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Characteristics and prediction of RNA structure.

Li H, Zhu D, Zhang C, Han H, Crandall KA - Biomed Res Int (2014)

Bottom Line: The performance of RNA structure prediction may be improved by considering dynamic and hierarchical folding mechanisms.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.

View Article: PubMed Central - PubMed

Affiliation: School of Computer Science and Technology, Shandong Provincial Key Laboratory of Digital Media Technology, Shandong University of Finance and Economics, Jinan 250014, China ; Computational Biology Institute, George Washington University, Ashburn, VA 20147, USA.

ABSTRACT
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, and L, where L is 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.

Show MeSH
Computation of sequence TMVup. (a) Status after the first fold of the GM algorithm. (b) Status after the second fold of the GM algorithm. (c) Intermediate result of the last step of the GM algorithm. (d) Final fold and predicted structure by GM. (e) Native structure of TMVup. (f) Final fold and predicted structure by PKNOTS. (g) The top shows the sequence of TMVup.
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fig3: Computation of sequence TMVup. (a) Status after the first fold of the GM algorithm. (b) Status after the second fold of the GM algorithm. (c) Intermediate result of the last step of the GM algorithm. (d) Final fold and predicted structure by GM. (e) Native structure of TMVup. (f) Final fold and predicted structure by PKNOTS. (g) The top shows the sequence of TMVup.

Mentions: A domain is closed by a helix or pseudoknot (Figure 3). A subdomain is an independently stable part of a domain. If the closed helix or pseudoknot of a domain is deleted, its subdomain will become a domain.


Characteristics and prediction of RNA structure.

Li H, Zhu D, Zhang C, Han H, Crandall KA - Biomed Res Int (2014)

Computation of sequence TMVup. (a) Status after the first fold of the GM algorithm. (b) Status after the second fold of the GM algorithm. (c) Intermediate result of the last step of the GM algorithm. (d) Final fold and predicted structure by GM. (e) Native structure of TMVup. (f) Final fold and predicted structure by PKNOTS. (g) The top shows the sequence of TMVup.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4109605&req=5

fig3: Computation of sequence TMVup. (a) Status after the first fold of the GM algorithm. (b) Status after the second fold of the GM algorithm. (c) Intermediate result of the last step of the GM algorithm. (d) Final fold and predicted structure by GM. (e) Native structure of TMVup. (f) Final fold and predicted structure by PKNOTS. (g) The top shows the sequence of TMVup.
Mentions: A domain is closed by a helix or pseudoknot (Figure 3). A subdomain is an independently stable part of a domain. If the closed helix or pseudoknot of a domain is deleted, its subdomain will become a domain.

Bottom Line: The performance of RNA structure prediction may be improved by considering dynamic and hierarchical folding mechanisms.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.

View Article: PubMed Central - PubMed

Affiliation: School of Computer Science and Technology, Shandong Provincial Key Laboratory of Digital Media Technology, Shandong University of Finance and Economics, Jinan 250014, China ; Computational Biology Institute, George Washington University, Ashburn, VA 20147, USA.

ABSTRACT
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, and L, where L is 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.

Show MeSH