Hierarchy and dynamics of RNA folding

Universal cold RNA phase transitions

RNA's diversity of structures and functions impacts all life forms since primordia. We use calorimetric force spectroscopy to investigate RNA folding landscapes in previously unexplored low-temperature conditions. We find that Watson-Crick RNA hairpins, the most basic secondary structure elements, undergo a glass-like transition below [Formula: see text]C where the heat capacity abruptly changes and the RNA folds into a diversity of misfolded structures. We hypothesize that an altered RNA biochemistry, determined by sequence-independent ribose-water interactions, outweighs sequence-dependent base pairing. The ubiquitous ribose-water interactions lead to universal RNA phase transitions below TG, such as maximum stability at [Formula: see text]C where water density is maximum, and cold denaturation at [Formula: see text]C. RNA cold biochemistry may have a profound impact on RNA function and evolution.

Context-dependent structure formation of hairpin motifs in bacteriophage MS2 genomic RNA

Many functions of ribonucleic acid (RNA) rely on its ability to assume specific sequence-structure motifs. Packaging signals found in certain RNA viruses are one such prominent example of functional RNA motifs. These signals are short hairpin loops that interact with coat proteins and drive viral self-assembly. As they are found in different positions along the much longer genomic RNA, the formation of their correct structure occurs as a part of a larger context. Any changes to this context can consequently lead to changes in the structure of the motifs themselves. In fact, previous studies have shown that structure and function of RNA motifs can be highly context sensitive to the flanking sequence surrounding them. However, in what ways different flanking sequences influence the structure of an RNA motif they surround has yet to be studied in detail. We focus on a hairpin-rich region of the RNA genome of bacteriophage MS2-a well-studied RNA virus with a wide potential for use in biotechnology-and systematically examine context-dependent structural stability of 14 previously identified hairpin motifs, which include putative and confirmed packaging signals. Combining secondary and tertiary RNA structure prediction of the hairpin motifs placed in different contexts, ranging from the native genomic sequence to random RNA sequences and unstructured poly-U sequences, we determine different measures of motif structural stability. In this way, we show that while some motif structures can be stable in any context, others require specific context provided by the genome. Our results demonstrate the importance of context in RNA structure formation and how changes in the flanking sequence of an RNA motif sometimes lead to drastic changes in its structure. Structural stability of a motif in different contexts could provide additional insights into its functionality as well as assist in determining whether it remains functional when intentionally placed in other contexts.

lociPARSE: a locality-aware invariant point attention model for scoring RNA 3D structures

A scoring function that can reliably assess the accuracy of a 3D RNA structural model in the absence of experimental structure is not only important for model evaluation and selection but also useful for scoring-guided conformational sampling. However, high-fidelity RNA scoring has proven to be difficult using conventional knowledge-based statistical potentials and currently-available machine learning-based approaches. Here we present lociPARSE, a locality-aware invariant point attention architecture for scoring RNA 3D structures. Unlike existing machine learning methods that estimate superposition-based root mean square deviation (RMSD), lociPARSE estimates Local Distance Difference Test (lDDT) scores capturing the accuracy of each nucleotide and its surrounding local atomic environment in a superposition-free manner, before aggregating information to predict global structural accuracy. Tested on multiple datasets including CASP15, lociPARSE significantly outperforms existing statistical potentials (rsRNASP, cgRNASP, DFIRE-RNA, and RASP) and machine learning methods (ARES and RNA3DCNN) across complementary assessment metrics. lociPARSE is freely available at https://github.com/Bhattacharya-Lab/lociPARSE.

RNA adapts its flexibility to efficiently fold and resist unfolding

Recent studies have demonstrated that the mechanisms through which biopolymers like RNA interconvert between multiple folded structures are critical for their cellular functions. A major obstacle to elucidating these mechanisms is the lack of experimental approaches that can resolve these interconversions between functionally relevant biomolecular structures. Here, using a nano-electronic device with microsecond time resolution, we dissect the complete set of structural rearrangements executed by an ultra-stable RNA, the UUCG stem-loop, at the single-molecule level. We show that the stem-loop samples at least four conformations along two folding pathways leading to two distinct folded structures, only one of which has been previously observed. By modulating its flexibility, the stem-loop can adaptively select between these pathways, enabling it to both fold rapidly and resist unfolding. This paradigm of stabilization through compensatory changes in flexibility broadens our understanding of stable RNA structures and is expected to serve as a general strategy employed by all biopolymers.

Frontiers and Challenges of Computing ncRNAs Biogenesis, Function and Modulation

Non-coding RNAs (ncRNAs), generated from nonprotein coding DNA sequences, constitute 98-99% of the human genome. Non-coding RNAs encompass diverse functional classes, including microRNAs, small interfering RNAs, PIWI-interacting RNAs, small nuclear RNAs, small nucleolar RNAs, and long non-coding RNAs. With critical involvement in gene expression and regulation across various biological and physiopathological contexts, such as neuronal disorders, immune responses, cardiovascular diseases, and cancer, non-coding RNAs are emerging as disease biomarkers and therapeutic targets. In this review, after providing an overview of non-coding RNAs' role in cell homeostasis, we illustrate the potential and the challenges of state-of-the-art computational methods exploited to study non-coding RNAs biogenesis, function, and modulation. This can be done by directly targeting them with small molecules or by altering their expression by targeting the cellular engines underlying their biosynthesis. Drawing from applications, also taken from our work, we showcase the significance and role of computer simulations in uncovering fundamental facets of ncRNA mechanisms and modulation. This information may set the basis to advance gene modulation tools and therapeutic strategies to address unmet medical needs.

Causes, functions, and therapeutic possibilities of RNA secondary structure ensembles and alternative states

RNA secondary structure plays essential roles in encoding RNA regulatory fate and function. Most RNAs populate ensembles of alternatively paired states and are continually unfolded and refolded by cellular processes. Measuring these structural ensembles and their contributions to cellular function has traditionally posed major challenges, but new methods and conceptual frameworks are beginning to fill this void. In this review, we provide a mechanism- and function-centric compendium of the roles of RNA secondary structural ensembles and minority states in regulating the RNA life cycle, from transcription to degradation. We further explore how dysregulation of RNA structural ensembles contributes to human disease and discuss the potential of drugging alternative RNA states to therapeutically modulate RNA activity. The emerging paradigm of RNA structural ensembles as central to RNA function provides a foundation for a deeper understanding of RNA biology and new therapeutic possibilities.

Classification and Identification of Non-canonical Base Pairs and Structural Motifs

The 3D structures of many ribonucleic acid (RNA) loops are characterized by highly organized networks of non-canonical interactions. Multiple computational methods have been developed to annotate structures with those interactions or automatically identify recurrent interaction networks. By contrast, the reverse problem that aims to retrieve the geometry of a look from its sequence or ensemble of interactions remains much less explored. In this chapter, we will describe how to retrieve and build families of conserved structural motifs using their underlying network of non-canonical interactions. Then, we will show how to assign sequence alignments to those families and use the software BayesPairing to build statistical models of structural motifs with their associated sequence alignments. From this model, we will apply BayesPairing to identify in new sequences regions where those loop geometries can occur.

