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

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.

At least three families of hyphosphere small secreted cysteine‐rich proteins can optimize surface properties to a moderately hydrophilic state suitable for fungal attachment

“…Genome mining for HFB‐encoding genes and the new SSCP family ‐ HFS‐encoding genes in the 42 Trichoderma whole‐genome sequenced strains (listed in Supporting Information 1 Table S2 ) ‐ was performed using RapidMiner (version 8.2, USA) with the HFB‐specific cysteine sequence pattern of C‐CC‐C‐C‐CC‐C or the HFS‐specific pattern of C‐CXXX‐C‐C‐C‐C‐C‐C (X represents any possible amino acid). In addition, EffHunter (Carreón‐Anguiano et al ., 2020 ) was used to search for effector proteins that were then manually verified by the presence of a secretory signal peptide (SignalP 5.0). The CP sequences were obtained from our previous report (Gao et al ., 2020 ).…”

Analysis of Pseudocercospora fijiensis genes upregulated during early interaction with Musa acuminata (var. Dwarf Cavendish)

“…Furthermore, not all effectors present with the same characteristics, although a few have been established to identify identification better. Canonical effectors are predicted based on the small size (<400 amino acids), richness in cysteine (>4 Cys or >2% per sequence), the occurrence of a signal peptide, and absence of a transmembrane domain 9 . Other criteria to identify in silico effectors include: high in planta expression, pathogenesis-related domains, and discontinuous taxonomic distribution 29 .…”

“…Interaction 7 . More recently, a group of small, cysteine-rich proteins called effectors has received particular attention in P. fijiensis; several effectors have been predicted: 172 sequences by Arango-Isaza et al, 105 sequences by Chang et al, and 136 sequences by Carreón-Anguiano et al 3,8,9 . As a hemibiotroph, it is expected that P. fijiensis would produce effector proteins and toxic secondary metabolites to manipulate the defense response of the host during late interaction, as well as prevent host cell death during the fungal biotrophic phase.…”

Understanding Host–Pathogen Interactions in Brassica napus in the Omics Era

“…Examples include EffectorP 2.0 [ 187 , 188 ], SignalP [ 189 ] and ApoplastP [ 190 ]. Bioinformatics tools specific for the identification of transposable elements (TEs) have also been developed [ 191 ]. Besides proteinaceous effector molecules, non-proteinaceous effectors in fungal pathogens, such as secondary metabolites, small noncoding RNAs and their biological roles in pathogenicity, have also been studied in plant–fungus interactions [ 192 ].…”