mimicINT: A workflow for microbe-host protein interaction inference [version 2; peer review: 2 approved]
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. Meth...
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F1000 Research Ltd
2025-03-01
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| Online Access: | https://f1000research.com/articles/14-128/v2 |
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| author | Andreas Zanzoni Lilian Drets Mégane Boujeant Marceau Cristianini Christine Brun Aurélie Bergon Lionel Spinelli Kevin Maldonado Sébastien A. Choteau |
| author_facet | Andreas Zanzoni Lilian Drets Mégane Boujeant Marceau Cristianini Christine Brun Aurélie Bergon Lionel Spinelli Kevin Maldonado Sébastien A. Choteau |
| author_sort | Andreas Zanzoni |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-443d829eee97486ea7d3d063e924367e |
| institution | DOAJ |
| issn | 2046-1402 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | F1000 Research Ltd |
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| series | F1000Research |
| spelling | doaj-art-443d829eee97486ea7d3d063e924367e2025-08-20T03:05:14ZengF1000 Research LtdF1000Research2046-14022025-03-011410.12688/f1000research.160063.2179574mimicINT: A workflow for microbe-host protein interaction inference [version 2; peer review: 2 approved]Andreas Zanzoni0https://orcid.org/0000-0002-4818-6161Lilian Drets1Mégane Boujeant2Marceau Cristianini3Christine Brun4https://orcid.org/0000-0002-5563-6765Aurélie Bergon5https://orcid.org/0000-0003-1518-4562Lionel Spinelli6Kevin Maldonado7Sébastien A. Choteau8https://orcid.org/0000-0002-5128-7086Aix-Marseille University, Inserm, TAGC, UMR_S1090, Turing Centre for Living Systems, Marseille, FranceAix-Marseille University, Inserm, TAGC, UMR_S1090, Turing Centre for Living Systems, Marseille, FranceAix-Marseille University, Inserm, TAGC, UMR_S1090, Turing Centre for Living Systems, Marseille, FranceAix-Marseille University, Inserm, TAGC, UMR_S1090, Turing Centre for Living Systems, Marseille, FranceAix-Marseille University, Inserm, TAGC, UMR_S1090, Turing Centre for Living Systems, Marseille, FranceAix-Marseille University, Inserm, TAGC, UMR_S1090, Turing Centre for Living Systems, Marseille, FranceAix-Marseille University, Inserm, TAGC, UMR_S1090, Turing Centre for Living Systems, Marseille, FranceAix-Marseille University, Inserm, TAGC, UMR_S1090, Turing Centre for Living Systems, Marseille, FranceAix-Marseille University, Inserm, TAGC, UMR_S1090, Turing Centre for Living Systems, Marseille, FranceBackground 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.https://f1000research.com/articles/14-128/v2Protein-protein interactions interaction inference microbe-host interactions molecular mimicry short linear motifseng |
| spellingShingle | Andreas Zanzoni Lilian Drets Mégane Boujeant Marceau Cristianini Christine Brun Aurélie Bergon Lionel Spinelli Kevin Maldonado Sébastien A. Choteau mimicINT: A workflow for microbe-host protein interaction inference [version 2; peer review: 2 approved] F1000Research Protein-protein interactions interaction inference microbe-host interactions molecular mimicry short linear motifs eng |
| title | mimicINT: A workflow for microbe-host protein interaction inference [version 2; peer review: 2 approved] |
| title_full | mimicINT: A workflow for microbe-host protein interaction inference [version 2; peer review: 2 approved] |
| title_fullStr | mimicINT: A workflow for microbe-host protein interaction inference [version 2; peer review: 2 approved] |
| title_full_unstemmed | mimicINT: A workflow for microbe-host protein interaction inference [version 2; peer review: 2 approved] |
| title_short | mimicINT: A workflow for microbe-host protein interaction inference [version 2; peer review: 2 approved] |
| title_sort | mimicint a workflow for microbe host protein interaction inference version 2 peer review 2 approved |
| topic | Protein-protein interactions interaction inference microbe-host interactions molecular mimicry short linear motifs eng |
| url | https://f1000research.com/articles/14-128/v2 |
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