51
|
Trewhella J. Small Angle Scattering and Structural Biology: Data Quality and Model Validation. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2018; 1105:77-100. [PMID: 30617825 DOI: 10.1007/978-981-13-2200-6_7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
This chapter provides a brief review of the current state-of-the-art in small-angle scattering (SAS) from biomolecules in solution in regard to: (1) sample preparation and instrumentation, (2) data reduction and analysis, and (3) three-dimensional structural modelling and validation. In this context, areas of ongoing research in regard to the interpretation of SAS data will be discussed with a particular focus on structural modelling using computational methods and data from different experimental techniques, including SAS (hybrid methods). Finally, progress made in establishing community accepted publication guidelines and a standard reporting framework that includes SAS data deposition in a public data bank will be described. Importantly, SAS data with associated meta-data can now be held in a format that supports exchange between data archives and seamless interoperability with the world-wide Protein Data Bank (wwPDB). Biomolecular SAS is thus well positioned to contribute to an envisioned federation of data archives in support of hybrid structural biology.
Collapse
Affiliation(s)
- Jill Trewhella
- School of Life and Environmental Sciences, The University of Sydney, NSW, Australia. .,Department of Chemistry, University of Utah, Salt Lake City, UT, USA.
| |
Collapse
|
52
|
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.
Collapse
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
| |
Collapse
|
53
|
Eschweiler JD, Frank AT, Ruotolo BT. Coming to Grips with Ambiguity: Ion Mobility-Mass Spectrometry for Protein Quaternary Structure Assignment. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2017; 28:1991-2000. [PMID: 28752478 PMCID: PMC5693686 DOI: 10.1007/s13361-017-1757-1] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/18/2017] [Revised: 07/04/2017] [Accepted: 07/05/2017] [Indexed: 05/21/2023]
Abstract
Multiprotein complexes are central to our understanding of cellular biology, as they play critical roles in nearly every biological process. Despite many impressive advances associated with structural characterization techniques, large and highly-dynamic protein complexes are too often refractory to analysis by conventional, high-resolution approaches. To fill this gap, ion mobility-mass spectrometry (IM-MS) methods have emerged as a promising approach for characterizing the structures of challenging assemblies due in large part to the ability of these methods to characterize the composition, connectivity, and topology of large, labile complexes. In this Critical Insight, we present a series of bioinformatics studies aimed at assessing the information content of IM-MS datasets for building models of multiprotein structure. Our computational data highlights the limits of current coarse-graining approaches, and compelled us to develop an improved workflow for multiprotein topology modeling, which we benchmark against a subset of the multiprotein complexes within the PDB. This improved workflow has allowed us to ascertain both the minimal experimental restraint sets required for generation of high-confidence multiprotein topologies, and quantify the ambiguity in models where insufficient IM-MS information is available. We conclude by projecting the future of IM-MS in the context of protein quaternary structure assignment, where we predict that a more complete knowledge of the ultimate information content and ambiguity within such models will undoubtedly lead to applications for a broader array of challenging biomolecular assemblies. Graphical Abstract ᅟ.
Collapse
Affiliation(s)
| | - Aaron T Frank
- Department of Chemistry, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Brandon T Ruotolo
- Department of Chemistry, University of Michigan, Ann Arbor, MI, 48109, USA.
| |
Collapse
|
54
|
Shevchuk R, Hub JS. Bayesian refinement of protein structures and ensembles against SAXS data using molecular dynamics. PLoS Comput Biol 2017; 13:e1005800. [PMID: 29045407 PMCID: PMC5662244 DOI: 10.1371/journal.pcbi.1005800] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2017] [Revised: 10/30/2017] [Accepted: 09/29/2017] [Indexed: 12/24/2022] Open
Abstract
Small-angle X-ray scattering is an increasingly popular technique used to detect protein structures and ensembles in solution. However, the refinement of structures and ensembles against SAXS data is often ambiguous due to the low information content of SAXS data, unknown systematic errors, and unknown scattering contributions from the solvent. We offer a solution to such problems by combining Bayesian inference with all-atom molecular dynamics simulations and explicit-solvent SAXS calculations. The Bayesian formulation correctly weights the SAXS data versus prior physical knowledge, it quantifies the precision or ambiguity of fitted structures and ensembles, and it accounts for unknown systematic errors due to poor buffer matching. The method further provides a probabilistic criterion for identifying the number of states required to explain the SAXS data. The method is validated by refining ensembles of a periplasmic binding protein against calculated SAXS curves. Subsequently, we derive the solution ensembles of the eukaryotic chaperone heat shock protein 90 (Hsp90) against experimental SAXS data. We find that the SAXS data of the apo state of Hsp90 is compatible with a single wide-open conformation, whereas the SAXS data of Hsp90 bound to ATP or to an ATP-analogue strongly suggest heterogenous ensembles of a closed and a wide-open state.
Collapse
Affiliation(s)
- Roman Shevchuk
- Institute for Microbiology and Genetics, University of Göttingen, Göttingen, Germany
- Göttingen Center for Molecular Biosciences (GZMB), University of Goettingen, Goettingen, Germany
| | - Jochen S. Hub
- Institute for Microbiology and Genetics, University of Göttingen, Göttingen, Germany
- Göttingen Center for Molecular Biosciences (GZMB), University of Goettingen, Goettingen, Germany
| |
Collapse
|
55
|
Heller GT, Aprile FA, Vendruscolo M. Methods of probing the interactions between small molecules and disordered proteins. Cell Mol Life Sci 2017; 74:3225-3243. [PMID: 28631009 PMCID: PMC5533867 DOI: 10.1007/s00018-017-2563-4] [Citation(s) in RCA: 47] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2017] [Accepted: 06/01/2017] [Indexed: 12/15/2022]
Abstract
It is generally recognized that a large fraction of the human proteome is made up of proteins that remain disordered in their native states. Despite the fact that such proteins play key biological roles and are involved in many major human diseases, they still represent challenging targets for drug discovery. A major bottleneck for the identification of compounds capable of interacting with these proteins and modulating their disease-promoting behaviour is the development of effective techniques to probe such interactions. The difficulties in carrying out binding measurements have resulted in a poor understanding of the mechanisms underlying these interactions. In order to facilitate further methodological advances, here we review the most commonly used techniques to probe three types of interactions involving small molecules: (1) those that disrupt functional interactions between disordered proteins; (2) those that inhibit the aberrant aggregation of disordered proteins, and (3) those that lead to binding disordered proteins in their monomeric states. In discussing these techniques, we also point out directions for future developments.
