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Accepted: 01/25/2022] [Indexed: 11/09/2022] Open
Abstract
Human metapneumovirus (hMPV) belongs to the Pneumoviridae family and is closely related to respiratory syncytial virus (RSV). The surface fusion (F) glycoprotein mediates viral fusion and is the primary target of neutralizing antibodies against hMPV. Here we report 113 hMPV-F specific monoclonal antibodies (mAbs) isolated from memory B cells of human donors. We characterize the antibodies' germline usage, epitopes, neutralization potencies, and binding specificities. We find that unlike RSV-F specific mAbs, antibody responses to hMPV F are less dominant against the apex of the antigen, and the majority of the potent neutralizing mAbs recognize epitopes on the side of hMPV F. Furthermore, neutralizing epitopes that differ from previously defined antigenic sites on RSV F are identified, and multiple binding modes of site V and II mAbs are discovered. Interestingly, mAbs that bind preferentially to the unprocessed prefusion F show poor neutralization potency. These results elucidate the immune recognition of hMPV infection and provide novel insights for future hMPV antibody and vaccine development.
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14
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DeVore K, Chiu PL. Probing Structural Perturbation of Biomolecules by Extracting Cryo-EM Data Heterogeneity. Biomolecules 2022; 12:biom12050628. [PMID: 35625556 PMCID: PMC9138638 DOI: 10.3390/biom12050628] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Revised: 04/15/2022] [Accepted: 04/18/2022] [Indexed: 02/04/2023] Open
Abstract
Single-particle cryogenic electron microscopy (cryo-EM) has become an indispensable tool to probe high-resolution structural detail of biomolecules. It enables direct visualization of the biomolecules and opens a possibility for averaging molecular images to reconstruct a three-dimensional Coulomb potential density map. Newly developed algorithms for data analysis allow for the extraction of structural heterogeneity from a massive and low signal-to-noise-ratio (SNR) cryo-EM dataset, expanding our understanding of multiple conformational states, or further implications in dynamics, of the target biomolecule. This review provides an overview that briefly describes the workflow of single-particle cryo-EM, including imaging and data processing, and new methods developed for analyzing the data heterogeneity to understand the structural variability of biomolecules.
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15
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Rabuck-Gibbons JN, Lyumkis D, Williamson JR. Quantitative mining of compositional heterogeneity in cryo-EM datasets of ribosome assembly intermediates. Structure 2022; 30:498-509.e4. [PMID: 34990602 PMCID: PMC9891661 DOI: 10.1016/j.str.2021.12.005] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Revised: 09/02/2021] [Accepted: 12/09/2021] [Indexed: 02/03/2023]
Abstract
Single-particle cryoelectron microscopy (cryo-EM) offers a unique opportunity to characterize macromolecular structural heterogeneity by virtue of its ability to place distinct particle populations into different groups through computational classification. However, there is a dearth of tools for surveying the heterogeneity landscape, quantitatively analyzing heterogeneous particle populations after classification, deciding how many unique classes are represented by the data, and accurately cross-comparing reconstructions. Here, we develop a workflow that contains discovery and analysis modules to quantitatively mine cryo-EM data for sets of structures with maximal diversity. This workflow was applied to a dataset of E. coli 50S ribosome assembly intermediates, which are characterized by significant structural heterogeneity. We identified more detailed branchpoints in the assembly process and characterized the interactions of an assembly factor with immature intermediates. While the tools described here were developed for ribosome assembly, they should be broadly applicable to the analysis of other heterogeneous cryo-EM datasets.
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Affiliation(s)
- Jessica N Rabuck-Gibbons
- Department of Integrative Structural and Computational Biology, Department of Chemistry, and The Skaggs Institute for Chemical Biology, The Scripps Research Institute, La Jolla, CA 92037, USA
| | - Dmitry Lyumkis
- Department of Integrative Structural and Computational Biology, Department of Chemistry, and The Skaggs Institute for Chemical Biology, The Scripps Research Institute, La Jolla, CA 92037, USA; Laboratory of Genetics and Helmsley Center for Genomic Medicine, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - James R Williamson
- Department of Integrative Structural and Computational Biology, Department of Chemistry, and The Skaggs Institute for Chemical Biology, The Scripps Research Institute, La Jolla, CA 92037, USA.
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16
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Gomez-Blanco J, Kaur S, Strauss M, Vargas J. Hierarchical autoclassification of cryo-EM samples and macromolecular energy landscape determination. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 216:106673. [PMID: 35149430 DOI: 10.1016/j.cmpb.2022.106673] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Revised: 01/10/2022] [Accepted: 01/28/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND AND OBJECTIVE Cryo-electron microscopy using single particle analysis is a powerful technique for obtaining 3D reconstructions of macromolecules in near native conditions. One of its major advances is its capacity to reveal conformations of dynamic molecular complexes. Most popular and successful current approaches to analyzing heterogeneous complexes are founded on Bayesian inference. However, these 3D classification methods require the tuning of specific parameters by the user and the use of complicated 3D re-classification procedures for samples affected by extensive heterogeneity. Thus, the success of these approaches highly depends on the user experience. We introduce a robust approach to identify many different conformations presented in a cryo-EM dataset based on Bayesian inference through Relion classification methods that does not require tuning of parameters and reclassification strategies. METHODS The algorithm allows both 2D and 3D classification and is based on a hierarchical clustering approach that runs automatically without requiring typical inputs, such as the number of conformations present in the dataset or the required classification iterations. This approach is applied to robustly determine the energy landscapes of macromolecules. RESULTS We tested the performance of the methods proposed here using four different datasets, comprising structurally homogeneous and highly heterogeneous cases. In all cases, the approach provided excellent results. The routines are publicly available as part of the CryoMethods plugin included in the Scipion package. CONCLUSIONS Our results show that the proposed method can be used to align and classify homogeneous and heterogeneous datasets without requiring previous alignment information or any prior knowledge about the number of co-existing conformations. The approach can be used for both 2D and 3D autoclassification and only requires an initial volume. In addition, the approach is robust to the "attractor" problem providing many different conformations/views for samples affected by extensive heterogeneity. The obtained 3D classes can render high resolution 3D structures, while the obtained energy landscapes can be used to determine structural trajectories.
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Affiliation(s)
- J Gomez-Blanco
- Departamento de Óptica, Universidad Complutense de Madrid, Plaza de Ciencias 1, 28040, Spain
| | - S Kaur
- Department of Anatomy and Cell Biology, McGill University, 3640 Rue University, Montréal, QC H3A 0C7, Canada
| | - M Strauss
- Department of Anatomy and Cell Biology, McGill University, 3640 Rue University, Montréal, QC H3A 0C7, Canada
| | - J Vargas
- Departamento de Óptica, Universidad Complutense de Madrid, Plaza de Ciencias 1, 28040, Spain.
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17
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Sorzano COS, Jiménez-Moreno A, Maluenda D, Martínez M, Ramírez-Aportela E, Krieger J, Melero R, Cuervo A, Conesa J, Filipovic J, Conesa P, del Caño L, Fonseca YC, Jiménez-de la Morena J, Losana P, Sánchez-García R, Strelak D, Fernández-Giménez E, de Isidro-Gómez FP, Herreros D, Vilas JL, Marabini R, Carazo JM. On bias, variance, overfitting, gold standard and consensus in single-particle analysis by cryo-electron microscopy. Acta Crystallogr D Struct Biol 2022; 78:410-423. [PMID: 35362465 PMCID: PMC8972802 DOI: 10.1107/s2059798322001978] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Accepted: 02/18/2022] [Indexed: 12/05/2022] Open
Abstract
Single-particle analysis (SPA) by cryo-electron microscopy comprises the estimation of many parameters along its image-processing pipeline. Overfitting observed in SPA is normally due to misestimated parameters, and the only way to identify these is by comparing the estimates of multiple algorithms or, at least, multiple executions of the same algorithm. Cryo-electron microscopy (cryoEM) has become a well established technique to elucidate the 3D structures of biological macromolecules. Projection images from thousands of macromolecules that are assumed to be structurally identical are combined into a single 3D map representing the Coulomb potential of the macromolecule under study. This article discusses possible caveats along the image-processing path and how to avoid them to obtain a reliable 3D structure. Some of these problems are very well known in the community. These may be referred to as sample-related (such as specimen denaturation at interfaces or non-uniform projection geometry leading to underrepresented projection directions). The rest are related to the algorithms used. While some have been discussed in depth in the literature, such as the use of an incorrect initial volume, others have received much less attention. However, they are fundamental in any data-analysis approach. Chiefly among them, instabilities in estimating many of the key parameters that are required for a correct 3D reconstruction that occur all along the processing workflow are referred to, which may significantly affect the reliability of the whole process. In the field, the term overfitting has been coined to refer to some particular kinds of artifacts. It is argued that overfitting is a statistical bias in key parameter-estimation steps in the 3D reconstruction process, including intrinsic algorithmic bias. It is also shown that common tools (Fourier shell correlation) and strategies (gold standard) that are normally used to detect or prevent overfitting do not fully protect against it. Alternatively, it is proposed that detecting the bias that leads to overfitting is much easier when addressed at the level of parameter estimation, rather than detecting it once the particle images have been combined into a 3D map. Comparing the results from multiple algorithms (or at least, independent executions of the same algorithm) can detect parameter bias. These multiple executions could then be averaged to give a lower variance estimate of the underlying parameters.
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18
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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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19
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Wu JG, Yan Y, Zhang DX, Liu BW, Zheng QB, Xie XL, Liu SQ, Ge SX, Hou ZG, Xia NS. Machine Learning for Structure Determination in Single-Particle Cryo-Electron Microscopy: A Systematic Review. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2022; 33:452-472. [PMID: 34932487 DOI: 10.1109/tnnls.2021.3131325] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Recently, single-particle cryo-electron microscopy (cryo-EM) has become an indispensable method for determining macromolecular structures at high resolution to deeply explore the relevant molecular mechanism. Its recent breakthrough is mainly because of the rapid advances in hardware and image processing algorithms, especially machine learning. As an essential support of single-particle cryo-EM, machine learning has powered many aspects of structure determination and greatly promoted its development. In this article, we provide a systematic review of the applications of machine learning in this field. Our review begins with a brief introduction of single-particle cryo-EM, followed by the specific tasks and challenges of its image processing. Then, focusing on the workflow of structure determination, we describe relevant machine learning algorithms and applications at different steps, including particle picking, 2-D clustering, 3-D reconstruction, and other steps. As different tasks exhibit distinct characteristics, we introduce the evaluation metrics for each task and summarize their dynamics of technology development. Finally, we discuss the open issues and potential trends in this promising field.
