Gisbert Schneider

ETH Zurich, Zürich, Zurich, Switzerland

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Publications (286)1184.12 Total impact

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    ABSTRACT: The computer-assisted design and optimization of peptides with selective cancer cell killing activity was achieved through merging the features of anticancer peptides, cell-penetrating peptides, and tumor-homing peptides. Machine-learning classifiers identified candidate peptides that possess the predicted properties. Starting from a template amino acid sequence, peptide cytotoxicity against a range of cancer cell lines was systematically optimized while minimizing the effects on primary human endothelial cells. The computer-generated sequences featured improved cancer-cell penetration, induced cancer-cell apoptosis, and were enabled a decrease in the cytotoxic concentration of co-administered chemotherapeutic agents in vitro. This study demonstrates the potential of multidimensional machine-learning methods for rapidly obtaining peptides with the desired cellular activities. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
    Angewandte Chemie International Edition 06/2015; DOI:10.1002/anie.201504018 · 11.34 Impact Factor
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    ABSTRACT: Sustained identification of innovative chemical entities is key for the success of chemical biology and drug discovery. We report the fragment-based, computer-assisted de novo design of a small molecule inhibiting Helicobacter pylori HtrA protease. Molecular binding of the designed compound to HtrA was confirmed through biophysical methods, supporting its functional activity in vitro. Hit expansion led to the identification of the currently best-in-class HtrA inhibitor. The results obtained reinforce the validity of ligand-based de novo design and binding-kinetics-guided optimization for the efficient discovery of pioneering lead structures and prototyping drug-like chemical probes with tailored bioactivity. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
    Angewandte Chemie International Edition in English 06/2015; DOI:10.1002/ange.201504035 · 13.45 Impact Factor
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    ABSTRACT: We present the crystal structures of the SEC14-like domain of supernatant protein factor (SPF) in complex with squalene and 2,3-oxidosqualene. The structures were resolved at 1.75Å (complex with squalene) and 1.6Å resolution (complex with 2,3-oxidosqualene), leading in both cases to clear images of the protein/substrate interactions. Ligand binding is facilitated by removal of the Golgi-dynamics (GOLD) C-terminal domain of SPF, which, as shown in previous structures of the apo-protein, blocked the opening of the binding pocket to the exterior. Both substrates bind into a large hydrophobic cavity, typical of such lipid-transporter family. Our structures report no specific recognition mode for the epoxide group. In fact, for both molecules, ligand affinity is dominated by hydrophobic interactions, and independent investigations by computational models or differential scanning micro-calorimetry reveal similar binding affinities for both ligands. Our findings elucidate the molecular bases of the role of SPF in sterol endo-synthesis, supporting the original hypothesis that SPF is a facilitator of substrate flow within the sterol synthetic pathway. Moreover, our results suggest that the GOLD domain acts as a regulator, as its conformational displacement must occur to favor ligand binding and release during the different synthetic steps. Copyright © 2015 Elsevier Inc. All rights reserved.
    Journal of Structural Biology 05/2015; 190(3). DOI:10.1016/j.jsb.2015.05.001 · 3.37 Impact Factor
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    ABSTRACT: Drug metabolism can produce metabolites with physicochemical and pharmacological properties that differ substantially from those of the parent drug, and consequently has important implications for both drug safety and efficacy. To reduce the risk of costly clinical-stage attrition due to the metabolic characteristics of drug candidates, there is a need for efficient and reliable ways to predict drug metabolism in vitro, in silico and in vivo. In this Perspective, we provide an overview of the state of the art of experimental and computational approaches for investigating drug metabolism. We highlight the scope and limitations of these methods, and indicate strategies to harvest the synergies that result from combining measurement and prediction of drug metabolism.
    dressNature Reviews Drug Discovery 04/2015; DOI:10.1038/nrd4581 · 37.23 Impact Factor
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    ABSTRACT: The ligand activated transcription factor farnesoid X receptor (FXR) is a crucial regulator of several metabolic and inflammatory pathways and its activation by agonistic ligands seems a valuable therapeutic approach for many disorders. Most known non-steroidal FXR agonists however, have limitations that hinder their clinical development and novel FXR ligands are required. Evaluation of the co-crystal structures of the widely used FXR agonist GW4064 and related compounds in complex with the FXR ligand binding domain indicated that their disubstituted isoxazole moiety is especially relevant for FXR activation. By investigation of GW4064-fragments missing the aromatic tail, we discovered a highly potent and soluble partial FXR agonist (14, ST-1892) as well as a fluorescent FXR ligand (15) as potential pharmacological tool. Copyright © 2015 Elsevier Ltd. All rights reserved.
