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.
Highlights
The growing number of sequenced fungal and oomycete plant pathogen genomes has enabled detailed reverse genetics studies into molecular pathogen-host interactions (Dean et al, 2012; Kamoun et al, 2014)
We assess the performance of popular software tools against known effectors of both the fungi and oomycetes and offer recommendations on which may be better suited to specialized applications
Such performance evaluations inevitably vary based on the test data sets used, and we advise readers to carefully consider the suitability of these recommendations to their own data
Summary
The growing number of sequenced fungal and oomycete plant pathogen genomes has enabled detailed reverse genetics studies into molecular pathogen-host interactions (Dean et al, 2012; Kamoun et al, 2014). This involves recognition of the N-terminal secretory signal peptide motif that directs proteins through the classical ER/Golgi-dependent pathway using tools such as SignalP (Petersen et al, 2011) Whilst this is a robust approach for defining a set of potential effector candidates, typically far more candidates are predicted for experimental validation than is feasible. Subcellular localization tools such as TargetP (Emanuelsson et al, 2000), WoLF PSORT (Horton et al, 2007), or ProtComp are frequently used to complement the predictions made by SignalP or Phobius, either through a union or intersection of predictions made by these methods (Table 1) This can serve to filter proteins that may be predicted to contain a signal peptide, yet that might not be fully secreted into the extracellular space due to being retained within the ER/Golgi. We highlight differences in performance for secretion prediction between fungal effectors and oomycete effectors and conclude by providing practical recommendations for the TABLE 1 | Examples for approaches used in eukaryotic plant pathogen genomic studies that predict secreted proteins
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