1
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Sali A. From integrative structural biology to cell biology. J Biol Chem 2021; 296:100743. [PMID: 33957123 PMCID: PMC8203844 DOI: 10.1016/j.jbc.2021.100743] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Revised: 04/09/2021] [Accepted: 04/30/2021] [Indexed: 12/16/2022] Open
Abstract
Integrative modeling is an increasingly important tool in structural biology, providing structures by combining data from varied experimental methods and prior information. As a result, molecular architectures of large, heterogeneous, and dynamic systems, such as the ∼52-MDa Nuclear Pore Complex, can be mapped with useful accuracy, precision, and completeness. Key challenges in improving integrative modeling include expanding model representations, increasing the variety of input data and prior information, quantifying a match between input information and a model in a Bayesian fashion, inventing more efficient structural sampling, as well as developing better model validation, analysis, and visualization. In addition, two community-level challenges in integrative modeling are being addressed under the auspices of the Worldwide Protein Data Bank (wwPDB). First, the impact of integrative structures is maximized by PDB-Development, a prototype wwPDB repository for archiving, validating, visualizing, and disseminating integrative structures. Second, the scope of structural biology is expanded by linking the wwPDB resource for integrative structures with archives of data that have not been generally used for structure determination but are increasingly important for computing integrative structures, such as data from various types of mass spectrometry, spectroscopy, optical microscopy, proteomics, and genetics. To address the largest of modeling problems, a type of integrative modeling called metamodeling is being developed; metamodeling combines different types of input models as opposed to different types of data to compute an output model. Collectively, these developments will facilitate the structural biology mindset in cell biology and underpin spatiotemporal mapping of the entire cell.
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Affiliation(s)
- Andrej Sali
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, the Department of Bioengineering and Therapeutic Sciences, the Quantitative Biosciences Institute (QBI), and the Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, California, USA.
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2
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Rout MP, Sali A. Principles for Integrative Structural Biology Studies. Cell 2020; 177:1384-1403. [PMID: 31150619 DOI: 10.1016/j.cell.2019.05.016] [Citation(s) in RCA: 165] [Impact Index Per Article: 41.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2019] [Revised: 04/24/2019] [Accepted: 05/06/2019] [Indexed: 12/22/2022]
Abstract
Integrative structure determination is a powerful approach to modeling the structures of biological systems based on data produced by multiple experimental and theoretical methods, with implications for our understanding of cellular biology and drug discovery. This Primer introduces the theory and methods of integrative approaches, emphasizing the kinds of data that can be effectively included in developing models and using the nuclear pore complex as an example to illustrate the practice and challenges involved. These guidelines are intended to aid the researcher in understanding and applying integrative structural methods to systems of their interest and thus take advantage of this rapidly evolving field.
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Affiliation(s)
- Michael P Rout
- Laboratory of Cellular and Structural Biology, The Rockefeller University, New York, NY 10065, USA.
| | - Andrej Sali
- Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, California Institute for Quantitative Biosciences, Byers Hall, 1700 4th Street, Suite 503B, University of California, San Francisco, San Francisco, CA 94158, USA.
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3
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Saltzberg DJ, Hepburn M, Pilla KB, Schriemer DC, Lees-Miller SP, Blundell TL, Sali A. SSEThread: Integrative threading of the DNA-PKcs sequence based on data from chemical cross-linking and hydrogen deuterium exchange. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2019; 147:92-102. [PMID: 31570166 DOI: 10.1016/j.pbiomolbio.2019.09.003] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/01/2019] [Revised: 08/09/2019] [Accepted: 09/10/2019] [Indexed: 01/19/2023]
Abstract
X-ray crystallography and electron microscopy maps resolved to 3-8 Å are generally sufficient for tracing the path of the polypeptide chain in space, while often insufficient for unambiguously registering the sequence on the path (i.e., threading). Frequently, however, additional information is available from other biophysical experiments, physical principles, statistical analyses, and other prior models. Here, we formulate an integrative approach for sequence assignment to a partial backbone model as an optimization problem, which requires three main components: the representation of the system, the scoring function, and the optimization method. The method is implemented in the open source Integrative Modeling Platform (IMP) (https://integrativemodeling.org), allowing a number of different terms in the scoring function. We apply this method to localizing the sequence assignment within a 199-residue disordered region of three structured and sequence unassigned helices in the DNA-PKcs crystallographic structure, using chemical crosslinks, hydrogen deuterium exchange, and sequence connectivity. The resulting ensemble of threading models provides two major solutions, one of which suggests that the crucial ABCDE cluster of phosphorylation sites cannot undergo intra-molecular autophosphorylation without a conformational rearrangement. The ensemble of solutions embodies the most accurate and precise sequence threading given the available information.
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Affiliation(s)
- Daniel J Saltzberg
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, USA.
| | - Morgan Hepburn
- Department of Biochemistry and Molecular Biology, University of Calgary, Calgary, Canada
| | - Kala Bharath Pilla
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, USA
| | - David C Schriemer
- Department of Biochemistry and Molecular Biology, University of Calgary, Calgary, Canada
| | - Susan P Lees-Miller
- Department of Biochemistry and Molecular Biology, University of Calgary, Calgary, Canada
| | - Tom L Blundell
- Department of Biochemistry, University of Cambridge, Cambridge, UK
| | - Andrej Sali
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, USA
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4
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Vallat B, Webb B, Westbrook J, Sali A, Berman HM. Archiving and disseminating integrative structure models. JOURNAL OF BIOMOLECULAR NMR 2019; 73:385-398. [PMID: 31278630 PMCID: PMC6692293 DOI: 10.1007/s10858-019-00264-2] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/06/2019] [Accepted: 06/25/2019] [Indexed: 05/04/2023]
Abstract
Limitations in the applicability, accuracy, and precision of individual structure characterization methods can sometimes be overcome via an integrative modeling approach that relies on information from all available sources, including all available experimental data and prior models. The open-source Integrative Modeling Platform (IMP) is one piece of software that implements all computational aspects of integrative modeling. To maximize the impact of integrative structures, the coordinates should be made publicly available, as is already the case for structures based on X-ray crystallography, NMR spectroscopy, and electron microscopy. Moreover, the associated experimental data and modeling protocols should also be archived, such that the original results can easily be reproduced. Finally, it is essential that the integrative structures are validated as part of their publication and deposition. A number of research groups have already developed software to implement integrative modeling and have generated a number of structures, prompting the formation of an Integrative/Hybrid Methods Task Force. Following the recommendations of this task force, the existing PDBx/mmCIF data representation used for atomic PDB structures has been extended to address the requirements for archiving integrative structural models. This IHM-dictionary adds a flexible model representation, including coarse graining, models in multiple states and/or related by time or other order, and multiple input experimental information sources. A prototype archiving system called PDB-Dev ( https://pdb-dev.wwpdb.org ) has also been created to archive integrative structural models, together with a Python library to facilitate handling of integrative models in PDBx/mmCIF format.
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Affiliation(s)
- Brinda Vallat
- Institute for Quantitative Biomedicine, Piscataway, USA
| | - Benjamin Webb
- Department of Bioengineering and Therapeutic Sciences, University of California at San Francisco, San Francisco, CA, 94143, USA
| | - John Westbrook
- Institute for Quantitative Biomedicine, Piscataway, USA
- RCSB Protein Data Bank, Piscataway, USA
| | - Andrej Sali
- RCSB Protein Data Bank, Piscataway, USA.
- Department of Bioengineering and Therapeutic Sciences, University of California at San Francisco, San Francisco, CA, 94143, USA.
- Department of Pharmaceutical Chemistry and California Institute for Quantitative Biosciences, University of California at San Francisco, San Francisco, CA, 94143, USA.
- Lead Contacts, San Francisco, USA.
| | - Helen M Berman
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ, 08854, USA.
- Lead Contacts, Piscataway, USA.