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20
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Semchonok DA, Mondal J, Cooper CJ, Schlum K, Li M, Amin M, Sorzano CO, Ramírez-Aportela E, Kastritis PL, Boekema EJ, Guskov A, Bruce BD. Cryo-EM structure of a tetrameric photosystem I from Chroococcidiopsis TS-821, a thermophilic, unicellular, non-heterocyst-forming cyanobacterium. PLANT COMMUNICATIONS 2022; 3:100248. [PMID: 35059628 PMCID: PMC8760143 DOI: 10.1016/j.xplc.2021.100248] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Revised: 09/03/2021] [Accepted: 10/08/2021] [Indexed: 05/19/2023]
Abstract
Photosystem I (PSI) is one of two photosystems involved in oxygenic photosynthesis. PSI of cyanobacteria exists in monomeric, trimeric, and tetrameric forms, in contrast to the strictly monomeric form of PSI in plants and algae. The tetrameric organization raises questions about its structural, physiological, and evolutionary significance. Here we report the ∼3.72 Å resolution cryo-electron microscopy structure of tetrameric PSI from the thermophilic, unicellular cyanobacterium Chroococcidiopsis sp. TS-821. The structure resolves 44 subunits and 448 cofactor molecules. We conclude that the tetramer is arranged via two different interfaces resulting from a dimer-of-dimers organization. The localization of chlorophyll molecules permits an excitation energy pathway within and between adjacent monomers. Bioinformatics analysis reveals conserved regions in the PsaL subunit that correlate with the oligomeric state. Tetrameric PSI may function as a key evolutionary step between the trimeric and monomeric forms of PSI organization in photosynthetic organisms.
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Affiliation(s)
- Dmitry A. Semchonok
- Groningen Biomolecular Sciences & Biotechnology Institute, University of Groningen, Groningen, the Netherlands
| | - Jyotirmoy Mondal
- Biochemistry & Cellular and Molecular Biology Department, University of Tennessee, Knoxville, TN, USA
| | - Connor J. Cooper
- Program in Genome Science and Technology, University of Tennessee, Knoxville, TN, USA
| | - Katrina Schlum
- Program in Genome Science and Technology, University of Tennessee, Knoxville, TN, USA
| | - Meng Li
- Biochemistry & Cellular and Molecular Biology Department, University of Tennessee, Knoxville, TN, USA
- Bredesen Center for Interdisciplinary Research & Education, University of Tennessee, Knoxville, TN, USA
| | - Muhamed Amin
- Department of Sciences, University College Groningen, Groningen, the Netherlands
| | - Carlos O.S. Sorzano
- Biocomputing Unit, National Center for Biotechnology (CSIC), Darwin 3, Campus Universidad Autónoma de Madrid, Cantoblanco, 28049 Madrid, Spain
- Universidad CEU San Pablo, Campus Urb. Montepríncipe, Boadilla del Monte, 28668 Madrid, Spain
| | - Erney Ramírez-Aportela
- Biocomputing Unit, National Center for Biotechnology (CSIC), Darwin 3, Campus Universidad Autónoma de Madrid, Cantoblanco, 28049 Madrid, Spain
| | - Panagiotis L. Kastritis
- Institute of Biochemistry and Biotechnology, Martin Luther University Halle-Wittenberg, Halle/Saale, Germany
| | - Egbert J. Boekema
- Groningen Biomolecular Sciences & Biotechnology Institute, University of Groningen, Groningen, the Netherlands
| | - Albert Guskov
- Groningen Biomolecular Sciences & Biotechnology Institute, University of Groningen, Groningen, the Netherlands
| | - Barry D. Bruce
- Biochemistry & Cellular and Molecular Biology Department, University of Tennessee, Knoxville, TN, USA
- Program in Genome Science and Technology, University of Tennessee, Knoxville, TN, USA
- Bredesen Center for Interdisciplinary Research & Education, University of Tennessee, Knoxville, TN, USA
- Microbiology Department, University of Tennessee, Knoxville, TN, USA
- Corresponding author
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21
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Hirn M, Little A. Wavelet invariants for statistically robust multi-reference alignment. INFORMATION AND INFERENCE : A JOURNAL OF THE IMA 2021; 10:1287-1351. [PMID: 35070296 PMCID: PMC8782248 DOI: 10.1093/imaiai/iaaa016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
We propose a nonlinear, wavelet-based signal representation that is translation invariant and robust to both additive noise and random dilations. Motivated by the multi-reference alignment problem and generalizations thereof, we analyze the statistical properties of this representation given a large number of independent corruptions of a target signal. We prove the nonlinear wavelet-based representation uniquely defines the power spectrum but allows for an unbiasing procedure that cannot be directly applied to the power spectrum. After unbiasing the representation to remove the effects of the additive noise and random dilations, we recover an approximation of the power spectrum by solving a convex optimization problem, and thus reduce to a phase retrieval problem. Extensive numerical experiments demonstrate the statistical robustness of this approximation procedure.
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Affiliation(s)
- Matthew Hirn
- Department of Computational Mathematics, Science and Engineering, Department of Mathematics and Center for Quantum Computing, Science and Engineering, Michigan State University, East Lansing, MI 48824
| | - Anna Little
- Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, MI 48824
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22
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A Fast Image Alignment Approach for 2D Classification of Cryo-EM Images Using Spectral Clustering. Curr Issues Mol Biol 2021; 43:1652-1668. [PMID: 34698131 PMCID: PMC8928942 DOI: 10.3390/cimb43030117] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Revised: 10/14/2021] [Accepted: 10/14/2021] [Indexed: 01/22/2023] Open
Abstract
Three-dimensional (3D) reconstruction in single-particle cryo-electron microscopy (cryo-EM) is a significant technique for recovering the 3D structure of proteins or other biological macromolecules from their two-dimensional (2D) noisy projection images taken from unknown random directions. Class averaging in single-particle cryo-EM is an important procedure for producing high-quality initial 3D structures, where image alignment is a fundamental step. In this paper, an efficient image alignment algorithm using 2D interpolation in the frequency domain of images is proposed to improve the estimation accuracy of alignment parameters of rotation angles and translational shifts between the two projection images, which can obtain subpixel and subangle accuracy. The proposed algorithm firstly uses the Fourier transform of two projection images to calculate a discrete cross-correlation matrix and then performs the 2D interpolation around the maximum value in the cross-correlation matrix. The alignment parameters are directly determined according to the position of the maximum value in the cross-correlation matrix after interpolation. Furthermore, the proposed image alignment algorithm and a spectral clustering algorithm are used to compute class averages for single-particle 3D reconstruction. The proposed image alignment algorithm is firstly tested on a Lena image and two cryo-EM datasets. Results show that the proposed image alignment algorithm can estimate the alignment parameters accurately and efficiently. The proposed method is also used to reconstruct preliminary 3D structures from a simulated cryo-EM dataset and a real cryo-EM dataset and to compare them with RELION. Experimental results show that the proposed method can obtain more high-quality class averages than RELION and can obtain higher reconstruction resolution than RELION even without iteration.
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23
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Chen YX, Xie R, Yang Y, He L, Feng D, Shen HB. Fast Cryo-EM Image Alignment Algorithm Using Power Spectrum Features. J Chem Inf Model 2021; 61:4795-4806. [PMID: 34523929 DOI: 10.1021/acs.jcim.1c00745] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Cryo-electron microscopy (cryo-EM) single-particle image analysis is a powerful technique to resolve structures of biomacromolecules, while the challenge is that the cryo-EM image is of a low signal-to-noise ratio. For both two-dimensional image analysis and three-dimensional density map analysis, image alignment is an important step to improve the precision of the image distance calculation. In this paper, we introduce a new algorithm for performing two-dimensional pairwise alignment for cryo-EM particle images, which is based on the Fourier transform and power spectrum analysis. Compared to the existing heuristic iterative alignment methods, our method utilizes the signal distribution and signal feature on images' power spectrum to directly compute the alignment parameters. It does not require iterative computations and is robust against the cryo-EM image noise. Both theoretical analysis and experimental results suggest that our power-spectrum-feature-based alignment method is highly computational-efficient and is capable of offering effective alignment results. This new alignment algorithm is publicly available at: www.csbio.sjtu.edu.cn/bioinf/EMAF/for academic use.
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Affiliation(s)
- Yu-Xuan Chen
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China
| | - Rui Xie
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China
| | - Yang Yang
- Department of Computer Science, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Lin He
- Instrumental Analysis Center, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Dagan Feng
- School of Computer Science, University of Sydney, Sydney 2006, Australia
| | - Hong-Bin Shen
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China
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24
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Sorzano COS, Jiménez-Moreno A, Maluenda D, Ramírez-Aportela E, Martínez M, Cuervo A, Melero R, Conesa JJ, Sánchez-García R, Strelak D, Filipovic J, Fernández-Giménez E, de Isidro-Gómez F, Herreros D, Conesa P, Del Caño L, Fonseca Y, de la Morena JJ, Macías JR, Losana P, Marabini R, Carazo JM. Image Processing in Cryo-Electron Microscopy of Single Particles: The Power of Combining Methods. Methods Mol Biol 2021; 2305:257-289. [PMID: 33950394 DOI: 10.1007/978-1-0716-1406-8_13] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Cryo-electron microscopy has established as a mature structural biology technique to elucidate the three-dimensional structure of biological macromolecules. The Coulomb potential of the sample is imaged by an electron beam, and fast semi-conductor detectors produce movies of the sample under study. These movies have to be further processed by a whole pipeline of image-processing algorithms that produce the final structure of the macromolecule. In this chapter, we illustrate this whole processing pipeline putting in value the strength of "meta algorithms," which are the combination of several algorithms, each one with different mathematical rationale, in order to distinguish correctly from incorrectly estimated parameters. We show how this strategy leads to superior performance of the whole pipeline as well as more confident assessments about the reconstructed structures. The "meta algorithms" strategy is common to many fields and, in particular, it has provided excellent results in bioinformatics. We illustrate this combination using the workflow engine, Scipion.
