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Autin L, Goodsell DS, Viola I, Olson A. CellVis2: a conference on visualizing the molecular cell. Trends Biochem Sci 2024:S0968-0004(24)00079-3. [PMID: 38670884 DOI: 10.1016/j.tibs.2024.03.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2024] [Accepted: 03/21/2024] [Indexed: 04/28/2024]
Abstract
In January 2024, a targeted conference, 'CellVis2', was held at Scripps Research in La Jolla, USA, the second in a series designed to explore the promise, practices, roadblocks, and prospects of creating, visualizing, sharing, and communicating physical representations of entire biological cells at scales down to the atom.
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Affiliation(s)
| | | | - Ivan Viola
- King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
| | - Arthur Olson
- The Scripps Research Institute, La Jolla, CA, USA.
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2
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Goodsell DS, Dutta S, Hudson BP, Voigt M, Burley SK, Zardecki C. Folding paper models of biostructures for outreach and education. Patterns (N Y) 2024; 5:100931. [PMID: 38370120 PMCID: PMC10873190 DOI: 10.1016/j.patter.2024.100931] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
Molecular origami offers an offline way to explore the 3D structures of biology. Visit PDB101.rcsb.org to download free paper models of DNA, green fluorescent protein, viruses, and more.
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Affiliation(s)
- David S. Goodsell
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Rutgers Cancer Institute of New Jersey, New Brunswick, NJ 08901, USA
- The Scripps Research Institute, La Jolla, CA 92037, USA
| | - Shuchismita Dutta
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Rutgers Cancer Institute of New Jersey, New Brunswick, NJ 08901, USA
| | - Brian P. Hudson
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Maria Voigt
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Stephen K. Burley
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Rutgers Cancer Institute of New Jersey, New Brunswick, NJ 08901, USA
- Research Collaboratory for Structural Biology 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, Piscataway, NJ 08854, USA
| | - Christine Zardecki
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
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3
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Goodsell DS, Autin L. Integrative modeling of JCVI-Syn3A nucleoids with a modular approach. Curr Res Struct Biol 2023; 7:100121. [PMID: 38221989 PMCID: PMC10784680 DOI: 10.1016/j.crstbi.2023.100121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Revised: 12/15/2023] [Accepted: 12/18/2023] [Indexed: 01/16/2024] Open
Abstract
A lattice-based method is presented for creating 3D models of entire bacterial nucleoids integrating ultrastructural information cryoelectron tomography, genomic and proteomic data, and experimental atomic structures of biomolecules and assemblies. The method is used to generate models of the minimal genome bacterium JCVI-Syn3A, producing a series of models that test hypotheses about transcription, condensation, and overall distribution of the genome. Lattice models are also used to generate atomic models of an entire JCVI-Syn3A cell.
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Affiliation(s)
- David S. Goodsell
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, 92037, USA
- Research Collaboratory for Structural Bioinformatics Protein Data and Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ, 08854, USA
| | - Ludovic Autin
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, 92037, USA
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4
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Liu D, Riggi M, Lee HO, Currie SL, Goodsell DS, Iwasa JH, Rog O. Depicting a cellular space occupied by condensates. Mol Biol Cell 2023; 34:tp2. [PMID: 37590933 PMCID: PMC10551707 DOI: 10.1091/mbc.e22-11-0519] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Revised: 06/20/2023] [Accepted: 07/10/2023] [Indexed: 08/19/2023] Open
Abstract
Condensates have emerged as a new way to understand how cells are organized, and have been invoked to play crucial roles in essentially all cellular processes. In this view, the cell is occupied by numerous assemblies, each composed of member proteins and nucleic acids that preferentially interact with each other. However, available visual representations of condensates fail to communicate the growing body of knowledge about how condensates form and function. The resulting focus on only a subset of the potential implications of condensates can skew interpretations of results and hinder the generation of new hypotheses. Here we summarize the discussion from a workshop that brought together cell biologists, visualization and computation specialists, and other experts who specialize in thinking about space and ways to represent it. We place the recent advances in condensate research in a historical perspective that describes evolving views of the cell; highlight different attributes of condensates that are not well-served by current visual conventions; and survey potential approaches to overcome these challenges. An important theme of these discussions is that the new understanding on the roles of condensates exposes broader challenges in visual representations that apply to cell biological research more generally.
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Affiliation(s)
- Daniel Liu
- Historisches Seminar, Abt. Wissenschaftsgeschichte, Ludwig-Maximilians-Universität München, Geschwister-Scholl-Platz 1, 80539 Munich, Germany
| | | | - Hyun O. Lee
- Department of Biochemistry, University of Toronto, Toronto, ON M5S 1A8, Canada
| | - Simon L. Currie
- Department of Biophysics, UT Southwestern Medical Center, Dallas, TX 75390
| | - David S. Goodsell
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037
- Institute for Quantitative Biomedicine and Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854
| | | | - Ofer Rog
- School of Biological Sciences and Center for Cell and Genome Science, University of Utah, Salt Lake City, UT 84112
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5
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Garrison LA, Goodsell DS, Bruckner S, Campbell B, Samsel F. Changing Aesthetics in Biomolecular Graphics. IEEE Comput Graph Appl 2023; 43:94-101. [PMID: 37195829 PMCID: PMC10288201 DOI: 10.1109/mcg.2023.3250680] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/19/2023]
Abstract
Aesthetics for the visualization of biomolecular structures have evolved over the years according to technological advances, user needs, and modes of dissemination. In this article, we explore the goals, challenges, and solutions that have shaped the current landscape of biomolecular imagery from the overlapping perspectives of computer science, structural biology, and biomedical illustration. We discuss changing approaches to rendering, color, human-computer interface, and narrative in the development and presentation of biomolecular graphics. With this historical perspective on the evolving styles and trends in each of these areas, we identify opportunities and challenges for future aesthetics in biomolecular graphics that encourage continued collaboration from multiple intersecting fields.
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6
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Goodsell DS, Lasker K. Integrative visualization of the molecular structure of a cellular microdomain. Protein Sci 2023; 32:e4577. [PMID: 36700303 PMCID: PMC9926476 DOI: 10.1002/pro.4577] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 01/16/2023] [Accepted: 01/23/2023] [Indexed: 01/27/2023]
Abstract
An integrative approach to visualization is used to create a visual snapshot of the structural biology of the polar microdomain of Caulobacter crescentus. The visualization is based on the current state of molecular and cellular knowledge of the microdomain and its cellular context. The collaborative process of researching and executing the visualization has identified aspects that are well determined and areas that require further study. The visualization is useful for dissemination, education, and outreach, and the study lays the groundwork for future 3D modeling and simulation of this well-studied example of a cellular condensate.
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Affiliation(s)
- David S Goodsell
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, California, USA.,Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, New Jersey, USA.,Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, New Jersey, USA.,Rutgers Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, New Jersey, USA
| | - Keren Lasker
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, California, USA
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7
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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 S, 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 (RCSB.org): delivery of experimentally-determined PDB structures alongside one million computed structure models of proteins from artificial intelligence/machine learning. Nucleic Acids Res 2023; 51:D488-D508. [PMID: 36420884 PMCID: PMC9825554 DOI: 10.1093/nar/gkac1077] [Citation(s) in RCA: 119] [Impact Index Per Article: 119.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 10/17/2022] [Accepted: 11/02/2022] [Indexed: 11/27/2022] Open
Abstract
The Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB), founding member of the Worldwide Protein Data Bank (wwPDB), is the US data center for the open-access PDB archive. As wwPDB-designated Archive Keeper, RCSB PDB is also responsible for PDB data security. Annually, RCSB PDB serves >10 000 depositors of three-dimensional (3D) biostructures working on all permanently inhabited continents. RCSB PDB delivers data from its research-focused RCSB.org web portal to many millions of PDB data consumers based in virtually every United Nations-recognized country, territory, etc. This Database Issue contribution describes upgrades to the research-focused RCSB.org web portal that created a one-stop-shop for open access to ∼200 000 experimentally-determined PDB structures of biological macromolecules alongside >1 000 000 incorporated Computed Structure Models (CSMs) predicted using artificial intelligence/machine learning methods. RCSB.org is a 'living data resource.' Every PDB structure and CSM is integrated weekly with related functional annotations from external biodata resources, providing up-to-date information for the entire corpus of 3D biostructure data freely available from RCSB.org with no usage limitations. Within RCSB.org, PDB structures and the CSMs are clearly identified as to their provenance and reliability. Both are fully searchable, and can be analyzed and visualized using the full complement of RCSB.org web portal capabilities.
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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
- Rutgers Cancer Institute of New Jersey, New Brunswick, NJ 08901, USA
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California San Diego, La Jolla, CA 92093, USA
| | - Charmi Bhikadiya
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California San Diego, La Jolla, CA 92093, USA
| | - Chunxiao Bi
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California San Diego, La Jolla, CA 92093, USA
| | - Sebastian Bittrich
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California San Diego, La Jolla, CA 92093, USA
| | - Henry Chao
- 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
| | - Li Chen
- 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
| | - Paul A Craig
- School of Chemistry and Materials Science, Rochester Institute of Technology, Rochester, NY 14623, USA
| | - Gregg V Crichlow
- 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
| | - Kenneth Dalenberg
- 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
| | - Jose M Duarte
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California San Diego, La Jolla, CA 92093, USA
| | - Shuchismita Dutta
- 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
- Rutgers Cancer Institute of New Jersey, New Brunswick, NJ 08901, USA
| | - Maryam Fayazi
- 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
| | - Zukang Feng
- 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
| | - 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
| | - Sai Ganesan
- 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
| | - Sutapa Ghosh
- 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
| | - David S Goodsell
- 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
- Rutgers Cancer Institute of New Jersey, New Brunswick, NJ 08901, USA
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA
| | - Rachel Kramer Green
- 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
| | - Vladimir Guranovic
- 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
| | - Jeremy Henry
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California San Diego, La Jolla, CA 92093, 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
| | - Igor Khokhriakov
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California San Diego, La Jolla, CA 92093, 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
| | - Yuhe Liang
- 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
| | - Robert Lowe
- 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
| | - Ezra Peisach
- 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
| | - Irina Persikova
- 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
| | - Dennis W Piehl
- 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
| | - Yana Rose
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California San Diego, La Jolla, CA 92093, 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
| | - Joan Segura
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California San Diego, La Jolla, CA 92093, USA
| | - Monica Sekharan
- 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
| | - Chenghua Shao
- 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
| | - 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
| | - Maria Voigt
- 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
| | - Ben Webb
- 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
| | - 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
- Rutgers Cancer Institute of New Jersey, New Brunswick, NJ 08901, USA
| | - Shamara Whetstone
- 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
| | - 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
| | - Arthur Zalevsky
- 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
| | - 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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8
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Holcomb M, Chang Y, Goodsell DS, Forli S. Evaluation of AlphaFold2 structures as docking targets. Protein Sci 2023; 32:e4530. [PMID: 36479776 PMCID: PMC9794023 DOI: 10.1002/pro.4530] [Citation(s) in RCA: 21] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 11/28/2022] [Accepted: 12/01/2022] [Indexed: 12/12/2022]
Abstract
AlphaFold2 is a promising new tool for researchers to predict protein structures and generate high-quality models, with low backbone and global root-mean-square deviation (RMSD) when compared with experimental structures. However, it is unclear if the structures predicted by AlphaFold2 will be valuable targets of docking. To address this question, we redocked ligands in the PDBbind datasets against the experimental co-crystallized receptor structures and against the AlphaFold2 structures using AutoDock-GPU. We find that the quality measure provided during structure prediction is not a good predictor of docking performance, despite accurately reflecting the quality of the alpha carbon alignment with experimental structures. Removing low-confidence regions of the predicted structure and making side chains flexible improves the docking outcomes. Overall, despite high-quality prediction of backbone conformation, fine structural details limit the naive application of AlphaFold2 models as docking targets.
