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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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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. |
format | Online Article Text |
id | pubmed-7277995 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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