Abstract
Pathogens secrete effector proteins and many operate inside plant cells to enable infection. Some effectors have been found to enter subcellular compartments by mimicking host targeting sequences. Although many computational methods exist to predict plant protein subcellular localization, they perform poorly for effectors. We introduce LOCALIZER for predicting plant and effector protein localization to chloroplasts, mitochondria, and nuclei. LOCALIZER shows greater prediction accuracy for chloroplast and mitochondrial targeting compared to other methods for 652 plant proteins. For 107 eukaryotic effectors, LOCALIZER outperforms other methods and predicts a previously unrecognized chloroplast transit peptide for the ToxA effector, which we show translocates into tobacco chloroplasts. Secretome-wide predictions and confocal microscopy reveal that rust fungi might have evolved multiple effectors that target chloroplasts or nuclei. LOCALIZER is the first method for predicting effector localisation in plants and is a valuable tool for prioritizing effector candidates for functional investigations. LOCALIZER is available at http://localizer.csiro.au/.
Highlights
Pathogens secrete effector proteins and many operate inside plant cells to enable infection
To predict if a plant or an effector protein localizes to chloroplasts or mitochondria, we applied a machine learning approach trained on plant proteins with experimentally verified localization data
LOCALIZER has been trained on transit peptides and a window of sequence harbouring a potential transit peptide should be presented to the classifier
Summary
Pathogens secrete effector proteins and many operate inside plant cells to enable infection. We introduce LOCALIZER for predicting plant and effector protein localization to chloroplasts, mitochondria, and nuclei. Plant proteins are translocated from the cytosol into specific organelles by means of N-terminal transit peptides in the case of chloroplasts and mitochondria[1,2], or nuclear localization signals (NLSs) in the case of nuclei. If effectors carry transit peptides these can be separated from N-terminal signal peptides by a pro-domain of varying length[13]. The ToxA effector localizes to chloroplasts, interacts with the chloroplast-localized protein ToxABP116 and contains a signal peptide followed by a pro-domain that are cleaved during secretion[17]. Effectors rarely share sequence similarity with other proteins[19] To address these challenges, we introduce a machine learning prediction tool called LOCALIZER, which can be run in two modes:
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