Multimodal SARS-CoV-2 interactome sketches the virus-host spatial organization
Guillaume Dugied, Estelle Mn Laurent, Mikaël Attia, Jean-Pascal Gimeno, Kamel Bachiri, Payman Samavarchi-Tehrani, Flora Donati, Yannis Rahou, Sandie Munier, Faustine Amara, Mélanie Dos Santos, Nicolas Soler, Stevenn Volant, Natalia Pietrosemoli, Anne-Claude Gingras, Georgios A Pavlopoulos, Sylvie Van Der Werf, Pascal Falter-Braun, Patrick Aloy, Yves Jacob, Anastassia Komarova, Yorgos Sofianatos, Etienne Coyaud, Caroline Demeret
Communications Biology, doi:10.1038/s42003-025-07933-z
An accurate spatial representation of protein-protein interaction networks is needed to achieve a realistic and biologically relevant representation of interactomes. Here, we leveraged the spatial information included in Proximity-Dependent Biotin Identification (BioID) interactomes of SARS-CoV-2 proteins to calculate weighted distances and model the organization of the SARS-CoV-2-human interactome in three dimensions (3D) within a cell-like volume. Cell regions with viral occupancy were highlighted, along with the coordination of viral proteins exploiting the cellular machinery. Profiling physical intra-virus and virus-host contacts enabled us to demonstrate both the accuracy and the predictive value of our 3D map for direct interactions, meaning that proteins in closer proximity tend to interact physically. Several functionally important virus-host complexes were detected, and robust structural models were obtained, opening the way to structure-directed drug discovery screens. This PPI discovery pipeline approach brings us closer to a realistic spatial representation of interactomes, which, when applied to viruses or other pathogens, can provide significant information for infection. Thus, it represents a promising tool for coping with emerging infectious diseases. Proteins are the main agents involved in the regulation of biological functions and don't act alone to mediate and regulate these functions; instead, they act in concert with other proteins through their interactions 1-5 . These protein-protein interactions (PPIs) are involved in the development of human diseases, either genetic, physiologic or infectious [6] [7] [8] . Therefore, mapping PPIs is essential to understanding their role in the development of diseases at the cellular level. Representation of networks of interactions is a critical component of the analysis, allowing the identification of proteins involved in similar complexes or pathways. However, PPI networks are often incomplete and hard to exploit, primarily due to the intrinsic nature of PPI detection methods and the two-dimensional network representations. Additionally, most interaction networks only represent a single study, performed with a single PPI detection method, while a complete network would require the aggregation of multiple studies. Moreover, distances within a protein interaction network are often empirical and without biological meaning, precluding the representation of actual volumes and regions. Contrasting with the huge increase in PPI detection methods sensitivity and deepness, little if any progress has been made toward the spatial representation of PPI networks. The tools developed to extrapolate distances from PPI networks dating back more than 10 years 9,10 . The inference of protein locations from interaction data to generate a spatial representation of interaction networks has so far remained out of reach. This step is yet a prerequisite towards an accurate three-dimensional representation..
Author contributions
Competing interests The authors declare no competing interests.
Additional information Supplementary information The online version contains supplementary material available at https://doi.org/10.1038/s42003-025-07933-z . Correspondence and requests for materials should be addressed to Yorgos Sofianatos, Etienne Coyaud or Caroline Demeret. Peer review information Communications Biology thanks Xinyu Cui and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editors: Xiaohui Frank Zhang and Tobias Goris. Reprints and permissions information is available at http://www.nature.com/reprints Publisher's note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the..
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