Chemical and functional diversity of small molecule ligands for RNA

Functional RNAs such as ribosomal RNA and structured domains of mRNA are targets for small molecule ligands that can act as modulators of the RNA biological activity. Natural ligands for RNA display a bewildering structural and chemical complexity that has yet to be matched by synthetic RNA binders. Comparison of natural and artificial ligands for RNA may help to direct future approaches to design and synthesize potent novel scaffolds for specific recognition of RNA targets.     

Computational approaches to RNA structure prediction, analysis, and design

RNA molecules are important cellular components involved in many fundamental biological processes. Understanding the mechanisms behind their functions requires RNA tertiary structure knowledge. Although modeling approaches for the study of RNA structures and dynamics lag behind efforts in protein folding, much progress has been achieved in the past two years. Here, we review recent advances in RNA folding algorithms, RNA tertiary motif discovery, applications of graph theory approaches to RNA structure and function, and in silico generation of RNA sequence pools for aptamer design. Advances within each area can be combined to impact many problems in RNA structure and function.     

Silencing of antiapoptotic survivin gene by multiple approaches of RNA interference technology

Silencing of mammalian gene expression by RNA interference (RNAi) technology can be achieved using small interfering RNA (siRNA) or short hairpin RNA (shRNA). However, the relative effectiveness of these two approaches is not known. It is also not clear whether gene-specific shRNA transcribed from an RNA polymerase II (Pol II)-directed promoter in a fusion form can disrupt the targeted gene expression. Here, we report that using both luciferase and antiapoptotic survivin genes as targets, both siRNA and shRNA approaches significantly silenced the targeted gene expression in cancer cells. We further demonstrated that shRNAs transcribed from an RNA Pol II-mediated promoter in a green fluorescent protein (GFP) fusion form at the 3'-untranslated region silenced luciferase and survivin expression as well, suggesting that the extra RNA sequence outside of the shRNA hairpin does not disrupt shRNA function. We also showed that silencing of survivin expression selectively induces apoptosis in transfected cells. Together, we have validated multiple approaches of RNAi technology using both survivin and luciferase genes as targets and demonstrated for the first time that GFP-shRNAs transcribed from an RNA Pol II-mediated promoter could mediate gene silencing, which may lead to new directions for the application of RNAi technology.     

RNA-protein interactions in the Escherichia coli ribosome.

Over the last two decades essentially three different approaches have been used to study the topography of RNA-protein interactions in the ribosome. These are: (a) the analysis of binding sites for individual ribosomal proteins or groups of proteins on the RNA; (b) the determination of protein footprint sites on the RNA by the application of higher order structure analytical techniques; and (c) the localisation of RNA-protein cross-link sites on the RNA. This article compares and contrasts the types of data that the three different approaches provide, and gives a brief and highly simplified summary of the results that have been obtained for both the 16S and 23S ribosomal RNA from E coli.     

Identifying complete RNA structural ensembles including pseudoknots

The close relationship between RNA structure and function underlines the significance of accurately predicting RNA structures from sequence information. Structural topologies such as pseudoknots are of particular interest due to their ubiquity and direct involvement in RNA function, but identifying pseudoknots is a computationally challenging problem and existing heuristic approaches usually perform poorly for RNA sequences of even a few hundred bases. We survey the performance of pseudoknot prediction methods on a data set of full-length RNA sequences representing varied sequence lengths, and biological RNA classes such as RNase P RNA, Group I Intron, tmRNA and tRNA. Pseudoknot prediction methods are compared with minimum free energy and suboptimal secondary structure prediction methods in terms of correct base-pairs, stems and pseudoknots and we find that the ensemble of suboptimal structure predictions succeeds in identifying correct structural elements in RNA that are usually missed in MFE and pseudoknot predictions. We propose a strategy to identify a comprehensive set of non-redundant stems in the suboptimal structure space of a RNA molecule by applying heuristics that reduce the structural redundancy of the predicted suboptimal structures by merging slightly varying stems that are predicted to form in local sequence regions. This reduced-redundancy set of structural elements consistently outperforms more specialized approaches.in data sets. Thus, the suboptimal folding space can be used to represent the structural diversity of an RNA molecule more comprehensively than optimal structure prediction approaches alone.     

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.     

