1
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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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Burley SK, Berman HM, Chiu W, Dai W, Flatt JW, Hudson BP, Kaelber JT, Khare SD, Kulczyk AW, Lawson CL, Pintilie GD, Sali A, Vallat B, Westbrook JD, Young JY, Zardecki C. Electron microscopy holdings of the Protein Data Bank: the impact of the resolution revolution, new validation tools, and implications for the future. Biophys Rev 2022; 14:1281-1301. [PMID: 36474933 PMCID: PMC9715422 DOI: 10.1007/s12551-022-01013-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Accepted: 11/06/2022] [Indexed: 12/04/2022] Open
Abstract
As a discipline, structural biology has been transformed by the three-dimensional electron microscopy (3DEM) "Resolution Revolution" made possible by convergence of robust cryo-preservation of vitrified biological materials, sample handling systems, and measurement stages operating a liquid nitrogen temperature, improvements in electron optics that preserve phase information at the atomic level, direct electron detectors (DEDs), high-speed computing with graphics processing units, and rapid advances in data acquisition and processing software. 3DEM structure information (atomic coordinates and related metadata) are archived in the open-access Protein Data Bank (PDB), which currently holds more than 11,000 3DEM structures of proteins and nucleic acids, and their complexes with one another and small-molecule ligands (~ 6% of the archive). Underlying experimental data (3DEM density maps and related metadata) are stored in the Electron Microscopy Data Bank (EMDB), which currently holds more than 21,000 3DEM density maps. After describing the history of the PDB and the Worldwide Protein Data Bank (wwPDB) partnership, which jointly manages both the PDB and EMDB archives, this review examines the origins of the resolution revolution and analyzes its impact on structural biology viewed through the lens of PDB holdings. Six areas of focus exemplifying the impact of 3DEM across the biosciences are discussed in detail (icosahedral viruses, ribosomes, integral membrane proteins, SARS-CoV-2 spike proteins, cryogenic electron tomography, and integrative structure determination combining 3DEM with complementary biophysical measurement techniques), followed by a review of 3DEM structure validation by the wwPDB that underscores the importance of community engagement.
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Affiliation(s)
- Stephen K. Burley
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, 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
- 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 San Diego, La Jolla, CA 92093 USA
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, 174 Frelinghuysen Road, Piscataway, NJ 08854 USA
| | - Helen M. Berman
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, 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
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, 174 Frelinghuysen Road, Piscataway, NJ 08854 USA
| | - Wah Chiu
- Department of Bioengineering, Stanford University, Stanford, CA USA
- Division of CryoEM and Bioimaging, SSRL, SLAC National Accelerator Laboratory, Stanford University, Menlo Park, CA USA
| | - Wei Dai
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854 USA
- Department of Cell Biology and Neuroscience, Rutgers, The State University of New Jersey, Piscataway, NJ 08854 USA
| | - Justin W. Flatt
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, 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
| | - Brian P. Hudson
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, 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
| | - Jason T. Kaelber
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854 USA
| | - Sagar D. Khare
- 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
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, 174 Frelinghuysen Road, Piscataway, NJ 08854 USA
| | - Arkadiusz W. Kulczyk
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854 USA
- Department of Biochemistry and Microbiology, Rutgers, The State University of New Jersey, Piscataway, NJ 08901 USA
| | - Catherine L. Lawson
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, 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
| | | | - Andrej Sali
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, Quantitative Biosciences Institute, University of California San Francisco, San Francisco, CA 94158 USA
| | - Brinda Vallat
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, 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
- Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, NJ 08901 USA
| | - John D. Westbrook
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, 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
- Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, NJ 08901 USA
| | - Jasmine Y. Young
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, 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
| | - Christine Zardecki
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, 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
