Structure determination of noncanonical RNA motifs guided by ¹H NMR chemical shifts

Recent Advances in NMR Protein Structure Prediction with ROSETTA

Nuclear magnetic resonance (NMR) spectroscopy is a powerful method for studying the structure and dynamics of proteins in their native state. For high-resolution NMR structure determination, the collection of a rich restraint dataset is necessary. This can be difficult to achieve for proteins with high molecular weight or a complex architecture. Computational modeling techniques can complement sparse NMR datasets (<1 restraint per residue) with additional structural information to elucidate protein structures in these difficult cases. The Rosetta software for protein structure modeling and design is used by structural biologists for structure determination tasks in which limited experimental data is available. This review gives an overview of the computational protocols available in the Rosetta framework for modeling protein structures from NMR data. We explain the computational algorithms used for the integration of different NMR data types in Rosetta. We also highlight new developments, including modeling tools for data from paramagnetic NMR and hydrogen-deuterium exchange, as well as chemical shifts in CS-Rosetta. Furthermore, strategies are discussed to complement and improve structure predictions made by the current state-of-the-art AlphaFold2 program using NMR-guided Rosetta modeling.

RNA 3D Modeling with FARFAR2, Online

Understanding the three-dimensional structure of an RNA molecule is often essential to understanding its function. Sampling algorithms and energy functions for RNA structure prediction are improving, due to the increasing diversity of structural data available for training statistical potentials and testing structural data, along with a steady supply of blind challenges through the RNA-Puzzles initiative. The recent FARFAR2 algorithm enables near-native structure predictions on fairly complex RNA structures, including automated selection of final candidate models and estimation of model accuracy. Here, we describe the use of a publicly available webserver for RNA modeling for realistic scenarios using FARFAR2, available at https://rosie.rosettacommons.org/farfar2 . We walk through two cases in some detail: a simple model pseudoknot from the frameshifting element of beet western yellows virus modeled using the "basic interface" to the webserver and a replication of RNA-Puzzle 20, a metagenomic twister sister ribozyme, using the "advanced interface." We also describe example runs of FARFAR2 modeling including two kinds of experimental data: a c-di-GMP riboswitch modeled with low-resolution restraints from MOHCA-seq experiments and a tandem GA motif modeled with 1H NMR chemical shifts.

Combining Experimental Restraints and RNA 3D Structure Prediction in RNA Nanotechnology

Precise RNA tertiary structure prediction can aid in the design of RNA nanoparticles. However, most existing RNA tertiary structure prediction methods are limited to small RNAs with relatively simple secondary structures. Large RNA molecules usually have complex secondary structures, including multibranched loops and pseudoknots, allowing for highly flexible RNA geometries and multiple stable states. Various experiments and bioinformatics analyses can often provide information about the distance between atoms (or residues) in RNA, which can be used to guide the prediction of RNA tertiary structure. In this chapter, we will introduce a platform, iFoldNMR, that can incorporate non-exchangeable imino protons resonance data from NMR as restraints for RNA 3D structure prediction. We also introduce an algorithm, DVASS, which optimizes distance restraints for better RNA 3D structure prediction.

Using Unassigned NMR Chemical Shifts to Model RNA Secondary Structure

NMR-derived chemical shifts are sensitive probes of RNA structure. However, the need to assign NMR spectra hampers their utility as a direct source of structural information. In this report, we describe a simple method that uses unassigned 2D NMR spectra to model the secondary structure of RNAs. As in the case of assigned chemical shifts, we could use unassigned chemical shift data to reweight conformational libraries such that the highest weighted structure closely resembles their reference NMR structure. Furthermore, the application of our approach to the 3'- and 5'-UTR of the SARS-CoV-2 genome yields structures that are, for the most part, consistent with the secondary structure models derived from chemical probing data. Therefore, we expect the framework we describe here will be useful as a general strategy for rapidly generating preliminary structural RNA models directly from unassigned 2D NMR spectra. As we demonstrated for the 337-nt and 472-nt UTRs of SARS-CoV-2, our approach could be especially valuable for modeling the secondary structures of large RNA.

Nuclear Magnetic Resonance Spectra and AMBER OL3 and ROC-RNA Simulations of UCUCGU Reveal Force Field Strengths and Weaknesses for Single-Stranded RNA

Single-stranded regions of RNA are important for folding of sequences into 3D structures and for design of therapeutics targeting RNA. Prediction of ensembles of 3D structures for single-stranded regions often involves classical mechanical approximations of interactions defined by quantum mechanical calculations on small model systems. Nuclear magnetic resonance (NMR) spectra and molecular dynamics (MD) simulations of short single strands provide tests for how well the approximations model many of the interactions. Here, the NMR spectra for UCUCGU at 2, 15, and 30 °C are compared to simulations with the AMBER force fields, OL3 and ROC-RNA. This is the first such comparison to an oligoribonucleotide containing an internal guanosine nucleotide (G). G is particularly interesting because of its many H-bonding groups, large dipole moment, and proclivity for both syn and anti conformations. Results reveal formation of a G amino to phosphate non-bridging oxygen H-bond. The results also demonstrate dramatic differences in details of the predicted structures. The variations emphasize the dependence of predictions on individual parameters and their balance with the rest of the force field. The NMR data can serve as a benchmark for future force fields.

Modeling Noncanonical RNA Base Pairs by a Coarse-Grained IsRNA2 Model

Noncanonical base pairs contribute crucially to the three-dimensional architecture of large RNA molecules; however, how to accurately model them remains an open challenge in RNA 3D structure prediction. Here, we report a promising coarse-grained (CG) IsRNA2 model to predict noncanonical base pairs in large RNAs through molecular dynamics simulations. By introducing a five-bead per nucleotide CG representation to reserve the three interacting edges of nucleobases, IsRNA2 accurately models various base-pairing interactions, including both canonical and noncanonical base pairs. A benchmark test indicated that IsRNA2 achieves a comparable performance to the atomic model in de novo modeling of noncanonical RNA structures. In addition, IsRNA2 was able to refine the 3D structure predictions for large RNAs in RNA-puzzle challenges. Finally, the graphics processing unit acceleration was introduced to speed up the sampling efficiency in IsRNA2 for very large RNA molecules. Therefore, the CG IsRNA2 model reported here offers a reliable approach to predict the structures and dynamics of large RNAs.

