Computational analysis and modeling of noncoding ribonucleic acid structure and function

背景 核糖核酸（RNA）除了作为基因和蛋白质之间的信使外，还具有多种细胞功能。哺乳动物基因组产生的非编码转录物比蛋白质编码转录物多10到20倍。此外，成千上万的人类基因组区域，不能在一级序列比对包含RNA结构。所有这些区域和非编码转录本是否都有功能仍是个问题。然而，现在很清楚，许多非编码RNA（ncRNA）是功能性的，并对基因活性的调节起作用。许多ncRNA在细胞分化、人诱导多能干细胞的重编程和发育过程中的细胞命运中至关重要。 假设和目标 特别是，大的ncRNA（lncRNA）创建一个相互作用网络，定义了细胞中遗传信息的可访问性和流动，一个地址代码，通过它蛋白质复合物，基因和染色体被有效地放置和检索，并携带在适当的位置。这项研究的长期目标是破译这种细胞地址代码，即确定控制细胞中分子可及性和交通的组织系统的性质和规则。如果ncRNA是地址码的核心，就像越来越多的证据所表明的那样，那么建立RNA相互作用网络，特别是与蛋白质的相互作用网络，就是首先要解决的问题之一。RNA的生物学功能依赖于其在不同时间尺度上发生的动态结构变化的固有性质，目前通常逃脱生物物理方法，如核磁共振（NMR）和荧光光谱。在这里，我们提出了一种计算方法，是免费的实验技术的限制，概括的RNA动力学模型，是坚定地支持NMR数据。利用这一模式，具体目标是： 目的1：确定ncRNA结构和动态谱 目的2：确定ncRNA：：蛋白质相互作用 方法 目标1.我们将使用SHAPE（引物延伸分析的选择性2 '-羟基酰化）化学和顺铂-RNA交联来探测体内ncRNA结构。SHAPE化学鉴定柔性RNA区域，顺铂交联鉴定结构化RNA区域。然后，我们将使用我们实验室开发的计算工具（MC-Tools）和SHAPE和顺铂数据确定ncRNA结构和动态概况。 目标2.我们将使用符号和数值计算的组合来确定ncRNA与蛋白质相互作用的可能性。如果ncRNA含有先前观察到的与蛋白质的结构域直接接触的结构基序，我们将预测ncRNA和蛋白质之间的相互作用。直接RNA：：蛋白质接触将从蛋白质数据库的实验解析结构中提取。然后，我们将使用分子对接和化学背景轮廓匹配来评估相互作用的真实性。我们将从这些预测中确定ncRNA相互作用。 相关性 该项目将提高我们对ncRNA在基因表达调控中的作用的理解。它将提供：（i）ncRNA的结构和动态概况;（ii）ncRNA与基因组DNA、其他细胞RNA和蛋白质相互作用的新预测。总的来说，这项研究的结果将通过提供对基因活性和细胞程序的新见解而使多个研究领域受益。我们提出的计算和实验相结合的方法将汇集和培养信息学，分子生物学和生物化学的学生和人员。

Background Ribonucleic acid (RNA) has diverse cellular functions beyond being a messenger between genes and proteins. Mammalian genomes produce 10 to 20 times more non-coding than protein-coding transcripts. Besides, thousands of human genomic regions that cannot be aligned in primary sequence contain RNA structure. Whether all of these regions and non-coding transcripts are functional is still in question. However, it is now clear that many noncoding RNAs (ncRNAs) are functional and act on the regulation of gene activity. Many ncRNAs have been shown crucial in cell differentiation, reprogramming of human induced pluripotent stem cells, and cell fates during development. Hypothesis and aims In particular, large ncRNAs (lncRNAs) create an interaction network that defines the accessibility and flow of genetic information in cells, an address code through which protein complexes, genes, and chromosomes are efficiently placed and retrieved, and carried in appropriate locations. The long-term goal of this research is to decipher this cellular address code, i.e. determine the nature and rules of the organizational system that control molecular accessibility and traffic in cells. If ncRNAs are central to the address code, as accumulating evidences suggest, then establishing the RNA interaction network, and in particular with proteins, is among the first problems to be solved. Biological functions of RNA rely on the inherent nature of its dynamic structural changes that occur at different timescales, which currently often escape biophysical methods such as nuclear magnetic resonance (NMR) and fluorescence spectroscopy. Here, we propose a computational approach that is free of the limitations of experimental techniques to generalize a model of RNA dynamics that is firmly supported by NMR data. Using this model, the specific aims are to: Aim 1: Determine ncRNA structure and dynamic profile Aim 2: Determine ncRNA::protein interactions Approach Aim 1. We will probe in vivo ncRNA structure using SHAPE (Selective 2'-Hydroxyl acylation Analyzed by Primer Extension) chemistry and cisplatin-RNA cross-linking. SHAPE chemistry identifies flexible RNA regions and cisplatin cross-linking identifies structured RNA regions. We will then determine ncRNA structure and dynamic profile using the computational tools developed in our laboratory (MC-Tools) and the SHAPE and cisplatin data. Aim 2. We will determine the likelihood of ncRNAs to interact with proteins using a combination of symbolic and numerical computation. We will predict an interaction between a ncRNA and a protein if the ncRNA contains a structural motif previously observed in direct contact with a structural domain of the protein. The direct RNA::protein contacts will be extracted from the experimentally resolved structures of the Protein DataBank. We will then assess the realism of the interaction using molecular docking and chemical context profile matching. We will determine ncRNA interactions from these predictions. Relevance This project will improve our understanding of the role of ncRNAs in the regulation of gene expression. It will deliver: (i) structural and dynamic profiles of ncRNAs; and, (ii) new predictions of ncRNA interactions with genomic DNA, other cellular RNAs, and proteins. Collectively, the results of this research will benefit multiple areas of research by providing new insights on gene activity and cellular programs. The combined computational and experimental approach we propose will bring and train together informatics, molecular biology and biochemistry students and personnel.