Tertiary motifs revealed in analyses of higher-order RNA junctions

Vfold2D-MC: A Physics-Based Hybrid Model for Predicting RNA Secondary Structure Folding

Accurate prediction of RNA structure and folding stability has a far-reaching impact on our understanding of RNA functions. Here we develop Vfold2D-MC, a new physics-based model, to predict RNA structure and folding thermodynamics from the sequence. The model employs virtual bond-based coarse-graining of RNA backbone conformation and generates RNA conformations through Monte Carlo sampling of the bond angles and torsional angles of the virtual bonds. Using a coarse-grained statistical potential derived from the known structures, we assign each conformation with a statistical weight. The weighted average over the conformational ensemble gives the entropy and free energy parameters for the hairpin, bulge, and internal loops, and multiway junctions. From the thermodynamic parameters, we predict RNA structures, melting curves, and structural changes from the sequence. Theory-experiment comparisons indicate that Vfold2D-MC not only gives improved structure predictions but also enables the interpretation of thermodynamic results for different RNA structures, including multibranched junctions. This new model sets a promising framework to treat more complicated RNA structures, such as pseudoknotted and intramolecular kissing loops, for which experimental thermodynamic parameters are often unavailable.

Features and Functions of the A-Minor Motif, the Most Common Motif in RNA Structure

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

A Fiedler Vector Scoring Approach for Novel RNA Motif Selection

Novel RNA motif design is of great practical importance for technology and medicine. Increasingly, computational design plays an important role in such efforts. Our coarse-grained RAG (RNA-As-Graphs) framework offers strategies for enumerating the universe of RNA 2D folds, selecting "RNA-like" candidates for design, and determining sequences that fold onto these candidates. In RAG, RNA secondary structures are represented as tree or dual graphs. Graphs with known RNA structures are called "existing", and the others are labeled "hypothetical". By using simplified features for RNA graphs, we have clustered the hypothetical graphs into "RNA-like" and "non-RNA-like" groups and proposed RNA-like graphs as candidates for design. Here, we propose a new way of designing graph features by using Fiedler vectors. The new features reflect graph shapes better, and they lead to a more clustered organization of existing graphs. We show significant increases in K-means clustering accuracy by using the new features (e.g., up to 95% and 98% accuracy for tree and dual graphs, respectively). In addition, we propose a scoring model for top graph candidate selection. This scoring model allows users to set a threshold for candidates, and it incorporates weighing of existing graphs based on their corresponding number of known RNAs. We include a list of top scored RNA-like candidates, which we hope will stimulate future novel RNA design.

Tuning the size, shape and structure of RNA nanoparticles for favorable cancer targeting and immunostimulation

The past decade has shown exponential growth in the field of RNA nanotechnology. The rapid advances of using RNA nanoparticles for biomedical applications, especially targeted cancer therapy, suggest its potential as a new generation of drug. After the first milestone of small molecule drugs and the second milestone of antibody drugs, it was predicted that RNA drugs, either RNA itself or chemicals/ligands that target RNA, will be the third milestone in drug development. Thus, a comprehensive assessment of the current therapeutic RNA nanoparticles is urgently needed to meet the drug evaluation criteria. Specifically, the pharmacological and immunological profiles of RNA nanoparticles need to be systematically studied to provide insights in rational design of RNA-based therapeutics. By virtue of its programmability and biocompatibility, RNA molecules can be designed to construct sophisticated nanoparticles with versatile functions/applications and highly tunable physicochemical properties. This intrinsic characteristic allows the systemic study of the effects of various properties of RNA nanoparticles on their in vivo behaviors such as cancer targeting and immune responses. This review will focus on the recent progress of RNA nanoparticles in cancer targeting, and summarize the effects of common physicochemical properties such as size and shape on the RNA nanoparticles' biodistribution and immunostimulation profiles. This article is categorized under: Biology-Inspired Nanomaterials > Nucleic Acid-Based Structures Diagnostic Tools > in vivo Nanodiagnostics and Imaging Therapeutic Approaches and Drug Discovery > Nanomedicine for Oncologic Disease.

Going beyond base-pairs: topology-based characterization of base-multiplets in RNA

Identification and characterization of base-multiplets, which are essentially mediated by base-pairing interactions, can provide insights into the diversity in the structure and dynamics of complex functional RNAs, and thus facilitate hypothesis driven biological research. The necessary nomenclature scheme, an extension of the geometric classification scheme for base-pairs by Leontis and Westhof, is however available only for base-triplets. In the absence of information on topology, this scheme is not applicable to quartets and higher order multiplets. Here we propose a topology-based classification scheme which, in conjunction with a graph-based algorithm, can be used for the automated identification and characterization of higher order base-multiplets in RNA structures. Here, the RNA structure is represented as a graph, where nodes represent nucleotides and edges represent base-pairing connectivity. Sets of connected components (of n nodes) within these graphs constitute subgraphs representing multiplets of "n" nucleotides. The different topological variants of the RNA multiplets thus correspond to different nonisomorphic forms of these subgraphs. To annotate RNA base-multiplets unambiguously, we propose a set of topology-based nomenclature rules for quartets, which are extendable to higher multiplets. We also demonstrate the utility of our approach toward the identification and annotation of higher order RNA multiplets, by investigating the occurrence contexts of selected examples in order to gain insights regarding their probable functional roles.