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3
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Burley SK, Bhikadiya C, Bi C, Bittrich S, Chao H, Chen L, Craig PA, Crichlow GV, Dalenberg K, Duarte JM, Dutta S, Fayazi M, Feng Z, Flatt JW, Ganesan SJ, Ghosh S, Goodsell DS, Green RK, Guranovic V, Henry J, Hudson BP, Khokhriakov I, Lawson CL, Liang Y, Lowe R, Peisach E, Persikova I, Piehl DW, Rose Y, Sali A, Segura J, Sekharan M, Shao C, Vallat B, Voigt M, Webb B, Westbrook JD, Whetstone S, Young JY, Zalevsky A, Zardecki C. RCSB Protein Data bank: Tools for visualizing and understanding biological macromolecules in 3D. Protein Sci 2022; 31:e4482. [PMID: 36281733 PMCID: PMC9667899 DOI: 10.1002/pro.4482] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 10/17/2022] [Accepted: 10/19/2022] [Indexed: 12/14/2022]
Abstract
Now in its 52nd year of continuous operations, the Protein Data Bank (PDB) is the premiere open-access global archive housing three-dimensional (3D) biomolecular structure data. It is jointly managed by the Worldwide Protein Data Bank (wwPDB) partnership. The Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB) is funded by the National Science Foundation, National Institutes of Health, and US Department of Energy and serves as the US data center for the wwPDB. RCSB PDB is also responsible for the security of PDB data in its role as wwPDB-designated Archive Keeper. Every year, RCSB PDB serves tens of thousands of depositors of 3D macromolecular structure data (coming from macromolecular crystallography, nuclear magnetic resonance spectroscopy, electron microscopy, and micro-electron diffraction). The RCSB PDB research-focused web portal (RCSB.org) makes PDB data available at no charge and without usage restrictions to many millions of PDB data consumers around the world. The RCSB PDB training, outreach, and education web portal (PDB101.RCSB.org) serves nearly 700 K educators, students, and members of the public worldwide. This invited Tools Issue contribution describes how RCSB PDB (i) is organized; (ii) works with wwPDB partners to process new depositions; (iii) serves as the wwPDB-designated Archive Keeper; (iv) enables exploration and 3D visualization of PDB data via RCSB.org; and (v) supports training, outreach, and education via PDB101.RCSB.org. New tools and features at RCSB.org are presented using examples drawn from high-resolution structural studies of proteins relevant to treatment of human cancers by targeting immune checkpoints.
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Affiliation(s)
- Stephen K. Burley
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA,Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA,Cancer Institute of New Jersey, Rutgers, The State University of New JerseyNew BrunswickNew JerseyUSA,Research Collaboratory for Structural Bioinformatics Protein Data BankSan Diego Supercomputer Center, University of CaliforniaLa JollaCaliforniaUSA,Department of Chemistry and Chemical Biology, RutgersThe State University of New JerseyPiscatawayNew JerseyUSA
| | - Charmi Bhikadiya
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA,Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Chunxiao Bi
- Research Collaboratory for Structural Bioinformatics Protein Data BankSan Diego Supercomputer Center, University of CaliforniaLa JollaCaliforniaUSA
| | - Sebastian Bittrich
- Research Collaboratory for Structural Bioinformatics Protein Data BankSan Diego Supercomputer Center, University of CaliforniaLa JollaCaliforniaUSA
| | - Henry Chao
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA,Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Li Chen
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA,Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Paul A. Craig
- School of Chemistry and Materials ScienceRochester Institute of TechnologyRochesterNew YorkUSA
| | - Gregg V. Crichlow
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA,Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Kenneth Dalenberg
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA,Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Jose M. Duarte
- Research Collaboratory for Structural Bioinformatics Protein Data BankSan Diego Supercomputer Center, University of CaliforniaLa JollaCaliforniaUSA
| | - Shuchismita Dutta
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA,Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA,Cancer Institute of New Jersey, Rutgers, The State University of New JerseyNew BrunswickNew JerseyUSA
| | - Maryam Fayazi
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA,Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Zukang Feng
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA,Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Justin W. Flatt
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA,Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Sai J. Ganesan
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Department of Bioengineering and Therapeutic SciencesQuantitative Biosciences Institute, University of CaliforniaSan FranciscoCaliforniaUSA,Research Collaboratory for Structural Bioinformatics Protein Data Bank, Department of Pharmaceutical ChemistryQuantitative Biosciences Institute, University of CaliforniaSan FranciscoCaliforniaUSA
