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

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 () 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.

Automated summary

This study introduces a novel tool and pipeline, Predector, for ranking predicted effector candidates, which outperforms traditional methods in ranking performance. Predector aggregates and reports additional supporting information relevant to effector and secretome prediction in a simple, efficient, and reproducible manner.