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Vallat B, Webb BM, Westbrook JD, Goddard TD, Hanke CA, Graziadei A, Peisach E, Zalevsky A, Sagendorf J, Tangmunarunkit H, Voinea S, Sekharan M, Yu J, Bonvin AAMJJ, DiMaio F, Hummer G, Meiler J, Tajkhorshid E, Ferrin TE, Lawson CL, Leitner A, Rappsilber J, Seidel CAM, Jeffries CM, Burley SK, Hoch JC, Kurisu G, Morris K, Patwardhan A, Velankar S, Schwede T, Trewhella J, Kesselman C, Berman HM, Sali A. IHMCIF: An Extension of the PDBx/mmCIF Data Standard for Integrative Structure Determination Methods. J Mol Biol 2024:168546. [PMID: 38508301 DOI: 10.1016/j.jmb.2024.168546] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Revised: 03/11/2024] [Accepted: 03/14/2024] [Indexed: 03/22/2024]
Abstract
IHMCIF (github.com/ihmwg/IHMCIF) is a data information framework that supports archiving and disseminating macromolecular structures determined by integrative or hybrid modeling (IHM), and making them Findable, Accessible, Interoperable, and Reusable (FAIR). IHMCIF is an extension of the Protein Data Bank Exchange/macromolecular Crystallographic Information Framework (PDBx/mmCIF) that serves as the framework for the Protein Data Bank (PDB) to archive experimentally determined atomic structures of biological macromolecules and their complexes with one another and small molecule ligands (e.g., enzyme cofactors and drugs). IHMCIF serves as the foundational data standard for the PDB-Dev prototype system, developed for archiving and disseminating integrative structures. It utilizes a flexible data representation to describe integrative structures that span multiple spatiotemporal scales and structural states with definitions for restraints from a variety of experimental methods contributing to integrative structural biology. The IHMCIF extension was created with the benefit of considerable community input and recommendations gathered by the Worldwide Protein Data Bank (wwPDB) Task Force for Integrative or Hybrid Methods (wwpdb.org/task/hybrid). Herein, we describe the development of IHMCIF to support evolving methodologies and ongoing advancements in integrative structural biology. Ultimately, IHMCIF will facilitate the unification of PDB-Dev data and tools with the PDB archive so that integrative structures can be archived and disseminated through PDB.
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Affiliation(s)
- Brinda Vallat
- Research Collaboratory for Structural Bioinformatics Protein Data Bank and the Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA; Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, NJ 08901, USA.
| | - Benjamin M Webb
- Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, the Quantitative Biosciences Institute (QBI), and the Research Collaboratory for Structural Bioinformatics Protein Data Bank, University of California, San Francisco, San Francisco, CA 94157, USA
| | - John D Westbrook
- Research Collaboratory for Structural Bioinformatics Protein Data Bank and the Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA; Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, NJ 08901, USA
| | - Thomas D Goddard
- Department of Pharmaceutical Chemistry, University of California, San Francisco, CA 94158, USA
| | - Christian A Hanke
- Molecular Physical Chemistry, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany
| | - Andrea Graziadei
- Bioanalytics, Institute of Biotechnology, Technische Universität Berlin, 10623 Berlin, Germany; Human Technopole, 20157 Milan, Italy
| | - Ezra Peisach
- Research Collaboratory for Structural Bioinformatics Protein Data Bank and the Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Arthur Zalevsky
- Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, the Quantitative Biosciences Institute (QBI), and the Research Collaboratory for Structural Bioinformatics Protein Data Bank, University of California, San Francisco, San Francisco, CA 94157, USA
| | - Jared Sagendorf
- Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, the Quantitative Biosciences Institute (QBI), and the Research Collaboratory for Structural Bioinformatics Protein Data Bank, University of California, San Francisco, San Francisco, CA 94157, USA
| | - Hongsuda Tangmunarunkit
- Information Sciences Institute, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, USA
| | - Serban Voinea
- Information Sciences Institute, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, USA
| | - Monica Sekharan
- Research Collaboratory for Structural Bioinformatics Protein Data Bank and the Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Jian Yu
- Protein Data Bank Japan, Institute for Protein Research, Osaka University, Suita, Osaka 565-0871, Japan
| | - Alexander A M J J Bonvin
- Bijvoet Centre for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, 3584 CH Utrecht, the Netherlands
| | - Frank DiMaio
- Department of Biochemistry and Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
| | - Gerhard Hummer
- Department of Theoretical Biophysics, Max Planck Institute of Biophysics, 60438 Frankfurt am Main, Germany; Institute for Biophysics, Goethe University Frankfurt, 60438 Frankfurt am Main, Germany
| | - Jens Meiler
- Center for Structural Biology, Vanderbilt University, 465 21st Avenue South, Nashville, TN 37221, USA; Institute for Drug Discovery, Leipzig University Medical School, 04103 Leipzig, Germany
| | - Emad Tajkhorshid
- NIH Resource for Macromolecular Modeling and Visualization, Beckman Institute for Advanced Science and Technology, Department of Biochemistry, and Center for Biophysics and Quantitative Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Thomas E Ferrin
- Department of Pharmaceutical Chemistry, University of California, San Francisco, CA 94158, USA
| | - Catherine L Lawson
- Research Collaboratory for Structural Bioinformatics Protein Data Bank and the Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Alexander Leitner
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, 8093 Zurich, Switzerland
| | - Juri Rappsilber
- Bioanalytics, Institute of Biotechnology, Technische Universität Berlin, 10623 Berlin, Germany; Wellcome Centre for Cell Biology, University of Edinburgh, Max Born Crescent, Edinburgh EH9 3BF, UK
| | - Claus A M Seidel
- Molecular Physical Chemistry, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany
| | - Cy M Jeffries
- European Molecular Biology Laboratory (EMBL), Hamburg Unit, c/o Deutsches Elektronen-Synchrotron (DESY), Notkestrasse 85, 22607 Hamburg, Germany
| | - Stephen K Burley
- Research Collaboratory for Structural Bioinformatics Protein Data Bank and the Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA; Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, NJ 08901, USA; Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California, La Jolla, CA 92093, USA; Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Jeffrey C Hoch
- Biological Magnetic Resonance Data Bank, Department of Molecular Biology and Biophysics, University of Connecticut, Farmington, CT 06030-3305, USA
| | - Genji Kurisu
- Protein Data Bank Japan, Institute for Protein Research, Osaka University, Suita, Osaka 565-0871, Japan
| | - Kyle Morris
- Electron Microscopy Data Bank, European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK
| | - Ardan Patwardhan
- Electron Microscopy Data Bank, European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK
| | - Sameer Velankar
- Protein Data Bank in Europe, European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, Cambridge CB10 1SD, UK
| | - Torsten Schwede
- Biozentrum, University of Basel, Basel, Switzerland; Computational Structural Biology & SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Jill Trewhella
- School of Life and Environmental Sciences, The University of Sydney, Sydney, NSW 2006, Australia; Department of Chemistry, University of Utah, Salt Lake City, UT 84112, USA
| | - Carl Kesselman
- Information Sciences Institute, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, USA
| | - Helen M Berman
- Research Collaboratory for Structural Bioinformatics Protein Data Bank and the Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA; Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA; Department of Quantitative and Computational Biology, University of Southern California, Los Angeles CA 90089, USA
| | - Andrej Sali
- Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, the Quantitative Biosciences Institute (QBI), and the Research Collaboratory for Structural Bioinformatics Protein Data Bank, University of California, San Francisco, San Francisco, CA 94157, USA
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2
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Hancock M, Peulen TO, Webb B, Poon B, Fraser JS, Adams P, Sali A. Integration of software tools for integrative modeling of biomolecular systems. J Struct Biol 2022; 214:107841. [PMID: 35149213 PMCID: PMC9278553 DOI: 10.1016/j.jsb.2022.107841] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 01/28/2022] [Accepted: 02/04/2022] [Indexed: 12/31/2022]
Abstract
Integrative modeling computes a model based on varied types of input information, be it from experiments or prior models. Often, a type of input information will be best handled by a specific modeling software package. In such a case, we desire to integrate our integrative modeling software package, Integrative Modeling Platform (IMP), with software specialized to the computational demands of the modeling problem at hand. After several attempts, however, we have concluded that even in collaboration with the software's developers, integration is either impractical or impossible. The reasons for the intractability of integration include software incompatibilities, differing modeling logic, the costs of collaboration, and academic incentives. In the integrative modeling software ecosystem, several large modeling packages exist with often redundant tools. We reason, therefore, that the other development groups have similarly concluded that the benefit of integration does not justify the cost. As a result, modelers are often restricted to the set of tools within a single software package. The inability to integrate tools from distinct software negatively impacts the quality of the models and the efficiency of the modeling. As the complexity of modeling problems grows, we seek to galvanize developers and modelers to consider the long-term benefit that software interoperability yields. In this article, we formulate a demonstrative set of software standards for implementing a model search using tools from independent software packages and discuss our efforts to integrate IMP and the crystallography suite Phenix within the Bayesian modeling framework.
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Affiliation(s)
- Matthew Hancock
- Biophysics Graduate Program, University of California, San Francisco, MC 2240 1600 16th St, San Francisco, CA 94143, United States; Department of Bioengineering and Therapeutic Sciences, UCSF Box 0775 1700 4th St, University of California, San Francisco, San Francisco, CA 94158, United States.
| | - Thomas-Otavio Peulen
- Department of Bioengineering and Therapeutic Sciences, UCSF Box 0775 1700 4th St, University of California, San Francisco, San Francisco, CA 94158, United States.
| | - Benjamin Webb
- Department of Bioengineering and Therapeutic Sciences, UCSF Box 0775 1700 4th St, University of California, San Francisco, San Francisco, CA 94158, United States.
| | - Billy Poon
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Building 33 1 Cyclotron Rd, Berkeley, CA 94270, United States.
| | - James S Fraser
- Department of Bioengineering and Therapeutic Sciences, UCSF Box 0775 1700 4th St, University of California, San Francisco, San Francisco, CA 94158, United States; Quantitative Biosciences Institute (QBI), University of California, San Francisco, 1700 4th St, San Francisco, CA, United States.
| | - Paul Adams
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Building 33 1 Cyclotron Rd, Berkeley, CA 94270, United States; Department of Bioengineering, University of California, Berkeley, MC 1762 306 Stanley Hall, Berkeley, CA 94720, United States.
| | - Andrej Sali
- Department of Bioengineering and Therapeutic Sciences, UCSF Box 0775 1700 4th St, University of California, San Francisco, San Francisco, CA 94158, United States; Department of Pharmaceutical Chemistry, University of California, San Francisco, UCSF Box 2880 600 16th St, San Francisco, CA 94143, United States; Quantitative Biosciences Institute (QBI), University of California, San Francisco, 1700 4th St, San Francisco, CA, United States.
