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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.

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Distribution of domains for sequences with lengths between 50 and 90. (a) For all NMR- or X-ray-validated data on 55 nonfragment and nonredundant sequences with lengths between 50 and 90 from RNA STRAND, except for synthetic RNA, the length ratio of the 3′-end of the domains to the sequence is computed and summarized. If one sequence has only one domain, subdomains are selected. (b) The x-axis represents the length ratio of the 3′-end of the domain to the sequence, and the y-axis represents the number of sequences.
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fig1: Distribution of domains for sequences with lengths between 50 and 90. (a) For all NMR- or X-ray-validated data on 55 nonfragment and nonredundant sequences with lengths between 50 and 90 from RNA STRAND, except for synthetic RNA, the length ratio of the 3′-end of the domains to the sequence is computed and summarized. If one sequence has only one domain, subdomains are selected. (b) The x-axis represents the length ratio of the 3′-end of the domain to the sequence, and the y-axis represents the number of sequences.

Mentions: The results of statistical analysis on these real secondary structures are shown in Figures 1 and 2 and Tables 1, 2, 3, and 4. In Tables 1 and 2, Num represents the number of domains, group is the 3′-end of group, Ratio 1 is the ratio of Group 1 to Group 2, and Ratio 2 is the ratio of Group 2 to Group 3. In Figures 1 and 2, the x-axis represents the length ratio of the 3′-end of domain to the sequence, and the y-axis represents the number of sequences. The number of complementary bases to form a helix at point x in the final structure is insufficient. Thus, we enlarge point x to region x, and the corresponding point y in the y-axis with x in the x-axis represents the number of sequences in the region of [x − 0.4, x].


Characteristics and prediction of RNA structure.

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

Distribution of domains for sequences with lengths between 50 and 90. (a) For all NMR- or X-ray-validated data on 55 nonfragment and nonredundant sequences with lengths between 50 and 90 from RNA STRAND, except for synthetic RNA, the length ratio of the 3′-end of the domains to the sequence is computed and summarized. If one sequence has only one domain, subdomains are selected. (b) The x-axis represents the length ratio of the 3′-end of the domain to the sequence, and the y-axis represents the number of sequences.
© Copyright Policy
Related In: Results  -  Collection

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

fig1: Distribution of domains for sequences with lengths between 50 and 90. (a) For all NMR- or X-ray-validated data on 55 nonfragment and nonredundant sequences with lengths between 50 and 90 from RNA STRAND, except for synthetic RNA, the length ratio of the 3′-end of the domains to the sequence is computed and summarized. If one sequence has only one domain, subdomains are selected. (b) The x-axis represents the length ratio of the 3′-end of the domain to the sequence, and the y-axis represents the number of sequences.
Mentions: The results of statistical analysis on these real secondary structures are shown in Figures 1 and 2 and Tables 1, 2, 3, and 4. In Tables 1 and 2, Num represents the number of domains, group is the 3′-end of group, Ratio 1 is the ratio of Group 1 to Group 2, and Ratio 2 is the ratio of Group 2 to Group 3. In Figures 1 and 2, the x-axis represents the length ratio of the 3′-end of domain to the sequence, and the y-axis represents the number of sequences. The number of complementary bases to form a helix at point x in the final structure is insufficient. Thus, we enlarge point x to region x, and the corresponding point y in the y-axis with x in the x-axis represents the number of sequences in the region of [x − 0.4, x].

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