| | - Sutapa Ghosh
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA,Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - David S. Goodsell
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA,Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA,Cancer Institute of New Jersey, Rutgers, The State University of New JerseyNew BrunswickNew JerseyUSA,Department of Integrative Structural and Computational BiologyThe Scripps Research InstituteLa JollaCaliforniaUSA
| | - Rachel Kramer Green
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA,Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Vladimir Guranovic
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA,Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Jeremy Henry
- Research Collaboratory for Structural Bioinformatics Protein Data BankSan Diego Supercomputer Center, University of CaliforniaLa JollaCaliforniaUSA
| | - Brian P. Hudson
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA,Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Igor Khokhriakov
- Research Collaboratory for Structural Bioinformatics Protein Data BankSan Diego Supercomputer Center, University of CaliforniaLa JollaCaliforniaUSA
| | - Catherine L. Lawson
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA,Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Yuhe Liang
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA,Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Robert Lowe
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA,Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Ezra Peisach
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA,Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Irina Persikova
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA,Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Dennis W. Piehl
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA,Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Yana Rose
- Research Collaboratory for Structural Bioinformatics Protein Data BankSan Diego Supercomputer Center, University of CaliforniaLa JollaCaliforniaUSA
| | - Andrej Sali
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Department of Bioengineering and Therapeutic SciencesQuantitative Biosciences Institute, University of CaliforniaSan FranciscoCaliforniaUSA,Research Collaboratory for Structural Bioinformatics Protein Data Bank, Department of Pharmaceutical ChemistryQuantitative Biosciences Institute, University of CaliforniaSan FranciscoCaliforniaUSA
| | - Joan Segura
- Research Collaboratory for Structural Bioinformatics Protein Data BankSan Diego Supercomputer Center, University of CaliforniaLa JollaCaliforniaUSA
| | - Monica Sekharan
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA,Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Chenghua Shao
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA,Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Brinda Vallat
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA,Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Maria Voigt
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA,Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Benjamin Webb
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Department of Bioengineering and Therapeutic SciencesQuantitative Biosciences Institute, University of CaliforniaSan FranciscoCaliforniaUSA,Research Collaboratory for Structural Bioinformatics Protein Data Bank, Department of Pharmaceutical ChemistryQuantitative Biosciences Institute, University of CaliforniaSan FranciscoCaliforniaUSA
| | - John D. Westbrook
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA,Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Shamara Whetstone
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA,Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Jasmine Y. Young
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA,Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Arthur Zalevsky
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Department of Bioengineering and Therapeutic SciencesQuantitative Biosciences Institute, University of CaliforniaSan FranciscoCaliforniaUSA,Research Collaboratory for Structural Bioinformatics Protein Data Bank, Department of Pharmaceutical ChemistryQuantitative Biosciences Institute, University of CaliforniaSan FranciscoCaliforniaUSA
| | - Christine Zardecki
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA,Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
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4
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Ullanat V, Kasukurthi N, Viswanath S. PrISM: Precision for Integrative Structural Models. Bioinformatics 2022; 38:3837-3839. [PMID: 35723541 DOI: 10.1093/bioinformatics/btac400] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Revised: 06/04/2022] [Accepted: 06/16/2022] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION A single precision value is currently reported for an integrative model. However, precision may vary for different regions of an integrative model owing to varying amounts of input information. RESULTS We develop PrISM (Precision for Integrative Structural Models), to efficiently identify high and low-precision regions for integrative models. AVAILABILITY PrISM is written in Python and available under the GNU General Public License v3.0 at https://github.com/isblab/prism; benchmark data used in this paper is available at doi:10.5281/zenodo.6241200. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Varun Ullanat
