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. In this context, we developed mimicINT, 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 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 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.