TBI-Preprint 99-NN-002


RNA in silico: The Computational Biology of RNA Secondary Structures

Christoph Flamm, Ivo L. Hofacker, Peter F. Stadler

Appeared in:
Adv. Complex Systems, 2, 65-90 (1999)

RNA secondary structures provide a unique computer model for investigating the most important aspects of structural and evolutionary biology. The existence of efficient algorithms for solving the folding problem, i.e., for predicting the secondary structure given only the sequence, allows the construction of realistic computer simulations. The notion of a "landscape" underlies both the structure formation (folding) and the (in vitro) evolution of RNA. Evolutionary adaptation may be seen as hill climbing process on a fitness landscape which is determined by the phenotype of the RNA molecule (within the model this is its secondary structure) and the selection constraints acting on the molecules. We find that a substantial fraction of point mutations do not change an RNA secondary structure. On the other hand, a comparable fraction of mutations leads to very different structures. This interplay of smoothness and ruggedness (or robustness and sensitivity) is a generic feature of both RNA and protein sequence-structure maps. Its consequences, "shape space covering" and "neutral networks" are inherited by the fitness landscapes and determine the dynamics of RNA evolution. Punctuated equilibria at phenotype level and a diffusion like evolution of the underlying genotypes are a characteristic feature of such models. As a practical application of these theoretical findings we have designed an algorithm that finds conserved (and therefore potentially functional) substructures of RNA virus genomes from sparse data sets. The folding dynamics of particular RNA molecule can also be studied successfully based on secondary structures. Given an RNA sequence, we consider the energy landscape formed by all possible conformations (secondary structures). A straight forward implementation of the Metropolis algorithm is sufficient to produce a quite realistic folding kinetics, allowing to identify meta-stable states and folding pathways. Just as in the protein case there are good and bad folders which can be distinguished by the properties of their energy landscapes.

RNA Secondary Structures, Fitness Landscapes, Energy Landscapes, Molecular Evolution, Punctuated Equilibria, Folding Kinetics, Folding Pathways.

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