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A-minor motifs are RNA tertiary structure motifs that generally involve a canonical base pair and an adenine base forming hydrogen bonds with the minor groove of the base pair. Such motifs are among the most common tertiary interactions in known RNA structures, comparable in number with the non-canonical base pairs. They are often found in functionally important regions of non-coding RNAs and, in particular, play a central role in protein synthesis. Here, we review local variations of the A-minor geometry and discuss difficulties associated with their annotation, as well as various structural contexts and common A-minor co-motifs, and diverse functions of A-minors in various processes in a living cell.

Higher-order structure governs function for many RNAs. However, discerning this structure for large RNA molecules in solution is an unresolved challenge. Here, we present SHAPE-JuMP (selective 2'-hydroxyl acylation analyzed by primer extension and juxtaposed merged pairs) to interrogate through-space RNA tertiary interactions. A bifunctional small molecule is used to chemically link proximal nucleotides in an RNA structure. The RNA cross-link site is then encoded into complementary DNA (cDNA) in a single, direct step using an engineered reverse transcriptase that "jumps" across cross-linked nucleotides. The resulting cDNAs contain a deletion relative to the native RNA sequence, which can be detected by sequencing, that indicates the sites of cross-linked nucleotides. SHAPE-JuMP measures RNA tertiary structure proximity concisely across large RNA molecules at nanometer resolution. SHAPE-JuMP is especially effective at measuring interactions in multihelix junctions and loop-to-helix packing, enables modeling of the global fold for RNAs up to several hundred nucleotides in length, facilitates ranking of structural models by consistency with through-space restraints, and is poised to enable solution-phase structural interrogation and modeling of complex RNAs.

Most of computational methods of building RNA tertiary structure are template-based. The template-based methods usually can give more accurate 3D structures due to the use of native 3D templates, but they cannot work if the 3D templates are not available. So, a more complete library of the native 3D templates is very important for this type of methods. 3dRNA is a template-based method for building RNA tertiary structure previously proposed by us. In this paper we report improved 3D template libraries of 3dRNA by using two different schemes that give two libraries 3dRNA_Lib1 and 3dRNA_Lib2. These libraries expand the original one by nearly ten times. Benchmark shows that they can significantly increase the accuracy of 3dRNA, especially in building complex and large RNA 3D structures.

Publishing, discussing, envisioning, modeling, designing and experimentally determining RNA three-dimensional (3D) structures involve preparation of two-dimensional (2D) drawings that depict critical functional features of the subject molecules, such as noncanonical base pairs and protein contacts. Here, we describe RiboDraw, new software for crafting these drawings. We illustrate the features of RiboDraw by applying it to several RNAs, including the Escherichia coli tRNA-Phe, the P4–P6 domain of Tetrahymena ribozyme, a −1 ribosomal frameshift stimulation element from beet western yellows virus and the 5′ untranslated region of SARS-CoV-2. We show secondary structure diagrams of the 23S and 16S subunits of the E. coli ribosome that reflect noncanonical base pairs, ribosomal proteins and structural motifs, and that convey the relative positions of these critical features in 3D space. This software is a MATLAB package freely available at https://github.com/DasLab/RiboDraw.

RNA nanotechnology seeks to create nanoscale machines by repurposing natural RNA modules. The field is slowed by the current need for human intuition during three-dimensional structural design. Here, we demonstrate that three distinct problems in RNA nanotechnology can be reduced to a pathfinding problem and automatically solved through an algorithm called RNAMake. First, RNAMake discovers highly stable single-chain solutions to the classic problem of aligning a tetraloop and its sequence-distal receptor, with experimental validation from chemical mapping, gel electrophoresis, solution X-ray scattering and crystallography with 2.55 A resolution. Second, RNAMake automatically generates structured tethers that integrate 16S and 23S ribosomal RNAs into single-chain ribosomal RNAs that remain uncleaved by ribonucleases and assemble onto messenger RNA. Third, RNAMake enables the automated stabilization of small-molecule binding RNAs, with designed tertiary contacts that improve the binding affinity of the ATP aptamer and improve the fluorescence and stability of the Spinach RNA in cell extracts and in living Escherichia coli cells. Automated 3D design produces rapid and near-atomically accurate predictions of RNA tertiary structure as well as the ability to generate complex RNA machines such as functional single-stranded tethered ribosomes, and enhancement of the binding properties of small-molecule RNA aptamers.

RNA structural complexity and flexibility present a challenge for computational modeling efforts. Experimental information and bioinformatics data can be used as restraints to improve the accuracy of RNA tertiary structure prediction. Regarding utilization of restraints, the fundamental questions are: (i) What is the limit in prediction accuracy that one can achieve with arbitrary number of restraints? (ii) Is there a strategy for selection of the minimal number of restraints that would result in the best structural model? We address the first question by testing the limits in prediction accuracy using native contacts as restraints. To address the second question, we develop an algorithm based on the distance variation allowed by secondary structure (DVASS), which ranks restraints according to their importance to RNA tertiary structure prediction. We find that due to kinetic traps, the greatest improvement in the structure prediction accuracy is achieved when we utilize only 40–60% of the total number of native contacts as restraints. When the restraints are sorted by DVASS algorithm, using only the first 20% ranked restraints can greatly improve the prediction accuracy. Our findings suggest that only a limited number of strategically selected distance restraints can significantly assist in RNA structure modeling.

Riboswitches are naturally occurring RNA aptamers that regulate gene expression by binding to specific small molecules. Riboswitches control the expression of essential bacterial genes and are important models for RNA-small molecule recognition. Here, we report the discovery of a class of synthetic small molecules that bind to PreQ1 riboswitch aptamers. These molecules bind specifically and reversibly to the aptamers with high affinity and induce a conformational change. Furthermore, the ligands modulate riboswitch activity through transcriptional termination despite no obvious chemical similarity to the cognate ligand. X-ray crystallographic studies reveal that the ligands share a binding site with the cognate ligand but make different contacts. Finally, alteration of the chemical structure of the ligand causes changes in the mode of RNA binding and affects regulatory function. Thus, target- and structure-based approaches can be used to identify and understand the mechanism of synthetic ligands that bind to and regulate complex, folded RNAs.

