INTARNAHELIX-composing RNA-RNA interactions from stable inter-molecular helices boosts bacterial sRNA target prediction
INTARNAHELIX-composing RNA-RNA interactions from stable inter-molecular helices boosts bacterial sRNA target prediction
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DOI：
10.1142/s0219720019400092
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发表时间：
2019-10-01
影响因子：
1
通讯作者：
Raden, Martin
中科院分区：
文献类型：
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作者：
Gelhausen, Rick;Will, Sebastian;Raden, Martin
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，通常-甚至在生物学上有利的-短的初始接吻发夹相互作用的延伸在动力学上被禁止，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不可能的弯曲。另一个来源是考虑不稳定的，因此不太可能的子交互，使更长的交互更好的评分。因此，有效的预测方法，不考虑这样的影响表现出很高的假阳性率。为了提高预测精度，我们设计了INTARNAHELIX，一个动态规划算法，长度限制运行的连续分子间碱基对（完美的典型堆叠），我们假设隐含模型的空间和动力学效应。新方法通过扩展最先进的工具INTARNA来实现。我们的综合细菌sRNA靶标预测基准表明，预测准确率显著提高，计算速度提高了40倍以上。这些结果表明-支持我们的假设-稳定的螺旋组成增加了相互作用预测模型的准确性相比，目前的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. Another source is the consideration of instable and thus unlikely subinteractions that enable better scoring of longer interactions. In consequence, the efficient prediction methods that do not consider such effects show a high false positive rate.To increase the prediction accuracy we devise INTARNAHELIX, a dynamic programming algorithm that length-restricts the runs of consecutive inter-molecular base pairs (perfect canonical stackings), which we hypothesize to implicitly 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 more than 40-times faster computations. These results indicate - supporting our hypothesis - that stable helix composition increases the accuracy of interaction prediction models compared to the current state-of-the-art approach.