NMR chemical shift assignments of RNA oligonucleotides to expand the RNA chemical shift database

RNAs play myriad functional and regulatory roles in the cell. Despite their significance, three-dimensional structure elucidation of RNA molecules lags significantly behind that of proteins. NMR-based studies are often rate-limited by the assignment of chemical shifts. Automation of the chemical shift assignment process can greatly facilitate structural studies, however, accurate chemical shift predictions rely on a robust and complete chemical shift database for training. We searched the Biological Magnetic Resonance Data Bank (BMRB) to identify sequences that had no (or limited) chemical shift information. Here, we report the chemical shift assignments for 12 RNA hairpins designed specifically to help populate the BMRB.

Rapid assessment of Watson-Crick to Hoogsteen exchange in unlabeled DNA duplexes using high-power SELOPE imino 1H CEST

In duplex DNA, Watson-Crick A-T and G-C base pairs (bp's) exist in dynamic equilibrium with an alternative Hoogsteen conformation, which is low in abundance and short-lived. Measuring how the Hoogsteen dynamics varies across different DNA sequences, structural contexts and physiological conditions is key for identifying potential Hoogsteen hot spots and for understanding the potential roles of Hoogsteen base pairs in DNA recognition and repair. However, such studies are hampered by the need to prepare 13 C or 15 N isotopically enriched DNA samples for NMR relaxation dispersion (RD) experiments. Here, using SELective Optimized Proton Experiments (SELOPE) 1 H CEST experiments employing high-power radiofrequency fields (B 1  >  250 Hz) targeting imino protons, we demonstrate accurate and robust characterization of Watson-Crick to Hoogsteen exchange, without the need for isotopic enrichment of the DNA. For 13 residues in three DNA duplexes under different temperature and pH conditions, the exchange parameters deduced from high-power imino 1 H CEST were in very good agreement with counterparts measured using off-resonance 13 C /  15 N spin relaxation in the rotating frame (R 1 ρ ). It is shown that 1 H-1 H NOE effects which typically introduce artifacts in 1 H-based measurements of chemical exchange can be effectively suppressed by selective excitation, provided that the relaxation delay is short (≤  100 ms). The 1 H CEST experiment can be performed with ∼  10× higher throughput and ∼  100× lower cost relative to 13 C /  15 N R 1 ρ and enabled Hoogsteen chemical exchange measurements undetectable by R 1 ρ . The results reveal an increased propensity to form Hoogsteen bp's near terminal ends and a diminished propensity within A-tract motifs. The 1 H CEST experiment provides a basis for rapidly screening Hoogsteen breathing in duplex DNA, enabling identification of unusual motifs for more in-depth characterization.

Developments in solution-state NMR yield broader and deeper views of the dynamic ensembles of nucleic acids

Nucleic acids do not fold into a single conformation, and dynamic ensembles are needed to describe their propensities to cycle between different conformations when performing cellular functions. We review recent advances in solution-state nuclear magnetic resonance (NMR) methods and their integration with computational techniques that are improving the ability to probe the dynamic ensembles of DNA and RNA. These include computational approaches for predicting chemical shifts from structure and generating conformational libraries from sequence, measurements of exact nuclear Overhauser effects, development of new probes to study chemical exchange using relaxation dispersion, faster and more sensitive real-time NMR techniques, and new NMR approaches to tackle large nucleic acid assemblies. We discuss how these advances are leading to new mechanistic insights into gene expression and regulation.

Chemical shift prediction of RNA imino groups: application toward characterizing RNA excited states

NH groups in proteins or nucleic acids are the most challenging target for chemical shift prediction. Here we show that the RNA base pair triplet motif dictates imino chemical shifts in its central base pair. A lookup table is established that links each type of base pair triplet to experimental chemical shifts of the central base pair, and can be used to predict imino chemical shifts of RNAs to remarkable accuracy. Strikingly, the semiempirical method can well interpret the variations of chemical shifts for different base pair triplets, and is even applicable to non-canonical motifs. This finding opens an avenue for predicting chemical shifts of more complicated RNA motifs. Furthermore, we combine the imino chemical shift prediction with NMR relaxation dispersion experiments targeting both ¹⁵N and ¹H^N of the imino group, and verify a previously characterized excited state of P5abc subdomain including an earlier speculated non-native G•G mismatch.

Prediction of chemical shifts is critical for extracting structural and dynamic information from biomolecular NMR data. Here the authors report an RNA imino group chemical shift predictor, showing that the imino chemical shifts of a residue are dictated by the surrounding base pair triplet.

Characterization of DNA G-quadruplex Topologies with NMR Chemical Shifts

G-quadruplexes are nucleic acid motifs formed by stacking of guanosine-tetrad pseudoplanes. They perform varied biological roles, and their distinctive structural features enable diverse applications. High-resolution structural characterization of G-quadruplexes is often time-consuming and expensive, calling for effective methods. Herein, we develop NMR chemical shifts and machine learning-based methodology that allows direct, rapid, and reliable analysis of canonical three-plane DNA G-quadruplexes sans isotopic enrichment. We show, for the first time, that each unique topology enforces a specific distribution of glycosidic torsion angles. Newly acquired carbon chemical shifts are exquisite probes for the dihedral angle distribution and provide immediate and unambiguous backbone topology assignment. The support vector machine learning methodology aids resonance assignment by providing plane indices for tetrad-forming guanosines. We further demonstrate the robustness by successful application of the methodology to a sequence that folds in two dissimilar topologies under different ionic conditions, providing its first atomic-level characterization.

