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    Evaluation of disorder predictions in CASP9

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    Type
    Article
    Authors
    Monastyrskyy, Bohdan
    Fidelis, Krzysztof cc
    Moult, John
    Tramontano, Anna
    Kryshtafovych, Andriy cc
    KAUST Grant Number
    KUK-I1-012-43
    Date
    2011-09-16
    Online Publication Date
    2011-09-16
    Print Publication Date
    2011
    Permanent link to this record
    http://hdl.handle.net/10754/598246
    
    Metadata
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    Abstract
    Lack of stable three-dimensional structure, or intrinsic disorder, is a common phenomenon in proteins. Naturally, unstructured regions are proven to be essential for carrying function by many proteins, and therefore identification of such regions is an important issue. CASP has been assessing the state of the art in predicting disorder regions from amino acid sequence since 2002. Here, we present the results of the evaluation of the disorder predictions submitted to CASP9. The assessment is based on the evaluation measures and procedures used in previous CASPs. The balanced accuracy and the Matthews correlation coefficient were chosen as basic measures for evaluating the correctness of binary classifications. The area under the receiver operating characteristic curve was the measure of choice for evaluating probability-based predictions of disorder. The CASP9 methods are shown to perform slightly better than the CASP7 methods but not better than the methods in CASP8. It was also shown that capability of most CASP9 methods to predict disorder decreases with increasing minimum disorder segment length.
    Citation
    Monastyrskyy B, Fidelis K, Moult J, Tramontano A, Kryshtafovych A (2011) Evaluation of disorder predictions in CASP9. Proteins: Structure, Function, and Bioinformatics 79: 107–118. Available: http://dx.doi.org/10.1002/prot.23161.
    Sponsors
    Grant sponsor: US National Library of Medicine (NIH/NLM); Grant number: LM007085; Grant sponsor: King Abdullah University of Science and Technology (KAUST); Grant number: KUK-I1-012-43.
    Publisher
    Wiley
    Journal
    Proteins: Structure, Function, and Bioinformatics
    DOI
    10.1002/prot.23161
    PubMed ID
    21928402
    PubMed Central ID
    PMC3212657
    ae974a485f413a2113503eed53cd6c53
    10.1002/prot.23161
    Scopus Count
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