背景:RNA分子在生命系统中起着许多至关重要的作用。RNA结构中存在的空间复杂性决定了它们的细胞功能。因此,理解RNA折叠构象,特别是,RNA二级结构,对于阐明生物学功能至关重要。现有文献将RNA设计集中在RNA结构预测问题或RNA逆折叠问题上,其中自由能发挥了关键作用。
结果:在这项研究中,我们提出了一个阳性未标记的数据驱动框架,称为ENTRNA。除了自由能和通常研究的序列和结构特征,我们提出了一个新功能,序列段熵(SSE),测量RNA序列的多样性。分别使用来自RNASTRAND数据库的1024个无假结的RNA和1060个假结的RNA训练和交叉验证ENTRNA。为了测试ENTRNA的稳健性,这些模型在PDB数据库中的206个无假结和93个假结RNA上进行了进一步的盲测试。对于无假结的RNA,ENTRNA在训练数据集上有86.5%的灵敏度,在测试数据集上有80.6%的灵敏度。对于假打结的RNA,ENTRNA在训练数据集上显示81.5%的灵敏度,在测试数据集上显示71.0%的灵敏度。为了测试ENTRNA对长结构复合物RNA的适用性,我们收集了5个实验室合成RNA,范围从1618到1790个核苷酸。ENTRNA能够预测4种RNA的折叠性。
结论:在本文中,我们将RNA设计问题重新表述为可折叠性预测问题,即预测序列-结构对共存的可能性。这种新的构建体具有RNA结构预测和反向折叠问题的潜力。此外,这种新构建体使我们能够探索RNA研究中的数据驱动方法。
BACKGROUND: RNA molecules play many crucial roles in living systems. The spatial complexity that exists in RNA structures determines their cellular functions. Therefore, understanding RNA folding conformations, in particular, RNA secondary structures, is critical for elucidating biological functions. Existing literature has focused on RNA design as either an RNA structure prediction problem or an RNA inverse folding problem where free energy has played a key role.
RESULTS: In this research, we propose a Positive-Unlabeled data- driven framework termed ENTRNA. Other than free energy and commonly studied sequence and structural features, we propose a new feature, Sequence Segment Entropy (SSE), to measure the diversity of RNA sequences. ENTRNA is trained and cross-validated using 1024 pseudoknot-free RNAs and 1060 pseudoknotted RNAs from the RNASTRAND database respectively. To test the robustness of the ENTRNA, the models are further blind tested on 206 pseudoknot-free and 93 pseudoknotted RNAs from the PDB database. For pseudoknot-free RNAs, ENTRNA has 86.5% sensitivity on the training dataset and 80.6% sensitivity on the testing dataset. For pseudoknotted RNAs, ENTRNA shows 81.5% sensitivity on the training dataset and 71.0% on the testing dataset. To test the applicability of ENTRNA to long structural-complex RNA, we collect 5 laboratory synthetic RNAs ranging from 1618 to 1790 nucleotides. ENTRNA is able to predict the
foldability of 4 RNAs.
CONCLUSIONS: In this article, we reformulate the RNA design problem as a
foldability prediction problem which is to predict the likelihood of the co-existence of a sequence-structure pair. This new construct has the potential for both RNA structure prediction and the inverse folding problem. In addition, this new construct enables us to explore data-driven approaches in RNA research.