Chemical shifts-based similarity restraints improve accuracy of RNA structures determined via NMR

Determination of structure of RNA via NMR is complicated in large part by the lack of a precise parameterization linking the observed chemical shifts to the underlying geometric parameters. In contrast to proteins, where numerous high-resolution crystal structures serve as coordinate templates for this mapping, such models are rarely available for smaller oligonucleotides accessible via NMR, or they exhibit crystal packing and counter-ion binding artifacts that prevent their use for the chemical shifts analysis. On the other hand, NMR-determined structures of RNA often are not solved at the density of restraints required to precisely define the variable degrees of freedom. In this study we sidestep the problems of direct parameterization of the RNA chemical shifts/structure relationship and examine the effects of imposing local fragmental coordinate similarity restraints based on similarities of the experimental secondary ribose ¹³C/¹H chemical shifts instead. The effect of such chemical shift similarity (CSS) restraints on the structural accuracy is assessed via residual dipolar coupling (RDC)-based cross-validation. Improvements in the coordinate accuracy are observed for all of the six RNA constructs considered here as test cases, which argues for routine inclusion of these terms during NMR-based oligonucleotide structure determination. Such accuracy improvements are expected to facilitate derivation of the chemical shift/structure relationships for RNA.

Dissecting Optical Response and Molecular Structure of Fluorescent Proteins With Non-canonical Chromophores

Tracking the structural dynamics of fluorescent protein chromophores holds the key to unlocking the fluorescence mechanisms in real time and enabling rational design principles of these powerful and versatile bioimaging probes. By combining recent chemical biology and ultrafast spectroscopy advances, we prepared the superfolder green fluorescent protein (sfGFP) and its non-canonical amino acid (ncAA) derivatives with a single chlorine, bromine, and nitro substituent at the ortho site to the phenolate oxygen of the embedded chromophore, and characterized them using an integrated toolset of femtosecond transient absorption and tunable femtosecond stimulated Raman spectroscopy (FSRS), aided by quantum calculations of the vibrational normal modes. A dominant vibrational cooling time constant of ~4 and 11 ps is revealed in Cl-GFP and Br-GFP, respectively, facilitating a ~30 and 12% increase of the fluorescent quantum yield vs. the parent sfGFP. Similar time constants were also retrieved from the transient absorption spectra, substantiating the correlated electronic and vibrational motions on the intrinsic molecular timescales. Key carbon-halogen stretching motions coupled with phenolate ring motions of the deprotonated chromophores at ca. 908 and 890 cm-1 in Cl-GFP and Br-GFP exhibit enhanced activities in the electronic excited state and blue-shift during a distinct vibrational cooling process on the ps timescale. The retrieved structural dynamics change due to targeted site-specific halogenation of the chromophore thus provides an effective means to design new GFP derivatives and enrich the bioimaging probe toolset for life and medical sciences.

Macromolecular modeling and design in Rosetta: recent methods and frameworks

The Rosetta software for macromolecular modeling, docking and design is extensively used in laboratories worldwide. During two decades of development by a community of laboratories at more than 60 institutions, Rosetta has been continuously refactored and extended. Its advantages are its performance and interoperability between broad modeling capabilities. Here we review tools developed in the last 5 years, including over 80 methods. We discuss improvements to the score function, user interfaces and usability. Rosetta is available at http://www.rosettacommons.org.

Integrating NMR and simulations reveals motions in the UUCG tetraloop

We provide an atomic-level description of the structure and dynamics of the UUCG RNA stem-loop by combining molecular dynamics simulations with experimental data. The integration of simulations with exact nuclear Overhauser enhancements data allowed us to characterize two distinct states of this molecule. The most stable conformation corresponds to the consensus three-dimensional structure. The second state is characterized by the absence of the peculiar non-Watson-Crick interactions in the loop region. By using machine learning techniques we identify a set of experimental measurements that are most sensitive to the presence of non-native states. We find that although our MD ensemble, as well as the consensus UUCG tetraloop structures, are in good agreement with experiments, there are remaining discrepancies. Together, our results show that (i) the MD simulation overstabilize a non-native loop conformation, (ii) eNOE data support its presence with a population of ≈10% and (iii) the structural interpretation of experimental data for dynamic RNAs is highly complex, even for a simple model system such as the UUCG tetraloop.

PyShifts: A PyMOL Plugin for Chemical Shift-Based Analysis of Biomolecular Ensembles

Here, we present PyShifts-a PyMOL plugin for chemical shift-based analysis of biomolecular ensembles. With PyShifts, users can compare and visualize differences between experimentally measured and computationally predicted chemical shifts. When analyzing multiple conformations of a biomolecule with PyShifts, users can also sort a set of conformations based on chemical shift differences and identify the conformers that exhibit the best agreement between measured and predicted chemical shifts. Although we have integrated PyShifts with the chemical shift predictors LARMOR^D and LARMOR^Cα, PyShifts can read in chemical shifts from any source, and so, users can employ PyShifts to analyze biomolecular structures using chemical shifts computed by any chemical shift predictor. We envision, therefore, that PyShifts (https://github.com/atfrank/PyShifts) will find utility as a general-purpose tool for exploring chemical shift-structure relationships in biomolecular ensembles.

Nuclear Magnetic Resonance of Single-Stranded RNAs and DNAs of CAAU and UCAAUC as Benchmarks for Molecular Dynamics Simulations

RNA and DNA are rapidly emerging as targets for therapeutics and as potential frameworks for nanotechnology. Accurate methods for predicting and designing structures and dynamics of nucleic acids would accelerate progress in these and other applications. Suitable approximations for modeling nucleic acids are being developed but require validation against disparate experimental observations. Here, nuclear magnetic resonance spectra for RNA and DNA single strands, CAAU and UCAAUC, are used as benchmarks to test molecular dynamics simulations with AMBER force fields OL3 and ROC-RNA for RNA and BSC1 for DNA. A detailed scheme for making comparisons is also presented. The results reflect recent progress in approximations and reveal remaining challenges.