Watching ion-driven kinetics of ribozyme folding and misfolding caused by energetic and topological frustration one molecule at a time

Folding of ribozymes into well-defined tertiary structures usually requires divalent cations. How Mg2+ ions direct the folding kinetics has been a long-standing unsolved problem because experiments cannot detect the positions and dynamics of ions. To address this problem, we used molecular simulations to dissect the folding kinetics of the Azoarcus ribozyme by monitoring the path each molecule takes to reach the folded state. We quantitatively establish that Mg2+ binding to specific sites, coupled with counter-ion release of monovalent cations, stimulate the formation of secondary and tertiary structures, leading to diverse pathways that include direct rapid folding and trapping in misfolded structures. In some molecules, key tertiary structural elements form when Mg2+ ions bind to specific RNA sites at the earliest stages of the folding, leading to specific collapse and rapid folding. In others, the formation of non-native base pairs, whose rearrangement is needed to reach the folded state, is the rate-limiting step. Escape from energetic traps, driven by thermal fluctuations, occurs readily. In contrast, the transition to the native state from long-lived topologically trapped native-like metastable states is extremely slow. Specific collapse and formation of energetically or topologically frustrated states occur early in the assembly process.

Magnesium Ion-Driven Folding and Conformational Switching Kinetics of Tetracycline Binding Aptamer: Implications for in vivo Riboswitch Engineering

Engineering in vitro selected RNA aptamers into in vivo functional riboswitches represents a long-standing challenge in molecular biology. The highly specific aptamer domain of the riboswitch undergoes a conformational adjustment in response to ligand sensing, which in turn exerts the regulatory function. Besides essential factors like structural complexity and ligand binding kinetics, the active role of magnesium ions in stabilizing RNA tertiary structures and assisting in ligand binding can be a vital criterion. We present spectroscopic studies on the magnesium ion-driven folding of the Tetracycline binding aptamer. Using fluorescent labels, the aptamer pre-folding and subsequent ligand binding is monitored by magnesium titration experiments and time-resolved stopped-flow measurements. A minimum concentration of 0.5 mM magnesium is required to fold into a magnesium ion-stabilized binding-competent state with a preformed binding pocket. Tetracycline binding causes a pronounced conformational change that results in the establishment of the triple helix core motif, and that further propagates towards the closing stem. By a dynamic acquisition of magnesium ions, a kink motif is formed at the intersection of the triple helix and closing stem regions. This ultimately entails a stabilization of the closing stem which is discussed as a key element in the regulatory function of the Tetracycline aptamer.

Structural Expansion of Catalytic RNA Nanostructures through Oligomerization of a Cyclic Trimer of Engineered Ribozymes

The multimolecular assembly of three-dimensionally structured proteins forms their quaternary structures, some of which have high geometric symmetry. The size and complexity of protein quaternary structures often increase in a hierarchical manner, with simpler, smaller structures serving as units for larger quaternary structures. In this study, we exploited oligomerization of a ribozyme cyclic trimer to achieve larger ribozyme-based RNA assembly. By installing kissing loop (KL) interacting units to one-, two-, or three-unit RNA molecules in the ribozyme trimer, we constructed dimers, open-chain oligomers, and branched oligomers of ribozyme trimer units. One type of open-chain oligomer preferentially formed a closed tetramer containing 12 component RNAs to provide 12 ribozyme units. We also observed large assembly of ribozyme trimers, which reached 1000 nm in size.

Comparative analysis of RNA secondary structure accuracy on predicted RNA 3D models

RNA structure is conformationally dynamic, and accurate all-atom tertiary (3D) structure modeling of RNA remains challenging with the prevailing tools. Secondary structure (2D) information is the standard prerequisite for most RNA 3D modeling. Despite several 2D and 3D structure prediction tools proposed in recent years, one of the challenges is to choose the best combination for accurate RNA 3D structure prediction. Here, we benchmarked seven small RNA PDB structures (40 to 90 nucleotides) with different topologies to understand the effects of different 2D structure predictions on the accuracy of 3D modeling. The current study explores the blind challenge of 2D to 3D conversions and highlights the performances of de novo RNA 3D modeling from their predicted 2D structure constraints. Our results show that conformational sampling-based methods such as SimRNA and IsRNA1 depend less on 2D accuracy, whereas motif-based methods account for 2D evidence. Our observations illustrate the disparities in available 3D and 2D prediction methods and may further offer insights into developing topology-specific or family-specific RNA structure prediction pipelines.

D-ORB: A Web Server to Extract Structural Features of Related But Unaligned RNA Sequences

Identifying the common structural elements of functionally related RNA sequences (family) is usually based on an alignment of the sequences, which is often subject to human bias and may not be accurate. The resulting covariance model (CM) provides probabilities for each base to covary with another, which allows to support evolutionarily the formation of double helical regions and possibly pseudoknots. The coexistence of alternative folds in RNA, resulting from its dynamic nature, may lead to the potential omission of motifs by CM. To overcome this limitation, we present D-ORB, a system of algorithms that identifies overrepresented motifs in the secondary conformational landscapes of a family when compared to those of unrelated sequences. The algorithms are bundled into an easy-to-use website allowing users to submit a family, and optionally provide unrelated sequences. D-ORB produces a non-pseudoknotted secondary structure based on the overrepresented motifs, a deep neural network classifier and two decision trees. When used to model an Rfam family, D-ORB fits overrepresented motifs in the corresponding Rfam structure; more than a hundred Rfam families have been modeled. The statistical approach behind D-ORB derives the structural composition of an RNA family, making it a valuable tool for analyzing and modeling it. Its easy-to-use interface and advanced algorithms make it an essential resource for researchers studying RNA structure. D-ORB is available at https://d-orb.major.iric.ca/.

Mono-valent salt corrections for RNA secondary structures in the ViennaRNA package

BACKGROUND

RNA features a highly negatively charged phosphate backbone that attracts a cloud of counter-ions that reduce the electrostatic repulsion in a concentration dependent manner. Ion concentrations thus have a large influence on folding and stability of RNA structures. Despite their well-documented effects, salt effects are not handled consistently by currently available secondary structure prediction algorithms. Combining Debye-Hückel potentials for line charges and Manning's counter-ion condensation theory, Einert et al. (Biophys J 100: 2745-2753, 2011) modeled the energetic contributions of monovalent cations on loops and helices.

RESULTS

The model of Einert et al. is adapted to match the structure of the dynamic programming recursion of RNA secondary structure prediction algorithms. An empirical term describing the salt dependence of the duplex initiation energy is added to improve co-folding predictions for two or more RNA strands. The slightly modified model is implemented in the ViennaRNA package in such way that only the energy parameters but not the algorithmic structure is affected. A comparison with data from the literature show that predicted free energies and melting temperatures are in reasonable agreement with experiments.