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Marzolf DR, Seffernick JT, Lindert S. Protein Structure Prediction from NMR Hydrogen-Deuterium Exchange Data. J Chem Theory Comput 2021; 17:2619-2629. [PMID: 33780620 DOI: 10.1021/acs.jctc.1c00077] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Amide hydrogen-deuterium exchange (HDX) has long been used to determine regional flexibility and binding sites in proteins; however, the data are too sparse for full structural characterization. Experiments that measure HDX rates, such as HDX-NMR, have far higher throughput compared to structure determination via X-ray crystallography, cryo-EM, or a full suite of NMR experiments. Data from HDX-NMR experiments encode information on the protein structure, making HDX a prime candidate to be supplemented by computational algorithms for protein structure prediction. We have developed a methodology to incorporate HDX-NMR data into ab initio protein structure prediction using the Rosetta software framework to predict structures based on experimental agreement. To demonstrate the efficacy of our algorithm, we examined 38 proteins with HDX-NMR data available, comparing the predicted model with and without the incorporation of HDX data into scoring. The root-mean-square deviation (rmsd, a measure of the average atomic distance between superimposed models) of the predicted model improved by 1.42 Å on average after incorporating the HDX-NMR data into scoring. The average rmsd improvement for the proteins where the selected model rmsd changed after incorporating HDX data was 3.63 Å, including one improvement of more than 11 Å and seven proteins improving by greater than 4 Å, with 12/15 proteins improving overall. Additionally, for independent verification, two proteins that were not part of the original benchmark were scored including HDX data, with a dramatic improvement of the selected model rmsd of nearly 9 Å for one of the proteins. Moreover, we have developed a confidence metric allowing us to successfully identify near-native models in the absence of a native structure. Improvement in model selection with a strong confidence measure demonstrates that protein structure prediction with HDX-NMR is a powerful tool which can be performed with minimal additional computational strain and expense.
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Affiliation(s)
- Daniel R Marzolf
- Department of Chemistry and Biochemistry, Ohio State University, Columbus, Ohio 43210, United States
| | - Justin T Seffernick
- Department of Chemistry and Biochemistry, Ohio State University, Columbus, Ohio 43210, United States
| | - Steffen Lindert
- Department of Chemistry and Biochemistry, Ohio State University, Columbus, Ohio 43210, United States
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4
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Del Alamo D, Fischer AW, Moretti R, Alexander NS, Mendenhall J, Hyman NJ, Meiler J. Efficient Sampling of Protein Loop Regions Using Conformational Hashing Complemented with Random Coordinate Descent. J Chem Theory Comput 2021; 17:560-570. [PMID: 33373213 DOI: 10.1021/acs.jctc.0c00836] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
De novo construction of loop regions is an important problem in computational structural biology. Compared to regions with well-defined secondary structure, loops tend to exhibit significant conformational heterogeneity. As a result, their structures are often ambiguous when determined using experimental data obtained by crystallography, cryo-EM, or NMR. Although structurally diverse models could provide a more relevant representation of proteins in their native states, obtaining large numbers of biophysically realistic and physiologically relevant loop conformations is a resource-consuming task. To address this need, we developed a novel loop construction algorithm, Hash/RCD, that combines knowledge-based conformational hashing with random coordinate descent (RCD). This hybrid approach achieved a closure rate of 100% on a benchmark set of 195 loops in 29 proteins that range from 3 to 31 residues. More importantly, the use of templates allows Hash/RCD to maintain the accuracy of state-of-the-art coordinate descent methods while reducing sampling time from over 400 to 141 ms. These results highlight how the integration of coordinate descent with knowledge-based sampling overcomes barriers inherent to either approach in isolation. This method may facilitate the identification of native-like loop conformations using experimental data or full-atom scoring functions by allowing rapid sampling of large numbers of loops. In this manuscript, we investigate and discuss the advantages, bottlenecks, and limitations of combining conformational hashing with RCD. By providing a detailed technical description of the Hash/RCD algorithm, we hope to facilitate its implementation by other researchers.
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Affiliation(s)
- Diego Del Alamo
- Department of Chemistry and Center for Structural Biology, Vanderbilt University, Nashville, 37235 Tennessee, United States
| | - Axel W Fischer
- Department of Chemistry and Center for Structural Biology, Vanderbilt University, Nashville, 37235 Tennessee, United States
| | - Rocco Moretti
- Department of Chemistry and Center for Structural Biology, Vanderbilt University, Nashville, 37235 Tennessee, United States
| | - Nathan S Alexander
- Department of Chemistry and Center for Structural Biology, Vanderbilt University, Nashville, 37235 Tennessee, United States
| | - Jeffrey Mendenhall
- Department of Chemistry and Center for Structural Biology, Vanderbilt University, Nashville, 37235 Tennessee, United States
| | - Nicholas J Hyman
- Department of Chemistry and Center for Structural Biology, Vanderbilt University, Nashville, 37235 Tennessee, United States
| | - Jens Meiler
- Department of Chemistry and Center for Structural Biology, Vanderbilt University, Nashville, 37235 Tennessee, United States.,Institut for Drug Discovery, Leipzig University, Leipzig SAC 04103, Germany
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5
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Sali A. From integrative structural biology to cell biology. J Biol Chem 2021; 296:100743. [PMID: 33957123 PMCID: PMC8203844 DOI: 10.1016/j.jbc.2021.100743] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Revised: 04/09/2021] [Accepted: 04/30/2021] [Indexed: 12/16/2022] Open
Abstract
Integrative modeling is an increasingly important tool in structural biology, providing structures by combining data from varied experimental methods and prior information. As a result, molecular architectures of large, heterogeneous, and dynamic systems, such as the ∼52-MDa Nuclear Pore Complex, can be mapped with useful accuracy, precision, and completeness. Key challenges in improving integrative modeling include expanding model representations, increasing the variety of input data and prior information, quantifying a match between input information and a model in a Bayesian fashion, inventing more efficient structural sampling, as well as developing better model validation, analysis, and visualization. In addition, two community-level challenges in integrative modeling are being addressed under the auspices of the Worldwide Protein Data Bank (wwPDB). First, the impact of integrative structures is maximized by PDB-Development, a prototype wwPDB repository for archiving, validating, visualizing, and disseminating integrative structures. Second, the scope of structural biology is expanded by linking the wwPDB resource for integrative structures with archives of data that have not been generally used for structure determination but are increasingly important for computing integrative structures, such as data from various types of mass spectrometry, spectroscopy, optical microscopy, proteomics, and genetics. To address the largest of modeling problems, a type of integrative modeling called metamodeling is being developed; metamodeling combines different types of input models as opposed to different types of data to compute an output model. Collectively, these developments will facilitate the structural biology mindset in cell biology and underpin spatiotemporal mapping of the entire cell.
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Affiliation(s)
- Andrej Sali
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, the Department of Bioengineering and Therapeutic Sciences, the Quantitative Biosciences Institute (QBI), and the Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, California, USA.
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6
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Seffernick JT, Lindert S. Hybrid methods for combined experimental and computational determination of protein structure. J Chem Phys 2020; 153:240901. [PMID: 33380110 PMCID: PMC7773420 DOI: 10.1063/5.0026025] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2020] [Accepted: 11/10/2020] [Indexed: 02/04/2023] Open
Abstract
Knowledge of protein structure is paramount to the understanding of biological function, developing new therapeutics, and making detailed mechanistic hypotheses. Therefore, methods to accurately elucidate three-dimensional structures of proteins are in high demand. While there are a few experimental techniques that can routinely provide high-resolution structures, such as x-ray crystallography, nuclear magnetic resonance (NMR), and cryo-EM, which have been developed to determine the structures of proteins, these techniques each have shortcomings and thus cannot be used in all cases. However, additionally, a large number of experimental techniques that provide some structural information, but not enough to assign atomic positions with high certainty have been developed. These methods offer sparse experimental data, which can also be noisy and inaccurate in some instances. In cases where it is not possible to determine the structure of a protein experimentally, computational structure prediction methods can be used as an alternative. Although computational methods can be performed without any experimental data in a large number of studies, inclusion of sparse experimental data into these prediction methods has yielded significant improvement. In this Perspective, we cover many of the successes of integrative modeling, computational modeling with experimental data, specifically for protein folding, protein-protein docking, and molecular dynamics simulations. We describe methods that incorporate sparse data from cryo-EM, NMR, mass spectrometry, electron paramagnetic resonance, small-angle x-ray scattering, Förster resonance energy transfer, and genetic sequence covariation. Finally, we highlight some of the major challenges in the field as well as possible future directions.
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Affiliation(s)
- Justin T. Seffernick
- Department of Chemistry and Biochemistry, Ohio State University, Columbus, Ohio 43210, USA
| | - Steffen Lindert
- Department of Chemistry and Biochemistry, Ohio State University, Columbus, Ohio 43210, USA
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Abstract
For two decades, Rosetta has consistently been at the forefront of protein structure
prediction. While it has become a very large package comprising programs, scripts, and tools, for
different types of macromolecular modelling such as ligand docking, protein-protein docking,
protein design, and loop modelling, it started as the implementation of an algorithm for ab initio
protein structure prediction. The term ’Rosetta’ appeared for the first time twenty years ago in the
literature to describe that algorithm and its contribution to the third edition of the community wide
Critical Assessment of techniques for protein Structure Prediction (CASP3). Similar to the Rosetta
stone that allowed deciphering the ancient Egyptian civilisation, David Baker and his co-workers
have been contributing to deciphering ’the second half of the genetic code’. Although the focus of
Baker’s team has expended to de novo protein design in the past few years, Rosetta’s ‘fame’ is
associated with its fragment-assembly protein structure prediction approach. Following a
presentation of the main concepts underpinning its foundation, especially sequence-structure
correlation and usage of fragments, we review the main stages of its developments and highlight
the milestones it has achieved in terms of protein structure prediction, particularly in CASP.
