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Updated: Jan 14, 2026
A Comparative Approach to Characterize the Landscape of Host-Pathogen Protein-Protein Interactions
Published on: July 18, 2013
mimicDetector: a pipeline for protein motif mimicry detection in host-pathogen interactions
Kaylee D Rich1, James D Wasmuth1
1Faculty of Veterinary Medicine, University of Calgary, Calgary, Alberta, T2N 4Z6, Canada.
Motivation:
Molecular mimicry is used by pathogens to evade the host immune system and manipulate other host cellular processes. It is often mediated by short motifs in non-homologous proteins, whose detection challenges the sensitivity and specificity of existing bioinformatics tools.
Results:
We present mimicDetector, a k-mer-based pipeline for identifying protein-level molecular mimicry between pathogens and their hosts. Applied to 17 globally important pathogens, mimicDetector identified a broad and biologically plausible set of mimicry candidates, including helminth proteins mimicking components of the human complement system and a Leishmania infantum mimic of Reticulon-4, a regulator of immune cell recruitment.
Availability And Implementation:
mimicDetector is freely available at https://github.com/kayleerich/mimicDetector/, implemented in Python and Snakemake, and compatible with Unix-based systems.
Insights
Pathogens use molecular mimicry to evade immune responses. A new bioinformatics tool, mimicDetector, effectively identifies these mimicry instances between pathogens and hosts, revealing potential therapeutic targets.
Area of Science:
- Bioinformatics
- Immunology
- Pathogen-Host Interactions
Background:
- Molecular mimicry is a strategy employed by pathogens to evade host immune defenses and manipulate cellular processes.
- Detection of molecular mimicry is challenging due to short sequence motifs in non-homologous proteins, requiring sensitive and specific bioinformatics tools.
Purpose of the Study:
- To develop and present mimicDetector, a novel k-mer-based pipeline for identifying protein-level molecular mimicry.
- To assess the capability of mimicDetector in identifying biologically plausible mimicry candidates between pathogens and hosts.
Main Methods:
- Developed a k-mer-based bioinformatics pipeline named mimicDetector.
- Implemented the pipeline using Python and Snakemake for compatibility with Unix-based systems.
- Applied mimicDetector to analyze molecular mimicry in 17 globally important pathogens.
Main Results:
- mimicDetector successfully identified a wide range of potential molecular mimicry candidates between pathogens and their hosts.
- Identified helminth proteins mimicking components of the human complement system.
- Discovered a Leishmania infantum mimic of Reticulon-4, a key regulator of immune cell recruitment.
Conclusions:
- mimicDetector is a sensitive and specific tool for identifying molecular mimicry.
- The identified mimicry candidates provide insights into pathogen evasion strategies and potential targets for therapeutic intervention.
- The tool is publicly available, facilitating further research in pathogen-host interactions.
