Biology subjects

Fernandez-Macgregor, J.

Publications and source records attributed to Fernandez-Macgregor, J..

3 recordsLinked to original sources

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↗

bioRxiv2025

A high throughput investigation of the binding specificity of carbohydrate-binding modules for synthetic and natural polymers

Carbohydrate-binding modules (CBMs) are non-catalytic domains that enhance enzyme binding to substrates, crucial for polysaccharide degradation. Type A CBMs also show potential for engineering plastic-degrading enzymes due to their high affinity for synthetic polymers. This study presents a high-throughput screening pipeline for characterizing the affinity and specificity of Type A CBMs to the synthetic polymers polyethylene terephthalate (PET), polystyrene (PS), and polyethylene (PE), and the natural polysaccharides cellulose, chitin, and starch. Approximately 800 CBMs from the families CBM2, CBM3, CBM10, and CBM64 were expressed as enhanced green fluorescent protein (EGFP)-fusion proteins and tested for binding using a modified holdup assay, which could produce up to 10,000 data points per day. The screening identified approximately 150 binders for PET and PE, around 250 for PS, and demonstrated family-specific binding patterns for avicel, chitin, and starch. Distinct patterns of substrate affinity and specificity were observed, which allowed for rationalizations of binding at the structural and phylogenetic levels. To demonstrate practical utility, CBMs with high PET affinity were fused to the PET hydrolase LCCICCG, enhancing enzymatic activity on crystalline PET powder by around 5-fold. Importantly, these CBM-enzyme fusions mitigated competitive binding to inert plastic impurities, improving performance in mixed plastic assays. This work significantly expands the known repertoire of CBMs capable of binding synthetic polymers, advances our understanding of CBM-substrate interactions, and provides knowledge for engineering enzymes to tackle plastic pollution, particularly in municipal solid waste where mixed plastics pose significant challenges. Significance StatementImproper recycling of plastic poses a severe environmental threat, with synthetic polymers persisting in ecosystems for decades. Enzymatic recycling of plastic offers a promising solution, with optimization of the enzymes and processes ongoing. To expand the toolkit for engineering enzymes via substrate-binding module fusion, we developed a high-throughput screening platform to identify carbohydrate-binding modules (CBMs) that selectively bind synthetic plastics such as PET, polystyrene, and polyethylene, as well as natural polysaccharides. By screening 797 CBMs, we discovered binding domains with distinct specificities, enabling the design of enzyme fusions that enhance plastic degradation, even in mixed plastic environments. This approach provides insights into protein-substrate interactions that can be leveraged for both waste management and bioengineering innovations.

bioengineering↗