Energy landscape of k-point mutants of an RNA molecule

P. Clote, J. Waldispuhl, B. Behzadi, J.-M. Steyaert
2005 Bioinformatics  
Motivation: A k-point mutant of a given RNA sequence s = s 1 , . . . , sn 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 (Schuster et al., 1994; Clote et al., 2005b) , 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
more » ... 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 (Waldispühl et al., 2002) , 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 is the Zuker (Zuker and Stiegler, 1981; Zuker, 2003 ) minimum free energy structure of the original (wild-type or unmutated) RNA.
doi:10.1093/bioinformatics/bti669 pmid:16159920 fatcat:wcmk3cgyhvejlpnzhwhxbc5wzu