Analyzing Secondary Structure Transition Paths of DNA/RNA Molecules

2004 ◽  
pp. 86-90 ◽  
Author(s):  
Hiroki Uejima ◽  
Masami Hagiya
Keyword(s):  
Secondary Structure ◽  
Structure Transition ◽  
Rna Molecules ◽  
Transition Paths

scholarly journals Structural relation matching: an algorithm to identify structural patterns into RNAs and their interactions

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Michela Quadrini
Keyword(s):  
Hydrogen Bonding ◽  
Secondary Structure ◽  
Nucleotide Sequence ◽  
Thermus Thermophilus ◽  
Three Dimensional ◽  
Secondary Structures ◽  
Structural Effect ◽  
Biological Processes ◽  
Structural Pattern ◽  
Rna Molecules

Abstract RNA molecules play crucial roles in various biological processes. Their three-dimensional configurations determine the functions and, in turn, influences the interaction with other molecules. RNAs and their interaction structures, the so-called RNA–RNA interactions, can be abstracted in terms of secondary structures, i.e., a list of the nucleotide bases paired by hydrogen bonding within its nucleotide sequence. Each secondary structure, in turn, can be abstracted into cores and shadows. Both are determined by collapsing nucleotides and arcs properly. We formalize all of these abstractions as arc diagrams, whose arcs determine loops. A secondary structure, represented by an arc diagram, is pseudoknot-free if its arc diagram does not present any crossing among arcs otherwise, it is said pseudoknotted. In this study, we face the problem of identifying a given structural pattern into secondary structures or the associated cores or shadow of both RNAs and RNA–RNA interactions, characterized by arbitrary pseudoknots. These abstractions are mapped into a matrix, whose elements represent the relations among loops. Therefore, we face the problem of taking advantage of matrices and submatrices. The algorithms, implemented in Python, work in polynomial time. We test our approach on a set of 16S ribosomal RNAs with inhibitors of Thermus thermophilus, and we quantify the structural effect of the inhibitors.

scholarly journals A method for RNA structure prediction shows evidence for structure in lncRNAs

2018 ◽  
Author(s):  
Riccardo Delli ponti ◽  
Alexandros Armaos ◽  
Stefanie Marti ◽  
Gian Gaetano Tartaglia
Keyword(s):  
Secondary Structure ◽  
Rna Structure ◽  
Structure Prediction ◽  
Sequence Information ◽  
Time Warping ◽  
Single Nucleotide ◽  
Rna Molecules ◽  
Rna Structure Prediction ◽  
Dynamic Time ◽  
Nucleotide Resolution

AbstractTo compare the secondary structures of RNA molecules we developed the CROSSalign method. CROSSalign is based on the combination of the Computational Recognition Of Secondary Structure (CROSS) algorithm to predict the RNA secondary structure at single-nucleotide resolution using sequence information, and the Dynamic Time Warping (DTW) method to align profiles of different lengths. We applied CROSSalign to investigate the structural conservation of long non-coding RNAs such as XIST and HOTAIR as well as ssRNA viruses including HIV. In a pool of sequences with the same secondary structure CROSSalign accurately recognizes repeat A of XIST and domain D2 of HOTAIR and outperforms other methods based on covariance modelling. CROSSalign can be applied to perform pair-wise comparisons and is able to find homologues between thousands of matches identifying the exact regions of similarity between profiles of different lengths. The algorithm is freely available at the webpage http://service.tartaglialab.com//new_submission/CROSSalign.

scholarly journals Viroids: unusual small pathogenic RNAs.