Collapse
Affiliation(s)
- Gabriella T Heller
- Department of Chemistry, University of Cambridge, Cambridge, CB2 1EW, UK
| | - Francesco A Aprile
- Department of Chemistry, University of Cambridge, Cambridge, CB2 1EW, UK
| | | |
Collapse
|
56
|
Trewhella J, Duff AP, Durand D, Gabel F, Guss JM, Hendrickson WA, Hura GL, Jacques DA, Kirby NM, Kwan AH, Pérez J, Pollack L, Ryan TM, Sali A, Schneidman-Duhovny D, Schwede T, Svergun DI, Sugiyama M, Tainer JA, Vachette P, Westbrook J, Whitten AE. 2017 publication guidelines for structural modelling of small-angle scattering data from biomolecules in solution: an update. Acta Crystallogr D Struct Biol 2017; 73:710-728. [PMID: 28876235 PMCID: PMC5586245 DOI: 10.1107/s2059798317011597] [Citation(s) in RCA: 181] [Impact Index Per Article: 25.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2017] [Accepted: 08/07/2017] [Indexed: 12/02/2022] Open
Abstract
In 2012, preliminary guidelines were published addressing sample quality, data acquisition and reduction, presentation of scattering data and validation, and modelling for biomolecular small-angle scattering (SAS) experiments. Biomolecular SAS has since continued to grow and authors have increasingly adopted the preliminary guidelines. In parallel, integrative/hybrid determination of biomolecular structures is a rapidly growing field that is expanding the scope of structural biology. For SAS to contribute maximally to this field, it is essential to ensure open access to the information required for evaluation of the quality of SAS samples and data, as well as the validity of SAS-based structural models. To this end, the preliminary guidelines for data presentation in a publication are reviewed and updated, and the deposition of data and associated models in a public archive is recommended. These guidelines and recommendations have been prepared in consultation with the members of the International Union of Crystallography (IUCr) Small-Angle Scattering and Journals Commissions, the Worldwide Protein Data Bank (wwPDB) Small-Angle Scattering Validation Task Force and additional experts in the field.
Collapse
Affiliation(s)
- Jill Trewhella
- School of Life and Environmental Sciences, The University of Sydney, NSW 2006, Australia
| | - Anthony P. Duff
- ANSTO, New Illawarra Road, Lucas Heights, NSW 2234, Australia
| | - Dominique Durand
- Institut de Biologie Intégrative de la Cellule, UMR 9198, Bâtiment 430, Université Paris-Sud, 91405 Orsay CEDEX, France
| | - Frank Gabel
- Université Grenoble Alpes, Commissariat à l’Energie Atomique (CEA), Centre National de la Recherche Scientifique (CNRS), Institut de Biologie Structurale (IBS), and Institut Laue–Langevin, 71 Avenue des Martyrs, 38000 Grenoble, France
| | - J. Mitchell Guss
- School of Life and Environmental Sciences, The University of Sydney, NSW 2006, Australia
| | - Wayne A. Hendrickson
- Department of Biochemistry and Molecular Biophysics, Columbia University, New York, NY 10032, USA
| | - Greg L. Hura
- Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - David A. Jacques
- University of Technology Sydney, ithree Institute, 15 Broadway, Ultimo, NSW 2007, Australia
| | - Nigel M. Kirby
- Australian Synchrotron, 800 Blackburn Road, Clayton, VIC 3168, Australia
| | - Ann H. Kwan
- School of Life and Environmental Sciences, The University of Sydney, NSW 2006, Australia
| | - Javier Pérez
- Synchrotron SOLEIL, L’Orme des Merisiers, Saint-Aubin BP48, 91192 Gif-sur-Yvette CEDEX, France
| | - Lois Pollack
- School of Applied and Engineering Physics, Cornell University, Ithaca, NY 14853-2501, USA
| | - Timothy M. Ryan
- Australian Synchrotron, 800 Blackburn Road, Clayton, VIC 3168, Australia
| | - Andrej Sali
- Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, and California Institute for Quantitative Biosciences (QB3), University of California San Francisco, San Francisco, California, USA
| | - Dina Schneidman-Duhovny
- School of Computer Science and Engineering, Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel
| | - Torsten Schwede
- Biozentrum, University of Basel and SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Dmitri I. Svergun
- European Molecular Biology Laboratory (EMBL) Hamburg, c/o DESY, Nokestrasse 85, 22607 Hamburg, Germany
| | - Masaaki Sugiyama
- Research Reactor Institute, Kyoto University, Kumatori, Sennan-gun, Osaka 590-0494, Japan
| | - John A. Tainer
- Basic Science Research Division, Molecular and Cellular Oncology, MD Anderson Cancer Center, University of Texas, Houston, Texas, USA
| | - Patrice Vachette
- Institut de Biologie Intégrative de la Cellule, UMR 9198, Bâtiment 430, Université Paris-Sud, 91405 Orsay CEDEX, France
| | - John Westbrook
- Department of Chemistry and Chemical Biology, Rutgers University, New Brunswick, NJ 07102, USA
| | | |
Collapse
|
57
|
The In Vivo Architecture of the Exocyst Provides Structural Basis for Exocytosis. Cell 2017; 168:400-412.e18. [PMID: 28129539 DOI: 10.1016/j.cell.2017.01.004] [Citation(s) in RCA: 56] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2016] [Revised: 10/18/2016] [Accepted: 01/05/2017] [Indexed: 11/21/2022]
Abstract
The structural characterization of protein complexes in their native environment is challenging but crucial for understanding the mechanisms that mediate cellular processes. We developed an integrative approach to reconstruct the 3D architecture of protein complexes in vivo. We applied this approach to the exocyst, a hetero-octameric complex of unknown structure that is thought to tether secretory vesicles during exocytosis with a poorly understood mechanism. We engineered yeast cells to anchor the exocyst on defined landmarks and determined the position of its subunit termini at nanometer precision using fluorescence microscopy. We then integrated these positions with the structural properties of the subunits to reconstruct the exocyst together with a vesicle bound to it. The exocyst has an open hand conformation made of rod-shaped subunits that are interlaced in the core. The exocyst architecture explains how the complex can tether secretory vesicles, placing them in direct contact with the plasma membrane.
Collapse
|
58
|
Perez A, Morrone JA, Dill KA. Accelerating physical simulations of proteins by leveraging external knowledge. WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE 2017; 7. [PMID: 28959358 DOI: 10.1002/wcms.1309] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
It is challenging to compute structure-function relationships of proteins using molecular physics. The problem arises from the exponential scaling of the computational searching and sampling of large conformational spaces. This scaling challenge is not met by today's methods, such as Monte Carlo, simulated annealing, genetic algorithms, or molecular dynamics (MD) or its variants such as replica exchange. Such methods of searching for optimal states on complex probabalistic landscapes are referred to more broadly as Explore-and-Exploit (EE), including in contexts such as computational learning, games, industrial planning and modeling military strategies. Here we describe a Bayesian method, called MELD, that 'melds' together explore-and-exploit approaches with externally added information that can be vague, combinatoric, noisy, intuitive, heuristic, or from experimental data. MELD is shown to accelerate physical MD simulations when using experimental data to determine protein structures; for predicting protein structures by using heuristic directives; and when predicting binding affinities of proteins from limited information about the binding site. Such Guided Explore-and-Exploit approaches might also be useful beyond proteins and beyond molecular science.
