Binding Kinetics in Transcription Activation and Repression

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Transcription regulation is a key determinant of how genetic variability is interpreted by a cell: more than 90% of traits- and disease-associated genetic variants map outside of the coding genome. While we now have a robust understanding of where human TFs might bind, enabling predictions of TF binding when data is unavailable, those approaches do not tell us how frequently a TF binds, how long it stays bound, and what it does once bound, which makes it difficult to predict the impact that TF or regulatory site variants will have on transcription. Critical regulatory architectures, primarily studied with activators, typically favor transient, lower affinity interactions between regulators and the DNA over stronger ones. This is consistent with observations that the transcription machinery can load very efficiently, within seconds. Whether similar strategies are adopted by repressors is unclear, given the much longer timescales over which repression unfolds. In this proposal, we will build upon a novel AI-based Zinc Finger design model to engineer synthetic mimics of endogenous DNA binding domains that bind arbitrary sequences with tunable affinity and measure their binding kinetics in reconstituted assays. We will deploy cutting-edge single-molecule tracking microscopy in order to measure the binding kinetics of these domains to their targets and at non-specific site inside living cells. These experiments will enable reconstructing the strategies deployed by Transcription Factors to find their targets in the genome haystack. We will fuse these DNA binding domains with activating or repressing domains in order to directly link Transcription Factor binding kinetics to the timing and output of transcription bursts synthesized by their target genes. Together, these data will provide mechanistic access to the strategies that repressors and activators have evolved to find and regulate their targets. The results constitute key design principles for novel biotechnologies based on Transcription Factor reprogramming.