mimicINT: a workflow for microbe-host protein interaction inference

BackgroundThe 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. MethodsIn this context, we developed mimicINT, an open-source computational workflow for large-scale protein-protein interaction inference between microbe and human by detecting putative molecular mimicry elements mediating 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 also provides (i) robust Monte-Carlo simulations to assess the statistical significance of SLiM detection which suffers from false positives, and (ii) an interaction specificity filter to account for differences between motif-binding domains of the same family. We have also made mimicINT available via a web server. ResultsIn two use cases, mimicINT can identify potential interfaces in experimentally detected interaction between pathogenic Escherichia coli type-3 secreted effectors and human proteins and infer biologically relevant interactions between Marburg virus and human proteins. ConclusionsThe mimicINT workflow can be instrumental to better understand the molecular details of microbe-host interactions.