Binding Kinetics in Transcription Activation and Repression

转录调控是细胞如何解释遗传变异的关键决定因素：超过90% 与性状和疾病相关的遗传变异图谱在编码基因组之外。虽然我们现在有一个 对人类TF可能结合的位置的强大理解，使得在数据被 这些方法无法告诉我们TF结合的频率，它保持结合的时间，以及它 一旦结合，这使得很难预测TF或调控位点变异对 转录。 关键的调控结构，主要是与激活剂研究，通常有利于短暂的，较低的亲和力 调节器和DNA之间的相互作用比更强的调节器和DNA之间的相互作用更强。这与观察结果一致， 转录机器可以在几秒钟内非常有效地加载。是否采取了类似的策略， 由于抑制作用的时间跨度要长得多，因此抑制剂的作用机制尚不清楚。 在这项提案中，我们将建立一个新的基于人工智能的锌指设计模型，以设计合成模拟物， 内源性DNA结合结构域，其以可调的亲和力结合任意序列，并测量它们的 重组测定中的结合动力学。我们将部署尖端的单分子跟踪显微镜， 为了测量这些结构域与它们的靶标以及在活体内的非特异性位点的结合动力学， 细胞这些实验将能够重建转录因子部署的策略， 在基因组的干草堆中寻找它们的目标。 我们将这些DNA结合结构域与激活或抑制结构域融合，以直接连接 转录因子结合动力学与其靶标合成的转录爆发的时间和输出 基因.总之，这些数据将提供机制的策略，阻遏和激活 已经进化到可以找到并调节它们的目标这些结果构成了新产品的关键设计原则。 基于转录因子重编程的生物技术。

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.