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A large number of studies have been dedicated to identify the structural and sequence based features of RNA thermometers, mRNAs that regulate their translation initiation rate with temperature. It has been shown that the melting of the ribosome-binding site (RBS) plays a prominent role in this thermosensing process. However, little is known as to how widespread this melting phenomenon is as earlier studies on the subject have worked with a small sample of known RNA thermometers. We have developed a novel method of studying the melting of RNAs with temperature by computationally sampling the distribution of the RNA structures at various temperatures using the RNA folding software Vienna. In this study, we compared the thermosensing property of 100 randomly selected mRNAs and three well known thermometers--rpoH, ibpA and agsA sequences from E. coli. We also compared the rpoH sequences from 81 mesophilic proteobacteria. Although both rpoH and ibpA show a higher rate of melting at their RBS compared with the mean of non-thermometers, contrary to our expectations these higher rates are not significant. Surprisingly, we also do not find any significant differences between rpoH thermometers from other gamma-proteobacteria and E. coli non-thermometers.

The prediction of functional RNA structures has attracted increased interest, as it allows us to study the potential functional roles of many genes. RNA structure prediction methods, however, assume that there is a unique functional RNA structure and also do not predict functional features required for in vivo folding. In order to understand how functional RNA structures form in vivo, we require sophisticated experiments or reliable prediction methods. So far, there exist only a few, experimentally validated transient RNA structures. On the computational side, there exist several computer programs which aim to predict the co-transcriptional folding pathway in vivo, but these make a range of simplifying assumptions and do not capture all features known to influence RNA folding in vivo. We want to investigate if evolutionarily related RNA genes fold in a similar way in vivo. To this end, we have developed a new computational method, Transat, which detects conserved helices of high statistical significance. We introduce the method, present a comprehensive performance evaluation and show that Transat is able to predict the structural features of known reference structures including pseudo-knotted ones as well as those of known alternative structural configurations. Transat can also identify unstructured sub-sequences bound by other molecules and provides evidence for new helices which may define folding pathways, supporting the notion that homologous RNA sequence not only assume a similar reference RNA structure, but also fold similarly. Finally, we show that the structural features predicted by Transat differ from those assuming thermodynamic equilibrium. Unlike the existing methods for predicting folding pathways, our method works in a comparative way. This has the disadvantage of not being able to predict features as function of time, but has the considerable advantage of highlighting conserved features and of not requiring a detailed knowledge of the cellular environment., Author Summary Many non-coding genes exert their function via an RNA structure which starts emerging while the RNA sequence is being transcribed from the genome. The resulting folding pathway is known to depend on a variety of features such as the transcription speed, the concentration of various ions and the binding of proteins and other molecules. Not all of these influences can be adequately captured by the existing computational methods which try to replicate what happens in vivo. So far, it has been challenging to experimentally investigate co-transcriptional folding pathways in vivo and only little data from in vitro experiments exists. In order to investigate if functionally similar RNA sequences from different organisms fold in a similar way, we have developed a new computational method, called Transat, which does not require the detailed computational modeling of the cellular environment. We show in a comprehensive analysis that our method is capable of detecting known structural features and provide evidence that structural features of the in vivo folding pathways have been conserved for several biologically interesting classes of RNA sequences.

