Establishing a Comprehensive Toolbox for Isotopic Labelling Studies on Terpene Synthases

The cumulative doctoral thesis "Establishing a Comprehensive Toolbox for Isotopic Labelling Studies on Terpene Synthases" describes the synthesis and application of isotopically labelled compounds for the systematic in vitro investigation of recombinant terpene synthases to target both cyclisation mechanism and product structure. Methodically, the known approach of enantioselectively deuterated oligoprenyl diphosphate substrates was further developed by the addition of 13C-labelling, which led to a more sensitive detection of the labelled product by NMR. With a stereochemical anchor of known absolute configuration installed in the substrate and untouched by the enzymatic cyclisation mechanism, it is possible to infer the absolute configuration of the terpene product by following the incorporation of deuterium into the diastereotopic hydrogen positions. By combining chemical and enzymatic synthesis, it was finally possible to label every methylene group of the common terpene precursors by 13C and 2H in an enantioselective fashion. These extensions improve both feasibility and robustness of this method, which contributes to the challenging structure elucidation of terpene natural products, including their difficult to address absolute configurations. Depending on the cyclisation mechanism, also the stereochemical course of hydrogen movements can be delineated. Connected to the expanding labelling possibilities, several newly identified terpene synthases from bacteria and fungi have been addressed covering various aspects of their catalysis such as substrate or product specificity, repetitive mechanistic motifs and stereochemical issues. The structural variety of the known and newly identified natural products thereby inspired further studies like tailored labelling experiments, site-directed mutagenesis, chemical modifications and the investigation of EI-MS fragmentation mechanisms. With few publications dealing with other aspects of natural product chemistry such as fungal aromatic volatiles, lignin degradation and selected aspects of the secondary metabolism of marine Roseobacter group bacteria also being included in this work, the main focus lays on a deepened understanding of terpene synthase reactions. The isotopically labelled substrates introduced in this study thereby represent a valuable experimental tool towards a comprehensive picture of these astonishing enzymes that create the largest group of natural products

Learning Relational Concepts with the Spatiotemporal Multidimensional Relational Framework

The real world can be seen as containing sets of objects that have multidimensional properties and relations. Whether an agent is planning the next course of action in a task or making predictions about the future state of some object, useful task-oriented concepts are often encoded in terms of the complex interactions between the multi-dimensional attributes of subsets of these objects and of the relationships that exist between them. In this dissertation, I present the Spatiotemporal Multi-dimensional Relational Framework (SMRF), a data mining technique that extends the successful Spatiotemporal Relational Probability Tree models. From a set of labeled, multi-object examples of some target concept, the SMRF learning algorithm infers both the set of objects that participate in the concept, as well as the key object and relational attributes that characterize the concept. In contrast to other relational model approaches, SMRF trees do not require that categorical relations between objects be defined a priori. Instead, the learning algorithm infers these categories from the continuous attributes of the objects and relations in the training data. In addition, the SMRF approach explicitly acknowledges the covariant, multi-dimensional nature of attributes, such as position, orientation, and color, in the creation of these categories. I demonstrate the effectiveness of the learning algorithm in three-dimensional domains that contain groups of objects related in various ways according to color, orientation, and spatial location. The learning algorithm is further shown to be robust to the addition of various kinds of noise to the data. I compare SMRF to other related algorithms and show that it outperforms each of them substantially on relational classification tasks, especially when noise is added to the data. I also show that SMRF handles the addition of extra objects to problem domains much more efficiently than most of its competitors, which empirically exhibit polynomial and exponential increases in running time