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5
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Lei D, Liu J, Liu H, Cleveland TE, Marino JP, Lei M, Ren G. Single-Molecule 3D Images of "Hole-Hole" IgG1 Homodimers by Individual-Particle Electron Tomography. Sci Rep 2019; 9:8864. [PMID: 31221961 PMCID: PMC6586654 DOI: 10.1038/s41598-019-44978-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2018] [Accepted: 05/30/2019] [Indexed: 12/20/2022] Open
Abstract
The engineering of immunoglobulin-G molecules (IgGs) is of wide interest for improving therapeutics, for example by modulating the activity or multiplexing the specificity of IgGs to recognize more than one antigen. Optimization of engineered IgG requires knowledge of three-dimensional (3D) structure of synthetic IgG. However, due to flexible nature of the molecules, their structural characterization is challenging. Here, we use our reported individual-particle electron tomography (IPET) method with optimized negative-staining (OpNS) for direct 3D reconstruction of individual IgG hole-hole homodimer molecules. The hole-hole homodimer is an undesired variant generated during the production of a bispecific antibody using the knob-into-hole heterodimer technology. A total of 64 IPET 3D density maps at ~15 Å resolutions were reconstructed from 64 individual molecules, revealing 64 unique conformations. In addition to the known Y-shaped conformation, we also observed an unusual X-shaped conformation. The 3D structure of the X-shaped conformation contributes to our understanding of the structural details of the interaction between two heavy chains in the Fc domain. The IPET approach, as an orthogonal technique to characterize the 3D structure of therapeutic antibodies, provides insight into the 3D structural variety and dynamics of heterogeneous IgG molecules.
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Affiliation(s)
- Dongsheng Lei
- The Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Jianfang Liu
- The Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Hongbin Liu
- Protein Analytical Chemistry, Genentech Inc., South San Francisco, CA, 94080, USA
| | - Thomas E Cleveland
- Institute for Bioscience and Biotechnology Research, National Institute of Standards and Technology and the University of Maryland, Rockville, MD, 20850, USA
| | - John P Marino
- Institute for Bioscience and Biotechnology Research, National Institute of Standards and Technology and the University of Maryland, Rockville, MD, 20850, USA
| | - Ming Lei
- Protein Analytical Chemistry, Genentech Inc., South San Francisco, CA, 94080, USA.
| | - Gang Ren
- The Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA.
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6
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Bonomi M, Vendruscolo M. Determination of protein structural ensembles using cryo-electron microscopy. Curr Opin Struct Biol 2019; 56:37-45. [DOI: 10.1016/j.sbi.2018.10.006] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2018] [Revised: 10/24/2018] [Accepted: 10/26/2018] [Indexed: 10/27/2022]
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7
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Braitbard M, Schneidman-Duhovny D, Kalisman N. Integrative Structure Modeling: Overview and Assessment. Annu Rev Biochem 2019; 88:113-135. [PMID: 30830798 DOI: 10.1146/annurev-biochem-013118-111429] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Integrative structure modeling computationally combines data from multiple sources of information with the aim of obtaining structural insights that are not revealed by any single approach alone. In the first part of this review, we survey the commonly used sources of structural information and the computational aspects of model building. Throughout the past decade, integrative modeling was applied to various biological systems, with a focus on large protein complexes. Recent progress in the field of cryo-electron microscopy (cryo-EM) has resolved many of these complexes to near-atomic resolution. In the second part of this review, we compare a range of published integrative models with their higher-resolution counterparts with the aim of critically assessing their accuracy. This comparison gives a favorable view of integrative modeling and demonstrates its ability to yield accurate and informative results. We discuss possible roles of integrative modeling in the new era of cryo-EM and highlight future challenges and directions.
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Affiliation(s)
- Merav Braitbard
- Department of Biological Chemistry, Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel;
| | - Dina Schneidman-Duhovny
- Department of Biological Chemistry, Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel; .,School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel;
| | - Nir Kalisman
- Department of Biological Chemistry, Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel;
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8
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Computational modeling of RNA 3D structure based on experimental data. Biosci Rep 2019; 39:BSR20180430. [PMID: 30670629 PMCID: PMC6367127 DOI: 10.1042/bsr20180430] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2018] [Revised: 01/19/2019] [Accepted: 01/21/2019] [Indexed: 01/02/2023] Open
Abstract
RNA molecules are master regulators of cells. They are involved in a variety of molecular processes: they transmit genetic information, sense cellular signals and communicate responses, and even catalyze chemical reactions. As in the case of proteins, RNA function is dictated by its structure and by its ability to adopt different conformations, which in turn is encoded in the sequence. Experimental determination of high-resolution RNA structures is both laborious and difficult, and therefore the majority of known RNAs remain structurally uncharacterized. To address this problem, predictive computational methods were developed based on the accumulated knowledge of RNA structures determined so far, the physical basis of the RNA folding, and taking into account evolutionary considerations, such as conservation of functionally important motifs. However, all theoretical methods suffer from various limitations, and they are generally unable to accurately predict structures for RNA sequences longer than 100-nt residues unless aided by additional experimental data. In this article, we review experimental methods that can generate data usable by computational methods, as well as computational approaches for RNA structure prediction that can utilize data from experimental analyses. We outline methods and data types that can be potentially useful for RNA 3D structure modeling but are not commonly used by the existing software, suggesting directions for future development.
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9
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Peter EK, Černý J. A Hybrid Hamiltonian for the Accelerated Sampling along Experimental Restraints. Int J Mol Sci 2019; 20:E370. [PMID: 30654563 PMCID: PMC6359555 DOI: 10.3390/ijms20020370] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2018] [Revised: 01/07/2019] [Accepted: 01/10/2019] [Indexed: 12/25/2022] Open
Abstract
In this article, we present an enhanced sampling method based on a hybrid Hamiltonian which combines experimental distance restraints with a bias dependent from multiple path-dependent variables. This simulation method determines the bias-coordinates on the fly and does not require a priori knowledge about reaction coordinates. The hybrid Hamiltonian accelerates the sampling of proteins, and, combined with experimental distance information, the technique considers the restraints adaptively and in dependency of the system's intrinsic dynamics. We validate the methodology on the dipole relaxation of two water models and the conformational landscape of dialanine. Using experimental NMR-restraint data, we explore the folding landscape of the TrpCage mini-protein and in a second example apply distance restraints from chemical crosslinking/mass spectrometry experiments for the sampling of the conformation space of the Killer Cell Lectin-like Receptor Subfamily B Member 1A (NKR-P1A). The new methodology has the potential to adaptively introduce experimental restraints without affecting the conformational space of the system along an ergodic trajectory. Since only a limited number of input- and no-order parameters are required for the setup of the simulation, the method is broadly applicable and has the potential to be combined with coarse-graining methods.
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Affiliation(s)
- Emanuel K Peter
- Institute of Biotechnology of the Czech Academy of Sciences, BIOCEV, Průmyslová 595, 252 50 Vestec, Prague West, Czech Republic.
| | - Jiří Černý
- Institute of Biotechnology of the Czech Academy of Sciences, BIOCEV, Průmyslová 595, 252 50 Vestec, Prague West, Czech Republic.
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10
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Cossio P, Allegretti M, Mayer F, Müller V, Vonck J, Hummer G. Bayesian inference of rotor ring stoichiometry from electron microscopy images of archaeal ATP synthase. Microscopy (Oxf) 2018; 67:266-273. [PMID: 30032235 DOI: 10.1093/jmicro/dfy033] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2017] [Accepted: 06/20/2018] [Indexed: 12/24/2022] Open
Abstract
The 'Bayesian inference of electron microscopy' (BioEM) framework makes it possible to determine the stoichiometry of protein complexes using 3D coarse-grained models and a relatively small number of cryo-electron microscopy images as input. We applied the method to determine the most probable rotor ring stoichiometry of the archaeal Na+ ATP synthase from Pyrococcus furiosus, a multisubunit complex able to produce ATP under extreme conditions. Archaeal ATP synthases consist of a catalytic A1 part and a membrane-embedded AO portion. The AO portion is composed of a rotor ring and the a-subunit. The rotor ring of P. furiosus ATP synthase is composed of 16-kDa c-subunits, each consisting of four helices forming a bundle, with only one Na+-binding site per bundle. This ring was proposed to be decameric from LILBID-MS analysis of the entire ATP synthase. By contrast, the BioEM posterior favors a c9 ring stoichiometry. With BioEM, we ranked coarse-grained models of the whole complex with different ring geometry, using 6400 unprocessed particle images of the A1AO complex collected in vitreous ice. BioEM makes it possible to probabilistically establish the domain stoichiometry using low-resolution information and comparably few particle images.
