scholarly journals Cotranscriptional kinetic folding of RNA secondary structures

2020 ◽  
Author(s):  
Vo Hong Thanh ◽  
Pekka Orponen
Keyword(s):  
Structure Formation ◽  
Rna Structure ◽  
Rna Folding ◽  
Computational Prediction ◽  
Simulation Method ◽  
Rna Structures ◽  
Rna Molecules ◽  
Primitive Operation ◽  
Nucleotide Resolution ◽  
Kinetics Of

Computational prediction of RNA structures is an important problem in computational structural biology. Studies of RNA structure formation often assume that the process starts from a fully synthesized sequence. Experimental evidence, however, has shown that RNA folds concurrently with its elongation. We investigate RNA structure formation, taking into account also the cotranscriptional effects. We propose a single-nucleotide resolution kinetic model of the folding process of RNA molecules, where the polymerase-driven elongation of an RNA strand by a new nucleotide is included as a primitive operation, together with a stochastic simulation method that implements this folding concurrently with the transcriptional synthesis. Numerical case studies show that our cotranscriptional RNA folding model can predict the formation of metastable conformations that are favored in actual biological systems. Our new computational tool can thus provide quantitative predictions and offer useful insights into the kinetics of RNA folding.

scholarly journals Distributed Biotin-Streptavidin Transcription Roadblocks for Mapping Cotranscriptional RNA Folding

2017 ◽  
Author(s):  
Eric J. Strobel ◽  
Kyle E. Watters ◽  
Julius B. Lucks
Keyword(s):  
Experimental Study ◽  
Rna Structure ◽  
Rna Folding ◽  
Folding Pathway ◽  
Decision Making Process ◽  
Important Process ◽  
Rna Structures ◽  
Rna Molecules ◽  
Fundamental Properties ◽  
Nucleotide Resolution

AbstractRNA molecules fold cotranscriptionally as they emerge from RNA polymerase. Cotranscriptional folding is an important process for proper RNA structure formation as the order of folding can determine an RNA molecule’s structure, and thus its functional properties. Despite its fundamental importance, the experimental study of RNA cotranscriptional folding has been limited by the lack of easily approachable methods that can interrogate nascent RNA structures at nucleotide resolution during transcription. We previously developed cotranscriptional selective 2’-hydroxyl acylation analyzed by primer extension sequencing (SHAPE-seq) to simultaneously probe all of the intermediate structures an RNA molecule transitions through during transcription elongation. Here, we improve the broad applicability of cotranscriptional SHAPE-Seq by developing a sequence-independent streptavidin roadblocking strategy to simplify the preparation of roadblocking transcription templates. We determine the fundamental properties of streptavidin roadblocks and show that randomly distributed streptavidin roadblocks can be used in cotranscriptional SHAPE-Seq experiments to measure the Bacillus cereus crcB fluoride riboswitch folding pathway. Comparison of EcoRIE111Q and streptavidin roadblocks in cotranscriptional SHAPE-Seq data shows that both strategies identify the same RNA structural transitions related to the riboswitch decision-making process. Finally, we propose guidelines to leverage the complementary strengths of each transcription roadblock for use in studying cotranscriptional folding.

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 The global and local distribution of RNA structure throughout the SARS-CoV-2 genome

2020 ◽  
Author(s):  
Rafael de Cesaris Araujo Tavares ◽  
Gandhar Mahadeshwar ◽  
Han Wan ◽  
Nicholas C. Huston ◽  
Anna Marie Pyle
Keyword(s):  
In Silico ◽  
Rna Structure ◽  
Drug Targets ◽  
Rna Folding ◽  
Rna Viruses ◽  
Viral Agent ◽  
Rna Structures ◽  
Viral Rnas ◽  
Viral Genomes ◽  
Rna Genome

