Physics-based modeling for RNA folding

Abstract

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] RNA (ribonucleic acid) molecules play a variety of crucial roles in cellular functions at the level of transcription, translation and gene regulation. Many RNAs have also been found to play catalytic and regulatory roles. RNA functions are tied to structures and dynamics. To understand the quantitative relationship between RNA functions and RNA 3D structures, stabilities and kinetics, we need a predictive model for RNA folding. We focus on the development of physics-based models for RNA folding. RNA molecules often undergo multiple conformational switches in order to perform their cellular functions, suggesting that RNA-involved processes can be kinetically controlled. Therefore, a full characterization of RNA folding and function should consider not only the native structure but also the folding kinetics. RNA hairpin is one of the most fundamental motifs in RNA structures. Predicting the physical mechanism of the folding and conformational switch for hairpins has far reaching impact on our understanding of more complex RNA folding problems. In the first project, we used Kinetic Monte Carlo method to explore the detailed kinetic mechanism for the conformational switches between bistable RNA hairpins. We found three types of conformational switch pathways for RNA hairpins: refolding after complete unfolding, folding through basepair-exchange pathways and through pseudoknot-assisted pathways, respectively. The study of this project led to new insights about RNA folding: tertiary structure such as pseudoknots can help secondary structure folding by lowering the kinetic barrier. The problem of RNA structure prediction from the nucleotide sequence is an unsolved problem for both 2D and 3D structures. One of the key issues in the current RNA structure prediction methods is the lack of the knowledge about the tertiary interactions. Tertiary interactions are crucial for RNA functions. Therefore, a model that can predict RNA structures with tertiary interactions is highly needed. In the second project, we developed a sta