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

Identifieur interne : 000096 ( Main/Merge ); précédent : 000095; suivant : 000097

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

Auteurs : Karla Gisel Carre N-Anguiano ; Ignacio Islas-Flores ; Julio Vega-Arreguín ; Luis Sáenz-Carbonell ; Blondy Canto-Canché

Source :

RBID : PMC:7277995

Abstract

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.


Url:
DOI: 10.3390/biom10050712
PubMed: 32375409
PubMed Central: 7277995

Links toward previous steps (curation, corpus...)


Links to Exploration step

PMC:7277995

Le document en format XML

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<p>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.</p>
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