RNA structure determination by NMR

Structures of small molecules bound to RNA repeat expansions that cause Huntington's disease-like 2 and myotonic dystrophy type 1

Trinucleotide repeat expansions fold into long, stable hairpins and cause a variety of incurable RNA gain-of-function diseases such as Huntington's disease, the myotonic dystrophies, and spinocerebellar ataxias. One approach for treating these diseases is to bind small molecules to the structured RNAs. Both Huntington's disease-like 2 (HDL2) and myotonic dystrophy type 1 (DM1) are caused by a r(CUG) repeat expansion, or r(CUG)exp. The RNA folds into a hairpin structure with a periodic array of 1 × 1 nucleotide UU loops (5'CUG/3'GUC; where the underlined nucleotides indicate the Us in the internal loop) that sequester various RNA-binding proteins (RBP) and hence the source of its gain-of-function. Here, we report NMR-refined structures of single 5'CUG/3'GUC motifs in complex with three different small molecules, a di-guandinobenzoate (1), a derivative of 1 where the guanidino groups have been exchanged for imidazole (2), and a quinoline with improved drug-like properties (3). These structures were determined using nuclear magnetic resonance (NMR) spectroscopy and simulated annealing with restrained molecular dynamics (MD). Compounds 1, 2, and 3 formed stacking and hydrogen bonding interactions with the 5'CUG/3'GUC motif. Compound 3 also formed van der Waals interactions with the internal loop. The global structure of each RNA-small molecule complexes retains an A-form conformation, while the internal loops are still dynamic but to a lesser extent compared to the unbound form. These results aid our understanding of ligand-RNA interactions and enable structure-based design of small molecules with improved binding affinity for and biological activity against r(CUG)exp. As the first ever reported structures of RNA r(CUG) repeats bound to ligands, these structures can enable virtual screening campaigns combined with machine learning assisted de novo design.

NMR structures of small molecules bound to a model of an RNA CUG repeat expansion

Trinucleotide repeat expansions fold into long, stable hairpins and cause a variety of incurable RNA gain-of-function diseases such as Huntington's disease, the myotonic dystrophies, and spinocerebellar ataxias. One approach for treating these diseases is to bind small molecules to the structured RNAs. Both Huntington's disease-like 2 (HDL2) and myotonic dystrophy type 1 (DM1) are caused by a r(CUG) repeat expansion, or r(CUG)exp. The RNA folds into a hairpin structure with a periodic array of 1×1 nucleotide UU loops (5'CUG/3'GUC; where the underlined nucleotides indicate the Us in the internal loop) that sequester various RNA-binding proteins (RBP) and hence the source of its gain-of-function. Here, we report NMR-refined structures of single 5'CUG/3'GUC motifs in complex with three different small molecules, a di-guandinobenzoate (1), a derivative of 1 where the guanidino groups have been exchanged for imidazole (2), and a quinoline with improved drug-like properties (3). These structures were determined using nuclear magnetic resonance (NMR) spectroscopy and simulated annealing with restrained molecular dynamics (MD). Compounds 1, 2, and 3 formed stacking and hydrogen bonding interactions with the 5'CUG/3'GUC motif. Compound 3 also formed van der Waals interactions with the internal loop. The global structure of each RNA-small molecule complexes retains an A-form conformation, while the internal loops are still dynamic but to a lesser extent compared to the unbound form. These results aid our understanding of ligand-RNA interactions and enable structure-based design of small molecules with improved binding affinity for and biological activity against r(CUG)exp. As the first ever reported structures of RNA r(CUG) repeats bound to ligands, these structures can enable virtual screening campaigns combined with machine learning assisted de novo design.

GATLGEMF: A graph attention model with line graph embedding multi-complex features for ncRNA-protein interactions prediction

Non-coding RNA (ncRNA) plays an important role in many fundamental biological processes, and it may be closely associated with many complex human diseases. NcRNAs exert their functions by interacting with proteins. Therefore, identifying novel ncRNA-protein interactions (NPIs) is important for understanding the mechanism of ncRNAs role. The computational approach has the advantage of low cost and high efficiency. Machine learning and deep learning have achieved great success by making full use of sequence information and structure information. Graph neural network (GNN) is a deep learning algorithm for complex network link prediction, which can extract and discover features in graph topology data. In this study, we propose a new computational model called GATLGEMF. We used a line graph transformation strategy to obtain the most valuable feature information and input this feature information into the attention network to predict NPIs. The results on four benchmark datasets show that our method achieves superior performance. We further compare GATLGEMF with the state-of-the-art existing methods to evaluate the model performance. GATLGEMF shows the best performance with the area under curve (AUC) of 92.41% and 98.93% on RPI2241 and NPInter v2.0 datasets, respectively. In addition, a case study shows that GATLGEMF has the ability to predict new interactions based on known interactions. The source code is available at https://github.com/JianjunTan-Beijing/GATLGEMF.

A guideline for the distance measurement plans of site-directed spin labels for structural prediction of nucleic acids

CONTEXT AND RESULTS

Site-directed spin labeling (SDSL) combined with electron paramagnetic resonance spectroscopy methods has been successfully used to predict the structures of nucleic acids. These methods measure the distances between spin labels yielding distance equations that are solved using numerical algorithms to provide one or several structural predictions. In this work, the minimum number of SDSL distance measurements and distance measurement types required to predict a unique nucleic acid structure were investigated. Our results indicate that at least six distance measurements should be obtained given that the distance measurements do not connect one SDSL on one arm with more than three SDSLs on the other arm. Moreover, there may be a preference for 1-to-1 SLs distance measurements rather than 1-to-many SLs as the latter was linked to undefined structures discussed in this study.

METHODS

Pairs of double-helical arms of nucleic acid were simulated using the finite element software Pro/ENGINEER (PTC Inc., Boston, MA). In each simulation, a specific SDSL distance measurement plan was adopted and the resulting structure was tested for movability. Immovable structures indicate that this plan will potentially result in a unique structural prediction of the nucleic acid. All the possible plans for SDSL distance measurements were investigated either by direct measurement or by extrapolation.

