RNA二级结构预测系统构建

运用下列RNA二级结构预测算法:碱基最大配对方法、Zuker极小化自由能方法、螺旋区最优堆积、螺旋区随机堆积和所有可能组合方法与基于一级螺旋区的RNA二级结构绘图技术, 构建了RNA二级结构预测系统Rnafold. 另外, 通过随机选取20个tRNA序列, 从自由能和三叶草结构两个方面比较了前4种二级结构预测算法, 并运用t检验方法分析了自由能的统计学差别. 从三叶草结构来看, 以随机堆积方法最好, 其次是螺旋区最优堆积方法和Zuker算法, 以碱基最大配对方法最差. 最后, 分析了两种极小化自由能方法之间的差别.     

基于局部茎搜索的RNA二级结构预测算法

RNA的二级结构预测是生物信息学中一个已经有30多年历史的经典问题,基于最小自由能模型(MFE)的优化算法是使用最为广泛的方法．但RNA结构中假结的存在使MFE问题理论上成为一个NP-hard问题,即使采用动态规划等优化算法也会面临时间复杂度高的困难,同时研究还发现,由于受RNA折叠动力学机制以及环境因素的影响,真实的RNA二级结构往往并不处于自由能最小状态．根据RNA折叠的特点,提出了一种启发式搜索算法来预测带假结的RNA二级结构．该算法以RNA的茎为基本单元,采用启发式搜索策略在茎的组合空间中搜索自由能最小并且出现频率最高的RNA二级结构,该算法不仅能显著降低搜索RNA二级结构的时间复杂度,还有助于弥补单纯依赖能量预测RNA二级结构的不足．在多种类型的RNA标准数据集上进行了检验,结果表明,该算法在预测的精度上优于目前国际上几个著名的RNA二级结构预测算法并且具有较高的运行效率．     

随机文法模型在RNA二级结构预测中的应用

RNA二级结构的研究是当今计算分子生物学的一个重要课题,基于比较序列分析方法的随机文法模型预测RNA二级结构具有准确率高,能对假结建模,但不易实施等特点,本文通过分析随机文法对RNA二级结构建模的过程,提出了一种综合利用比较序列方法,随机文法方法,词条方法预测RNA二级结构的方案.     

p53蛋白质C端的三维结构及生物功能区

利用p53 C端118个氨基酸的mRNA二级结构和Chou-Fasman蛋白质二级结构预测原则,预测p53蛋白质C端289～325为卷曲肽段,368～393段包括两段螺旋结构: α₁ 368～373, α₂ 381～388.其中三段已知的蛋白质二级结构与此mRNA二级结构单元间有准确的对应关系.与四种以多重序列联配为基础的蛋白质二级结构预测方法（准确率均为73.20%左右）相对照,预测结果基本一致.结合单体聚合区31个氨基酸晶体结构,在SGI INDIGO²工作站上构建了p53 C端108个残基的三维结构.进一步揭示了p53 C端诸多生物功能区之间的空间构象关系.     

非编码RNA的生物信息学研究方法:RNA结构预测及其应用

近10多年来的研究逐步揭示了RNA的各种生物学功能。RNA不仅是信息从DNA传递到蛋白质的中间体,还直接参与基因沉默、表观遗传学修饰等生物学过程。单链的RNA在体内通过碱基配对折叠成一定的二级结构。介绍了现在预测RNA二级结构的主要算法及其应用,其中包括基于热力学、同源比对和统计学习的各种算法,以及如何引入实验数据辅助预测。二级结构预测算法被广泛用于寻找RNA功能单元和预测新非编码RNA等各种问题。如何利用高通量实验数据帮助结构预测,探索长非编码RNA功能,研究RNA与蛋白质相互作用,是RNA二级结构预测算法和应用的一些前沿方向。     

简单的一致性方法预测蛋白质二级结构

蛋白质二级结构预测对于我们了解蛋白质空间结构是至关重要的一步。文章提出了一种简单的二级结构预测方法,该方法采用多数投票法将现有的3种较好的二级结构预测方法的预测结果汇集形成一致性预测结果。从PDB数据库中随机选取近两年新测定结构的57条相似性小于30%的蛋白质,对该方法的预测结果进行测试,其Q3准确率比3种独立的方法提高了1.12—2.29%,相关系数及SOV准确率也有相应的提高。并且各项准确率均比同样采用一致性方法的Jpred二级结构预测程序准确率要高。这种预测方法虽然原理简单,但无须使用额外的参数,计算量小,易于实现,最重要的前提就是必须选用目前准确性比较出色的蛋白质二级结构预测方法。     

用模式识别方法预测膜蛋白RC、BR和RH的二级结构

膜蛋白的结构预测在目前比较困难.本文利用已建立的模式识别方法预测了三个典型的膜蛋白RC,BR和RH的二级结构,预测结果与实验资料的符合率与该方法用于球蛋白时的结果相仿,是成功的.本文进一步完善了模式识别预测蛋白质二级结构的方法.建立了针对球蛋白二级结构预测的多分类方法,预测精度大于60%.事实证明这是一种较好的结构预测方法,鉴于目前国内外运用模式识别方法进行结构预测研究的还不多见,我们拟进一步发展完善这一方法.     