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Affiliation(s)
- Pilar Cossio
- Department of Theoretical Biophysics, Max Planck Institute of Biophysics, Frankfurt am Main, Germany.,Biophysics of Tropical Diseases, Max Planck Tandem Group, University of Antioquia, Medellín, Colombia
| | - Matteo Allegretti
- Department of Structural Biology, Max Planck Institute of Biophysics, Frankfurt am Main, Germany
| | - Florian Mayer
- Department of Molecular Microbiology & Bioenergetics, Goethe University Frankfurt, Max-von-Laue-Strasse 9, Frankfurt am Main, Germany
| | - Volker Müller
- Department of Molecular Microbiology & Bioenergetics, Goethe University Frankfurt, Max-von-Laue-Strasse 9, Frankfurt am Main, Germany
| | - Janet Vonck
- Department of Structural Biology, Max Planck Institute of Biophysics, Frankfurt am Main, Germany
| | - Gerhard Hummer
- Department of Theoretical Biophysics, Max Planck Institute of Biophysics, Frankfurt am Main, Germany.,Department of Physics, Goethe University Frankfurt, Max-von-Laue-Strasse 9, Frankfurt am Main, Germany
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11
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Advances in image processing for single-particle analysis by electron cryomicroscopy and challenges ahead. Curr Opin Struct Biol 2018; 52:127-145. [PMID: 30509756 DOI: 10.1016/j.sbi.2018.11.004] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2018] [Revised: 10/26/2018] [Accepted: 11/17/2018] [Indexed: 12/20/2022]
Abstract
Electron cryomicroscopy (cryoEM) is essential for the study and functional understanding of non-crystalline macromolecules such as proteins. These molecules cannot be imaged using X-ray crystallography or other popular methods. CryoEM has been successfully used to visualize macromolecular complexes such as ribosomes, viruses, and ion channels. Determination of structural models of these at various conformational states leads to insight on how these molecules function. Recent advances in imaging technology have given cryoEM a scientific rebirth. As a result of these technological advances image processing and analysis have yielded molecular structures at atomic resolution. Nevertheless there continue to be challenges in image processing, and in this article we will touch on the most essential in order to derive an accurate three-dimensional model from noisy projection images. Traditional approaches, such as k-means clustering for class averaging, will be provided as background. We will then highlight new approaches for each image processing subproblem, including a 3D reconstruction method for asymmetric molecules using just two projection images and deep learning algorithms for automated particle picking.
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12
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Bonomi M, Hanot S, Greenberg CH, Sali A, Nilges M, Vendruscolo M, Pellarin R. Bayesian Weighing of Electron Cryo-Microscopy Data for Integrative Structural Modeling. Structure 2018; 27:175-188.e6. [PMID: 30393052 DOI: 10.1016/j.str.2018.09.011] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2018] [Revised: 08/07/2018] [Accepted: 09/19/2018] [Indexed: 10/28/2022]
Abstract
Cryo-electron microscopy (cryo-EM) has become a mainstream technique for determining the structures of complex biological systems. However, accurate integrative structural modeling has been hampered by the challenges in objectively weighing cryo-EM data against other sources of information due to the presence of random and systematic errors, as well as correlations, in the data. To address these challenges, we introduce a Bayesian scoring function that efficiently and accurately ranks alternative structural models of a macromolecular system based on their consistency with a cryo-EM density map as well as other experimental and prior information. The accuracy of this approach is benchmarked using complexes of known structure and illustrated in three applications: the structural determination of the GroEL/GroES, RNA polymerase II, and exosome complexes. The approach is implemented in the open-source Integrative Modeling Platform (http://integrativemodeling.org), thus enabling integrative structure determination by combining cryo-EM data with other sources of information.
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Affiliation(s)
| | - Samuel Hanot
- Institut Pasteur, Structural Bioinformatics Unit, Department of Structural Biology and Chemistry, CNRS UMR 3528, C3BI USR 3756 CNRS & IP, Paris, France
| | - Charles H Greenberg
- Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Sciences, and California Institute for Quantitative Biomedical Sciences, University of California, San Francisco, CA 94158, USA
| | - Andrej Sali
- Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Sciences, and California Institute for Quantitative Biomedical Sciences, University of California, San Francisco, CA 94158, USA
| | - Michael Nilges
- Institut Pasteur, Structural Bioinformatics Unit, Department of Structural Biology and Chemistry, CNRS UMR 3528, C3BI USR 3756 CNRS & IP, Paris, France
| | | | - Riccardo Pellarin
- Institut Pasteur, Structural Bioinformatics Unit, Department of Structural Biology and Chemistry, CNRS UMR 3528, C3BI USR 3756 CNRS & IP, Paris, France.
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13
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Cossio P, Hummer G. Likelihood-based structural analysis of electron microscopy images. Curr Opin Struct Biol 2018; 49:162-168. [PMID: 29579548 DOI: 10.1016/j.sbi.2018.03.004] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2017] [Revised: 01/24/2018] [Accepted: 03/06/2018] [Indexed: 10/17/2022]
Abstract
Likelihood-based analysis of single-particle electron microscopy images has contributed much to the recent improvements in resolution. By treating particle orientations and classes probabilistically, uncertainties in the reconstruction process are explicitly accounted for, and the risk of bias towards the initial model is diminished. As a result, the quality and reliability of the reconstructions have greatly improved at manageable computational cost. Likelihood-based analysis of electron microscopy images also offers a route to direct coordinate refinement for dynamic systems, as an alternative to 3D density reconstruction. Here, we review recent developments in the algorithms used for reconstructions of high-resolution maps, and in the integrative framework of combining likelihood methods with simulations to address conformational variability in cryo-electron microscopy.
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Affiliation(s)
- Pilar Cossio
- Biophysics of Tropical Diseases, Max Planck Tandem Group, University of Antioquia, Medellín, Colombia; Department of Theoretical Biophysics, Max Planck Institute of Biophysics, 60438 Frankfurt am Main, Germany.
| | - Gerhard Hummer
- Department of Theoretical Biophysics, Max Planck Institute of Biophysics, 60438 Frankfurt am Main, Germany; Institute of Biophysics, Goethe University Frankfurt, 60438 Frankfurt am Main, Germany
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14
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Abstract
In eukaryotes, RNA polymerase II (pol II) transcribes all protein-coding genes and many noncoding RNAs. Whereas many factors contribute to the regulation of pol II activity, the Mediator complex is required for expression of most, if not all, pol II transcripts. Structural characterization of Mediator is challenging due to its large size (∼20 subunits in yeast and 26 subunits in humans) and conformational flexibility. However, recent studies have revealed structural details at higher resolution. Here, we summarize recent findings and place in context with previous results, highlighting regions within Mediator that are important for regulating its structure and function.
