New computational tools for predicting ion effects in RNA structures

项目摘要 目前的计算工具无法跟上稳步出现的RNA序列和新的 例如核糖开关介导的细菌基因表达调控，RNA指导的基因组工程， CRISPR和基于RNA的药物设计和递送。其中一个尚未解决的关键问题是预测和 了解金属离子在RNA结构形成中的作用。这个问题对于RNA来说至关重要 因为RNA是高度带电的;因此，在溶液中没有金属离子的参与，RNA 就是不会屈服此外，越来越多的人支持细胞环境中的金属离子 可以通过引起RNA结构变化在基因表达调控中发挥重要作用。生物 离子效应的重要性强调了对计算工具的迫切要求， 离子效应 该项目的目标是开发成功的计算工具，包括模型，软件包- 年龄和网络服务器来预测和理解金属离子在RNA结构和功能中的作用。通过 包括金属离子的关联效应和涨落效应， 影响小而简单的RNA结构。然而，准确预测离子（特别是Mg 2+）的影响， 对于大的，生物学上重要的RNA来说是不可能的。我们现在建议开发计算工具 能够提供如此精确的预测。我们的目标是（a）开发和验证一种新的采样算法 能够预测大RNA结构的离子效应，（B）开发和验证新模型 用于预测RNA中的金属离子结合位点，（c）通过与RNA结构生物学实验室合作， 开发一个离子效应模型，用于可降解的RNA结构，以及（d）将计算模型转换为用户 友好的、可自由访问的开放源码软件包和网络服务器。 所提出的新算法将直接应用于与人类疾病相关的重要问题， 丙型肝炎病毒基因组RNA的结构和稳定性。此外，预测金属的能力 离子效应将使我们不仅能够理解RNA功能结构的形成， 通过削弱或加强离子结合并因此改变结构的治疗策略， 人类疾病相关RNA的稳定性。

Project Summary The current computational tools cannot keep up the pace with steadily emerging RNA sequences and new functions such as riboswitch-mediated regulation of gene expression in bacteria, RNA-guided genome engineer- ing (CRISPR), and RNA-based drug design and delivery. One of the unsolved key issues is the prediction and understanding of the role of metal ions in RNA structure formation. The problem is critically important for RNA functions because RNAs are highly charged; thus, without the participation of the metal ions in the solution, RNAs simply won't fold. Furthermore, there is increasing support for the idea that metal ions in the cellular environment can play a significant role in the regulation of gene expression by causing RNA structure changes. The biological importance of ion effects underscores the urgent request for computational tools for accurate prediction of the ion effects. The objective of this project is to develop successful computational tools, including models, software pack- ages, and web servers to predict and understand the role of metal ions in RNA structures and functions. By including the correlation and fluctuation effects for metal ions, considerable progress has been made for the ion effects in small and simple RNA structures. However, accurate prediction for the ion (especially Mg2+) effects has not been possible for large, biologically important RNAs. We now propose to develop computational tools that can provide such accurate predictions. Our goals are (a) to develop and validate a novel sampling algorithm that enables predictions of the ion effects for large RNA structures, (b) to develop and validate a new model for predicting metal ion binding sites in RNA, (c) through collaboration with RNA structural biology laboratory to develop an ion effect model for flexible RNA structures, and (d) to convert the computational models into user friendly, freely accessible, open-source software package and web servers. The proposed new algorithms will be directly applied to important problems related to human diseases such as the structure and stability of Hepatitis C virus genome RNA. Furthermore, the ability to predict the metal ion effects will allow us not only to understand the formation of RNA functional structures but also to design therapeutic strategies by weakening or strengthening ion binding and consequently changing the structures and stabilities of human disease-related RNAs.