RNA Structure Prediction Research Articles

RNA motif discovery: a computational overview.

Genomic studies have greatly expanded our knowledge of structural non-coding RNAs (ncRNAs). These RNAs fold into characteristic secondary structures and perform specific-structure dependent biological functions. Hence RNA secondary structure prediction is one of the most well studied problems in computational RNA biology. Comparative sequence analysis is one of the more reliable RNA structure prediction approaches as it exploits information of multiple related sequences to infer the consensus secondary structure. This class of methods essentially learns a global secondary structure from the input sequences. In this paper, we consider the more general problem of unearthing common local secondary structure based patterns from a set of related sequences. The input sequences for example could correspond to 3′ or 5′ untranslated regions of a set of orthologous genes and the unearthed local patterns could correspond to regulatory motifs found in these regions. These sequences could also correspond to in vitro selected RNA, genomic segments housing ncRNA genes from the same family and so on. Here, we give a detailed review of the various computational techniques proposed in literature attempting to solve this general motif discovery problem. We also give empirical comparisons of some of the current state of the art methods and point out future directions of research.ReviewersThis article was reviewed by Dr. Erez Levanon, Dr. Sebastian Maurer-Stroh and Dr. Weixiong Zhang.

New in silico approach to assessing RNA secondary structures with non-canonical base pairs.

BackgroundThe function of RNA is strongly dependent on its structure, so an appropriate recognition of this structure, on every level of organization, is of great importance. One particular concern is the assessment of base-base interactions, described as the secondary structure, the knowledge of which greatly facilitates an interpretation of RNA function and allows for structure analysis on the tertiary level. The RNA secondary structure can be predicted from a sequence using in silico methods often adjusted with experimental data, or assessed from 3D structure atom coordinates. Computational approaches typically consider only canonical, Watson-Crick and wobble base pairs. Handling of non-canonical interactions, important for a full description of RNA structure, is still very difficult.ResultsWe introduce our novel approach to assessing an extended RNA secondary structure, which characterizes both canonical and non-canonical base pairs, along with their type classification. It is based on predicting the RNA 3D structure from a user-provided sequence or a secondary structure that only describes canonical base pairs, and then deriving the extended secondary structure from atom coordinates. In our example implementation, this was achieved by integrating the functionality of two fully automated, high fidelity methods in a computational pipeline: RNAComposer for the 3D RNA structure prediction and RNApdbee for base-pair annotation.ConclusionsThe presented methodology ties together existing applications for RNA 3D structure prediction and base-pair annotation. The example performance, applying RNAComposer and RNApdbee, reveals better accuracy in non-canonical base pair assessment than the compared methods that directly predict RNA secondary structure.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0718-6) contains supplementary material, which is available to authorized users.

GARN: Sampling RNA 3D Structure Space with Game Theory and Knowledge-Based Scoring Strategies.

Cellular processes involve large numbers of RNA molecules. The functions of these RNA molecules and their binding to molecular machines are highly dependent on their 3D structures. One of the key challenges in RNA structure prediction and modeling is predicting the spatial arrangement of the various structural elements of RNA. As RNA folding is generally hierarchical, methods involving coarse-grained models hold great promise for this purpose. We present here a novel coarse-grained method for sampling, based on game theory and knowledge-based potentials. This strategy, GARN (Game Algorithm for RNa sampling), is often much faster than previously described techniques and generates large sets of solutions closely resembling the native structure. GARN is thus a suitable starting point for the molecular modeling of large RNAs, particularly those with experimental constraints. GARN is available from: http://garn.lri.fr/.