SARS-CoV-2 is the causative viral agent of COVID-19, the disease at the center of the current global pandemic. While knowledge of highly structured regions is integral for mechanistic insights into the viral infection cycle, very little is known about the location and folding stability of functional elements within the massive, ∼30kb SARS-CoV-2 RNA genome. In this study, we analyze the folding stability of this RNA genome relative to the structural landscape of other well-known viral RNAs. We present an in-silico pipeline to predict regions of high base pair content across long genomes and to pinpoint hotspots of well-defined RNA structures, a method that allows for direct comparisons of RNA structural complexity within the several domains in SARS-CoV-2 genome. We report that the SARS-CoV-2 genomic propensity for stable RNA folding is exceptional among RNA viruses, superseding even that of HCV, one of the most structured viral RNAs in nature. Furthermore, our analysis suggests varying levels of RNA structure across genomic functional regions, with accessory and structural ORFs containing the highest structural density in the viral genome. Finally, we take a step further to examine how individual RNA structures formed by these ORFs are affected by the differences in genomic and subgenomic contexts, which given the technical difficulty of experimentally separating cellular mixtures of sgRNA from gRNA, is a unique advantage of our in-silico pipeline. The resulting findings provide a useful roadmap for planning focused empirical studies of SARS-CoV-2 RNA biology, and a preliminary guide for exploring potential SARS-CoV-2 RNA drug targets. Importance The RNA genome of SARS-CoV-2 is among the largest and most complex viral genomes, and yet its RNA structural features remain relatively unexplored. Since RNA elements guide function in most RNA viruses, and they represent potential drug targets, it is essential to chart the architectural features of SARS-CoV-2 and pinpoint regions that merit focused study. Here we show that RNA folding stability of SARS-CoV-2 genome is exceptional among viral genomes and we develop a method to directly compare levels of predicted secondary structure across SARS-CoV-2 domains. Remarkably, we find that coding regions display the highest structural propensity in the genome, forming motifs that differ between the genomic and subgenomic contexts. Our approach provides an attractive strategy to rapidly screen for candidate structured regions based on base pairing potential and provides a readily interpretable roadmap to guide functional studies of RNA viruses and other pharmacologically relevant RNA transcripts.

Computational Design of a Circular RNA with Prionlike Behavior

2016 ◽  
Vol 22 (2) ◽  
pp. 172-184 ◽  
Author(s):  
Stefan Badelt ◽  
Christoph Flamm ◽  
Ivo L. Hofacker
Keyword(s):  
Synthetic Biology ◽  
Structure Formation ◽  
Computational Methods ◽  
Computational Design ◽  
Circular Rna ◽  
Equilibrium Thermodynamics ◽  
Rna Molecules ◽  
Rna Design ◽  
Kinetics Of ◽  
Functional Rna

RNA molecules engineered to fold into predefined conformations have enabled the design of a multitude of functional RNA devices in the field of synthetic biology and nanotechnology. More complex designs require efficient computational methods, which need to consider not only equilibrium thermodynamics but also the kinetics of structure formation. Here we present a novel type of RNA design that mimics the behavior of prions, that is, sequences capable of interaction-triggered autocatalytic replication of conformations. Our design was computed with the ViennaRNA package and is based on circular RNA that embeds domains amenable to intermolecular kissing interactions.

scholarly journals A novel SHAPE reagent enables the analysis of RNA structure in living cells with unprecedented accuracy

2021 ◽  
Author(s):  
Tycho Marinus ◽  
Adam B Fessler ◽  
Craig A Ogle ◽  
Danny Incarnato
Keyword(s):  
Rna Structure ◽  
Structure Prediction ◽  
Critical Role ◽  
Living Cells ◽  
Pathological Process ◽  
Rna Structures ◽  
Rna Molecules ◽  
Derived Data

Abstract Due to the mounting evidence that RNA structure plays a critical role in regulating almost any physiological as well as pathological process, being able to accurately define the folding of RNA molecules within living cells has become a crucial need. We introduce here 2-aminopyridine-3-carboxylic acid imidazolide (2A3), as a general probe for the interrogation of RNA structures in vivo. 2A3 shows moderate improvements with respect to the state-of-the-art selective 2′-hydroxyl acylation analyzed by primer extension (SHAPE) reagent NAI on naked RNA under in vitro conditions, but it significantly outperforms NAI when probing RNA structure in vivo, particularly in bacteria, underlining its increased ability to permeate biological membranes. When used as a restraint to drive RNA structure prediction, data derived by SHAPE-MaP with 2A3 yields more accurate predictions than NAI-derived data. Due to its extreme efficiency and accuracy, we can anticipate that 2A3 will rapidly take over conventional SHAPE reagents for probing RNA structures both in vitro and in vivo.