Dynamic geometry design of cyclic peptide architectures for RNA structure

Designing inhibitors for RNA is still challenging due to the bottleneck of maintaining the binding interaction of inhibitor-RNA accompanied by subtle RNA flexibility. Thus, the current approach usually needs to screen thousands of candidate inhibitors for binding. Here, we propose a dynamic geometry design approach to enrich the hits with only a tiny pool of designed geometrically compatible scaffold candidates. First, our method uses graph-based tree decomposition to explore the complementarity rigid binding cyclic peptide and design the amino acid side chain length and charge to fit the RNA pocket. Then, we perform an energy-based dynamical network algorithm to optimize the inhibitor-RNA hydrogen bonds. Dynamic geometry-guided design yields successful inhibitors with low micromolar binding affinity scaffolds and experimentally competes with the natural RNA chaperone. The results indicate that the dynamic geometry method yields higher efficiency and accuracy than traditional methods. The strategy could be further optimized to design the length and chirality by adopting nonstandard amino acids and facilitating RNA engineering for biological or medical applications.

RNA structure determination: From 2D to 3D

RNA molecules serve a wide range of functions that are closely linked to their structures. The basic structural units of RNA consist of single- and double-stranded regions. In order to carry out advanced functions such as catalysis and ligand binding, certain types of RNAs can adopt higher-order structures. The analysis of RNA structures has progressed alongside advancements in structural biology techniques, but it comes with its own set of challenges and corresponding solutions. In this review, we will discuss recent advances in RNA structure analysis techniques, including structural probing methods, X-ray crystallography, nuclear magnetic resonance, cryo-electron microscopy, and small-angle X-ray scattering. Often, a combination of multiple techniques is employed for the integrated analysis of RNA structures. We also survey important RNA structures that have been recently determined using various techniques.

Recognizing the power of machine learning and other computational methods to accelerate progress in small molecule targeting of RNA

RNA structures regulate a wide range of processes in biology and disease, yet small molecule chemical probes or drugs that can modulate these functions are rare. Machine learning and other computational methods are well poised to fill gaps in knowledge and overcome the inherent challenges in RNA targeting, such as the dynamic nature of RNA and the difficulty of obtaining RNA high-resolution structures. Successful tools to date include principal component analysis, linear discriminate analysis, k-nearest neighbor, artificial neural networks, multiple linear regression, and many others. Employment of these tools has revealed critical factors for selective recognition in RNA:small molecule complexes, predictable differences in RNA- and protein-binding ligands, and quantitative structure activity relationships that allow the rational design of small molecules for a given RNA target. Herein we present our perspective on the value of using machine learning and other computation methods to advance RNA:small molecule targeting, including select examples and their validation as well as necessary and promising future directions that will be key to accelerate discoveries in this important field.

Sulfonylation of RNA 2'-OH groups

The nucleophilic reactivity of RNA 2'-OH groups in water has proven broadly useful in probing, labeling, and conjugating RNA. To date, reactions selective to ribose 2'-OH have been limited to bond formation with short-lived carbonyl electrophiles. Here we report that many activated small-molecule sulfonyl species can exhibit extended lifetimes in water and retain 2'-OH reactivity. The data establish favorable aqueous solubility for selected reagents and successful RNA-selective reactions at stoichiometric and superstoichiometric yields, particularly for aryl sulfonyltriazole species. We report that the latter are considerably more stable than most prior carbon electrophiles in aqueous environments and tolerate silica chromatography. Furthermore, an azide-substituted sulfonyltriazole reagent is developed to introduce labels into RNA via click chemistry. In addition to high-yield reactions, we find that RNA sulfonylation can also be performed under conditions that give trace yields necessary for structure mapping. Like acylation, the reaction occurs with selectivity for unpaired nucleotides over those in the duplex structure, and a sulfonate adduct causes reverse transcriptase stops, suggesting potential use in RNA structure analysis. Probing of rRNA is demonstrated in human cells, indicating possible cell permeability. The sulfonyl reagent class enables a new level of control, selectivity, versatility, and ease of preparation for RNA applications.

Multi-model predictive analysis of RNA solvent accessibility based on modified residual attention mechanism

Predicting RNA solvent accessibility using only primary sequence data can be regarded as sequence-based prediction work. Currently, the established studies for sequence-based RNA solvent accessibility prediction are limited due to the available number of datasets and black box prediction. To improve these issues, we first expanded the available RNA structures and then developed a sequence-based model using modified attention layers with different receptive fields to conform to the stem-loop structure of RNA chains. We measured the improvement with an extended dataset and further explored the model's interpretability by analysing the model structures, attention values and hyperparameters. Finally, we found that the developed model regarded the pieces of a sequence as templates during the training process. This work will be helpful for researchers who would like to build RNA attribute prediction models using deep learning in the future.

The promise of cryo-EM to explore RNA structural dynamics

Conformational dynamics are essential to macromolecular function. This is certainly true of RNA, whose ability to undergo programmed conformational dynamics is essential to create and regulate complex biological processes. However, methods to easily and simultaneously interrogate both the structure and conformational dynamics of fully functional RNAs in isolation and in complex with proteins have not historically been available. Due to its ability to image and classify single particles, cryogenic electron microscopy (cryo-EM) has the potential to address this gap and may be particularly amenable to exploring structural dynamics within the three-dimensional folds of biologically active RNAs. We discuss the possibilities and current limitations of applying cryo-EM to simultaneously study RNA structure and conformational dynamics, and present one example that illustrates this (as of yet) not fully realized potential.

