@inproceedings{Gelhausen-helixLength-2019,
author = {Gelhausen, Rick and Will, Sebastian and Hofacker, Ivo L. and Backofen, Rolf and Raden, Martin},
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},
doi = {10.5220/0007689701310140},
user = {mmann},
publisher = {SciTePress},
pages = {131-140},
organization = {INSTICC},
location = {Prague, Czech Republic},
isbn = {978-989-758-353-7},
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.}
}