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Affiliation(s)
| | | | | | | | | | - Ana Cuervo
- National Centre for Biotechnology (CSIC), Madrid, Spain
| | - Robert Melero
- National Centre for Biotechnology (CSIC), Madrid, Spain
| | | | | | - David Strelak
- National Centre for Biotechnology (CSIC), Madrid, Spain
| | | | | | | | | | - Pablo Conesa
- National Centre for Biotechnology (CSIC), Madrid, Spain
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25
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Rashid I, Hammel M, Sverzhinsky A, Tsai MS, Pascal JM, Tainer JA, Tomkinson AE. Direct interaction of DNA repair protein tyrosyl DNA phosphodiesterase 1 and the DNA ligase III catalytic domain is regulated by phosphorylation of its flexible N-terminus. J Biol Chem 2021; 297:100921. [PMID: 34181949 PMCID: PMC8318918 DOI: 10.1016/j.jbc.2021.100921] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Revised: 06/10/2021] [Accepted: 06/23/2021] [Indexed: 02/07/2023] Open
Abstract
Tyrosyl DNA phosphodiesterase 1 (TDP1) and DNA Ligase IIIα (LigIIIα) are key enzymes in single-strand break (SSB) repair. TDP1 removes 3'-tyrosine residues remaining after degradation of DNA topoisomerase (TOP) 1 cleavage complexes trapped by either DNA lesions or TOP1 inhibitors. It is not known how TDP1 is linked to subsequent processing and LigIIIα-catalyzed joining of the SSB. Here we define a direct interaction between the TDP1 catalytic domain and the LigIII DNA-binding domain (DBD) regulated by conformational changes in the unstructured TDP1 N-terminal region induced by phosphorylation and/or alterations in amino acid sequence. Full-length and N-terminally truncated TDP1 are more effective at correcting SSB repair defects in TDP1 null cells compared with full-length TDP1 with amino acid substitutions of an N-terminal serine residue phosphorylated in response to DNA damage. TDP1 forms a stable complex with LigIII170-755, as well as full-length LigIIIα alone or in complex with the DNA repair scaffold protein XRCC1. Small-angle X-ray scattering and negative stain electron microscopy combined with mapping of the interacting regions identified a TDP1/LigIIIα compact dimer of heterodimers in which the two LigIII catalytic cores are positioned in the center, whereas the two TDP1 molecules are located at the edges of the core complex flanked by highly flexible regions that can interact with other repair proteins and SSBs. As TDP1and LigIIIα together repair adducts caused by TOP1 cancer chemotherapy inhibitors, the defined interaction architecture and regulation of this enzyme complex provide insights into a key repair pathway in nonmalignant and cancer cells.
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Affiliation(s)
- Ishtiaque Rashid
- Departments of Internal Medicine, Molecular Genetics and Microbiology and the University of New Mexico Comprehensive Cancer Center, University of New Mexico, Albuquerque, New Mexico, USA
| | - Michal Hammel
- Molecular Biophysics & Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA
| | - Aleksandr Sverzhinsky
- Department of Biochemistry and Molecular Medicine, Université de Montréal, Montréal, Québec, Canada
| | - Miaw-Sheue Tsai
- Molecular Biophysics & Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA
| | - John M Pascal
- Department of Biochemistry and Molecular Medicine, Université de Montréal, Montréal, Québec, Canada
| | - John A Tainer
- Departments of Cancer Biology and of Molecular and Cellular Oncology, University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
| | - Alan E Tomkinson
- Departments of Internal Medicine, Molecular Genetics and Microbiology and the University of New Mexico Comprehensive Cancer Center, University of New Mexico, Albuquerque, New Mexico, USA.
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26
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Lapenta F, Aupič J, Vezzoli M, Strmšek Ž, Da Vela S, Svergun DI, Carazo JM, Melero R, Jerala R. Self-assembly and regulation of protein cages from pre-organised coiled-coil modules. Nat Commun 2021; 12:939. [PMID: 33574245 PMCID: PMC7878516 DOI: 10.1038/s41467-021-21184-6] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2020] [Accepted: 01/13/2021] [Indexed: 11/09/2022] Open
Abstract
Coiled-coil protein origami (CCPO) is a modular strategy for the de novo design of polypeptide nanostructures. CCPO folds are defined by the sequential order of concatenated orthogonal coiled-coil (CC) dimer-forming peptides, where a single-chain protein is programmed to fold into a polyhedral cage. Self-assembly of CC-based nanostructures from several chains, similarly as in DNA nanotechnology, could facilitate the design of more complex assemblies and the introduction of functionalities. Here, we show the design of a de novo triangular bipyramid fold comprising 18 CC-forming segments and define the strategy for the two-chain self-assembly of the bipyramidal cage from asymmetric and pseudo-symmetric pre-organised structural modules. In addition, by introducing a protease cleavage site and masking the interfacial CC-forming segments in the two-chain bipyramidal cage, we devise a proteolysis-mediated conformational switch. This strategy could be extended to other modular protein folds, facilitating the construction of dynamic multi-chain CC-based complexes. Coiled-coil protein origami is a strategy for the de novo design of polypeptide nanostructures based on coiled-coil dimer forming peptides, where a single chain protein folds into a polyhedral cage. Here, the authors design a single-chain triangular bipyramid and also demonstrate that the bipyramid can be self-assembled as a heterodimeric complex, comprising pre-defined subunits.
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Affiliation(s)
- Fabio Lapenta
- Department of Synthetic Biology and Immunology, National Institute of Chemistry, Ljubljana, Slovenia.,EN-FIST Centre of Excellence, Ljubljana, Slovenia
| | - Jana Aupič
- Department of Synthetic Biology and Immunology, National Institute of Chemistry, Ljubljana, Slovenia
| | - Marco Vezzoli
- Department of Chemistry, Life Sciences and Environmental Sustainability, University of Parma, Parma, Italy
| | - Žiga Strmšek
- Department of Synthetic Biology and Immunology, National Institute of Chemistry, Ljubljana, Slovenia
| | | | | | | | - Roberto Melero
- Centro Nacional de Biotecnología (CNB-CSIC), Madrid, Spain
| | - Roman Jerala
- Department of Synthetic Biology and Immunology, National Institute of Chemistry, Ljubljana, Slovenia. .,EN-FIST Centre of Excellence, Ljubljana, Slovenia.
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Lei H, Yang Y. CDAE: A Cascade of Denoising Autoencoders for Noise Reduction in the Clustering of Single-Particle Cryo-EM Images. Front Genet 2021; 11:627746. [PMID: 33552141 PMCID: PMC7854571 DOI: 10.3389/fgene.2020.627746] [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: 11/10/2020] [Accepted: 12/21/2020] [Indexed: 11/16/2022] Open
Abstract
As an emerging technology, cryo-electron microscopy (cryo-EM) has attracted more and more research interests from both structural biology and computer science, because many challenging computational tasks are involved in the processing of cryo-EM images. An important image processing step is to cluster the 2D cryo-EM images according to their projection angles, then the cluster mean images are used for the subsequent 3D reconstruction. However, cryo-EM images are quite noisy and denoising them is not easy, because the noise is a complicated mixture from samples and hardware. In this study, we design an effective cryo-EM image denoising model, CDAE, i.e., a cascade of denoising autoencoders. The new model comprises stacked blocks of deep neural networks to reduce noise in a progressive manner. Each block contains a convolutional autoencoder, pre-trained by simulated data of different SNRs and fine-tuned by target data set. We assess this new model on three simulated test sets and a real data set. CDAE achieves very competitive PSNR (peak signal-to-noise ratio) in the comparison of the state-of-the-art image denoising methods. Moreover, the denoised images have significantly enhanced clustering results compared to original image features or high-level abstraction features obtained by other deep neural networks. Both quantitative and visualized results demonstrate the good performance of CDAE for the noise reduction in clustering single-particle cryo-EM images.
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Affiliation(s)
- Houchao Lei
- Center for Brain-Like Computing and Machine Intelligence, Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Yang Yang
- Center for Brain-Like Computing and Machine Intelligence, Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China
- Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering, Shanghai, China
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28
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Sorzano COS, Semchonok D, Lin SC, Lo YC, Vilas JL, Jiménez-Moreno A, Gragera M, Vacca S, Maluenda D, Martínez M, Ramírez-Aportela E, Melero R, Cuervo A, Conesa JJ, Conesa P, Losana P, Caño LD, de la Morena JJ, Fonseca YC, Sánchez-García R, Strelak D, Fernández-Giménez E, de Isidro F, Herreros D, Kastritis PL, Marabini R, Bruce BD, Carazo JM. Algorithmic robustness to preferred orientations in single particle analysis by CryoEM. J Struct Biol 2021; 213:107695. [PMID: 33421545 DOI: 10.1016/j.jsb.2020.107695] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Revised: 12/18/2020] [Accepted: 12/24/2020] [Indexed: 01/30/2023]
Abstract
The presence of preferred orientations in single particle analysis (SPA) by cryo-Electron Microscopy (cryoEM) is currently one of the hurdles preventing many structural analyses from yielding high-resolution structures. Although the existence of preferred orientations is mostly related to the grid preparation, in this technical note, we show that some image processing algorithms used for angular assignment and three-dimensional (3D) reconstruction are more robust than others to these detrimental conditions. We exemplify this argument with three different data sets in which the presence of preferred orientations hindered achieving a 3D reconstruction without artifacts or, even worse, a 3D reconstruction could never be achieved.
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Affiliation(s)
- C O S Sorzano
- Biocomputing Unit, Centro Nacional de Biotecnologia (CNB-CSIC), Darwin, 3, Campus Universidad Autonoma, 28049 Cantoblanco, Madrid, Spain.
