Characterization of the mechanical unfolding of RNA pseudoknots

The pseudoknot is an important RNA structural element that provides an excellent model system for studying the contributions of tertiary interactions to RNA stability and to folding kinetics. RNA pseudoknots are also of interest because of their key role in the control of ribosomal frameshifting by viral RNAs. Their mechanical properties are directly relevant to their unfolding by ribosomes during translation. We have used optical tweezers to study the kinetics and thermodynamics of mechanical unfolding and refolding of single RNA molecules. Here we describe the unfolding of the frameshifting pseudoknot from infectious bronchitis virus (IBV), three constituent hairpins, and three mutants of the IBV pseudoknot. All four pseudoknots cause −1 programmed ribosomal frameshifting. We have measured the free energies and rates of mechanical unfolding and refolding of the four frameshifting pseudoknots. Our results show that the IBV pseudoknot requires a higher force than its corresponding hairpins to unfold. Furthermore, its rate of unfolding changes little with increasing force, in contrast with the rate of hairpin unfolding. The presence of Mg²⁺ significantly increases the kinetic barriers to unfolding the IBV pseudoknot, but has only a minor effect on the hairpin unfolding. The greater mechanical stability of pseudoknots compared to hairpins, and their kinetic insensitivity to force supports the hypothesis that −1 frameshifting depends on the difficulty of unfolding the mRNA.     

Possible involvement of coaxially stacked double pseudoknots in the regulation of −1 programmed ribosomal frameshifting in RNA viruses

?1 programmed ribosomal frameshifting (PRF) in viruses is often stimulated by a pseudoknot downstream from the slippery sequence. At the PRF junction of HIV-1, transmissible gastroenteritis virus (TGEV), Barmah Forest virus (BFV), Fort Morgan virus (FMV), and Equine arteritis virus (EAV), we identified potential double pseudoknots in either a tandem mode or embedded mode. In viruses with tandem pseudoknots (5′PK & 3′PK), the slippery sequence is encompassed in the 5′PK. The ribosome needs to unwind the 5′PK to get to the slippery sequence. In HIV-1, the 3′PK and several alternative structures are mutually exclusive. Disruption of the tandem pseudoknots may enable one of the alternative structures to form as the effective frameshift stimulator. In TGEV/BFV/FMV, the 3′PK is a conventional frameshift stimulator. In all cases, the tandem pseudoknots may slow down the ribosome before it reaches the conventional PRF signals. In EAV, a compact pseudoknot is embedded within loop2 of the otherwise conventional frameshift-stimulating pseudoknot. All double pseudoknots have the potential to stack their stems coaxially. We built structural models of the HIV-1 and EAV double pseudoknots to show that both the tandem and embedded modes are feasible and reasonable. We hypothesize that the fundamental reason for the viruses to utilize coaxially stacked double pseudoknots is to increase the overall stability of the frameshift regulating structure, and avoid an ultra-stable single pseudoknot which may become a ribosomal roadblock. Our results significantly expand the repertoire of RNA structures and dynamics that may potentially involve in ?1 PRF regulation.     

基于最小自由能和协变信息预测带伪结RNA二级结构的迭代化方法

多数RNA分子的结构在进化中是高度保守的, 其中很多包含伪结。而RNA伪结的预测一直是一个棘手问题, 很多RNA 二级结构预测算法都不能预测伪结。文章提出一种基于迭代法预测带伪结RNA 二级结构的新方法。该方法在给潜在碱基对打分时综合了热力学和协变信息, 通过基于最小自由能RNA折叠算法的多次迭代选出所有的碱基对。测试结果表明: 此方法几乎能预测到所有的伪结。与其他方法相比, 敏感度接近最优, 而特异性达到最优。     

基于堆积协变信息与最小自由能预测含伪结的RNA二级结构

RNA伪结预测是RNA研究的一个难点问题。文中提出一种基于堆积协变信息与最小自由能的RNA伪结预测方法。该方法使用已知结构的RNA比对序列(ClustalW比对和结构比对)测试此方法, 侧重考虑相邻碱基对之间相互作用形成的堆积协变信息, 并结合最小自由能方法对碱基配对综合评分, 通过逐步迭代求得含伪结的RNA二级结构。结果表明, 此方法能正确预测伪结, 其平均敏感性和特异性优于参考算法, 并且结构比对的预测性能比ClustalW比对的预测性能更加稳定。文中同时讨论了不同协变信息权重因子对预测性能的影响, 发现权重因子比值在l1: l2=5:1时, 预测性能达到最优。     

Heuristic RNA pseudoknot prediction including intramolecular kissing hairpins

Pseudoknots are an essential feature of RNA tertiary structures. Simple H-type pseudoknots have been studied extensively in terms of biological functions, computational prediction, and energy models. Intramolecular kissing hairpins are a more complex and biologically important type of pseudoknot in which two hairpin loops form base pairs. They are hard to predict using free energy minimization due to high computational requirements. Heuristic methods that allow arbitrary pseudoknots strongly depend on the quality of energy parameters, which are not yet available for complex pseudoknots. We present an extension of the heuristic pseudoknot prediction algorithm DotKnot, which covers H-type pseudoknots and intramolecular kissing hairpins. Our framework allows for easy integration of advanced H-type pseudoknot energy models. For a test set of RNA sequences containing kissing hairpins and other types of pseudoknot structures, DotKnot outperforms competing methods from the literature. DotKnot is available as a web server under http://dotknot.csse.uwa.edu.au.     