A new entropy model for RNA: part V, Incorporating the Flory-Huggins model in structure prediction and folding

The effect of solvent-biopolymer interactions is hardly negligible. Whereas the ideal (non-interacting) polymer consisting of N monomers in an ideal solvent is expected to have the terminal ends of its chain with a root-mean-squared (rms) end-to-end separation distance (<em>rms</em>) proportional to the square root of <em>N</em>, real interactions of a <em>rms</em> polymer both with itself and with the solvent often tend to strongly perturb <em>rms</em>. In <em>rms</em> poor solvent, the biopolymer can collapse into a small globule much smaller than the ideal <em>rms</em> due to excluding solvent. In good solvent, the biopolymer can swell to a size much larger than the ideal r due to favoring solvent. These effects require rms corrections to an ideal polymer equation. We have been developing the cross linking entropy (CLE) model in this series. The model attempts find the maximum entropy of a folded polymer by taking into account the correlation caused by bonding and other interactions of the structure. In RNA, this mostly occurs in the stems. Here we adapt CLE model to handle polymer swelling and collapse for RNA molecules both in good and in poor solvent. This work is intended to introduce this type of study and to allow its systematic application in problems of RNA folding and structure prediction. The current study suggests that there may be some tendency for RNA to behave as a polymer in poor solvent and that this collapse may happen in sequences longer than 50 nt.

Physics-based RNA structure prediction.

Despite the success of RNA secondary structure prediction for simple, short RNAs, the problem of predicting RNAs with long-range tertiary folds remains. Furthermore, RNA 3D structure prediction is hampered by the lack of the knowledge about the tertiary contacts and their thermodynamic parameters. Low-resolution structural modeling enables us to estimate the conformational entropies for a number of tertiary folds through rigorous statistical mechanical calculations. The models lead to 3D tertiary folds at coarse-grained level. The coarse-grained structures serve as the initial structures for all-atom molecular dynamics refinement to build the final all-atom 3D structures. In this paper, we present an overview of RNA computational models for secondary and tertiary structures’ predictions and then focus on a recently developed RNA statistical mechanical model—the Vfold model. The main emphasis is placed on the physics behind the models, including the treatment of the non-canonical interactions in secondary and tertiary structure modelings, and the correlations to RNA functions.

Pareto optimization in algebraic dynamic programming.

Pareto optimization combines independent objectives by computing the Pareto front of its search space, defined as the set of all solutions for which no other candidate solution scores better under all objectives. This gives, in a precise sense, better information than an artificial amalgamation of different scores into a single objective, but is more costly to compute. Pareto optimization naturally occurs with genetic algorithms, albeit in a heuristic fashion. Non-heuristic Pareto optimization so far has been used only with a few applications in bioinformatics. We study exact Pareto optimization for two objectives in a dynamic programming framework. We define a binary Pareto product operator {*}_{text {Par}} on arbitrary scoring schemes. Independent of a particular algorithm, we prove that for two scoring schemes A and B used in dynamic programming, the scoring scheme A {*}_{text {Par}}B correctly performs Pareto optimization over the same search space. We study different implementations of the Pareto operator with respect to their asymptotic and empirical efficiency. Without artificial amalgamation of objectives, and with no heuristics involved, Pareto optimization is faster than computing the same number of answers separately for each objective. For RNA structure prediction under the minimum free energy versus the maximum expected accuracy model, we show that the empirical size of the Pareto front remains within reasonable bounds. Pareto optimization lends itself to the comparative investigation of the behavior of two alternative scoring schemes for the same purpose. For the above scoring schemes, we observe that the Pareto front can be seen as a composition of a few macrostates, each consisting of several microstates that differ in the same limited way. We also study the relationship between abstract shape analysis and the Pareto front, and find that they extract information of a different nature from the folding space and can be meaningfully combined.Electronic supplementary materialThe online version of this article (doi:10.1186/s13015-015-0051-7) contains supplementary material, which is available to authorized users.

Coarse-Grained HiRE-RNA Model for ab Initio RNA Folding beyond Simple Molecules, Including Noncanonical and Multiple Base Pairings

HiRE-RNA is a coarse-grained model for RNA structure prediction and the dynamical study of RNA folding. Using a reduced set of particles and detailed interactions accounting for base-pairing and stacking, we show that noncanonical and multiple base interactions are necessary to capture the full physical behavior of complex RNAs. In this paper, we give a full account of the model and present results on the folding, stability, and free energy surfaces of 16 systems with 12 to 76 nucleotides of increasingly complex architectures, ranging from monomers to dimers, using a total of 850 μs of simulation time.

