Journal: ChemistryOpen

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Wiley

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2191-1363

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2014 - 201952020 - 20211
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Publications 1 - 6 of 6
  • Electronic and Geometric Structures of Paramagnetic Diazadiene Complexes of Lithium and Sodium
    Item type: Journal Article
    Haeri, Haleh H.; Duraisam, Ramesh; Harmgarth, Nicole; et al. (2018)
    ChemistryOpen
    The electronic and molecular structures of the lithium and sodium complexes of 1,4‐bis(2,6‐diisopropylphenyl)‐2,3‐dimethyl‐1,4‐diazabutadiene (Me2DADDipp) were fully characterized by using a multi‐frequency electron paramagnetic resonance (EPR) spectroscopy approach and crystallography, together with density functional theory (DFT) calculations. EPR measurements, using T1 relaxation‐time‐filtered pulse EPR spectroscopy, revealed the diagonal elements of the A and g tensors for the metal and ligand sites. It was found that the central metals in the lithium complexes had sizable contributions to the SOMO, whereas this contribution was less strongly observed for the sodium complex. Such strong contributions were attributed to structural specifications (e.g. geometrical data and atomic size) rather than electronic effects.
  • Synthetic Activators of Cell Migration Designed by Constructive Machine Learning
    Item type: Journal Article
    Bruns, Dominique; Merk, Daniel; Kumar, Karthiga S.; et al. (2019)
    ChemistryOpen
    Constructive machine learning aims to create examples from its learned domain which are likely to exhibit similar properties. Here, a recurrent neural network was trained with the chemical structures of known cell‐migration modulators. This machine learning model was used to generate new molecules that mimic the training compounds. Two top‐scoring designs were synthesized, and tested for functional activity in a phenotypic spheroid cell migration assay. These computationally generated small molecules significantly increased the migration of medulloblastoma cells. The results further corroborate the applicability of constructive machine learning to the de novo design of druglike molecules with desired properties.
  • Reaction Mechanisms and Rate Constants of Auto-Catalytic Urethane Formation and Cleavage Reactions
    Item type: Journal Article
    Gertig, Christoph; Erdkamp, Eric; Ernst, Andreas; et al. (2021)
    ChemistryOpen
    The chemistry of urethanes plays a key role in important industrial processes. Although catalysts are often used, the study of the reactions without added catalysts provides the basis for a deeper understanding. For the non-catalytic urethane formation and cleavage reactions, the dominating reaction mechanism has long been debated. To our knowledge, the reaction kinetics have not been predicted quantitatively so far. Therefore, we report a new computational study of urethane formation and cleavage reactions. To analyze various potential reaction mechanisms and to predict the reaction rate constants quantum chemistry and transition state theory were employed. For validation, experimental data from literature and from own experiments were used. Quantitative agreement of experiments and predictions could be demonstrated. The calculations confirm earlier assumptions that urethane formation reactions proceed via mechanisms where alcohol molecules act as auto-catalysts. Our results show that it is essential to consider several transition states corresponding to different reaction orders to enable agreement with experimental observations. Urethane cleavage seems to be catalyzed by an isourethane, leading to an observed 2nd-order dependence of the reaction rate on the urethane concentration. The results of our study support a deeper understanding of the reactions as well as a better description of reaction kinetics and will therefore help in catalyst development and process optimization.
  • Discovery of Novel Molecular Frameworks of Farnesoid X Receptor Modulators by Ensemble Machine Learning
    Item type: Journal Article
    Merk, Daniel; Grisoni, Francesca; Schaller, Kay; et al. (2019)
    ChemistryOpen
    The bile acid activated transcription factor farnesoid X receptor (FXR) has revealed therapeutic potential as a molecular drug target for the treatment of hepatic and metabolic disorders. Despite strong efforts in FXR ligand development, the structural diversity among the known FXR modulators is limited. Only four molecular frameworks account for more than 50 % of the FXR modulators annotated in ChEMBL. Here, we leverage machine learning methods to expand the chemical space of FXR‐targeting small molecules by employing an ensemble of three complementary machine learning approaches. A counter‐propagation artificial neural network, a k‐nearest neighbor learner, and a three‐dimensional pharmacophore descriptor were combined to retrieve novel FXR ligands from a collection of more than 3 million compounds. The ensemble machine learning model identified six new FXR modulators among ten top‐ranked candidates. These active hits comprise both FXR activators and antagonists with micromolar potencies. With four novel FXR ligand scaffolds, these computationally identified bioactive compounds appreciably expand the chemical space of known FXR modulators and may serve as starting points for hit‐to‐lead expansion.
  • gem‐Diethyl Pyrroline Nitroxide Spin Labels: Synthesis, EPR Characterization, Rotamer Libraries and Biocompatibility
    Item type: Journal Article
    Bleicken, Stephanie; Assafa, Tufa E.; Zhang, Hui; et al. (2019)
    ChemistryOpen
    The availability of bioresistant spin labels is crucial for the optimization of site‐directed spin labeling protocols for EPR structural studies of biomolecules in a cellular context. As labeling can affect proteins’ fold and/or function, having the possibility to choose between different spin labels will increase the probability to produce spin‐labeled functional proteins. Here, we report the synthesis and characterization of iodoacetamide‐ and maleimide‐functionalized spin labels based on the gem‐diethyl pyrroline structure. The two nitroxide labels are compared to conventional gem‐dimethyl analogs by site‐directed spin labeling (SDSL) electron paramagnetic resonance (EPR) spectroscopy, using two water soluble proteins: T4 lysozyme and Bid. To foster their use for structural studies, we also present rotamer libraries for these labels, compatible with the MMM software. Finally, we investigate the “true” biocompatibility of the gem‐diethyl probes comparing the resistance towards chemical reduction of the NO group in ascorbate solutions and E. coli cytosol at different spin concentrations.
  • Structure-Based Evolution of Subtype-Selective Neurotensin Receptor Ligands
    Item type: Journal Article
    Schaab, Carolin; Kling, Ralf Christian; Einsiedel, Jürgen; et al. (2014)
    ChemistryOpen
    Subtype‐selective agonists of the neurotensin receptor NTS2 represent a promising option for the treatment of neuropathic pain, as NTS2 is involved in the mediation of μ‐opioid‐independent anti‐nociceptive effects. Based on the crystal structure of the subtype NTS1 and previous structure–activity relationships (SARs) indicating a potential role for the sub‐pocket around Tyr11 of NT(8–13) in subtype‐specific ligand recognition, we have developed new NTS2‐selective ligands. Starting from NT(8–13), we replaced the tyrosine unit by β2‐amino acids (type 1), by heterocyclic tyrosine bioisosteres (type 2) and peptoid analogues (type 3). We were able to evolve an asymmetric synthesis of a 5‐substituted azaindolylalanine and its application as a bioisostere of tyrosine capable of enhancing NTS2 selectivity. The S‐configured test compound 2 a, [(S)‐3‐(pyrazolo[1,5‐a]pyridine‐5‐yl)‐propionyl11]NT(8–13), exhibits substantial NTS2 affinity (4.8 nm) and has a nearly 30‐fold NTS2 selectivity over NTS1. The (R)‐epimer 2 b showed lower NTS2 affinity but more than 600‐fold selectivity over NTS1.
Publications 1 - 6 of 6