RNA结构与生物信息和记忆

RNA结构具有多样性,RNA功能依赖于RNA结构,RNA结构承载着重要的生物信息。在人脑中,RNA结构可能承载着记忆信息编码的重要功能。对RNA结构作为生物信息的载体和人脑中记忆信息的载体,特别是瞬时记忆信息编码的载体进行了探讨。该假说的提出对阐释记忆的分子机制具有重要意义。     

RNA二级结构数据库

RNA分子众多、结构复杂、功能重要,已经成为当前重要的研究热点之一。RNA的功能与结构密切相关,伴随RNA分子及功能的发现,建立了有关RNA二级结构的数据库,一方面有助于理解RNA功能的结构基础,一方面有助于开发各种有关RNA结构的预测模型。本文对近年常见的RNA二级结构数据库作一概述,希望有助于相关工作者更好地了解与应用相关数据。     

RNA二级结构数据库北大核心CSCD

RNA分子众多、结构复杂、功能重要,已经成为当前重要的研究热点之一。RNA的功能与结构密切相关,伴随RNA分子及功能的发现,建立了有关RNA二级结构的数据库,一方面有助于理解RNA功能的结构基础,一方面有助于开发各种有关RNA结构的预测模型。本文对近年常见的RNA二级结构数据库作一概述,希望有助于相关工作者更好地了解与应用相关数据。     

环形RNA分子揭秘

环形RNA分子是一类不具有5'末端帽子和3'末端poly(A)尾巴、并以共价键形成环形结构的非编码RNA分子。利用针对环形结构RNA分子的实验和计算方法,并结合新一代高通量测序,现已在多种细胞内发现了大量环形RNA分子的稳定存在。目前,尽管环形RNA分子的产生机制及其代谢仍然不清楚,但是已有的研究表明环形RNA分子具有调控基因表达的功能。对环形RNA分子的产生机制和功能等的进一步研究,将为人们深入理解生命活动在转录水平的复杂性调控提供重要的分子基础和研究依据。     

基于CRISPR基因文库筛选前列腺癌中的RNA结合蛋白及其功能性研究

<正>RNA在细胞生命活动中发挥着重要作用,既能作为遗传信息的传递者,又能作为重要的结构和功能性分子,参与RNA转录、蛋白质翻译、基因表达调控等生物学进程。RNA一经转录,通常就会和多种特异性的RNA结合蛋白相结合,其命运和功能受到后者的严密调控。因此,RNA结合蛋白对于RNA     

决定RNA三维结构的规则

美国科学家发现决定RNA分子三维结构的规则——RNA分子三维结构不是由复杂的化学作用决定,而仅仅取决于几何学特性。 RNA分子非常松散,经常通过与其他分子结合改变其形状来发挥功能。RNA的形状改变后,会引发一连串反应,如打开或关闭特定基因的表达等。     

环状RNA的生物学功能及其在疾病发生中的作用

环状RNA(circular RNA,circRNA)主要包括由外显子转录本构成的、经非线性反向剪接形成的内源性RNA分子和内含子来源的环状RNA分子等两类。研究发现,circRNA在人体细胞中广泛表达,在转录后水平具有调控基因表达的重要功能。有些circRNA具有天然微小RNA(microRNA,miRNA)海绵作用,可通过与miRNA结合而抑制其活性,从而调控miRNA靶标发挥作用。circRNA在动脉粥样硬化、神经系统紊乱、糖尿病和肿瘤等疾病发生过程中起着较为重要的作用,深入研究circRNA的结构和功能可使我们更好地了解疾病的发生机制,提高相关疾病的预防和诊断水平。文章就circRNA的形成、功能及其在疾病发生中的作用等做一综述。     

蛋白质一级结构的“远缘”比较

由于核苷酸顺序测定方法的突破,致使蛋白质(尤其是分子量大、低含量的蛋白质)的一级结构测定大为简便,越来越多的蛋白质的一级结构已被测定。与此同时,电子计算机技术发展迅速,并广泛地应用于生物科学。这两者的结合,开拓了蛋白质一级结构比较研究的领域,在一些尚未想到会有关系的蛋白质一级结构之间也发现有同源性。这种比较研究可称为“远缘”比较,其结果是建立了蛋白质一级结构的“家谱”。本文例举七个类型蛋白质一级结构的“远缘”比较,并对“远缘”比较给予蛋白质结构和功能关系,分子进化的研究提供的启迪作一概要的讨论。     

细菌病毒phi29DNA—装运泵六聚体RNA结构和功能的研究方法

在双链DNA病毒增殖和成熟的过程中 ,需要将相当长的子代DNA装入一个极为有限空间的新生病毒衣壳。整个核酸装壳过程是耗能的过程 ,必需依靠生物泵来将DNA推入壳中。在细菌病毒phi2 9的核酸装壳过程中 ,需要RNA分子作为此生物泵的重要构成组分。6个RNA分子构成一个六边形样螺帽 ,将DNA如螺栓般装入病毒衣壳。6个RNA的这种依次运动的轮流作用模型如同汽车发动机的 6个气缸依次起火的原理一样 ,只是能源来自ATP而不是汽油。综述了此RNA的结构 ,及其结构对其功能所起的重要作用 ,并着重阐述研究 pRNA结构的独特构思和方法     

