RNA-Puzzles Round II: assessment of RNA structure prediction programs applied to three large RNA structures

RNA CoSSMos 2.0: an improved searchable database of secondary structure motifs in RNA three-dimensional structures

The RNA Characterization of Secondary Structure Motifs, RNA CoSSMos, database is a freely accessible online database that allows users to identify secondary structure motifs among RNA 3D structures and explore their structural features. RNA CoSSMos 2.0 now requires two closing base pairs for all RNA loop motifs to create a less redundant database of secondary structures. Furthermore, RNA CoSSMos 2.0 represents an upgraded database with new features that summarize search findings and aid in the search for 3D structural patterns among RNA secondary structure motifs. Previously, users were limited to viewing search results individually, with no built-in tools to compare search results. RNA CoSSMos 2.0 provides two new features, allowing users to summarize, analyze and compare their search result findings. A function has been added to the website that calculates the average and representative structures of the search results. Additionally, users can now view a summary page of their search results that reports percentages of each structural feature found, including sugar pucker, glycosidic linkage, hydrogen bonding patterns and stacking interactions. Other upgrades include a newly embedded NGL structural viewer, the option to download the clipped structure coordinates in *.pdb format and improved NMR structure results. RNA CoSSMos 2.0 is no longer simply a search engine for a structure database; it now has the capability of analyzing, comparing and summarizing search results.

Database URL: http://rnacossmos.com

Advances in RNA 3D Structure Modeling Using Experimental Data

RNA is a unique bio-macromolecule that can both record genetic information and perform biological functions in a variety of molecular processes, including transcription, splicing, translation, and even regulating protein function. RNAs adopt specific three-dimensional conformations to enable their functions. Experimental determination of high-resolution RNA structures using x-ray crystallography is both laborious and demands expertise, thus, hindering our comprehension of RNA structural biology. The computational modeling of RNA structure was a milestone in the birth of bioinformatics. Although computational modeling has been greatly improved over the last decade showing many successful cases, the accuracy of such computational modeling is not only length-dependent but also varies according to the complexity of the structure. To increase credibility, various experimental data were integrated into computational modeling. In this review, we summarize the experiments that can be integrated into RNA structure modeling as well as the computational methods based on these experimental data. We also demonstrate how computational modeling can help the experimental determination of RNA structure. We highlight the recent advances in computational modeling which can offer reliable structure models using high-throughput experimental data.

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

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

3dRNA: Building RNA 3D structure with improved template library

Most of computational methods of building RNA tertiary structure are template-based. The template-based methods usually can give more accurate 3D structures due to the use of native 3D templates, but they cannot work if the 3D templates are not available. So, a more complete library of the native 3D templates is very important for this type of methods. 3dRNA is a template-based method for building RNA tertiary structure previously proposed by us. In this paper we report improved 3D template libraries of 3dRNA by using two different schemes that give two libraries 3dRNA_Lib1 and 3dRNA_Lib2. These libraries expand the original one by nearly ten times. Benchmark shows that they can significantly increase the accuracy of 3dRNA, especially in building complex and large RNA 3D structures.

Sampling Performance of Multiple Independent Molecular Dynamics Simulations of an RNA Aptamer

Using multiple independent simulations instead of one long simulation has been shown to improve the sampling performance attained with the molecular dynamics (MD) simulation method. However, it is generally not known how long each independent simulation should be, how many independent simulations should be used, or to what extent either of these factors affects the overall sampling performance achieved for a given system. The goal of the present study was to assess the sampling performance of multiple independent MD simulations, where each independent simulation begins from a different initial molecular conformation. For this purpose, we used an RNA aptamer that is 25 nucleotides long as a case study. The initial conformations of the aptamer are derived from six de novo predicted 3D structures. Each of the six de novo predicted structures is energy minimized in solution and equilibrated with MD simulations at high temperature. Ten conformations from these six high-temperature equilibration runs are selected as initial conformations for further simulations at ambient temperature. In total, we conducted 60 independent MD simulations, each with a duration of 100 ns, to study the conformation and dynamics of the aptamer. For each group of 10 independent simulations that originated from a particular de novo predicted structure, we evaluated the potential energy distribution of the RNA and used recurrence quantification analysis to examine the sampling of RNA conformational transitions. To assess the impact of starting from different de novo predicted structures, we computed the density of structure projection on principal components to compare the regions sampled by the different groups of ten independent simulations. The recurrence rate and dependence of initial conformation among the groups were also compared. We stress the necessity of using different initial configurations as simulation starting points by showing long simulations from different initial structures suffer from being trapped in different states. Finally, we summarized the sampling efficiency for the complete set of 60 independent simulations and determined regions of under-sampling on the potential energy landscape. The results suggest that conducting multiple independent simulations using a diverse set of de novo predicted structures is a promising approach to achieve sufficient sampling. This approach avoids undesirable outcomes, such as the problem of the RNA aptamer being trapped in a local minimum. For others wishing to conduct multiple independent simulations, the analysis protocol presented in this study is a guide for examining overall sampling and determining if more simulations are necessary for sufficient sampling.