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Affiliation(s)
- Jad Abbass
- Department of Computer Science, Lebanese International University, Bekaa, Lebanon
| | - Jean-Christophe Nebel
- Faculty of Science, Engineering and Computing, Kingston University, London, KT1 2EE, United Kingdom
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Berman HM, Adams PD, Bonvin AA, Burley SK, Carragher B, Chiu W, DiMaio F, Ferrin TE, Gabanyi MJ, Goddard TD, Griffin PR, Haas J, Hanke CA, Hoch JC, Hummer G, Kurisu G, Lawson CL, Leitner A, Markley JL, Meiler J, Montelione GT, Phillips GN, Prisner T, Rappsilber J, Schriemer DC, Schwede T, Seidel CAM, Strutzenberg TS, Svergun DI, Tajkhorshid E, Trewhella J, Vallat B, Velankar S, Vuister GW, Webb B, Westbrook JD, White KL, Sali A. Federating Structural Models and Data: Outcomes from A Workshop on Archiving Integrative Structures. Structure 2019; 27:1745-1759. [PMID: 31780431 DOI: 10.1016/j.str.2019.11.002] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2019] [Revised: 10/31/2019] [Accepted: 11/06/2019] [Indexed: 12/23/2022]
Abstract
Structures of biomolecular systems are increasingly computed by integrative modeling. In this approach, a structural model is constructed by combining information from multiple sources, including varied experimental methods and prior models. In 2019, a Workshop was held as a Biophysical Society Satellite Meeting to assess progress and discuss further requirements for archiving integrative structures. The primary goal of the Workshop was to build consensus for addressing the challenges involved in creating common data standards, building methods for federated data exchange, and developing mechanisms for validating integrative structures. The summary of the Workshop and the recommendations that emerged are presented here.
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Affiliation(s)
- Helen M Berman
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA; Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA; Bridge Institute, Michelson Center, University of Southern California, Los Angeles, CA 90089, USA.
| | - Paul D Adams
- Physical Biosciences Division, Lawrence Berkeley Laboratory, Berkeley, CA 94720-8235, USA; Department of Bioengineering, University of California-Berkeley, Berkeley, CA 94720, USA
| | - Alexandre A Bonvin
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, 3584 CH Utrecht, the Netherlands
| | - Stephen K Burley
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, The State University of New Jersey, Piscataway, NJ 08854, USA; Institute for Quantitative Biomedicine, 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; Rutgers Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, NJ 08903, USA
| | - Bridget Carragher
- Simons Electron Microscopy Center, New York Structural Biology Center, New York, NY 10027, USA; Department of Biochemistry and Molecular Biophysics, Columbia University, New York, NY 10032, USA
| | - Wah Chiu
- Department of Bioengineering, Department of Microbiology and Immunology, Stanford University, Stanford, CA 94305-5447, USA; SLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA
| | - Frank DiMaio
- Department of Biochemistry and Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
| | - Thomas E Ferrin
- Department of Pharmaceutical Chemistry, University of California, San Francisco, CA 94158, USA
| | - Margaret J Gabanyi
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Thomas D Goddard
- Department of Pharmaceutical Chemistry, University of California, San Francisco, CA 94158, USA
| | | | - Juergen Haas
- Swiss Institute of Bioinformatics and Biozentrum, University of Basel, 4056 Basel, Switzerland
| | - Christian A Hanke
- Molecular Physical Chemistry, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany
| | - Jeffrey C Hoch
- Department of Molecular Biology and Biophysics, UConn Health, Farmington, CT 06030, USA
| | - Gerhard Hummer
- Department of Theoretical Biophysics, Max Planck Institute of Biophysics, 60438 Frankfurt am Main, Germany; Institute for Biophysics, Goethe University Frankfurt, 60438 Frankfurt am Main, Germany
| | - Genji Kurisu
- Protein Data Bank Japan (PDBj), Institute for Protein Research, Osaka University, Osaka 565-0871, Japan
| | - Catherine L Lawson
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Alexander Leitner
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, 8093 Zurich, Switzerland
| | - John L Markley
- BioMagResBank (BMRB), Biochemistry Department, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Jens Meiler
- Center for Structural Biology, Vanderbilt University, 465 21st Avenue South, Nashville, TN 37221, USA
| | - 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; Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytech Institute, Troy, NY 12180, USA
| | - George N Phillips
- BioSciences at Rice and Department of Chemistry, Rice University, Houston, TX 77251, USA
| | - Thomas Prisner
- Institute of Physical and Theoretical Chemistry and Center of Biomolecular Magnetic Resonance, Goethe University Frankfurt, 60438 Frankfurt am Main, Germany
| | - Juri Rappsilber
- Wellcome Trust Centre for Cell Biology, Edinburgh EH9 3JR, Scotland
| | - David C Schriemer
- Department of Biochemistry & Molecular Biology, Robson DNA Science Centre, University of Calgary, Calgary, AB T2N 4N1, Canada
| | - Torsten Schwede
- Swiss Institute of Bioinformatics and Biozentrum, University of Basel, 4056 Basel, Switzerland
| | - Claus A M Seidel
- Molecular Physical Chemistry, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany
| | | | - Dmitri I Svergun
- European Molecular Biology Laboratory (EMBL), Hamburg Outstation, Notkestrasse 85, 22607 Hamburg, Germany
| | - Emad Tajkhorshid
- Department of Biochemistry, NIH Center for Macromolecular Modeling and Bioinformatics, Center for Biophysics and Quantitative Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA; Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Jill Trewhella
- School of Life and Environmental Sciences, The University of Sydney, Sydney, NSW 2006, Australia; Department of Chemistry, University of Utah, Salt Lake City, UT 84112, USA
| | - Brinda Vallat
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Sameer Velankar
- Protein Data Bank in Europe (PDBe), European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire CB10 1SD, UK
| | - Geerten W Vuister
- Department of Molecular and Cell Biology, Leicester Institute of Structural and Chemical Biology, University of Leicester, Leicester LE1 9HN, UK
| | - Benjamin Webb
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - John D Westbrook
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, The State University of New Jersey, Piscataway, NJ 08854, USA; Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Kate L White
- Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA; Bridge Institute, Michelson Center, University of Southern California, Los Angeles, CA 90089, USA
| | - Andrej Sali
- Department of Pharmaceutical Chemistry, University of California, San Francisco, CA 94158, USA; Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA 94158, USA; California Institute for Quantitative Biosciences, University of California, San Francisco, San Francisco, CA 94158, USA.
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9
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Brown BP, Mendenhall J, Meiler J. BCL::MolAlign: Three-Dimensional Small Molecule Alignment for Pharmacophore Mapping. J Chem Inf Model 2019; 59:689-701. [PMID: 30707580 DOI: 10.1021/acs.jcim.9b00020] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
Small molecule flexible alignment is a critical component of both ligand- and structure-based methods in computer-aided drug discovery. Despite its importance, the availability of high-quality flexible alignment software packages is limited. Here, we present BCL::MolAlign, a freely available property-based molecular alignment program. BCL::MolAlign accommodates ligand flexibility through a combination of pregenerated conformers and on-the-fly bond rotation. BCL::MolAlign converges on alignment poses by sampling the relative orientations of mutually matching atom pairs between molecules through Monte Carlo Metropolis sampling. Across six diverse ligand data sets, BCL::MolAlign flexible alignment outperforms MOE, ROCS, and FLEXS in recovering native ligand binding poses. Moreover, the BCL::MolAlign alignment score is more predictive of ligand activity than maximum common substructure similarity across 10 data sets. Finally, on a recently published benchmark set of 20 high quality congeneric ligand-protein complexes, BCL::MolAlign is able to recover a larger fraction of native binding poses than maximum common substructure-based alignment and RosettaLigand. BCL::MolAlign can be obtained as part of the Biology and Chemistry Library (BCL) software package freely with an academic license or can be accessed via Web server at http://meilerlab.org/index.php/servers/molalign .
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Affiliation(s)
- Benjamin P Brown
- Chemical and Physical Biology Program, Medical Scientist Training Program, Center for Structural Biology , Vanderbilt University , Nashville , Tennessee 37232 , United States
| | - Jeffrey Mendenhall
- Department of Chemistry, Center for Structural Biology , Vanderbilt University , Nashville , Tennessee 37232 , United States
| | - Jens Meiler
- Department of Chemistry, Center for Structural Biology , Vanderbilt University , Nashville , Tennessee 37232 , United States.,Departments of Pharmacology and Biomedical Informatics , Vanderbilt University , Nashville , Tennessee 37212 , United States
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10
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Identification and Verification of Ubiquitin-Activated Bacterial Phospholipases. J Bacteriol 2019; 201:JB.00623-18. [PMID: 30455285 DOI: 10.1128/jb.00623-18] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2018] [Accepted: 11/16/2018] [Indexed: 12/22/2022] Open
Abstract
ExoU is a potent type III secretion system effector that is injected directly into mammalian cells by the opportunistic pathogen Pseudomonas aeruginosa As a ubiquitin-activated phospholipase A2 (PLA2), ExoU exhibits cytotoxicity by cleaving membrane phospholipids, resulting in lysis of the host cells and inhibition of the innate immune response. Recently, ExoU has been established as a model protein for a group of ubiquitin-activated PLA2 enzymes encoded by a variety of bacteria. Bioinformatic analyses of homologous proteins is a powerful approach that can complement and enhance the overall understanding of protein structure and function. To conduct homology studies, it is important to have efficient and effective tools to screen and to validate the putative homologs of interest. Here we make use of an Escherichia coli-based dual expression system to screen putative ubiquitin-activated PLA2 enzymes from a variety of bacteria that are known to colonize humans and to cause human infections. The screen effectively identified multiple ubiquitin-activated phospholipases, which were validated using both biological and biochemical techniques. In this study, two new ExoU orthologs were identified and the ubiquitin activation of the rickettsial enzyme RP534 was verified. Conversely, ubiquitin was not found to regulate the activity of several other tested enzymes. Based on structural homology analyses, functional properties were predicted for AxoU, a unique member of the group expressed by Achromobacter xylosoxidans IMPORTANCE Bacterial phospholipases act as intracellular and extracellular enzymes promoting the destruction of phospholipid barriers and inflammation during infections. Identifying enzymes with a common mechanism of activation is an initial step in understanding structural and functional properties. These properties serve as critical information for the design of specific inhibitors to reduce enzymatic activity and ameliorate host cell death. In this study, we identify and verify cytotoxic PLA2 enzymes from several bacterial pathogens. Similar to the founding member of the group, ExoU, these enzymes share the property of ubiquitin-mediated activation. The identification and validation of potential toxins from multiple bacterial species provide additional proteins from which to derive structural insights that could lead to paninhibitors useful for treating a variety of infections.