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Affiliation(s)
- Thomas M Harper
- From the Department of Biochemistry, University of Colorado, Boulder, Colorado 80303
| | - Dylan J Taatjes
- From the Department of Biochemistry, University of Colorado, Boulder, Colorado 80303
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15
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Viswanath S, Bonomi M, Kim SJ, Klenchin VA, Taylor KC, Yabut KC, Umbreit NT, Van Epps HA, Meehl J, Jones MH, Russel D, Velazquez-Muriel JA, Winey M, Rayment I, Davis TN, Sali A, Muller EG. The molecular architecture of the yeast spindle pole body core determined by Bayesian integrative modeling. Mol Biol Cell 2017; 28:3298-3314. [PMID: 28814505 PMCID: PMC5687031 DOI: 10.1091/mbc.e17-06-0397] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2017] [Revised: 08/07/2017] [Accepted: 08/09/2017] [Indexed: 12/31/2022] Open
Abstract
A model of the core of the yeast spindle pole body (SPB) was created by a Bayesian modeling approach that integrated a diverse data set of biophysical, biochemical, and genetic information. The model led to a proposed pathway for the assembly of Spc110, a protein related to pericentrin, and a mechanism for how calmodulin strengthens the SPB during mitosis. Microtubule-organizing centers (MTOCs) form, anchor, and stabilize the polarized network of microtubules in a cell. The central MTOC is the centrosome that duplicates during the cell cycle and assembles a bipolar spindle during mitosis to capture and segregate sister chromatids. Yet, despite their importance in cell biology, the physical structure of MTOCs is poorly understood. Here we determine the molecular architecture of the core of the yeast spindle pole body (SPB) by Bayesian integrative structure modeling based on in vivo fluorescence resonance energy transfer (FRET), small-angle x-ray scattering (SAXS), x-ray crystallography, electron microscopy, and two-hybrid analysis. The model is validated by several methods that include a genetic analysis of the conserved PACT domain that recruits Spc110, a protein related to pericentrin, to the SPB. The model suggests that calmodulin can act as a protein cross-linker and Spc29 is an extended, flexible protein. The model led to the identification of a single, essential heptad in the coiled-coil of Spc110 and a minimal PACT domain. It also led to a proposed pathway for the integration of Spc110 into the SPB.
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Affiliation(s)
- Shruthi Viswanath
- Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, California Institute for Quantitative Biosciences, University of California, San Francisco, San Francisco, CA 94158
| | - Massimiliano Bonomi
- Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, California Institute for Quantitative Biosciences, University of California, San Francisco, San Francisco, CA 94158 .,Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, UK
| | - Seung Joong Kim
- Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, California Institute for Quantitative Biosciences, University of California, San Francisco, San Francisco, CA 94158
| | - Vadim A Klenchin
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI 53706
| | - Keenan C Taylor
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI 53706
| | - King C Yabut
- Department of Biochemistry, University of Washington, Seattle, WA 98195
| | - Neil T Umbreit
- Department of Biochemistry, University of Washington, Seattle, WA 98195
| | | | - Janet Meehl
- Department of Molecular, Cellular and Developmental Biology, University of Colorado-Boulder, Boulder, CO 80309
| | - Michele H Jones
- Department of Molecular, Cellular and Developmental Biology, University of Colorado-Boulder, Boulder, CO 80309
| | - Daniel Russel
- Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, California Institute for Quantitative Biosciences, University of California, San Francisco, San Francisco, CA 94158
| | - Javier A Velazquez-Muriel
- Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, California Institute for Quantitative Biosciences, University of California, San Francisco, San Francisco, CA 94158
| | - Mark Winey
- Department of Molecular, Cellular and Developmental Biology, University of Colorado-Boulder, Boulder, CO 80309
| | - Ivan Rayment
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI 53706
| | - Trisha N Davis
- Department of Biochemistry, University of Washington, Seattle, WA 98195
| | - Andrej Sali
- Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, California Institute for Quantitative Biosciences, University of California, San Francisco, San Francisco, CA 94158
| | - Eric G Muller
- Department of Biochemistry, University of Washington, Seattle, WA 98195
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16
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Reconstruction of 3D structures of MET antibodies from electron microscopy 2D class averages. PLoS One 2017; 12:e0175758. [PMID: 28406969 PMCID: PMC5391116 DOI: 10.1371/journal.pone.0175758] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2016] [Accepted: 03/30/2017] [Indexed: 11/19/2022] Open
Abstract
Dynamics of three MET antibody constructs (IgG1, IgG2, and IgG4) and the IgG4-MET antigen complex was investigated by creating their atomic models with an integrative experimental and computational approach. In particular, we used two-dimensional (2D) Electron Microscopy (EM) images, image class averaging, homology modeling, Rapidly exploring Random Tree (RRT) structure sampling, and fitting of models to images, to find the relative orientations of antibody domains that are consistent with the EM images. We revealed that the conformational preferences of the constructs depend on the extent of the hinge flexibility. We also quantified how the MET antigen impacts on the conformational dynamics of IgG4. These observations allow to create testable hypothesis to investigate MET biology. Our protocol may also help describe structural diversity of other antigen systems at approximately 5 Å precision, as quantified by Root-Mean-Square Deviation (RMSD) among good-scoring models.
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17
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Bonomi M, Camilloni C, Vendruscolo M. Metadynamic metainference: Enhanced sampling of the metainference ensemble using metadynamics. Sci Rep 2016; 6:31232. [PMID: 27561930 PMCID: PMC4999896 DOI: 10.1038/srep31232] [Citation(s) in RCA: 52] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2016] [Accepted: 07/11/2016] [Indexed: 01/23/2023] Open
Abstract
Accurate and precise structural ensembles of proteins and macromolecular complexes can be obtained with metainference, a recently proposed Bayesian inference method that integrates experimental information with prior knowledge and deals with all sources of errors in the data as well as with sample heterogeneity. The study of complex macromolecular systems, however, requires an extensive conformational sampling, which represents a separate challenge. To address such challenge and to exhaustively and efficiently generate structural ensembles we combine metainference with metadynamics and illustrate its application to the calculation of the free energy landscape of the alanine dipeptide.
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Affiliation(s)
- Massimiliano Bonomi
- Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, UK
| | - Carlo Camilloni
- Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, UK
- Department of Chemistry and Institute for Advanced Study, Technische Universität München, Lichtenbergstrasse 4, D-85747 Garching, Germany
| | - Michele Vendruscolo
- Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, UK
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18
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Computational Refinement and Validation Protocol for Proteins with Large Variable Regions Applied to Model HIV Env Spike in CD4 and 17b Bound State. Structure 2016; 23:1138-49. [PMID: 26039348 DOI: 10.1016/j.str.2015.03.026] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2014] [Revised: 03/11/2015] [Accepted: 03/13/2015] [Indexed: 12/28/2022]
Abstract
Envelope glycoprotein gp120 of HIV-1 possesses several variable regions; their precise structure has been difficult to establish. We report a new model of gp120, in complex with antibodies CD4 and 17b, complete with its variable regions. The model was produced by a computational protocol that uses cryo-electron microscopy (EM) maps, atomic-resolution structures of the core, and information on binding interactions. Our model has excellent fit with EMD: 5020, is stereochemically and energetically favorable, and has the expected binding interfaces. Comparison of the ternary arrangement of the loops in this model with those bound to PGT122 and PGV04 suggested a possible motion of the V1V2 away from the CCR5 binding site and toward CD4. Our study also revealed that the CD4-bound state of the V1V2 loop is not optimal for gp120 bound with several neutralizing antibodies.
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19
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Zelter A, Bonomi M, Kim JO, Umbreit NT, Hoopmann MR, Johnson R, Riffle M, Jaschob D, MacCoss MJ, Moritz RL, Davis TN. The molecular architecture of the Dam1 kinetochore complex is defined by cross-linking based structural modelling. Nat Commun 2015; 6:8673. [PMID: 26560693 PMCID: PMC4660060 DOI: 10.1038/ncomms9673] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2015] [Accepted: 09/18/2015] [Indexed: 12/28/2022] Open
Abstract
Accurate segregation of chromosomes during cell division is essential. The Dam1 complex binds kinetochores to microtubules and its oligomerization is required to form strong attachments. It is a key target of Aurora B kinase, which destabilizes erroneous attachments allowing subsequent correction. Understanding the roles and regulation of the Dam1 complex requires structural information. Here we apply cross-linking/mass spectrometry and structural modelling to determine the molecular architecture of the Dam1 complex. We find microtubule attachment is accompanied by substantial conformational changes, with direct binding mediated by the carboxy termini of Dam1p and Duo1p. Aurora B phosphorylation of Dam1p C terminus weakens direct interaction with the microtubule. Furthermore, the Dam1p amino terminus forms an interaction interface between Dam1 complexes, which is also disrupted by phosphorylation. Our results demonstrate that Aurora B inhibits both direct interaction with the microtubule and oligomerization of the Dam1 complex to drive error correction during mitosis.
