RNA Tertiary Structure Research Articles

Soluble Siglec-14 glycan-recognition protein is generated by alternative splicing and suppresses myeloid inflammatory responses

Human sialic acid-binding immunoglobulin-like lectin 14 (Siglec-14) is a glycan-recognition protein that is expressed on myeloid cells, recognizes bacterial pathogens, and elicits pro-inflammatory responses. Although Siglec-14 is a transmembrane protein, a soluble form of Siglec-14 is also present in human blood. However, the mechanism that generates soluble Siglec-14 and what role this protein form may play remain unknown. Here, investigating the generation and function of soluble Siglec-14, we found that soluble Siglec-14 is derived from an alternatively spliced mRNA that retains intron 5, containing a termination codon and thus preventing the translation of exon 6, which encodes Siglec-14's transmembrane domain. We also note that the translated segment in intron 5 encodes a unique C-terminal 7-amino acid extension, which allowed the specific antibody-mediated detection of this isoform in human blood. Moreover, soluble Siglec-14 dose-dependently suppressed pro-inflammatory responses of myeloid cells that expressed membrane-bound Siglec-14, likely by interfering with the interaction between membrane-bound Siglec-14 and Toll-like receptor 2 on the cell surface. We also found that intron 5 contains a G-rich segment that assumes an RNA tertiary structure called a G-quadruplex, which may regulate the efficiency of intron 5 splicing. Taken together, we propose that soluble Siglec-14 suppresses pro-inflammatory responses triggered by membrane-bound Siglec-14.

Antifungal drugs: Small molecules targeting a tertiary RNA structure fight fungi.

Antifungal drugs: Small molecules targeting a tertiary RNA structure fight fungi.

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.

Polymorphisms in KSHV-encoded microRNA sequences affect levels of mature viral microRNA in Kaposi Sarcoma lesions.

BackgroundWe previously reported Kaposi sarcoma-associated herpesvirus (KSHV) microRNA sequence variants in clinical samples correlated with increased risk of multicentric Castleman's disease (MCD). We then demonstrated that microRNAs with variant sequence have different maturation and mature microRNA expression in vitro. Here, we illustrate the association between microRNA sequence and changes in mature microRNA levels within Kaposi sarcoma (KS) lesions.MethodsKSHV microRNA sequences were determined from 20 KS lesions and 4 control skin biopsies from individuals evaluated for KS. Levels of mature KSHV microRNAs were measured with 21 custom small RNA qRT-PCR assays using RNA RNU6B as endogenous control.ResultsThe levels of 13 KSHV-encoded microRNAs were elevated in KS lesions compared to control biopsies. MicroRNA 9-5p was strongly down regulated in South African vs. US biopsies. Low levels of K12-9-5p were associated with single nucleotide polymorphisms (SNPs) in miR-K12-9-5p, 4-5p, 5-3p, 7-3p and pri-miR-K12-3. One SNP in pri-miR-K12-3 resulted in down regulation of miR-K12-6-3p, 8-3p, 10-3p, 12-5p and the upregulation of 5-5p, illustrating sequence variants outside pre-microRNAs were also associated with changes in mature microRNA levels.ConclusionsThe levels of mature KSHV-encoded microRNAs in KS lesions correlate with sequence variation reflecting changes in secondary and tertiary RNA structure.

Interactive Visualization of RNA and DNA Structures.

The analysis and visualization of nucleic acids (RNA and DNA) is playing an increasingly important role due to their fundamental importance for all forms of life and the growing number of known 3D structures of such molecules. The great complexity of these structures, in particular, those of RNA, demands interactive visualization to get deeper insights into the relationship between the 2D secondary structure motifs and their 3D tertiary structures. Over the last decades, a lot of research in molecular visualization has focused on the visual exploration of protein structures while nucleic acids have only been marginally addressed. In contrast to proteins, which are composed of amino acids, the ingredients of nucleic acids are nucleotides. They form structuring patterns that differ from those of proteins and, hence, also require different visualization and exploration techniques. In order to support interactive exploration of nucleic acids, the computation of secondary structure motifs as well as their visualization in 2D and 3D must be fast. Therefore, in this paper, we focus on the performance of both the computation and visualization of nucleic acid structure. We present a ray casting-based visualization of RNA and DNA secondary and tertiary structures, which enables for the first time real-time visualization of even large molecular dynamics trajectories. Furthermore, we provide a detailed description of all important aspects to visualize nucleic acid secondary and tertiary structures. With this, we close an important gap in molecular visualization.

