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

Area of Science:

• Microbiology
• Bioinformatics
• Genomics

Background:

• Type-III secretion systems (T3SS) are crucial virulence factors in Gram-negative bacteria.
• T3SS inject type-3 effectors (T3Es) into host cells, manipulating cellular processes and promoting disease.
• Identifying T3E repertoires is essential for understanding pathogen virulence and developing targeted therapies.

Purpose of the Study:

• To develop a computational tool for predicting T3Es in bacterial genomes.
• To provide a user-friendly web server for T3E identification.
• To enhance the study of bacterial pathogenesis and host-pathogen interactions.

Main Methods:

• Development of Effectidor, a web server utilizing multiple machine-learning algorithms.
• Comparative performance analysis of Effectidor against existing T3E prediction tools.
• Validation on effector repertoires of three pathogenic bacterial species.

Main Results:

• Effectidor demonstrates high accuracy in predicting T3Es, achieving an area under the precision-recall curve > 0.98.
• The tool significantly outperforms other available methods for T3E identification.
• Effectidor provides a reliable and efficient means to identify effector proteins.

Conclusions:

• Effectidor is a powerful and accurate tool for predicting type-3 effectors in bacterial genomes.
• This resource facilitates the identification of bacterial virulence factors and aids in the development of novel antimicrobial strategies.
• The availability of Effectidor accelerates research in bacterial pathogenesis and host-pathogen interactions.