Aptamer-Based Affinity Chromatography for Protein Extraction and Purification

Aptamers are oligonucleotide molecules able to recognize very specifically proteins. Among the possible applications, aptamers have been used for affinity chromatography with effective results and advantages over most advanced protein separation technologies. This chapter first discusses the context of the affinity chromatography with aptamer ligands. With the adaptation of SELEX, the chemical modifications of aptamers to comply with the covalent coupling and the separation process are then extensively presented. A focus is then made about the most important applications for protein separation with real-life examples and the comparison with immunoaffinity chromatography. In spite of well-advanced demonstrations and the extraordinary potential developments, a significant optimization work is still due to deserve large-scale applications with all necessary validations. Graphical Abstract Aptamer-protein complexes by X-ray crystallography.

FARFAR2: Improved De Novo Rosetta Prediction of Complex Global RNA Folds

Predicting RNA three-dimensional structures from sequence could accelerate understanding of the growing number of RNA molecules being discovered across biology. Rosetta's Fragment Assembly of RNA with Full-Atom Refinement (FARFAR) has shown promise in community-wide blind RNA-Puzzle trials, but lack of a systematic and automated benchmark has left unclear what limits FARFAR performance. Here, we benchmark FARFAR2, an algorithm integrating RNA-Puzzle-inspired innovations with updated fragment libraries and helix modeling. In 16 of 21 RNA-Puzzles revisited without experimental data or expert intervention, FARFAR2 recovers native-like structures more accurate than models submitted during the RNA-Puzzles trials. Remaining bottlenecks include conformational sampling for >80-nucleotide problems and scoring function limitations more generally. Supporting these conclusions, preregistered blind models for adenovirus VA-I RNA and five riboswitch complexes predicted native-like folds with 3- to 14 Å root-mean-square deviation accuracies. We present a FARFAR2 webserver and three large model archives (FARFAR2-Classics, FARFAR2-Motifs, and FARFAR2-Puzzles) to guide future applications and advances.

RNA-Puzzles Round IV: 3D structure predictions of four ribozymes and two aptamers

RNA-Puzzles is a collective endeavor dedicated to the advancement and improvement of RNA 3D structure prediction. With agreement from crystallographers, the RNA structures are predicted by various groups before the publication of the crystal structures. We now report the prediction of 3D structures for six RNA sequences: four nucleolytic ribozymes and two riboswitches. Systematic protocols for comparing models and crystal structures are described and analyzed. In these six puzzles, we discuss (i) the comparison between the automated web servers and human experts; (ii) the prediction of coaxial stacking; (iii) the prediction of structural details and ligand binding; (iv) the development of novel prediction methods; and (v) the potential improvements to be made. We show that correct prediction of coaxial stacking and tertiary contacts is essential for the prediction of RNA architecture, while ligand binding modes can only be predicted with low resolution and simultaneous prediction of RNA structure with accurate ligand binding still remains out of reach. All the predicted models are available for the future development of force field parameters and the improvement of comparison and assessment tools.

Evaluating DCA-based method performances for RNA contact prediction by a well-curated data set

RNA molecules play many pivotal roles in a cell that are still not fully understood. Any detailed understanding of RNA function requires knowledge of its three-dimensional structure, yet experimental RNA structure resolution remains demanding. Recent advances in sequencing provide unprecedented amounts of sequence data that can be statistically analyzed by methods such as direct coupling analysis (DCA) to determine spatial proximity or contacts of specific nucleic acid pairs, which improve the quality of structure prediction. To quantify this structure prediction improvement, we here present a well curated data set of about 70 RNA structures of high resolution and compare different nucleotide-nucleotide contact prediction methods available in the literature. We observe only minor differences between the performances of the different methods. Moreover, we discuss how robust these predictions are for different contact definitions and how strongly they depend on procedures used to curate and align the families of homologous RNA sequences.

A community proposal to integrate structural bioinformatics activities in ELIXIR (3D-Bioinfo Community)

Structural bioinformatics provides the scientific methods and tools to analyse, archive, validate, and present the biomolecular structure data generated by the structural biology community. It also provides an important link with the genomics community, as structural bioinformaticians also use the extensive sequence data to predict protein structures and their functional sites. A very broad and active community of structural bioinformaticians exists across Europe, and 3D-Bioinfo will establish formal platforms to address their needs and better integrate their activities and initiatives. Our mission will be to strengthen the ties with the structural biology research communities in Europe covering life sciences, as well as chemistry and physics and to bridge the gap between these researchers in order to fully realize the potential of structural bioinformatics. Our Community will also undertake dedicated educational, training and outreach efforts to facilitate this, bringing new insights and thus facilitating the development of much needed innovative applications e.g. for human health, drug and protein design. Our combined efforts will be of critical importance to keep the European research efforts competitive in this respect.

Here we highlight the major European contributions to the field of structural bioinformatics, the most pressing challenges remaining and how Europe-wide interactions, enabled by ELIXIR and its platforms, will help in addressing these challenges and in coordinating structural bioinformatics resources across Europe. In particular, we present recent activities and future plans to consolidate an ELIXIR 3D-Bioinfo Community in structural bioinformatics and propose means to develop better links across the community. These include building new consortia, organising workshops to establish data standards and seeking community agreement on benchmark data sets and strategies. We also highlight existing and planned collaborations with other ELIXIR Communities and other European infrastructures, such as the structural biology community supported by Instruct-ERIC, with whom we have synergies and overlapping common interests.

