The tmRDB and SRPDB resources

Detection of a 7SL RNA-derived small non-coding RNA using Molecular Beacons in vitro and in cells

Small non-coding RNAs (sncRNA) are involved in many steps of the gene expression cascade and regulate processing and expression of mRNAs by the formation of ribonucleoprotein complexes (RNP) such as the RNA-induced silencing complex (RISC). By analyzing small RNA Seq data sets, we identified a sncRNA annotated as piR-hsa-1254, which is likely derived from the 3'-end of 7SL RNA2 (RN7SL2), herein referred to as snc7SL RNA. The 7SL RNA is an abundant long non-coding RNA polymerase III transcript and serves as structural component of the cytoplasmic signal recognition particle (SRP). To evaluate a potential functional role of snc7SL RNA, we aimed to define its cellular localization by live cell imaging. Therefore, a Molecular Beacon (MB)-based method was established to compare the subcellular localization of snc7SL RNA with its precursor 7SL RNA. We designed and characterized several MBs in vitro and tested those by live cell fluorescence microscopy. Using a multiplex approach, we show that 7SL RNA localizes mainly to the endoplasmic reticulum (ER), as expected for the SRP, whereas snc7SL RNA predominately localizes to the nucleus. This finding suggests a fundamentally different function of 7SL RNA and its derivate snc7SL RNA.

Recent trends in RNA informatics: a review of machine learning and deep learning for RNA secondary structure prediction and RNA drug discovery

Computational analysis of RNA sequences constitutes a crucial step in the field of RNA biology. As in other domains of the life sciences, the incorporation of artificial intelligence and machine learning techniques into RNA sequence analysis has gained significant traction in recent years. Historically, thermodynamics-based methods were widely employed for the prediction of RNA secondary structures; however, machine learning-based approaches have demonstrated remarkable advancements in recent years, enabling more accurate predictions. Consequently, the precision of sequence analysis pertaining to RNA secondary structures, such as RNA-protein interactions, has also been enhanced, making a substantial contribution to the field of RNA biology. Additionally, artificial intelligence and machine learning are also introducing technical innovations in the analysis of RNA-small molecule interactions for RNA-targeted drug discovery and in the design of RNA aptamers, where RNA serves as its own ligand. This review will highlight recent trends in the prediction of RNA secondary structure, RNA aptamers and RNA drug discovery using machine learning, deep learning and related technologies, and will also discuss potential future avenues in the field of RNA informatics.

KnotAli: informed energy minimization through the use of evolutionary information

BACKGROUND

Improving the prediction of structures, especially those containing pseudoknots (structures with crossing base pairs) is an ongoing challenge. Homology-based methods utilize structural similarities within a family to predict the structure. However, their prediction is limited to the consensus structure, and by the quality of the alignment. Minimum free energy (MFE) based methods, on the other hand, do not rely on familial information and can predict structures of novel RNA molecules. Their prediction normally suffers from inaccuracies due to their underlying energy parameters.

RESULTS

We present a new method for prediction of RNA pseudoknotted secondary structures that combines the strengths of MFE prediction and alignment-based methods. KnotAli takes a multiple RNA sequence alignment as input and uses covariation and thermodynamic energy minimization to predict possibly pseudoknotted secondary structures for each individual sequence in the alignment. We compared KnotAli's performance to that of three other alignment-based programs, two that can handle pseudoknotted structures and one control, on a large data set of 3034 RNA sequences with varying lengths and levels of sequence conservation from 10 families with pseudoknotted and pseudoknot-free reference structures. We produced sequence alignments for each family using two well-known sequence aligners (MUSCLE and MAFFT).

CONCLUSIONS

We found KnotAli's performance to be superior in 6 of the 10 families for MUSCLE and 7 of the 10 for MAFFT. While both KnotAli and Cacofold use background noise correction strategies, we found KnotAli's predictions to be less dependent on the alignment quality. KnotAli can be found online at the Zenodo image: https://doi.org/10.5281/zenodo.5794719.

UFold: fast and accurate RNA secondary structure prediction with deep learning

For many RNA molecules, the secondary structure is essential for the correct function of the RNA. Predicting RNA secondary structure from nucleotide sequences is a long-standing problem in genomics, but the prediction performance has reached a plateau over time. Traditional RNA secondary structure prediction algorithms are primarily based on thermodynamic models through free energy minimization, which imposes strong prior assumptions and is slow to run. Here, we propose a deep learning-based method, called UFold, for RNA secondary structure prediction, trained directly on annotated data and base-pairing rules. UFold proposes a novel image-like representation of RNA sequences, which can be efficiently processed by Fully Convolutional Networks (FCNs). We benchmark the performance of UFold on both within- and cross-family RNA datasets. It significantly outperforms previous methods on within-family datasets, while achieving a similar performance as the traditional methods when trained and tested on distinct RNA families. UFold is also able to predict pseudoknots accurately. Its prediction is fast with an inference time of about 160 ms per sequence up to 1500 bp in length. An online web server running UFold is available at https://ufold.ics.uci.edu. Code is available at https://github.com/uci-cbcl/UFold.

SRPassing Co-translational Targeting: The Role of the Signal Recognition Particle in Protein Targeting and mRNA Protection

Signal recognition particle (SRP) is an RNA and protein complex that exists in all domains of life. It consists of one protein and one noncoding RNA in some bacteria. It is more complex in eukaryotes and consists of six proteins and one noncoding RNA in mammals. In the eukaryotic cytoplasm, SRP co-translationally targets proteins to the endoplasmic reticulum and prevents misfolding and aggregation of the secretory proteins in the cytoplasm. It was demonstrated recently that SRP also possesses an earlier unknown function, the protection of mRNAs of secretory proteins from degradation. In this review, we analyze the progress in studies of SRPs from different organisms, SRP biogenesis, its structure, and function in protein targeting and mRNA protection.

Structural analysis of the SRP Alu domain from Plasmodium falciparum reveals a non-canonical open conformation

The eukaryotic signal recognition particle (SRP) contains an Alu domain, which docks into the factor binding site of translating ribosomes and confers translation retardation. The canonical Alu domain consists of the SRP9/14 protein heterodimer and a tRNA-like folded Alu RNA that adopts a strictly 'closed' conformation involving a loop-loop pseudoknot. Here, we study the structure of the Alu domain from Plasmodium falciparum (PfAlu), a divergent apicomplexan protozoan that causes human malaria. Using NMR, SAXS and cryo-EM analyses, we show that, in contrast to its prokaryotic and eukaryotic counterparts, the PfAlu domain adopts an 'open' Y-shaped conformation. We show that cytoplasmic P. falciparum ribosomes are non-discriminative and recognize both the open PfAlu and closed human Alu domains with nanomolar affinity. In contrast, human ribosomes do not provide high affinity binding sites for either of the Alu domains. Our analyses extend the structural database of Alu domains to the protozoan species and reveal species-specific differences in the recognition of SRP Alu domains by ribosomes.