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Affiliation(s)
- Matthew Holcomb
- Department of Integrative Structural and Computational BiologyThe Scripps Research InstituteLa JollaCaliforniaUSA
| | - Ya‐Ting Chang
- Department of Integrative Structural and Computational BiologyThe Scripps Research InstituteLa JollaCaliforniaUSA
| | - David S. Goodsell
- Department of Integrative Structural and Computational BiologyThe Scripps Research InstituteLa JollaCaliforniaUSA,Research Collaboratory for Structural Bioinformatics Protein Data Bank, RutgersThe State University of New JerseyPiscatawayNew JerseyUSA,Institute for Quantitative Biomedicine, RutgersThe State University of New JerseyPiscatawayNew JerseyUSA,Rutgers Cancer Institute of New Jersey, RutgersThe State University of New JerseyNew BrunswickNew JerseyUSA
| | - Stefano Forli
- Department of Integrative Structural and Computational BiologyThe Scripps Research InstituteLa JollaCaliforniaUSA
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9
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Lyall K, Iwasa JH, Goodsell DS, Holt L. Communicating science through visual means. Trends Biochem Sci 2023; 48:2-4. [PMID: 36563656 DOI: 10.1016/j.tibs.2022.08.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Accepted: 08/18/2022] [Indexed: 12/27/2022]
Affiliation(s)
- Kip Lyall
- Illustration & Design Program Manager, Cell Press, Cambridge, MA, USA.
| | | | - David S Goodsell
- The Scripps Research Institute, La Jolla, CA, USA; RCSB Protein Data Bank.
| | - Liam Holt
- New York University School of Medicine, Institute for Systems Genetics, New York, NY, USA; New York University Tandon School of Engineering, Department of Biomolecular Engineering, Brooklyn, NY, USA.
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10
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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11
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Abstract
Data from genomics, proteomics, structural biology and cryo-electron microscopy are integrated into a structural illustration of a cross section through an entire JCVI-syn3.0 minimal cell. The illustration is designed with several goals: to inspire excitement in science, to depict the underlying scientific results accurately, and to be feasible in traditional media. Design choices to achieve these goals include reduction of visual complexity with simplified representations, use of orthographic projection to retain scale relationships, and an approach to color that highlights functional compartments of the cell. Given that this simple cell provides an attractive laboratory for exploring the central processes needed for life, several functional narratives are included in the illustration, including division of the cell and the first depiction of an entire cellular proteome. The illustration lays the foundation for 3D molecular modeling of this cell.
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Affiliation(s)
- David S. Goodsell
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA92037, USA
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ08854, USA
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12
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Zardecki C, Goodsell DS, Dutta S, Voigt M, Burley SK. PDB‐101: Molecular Explorations through Biology and Medicine. FASEB J 2022. [DOI: 10.1096/fasebj.2022.36.s1.r4952] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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13
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Lubin JH, Zardecki C, Dolan EM, Lu C, Shen Z, Dutta S, Westbrook JD, Hudson BP, Goodsell DS, Williams JK, Voigt M, Sarma V, Xie L, Venkatachalam T, Arnold S, Alfaro Alvarado LH, Catalfano K, Khan A, McCarthy E, Staggers S, Tinsley B, Trudeau A, Singh J, Whitmore L, Zheng H, Benedek M, Currier J, Dresel M, Duvvuru A, Dyszel B, Fingar E, Hennen EM, Kirsch M, Khan AA, Labrie‐Cleary C, Laporte S, Lenkeit E, Martin K, Orellana M, Ortiz‐Alvarez de la Campa M, Paredes I, Wheeler B, Rupert A, Sam A, See K, Soto Zapata S, Craig PA, Hall BL, Jiang J, Koeppe JR, Mills SA, Pikaart MJ, Roberts R, Bromberg Y, Hoyer JS, Duffy S, Tischfield J, Ruiz FX, Arnold E, Baum J, Sandberg J, Brannigan G, Khare SD, Burley SK. Evolution of the SARS-CoV-2 proteome in three dimensions (3D) during the first 6 months of the COVID-19 pandemic. Proteins 2022; 90:1054-1080. [PMID: 34580920 PMCID: PMC8661935 DOI: 10.1002/prot.26250] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Revised: 08/26/2021] [Accepted: 09/16/2021] [Indexed: 01/18/2023]
Abstract
Understanding the molecular evolution of the SARS-CoV-2 virus as it continues to spread in communities around the globe is important for mitigation and future pandemic preparedness. Three-dimensional structures of SARS-CoV-2 proteins and those of other coronavirusess archived in the Protein Data Bank were used to analyze viral proteome evolution during the first 6 months of the COVID-19 pandemic. Analyses of spatial locations, chemical properties, and structural and energetic impacts of the observed amino acid changes in >48 000 viral isolates revealed how each one of 29 viral proteins have undergone amino acid changes. Catalytic residues in active sites and binding residues in protein-protein interfaces showed modest, but significant, numbers of substitutions, highlighting the mutational robustness of the viral proteome. Energetics calculations showed that the impact of substitutions on the thermodynamic stability of the proteome follows a universal bi-Gaussian distribution. Detailed results are presented for potential drug discovery targets and the four structural proteins that comprise the virion, highlighting substitutions with the potential to impact protein structure, enzyme activity, and protein-protein and protein-nucleic acid interfaces. Characterizing the evolution of the virus in three dimensions provides testable insights into viral protein function and should aid in structure-based drug discovery efforts as well as the prospective identification of amino acid substitutions with potential for drug resistance.
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Affiliation(s)
- Joseph H. Lubin
- Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Department of Chemistry and Chemical BiologyRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Christine Zardecki
- Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Research Collaboratory for Structural Bioinformatics Protein Data BankRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Elliott M. Dolan
- Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Department of Chemistry and Chemical BiologyRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Changpeng Lu
- Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Zhuofan Shen
- Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Department of Chemistry and Chemical BiologyRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Shuchismita Dutta
- Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Research Collaboratory for Structural Bioinformatics Protein Data BankRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Rutgers Cancer Institute of New Jersey, Robert Wood Johnson Medical School, Rutgers, The State University of New JerseyNew BrunswickNew JerseyUSA
| | - John D. Westbrook
- Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Research Collaboratory for Structural Bioinformatics Protein Data BankRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Rutgers Cancer Institute of New Jersey, Robert Wood Johnson Medical School, Rutgers, The State University of New JerseyNew BrunswickNew JerseyUSA
| | - Brian P. Hudson
- Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Research Collaboratory for Structural Bioinformatics Protein Data BankRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - David S. Goodsell
- Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Research Collaboratory for Structural Bioinformatics Protein Data BankRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Rutgers Cancer Institute of New Jersey, Robert Wood Johnson Medical School, Rutgers, The State University of New JerseyNew BrunswickNew JerseyUSA
- The Scripps Research InstituteLa JollaCaliforniaUSA
| | - Jonathan K. Williams
- Department of Chemistry and Chemical BiologyRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Maria Voigt
- Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Research Collaboratory for Structural Bioinformatics Protein Data BankRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Vidur Sarma
- Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Lingjun Xie
- Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Department of Chemistry and Chemical BiologyRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Thejasvi Venkatachalam
- Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Steven Arnold
- Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | | | | | - Aaliyah Khan
- University of Maryland Baltimore CountyBaltimoreMarylandUSA
| | | | | | | | | | | | | | - Helen Zheng
- Watchung Hills Regional High SchoolWarrenNew JerseyUSA
| | | | | | - Mark Dresel
- Department of Chemistry and Chemical BiologyRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | | | | | | | | | | | | | | | | | - Evan Lenkeit
- Department of Chemistry and Chemical BiologyRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | | | | | | | | | | | | | - Andrew Sam
- Department of Chemistry and Chemical BiologyRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Katherine See
- Rochester Institute of TechnologyRochesterNew YorkUSA
| | | | - Paul A. Craig
- Rochester Institute of TechnologyRochesterNew YorkUSA
| | | | - Jennifer Jiang
- Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | | | | | | | | | - Yana Bromberg
- Department of Biochemistry and MicrobiologyRutgers, The State University of New JerseyNew BrunswickNew JerseyUSA
| | - J. Steen Hoyer
- Department of Ecology, Evolution and Natural Resources, School of Environmental and Biological SciencesRutgers, The State University of New JerseyNew BrunswickNew JerseyUSA
| | - Siobain Duffy
- Department of Ecology, Evolution and Natural Resources, School of Environmental and Biological SciencesRutgers, The State University of New JerseyNew BrunswickNew JerseyUSA
| | - Jay Tischfield
- Department of GeneticsRutgers, The State University of New Jersey, and Human Genetics Institute of New JerseyPiscatawayNew JerseyUSA
| | - Francesc X. Ruiz
- Center for Advanced Biotechnology and MedicineRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Eddy Arnold
- Department of Chemistry and Chemical BiologyRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Center for Advanced Biotechnology and MedicineRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Jean Baum
- Department of Chemistry and Chemical BiologyRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Jesse Sandberg
- Center for Computational and Integrative BiologyRutgers, The State University of New JerseyCamdenNew JerseyUSA
| | - Grace Brannigan
- Center for Computational and Integrative BiologyRutgers, The State University of New JerseyCamdenNew JerseyUSA
- Department of PhysicsRutgers, The State University of New JerseyCamdenNew JerseyUSA
| | - Sagar D. Khare
- Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Department of Chemistry and Chemical BiologyRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Rutgers Cancer Institute of New Jersey, Robert Wood Johnson Medical School, Rutgers, The State University of New JerseyNew BrunswickNew JerseyUSA
| | - Stephen K. Burley
- Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Department of Chemistry and Chemical BiologyRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Research Collaboratory for Structural Bioinformatics Protein Data BankRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Rutgers Cancer Institute of New Jersey, Robert Wood Johnson Medical School, Rutgers, The State University of New JerseyNew BrunswickNew JerseyUSA
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer CenterUniversity of CaliforniaSan Diego, La JollaCaliforniaUSA
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14
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Maritan M, Autin L, Karr J, Covert MW, Olson AJ, Goodsell DS. Building Structural Models of a Whole Mycoplasma Cell. J Mol Biol 2022; 434:167351. [PMID: 34774566 PMCID: PMC8752489 DOI: 10.1016/j.jmb.2021.167351] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Revised: 11/04/2021] [Accepted: 11/05/2021] [Indexed: 02/01/2023]
Abstract
Building structural models of entire cells has been a long-standing cross-discipline challenge for the research community, as it requires an unprecedented level of integration between multiple sources of biological data and enhanced methods for computational modeling and visualization. Here, we present the first 3D structural models of an entire Mycoplasma genitalium (MG) cell, built using the CellPACK suite of computational modeling tools. Our model recapitulates the data described in recent whole-cell system biology simulations and provides a structural representation for all MG proteins, DNA and RNA molecules, obtained by combining experimental and homology-modeled structures and lattice-based models of the genome. We establish a framework for gathering, curating and evaluating these structures, exposing current weaknesses of modeling methods and the boundaries of MG structural knowledge, and visualization methods to explore functional characteristics of the genome and proteome. We compare two approaches for data gathering, a manually-curated workflow and an automated workflow that uses homologous structures, both of which are appropriate for the analysis of mesoscale properties such as crowding and volume occupancy. Analysis of model quality provides estimates of the regularization that will be required when these models are used as starting points for atomic molecular dynamics simulations.
