Secondary Structure Of RNA Molecules Research Articles

RNA Folding, Mutation, and Detection.

The structure of RNA molecules is absolutely critical to their functions in a biological system. RNA structure is dynamic and changes in response to cellular needs. Within the last few decades, there has been an increased interest in studying the structure of RNA molecules and how they change to support the needs of the cell in different conditions. Selective 2'-hydroxyl acylation-based mutational profiling using high-throughput sequencing is a powerful method to predict the secondary structure of RNA molecules both in vivo and in immunopurified samples. Selective 2'-hydroxyl acylation-based mutational profiling using high-throughput sequencing works by adding bulky groups onto accessible "flexible" bases in an RNA molecule that are not involved in any base-pairing or RNA-protein interactions. When the RNA is reverse transcribed into cDNA, the bulky groups are incorporated as base mutations, which can be compared to an unmodified control to identify the locations of flexible bases. The comparison ofsequence data between modified and unmodified samples allows the computer software program (developed to generate reactivity profiles) to generate RNA secondary structure models. These models can be compared in a variety of conditions to determine how specific stimuli influence RNA secondary structures.

The Evaluation of SHAPE-MaP RNA Structure Probing Protocols Reveals a Novel Role of Mn2+ in the Detection of 2'-OH Adducts.

Chemical probing, for decades, has been one of the most popular tools for studying the secondary structure of RNA molecules. Recently, protocols for simultaneous analysis of multiple RNAs have been developed, enabling in vivo transcriptome-wide interrogation of the RNA structure dynamics. One of the most popular methods is the selective 2'-hydroxyl acylation analyzed by primer extension and mutational profiling (SHAPE-MaP). In this study, we describe the evaluation of this protocol by addressing the influence of the reverse transcription enzymes, buffer conditions, and chemical probes on the properties of the cDNA library and the quality of mutational profiling-derived structural signals. Our results reveal a SuperScript IV (SSIV) reverse transcriptase as a more efficient enzyme for mutational profiling of SHAPE adducts and shed new light on the role of Mn2+ cations in the modulation of SSIV readthrough efficiency.

Rtools: A Web Server for Various Secondary Structural Analyses on Single RNA Sequences.

Predicting the secondary structures of RNA molecules is an essential step to characterize their functions, but the thermodynamic probability of any prediction is generally small. On the other hand, there are a few tools for calculating and visualizing various secondary structural information from RNA sequences. We implemented a web server that calculates in parallel various features of secondary structures: different types of secondary structure predictions, the marginal probabilities for local structural contexts, accessibilities of the subsequences, the energy changes by arbitrary base mutations, and the measures for validations of the predicted secondary structures. The web server is available at http://rtools.cbrc.jp , which integrates software tools, CentroidFold, CentroidHomfold, IPknot, CapR, Raccess, Rchange, RintD, and RintW.

The in vivo RNA structurome of the malaria parasite Plasmodium falciparum, a protozoan with an A/U-rich transcriptome.

Plasmodium falciparum, a protozoan parasite and causative agent of human malaria, has one of the most A/T-biased genomes sequenced to date. This may give the genome and the transcriptome unusual structural features. Recent progress in sequencing techniques has made it possible to study the secondary structures of RNA molecules at the transcriptomic level. Thus, in this study we produced the in vivo RNA structurome of a protozoan parasite with a highly A/U-biased transcriptome. We showed that it is possible to probe the secondary structures of P. falciparum RNA molecules in vivo using two different chemical probes, and obtained structures for more than half of all transcripts in the transcriptome. These showed greater stability (lower free energy) than the same structures modelled in silico, and structural features appeared to influence translation efficiency and RNA decay. Finally, we compared the P. falciparum RNA structurome with the predicted RNA structurome of an A/U-balanced species, P. knowlesi, finding a bias towards lower overall transcript stability and more hairpins and multi-stem loops in P. falciparum. This unusual protozoan RNA structurome will provide a basis for similar studies in other protozoans and also in other unusual genomes.

