The current experiments on structural determination cannot keep up the pace with the steadily emerging RNA sequences and new functions. This underscores the urgent request for an accurate free energy model for RNA tertiary folds, from which one can predict structures from sequences. Furthermore, there is increasing support for the idea that large structured RNAs may adopt a variety of conformational states, rather than just one, during the course of performing its biological function, this is particularly so in the replication of RNA viruses and a central tenet of riboswitch-mediated regulation of gene expression in bacteria. Although considerable progress has been made in mechanistic studies, accurate prediction for RNA tertiary folding from sequence remains an unsolved problem. The first and most important requirement for understanding and predicting of RNA folding from RNA structural fluctuations to large conformational changes is an accurate free energy model. Support from this grant has allowed us to develop a novel virtual bond-based RNA free energy model that enables much better predictions than other existing models for simple tertiary structures (pseudoknots). We now propose to go beyond the simple pseudoknots by studying all-atom, larger, more complex RNA tertiary folds. Our approach will be based on rigorous, first principles analytical calculations. A key advantage of the approach is the completeness and certainty in conformational sampling (entropy). Incorrect entropy results in poor predictions. Preliminary tests using experimental data have shown significant improvements from our approach in both accuracy and specificity than existing folding algorithms. The success attests the high promise of the new approach proposed in this grant.
Our specific aims are: (a) Systematic model development for tertiary folding free energies. (b) Developing a novel approach for complex, larger tertiary folds. (c) Developing a 3D all-atom model. (d) Systematic test and refinement of the model using experimental structural data.

Public Health Relevance

This project will develop a model for accurate predictions of all-atom structures and free energy landscapes for RNA tertiary folds. This predictive model will contribute to the quantitative understanding of RNA mechanisms in cellular functions as well as the rational design of RNA-based therapeutics.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM063732-08
Application #
8230580
Study Section
Macromolecular Structure and Function D Study Section (MSFD)
Program Officer
Preusch, Peter C
Project Start
2003-03-01
Project End
2014-02-28
Budget Start
2012-03-01
Budget End
2013-02-28
Support Year
8
Fiscal Year
2012
Total Cost
$258,813
Indirect Cost
$85,563
Name
University of Missouri-Columbia
Department
Physics
Type
Schools of Arts and Sciences
DUNS #
153890272
City
Columbia
State
MO
Country
United States
Zip Code
65211
Urak, Kevin T; Shore, Sabrina; Rockey, William M et al. (2016) In vitro RNA SELEX for the generation of chemically-optimized therapeutic RNA drugs. Methods 103:167-74
Xu, Xiaojun; Chen, Shi-Jie (2016) VfoldCPX Server: Predicting RNA-RNA Complex Structure and Stability. PLoS One 11:e0163454
Xu, Xiaojun; Yu, Tao; Chen, Shi-Jie (2016) Understanding the kinetic mechanism of RNA single base pair formation. Proc Natl Acad Sci U S A 113:116-21
Zhu, Yuhong; He, Zhaojian; Chen, Shi-Jie (2015) TBI server: a web server for predicting ion effects in RNA folding. PLoS One 10:e0119705
Miao, Zhichao; Adamiak, Ryszard W; Blanchet, Marc-Frédérick et al. (2015) RNA-Puzzles Round II: assessment of RNA structure prediction programs applied to three large RNA structures. RNA 21:1066-84
Xu, Xiaojun; Chen, Shi-Jie (2015) Physics-based RNA structure prediction. Biophys Rep 1:2-13
Zhang, Xinyue; Xu, Xiaojun; Yang, Zhiyu et al. (2015) Mimicking Ribosomal Unfolding of RNA Pseudoknot in a Protein Channel. J Am Chem Soc 137:15742-52
Xu, Xiaojun; Chen, Shi-Jie (2015) A Method to Predict the 3D Structure of an RNA Scaffold. Methods Mol Biol 1316:1-11
He, Zhaojian; Zhu, Yuhong; Chen, Shi-Jie (2014) Exploring the electrostatic energy landscape for tetraloop-receptor docking. Phys Chem Chem Phys 16:6367-75
Xu, Xiaojun; Zhao, Peinan; Chen, Shi-Jie (2014) Vfold: a web server for RNA structure and folding thermodynamics prediction. PLoS One 9:e107504

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