Sequence-specific cleavage of RNA using chimeric DNA splints and RNase H

To cleave RNA molecules using E. coli RNase H in a site-specific manner, a short oligodeoxyribonucleotide (3-5 mer) linked with oligo(2'-O-methyl)ribonucleotide(s) was designed to be used as a DNA splint. Our model experiments with ribooligomer the splint duplexes (9 mers) and RNase H demonstrated that a tetradeoxynucleotide cluster seems to be sufficient for the enzyme recognition and the short DNA-containing splint directs a unique cleavage of RNA by RNase H. The method could be applied to longer ribooligonucleotide substrates. For example, when 3'm (GA)d(AGAA)m(GGU)5' was used as a hybridization strand, 32pUCUUUCUUCUUCCAGGAU was cleaved specifically between U11 and C12 to yield 32pUCUUUCUUCUU. This method will have a variety of applications for the study of RNA.     

RNase H cleavage of RNA hybridized to oligonucleotides containing methylphosphonate, phosphorothioate and phosphodiester bonds.

Three types of 14-mer oligonucleotides were hybridized to human beta-globin pre-mRNA and the resultant duplexes were tested for susceptibility to cleavage by RNase H from E. coli or from HeLa cell nuclear extract. The oligonucleotides contained normal deoxynucleotides, phosphorothioate analogs alternating with normal deoxynucleotides, or one to six methylphosphonate deoxynucleosides. Duplexes formed with deoxyoligonucleotides or phosphorothioate analogs were susceptible to cleavage by RNase H from both sources, whereas a duplex formed with an oligonucleotide containing six methylphosphonate deoxynucleosides alternating with normal deoxynucleotides was resistant. Susceptibility to cleavage by RNase H increased parallel to a reduction in the number of methylphosphonate residues in the oligonucleotide. Stability of the oligonucleotides in the nuclear extract from HeLa cells was also tested. Whereas deoxyoligonucleotides were rapidly degraded, oligonucleotides containing alternating methylphosphonate residues remained unchanged after 70 minutes of incubation. Other oligonucleotides exhibited intermediate stability.     

Improvement of protein secondary structure prediction using binary word encoding

We propose a binary word encoding to improve the protein secondary structure prediction. A binary word encoding encodes a local amino acid sequence to a binary word, which consists of 0 or 1. We use an encoding function to map an amino acid to 0 or 1. Using the binary word encoding, we can statistically extract the multiresidue information, which depends on more than one residue. We combine the binary word encoding with the GOR method, its modified version, which shows better accuracy, and the neural network method. The binary word encoding improves the accuracy of GOR by 2.8%. We obtain similar improvement when we combine this with the modified GOR method and the neural network method. When we use multiple sequence alignment data, the binary word encoding similarly improves the accuracy. The accuracy of our best combined method is 68.2%. In this paper, we only show improvement of the GOR and neural network method, we cannot say that the encoding improves the other methods. But the improvement by the encoding suggests that the multiresidue interaction affects the formation of secondary structure. In addition, we find that the optimal encoding function obtained by the simulated annealing method relates to non-polarity. This means that nonpolarity is important to the multiresidue interaction. Proteins 27:36–46 © 1997 Wiley-Liss, Inc.     

Multithreaded comparative RNA secondary structure prediction using stochastic context-free grammars

Background  

The prediction of the structure of large RNAs remains a particular challenge in bioinformatics, due to the computational complexity and low levels of accuracy of state-of-the-art algorithms. The pfold model couples a stochastic context-free grammar to phylogenetic analysis for a high accuracy in predictions, but the time complexity of the algorithm and underflow errors have prevented its use for long alignments. Here we present PPfold, a multithreaded version of pfold, which is capable of predicting the structure of large RNA alignments accurately on practical timescales.     

Pfold: RNA secondary structure prediction using stochastic context-free grammars

RNA secondary structures are important in many biological processes and efficient structure prediction can give vital directions for experimental investigations. Many available programs for RNA secondary structure prediction only use a single sequence at a time. This may be sufficient in some applications, but often it is possible to obtain related RNA sequences with conserved secondary structure. These should be included in structural analyses to give improved results. This work presents a practical way of predicting RNA secondary structure that is especially useful when related sequences can be obtained. The method improves a previous algorithm based on an explicit evolutionary model and a probabilistic model of structures. Predictions can be done on a web server at http://www.daimi.au.dk/~compbio/pfold.     

Novel and efficient RNA secondary structure prediction using hierarchical folding.