Classification of RNA backbone conformations into rotamers using 13C' chemical shifts: exploring how far we can go

The conformational space of the ribose-phosphate backbone is very complex as it is defined in terms of six torsional angles. To help delimit the RNA backbone conformational preferences, 46 rotamers have been defined in terms of these torsional angles. In the present work, we use the ribose experimental and theoretical ¹³C′ chemical shifts data and machine learning methods to classify RNA backbone conformations into rotamers and families of rotamers. We show to what extent the experimental ¹³C′ chemical shifts can be used to identify rotamers and discuss some problem with the theoretical computations of ¹³C′ chemical shifts.

Limits in accuracy and a strategy of RNA structure prediction using experimental information

RNA structural complexity and flexibility present a challenge for computational modeling efforts. Experimental information and bioinformatics data can be used as restraints to improve the accuracy of RNA tertiary structure prediction. Regarding utilization of restraints, the fundamental questions are: (i) What is the limit in prediction accuracy that one can achieve with arbitrary number of restraints? (ii) Is there a strategy for selection of the minimal number of restraints that would result in the best structural model? We address the first question by testing the limits in prediction accuracy using native contacts as restraints. To address the second question, we develop an algorithm based on the distance variation allowed by secondary structure (DVASS), which ranks restraints according to their importance to RNA tertiary structure prediction. We find that due to kinetic traps, the greatest improvement in the structure prediction accuracy is achieved when we utilize only 40-60% of the total number of native contacts as restraints. When the restraints are sorted by DVASS algorithm, using only the first 20% ranked restraints can greatly improve the prediction accuracy. Our findings suggest that only a limited number of strategically selected distance restraints can significantly assist in RNA structure modeling.

Computational modeling of RNA 3D structure based on experimental data

RNA molecules are master regulators of cells. They are involved in a variety of molecular processes: they transmit genetic information, sense cellular signals and communicate responses, and even catalyze chemical reactions. As in the case of proteins, RNA function is dictated by its structure and by its ability to adopt different conformations, which in turn is encoded in the sequence. Experimental determination of high-resolution RNA structures is both laborious and difficult, and therefore the majority of known RNAs remain structurally uncharacterized. To address this problem, predictive computational methods were developed based on the accumulated knowledge of RNA structures determined so far, the physical basis of the RNA folding, and taking into account evolutionary considerations, such as conservation of functionally important motifs. However, all theoretical methods suffer from various limitations, and they are generally unable to accurately predict structures for RNA sequences longer than 100-nt residues unless aided by additional experimental data. In this article, we review experimental methods that can generate data usable by computational methods, as well as computational approaches for RNA structure prediction that can utilize data from experimental analyses. We outline methods and data types that can be potentially useful for RNA 3D structure modeling but are not commonly used by the existing software, suggesting directions for future development.

Nuclear Magnetic Resonance Reveals That GU Base Pairs Flanking Internal Loops Can Adopt Diverse Structures

RNA thermodynamics play an important role in determining the two- and three-dimensional structures of RNA. Internal loops of the sequence 5'-GMNU/3'-UNMG are relatively unstable thermodynamically. Here, five duplexes with GU-flanked 2 × 2 nucleotide internal loops were structurally investigated to reveal determinants of their instability. The following internal loops were investigated: 5'-GCAU/3'-UACG, 5'-UUCG/3'-GCUU, 5'-GCUU/3'-UUCG, 5'-GUCU/3'-UCUG, and 5'-GCCU/3'-UCCG. Two-dimensional nuclear magnetic resonance spectra indicate the absence of GU wobble base pairing in 5'-GCUU/3'-UUCG, 5'-GUCU/3'-UCUG, and 5'-GCCU/3'-UCCG. The 5'-GCUU/3'-UUCG loop has an unusual conformation of the GU base pairs, in which U's O2 carbonyl forms a bifurcated hydrogen bond with G's amino and imino protons. The internal loop of 5'-GUCU/3'-UCUG displays a shifted configuration in which GC pairs flank a U-U pair and several U's are in fast exchange between positions inside and outside the helix. In contrast, 5'-GCAU/3'-UACG and 5'-UUCG/3'-GCUU both have the expected GU wobble base pairs flanking the internal loop. Evidently, GU base pairs flanking internal loops are more likely to display atypical structures relative to Watson-Crick base pairs flanking internal loops. This appears to be more likely when the G of the GU pair is 5' to the loop. Such unusual structures could serve as recognition elements for biological function and as benchmarks for structure prediction methods.

Chemical shift-based methods in NMR structure determination

Chemical shifts are highly sensitive probes harnessed by NMR spectroscopists and structural biologists as conformational parameters to characterize a range of biological molecules. Traditionally, assignment of chemical shifts has been a labor-intensive process requiring numerous samples and a suite of multidimensional experiments. Over the past two decades, the development of complementary computational approaches has bolstered the analysis, interpretation and utilization of chemical shifts for elucidation of high resolution protein and nucleic acid structures. Here, we review the development and application of chemical shift-based methods for structure determination with a focus on ab initio fragment assembly, comparative modeling, oligomeric systems, and automated assignment methods. Throughout our discussion, we point out practical uses, as well as advantages and caveats, of using chemical shifts in structure modeling. We additionally highlight (i) hybrid methods that employ chemical shifts with other types of NMR restraints (residual dipolar couplings, paramagnetic relaxation enhancements and pseudocontact shifts) that allow for improved accuracy and resolution of generated 3D structures, (ii) the utilization of chemical shifts to model the structures of sparsely populated excited states, and (iii) modeling of sidechain conformations. Finally, we briefly discuss the advantages of contemporary methods that employ sparse NMR data recorded using site-specific isotope labeling schemes for chemical shift-driven structure determination of larger molecules. With this review, we aim to emphasize the accessibility and versatility of chemical shifts for structure determination of challenging biological systems, and to point out emerging areas of development that lead us towards the next generation of tools.

