Transcription factors (TFs) have been a focal point of molecular biology research for decades, with evolving methodologies offering progressively deeper insights into their critical roles in gene regulation. Recent advancements in experimental and computational techniques have significantly enhanced our understanding of TF functionality, yet this depth of knowledge varies widely across the spectrum of known TFs — from extensively characterized ones with quantitative binding affinity data to those scarcely studied or understood. In this work, we systematically carried out binding and expression experiments on all Escherichia coli TFs using a standardized computational pipeline to identify direct and indirect regulatory targets. We further leveraged our binding data to develop a novel biophysically motivated neural network capable of predicting TF-DNA binding affinity from DNA sequence. This approach allowed us to design binding sites with specified affinities, including those stronger than any sequence observed in nature, which we validate experimentally using an in vitro binding assay. We further optimized this assay to provide insight into complex TF binding regimes, where chemical signals can modulate TF binding affinity. Finally, we demonstrate the utility of systematically mapping TF binding sites through a case study on a previously thought dormant TF acquired from viral infection, revealing an unexpected phenotype where it can hijack the host cell. This work not only offers broad insights into the determinants of TF binding and regulation, but also provides a means to predictively engineer binding sites with desired affinity, while demonstrating the power of efficient data processing in uncovering intricate biological processes. / 2025-05-23T00:00:00Z