Complexity Of RNA Structures Research Articles

Quantifying RNA structures and interactions with a unified reduced chain representation model

RNA is a pivotal molecule that plays critical roles in various cellular processes. Quantifying RNA structures and interactions is essential to understanding RNA function and developing RNA-based therapeutics. Using a unified five-bead model and a non-redundant database, this paper investigates the structural features and interactions of five commonly occurring RNA motifs, i.e., double-stranded helices, hairpin loops, internal/bulge loops, multi-branched junctions, and single-stranded terminal tails. Analyzing detailed distributions of RNA local structural features and base-base interactions reveals a preference for helical structures in both local backbone structures and base orientations. The interactions between adjacent bases exhibit motif-specific and sequence-dependent characteristics, reflecting the distinct topological constraints imposed by different loop-helix connection modes and the varying pairing and stacking interactions among different sequences. These findings shed light on the stability of RNA helices, emphasizing their significance in providing dominant base pairing and stacking interactions for RNA structures and stability. The four non-helix motifs encompass unpaired nucleotide loops and exhibit diverse base-base interactions, contributing to the structural diversity observed in RNA. Overall, the complexity of RNA structure arises from the intricate interplay of base-base interactions.

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.

In Cellulo Protein-mRNA Interaction Assay to Determine the Action of G-Quadruplex-Binding Molecules.

Protein-RNA interactions (PRIs) control pivotal steps in RNA biogenesis, regulate multiple physiological and pathological cellular networks, and are emerging as important drug targets. However, targeting of specific protein-RNA interactions for therapeutic developments is still poorly advanced. Studies and manipulation of these interactions are technically challenging and in vitro drug screening assays are often hampered due to the complexity of RNA structures. The binding of nucleolin (NCL) to a G-quadruplex (G4) structure in the messenger RNA (mRNA) of the Epstein-Barr virus (EBV)-encoded EBNA1 has emerged as an interesting therapeutic target to interfere with immune evasion of EBV-associated cancers. Using the NCL-EBNA1 mRNA interaction as a model, we describe a quantitative proximity ligation assay (PLA)-based in cellulo approach to determine the structure activity relationship of small chemical G4 ligands. Our results show how different G4 ligands have different effects on NCL binding to G4 of the EBNA1 mRNA and highlight the importance of in-cellulo screening assays for targeting RNA structure-dependent interactions.

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.

Study of RNA structures with a connection to random matrix theory

This manuscript investigates the level of complexity and thermodynamic properties of the real RNA structures and compares the properties with the random RNA sequences. A discussion on the similarities of thermodynamical properties of the real structures with the non linear random matrix model of RNA folding is presented. The structural information contained in the PDB file is exploited to get the base pairing information. The complexity of an RNA structure is defined by a topological quantity called genus which is calculated from the base pairing information. Thermodynamic analysis of the real structures is done numerically. The real structures have a minimum free energy which is very small compared to the randomly generated sequences of the same length. This analysis suggests that there are specific patterns in the structures which are preserved during the evolution of the sequences and certain sequences are discarded by the evolutionary process. Further analyzing the sequences of a fixed length reveal that the RNA structures exist in ensembles i.e. although all the sequences in the ensemble have different series of nucleotides (sequence) they fold into structures that have the same pairs of hydrogen bonding as well as the same minimum free energy. The specific heat of the RNA molecule is numerically estimated at different lengths. The specific heat curve with temperature shows a bump and for some RNA, a double peak behavior is observed. The same behavior is seen in the study of the random matrix model with non linear interaction of RNA folding. The bump in the non linear matrix model can be controlled by the change in the interaction strength.

A contact-waiting-time metric and RNA folding rates

Metrics for indirectly predicting the folding rates of RNA sequences are of interest. In this letter, we introduce a simple metric of RNA structural complexity, which accounts for differences in the energetic contributions of RNA base contacts toward RNA structure formation. We apply the metric to RNA sequences whose folding rates were previously determined experimentally. We find that the metric has good correlation (correlation coefficient: −0.95, p ≪ 0.01 ) with the logarithmically transformed folding rates of those RNA sequences. This suggests that the metric can be useful for predicting RNA folding rates. We use the metric to predict the folding rates of bacterial and eukaryotic group II introns. Future applications of the metric (e.g., to predict structural RNAs) could prove fruitful.

