Abstract

The diverse landscape of RNA conformational space includes many canyons and crevices that are distant from the lowest minimum free energy valley and remain unexplored by traditional RNA structure prediction methods. A complete description of the entire RNA folding landscape can facilitate identification of biologically important conformations. The Crumple algorithm rapidly enumerates all possible non-pseudoknotted structures for an RNA sequence without consideration of thermodynamics while filtering the output with experimental data. The Crumple algorithm provides an alternative approach to traditional free energy minimization programs for RNA secondary structure prediction. A complete computation of all non-pseudoknotted secondary structures can reveal structures that would not be predicted by methods that sample the RNA folding landscape based on thermodynamic predictions. The free energy minimization approach is often successful but is limited by not considering RNA tertiary and protein interactions and the possibility that kinetics rather than thermodynamics determines the functional RNA fold. Efficient parallel computing and filters based on experimental data make practical the complete enumeration of all non-pseudoknotted structures. Efficient parallel computing for Crumple is implemented in a ring graph approach. Filters for experimental data include constraints from chemical probing of solvent accessibility, enzymatic cleavage of paired or unpaired nucleotides, phylogenetic covariation, and the minimum number and lengths of helices determined from crystallography or cryo-electron microscopy. The minimum number and length of helices has a significant effect on reducing conformational space. Pairing constraints reduce conformational space more than single nucleotide constraints. Examples with Alfalfa Mosaic Virus RNA and Trypanosome brucei guide RNA demonstrate the importance of evaluating all possible structures when pseduoknots, RNA-protein interactions, and metastable structures are important for biological function. Crumple software is freely available at http://adenosine.chem.ou.edu/software.html.

Highlights

  • Amidst a flood of RNA sequence information and a tidal wave of new roles for small noncoding RNAs, successful exploration and navigation of the RNA world requires effective tools to evaluate structure and function from sequence

  • Efficient parallel computing and effective experimental filters make the complete enumeration of all structures for an RNA sequence a reasonable approach

  • Experimental filters from chemical or enzymatic probing, phylogenetic analysis, crystallography, or cryoelectron microscopy can reduce the conformational space without overlooking structures that may be stabilized by tertiary and quaternary interactions

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Summary

Introduction

Amidst a flood of RNA sequence information and a tidal wave of new roles for small noncoding RNAs, successful exploration and navigation of the RNA world requires effective tools to evaluate structure and function from sequence. The Crumple algorithm provides a method to compute completely and efficiently all possible non-pseudoknotted secondary structures for a given RNA sequence without consideration of thermodynamic parameters. Efficient parallel computing and effective experimental filters make the complete enumeration of all structures for an RNA sequence a reasonable approach.

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