Research ArticleBIOCHEMISTRY

An ambiguity principle for assigning protein structural domains

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Science Advances  13 Jan 2017:
Vol. 3, no. 1, e1600552
DOI: 10.1126/sciadv.1600552
  • Fig. 1 Analogy between recognition of structural domains and image interpretation.

    (A) Three equally valid assignments of structural domains for the actin protein (PDB: 1ATNA), each resulting from different partitioning criteria. (B) Ambiguous image that can be interpreted as either the letters “KB,” the mathematical inequality “1 < 13,” or the letters “VD” with their mirror image.

  • Fig. 2 Benchmark results for the SWORD algorithm.

    (A) Partitioning accuracies of SWORD, PDP, DomainParser (DP), and DDomain (DD) calculated for the four data sets of structural domain annotations (values are given in fig. S1). (B) Agreement between SWORD and either CATH or SCOP annotations, depending on the number of assignments provided, for the structures of the Dissensus data set (values are given in table S1).

  • Fig. 3 Practical cases requiring alternative domain decompositions.

    (A to X) Examples illustrating the usefulness of SWORD structural partitioning (for details, please refer to the main text).

  • Fig. 4 Assessement of the structural ambiguity measure.

    (A) Box plots of the A-indexes calculated by SWORD for the two-domain protein structures (SCOP boundaries) of the Consensus, Dissensus, and Strong-dissensus data sets (statistics in table S3). (B) A-index distributions of the structures from the Consensus and Dissensus data sets for four categories of chain lengths from 100 to 200 amino acids to >400 amino acids (statistics in table S4).

  • Fig. 5 Revealing the structural ambiguity with the A-index.

    (A to D) Examples of domain assignments for protein structures from the Consensus set that are detected as ambiguous by SWORD.

Supplementary Materials

  • Supplementary material for this article is available at http://advances.sciencemag.org/cgi/content/full/3/1/e1600552/DC1

    fig. S1. Monopartitioning accuracies of SWORD, PDP, DomainParser, and DDomain.

    fig. S2. Rate of agreement between SWORD and CATH, SCOP, or ECOD annotations, depending on the number of assignments provided, for the structures of the Strong-dissensus data set.

    fig. S3. Representation of the domain assignment model.

    fig. S4. Domain assignments of the 1A8YA protein structure, as displayed by SWORD.

    table S1. Rate of agreement between SWORD and annotations from the five data sets of structural domains, depending on the number of assignments provided.

    table S2. The 34 most ambiguous protein structures of the Consensus set.

    table S3. The P values of the Mann-Whitney-Wilcoxon tests comparing the A-index means of the Consensus, Dissensus, and Strong-dissensus sets.

    table S4. The P values of the Mann-Whitney-Wilcoxon and Pearson’s χ2 tests comparing the A-index distributions of the Consensus and Dissensus sets.

    equation S1. The contact probability between two PUs.

  • Supplementary Materials

    This PDF file includes:

    • fig. S1. Monopartitioning accuracies of SWORD, PDP, DomainParser, and DDomain.
    • fig. S2. Rate of agreement between SWORD and CATH, SCOP, or ECOD annotations, depending on the number of assignments provided, for the structures of the Strong-dissensus data set.
    • fig. S3. Representation of the domain assignment model.
    • fig. S4. Domain assignments of the 1A8YA protein structure, as displayed by SWORD.
    • table S1. Rate of agreement between SWORD and annotations from the five data sets of structural domains, depending on the number of assignments provided.
    • table S2. The 34 most ambiguous protein structures of the Consensus set.
    • table S3. The P values of the Mann-Whitney-Wilcoxon tests comparing the A-index means of the Consensus, Dissensus, and Strong-dissensus sets.
    • table S4. The P values of the Mann-Whitney-Wilcoxon and Pearson’s χ2 tests comparing the A-index distributions of the Consensus and Dissensus sets.
    • equation S1. The contact probability between two PUs.

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