@phdthesis{Schroeder2016,
  author    = {Schr{\"o}der, Henning},
  title     = {Ultrafast electron dynamics in Fe(CO)5 and Cr(CO)6},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-94589},
  school      = {Universit{\"a}t Potsdam},
  pages     = {v, 87},
  year      = {2016},
  abstract  = {In this thesis, the two prototype catalysts Fe(CO)₅ and Cr(CO)₆ are investigated with time-resolved photoelectron spectroscopy at a high harmonic setup. In both of these metal carbonyls, a UV photon can induce the dissociation of one or more ligands of the complex. The mechanism of the dissociation has been debated over the last decades. The electronic dynamics of the first dissociation occur on the femtosecond timescale. For the experiment, an existing high harmonic setup was moved to a new location, was extended, and characterized. The modified setup can induce dynamics in gas phase samples with photon energies of 1.55eV, 3.10eV, and 4.65eV. The valence electronic structure of the samples can be probed with photon energies between 20eV and 40eV. The temporal resolution is 111fs to 262fs, depending on the combination of the two photon energies. The electronically excited intermediates of the two complexes, as well as of the reaction product Fe(CO)₄, could be observed with photoelectron spectroscopy in the gas phase for the first time. However, photoelectron spectroscopy gives access only to the final ionic states. Corresponding calculations to simulate these spectra are still in development. The peak energies and their evolution in time with respect to the initiation pump pulse have been determined, these peaks have been assigned based on literature data. The spectra of the two complexes show clear differences. The dynamics have been interpreted with the assumption that the motion of peaks in the spectra relates to the movement of the wave packet in the multidimensional energy landscape. The results largely confirm existing models for the reaction pathways. In both metal carbonyls, this pathway involves a direct excitation of the wave packet to a metal-to-ligand charge transfer state and the subsequent crossing to a dissociative ligand field state. The coupling of the electronic dynamics to the nuclear dynamics could explain the slower dissociation in Fe(CO)₅ as compared to Cr(CO)₆.},
  language  = {en}
}
@article{RakersSchumacherMeinletal.2016,
  author    = {Rakers, Christin and Schumacher, Fabian and Meinl, Walter and Glatt, Hansruedi and Kleuser, Burkhard and Wolber, Gerhard},
  title     = {In Silico Prediction of Human Sulfotransferase 1E1 Activity Guided by Pharmacophores from Molecular Dynamics Simulations},
  series = {The journal of biological chemistry},
  volume    = {291},
  journal   = {The journal of biological chemistry},
  publisher = {American Society for Biochemistry and Molecular Biology},
  address   = {Bethesda},
  issn      = {0021-9258},
  doi       = {10.1074/jbc.M115.685610},
  pages     = {58 -- 71},
  year      = {2016},
  abstract  = {Acting during phase II metabolism, sulfotransferases (SULTs) serve detoxification by transforming a broad spectrum of compounds from pharmaceutical, nutritional, or environmental sources into more easily excretable metabolites. However, SULT activity has also been shown to promote formation of reactive metabolites that may have genotoxic effects. SULT subtype 1E1 (SULT1E1) was identified as a key player in estrogen homeostasis, which is involved in many physiological processes and the pathogenesis of breast and endometrial cancer. The development of an in silico prediction model for SULT1E1 ligands would therefore support the development of metabolically inert drugs and help to assess health risks related to hormonal imbalances. Here, we report on a novel approach to develop a model that enables prediction of substrates and inhibitors of SULT1E1. Molecular dynamics simulations were performed to investigate enzyme flexibility and sample protein conformations. Pharmacophores were developed that served as a cornerstone of the model, and machine learning techniques were applied for prediction refinement. The prediction model was used to screen the DrugBank (a database of experimental and approved drugs): 28\% of the predicted hits were reported in literature as ligands of SULT1E1. From the remaining hits, a selection of nine molecules was subjected to biochemical assay validation and experimental results were in accordance with the in silico prediction of SULT1E1 inhibitors and substrates, thus affirming our prediction hypotheses.},
  language  = {en}
}