Unraveling the structural complexity in a single-stranded RNA tail: implications for efficient ligand binding in the prequeuosine riboswitch.
Unraveling the structural complexity in a single-stranded RNA tail: implications for efficient ligand binding in the prequeuosine riboswitch.
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DOI：
10.1093/nar/gkr833
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发表时间：
2012-02
影响因子：
14.9
通讯作者：
Al-Hashimi HM
中科院分区：
文献类型：
--
作者：
Eichhorn CD;Feng J;Suddala KC;Walter NG;Brooks CL 3rd;Al-Hashimi HM
单链RNA（ssRNA）是普遍存在的RNA元件，具有多种功能作用。我们对ssRNA构象行为的大部分理解仅限于ssRNA直接参与三级相互作用或被蛋白质识别的结构。关于原子分辨率下游离ssRNA的结构和动力学行为知之甚少。在这里，我们报告的合作应用核磁共振（NMR）和复制交换分子动力学（REMD）模拟表征的12 nt的ssRNA尾来自前腺苷核糖开关。核磁共振碳自旋弛豫数据和残余偶极耦合测量揭示了一个灵活的，但堆叠的核心，采用A-形式的构象，与水平的顺序朝着终端下降。多聚腺嘌呤束内的A至C突变改变了所观察到的动态，与引入动态扭结一致。尾部的预排序可以增加配体结合的功效，高于通过随机卷曲ssRNA实现的功效。REMD模拟概括了NMR数据中的重要趋势，但建议更多的内部运动比从NMR分析推断。我们的研究揭示了ssRNA中以前未被认识到的复杂性水平，我们相信它也将作为测试和开发计算力场的优秀模型系统。
Single-stranded RNAs (ssRNAs) are ubiquitous RNA elements that serve diverse functional roles. Much of our understanding of ssRNA conformational behavior is limited to structures in which ssRNA directly engages in tertiary interactions or is recognized by proteins. Little is known about the structural and dynamic behavior of free ssRNAs at atomic resolution. Here, we report the collaborative application of nuclear magnetic resonance (NMR) and replica exchange molecular dynamics (REMD) simulations to characterize the 12 nt ssRNA tail derived from the prequeuosine riboswitch. NMR carbon spin relaxation data and residual dipolar coupling measurements reveal a flexible yet stacked core adopting an A-form-like conformation, with the level of order decreasing toward the terminal ends. An A-to-C mutation within the polyadenine tract alters the observed dynamics consistent with the introduction of a dynamic kink. Pre-ordering of the tail may increase the efficacy of ligand binding above that achieved by a random-coil ssRNA. The REMD simulations recapitulate important trends in the NMR data, but suggest more internal motions than inferred from the NMR analysis. Our study unmasks a previously unappreciated level of complexity in ssRNA, which we believe will also serve as an excellent model system for testing and developing computational force fields.
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