一种新的RNA二级结构三维图形表示及其应用

本研究提出了一种新的RNA二级结构的图形表示方法,这种方法不同于以往的表示方式。根据所提出的RNA二级结构的图形表示,将对9种病毒的RNA二级结构进行图形表示,构建系统进化树,进行序列间相似性的比较和分析。根据最终结果,可以很清晰地发现,AVII与LRMV两种病毒是最为相似的,另外,较大的距离值出现在了APMV与ALMV;PDV与AVII中,这说明这几种RNA二级结构明显不相似。这一研究结果与前人相似性分析的结果是十分相似的,同时,所采取的方法更加简单易于区分观察且得到的结果又是十分可靠的,因此,这些更加证明了该方法是有效的。     

绒泡菌目黏菌的ITS1-5.8S-ITS2二级结构的比较分析

为探讨核糖体DNA转录间隔区(r DNA ITS)的RNA二级结构在黏菌系统发育研究中的作用,以黏菌ITS通用引物PHYS4和PHYS5对绒泡菌目5属8种黏菌的r DNA ITS进行扩增和测序,利用RNA structure构建了ITS区的RNA二级结构模型。结果表明:ITS1在绒泡菌目黏菌中不能形成一个紧实的结构,但大部分物种都具有一段稳定的螺旋结构,可能对r RNA的成熟具有作用;5.8S r RNA的二级结构相似,由4个螺旋组成,主要为两种类型;基于5.8S r RNA和28S r RNA相互作用构建的ITS2的二级结构模型显示,它由一个封闭的多分支环和至少4个主要的螺旋组成,其中螺旋IV结构相对比较保守。由于ITS区的二级结构相比核苷酸序列更加保守,因此深入地分析其二级结构有助于认识其结构与进化的关系。     

RNA二级结构的最小自由能算法

RNA(即tRNA,rRNA,mRNA和SnRNA)有两大主要功能：一是某些病毒的遗传物质;二是参与蛋白质的合成,这些与细胞分化、代谢、记忆的储存等有重要关系,这些功能与RNA二级结构的稳定性。自由能密切相关．常用的计算自由能的方法有热力学微扰法及热力学微积分法等．本文以寻找最小自由能二级结构为目的,给出了RNA二级结构的最小自由能算法,该算法的时间复杂性不超过O(n^4)。     

Improvement of RNA secondary structure prediction using RNase H cleavage and randomized oligonucleotides

RNA secondary structure prediction using free energy minimization is one method to gain an approximation of structure. Constraints generated by enzymatic mapping or chemical modification can improve the accuracy of secondary structure prediction. We report a facile method that identifies single-stranded regions in RNA using short, randomized DNA oligonucleotides and RNase H cleavage. These regions are then used as constraints in secondary structure prediction. This method was used to improve the secondary structure prediction of Escherichia coli 5S rRNA. The lowest free energy structure without constraints has only 27% of the base pairs present in the phylogenetic structure. The addition of constraints from RNase H cleavage improves the prediction to 100% of base pairs. The same method was used to generate secondary structure constraints for yeast tRNA^Phe, which is accurately predicted in the absence of constraints (95%). Although RNase H mapping does not improve secondary structure prediction, it does eliminate all other suboptimal structures predicted within 10% of the lowest free energy structure. The method is advantageous over other single-stranded nucleases since RNase H is functional in physiological conditions. Moreover, it can be used for any RNA to identify accessible binding sites for oligonucleotides or small molecules.     

Enhancement of accuracy and efficiency for RNA secondary structure prediction by sequence segmentation and MapReduce

Background

Ribonucleic acid (RNA) molecules play important roles in many biological processes including gene expression and regulation. Their secondary structures are crucial for the RNA functionality, and the prediction of the secondary structures is widely studied. Our previous research shows that cutting long sequences into shorter chunks, predicting secondary structures of the chunks independently using thermodynamic methods, and reconstructing the entire secondary structure from the predicted chunk structures can yield better accuracy than predicting the secondary structure using the RNA sequence as a whole. The chunking, prediction, and reconstruction processes can use different methods and parameters, some of which produce more accurate predictions than others. In this paper, we study the prediction accuracy and efficiency of three different chunking methods using seven popular secondary structure prediction programs that apply to two datasets of RNA with known secondary structures, which include both pseudoknotted and non-pseudoknotted sequences, as well as a family of viral genome RNAs whose structures have not been predicted before. Our modularized MapReduce framework based on Hadoop allows us to study the problem in a parallel and robust environment.