RNA-Puzzles toolkit: a computational resource of RNA 3D structure benchmark datasets, structure manipulation, and evaluation tools

Significant improvements have been made in the efficiency and accuracy of RNA 3D structure prediction methods during the succeeding challenges of RNA-Puzzles, a community-wide effort on the assessment of blind prediction of RNA tertiary structures. The RNA-Puzzles contest has shown, among others, that the development and validation of computational methods for RNA fold prediction strongly depend on the benchmark datasets and the structure comparison algorithms. Yet, there has been no systematic benchmark set or decoy structures available for the 3D structure prediction of RNA, hindering the standardization of comparative tests in the modeling of RNA structure. Furthermore, there has not been a unified set of tools that allows deep and complete RNA structure analysis, and at the same time, that is easy to use. Here, we present RNA-Puzzles toolkit, a computational resource including (i) decoy sets generated by different RNA 3D structure prediction methods (raw, for-evaluation and standardized datasets), (ii) 3D structure normalization, analysis, manipulation, visualization tools (RNA_format, RNA_normalizer, rna-tools) and (iii) 3D structure comparison metric tools (RNAQUA, MCQ4Structures). This resource provides a full list of computational tools as well as a standard RNA 3D structure prediction assessment protocol for the community.

All-Atom Knowledge-Based Potential for RNA Structure Discrimination Based on the Distance-Scaled Finite Ideal-Gas Reference State

Noncoding RNAs are increasingly found to play a wide variety of roles in living organisms. Yet, their functional mechanisms are poorly understood because their structures are difficult to determine experimentally. As a result, developing more effective computational techniques to predict RNA structures becomes increasingly an urgent task. One key challenge in RNA structure prediction is the lack of an accurate free energy function to guide RNA folding and discriminate native and near-native structures from decoy conformations. In this study, we developed an all-atom distance-dependent knowledge-based energy function for RNA that is based on a reference state (distance-scaled finite ideal-gas reference state, DFIRE) proven successful for protein structure discrimination. Using four separate benchmarks including RNA puzzles, we found that this DFIRE-based RNA statistical energy function is able to discriminate native and near-native structures against decoys with performance comparable with or better than several existing scoring functions compared. The energy function is expected to be useful for improving the detection of RNA near-native structures.

RNA 3D structure prediction guided by independent folding of homologous sequences

BACKGROUND

The understanding of the importance of RNA has dramatically changed over recent years. As in the case of proteins, the function of an RNA molecule is encoded in its tertiary structure, which in turn is determined by the molecule's sequence. The prediction of tertiary structures of complex RNAs is still a challenging task.

RESULTS

Using the observation that RNA sequences from the same RNA family fold into conserved structure, we test herein whether parallel modeling of RNA homologs can improve ab initio RNA structure prediction. EvoClustRNA is a multi-step modeling process, in which homologous sequences for the target sequence are selected using the Rfam database. Subsequently, independent folding simulations using Rosetta FARFAR and SimRNA are carried out. The model of the target sequence is selected based on the most common structural arrangement of the common helical fragments. As a test, on two blind RNA-Puzzles challenges, EvoClustRNA predictions ranked as the first of all submissions for the L-glutamine riboswitch and as the second for the ZMP riboswitch. Moreover, through a benchmark of known structures, we discovered several cases in which particular homologs were unusually amenable to structure recovery in folding simulations compared to the single original target sequence.

CONCLUSION

This work, for the first time to our knowledge, demonstrates the importance of the selection of the target sequence from an alignment of an RNA family for the success of RNA 3D structure prediction. These observations prompt investigations into a new direction of research for checking 3D structure "foldability" or "predictability" of related RNA sequences to obtain accurate predictions. To support new research in this area, we provide all relevant scripts in a documented and ready-to-use form. By exploring new ideas and identifying limitations of the current RNA 3D structure prediction methods, this work is bringing us closer to the near-native computational RNA 3D models.

Whole-Cell Models and Simulations in Molecular Detail

Comprehensive data about the composition and structure of cellular components have enabled the construction of quantitative whole-cell models. While kinetic network-type models have been established, it is also becoming possible to build physical, molecular-level models of cellular environments. This review outlines challenges in constructing and simulating such models and discusses near- and long-term opportunities for developing physical whole-cell models that can connect molecular structure with biological function.

Updates to the RNA mapping database (RMDB), version 2

Chemical mapping is a broadly utilized technique for probing the structure and function of RNAs. The volume of chemical mapping data continues to grow as more researchers routinely employ this information and as experimental methods increase in throughput and information content. To create a central location for these data, we established an RNA mapping database (RMDB) 5 years ago. The RMDB, which is available at http://rmdb.stanford.edu, now contains chemical mapping data for over 800 entries, involving 134 000 natural and engineered RNAs, in vitro and in cellulo. The entries include large data sets from multidimensional techniques that focus on RNA tertiary structure and co-transcriptional folding, resulting in over 15 million residues probed. The database interface has been redesigned and now offers interactive graphical browsing of structural, thermodynamic and kinetic data at single-nucleotide resolution. The front-end interface now uses the force-directed RNA applet for secondary structure visualization and other JavaScript-based views of bar graphs and annotations. A new interface also streamlines the process for depositing new chemical mapping data to the RMDB.

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.

Using Rosetta for RNA homology modeling

The three-dimensional structures of RNA molecules provide rich and often critical information for understanding their functions, including how they recognize small molecule and protein partners. Computational modeling of RNA 3D structure is becoming increasingly accurate, particularly with the availability of growing numbers of template structures already solved experimentally and the development of sequence alignment and 3D modeling tools to take advantage of this database. For several recent "RNA puzzle" blind modeling challenges, we have successfully identified useful template structures and achieved accurate structure predictions through homology modeling tools developed in the Rosetta software suite. We describe our semi-automated methodology here and walk through two illustrative examples: an adenine riboswitch aptamer, modeled from a template guanine riboswitch structure, and a SAM I/IV riboswitch aptamer, modeled from a template SAM I riboswitch structure.