Predicting RNA solvent accessibility from multi-scale context feature via multi-shot neural network

Knowledge of RNA solvent accessibility has recently become attractive due to the increasing awareness of its importance for key biological process. Accurately predicting the solvent accessibility of RNA is crucial for understanding its 3D structure and biological function. In this study, we develop a novel computational method, termed M²pred, for accurately predicting the solvent accessibility of RNA from sequence-based multi-scale context feature. In M²pred, three single-view features, i.e., base-pairing probabilities, position-specific frequency matrix, and a binary one-hot encoding, are first generated as three feature sources, and immediately concatenated to engender a super feature. Secondly, for the super feature, the matrix-format features of each nucleotide are extracted using an initialized sliding window technique, and regularly stacked into a cube-format feature. Then, using multi-scale context feature extraction strategy, a pyramid feature constructed of contextual feature of four scales related to target nucleotides is extracted from the cube-format feature. Finally, a customized multi-shot neural network framework, which is equipped with four different scales of receptive fields mainly integrating several residual attention blocks, is designed to dig discrimination information from the contextual pyramid feature. Experimental results demonstrate that the proposed M²pred achieve a high prediction performance and outperforms existing state-of-the-art prediction methods of RNA solvent accessibility.

Strategies for RNA Resonance Assignment by 13C/15N- and 1H-Detected Solid-State NMR Spectroscopy

Magic angle spinning (MAS) solid-state NMR (ssNMR) is an established tool that can be applied to non-soluble or non-crystalline biomolecules of any size or complexity. The ssNMR method advances rapidly due to technical improvements and the development of advanced isotope labeling schemes. While ssNMR has shown significant progress in structural studies of proteins, the number of RNA studies remains limited due to ssNMR methodology that is still underdeveloped. Resonance assignment is the most critical and limiting step in the structure determination protocol that defines the feasibility of NMR studies. In this review, we summarize the recent progress in RNA resonance assignment methods and approaches for secondary structure determination by ssNMR. We critically discuss advantages and limitations of conventional 13C- and 15N-detected experiments and novel 1H-detected methods, identify optimal regimes for RNA studies by ssNMR, and provide our view on future ssNMR studies of RNA in large RNP complexes.

EDLMFC: an ensemble deep learning framework with multi-scale features combination for ncRNA-protein interaction prediction

Background

Non-coding RNA (ncRNA) and protein interactions play essential roles in various physiological and pathological processes. The experimental methods used for predicting ncRNA–protein interactions are time-consuming and labor-intensive. Therefore, there is an increasing demand for computational methods to accurately and efficiently predict ncRNA–protein interactions.

Results

In this work, we presented an ensemble deep learning-based method, EDLMFC, to predict ncRNA–protein interactions using the combination of multi-scale features, including primary sequence features, secondary structure sequence features, and tertiary structure features. Conjoint k-mer was used to extract protein/ncRNA sequence features, integrating tertiary structure features, then fed into an ensemble deep learning model, which combined convolutional neural network (CNN) to learn dominating biological information with bi-directional long short-term memory network (BLSTM) to capture long-range dependencies among the features identified by the CNN. Compared with other state-of-the-art methods under five-fold cross-validation, EDLMFC shows the best performance with accuracy of 93.8%, 89.7%, and 86.1% on RPI1807, NPInter v2.0, and RPI488 datasets, respectively. The results of the independent test demonstrated that EDLMFC can effectively predict potential ncRNA–protein interactions from different organisms. Furtherly, EDLMFC is also shown to predict hub ncRNAs and proteins presented in ncRNA–protein networks of Mus musculus successfully.

Conclusions

In general, our proposed method EDLMFC improved the accuracy of ncRNA–protein interaction predictions and anticipated providing some helpful guidance on ncRNA functions research. The source code of EDLMFC and the datasets used in this work are available at https://github.com/JingjingWang-87/EDLMFC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04069-9.

Site-Specific Spin Labeling of RNA for NMR and EPR Structural Studies

Many RNA architectures were discovered to be involved in essential biological pathways acting as catalysts and/or regulators of gene expression, transcription, translation, splicing, or viral infection. The key to understand their diverse biological functions is to investigate their structure and dynamic. Nuclear Magnetic Resonance (NMR) is a powerful method to gain insight into these properties. However, the study of high-molecular-weight RNAs by NMR remains challenging. Advances in biochemical and NMR methods over the recent years allow to overcome the limitation of NMR. In particular, the incorporation of paramagnetic probes, coupled to the measurement of the induced effects on nuclear spins, has become an efficient tool providing long-range distance restraints and information on dynamic in solution. At the same time, the use of spin label enabled the application of Electron Paramagnetic Resonance (EPR) to study biological macromolecules. Combining NMR and EPR is emerging as a new approach to investigate the architecture of biological systems.Here, we describe an efficient protocol to introduce a paramagnetic probe into a RNA at a specific position. This method enables various combinations of isotopic labeling for NMR and is also of interest for EPR studies.

Prediction of RNA-protein interactions by combining deep convolutional neural network with feature selection ensemble method

RNA-protein interaction (RPI) plays an important role in the basic cellular processes of organisms. Unfortunately, due to time and cost constraints, it is difficult for biological experiments to determine the relationship between RNA and protein to a large extent. So there is an urgent need for reliable computational methods to quickly and accurately predict RNA-protein interaction. In this study, we propose a novel computational method RPIFSE (predicting RPI with Feature Selection Ensemble method) based on RNA and protein sequence information to predict RPI. Firstly, RPIFSE disturbs the features extracted by the convolution neural network (CNN) and generates multiple data sets according to the weight of the feature, and then use extreme learning machine (ELM) classifier to classify these data sets. Finally, the results of each classifier are combined, and the highest score is chosen as the final prediction result by weighting voting method. In 5-fold cross-validation experiments, RPIFSE achieved 91.87%, 89.74%, 97.76% and 98.98% accuracy on RPI369, RPI2241, RPI488 and RPI1807 data sets, respectively. To further evaluate the performance of RPIFSE, we compare it with the state-of-the-art support vector machine (SVM) classifier and other exiting methods on those data sets. Furthermore, we also predicted the RPI on the independent data set NPInter2.0 and drew the network graph based on the prediction results. These promising comparison results demonstrated the effectiveness of RPIFSE and indicated that RPIFSE could be a useful tool for predicting RPI.

