1
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Schaffer LV, Ideker T. Mapping the multiscale structure of biological systems. Cell Syst 2021; 12:622-635. [PMID: 34139169 PMCID: PMC8245186 DOI: 10.1016/j.cels.2021.05.012] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Revised: 05/04/2021] [Accepted: 05/14/2021] [Indexed: 01/14/2023]
Abstract
Biological systems are by nature multiscale, consisting of subsystems that factor into progressively smaller units in a deeply hierarchical structure. At any level of the hierarchy, an ever-increasing diversity of technologies can be applied to characterize the corresponding biological units and their relations, resulting in large networks of physical or functional proximities-e.g., proximities of amino acids within a protein, of proteins within a complex, or of cell types within a tissue. Here, we review general concepts and progress in using network proximity measures as a basis for creation of multiscale hierarchical maps of biological systems. We discuss the functionalization of these maps to create predictive models, including those useful in translation of genotype to phenotype, along with strategies for model visualization and challenges faced by multiscale modeling in the near future. Collectively, these approaches enable a unified hierarchical approach to biological data, with application from the molecular to the macroscopic.
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Affiliation(s)
- Leah V Schaffer
- Division of Genetics, Department of Medicine, University of California San Diego, San Diego, La Jolla, CA 92093, USA
| | - Trey Ideker
- Division of Genetics, Department of Medicine, University of California San Diego, San Diego, La Jolla, CA 92093, USA.
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2
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Filella-Merce I, Bardiaux B, Nilges M, Bouvier G. Quantitative Structural Interpretation of Protein Crosslinks. Structure 2020; 28:75-82.e4. [PMID: 31753619 DOI: 10.1016/j.str.2019.10.018] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2019] [Revised: 09/11/2019] [Accepted: 10/28/2019] [Indexed: 11/28/2022]
Abstract
Chemical crosslinking, combined with mass spectrometry analysis, is a key source of information for characterizing the structure of large protein assemblies, in the context of molecular modeling. In most approaches, the interpretation is limited to simple spatial restraints, neglecting physico-chemical interactions between the crosslinker and the protein and their flexibility. Here we present a method, named NRGXL (new realistic grid for crosslinks), which models the flexibility of the crosslinker and the linked side-chains, by explicitly sampling many conformations. Also, the method can efficiently deal with overall protein dynamics. This method creates a physical model of the crosslinker and associated energy. A classifier based on it outperforms others, based on Euclidean distance or solvent-accessible distance and its efficiency makes it usable for validating 3D models from crosslinking data. NRGXL is freely available as a web server at: https://nrgxl.pasteur.fr.
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Affiliation(s)
- Isaac Filella-Merce
- Structural Bioinformatics Unit, Department of Structural Biology and Chemistry, Institut Pasteur, CNRS UMR3528, C3BI, USR3756 Paris, France; Faculty of Health and Life Sciences, University Pompeu Fabra, Carrer del Doctor Aiguader 80, Barcelona 08003, Spain
| | - Benjamin Bardiaux
- Structural Bioinformatics Unit, Department of Structural Biology and Chemistry, Institut Pasteur, CNRS UMR3528, C3BI, USR3756 Paris, France
| | - Michael Nilges
- Structural Bioinformatics Unit, Department of Structural Biology and Chemistry, Institut Pasteur, CNRS UMR3528, C3BI, USR3756 Paris, France
| | - Guillaume Bouvier
- Structural Bioinformatics Unit, Department of Structural Biology and Chemistry, Institut Pasteur, CNRS UMR3528, C3BI, USR3756 Paris, France.
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3
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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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4
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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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5
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Schlessinger A, Welch MA, van Vlijmen H, Korzekwa K, Swaan PW, Matsson P. Molecular Modeling of Drug-Transporter Interactions-An International Transporter Consortium Perspective. Clin Pharmacol Ther 2018; 104:818-835. [PMID: 29981151 PMCID: PMC6197929 DOI: 10.1002/cpt.1174] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2018] [Accepted: 06/30/2018] [Indexed: 12/31/2022]
Abstract
Membrane transporters play diverse roles in the pharmacokinetics and pharmacodynamics of small-molecule drugs. Understanding the mechanisms of drug-transporter interactions at the molecular level is, therefore, essential for the design of drugs with optimal therapeutic effects. This white paper examines recent progress, applications, and challenges of molecular modeling of membrane transporters, including modeling techniques that are centered on the structures of transporter ligands, and those focusing on the structures of the transporters. The goals of this article are to illustrate current best practices and future opportunities in using molecular modeling techniques to understand and predict transporter-mediated effects on drug disposition and efficacy.Membrane transporters from the solute carrier (SLC) and ATP-binding cassette (ABC) superfamilies regulate the cellular uptake, efflux, and homeostasis of many essential nutrients and significantly impact the pharmacokinetics of drugs; further, they may provide targets for novel therapeutics as well as facilitate prodrug approaches. Because of their often broad substrate selectivity they are also implicated in many undesirable and sometimes life-threatening drug-drug interactions (DDIs).5,6.
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Affiliation(s)
- Avner Schlessinger
- Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Matthew A. Welch
- Department of Pharmaceutical Sciences, University of Maryland, Baltimore, MD
| | - Herman van Vlijmen
- Computational Chemistry, Discovery Sciences, Janssen Research & Development, Beerse, Belgium
| | - Ken Korzekwa
- Department of Pharmaceutical Sciences, Temple University, Philadelphia, PA
| | - Peter W. Swaan
- Department of Pharmaceutical Sciences, University of Maryland, Baltimore, MD
| | - Pär Matsson
- Department of Pharmacy, Uppsala University, Sweden
,Address correspondence to: Pär Matsson, Department of Pharmacy, Uppsala University, Box 580, SE-75123 Uppsala, Sweden, Phone: +46-(0)18-471 46 30, Fax: +46-(0)18-471 42 23,
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6
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Chu F, Thornton DT, Nguyen HT. Chemical cross-linking in the structural analysis of protein assemblies. Methods 2018; 144:53-63. [PMID: 29857191 DOI: 10.1016/j.ymeth.2018.05.023] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2018] [Revised: 05/22/2018] [Accepted: 05/25/2018] [Indexed: 12/31/2022] Open
Abstract
For decades, chemical cross-linking of proteins has been an established method to study protein interaction partners. The chemical cross-linking approach has recently been revived by mass spectrometric analysis of the cross-linking reaction products. Chemical cross-linking and mass spectrometric analysis (CXMS) enables the identification of residues that are close in three-dimensional (3D) space but not necessarily close in primary sequence. Therefore, this approach provides medium resolution information to guide de novo structure prediction, protein interface mapping and protein complex model building. The robustness and compatibility of the CXMS approach with multiple biochemical methods have made it especially appealing for challenging systems with multiple biochemical compositions and conformation states. This review provides an overview of the CXMS approach, describing general procedures in sample processing, data acquisition and analysis. Selection of proper chemical cross-linking reagents, strategies for cross-linked peptide identification, and successful application of CXMS in structural characterization of proteins and protein complexes are discussed.
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Affiliation(s)
- Feixia Chu
- Department of Molecular, Cellular & Biomedical Sciences, University of New Hampshire, Durham, NH 03824, United States; Hubbard Center for Genome Studies, University of New Hampshire, Durham, NH 03824, United States.
| | - Daniel T Thornton
- Department of Molecular, Cellular & Biomedical Sciences, University of New Hampshire, Durham, NH 03824, United States
| | - Hieu T Nguyen
- Department of Molecular, Cellular & Biomedical Sciences, University of New Hampshire, Durham, NH 03824, United States
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7
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Orbán-Németh Z, Beveridge R, Hollenstein DM, Rampler E, Stranzl T, Hudecz O, Doblmann J, Schlögelhofer P, Mechtler K. Structural prediction of protein models using distance restraints derived from cross-linking mass spectrometry data. Nat Protoc 2018; 13:478-494. [PMID: 29419816 PMCID: PMC5999019 DOI: 10.1038/nprot.2017.146] [Citation(s) in RCA: 46] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
This protocol describes a workflow for creating structural models of proteins or protein complexes using distance restraints derived from cross-linking mass spectrometry experiments. The distance restraints are used (i) to adjust preliminary models that are calculated on the basis of a homologous template and primary sequence, and (ii) to select the model that is in best agreement with the experimental data. In the case of protein complexes, the cross-linking data are further used to dock the subunits to one another to generate models of the interacting proteins. Predicting models in such a manner has the potential to indicate multiple conformations and dynamic changes that occur in solution. This modeling protocol is compatible with many cross-linking workflows and uses open-source programs or programs that are free for academic users and do not require expertise in computational modeling. This protocol is an excellent additional application with which to use cross-linking results for building structural models of proteins. The established protocol is expected to take 6-12 d to complete, depending on the size of the proteins and the complexity of the cross-linking data.
