Fungal 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.
EffectorP is a machine learning method for fungal effector prediction in secretomes and has been trained to distinguish secreted proteins from secreted effectors in plant-pathogenic fungi. EffectorP improves fungal effector prediction from secretomes based on a robust signal of sequence-derived properties.
It is highly recommended to make the switch to the more accurate EffectorP 2.0. EffectorP 2.0 achieves an accuracy of 89%, compared with 82% for EffectorP 1.0 and 59.8% for a small size classifier.
EffectorP is not a tool for secretome prediction.
EffectorP has been trained to find fungal effectors in secretomes, so please run it on a FASTA file of secreted fungal proteins to test if they are predicted effectors. It is recommended to use tools such as SignalP or Phobius to predict first if a protein is likely to be secreted. Alternatively, experimentally determined secretomes instead of computationally predicted secretomes can be submitted to EffectorP.
You can submit secreted fungal proteins to the webserver at http://effectorp.csiro.au/.
Alternatively, you can install EffectorP on your machine to run it locally.
All training and evaluation data can be found here.
EffectorP has been written in Python and uses pepstats from the EMBOSS software and the WEKA 3.6.12 software. To get EffectorP to work on your local machine, you need to install the EMBOSS and WEKA softwares from source. Both are already provided in the EffectorP distribution to ensure that compatible versions are used. Effector from version 1.0.1 inclusive uses Python 3.
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Download the latest release from this github repo (or alternatively you can clone the github repo and skip step 1).
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Make sure EffectorP has the permission to execute. Then unpack EffectorP in your desired location
tar xvf EffectorP-1.0.1.tar.gz
chmod -R 755 EffectorP-1.0.1/
cd EffectorP-1.0.1
- For the EMBOSS installation, you need to switch to the Scripts directory and unpack, configure and make. Alternatively, if you are on a computer cluster and EMBOSS is already installed, you can change the variable PEPSTATS_PATH in the EffectorP.py script to the EMBOSS directory that contains pepstats on the machine you are using.
cd Scripts
tar xvf emboss-latest.tar.gz
cd EMBOSS-6.5.7/
./configure
make
cd ../
- For WEKA, you need to simply unzip the file weka-3-6-12.zip
unzip weka-3-6-12.zip
If you are having troube installing EMBOSS, please see here for help. If you are having troube installing WEKA, please see here for help.
- Test if EffectorP is working
python EffectorP.py -i Effector_Testing.fasta