RNA Structure-A Neglected Puppet Master for the Evolution of Virus and Host Immunity

The central dogma of molecular biology describes the flow of genetic information from DNA to protein via an RNA intermediate. For many years, RNA has been considered simply as a messenger relaying information between DNA and proteins. Recent advances in next generation sequencing technology, bioinformatics, and non-coding RNA biology have highlighted the many important roles of RNA in virtually every biological process. Our understanding of RNA biology has been further enriched by a number of significant advances in probing RNA structures. It is now appreciated that many cellular and viral biological processes are highly dependent on specific RNA structures and/or sequences, and such reliance will undoubtedly impact on the evolution of both hosts and viruses. As a contribution to this special issue on host immunity and virus evolution, it is timely to consider how RNA sequences and structures could directly influence the co-evolution between hosts and viruses. In this manuscript, we begin by stating some of the basic principles of RNA structures, followed by describing some of the critical RNA structures in both viruses and hosts. More importantly, we highlight a number of available new tools to predict and to evaluate novel RNA structures, pointing out some of the limitations readers should be aware of in their own analyses.

Bioinformatics Tools and Benchmarks for Computational Docking and 3D Structure Prediction of RNA-Protein Complexes

RNA-protein (RNP) interactions play essential roles in many biological processes, such as regulation of co-transcriptional and post-transcriptional gene expression, RNA splicing, transport, storage and stabilization, as well as protein synthesis. An increasing number of RNP structures would aid in a better understanding of these processes. However, due to the technical difficulties associated with experimental determination of macromolecular structures by high-resolution methods, studies on RNP recognition and complex formation present significant challenges. As an alternative, computational prediction of RNP interactions can be carried out. Structural models obtained by theoretical predictive methods are, in general, less reliable compared to models based on experimental measurements but they can be sufficiently accurate to be used as a basis for to formulating functional hypotheses. In this article, we present an overview of computational methods for 3D structure prediction of RNP complexes. We discuss currently available methods for macromolecular docking and for scoring 3D structural models of RNP complexes in particular. Additionally, we also review benchmarks that have been developed to assess the accuracy of these methods.

A guide to large-scale RNA sample preparation

RNA is becoming more important as an increasing number of functions, both regulatory and enzymatic, are being discovered on a daily basis. As the RNA boom has just begun, most techniques are still in development and changes occur frequently. To understand RNA functions, revealing the structure of RNA is of utmost importance, which requires sample preparation. We review the latest methods to produce and purify a variation of RNA molecules for different purposes with the main focus on structural biology and biophysics. We present a guide aimed at identifying the most suitable method for your RNA and your biological question and highlighting the advantages of different methods. Graphical abstract In this review we present different methods for large-scale production and purification of RNAs for structural and biophysical studies.

CCR5 RNA Pseudoknots: Residue and Site-Specific Labeling correlate Internal Motions with microRNA Binding

Conformational dynamics of RNA molecules play a critical role in governing their biological functions. Measurements of RNA dynamic behavior sheds important light on sites that interact with their binding partners or cellular stimulators. However, such measurements using solution-state NMR are difficult for large RNA molecules (>70 nt; nt=nucleotides) owing to severe spectral overlap, homonuclear 13 C scalar couplings, and line broadening. Herein, a strategic combination of solid-phase synthesis, site-specific isotopic labeled phosphoramidites, and enzymatic ligation is introduced. This approach allowed the position-specific insertion of isotopic probes into a 96 nt CCR5 RNA fragment. Accurate measurements of functional dynamics using the Carr-Purcell-Meiboom-Gill (CPMG) relaxation dispersion (RD) experiments enabled extraction of the exchange rates and populations of this RNA. NMR chemical shift perturbation analysis of the RNA/microRNA-1224 complex indicated that A90-C1' of the pseudoknot exhibits similar changes in chemical shift observed in the excited state. This work demonstrates the general applicability of a NMR-labeling strategy to probe functional RNA structural dynamics.

A pair-conformation-dependent scoring function for evaluating 3D RNA-protein complex structures

Computational prediction of RNA-protein complex 3D structures includes two basic steps: one is sampling possible structures and another is scoring the sampled structures to pick out the correct one. At present, constructing accurate scoring functions is still not well solved and the performances of the scoring functions usually depend on used benchmarks. Here we propose a pair-conformation-dependent scoring function, 3dRPC-Score, for 3D RNA-protein complex structure prediction by considering the nucleotide-residue pairs having the same energy if their conformations are similar, instead of the distance-only dependence of the most existing scoring functions. Benchmarking shows that 3dRPC-Score has a consistent performance in three test sets.

Inforna 2.0: A Platform for the Sequence-Based Design of Small Molecules Targeting Structured RNAs

The development of small molecules that target RNA is challenging yet, if successful, could advance the development of chemical probes to study RNA function or precision therapeutics to treat RNA-mediated disease. Previously, we described Inforna, an approach that can mine motifs (secondary structures) within target RNAs, which is deduced from the RNA sequence, and compare them to a database of known RNA motif-small molecule binding partners. Output generated by Inforna includes the motif found in both the database and the desired RNA target, lead small molecules for that target, and other related meta-data. Lead small molecules can then be tested for binding and affecting cellular (dys)function. Herein, we describe Inforna 2.0, which incorporates all known RNA motif-small molecule binding partners reported in the scientific literature, a chemical similarity searching feature, and an improved user interface and is freely available via an online web server. By incorporation of interactions identified by other laboratories, the database has been doubled, containing 1936 RNA motif-small molecule interactions, including 244 unique small molecules and 1331 motifs. Interestingly, chemotype analysis of the compounds that bind RNA in the database reveals features in small molecule chemotypes that are privileged for binding. Further, this updated database expanded the number of cellular RNAs to which lead compounds can be identified.

Accurate prediction of RNA-binding protein residues with two discriminative structural descriptors

BACKGROUND

RNA-binding proteins participate in many important biological processes concerning RNA-mediated gene regulation, and several computational methods have been recently developed to predict the protein-RNA interactions of RNA-binding proteins. Newly developed discriminative descriptors will help to improve the prediction accuracy of these prediction methods and provide further meaningful information for researchers.