转移核糖核酸(tRNA)与癌症

转移核糖核酸(transfer RNA, tRNA)在蛋白质生物合成过程中起关键作用,是将遗传信息翻译成蛋白质一级结构的接头分子。tRNA长久以来一直被认为是基因表达调控过程中的执行者而不具备调控功能,更不曾与癌症的发生联系起来。最新研究表明,某些tRNA在癌细胞中异常表达,与癌症的发生和发展有密切联系。tRNA来源的小分子非编码RNA (tRFs和tiRNAs)是一类新的基因表达调控分子,tRFs可以调控癌基因的表达或者与RNA结合蛋白相互作用来调控癌细胞增殖和细胞周期进程。tRNA的转录后修饰能够调控mRNA翻译过程,进而影响癌细胞的生长。随着测序技术的发展,tRNA在癌症发生和发展中的调控作用成为近年来的研究热点,现将从"tRNA分子调控癌症的发生和发展"、"tRNA来源的小分子非编码RNA与癌症"以及"tRNA修饰与癌症"三个方面综述tRNA分子在癌症发生和发展中的调控功能。     

基因组结构信息在动物系统发育研究中的应用

随着人类基因组和一些模式生物、重要经济生物以及大量微生物基因组测序的完成,生物学整体研究业已进入基因组时代.最近5～10年以来,利用基因组结构信息进行系统发育推断的研究形成了分类学和进化生物学中的前沿领域之一.相对于核苷酸或氨基酸序列中的突变而言,基因组的结构变化--内含子的插入/缺失、反转录子的整合、签名序列、基因重复以及基因排序等--是更大空间(或者时间空间)尺度上的相对稀缺的系统发育信息,一般用于科和科以上阶元间的亲缘关系研究.基因组全序列的获得和其中各基因位置的确定有利于将基因组中不同层次的系统发育信息综合起来,利用全面分子证据(total molecular evidence;包括基因组信息,DNA、RNA、蛋白质的序列信息,RNA和蛋白质的高级结构等)进行分子系统学研究.     

R-CHIE: a web server and R package for visualizing RNA secondary structures

Visually examining RNA structures can greatly aid in understanding their potential functional roles and in evaluating the performance of structure prediction algorithms. As many functional roles of RNA structures can already be studied given the secondary structure of the RNA, various methods have been devised for visualizing RNA secondary structures. Most of these methods depict a given RNA secondary structure as a planar graph consisting of base-paired stems interconnected by roundish loops. In this article, we present an alternative method of depicting RNA secondary structure as arc diagrams. This is well suited for structures that are difficult or impossible to represent as planar stem-loop diagrams. Arc diagrams can intuitively display pseudo-knotted structures, as well as transient and alternative structural features. In addition, they facilitate the comparison of known and predicted RNA secondary structures. An added benefit is that structure information can be displayed in conjunction with a corresponding multiple sequence alignments, thereby highlighting structure and primary sequence conservation and variation. We have implemented the visualization algorithm as a web server R-chie as well as a corresponding R package called R4RNA, which allows users to run the software locally and across a range of common operating systems.     

Automatic detection of conserved RNA structure elements in complete RNA virus genomes.

We propose a new method for detecting conserved RNA secondary structures in a family of related RNA sequences. Our method is based on a combination of thermodynamic structure prediction and phylogenetic comparison. In contrast to purely phylogenetic methods, our algorithm can be used for small data sets of approximately 10 sequences, efficiently exploiting the information contained in the sequence variability. The procedure constructs a prediction only for those parts of sequences that are consistent with a single conserved structure. Our implementation produces reasonable consensus structures without user interference. As an example we have analysed the complete HIV-1 and hepatitis C virus (HCV) genomes as well as the small segment of hantavirus. Our method confirms the known structures in HIV-1 and predicts previously unknown conserved RNA secondary structures in HCV.     

RNA pseudoknot prediction in energy-based models.

RNA molecules are sequences of nucleotides that serve as more than mere intermediaries between DNA and proteins, e.g., as catalytic molecules. Computational prediction of RNA secondary structure is among the few structure prediction problems that can be solved satisfactorily in polynomial time. Most work has been done to predict structures that do not contain pseudoknots. Allowing pseudoknots introduces modeling and computational problems. In this paper we consider the problem of predicting RNA secondary structures with pseudoknots based on free energy minimization. We first give a brief comparison of energy-based methods for predicting RNA secondary structures with pseudoknots. We then prove that the general problem of predicting RNA secondary structures containing pseudoknots is NP complete for a large class of reasonable models of pseudoknots.     