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Affiliation(s)
- Alex Zelter
- Department of Biochemistry, University of Washington, Seattle, Washington 98195, USA
| | | | - Jae Ook Kim
- Department of Biochemistry, University of Washington, Seattle, Washington 98195, USA
| | - Neil T Umbreit
- Department of Biochemistry, University of Washington, Seattle, Washington 98195, USA
| | | | - Richard Johnson
- Department of Genome Sciences, University of Washington, Seattle, Washington 98195, USA
| | - Michael Riffle
- Department of Biochemistry, University of Washington, Seattle, Washington 98195, USA
| | - Daniel Jaschob
- Department of Biochemistry, University of Washington, Seattle, Washington 98195, USA
| | - Michael J MacCoss
- Department of Genome Sciences, University of Washington, Seattle, Washington 98195, USA
| | - Robert L Moritz
- Institute for Systems Biology, Seattle, Washington 98109, USA
| | - Trisha N Davis
- Department of Biochemistry, University of Washington, Seattle, Washington 98195, USA
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20
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Liu J, Yu C, Hu X, Kim JK, Bierma JC, Jun HI, Rychnovsky SD, Huang L, Qiao F. Dissecting Fission Yeast Shelterin Interactions via MICro-MS Links Disruption of Shelterin Bridge to Tumorigenesis. Cell Rep 2015; 12:2169-80. [PMID: 26365187 PMCID: PMC4591219 DOI: 10.1016/j.celrep.2015.08.043] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2015] [Revised: 07/12/2015] [Accepted: 08/11/2015] [Indexed: 11/16/2022] Open
Abstract
Shelterin, a six-member complex, protects telomeres from nucleolytic attack and regulates their elongation by telomerase. Here, we have developed a strategy, called MICro-MS (Mapping Interfaces via Crosslinking-Mass Spectrometry), that combines crosslinking-mass spectrometry and phylogenetic analysis to identify contact sites within the complex. This strategy allowed identification of separation-of-function mutants of fission yeast Ccq1, Poz1, and Pot1 that selectively disrupt their respective interactions with Tpz1. The various telomere dysregulation phenotypes observed in these mutants further emphasize the critical regulatory roles of Tpz1-centered shelterin interactions in telomere homeostasis. Furthermore, the conservation between fission yeast Tpz1-Pot1 and human TPP1-POT1 interactions led us to map a human melanoma-associated POT1 mutation (A532P) to the TPP1-POT1 interface. Diminished TPP1-POT1 interaction caused by hPOT1-A532P may enable unregulated telomere extension, which, in turn, helps cancer cells to achieve replicative immortality. Therefore, our study reveals a connection between shelterin connectivity and tumorigenicity.
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Affiliation(s)
- Jinqiang Liu
- Department of Biological Chemistry, School of Medicine, University of California, Irvine, Irvine, CA 92697-1700, USA
| | - Clinton Yu
- Department of Physiology and Biophysics, School of Medicine, University of California, Irvine, Irvine, CA 92697-4560, USA
| | - Xichan Hu
- Department of Biological Chemistry, School of Medicine, University of California, Irvine, Irvine, CA 92697-1700, USA
| | - Jin-Kwang Kim
- Department of Biological Chemistry, School of Medicine, University of California, Irvine, Irvine, CA 92697-1700, USA
| | - Jan C Bierma
- Department of Biological Chemistry, School of Medicine, University of California, Irvine, Irvine, CA 92697-1700, USA
| | - Hyun-Ik Jun
- Department of Biological Chemistry, School of Medicine, University of California, Irvine, Irvine, CA 92697-1700, USA
| | - Scott D Rychnovsky
- Department of Chemistry, University of California, Irvine, Irvine, CA 92697-2025, USA
| | - Lan Huang
- Department of Physiology and Biophysics, School of Medicine, University of California, Irvine, Irvine, CA 92697-4560, USA
| | - Feng Qiao
- Department of Biological Chemistry, School of Medicine, University of California, Irvine, Irvine, CA 92697-1700, USA.
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21
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Integrative Modeling of Biomolecular Complexes: HADDOCKing with Cryo-Electron Microscopy Data. Structure 2015; 23:949-960. [DOI: 10.1016/j.str.2015.03.014] [Citation(s) in RCA: 60] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2014] [Revised: 03/12/2015] [Accepted: 03/13/2015] [Indexed: 12/13/2022]
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22
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Politis A, Borysik AJ. Assembling the pieces of macromolecular complexes: Hybrid structural biology approaches. Proteomics 2015; 15:2792-803. [DOI: 10.1002/pmic.201400507] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2014] [Revised: 01/26/2015] [Accepted: 02/24/2015] [Indexed: 01/14/2023]
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23
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Schröder GF. Hybrid methods for macromolecular structure determination: experiment with expectations. Curr Opin Struct Biol 2015; 31:20-7. [DOI: 10.1016/j.sbi.2015.02.016] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2014] [Revised: 02/22/2015] [Accepted: 02/26/2015] [Indexed: 12/15/2022]
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24
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Bonomi M, Pellarin R, Kim SJ, Russel D, Sundin BA, Riffle M, Jaschob D, Ramsden R, Davis TN, Muller EGD, Sali A. Determining protein complex structures based on a Bayesian model of in vivo Förster resonance energy transfer (FRET) data. Mol Cell Proteomics 2014; 13:2812-23. [PMID: 25139910 PMCID: PMC4223474 DOI: 10.1074/mcp.m114.040824] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2014] [Revised: 08/13/2014] [Indexed: 12/24/2022] Open
Abstract
The use of in vivo Förster resonance energy transfer (FRET) data to determine the molecular architecture of a protein complex in living cells is challenging due to data sparseness, sample heterogeneity, signal contributions from multiple donors and acceptors, unequal fluorophore brightness, photobleaching, flexibility of the linker connecting the fluorophore to the tagged protein, and spectral cross-talk. We addressed these challenges by using a Bayesian approach that produces the posterior probability of a model, given the input data. The posterior probability is defined as a function of the dependence of our FRET metric FRETR on a structure (forward model), a model of noise in the data, as well as prior information about the structure, relative populations of distinct states in the sample, forward model parameters, and data noise. The forward model was validated against kinetic Monte Carlo simulations and in vivo experimental data collected on nine systems of known structure. In addition, our Bayesian approach was validated by a benchmark of 16 protein complexes of known structure. Given the structures of each subunit of the complexes, models were computed from synthetic FRETR data with a distance root-mean-squared deviation error of 14 to 17 Å. The approach is implemented in the open-source Integrative Modeling Platform, allowing us to determine macromolecular structures through a combination of in vivo FRETR data and data from other sources, such as electron microscopy and chemical cross-linking.
