Effectidor: an automated machine-learning-based web server for the prediction of type-III secretion system effectors

Wagner, Naama; Avram, Oren; Gold-Binshtok, Dafna; Zerah, Ben; Teper, Doron; Pupko, Tal

doi:10.1093/bioinformatics/btac087

Cited by 25 publications

(27 citation statements)

References 26 publications

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“…This trained LME model provides for each possible protein a score that reflects its propensity to harbor a type III secretion signal within its 100 N-terminal positions. This score was added as an optional feature within the Effectidor web server ( Wagner et al., 2022 ).…”

Section: Methodsmentioning

confidence: 99%

“…Positive and negative datasets. For preparing the positive data, a total of 1,857 known effectors from plant and animal pathogens were first retrieved from the Effectidor web server (Wagner et al, 2022) at https://effectidor.tau.ac.il/T3Es_data/ T3Es.faa. These data were filtered to remove closely related homologs by conducting a blastP search (all-against-all with an E value cutoff of 10 -4 ) and randomly selecting a single representative from each connected component and by only considering effectors of length equal or higher than 100 amino acids.…”

Section: Datamentioning

confidence: 99%

“…Over a decade ago, others and we were the first to formulate the problem of effector identification as a machine-learning (ML) classification problem ( Arnold et al., 2009 ; Burstein et al., 2009 ; Löwer and Schneider, 2009 ; Samudrala et al., 2009 ). Since then, numerous ML approaches have been applied to predict effectors for T3SS, T4SS and T6SS, for various animal and plant pathogenic bacteria ( Yang et al., 2010 ; Niemann et al., 2011 ; Sato et al., 2011 ; Yang, 2012 ; Dong et al., 2013 ; Zou et al., 2013 ; Lifshitz et al., 2014 ; Burstein et al., 2015 ; Burstein et al., 2016 ; Hobbs et al., 2016 ; Teper et al., 2016 ; Ashari et al., 2018 ; Jiaweiwang et al., 2018 ; Nissan et al., 2018 ; Jiménez-Guerrero et al., 2020 ; Ruano-Gallego et al., 2021 ; Wagner et al., 2022 ).…”

Section: Introductionmentioning

confidence: 99%

“…In addition to previously-described features, we also aimed to test the utility of encoding the secretion signal using Natural Language Processing (NLP) approaches, specifically using large pre-trained models. Finally, we incorporated the optimal characterization of the secretion signal into Effectidor, our previously developed ML algorithm for predicting T3Es, which uses a host of features including regulation, spatial distribution of effectors within the genome, homology to known effectors and to non-effectors, to name but a few ( Wagner et al., 2022 ).…”

Section: Introductionmentioning

confidence: 99%

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Natural language processing approach to model the secretion signal of type III effectors

et al. 2022

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Type III effectors are proteins injected by Gram-negative bacteria into eukaryotic hosts. In many plant and animal pathogens, these effectors manipulate host cellular processes to the benefit of the bacteria. Type III effectors are secreted by a type III secretion system that must “classify” each bacterial protein into one of two categories, either the protein should be translocated or not. It was previously shown that type III effectors have a secretion signal within their N-terminus, however, despite numerous efforts, the exact biochemical identity of this secretion signal is generally unknown. Computational characterization of the secretion signal is important for the identification of novel effectors and for better understanding the molecular translocation mechanism. In this work we developed novel machine-learning algorithms for characterizing the secretion signal in both plant and animal pathogens. Specifically, we represented each protein as a vector in high-dimensional space using Facebook’s protein language model. Classification algorithms were next used to separate effectors from non-effector proteins. We subsequently curated a benchmark dataset of hundreds of effectors and thousands of non-effector proteins. We showed that on this curated dataset, our novel approach yielded substantially better classification accuracy compared to previously developed methodologies. We have also tested the hypothesis that plant and animal pathogen effectors are characterized by different secretion signals. Finally, we integrated the novel approach in Effectidor, a web-server for predicting type III effector proteins, leading to a more accurate classification of effectors from non-effectors.

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Section: Methodsmentioning

confidence: 99%

Section: Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Natural language processing approach to model the secretion signal of type III effectors

et al. 2022

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“…More recently, the EffHunter algorithm [ 85 ] and machine learning (ML) tools trained to predict effectors based on shared physiochemical protein properties are facilitating easier high-throughput effector identification from pathogen genomes. EffectorP versions 1, 2 and 3 ( ; [ 214 , 215 , 216 ], ApoplastP v. 1.0 ( ; [ 217 ] and FunEffector-Pred ( ; [ 218 ] are available tools for fungi, and EffectorO for oomycetes ( ; [ 212 ]; while Effectidor ( ; [ 219 ], is a recent example of a ML predictor for T3SS effectors of bacteria.…”

Section: Effectors and Plant Defensementioning

confidence: 99%

Microbial Effectors: Key Determinants in Plant Health and Disease

et al. 2022

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Effectors are small, secreted molecules that alter host cell structure and function, thereby facilitating infection or triggering a defense response. Effectoromics studies have focused on effectors in plant–pathogen interactions, where their contributions to virulence are determined in the plant host, i.e., whether the effector induces resistance or susceptibility to plant disease. Effector molecules from plant pathogenic microorganisms such as fungi, oomycetes and bacteria are major disease determinants. Interestingly, the effectors of non-pathogenic plant organisms such as endophytes display similar functions but have different outcomes for plant health. Endophyte effectors commonly aid in the establishment of mutualistic interactions with the plant and contribute to plant health through the induction of systemic resistance against pathogens, while pathogenic effectors mainly debilitate the plant’s immune response, resulting in the establishment of disease. Effectors of plant pathogens as well as plant endophytes are tools to be considered in effectoromics for the development of novel strategies for disease management. This review aims to present effectors in their roles as promotors of health or disease for the plant host.

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