    Bioorganic & medicinal chemistry 04/2015; 23(13). DOI:10.1016/j.bmc.2015.04.035 · 2.95 Impact Factor
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    ABSTRACT: We have recently demonstrated that Taspase1-mediated cleavage of the AF4–MLL oncoprotein results in the formation of a stable multiprotein complex which forms the key event for the onset of acute proB leukemia in mice. Therefore, Taspase1 represents a conditional oncoprotein in the context of t(4;11) leukemia. In this report, we used site-directed mutagenesis to unravel the molecular events by which Taspase1 becomes sequentially activated. Monomeric pro-enzymes form dimers which are autocatalytically processed into the enzymatically active form of Taspase1 (αββα). The active enzyme cleaves only very few target proteins, e.g., MLL, MLL4 and TFIIA at their corresponding consensus cleavage sites (CSTasp1) as well as AF4–MLL in the case of leukemogenic translocation. This knowledge was translated into the design of a dominant-negative mutant of Taspase1 (dnTASP1). As expected, simultaneous expression of the leukemogenic AF4–MLL and dnTASP1 causes the disappearance of the leukemogenic oncoprotein, because the uncleaved AF4–MLL protein (328 kDa) is subject to proteasomal degradation, while the cleaved AF4–MLL forms a stable oncogenic multi-protein complex with a very long half-life. Moreover, coexpression of dnTASP1 with a BFP-CSTasp1-GFP FRET biosensor effectively inhibits cleavage. The impact of our findings on future drug development and potential treatment options for t(4;11) leukemia will be discussed.
    04/2015; 25(5). DOI:10.1016/j.ebiom.2015.04.009
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    ABSTRACT: Using computational bioactivity prediction models we identified phosphodiesterase 3B (PDE3B) and cathepsin L as macromolecular targets of de novo designed compounds. By disclosing the most potent cathepsin L activator known to date, small molecule repurposing by target panel prediction represents a feasible route towards innovative leads for chemical biology and molecular medicine.
    Chemical Communications 04/2015; DOI:10.1039/c5cc01376c · 6.72 Impact Factor
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    ABSTRACT: We present the application of the generative topographic map algorithm to visualize the chemical space populated by natural products and synthetic drugs. Generative topographic maps may be used for nonlinear dimensionality reduction and probabilistic modeling. For compound mapping, we represented the molecules by two-dimensional pharmacophore features (chemically advanced template search descriptor). The results obtained suggest a close resemblance of synthetic drugs with natural products in terms of their pharmacophore features, despite pronounced differences in chemical structure. Generative topographic map-based cluster analysis revealed both known and new potential activities of natural products and drug-like compounds. We conclude that the generative topographic map method is suitable for inferring functional similarities between these two classes of compounds and predicting macromolecular targets of natural products. Georg Thieme Verlag KG Stuttgart · New York.
    Planta Medica 02/2015; 81(06). DOI:10.1055/s-0034-1396322 · 2.34 Impact Factor
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    ABSTRACT: We report a multi-objective de novo design study driven by synthetic tractability and aimed at the prioritization of computer-generated 5-HT2B receptor ligands with accurately predicted target-binding affinities. Relying on quantitative bioactivity models we designed and synthesized structurally novel, selective, nanomolar, and ligand-efficient 5-HT2B modulators with sustained cell-based effects. Our results suggest that seamless amalgamation of computational activity prediction and molecular design with microfluidics-assisted synthesis enables the swift generation of small molecules with the desired polypharmacology. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
    Angewandte Chemie International Edition in English 01/2015; 127(5). DOI:10.1002/anie.201410201 · 13.45 Impact Factor
  • 01/2015; 34(1). DOI:10.1002/minf.201580131
  • Daniel Reker, Gisbert Schneider
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    ABSTRACT: High-throughput compound screening is time and resource consuming, and considerable effort is invested into screening compound libraries, profiling, and selecting the most promising candidates for further testing. Active-learning methods assist the selection process by focusing on areas of chemical space that have the greatest chance of success while considering structural novelty. The core feature of these algorithms is their ability to adapt the structure-activity landscapes through feedback. Instead of full-deck screening, only focused subsets of compounds are tested, and the experimental readout is used to refine molecule selection for subsequent screening cycles. Once implemented, these techniques have the potential to reduce costs and save precious materials. Here, we provide a comprehensive overview of the various computational active-learning approaches and outline their potential for drug discovery. Copyright © 2014. Published by Elsevier Ltd.
