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Heuristic RNA pseudoknot detection in long sequences
There are two types of nucleic acids in the living cell: deoxyribonucleic acid (DNA) and ribonucleic acid (RNA). RNA is a versatile macromolecule which is no longer seen as the passive intermediate between DNA and proteins. Numerous functional RNA molecules with an astonishing variety have been uncovered in the last decade. Macromolecule function is closely connected to its three-dimensional folding and therefore, structure prediction from the base sequence is of great importance. Biologists demand computational RNA structure prediction methods as laboratory techniques are intricate. Several robust and efficient algorithms for RNA secondary structure prediction are available which neglect crossing structure elements, so-called pseudoknots, for ease of computation. However, pseudoknots have turned out to be of great biological relevance over the last decade. Pseudoknots are functional structure elements which occur in most classes of RNA and in many viruses. In my thesis, I am developing methods for RNA structure prediction including pseudoknots.
Pseudoknots play key roles in viral genome replication and regulation of protein synthesis. Therefore, prediction of these crossing structures in RNA molecules has important implications in antiviral drug design. From a practical point of view, goal of this PhD project is to look for pseudoknots in a wide range of viral genomes and, where found, note their locations. As structure is strongly related to function, computational prediction can deliver the basis for laboratory experiments on detected pseudoknots.