Algorithms for prediction of RNA secondary structure-the set of base pairs that form when an RNA molecule folds-are valuable to biologists who aim to understand RNA structure and function. Improving the accuracy and efficiency of prediction methods is an ongoing challenge, particularly for pseudoknotted secondary structures, in which base pairs overlap. This challenge is biologically important, since pseudoknotted structures play essential roles in functions of many RNA molecules, such as splicing and ribosomal frameshifting. State-of-the-art methods, which are based on free energy minimization, have high run-time complexity (typically Theta(n(5)) or worse), and can handle (minimize over) only limited types of pseudoknotted structures. We propose a new approach for prediction of pseudoknotted structures, motivated by the hypothesis that RNA structures fold hierarchically, with pseudoknot-free (non-overlapping) base pairs forming first, and pseudoknots forming later so as to minimize energy relative to the folded pseudoknot-free structure. Our HFold algorithm uses two-phase energy minimization to predict hierarchically formed secondary structures in O(n(3)) time, matching the complexity of the best algorithms for pseudoknot-free secondary structure prediction via energy minimization. Our algorithm can handle a wide range of biological structures, including kissing hairpins and nested kissing hairpins, which have previously required Theta(n(6)) time.     

Revolutions in RNA secondary structure prediction

RNA structure formation is hierarchical and, therefore, secondary structure, the sum of canonical base-pairs, can generally be predicted without knowledge of the three-dimensional structure. Secondary structure prediction algorithms evolved from predicting a single, lowest free energy structure to their current state where statistics can be determined from the thermodynamic ensemble. This article reviews the free energy minimization technique and the salient revolutions in the dynamic programming algorithm methods for secondary structure prediction. Emphasis is placed on highlighting the recently developed method, which statistically samples structures from the complete Boltzmann ensemble.     

Site-specific cleavage of MS2 RNA by a thermostable DNA-linked RNase H

A series of DNA-linked RNases H, in which the 15-mer DNA is cross-linked to the Thermus thermophilus RNase HI (TRNH) variants at positions 135, 136, 137 and 138, were constructed and analyzed for their abilities to cleave the complementary 15-mer RNA. Of these, that with the DNA adduct at position 135 most efficiently cleaved the RNA substrate, indicating that position 135 is the most appropriate cross-linking site among those examined. To examine whether DNA-linked RNase H also site-specifically cleaves a highly structured natural RNA, DNA-linked TRNHs with a series of DNA adducts varying in size at position 135 were constructed and analyzed for their abilities to cleave MS2 RNA. These DNA adducts were designed such that DNA-linked enzymes cleave MS2 RNA at a loop around residue 2790. Of the four DNA-linked TRNHs with the 8-, 12-, 16- and 20-mer DNA adducts, only that with the 16-mer DNA adduct efficiently and site-specifically cleaved MS2 RNA. Primer extension revealed that this DNA-linked TRNH cleaved MS2 RNA within the target sequence.     

RNA secondary structure prediction using stochastic context-free grammars and evolutionary history.

MOTIVATION: Many computerized methods for RNA secondary structure prediction have been developed. Few of these methods, however, employ an evolutionary model, thus relevant information is often left out from the structure determination. This paper introduces a method which incorporates evolutionary history into RNA secondary structure prediction. The method reported here is based on stochastic context-free grammars (SCFGs) to give a prior probability distribution of structures. RESULTS: The phylogenetic tree relating the sequences can be found by maximum likelihood (ML) estimation from the model introduced here. The tree is shown to reveal information about the structure, due to mutation patterns. The inclusion of a prior distribution of RNA structures ensures good structure predictions even for a small number of related sequences. Prediction is carried out using maximum a posteriori estimation (MAP) estimation in a Bayesian approach. For small sequence sets, the method performs very well compared to current automated methods.     

Structural basis of cleavage by RNase H of hybrids of arabinonucleic acids and RNA