CONCLUSION

The new feature in the ViennaRNA package makes it possible to study effects of salt concentrations on RNA folding in a systematic manner. Strictly speaking, the model pertains only to mono-valent cations, and thus covers the most important parameter, i.e., the NaCl concentration. It remains a question for future research to what extent unspecific effects of bi- and tri-valent cations can be approximated in a similar manner.

AVAILABILITY

Corrections for the concentration of monovalent cations are available in the ViennaRNA package starting from version 2.6.0.

Single-Molecule Correlated Chemical Probing: A Revolution in RNA Structure Analysis

RNA molecules convey biological information both in their linear sequence and in their base-paired secondary and tertiary structures. Chemical probing experiments, which involve treating an RNA with a reagent that modifies conformationally dynamic nucleotides, have broadly enabled examination of short- and long-range RNA structure in diverse contexts, including in living cells. For decades, chemical probing experiments have been interpreted in a per-nucleotide way, such that the reactivity measured at each nucleotide reports the average structure at a position over all RNA molecules within a sample. However, there are numerous important cases where per-nucleotide chemical probing falls short, including for RNAs that are bound by proteins, RNAs that form complex higher order structures, and RNAs that sample multiple conformations.Recent experimental and computational innovations have started a revolution in RNA structure analysis by transforming chemical probing into a massively parallel, single-molecule experiment. Enabled by a specialized reverse transcription strategy called mutational profiling (MaP), multiple chemical modification events can be measured within individual RNA molecules. Nucleotides that communicate structurally through direct base pairing or large-scale folding-unfolding transitions will react with chemical probes in a correlated manner, thereby revealing structural complexity hidden to conventional approaches. These single-molecule correlated chemical probing (smCCP) experiments can be interpreted to directly identify nucleotides that base pair (the PAIR-MaP strategy) and to reveal long-range, through-space structural communication (RING-MaP). Correlated probing can also define the thermodynamic populations of complex RNA ensembles (DANCE-MaP). Complex RNA-protein networks can be interrogated by cross-linking proteins to RNA and measuring correlations between cross-linked positions (RNP-MaP).smCCP thus visualizes RNA secondary and higher-order structure with unprecedented accuracy, defining novel structures, RNA-protein interaction networks, time-resolved dynamics, and allosteric structural switches. These strategies are not mutually exclusive; in favorable cases, multiple levels of RNA structure ─ base pairing, through-space structural communication, and equilibrium ensembles ─ can be resolved concurrently. The physical experimentation required for smCCP is profoundly simple, and experiments are readily performed in cells on RNAs of any size, including large noncoding RNAs and mRNAs. Single-molecule correlated chemical probing is paving the way for a new generation of biophysical studies on RNA in living systems.

Pseudoknots in RNA Structure Prediction

RNA molecules play active roles in the cell and are important for numerous applications in biotechnology and medicine. The function of an RNA molecule stems from its structure. RNA structure determination is time consuming, challenging, and expensive using experimental methods. Thus, much research has been directed at RNA structure prediction through computational means. Many of these methods focus primarily on the secondary structure of the molecule, ignoring the possibility of pseudoknotted structures. However, pseudoknots are known to play functional roles in many RNA molecules or in their method of interaction with other molecules. Improving the accuracy and efficiency of computational methods that predict pseudoknots is an ongoing challenge for single RNA molecules, RNA-RNA interactions, and RNA-protein interactions. To improve the accuracy of prediction, many methods focus on specific applications while restricting the length and the class of the pseudoknotted structures they can identify. In recent years, computational methods for structure prediction have begun to catch up with the impressive developments seen in biotechnology. Here, we provide a non-comprehensive overview of available pseudoknot prediction methods and their best-use cases. © 2023 Wiley Periodicals LLC.

Characterizing the Conformational Free-Energy Landscape of RNA Stem-Loops Using Single-Molecule Field-Effect Transistors

We have developed and used single-molecule field-effect transistors (smFETs) to characterize the conformational free-energy landscape of RNA stem-loops. Stem-loops are one of the most common RNA structural motifs and serve as building blocks for the formation of complex RNA structures. Given their prevalence and integral role in RNA folding, the kinetics of stem-loop (un)folding has been extensively characterized using both experimental and computational approaches. Interestingly, these studies have reported vastly disparate timescales of (un)folding, which has been interpreted as evidence that (un)folding of even simple stem-loops occurs on a highly rugged conformational energy landscape. Because smFETs do not rely on fluorophore reporters of conformation or mechanical (un)folding forces, they provide a unique approach that has allowed us to directly monitor tens of thousands of (un)folding events of individual stem-loops at a 200 μs time resolution. Our results show that under our experimental conditions, stem-loops (un)fold over a 1-200 ms timescale during which they transition between ensembles of unfolded and folded conformations, the latter of which is composed of at least two sub-populations. The 1-200 ms timescale of (un)folding we observe here indicates that smFETs report on complete (un)folding trajectories in which unfolded conformations of the RNA spend long periods of time wandering the free-energy landscape before sampling one of several misfolded conformations or the natively folded conformation. Our findings highlight the extremely rugged landscape on which even the simplest RNA structural elements fold and demonstrate that smFETs are a unique and powerful approach for characterizing the conformational free-energy of RNA.

An overview of structural approaches to study therapeutic RNAs

RNAs provide considerable opportunities as therapeutic agent to expand the plethora of classical therapeutic targets, from extracellular and surface proteins to intracellular nucleic acids and its regulators, in a wide range of diseases. RNA versatility can be exploited to recognize cell types, perform cell therapy, and develop new vaccine classes. Therapeutic RNAs (aptamers, antisense nucleotides, siRNA, miRNA, mRNA and CRISPR-Cas9) can modulate or induce protein expression, inhibit molecular interactions, achieve genome editing as well as exon-skipping. A common RNA thread, which makes it very promising for therapeutic applications, is its structure, flexibility, and binding specificity. Moreover, RNA displays peculiar structural plasticity compared to proteins as well as to DNA. Here we summarize the recent advances and applications of therapeutic RNAs, and the experimental and computational methods to analyze their structure, by biophysical techniques (liquid-state NMR, scattering, reactivity, and computational simulations), with a focus on dynamic and flexibility aspects and to binding analysis. This will provide insights on the currently available RNA therapeutic applications and on the best techniques to evaluate its dynamics and reactivity.

Box-shaped ribozyme octamer formed by face-to-face dimerization of a pair of square-shaped ribozyme tetramers

Naturally occurring ribozymes with defined three-dimensional (3D) structures serve as promising platforms for the design and construction of artificial RNA nanostructures. We constructed a hexameric ribozyme nanostructure by face-to-face dimerization of a pair of triangular ribozyme trimers, unit RNAs of which were derived from the Tetrahymena group I ribozyme. In this study, we have expanded the dimerization strategy to a square-shaped ribozyme tetramer by introducing four pillar units. The resulting box-shaped nanostructures, which contained eight ribozyme units, can be assembled from either four or two components of their unit RNAs.