Sequence-controlled RNA self-processing: computational design, biochemical analysis, and visualization by AFM.

Reversible chemistry allowing for assembly and disassembly of molecular entities is important for biological self-organization. Thus, ribozymes that support both cleavage and formation of phosphodiester bonds may have contributed to the emergence of functional diversity and increasing complexity of regulatory RNAs in early life. We have previously engineered a variant of the hairpin ribozyme that shows how ribozymes may have circularized or extended their own length by forming concatemers. Using the Vienna RNA package, we now optimized this hairpin ribozyme variant and selected four different RNA sequences that were expected to circularize more efficiently or form longer concatemers upon transcription. (Two-dimensional) PAGE analysis confirms that (i) all four selected ribozymes are catalytically active and (ii) high yields of cyclic species are obtained. AFM imaging in combination with RNA structure prediction enabled us to calculate the distributions of monomers and self-concatenated dimers and trimers. Our results show that computationally optimized molecules do form reasonable amounts of trimers, which has not been observed for the original system so far, and we demonstrate that the combination of theoretical prediction, biochemical and physical analysis is a promising approach toward accurate prediction of ribozyme behavior and design of ribozymes with predefined functions.

StructureFold: genome-wide RNA secondary structure mapping and reconstruction in vivo.

RNAs fold into complex structures that are integral to the diverse mechanisms underlying RNA regulation of gene expression. Recent development of transcriptome-wide RNA structure profiling through the application of structure-probing enzymes or chemicals combined with high-throughput sequencing has opened a new field that greatly expands the amount of in vitro and in vivo RNA structural information available. The resultant datasets provide the opportunity to investigate RNA structural information on a global scale. However, the analysis of high-throughput RNA structure profiling data requires considerable computational effort and expertise. We present a new platform, StructureFold, that provides an integrated computational solution designed specifically for large-scale RNA structure mapping and reconstruction across any transcriptome. StructureFold automates the processing and analysis of raw high-throughput RNA structure profiling data, allowing the seamless incorporation of wet-bench structural information from chemical probes and/or ribonucleases to restrain RNA secondary structure prediction via the RNAstructure and ViennaRNA package algorithms. StructureFold performs reads mapping and alignment, normalization and reactivity derivation, and RNA structure prediction in a single user-friendly web interface or via local installation. The variation in transcript abundance and length that prevails in living cells and consequently causes variation in the counts of structure-probing events between transcripts is accounted for. Accordingly, StructureFold is applicable to RNA structural profiling data obtained in vivo as well as to in vitro or in silico datasets. StructureFold is deployed via the Galaxy platform. StructureFold is freely available as a component of Galaxy available at: https://usegalaxy.org/. yxt148@psu.edu or sma3@psu.edu Supplementary data are available at Bioinformatics online.

First description of small proteins encoded by spRNAs in Methanosarcina mazei strain Gö1.