Adventures with RNA graphs

The structure of RNA has been a natural subject for mathematical modeling, inviting many innovative computational frameworks. This single-stranded polynucleotide chain can fold upon itself in numerous ways to form hydrogen-bonded segments, imperfect with single-stranded loops. Illustrating these paired and non-paired interaction networks, known as RNA's secondary (2D) structure, using mathematical graph objects has been illuminating for RNA structure analysis. Building upon such seminal work from the 1970s and 1980s, graph models are now used to study not only RNA structure but also describe RNA's recurring modular units, sample the conformational space accessible to RNAs, predict RNA's three-dimensional folds, and apply the combined aspects to novel RNA design. In this article, we outline the development of the RNA-As-Graphs (or RAG) approach and highlight current applications to RNA structure prediction and design.

RNA Structural Dynamics As Captured by Molecular Simulations: A Comprehensive Overview

With both catalytic and genetic functions, ribonucleic acid (RNA) is perhaps the most pluripotent chemical species in molecular biology, and its functions are intimately linked to its structure and dynamics. Computer simulations, and in particular atomistic molecular dynamics (MD), allow structural dynamics of biomolecular systems to be investigated with unprecedented temporal and spatial resolution. We here provide a comprehensive overview of the fast-developing field of MD simulations of RNA molecules. We begin with an in-depth, evaluatory coverage of the most fundamental methodological challenges that set the basis for the future development of the field, in particular, the current developments and inherent physical limitations of the atomistic force fields and the recent advances in a broad spectrum of enhanced sampling methods. We also survey the closely related field of coarse-grained modeling of RNA systems. After dealing with the methodological aspects, we provide an exhaustive overview of the available RNA simulation literature, ranging from studies of the smallest RNA oligonucleotides to investigations of the entire ribosome. Our review encompasses tetranucleotides, tetraloops, a number of small RNA motifs, A-helix RNA, kissing-loop complexes, the TAR RNA element, the decoding center and other important regions of the ribosome, as well as assorted others systems. Extended sections are devoted to RNA-ion interactions, ribozymes, riboswitches, and protein/RNA complexes. Our overview is written for as broad of an audience as possible, aiming to provide a much-needed interdisciplinary bridge between computation and experiment, together with a perspective on the future of the field.

The RNA 3D Motif Atlas: Computational methods for extraction, organization and evaluation of RNA motifs

RNA 3D motifs occupy places in structured RNA molecules that correspond to the hairpin, internal and multi-helix junction "loops" of their secondary structure representations. As many as 40% of the nucleotides of an RNA molecule can belong to these structural elements, which are distinct from the regular double helical regions formed by contiguous AU, GC, and GU Watson-Crick basepairs. With the large number of atomic- or near atomic-resolution 3D structures appearing in a steady stream in the PDB/NDB structure databases, the automated identification, extraction, comparison, clustering and visualization of these structural elements presents an opportunity to enhance RNA science. Three broad applications are: (1) identification of modular, autonomous structural units for RNA nanotechnology, nanobiology and synthetic biology applications; (2) bioinformatic analysis to improve RNA 3D structure prediction from sequence; and (3) creation of searchable databases for exploring the binding specificities, structural flexibility, and dynamics of these RNA elements. In this contribution, we review methods developed for computational extraction of hairpin and internal loop motifs from a non-redundant set of high-quality RNA 3D structures. We provide a statistical summary of the extracted hairpin and internal loop motifs in the most recent version of the RNA 3D Motif Atlas. We also explore the reliability and accuracy of the extraction process by examining its performance in clustering recurrent motifs from homologous ribosomal RNA (rRNA) structures. We conclude with a summary of remaining challenges, especially with regard to extraction of multi-helix junction motifs.

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/.

