Derivation of a structural model for the c-myc IRES.

We have derived a secondary structure model for the c-myc internal ribosome entry segment (IRES) by using information from chemical probing of the c-myc IRES RNA to constrain structure prediction programs. Our data suggest that the IRES is modular in nature, and can be divided into two structural domains linked by a long unstructured region. Both domains are required for full IRES function. Domain 1 is a complex element that contains a GNNRA apical loop and an overlapping double pseudoknot motif that is topologically unique amongst published RNA structures. Domain 2, the smaller of the two, contains an apical AUUU loop. We have located the ribosome landing site and have shown that ribosomes enter in a 16 nt region downstream of the pseudoknots in a situation similar to that observed in several viral IRESs. To test the structure, several key regions of the IRES were mutated and, interestingly, it appears that some of the structural elements that we have identified function to repress c-myc IRES function. This has profound implications for de-regulation of c-myc expression by mutations occurring in the IRES.     

The computer simulation of RNA folding involving pseudoknot formation.

The algorithm and the program for the prediction of RNA secondary structure with pseudoknot formation have been proposed. The algorithm simulates stepwise folding by generating random structures using Monte Carlo method, followed by the selection of helices to final structure on the basis of both their probabilities of occurrence in a random structure and free energy parameters. The program versions have been tested on ribosomal RNA structures and on RNAs with pseudoknots evidenced by experimental data. It is shown that the simulation of folding during RNA synthesis improves the results. The introduction of pseudoknot formation permits to predict the pseudoknotted structures and to improve the prediction of long-range interactions. The computer program is rather fast and allows to predict the structures for long RNAs without using large memory volumes in usual personal computer.     

Prediction of RNA pseudoknots using heuristic modeling with mapping and sequential folding

Predicting RNA secondary structure is often the first step to determining the structure of RNA. Prediction approaches have historically avoided searching for pseudoknots because of the extreme combinatorial and time complexity of the problem. Yet neglecting pseudoknots limits the utility of such approaches. Here, an algorithm utilizing structure mapping and thermodynamics is introduced for RNA pseudoknot prediction that finds the minimum free energy and identifies information about the flexibility of the RNA. The heuristic approach takes advantage of the 5' to 3' folding direction of many biological RNA molecules and is consistent with the hierarchical folding hypothesis and the contact order model. Mapping methods are used to build and analyze the folded structure for pseudoknots and to add important 3D structural considerations. The program can predict some well known pseudoknot structures correctly. The results of this study suggest that many functional RNA sequences are optimized for proper folding. They also suggest directions we can proceed in the future to achieve even better results.     

The HCV IRES pseudoknot positions the initiation codon on the 40S ribosomal subunit

The hepatitis C virus (HCV) genomic RNA contains an internal ribosome entry site (IRES) in its 5′ untranslated region, the structure of which is essential for viral protein translation. The IRES includes a predicted pseudoknot interaction near the AUG start codon, but the results of previous studies of its structure have been conflicting. Using mutational analysis coupled with activity and functional assays, we verified the importance of pseudoknot base pairings for IRES-mediated translation and, using 35 mutants, conducted a comprehensive study of the structural tolerance and functional contributions of the pseudoknot. Ribosomal toeprinting experiments show that the entirety of the pseudoknot element positions the initiation codon in the mRNA binding cleft of the 40S ribosomal subunit. Optimal spacing between the pseudoknot and the start site AUG resembles that between the Shine–Dalgarno sequence and the initiation codon in bacterial mRNAs. Finally, we validated the HCV IRES pseudoknot as a potential drug target using antisense 2′-OMe oligonucleotides.     

基于支持向量机分类的RNA共同二级结构预测

比较序列分析作为RNA二级结构预测的最可靠途径, 已经发展出许多算法。将基于此方法的结构预测视为一个二值分类问题: 根据序列比对给出的可用信息, 判断比对中任意两列能否构成碱基对。分类器采用支持向量机方法, 特征向量包括共变信息、热力学信息和碱基互补比例。考虑到共变信息对序列相似性的要求, 通过引入一个序列相似度影响因子, 来调整不同序列相似度情况下共变信息和热力学信息对预测过程的影响, 提高了预测精度。通过49组Rfam-seed比对的验证, 显示了该方法的有效性, 算法的预测精度优于多数同类算法, 并且可以预测简单的假节。     

A tertiary structure model of the internal ribosome entry site (IRES) for methionine-independent initiation of translation