Role of RNA Motifs in RNA Interaction with Membrane Lipid Rafts: Implications for Therapeutic Applications of Exosomal RNAs.

RNA motifs may promote interactions with exosomes (EXO-motifs) and lipid rafts (RAFT-motifs) that are enriched in exosomal membranes. These interactions can promote selective RNA loading into exosomes. We quantified the affinity between RNA aptamers containing various EXO- and RAFT-motifs and membrane lipid rafts in a liposome model of exosomes by determining the dissociation constants. Analysis of the secondary structure of RNA molecules provided data about the possible location of EXO- and RAFT-motifs within the RNA structure. The affinity of RNAs containing RAFT-motifs (UUGU, UCCC, CUCC, CCCU) and some EXO-motifs (CCCU, UCCU) to rafted liposomes is higher in comparison to aptamers without these motifs, suggesting direct RNA-exosome interaction. We have confirmed these results through the determination of the dissociation constant values of exosome-RNA aptamer complexes. RNAs containing EXO-motifs GGAG or UGAG have substantially lower affinity to lipid rafts, suggesting indirect RNA-exosome interaction via RNA binding proteins. Bioinformatics analysis revealed RNA aptamers containing both raft- and miRNA-binding motifs and involvement of raft-binding motifs UCCCU and CUCCC. A strategy is proposed for using functional RNA aptamers (fRNAa) containing both RAFT-motif and a therapeutic motif (e.g., miRNA inhibitor) to selectively introduce RNAs into exosomes for fRNAa delivery to target cells for personalized therapy.

Developing effective siRNAs to reduce the expression of key viral genes of COVID-19.

The COVID-19 pandemic has been raging worldwide for more than a year. Many efforts have been made to create vaccines and develop new antiviral drugs to cope with the disease. Here, we propose the application of short interfering RNAs (siRNAs) to degrade the viral genome, thus reducing viral infection. By introducing the concept of the probability of binding efficiency (PBE) and combining the secondary structures of RNA molecules, we designed 11 siRNAs that target the consensus regions of three key viral genes: the spike (S), nucleocapsid (N) and membrane (M) genes of SARS-CoV-2. The silencing efficiencies of the siRNAs were determined in human lung and endothelial cells overexpressing these viral genes. The results suggested that most of the siRNAs could significantly reduce the expression of the viral genes with inhibition rates above 50% in 24 hours. This work not only provides a strategy for designing potentially effective siRNAs against target genes but also validates several potent siRNAs that can be used in the clinical development of preventative medication for COVID-19 in the future.

Математический феномен генетической связи G-U

As you know, the secondary structure of RNA molecules, unlike DNA, in addition to canonical or Watson-Crick pairs, non-canonical G-U pairs, this is guanine - uracil. The last pairs were established experimentally by X-ray diffraction analysis and are still a mystery to researchers. The fact is that such pairs are 5-10 times energetically weaker than the canonical pairs A-U, C-G. The question arises: why are such pairs needed? The study was carried out in the framework of the strict mathematical analogy established earlier by the authors of some cases of genetic overlaps and stems of the secondary structure of messenger RNAs. In this work, it is shown that due to the non-canonical G-U pair, using the new ambiguities found, it is possible to substantially “regulate” the stem free energy (even for small stems) - the most important biochemical characteristic. It turned out that the discovered effect for G-U pairs significantly exceeds a similar effect for canonical pairs A-U, C-G.

CHSalign: A Web Server That Builds upon Junction-Explorer and RNAJAG for Pairwise Alignment of RNA Secondary Structures with Coaxial Helical Stacking.

