Coarse-grained prediction of RNA loop structures.
Coarse-grained prediction of RNA loop structures.
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DOI：
10.1371/journal.pone.0048460
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发表时间：
2012
期刊：
影响因子：
3.7
通讯作者：
Chen SJ
中科院分区：
文献类型：
--
作者：
Liu L;Chen SJ
RNA折叠理论预测的关键问题之一是从序列预测环结构。RNA环自由能取决于环序列含量。然而，目前大多数模型只考虑了循环长度依赖性。先前开发的“Vfold”模型（粗粒度RNA折叠模型）提供了一种有效的方法来生成完整的粗粒度RNA环和连接构象的集合。然而，由于缺乏序列依赖性评分参数，该方法无法从序列中鉴定天然和近天然结构。在这项研究中，使用先前开发的迭代方法提取的知识为基础的潜在参数从已知的结构，我们推导出一组基于二核苷酸的统计潜力的RNA环和路口。该方法的一个独特优势是它能够超越（已知的）原生结构，通过考虑完整的自由能景观，包括所有的非原生折叠。基准测试表明，对于给定的环/交界序列，统计潜力，使成功的预测粗粒度的3D结构从完整的构象合奏产生的Vfold模型。预测的粗粒度结构可以提供有用的初始褶皱进一步详细的结构细化。
One of the key issues in the theoretical prediction of RNA folding is the prediction of loop structure from the sequence. RNA loop free energies are dependent on the loop sequence content. However, most current models account only for the loop length-dependence. The previously developed “Vfold” model (a coarse-grained RNA folding model) provides an effective method to generate the complete ensemble of coarse-grained RNA loop and junction conformations. However, due to the lack of sequence-dependent scoring parameters, the method is unable to identify the native and near-native structures from the sequence. In this study, using a previously developed iterative method for extracting the knowledge-based potential parameters from the known structures, we derive a set of dinucleotide-based statistical potentials for RNA loops and junctions. A unique advantage of the approach is its ability to go beyond the the (known) native structures by accounting for the full free energy landscape, including all the nonnative folds. The benchmark tests indicate that for given loop/junction sequences, the statistical potentials enable successful predictions for the coarse-grained 3D structures from the complete conformational ensemble generated by the Vfold model. The predicted coarse-grained structures can provide useful initial folds for further detailed structural refinement.
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