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Martinez K, Agirre J, Akune Y, Aoki-Kinoshita KF, Arighi C, Axelsen KB, Bolton E, Bordeleau E, Edwards NJ, Fadda E, Feizi T, Hayes C, Ives CM, Joshi HJ, Krishna Prasad K, Kossida S, Lisacek F, Liu Y, Lütteke T, Ma J, Malik A, Martin M, Mehta AY, Neelamegham S, Panneerselvam K, Ranzinger R, Ricard-Blum S, Sanou G, Shanker V, Thomas PD, Tiemeyer M, Urban J, Vita R, Vora J, Yamamoto Y, Mazumder R. Functional implications of glycans and their curation: insights from the workshop held at the 16th Annual International Biocuration Conference in Padua, Italy. Database (Oxford) 2024; 2024:baae073. [PMID: 39137905 PMCID: PMC11321244 DOI: 10.1093/database/baae073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2024] [Revised: 06/24/2024] [Accepted: 07/10/2024] [Indexed: 08/15/2024]
Abstract
Dynamic changes in protein glycosylation impact human health and disease progression. However, current resources that capture disease and phenotype information focus primarily on the macromolecules within the central dogma of molecular biology (DNA, RNA, proteins). To gain a better understanding of organisms, there is a need to capture the functional impact of glycans and glycosylation on biological processes. A workshop titled "Functional impact of glycans and their curation" was held in conjunction with the 16th Annual International Biocuration Conference to discuss ongoing worldwide activities related to glycan function curation. This workshop brought together subject matter experts, tool developers, and biocurators from over 20 projects and bioinformatics resources. Participants discussed four key topics for each of their resources: (i) how they curate glycan function-related data from publications and other sources, (ii) what type of data they would like to acquire, (iii) what data they currently have, and (iv) what standards they use. Their answers contributed input that provided a comprehensive overview of state-of-the-art glycan function curation and annotations. This report summarizes the outcome of discussions, including potential solutions and areas where curators, data wranglers, and text mining experts can collaborate to address current gaps in glycan and glycosylation annotations, leveraging each other's work to improve their respective resources and encourage impactful data sharing among resources. Database URL: https://wiki.glygen.org/Glycan_Function_Workshop_2023.
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Affiliation(s)
- Karina Martinez
- Department of Biochemistry & Molecular Medicine, The George Washington University School of Medicine and Health Sciences, 2300 I St. NW, Washington, DC 20052, United States
| | - Jon Agirre
- York Structural Biology Laboratory, Department of Chemistry, University of York, Wentworth Way, York YO10 5DD, United Kingdom
| | - Yukie Akune
- The Glycosciences Laboratory, Imperial College London, Hammersmith Campus, Du Cane Road, London W12 0NN, United Kingdom
| | - Kiyoko F Aoki-Kinoshita
- Glycan and Life Systems Integration Center (GaLSIC), Soka University, 1-236 Tangi-machi, Hachioji, Tokyo 192-8577, Japan
| | - Cecilia Arighi
- Department of Computer and Information Sciences, University of Delaware, 18 Amstel Ave, Newark, DE 19716, United States
| | - Kristian B Axelsen
- Swiss-Prot Group, Swiss Institute of Bioinformatics (SIB), CMU, 1 rue Michel Servet, Geneva 4 1211, Switzerland
| | - Evan Bolton
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, 8600 Rockville Pike, Bethesda, MD 20894, United States
| | - Emily Bordeleau
- Michael Smith Laboratories, The University of British Columbia, 2185 East Mall, Vancouver, British Columbia V6T 1Z4, Canada
| | - Nathan J Edwards
- Department of Biochemistry and Molecular & Cellular Biology, Georgetown University, 2115 Wisconsin Ave NW, Washington, DC 20007, United States
| | - Elisa Fadda
- Department of Chemistry and Hamilton Institute, Maynooth University, Kilcock Road, Maynooth, Co. Kildare W23 AH3Y, Ireland
| | - Ten Feizi
- The Glycosciences Laboratory, Imperial College London, Hammersmith Campus, Du Cane Road, London W12 0NN, United Kingdom
| | - Catherine Hayes
- Proteome Informatics Group, Swiss Institute of Bioinformatics (SIB), route de Drize 7, Geneva CH-1227, Switzerland
| | - Callum M Ives
- Department of Chemistry and Hamilton Institute, Maynooth University, Kilcock Road, Maynooth, Co. Kildare W23 AH3Y, Ireland
| | - Hiren J Joshi