scholarly journals Computational Design of Asymmetric Three-dimensional RNA Structures and Machines

2017 ◽  
Author(s):  
Joseph D. Yesselman ◽  
Daniel Eiler ◽  
Erik D. Carlson ◽  
Alexandra N. Ooms ◽  
Wipapat Kladwang ◽  
...  
Keyword(s):  
Three Dimensional ◽  
Computational Design ◽  
Chemical Mapping ◽  
Rna Structures ◽  
Single Nucleotide ◽  
Rna Molecules ◽  
Rna Nanotechnology ◽  
X Ray Scattering ◽  
Nucleotide Resolution ◽  
Large Sections

AbstractThe emerging field of RNA nanotechnology seeks to create nanoscale 3D machines by repurposing natural RNA modules, but successes have been limited to symmetric assemblies of single repeating motifs. We present RNAMake, a suite that automates design of RNA molecules with complex 3D folds. We first challenged RNAMake with the paradigmatic problem of aligning a tetraloop and sequence-distal receptor, previously only solved via symmetry. Single-nucleotide-resolution chemical mapping, native gel electrophoresis, and solution x-ray scattering confirmed that 11 of the 16 ‘miniTTR’ designs successfully achieved clothespin-like folds. A 2.55 Å diffraction-resolution crystal structure of one design verified formation of the target asymmetric nanostructure, with large sections achieving near-atomic accuracy (< 2.0 Å). Finally, RNAMake designed asymmetric segments to tether the 16S and 23S rRNAs together into a synthetic singlestranded ribosome that remains uncleaved by ribonucleases and supports life in Escherichia coli, a challenge previously requiring several rounds of trial-and-error.

scholarly journals Estimating RNA structure chemical probing reactivities from reverse transcriptase stops and mutations

2018 ◽  
Author(s):  
Angela M Yu ◽  
Molly E. Evans ◽  
Julius B. Lucks
Keyword(s):  
Statistical Analysis ◽  
Reverse Transcriptase ◽  
Rna Structure ◽  
Rna Structures ◽  
Modeling Framework ◽  
Single Experiment ◽  
Complementary Dna ◽  
Rna Molecules ◽  
Chemical Probing ◽  
Covalent Adducts

ABSTRACTChemical probing experiments interrogate RNA structures by creating covalent adducts on RNA molecules in structure-dependent patterns. Adduct positions are then detected through conversion of the modified RNAs into complementary DNA (cDNA) by reverse transcription (RT) as either stops (RT-stops) or mutations (RT-mutations). Statistical analysis of the frequencies of RT-stops and RT-mutations can then be used to estimate a measure of chemical probing reactivity at each nucleotide of an RNA, which reveals properties of the underlying RNA structure. Inspired by recent work that showed that different reverse transcriptase enzymes show distinct biases for detecting adducts as either RT-stops or RT-mutations, here we use a statistical modeling framework to derive an equation for chemical probing reactivity using experimental signatures from both RT-stops and RT-mutations within a single experiment. The resulting formula intuitively matches the expected result from considering reactivity to be defined as the fraction of adduct observed at each position in an RNA at the end of a chemical probing experiment. We discuss assumptions and implementation of the model, as well as ways in which the model may be experimentally validated.

scholarly journals A novel SHAPE reagent enables the analysis of RNA structure in living cells with unprecedented accuracy

2020 ◽  
Author(s):  
Tycho Marinus ◽  
Adam B. Fessler ◽  
Craig A. Ogle ◽  
Danny Incarnato
Keyword(s):  
Rna Structure ◽  
Structure Prediction ◽  
Critical Role ◽  
Living Cells ◽  
Pathological Process ◽  
Rna Structures ◽  
Rna Molecules ◽  
Derived Data

