A heuristic method for computational RNA pseudoknot prediction

Jana Sperschneider

    Research output: ThesisDoctoral Thesis

    282 Downloads (Pure)

    Abstract

    [Truncated abstract] Macromolecules such as DNA, RNA and proteins have the ability to form diverse threedimensional structures which enable functionality and thus, life. For many decades, proteins were deemed the global players in the cell until RNA entered the spotlight. For example, RNA structures have been found to be catalytically active which was assumed to be the privilege of proteins. Furthermore, small RNAs are known to regulate gene expression and RNA viruses employ a plethora of structure elements to invade the host cell. To gain insight into macromolecule function one must investigate the structure. The first step in RNA folding is stable base pairing which leads to a secondary structure. As RNA structure formation is of hierarchical nature, secondary structure is the basis for the tertiary fold, that produces the functional structure (Tinoco & Bustamante, 1999). Especially for RNAs, structure determination by experimental means is an intricate and expensive task. Computational RNA structure prediction is therefore an invaluable tool for biologists. However, from a computer scientist's point of view an exponential search space of possible structures for a single sequence makes it not an easy problem to solve. Computational RNA structure prediction started to develop in the 1980s simultaneously to the ground-breaking discovery of the ability of RNA to act as a catalyst.
    Original languageEnglish
    QualificationDoctor of Philosophy
    Publication statusUnpublished - 2012

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