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Affiliation(s)
- Martina Maritan
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, 92037 USA. https://twitter.com/MartinaMaritan
| | - Ludovic Autin
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, 92037 USA. https://twitter.com/grinche
| | - Jonathan Karr
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Markus W Covert
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | - Arthur J Olson
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, 92037 USA
| | - David S Goodsell
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, 92037 USA; RCSB Protein Data Bank and Institute for Quantitative Biomedicine, Rutgers, the State University of New Jersey, Piscataway, NJ 08854, USA.
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15
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Goodsell DS, Burley SK. RCSB Protein Data Bank resources for structure-facilitated design of mRNA vaccines for existing and emerging viral pathogens. Structure 2022; 30:55-68.e2. [PMID: 34739839 PMCID: PMC8567414 DOI: 10.1016/j.str.2021.10.008] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Revised: 09/17/2021] [Accepted: 10/14/2021] [Indexed: 01/11/2023]
Abstract
Structural biologists provide direct insights into the molecular bases of human health and disease. The open-access Protein Data Bank (PDB) stores and delivers three-dimensional (3D) biostructure data that facilitate discovery and development of therapeutic agents and diagnostic tools. We are in the midst of a revolution in vaccinology. Non-infectious mRNA vaccines have been proven during the coronavirus disease 2019 (COVID-19) pandemic. This new technology underpins nimble discovery and clinical development platforms that use knowledge of 3D viral protein structures for societal benefit. The RCSB PDB supports vaccine designers through expert biocuration and rigorous validation of 3D structures; open-access dissemination of structure information; and search, visualization, and analysis tools for structure-guided design efforts. This resource article examines the structural biology underpinning the success of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) mRNA vaccines and enumerates some of the many protein structures in the PDB archive that could guide design of new countermeasures against existing and emerging viral pathogens.
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Affiliation(s)
- David S Goodsell
- RCSB Protein Data Bank and Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA; Rutgers Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, NJ 08903, USA; Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA
| | - Stephen K Burley
- RCSB Protein Data Bank and Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA; Rutgers Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, NJ 08903, USA; Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California, San Diego, CA 92093, USA; Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA.
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16
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Burley SK, Bhikadiya C, Bi C, Bittrich S, Chen L, Crichlow GV, Duarte JM, Dutta S, Fayazi M, Feng Z, Flatt JW, Ganesan SJ, Goodsell DS, Ghosh S, Kramer Green R, Guranovic V, Henry J, Hudson BP, 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, Westbrook JD, Whetstone S, Young JY, Zardecki C. RCSB Protein Data Bank: Celebrating 50 years of the PDB with new tools for understanding and visualizing biological macromolecules in 3D. Protein Sci 2022; 31:187-208. [PMID: 34676613 PMCID: PMC8740825 DOI: 10.1002/pro.4213] [Citation(s) in RCA: 63] [Impact Index Per Article: 31.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Revised: 10/12/2021] [Accepted: 10/12/2021] [Indexed: 01/03/2023]
Abstract
The Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB), funded by the US National Science Foundation, National Institutes of Health, and Department of Energy, has served structural biologists and Protein Data Bank (PDB) data consumers worldwide since 1999. RCSB PDB, a founding member of the Worldwide Protein Data Bank (wwPDB) partnership, is the US data center for the global PDB archive housing biomolecular structure data. RCSB PDB is also responsible for the security of PDB data, as the wwPDB-designated Archive Keeper. Annually, RCSB PDB serves tens of thousands of three-dimensional (3D) macromolecular structure data depositors (using macromolecular crystallography, nuclear magnetic resonance spectroscopy, electron microscopy, and micro-electron diffraction) from all inhabited continents. RCSB PDB makes PDB data available from its research-focused RCSB.org web portal at no charge and without usage restrictions to millions of PDB data consumers working in every nation and territory worldwide. In addition, RCSB PDB operates an outreach and education PDB101.RCSB.org web portal that was used by more than 800,000 educators, students, and members of the public during calendar year 2020. This invited Tools Issue contribution describes (i) how the archive is growing and evolving as new experimental methods generate ever larger and more complex biomolecular structures; (ii) the importance of data standards and data remediation in effective management of the archive and facile integration with more than 50 external data resources; and (iii) new tools and features for 3D structure analysis and visualization made available during the past year via the RCSB.org web portal.
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Affiliation(s)
- Stephen K. Burley
- Research Collaboratory for Structural Bioinformatics Protein Data BankRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Institute for Quantitative BiomedicineRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Cancer Institute of New JerseyRutgers, The State University of New JerseyNew BrunswickNew JerseyUSA
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer CenterUniversity of CaliforniaLa JollaCaliforniaUSA
- Department of Chemistry and Chemical BiologyRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Charmi Bhikadiya
- Research Collaboratory for Structural Bioinformatics Protein Data BankRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Institute for Quantitative BiomedicineRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Chunxiao Bi
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer CenterUniversity of CaliforniaLa JollaCaliforniaUSA
| | - Sebastian Bittrich
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer CenterUniversity of CaliforniaLa JollaCaliforniaUSA
| | - Li Chen
- Research Collaboratory for Structural Bioinformatics Protein Data BankRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Institute for Quantitative BiomedicineRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Gregg V. Crichlow
- Research Collaboratory for Structural Bioinformatics Protein Data BankRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Institute for Quantitative BiomedicineRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Jose M. Duarte
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer CenterUniversity of CaliforniaLa JollaCaliforniaUSA
| | - Shuchismita Dutta
- Research Collaboratory for Structural Bioinformatics Protein Data BankRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Institute for Quantitative BiomedicineRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Cancer Institute of New JerseyRutgers, The State University of New JerseyNew BrunswickNew JerseyUSA
| | - Maryam Fayazi
- Research Collaboratory for Structural Bioinformatics Protein Data BankRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Institute for Quantitative BiomedicineRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Zukang Feng
- Research Collaboratory for Structural Bioinformatics Protein Data BankRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Institute for Quantitative BiomedicineRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Justin W. Flatt
- Research Collaboratory for Structural Bioinformatics Protein Data BankRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Institute for Quantitative BiomedicineRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Sai J. Ganesan
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, Quantitative Biosciences InstituteUniversity of CaliforniaSan FranciscoCaliforniaUSA
| | - David S. Goodsell
- Research Collaboratory for Structural Bioinformatics Protein Data BankRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Institute for Quantitative BiomedicineRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Cancer Institute of New JerseyRutgers, The State University of New JerseyNew BrunswickNew JerseyUSA
- Department of Integrative Structural and Computational BiologyThe Scripps Research InstituteLa JollaCaliforniaUSA
| | - Sutapa Ghosh
- Research Collaboratory for Structural Bioinformatics Protein Data BankRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Institute for Quantitative BiomedicineRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Rachel Kramer Green
- Research Collaboratory for Structural Bioinformatics Protein Data BankRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Institute for Quantitative BiomedicineRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Vladimir Guranovic
- Research Collaboratory for Structural Bioinformatics Protein Data BankRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Institute for Quantitative BiomedicineRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Jeremy Henry
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer CenterUniversity of CaliforniaLa JollaCaliforniaUSA
| | - Brian P. Hudson
- Research Collaboratory for Structural Bioinformatics Protein Data BankRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Institute for Quantitative BiomedicineRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Catherine L. Lawson
- Research Collaboratory for Structural Bioinformatics Protein Data BankRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Institute for Quantitative BiomedicineRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Yuhe Liang
- Research Collaboratory for Structural Bioinformatics Protein Data BankRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Institute for Quantitative BiomedicineRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Robert Lowe
- Research Collaboratory for Structural Bioinformatics Protein Data BankRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Institute for Quantitative BiomedicineRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Ezra Peisach
- Research Collaboratory for Structural Bioinformatics Protein Data BankRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Institute for Quantitative BiomedicineRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Irina Persikova
- Research Collaboratory for Structural Bioinformatics Protein Data BankRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Institute for Quantitative BiomedicineRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Dennis W. Piehl
- Research Collaboratory for Structural Bioinformatics Protein Data BankRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Institute for Quantitative BiomedicineRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Yana Rose
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer CenterUniversity of CaliforniaLa JollaCaliforniaUSA
| | - Andrej Sali
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, Quantitative Biosciences InstituteUniversity of CaliforniaSan FranciscoCaliforniaUSA
| | - Joan Segura
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer CenterUniversity of CaliforniaLa JollaCaliforniaUSA
| | - Monica Sekharan
- Research Collaboratory for Structural Bioinformatics Protein Data BankRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Institute for Quantitative BiomedicineRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Chenghua Shao
- Research Collaboratory for Structural Bioinformatics Protein Data BankRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Institute for Quantitative BiomedicineRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Brinda Vallat
- Research Collaboratory for Structural Bioinformatics Protein Data BankRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Institute for Quantitative BiomedicineRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Maria Voigt
- Research Collaboratory for Structural Bioinformatics Protein Data BankRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Institute for Quantitative BiomedicineRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - John D. Westbrook
- Research Collaboratory for Structural Bioinformatics Protein Data BankRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Institute for Quantitative BiomedicineRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Cancer Institute of New JerseyRutgers, The State University of New JerseyNew BrunswickNew JerseyUSA
| | - Shamara Whetstone
- Research Collaboratory for Structural Bioinformatics Protein Data BankRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Institute for Quantitative BiomedicineRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Jasmine Y. Young
- Research Collaboratory for Structural Bioinformatics Protein Data BankRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Institute for Quantitative BiomedicineRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Christine Zardecki
- Research Collaboratory for Structural Bioinformatics Protein Data BankRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Institute for Quantitative BiomedicineRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
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17
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Goodsell DS, Dutta S, Voigt M, Zardecki C, Burley SK. Molecular explorations of cancer biology and therapeutics at PDB-101. Oncogene 2022; 41:4333-4335. [PMID: 35948649 PMCID: PMC9364277 DOI: 10.1038/s41388-022-02424-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Revised: 07/19/2022] [Accepted: 07/21/2022] [Indexed: 01/29/2023]
Affiliation(s)
- David S. Goodsell
- grid.430387.b0000 0004 1936 8796Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854 USA ,grid.430387.b0000 0004 1936 8796Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854 USA ,grid.430387.b0000 0004 1936 8796Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, NJ 08901 USA ,grid.214007.00000000122199231Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037 USA
| | - Shuchismita Dutta
- grid.430387.b0000 0004 1936 8796Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854 USA ,grid.430387.b0000 0004 1936 8796Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854 USA ,grid.430387.b0000 0004 1936 8796Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, NJ 08901 USA
| | - Maria Voigt
- grid.430387.b0000 0004 1936 8796Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854 USA ,grid.430387.b0000 0004 1936 8796Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854 USA
| | - Christine Zardecki
- grid.430387.b0000 0004 1936 8796Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854 USA ,grid.430387.b0000 0004 1936 8796Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854 USA
| | - Stephen K. Burley
- grid.430387.b0000 0004 1936 8796Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854 USA ,grid.430387.b0000 0004 1936 8796Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854 USA ,grid.430387.b0000 0004 1936 8796Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, NJ 08901 USA ,grid.266102.10000 0001 2297 6811Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California, La Jolla, CA 92093 USA ,grid.430387.b0000 0004 1936 8796Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ 08854 USA
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18
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Zardecki C, Dutta S, Goodsell DS, Lowe R, Voigt M, Burley SK. PDB-101: Educational resources supporting molecular explorations through biology and medicine. Protein Sci 2022; 31:129-140. [PMID: 34601771 PMCID: PMC8740840 DOI: 10.1002/pro.4200] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Revised: 09/24/2021] [Accepted: 09/28/2021] [Indexed: 01/03/2023]
Abstract
The Protein Data Bank (PDB) archive is a rich source of information in the form of atomic-level three-dimensional (3D) structures of biomolecules experimentally determined using macromolecular crystallography, nuclear magnetic resonance (NMR) spectroscopy, and electron microscopy (3DEM). Originally established in 1971 as a resource for protein crystallographers to freely exchange data, today PDB data drive research and education across scientific disciplines. In 2011, the online portal PDB-101 was launched to support teachers, students, and the general public in PDB archive exploration (pdb101.rcsb.org). Maintained by the Research Collaboratory for Structural Bioinformatics PDB, PDB-101 aims to help train the next generation of PDB users and to promote the overall importance of structural biology and protein science to nonexperts. Regularly published features include the highly popular Molecule of the Month series, 3D model activities, molecular animation videos, and educational curricula. Materials are organized into various categories (Health and Disease, Molecules of Life, Biotech and Nanotech, and Structures and Structure Determination) and searchable by keyword. A biennial health focus frames new resource creation and provides topics for annual video challenges for high school students. Web analytics document that PDB-101 materials relating to fundamental topics (e.g., hemoglobin, catalase) are highly accessed year-on-year. In addition, PDB-101 materials created in response to topical health matters (e.g., Zika, measles, coronavirus) are well received. PDB-101 shows how learning about the diverse shapes and functions of PDB structures promotes understanding of all aspects of biology, from the central dogma of biology to health and disease to biological energy.
