Quantitative Modeling of Transcription Factor-DNA Binding

转录因子-DNA 结合的定量建模

• 批准号: 9975181
• 负责人: Remo Rohs
• 金额: $ 52.37万
• 依托单位: UNIVERSITY OF SOUTHERN CALIFORNIA
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-07-09 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9975181
• 关键词: 3-Dimensional; Amino Acids; Base Pairing; Base Sequence; Binding; Binding Proteins; Binding Sites; Biological Assay; Biophysics; Chromatin; Chromatin Structure; Computing Methodologies; DNA; DNA Binding; DNA Methylation; DNA Sequence; DNA Structure; Data; Development; Epitopes; Family; Gene Expression Regulation; Generations; Genes; Genome; Genomics; Hydrogen Bonding; Hydrophobicity; In Vitro; Individual; Knowledge; Major Groove; Mentors; Methods; Modeling; Molecular; Mutation; Nature; Nucleosomes; Protein Family; Proteins; Regulator Genes; Scientist; Shapes; Site; Specificity; Structure; Technology; Time; Training; Transcriptional Regulation; base; cell type; cofactor; convolutional neural network; deep learning; dimer; experimental study; feature selection; functional group; histone modification; in vivo; innovation; insight; learning strategy; transcription factor

Title: Quantitative Modeling of Transcription Factor–DNA Binding PI: Rohs, Remo PROJECT SUMMARY Genes are regulated through transcription factor (TF) binding to specific DNA target sites in the genome. These target sites are recognized through several layers of specificity determinants. The most extensively studied layer of binding specificity are hydrogen bonds and hydrophobic contacts between protein amino acids and functional groups of the base pairs mainly in the major groove. Base readout recognizes nucleotide sequence within a short core-binding site of only a few base pairs. However, these distinct sequence combinations in a TF binding motif occur many times in the genome and only a very small fraction of putative binding sites are functional. It is still unknown how a TF locates and identifies its in vivo binding sites in the plethora of possible genomic target sites. Recognition of three-dimensional DNA structure is an additional layer that refines base readout. While the latter is restricted to direct contacts with the core motif, shape readout is a mechanism through which flanking regions of the core motif or spacer regions between half-sites of dimeric TFs contribute to binding specificity. Other layers of in vivo TF binding determinants are chromatin structure, DNA accessibility, histone modifications, DNA methylation, cofactors and cooperative binding, and cell type. Given this multi-layer nature of TF recognition, we will develop quantitative models to predict TF binding with high accuracy. More important, however, is that our models will reveal recognition mechanisms in the absence of experiment-based structural information. We will build models where each distinct layer of TF binding specificity determinants is added to a base-line model combining DNA sequence and shape. Since it is expected that the importance of each of these TF binding specificity determinants will vary dramatically across protein families, we will use feature selection to identify relative contributions of each feature group as a function of TF or TF family. We will also develop a deep learning framework where individual feature modules can be added or removed from the input layer of convolutional neural networks. This approach will leverage the advantages of deep learning while circumventing the “black box” nature of standard deep learning methods. We will also generate experimental data for specific TFs using the SELEX-seq technology. This approach is currently able to probe the effect of cofactors, cooperative binding, and protein mutations on the binding specificity of a TF. We will add nucleosomes to the SELEX-seq binding assay and, thereby, probe chromatin effects on TF binding using an in vitro experiment in the absence of other cellular contributions. This project will result in a better mechanistic understanding of TF-DNA binding and reveal the impact of various specificity determinants across multiple scales. The new insights will describe different combinations of readout mechanisms on a protein-family specific basis. Our new methods will yield progress in biomedical innovation that is based on transcription and gene regulation. The generated knowledge will better integrate genomics and biophysics, and the project will contribute to the training and mentoring of a new generation of scientists.

转录因子-DNA结合的定量模型 圆周率：RoHS，Remo 项目总结 基因通过转录因子(TF)与基因组中特定的DNA靶点结合来调节。 这些靶点是通过几层特异性决定因素识别的。最广泛的 研究的结合特异层是蛋白质氨基酸之间的氢键和疏水接触 碱基对的官能团主要在主槽中。碱基读数识别核苷酸 只有几个碱基对的短核心结合位点内的序列。然而，这些不同序列 Tf结合基序中的结合在基因组中出现多次，并且只有很小一部分推定 结合位点具功能性。目前还不清楚转铁蛋白如何定位和识别其在体内的结合部位。 过多的可能的基因组靶点。识别三维DNA结构是额外的一层 这改进了基本读数。虽然后者仅限于与核心主题的直接接触，但形状读出是一种 核心基序侧翼区域或二聚体半位点间隔区的作用机制 TFS有助于结合特异性。体内转铁蛋白结合决定簇的其他层是染色质结构， DNA可及性、组蛋白修饰、DNA甲基化、辅因子和协同结合，以及细胞类型。 鉴于转铁蛋白识别的这种多层性质，我们将开发定量模型来预测转铁蛋白与 精确度高。然而，更重要的是，我们的模型将揭示在没有识别机制的情况下 基于实验的结构信息。我们将构建模型，其中每个不同的TF绑定层 将特异性决定因素添加到结合DNA序列和形状的基线模型中。因为它是 预计这些转铁蛋白结合特异性决定因素中的每一个的重要性将在 蛋白质家族，我们将使用特征选择来识别每个特征组的相对贡献 Tf或Tf家族的功能。我们还将开发一个深度学习框架，其中各个功能模块 可以从卷积神经网络的输入层中添加或删除。这一方法将利用 深度学习的优势，同时绕过了标准深度学习的黑匣子性质 方法：研究方法。我们还将使用SELEX-SEQ技术为特定的TF生成实验数据。这 方法目前能够探索辅因子、协同结合和蛋白质突变对 转铁蛋白的结合特异性。我们将在SELEX-SEQ结合分析中加入核小体，从而探测 在没有其他细胞贡献的情况下，用体外实验研究染色质对转铁蛋白结合的影响。这 该项目将导致对TF-DNA结合的更好的机制理解，并揭示各种 多个尺度上的特异性决定因素。新的见解将描述读数的不同组合 在蛋白质家族特定基础上的机制。我们的新方法将在生物医学创新方面取得进展 这是基于转录和基因调控。产生的知识将更好地整合基因组学 和生物物理学，该项目将有助于培训和指导新一代科学家。