Cricket paralysis-like viruses have a dicistronic positive-strand RNA genome. These viruses produce capsid proteins through internal ribosome entry site (IRES)-mediated translation. The IRES element of one of these viruses, Plautia stall intestine virus (PSIV), forms a pseudoknot immediately upstream from the capsid coding sequence, and initiates translation from other than methionine. Previously, we estimated that the IRES element of PSIV consists of seven stem-loops using the program MFOLD; however, experimental evidence of the predicted structures was not shown, except for stem-loop VI, which was responsible for formation of the pseudoknot. To determine the whole structure of the PSIV-IRES element, we introduced compensatory mutations into the upstream MFOLD-predicted helical segments. Mutation analysis showed that stem-loop V exists as predicted, but stem-loop IV is shorter than predicted. The structure of stem-loop III is different from predicted, and stem-loops I and II are not necessary for IRES activity. In addition, we identified two new pseudoknots in the IRES element of PSIV. The complementary sequence segments that are responsible for formation of the two pseudoknots are also observed in cricket paralysis virus (CrPV) and CrPV-like viruses such as Drosophila C virus (DCV), Rhopalosiphum padi virus (RhPV), himetobi P virus (HiPV), Triatoma virus (TrV), and black queen-cell virus (BQCV), although each sequence is distinct in each virus. Considering the three pseudoknots, we constructed a tertiary structure model of the PSIV-IRES element. This structural model is applicable to other CrPV-like viruses, indicating that other CrPV-like viruses can also initiate translation from other than methionine.     

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.     

Prediction of RNA secondary structure, including pseudoknotting, by computer simulation.

A computer program is presented which determines the secondary structure of linear RNA molecules by simulating a hypothetical process of folding. This process implies the concept of 'nucleation centres', regions in RNA which locally trigger the folding. During the simulation, the RNA is allowed to fold into pseudoknotted structures, unlike all other programs predicting RNA secondary structure. The simulation uses published, experimentally determined free energy values for nearest neighbour base pair stackings and loop regions, except for new extrapolated values for loops larger than seven nucleotides. The free energy value for a loop arising from pseudoknot formation is set to a single, estimated value of 4.2 kcal/mole. Especially in the case of long RNA sequences, our program appears superior to other secondary structure predicting programs described so far, as tests on tRNAs, the LSU intron of Tetrahymena thermophila and a number of plant viral RNAs show. In addition, pseudoknotted structures are often predicted successfully. The program is written in mainframe APL and is adapted to run on IBM compatible PCs, Atari ST and Macintosh personal computers. On an 8 MHz 8088 standard PC without coprocessor, using STSC APL, it folds a sequence of 700 nucleotides in one and a half hour.     

ProbKnot: Fast prediction of RNA secondary structure including pseudoknots

It is a significant challenge to predict RNA secondary structures including pseudoknots. Here, a new algorithm capable of predicting pseudoknots of any topology, ProbKnot, is reported. ProbKnot assembles maximum expected accuracy structures from computed base-pairing probabilities in O(N²) time, where N is the length of the sequence. The performance of ProbKnot was measured by comparing predicted structures with known structures for a large database of RNA sequences with fewer than 700 nucleotides. The percentage of known pairs correctly predicted was 69.3%. Additionally, the percentage of predicted pairs in the known structure was 61.3%. This performance is the highest of four tested algorithms that are capable of pseudoknot prediction. The program is available for download at: http://rna.urmc.rochester.edu/RNAstructure.html.     

IRSS: a web-based tool for automatic layout and analysis of IRES secondary structure prediction and searching system in silico

Background  

Internal ribosomal entry sites (IRESs) provide alternative, cap-independent translation initiation sites in eukaryotic cells. IRES elements are important factors in viral genomes and are also useful tools for bi-cistronic expression vectors. Most existing RNA structure prediction programs are unable to deal with IRES elements.     

Pair stochastic tree adjoining grammars for aligning and predicting pseudoknot RNA structures

MOTIVATION: Since the whole genome sequences of many species have been determined, computational prediction of RNA secondary structures and computational identification of those non-coding RNA regions by comparative genomics become important. Therefore, more advanced alignment methods are required. Recently, an approach of structural alignment for RNA sequences has been introduced to solve these problems. Pair hidden Markov models on tree structures (PHMMTSs) proposed by Sakakibara are efficient automata-theoretic models for structural alignment of RNA secondary structures, although PHMMTSs are incapable of handling pseudoknots. On the other hand, tree adjoining grammars (TAGs), a subclass of context-sensitive grammars, are suitable for modeling pseudoknots. Our goal is to extend PHMMTSs by incorporating TAGs to be able to handle pseudoknots. RESULTS: We propose pair stochastic TAGs (PSTAGs) for aligning and predicting RNA secondary structures including a simple type of pseudoknot which can represent most known pseudoknot structures. First, we extend PHMMTSs defined on alignment of 'trees' to PSTAGs defined on alignment of 'TAG trees' which represent derivation processes of TAGs and are functionally equivalent to derived trees of TAGs. Then, we develop an efficient dynamic programming algorithm of PSTAGs for obtaining an optimal structural alignment including pseudoknots. We implement the PSTAG algorithm and demonstrate the properties of the algorithm by using it to align and predict several small pseudoknot structures. We believe that our implemented program based on PSTAGs is the first grammar-based and practically executable software for comparative analyses of RNA pseudoknot structures, and, further, non-coding RNAs.     

