Changes in Striatal N-methyl-D-aspartate (NMDA) Stimulation of Dopamine Release and Receptor Subunit Expression During Expression of and Recovery from MPTP-Induced Parkinsonism

Normal and 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine(MPTP)-treated cats were used to examine changes in N-methyl-D-aspartate (NMDA) receptor function. In vivo microdialysis studies showed that NMDA-stimulated dopamine (DA) release was similar in the normal dorso-lateral and ventro-medial caudate nucleus. In symptomatic animals, NMDA-stimulated DA release was significantly decreased in both striatal regions. In symptomatic animals, NMDA-stimulated dopamine release was significantly decreased in both striatal regions. In recovered animals, the dorsal striatum and ventral striatum demonstrated an upregulation in NMDA-stimulated dopamine release compared to symptomatic animals. Receptor autoradiography showed no significant differences in NMDA receptor binding between normal, symptomatic, and recovered animals in the dorso-lateral caudate. NMDA receptor binding was, however, upregulated in the ventro-medial caudate of recovered animals. With Western analysis, NR1 and NR2A subunit levels in the dorso-lateral caudate were shown to decrease significantly in symptomatic animals compared to normal and then increase in recovered animals compared to symptomatic animals. In the ventro-medial caudate, NR1 and NR2A levels in the symptomatic group were significantly increased compared to normal and recovered groups. These data suggest that there may be recovery-induced changes in the functional regulation of the NMDA receptors in the striatum contributing to the behavioral recovery seen in this model

Virtual Institutes: Between Immersion and Communication

In the two expressions "virtual reality" and "virtual community", the term "virtual" has different meanings. A virtual reality is a depiction or, more generally speaking, a sensuous representation of reality that allows - mainly by means of interactivity - to experience various features of reality without actually being in contact with the reality depicted. Therefore, any interactive depiction that is able to imitate reality to such an extent that a high degree of sensory-motor immersion becomes possible is called a virtual reality (Heim 1998, 6f). Since reality is always much more complex than its depiction and full of unpredictable surprises, hardly ever a user has doubts about the difference between the depiction and the thing depicted. Nevertheless, there are good reasons for preferring the imitation to the reality: at least, the imitation is usually not as dangerous as reality sometimes turns out to be. Accordingly, quite different platforms for virtual institutes may be used emphasizing either the immersion aspect or the communication aspect. The decision for a platform depends on the goals pursued with the institute: text-based chat systems allow virtual communities to flourish, single-user VRML scenes convey a highly immersive 3D impression to its users. This is particularly true for virtual institutes realized as a 3D environment, as well as for corresponding virtual communities since 3D environments are adequate for certain tasks only. As an overall framework for the evaluation it is helpful to distinguish three major application areas: research, presentation, and communicative work. The Virtual Institute for Image Science (VIB), which we would like to describe in the following (3) as a case study, is almost exclusively designed for the third task: communicative working. It intends to provide a working space persons can share for joint projects despite being physically separated. Before describing the VIB in more detail we would like to give an overview of virtual institutes between the poles of realistic immersion and of communication in a community (2). The discussion of the case study leads to some more general considerations about the balance virtual institutes must find along that bi-polar dimension (4). In the concluding remarks we focus on the technical tools for virtual communities in 3D presently available

Possibilities and limitations of protein supply in organic poultry and pig production

Organic poultry and pig production has to face severe restrictions in the availability of feedstuffs of high quality protein. The objective of the project was to assess by meta-analysis of the literature whether the restrictions can be compensated by others measures without jeopardizing the goal of a high level of product- and process-related quality. Calculations showed that, in general, it is possible to formulate diets for poultry and pigs without the use of non-organic feedstuffs. However, there is a huge variation between farms on the local, regional or national level in their ability to provide organic diets. Several measures are outlined that are at the organic farmer's disposal to adapt to the restricted availability of high protein feedstuffs. The risk of the occurrence of diseases and welfare problems in organic livestock production due to suboptimal nutrient provision by the farmer is compa-rably low, and can be handled by a proper manage-ment. Intensification of meat production, however, encloses a system-related increase in the risks of animal health disorders. From the animal health and welfare point of view, organic farming should be protected towards the negative side effects of an intensified meat production by setting limits with respect to the intensification process

Domain organization of long autotransporter signal sequences

Bacterial autotransporters represent a diverse family of proteins that autonomously translocate across the inner membrane of Gram-negative bacteria via the Sec complex and across the outer bacterial membrane. They often possess exceptionally long N-terminal signal sequences. We analyzed 90 long signal sequences of bacterial autotransporters and members of the two-partner secretion pathway in silico and describe common domain organization found in 79 of these sequences. The domains are in agreement with previously published experimental data. Our algorithmic approach allows for the systematic identification of functionally different domains in long signal sequences. Keywords: bacterial autotransporter, sequence analysis, pattern, protein targeting, signal peptide, protein traffickin

Molecular similarity for machine learning in drug development : poster presentation