RNA junctions are important structural elements of RNA molecules. They are formed when three or more helices come together in three-dimensional space. Recent studies have focused on the annotation and prediction of coaxial helical stacking (CHS) motifs within junctions. Here we exploit such predictions to develop an efficient alignment tool to handle RNA secondary structures with CHS motifs. Specifically, we build upon our Junction-Explorer software for predicting coaxial stacking and RNAJAG for modelling junction topologies as tree graphs to incorporate constrained tree matching and dynamic programming algorithms into a new method, called CHSalign, for aligning the secondary structures of RNA molecules containing CHS motifs. Thus, CHSalign is intended to be an efficient alignment tool for RNAs containing similar junctions. Experimental results based on thousands of alignments demonstrate that CHSalign can align two RNA secondary structures containing CHS motifs more accurately than other RNA secondary structure alignment tools. CHSalign yields a high score when aligning two RNA secondary structures with similar CHS motifs or helical arrangement patterns, and a low score otherwise. This new method has been implemented in a web server, and the program is also made freely available, at http://bioinformatics.njit.edu/CHSalign/.

Predicting the 3D Secondary Structure of RNA Molecules

From a number of crystallographic structures of high molecular weight RNA molecules, we derived a set of effective potentials that describe the interactions between nucleotide centroids. Using this information, we developed a new Force Field that was implemented in a Molecular Dynamics code whose objective is to describe the three-dimensional folding of RNA molecules. The acceptance criterion for the resulting structures from MD simulations is through the minimum potential energy. Our simulated structures were compared to experimental NMR results and we found a good structural similarity measured by the RMSD.

DPSO‐Vis: Topology‐based Visualization of Discrete Particle Swarm Optimization

AbstractParticle swarm optimization (PSO) is a metaheuristic that has been applied successfully to many continuous and combinatorial optimization problems, e.g., in the fields of economics, engineering, and natural sciences. In PSO, a swarm of particles moves within a search space in order to find an optimal solution. Unfortunately, it is hard to understand in detail why and how changes in the design of PSO algorithms affect the optimization behavior. Visualizing the particle states could provide substantially better insight into PSO algorithms. Though in case of combinatorial optimization problems, it often raises the problem of illustrating the states within the discrete search space that cannot be embedded spatially. We propose a visualization approach to depict the optimization problem topologically using a landscape metaphor. This visualization is augmented by an illustration of the time‐dependent states of the particles. Thus, the user of dPSO‐Vis is able to analyze the swarm's behavior within the search space. In principle, our method can be used for any optimization algorithm where a swarm of individuals searches within a discrete search space. Our approach is verified with a case study for the PSO algorithm HelixPSO that predicts the secondary structure of RNA molecules.

Evaluation of a sophisticated SCFG design for RNA secondary structure prediction

Predicting secondary structures of RNA molecules is one of the fundamental problems of and thus a challenging task in computational structural biology. Over the past decades, mainly two different approaches have been considered to compute predictions of RNA secondary structures from a single sequence: the first one relies on physics-based and the other on probabilistic RNA models. Particularly, the free energy minimization (MFE) approach is usually considered the most popular and successful method. Moreover, based on the paradigm-shifting work by McCaskill which proposes the computation of partition functions (PFs) and base pair probabilities based on thermodynamics, several extended partition function algorithms, statistical sampling methods and clustering techniques have been invented over the last years. However, the accuracy of the corresponding algorithms is limited by the quality of underlying physics-based models, which include a vast number of thermodynamic parameters and are still incomplete. The competing probabilistic approach is based on stochastic context-free grammars (SCFGs) or corresponding generalizations, like conditional log-linear models (CLLMs). These methods abstract from free energies and instead try to learn about the structural behavior of the molecules by learning (a manageable number of) probabilistic parameters from trusted RNA structure databases. In this work, we introduce and evaluate a sophisticated SCFG design that mirrors state-of-the-art physics-based RNA structure prediction procedures by distinguishing between all features of RNA that imply different energy rules. This SCFG actually serves as the foundation for a statistical sampling algorithm for RNA secondary structures of a single sequence that represents a probabilistic counterpart to the sampling extension of the PF approach. Furthermore, some new ways to derive meaningful structure predictions from generated sample sets are presented. They are used to compare the predictive accuracy of our model to that of other probabilistic and energy-based prediction methods. Particularly, comparisons to lightweight SCFGs and corresponding CLLMs for RNA structure prediction indicate that more complex SCFG designs might yield higher accuracy but eventually require more comprehensive and pure training sets. Investigations on both the accuracies of predicted foldings and the overall quality of generated sample sets (especially on an abstraction level, called abstract shapes of generated structures, that is relevant for biologists) yield the conclusion that the Boltzmann distribution of the PF sampling approach is more centered than the ensemble distribution induced by the sophisticated SCFG model, which implies a greater structural diversity within generated samples. In general, neither of the two distinct ensemble distributions is more adequate than the other and the corresponding results obtained by statistical sampling can be expected to bare fundamental differences, such that the method to be preferred for a particular input sequence strongly depends on the considered RNA type.