Results

On average, the maximum accuracy retention values are larger than one for our chunking methods and the seven prediction programs over 50 non-pseudoknotted sequences, meaning that the secondary structure predicted using chunking is more similar to the real structure than the secondary structure predicted by using the whole sequence. We observe similar results for the 23 pseudoknotted sequences, except for the NUPACK program using the centered chunking method. The performance analysis for 14 long RNA sequences from the Nodaviridae virus family outlines how the coarse-grained mapping of chunking and predictions in the MapReduce framework exhibits shorter turnaround times for short RNA sequences. However, as the lengths of the RNA sequences increase, the fine-grained mapping can surpass the coarse-grained mapping in performance.

Conclusions

By using our MapReduce framework together with statistical analysis on the accuracy retention results, we observe how the inversion-based chunking methods can outperform predictions using the whole sequence. Our chunk-based approach also enables us to predict secondary structures for very long RNA sequences, which is not feasible with traditional methods alone.

     

No evidence that mRNAs have lower folding free energies than random sequences with the same dinucleotide distribution

This work investigates whether mRNA has a lower estimated folding free energy than random sequences. The free energy estimates are calculated by the mfold program for prediction of RNA secondary structures. For a set of 46 mRNAs it is shown that the predicted free energy is not significantly different from random sequences with the same dinucleotide distribution. For random sequences with the same mononucleotide distribution it has previously been shown that the native mRNA sequences have a lower predicted free energy, which indicates a more stable structure than random sequences. However, dinucleotide content is important when assessing the significance of predicted free energy as the physical stability of RNA secondary structure is known to depend on dinucleotide base stacking energies. Even known RNA secondary structures, like tRNAs, can be shown to have predicted free energies indistinguishable from randomized sequences. This suggests that the predicted free energy is not always a good determinant for RNA folding.     

A set of nearest neighbor parameters for predicting the enthalpy change of RNA secondary structure formation

A complete set of nearest neighbor parameters to predict the enthalpy change of RNA secondary structure formation was derived. These parameters can be used with available free energy nearest neighbor parameters to extend the secondary structure prediction of RNA sequences to temperatures other than 37°C. The parameters were tested by predicting the secondary structures of sequences with known secondary structure that are from organisms with known optimal growth temperatures. Compared with the previous set of enthalpy nearest neighbor parameters, the sensitivity of base pair prediction improved from 65.2 to 68.9% at optimal growth temperatures ranging from 10 to 60°C. Base pair probabilities were predicted with a partition function and the positive predictive value of structure prediction is 90.4% when considering the base pairs in the lowest free energy structure with pairing probability of 0.99 or above. Moreover, a strong correlation is found between the predicted melting temperatures of RNA sequences and the optimal growth temperatures of the host organism. This indicates that organisms that live at higher temperatures have evolved RNA sequences with higher melting temperatures.     

RGRNA: prediction of RNA secondary structure based on replacement and growth of stems

Owing to their structural diversity, RNAs perform many diverse biological functions in the cell. RNA secondary structure is thus important for predicting RNA function. Here, we propose a new combinatorial optimization algorithm, named RGRNA, to improve the accuracy of predicting RNA secondary structure. Following the establishment of a stempool, the stems are sorted by length, and chosen from largest to smallest. If the stem selected is the true stem, the secondary structure of this stem when combined with another stem selected at random will have low free energy, and the free energy will tend to gradually diminish. The free energy is considered as a parameter and the structure is converted into binary numbers to determine stem compatibility, for step-by-step prediction of the secondary structure for all combinations of stems. The RNA secondary structure can be predicted by the RGRNA method. Our experimental results show that the proposed algorithm outperforms RNAfold in terms of sensitivity, specificity, and Matthews correlation coefficient value.     

Prediction of RNA secondary structure by free energy minimization

RNA secondary structure is often predicted from sequence by free energy minimization. Over the past two years, advances have been made in the estimation of folding free energy change, the mapping of secondary structure and the implementation of computer programs for structure prediction. The trends in computer program development are: efficient use of experimental mapping of structures to constrain structure prediction; use of statistical mechanics to improve the fidelity of structure prediction; inclusion of pseudoknots in secondary structure prediction; and use of two or more homologous sequences to find a common structure.     