In Methanosarcina mazei several small RNAs have been identified containing a small putative open reading frame (sORF) and thus classified as spRNAs. Here, we report on the first detection of three small proteins in M.mazei encoded by spRNAs using LC-MS/MS analysis of total protein extracts of cells grown under various stress conditions. Each spRNA shows high conservation in Methanosarcina species with regard to the sORF and the flanking non-coding RNA regions, moreover the predicted RNA structures are as well highly conserved. Characterizing the respective transcript levels in response to several stress conditions by northern blots demonstrated an enormous decrease of spRNA36 and spRNA44 during stationary growth (to less than 5%), and a significant increase of spRNA36 (2.5-fold) in response to nitrogen limitation. spRNA41, however, was only detected by RNA-Seq approaches. Quantification of the small proteins by LC-MS/MS using synthetic stable isotope labeled oligopeptides as standards indicated that the concentration of oligopetide36 and 41 in mid exponential phase is induced under nitrogen limitation, which in case of oligopeptide36 is in accordance with its transcript level. The relative amount of the three oligopeptides did not change upon entering stationary growth phase, even though the transcript levels decreased dramatically. Additional production of the oligopeptides in M.mazei did not result in any evident phenotype under standard or nitrogen limiting growth conditions. However, overall the transcript levels of several genes involved in carbon metabolism or in heat shock response were reduced 2-3 fold due to the overproduction, though no sORF specific change was observed. Based on our findings we hypothesize that oligopeptide36 might have a regulatory function in nitrogen metabolism by modulating the activity of a yet unknown target protein involved in the central nitrogen metabolism.

3dRNAscore: a distance and torsion angle dependent evaluation function of 3D RNA structures.

Model evaluation is a necessary step for better prediction and design of 3D RNA structures. For proteins, this has been widely studied and the knowledge-based statistical potential has been proved to be one of effective ways to solve this problem. Currently, a few knowledge-based statistical potentials have also been proposed to evaluate predicted models of RNA tertiary structures. The benchmark tests showed that they can identify the native structures effectively but further improvements are needed to identify near-native structures and those with non-canonical base pairs. Here, we present a novel knowledge-based potential, 3dRNAscore, which combines distance-dependent and dihedral-dependent energies. The benchmarks on different testing datasets all show that 3dRNAscore are more efficient than existing evaluation methods in recognizing native state from a pool of near-native states of RNAs as well as in ranking near-native states of RNA models.

FRESCo: finding regions of excess synonymous constraint in diverse viruses.

BackgroundThe increasing availability of sequence data for many viruses provides power to detect regions under unusual evolutionary constraint at a high resolution. One approach leverages the synonymous substitution rate as a signature to pinpoint genic regions encoding overlapping or embedded functional elements. Protein-coding regions in viral genomes often contain overlapping RNA structural elements, reading frames, regulatory elements, microRNAs, and packaging signals. Synonymous substitutions in these regions would be selectively disfavored and thus these regions are characterized by excess synonymous constraint. Codon choice can also modulate transcriptional efficiency, translational accuracy, and protein folding.ResultsWe developed a phylogenetic codon model-based framework, FRESCo, designed to find regions of excess synonymous constraint in short, deep alignments, such as individual viral genes across many sequenced isolates. We demonstrated the high specificity of our approach on simulated data and applied our framework to the protein-coding regions of approximately 30 distinct species of viruses with diverse genome architectures.ConclusionsFRESCo recovers known multifunctional regions in well-characterized viruses such as hepatitis B virus, poliovirus, and West Nile virus, often at a single-codon resolution, and predicts many novel functional elements overlapping viral genes, including in Lassa and Ebola viruses. In a number of viruses, the synonymously constrained regions that we identified also display conserved, stable predicted RNA structures, including putative novel elements in multiple viral species.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-015-0603-7) contains supplementary material, which is available to authorized users.

Towards de novo RNA 3D Structure Prediction

RNA is a fundamental class of biomolecules that mediate a large variety of molecular processes within the cell. Computational algorithms can be of great help in the understanding of RNA structure-function relationship. One of the main challenges in this field is the development of structure-prediction algorithms, which aim at the prediction of the three-dimensional (3D) native fold from the sole knowledge of the sequence. In a recent paper, we have introduced a scoring function for RNA structure prediction. Here, we analyze in detail the performance of the method, we underline strengths and shortcomings, and we discuss the results with respect to state-of-the-art techniques. These observations provide a starting point for improving current methodologies, thus paving the way to the advances of more accurate approaches for RNA 3D structure prediction.

