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Related Concept Videos

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Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...
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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
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Updated: May 15, 2025

Label-Free Quantitative Proteomics Workflow for Discovery-Driven Host-Pathogen Interactions
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mimicINT: A workflow for microbe-host protein interaction inference.

Sébastien A Choteau1, Kevin Maldonado1, Aurélie Bergon1

  • 1Aix-Marseille University, Inserm, TAGC, UMR_S1090, Turing Centre for Living Systems, Marseille, France.

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|April 11, 2025
PubMed
Summary
This summary is machine-generated.

A new computational workflow, mimicINT, infers microbe-host protein interactions by identifying molecular mimicry elements. This tool aids in understanding infectious disease mechanisms and potential therapeutic targets.

Keywords:
Protein-protein interactionsinteraction inferencemicrobe-host interactionsmolecular mimicryshort linear motifs

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Area of Science:

  • Computational Biology
  • Infectious Disease Research
  • Bioinformatics

Background:

  • Emerging infectious diseases present significant global health challenges.
  • Characterizing molecular mechanisms of microbial infections requires advanced computational tools.
  • There is a need to accelerate experimental research through computational methods.

Purpose of the Study:

  • To develop an open-source computational workflow, mimicINT, for inferring protein-protein interactions between microbes and humans.
  • To identify molecular mimicry elements, such as short linear motifs (SLiMs) and host-like globular domains, mediating these interactions.
  • To provide a web server for accessible use of the mimicINT workflow.

Main Methods:

  • mimicINT utilizes known domain-domain and SLiM-domain interaction templates to infer microbe-host protein interactions.
  • It detects putative molecular mimicry elements mediating interactions.
  • Includes Monte-Carlo simulations for SLiM detection statistical significance and an interaction specificity filter.

Main Results:

  • mimicINT successfully identified potential interaction interfaces between pathogenic Escherichia coli effectors and human proteins.
  • The workflow inferred biologically relevant interactions between Marburg virus and human proteins in use case studies.
  • Demonstrated capability in analyzing specific microbial interactions.

Conclusions:

  • The mimicINT workflow offers a valuable tool for understanding the molecular intricacies of microbe-host interactions.
  • It can significantly aid in the characterization of infectious disease mechanisms.
  • Facilitates the identification of novel interaction pathways relevant to disease pathogenesis.