Bioinformatics Advance Access originally published online on September 13, 2005
Bioinformatics 2005 21(22):4140-4147; doi:10.1093/bioinformatics/bti669
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Energy landscape of k-point mutants of an RNA molecule
1Department of Biology Higgins 355 Boston College Chestnut Hill, MA 02467, USA
2Department of Computer Science (courtesy appointment) Higgins 355 Boston College Chestnut Hill, MA 02467, USA
3LIX, Ecole Polytechnique 91128 Palaiseau Cedex, France
4LIAFA, Université Denis Diderot 2 place Jussieu, 75251 Paris Cedex, France
*To whom correspondence should be addressed.
Motivation: A k-point mutant of a given RNA sequence s = s1, ..., sn is an RNA sequence
obtained by mutating exactly k-positions in s; i.e. Hamming distance between s and s' equals k. To understand the effect of pointwise mutation in RNA, we consider the distribution of energies of all secondary structures of k-point mutants of a given RNA sequence.
Results: Here we describe a novel algorithm to compute the mean and standard deviation of energies of all secondary structures of k-point mutants of a given RNA sequence. We then focus on the tail of the energy distribution and compute, using the algorithm AMSAG, the k-superoptimal structure; i.e. the secondary structure of a
k-point mutant having least free energy over all secondary structures of all k'-point mutants of a given RNA sequence, for k'
k. Evidence is presented that the k-superoptimal secondary structure is often closer, as measured by base pair distance and two additional distance measures, to the secondary structure derived by comparative sequence analysis than that derived by the Zuker minimum free energy structure of the original (wild type or unmutated) RNA.
Contact: clote{at}bc.edu
Supplementary information: http://clavius.bc.edu/~clotelab/RNAmutants/
Received on June 7, 2005; revised on September 6, 2005; accepted on September 7, 2005
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