Defining the protein sequence features that control transcriptional activation domain function

定义控制转录激活域功能的蛋白质序列特征

• 批准号: 10714062
• 负责人: Max Valentin Staller
• 金额: $ 37.92万
• 依托单位: UNIVERSITY OF CALIFORNIA BERKELEY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2028-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10714062
• 关键词: Amino Acid Sequence; Binding; Biophysics; Cataloging; Cell Culture Techniques; Cell Nucleus; Cells; Computer Models; Data; Disease; Enhancers; Evolution; Family; Gene Expression; Genes; Genome; Goals; Growth; Human; Individual; Machine Learning; Movement; Mutation; Orthologous Gene; Proteins; Surveys; Tertiary Protein Structure; Testing; Transcriptional Activation Domain; Transcriptional Regulation; Work; Yeasts; cell type; convolutional neural network; data integration; high resolution imaging; high throughput screening; predictive modeling; promoter; rational design; recruit; simulation; three dimensional structure; transcription factor

Project Summary/Abstract Most cell-type specific gene expression arises from transcription factors binding to enhancers and promoters12,13. The last decade has seen explosive growth in the identification of enhancers and cataloging of transcription factors binding data14, but it is still not possible to predict gene expression from genome sequence, in large part because we still have a limited understanding of transcriptional activation domains, the regions that bind coactivator proteins. Our group studies the protein sequence features that control the function of transcriptional activation domains. We seek to understand how activation domains work, how they evolve and how we can predict them from protein sequence. To study how activation domains work, we rationally design mutations to test specific hypotheses. To study activation domain evolution, we survey the diversity of extant orthologs to find highly diverse functional sequences. To predict activation domains from protein sequence, we integrate all these data with interpretable mechanistic predictors and with convolutional neural networks. We combine three approaches to study activation domain function. First, we use high throughput assays in yeast and human cell culture, rationally designed thousands of mutations to test specific hypotheses about function, and integrate these data with machine learning. Second, we use biophysical simulations to study how mutations in activation domains change the 3D structures of these intrinsically disordered regions. Third, we use high-resolution imaging to study how activation domains modulate the movements of individual transcription factor molecules in the nuclei of living cells. These three approaches will reveal the amino acid sequence features that control how activation domains control transcription. Our long term goal is to build a family of computational models that predict activation domains from protein sequence, predict the coactivators each activation domain recruits, and predict how activation domains evolve.

项目摘要/摘要 大多数细胞类型特异性基因表达来自转录因子与 增强剂和启动子12,13。在过去的十年中，识别的爆炸性增长 增强器和转录因子结合数据的分类14，但仍然不可能 从基因组序列预测基因表达，在很大程度上是因为我们仍然有限 了解转录激活域，即结合共激活蛋白的区域。 我们的小组研究了控制功能的蛋白质序列特征 转录激活域。我们试图了解激活域的工作方式，如何 它们发展以及我们如何从蛋白质序列中预测它们。研究如何激活 领域起作用，我们合理地设计突变以检验特定的假设。研究激活 域的演变，我们调查了现有直系同源物的多样性，以找到高度多样的功能 序列。为了预测蛋白质序列的激活域，我们整合了所有这些数据 具有可解释的机械预测因子和卷积神经网络。 我们结合了研究激活域功能的三种方法。首先，我们使用高 酵母和人类细胞培养的吞吐量测定，理性设计数千种突变 测试有关功能的特定假设，并将这些数据与机器学习集成在一起。 其次，我们使用生物物理模拟研究激活结构域中的突变如何变化 这些本质上无序区域的3D结构。第三，我们使用高分辨率成像 研究激活域如何调节单个转录因子的运动 活细胞核中的分子。这三种方法将揭示氨基酸 序列具有控制激活域如何控制转录的序列。 我们的长期目标是建立一个预测激活的计算模型家族 来自蛋白质序列的域，预测每个激活域募集的共激活因子，并且 预测激活结构域的演变。