- Copenhagen Center for Glycomics, Department of Cellular and Molecular Medicine, Faculty of Health Sciences, University of Copenhagen, Blegdamsvej 3, Copenhagen DK-2200, Denmark
| | - Khakurel Krishna Prasad
- ELI Beamlines Facility, The Extreme Light Infrastructure ERIC, Za Radnicí 835, Dolní Břežany 25241, Czech Republic
| | - Sofia Kossida
- IMGT, The International ImMunoGeneTics Information System, National Center for Scientific Research (CNRS), Institute of Human Genetics (IGH), University of Montpellier (UM), 141 rue de la Cardonille, Montpellier 34 090, France
| | - Frederique Lisacek
- Proteome Informatics Group, Swiss Institute of Bioinformatics (SIB), route de Drize 7, Geneva CH-1227, Switzerland
| | - Yan Liu
- The Glycosciences Laboratory, Imperial College London, Hammersmith Campus, Du Cane Road, London W12 0NN, United Kingdom
| | - Thomas Lütteke
- Institute of Veterinary Physiology and Biochemistry, Justus-Liebig-University Gießen, Frankfurter Str. 100, Gießen 35392, Germany
| | - Junfeng Ma
- Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, 3900 Reservior Road NW, Washington, DC 20007, United States
| | - Adnan Malik
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom
| | - Maria Martin
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom
| | - Akul Y Mehta
- Department of Surgery, Beth Israel Deaconess Medical Center, National Center for Functional Glycomics, Harvard Medical School, 330 Brookline Avenue, Boston, MA 02215, United States
| | - Sriram Neelamegham
- Departments of Chemical & Biological Engineering, Biomedical Engineering and Medicine, University at Buffalo, State University of New York, 906 Furnas Hall, Buffalo, NY 14260, United States
| | - Kalpana Panneerselvam
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom
| | - René Ranzinger
- Complex Carbohydrate Research Center, University of Georgia, 315 Riverbend Rd, Athens, GA 30602, United States
| | - Sylvie Ricard-Blum
- Institute of Molecular and Supramolecular Chemistry and Biochemistry (ICBMS), UMR 5246, University Lyon 1, CNRS, 43 Boulevard du 11 novembre 1918, Villeurbanne cedex F-69622, France
| | - Gaoussou Sanou
- IMGT, The International ImMunoGeneTics Information System, National Center for Scientific Research (CNRS), Institute of Human Genetics (IGH), University of Montpellier (UM), 141 rue de la Cardonille, Montpellier 34 090, France
| | - Vijay Shanker
- Department of Computer and Information Sciences, University of Delaware, 18 Amstel Ave, Newark, DE 19716, United States
| | - Paul D Thomas
- Department of Population and Public Health Sciences, University of Southern California, 2001 N Soto Street, Los Angeles, CA 90032, United States
| | - Michael Tiemeyer
- Complex Carbohydrate Research Center, University of Georgia, 315 Riverbend Rd, Athens, GA 30602, United States
| | - James Urban
- Department of Chemistry and Molecular Biology, University of Gothenburg, Medicinaregatan 7 B, Gothenburg 41390, Sweden
| | - Randi Vita
- Immune Epitope Database and Analysis Project, La Jolla Institute for Allergy & Immunology, 9420 Athena Circle, La Jolla, CA 92037, United States
| | - Jeet Vora
- Department of Biochemistry & Molecular Medicine, The George Washington University School of Medicine and Health Sciences, 2300 I St. NW, Washington, DC 20052, United States
| | - Yasunori Yamamoto
- Database Center for Life Science, Joint Support-Center for Data Science Research, Research Organization of Information and Systems, 178-4-4 Wakashiba, Kashiwa, Chiba 277-0871, Japan
| | - Raja Mazumder
- Department of Biochemistry & Molecular Medicine, The George Washington University School of Medicine and Health Sciences, 2300 I St. NW, Washington, DC 20052, United States
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2
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Xu W, Velankar S, Patwardhan A, Hoch JC, Burley SK, Kurisu G. Announcing the launch of Protein Data Bank China as an Associate Member of the Worldwide Protein Data Bank Partnership. Acta Crystallogr D Struct Biol 2023; 79:792-795. [PMID: 37561405 PMCID: PMC10478634 DOI: 10.1107/s2059798323006381] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2023] [Accepted: 07/21/2023] [Indexed: 08/11/2023] Open
Abstract
