scholarly journals Diverse RNA pseudoknots exist for short stems only

2018 ◽  
Vol 13 (2) ◽  
pp. 526-533
Author(s):  
E. Baulin ◽  
A. Korinevskaya ◽  
P. Tikhonova ◽  
M. Roytberg
Keyword(s):  
Free Energy ◽  
Structure Prediction ◽  
Rna Secondary Structure ◽  
Secondary Structure Prediction ◽  
Base Pairs ◽  
Rna Secondary Structure Prediction ◽  
Rna Pseudoknots ◽  
Np Hard Problem ◽  
Short Stems ◽  
Folded Rna

RNA secondary structure prediction including pseudoknotted structures of arbitrary types is a well-known NP-hard problem of computational biology. By limiting the possible types of pseudoknots the problem can be solved in polynomial time. According to the empirical thermodynamic parameters, the formation of a stem starts to decrease free energy of the structure only after the formation of the third stack of base pairs. Thus, the short stems may be unstable and provide a limited contribution to the overall free energy of a folded RNA molecule. Therefore, detailed analysis of stems in pseudoknots could facilitate reducing pseudoknots complexity. In this paper, we show that the pseudoknots from experimentally determined RNA spatial structures are primarily formed by short stems of 2-3 base pairs. The short stems tend to avoid hairpins and prefer internal loops that indicates that they could be energetically insignificant. An exclusion of short stems reduces the diversity of pseudoknots to two basic types which are H-knots and kissing loops.

scholarly journals An efficient simulated annealing algorithm for the RNA secondary structure prediction with Pseudoknots

BMC Genomics ◽  
2019 ◽  
Vol 20 (S13) ◽  
Author(s):  
Zhang Kai ◽  
Wang Yuting ◽  
Lv Yulin ◽  
Liu Jun ◽  
He Juanjuan
Keyword(s):  
Free Energy ◽  
Secondary Structure ◽  
Structure Prediction ◽  
Rna Secondary Structure ◽  
Simulated Annealing Algorithm ◽  
Secondary Structure Prediction ◽  
Stem Length ◽  
Base Pairs ◽  
Rna Secondary Structure Prediction ◽  
Prediction Algorithms

Abstract Background RNA pseudoknot structures play an important role in biological processes. However, existing RNA secondary structure prediction algorithms cannot predict the pseudoknot structure efficiently. Although random matching can improve the number of base pairs, these non-consecutive base pairs cannot make contributions to reduce the free energy. Result In order to improve the efficiency of searching procedure, our algorithm take consecutive base pairs as the basic components. Firstly, our algorithm calculates and archive all the consecutive base pairs in triplet data structure, if the number of consecutive base pairs is greater than given minimum stem length. Secondly, the annealing schedule is adapted to select the optimal solution that has minimum free energy. Finally, the proposed algorithm is evaluated with the real instances in PseudoBase. Conclusion The experimental results have been demonstrated to provide a competitive and oftentimes better performance when compared against some chosen state-of-the-art RNA structure prediction algorithms.

scholarly journals An Improved Algorithm for RNA Secondary Structure Prediction

1999 ◽  
Vol 6 (15) ◽  
Author(s):  
Rune B. Lyngsø ◽  
Michael Zuker ◽  
Christian N. S. Pedersen
Keyword(s):  
Free Energy ◽  
Secondary Structure ◽  
Structure Prediction ◽  
Rna Secondary Structure ◽  
Secondary Structure Prediction ◽  
Biological Processes ◽  
Loop Formation ◽  
Rna Secondary Structure Prediction ◽  
Rna Molecules ◽  
Equilibrium Partition

Though not as abundant in known biological processes as proteins,<br />RNA molecules serve as more than mere intermediaries between<br />DNA and proteins, e.g. as catalytic molecules. Furthermore,<br />RNA secondary structure prediction based on free energy<br />rules for stacking and loop formation remains one of the few major<br />breakthroughs in the field of structure prediction. We present a<br />new method to evaluate all possible internal loops of size at most<br />k in an RNA sequence, s, in time O(k|s|^2); this is an improvement<br />from the previously used method that uses time O(k^2|s|^2).<br />For unlimited loop size this improves the overall complexity of<br />evaluating RNA secondary structures from O(|s|^4) to O(|s|^3) and<br />the method applies equally well to finding the optimal structure<br />and calculating the equilibrium partition function. We use our<br />method to examine the soundness of setting k = 30, a commonly<br />used heuristic.

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