Bioinformatic prediction of plant-pathogenicity effector proteins of fungi

Abstract

Effector proteins are important virulence factors of fungal plant pathogens and their prediction largely relies on bioinformatic methods. In this review we outline the current methods for the prediction of fungal plant pathogenicity effector proteins. Some fungal effectors have been characterised and are represented by conserved motifs or in sequence repositories, however most fungal effectors do not generally exhibit high conservation of amino acid sequence. Therefore various predictive methods have been developed around: general properties, structure, position in the genomic landscape, and detection of mutations including repeat-induced point mutations and positive selection. A combinatorial approach incorporating several of these methods is often employed and candidates can be prioritised by either ranked scores or hierarchical clustering.