Because of the present pandemic situation that world is exploring cellular biology, the importance of RNA structure prediction has increased exponentially. RNA plays a vital role in most of the functionalities in cellular reaction. Significantly, it is important in metabolic and protein reaction. Scientifically, it has been proven that RNA structure prediction methods are useful in the treatment of many syndromes like Tumor, HIV, Covid, etc. As an outcome, lots of scholars are exploring this area to develop RNA structure prediction procedures. In this paper, working of the newly developed algorithm, “umFold,” is explained. To accelerate the execution time of the algorithm, parallel techniques are applied to the algorithm. This paper is divided into three main sections. Introduction part elaborates the basic terminologies in RNA and types of RNA structures available. In second section, a brief about existing algorithms with their basic working strategies is given in short. In last section, the detail working of “umFold,” a newly developed RNA tertiary structure prediction algorithm, is presented. After testing the experimental results, it was found that this newly developed algorithm is proficient of predicting tertiary structure for very lengthy sequences, where other existing methods face a problem of memory crash. The web server “www.umFold.in” is developed to compare the execution time with the existing webserver. It was observed that in many cases, algorithm works in better manner as compared to existing algorithm.

The preponderance of a specific d- or l-chirality in fats, sugars, amino acids, nucleic acids, and so on is ubiquitous in nature, yet the biological origin of such chiral dominance (i.e., with one enantiomer overwhelmingly present) remains an open question. One plausible proposal for the predominance of l-chirality in amino acids could be through evolutionary templating of chiral RNA-folding via chaperone activity. To help evaluate this possibility, single molecule fluorescence experiments have been performed that measure the chiral dependence of chaperone folding dynamics for the simple tetraloop-tetraloop receptor (TL-TLR) tertiary binding motif in the presence of a series of chiral amino acids. Specifically, d- vs l-arginine is found to accelerate the unfolding of this RNA motif in a chirally selective fashion, with temperature-dependent studies of the kinetics performed to extract free energy, enthalpy, and entropy landscapes for the underlying thermodynamics. Furthermore, all-atom molecular dynamics (MD) simulations are pursued to provide additional physical insight into this chiral sensitivity, which reveal enantiomer-specific sampling of nucleic acid surfaces by d- vs l-arginine and support a putative mechanism for chirally specific denaturation of RNA tertiary structure by arginine but not other amino acids.

Accurate RNA secondary structure information is the cornerstone of gene function research and RNA tertiary structure prediction. However, most traditional RNA secondary structure prediction algorithms are based on the dynamic programming (DP) algorithm, according to the minimum free energy theory, with both hard and soft constraints. The accuracy is particularly dependent on the accuracy of soft constraints (from experimental data like chemical and enzyme detection). With the elongation of the RNA sequence, the time complexity of DP-based algorithms will increase geometrically, as a result, they are not good at coping with relatively long sequences. Furthermore, due to the complexity of the pseudoknots structure, the secondary structure prediction method, based on traditional algorithms, has great defects which cannot predict the secondary structure with pseudoknots well. Therefore, few algorithms have been available for pseudoknots prediction in the past. The ATTfold algorithm proposed in this article is a deep learning algorithm based on an attention mechanism. It analyzes the global information of the RNA sequence via the characteristics of the attention mechanism, focuses on the correlation between paired bases, and solves the problem of long sequence prediction. Moreover, this algorithm also extracts the effective multi-dimensional features from a great number of RNA sequences and structure information, by combining the exclusive hard constraints of RNA secondary structure. Hence, it accurately determines the pairing position of each base, and obtains the real and effective RNA secondary structure, including pseudoknots. Finally, after training the ATTfold algorithm model through tens of thousands of RNA sequences and their real secondary structures, this algorithm was compared with four classic RNA secondary structure prediction algorithms. The results show that our algorithm significantly outperforms others and more accurately showed the secondary structure of RNA. As the data in RNA sequence databases increase, our deep learning-based algorithm will have superior performance. In the future, this kind of algorithm will be more indispensable.

RNA tertiary structure is crucial to its many non-coding molecular functions. RNA architecture is shaped by its secondary structure composed of stems, stacked canonical base pairs, enclosing loops. While stems are precisely captured by free-energy models, loops composed of non-canonical base pairs are not. Nor are distant interactions linking together those secondary structure elements (SSEs). Databases of conserved 3D geometries (a.k.a. modules) not captured by energetic models are leveraged for structure prediction and design, but the computational complexity has limited their study to local elements, loops. Representing the RNA structure as a graph has recently allowed to expend this work to pairs of SSEs, uncovering a hierarchical organization of these 3D modules, at great computational cost. Systematically capturing recurrent patterns on a large scale is a main challenge in the study of RNA structures. In this paper, we present an efficient algorithm to compute maximal isomorphisms in edge colored graphs. We extend this algorithm to a framework well suited to identify RNA modules, and fast enough to considerably generalize previous approaches. To exhibit the versatility of our framework, we first reproduce results identifying all common modules spanning more than 2 SSEs, in a few hours instead of weeks. The efficiency of our new algorithm is demonstrated by computing the maximal modules between any pair of entire RNA in the non-redundant corpus of known RNA 3D structures. We observe that the biggest modules our method uncovers compose large shared sub-structure spanning hundreds of nucleotides and base pairs between the ribosomes of Thermus thermophilus, Escherichia Coli, and Pseudomonas aeruginosa., Author summary Ribonucleic Acids (RNAs) are performing a broad range of essential molecular functions in cells, many of which rely on intricate folding properties of the molecule. Watson-Crick and Wobble base pairs form early, stack onto each other to create stems connected by loops, which are themselves stabilized by more sophisticated base interaction patterns. These networks are essential to shape RNA 3D structures but unfortunately still poorly understood. Here, we undertake the task to build a catalog of base interaction networks occurring in multiple structures. However, a pairwise comparison of all RNA structures is computationally heavy. Therefore, we devise an algorithm leveraging intrinsic properties of RNA base interaction networks that enables us to quickly mine full databases of 3D structures. Compared to previous methods, our techniques bring the total running time of the analysis from months to hours while performing more general searches. The data collected though this work will benefit molecular evolution studies and serve in structure prediction tools.

RNA binding proteins (RBPs) commonly feature multiple RNA binding domains (RBDs), which provides these proteins with a modular architecture. Accumulating evidence supports that RBP architectural modularity and adaptability defines the specificity of their interactions with RNA. However, how multiple RBDs recognize their cognate single-stranded RNA (ssRNA) sequences in concert remains poorly understood. Here, we use Upstream of N-Ras (Unr) as a model system to address this question. Although reported to contain five ssRNA binding cold-shock domains (CSDs), we now demonstrate that Unr includes an additional four CSDs that do not bind RNA (pseudo-RBDs), but are involved in mediating RNA tertiary structure specificity by reducing the conformational heterogeneity of Unr. Disrupting canonical/non-canonical CSD interactions impacted RNA binding, Unr-mediated translation regulation, and the Unr-dependent RNA interactome. Taken together, our studies revealed a new paradigm in protein-RNA recognition, where interactions between RBDs and pseudo- RBDs select RNA tertiary structures, influence RNP assembly and define target specificity.

