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

ABSTRACT: 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

SUBMITTER: Jones DAB 

PROVIDER: S-EPMC8492765 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature