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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
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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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“the experimentally determined structures of ~100,000 proteins1 represent a very small fraction of the size and diversity of the universe of proteins.”
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Basic local alignment search tool
Altschul, S. F., Gish, W., Miller, W., Myers, E. W., & Lipman, D. J. (1990) · Journal of Molecular Biology · doi:10.1006/jmbi.1990.9999 · Cited by 98,669
Berman, H. M. (2000) · Nucleic Acids Research · doi:10.1093/nar/28.1.235 · Cited by 40,196
The FAIR Guiding Principles for scientific data management and stewardship
Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., Appleton, G., Axton, M., Baak, A., Blomberg, N., Boiten, J.-W., da Silva Santos, L. B., Bourne, P. E., et al. (2016) · Scientific Data · doi:10.1038/sdata.2016.18 · Cited by 18,423
Cryo-EM structure of the 2019-nCoV spike in the prefusion conformation
Wrapp, D., Wang, N., Corbett, K. S., Goldsmith, J. A., Hsieh, C.-L., Abiona, O., Graham, B. S., & McLellan, J. S. (2020) · Science · doi:10.1126/science.abb2507 · Cited by 8,897
Structure, function and antigenicity of the SARS-CoV-2 spike glycoprotein
Walls, A. C., Park, Y.-J., Tortorici, M. A., Wall, A., McGuire, A. T., & Veesler, D. (2020) · doi:10.1101/2020.02.19.956581 · Cited by 8,586
DrugBank 5.0: a major update to the DrugBank database for 2018
Wishart, D. S., Feunang, Y. D., Guo, A. C., Lo, E. J., Marcu, A., Grant, J. R., Sajed, T., Johnson, D., Li, C., Sayeeda, Z., et al. (2018) · Nucleic Acids Research · doi:10.1093/nar/gkx1037 · Cited by 8,404
UniProt: a worldwide hub of protein knowledge
The UniProt Consortium (2019) · Nucleic Acids Research · doi:10.1093/nar/gky1049 · Cited by 7,698
O'Leary, N. A., Wright, M. W., Brister, J. R., Ciufo, S., Haddad, D., McVeigh, R., Rajput, B., Robbertse, B., Smith-White, B., Ako-Adjei, D., et al. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkv1189 · Cited by 6,357
The Pfam protein families database: towards a more sustainable future
Finn, R. D., Coggill, P., Eberhardt, R. Y., Eddy, S. R., Mistry, J., Mitchell, A. L., Potter, S. C., Punta, M., Qureshi, M., Sangrador-Vegas, A., et al. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkv1344 · Cited by 5,975
The GTEx Consortium atlas of genetic regulatory effects across human tissues
The GTEx Consortium, Aguet, F., Anand, S., Ardlie, K. G., Gabriel, S., Getz, G. A., Graubert, A., Hadley, K., Handsaker, R. E., Huang, K. H., et al. (2020) · Science · doi:10.1126/science.aaz1776 · Cited by 5,547
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Research works printed but not linked: 3
- Westbrook, 2005, 3.6.2 The Protein Data Bank exchange data dictionary in International Tables for Crystallography, Vol. G. Definition and Exchange of Crystallographic Data, 195
- Fitzgerald, 2005, 4.5 Macromolecular dictionary (mmCIF) in International Tables for Crystallography, Vol. G. Definition and Exchange of Crystallographic Data, 295
- Rose, 2017, The RCSB Protein Data Bank: integrative view of protein, gene and 3D structural information, Nucleic Acids Res., 45, D271
Cited by
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Yi, Y., Liu, S., Hao, Y., Sun, Q., Lei, X., Wang, Y., Wang, J., Zhang, M., Tang, S., Tang, Q., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-41699-4 · Cited by 50
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Ji, Y., Zhang, L., Wu, J., Wu, B., Li, L., Huang, L.-K., Xu, T., Rong, Y., Ren, J., Ding, X., et al. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i7.25970 · Cited by 122
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Yu, H., Huang, X. X., Zhu, H.-H., Wang, N., Xie, C. H., Zhou, Y., Shi, H., Chen, M., Wu, Y., Ruan, Z., et al. (2023) · Phytomedicine · doi:10.1016/j.phymed.2023.154646 · Cited by 54
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Ou, S., Chen, H., Wang, H., Ye, J., Liu, H., Tao, Y., Ran, S., Mu, X., Liu, F., Zhu, S., et al. (2023) · Journal of Translational Medicine · doi:10.1186/s12967-023-04527-3 · Cited by 47