Folding of an RNA from secondary to tertiary structure often depends on divalent ions for efficient electrostatic charge screening (nonspecific association) or binding (specific association). To measure how different divalent cations modify folding kinetics of the 60 nucleotide E. coli rRNA GTPase center, we combined stopped-flow fluorescence in the presence of Mg2+, Ca2+, or Sr2+ together with time-resolved small angle X-ray scattering (SAXS) in the presence of Mg2+ to observe the folding process. Immediately upon addition of each divalent ion, the RNA undergoes a transition from an extended state with secondary structure to a more compact structure. Subsequently, specific divalent ions modulate populations of intermediates in conformational ensembles along the folding pathway with transition times longer than 10 msec. Rate constants for the five folding transitions act on timescales from submillisecond to tens of seconds. The sensitivity of RNA tertiary structure to divalent cation identity affects all but the fastest events in RNA folding, and allowed us to identify those states that prefer Mg2+. The GTPase center RNA appears to have optimized its folding trajectory to specifically utilize this most abundant intracellular divalent ion.

Scientific breakthroughs in recent decades have uncovered the capability of RNA molecules to fulfill a wide array of structural, functional, and regulatory roles in living cells, leading to a concomitantly significant increase in both the number and diversity of experimentally determined RNA three-dimensional (3D) structures. Atomic coordinates from a representative training set of solved RNA structures, displaying low sequence and structure similarity, facilitate derivation of knowledge-based energy functions. Here we develop an all-atom four-body statistical potential and evaluate its capacity to distinguish native RNA 3D structures from nonnative folds based on calculated free energy scores. Atomic four-body nearest-neighbors are objectively identified by their occurrence as tetrahedral vertices in the Delaunay tessellations of RNA structures, and rates of atomic quadruplet interactions expected by chance are obtained from a multinomial reference distribution. Our four-body energy function, referred to as RAMP (ribonucleic acids multibody potential), is subsequently derived by applying the inverted Boltzmann principle to the frequency data, yielding an energy score for each type of atomic quadruplet interaction. Several well-known benchmark datasets reveal that RAMP is comparable with, and often outperforms, existing knowledge- and physics-based energy functions. To the best of our knowledge, this is the first study detailing an RNA tertiary structure-based multibody statistical potential and its comparative evaluation.

SUMMARY Large-scale, cooperative rearrangements underlie the functions of RNA in RNA-protein machines and gene regulation. To understand how such rearrangements are orchestrated, we used high-throughput chemical footprinting to dissect a seemingly concerted rearrangement in P5abc RNA, a paradigm of RNA folding studies. With mutations that systematically disrupt or restore putative structural elements, we found that this transition reflects local folding of structural modules, with modest and incremental cooperativity that results in concerted behavior. First, two distant secondary structure changes are coupled through a bridging three-way junction and Mg2+-dependent tertiary structure. Second, long-range contacts are formed between modules, resulting in additional cooperativity. Given the sparseness of RNA tertiary contacts after secondary structure formation, we expect that modular folding and incremental cooperativity are generally important for specifying functional structures while also providing productive kinetic paths to these structures. Additionally, we expect our approach to be useful for uncovering modularity in other complex RNAs., Graphical abstract In Brief: Gracia et al. dissect a complex RNA folding transition with changes in secondary and tertiary structure. They show that modular structures fold and interact with limited, incremental cooperativity. This cooperativity is sufficient to give a concerted folding transition and may balance competing biological demands on RNA stability and folding efficiency.

Three-dimensional RNA domain reconstruction is important for the assembly, disassembly and delivery functionalities of a packed proteinaceus capsid. However, to date, the self-association of RNA molecules is still an open problem. Recent chemical probing reports provide, with high reliability, the secondary structure of diverse RNA ensembles, such as those of viral genomes. Here, we present a method for reconstructing the complete 3D structure of RNA genomes, which combines a coarse-grained model with a subdomain composition scheme to obtain the entire genome inside proteinaceus capsids based on secondary structures from experimental techniques. Despite the amount of sampling involved in the folded and also unfolded RNA molecules, advanced microscope techniques can provide points of anchoring, which enhance our model to include interactions between capsid pentamers and RNA subdomains. To test our method, we tackle the satellite tobacco mosaic virus (STMV) genome, which has been widely studied by both experimental and computational communities. We provide not only a methodology to structurally analyze the tertiary conformations of the RNA genome inside capsids, but a flexible platform that allows the easy implementation of features/descriptors coming from both theoretical and experimental approaches.

Chemical mapping is a broadly utilized technique for probing the structure and function of RNAs. The volume of chemical mapping data continues to grow as more researchers routinely employ this information and as experimental methods increase in throughput and information content. To create a central location for these data, we established an RNA mapping database (RMDB) 5 years ago. The RMDB, which is available at http://rmdb.stanford.edu, now contains chemical mapping data for over 800 entries, involving 134 000 natural and engineered RNAs, in vitro and in cellulo. The entries include large data sets from multidimensional techniques that focus on RNA tertiary structure and co-transcriptional folding, resulting in over 15 million residues probed. The database interface has been redesigned and now offers interactive graphical browsing of structural, thermodynamic and kinetic data at single-nucleotide resolution. The front-end interface now uses the force-directed RNA applet for secondary structure visualization and other JavaScript-based views of bar graphs and annotations. A new interface also streamlines the process for depositing new chemical mapping data to the RMDB.

Direct coupling analysis of nucleotide coevolution provides a novel approach to identify which nucleotides in an RNA molecule are likely in direct contact, and this information obtained from sequence only can be used to predict RNA 3D structures with much improved accuracy. Here we present an efficient method that incorporates this information into current RNA 3D structure prediction methods, specifically 3dRNA. Our method makes much more accurate RNA 3D structure prediction than the original 3dRNA as well as other existing prediction methods that used the direct coupling analysis. In particular our method demonstrates a significant improvement in predicting multi-branch junction conformations, a major bottleneck for RNA 3D structure prediction. We also show that our method can be used to optimize the predictions by other methods. These results indicate that optimization of RNA 3D structure prediction using evolutionary restraints of nucleotide–nucleotide interactions from direct coupling analysis offers an efficient way for accurate RNA tertiary structure predictions.

