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Updated: Sep 21, 2025

A Protocol for Computer-Based Protein Structure and Function Prediction

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

Predicting Type III Effector Proteins Using the Effectidor Web Server.

Naama Wagner¹, Doron Teper², Tal Pupko³

¹The Shmunis School of Biomedicine and Cancer Research, Tel Aviv University, Tel Aviv, Israel.

Methods in Molecular Biology (Clifton, N.J.)

|May 26, 2022

Summary

This summary is machine-generated.

This study introduces Effectidor, a web server that uses machine learning to predict bacterial type III effector proteins. Effectidor helps identify novel effectors by analyzing protein features to distinguish them from non-effectors.

Keywords:

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

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Identification of Growth Inhibition Phenotypes Induced by Expression of Bacterial Type III Effectors in Yeast

Identification of Growth Inhibition Phenotypes Induced by Expression of Bacterial Type III Effectors in Yeast

Published on: March 30, 2010

Area of Science:

Microbiology
Bioinformatics
Computational Biology

Background:

Gram-negative bacteria utilize secretion systems to deliver effector proteins into host cells, manipulating host pathways for bacterial advantage.
Machine learning (ML) approaches have been developed to predict novel effector proteins based on distinguishing features.
Previous ML algorithms require training with known effectors and non-effectors to identify predictive characteristics.

Purpose of the Study:

To introduce Effectidor, a novel web server designed for the prediction of type III effector proteins.
To provide a user-friendly, step-by-step guide for applying Effectidor in research.
To facilitate the identification of novel bacterial virulence factors.

Main Methods:

Development of a machine-learning algorithm trained on known effector and non-effector protein datasets.
Feature extraction to identify characteristics that differentiate effector proteins.
Application of the trained ML model to predict effector likelihood in unknown open reading frames (ORFs).
Implementation of the prediction model into the Effectidor web server.

Main Results:

Effectidor provides a scoring system indicating the probability of an ORF being a type III effector.
The web server enables researchers to input data and analyze prediction results efficiently.
The study details the process from data selection to result interpretation for Effectidor users.

Conclusions:

Effectidor serves as a valuable tool for predicting type III effectors in Gram-negative bacteria.
The web server simplifies the application of machine learning for effector identification.
This resource aids in the discovery of bacterial proteins that modulate host functions.