The Protein Data Bank (PDB) is the single global archive of atomic-level, three-dimensional structures of biological macromolecules experimentally determined by macromolecular crystallography, nuclear magnetic resonance spectroscopy or three-dimensional cryo-electron microscopy. The PDB is growing continuously, with a recent rapid increase in new structure depositions from Asia. In 2022, the Worldwide Protein Data Bank (wwPDB; https://www.wwpdb.org/) partners welcomed Protein Data Bank China (PDBc; https://www.pdbc.org.cn) to the organization as an Associate Member. PDBc is based in the National Facility for Protein Science in Shanghai which is associated with the Shanghai Advanced Research Institute of Chinese Academy of Sciences, the Shanghai Institute for Advanced Immunochemical Studies and the iHuman Institute of ShanghaiTech University. This letter describes the history of the wwPDB, recently established mechanisms for adding new wwPDB data centers and the processes developed to bring PDBc into the partnership.
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Affiliation(s)
- Wenqing Xu
- Protein Data Bank China, ShanghaiTech University and National Facility for Protein Science in Shanghai, Shanghai, People’s Republic of China
| | - Sameer Velankar
- Protein Data Bank in Europe, European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom
| | - Ardan Patwardhan
- Electron Microscopy Data Bank, European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom
| | - Jeffrey C. Hoch
- Biological Magnetic Resonance Data Bank, UConn Health, Farmington, CT 06030-3305, USA
| | - Stephen K. Burley
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Research Collaboratory for Structural Biology Protein Data Bank, San Diego Supercomputer Center, University of California San Diego, La Jolla, CA 92093, USA
| | - Genji Kurisu
- Protein Data Bank Japan, Institute for Protein Research, Osaka University, Osaka 565-0871, Japan
- Protein Data Bank Japan, Protein Research Foundation, Minoh, Osaka 562-8686, Japan
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3
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Schormann N, Patel M, Thannickal L, Purushotham S, Wu R, Mieher JL, Wu H, Deivanayagam C. The catalytic domains of Streptococcus mutans glucosyltransferases: a structural analysis. Acta Crystallogr F Struct Biol Commun 2023; 79:119-127. [PMID: 37158310 PMCID: PMC10167749 DOI: 10.1107/s2053230x23003199] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Accepted: 04/06/2023] [Indexed: 05/10/2023] Open
Abstract
Streptococcus mutans, found in the human oral cavity, is a significant contributor to the pathogenesis of dental caries. This bacterium expresses three genetically distinct types of glucosyltransferases named GtfB (GTF-I), GtfC (GTF-SI) and GtfD (GTF-S) that play critical roles in the development of dental plaque. The catalytic domains of GtfB, GtfC and GtfD contain conserved active-site residues for the overall enzymatic activity that relate to hydrolytic glycosidic cleavage of sucrose to glucose and fructose, release of fructose and generation of a glycosyl-enzyme intermediate in the reducing end. In a subsequent transglycosylation step, the glucosyl moiety is transferred to the nonreducing end of an acceptor to form a growing glucan polymer chain made up of glucose molecules. It has been proposed that both sucrose breakdown and glucan synthesis occur in the same active site of the catalytic domain, although the active site does not appear to be large enough to accommodate both functions. These three enzymes belong to glycoside hydrolase family 70 (GH70), which shows homology to glycoside hydrolase family 13 (GH13). GtfC synthesizes both soluble and insoluble glucans (α-1,3 and α-1,6 glycosidic linkages), while GtfB and GtfD synthesize only insoluble or soluble glucans, respectively. Here, crystal structures of the catalytic domains of GtfB and GtfD are reported. These structures are compared with previously determined structures of the catalytic domain of GtfC. With this work, apo structures and inhibitor-complex structures with acarbose are now available for the catalytic domains of GtfC and GtfB. The structure of GtfC with maltose allows further identification and comparison of active-site residues. A model of sucrose binding to GtfB is also included. The new structure of the catalytic domain of GtfD affords a structural comparison of the three S. mutans glycosyltransferases. Unfortunately, the catalytic domain of GtfD is not complete since crystallization resulted in the structure of a truncated protein lacking approximately 200 N-terminal residues of domain IV.