Improved RNA secondary structure prediction by maximizing expected pair accuracy

Free energy minimization has been the most popular method for RNA secondary structure prediction for decades. It is based on a set of empirical free energy change parameters derived from experiments using a nearest-neighbor model. In this study, a program, MaxExpect, that predicts RNA secondary structure by maximizing the expected base-pair accuracy, is reported. This approach was first pioneered in the program CONTRAfold, using pair probabilities predicted with a statistical learning method. Here, a partition function calculation that utilizes the free energy change nearest-neighbor parameters is used to predict base-pair probabilities as well as probabilities of nucleotides being single-stranded. MaxExpect predicts both the optimal structure (having highest expected pair accuracy) and suboptimal structures to serve as alternative hypotheses for the structure. Tested on a large database of different types of RNA, the maximum expected accuracy structures are, on average, of higher accuracy than minimum free energy structures. Accuracy is measured by sensitivity, the percentage of known base pairs correctly predicted, and positive predictive value (PPV), the percentage of predicted pairs that are in the known structure. By favoring double-strandedness or single-strandedness, a higher sensitivity or PPV of prediction can be favored, respectively. Using MaxExpect, the average PPV of optimal structure is improved from 66% to 68% at the same sensitivity level (73%) compared with free energy minimization.     

Predicting RNA Structure Using Mutual Information

BACKGROUND: With the ever-increasing number of sequenced RNAs and the establishment of new RNA databases, such as the Comparative RNA Web Site and Rfam, there is a growing need for accurately and automatically predicting RNA structures from multiple alignments. Since RNA secondary structure is often conserved in evolution, the well known, but underused, mutual information measure for identifying covarying sites in an alignment can be useful for identifying structural elements. This article presents MIfold, a MATLAB toolbox that employs mutual information, or a related covariation measure, to display and predict conserved RNA secondary structure (including pseudoknots) from an alignment. RESULTS: We show that MIfold can be used to predict simple pseudoknots, and that the performance can be adjusted to make it either more sensitive or more selective. We also demonstrate that the overall performance of MIfold improves with the number of aligned sequences for certain types of RNA sequences. In addition, we show that, for these sequences, MIfold is more sensitive but less selective than the related RNAalifold structure prediction program and is comparable with the COVE structure prediction package. CONCLUSION: MIfold provides a useful supplementary tool to programs such as RNA Structure Logo, RNAalifold and COVE, and should be useful for automatically generating structural predictions for databases such as Rfam.     

Structure of the hepatitis C virus IRES bound to the human 80S ribosome: remodeling of the HCV IRES

Initiation of translation of the hepatitis C virus (HCV) polyprotein is driven by an internal ribosome entry site (IRES) RNA that bypasses much of the eukaryotic translation initiation machinery. Here, single-particle electron cryomicroscopy has been used to study the mechanism of HCV IRES-mediated initiation. A HeLa in vitro translation system was used to assemble human IRES-80S ribosome complexes under near physiological conditions; these were stalled before elongation. Domain 2 of the HCV IRES is bound to the tRNA exit site, touching the L1 stalk of the 60S subunit, suggesting a mechanism for the removal of the HCV IRES in the progression to elongation. Domain 3 of the HCV IRES positions the initiation codon in the ribosomal mRNA binding cleft by binding helix 28 at the head of the 40S subunit. The comparison with the previously published binary 40S-HCV IRES complex reveals structural rearrangements in the two pseudoknot structures of the HCV IRES in translation initiation.     

Inhibition of Hepatitis C Virus in Mice by a Small Interfering RNA Targeting a Highly Conserved Sequence in Viral IRES Pseudoknot