How does RNA fold dynamically?

Recent advances in interrogating RNA folding dynamics have shown the classical model of RNA folding to be incomplete. Here, we pose three prominent questions for the field that are at the forefront of our understanding of the importance of RNA folding dynamics for RNA function. The first centers on the most appropriate biophysical framework to describe changes to the RNA folding energy landscape that a growing RNA chain encounters during transcriptional elongation. The second focuses on the potential ubiquity of strand displacement - a process by which RNA can rapidly change conformations - and how this process may be generally present in broad classes of seemingly different RNAs. The third raises questions about the potential importance and roles of cellular protein factors in RNA conformational switching. Answers to these questions will greatly improve our fundamental knowledge of RNA folding and function, drive biotechnological advances that utilize engineered RNAs, and potentially point to new areas of biology yet to be discovered.

Toward Convergence in Folding Simulations of RNA Tetraloops: Comparison of Enhanced Sampling Techniques and Effects of Force Field Modifications

Atomistic molecular dynamics simulations represent an established technique for investigation of RNA structural dynamics. Despite continuous development, contemporary RNA simulations still suffer from suboptimal accuracy of empirical potentials (force fields, ffs) and sampling limitations. Development of efficient enhanced sampling techniques is important for two reasons. First, they allow us to overcome the sampling limitations, and second, they can be used to quantify ff imbalances provided they reach a sufficient convergence. Here, we study two RNA tetraloops (TLs), namely the GAGA and UUCG motifs. We perform extensive folding simulations and calculate folding free energies (ΔG_fold^°) with the aim to compare different enhanced sampling techniques and to test several modifications of the nonbonded terms extending the AMBER OL3 RNA ff. We demonstrate that replica-exchange solute tempering (REST2) simulations with 12-16 replicas do not show any sign of convergence even when extended to a timescale of 120 μs per replica. However, the combination of REST2 with well-tempered metadynamics (ST-MetaD) achieves good convergence on a timescale of 5-10 μs per replica, improving the sampling efficiency by at least 2 orders of magnitude. Effects of ff modifications on ΔG_fold^° energies were initially explored by the reweighting approach and then validated by new simulations. We tested several manually prepared variants of the gHBfix potential which improve stability of the native state of both TLs by ∼2 kcal/mol. This is sufficient to conveniently stabilize the folded GAGA TL while the UUCG TL still remains under-stabilized. Appropriate adjustment of van der Waals parameters for C-H···O5' base-phosphate interaction may further stabilize the native states of both TLs by ∼0.6 kcal/mol.

Cotranscriptional RNA strand exchange underlies the gene regulation mechanism in a purine-sensing transcriptional riboswitch

RNA folds cotranscriptionally to traverse out-of-equilibrium intermediate structures that are important for RNA function in the context of gene regulation. To investigate this process, here we study the structure and function of the Bacillus subtilis yxjA purine riboswitch, a transcriptional riboswitch that downregulates a nucleoside transporter in response to binding guanine. Although the aptamer and expression platform domain sequences of the yxjA riboswitch do not completely overlap, we hypothesized that a strand exchange process triggers its structural switching in response to ligand binding. In vivo fluorescence assays, structural chemical probing data and experimentally informed secondary structure modeling suggest the presence of a nascent intermediate central helix. The formation of this central helix in the absence of ligand appears to compete with both the aptamer's P1 helix and the expression platform's transcriptional terminator. All-atom molecular dynamics simulations support the hypothesis that ligand binding stabilizes the aptamer P1 helix against central helix strand invasion, thus allowing the terminator to form. These results present a potential model mechanism to explain how ligand binding can induce downstream conformational changes by influencing local strand displacement processes of intermediate folds that could be at play in multiple riboswitch classes.

Structural basis for impaired 5' processing of a mutant tRNA associated with defects in neuronal homeostasis

SignificanceUnderstanding and treating neurological disorders are global priorities. Some of these diseases are engendered by mutations that cause defects in the cellular synthesis of transfer RNAs (tRNAs), which function as adapter molecules that translate messenger RNAs into proteins. During tRNA biogenesis, ribonuclease P catalyzes removal of the transcribed sequence upstream of the mature tRNA. Here, we focus on a cytoplasmic tRNAArgUCU that is expressed specifically in neurons and, when harboring a particular point mutation, contributes to neurodegeneration in mice. Our results suggest that this mutation favors stable alternative structures that are not cleaved by mouse ribonuclease P and motivate a paradigm that may help to understand the molecular basis for disease-associated mutations in other tRNAs.

RNAStat: An Integrated Tool for Statistical Analysis of RNA 3D Structures

The 3D architectures of RNAs are essential for understanding their cellular functions. While an accurate scoring function based on the statistics of known RNA structures is a key component for successful RNA structure prediction or evaluation, there are few tools or web servers that can be directly used to make comprehensive statistical analysis for RNA 3D structures. In this work, we developed RNAStat, an integrated tool for making statistics on RNA 3D structures. For given RNA structures, RNAStat automatically calculates RNA structural properties such as size and shape, and shows their distributions. Based on the RNA structure annotation from DSSR, RNAStat provides statistical information of RNA secondary structure motifs including canonical/non-canonical base pairs, stems, and various loops. In particular, the geometry of base-pairing/stacking can be calculated in RNAStat by constructing a local coordinate system for each base. In addition, RNAStat also supplies the distribution of distance between any atoms to the users to help build distance-based RNA statistical potentials. To test the usability of the tool, we established a non-redundant RNA 3D structure dataset, and based on the dataset, we made a comprehensive statistical analysis on RNA structures, which could have the guiding significance for RNA structure modeling. The python code of RNAStat, the dataset used in this work, and corresponding statistical data files are freely available at GitHub (https://github.com/RNA-folding-lab/RNAStat).

