ParCrys: a Parzen window density estimation approach to protein crystallization propensity prediction

Ian M. Overton, Gianandrea Padovani, Mark A. Girolami, Geoffrey J. Barton

    Research output: Contribution to journalArticlepeer-review

    52 Citations (Scopus)

    Abstract

    The ability to rank proteins by their likely success in crystallization is useful in current Structural Biology efforts and in particular in high-throughput Structural Genomics initiatives. We present ParCrys, a Parzen Window approach to estimate a proteins propensity to produce diffraction-quality crystals. The Protein Data Bank (PDB) provided training data whilst the databases TargetDB and PepcDB were used to define feature selection data as well as test data independent of feature selection and training. ParCrys outperforms the OB-Score, SECRET and CRYSTALP on the data examined, with accuracy and Matthews correlation coefficient values of 79.1 and 0.582, respectively (74.0 and 0.227, respectively, on data with a real-world ratio of positive:negative examples). ParCrys predictions and associated data are available from www.compbio.dundee.ac.uk/parcrys.

    Original languageEnglish
    Pages (from-to)901-907
    Number of pages7
    JournalBMC Bioinformatics
    Volume24
    Issue number7
    DOIs
    Publication statusPublished - 1 Apr 2008

    Keywords

    • STRUCTURAL-GENOMICS
    • TARGET SELECTION
    • INHIBITOR
    • SEQUENCES
    • THROUGHPUT
    • STRATEGIES
    • SCIENCE
    • COMPLEX
    • BIOLOGY
    • DESIGN

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