Physics Based Modeling For Rna Folding PDF Download

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 statistical mechanical model for the folding of tertiary structures with loop-loop kissing interactions. Based on a coarse-grained RNA conformational model, we developed an algorithm to compute the entropy parameters for the formation of the kissing structures. The model enables computational prediction of the structure and thermodynamic stability of RNA tertiary folds with long-range kissing loop-loop kissing contacts. The validity of the model is supported by the good theory-experiment agreements for the native structures and thermodynamic stabilities for a series of RNA/RNA complex systems. For large RNAs, due to the increased complexity of the structures, the accuracy of computational predictions for RNA folding decreases. For such complex RNA structures, a novel structure determination strategy is to combine computational modeling with experimental data such as the reactivity profile of Selective 2'-hydroxyl acylation analyzed by primer extension (SHAPE), an method for chemical probing of RNA structure. Although the SHAPE technology has been widely applied to a broad range of RNA structure probing problems, the quantitative relationship between the structure/dynamics and SHAPE reactivity is not understood. In the third project, we developed a model to quantify the relationship between RNA three-dimensional structure and SHAPE reactivity. We developed an analytical function to rebuild SHAPE profiles from three-dimensional structures. The algorithm starts from RNA structures and combines nucleotide interaction strength and conformational propensity, ligand (SHAPE reagent) accessibility, and base-pairing pattern through a composite function to quantify the correlation between SHAPE reactivity and nucleotide conformational stability. Comparisons between predicted SHAPE profiles and experimental SHAPE data show high correlations, suggesting the validity of the extracted analytical function. The result supports our analysis for the key factors that determine the SHAPE reactivity from structure.