From SHAPE Signatures to 3-D Structures
Structure is for function, so the challenge is to determine the exact structure of the RNAs. Here is the structure of the stem-loop II motif RNA element from the SARS virus genome.
RNAs undergo extensive folding to form sophisticated based-paired secondary structures that are, in part, indicators of more complex three-dimensional structures. These 3-D shapes are an integral part of the cellular gene-expression machinery. Deconstructing these structures is no small matter, yet it is critical to understanding their function.
Bruce A. Shapiro, Ph.D., a CCR investigator who works in the Nanobiology Program at the National Cancer Institute-Frederick, has expanded upon an existing methodology such that it can predict these RNA structures in a more rapid and accurate manner. He reported his findings in RNA, a Cold Spring Harbor publication of the RNA Society.
Shapiro and his colleagues started with technology developed by Kevin Weeks and colleagues at the University of North Carolina, Chapel Hill. The methodology called SHAPE (Selective 2´-Hydroxyl Acylation analyzed by Primer Extension) provides quantitative nucleotide resolution data that can determine RNA secondary structure. A low signal, using this system, indicates that there is base-pairing, but it doesn’t disclose the base-pairing partner or the type of base pair.
In order to adapt SHAPE to their research needs, Shapiro utilized seven RNAs taken from the Protein Data Bank whose structure had already been solved. He and his collaborators prepared RNAs by in vitro transcription, and folded the RNA into its native structure. Next they chemically modified the RNAs with N-methyl-7-nitroisatoic anhydride (NMIA) and subjected the sequences to primer extension. Due to stops in primer extension caused by NMIA-modified unpaired nucleotides, the researchers obtained bands on gels indicating the positions of these stops. The intensities of these bands were then converted to SHAPE values.
The Shapiro team discovered that the SHAPE signal is impacted to a large extent by the base-pairing state of a residue. They also found significant correlations with base-pair stacking. By comparing known structures with SHAPE data, the team developed a method to convert raw SHAPE values into probabilities of base pairing. Overall this new adaptation enhances SHAPE analysis. This adapted SHAPE technology can help inform development of therapeutics that target specific RNAs involved in supporting cancer processes.
Shapiro and his collaborators are still honing their predictive methods. They hope their new improved tool will aid the interpretation of RNA structure for basic science as well as for pharmaceutical research.Summary Posted: 07/2011
RNA 2011 17: 1688-1696 originally published online July 13, 2011. http://rnajournal.cshlp.org/content/17/9/1688/ Reviewed by Donna Kerrigan