Riboswitches, RNA conformational switches and prokaryotic gene regulation
Speaker: Peter Clote , Boston College
Date: February 13 2008
Time: 11:30AM to 1:00PM
Location: 32-G575
Host: Bonnie Berger & Peter Clote, MIT - BC
Contact: Patrice Macaluso, 617-253-3037, macaluso@csail.mit.edu
Relevant URL: http://www-math.mit.edu/compbiosem/Metabolite-sensing 50-UTR (untranlated regions) of certain mRNAs, called
riboswitches, have been discovered to undergo a conformational change upon
ligand-binding, which thereby can up- or down-regulate the corresponding protein product. For instance, upon the binding of nucleotide guanine, the G-box riboswitch in the 50 UTR of the XPT gene of Bacillus subtillis undergoes a conformational change to create a terminator loop, thereby prematurely
terminating transcription of the XPT gene. Since XPT is involved in guanine metabolism, this is an example of negative autoregulation by a riboswitch. Although riboswitches have been postulated to be an ancient genetic regulatory system, ¯rst developed in bacteria, the remarkable discovery
of Cheah et al. in Nature 2007 suggests that eukaryotes may have co-opted riboswitches to control alternative splicing of genes.
Here we describe a new algorithm RNAbor (Freyhult, Moulton, Clote Bioinformatics 2007) which gives information on possible conformational switches by computing the Boltzmann probability of structural neighbors of a given RNA secondary structure. A secondary structure T of a given RNA sequence s is called a ±-neighbor of S if T and S di®er by exactly ± base pairs.
RNAbor computes the number (N±), the Boltzmann partition function (Z±)
and the minimum free energy (MFE±) and corresponding structure over the
collection of all ±-neighbors of S. This computation is done simultaneously
for all ± · m, in run time O(m2n3) and memory O(mn2), where n is the sequence length. We apply RNAbor for the detection of possible RNA conformational
switches, and compare RNAbor with an existent switch detection method. We also provide examples of how RNAbor can at times improve the accuracy of secondary structure prediction.
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