RNA folding and combinatory landscapes
- Santa Fe Institute, 1660 Old Pecos Trail, Santa Fe, New Mexico 87501 (United States) Theoretical Division T-13, Los Alamos National Laboratory, Los Alamos, New Mexico 87545 (United States)
- Institut fuer Theoretische Chemie, Universitaet Wien, Waehringerstra (27e 17, A-1090) Wien (Austria) Max-Planck-Institut fuer Biophysikalische Chemie, Am Fassberg, D-3400 Goettingen (Germany) Santa Fe Institute, 1660 Old Pecos Trail, Santa Fe, New Mexico 87501 (United States)
- Institut fuer Theoretische Chemie, Universitaet Wien, Waehringerstra (27e 17, A-1090) Wien (Austria)
- Institut fuer Theoretische Chemie, Universitaet Wien, Waehringerstra (27e 17, A-1090) Wien (Austria) Departamento de Fisica de la Materia Condensada C-XII, Universidad Autonoma de Madrid, E-28049 Madrid (Spain)
- Max-Planck-Institut fuer Biophysikalische Chemie, Am Fassberg, D-3400 Goettingen (G
In this paper we view the folding of polynucleotide (RNA) sequences as a map that assigns to each sequence a minimum-free-energy pattern of base pairings, known as secondary structure. Considering only the free energy leads to an energy landscape over the sequence space. Taking into account structure generates a less visualizable nonscalar landscape,'' where a sequence space is mapped into a space of discrete shapes.'' We investigate the statistical features of both types of landscapes by computing autocorrelation functions, as well as distributions of energy and structure distances, as a function of distance in sequence space. RNA folding is characterized by very short structure correlation lengths compared to the diameter of the sequence space. The correlation lengths depend strongly on the size and the pairing rules of the underlying nucleotide alphabet. Our data suggest that almost every minimum-free-energy structure is found within a small neighborhood of any random sequence. The interest in such landscapes results from the fact that they govern natural and artificial processes of optimization by mutation and selection. Simple statistical model landscapes, like Kauffman and Levin's [ital n]-[ital k] model [J. Theor. Biol. 128, 11 (1987)], are often used as a proxy for understanding realistic landscapes, like those induced by RNA folding. We make a detailed comparison between the energy landscapes derived from RNA folding and those obtained from the [ital n]-[ital k] model. We derive autocorrelation functions for several variants of the [ital n]-[ital k] model, and briefly summarize work on its fine structure. The comparison leads to an estimate for [ital k]=7--8, independent of [ital n], where [ital n] is the chain length. While the scaling behaviors agree, the fine structure is considerably different in the two cases.
- DOE Contract Number:
- FG05-88ER25054
- OSTI ID:
- 6707828
- Journal Information:
- Physical Review E; (United States), Journal Name: Physical Review E; (United States) Vol. 47:3; ISSN 1063-651X; ISSN PLEEE8
- Country of Publication:
- United States
- Language:
- English
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