Pseudoknot
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A pseudoknot is an RNA secondary structure containing two stem-loop structures in which the first stem's loop forms part of the second stem. The pseudoknot was first recognized in the turnip yellow mosaic virus in 1982.[2] Pseudoknots fold into knot-shaped three-dimensional conformations but are not true topological knots.
[edit] Prediction and identification
The base pairing in pseudoknots is not well nested; that is, base pairs occur that "overlap" one another in sequence position. This makes the presence of pseudoknots in RNA sequences impossible to predict by the standard method of dynamic programming, which uses a recursive scoring system to identify paired stems and consequently cannot detect non-nested base pairs under most circumstances, or by the newer method of stochastic context-free grammars. Thus popular secondary structure prediction methods like Mfold and Pfold will not predict pseudoknot structures present in a query sequence; they will only identify the more stable of the two pseudoknot stems.
It is possible to contrive situations in which dynamic programming-like methods can identify pseudoknots, but these methods are not general and are extremely inefficient.[3] The general problem of pseudoknot prediction has been shown to be NP-complete.[4]
[edit] Biological significance
Several important biological processes rely on RNA molecules that form pseudoknots. For example, the RNA component of human telomerase contains a pseudoknot that is critical for activity.[1]
[edit] References
- ^ a b Chen JL, Greiger CW. (2005). "Functional analysis of the pseudoknot structure in human telomerase RNA". Proc Natl Acad Sci USA 102(23): 8080–5.
- ^ Staple DW, Butcher SE. (2005). "Pseudoknots: RNA Structures with Diverse Functions". PLoS Biology 3(6). 2005 Online Article Open access publication
- ^ Rivas E, Eddy S. (1999). "A dynamic programming algorithm for RNA structure prediction including pseudoknots". J Mol Biol 285(5): 2053–2068.
- ^ Lyngsø RB, Pedersen CN. (2000). "RNA pseudoknot prediction in energy-based models". J Comput Biol 7(3–4): 409–427.