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Affiliation(s)
- Zsuzsanna Orbán-Németh
- Mass Spectrometry and Protein Chemistry, Research Institute of Molecular Pathology (IMP), Vienna Biocenter (VBC), Vienna, Austria
- Mass Spectrometry and Protein Chemistry, Institute of Molecular Biotechnology of the Austrian Academy of Sciences (IMBA), Vienna Biocenter (VBC), Vienna, Austria
| | - Rebecca Beveridge
- Mass Spectrometry and Protein Chemistry, Research Institute of Molecular Pathology (IMP), Vienna Biocenter (VBC), Vienna, Austria
- Mass Spectrometry and Protein Chemistry, Institute of Molecular Biotechnology of the Austrian Academy of Sciences (IMBA), Vienna Biocenter (VBC), Vienna, Austria
| | - David M. Hollenstein
- Department of Biochemistry and Cell Biology, Max F. Perutz Laboratories, University of Vienna, Vienna, Austria
| | - Evelyn Rampler
- Mass Spectrometry and Protein Chemistry, Research Institute of Molecular Pathology (IMP), Vienna Biocenter (VBC), Vienna, Austria
- Mass Spectrometry and Protein Chemistry, Institute of Molecular Biotechnology of the Austrian Academy of Sciences (IMBA), Vienna Biocenter (VBC), Vienna, Austria
- Department of Analytical Chemistry, Faculty of Chemistry, University of Vienna, Vienna, Austria
| | - Thomas Stranzl
- Mass Spectrometry and Protein Chemistry, Research Institute of Molecular Pathology (IMP), Vienna Biocenter (VBC), Vienna, Austria
- Mass Spectrometry and Protein Chemistry, Institute of Molecular Biotechnology of the Austrian Academy of Sciences (IMBA), Vienna Biocenter (VBC), Vienna, Austria
| | - Otto Hudecz
- Mass Spectrometry and Protein Chemistry, Research Institute of Molecular Pathology (IMP), Vienna Biocenter (VBC), Vienna, Austria
- Mass Spectrometry and Protein Chemistry, Institute of Molecular Biotechnology of the Austrian Academy of Sciences (IMBA), Vienna Biocenter (VBC), Vienna, Austria
| | - Johannes Doblmann
- Mass Spectrometry and Protein Chemistry, Research Institute of Molecular Pathology (IMP), Vienna Biocenter (VBC), Vienna, Austria
- Mass Spectrometry and Protein Chemistry, Institute of Molecular Biotechnology of the Austrian Academy of Sciences (IMBA), Vienna Biocenter (VBC), Vienna, Austria
| | - Peter Schlögelhofer
- Department of Chromosome Biology, Max F. Perutz Laboratories, University of Vienna, Vienna, Austria
| | - Karl Mechtler
- Mass Spectrometry and Protein Chemistry, Research Institute of Molecular Pathology (IMP), Vienna Biocenter (VBC), Vienna, Austria
- Mass Spectrometry and Protein Chemistry, Institute of Molecular Biotechnology of the Austrian Academy of Sciences (IMBA), Vienna Biocenter (VBC), Vienna, Austria
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8
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Politis A, Schmidt C. Structural characterisation of medically relevant protein assemblies by integrating mass spectrometry with computational modelling. J Proteomics 2018; 175:34-41. [DOI: 10.1016/j.jprot.2017.04.019] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2016] [Revised: 04/13/2017] [Accepted: 04/18/2017] [Indexed: 01/14/2023]
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9
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Webb B, Viswanath S, Bonomi M, Pellarin R, Greenberg CH, Saltzberg D, Sali A. Integrative structure modeling with the Integrative Modeling Platform. Protein Sci 2017; 27:245-258. [PMID: 28960548 DOI: 10.1002/pro.3311] [Citation(s) in RCA: 73] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2017] [Revised: 09/23/2017] [Accepted: 09/25/2017] [Indexed: 11/06/2022]
Abstract
Building models of a biological system that are consistent with the myriad data available is one of the key challenges in biology. Modeling the structure and dynamics of macromolecular assemblies, for example, can give insights into how biological systems work, evolved, might be controlled, and even designed. Integrative structure modeling casts the building of structural models as a computational optimization problem, for which information about the assembly is encoded into a scoring function that evaluates candidate models. Here, we describe our open source software suite for integrative structure modeling, Integrative Modeling Platform (https://integrativemodeling.org), and demonstrate its use.
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Affiliation(s)
- Benjamin Webb
- California Institute for Quantitative Biosciences, University of California, San Francisco, California, 94158
| | - Shruthi Viswanath
- California Institute for Quantitative Biosciences, University of California, San Francisco, California, 94158
| | | | - Riccardo Pellarin
- Structural Bioinformatics Unit, Institut Pasteur, CNRS UMR 3528, Paris, France
| | - Charles H Greenberg
- California Institute for Quantitative Biosciences, University of California, San Francisco, California, 94158
| | - Daniel Saltzberg
- California Institute for Quantitative Biosciences, University of California, San Francisco, California, 94158
| | - Andrej Sali
- California Institute for Quantitative Biosciences, University of California, San Francisco, California, 94158
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10
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Sønderby P, Rinnan Å, Madsen JJ, Harris P, Bukrinski JT, Peters GHJ. Small-Angle X-ray Scattering Data in Combination with RosettaDock Improves the Docking Energy Landscape. J Chem Inf Model 2017; 57:2463-2475. [DOI: 10.1021/acs.jcim.6b00789] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Affiliation(s)
- Pernille Sønderby
- Department
of Chemistry, Technical University of Denmark, DK-2800 Kongens
Lyngby, Denmark
| | - Åsmund Rinnan
- Department
of Food Science, Faculty of Science, University of Copenhagen, DK-1958 Frederiksberg C, Denmark
| | - Jesper J. Madsen
- Department
of Chemistry, The University of Chicago, Chicago, Illinois 60637, United States
| | - Pernille Harris
- Department
of Chemistry, Technical University of Denmark, DK-2800 Kongens
Lyngby, Denmark
| | | | - Günther H. J. Peters
- Department
of Chemistry, Technical University of Denmark, DK-2800 Kongens
Lyngby, Denmark
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11
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Computational modeling of protein assemblies. Curr Opin Struct Biol 2017; 44:179-189. [DOI: 10.1016/j.sbi.2017.04.006] [Citation(s) in RCA: 39] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2016] [Revised: 04/07/2017] [Accepted: 04/11/2017] [Indexed: 01/18/2023]
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12
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Molecular Architecture of the Major Membrane Ring Component of the Nuclear Pore Complex. Structure 2017; 25:434-445. [PMID: 28162953 DOI: 10.1016/j.str.2017.01.006] [Citation(s) in RCA: 43] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2016] [Revised: 01/06/2017] [Accepted: 01/19/2017] [Indexed: 02/06/2023]
Abstract
The membrane ring that equatorially circumscribes the nuclear pore complex (NPC) in the perinuclear lumen of the nuclear envelope is composed largely of Pom152 in yeast and its ortholog Nup210 (or Gp210) in vertebrates. Here, we have used a combination of negative-stain electron microscopy, nuclear magnetic resonance, and small-angle X-ray scattering methods to determine an integrative structure of the ∼120 kDa luminal domain of Pom152. Our structural analysis reveals that the luminal domain is formed by a flexible string-of-pearls arrangement of nine repetitive cadherin-like Ig-like domains, indicating an evolutionary connection between NPCs and the cell adhesion machinery. The 16 copies of Pom152 known to be present in the yeast NPC are long enough to form the observed membrane ring, suggesting how interactions between Pom152 molecules help establish and maintain the NPC architecture.
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13
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Venko K, Roy Choudhury A, Novič M. Computational Approaches for Revealing the Structure of Membrane Transporters: Case Study on Bilitranslocase. Comput Struct Biotechnol J 2017; 15:232-242. [PMID: 28228927 PMCID: PMC5312651 DOI: 10.1016/j.csbj.2017.01.008] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2016] [Revised: 01/19/2017] [Accepted: 01/20/2017] [Indexed: 11/23/2022] Open
Abstract
The structural and functional details of transmembrane proteins are vastly underexplored, mostly due to experimental difficulties regarding their solubility and stability. Currently, the majority of transmembrane protein structures are still unknown and this present a huge experimental and computational challenge. Nowadays, thanks to X-ray crystallography or NMR spectroscopy over 3000 structures of membrane proteins have been solved, among them only a few hundred unique ones. Due to the vast biological and pharmaceutical interest in the elucidation of the structure and the functional mechanisms of transmembrane proteins, several computational methods have been developed to overcome the experimental gap. If combined with experimental data the computational information enables rapid, low cost and successful predictions of the molecular structure of unsolved proteins. The reliability of the predictions depends on the availability and accuracy of experimental data associated with structural information. In this review, the following methods are proposed for in silico structure elucidation: sequence-dependent predictions of transmembrane regions, predictions of transmembrane helix–helix interactions, helix arrangements in membrane models, and testing their stability with molecular dynamics simulations. We also demonstrate the usage of the computational methods listed above by proposing a model for the molecular structure of the transmembrane protein bilitranslocase. Bilitranslocase is bilirubin membrane transporter, which shares similar tissue distribution and functional properties with some of the members of the Organic Anion Transporter family and is the only member classified in the Bilirubin Transporter Family. Regarding its unique properties, bilitranslocase is a potentially interesting drug target.
