Most RNAs undergo extensive folding to form sophisticated based-paired secondary structures that become an integral part of cellular gene-expression machinery. Accurately deducing these structures is no small matter, yet this is a critical aspect of understanding structure/function.
“Structure is function,” noted Bruce A. Shapiro, Ph.D., principle investigator, Center for Cancer Research Nanobiology Program, National Cancer Institute-Frederick. “Predicting RNA structure in a rapid and accurate way remains a challenge.
“Several years ago, Kevin Weeks and colleagues at the University of North Carolina, Chapel Hill developed a methodology called SHAPE (Selective 2´-Hydroxyl Acylation analyzed by Primer Extension). This method provides quantitative nucleotide resolution data that can be utilized to determine RNA secondary structure.”
The problem, according to Dr. Shapiro, is how to interpret the data. “A low signal indicates that there is base-pairing, but it doesn’t disclose the base-pairing partner or the type of base pair.
“In order to better understand the complexities of SHAPE, we utilized seven RNAs taken from the Protein Data Bank whose structure had already been solved. We prepared the RNAs by in vitro transcription, and folded the RNA into its native structure. This was followed by chemical modification of the RNAs with N-methyl-7-nitroisatoic anhydride (NMIA).
“Subsequently these sequences were subjected to primer extension. Due to stops in primer extension caused by NMIA-modified unpaired nucleotides, one is able to obtain bands on gels indicating the positions of these stops. The intensities of these bands are then converted to SHAPE values.”
Dr. Shapiro and his colleagues arrived at several conclusions. “First, the SHAPE signal is impacted to a large extent by the base-pairing state of a residue. We also found significant correlations with base-pair stacking. By comparing the known structures with the SHAPE data, we developed a method that converts raw SHAPE values into probabilities of base pairing.”
Overall the new methodology enhances understanding of the SHAPE methodology and could be particularly important for pharmaceutical applications.
“One could imagine applying this enhanced SHAPE analysis to help in the development of therapeutics that target specific RNAs that are involved in disease processes. Although we are still developing predictive methods, ultimately such probabilistic models will aid the interpretation of RNA structure for basic science as well as pharmaceutical development.”