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Xu Q, Dunbrack R. The protein common assembly database (ProtCAD)-a comprehensive structural resource of protein complexes. Nucleic Acids Res 2022; 51:D466-D478. [PMID: 36300618 PMCID: PMC9825537 DOI: 10.1093/nar/gkac937] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 10/04/2022] [Accepted: 10/11/2022] [Indexed: 01/29/2023] Open
Abstract
Proteins often act through oligomeric interactions with other proteins. X-ray crystallography and cryo-electron microscopy provide detailed information on the structures of biological assemblies, defined as the most likely biologically relevant structures derived from experimental data. In crystal structures, the most relevant assembly may be ambiguously determined, since multiple assemblies observed in the crystal lattice may be plausible. It is estimated that 10-15% of PDB entries may have incorrect or ambiguous assembly annotations. Accurate assemblies are required for understanding functional data and training of deep learning methods for predicting assembly structures. As with any other kind of biological data, replication via multiple independent experiments provides important validation for the determination of biological assembly structures. Here we present the Protein Common Assembly Database (ProtCAD), which presents clusters of protein assembly structures observed in independent structure determinations of homologous proteins in the Protein Data Bank (PDB). ProtCAD is searchable by PDB entry, UniProt identifiers, or Pfam domain designations and provides downloads of coordinate files, PyMol scripts, and publicly available assembly annotations for each cluster of assemblies. About 60% of PDB entries contain assemblies in clusters of at least 2 independent experiments. All clusters and coordinates are available on ProtCAD web site (http://dunbrack2.fccc.edu/protcad).
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Affiliation(s)
- Qifang Xu
- Institute for Cancer Research, Fox Chase Cancer Center, Philadelphia, PA 19111, USA
| | - Roland L Dunbrack
- To whom correspondence should be addressed. Tel: +1 215 728 2434; Fax: +1 215 728 2412;
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2
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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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Goodsell DS, Burley SK. RCSB Protein Data Bank tools for 3D structure-guided cancer research: human papillomavirus (HPV) case study. Oncogene 2020; 39:6623-6632. [PMID: 32939013 PMCID: PMC7581513 DOI: 10.1038/s41388-020-01461-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2020] [Revised: 07/30/2020] [Accepted: 09/04/2020] [Indexed: 11/21/2022]
Abstract
Atomic-level three-dimensional (3D) structure data for biological macromolecules often prove critical to dissecting and understanding the precise mechanisms of action of cancer-related proteins and their diverse roles in oncogenic transformation, proliferation, and metastasis. They are also used extensively to identify potentially druggable targets and facilitate discovery and development of both small-molecule and biologic drugs that are today benefiting individuals diagnosed with cancer around the world. 3D structures of biomolecules (including proteins, DNA, RNA, and their complexes with one another, drugs, and other small molecules) are freely distributed by the open-access Protein Data Bank (PDB). This global data repository is used by millions of scientists and educators working in the areas of drug discovery, vaccine design, and biomedical and biotechnology research. The US Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB) provides an integrated portal to the PDB archive that streamlines access for millions of worldwide PDB data consumers worldwide. Herein, we review online resources made available free of charge by the RCSB PDB to basic and applied researchers, healthcare providers, educators and their students, patients and their families, and the curious public. We exemplify the value of understanding cancer-related proteins in 3D with a case study focused on human papillomavirus.
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Affiliation(s)
- David S Goodsell
- Research Collaboratory for Structural Bioinformatics Protein Data Bank and Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ, 08854, USA. .,Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, 92037, USA.
| | - Stephen K Burley
- Research Collaboratory for Structural Bioinformatics Protein Data Bank and Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ, 08854, USA. .,Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ, 08854, USA. .,Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, and the Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, 92093, USA. .,Rutgers Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, NJ, 08903, USA.