Collapse
Affiliation(s)
- Alberto Perez
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York 11794, United States
| | - Joseph A Morrone
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York 11794, United States
| | - Ken A Dill
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York 11794, United States.,Chemistry Department, Stony Brook University, Stony Brook, New York 11794, United States.,Physics and Astronomy Department, Stony Brook University, Stony Brook, New York 11794, United States
| |
Collapse
|
59
|
Bonomi M, Heller GT, Camilloni C, Vendruscolo M. Principles of protein structural ensemble determination. Curr Opin Struct Biol 2017; 42:106-116. [PMID: 28063280 DOI: 10.1016/j.sbi.2016.12.004] [Citation(s) in RCA: 227] [Impact Index Per Article: 32.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2016] [Revised: 11/18/2016] [Accepted: 12/06/2016] [Indexed: 01/19/2023]
Abstract
The biological functions of protein molecules are intimately dependent on their conformational dynamics. This aspect is particularly evident for disordered proteins, which constitute perhaps one-third of the human proteome. Therefore, structural ensembles often offer more useful representations of proteins than individual conformations. Here, we describe how the well-established principles of protein structure determination should be extended to the case of protein structural ensembles determination. These principles concern primarily how to deal with conformationally heterogeneous states, and with experimental measurements that are averaged over such states and affected by a variety of errors. We first review the growing literature of recent methods that combine experimental and computational information to model structural ensembles, highlighting their similarities and differences. We then address some conceptual problems in the determination of structural ensembles and define future goals towards the establishment of objective criteria for the comparison, validation, visualization and dissemination of such ensembles.
Collapse
Affiliation(s)
| | | | - Carlo Camilloni
- Department of Chemistry and Institute for Advanced Study, Technische Universität München, D-85747 Garching, Germany
| | | |
Collapse
|
60
|
Dimura M, Peulen TO, Hanke CA, Prakash A, Gohlke H, Seidel CA. Quantitative FRET studies and integrative modeling unravel the structure and dynamics of biomolecular systems. Curr Opin Struct Biol 2016; 40:163-185. [PMID: 27939973 DOI: 10.1016/j.sbi.2016.11.012] [Citation(s) in RCA: 118] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2016] [Revised: 11/11/2016] [Accepted: 11/11/2016] [Indexed: 01/11/2023]
Abstract
Förster Resonance Energy Transfer (FRET) combined with single-molecule spectroscopy probes macromolecular structure and dynamics and identifies coexisting conformational states. We review recent methodological developments in integrative structural modeling by satisfying spatial restraints on networks of FRET pairs (hybrid-FRET). We discuss procedures to incorporate prior structural knowledge and to obtain optimal distance networks. Finally, a workflow for hybrid-FRET is presented that automates integrative structural modeling and experiment planning to put hybrid-FRET on rails. To test this workflow, we simulate realistic single-molecule experiments and resolve three protein conformers, exchanging at 30μs and 10ms, with accuracies of 1-3Å RMSD versus the target structure. Incorporation of data from other spectroscopies and imaging is also discussed.
Collapse
Affiliation(s)
- Mykola Dimura
- Chair for Molecular Physical Chemistry, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany; Institute for Pharmaceutical and Medicinal Chemistry, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany
| | - Thomas O Peulen
- Chair for Molecular Physical Chemistry, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany
| | - Christian A Hanke
- Chair for Molecular Physical Chemistry, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany
| | - Aiswaria Prakash
- Chair for Molecular Physical Chemistry, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany
| | - Holger Gohlke
- Institute for Pharmaceutical and Medicinal Chemistry, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany
| | - Claus Am Seidel
- Chair for Molecular Physical Chemistry, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany.
| |
Collapse
|
61
|
Fernandez-Martinez J, Kim SJ, Shi Y, Upla P, Pellarin R, Gagnon M, Chemmama IE, Wang J, Nudelman I, Zhang W, Williams R, Rice WJ, Stokes DL, Zenklusen D, Chait BT, Sali A, Rout MP. Structure and Function of the Nuclear Pore Complex Cytoplasmic mRNA Export Platform. Cell 2016; 167:1215-1228.e25. [PMID: 27839866 DOI: 10.1016/j.cell.2016.10.028] [Citation(s) in RCA: 120] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2016] [Revised: 07/20/2016] [Accepted: 10/14/2016] [Indexed: 12/28/2022]
Abstract
The last steps in mRNA export and remodeling are performed by the Nup82 complex, a large conserved assembly at the cytoplasmic face of the nuclear pore complex (NPC). By integrating diverse structural data, we have determined the molecular architecture of the native Nup82 complex at subnanometer precision. The complex consists of two compositionally identical multiprotein subunits that adopt different configurations. The Nup82 complex fits into the NPC through the outer ring Nup84 complex. Our map shows that this entire 14-MDa Nup82-Nup84 complex assembly positions the cytoplasmic mRNA export factor docking sites and messenger ribonucleoprotein (mRNP) remodeling machinery right over the NPC's central channel rather than on distal cytoplasmic filaments, as previously supposed. We suggest that this configuration efficiently captures and remodels exporting mRNP particles immediately upon reaching the cytoplasmic side of the NPC.
Collapse
Affiliation(s)
| | - Seung Joong Kim
- Departments of Bioengineering and Therapeutic Sciences and Pharmaceutical Chemistry, California Institute for Quantitative Biosciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Yi Shi
- Laboratory of Mass Spectrometry and Gaseous Ion Chemistry, The Rockefeller University, New York, NY 10065, USA
| | - Paula Upla
- Skirball Institute of Biomolecular Medicine, Department of Cell Biology, New York University School of Medicine, New York, NY 10016, USA
| | - Riccardo Pellarin
- Departments of Bioengineering and Therapeutic Sciences and Pharmaceutical Chemistry, California Institute for Quantitative Biosciences, University of California, San Francisco, San Francisco, CA 94158, USA; Structural Bioinformatics Unit, Institut Pasteur, CNRS UMR 3528, 75015 Paris, France
| | - Michael Gagnon
- Département de Biochimie et Médecine Moléculaire, University of Montréal, Montréal, QC H3C3J7, Canada
| | - Ilan E Chemmama
- Departments of Bioengineering and Therapeutic Sciences and Pharmaceutical Chemistry, California Institute for Quantitative Biosciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Junjie Wang
- Laboratory of Mass Spectrometry and Gaseous Ion Chemistry, The Rockefeller University, New York, NY 10065, USA
| | - Ilona Nudelman
- Laboratory of Cellular and Structural Biology, The Rockefeller University, New York, NY 10065, USA
| | - Wenzhu Zhang
- Laboratory of Mass Spectrometry and Gaseous Ion Chemistry, The Rockefeller University, New York, NY 10065, USA
| | - Rosemary Williams
- Laboratory of Cellular and Structural Biology, The Rockefeller University, New York, NY 10065, USA
| | - William J Rice
- Simons Electron Microscopy Center at New York Structural Biology Center, New York, NY 10027, USA
| | - David L Stokes
- Skirball Institute of Biomolecular Medicine, Department of Cell Biology, New York University School of Medicine, New York, NY 10016, USA
| | - Daniel Zenklusen
- Département de Biochimie et Médecine Moléculaire, University of Montréal, Montréal, QC H3C3J7, Canada
| | - Brian T Chait
- Laboratory of Mass Spectrometry and Gaseous Ion Chemistry, The Rockefeller University, New York, NY 10065, USA.
