Predicting Type III Effector Proteins Using the Effectidor Web Server

Naama Wagner, Doron Teper, Tal Pupko

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
Number of pages12
StatePublished - 2022

Publication series

NameMethods in Molecular Biology


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

All Science Journal Classification (ASJC) codes

  • Molecular Biology
  • Genetics


Dive into the research topics of 'Predicting Type III Effector Proteins Using the Effectidor Web Server'. Together they form a unique fingerprint.

Cite this