Displaying 3D data on RNA secondary structures: coloRNA

RNA performs a variety of diverse functions and therefore must adopt many different three-dimensional conformations. The number and complexity of RNA structures that are currently available are steadily increasing, necessitating the generation of versatile structure visualization tools. Here, we describe a new RNA secondary and tertiary structure visualization tool, the display program coloRNA. This program colors each nucleotide in a secondary structure schematic according to the value of an assigned property of the corresponding backbone phosphate group, such as the distance between corresponding residues in two atomic models of the same RNA molecule. To assist in analyzing tertiary structure, coloRNA also colors nucleotides based on the three-dimensional distances between a user-selected nucleotide and all others. Minimum and maximum thresholds can be used to focus in on, or eliminate, a particular value range. coloRNA can display a user-specified group of nucleotides by outlining the structure in an automatically assigned, but user-changeable color. As an example, we have used coloRNA to analyze a pair of recently published structures of the Escherichia coli 70S ribosome. When coloRNA is used to display the conformational difference between the two structures, the large movement of the small subunit head stands visually out from the background changes in the remaining domains of the small subunit.

Informational Complexity and Functional Activity of RNA Structures

Very little is known about the distribution of functional DNA, RNA, and protein molecules in sequence space. The question of how the number and complexity of distinct solutions to a particular biochemical problem varies with activity is an important aspect of this general problem. Here we present a comparison of the structures and activities of eleven distinct GTP-binding RNAs (aptamers). By experimentally measuring the amount of information required to specify each optimal binding structure, we show that defining a structure capable of 10-fold tighter binding requires approximately 10 additional bits of information. This increase in information content is equivalent to specifying the identity of five additional nucleotide positions and corresponds to an ∼1000-fold decrease in abundance in a sample of random sequences. We observe a similar relationship between structural complexity and activity in a comparison of two catalytic RNAs (ribozyme ligases), raising the possibility of a general relationship between the complexity of RNA structures and their functional activity. Describing how information varies with activity in other heteropolymers, both biological and synthetic, may lead to an objective means of comparing their functional properties. This approach could be useful in predicting the functional utility of novel heteropolymers.

Specific HIV-1 TAR RNA loop sequence and functional groups are required for human cyclin T1-Tat-TAR ternary complex formation.

Replication of human immunodeficiency virus requires Tat protein which activates elongation of RNA polymerase II transcription at the HIV-1 promoter through interaction with the cyclin T1 (CycT1) subunit of the positive transcription elongation factor complex (P-TEFb). Tat binds directly through its transactivation domain to the CycT1 subunit of the P-TEFb and induces loop sequence specific binding of the P-TEFb onto nascent HIV-1 TAR RNA. By using a gel electrophoresis method and a comprehensive set of TAR loop mutants, we have identified the sequence and structural determinants for high-affinity CycT1-Tat-TAR ternary complex formation. Our results show that CycT1 and Tat binding to TAR RNA is highly cooperative, and a capacity of 85%, a Hill coefficient of 2.7, and a dissociation constant (K(D)) of 2.45 nM were observed. These results indicate that there are three binding sites on TAR RNA. CycT1 does not bind TAR RNA in the absence of Tat, and Tat binding to TAR, while detectable, is very inefficient in the absence of CycT1. It is conceivable that the CycT1-Tat heterodimer directly binds to TAR RNA in the U-rich RNA bulge region and this binding facilitates the interactions of the CycT1-Tat heterodimer at the other two sites in the RNA loop region. On the basis of our results, we suggest a model where CycT1 interacts with Tat protein and positions the protein complex to make contacts with the G34 region of the loop sequence; G34 is critical for CycT1-Tat binding and forms a C30.G34 base pair. Two functional groups, O6 and N7, at nucleotide positions 32 and 34 in the TAR loop are essential for CycT1-Tat interactions with TAR RNA. The identity of two nucleotides, U31 and G33, is not critical, but they contribute to the stabilization of the RNA-protein complex. The presence of a single-nucleotide bulge of A35 or C35 is essential for distortion of the backbone RNA structure as well as the accessibility of functional groups in the major groove of the double-helical region. CycT1-Tat interaction with TAR RNA represents another example of the flexibility and complexity of RNA structure involved in protein recognition.

Probing RNA-protein interactions by psoralen photocrosslinking.

RNA molecules can fold into extensive structures containing regions of double-stranded duplex, hairpins, internal loops, bulged bases, and pseudo-knotted structures (). Owing to the complexity of RNA structure, the rules governing sequence-specific RNA-protein recognition are not well understood. RNA-protein interactions are vital for many regulatory processes, especially in gene regulation where proteins specifically interact with binding sites found within RNA transcripts. In the absence of high-resolution crystallographic and nuclear magnetic resonance data, new methods are needed to determine the topology of RNA-protein complexes under physiological conditions. We have devised a new method based on psoralen photochemistry to identify specific contacts in RNA-protein complexes (,).