| | - Andrej Sali
- Departments of Bioengineering and Therapeutic Sciences and Pharmaceutical Chemistry, California Institute for Quantitative Biosciences, University of California, San Francisco, San Francisco, CA 94158, USA.
| | - Michael P Rout
- Laboratory of Cellular and Structural Biology, The Rockefeller University, New York, NY 10065, USA.
| |
Collapse
|
62
|
Schulze-Gahmen U, Echeverria I, Stjepanovic G, Bai Y, Lu H, Schneidman-Duhovny D, Doudna JA, Zhou Q, Sali A, Hurley JH. Insights into HIV-1 proviral transcription from integrative structure and dynamics of the Tat:AFF4:P-TEFb:TAR complex. eLife 2016; 5. [PMID: 27731797 PMCID: PMC5072841 DOI: 10.7554/elife.15910] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2016] [Accepted: 10/07/2016] [Indexed: 01/04/2023] Open
Abstract
HIV-1 Tat hijacks the human superelongation complex (SEC) to promote proviral transcription. Here we report the 5.9 Å structure of HIV-1 TAR in complex with HIV-1 Tat and human AFF4, CDK9, and CycT1. The TAR central loop contacts the CycT1 Tat-TAR recognition motif (TRM) and the second Tat Zn2+-binding loop. Hydrogen-deuterium exchange (HDX) shows that AFF4 helix 2 is stabilized in the TAR complex despite not touching the RNA, explaining how it enhances TAR binding to the SEC 50-fold. RNA SHAPE and SAXS data were used to help model the extended (Tat Arginine-Rich Motif) ARM, which enters the TAR major groove between the bulge and the central loop. The structure and functional assays collectively support an integrative structure and a bipartite binding model, wherein the TAR central loop engages the CycT1 TRM and compact core of Tat, while the TAR major groove interacts with the extended Tat ARM. DOI:http://dx.doi.org/10.7554/eLife.15910.001
Collapse
Affiliation(s)
- Ursula Schulze-Gahmen
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, United States.,California Institute of Quantitative Biosciences, University of California, Berkeley, Berkeley, United States
| | - Ignacia Echeverria
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, United States.,Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, United States.,California Institute of Quantitative Biosciences, University of California San, Francisco, San Francisco, United States
| | - Goran Stjepanovic
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, United States.,California Institute of Quantitative Biosciences, University of California, Berkeley, Berkeley, United States.,Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, United States
| | - Yun Bai
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, United States.,California Institute of Quantitative Biosciences, University of California, Berkeley, Berkeley, United States
| | - Huasong Lu
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, United States.,California Institute of Quantitative Biosciences, University of California, Berkeley, Berkeley, United States
| | - Dina Schneidman-Duhovny
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, United States.,Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, United States.,California Institute of Quantitative Biosciences, University of California San, Francisco, San Francisco, United States
| | - Jennifer A Doudna
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, United States.,California Institute of Quantitative Biosciences, University of California, Berkeley, Berkeley, United States.,Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, United States.,Howard Hughes Medical Institute, University of California, Berkeley, Berkeley, United States.,Department of Chemistry, University of California, Berkeley, Berkeley, United States
| | - Qiang Zhou
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, United States.,California Institute of Quantitative Biosciences, University of California, Berkeley, Berkeley, United States
| | - Andrej Sali
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, United States.,Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, United States.,California Institute of Quantitative Biosciences, University of California San, Francisco, San Francisco, United States
| | - James H Hurley
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, United States.,California Institute of Quantitative Biosciences, University of California, Berkeley, Berkeley, United States.,Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, United States
| |
Collapse
|
63
|
Hummer G, Köfinger J. Bayesian ensemble refinement by replica simulations and reweighting. J Chem Phys 2016; 143:243150. [PMID: 26723635 DOI: 10.1063/1.4937786] [Citation(s) in RCA: 137] [Impact Index Per Article: 17.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023] Open
Abstract
We describe different Bayesian ensemble refinement methods, examine their interrelation, and discuss their practical application. With ensemble refinement, the properties of dynamic and partially disordered (bio)molecular structures can be characterized by integrating a wide range of experimental data, including measurements of ensemble-averaged observables. We start from a Bayesian formulation in which the posterior is a functional that ranks different configuration space distributions. By maximizing this posterior, we derive an optimal Bayesian ensemble distribution. For discrete configurations, this optimal distribution is identical to that obtained by the maximum entropy "ensemble refinement of SAXS" (EROS) formulation. Bayesian replica ensemble refinement enhances the sampling of relevant configurations by imposing restraints on averages of observables in coupled replica molecular dynamics simulations. We show that the strength of the restraints should scale linearly with the number of replicas to ensure convergence to the optimal Bayesian result in the limit of infinitely many replicas. In the "Bayesian inference of ensembles" method, we combine the replica and EROS approaches to accelerate the convergence. An adaptive algorithm can be used to sample directly from the optimal ensemble, without replicas. We discuss the incorporation of single-molecule measurements and dynamic observables such as relaxation parameters. The theoretical analysis of different Bayesian ensemble refinement approaches provides a basis for practical applications and a starting point for further investigations.
Collapse
Affiliation(s)
- Gerhard Hummer
- Department of Theoretical Biophysics, Max Planck Institute of Biophysics, Max-von-Laue Str. 3, 60438 Frankfurt am Main, Germany
| | - Jürgen Köfinger
- Department of Theoretical Biophysics, Max Planck Institute of Biophysics, Max-von-Laue Str. 3, 60438 Frankfurt am Main, Germany
| |
Collapse
|
64
|
Palamini M, Canciani A, Forneris F. Identifying and Visualizing Macromolecular Flexibility in Structural Biology. Front Mol Biosci 2016; 3:47. [PMID: 27668215 PMCID: PMC5016524 DOI: 10.3389/fmolb.2016.00047] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2016] [Accepted: 08/22/2016] [Indexed: 12/29/2022] Open
Abstract
Structural biology comprises a variety of tools to obtain atomic resolution data for the investigation of macromolecules. Conventional structural methodologies including crystallography, NMR and electron microscopy often do not provide sufficient details concerning flexibility and dynamics, even though these aspects are critical for the physiological functions of the systems under investigation. However, the increasing complexity of the molecules studied by structural biology (including large macromolecular assemblies, integral membrane proteins, intrinsically disordered systems, and folding intermediates) continuously demands in-depth analyses of the roles of flexibility and conformational specificity involved in interactions with ligands and inhibitors. The intrinsic difficulties in capturing often subtle but critical molecular motions in biological systems have restrained the investigation of flexible molecules into a small niche of structural biology. Introduction of massive technological developments over the recent years, which include time-resolved studies, solution X-ray scattering, and new detectors for cryo-electron microscopy, have pushed the limits of structural investigation of flexible systems far beyond traditional approaches of NMR analysis. By integrating these modern methods with powerful biophysical and computational approaches such as generation of ensembles of molecular models and selective particle picking in electron microscopy, more feasible investigations of dynamic systems are now possible. Using some prominent examples from recent literature, we review how current structural biology methods can contribute useful data to accurately visualize flexibility in macromolecular structures and understand its important roles in regulation of biological processes.