There is an abundance of RNA sequence information available due to the efforts of sequencing projects. However, current techniques implemented to solve the tertiary structures of RNA, such as NMR and X-ray crystallography, are difficult and time-consuming. Therefore, biophysical techniques are not able to keep pace with the abundance of sequence information available. Because of this, there is a need to develop quick and efficient ways to predict RNA tertiary structure from sequence. One promising approach is to identify structural patterns within previously solved 3D structures and apply these patterns to new sequences. RNA tetraloops are one of the most common naturally occurring secondary structure motifs. Here, we use RNA Characterization of Secondary Structure Motifs (CoSSMos), Dissecting the Spatial Structure of RNA (DSSR), and a bioinformatic approach to search for and characterize tertiary structure patterns among tetraloops. Not surprising, we identified the well-known GNRA and UNCG tetraloops, as well as the previously identified RNYA tetraloop. However, some previously identified characteristics of these families were not observed in this data set, and some new characteristics were identified. In addition, we also identified and characterized three new tetraloop sequence families: YGAR, UGGU, and RMSA. This new structural information sheds light on the tertiary structure of tetraloops and contributes to the efforts of RNA tertiary structure prediction from sequence.

Internal ribosome entry sites (IRESes) were first reported in RNA viruses and subsequently identified in cellular mRNAs. In this study, IRES activity of the 5′-UTR in Wheat yellow mosaic virus (WYMV) RNA1 was identified, and the 3′-UTR synergistically enhanced this IRES activity via long-distance RNA–RNA interaction between C80U81and A7574G7575. Within the 5′-UTR, the hairpin 1(H1), flexible hairpin 2 (H2) and linker region (LR1) between H1 and H2 played an essential role in cap-independent translation, which is associated with the structural stability of H1, length of discontinuous stems and nucleotide specificity of the H2 upper loop and the long-distance RNA–RNA interaction sites in LR1. The H2 upper loop is a target region of the eIF4E. Cytosines (C55, C66, C105 and C108) in H1 and H2 and guanines (G73, G79 and G85) in LR1 form discontinuous and alternative base pairing to maintain the dynamic equilibrium state, which is used to elaborately regulate translation at a suitable level. The WYMV RNA1 5′-UTR contains a novel IRES, which is different from reported IRESes because of the dynamic equilibrium state. It is also suggested that robustness not at the maximum level of translation is the selection target during evolution of WYMV RNA1.

Structured RNAs and RNA complexes underlie biological processes ranging from control of gene expression to protein translation. Approximately 50% of nucleotides within known structured RNAs are folded into Watson-Crick (WC) base pairs, and sequence changes that preserve these pairs are typically assumed to preserve higher-order RNA structure and binding of macromolecule partners. Here, we report that indirect effects of the helix sequence on RNA tertiary stability are, in fact, significant but are nevertheless predictable from a simple computational model called RNAMake-∆∆G. When tested through the RNA on a massively parallel array (RNA-MaP) experimental platform, blind predictions for >1500 variants of the tectoRNA heterodimer model system achieve high accuracy (rmsd 0.34 and 0.77 kcal/mol for sequence and length changes, respectively). Detailed comparison of predictions to experiments support a microscopic picture of how helix sequence changes subtly modulate conformational fluctuations at each base-pair step, which accumulate to impact RNA tertiary structure stability. Our study reveals a previously overlooked phenomenon in RNA structure formation and provides a framework of computation and experiment for understanding helix conformational preferences and their impact across biological RNA and RNA-protein assemblies.

In recent years, great progress has been made in the research of RNA function, and more and more RNA functions have been discovered. The function of RNA is highly dependent on its 3D structure, the RNA tertiary structure includes the RNA 3D structure and RNA tertiary interaction, so the RNA tertiary structure prediction has also attracted extensive attention. There are many RNA tertiary structure prediction algorithms. According to the traditional classification methods, the existing RNA tertiary structure prediction algorithms can be divided into two categories: the RNA tertiary structure prediction algorithm based on knowledge mining and the RNA tertiary structure prediction algorithm based on physics. On this basis, this paper further refines the RNA tertiary structure prediction algorithm based on physics in traditional classification, and proposes a new refinement classification method based on conformational sampling method, namely RNA tertiary structure prediction algorithm based on physical fragment assembly conformational sampling method and RNA tertiary structure prediction algorithm based on Stepwise ansatz conformational sampling method. We make a comparative analysis of RNA tertiary structure prediction algorithms, and put forward some suggestions for improving the energy function in the next step, in order to find an RNA tertiary structure prediction algorithm that can achieve atomic accuracy.

RNA tertiary structure motifs are stabilized by a wide variety of hydrogen-bonding interactions. Protonated A and C nucleotides are normally not considered to be suitable building blocks for such motifs since their pKa values are far from physiological pH. Here, we report the NMR solution structure of an in vitro selected GTP-binding RNA aptamer bound to GTP with an intricate tertiary structure. It contains a novel kind of base quartet stabilized by a protonated A residue. Owing to its unique structural environment in the base quartet, the pKa value for the protonation of this A residue in the complex is shifted by more than 5 pH units compared to the pKa for A nucleotides in single-stranded RNA. This is the largest pKa shift for an A residue in structured nucleic acids reported so far, and similar in size to the largest pKa shifts observed for amino acid side chains in proteins. Both RNA pre-folding and ligand binding contribute to the pKa shift.

RNAs adopt specific, stable tertiary architectures to perform their activities. Knowledge of RNA tertiary structure is fundamental to understand RNA functions beginning with transcription and ending with turnover. Contrary to advanced RNA secondary structure prediction algorithms, which allow good accuracy when experimental data are integrated into the prediction, tertiary structure prediction of large RNAs still remains a significant challenge. However, the field of RNA tertiary structure prediction is rapidly developing and new computational methods based on different strategies are emerging. RNAComposer is a user-friendly and freely available server for 3D structure prediction of RNA up to 500 nucleotide residues. RNAComposer employs fully automated fragment assembly based on RNA secondary structure specified by the user. Importantly, this method allows incorporation of distance restraints derived from the experimental data to strengthen the 3D predictions. The potential and limitations of RNAComposer are discussed and an application to RNA design for nanotechnology is presented.

Recently, multiple researches of solvent accessibility on protein are well established. However, research on solvent accessibility of RNA faces a few challenges such as instability and diversity of RNA tertiary structure. Nowadays, no study has examined the predicting performance from different datasets built from multi-models and multi-attributes, but it is an important part of measuring the overall performance of modeling. Therefore, we performed a comprehensive comparison for predicting RNA solvent accessibility based on two datasets. 15,923 (12,229 ​+ ​3694) samples and 512(336 ​+ ​176) attributes were generated for attribute selection, finally 336 models were built for predicting. 12 modeling methods and 2 attribute selection methods were used for modeling and evaluating. This work provided a strategy for getting stable expectation when predicting RNA solvent accessibility. These results would be useful in further experimental or computational design for RNA solvent accessibility predicting and we hope this work could help the related researches which need a workflow for stable prediction.

