title: Computational Prediction of Effector Proteins in Fungi: Opportunities and Challenges.
abstract: Effector proteins are mostly secretory proteins that stimulate plant infection by manipulating the host response. Identifying fungal effector proteins and understanding their function is of great importance in efforts to curb losses to plant diseases. Recent advances in high-throughput sequencing technologies have facilitated the availability of several fungal genomes and 1000s of transcriptomes. As a result, the growing amount of genomic information has provided great opportunities to identify putative effector proteins in different fungal species. There is little consensus over the annotation and functionality of effector proteins, and mostly small secretory proteins are considered as effector proteins, a concept that tends to overestimate the number of proteins involved in a plant-pathogen interaction. With the characterization of Avr genes, criteria for computational prediction of effector proteins are becoming more efficient. There are 100s of tools available for the identification of conserved motifs, signature sequences and structural features in the proteins. Many pipelines and online servers, which combine several tools, are made available to perform genome-wide identification of effector proteins. In this review, available tools and pipelines, their strength and limitations for effective identification of fungal effector proteins are discussed. We also present an exhaustive list of classically secreted proteins along with their key conserved motifs found in 12 common plant pathogens (11 fungi and one oomycete) through an analytical pipeline.
authors.affiliation: Département de Phytologie, Faculté des Sciences de l'Agriculture et de l'Alimentation, Centre de Recherche en Horticulture, Université Laval, Québec QC, Canada.
authors.forename: Humira
authors.initials: H
authors.lastname: Sonah
authors.affiliation: Département de Phytologie, Faculté des Sciences de l'Agriculture et de l'Alimentation, Centre de Recherche en Horticulture, Université Laval, Québec QC, Canada.
authors.forename: Rupesh K
authors.initials: RK
authors.lastname: Deshmukh
authors.affiliation: Département de Phytologie, Faculté des Sciences de l'Agriculture et de l'Alimentation, Centre de Recherche en Horticulture, Université Laval, Québec QC, Canada.
authors.forename: Richard R
authors.initials: RR
authors.lastname: Bélanger
keywords: classification and prediction; computational tool and servers; effector proteins; fungal secretome; host–pathogen interaction