A Nucleobase-Centric Coarse-Grained Model for Structure Prediction of RNA Fragments

The structural characterization of RNA usually poses additional challenges when compared to other biomolecular systems. For example, there is a relatively scarce amount of structural data available, which demands the development of three-dimensional structure prediction tools. In addition, full-resolution simulations can be a hard task not only because of the complexity of the interactions involved, but also due to the limitations of the current force fields. At this point, coarse-grained simulations are a good candidate to fill the gaps in this growing research field. The use of these techniques has rapidly increased in the past decades in the study of biological systems on which the experiments require an additional interpretation or where an atomistic computational approach results difficult or unfeasible. Nevertheless, the development of coarse-grained models involves the understanding of the main structural features of the original system, which can represent a challenge by itself.In this work, we present a knowledge-base coarse-grained model for RNA structure prediction, representing each nucleotide by a single anisotropic particle. The mapping and the main interactions are designed to reproduce the geometrical distribution of the closest pairs of nucleotides, extracted from a set of ribosomal structures. The model is inspired in the ESCORE function [1], a knowledge-based scoring function that has been shown to perform better compared to fully atomistic techniques in identifying native-like structures from a set of decoys. Its minimalistic nature and successful application on a broad range of structures straightforwardly suggest a representation for a coarse-grained approach. We show the preliminary results of our simulations and discuss the role of pair interactions in the prediction of RNA structures.[1] S. Bottaro, F. Di Palma and G. Bussi, “The Role of Nucleobase Interactions in RNA Structure and Dynamics”, Nucleic Acids Res., accepted.

Modeling complex RNA tertiary folds with Rosetta.

Reliable modeling of RNA tertiary structures is key to both understanding these structures' roles in complex biological machines and to eventually facilitating their design for molecular computing and robotics. In recent years, a concerted effort to improve computational prediction of RNA structure through the RNA-Puzzles blind prediction trials has accelerated advances in the field. Among other approaches, the versatile and expanding Rosetta molecular modeling software now permits modeling of RNAs in the 100-300 nucleotide size range at consistent subhelical (~1 nm) resolution. Our laboratory's current state-of-the-art methods for RNAs in this size range involve Fragment Assembly of RNA with Full-Atom Refinement (FARFAR), which optimizes RNA conformations in the context of a physically realistic energy function, as well as hybrid techniques that leverage experimental data to inform computational modeling. In this chapter, we give a practical guide to our current workflow for modeling RNA three-dimensional structures using FARFAR, including strategies for using data from multidimensional chemical mapping experiments to focus sampling and select accurate conformations.

RNA-Puzzles Round II: assessment of RNA structure prediction programs applied to three large RNA structures

RNA-Puzzles Round II: assessment of RNA structure prediction programs applied to three large RNA structures

A Method to Predict the 3D Structure of an RNA Scaffold.

The ever increasing discoveries of noncoding RNA functions draw a strong demand for RNA structure determination from the sequence. In recently years, computational studies for RNA structures, at both the two-dimensional and the three-dimensional levels, led to several highly promising new developments. In this chapter, we describe a recently developed RNA structure prediction method based on the virtual bond-based coarse-grained folding model (Vfold). The main emphasis in the Vfold method is placed on the loop entropy calculations, the treatment of noncanonical (mismatch) interactions and the 3D structure assembly from motif-based template library. As case studies, we use the glycine riboswitch and the G310-U376 domain of MLV RNA to illustrate the Vfold-based prediction of RNA 3D structures from the sequences.

Thermodynamic and kinetic folding of riboswitches.

Riboswitches are structured RNA regulatory elements located in the 5'-UTRs of mRNAs. Ligand-binding induces a structural rearrangement in these RNA elements, effecting events in downstream located coding sequences. Since they do not require proteins for their functions, they are ideally suited for computational analysis using the toolbox of RNA structure prediction methods. By their very definition riboswitch function depends on structural change. Methods that consider only the thermodynamic equilibrium of an RNA are therefore of limited use. Instead, one needs to employ computationally more expensive methods that consider the energy landscape and the folding dynamics on that landscape. Moreover, for the important class of kinetic riboswitches, the mechanism of riboswitch function can only be understood in the context of co-transcriptional folding. We present a computational approach to simulate the dynamic behavior of riboswitches during co-transcriptional folding in the presence and absence of a ligand. Our investigations show that the abstraction level of RNA secondary structure in combination with a dynamic folding landscape approach is expressive enough to understand how riboswitches perform their function. We apply our approach to a experimentally validated theophylline-binding riboswitch.

