mimicINTweb

mimicINTweb enables large-scale protein-protein interaction inference between microbe and human by detecting putative molecular mimicry elements mediating the interaction with host proteins.

Key Features:

• Molecular Mimicry Detection: Identifies short linear motifs (SLiMs) and host-like globular domains as putative elements mediating microbe-host protein interactions.
• Template-Based Interaction Inference: Exploits detected mimicry elements to infer interactions with human proteins using known templates of domain-domain and SLiM-domain interaction templates.
• Statistical Significance Assessment: Implements robust Monte-Carlo simulations to assess the statistical significance of SLiM detection, addressing the problem of false positives.
• Interaction Specificity Filtering: Provides a filter to account for differences between motif-binding domains of the same family.
• Customizable Submission: Allows users to submit protein sequences and tune running parameters for interaction prediction.

Scientific Applications:

• Pathogenic Effector Interface Identification: Identifies potential interfaces in experimentally detected interactions between pathogenic Escherichia coli type-3 secreted effectors and human proteins.
• Viral-Host Interaction Inference: Infers biologically relevant interactions between Marburg virus and human proteins.
• Microbial Infection Mechanism Characterization: Assists computational and experimental research to better characterize the molecular mechanisms of microbial infections.

Methodology:

The workflow detects putative molecular mimicry elements including short linear motifs and host-like globular domains, then uses known templates of domain-domain and SLiM-domain interactions to infer protein-protein interactions with human proteins, with Monte-Carlo simulations for statistical significance assessment of SLiM detection and an interaction specificity filter for motif-binding domain family differences.