2004 ◽  
Vol 51 (3) ◽  
pp. 587-607 ◽  
Author(s):  
Anna Góra-Sochacka
Keyword(s):  
Secondary Structure ◽  
Hammerhead Ribozyme ◽  
Direct Interaction ◽  
Plant Diseases ◽  
Plant Host ◽  
Rolling Circle ◽  
Rna Molecules ◽  
Viroid Replication ◽  
Plant Genes ◽  
Rolling Circle Mechanism

Viroids are small (about 300 nucleotides), single-stranded, circular, non-encapsidated pathogenic RNA molecules. They do not code for proteins and thus depend on plant host enzymes for their replication and other functions. They induce plant diseases by direct interaction with host factors but the mechanism of pathogenicity is still unknown. They can alter the expression of selected plant genes important for growth and development. Viroids belong to two families, the Avsunviroidae and the Pospiviroidae. Viroids of the Avsunviroidae family adopt a branched or quasi rod-like secondary structure in their native state. Members of the Pospiviroidae family adopt a rod-like secondary structure. In such native structures five structural/functional domains have been identified: central (C), pathogenicity, variable and two terminal domains. The central conserved region (CCR) within the C domain characterizes viroids of the Pospiviroidae. Specific secondary structures of this region play an important role in viroid replication and processing. Viroids of the Avsunviroidae family lack a CCR but possess self-cleaving properties by forming hammerhead ribozyme structures; they accumulate and replicate in chloroplasts, whereas members of the Pospiviroidae family have a nuclear localization. Viroid replication occurs via a rolling circle mechanism using either a symmetric or asymmetric pathway in three steps, RNA transcription, processing and ligation.

RNA Structure Analysis: A Multifaceted Approach

1999 ◽  
Author(s):  
Bruce A. Shapiro ◽  
Wojciech Kasprzak
Keyword(s):  
Nucleic Acid ◽  
Secondary Structure ◽  
Tertiary Structure ◽  
Hammerhead Ribozyme ◽  
3D Structure ◽  
Computer Prediction ◽  
Rna Molecules ◽  
Tertiary Interactions ◽  
The One ◽  
Secondary And Tertiary Structure

Genomic information (nucleic acid and amino acid sequences) completely determines the characteristics of the nucleic acid and protein molecules that express a living organism’s function. One of the greatest challenges in which computation is playing a role is the prediction of higher order structure from the one-dimensional sequence of genes. Rules for determining macromolecule folding have been continually evolving. Specifically in the case of RNA (ribonucleic acid) there are rules and computer algorithms/systems (see below) that partially predict and can help analyze the secondary and tertiary interactions of distant parts of the polymer chain. These successes are very important for determining the structural and functional characteristics of RNA in disease processes and hi the cell life cycle. It has been shown that molecules with the same function have the potential to fold into similar structures though they might differ in their primary sequences. This fact also illustrates the importance of secondary and tertiary structure in relation to function. Examples of such constancy in secondary structure exist in transfer RNAs (tRNAs), 5s RNAs, 16s RNAs, viroid RNAs, and portions of retroviruses such as HIV. The secondary and tertiary structure of tRNA Phe (Kim et al., 1974), of a hammerhead ribozyme (Pley et al., 1994), and of Tetrahymena (Cate et al., 1996a, 1996b) have been shown by their crystal structure. Currently little is known of tertiary interactions, but studies on tRNA indicate these are weaker than secondary structure interactions (Riesner and Romer, 1973; Crothers and Cole, 1978; Jaeger et al., 1989b). It is very difficult to crystallize and/or get nuclear magnetic resonance spectrum data for large RNA molecules. Therefore, a logical place to start in determining the 3D structure of RNA is computer prediction of the secondary structure. The sequence (primary structure) of an RNA molecule is relatively easy to produce. Because experimental methods for determining RNA secondary and tertiary structure (when the primary sequence folds back on itself and forms base pairs) have not kept pace with the rapid discovery of RNA molecules and their function, use of and methods for computer prediction of secondary and tertiary structures have increasingly been developed.

scholarly journals Base-pair ambiguity and the kinetics of RNA folding

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Guangyao Zhou ◽  
Jackson Loper ◽  
Stuart Geman
Keyword(s):  
Free Energy ◽  
Secondary Structure ◽  
Thermodynamic Equilibrium ◽  
Secondary Structures ◽  
Minimum Free Energy ◽  
Nucleotide Sequences ◽  
Biologically Relevant ◽  
Rna Molecules ◽  
Comparative Structure ◽  
Kinetic Traps