Computational methods toward accurate RNA structure prediction using coarse-grained and all-atom models.

Computational methods can provide significant insights into RNA structure and dynamics, bridging the gap in our understanding of the relationship between structure and biological function. Simulations enrich and enhance our understanding of data derived on the bench, as well as provide feasible alternatives to costly or technically challenging experiments. Coarse-grained computational models of RNA are especially important in this regard, as they allow analysis of events occurring in timescales relevant to RNA biological function, which are inaccessible through experimental methods alone. We have developed a three-bead coarse-grained model of RNA for discrete molecular dynamics simulations. This model is efficient in de novo prediction of short RNA tertiary structure, starting from RNA primary sequences of less than 50 nucleotides. To complement this model, we have incorporated additional base-pairing constraints and have developed a bias potential reliant on data obtained from hydroxyl probing experiments that guide RNA folding to its correct state. By introducing experimentally derived constraints to our computer simulations, we are able to make reliable predictions of RNA tertiary structures up to a few hundred nucleotides. Our refined model exemplifies a valuable benefit achieved through integration of computation and experimental methods.

Structural basis of genomic RNA (gRNA) dimerization and packaging determinants of mouse mammary tumor virus (MMTV).

BackgroundOne of the hallmarks of retroviral life cycle is the efficient and specific packaging of two copies of retroviral gRNA in the form of a non-covalent RNA dimer by the assembling virions. It is becoming increasingly clear that the process of dimerization is closely linked with gRNA packaging, and in some retroviruses, the latter depends on the former. Earlier mutational analysis of the 5’ end of the MMTV genome indicated that MMTV gRNA packaging determinants comprise sequences both within the 5’ untranslated region (5’ UTR) and the beginning of gag.ResultsThe RNA secondary structure of MMTV gRNA packaging sequences was elucidated employing selective 2’hydroxyl acylation analyzed by primer extension (SHAPE). SHAPE analyses revealed the presence of a U5/Gag long-range interaction (U5/Gag LRI), not predicted by minimum free-energy structure predictions that potentially stabilizes the global structure of this region. Structure conservation along with base-pair covariations between different strains of MMTV further supported the SHAPE-validated model. The 5’ region of the MMTV gRNA contains multiple palindromic (pal) sequences that could initiate intermolecular interaction during RNA dimerization. In vitro RNA dimerization, SHAPE analysis, and structure prediction approaches on a series of pal mutants revealed that MMTV RNA utilizes a palindromic point of contact to initiate intermolecular interactions between two gRNAs, leading to dimerization. This contact point resides within pal II (5’ CGGCCG 3’) at the 5’ UTR and contains a canonical “GC” dyad and therefore likely constitutes the MMTV RNA dimerization initiation site (DIS). Further analyses of these pal mutants employing in vivo genetic approaches indicate that pal II, as well as pal sequences located in the primer binding site (PBS) are both required for efficient MMTV gRNA packaging.ConclusionsEmploying structural prediction, biochemical, and genetic approaches, we show that pal II functions as a primary point of contact between two MMTV RNAs, leading to gRNA dimerization and its subsequent encapsidation into the assembling virus particles. The results presented here enhance our understanding of the MMTV gRNA dimerization and packaging processes and the role of structural motifs with respect to RNA-RNA and possibly RNA-protein interactions that might be taking place during MMTV life cycle.Electronic supplementary materialThe online version of this article (doi:10.1186/s12977-014-0096-6) contains supplementary material, which is available to authorized users.