Collapse
Affiliation(s)
| | | | - Federico Forneris
- The Armenise-Harvard Laboratory of Structural Biology, Department of Biology and Biotechnology, University of PaviaPavia, Italy
| |
Collapse
|
65
|
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.
Collapse
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
| |
Collapse
|
66
|
Peng J, Zhang Z. Unraveling low-resolution structural data of large biomolecules by constructing atomic models with experiment-targeted parallel cascade selection simulations. Sci Rep 2016; 6:29360. [PMID: 27377017 PMCID: PMC4932515 DOI: 10.1038/srep29360] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2016] [Accepted: 06/17/2016] [Indexed: 11/09/2022] Open
Abstract
Various low-resolution experimental techniques have gained more and more popularity in obtaining structural information of large biomolecules. In order to interpret the low-resolution structural data properly, one may need to construct an atomic model of the biomolecule by fitting the data using computer simulations. Here we develop, to our knowledge, a new computational tool for such integrative modeling by taking the advantage of an efficient sampling technique called parallel cascade selection (PaCS) simulation. For given low-resolution structural data, this PaCS-Fit method converts it into a scoring function. After an initial simulation starting from a known structure of the biomolecule, the scoring function is used to pick conformations for next cycle of multiple independent simulations. By this iterative screening-after-sampling strategy, the biomolecule may be driven towards a conformation that fits well with the low-resolution data. Our method has been validated using three proteins with small-angle X-ray scattering data and two proteins with electron microscopy data. In all benchmark tests, high-quality atomic models, with generally 1-3 Å from the target structures, are obtained. Since our tool does not need to add any biasing potential in the simulations to deform the structure, any type of low-resolution data can be implemented conveniently.
Collapse
Affiliation(s)
- Junhui Peng
- Hefei National Laboratory for Physical Science at Microscale and School of Life Sciences, University of Science and Technology of China, Hefei, Anhui 230026, People’s Republic of China
| | - Zhiyong Zhang
- Hefei National Laboratory for Physical Science at Microscale and School of Life Sciences, University of Science and Technology of China, Hefei, Anhui 230026, People’s Republic of China
| |
Collapse
|
67
|
Maximova T, Moffatt R, Ma B, Nussinov R, Shehu A. Principles and Overview of Sampling Methods for Modeling Macromolecular Structure and Dynamics. PLoS Comput Biol 2016; 12:e1004619. [PMID: 27124275 PMCID: PMC4849799 DOI: 10.1371/journal.pcbi.1004619] [Citation(s) in RCA: 132] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
Investigation of macromolecular structure and dynamics is fundamental to understanding how macromolecules carry out their functions in the cell. Significant advances have been made toward this end in silico, with a growing number of computational methods proposed yearly to study and simulate various aspects of macromolecular structure and dynamics. This review aims to provide an overview of recent advances, focusing primarily on methods proposed for exploring the structure space of macromolecules in isolation and in assemblies for the purpose of characterizing equilibrium structure and dynamics. In addition to surveying recent applications that showcase current capabilities of computational methods, this review highlights state-of-the-art algorithmic techniques proposed to overcome challenges posed in silico by the disparate spatial and time scales accessed by dynamic macromolecules. This review is not meant to be exhaustive, as such an endeavor is impossible, but rather aims to balance breadth and depth of strategies for modeling macromolecular structure and dynamics for a broad audience of novices and experts.
Collapse
Affiliation(s)
- Tatiana Maximova
- Department of Computer Science, George Mason University, Fairfax, Virginia, United States of America
| | - Ryan Moffatt
- Department of Computer Science, George Mason University, Fairfax, Virginia, United States of America
| | - Buyong Ma
- Basic Science Program, Leidos Biomedical Research, Inc. Cancer and Inflammation Program, National Cancer Institute, Frederick, Maryland, United States of America
| | - Ruth Nussinov
- Basic Science Program, Leidos Biomedical Research, Inc. Cancer and Inflammation Program, National Cancer Institute, Frederick, Maryland, United States of America
- Sackler Institute of Molecular Medicine, Department of Human Genetics and Molecular Medicine, Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Amarda Shehu
- Department of Computer Science, George Mason University, Fairfax, Virginia, United States of America
- Department of Biongineering, George Mason University, Fairfax, Virginia, United States of America
- School of Systems Biology, George Mason University, Manassas, Virginia, United States of America
| |
Collapse
|
68
|
Latek D, Bajda M, Filipek S. A Hybrid Approach to Structure and Function Modeling of G Protein-Coupled Receptors. J Chem Inf Model 2016; 56:630-41. [PMID: 26978043 DOI: 10.1021/acs.jcim.5b00451] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
The recent GPCR Dock 2013 assessment of serotonin receptor 5-HT1B and 5-HT2B, and smoothened receptor SMO targets, exposed the strengths and weaknesses of the currently used computational approaches. The test cases of 5-HT1B and 5-HT2B demonstrated that both the receptor structure and the ligand binding mode can be predicted with the atomic-detail accuracy, as long as the target-template sequence similarity is relatively high. On the other hand, the observation of a low target-template sequence similarity, e.g., between SMO from the frizzled GPCR family and members of the rhodopsin family, hampers the GPCR structure prediction and ligand docking. Indeed, in GPCR Dock 2013, accurate prediction of the SMO target was still beyond the capabilities of most research groups. Another bottleneck in the current GPCR research, as demonstrated by the 5-HT2B target, is the reliable prediction of global conformational changes induced by activation of GPCRs. In this work, we report details of our protocol used during GPCR Dock 2013. Our structure prediction and ligand docking protocol was especially successful in the case of 5-HT1B and 5-HT2B-ergotamine complexes for which we provide one of the most accurate predictions. In addition to a description of the GPCR Dock 2013 results, we propose a novel hybrid computational methodology to improve GPCR structure and function prediction. This computational methodology employs two separate rankings for filtering GPCR models. The first ranking is ligand-based while the second is based on the scoring scheme of the recently published BCL method. In this work, we prove that the use of knowledge-based potentials implemented in BCL is an efficient way to cope with major bottlenecks in the GPCR structure prediction. Thereby, we also demonstrate that the knowledge-based potentials for membrane proteins were significantly improved, because of the recent surge in available experimental structures.
