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首页> 外文期刊>Nucleic Acids Research >Characterizing RNA ensembles from NMR data with kinematic models
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Characterizing RNA ensembles from NMR data with kinematic models

机译：使用运动学模型从NMR数据表征RNA集合

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Functional mechanisms of biomolecules often manifest themselves precisely in transient conformational substates. Researchers have long sought to structurally characterize dynamic processes in noncoding RNA, combining experimental data with computer algorithms. However, adequate exploration of conformational space for these highly dynamic molecules, starting from static crystal structures, remains challenging. Here, we report a new conformational sampling procedure, KGSrna, which can efficiently probe the native ensemble of RNA molecules in solution. We found that KGSrna ensembles accurately represent the conformational landscapes of 3D RNA encoded by NMR proton chemical shifts. KGSrna resolves motionally averaged NMR data into structural contributions; when coupled with residual dipolar coupling data, a KGSrna ensemble revealed a previously uncharacterized transient excited state of the HIV-1 trans-activation response element stem-loop. Ensemble-based interpretations of averaged data can aid in formulating and testing dynamic, motion-based hypotheses of functional mechanisms in RNAs with broad implications for RNA engineering and therapeutic intervention.
机译：生物分子的功能机制通常以短暂的构象亚状态精确地表现出来。长期以来，研究人员一直试图通过将实验数据与计算机算法相结合来对非编码RNA中的动态过程进行结构表征。然而，从静态晶体结构开始，对这些高动态分子的构象空间的充分探索仍然具有挑战性。在这里，我们报告了一种新的构象采样程序KGSrna，它可以有效地探测溶液中RNA分子的天然集合。我们发现，KGSrna集成体准确地代表了由NMR质子化学位移编码的3D RNA的构象景观。 KGSrna将运动平均NMR数据解析为结构贡献;当与残留的偶极耦合数据耦合时，KGSrna集合揭示了HIV-1反式激活反应元件茎环先前未表征的瞬时激发态。基于集合的平均数据解释可以帮助制定和测试RNA中功能机制的动态，基于运动的假设，这对RNA工程和治疗干预具有广泛的意义。

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