    Drug Discovery Today 12/2014; 20(4). DOI:10.1016/j.drudis.2014.12.004 · 5.96 Impact Factor
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    ABSTRACT: Natural products have long been a source of useful biological activity for the development of new drugs. Their macromolecular targets are, however, largely unknown, which hampers rational drug design and optimization. Here we present the development and experimental validation of a computational method for the discovery of such targets. The technique does not require three-dimensional target models and may be applied to structurally complex natural products. The algorithm dissects the natural products into fragments and infers potential pharmacological targets by comparing the fragments to synthetic reference drugs with known targets. We demonstrate that this approach results in confident predictions. In a prospective validation, we show that fragments of the potent antitumour agent archazolid A, a macrolide from the myxobacterium Archangium gephyra, contain relevant information regarding its polypharmacology. Biochemical and biophysical evaluation confirmed the predictions. The results obtained corroborate the practical applicability of the computational approach to natural product 'de-orphaning'.
    Nature Chemistry 11/2014; 6:1072-1078. DOI:10.1038/nchem.2095 · 23.30 Impact Factor
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    ABSTRACT: Antimicrobial peptides (AMPs) show remarkable selectivity toward lipid membranes and possess promising antibiotic potential. Their modes of action are diverse and not fully understood, and innovative peptide design strategies are needed to generate AMPs with improved properties. We present a de novo peptide design approach that resulted in new AMPs possessing low-nanomolar membranolytic activities. Thermal analysis revealed an entropy-driven mechanism of action. The study demonstrates sustained potential of advanced computational methods for designing peptides with the desired activity.
    ChemBioChem 10/2014; 15(15). DOI:10.1002/cbic.201402231 · 3.06 Impact Factor
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    ABSTRACT: The ligand activated transcription factor nuclear farnesoid X receptor (FXR) is involved as a regulator in many metabolic pathways including bile acid and glucose homeostasis. Therefore, pharmacological activation of FXR seems a valuable therapeutic approach for several conditions including metabolic diseases linked to insulin resistance, liver disorders such as primary biliary cirrhosis or nonalcoholic steatohepatitis, and certain forms of cancer. The available FXR agonists, however, activate the receptor to the full extent which might be disadvantageous over a longer time period. Hence, partial FXR activators are required for long-term treatment of metabolic disorders. We here report the SAR of anthranilic acid derivatives as FXR modulators and development, synthesis, and characterization of compound 51, which is a highly potent partial FXR agonist in a reporter gene assay with an EC50 value of 8 ± 3 nM and on mRNA level in liver cells.
    Journal of Medicinal Chemistry 09/2014; 57(19). DOI:10.1021/jm500937v · 5.48 Impact Factor
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    ABSTRACT: We synthesized a series of vanillin-derived compounds and analyzed them in HeLa cells for their effects on the proliferation of cancer cells. The molecules are derivatives of the lead compound SBE13, a potent inhibitor of the inactive conformation of human polo-like kinase 1 (Plk1). Some of the new designs were able to inhibit cancer cell proliferation to a similar extent as the lead structure. Two of the compounds ((({4-[(6-chloropyridin-3-yl)methoxy]-3-methoxyphenyl}methyl)(pyridin-4-ylmethyl)amine) and (({4-[(4-chlorophenyl)methoxy]-3-methoxyphenyl}methyl)(pyridin-4-ylmethyl)amine)) were much stronger in their capacity to reduce HeLa cell proliferation and turned out to potently induce apoptosis and reduce Plk1 kinase activity in vitro.
    Bioorganic & Medicinal Chemistry Letters 09/2014; 24(21). DOI:10.1016/j.bmcl.2014.09.015 · 2.33 Impact Factor
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    ABSTRACT: Quantifying the properties of macromolecules is a prerequisite for understanding their roles in biochemical processes. One of the less‐explored geometric features of macromolecules is molecular surface irregularity, or ‘roughness’, which can be measured in terms of fractal dimension (D). In this study, we demonstrate that surface roughness correlates with ligand binding potential. We quantified the surface roughnesses of biological macromolecules in a large‐scale survey that revealed D values between 2.0 and 2.4. The results of our study imply that surface patches involved in molecular interactions, such as ligand‐binding pockets and protein‐protein interfaces, exhibit greater local fluctuations in their fractal dimensions than ‘inert’ surface areas. We expect approximately 22 % of a protein’s surface outside of the crystallographically known ligand binding sites to be ligandable. These findings provide a fresh perspective on macromolecular structure and have considerable implications for drug design as well as chemical and systems biology.
