效应器
分泌物
生物
计算生物学
功能(生物学)
同源(生物学)
计算机科学
遗传学
细胞生物学
基因
生物化学
作者
Ziyi Zhao,Yixue Hu,Yueming Hu,Aaron P. White,Yejun Wang
标识
DOI:10.1016/j.tim.2023.05.011
摘要
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.
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