scholarly journals SHAPE directed RNA folding

2015 ◽  
Author(s):  
Dominik Luntzer ◽  
Ronny Lorenz ◽  
Ivo L Hofacker ◽  
Peter F Stadler ◽  
Michael T. Wolfinger
Keyword(s):  
Rna Structure ◽  
Structure Prediction ◽  
Rna Folding ◽  
Secondary Structure Prediction ◽  
Reference Structure ◽  
Chemical Mapping ◽  
Rna Secondary Structure Prediction ◽  
New Feature ◽  
Nucleotide Resolution ◽  
Standard Energy

Summary: Chemical mapping experiments allow for nucleotide resolution assessment of RNA structure. We demonstrate that different strategies of integrating probing data with thermodynamics- based RNA secondary structure prediction algorithms can be implemented by means of soft constraints. This amounts to incorporating suitable pseudo-energies into the standard energy model for RNA secondary structures. As a showcase application for this new feature of the ViennaRNA Package we compare three distinct, previously published strategies to utilize SHAPE reactivities for structure prediction. The new tool is benchmarked on a set of RNAs with known reference structure. Availability and implementation: The capability for SHAPE directed RNA folding is part of the upcoming release of the ViennaRNA Package 2.2, for which a preliminary release is already freely available at http://www.tbi.univie.ac.at/RNA.

scholarly journals Blind prediction of noncanonical RNA structure at atomic accuracy

2017 ◽  
Author(s):  
Andrew Watkins ◽  
Caleb Geniesse ◽  
Wipapat Kladwang ◽  
Paul Zakrevsky ◽  
Luc Jaeger ◽  
...  
Keyword(s):  
Rna Structure ◽  
Structure Prediction ◽  
Grand Challenge ◽  
Chemical Mapping ◽  
Base Pairs ◽  
General Ability ◽  
Rna Structural Motifs ◽  
Nucleotide Resolution ◽  
Noncanonical Base Pairs

AbstractPrediction of RNA structure from nucleotide sequence remains an unsolved grand challenge of biochemistry and requires distinct concepts from protein structure prediction. Despite extensive algorithmic development in recent years, modeling of noncanonical base pairs of new RNA structural motifs has not been achieved in blind challenges. We report herein a stepwise Monte Carlo (SWM) method with a unique add-and-delete move set that enables predictions of noncanonical base pairs of complex RNA structures. A benchmark of 82 diverse motifs establishes the method’s general ability to recover noncanonical pairs ab initio, including multistrand motifs that have been refractory to prior approaches. In a blind challenge, SWM models predicted nucleotide-resolution chemical mapping and compensatory mutagenesis experiments for three in vitro selected tetraloop/receptors with previously unsolved structures (C7.2, C7.10, and R1). As a final test, SWM blindly and correctly predicted all noncanonical pairs of a Zika virus double pseudoknot during a recent community-wide RNA-puzzle. Stepwise structure formation, as encoded in the SWM method, enables modeling of noncanonical RNA structure in a variety of previously intractable problems.

Statistical Mechanical Prediction of Ligand Perturbation to RNA Secondary Structure and Application to the SAM-I Riboswitch

2018 ◽  
Author(s):  
Osama Alaidi ◽  
Fareed Aboul-ela
Keyword(s):  
Secondary Structure ◽  
Ligand Binding ◽  
Rna Structure ◽  
Structure Prediction ◽  
Rna Secondary Structure ◽  
Rna Folding ◽  
Tertiary Structure ◽  
Secondary Structure Prediction ◽  
Key Factor ◽  
Statistical Mechanical

ABSTRACTThe realization that non protein-coding RNA (ncRNA) is implicated in an increasing number of cellular processes, many related to human disease, makes it imperative to understand and predict RNA folding. RNA secondary structure prediction is more tractable than tertiary structure or protein structure. Yet insights into RNA structure-function relationships are complicated by coupling between RNA folding and ligand binding. Here, we introduce a simple statistical mechanical formalism to calculate perturbations to equilibrium secondary structure conformational distributions for RNA, in the presence of bound cognate ligands. For the first time, this formalism incorporates a key factor in coupling ligand binding to RNA conformation: the differential affinity of the ligand for a range of RNA-folding intermediates. We apply the approach to the SAM-I riboswitch, for which binding data is available for analogs of intermediate secondary structure conformers. Calculations of equilibrium secondary structure distributions during the transcriptional “decision window” predict subtle shifts due to the ligand, rather than an on/off switch. The results suggest how ligand perturbation can release a kinetic block to the formation of a terminator hairpin in the full-length riboswitch. Such predictions identify aspects of folding that are most affected by ligand binding, and can readily be compared with experiment.