Conformational ensembles of RNA oligonucleotides from integrating NMR and molecular simulations

RNA molecules are key players in numerous cellular processes and are characterized by a complex relationship between structure, dynamics, and function. Despite their apparent simplicity, RNA oligonucleotides are very flexible molecules, and understanding their internal dynamics is particularly challenging using experimental data alone. We show how to reconstruct the conformational ensemble of four RNA tetranucleotides by combining atomistic molecular dynamics simulations with nuclear magnetic resonance spectroscopy data. The goal is achieved by reweighting simulations using a maximum entropy/Bayesian approach. In this way, we overcome problems of current simulation methods, as well as in interpreting ensemble- and time-averaged experimental data. We determine the populations of different conformational states by considering several nuclear magnetic resonance parameters and point toward properties that are not captured by state-of-the-art molecular force fields. Although our approach is applied on a set of model systems, it is fully general and may be used to study the conformational dynamics of flexible biomolecules and to detect inaccuracies in molecular dynamics force fields.

Blind prediction of noncanonical RNA structure at atomic accuracy

Prediction of RNA structure from nucleotide sequence remains an unsolved grand challenge of biochemistry and requires distinct concepts from protein structure prediction. Despite extensive algorithmic development in recent years, modeling of noncanonical base pairs of new RNA structural motifs has not been achieved in blind challenges. We report a stepwise Monte Carlo (SWM) method with a unique add-and-delete move set that enables predictions of noncanonical base pairs of complex RNA structures. A benchmark of 82 diverse motifs establishes the method's general ability to recover noncanonical pairs ab initio, including multistrand motifs that have been refractory to prior approaches. In a blind challenge, SWM models predicted nucleotide-resolution chemical mapping and compensatory mutagenesis experiments for three in vitro selected tetraloop/receptors with previously unsolved structures (C7.2, C7.10, and R1). As a final test, SWM blindly and correctly predicted all noncanonical pairs of a Zika virus double pseudoknot during a recent community-wide RNA-Puzzle. Stepwise structure formation, as encoded in the SWM method, enables modeling of noncanonical RNA structure in a variety of previously intractable problems.

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.

Efficient Detection of Structure and Dynamics in Unlabeled RNAs: The SELOPE Approach

The knowledge of structure and dynamics is crucial to explain the function of RNAs. While nuclear magnetic resonance (NMR) is well suited to probe these for complex biomolecules, it requires expensive, isotopically labeled samples, and long measurement times. Here we present SELOPE, a new robust, proton-only NMR method that allows us to obtain site-specific overview of structure and dynamics in an entire RNA molecule using an unlabeled sample. SELOPE simplifies assignment and allows for cost-effective screening of the response of nucleic acids to physiological changes (e.g. ion concentration) or screening of drugs in a high throughput fashion. This single technique allows us to probe an unprecedented range of exchange time scales (the whole μs to ms motion range) with increased sensitivity, surpassing all current experiments to detect chemical exchange. For the first time we could describe an RNA excited state using an unlabeled RNA.

Surprising Sequence Effects on GU Closure of Symmetric 2 × 2 Nucleotide RNA Internal Loops

GU base pairs are important RNA structural motifs and often close loops. Accurate prediction of RNA structures relies upon understanding the interactions determining structure. The thermodynamics of some 2 × 2 nucleotide internal loops closed by GU pairs are not well understood. Here, several self-complementary oligonucleotide sequences expected to form duplexes with 2 × 2 nucleotide internal loops closed by GU pairs were investigated. Surprisingly, nuclear magnetic resonance revealed that many of the sequences exist in equilibrium between hairpin and duplex conformations. This equilibrium is not observed with loops closed by Watson-Crick pairs. To measure the thermodynamics of some 2 × 2 nucleotide internal loops closed by GU pairs, non-self-complementary sequences that preclude formation of hairpins were designed. The measured thermodynamics indicate that some internal loops closed by GU pairs are unusually unstable. This instability accounts for the observed equilibria between duplex and hairpin conformations. Moreover, it suggests that future three-dimensional structures of loops closed by GU pairs may reveal interactions that unexpectedly destabilize folding.

Physics-based all-atom modeling of RNA energetics and structure

The database of RNA sequences is exploding, but knowledge of energetics, structures, and dynamics lags behind. All-atom computational methods, such as molecular dynamics, hold promise for closing this gap. New algorithms and faster computers have accelerated progress in improving the reliability and accuracy of predictions. Currently, the methods can facilitate refinement of experimentally determined nuclear magnetic resonance and x-ray structures, but are 'unreliable' for predictions based only on sequence. Much remains to be discovered, however, about the many molecular interactions driving RNA folding and the best way to approximate them quantitatively. The large number of parameters required means that a wide variety of experimental results will be required to benchmark force fields and different approaches. As computational methods become more reliable and accessible, they will be used by an increasing number of biologists, much as x-ray crystallography has expanded. Thus, many fundamental physical principles underlying the computational methods are described. This review presents a summary of the current state of molecular dynamics as applied to RNA. It is designed to be helpful to students, postdoctoral fellows, and faculty who are considering or starting computational studies of RNA. WIREs RNA 2017, 8:e1422. doi: 10.1002/wrna.1422.