Abstract Background A folding RNA molecule encounters multiple opportunities to form non-native yet energetically favorable pairings of nucleotide sequences. Given this forbidding free-energy landscape, mechanisms have evolved that contribute to a directed and efficient folding process, including catalytic proteins and error-detecting chaperones. Among structural RNA molecules we make a distinction between “bound” molecules, which are active as part of ribonucleoprotein (RNP) complexes, and “unbound,” with physiological functions performed without necessarily being bound in RNP complexes. We hypothesized that unbound molecules, lacking the partnering structure of a protein, would be more vulnerable than bound molecules to kinetic traps that compete with native stem structures. We defined an “ambiguity index”—a normalized function of the primary and secondary structure of an individual molecule that measures the number of kinetic traps available to nucleotide sequences that are paired in the native structure, presuming that unbound molecules would have lower indexes. The ambiguity index depends on the purported secondary structure, and was computed under both the comparative (“gold standard”) and an equilibrium-based prediction which approximates the minimum free energy (MFE) structure. Arguing that kinetically accessible metastable structures might be more biologically relevant than thermodynamic equilibrium structures, we also hypothesized that MFE-derived ambiguities would be less effective in separating bound and unbound molecules. Results We have introduced an intuitive and easily computed function of primary and secondary structures that measures the availability of complementary sequences that could disrupt the formation of native stems on a given molecule—an ambiguity index. Using comparative secondary structures, the ambiguity index is systematically smaller among unbound than bound molecules, as expected. Furthermore, the effect is lost when the presumably more accurate comparative structure is replaced instead by the MFE structure. Conclusions A statistical analysis of the relationship between the primary and secondary structures of non-coding RNA molecules suggests that stem-disrupting kinetic traps are substantially less prevalent in molecules not participating in RNP complexes. In that this distinction is apparent under the comparative but not the MFE secondary structure, the results highlight a possible deficiency in structure predictions when based upon assumptions of thermodynamic equilibrium.

Secondary structure of U6 small nuclear RNA: implications for spliceosome assembly

2010 ◽  
Vol 38 (4) ◽  
pp. 1099-1104 ◽  
Author(s):  
Elizabeth A. Dunn ◽  
Stephen D. Rader
Keyword(s):  
Secondary Structure ◽  
Small Nuclear Rna ◽  
Stem Loop ◽  
New Model ◽  
Rna Molecules ◽  
U6 Snrna ◽  
Small Nuclear Ribonucleoprotein ◽  
Nuclear Rna ◽  
High Level ◽  
And Function

U6 snRNA (small nuclear RNA), one of five RNA molecules that are required for the essential process of pre-mRNA splicing, is notable for its high level of sequence conservation and the important role it is thought to play in the splicing reaction. Nevertheless, the secondary structure of U6 in the free snRNP (small nuclear ribonucleoprotein) form has remained elusive, with predictions changing substantially over the years. In the present review we discuss the evidence for existing models and critically evaluate a fundamental assumption of these models, namely whether the important 3′ ISL (3′ internal stem–loop) is present in the free U6 particle, as well as in the active splicing complex. We compare existing models of free U6 with a newly proposed model lacking the 3′ ISL and evaluate the implications of the new model for the structure and function of U6's base-pairing partner U4 snRNA. Intriguingly, the new model predicts a role for U4 that was unanticipated previously, namely as an activator of U6 for assembly into the splicing machinery.

Secondary Structure Prediction of Interacting RNA Molecules

2005 ◽  
Vol 345 (5) ◽  
pp. 987-1001 ◽  
Author(s):  
Mirela Andronescu ◽  
Zhi Chuan Zhang ◽  
Anne Condon
Keyword(s):  
Secondary Structure ◽  
Structure Prediction ◽  
Secondary Structure Prediction ◽  
Rna Molecules
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