DNA-Binding Activity of Human Proteins

描述(由申请人提供)： 长期以来，人们一直感到困惑的是，在人类中，相对较少的转录因子(TF)如何准确地控制~21,000个ORF的表达，甚至可能是10倍以上的非编码RNA。转录领域的另一个主要差距是缺乏一套简单的规则来解释蛋白质-DNA相互作用的特异性。这些空白代表着一个大问题，因为在它们被填补之前，对转录电路及其基本原理的理解仍然非常不完整。长期目标是利用蛋白质微阵列技术和生物信息学的组合来表征人类蛋白质-DNA相互作用(PDI)网络，并阐明转录调控的基本分子机制。这一特殊应用的目标是确定序列特定的非传统DNA结合蛋白(UDBPs)的全面列表，并更好地定义指定TF-DNA相互作用的规则。中心假设是，无偏见的，高通量的PDI简档将揭示转录调控网络和途径的规则和组织。这一假设是根据申请者实验室提供的初步数据提出的。这项研究的基本原理是，一旦生成了一份全面的序列特异性uDBPs列表，一旦完成了对PDI和TF晶体结构的全面分析，我们将能够预测和测试uDBPs的新的生理作用，并生成更好的规则来定义人类许多TF亚家族的DNA结合特异性。在强大的初步数据的指导下，这一假说将通过追求两个具体目标来检验：1)使用人类蛋白质组微阵列全面鉴定DNA结合蛋白；2)鉴定和表征定义DNA结合偏好的TF识别结构域。在第一个目标下，500个预测和已知的DNA基序将被探测到由约17,000个单独纯化的蛋白质组成的人类蛋白质组微阵列，这是申请人实验室制造的一种新工具，以生成一份全面的uDBP列表。在一系列生物信息学分析和预测的基础上，将对1-2个uDBPs进行深入的鉴定，以阐明申请人实验室中转录调控的生理作用。在第二个目标下，将分析各种物种的Tf-DNA络合物的现有晶体结构，以确定决定PDI的Tf的非接触性氨基酸残基(AAs)。选定的一组已确定的残留物将进一步进行实验测试。这种方法是创新的，因为它利用基于活动的筛选来检测人类蛋白质组中的uDBP，并对非接触性氨基酸的贡献进行无偏见的调查，以确定DNA结合的特异性。这项拟议的研究具有重要意义，因为它将是第一次对人类DNA结合活动的系统分析，将提供转录控制和调控参与者的全面名单，还因为一套定义更好的规则最终将为科学界提供一块罗塞塔石头，用于解码人类转录调控电路。最终，这些知识有可能为开发更好的治疗TF相关疾病的疗法提供信息。

DESCRIPTION (provided by applicant): It has long been puzzling how a relatively small number of transcription factors (TFs) can precisely control expression of ~21,000 ORFs and probably 10-fold more non-coding RNAs in humans. Another major gap in the transcription field is a lack of a simple set of rules that explain the specificity of protein-DNA interactions. These gaps represent a major problem because, until they are filled, understanding of the transcription circuitry and its underlining principles will remain highly incomplete. The long-term goals are to characterize the human protein-DNA interaction (PDI) network and elucidate the underlining molecular mechanisms of transcriptional regulation using the combined force of protein microarray technologies and bioinformatics. The objectives of this particular application are to identify a comprehensive list of sequence-specific unconventional DNA-binding proteins (uDBPs) and to better define rules that specify TF-DNA interactions. The central hypothesis is that unbiased, high-throughput profiling of PDIs will reveal rules and organization of transcriptional regulatory networks and pathways. This hypothesis has been formulated on the basis of preliminary data produced in the applicants< laboratories. The rationale for the proposed research is that, once a comprehensive list of sequence-specific uDBPs is generated and once a comprehensive analysis of PDIs and crystal structures of TFs is completed, we will be able to predict and test novel physiological roles of uDBPs and generate better rules that define DNA-binding specificity for many TF subfamilies in humans. Guided by strong preliminary data, this hypothesis will be tested by pursuing two specific aims: 1) Comprehensively identify DNA-binding proteins using human proteome microarrays; and 2) Identify and characterize TF recognition domains that define DNA-binding preference. Under the first aim, 500 predicted and known DNA motifs will be probed to a human proteome microarray composed of ~17,000 individually purified proteins, a new tool fabricated at the applicants' laboratory, to generate a comprehensive list of uDBPs. On the basis of a series of bioinformatics analysis and prediction, 1-2 uDBPs will be characterized in-depth to elucidate the physiological roles in transcription regulation in the applicants' laboratories. Under the second aim, the existing crystal structures of TF-DNA complexes of various species will be analyzed to identify non-contacting amino acid residues (AAs) of TFs that dictate PDIs. A selected set of the identified residues will be further tested experimentally. The approach is innovative, because it utilizes activity-based screens for uDBPs in the human proteome and an unbiased survey for contribution by non-contacting AAs to dictate DNA-binding specificity. This proposed research is significant, because it will be the first systematic profiling of DNA-binding activities in humans that will offer a comprehensive list of participants in transcriptional control and regulation, and because a set of better-defined rules will ultimately provide the scientific community with a Rosetta Stone for decoding human transcriptional regulatory circuitry. Ultimately, such knowledge has the potential to inform the development of better therapeutics for TF-related diseases.