Structator: fast index-based search for RNA sequence-structure patterns

BackgroundThe secondary structure of RNA molecules is intimately related to their function and often more conserved than the sequence. Hence, the important task of searching databases for RNAs requires to match sequence-structure patterns. Unfortunately, current tools for this task have, in the best case, a running time that is only linear in the size of sequence databases. Furthermore, established index data structures for fast sequence matching, like suffix trees or arrays, cannot benefit from the complementarity constraints introduced by the secondary structure of RNAs.ResultsWe present a novel method and readily applicable software for time efficient matching of RNA sequence-structure patterns in sequence databases. Our approach is based on affix arrays, a recently introduced index data structure, preprocessed from the target database. Affix arrays support bidirectional pattern search, which is required for efficiently handling the structural constraints of the pattern. Structural patterns like stem-loops can be matched inside out, such that the loop region is matched first and then the pairing bases on the boundaries are matched consecutively. This allows to exploit base pairing information for search space reduction and leads to an expected running time that is sublinear in the size of the sequence database. The incorporation of a new chaining approach in the search of RNA sequence-structure patterns enables the description of molecules folding into complex secondary structures with multiple ordered patterns. The chaining approach removes spurious matches from the set of intermediate results, in particular of patterns with little specificity. In benchmark experiments on the Rfam database, our method runs up to two orders of magnitude faster than previous methods.ConclusionsThe presented method's sublinear expected running time makes it well suited for RNA sequence-structure pattern matching in large sequence databases. RNA molecules containing several stem-loop substructures can be described by multiple sequence-structure patterns and their matches are efficiently handled by a novel chaining method. Beyond our algorithmic contributions, we provide with Structator a complete and robust open-source software solution for index-based search of RNA sequence-structure patterns. The Structator software is available at http://www.zbh.uni-hamburg.de/Structator.

Distinguishable Populations Report on the Interactions of Single DNA Molecules with Solid-State Nanopores

Solid-state nanopores have received increasing interest over recent years because of their potential for genomic screening and sequencing. In particular, small nanopores (2–5 nm in diameter) allow the detection of local structure along biological molecules, such as proteins bound to DNA or possibly the secondary structure of RNA molecules. In a typical experiment, individual molecules are translocated through a single nanopore, thereby causing a small deviation in the ionic conductance. A correct interpretation of these conductance changes is essential for our understanding of the process of translocation, and for further sophistication of this technique. Here, we present translocation measurements of double-stranded DNA through nanopores down to the diameter of the DNA itself (1.8–7 nm at the narrowest constriction). In contrast to previous findings on such small nanopores, we find that single molecules interacting with these pores can cause three distinct levels of conductance blockades. We attribute the smallest conductance blockades to molecules that briefly skim the nanopore entrance without translocating, the intermediate level of conductance blockade to regular head-to-tail translocations, and the largest conductance blockades to obstruction of the nanopore entrance by one or multiple (duplex) DNA strands. Our measurements are an important step toward understanding the conductance blockade of biomolecules in such small nanopores, which will be essential for future applications involving solid-state nanopores.