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Affiliation(s)
- Norbert Schormann
- Department of Biochemistry and Molecular Genetics, University of Alabama at Birmingham, Birmingham, AL 35294, USA
| | - Manisha Patel
- Department of Biochemistry and Molecular Genetics, University of Alabama at Birmingham, Birmingham, AL 35294, USA
| | - Luke Thannickal
- Department of Biochemistry and Molecular Genetics, University of Alabama at Birmingham, Birmingham, AL 35294, USA
| | - Sangeetha Purushotham
- Department of Biochemistry and Molecular Genetics, University of Alabama at Birmingham, Birmingham, AL 35294, USA
| | - Ren Wu
- Department of Biochemistry and Molecular Genetics, University of Alabama at Birmingham, Birmingham, AL 35294, USA
| | - Joshua L. Mieher
- Department of Biochemistry and Molecular Genetics, University of Alabama at Birmingham, Birmingham, AL 35294, USA
| | - Hui Wu
- School of Dentistry, Oregon Health and Science University, Portland, OR 97201, USA
| | - Champion Deivanayagam
- Department of Biochemistry and Molecular Genetics, University of Alabama at Birmingham, Birmingham, AL 35294, USA
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4
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J N C, Mallajosyula SS. Impact of Polarization on the Ring Puckering Dynamics of Hexose Monosaccharides. J Chem Inf Model 2023; 63:208-223. [PMID: 36475659 DOI: 10.1021/acs.jcim.2c01286] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Analysis of crystal structures of hexose monosaccharides α-d-mannose (α-MAN), β-d-mannose (β-MAN), α-d-glucose (α-GLC), β-d-glucose (β-GLC), α-d-galactose (α-GAL), β-d-galactose (β-GAL), α-d-altrose (α-ALT), β-d-altrose (β-ALT), α-d-idose (α-IDO), and β-d-idose (β-IDO) reveals that the monosaccharide ring adopts multiple ring conformations. These ring conformations can be broadly classified as chair, half-chair, envelope, boat, and skew-boat conformations. The ability of the monosaccharide ring to adopt multiple conformations has been closely tied with their bioactivity. However, it has been difficult to capture the dynamic information of these conformations from experimental studies. Even from simulations, capturing these different conformations is challenging because of the energy barriers involved in the transitions between the stable 4C1 and 1C4 chair forms. In this study, we analyze the influence of the polarizable force field on the ring dynamics of five major types of unsubstituted aldohexoses─glucose, mannose, galactose, altrose, and idose─and their anomers. We simulate microsecond trajectories to capture the influence of the CHARMM36 additive and polarizable carbohydrate force fields on the ring dynamics. The microsecond trajectories allow us to comment on the issues associated with equilibrium molecular dynamics simulations. Further, we use the extended system adaptive biasing force (eABF) method to compare the conformational sampling efficiencies of the additive and polarizable force fields. Our studies reveal that inclusion of polarization enhances the sampling of ring conformations and lowers the energy barriers between the 4C1 and 1C4 conformations. Overall, the CHARMM36 additive force field is observed to be rigid and favor the 4C1 conformations. Although the inclusion of polarizability results in enhancing ring flexibility, we observe sampling that does not agree with experimental results, warranting a revision of the polarizable Drude parameters.
