Protein-Protein Docking Using Starting Points Based On Structural Homology
Protein-protein interactions build large networks which are essential in understanding complex diseases. Due to limitations of experimental methodology there are problems with large amounts of false negative and positive interactions; and a large gap in the amount of known interactions and structurally determined interactions. By using computational methods these problems can be alleviated.
In this thesis the quality of a newly developed pipeline (InterPred) were investigated for its ability to generate coarse interaction models and score them. This ability was investigated by performing docking experiments in Rosetta on models generated in InterPred.
The results suggest that InterPred is highly successful in generating good starting points for docking proteins in silico and to distinguish the quality of models.
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