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11
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Vallat B, Webb B, Westbrook JD, Sali A, Berman HM. Development of a Prototype System for Archiving Integrative/Hybrid Structure Models of Biological Macromolecules. Structure 2018; 26:894-904.e2. [PMID: 29657133 DOI: 10.1016/j.str.2018.03.011] [Citation(s) in RCA: 69] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2017] [Revised: 02/16/2018] [Accepted: 03/20/2018] [Indexed: 10/17/2022]
Abstract
Essential processes in biology are carried out by large macromolecular assemblies, whose structures are often difficult to determine by traditional methods. Increasingly, researchers combine measured data and computed information from several complementary methods to obtain "hybrid" or "integrative" structural models of macromolecules and their assemblies. These integrative/hybrid (I/H) models are not archived in the PDB because of the absence of standard data representations and processing mechanisms. Here we present the development of data standards and a prototype system for archiving I/H models. The data standards provide the definitions required for representing I/H models that span multiple spatiotemporal scales and conformational states, as well as spatial restraints derived from different experimental techniques. Based on these data definitions, we have built a prototype system called PDB-Dev, which provides the infrastructure necessary to archive I/H structural models. PDB-Dev is now accepting structures and is open to the community for new submissions.
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Affiliation(s)
- Brinda Vallat
- Research Collaboratory for Structural Bioinformatics, Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA.
| | - Benjamin Webb
- Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry and California Institute for Quantitative Biosciences, University of California at San Francisco, San Francisco, CA 94143, USA
| | - John D Westbrook
- Research Collaboratory for Structural Bioinformatics, Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Andrej Sali
- Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry and California Institute for Quantitative Biosciences, University of California at San Francisco, San Francisco, CA 94143, USA
| | - Helen M Berman
- Research Collaboratory for Structural Bioinformatics, Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
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12
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Xia Y, Fischer AW, Teixeira P, Weiner B, Meiler J. Integrated Structural Biology for α-Helical Membrane Protein Structure Determination. Structure 2018; 26:657-666.e2. [PMID: 29526436 PMCID: PMC5884713 DOI: 10.1016/j.str.2018.02.006] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2017] [Revised: 06/14/2017] [Accepted: 02/05/2018] [Indexed: 01/12/2023]
Abstract
While great progress has been made, only 10% of the nearly 1,000 integral, α-helical, multi-span membrane protein families are represented by at least one experimentally determined structure in the PDB. Previously, we developed the algorithm BCL::MP-Fold, which samples the large conformational space of membrane proteins de novo by assembling predicted secondary structure elements guided by knowledge-based potentials. Here, we present a case study of rhodopsin fold determination by integrating sparse and/or low-resolution restraints from multiple experimental techniques including electron microscopy, electron paramagnetic resonance spectroscopy, and nuclear magnetic resonance spectroscopy. Simultaneous incorporation of orthogonal experimental restraints not only significantly improved the sampling accuracy but also allowed identification of the correct fold, which is demonstrated by a protein size-normalized transmembrane root-mean-square deviation as low as 1.2 Å. The protocol developed in this case study can be used for the determination of unknown membrane protein folds when limited experimental restraints are available.
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Affiliation(s)
- Yan Xia
- Department of Chemistry, Vanderbilt University, Stevenson Center, Station B 351822, Room 7330, Nashville, TN 37232, USA; Center for Structural Biology, Vanderbilt University, Nashville, TN 37232, USA
| | - Axel W Fischer
- Department of Chemistry, Vanderbilt University, Stevenson Center, Station B 351822, Room 7330, Nashville, TN 37232, USA; Center for Structural Biology, Vanderbilt University, Nashville, TN 37232, USA
| | - Pedro Teixeira
- Center for Structural Biology, Vanderbilt University, Nashville, TN 37232, USA
| | - Brian Weiner
- Center for Structural Biology, Vanderbilt University, Nashville, TN 37232, USA
| | - Jens Meiler
- Department of Chemistry, Vanderbilt University, Stevenson Center, Station B 351822, Room 7330, Nashville, TN 37232, USA; Center for Structural Biology, Vanderbilt University, Nashville, TN 37232, USA.
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13
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Li B, Fooksa M, Heinze S, Meiler J. Finding the needle in the haystack: towards solving the protein-folding problem computationally. Crit Rev Biochem Mol Biol 2018; 53:1-28. [PMID: 28976219 PMCID: PMC6790072 DOI: 10.1080/10409238.2017.1380596] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2017] [Revised: 08/22/2017] [Accepted: 09/13/2017] [Indexed: 12/22/2022]
Abstract
Prediction of protein tertiary structures from amino acid sequence and understanding the mechanisms of how proteins fold, collectively known as "the protein folding problem," has been a grand challenge in molecular biology for over half a century. Theories have been developed that provide us with an unprecedented understanding of protein folding mechanisms. However, computational simulation of protein folding is still difficult, and prediction of protein tertiary structure from amino acid sequence is an unsolved problem. Progress toward a satisfying solution has been slow due to challenges in sampling the vast conformational space and deriving sufficiently accurate energy functions. Nevertheless, several techniques and algorithms have been adopted to overcome these challenges, and the last two decades have seen exciting advances in enhanced sampling algorithms, computational power and tertiary structure prediction methodologies. This review aims at summarizing these computational techniques, specifically conformational sampling algorithms and energy approximations that have been frequently used to study protein-folding mechanisms or to de novo predict protein tertiary structures. We hope that this review can serve as an overview on how the protein-folding problem can be studied computationally and, in cases where experimental approaches are prohibitive, help the researcher choose the most relevant computational approach for the problem at hand. We conclude with a summary of current challenges faced and an outlook on potential future directions.
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Affiliation(s)
- Bian Li
- Department of Chemistry, Vanderbilt University, Nashville, TN, USA
- Center for Structural Biology, Vanderbilt University, Nashville, TN, USA
| | - Michaela Fooksa
- Center for Structural Biology, Vanderbilt University, Nashville, TN, USA
- Chemical and Physical Biology Graduate Program, Vanderbilt University, Nashville, TN, USA
| | - Sten Heinze
- Department of Chemistry, Vanderbilt University, Nashville, TN, USA
- Center for Structural Biology, Vanderbilt University, Nashville, TN, USA
| | - Jens Meiler
- Department of Chemistry, Vanderbilt University, Nashville, TN, USA
- Center for Structural Biology, Vanderbilt University, Nashville, TN, USA
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14
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Li B, Mendenhall J, Nguyen ED, Weiner BE, Fischer AW, Meiler J. Improving prediction of helix-helix packing in membrane proteins using predicted contact numbers as restraints. Proteins 2017; 85:1212-1221. [PMID: 28263405 PMCID: PMC5476507 DOI: 10.1002/prot.25281] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2016] [Revised: 01/20/2017] [Accepted: 02/17/2017] [Indexed: 01/21/2023]
Abstract
One of the challenging problems in tertiary structure prediction of helical membrane proteins (HMPs) is the determination of rotation of α-helices around the helix normal. Incorrect prediction of helix rotations substantially disrupts native residue-residue contacts while inducing only a relatively small effect on the overall fold. We previously developed a method for predicting residue contact numbers (CNs), which measure the local packing density of residues within the protein tertiary structure. In this study, we tested the idea of incorporating predicted CNs as restraints to guide the sampling of helix rotation. For a benchmark set of 15 HMPs with simple to rather complicated folds, the average contact recovery (CR) of best-sampled models was improved for all targets, the likelihood of sampling models with CR greater than 20% was increased for 13 targets, and the average RMSD100 of best-sampled models was improved for 12 targets. This study demonstrated that explicit incorporation of CNs as restraints improves the prediction of helix-helix packing. Proteins 2017; 85:1212-1221. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
- Bian Li
- Department of Chemistry, Vanderbilt University, Nashville, TN 37232, USA
- Center for Structural Biology, Vanderbilt University, Nashville, TN 37232, USA
| | - Jeffrey Mendenhall
- Department of Chemistry, Vanderbilt University, Nashville, TN 37232, USA
- Center for Structural Biology, Vanderbilt University, Nashville, TN 37232, USA
| | | | - Brian E. Weiner
- Department of Chemistry, Vanderbilt University, Nashville, TN 37232, USA
- Center for Structural Biology, Vanderbilt University, Nashville, TN 37232, USA
| | - Axel W. Fischer
- Department of Chemistry, Vanderbilt University, Nashville, TN 37232, USA
- Center for Structural Biology, Vanderbilt University, Nashville, TN 37232, USA
| | - Jens Meiler
- Department of Chemistry, Vanderbilt University, Nashville, TN 37232, USA
- Center for Structural Biology, Vanderbilt University, Nashville, TN 37232, USA
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15
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Fischer AW, Anderson DM, Tessmer MH, Frank DW, Feix JB, Meiler J. Structure and Dynamics of Type III Secretion Effector Protein ExoU As determined by SDSL-EPR Spectroscopy in Conjunction with De Novo Protein Folding. ACS OMEGA 2017; 2:2977-2984. [PMID: 28691114 PMCID: PMC5494639 DOI: 10.1021/acsomega.7b00349] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/23/2017] [Accepted: 06/15/2017] [Indexed: 05/24/2023]
Abstract
ExoU is a 74 kDa cytotoxin that undergoes substantial conformational changes as part of its function, that is, it has multiple thermodynamically stable conformations that interchange depending on its environment. Such flexible proteins pose unique challenges to structural biology: (1) not only is it often difficult to determine structures by X-ray crystallography for all biologically relevant conformations because of the flat energy landscape (2) but also experimental conditions can easily perturb the biologically relevant conformation. The first challenge can be overcome by applying orthogonal structural biology techniques that are capable of observing alternative, biologically relevant conformations. The second challenge can be addressed by determining the structure in the same biological state with two independent techniques under different experimental conditions. If both techniques converge to the same structural model, the confidence that an unperturbed biologically relevant conformation is observed increases. To this end, we determine the structure of the C-terminal domain of the effector protein, ExoU, from data obtained by electron paramagnetic resonance spectroscopy in conjunction with site-directed spin labeling and in silico de novo structure determination. Our protocol encompasses a multimodule approach, consisting of low-resolution topology sampling, clustering, and high-resolution refinement. The resulting model was compared with an ExoU model in complex with its chaperone SpcU obtained previously by X-ray crystallography. The two models converged to a minimal RMSD100 of 3.2 Å, providing evidence that the unbound structure of ExoU matches the fold observed in complex with SpcU.
