Department of Genome Sciences, University of Washington, Seattle, Washington 98195, USA.
RNA. 2010 Jan;16(1):26-42. doi: 10.1261/rna.1689910. Epub 2009 Nov 20.
Accurate prediction of RNA pseudoknotted secondary structures from the base sequence is a challenging computational problem. Since prediction algorithms rely on thermodynamic energy models to identify low-energy structures, prediction accuracy relies in large part on the quality of free energy change parameters. In this work, we use our earlier constraint generation and Boltzmann likelihood parameter estimation methods to obtain new energy parameters for two energy models for secondary structures with pseudoknots, namely, the Dirks-Pierce (DP) and the Cao-Chen (CC) models. To train our parameters, and also to test their accuracy, we create a large data set of both pseudoknotted and pseudoknot-free secondary structures. In addition to structural data our training data set also includes thermodynamic data, for which experimentally determined free energy changes are available for sequences and their reference structures. When incorporated into the HotKnots prediction algorithm, our new parameters result in significantly improved secondary structure prediction on our test data set. Specifically, the prediction accuracy when using our new parameters improves from 68% to 79% for the DP model, and from 70% to 77% for the CC model.
准确预测 RNA 假结二级结构的碱基序列是一个具有挑战性的计算问题。由于预测算法依赖于热力学能量模型来识别低能结构，因此预测准确性在很大程度上取决于自由能变化参数的质量。在这项工作中，我们使用早期的约束生成和玻尔兹曼似然参数估计方法，为具有假结的两种二级结构的能量模型，即 Dirks-Pierce (DP) 和 Cao-Chen (CC) 模型，获得新的能量参数。为了训练我们的参数，并且测试它们的准确性，我们创建了一个大型的假结和非假结二级结构数据集。除了结构数据，我们的训练数据集还包括热力学数据，对于这些数据，序列及其参考结构的实验确定的自由能变化是可用的。当将我们的新参数纳入 HotKnots 预测算法中时，我们的新参数显著提高了我们测试数据集上的二级结构预测准确性。具体来说，当使用我们的新参数时，DP 模型的预测准确性从 68%提高到 79%，CC 模型的预测准确性从 70%提高到 77%。
