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首页> 美国卫生研究院文献>Nucleic Acids Research >Statistical prediction of single-stranded regions in RNA secondary structure and application to predicting effective antisense target sites and beyond
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Statistical prediction of single-stranded regions in RNA secondary structure and application to predicting effective antisense target sites and beyond

机译：单链统计预测 RNA二级结构中的区域及其在预测有效中的应用 反义目标位点及其他

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摘要

Single-stranded regions in RNA secondary structure are important for RNA–RNA and RNA–protein interactions. We present a probability profile approach for the prediction of these regions based on a statistical algorithm for sampling RNA secondary structures. For the prediction of phylogenetically-determined single-stranded regions in secondary structures of representative RNA sequences, the probability profile offers substantial improvement over the minimum free energy structure. In designing antisense oligonucleotides, a practical problem is how to select a secondary structure for the target mRNA from the optimal structure(s) and many suboptimal structures with similar free energies. By summarizing the information from a statistical sample of probable secondary structures in a single plot, the probability profile not only presents a solution to this dilemma, but also reveals ‘well-determined’ single-stranded regions through the assignment of probabilities as measures of confidence in predictions. In antisense application to the rabbit β-globin mRNA, a significant correlation between hybridization potential predicted by the probability profile and the degree of inhibition of in vitro translation suggests that the probability profile approach is valuable for the identification of effective antisense target sites. Coupling computational design with DNA–RNA array technique provides a rational, efficient framework for antisense oligonucleotide screening. This framework has the potential for high-throughput applications to functional genomics and drug target validation.
机译：RNA二级结构中的单链区域对于RNA-RNA和RNA-蛋白质的相互作用非常重要。我们提出了一种概率统计方法，用于基于对RNA二级结构采样的统计算法来预测这些区域。为了预测代表性RNA序列二级结构中的系统发育决定的单链区，概率分布图提供了相对于最小自由能结构的实质性改进。在设计反义寡核苷酸时，一个实际的问题是如何从最佳结构和具有类似自由能的许多次优结构中选择靶mRNA的二级结构。通过汇总单个图中可能的二级结构的统计样本中的信息，概率分布图不仅可以解决该难题，还可以通过将概率分配为置信度来揭示“确定的”单链区域在预测中。在反义应用于兔β-珠蛋白mRNA时，通过概率分布预测的杂交潜力与抑制程度之间存在显着相关性 的体外翻译表明 简介方法对于识别有效 反义目标网站。 DNA-RNA的耦合计算设计 阵列技术为反义提供了合理，有效的框架 寡核苷酸筛选。该框架有潜力 高通量对功能基因组学和药物靶标的应用 验证。

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