Summary RNA-binding proteins (RBPs) commonly feature multiple RNA-binding domains (RBDs), which provide these proteins with a modular architecture. Accumulating evidence supports that RBP architectural modularity and adaptability define the specificity of their interactions with RNA. However, how multiple RBDs recognize their cognate single-stranded RNA (ssRNA) sequences in concert remains poorly understood. Here, we use Upstream of N-Ras (Unr) as a model system to address this question. Although reported to contain five ssRNA-binding cold-shock domains (CSDs), we demonstrate that Unr includes an additional four CSDs that do not bind RNA (pseudo-RBDs) but are involved in mediating RNA tertiary structure specificity by reducing the conformational heterogeneity of Unr. Disrupting the interactions between canonical and non-canonical CSDs impacts RNA binding, Unr-mediated translation regulation, and the Unr-dependent RNA interactome. Taken together, our studies reveal a new paradigm in protein-RNA recognition, where interactions between RBDs and pseudo-RBDs select RNA tertiary structures, influence RNP assembly, and define target specificity., Graphical Abstract, Highlights • Discovery of non-canonical cold-shock domains • Non-canonical cold-shock domains do not bind RNA independently • Interdomain contacts mediate RNA structure specificity and impact translation • Determination of an Unr-dependent ribonucleoprotein (RNP) interactome, Hollmann et al. show how non-RNA-binding domains within Drosophila Unr, an RNA-binding protein, contribute to its RNA target specificity. The selectivity is mediated by interdomain contacts to the RNA-binding cold-shock domains, which restrict the protein shape.

Many pathogenic viruses use programmed −1 ribosomal frameshifting to regulate translation of their structural and enzymatic proteins from polycistronic mRNAs. Frameshifting is commonly stimulated by a pseudoknot located downstream from a slippery sequence, the latter positioned at the ribosomal A and P sites. We report here the structures of two crystal forms of the frameshifting RNA pseudoknot from beet western yellow virus at resolutions of 1.25 and 2.85 Å. Because of the very high resolution of 1.25 Å, ten mono- and divalent metal ions per asymmetric unit could be identified, giving insight into potential roles of metal ions in stabilizing the pseudoknot. A magnesium ion located at the junction of the two pseudoknot stems appears to play a crucial role in stabilizing the structure. Because the two crystal forms exhibit mostly unrelated packing interactions and local crystallographic disorder in the high-resolution form was resolvable, the two structures offer the most detailed view yet of the conformational preference and flexibility of an RNA pseudoknot.

RNA virus genomes are efficient and compact carriers of biological information, encoding information required for replication both in their primary sequences and in higher-order RNA structures. However, the ubiquity of RNA elements with higher-order folds—in which helices pack together to form complex 3D structures—and the extent to which these elements affect viral fitness are largely unknown. Here we used single-molecule correlated chemical probing to define secondary and tertiary structures across the RNA genome of dengue virus serotype 2 (DENV2). Higher-order RNA structures are pervasive and involve more than one-third of nucleotides in the DENV2 genomic RNA. These 3D structures promote a compact overall architecture and contribute to viral fitness. Disrupting RNA regions with higher-order structures leads to stable, nonreverting mutants and could guide the development of vaccines based on attenuated RNA viruses. The existence of extensive regions of functional RNA elements with tertiary folds in viral RNAs, and likely many other messenger and noncoding RNAs, means that there are significant regions with pocket-containing surfaces that may serve as novel RNA-directed drug targets.

High-throughput sequencing methods have revolutionized our ability to catalog the diversity of RNAs and RNA-protein interactions that can exist in our cells. However, the relationship between RNA sequence, structure, and function is enormously complex, demonstrating the need for methods that can provide quantitative thermodynamic and kinetic measurements of macromolecular interaction with RNA, at a scale commensurate with the sequence diversity of RNA. Here, we discuss a class of methods that extend the core functionality of DNA sequencers to enable high-throughput measurements of RNA folding and RNA-protein interactions. Topics discussed include a description of the method and multiple applications to RNA-binding proteins, riboswitch design and engineering, and RNA tertiary structure energetics.

Over 50% of residues within functional structured RNAs are base-paired in Watson-Crick helices, but it is not fully understood how these helices’ geometric preferences and flexibility might influence RNA tertiary structure. Here, we show experimentally and computationally that the ensemble fluctuations of RNA helices substantially impact RNA tertiary structure stability. We updated a model for the conformational ensemble of the RNA helix using crystallographic structures of Watson-Crick base pair steps. To test this model, we made blind predictions of the thermodynamic stability of >1500 tertiary assemblies with differing helical sequences and compared calculations to independent measurements from a high-throughput experimental platform. The blind predictions accounted for thermodynamic effects from changing helix sequence and length with unexpectedly tight accuracies (RMSD of 0.34 and 0.77 kcal/mol, respectively). These comparisons lead to a detailed picture of how RNA base pair steps fluctuate within complex assemblies and suggest a new route toward predicting RNA tertiary structure formation and energetics.

Osmolytes and macromolecular crowders have the potential to influence the stability of secondary structure motifs and alter preferences for conserved nucleic acid sequences in vivo. To further understand the cellular function of RNA we observed the effects of a model osmolyte, polyethylene glycol (PEG) 200, and a model macromolecular crowding agent, PEG 8000, on the GAAA tetraloop motif. GAAA tetraloops are conserved, stable tetraloops, and are critical participants in RNA tertiary structure. They also have a thermodynamic preference for a CG closing base pair. The thermal denaturation of model hairpins containing GAAA loops was monitored using UV-Vis spectroscopy in the presence and absence of PEG 200 or PEG 8000. Both of the cosolutes tested influenced the thermodynamic preference for a CG base pair by destabilizing the loop with a CG closing base pair relative to the loop with a GC closing base pair. This result also extended to a related DNA triloop, which provides further evidence that the interactions between the loop and closing base pair are identical for the d(GCA) triloop and the GAAA tetraloop. Our results suggest that in the presence of model PEG molecules, loops with a GC closing base pair may retain some preferential interactions with the cosolutes that are lost in the presence of the CG closing base pair. These results reveal that relatively small structural changes could influence how neutral cosolutes tune the stability and function of secondary structure motifs in vivo.

