title: EffectorP: predicting fungal effector proteins from secretomes using machine learning.
abstract: 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. We present EffectorP which pioneers the application of machine learning to fungal effector prediction. EffectorP improves fungal effector prediction from secretomes based on a robust signal of sequence-derived properties, achieving sensitivity and specificity of over 80%. Features that discriminate fungal effectors from secreted noneffectors are predominantly sequence length, molecular weight and protein net charge, as well as cysteine, serine and tryptophan content. We demonstrate that EffectorP is powerful when combined with in planta expression data for predicting high-priority effector candidates. EffectorP is the first prediction program for fungal effectors based on machine learning. Our findings will facilitate functional fungal effector studies and improve our understanding of effectors in plant-pathogen interactions. EffectorP is available at http://effectorp.csiro.au.
authors.affiliation: Centre for Environment and Life Sciences, CSIRO Agriculture, Perth, 6014, WA, Australia.
authors.forename: Jana
authors.initials: J
authors.lastname: Sperschneider
authors.affiliation: Queensland Bioscience Precinct, CSIRO Agriculture, Brisbane, 4067, QLD, Australia.
authors.forename: Donald M
authors.initials: DM
authors.lastname: Gardiner
authors.affiliation: Black Mountain Laboratories, CSIRO Agriculture, Canberra, 2601, ACT, Australia.
authors.forename: Peter N
authors.initials: PN
authors.lastname: Dodds
authors.affiliation: Queensland Bioscience Precinct, CSIRO Agriculture, Brisbane, 4067, QLD, Australia.|Department of Agricultural, Food and Environmental Sciences, University of Perugia, Perugia, 06121, Umbria, Italy.
authors.forename: Francesco
authors.initials: F
authors.lastname: Tini
authors.affiliation: Department of Agricultural, Food and Environmental Sciences, University of Perugia, Perugia, 06121, Umbria, Italy.
authors.forename: Lorenzo
authors.initials: L
authors.lastname: Covarelli
authors.affiliation: Centre for Environment and Life Sciences, CSIRO Agriculture, Perth, 6014, WA, Australia.
authors.forename: Karam B
authors.initials: KB
authors.lastname: Singh
authors.affiliation: Black Mountain Laboratories, CSIRO Agriculture, Canberra, 2601, ACT, Australia.
authors.forename: John M
authors.initials: JM
authors.lastname: Manners
authors.affiliation: Black Mountain Laboratories, CSIRO Agriculture, Canberra, 2601, ACT, Australia.
authors.forename: Jennifer M
authors.initials: JM
authors.lastname: Taylor
mesh_terms: D000465:Algorithms; D000596:Amino Acids / Q000378:metabolism; D019295:Computational Biology / Q000379:methods*; D003593:Cytoplasm / Q000378:metabolism; D005656:Fungal Proteins / Q000737:chemistry / Q000378:metabolism*; D005670:Fusarium / Q000378:metabolism; D016681:Genome, Fungal; D000069550:Machine Learning; D008970:Molecular Weight; D015203:Reproducibility of Results; D013045:Species Specificity
chemical_list: D000596:Amino Acids; D005656:Fungal Proteins
keywords: EffectorP; effector; fungal effector prediction; fungal pathogen; machine learning; secretomes