基于堆积协变信息与最小自由能预测含伪结的RNA二级结构

RNA伪结预测是RNA研究的一个难点问题。文中提出一种基于堆积协变信息与最小自由能的RNA伪结预测方法。该方法使用已知结构的RNA比对序列(ClustalW比对和结构比对)测试此方法, 侧重考虑相邻碱基对之间相互作用形成的堆积协变信息, 并结合最小自由能方法对碱基配对综合评分, 通过逐步迭代求得含伪结的RNA二级结构。结果表明, 此方法能正确预测伪结, 其平均敏感性和特异性优于参考算法, 并且结构比对的预测性能比ClustalW比对的预测性能更加稳定。文中同时讨论了不同协变信息权重因子对预测性能的影响, 发现权重因子比值在l1: l2=5:1时, 预测性能达到最优。     

Prediction of geometrically feasible three-dimensional structures of pseudoknotted RNA through free energy estimation

Accurate free energy estimation is essential for RNA structure prediction. The widely used Turner''s energy model works well for nested structures. For pseudoknotted RNAs, however, there is no effective rule for estimation of loop entropy and free energy. In this work we present a new free energy estimation method, termed the pseudoknot predictor in three-dimensional space (pk3D), which goes beyond Turner''s model. Our approach treats nested and pseudoknotted structures alike in one unifying physical framework, regardless of how complex the RNA structures are. We first test the ability of pk3D in selecting native structures from a large number of decoys for a set of 43 pseudoknotted RNA molecules, with lengths ranging from 23 to 113. We find that pk3D performs slightly better than the Dirks and Pierce extension of Turner''s rule. We then test pk3D for blind secondary structure prediction, and find that pk3D gives the best sensitivity and comparable positive predictive value (related to specificity) in predicting pseudoknotted RNA secondary structures, when compared with other methods. A unique strength of pk3D is that it also generates spatial arrangement of structural elements of the RNA molecule. Comparison of three-dimensional structures predicted by pk3D with the native structure measured by nuclear magnetic resonance or X-ray experiments shows that the predicted spatial arrangement of stems and loops is often similar to that found in the native structure. These close-to-native structures can be used as starting points for further refinement to derive accurate three-dimensional structures of RNA molecules, including those with pseudoknots.     

Improved free energy parameters for RNA pseudoknotted secondary structure prediction

Accurate prediction of RNA pseudoknotted secondary structures from the base sequence is a challenging computational problem. Since prediction algorithms rely on thermodynamic energy models to identify low-energy structures, prediction accuracy relies in large part on the quality of free energy change parameters. In this work, we use our earlier constraint generation and Boltzmann likelihood parameter estimation methods to obtain new energy parameters for two energy models for secondary structures with pseudoknots, namely, the Dirks–Pierce (DP) and the Cao–Chen (CC) models. To train our parameters, and also to test their accuracy, we create a large data set of both pseudoknotted and pseudoknot-free secondary structures. In addition to structural data our training data set also includes thermodynamic data, for which experimentally determined free energy changes are available for sequences and their reference structures. When incorporated into the HotKnots prediction algorithm, our new parameters result in significantly improved secondary structure prediction on our test data set. Specifically, the prediction accuracy when using our new parameters improves from 68% to 79% for the DP model, and from 70% to 77% for the CC model.     

Dissecting non-canonical interactions in frameshift-stimulating mRNA pseudoknots

A variety of powerful NMR experiments have been introduced over the last few years that allow for the direct identification of different combinations of donor and acceptor atoms involved in hydrogen bonds in biomolecules. This ability to directly observe tertiary structural hydrogen bonds in solution tremendously facilitates structural studies of nucleic acids. We show here that an adiabatic HNN-COSY pulse scheme permits observation and measurement of J(N,N) couplings for nitrogen sites that are separated by up to 140 ppm in a single experiment at a proton resonance frequency of 500 MHz. Crucial hydrogen bond acceptor sites in nucleic acids, such as cytidine N3 nitrogens, can be unambiguously identified even in the absence of detectable H41 and H42 amino protons using a novel triple-resonance two-dimensional experiment, denoted H5(C5C4)N3. The unambiguous identification of amino nitrogen donor and aromatic nitrogen acceptor sites associated with both major groove as well as minor groove triple base pairs reveal the details of hydrogen bonding networks that stabilize the complex architecture of frameshift-stimulating mRNA pseudoknots. Another key tertiary interaction involving a 2′-OH hydroxyl proton that donates a hydrogen bond to an aromatic nitrogen acceptor in a cis Watson–Crick/sugar edge interaction can also be directly detected using a quantitative J(H,N) ¹H,¹⁵N-HSQC experiment.