Secondary structure encodes a cooperative tertiary folding funnel in the Azoarcus ribozyme

A requirement for specific RNA folding is that the free-energy landscape discriminate against non-native folds. While tertiary interactions are critical for stabilizing the native fold, they are relatively non-specific, suggesting additional mechanisms contribute to tertiary folding specificity. In this study, we use coarse-grained molecular dynamics simulations to explore how secondary structure shapes the tertiary free-energy landscape of the Azoarcus ribozyme. We show that steric and connectivity constraints posed by secondary structure strongly limit the accessible conformational space of the ribozyme, and that these so-called topological constraints in turn pose strong free-energy penalties on forming different tertiary contacts. Notably, native A-minor and base-triple interactions form with low conformational free energy, while non-native tetraloop/tetraloop–receptor interactions are penalized by high conformational free energies. Topological constraints also give rise to strong cooperativity between distal tertiary interactions, quantitatively matching prior experimental measurements. The specificity of the folding landscape is further enhanced as tertiary contacts place additional constraints on the conformational space, progressively funneling the molecule to the native state. These results indicate that secondary structure assists the ribozyme in navigating the otherwise rugged tertiary folding landscape, and further emphasize topological constraints as a key force in RNA folding.

It's a loop world - single strands in RNA as structural and functional elements

Unpaired regions in RNA molecules - loops - are centrally involved in defining the characteristic three-dimensional (3D) architecture of RNAs and are of high interest in RNA engineering and design. Loops adopt diverse, but specific conformations stabilised by complex tertiary structural interactions that provide structural flexibility to RNA structures that would otherwise not be possible if they only consisted of the rigid A-helical shapes usually formed by canonical base pairing. By participating in sequence-non-local contacts, they furthermore contribute to stabilising the overall fold of RNA molecules. Interactions between RNAs and other nucleic acids, proteins, or small molecules are also generally mediated by RNA loop structures. Therefore, the function of an RNA molecule is generally dependent on its loops. Examples include intermolecular interactions between RNAs as part of the microRNA processing pathways, ribozymatic activity, or riboswitch-ligand interactions. Bioinformatics approaches have been successfully applied to the identification of novel RNA structural motifs including loops, local and global RNA 3D structure prediction, and structural and conformational analysis of RNAs and have contributed to a better understanding of the sequence-structure-function relationships in RNA loops.

GARN: Sampling RNA 3D Structure Space with Game Theory and Knowledge-Based Scoring Strategies

Cellular processes involve large numbers of RNA molecules. The functions of these RNA molecules and their binding to molecular machines are highly dependent on their 3D structures. One of the key challenges in RNA structure prediction and modeling is predicting the spatial arrangement of the various structural elements of RNA. As RNA folding is generally hierarchical, methods involving coarse-grained models hold great promise for this purpose. We present here a novel coarse-grained method for sampling, based on game theory and knowledge-based potentials. This strategy, GARN (Game Algorithm for RNa sampling), is often much faster than previously described techniques and generates large sets of solutions closely resembling the native structure. GARN is thus a suitable starting point for the molecular modeling of large RNAs, particularly those with experimental constraints. GARN is available from: http://garn.lri.fr/.

Computing the origin and evolution of the ribosome from its structure - Uncovering processes of macromolecular accretion benefiting synthetic biology

Accretion occurs pervasively in nature at widely different timeframes. The process also manifests in the evolution of macromolecules. Here we review recent computational and structural biology studies of evolutionary accretion that make use of the ideographic (historical, retrodictive) and nomothetic (universal, predictive) scientific frameworks. Computational studies uncover explicit timelines of accretion of structural parts in molecular repertoires and molecules. Phylogenetic trees of protein structural domains and proteomes and their molecular functions were built from a genomic census of millions of encoded proteins and associated terminal Gene Ontology terms. Trees reveal a ‘metabolic-first’ origin of proteins, the late development of translation, and a patchwork distribution of proteins in biological networks mediated by molecular recruitment. Similarly, the natural history of ancient RNA molecules inferred from trees of molecular substructures built from a census of molecular features shows patchwork-like accretion patterns. Ideographic analyses of ribosomal history uncover the early appearance of structures supporting mRNA decoding and tRNA translocation, the coevolution of ribosomal proteins and RNA, and a first evolutionary transition that brings ribosomal subunits together into a processive protein biosynthetic complex. Nomothetic structural biology studies of tertiary interactions and ancient insertions in rRNA complement these findings, once concentric layering assumptions are removed. Patterns of coaxial helical stacking reveal a frustrated dynamics of outward and inward ribosomal growth possibly mediated by structural grafting. The early rise of the ribosomal ‘turnstile’ suggests an evolutionary transition in natural biological computation. Results make explicit the need to understand processes of molecular growth and information transfer of macromolecules.