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Affiliation(s)
- Katja Venko
- Department of Cheminformatics, National Institute of Chemistry, Ljubljana, Slovenia
| | - A Roy Choudhury
- Department of Cheminformatics, National Institute of Chemistry, Ljubljana, Slovenia
| | - Marjana Novič
- Department of Cheminformatics, National Institute of Chemistry, Ljubljana, Slovenia
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14
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Laine E, Carbone A. Protein social behavior makes a stronger signal for partner identification than surface geometry. Proteins 2016; 85:137-154. [PMID: 27802579 PMCID: PMC5242317 DOI: 10.1002/prot.25206] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2016] [Revised: 10/10/2016] [Accepted: 10/20/2016] [Indexed: 01/26/2023]
Abstract
Cells are interactive living systems where proteins movements, interactions and regulation are substantially free from centralized management. How protein physico‐chemical and geometrical properties determine who interact with whom remains far from fully understood. We show that characterizing how a protein behaves with many potential interactors in a complete cross‐docking study leads to a sharp identification of its cellular/true/native partner(s). We define a sociability index, or S‐index, reflecting whether a protein likes or not to pair with other proteins. Formally, we propose a suitable normalization function that accounts for protein sociability and we combine it with a simple interface‐based (ranking) score to discriminate partners from non‐interactors. We show that sociability is an important factor and that the normalization permits to reach a much higher discriminative power than shape complementarity docking scores. The social effect is also observed with more sophisticated docking algorithms. Docking conformations are evaluated using experimental binding sites. These latter approximate in the best possible way binding sites predictions, which have reached high accuracy in recent years. This makes our analysis helpful for a global understanding of partner identification and for suggesting discriminating strategies. These results contradict previous findings claiming the partner identification problem being solvable solely with geometrical docking. Proteins 2016; 85:137–154. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Elodie Laine
- Sorbonne Universités, UPMC-Univ P6, CNRS, Laboratoire de Biologie Computationnelle et Quantitative - UMR 7238, Paris, 75005, France
| | - Alessandra Carbone
- Sorbonne Universités, UPMC-Univ P6, CNRS, Laboratoire de Biologie Computationnelle et Quantitative - UMR 7238, Paris, 75005, France.,Institut Universitaire de France, Paris, 75005, France
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15
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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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16
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Chait BT, Cadene M, Olinares PD, Rout MP, Shi Y. Revealing Higher Order Protein Structure Using Mass Spectrometry. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2016; 27:952-65. [PMID: 27080007 PMCID: PMC5125627 DOI: 10.1007/s13361-016-1385-1] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/13/2016] [Revised: 03/14/2016] [Accepted: 03/15/2016] [Indexed: 05/24/2023]
Abstract
The development of rapid, sensitive, and accurate mass spectrometric methods for measuring peptides, proteins, and even intact protein assemblies has made mass spectrometry (MS) an extraordinarily enabling tool for structural biology. Here, we provide a personal perspective of the increasingly useful role that mass spectrometric techniques are exerting during the elucidation of higher order protein structures. Areas covered in this brief perspective include MS as an enabling tool for the high resolution structural biologist, for compositional analysis of endogenous protein complexes, for stoichiometry determination, as well as for integrated approaches for the structural elucidation of protein complexes. We conclude with a vision for the future role of MS-based techniques in the development of a multi-scale molecular microscope. Graphical Abstract ᅟ.
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Affiliation(s)
- Brian T Chait
- Laboratory of Mass Spectrometry and Gaseous Ion Chemistry, The Rockefeller University, New York, NY, 10065, USA.
| | - Martine Cadene
- CBM, CNRS UPR4301, Rue Charles Sadron, CS 80054, 45071, Orleans Cedex 2, France
| | - Paul Dominic Olinares
- Laboratory of Mass Spectrometry and Gaseous Ion Chemistry, The Rockefeller University, New York, NY, 10065, USA
| | - Michael P Rout
- Laboratory of Cellular and Structural Biology, The Rockefeller University, New York, NY, 10065, USA
| | - Yi Shi
- Laboratory of Mass Spectrometry and Gaseous Ion Chemistry, The Rockefeller University, New York, NY, 10065, USA
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17
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Liu F, Koval M, Ranganathan S, Fanayan S, Hancock WS, Lundberg EK, Beavis RC, Lane L, Duek P, McQuade L, Kelleher NL, Baker MS. Systems Proteomics View of the Endogenous Human Claudin Protein Family. J Proteome Res 2016; 15:339-59. [PMID: 26680015 DOI: 10.1021/acs.jproteome.5b00769] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Claudins are the major transmembrane protein components of tight junctions in human endothelia and epithelia. Tissue-specific expression of claudin members suggests that this protein family is not only essential for sustaining the role of tight junctions in cell permeability control but also vital in organizing cell contact signaling by protein-protein interactions. How this protein family is collectively processed and regulated is key to understanding the role of junctional proteins in preserving cell identity and tissue integrity. The focus of this review is to first provide a brief overview of the functional context, on the basis of the extensive body of claudin biology research that has been thoroughly reviewed, for endogenous human claudin members and then ascertain existing and future proteomics techniques that may be applicable to systematically characterizing the chemical forms and interacting protein partners of this protein family in human. The ability to elucidate claudin-based signaling networks may provide new insight into cell development and differentiation programs that are crucial to tissue stability and manipulation.
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Affiliation(s)
| | - Michael Koval
- Department of Medicine, Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, and Department of Cell Biology, Emory University School of Medicine , 205 Whitehead Biomedical Research Building, 615 Michael Street, Atlanta, Georgia 30322, United States
| | | | | | - William S Hancock
- Barnett Institute and Department of Chemistry and Chemical Biology, Northeastern University , Boston, Massachusetts 02115, United States
| | - Emma K Lundberg
- SciLifeLab, School of Biotechnology, Royal Institute of Technology (KTH) , SE-171 21 Solna, Stockholm, Sweden
| | - Ronald C Beavis
- Department of Biochemistry and Medical Genetics, University of Manitoba , 744 Bannatyne Avenue, Winnipeg, Manitoba R3E 0W3, Canada
| | - Lydie Lane
- SIB-Swiss Institute of Bioinformatics , CMU - Rue Michel-Servet 1, 1211 Geneva, Switzerland
| | - Paula Duek
- SIB-Swiss Institute of Bioinformatics , CMU - Rue Michel-Servet 1, 1211 Geneva, Switzerland
| | | | - Neil L Kelleher
- Department of Chemistry, Department of Molecular Biosciences, and Proteomics Center of Excellence, Northwestern University , 2145 North Sheridan Road, Evanston, Illinois 60208, United States
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18
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Robinson PJ, Trnka MJ, Pellarin R, Greenberg CH, Bushnell DA, Davis R, Burlingame AL, Sali A, Kornberg RD. Molecular architecture of the yeast Mediator complex. eLife 2015; 4. [PMID: 26402457 PMCID: PMC4631838 DOI: 10.7554/elife.08719] [Citation(s) in RCA: 122] [Impact Index Per Article: 13.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2015] [Accepted: 09/23/2015] [Indexed: 12/18/2022] Open
Abstract
The 21-subunit Mediator complex transduces regulatory information from enhancers to promoters, and performs an essential role in the initiation of transcription in all eukaryotes. Structural information on two-thirds of the complex has been limited to coarse subunit mapping onto 2-D images from electron micrographs. We have performed chemical cross-linking and mass spectrometry, and combined the results with information from X-ray crystallography, homology modeling, and cryo-electron microscopy by an integrative modeling approach to determine a 3-D model of the entire Mediator complex. The approach is validated by the use of X-ray crystal structures as internal controls and by consistency with previous results from electron microscopy and yeast two-hybrid screens. The model shows the locations and orientations of all Mediator subunits, as well as subunit interfaces and some secondary structural elements. Segments of 20–40 amino acid residues are placed with an average precision of 20 Å. The model reveals roles of individual subunits in the organization of the complex. DOI:http://dx.doi.org/10.7554/eLife.08719.001 Inside a cell, proteins are made from instructions encoded by DNA. To produce a particular protein, a section of DNA within a gene is copied into a molecule of messenger ribonucleic acid (or mRNA). This process is called transcription and is carried out by an enzyme known as RNA polymerase. Transcription begins in a region of DNA called a promoter, which is found at the start of the gene. RNA polymerase is brought to the DNA by many proteins, including the so-called Mediator complex. Mediator receives signals from within the cell and from the environment, processes the information, and instructs RNA polymerase whether to transcribe the gene or not. Mediator performs this important role in all organisms from yeast to humans, but it is not clear how it works. A crucial step towards the solution of this problem is to understand the three-dimensional structure of the complex. Previous research using a technique called ‘electron microscopy’ showed that Mediator is composed of three modules, referred to as Head, Middle and Tail. The images from electron microscopy were not sufficiently detailed to reveal the organization of the proteins within these modules. An open-source Integrative Modeling Platform (IMP for short) was recently developed to arrive at structural models of large protein complexes from a combination of experimental data and computer models. Now, Robinson, Trnka, Pellarin et al. have used this platform to study the Mediator complex. First, Robinson, Trnka, Pellarin et al. collected experimental data on the structure of the Mediator complex using two approaches called ‘chemical cross-linking’ and ‘mass spectrometry’. This data was combined with biochemical and structural information from previous studies to generate a three-dimensional model of the structure of the entire Mediator using IMP. The model is detailed enough to show the location and orientation of all the proteins in the complex. For example, a protein called Med17 connects the Head and Middle modules, while another subunit—known as Med14—spans the entire complex and makes extensive contacts with other proteins in all three modules. DOI:http://dx.doi.org/10.7554/eLife.08719.002
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Affiliation(s)
- Philip J Robinson
- Department of Structural Biology, Stanford University School of Medicine, Stanford, United States
| | - Michael J Trnka
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, United States
| | - Riccardo Pellarin
- Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, California Institute for Quantitative Biosciences, University of California, San Francisco, San Francisco, United States.,Structural Bioinformatics Unit, Paris, France
| | - Charles H Greenberg
- Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, California Institute for Quantitative Biosciences, University of California, San Francisco, San Francisco, United States
| | - David A Bushnell
- Department of Structural Biology, Stanford University School of Medicine, Stanford, United States
| | - Ralph Davis
- Department of Structural Biology, Stanford University School of Medicine, Stanford, United States
| | - Alma L Burlingame
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, United States
| | - Andrej Sali
- Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, California Institute for Quantitative Biosciences, University of California, San Francisco, San Francisco, United States
| | - Roger D Kornberg
- Department of Structural Biology, Stanford University School of Medicine, Stanford, United States
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19
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Nepomnyachiy S, Ben-Tal N, Kolodny R. CyToStruct: Augmenting the Network Visualization of Cytoscape with the Power of Molecular Viewers. Structure 2015; 23:941-948. [PMID: 25865247 DOI: 10.1016/j.str.2015.02.013] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2014] [Revised: 02/20/2015] [Accepted: 02/24/2015] [Indexed: 12/18/2022]
Abstract
It can be informative to view biological data, e.g., protein-protein interactions within a large complex, in a network representation coupled with three-dimensional structural visualizations of individual molecular entities. CyToStruct, introduced here, provides a transparent interface between the Cytoscape platform for network analysis and molecular viewers, including PyMOL, UCSF Chimera, VMD, and Jmol. CyToStruct launches and passes scripts to molecular viewers from the network's edges and nodes. We provide demonstrations to analyze interactions among subunits in large protein/RNA/DNA complexes, and similarities among proteins. CyToStruct enriches the network tools of Cytoscape by adding a layer of structural analysis, offering all capabilities implemented in molecular viewers. CyToStruct is available at https://bitbucket.org/sergeyn/cytostruct/wiki/Home and in the Cytoscape App Store. Given the coordinates of a molecular complex, our web server (http://trachel-srv.cs.haifa.ac.il/rachel/ppi/) automatically generates all files needed to visualize the complex as a Cytoscape network with CyToStruct bridging to PyMOL, UCSF Chimera, VMD, and Jmol.