ABSTRACTDue to the mounting evidence that RNA structure plays a critical role in regulating almost any physiological as well as pathological process, being able to accurately define the folding of RNA molecules within living cells has become a crucial need. We introduce here 2-aminopyridine-3-carboxylic acid imidazolide (2A3), as a general probe for the interrogation of RNA structures in vivo. 2A3 shows moderate improvements with respect to the state-of-the-art SHAPE reagent NAI on naked RNA under in vitro conditions, but it significantly outperforms NAI when probing RNA structure in vivo, particularly in bacteria, underlining its increased ability to permeate biological membranes. When used as a restraint to drive RNA structure prediction, data derived by SHAPE-MaP with 2A3 yields more accurate predictions than NAI-derived data. Due to its extreme efficiency and accuracy, we can anticipate that 2A3 will rapidly take over conventional SHAPE reagents for probing RNA structures both in vitro and in vivo.

scholarly journals Characteristics and Prediction of RNA Structure

2014 ◽  
Vol 2014 ◽  
pp. 1-10
Author(s):  
Hengwu Li ◽  
Daming Zhu ◽  
Caiming Zhang ◽  
Huijian Han ◽  
Keith A. Crandall
Keyword(s):  
Rna Structure ◽  
Structure Prediction ◽  
Rna Folding ◽  
Golden Section ◽  
Hierarchical Structures ◽  
Secondary Structures ◽  
Sequence Length ◽  
Rna Structures ◽  
Rna Secondary Structures ◽  
Hierarchical Folding

RNA secondary structures with pseudoknots are often predicted by minimizing free energy, which is NP-hard. Most RNAs fold during transcription from DNA into RNA through a hierarchical pathway wherein secondary structures form prior to tertiary structures. Real RNA secondary structures often have local instead of global optimization because of kinetic reasons. The performance of RNA structure prediction may be improved by considering dynamic and hierarchical folding mechanisms. This study is a novel report on RNA folding that accords with the golden mean characteristic based on the statistical analysis of the real RNA secondary structures of all 480 sequences from RNA STRAND, which are validated by NMR or X-ray. The length ratios of domains in these sequences are approximately 0.382L, 0.5L, 0.618L, andL, whereLis the sequence length. These points are just the important golden sections of sequence. With this characteristic, an algorithm is designed to predict RNA hierarchical structures and simulate RNA folding by dynamically folding RNA structures according to the above golden section points. The sensitivity and number of predicted pseudoknots of our algorithm are better than those of the Mfold, HotKnots, McQfold, ProbKnot, and Lhw-Zhu algorithms. Experimental results reflect the folding rules of RNA from a new angle that is close to natural folding.

scholarly journals EVALUATING MIXTURE MODELS FOR BUILDING RNA KNOWLEDGE-BASED POTENTIALS

2012 ◽  
Vol 10 (02) ◽  
pp. 1241010 ◽  
Author(s):  
ADELENE Y. L. SIM ◽  
OLIVIER SCHWANDER ◽  
MICHAEL LEVITT ◽  
JULIE BERNAUER
Keyword(s):  
Mixture Models ◽  
Dirichlet Process ◽  
Rna Structure ◽  
Structural Information ◽  
A Priori ◽  
Comparable Efficacy ◽  
Rna Structures ◽  
Rna Molecules ◽  
Knowledge Based ◽  
Spline Fitting

Ribonucleic acid (RNA) molecules play important roles in a variety of biological processes. To properly function, RNA molecules usually have to fold to specific structures, and therefore understanding RNA structure is vital in comprehending how RNA functions. One approach to understanding and predicting biomolecular structure is to use knowledge-based potentials built from experimentally determined structures. These types of potentials have been shown to be effective for predicting both protein and RNA structures, but their utility is limited by their significantly rugged nature. This ruggedness (and hence the potential's usefulness) depends heavily on the choice of bin width to sort structural information (e.g. distances) but the appropriate bin width is not known a priori. To circumvent the binning problem, we compared knowledge-based potentials built from inter-atomic distances in RNA structures using different mixture models (Kernel Density Estimation, Expectation Minimization and Dirichlet Process). We show that the smooth knowledge-based potential built from Dirichlet process is successful in selecting native-like RNA models from different sets of structural decoys with comparable efficacy to a potential developed by spline-fitting — a commonly taken approach — to binned distance histograms. The less rugged nature of our potential suggests its applicability in diverse types of structural modeling.

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