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Updated: May 15, 2025
Label-Free Quantitative Proteomics Workflow for Discovery-Driven Host-Pathogen Interactions
Published on: October 20, 2020
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.
Background:
The increasing incidence of emerging infectious diseases is posing serious global threats. Therefore, there is a clear need for developing computational methods that can assist and speed up experimental research to better characterize the molecular mechanisms of microbial infections.
Methods:
In this context, we developed mimicINT, an open-source computational workflow for large-scale protein-protein interaction inference between microbe and human by detecting putative molecular mimicry elements mediating the interaction with host proteins: short linear motifs (SLiMs) and host-like globular domains. mimicINT exploits these putative elements to infer the interaction with human proteins by using known templates of domain-domain and SLiM-domain interaction templates. mimicINT also provides (i) robust Monte-Carlo simulations to assess the statistical significance of SLiM detection which suffers from false positives, and (ii) an interaction specificity filter to account for differences between motif-binding domains of the same family. We have also made mimicINT available via a web server.
Results:
In two use cases, mimicINT can identify potential interfaces in experimentally detected interaction between pathogenic Escherichia coli type-3 secreted effectors and human proteins and infer biologically relevant interactions between Marburg virus and human proteins.
Conclusions:
The mimicINT workflow can be instrumental to better understand the molecular details of microbe-host interactions.
Insights
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.
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.
Related Concept Videos
02:04Protein-protein Interfaces
02:26Protein Networks
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...