Use of Bastion for the Identification of Secreted Substrates.

Autor:	Wang J; European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, UK. jwang@ebi.ac.uk.; Infection Program, Biomedicine Discovery Institute and Department of Microbiology, Monash University, Melbourne, VIC, Australia. jwang@ebi.ac.uk.; Centre to Impact AMR, Monash University, Melbourne, VIC, Australia. jwang@ebi.ac.uk., Li J; Infection Program, Biomedicine Discovery Institute and Department of Microbiology, Monash University, Melbourne, VIC, Australia.; Centre to Impact AMR, Monash University, Melbourne, VIC, Australia., Stubenrauch CJ; Infection Program, Biomedicine Discovery Institute and Department of Microbiology, Monash University, Melbourne, VIC, Australia.; Centre to Impact AMR, Monash University, Melbourne, VIC, Australia.
Jazyk:	angličtina
Zdroj:	Methods in molecular biology (Clifton, N.J.) [Methods Mol Biol] 2024; Vol. 2715, pp. 519-531.
DOI:	10.1007/978-1-0716-3445-5_31
Abstrakt:	Bacteria use secretion systems to translocate numerous proteins into and across cell membranes, but have evolved more specialized secretion systems that can disrupt the normal cellular processes of host cells and compete bacteria or protect the bacteria from host defenses. Among them, Gram-negative bacteria utilize a variety of different proteins secreted by Type 1 to Type 6 secretion systems to transfer substrates into target cells or the surrounding environment, which play key roles in disease and survival. Therefore, these secreted proteins have attracted the attention of a wealth of researchers. The first step to characterizing new substrates of secretion systems is typically identifying candidates bioinformatically, and the Bastion series of substrate predictors provide biologists machine learning tools that can accurately predict these substrates. This chapter will explain how to use the Bastion series for identifying and analyzing secreted substrates in Gram-negative bacteria. (© 2024. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)
Databáze:	MEDLINE
Externí odkaz:	Zobrazit plný text záznamu