Small molecules that target group II introns are potent antifungal agents.

Specific RNA structures control numerous metabolic processes that impact human health, and yet efforts to target RNA structures de-novo have been limited. In eukaryotes, the self-splicing group II intron is a mitochondrial RNA tertiary structure that is absent in vertebrates but essential for respiration in plants, fungi and yeast. Here we show that this RNA can be targeted through a process of high-throughput in vitro screening, SAR and lead optimization, resulting in high affinity compounds that specifically inhibit group IIB intron splicing in vitro and in vivo and lack toxicity in human cells. The compounds are potent growth inhibitors of the pathogen Candida parapsilosis, displaying antifungal activity comparable with Amphotericin B. These studies demonstrate that RNA tertiary structures can be successfully targeted de-novo, resulting in pharmacologically valuable compounds.

Enhanced prediction of RNA solvent accessibility with long short-term memory neural networks and improved sequence profiles.

The de novo prediction of RNA tertiary structure remains a grand challenge. Predicted RNA solvent accessibility provides an opportunity to address this challenge. To the best of our knowledge, there is only one method (RNAsnap) available for RNA solvent accessibility prediction. However, its performance is unsatisfactory for protein-free RNAs. We developed RNAsol, a new algorithm to predict RNA solvent accessibility. RNAsol was built based on improved sequence profiles from the covariance models and trained with the long short-term memory (LSTM) neural networks. Independent tests on the same datasets from RNAsnap show that RNAsol achieves the mean Pearson's correlation coefficient (PCC) of 0.43/0.26 for the protein-bound/protein-free RNA molecules, which is 26.5%/136.4% higher than that of RNAsnap. When the training set is enlarged to include both types of RNAs, the PCCs increase to 0.49 and 0.46 for protein-bound and protein-free RNAs, respectively. The success of RNAsol is attributed to two aspects, including the improved sequence profiles constructed by the sequence-profile alignment and the enhanced training by the LSTM neural networks. http://yanglab.nankai.edu.cn/RNAsol/. Supplementary data are available at Bioinformatics online.

Linking RNA Sequence, Structure, and Function on Massively Parallel High-Throughput Sequencers.

High-throughput sequencing methods have revolutionized our ability to catalog the diversity of RNAs and RNA-protein interactions that can exist in our cells. However, the relationship between RNA sequence, structure, and function is enormously complex, demonstrating the need for methods that can provide quantitative thermodynamic and kinetic measurements of macromolecular interaction with RNA, at a scale commensurate with the sequence diversity of RNA. Here, we discuss a class of methods that extend the core functionality of DNA sequencers to enable high-throughput measurements of RNA folding and RNA-protein interactions. Topics discussed include a description of the method and multiple applications to RNA-binding proteins, riboswitch design and engineering, and RNA tertiary structure energetics.

Divalent ions tune the kinetics of a bacterial GTPase center rRNA folding transition from secondary to tertiary structure.

Folding of an RNA from secondary to tertiary structure often depends on divalent ions for efficient electrostatic charge screening (nonspecific association) or binding (specific association). To measure how different divalent cations modify folding kinetics of the 60 nucleotide E. coli rRNA GTPase center, we combined stopped-flow fluorescence in the presence of Mg2+, Ca2+, or Sr2+ together with time-resolved small angle X-ray scattering (SAXS) in the presence of Mg2+ to observe the folding process. Immediately upon addition of each divalent ion, the RNA undergoes a transition from an extended state with secondary structure to a more compact structure. Subsequently, specific divalent ions modulate populations of intermediates in conformational ensembles along the folding pathway with transition times longer than 10 msec. Rate constants for the five folding transitions act on timescales from submillisecond to tens of seconds. The sensitivity of RNA tertiary structure to divalent cation identity affects all but the fastest events in RNA folding, and allowed us to identify those states that prefer Mg2+. The GTPase center RNA appears to have optimized its folding trajectory to specifically utilize this most abundant intracellular divalent ion.

Prediction of secondary and tertiary structures of human BC200 RNA (BCYRN1) based on experimental and bioinformatic cross-validation.

