RNA plays important roles in many areas of Biology. Functional RNA sequences that work at the level of RNA, i.e. not as an mRNA coding region, are called non-coding RNA (ncRNA). ncRNA sequences serve diverse roles from sequence recognition, such as hybridization in RNA interference, to catalyzing reactions, such as peptide bond formation by ribosomal RNA. To understand RNA function and to harness the power of RNA, with RNA interference, therapeutic RNA enzymes, or RNA nanostructures, an understanding of RNA structure is required. RNAstructure is a software package for RNA secondary structure prediction and analysis. It has been downloaded by over 17,000 different users and is available at the Mathews lab website http://rna.urmc.rochester.edu. It provides state-of-the-art algorithms for RNA structure prediction using the most current understanding of RNA folding thermodynamics. It also provides algorithms for finding the optimal secondary structure shared by two sequences, which is, on average, much more accurate than predicting a structure for a single sequence. Furthermore, it provides methods to predict the affinity of structured oligonucleotides (DNA or RNA) annealing to a structured RNA target. These predictions are important for the selection of effective small interfering RNA (siRNA) for gene silencing. RNAstructure is provided with user-friendly graphical user interfaces in C++ for Microsoft Windows and in JAVA for Linux or Mac OS-X. Text interfaces are provided for scripting or command line use. These can be compiled on Unix/Linux/Mac OS-X and executables are provided for Windows. Finally, a shared C++ class library is available for programmers to include algorithms in new programs. The proposal is to extend and maintain RNAstructure. Four formal aims are proposed for the next period of support.
Aim 1 is to provide a new wizard mode for RNAstructure to guide users through the analysis of their sequence(s).
Aim 2 is to update the nearest neighbor parameters for predicting conformational stability using the most recent experimental data.
Aim 3 is to provide all the RNAstructure components via web interfaces.
Aim 4 is to provide fast and accurate prediction of secondary structures that include pseudoknots, which are currently not predicted by most algorithms.
This proposal has direct public health relevance. We provide software tools for predicting, understanding, and targeting RNA structure. These can be applied to understanding the biology of infectious diseases because some viruses, including influenza and HIV, are RNA viruses. Furthermore, they can be used to design novel therapeutics, such as antisense oligonucleotides or small interfering RNA that both target RNA. This RNA-targeting therapeutics could be used for diseases such as cancer or inherited diseases.
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|Aytenfisu, Asaminew H; Spasic, Aleksandar; Grossfield, Alan et al. (2017) Revised RNA Dihedral Parameters for the Amber Force Field Improve RNA Molecular Dynamics. J Chem Theory Comput 13:900-915|
|Smith, Louis G; Zhao, Jianbo; Mathews, David H et al. (2017) Physics-based all-atom modeling of RNA energetics and structure. Wiley Interdiscip Rev RNA 8:|
|Gamache, Eric R; Doh, Jung H; Ritz, Justin et al. (2017) Structure-Function Model for Kissing Loop Interactions That Initiate Dimerization of Ty1 RNA. Viruses 9:|
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|Tan, Zhen; Sharma, Gaurav; Mathews, David H (2017) Modeling RNA Secondary Structure with Sequence Comparison and Experimental Mapping Data. Biophys J 113:330-338|
|Xu, Zhenjiang Zech; Mathews, David H (2016) Secondary Structure Prediction of Single Sequences Using RNAstructure. Methods Mol Biol 1490:15-34|
|Sloma, Michael F; Mathews, David H (2016) Exact calculation of loop formation probability identifies folding motifs in RNA secondary structures. RNA 22:1808-1818|
|Mathews, David H; Turner, Douglas H; Watson, Richard M (2016) RNA Secondary Structure Prediction. Curr Protoc Nucleic Acid Chem 67:11.2.1-11.2.19|
|DiChiacchio, Laura; Sloma, Michael F; Mathews, David H (2016) AccessFold: predicting RNA-RNA interactions with consideration for competing self-structure. Bioinformatics 32:1033-9|
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