A novel algorithm for ranking RNA structure candidates

RNA research is advancing at an ever increasing pace. The newest and most state-of-the-art instruments and techniques have made possible the discoveries of new RNAs, and they have carried the field to new frontiers of disease research, vaccine development, therapeutics, and architectonics. Like proteins, RNAs show a marked relationship between structure and function. A deeper grasp of RNAs requires a finer understanding of their elaborate structures. In pursuit of this, cutting-edge experimental and computational structure-probing techniques output several candidate geometries for a given RNA, each of which is perfectly aligned with experimentally determined parameters. Identifying which structure is the most accurate, however, remains a major obstacle. In recent years, several algorithms have been developed for ranking candidate RNA structures in order from most to least probable, though their levels of accuracy and transparency leave room for improvement. Most recently, advances in both areas are demonstrated by rsRNASP, a novel algorithm proposed by Tan et al. rsRNASP is a residue-separation-based statistical potential for three-dimensional structure evaluation, and it outperforms the leading algorithms in the field.

rsRNASP: A residue-separation-based statistical potential for RNA 3D structure evaluation

Knowledge-based statistical potentials have been shown to be rather effective in protein 3-dimensional (3D) structure evaluation and prediction. Recently, several statistical potentials have been developed for RNA 3D structure evaluation, while their performances are either still at a low level for the test datasets from structure prediction models or dependent on the "black-box" process through neural networks. In this work, we have developed an all-atom distance-dependent statistical potential based on residue separation for RNA 3D structure evaluation, namely rsRNASP, which is composed of short- and long-ranged potentials distinguished by residue separation. The extensive examinations against available RNA test datasets show that rsRNASP has apparently higher performance than the existing statistical potentials for the realistic test datasets with large RNAs from structure prediction models, including the newly released RNA-Puzzles dataset, and is comparable to the existing top statistical potentials for the test datasets with small RNAs or near-native decoys. In addition, rsRNASP is superior to RNA3DCNN, a recently developed scoring function through 3D convolutional neural networks. rsRNASP and the relevant databases are available to the public.

A Computational Study of RNA Tetraloop Thermodynamics, Including Misfolded States

An important characteristic of RNA folding is the adoption of alternative configurations of similar stability, often referred to as misfolded configurations. These configurations are considered to compete with correctly folded configurations, although their rigorous thermodynamic and structural characterization remains elusive. Tetraloop motifs found in large ribozymes are ideal systems for an atomistically detailed computational quantification of folding free energy landscapes and the structural characterization of their constituent free energy basins, including nonnative states. In this work, we studied a group of closely related 10-mer tetraloops using a combined parallel tempering and metadynamics technique that allows a reliable sampling of the free energy landscapes, requiring only knowledge that the stem folds into a canonical A-RNA configuration. We isolated and analyzed unfolded, folded, and misfolded populations that correspond to different free energy basins. We identified a distinct misfolded state that has a stability very close to that of the correctly folded state. This misfolded state contains a predominant population that shares the same structural features across all tetraloops studied here and lacks the noncanonical A-G base pair in its loop portion. Further analysis performed with biased trajectories showed that although this competitive misfolded state is not an essential intermediate, it is visited in most of the transitions from unfolded to correctly folded states. Moreover, the tetraloops can transition from this misfolded state to the correctly folded state without requiring extensive unfolding.

RNA Secondary Structures Regulate Adsorption of Fragments onto Flat Substrates

RNA is a functionally rich molecule with multilevel, hierarchical structures whose role in the adsorption to molecular substrates is only beginning to be elucidated. Here, we introduce a multiscale simulation approach that combines a tractable coarse-grained RNA structural model with an interaction potential of a structureless flat adsorbing substrate. Within this approach, we study the specific role of stem-hairpin and multibranch RNA secondary structure motifs on its adsorption phenomenology. Our findings identify a dual regime of adsorption for short RNA fragments with and without the secondary structure and underline the adsorption efficiency in both cases as a function of the surface interaction strength. The observed behavior results from an interplay between the number of contacts formed at the surface and the conformational entropy of the RNA molecule. The adsorption phenomenology of RNA seems to persist also for much longer RNAs as qualitatively observed by comparing the trends of our simulations with a theoretical approach based on an ideal semiflexible polymer chain.

Transcription complexes as RNA chaperones

To exert their functions, RNAs adopt diverse structures, ranging from simple secondary to complex tertiary and quaternary folds. In vivo, RNA folding starts with RNA transcription, and a wide variety of processes are coupled to co-transcriptional RNA folding events, including the regulation of fundamental transcription dynamics, gene regulation by mechanisms like attenuation, RNA processing or ribonucleoprotein particle formation. While co-transcriptional RNA folding and associated co-transcriptional processes are by now well accepted as pervasive regulatory principles in all organisms, investigations into the role of the transcription machinery in co-transcriptional folding processes have so far largely focused on effects of the order in which RNA regions are produced and of transcription kinetics. Recent structural and structure-guided functional analyses of bacterial transcription complexes increasingly point to an additional role of RNA polymerase and associated transcription factors in supporting co-transcriptional RNA folding by fostering or preventing strategic contacts to the nascent transcripts. In general, the results support the view that transcription complexes can act as RNA chaperones, a function that has been suggested over 30 years ago. Here, we discuss transcription complexes as RNA chaperones based on recent examples from bacterial transcription.

Exploring the energy landscape of a SAM-I riboswitch

SAM-I riboswitches regulate gene expression through transcription termination upon binding a S-adenosyl-L-methionine (SAM) ligand. In previous work, we characterized the conformational energy landscape of the full-length Bacillus subtilis yitJ SAM-I riboswitch as a function of Mg²⁺ and SAM ligand concentrations. Here, we have extended this work with measurements on a structurally similar ligand, S-adenosyl-l-homocysteine (SAH), which has, however, a much lower binding affinity. Using single-molecule Förster resonance energy transfer (smFRET) microscopy and hidden Markov modeling (HMM) analysis, we identified major conformations and determined their fractional populations and dynamics. At high Mg²⁺ concentration, FRET analysis yielded four distinct conformations, which we assigned to two terminator and two antiterminator states. In the same solvent, but with SAM added at saturating concentrations, four states persisted, although their populations, lifetimes and interconversion dynamics changed. In the presence of SAH instead of SAM, HMM revealed again four well-populated states and, in addition, a weakly populated ‘hub’ state that appears to mediate conformational transitions between three of the other states. Our data show pronounced and specific effects of the SAM and SAH ligands on the RNA conformational energy landscape. Interestingly, both SAM and SAH shifted the fractional populations toward terminator folds, but only gradually, so the effect cannot explain the switching action. Instead, we propose that the noticeably accelerated dynamics of interconversion between terminator and antiterminator states upon SAM binding may be essential for control of transcription.

Predicting RNA Secondary Structure Using In Vitro and In Vivo Data

The new flow of high-throughput RNA secondary structure data coming from different techniques allowed the further development of machine learning approaches. We developed CROSS and CROSSalive, two algorithms trained on experimental data able to predict the RNA secondary structure propensity both in vitro and in vivo. Since the in vivo folding of RNA molecules depends on multiple factors due to the cellular crowded environment, prediction is a complex problem that needs additional calculations for the interaction with proteins and other molecules. In the following chapter, we will describe the differences in predicting RNA secondary structure propensity using experimental data as input for an Artificial Neural Network (ANN) in vitro and in vivo.

