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Use of Bastion for the Identification of Secreted Substrates

Jiawei Wang, Jiahui Li, Christopher J. Stubenrauch

Research output: Chapter or section in a book/report/conference proceedingBook chapter

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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: Methods and Protocols
Place of PublicationNew York, U.S.A
PublisherSpringer
Chapter31
Pages519-531
Number of pages13
Volume2715
Edition2
ISBN (Electronic)9781071634455
ISBN (Print)9781071634448, 9781071634479
DOIs
Publication statusPublished - 2024

Publication series

NameMethods in molecular biology (Clifton, N.J.)
PublisherHumana Press
ISSN (Print)1064-3745

Bibliographical note

Publisher Copyright:
© 2024. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.

Acknowledgements

J.W. is a recipient of Marie Skłodowska-Curie Postdoctoral Fellowship, EMBL Interdisciplinary Postdoctoral (EIPOD) Fellowship and EMBO Non-Stipendiary Fellowship (EMBO ALTF 400-2022), and a Junior Research Fellow at Wolfson College, the University of Cambridge, UK. C.J.S. is an Australian Research Council (ARC) Discovery Early Career Researcher Award (DECRA) Fellow (DE230100700).

Keywords

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

ASJC Scopus subject areas

  • Molecular Biology
  • Genetics

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