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.

标题：转录因子 - DNA结合的定量建模 PI：Rohs，Remo 项目摘要 基因通过转录因子（TF）与基因组中特定的DNA靶位点结合进行调节。 这些目标位点通过多个特异性确定剂识别。最广泛的 结合特异性的研究层是氢键和蛋白质氨基酸之间的疏水接触 基本对的功能组主要在主要凹槽中。基本读数识别核苷酸 仅几个碱基对的短核结合位点内的序列。但是，这些独特的序列 TF结合基序中的组合在基因组中多次发生，只有很小的推定。 结合位点功能性。 TF如何定位和识别其体内绑定位点仍然未知 大量可能的基因组靶位点。识别三维DNA结构是一个额外的层 完善基本读数。虽然后者仅限于与核心图案进行直接接触，但形状读数为 核心基序或间隔区域的侧面区域之间的机制，二聚体的半位置之间 TFS有助于结合特异性。体内TF结合确定剂的其他层是染色质结构， DNA可及性，组蛋白修饰，DNA甲基化，辅助因子和合作结合以及细胞类型。 鉴于TF识别的这种多层性质，我们将开发定量模型，以预测与TF结合 高精度。但是，更重要的是，我们的模型将在不存在的情况下揭示识别机制 基于实验的结构信息。我们将构建模型，其中TF绑定的每个不同层 将特异性确定剂添加到结合​​DNA序列和形状的基线模型中。因为是 期望每种TF结合特异性的重要性都会在跨越 蛋白质家族，我们将使用特征选择来确定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 可以从卷积神经网络的输入层中添加或删除。这种方法将利用 深度学习的优势，同时规避标准深度学习的“黑匣子”性质 方法。我们还将使用SELEX-SEQ技术生成特定TF的实验数据。这 目前，方法能够探测辅助因子，合作结合和蛋白质突变对 TF的结合特异性。我们将在SELEX-SEQ结合测定中添加核小体，从而证明 在没有其他细胞贡献的情况下，使用体外实验对TF结合的影响。这 项目将对TF-DNA结合有更好的机械理解，并揭示各种影响 特异性决定了多个量表。新见解将描述读数的不同组合 基于蛋白质家庭特异性的机制。我们的新方法将在生物医学创新方面产生进展 这是基于转录和基因调节的。生成的知识将更好地整合基因组学 和生物物理学，该项目将有助于对新一代科学家的培训和心理。