Ribosomal accretion, apriorism and the phylogenetic method: a response to Petrov and Williams

Historical (ideographic) and non-historical (nomothetic) studies of ribosomal accretion appear to arrive at diametrically opposite conclusions. Phylogenetic analysis of thousands of RNA molecules and protein structures in hundreds of genomes supports the structural origin of the ribosome in RNA decoding and ribosomal mechanics. Predictions from extant features in a handful of rRNA structural models of the large ribosomal subunit support its origin in protein biosynthesis. In recent correspondence, one of us reported that correcting dismissals of conflicting data and avoiding unwarranted assumptions of the nomothetic method reconciled conclusions. In response, Petrov and Williams dismissed our arguments claiming we did not understand their algorithmic model of ribosomal apical growth. Instead, they controverted the historical approach. Here we show that their objections to the phylogenetic method are unjustified, that their algorithm subjectively guarantees back-in-time molecular deconstructions toward the protein biosynthetic core, and that processes of ribosomal growth are much more complex. We prompt abandoning apriorism, decreasing ad hoc hypotheses and integrating historical and non-historical scientific methods.

Interconversion between parallel and antiparallel conformations of a 4H RNA junction in domain 3 of foot-and-mouth disease virus IRES captured by dynamics simulations

RNA junctions are common secondary structural elements present in a wide range of RNA species. They play crucial roles in directing the overall folding of RNA molecules as well as in a variety of biological functions. In particular, there has been great interest in the dynamics of RNA junctions, including conformational pathways of fully base-paired 4-way (4H) RNA junctions. In such constructs, all nucleotides participate in one of the four double-stranded stem regions, with no connecting loops. Dynamical aspects of these 4H RNAs are interesting because frequent interchanges between parallel and antiparallel conformations are thought to occur without binding of other factors. Gel electrophoresis and single-molecule fluorescence resonance energy transfer experiments have suggested two possible pathways: one involves a helical rearrangement via disruption of coaxial stacking, and the other occurs by a rotation between the helical axes of coaxially stacked conformers. Employing molecular dynamics simulations, we explore this conformational variability in a 4H junction derived from domain 3 of the foot-and-mouth disease virus internal ribosome entry site (IRES); this junction contains highly conserved motifs for RNA-RNA and RNA-protein interactions, important for IRES activity. Our simulations capture transitions of the 4H junction between parallel and antiparallel conformations. The interconversion is virtually barrier-free and occurs via a rotation between the axes of coaxially stacked helices with a transient perpendicular intermediate. We characterize this transition, with various interhelical orientations, by pseudodihedral angle and interhelical distance measures. The high flexibility of the junction, as also demonstrated experimentally, is suitable for IRES activity. Because foot-and-mouth disease virus IRES structure depends on long-range interactions involving domain 3, the perpendicular intermediate, which maintains coaxial stacking of helices and thereby consensus primary and secondary structure information, may be beneficial for guiding the overall organization of the RNA system in domain 3.

Topological constraints are major determinants of tRNA tertiary structure and dynamics and provide basis for tertiary folding cooperativity

Recent studies have shown that basic steric and connectivity constraints encoded at the secondary structure level are key determinants of 3D structure and dynamics in simple two-way RNA junctions. However, the role of these topological constraints in higher order RNA junctions remains poorly understood. Here, we use a specialized coarse-grained molecular dynamics model to directly probe the thermodynamic contributions of topological constraints in defining the 3D architecture and dynamics of transfer RNA (tRNA). Topological constraints alone restrict tRNA's allowed conformational space by over an order of magnitude and strongly discriminate against formation of non-native tertiary contacts, providing a sequence independent source of folding specificity. Topological constraints also give rise to long-range correlations between the relative orientation of tRNA's helices, which in turn provides a mechanism for encoding thermodynamic cooperativity between distinct tertiary interactions. These aspects of topological constraints make it such that only several tertiary interactions are needed to confine tRNA to its native global structure and specify functionally important 3D dynamics. We further show that topological constraints are conserved across tRNA's different naturally occurring secondary structures. Taken together, our results emphasize the central role of secondary-structure-encoded topological constraints in defining RNA 3D structure, dynamics and folding.

TOPOFOLD, the designed modular biomolecular folds: polypeptide-based molecular origami nanostructures following the footsteps of DNA

Biopolymers, the essential components of life, are able to form many complex nanostructures, and proteins in particular are the material of choice for most cellular processes. Owing to numerous cooperative interactions, rational design of new protein folds remains extremely challenging. An alternative strategy is to design topofolds-nanostructures built from polypeptide arrays of interacting modules that define their topology. Over the course of the last several decades DNA has successfully been repurposed from its native role of information storage to a smart nanomaterial used for nanostructure self-assembly of almost any shape, which is largely because of its programmable nature. Unfortunately, polypeptides do not possess the straightforward complementarity as do nucleic acids. However, a modular approach can nevertheless be used to assemble polypeptide nanostructures, as was recently demonstrated on a single-chain polypeptide tetrahedron. This review focuses on the current state-of-the-art in the field of topological polypeptide folds. It starts with a brief overview of the field of structural DNA and RNA nanotechnology, from which it draws parallels and possible directions of development for the emerging field of polypeptide-based nanotechnology. The principles of topofold strategy and unique properties of such polypeptide nanostructures in comparison to native protein folds are discussed. Reasons for the apparent absence of such folds in nature are also examined. Physicochemical versatility of amino acid residues and cost-effective production makes polypeptides an attractive platform for designed functional bionanomaterials.