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Affiliation(s)
- Sergey Nepomnyachiy
- Department of Computer Science & Engineering, Polytechnic Institute of NYU, Brooklyn, NY 11201, USA
| | - Nir Ben-Tal
- Department of Biochemistry and Molecular Biochemistry, George S. Wise Faculty of Life Sciences, Tel Aviv University, Ramat Aviv 69978, Israel.
| | - Rachel Kolodny
- Department of Computer Science, University of Haifa, Mount Carmel 31905, Israel.
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20
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Erzberger JP, Stengel F, Pellarin R, Zhang S, Schaefer T, Aylett CHS, Cimermančič P, Boehringer D, Sali A, Aebersold R, Ban N. Molecular architecture of the 40S⋅eIF1⋅eIF3 translation initiation complex. Cell 2015; 158:1123-1135. [PMID: 25171412 PMCID: PMC4151992 DOI: 10.1016/j.cell.2014.07.044] [Citation(s) in RCA: 165] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2014] [Revised: 05/29/2014] [Accepted: 07/29/2014] [Indexed: 11/25/2022]
Abstract
Eukaryotic translation initiation requires the recruitment of the large, multiprotein eIF3 complex to the 40S ribosomal subunit. We present X-ray structures of all major components of the minimal, six-subunit Saccharomyces cerevisiae eIF3 core. These structures, together with electron microscopy reconstructions, cross-linking coupled to mass spectrometry, and integrative structure modeling, allowed us to position and orient all eIF3 components on the 40S⋅eIF1 complex, revealing an extended, modular arrangement of eIF3 subunits. Yeast eIF3 engages 40S in a clamp-like manner, fully encircling 40S to position key initiation factors on opposite ends of the mRNA channel, providing a platform for the recruitment, assembly, and regulation of the translation initiation machinery. The structures of eIF3 components reported here also have implications for understanding the architecture of the mammalian 43S preinitiation complex and the complex of eIF3, 40S, and the hepatitis C internal ribosomal entry site RNA. X-ray structures of major yeast eIF3 components and subcomplexes Crosslinking coupled to mass-spectrometry analysis of 40S⋅eIF1⋅eIF3 complex Integrative modeling reveals architecture of 40S⋅eIF1⋅eIF3 complex
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Affiliation(s)
- Jan P Erzberger
- Department of Biology, Institute of Molecular Biology and Biophysics, ETH Zurich, Otto-Stern-Weg 5, 8093 Zurich, Switzerland.
| | - Florian Stengel
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Auguste-Piccard-Hof 1, 8093 Zurich, Switzerland
| | - Riccardo Pellarin
- Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, and California Institute for Quantitative Biosciences (QB3), University of California, San Francisco, UCSF MC 2552, Byers Hall Room 503B, 1700 4th Street, San Francisco, CA 94158-2330, USA
| | - Suyang Zhang
- Department of Biology, Institute of Molecular Biology and Biophysics, ETH Zurich, Otto-Stern-Weg 5, 8093 Zurich, Switzerland
| | - Tanja Schaefer
- Department of Biology, Institute of Molecular Biology and Biophysics, ETH Zurich, Otto-Stern-Weg 5, 8093 Zurich, Switzerland
| | - Christopher H S Aylett
- Department of Biology, Institute of Molecular Biology and Biophysics, ETH Zurich, Otto-Stern-Weg 5, 8093 Zurich, Switzerland
| | - Peter Cimermančič
- Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, and California Institute for Quantitative Biosciences (QB3), University of California, San Francisco, UCSF MC 2552, Byers Hall Room 503B, 1700 4th Street, San Francisco, CA 94158-2330, USA
| | - Daniel Boehringer
- Department of Biology, Institute of Molecular Biology and Biophysics, ETH Zurich, Otto-Stern-Weg 5, 8093 Zurich, Switzerland
| | - Andrej Sali
- Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, and California Institute for Quantitative Biosciences (QB3), University of California, San Francisco, UCSF MC 2552, Byers Hall Room 503B, 1700 4th Street, San Francisco, CA 94158-2330, USA
| | - Ruedi Aebersold
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Auguste-Piccard-Hof 1, 8093 Zurich, Switzerland; Faculty of Science, University of Zurich, 8006 Zurich, Switzerland
| | - Nenad Ban
- Department of Biology, Institute of Molecular Biology and Biophysics, ETH Zurich, Otto-Stern-Weg 5, 8093 Zurich, Switzerland.
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21
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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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22
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Algret R, Fernandez-Martinez J, Shi Y, Kim SJ, Pellarin R, Cimermancic P, Cochet E, Sali A, Chait BT, Rout MP, Dokudovskaya S. Molecular architecture and function of the SEA complex, a modulator of the TORC1 pathway. Mol Cell Proteomics 2014; 13:2855-70. [PMID: 25073740 DOI: 10.1074/mcp.m114.039388] [Citation(s) in RCA: 59] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023] Open
Abstract
The TORC1 signaling pathway plays a major role in the control of cell growth and response to stress. Here we demonstrate that the SEA complex physically interacts with TORC1 and is an important regulator of its activity. During nitrogen starvation, deletions of SEA complex components lead to Tor1 kinase delocalization, defects in autophagy, and vacuolar fragmentation. TORC1 inactivation, via nitrogen deprivation or rapamycin treatment, changes cellular levels of SEA complex members. We used affinity purification and chemical cross-linking to generate the data for an integrative structure modeling approach, which produced a well-defined molecular architecture of the SEA complex and showed that the SEA complex comprises two regions that are structurally and functionally distinct. The SEA complex emerges as a platform that can coordinate both structural and enzymatic activities necessary for the effective functioning of the TORC1 pathway.
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Affiliation(s)
- Romain Algret
- From the ‡CNRS UMR 8126, Université Paris-Sud 11, Institut Gustave Roussy, 114, rue Edouard Vaillant, 94805, Villejuif, France
| | - Javier Fernandez-Martinez
- §Laboratory of Cellular and Structural Biology, The Rockefeller University, 1230 York Avenue, New York, New York 10065
| | - Yi Shi
- ¶Laboratory of Mass Spectrometry and Gaseous Ion Chemistry, The Rockefeller University, 1230 York Avenue, New York, New York 10065
| | - Seung Joong Kim
- ‖Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, and California Institute for Quantitative Biosciences (QB3), University of California, San Francisco, UCSF MC 2552, Byers Hall Room 503B, 1700 4th Street, San Francisco, California 94158-2330
| | - Riccardo Pellarin
- ‖Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, and California Institute for Quantitative Biosciences (QB3), University of California, San Francisco, UCSF MC 2552, Byers Hall Room 503B, 1700 4th Street, San Francisco, California 94158-2330
| | - Peter Cimermancic
- ‖Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, and California Institute for Quantitative Biosciences (QB3), University of California, San Francisco, UCSF MC 2552, Byers Hall Room 503B, 1700 4th Street, San Francisco, California 94158-2330
| | - Emilie Cochet
- From the ‡CNRS UMR 8126, Université Paris-Sud 11, Institut Gustave Roussy, 114, rue Edouard Vaillant, 94805, Villejuif, France
| | - Andrej Sali
- ‖Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, and California Institute for Quantitative Biosciences (QB3), University of California, San Francisco, UCSF MC 2552, Byers Hall Room 503B, 1700 4th Street, San Francisco, California 94158-2330
| | - Brian T Chait
- ¶Laboratory of Mass Spectrometry and Gaseous Ion Chemistry, The Rockefeller University, 1230 York Avenue, New York, New York 10065
| | - Michael P Rout
- §Laboratory of Cellular and Structural Biology, The Rockefeller University, 1230 York Avenue, New York, New York 10065
| | - Svetlana Dokudovskaya
- From the ‡CNRS UMR 8126, Université Paris-Sud 11, Institut Gustave Roussy, 114, rue Edouard Vaillant, 94805, Villejuif, France;
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23
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Liu Z, Szarecka A, Yonkunas M, Speranskiy K, Kurnikova M, Cascio M. Crosslinking constraints and computational models as complementary tools in modeling the extracellular domain of the glycine receptor. PLoS One 2014; 9:e102571. [PMID: 25025226 PMCID: PMC4099341 DOI: 10.1371/journal.pone.0102571] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2014] [Accepted: 06/20/2014] [Indexed: 01/03/2023] Open
Abstract
The glycine receptor (GlyR), a member of the pentameric ligand-gated ion channel superfamily, is the major inhibitory neurotransmitter-gated receptor in the spinal cord and brainstem. In these receptors, the extracellular domain binds agonists, antagonists and various other modulatory ligands that act allosterically to modulate receptor function. The structures of homologous receptors and binding proteins provide templates for modeling of the ligand-binding domain of GlyR, but limitations in sequence homology and structure resolution impact on modeling studies. The determination of distance constraints via chemical crosslinking studies coupled with mass spectrometry can provide additional structural information to aid in model refinement, however it is critical to be able to distinguish between intra- and inter-subunit constraints. In this report we model the structure of GlyBP, a structural and functional homolog of the extracellular domain of human homomeric α1 GlyR. We then show that intra- and intersubunit Lys-Lys crosslinks in trypsinized samples of purified monomeric and oligomeric protein bands from SDS-polyacrylamide gels may be identified and differentiated by MALDI-TOF MS studies of limited resolution. Thus, broadly available MS platforms are capable of providing distance constraints that may be utilized in characterizing large complexes that may be less amenable to NMR and crystallographic studies. Systematic studies of state-dependent chemical crosslinking and mass spectrometric identification of crosslinked sites has the potential to complement computational modeling efforts by providing constraints that can validate and refine allosteric models.
