Data-Driven Prediction and Design of bZIP Coiled-Coil Interactions

Selective dimerization of the basic-region leucine-zipper (bZIP) transcription factors presents a vivid example of how a high degree of interaction specificity can be achieved within a family of structurally similar proteins. The coiled-coil motif that mediates homo- or hetero-dimerization of the bZIP proteins has been intensively studied, and a variety of methods have been proposed to predict these interactions from sequence data. In this work, we used a large quantitative set of 4,549 bZIP coiled-coil interactions to develop a predictive model that exploits knowledge of structurally conserved residue-residue interactions in the coiled-coil motif. Our model, which expresses interaction energies as a sum of interpretable residue-pair and triplet terms, achieves a correlation with experimental binding free energies of R = 0.68 and significantly out-performs other scoring functions. To use our model in protein design applications, we devised a strategy in which synthetic peptides are built by assembling 7-residue native-protein heptad modules into new combinations. An integer linear program was used to find the optimal combination of heptads to bind selectively to a target human bZIP coiled coil, but not to target paralogs. Using this approach, we designed peptides to interact with the bZIP domains from human JUN, XBP1, ATF4 and ATF5. Testing more than 132 candidate protein complexes using a fluorescence resonance energy transfer assay confirmed the formation of tight and selective heterodimers between the designed peptides and their targets. This approach can be used to make inhibitors of native proteins, or to develop novel peptides for applications in synthetic biology or nanotechnology.Author Summary: Protein interactions are important for all life processes, and an ability to rationally control or selectively inhibit protein complexes would impact studies of cellular structure, biological information processing, molecular regulatory processes and other phenomena. Rational protein design has applications in developing biotherapeutics and in advancing synthetic biology and nanotechnology. Rational protein-interaction design has been approached in many ways over the past decades, but it remains a challenge. Computational methods require models to predict binding and tools for applying predictive models in design. Many such methods are based on modeling and evaluating protein structure using physical or semi-physical energy terms. In this work, we used a different strategy, deriving a binding model that describes the protein-protein interactions of basic-region leucine-zipper (bZIP) transcriptions factors directly from a large body of experimental interaction data. Our model showed much better performance than previously published predictors. We used our model, in conjunction with a protein-design strategy that builds new proteins from modular parts of known proteins, to successfully design novel bZIP-like protein domains. We demonstrated experimentally that the designed proteins bind tightly and specifically to a number of human bZIPs that regulate important processes including stress responses and oncogenesis.