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Features and algorithms: facilitating investigation of secreted effectors in Gram-negative bacteria. | LitMetric

Features and algorithms: facilitating investigation of secreted effectors in Gram-negative bacteria.

Trends Microbiol

Youth Innovation Team of Medical Bioinformatics, Shenzhen University Medical School, Shenzhen 518060, China; Department of Cell Biology and Genetics, College of Basic Medicine, Shenzhen University Medical School, Shenzhen 518060, China. Electronic address:

Published: November 2023

AI Article Synopsis

Article Abstract

Gram-negative bacteria deliver effector proteins through type III, IV, or VI secretion systems (T3SSs, T4SSs, and T6SSs) into host cells, causing infections and diseases. In general, effector proteins for each of these distinct secretion systems lack homology and are difficult to identify. Sequence analysis has disclosed many common features, helping us to understand the evolution, function, and secretion mechanisms of the effectors. In combination with various algorithms, the known common features have facilitated accurate prediction of new effectors. Ensemblers or integrated pipelines achieve a better prediction of performance, which combines multiple computational models or modules with multidimensional features. Natural language processing (NLP) models also show the merits, which could enable discovery of novel features and, in turn, facilitate more precise effector prediction, extending our knowledge about each secretion mechanism.

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.tim.2023.05.011DOI Listing

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