Poster presentation In pharmaceutical research and drug development, machine learning methods play an important role in virtual screening and ADME/Tox prediction. For the application of such methods, a formal measure of similarity between molecules is essential. Such a measure, in turn, depends on the underlying molecular representation. Input samples have traditionally been modeled as vectors. Consequently, molecules are represented to machine learning algorithms in a vectorized form using molecular descriptors. While this approach is straightforward, it has its shortcomings. Amongst others, the interpretation of the learned model can be difficult, e.g. when using fingerprints or hashing. Structured representations of the input constitute an alternative to vector based representations, a trend in machine learning over the last years. For molecules, there is a rich choice of such representations. Popular examples include the molecular graph, molecular shape and the electrostatic field. We have developed a molecular similarity measure defined directly on the (annotated) molecular graph, a long-standing established topological model for molecules. It is based on the concepts of optimal atom assignments and iterative graph similarity. In the latter, two atoms are considered similar if their neighbors are similar. This recursive definition leads to a non-linear system of equations. We show how to iteratively solve these equations and give bounds on the computational complexity of the procedure. Advantages of our similarity measure include interpretability (atoms of two molecules are assigned to each other, each pair with a score expressing local similarity; this can be visualized to show similar regions of two molecules and the degree of their similarity) and the possibility to introduce knowledge about the target where available. We retrospectively tested our similarity measure using support vector machines for virtual screening on several pharmaceutical and toxicological datasets, with encouraging results. Prospective studies are under way

PocketPicker: analysis of ligand binding-sites with shape descriptors

Background Identification and evaluation of surface binding-pockets and occluded cavities are initial steps in protein structure-based drug design. Characterizing the active site's shape as well as the distribution of surrounding residues plays an important role for a variety of applications such as automated ligand docking or in situ modeling. Comparing the shape similarity of binding site geometries of related proteins provides further insights into the mechanisms of ligand binding. Results We present PocketPicker, an automated grid-based technique for the prediction of protein binding pockets that specifies the shape of a potential binding-site with regard to its buriedness. The method was applied to a representative set of protein-ligand complexes and their corresponding apo-protein structures to evaluate the quality of binding-site predictions. The performance of the pocket detection routine was compared to results achieved with the existing methods CAST, LIGSITE, LIGSITEcs, PASS and SURFNET. Success rates PocketPicker were comparable to those of LIGSITEcs and outperformed the other tools. We introduce a descriptor that translates the arrangement of grid points delineating a detected binding-site into a correlation vector. We show that this shape descriptor is suited for comparative analyses of similar binding-site geometry by examining induced-fit phenomena in aldose reductase. This new method uses information derived from calculations of the buriedness of potential binding-sites. Conclusions The pocket prediction routine of PocketPicker is a useful tool for identification of potential protein binding-pockets. It produces a convenient representation of binding-site shapes including an intuitive description of their accessibility. The shape-descriptor for automated classification of binding-site geometries can be used as an additional tool complementing elaborate manual inspections

PocketGraph : graph representation of binding site volumes

The representation of small molecules as molecular graphs is a common technique in various fields of cheminformatics. This approach employs abstract descriptions of topology and properties for rapid analyses and comparison. Receptor-based methods in contrast mostly depend on more complex representations impeding simplified analysis and limiting the possibilities of property assignment. In this study we demonstrate that ligand-based methods can be applied to receptor-derived binding site analysis. We introduce the new method PocketGraph that translates representations of binding site volumes into linear graphs and enables the application of graph-based methods to the world of protein pockets. The method uses the PocketPicker algorithm for characterization of binding site volumes and employs a Growing Neural Gas procedure to derive graph representations of pocket topologies. Self-organizing map (SOM) projections revealed a limited number of pocket topologies. We argue that there is only a small set of pocket shapes realized in the known ligand-receptor complexes

SQUIRRELnovo : de novo design of a PPARalpha agonist by bioisosteric replacement

Shape complementarity is a compulsory condition for molecular recognition. In our 3D ligand-based virtual screening approach called SQUIRREL, we combine shape-based rigid body alignment with fuzzy pharmacophore scoring. Retrospective validation studies demonstrate the superiority of methods which combine both shape and pharmacophore information on the family of peroxisome proliferator-activated receptors (PPARs). We demonstrate the real-life applicability of SQUIRREL by a prospective virtual screening study, where a potent PPARalpha agonist with an EC50 of 44 nM and 100-fold selectivity against PPARgamma has been identified..

Fuzzy virtual ligands for virtual screening

A new method to bridge the gap between ligand and receptor-based methods in virtual screening (VS) is presented. We introduce a structure-derived virtual ligand (VL) model as an extension to a previously published pseudo-ligand technique [1]: LIQUID [2] fuzzy pharmacophore virtual screening is combined with grid-based protein binding site predictions of PocketPicker [3]. This approach might help reduce bias introduced by manual selection of binding site residues and introduces pocket shape information to the VL. It allows for a combination of several protein structure models into a single "fuzzy" VL representation, which can be used to scan screening compound collections for ligand structures with a similar potential pharmacophore. PocketPicker employs an elaborate grid-based scanning procedure to determine buried cavities and depressions on the protein's surface. Potential binding sites are represented by clusters of grid probes characterizing the shape and accessibility of a cavity. A rule-based system is then applied to project reverse pharmacophore types onto the grid probes of a selected pocket. The pocket pharmacophore types are assigned depending on the properties and geometry of the protein residues surrounding the pocket with regard to their relative position towards the grid probes. LIQUID is used to cluster representative pocket probes by their pharmacophore types describing a fuzzy VL model. The VL is encoded in a correlation vector, which can then be compared to a database of pre-calculated ligand models. A retrospective screening using the fuzzy VL and several protein structures was evaluated by ten fold cross-validation with ROC-AUC and BEDROC metrics, obtaining a significant enrichment of actives. Future work will be devoted to prospective screening using a novel protein target of Helicobacter pylori and compounds from commercial providers