基于精确聚类随机模拟的RNA结构中伪结点的预测与统计
Prediction and statistics of pseudoknots in RNA structures using exactly clustered stochastic simulations
摘要：
RNA二级结构的从头计算预测长期以来一直不考虑环内部的螺旋，即所谓的假结，尽管它们在结构上很重要。在这里，我们报告说，许多假结可以预测通过长时间尺度RNA折叠模拟，遵循随机关闭和开放的个人RNA螺旋。这些随机模拟的数值效果依赖于一个O（n^2）的聚类算法，该算法计算连续更新的n个参考结构的时间平均值。应用这种精确的随机聚类方法，我们通常可以获得高达400个碱基的RNA序列的5到100倍的模拟加速，而对于短的多稳定分子（<150个碱基），有效加速可以高达100，000倍。我们对随机和天然RNA序列进行了广泛的折叠统计，发现假结在RNA结构中不均匀分布，并且在富含G+C的RNA序列中占高达30%的碱基对（包括假结的在线RNA折叠动力学服务器：http://kinefold.u-strasbg.fr/）。
英文摘要：
Ab initio RNA secondary structure predictions have long dismissed helices interior to loops, so-called pseudoknots, despite their structural importance. Here, we report that many pseudoknots can be predicted through long time scales RNA folding simulations, which follow the stochastic closing and opening of individual RNA helices. The numerical efficacy of these stochastic simulations relies on an O(n^2) clustering algorithm which computes time averages over a continously updated set of n reference structures. Applying this exact stochastic clustering approach, we typically obtain a 5- to 100-fold simulation speed-up for RNA sequences up to 400 bases, while the effective acceleration can be as high as 100,000-fold for short multistable molecules (<150 bases). We performed extensive folding statistics on random and natural RNA sequences, and found that pseudoknots are unevenly distributed amongst RNAstructures and account for up to 30% of base pairs in G+C rich RNA sequences (Online RNA folding kinetics server including pseudoknots : http://kinefold.u-strasbg.fr/ ).
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