Correlation of RNA secondary structure statistics with thermodynamic stability and applications to folding

Poly(G)7 box: a functional element of mammalian 18S rRNA involved in translation

In eukaryotes, the ribosomal small subunit (40S) is composed of 18S rRNA and 33 ribosomal proteins. 18S rRNA has a special secondary structure and is an indispensable part of the translation process. Herein, a special sequence located in mammalian 18S rRNA named Poly(G)7box, which is composed of seven guanines, was found. Poly(G)7 can form a special and stable secondary structure by binding to the translation elongation factor subunit eEF1D and the ribosomal protein RPL32. Poly(G)7box was transfected into cells, and the translation efficiency of cells was inhibited. We believe that Poly(G)7box is an important translation-related functional element located on mammalian 18S rRNA, meanwhile the Poly(G)7 located on mRNA 5' and 3' box does not affect mRNA translation.

Computational study on the binding of Mango-II RNA aptamer and fluorogen using the polarizable force field AMOEBA

Fluorescent light-up aptamers (FLAPs) are well-performed biosensors for cellular imaging and the detection of different targets of interest, including RNA, non-nucleic acid molecules, metal ions, and so on. They could be easily designed and emit a strong fluorescence signal once bound to specified fluorogens. Recently, one unique aptamer called Mango-II has been discovered to possess a strong affinity and excellent fluorescent properties with fluorogens TO1-Biotin and TO3-Biotin. To explore the binding mechanisms, computational simulations have been performed to obtain structural and thermodynamic information about FLAPs at atomic resolution. AMOEBA polarizable force field, with the capability of handling the highly charged and flexible RNA system, was utilized for the simulation of Mango-II with TO1-Biotin and TO3-Biotin in this work. The calculated binding free energy using published crystal structures is in excellent agreement with the experimental values. Given the challenges in modeling complex RNA dynamics, our work demonstrates that MD simulation with a polarizable force field is valuable for understanding aptamer-fluorogen binding and potentially designing new aptamers or fluorogens with better performance.

Modeling Loop Composition and Ion Concentration Effects in RNA Hairpin Folding Stability

The ability to accurately predict RNA hairpin structure and stability for different loop sequences and salt conditions is important for understanding, modeling, and designing larger RNA folds. However, traditional RNA secondary structure models cannot treat loop-sequence and ionic effects on RNA hairpin folding. Here, we describe a general, three-dimensional (3D) conformation-based computational method for modeling salt concentration-dependent conformational distributions and the detailed 3D structures for a set of three RNA hairpins that contain a variable, 15-nucleotide loop sequence. For a given RNA sequence, the new, to our knowledge, method integrates a Vfold2D two-dimensional structure folding model with IsRNA coarse-grained molecular dynamics 3D folding simulations and Monte Carlo tightly bound ion estimations of ion-mediated electrostatic interactions. The model predicts free-energy landscapes for the different RNA hairpin-forming sequences with variable salt conditions. The theoretically predicted results agree with the experimental fluorescence measurements, validating the strategy. Furthermore, the theoretical model goes beyond the experimental results by enabling in-depth 3D structural analysis, revealing energetic mechanisms for the sequence- and salt-dependent folding stability. Although the computational framework presented here is developed for RNA hairpin systems, the general method may be applied to investigate other RNA systems, such as multiway junctions or pseudoknots in mixed metal ion solutions.

Developments of Riboswitches and Toehold Switches for Molecular Detection-Biosensing and Molecular Diagnostics

Riboswitches and toehold switches are considered to have potential for implementation in various fields, i.e., biosensing, metabolic engineering, and molecular diagnostics. The specific binding, programmability, and manipulability of these RNA-based molecules enable their intensive deployments in molecular detection as biosensors for regulating gene expressions, tracking metabolites, or detecting RNA sequences of pathogenic microorganisms. In this review, we will focus on the development of riboswitches and toehold switches in biosensing and molecular diagnostics. This review introduces the operating principles and the notable design features of riboswitches as well as toehold switches. Moreover, we will describe the advances and future directions of riboswitches and toehold switches in biosensing and molecular diagnostics.

bpRNA: large-scale automated annotation and analysis of RNA secondary structure

While RNA secondary structure prediction from sequence data has made remarkable progress, there is a need for improved strategies for annotating the features of RNA secondary structures. Here, we present bpRNA, a novel annotation tool capable of parsing RNA structures, including complex pseudoknot-containing RNAs, to yield an objective, precise, compact, unambiguous, easily-interpretable description of all loops, stems, and pseudoknots, along with the positions, sequence, and flanking base pairs of each such structural feature. We also introduce several new informative representations of RNA structure types to improve structure visualization and interpretation. We have further used bpRNA to generate a web-accessible meta-database, ‘bpRNA-1m’, of over 100 000 single-molecule, known secondary structures; this is both more fully and accurately annotated and over 20-times larger than existing databases. We use a subset of the database with highly similar (≥90% identical) sequences filtered out to report on statistical trends in sequence, flanking base pairs, and length. Both the bpRNA method and the bpRNA-1m database will be valuable resources both for specific analysis of individual RNA molecules and large-scale analyses such as are useful for updating RNA energy parameters for computational thermodynamic predictions, improving machine learning models for structure prediction, and for benchmarking structure-prediction algorithms.