RNA design rules from a massive open laboratory

Self-assembling RNA molecules present compelling substrates for the rational interrogation and control of living systems. However, imperfect in silico models--even at the secondary structure level--hinder the design of new RNAs that function properly when synthesized. Here, we present a unique and potentially general approach to such empirical problems: the Massive Open Laboratory. The EteRNA project connects 37,000 enthusiasts to RNA design puzzles through an online interface. Uniquely, EteRNA participants not only manipulate simulated molecules but also control a remote experimental pipeline for high-throughput RNA synthesis and structure mapping. We show herein that the EteRNA community leveraged dozens of cycles of continuous wet laboratory feedback to learn strategies for solving in vitro RNA design problems on which automated methods fail. The top strategies--including several previously unrecognized negative design rules--were distilled by machine learning into an algorithm, EteRNABot. Over a rigorous 1-y testing phase, both the EteRNA community and EteRNABot significantly outperformed prior algorithms in a dozen RNA secondary structure design tests, including the creation of dendrimer-like structures and scaffolds for small molecule sensors. These results show that an online community can carry out large-scale experiments, hypothesis generation, and algorithm design to create practical advances in empirical science.

Novel features for identifying A-minors in three-dimensional RNA molecules

RNA tertiary interactions or tertiary motifs are conserved structural patterns formed by pairwise interactions between nucleotides. They include base-pairing, base-stacking, and base-phosphate interactions. A-minor motifs are the most common tertiary interactions in the large ribosomal subunit. The A-minor motif is a nucleotide triple in which minor groove edges of an adenine base are inserted into the minor groove of neighboring helices, leading to interaction with a stabilizing base pair. We propose here novel features for identifying and predicting A-minor motifs in a given three-dimensional RNA molecule. By utilizing the features together with machine learning algorithms including random forests and support vector machines, we show experimentally that our approach is capable of predicting A-minor motifs in the given RNA molecule effectively, demonstrating the usefulness of the proposed approach. The techniques developed from this work will be useful for molecular biologists and biochemists to analyze RNA tertiary motifs, specifically A-minor interactions.

Network Theory Tools for RNA Modeling

An introduction into the usage of graph or network theory tools for the study of RNA molecules is presented. By using vertices and edges to define RNA secondary structures as tree and dual graphs, we can enumerate, predict, and design RNA topologies. Graph connectivity and associated Laplacian eigenvalues relate to biological properties of RNA and help understand RNA motifs as well as build, by computational design, various RNA target structures. Importantly, graph theoretical representations of RNAs reduce drastically the conformational space size and therefore simplify modeling and prediction tasks. Ongoing challenges remain regarding general RNA design, representation of RNA pseudoknots, and tertiary structure prediction. Thus, developments in network theory may help advance RNA biology.

Predicting helical topologies in RNA junctions as tree graphs

RNA molecules are important cellular components involved in many fundamental biological processes. Understanding the mechanisms behind their functions requires knowledge of their tertiary structures. Though computational RNA folding approaches exist, they often require manual manipulation and expert intuition; predicting global long-range tertiary contacts remains challenging. Here we develop a computational approach and associated program module (RNAJAG) to predict helical arrangements/topologies in RNA junctions. Our method has two components: junction topology prediction and graph modeling. First, junction topologies are determined by a data mining approach from a given secondary structure of the target RNAs; second, the predicted topology is used to construct a tree graph consistent with geometric preferences analyzed from solved RNAs. The predicted graphs, which model the helical arrangements of RNA junctions for a large set of 200 junctions using a cross validation procedure, yield fairly good representations compared to the helical configurations in native RNAs, and can be further used to develop all-atom models as we show for two examples. Because junctions are among the most complex structural elements in RNA, this work advances folding structure prediction methods of large RNAs. The RNAJAG module is available to academic users upon request.

An algorithmic game-theory approach for coarse-grain prediction of RNA 3D structure

We present a new approach for the prediction of the coarse-grain 3D structure of RNA molecules. We model a molecule as being made of helices and junctions. Those junctions are classified into topological families that determine their preferred 3D shapes. All the parts of the molecule are then allowed to establish long-distance contacts that induce a 3D folding of the molecule. An algorithm relying on game theory is proposed to discover such long-distance contacts that allow the molecule to reach a Nash equilibrium. As reported by our experiments, this approach allows one to predict the global shape of large molecules of several hundreds of nucleotides that are out of reach of the state-of-the-art methods.

