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Toxoplasma gondii (T. gondii) is an obligate intracellular parasite and the causative agent of toxoplasmosis. During host cell invasion, T. gondii forms an unique membranous compartment called the parasitophorous vacuole (PV). Members of the murine guanylate binding protein (mGBP) family localize around the PV leading to its disruption, but the underlying molecular mechanisms are poorly understood. The aim of this project is to provide a better understanding of the biochemistry of mGBPs as well as to elucidate the molecular mechanisms by which this IFNγ-inducible protein family is able to attack the parasite directly. The GTPase activity of mGBP7 and its oligomerization status were analyzed using a colorimetric assay and multi-angle light scattering (MALS). The structure model of mGBP7 was obtained using I-TASSER homology modeling. For the identification of potential mGBP7 interaction partners co-immunoprecipitation (IP) and mass spectrometry (MS) experiments were performed. The obtained biochemical data for mGBP7 display a GTPase activity in the μM range and a positive cooperativity of GTP hydrolysis. Furthermore, the MALS results indicate a transient oligomer formation independent of the presence of nucleotides. Homology modeling reveals an extended C-terminal domain for mGBP7 probably responsible for its oligomerization pattern. The initial mGBP7 IP-MS results offer hints about potential mGBP7 interaction partners and indicate that mGBP7 is ubiquitinated and ISGylated even in the absence of T. gondii infection. The biochemical characterization of mGBP7 as well as the identification of potential mGBP7 interacting proteins will provide new insights into the dynamic interactions at the interface of parasite and host.

The importance of structural biology has been highlighted in the past few years not only as part of drug discovery programs in the pharmaceutical industry but also by structural genomics programs. Although the function of a protein can be studied by several biochemical and or biophysical techniques a molecular understanding of a protein can only be obtained by combining functional data with the three-dimensional structure. In principle three techniques exist to determine a protein structure, namely X-ray crystallography, nuclear magnetic resonance (NMR) and electron microscopy (EM). X-ray crystallography contributes over 90 % of all structures in the protein data bank (PDB) and emphasis the importance of this technique. Crystallization of a protein is a tedious route and although a lot of knowledge about crystallization has been gained in the last decades, one still cannot predict the outcome. The sometimes unexpected bottlenecks in protein purification and crystallization have recently been summarized and possible strategies to obtain a protein crystal were postulated [1]. This book chapter will tackle the next step: How to crystallize proteinligand complexes or intermediate steps of the reaction cycle?

This paper analyses a class of route choice models with closed-form probability expressions, namely, Generalized Multivariate Extreme Value (GMEV) models. A large group of these models emerge from different utility formulas that combine systematic utility and random error terms. Twelve models are captured in a single discrete choice framework. The additive utility formula leads to the known logit family, being multinomial, path-size, paired combinatorial and link-nested. For the multiplicative formulation only the multinomial and path-size weibit models have been identified; this study also identifies the paired combinatorial and link-nested variations, and generalizes the path-size variant. Furthermore, a new traveller's decision rule based on the multiplicative utility formula with a reference route is presented. Here the traveller chooses exclusively based on the differences between routes. This leads to four new GMEV models. We assess the models qualitatively based on a generic structure of route utility with random foreseen travel times, for which we empirically identify that the variance of utility should be different from thus far assumed for multinomial probit and logit-kernel models. The expected travellers' behaviour and model-behaviour under simple network changes are analysed. Furthermore, all models are estimated and validated on an illustrative network example with long distance and short distance origin-destination pairs. The new multiplicative models based on differences outperform the additive models in both tests.