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The genetic architecture of the human bZIP interaction network

ABSTRACT: 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.

ORGANISM(S): Escherichia coli Saccharomyces cerevisiae

PROVIDER: GSE306110 | GEO | 2025/08/28

REPOSITORIES: GEO

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Project description:Gene expression microarray has been the primary biomarker platform ubiquitously applied in biomedical research, resulting in enormous data, predictive models and biomarkers accrued. Recently, RNA-seq has looked likely to replace microarrays, but there will be a period where both technologies coexist. This raises two important questions: can microarray-based models and biomarkers be directly applied to RNA-Seq data? Can future RNA-Seq-based predictive models and biomarkers be applied to microarray data to leverage past investment? We systematically evaluated the transferability of predictive models and signature genes between microarray and RNA-seq using two large clinical data sets. The complexity of cross-platform sequence correspondence was considered in the analysis and examined using three human and two rat data sets, and three levels of mapping complexity were revealed. Three algorithms representing different modeling complexity were applied to the three levels of mappings for each of the eight binary endpoints and Cox regression was used to model survival times with expression data. In total, 240,096 predictive models were examined. Signature genes of predictive models are reciprocally transferable between microarray and RNA-seq data for model development, and microarray-based models can accurately predict RNA-seq-profiled samples; while RNA-seq-based models are less accurate in predicting microarray-profiled samples and are affected both by the choice of modeling algorithm and the gene mapping complexity. The results suggest continued usefulness of legacy microarray data and established microarray biomarkers and predictive models in the forthcoming RNA-seq era. Definitions of characteristics: EFS day: number of days for event free survival EFS bin: binary classification of event free survival OS day: number of days for overall survival OS bin: binary classification of overall survival High Risk: Indicating whether a sample belongs to high risk group or not A_EFS_All: binary class label for event free survival for all samples B_OS_All: binary class label for overall survival for all samples C_SEX_All: binary class label for sex D_FAV_All: binary class label for favorable and unfavorable samples E_EFS_HR: binary class label for event free survival of High Risk group F_OS_HR: binary class label for overall survival of High Risk group. The same set of Samples is submitted under GEO accession GSE49711. This Series is a reanalysis of the data. The same set of RNA samples were profiled with microarray and RNA-Seq platforms. We explore the transferability of predictive models and signature genes between microarray and RNA-Seq data

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The genetic architecture of the human bZIP interaction network

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