Biology subjects

Bergogne, L.

Publications and source records attributed to Bergogne, L..

3 recordsLinked to original sources

bioRxiv2026

Capturing domain-level detail of molecular interactions in the IMEx Consortium binding domain data set

The machinery of the cell depends on physical interactions, between proteins themselves, and between proteins and other biomolecules. The detailed characterisation of these interactions, including the identification of the precise sequence regions mediating binding events, is essential to understand both normal cell biology and disease mechanisms. Protein binding domains are specific regions of a protein sequence that mediate direct contact with a partner and represent a critical layer of functional annotation, bridging sequence, structure, and cellular function. Here we present the IMEx binding domain dataset, a large-scale, manually curated open-access set of molecular interactions assembled over more than two decades by the International Molecular Exchange (IMEx) Consortium. The dataset compiles experimentally derived binding region annotations from peer-reviewed literature, systematically linked to protein-protein interaction (PPI) records and mapped to standardised protein sequences in UniProtKB. We describe the curation model, data format, and content of the dataset, provide a descriptive and comparative analysis, and demonstrate the utility of the dataset as a gold standard for benchmarking interface prediction and variant effect annotation methods. Its integration with genomic variations further offers a route to elucidate the mechanistic basis of disease-associated mutations. The datasets are freely available through IntAct in the dedicated PSI-MI FeatureTAB format at https://ftp.ebi.ac.uk/pub/databases/intact/current/psimitab/features/ and are updated and enhanced with every release.

bioinformatics↗

bioRxiv2026

BactMentha: a database of annotated bacteria-host protein interactions

Bacterial pathogens ensure their successful dissemination by employing specialized secretion systems to deliver a wide range of virulence proteins that interact with specific host proteins to modulate the response during infection. These interactions are often mediated by host-like elements, like short linear motifs, which can interfere with host interaction interfaces, enabling bacterial secreted proteins to compete and displace native partners of the targeted proteins. Several resources provide access to bacteria-host protein-protein interaction data. However, some are neither fully functional or up to date. Moreover, most of these databases lack virulence protein annotations, thus hampering hypothesis formulation based on protein interaction analyses. In this context, we have developed BactMentha, a unified resource that integrates bacterial-host protein-protein interactions for three organisms (human, mouse, and rat) with virulence protein annotations and bacterial strain clinical relevance information. BactMentha provides easy access to available experimentally identified binding regions of the interaction partners, complemented with putative interaction interfaces based on predicted structure complexes and on the detection of host-like elements on bacterial protein sequences interacting with human proteins. BactMentha currently contains 11 776 unique protein interactions between 2970 bacterial proteins, of which 684 have at least one virulence annotation, and 5088 host proteins. Experimentally identified binding regions are available for 532 bacteria-host interactions, most of which involve human proteins (440 interactions). Predicted interaction interfaces enabled a 3.7-fold increase in known and putative binding regions in the bacteria-human protein interaction data. By providing easy access to functionally annotated and clinically relevant protein interaction data, enriched with experimentally and predicted interaction interfaces, BactMentha can be instrumental in formulating novel hypotheses and speeding up wet lab research on the molecular mechanisms underlying pathogenic bacterial infections.

bioinformatics↗

bioRxiv2026

A quantitative interactome map reveals affinity tailored hijacking of human cellular functions by resident and pathogenic bacteria

Bacteria employ molecular mimicry to hijack host cellular functions for their own benefit. Despite the wealth of data mapping human-bacteria interactions, a comprehensive view of how these networks are fine-tuned remains elusive. In particular, there have been no large-scale screenings measuring human-bacterial protein-protein binding affinities to explore their role in functional and pathological outcomes. Here, we report the largest quantitative interactome to date between the family of human PDZ domains and C-terminal PDZ-binding motifs (PBMs) in effectors from pathogenic and resident gut bacteria. We show that bacteria tailor effector sequences to bind PDZ domains with similar or higher binding affinities than human PBMs to hijack their associated cellular functions. For a subset of the dataset, we validate PDZ-PBM interactions with the corresponding full-length proteins and show that for pathogenic E. coli effector NleA, its C-terminal PBM is necessary for interactions with proteins involved in intracellular transport and membrane organization, probably impacting the host glycocalyx to favor infection. Finally, we show that motifs overlapping with effector PBMs target interaction modules other than PDZ domains, adding an additional layer of complexity to the host-microbe molecular dialogue.

systems biology↗