EffHunter: A Tool for Prediction of Effector Protein Candidates in Fungal Proteomic Databases
Identifieur interne : 000096 ( Main/Merge ); précédent : 000095; suivant : 000097EffHunter: 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-CanchéSource :
- Biomolecules [ 2218-273X ] ; 2020.
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...)
- to stream Pmc, to step Corpus: 000602
- to stream Pmc, to step Curation: 000602
- to stream Pmc, to step Checkpoint: 000092
- to stream Ncbi, to step Merge: 000B50
- to stream Ncbi, to step Curation: 000B50
- to stream Ncbi, to step Checkpoint: 000B50
Links to Exploration step
PMC:7277995Le document en format XML
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<author><name sortKey="Islas Flores, Ignacio" sort="Islas Flores, Ignacio" uniqKey="Islas Flores I" first="Ignacio" last="Islas-Flores">Ignacio Islas-Flores</name>
<affiliation><nlm:aff id="af2-biomolecules-10-00712">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;<email>islasign@cicy.mx</email>
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(K.G.C.-A.);<email>vyca@cicy.mx</email>
(L.S.-C.)</nlm:aff>
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<author><name sortKey="Canto Canche, Blondy" sort="Canto Canche, Blondy" uniqKey="Canto Canche B" first="Blondy" last="Canto-Canché">Blondy Canto-Canché</name>
<affiliation><nlm:aff id="af1-biomolecules-10-00712">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;<email>karla.carreon@cicy.mx</email>
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<front><div type="abstract" xml:lang="en"><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>
</div>
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