rRNA Mimicry in RNA Regulation of Gene Expression

The rRNA is the largest and most abundant RNA in bacterial and archaeal cells. It is also one of the best-characterized RNAs in terms of its structural motifs and sequence variation. Production of ribosome components including >50 ribosomal proteins (r-proteins) consumes significant cellular resources. Thus, RNA cis-regulatory structures that interact with r-proteins to repress further r-protein synthesis play an important role in maintaining appropriate stoichiometry between r-proteins and rRNA. Classically, such mRNA structures were thought to directly mimic the rRNA. However, more than 30 years of research has demonstrated that a variety of different recognition and regulatory paradigms are present. This review will demonstrate how structural mimicry between the rRNA and mRNA cis-regulatory structures may take many different forms. The collection of mRNA structures that interact with r-proteins to regulate r-protein operons are best characterized in Escherichia coli, but are increasingly found within species from nearly all phyla of bacteria and several archaea. Furthermore, they represent a unique opportunity to assess the plasticity of RNA structure in the context of RNA-protein interactions. The binding determinants imposed by r-proteins to allow regulation can be fulfilled in many ways. Some r-protein-interacting mRNAs are immediately obvious as rRNA mimics from primary sequence similarity, others are identifiable only after secondary or tertiary structure determination, and some show no obvious similarity. In addition, across different bacterial species a host of different mechanisms of action have been characterized, showing that there is no simple one-size-fits-all solution.

Antisense targeting of 3' end elements involved in DUX4 mRNA processing is an efficient therapeutic strategy for facioscapulohumeral dystrophy: a new gene-silencing approach

Defects in mRNA 3'end formation have been described to alter transcription termination, transport of the mRNA from the nucleus to the cytoplasm, stability of the mRNA and translation efficiency. Therefore, inhibition of polyadenylation may lead to gene silencing. Here, we choose facioscapulohumeral dystrophy (FSHD) as a model to determine whether or not targeting key 3' end elements involved in mRNA processing using antisense oligonucleotide drugs can be used as a strategy for gene silencing within a potentially therapeutic context. FSHD is a gain-of-function disease characterized by the aberrant expression of the Double homeobox 4 (DUX4) transcription factor leading to altered pathogenic deregulation of multiple genes in muscles. Here, we demonstrate that targeting either the mRNA polyadenylation signal and/or cleavage site is an efficient strategy to down-regulate DUX4 expression and to decrease the abnormally high-pathological expression of genes downstream of DUX4. We conclude that targeting key functional 3' end elements involved in pre-mRNA to mRNA maturation with antisense drugs can lead to efficient gene silencing and is thus a potentially effective therapeutic strategy for at least FSHD. Moreover, polyadenylation is a crucial step in the maturation of almost all eukaryotic mRNAs, and thus all mRNAs are virtually eligible for this antisense-mediated knockdown strategy.

Flavivirus RNA synthesis in vitro

Establishment of in vitro systems to study mechanisms of RNA synthesis for positive strand RNA viruses have been very useful in the past and have shed light on the composition of protein and RNA components, optimum conditions, the nature of the products formed, cis-acting RNA elements and trans-acting protein factors required for efficient synthesis. In this review, we summarize our current understanding regarding the requirements for flavivirus RNA synthesis in vitro. We describe details of reaction conditions, the specificity of template used by either the multi-component membrane-bound viral replicase complex or by purified, recombinant RNA-dependent RNA polymerase. We also discuss future perspectives to extend the boundaries of our knowledge.

RNAsnp: efficient detection of local RNA secondary structure changes induced by SNPs

Structural characteristics are essential for the functioning of many noncoding RNAs and cis-regulatory elements of mRNAs. SNPs may disrupt these structures, interfere with their molecular function, and hence cause a phenotypic effect. RNA folding algorithms can provide detailed insights into structural effects of SNPs. The global measures employed so far suffer from limited accuracy of folding programs on large RNAs and are computationally too demanding for genome-wide applications. Here, we present a strategy that focuses on the local regions of maximal structural change between mutant and wild-type. These local regions are approximated in a “screening mode” that is intended for genome-wide applications. Furthermore, localized regions are identified as those with maximal discrepancy. The mutation effects are quantified in terms of empirical P values. To this end, the RNAsnp software uses extensive precomputed tables of the distribution of SNP effects as function of length and GC content. RNAsnp thus achieves both a noise reduction and speed-up of several orders of magnitude over shuffling-based approaches. On a data set comprising 501 SNPs associated with human-inherited diseases, we predict 54 to have significant local structural effect in the untranslated region of mRNAs. RNAsnp is available at http://rth.dk/resources/rnasnp.

