mimicINT: a workflow for microbe-host protein interaction inference

Abstract

AbstractThe 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. In this context, we developedmimicINT, a freely available computational workflow for large-scale protein-protein interaction inference between microbe and human by detecting putative molecular mimicry elements that can mediate the interaction with host proteins: short linear motifs (SLiMs) and hostlike 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 provides(i)robust Monte-Carlo simulations to assess the statistical significance of SLiM detection which suffers from false positive, and(ii)interaction specificity filter to account for differences between motif-binding domains of the same family.mimicINT is implemented in Python and R, and it is available at:https://github.com/TAGC-NetworkBiology/mimicINT.