BSGatlas: a unified Bacillus subtilis genome and transcriptome annotation atlas with enhanced information access

A large part of our current understanding of gene regulation in Gram-positive bacteria is based on Bacillus subtilis, as it is one of the most well studied bacterial model systems. The rapid growth in data concerning its molecular and genomic biology is distributed across multiple annotation resources. Consequently, the interpretation of data from further B. subtilis experiments becomes increasingly challenging in both low- and large-scale analyses. Additionally, B. subtilis annotation of structured RNA and non-coding RNA (ncRNA), as well as the operon structure, is still lagging behind the annotation of the coding sequences. To address these challenges, we created the B. subtilis genome atlas, BSGatlas, which integrates and unifies multiple existing annotation resources. Compared to any of the individual resources, the BSGatlas contains twice as many ncRNAs, while improving the positional annotation for 70 % of the ncRNAs. Furthermore, we combined known transcription start and termination sites with lists of known co-transcribed gene sets to create a comprehensive transcript map. The combination with transcription start/termination site annotations resulted in 717 new sets of co-transcribed genes and 5335 untranslated regions (UTRs). In comparison to existing resources, the number of 5' and 3' UTRs increased nearly fivefold, and the number of internal UTRs doubled. The transcript map is organized in 2266 operons, which provides transcriptional annotation for 92 % of all genes in the genome compared to the at most 82 % by previous resources. We predicted an off-target-aware genome-wide library of CRISPR-Cas9 guide RNAs, which we also linked to polycistronic operons. We provide the BSGatlas in multiple forms: as a website (https://rth.dk/resources/bsgatlas/), an annotation hub for display in the UCSC genome browser, supplementary tables and standardized GFF3 format, which can be used in large scale -omics studies. By complementing existing resources, the BSGatlas supports analyses of the B. subtilis genome and its molecular biology with respect to not only non-coding genes but also genome-wide transcriptional relationships of all genes.

Signal Recognition Particle RNA Contributes to Oxidative Stress Response in Deinococcus radiodurans by Modulating Catalase Localization

The proper functioning of many proteins requires their transport to the correct cellular compartment or their secretion. Signal recognition particle (SRP) is a major protein transport pathway responsible for the co-translational movement of integral membrane proteins as well as periplasmic proteins. Deinococcus radiodurans is a ubiquitous bacterium that expresses a complex phenotype of extreme oxidative stress resistance, which depends on proteins involved in DNA repair, metabolism, gene regulation, and antioxidant defense. These proteins are located extracellularly or subcellularly, but the molecular mechanism of protein localization in D. radiodurans to manage oxidative stress response remains unexplored. In this study, we characterized the SRP complex in D. radiodurans R1 and showed that the knockdown (KD) of the SRP RNA (Qpr6) reduced bacterial survival under hydrogen peroxide and growth under chronic ionizing radiation. Through LC-mass spectrometry (MS/MS) analysis, we detected 162 proteins in the periplasm of wild-type D. radiodurans, of which the transport of 65 of these proteins to the periplasm was significantly reduced in the Qpr6 KD strain. Through Western blotting, we further demonstrated the localization of the catalases in D. radiodurans, DR_1998 (KatE1) and DR_A0259 (KatE2), in both the cytoplasm and periplasm, respectively, and showed that the accumulation of KatE1 and KatE2 in the periplasm was reduced in the SRP-defective strains. Collectively, this study establishes the importance of the SRP pathway in the survival and the transport of antioxidant proteins in D. radiodurans under oxidative stress.

Base-pair ambiguity and the kinetics of RNA folding

Background

A pairings of nucleotide sequences. Given this forbidding free-energy landscape, mechanisms have evolved that contribute to a directed and efficient folding process, including catalytic proteins and error-detecting chaperones. Among structural RNA molecules we make a distinction between “bound” molecules, which are active as part of ribonucleoprotein (RNP) complexes, and “unbound,” with physiological functions performed without necessarily being bound in RNP complexes. We hypothesized that unbound molecules, lacking the partnering structure of a protein, would be more vulnerable than bound molecules to kinetic traps that compete with native stem structures. We defined an “ambiguity index”—a normalized function of the primary and secondary structure of an individual molecule that measures the number of kinetic traps available to nucleotide sequences that are paired in the native structure, presuming that unbound molecules would have lower indexes. The ambiguity index depends on the purported secondary structure, and was computed under both the comparative (“gold standard”) and an equilibrium-based prediction which approximates the minimum free energy (MFE) structure. Arguing that kinetically accessible metastable structures might be more biologically relevant than thermodynamic equilibrium structures, we also hypothesized that MFE-derived ambiguities would be less effective in separating bound and unbound molecules.

Results

We have introduced an intuitive and easily computed function of primary and secondary structures that measures the availability of complementary sequences that could disrupt the formation of native stems on a given molecule—an ambiguity index. Using comparative secondary structures, the ambiguity index is systematically smaller among unbound than bound molecules, as expected. Furthermore, the effect is lost when the presumably more accurate comparative structure is replaced instead by the MFE structure.

Conclusions

A statistical analysis of the relationship between the primary and secondary structures of non-coding RNA molecules suggests that stem-disrupting kinetic traps are substantially less prevalent in molecules not participating in RNP complexes. In that this distinction is apparent under the comparative but not the MFE secondary structure, the results highlight a possible deficiency in structure predictions when based upon assumptions of thermodynamic equilibrium.

Structure, dynamics and interactions of large SRP variants

Co-translational protein targeting to membranes relies on the signal recognition particle (SRP) system consisting of a cytosolic ribonucleoprotein complex and its membrane-associated receptor. SRP recognizes N-terminal cleavable signals or signal anchor sequences, retards translation, and delivers ribosome-nascent chain complexes (RNCs) to vacant translocation channels in the target membrane. While our mechanistic understanding is well advanced for the small bacterial systems it lags behind for the large bacterial, archaeal and eukaryotic SRP variants including an Alu and an S domain. Here we describe recent advances on structural and functional insights in domain architecture, particle dynamics and interplay with RNCs and translocon and GTP-dependent regulation of co-translational protein targeting stimulated by SRP RNA.