RNA 3D structure prediction by using a coarse-grained model and experimental data

RNAs form complex secondary and three-dimensional structures, and their biological functions highly rely on their structures and dynamics. Here we developed a general coarse-grained framework for RNA 3D structure prediction. A new, hybrid coarse-grained model that explicitly describes the electrostatics and hydrogen-bond interactions has been constructed based on experimental structural statistics. With the simulated annealing simulation protocol, several RNAs of less than 30-nt were folded to within 4.0 Å of the native structures. In addition, with limited restraints on Watson-Crick basepairing based on the data from NMR spectroscopy and small-angle X-ray scattering (SAXS) information, the current model was able to characterize the complex tertiary structures of large size RNAs, such as 5S ribosome and U2/U6 snRNA. We also demonstrated that the pseudoknot structure was better captured when the coordinating Mg(2+) cations and limited basepairing restraints were included. The accuracy of our model has been compared favorably with other RNA structure prediction methods presented in the previous study of RNA-Puzzles. Therefore the coarse-grained model presented here offers a unique approach for accurate prediction and modeling of RNA structures.

Coarse-grained prediction of RNA loop structures

One of the key issues in the theoretical prediction of RNA folding is the prediction of loop structure from the sequence. RNA loop free energies are dependent on the loop sequence content. However, most current models account only for the loop length-dependence. The previously developed “Vfold” model (a coarse-grained RNA folding model) provides an effective method to generate the complete ensemble of coarse-grained RNA loop and junction conformations. However, due to the lack of sequence-dependent scoring parameters, the method is unable to identify the native and near-native structures from the sequence. In this study, using a previously developed iterative method for extracting the knowledge-based potential parameters from the known structures, we derive a set of dinucleotide-based statistical potentials for RNA loops and junctions. A unique advantage of the approach is its ability to go beyond the the (known) native structures by accounting for the full free energy landscape, including all the nonnative folds. The benchmark tests indicate that for given loop/junction sequences, the statistical potentials enable successful predictions for the coarse-grained 3D structures from the complete conformational ensemble generated by the Vfold model. The predicted coarse-grained structures can provide useful initial folds for further detailed structural refinement.

Gay-Berne and electrostatic multipole based coarse-grain potential in implicit solvent

A general, transferable coarse-grain (CG) framework based on the Gay-Berne potential and electrostatic point multipole expansion is presented for polypeptide simulations. The solvent effect is described by the Generalized Kirkwood theory. The CG model is calibrated using the results of all-atom simulations of model compounds in solution. Instead of matching the overall effective forces produced by atomic models, the fundamental intermolecular forces such as electrostatic, repulsion-dispersion, and solvation are represented explicitly at a CG level. We demonstrate that the CG alanine dipeptide model is able to reproduce quantitatively the conformational energy of all-atom force fields in both gas and solution phases, including the electrostatic and solvation components. Replica exchange molecular dynamics and microsecond dynamic simulations of polyalanine of 5 and 12 residues reveal that the CG polyalanines fold into "alpha helix" and "beta sheet" structures. The 5-residue polyalanine displays a substantial increase in the "beta strand" fraction relative to the 12-residue polyalanine. The detailed conformational distribution is compared with those reported from recent all-atom simulations and experiments. The results suggest that the new coarse-graining approach presented in this study has the potential to offer both accuracy and efficiency for biomolecular modeling.

R-PASS: A Fast Structure-based RNA Sequence Alignment Algorithm

We present a fast pairwise RNA sequence alignment method using structural information, named R-PASS (RNA Pairwise Alignment of Structure and Sequence), which shows good accuracy on sequences with low sequence identity and significantly faster than alternative methods. The method begins by representing RNA secondary structure as a set of structure motifs. The motifs from two RNAs are then used as input into a bipartite graph-matching algorithm, which determines the structure matches. The matches are then used as constraints in a constrained dynamic programming sequence alignment procedure. The R-PASS method has an O(nm) complexity. We compare our method with two other structure-based alignment methods, LARA and ExpaLoc, and with a sequence-based alignment method, MAFFT, across three benchmarks and obtain favorable results in accuracy and orders of magnitude faster in speed.

rCAD: A Novel Database Schema for the Comparative Analysis of RNA

Beyond its direct involvement in protein synthesis with mRNA, tRNA, and rRNA, RNA is now being appreciated for its significance in the overall metabolism and regulation of the cell. Comparative analysis has been very effective in the identification and characterization of RNA molecules, including the accurate prediction of their secondary structure. We are developing an integrative scalable data management and analysis system, the RNA Comparative Analysis Database (rCAD), implemented with SQL Server to support RNA comparative analysis. The platformagnostic database schema of rCAD captures the essential relationships between the different dimensions of information for RNA comparative analysis datasets. The rCAD implementation enables a variety of comparative analysis manipulations with multiple integrated data dimensions for advanced RNA comparative analysis workflows. In this paper, we describe details of the rCAD schema design and illustrate its usefulness with two usage scenarios.

