Identification, Discovery, and Public Archiving of RNA Structural Motifs

描述（申请人提供）：非编码RNA在生物过程中发挥着许多功能作用，如催化、基因表达调控和RNA剪接。非编码RNA的各种作用是由它们的特征结构决定的。RNA结构基序是非编码RNA中反复出现的结构成分。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中发现了扭结旋转基序，它们都负责蛋白质结合活性。它们结构的改变会导致RNA结构基序的功能丧失，在某些情况下会导致严重的疾病。例如，小核核RNA （small nucleolar RNA, snoRNA）中扭结-转动基序的破坏会阻止其募集L7Ae蛋白，从而导致先天性角化不良症和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结构基序的基本研究包括以下几个问题：1)识别给定基序的所有出现（搜索）；2)根据结构和功能对已知基序实例进行分类（分类）；3)定义新的RNA结构基序家族（de novo discovery）。在本提案中，我们旨在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。现有的大多数RNA结构基序搜索工具在检测具有柔性几何结构的基序实例方面存在局限性。包含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来同时解决分类和从头发现问题。现有的聚类策略采用长度相关的结构对齐分数（表示两个候选基序实例之间的结构相似性）作为距离度量，并采用分层聚类算法来识别密切相关的基序聚类。我们计划包含一个统计框架，它可以使对齐分数正常化，从而解决这个问题。此外，我们将在聚类策略中采用clique-finding算法，而不是分层聚类算法，使其适用于大数据集。我们将检查得到的簇，并将它们与已知的基序进行比较，然后提出新的RNA结构基序家族。为了实现这两个目标，我们建议建立一个数据库来归档我们的新搜索工具所识别的motif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。我们期望这项工作将有助于更好地了解RNA结构基序，并显著促进生物医学研究。

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. PUBLIC HEALTH RELEVANCE: RNA structural motifs are components in non-coding RNAs, which play catalytic, regulatory and other important roles in many biological processes. The dysfunction of RNA structural motif will result in physiological disorders and cause diseases (such as Dyskeratosis congenita and Prader-Willi syndrome). We plan to devise a suite of computational methods for RNA structural motif search, classification, and discovery, so as to elucidate the mechanisms of RNA structural motif related diseases and push forward the development of their treatment strategies.