@conference{Gelhausen-helixLength-2019,
author={Rick Gelhausen and Sebastian Will and Hofacker, Ivo L. and Rolf Backofen and Martin Raden},
title={Constraint Maximal Inter-molecular Helix Lengths within {RNA-RNA} Interaction Prediction Improves Bacterial {sRNA} Target Prediction},
booktitle={Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: BIOINFORMATICS,},
year={2019},
pages={131-140},
publisher={SciTePress},
organization={INSTICC},
  location =     {Prague, Czech Republic},
doi={10.5220/0007689701310140},
isbn={978-989-758-353-7},
  user =         {mmann},
  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 in-
teraction 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 kineti-
cally 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-restricts 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 in-
crease the accuracy of interaction prediction models compared to the current state-of-the-art approach.}

}