RNA Binding: Getting Specific about Specificity

Sequence-specific nucleic acid binding proteins do not recognize one sequence out of all possibilities; rather, they bind to many sequences with a range of affinities. In this issue of Cell Chemical Biology, Lin et al. (2016), describe the entire landscape of affinities between different RNA molecules and an RNA-binding protein, thus providing a comprehensive description of the factors affecting specificity.

QRNAS: software tool for refinement of nucleic acid structures

BACKGROUND

Computational models of RNA 3D structure often present various inaccuracies caused by simplifications used in structure prediction methods, such as template-based modeling or coarse-grained simulations. To obtain a high-quality model, the preliminary RNA structural model needs to be refined, taking into account atomic interactions. The goal of the refinement is not only to improve the local quality of the model but to bring it globally closer to the true structure.

RESULTS

We present QRNAS, a software tool for fine-grained refinement of nucleic acid structures, which is an extension of the AMBER simulation method with additional restraints. QRNAS is capable of handling RNA, DNA, chimeras, and hybrids thereof, and enables modeling of nucleic acids containing modified residues.

CONCLUSIONS

We demonstrate the ability of QRNAS to improve the quality of models generated with different methods. QRNAS was able to improve MolProbity scores of NMR structures, as well as of computational models generated in the course of the RNA-Puzzles experiment. The overall geometry improvement may be associated with increased model accuracy, especially on the level of correctly modeled base-pairs, but the systematic improvement of root mean square deviation to the reference structure should not be expected. The method has been integrated into a computational modeling workflow, enabling improved RNA 3D structure prediction.

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.

Predicting Site-Binding Modes of Ions and Water to Nucleic Acids Using Molecular Solvation Theory

Site binding of ions and water shapes nucleic acids folding, dynamics, and biological function, complementing the more diffuse, nonspecific "territorial" ion binding. Unlike territorial binding, prediction of site-specific binding to nucleic acids remains an unsolved challenge in computational biophysics. This work presents a new toolset based on the 3D-RISM molecular solvation theory and topological analysis that predicts cation and water site binding to nucleic acids. 3D-RISM is shown to accurately capture alkali cations and water binding to the central channel, transversal loops, and grooves of the Oxytricha nova's telomeres' G-quadruplex ( Oxy-GQ), in agreement with high-resolution crystallographic data. To improve the computed cation occupancy along the Oxy-GQ central channel, it was necessary to refine and validate new cation-oxygen parameters using structural and thermodynamic data available for crown ethers and ion channels. This single set of parameters that describes both localized and delocalized binding to various biological systems is used to gain insight into cation occupancy along the Oxy-GQ channel under various salt conditions. The paper concludes with prospects for extending the method to predict divalent cation binding to nucleic acids. This work advances the forefront of theoretical methods able to provide predictive insight into ion atmosphere effects on nucleic acids function.

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.

The evolution of RNA structural probing methods: From gels to next-generation sequencing

RNA molecules are important players in all domains of life and the study of the relationship between their multiple flexible states and the associated biological roles has increased in recent years. For several decades, chemical and enzymatic structural probing experiments have been used to determine RNA structure. During this time, there has been a steady improvement in probing reagents and experimental methods, and today the structural biologist community has a large range of tools at its disposal to probe the secondary structure of RNAs in vitro and in cells. Early experiments used radioactive labeling and polyacrylamide gel electrophoresis as read-out methods. This was superseded by capillary electrophoresis, and more recently by next-generation sequencing. Today, powerful structural probing methods can characterize RNA structure on a genome-wide scale. In this review, we will provide an overview of RNA structural probing methodologies from a historical and technical perspective. This article is categorized under: RNA Structure and Dynamics > RNA Structure, Dynamics, and Chemistry RNA Methods > RNA Analyses in vitro and In Silico RNA Methods > RNA Analyses in Cells.

RNA3DCNN: Local and global quality assessments of RNA 3D structures using 3D deep convolutional neural networks

Quality assessment is essential for the computational prediction and design of RNA tertiary structures. To date, several knowledge-based statistical potentials have been proposed and proved to be effective in identifying native and near-native RNA structures. All these potentials are based on the inverse Boltzmann formula, while differing in the choice of the geometrical descriptor, reference state, and training dataset. Via an approach that diverges completely from the conventional statistical potentials, our work explored the power of a 3D convolutional neural network (CNN)-based approach as a quality evaluator for RNA 3D structures, which used a 3D grid representation of the structure as input without extracting features manually. The RNA structures were evaluated by examining each nucleotide, so our method can also provide local quality assessment. Two sets of training samples were built. The first one included 1 million samples generated by high-temperature molecular dynamics (MD) simulations and the second one included 1 million samples generated by Monte Carlo (MC) structure prediction. Both MD and MC procedures were performed for a non-redundant set of 414 RNAs. For two training datasets (one including only MD training samples and the other including both MD and MC training samples), we trained two neural networks, named RNA3DCNN_MD and RNA3DCNN_MDMC, respectively. The former is suitable for assessing near-native structures, while the latter is suitable for assessing structures covering large structural space. We tested the performance of our method and made comparisons with four other traditional scoring functions. On two of three test datasets, our method performed similarly to the state-of-the-art traditional scoring function, and on the third test dataset, our method was far superior to other scoring functions. Our method can be downloaded from https://github.com/lijunRNA/RNA3DCNN.

