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- Title
An automated and combinative method for the predictive ranking of candidate effector proteins of fungal plant pathogens.
- Authors
Jones, Darcy A. B.; Rozano, Lina; Debler, Johannes W.; Mancera, Ricardo L.; Moolhuijzen, Paula M.; Hane, James K.
- 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 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.
- Subjects
FUNGAL proteins; PHYTOPATHOGENIC microorganisms; PLANT proteins; DISEASE resistance of plants; PLANT diseases; PLANT growth
- Publication
Scientific Reports, 2021, Vol 11, Issue 1, p1
- ISSN
2045-2322
- Publication type
Article
- DOI
10.1038/s41598-021-03673-2