利用模拟和动力学网络模型揭示核糖开关的动力学和功能
Using Simulations and kinetic network models to reveal the dynamics and functions of Riboswitches
摘要：
核糖开关是存在于非翻译区的RNA元件，通过与靶代谢物特异性结合来调节基因表达。代谢物与保守适体结构域的结合变构地改变下游表达平台中的构象。基因表达的命运由下游RNA序列的变化决定。由于核糖开关的代谢产物依赖的共转录折叠和去折叠动力学是基因表达的关键决定因素，因此研究核糖开关在代谢产物存在和不存在的情况下的热力学和动力学是非常重要的。单分子力实验从核糖开关的机械响应、理论和计算研究中揭示了核糖开关的自由能景观，揭示了不同类型核糖开关的折叠动力学的不同机制。在这里，我们首先讨论核糖开关周围的水的动力学，强调水动力学可以增强核酸结构的波动。为了超越自然状态的波动，我们使用自组织聚合物（SOP）模型来预测机械力下的腺嘌呤核糖开关的动力学。除了定量预测的折叠景观添加核糖开关，我们的模拟还解释了pbuE腺嘌呤和添加腺嘌呤核糖开关之间的动力学差异。为了探测功能{ \it in vivo}，我们使用折叠景观，提出了一个系统水平的动力学网络模型，以定量预测基因表达是如何调节核糖开关，动力学控制下。
英文摘要：
Riboswitches, RNA elements found in the untranslated region, regulate gene expression by binding to target metaboloites with exquisite specificity. Binding of metabolites to the conserved aptamer domain allosterically alters the conformation in the downstream expression platform. The fate of gene expression is determined by the changes in the downstream RNA sequence. As the metabolite-dependent cotranscriptional folding and unfolding dynamics of riboswitches is the key determinant of gene expression, it is important to investigate both the thermodynamics and kinetics of riboswitches both in the presence and absence of metabolite. Single molecule force experiments that decipher the free energy landscape of riboswitches from their mechanical responses, theoretical and computational studies have recently shed light on the distinct mechanism of folding dynamics in different classes of riboswitches. Here we first discuss the dynamics of water around riboswitch, highlighting that water dynamics can enhance the fluctuation of nucleic acid structure. To go beyond native state fluctuations we used the Self-Organized Polymer (SOP) model to predict the dynamics of add adenine riboswitch under mechanical forces. In addition to quantitatively predicting the folding landscape of add-riboswitch our simulations also explain the difference in the dynamics between pbuE adenine- and add adenine-riboswitches. In order to probe the function {\it in vivo} we use the folding landscape to propose a system level kinetic network model to quantitatively predict how gene expression is regulated for riboswitches that are under kinetic control.
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