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Affiliation(s)
- Chythra J N
- Discipline of Chemistry, Indian Institute of Technology Gandhinagar, Gandhinagar, Gujarat382355, India
| | - Sairam S Mallajosyula
- Discipline of Chemistry, Indian Institute of Technology Gandhinagar, Gandhinagar, Gujarat382355, India
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5
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Burley SK, Berman HM, Chiu W, Dai W, Flatt JW, Hudson BP, Kaelber JT, Khare SD, Kulczyk AW, Lawson CL, Pintilie GD, Sali A, Vallat B, Westbrook JD, Young JY, Zardecki C. Electron microscopy holdings of the Protein Data Bank: the impact of the resolution revolution, new validation tools, and implications for the future. Biophys Rev 2022; 14:1281-1301. [PMID: 36474933 PMCID: PMC9715422 DOI: 10.1007/s12551-022-01013-w] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Accepted: 11/06/2022] [Indexed: 12/04/2022] Open
Abstract
As a discipline, structural biology has been transformed by the three-dimensional electron microscopy (3DEM) "Resolution Revolution" made possible by convergence of robust cryo-preservation of vitrified biological materials, sample handling systems, and measurement stages operating a liquid nitrogen temperature, improvements in electron optics that preserve phase information at the atomic level, direct electron detectors (DEDs), high-speed computing with graphics processing units, and rapid advances in data acquisition and processing software. 3DEM structure information (atomic coordinates and related metadata) are archived in the open-access Protein Data Bank (PDB), which currently holds more than 11,000 3DEM structures of proteins and nucleic acids, and their complexes with one another and small-molecule ligands (~ 6% of the archive). Underlying experimental data (3DEM density maps and related metadata) are stored in the Electron Microscopy Data Bank (EMDB), which currently holds more than 21,000 3DEM density maps. After describing the history of the PDB and the Worldwide Protein Data Bank (wwPDB) partnership, which jointly manages both the PDB and EMDB archives, this review examines the origins of the resolution revolution and analyzes its impact on structural biology viewed through the lens of PDB holdings. Six areas of focus exemplifying the impact of 3DEM across the biosciences are discussed in detail (icosahedral viruses, ribosomes, integral membrane proteins, SARS-CoV-2 spike proteins, cryogenic electron tomography, and integrative structure determination combining 3DEM with complementary biophysical measurement techniques), followed by a review of 3DEM structure validation by the wwPDB that underscores the importance of community engagement.
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Affiliation(s)
- Stephen K. Burley
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854 USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854 USA
- Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, NJ 08901 USA
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California San Diego, La Jolla, CA 92093 USA
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, 174 Frelinghuysen Road, Piscataway, NJ 08854 USA
| | - Helen M. Berman
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854 USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854 USA
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, 174 Frelinghuysen Road, Piscataway, NJ 08854 USA
| | - Wah Chiu
- Department of Bioengineering, Stanford University, Stanford, CA USA
- Division of CryoEM and Bioimaging, SSRL, SLAC National Accelerator Laboratory, Stanford University, Menlo Park, CA USA
| | - Wei Dai
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854 USA
- Department of Cell Biology and Neuroscience, Rutgers, The State University of New Jersey, Piscataway, NJ 08854 USA
| | - Justin W. Flatt
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854 USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854 USA
| | - Brian P. Hudson
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854 USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854 USA
| | - Jason T. Kaelber
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854 USA
| | - Sagar D. Khare
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854 USA
- Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, NJ 08901 USA
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, 174 Frelinghuysen Road, Piscataway, NJ 08854 USA
| | - Arkadiusz W. Kulczyk
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854 USA
- Department of Biochemistry and Microbiology, Rutgers, The State University of New Jersey, Piscataway, NJ 08901 USA
| | - Catherine L. Lawson
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854 USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854 USA
| | | | - Andrej Sali
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, Quantitative Biosciences Institute, University of California San Francisco, San Francisco, CA 94158 USA
| | - Brinda Vallat
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854 USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854 USA
- Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, NJ 08901 USA
