Constraint Maximal Inter-molecular Helix Lengths within RNA-RNA Interaction Prediction Improves Bacterial sRNA Target Prediction
Constraint Maximal Inter-molecular Helix Lengths within RNA-RNA Interaction Prediction Improves Bacterial sRNA Target Prediction
复制标题
限制 RNA-RNA 相互作用预测中的最大分子间螺旋长度可改善细菌 sRNA 靶点预测
作者：
R. Gelhausen;S. Will;I. Hofacker;R. Backofen;Martin Raden
用于鉴定由原核sRNA调控的推定靶RNA的有效计算工具依赖于RNA二级结构的热力学模型。虽然它们通常准确地预测RNA-RNA相互作用复合物，但它们在靶筛选中产生许多高等级的假阳性。这种低特异性的一个明显来源似乎是目前的二级结构为基础的模型，以反映空间的限制，这仍然支配的RNA-RNA相互作用的动力学形成的残疾。例如，短的初始接吻发夹相互作用的通常甚至是在化学上可互换的延伸在动力学上是被禁止的，因为这将需要分子内螺旋的解旋以及相互作用螺旋的空间上不可能的弯曲。因此，有效的预测方法，不考虑这种影响，预测过长的螺旋。为了提高预测精度，我们设计了一个动态规划算法，长度限制连续的分子间碱基对（完美的典型堆叠），我们假设隐含模型的空间和动力学效应的运行。新的方法是通过扩展国家的最先进的工具INTARNA。我们全面的细菌sRNA靶标预测基准测试表明，预测准确性显著提高，计算速度提高了3-4倍。这些结果表明，支持我们的假设，即分子间亚螺旋的长度限制增加了相互作用预测模型的准确性相比，目前国家的最先进的方法。
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-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 increase the accuracy of interaction prediction models compared to the current state-of-the-art approach.