The worldwide Protein Data Bank (wwPDB): ensuring a single, uniform archive of PDB data

By Helen M. Berman, K. Henrick, Haruki Nakamura, John L. Markley. 2006

1,102 citations recorded by the source corpus.

The worldwide Protein Data Bank (wwPDB) is the international collaboration that manages the deposition, processing and distribution of the PDB archive. The online PDB archive is a repository for the coordinates and related information for more than 38 000 structures, including proteins, nucleic acids and large macromolecular complexes that have been determined using X-ray crystallography, NMR and electron microscopy techniques. The founding members of the wwPDB are RCSB PDB (USA), MSD-EBI (Europe) and PDBj (Japan) [H.M. Berman, K. Henrick and H. Nakamura (2003) Nature Struct. Biol., 10, 980]. The BMRB group (USA) joined the wwPDB in 2006. The mission of the wwPDB is to maintain a single archive of macromolecular structural data that are freely and publicly available to the global community. Additionally, the wwPDB provides a variety of services to a broad community of users. The wwPDB website at provides information about services provided by the individual member organizations and about projects undertaken by the wwPDB.

Subfields: Materials Chemistry, Molecular Biology, Evaluation & Benchmarks, Computational Biology & Bioinformatics

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Citations in the corpus

The Pfam protein families database in 2019 (2018, methodology): “As such, Pfam 32.0 now covers 87.1% of sequences and 73.8% of all residues represented by known structures found in PDB (15,16) (9 May 2018).”
SWISS-MODEL: modelling protein tertiary and quaternary structure using evolutionary information (2014, background): “Modelling servers are blindly assessed based on sequences pre-released by the PDB for proteins whose structure will be published in the next release.”
Assessment of ligand binding residue predictions in CASP9 (2011, methodology): “In summary, all top performing groups have applied a similar approach, using ligand information derived from homologous structures in the PDB. 26 In comparison with CASP8,20 we could not observe a significant progress by the top groups, but rather a larger number of groups performing at the same…”
The ProteomeXchange consortium in 2017: supporting the cultural change in proteomics public data deposition (2016, background): “Prime examples of long-running and successful consortia to systematically collect, exchange and disseminate biomolecular data are the International Nucleotide Sequence Database Collaboration (1) for DNA sequence data, and the worldwide Protein Data Bank (2) for protein structure data.”
FireDB: a compendium of biological and pharmacologically relevant ligands (2013, background): “Much of the untapped functional information in the main repository for protein 3D structures, the Protein Data Bank [PDB, (3)], can be found at the residue level in the form of the amino acid residues involved in ligand binding and implicated in catalysis.”
The RING 2.0 web server for high quality residue interaction networks (2016, methodology): “Moreover, distance W368 Nucleic Acids Research, 2016, Vol. 44, Web Server issue thresholds have been optimized to maximize network reliability on a large-scale by analysing the entire Protein Data Bank (PDB) repository (22).”
Protein-Protein Interaction Detection: Methods and Analysis (2014, background): “The PDB database offers useful tools and information resources for researchers to build the structure for a query protein [34].”
HMMER web server: 2015 update (2015, background): “We also include sequences from known structures that have been deposited in the Protein Data Bank (13)).”
Automated Protein Structure Modeling with SWISS-MODEL Workspace and the Protein Model Portal (2012, methodology): “Models built based on a specific template structure can be also retrieved by entering either PDB accession codes (54) or structural genomics targets identifiers (101).”
PONDEROSA-C/S: client–server based software package for automated protein 3D structure determination (2014, background): “The growing gap between known sequences of proteins [ [ 1.6 9 10 8 in GenBank (Benson et al. 2008)] and 3D structures [ * 1 9 10 5 in PDB (Protein Data Bank; Berman et al. 2007)] is motivating the development of improved approaches to experimental structure determination.”
Evaluation of the template-based modeling in CASP12 (2017, methodology): “This CASP we saw signs of measurable progress in at least four areas of TBM: (1) Overall backbone accuracy, (2) Overall accuracy as measured by improvement over the best template, (3) Accuracy of regions structurally divergent from a principal template, and (4) Overall alignment accuracy.”