| | - D Semchonok
- ZIK HALOMEM & Institute of Biochemistry and Biotechnology, Martin Luther University Halle-Wittenberg, Biozentrum, Halle (Saale), Germany
| | - S-C Lin
- Genomics Research Center, Academia Sinica, Taipei 11529, Taiwan
| | - Y-C Lo
- Dept. Biotechnology and Bioindustry Sciences, College of Bioscience and Biotechnology, National Cheng Kung University, Tainan 70101, Taiwan
| | - J L Vilas
- Dept. of Biomedical Engineering, Yale University, New Haven, United States
| | - A Jiménez-Moreno
- Biocomputing Unit, Centro Nacional de Biotecnologia (CNB-CSIC), Darwin, 3, Campus Universidad Autonoma, 28049 Cantoblanco, Madrid, Spain
| | - M Gragera
- Biocomputing Unit, Centro Nacional de Biotecnologia (CNB-CSIC), Darwin, 3, Campus Universidad Autonoma, 28049 Cantoblanco, Madrid, Spain
| | - S Vacca
- Dept. of Biochemistry, Univ. Zurich, Winterthurerstr. 190, CH-8057 Zurich, Switzerland
| | - D Maluenda
- Biocomputing Unit, Centro Nacional de Biotecnologia (CNB-CSIC), Darwin, 3, Campus Universidad Autonoma, 28049 Cantoblanco, Madrid, Spain
| | - M Martínez
- Biocomputing Unit, Centro Nacional de Biotecnologia (CNB-CSIC), Darwin, 3, Campus Universidad Autonoma, 28049 Cantoblanco, Madrid, Spain
| | - E Ramírez-Aportela
- Biocomputing Unit, Centro Nacional de Biotecnologia (CNB-CSIC), Darwin, 3, Campus Universidad Autonoma, 28049 Cantoblanco, Madrid, Spain
| | - R Melero
- Biocomputing Unit, Centro Nacional de Biotecnologia (CNB-CSIC), Darwin, 3, Campus Universidad Autonoma, 28049 Cantoblanco, Madrid, Spain
| | - A Cuervo
- Biocomputing Unit, Centro Nacional de Biotecnologia (CNB-CSIC), Darwin, 3, Campus Universidad Autonoma, 28049 Cantoblanco, Madrid, Spain
| | - J J Conesa
- Biocomputing Unit, Centro Nacional de Biotecnologia (CNB-CSIC), Darwin, 3, Campus Universidad Autonoma, 28049 Cantoblanco, Madrid, Spain
| | - P Conesa
- Biocomputing Unit, Centro Nacional de Biotecnologia (CNB-CSIC), Darwin, 3, Campus Universidad Autonoma, 28049 Cantoblanco, Madrid, Spain
| | - P Losana
- Biocomputing Unit, Centro Nacional de Biotecnologia (CNB-CSIC), Darwin, 3, Campus Universidad Autonoma, 28049 Cantoblanco, Madrid, Spain
| | - L Del Caño
- Biocomputing Unit, Centro Nacional de Biotecnologia (CNB-CSIC), Darwin, 3, Campus Universidad Autonoma, 28049 Cantoblanco, Madrid, Spain
| | - J Jiménez de la Morena
- Biocomputing Unit, Centro Nacional de Biotecnologia (CNB-CSIC), Darwin, 3, Campus Universidad Autonoma, 28049 Cantoblanco, Madrid, Spain
| | - Y C Fonseca
- Biocomputing Unit, Centro Nacional de Biotecnologia (CNB-CSIC), Darwin, 3, Campus Universidad Autonoma, 28049 Cantoblanco, Madrid, Spain
| | - R Sánchez-García
- Biocomputing Unit, Centro Nacional de Biotecnologia (CNB-CSIC), Darwin, 3, Campus Universidad Autonoma, 28049 Cantoblanco, Madrid, Spain
| | - D Strelak
- Biocomputing Unit, Centro Nacional de Biotecnologia (CNB-CSIC), Darwin, 3, Campus Universidad Autonoma, 28049 Cantoblanco, Madrid, Spain
| | - E Fernández-Giménez
- Biocomputing Unit, Centro Nacional de Biotecnologia (CNB-CSIC), Darwin, 3, Campus Universidad Autonoma, 28049 Cantoblanco, Madrid, Spain
| | - F de Isidro
- Biocomputing Unit, Centro Nacional de Biotecnologia (CNB-CSIC), Darwin, 3, Campus Universidad Autonoma, 28049 Cantoblanco, Madrid, Spain
| | - D Herreros
- Biocomputing Unit, Centro Nacional de Biotecnologia (CNB-CSIC), Darwin, 3, Campus Universidad Autonoma, 28049 Cantoblanco, Madrid, Spain
| | - P L Kastritis
- Biocomputing Unit, Centro Nacional de Biotecnologia (CNB-CSIC), Darwin, 3, Campus Universidad Autonoma, 28049 Cantoblanco, Madrid, Spain
| | - R Marabini
- Escuela Politecnica Superior, Universidad Autónoma de Madrid, 28049 Cantoblanco, Madrid, Spain
| | - B D Bruce
- Dept. Biochemistry & Cellular and Molecular Biology, Univ. Tennessee Knoxville, Knoxville, TN 37996, United States
| | - J M Carazo
- Biocomputing Unit, Centro Nacional de Biotecnologia (CNB-CSIC), Darwin, 3, Campus Universidad Autonoma, 28049 Cantoblanco, Madrid, Spain
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The Smc5/6 Core Complex Is a Structure-Specific DNA Binding and Compacting Machine. Mol Cell 2020; 80:1025-1038.e5. [PMID: 33301731 DOI: 10.1016/j.molcel.2020.11.011] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2019] [Revised: 10/13/2020] [Accepted: 11/04/2020] [Indexed: 01/09/2023]
Abstract
The structural organization of chromosomes is a crucial feature that defines the functional state of genes and genomes. The extent of structural changes experienced by genomes of eukaryotic cells can be dramatic and spans several orders of magnitude. At the core of these changes lies a unique group of ATPases-the SMC proteins-that act as major effectors of chromosome behavior in cells. The Smc5/6 proteins play essential roles in the maintenance of genome stability, yet their mode of action is not fully understood. Here we show that the human Smc5/6 complex recognizes unusual DNA configurations and uses the energy of ATP hydrolysis to promote their compaction. Structural analyses reveal subunit interfaces responsible for the functionality of the Smc5/6 complex and how mutations in these regions may lead to chromosome breakage syndromes in humans. Collectively, our results suggest that the Smc5/6 complex promotes genome stability as a DNA micro-compaction machine.
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Alvira S, Watkins DW, Troman LA, Allen WJ, Lorriman JS, Degliesposti G, Cohen EJ, Beeby M, Daum B, Gold VAM, Skehel JM, Collinson I. Inter-membrane association of the Sec and BAM translocons for bacterial outer-membrane biogenesis. eLife 2020; 9:e60669. [PMID: 33146611 PMCID: PMC7695460 DOI: 10.7554/elife.60669] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Accepted: 11/03/2020] [Indexed: 12/24/2022] Open
Abstract
The outer-membrane of Gram-negative bacteria is critical for surface adhesion, pathogenicity, antibiotic resistance and survival. The major constituent - hydrophobic β-barrel Outer-Membrane Proteins (OMPs) - are first secreted across the inner-membrane through the Sec-translocon for delivery to periplasmic chaperones, for example SurA, which prevent aggregation. OMPs are then offloaded to the β-Barrel Assembly Machinery (BAM) in the outer-membrane for insertion and folding. We show the Holo-TransLocon (HTL) - an assembly of the protein-channel core-complex SecYEG, the ancillary sub-complex SecDF, and the membrane 'insertase' YidC - contacts BAM through periplasmic domains of SecDF and YidC, ensuring efficient OMP maturation. Furthermore, the proton-motive force (PMF) across the inner-membrane acts at distinct stages of protein secretion: (1) SecA-driven translocation through SecYEG and (2) communication of conformational changes via SecDF across the periplasm to BAM. The latter presumably drives efficient passage of OMPs. These interactions provide insights of inter-membrane organisation and communication, the importance of which is becoming increasingly apparent.
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Affiliation(s)
- Sara Alvira
- School of Biochemistry, University of BristolBristolUnited Kingdom
| | - Daniel W Watkins
- School of Biochemistry, University of BristolBristolUnited Kingdom
| | - Luca A Troman
- School of Biochemistry, University of BristolBristolUnited Kingdom
| | - William J Allen
- School of Biochemistry, University of BristolBristolUnited Kingdom
| | - James S Lorriman
- School of Biochemistry, University of BristolBristolUnited Kingdom
| | - Gianluca Degliesposti
- Biological Mass Spectrometry and Proteomics, MRC Laboratory of Molecular BiologyCambridgeUnited Kingdom
| | - Eli J Cohen
- Department of Life Sciences, Imperial College LondonLondonUnited Kingdom
| | - Morgan Beeby
- Department of Life Sciences, Imperial College LondonLondonUnited Kingdom
| | - Bertram Daum
- Living Systems Institute, University of ExeterExeterUnited Kingdom
- College of Life and Environmental Sciences, University of ExeterExeterUnited Kingdom
| | - Vicki AM Gold
- Living Systems Institute, University of ExeterExeterUnited Kingdom
- College of Life and Environmental Sciences, University of ExeterExeterUnited Kingdom
| | - J Mark Skehel
- Biological Mass Spectrometry and Proteomics, MRC Laboratory of Molecular BiologyCambridgeUnited Kingdom
| | - Ian Collinson
- School of Biochemistry, University of BristolBristolUnited Kingdom
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31
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Chung SC, Lin HH, Niu PY, Huang SH, Tu IP, Chang WH. Pre-pro is a fast pre-processor for single-particle cryo-EM by enhancing 2D classification. Commun Biol 2020; 3:508. [PMID: 32917929 PMCID: PMC7486923 DOI: 10.1038/s42003-020-01229-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2019] [Accepted: 07/13/2020] [Indexed: 01/04/2023] Open
Abstract
2D classification plays a pivotal role in analyzing single particle cryo-electron microscopy images. Here, we introduce a simple and loss-less pre-processor that incorporates a fast dimension-reduction (2SDR) de-noiser to enhance 2D classification. By implementing this 2SDR pre-processor prior to a representative classification algorithm like RELION and ISAC, we compare the performances with and without the pre-processor. Tests on multiple cryo-EM experimental datasets show the pre-processor can make classification faster, improve yield of good particles and increase the number of class-average images to generate better initial models. Testing on the nanodisc-embedded TRPV1 dataset with high heterogeneity using a 3D reconstruction workflow with an initial model from class-average images highlights the pre-processor improves the final resolution to 2.82 Å, close to 0.9 Nyquist. Those findings and analyses suggest the 2SDR pre-processor, of minimal cost, is widely applicable for boosting 2D classification, while its generalization to accommodate neural network de-noisers is envisioned.
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Affiliation(s)
- Szu-Chi Chung
- Institute of Statistical Science, Academia Sinica, 128 Academia Road, Section 2, Nankang, Taipei, 11529, Taiwan
| | - Hsin-Hung Lin
- Institute of Chemistry, Academia Sinica, 128 Academia Road, Section 2, Nankang, Taipei, 11529, Taiwan
| | - Po-Yao Niu
- Institute of Statistical Science, Academia Sinica, 128 Academia Road, Section 2, Nankang, Taipei, 11529, Taiwan
| | - Shih-Hsin Huang
- Institute of Chemistry, Academia Sinica, 128 Academia Road, Section 2, Nankang, Taipei, 11529, Taiwan
| | - I-Ping Tu
- Institute of Statistical Science, Academia Sinica, 128 Academia Road, Section 2, Nankang, Taipei, 11529, Taiwan.
| | - Wei-Hau Chang
- Institute of Chemistry, Academia Sinica, 128 Academia Road, Section 2, Nankang, Taipei, 11529, Taiwan.
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32
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Abstract
We present an approach for preparing cryo-electron microscopy (cryo-EM) grids to study short-lived molecular states. Using piezoelectric dispensing, two independent streams of ~50-pl droplets of sample are deposited within 10 ms of each other onto the surface of a nanowire EM grid, and the mixing reaction stops when the grid is vitrified in liquid ethane ~100 ms later. We demonstrate this approach for four biological systems where short-lived states are of high interest.
