The structure of an RNA sequence encodes information about its biological function. A sequence is typically predicted to fold to a single minimum free energy conformation. But, an increasing number of RNA molecules are now known to fold into multiple stable structures. Discrete optimization methods are commonly used to predict foldings, and adding experimental data as auxiliary information improves prediction accuracy when there is a single dominant conformation. In this paper, we analyze the outputs of existing structural prediction models when they receive auxiliary data derived from a mixture of structures. Under a binary model of auxiliary data, we find that current structural prediction methods typically favor distributions with one dominant structure, and hence cannot guarantee accurate reconstruction of multimodal distributions. Additionally, we analyze empirical distributions of auxiliary data used in current prediction models. We show that even when the structures in a distribution are known in advance, it is difficult to determine the weightings of the structures using auxiliary data.

中文翻译：

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关于重构rna结构混合物的问题

RNA 序列的结构编码有关其生物学功能的信息。通常预测一个序列会折叠成单个最小自由能构象。但是，现在已知越来越多的 RNA 分子折叠成多个稳定结构。离散优化方法通常用于预测折叠，当存在单一主导构象时，添加实验数据作为辅助信息可以提高预测精度。在本文中，我们分析了现有结构预测模型在接收来自混合结构的辅助数据时的输出。在辅助数据的二元模型下，我们发现当前的结构预测方法通常倾向于具有一个主导结构的分布，因此不能保证多模态分布的准确重建。此外，我们分析了当前预测模型中使用的辅助数据的经验分布。我们表明，即使预先知道分布中的结构，也很难使用辅助数据确定结构的权重。