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What is EffectorP?

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.

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, achieving sensitivity and specificity of over 80%.

For more details on the underlying method, please see our paper:

  • Sperschneider J, Gardiner DM, Dodds PN, Tini F, Covarelli L, Singh KB, Manners JM, Taylor JM (2015) EffectorP: Predicting Fungal Effector Proteins from Secretomes Using Machine Learning. New Phytologist, doi:10.1111/nph.13794. Abstract

  • Input

    EffectorP has been trained to find fungal effectors in secretomes, so please submit 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.

    Do not use EffectorP to scan the whole protein set of a fungal genome, you will get results that make no sense.