title: Evaluation of Secretion Prediction Highlights Differing Approaches Needed for Oomycete and Fungal Effectors.
abstract: The steadily increasing number of sequenced fungal and oomycete genomes has enabled detailed studies of how these eukaryotic microbes infect plants and cause devastating losses in food crops. During infection, fungal and oomycete pathogens secrete effector molecules which manipulate host plant cell processes to the pathogen's advantage. Proteinaceous effectors are synthesized intracellularly and must be externalized to interact with host cells. Computational prediction of secreted proteins from genomic sequences is an important technique to narrow down the candidate effector repertoire for subsequent experimental validation. In this study, we benchmark secretion prediction tools on experimentally validated fungal and oomycete effectors. We observe that for a set of fungal SwissProt protein sequences, SignalP 4 and the neural network predictors of SignalP 3 (D-score) and SignalP 2 perform best. For effector prediction in particular, the use of a sensitive method can be desirable to obtain the most complete candidate effector set. We show that the neural network predictors of SignalP 2 and 3, as well as TargetP were the most sensitive tools for fungal effector secretion prediction, whereas the hidden Markov model predictors of SignalP 2 and 3 were the most sensitive tools for oomycete effectors. Thus, previous versions of SignalP retain value for oomycete effector prediction, as the current version, SignalP 4, was unable to reliably predict the signal peptide of the oomycete Crinkler effectors in the test set. Our assessment of subcellular localization predictors shows that cytoplasmic effectors are often predicted as not extracellular. This limits the reliability of secretion predictions that depend on these tools. We present our assessment with a view to informing future pathogenomics studies and suggest revised pipelines for secretion prediction to obtain optimal effector predictions in fungi and oomycetes.
authors.affiliation: CSIRO Agriculture Flagship, Centre for Environment and Life Sciences Perth, WA, Australia.
authors.forename: Jana
authors.initials: J
authors.lastname: Sperschneider
authors.affiliation: CSIRO Agriculture Flagship, Centre for Environment and Life SciencesPerth, WA, Australia; The Institute of Agriculture, The University of Western AustraliaCrawley, WA, Australia.
authors.forename: Angela H
authors.initials: AH
authors.lastname: Williams
authors.affiliation: Department of Environment and Agriculture, CCDM Bioinformatics, Centre for Crop and Disease Management, Curtin UniversityPerth, WA, Australia; Curtin Institute for Computation, Curtin UniversityPerth, WA, Australia.
authors.forename: James K
authors.initials: JK
authors.lastname: Hane
authors.affiliation: CSIRO Agriculture Flagship, Centre for Environment and Life SciencesPerth, WA, Australia; The Institute of Agriculture, The University of Western AustraliaCrawley, WA, Australia.
authors.forename: Karam B
authors.initials: KB
authors.lastname: Singh
authors.affiliation: CSIRO Agriculture, Black Mountain Laboratories Canberra, ACT, Australia.
authors.forename: Jennifer M
authors.initials: JM
authors.lastname: Taylor
keywords: effectors; fungi; oomycetes; plant pathogens; protein secretion; signal peptide prediction