RESULTS

In this work, we designed two structural features (residue electrostatic surface potential and triplet interface propensity) and according to the statistical and structural analysis of protein-RNA complexes, the two features were powerful for identifying RNA-binding protein residues. Using these two features and other excellent structure- and sequence-based features, a random forest classifier was constructed to predict RNA-binding residues. The area under the receiver operating characteristic curve (AUC) of five-fold cross-validation for our method on training set RBP195 was 0.900, and when applied to the test set RBP68, the prediction accuracy (ACC) was 0.868, and the F-score was 0.631.

CONCLUSIONS

The good prediction performance of our method revealed that the two newly designed descriptors could be discriminative for inferring protein residues interacting with RNAs. To facilitate the use of our method, a web-server called RNAProSite, which implements the proposed method, was constructed and is freely available at http://lilab.ecust.edu.cn/NABind .

Chemo-enzymatic labeling for rapid assignment of RNA molecules

Even though Nuclear Magnetic Resonance (NMR) spectroscopy is one of the few techniques capable of determining atomic resolution structures of RNA, it is constrained by two major problems of chemical shift overlap of resonances and rapid signal loss due to line broadening. Emerging tools to tackle these problems include synthesis of atom specifically labeled or chemically modified nucleotides. Herein we review the synthesis of these nucleotides, the design and production of appropriate RNA samples, and the application and analysis of the NMR experiments that take advantage of these labels.

Screening of Pre-miRNA-155 Binding Peptides for Apoptosis Inducing Activity Using Peptide Microarrays

MicroRNA-155, one of the most potent miRNAs that suppress apoptosis in human cancer, is overexpressed in numerous cancers, and it displays oncogenic activity. Peptide microarrays, constructed by immobilizing 185 peptides containing the C-terminal hydrazide onto epoxide-derivatized glass slides, were employed to evaluate peptide binding properties of pre-miRNA-155 and to identify its binding peptides. Two peptides, which were identified based on the results of peptide microarray and in vitro Dicer inhibition studies, were found to inhibit generation of mature miRNA-155 catalyzed by Dicer and to enhance expression of miRNA-155 target genes in cells. In addition, the results of cell experiments indicate that peptide inhibitors promote apoptotic cell death via a caspase-dependent pathway. Finally, observations made in NMR and molecular modeling studies suggest that a peptide inhibitor preferentially binds to the upper bulge and apical stem-loop region of pre-miRNA-155, thereby suppressing Dicer-mediated miRNA-155 processing.

Pairwise RNA secondary structure alignment with conserved stem pattern

MOTIVATION

The regulatory functions performed by non-coding RNAs are related to their 3D structures, which are, in turn, determined by their secondary structures. Pairwise secondary structure alignment gives insight into the functional similarity between a pair of RNA sequences. Numerous exact or heuristic approaches have been proposed for computational alignment. However, the alignment becomes intractable when arbitrary pseudoknots are allowed. Also, since non-coding RNAs are, in general, more conserved in structures than sequences, it is more effective to perform alignment based on the common structural motifs discovered.

RESULTS

We devised a method to approximate the true conserved stem pattern for a secondary structure pair, and constructed the alignment from it. Experimental results suggest that our method identified similar RNA secondary structures better than the existing tools, especially for large structures. It also successfully indicated the conservation of some pseudoknot features with biological significance. More importantly, even for large structures with arbitrary pseudoknots, the alignment can usually be obtained efficiently.

AVAILABILITY AND IMPLEMENTATION

Our algorithm has been implemented in a tool called PSMAlign. The source code of PSMAlign is freely available at http://homepage.cs.latrobe.edu.au/ypchen/psmalign/.

The "Speedy" Synthesis of Atom-Specific (15)N Imino/Amido-Labeled RNA

Although numerous reports on the synthesis of atom-specific (15)N-labeled nucleosides exist, fast and facile access to the corresponding phosphoramidites for RNA solid-phase synthesis is still lacking. This situation represents a severe bottleneck for NMR spectroscopic investigations on functional RNAs. Here, we present optimized procedures to speed up the synthesis of (15)N(1) adenosine and (15)N(1) guanosine amidites, which are the much needed counterparts of the more straightforward-to-achieve (15)N(3) uridine and (15)N(3) cytidine amidites in order to tap full potential of (1)H/(15)N/(15)N-COSY experiments for directly monitoring individual Watson-Crick base pairs in RNA. Demonstrated for two preQ1 riboswitch systems, we exemplify a versatile concept for individual base-pair labeling in the analysis of conformationally flexible RNAs when competing structures and conformational dynamics are encountered.

Conjoined use of EM and NMR in RNA structure refinement

More than 40% of the RNA structures have been determined using nuclear magnetic resonance (NMR) technique. NMR mainly provides local structural information of protons and works most effectively on relatively small biomacromolecules. Hence structural characterization of large RNAs can be difficult for NMR alone. Electron microscopy (EM) provides global shape information of macromolecules at nanometer resolution, which should be complementary to NMR for RNA structure determination. Here we developed a new energy term in Xplor-NIH against the density map obtained by EM. We conjointly used NMR and map restraints for the structure refinement of three RNA systems — U2/U6 small-nuclear RNA, genome-packing motif (Ψ^CD)₂ from Moloney murine leukemia virus, and ribosome-binding element from turnip crinkle virus. In all three systems, we showed that the incorporation of a map restraint, either experimental or generated from known PDB structure, greatly improves structural precision and accuracy. Importantly, our method does not rely on an initial model assembled from RNA duplexes, and allows full torsional freedom for each nucleotide in the torsion angle simulated annealing refinement. As increasing number of macromolecules can be characterized by both NMR and EM, the marriage between the two techniques would enable better characterization of RNA three-dimensional structures.

