Vergleich von Proteinbindetaschen unter Verwendung neuronaler Netze: von der Struktur zur Funktion und zum Ligandendesign

Das Projekt ist den Bereichen Proteinfunktionsanalyse und computergestütztes Wirkstoffdesign zuzuordnen. Durch die Auflösung des menschlichen Genoms und die Verbesserung experimenteller Techniken wie Röntgenstrukturanalyse und NMR-Spektroskopie ist eine Vielzahl von Proteinstrukturen gelöst worden....

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Bibliographic Details
Main Author: Kupas, Katrin
Contributors: Ultsch, Alfred (Prof. Dr.) (Thesis advisor)
Format: Dissertation
Language:German
Published: Philipps-Universität Marburg 2006
Mathematik und Informatik
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Table of Contents: In a biological system multiple biochemical pathways are proceeded and regulated via the complementary recognition properties of proteins and their substrates. The binding of this substrate takes place in the so called binding cavity or active site. It can be assumed, that proteins having similar binding cavities also bind similar ligands and exhibit a similar function. The shape and function of a protein, e.g. of an enzyme together with its active site is not exclusively represented by a unique amino acid sequence. Proteins with deviating amino acid sequence, even adopting a different folding pattern, can nevertheless exhibit related binding cavities to accommodate a ligand. Low sequence homology does not imply any conclusions on binding site differences or similarities. For this reason one has to regard the three-dimensional structure as a prerequisite for a reliable comparison of proteins. Such structures are available for many examples from X-ray crystallography. The project is associated with the research topics Computer-Aided Drug Design and Molecular Modeling. It deals with the problem to use the information of the high number of available protein structures for a rational drug design and a classification of proteins with respect to their function. With the comparison of a new protein with well known structures the function and a suitable ligand of the new protein can be predicted.