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Weltentwicklungspolitik – Aufgabe kompetenter und demokratisch legitimierter Globaler Staatlichkeit
(2010)
The glomerulosclerosis gene Mpv17 encodes a peroxisomal protein producing reactive oxygen species
(1994)
RNA is one of the most important molecules in living organisms. One of its main functions is to regulate gene expression. This involves binding to and forming a joint structure with a messenger RNA. An RNAs functions is determined by its sequence and the structure it folds into. Accordingly, the prediction of individual as well as joint structures is an important area of research. In this thesis a method for the prediction of RNA-RNA joint structure using their minimum free energy (mfe) structures was developed. It is able to extensively explore the joint structural landscape of two interacting RNAs by taking advantage of the locality of changes in the RNAs structures as well as natural and energetic constraints. The method predicts the mfe joint structure as well as alternative stable joint structures while also computing non-optimal folding pathways from the unbound individual mfe structures to the predicted joint structures. It is shown how an enumeration approach is used which is able to deal with the enormous search space as well as to avoid any cyclic behaviour. The method is evaluated using two standard datasets of known interacting RNAs and shows good results.
MOTIVATION: The genome projects produce a wealth of protein sequences. Theoretical methods to predict possible structures and functions are needed for screening purposes, large-scale comparisons and in-depth analysis to identify worthwhile targets for further experimental research. Sequence-structure alignment is a basic tool for the identification of model folds for protein sequences and the construction of crude structural models. Empirical contact potentials (potentials of mean force) are used to optimize and evaluate such alignments. RESULTS: We propose new scoring schemes based on a contact definition derived from Voronoi decompositions of the three-dimensional coordinates of protein structures. We demonstrate that Voronoi potentials are superior to pure distance-based contact potentials with respect to recognition rate and significance for native folds. Moreover, the scoring scheme has the potential to provide a reasonable balance of detail and ion such that it is also useful for the recognition of distantly related (both homologous and non-homologous) proteins. This is demonstrated here on a set of structural alignments showing much better correspondence of native and model scores for the Voronoi potentials as compared to conventional distance-based potentials.
Scientific or statistical research has long been the domain of dedicated programming languages such as R, SPSS or SAS. A few years other competitors entered the arena, among them Python with its powerful SciPy package. The following article introduces SciPy by applying a small subset of its functionality to a well-known dataset.