Understanding the Thermodynamics and Structure of RNA Secondary Structure Motifs

Project Summary While many important RNA sequences have been determined, there is little definitive secondary and three-dimensional (3D) structure information about RNA. Several algorithms predict RNA secondary structure from sequence. However, they are limited by a lack of experimental parameters for non-Watson-Crick regions, the inability to incorporate non-standard nucleotides, and a lack of knowledge about how in vivo-like conditions affect stability. NMR and X-ray crystallography are powerful tools to determine RNA 3D structure but are time- and labor-intensive. With the millions of RNA sequences available, there is a need for reliable, rapid methods to predict secondary and 3D structures of RNA from sequence. Therefore, the broad, long-term objective of the PI’s laboratory is to improve RNA secondary and tertiary structure prediction from sequence. To do so, it is essential to understand RNA thermodynamics and structure. Improved nearest neighbor parameters derived from thermodynamic data can improve secondary structure prediction from sequence. To improve tertiary structure prediction, it would help to know the structural features of secondary structure motifs in solved 3D structures. Therefore, this proposal continues to investigate the thermodynamics and structures of common RNA secondary structure motifs. Specific objectives are: 1) derive improved nearest neighbor parameters to better predict RNA stability and secondary structure from sequence; 2) identify structural patterns in previously solved RNA 3D structures to improve 3D structure prediction. Design and methods include optical melting experiments and free energy parameter derivation, online tool development to annotate and compare 3D structures, and an in-depth analysis of the structural features of secondary structure motifs. This research is relevant to the NIH mission and AREA grant program objectives. An improved method to predict RNA secondary and tertiary structure from sequence is essential to move RNA research forward. Further, the work should impact researchers in any field relying on RNA structure prediction, especially those attempting to understand the structure-function relationship of RNA, understand RNA interactions with other biological molecules, target RNA with therapeutics, and utilize RNA as a therapeutic. As a result, the proposed research will advance the nation’s capacity to protect and improve health, expand knowledge in medical and associated sciences, and benefit students through exposure to and participation in research in the biomedical sciences.