Fuzzy Kernel Clustering of RNA Secondary Structure Ensemble Using a Novel Similarity Metric

Abstract

Measuring the (dis)similarity between RNA secondary structures is critical for the study of RNA secondary structures and has implications to RNA functional characterization. Although a number of methods have been developed for comparing RNA structural similarities, their applications have been limited by the complexity of the required computation. In this paper, we present a novel method for comparing the similarity of RNA secondary structures generated from the same RNA sequence, i.e., a secondary structure ensemble, using a matrix representation of the RNA structures. Relevant features of the RNA secondary structures can be easily extracted through singular value decomposition (SVD) of the representing matrices. We have mapped the feature vectors of the singular values to a kernel space, where (dis)similarities among the mapped feature vectors become more evident, making clustering of RNA secondary structures easier to handle. The pair-wise comparison of RNA structures is achieved through computing the distance between the singular value vectors in the kernel space. We have applied a fuzzy kernel clustering method, using this similarity metric, to cluster the RNA secondary structure ensembles. Our application results suggest that our fuzzy kernel clustering method is highly promising for classifications of RNA structure ensembles, because of its low computational complexity and high clustering accuracy.     

Fuzzy kernel clustering of RNA secondary structure ensemble using a novel similarity metric

Measuring the (dis)similarity between RNA secondary structures is critical for the study of RNA secondary structures and has implications to RNA functional characterization. Although a number of methods have been developed for comparing RNA structural similarities, their applications have been limited by the complexity of the required computation. In this paper, we present a novel method for comparing the similarity of RNA secondary structures generated from the same RNA sequence, i.e., a secondary structure ensemble, using a matrix representation of the RNA structures. Relevant features of the RNA secondary structures can be easily extracted through singular value decomposition (SVD) of the representing matrices. We have mapped the feature vectors of the singular values to a kernel space, where (dis)similarities among the mapped feature vectors become more evident, making clustering of RNA secondary structures easier to handle. The pair-wise comparison of RNA structures is achieved through computing the distance between the singular value vectors in the kernel space. We have applied a fuzzy kernel clustering method, using this similarity metric, to cluster the RNA secondary structure ensembles. Our application results suggest that our fuzzy kernel clustering method is highly promising for classifications of RNA structure ensembles, because of its low computational complexity and high clustering accuracy.     

Pure multiple RNA secondary structure alignments: a progressive profile approach

In functional, noncoding RNA, structure is often essential to function. While the full 3D structure is very difficult to determine, the 2D structure of an RNA molecule gives good clues to its 3D structure, and for molecules of moderate length, it can be predicted with good reliability. Structure comparison is, in analogy to sequence comparison, the essential technique to infer related function. We provide a method for computing multiple alignments of RNA secondary structures under the tree alignment model, which is suitable to cluster RNA molecules purely on the structural level, i.e., sequence similarity is not required. We give a systematic generalization of the profile alignment method from strings to trees and forests. We introduce a tree profile representation of RNA secondary structure alignments which allows reasonable scoring in structure comparison. Besides the technical aspects, an RNA profile is a useful data structure to represent multiple structures of RNA sequences. Moreover, we propose a visualization of RNA consensus structures that is enriched by the full sequence information.     

Consensus folding of unaligned RNA sequences revisited.

As one of the earliest problems in computational biology, RNA secondary structure prediction (sometimes referred to as "RNA folding") problem has attracted attention again, thanks to the recent discoveries of many novel non-coding RNA molecules. The two common approaches to this problem are de novo prediction of RNA secondary structure based on energy minimization and the consensus folding approach (computing the common secondary structure for a set of unaligned RNA sequences). Consensus folding algorithms work well when the correct seed alignment is part of the input to the problem. However, seed alignment itself is a challenging problem for diverged RNA families. In this paper, we propose a novel framework to predict the common secondary structure for unaligned RNA sequences. By matching putative stacks in RNA sequences, we make use of both primary sequence information and thermodynamic stability for prediction at the same time. We show that our method can predict the correct common RNA secondary structures even when we are given only a limited number of unaligned RNA sequences, and it outperforms current algorithms in sensitivity and accuracy.     

Nucleotide sequences of the 5.8S rRNAs of a mollusc and a porifer, and considerations regarding the secondary structure of 5.8S rRNA and its interaction with 28S rRNA

We report the primary structures of the 5.8 S ribosomal RNAs isolated from the sponge Hymeniacidon sanguinea and the snail Arion rufus. We had previously proposed (Ursi et al., Nucl. Acids Res. 10, 3517-3530 (1982)) a secondary structure model on the basis of a comparison of twelve 5.8 S RNA sequences then known, and a matching model for the interaction of 5.8 S RNA with 26 S RNA in yeast. Here we show that the secondary structure model can be extended to the 25 sequences presently available, and that the interaction model can be extended to the binding of 5.8 S RNA to the 5'-terminal domain of 28 S (26 S) RNA in three species.