- National Center for Biological Sciences, Tata Institute of Fundamental Research, Bangalore, India
| | - Nikhil Kasukurthi
- National Center for Biological Sciences, Tata Institute of Fundamental Research, Bangalore, India
| | - Shruthi Viswanath
- National Center for Biological Sciences, Tata Institute of Fundamental Research, Bangalore, India
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5
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Devaurs D, Antunes DA, Borysik AJ. Computational Modeling of Molecular Structures Guided by Hydrogen-Exchange Data. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2022; 33:215-237. [PMID: 35077179 DOI: 10.1021/jasms.1c00328] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Data produced by hydrogen-exchange monitoring experiments have been used in structural studies of molecules for several decades. Despite uncertainties about the structural determinants of hydrogen exchange itself, such data have successfully helped guide the structural modeling of challenging molecular systems, such as membrane proteins or large macromolecular complexes. As hydrogen-exchange monitoring provides information on the dynamics of molecules in solution, it can complement other experimental techniques in so-called integrative modeling approaches. However, hydrogen-exchange data have often only been used to qualitatively assess molecular structures produced by computational modeling tools. In this paper, we look beyond qualitative approaches and survey the various paradigms under which hydrogen-exchange data have been used to quantitatively guide the computational modeling of molecular structures. Although numerous prediction models have been proposed to link molecular structure and hydrogen exchange, none of them has been widely accepted by the structural biology community. Here, we present as many hydrogen-exchange prediction models as we could find in the literature, with the aim of providing the first exhaustive list of its kind. From purely structure-based models to so-called fractional-population models or knowledge-based models, the field is quite vast. We aspire for this paper to become a resource for practitioners to gain a broader perspective on the field and guide research toward the definition of better prediction models. This will eventually improve synergies between hydrogen-exchange monitoring and molecular modeling.
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Affiliation(s)
- Didier Devaurs
- MRC Institute of Genetics and Cancer, University of Edinburgh, Edinburgh EH4 2XU, U.K
| | - Dinler A Antunes
- Department of Biology and Biochemistry, University of Houston, Houston, Texas 77005, United States
| | - Antoni J Borysik
- Department of Chemistry, King's College London, London SE1 1DB, U.K
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6
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Graziadei A, Rappsilber J. Leveraging crosslinking mass spectrometry in structural and cell biology. Structure 2021; 30:37-54. [PMID: 34895473 DOI: 10.1016/j.str.2021.11.007] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Revised: 10/11/2021] [Accepted: 11/17/2021] [Indexed: 12/18/2022]
Abstract
Crosslinking mass spectrometry (crosslinking-MS) is a versatile tool providing structural insights into protein conformation and protein-protein interactions. Its medium-resolution residue-residue distance restraints have been used to validate protein structures proposed by other methods and have helped derive models of protein complexes by integrative structural biology approaches. The use of crosslinking-MS in integrative approaches is underpinned by progress in estimating error rates in crosslinking-MS data and in combining these data with other information. The flexible and high-throughput nature of crosslinking-MS has allowed it to complement the ongoing resolution revolution in electron microscopy by providing system-wide residue-residue distance restraints, especially for flexible regions or systems. Here, we review how crosslinking-MS information has been leveraged in structural model validation and integrative modeling. Crosslinking-MS has also been a key technology for cell biology studies and structural systems biology where, in conjunction with cryoelectron tomography, it can provide structural and mechanistic insights directly in situ.
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Affiliation(s)
- Andrea Graziadei
- Bioanalytics, Institute of Biotechnology, Technische Universität Berlin, 13355 Berlin, Germany
| | - Juri Rappsilber
- Bioanalytics, Institute of Biotechnology, Technische Universität Berlin, 13355 Berlin, Germany; Wellcome Centre for Cell Biology, University of Edinburgh, Max Born Crescent, Edinburgh EH9 3BF, UK.
