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    EffectorP: Predicting fungal effector proteins from secretomes using machine learning

    Access Status
    Open access via publisher
    Authors
    Sperschneider, J.
    Gardiner, D.
    Dodds, P.
    Tini, F.
    Covarelli, L.
    Singh, Karambir
    Manners, J.
    Taylor, J.
    Date
    2016
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Sperschneider, J. and Gardiner, D. and Dodds, P. and Tini, F. and Covarelli, L. and Singh, K. and Manners, J. et al. 2016. EffectorP: Predicting fungal effector proteins from secretomes using machine learning. New Phytologist. 210 (2): pp. 743-761.
    Source Title
    New Phytologist
    DOI
    10.1111/nph.13794
    ISSN
    0028-646X
    School
    Centre for Crop Disease Management
    URI
    http://hdl.handle.net/20.500.11937/9851
    Collection
    • Curtin Research Publications
    Abstract

    Eukaryotic filamentous plant pathogens secrete effector proteins that modulate the host cell to facilitate infection. Computational effector candidate identification and subsequent functional characterization delivers valuable insights into plant-pathogen interactions. However, effector prediction in fungi has been challenging due to a lack of unifying sequence features such as conserved N-terminal sequence motifs. Fungal effectors are commonly predicted from secretomes based on criteria such as small size and cysteine-rich, which suffers from poor accuracy. We present EffectorP which pioneers the application of machine learning to fungal effector prediction. EffectorP improves fungal effector prediction from secretomes based on a robust signal of sequence-derived properties, achieving sensitivity and specificity of over 80%. Features that discriminate fungal effectors from secreted noneffectors are predominantly sequence length, molecular weight and protein net charge, as well as cysteine, serine and tryptophan content. We demonstrate that EffectorP is powerful when combined with in planta expression data for predicting high-priority effector candidates. EffectorP is the first prediction program for fungal effectors based on machine learning. Our findings will facilitate functional fungal effector studies and improve our understanding of effectors in plant-pathogen interactions. EffectorP is available at http://effectorp.csiro.au.

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