Candidate RNA structures for domain 3 of the foot-and-mouth-disease virus internal ribosome entry site

The foot-and-mouth-disease virus (FMDV) utilizes non-canonical translation initiation for viral protein synthesis, by forming a specific RNA structure called internal ribosome entry site (IRES). Domain 3 in FMDV IRES is phylogenetically conserved and highly structured; it contains four-way junctions where intramolecular RNA–RNA interactions serve as a scaffold for the RNA to fold for efficient IRES activity. Although the 3D structure of domain 3 is crucial to exploring and deciphering the initiation mechanism of translation, little is known. Here, we employ a combination of various modeling approaches to propose candidate tertiary structures for the apical region of domain 3, thought to be crucial for IRES function. We begin by modeling junction topology candidates and build atomic 3D models consistent with available experimental data. We then investigate each of the four candidate 3D structures by molecular dynamics simulations to determine the most energetically favorable configurations and to analyze specific tertiary interactions. Only one model emerges as viable containing not only the specific binding site for the GNRA tetraloop but also helical arrangements which enhance the stability of domain 3. These collective findings, together with available experimental data, suggest a plausible theoretical tertiary structure of the apical region in FMDV IRES domain 3.

Exploring Ty1 retrotransposon RNA structure within virus-like particles

Ty1, a long terminal repeat retrotransposon of Saccharomyces, is structurally and functionally related to retroviruses. However, a differentiating aspect between these retroelements is the diversity of the replication strategies used by long terminal repeat retrotransposons. To understand the structural organization of cis-acting elements present on Ty1 genomic RNA from the GAG region that control reverse transcription, we applied chemoenzymatic probing to RNA/tRNA complexes assembled in vitro and to the RNA in virus-like particles. By comparing different RNA states, our analyses provide a comprehensive structure of the primer-binding site, a novel pseudoknot adjacent to the primer-binding sites, three regions containing palindromic sequences that may be involved in RNA dimerization or packaging and candidate protein interaction sites. In addition, we determined the impact of a novel form of transposon control based on Ty1 antisense transcripts that associate with virus-like particles. Our results support the idea that antisense RNAs inhibit retrotransposition by targeting Ty1 protein function rather than annealing with the RNA genome.

Simultaneous prediction of RNA secondary structure and helix coaxial stacking

Background

RNA secondary structure plays a scaffolding role for RNA tertiary conformation. Accurate secondary structure prediction can not only identify double-stranded helices and single stranded-loops but also help provide information for potential tertiary interaction motifs critical to the 3D conformation. The average accuracy in ab initio prediction remains 70%; performance improvement has only been limited to short RNA sequences. The prediction of tertiary interaction motifs is difficult without multiple, related sequences that are usually not available. This paper presents research that aims to improve the secondary structure prediction performance and to develop a capability to predict coaxial stacking between helices. Coaxial stacking positions two helices on the same axis, a tertiary motif present in almost all junctions that account for a high percentage of RNA tertiary structures.

Results

This research identified energetic rules for coaxial stacks and geometric constraints on stack combinations, which were applied to developing an efficient dynamic programming application for simultaneous prediction of secondary structure and coaxial stacking. Results on a number of non-coding RNA data sets, of short and moderately long lengths, show a performance improvement (specially on tRNAs) for secondary structure prediction when compared with existing methods. The program also demonstrates a capability for prediction of coaxial stacking.

Conclusions

The significant leap of performance on tRNAs demonstrated in this work suggests that a breakthrough to a higher performance in RNA secondary structure prediction may lie in understanding contributions from tertiary motifs critical to the structure, as such information can be used to constrain geometrically as well as energetically the space of RNA secondary structure.

New insights into the fundamental role of topological constraints as a determinant of two-way junction conformation

Recent studies have shown that topological constraints encoded at the RNA secondary structure level involving basic steric and stereochemical forces can significantly restrict the orientations sampled by helices across two-way RNA junctions. Here, we formulate these topological constraints in greater quantitative detail and use this topological framework to rationalize long-standing but poorly understood observations regarding the basic behavior of RNA two-way junctions. Notably, we show that the asymmetric nature of the A-form helix and the finite length of a bulge provide a physical basis for the experimentally observed directionality and bulge-length amplitude dependence of bulge induced inter-helical bends. We also find that the topologically allowed space can be modulated by variations in sequence, particularly with the addition of non-canonical GU base pairs at the junction, and, surprisingly, by the length of the 5′ and 3′ helices. A survey of two-way RNA junctions in the protein data bank confirms that junction residues have a strong preference to adopt looped-in, non-canonically base-paired conformations, providing a route for extending our bulge-directed framework to internal loop motifs and implying a simplified link between secondary and tertiary structure. Finally, our results uncover a new simple mechanism for coupling junction-induced topological constraints with tertiary interactions.