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Rao, P., Goswami, D., & Rawal, R. (2021) · Scientific Reports · doi:10.1038/s41598-021-96963-8 · Cited by 44
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Padhi, A. K., Janežič, M., & Zhang, K. Y. J. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-90264-9.00026-x · Cited by 47
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Ramalli, S. G., Miles, A. J., Janes, R. W., & Wallace, B. A. (2022) · Journal of Molecular Biology · doi:10.1016/j.jmb.2022.167441 · Cited by 42
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Ho, T. Y. H., Shao, A., Lu, Z. M., Savilahti, H., Menolascina, F., Wang, L., Dalchau, N., & Wang, B. (2021) · Nature Communications · doi:10.1038/s41467-021-22404-9 · Cited by 39
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Shi, M., Chen, T., Wei, S., Zhao, C., Zhang, X., Li, X., Tang, X., Liu, Y., Yang, Z., & Chen, L. (2022) · ACS Omega · doi:10.1021/acsomega.2c03951 · Cited by 36
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Metadata
- Authors
- Burley, S. K., Bhikadiya, C., Bi, C., Bittrich, S., Chen, L., Crichlow, G. V., Christie, C. H., Dalenberg, K., Di Costanzo, L., Duarte, J. M., Dutta, S., Feng, Z., Ganesan, S., Goodsell, D. S., Ghosh, S., Green, R. K., Guranović, V., Guzenko, D., Hudson, B. P., Lawson, C. L., 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, Y.-P., Voigt, M., Westbrook, J. D., Young, J. Y., Zardecki, C., & Zhuravleva, M.
- Affiliations
- Rutgers, The State University of New Jersey (US)
- San Diego Supercomputer Center (US)
- 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, La Jolla, CA 92093, USA
- University of California, San Francisco (US)
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Department of Biotherapeutic Sciences, University of California, San Francisco, San Francisco, CA 94158, USA
- Corresponding author
- Burley, Stephen K
- ORCID
- Burley, Stephen K
- Bhikadiya, Charmi
- Bittrich, Sebastian
- Chen, Li
- Crichlow, Gregg V
- Duarte, Jose M
- Dutta, Shuchismita
- Feng, Zukang
- Ganesan, Sai
- Goodsell, David S
- Ghosh, Sutapa
- Guranović, Vladimir
- Guzenko, Dmytro
- Hudson, Brian P
- Lawson, Catherine L
- Liang, Yuhe
- Peisach, Ezra
- Persikova, Irina
- Rose, Alexander
- Rose, Yana
- Segura, Joan
- Sekharan, Monica
- Shao, Chenghua
- Voigt, Maria
- Westbrook, John D
- Young, Jasmine Y
- Zardecki, Christine
- Venue
- Nucleic Acids Research
- Publisher
- Oxford University Press (OUP)
- Published
- 2020-11-19
- Volume
- 49
- Issue
- D1
- Pages
- D437-D451
- ISSN
- 0305-1048 (ISSN-L)
- 1362-4962
- PMC
- PMC7779003
- OpenAlex
- W3104537585
- Cited by
- 1,581
- References
- 80
- Citations by year
- 2026: 188 · 2025: 196 · 2024: 272 · 2023: 352 · 2022: 374 · 2021: 194 · 2020: 3
- FWCI
- 91.93
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Nucleic Acids Research (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Rutgers University Community Repository (Rutgers University) (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion, cc-by)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- Funders
- National Science Foundation (DBI-1832184)
- U.S. Department of Energy (DE-SC0019749)
- National Cancer Institute
- National Institute of Allergy and Infectious Diseases
- National Institute of General Medical Sciences (R01GM133198)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Structural Biology › Enzyme Structure and Function
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Physical Sciences › Computer Science › Information Systems › Research Data Management Practices
- Keywords
- structural bioinformatics, Boolean search, SARS-CoV-2 proteins, COVID-19 pandemic, host cell proteins, macromolecular crystallography, NMR spectroscopy, 3D electron microscopy, micro-electron diffraction, structural biology education
- MeSH terms
- COVID-19, SARS-CoV-2, Biotechnology, Humans, Protein Conformation, Proteins, Software, Viral Proteins, Computational Biology, Databases, Protein, Biomedical Research, Macromolecular Substances, Bioengineering, Pandemics
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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