Collapse
Affiliation(s)
- Dorota Latek
- Faculty of Chemistry, Biological and Chemical Research Centre, University of Warsaw , Pasteura 1, 02-093 Warsaw, Poland
| | - Marek Bajda
- Faculty of Chemistry, Biological and Chemical Research Centre, University of Warsaw , Pasteura 1, 02-093 Warsaw, Poland.,Department of Physicochemical Drug Analysis, Faculty of Pharmacy, Medical College, Jagiellonian University , Medyczna 9, 30-688 Cracow, Poland
| | - Sławomir Filipek
- Faculty of Chemistry, Biological and Chemical Research Centre, University of Warsaw , Pasteura 1, 02-093 Warsaw, Poland
| |
Collapse
|
69
|
Greenberg CH, Kollman J, Zelter A, Johnson R, MacCoss MJ, Davis TN, Agard DA, Sali A. Structure of γ-tubulin small complex based on a cryo-EM map, chemical cross-links, and a remotely related structure. J Struct Biol 2016; 194:303-10. [PMID: 26968363 DOI: 10.1016/j.jsb.2016.03.006] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2016] [Revised: 03/06/2016] [Accepted: 03/07/2016] [Indexed: 11/26/2022]
Abstract
Modeling protein complex structures based on distantly related homologues can be challenging due to poor sequence and structure conservation. Therefore, utilizing even low-resolution experimental data can significantly increase model precision and accuracy. Here, we present models of the two key functional states of the yeast γ-tubulin small complex (γTuSC): one for the low-activity "open" state and another for the higher-activity "closed" state. Both models were computed based on remotely related template structures and cryo-EM density maps at 6.9Å and 8.0Å resolution, respectively. For each state, extensive sampling of alignments and conformations was guided by the fit to the corresponding cryo-EM density map. The resulting good-scoring models formed a tightly clustered ensemble of conformations in most regions. We found significant structural differences between the two states, primarily in the γ-tubulin subunit regions where the microtubule binds. We also report a set of chemical cross-links that were found to be consistent with equilibrium between the open and closed states. The protocols developed here have been incorporated into our open-source Integrative Modeling Platform (IMP) software package (http://integrativemodeling.org), and can therefore be applied to many other systems.
Collapse
Affiliation(s)
- Charles H Greenberg
- Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, California Institute for Quantitative Biosciences, University of California at San Francisco, San Francisco, CA, USA.
| | - Justin Kollman
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Alex Zelter
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Richard Johnson
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Michael J MacCoss
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Trisha N Davis
- Department of Genome Sciences, University of Washington, Seattle, WA, USA.
| | - David A Agard
- Department of Biochemistry and Biophysics, University of California at San Francisco, San Francisco, CA, USA; Howard Hughes Medical Institute, University of California at San Francisco, San Francisco, CA, USA.
| | - Andrej Sali
- Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, California Institute for Quantitative Biosciences, University of California at San Francisco, San Francisco, CA, USA.
| |
Collapse
|
70
|
de Vries SJ, Chauvot de Beauchêne I, Schindler CEM, Zacharias M. Cryo-EM Data Are Superior to Contact and Interface Information in Integrative Modeling. Biophys J 2016; 110:785-97. [PMID: 26846888 DOI: 10.1016/j.bpj.2015.12.038] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2015] [Revised: 11/18/2015] [Accepted: 12/14/2015] [Indexed: 12/29/2022] Open
Abstract
Protein-protein interactions carry out a large variety of essential cellular processes. Cryo-electron microscopy (cryo-EM) is a powerful technique for the modeling of protein-protein interactions at a wide range of resolutions, and recent developments have caused a revolution in the field. At low resolution, cryo-EM maps can drive integrative modeling of the interaction, assembling existing structures into the map. Other experimental techniques can provide information on the interface or on the contacts between the monomers in the complex. This inevitably raises the question regarding which type of data is best suited to drive integrative modeling approaches. Systematic comparison of the prediction accuracy and specificity of the different integrative modeling paradigms is unavailable to date. Here, we compare EM-driven, interface-driven, and contact-driven integrative modeling paradigms. Models were generated for the protein docking benchmark using the ATTRACT docking engine and evaluated using the CAPRI two-star criterion. At 20 Å resolution, EM-driven modeling achieved a success rate of 100%, outperforming the other paradigms even with perfect interface and contact information. Therefore, even very low resolution cryo-EM data is superior in predicting heterodimeric and heterotrimeric protein assemblies. Our study demonstrates that a force field is not necessary, cryo-EM data alone is sufficient to accurately guide the monomers into place. The resulting rigid models successfully identify regions of conformational change, opening up perspectives for targeted flexible remodeling.
Collapse
Affiliation(s)
- Sjoerd J de Vries
- Physik-Department T38, Technische Universität München, Garching, Germany.
| | | | - Christina E M Schindler
- Physik-Department T38, Technische Universität München, Garching, Germany; Center for Integrated Protein Science Munich (CIPSM) at the Physics Department, Technische Universität München, Garching, Germany
| | - Martin Zacharias
- Physik-Department T38, Technische Universität München, Garching, Germany; Center for Integrated Protein Science Munich (CIPSM) at the Physics Department, Technische Universität München, Garching, Germany
| |
Collapse
|
71
|
Protein Structural Analysis via Mass Spectrometry-Based Proteomics. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2016; 919:397-431. [PMID: 27975228 DOI: 10.1007/978-3-319-41448-5_19] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
Abstract
Modern mass spectrometry (MS) technologies have provided a versatile platform that can be combined with a large number of techniques to analyze protein structure and dynamics. These techniques include the three detailed in this chapter: (1) hydrogen/deuterium exchange (HDX), (2) limited proteolysis, and (3) chemical crosslinking (CX). HDX relies on the change in mass of a protein upon its dilution into deuterated buffer, which results in varied deuterium content within its backbone amides. Structural information on surface exposed, flexible or disordered linker regions of proteins can be achieved through limited proteolysis, using a variety of proteases and only small extents of digestion. CX refers to the covalent coupling of distinct chemical species and has been used to analyze the structure, function and interactions of proteins by identifying crosslinking sites that are formed by small multi-functional reagents, termed crosslinkers. Each of these MS applications is capable of revealing structural information for proteins when used either with or without other typical high resolution techniques, including NMR and X-ray crystallography.
