An automated and combinative method for the predictive ranking of candidate effector proteins of fungal plant pathogens.

Darcy A B Jones, Lina Rozano, Johannes W Debler, Ricardo L Mancera, Paula M Moolhuijzen, James K Hane

Scientific reports 2021 Oct 05

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Fungal plant-pathogens promote infection of their hosts through the release of 'effectors'-a broad class of cytotoxic or virulence-promoting molecules. Effectors may be recognised by resistance or sensitivity receptors in the host, which can determine disease outcomes. Accurate prediction of effectors remains a major challenge in plant pathology, but if achieved will facilitate rapid improvements to host disease resistance. This study presents a novel tool and pipeline for the ranking of predicted effector candidates-Predector-which interfaces with multiple software tools and methods, aggregates disparate features that are relevant to fungal effector proteins, and applies a pairwise learning to rank approach. Predector outperformed a typical combination of secretion and effector prediction methods in terms of ranking performance when applied to a curated set of confirmed effectors derived from multiple species. We present Predector ( https://github.com/ccdmb/predector ) as a useful tool for the ranking of predicted effector candidates, which also aggregates and reports additional supporting information relevant to effector and secretome prediction in a simple, efficient, and reproducible manner. © 2021. The Author(s).

Citation

Darcy A B Jones, Lina Rozano, Johannes W Debler, Ricardo L Mancera, Paula M Moolhuijzen, James K Hane. An automated and combinative method for the predictive ranking of candidate effector proteins of fungal plant pathogens. Scientific reports. 2021 Oct 05;11(1):19731