In the natural sciences, research in many areas is based on the collection and analysis of large, complex and, in recent years, drastically increasing amounts of data. This is due to the increasing automation of experimental research, the digitalisation of complex technical systems and the growing importance of simulations. These challenges can only be met by closely linking the natural sciences with methodologically oriented research in computer science and applied mathematics. For this reason, the Center for Data and Computing in Natural Science (CDCS) is being established in the Science City Bahrenfeld (SCB) - covering science from basic and structural research to systems biology - in cooperation with the University of Hamburg (UHH), the German Electron Synchrotron (DESY) and the Hamburg University of Technology (TUHH). The CDCS initially consists of four application-focused, cross-disciplinary laboratories (CDLs), which are supported by an Informatics Core Unit (ICU). The CDLs focus on the following areas: Computational Astro and Particle Physics - Computational Photon Science - Computational Systems Biology - Computational Controls of Accelerators. The overall aim is to significantly strengthen the conditions for excellent research at the SCB in the field of computation.

DASHH is a newly established Helmholtz graduate school. In DASHH we harness data, computer and applied mathematical science to advance our understanding of nature. We aim to educate the future generation of data- and information- scientists that will tackle tomorrow’s scientific challenges that come along with large-scale experiments.

Das Ziel des EnzymeStructures-Projektes ist es, Proteinstrukturdaten allen Wissenschaftlern im Bereich der modernen Lebenswissenschaften einfach zugänglich und nutzbar zu machen. Die Protein-Datenbank PDB hat sich bereits als weltweit zentraler Knotenpunkt für Experimentaldaten in diesem Bereich etabliert. EnzymeStructures wird die Strukturdaten mit wichtigen, automatisierten Präprozessierungsverfahren anreichern, die für die Nutzung der Daten in der biotechnologischen Forschung notwendig sind. Insbesondere werden Suchverfahren, die eine gezielte Auswahl forschungsrelevanter Proteine auf der Basis von Struktureigenschaften ermöglichen, entwickelt. Die Schwerpunkte liegen dabei auf der Betrachtung aktiver Zentren und Interaktionen zu niedermolekularen Substanzen wie Subtrate, Co-Faktoren und Inhibitoren.
Als Entwicklungsbasis dient der am ZBH bereits etablierte ProteinsPlus-Server, der insbesondere um geometrisch und strukturell motivierte Suchanfragen erweitert wird. So werden Verfahren für den Vergleich aktiver Zentren und auch für das inverse Screening in das System integriert. Neben dem bereits etablierten Web-Frontend des ProteinsPlus-Servers wird ein sogenannter RESTful Service aufgebaut, der die Übernahme von ProteinsPlus-Funktionalitäten in andere Web-Services einfach ermöglicht. Auf diese Weise werden die ProteinsPlus-Funktionalitäten in das Frontend der weltweit verbreiteten Enzym-Datenbank BRENDA integriert.

1. Bietz, S.; Fährrolfes, R.; Rarey, M. (2016) The Art of Compiling Protein Binding Site Ensembles . Molecular Informatics, 35(11/12):593-598.

The project aims at further development of the Hyde scoring function to allow the estimation of interaction energies within proteins and of protein-protein complexes. These improvements enable a systematic optimization of enzymes and mutation analysis of protein complexes with regard to questions in the biocatalysis field.

1. Schneider, N., Lange, G., Hindle, S., Klein, R., Rarey, M. (2013) A consistent description of HYdrogen bond and DEhydration energies in protein–ligand complexes: methods behind the HYDE scoring function . Journal of Computer-Aided Molecular Design, 27(1):15-29.
2. Schneider, N., Klein, R., Lange, G., Rarey, M. (2012) Nearly no Scoring Function without a Hansch-Analysis . Molecular Informatics, 31(6-7):503–507.
3. Schneider, N., Hindle, S., Lange, G., Klein, R., Albrecht, J., Briem, H., Beyer, K., Claußen, H., Gastreich, M., Lemmen, C., Rarey, M. (2011) Substantial improvements in large-scale redocking and screening using the novel HYDE scoring function . Journal of Computer-Aided Molecular Design, 26(6):701-723.

1. Schomburg, K. T., Ardao, I., Götz, K., Rieckenberg, F., Liese, A., Zeng, A.-P., M., Rarey. (2012) Computational biotechnology: Prediction of competitive substrate inhibition
   of enzymes by buffer compounds with protein-ligand docking . Journal of Biotechnology, 161:391-401.