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33
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Abstract
Single-particle electron cryomicroscopy (cryo-EM) is an increasingly popular technique for elucidating the three-dimensional structure of proteins and other biologically significant complexes at near-atomic resolution. It is an imaging method that does not require crystallization and can capture molecules in their native states. In single-particle cryo-EM, the three-dimensional molecular structure needs to be determined from many noisy two-dimensional tomographic projections of individual molecules, whose orientations and positions are unknown. The high level of noise and the unknown pose parameters are two key elements that make reconstruction a challenging computational problem. Even more challenging is the inference of structural variability and flexible motions when the individual molecules being imaged are in different conformational states. This review discusses computational methods for structure determination by single-particle cryo-EM and their guiding principles from statistical inference, machine learning, and signal processing that also play a significant role in many other data science applications.
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Affiliation(s)
- Amit Singer
- Department of Mathematics and Program in Applied and Computational Mathematics, Princeton University, Princeton, NJ 08544, USA
| | - Fred J Sigworth
- Departments of Cellular and Molecular Physiology, Biomedical Engineering, and Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
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34
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Structural insights into influenza A virus ribonucleoproteins reveal a processive helical track as transcription mechanism. Nat Microbiol 2020; 5:727-734. [PMID: 32152587 DOI: 10.1038/s41564-020-0675-3] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2018] [Accepted: 01/24/2020] [Indexed: 01/10/2023]
Abstract
The influenza virus genome consists of eight viral ribonucleoproteins (vRNPs), each consisting of a copy of the polymerase, one of the genomic RNA segments and multiple copies of the nucleoprotein arranged in a double helical conformation. vRNPs are macromolecular machines responsible for messenger RNA synthesis and genome replication, that is, the formation of progeny vRNPs. Here, we describe the structural basis of the transcription process. The mechanism, which we call the 'processive helical track', is based on the extreme flexibility of the helical part of the vRNP that permits a sliding movement between both antiparallel nucleoprotein-RNA strands, thereby allowing the polymerase to move over the genome while bound to both RNA ends. Accordingly, we demonstrate that blocking this movement leads to inhibition of vRNP transcriptional activity. This mechanism also reveals a critical role of the nucleoprotein in maintaining the double helical structure throughout the copying process to make the RNA template accessible to the polymerase.
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35
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Shi J, Zeng X, Jiang R, Jiang T, Xu M. A simulated annealing approach for resolution guided homogeneous cryo-electron microscopy image selection. QUANTITATIVE BIOLOGY 2020; 8:51-63. [PMID: 32477613 PMCID: PMC7259590 DOI: 10.1007/s40484-019-0191-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2019] [Revised: 09/10/2019] [Accepted: 11/08/2019] [Indexed: 10/24/2022]
Abstract
BACKGROUND Cryo-electron microscopy (Cryo-EM) and tomography (Cryo-ET) have emerged as important imaging techniques for studying structures of macromolecular complexes. In 3D reconstruction of large macromolecular complexes, many 2D projection images of macromolecular complex particles are usually acquired with low signal-to-noise ratio. Therefore, it is meaningful to select multiple images containing the same structure with identical orientation. The selected images are averaged to produce a higher-quality representation of the underlying structure with improved resolution. Existing approaches of selecting such images have limited accuracy and speed. METHODS We propose a simulated annealing-based algorithm (SA) to pick the homogeneous image set with best average. Its performance is compared with two baseline methods based on both 2D and 3D datasets. When tested on simulated and experimental 3D Cryo-ET images of Ribosome complex, SA sometimes stopped at a local optimal solution. Restarting is applied to settle this difficulty and significantly improved the performance of SA on 3D datasets. RESULTS Experimented on simulated and experimental 2D Cryo-EM images of Ribosome complex datasets respectively with SNR = 10 and SNR = 0.5, our method achieved better accuracy in terms of F-measure, resolution score, and time cost than two baseline methods. Additionally, SA shows its superiority when the proportion of homogeneous images decreases. CONCLUSIONS SA is introduced for homogeneous image selection to realize higher accuracy with faster processing speed. Experiments on both simulated and real 2D Cryo-EM and 3D Cryo-ET images demonstrated that SA achieved expressively better performance. This approach serves as an important step for improving the resolution of structural recovery of macromolecular complexes captured by Cryo-EM and Cryo-ET.
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Affiliation(s)
- Jie Shi
- Department of Computer Science, The University of Hong Kong, Hong Kong 999077, China
| | - Xiangrui Zeng
- Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Rui Jiang
- Department of Automation, Tsinghua University, Beijing 100084, China
| | - Tao Jiang
- Department of Computer Science and Engineering, University of California-Riverside, Riverside, CA 92521, USA
| | - Min Xu
- Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA
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36
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Bendory T, Bartesaghi A, Singer A. Single-particle cryo-electron microscopy: Mathematical theory, computational challenges, and opportunities. IEEE SIGNAL PROCESSING MAGAZINE 2020; 37:58-76. [PMID: 32395065 PMCID: PMC7213211 DOI: 10.1109/msp.2019.2957822] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/09/2023]
Abstract
In recent years, an abundance of new molecular structures have been elucidated using cryo-electron microscopy (cryo-EM), largely due to advances in hardware technology and data processing techniques. Owing to these new exciting developments, cryo-EM was selected by Nature Methods as Method of the Year 2015, and the Nobel Prize in Chemistry 2017 was awarded to three pioneers in the field. The main goal of this article is to introduce the challenging and exciting computational tasks involved in reconstructing 3-D molecular structures by cryo-EM. Determining molecular structures requires a wide range of computational tools in a variety of fields, including signal processing, estimation and detection theory, high-dimensional statistics, convex and non-convex optimization, spectral algorithms, dimensionality reduction, and machine learning. The tools from these fields must be adapted to work under exceptionally challenging conditions, including extreme noise levels, the presence of missing data, and massively large datasets as large as several Terabytes. In addition, we present two statistical models: multi-reference alignment and multi-target detection, that abstract away much of the intricacies of cryo-EM, while retaining some of its essential features. Based on these abstractions, we discuss some recent intriguing results in the mathematical theory of cryo-EM, and delineate relations with group theory, invariant theory, and information theory.
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Affiliation(s)
- Tamir Bendory
- Tel Aviv University, Electrical Engineering, Tel Aviv, Israel
| | - Alberto Bartesaghi
- Computer Science, Biochemistry, and Electrical and Computer Engineering, Durham, NC, USA, Duke University
| | - Amit Singer
- Princeton University, Applied and Computational Mathematics, Princeton, NJ USA
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37
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Xie R, Chen YX, Cai JM, Yang Y, Shen HB. SPREAD: A Fully Automated Toolkit for Single-Particle Cryogenic Electron Microscopy Data 3D Reconstruction with Image-Network-Aided Orientation Assignment. J Chem Inf Model 2020; 60:2614-2625. [DOI: 10.1021/acs.jcim.9b01099] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Affiliation(s)
- Rui Xie
- Institute of Image Processing and Pattern Recognition and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Yu-Xuan Chen
- Institute of Image Processing and Pattern Recognition and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Jia-Ming Cai
- Institute of Image Processing and Pattern Recognition and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Yang Yang
- Department of Computer Science, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Hong-Bin Shen
- Institute of Image Processing and Pattern Recognition and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai Jiao Tong University, Shanghai 200240, China
- Department of Computer Science, Shanghai Jiao Tong University, Shanghai 200240, China
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38
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Saw WG, Wong CF, Dick T, Grüber G. Overexpression, purification, enzymatic and microscopic characterization of recombinant mycobacterial F-ATP synthase. Biochem Biophys Res Commun 2019; 522:374-380. [PMID: 31761325 DOI: 10.1016/j.bbrc.2019.11.098] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2019] [Accepted: 11/15/2019] [Indexed: 01/16/2023]
Abstract
The F-ATP synthase is an essential enzyme in mycobacteria, including the pathogenic Mycobacterium tuberculosis. Several new compounds in the TB-drug pipeline target the F-ATP synthase. In light of the importance and pharmacological attractiveness of this novel antibiotic target, tools have to be developed to generate a recombinant mycobacterial F1FO ATP synthase to achieve atomic insight and mutants for mechanistic and regulatory understanding as well as structure-based drug design. Here, we report the first genetically engineered, purified and enzymatically active recombinant M. smegmatis F1FO ATP synthase. The projected 2D- and 3D structures of the recombinant enzyme derived from negatively stained electron micrographs are presented. Furthermore, the first 2D projections from cryo-electron images are revealed, paving the way for an atomic resolution structure determination.
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Affiliation(s)
- Wuan-Geok Saw
- Nanyang Technological University, School of Biological Sciences, 60 Nanyang Drive, Singapore, 637551, Republic of Singapore
| | - Chui-Fann Wong
- Nanyang Technological University, School of Biological Sciences, 60 Nanyang Drive, Singapore, 637551, Republic of Singapore
| | - Thomas Dick
- Center for Discovery and Innovation, Hackensack Meridian Health, Nutley, NJ, USA; Department of Microbiology and Immunology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Republic of Singapore
| | - Gerhard Grüber
- Nanyang Technological University, School of Biological Sciences, 60 Nanyang Drive, Singapore, 637551, Republic of Singapore.
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39
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Harastani M, Sorzano COS, Jonić S. Hybrid Electron Microscopy Normal Mode Analysis with Scipion. Protein Sci 2019; 29:223-236. [PMID: 31693263 DOI: 10.1002/pro.3772] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2019] [Revised: 11/03/2019] [Accepted: 11/04/2019] [Indexed: 12/12/2022]
Abstract
Hybrid Electron Microscopy Normal Mode Analysis (HEMNMA) method was introduced in 2014. HEMNMA computes normal modes of a reference model (an atomic structure or an electron microscopy map) of a molecular complex and uses this model and its normal modes to analyze single-particle images of the complex to obtain information on its continuous conformational changes, by determining the full distribution of conformational variability from the images. An advantage of HEMNMA is a simultaneous determination of all parameters of each image (particle conformation, orientation, and shift) through their iterative optimization, which allows applications of HEMNMA even when the effects of conformational changes dominate those of orientational changes. HEMNMA was first implemented in Xmipp and was using MATLAB for statistical analysis of obtained conformational distributions and for fitting of underlying trajectories of conformational changes. A HEMNMA implementation independent of MATLAB is now available as part of a plugin of Scipion V2.0 (http://scipion.i2pc.es). This plugin, named ContinuousFlex, can be installed by following the instructions at https://pypi.org/project/scipion-em-continuousflex. In this article, we present this new HEMNMA software, which is user-friendly, totally free, and open-source. STATEMENT FOR A BROADER AUDIENCE: This article presents Hybrid Electron Microscopy Normal Mode Analysis (HEMNMA) software that allows analyzing single-particle images of a complex to obtain information on continuous conformational changes of the complex, by determining the full distribution of conformational variability from the images. The HEMNMA software is user-friendly, totally free, open-source, and available as part of ContinuousFlex plugin (https://pypi.org/project/scipion-em-continuousflex) of Scipion V2.0 (http://scipion.i2pc.es).