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7
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Vallat B, Webb B, Fayazi M, Voinea S, Tangmunarunkit H, Ganesan SJ, Lawson CL, Westbrook JD, Kesselman C, Sali A, Berman HM. New system for archiving integrative structures. Acta Crystallogr D Struct Biol 2021; 77:1486-1496. [PMID: 34866606 PMCID: PMC8647179 DOI: 10.1107/s2059798321010871] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Accepted: 10/19/2021] [Indexed: 11/30/2022] Open
Abstract
Structures of many complex biological assemblies are increasingly determined using integrative approaches, in which data from multiple experimental methods are combined. A standalone system, called PDB-Dev, has been developed for archiving integrative structures and making them publicly available. Here, the data standards and software tools that support PDB-Dev are described along with the new and updated components of the PDB-Dev data-collection, processing and archiving infrastructure. Following the FAIR (Findable, Accessible, Interoperable and Reusable) principles, PDB-Dev ensures that the results of integrative structure determinations are freely accessible to everyone.
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Affiliation(s)
- Brinda Vallat
- RCSB PDB, Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, New Jersey, 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, California, USA
| | - Maryam Fayazi
- RCSB PDB, Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, New Jersey, USA
| | - Serban Voinea
- Information Sciences Institute, Viterbi School of Engineering, University of Southern California, Los Angeles, California, USA
| | - Hongsuda Tangmunarunkit
- Information Sciences Institute, Viterbi School of Engineering, University of Southern California, Los Angeles, California, USA
| | - Sai J. Ganesan
- Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, and California Institute for Quantitative Biosciences, University of California at San Francisco, San Francisco, California, USA
| | - Catherine L. Lawson
- RCSB PDB, Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, New Jersey, USA
| | - John D. Westbrook
- RCSB PDB, Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, New Jersey, USA
| | - Carl Kesselman
- RCSB PDB, Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, New Jersey, 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, California, USA
| | - Helen M. Berman
- Department of Chemistry and Chemical Biology and Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, New Jersey, USA
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8
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Structure-based protein function prediction using graph convolutional networks. Nat Commun 2021; 12:3168. [PMID: 34039967 PMCID: PMC8155034 DOI: 10.1038/s41467-021-23303-9] [Citation(s) in RCA: 211] [Impact Index Per Article: 70.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2020] [Accepted: 04/22/2021] [Indexed: 02/04/2023] Open
Abstract
The rapid increase in the number of proteins in sequence databases and the diversity of their functions challenge computational approaches for automated function prediction. Here, we introduce DeepFRI, a Graph Convolutional Network for predicting protein functions by leveraging sequence features extracted from a protein language model and protein structures. It outperforms current leading methods and sequence-based Convolutional Neural Networks and scales to the size of current sequence repositories. Augmenting the training set of experimental structures with homology models allows us to significantly expand the number of predictable functions. DeepFRI has significant de-noising capability, with only a minor drop in performance when experimental structures are replaced by protein models. Class activation mapping allows function predictions at an unprecedented resolution, allowing site-specific annotations at the residue-level in an automated manner. We show the utility and high performance of our method by annotating structures from the PDB and SWISS-MODEL, making several new confident function predictions. DeepFRI is available as a webserver at https://beta.deepfri.flatironinstitute.org/ .