Evaluation of the information content of RNA structure mapping data for secondary structure prediction.

Structure mapping experiments (using probes such as dimethyl sulfate [DMS], kethoxal, and T1 and V1 RNases) are used to determine the secondary structures of RNA molecules. The process is iterative, combining the results of several probes with constrained minimum free-energy calculations to produce a model of the structure. We aim to evaluate whether particular probes provide more structural information, and specifically, how noise in the data affects the predictions. Our approach involves generating "decoy" RNA structures (using the sFold Boltzmann sampling procedure) and evaluating whether we are able to identify the correct structure from this ensemble of structures. We show that with perfect information, we are always able to identify the optimal structure for five RNAs of known structure. We then collected orthogonal structure mapping data (DMS and RNase T1 digest) under several solution conditions using our high-throughput capillary automated footprinting analysis (CAFA) technique on two group I introns of known structure. Analysis of these data reveals the error rates in the data under optimal (low salt) and suboptimal solution conditions (high MgCl(2)). We show that despite these errors, our computational approach is less sensitive to experimental noise than traditional constraint-based structure prediction algorithms. Finally, we propose a novel approach for visualizing the interaction of chemical and enzymatic mapping data with RNA structure. We project the data onto the first two dimensions of a multidimensional scaling of the sFold-generated decoy structures. We are able to directly visualize the structural information content of structure mapping data and reconcile multiple data sets.

NOBAI: a web server for character coding of geometrical and statistical features in RNA structure

The Numeration of Objects in Biology: Alignment Inferences (NOBAI) web server provides a web interface to the applications in the NOBAI software package. This software codes topological and thermodynamic information related to the secondary structure of RNA molecules as multi-state phylogenetic characters, builds character matrices directly in NEXUS format and provides sequence randomization options. The web server is an effective tool that facilitates the search for evolutionary history embedded in the structure of functional RNA molecules. The NOBAI web server is accessible at ‘http://www.manet.uiuc.edu/nobai/nobai.php’. This web site is free and open to all users and there is no login requirement.

Detecting conserved secondary structures in RNA molecules using constrained structural alignment

Constrained sequence alignment has been studied extensively in the past. Different forms of constraints have been investigated, where a constraint can be a subsequence, a regular expression, or a probability matrix of symbols and positions. However, constrained structural alignment has been investigated to a much lesser extent. In this paper, we present an efficient method for constrained structural alignment and apply the method to detecting conserved secondary structures, or structural motifs, in a set of RNA molecules. The proposed method combines both sequence and structural information of RNAs to find an optimal local alignment between two RNA secondary structures, one of which is a query and the other is a subject structure in the given set. The method allows a biologist to annotate conserved regions, or constraints, in the query RNA structure and incorporates these regions into the alignment process to obtain biologically more meaningful alignment scores. A statistical measure is developed to assess the significance of the scores. Experimental results based on detecting internal ribosome entry sites in the RNA molecules of hepatitis C virus and Trypanosoma brucei demonstrate the effectiveness of the proposed method and its superiority over existing techniques.

The complete mitochondrial genome of the common sea slater, Ligia oceanica (Crustacea, Isopoda) bears a novel gene order and unusual control region features