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Affiliation(s)
- Zhenyu Liu
- Center for Neuroscience, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Agnieszka Szarecka
- Department of Chemistry, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
- Department of Cell and Molecular Biology, Grand Valley State University, Allendale, Michigan, United States of America
| | - Michael Yonkunas
- Department of Chemistry, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
| | - Kirill Speranskiy
- Department of Chemistry, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
| | - Maria Kurnikova
- Department of Chemistry, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
| | - Michael Cascio
- Center for Neuroscience, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
- Department of Chemistry and Biochemistry, Duquesne University, Pittsburgh, Pennsylvania, United States of America
- * E-mail:
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24
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Villa E, Lasker K. Finding the right fit: chiseling structures out of cryo-electron microscopy maps. Curr Opin Struct Biol 2014; 25:118-25. [PMID: 24814094 DOI: 10.1016/j.sbi.2014.04.001] [Citation(s) in RCA: 42] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2014] [Revised: 04/08/2014] [Accepted: 04/09/2014] [Indexed: 11/19/2022]
Abstract
Cryo-electron microscopy is a central tool for studying the architecture of macromolecular complexes at subnanometer resolution. Interpretation of an electron microscopy map requires its computational integration with data about the structure's components from all available sources, notably atomic models. Selecting a protocol for EM density-guided integrative structural modeling depends on the resolution and quality of the EM map as well as the available complimentary datasets. Here, we review rigid, flexible, and de novo integrative fitting into EM maps and provide guidelines and considerations for the design of modeling experiments. Finally, we discuss efforts towards establishing unified criteria for map and model assessment and validation.
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Affiliation(s)
- Elizabeth Villa
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, CA 92093, United States.
| | - Keren Lasker
- Department of Developmental Biology, Stanford University, Stanford, CA 94305, United States.
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25
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Joseph AP, de Brevern AG. From local structure to a global framework: recognition of protein folds. J R Soc Interface 2014; 11:20131147. [PMID: 24740960 DOI: 10.1098/rsif.2013.1147] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Protein folding has been a major area of research for many years. Nonetheless, the mechanisms leading to the formation of an active biological fold are still not fully apprehended. The huge amount of available sequence and structural information provides hints to identify the putative fold for a given sequence. Indeed, protein structures prefer a limited number of local backbone conformations, some being characterized by preferences for certain amino acids. These preferences largely depend on the local structural environment. The prediction of local backbone conformations has become an important factor to correctly identifying the global protein fold. Here, we review the developments in the field of local structure prediction and especially their implication in protein fold recognition.
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Affiliation(s)
- Agnel Praveen Joseph
- Science and Technology Facilities Council, Rutherford Appleton Laboratory, Harwell Oxford, , Didcot OX11 0QX, UK
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26
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Kuzu G, Keskin O, Nussinov R, Gursoy A. Modeling protein assemblies in the proteome. Mol Cell Proteomics 2014; 13:887-96. [PMID: 24445405 PMCID: PMC3945916 DOI: 10.1074/mcp.m113.031294] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2013] [Revised: 12/13/2013] [Indexed: 11/06/2022] Open
Abstract
Most (if not all) proteins function when associated in multimolecular assemblies. Attaining the structures of protein assemblies at the atomic scale is an important aim of structural biology. Experimentally, structures are increasingly available, and computations can help bridge the resolution gap between high- and low-resolution scales. Existing computational methods have made substantial progress toward this aim; however, current approaches are still limited. Some involve manual adjustment of experimental data; some are automated docking methods, which are computationally expensive and not applicable to large-scale proteome studies; and still others exploit the symmetry of the complexes and thus are not applicable to nonsymmetrical complexes. Our study aims to take steps toward overcoming these limitations. We have developed a strategy for the construction of protein assemblies computationally based on binary interactions predicted by a motif-based protein interaction prediction tool, PRISM (Protein Interactions by Structural Matching). Previously, we have shown its power in predicting pairwise interactions. Here we take a step toward multimolecular assemblies, reflecting the more prevalent cellular scenarios. With this method we are able to construct homo-/hetero-complexes and symmetric/asymmetric complexes without a limitation on the number of components. The method considers conformational changes and is applicable to large-scale studies. We also exploit electron microscopy density maps to select a solution from among the predictions. Here we present the method, illustrate its results, and highlight its current limitations.
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Affiliation(s)
- Guray Kuzu
- From the ‡Center for Computational Biology and Bioinformatics and College of Engineering, Koc University Rumelifeneri Yolu, 34450 Sariyer Istanbul, Turkey
| | - Ozlem Keskin
- From the ‡Center for Computational Biology and Bioinformatics and College of Engineering, Koc University Rumelifeneri Yolu, 34450 Sariyer Istanbul, Turkey
| | - Ruth Nussinov
- §Cancer and Inflammation Program, Leidos Biomedical Research, Inc., National Cancer Institute, Frederick National Laboratory for Cancer Research, Frederick, Maryland 21702
- ¶Sackler Institute of Molecular Medicine Department of Human Genetics and Molecular Medicine Sackler School of Medicine, Tel Aviv University, Tel Aviv 69978, Israel
| | - Attila Gursoy
- From the ‡Center for Computational Biology and Bioinformatics and College of Engineering, Koc University Rumelifeneri Yolu, 34450 Sariyer Istanbul, Turkey
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27
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The binary protein-protein interaction landscape of Escherichia coli. Nat Biotechnol 2014; 32:285-290. [PMID: 24561554 PMCID: PMC4123855 DOI: 10.1038/nbt.2831] [Citation(s) in RCA: 162] [Impact Index Per Article: 16.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2013] [Accepted: 01/16/2014] [Indexed: 11/09/2022]
Abstract
Efforts to map the Escherichia coli interactome have identified several hundred macromolecular complexes, but direct binary protein-protein interactions (PPIs) have not been surveyed on a large scale. Here we performed yeast two-hybrid screens of 3,305 baits against 3,606 preys (∼70% of the E. coli proteome) in duplicate to generate a map of 2,234 interactions, which approximately doubles the number of known binary PPIs in E. coli. Integration of binary PPI and genetic-interaction data revealed functional dependencies among components involved in cellular processes, including envelope integrity, flagellum assembly and protein quality control. Many of the binary interactions that we could map in multiprotein complexes were informative regarding internal topology of complexes and indicated that interactions in complexes are substantially more conserved than those interactions connecting different complexes. This resource will be useful for inferring bacterial gene function and provides a draft reference of the basic physical wiring network of this evolutionarily important model microbe.
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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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On the use of knowledge-based potentials for the evaluation of models of protein-protein, protein-DNA, and protein-RNA interactions. ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2014; 94:77-120. [PMID: 24629186 DOI: 10.1016/b978-0-12-800168-4.00004-4] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Proteins are the bricks and mortar of cells, playing structural and functional roles. In order to perform their function, they interact with each other as well as with other biomolecules such as DNA or RNA. Therefore, to fathom the function of a protein, we require knowing its partners and the atomic details of its interactions (i.e., the structure of the complex). However, the amount of protein interactions with an experimentally determined three-dimensional structure is scarce. Therefore, computational techniques such as homology modeling are foremost to fill this gap. Protein interactions can be modeled using as templates the interactions of homologous proteins, if the structure of the complex is known, or using docking methods. In both approaches, the estimation of the quality of models is essential. There are several ways to address this problem. In this review, we focus on the use of knowledge-based potentials for the analysis of protein interactions. We describe the procedure to derive statistical potentials and split them into different energetic terms that can be used for different purposes. We extensively discuss the fields where knowledge-based potentials have been successfully applied to (1) model protein-protein, protein-DNA, and protein-RNA interactions and (2) predict binding sites (in the protein and in the DNA). Moreover, we provide ready-to-use resources for docking and benchmarking protein interactions.
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30
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Template-based structure modeling of protein-protein interactions. Curr Opin Struct Biol 2013; 24:10-23. [PMID: 24721449 DOI: 10.1016/j.sbi.2013.11.005] [Citation(s) in RCA: 116] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2013] [Revised: 10/29/2013] [Accepted: 11/21/2013] [Indexed: 01/21/2023]
Abstract
The structure of protein-protein complexes can be constructed by using the known structure of other protein complexes as a template. The complex structure templates are generally detected either by homology-based sequence alignments or, given the structure of monomer components, by structure-based comparisons. Critical improvements have been made in recent years by utilizing interface recognition and by recombining monomer and complex template libraries. Encouraging progress has also been witnessed in genome-wide applications of template-based modeling, with modeling accuracy comparable to high-throughput experimental data. Nevertheless, bottlenecks exist due to the incompleteness of the protein-protein complex structure library and the lack of methods for distant homologous template identification and full-length complex structure refinement.
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31
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Mass spectrometry coupled experiments and protein structure modeling methods. Int J Mol Sci 2013; 14:20635-57. [PMID: 24132151 PMCID: PMC3821635 DOI: 10.3390/ijms141020635] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2013] [Revised: 09/17/2013] [Accepted: 09/19/2013] [Indexed: 01/02/2023] Open
Abstract
With the accumulation of next generation sequencing data, there is increasing interest in the study of intra-species difference in molecular biology, especially in relation to disease analysis. Furthermore, the dynamics of the protein is being identified as a critical factor in its function. Although accuracy of protein structure prediction methods is high, provided there are structural templates, most methods are still insensitive to amino-acid differences at critical points that may change the overall structure. Also, predicted structures are inherently static and do not provide information about structural change over time. It is challenging to address the sensitivity and the dynamics by computational structure predictions alone. However, with the fast development of diverse mass spectrometry coupled experiments, low-resolution but fast and sensitive structural information can be obtained. This information can then be integrated into the structure prediction process to further improve the sensitivity and address the dynamics of the protein structures. For this purpose, this article focuses on reviewing two aspects: the types of mass spectrometry coupled experiments and structural data that are obtainable through those experiments; and the structure prediction methods that can utilize these data as constraints. Also, short review of current efforts in integrating experimental data in the structural modeling is provided.