| | - John D. Westbrook
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854 USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854 USA
- Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, NJ 08901 USA
| | - Jasmine Y. Young
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854 USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854 USA
| | - Christine Zardecki
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854 USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854 USA
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6
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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: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [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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7
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Burley SK, Berman HM, Duarte JM, Feng Z, Flatt JW, Hudson BP, Lowe R, Peisach E, Piehl DW, Rose Y, Sali A, Sekharan M, Shao C, Vallat B, Voigt M, Westbrook JD, Young JY, Zardecki C. Protein Data Bank: A Comprehensive Review of 3D Structure Holdings and Worldwide Utilization by Researchers, Educators, and Students. Biomolecules 2022; 12:1425. [PMID: 36291635 PMCID: PMC9599165 DOI: 10.3390/biom12101425] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 09/23/2022] [Accepted: 09/26/2022] [Indexed: 11/18/2022] Open
Abstract
The Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB), funded by the United States National Science Foundation, National Institutes of Health, and Department of Energy, supports structural biologists and Protein Data Bank (PDB) data users around the world. The RCSB PDB, a founding member of the Worldwide Protein Data Bank (wwPDB) partnership, serves as the US data center for the global PDB archive housing experimentally-determined three-dimensional (3D) structure data for biological macromolecules. As the wwPDB-designated Archive Keeper, RCSB PDB is also responsible for the security of PDB data and weekly update of the archive. RCSB PDB serves tens of thousands of data depositors (using macromolecular crystallography, nuclear magnetic resonance spectroscopy, electron microscopy, and micro-electron diffraction) annually working on all permanently inhabited continents. RCSB PDB makes PDB data available from its research-focused web portal at no charge and without usage restrictions to many millions of PDB data consumers around the globe. It also provides educators, students, and the general public with an introduction to the PDB and related training materials through its outreach and education-focused web portal. This review article describes growth of the PDB, examines evolution of experimental methods for structure determination viewed through the lens of the PDB archive, and provides a detailed accounting of PDB archival holdings and their utilization by researchers, educators, and students worldwide.
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Affiliation(s)
- Stephen K. Burley
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, NJ 08901, USA
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California San Diego, La Jolla, CA 92093, USA
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Helen M. Berman
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, 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
| | - 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
| | - 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
| | - 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
| | - 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
| | - 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
- Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, NJ 08901, 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
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8
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Exploring protein symmetry at the RCSB Protein Data Bank. Emerg Top Life Sci 2022; 6:231-243. [PMID: 35801924 PMCID: PMC9472815 DOI: 10.1042/etls20210267] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Revised: 06/15/2022] [Accepted: 06/20/2022] [Indexed: 11/17/2022]
Abstract
The symmetry of biological molecules has fascinated structural biologists ever since the structure of hemoglobin was determined. The Protein Data Bank (PDB) archive is the central global archive of three-dimensional (3D), atomic-level structures of biomolecules, providing open access to the results of structural biology research with no limitations on usage. Roughly 40% of the structures in the archive exhibit some type of symmetry, including formal global symmetry, local symmetry, or pseudosymmetry. The Research Collaboratory for Structural Bioinformatics (RCSB) Protein Data Bank (founding member of the Worldwide Protein Data Bank partnership that jointly manages, curates, and disseminates the archive) provides a variety of tools to assist users interested in exploring the symmetry of biological macromolecules. These tools include multiple modalities for searching and browsing the archive, turnkey methods for biomolecular visualization, documentation, and outreach materials for exploring functional biomolecular symmetry.
