DNA Binding Properties of Zinc Finger Proteins

锌指蛋白的 DNA 结合特性

• 批准号: 10237910
• 负责人: Petko M Petkov
• 金额: $ 36.76万
• 依托单位: JACKSON LABORATORY
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10237910
• 关键词: Address; Affect; Affinity; Amino Acid Substitution; Amino Acids; Base Pairing; Binding; Binding Proteins; Binding Sites; Biological; Biological Assay; Cell Culture System; Cell Culture Techniques; ChIP-seq; Chromatin Remodeling Factor; Computational algorithm; DNA; DNA Binding; DNA Methylation; DNA Sequence; DNA analysis; DNA-Binding Proteins; Data; Development; Disease; Engineering; Evolution; Fingers; Genes; Genetic; Genetic Engineering; Genetic Recombination; Genomic DNA; Human; Human Genome; In Vitro; Infertility; Knowledge; Malignant Neoplasms; Measures; Meiotic Recombination; Metabolic Diseases; Methods; Methylation; Modeling; Mus; Mutation Analysis; Nucleotides; Oligonucleotides; Orthologous Gene; Physiological; Positioning Attribute; Process; Property; Protein Array; Proteins; Regulation; Sensitivity and Specificity; Site; Source; Specificity; Study models; System; Testing; Variant; Zinc Fingers; base; cost; deep sequencing; ds-DNA; gene function; genome-wide; human disease; improved; in vivo; mammalian genome; novel; paralogous gene; prediction algorithm; predictive modeling; programs; transcription factor

The human genome contains over 700 genes encoding proteins with zinc finger domains, more than half of which contain eight or more fingers organized in a tandem fashion. Many of these genes function as transcription factors, insulator binding proteins, or chromatin modifiers. Despite their importance, we still lack a comprehensive knowledge on the rules that determine these proteins’ binding to DNA, and the existing prediction programs do not perform satisfactorily. Recently, we have developed two new methods for isolation and deep sequencing of zinc finger protein binding sites. The first, Affinity-seq directly determines the relative affinity of tens of thousands of binding sites genome-wide with high binding specificity. It also provides the opportunity for mutational analysis of binding site specificities using alternate sources of genomic DNA. The second, Spec-sec, determines the changes in binding energy for thousands of variants of a preferred sequence, and their sensitivity to DNA methylation. We propose to apply these methods for comprehensive analysis of DNA binding sites of over twenty mouse and human natural protein variants of the recombination regulator PRDM9, as well as over one hundred other human and mouse zinc finger proteins, which represent different groups of long zinc finger array proteins, and whose binding sites has not been determined previously. In Aim 1, we will determine the specificities of PRDM9 protein variants binding to DNA. Aim 1a will determine how systematic changes in contact amino acids, numbers, and interactions between ZFs in PRDM9 protein variants affect their DNA binding by Affinity-seq. Aim 1b will determine the quantitative specificity and sensitivity to DNA methylation of each PRDM9 protein variant by Spec-seq. Aim 1c will use cell culture approaches to determine how conserved features of ZF arrays and combinations of motifs in the same array affect the biological activity of engineered PRDM9 protein variants. In Aim 2, we will determine whether DNA- binding specificities of different laZFP groups co-evolve with their additional domains. Aim 2a will determine the commonality or uniqueness of the rules governing binding to DNA of laZFPs belonging to BTB-, SCAN-, SET-, and KRAB- containing groups, and those without additional domains, by Affinity-seq. Aim 2b will determine their quantitative specificity and sensitivity to CpG methylation (mCpG) status by Spec-seq. In Aim 3, we will develop new and improved computational algorithms for binding site modeling and motif prediction based on laZFP sequences, including mCpG sensitivity. Aim 3a will develop enhanced specificity representations of ZFPs that take full advantage of the Spec-seq data and don’t impose the positional independence inherent in PWM models. Aim 3b will develop improved motif prediction models including methylation sensitivity.

人类基因组包含700多个基因，编码带有锌指结构域的蛋白质，超过一半 其包含以串联方式组织的八个或更多个手指。其中许多基因的功能是 转录因子、绝缘体结合蛋白或染色质修饰物。尽管它们很重要，但我们仍然缺乏 全面了解决定这些蛋白质与DNA结合的规则，以及现有的 预测程序的表现并不令人满意。最近，我们开发了两种新的分离方法 以及锌指蛋白结合位点的深度测序。第一，亲和力-序号直接确定相对 全基因组数万个结合位点的亲和力，具有很高的结合特异性。它还提供了 利用基因组DNA的替代来源突变分析结合部位特异性的机会。这个 其次，Spec-SEC确定了首选的数千个变种的结合能的变化 序列，以及它们对DNA甲基化的敏感性。我们建议将这些方法应用于综合 20多种重组鼠和人天然蛋白变异体的DNA结合位点分析 调节子PRDM9，以及100多个其他人和小鼠锌指蛋白，它们代表 不同基团的长锌指阵列蛋白，其结合位置此前尚未确定。 在目标1中，我们将确定PRDM9蛋白变体与DNA结合的特异性。目标1a将决定 PRDM9蛋白质中接触氨基酸、数量以及ZF之间相互作用的系统性变化 突变体通过亲和力序列影响其DNA结合。目标1b将确定数量特异性和 通过Spec-Seq检测每个PRDM9蛋白变体对DNA甲基化的敏感性。Aim 1c将使用细胞培养 确定ZF阵列和同一阵列中的基元组合的保守性的方法 影响工程PRDM9蛋白变异体的生物活性。在目标2中，我们将确定DNA- 不同laZFP基团的结合特异性与其额外的结构域共同进化。目标2a将决定 管理属于BTB-、SCAN-、SET-的LaZFP与DNA结合的规则的共性或唯一性， 和含有KRAB的基团，以及那些没有额外结构域的基团，通过亲和力序列。目标2b将决定 通过Spec-Seq检测它们对CpG甲基化(MCpG)状态的定量特异性和敏感性。在《目标3》中，我们将 开发新的和改进的计算算法，用于结合位点建模和基序预测 LaZFP序列，包括mCpG敏感性。目标3a将开发增强的特异性表示法 ZFP充分利用Spec-Seq数据，并且不强制 脉宽调制模型。Aim 3b将开发改进的基序预测模型，包括甲基化敏感性。