Exploiting post-transcriptional regulation to probe RNA structures in vivo via fluorescence

While RNA structures have been extensively characterized in vitro, very few techniques exist to probe RNA structures inside cells. Here, we have exploited mechanisms of post-transcriptional regulation to synthesize fluorescence-based probes that assay RNA structures in vivo. Our probing system involves the co-expression of two constructs: (i) a target RNA and (ii) a reporter containing a probe complementary to a region in the target RNA attached to an RBS-sequestering hairpin and fused to a sequence encoding the green fluorescent protein (GFP). When a region of the target RNA is accessible, the area can interact with its complementary probe, resulting in fluorescence. By using this system, we observed varied patterns of structural accessibility along the length of the Tetrahymena group I intron. We performed in vivo DMS footprinting which, along with previous footprinting studies, helped to explain our probing results. Additionally, this novel approach represents a valuable tool to differentiate between RNA variants and to detect structural changes caused by subtle mutations. Our results capture some differences from traditional footprinting assays that could suggest that probing in vivo via oligonucleotide hybridization facilitates the detection of folding intermediates. Importantly, our data indicate that intracellular oligonucleotide probing can be a powerful complement to existing RNA structural probing methods.

Protein-RNA complexes and efficient automatic docking: expanding RosettaDock possibilities

Protein-RNA complexes provide a wide range of essential functions in the cell. Their atomic experimental structure solving, despite essential to the understanding of these functions, is often difficult and expensive. Docking approaches that have been developed for proteins are often challenging to adapt for RNA because of its inherent flexibility and the structural data available being relatively scarce. In this study we adapted the RosettaDock protocol for protein-RNA complexes both at the nucleotide and atomic levels. Using a genetic algorithm-based strategy, and a non-redundant protein-RNA dataset, we derived a RosettaDock scoring scheme able not only to discriminate but also score efficiently docking decoys. The approach proved to be both efficient and robust for generating and identifying suitable structures when applied to two protein-RNA docking benchmarks in both bound and unbound settings. It also compares well to existing strategies. This is the first approach that currently offers a multi-level optimized scoring approach integrated in a full docking suite, leading the way to adaptive fully flexible strategies.

Multivalent Amino Sugars to Recognize Different TAR RNA Conformations

Neomycin dimers synthesized using "click chemistry" with varying functionality and length in the linker region have been shown to be effective in targeting the HIV-1 TAR RNA region of the HIV virus. TAR (Transactivation Response) RNA region, a 59 base pair stem loop structure located at the 5'-end of all nascent viral transcripts interacts with its target, a key regulatory protein, Tat, and necessitates the replication of HIV-1 virus. Ethidium bromide displacement and FRET competition assays have revealed nanomolar binding affinity between neomycin dimers and wildtype TAR RNA while in case of neomycin, only a weak binding was detected. Here, NMR and FID-based comparisons reveal an extended binding interface for neomycin dimers involving the upper stem of the TAR RNA thereby offering an explanation for increased affinities. To further explore the potential of these modified aminosugars we have extended binding studies to include four TAR RNA mutants that display conformational differences with minimal sequence variation. The differences in binding between neomycin and neomycin dimers is characterized with TAR RNA mutants that include mutations to the bulge region, hairpin region, and both the bulge and hairpin regions. Our results demonstrate the effect of these mutations on neomycin binding and our results show that linker functionalities between dimeric units of neomycin can distinguish between the conformational differences of mutant TAR RNA structures.

The arginine-rich RNA-binding motif of HIV-1 Rev is intrinsically disordered and folds upon RRE binding

Arginine-rich motifs (ARMs) capable of binding diverse RNA structures play critical roles in transcription, translation, RNA trafficking, and RNA packaging. The regulatory HIV-1 protein Rev is essential for viral replication and belongs to the ARM family of RNA-binding proteins. During the early stages of the HIV-1 life cycle, incompletely spliced and full-length viral mRNAs are very inefficiently recognized by the splicing machinery of the host cell and are subject to degradation in the cell nucleus. These transcripts harbor the Rev Response Element (RRE), which orchestrates the interaction with the Rev ARM and the successive Rev-dependent mRNA export pathway. Based on established criteria for predicting intrinsic disorder, such as hydropathy, combined with significant net charge, the very basic primary sequences of ARMs are expected to adopt coil-like structures. Thus, we initiated this study to investigate the conformational changes of the Rev ARM associated with RNA binding. We used multidimensional NMR and circular dichroism spectroscopy to monitor the observed structural transitions, and described the conformational landscapes using statistical ensemble and molecular-dynamics simulations. The combined spectroscopic and simulated results imply that the Rev ARM is intrinsically disordered not only as an isolated peptide but also when it is embedded into an oligomerization-deficient Rev mutant. RRE recognition triggers a crucial coil-to-helix transition employing an induced-fit mechanism.

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

Structured noncoding RNAs underlie fundamental cellular processes, but determining their three-dimensional structures remains challenging. We demonstrate that integrating ¹H NMR chemical shift data with Rosetta de novo modeling can be used to consistently determine high-resolution RNA structures. On a benchmark set of 23 noncanonical RNA motifs, including 11 'blind' targets, chemical-shift Rosetta for RNA (CS-Rosetta-RNA) recovered experimental structures with high accuracy (0.6-2.0 Å all-heavy-atom r.m.s. deviation) in 18 cases.

Tackling structures of long noncoding RNAs

RNAs are important catalytic machines and regulators at every level of gene expression. A new class of RNAs has emerged called long non-coding RNAs, providing new insights into evolution, development and disease. Long non-coding RNAs (lncRNAs) predominantly found in higher eukaryotes, have been implicated in the regulation of transcription factors, chromatin-remodeling, hormone receptors and many other processes. The structural versatility of RNA allows it to perform various functions, ranging from precise protein recognition to catalysis and metabolite sensing. While major housekeeping RNA molecules have long been the focus of structural studies, lncRNAs remain the least characterized class, both structurally and functionally. Here, we review common methodologies used to tackle RNA structure, emphasizing their potential application to lncRNAs. When considering the complexity of lncRNAs and lack of knowledge of their structure, chemical probing appears to be an indispensable tool, with few restrictions in terms of size, quantity and heterogeneity of the RNA molecule. Probing is not constrained to in vitro analysis and can be adapted to high-throughput sequencing platforms. Significant efforts have been applied to develop new in vivo chemical probing reagents, new library construction protocols for sequencing platforms and improved RNA prediction software based on the experimental evidence.