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Affiliation(s)
- Axel W. Fischer
- Department
of Chemistry and Center for Structural Biology, Vanderbilt
University, Nashville, Tennessee 37232, United States
| | - David M. Anderson
- Department
of Pathology, Microbiology and Immunology, Vanderbilt University School of Medicine, Nashville, Tennessee 37232, United States
| | - Maxx H. Tessmer
- Department of Biophysics and Department of
Microbiology and Immunology, Medical College
of Wisconsin, Milwaukee, Wisconsin 53226, United States
| | - Dara W. Frank
- Department of Biophysics and Department of
Microbiology and Immunology, Medical College
of Wisconsin, Milwaukee, Wisconsin 53226, United States
| | - Jimmy B. Feix
- Department of Biophysics and Department of
Microbiology and Immunology, Medical College
of Wisconsin, Milwaukee, Wisconsin 53226, United States
| | - Jens Meiler
- Department
of Chemistry and Center for Structural Biology, Vanderbilt
University, Nashville, Tennessee 37232, United States
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16
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Teixeira PL, Mendenhall JL, Heinze S, Weiner B, Skwark MJ, Meiler J. Membrane protein contact and structure prediction using co-evolution in conjunction with machine learning. PLoS One 2017; 12:e0177866. [PMID: 28542325 PMCID: PMC5443516 DOI: 10.1371/journal.pone.0177866] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2016] [Accepted: 05/04/2017] [Indexed: 11/18/2022] Open
Abstract
De novo membrane protein structure prediction is limited to small proteins due to the conformational search space quickly expanding with length. Long-range contacts (24+ amino acid separation)-residue positions distant in sequence, but in close proximity in the structure, are arguably the most effective way to restrict this conformational space. Inverse methods for co-evolutionary analysis predict a global set of position-pair couplings that best explain the observed amino acid co-occurrences, thus distinguishing between evolutionarily explained co-variances and these arising from spurious transitive effects. Here, we show that applying machine learning approaches and custom descriptors improves evolutionary contact prediction accuracy, resulting in improvement of average precision by 6 percentage points for the top 1L non-local contacts. Further, we demonstrate that predicted contacts improve protein folding with BCL::Fold. The mean RMSD100 metric for the top 10 models folded was reduced by an average of 2 Å for a benchmark of 25 membrane proteins.
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Affiliation(s)
- Pedro L. Teixeira
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
| | - Jeff L. Mendenhall
- Department of Chemistry, Center for Structural Biology, Vanderbilt University, Nashville Tennessee, United States of America
| | - Sten Heinze
- Department of Chemistry, Center for Structural Biology, Vanderbilt University, Nashville Tennessee, United States of America
| | - Brian Weiner
- Department of Chemistry, Center for Structural Biology, Vanderbilt University, Nashville Tennessee, United States of America
| | - Marcin J. Skwark
- Department of Chemistry, Center for Structural Biology, Vanderbilt University, Nashville Tennessee, United States of America
| | - Jens Meiler
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
- Department of Chemistry, Center for Structural Biology, Vanderbilt University, Nashville Tennessee, United States of America
- * E-mail:
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17
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Lindert S, McCammon JA. Improved cryoEM-Guided Iterative Molecular Dynamics--Rosetta Protein Structure Refinement Protocol for High Precision Protein Structure Prediction. J Chem Theory Comput 2016; 11:1337-46. [PMID: 25883538 PMCID: PMC4393324 DOI: 10.1021/ct500995d] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2014] [Indexed: 12/13/2022]
Abstract
![]()
Many excellent methods exist that
incorporate cryo-electron microscopy
(cryoEM) data to constrain computational protein structure prediction
and refinement. Previously, it was shown that iteration of two such
orthogonal sampling and scoring methods – Rosetta and molecular
dynamics (MD) simulations – facilitated exploration of conformational
space in principle. Here, we go beyond a proof-of-concept study and
address significant remaining limitations of the iterative MD–Rosetta
protein structure refinement protocol. Specifically, all parts of
the iterative refinement protocol are now guided by medium-resolution
cryoEM density maps, and previous knowledge about the native structure
of the protein is no longer necessary. Models are identified solely
based on score or simulation time. All four benchmark proteins showed
substantial improvement through three rounds of the iterative refinement
protocol. The best-scoring final models of two proteins had sub-Ångstrom
RMSD to the native structure over residues in secondary structure
elements. Molecular dynamics was most efficient in refining secondary
structure elements and was thus highly complementary to the Rosetta
refinement which is most powerful in refining side chains and loop
regions.
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18
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Figueroa M, Sleutel M, Vandevenne M, Parvizi G, Attout S, Jacquin O, Vandenameele J, Fischer AW, Damblon C, Goormaghtigh E, Valerio-Lepiniec M, Urvoas A, Durand D, Pardon E, Steyaert J, Minard P, Maes D, Meiler J, Matagne A, Martial JA, Van de Weerdt C. The unexpected structure of the designed protein Octarellin V.1 forms a challenge for protein structure prediction tools. J Struct Biol 2016; 195:19-30. [PMID: 27181418 DOI: 10.1016/j.jsb.2016.05.004] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2016] [Revised: 04/19/2016] [Accepted: 05/12/2016] [Indexed: 12/26/2022]
Abstract
Despite impressive successes in protein design, designing a well-folded protein of more 100 amino acids de novo remains a formidable challenge. Exploiting the promising biophysical features of the artificial protein Octarellin V, we improved this protein by directed evolution, thus creating a more stable and soluble protein: Octarellin V.1. Next, we obtained crystals of Octarellin V.1 in complex with crystallization chaperons and determined the tertiary structure. The experimental structure of Octarellin V.1 differs from its in silico design: the (αβα) sandwich architecture bears some resemblance to a Rossman-like fold instead of the intended TIM-barrel fold. This surprising result gave us a unique and attractive opportunity to test the state of the art in protein structure prediction, using this artificial protein free of any natural selection. We tested 13 automated webservers for protein structure prediction and found none of them to predict the actual structure. More than 50% of them predicted a TIM-barrel fold, i.e. the structure we set out to design more than 10years ago. In addition, local software runs that are human operated can sample a structure similar to the experimental one but fail in selecting it, suggesting that the scoring and ranking functions should be improved. We propose that artificial proteins could be used as tools to test the accuracy of protein structure prediction algorithms, because their lack of evolutionary pressure and unique sequences features.
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Affiliation(s)
- Maximiliano Figueroa
- GIGA-Research, Molecular Biomimetics and Protein Engineering, University of Liège, Liège, Belgium.
| | - Mike Sleutel
- Structural Biology Brussels, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium
| | - Marylene Vandevenne
- GIGA-Research, Molecular Biomimetics and Protein Engineering, University of Liège, Liège, Belgium
| | - Gregory Parvizi
- GIGA-Research, Molecular Biomimetics and Protein Engineering, University of Liège, Liège, Belgium
| | - Sophie Attout
- GIGA-Research, Molecular Biomimetics and Protein Engineering, University of Liège, Liège, Belgium
| | - Olivier Jacquin
- GIGA-Research, Molecular Biomimetics and Protein Engineering, University of Liège, Liège, Belgium
| | - Julie Vandenameele
- Laboratoire d'Enzymologie et Repliement des Protéines, Centre for Protein Engineering, University of Liège, Liège, Belgium
| | - Axel W Fischer
- Department of Chemistry, Center for Structural Biology, Vanderbilt University, Nashville, TN, United States
| | | | - Erik Goormaghtigh
- Laboratory for the Structure and Function of Biological Membranes, Center for Structural Biology and Bioinformatics, Université Libre de Bruxelles, Brussels, Belgium
| | - Marie Valerio-Lepiniec
- Institute for Integrative Biology of the Cell (I2BC), UMT 9198, CEA, CNRS, Université Paris-Sud, Orsay, France
| | - Agathe Urvoas
- Institute for Integrative Biology of the Cell (I2BC), UMT 9198, CEA, CNRS, Université Paris-Sud, Orsay, France
| | - Dominique Durand
- Institute for Integrative Biology of the Cell (I2BC), UMT 9198, CEA, CNRS, Université Paris-Sud, Orsay, France
| | - Els Pardon
- Structural Biology Brussels, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium; Structural Biology Research Center, VIB, Pleinlaan 2, 1050 Brussels, Belgium
| | - Jan Steyaert
- Structural Biology Brussels, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium; Structural Biology Research Center, VIB, Pleinlaan 2, 1050 Brussels, Belgium
| | - Philippe Minard
- Institute for Integrative Biology of the Cell (I2BC), UMT 9198, CEA, CNRS, Université Paris-Sud, Orsay, France
| | - Dominique Maes
- Structural Biology Brussels, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium
| | - Jens Meiler
- Department of Chemistry, Center for Structural Biology, Vanderbilt University, Nashville, TN, United States
| | - André Matagne
- Laboratoire d'Enzymologie et Repliement des Protéines, Centre for Protein Engineering, University of Liège, Liège, Belgium
| | - Joseph A Martial
- GIGA-Research, Molecular Biomimetics and Protein Engineering, University of Liège, Liège, Belgium
| | - Cécile Van de Weerdt
- GIGA-Research, Molecular Biomimetics and Protein Engineering, University of Liège, Liège, Belgium.