Prediction of efficient oligonucleotides for RNA interference presents a serious challenge, especially for the development of genome-wide RNAi libraries which encounter difficulties and limitations due to ambiguities in the results and the requirement for significant computational resources. Here we present a fast and practical algorithm for shRNA design based on the thermodynamic parameters. In order to identify shRNA and siRNA features universally associated with high silencing efficiency, we analyzed structure-activity relationships in thousands of individual RNAi experiments from publicly available databases (ftp://ftp.ncbi.nlm.nih.gov/pub/shabalin/​siRNA/si_shRNA_selector/ ). Using this statistical analysis, we found free energy ranges for the terminal duplex asymmetry and for fully paired duplex stability, such that shRNAs or siRNAs falling in both ranges have a high probability of being efficient. When combined, these two parameters yield a ~72% success rate on shRNAs from the siRecords database, with the target RNA levels reduced to below 20% of the control. Two other parameters correlate well with silencing efficiency: the stability of target RNA and the antisense strand secondary structure. Both parameters also correlate with the short RNA duplex stability; as a consequence, adding these parameters to our prediction scheme did not substantially improve classification accuracy. To test the validity of our predictions, we designed 83 shRNAs with optimal terminal asymmetry, and experimentally verified that small shifts in duplex stability strongly affected silencing efficiency. We showed that shRNAs with short fully paired stems could be successfully selected by optimizing only two parameters: terminal duplex asymmetry and duplex stability of the hypothetical cleavage product, which also relates to the specificity of mRNA target recognition. Our approach performs at the level of the best currently utilized algorithms that take into account prediction of the secondary structure of the target and antisense RNAs, but at significantly lower computational costs. Based on this study, we created the si-shRNA Selector program that predicts both highly efficient shRNAs and functional siRNAs (ftp://ftp.ncbi.nlm.nih.gov/pub/shabalin/​siRNA/si_shRNA_selector/ )., National Institutes of Health, National Library of Medicine

Since the outbreak of severe acute respiratory syndrome (SARS) in 2003, the three-dimensional structures of several of the replicase/transcriptase components of SARS coronavirus (SARS-CoV), the non-structural proteins (Nsps), have been determined. However, within the large Nsp3 (1922 amino-acid residues), the structure and function of the so-called SARS-unique domain (SUD) have remained elusive. SUD occurs only in SARS-CoV and the highly related viruses found in certain bats, but is absent from all other coronaviruses. Therefore, it has been speculated that it may be involved in the extreme pathogenicity of SARS-CoV, compared to other coronaviruses, most of which cause only mild infections in humans. In order to help elucidate the function of the SUD, we have determined crystal structures of fragment 389–652 (“SUDcore”) of Nsp3, which comprises 264 of the 338 residues of the domain. Both the monoclinic and triclinic crystal forms (2.2 and 2.8 Å resolution, respectively) revealed that SUDcore forms a homodimer. Each monomer consists of two subdomains, SUD-N and SUD-M, with a macrodomain fold similar to the SARS-CoV X-domain. However, in contrast to the latter, SUD fails to bind ADP-ribose, as determined by zone-interference gel electrophoresis. Instead, the entire SUDcore as well as its individual subdomains interact with oligonucleotides known to form G-quadruplexes. This includes oligodeoxy- as well as oligoribonucleotides. Mutations of selected lysine residues on the surface of the SUD-N subdomain lead to reduction of G-quadruplex binding, whereas mutations in the SUD-M subdomain abolish it. As there is no evidence for Nsp3 entering the nucleus of the host cell, the SARS-CoV genomic RNA or host-cell mRNA containing long G-stretches may be targets of SUD. The SARS-CoV genome is devoid of G-stretches longer than 5–6 nucleotides, but more extended G-stretches are found in the 3′-nontranslated regions of mRNAs coding for certain host-cell proteins involved in apoptosis or signal transduction, and have been shown to bind to SUD in vitro. Therefore, SUD may be involved in controlling the host cell's response to the viral infection. Possible interference with poly(ADP-ribose) polymerase-like domains is also discussed., Author Summary The genome of the SARS coronavirus codes for 16 non-structural proteins that are involved in replicating this huge RNA (approximately 29 kilobases). The roles of many of these in replication (and/or transcription) are unknown. We attempt to derive conclusions concerning the possible functions of these proteins from their three-dimensional structures, which we determine by X-ray crystallography. Non-structural protein 3 contains at least seven different functional modules within its 1922-amino-acid polypeptide chain. One of these is the so-called SARS-unique domain, a stretch of about 338 residues that is completely absent from any other coronavirus. It may thus be responsible for the extraordinarily high pathogenicity of the SARS coronavirus, compared to other viruses of this family. We describe here the three-dimensional structure of the SARS-unique domain and show that it consists of two modules with a known fold, the so-called macrodomain. Furthermore, we demonstrate that these domains bind unusual nucleic-acid structures formed by consecutive guanosine nucleotides, where four strands of nucleic acid are forming a superhelix (so-called G-quadruplexes). SUD may be involved in binding to viral or host-cell RNA bearing this peculiar structure and thereby regulate viral replication or fight the immune response of the infected host cell.