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Affiliation(s)
- Massimiliano Bonomi
- From the ‡Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, California Institute for Quantitative Biosciences, University of California, San Francisco, California 94158; §Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, United Kingdom;
| | - Riccardo Pellarin
- From the ‡Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, California Institute for Quantitative Biosciences, University of California, San Francisco, California 94158
| | - Seung Joong Kim
- From the ‡Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, California Institute for Quantitative Biosciences, University of California, San Francisco, California 94158
| | - Daniel Russel
- From the ‡Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, California Institute for Quantitative Biosciences, University of California, San Francisco, California 94158
| | - Bryan A Sundin
- ‖Department of Biochemistry, University of Washington, Seattle, Washington 98195
| | - Michael Riffle
- ‖Department of Biochemistry, University of Washington, Seattle, Washington 98195
| | - Daniel Jaschob
- ‖Department of Biochemistry, University of Washington, Seattle, Washington 98195
| | - Richard Ramsden
- ‖Department of Biochemistry, University of Washington, Seattle, Washington 98195
| | - Trisha N Davis
- ‖Department of Biochemistry, University of Washington, Seattle, Washington 98195
| | - Eric G D Muller
- ‖Department of Biochemistry, University of Washington, Seattle, Washington 98195
| | - Andrej Sali
- From the ‡Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, California Institute for Quantitative Biosciences, University of California, San Francisco, California 94158;
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25
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Hopf TA, Schärfe CPI, Rodrigues JPGLM, Green AG, Kohlbacher O, Sander C, Bonvin AMJJ, Marks DS. Sequence co-evolution gives 3D contacts and structures of protein complexes. eLife 2014; 3. [PMID: 25255213 PMCID: PMC4360534 DOI: 10.7554/elife.03430] [Citation(s) in RCA: 332] [Impact Index Per Article: 33.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2014] [Accepted: 09/23/2014] [Indexed: 12/24/2022] Open
Abstract
Protein-protein interactions are fundamental to many biological processes. Experimental screens have identified tens of thousands of interactions, and structural biology has provided detailed functional insight for select 3D protein complexes. An alternative rich source of information about protein interactions is the evolutionary sequence record. Building on earlier work, we show that analysis of correlated evolutionary sequence changes across proteins identifies residues that are close in space with sufficient accuracy to determine the three-dimensional structure of the protein complexes. We evaluate prediction performance in blinded tests on 76 complexes of known 3D structure, predict protein-protein contacts in 32 complexes of unknown structure, and demonstrate how evolutionary couplings can be used to distinguish between interacting and non-interacting protein pairs in a large complex. With the current growth of sequences, we expect that the method can be generalized to genome-wide elucidation of protein-protein interaction networks and used for interaction predictions at residue resolution.
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Affiliation(s)
- Thomas A Hopf
- Department of Systems Biology, Harvard University, Boston, United States
| | | | - João P G L M Rodrigues
- Computational Structural Biology Group, Bijvoet Center for Biomolecular Research, Utrecht University, Utrecht, Netherlands
| | - Anna G Green
- Department of Systems Biology, Harvard University, Boston, United States
| | - Oliver Kohlbacher
- Applied Bioinformatics, Quantitative Biology Center, University of Tübingen, Tübingen, Germany
| | - Chris Sander
- Computational Biology Center, Memorial Sloan Kettering Cancer Center, New York, United States
| | - Alexandre M J J Bonvin
- Computational Structural Biology Group, Bijvoet Center for Biomolecular Research, Utrecht University, Utrecht, Netherlands
| | - Debora S Marks
- Department of Systems Biology, Harvard University, Boston, United States
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26
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López-Blanco JR, Chacón P. Structural modeling from electron microscopy data. WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE 2014. [DOI: 10.1002/wcms.1199] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Affiliation(s)
- José Ramón López-Blanco
- Department of Biological Physical Chemistry; Rocasolano Physical Chemistry Institute, CSIC; Madrid Spain
| | - Pablo Chacón
- Department of Biological Physical Chemistry; Rocasolano Physical Chemistry Institute, CSIC; Madrid Spain
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27
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Shi Y, Fernandez-Martinez J, Tjioe E, Pellarin R, Kim SJ, Williams R, Schneidman-Duhovny D, Sali A, Rout MP, Chait BT. Structural characterization by cross-linking reveals the detailed architecture of a coatomer-related heptameric module from the nuclear pore complex. Mol Cell Proteomics 2014; 13:2927-43. [PMID: 25161197 DOI: 10.1074/mcp.m114.041673] [Citation(s) in RCA: 131] [Impact Index Per Article: 13.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
Abstract
Most cellular processes are orchestrated by macromolecular complexes. However, structural elucidation of these endogenous complexes can be challenging because they frequently contain large numbers of proteins, are compositionally and morphologically heterogeneous, can be dynamic, and are often of low abundance in the cell. Here, we present a strategy for the structural characterization of such complexes that has at its center chemical cross-linking with mass spectrometric readout. In this strategy, we isolate the endogenous complexes using a highly optimized sample preparation protocol and generate a comprehensive, high-quality cross-linking dataset using two complementary cross-linking reagents. We then determine the structure of the complex using a refined integrative method that combines the cross-linking data with information generated from other sources, including electron microscopy, X-ray crystallography, and comparative protein structure modeling. We applied this integrative strategy to determine the structure of the native Nup84 complex, a stable hetero-heptameric assembly (∼ 600 kDa), 16 copies of which form the outer rings of the 50-MDa nuclear pore complex (NPC) in budding yeast. The unprecedented detail of the Nup84 complex structure reveals previously unseen features in its pentameric structural hub and provides information on the conformational flexibility of the assembly. These additional details further support and augment the protocoatomer hypothesis, which proposes an evolutionary relationship between vesicle coating complexes and the NPC, and indicates a conserved mechanism by which the NPC is anchored in the nuclear envelope.
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Affiliation(s)
- Yi Shi
- From the ‡Laboratory of Mass Spectrometry and Gaseous Ion Chemistry, The Rockefeller University, New York, New York 10065
| | - Javier Fernandez-Martinez
- ¶Laboratory of Cellular and Structural Biology, The Rockefeller University, New York, New York 10065
| | - Elina Tjioe
- ‖Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, California Institute for Quantitative Biosciences, Byers Hall, 1700 4th Street, Suite 503B, University of California, San Francisco, San Francisco, California 94158
| | - Riccardo Pellarin
- ‖Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, California Institute for Quantitative Biosciences, Byers Hall, 1700 4th Street, Suite 503B, University of California, San Francisco, San Francisco, California 94158
| | - Seung Joong Kim
- ‖Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, California Institute for Quantitative Biosciences, Byers Hall, 1700 4th Street, Suite 503B, University of California, San Francisco, San Francisco, California 94158
| | - Rosemary Williams
- ¶Laboratory of Cellular and Structural Biology, The Rockefeller University, New York, New York 10065
| | - Dina Schneidman-Duhovny
- ‖Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, California Institute for Quantitative Biosciences, Byers Hall, 1700 4th Street, Suite 503B, University of California, San Francisco, San Francisco, California 94158
| | - Andrej Sali
- ‖Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, California Institute for Quantitative Biosciences, Byers Hall, 1700 4th Street, Suite 503B, University of California, San Francisco, San Francisco, California 94158
| | - Michael P Rout
- ¶Laboratory of Cellular and Structural Biology, The Rockefeller University, New York, New York 10065;
| | - Brian T Chait
- From the ‡Laboratory of Mass Spectrometry and Gaseous Ion Chemistry, The Rockefeller University, New York, New York 10065;
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28
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Kim SJ, Fernandez-Martinez J, Sampathkumar P, Martel A, Matsui T, Tsuruta H, Weiss TM, Shi Y, Markina-Inarrairaegui A, Bonanno JB, Sauder JM, Burley SK, Chait BT, Almo SC, Rout MP, Sali A. Integrative structure-function mapping of the nucleoporin Nup133 suggests a conserved mechanism for membrane anchoring of the nuclear pore complex. Mol Cell Proteomics 2014; 13:2911-26. [PMID: 25139911 DOI: 10.1074/mcp.m114.040915] [Citation(s) in RCA: 61] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023] Open
Abstract
The nuclear pore complex (NPC) is the sole passageway for the transport of macromolecules across the nuclear envelope. Nup133, a major component in the essential Y-shaped Nup84 complex, is a large scaffold protein of the NPC's outer ring structure. Here, we describe an integrative modeling approach that produces atomic models for multiple states of Saccharomyces cerevisiae (Sc) Nup133, based on the crystal structures of the sequence segments and their homologs, including the related Vanderwaltozyma polyspora (Vp) Nup133 residues 55 to 502 (VpNup133(55-502)) determined in this study, small angle X-ray scattering profiles for 18 constructs of ScNup133 and one construct of VpNup133, and 23 negative-stain electron microscopy class averages of ScNup133(2-1157). Using our integrative approach, we then computed a multi-state structural model of the full-length ScNup133 and validated it with mutational studies and 45 chemical cross-links determined via mass spectrometry. Finally, the model of ScNup133 allowed us to annotate a potential ArfGAP1 lipid packing sensor (ALPS) motif in Sc and VpNup133 and discuss its potential significance in the context of the whole NPC; we suggest that ALPS motifs are scattered throughout the NPC's scaffold in all eukaryotes and play a major role in the assembly and membrane anchoring of the NPC in the nuclear envelope. Our results are consistent with a common evolutionary origin of Nup133 with membrane coating complexes (the protocoatomer hypothesis); the presence of the ALPS motifs in coatomer-like nucleoporins suggests an ancestral mechanism for membrane recognition present in early membrane coating complexes.