RNA molecules are essential players in many fundamental biological processes. Prokaryotes and eukaryotes have distinct RNA classes with specific structural features and functional roles. Computational prediction of protein structures is a research field in which high confidence three-dimensional protein models can be proposed based on the sequence alignment between target and templates. However, to date, only a few approaches have been developed for the computational prediction of RNA structures. Similar to proteins, RNA structures may be altered due to the interaction with various ligands, including proteins, other RNAs, and metabolites. A riboswitch is a molecular mechanism, found in the three kingdoms of life, in which the RNA structure is modified by the binding of a metabolite. It can regulate multiple gene expression mechanisms, such as transcription, translation initiation, and mRNA splicing and processing. Due to their nature, these entities also act on the regulation of gene expression and detection of small metabolites and have the potential to helping in the discovery of new classes of antimicrobial agents. In this review, we describe software and web servers currently available for riboswitch aptamer identification and secondary and tertiary structure prediction, including applications.

Pseudoknots are a fundamental RNA tertiary structure with important roles in regulation of mRNA translation. Molecular force spectroscopic approaches such as optical tweezers can track the pseudoknot’s unfolding intermediate states by pulling the RNA chain from both ends, but the kinetic unfolding pathway induced by this method may be different from that in vivo, which occurs during translation and proceeds from the 5′ to 3′ end. Here we developed a ribosome-mimicking, nanopore pulling assay for dissecting the vectorial unfolding mechanism of pseudoknots. The pseudoknot unfolding pathway in the nanopore, either from the 5′ to 3′ end or in the reverse direction, can be controlled by a DNA leader that is attached to the pseudoknot at the 5′ or 3′ ends. The different nanopore conductance between DNA and RNA translocation serves as a marker for the position and structure of the unfolding RNA in the pore. With this design, we provided evidence that the pseudoknot unfolding is a two-step, multistate, metal ion-regulated process depending on the pulling direction. Most notably, unfolding in both directions is rate-limited by the unzipping of the first helix domain (first step), which is Helix-1 in the 5′ → 3′ direction and Helix-2 in the 3′ → 5′ direction, suggesting that the initial unfolding step in either pulling direction needs to overcome an energy barrier contributed by the noncanonical triplex base-pairs and coaxial stacking interactions for the tertiary structure stabilization. These findings provide new insights into RNA vectorial unfolding mechanisms, which play an important role in biological functions including frameshifting.

The functions of RNA molecules are defined by their spatial structure, whose folding is regulated by numerous factors making RNA very similar to proteins. Prediction of RNA folding nuclei gives the possibility to take a fresh look at the problems of the multiple folding pathways of RNA molecules and RNA stability. The algorithm previously developed for prediction of protein folding nuclei has been successfully applied to ~150 various RNA structures: hairpins, tRNAs, structures with pseudoknots, and the large structured P4-P6 domain of the Tetrahymena group I intron RNA. The calculated Φ-values for tRNA structures agree with the experimental data obtained earlier. According to the experiment the nucleotides of the D and T hairpin loops are the last to be involved in the tRNA tertiary structure. Such agreement allowed us to do a prediction for an example of large structured RNA, the P4-P6 RNA domain. One of the advantages of our method is that it allows us to make predictions about the folding nucleus for nontrivial RNA motifs: pseudoknots and tRNA.

As one of its goals, synthetic biology seeks to increase the number of building blocks in nucleic acids. While efforts towards this goal are well advanced for DNA, they have hardly begun for RNA. Herein, we present a crystal structure for an RNA riboswitch where a stem C:G pair has been replaced by a pair between two components of an artificially expanded genetic-information system (AEGIS), Z and P, (6-amino-5-nitro-2(1H)-pyridone and 2-amino-imidazo[1,2-a]-1,3,5-triazin-4-(8H)-one). The structure shows that the Z:P pair does not greatly change the conformation of the RNA molecule nor the details of its interaction with a hypoxanthine ligand. This was confirmed in solution by in-line probing, which also measured a 3.7 nM affinity of the riboswitch for guanine. These data show that the Z:P pair mimics the natural Watson–Crick geometry in RNA in the first example of a crystal structure of an RNA molecule that contains an orthogonal added nucleobase pair.

Recent advances in RNA research and the steady growth of available RNA structures call for bioinformatics methods for handling and analyzing RNA structural data. Recently, we introduced SETTER--a fast and accurate method for RNA pairwise structure alignment. In this paper, we describe MultiSETTER, SETTER extension for multiple RNA structure alignment. MultiSETTER combines SETTER's decomposition of RNA structures into non-overlapping structural subunits with the multiple sequence alignment algorithm ClustalW adapted for the structure alignment. The accuracy of MultiSETTER was assessed by the automatic classification of RNA structures and its comparison to SCOR annotations. In addition, MultiSETTER classification was also compared to multiple sequence alignment-based and secondary structure alignment-based classifications provided by LocARNA and RNADistance tools, respectively. MultiSETTER precompiled Windows libraries, as well as the C++ source code, are freely available from http://siret.cz/multisetter.

A 3D model of RNA structure can provide information about its function and regulation that is not possible with just the sequence or secondary structure. Current models suffer from low accuracy and long running times and either neglect or presume knowledge of the long-range interactions which stabilize the tertiary structure. Our coarse-grained, helix-based, tertiary structure model operates with only a few degrees of freedom compared with all-atom models while preserving the ability to sample tertiary structures given a secondary structure. It strikes a balance between the precision of an all-atom tertiary structure model and the simplicity and effectiveness of a secondary structure representation. It provides a simplified tool for exploring global arrangements of helices and loops within RNA structures. We provide an example of a novel energy function relying only on the positions of stems and loops. We show that coupling our model to this energy function produces predictions as good as or better than the current state of the art tools. We propose that given the wide range of conformational space that needs to be explored, a coarse-grain approach can explore more conformations in less iterations than an all-atom model coupled to a fine-grain energy function. Finally, we emphasize the overarching theme of providing an ensemble of predicted structures, something which our tool excels at, rather than providing a handful of the lowest energy structures.