A range of complex probabilistic models for RNA secondary structure prediction that includes the nearest-neighbor model and more

The standard approach for single-sequence RNA secondary structure prediction uses a nearest-neighbor thermodynamic model with several thousand experimentally determined energy parameters. An attractive alternative is to use statistical approaches with parameters estimated from growing databases of structural RNAs. Good results have been reported for discriminative statistical methods using complex nearest-neighbor models, including CONTRAfold, Simfold, and ContextFold. Little work has been reported on generative probabilistic models (stochastic context-free grammars [SCFGs]) of comparable complexity, although probabilistic models are generally easier to train and to use. To explore a range of probabilistic models of increasing complexity, and to directly compare probabilistic, thermodynamic, and discriminative approaches, we created TORNADO, a computational tool that can parse a wide spectrum of RNA grammar architectures (including the standard nearest-neighbor model and more) using a generalized super-grammar that can be parameterized with probabilities, energies, or arbitrary scores. By using TORNADO, we find that probabilistic nearest-neighbor models perform comparably to (but not significantly better than) discriminative methods. We find that complex statistical models are prone to overfitting RNA structure and that evaluations should use structurally nonhomologous training and test data sets. Overfitting has affected at least one published method (ContextFold). The most important barrier to improving statistical approaches for RNA secondary structure prediction is the lack of diversity of well-curated single-sequence RNA secondary structures in current RNA databases.     

The Ligand-Free State of the TPP Riboswitch: A Partially Folded RNA Structure

Riboswitches are elements of mRNA that regulate gene expression by undergoing structural changes upon binding of small ligands. Although the structures of several riboswitches have been solved with their ligands bound, the ligand-free states of only a few riboswitches have been characterized. The ligand-free state is as important for the functionality of the riboswitch as the ligand-bound form, but the ligand-free state is often a partially folded structure of the RNA, with conformational heterogeneity that makes it particularly challenging to study. Here, we present models of the ligand-free state of a thiamine pyrophosphate riboswitch that are derived from a combination of complementary experimental and computational modeling approaches. We obtain a global picture of the molecule using small-angle X-ray scattering data and use an RNA structure modeling software, MC-Sym, to fit local structural details to these data on an atomic scale. We have used two different approaches to obtaining these models. Our first approach develops a model of the RNA from the structures of its constituent junction fragments in isolation. The second approach treats the RNA as a single entity, without bias from the structure of its individual constituents. We find that both approaches give similar models for the ligand-free form, but the ligand-bound models differ for the two approaches, and only the models from the second approach agree with the ligand-bound structure known previously from X-ray crystallography. Our models provide a picture of the conformational changes that may occur in the riboswitch upon binding of its ligand. Our results also demonstrate the power of combining experimental small-angle X-ray scattering data with theoretical structure prediction tools in the determination of RNA structures beyond riboswitches.     

Enzymatically prepared RNAi libraries

Large-scale RNA interference (RNAi) screens in mammalian cells have mainly used synthetic small interfering RNA (siRNA) or short hairpin RNA (shRNA) libraries. The RNAi triggers for both of these approaches were designed with algorithm-based predictions to identify single sequences for mRNA knockdown. Alternatives to these approaches have recently been developed using enzymatic methods. Here we describe the concepts of enzymatically prepared shRNA and siRNA libraries, and discuss their strengths and limitations.     

Determination of relative rate constants for in vitro RNA processing reactions by internal competition

Studies of RNA recognition and catalysis typically involve measurement of rate constants for reactions of individual RNA sequence variants by fitting changes in substrate or product concentration to exponential or linear functions. A complementary approach is determination of relative rate constants by internal competition, which involves quantifying the time-dependent changes in substrate or product ratios in reactions containing multiple substrates. Here, we review approaches for determining relative rate constants by analysis of both substrate and product ratios and illustrate their application using the in vitro processing of precursor transfer RNA (tRNA) by ribonuclease P as a model system. The presence of inactive substrate populations is a common complicating factor in analysis of reactions involving RNA substrates, and approaches for quantitative correction of observed rate constants for these effects are illustrated. These results, together with recent applications in the literature, indicate that internal competition offers an alternate method for analyzing RNA processing kinetics using standard molecular biology methods that directly quantifies substrate specificity and may be extended to a range of applications.     