Based on experimental and bioinformatic approaches, we present the first empirically established complete secondary structure of human BC200 RNA. BC200 RNA is a brain-specific non-messenger RNA with a confirmed regulatory role in dendritic translation in neurons. Although the involvement of human BC200 RNA in various types of tumour and Alzheimer's disease has been repeatedly confirmed, the exact secondary structure remains not fully elucidated. To determine the secondary structure of BC200 RNA in vitro, we performed partial hydrolysis with sequence-specific nucleases and lead-induced cleavage. We also examined the availabilities of putative single-stranded regions and base-pairing interactions via specific DNAzymes and RNase H assay. To determine the complete spatial folding of BC200 RNA, we used experimental data as constraints in structure prediction programs and performed a comparison of results obtained by several algorithms using different criteria. Based on the experimental-derived secondary structure of BC200 RNA, we also predicted the tertiary structure of BC200 RNA. The presented combination of experimental and bioinformatic approaches not only enabled the determination of the most reliable secondary and tertiary structures of human BC200 RNA (largely in agreement with the previous phylogenetic model), but also verified the compatibility and potential disadvantages of utilizing in silico structure prediction programs.

Predicting RNA Tertiary Structure Using Parallel Algorithm

Predicting RNA Tertiary Structure Using Parallel Algorithm

A path to predict RNA tertiary structures.

A high-throughput platform allows biophysical measurements to probe RNA tertiary folding.

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

BackgroundComputational 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.ResultsHere, 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/ConclusionsRNAfitme 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.

High-Throughput Investigation of Diverse Junction Elements in RNA Tertiary Folding

RNAs fold into defined tertiary structures to function in critical biological processes. While quantitative models can predict RNA secondary structure stability, we are still unable to predict the thermodynamic stability of RNA tertiary structure. Here, we probe conformational preferences of diverse RNA two-way junctions to develop a predictive model for the formation of RNA tertiary structure. We quantitatively measured tertiary assembly energetics of >1,000 of RNA junctions inserted in multiple structural scaffolds to generate a "thermodynamic fingerprint" for each junction. Thermodynamic fingerprints enabled comparison of junction conformational preferences, revealing principles for how sequence influences 3-dimensional conformations. Utilizing fingerprints of junctions with known crystal structures, we generated ensembles for related junctions that predicted their thermodynamic effects on assembly formation. This work reveals sequence-structure-energeticrelationships in RNA, demonstrates the capacity fordiverse compensation strategies within tertiary structures, and provides a path to quantitative modeling of RNA folding energetics based on "ensemble modularity."

Time-Resolved Hydroxyl Radical Footprinting of RNA with X-Rays.

RNA footprinting by hydroxyl radical cleavage provides 'snapshots' of RNA tertiary structure or protein interactions that bury the RNA backbone. Generation of hydroxyl radicals with a high-flux synchrotron X-ray beam provides analysis on a short timescale (5-100 msec), which enables the structures of folding intermediates or other transient conformational states to be determined in biochemical solutions or cells. This article provides protocols for using synchrotron beamlines for hydroxyl radical footprinting. © 2018 by John Wiley & Sons, Inc.

All-atom four-body knowledge-based statistical potential to distinguish native tertiary RNA structures from nonnative folds

Scientific breakthroughs in recent decades have uncovered the capability of RNA molecules to fulfill a wide array of structural, functional, and regulatory roles in living cells, leading to a concomitantly significant increase in both the number and diversity of experimentally determined RNA three-dimensional (3D) structures. Atomic coordinates from a representative training set of solved RNA structures, displaying low sequence and structure similarity, facilitate derivation of knowledge-based energy functions. Here we develop an all-atom four-body statistical potential and evaluate its capacity to distinguish native RNA 3D structures from nonnative folds based on calculated free energy scores. Atomic four-body nearest-neighbors are objectively identified by their occurrence as tetrahedral vertices in the Delaunay tessellations of RNA structures, and rates of atomic quadruplet interactions expected by chance are obtained from a multinomial reference distribution. Our four-body energy function, referred to as RAMP (ribonucleic acids multibody potential), is subsequently derived by applying the inverted Boltzmann principle to the frequency data, yielding an energy score for each type of atomic quadruplet interaction. Several well-known benchmark datasets reveal that RAMP is comparable with, and often outperforms, existing knowledge- and physics-based energy functions. To the best of our knowledge, this is the first study detailing an RNA tertiary structure-based multibody statistical potential and its comparative evaluation.

