We present a comprehensive and efficient gene set analysis tool, called ‘GeneTrail’ that offers a rich functionality and is easy to use. Our web-based application facilitates the statistical evaluation of high-throughput genomic or proteomic data sets with respect to enrichment of functional categories. GeneTrail covers a wide variety of biological categories and pathways, among others KEGG, TRANSPATH, TRANSFAC, and GO. Our web server provides two common statistical approaches, ‘Over-Representation Analysis’ (ORA) comparing a reference set of genes to a test set, and ‘Gene Set Enrichment Analysis’ (GSEA) scoring sorted lists of genes. Besides other newly developed features, GeneTrail's statistics module includes a novel dynamic-programming algorithm that improves the P-value computation of GSEA methods considerably. GeneTrail is freely accessible at http://genetrail.bioinf.uni-sb.de

The reasons for distortions from optimal α-helical geometry are widely unknown, but their influences on structural changes of proteins are significant. Hence, their prediction is a crucial problem in structural bioinformatics. For the particular case of kink prediction, we generated a data set of 132 membrane proteins containing 1014 manually labeled helices and examined the environment of kinks. Our sequence analysis confirms the great relevance of proline and reveals disproportionately high occurrences of glycine and serine at kink positions. The structural analysis shows significantly different solvent accessible surface area mean values for kinked and nonkinked helices. More important, we used this data set to validate string kernels for support vector machines as a new kink prediction method. Applying the new predictor, about 80% of all helices could be correctly predicted as kinked or nonkinked even when focusing on small helical fragments. The results exceed recently reported accuracies of alternative approaches and are a consequence of both the method and the data set.

The feasibility of automated procedures for the modeling of G-protein coupled receptors (GPCR) is investigated on the example of the human neurokinin-1 (NK1) receptor. We use a combined method of homology modeling and molecular docking and analyze the information content of the resulting docking complexes regarding the binding mode for further refinements. Moreover, we explore the impact of different template structures, the bovine rhodopsin structure, the human beta(2) adrenergic receptor, and in particular a combination of both templates to include backbone flexibility in the target conformational space. Our results for NK1 modeling demonstrate that model selection from a set of decoys can in general not solely rely on docking experiments but still requires additional mutagenesis data. However, an enrichment factor of 2.6 in a nearly fully automated approach indicates that reasonable models can be created automatically if both available templates are used for model construction. Thus, the recently resolved GPCR structures open new ways to improve the model building fundamentally.

The Tyrolean Iceman, a Copper-age ice mummy, is one of the best-studied human individuals. While the genome of the Iceman has largely been decoded, tissue-specific proteomes have not yet been investigated. We studied the proteome of two distinct brain samples using gel-based and liquid chromatography-mass spectrometry-based proteomics technologies together with a multiple-databases and -search algorithms-driven data-analysis approach. Thereby, we identified a total of 502 different proteins. Of these, 41 proteins are known to be highly abundant in brain tissue and 9 are even specifically expressed in the brain. Furthermore, we found 10 proteins related to blood and coagulation. An enrichment analysis revealed a significant accumulation of proteins related to stress response and wound healing. Together with atomic force microscope scans, indicating clustered blood cells, our data reopens former discussions about a possible injury of the Iceman's head near the site where the tissue samples have been extracted.

Shotgun proteome analysis of the myxobacterial model strain for secondary metabolite biosynthesis Sorangium cellulosum was performed employing off-line two-dimensional high-pH reversed-phase HPLC x low-pH ion-pair reversed-phase HPLC and dual tandem mass spectrometry with collision-induced dissociation (CID) and electron transfer dissociation (ETD) as complementary fragmentation techniques. Peptide identification using database searching was optimized for ETD fragment spectra to obtain the maximum number of identifications at equivalent false discovery rates (1.0%) in the evaluation of both fragmentation techniques. In the database search of the CID MS/MS data, the mass tolerance was set to the well-established 0.3 Da window, whereas for ETD data, it was widened to 1.1 Da to account for hydrogen-rearrangement in the radical-intermediate of the peptide precursor ion. To achieve a false discovery rate comparable to the CID results, we increased the significance threshold for peptide identification to 0.001 for the ETD data. The ETD based analysis yielded about 74% of all peptides and about 78% of all proteins compared to the CID-method. In the combined data set, 952 proteins of S. cellulosum were confidently identified by at least two peptides per protein, facilitating the study of the function of regulatory proteins in the social myxobacteria and their role in secondary metabolism.