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

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 c...

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Autores principales: Carreón-Anguiano, Karla Gisel, Islas-Flores, Ignacio, Vega-Arreguín, Julio, Sáenz-Carbonell, Luis, Canto-Canché, Blondy
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7277995/
https://www.ncbi.nlm.nih.gov/pubmed/32375409
http://dx.doi.org/10.3390/biom10050712
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author Carreón-Anguiano, Karla Gisel
Islas-Flores, Ignacio
Vega-Arreguín, Julio
Sáenz-Carbonell, Luis
Canto-Canché, Blondy
author_facet Carreón-Anguiano, Karla Gisel
Islas-Flores, Ignacio
Vega-Arreguín, Julio
Sáenz-Carbonell, Luis
Canto-Canché, Blondy
author_sort Carreón-Anguiano, Karla Gisel
collection PubMed
description 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.
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spelling pubmed-72779952020-06-12 EffHunter: A Tool for Prediction of Effector Protein Candidates in Fungal Proteomic Databases Carreón-Anguiano, Karla Gisel Islas-Flores, Ignacio Vega-Arreguín, Julio Sáenz-Carbonell, Luis Canto-Canché, Blondy Biomolecules Article 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. MDPI 2020-05-04 /pmc/articles/PMC7277995/ /pubmed/32375409 http://dx.doi.org/10.3390/biom10050712 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Carreón-Anguiano, Karla Gisel
Islas-Flores, Ignacio
Vega-Arreguín, Julio
Sáenz-Carbonell, Luis
Canto-Canché, Blondy
EffHunter: A Tool for Prediction of Effector Protein Candidates in Fungal Proteomic Databases
title EffHunter: A Tool for Prediction of Effector Protein Candidates in Fungal Proteomic Databases
title_full EffHunter: A Tool for Prediction of Effector Protein Candidates in Fungal Proteomic Databases
title_fullStr EffHunter: A Tool for Prediction of Effector Protein Candidates in Fungal Proteomic Databases
title_full_unstemmed EffHunter: A Tool for Prediction of Effector Protein Candidates in Fungal Proteomic Databases
title_short EffHunter: A Tool for Prediction of Effector Protein Candidates in Fungal Proteomic Databases
title_sort effhunter: a tool for prediction of effector protein candidates in fungal proteomic databases
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7277995/
https://www.ncbi.nlm.nih.gov/pubmed/32375409
http://dx.doi.org/10.3390/biom10050712
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