Computational analysis and modeling of noncoding ribonucleic acid structure and function

背景核糖核酸(RNA)除了是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产物是蛋白质编码转录本的10到20倍。此外，数以千计的人类基因组区域在初级序列中无法比对，其中包含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，现在已经清楚的是，许多非编码RNA(NcRNAs)具有功能并作用于基因活性的调节。许多ncRNAs在细胞分化、人类诱导的多能干细胞的重新编程以及细胞发育过程中起着至关重要的作用。尤其是，大的ncRNAs(LncRNAs)创建了一个相互作用的网络，它定义了细胞中遗传信息的可及性和流动，一个地址代码，通过这个地址代码，蛋白质复合体、基因和染色体被有效地放置和检索，并被携带在适当的位置。这项研究的长期目标是破译这种蜂窝地址代码，即确定控制细胞内分子可及性和通信量的组织系统的性质和规则。如果正如越来越多的证据所表明的那样，如果ncRNAs是地址编码的中心，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，是首先要解决的问题之一。RNA的生物学功能依赖于其动态结构变化的固有性质，这些变化发生在不同的时间尺度上，目前通常无法通过核磁共振(NMR)和荧光光谱等生物物理方法进行检测。在这里，我们提出了一种不受实验技术限制的计算方法，以推广由核磁共振数据坚定支持的RNA动力学模型。使用该模型的具体目的是：**目标1：确定ncRNA的结构和动态图谱*目标2：确定ncRNA：：蛋白质相互作用**方法**目标1.我们将利用SHAPE(选择性2‘-羟基酰化分析)化学和顺铂-RNA交联法在体内探索ncRNA的结构。形状化学识别柔性RNA区域，顺铂交联识别结构化RNA区域。然后，我们将使用我们实验室开发的计算工具(MC-TOOLS)以及SHAPE和顺铂数据来确定ncRNA的结构和动态分布。**目标2.我们将使用符号计算和数值计算相结合的方法来确定ncRNA与蛋白质相互作用的可能性。如果ncRNA包含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与蛋白质之间的相互作用。直接的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上下文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的相互作用。**相关性**这个项目将提高我们对ncRNAs在基因表达调控中的作用的理解。它将提供：(I)ncRNAs的结构和动态轮廓；以及(Ii)ncRNA与基因组DNA、其他细胞RNA和蛋白质相互作用的新预测。总而言之，这项研究的结果将为多个研究领域提供关于基因活动和细胞程序的新见解，从而使其受益。我们提出的计算和实验相结合的方法将把信息学、分子生物学和生物化学的学生和人员聚集在一起并进行培训。

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.