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Affiliation(s)
- Christine Zardecki
- Research Collaboratory for Structural Bioinformatics Protein Data BankRutgers, The State University of New JerseyPiscatawayNew JerseyUSA,Institute for Quantitative BiomedicineRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Shuchismita Dutta
- Research Collaboratory for Structural Bioinformatics Protein Data BankRutgers, The State University of New JerseyPiscatawayNew JerseyUSA,Institute for Quantitative BiomedicineRutgers, The State University of New JerseyPiscatawayNew JerseyUSA,Rutgers Cancer Institute of New JerseyRutgers, The State University of New JerseyNew BrunswickNew JerseyUSA
| | - David S. Goodsell
- Research Collaboratory for Structural Bioinformatics Protein Data BankRutgers, The State University of New JerseyPiscatawayNew JerseyUSA,Institute for Quantitative BiomedicineRutgers, The State University of New JerseyPiscatawayNew JerseyUSA,Rutgers Cancer Institute of New JerseyRutgers, The State University of New JerseyNew BrunswickNew JerseyUSA,Department of Integrative Structural and Computational BiologyThe Scripps Research InstituteLa JollaCaliforniaUSA
| | - Robert Lowe
- Research Collaboratory for Structural Bioinformatics Protein Data BankRutgers, The State University of New JerseyPiscatawayNew JerseyUSA,Institute for Quantitative BiomedicineRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Maria Voigt
- Research Collaboratory for Structural Bioinformatics Protein Data BankRutgers, The State University of New JerseyPiscatawayNew JerseyUSA,Institute for Quantitative BiomedicineRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Stephen K. Burley
- Research Collaboratory for Structural Bioinformatics Protein Data BankRutgers, The State University of New JerseyPiscatawayNew JerseyUSA,Institute for Quantitative BiomedicineRutgers, The State University of New JerseyPiscatawayNew JerseyUSA,Rutgers Cancer Institute of New JerseyRutgers, The State University of New JerseyNew BrunswickNew JerseyUSA,Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer CenterUniversity of California San DiegoLa JollaCaliforniaUSA,Department of Chemistry and Chemical BiologyRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
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19
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Affiliation(s)
- David S Goodsell
- Department of Integrative Structural and Computational Biology, the Scripps Research Institute, La Jolla, CA, USA. .,Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, the State University of New Jersey, Piscataway, NJ, USA.
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20
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Abstract
CellPAINT is an interactive digital tool that allows non-expert users to create illustrations of the molecular structure of cells and viruses. We present a new release with several key enhancements, including the ability to generate custom ingredients from structure information in the Protein Data Bank, and interaction, grouping, and locking functions that streamline the creation of assemblies and illustration of large, complex scenes. An example of CellPAINT as a tool for hypothesis generation in the interpretation of cryoelectron tomograms is presented. CellPAINT is freely available at http://ccsb.scripps.edu/cellpaint.
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Affiliation(s)
- Adam Gardner
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, United States
| | - Ludovic Autin
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, United States
| | - Daniel Fuentes
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, United States
| | - Martina Maritan
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, United States
| | - Benjamin A. Barad
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, United States
| | - Michaela Medina
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, United States
| | - Arthur J. Olson
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, United States
| | - Danielle A. Grotjahn
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, United States
| | - David S. Goodsell
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, United States
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ, United States
- *Correspondence: David S. Goodsell
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21
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Goodsell DS, Dutta S, Voigt M, Zardecki C. Molecular storytelling for online structural biology outreach and education. Struct Dyn 2021; 8:020401. [PMID: 33728361 PMCID: PMC7936881 DOI: 10.1063/4.0000077] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/19/2020] [Accepted: 01/25/2021] [Indexed: 06/12/2023]
Abstract
Knowledge about the structure and function of biomolecules continues to grow exponentially, enabling us to "see" structural snapshots of biomolecular interactions and functional assemblies. At PDB-101, the educational portal of the RCSB Protein Data Bank, we have taken a storytelling approach to make this body of knowledge accessible and comprehensible to a wide community of students, educators, and the general public. For over 20 years, the Molecule of the Month series has utilized a traditional illustrated storytelling approach that is regularly adapted for classroom instruction. Similar visual and interactive storytelling approaches are used to present topical subjects at PDB-101 and full curricular materials and case studies for building a detailed narrative around topics of particular interest. This emphasis on storytelling led to the Video Challenge for High School students, now in its 8th year. In this Article, we will present some of the lessons we have learned for teaching and communicating structural biology using the PDB archive of biomolecular structures.
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Affiliation(s)
| | | | - Maria Voigt
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, the State University of New Jersey, Piscataway, New Jersey 08854, USA
| | - Christine Zardecki
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, the State University of New Jersey, Piscataway, New Jersey 08854, USA
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22
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Jewett AI, Stelter D, Lambert J, Saladi SM, Roscioni OM, Ricci M, Autin L, Maritan M, Bashusqeh SM, Keyes T, Dame RT, Shea JE, Jensen GJ, Goodsell DS. Moltemplate: A Tool for Coarse-Grained Modeling of Complex Biological Matter and Soft Condensed Matter Physics. J Mol Biol 2021; 433:166841. [PMID: 33539886 DOI: 10.1016/j.jmb.2021.166841] [Citation(s) in RCA: 93] [Impact Index Per Article: 31.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Revised: 01/19/2021] [Accepted: 01/20/2021] [Indexed: 02/01/2023]
Abstract
Coarse-grained models have long been considered indispensable tools in the investigation of biomolecular dynamics and assembly. However, the process of simulating such models is arduous because unconventional force fields and particle attributes are often needed, and some systems are not in thermal equilibrium. Although modern molecular dynamics programs are highly adaptable, software designed for preparing all-atom simulations typically makes restrictive assumptions about the nature of the particles and the forces acting on them. Consequently, the use of coarse-grained models has remained challenging. Moltemplate is a file format for storing coarse-grained molecular models and the forces that act on them, as well as a program that converts moltemplate files into input files for LAMMPS, a popular molecular dynamics engine. Moltemplate has broad scope and an emphasis on generality. It accommodates new kinds of forces as they are developed for LAMMPS, making moltemplate a popular tool with thousands of users in computational chemistry, materials science, and structural biology. To demonstrate its wide functionality, we provide examples of using moltemplate to prepare simulations of fluids using many-body forces, coarse-grained organic semiconductors, and the motor-driven supercoiling and condensation of an entire bacterial chromosome.
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Affiliation(s)
- Andrew I Jewett
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, USA.
| | | | - Jason Lambert
- Department of Chemistry, University of Tennessee, Knoxville, TN, USA
| | - Shyam M Saladi
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | | | | | - Ludovic Autin
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, USA
| | - Martina Maritan
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, USA
| | - Saeed M Bashusqeh
- School of Mechanical Engineering, College of Engineering, University of Tehran, Tehran, Iran
| | - Tom Keyes
- Department of Chemistry, Boston University, MA, USA
| | - Remus T Dame
- Leiden Institute of Chemistry, Leiden University, Leiden, Netherlands
| | - Joan-Emma Shea
- Departments of Chemistry and Biochemistry and Physics, University of California, Santa Barbara, CA, USA
| | - Grant J Jensen
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA; Howard Hughes Medical Institute, California Institute of Technology, Pasadena, CA, USA
| | - David S Goodsell
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, USA; RCSB Protein Data Bank and Institute for Quantitative Biomedicine, Rutgers, the State University of New Jersey, Piscataway, NJ, USA.
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23
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Nguyen N, Strnad O, Klein T, Luo D, Alharbi R, Wonka P, Maritan M, Mindek P, Autin L, Goodsell DS, Viola I. Modeling in the Time of COVID-19: Statistical and Rule-based Mesoscale Models. IEEE Trans Vis Comput Graph 2021; 27:722-732. [PMID: 33055034 PMCID: PMC8642830 DOI: 10.1109/tvcg.2020.3030415] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 07/31/2020] [Accepted: 08/14/2020] [Indexed: 06/11/2023]
Abstract
We present a new technique for the rapid modeling and construction of scientifically accurate mesoscale biological models. The resulting 3D models are based on a few 2D microscopy scans and the latest knowledge available about the biological entity, represented as a set of geometric relationships. Our new visual-programming technique is based on statistical and rule-based modeling approaches that are rapid to author, fast to construct, and easy to revise. From a few 2D microscopy scans, we determine the statistical properties of various structural aspects, such as the outer membrane shape, the spatial properties, and the distribution characteristics of the macromolecular elements on the membrane. This information is utilized in the construction of the 3D model. Once all the imaging evidence is incorporated into the model, additional information can be incorporated by interactively defining the rules that spatially characterize the rest of the biological entity, such as mutual interactions among macromolecules, and their distances and orientations relative to other structures. These rules are defined through an intuitive 3D interactive visualization as a visual-programming feedback loop. We demonstrate the applicability of our approach on a use case of the modeling procedure of the SARS-CoV-2 virion ultrastructure. This atomistic model, which we present here, can steer biological research to new promising directions in our efforts to fight the spread of the virus.