scholarly journals A Novel Framework Based on ACO and PSO for RNA Secondary Structure Prediction

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Gang Wang ◽  
Wen-yi Zhang ◽  
Qiao Ning ◽  
Hui-ling Chen
Keyword(s):  
Secondary Structure ◽  
Rna Structure ◽  
Structure Prediction ◽  
Rna Secondary Structure ◽  
Secondary Structure Prediction ◽  
Genetic Diseases ◽  
New Drugs ◽  
Accuracy Rate ◽  
Rna Secondary Structure Prediction ◽  
Comparison Results

Prediction of RNA structure is a useful process for creating new drugs and understanding genetic diseases. In this paper, we proposed a particle swarm optimization (PSO) and ant colony optimization (ACO) based framework (PAF) for RNA secondary structure prediction. PAF consists of crucial stem searching (CSS) and global sequence building (GSB). In CSS, a modified ACO (MACO) is used to search the crucial stems, and then a set of stems are generated. In GSB, we used a modified PSO (MPSO) to construct all the stems in one sequence. We evaluated the performance of PAF on ten sequences, which have length from 122 to 1494. We also compared the performance of PAF with the results obtained from six existing well-known methods, SARNA-Predict, RnaPredict, ACRNA, PSOfold, IPSO, and mfold. The comparison results show that PAF could not only predict structures with higher accuracy rate but also find crucial stems.

New Algorithm for Predicting RNA Secondary Structure with Pseudoknots

2012 ◽  
Vol 532-533 ◽  
pp. 1796-1799 ◽  
Author(s):  
Zhen Dong Liu ◽  
Da Ming Zhu
Keyword(s):  
Secondary Structure ◽  
Rna Structure ◽  
Structure Prediction ◽  
Rna Secondary Structure ◽  
Secondary Structure Prediction ◽  
Experimental Results ◽  
Rna Secondary Structure Prediction ◽  
Rna Molecules ◽  
Specificity And Sensitivity

Pseudoknots are complicated and stable RNA structure. Based on the idea of iteratively forming stable stems, and the character that the stems in RNA molecules are relatively stable, an algorithm is presented to predict RNA secondary structure including pseudoknots, it is an improvement from the previously used algorithm ,the algorithm takes O(n3) time and O(n2) sapce , in predicting accuracy, it outperforms other known algorithm of RNA secondary structure prediction, its performance is tested with the RNA sub-sequences in PseudoBase. The experimental results indicate that the algorithm has good specificity and sensitivity.

FINE-GRAINED PARALLEL APPLICATION SPECIFIC COMPUTING FOR RNA SECONDARY STRUCTURE PREDICTION ON FPGA

2014 ◽  
Vol 23 (03) ◽  
pp. 1450031
Author(s):  
QIANGHUA ZHU ◽  
FEI XIA ◽  
GUOQING JIN
Keyword(s):  
Dynamic Programming ◽  
Secondary Structure ◽  
Structure Prediction ◽  
Rna Secondary Structure ◽  
Rna Folding ◽  
Secondary Structure Prediction ◽  
Data Reuse ◽  
Data Dependency ◽  
Rna Secondary Structure Prediction ◽  
Fine Grained

RNA secondary structure prediction is one of the important research areas in modern bioinformatics and computational biology. PKNOTS is the most famous benchmark program and has been widely used to predict RNA secondary structure including pseudoknots. It adopts the standard 4D dynamic programming method and is the basis of many variants and improved algorithms. Unfortunately, the O(N6) computing requirements and complicated data dependency greatly limits the usefulness of PKNOTS package with the explosion in gene database size. In this paper, we present a fine-grained parallel PKNOTS algorithm and prototype system for accelerating RNA folding application on field programmable gate-array (FPGA) platform. We improved data locality by converting cycle nested relationship and reorganizing computing order of the elements in source code. We aggressively exploit data reuse, data dependency elimination and memory access scheduling strategies to minimize the need for loading data from external memory. To the best of our knowledge, our design is the first FPGA implementation for accelerating 4D dynamic programming problem for RNA folding application including pseudoknots. The experimental results show a factor of more than 11 × average speedup over the PKNOTS-1.05 software running on a PC platform with AMD Phenom 9650 Quad CPU for input RNA sequences. However, the power consumption of our FPGA accelerator is only about 50% of the general-purpose micro-processors.

Sign in / Sign up

Export Citation Format

Share Document