BackgroundSequence data and other characters from mitochondrial genomes (gene translocations, secondary structure of RNA molecules) are useful in phylogenetic studies among metazoan animals from population to phylum level. Moreover, the comparison of complete mitochondrial sequences gives valuable information about the evolution of small genomes, e.g. about different mechanisms of gene translocation, gene duplication and gene loss, or concerning nucleotide frequency biases.The Peracarida (gammarids, isopods, etc.) comprise about 21,000 species of crustaceans, living in many environments from deep sea floor to arid terrestrial habitats. Ligia oceanica is a terrestrial isopod living at rocky seashores of the european North Sea and Atlantic coastlines.ResultsThe study reveals the first complete mitochondrial DNA sequence from a peracarid crustacean. The mitochondrial genome of Ligia oceanica is a circular double-stranded DNA molecule, with a size of 15,289 bp. It shows several changes in mitochondrial gene order compared to other crustacean species. An overview about mitochondrial gene order of all crustacean taxa yet sequenced is also presented. The largest non-coding part (the putative mitochondrial control region) of the mitochondrial genome of Ligia oceanica is unexpectedly not AT-rich compared to the remainder of the genome. It bears two repeat regions (4× 10 bp and 3× 64 bp), and a GC-rich hairpin-like secondary structure. Some of the transfer RNAs show secondary structures which derive from the usual cloverleaf pattern. While some tRNA genes are putative targets for RNA editing, trnR could not be localized at all.ConclusionGene order is not conserved among Peracarida, not even among isopods. The two isopod species Ligia oceanica and Idotea baltica show a similarly derived gene order, compared to the arthropod ground pattern and to the amphipod Parhyale hawaiiensis, suggesting that most of the translocation events were already present the last common ancestor of these isopods. Beyond that, the positions of three tRNA genes differ in the two isopod species. Strand bias in nucleotide frequency is reversed in both isopod species compared to other Malacostraca. This is probably due to a reversal of the replication origin, which is further supported by the fact that the hairpin structure typically found in the control region shows a reversed orientation in the isopod species, compared to other crustaceans.

Analysis of internal loops within the RNA secondary structure in almost quadratic time

Evaluating all possible internal loops is one of the key steps in predicting the optimal secondary structure of an RNA molecule. The best algorithm available runs in time O(L(3)), L is the length of the RNA. We propose a new algorithm for evaluating internal loops, its run-time is O(M(*)log(2)L), M < L(2) is a number of possible nucleotide pairings. We created a software tool Afold which predicts the optimal secondary structure of RNA molecules of lengths up to 28 000 nt, using a computer with 2 Gb RAM. We also propose algorithms constructing sets of conditionally optimal multi-branch loop free (MLF) structures, e.g. the set that for every possible pairing (x, y) contains an optimal MLF structure in which nucleotides x and y form a pair. All the algorithms have run-time O(M(*)log(2)L).

Single molecule studies of RNA secondary structure: AFM of TYMV viral RNA

Nowadays, the development of experimental procedures for the determination of the secondary structure of RNA molecules is taking advantage of the novel single-molecule probing and imaging techniques. We report a method for the mapping of the secondary structure of RNA molecules spread on a flat surface by means of the atomic force microscope. Globular domains comprising groups of RNA secondary and tertiary structure elements separated by unstructured domains can be discerned in the micrographs and their position along the molecule contour can be measured directly on unstained specimens. We have analyzed the morphology of a population of single molecules of 3' fragments of the Turnip Yellow Mosaic Virus RNA shorter than 1 kb in different temperature and electrolytic conditions. We found a satisfying agreement of the shape of the imaged structures with previously available evidence. The method we have developed can be used to map also different types of RNA molecules and has the advantage of showing the distribution of the single molecule conformations within the population.

Identifying good predictions of RNA secondary structure.

Predicting the secondary structure of RNA molecules from the knowledge of the primary structure (the sequence of bases) is still a challenging task. There are algorithms that provide good results e.g. based on the search for an energetic optimal configuration. However the output of such algorithms does not always give the real folding of the molecule and therefore a feature to judge the reliability of the prediction would be appreciated. In this paper we present results on the expected structural behavior of LSU rRNA derived using a stochastic context-free grammar and generating functions. We show how these results can be used to judge the predictions made for LSU rRNA by any algorithm. In this way it will be possible to identify those predictions which are close to the natural folding of the molecule with a probability of 97% of success.