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Affiliation(s)
- Mohamad Harastani
- Sorbonne Université, UMR CNRS 7590, Muséum National d'Histoire Naturelle, IRD, Institut de Minéralogie, de Physique des Matériaux et de Cosmochimie, IMPMC, Paris, France
| | | | - Slavica Jonić
- Sorbonne Université, UMR CNRS 7590, Muséum National d'Histoire Naturelle, IRD, Institut de Minéralogie, de Physique des Matériaux et de Cosmochimie, IMPMC, Paris, France
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40
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Ma C, Bendory T, Boumal N, Sigworth F, Singer A. Heterogeneous multireference alignment for images with application to 2-D classification in single particle reconstruction. IEEE TRANSACTIONS ON IMAGE PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 2019; 29:1699-1710. [PMID: 31613760 DOI: 10.1109/tip.2019.2945686] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Motivated by the task of 2-D classification in single particle reconstruction by cryo-electron microscopy (cryo-EM), we consider the problem of heterogeneous multireference alignment of images. In this problem, the goal is to estimate a (typically small) set of target images from a (typically large) collection of observations. Each observation is a rotated, noisy version of one of the target images. For each individual observation, neither the rotation nor which target image has been rotated are known. As the noise level in cryo-EM data is high, clustering the observations and estimating individual rotations is challenging. We propose a framework to estimate the target images directly from the observations, completely bypassing the need to cluster or register the images. The framework consists of two steps. First, we estimate rotation-invariant features of the images, such as the bispectrum. These features can be estimated to any desired accuracy, at any noise level, provided sufficiently many observations are collected. Then, we estimate the images from the invariant features. Numerical experiments on synthetic cryo-EM datasets demonstrate the effectiveness of the method. Ultimately, we outline future developments required to apply this method to experimental data.
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41
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Dandey VP, Wei H, Zhang Z, Tan YZ, Acharya P, Eng ET, Rice WJ, Kahn PA, Potter CS, Carragher B. Spotiton: New features and applications. J Struct Biol 2019; 202:161-169. [PMID: 29366716 DOI: 10.1016/j.jsb.2018.01.002] [Citation(s) in RCA: 99] [Impact Index Per Article: 19.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2017] [Revised: 01/02/2018] [Accepted: 01/04/2018] [Indexed: 10/17/2022]
Abstract
We present an update describing new features and applications of Spotiton, a novel instrument for vitrifying samples for cryoEM. We have used Spotiton to prepare several test specimens that can be reconstructed using routine single particle analysis to ∼3 Å resolution, indicating that the process has no apparent deleterious effect on the sample integrity. The system is now in routine and continuous use in our lab and has been used to successfully vitrify a wide variety of samples.
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Affiliation(s)
- Venkata P Dandey
- The National Resource for Automated Molecular Microscopy, Simons Electron Microscopy Center, New York Structural Biology Center, 89 Convent Ave, New York, NY, USA
| | - Hui Wei
- The National Resource for Automated Molecular Microscopy, Simons Electron Microscopy Center, New York Structural Biology Center, 89 Convent Ave, New York, NY, USA
| | - Zhening Zhang
- The National Resource for Automated Molecular Microscopy, Simons Electron Microscopy Center, New York Structural Biology Center, 89 Convent Ave, New York, NY, USA
| | - Yong Zi Tan
- The National Resource for Automated Molecular Microscopy, Simons Electron Microscopy Center, New York Structural Biology Center, 89 Convent Ave, New York, NY, USA; Department of Biochemistry and Molecular Biophysics, Columbia University, New York, NY, USA
| | - Priyamvada Acharya
- The National Resource for Automated Molecular Microscopy, Simons Electron Microscopy Center, New York Structural Biology Center, 89 Convent Ave, New York, NY, USA; Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Edward T Eng
- The National Resource for Automated Molecular Microscopy, Simons Electron Microscopy Center, New York Structural Biology Center, 89 Convent Ave, New York, NY, USA
| | - William J Rice
- The National Resource for Automated Molecular Microscopy, Simons Electron Microscopy Center, New York Structural Biology Center, 89 Convent Ave, New York, NY, USA
| | | | - Clinton S Potter
- The National Resource for Automated Molecular Microscopy, Simons Electron Microscopy Center, New York Structural Biology Center, 89 Convent Ave, New York, NY, USA; Department of Biochemistry and Molecular Biophysics, Columbia University, New York, NY, USA
| | - Bridget Carragher
- The National Resource for Automated Molecular Microscopy, Simons Electron Microscopy Center, New York Structural Biology Center, 89 Convent Ave, New York, NY, USA; Department of Biochemistry and Molecular Biophysics, Columbia University, New York, NY, USA.
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42
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Jain R, Rice WJ, Malik R, Johnson RE, Prakash L, Prakash S, Ubarretxena-Belandia I, Aggarwal AK. Cryo-EM structure and dynamics of eukaryotic DNA polymerase δ holoenzyme. Nat Struct Mol Biol 2019; 26:955-962. [PMID: 31582849 DOI: 10.1038/s41594-019-0305-z] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2019] [Accepted: 08/19/2019] [Indexed: 11/09/2022]
Abstract
DNA polymerase δ (Polδ) plays pivotal roles in eukaryotic DNA replication and repair. Polδ is conserved from yeast to humans, and mutations in human Polδ have been implicated in various cancers. Saccharomyces cerevisiae Polδ consists of catalytic Pol3 and the regulatory Pol31 and Pol32 subunits. Here, we present the near atomic resolution (3.2 Å) cryo-EM structure of yeast Polδ holoenzyme in the act of DNA synthesis. The structure reveals an unexpected arrangement in which the regulatory subunits (Pol31 and Pol32) lie next to the exonuclease domain of Pol3 but do not engage the DNA. The Pol3 C-terminal domain contains a 4Fe-4S cluster and emerges as the keystone of Polδ assembly. We also show that the catalytic and regulatory subunits rotate relative to each other and that this is an intrinsic feature of the Polδ architecture. Collectively, the structure provides a framework for understanding DNA transactions at the replication fork.
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Affiliation(s)
- Rinku Jain
- Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
| | - William J Rice
- Simons Electron Microscopy Center, New York Structural Biology Center, New York, NY, USA
| | - Radhika Malik
- Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Robert E Johnson
- Department of Biochemistry and Molecular Biology, University of Texas Medical Branch, Galveston, TX, USA
| | - Louise Prakash
- Department of Biochemistry and Molecular Biology, University of Texas Medical Branch, Galveston, TX, USA
| | - Satya Prakash
- Department of Biochemistry and Molecular Biology, University of Texas Medical Branch, Galveston, TX, USA
| | - Iban Ubarretxena-Belandia
- Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Instituto Biofisika (UPV/EHU, CSIC), University of the Basque Country, Leioa, Spain.,Ikerbasque, Basque Foundation for Science, Bilbao, Spain
| | - Aneel K Aggarwal
- Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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Maluenda D, Majtner T, Horvath P, Vilas JL, Jiménez-Moreno A, Mota J, Ramírez-Aportela E, Sánchez-García R, Conesa P, del Caño L, Rancel Y, Fonseca Y, Martínez M, Sharov G, García C, Strelak D, Melero R, Marabini R, Carazo JM, Sorzano COS. Flexible workflows for on-the-fly electron-microscopy single-particle image processing using Scipion. Acta Crystallogr D Struct Biol 2019; 75:882-894. [PMID: 31588920 PMCID: PMC6778851 DOI: 10.1107/s2059798319011860] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2019] [Accepted: 08/28/2019] [Indexed: 01/18/2023] Open
Abstract
Electron microscopy of macromolecular structures is an approach that is in increasing demand in the field of structural biology. The automation of image acquisition has greatly increased the potential throughput of electron microscopy. Here, the focus is on the possibilities in Scipion to implement flexible and robust image-processing workflows that allow the electron-microscope operator and the user to monitor the quality of image acquisition, assessing very simple acquisition measures or obtaining a first estimate of the initial volume, or the data resolution and heterogeneity, without any need for programming skills. These workflows can implement intelligent automatic decisions and they can warn the user of possible acquisition failures. These concepts are illustrated by analysis of the well known 2.2 Å resolution β-galactosidase data set.