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19
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Pushing the size limit of de novo structure ensemble prediction guided by sparse SDSL-EPR restraints to 200 residues: The monomeric and homodimeric forms of BAX. J Struct Biol 2016; 195:62-71. [PMID: 27129417 DOI: 10.1016/j.jsb.2016.04.014] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2015] [Revised: 04/25/2016] [Accepted: 04/26/2016] [Indexed: 01/24/2023]
Abstract
Structure determination remains a challenge for many biologically important proteins. In particular, proteins that adopt multiple conformations often evade crystallization in all biologically relevant states. Although computational de novo protein folding approaches often sample biologically relevant conformations, the selection of the most accurate model for different functional states remains a formidable challenge, in particular, for proteins with more than about 150 residues. Electron paramagnetic resonance (EPR) spectroscopy can obtain limited structural information for proteins in well-defined biological states and thereby assist in selecting biologically relevant conformations. The present study demonstrates that de novo folding methods are able to accurately sample the folds of 192-residue long soluble monomeric Bcl-2-associated X protein (BAX). The tertiary structures of the monomeric and homodimeric forms of BAX were predicted using the primary structure as well as 25 and 11 EPR distance restraints, respectively. The predicted models were subsequently compared to respective NMR/X-ray structures of BAX. EPR restraints improve the protein-size normalized root-mean-square-deviation (RMSD100) of the most accurate models with respect to the NMR/crystal structure from 5.9Å to 3.9Å and from 5.7Å to 3.3Å, respectively. Additionally, the model discrimination is improved, which is demonstrated by an improvement of the enrichment from 5% to 15% and from 13% to 21%, respectively.
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20
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Fischer AW, Heinze S, Putnam DK, Li B, Pino JC, Xia Y, Lopez CF, Meiler J. CASP11--An Evaluation of a Modular BCL::Fold-Based Protein Structure Prediction Pipeline. PLoS One 2016; 11:e0152517. [PMID: 27046050 PMCID: PMC4821492 DOI: 10.1371/journal.pone.0152517] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2015] [Accepted: 03/15/2016] [Indexed: 11/18/2022] Open
Abstract
In silico prediction of a protein's tertiary structure remains an unsolved problem. The community-wide Critical Assessment of Protein Structure Prediction (CASP) experiment provides a double-blind study to evaluate improvements in protein structure prediction algorithms. We developed a protein structure prediction pipeline employing a three-stage approach, consisting of low-resolution topology search, high-resolution refinement, and molecular dynamics simulation to predict the tertiary structure of proteins from the primary structure alone or including distance restraints either from predicted residue-residue contacts, nuclear magnetic resonance (NMR) nuclear overhauser effect (NOE) experiments, or mass spectroscopy (MS) cross-linking (XL) data. The protein structure prediction pipeline was evaluated in the CASP11 experiment on twenty regular protein targets as well as thirty-three 'assisted' protein targets, which also had distance restraints available. Although the low-resolution topology search module was able to sample models with a global distance test total score (GDT_TS) value greater than 30% for twelve out of twenty proteins, frequently it was not possible to select the most accurate models for refinement, resulting in a general decay of model quality over the course of the prediction pipeline. In this study, we provide a detailed overall analysis, study one target protein in more detail as it travels through the protein structure prediction pipeline, and evaluate the impact of limited experimental data.
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Affiliation(s)
- Axel W. Fischer
- Department of Chemistry, Vanderbilt University, Nashville, TN, 37232, United States of America
- Center for Structural Biology, Vanderbilt University, Nashville, TN, 37232, United States of America
| | - Sten Heinze
- Department of Chemistry, Vanderbilt University, Nashville, TN, 37232, United States of America
- Center for Structural Biology, Vanderbilt University, Nashville, TN, 37232, United States of America
| | - Daniel K. Putnam
- Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, 37232, United States of America
| | - Bian Li
- Department of Chemistry, Vanderbilt University, Nashville, TN, 37232, United States of America
- Center for Structural Biology, Vanderbilt University, Nashville, TN, 37232, United States of America
| | - James C. Pino
- Chemical and Physical Biology Graduate Program, Vanderbilt University, Nashville, TN, 37232, United States of America
| | - Yan Xia
- Department of Chemistry, Vanderbilt University, Nashville, TN, 37232, United States of America
- Center for Structural Biology, Vanderbilt University, Nashville, TN, 37232, United States of America
| | - Carlos F. Lopez
- Center for Structural Biology, Vanderbilt University, Nashville, TN, 37232, United States of America
- Department of Cancer Biology and Center for Quantitative Sciences, Vanderbilt University, Nashville, TN, 37232, United States of America
| | - Jens Meiler
- Department of Chemistry, Vanderbilt University, Nashville, TN, 37232, United States of America
- Center for Structural Biology, Vanderbilt University, Nashville, TN, 37232, United States of America
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21
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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.
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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
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22
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Protonation-dependent conformational dynamics of the multidrug transporter EmrE. Proc Natl Acad Sci U S A 2016; 113:1220-5. [PMID: 26787875 DOI: 10.1073/pnas.1520431113] [Citation(s) in RCA: 49] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
The small multidrug transporter from Escherichia coli, EmrE, couples the energetically uphill extrusion of hydrophobic cations out of the cell to the transport of two protons down their electrochemical gradient. Although principal mechanistic elements of proton/substrate antiport have been described, the structural record is limited to the conformation of the substrate-bound state, which has been shown to undergo isoenergetic alternating access. A central but missing link in the structure/mechanism relationship is a description of the proton-bound state, which is an obligatory intermediate in the transport cycle. Here we report a systematic spin labeling and double electron electron resonance (DEER) study that uncovers the conformational changes of EmrE subsequent to protonation of critical acidic residues in the context of a global description of ligand-induced structural rearrangements. We find that protonation of E14 leads to extensive rotation and tilt of transmembrane helices 1-3 in conjunction with repacking of loops, conformational changes that alter the coordination of the bound substrate and modulate its access to the binding site from the lipid bilayer. The transport model that emerges from our data posits a proton-bound, but occluded, resting state. Substrate binding from the inner leaflet of the bilayer releases the protons and triggers alternating access between inward- and outward-facing conformations of the substrate-loaded transporter, thus enabling antiport without dissipation of the proton gradient.
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23
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Characterization of the Domain Orientations of E. coli 5'-Nucleotidase by Fitting an Ensemble of Conformers to DEER Distance Distributions. Structure 2015; 24:43-56. [PMID: 26724996 DOI: 10.1016/j.str.2015.11.007] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2015] [Revised: 10/20/2015] [Accepted: 11/13/2015] [Indexed: 01/04/2023]
Abstract
Escherichia coli 5'-nucleotidase is a two-domain enzyme exhibiting a unique 96° domain motion that is required for catalysis. Here we present an integrated structural biology study that combines DEER distance distributions with structural information from X-ray crystallography and computational biology to describe the population of presumably almost isoenergetic open and closed states in solution. Ensembles of models that best represent the experimental distance distributions are determined by a Monte Carlo search algorithm. As a result, predominantly open conformations are observed in the unliganded state indicating that the majority of enzyme molecules await substrate binding for the catalytic cycle. The addition of a substrate analog yields ensembles with an almost equal mixture of open and closed states. Thus, in the presence of substrate, efficient catalysis is provided by the simultaneous appearance of open conformers (binding substrate or releasing product) and closed conformers (enabling the turnover of the substrate).
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24
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Heinze S, Putnam DK, Fischer AW, Kohlmann T, Weiner BE, Meiler J. CASP10-BCL::Fold efficiently samples topologies of large proteins. Proteins 2015; 83:547-63. [PMID: 25581562 DOI: 10.1002/prot.24733] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2014] [Revised: 10/15/2014] [Accepted: 11/03/2014] [Indexed: 12/26/2022]
Abstract
During CASP10 in summer 2012, we tested BCL::Fold for prediction of free modeling (FM) and template-based modeling (TBM) targets. BCL::Fold assembles the tertiary structure of a protein from predicted secondary structure elements (SSEs) omitting more flexible loop regions early on. This approach enables the sampling of conformational space for larger proteins with more complex topologies. In preparation of CASP11, we analyzed the quality of CASP10 models throughout the prediction pipeline to understand BCL::Fold's ability to sample the native topology, identify native-like models by scoring and/or clustering approaches, and our ability to add loop regions and side chains to initial SSE-only models. The standout observation is that BCL::Fold sampled topologies with a GDT_TS score > 33% for 12 of 18 and with a topology score > 0.8 for 11 of 18 test cases de novo. Despite the sampling success of BCL::Fold, significant challenges still exist in clustering and loop generation stages of the pipeline. The clustering approach employed for model selection often failed to identify the most native-like assembly of SSEs for further refinement and submission. It was also observed that for some β-strand proteins model refinement failed as β-strands were not properly aligned to form hydrogen bonds removing otherwise accurate models from the pool. Further, BCL::Fold samples frequently non-natural topologies that require loop regions to pass through the center of the protein.