Computational modeling of protein-RNA complex structures

Protein-RNA interactions play fundamental roles in many biological processes, such as regulation of gene expression, RNA splicing, and protein synthesis. The understanding of these processes improves as new structures of protein-RNA complexes are solved and the molecular details of interactions analyzed. However, experimental determination of protein-RNA complex structures by high-resolution methods is tedious and difficult. Therefore, studies on protein-RNA recognition and complex formation present major technical challenges for macromolecular structural biology. Alternatively, protein-RNA interactions can be predicted by computational methods. Although less accurate than experimental measurements, theoretical models of macromolecular structures can be sufficiently accurate to prompt functional hypotheses and guide e.g. identification of important amino acid or nucleotide residues. In this article we present an overview of strategies and methods for computational modeling of protein-RNA complexes, including software developed in our laboratory, and illustrate it with practical examples of structural predictions.

Nucleic acid structure characterization by small angle X-ray scattering (SAXS)

Small angle X-ray scattering (SAXS) is a powerful method for investigating macromolecular structure in solution. SAXS data provide information about the size and shape of a molecule with a resolution of ∼2 to 3 nm. SAXS is particularly useful for the investigation of nucleic acids, which scatter X-rays strongly due to the electron-rich phosphate backbone. Therefore, SAXS has become an increasingly popular method for modeling nucleic acid structures, an endeavor made tractable by the highly regular helical nature of nucleic acid secondary structures. Recently, SAXS was used in combination with NMR to filter and refine all-atom models of a U2/U6 small nuclear RNA complex. In this unit, general protocols for sample preparation, data acquisition, and data analysis and processing are given. Additionally, examples of correctly and incorrectly processed SAXS data and expected results are provided.

Biosynthetic preparation of 13C/15N-labeled rNTPs for high-resolution NMR studies of RNAs

High-resolution investigations of the structure and dynamics of RNA molecules by nuclear magnetic resonance (NMR) methodologies require the production of (13)C/(15)N-isotopically labeled samples. A common strategy entails the preparation of (13)C/(15)N-enriched ribonucleoside 5'-triphosphates (rNTPs) to be incorporated into RNA oligomers by in vitro transcription. Here, we describe the methods to obtain isotopically labeled rNTP in a uniform or selective fashion from bacterial cultures, using common and versatile E. coli strains. This chapter also covers procedures for extraction and digestion of the total RNA from bacterial cells, purification of the ribonucleoside 5'-monophosphates and their enzymatic phosphorylation to rNTPs.

RNA dimerization plays a role in ribosomal frameshifting of the SARS coronavirus

Messenger RNA encoded signals that are involved in programmed -1 ribosomal frameshifting (-1 PRF) are typically two-stemmed hairpin (H)-type pseudoknots (pks). We previously described an unusual three-stemmed pseudoknot from the severe acute respiratory syndrome (SARS) coronavirus (CoV) that stimulated -1 PRF. The conserved existence of a third stem–loop suggested an important hitherto unknown function. Here we present new information describing structure and function of the third stem of the SARS pseudoknot. We uncovered RNA dimerization through a palindromic sequence embedded in the SARS-CoV Stem 3. Further in vitro analysis revealed that SARS-CoV RNA dimers assemble through ‘kissing’ loop–loop interactions. We also show that loop–loop kissing complex formation becomes more efficient at physiological temperature and in the presence of magnesium. When the palindromic sequence was mutated, in vitro RNA dimerization was abolished, and frameshifting was reduced from 15 to 5.7%. Furthermore, the inability to dimerize caused by the silent codon change in Stem 3 of SARS-CoV changed the viral growth kinetics and affected the levels of genomic and subgenomic RNA in infected cells. These results suggest that the homodimeric RNA complex formed by the SARS pseudoknot occurs in the cellular environment and that loop–loop kissing interactions involving Stem 3 modulate -1 PRF and play a role in subgenomic and full-length RNA synthesis.

RNAmap2D - calculation, visualization and analysis of contact and distance maps for RNA and protein-RNA complex structures

BACKGROUND

The structures of biological macromolecules provide a framework for studying their biological functions. Three-dimensional structures of proteins, nucleic acids, or their complexes, are difficult to visualize in detail on flat surfaces, and algorithms for their spatial superposition and comparison are computationally costly. Molecular structures, however, can be represented as 2D maps of interactions between the individual residues, which are easier to visualize and compare, and which can be reconverted to 3D structures with reasonable precision. There are many visualization tools for maps of protein structures, but few for nucleic acids.

RESULTS

We developed RNAmap2D, a platform-independent software tool for calculation, visualization and analysis of contact and distance maps for nucleic acid molecules and their complexes with proteins or ligands. The program addresses the problem of paucity of bioinformatics tools dedicated to analyzing RNA 2D maps, given the growing number of experimentally solved RNA structures in the Protein Data Bank (PDB) repository, as well as the growing number of tools for RNA 2D and 3D structure prediction. RNAmap2D allows for calculation and analysis of contacts and distances between various classes of atoms in nucleic acid, protein, and small ligand molecules. It also discriminates between different types of base pairing and stacking.

CONCLUSIONS

RNAmap2D is an easy to use method to visualize, analyze and compare structures of nucleic acid molecules and their complexes with other molecules, such as proteins or ligands and metal ions. Its special features make it a very useful tool for analysis of tertiary structures of RNAs. RNAmap2D for Windows/Linux/MacOSX is freely available for academic users at http://iimcb.genesilico.pl/rnamap2d.html.

