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. Thus, 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 and how these are related. Improved nearest neighbor parameters derived from thermodynamic data and computations 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 three-dimensional structures. Therefore, this proposal continues to investigate the thermodynamics, energetics, and structures of common RNA secondary structure motifs. Specific objectives are: 1) derive or improve nearest neighbor parameters for naturally occurring motifs; 2) determine how in vivo-like ions, crowders, cosolvents, and cosolutes affect Watson-Crick nearest neighbor parameters; 3) identify patterns of secondary structure motifs in three-dimensional structures. Design and methods include optical melting experiments, hydrogen bonding and base stacking calculations, 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, and target RNA with therapeutics. 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.
The proposed research will provide essential information to advance RNA research. The data collected can be used to better understand humans, bacteria, and viruses and can be used by other scientists researching the causes, diagnosis, prevention, and cure of human diseases.
Showing the most recent 10 out of 18 publications
