XANNpred: Neural nets that predict the propensity of a protein to yield diffraction-quality crystals

Ian M. Overton, C. A. Johannes van Niekerk, Geoffrey J. Barton

    Research output: Contribution to journalArticle

    16 Citations (Scopus)

    Abstract

    Production of diffracting crystals is a critical step in determining the three-dimensional structure of a protein by X-ray crystallography. Computational techniques to rank proteins by their propensity to yield diffraction-quality crystals can improve efficiency in obtaining structural data by guiding both protein selection and construct design. XANNpred comprises a pair of artificial neural networks that each predict the propensity of a selected protein sequence to produce diffraction- quality crystals by current structural biology techniques. Blind tests show XANNpred has accuracy and Matthews correlation values ranging from 75% to 81% and 0.50 to 0.63 respectively; values of area under the receiver operator characteristic (ROC) curve range from 0.81 to 0.88. On blind test data XANNpred outperforms the other available algorithms XtalPred, PXS, OB-Score, and ParCrys. XANNpred also guides construct design by presenting graphs of predicted propensity for diffraction-quality crystals against residue sequence position. The XANNpred-SG algorithm is likely to be most useful to target selection in structural genomics consortia, while the XANNpred-PDB algorithm is more suited to the general structural biology community. XANNpred predictions that include sliding window graphs are freely available from http://www.compbio.dundee.ac.uk/xannpred

    Original languageEnglish
    Pages (from-to)1027-1033
    Number of pages7
    JournalProteins: Structure, Function, and Bioinformatics
    Volume79
    Issue number4
    DOIs
    Publication statusPublished - Apr 2011

    Keywords

    • computational biology
    • bioinformatics
    • crystallization
    • software
    • artificial neural network
    • predictor
    • GENOMICS TARGET SELECTION
    • X-RAY CRYSTALLOGRAPHY
    • STRUCTURAL GENOMICS
    • SEQUENCE ALIGNMENTS
    • CRYSTALLIZATION
    • DATABASE
    • THROUGHPUT
    • PROTEOMICS
    • STRATEGY
    • IMPACT

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