The hepatitis C virus (HCV) internal ribosome entry site (IRES) that directs cap-independent viral translation is a primary target for small interfering RNA (siRNA)-based HCV antiviral therapy. However, identification of potent siRNAs against HCV IRES by bioinformatics-based siRNA design is a challenging task given the complexity of HCV IRES secondary and tertiary structures and association with multiple proteins, which can also dynamically change the structure of this cis-acting RNA element. In this work, we utilized siRNA tiling approach whereby siRNAs were tiled with overlapping sequences that were shifted by one or two nucleotides over the HCV IRES stem-loop structures III and IV spanning nucleotides (nts) 277–343. Based on their antiviral activity, we mapped a druggable region (nts 313–343) where the targets of potent siRNAs were enriched. siIE22, which showed the greatest anti-HCV potency, targeted a highly conserved sequence across diverse HCV genotypes, locating within the IRES subdomain IIIf involved in pseudoknot formation. Stepwise target shifting toward the 5′ or 3′ direction by 1 or 2 nucleotides reduced the antiviral potency of siIE22, demonstrating the importance of siRNA accessibility to this highly structured and sequence-conserved region of HCV IRES for RNA interference. Nanoparticle-mediated systemic delivery of the stability-improved siIE22 derivative gs_PS1 siIE22, which contains a single phosphorothioate linkage on the guide strand, reduced the serum HCV genome titer by more than 4 log₁₀ in a xenograft mouse model for HCV replication without generation of resistant variants. Our results provide a strategy for identifying potent siRNA species against a highly structured RNA target and offer a potential pan-HCV genotypic siRNA therapy that might be beneficial for patients resistant to current treatment regimens.     

An iterated loop matching approach to the prediction of RNA secondary structures with pseudoknots

MOTIVATION: Pseudoknots have generally been excluded from the prediction of RNA secondary structures due to its difficulty in modeling. Although, several dynamic programming algorithms exist for the prediction of pseudoknots using thermodynamic approaches, they are neither reliable nor efficient. On the other hand, comparative methods are more reliable, but are often done in an ad hoc manner and require expert intervention. Maximum weighted matching, an algorithm for pseudoknot prediction with comparative analysis, suffers from low-prediction accuracy in many cases. RESULTS: Here we present an algorithm, iterated loop matching, for reliably and efficiently predicting RNA secondary structures including pseudoknots. The method can utilize either thermodynamic or comparative information or both, thus is able to predict pseudoknots for both aligned and individual sequences. We have tested the algorithm on a number of RNA families. Using 8-12 homologous sequences, the algorithm correctly identifies more than 90% of base-pairs for short sequences and 80% overall. It correctly predicts nearly all pseudoknots and produces very few spurious base-pairs for sequences without pseudoknots. Comparisons show that our algorithm is both more sensitive and more specific than the maximum weighted matching method. In addition, our algorithm has high-prediction accuracy on individual sequences, comparable with the PKNOTS algorithm, while using much less computational resources. AVAILABILITY: The program has been implemented in ANSI C and is freely available for academic use at http://www.cse.wustl.edu/~zhang/projects/rna/ilm/ Supplementary information: http://www.cse.wustl.edu/~zhang/projects/rna/ilm/     

RNA secondary structure prediction from sequence alignments using a network of k-nearest neighbor classifiers

We present a machine learning method (a hierarchical network of k-nearest neighbor classifiers) that uses an RNA sequence alignment in order to predict a consensus RNA secondary structure. The input to the network is the mutual information, the fraction of complementary nucleotides, and a novel consensus RNAfold secondary structure prediction of a pair of alignment columns and its nearest neighbors. Given this input, the network computes a prediction as to whether a particular pair of alignment columns corresponds to a base pair. By using a comprehensive test set of 49 RFAM alignments, the program KNetFold achieves an average Matthews correlation coefficient of 0.81. This is a significant improvement compared with the secondary structure prediction methods PFOLD and RNAalifold. By using the example of archaeal RNase P, we show that the program can also predict pseudoknot interactions.     

PseudoViewer: automatic visualization of RNA pseudoknots

MOTIVATION: Several algorithms have been developed for drawing RNA secondary structures, however none of these can be used to draw RNA pseudoknot structures. In the sense of graph theory, a drawing of RNA secondary structures is a tree, whereas a drawing of RNA pseudoknots is a graph with inner cycles within a pseudoknot as well as possible outer cycles formed between a pseudoknot and other structural elements. Thus, RNA pseudoknots are more difficult to visualize than RNA secondary structures. Since no automatic method for drawing RNA pseudoknots exists, visualizing RNA pseudoknots relies on significant amount of manual work and does not yield satisfactory results. The task of visualizing RNA pseudoknots by hand becomes more challenging as the size and complexity of the RNA pseudoknots increase. RESULTS: We have developed a new representation and an algorithm for drawing H-type pseudoknots with RNA secondary structures. Compared to existing representations of H-type pseudoknots, the new representation ensures uniform and clear drawings with no edge crossing for any H-type pseudoknots. To the best of our knowledge, this is the first algorithm for automatically drawing RNA pseudoknots with RNA secondary structures. The algorithm has been implemented in a Java program, which can be executed on any computing system. Experimental results demonstrate that the algorithm generates an aesthetically pleasing drawing of all H-type pseudoknots. The results have also shown that the drawing has high readability, enabling the user to quickly and easily recognize the whole RNA structure as well as the pseudoknots themselves.