Modular calibrant sets for the structural analysis of nucleic acids by ion mobility spectrometry mass spectrometry

This study explored the use of modular nucleic acid (NA) standards to generate calibration curves capable of translating primary ion mobility readouts into corresponding collision cross section (CCS) data. Putative calibrants consisted of single- (ss) and double-stranded (ds) oligo-deoxynucleotides reaching up to ∼40 kDa in size (i.e., 64 bp) and ∼5700 Å(2) in CCS. To ensure self-consistency among reference CCS values, computational data obtained in house were preferred to any experimental or computational data from disparate sources. Such values were obtained by molecular dynamics (MD) simulations and either the exact hard sphere scattering (EHSS) or the projection superposition approximation (PSA) methods, and then plotted against the corresponding experimental values to generate separate calibration curves. Their performance was evaluated on the basis of their correlation coefficients and ability to provide values that matched the CCS of selected test samples mimicking typical unknowns. The results indicated that the predictive power benefited from the exclusion of higher charged species that were more susceptible to the destabilizing effects of Coulombic repulsion. The results revealed discrepancies between EHSS and PSA data that were ascribable to the different approximations used to describe the ion mobility process. Within the boundaries defined by these approximations and the challenges of modeling NA structure in a solvent-free environment, the calibrant sets enabled the experimental determination of CCS with excellent reproducibility (precision) and error (accuracy), which will support the analysis of progressively larger NA samples of biological significance.

RNAfitme: a webserver for modeling nucleobase and nucleoside residue conformation in fixed-backbone RNA structures

BACKGROUND

Computational RNA 3D structure prediction and modeling are rising as complementary approaches to high-resolution experimental techniques for structure determination. They often apply to substitute or complement them. Recently, researchers' interests have directed towards in silico methods to fit, remodel and refine RNA tertiary structure models. Their power lies in a problem-specific exploration of RNA conformational space and efficient optimization procedures. The aim is to improve the accuracy of models obtained either computationally or experimentally.

RESULTS

Here, we present RNAfitme, a versatile webserver tool for remodeling of nucleobase- and nucleoside residue conformations in the fixed-backbone RNA 3D structures. Our approach makes use of dedicated libraries that define RNA conformational space. They have been built upon torsional angle characteristics of PDB-deposited RNA structures. RNAfitme can be applied to reconstruct full-atom model of RNA from its backbone; remodel user-selected nucleobase/nucleoside residues in a given RNA structure; predict RNA 3D structure based on the sequence and the template of a homologous molecule of the same size; refine RNA 3D model by reducing steric clashes indicated during structure quality assessment. RNAfitme is a publicly available tool with an intuitive interface. It is freely accessible at http://rnafitme.cs.put.poznan.pl/ CONCLUSIONS: RNAfitme has been applied in various RNA 3D remodeling scenarios for several types of input data. Computational experiments proved its efficiency, accuracy, and usefulness in the processing of RNAs of any size. Fidelity of RNAfitme predictions has been thoroughly tested for RNA 3D structures determined experimentally and modeled in silico.

Prediction of DNA and RNA structure with the NARES-2P force field and conformational space annealing

A physics-based method for the prediction of the structures of nucleic acids, which is based on the physics-based 2-bead NARES-2P model of polynucleotides and global-optimization Conformational Space Annealing (CSA) algorithm has been proposed. The target structure is sought as the global-energy-minimum structure, which ignores the entropy component of the free energy but spares expensive multicanonical simulations necessary to find the conformational ensemble with the lowest free energy. The CSA algorithm has been modified to optimize its performance when treating both single and multi-chain nucleic acids. It was shown that the method finds the native fold for simple RNA molecules and DNA duplexes and with limited distance restraints, which can easily be obtained from the secondary-structure-prediction servers, complex RNA folds can be treated with using moderate computer resources.

Adventures with RNA graphs

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

One-bead coarse-grained model for RNA dynamics

We present a revised version of a coarse-grained model for RNA dynamics. In such approach, the description of nucleotides is reduced to single points that interact between them through a series of effective pair potentials that were obtained from an improved analysis of RNA structures from the Protein Data Bank. These interaction potentials are the main constituents of a Brownian dynamics simulation algorithm that allows to perform a variety of tasks by taking advantage of the reduced number of variables. Such tasks include the prediction of the three-dimensional configuration of a series of test molecules. Moreover, the model permits the inclusion of effective magnesium ions and the ends of the RNA chains can be pulled with an external force to study the process of unfolding. In spite of the simplicity of the model, we obtain a good agreement with the experimental results.

