Predicting Type III Effector Proteins Using the Effectidor Web Server

Naama Wagner, Doron Teper, Tal Pupko*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review


Various Gram-negative bacteria use secretion systems to secrete effector proteins that manipulate host biochemical pathways to their benefit. We and others have previously developed machine-learning algorithms to predict novel effectors. Specifically, given a set of known effectors and a set of known non-effectors, the machine-learning algorithm extracts features that distinguish these two protein groups. In the training phase, the machine learning learns how to best combine the features to separate the two groups. The trained machine learning is then applied to open reading frames (ORFs) with unknown functions, resulting in a score for each ORF, which is its likelihood to be an effector. We developed Effectidor, a web server for predicting type III effectors. In this book chapter, we provide a step-by-step introduction to the application of Effectidor, from selecting input data to analyzing the obtained predictions.

Original languageEnglish
Title of host publicationMethods in Molecular Biology
PublisherHumana Press Inc.
Number of pages12
StatePublished - 2022

Publication series

NameMethods in Molecular Biology
ISSN (Print)1064-3745
ISSN (Electronic)1940-6029


  • Bacterial pathogenicity
  • Effectidor
  • Effector proteins
  • Machine learning
  • Pathogenicity
  • Secretion system
  • Type III effectors


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