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Three-dimensional Structure Databases of Biological Macromolecules

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Data Mining Techniques for the Life Sciences

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2449))

Abstract

Databases of three-dimensional structures of proteins (and their associated molecules) provide:

  1. (a)

    Curated repositories of coordinates of experimentally determined structures, including extensive metadata; for instance information about provenance, details about data collection and interpretation, and validation of results.

  2. (b)

    Information-retrieval tools to allow searching to identify entries of interest and provide access to them.

  3. (c)

    Links among databases, especially to databases of amino-acid and genetic sequences, and of protein function; and links to software for analysis of amino-acid sequence and protein structure, and for structure prediction.

  4. (d)

    Collections of predicted three-dimensional structures of proteins. These will become more and more important after the breakthrough in structure prediction achieved by AlphaFold2.

The single global archive of experimentally determined biomacromolecular structures is the Protein Data Bank (PDB). It is managed by wwPDB, a consortium of five partner institutions: the Protein Data Bank in Europe (PDBe), the Research Collaboratory for Structural Bioinformatics (RCSB), the Protein Data Bank Japan (PDBj), the BioMagResBank (BMRB), and the Electron Microscopy Data Bank (EMDB). In addition to jointly managing the PDB repository, the individual wwPDB partners offer many tools for analysis of protein and nucleic acid structures and their complexes, including providing computer-graphic representations. Their collective and individual websites serve as hubs of the community of structural biologists, offering newsletters, reports from Task Forces, training courses, and “helpdesks,” as well as links to external software.

Many specialized projects are based on the information contained in the PDB. Especially important are SCOP, CATH, and ECOD, which present classifications of protein domains.

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Notes

  1. 1.

    A (very rough) back-of-the-envelope calculation suggests that to store ∼50 PDB entries on punched cards in mmCIF format rather than old PDB format would cost one 8-inch-diameter tree.

  2. 2.

    In this example, the subgraph shown could be regarded as a tree, starting with the family node as the root. (Indeed, having the root at the bottom would more accurately reflect the botanical metaphor.) However, in many cases the ancestry subgraph is a directed acyclic graph but not a tree.

  3. 3.

    Sydney Brenner used to chaff Aaron Klug, saying: “Why don’t you crystallize E. coli?”

  4. 4.

    Many readers will recall that Paul Dirac famously made a similar-sounding claim about chemistry, in 1929, but this has not happened.

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Acknowledgements

We thank A.G. Murzin and A. Andreeva for helpful advice.

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Authors and Affiliations

  1. Institute of Structural and Molecular Biology, University College London, London, UK

    Vaishali P. Waman & Christine Orengo

  2. European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK

    Gerard J. Kleywegt

  3. Department of Biochemistry and Molecular Biology and Center for Computational Biology and Bioinformatics, The Pennsylvania State University, University Park, PA, USA

    Arthur M. Lesk

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  1. Vaishali P. Waman
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  2. Christine Orengo
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  4. Arthur M. Lesk
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Correspondence to Arthur M. Lesk .

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Editors and Affiliations

  1. University of Pavia, Pavia, Italy

    Oliviero Carugo

  2. Genome Institute and Bioinformatics Institute, Singapore, Singapore

    Frank Eisenhaber

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Waman, V.P., Orengo, C., Kleywegt, G.J., Lesk, A.M. (2022). Three-dimensional Structure Databases of Biological Macromolecules. In: Carugo, O., Eisenhaber, F. (eds) Data Mining Techniques for the Life Sciences. Methods in Molecular Biology, vol 2449. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-2095-3_3

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