Defining the multi-dimensional code of zinc finger specificity-Resubmission-1

项目摘要 Cys 2 His 2锌指DNA结合结构域是人类中最常见的结构域， 这些蛋白质中的绝大多数的特异性仍然不确定。在许多这些领域的突变， 无论有没有已知的DNA结合数据，都与阿尔茨海默氏症的一系列疾病有关 （REST）至癌症（例如，Slug、WT 1、CTCF）。因此，这些蛋白质的表征具有很大的价值。 不幸的是，用于确定转录因子的DNA结合特异性的常用方法 未能解决锌指问题，至少部分原因是无法完全确定大目标 哺乳动物锌指蛋白所需的特异性。即使存在ChIP-Seq数据， 限制，因为基因组的大小不允许我们捕获因子的全部结合潜力， 可提供≥ 21 bp的靶序列。因此，如果没有对蛋白质结合的全面了解， 潜在的，基因组中的SNP将继续代表我们无法识别的潜在结合位点。 预测。总之，几十年的研究已经启发了我们对这一领域的理解，但我们仍然处于 当涉及到它作为转录因子的功能时，最近，我们采取了另一种方法， 定义这个域，证明了一个合成的，一个接一个的单个锌指的屏幕允许我们 预测多指蛋白质的特异性，其准确性与所有先前预测相似或更高 算法然而，这种方法未能考虑相邻手指对系统的影响。 彼此我们已经制作了相当于锌指的全面快照 在许多潜在的背景环境中的一个。在这里，我们建议扩展这种方法和屏幕 锌指在一组包容性的上下文环境下。我们将考虑最常见的直接和 间接影响相邻的手指绑定以及影响几何形状的因素， 手指接触DNA我们将使用这些结果来提供一个完整的图片如何相邻的锌指 确定它们的特异性，并通过构建这些双指模型，预测和设计 大型多指蛋白质这样，我们将定义一个锌指特异性的多维编码 这使我们能够预测所有的锌指DNA结合特异性， 会改变这种特异性，以及导致相邻手指不相容和丧失的因素。 DNA结合功能。我们将应用这个模型来预测所有人类锌指蛋白的特异性， 通过对一组已知的转录因子进行体内表征来验证这些预测，并测试 多指结合的预测机制与设计师，人为因素。

Project Summary The Cys2His2 zinc finger DNA-binding domain is the most common domain in human yet the DNA-binding specificities for the great majority of these proteins remain undefined. Mutations in many of these domains, both with and without known DNA-binding data, have been linked to a host of diseases from Alzheimers (REST) to Cancer (e.g. Slug, WT1, CTCF). Therefore, the characterization of these proteins holds great value. Unfortunately common methodologies used to determine the DNA-binding specificity of transcription factors have failed to address the zinc finger, at least in part because of an inability to fully define the large target specificities required of the average mammalian zinc finger protein. Even when ChIP-Seq data exists it is limited because the size of the genome does not allow us to capture the full binding potential of a factor that could offer a ≥21bp target sequence. As a result, without a comprehensive understanding of a protein’s binding potential, SNPs across the genome will continue to represent potential binding sites that we are unable to predict. In sum, decades of research have enlightened our understanding of this domain but we are still in the dark when it comes to its function as a transcription factors. Recently we have taken an alternative approach to define this domain, demonstrating that a synthetic, one-by-one screen of individual zinc fingers allows us to predict the specificity of multi-fingered proteins with similar or greater accuracy than all prior prediction algorithms. However, this approach fails to take into consideration the influences that adjacent fingers have on one another. We have produced the equivalent of a comprehensive snapshot of what a zinc finger is capable of in just one of many potential contextual environments. Here we propose to scale this approach and screen the zinc finger under an inclusive set of contextual environments. We will consider the most common direct and indirect influences on adjacent finger binding as well as factors that impact the geometry with which the zinc fingers engage the DNA. We will use these results to provide a complete picture of how adjacent zinc fingers determine their specificity and by scaffolding these two-fingered models, predict and design the specificity of large, multi-fingered proteins. In this way, we will define a multi-dimensional code of zinc finger specificity that allows us to predict all zinc finger DNA-binding specificities, how any neighbor finger context would modify this specificity, and the factors that result in adjacent finger incompatibility and loss of DNA-binding function. We will apply this model to predict the specificity of all human zinc finger proteins, validate these predictions through in vivo characterization of an informed set of transcription factors, and test predicted mechanisms of multi-fingered binding with designer, artificial factors.