Molecular dynamics correctly models the unusual major conformation of the GAGU RNA internal loop and with NMR reveals an unusual minor conformation

The RNA "GAGU" duplex, (5'GACGAGUGUCA)2, contains the internal loop (5'-GAGU-3')2 , which has two conformations in solution as determined by NMR spectroscopy. The major conformation has a loop structure consisting of trans-Watson-Crick/Hoogsteen GG pairs, A residues stacked on each other, U residues bulged outside the helix, and all sugars with a C2'-endo conformation. This differs markedly from the internal loops, (5'-GAGC-3')2, (5'-AAGU-3')2, and (5'-UAGG-3')2, which all have cis-Watson-Crick/Watson-Crick AG "imino" pairs flanked by cis-Watson-Crick/Watson-Crick canonical pairs resulting in maximal hydrogen bonding. Here, molecular dynamics was used to test whether the Amber force field (ff99 + bsc0 + OL3) approximates molecular interactions well enough to keep stable the unexpected conformation of the GAGU major duplex structure and the NMR structures of the duplexes containing (5'-GAGC-3')2, (5'-AAGU-3')2, and (5'-UAGG-3')2 internal loops. One-microsecond simulations were repeated four times for each of the duplexes starting in their NMR conformations. With the exception of (5'-UAGG-3')2, equivalent simulations were also run starting with alternative conformations. Results indicate that the Amber force field keeps the NMR conformations of the duplexes stable for at least 1 µsec. They also demonstrate an unexpected minor conformation for the (5'-GAGU-3')2 loop that is consistent with newly measured NMR spectra of duplexes with natural and modified nucleotides. Thus, unrestrained simulations led to the determination of the previously unknown minor conformation. The stability of the native (5'-GAGU-3')2 internal loop as compared to other loops can be explained by changes in hydrogen bonding and stacking as the flanking bases are changed.

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.

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.

Classification of RNA structure change by 'gazing' at experimental data

Motivation

Mutations (or Single Nucleotide Variants) in folded RiboNucleic Acid structures that cause local or global conformational change are riboSNitches. Predicting riboSNitches is challenging, as it requires making two, albeit related, structure predictions. The data most often used to experimentally validate riboSNitch predictions is Selective 2' Hydroxyl Acylation by Primer Extension, or SHAPE. Experimentally establishing a riboSNitch requires the quantitative comparison of two SHAPE traces: wild-type (WT) and mutant. Historically, SHAPE data was collected on electropherograms and change in structure was evaluated by 'gel gazing.' SHAPE data is now routinely collected with next generation sequencing and/or capillary sequencers. We aim to establish a classifier capable of simulating human 'gazing' by identifying features of the SHAPE profile that human experts agree 'looks' like a riboSNitch.

Results

We find strong quantitative agreement between experts when RNA scientists 'gaze' at SHAPE data and identify riboSNitches. We identify dynamic time warping and seven other features predictive of the human consensus. The classSNitch classifier reported here accurately reproduces human consensus for 167 mutant/WT comparisons with an Area Under the Curve (AUC) above 0.8. When we analyze 2019 mutant traces for 17 different RNAs, we find that features of the WT SHAPE reactivity allow us to improve thermodynamic structure predictions of riboSNitches. This is significant, as accurate RNA structural analysis and prediction is likely to become an important aspect of precision medicine.

Availability and Implementation

The classSNitch R package is freely available at http://classsnitch.r-forge.r-project.org .

Contact

alain@email.unc.edu.

Supplementary information

Supplementary data are available at Bioinformatics online.

A nucleobase-centered coarse-grained representation for structure prediction of RNA motifs

We introduce the SPlit-and-conQueR (SPQR) model, a coarse-grained (CG) representation of RNA designed for structure prediction and refinement. In our approach, the representation of a nucleotide consists of a point particle for the phosphate group and an anisotropic particle for the nucleoside. The interactions are, in principle, knowledge-based potentials inspired by the $\mathcal {E}$SCORE function, a base-centered scoring function. However, a special treatment is given to base-pairing interactions and certain geometrical conformations which are lost in a raw knowledge-based model. This results in a representation able to describe planar canonical and non-canonical base pairs and base-phosphate interactions and to distinguish sugar puckers and glycosidic torsion conformations. The model is applied to the folding of several structures, including duplexes with internal loops of non-canonical base pairs, tetraloops, junctions and a pseudoknot. For the majority of these systems, experimental structures are correctly predicted at the level of individual contacts. We also propose a method for efficiently reintroducing atomistic detail from the CG representation.

Using RNA Sequence and Structure for the Prediction of Riboswitch Aptamer: A Comprehensive Review of Available Software and Tools

RNA molecules are essential players in many fundamental biological processes. Prokaryotes and eukaryotes have distinct RNA classes with specific structural features and functional roles. Computational prediction of protein structures is a research field in which high confidence three-dimensional protein models can be proposed based on the sequence alignment between target and templates. However, to date, only a few approaches have been developed for the computational prediction of RNA structures. Similar to proteins, RNA structures may be altered due to the interaction with various ligands, including proteins, other RNAs, and metabolites. A riboswitch is a molecular mechanism, found in the three kingdoms of life, in which the RNA structure is modified by the binding of a metabolite. It can regulate multiple gene expression mechanisms, such as transcription, translation initiation, and mRNA splicing and processing. Due to their nature, these entities also act on the regulation of gene expression and detection of small metabolites and have the potential to helping in the discovery of new classes of antimicrobial agents. In this review, we describe software and web servers currently available for riboswitch aptamer identification and secondary and tertiary structure prediction, including applications.

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.

