Identification, Discovery, and Public Archiving of RNA Structural Motifs

描述（申请人提供）：非编码RNA在生物过程中发挥多种功能作用，如催化、基因表达调控和RNA剪接。非编码RNA所起的各种作用由其特征结构决定。RNA结构基序是非编码RNA中重复出现的结构成分。RNA结构基序具有保守的结构，因此具有保守的生物学或结构功能。例如，在不同种类的非编码RNA中发现了扭结-转角基序，并且它们都负责蛋白质结合活性。其结构的改变将导致RNA结构基序的功能丧失，并且在某些情况下导致严重的疾病。例如，小核仁RNA（snoRNA）中的扭结-转角基序的破坏将阻止其招募L7 Ae蛋白，从而导致先天性角化不良和Prader-Willi综合征。因此，对RNA结构基序的研究将有助于我们阐明许多疾病的机制，并导致新的治疗策略的发展。目前，RNA结构基序的研究主要包括以下几个方面：1）识别所有出现的已知基序（搜索）; 2）根据已知基序的结构和功能对其进行分类（分类）; 3）定义新的RNA结构基序家族（从头发现）。在本提案中，我们的目标是设计一套计算方法来解决这三个问题。首先，我们将开发一种新的计算搜索工具，除了3D几何形状外，还将考虑碱基配对（氢键力）和碱基堆叠（磁力和静电力）信息。大多数现有的RNA结构基序搜索工具在检测具有灵活几何形状的基序实例时显示出局限性。包括碱基配对和碱基堆叠将解决这个问题。其次，我们将开发一种新的聚类策略，同时解决分类和从头发现问题。现有的聚类策略采用长度依赖的结构比对得分（表示两个候选模体实例之间的结构相似性）作为距离度量，并应用层次聚类算法来识别紧密相关的模体簇。我们计划包括一个统计框架，可以规范化对齐分数，从而解决这个问题。此外，在聚类策略中，我们将采用聚类发现算法代替层次聚类算法，使其适用于大数据集。我们将检查所产生的集群，并与已知的图案进行比较，然后提出新的RNA结构基序家族。随着这两个目标的实现，我们建议建立一个数据库，用于归档我们的新搜索工具确定的基序实例。最后，我们将报告潜在的新型RNA结构基序家族，并鼓励对其功能进行实验研究。我们期望所提出的工作将导致更好地了解RNA结构基序，并显着促进生物医学研究。

DESCRIPTION (provided by applicant): The non-coding RNAs play many functional roles in biological processes, such as catalysis, gene expression regulation and RNA splicing. The various roles played by non-coding RNA are determined by their character- istic structure. RNA structural motifs are recurrent structural components in the non-coding RNAs. The RNA structural motifs have conserved structures, and therefore, have conserved biological or structural functions. For instance, the kink-turn motif is found in different kinds of non-coding RNAs and all of them are responsible for protein binding activities. The alternation of their structures will result in loss-of-function of the RNA structural motif, and in some cases severe diseases. For example, the destruction of kink-turn motif in small nucleolar RNA (snoRNA) will prevent it from recruiting the L7Ae protein, and thus lead to Dyskeratosis congenita and Prader-Willi syndrome. Therefore, the study of RNA structural motif will help us to elucidate the mechanisms of many diseases and lead to the development of novel treatment strategies. Currently, the essential RNA struc- tural motif research includes the following problems: 1) identifying all occurrences of the given motif (search), 2), classifying known motif instances based on their structures and functionalities (classification), and 3) defin- ing novel RNA structural motif families (de novo discovery). In this proposal, we aim at devising a suite of computational methods to solve these three problems. First, we will develop a new computational search tool which will, in addition to 3D geometry, take into account base pairing (hydrogen bonding forces) and base stack- ing (magnetic and electrostatic forces) information. Most of the existing RNA structural motif search tools show limitations in detecting motif instances with flexible geometry. The inclusion of base pairing and base stacking will resolve this issue. Second, we will develop a novel clustering strategy to solve the classification and de novo discovery problems simultaneously. Existing clustering strategies adopt length-dependent structural alignment score (which indicates the structural similarity between two candidate motif instances) as the distance measure- ment, and apply hierarchical clustering algorithm to identify closely related motif clusters. We plan to include a statistical framework that can normalize the alignment score, and thus resolve this issue. In addition, instead of hierarchical clustering algorithm, we will adopt clique-finding algorithm in our clustering strategy, so as to make it applicable to large data sets. We will examine the resulting clusters and compare them with known motifs, and then suggest novel RNA structural motif families. With the achievement of these two goals, we propose to build a database for archiving motif instances identified by our new search tool. Finally, we will report potential novel RNA structural motif families and encourage experimental investigation of their functionalities. We expect that the proposed work will lead to better understanding of the RNA structural motifs, and significantly promote biomedical research.