Statistical potentials for hairpin and internal loops improve the accuracy of the predicted RNA structure

RNA is directly associated with a growing number of functions within the cell. The accurate prediction of different RNA higher-order structures from their nucleic acid sequences will provide insight into their functions and molecular mechanics. We have been determining statistical potentials for a collection of structural elements that is larger than the number of structural elements determined with experimentally determined energy values. The experimentally derived free energies and the statistical potentials for canonical base-pair stacks are analogous, demonstrating that statistical potentials derived from comparative data can be used as an alternative energetic parameter. A new computational infrastructure-RNA Comparative Analysis Database (rCAD)-that utilizes a relational database was developed to manipulate and analyze very large sequence alignments and secondary-structure data sets. Using rCAD, we determined a richer set of energetic parameters for RNA fundamental structural elements including hairpin and internal loops. A new version of RNAfold was developed to utilize these statistical potentials. Overall, these new statistical potentials for hairpin and internal loops integrated into the new version of RNAfold demonstrated significant improvements in the prediction accuracy of RNA secondary structure.

Fluorescence competition and optical melting measurements of RNA three-way multibranch loops provide a revised model for thermodynamic parameters

Three-way multibranch loops (junctions) are common in RNA secondary structures. Computer algorithms such as RNAstructure and MFOLD do not consider the identity of unpaired nucleotides in multibranch loops when predicting secondary structure. There is limited experimental data, however, to parametrize this aspect of these algorithms. In this study, UV optical melting and a fluorescence competition assay are used to measure stabilities of multibranch loops containing up to five unpaired adenosines or uridines or a loop E motif. These results provide a test of our understanding of the factors affecting multibranch loop stability and provide revised parameters for predicting stability. The results should help to improve predictions of RNA secondary structure.

Coarse-grained model for simulation of RNA three-dimensional structures

The accurate prediction of an RNA's three-dimensional structure from its "primary structure" will have a tremendous influence on the experimental design and its interpretation and ultimately our understanding of the many functions of RNA. This paper presents a general coarse-grained (CG) potential for modeling RNA 3-D structures. Each nucleotide is represented by five pseudo atoms, two for the backbone (one for the phosphate and another for the sugar) and three for the base to represent base-stacking interactions. The CG potential has been parametrized from statistical analysis of 688 RNA experimental structures. Molecular dynamic simulations of 15 RNA molecules with the length of 12-27 nucleotides have been performed using the CG potential, with performance comparable to that from all-atom simulations. For ~75% of systems tested, simulated annealing led to native-like structures at least once out of multiple repeated runs. Furthermore, with weak distance restraints based on the knowledge of three to five canonical Watson-Crick pairs, all 15 RNAs tested are successfully folded to within 6.5 Å of native structures using the CG potential and simulated annealing. The results reveal that with a limited secondary structure model the current CG potential can reliably predict the 3-D structures for small RNA molecules. We also explored an all-atom force field to construct atomic structures from the CG simulations.

Elucidating the higher-order structure of biopolymers by structural probing and mass spectrometry: MS3D

Chemical probing represents a very versatile alternative for studying the structure and dynamics of substrates that are intractable by established high-resolution techniques. The implementation of MS-based strategies for the characterization of probing products has not only extended the range of applicability to virtually all types of biopolymers but has also paved the way for the introduction of new reagents that would not have been viable with traditional analytical platforms. As the availability of probing data is steadily increasing on the wings of the development of dedicated interpretation aids, powerful computational approaches have been explored to enable the effective utilization of such information to generate valid molecular models. This combination of factors has contributed to making the possibility of obtaining actual 3D structures by MS-based technologies (MS3D) a reality. Although approaches for achieving structure determination of unknown targets or assessing the dynamics of known structures may share similar reagents and development trajectories, they clearly involve distinctive experimental strategies, analytical concerns and interpretation paradigms. This Perspective offers a commentary on methods aimed at obtaining distance constraints for the modeling of full-fledged structures while highlighting common elements, salient distinctions and complementary capabilities exhibited by methods used in dynamics studies. We discuss critical factors to be addressed for completing effective structural determinations and expose possible pitfalls of chemical methods. We survey programs developed for facilitating the interpretation of experimental data and discuss possible computational strategies for translating sparse spatial constraints into all-atom models. Examples are provided to illustrate how the concerted application of very diverse probing techniques can lead to the solution of actual biological systems.