The increasing importance of non-coding RNA in biology and medicine has led to a growing interest in the problem of RNA 3-D structure prediction. As is the case for proteins, RNA 3-D structure prediction methods require two key ingredients: an accurate energy function and a conformational sampling procedure. Both are only partly solved problems. Here, we focus on the problem of conformational sampling. The current state of the art solution is based on fragment assembly methods, which construct plausible conformations by stringing together short fragments obtained from experimental structures. However, the discrete nature of the fragments necessitates the use of carefully tuned, unphysical energy functions, and their non-probabilistic nature impairs unbiased sampling. We offer a solution to the sampling problem that removes these important limitations: a probabilistic model of RNA structure that allows efficient sampling of RNA conformations in continuous space, and with associated probabilities. We show that the model captures several key features of RNA structure, such as its rotameric nature and the distribution of the helix lengths. Furthermore, the model readily generates native-like 3-D conformations for 9 out of 10 test structures, solely using coarse-grained base-pairing information. In conclusion, the method provides a theoretical and practical solution for a major bottleneck on the way to routine prediction and simulation of RNA structure and dynamics in atomic detail., Author Summary The importance of RNA in biology and medicine has increased immensely over the last several years, due to the discovery of a wide range of important biological processes that are under the guidance of non-coding RNA. As is the case with proteins, the function of an RNA molecule is encoded in its three-dimensional (3-D) structure, which in turn is determined by the molecule's sequence. Therefore, interest in the computational prediction of the 3-D structure of RNA from sequence is great. One of the main bottlenecks in routine prediction and simulation of RNA structure and dynamics is sampling, the efficient generation of RNA-like conformations, ideally in a mathematically and physically sound way. Current methods require the use of unphysical energy functions to amend the shortcomings of the sampling procedure. We have developed a mathematical model that describes RNA's conformational space in atomic detail, without the shortcomings of other sampling methods. As an illustration of its potential, we describe a simple yet efficient method to sample conformations that are compatible with a given secondary structure. An implementation of the sampling method, called BARNACLE, is freely available.

The diversity and importance of the role played by RNAs in the regulation and development of the cell are now well-known and well-documented. This broad range of functions is achieved through specific structures that have been (presumably) optimized through evolution. State-of-the-art methods, such as McCaskill's algorithm, use a statistical mechanics framework based on the computation of the partition function over the canonical ensemble of all possible secondary structures on a given sequence. Although secondary structure predictions from thermodynamics-based algorithms are not as accurate as methods employing comparative genomics, the former methods are the only available tools to investigate novel RNAs, such as the many RNAs of unknown function recently reported by the ENCODE consortium. In this paper, we generalize the McCaskill partition function algorithm to sum over the grand canonical ensemble of all secondary structures of all mutants of the given sequence. Specifically, our new program, RNAmutants, simultaneously computes for each integer k the minimum free energy structure MFE(k) and the partition function Z(k) over all secondary structures of all k-point mutants, even allowing the user to specify certain positions required not to mutate and certain positions required to base-pair or remain unpaired. This technically important extension allows us to study the resilience of an RNA molecule to pointwise mutations. By computing the mutation profile of a sequence, a novel graphical representation of the mutational tendency of nucleotide positions, we analyze the deleterious nature of mutating specific nucleotide positions or groups of positions. We have successfully applied RNAmutants to investigate deleterious mutations (mutations that radically modify the secondary structure) in the Hepatitis C virus cis-acting replication element and to evaluate the evolutionary pressure applied on different regions of the HIV trans-activation response element. In particular, we show qualitative agreement between published Hepatitis C and HIV experimental mutagenesis studies and our analysis of deleterious mutations using RNAmutants. Our work also predicts other deleterious mutations, which could be verified experimentally. Finally, we provide evidence that the 3′ UTR of the GB RNA virus C has been optimized to preserve evolutionarily conserved stem regions from a deleterious effect of pointwise mutations. We hope that there will be long-term potential applications of RNAmutants in de novo RNA design and drug design against RNA viruses. This work also suggests potential applications for large-scale exploration of the RNA sequence-structure network. Binary distributions are available at http://RNAmutants.csail.mit.edu/., Author Summary Evolution is a central concept in biology. This phenomenon can be observed at all levels of the organization of life—from single molecules to multicellular organisms. Here, we focus our attention on the implication of evolution at the level of nucleic acid sequences. In this context, RNA sequences presumably have been optimized by evolution to achieve specific functions. These functions are supported by a structure that can be determined using thermodynamics-based models and energy minimization techniques. In this work, we develop efficient algorithms for predicting energetically favorable mutations and study their impact on the stability of the structure. We use these techniques to reveal sequences under evolutionary pressure and design new methods to predict lethal mutations. Applications of our tool lead to a better understanding of the mutational process of some key regulatory elements of two important pathogenic RNA viruses—human immunodeficiency virus and hepatitis C virus.