Apolar chemical environments compact unfolded RNAs and can promote folding

It is well documented that the structure, and thus function, of nucleic acids depends on the chemical environment surrounding them, which often includes potential proteinaceous binding partners. The nonpolar amino acid side chains of these proteins will invariably alter the polarity of the local chemical environment around the nucleic acid. However, we are only beginning to understand how environmental polarity generally influences the structural and energetic properties of RNA folding. Here, we use a series of aqueous-organic cosolvent mixtures to systematically modulate the solvent polarity around two different RNA folding constructs that can form either secondary or tertiary structural elements. Using single-molecule Förster resonance energy transfer spectroscopy to simultaneously monitor the structural and energetic properties of these RNAs, we show that the unfolded conformations of both model RNAs become more compact in apolar environments characterized by dielectric constants less than that of pure water. In the case of tertiary structure formation, this compaction also gives rise to more energetically favorable folding. We propose that these physical changes arise from an enhanced accumulation of counterions in the low dielectric environment surrounding the unfolded RNA.

High-throughput dissection of the thermodynamic and conformational properties of a ubiquitous class of RNA tertiary contact motifs

Despite RNA's diverse secondary and tertiary structures and its complex conformational changes, nature utilizes a limited set of structural "motifs"-helices, junctions, and tertiary contact modules-to build diverse functional RNAs. Thus, in-depth descriptions of a relatively small universe of RNA motifs may lead to predictive models of RNA tertiary conformational landscapes. Motifs may have different properties depending on sequence and secondary structure, giving rise to subclasses that expand the universe of RNA building blocks. Yet we know very little about motif subclasses, given the challenges in mapping conformational properties in high throughput. Previously, we used "RNA on a massively parallel array" (RNA-MaP), a quantitative, high-throughput technique, to study thousands of helices and two-way junctions. Here, we adapt RNA-MaP to study the thermodynamic and conformational properties of tetraloop/tetraloop receptor (TL/TLR) tertiary contact motifs, analyzing 1,493 TLR sequences from different classes. Clustering analyses revealed variability in TL specificity, stability, and conformational behavior. Nevertheless, natural GAAA/11ntR TL/TLRs, while varying in tertiary stability by ∼2.5 kcal/mol, exhibited conserved TL specificity and conformational properties. Thus, RNAs may tune stability without altering the overall structure of these TL/TLRs. Furthermore, their stability correlated with natural frequency, suggesting thermodynamics as the dominant selection pressure. In contrast, other TL/TLRs displayed heterogenous conformational behavior and appear to not be under strong thermodynamic selection. Our results build toward a generalizable model of RNA-folding thermodynamics based on the properties of isolated motifs, and our characterized TL/TLR library can be used to engineer RNAs with predictable thermodynamic and conformational behavior.

Structural 3D Domain Reconstruction of the RNA Genome from Viruses with Secondary Structure Models

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

Formation of frameshift-stimulating RNA pseudoknots is facilitated by remodeling of their folding intermediates

Programmed –1 ribosomal frameshifting is an essential regulation mechanism of translation in viruses and bacteria. It is stimulated by mRNA structures inside the coding region. As the structure is unfolded repeatedly by consecutive translating ribosomes, whether it can refold properly each time is important in performing its function. By using single-molecule approaches and molecular dynamics simulations, we found that a frameshift-stimulating RNA pseudoknot folds sequentially through its upstream stem S1 and downstream stem S2. In this pathway, S2 folds from the downstream side and tends to be trapped in intermediates. By masking the last few nucleotides to mimic their gradual emergence from translating ribosomes, S2 can be directed to fold from the upstream region. The results show that the intermediates are greatly suppressed, suggesting that mRNA refolding may be modulated by ribosomes. Moreover, masking the first few nucleotides of S1 favors the folding from S2 and yields native pseudoknots, which are stable enough to retrieve the masked nucleotides. We hypothesize that translating ribosomes can remodel an intermediate mRNA structure into a stable conformation, which may in turn stimulate backward slippage of the ribosome. This supports an interactive model of ribosomal frameshifting and gives an insightful account addressing previous experimental observations.

Direct Mapping of Higher-Order RNA Interactions by SHAPE-JuMP

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

Translocation of a looped polymer threading through a nanopore

Recent experiments reported that the complicated translocation dynamics of a looped DNA chain through a nanopore can be detected by ionic current blockade profiles. Inspired by the experimental results, we systematically study the translocation dynamics of a looped polymer, formed by three building blocks of a loop in the middle and two tails of the same length connected with the loop, by using Langevin dynamics simulations. Based on two entering modes (tail-leading and loop-leading) and three translocation orders (loop-tail-tail, tail-loop-tail, and tail-tail-loop), the translocation of the looped polymer is classified into six translocation pathways, corresponding to different current blockade profiles. The probabilities of the six translocation pathways are dependent on the loop length, polymer length, and pore radius. Moreover, the translocation times of the entire polymer and the loop are investigated. We find that the two translocation times show different dependencies on the translocation pathways and on the lengths of the loop and the entire polymer.

Smaller molecules crowd better: Crowder size dependence revealed by single-molecule FRET studies and depletion force modeling analysis

The cell is an extremely crowded environment, which is known to have a profound impact on the thermodynamics, functionality, and conformational stability of biomolecules. Speculations from recent theoretical molecular dynamics studies suggest an intriguing size dependence to such purely entropic crowding effects, whereby small molecular weight crowders under constant enthalpy conditions are more effective than larger crowders on a per volume basis. If experimentally confirmed, this would be profoundly significant, as the cellular cytoplasm is also quite concentrated in smaller molecular weight solutes such as inorganic ions, amino acids, and various metabolites. The challenge is to perform such studies isolating entropic effects under isoenthalpic conditions. In this work, we first present results from single-molecule FRET spectroscopy (smFRET) on the molecular size-dependent crowding stabilization of a simple RNA tertiary motif (the GAAA tetraloop-tetraloop receptor), indeed providing evidence in support of the surprising notion in the crowding literature that "smaller is better." Specifically, systematic smFRET studies as a function of crowder solute size reveal that smaller molecules both significantly increase the RNA tertiary folding rate and, yet, simultaneously decrease the unfolding rate, predicting strongly size-dependent stabilization of RNA tertiary structures under crowded cellular conditions. The size dependence of these effects has been explored via systematic variation of crowder size over a broad range of molecular weights (90-3000 amu). Furthermore, corresponding temperature dependent studies indicate the systematic changes in the folding equilibrium to be predominantly entropic in origin, i.e., consistent with a fundamental picture of entropic molecular crowding without additional enthalpic interactions. Most importantly, all trends in the single-molecule crowding data can be quantitatively recapitulated by a simple analytic depletion force model, whereby excluded volume interactions represent the major thermodynamic driving force toward folding. Our study, thus, not only provides experimental evidence and theoretical support for small molecule crowding but also predicts further enhancement of crowding effects for even smaller molecules on a per volume basis.

