Name
Abstract | ||
|---|---|---|
Motivation: The function of an RNA molecule is not only linked to its native structure, which is usually taken to be the ground state of its folding landscape, but also in many cases crucially depends on the details of the folding pathways such as stable folding intermediates or the timing of the folding process itself. To model and understand these processes, it is necessary to go beyond ground state structures. The study of rugged RNA folding landscapes holds the key to answer these questions. Efficient coarse-graining methods are required to reduce the intractably vast energy landscapes into condensed representations such as barrier trees or basin hopping graphs (BHG) that convey an approximate but comprehensive picture of the folding kinetics. So far, exact and heuristic coarse-graining methods have been mostly restricted to the pseudoknot-free secondary structures. Pseudoknots, which are common motifs and have been repeatedly hypothesized to play an important role in guiding folding trajectories, were usually excluded. Results: We generalize the BHG framework to include pseudoknotted RNA structures and systematically study the differences in predicted folding behavior depending on whether pseudoknotted structures are allowed to occur as folding intermediates or not. We observe that RNAs with pseudoknotted ground state structures tend to have more pseudoknotted folding intermediates than RNAs with pseudoknot-free ground state structures. The occurrence and influence of pseudoknotted intermediates on the folding pathway, however, appear to depend very strongly on the individual RNAs so that no general rule can be inferred. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1093/bioinformatics/btv572 | BIOINFORMATICS |
Field | DocType | Volume |
RNA,Graph,Heuristic,Ground state,Source code,RNA molecule,Computer science,Rna folding,Bioinformatics | Journal | 32 |
Issue | ISSN | Citations |
2 | 1367-4803 | 2 |
PageRank | References | Authors |
0.41 | 10 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Marcel Kucharík | 1 | 10 | 2.10 |
| Ivo L. Hofacker | 2 | 1669 | 131.57 |
| Peter F. Stadler | 3 | 1839 | 152.96 |
| Jing Qin | 4 | 69 | 4.39 |