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Affiliation(s)
- Seung Joong Kim
- From the ‡Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, California Institute for Quantitative Biosciences, Byers Hall, 1700 4th Street, Suite 503B, University of California San Francisco, San Francisco, California 94158
| | - Javier Fernandez-Martinez
- ¶Laboratory of Cellular and Structural Biology, The Rockefeller University, New York, New York 10065
| | - Parthasarathy Sampathkumar
- ‖Department of Biochemistry, Ullmann Building, Room 409, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, New York 10461
| | - Anne Martel
- **Stanford Synchrotron Radiation Lightsource, SLAC National Accelerator Laboratory, 2575 Sand Hill Road, MS 69, Menlo Park, California 94025
| | - Tsutomu Matsui
- **Stanford Synchrotron Radiation Lightsource, SLAC National Accelerator Laboratory, 2575 Sand Hill Road, MS 69, Menlo Park, California 94025
| | - Hiro Tsuruta
- **Stanford Synchrotron Radiation Lightsource, SLAC National Accelerator Laboratory, 2575 Sand Hill Road, MS 69, Menlo Park, California 94025
| | - Thomas M Weiss
- **Stanford Synchrotron Radiation Lightsource, SLAC National Accelerator Laboratory, 2575 Sand Hill Road, MS 69, Menlo Park, California 94025
| | - Yi Shi
- ‡‡Laboratory of Mass Spectrometry and Gaseous Ion Chemistry, The Rockefeller University, New York, New York 10065
| | - Ane Markina-Inarrairaegui
- §§Laboratorio de Genetica Molecular de Aspergillus, Departamento de Biología Celular y Molecular, Centro de Investigaciones Biológicas (CSIC), Ramiro de Maeztu 9, 28040, Madrid, Spain
| | - Jeffery B Bonanno
- ‖Department of Biochemistry, Ullmann Building, Room 409, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, New York 10461
| | - J Michael Sauder
- ¶¶Discovery Chemistry Research and Technologies (DCR&T), Eli Lilly and Company, Lilly Biotechnology Center, 10300 Campus Point Drive, Suite 200, San Diego, California 92121
| | - Stephen K Burley
- ‖‖Center for Integrative Proteomics Research, Department of Chemistry and Chemical Biology, Rutgers, the State University of New Jersey, 174 Frelinghuysen Road, Piscataway, New Jersey 08854
| | - Brian T Chait
- ‡‡Laboratory of Mass Spectrometry and Gaseous Ion Chemistry, The Rockefeller University, New York, New York 10065
| | - Steven C Almo
- ‖Department of Biochemistry, Ullmann Building, Room 409, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, New York 10461;
| | - Michael P Rout
- ¶Laboratory of Cellular and Structural Biology, The Rockefeller University, New York, New York 10065;
| | - Andrej Sali
- From the ‡Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, California Institute for Quantitative Biosciences, Byers Hall, 1700 4th Street, Suite 503B, University of California San Francisco, San Francisco, California 94158;
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29
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Schwede T. Protein modeling: what happened to the "protein structure gap"? Structure 2014; 21:1531-40. [PMID: 24010712 DOI: 10.1016/j.str.2013.08.007] [Citation(s) in RCA: 83] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2013] [Revised: 08/12/2013] [Accepted: 08/12/2013] [Indexed: 11/27/2022]
Abstract
Computational modeling of three-dimensional macromolecular structures and complexes from their sequence has been a long-standing vision in structural biology. Over the last 2 decades, a paradigm shift has occurred: starting from a large "structure knowledge gap" between the huge number of protein sequences and small number of known structures, today, some form of structural information, either experimental or template-based models, is available for the majority of amino acids encoded by common model organism genomes. With the scientific focus of interest moving toward larger macromolecular complexes and dynamic networks of interactions, the integration of computational modeling methods with low-resolution experimental techniques allows the study of large and complex molecular machines. One of the open challenges for computational modeling and prediction techniques is to convey the underlying assumptions, as well as the expected accuracy and structural variability of a specific model, which is crucial to understanding its limitations.
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Affiliation(s)
- Torsten Schwede
- Biozentrum, University of Basel, Klingelbergstrasse 50-70, 4056 Basel, Switzerland; Computational Structural Biology, SIB Swiss Institute of Bioinformatics, Klingelbergstrasse 50-70, 4056 Basel, Switzerland.
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30
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Thalassinos K, Pandurangan AP, Xu M, Alber F, Topf M. Conformational States of macromolecular assemblies explored by integrative structure calculation. Structure 2014; 21:1500-8. [PMID: 24010709 PMCID: PMC3988990 DOI: 10.1016/j.str.2013.08.006] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2013] [Revised: 08/10/2013] [Accepted: 08/12/2013] [Indexed: 12/22/2022]
Abstract
A detailed description of macromolecular assemblies in multiple conformational states can be very valuable for understanding cellular processes. At present, structural determination of most assemblies in different biologically relevant conformations cannot be achieved by a single technique and thus requires an integrative approach that combines information from multiple sources. Different techniques require different computational methods to allow efficient and accurate data processing and analysis. Here, we summarize the latest advances and future challenges in computational methods that help the interpretation of data from two techniques—mass spectrometry and three-dimensional cryo-electron microscopy (with focus on alignment and classification of heterogeneous subtomograms from cryo-electron tomography). We evaluate how new developments in these two broad fields will lead to further integration with atomic structures to broaden our picture of the dynamic behavior of assemblies in their native environment.
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Affiliation(s)
- Konstantinos Thalassinos
- Institute of Structural and Molecular Biology, Division of Biosciences, University College London, London WC1E 6BT, UK
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31
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Webb B, Lasker K, Velázquez-Muriel J, Schneidman-Duhovny D, Pellarin R, Bonomi M, Greenberg C, Raveh B, Tjioe E, Russel D, Sali A. Modeling of proteins and their assemblies with the Integrative Modeling Platform. Methods Mol Biol 2014; 1091:277-95. [PMID: 24203340 DOI: 10.1007/978-1-62703-691-7_20] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
To understand the workings of the living cell, we need to characterize protein assemblies that constitute the cell (for example, the ribosome, 26S proteasome, and the nuclear pore complex). A reliable high-resolution structural characterization of these assemblies is frequently beyond the reach of current experimental methods, such as X-ray crystallography, NMR spectroscopy, electron microscopy, footprinting, chemical cross-linking, FRET spectroscopy, small angle X-ray scattering, and proteomics. However, the information garnered from different methods can be combined and used to build models of the assembly structures that are consistent with all of the available datasets, and therefore more accurate, precise, and complete. Here, we describe a protocol for this integration, whereby the information is converted to a set of spatial restraints and a variety of optimization procedures can be used to generate models that satisfy the restraints as well as possible. These generated models can then potentially inform about the precision and accuracy of structure determination, the accuracy of the input datasets, and further data generation. We also demonstrate the Integrative Modeling Platform (IMP) software, which provides the necessary computational framework to implement this protocol, and several applications for specific use cases.