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

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

Deep genome annotation of the opportunistic human pathogen Streptococcus pneumoniae D39

A precise understanding of the genomic organization into transcriptional units and their regulation is essential for our comprehension of opportunistic human pathogens and how they cause disease. Using single-molecule real-time (PacBio) sequencing we unambiguously determined the genome sequence of Streptococcus pneumoniae strain D39 and revealed several inversions previously undetected by short-read sequencing. Significantly, a chromosomal inversion results in antigenic variation of PhtD, an important surface-exposed virulence factor. We generated a new genome annotation using automated tools, followed by manual curation, reflecting the current knowledge in the field. By combining sequence-driven terminator prediction, deep paired-end transcriptome sequencing and enrichment of primary transcripts by Cappable-Seq, we mapped 1015 transcriptional start sites and 748 termination sites. We show that the pneumococcal transcriptional landscape is complex and includes many secondary, antisense and internal promoters. Using this new genomic map, we identified several new small RNAs (sRNAs), RNA switches (including sixteen previously misidentified as sRNAs), and antisense RNAs. In total, we annotated 89 new protein-encoding genes, 34 sRNAs and 165 pseudogenes, bringing the S. pneumoniae D39 repertoire to 2146 genetic elements. We report operon structures and observed that 9% of operons are leaderless. The genome data are accessible in an online resource called PneumoBrowse (https://veeninglab.com/pneumobrowse) providing one of the most complete inventories of a bacterial genome to date. PneumoBrowse will accelerate pneumococcal research and the development of new prevention and treatment strategies.

How to benchmark RNA secondary structure prediction accuracy

RNA secondary structure prediction is widely used. As new methods are developed, these are often benchmarked for accuracy against existing methods. This review discusses good practices for performing these benchmarks, including the choice of benchmarking structures, metrics to quantify accuracy, the importance of allowing flexibility for pairs in the accepted structure, and the importance of statistical testing for significance.

The cellular landscape of mid-size noncoding RNA

Noncoding RNA plays an important role in all aspects of the cellular life cycle, from the very basic process of protein synthesis to specialized roles in cell development and differentiation. However, many noncoding RNAs remain uncharacterized and the function of most of them remains unknown. Mid-size noncoding RNAs (mncRNAs), which range in length from 50 to 400 nucleotides, have diverse regulatory functions but share many fundamental characteristics. Most mncRNAs are produced from independent promoters although others are produced from the introns of other genes. Many are found in multiple copies in genomes. mncRNAs are highly structured and carry many posttranscriptional modifications. Both of these facets dictate their RNA-binding protein partners and ultimately their function. mncRNAs have already been implicated in translation, catalysis, as guides for RNA modification, as spliceosome components and regulatory RNA. However, recent studies are adding new mncRNA functions including regulation of gene expression and alternative splicing. In this review, we describe the different classes, characteristics and emerging functions of mncRNAs and their relative expression patterns. Finally, we provide a portrait of the challenges facing their detection and annotation in databases. This article is categorized under: Regulatory RNAs/RNAi/Riboswitches > Regulatory RNAs RNA Structure and Dynamics > RNA Structure, Dynamics, and Chemistry RNA Structure and Dynamics > Influence of RNA Structure in Biological Systems RNA Evolution and Genomics > RNA and Ribonucleoprotein Evolution.

Noncoding RNAs Databases: Current Status and Trends

One of the most important resources for researchers of noncoding RNAs is the information available in public databases spread over the internet. However, the effective exploration of this data can represent a daunting task, given the large amount of databases available and the variety of stored data. This chapter describes a classification of databases based on information source, type of RNA, source organisms, data formats, and the mechanisms for information retrieval, detailing the relevance of each of these classifications and its usability by researchers. This classification is used to update a 2012 review, indexing now more than 229 public databases. This review will include an assessment of the new trends for ncRNA research based on the information that is being offered by the databases. Additionally, we will expand the previous analysis focusing on the usability and application of these databases in pathogen and disease research. Finally, this chapter will analyze how currently available database schemas can help the development of new and improved web resources.

Exploring the sound-modulated delay in tomato ripening through expression analysis of coding and non-coding RNAs

Background and Aims

Sound is omnipresent in nature. Recent evidence supports the notion that naturally occurring and artificially generated sound waves induce inter- and intracellular changes in plants. These changes, in turn, lead to diverse physiological changes, such as enhanced biotic and abiotic stress responses, in both crops and model plants.

Methods

We previously observed delayed ripening in tomato fruits exposed to 1 kHz sound vibrations for 6 h. Here, we evaluated the molecular mechanism underlying this delaying fruit ripening by performing RNA-sequencing analysis of tomato fruits at 6 h, 2 d, 5 d and 7 d after 1 kHz sound vibration treatment.

Key Results

Bioinformatic analysis of differentially expressed genes and non-coding small RNAs revealed that some of these genes are involved in plant hormone and cell wall modification processes. Ethylene and cytokinin biosynthesis and signalling-related genes were downregulated by sound vibration treatment, whereas genes involved in flavonoid, phenylpropanoid and glucan biosynthesis were upregulated. Furthermore, we identified two sound-specific microRNAs and validated the expression of the pre-microRNAs and the mRNAs of their target genes.

Conclusions

Our results indicate that sound vibration helps to delay fruit ripening through the sophisticated regulation of coding and non-coding RNAs and transcription factor genes.

Human apo-SRP72 and SRP68/72 complex structures reveal the molecular basis of protein translocation

The co-translational targeting or insertion of secretory and membrane proteins into the endoplasmic reticulum (ER) is a key biological process mediated by the signal recognition particle (SRP). In eukaryotes, the SRP68–SRP72 (SRP68/72) heterodimer plays an essential role in protein translocation. However, structural information on the two largest SRP proteins, SRP68 and SRP72, is limited, especially regarding their interaction. Herein, we report the first crystal structures of human apo-SRP72 and the SRP68/72 complex at 2.91Å and 1.7Å resolution, respectively. The SRP68-binding domain of SRP72 contains four atypical tetratricopeptide repeats (TPR) and a flexible C-terminal cap. Apo-SRP72 exists mainly as dimers in solution. To bind to SRP68, the SRP72 homodimer disassociates, and the indispensable C-terminal cap undergoes a pronounced conformational change to assist formation of the SRP68/72 heterodimer. A 23-residue polypeptide of SRP68 is sufficient for tight binding to SRP72 through its unusually hydrophobic and extended surface. Structural, biophysical, and mutagenesis analyses revealed that cancer-associated mutations disrupt the SRP68–SRP72 interaction and their co-localization with ER in mammalian cells. The results highlight the essential role of the SRP68–SRP72 interaction in SRP-mediated protein translocation and provide a structural basis for disease diagnosis, pathophysiology, and drug design.

Base pair probability estimates improve the prediction accuracy of RNA non-canonical base pairs

Prediction of RNA tertiary structure from sequence is an important problem, but generating accurate structure models for even short sequences remains difficult. Predictions of RNA tertiary structure tend to be least accurate in loop regions, where non-canonical pairs are important for determining the details of structure. Non-canonical pairs can be predicted using a knowledge-based model of structure that scores nucleotide cyclic motifs, or NCMs. In this work, a partition function algorithm is introduced that allows the estimation of base pairing probabilities for both canonical and non-canonical interactions. Pairs that are predicted to be probable are more likely to be found in the true structure than pairs of lower probability. Pair probability estimates can be further improved by predicting the structure conserved across multiple homologous sequences using the TurboFold algorithm. These pairing probabilities, used in concert with prior knowledge of the canonical secondary structure, allow accurate inference of non-canonical pairs, an important step towards accurate prediction of the full tertiary structure. Software to predict non-canonical base pairs and pairing probabilities is now provided as part of the RNAstructure software package.