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9
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Westbrook JD, Young JY, Shao C, Feng Z, Guranovic V, Lawson CL, Vallat B, Adams PD, Berrisford JM, Bricogne G, Diederichs K, Joosten RP, Keller P, Moriarty NW, Sobolev OV, Velankar S, Vonrhein C, Waterman DG, Kurisu G, Berman HM, Burley SK, Peisach E. PDBx/mmCIF Ecosystem: Foundational Semantic Tools for Structural Biology. J Mol Biol 2022; 434:167599. [PMID: 35460671 DOI: 10.1016/j.jmb.2022.167599] [Citation(s) in RCA: 34] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 03/31/2022] [Accepted: 04/13/2022] [Indexed: 02/07/2023]
Abstract
PDBx/mmCIF, Protein Data Bank Exchange (PDBx) macromolecular Crystallographic Information Framework (mmCIF), has become the data standard for structural biology. With its early roots in the domain of small-molecule crystallography, PDBx/mmCIF provides an extensible data representation that is used for deposition, archiving, remediation, and public dissemination of experimentally determined three-dimensional (3D) structures of biological macromolecules by the Worldwide Protein Data Bank (wwPDB, wwpdb.org). Extensions of PDBx/mmCIF are similarly used for computed structure models by ModelArchive (modelarchive.org), integrative/hybrid structures by PDB-Dev (pdb-dev.wwpdb.org), small angle scattering data by Small Angle Scattering Biological Data Bank SASBDB (sasbdb.org), and for models computed generated with the AlphaFold 2.0 deep learning software suite (alphafold.ebi.ac.uk). Community-driven development of PDBx/mmCIF spans three decades, involving contributions from researchers, software and methods developers in structural sciences, data repository providers, scientific publishers, and professional societies. Having a semantically rich and extensible data framework for representing a wide range of structural biology experimental and computational results, combined with expertly curated 3D biostructure data sets in public repositories, accelerates the pace of scientific discovery. Herein, we describe the architecture of the PDBx/mmCIF data standard, tools used to maintain representations of the data standard, governance, and processes by which data content standards are extended, plus community tools/software libraries available for processing and checking the integrity of PDBx/mmCIF data. Use cases exemplify how the members of the Worldwide Protein Data Bank have used PDBx/mmCIF as the foundation for its pipeline for delivering Findable, Accessible, Interoperable, and Reusable (FAIR) data to many millions of users worldwide.
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Affiliation(s)
- 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
| | - 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
| | - 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
| | - 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
| | - 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
| | - 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
| | - Paul D Adams
- Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA; Department of Bioengineering, University of California at Berkeley, Berkeley, CA 94720, USA
| | - John M Berrisford
- Protein Data Bank in Europe, European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Gerard Bricogne
- Global Phasing Ltd, Sheraton House, Castle Park, Cambridge CB3 0AK, UK
| | | | - Robbie P Joosten
- Department of Biochemistry, Netherlands Cancer Institute, Amsterdam, the Netherlands; Oncode Institute, 3521 AL Utrecht, the Netherlands. https://www.twitter.com/Robbie_Joosten
| | - Peter Keller
- Global Phasing Ltd, Sheraton House, Castle Park, Cambridge CB3 0AK, UK
| | - Nigel W Moriarty
- Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Oleg V Sobolev
- Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Sameer Velankar
- Protein Data Bank in Europe, European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Clemens Vonrhein
- Global Phasing Ltd, Sheraton House, Castle Park, Cambridge CB3 0AK, UK
| | - David G Waterman
- UKRI-STFC Rutherford Appleton Laboratory, Didcot OX11 0FA, UK; CCP4, Research Complex at Harwell, Rutherford Appleton Laboratory, Didcot OX11 0FA, UK. https://www.twitter.com/upintheair
| | - Genji Kurisu
- Protein Data Bank Japan, Institute for Protein Research, Osaka University, Suita, Osaka 565-0871, Japan
| | - Helen M Berman
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, 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; The Bridge Institute, Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA, USA
| | - 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; 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, La Jolla, CA 92093, 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.
