EffectorP: Predicting fungal effector proteins from secretomes using machine learning
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 i...
| Main Authors: | , , , , , , , |
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| Format: | Journal Article |
| Published: |
Wiley-Blackwell Publishing Ltd.
2016
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| Online Access: | http://hdl.handle.net/20.500.11937/9851 |
| _version_ | 1848746069605744640 |
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| author | Sperschneider, J. Gardiner, D. Dodds, P. Tini, F. Covarelli, L. Singh, Karambir Manners, J. Taylor, J. |
| author_facet | Sperschneider, J. Gardiner, D. Dodds, P. Tini, F. Covarelli, L. Singh, Karambir Manners, J. Taylor, J. |
| author_sort | Sperschneider, J. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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 <a href="http://effectorp.csiro.au.">http://effectorp.csiro.au.</a> |
| first_indexed | 2025-11-14T06:27:24Z |
| format | Journal Article |
| id | curtin-20.500.11937-9851 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T06:27:24Z |
| publishDate | 2016 |
| publisher | Wiley-Blackwell Publishing Ltd. |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-98512017-09-13T14:49:26Z EffectorP: Predicting fungal effector proteins from secretomes using machine learning Sperschneider, J. Gardiner, D. Dodds, P. Tini, F. Covarelli, L. Singh, Karambir Manners, J. Taylor, J. 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 <a href="http://effectorp.csiro.au.">http://effectorp.csiro.au.</a> 2016 Journal Article http://hdl.handle.net/20.500.11937/9851 10.1111/nph.13794 Wiley-Blackwell Publishing Ltd. unknown |
| spellingShingle | Sperschneider, J. Gardiner, D. Dodds, P. Tini, F. Covarelli, L. Singh, Karambir Manners, J. Taylor, J. EffectorP: Predicting fungal effector proteins from secretomes using machine learning |
| title | EffectorP: Predicting fungal effector proteins from secretomes using machine learning |
| title_full | EffectorP: Predicting fungal effector proteins from secretomes using machine learning |
| title_fullStr | EffectorP: Predicting fungal effector proteins from secretomes using machine learning |
| title_full_unstemmed | EffectorP: Predicting fungal effector proteins from secretomes using machine learning |
| title_short | EffectorP: Predicting fungal effector proteins from secretomes using machine learning |
| title_sort | effectorp: predicting fungal effector proteins from secretomes using machine learning |
| url | http://hdl.handle.net/20.500.11937/9851 |