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9
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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: 39] [Impact Index Per Article: 13.0] [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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10
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Saltzberg DJ, Viswanath S, Echeverria I, Chemmama IE, Webb B, Sali A. Using Integrative Modeling Platform to compute, validate, and archive a model of a protein complex structure. Protein Sci 2020; 30:250-261. [PMID: 33166013 DOI: 10.1002/pro.3995] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Revised: 11/06/2020] [Accepted: 11/06/2020] [Indexed: 12/18/2022]
Abstract
Biology is advanced by producing structural models of biological systems, such as protein complexes. Some systems are recalcitrant to traditional structure determination methods. In such cases, it may still be possible to produce useful models by integrative structure determination that depends on simultaneous use of multiple types of data. An ensemble of models that are sufficiently consistent with the data is produced by a structural sampling method guided by a data-dependent scoring function. The variation in the ensemble of models quantified the uncertainty of the structure, generally resulting from the uncertainty in the input information and actual structural heterogeneity in the samples used to produce the data. Here, we describe how to generate, assess, and interpret ensembles of integrative structural models using our open source Integrative Modeling Platform program (https://integrativemodeling.org).
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Affiliation(s)
- Daniel J Saltzberg
- Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, and California Institute for Quantitative Biosciences, University of California, San Francisco, California, USA
| | - Shruthi Viswanath
- National Center for Biological Sciences, Tata Institute of Fundamental Research, Bangalore, India
| | - Ignacia Echeverria
- Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, and California Institute for Quantitative Biosciences, University of California, San Francisco, California, USA.,Department of Cellular and Molecular Pharmacology, University of California, San Francisco, California, USA
| | - Ilan E Chemmama
- Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, and California Institute for Quantitative Biosciences, University of California, San Francisco, California, USA
| | - Ben Webb
- Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, and California Institute for Quantitative Biosciences, University of California, San Francisco, California, USA
| | - Andrej Sali
- Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, and California Institute for Quantitative Biosciences, University of California, San Francisco, California, USA
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11
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Berman HM, Vallat B, Lawson CL. The data universe of structural biology. IUCRJ 2020; 7:630-638. [PMID: 32695409 PMCID: PMC7340255 DOI: 10.1107/s205225252000562x] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/20/2020] [Accepted: 04/21/2020] [Indexed: 05/05/2023]
Abstract
The Protein Data Bank (PDB) has grown from a small data resource for crystallographers to a worldwide resource serving structural biology. The history of the growth of the PDB and the role that the community has played in developing standards and policies are described. This article also illustrates how other biophysics communities are collaborating with the worldwide PDB to create a network of interoperating data resources. This network will expand the capabilities of structural biology and enable the determination and archiving of increasingly complex structures.
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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 and Bridge Institute, University of Southern California, Los Angeles, CA 90089, USA
| | - Brinda Vallat
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Catherine L. Lawson
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
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12
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Srivastava A, Tiwari SP, Miyashita O, Tama F. Integrative/Hybrid Modeling Approaches for Studying Biomolecules. J Mol Biol 2020; 432:2846-2860. [DOI: 10.1016/j.jmb.2020.01.039] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2019] [Revised: 01/20/2020] [Accepted: 01/24/2020] [Indexed: 12/12/2022]
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13
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Abriata LA, Lepore R, Dal Peraro M. About the need to make computational models of biological macromolecules available and discoverable. Bioinformatics 2020; 36:2952-2954. [DOI: 10.1093/bioinformatics/btaa086] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2019] [Revised: 01/13/2020] [Accepted: 02/06/2020] [Indexed: 12/19/2022] Open
Affiliation(s)
- Luciano A Abriata
- Laboratory for Biomolecular Modeling
- Protein Production and Structure Core Facility, School of Life Sciences, École Polytechnique Fédérale de Lausanne and Swiss Institute of Bioinformatics, Lausanne CH-1015, Switzerland
| | - Rosalba Lepore
- BSC-CNS Barcelona Supercomputing Center, Barcelona, Spain
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14
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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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15
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Kaptein R, Wagner G. Integrative methods in structural biology. JOURNAL OF BIOMOLECULAR NMR 2019; 73:261-263. [PMID: 31313058 DOI: 10.1007/s10858-019-00267-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Affiliation(s)
- Rob Kaptein
- Bijvoet Centre, Utrecht University, 3584 CH, Utrecht, The Netherlands.
| | - Gerhard Wagner
- Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA, 02115, USA
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