Predicting coaxial helical stacking in RNA junctions

RNA junctions are important structural elements that form when three or more helices come together in space in the tertiary structures of RNA molecules. Determining their structural configuration is important for predicting RNA 3D structure. We introduce a computational method to predict, at the secondary structure level, the coaxial helical stacking arrangement in junctions, as well as classify the junction topology. Our approach uses a data mining approach known as random forests, which relies on a set of decision trees trained using length, sequence and other variables specified for any given junction. The resulting protocol predicts coaxial stacking within three- and four-way junctions with an accuracy of 81% and 77%, respectively; the accuracy increases to 83% and 87%, respectively, when knowledge from the junction family type is included. Coaxial stacking predictions for the five to ten-way junctions are less accurate (60%) due to sparse data available for training. Additionally, our application predicts the junction family with an accuracy of 85% for three-way junctions and 74% for four-way junctions. Comparisons with other methods, as well applications to unsolved RNAs, are also presented. The web server Junction-Explorer to predict junction topologies is freely available at: http://bioinformatics.njit.edu/junction.

Computational approaches to RNA structure prediction, analysis, and design

RNA molecules are important cellular components involved in many fundamental biological processes. Understanding the mechanisms behind their functions requires RNA tertiary structure knowledge. Although modeling approaches for the study of RNA structures and dynamics lag behind efforts in protein folding, much progress has been achieved in the past two years. Here, we review recent advances in RNA folding algorithms, RNA tertiary motif discovery, applications of graph theory approaches to RNA structure and function, and in silico generation of RNA sequence pools for aptamer design. Advances within each area can be combined to impact many problems in RNA structure and function.

Topological constraints: using RNA secondary structure to model 3D conformation, folding pathways, and dynamic adaptation

Accompanying recent advances in determining RNA secondary structure is the growing appreciation for the importance of relatively simple topological constraints, encoded at the secondary structure level, in defining the overall architecture, folding pathways, and dynamic adaptability of RNA. A new view is emerging in which tertiary interactions do not define RNA 3D structure, but rather, help select specific conformers from an already narrow, topologically pre-defined conformational distribution. Studies are providing fundamental insights into the nature of these topological constraints, how they are encoded by the RNA secondary structure, and how they interplay with other interactions, breathing new meaning to RNA secondary structure. New approaches have been developed that take advantage of topological constraints in determining RNA backbone conformation based on secondary structure, and a limited set of other, easily accessible constraints. Topological constraints are also providing a much-needed framework for rationalizing and describing RNA dynamics and structural adaptation. Finally, studies suggest that topological constraints may play important roles in steering RNA folding pathways. Here, we review recent advances in our understanding of topological constraints encoded by the RNA secondary structure.

Clustering to identify RNA conformations constrained by secondary structure

RNA often folds hierarchically, so that its sequence defines its secondary structure (helical base-paired regions connected by single-stranded junctions), which subsequently defines its tertiary fold. To preserve base-pairing and chain connectivity, the three-dimensional conformations that RNA can explore are strongly confined compared to when secondary structure constraints are not enforced. Using three examples, we studied how secondary structure confines and dictates an RNA's preferred conformations. We made use of Macromolecular Conformations by SYMbolic programming (MC-Sym) fragment assembly to generate RNA conformations constrained by secondary structure. Then, to understand the correlations between different helix placements and orientations, we robustly clustered all RNA conformations by employing unique methods to remove outliers and estimate the best number of conformational clusters. We observed that the preferred conformation (as judged by largest cluster size) for each type of RNA junction molecule tested is consistent with its biological function. Further, the improved quality of models in our pruned datasets facilitates subsequent discrimination using scoring functions based either on statistical analysis (knowledge based) or experimental data.

Identification of a tertiary interaction important for cooperative ligand binding by the glycine riboswitch

The glycine riboswitch has a tandem dual aptamer configuration, where each aptamer is a separate ligand-binding domain, but the aptamers function together to bind glycine cooperatively. We sought to understand the molecular basis of glycine riboswitch cooperativity by comparing sites of tertiary contacts in a series of cooperative and noncooperative glycine riboswitch mutants using hydroxyl radical footprinting, in-line probing, and native gel-shift studies. The results illustrate the importance of a direct or indirect interaction between the P3b hairpin of aptamer 2 and the P1 helix of aptamer 1 in cooperative glycine binding. Furthermore, our data support a model in which glycine binding is sequential; where the binding of glycine to the second aptamer allows tertiary interactions to be made that facilitate binding of a second glycine molecule to the first aptamer. These results provide insight into cooperative ligand binding in RNA macromolecules.