Hierarchical Assembly of RNA Three-Dimensional Structures Based on Loop Templates

The current RNA structure prediction methods cannot keep up the pace of the rapidly increasing number of sequences and the emerging new functions of RNAs. Template-based RNA three-dimensional structure prediction methods are restricted by the limited number of known RNA structures, and traditional motif-based search for the templates does not always lead to successful results. Here we report a new template search and assembly algorithm, the hierarchical loop template-assembly method (VfoldLA). The method searches for templates for single strand loop/junctions instead of the whole motifs, which often renders no available templates, or short fragments (several nucleotides), which requires a long computational time to assemble and refine. The VfoldLA method has the advantage of accounting for local and nonlocal interloop interactions. Benchmark tests indicate that this new method can provide low-resolution predictions for RNA conformations at different levels of structural complexities. Furthermore, the VfoldLA-predicted conformations may also serve as reliable putative models for further structure prediction and refinements. VfoldLA is accessible at http://rna.physics.missouri.edu/vfoldLA .

F-RAG: Generating Atomic Coordinates from RNA Graphs by Fragment Assembly

Coarse-grained models represent attractive approaches to analyze and simulate ribonucleic acid (RNA) molecules, for example, for structure prediction and design, as they simplify the RNA structure to reduce the conformational search space. Our structure prediction protocol RAGTOP (RNA-As-Graphs Topology Prediction) represents RNA structures as tree graphs and samples graph topologies to produce candidate graphs. However, for a more detailed study and analysis, construction of atomic from coarse-grained models is required. Here we present our graph-based fragment assembly algorithm (F-RAG) to convert candidate three-dimensional (3D) tree graph models, produced by RAGTOP into atomic structures. We use our related RAG-3D utilities to partition graphs into subgraphs and search for structurally similar atomic fragments in a data set of RNA 3D structures. The fragments are edited and superimposed using common residues, full atomic models are scored using RAGTOP's knowledge-based potential, and geometries of top scoring models is optimized. To evaluate our models, we assess all-atom RMSDs and Interaction Network Fidelity (a measure of residue interactions) with respect to experimentally solved structures and compare our results to other fragment assembly programs. For a set of 50 RNA structures, we obtain atomic models with reasonable geometries and interactions, particularly good for RNAs containing junctions. Additional improvements to our protocol and databases are outlined. These results provide a good foundation for further work on RNA structure prediction and design applications.

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

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

Optimization of RNA 3D structure prediction using evolutionary restraints of nucleotide-nucleotide interactions from direct coupling analysis

Direct coupling analysis of nucleotide coevolution provides a novel approach to identify which nucleotides in an RNA molecule are likely in direct contact, and this information obtained from sequence only can be used to predict RNA 3D structures with much improved accuracy. Here we present an efficient method that incorporates this information into current RNA 3D structure prediction methods, specifically 3dRNA. Our method makes much more accurate RNA 3D structure prediction than the original 3dRNA as well as other existing prediction methods that used the direct coupling analysis. In particular our method demonstrates a significant improvement in predicting multi-branch junction conformations, a major bottleneck for RNA 3D structure prediction. We also show that our method can be used to optimize the predictions by other methods. These results indicate that optimization of RNA 3D structure prediction using evolutionary restraints of nucleotide-nucleotide interactions from direct coupling analysis offers an efficient way for accurate RNA tertiary structure predictions.

SupeRNAlign: a new tool for flexible superposition of homologous RNA structures and inference of accurate structure-based sequence alignments

RNA has been found to play an ever-increasing role in a variety of biological processes. The function of most non-coding RNA molecules depends on their structure. Comparing and classifying macromolecular 3D structures is of crucial importance for structure-based function inference and it is used in the characterization of functional motifs and in structure prediction by comparative modeling. However, compared to the numerous methods for protein structure superposition, there are few tools dedicated to the superimposing of RNA 3D structures. Here, we present SupeRNAlign (v1.3.1), a new method for flexible superposition of RNA 3D structures, and SupeRNAlign-Coffee—a workflow that combines SupeRNAlign with T-Coffee for inferring structure-based sequence alignments. The methods have been benchmarked with eight other methods for RNA structural superposition and alignment. The benchmark included 151 structures from 32 RNA families (with a total of 1734 pairwise superpositions). The accuracy of superpositions was assessed by comparing structure-based sequence alignments to the reference alignments from the Rfam database. SupeRNAlign and SupeRNAlign-Coffee achieved significantly higher scores than most of the benchmarked methods: SupeRNAlign generated the most accurate sequence alignments among the structure superposition methods, and SupeRNAlign-Coffee performed best among the sequence alignment methods.

Impact of the structural integrity of the three-way junction of adenovirus VAI RNA on PKR inhibition

Highly structured RNA derived from viral genomes is a key cellular indicator of viral infection. In response, cells produce the interferon inducible RNA-dependent protein kinase (PKR) that, when bound to viral dsRNA, phosphorylates eukaryotic initiation factor 2α and attenuates viral protein translation. Adenovirus can evade this line of defence through transcription of a non-coding RNA, VA_I, an inhibitor of PKR. VA_I consists of three base-paired regions that meet at a three-way junction; an apical stem responsible for the interaction with PKR, a central stem required for inhibition, and a terminal stem. Recent studies have highlighted the potential importance of the tertiary structure of the three-way junction to PKR inhibition by enabling interaction between regions of the central and terminal stems. To further investigate the role of the three-way junction, we characterized the binding affinity and inhibitory potential of central stem mutants designed to introduce subtle alterations. These results were then correlated with small-angle X-ray scattering solution studies and computational tertiary structural models. Our results demonstrate that while mutations to the central stem have no observable effect on binding affinity to PKR, mutations that appear to disrupt the structure of the three-way junction prevent inhibition of PKR. Therefore, we propose that instead of simply sequestering PKR, a specific structural conformation of the PKR-VA_I complex may be required for inhibition.