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Affiliation(s)
- Ngan Nguyen
- King Abdullah University of Science and Technology (KAUST)Saudi Arabia
| | - Ondřej Strnad
- King Abdullah University of Science and Technology (KAUST)Saudi Arabia
| | | | - Deng Luo
- King Abdullah University of Science and Technology (KAUST)Saudi Arabia
| | - Ruwayda Alharbi
- King Abdullah University of Science and Technology (KAUST)Saudi Arabia
| | - Peter Wonka
- King Abdullah University of Science and Technology (KAUST)Saudi Arabia
| | | | | | | | | | - Ivan Viola
- King Abdullah University of Science and Technology (KAUST)Saudi Arabia
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24
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Burley SK, Bhikadiya C, Bi C, Bittrich S, Chen L, Crichlow GV, Christie CH, Dalenberg K, Di Costanzo L, Duarte JM, Dutta S, Feng Z, Ganesan S, Goodsell DS, Ghosh S, Green RK, Guranović V, Guzenko D, Hudson BP, Lawson C, Liang Y, Lowe R, Namkoong H, Peisach E, Persikova I, Randle C, Rose A, Rose Y, Sali A, Segura J, Sekharan M, Shao C, Tao YP, Voigt M, Westbrook J, Young JY, Zardecki C, Zhuravleva M. RCSB Protein Data Bank: powerful new tools for exploring 3D structures of biological macromolecules for basic and applied research and education in fundamental biology, biomedicine, biotechnology, bioengineering and energy sciences. Nucleic Acids Res 2021; 49:D437-D451. [PMID: 33211854 PMCID: PMC7779003 DOI: 10.1093/nar/gkaa1038] [Citation(s) in RCA: 731] [Impact Index Per Article: 243.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Revised: 10/14/2020] [Accepted: 11/17/2020] [Indexed: 12/14/2022] Open
Abstract
The Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB), the US data center for the global PDB archive and a founding member of the Worldwide Protein Data Bank partnership, serves tens of thousands of data depositors in the Americas and Oceania and makes 3D macromolecular structure data available at no charge and without restrictions to millions of RCSB.org users around the world, including >660 000 educators, students and members of the curious public using PDB101.RCSB.org. PDB data depositors include structural biologists using macromolecular crystallography, nuclear magnetic resonance spectroscopy, 3D electron microscopy and micro-electron diffraction. PDB data consumers accessing our web portals include researchers, educators and students studying fundamental biology, biomedicine, biotechnology, bioengineering and energy sciences. During the past 2 years, the research-focused RCSB PDB web portal (RCSB.org) has undergone a complete redesign, enabling improved searching with full Boolean operator logic and more facile access to PDB data integrated with >40 external biodata resources. New features and resources are described in detail using examples that showcase recently released structures of SARS-CoV-2 proteins and host cell proteins relevant to understanding and addressing the COVID-19 global pandemic.
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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, La Jolla, CA 92093, USA
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Charmi Bhikadiya
- 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
| | - Chunxiao Bi
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California, La Jolla, CA 92093, USA
| | - Sebastian Bittrich
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California, La Jolla, CA 92093, USA
| | - Li Chen
- 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
| | - Gregg V Crichlow
- 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
| | - Cole H Christie
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California, La Jolla, CA 92093, USA
| | - Kenneth Dalenberg
- 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
| | - Luigi Di Costanzo
- 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
| | - Jose M Duarte
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California, La Jolla, CA 92093, USA
| | - Shuchismita Dutta
- 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
| | - Zukang Feng
- 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
| | - Sai Ganesan
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Department of Biotherapeutic Sciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - David S Goodsell
- 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
- Center for Computational Structural Biology, The Scripps Research Institute, La Jolla, CA 92037, USA
| | - Sutapa Ghosh
- 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
| | - Rachel Kramer Green
- 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
| | - Vladimir Guranović
- 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
| | - Dmytro Guzenko
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California, La Jolla, CA 92093, 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
| | - 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
| | - Yuhe Liang
- 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
| | - Robert Lowe
- 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
| | - Harry Namkoong
- 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
| | - Ezra Peisach
- 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
| | - Irina Persikova
- 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
| | - Chris Randle
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California, La Jolla, CA 92093, USA
| | - Alexander Rose
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California, La Jolla, CA 92093, USA
| | - Yana Rose
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California, La Jolla, CA 92093, USA
| | - Andrej Sali
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Department of Biotherapeutic Sciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Joan Segura
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California, La Jolla, CA 92093, USA
| | - Monica Sekharan
- 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
| | - Chenghua Shao
- 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
| | - Yi-Ping Tao
- 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
| | - Maria Voigt
- 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
| | - 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
| | - Marina Zhuravleva
- 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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25
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Abstract
Ever since the first structures of proteins were determined in the 1960s, structural biologists have required methods to visualize biomolecular structures, both as an essential tool for their research and also to promote 3D comprehension of structural results by a wide audience of researchers, students, and the general public. In this review to celebrate the 50th anniversary of the Protein Data Bank, we present our own experiences in developing and applying methods of visualization and analysis to the ever-expanding archive of protein and nucleic acid structures in the worldwide Protein Data Bank. Across that timespan, Jane and David Richardson have concentrated on the organization inside and between the macromolecules, with ribbons to show the overall backbone "fold" and contact dots to show how the all-atom details fit together locally. David Goodsell has explored surface-based representations to present and explore biological subjects that range from molecules to cells. This review concludes with some ideas about the current challenges being addressed by the field of biomolecular visualization.
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Affiliation(s)
- Jane S Richardson
- Department of Biochemistry, Duke University, Durham, North Carolina, USA.
| | - David C Richardson
- Department of Biochemistry, Duke University, Durham, North Carolina, USA
| | - David S Goodsell
- Department of Integrative and Computational Biology, The Scripps Research Institute, La Jolla, California, USA; Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, the State University of New Jersey, Piscataway, New Jersey, USA.
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26
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Lubin JH, Zardecki C, Dolan EM, Lu C, Shen Z, Dutta S, Westbrook JD, Hudson BP, Goodsell DS, Williams JK, Voigt M, Sarma V, Xie L, Venkatachalam T, Arnold S, Alvarado LHA, Catalfano K, Khan A, McCarthy E, Staggers S, Tinsley B, Trudeau A, Singh J, Whitmore L, Zheng H, Benedek M, Currier J, Dresel M, Duvvuru A, Dyszel B, Fingar E, Hennen EM, Kirsch M, Khan AA, Labrie-Cleary C, Laporte S, Lenkeit E, Martin K, Orellana M, de la Campa MOA, Paredes I, Wheeler B, Rupert A, Sam A, See K, Zapata SS, Craig PA, Hall BL, Jiang J, Koeppe JR, Mills SA, Pikaart MJ, Roberts R, Bromberg Y, Hoyer JS, Duffy S, Tischfield J, Ruiz FX, Arnold E, Baum J, Sandberg J, Brannigan G, Khare SD, Burley SK. Evolution of the SARS-CoV-2 proteome in three dimensions (3D) during the first six months of the COVID-19 pandemic. bioRxiv 2020. [PMID: 33299989 DOI: 10.1101/2020.12.01.406637] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Three-dimensional structures of SARS-CoV-2 and other coronaviral proteins archived in the Protein Data Bank were used to analyze viral proteome evolution during the first six months of the COVID-19 pandemic. Analyses of spatial locations, chemical properties, and structural and energetic impacts of the observed amino acid changes in >48,000 viral proteome sequences showed how each one of the 29 viral study proteins have undergone amino acid changes. Structural models computed for every unique sequence variant revealed that most substitutions map to protein surfaces and boundary layers with a minority affecting hydrophobic cores. Conservative changes were observed more frequently in cores versus boundary layers/surfaces. Active sites and protein-protein interfaces showed modest numbers of substitutions. Energetics calculations showed that the impact of substitutions on the thermodynamic stability of the proteome follows a universal bi-Gaussian distribution. Detailed results are presented for six drug discovery targets and four structural proteins comprising the virion, highlighting substitutions with the potential to impact protein structure, enzyme activity, and functional interfaces. Characterizing the evolution of the virus in three dimensions provides testable insights into viral protein function and should aid in structure-based drug discovery efforts as well as the prospective identification of amino acid substitutions with potential for drug resistance.
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27
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Bianco G, Goodsell DS, Forli S. Selective and Effective: Current Progress in Computational Structure-Based Drug Discovery of Targeted Covalent Inhibitors. Trends Pharmacol Sci 2020; 41:1038-1049. [PMID: 33153778 PMCID: PMC7669701 DOI: 10.1016/j.tips.2020.10.005] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Revised: 10/09/2020] [Accepted: 10/12/2020] [Indexed: 12/28/2022]
Abstract
Targeted covalent inhibitors are currently showing great promise for systems that are normally difficult to target with small molecule therapies. This renewed interest has spurred the refinement of existing computational methods as well as the designof new ones, expanding the toolbox for discovery and optimization of selectiveand effective covalent inhibitors. Commonly applied approaches are covalentdocking methods that predict the conformation of the covalent complex with known residues. More recently, a new predictive method, reactive docking, was developed, building on the growing corpus of data generated by large proteomics experiments. This method was successfully used in several 'inverse drug discovery' programs that use high-throughput techniques to isolate effective compounds based on screening of entire compound libraries based on desired phenotypes.
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Affiliation(s)
- Giulia Bianco
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA
| | - David S Goodsell
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA; Research Collaboratory for Structure Bioinformatics Protein Data Bank, Rutgers, the State University of New Jersey, Piscataway, NJ 08854, USA
| | - Stefano Forli
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA.
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28
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Goodsell DS, Burley SK. RCSB Protein Data Bank tools for 3D structure-guided cancer research: human papillomavirus (HPV) case study. Oncogene 2020; 39:6623-6632. [PMID: 32939013 PMCID: PMC7581513 DOI: 10.1038/s41388-020-01461-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2020] [Revised: 07/30/2020] [Accepted: 09/04/2020] [Indexed: 11/21/2022]
Abstract
Atomic-level three-dimensional (3D) structure data for biological macromolecules often prove critical to dissecting and understanding the precise mechanisms of action of cancer-related proteins and their diverse roles in oncogenic transformation, proliferation, and metastasis. They are also used extensively to identify potentially druggable targets and facilitate discovery and development of both small-molecule and biologic drugs that are today benefiting individuals diagnosed with cancer around the world. 3D structures of biomolecules (including proteins, DNA, RNA, and their complexes with one another, drugs, and other small molecules) are freely distributed by the open-access Protein Data Bank (PDB). This global data repository is used by millions of scientists and educators working in the areas of drug discovery, vaccine design, and biomedical and biotechnology research. The US Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB) provides an integrated portal to the PDB archive that streamlines access for millions of worldwide PDB data consumers worldwide. Herein, we review online resources made available free of charge by the RCSB PDB to basic and applied researchers, healthcare providers, educators and their students, patients and their families, and the curious public. We exemplify the value of understanding cancer-related proteins in 3D with a case study focused on human papillomavirus.
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Affiliation(s)
- David S Goodsell
- Research Collaboratory for Structural Bioinformatics Protein Data Bank and Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ, 08854, USA. .,Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, 92037, USA.
| | - Stephen K Burley
- Research Collaboratory for Structural Bioinformatics Protein Data Bank and 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. .,Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, and the Skaggs School of Pharmacy and Pharmaceutical Sciences, 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.
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29
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Abstract
The AutoDock suite provides a comprehensive toolset for computational ligand docking and drug design and development. The suite builds on 30 years of methods development, including empirical free energy force fields, docking engines, methods for site prediction, and interactive tools for visualization and analysis. Specialized tools are available for challenging systems, including covalent inhibitors, peptides, compounds with macrocycles, systems where ordered hydration plays a key role, and systems with substantial receptor flexibility. All methods in the AutoDock suite are freely available for use and reuse, which has engendered the continued growth of a diverse community of primary users and third-party developers.
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Affiliation(s)
- David S Goodsell
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, California, USA.,Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, New Jersey, USA
| | - Michel F Sanner
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, California, USA
| | - Arthur J Olson
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, California, USA
| | - Stefano Forli
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, California, USA
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30
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Abstract
Two illustrations integrate current knowledge about severe acute respiratory syndrome (SARS) coronaviruses and their life cycle. They have been widely used in education and outreach through free distribution as part of a coronavirus-related resource at Protein Data Bank (PDB)-101, the education portal of the RCSB PDB. Scientific sources for creation of the illustrations and examples of dissemination and response are presented.
