Many fungi and oomycetes species are devastating plant pathogens and secrete effector proteins to facilitate plant infection. Fungi and oomycete pathogens have diverse infection strategies and their effectors do not share sequence homology. However, effectors still have unifying properties: they either localize extracellularly to the plant apoplast or intracellularly to the plant cytoplasm. EffectorP 3.0 exploits this biological signal and uses two machine learning models trained on apoplastic and cytoplasmic effectors, respectively.
For a given set of secreted fungal/oomycete pathogen proteins, EffectorP 3.0 will predict if a protein is:
- an apoplastic effector
- a cytoplasmic effector
- a dual-localized apoplastic/cytoplasmic effector
- a non-effector
Note: EffectorP does not use sequence motif searches (such as RxLR) for effector prediction in oomycetes.
EffectorP is not a tool for secretome prediction and not a tool for bacterial effector prediction.
EffectorP has been trained to find fungal/oomycete effectors in secretomes, so please run it on a FASTA file of secreted proteins to test if they are predicted effectors. It is essential to use tools such as SignalP, Phobius and TMHMM to predict first if a protein is likely to be secreted. Alternatively, high-confidence experimentally determined secretomes instead of computationally predicted secretomes can be submitted to EffectorP.
EffectorP has been written in Python3 and uses the WEKA 3.8.4 software. To get EffectorP to work on your local machine, you need to unzip the WEKA software, which is already provided in the EffectorP distribution to ensure that a compatible version is used. You also need an installation of Python 3.
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Download the latest release from this github repo (e.g. EffectorP_3.0.zip) or alternatively you can clone the github repo (git clone https://github.com/JanaSperschneider/EffectorP-3.0.git) and skip step 1.
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Unpack EffectorP in your desired location
unzip EffectorP_3.0.zip
cd EffectorP-3.0
- For WEKA, you need to simply unzip the file weka-3-8-4.zip
unzip weka-3-8-4.zip
If you are having troube installing WEKA, please see here for help.
- Test if EffectorP is working
python EffectorP.py -i Effectors.fasta
Run this to get a feel for the output format:
python EffectorP.py -i Effectors.fasta
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EffectorP 3.0 is running for 9 proteins given in FASTA file Effectors.fasta
Ensemble classification
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All done.
# Identifier Cytoplasmic effector Apoplastic effector Non-effector Prediction
AvrM Melampsora lini Y (1.0) - - Cytoplasmic effector
Avr1-CO39 Magnaporthe oryzae Y (0.945) Y (0.667) - Cytoplasmic/apoplastic effector
ToxA Parastagonospora nodorum Y (0.551) Y (0.767) - Apoplastic/cytoplasmic effector
AVR3a Phytophthora infestans Y (0.985) - - Cytoplasmic effector
Pit2 Ustilago maydis Y (0.779) - - Cytoplasmic effector
Zt6 Zymoseptoria tritici - Y (0.944) - Apoplastic effector
INF1 Phytophthora infestans - Y (0.837) - Apoplastic effector
Zinc transporter 3 Arabidopsis thaliana - - Y (0.737) Non-effector
GPI-anchored protein 13 Candida albicans - - Y (0.708) Non-effector
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9 proteins were provided as input in the following file: Effectors.fasta
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Number of predicted effectors: 7
Number of predicted cytoplasmic effectors: 4
Number of predicted apoplastic effectors: 3
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77.8 percent are predicted effectors.
44.4 percent are predicted cytoplasmic effectors.
33.3 percent are predicted apoplastic effectors.
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EffectorP will return the output as shown in the example above. A summary table will be shown which shows the predictions for each submitted protein. For each protein, its most likely localization (apoplastic or cytoplasmic) will be returned, with an associated probability.
In the above example, four proteins are predicted as cytoplasmic effectors and three as apoplastic effectors. The Avr1-CO39 protein is predicted as a cytoplasmic effector (probability: 0.945), but also has a weaker prediction as an apoplastic effector (probability: 0.667). The ToxA protein is predicted as an apoplastic effector (probability: 0.767), but also has a weaker prediction as a cytoplasmic effector (probability: 0.551). We included two non-effectors to illustrate the output format.
Please note that whilst EffectorP returns a probability that a tested instance will belong to either the effector or non-effector class, these are known to be only rough estimations and should therefore not be overinterpreted.
We deliberately did not recommend a probability threshold over which a protein would be classified as an effector candidate, as we believe it should remain up to the individual user to interpret their results in the context of additional resources available. For example, a researcher might like to predict the full effector candidate complement using EffectorP and overlay this with in planta expression data to prioritize candidates, whereas in other situations without additional information a list of high-priority candidates as determined by the EffectorP probabilities might be more appropriate.
Please cite the EffectorP 3.0 paper as follows:
Sperschneider J, Dodds P. EffectorP 3.0: prediction of apoplastic and cytoplasmic effectors in fungi and oomycetes. Mol Plant Microbe Interact. 2021. doi: 10.1094/MPMI-08-21-0201-R
Preprint also available at biorxiv.