Unidad de Biotecnología, Centro de Investigación Científica de Yucatán, A.C., Calle 43 No. 130 X 32 y 34, Col. Chuburná de Hidalgo, C.P. 97205 Mérida, México.
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, C.P. 97205 Mérida, México.
Biomolecules. 2020 May 4;10(5):712. doi: 10.3390/biom10050712.
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.
病原体能够将小分泌、富含半胱氨酸的蛋白质递送到植物细胞中，以实现感染。效应蛋白的计算预测仍然是植物真菌相互作用研究中最具挑战性的领域之一。目前，有几个生物信息学程序可以帮助识别这些蛋白质；然而，在大多数情况下，这些程序是独立管理的。在这里，我们提出了 EffHunter，这是一种用于鉴定效应物的简单快速的生物信息学工具。该预测器使用大小、半胱氨酸残基含量、分泌信号和跨膜结构域等特征，在 88 个蛋白质组中鉴定了可能的效应物。
