Automated de novo prediction of native-like RNA tertiary structures
Automated de novo prediction of native-like RNA tertiary structures
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DOI：
10.1073/pnas.0703836104
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发表时间：
2007-09-11
影响因子：
11.1
通讯作者：
Baker, David
中科院分区：
文献类型：
--
作者：
Das, Rhiju;Baker, David
RNA的三级结构预测几乎完全基于来自系统发育协变分析的碱基配对约束。我们在这里描述了一种补充方法，该方法受Rosetta低分辨率蛋白质结构预测方法的启发，在不使用进化信息的情况下为给定的RNA序列寻找最低能量的三级结构。在对20个已知结构和长度约30个核苷酸的RNA序列的基准测试中，新方法的重现率高于90%的Watson-Crick碱基对，与二级结构预测方法的精度相当。在超过一半的情况下，排名前五的型号中至少有一个与原生结构一致，在主干上的RMSD好于4 A。最重要的是，该方法概括了天然结构中超过三分之一的非Watson-Crick碱基对。“剪切的”碱基对、碱基三联体和假结的串联堆叠是模型中再现的非正则特征之一。在前五个模型都不是天然的、能量更高的构象的情况下。与原生结构类似，结构仍然被频繁地采样，但没有被分配低能量。这些结果表明，能量函数的适度改善，加上来自系统发育协方差的信息的纳入，可能会使更大、更复杂的RNA链的结构预测变得自信和准确。
RNA tertiary structure prediction has been based almost entirely on base-pairing constraints derived from phylogenetic covariation analysis. We describe here a complementary approach, inspired by the Rosetta low-resolution protein structure prediction method, that seeks the lowest energy tertiary structure for a given RNA sequence without using evolutionary information. In a benchmark test of 20 RNA sequences with known structure and lengths of approximate to 30 nt, the new method reproduces better than 90% of Watson-Crick base pairs, comparable with the accuracy of secondary structure prediction methods. In more than half the cases, at least one of the top five models agrees with the native structure to better than 4 A rmsd over the backbone. Most importantly, the method recapitulates more than one-third of non-Watson-Crick base pairs seen in the native structures. Tandem stacks of "sheared" base pairs, base triplets, and pseudoknots are among the noncanonical features reproduced in the models. In the cases in which none of the top five models were native-like, higher energy, conformations. similar to the native structures are still sampled frequently but not assigned low energies. These results suggest that modest improvements in the energy function, together with the incorporation of information from phylogenetic covariance, may allow confident and accurate structure prediction for larger and more complex RNA chains.