The origins of the substrate specificity of Escherichia coli RNase H1 (termed RNase H here), an enzyme that hydrolyzes the RNA strand of DNA-RNA hybrids, are not understood at present. Although the enzyme binds double-stranded RNA, no cleavage occurs with such duplexes [Lima, W. F., and Crooke, S. T. (1997) Biochemistry 36, 390]. Therefore, the hybrid substrates may not adopt a canonical A-form geometry. Furthermore, RNase H is exquisitely sensitive to chemical modification of the DNA strands in hybrid duplexes. This is particularly relevant to the RNase H-dependent pathway of antisense action. Thus, only very few of the modifications currently being evaluated as antisense therapeutics are tolerated by the enzyme, among them phosphorothioate DNA (PS-DNA). Recently, hybrids of RNA and arabinonucleic acid (ANA) as well as the 2'F-ANA analogue were shown to be substrates of RNase H [Damha, M. J., et al. (1998) J. Am. Chem. Soc. 120, 12976]. Using X-ray crystallography, we demonstrate here that ANA analogues, such as 2'F-ANA [Berger, I., et al. (1998) Nucleic Acids Res. 26, 2473] and [3.3.0]bicyclo-ANA (bc-ANA), may not be able to adopt sugar puckers that are compatible with pure A- or a B-form duplex geometries, but rather prefer the intermediate O4'-endo conformation. On the basis of the observed conformations of these ANA analogues in a DNA dodecamer duplex, we have modeled a duplex of an all-C3'-endo RNA strand and an all-O4'-endo 2'F-ANA strand. This duplex exhibits a minor groove width that is intermediate between that of A-form RNA and B-form DNA, a feature that may be exploited by the enzyme in differentiating between RNA duplexes and DNA-RNA hybrids. Therefore, the combination of the established structural and functional properties of ANA analogues helps settle existing controversies concerning the discrimination of substrates by RNase H. Knowlegde of the structure of an analogue that exhibits enhanced RNA affinity while not interfering with RNase H activity may prove helpful in the design of future antisense modifications.     

RNase H mediated cleavage of RNA by cyclohexene nucleic acid (CeNA)

Cyclohexene nucleic acid (CeNA) forms a duplex with RNA that is more stable than a DNA–RNA duplex (ΔT_m per modification: +2°C). A cyclohexenyl A nucleotide adopts a 3′-endo conformation when introduced in dsDNA. The neighbouring deoxynucleotide adopts an O4′-endo conformation. The CeNA:RNA duplex is cleaved by RNase H. The V_max and K_m of the cleavage reaction for CeNA:RNA and DNA:RNA is in the same range, although the k_cat value is about 600 times lower in the case of CeNA:RNA.     

Statistical and Bayesian approaches to RNA secondary structure prediction

Prediction of RNA secondary structure is a fundamental problem in computational structural biology. For several decades, free energy minimization has been the most popular method for prediction from a single sequence. In recent years, the McCaskill algorithm for computation of partition function and base-pair probabilities has become increasingly appreciated. This paradigm-shifting work has inspired the developments of extended partition function algorithms, statistical sampling and clustering, and application of Bayesian statistical inference. The performance of thermodynamics-based methods is limited by thermodynamic rules and parameters. However, further improvements may come from statistical estimates derived from structural databases for thermodynamics parameters with weak or little experimental data. The Bayesian inference approach appears to be promising in this context.     

SARNA-Predict: accuracy improvement of RNA secondary structure prediction using permutation-based simulated annealing

Ribonucleic acid (RNA), a single-stranded linear molecule, is essential to all biological systems. Different regions of the same RNA strand will fold together via base pair interactions to make intricate secondary and tertiary structures that guide crucial homeostatic processes in living organisms. Since the structure of RNA molecules is the key to their function, algorithms for the prediction of RNA structure are of great value. In this article, we demonstrate the usefulness of SARNA-Predict, an RNA secondary structure prediction algorithm based on Simulated Annealing (SA). A performance evaluation of SARNA-Predict in terms of prediction accuracy is made via comparison with eight state-of-the-art RNA prediction algorithms: mfold, Pseudoknot (pknotsRE), NUPACK, pknotsRG-mfe, Sfold, HotKnots, ILM, and STAR. These algorithms are from three different classes: heuristic, dynamic programming, and statistical sampling techniques. An evaluation for the performance of SARNA-Predict in terms of prediction accuracy was verified with native structures. Experiments on 33 individual known structures from eleven RNA classes (tRNA, viral RNA, antigenomic HDV, telomerase RNA, tmRNA, rRNA, RNaseP, 5S rRNA, Group I intron 23S rRNA, Group I intron 16S rRNA, and 16S rRNA) were performed. The results presented in this paper demonstrate that SARNA-Predict can out-perform other state-of-the-art algorithms in terms of prediction accuracy. Furthermore, there is substantial improvement of prediction accuracy by incorporating a more sophisticated thermodynamic model (efn2).     

Sequence-dependent hydrolysis of RNA using modified oligonucleotide splints and RNase H

To cleave RNA molecules using RNase H in a site-specific manner, a short deoxyoligonucleotide (3-5mer) joining with 2'-O-methyl oligonucleotide(s) was designed as a DNA splint to be used. Model experiments were carried out using ribooligonucleotide substrates (9 and 18 mer). It was found that the use of this type of splints (9 mer) causes a unique cleavage by RNase H. For example, when 3'm (GA)d(AGAA)m(GGU)5' was used as a hybridization strand, 32pUCUUUCUUCUUCCAGGAU was cleaved specifically between U11 and C12 to yield 32pUCUUUCUUCUU. This method will have a variety of applications for the study of RNA.