Biokatalytische Daten aus enzymatischen Kaskadenreaktionen: Integration von Datenerfassung, Data Mining und mechanistischer Modellierung
Zusammenfassung der Projektergebnisse
Synthetic chemistry is challenged by the increasing need for sustainability and efficiency. Enzymes catalyze chemical reactions with high efficiency and selectivity under ambient conditions, and therefore are promising catalysts for a greener chemistry. The structure and function of enzymes is determined by their sequence, and an exponentially increasing number of protein sequences have become available as a rich resource for the discovery of novel biocatalysts. Our previously developed "BioCatNet" database system collects and analyzes the sequences and structures of large protein families and integrates this information with experimental bio-catalytic measurement data, reaction conditions, and kinetic modelling. In a previous project, the BioCatNet database system was extended and applied to model enzymatic reactions and to analyze four large enzyme families. The selection of kinetic models and the estimation of kinetic parameters provide valuable insights into the function of enzymes and identify kinetic bottlenecks. In a collaborative project, we compared different kinetic models for their ability to describe benzaldehyde lyase catalyzed C-C coupling reactions. The experimental data and information on reaction conditions was stored in our database system. The sequences and structures of short-chain dehydrogenases/reductases and alpha/beta-hydrolases were systematically analyzed to study the relationships between sequence, structure, and function. By applying a comprehensive sequence comparison and a correlation analysis of amino acid positions, a new subfamily within the chorismatase family with interesting properties was identified. A navigation tool for the comparison of local and global properties of sequences and structures was applied to study the large family of thiamine-dependent decarboxylases. Finally, the analysis of different enzyme families by sequence networks identified robust and highly mutable proteins that could serve as promising starting points for protein engineering.
Projektbezogene Publikationen (Auswahl)
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2018. The scale-free nature of protein sequence space. PLoS One 13: e0200815
Buchholz PCF, Zeil C, Pleiss J
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2019. Navigating within thiamine diphosphate-dependent decarboxylases: Sequences, structures, functional positions, and binding sites. Proteins 87: 774 -785
Buchholz PCF, Ferrario V, Pohl M, Gardossi L, Pleiss J
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2019. Progress curve analysis within BioCatNet: comparing kinetic models for enzyme-catalyzed self-ligation. Biotechnol J 14: e1800183
Buchholz PCF, Ohs R, Spieß A, Pleiss J
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2019. The Short-chain Dehydrogenase/Reductase Engineering Database (SDRED): A classification and analysis system for a highly diverse enzyme family. Proteins 87: 443 -451
Gräff M, Buchholz PCF, Stockinger P, Bommarius B, Bommarius AS, Pleiss J
