New computational tools for predicting ion effects in RNA structures

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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.