RNAnue: efficient data analysis for RNA–RNA interactomics

RNA–RNA inter- and intramolecular interactions are fundamental for numerous biological processes. While there are reasonable approaches to map RNA secondary structures genome-wide, understanding how different RNAs interact to carry out their regulatory functions requires mapping of intermolecular base pairs. Recently, different strategies to detect RNA–RNA duplexes in living cells, so called direct duplex detection (DDD) methods, have been developed. Common to all is the Psoralen-mediated in vivo RNA crosslinking followed by RNA Proximity Ligation to join the two interacting RNA strands. Sequencing of the RNA via classical RNA-seq and subsequent specialised bioinformatic analyses the result in the prediction of inter- and intramolecular RNA–RNA interactions. Existing approaches adapt standard RNA-seq analysis pipelines, but often neglect inherent features of RNA–RNA interactions that are useful for filtering and statistical assessment. Here we present RNAnue, a general pipeline for the inference of RNA–RNA interactions from DDD experiments that takes into account hybridisation potential and statistical significance to improve prediction accuracy. We applied RNAnue to data from different DDD studies and compared our results to those of the original methods. This showed that RNAnue performs better in terms of quantity and quality of predictions.     

RNA as a small molecule druggable target

Small molecule drugs have readily been developed against many proteins in the human proteome, but RNA has remained an elusive target for drug discovery. Increasingly, we see that RNA, and to a lesser extent DNA elements, show a persistent tertiary structure responsible for many diverse and complex cellular functions. In this digest, we have summarized recent advances in screening approaches for RNA targets and outlined the discovery of novel, drug-like small molecules against RNA targets from various classes and therapeutic areas. The link of structure, function, and small-molecule Druggability validates now for the first time that RNA can be the targets of therapeutic agents.     

Thermodynamic heuristics with case-based reasoning: combined insights for RNA pseudoknot secondary structure

The secondary structure of RNA pseudoknots has been extensively inferred and scrutinized by computational approaches. Experimental methods for determining RNA structure are time consuming and tedious; therefore, predictive computational approaches are required. Predicting the most accurate and energy-stable pseudoknot RNA secondary structure has been proven to be an NP-hard problem. In this paper, a new RNA folding approach, termed MSeeker, is presented; it includes KnotSeeker (a heuristic method) and Mfold (a thermodynamic algorithm). The global optimization of this thermodynamic heuristic approach was further enhanced by using a case-based reasoning technique as a local optimization method. MSeeker is a proposed algorithm for predicting RNA pseudoknot structure from individual sequences, especially long ones. This research demonstrates that MSeeker improves the sensitivity and specificity of existing RNA pseudoknot structure predictions. The performance and structural results from this proposed method were evaluated against seven other state-of-the-art pseudoknot prediction methods. The MSeeker method had better sensitivity than the DotKnot, FlexStem, HotKnots, pknotsRG, ILM, NUPACK and pknotsRE methods, with 79% of the predicted pseudoknot base-pairs being correct.     

Quantitative study of HIV-1 Tat peptide and TAR RNA interaction inhibited by poly(allylamine hydrochloride)

The interaction of poly(allylamine hydrochloride) (PAH) with TAR RNA has been studied by quartz crystal microbalance (QCM) cooperating with capillary electrophoresis (CE). Experimental results showed that PAH had high affinity for TAR RNA. In particular, PAH could disrupt the interaction of Tat peptide with TAR RNA, which is critical for HIV-1 virus replication. The approaches described here indicate that they are powerful for studying the binding processes of Tat peptide-TAR RNA and drug-TAR RNA, having great significance for the design of new drug.     

Computational approaches for RNA energy parameter estimation

Methods for efficient and accurate prediction of RNA structure are increasingly valuable, given the current rapid advances in understanding the diverse functions of RNA molecules in the cell. To enhance the accuracy of secondary structure predictions, we developed and refined optimization techniques for the estimation of energy parameters. We build on two previous approaches to RNA free-energy parameter estimation: (1) the Constraint Generation (CG) method, which iteratively generates constraints that enforce known structures to have energies lower than other structures for the same molecule; and (2) the Boltzmann Likelihood (BL) method, which infers a set of RNA free-energy parameters that maximize the conditional likelihood of a set of reference RNA structures. Here, we extend these approaches in two main ways: We propose (1) a max-margin extension of CG, and (2) a novel linear Gaussian Bayesian network that models feature relationships, which effectively makes use of sparse data by sharing statistical strength between parameters. We obtain significant improvements in the accuracy of RNA minimum free-energy pseudoknot-free secondary structure prediction when measured on a comprehensive set of 2518 RNA molecules with reference structures. Our parameters can be used in conjunction with software that predicts RNA secondary structures, RNA hybridization, or ensembles of structures. Our data, software, results, and parameter sets in various formats are freely available at http://www.cs.ubc.ca/labs/beta/Projects/RNA-Params.