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Affiliation(s)
- David S. Goodsell
- RCSB Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, New Jersey, United States of America
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, California, United States of America
- * E-mail:
| | - Maria Voigt
- RCSB Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, New Jersey, United States of America
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, New Jersey, United States of America
| | - Christine Zardecki
- RCSB Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, New Jersey, United States of America
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, New Jersey, United States of America
| | - Stephen K. Burley
- RCSB Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, New Jersey, United States of America
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, New Jersey, United States of America
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California, San Diego, California, United States of America
- Rutgers Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, New Jersey, United States of America
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31
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Goodsell DS, Zardecki C, Berman HM, Burley SK. Insights from 20 years of the Molecule of the Month. Biochem Mol Biol Educ 2020; 48:350-355. [PMID: 32558264 PMCID: PMC7496199 DOI: 10.1002/bmb.21360] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/04/2019] [Revised: 11/25/2019] [Accepted: 04/20/2020] [Indexed: 06/11/2023]
Abstract
For 20 years, Molecule of the Month articles have highlighted the functional stories of 3D structures found in the Protein Data Bank (PDB). The PDB is the primary archive of atomic structures of biological molecules, currently providing open access to more than 150,000 structures studied by researchers around the world. The wealth of knowledge embodied in this resource is remarkable, with structures that allow exploration of nearly any biomolecular topic, including the basic science of genetic mechanisms, mechanisms of photosynthesis and bioenergetics, and central biomedical topics like cancer therapy and the fight against infectious disease. The central motivation behind the Molecule of the Month is to provide a user-friendly introduction to this rich body of data, charting a path for users to get started with finding and exploring the many available structures. The Molecule of the Month and related materials are updated regularly at the education portal PDB-101 (http://pdb101.rcsb.org/), offering an ongoing resource for molecular biology educators and students around the world.
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Affiliation(s)
- David S. Goodsell
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, RutgersThe State University of New JerseyPiscatawayNew JerseyUSA
- Institute for Quantitative Biomedicine, RutgersThe State University of New JerseyPiscatawayNew JerseyUSA
- Department of Integrative Structural and Computational BiologyThe Scripps Research InstituteLa JollaCaliforniaUSA
| | - Christine Zardecki
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, RutgersThe State University of New JerseyPiscatawayNew JerseyUSA
- Institute for Quantitative Biomedicine, RutgersThe State University of New JerseyPiscatawayNew JerseyUSA
| | - Helen M. Berman
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, RutgersThe State University of New JerseyPiscatawayNew JerseyUSA
- Department of Chemistry and Chemical BiologyThe State University of New JerseyPiscatawayNew JerseyUSA
- Department of Biological SciencesUniversity of Southern CaliforniaCaliforniaLos AngelesUSA
| | - Stephen K. Burley
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, RutgersThe State University of New JerseyPiscatawayNew JerseyUSA
- Institute for Quantitative Biomedicine, RutgersThe State University of New JerseyPiscatawayNew JerseyUSA
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer CenterUniversity of California, San DiegoLa JollaCaliforniaUSA
- Rutgers Cancer Institute of New Jersey, RutgersThe State University of New JerseyNew BrunswickNew JerseyUSA
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32
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Abstract
Experimental information from microscopy, structural biology, and bioinformatics may be integrated to build structural models of entire cells with molecular detail. This integrative modeling is challenging in several ways: the intrinsic complexity of biology results in models with many closely packed and heterogeneous components; the wealth of available experimental data is scattered among multiple resources and must be gathered, reconciled, and curated; and computational infrastructure is only now gaining the capability of modeling and visualizing systems of this complexity. We present recent efforts to address these challenges, both with artistic approaches to depicting the cellular mesoscale, and development and application of methods to build quantitative models.
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Affiliation(s)
- David S Goodsell
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA; Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, the State University of New Jersey, Piscataway, NJ 08854, USA.
| | - Arthur J Olson
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA
| | - Stefano Forli
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA
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33
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Burley SK, Berman HM, Bhikadiya C, Bi C, Chen L, Di Costanzo L, Christie C, Dalenberg K, Duarte JM, Dutta S, Feng Z, Ghosh S, Goodsell DS, Green RK, Guranovic V, Guzenko D, Hudson BP, Kalro T, Liang Y, Lowe R, Namkoong H, Peisach E, Periskova I, Prlic A, Randle C, Rose A, Rose P, Sala R, Sekharan M, Shao C, Tan L, Tao YP, Valasatava Y, Voigt M, Westbrook J, Woo J, Yang H, Young J, Zhuravleva M, Zardecki C. RCSB Protein Data Bank: biological macromolecular structures enabling research and education in fundamental biology, biomedicine, biotechnology and energy. Nucleic Acids Res 2020; 47:D464-D474. [PMID: 30357411 PMCID: PMC6324064 DOI: 10.1093/nar/gky1004] [Citation(s) in RCA: 713] [Impact Index Per Article: 178.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2018] [Accepted: 10/11/2018] [Indexed: 02/06/2023] Open
Abstract
The Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB, rcsb.org), the US data center for the global PDB archive, serves thousands of Data Depositors in the Americas and Oceania and makes 3D macromolecular structure data available at no charge and without usage restrictions to more than 1 million rcsb.org Users worldwide and 600 000 pdb101.rcsb.org education-focused Users around the globe. PDB Data Depositors include structural biologists using macromolecular crystallography, nuclear magnetic resonance spectroscopy and 3D electron microscopy. PDB Data Consumers include researchers, educators and students studying Fundamental Biology, Biomedicine, Biotechnology and Energy. Recent reorganization of RCSB PDB activities into four integrated, interdependent services is described in detail, together with tools and resources added over the past 2 years to RCSB PDB web portals in support of a ‘Structural View of Biology.’
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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.,Research Collaboratory for Structural Bioinformatics Protein Data Bank, 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.,Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, 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
| | - Charmi Bhikadiya
- 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
| | - Chunxiao Bi
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California, San Diego, La Jolla, CA 92093, USA
| | - Li Chen
- 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
| | - Luigi Di Costanzo
- 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
| | - Cole Christie
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California, San Diego, La Jolla, CA 92093, USA
| | - Ken Dalenberg
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Jose M Duarte
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California, San Diego, La Jolla, CA 92093, USA
| | - Shuchismita Dutta
- 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
| | - Zukang Feng
- 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
| | - Sutapa Ghosh
- 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
| | - David S Goodsell
- 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.,The Scripps Research Institute, La Jolla, CA 92037, USA
| | - Rachel K Green
- 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
| | - Vladimir Guranovic
- 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
| | - Dmytro Guzenko
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California, San Diego, La Jolla, CA 92093, 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
| | - Tara Kalro
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California, San Diego, La Jolla, CA 92093, USA
| | - Yuhe Liang
- 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
| | - Robert Lowe
- 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
| | - Harry Namkoong
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Ezra Peisach
- 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
| | - Irina Periskova
- 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
| | - Andreas Prlic
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California, San Diego, La Jolla, CA 92093, USA
| | - Chris Randle
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California, San Diego, La Jolla, CA 92093, USA
| | - Alexander Rose
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California, San Diego, La Jolla, CA 92093, USA
| | - Peter Rose
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California, San Diego, La Jolla, CA 92093, USA
| | - Raul Sala
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Monica Sekharan
- 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
| | - Chenghua Shao
- 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
| | - Lihua Tan
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Yi-Ping Tao
- 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
| | - Yana Valasatava
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California, San Diego, La Jolla, CA 92093, USA
| | - Maria Voigt
- 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
| | - John 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
| | - Jesse Woo
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California, San Diego, La Jolla, CA 92093, USA
| | - Huanwang Yang
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Jasmine 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
| | - Marina Zhuravleva
- 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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34
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Autin L, Maritan M, Barbaro BA, Gardner A, Olson AJ, Sanner M, Goodsell DS. Mesoscope: A Web-based Tool for Mesoscale Data Integration and Curation. MolVa (2020) 2020; 2020:23-31. [PMID: 37928321 PMCID: PMC10624244 DOI: 10.2312/molva.20201098] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 11/07/2023]
Abstract
Interest is growing for 3D models of the biological mesoscale, the intermediate scale between the nanometer scale of molecular structure and micrometer scale of cellular biology. However, it is currently difficult to gather, curate and integrate all the data required to define such models. To address this challenge we developed Mesoscope (mesoscope.scripps.edu/beta), a web-based data integration and curation tool. Mesoscope allows users to begin with a listing of molecules (such as data from proteomics), and to use resources at UniProt and the PDB to identify, prepare and validate appropriate structures and representations for each molecule, ultimately producing a portable output file used by CellPACK and other modeling tools for generation of 3D models of the biological mesoscale. The availability of this tool has proven essential in several exploratory applications, given the high complexity of mesoscale models and the heterogeneity of the available data sources.
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Affiliation(s)
- L Autin
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA
| | - M Maritan
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA
| | - B A Barbaro
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA
| | - A Gardner
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA
| | - A J Olson
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA
| | - M Sanner
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA
| | - D S Goodsell
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA
- RCSB Protein Data Bank and Center for Integrative Proteomics Research, Rutgers, the State University of New Jersey, Piscataway, NJ 08854, USA
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35
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Zardecki C, Berman HM, Burley SK, Goodsell DS. Insights from 20 Years of the Molecule of the Month and PDB‐101. FASEB J 2020. [DOI: 10.1096/fasebj.2020.34.s1.02238] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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36
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Goodsell DS, Zardecki C, Di Costanzo L, Duarte JM, Hudson BP, Persikova I, Segura J, Shao C, Voigt M, Westbrook JD, Young JY, Burley SK. RCSB Protein Data Bank: Enabling biomedical research and drug discovery. Protein Sci 2019; 29:52-65. [PMID: 31531901 DOI: 10.1002/pro.3730] [Citation(s) in RCA: 179] [Impact Index Per Article: 35.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2019] [Revised: 09/09/2019] [Accepted: 09/10/2019] [Indexed: 12/11/2022]
Abstract
Analyses of publicly available structural data reveal interesting insights into the impact of the three-dimensional (3D) structures of protein targets important for discovery of new drugs (e.g., G-protein-coupled receptors, voltage-gated ion channels, ligand-gated ion channels, transporters, and E3 ubiquitin ligases). The Protein Data Bank (PDB) archive currently holds > 155,000 atomic-level 3D structures of biomolecules experimentally determined using crystallography, nuclear magnetic resonance spectroscopy, and electron microscopy. The PDB was established in 1971 as the first open-access, digital-data resource in biology, and is now managed by the Worldwide PDB partnership (wwPDB; wwPDB.org). US PDB operations are the responsibility of the Research Collaboratory for Structural Bioinformatics PDB (RCSB PDB). The RCSB PDB serves millions of RCSB.org users worldwide by delivering PDB data integrated with ∼40 external biodata resources, providing rich structural views of fundamental biology, biomedicine, and energy sciences. Recently published work showed that the PDB archival holdings facilitated discovery of ∼90% of the 210 new drugs approved by the US Food and Drug Administration 2010-2016. We review user-driven development of RCSB PDB services, examine growth of the PDB archive in terms of size and complexity, and present examples and opportunities for structure-guided drug discovery for challenging targets (e.g., integral membrane proteins).