Structure modeling of RNA using sparse NMR constraints

RNAs fold into distinct molecular conformations that are often essential for their functions. Accurate structure modeling of complex RNA motifs, including ubiquitous non-canonical base pairs and pseudoknots, remains a challenge. Here, we present an NMR-guided all-atom discrete molecular dynamics (DMD) platform, iFoldNMR, for rapid and accurate structure modeling of complex RNAs. We show that sparse distance constraints from imino resonances, which can be readily obtained from routine NMR experiments and easier to compile than laborious assignments of non-solvent-exchangeable protons, are sufficient to direct a DMD search for low-energy RNA conformers. Benchmarking on a set of RNAs with complex folds spanning up to 56 nucleotides in length yields structural models that recapitulate experimentally determined structures with all-heavy-atom RMSDs ranging from 2.4 to 6.5 Å. This platform represents an efficient approach for high-throughput RNA structure modeling and will facilitate analysis of diverse, newly discovered functional RNAs.

Modeling Small Noncanonical RNA Motifs with the Rosetta FARFAR Server

Noncanonical RNA motifs help define the vast complexity of RNA structure and function, and in many cases, these loops and junctions are on the order of only ten nucleotides in size. Unfortunately, despite their small size, there is no reliable method to determine the ensemble of lowest energy structures of junctions and loops at atomic accuracy. This chapter outlines straightforward protocols using a webserver for Rosetta Fragment Assembly of RNA with Full Atom Refinement (FARFAR) ( http://rosie.rosettacommons.org/rna_denovo/submit ) to model the 3D structure of small noncanonical RNA motifs for use in visualizing motifs and for further refinement or filtering with experimental data such as NMR chemical shifts.

RNA structure refinement using NMR solvent accessibility data

NMR spectroscopy is a powerful technique to study ribonucleic acids (RNAs) which are key players in a plethora of cellular processes. Although the NMR toolbox for structural studies of RNAs expanded during the last decades, they often remain challenging. Here, we show that solvent paramagnetic relaxation enhancements (sPRE) induced by the soluble, paramagnetic compound Gd(DTPA-BMA) provide a quantitative measure for RNA solvent accessibility and encode distance-to-surface information that correlates well with RNA structure and improves accuracy and convergence of RNA structure determination. Moreover, we show that sPRE data can be easily obtained for RNAs with any isotope labeling scheme and is advantageous regarding sample preparation, stability and recovery. sPRE data show a large dynamic range and reflect the global fold of the RNA suggesting that they are well suited to identify interaction surfaces, to score structural models and as restraints in RNA structure determination.

Nuclear Magnetic Resonance Structure of an 8 × 8 Nucleotide RNA Internal Loop Flanked on Each Side by Three Watson-Crick Pairs and Comparison to Three-Dimensional Predictions

The prediction of RNA three-dimensional structure from sequence alone has been a long-standing goal. High-resolution, experimentally determined structures of simple noncanonical pairings and motifs are critical to the development of prediction programs. Here, we present the nuclear magnetic resonance structure of the (5'CCAGAAACGGAUGGA)₂ duplex, which contains an 8 × 8 nucleotide internal loop flanked by three Watson-Crick pairs on each side. The loop is comprised of a central 5'AC/3'CA nearest neighbor flanked by two 3RRs motifs, a known stable motif consisting of three consecutive sheared GA pairs. Hydrogen bonding patterns between base pairs in the loop, the all-atom root-mean-square deviation for the loop, and the deformation index were used to compare the structure to automated predictions by MC-sym, RNA FARFAR, and RNAComposer.

Structure and Dynamics of RNA Repeat Expansions That Cause Huntington's Disease and Myotonic Dystrophy Type 1

RNA repeat expansions cause a host of incurable, genetically defined diseases. The most common class of RNA repeats consists of trinucleotide repeats. These long, repeating transcripts fold into hairpins containing 1 × 1 internal loops that can mediate disease via a variety of mechanism(s) in which RNA is the central player. Two of these disorders are Huntington's disease and myotonic dystrophy type 1, which are caused by r(CAG) and r(CUG) repeats, respectively. We report the structures of two RNA constructs containing three copies of a r(CAG) [r(3×CAG)] or r(CUG) [r(3×CUG)] motif that were modeled with nuclear magnetic resonance spectroscopy and simulated annealing with restrained molecular dynamics. The 1 × 1 internal loops of r(3×CAG) are stabilized by one-hydrogen bond (cis Watson-Crick/Watson-Crick) AA pairs, while those of r(3×CUG) prefer one- or two-hydrogen bond (cis Watson-Crick/Watson-Crick) UU pairs. Assigned chemical shifts for the residues depended on the identity of neighbors or next nearest neighbors. Additional insights into the dynamics of these RNA constructs were gained by molecular dynamics simulations and a discrete path sampling method. Results indicate that the global structures of the RNA are A-form and that the loop regions are dynamic. The results will be useful for understanding the dynamic trajectory of these RNA repeats but also may aid in the development of therapeutics.

Theory and Modeling of RNA Structure and Interactions with Metal Ions and Small Molecules

In addition to continuous rapid progress in RNA structure determination, probing, and biophysical studies, the past decade has seen remarkable advances in the development of a new generation of RNA folding theories and models. In this article, we review RNA structure prediction models and models for ion-RNA and ligand-RNA interactions. These new models are becoming increasingly important for a mechanistic understanding of RNA function and quantitative design of RNA nanotechnology. We focus on new methods for physics-based, knowledge-based, and experimental data-directed modeling for RNA structures and explore the new theories for the predictions of metal ion and ligand binding sites and metal ion-dependent RNA stabilities. The integration of these new methods with theories about the cellular environment effects in RNA folding, such as molecular crowding and cotranscriptional kinetic effects, may ultimately lead to an all-encompassing RNA folding model.