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Förster F, Unverdorben P, Śledź P, Baumeister W. Unveiling the Long-Held Secrets of the 26S Proteasome. Structure 2013; 21:1551-62. [DOI: 10.1016/j.str.2013.08.010] [Citation(s) in RCA: 54] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2013] [Revised: 08/15/2013] [Accepted: 08/16/2013] [Indexed: 01/23/2023]
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Rozbesky D, Sovova Z, Marcoux J, Man P, Ettrich R, Robinson CV, Novak P. Structural model of lymphocyte receptor NKR-P1C revealed by mass spectrometry and molecular modeling. Anal Chem 2013; 85:1597-604. [PMID: 23249299 DOI: 10.1021/ac302860m] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
NKR-P1C is an activating immune receptor expressed on the surface of mouse natural killer cells. It has been widely used as a marker for NK cell identification in different mice strains. Recently we solved a crystal structure of the C-type lectin-like domain of a homologous protein, NKR-P1A, using X-ray crystallography and also described the strategy for rapid characterization of the protein conformation in solution. This procedure utilized chemical cross-linking, hydrogen/deuterium exchange, and molecular modeling. It was found that the solution structure differs from the crystal structure in the conformation of the loop region. The loop, detached from the protein compact core in the crystal structure, is closely attached to the core of the protein in solution. Here we present and interpret the solution structure of the C-type lectin-like domain of NKR-P1C using chemical cross-linking and molecular modeling. The validation of the model and conformation of the loop region in NKR-P1C were addressed using ion-mobility mass spectrometry.
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Affiliation(s)
- Daniel Rozbesky
- Department of Biochemistry, Charles University, Prague, Czech Republic
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34
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Conventional electron microscopy, cryo-electron microscopy and cryo-electron tomography of viruses. Subcell Biochem 2013; 68:79-115. [PMID: 23737049 DOI: 10.1007/978-94-007-6552-8_3] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Electron microscopy (EM) techniques have been crucial for understanding the structure of biological specimens such as cells, tissues and macromolecular assemblies. Viruses and related viral assemblies are ideal targets for structural studies that help to define essential biological functions. Whereas conventional EM methods use chemical fixation, dehydration, and staining of the specimens, cryo-electron microscopy (cryo-EM) preserves the native hydrated state. Combined with image processing and three-dimensional reconstruction techniques, cryo-EM provides 3D maps of these macromolecular complexes from projection images, at subnanometer to near-atomic resolutions. Cryo-EM is also a major technique in structural biology for dynamic studies of functional complexes, which are often unstable, flexible, scarce or transient in their native environments. As a tool, cryo-EM complements high-resolution techniques such as X-ray diffraction and NMR spectroscopy; these synergistic hybrid approaches provide important new information. Three-dimensional cryo-electron tomography goes further, and allows the study of viruses not only in their physiological state, but also in their natural environment in the cell, thereby bridging structural studies at the molecular and cellular levels.
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35
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Shih ESC, Hwang MJ. A critical assessment of information-guided protein-protein docking predictions. Mol Cell Proteomics 2012; 12:679-86. [PMID: 23242549 DOI: 10.1074/mcp.m112.020198] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
Abstract
The structures of protein complexes are increasingly predicted via protein-protein docking (PPD) using ambiguous interaction data to help guide the docking. These data often are incomplete and contain errors and therefore could lead to incorrect docking predictions. In this study, we performed a series of PPD simulations to examine the effects of incompletely and incorrectly assigned interface residues on the success rate of PPD predictions. The results for a widely used PPD benchmark dataset obtained using a new interface information-driven PPD (IPPD) method developed in this work showed that the success rate for an acceptable top-ranked model varied, depending on the information content used, from as high as 95% when contact relationships (though not contact distances) were known for all residues to 78% when only the interface/non-interface state of the residues was known. However, the success rates decreased rapidly to ∼40% when the interface/non-interface state of 20% of the residues was assigned incorrectly, and to less than 5% for a 40% incorrect assignment. Comparisons with results obtained by re-ranking a global search and with those reported for other data-guided PPD methods showed that, in general, IPPD performed better than re-ranking when the information used was more complete and more accurate, but worse when it was not, and that when using bioinformatics-predicted information on interface residues, IPPD and other data-guided PPD methods performed poorly, at a level similar to simulations with a 40% incorrect assignment. These results provide guidelines for using information about interface residues to improve PPD predictions and reveal a bottleneck for such improvement imposed by the low accuracy of current bioinformatic interface residue predictions.
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Affiliation(s)
- Edward S C Shih
- ‡Institute of Biomedical Sciences, Academia Sinica, Nankang, Taipei 115, Taiwan
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36
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Margaret Sunitha S, Mercer JA, Spudich JA, Sowdhamini R. Integrative structural modelling of the cardiac thin filament: energetics at the interface and conservation patterns reveal a spotlight on period 2 of tropomyosin. Bioinform Biol Insights 2012; 6:203-23. [PMID: 23071391 PMCID: PMC3468436 DOI: 10.4137/bbi.s9798] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
Cardiomyopathies are a major health problem, with inherited cardiomyopathies, many of which are caused by mutations in genes encoding sarcomeric proteins, constituting an ever-increasing fraction of cases. To begin to study the mechanisms by which these mutations cause disease, we have employed an integrative modelling approach to study the interactions between tropomyosin and actin. Starting from the existing blocked state model, we identified a specific zone on the actin surface which is highly favourable to support tropomyosin sliding from the blocked/closed states to the open state. We then analysed the predicted actin-tropomyosin interface regions for the three states. Each quasi-repeat of tropomyosin was studied for its interaction strength and evolutionary conservation to focus on smaller surface zones. Finally, we show that the distribution of the known cardiomyopathy mutations of α-tropomyosin is consistent with our model. This analysis provides structural insights into the possible mode of interactions between tropomyosin and actin in the open state for the first time.
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Affiliation(s)
- S Margaret Sunitha
- National Centre for Biological Sciences (TIFR), GKVK Campus, Bellary Road, Bangalore, India
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37
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Henderson R, Sali A, Baker ML, Carragher B, Devkota B, Downing KH, Egelman EH, Feng Z, Frank J, Grigorieff N, Jiang W, Ludtke SJ, Medalia O, Penczek PA, Rosenthal PB, Rossmann MG, Schmid MF, Schröder GF, Steven AC, Stokes DL, Westbrook JD, Wriggers W, Yang H, Young J, Berman HM, Chiu W, Kleywegt GJ, Lawson CL. Outcome of the first electron microscopy validation task force meeting. Structure 2012; 20:205-14. [PMID: 22325770 PMCID: PMC3328769 DOI: 10.1016/j.str.2011.12.014] [Citation(s) in RCA: 361] [Impact Index Per Article: 30.1] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2011] [Revised: 12/29/2011] [Accepted: 12/29/2011] [Indexed: 11/10/2022]
Abstract
This Meeting Review describes the proceedings and conclusions from the inaugural meeting of the Electron Microscopy Validation Task Force organized by the Unified Data Resource for 3DEM (http://www.emdatabank.org) and held at Rutgers University in New Brunswick, NJ on September 28 and 29, 2010. At the workshop, a group of scientists involved in collecting electron microscopy data, using the data to determine three-dimensional electron microscopy (3DEM) density maps, and building molecular models into the maps explored how to assess maps, models, and other data that are deposited into the Electron Microscopy Data Bank and Protein Data Bank public data archives. The specific recommendations resulting from the workshop aim to increase the impact of 3DEM in biology and medicine.
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Affiliation(s)
- Richard Henderson
- MRC Laboratory of Molecular Biology, Hills Road, Cambridge CB2 0QH, UK
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38
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Trinh MH, Odorico M, Pique ME, Teulon JM, Roberts VA, Ten Eyck LF, Getzoff ED, Parot P, Chen SWW, Pellequer JL. Computational reconstruction of multidomain proteins using atomic force microscopy data. Structure 2012; 20:113-20. [PMID: 22244760 DOI: 10.1016/j.str.2011.10.023] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2011] [Revised: 10/05/2011] [Accepted: 10/10/2011] [Indexed: 01/10/2023]
Abstract
Classical structural biology techniques face a great challenge to determine the structure at the atomic level of large and flexible macromolecules. We present a novel methodology that combines high-resolution AFM topographic images with atomic coordinates of proteins to assemble very large macromolecules or particles. Our method uses a two-step protocol: atomic coordinates of individual domains are docked beneath the molecular surface of the large macromolecule, and then each domain is assembled using a combinatorial search. The protocol was validated on three test cases: a simulated system of antibody structures; and two experimentally based test cases: Tobacco mosaic virus, a rod-shaped virus; and Aquaporin Z, a bacterial membrane protein. We have shown that AFM-intermediate resolution topography and partial surface data are useful constraints for building macromolecular assemblies. The protocol is applicable to multicomponent structures connected in the polypeptide chain or as disjoint molecules. The approach effectively increases the resolution of AFM beyond topographical information down to atomic-detail structures.