Motivation: Given the importance of non-coding RNAs to cellular regulatory functions, it would be highly desirable to have accurate computational prediction of RNA 3D structure, a task which remains challenging. Even for a short RNA sequence, the space of tertiary conformations is immense; existing methods to identify native-like conformations mostly resort to random sampling of conformations to achieve computational feasibility. However, native conformations may not be examined and prediction accuracy may be compromised due to sampling. State-of-the-art methods have yet to deliver satisfactory predictions for RNAs of length beyond 50 nucleotides. Results: This paper presents a method to tackle a key step in the RNA 3D structure prediction problem, the prediction of the nucleotide interactions that constitute the desired 3D structure. The research is based on a novel graph model, called a backbone k-tree, to tightly constrain the nucleotide interaction relationships considered for RNA 3D structures. It is shown that the new model makes it possible to efficiently predict the optimal set of nucleotide interactions (including the non-canonical interactions in all recently revealed families) from the query sequence along with known or predicted canonical basepairs. The preliminary results indicate that in most cases the new method can predict with a high accuracy the nucleotide interactions that constitute the 3D structure of the query sequence. It thus provides a useful tool for the accurate prediction of RNA 3D structure. Availability and Implementation: The source package for BkTree is available at http://rna-informatics.uga.edu/index.php?f=software&p=BkTree. Contact: lding@uga.edu or cai@cs.uga.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Binase, the ribonuclease secreted by Bacillus pumilus, is an endonuclease that cleaves the phosphodiester bond between the 3′-guanyl residue and 5′-OH residue of an adjacent nucleotide, with the formation of a corresponding intermediate, 2′,3′-cGMP on the first stage of a catalytic reaction. Binase possesses selective antitumor effect and induces apoptosis of lung carcinoma A549 cells. It was shown that the 2′,3′-cGMP messenger could exist in the reaction mixture over an hour. Furthermore, the addition of divalent non-transition metal Mg2+ increases the level of 2′,3′-cGMP formed by binase and that could be associated with the stabilization of RNA tertiary structure by this metal. It has been shown that exogenous 2′,3′-cGMP does not induce apoptosis of A549 cells, which are sensitive to binase. However, taking into account the cell-penetrating ability of binase, it can be concluded that 2′,3′-cGMP contributes to apoptogenic binase action only when it is formed intracellularly.

Motivation The de novo prediction of RNA tertiary structure remains a grand challenge. Predicted RNA solvent accessibility provides an opportunity to address this challenge. To the best of our knowledge, there is only one method (RNAsnap) available for RNA solvent accessibility prediction. However, its performance is unsatisfactory for protein-free RNAs. Results We developed RNAsol, a new algorithm to predict RNA solvent accessibility. RNAsol was built based on improved sequence profiles from the covariance models and trained with the long short-term memory (LSTM) neural networks. Independent tests on the same datasets from RNAsnap show that RNAsol achieves the mean Pearson’s correlation coefficient (PCC) of 0.43/0.26 for the protein-bound/protein-free RNA molecules, which is 26.5%/136.4% higher than that of RNAsnap. When the training set is enlarged to include both types of RNAs, the PCCs increase to 0.49 and 0.46 for protein-bound and protein-free RNAs, respectively. The success of RNAsol is attributed to two aspects, including the improved sequence profiles constructed by the sequence-profile alignment and the enhanced training by the LSTM neural networks. Availability and implementation http://yanglab.nankai.edu.cn/RNAsol/ Supplementary information Supplementary data are available at Bioinformatics online.

We present improved methodology for prediction of medium and large sized RNA three dimensional structures, based on comparative modeling approach. Method is enriched by the prediction of secondary structure, which is then used in tertiary structure prediction.

Decades of study of the architecture and function of structured RNAs have led to the perspective that RNA tertiary structure is modular, made of locally stable domains that retain their structure across RNAs. We formalize a hypothesis inspired by this modularity—that RNA folding thermodynamics and kinetics can be quantitatively predicted from separable energetic contributions of the individual components of a complex RNA. This reconstitution hypothesis considers RNA tertiary folding in terms of ΔGalign, the probability of aligning tertiary contact partners, and ΔGtert, the favorable energetic contribution from the formation of tertiary contacts in an aligned state. This hypothesis predicts that changes in the alignment of tertiary contacts from different connecting helices and junctions (ΔGHJH) or from changes in the electrostatic environment (ΔG+/−) will not affect the energetic perturbation from a mutation in a tertiary contact (ΔΔGtert). Consistent with these predictions, single-molecule FRET measurements of folding of model RNAs revealed constant ΔΔGtert values for mutations in a tertiary contact embedded in different structural contexts and under different electrostatic conditions. The kinetic effects of these mutations provide further support for modular behavior of RNA elements and suggest that tertiary mutations may be used to identify rate-limiting steps and dissect folding and assembly pathways for complex RNAs. Overall, our model and results are foundational for a predictive understanding of RNA folding that will allow manipulation of RNA folding thermodynamics and kinetics. Conversely, the approaches herein can identify cases where an independent, additive model cannot be applied and so require additional investigation.

The hairpin ribozyme consists of two RNA internal loops that interact to form the catalytically active structure. This docking transition is a rare example of intermolecular formation of RNA tertiary structure without coupling to helix annealing. We have used temperature-dependent surface plasmon resonance (SPR) to characterize the thermodynamics and kinetics of RNA tertiary structure formation for the junctionless form of the ribozyme, in which loops A and B reside on separate molecules. We find docking to be strongly enthalpy-driven and to be accompanied by substantial activation barriers for association and dissociation, consistent with the structural reorganization of both internal loops upon complex formation. Comparisons with the parallel analysis of a ribozyme variant carrying a 2'-O-methyl modification at the self-cleavage site and with published data in other systems reveal a surprising diversity of thermodynamic signatures, emphasizing the delicate balance of contributions to the free energy of formation of RNA tertiary structure.

Circular dichroism (CD) was used to assess the stabilization/destabilization imposed by oxidative lesion 7,8-dihydro-8-hydroxyadenosine (8-oxoA) on strands of RNA with different structural motifs. RNA:RNA homoduplex destabilization was observed in a position dependent manner using 10-mers as models that displayed differences between 12.7 and 15.1°C. We found that increasing the number of modifications resulted in depressed Tm values of about 12–15°C per lesion. The same effect was observed on RNA:DNA heteroduplex samples. We also tested the effects of this lesion in short hairpins containing the tetraloop UUCX (X = A, 8-oxoA). We found that the stem was hypersensitive to substitution of A by 8-oxoA and that it destabilized the structure by >23°C. Concomitant substitution at the stem and loop prevented formation of this secondary structure or lead to other less-stable hairpins. Incorporation of this lesion at the first base of the loop had no effect on either structure. Overall, we found that the effects of 8-oxoA on RNA structure are position dependent and that its stabilization may vary from sharp decreases to small increments, in some cases, leading to the formation of other more/less stable structures. These structural changes may have larger biological implications, particularly if the oxidatively modified RNA persists, thus leading to changes in RNA reactivity and function. © 2014 Wiley Periodicals, Inc. Biopolymers 103: 167–174, 2015.