1. Urbaczek, S., Kolodzik, A., Fischer, J. R., Lippert, T., Heuser, S., Schulz-Gasch, T., Rarey, M. (2011) NAOMI: On the Almost Trivial Task of Reading Molecules
   from Different File formats . Journal of Chemical Information and Modeling, 51(12):3199-3207.
2. Henzler, A. M., Rarey, M. (2010) In Pursuit of Fully Flexible Protein-Ligand Docking: Modeling the Bilateral Mechanism of Binding . Molecular Informatics, 29(3):164-173.
3. Schlosser, J., Rarey, M. (2009) Beyond the Virtual Screening Paradigm: Structure-Based Searching for New Lead Compounds . Journal of Chemical Information and Modeling, 49(4):800-809.

1. Schlosser, J., Rarey, M. (2009) Beyond the Virtual Screening Paradigm: Structure-Based Searching for New Lead Compounds . Journal of Chemical Information and Modeling, 49(4):800-809.

1. Volkamer, A., Kuhn, D., Rippmann, F., Rarey, M. (2012) DoGSiteScorer: A web-server for automatic binding site prediction, analysis, and druggability assessment . Bioinformatics, 28(15):2074–2075.
2. Volkamer, A., Kuhn, D., Grombacher, T., Rippmann, F., Rarey, M. (2012) Combining Global and Local Measures for Structure-Based Druggability Predictions . Journal of Chemical Information and Modeling, 52(2):360-372.
3. Volkamer, A., Griewel, A., Grombacher, T., Rarey, M. (2010) Analyzing the Topology of Active Sites: On the Prediction of Pockets and Sub-pockets . Journal of Chemical Information and Modeling, 50(11):2041-2052.

1. Ziegler, P., Chahoud, T., Wilhelm, T., Pällman, N., Braig, M., Wiehle, V., Ziegler, S., Schröder, M., Meier, C., Kolodzik, A., Rarey, M., Panse, J., Hauber, J., Balabanov, S., Brümmendorf, T. H. (2012) Evaluation of deoxyhypusine synthase inhibitors targeting BCR-ABL positive leukemias . Investigational New Drugs

1. Schneider, N., Lange, G., Hindle, S., Klein, R., Rarey, M. (2013) A consistent description of HYdrogen bond and DEhydration energies in protein–ligand complexes: methods behind the HYDE scoring function . Journal of Computer-Aided Molecular Design, 27(1):15-29.
2. Schneider, N., Klein, R., Lange, G., Rarey, M. (2012) Nearly no Scoring Function without a Hansch-Analysis . Molecular Informatics, 31(6-7):503–507.
3. Schneider, N., Hindle, S., Lange, G., Klein, R., Albrecht, J., Briem, H., Beyer, K., Claußen, H., Gastreich, M., Lemmen, C., Rarey, M. (2011) Substantial improvements in large-scale redocking and screening using the novel HYDE scoring function . Journal of Computer-Aided Molecular Design, 26(6):701-723.

1. Seebeck, B., Wagener, M., Rarey, M. (2011) From Activity Cliffs to Target-Specific Scoring Models and Pharmacophore Hypotheses . ChemMedChem, 6(9):1630-1639.

1. Lemcke, T., Dreher, J., Rarey, M., Totzke, F., Schächtele, C., Kubbutat, M. H. G., Kunick, C. (2011) Identification of Inhibitors of the Tyrosine Kinase c-Met by
   Structure-Based Virtual Screening . Molecular Informatics, 30(2-3):145-150.

1. Schellhammer, I., Rarey, M. (2007) TrixX: Structure-Based Molecule Indexing for Large-Scale Virtual Screening in Sublinear Time . Journal of Computer-Aided Molecular Design, 21(5):223-238.
2. Schellhammer, I., Rarey, M. (2004) FlexX-Scan: Fast Structure-Based Virtual Screening . PROTEINS: Structure, Function, and Genetics, 57:504-517.

1. Reulecke, I., Lange, G., Albrecht, J., Klein, R., Rarey, M. (2008) Towards an integrated description of hydrogen bonding and dehydration: II. Reducing false positives in virtual screening using the HYDE scoring function . ChemMedChem, 3(6):885-897.