An Algorithm for Template-Based Prediction of Secondary Structures of Individual RNA Sequences

While understanding the structure of RNA molecules is vital for deciphering their functions, determining RNA structures experimentally is exceptionally hard. At the same time, extant approaches to computational RNA structure prediction have limited applicability and reliability. In this paper we provide a method to solve a simpler yet still biologically relevant problem: prediction of secondary RNA structure using structure of different molecules as a template. Our method identifies conserved and unconserved subsequences within an RNA molecule. For conserved subsequences, the template structure is directly transferred into the generated structure and combined with de-novo predicted structure for the unconserved subsequences with low evolutionary conservation. The method also determines, when the generated structure is unreliable. The method is validated using experimentally identified structures. The accuracy of the method exceeds that of classical prediction algorithms and constrained prediction methods. This is demonstrated by comparison using large number of heterogeneous RNAs. The presented method is fast and robust, and useful for various applications requiring knowledge of secondary structures of individual RNA sequences.

Structures of human SRP72 complexes provide insights into SRP RNA remodeling and ribosome interaction

Co-translational protein targeting and membrane protein insertion is a fundamental process and depends on the signal recognition particle (SRP). In mammals, SRP is composed of the SRP RNA crucial for SRP assembly and function and six proteins. The two largest proteins SRP68 and SRP72 form a heterodimer and bind to a regulatory site of the SRP RNA. Despite their essential roles in the SRP pathway, structural information has been available only for the SRP68 RNA-binding domain (RBD). Here we present the crystal structures of the SRP68 protein-binding domain (PBD) in complex with SRP72-PBD and of the SRP72-RBD bound to the SRP S domain (SRP RNA, SRP19 and SRP68) detailing all interactions of SRP72 within SRP. The SRP72-PBD is a tetratricopeptide repeat, which binds an extended linear motif of SRP68 with high affinity. The SRP72-RBD is a flexible peptide crawling along the 5e- and 5f-loops of SRP RNA. A conserved tryptophan inserts into the 5e-loop forming a novel type of RNA kink-turn stabilized by a potassium ion, which we define as K⁺-turn. In addition, SRP72-RBD remodels the 5f-loop involved in ribosome binding and visualizes SRP RNA plasticity. Docking of the S domain structure into cryo-electron microscopy density maps reveals multiple contact sites between SRP68/72 and the ribosome, and explains the role of SRP72 in the SRP pathway.

A New Method to Predict RNA Secondary Structure Based on RNA Folding Simulation

RNA plays an important role in various biological processes; hence, it is essential when determining the functions of RNA to research its secondary structures. So far, the accuracy of RNA secondary structure prediction remains an area in need of improvement. This paper presents a novel method for predicting RNA secondary structure based on an RNA folding simulation model. This model assumes that the process of RNA folding from the random coil state to full structure is staged and in every stage of folding, the final state of an RNA is determined by the optimal combination of helical regions, which are urgently essential to dynamics of RNA formation. This paper proposes the First Large Free Energy Difference (FLED) in order to find the helical regions most urgently needed for optimal final state formation among all the possible helical regions. Tests on the datasets with known structures from public databases demonstrate that our method can outperform other current RNA secondary structure prediction methods in terms of prediction accuracy.

The Kink Turn, a Key Architectural Element in RNA Structure

Kink turns (k-turns) are widespread structural elements that introduce an axial bend into duplex RNA with an included angle of 50°. These mediate key tertiary interactions and bind specific proteins including members of the L7Ae family. The standard k-turn comprises a three-nucleotide bulge followed by G·A and A·G pairs. The RNA kinks by an association of the two minor grooves, stabilized by the formation of a number of key cross-strand hydrogen bonds mostly involving the adenine bases of the G·A and A·G pairs. The k-turns may be divided into two conformational classes, depending on the receptor for one of these hydrogen bonds. k-turns become folded by one of three different processes. Some, but not all, k-turns become folded in the presence of metal ions. Whether or not a given k-turn is folded under these conditions is determined by its sequence. We present a set of rules for the prediction of folding properties and the structure adopted on local sequence.

Noncanoncial signal recognition particle RNAs in a major eukaryotic phylum revealed by purification of SRP from the human pathogen Cryptococcus neoformans

Despite conservation of the signal recognition particle (SRP) from bacteria to man, computational approaches have failed to identify SRP components from genomes of many lower eukaryotes, raising the possibility that they have been lost or altered in those lineages. We report purification and analysis of SRP in the human pathogen Cryptococcus neoformans, providing the first description of SRP in basidiomycetous yeast. The C. neoformans SRP RNA displays a predicted structure in which the universally conserved helix 8 contains an unprecedented stem-loop insertion. Guided by this sequence, we computationally identified 152 SRP RNAs throughout the phylum Basidiomycota. This analysis revealed additional helix 8 alterations including single and double stem-loop insertions as well as loop diminutions affecting RNA structural elements that are otherwise conserved from bacteria to man. Strikingly, these SRP RNA features in Basidiomycota are accompanied by phylum-specific alterations in the RNA-binding domain of Srp54, the SRP protein subunit that directly interacts with helix 8. Our findings reveal unexpected fungal SRP diversity and suggest coevolution of the two most conserved SRP features-SRP RNA helix 8 and Srp54-in basidiomycetes. Because members of this phylum include important human and plant pathogens, these noncanonical features provide new targets for antifungal compound development.

Signal-sequence induced conformational changes in the signal recognition particle

Co-translational protein targeting is an essential, evolutionarily conserved pathway for delivering nascent proteins to the proper cellular membrane. In this pathway, the signal recognition particle (SRP) first recognizes the N-terminal signal sequence of nascent proteins and subsequently interacts with the SRP receptor. For this, signal sequence binding in the SRP54 M domain must be effectively communicated to the SRP54 NG domain that interacts with the receptor. Here we present the 2.9 Å crystal structure of unbound- and signal sequence bound SRP forms, both present in the asymmetric unit. The structures provide evidence for a coupled binding and folding mechanism in which signal sequence binding induces the concerted folding of the GM linker helix, the finger loop, and the C-terminal alpha helix αM6. This mechanism allows for a high degree of structural adaptability of the binding site and suggests how signal sequence binding in the M domain is coupled to repositioning of the NG domain.