In vivo structural characterization of the SARS-CoV-2 RNA genome identifies host proteins vulnerable to repurposed drugs

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the cause of the ongoing coronavirus disease 2019 (COVID-19) pandemic. Understanding of the RNA virus and its interactions with host proteins could improve therapeutic interventions for COVID-19. By using icSHAPE, we determined the structural landscape of SARS-CoV-2 RNA in infected human cells and from refolded RNAs, as well as the regulatory untranslated regions of SARS-CoV-2 and six other coronaviruses. We validated several structural elements predicted in silico and discovered structural features that affect the translation and abundance of subgenomic viral RNAs in cells. The structural data informed a deep-learning tool to predict 42 host proteins that bind to SARS-CoV-2 RNA. Strikingly, antisense oligonucleotides targeting the structural elements and FDA-approved drugs inhibiting the SARS-CoV-2 RNA binding proteins dramatically reduced SARS-CoV-2 infection in cells derived from human liver and lung tumors. Our findings thus shed light on coronavirus and reveal multiple candidate therapeutics for COVID-19 treatment.

IsRNA1: De Novo Prediction and Blind Screening of RNA 3D Structures

Modeling structures and functions of large ribonucleic acid (RNAs) especially with complicated topologies is highly challenging due to the inefficiency of large conformational sampling and the presence of complicated tertiary interactions. To address this problem, one highly promising approach is coarse-grained modeling. Here, following an iterative simulated reference state approach to decipher the correlations between different structural parameters, we developed a potent coarse-grained RNA model named as IsRNA1 for RNA studies. Molecular dynamics simulations in the IsRNA1 can predict the native structures of small RNAs from a sequence and fold medium-sized RNAs into near-native tertiary structures with the assistance of secondary structure constraints. A large-scale benchmark test on RNA 3D structure prediction shows that IsRNA1 exhibits improved performance for relatively large RNAs of complicated topologies, such as large stem-loop structures and structures containing long-range tertiary interactions. The advantages of IsRNA1 include the consideration of the correlations between the different structural variables, the appropriate characterization of canonical base-pairing and base-stacking interactions, and the better sampling for the backbone conformations. Moreover, a blind screening protocol was developed based on IsRNA1 to identify good structural models from a pool of candidates without prior knowledge of the native structures.

How Many Messenger RNAs Can Be Translated by the START Mechanism?

Translation initiation is a key step in the protein synthesis stage of the gene expression pathway of all living cells. In this important process, ribosomes have to accurately find the AUG start codon in order to ensure the integrity of the proteome. “Structure Assisted RNA Translation”, or “START”, has been proposed to use stable secondary structures located in the coding sequence to augment start site selection by steric hindrance of the progression of pre-initiation complex on messenger RNA. This implies that such structures have to be located downstream and at on optimal distance from the AUG start codon (i.e., downstream nucleotide +16). In order to assess the importance of the START mechanism in the overall mRNA translation process, we developed a bioinformatic tool to screen coding sequences for such stable structures in a 50 nucleotide-long window spanning the nucleotides from +16 to +65. We screened eight bacterial genomes and six eukaryotic genomes. We found stable structures in 0.6–2.5% of eukaryotic coding sequences. Among these, approximately half of them were structures predicted to form G-quadruplex structures. In humans, we selected 747 structures. In bacteria, the coding sequences from Gram-positive bacteria contained 2.6–4.2% stable structures, whereas the structures were less abundant in Gram-negative bacteria (0.2–2.7%). In contrast to eukaryotes, putative G-quadruplex structures are very rare in the coding sequence of bacteria. Altogether, our study reveals that the START mechanism seems to be an ancient strategy to facilitate the start codon recognition that is used in different kingdoms of life.

RLDOCK: A New Method for Predicting RNA-Ligand Interactions

The ability to accurately predict the binding site, binding pose, and binding affinity for ligand-RNA binding is important for RNA-targeted drug design. Here, we describe a new computational method, RLDOCK, for predicting the binding site and binding pose for ligand-RNA binding. By developing an energy-based scoring function, we sample exhaustively all of the possible binding sites with flexible ligand conformations for a ligand-RNA pair based on the geometric and energetic scores. The model distinguishes from other approaches in three notable features. First, the model enables exhaustive scanning of all of the possible binding sites, including multiple alternative or coexisting binding sites, for a given ligand-RNA pair. Second, the model is based on a new energy-based scoring function developed here. Third, the model employs a novel multistep screening algorithm to improve computational efficiency. Specifically, first, for each binding site, we use a gird-based energy map to rank the binding sites according to the minimum Lennard-Jones potential energy for the different ligand poses. Second, for a given selected binding site, we predict the ligand pose using a two-step algorithm. In the first step, we quickly identify the probable ligand poses using a coarse-grained simplified energy function. In the second step, for each of the probable ligand poses, we predict the ligand poses using a refined energy function. Tests of the RLDOCK for a set of 230 RNA-ligand-bound structures indicate that RLDOCK can successfully predict ligand poses for 27.8, 58.3, and 69.6% of all of the test cases with the root-mean-square deviation within 1.0, 2.0, and 3.0 Å, respectively, for the top three predicted docking poses. The computational method presented here may enable the development of a new, more comprehensive framework for the prediction of ligand-RNA binding with an ensemble of RNA conformations and the metal-ion effects.

Time-Resolved, Single-Molecule, Correlated Chemical Probing of RNA

Capturing the folding dynamics of large, functionally important RNAs has relied primarily on global measurements of structure or on per-nucleotide chemical probing. These approaches infer, but do not directly measure, through-space structural interactions. Here we introduce trimethyloxonium (TMO) as a chemical probe for RNA. TMO alkylates RNA at high levels in seconds, and thereby enables time-resolved, single-molecule, through-space probing of RNA folding using the RING-MaP correlated chemical probing framework. Time-resolved correlations in the RNase P RNA-a functional RNA with a complex structure stabilized by multiple noncanonical interactions-revealed that a long-range tertiary interaction guides native RNA folding for both secondary and tertiary structure. This unanticipated nonhierarchical folding mechanism was directly validated by examining the consequences of concise disruption of the through-space interaction. Single-molecule, time-resolved RNA structure probing with TMO is poised to reveal a wide range of dynamic RNA folding processes and principles of RNA folding.

Modeling Loop Composition and Ion Concentration Effects in RNA Hairpin Folding Stability

The ability to accurately predict RNA hairpin structure and stability for different loop sequences and salt conditions is important for understanding, modeling, and designing larger RNA folds. However, traditional RNA secondary structure models cannot treat loop-sequence and ionic effects on RNA hairpin folding. Here, we describe a general, three-dimensional (3D) conformation-based computational method for modeling salt concentration-dependent conformational distributions and the detailed 3D structures for a set of three RNA hairpins that contain a variable, 15-nucleotide loop sequence. For a given RNA sequence, the new, to our knowledge, method integrates a Vfold2D two-dimensional structure folding model with IsRNA coarse-grained molecular dynamics 3D folding simulations and Monte Carlo tightly bound ion estimations of ion-mediated electrostatic interactions. The model predicts free-energy landscapes for the different RNA hairpin-forming sequences with variable salt conditions. The theoretically predicted results agree with the experimental fluorescence measurements, validating the strategy. Furthermore, the theoretical model goes beyond the experimental results by enabling in-depth 3D structural analysis, revealing energetic mechanisms for the sequence- and salt-dependent folding stability. Although the computational framework presented here is developed for RNA hairpin systems, the general method may be applied to investigate other RNA systems, such as multiway junctions or pseudoknots in mixed metal ion solutions.