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Affiliation(s)
- Minh-Hieu Trinh
- CEA, iBEB, Department of Biochemistry and Nuclear Toxicology, F-30207 Bagnols sur Cèze, France
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39
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Sinitskiy AV, Saunders MG, Voth GA. Optimal number of coarse-grained sites in different components of large biomolecular complexes. J Phys Chem B 2012; 116:8363-74. [PMID: 22276676 DOI: 10.1021/jp2108895] [Citation(s) in RCA: 48] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
The computational study of large biomolecular complexes (molecular machines, cytoskeletal filaments, etc.) is a formidable challenge facing computational biophysics and biology. To achieve biologically relevant length and time scales, coarse-grained (CG) models of such complexes usually must be built and employed. One of the important early stages in this approach is to determine an optimal number of CG sites in different constituents of a complex. This work presents a systematic approach to this problem. First, a universal scaling law is derived and numerically corroborated for the intensity of the intrasite (intradomain) thermal fluctuations as a function of the number of CG sites. Second, this result is used for derivation of the criterion for the optimal number of CG sites in different parts of a large multibiomolecule complex. In the zeroth-order approximation, this approach validates the empirical rule of taking one CG site per fixed number of atoms or residues in each biomolecule, previously widely used for smaller systems (e.g., individual biomolecules). The first-order corrections to this rule are derived and numerically checked by the case studies of the Escherichia coli ribosome and Arp2/3 actin filament junction. In different ribosomal proteins, the optimal number of amino acids per CG site is shown to differ by a factor of 3.5, and an even wider spread may exist in other large biomolecular complexes. Therefore, the method proposed in this paper is valuable for the optimal construction of CG models of such complexes.
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Affiliation(s)
- Anton V Sinitskiy
- Department of Chemistry, Institute for Biophysical Dynamics, University of Chicago, Chicago, Illinois 60637, United States
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40
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Fernandez-Martinez J, Phillips J, Sekedat MD, Diaz-Avalos R, Velazquez-Muriel J, Franke JD, Williams R, Stokes DL, Chait BT, Sali A, Rout MP. Structure-function mapping of a heptameric module in the nuclear pore complex. ACTA ACUST UNITED AC 2012; 196:419-34. [PMID: 22331846 PMCID: PMC3283990 DOI: 10.1083/jcb.201109008] [Citation(s) in RCA: 103] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Integration of EM, protein–protein interaction, and phenotypic data reveals novel insights into the structure and function of the nuclear pore complex’s ∼600-kD heptameric Nup84 complex. The nuclear pore complex (NPC) is a multiprotein assembly that serves as the sole mediator of nucleocytoplasmic exchange in eukaryotic cells. In this paper, we use an integrative approach to determine the structure of an essential component of the yeast NPC, the ∼600-kD heptameric Nup84 complex, to a precision of ∼1.5 nm. The configuration of the subunit structures was determined by satisfaction of spatial restraints derived from a diverse set of negative-stain electron microscopy and protein domain–mapping data. Phenotypic data were mapped onto the complex, allowing us to identify regions that stabilize the NPC’s interaction with the nuclear envelope membrane and connect the complex to the rest of the NPC. Our data allow us to suggest how the Nup84 complex is assembled into the NPC and propose a scenario for the evolution of the Nup84 complex through a series of gene duplication and loss events. This work demonstrates that integrative approaches based on low-resolution data of sufficient quality can generate functionally informative structures at intermediate resolution.
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41
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A jumbo problem: mapping the structure and functions of the nuclear pore complex. Curr Opin Cell Biol 2012; 24:92-9. [PMID: 22321828 DOI: 10.1016/j.ceb.2011.12.013] [Citation(s) in RCA: 43] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2011] [Revised: 12/23/2011] [Accepted: 12/24/2011] [Indexed: 01/16/2023]
Abstract
Macromolecular assemblies can be intrinsically refractive to classical structural analysis, due to their size, complexity, plasticity and dynamic nature. One such assembly is the nuclear pore complex (NPC). The NPC is formed from ∼450 copies of 30 different proteins, called nucleoporins, and is the sole mediator of exchange between the nucleus and the cytoplasm in eukaryotic cells. Despite significant progress, it has become increasingly clear that new approaches, integrating different sources of structural and functional data, will be needed to understand the functional biology of the NPC. Here, we discuss the latest approaches trying to address this challenge.
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42
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Lasker K, Förster F, Bohn S, Walzthoeni T, Villa E, Unverdorben P, Beck F, Aebersold R, Sali A, Baumeister W. Molecular architecture of the 26S proteasome holocomplex determined by an integrative approach. Proc Natl Acad Sci U S A 2012; 109:1380-7. [PMID: 22307589 PMCID: PMC3277140 DOI: 10.1073/pnas.1120559109] [Citation(s) in RCA: 376] [Impact Index Per Article: 31.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
The 26S proteasome is at the executive end of the ubiquitin-proteasome pathway for the controlled degradation of intracellular proteins. While the structure of its 20S core particle (CP) has been determined by X-ray crystallography, the structure of the 19S regulatory particle (RP), which recruits substrates, unfolds them, and translocates them to the CP for degradation, has remained elusive. Here, we describe the molecular architecture of the 26S holocomplex determined by an integrative approach based on data from cryoelectron microscopy, X-ray crystallography, residue-specific chemical cross-linking, and several proteomics techniques. The "lid" of the RP (consisting of Rpn3/5/6/7/8/9/11/12) is organized in a modular fashion. Rpn3/5/6/7/9/12 form a horseshoe-shaped heterohexamer, which connects to the CP and roofs the AAA-ATPase module, positioning the Rpn8/Rpn11 heterodimer close to its mouth. Rpn2 is rigid, supporting the lid, while Rpn1 is conformationally variable, positioned at the periphery of the ATPase ring. The ubiquitin receptors Rpn10 and Rpn13 are located in the distal part of the RP, indicating that they were recruited to the complex late in its evolution. The modular structure of the 26S proteasome provides insights into the sequence of events prior to the degradation of ubiquitylated substrates.
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Affiliation(s)
- Keren Lasker
- Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, and California Institute of Quantitative Biosciences, 1700 4th Street, University of California, San Francisco, CA 94158
- Blavatnik School of Computer Science, Raymond and Beverly Sackler Faculty of Exact Sciences, Tel Aviv University, Tel Aviv 69978, Israel
| | - Friedrich Förster
- Department of Molecular Structural Biology, Max-Planck-Institute of Biochemistry, Am Klopferspitz 18, 82152 Martinsried, Germany
| | - Stefan Bohn
- Department of Molecular Structural Biology, Max-Planck-Institute of Biochemistry, Am Klopferspitz 18, 82152 Martinsried, Germany
| | - Thomas Walzthoeni
- Department of Biology, Institute of Molecular Systems Biology, Eidgenössische, Technische Hochschule, 8093 Zürich, Switzerland
- PhD Program in Molecular Life Sciences, University of Zurich/ETH Zurich, Winterthurerstrasse 190, 8057 Zürich, Switzerland; and
| | - Elizabeth Villa
- Department of Molecular Structural Biology, Max-Planck-Institute of Biochemistry, Am Klopferspitz 18, 82152 Martinsried, Germany
| | - Pia Unverdorben
- Department of Molecular Structural Biology, Max-Planck-Institute of Biochemistry, Am Klopferspitz 18, 82152 Martinsried, Germany
| | - Florian Beck
- Department of Molecular Structural Biology, Max-Planck-Institute of Biochemistry, Am Klopferspitz 18, 82152 Martinsried, Germany
| | - Ruedi Aebersold
- Department of Biology, Institute of Molecular Systems Biology, Eidgenössische, Technische Hochschule, 8093 Zürich, Switzerland
- Faculty of Science, University of Zürich, 8093 Zürich, Switzerland
| | - Andrej Sali
- Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, and California Institute of Quantitative Biosciences, 1700 4th Street, University of California, San Francisco, CA 94158
| | - Wolfgang Baumeister
- Department of Molecular Structural Biology, Max-Planck-Institute of Biochemistry, Am Klopferspitz 18, 82152 Martinsried, Germany
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Russel D, Lasker K, Webb B, Velázquez-Muriel J, Tjioe E, Schneidman-Duhovny D, Peterson B, Sali A. Putting the pieces together: integrative modeling platform software for structure determination of macromolecular assemblies. PLoS Biol 2012; 10:e1001244. [PMID: 22272186 PMCID: PMC3260315 DOI: 10.1371/journal.pbio.1001244] [Citation(s) in RCA: 381] [Impact Index Per Article: 31.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023] Open
Abstract
A set of software tools for building and distributing models of macromolecular assemblies uses an integrative structure modeling approach, which casts the building of models as a computational optimization problem where information is encoded into a scoring function used to evaluate candidate models.
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Affiliation(s)
- Daniel Russel
- Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, California Institute for Quantitative Biosciences (QB3), University of California, San Francisco, San Francisco, California, United States of America
| | - Keren Lasker
- Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, California Institute for Quantitative Biosciences (QB3), University of California, San Francisco, San Francisco, California, United States of America
- Raymond and Beverly Sackler Faculty of Exact Sciences, Blavatnik School of Computer Science, Tel Aviv University, Tel-Aviv, Israel
| | - Ben Webb
- Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, California Institute for Quantitative Biosciences (QB3), University of California, San Francisco, San Francisco, California, United States of America
| | - Javier Velázquez-Muriel
- Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, California Institute for Quantitative Biosciences (QB3), University of California, San Francisco, San Francisco, California, United States of America
| | - Elina Tjioe
- Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, California Institute for Quantitative Biosciences (QB3), University of California, San Francisco, San Francisco, California, United States of America
| | - Dina Schneidman-Duhovny
- Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, California Institute for Quantitative Biosciences (QB3), University of California, San Francisco, San Francisco, California, United States of America
| | - Bret Peterson
- Google, Mountain View, California, United States of America
| | - Andrej Sali
- Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, California Institute for Quantitative Biosciences (QB3), University of California, San Francisco, San Francisco, California, United States of America
- * E-mail:
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Tjioe E, Lasker K, Webb B, Wolfson HJ, Sali A. MultiFit: a web server for fitting multiple protein structures into their electron microscopy density map. Nucleic Acids Res 2011; 39:W167-70. [PMID: 21715383 PMCID: PMC3125811 DOI: 10.1093/nar/gkr490] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Advances in electron microscopy (EM) allow for structure determination of large biological assemblies at increasingly higher resolutions. A key step in this process is fitting multiple component structures into an EM-derived density map of their assembly. Here, we describe a web server for this task. The server takes as input a set of protein structures in the PDB format and an EM density map in the MRC format. The output is an ensemble of models ranked by their quality of fit to the density map. The models can be viewed online or downloaded from the website. The service is available at; http://salilab.org/multifit/ and http://bioinfo3d.cs.tau.ac.il/.