Using sequence signatures and kink-turn motifs in knowledge-based statistical potentials for RNA structure prediction

Kink turns are widely occurring motifs in RNA, located in internal loops and associated with many biological functions including translation, regulation and splicing. The associated sequence pattern, a 3-nt bulge and G-A, A-G base-pairs, generates an angle of ∼50° along the helical axis due to A-minor interactions. The conserved sequence and distinct secondary structures of kink-turns (k-turn) suggest computational folding rules to predict k-turn-like topologies from sequence. Here, we annotate observed k-turn motifs within a non-redundant RNA dataset based on sequence signatures and geometrical features, analyze bending and torsion angles, and determine distinct knowledge-based potentials with and without k-turn motifs. We apply these scoring potentials to our RAGTOP (RNA-As-Graph-Topologies) graph sampling protocol to construct and sample coarse-grained graph representations of RNAs from a given secondary structure. We present graph-sampling results for 35 RNAs, including 12 k-turn and 23 non k-turn internal loops, and compare the results to solved structures and to RAGTOP results without special k-turn potentials. Significant improvements are observed with the updated scoring potentials compared to the k-turn-free potentials. Because k-turns represent a classic example of sequence/structure motif, our study suggests that other such motifs with sequence signatures and unique geometrical features can similarly be utilized for RNA structure prediction and design.

RNA structure prediction: from 2D to 3D

We summarize different levels of RNA structure prediction, from classical 2D structure to extended secondary structure and motif-based research toward 3D structure prediction of RNA. We outline the importance of classical secondary structure during all those levels of structure prediction.

Structural signatures of thermal adaptation of bacterial ribosomal RNA, transfer RNA, and messenger RNA

Temperature adaptation of bacterial RNAs is a subject of both fundamental and practical interest because it will allow a better understanding of molecular mechanism of RNA folding with potential industrial application of functional thermophilic or psychrophilic RNAs. Here, we performed a comprehensive study of rRNA, tRNA, and mRNA of more than 200 bacterial species with optimal growth temperatures (OGT) ranging from 4°C to 95°C. We investigated temperature adaptation at primary, secondary and tertiary structure levels. We showed that unlike mRNA, tRNA and rRNA were optimized for their structures at compositional levels with significant tertiary structural features even for their corresponding randomly permutated sequences. tRNA and rRNA are more exposed to solvent but remain structured for hyperthermophiles with nearly OGT-independent fluctuation of solvent accessible surface area within a single RNA chain. mRNA in hyperthermophiles is essentially the same as random sequences without tertiary structures although many mRNA in mesophiles and psychrophiles have well-defined tertiary structures based on their low overall solvent exposure with clear separation of deeply buried from partly exposed bases as in tRNA and rRNA. These results provide new insight into temperature adaptation of different RNAs.

Topological Structure Determination of RNA Using Small-Angle X-Ray Scattering

Knowledge of RNA three-dimensional topological structures provides important insight into the relationship between RNA structural components and function. It is often likely that near-complete sets of biochemical and biophysical data containing structural restraints are not available, but one still wants to obtain knowledge about approximate topological folding of RNA. In this regard, general methods for determining such topological structures with minimum readily available restraints are lacking. Naked RNAs are difficult to crystallize and NMR spectroscopy is generally limited to small RNA fragments. By nature, sequence determines structure and all interactions that drive folding are self-contained within sequence. Nevertheless, there is little apparent correlation between primary sequences and three-dimensional folding unless supplemented with experimental or phylogenetic data. Thus, there is an acute need for a robust high-throughput method that can rapidly determine topological structures of RNAs guided by some experimental data. We present here a novel method (RS3D) that can assimilate the RNA secondary structure information, small-angle X-ray scattering data, and any readily available tertiary contact information to determine the topological fold of RNA. Conformations are firstly sampled at glob level where each glob represents a nucleotide. Best-ranked glob models can be further refined against solvent accessibility data, if available, and then converted to explicit all-atom coordinates for refinement against SAXS data using the Xplor-NIH program. RS3D is widely applicable to a variety of RNA folding architectures currently present in the structure database. Furthermore, we demonstrate applicability and feasibility of the program to derive low-resolution topological structures of relatively large multi-domain RNAs.

RNA structure inference through chemical mapping after accidental or intentional mutations

Despite the critical roles RNA structures play in regulating gene expression, sequencing-based methods for experimentally determining RNA base pairs have remained inaccurate. Here, we describe a multidimensional chemical-mapping method called "mutate-and-map read out through next-generation sequencing" (M2-seq) that takes advantage of sparsely mutated nucleotides to induce structural perturbations at partner nucleotides and then detects these events through dimethyl sulfate (DMS) probing and mutational profiling. In special cases, fortuitous errors introduced during DNA template preparation and RNA transcription are sufficient to give M2-seq helix signatures; these signals were previously overlooked or mistaken for correlated double-DMS events. When mutations are enhanced through error-prone PCR, in vitro M2-seq experimentally resolves 33 of 68 helices in diverse structured RNAs including ribozyme domains, riboswitch aptamers, and viral RNA domains with a single false positive. These inferences do not require energy minimization algorithms and can be made by either direct visual inspection or by a neural-network-inspired algorithm called M2-net. Measurements on the P4-P6 domain of the Tetrahymena group I ribozyme embedded in Xenopus egg extract demonstrate the ability of M2-seq to detect RNA helices in a complex biological environment.