Biocatalytic data from enzymatic cascade reactions: integration of data acquisition, data mining, and mechanistic modeling

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Our project proposal complements the ongoing project of Dr. Selin Kara since 4/2016. The two projects are closely integrated and the project plan is agreed by both partners. By applying our BioCatNet database system, our project expands the project of Dr. Kara by two work packages: (1) Acquisition, storage and analysis of large datasets from biocatalytic experiments, (2) removal of reaction bottlenecks by optimization of the biocatalysts.BioCatNet is a database system for enzyme families integrating protein sequences, structures and biocatalytic data. The BioCatNet system allows for an acquisition of experimental data in a standardized and consistent manner, its exchange with other groups, as well as long-term archiving, thus making the data accessible to a later analysis by alternative models or other research groups.We discriminate between original data (time courses of substrates, intermediates and products) and derived, model-based data (kinetic parameters depending on a chosen kinetic model). The method, which will be developed within the proposed project, acquires data from enzymatic cascade reactions of Baeyer-Villiger monooxygenases and alcohol dehydrogenases and can be transferred to further biocatalytic reactions.The project of Dr. Kara aims at optimizing reaction conditions. In the framework of the proposed project, the next optimization step, the removal of enzyme-dependent reaction bottlenecks, will be prepared. Potential targets for this next optimization step are the stability of the applied Baeyer-Villiger monooxygenase under the chosen reaction conditions and the ratio between oxidation and reduction kinetics of the dehydrogenase, each of which are to be increased. Biocatalysts with the desired properties will be selected and developed by a systematic analysis of protein family databases and molecular modeling of substrate binding. Variable positions and positions involved in substrate binding are identified by a systematic comparison of sequences from the respective enzyme family.The proposed project contributes to the integration of data mining, kinetic and molecular modeling and establishes a concise process for the sustainable management of research data. We expect this process to significantly facilitate the handling and usage of biocatalytic data and to support their broad usage.