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Affiliation(s)
- David S Goodsell
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, New Jersey.,Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, New Jersey.,The Scripps Research Institute, La Jolla, California
| | - Christine Zardecki
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, New Jersey.,Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, New Jersey
| | - Luigi Di Costanzo
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, New Jersey.,Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, New Jersey
| | - Jose M Duarte
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California, San Diego, California
| | - Brian P Hudson
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, New Jersey.,Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, New Jersey
| | - Irina Persikova
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, New Jersey.,Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, New Jersey
| | - Joan Segura
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California, San Diego, California
| | - Chenghua Shao
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, New Jersey.,Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, New Jersey
| | - Maria Voigt
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, New Jersey.,Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, New Jersey
| | - John D Westbrook
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, New Jersey.,Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, New Jersey
| | - Jasmine Y Young
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, New Jersey.,Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, New Jersey
| | - Stephen K Burley
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, New Jersey.,Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, New Jersey.,Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California, San Diego, California.,Rutgers Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, New Jersey
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37
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Abstract
Information from proteomics, microscopy, and structural biology are integrated to create structural models of exosomes, small vesicles released from cells. Three visualization methods are employed and compared: 2D painting of a cross section using traditional media, manual creation of a cross section using the mesoscale 2.5D digital painting software cellPAINT, and generation of a 3D atomic model using the mesoscale modeling program cellPACK.
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Affiliation(s)
- Julia Jiménez
- The Sanitary Research Institution IdiPAZ
- University Hospital La Paz, Madrid
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38
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Goodsell DS, Autin L, Olson AJ. Illustrate: Software for Biomolecular Illustration. Structure 2019; 27:1716-1720.e1. [PMID: 31519398 DOI: 10.1016/j.str.2019.08.011] [Citation(s) in RCA: 72] [Impact Index Per Article: 14.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2019] [Revised: 08/12/2019] [Accepted: 08/20/2019] [Indexed: 10/26/2022]
Abstract
The small program Illustrate generates non-photorealistic images of biological molecules for use in dissemination, outreach, and education. The method has been used as part of the "Molecule of the Month," an ongoing educational column at the RCSB Protein Data Bank (http://rcsb.org). Insights from 20 years of application of the program are presented, and the program has been released both as open-source Fortran at GitHub and through an interactive web-based interface.
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Affiliation(s)
- David S Goodsell
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, CA 92037, USA; RCSB Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA.
| | - Ludovic Autin
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, CA 92037, USA
| | - Arthur J Olson
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, CA 92037, USA
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39
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Zardecki C, Bhikadiya C, Dutta S, Goodsell DS, Lowe R, Randle C, Tao W, Voigt M, Burley SK. Exploring biology and medicine using 3D biomacromolecules with PDB-101. Acta Crystallogr A Found Adv 2019. [DOI: 10.1107/s0108767319099732] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
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40
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Goodsell DS, Jewett A, Olson AJ, Forli S. Integrative modeling of the HIV-1 ribonucleoprotein complex. PLoS Comput Biol 2019; 15:e1007150. [PMID: 31194731 PMCID: PMC6592547 DOI: 10.1371/journal.pcbi.1007150] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2018] [Revised: 06/25/2019] [Accepted: 05/31/2019] [Indexed: 12/22/2022] Open
Abstract
A coarse-grain computational method integrates biophysical and structural data to generate models of HIV-1 genomic RNA, nucleocapsid and integrase condensed into a mature ribonucleoprotein complex. Several hypotheses for the initial structure of the genomic RNA and oligomeric state of integrase are tested. In these models, integrase interaction captures features of the relative distribution of gRNA in the immature virion and increases the size of the RNP globule, and exclusion of nucleocapsid from regions with RNA secondary structure drives an asymmetric placement of the dimerized 5’UTR at the surface of the RNP globule. The genome of HIV-1 is composed of two strands of RNA that are packaged in the mature virion as a condensed ribonucleoprotein complex with nucleocapsid, integrase, and other proteins. We have generated models of the HIV-1 ribonucleoprotein that integrate experimental results from multiple structural and biophysical experiments, exploring several hypotheses about the state of the RNA before condensation, and the role of crosslinking by integrase. The models suggest that the 5’UTR, which shows extensive secondary structure, has a propensity to be placed on the surface of the condensed globule, due to reduced binding of nucleocapsid to double-stranded regions within the 5’UTR. This unexpected localization of the 5’UTR may have consequences for the subsequent structural transitions that occur during the process of reverse transcription.
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Affiliation(s)
- David S. Goodsell
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, California, United States of America
- Center for Integrative Proteomics Research, Rutgers State University, Piscataway, New Jersey, United States of America
- * E-mail:
| | - Andrew Jewett
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, California, United States of America
| | - Arthur J. Olson
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, California, United States of America
| | - Stefano Forli
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, California, United States of America
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41
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Burley SK, Berman HM, Bhikadiya C, Bi C, Chen L, Costanzo LD, Christie C, Duarte JM, Dutta S, Feng Z, Ghosh S, Goodsell DS, Green RK, Guranovic V, Guzenko D, Hudson BP, Liang Y, Lowe R, Peisach E, Periskova I, Randle C, Rose A, Sekharan M, Shao C, Tao YP, Valasatava Y, Voigt M, Westbrook J, Young J, Zardecki C, Zhuravleva M, Kurisu G, Nakamura H, Kengaku Y, Cho H, Sato J, Kim JY, Ikegawa Y, Nakagawa A, Yamashita R, Kudou T, Bekker GJ, Suzuki H, Iwata T, Yokochi M, Kobayashi N, Fujiwara T, Velankar S, Kleywegt GJ, Anyango S, Armstrong DR, Berrisford JM, Conroy MJ, Dana JM, Deshpande M, Gane P, Gáborová R, Gupta D, Gutmanas A, Koča J, Mak L, Mir S, Mukhopadhyay A, Nadzirin N, Nair S, Patwardhan A, Paysan-Lafosse T, Pravda L, Salih O, Sehnal D, Varadi M, Vařeková R, Markley JL, Hoch JC, Romero PR, Baskaran K, Maziuk D, Ulrich EL, Wedell JR, Yao H, Livny M, Ioannidis YE. Protein Data Bank: the single global archive for 3D macromolecular structure data. Nucleic Acids Res 2019; 47:D520-D528. [PMID: 30357364 PMCID: PMC6324056 DOI: 10.1093/nar/gky949] [Citation(s) in RCA: 505] [Impact Index Per Article: 101.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2018] [Revised: 09/28/2018] [Accepted: 10/05/2018] [Indexed: 01/10/2023] Open
Abstract
The Protein Data Bank (PDB) is the single global archive of experimentally determined three-dimensional (3D) structure data of biological macromolecules. Since 2003, the PDB has been managed by the Worldwide Protein Data Bank (wwPDB; wwpdb.org), an international consortium that collaboratively oversees deposition, validation, biocuration, and open access dissemination of 3D macromolecular structure data. The PDB Core Archive houses 3D atomic coordinates of more than 144 000 structural models of proteins, DNA/RNA, and their complexes with metals and small molecules and related experimental data and metadata. Structure and experimental data/metadata are also stored in the PDB Core Archive using the readily extensible wwPDB PDBx/mmCIF master data format, which will continue to evolve as data/metadata from new experimental techniques and structure determination methods are incorporated by the wwPDB. Impacts of the recently developed universal wwPDB OneDep deposition/validation/biocuration system and various methods-specific wwPDB Validation Task Forces on improving the quality of structures and data housed in the PDB Core Archive are described together with current challenges and future plans.
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42
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Kouril D, Cmolik L, Kozlikova B, Wu HY, Johnson G, Goodsell DS, Olson A, Groller ME, Viola I. Labels on Levels: Labeling of Multi-Scale Multi-Instance and Crowded 3D Biological Environments. IEEE Trans Vis Comput Graph 2019; 25:977-986. [PMID: 30130195 PMCID: PMC8620714 DOI: 10.1109/tvcg.2018.2864491] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Labeling is intrinsically important for exploring and understanding complex environments and models in a variety of domains. We present a method for interactive labeling of crowded 3D scenes containing very many instances of objects spanning multiple scales in size. In contrast to previous labeling methods, we target cases where many instances of dozens of types are present and where the hierarchical structure of the objects in the scene presents an opportunity to choose the most suitable level for each placed label. Our solution builds on and goes beyond labeling techniques in medical 3D visualization, cartography, and biological illustrations from books and prints. In contrast to these techniques, the main characteristics of our new technique are: 1) a novel way of labeling objects as part of a bigger structure when appropriate, 2) visual clutter reduction by labeling only representative instances for each type of an object, and a strategy of selecting those. The appropriate level of label is chosen by analyzing the scene's depth buffer and the scene objects' hierarchy tree. We address the topic of communicating the parent-children relationship between labels by employing visual hierarchy concepts adapted from graphic design. Selecting representative instances considers several criteria tailored to the character of the data and is combined with a greedy optimization approach. We demonstrate the usage of our method with models from mesoscale biology where these two characteristics-multi-scale and multi-instance-are abundant, along with the fact that these scenes are extraordinarily dense.
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43
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Gardner A, Autin L, Barbaro B, Olson AJ, Goodsell DS. CellPAINT: Interactive Illustration of Dynamic Mesoscale Cellular Environments. IEEE Comput Graph Appl 2018; 38:51-66. [PMID: 30668455 PMCID: PMC6456043 DOI: 10.1109/mcg.2018.2877076] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/13/2023]
Abstract
CellPAINT allows nonexpert users to create interactive mesoscale illustrations that integrate a variety of biological data. Like popular digital painting software, scenes are created using a palette of molecular "brushes." The current release allows creation of animated scenes with an HIV virion, blood plasma, and a simplified T-cell.
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44
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Goodsell DS, Franzen MA, Herman T. From Atoms to Cells: Using Mesoscale Landscapes to Construct Visual Narratives. J Mol Biol 2018; 430:3954-3968. [PMID: 29885327 PMCID: PMC6186495 DOI: 10.1016/j.jmb.2018.06.009] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2018] [Revised: 05/31/2018] [Accepted: 06/01/2018] [Indexed: 10/14/2022]
Abstract
Modeling and visualization of the cellular mesoscale, bridging the nanometer scale of molecules to the micrometer scale of cells, is being studied by an integrative approach. Data from structural biology, proteomics, and microscopy are combined to simulate the molecular structure of living cells. These cellular landscapes are used as research tools for hypothesis generation and testing, and to present visual narratives of the cellular context of molecular biology for dissemination, education, and outreach.
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Affiliation(s)
- David S Goodsell
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA; RCSB Protein Data Bank & Center for Integrative Proteomics Research, Rutgers State University, Piscataway, NJ 08854, USA.
| | - Margaret A Franzen
- Center for BioMolecular Modeling, Milwaukee School of Engineering, Milwaukee, WI 53202, USA
| | - Tim Herman
- Center for BioMolecular Modeling, Milwaukee School of Engineering, Milwaukee, WI 53202, USA
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45
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Goodsell DS, Jenkinson J. Molecular Illustration in Research and Education: Past, Present, and Future. J Mol Biol 2018; 430:3969-3981. [PMID: 29752966 DOI: 10.1016/j.jmb.2018.04.043] [Citation(s) in RCA: 45] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2018] [Revised: 04/26/2018] [Accepted: 04/30/2018] [Indexed: 01/26/2023]
Abstract
Two-dimensional illustration is used extensively to study and disseminate the results of structural molecular biology. Molecular graphics methods have been and continue to be developed to address the growing needs of the structural biology community, and there are currently many effective, turn-key methods for displaying and exploring molecular structure. Building on decades of experience in design, best-practice resources are available to guide creation of illustrations that are effective for research and education communities.
