The genetic architecture of the human bZIP interaction network
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PROVIDER: PRJNA1309138 | ENA |
REPOSITORIES: ENA
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Project description:nderstanding the complexity of the genetic architecture underlying protein function is key to 20building accurate predictive models for therapeutic and bioengineering applications. Here, we systematically mutagenized all 54 human basic-leucine zipper (bZIP)domains and quantified their interactions with each other using bindingPCA, a quantitative deep mutational scanning assay. This resulted in ~2 million interaction measurements, capturing the effect of all single amino acid substitutions at each of the 35 interfacial positions. We found that mutation effects 25are largely additive in the vicinity of each wild-type bZIP, but diverge across the family, indicating strong context dependency. A global additive thermodynamic model provided moderate prediction of mutation effects, while individual models per bZIP achieved higher performance, supporting a model of local simplicity and global complexity. A convolutional neural network trained on this dataset accurately predicted binding scores from sequence alone. 30Furthermore, the model enabled the design of synthetic bZIPs with high target specificity, demonstrating practical applicability for bioengineering purposes. Our study shows that capturing family-wide diversity is essential to reveal context dependencies and train accurate quantitative models of protein-protein interactions.
2025-08-28 | GSE306110 | GEO
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Project description:Genome-wide association studies (GWAS) have identified hundreds of susceptibility loci for chronic and inflammatory disease phenotypes in humans. There is increasing evidence that chronic inflammation is a crucial driver in the pathogenesis of cardiovascular diseases (CVD), which may be genetically determined. To understand the genetic architecture underlying chronic inflammation and CVD we performed a systematic analysis of (1) common risk alleles coming from published GWAS, (2) of protein-protein interaction (PPI) networks informed by (3) gene expression data with a defined molecular target involved in the inflammatory processes promoting CVD, MRP8. (4) through analysis of integrated haplotype scores (iHS) and FST values in HapMap phase 2 data, we investigated whether recent selection pressure acting upon inflammatory genes affected CVD susceptibility loci. Our findings provide significant evidence for a PPI network, which connects inflammatory and cardiovascular susceptibility genes, and establish a genetic framework of inflammatory CVD. 41.59% of PPI genes are associated with immune functions. 28.3% of integrated genes can be linked to both, an inflammatory and cardiovascular disease phenotype. Interestingly, CDKN2B, and CELSR2/PSRC1/MYBPHL/SORT1, unequivocally replicated CVD loci, are integrated within this network as are several SNPs located in transcription factor recognition sequences, i.e. NFKB1, STAT3, which are key factors in inflammation. Finally, we observed a significant enrichment of inflammatory variants within CVD cluster loci that are targets of selection. Overall, 32 genes exhibit traces of selection, 16 of which are part of the PPI, further suggesting that recent selective sweeps may have affected the genomic architecture underlying CVD. 6 samples, no replicates.
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Project description:Genetic interactions have long informed our understanding of the coordinated proteins and pathways that respond to DNA damage in mammalian cells, but systematic interrogation of the genetic network underlying this system has yet to be achieved. Towards this goal, we measured 147,153 pairwise interactions among genes implicated in PARP inhibitor (PARPi) response with and without exposure to PARPi. By applying an analytical framework we defined differential genetic interactions at scale, identified novel interactions belonging to different interaction categories, and revealed PARP1-trapping as a common mode of sensitization of PARPi across genetic backgrounds. We uncovered the minimally characterized gene, AUNIP, while not essential for genome stability under normal conditions, plays a key role in repairing PARPi induced DNA damage together with FA pathway genes and is antagonized by the BRCA1-A complex. Our work thus establishes a foundation for mapping differential genetic interactions in mammalian cells and provides a comprehensive resource for future studies of DNA repair and PARP inhibitors.
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Project description:Proteins function in crowded cellular environments in which they must bind to specific target proteins but also avoid binding to many other off-target proteins. In large protein families this task is particularly challenging because many off-target proteins have very similar structures. How this specificity of physical protein-protein interactions in cellular networks is encoded and evolves is not very well understood. Here we address the question of specificity-encoding by comprehensively quantifying the effects of all mutations in one protein, JUN, on its binding to all other members of a protein family, the 54 human basic leucine zipper transcription factors. Fitting a global thermodynamic model to the data reveals that most affinity changing mutations equally affect JUN’s propensity to bind to all its interaction partners. Mutations that alter the specificity of binding are much rarer. These specificity-altering mutations are, however, distributed throughout the JUN interaction interface. JUN’s interaction specificity is encoded by both positive determinants that promote on-target interactions and negative determinants that prevent off-target interactions. Indeed, about half of the specificity-defining residues in JUN have dual functions and both promote on-target binding and prevent off-target binding. Whereas nearly all mutations that alter specificity are pleiotropic and also alter the affinity of binding to all interaction partners, the converse is not true with mutations outside of the interface able to tune affinity without affecting specificity. Our results provide the first global view of how mutations in a protein affect binding to all its potential interaction partners and reveal the distributed encoding of specificity and affinity in an interaction interface. They also show how the modular architecture of coiled-coils provides an elegant solution to the challenge of optimising specificity and affinity in a large protein family.
2024-05-02 | GSE245326 | GEO
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