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Affiliation(s)
- Sten Heinze
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee, 37240
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25
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Fischer AW, Alexander NS, Woetzel N, Karakas M, Weiner BE, Meiler J. BCL::MP-fold: Membrane protein structure prediction guided by EPR restraints. Proteins 2015; 83:1947-62. [PMID: 25820805 PMCID: PMC5064833 DOI: 10.1002/prot.24801] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2014] [Revised: 03/11/2015] [Accepted: 03/20/2015] [Indexed: 11/05/2022]
Abstract
For many membrane proteins, the determination of their topology remains a challenge for methods like X-ray crystallography and nuclear magnetic resonance (NMR) spectroscopy. Electron paramagnetic resonance (EPR) spectroscopy has evolved as an alternative technique to study structure and dynamics of membrane proteins. The present study demonstrates the feasibility of membrane protein topology determination using limited EPR distance and accessibility measurements. The BCL::MP-Fold (BioChemical Library membrane protein fold) algorithm assembles secondary structure elements (SSEs) in the membrane using a Monte Carlo Metropolis (MCM) approach. Sampled models are evaluated using knowledge-based potential functions and agreement with the EPR data and a knowledge-based energy function. Twenty-nine membrane proteins of up to 696 residues are used to test the algorithm. The RMSD100 value of the most accurate model is better than 8 Å for 27, better than 6 Å for 22, and better than 4 Å for 15 of the 29 proteins, demonstrating the algorithms' ability to sample the native topology. The average enrichment could be improved from 1.3 to 2.5, showing the improved discrimination power by using EPR data.
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Affiliation(s)
- Axel W Fischer
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee, 37232
- Center for Structural Biology, Vanderbilt University, Nashville, Tennessee, 37232
| | - Nathan S Alexander
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee, 37232
- Center for Structural Biology, Vanderbilt University, Nashville, Tennessee, 37232
| | - Nils Woetzel
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee, 37232
- Center for Structural Biology, Vanderbilt University, Nashville, Tennessee, 37232
| | - Mert Karakas
- Center for Structural Biology, Vanderbilt University, Nashville, Tennessee, 37232
| | - Brian E Weiner
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee, 37232
- Center for Structural Biology, Vanderbilt University, Nashville, Tennessee, 37232
| | - Jens Meiler
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee, 37232
- Center for Structural Biology, Vanderbilt University, Nashville, Tennessee, 37232
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26
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Putnam DK, Weiner BE, Woetzel N, Lowe EW, Meiler J. BCL::SAXS: GPU accelerated Debye method for computation of small angle X-ray scattering profiles. Proteins 2015; 83:1500-12. [PMID: 26018949 PMCID: PMC4797635 DOI: 10.1002/prot.24838] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2015] [Revised: 05/08/2015] [Accepted: 05/19/2015] [Indexed: 12/25/2022]
Abstract
Small angle X-ray scattering (SAXS) is an experimental technique used for structural characterization of macromolecules in solution. Here, we introduce BCL::SAXS--an algorithm designed to replicate SAXS profiles from rigid protein models at different levels of detail. We first show our derivation of BCL::SAXS and compare our results with the experimental scattering profile of hen egg white lysozyme. Using this protein we show how to generate SAXS profiles representing: (1) complete models, (2) models with approximated side chain coordinates, and (3) models with approximated side chain and loop region coordinates. We evaluated the ability of SAXS profiles to identify a correct protein topology from a non-redundant benchmark set of proteins. We find that complete SAXS profiles can be used to identify the correct protein by receiver operating characteristic (ROC) analysis with an area under the curve (AUC) > 99%. We show how our approximation of loop coordinates between secondary structure elements improves protein recognition by SAχS for protein models without loop regions and side chains. Agreement with SAXS data is a necessary but not sufficient condition for structure determination. We conclude that experimental SAXS data can be used as a filter to exclude protein models with large structural differences from the native.
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Affiliation(s)
- Daniel K. Putnam
- Center for Structural Biology, Vanderbilt University, Nashville, TN 37235, USA
- Department of Biomedical Informatics, Vanderbilt University, Nashville, TN 37235, USA
| | - Brian E. Weiner
- Center for Structural Biology, Vanderbilt University, Nashville, TN 37235, USA
| | - Nils Woetzel
- Center for Structural Biology, Vanderbilt University, Nashville, TN 37235, USA
| | - Edward W. Lowe
- Center for Structural Biology, Vanderbilt University, Nashville, TN 37235, USA
| | - Jens Meiler
- Center for Structural Biology, Vanderbilt University, Nashville, TN 37235, USA
- Department of Biomedical Informatics, Vanderbilt University, Nashville, TN 37235, USA
- Department of Chemistry, Vanderbilt University, Nashville, TN 37235, USA
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27
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Hofmann T, Fischer AW, Meiler J, Kalkhof S. Protein structure prediction guided by crosslinking restraints--A systematic evaluation of the impact of the crosslinking spacer length. Methods 2015; 89:79-90. [PMID: 25986934 DOI: 10.1016/j.ymeth.2015.05.014] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2015] [Revised: 04/21/2015] [Accepted: 05/12/2015] [Indexed: 11/15/2022] Open
Abstract
Recent development of high-resolution mass spectrometry (MS) instruments enables chemical crosslinking (XL) to become a high-throughput method for obtaining structural information about proteins. Restraints derived from XL-MS experiments have been used successfully for structure refinement and protein-protein docking. However, one formidable question is under which circumstances XL-MS data might be sufficient to determine a protein's tertiary structure de novo? Answering this question will not only include understanding the impact of XL-MS data on sampling and scoring within a de novo protein structure prediction algorithm, it must also determine an optimal crosslinker type and length for protein structure determination. While a longer crosslinker will yield more restraints, the value of each restraint for protein structure prediction decreases as the restraint is consistent with a larger conformational space. In this study, the number of crosslinks and their discriminative power was systematically analyzed in silico on a set of 2055 non-redundant protein folds considering Lys-Lys, Lys-Asp, Lys-Glu, Cys-Cys, and Arg-Arg reactive crosslinkers between 1 and 60Å. Depending on the protein size a heuristic was developed that determines the optimal crosslinker length. Next, simulated restraints of variable length were used to de novo predict the tertiary structure of fifteen proteins using the BCL::Fold algorithm. The results demonstrate that a distinct crosslinker length exists for which information content for de novo protein structure prediction is maximized. The sampling accuracy improves on average by 1.0 Å and up to 2.2 Å in the most prominent example. XL-MS restraints enable consistently an improved selection of native-like models with an average enrichment of 2.1.
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Affiliation(s)
- Tommy Hofmann
- Department of Proteomics, Helmholtz-Centre for Environmental Research - UFZ, Leipzig D-04318, Germany
| | - Axel W Fischer
- Department of Chemistry and Center for Structural Biology, Vanderbilt University, Nashville, TN 37232, USA
| | - Jens Meiler
- Department of Chemistry and Center for Structural Biology, Vanderbilt University, Nashville, TN 37232, USA.
| | - Stefan Kalkhof
- Department of Proteomics, Helmholtz-Centre for Environmental Research - UFZ, Leipzig D-04318, Germany; Department of Bioanalytics, University of Applied Sciences and Arts of Coburg, D-96450 Coburg, Germany.
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28
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H. DeLuca S, L. DeLuca S, Leaver-Fay A, Meiler J. RosettaTMH: a method for membrane protein structure elucidation combining EPR distance restraints with assembly of transmembrane helices. AIMS BIOPHYSICS 2015. [DOI: 10.3934/biophy.2016.1.1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
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29
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Leman JK, Ulmschneider MB, Gray JJ. Computational modeling of membrane proteins. Proteins 2015; 83:1-24. [PMID: 25355688 PMCID: PMC4270820 DOI: 10.1002/prot.24703] [Citation(s) in RCA: 81] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2014] [Revised: 10/01/2014] [Accepted: 10/18/2014] [Indexed: 02/06/2023]
Abstract
The determination of membrane protein (MP) structures has always trailed that of soluble proteins due to difficulties in their overexpression, reconstitution into membrane mimetics, and subsequent structure determination. The percentage of MP structures in the protein databank (PDB) has been at a constant 1-2% for the last decade. In contrast, over half of all drugs target MPs, only highlighting how little we understand about drug-specific effects in the human body. To reduce this gap, researchers have attempted to predict structural features of MPs even before the first structure was experimentally elucidated. In this review, we present current computational methods to predict MP structure, starting with secondary structure prediction, prediction of trans-membrane spans, and topology. Even though these methods generate reliable predictions, challenges such as predicting kinks or precise beginnings and ends of secondary structure elements are still waiting to be addressed. We describe recent developments in the prediction of 3D structures of both α-helical MPs as well as β-barrels using comparative modeling techniques, de novo methods, and molecular dynamics (MD) simulations. The increase of MP structures has (1) facilitated comparative modeling due to availability of more and better templates, and (2) improved the statistics for knowledge-based scoring functions. Moreover, de novo methods have benefited from the use of correlated mutations as restraints. Finally, we outline current advances that will likely shape the field in the forthcoming decade.
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Affiliation(s)
- Julia Koehler Leman
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Martin B. Ulmschneider
- Department of Materials Science and Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Jeffrey J. Gray
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
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30
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Hoffmann F, Vancea I, Kamat SG, Strodel B. Protein structure prediction: assembly of secondary structure elements by basin-hopping. Chemphyschem 2014; 15:3378-90. [PMID: 25056272 DOI: 10.1002/cphc.201402247] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2014] [Indexed: 12/30/2022]
Abstract
The prediction of protein tertiary structure from primary structure remains a challenging task. One possible approach to this problem is the application of basin-hopping global optimization combined with an all-atom force field. In this work, the efficiency of basin-hopping is improved by introducing an approach that derives tertiary structures from the secondary structure assignments of individual residues. This approach is termed secondary-to-tertiary basin-hopping and benchmarked for three miniproteins: trpzip, trp-cage and ER-10. For each of the three miniproteins, the secondary-to-tertiary basin-hopping approach successfully and reliably predicts their three-dimensional structure. When it is applied to larger proteins, correctly folded structures are obtained. It can be concluded that the assembly of secondary structure elements using basin-hopping is a promising tool for de novo protein structure prediction.
