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Protein sociology of ProA, Mip and other secreted virulence factors at the Legionella pneumophila surface

Scheithauer, Lina ; Karagöz, Mustafa Safa ; Mayer, Benjamin E. ; Steinert, Michael (2023)
Protein sociology of ProA, Mip and other secreted virulence factors at the Legionella pneumophila surface.
In: Frontiers in Cellular and Infection Microbiology, 2023, 13
doi: 10.26083/tuprints-00023383
Article, Secondary publication, Publisher's Version

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Item Type: Article
Type of entry: Secondary publication
Title: Protein sociology of ProA, Mip and other secreted virulence factors at the Legionella pneumophila surface
Language: English
Date: 11 April 2023
Place of Publication: Darmstadt
Year of primary publication: 2023
Publisher: Frontiers Media S.A.
Journal or Publication Title: Frontiers in Cellular and Infection Microbiology
Volume of the journal: 13
Collation: 21 Seiten
DOI: 10.26083/tuprints-00023383
Corresponding Links:
Origin: Secondary publication DeepGreen
Abstract:

The pathogenicity of L. pneumophila, the causative agent of Legionnaires’ disease, depends on an arsenal of interacting proteins. Here we describe how surface-associated and secreted virulence factors of this pathogen interact with each other or target extra- and intracellular host proteins resulting in host cell manipulation and tissue colonization. Since progress of computational methods like AlphaFold, molecular dynamics simulation, and docking allows to predict, analyze and evaluate experimental proteomic and interactomic data, we describe how the combination of these approaches generated new insights into the multifaceted “protein sociology” of the zinc metalloprotease ProA and the peptidyl-prolyl cis/trans isomerase Mip (macrophage infectivity potentiator). Both virulence factors of L. pneumophila interact with numerous proteins including bacterial flagellin (FlaA) and host collagen, and play important roles in virulence regulation, host tissue degradation and immune evasion. The recent progress in protein-ligand analyses of virulence factors suggests that machine learning will also have a beneficial impact in early stages of drug discovery.

Uncontrolled Keywords: Legionella pneumophila, surface-associated proteins, secreted effectors, zinc metalloprotease ProA, macrophage infectivity potentiator, interactomics, computational biology
Status: Publisher's Version
URN: urn:nbn:de:tuda-tuprints-233837
Classification DDC: 500 Science and mathematics > 570 Life sciences, biology
Divisions: 10 Department of Biology > Computational Biology and Simulation
Date Deposited: 11 Apr 2023 11:55
Last Modified: 14 Nov 2023 19:05
SWORD Depositor: Deep Green
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/23383
PPN: 509033512
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