mimicINT: A workflow for microbe-host protein interaction inference.

Abstract

Background: 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.

Methods: In 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.

Results: In 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.

Conclusions: The mimicINT workflow can be instrumental to better understand the molecular details of microbe-host interactions.