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Affiliation(s)
- Elina Tjioe
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA, USA
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45
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Shih ESC, Hwang MJ. On the use of distance constraints in protein-protein docking computations. Proteins 2011; 80:194-205. [DOI: 10.1002/prot.23179] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2011] [Revised: 08/24/2011] [Accepted: 09/04/2011] [Indexed: 12/29/2022]
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46
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Yang Z, Lasker K, Schneidman-Duhovny D, Webb B, Huang CC, Pettersen EF, Goddard TD, Meng EC, Sali A, Ferrin TE. UCSF Chimera, MODELLER, and IMP: an integrated modeling system. J Struct Biol 2011; 179:269-78. [PMID: 21963794 DOI: 10.1016/j.jsb.2011.09.006] [Citation(s) in RCA: 453] [Impact Index Per Article: 34.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2011] [Revised: 09/16/2011] [Accepted: 09/18/2011] [Indexed: 02/02/2023]
Abstract
Structural modeling of macromolecular complexes greatly benefits from interactive visualization capabilities. Here we present the integration of several modeling tools into UCSF Chimera. These include comparative modeling by MODELLER, simultaneous fitting of multiple components into electron microscopy density maps by IMP MultiFit, computing of small-angle X-ray scattering profiles and fitting of the corresponding experimental profile by IMP FoXS, and assessment of amino acid sidechain conformations based on rotamer probabilities and local interactions by Chimera.
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Affiliation(s)
- Zheng Yang
- Resource for Biocomputing, Visualization, and Informatics, Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94158, USA
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47
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Han L, Hyung SJ, Mayers JJ, Ruotolo BT. Bound anions differentially stabilize multiprotein complexes in the absence of bulk solvent. J Am Chem Soc 2011; 133:11358-67. [PMID: 21675748 PMCID: PMC3140617 DOI: 10.1021/ja203527a] [Citation(s) in RCA: 82] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
The combination of ion mobility separation with mass spectrometry is an emergent and powerful structural biology tool, capable of simultaneously assessing the structure, topology, dynamics, and composition of large protein assemblies within complex mixtures. An integral part of the ion mobility-mass spectrometry measurement is the ionization of intact multiprotein complexes and their removal from bulk solvent. This process, during which a substantial portion of protein structure and organization is likely to be preserved, imposes a foreign environment on proteins that may cause structural rearrangements to occur. Thus, a general means must be identified to stabilize protein structures in the absence of bulk solvent. Our approach to this problem involves the protection of protein complex structure through the addition of salts in solution prior to desorption/ionization. Anionic components of the added salts bind to the complex either in solution or during the electrospray process, and those that remain bound in the gas phase tend to have high gas phase acidities. The resulting 'shell' of counterions is able to carry away excess energy from the protein complex ion upon activation and can result in significant structural stabilization of the gas-phase protein assembly overall. By using ion mobility-mass spectrometry, we observe both the dissociation and unfolding transitions for four tetrameric protein complexes bound to populations of 12 different anions using collisional activation. The data presented here quantifies, for the first time, the influence of a large range of counterions on gas-phase protein structure and allows us to rank and classify counterions as structure stabilizers in the absence of bulk solvent. Our measurements indicate that tartrate, citrate, chloride, and nitrate anions are among the strongest stabilizers of gas-phase protein structure identified in this screen. The rank order determined by our data is substantially different when compared to the known Hofmeister salt series in solution. While this is an expected outcome of our work, due to the diminished influence of anion and protein solvation by water, our data correlates well to expected anion binding in solution and highlights the fact that both hydration layer and anion-protein binding effects are critical for Hofmeister-type stabilization in solution. Finally, we present a detailed mechanism of action for counterion stabilization of proteins and their complexes in the gas-phase, which indicates that anions must bind with high affinity, but must dissociate readily from the protein in order to be an effective stabilizer. Anion-resolved data acquired for smaller protein systems allows us to classify anions into three categories based on their ability to stabilize protein and protein complex structure in the absence of bulk solvent.
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Affiliation(s)
| | | | - Jonathan J.S. Mayers
- Department of Chemistry, University of Michigan, 930 N. University Ave., Ann Arbor, MI 48109
| | - Brandon T. Ruotolo
- Department of Chemistry, University of Michigan, 930 N. University Ave., Ann Arbor, MI 48109
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48
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Tuncbag N, Gursoy A, Keskin O. Prediction of protein-protein interactions: unifying evolution and structure at protein interfaces. Phys Biol 2011; 8:035006. [PMID: 21572173 DOI: 10.1088/1478-3975/8/3/035006] [Citation(s) in RCA: 49] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
The vast majority of the chores in the living cell involve protein-protein interactions. Providing details of protein interactions at the residue level and incorporating them into protein interaction networks are crucial toward the elucidation of a dynamic picture of cells. Despite the rapid increase in the number of structurally known protein complexes, we are still far away from a complete network. Given experimental limitations, computational modeling of protein interactions is a prerequisite to proceed on the way to complete structural networks. In this work, we focus on the question 'how do proteins interact?' rather than 'which proteins interact?' and we review structure-based protein-protein interaction prediction approaches. As a sample approach for modeling protein interactions, PRISM is detailed which combines structural similarity and evolutionary conservation in protein interfaces to infer structures of complexes in the protein interaction network. This will ultimately help us to understand the role of protein interfaces in predicting bound conformations.
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Affiliation(s)
- Nurcan Tuncbag
- Koc University, Center for Computational Biology and Bioinformatics, and College of Engineering, Rumelifeneri Yolu, 34450 Sariyer Istanbul, Turkey
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49
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Beck M, Topf M, Frazier Z, Tjong H, Xu M, Zhang S, Alber F. Exploring the spatial and temporal organization of a cell's proteome. J Struct Biol 2011; 173:483-96. [PMID: 21094684 PMCID: PMC3784337 DOI: 10.1016/j.jsb.2010.11.011] [Citation(s) in RCA: 25] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2010] [Revised: 11/05/2010] [Accepted: 11/08/2010] [Indexed: 10/18/2022]
Abstract
To increase our current understanding of cellular processes, such as cell signaling and division, knowledge is needed about the spatial and temporal organization of the proteome at different organizational levels. These levels cover a wide range of length and time scales: from the atomic structures of macromolecules for inferring their molecular function, to the quantitative description of their abundance, and spatial distribution in the cell. Emerging new experimental technologies are greatly increasing the availability of such spatial information on the molecular organization in living cells. This review addresses three fields that have significantly contributed to our understanding of the proteome's spatial and temporal organization: first, methods for the structure determination of individual macromolecular assemblies, specifically the fitting of atomic structures into density maps generated from electron microscopy techniques; second, research that visualizes the spatial distributions of these complexes within the cellular context using cryo electron tomography techniques combined with computational image processing; and third, methods for the spatial modeling of the dynamic organization of the proteome, specifically those methods for simulating reaction and diffusion of proteins and complexes in crowded intracellular fluids. The long-term goal is to integrate the varied data about a proteome's organization into a spatially explicit, predictive model of cellular processes.
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Affiliation(s)
- Martin Beck
- European Molecular Biology Laboratory, Meyerhofstr. 1, 69117 Heidelberg, Germany
| | - Maya Topf
- Molecular Biology, Crystallography, Department of Biological Sciences, Birkbeck College, University of London, London, UK
| | - Zachary Frazier
- Program in Molecular and Computational Biology, University of Southern California, 1050 Childs Way, RRI 413E, Los Angeles, CA 90068, USA
| | - Harianto Tjong
- Program in Molecular and Computational Biology, University of Southern California, 1050 Childs Way, RRI 413E, Los Angeles, CA 90068, USA
| | - Min Xu
- Program in Molecular and Computational Biology, University of Southern California, 1050 Childs Way, RRI 413E, Los Angeles, CA 90068, USA
| | - Shihua Zhang
- Program in Molecular and Computational Biology, University of Southern California, 1050 Childs Way, RRI 413E, Los Angeles, CA 90068, USA
| | - Frank Alber
- Program in Molecular and Computational Biology, University of Southern California, 1050 Childs Way, RRI 413E, Los Angeles, CA 90068, USA
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50
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Lasker K, Sali A, Wolfson HJ. Determining macromolecular assembly structures by molecular docking and fitting into an electron density map. Proteins 2011; 78:3205-11. [PMID: 20827723 DOI: 10.1002/prot.22845] [Citation(s) in RCA: 60] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Structural models of macromolecular assemblies are instrumental for gaining a mechanistic understanding of cellular processes. Determining these structures is a major challenge for experimental techniques, such as X-ray crystallography, NMR spectroscopy and electron microscopy (EM). Thus, computational modeling techniques, including molecular docking, are required. The development of most molecular docking methods has so far been focused on modeling of binary complexes. We have recently introduced the MultiFit method for modeling the structure of a multisubunit complex by simultaneously optimizing the fit of the model into an EM density map of the entire complex and the shape complementarity between interacting subunits. Here, we report algorithmic advances of the MultiFit method that result in an efficient and accurate assembly of the input subunits into their density map. The successful predictions and the increasing number of complexes being characterized by EM suggests that the CAPRI challenge could be extended to include docking-based modeling of macromolecular assemblies guided by EM.
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Affiliation(s)
- Keren Lasker
- Raymond and Beverly Sackler Faculty of Exact Sciences, Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv 69978, Israel
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