Journal article

Systematic analysis and prediction of type IV secreted effector proteins by machine learning approaches

J Wang, B Yang, Y An, T Marquez-Lago, A Leier, J Wilksch, Q Hong, Y Zhang, M Hayashida, T Akutsu, GI Webb, RA Strugnell, J Song, T Lithgow

Briefings in Bioinformatics | OXFORD UNIV PRESS | Published : 2017

Abstract

In the course of infecting their hosts, pathogenic bacteria secrete numerous effectors, namely, bacterial proteins that pervert host cell biology. Many Gram-negative bacteria, including context-dependent human pathogens, use a type IV secretion system T4SS) to translocate effectors directly into the cytosol of host cells. Various type IV secreted effectors T4SEs) have been experimentally validated to play crucial roles in virulence by manipulating host cell gene expression and other processes. Consequently, the identification of novel effector proteins is an important step in increasing our understanding of host-pathogen interactions and bacterial pathogenesis. Here, we train and compare six..

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Grants

Awarded by National Institute of Allergy and Infectious Diseases


Funding Acknowledgements

This work was supported by grants from the National Health and Medical Research Council of Australia (NHMRC) (grant number 1092262), the Australian Research Council (ARC) (grant numbers LP110200333 and DP120104460) and the National Institute of Allergy and Infectious Diseases of the National Institute of Health (grant number R01 AI111965). G.I.W. is a recipient of the Discovery Outstanding Research Award (DORA) of the ARC. T.L. is an ARC Australian Laureate Fellow (grant number FL130100038).