ApoplastP: Prediction of effectors and plant proteins in the apoplast using machine learning
© 2017 New Phytologist Trust. The plant apoplast is integral to intercellular signalling, transport and plant-pathogen interactions. Plant pathogens deliver effectors both into the apoplast and inside host cells, but no computational method currently exists to discriminate between these localization...
| Main Authors: | , , , |
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| Format: | Journal Article |
| Published: |
Wiley-Blackwell Publishing Ltd.
2017
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| Online Access: | http://hdl.handle.net/20.500.11937/62370 |
| _version_ | 1848760837615910912 |
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| author | Sperschneider, J. Dodds, P. Singh, Karam Taylor, J. |
| author_facet | Sperschneider, J. Dodds, P. Singh, Karam Taylor, J. |
| author_sort | Sperschneider, J. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | © 2017 New Phytologist Trust. The plant apoplast is integral to intercellular signalling, transport and plant-pathogen interactions. Plant pathogens deliver effectors both into the apoplast and inside host cells, but no computational method currently exists to discriminate between these localizations. We present ApoplastP, the first method for predicting whether an effector or plant protein localizes to the apoplast. ApoplastP uncovers features of apoplastic localization common to both effectors and plant proteins, namely depletion in glutamic acid, acidic amino acids and charged amino acids and enrichment in small amino acids. ApoplastP predicts apoplastic localization in effectors with a sensitivity of 75% and a false positive rate of 5%, improving the accuracy of cysteine-rich classifiers by > 13%. ApoplastP does not depend on the presence of a signal peptide and correctly predicts the localization of unconventionally secreted proteins. The secretomes of fungal saprophytes as well as necrotrophic, hemibiotrophic and extracellular fungal pathogens are enriched for predicted apoplastic proteins. Rust pathogens have low proportions of predicted apoplastic proteins, but these are highly enriched for predicted effectors. ApoplastP pioneers apoplastic localization prediction using machine learning. It will facilitate functional studies and will be valuable for predicting if an effector localizes to the apoplast or if it enters plant cells. |
| first_indexed | 2025-11-14T10:22:07Z |
| format | Journal Article |
| id | curtin-20.500.11937-62370 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T10:22:07Z |
| publishDate | 2017 |
| publisher | Wiley-Blackwell Publishing Ltd. |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-623702018-02-01T05:57:29Z ApoplastP: Prediction of effectors and plant proteins in the apoplast using machine learning Sperschneider, J. Dodds, P. Singh, Karam Taylor, J. © 2017 New Phytologist Trust. The plant apoplast is integral to intercellular signalling, transport and plant-pathogen interactions. Plant pathogens deliver effectors both into the apoplast and inside host cells, but no computational method currently exists to discriminate between these localizations. We present ApoplastP, the first method for predicting whether an effector or plant protein localizes to the apoplast. ApoplastP uncovers features of apoplastic localization common to both effectors and plant proteins, namely depletion in glutamic acid, acidic amino acids and charged amino acids and enrichment in small amino acids. ApoplastP predicts apoplastic localization in effectors with a sensitivity of 75% and a false positive rate of 5%, improving the accuracy of cysteine-rich classifiers by > 13%. ApoplastP does not depend on the presence of a signal peptide and correctly predicts the localization of unconventionally secreted proteins. The secretomes of fungal saprophytes as well as necrotrophic, hemibiotrophic and extracellular fungal pathogens are enriched for predicted apoplastic proteins. Rust pathogens have low proportions of predicted apoplastic proteins, but these are highly enriched for predicted effectors. ApoplastP pioneers apoplastic localization prediction using machine learning. It will facilitate functional studies and will be valuable for predicting if an effector localizes to the apoplast or if it enters plant cells. 2017 Journal Article http://hdl.handle.net/20.500.11937/62370 10.1111/nph.14946 Wiley-Blackwell Publishing Ltd. restricted |
| spellingShingle | Sperschneider, J. Dodds, P. Singh, Karam Taylor, J. ApoplastP: Prediction of effectors and plant proteins in the apoplast using machine learning |
| title | ApoplastP: Prediction of effectors and plant proteins in the apoplast using machine learning |
| title_full | ApoplastP: Prediction of effectors and plant proteins in the apoplast using machine learning |
| title_fullStr | ApoplastP: Prediction of effectors and plant proteins in the apoplast using machine learning |
| title_full_unstemmed | ApoplastP: Prediction of effectors and plant proteins in the apoplast using machine learning |
| title_short | ApoplastP: Prediction of effectors and plant proteins in the apoplast using machine learning |
| title_sort | apoplastp: prediction of effectors and plant proteins in the apoplast using machine learning |
| url | http://hdl.handle.net/20.500.11937/62370 |