Collapse
|
72
|
LoPiccolo J, Kim SJ, Shi Y, Wu B, Wu H, Chait BT, Singer RH, Sali A, Brenowitz M, Bresnick AR, Backer JM. Assembly and Molecular Architecture of the Phosphoinositide 3-Kinase p85α Homodimer. J Biol Chem 2015; 290:30390-405. [PMID: 26475863 DOI: 10.1074/jbc.m115.689604] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2015] [Indexed: 11/06/2022] Open
Abstract
Phosphoinositide 3-kinases (PI3Ks) are a family of lipid kinases that are activated by growth factor and G-protein-coupled receptors and propagate intracellular signals for growth, survival, proliferation, and metabolism. p85α, a modular protein consisting of five domains, binds and inhibits the enzymatic activity of class IA PI3K catalytic subunits. Here, we describe the structural states of the p85α dimer, based on data from in vivo and in vitro solution characterization. Our in vitro assembly and structural analyses have been enabled by the creation of cysteine-free p85α that is functionally equivalent to native p85α. Analytical ultracentrifugation studies showed that p85α undergoes rapidly reversible monomer-dimer assembly that is highly exothermic in nature. In addition to the documented SH3-PR1 dimerization interaction, we identified a second intermolecular interaction mediated by cSH2 domains at the C-terminal end of the polypeptide. We have demonstrated in vivo concentration-dependent dimerization of p85α using fluorescence fluctuation spectroscopy. Finally, we have defined solution conditions under which the protein is predominantly monomeric or dimeric, providing the basis for small angle x-ray scattering and chemical cross-linking structural analysis of the discrete dimer. These experimental data have been used for the integrative structure determination of the p85α dimer. Our study provides new insight into the structure and assembly of the p85α homodimer and suggests that this protein is a highly dynamic molecule whose conformational flexibility allows it to transiently associate with multiple binding proteins.
Collapse
Affiliation(s)
| | - Seung Joong Kim
- the Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, and California Institute for Quantitative Biosciences, University of California, San Francisco, San Francisco, California 94158, and
| | - Yi Shi
- the Laboratory of Mass Spectrometry and Gaseous Ion Chemistry, The Rockefeller University, New York, New York 10065
| | - Bin Wu
- Department of Anatomy and Structural Biology, Albert Einstein College of Medicine, Bronx, New York 10461
| | - Haiyan Wu
- From the Department of Molecular Pharmacology
| | - Brian T Chait
- the Laboratory of Mass Spectrometry and Gaseous Ion Chemistry, The Rockefeller University, New York, New York 10065
| | - Robert H Singer
- Department of Anatomy and Structural Biology, Albert Einstein College of Medicine, Bronx, New York 10461
| | - Andrej Sali
- the Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, and California Institute for Quantitative Biosciences, University of California, San Francisco, San Francisco, California 94158, and
| | | | | | - Jonathan M Backer
- From the Department of Molecular Pharmacology, Department of Biochemistry,
| |
Collapse
|
73
|
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
Collapse
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
| |
Collapse
|
74
|
Sali A, Berman HM, Schwede T, Trewhella J, Kleywegt G, Burley SK, Markley J, Nakamura H, Adams P, Bonvin AMJJ, Chiu W, Peraro MD, Di Maio F, Ferrin TE, Grünewald K, Gutmanas A, Henderson R, Hummer G, Iwasaki K, Johnson G, Lawson CL, Meiler J, Marti-Renom MA, Montelione GT, Nilges M, Nussinov R, Patwardhan A, Rappsilber J, Read RJ, Saibil H, Schröder GF, Schwieters CD, Seidel CAM, Svergun D, Topf M, Ulrich EL, Velankar S, Westbrook JD. Outcome of the First wwPDB Hybrid/Integrative Methods Task Force Workshop. Structure 2015; 23:1156-67. [PMID: 26095030 PMCID: PMC4933300 DOI: 10.1016/j.str.2015.05.013] [Citation(s) in RCA: 141] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2015] [Revised: 05/11/2015] [Accepted: 05/18/2015] [Indexed: 01/20/2023]
Abstract
Structures of biomolecular systems are increasingly computed by integrative modeling that relies on varied types of experimental data and theoretical information. We describe here the proceedings and conclusions from the first wwPDB Hybrid/Integrative Methods Task Force Workshop held at the European Bioinformatics Institute in Hinxton, UK, on October 6 and 7, 2014. At the workshop, experts in various experimental fields of structural biology, experts in integrative modeling and visualization, and experts in data archiving addressed a series of questions central to the future of structural biology. How should integrative models be represented? How should the data and integrative models be validated? What data should be archived? How should the data and models be archived? What information should accompany the publication of integrative models?
Collapse
Affiliation(s)
- Andrej Sali
- Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, California Institute for Quantitative Biosciences, Byers Hall Room 503B, University of California, San Francisco, 1700 4(th) Street, San Francisco, CA 94158-2330, USA.
| | - Helen M Berman
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Center for Integrative Proteomics Research, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Torsten Schwede
- Swiss Institute of Bioinformatics Biozentrum, University of Basel, Klingelbergstrasse 50-70, 4056 Basel, Switzerland
| | - Jill Trewhella
- School of Molecular Bioscience, The University of Sydney, NSW 2006, Australia
| | - Gerard Kleywegt
- Protein Data Bank in Europe, European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Stephen K Burley
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Center for Integrative Proteomics Research, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA; Skaggs School of Pharmacy and Pharmaceutical Sciences and San Diego Supercomputer Center, University of California, San Diego, La Jolla, CA 92093, USA
| | - John Markley
- BioMagResBank, Department of Biochemistry, University of Wisconsin-Madison, Madison, WI 53706-1544, USA
| | - Haruki Nakamura
- Protein Data Bank Japan, Institute for Protein Research, Osaka University, 3-2 Yamadaoka, Suita, Osaka 565-0871, Japan
| | - Paul Adams
- Physical Biosciences Division, Lawrence Berkeley Laboratory, Berkeley, CA 94720-8235, USA; Department of Bioengineering, UC Berkeley, Berkeley, CA 94720, USA
| | - Alexandre M J J Bonvin
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, Utrecht, 3584 CH, the Netherlands
| | - Wah Chiu
- National Center for Macromolecular Imaging, Baylor College of Medicine, Houston, TX 77030, USA
| | - Matteo Dal Peraro
- Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL) and Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Frank Di Maio
- Department of Biochemistry, University of Washington, Seattle, WA 98195-7370, USA
| | - Thomas E Ferrin
- Department of Pharmaceutical Chemistry and Department of Bioengineering and Therapeutic Sciences, California Institute for Quantitative Biosciences, University of California, San Francisco, 600 16(th) Street, San Francisco, CA 94158-2517, USA
| | - Kay Grünewald
- Division of Structural Biology, Wellcome Trust Centre of Human Genetics, University of Oxford, OX3 7BN Oxford, UK
| | - Aleksandras Gutmanas
