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.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Research Project (R01)
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Macromolecular Structure and Function D Study Section (MSFD)
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Preusch, Peter C
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University of Missouri-Columbia
Schools of Arts and Sciences
United States
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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
Zhu, Yuhong; Chen, Shi-Jie (2014) Many-body effect in ion binding to RNA. J Chem Phys 141:055101
Cao, Song; Xu, Xiaojun; Chen, Shi-Jie (2014) Predicting structure and stability for RNA complexes with intermolecular loop-loop base-pairing. RNA 20:835-45
He, Zhaojian; Chen, Shi-Jie (2013) Quantifying Coulombic and solvent polarization-mediated forces between DNA helices. J Phys Chem B 117:7221-7
Cao, Song; Chen, Shi-Jie (2012) A domain-based model for predicting large and complex pseudoknotted structures. RNA Biol 9:200-11
Cruz, Jose Almeida; Blanchet, Marc-Frederick; Boniecki, Michal et al. (2012) RNA-Puzzles: a CASP-like evaluation of RNA three-dimensional structure prediction. RNA 18:610-25
Cao, Song; Chen, Shi-Jie (2012) Predicting kissing interactions in microRNA-target complex and assessment of microRNA activity. Nucleic Acids Res 40:4681-90
Stammler, Suzanne N; Cao, Song; Chen, Shi-Jie et al. (2011) A conserved RNA pseudoknot in a putative molecular switch domain of the 3'-untranslated region of coronaviruses is only marginally stable. RNA 17:1747-59
Rockey, William M; Hernandez, Frank J; Huang, Sheng-You et al. (2011) Rational truncation of an RNA aptamer to prostate-specific membrane antigen using computational structural modeling. Nucleic Acid Ther 21:299-314

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