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Affiliation(s)
- Falk Hoffmann
- Institute of Complex Systems: Structural Biochemistry, Forschungszentrum Jülich, 52425 Jülich (Germany)
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31
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Maurice KJ. SSThread: Template-free protein structure prediction by threading pairs of contacting secondary structures followed by assembly of overlapping pairs. J Comput Chem 2014; 35:644-56. [PMID: 24523210 DOI: 10.1002/jcc.23543] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2013] [Revised: 11/15/2013] [Accepted: 01/05/2014] [Indexed: 11/12/2022]
Abstract
Acquiring the three-dimensional structure of a protein from its amino acid sequence alone, despite a great deal of work and significant progress on the subject, is still an unsolved problem. SSThread, a new template-free algorithm is described here that consists of making several predictions of contacting pairs of α-helices and β-strands derived from a database of experimental structures using a knowledge-based potential, secondary structure prediction, and contact map prediction followed by assembly of overlapping pair predictions to create an ensemble of core structure predictions whose loops are then predicted. In a set of seven CASP10 targets SSThread outperformed the two leading methods for two targets each. The targets were all β-strand containing structures and most of them have a high relative contact order which demonstrates the advantages of SSThread. The primary bottlenecks based on sets of 74 and 21 test cases are the pair prediction and loop prediction stages.
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32
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Bibby J, Keegan RM, Mayans O, Winn MD, Rigden DJ. Application of the AMPLE cluster-and-truncate approach to NMR structures for molecular replacement. ACTA CRYSTALLOGRAPHICA. SECTION D, BIOLOGICAL CRYSTALLOGRAPHY 2013; 69:2194-201. [PMID: 24189230 PMCID: PMC3817692 DOI: 10.1107/s0907444913018453] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/11/2013] [Accepted: 07/03/2013] [Indexed: 12/27/2022]
Abstract
AMPLE is a program developed for clustering and truncating ab initio protein structure predictions into search models for molecular replacement. Here, it is shown that its core cluster-and-truncate methods also work well for processing NMR ensembles into search models. Rosetta remodelling helps to extend success to NMR structures bearing low sequence identity or high structural divergence from the target protein. Potential future routes to improved performance are considered and practical, general guidelines on using AMPLE are provided.
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Affiliation(s)
- Jaclyn Bibby
- Institute of Integrative Biology, University of Liverpool, Liverpool L69 7ZB, England
| | - Ronan M. Keegan
- Research Complex at Harwell, STFC Rutherford Appleton Laboratory, Didcot OX11 0FA, England
| | - Olga Mayans
- Institute of Integrative Biology, University of Liverpool, Liverpool L69 7ZB, England
| | - Martyn D. Winn
- Science and Technology Facilities Council Daresbury Laboratory, Warrington WA4 4AD, England
| | - Daniel J. Rigden
- Institute of Integrative Biology, University of Liverpool, Liverpool L69 7ZB, England
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33
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Weiner BE, Alexander N, Akin LR, Woetzel N, Karakas M, Meiler J. BCL::Fold--protein topology determination from limited NMR restraints. Proteins 2013; 82:587-95. [PMID: 24123100 DOI: 10.1002/prot.24427] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2013] [Revised: 09/03/2013] [Accepted: 09/10/2013] [Indexed: 02/02/2023]
Abstract
When experimental protein NMR data are too sparse to apply traditional structure determination techniques, de novo protein structure prediction methods can be leveraged. Here, we describe the incorporation of NMR restraints into the protein structure prediction algorithm BCL::Fold. The method assembles discreet secondary structure elements using a Monte Carlo sampling algorithm with a consensus knowledge-based energy function. New components were introduced into the energy function to accommodate chemical shift, nuclear Overhauser effect, and residual dipolar coupling data. In particular, since side chains are not explicitly modeled during the minimization process, a knowledge based potential was created to relate experimental side chain proton-proton distances to Cβ -Cβ distances. In a benchmark test of 67 proteins of known structure with the incorporation of sparse NMR restraints, the correct topology was sampled in 65 cases, with an average best model RMSD100 of 3.4 ± 1.3 Å versus 6.0 ± 2.0 Å produced with the de novo method. Additionally, the correct topology is present in the best scoring 1% of models in 61 cases. The benchmark set includes both soluble and membrane proteins with up to 565 residues, indicating the method is robust and applicable to large and membrane proteins that are less likely to produce rich NMR datasets.
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Affiliation(s)
- Brian E Weiner
- Department of Chemistry, Center for Structural Biology, Vanderbilt University, Nashville, Tennessee, 37232
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34
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Li F, Xia Y, Meiler J, Ferguson-Miller S. Characterization and modeling of the oligomeric state and ligand binding behavior of purified translocator protein 18 kDa from Rhodobacter sphaeroides. Biochemistry 2013; 52:5884-99. [PMID: 23952237 PMCID: PMC3756528 DOI: 10.1021/bi400431t] [Citation(s) in RCA: 45] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/04/2022]
Abstract
![]()
Translocator
Protein 18 kDa (TSPO), previously known as the peripheral-type
benzodiazepine receptor (PBR), is a mitochondrial outer membrane protein
that has been identified as a key player in cholesterol and porphyrin
transport, apoptotic signaling, and cancer development, as well as
neurological inflammation and disease. Despite a number of TSPO ligands
whose effects have been studied with respect to these varied biological
activities, the nature of their interactions with TSPO and the molecular
mechanism of their effects remain controversial, in part because of
the lack of an atomic-resolution structure. We expressed and purified
the homologue of mammalian TSPO from Rhodobacter sphaeroides (RsTSPO), as well as a mutant form in a proposed
drug binding loop, RsTSPOW38C. We characterized their
binding behaviors with endogenous ligands and a series of compounds
that affect apoptosis by using a sensitive tryptophan fluorescence
quenching assay. Our results show that RsTSPO behaves
as a dimer in the purified state and binds with low micromolar affinity
to many of these ligands, including retinoic acid, curcumin, and a
known Bcl-2 inhibitor, gossypol, suggesting a possible direct role
for TSPO in their regulation of apoptosis. A computational model of
the RsTSPO dimer is constructed using EM-Fold, Rosetta,
and a cryo-electron microscopy density map. Binding behaviors of known
ligands are discussed in the context of the model with respect to
regions that may be involved in binding.
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Affiliation(s)
- Fei Li
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan 48824, United States
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Lindert S, Meiler J, McCammon JA. Iterative Molecular Dynamics-Rosetta Protein Structure Refinement Protocol to Improve Model Quality. J Chem Theory Comput 2013; 9:3843-3847. [PMID: 23956701 PMCID: PMC3744128 DOI: 10.1021/ct400260c] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2013] [Indexed: 02/02/2023]
Abstract
Rosetta is one of the prime tools for high resolution protein structure refinement. While its scoring function can distinguish native-like from non-native-like conformations in many cases, the method is limited by conformational sampling for larger proteins, that is, leaving a local energy minimum in which the search algorithm may get stuck. Here, we test the hypothesis that iteration of Rosetta with an orthogonal sampling and scoring strategy might facilitate exploration of conformational space. Specifically, we run short molecular dynamics (MD) simulations on models created by de novo folding of large proteins into cryoEM density maps to enable sampling of conformational space not directly accessible to Rosetta and thus provide an escape route from the conformational traps. We present a combined MD-Rosetta protein structure refinement protocol that can overcome some of these sampling limitations. Two of four benchmark proteins showed incremental improvement through all three rounds of the iterative refinement protocol. Molecular dynamics is most efficient in applying subtle but important rearrangements within secondary structure elements and is thus highly complementary to the Rosetta refinement, which focuses on side chains and loop regions.
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Affiliation(s)
- Steffen Lindert
- Department of Pharmacology, University of California
San Diego, La Jolla, California
92093, United States
- Center for Theoretical
Biological Physics, La Jolla, California 92093, United
States
| | - Jens Meiler
- Department of Chemistry
and Center
for Structural Biology, Vanderbilt University, Nashville, Tennessee 37212,
United States
| | - J. Andrew McCammon
- Department of Pharmacology, University of California
San Diego, La Jolla, California
92093, United States
- Center for Theoretical
Biological Physics, La Jolla, California 92093, United
States
- Howard Hughes
Medical Institute, University of California San Diego, La Jolla, California
92093, United States
- Department
of Chemistry and
Biochemistry, NSF Center for Theoretical Biological Physics, National
Biomedical Computation Resource, University of California
San Diego, La Jolla, California
92093, United States
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Weiner BE, Woetzel N, Karakas M, Alexander N, Meiler J. BCL::MP-fold: folding membrane proteins through assembly of transmembrane helices. Structure 2013; 21:1107-17. [PMID: 23727232 PMCID: PMC3738745 DOI: 10.1016/j.str.2013.04.022] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2013] [Revised: 04/10/2013] [Accepted: 04/25/2013] [Indexed: 12/01/2022]
Abstract
Membrane protein structure determination remains a challenging endeavor. Computational methods that predict membrane protein structure from sequence can potentially aid structure determination for such difficult target proteins. The de novo protein structure prediction method BCL::Fold rapidly assembles secondary structure elements into three-dimensional models. Here, we describe modifications to the algorithm, named BCL::MP-Fold, in order to simulate membrane protein folding. Models are built into a static membrane object and are evaluated using a knowledge-based energy potential, which has been modified to account for the membrane environment. Additionally, a symmetry folding mode allows for the prediction of obligate homomultimers, a common property among membrane proteins. In a benchmark test of 40 proteins of known structure, the method sampled the correct topology in 34 cases. This demonstrates that the algorithm can accurately predict protein topology without the need for large multiple sequence alignments, homologous template structures, or experimental restraints.
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Affiliation(s)
- Brian E. Weiner
- Department of Chemistry, Center for Structural Biology, Vanderbilt University, Nashville TN, 37232, USA
| | - Nils Woetzel
- Department of Chemistry, Center for Structural Biology, Vanderbilt University, Nashville TN, 37232, USA
| | - Mert Karakas
- Department of Chemistry, Center for Structural Biology, Vanderbilt University, Nashville TN, 37232, USA
| | - Nathan Alexander
- Department of Chemistry, Center for Structural Biology, Vanderbilt University, Nashville TN, 37232, USA
| | - Jens Meiler
- Department of Chemistry, Center for Structural Biology, Vanderbilt University, Nashville TN, 37232, USA
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