Pseudoknots in RNA folding landscapes

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.Availability and implementation: The algorithms described here are implemented in C++ as standalone programs. Its source code and Supplemental material can be freely downloaded from http://www.tbi.univie.ac.at/bhg.html.Contact: qin@bioinf.uni-leipzig.deSupplementary information: Supplementary data are available at Bioinformatics online.

Dual Graph Partitioning Highlights a Small Group of Pseudoknot-Containing RNA Submotifs

“…We are also working to modify our current partitioning methodology to differentiate between non-recursive and recursive pseudoknot blocks; recursive pseudoknot blocks can be further partitioned (with the new partitioning methodology being developed) into pseudoknot-free structure and/or isolated pseudoknots [ 59 ]. Also useful for RNA submotif search is annotating the pseudoknot containing subgraphs and corresponding substructures based on types of loop residues involved (i.e., hairpins, internal loops, or junctions [ 60 , 61 ]) (see below).…”

Efficient computation of co-transcriptional RNA-ligand interaction dynamics

“…Since barriers relies on an exhaustive enumeration of low energy structures, the computational effort grows exponentially with sequence size, which limits the length of RNAs that can be studied to a little over 100 nt. Recently, a number of heuristic approaches have been reported that attempt to raise this limit based on flooding techniques [34,24] or sampling of local minima [27,28,20,21].…”

Efficient computation of co-transcriptional RNA-ligand interaction dynamics

“…Since barriers relies on an exhaustive enumeration of low energy structures, the computational effort grows exponentially with sequence size, which limits the length of RNAs that can be studied to a little over 100 nt. Recently, a number of heuristic approaches have been reported that attempt to raise this limit based on flooding techniques [34,24] or sampling of local minima [27,28,20,21].…”