基于可解释性集成近似和动态分解的RNA设计性概率方法
Probabilistic RNA Designability via Interpretable Ensemble Approximation and Dynamic Decomposition
AI总结：
本文提出了一种基于可解释性集成近似和动态分解的RNA设计性概率方法，通过线性时间动态规划算法高效搜索并确定最优分解，为RNA结构的折叠概率提供更紧的界。
摘要：
动机：RNA设计旨在寻找能折叠成给定目标二级结构的RNA序列，也称为RNA反折叠问题。然而，并非所有目标结构都是可设计的。近年来，RNA设计性主要集中在基于最小自由能（MFE）的标准上，而基于集成的可设计性概念仍被忽视。为填补这一空白，我们引入了一种集成近似理论和一个概率分解框架，以可解释的方式对RNA结构的折叠概率进行界。我们进一步开发了一种线性时间动态规划算法，该算法高效地搜索指数级众多的分解，并识别出最优的一个，以获得给定结构的最紧的概率界。结果：将我们的方法应用于ArchiveII和Eterna100基准中的原生和人工RNA结构，我们获得了比先前方法更紧的概率界。此外，我们的方法还提供了分析RNA结构和理解在motif层面设计困难来源的解剖工具。可用性：源代码和数据可在https://github.com/shanry/RNA-Undesign上获得。补充信息：补充文本和数据可在单独的PDF中获得。
英文摘要：
Motivation: RNA design aims to find RNA sequences that fold into a given target secondary structure, a problem also known as RNA inverse folding. However, not all target structures are designable. Recent advances in RNA designability have focused primarily on minimum free energy (MFE)-based criteria, while ensemble-based notions of designability remain largely underexplored. To address this gap, we introduce a theory of ensemble approximation and a probability decomposition framework for bounding the folding probabilities of RNA structures in an explainable way. We further develop a linear-time dynamic programming algorithm that efficiently searches over exponentially many decompositions and identifies the optimal one that yields the tightest probabilistic bound for a given structure. Results: Applying our methods to both native and artificial RNA structures in the ArchiveII and Eterna100 benchmarks, we obtained probability bounds that are much tighter than prior approaches. In addition, our methods further provide anatomical tools for analyzing RNA structures and understanding the sources of design difficulty at the motif level. Availability: Source code and data are available at https://github.com/shanry/RNA-Undesign. Supplementary information: Supplementary text and data are available in a separate PDF.
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