Use of Bastion for the Identification of Secreted Substrates

Jiawei Wang, Jiahui Li, Christopher J. Stubenrauch

Research output: Chapter in Book/Report/Conference proceedingChapter (Book)Otherpeer-review

2 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationBacterial Secretion Systems
EditorsLaure Journet, Eric Cascales
Place of PublicationNew York NY USA
PublisherHumana Press
Chapter31
Pages519-531
Number of pages13
Edition2nd
ISBN (Electronic)9781071634455
ISBN (Print)9781071634448
DOIs
Publication statusPublished - 2024

Publication series

NameMethods in Molecular Biology
PublisherHumana Press
Volume2715
ISSN (Print)1064-3745

Keywords

  • Bastion
  • Bioinformatics
  • Effector prediction
  • Machine learning
  • Secretion system

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