This research project will use advanced fluorescence fluctuation spectroscopy (FFS) techniques combined with state-of-the-art biophysical theory and modeling to develop an elementary reaction rate model for intramolecular base-pairing and base-stacking in RNA secondary structure formation. The model to be developed will predict all experimentally observable kinetics properties of RNA secondary structure formation for any given sequence, including reaction rates, reaction mechanisms, and the structures and stabilities of the reaction intermediates. Development of the model will be based on experimental-theoretical comparisons of the folding and unfolding kinetics of designed RNA hairpins. Several competing theoretical reaction rate models will be used to predict the temperature dependent folding kinetics of the hairpins. The kinetics of these reactions will then be assessed experimentally using FFS. The experiments will measure reaction rates and identify reaction intermediates occurring over a broad range of time scales, from nanoseconds to hundreds of milliseconds. Different reaction rate models predict qualitatively and quantitatively different folding kinetics, including different Arrhenius plots, reaction intermediates, and reaction time scales. It is not possible to determine which reaction rate model is accurate based on theoretical calculations alone. Experimental comparisons to different model predictions will thus reveal which rate model is most consistent with experimental observations. Once the most likely model has been identified, it will be refined to account for the solvent viscosity, heat capacity, temperature, and counterion dependencies of the kinetic and thermodynamic parameters. The model will be validated by predicting the folding kinetics of RNA hairpins with arbitrary length and sequence and verifying the predictions experimentally. Modeling of more complicated tertiary structure forming RNA will proceed from that point.
The broader impacts of this project include the development of a model that can aid researchers in uncovering the biological function of any given RNA sequence. RNA is one of the fundamental molecular building blocks of living systems and plays a crucial role in a host of biological processes. This research will also contribute to the education and training of a new generation of scientists capable of applying rigorous quantitative experimental and theoretical research tools to important biological problems. Finally, new pedagogical materials based on this research will be developed for an emerging curriculum at the interface of the biological and physical sciences.
Supported by this NSF grant, we developed a computational strategy to investigate the elementary rate process for RNA folding, namely, the formation and disruption of a single base pair and base stack. We combined the molecular dynamics simulation, kinetic Monte Carlo simulation, and the master equation equation methods to model the folding process at the atomic scale. We discovered several discrete intermediate states in the folding process. Moreover, through detailed analysis of the folding trajectories, we were able to compute the rate constants and pathways. These results, especially the results for the rate constants, the kinetic barriers and the pathways, would form the foundation for further systematic development of accurate models for RNA folding kinetics. A successful development of these models would significantly impact our ability of molecular design for RNA-based nanomachine and RNA-based synthetic biology. This grant supported a graduate student, who was a core participant in the project, to receive extensive training and education on quantitative modeling and critical analysis of experimental data. The project motivated the student to learn advanced skills of computer modeling for biological system and the basic knowledge of RNA biology. In the process of testing and validating the computational models through comparisons with experimental data, the student received rigorous training for the interpretation and analysis of the raw data from experiments. Therefore, this project provided an important training and education opportunity for the student and contributed to the NSF goal of STEM education.
