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Predicting the sizes of large RNA molecules.

ABSTRACT: We present a theory of the dependence on sequence of the three-dimensional size of large single-stranded (ss) RNA molecules. The work is motivated by the fact that the genomes of many viruses are large ssRNA molecules-often several thousand nucleotides long-and that these RNAs are spontaneously packaged into small rigid protein shells. We argue that there has been evolutionary pressure for the genome to have overall spatial properties-including an appropriate radius of gyration, R(g)-that facilitate this assembly process. For an arbitrary RNA sequence, we introduce the (thermal) average maximum ladder distance (MLD) and use it as a measure of the "extendedness" of the RNA secondary structure. The MLD values of viral ssRNAs that package into capsids of fixed size are shown to be consistently smaller than those for randomly permuted sequences of the same length and base composition, and also smaller than those of natural ssRNAs that are not under evolutionary pressure to have a compact native form. By mapping these secondary structures onto a linear polymer model and by using MLD as a measure of effective contour length, we predict the R(g) values of viral ssRNAs are smaller than those of nonviral sequences. More generally, we predict the average MLD values of large nonviral ssRNAs scale as N(0.67+/-0.01), where N is the number of nucleotides, and that their R(g) values vary as MLD(0.5) in an ideal solvent, and hence as N(0.34). An alternative analysis, which explicitly includes all branches, is introduced and shown to yield consistent results.

SUBMITTER: Yoffe AM

PROVIDER: S-EPMC2570976 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:MotivationThe prediction of RNA 3D structures from its sequence only is a milestone to RNA function analysis and prediction. In recent years, many methods addressed this challenge, ranging from cycle decomposition and fragment assembly to molecular dynamics simulations. However, their predictions remain fragile and limited to small RNAs. To expand the range and accuracy of these techniques, we need to develop algorithms that will enable to use all the structural information available. In particular, the energetic contribution of secondary structure interactions is now well documented, but the quantification of non-canonical interactions-those shaping the tertiary structure-is poorly understood. Nonetheless, even if a complete RNA tertiary structure energy model is currently unavailable, we now have catalogues of local 3D structural motifs including non-canonical base pairings. A practical objective is thus to develop techniques enabling us to use this knowledge for robust RNA tertiary structure predictors.ResultsIn this work, we introduce RNA-MoIP, a program that benefits from the progresses made over the last 30 years in the field of RNA secondary structure prediction and expands these methods to incorporate the novel local motif information available in databases. Using an integer programming framework, our method refines predicted secondary structures (i.e. removes incorrect canonical base pairs) to accommodate the insertion of RNA 3D motifs (i.e. hairpins, internal loops and k-way junctions). Then, we use predictions as templates to generate complete 3D structures with the MC-Sym program. We benchmarked RNA-MoIP on a set of 9 RNAs with sizes varying from 53 to 128 nucleotides. We show that our approach (i) improves the accuracy of canonical base pair predictions; (ii) identifies the best secondary structures in a pool of suboptimal structures; and (iii) predicts accurate 3D structures of large RNA molecules.AvailabilityRNA-MoIP is publicly available at: http://csb.cs.mcgill.ca/RNAMoIP.

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Predicting the sizes of large RNA molecules.

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