High-throughput data and modeling reveal insights into the mechanisms of cooperative DNA-binding by transcription factor proteins.
High-throughput data and modeling reveal insights into the mechanisms of cooperative DNA-binding by transcription factor proteins.
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DOI：
10.1093/nar/gkad872
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发表时间：
2023-11-27
影响因子：
14.9
通讯作者：
中科院分区：
文献类型：
--
作者：
转录因子（TF）蛋白的协同dna结合是真核生物基因调控的关键。在人类基因组中，许多调控区域都含有相互靠近的tf结合位点，这可以促进合作相互作用。然而，结合位点接近并不一定意味着合作结合，因为tf也可以独立地结合到它们相邻的每个目标位点。目前，驱动协作TF绑定的规则还没有得到很好的理解。此外，通常很难从现有的dna结合数据中推断出TF-TF的直接协同性。在这里，我们展示了使用数千个基因组序列的DNA文库进行体外结合试验，这些DNA文库具有假定的协同tf结合事件，可用于开发精确的协同模型，并深入了解协同结合机制。以因子ETS1和RUNX1为例，我们发现ETS1位点之间的距离和方向是ETS1 - ETS1合作结合的关键决定因素，而ETS1 - RUNX1合作相互作用在距离和方向上具有更大的灵活性，可以根据结合位点的亲和力和序列/形状特征准确预测。本文描述的方法将定制实验设计与机器学习建模相结合，可以很容易地应用于研究任何tf的合作dna结合模式。
Cooperative DNA-binding by transcription factor (TF) proteins is critical for eukaryotic gene regulation. In the human genome, many regulatory regions contain TF-binding sites in close proximity to each other, which can facilitate cooperative interactions. However, binding site proximity does not necessarily imply cooperative binding, as TFs can also bind independently to each of their neighboring target sites. Currently, the rules that drive cooperative TF binding are not well understood. In addition, it is oftentimes difficult to infer direct TF–TF cooperativity from existing DNA-binding data. Here, we show that in vitro binding assays using DNA libraries of a few thousand genomic sequences with putative cooperative TF-binding events can be used to develop accurate models of cooperativity and to gain insights into cooperative binding mechanisms. Using factors ETS1 and RUNX1 as our case study, we show that the distance and orientation between ETS1 sites are critical determinants of cooperative ETS1–ETS1 binding, while cooperative ETS1–RUNX1 interactions show more flexibility in distance and orientation and can be accurately predicted based on the affinity and sequence/shape features of the binding sites. The approach described here, combining custom experimental design with machine-learning modeling, can be easily applied to study the cooperative DNA-binding patterns of any TFs.
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作者：
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10.1016/j.csbj.2014.07.005
发表时间：
2014-07
影响因子：
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通讯作者：
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