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Affiliation(s)
- D. Maluenda
- National Center for Biotechnology (CSIC), 28049 Cantoblanco, Madrid, Spain
| | - T. Majtner
- National Center for Biotechnology (CSIC), 28049 Cantoblanco, Madrid, Spain
| | - P. Horvath
- National Center for Biotechnology (CSIC), 28049 Cantoblanco, Madrid, Spain
| | - J. L. Vilas
- National Center for Biotechnology (CSIC), 28049 Cantoblanco, Madrid, Spain
| | - A. Jiménez-Moreno
- National Center for Biotechnology (CSIC), 28049 Cantoblanco, Madrid, Spain
| | - J. Mota
- National Center for Biotechnology (CSIC), 28049 Cantoblanco, Madrid, Spain
| | | | - R. Sánchez-García
- National Center for Biotechnology (CSIC), 28049 Cantoblanco, Madrid, Spain
| | - P. Conesa
- National Center for Biotechnology (CSIC), 28049 Cantoblanco, Madrid, Spain
| | - L. del Caño
- National Center for Biotechnology (CSIC), 28049 Cantoblanco, Madrid, Spain
| | - Y. Rancel
- National Center for Biotechnology (CSIC), 28049 Cantoblanco, Madrid, Spain
| | - Y. Fonseca
- National Center for Biotechnology (CSIC), 28049 Cantoblanco, Madrid, Spain
| | - M. Martínez
- National Center for Biotechnology (CSIC), 28049 Cantoblanco, Madrid, Spain
| | - G. Sharov
- MRC Laboratory of Molecular Biology, Cambridge Biomedical Campus, England
| | | | - D. Strelak
- National Center for Biotechnology (CSIC), 28049 Cantoblanco, Madrid, Spain
| | - R. Melero
- National Center for Biotechnology (CSIC), 28049 Cantoblanco, Madrid, Spain
| | - R. Marabini
- Universidad Autónoma de Madrid, Madrid, Spain
| | - J. M. Carazo
- National Center for Biotechnology (CSIC), 28049 Cantoblanco, Madrid, Spain
| | - C. O. S. Sorzano
- National Center for Biotechnology (CSIC), 28049 Cantoblanco, Madrid, Spain
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Ludlam WG, Aoba T, Cuéllar J, Bueno-Carrasco MT, Makaju A, Moody JD, Franklin S, Valpuesta JM, Willardson BM. Molecular architecture of the Bardet-Biedl syndrome protein 2-7-9 subcomplex. J Biol Chem 2019; 294:16385-16399. [PMID: 31530639 DOI: 10.1074/jbc.ra119.010150] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2019] [Revised: 09/10/2019] [Indexed: 02/04/2023] Open
Abstract
Bardet-Biedl syndrome (BBS) is a genetic disorder characterized by malfunctions in primary cilia resulting from mutations that disrupt the function of the BBSome, an 8-subunit complex that plays an important role in protein transport in primary cilia. To better understand the molecular basis of BBS, here we used an integrative structural modeling approach consisting of EM and chemical cross-linking coupled with MS analyses, to analyze the structure of a BBSome 2-7-9 subcomplex consisting of three homologous BBS proteins, BBS2, BBS7, and BBS9. The resulting molecular model revealed an overall structure that resembles a flattened triangle. We found that within this structure, BBS2 and BBS7 form a tight dimer through a coiled-coil interaction and that BBS9 associates with the dimer via an interaction with the α-helical domain of BBS2. Interestingly, a BBS-associated mutation of BBS2 (R632P) is located in its α-helical domain at the interface between BBS2 and BBS9, and binding experiments indicated that this mutation disrupts the BBS2-BBS9 interaction. This finding suggests that BBSome assembly is disrupted by the R632P substitution, providing molecular insights that may explain the etiology of BBS in individuals harboring this mutation.
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Affiliation(s)
- W Grant Ludlam
- Department of Chemistry and Biochemistry, Brigham Young University, Provo, Utah 84602
| | - Takuma Aoba
- Department of Chemistry and Biochemistry, Brigham Young University, Provo, Utah 84602
| | - Jorge Cuéllar
- Centro Nacional de Biotecnología (CNB-CSIC), Campus de la Universidad Autónoma de Madrid, 28049 Madrid, Spain
| | - M Teresa Bueno-Carrasco
- Centro Nacional de Biotecnología (CNB-CSIC), Campus de la Universidad Autónoma de Madrid, 28049 Madrid, Spain
| | - Aman Makaju
- Department of Internal Medicine, Nora Eccles Harrison Cardiovascular Research and Training Institute, University of Utah, Salt Lake City, Utah 84112
| | - James D Moody
- Department of Chemistry and Biochemistry, Brigham Young University, Provo, Utah 84602
| | - Sarah Franklin
- Department of Internal Medicine, Nora Eccles Harrison Cardiovascular Research and Training Institute, University of Utah, Salt Lake City, Utah 84112
| | - José M Valpuesta
- Centro Nacional de Biotecnología (CNB-CSIC), Campus de la Universidad Autónoma de Madrid, 28049 Madrid, Spain
| | - Barry M Willardson
- Department of Chemistry and Biochemistry, Brigham Young University, Provo, Utah 84602
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Peschiera I, Giuliani M, Giusti F, Melero R, Paccagnini E, Donnarumma D, Pansegrau W, Carazo JM, Sorzano COS, Scarselli M, Masignani V, Liljeroos LJ, Ferlenghi I. Structural basis for cooperativity of human monoclonal antibodies to meningococcal factor H-binding protein. Commun Biol 2019; 2:241. [PMID: 31263785 PMCID: PMC6595007 DOI: 10.1038/s42003-019-0493-4] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2018] [Accepted: 05/29/2019] [Indexed: 11/23/2022] Open
Abstract
Monoclonal antibody (mAb) cooperativity is a phenomenon triggered when mAbs couples promote increased bactericidal killing compared to individual partners. Cooperativity has been deeply investigated among mAbs elicited by factor H-binding protein (fHbp), a Neisseria meningitidis surface-exposed lipoprotein and one of the key antigens included in both serogroup B meningococcus vaccine Bexsero and Trumenba. Here we report the structural and functional characterization of two cooperative mAbs pairs isolated from Bexsero vaccines. The 3D electron microscopy structures of the human mAb-fHbp-mAb cooperative complexes indicate that the angle formed between the antigen binding fragments (fAbs) assume regular angle and that fHbp is able to bind simultaneously and stably the cooperative mAbs pairs and human factor H (fH) in vitro. These findings shed light on molecular basis of the antibody-based mechanism of protection driven by simultaneous recognition of the different epitopes of the fHbp and underline that cooperativity is crucial in vaccine efficacy.
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Native top-down mass spectrometry provides insights into the copper centers of membrane-bound methane monooxygenase. Nat Commun 2019; 10:2675. [PMID: 31209220 PMCID: PMC6572826 DOI: 10.1038/s41467-019-10590-6] [Citation(s) in RCA: 64] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2019] [Accepted: 05/15/2019] [Indexed: 01/12/2023] Open
Abstract
Aerobic methane oxidation is catalyzed by particulate methane monooxygenase (pMMO), a copper-dependent, membrane metalloenzyme composed of subunits PmoA, PmoB, and PmoC. Characterization of the copper active site has been limited by challenges in spectroscopic analysis stemming from the presence of multiple copper binding sites, effects of detergent solubilization on activity and crystal structures, and the lack of a heterologous expression system. Here we utilize nanodiscs coupled with native top-down mass spectrometry (nTDMS) to determine the copper stoichiometry in each pMMO subunit and to detect post-translational modifications (PTMs). These results indicate the presence of a mononuclear copper center in both PmoB and PmoC. pMMO-nanodisc complexes with a higher stoichiometry of copper-bound PmoC exhibit increased activity, suggesting that the PmoC copper site plays a role in methane oxidation activity. These results provide key insights into the pMMO copper centers and demonstrate the ability of nTDMS to characterize complex membrane-bound metalloenzymes.
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Cirelli KM, Carnathan DG, Nogal B, Martin JT, Rodriguez OL, Upadhyay AA, Enemuo CA, Gebru EH, Choe Y, Viviano F, Nakao C, Pauthner MG, Reiss S, Cottrell CA, Smith ML, Bastidas R, Gibson W, Wolabaugh AN, Melo MB, Cossette B, Kumar V, Patel NB, Tokatlian T, Menis S, Kulp DW, Burton DR, Murrell B, Schief WR, Bosinger SE, Ward AB, Watson CT, Silvestri G, Irvine DJ, Crotty S. Slow Delivery Immunization Enhances HIV Neutralizing Antibody and Germinal Center Responses via Modulation of Immunodominance. Cell 2019; 177:1153-1171.e28. [PMID: 31080066 PMCID: PMC6619430 DOI: 10.1016/j.cell.2019.04.012] [Citation(s) in RCA: 250] [Impact Index Per Article: 50.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2018] [Revised: 02/26/2019] [Accepted: 04/05/2019] [Indexed: 12/14/2022]
Abstract
Conventional immunization strategies will likely be insufficient for the development of a broadly neutralizing antibody (bnAb) vaccine for HIV or other difficult pathogens because of the immunological hurdles posed, including B cell immunodominance and germinal center (GC) quantity and quality. We found that two independent methods of slow delivery immunization of rhesus monkeys (RMs) resulted in more robust T follicular helper (TFH) cell responses and GC B cells with improved Env-binding, tracked by longitudinal fine needle aspirates. Improved GCs correlated with the development of >20-fold higher titers of autologous nAbs. Using a new RM genomic immunoglobulin locus reference, we identified differential IgV gene use between immunization modalities. Ab mapping demonstrated targeting of immunodominant non-neutralizing epitopes by conventional bolus-immunized animals, whereas slow delivery-immunized animals targeted a more diverse set of epitopes. Thus, alternative immunization strategies can enhance nAb development by altering GCs and modulating the immunodominance of non-neutralizing epitopes.