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Affiliation(s)
- Benjamin Webb
- Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, and California Institute for Quanstitative Biosciences (QB3), University of California San Francisco, San Francisco, CA, USA
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32
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Cossio P, Hummer G. Bayesian analysis of individual electron microscopy images: towards structures of dynamic and heterogeneous biomolecular assemblies. J Struct Biol 2013; 184:427-37. [PMID: 24161733 DOI: 10.1016/j.jsb.2013.10.006] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2013] [Revised: 10/05/2013] [Accepted: 10/09/2013] [Indexed: 10/26/2022]
Abstract
We develop a method to extract structural information from electron microscopy (EM) images of dynamic and heterogeneous molecular assemblies. To overcome the challenge of disorder in the imaged structures, we analyze each image individually, avoiding information loss through clustering or averaging. The Bayesian inference of EM (BioEM) method uses a likelihood-based probabilistic measure to quantify the consistency between each EM image and given structural models. The likelihood function accounts for uncertainties in the molecular position and orientation, variations in the relative intensities and noise in the experimental images. The BioEM formalism is physically intuitive and mathematically simple. We show that for experimental GroEL images, BioEM correctly identifies structures according to the functional state. The top-ranked structure is the corresponding X-ray crystal structure, followed by an EM structure generated previously from a superset of the EM images used here. To analyze EM images of highly flexible molecules, we propose an ensemble refinement procedure, and validate it with synthetic EM maps of the ESCRT-I-II supercomplex. Both the size of the ensemble and its structural members are identified correctly. BioEM offers an alternative to 3D-reconstruction methods, extracting accurate population distributions for highly flexible structures and their assemblies. We discuss limitations of the method, and possible applications beyond ensemble refinement, including the cross-validation and unbiased post-assessment of model structures, and the structural characterization of systems where traditional approaches fail. Overall, our results suggest that the BioEM framework can be used to analyze EM images of both ordered and disordered molecular systems.
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Affiliation(s)
- Pilar Cossio
- Department of Theoretical Biophysics, Max Planck Institute of Biophysics, 60438 Frankfurt am Main, Germany; Laboratory of Chemical Physics, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD 20892-0520, USA
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33
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Kundrotas PJ, Vakser IA, Janin J. Structural templates for modeling homodimers. Protein Sci 2013; 22:1655-63. [PMID: 23996787 DOI: 10.1002/pro.2361] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2013] [Revised: 08/23/2013] [Accepted: 08/23/2013] [Indexed: 12/17/2022]
Abstract
Oligomeric proteins are more abundant in nature than monomeric proteins, and involved in all biological processes. In the absence of an experimental structure, their subunits can be modeled from their sequence like monomeric proteins, but reliable procedures to build the oligomeric assembly are scarce. Template-based methods, which start from known protein structures, are commonly applied to model subunits. We present a method to model homodimers that relies on a structural alignment of the subunits, and test it on a set of 511 target structures recently released by the Protein Data Bank, taking as templates the earlier released structures of 3108 homodimeric proteins (H-set), and 2691 monomeric proteins that form dimer-like assemblies in crystals (M-set). The structural alignment identifies a H-set template for 97% of the targets, and in half of the cases, it yields a correct model of the dimer geometry and residue-residue contacts in the target. It also identifies a M-set template for most of the targets, and some of the crystal dimers are very similar to the target homodimers. The procedure efficiently detects homology at low levels of sequence identities, and points to erroneous quaternary structures in the Protein Data Bank. The high coverage of the target set suggests that the content of the Protein Data Bank already approaches the structural diversity of protein assemblies in nature, and that template-based methods should become the choice method for modeling oligomeric as well as monomeric proteins.
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Affiliation(s)
- Petras J Kundrotas
- Center for Bioinformatics, The University of Kansas, 2030 Becker Dr., Lawrence, Kansas, 66047
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34
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Granata D, Camilloni C, Vendruscolo M, Laio A. Characterization of the free-energy landscapes of proteins by NMR-guided metadynamics. Proc Natl Acad Sci U S A 2013; 110:6817-22. [PMID: 23572592 PMCID: PMC3637744 DOI: 10.1073/pnas.1218350110] [Citation(s) in RCA: 110] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
The use of free-energy landscapes rationalizes a wide range of aspects of protein behavior by providing a clear illustration of the different states accessible to these molecules, as well as of their populations and pathways of interconversion. The determination of the free-energy landscapes of proteins by computational methods is, however, very challenging as it requires an extensive sampling of their conformational spaces. We describe here a technique to achieve this goal with relatively limited computational resources by incorporating nuclear magnetic resonance (NMR) chemical shifts as collective variables in metadynamics simulations. As in this approach the chemical shifts are not used as structural restraints, the resulting free-energy landscapes correspond to the force fields used in the simulations. We illustrate this approach in the case of the third Ig-binding domain of protein G from streptococcal bacteria (GB3). Our calculations reveal the existence of a folding intermediate of GB3 with nonnative structural elements. Furthermore, the availability of the free-energy landscape enables the folding mechanism of GB3 to be elucidated by analyzing the conformational ensembles corresponding to the native, intermediate, and unfolded states, as well as the transition states between them. Taken together, these results show that, by incorporating experimental data as collective variables in metadynamics simulations, it is possible to enhance the sampling efficiency by two or more orders of magnitude with respect to standard molecular dynamics simulations, and thus to estimate free-energy differences among the different states of a protein with a k(B)T accuracy by generating trajectories of just a few microseconds.
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Affiliation(s)
- Daniele Granata
- International School for Advanced Studies (SISSA), Trieste 34136, Italy; and
| | - Carlo Camilloni
- Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, United Kingdom
| | - Michele Vendruscolo
- Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, United Kingdom
| | - Alessandro Laio
- International School for Advanced Studies (SISSA), Trieste 34136, Italy; and
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Sampathkumar P, Kim SJ, Upla P, Rice WJ, Phillips J, Timney BL, Pieper U, Bonanno JB, Fernandez-Martinez J, Hakhverdyan Z, Ketaren NE, Matsui T, Weiss TM, Stokes DL, Sauder JM, Burley SK, Sali A, Rout MP, Almo SC. Structure, dynamics, evolution, and function of a major scaffold component in the nuclear pore complex. Structure 2013; 21:560-71. [PMID: 23499021 DOI: 10.1016/j.str.2013.02.005] [Citation(s) in RCA: 50] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2012] [Revised: 01/29/2013] [Accepted: 02/08/2013] [Indexed: 01/18/2023]
Abstract
The nuclear pore complex, composed of proteins termed nucleoporins (Nups), is responsible for nucleocytoplasmic transport in eukaryotes. Nuclear pore complexes (NPCs) form an annular structure composed of the nuclear ring, cytoplasmic ring, a membrane ring, and two inner rings. Nup192 is a major component of the NPC's inner ring. We report the crystal structure of Saccharomyces cerevisiae Nup192 residues 2-960 [ScNup192(2-960)], which adopts an α-helical fold with three domains (i.e., D1, D2, and D3). Small angle X-ray scattering and electron microscopy (EM) studies reveal that ScNup192(2-960) could undergo long-range transition between "open" and "closed" conformations. We obtained a structural model of full-length ScNup192 based on EM, the structure of ScNup192(2-960), and homology modeling. Evolutionary analyses using the ScNup192(2-960) structure suggest that NPCs and vesicle-coating complexes are descended from a common membrane-coating ancestral complex. We show that suppression of Nup192 expression leads to compromised nuclear transport and hypothesize a role for Nup192 in modulating the permeability of the NPC central channel.
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Affiliation(s)
- Parthasarathy Sampathkumar
- Department of Biochemistry, Ullmann Building, Room 409, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY 10461, USA
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