- Protein Data Bank in Europe, European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Richard Henderson
- MRC Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge CB2 0QH, UK
| | - Gerhard Hummer
- Department of Theoretical Biophysics, Max Planck Institute of Biophysics, Max-von-Laue Straße 3, 60438 Frankfurt am Main, Germany
| | - Kenji Iwasaki
- Institute for Protein Research, Osaka University, 3-2 Yamadaoka, Suita, Osaka 565-0871, Japan
| | - Graham Johnson
- Department of Bioengineering and Therapeutic Sciences, California Institute for Quantitative Biosciences, University of California, San Francisco, 600 16(th) Street, San Francisco, CA 94158-2330, USA
| | - Catherine L Lawson
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Center for Integrative Proteomics Research, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Jens Meiler
- Department of Chemistry, Center for Structural Biology, Vanderbilt University, Nashville, TN 37235, USA
| | - Marc A Marti-Renom
- Genome Biology Group, Centre Nacional d'Anàlisi Genòmica (CNAG), Gene Regulation, Stem Cells and Cancer Program, Center for Genomic Regulation (CRG) and Institució Catalana de Recerca i Estudis Avançats (ICREA), 08028 Barcelona, Spain
| | - Gaetano T Montelione
- Center for Advanced Biotechnology and Medicine, Department of Molecular Biology and Biochemistry, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA; Department of Biochemistry, Robert Wood Johnson Medical School, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Michael Nilges
- Département de Biologie Structurale et Chimie, Unité de Bioinformatique Structurale, Institut Pasteur, F-75015 Paris, France; Unité Mixte de Recherche 3258, Centre National de la Recherche Scientifique, F-75015 Paris, France
| | - Ruth Nussinov
- Cancer and Inflammation Program, Leidos Biomedical Research Inc., Frederick National Laboratory, National Cancer Institute, Frederick, MD 21702, USA; Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv 69978, Israel
| | - Ardan Patwardhan
- Protein Data Bank in Europe, European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Juri Rappsilber
- Wellcome Trust Centre for Cell Biology, Institute of Cell Biology, University of Edinburgh, Edinburgh EH9 3BF, UK; Department of Bioanalytics, Institute of Biotechnology, Technische Universität Berlin, 13355 Berlin, Germany
| | - Randy J Read
- Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Cambridge CB2 0XY, UK
| | - Helen Saibil
- Institute of Structural and Molecular Biology, Department of Biological Sciences, Birkbeck College, Malet Street, London WC1E 7HX, UK
| | - Gunnar F Schröder
- Institute of Complex Systems (ICS-6), Forschungszentrum Jülich, 52425 Jülich, Germany; Physics Department, Heinrich-Heine University Düsseldorf, 40225 Düsseldorf, Germany
| | - Charles D Schwieters
- Division of Computational Bioscience, Center for Information Technology, National Institutes of Health, Bethesda, MD 20892-0520, USA
| | - Claus A M Seidel
- Chair for Molecular Physical Chemistry, Heinrich-Heine-Universität, Universitätsstraße 1, 40225 Düsseldorf, Germany
| | - Dmitri Svergun
- European Molecular Biology Laboratory, Hamburg Unit, Notkestrasse 85, 22607 Hamburg, Germany
| | - Maya Topf
- Institute of Structural and Molecular Biology, Department of Biological Sciences, Birkbeck College, Malet Street, London WC1E 7HX, UK
| | - Eldon L Ulrich
- BioMagResBank, Department of Biochemistry, University of Wisconsin-Madison, Madison, WI 53706-1544, USA
| | - Sameer Velankar
- Protein Data Bank in Europe, European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - John D Westbrook
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Center for Integrative Proteomics Research, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| |
Collapse
|
75
|
Farabella I, Vasishtan D, Joseph AP, Pandurangan AP, Sahota H, Topf M. TEMPy: a Python library for assessment of three-dimensional electron microscopy density fits. J Appl Crystallogr 2015; 48:1314-1323. [PMID: 26306092 PMCID: PMC4520291 DOI: 10.1107/s1600576715010092] [Citation(s) in RCA: 61] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2014] [Accepted: 05/24/2015] [Indexed: 12/21/2022] Open
Abstract
TEMPy is an object-oriented Python library that provides the means to validate density fits in electron microscopy reconstructions. This article highlights several features of particular interest for this purpose and includes some customized examples. Three-dimensional electron microscopy is currently one of the most promising techniques used to study macromolecular assemblies. Rigid and flexible fitting of atomic models into density maps is often essential to gain further insights into the assemblies they represent. Currently, tools that facilitate the assessment of fitted atomic models and maps are needed. TEMPy (template and electron microscopy comparison using Python) is a toolkit designed for this purpose. The library includes a set of methods to assess density fits in intermediate-to-low resolution maps, both globally and locally. It also provides procedures for single-fit assessment, ensemble generation of fits, clustering, and multiple and consensus scoring, as well as plots and output files for visualization purposes to help the user in analysing rigid and flexible fits. The modular nature of TEMPy helps the integration of scoring and assessment of fits into large pipelines, making it a tool suitable for both novice and expert structural biologists.
Collapse
Affiliation(s)
- Irene Farabella
- Institute of Structural and Molecular Biology, Department of Biological Sciences, Birkbeck, University of London , Malet street, London WC1E 7HX, UK
| | - Daven Vasishtan
- Oxford Particle Imaging Centre, Division of Structural Biology, Wellcome Trust Centre for Human Genetics, University of Oxford , Oxford OX3 7BN, UK
| | - Agnel Praveen Joseph
- Scientific Computing Department, Science and Technology Facilities Council, Research Complex at Harwell , Didcot, Oxon OX11 0QX, UK
| | - Arun Prasad Pandurangan
- Institute of Structural and Molecular Biology, Department of Biological Sciences, Birkbeck, University of London , Malet street, London WC1E 7HX, UK
| | - Harpal Sahota
- Institute of Structural and Molecular Biology, Department of Biological Sciences, Birkbeck, University of London , Malet street, London WC1E 7HX, UK
| | - Maya Topf
- Institute of Structural and Molecular Biology, Department of Biological Sciences, Birkbeck, University of London , Malet street, London WC1E 7HX, UK
| |
Collapse
|
76
|
Politis A, Borysik AJ. Assembling the pieces of macromolecular complexes: Hybrid structural biology approaches. Proteomics 2015; 15:2792-803. [DOI: 10.1002/pmic.201400507] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2014] [Revised: 01/26/2015] [Accepted: 02/24/2015] [Indexed: 01/14/2023]
|
77
|
Schröder GF. Hybrid methods for macromolecular structure determination: experiment with expectations. Curr Opin Struct Biol 2015; 31:20-7. [DOI: 10.1016/j.sbi.2015.02.016] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2014] [Revised: 02/22/2015] [Accepted: 02/26/2015] [Indexed: 12/15/2022]
|
78
|
Hirst JD, Glowacki DR, Baaden M. Molecular simulations and visualization: introduction and overview. Faraday Discuss 2014; 169:9-22. [DOI: 10.1039/c4fd90024c] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
|