Engineering cooperative tecto-RNA complexes having programmable stoichiometries

High affinity and specificity RNA-RNA binding interfaces can be constructed by combining pairs of GNRA loop/loop-receptor interaction motifs. These interactions can be fused using flexible four-way junction motifs to create divalent, self-assembling scaffolding units ('tecto-RNA') that have favorable properties for nanomedicine and other applications. We describe the design and directed assembly of tecto-RNA units ranging from closed, cooperatively assembling ring-shaped complexes of programmable stoichiometries (dimers, trimers and tetramers) to open multimeric structures. The novelty of this work is that tuning of the stoichiometries of self-assembled complexes is achieved by precise positioning of the interaction motifs in the monomer units rather than changing their binding specificities. Structure-probing and transmission electron microscopy studies as well as thermodynamic analysis support formation of closed cooperative complexes that are highly resistant to nuclease digestion. The present designs provide two helical arms per RNA monomer for further functionalization aims.

Computational approaches to 3D modeling of RNA

Many exciting discoveries have recently revealed the versatility of RNA and its importance in a variety of functions within the cell. Since the structural features of RNA are of major importance to their biological function, there is much interest in predicting RNA structure, either in free form or in interaction with various ligands, including proteins, metabolites and other molecules. In recent years, an increasing number of researchers have developed novel RNA algorithms for predicting RNA secondary and tertiary structures. In this review, we describe current experimental and computational advances and discuss recent ideas that are transforming the traditional view of RNA folding. To evaluate the performance of the most recent RNA 3D folding algorithms, we provide a comparative study in order to test the performance of available 3D structure prediction algorithms for an RNA data set of 43 structures of various lengths and motifs. We find that the algorithms vary widely in terms of prediction quality across different RNA lengths and topologies; most predictions have very large root mean square deviations from the experimental structure. We conclude by outlining some suggestions for future RNA folding research.

Exploring RNA structure by integrative molecular modelling

RNA molecular modelling is adequate to rapidly tackle the structure of RNA molecules. With new structured RNAs constituting a central class of cellular regulators discovered every year, the need for swift and reliable modelling methods is more crucial than ever. The pragmatic method based on interactive all-atom molecular modelling relies on the observation that specific structural motifs are recurrently found in RNA sequences. Once identified by a combination of comparative sequence analysis and biochemical data, the motifs composing the secondary structure of a given RNA can be extruded in three dimensions (3D) and used as building blocks assembled manually during a bioinformatic interactive process. Comparing the models to the corresponding crystal structures has validated the method as being powerful to predict the RNA topology and architecture while being less accurate regarding the prediction of base-base interactions. These aspects as well as the necessary steps towards automation will be discussed.

RNA structural motifs that entail hydrogen bonds involving sugar-phosphate backbone atoms of RNA

The growing number of high-resolution crystal structures of large RNA molecules provides much information for understanding the principles of structural organization of these complex molecules. Several in-depth analyses of nucleobase-centered RNA structural motifs and backbone conformations have been published based on this information, including a systematic classification of base pairs by Leontis and Westhof. However, hydrogen bonds involving sugar-phosphate backbone atoms of RNA have not been analyzed systematically until recently, although such hydrogen bonds appear to be common both in local and tertiary interactions. Here we review some backbone structural motifs discussed in the literature and analyze a set of eight high-resolution multi-domain RNA structures. The analyzed RNAs are highly structured: among 5372 nucleotides in this set, 89% are involved in at least one "long-range" RNA-RNA hydrogen bond, i.e., hydrogen bonds between atoms in the same residue or sequential residues are ignored. These long-range hydrogen bonds frequently use backbone atoms as hydrogen bond acceptors, i.e., OP1, OP2, O2', O3', O4', or O5', or as a donor (2'OH). A surprisingly large number of such hydrogen bonds are found, considering that neither single-stranded nor double-stranded regions will contain such hydrogen bonds unless additional interactions with other residues exist. Among 8327 long-range hydrogen bonds found in this set of structures, 2811, or about one-third, are hydrogen bonds entailing RNA backbone atoms; they involve 39% of all nucleotides in the structures. The majority of them (2111) are hydrogen bonds entailing ribose hydroxyl groups, which can be used either as a donor or an acceptor; they constitute 25% of all hydrogen bonds and involve 31% of all nucleotides. The phosphate oxygens OP1 or OP2 are used as hydrogen bond acceptors in 12% of all nucleotides, and the ribose ring oxygen O4' and phosphodiester oxygens O3' and O5' are used in 4%, 4%, and 1% of all nucleotides, respectively. Distributions of geometric parameters and some examples of such hydrogen bonds are presented in this report. A novel motif involving backbone hydrogen bonds, the ribose-phosphate zipper, is also identified.