A New Approach to 3D Modeling of Inhomogeneous Populations of Viral Regulatory RNA

Tertiary structure (3D) is the physical context of RNA regulatory activity. Retroviruses are RNA viruses that replicate through the proviral DNA intermediate transcribed by hosts. Proviral transcripts form inhomogeneous populations due to variable structural ensembles of overlapping regulatory RNA motifs in the 5′-untranslated region (UTR), which drive RNAs to be spliced or translated, and/or dimerized and packaged into virions. Genetic studies and structural techniques have provided fundamental input constraints to begin predicting HIV 3D conformations in silico. Using SimRNA and sets of experimentally-determined input constraints of HIV^NL4-3 trans-activation responsive sequence (TAR) and pairings of unique-5′ (U5) with dimerization (DIS) or AUG motifs, we calculated a series of 3D models that differ in proximity of 5′-Cap and the junction of TAR and PolyA helices; configuration of primer binding site (PBS)-segment; and two host cofactors binding sites. Input constraints on U5-AUG pairings were most compatible with intramolecular folding of 5′-UTR motifs in energetic minima. Introducing theoretical constraints predicted metastable PolyA region drives orientation of 5′-Cap with TAR, U5 and PBS-segment helices. SimRNA and the workflow developed herein provides viable options to predict 3D conformations of inhomogeneous populations of large RNAs that have been intractable to conventional ensemble methods.

2'-O-Trifluoromethylated RNA - a powerful modification for RNA chemistry and NMR spectroscopy

New RNA modifications are needed to advance our toolbox for targeted manipulation of RNA. In particular, the development of high-performance reporter groups facilitating spectroscopic analysis of RNA structure and dynamics, and of RNA-ligand interactions has attracted considerable interest. To this end, fluorine labeling in conjunction with 19F-NMR spectroscopy has emerged as a powerful strategy. Appropriate probes for RNA previously focused on single fluorine atoms attached to the 5-position of pyrimidine nucleobases or at the ribose 2'-position. To increase NMR sensitivity, trifluoromethyl labeling approaches have been developed, with the ribose 2'-SCF3 modification being the most prominent one. A major drawback of the 2'-SCF3 group, however, is its strong impact on RNA base pairing stability. Interestingly, RNA containing the structurally related 2'-OCF3 modification has not yet been reported. Therefore, we set out to overcome the synthetic challenges toward 2'-OCF3 labeled RNA and to investigate the impact of this modification. We present the syntheses of 2'-OCF3 adenosine and cytidine phosphoramidites and their incorporation into oligoribonucleotides by solid-phase synthesis. Importantly, it turns out that the 2'-OCF3 group has only a slight destabilizing effect when located in double helical regions which is consistent with the preferential C3'-endo conformation of the 2'-OCF3 ribose as reflected in the 3 J (H1'-H2') coupling constants. Furthermore, we demonstrate the exceptionally high sensitivity of the new label in 19F-NMR analysis of RNA structure equilibria and of RNA-small molecule interactions. The study is complemented by a crystal structure at 0.9 Å resolution of a 27 nt hairpin RNA containing a single 2'-OCF3 group that well integrates into the minor groove. The new label carries high potential to outcompete currently applied fluorine labels for nucleic acid NMR spectroscopy because of its significantly advanced performance.

Topological constraints of RNA pseudoknotted and loop-kissing motifs: applications to three-dimensional structure prediction

An RNA global fold can be described at the level of helix orientations and relatively flexible loop conformations that connect the helices. The linkage between the helices plays an essential role in determining the structural topology, which restricts RNA local and global folds, especially for RNA tertiary structures involving cross-linked base pairs. We quantitatively analyze the topological constraints on RNA 3D conformational space, in particular, on the distribution of helix orientations, for pseudoknots and loop-loop kissing structures. The result shows that a viable conformational space is predominantly determined by the motif type, helix size, and loop size, indicating a strong topological coupling between helices and loops in RNA tertiary motifs. Moreover, the analysis indicates that (cross-linked) tertiary contacts can cause much stronger topological constraints on RNA global fold than non-cross-linked base pairs. Furthermore, based on the topological constraints encoded in the 2D structure and the 3D templates, we develop a 3D structure prediction approach. This approach can be further combined with structure probing methods to expand the capability of computational prediction for large RNA folds.

The RNA secondary structural variation in the cyclization elements of the dengue genome and the possible implications in pathogenicity

Dengue virus (DENV), the causative agent of dengue fever and severe dengue, exists as four antigenically different serotypes. These serotypes are further classified into genotypes and have varying degrees of pathogenicity. The 5' and 3' ends of the genomic RNA play a critical role in the viral life cycle. A global scale study of the RNA structural variation among the sero- and genotypes was carried out to correlate RNA structure with pathogenicity. We found that the GC rich stem and rigid loop structure of the 5' end of the genomic RNA of DENV 2 differs significantly from the others. The observed variation in base composition and base pairing may confer structural and functional advantage in highly virulent strains. This variation in the structure may influence the ease of cyclization and recruitment of viral RNA polymerase, NS5 RdRp, thereby affecting the pathogenicity of these strains.

José M. The Ancient History of Peptidyl Transferase Center Formation as Told by Conservation and Information Analyses

The peptidyl transferase center (PTC) is the catalytic center of the ribosome and forms part of the 23S ribosomal RNA. The PTC has been recognized as the earliest ribosomal part and its origins embodied the First Universal Common Ancestor (FUCA). The PTC is frequently assumed to be highly conserved along all living beings. In this work, we posed the following questions: (i) How many 100% conserved bases can be found in the PTC? (ii) Is it possible to identify clusters of informationally linked nucleotides along its sequence? (iii) Can we propose how the PTC was formed? (iv) How does sequence conservation reflect on the secondary and tertiary structures of the PTC? Aiming to answer these questions, all available complete sequences of 23S ribosomal RNA from Bacteria and Archaea deposited on GenBank database were downloaded. Using a sequence bait of 179 bp from the PTC of Thermus termophilus, we performed an optimum pairwise alignment to retrieve the PTC region from 1424 filtered 23S rRNA sequences. These PTC sequences were multiply aligned, and the conserved regions were assigned and observed along the primary, secondary, and tertiary structures. The PTC structure was observed to be more highly conserved close to the adenine located at the catalytical site. Clusters of interrelated, co-evolving nucleotides reinforce previous assumptions that the PTC was formed by the concatenation of proto-tRNAs and important residues responsible for its assembly were identified. The observed sequence variation does not seem to significantly affect the 3D structure of the PTC ribozyme.