Physics-based de novo prediction of RNA 3D structures.
Physics-based de novo prediction of RNA 3D structures.
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DOI：
10.1021/jp112059y
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发表时间：
2011-04-14
影响因子：
3.3
通讯作者：
Chen, Shi-Jie
中科院分区：
文献类型：
--
作者：
Cao, Song;Chen, Shi-Jie
目前的结构测定实验跟不上不断涌现的RNA序列和新功能的步伐。这强调了对RNA三维(3D)结构预测的准确模型的要求。尽管在机理研究方面已经取得了长足的进展，但从序列中准确预测RNA三级折叠仍然是一个悬而未决的问题。从物理原理预测RNA结构的第一个也是最重要的要求是一个准确的自由能模型。最近发展起来的基于三向量虚拟键的RNA折叠模型(“Vold”)使我们能够计算RNA二级结构和简单假结的链熵，并预测折叠自由能和结构。在这里，我们开发了一种基于自由能的方法来预测更大、更复杂的RNA三级折叠。该方法基于多尺度策略：从核苷酸序列预测二维结构(由碱基对和三级接触定义)；基于二维结构构建三维支架；以三维支架为初始状态，将琥珀能量最小化和基于PDB的片段搜索相结合来预测全原子结构。该方法的一个主要优点是对RNA结构的构象熵进行了统计机械计算，包括那些具有交联环的结构。基准测试表明，该模型显著提高了RNA3D结构预测的准确率。
Current experiments on structural determination cannot keep up the pace with the steadily emerging RNA sequences and new functions. This underscores the request for an accurate model for RNA three-dimensional (3D) structural prediction. Although considerable progress has been made in mechanistic studies, accurate prediction for RNA tertiary folding from sequence remains an unsolved problem. The first and most important requirement for the prediction of RNA structure from physical principles is an accurate free energy model. A recently developed three-vector virtual bond-based RNA folding model (“Vfold”) has allowed us to compute the chain entropy and predict folding free energies and structures for RNA secondary structures and simple pseudoknots. Here we develop a free energy-based method to predict larger more complex RNA tertiary folds. The approach is based on a multiscaling strategy: from the nucleotide sequence, we predict the two-dimensional (2D) structures (defined by the base pairs and tertiary contacts); based on the 2D structure, we construct a 3D scaffold; with the 3D scaffold as the initial state, we combine AMBER energy minimization and PDB-based fragment search to predict the all-atom structure. A key advantage of the approach is the statistical mechanical calculation for the conformational entropy of RNA structures, including those with cross-linked loops. Benchmark tests show that the model leads to significant improvements in RNA 3D structure prediction.
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影响因子：
5.6
作者：
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通讯作者：
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作者：
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影响因子：
14.9
作者：
Cao, Song;Chen, Shi-Jie
通讯作者：
Chen, Shi-Jie