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Affiliation(s)
- David S Goodsell
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA; RCSB Protein Data Bank & Center for Integrative Proteomics Research, Rutgers State University, Piscataway, NJ 08854, USA.
| | - Jodie Jenkinson
- Biomedical Communications, Department of Biology, University of Toronto, Mississauga, ON L5L 1C6, Canada
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46
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Engholm DH, Kilian M, Goodsell DS, Andersen ES, Kjærgaard RS. A visual review of the human pathogen Streptococcus pneumoniae. FEMS Microbiol Rev 2018; 41:854-879. [PMID: 29029129 DOI: 10.1093/femsre/fux037] [Citation(s) in RCA: 56] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2017] [Accepted: 09/04/2017] [Indexed: 11/12/2022] Open
Abstract
Being the principal causative agent of bacterial pneumonia, otitis media, meningitis and septicemia, the bacterium Streptococcus pneumoniae is a major global health problem. To highlight the molecular basis of this problem, we have portrayed essential biological processes of the pneumococcal life cycle in eight watercolor paintings. The paintings are done to a consistent nanometer scale based on currently available data from structural biology and proteomics. In this review article, the paintings are used to provide a visual review of protein synthesis, carbohydrate metabolism, cell wall synthesis, cell division, teichoic acid synthesis, virulence, transformation and pilus synthesis based on the available scientific literature within the field of pneumococcal biology. Visualization of the molecular details of these processes reveals several scientific questions about how molecular components of the pneumococcal cell are organized to allow biological function to take place. By the presentation of this visual review, we intend to stimulate scientific discussion, aid in the generation of scientific hypotheses and increase public awareness. A narrated video describing the biological processes in the context of a whole-cell illustration accompany this article.
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Affiliation(s)
- Ditte Høyer Engholm
- Department of Molecular Biology and Genetics, Aarhus University, 8000 Aarhus, Denmark
| | - Mogens Kilian
- Department of Biomedicine, Aarhus University, 8000 Aarhus, Denmark
| | - David S Goodsell
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA.,Rutgers, the State University of New Jersey, NJ 08901, USA
| | - Ebbe Sloth Andersen
- Department of Molecular Biology and Genetics, Aarhus University, 8000 Aarhus, Denmark.,Interdisciplinary Nanoscience Center, Aarhus University, 8000 Aarhus, Denmark
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47
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Abstract
Mesoscale molecular modeling is providing a new window into the inner workings of living cells. Modeling of genomes, however, remains a technical challenge, due to their large size and complexity. We describe a lattice method for rapid generation of bacterial nucleoid models that integrates experimental data from a variety of biophysical techniques and provides a starting point for simulation and hypothesis generation. The current method builds models of a circular bacterial genome with supercoiled plectonemes, packed within the small space of the bacterial cell. Lattice models are generated for Mycoplasma genitalium and Escherichia coli nucleoids, and used to simulate interaction data. The method is rapid enough to allow generation of multiple models when analyzing structure/function relationships, and we demonstrate use of the lattice models in creation of an all-atom representation of an entire cell.
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Affiliation(s)
- David S Goodsell
- Department of Integrative Structural and Computational Biology , The Scripps Research Institute , 10550 North Torrey Pines Road , La Jolla , California , United States.,Center for Integrative Proteomics Research , Rutgers State University , 174 Frelinghuysen Road , Piscataway , New Jersey , United States
| | - Ludovic Autin
- Department of Integrative Structural and Computational Biology , The Scripps Research Institute , 10550 North Torrey Pines Road , La Jolla , California , United States
| | - Arthur J Olson
- Department of Integrative Structural and Computational Biology , The Scripps Research Institute , 10550 North Torrey Pines Road , La Jolla , California , United States
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48
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Klein T, Autin L, Kozlikova B, Goodsell DS, Olson A, Groller ME, Viola I. Instant Construction and Visualization of Crowded Biological Environments. IEEE Trans Vis Comput Graph 2018; 24:862-872. [PMID: 28866533 PMCID: PMC5746312 DOI: 10.1109/tvcg.2017.2744258] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
We present the first approach to integrative structural modeling of the biological mesoscale within an interactive visual environment. These complex models can comprise up to millions of molecules with defined atomic structures, locations, and interactions. Their construction has previously been attempted only within a non-visual and non-interactive environment. Our solution unites the modeling and visualization aspect, enabling interactive construction of atomic resolution mesoscale models of large portions of a cell. We present a novel set of GPU algorithms that build the basis for the rapid construction of complex biological structures. These structures consist of multiple membrane-enclosed compartments including both soluble molecules and fibrous structures. The compartments are defined using volume voxelization of triangulated meshes. For membranes, we present an extension of the Wang Tile concept that populates the bilayer with individual lipids. Soluble molecules are populated within compartments distributed according to a Halton sequence. Fibrous structures, such as RNA or actin filaments, are created by self-avoiding random walks. Resulting overlaps of molecules are resolved by a forced-based system. Our approach opens new possibilities to the world of interactive construction of cellular compartments. We demonstrate its effectiveness by showcasing scenes of different scale and complexity that comprise blood plasma, mycoplasma, and HIV.
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49
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Zardecki C, Christie C, Dutta S, Goodsell DS, Lowe R, Randle C, Tao W, Voigt M, Woo J, Burley SK. PDB-101: educational portal for molecular explorations through biology and medicine. Acta Crystallogr A Found Adv 2017. [DOI: 10.1107/s2053273317089033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
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50
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Rose PW, Prlić A, Altunkaya A, Bi C, Bradley AR, Christie CH, Costanzo LD, Duarte JM, Dutta S, Feng Z, Green RK, Goodsell DS, Hudson B, Kalro T, Lowe R, Peisach E, Randle C, Rose AS, Shao C, Tao YP, Valasatava Y, Voigt M, Westbrook JD, Woo J, Yang H, Young JY, Zardecki C, Berman HM, Burley SK. The RCSB protein data bank: integrative view of protein, gene and 3D structural information. Nucleic Acids Res 2016; 45:D271-D281. [PMID: 27794042 PMCID: PMC5210513 DOI: 10.1093/nar/gkw1000] [Citation(s) in RCA: 375] [Impact Index Per Article: 46.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2016] [Revised: 10/12/2016] [Accepted: 10/14/2016] [Indexed: 12/02/2022] Open
Abstract
The Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB, http://rcsb.org), the US data center for the global PDB archive, makes PDB data freely available to all users, from structural biologists to computational biologists and beyond. New tools and resources have been added to the RCSB PDB web portal in support of a ‘Structural View of Biology.’ Recent developments have improved the User experience, including the high-speed NGL Viewer that provides 3D molecular visualization in any web browser, improved support for data file download and enhanced organization of website pages for query, reporting and individual structure exploration. Structure validation information is now visible for all archival entries. PDB data have been integrated with external biological resources, including chromosomal position within the human genome; protein modifications; and metabolic pathways. PDB-101 educational materials have been reorganized into a searchable website and expanded to include new features such as the Geis Digital Archive.
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Affiliation(s)
- Peter W Rose
- RCSB Protein Data Bank, San Diego Supercomputer Center, University of California, San Diego, La Jolla, CA 92093, USA
| | - Andreas Prlić
- RCSB Protein Data Bank, San Diego Supercomputer Center, University of California, San Diego, La Jolla, CA 92093, USA
| | - Ali Altunkaya
- RCSB Protein Data Bank, San Diego Supercomputer Center, University of California, San Diego, La Jolla, CA 92093, USA
| | - Chunxiao Bi
- RCSB Protein Data Bank, San Diego Supercomputer Center, University of California, San Diego, La Jolla, CA 92093, USA
| | - Anthony R Bradley
- RCSB Protein Data Bank, San Diego Supercomputer Center, University of California, San Diego, La Jolla, CA 92093, USA
| | - Cole H Christie
- RCSB Protein Data Bank, San Diego Supercomputer Center, University of California, San Diego, La Jolla, CA 92093, USA
| | - Luigi Di Costanzo
- RCSB Protein Data Bank, Department of Chemistry and Chemical Biology, Center for Integrative Proteomics Research, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Jose M Duarte
- RCSB Protein Data Bank, San Diego Supercomputer Center, University of California, San Diego, La Jolla, CA 92093, USA
| | - Shuchismita Dutta
- RCSB Protein Data Bank, Department of Chemistry and Chemical Biology, Center for Integrative Proteomics Research, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Zukang Feng
- RCSB Protein Data Bank, Department of Chemistry and Chemical Biology, Center for Integrative Proteomics Research, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Rachel Kramer Green
- RCSB Protein Data Bank, Department of Chemistry and Chemical Biology, Center for Integrative Proteomics Research, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - David S Goodsell
- RCSB Protein Data Bank, Department of Chemistry and Chemical Biology, Center for Integrative Proteomics Research, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA.,Department of Molecular Biology, The Scripps Research Institute, La Jolla, CA 92037, USA
| | - Brian Hudson
- RCSB Protein Data Bank, Department of Chemistry and Chemical Biology, Center for Integrative Proteomics Research, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Tara Kalro
- RCSB Protein Data Bank, San Diego Supercomputer Center, University of California, San Diego, La Jolla, CA 92093, USA
| | - Robert Lowe
- RCSB Protein Data Bank, Department of Chemistry and Chemical Biology, Center for Integrative Proteomics Research, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Ezra Peisach
- RCSB Protein Data Bank, Department of Chemistry and Chemical Biology, Center for Integrative Proteomics Research, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Christopher Randle
- RCSB Protein Data Bank, San Diego Supercomputer Center, University of California, San Diego, La Jolla, CA 92093, USA
| | - Alexander S Rose
- RCSB Protein Data Bank, San Diego Supercomputer Center, University of California, San Diego, La Jolla, CA 92093, USA
| | - Chenghua Shao
- RCSB Protein Data Bank, Department of Chemistry and Chemical Biology, Center for Integrative Proteomics Research, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Yi-Ping Tao
- RCSB Protein Data Bank, Department of Chemistry and Chemical Biology, Center for Integrative Proteomics Research, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Yana Valasatava
- RCSB Protein Data Bank, San Diego Supercomputer Center, University of California, San Diego, La Jolla, CA 92093, USA
| | - Maria Voigt
- RCSB Protein Data Bank, Department of Chemistry and Chemical Biology, Center for Integrative Proteomics Research, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - John D Westbrook
- RCSB Protein Data Bank, Department of Chemistry and Chemical Biology, Center for Integrative Proteomics Research, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Jesse Woo
- RCSB Protein Data Bank, San Diego Supercomputer Center, University of California, San Diego, La Jolla, CA 92093, USA
| | - Huangwang Yang
- RCSB Protein Data Bank, Department of Chemistry and Chemical Biology, Center for Integrative Proteomics Research, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Jasmine Y Young
- RCSB Protein Data Bank, Department of Chemistry and Chemical Biology, Center for Integrative Proteomics Research, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Christine Zardecki
- RCSB Protein Data Bank, Department of Chemistry and Chemical Biology, Center for Integrative Proteomics Research, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Helen M Berman
- RCSB Protein Data Bank, Department of Chemistry and Chemical Biology, Center for Integrative Proteomics Research, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Stephen K Burley
- RCSB Protein Data Bank, San Diego Supercomputer Center, University of California, San Diego, La Jolla, CA 92093, USA .,RCSB Protein Data Bank, Department of Chemistry and Chemical Biology, Center for Integrative Proteomics Research, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA.,Institute for Quantitative BioMedicine and Rutgers Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, NJ 08901, USA.,Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA 92093, USA
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