Toward RNA Genomics: A Pilot Study in the Analysis, Design, and Prediction of RNA Structures

RNA 基因组学：RNA 结构分析、设计和预测的初步研究

• 批准号: 0201160
• 负责人: Tamar Schlick
• 金额: --
• 依托单位: New York University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-10-01 至 2009-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0201160&HistoricalAwards=false
• 关键词: Toward; RNA; Genomics; Pilot; Study

We propose a pilot study to analyze, design, and predict RNA structures using a combination of graph theory and computational methods. The design and prediction work aims to expand the set of RNA sequences and structures available, and hence our knowledge of RNA functions. By representing RNA secondary structures as tree or pseudoknot graphs, we exploit the mathematical results in graph theory for graph comparison, enumeration, and construction. This approach allows us to enumerate all possible RNA graphs which may represent both natural and hypothetical RNA topologies. Our proposed investigations consist of three related stages: (1) survey and analyze existing RNAs; (2) design RNA sequences for novel RNA tree topologies; and (3) predict the three-dimensional structures of designed RNA sequences. In survey and analysis (goal 1), we will systematically search for the occurrence of RNAs within a larger RNA system, especially for ribosome structures; use RNA topological characteristics to survey and classify functional RNA families; and develop graph theory algorithms to estimate the probable size and diversity of various functional RNA groups within genomes. Our RNA search tools, survey results, and classification methods will be made available to the public through the Nucleic Acid Database. To generate novel RNA topologies for three-dimensional structure prediction (goal 2), we will apply two strategies: sequence mutation of known RNAs and de novo sequence design. Finally, our goal of predicting the three-dimensional structures of designed sequences (goal 3) will be accomplished through the development of empirical force fields and folding algorithms for reduced RNA models. For this challenging goal, we will consider several computational approaches, including the use of elastic energy models with Brownian dynamics as employed for long DNAs. RNA molecules play vital and diverse biological roles in the cell. In parallel with protein genomics efforts, the development of RNA genomics or ribonomics --- a systematic analysis and prediction of RNA sequence/structure relationships --- will help advance our understanding of the biological functions of RNAs. In marked contrast to proteins, only a small number of distinct RNA structures is currently known. The methodologies currently used to search for novel functional RNAs are either restricted to small RNAs or to known RNA classes. In an effort to overcome these limitations, we have developed a novel approach based on an area in mathematics called graph theory and computational biology methods to allow comprehensive search through RNA's structural possibilities. Since known RNA structures represent only a small subset of all distinct structures, we postulate that some of the missing structures represent novel functional RNAs which may occur naturally in cells or be generated in the laboratory, but as yet unidentified or do not exist. The three-dimensional structures of novel RNAs will be predicted through design and folding methodologies. Our approaches will likely expand the number of RNA types and lead to the identification of unknown RNA families. Designed RNAs with novel properties have potential applications as catalysts, molecular sensors, and therapeutic agents in biotechnology and molecular medicine. In addition to RNA structure prediction, graph theory will allow us to systematically characterize, classify and establish structural/functional relationships between existing RNAs. The results and tools developed for this analysis will be made available through the publicly accessible Nucleic Acid Database. Our proposed investigations will contribute to the development of RNA genomics, a field currently lagging behind protein genomics or proteomics. This grant is made under the Joint DMS/NIGMS Initiative to Support Research Grants in the Area of Mathematical Biology. This is a joint competition sponsored by the Division of Mathematical Sciences (DMS) at the National Science Foundation and the National Institute of General Medical Sciences (NIGMS) at the National Institutes of Health.

我们提出了一个试点研究，分析，设计和预测RNA结构，使用图论和计算方法的组合。设计和预测工作的目的是扩大现有的RNA序列和结构的集合，从而扩大我们对RNA功能的了解。通过将RNA二级结构表示为树或伪结图，我们利用图论中的数学结果进行图的比较、枚举和构建。这种方法使我们能够枚举所有可能的RNA图，这可能代表自然和假设的RNA拓扑结构。我们提出的研究包括三个相关的阶段：（1）调查和分析现有的RNA;（2）设计新的RNA树拓扑结构的RNA序列;（3）预测设计的RNA序列的三维结构。在调查和分析（目标1）中，我们将系统地搜索RNA在更大的RNA系统中的出现，特别是核糖体结构;使用RNA拓扑特征来调查和分类功能RNA家族;并开发图论算法来估计基因组中各种功能RNA组的可能大小和多样性。我们的RNA搜索工具、调查结果和分类方法将通过核酸数据库向公众提供。为了生成新的RNA拓扑结构用于三维结构预测（目标2），我们将采用两种策略：已知RNA的序列突变和从头序列设计。最后，我们的目标是预测设计的序列的三维结构（目标3）将通过减少RNA模型的经验力场和折叠算法的发展来实现。对于这个具有挑战性的目标，我们将考虑几种计算方法，包括使用弹性能量模型与布朗动力学的长DNA。 RNA分子在细胞中发挥着重要而多样的生物学作用。与蛋白质基因组学的努力相平行，RNA基因组学或核糖体组学（ribonomics）的发展-系统地分析和预测RNA序列/结构关系-将有助于促进我们对RNA生物学功能的理解。与蛋白质形成鲜明对比的是，目前只知道少量不同的RNA结构。目前用于寻找新功能RNA的方法要么局限于小RNA，要么局限于已知的RNA类别。为了克服这些限制，我们开发了一种基于数学领域的新方法，称为图论和计算生物学方法，以允许通过RNA的结构可能性进行全面搜索。由于已知的RNA结构只代表了所有不同结构的一小部分，我们假设一些缺失的结构代表了新的功能性RNA，它们可能在细胞中天然存在或在实验室中产生，但尚未鉴定或不存在。新RNA的三维结构将通过设计和折叠方法来预测。我们的方法可能会扩大RNA类型的数量，并导致未知RNA家族的鉴定。设计的具有新特性的RNA在生物技术和分子医学中作为催化剂、分子传感器和治疗剂具有潜在的应用前景。除了RNA结构预测，图论将使我们能够系统地表征，分类和建立现有RNA之间的结构/功能关系。为这项分析开发的结果和工具将通过可公开访问的核酸数据库提供。我们提出的研究将有助于RNA基因组学的发展，这是一个目前落后于蛋白质基因组学或蛋白质组学的领域。这项赠款是根据联合DMS/NIGMS倡议，以支持研究赠款在数学生物学领域。这是一项由美国国家科学基金会数学科学部（DMS）和美国国立卫生研究院国家普通医学科学研究所（NIGMS）赞助的联合竞赛。