EffectorP 3.0: prediction of apoplastic and cytoplasmic effectors in fungi and oomycetes

Abstract

Many fungi and oomycete species are devasting plant pathogens. These eukaryotic filamentous pathogens secrete effector proteins to facilitate plant infection. Fungi and oomycete pathogens have diverse infection strategies and their effectors generally do not share sequence homology. However, they occupy similar host environments, either the plant apoplast or plant cytoplasm, and may therefore share some unifying properties based on the requirements of these host compartments. Here we exploit these biological signals and present the first classifier (EffectorP 3.0) that uses two machine learning models: one trained on apoplastic effectors and one trained on cytoplasmic effectors. EffectorP 3.0 accurately predicts known apoplastic and cytoplasmic effectors in fungal and oomycete secretomes with low estimated false positive rates of 3% and 8%, respectively. Cytoplasmic effectors have a higher proportion of positively charged amino acids, whereas apoplastic effectors are enriched for cysteine residues. The combination of fungal and oomycete effectors in training leads to a higher number of predicted cytoplasmic effectors in biotrophic fungi. EffectorP 3.0 expands predicted effector repertoires beyond small, cysteine-rich secreted proteins in fungi and RxLR-motif containing secreted proteins in oomycetes. We show that signal peptide prediction is essential for accurate effector prediction, as EffectorP 3.0 recognizes a cytoplasmic signal also in intracellular, non-secreted proteins. EffectorP 3.0 is available at http://effectorp.csiro.au.

A chromosome-level, fully phased genome assembly of the oat crown rust fungus Puccinia coronata f. sp. avenae: a resource to enable comparative genomics in the cereal rusts

“…A fungal protein was called secreted if it was predicted to have a signal peptide and has no transmembrane domains. Effector proteins were predicted with EffectorP version 3.0 (Sperschneider and Dodds 2021). Hi-C contact maps were produced with HiC-Pro 2.11.1 (MAPQ = 10) (Servant et al, 2015) and Hicexplorer 3.6 (Ramírez et al, 2018;Winter et al, 2018;Wolff et al, 2018Wolff et al, , 2020.…”

Candidate effector proteins from the maize tar spot pathogenPhyllachora maydislocalize to diverse plant cell compartments

“…To select candidate effector proteins, we began with the effector predictions generated previously by Telenko et al, (2020) from the predicted P. maydis secretome. We extracted all 59 candidate effector protein sequences and used EffectorP (v3.0) (Sperschneider and Dodds, 2022) (http://effectorp.csiro.au/) to further improve the effector prediction performed by Telenko et al (2020). We also employed SignalP (v6.0) (Teufel et al, 2022) (https://services.healthtech.dtu.dk/service.php?SignalP) to predict the presence of signal peptide sequences.…”

The Necrosis- and Ethylene-inducing peptide 1-like protein (NLP) gene family of the plant pathogen Corynespora cassiicola

“…Finally, we subject the predicted NLPs to EffectorP v3.0, which uses machine learning in order to classify proteins into three classes (effectors, unlikely effectors, or non-effectors) and to predict the localization of the effectors into the host cell (cytoplasm or apoplast) (Sperschneider and Dodds 2022).…”