EffHunter: A Tool for Prediction of Effector Protein Candidates in Fungal Proteomic Databases
Karla Gisel Carreón-Anguiano,
Ignacio Islas-Flores,
Julio Vega-Arreguín,
Luis Sáenz-Carbonell,
Blondy Canto-Canché
Affiliations
Karla Gisel Carreón-Anguiano
Unidad de Biotecnología, Centro de Investigación Científica de Yucatán, A.C., Calle 43 No. 130 X 32 y 34, Col. Chuburná de Hidalgo, Mérida C.P. 97205, Mexico
Ignacio Islas-Flores
Unidad de Bioquímica y Biología Molecular de Plantas, Centro de Investigación Científica de Yucatán, A.C., Calle 43 No. 130 X 32 y 34, Col. Chuburná de Hidalgo, Mérida C.P. 97205, Mexico
Julio Vega-Arreguín
Laboratorio de Ciencias AgroGenómicas, Escuela Nacional de Estudios Superiores-UNAM, León 37689, Mexico
Luis Sáenz-Carbonell
Unidad de Biotecnología, Centro de Investigación Científica de Yucatán, A.C., Calle 43 No. 130 X 32 y 34, Col. Chuburná de Hidalgo, Mérida C.P. 97205, Mexico
Blondy Canto-Canché
Unidad de Biotecnología, Centro de Investigación Científica de Yucatán, A.C., Calle 43 No. 130 X 32 y 34, Col. Chuburná de Hidalgo, Mérida C.P. 97205, Mexico
Pathogens are able to deliver small-secreted, cysteine-rich proteins into plant cells to enable infection. The computational prediction of effector proteins remains one of the most challenging areas in the study of plant fungi interactions. At present, there are several bioinformatic programs that can help in the identification of these proteins; however, in most cases, these programs are managed independently. Here, we present EffHunter, an easy and fast bioinformatics tool for the identification of effectors. This predictor was used to identify putative effectors in 88 proteomes using characteristics such as size, cysteine residue content, secretion signal and transmembrane domains.