Background Recent evidence suggests that the number and variety of functional RNAs (ncRNAs as well as cis-acting RNA elements within mRNAs ) is much higher than previously thought; thus, the ability to computationally predict and analyze RNAs has taken on new importance. We have computationally studied the secondary structures in an alignment of six Aspergillus genomes. Little is known about the RNAs present in this set of fungi, and this diverse set of genomes has an optimal level of sequence conservation for observing the correlated evolution of base-pairs seen in RNAs. Methodology/Principal Findings We report the results of a whole-genome search for evolutionarily conserved secondary structures, as well as the results of clustering these predicted secondary structures by structural similarity. We find a total of 7450 predicted secondary structures, including a new predicted ∼60 bp long hairpin motif found primarily inside introns. We find no evidence for microRNAs. Different types of genomic regions are over-represented in different classes of predicted secondary structures. Exons contain the longest motifs (primarily long, branched hairpins), 5′ UTRs primarily contain groupings of short hairpins located near the start codon, and 3′ UTRs contain very little secondary structure compared to other regions. There is a large concentration of short hairpins just inside the boundaries of exons. The density of predicted intronic RNAs increases with the length of introns, and the density of predicted secondary structures within mRNA coding regions increases with the number of introns in a gene. Conclusions/Sigificance There are many conserved, high-confidence RNAs of unknown function in these Aspergillus genomes, as well as interesting spatial distributions of predicted secondary structures. This study increases our knowledge of secondary structure in these aspergillus organisms.

Background Decoding of mRNAs is performed by aminoacyl tRNAs (aa-tRNAs). This process is highly accurate, however, at low frequencies (10−3 – 10−4) the wrong aa-tRNA can be selected, leading to incorporation of aberrant amino acids. Although our understanding of what constitutes the correct or cognate aa-tRNA:mRNA interaction is well defined, a functional distinction between near-cognate or single mismatched, and unpaired or non-cognate interactions is lacking. Methodology/Principal Findings Misreading of several synonymous codon substitutions at the catalytic site of firefly luciferase was assayed in Saccharomyces cerevisiae. Analysis of the results in the context of current kinetic and biophysical models of aa-tRNA selection suggests that the defining feature of near-cognate aa-tRNAs is their potential to form mini-helical structures with A-site codons, enabling stimulation of GTPase activity of eukaryotic Elongation Factor 1A (eEF1A). Paromomycin specifically stimulated misreading of near-cognate but not of non-cognate aa-tRNAs, providing a functional probe to distinguish between these two classes. Deletion of the accessory elongation factor eEF1Bγ promoted increased misreading of near-cognate, but hyperaccurate reading of non-cognate codons, suggesting that this factor also has a role in tRNA discrimination. A mutant of eEF1Bα, the nucleotide exchange factor for eEF1A, promoted a general increase in fidelity, suggesting that the decreased rates of elongation may provide more time for discrimination between aa-tRNAs. A mutant form of ribosomal protein L5 promoted hyperaccurate decoding of both types of codons, even though it is topologically distant from the decoding center. Conclusions/Signficance It is important to distinguish between near-cognate and non-cognate mRNA:tRNA interactions, because such a definition may be important for informing therapeutic strategies for suppressing these two different categories of mutations underlying many human diseases. This study suggests that the defining feature of near-cognate aa-tRNAs is their potential to form mini-helical structures with A-site codons in the ribosomal decoding center. An aminoglycoside and a ribosomal factor can be used to distinguish between near-cognate and non-cognate interactions.