Exploring RNA conformational space under sparse distance restraints

We show that the application of a small number of restraints predicted by coevolution analysis can provide a powerful restriction on the conformational freedom of an RNA molecule. The greatest degree of restriction occurs when a contact is predicted between the distal ends of a pair of adjacent stemloops but even with this location additional flexibilities in the molecule can mask the contribution. Multiple cross-links, especially those including a pseudoknot provided the strongest restraint on conformational freedom with the effect being most apparent in topologically simple folds and less so if the fold is more topologically entwined. Little was expected for large structures (over 300 bases) and although a few strong localised restrictions were observed, they contributed little to the restraint of the overall fold. Although contacts predicted using a correlated mutation analysis can provide some powerful restrictions on the conformational freedom of RNA molecules, they are too erratic in their occurrence and distribution to provide a general approach to the problem of RNA 3D structure prediction from sequence.

Simultaneous NMR characterisation of multiple minima in the free energy landscape of an RNA UUCG tetraloop

RNA molecules in solution tend to undergo structural fluctuations of relatively large amplitude and to populate a range of different conformations some of which with low populations. It is still very challenging, however, to characterise the structures of these low populated states and to understand their functional roles. In the present study, we address this problem by using NMR residual dipolar couplings (RDCs) as structural restraints in replica-averaged metadynamics (RAM) simulations. By applying this approach to a 14-mer RNA hairpin containing the prototypical UUCG tetraloop motif, we show that it is possible to construct the free energy landscape of this RNA molecule. This free energy landscapes reveals the surprisingly rich dynamics of the UUCG tetraloop and identifies the multiple substates that exist in equilibrium owing to thermal fluctuations. The approach that we present is general and can be applied to the study of the free energy landscapes of other RNA or RNA-protein systems.

Capturing Excited States in the Fast-Intermediate Exchange Limit in Biological Systems Using 1 H NMR Spectroscopy

Changes in molecular structure are essential for the function of biomolecules. Characterization of these structural fluctuations can illuminate alternative states and help in correlating structure to function. NMR relaxation dispersion (RD) is currently the only method for detecting these alternative, high-energy states. In this study, we present a versatile ¹ H R_1ρ RD experiment that not only extends the exchange timescales at least three times beyond the rate limits of ¹³ C/¹⁵ N R_1ρ and ten times for CPMG experiments, but also makes use of easily accessible probes, thus allowing a general description of biologically important excited states. This technique can be used to extract chemical shifts for the structural characterization of excited states and to elucidate complex excited states.

Excited States of Nucleic Acids Probed by Proton Relaxation Dispersion NMR Spectroscopy

In this work an improved stable isotope labeling protocol for nucleic acids is introduced. The novel building blocks eliminate/minimize homonuclear (13) C and (1) H scalar couplings thus allowing proton relaxation dispersion (RD) experiments to report accurately on the chemical exchange of nucleic acids. Using site-specific (2) H and (13) C labeling, spin topologies are introduced into DNA and RNA that make (1) H relaxation dispersion experiments applicable in a straightforward manner. The novel RNA/DNA building blocks were successfully incorporated into two nucleic acids. The A-site RNA was previously shown to undergo a two site exchange process in the micro- to millisecond time regime. Using proton relaxation dispersion experiments the exchange parameters determined earlier could be recapitulated, thus validating the proposed approach. We further investigated the dynamics of the cTAR DNA, a DNA transcript that is involved in the viral replication cycle of HIV-1. Again, an exchange process could be characterized and quantified. This shows the general applicablility of the novel labeling scheme for (1) H RD experiments of nucleic acids.

Protocols for Molecular Modeling with Rosetta3 and RosettaScripts

Previously, we published an article providing an overview of the Rosetta suite of biomacromolecular modeling software and a series of step-by-step tutorials [Kaufmann, K. W., et al. (2010) Biochemistry 49, 2987–2998]. The overwhelming positive response to this publication we received motivates us to here share the next iteration of these tutorials that feature de novo folding, comparative modeling, loop construction, protein docking, small molecule docking, and protein design. This updated and expanded set of tutorials is needed, as since 2010 Rosetta has been fully redesigned into an object-oriented protein modeling program Rosetta3. Notable improvements include a substantially improved energy function, an XML-like language termed “RosettaScripts” for flexibly specifying modeling task, new analysis tools, the addition of the TopologyBroker to control conformational sampling, and support for multiple templates in comparative modeling. Rosetta’s ability to model systems with symmetric proteins, membrane proteins, noncanonical amino acids, and RNA has also been greatly expanded and improved.

NMR Methods for Characterization of RNA Secondary Structure

Knowledge of RNA secondary structure is often sufficient to identify relationships between the structure of RNA and processing pathways, and the design of therapeutics. Nuclear magnetic resonance (NMR) can identify types of nucleotide base pairs and the sequence, thus limiting possible secondary structures. Because NMR experiments, like chemical mapping, are performed in solution, not in single crystals, experiments can be initiated as soon as the biomolecule is expressed and purified. This chapter summarizes NMR methods that permit rapid identification of RNA secondary structure, information that can be used as supplements to chemical mapping, and/or as preliminary steps required for 3D structure determination. The primary aim is to provide guidelines to enable a researcher with minimal knowledge of NMR to quickly extract secondary structure information from basic datasets. Instrumental and sample considerations that can maximize data quality are discussed along with some details for optimal data acquisition and processing parameters. Approaches for identifying base pair types in both unlabeled and isotopically labeled RNA are covered. Common problems, such as missing signals and overlaps, and approaches to address them are considered. Programs under development for merging NMR data with structure prediction algorithms are briefly discussed.

A systematic study on RNA NMR chemical shift calculation based on the automated fragmentation QM/MM approach

¹H, ¹³C and ¹⁵N NMR chemical shift calculations on RNAs were performed using the automated fragmentation quantum mechanics/molecular mechanics (AF-QM/MM) approach.

