Collaborative Research: Experimental and Computational Studies of DNA Binding by Human Paralogous Transcription Factors

一个人的所有细胞都含有相同的遗传信息。然而，不同的细胞以不同的方式使用这些信息，每个细胞只表达一小部分基因来产生相应的蛋白质。这一过程受到称为转录因子的特殊蛋白质的严格调控，转录因子结合特定基因附近的DNA并影响其表达。该项目研究人类转录因子，以了解它们如何在基因组中识别其特定的DNA靶标。该项目侧重于具有相似结构的人类转录因子，但与细胞中不同的基因组区域相互作用，从而执行不同的功能。这项研究的目的是了解密切相关的因素如何能够识别不同的基因组位点，这是一个无法用目前的DNA结合特异性模型彻底解决的问题。在项目过程中，将产生高质量的转录因子-DNA结合数据和DNA结合特异性模型。这些数据和模型将提供给科学界。本研究产生的数据有望成为未来开发和测试蛋白质- dna结合模型以及研究相关转录因子之间差异的宝贵资源。研究生、本科生和高中生将参与数据生成和分析，并使用可视化软件、网络平台、科学海报和文章传播结果。因此，不同年龄组的学生将通过蛋白质- dna相互作用的实际研究介绍分子和结构生物学。此外，学生将成为生物信息学研究数据库的贡献者，并将有机会在他们的职业生涯早期共同撰写科学出版物。描述蛋白质-DNA的相互作用，了解蛋白质和DNA的作用，对于解释基因组中的调控元件，以及了解蛋白质或DNA结合位点的变化如何影响细胞功能至关重要。该项目的目标是了解来自6个不同蛋白质家族的16个转录因子（tf）是如何识别不同的基因组位点的，这是一个使用当前数据和模型无法解决的问题。该项目将使用实验和计算相结合的方法来确定DNA序列和形状如何影响同源tf的差异DNA结合。首先，该项目将使用精心设计的高通量分析来测量相关tf与数千个假定的基因组结合位点的体外结合。这些被称为基因组-上下文蛋白结合微阵列（gcPBM）的检测方法，最大限度地减少了实验测量中的噪声和偏差，使数据成为比较密切相关因素的内在序列偏好的理想选择。然后，这些高质量的数据将用于使用基于假定结合位点的DNA序列内容的回归模型来表征旁系tf的DNA结合偏好。接下来，该项目将研究与高阶DNA序列特征相比，DNA形状对同源tf的差异DNA结合特异性的贡献。最后，新的DNA结合特异性计算模型将通过两种方法进行验证：1)模型将通过在DNA结合位点引入突变并在新的gcPBM分析中进行测试；2)将根据体内TF结合数据对模型进行验证，以验证新模型能够解释（至少部分解释）相似TF在体内的不同结合模式。通过鉴定导致密切相关的tf差异DNA结合的特征，该项目在理解这些因子如何能够选择不同的基因组结合位点方面迈出了重要的一步，尽管它们共享一个共同的DNA结合域。这将最终导致更好地理解tf如何进化以调节不同的靶基因并在细胞中执行不同的功能。

All cells in an individual contain the same genetic information. However, different cells use this information differently, and each cell expresses only a small fraction of genes to produce the corresponding proteins. This process is tightly regulated by specialized proteins called transcription factors, which bind DNA in the neighborhood of specific genes and influence their expression. This project studies human transcription factors to understand how they identify their specific DNA targets across the genome. The project focuses on human transcription factors that have similar structures but interact with different genomic regions in the cell, and thus perform different functions. The goal of this research is to understand how closely related factors are able to recognize distinct genomic sites, a question that cannot be thoroughly addressed using current DNA binding specificity models. During the course of the project, high-quality transcription factor-DNA binding data and DNA binding specificity models will be generated. The data and models will be made available to the scientific community. The data generated in this study is expected to become a valuable resource for future development and testing of protein-DNA binding models, and for studies of differences among related transcription factors. Graduate, undergraduate, and high school students will participate in data generation and analysis, as well as dissemination of the results using visualization software, web platforms, and scientific posters and articles. Thus, students of various age groups will be introduced to molecular and structural biology through practical studies of protein-DNA interactions. In addition, students will become contributors to a bioinformatics research database and will have the opportunity to co-author scientific publications early in their career.Characterizing protein-DNA interactions and understanding the role of both proteins and DNA is vital to interpreting regulatory elements in the genome, and to understanding how changes in the protein or the DNA binding sites will affect cell function. Focusing on 16 transcription factors (TFs) from six different protein families, the goal of this project is to understand how paralogous TFs from each family are able to recognize distinct genomic sites, a question that cannot be addressed using current data and models. The project will use a combined experimental and computational approach to determine how DNA sequence and shape contribute to differential DNA binding by paralogous TFs. First, the project will use carefully designed high-throughput assays to measure in vitro binding of related TFs to thousands of putative genomic binding sites. These assays, called genomic-context protein-binding microarrays (gcPBM), minimize the noise and bias in the experimental measurements, making the data ideal for comparing the intrinsic sequence preferences of closely related factors. These high quality data will then be used to characterize the DNA binding preferences of paralogous TFs using regression models based on the DNA sequence content of putative binding sites. Next, the project will investigate the contribution of DNA shape, compared to high order DNA sequence features, to differential DNA binding specificities of paralogous TFs. Finally, the new computational models of DNA binding specificity will be validated using two approaches: 1) the models will be tested in vitro by introducing mutations in the DNA binding sites and testing them in new gcPBM assays; and 2) the models will be validated against in vivo TF binding data to verify that the new models are able to explain, at least in part, the differential in vivo binding patterns of paralogous TFs. Through the identification of characteristics that contribute to differential DNA binding of closely related TFs, this project represents a significant step forward in understanding how these factors are able to select different genomic binding sites, despite sharing a common DNA binding domain. This will ultimately lead to a better understanding of how TFs have evolved to regulate different target genes and perform different functions in the cell.