Protein-RNA footprinting: an evolving tool

As more RNA molecules with important cellular functions are discovered, there is a strong need to characterize their structures, functions, and interactions. Chemical and enzymatic footprinting methods are used to map RNA secondary and tertiary structure, to monitor ligand interactions and conformational changes, and in the study of protein-RNA interactions. These methods provide data at single-nucleotide resolution that nicely complements the structural information available from X-ray diffraction, nuclear magnetic resonance spectroscopy (NMR), or cryo-electron microscopy. Footprinting methods also complement the dynamic information derived from single-molecule Förster resonance energy transfer. RNA footprinting tools have been used for decades, but we have recently seen spectacular advances, for instance, the use in combination with massive parallel sequencing techniques. Large libraries of RNA molecules (small or large in size) can now be probed in high-throughput manner when RNA footprinting methods are combined with fluorescent probe technologies and automation. In this article, after a brief historical overview, we summarize recent advances in RNA-protein footprinting methodologies that now integrate tools for massive parallel analysis.

Computational methods for prediction of protein-RNA interactions

Understanding the molecular mechanism of protein-RNA recognition and complex formation is a major challenge in structural biology. Unfortunately, the experimental determination of protein-RNA complexes by X-ray crystallography and nuclear magnetic resonance spectroscopy (NMR) is tedious and difficult. Alternatively, protein-RNA interactions can be predicted by computational methods. Although less accurate than experimental observations, computational predictions can be sufficiently accurate to prompt functional hypotheses and guide experiments, e.g. to identify individual amino acid or nucleotide residues. In this article we review 10 methods for predicting protein-RNA interactions, seven of which predict RNA-binding sites from protein sequences and three from structures. We also developed a meta-predictor that uses the output of top three sequence-based primary predictors to calculate a consensus prediction, which outperforms all the primary predictors. In order to fully cover the software for predicting protein-RNA interactions, we also describe five methods for protein-RNA docking. The article highlights the strengths and shortcomings of existing methods for the prediction of protein-RNA interactions and provides suggestions for their further development.

DARS-RNP and QUASI-RNP: new statistical potentials for protein-RNA docking

Background

Protein-RNA interactions play fundamental roles in many biological processes. Understanding the molecular mechanism of protein-RNA recognition and formation of protein-RNA complexes is a major challenge in structural biology. Unfortunately, the experimental determination of protein-RNA complexes is tedious and difficult, both by X-ray crystallography and NMR. For many interacting proteins and RNAs the individual structures are available, enabling computational prediction of complex structures by computational docking. However, methods for protein-RNA docking remain scarce, in particular in comparison to the numerous methods for protein-protein docking.

Results

We developed two medium-resolution, knowledge-based potentials for scoring protein-RNA models obtained by docking: the quasi-chemical potential (QUASI-RNP) and the Decoys As the Reference State potential (DARS-RNP). Both potentials use a coarse-grained representation for both RNA and protein molecules and are capable of dealing with RNA structures with posttranscriptionally modified residues. We compared the discriminative power of DARS-RNP and QUASI-RNP for selecting rigid-body docking poses with the potentials previously developed by the Varani and Fernandez groups.

Conclusions

In both bound and unbound docking tests, DARS-RNP showed the highest ability to identify native-like structures. Python implementations of DARS-RNP and QUASI-RNP are freely available for download at http://iimcb.genesilico.pl/RNP/

NMR resonance assignments of an engineered neomycin-sensing riboswitch RNA bound to ribostamycin and tobramycin

The neomycin-sensing riboswitch is an engineered riboswitch developed to regulate gene expression in vivo in the lower eukaryote Saccharomyces cerevisiae upon binding to neomycin B. With a size of only 27nt it is the smallest functional riboswitch element identified so far. It binds not only neomycin B but also related aminoglycosides of the 2'-deoxystreptamine class with high affinity. The regulatory activity, however, strongly depends on the identity of the aminoglycoside. As a prerequisite for the structure determination of riboswitch-ligand complexes we report here the (1)H, (15)N, (13)C and partial (31)P chemical shift assignments for the minimal functional 27nt neomycin sensing riboswitch RNA in complex with the 4,5-linked neomycin analog ribostamycin and the 4,6-linked aminoglycoside tobramycin.

A reverse transcriptase stop assay revealed diverse quadruplex formations in UTRs in mRNA

Here, we developed a reverse transcriptase based method (RTase stop assay) to characterize quadruplex formations in guanine-rich RNAs with high sensitivity and specificity. By using the RTase stop assay, we also revealed a plausible structural polymorphism in biologically important RNAs. The RTase stop assay would provide helpful insight into RNA quadruplex structures and functions, together with other analytical methods, including various footprinting techniques.

Raman crystallography of RNA

Raman crystallography is the application of Raman spectroscopy to single crystals. This technique has been applied to a variety of protein molecules where it has provided unique information about biopolymer folding, substrate binding, and catalysis. Here, we describe the application of Raman crystallography to functional RNA molecules. RNA represents unique opportunities and challenges for Raman crystallography. One issue that confounds studies of RNA is its tendency to adopt multiple non-functional folds. Raman crystallography has the advantage that it isolates a single state of the RNA within the crystal and can evaluate its fold, metal ion binding properties (ligand identity, stoichiometry, and affinity), proton binding properties (identity, stoichiometry, and affinity), and catalytic potential. In particular, base-specific stretches can be identified and then associated with the binding of metal ions and protons. Because measurements are carried out in the hanging drop at ambient, rather than cryo, conditions and because RNA crystals tend to be approximately 70% solvent, RNA dynamics and conformational changes become experimentally accessible. This review focuses on experimental setup and procedures, acquisition and interpretation of Raman data, and determination of physicochemical properties of the RNA. Raman crystallographic and solution biochemical experiments on the HDV RNA enzyme are summarized and found to be in excellent agreement. Remarkably, characterization of the crystalline state has proven to help rather than hinder functional characterization of functional RNA, most likely because the tendency of RNA to fold heterogeneously is limited in a crystalline environment. Future applications of Raman crystallography to RNA are briefly discussed.