Recognition of cyclic-di-GMP by a riboswitch conducts translational repression through masking the ribosome-binding site distant from the aptamer domain.

The riboswitch is a class of RNA-based gene regulatory machinery that is dependent on recognition of its target ligand by RNA tertiary structures. Ligand recognition is achieved by the aptamer domain, and ligand-dependent structural changes of the expression platform then usually mediate termination of transcription or translational initiation. Ligand-dependent structural changes of the aptamer domain and expression platform have been reported for several riboswitches with short (<40 nucleotides) expression platforms. In this study, we characterized structural changes of the Vc2 c-di-GMP riboswitch that represses translation of downstream open reading frames in a ligand-dependent manner. The Vc2 riboswitch has a long (97 nucleotides) expression platform, but its structure and function are largely unknown. Through mutational analysis and chemical probing, we identified its secondary structures that are possibly responsible for switch-OFF and switch-ON states of translational initiation.

Competitive Binding of Mg2+ and Na+ Ions to Nucleic Acids: From Helices to Tertiary Structures

Nucleic acids generally reside in cellular aqueous solutions with mixed divalent/monovalent ions, and the competitive binding of divalent and monovalent ions is critical to the structures of nucleic acids because of their polyanionic nature. In this work, we first proposed a general and effective method for simulating a nucleic acid in mixed divalent/monovalent ion solutions with desired bulk ion concentrations via molecular dynamics (MD) simulations and investigated the competitive binding of Mg2+/Na+ ions to various nucleic acids by all-atom MD simulations. The extensive MD-based examinations show that single MD simulations conducted using the proposed method can yield desired bulk divalent/monovalent ion concentrations for various nucleic acids, including RNA tertiary structures. Our comprehensive analyses show that the global binding of Mg2+/Na+ to a nucleic acid is mainly dependent on its structure compactness, as well as Mg2+/Na+ concentrations, rather than the specific structure of the nucleic acid. Specifically, the relative global binding of Mg2+ over Na+ is stronger for a nucleic acid with higher effective surface charge density and higher relative Mg2+/Na+ concentrations. Furthermore, the local binding of Mg2+/Na+ to a phosphate of a nucleic acid mainly depends on the local phosphate density in addition to Mg2+/Na+ concentrations.

RNA thermosensors: how might animals exploit their regulatory potential?

The secondary and tertiary orders of RNA structure are crucial for a suite of RNA-related functions, including regulation of translation, gene expression and RNA turnover. The temperature sensitivity of RNA secondary and tertiary structures is exploited by bacteria to fabricate RNA thermosensing systems that allow a rapid adaptive response to temperature change. RNA thermometers (RNATs) present in non-coding regions of certain mRNAs of pathogenic bacteria enable rapid upregulation of translation of virulence proteins when the temperature of the bacterium rises after entering a mammalian host. Rapid upregulation of translation of bacterial heat-shock proteins likewise is governed in part by RNATs. Turnover of mRNA may be regulated by temperature-sensitive RNA structures. Whereas the roles of temperature-sensitive RNA structures similar to RNATs in Eukarya and Archaea are largely unknown, there would appear to be a potential for all taxa to adaptively regulate their thermal physiology through exploitation of RNA-based thermosensory responses akin to those of bacteria. In animals, these responses might include regulation of translation of stress-induced proteins, alternative splicing of messenger RNA precursors, differential expression of allelic proteins, modulation of activities of small non-coding RNAs, regulation of mRNA turnover and control of RNA editing. New methods for predicting, detecting and experimentally modifying RNA secondary structure offer promising windows into these fascinating aspects of RNA biochemistry. Elucidating whether animals too have exploited the types of RNA thermosensing tools that are used so effectively by bacteria seems likely to provide exciting new insights into the mechanisms of evolutionary adaptation and acclimatization to temperature.

RNA structure: A LASER-focused view into cells.

RNA structure is irrevocably linked to function. A new method, termed 'LASER', utilizes a light-activated chemical probe to query RNA tertiary structure and illuminate RNA–protein interactions in the living cell.