Exportin-5 mediates nuclear export of SRP RNA in vertebrates

The signal recognition particle is a ribonucleoprotein complex that is essential for the translocation of nascent proteins into the endoplasmic reticulum. It has been shown that the RNA component (SRP RNA) is exported from the nucleus by CRM1 in the budding yeast. However, how SRP RNA is exported in higher species has been elusive. Here, we show that SRP RNA does not use the CRM1 pathway in Xenopus oocytes. Instead, SRP RNA uses the same export pathway as pre-miRNA and tRNA as showed by cross-competition experiments. Consistently, the recombinant Exportin-5 protein specifically stimulated export of SRP RNA as well as of pre-miRNA and tRNA, whereas an antibody raised against Exportin-5 specifically inhibited export of the same RNA species. Moreover, biotinylated SRP RNA can pull down Exportin-5 but not CRM1 from HeLa cell nuclear extracts in a RanGTP-dependent manner. These results, taken together, strongly suggest that the principal export receptor for SRP RNA in vertebrates is Exportin-5 unlike in the budding yeast.

The tmRNA website

The transfer-messenger RNA (tmRNA) and its partner protein SmpB act together in resolving problems arising when translating bacterial ribosomes reach the end of mRNA with no stop codon. Their genes have been found in nearly all bacterial genomes and in some organelles. The tmRNA Website serves tmRNA sequences, alignments and feature annotations, and has recently moved to http://bioinformatics.sandia.gov/tmrna/. New features include software used to find the sequences, an update raising the number of unique tmRNA sequences from 492 to 1716, and a database of SmpB sequences which are served along with the tmRNA sequence from the same organism.

RNAcentral: an international database of ncRNA sequences

The field of non-coding RNA biology has been hampered by the lack of availability of a comprehensive, up-to-date collection of accessioned RNA sequences. Here we present the first release of RNAcentral, a database that collates and integrates information from an international consortium of established RNA sequence databases. The initial release contains over 8.1 million sequences, including representatives of all major functional classes. A web portal (http://rnacentral.org) provides free access to data, search functionality, cross-references, source code and an integrated genome browser for selected species.

Structure of the complete bacterial SRP Alu domain

The Alu domain of the signal recognition particle (SRP) arrests protein biosynthesis by competition with elongation factor binding on the ribosome. The mammalian Alu domain is a protein-RNA complex, while prokaryotic Alu domains are protein-free with significant extensions of the RNA. Here we report the crystal structure of the complete Alu domain of Bacillus subtilis SRP RNA at 2.5 Å resolution. The bacterial Alu RNA reveals a compact fold, which is stabilized by prokaryote-specific extensions and interactions. In this 'closed' conformation, the 5' and 3' regions are clamped together by the additional helix 1, the connecting 3-way junction and a novel minor groove interaction, which we term the 'minor-saddle motif' (MSM). The 5' region includes an extended loop-loop pseudoknot made of five consecutive Watson-Crick base pairs. Homology modeling with the human Alu domain in context of the ribosome shows that an additional lobe in the pseudoknot approaches the large subunit, while the absence of protein results in the detachment from the small subunit. Our findings provide the structural basis for purely RNA-driven elongation arrest in prokaryotes, and give insights into the structural adaption of SRP RNA during evolution.

Requirements for resuming translation in chimeric transfer-messenger RNAs of Escherichia coli and Mycobacterium tuberculosis

BACKGROUND

Trans-translation is catalyzed by ribonucleprotein complexes composed of SmpB protein and transfer-messenger RNA. They release stalled ribosomes from truncated mRNAs and tag defective proteins for proteolytic degradation. Comparative sequence analysis of bacterial tmRNAs provides considerable insights into their secondary structures in which a tRNA-like domain and an mRNA-like region are connected by a variable number of pseudoknots. Progress toward understanding the molecular mechanism of trans-translation is hampered by our limited knowledge about the structure of tmRNA:SmpB complexes.

RESULTS

Complexes consisting of M. tuberculosis tmRNA and E. coli SmpB tag truncated proteins poorly in E. coli. In contrast, the tagging activity of E. coli tmRNA is well supported by M. tuberculosis SmpB that is expressed in E. coli. To investigate this incompatibility, we constructed 12 chimeric tmRNA molecules composed of structural features derived from both E. coli and M. tuberculosis. Our studies demonstrate that replacing the hp5-pk2-pk3-pk4 segment of E. coli tmRNA with the equivalent segment of M. tuberculosis tmRNA has no significant effect on the tagging efficiency of chimeric tmRNAs in the presence of E. coli SmpB. Replacing either helices 2b-2d, the single-stranded part of the ORF, pk1, or residues 79-89 of E. coli tmRNA with the equivalent features of M. tuberculosis tmRNA yields chimeric tmRNAs that are tagged at 68 to 88 percent of what is observed with E. coli tmRNA. Exchanging segments composed of either pk1 and the single-stranded segment upstream of the ORF or helices 2b-2d and pk1 results in markedly impaired tagging activity.

CONCLUSION

Our observations demonstrate the existence of functionally important but as yet uncharacterized structural constraints in the segment of tmRNA that connects its TLD to the ORF used for resuming translation. As trans-translation is important for the survival of M. tuberculosis, our work provides a new target for pharmacological intervention against multidrug-resistant tuberculosis.

The SmpB C-terminal tail helps tmRNA to recognize and enter stalled ribosomes

In bacteria, transfer-messenger RNA (tmRNA) and SmpB comprise the most common and effective system for rescuing stalled ribosomes. Ribosomes stall on mRNA transcripts lacking stop codons and are rescued as the defective mRNA is swapped for the tmRNA template in a process known as trans-translation. The tmRNA–SmpB complex is recruited to the ribosome independent of a codon–anticodon interaction. Given that the ribosome uses robust discriminatory mechanisms to select against non-cognate tRNAs during canonical decoding, it has been hard to explain how this can happen. Recent structural and biochemical studies show that SmpB licenses tmRNA entry through its interactions with the decoding center and mRNA channel. In particular, the C-terminal tail of SmpB promotes both EFTu activation and accommodation of tmRNA, the former through interactions with 16S rRNA nucleotide G530 and the latter through interactions with the mRNA channel downstream of the A site. Here we present a detailed model of the earliest steps in trans-translation, and in light of these mechanistic considerations, revisit the question of how tmRNA preferentially reacts with stalled, non-translating ribosomes.