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Affiliation(s)
- Kimberly M Cirelli
- Division of Vaccine Discovery, La Jolla Institute for Immunology (LJI), La Jolla, CA 92037, USA; Center for HIV/AIDS Vaccine Immunology and Immunogen Discovery (Scripps CHAVI-ID), The Scripps Research Institute, La Jolla, CA 92037, USA
| | - Diane G Carnathan
- Center for HIV/AIDS Vaccine Immunology and Immunogen Discovery (Scripps CHAVI-ID), The Scripps Research Institute, La Jolla, CA 92037, USA; Yerkes National Primate Research Center, Emory University, Atlanta, GA 30322, USA; Emory Vaccine Center, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Bartek Nogal
- Center for HIV/AIDS Vaccine Immunology and Immunogen Discovery (Scripps CHAVI-ID), The Scripps Research Institute, La Jolla, CA 92037, USA; Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA
| | - Jacob T Martin
- Center for HIV/AIDS Vaccine Immunology and Immunogen Discovery (Scripps CHAVI-ID), The Scripps Research Institute, La Jolla, CA 92037, USA; Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Oscar L Rodriguez
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Amit A Upadhyay
- Yerkes National Primate Research Center, Emory University, Atlanta, GA 30322, USA
| | - Chiamaka A Enemuo
- Yerkes National Primate Research Center, Emory University, Atlanta, GA 30322, USA; Emory Vaccine Center, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Etse H Gebru
- Yerkes National Primate Research Center, Emory University, Atlanta, GA 30322, USA; Emory Vaccine Center, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Yury Choe
- Yerkes National Primate Research Center, Emory University, Atlanta, GA 30322, USA; Emory Vaccine Center, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Federico Viviano
- Yerkes National Primate Research Center, Emory University, Atlanta, GA 30322, USA; Emory Vaccine Center, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Catherine Nakao
- Division of Vaccine Discovery, La Jolla Institute for Immunology (LJI), La Jolla, CA 92037, USA
| | - Matthias G Pauthner
- Center for HIV/AIDS Vaccine Immunology and Immunogen Discovery (Scripps CHAVI-ID), The Scripps Research Institute, La Jolla, CA 92037, USA; Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, CA 92037, USA
| | - Samantha Reiss
- Division of Vaccine Discovery, La Jolla Institute for Immunology (LJI), La Jolla, CA 92037, USA; Center for HIV/AIDS Vaccine Immunology and Immunogen Discovery (Scripps CHAVI-ID), The Scripps Research Institute, La Jolla, CA 92037, USA
| | - Christopher A Cottrell
- Center for HIV/AIDS Vaccine Immunology and Immunogen Discovery (Scripps CHAVI-ID), The Scripps Research Institute, La Jolla, CA 92037, USA; Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA
| | - Melissa L Smith
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Raiza Bastidas
- Center for HIV/AIDS Vaccine Immunology and Immunogen Discovery (Scripps CHAVI-ID), The Scripps Research Institute, La Jolla, CA 92037, USA; Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, CA 92037, USA
| | - William Gibson
- Department of Biochemistry and Molecular Genetics, University of Louisville School of Medicine, Louisville, KY 40202, USA
| | - Amber N Wolabaugh
- Yerkes National Primate Research Center, Emory University, Atlanta, GA 30322, USA
| | - Mariane B Melo
- Center for HIV/AIDS Vaccine Immunology and Immunogen Discovery (Scripps CHAVI-ID), The Scripps Research Institute, La Jolla, CA 92037, USA; Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Benjamin Cossette
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Venkatesh Kumar
- Department of Medicine, University of California, San Diego, La Jolla, CA 92037, USA
| | - Nirav B Patel
- Yerkes NHP Genomics Core Laboratory, Yerkes National Primate Research Center, Atlanta, GA 30329, USA
| | - Talar Tokatlian
- Center for HIV/AIDS Vaccine Immunology and Immunogen Discovery (Scripps CHAVI-ID), The Scripps Research Institute, La Jolla, CA 92037, USA; Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Sergey Menis
- Center for HIV/AIDS Vaccine Immunology and Immunogen Discovery (Scripps CHAVI-ID), The Scripps Research Institute, La Jolla, CA 92037, USA; Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, CA 92037, USA
| | - Daniel W Kulp
- Center for HIV/AIDS Vaccine Immunology and Immunogen Discovery (Scripps CHAVI-ID), The Scripps Research Institute, La Jolla, CA 92037, USA; Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, CA 92037, USA; Vaccine and Immunotherapy Center, Wistar Institute, Philadelphia, PA 19104, USA
| | - Dennis R Burton
- Center for HIV/AIDS Vaccine Immunology and Immunogen Discovery (Scripps CHAVI-ID), The Scripps Research Institute, La Jolla, CA 92037, USA; Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, CA 92037, USA; Ragon Institute of Massachusetts General Hospital, Massachusetts Institute of Technology, and Harvard University, Cambridge, MA 02139, USA
| | - Ben Murrell
- Department of Medicine, University of California, San Diego, La Jolla, CA 92037, USA; Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Stockholm, Sweden
| | - William R Schief
- Center for HIV/AIDS Vaccine Immunology and Immunogen Discovery (Scripps CHAVI-ID), The Scripps Research Institute, La Jolla, CA 92037, USA; Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, CA 92037, USA; Ragon Institute of Massachusetts General Hospital, Massachusetts Institute of Technology, and Harvard University, Cambridge, MA 02139, USA
| | - Steven E Bosinger
- Yerkes National Primate Research Center, Emory University, Atlanta, GA 30322, USA; Yerkes NHP Genomics Core Laboratory, Yerkes National Primate Research Center, Atlanta, GA 30329, USA
| | - Andrew B Ward
- Center for HIV/AIDS Vaccine Immunology and Immunogen Discovery (Scripps CHAVI-ID), The Scripps Research Institute, La Jolla, CA 92037, USA; Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA
| | - Corey T Watson
- Department of Biochemistry and Molecular Genetics, University of Louisville School of Medicine, Louisville, KY 40202, USA
| | - Guido Silvestri
- Center for HIV/AIDS Vaccine Immunology and Immunogen Discovery (Scripps CHAVI-ID), The Scripps Research Institute, La Jolla, CA 92037, USA; Yerkes National Primate Research Center, Emory University, Atlanta, GA 30322, USA; Emory Vaccine Center, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Darrell J Irvine
- Center for HIV/AIDS Vaccine Immunology and Immunogen Discovery (Scripps CHAVI-ID), The Scripps Research Institute, La Jolla, CA 92037, USA; Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Ragon Institute of Massachusetts General Hospital, Massachusetts Institute of Technology, and Harvard University, Cambridge, MA 02139, USA; Departments of Biological Engineering and Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Shane Crotty
- Division of Vaccine Discovery, La Jolla Institute for Immunology (LJI), La Jolla, CA 92037, USA; Center for HIV/AIDS Vaccine Immunology and Immunogen Discovery (Scripps CHAVI-ID), The Scripps Research Institute, La Jolla, CA 92037, USA; Department of Medicine, University of California, San Diego, La Jolla, CA 92037, USA.
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Serna M. Hands on Methods for High Resolution Cryo-Electron Microscopy Structures of Heterogeneous Macromolecular Complexes. Front Mol Biosci 2019; 6:33. [PMID: 31157234 PMCID: PMC6529575 DOI: 10.3389/fmolb.2019.00033] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2018] [Accepted: 04/24/2019] [Indexed: 01/23/2023] Open
Abstract
Electron microscopy of frozen hydrated samples (cryo-EM) is a powerful structural technique that allows the direct study of functional macromolecular complexes in an almost physiological environment. Protein macromolecular complexes are dynamic structures that usually hold together by an intricate network of protein-protein interactions that can be weak and transient. Moreover, a standard feature of many of these complexes is that they behave as nanomachines able to undergo functionally relevant conformational changes in one or several complex components. Among all the other main structural biology techniques, only cryo-EM has the potential of successfully dealing at the same time with both sample heterogeneity and inherent flexibility. The cryo-EM field is currently undergoing a revolution thanks to groundbreaking technical developments that have brought within our reach the possibility of solving the structure of biological complexes at atomic resolution. These technical developments have been mostly focused on new direct electron detector technology and improved sample preparation methods leading to better image quality. This fact has in turn required the development of new and better image processing algorithms to make the most of the higher quality data. The aim of this review is to provide a brief overview of some reported examples of single particle analysis strategies designed to find different conformational and compositional states within target macromolecular complex and specifically to deal with it to reach higher resolution information. Different image processing methodologies specifically aimed to symmetric or pseudo-symmetric protein complexes will also be discussed.
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Affiliation(s)
- Marina Serna
- Structural Biology Program, Spanish National Cancer Research Centre (CNIO), Madrid, Spain
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49
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Yin S, Zhang B, Yang Y, Huang Y, Shen HB. Clustering Enhancement of Noisy Cryo-Electron Microscopy Single-Particle Images with a Network Structural Similarity Metric. J Chem Inf Model 2019; 59:1658-1667. [DOI: 10.1021/acs.jcim.8b00853] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Affiliation(s)
- Shuo Yin
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key
Laboratory of System Control and Information Processing, Ministry
of Education of China, Shanghai 200240, China
| | - Biao Zhang
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key
Laboratory of System Control and Information Processing, Ministry
of Education of China, Shanghai 200240, China
| | - Yang Yang
- Department of Computer Science, Shanghai Jiao Tong University, and Key Laboratory
of Shanghai Education Commission for Intelligent Interaction and Cognitive
Engineering, Shanghai 200240, China
| | - Yan Huang
- State Key Laboratory of Infrared Physics Shanghai Institute of Technical Physics, Chinese Academy of Sciences, 500 Yutian Road, Shanghai 200083, China
| | - Hong-Bin Shen
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key
Laboratory of System Control and Information Processing, Ministry
of Education of China, Shanghai 200240, China
- Department of Computer Science, Shanghai Jiao Tong University, and Key Laboratory
of Shanghai Education Commission for Intelligent Interaction and Cognitive
Engineering, Shanghai 200240, China
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Fernández-Justel D, Núñez R, Martín-Benito J, Jimeno D, González-López A, Soriano EM, Revuelta JL, Buey RM. A Nucleotide-Dependent Conformational Switch Controls the Polymerization of Human IMP Dehydrogenases to Modulate their Catalytic Activity. J Mol Biol 2019; 431:956-969. [PMID: 30664871 DOI: 10.1016/j.jmb.2019.01.020] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2018] [Revised: 01/09/2019] [Accepted: 01/10/2019] [Indexed: 12/29/2022]
Abstract
Inosine 5'-monophosphate dehydrogenase (IMPDH) catalyzes the rate-limiting step in the de novo GTP biosynthetic pathway and plays essential roles in cell proliferation. As a clinical target, IMPDH has been studied for decades, but it has only been within the last years that we are starting to understand the complexity of the mechanisms of its physiological regulation. Here, we report structural and functional insights into how adenine and guanine nucleotides control a conformational switch that modulates the assembly of the two human IMPDH enzymes into cytoophidia and allosterically regulates their catalytic activity. In vitro reconstituted micron-length cytoophidia-like structures show catalytic activity comparable to unassembled IMPDH but, in turn, are more resistant to GTP/GDP allosteric inhibition. Therefore, IMPDH cytoophidia formation facilitates the accumulation of high levels of guanine nucleotides when the cell requires it. Finally, we demonstrate that most of the IMPDH retinopathy-associated mutations abrogate GTP/GDP-induced allosteric inhibition and alter cytoophidia dynamics.
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Affiliation(s)
- David Fernández-Justel
- Metabolic Engineering Group, Dpto. Microbiología y Genética, Universidad de Salamanca, Campus Miguel de Unamuno, 37007, Salamanca, Spain
| | - Rafael Núñez
- Centro de Investigaciones Biológicas (CIB), Spanish National Research Council (CSIC), Ramiro de Maeztu 9, 28040 Madrid, Spain
| | - Jaime Martín-Benito
- Centro Nacional de Biotecnología (CNB), Spanish National Research Council (CSIC), Darwin 3, 28039 Madrid, Spain
| | - David Jimeno
- Instituto de Biología Molecular y Celular del Cáncer (CSIC-Universidad de Salamanca), Campus Miguel de Unamuno, 37007 Salamanca, Spain
| | - Adrián González-López
- Metabolic Engineering Group, Dpto. Microbiología y Genética, Universidad de Salamanca, Campus Miguel de Unamuno, 37007, Salamanca, Spain
| | - Eva María Soriano
- Metabolic Engineering Group, Dpto. Microbiología y Genética, Universidad de Salamanca, Campus Miguel de Unamuno, 37007, Salamanca, Spain
| | - José Luis Revuelta
- Metabolic Engineering Group, Dpto. Microbiología y Genética, Universidad de Salamanca, Campus Miguel de Unamuno, 37007, Salamanca, Spain.
| | - Rubén M Buey
- Metabolic Engineering Group, Dpto. Microbiología y Genética, Universidad de Salamanca, Campus Miguel de Unamuno, 37007, Salamanca, Spain.
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