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Article

EffHunter: A Tool for Prediction of Effector Protein Candidates in Fungal Proteomic Databases

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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
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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
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Laboratorio de Ciencias AgroGenómicas, Escuela Nacional de Estudios Superiores-UNAM, León 37689, Mexico
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Author to whom correspondence should be addressed.
Biomolecules 2020, 10(5), 712; https://doi.org/10.3390/biom10050712
Received: 9 January 2020 / Revised: 17 March 2020 / Accepted: 21 March 2020 / Published: 4 May 2020
(This article belongs to the Section Bioinformatics and Systems Biology)
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. View Full-Text
Keywords: computational prediction; host-pathogen interaction; effector proteins; fungal secretome computational prediction; host-pathogen interaction; effector proteins; fungal secretome
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MDPI and ACS Style

Carreón-Anguiano, K.G.; Islas-Flores, I.; Vega-Arreguín, J.; Sáenz-Carbonell, L.; Canto-Canché, B. EffHunter: A Tool for Prediction of Effector Protein Candidates in Fungal Proteomic Databases. Biomolecules 2020, 10, 712. https://doi.org/10.3390/biom10050712

AMA Style

Carreón-Anguiano KG, Islas-Flores I, Vega-Arreguín J, Sáenz-Carbonell L, Canto-Canché B. EffHunter: A Tool for Prediction of Effector Protein Candidates in Fungal Proteomic Databases. Biomolecules. 2020; 10(5):712. https://doi.org/10.3390/biom10050712

Chicago/Turabian Style

Carreón-Anguiano, Karla G., Ignacio Islas-Flores, Julio Vega-Arreguín, Luis Sáenz-Carbonell, and Blondy Canto-Canché. 2020. "EffHunter: A Tool for Prediction of Effector Protein Candidates in Fungal Proteomic Databases" Biomolecules 10, no. 5: 712. https://doi.org/10.3390/biom10050712

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