The ability of ribonucleic acids (RNAs) to sample distinct structures underlie many of their biological roles. Many RNAs that regulate gene expression in bacteria, viruses, and humans do so by changing their structure in response to stimuli. In bacteria, for instance, RNA elements switch their molecular structure in the presence of specific metabolites, such as vitamin B12. The binding of such metabolites and the subsequent structural changes enable organisms that express these RNAs to respond to their environment and turn ON or OFF gene expression as needed. However, visualizing the structures that RNAs adopt remains a fundamental challenge in molecular biophysics. This project seeks to develop and apply new methods to integrate experimental and computational data to describe the structure of RNAs. This project will also tightly integrate research and education to train high school, undergraduate, and graduate students in the fundamentals of applied computational molecular biophysics. During this project, students will leverage modern machine learning techniques, similar to techniques used in facial recognition software, to extract RNA structural information from biophysical measurements. Outreach activities will be aimed at actively engaging high school students in computationally-intensive research, especially those from historically under-recruited groups.
Specifically, this project aims to develop methods to determine the dynamical ensemble, i.e., the collection of conformational states that a given RNA can populate. To understand how RNAs fold and function, one must determine their ensembles and how they change during the recognition of proteins or ligands or during the introduction of mutations or chemical modifications. Characterizing dynamical ensembles of RNA, however, remains a formidable task because the number of unknown variables exceeds that which can be measured experimentally. Additionally, many conformers exist in low-abundance and have short lifetimes and are therefore difficult to detect experimentally. Even more challenging is determining how different conformers in an ensemble interconvert as this requires characterization of exceptionally lowly-populated transition states and complex pathways that can be difficult to tease out. In this project, the dynamical ensembles of RNAs will be determined using novel integrative approaches that are initialized using readily available experimental data. In this project, NMR chemical shifts will be used to guide simulations of RNA and to determine their dynamical ensembles. Therefore, the first objective is to develop multi-scale methods to estimate chemical shifts from structures of RNA. The second objective is to leverage multi-scale chemical shift prediction methods to construct multi-scale dynamical ensembles of RNA. The last objective is to develop a methodology that uses multi-scale dynamical ensembles to construct transition-path ensembles of RNA. Ultimately, this project will lead to robust, streamlined, and integrated methodologies that go beyond static structure determination to construct dynamical ensembles that capture the range of conformational states accessible to functional RNAs, while simultaneously describing the transitions between these states. This structural information will advance the understanding of RNA-driven biochemical processes from descriptive to predictive.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
