Authors:
Rick Gelhausen
1
;
Sebastian Will
2
;
Ivo L. Hofacker
2
;
Rolf Backofen
1
;
3
and
Martin Raden
1
Affiliations:
1
Bioinformatics Group, University of Freiburg, Georges-Koehler-Allee 106, 79110 Freiburg, Germany
;
2
Institute for Theoretical Chemistry, University of Vienna, Waehringer Strasse 17, 1090 Wien, Austria
;
3
Centre for Biological Signalling Studies (BIOSS), University of Freiburg, Schaenzlestr. 18, 79104 Freiburg, Germany
Keyword(s):
RNA-RNA Interaction Prediction, Steric Constraints, Constrained Helix Length, Canonical Helix, Seed.
Abstract:
Efficient computational tools for the identification of putative target RNAs regulated by prokaryotic sRNAs rely on thermodynamic models of RNA secondary structures. While they typically predict RNA–RNA interaction complexes accurately, they yield many highly-ranked false positives in target screens. One obvious source of this low specificity appears to be the disability of current secondary-structure-based models to reflect steric constraints, which nevertheless govern the kinetic formation of RNA–RNA interactions. For example, often—even thermodynamically favorable—extensions of short initial kissing hairpin interactions are kinetically prohibited, since this would require unwinding of intra-molecular helices as well as sterically impossible bending of the interaction helix. In consequence, the efficient prediction methods, which do not consider such effects, predict over-long helices. To increase the prediction accuracy, we devise a dynamic programming algorithm that length-restri
cts the runs of consecutive inter-molecular base pairs (perfect canonical stackings), which we hypothesize to implicitely model the steric and kinetic effects. The novel method is implemented by extending the state-of-the-art tool INTARNA. Our comprehensive bacterial sRNA target prediction benchmark demonstrates significant improvements of the prediction accuracy and enables 3-4 times faster computations. These results indicate—supporting our hypothesis—that length-limitations on inter-molecular subhelices increase the accuracy of interaction prediction models compared to the current state-of-the-art approach.
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