mimicDetector: a pipeline for protein motif mimicry detection in host-pathogen systems

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Abstract Motivation Molecular mimicry is a widespread strategy used by pathogens to evade the host immune system and manipulate other host cellular processes. Detecting these events—where pathogen proteins resemble host molecules—is challenging due to limitations in the sensitivity, specificity, and scalability of current bioinformatics tools. The challenges are pronounced when identifying subtle similarities in short protein fragments. Results We present mimicDetector, an optimized bioinformatic pipeline for systematically identifying protein-level molecular mimicry between pathogens and their hosts. mimicDetector builds on existing k -mer-based approaches with three key improvements: (i) improved sensitivity for short-sequence alignments using the PAM30 substitution matrix and tuned BLASTP parameters; (ii) a revised k -mer filtering strategy based on bitscore differences rather than percent identity; (iii) the removal of overly conservative homologue exclusion steps. 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 compatible with Unix-based systems.

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