Nuclear Magnetic Resonance-Assisted Prediction of Secondary Structure for RNA: Incorporation of Direction-Dependent Chemical Shift Constraints

Knowledge of RNA structure is necessary to determine structure–function relationships and to facilitate design of potential therapeutics. RNA secondary structure prediction can be improved by applying constraints from nuclear magnetic resonance (NMR) experiments to a dynamic programming algorithm. Imino proton walks from NOESY spectra reveal double-stranded regions. Chemical shifts of protons in GH1, UH3, and UH5 of GU pairs, UH3, UH5, and AH2 of AU pairs, and GH1 of GC pairs were analyzed to identify constraints for the 5′ to 3′ directionality of base pairs in helices. The 5′ to 3′ directionality constraints were incorporated into an NMR-assisted prediction of secondary structure (NAPSS-CS) program. When it was tested on 18 structures, including nine pseudoknots, the sensitivity and positive predictive value were improved relative to those of three unrestrained programs. The prediction accuracy for the pseudoknots improved the most. The program also facilitates assignment of chemical shifts to individual nucleotides, a necessary step for determining three-dimensional structure.

Measuring Residual Dipolar Couplings in Excited Conformational States of Nucleic Acids by CEST NMR Spectroscopy

Nucleic acids undergo structural transitions to access sparsely populated and transiently lived conformational states--or excited conformational states--that play important roles in diverse biological processes. Despite ever-increasing detection of these functionally essential states, 3D structure determination of excited states (ESs) of RNA remains elusive. This is largely due to challenges in obtaining high-resolution structural constraints in these ESs by conventional structural biology approaches. Here, we present nucleic-acid-optimized chemical exchange saturation transfer (CEST) NMR spectroscopy for measuring residual dipolar couplings (RDCs), which provide unique long-range angular constraints in ESs of nucleic acids. We demonstrate these approaches on a fluoride riboswitch, where one-bond (13)C-(1)H RDCs from both base and sugar moieties provide direct structural probes into an ES of the ligand-free riboswitch.

Direct-Coupling Analysis of nucleotide coevolution facilitates RNA secondary and tertiary structure prediction

Despite the biological importance of non-coding RNA, their structural characterization remains challenging. Making use of the rapidly growing sequence databases, we analyze nucleotide coevolution across homologous sequences via Direct-Coupling Analysis to detect nucleotide-nucleotide contacts. For a representative set of riboswitches, we show that the results of Direct-Coupling Analysis in combination with a generalized Nussinov algorithm systematically improve the results of RNA secondary structure prediction beyond traditional covariance approaches based on mutual information. Even more importantly, we show that the results of Direct-Coupling Analysis are enriched in tertiary structure contacts. By integrating these predictions into molecular modeling tools, systematically improved tertiary structure predictions can be obtained, as compared to using secondary structure information alone.

Prediction of hydrogen and carbon chemical shifts from RNA using database mining and support vector regression

The Biological Magnetic Resonance Data Bank (BMRB) contains NMR chemical shift depositions for over 200 RNAs and RNA-containing complexes. We have analyzed the (1)H NMR and (13)C chemical shifts reported for non-exchangeable protons of 187 of these RNAs. Software was developed that downloads BMRB datasets and corresponding PDB structure files, and then generates residue-specific attributes based on the calculated secondary structure. Attributes represent properties present in each sequential stretch of five adjacent residues and include variables such as nucleotide type, base-pair presence and type, and tetraloop types. Attributes and (1)H and (13)C NMR chemical shifts of the central nucleotide are then used as input to train a predictive model using support vector regression. These models can then be used to predict shifts for new sequences. The new software tools, available as stand-alone scripts or integrated into the NMR visualization and analysis program NMRViewJ, should facilitate NMR assignment and/or validation of RNA (1)H and (13)C chemical shifts. In addition, our findings enabled the re-calibration a ring-current shift model using published NMR chemical shifts and high-resolution X-ray structural data as guides.

Dynamic nuclear polarization of nucleic acid with endogenously bound manganese

We report the direct dynamic nuclear polarization (DNP) of (13)C nuclei of a uniformly [(13)C,(15)N]-labeled, paramagnetic full-length hammerhead ribozyme (HHRz) complex with Mn(2+) where the enhanced polarization is fully provided by the endogenously bound metal ion and no exogenous polarizing agent is added. A (13)C enhancement factor of ε = 8 was observed by intra-complex DNP at 9.4 T. In contrast, "conventional" indirect and direct DNP experiments were performed using AMUPol as polarizing agent where we obtained a (1)H enhancement factor of ε ≈ 250. Comparison with the diamagnetic (Mg(2+)) HHRz complex shows that the presence of Mn(2+) only marginally influences the (DNP-enhanced) NMR properties of the RNA. Furthermore two-dimensional correlation spectra ((15)N-(13)C and (13)C-(13)C) reveal structural inhomogeneity in the frozen, amorphous state indicating the coexistence of several conformational states. These demonstrations of intra-complex DNP using an endogenous metal ion as well as DNP-enhanced MAS NMR of RNA in general yield important information for the development of new methods in structural biology.

RNA Structures as Mediators of Neurological Diseases and as Drug Targets

RNAs adopt diverse folded structures that are essential for function and thus play critical roles in cellular biology. A striking example of this is the ribosome, a complex, three-dimensionally folded macromolecular machine that orchestrates protein synthesis. Advances in RNA biochemistry, structural and molecular biology, and bioinformatics have revealed other non-coding RNAs whose functions are dictated by their structure. It is not surprising that aberrantly folded RNA structures contribute to disease. In this Review, we provide a brief introduction into RNA structural biology and then describe how RNA structures function in cells and cause or contribute to neurological disease. Finally, we highlight successful applications of rational design principles to provide chemical probes and lead compounds targeting structured RNAs. Based on several examples of well-characterized RNA-driven neurological disorders, we demonstrate how designed small molecules can facilitate the study of RNA dysfunction, elucidating previously unknown roles for RNA in disease, and provide lead therapeutics.