RNA secondary structure diagrams familiar to molecular biologists summarize at a glance the folding of RNA chains to form Watson–Crick paired double helices. However, they can be misleading: First of all, they imply that the nucleotides in loops and linker segments, which can amount to 35% to 50% of a structured RNA, do not significantly interact with other nucleotides. Secondly, they give the impression that RNA molecules are loosely organized in three-dimensional (3D) space. In fact, structured RNAs are compactly folded as a result of numerous long-range, sequence-specific interactions, many of which involve loop or linker nucleotides. Here, we provide an introduction for students and researchers of RNA on the types, prevalence, and sequence variations of inter-nucleotide interactions that structure and stabilize RNA 3D motifs and architectures, using Escherichia coli (E. coli) 16S ribosomal RNA as a concrete example. The picture that emerges is that almost all nucleotides in structured RNA molecules, including those in nominally single-stranded loop or linker regions, form specific interactions that stabilize functional structures or mediate interactions with other molecules. The small number of noninteracting, ‘looped-out’ nucleotides make it possible for the RNA chain to form sharp turns. Base-pairing is the most specific interaction in RNA as it involves edge-to-edge hydrogen bonding (H-bonding) of the bases. Non-Watson–Crick base pairs are a significant fraction (30% or more) of base pairs in structured RNAs.

While hydroxyl radical cleavage is widely used to map RNA tertiary structure, lack of mechanistic understanding of strand break formation limits the degree of structural insight that can be obtained from this experiment. Here, we determine how individual ribose hydrogens of sarcin/ricin loop RNA participate in strand cleavage. We find that substituting deuterium for hydrogen at a ribose 5'-carbon produces a kinetic isotope effect on cleavage; the major cleavage product is an RNA strand terminated by a 5'-aldehyde. We conclude that hydroxyl radical abstracts a 5'-hydrogen atom, leading to RNA strand cleavage. We used this approach to obtain structural information for a GUA base triple, a common tertiary structural feature of RNA. Cleavage at U exhibits a large 5' deuterium kinetic isotope effect, a potential signature of a base triple. Others had noted a ribose-phosphate hydrogen bond involving the G 2'-OH and the U phosphate of the GUA triple, and suggested that this hydrogen bond contributes to backbone rigidity. Substituting deoxyguanosine for G, to eliminate this hydrogen bond, results in a substantial decrease in cleavage at G and U of the triple. We conclude that this hydrogen bond is a linchpin of backbone structure around the triple.

Background: RNA plays key role in many aspects of biological processes and its tertiary structure is critical for its biological function. RNA secondary structure represents various significant portions of RNA tertiary structure. Since the biological function of RNA is concluded indirectly from its primary structure, it would be important to analyze the relations between the RNA sequences and their structures. One important tool to perform this kind of analysis is the neutral network which is a collection of RNA sequences, all coding the same secondary structure, where each RNA sequence is distinguished from the others by no more than a single base mutation. Another high level and useful representation of an RNA secondary structure is the RNA shape, where it is holding the vicinity and nesting of structural components and reducing their lengths to one unit. This allows us to analyze the huge structural space corresponding to the larger RNA sequences. Objectives: In this study, a new concept, entitled Variation Network, over the set of all RNA shapes is introduced. Based on this concept, the potential relations between random and natural RNA sequences, as well as their corresponding structures are analyzed. Materials and Methods: To explore the relations between random and natural RNA sequences and their corresponding structures, different properties including frequency, normalized frequency, shape energy average, variation rate, normalized variation rate, neighborhood energy average, and stability were obtained and analyzed. Results: The correlations among these properties of random and natural Variation Networks are presented. Base on the obtained correlations, all the employed datasets are highly correlated to each other from the frequency point of view, whereas they are not well correlated from the thermodynamic energy point of view. Conclusions: Since the thermodynamic energy value of an RNA sequence over its secondary structure plays a key role in its function, this research conclude that the natural RNA sequences are not generated randomly.

The diverse biological functions of RNA are determined by the complex structures of RNA stabilized by both secondary and tertiary interactions. An RNA triplex is an important tertiary structure motif that is found in many pseudoknots and other structured RNAs. A triplex structure usually forms through tertiary interactions in the major or minor groove of a Watson-Crick base-paired stem. A major-groove RNA triplex structure is stable in isolation by forming consecutive major-groove base triples such as U·A-U and C(+) ·G-C. Minor-groove RNA triplexes, e.g., A-minor motif triplexes, are found in almost all large structured RNAs. As double-stranded RNA stem regions are often involved in biologically important tertiary triplex structure formation and protein binding, the ability to sequence specifically target any desired RNA duplexes by triplex formation would have great potential for biomedical applications. Programmable chemically modified triplex-forming oligonucleotides (TFOs) and triplex-forming peptide nucleic acids (PNAs) have been developed to form TFO·RNA2 and PNA·RNA2 triplexes, respectively, with enhanced binding affinity and sequence specificity at physiological conditions. Here, we (1) provide an overview of naturally occurring RNA triplexes, (2) summarize the experimental methods for studying triplexes, and (3) review the development of TFOs and triplex-forming PNAs for targeting an HIV-1 ribosomal frameshift-inducing RNA, a bacterial ribosomal A-site RNA, and a human microRNA hairpin precursor, and for inhibiting the RNA-protein interactions involving human RNA-dependent protein kinase and HIV-1 viral protein Rev.

Motivation: RNAs play fundamental roles in cellular processes. The function of an RNA is highly dependent on its 3D conformation, which is referred to as the RNA tertiary structure. Because the prediction or experimental determination of these structures is difficult, so many works focus on the problems associated with the RNA secondary structure. Here, we consider the RNA inverse folding problem, in which an RNA secondary structure is given as a target structure and the goal is to design an RNA sequence that folds into the target structure. In this article, we introduce a new evolutionary algorithm for the RNA inverse folding problem. Our algorithm, entitled Evolutionary RNA Design, generates a sequence whose minimum free energy structure is the same as the target structure. Results: We compare our algorithm with INFO-RNA, MODENA, RNAiFold and NUPACK approaches for some biological test sets. The results presented in this article indicate that for longer structures, our algorithm performs better than the other mentioned algorithms in terms of the energy range, accuracy, speedup and nucleotide distribution. Particularly, the generated RNA sequences in our method are much more reliable and similar to the natural RNA sequences. Availability and implementation: The web server and source code are available at http://mostafa.ut.ac.ir/corna/erd. Contact: mgtabesh@ut.ac.ir