@inbook{295ca8039f844f2fa76219b4b8edb21e,
title = "Use of Bastion for the Identification of Secreted Substrates",
abstract = "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.",
keywords = "Bastion, Bioinformatics, Effector prediction, Machine learning, Secretion system",
author = "Jiawei Wang and Jiahui Li and Stubenrauch, {Christopher J.}",
note = "Publisher Copyright: {\textcopyright} 2024. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.",
year = "2024",
doi = "10.1007/978-1-0716-3445-5_31",
language = "English",
isbn = "9781071634448",
series = "Methods in Molecular Biology",
publisher = "Humana Press",
pages = "519--531",
editor = "Laure Journet and Eric Cascales",
booktitle = "Bacterial Secretion Systems",
address = "United States of America",
edition = "2nd",
}