Ends of the line for tmRNA-SmpB

Genes for the RNA tmRNA and protein SmpB, partners in the trans-translation process that rescues stalled ribosomes, have previously been found in all bacteria and some organelles. During a major update of The tmRNA Website (relocated to http://bioinformatics.sandia.gov/tmrna), including addition of an SmpB sequence database, we found some bacteria that lack functionally significant regions of SmpB. Three groups with reduced genomes have lost the central loop of SmpB, which is thought to improve alanylation and EF-Tu activation: Carsonella, Hodgkinia, and the hemoplasmas (hemotropic Mycoplasma). Carsonella has also lost the SmpB C-terminal tail, thought to stimulate the decoding center of the ribosome. We validate recent identification of tmRNA homologs in oomycete mitochondria by finding partner genes from oomycete nuclei that target SmpB to the mitochondrion. We have moreover identified through exhaustive search a small number of complete, but often highly derived, bacterial genomes that appear to lack a functional copy of either the tmRNA or SmpB gene (but not both). One Carsonella isolate exhibits complete degradation of the tmRNA gene sequence yet its smpB shows no evidence for relaxed selective constraint, relative to other genes in the genome. After loss of the SmpB central loop in the hemoplasmas, one subclade apparently lost tmRNA. Carsonella also exhibits gene overlap such that tmRNA maturation should produce a non-stop smpB mRNA. At least some of the tmRNA/SmpB-deficient strains appear to further lack the ArfA and ArfB backup systems for ribosome rescue. The most frequent neighbors of smpB are the tmRNA gene, a ratA/rnfH unit, and the gene for RNaseR, a known physical and functional partner of tmRNA-SmpB.

The K-turn motif in riboswitches and other RNA species

The kink turn is a widespread structure motif that introduces a tight bend into the axis of duplex RNA. This generally functions to mediate tertiary interactions, and to serve as a specific protein binding site. K-turns or closely related structures are found in at least seven different riboswitch structures, where they function as key architectural elements that help generate the ligand binding pocket. This article is part of a Special Issue entitled: Riboswitches.

An unusual mechanism for EF-Tu activation during tmRNA-mediated ribosome rescue

In bacteria, ribosomes stalled on truncated mRNAs are rescued by transfer-messenger RNA (tmRNA) and its protein partner SmpB. Acting like tRNA, the aminoacyl-tmRNA/SmpB complex is delivered to the ribosomal A site by EF-Tu and accepts the transfer of the nascent polypeptide. Although SmpB binding within the decoding center is clearly critical for licensing tmRNA entry into the ribosome, it is not known how activation of EF-Tu occurs in the absence of a codon-anticodon interaction. A recent crystal structure revealed that SmpB residue His136 stacks on 16S rRNA nucleotide G530, a critical player in the canonical decoding mechanism. Here we use pre-steady-state kinetic methods to probe the role of this interaction in ribosome rescue. We find that although mutation of His136 does not reduce SmpB's affinity for the ribosomal A-site, it dramatically reduces the rate of GTP hydrolysis by EF-Tu. Surprisingly, the same mutation has little effect on the apparent rate of peptide-bond formation, suggesting that release of EF-Tu from the tmRNA/SmpB complex on the ribosome may occur prior to GTP hydrolysis. Consistent with this idea, we find that peptidyl transfer to tmRNA is relatively insensitive to the antibiotic kirromycin. Taken together, our studies provide a model for the initial stages of ribosomal rescue by tmRNA.

Plasmodium falciparum signal recognition particle components and anti-parasitic effect of ivermectin in blocking nucleo-cytoplasmic shuttling of SRP

Signal recognition particle (SRP) is a ubiquitous ribonucleoprotein complex that targets proteins to endoplasmic reticulum (ER) in eukaryotes. Here we report that Plasmodium falciparum SRP is composed of six polypeptides; SRP9, SRP14, SRP19, SRP54, SRP68 and SRP72 and a 303nt long SRP RNA. We generated four transgenic parasite lines expressing SRP-GFP chimeric proteins and co-localization studies showed the nucleo-cytoplasmic localization for these proteins. The evaluation of the effect of known SRP and nuclear import/export inhibitors on P. falciparum revealed that ivermectin, an inhibitor of importin α/β mediated nuclear import inhibited the nuclear import of PfSRP polypeptides at submicromolar concentration, thereby killing the parasites. These findings provide insights into dynamic structure of P. falciparum SRP and also raise the possibility that ivermectin could be used in combination with other antimalarial agents to control the disease.

The determination of RNA folding nearest neighbor parameters

The stability of RNA secondary structure can be predicted using a set of nearest neighbor parameters. These parameters are widely used by algorithms that predict secondary structure. This contribution introduces the UV optical melting experiments that are used to determine the folding stability of short RNA strands. It explains how the nearest neighbor parameters are chosen and how the values are fit to the data. A sample nearest neighbor calculation is provided. The contribution concludes with new methods that use the database of sequences with known structures to determine parameter values.

Assembly information services in the European Nucleotide Archive

The European Nucleotide Archive (ENA; http://www.ebi.ac.uk/ena) is a repository for the world public domain nucleotide sequence data output. ENA content covers a spectrum of data types including raw reads, assembly data and functional annotation. ENA has faced a dramatic growth in genome assembly submission rates, data volumes and complexity of datasets. This has prompted a broad reworking of assembly submission services, for which we now reach the end of a major programme of work and many enhancements have already been made available over the year to components of the submission service. In this article, we briefly review ENA content and growth over 2013, describe our rapidly developing services for genome assembly information and outline further major developments over the last year.

The structural basis of FtsY recruitment and GTPase activation by SRP RNA

The universally conserved signal recognition particle (SRP) system mediates the targeting of membrane proteins to the translocon in a multistep process controlled by GTP hydrolysis. Here we present the 2.6 Å crystal structure of the GTPase domains of the E. coli SRP protein (Ffh) and its receptor (FtsY) in complex with the tetraloop and the distal region of SRP-RNA, trapped in the activated state in presence of GDP:AlF4. The structure reveals the atomic details of FtsY recruitment and, together with biochemical experiments, pinpoints G83 as the key RNA residue that stimulates GTP hydrolysis. Insertion of G83 into the FtsY active site orients a single glutamate residue provided by Ffh (E277), triggering GTP hydrolysis and complex disassembly at the end of the targeting cycle. The complete conservation of the key residues of the SRP-RNA and the SRP protein implies that the suggested chemical mechanism of GTPase activation is applicable across all kingdoms.

The four ingredients of single-sequence RNA secondary structure prediction. A unifying perspective

Any method for RNA secondary structure prediction is determined by four ingredients. The architecture is the choice of features implemented by the model (such as stacked basepairs, loop length distributions, etc.). The architecture determines the number of parameters in the model. The scoring scheme is the nature of those parameters (whether thermodynamic, probabilistic, or weights). The parameterization stands for the specific values assigned to the parameters. These three ingredients are referred to as "the model." The fourth ingredient is the folding algorithms used to predict plausible secondary structures given the model and the sequence of a structural RNA. Here, I make several unifying observations drawn from looking at more than 40 years of methods for RNA secondary structure prediction in the light of this classification. As a final observation, there seems to be a performance ceiling that affects all methods with complex architectures, a ceiling that impacts all scoring schemes with remarkable similarity. This suggests that modeling RNA secondary structure by using intrinsic sequence-based plausible "foldability" will require the incorporation of other forms of information in order to constrain the folding space and to improve prediction accuracy. This could give an advantage to probabilistic scoring systems since a probabilistic framework is a natural platform to incorporate different sources of information into one single inference problem.