A phylogenetically based secondary structure for the yeast telomerase RNA

BACKGROUND: Telomerase is a ribonucleoprotein complex whose RNA moiety dictates the addition of specific simple sequences onto chromosomes ends. While relevant for certain human genetic diseases, the contribution of the essential telomerase RNA to RNP assembly still remains unclear. Phylogenetic analyses of vertebrate and ciliate telomerase RNAs revealed conserved elements that potentially organize protein subunits for RNP function. In contrast, the yeast telomerase RNA could not be fitted to any known structural model, and the limited number of known sequences from Saccharomyces species did not permit the prediction of a yeast specific conserved structure. RESULTS: We cloned and analyzed the complete telomerase RNA loci (TLC1) from all known Saccharomyces species belonging to the "sensu stricto" group. Complementation analyses in S. cerevisiae and end mappings of mature RNAs ensured the relevance of the cloned sequences. By using phylogenetic comparative analysis coupled with in vitro enzymatic probing, we derived a secondary structure prediction of the Saccharomyces cerevisiae TLC1 RNA. This conserved secondary structure prediction includes a central domain that is likely to orchestrate DNA synthesis and at least two accessory domains important for RNA stability and telomerase recruitment. The structure also reveals a potential tertiary interaction between two loops in the central core. CONCLUSIONS: The predicted secondary structure of the TLC1 RNA of S. cerevisiae reveals a distinct folding pattern featuring well-separated but conserved functional elements. The predicted structure now allows for a detailed and rationally designed study to the structure-function relationships within the telomerase RNP-complex in a genetically tractable system.     

RBPBind: Quantitative Prediction of Protein-RNA Interactions

There are hundreds of RNA binding proteins in the human genome alone and their interactions with messenger and other RNAs in a cell regulate every step in an RNA's life cycle. To understand this interplay of proteins and RNA it is important to be able to know which protein binds which RNA how strongly and where. Here, we introduce RBPBind, a web-based tool for the quantitative prediction of the interaction of single-stranded RNA binding proteins with target RNAs that fully takes into account the effect of RNA secondary structure on binding affinity. Given a user-specified RNA and a protein selected from a set of several RNA-binding proteins, RBPBind computes their binding curve and effective binding constant. The server also computes the probability that, at a given protein concentration, a protein molecule will bind to any particular nucleotide along the RNA. The sequence specificity of the protein-RNA interaction is parameterized from public RNAcompete experiments and integrated into the recursions of the Vienna RNA package to simultaneously take into account protein binding and RNA secondary structure. We validate our approach by comparison to experimentally determined binding affinities of the HuR protein for several RNAs of different sequence contexts from the literature, showing that integration of raw sequence affinities into RNA secondary structure prediction significantly improves the agreement between computationally predicted and experimentally measured binding affinities. Our resource thus provides a quick and easy way to obtain reliable predicted binding affinities and locations for single-stranded RNA binding proteins based on RNA sequence alone.     

RNA-PAIRS: RNA probabilistic assignment of imino resonance shifts

The significant biological role of RNA has further highlighted the need for improving the accuracy, efficiency and the reach of methods for investigating RNA structure and function. Nuclear magnetic resonance (NMR) spectroscopy is vital to furthering the goals of RNA structural biology because of its distinctive capabilities. However, the dispersion pattern in the NMR spectra of RNA makes automated resonance assignment, a key step in NMR investigation of biomolecules, remarkably challenging. Herein we present RNA Probabilistic Assignment of Imino Resonance Shifts (RNA-PAIRS), a method for the automated assignment of RNA imino resonances with synchronized verification and correction of predicted secondary structure. RNA-PAIRS represents an advance in modeling the assignment paradigm because it seeds the probabilistic network for assignment with experimental NMR data, and predicted RNA secondary structure, simultaneously and from the start. Subsequently, RNA-PAIRS sets in motion a dynamic network that reverberates between predictions and experimental evidence in order to reconcile and rectify resonance assignments and secondary structure information. The procedure is halted when assignments and base-parings are deemed to be most consistent with observed crosspeaks. The current implementation of RNA-PAIRS uses an initial peak list derived from proton-nitrogen heteronuclear multiple quantum correlation (¹H–¹⁵N 2D HMQC) and proton–proton nuclear Overhauser enhancement spectroscopy (¹H–¹H 2D NOESY) experiments. We have evaluated the performance of RNA-PAIRS by using it to analyze NMR datasets from 26 previously studied RNAs, including a 111-nucleotide complex. For moderately sized RNA molecules, and over a range of comparatively complex structural motifs, the average assignment accuracy exceeds 90%, while the average base pair prediction accuracy exceeded 93%. RNA-PAIRS yielded accurate assignments and base pairings consistent with imino resonances for a majority of the NMR resonances, even when the initial predictions are only modestly accurate. RNA-PAIRS is available as a public web-server at .