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10
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Scherbinina SI, Frank M, Toukach PV. Carbohydrate structure database (CSDB) oligosaccharide conformation tool. Glycobiology 2022; 32:460-468. [PMID: 35275211 DOI: 10.1093/glycob/cwac011] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2021] [Revised: 02/17/2022] [Accepted: 03/04/2022] [Indexed: 11/13/2022] Open
Abstract
Population analysis in terms of glycosidic torsion angles is frequently used to reveal preferred conformers of glycans. However, due to high structural diversity and flexibility of carbohydrates, conformational characterization of complex glycans can be a challenging task. Herein we present a conformation module of oligosaccharide fragments occurring in natural glycan structures developed on the platform of the Carbohydrate Structure Database (CSDB). Currently, this module deposits free energy surface and conformer abundance maps plotted as a function of glycosidic torsions for 194 inter-residue bonds. Data are automatically and continuously derived from explicit-solvent molecular dynamics (MD) simulations. The module was also supplemented with high-temperature MD data of saccharides (2403 maps) provided by GlycoMapsDB (hosted by GLYCOSCIENCES.de project). Conformational data defined by up to four torsional degrees of freedom can be freely explored using a web interface of the module available at http://csdb.glycoscience.ru/database/core/search_conf.html.
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Affiliation(s)
- S I Scherbinina
- Higher Chemical College, D. Mendeleev University of Chemical Technology of Russia, Miusskaya Square 9, 125047 Moscow, Russia
| | - M Frank
- Biognos AB, Box 8963, 40274 Göteborg, Sweden
| | - P V Toukach
- N.D. Zelinsky Institute of Organic Chemistry, Russian Academy of Science, Leninsky prospect 47, 119991 Moscow, Russia
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11
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Simplified quality assessment for small-molecule ligands in the Protein Data Bank. Structure 2022; 30:252-262.e4. [PMID: 35026162 PMCID: PMC8849442 DOI: 10.1016/j.str.2021.10.003] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Revised: 09/14/2021] [Accepted: 10/06/2021] [Indexed: 02/05/2023]
Abstract
More than 70% of the experimentally determined macromolecular structures in the Protein Data Bank (PDB) contain small-molecule ligands. Quality indicators of ∼643,000 ligands present in ∼106,000 PDB X-ray crystal structures have been analyzed. Ligand quality varies greatly with regard to goodness of fit between ligand structure and experimental data, deviations in bond lengths and angles from known chemical structures, and inappropriate interatomic clashes between the ligand and its surroundings. Based on principal component analysis, correlated quality indicators of ligand structure have been aggregated into two largely orthogonal composite indicators measuring goodness of fit to experimental data and deviation from ideal chemical structure. Ranking of the composite quality indicators across the PDB archive enabled construction of uniformly distributed composite ranking score. This score is implemented at RCSB.org to compare chemically identical ligands in distinct PDB structures with easy-to-interpret two-dimensional ligand quality plots, allowing PDB users to quickly assess ligand structure quality and select the best exemplars.
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12
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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] [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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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: 76] [Impact Index Per Article: 38.0] [Reference Citation Analysis] [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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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: 39] [Impact Index Per Article: 19.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [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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SugarDrawer: A Web-Based Database Search Tool with Editing Glycan Structures. Molecules 2021; 26:molecules26237149. [PMID: 34885724 PMCID: PMC8659005 DOI: 10.3390/molecules26237149] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2021] [Revised: 11/21/2021] [Accepted: 11/22/2021] [Indexed: 11/17/2022] Open
Abstract
In life science fields, database integration is progressing and contributing to collaboration between different research fields, including the glycosciences. The integration of glycan databases has greatly progressed collaboration worldwide with the development of the international glycan structure repository, GlyTouCan. This trend has increased the need for a tool by which researchers in various fields can easily search glycan structures from integrated databases. We have developed a web-based glycan structure search tool, SugarDrawer, which supports the depiction of glycans including ambiguity, such as glycan fragments which contain underdetermined linkages, and a database search for glycans drawn on the canvas. This tool provides an easy editing feature for various glycan structures in just a few steps using template structures and pop-up windows which allow users to select specific information for each structure element. This tool has a unique feature for selecting possible attachment sites, which is defined in the Symbol Nomenclature for Glycans (SNFG). In addition, this tool can input and output glycans in WURCS and GlycoCT formats, which are the most commonly-used text formats for glycan structures.
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