An Approximation Scheme for RNA Folding Structure Prediction Including Pseudoknots

2013 ◽  
Author(s):  
Zhendong Liu ◽  
Daming Zhu ◽  
Wei Cui ◽  
Nan Liu
Keyword(s):  
Structure Prediction ◽  
Approximation Scheme ◽  
Rna Folding ◽  
Folding Structure

scholarly journals Predicting Model and Algorithm in RNA Folding Structure Including Pseudoknots

2018 ◽  
Vol 32 (10) ◽  
pp. 1851005 ◽  
Author(s):  
Zhendong Liu ◽  
Daming Zhu ◽  
Qionghai Dai
Keyword(s):  
Polynomial Time ◽  
Rna Structure ◽  
Approximation Scheme ◽  
Rna Folding ◽  
Experimental Tests ◽  
Minimum Free Energy ◽  
Pseudoknotted Structure ◽  
Np Hard Problem ◽  
Folding Structure ◽  
Maximum Weight Matching

The prediction of RNA structure with pseudoknots is a nondeterministic polynomial-time hard (NP-hard) problem; according to minimum free energy models and computational methods, we investigate the RNA-pseudoknotted structure. Our paper presents an efficient algorithm for predicting RNA structure with pseudoknots, and the algorithm takes O([Formula: see text]) time and O([Formula: see text]) space, the experimental tests in Rfam10.1 and PseudoBase indicate that the algorithm is more effective and precise. The predicting accuracy, the time complexity and space complexity outperform existing algorithms, such as Maximum Weight Matching (MWM) algorithm, PKNOTS algorithm and Inner Limiting Layer (ILM) algorithm, and the algorithm can predict arbitrary pseudoknots. And there exists a [Formula: see text] ([Formula: see text]) polynomial time approximation scheme in searching maximum number of stackings, and we give the proof of the approximation scheme in RNA-pseudoknotted structure. We have improved several types of pseudoknots considered in RNA folding structure, and analyze their possible transitions between types of pseudoknots.

The Algorithm and Scheme of Prediction in RNA Folding Structure with Pseudoknots

2017 ◽  
Author(s):  
Zhendong Liu ◽  
Qingxia Kong ◽  
Yaoyao Fu ◽  
Hongchao Ye ◽  
Shimin Zhao ◽  
...  
Keyword(s):  
Rna Folding ◽  
Folding Structure

Predicting scheme of RNA folding structure including pseudoknots

2014 ◽  
Vol 16 (4) ◽  
pp. 229 ◽  
Author(s):  
Zhendong Liu ◽  
Daming Zhu ◽  
Hongwei Ma
Keyword(s):  
Rna Folding ◽  
Folding Structure

scholarly journals Transcriptional and Translational Regulation of α-Acetolactate Decarboxylase of Lactococcus lactissubsp. lactis

2000 ◽  
Vol 182 (19) ◽  
pp. 5399-5408 ◽  
Author(s):  
Nathalie Goupil-Feuillerat ◽  
Gérard Corthier ◽  
Jean-Jacques Godon ◽  
S. Dusko Ehrlich ◽  
Pierre Renault
Keyword(s):  
Translational Regulation ◽  
Rna Folding ◽  
Stringent Response ◽  
Second Step ◽  
Stem Loop ◽  
Ribosome Binding ◽  
Branched Chain Amino Acid ◽  
Folding Structure ◽  
Branched Chain ◽  
Favorable Conditions

ABSTRACT The α-acetolactate decarboxylase (ALDC) gene, aldB, is the penultimate gene of the leu-ilv-ald operon, which encodes the three branched-chain amino acid (BCAA) biosynthesis genes in Lactococcus lactis. Its product plays a dual role in the cell: (i) it catalyzes the second step of the acetoin pathway, and (ii) it controls the pool of α-acetolactate during leucine and valine synthesis. It can be transcribed from the two promoters present upstream of the leu and ilv genes (P1 and P2) or independently under the control of its own promoter (P3). In this paper we show that the production of ALDC is limited by two mechanisms. First, the strength of P3 decreases greatly during starvation for BCAAs and under other conditions that generally provoke the stringent response. Second, although aldB is actively transcribed from P1 and P2 during BCAA starvation, ALDC is not significantly produced from these transcripts. The aldB ribosome binding site (RBS) appears to be entrapped in a stem-loop, which is itself part of a more complex RNA folding structure. The function of the structure was studied by mutagenesis, using translational fusions with luciferase genes to assess its activity. The presence of the single stem-loop entrapping the aldB RBS was responsible for a 100-fold decrease in the level of aldB translation. The presence of a supplementary secondary structure upstream of the stem-loop led to an additional fivefold decrease of aldB translation. Finally, the translation of the ilvA gene terminating in the latter structure decreased the level of translation of aldBfivefold more, leading to the complete extinction of the reporter gene activity. Since three leucines and one valine are present among the last six amino acids of the ilvA product, we propose that pausing of the ribosomes during translation could modulate the folding of the messenger, as a function of BCAA availability. The purpose of the structure-dependent regulation could be to ensure the minimal production of ALDC required for the control of the acetolactate pool during BCAA synthesis but to avoid its overproduction, which would dissipate acetolactate. Large amounts of ALDC, necessary for operation of the acetoin pathway, could be produced under favorable conditions from the P3 transcripts, which do not contain the secondary structures.

scholarly journals RAFFT: Efficient prediction of RNA folding pathways using the fast Fourier transform

2021 ◽  
Author(s):  
vaitea opuu ◽  
Cyrille Merleau Nono Saha ◽  
Matteo Smerlak
Keyword(s):  
Fourier Transform ◽  
Fast Fourier Transform ◽  
Structure Prediction ◽  
Rna Folding ◽  
Prediction Methods ◽  
Free Energies ◽  
Native Structure ◽  
Metastable Structures ◽  
Folding Pathways ◽  
Efficient Prediction

We propose a novel heuristic to predict RNA secondary structures. The algorithm is inspired by the kinetic partitioning mechanism, by which molecules follow alternative folding pathways to their native structure, some much faster than others. Similarly, our algorithm RAFFT generates an ensemble of concurrent folding pathways ending in multiple metastable structures for each given sequence; this is in contrast with traditional thermodynamic approaches, which are based on aim to find single structures with minimal free energies. When analyzing 50 predicted folds per sequence, we found near-native predictions (79% PPV and 81% sensitivity) for RNAs of length < 200 nucleotides, matching the performance of recent deep-learning-based structure prediction methods. Our algorithm also acts as a folding kinetic ansatz, which we tested on two RNAs: the coronavirus frameshifting stimulation element (CFSE) and a classic bi-stable sequence. For the CFSE, an ensemble of 68 distinct structures computed by RAFFT allowed us to produce complete folding kinetic trajectories, whereas known methods require evaluating millions of sub-optimal structures to achieve this result. For the second application, only 46 distinct structures were required to reproduce the kinetics, whereas known methods required a sample of 20,000 structures. Thanks to the efficiency of the fast Fourier transform on which RAFFT is based, these computations are efficient, with complexity O(L^2 log L).

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