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Bioinformatics. 2005 Nov 15;21(22):4140-7. Epub 2005 Sep 13.

Energy landscape of k-point mutants of an RNA molecule.

Author information

1
Department of Biology, Boston College, Chestnut Hill, MA 02467, USA. clote@bc.edu

Abstract

MOTIVATION:

A k-point mutant of a given RNA sequence s = s(1), ..., s(n) is an RNA sequence s' = s'(1),..., s'(n) 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 < or =k-point mutant having least free energy over all secondary structures of all k'-point mutants of a given RNA sequence, for k' < or = 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.

PMID:
16159920
DOI:
10.1093/bioinformatics/bti669
[Indexed for MEDLINE]
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