Ensemble-based prediction of RNA secondary structures

BACKGROUND

Accurate structure prediction methods play an important role for the understanding of RNA function. Energy-based, pseudoknot-free secondary structure prediction is one of the most widely used and versatile approaches, and improved methods for this task have received much attention over the past five years. Despite the impressive progress that as been achieved in this area, existing evaluations of the prediction accuracy achieved by various algorithms do not provide a comprehensive, statistically sound assessment. Furthermore, while there is increasing evidence that no prediction algorithm consistently outperforms all others, no work has been done to exploit the complementary strengths of multiple approaches.

RESULTS

In this work, we present two contributions to the area of RNA secondary structure prediction. Firstly, we use state-of-the-art, resampling-based statistical methods together with a previously published and increasingly widely used dataset of high-quality RNA structures to conduct a comprehensive evaluation of existing RNA secondary structure prediction procedures. The results from this evaluation clarify the performance relationship between ten well-known existing energy-based pseudoknot-free RNA secondary structure prediction methods and clearly demonstrate the progress that has been achieved in recent years. Secondly, we introduce AveRNA, a generic and powerful method for combining a set of existing secondary structure prediction procedures into an ensemble-based method that achieves significantly higher prediction accuracies than obtained from any of its component procedures.

CONCLUSIONS

Our new, ensemble-based method, AveRNA, improves the state of the art for energy-based, pseudoknot-free RNA secondary structure prediction by exploiting the complementary strengths of multiple existing prediction procedures, as demonstrated using a state-of-the-art statistical resampling approach. In addition, AveRNA allows an intuitive and effective control of the trade-off between false negative and false positive base pair predictions. Finally, AveRNA can make use of arbitrary sets of secondary structure prediction procedures and can therefore be used to leverage improvements in prediction accuracy offered by algorithms and energy models developed in the future. Our data, MATLAB software and a web-based version of AveRNA are publicly available at http://www.cs.ubc.ca/labs/beta/Software/AveRNA.

Frnakenstein: multiple target inverse RNA folding

BACKGROUND

RNA secondary structure prediction, or folding, is a classic problem in bioinformatics: given a sequence of nucleotides, the aim is to predict the base pairs formed in its three dimensional conformation. The inverse problem of designing a sequence folding into a particular target structure has only more recently received notable interest. With a growing appreciation and understanding of the functional and structural properties of RNA motifs, and a growing interest in utilising biomolecules in nano-scale designs, the interest in the inverse RNA folding problem is bound to increase. However, whereas the RNA folding problem from an algorithmic viewpoint has an elegant and efficient solution, the inverse RNA folding problem appears to be hard.

RESULTS

In this paper we present a genetic algorithm approach to solve the inverse folding problem. The main aims of the development was to address the hitherto mostly ignored extension of solving the inverse folding problem, the multi-target inverse folding problem, while simultaneously designing a method with superior performance when measured on the quality of designed sequences. The genetic algorithm has been implemented as a Python program called Frnakenstein. It was benchmarked against four existing methods and several data sets totalling 769 real and predicted single structure targets, and on 292 two structure targets. It performed as well as or better at finding sequences which folded in silico into the target structure than all existing methods, without the heavy bias towards CG base pairs that was observed for all other top performing methods. On the two structure targets it also performed well, generating a perfect design for about 80% of the targets.

CONCLUSIONS

Our method illustrates that successful designs for the inverse RNA folding problem does not necessarily have to rely on heavy biases in base pair and unpaired base distributions. The design problem seems to become more difficult on larger structures when the target structures are real structures, while no deterioration was observed for predicted structures. Design for two structure targets is considerably more difficult, but far from impossible, demonstrating the feasibility of automated design of artificial riboswitches. The Python implementation is available at http://www.stats.ox.ac.uk/research/genome/software/frnakenstein.

Conformational features of topologically classified RNA secondary structures

Background

Current RNA secondary structure prediction approaches predict prevalent pseudoknots such as the H-pseudoknot and kissing hairpin. The number of possible structures increases drastically when more complex pseudoknots are considered, thus leading to computational limitations. On the other hand, the enormous population of possible structures means not all of them appear in real RNA molecules. Therefore, it is of interest to understand how many of them really exist and the reasons for their preferred existence over the others, as any new findings revealed by this study might enhance the capability of future structure prediction algorithms for more accurate prediction of complex pseudoknots.

Methodology/Principal Findings

A novel algorithm was devised to estimate the exact number of structural possibilities for a pseudoknot constructed with a specified number of base pair stems. Then, topological classification was applied to classify RNA pseudoknotted structures from data in the RNA STRAND database. By showing the vast possibilities and the real population, it is clear that most of these plausible complex pseudoknots are not observed. Moreover, from these classified motifs that exist in nature, some features were identified for further investigation. It was found that some features are related to helical stacking. Other features are still left open to discover underlying tertiary interactions.

Conclusions

Results from topological classification suggest that complex pseudoknots are usually some well-known motifs that are themselves complex or the interaction results of some special motifs. Heuristics can be proposed to predict the essential parts of these complex motifs, even if the required thermodynamic parameters are currently unknown.

Evolving stochastic context--free grammars for RNA secondary structure prediction

BACKGROUND

Stochastic Context-Free Grammars (SCFGs) were applied successfully to RNA secondary structure prediction in the early 90s, and used in combination with comparative methods in the late 90s. The set of SCFGs potentially useful for RNA secondary structure prediction is very large, but a few intuitively designed grammars have remained dominant. In this paper we investigate two automatic search techniques for effective grammars - exhaustive search for very compact grammars and an evolutionary algorithm to find larger grammars. We also examine whether grammar ambiguity is as problematic to structure prediction as has been previously suggested.

RESULTS

These search techniques were applied to predict RNA secondary structure on a maximal data set and revealed new and interesting grammars, though none are dramatically better than classic grammars. In general, results showed that many grammars with quite different structure could have very similar predictive ability. Many ambiguous grammars were found which were at least as effective as the best current unambiguous grammars.

CONCLUSIONS

Overall the method of evolving SCFGs for RNA secondary structure prediction proved effective in finding many grammars that had strong predictive accuracy, as good or slightly better than those designed manually. Furthermore, several of the best grammars found were ambiguous, demonstrating that such grammars should not be disregarded.

Decoding in the absence of a codon by tmRNA and SmpB in the ribosome

In bacteria, ribosomes stalled at the end of truncated messages are rescued by transfer-messenger RNA (tmRNA), a bifunctional molecule that acts as both a transfer RNA (tRNA) and a messenger RNA (mRNA), and SmpB, a small protein that works in concert with tmRNA. Here, we present the crystal structure of a tmRNA fragment, SmpB and elongation factor Tu bound to the ribosome at 3.2 angstroms resolution. The structure shows how SmpB plays the role of both the anticodon loop of tRNA and portions of mRNA to facilitate decoding in the absence of an mRNA codon in the A site of the ribosome and explains why the tmRNA-SmpB system does not interfere with normal translation.