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Pseudo-Symmetric Assembly of Protodomains as a Common Denominator in the Evolution of Polytopic Helical Membrane Proteins. J Mol Evol 2020; 88:319-344. [PMID: 32189026 PMCID: PMC7162841 DOI: 10.1007/s00239-020-09934-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2019] [Accepted: 02/16/2020] [Indexed: 11/05/2022]
Abstract
The polytopic helical membrane proteome is dominated by proteins containing seven transmembrane helices (7TMHs). They cannot be grouped under a monolithic fold or superfold. However, a parallel structural analysis of folds around that magic number of seven in distinct protein superfamilies (SWEET, PnuC, TRIC, FocA, Aquaporin, GPCRs) reveals a common homology, not in their structural fold, but in their systematic pseudo-symmetric construction during their evolution. Our analysis leads to guiding principles of intragenic duplication and pseudo-symmetric assembly of ancestral transmembrane helical protodomains, consisting of 3 (or 4) helices. A parallel deconstruction and reconstruction of these domains provides a structural and mechanistic framework for their evolutionary paths. It highlights the conformational plasticity inherent to fold formation itself, the role of structural as well as functional constraints in shaping that fold, and the usefulness of protodomains as a tool to probe convergent vs divergent evolution. In the case of FocA vs. Aquaporin, this protodomain analysis sheds new light on their potential divergent evolution at the protodomain level followed by duplication and parallel evolution of the two folds. GPCR domains, whose function does not seem to require symmetry, nevertheless exhibit structural pseudo-symmetry. Their construction follows the same protodomain assembly as any other pseudo-symmetric protein suggesting their potential evolutionary origins. Interestingly, all the 6/7/8TMH pseudo-symmetric folds in this study also assemble as oligomeric forms in the membrane, emphasizing the role of symmetry in evolution, revealing self-assembly and co-evolution not only at the protodomain level but also at the domain level.
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Liu H, Cao M, Wang Y, Lv B, Li C. Bioengineering oligomerization and monomerization of enzymes: learning from natural evolution to matching the demands for industrial applications. Crit Rev Biotechnol 2020; 40:231-246. [PMID: 31914816 DOI: 10.1080/07388551.2019.1711014] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Abstract
It is generally accepted that oligomeric enzymes evolve from their monomeric ancestors, and the evolution process generates superior structural benefits for functional advantages. Furthermore, adjusting the transition between different oligomeric states is an important mechanism for natural enzymes to regulate their catalytic functions for adapting environmental fluctuations in nature, which inspires researchers to mimic such a strategy to develop artificially oligomerized enzymes through protein engineering for improved performance under specific conditions. On the other hand, transforming oligomeric enzymes into their monomers is needed in fundamental research for deciphering catalytic mechanisms as well as exploring their catalytic capacities for better industrial applications. In this article, strategies for developing artificially oligomerized and monomerized enzymes are reviewed and highlighted by their applications. Furthermore, advances in the computational prediction of oligomeric structures are introduced, which would accelerate the systematic design of oligomeric and monomeric enzymes. Finally, the current challenges and future directions in this field are discussed.
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Affiliation(s)
- Hu Liu
- Institute for Synthetic Biosystem, Department of Biochemical Engineering, School of Chemistry and Chemical Engineering, Beijing Institute of Technology, Beijing, China
| | - Mingming Cao
- Institute for Synthetic Biosystem, Department of Biochemical Engineering, School of Chemistry and Chemical Engineering, Beijing Institute of Technology, Beijing, China
| | - Ying Wang
- Institute for Synthetic Biosystem, Department of Biochemical Engineering, School of Chemistry and Chemical Engineering, Beijing Institute of Technology, Beijing, China
| | - Bo Lv
- Institute for Synthetic Biosystem, Department of Biochemical Engineering, School of Chemistry and Chemical Engineering, Beijing Institute of Technology, Beijing, China
| | - Chun Li
- Institute for Synthetic Biosystem, Department of Biochemical Engineering, School of Chemistry and Chemical Engineering, Beijing Institute of Technology, Beijing, China
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Principles and characteristics of biological assemblies in experimentally determined protein structures. Curr Opin Struct Biol 2019; 55:34-49. [PMID: 30965224 DOI: 10.1016/j.sbi.2019.03.006] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2019] [Accepted: 03/01/2019] [Indexed: 12/27/2022]
Abstract
More than half of all structures in the PDB are assemblies of two or more proteins, including both homooligomers and heterooligomers. Structural information on these assemblies comes from X-ray crystallography, NMR, and cryo-EM spectroscopy. The correct assembly in an X-ray structure is often ambiguous, and computational methods have been developed to identify the most likely biologically relevant assembly based on physical properties of assemblies and sequence conservation in interfaces. Taking advantage of the large number of structures now available, some of the most recent methods have relied on similarity of interfaces and assemblies across structures of homologous proteins.
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