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Statistical Mechanics of RNA Folding

Statistical Mechanics of RNA Folding
RNA 折叠的统计力学
批准号:
7026387
负责人:
SHI-JIE CHEN
金额:
$21.24万
依托单位国家:
美国
项目类别:
财政年份:
2003
资助国家:
美国
项目状态:
已结题
起止时间:
2003-03-01 至 2008-02-29

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中文摘要
翻译
描述(由申请人提供):RNA三级折叠的准确模型的可用性可以极大地帮助我们理解RNA的功能和RNA靶向药物的发现。然而,与几十年来一直被广泛研究的蛋白质折叠问题不同,三级RNA折叠的建模是一个相对较新的努力领域。我们目前的图论热力学模型使我们能够比其他现有模型对RNA二级结构熔化曲线做出更好的预测。在这个建议中,我们的目标是超越二级结构模型来研究三级RNA折叠。我们方法的一个关键优势是我们的构象采样的完整性和确定性。不完整的构象采样可能会导致无法接受的精度损失。我们的热力学和动力学模型将在这个项目中并行开发。在热力学模型中,用户可以提供能量参数,使我们能够从实验熔化曲线中提取RNA三级相互作用的参数。自从这一提议首次提交以来,我们已经产生了新的结果,表明我们可以准确地捕捉三级折叠和非晶格构象,以给出可靠的配分函数。动力学模型将使用主方程方法来建立,我们已经成功地使用该方法来处理RNA发夹折叠动力学。我们的长期目标是超越发夹,转向治疗大型复杂RNA。我们的近期目标是消除通往长期目标道路上的两个关键“路障”:从速率方程中寻找过渡态,以及通过构象聚类来简化速率方程。实验测试将集中在一系列设计合理的RNA系统上。关键的一点是,实验将在理论预测中使用的完全相同的盐条件下进行。
英文摘要
DESCRIPTION (provided by applicant): The availability of an accurate model for RNA tertiary folding can greatly aid in our understanding of RNA functions and in RNA-targeted drug discovery. However, unlike the protein folding problem which has been extensively studied for decades, modeling of tertiary RNA folding is a relatively new field of endeavor. Our current graph-theoretic thermodynamic model has allowed us to make better predictions than other existing models on RNA secondary structure melting curves. In this proposal, we aim to move beyond the secondary structure model to study tertiary RNA folding. A key advantage of our approach is the completeness and certainty in our conformational sampling. Incomplete conformational sampling may result in unacceptable loss of accuracy. Our thermodynamic and kinetic models will be developed in parallel in this project. The thermodynamic model, in which user can supply energy parameters, will enable us to extract parameters for RNA tertiary interactions from experimental melting curves. Since the first submission of this proposal, we have generated new results indicating that we can accurately capture tertiary folds and off-lattice conformations to give reliable partition functions. The kinetic model will be developed using a master equation approach, which we have successfully used to treat RNA hairpin folding kinetics. Our long-term goal is to move beyond hairpin to treat large complex RNAs. Our immediate goal is to remove two key "roadblocks" on the road toward our long-term goal: to search for transition states from rate equations, and to reduce the rate equations by conformational clustering. Experimental tests will focus on a series of rationally designed RNA systems. A key point is that the experiments will be performed under exactly the same salt condition used in theoretical predictions.
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海外基金