The genetic architecture of the human bZIP interaction network

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. Deep Mutational Scanning of the entire human bZIP domains and measurement of the effect on protein-protein interactions. Provided is NGS data that serves as readout for protein interaction.