RNAcentral: A vision for an international database of RNA sequences

During the last decade there has been a great increase in the number of noncoding RNA genes identified, including new classes such as microRNAs and piRNAs. There is also a large growth in the amount of experimental characterization of these RNA components. Despite this growth in information, it is still difficult for researchers to access RNA data, because key data resources for noncoding RNAs have not yet been created. The most pressing omission is the lack of a comprehensive RNA sequence database, much like UniProt, which provides a comprehensive set of protein knowledge. In this article we propose the creation of a new open public resource that we term RNAcentral, which will contain a comprehensive collection of RNA sequences and fill an important gap in the provision of biomedical databases. We envision RNA researchers from all over the world joining a federated RNAcentral network, contributing specialized knowledge and databases. RNAcentral would centralize key data that are currently held across a variety of databases, allowing researchers instant access to a single, unified resource. This resource would facilitate the next generation of RNA research and help drive further discoveries, including those that improve food production and human and animal health. We encourage additional RNA database resources and research groups to join this effort. We aim to obtain international network funding to further this endeavor.

The role of SmpB and the ribosomal decoding center in licensing tmRNA entry into stalled ribosomes

In bacteria, stalled ribosomes are recycled by a hybrid transfer-messenger RNA (tmRNA). Like tRNA, tmRNA is aminoacylated with alanine and is delivered to the ribosome by EF-Tu, where it reacts with the growing polypeptide chain. tmRNA entry into stalled ribosomes poses a challenge to our understanding of ribosome function because it occurs in the absence of a codon-anticodon interaction. Instead, tmRNA entry is licensed by the binding of its protein partner, SmpB, to the ribosomal decoding center. We analyzed a series of SmpB mutants and found that its C-terminal tail is essential for tmRNA accommodation but not for EF-Tu activation. We obtained evidence that the tail likely functions as a helix on the ribosome to promote accommodation and identified key residues in the tail essential for this step. In addition, our mutational analysis points to a role for the conserved K(131)GKK tail residues in trans-translation after peptidyl transfer to tmRNA, presumably EF-G-mediated translocation or translation of the tmRNA template. Surprisingly, analysis of A1492, A1493, and G530 mutants reveals that while these ribosomal nucleotides are essential for normal tRNA selection, they play little to no role in peptidyl transfer to tmRNA. These studies clarify how SmpB interacts with the ribosomal decoding center to license tmRNA entry into stalled ribosomes.

From structure prediction to genomic screens for novel non-coding RNAs

Non-coding RNAs (ncRNAs) are receiving more and more attention not only as an abundant class of genes, but also as regulatory structural elements (some located in mRNAs). A key feature of RNA function is its structure. Computational methods were developed early for folding and prediction of RNA structure with the aim of assisting in functional analysis. With the discovery of more and more ncRNAs, it has become clear that a large fraction of these are highly structured. Interestingly, a large part of the structure is comprised of regular Watson-Crick and GU wobble base pairs. This and the increased amount of available genomes have made it possible to employ structure-based methods for genomic screens. The field has moved from folding prediction of single sequences to computational screens for ncRNAs in genomic sequence using the RNA structure as the main characteristic feature. Whereas early methods focused on energy-directed folding of single sequences, comparative analysis based on structure preserving changes of base pairs has been efficient in improving accuracy, and today this constitutes a key component in genomic screens. Here, we cover the basic principles of RNA folding and touch upon some of the concepts in current methods that have been applied in genomic screens for de novo RNA structures in searches for novel ncRNA genes and regulatory RNA structure on mRNAs. We discuss the strengths and weaknesses of the different strategies and how they can complement each other.

TurboFold: iterative probabilistic estimation of secondary structures for multiple RNA sequences

Background

The prediction of secondary structure, i.e. the set of canonical base pairs between nucleotides, is a first step in developing an understanding of the function of an RNA sequence. The most accurate computational methods predict conserved structures for a set of homologous RNA sequences. These methods usually suffer from high computational complexity. In this paper, TurboFold, a novel and efficient method for secondary structure prediction for multiple RNA sequences, is presented.

Results

TurboFold takes, as input, a set of homologous RNA sequences and outputs estimates of the base pairing probabilities for each sequence. The base pairing probabilities for a sequence are estimated by combining intrinsic information, derived from the sequence itself via the nearest neighbor thermodynamic model, with extrinsic information, derived from the other sequences in the input set. For a given sequence, the extrinsic information is computed by using pairwise-sequence-alignment-based probabilities for co-incidence with each of the other sequences, along with estimated base pairing probabilities, from the previous iteration, for the other sequences. The extrinsic information is introduced as free energy modifications for base pairing in a partition function computation based on the nearest neighbor thermodynamic model. This process yields updated estimates of base pairing probability. The updated base pairing probabilities in turn are used to recompute extrinsic information, resulting in the overall iterative estimation procedure that defines TurboFold.

TurboFold is benchmarked on a number of ncRNA datasets and compared against alternative secondary structure prediction methods. The iterative procedure in TurboFold is shown to improve estimates of base pairing probability with each iteration, though only small gains are obtained beyond three iterations. Secondary structures composed of base pairs with estimated probabilities higher than a significance threshold are shown to be more accurate for TurboFold than for alternative methods that estimate base pairing probabilities. TurboFold-MEA, which uses base pairing probabilities from TurboFold in a maximum expected accuracy algorithm for secondary structure prediction, has accuracy comparable to the best performing secondary structure prediction methods. The computational and memory requirements for TurboFold are modest and, in terms of sequence length and number of sequences, scale much more favorably than joint alignment and folding algorithms.

Conclusions

TurboFold is an iterative probabilistic method for predicting secondary structures for multiple RNA sequences that efficiently and accurately combines the information from the comparative analysis between sequences with the thermodynamic folding model. Unlike most other multi-sequence structure prediction methods, TurboFold does not enforce strict commonality of structures and is therefore useful for predicting structures for homologous sequences that have diverged significantly. TurboFold can be downloaded as part of the RNAstructure package at http://rna.urmc.rochester.edu.

RNA interactions

Noncoding RNAs form an indispensible component of the cellular information processing networks, a role that crucially depends on the specificity of their interactions among each other as well as with DNA and protein. Patterns of intramolecular and intermolecular base pairs govern most RNA interactions. Specific base pairs dominate the structure formation of nucleic acids. Only little details distinguish intramolecular secondary structures from those cofolding molecules. RNA-protein interactions, on the other hand, are strongly dependent on the RNA structure as well since the sequence content of helical regions is largely unreadable, so that sequence specificity is mostly restricted to unpaired loop regions. Conservation of both sequence and structure thus this can give indications of the functioning of the diversity of ncRNAs.