    Molecular Informatics 09/2014; 33(9). DOI:10.1002/minf.201400090 · 2.01 Impact Factor
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    ABSTRACT: Predicting the macromolecular targets of drug-like molecules has become everyday practice in medicinal chemistry. We present an overview of our recent research activities in the area of polypharmacology-guided drug design. A focus is put on the self-organizing map (SOM) as a tool for compound clustering and visualization. We show how the SOM can be efficiently used for target-panel prediction, drug re-purposing, and the design of focused compound libraries. We also present the concept of virtual organic synthesis in combination with quantitative estimates of ligand-receptor binding, which we used for de novo designing target-selective ligands. We expect these and related approaches to enable the future discovery of personalized medicines.
    CHIMIA International Journal for Chemistry 09/2014; 68(9). DOI:10.2533/chimia.2014.648 · 1.35 Impact Factor
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    ABSTRACT: The concept of dual PPARα/γ activation was originally proposed as a new approach for the treatment of the metabolic syndrome. However, recent results indicated that PPARα as well as PPARγ activation might also be beneficial in the treatment of inflammatory diseases and cancer. We have recently identified aminothiazole-featured pirinixic acids as dual 5-lipoxygenase (5-LO) and microsomal prostaglandin E2 synthase-1 (mPGES-1) inhibitors. Here we present the structure-activity relationship of these aminothiazole-featured pirinixic acids as dual PPARα/γ agonists and discuss their advantages with their potential as dual 5-LO/mPGES-1 inhibitors in inflammatory and cancer diseases. Various pirinixic acid derivatives had already been identified as dual PPARα/γ agonists. However, within this series of aminothiazole-featured pirinixic acids we were able to identify the most potent selective PPARγ agonistic pirinixic acid derivative (compound 13, (2-[(4-chloro-6-{[4-(naphthalen-2-yl)-1,3-thiazol-2-yl]amino}pyrimidin-2-yl)sulfanyl]octanoic acid)). Therefore, docking of 13 on PPARγ was performed to determine the potential binding mode.
    Bioorganic & Medicinal Chemistry Letters 07/2014; 24(16). DOI:10.1016/j.bmcl.2014.06.077 · 2.33 Impact Factor
  • Angewandte Chemie 07/2014; 126(27). DOI:10.1002/ange.201311162
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    ABSTRACT: The discovery of pyrrolopyrazines as potent antimalarial agents is presented, with the most effective compounds exhibiting EC50 values in the low nanomolar range against asexual blood stages of Plasmodium falciparum in human red blood cells, and Plasmodium berghei liver schizonts, with negligible HepG2 cytotoxicity. Their potential mode of action is uncovered by predicting macromolecular targets through avant-garde computer modeling. The consensus prediction method suggested a functional resemblance between ligand binding sites in non-homologous target proteins, linking the observed parasite elimination to IspD, an enzyme from the non-mevalonate pathway of isoprenoid biosynthesis, and multi-kinase inhibition. Further computational analysis suggested essential P. falciparum kinases as likely targets of our lead compound. The results obtained validate our methodology for ligand- and structure-based target prediction, expand the bioinformatics toolbox for proteome mining, and provide unique access to deciphering polypharmacological effects of bioactive chemical agents.
    Angewandte Chemie International Edition in English 07/2014; 53(27). DOI:10.1002/anie.201311162 · 13.45 Impact Factor

Publication Stats

6k Citations
1,184.12 Total Impact Points


  • 2010–2015
    • ETH Zurich
      • • Department of Chemistry and Applied Biosciences
      • • Institute of Pharmaceutical Sciences
      Zürich, Zurich, Switzerland
  • 2014
    • Eawag: Das Wasserforschungs-Institut des ETH-Bereichs
      Duebendorf, Zurich, Switzerland
  • 2012
    • University of Salzburg
      • Division of Microbiology
      Salzburg, Salzburg, Austria
  • 2011
    • Justus-Liebig-Universität Gießen
      Gieben, Hesse, Germany
    • University of British Columbia - Vancouver
      Vancouver, British Columbia, Canada
    • Friedrich Schiller University Jena
      Jena, Thuringia, Germany
  • 2003–2011
    • Goethe-Universität Frankfurt am Main
      • Institut für Organische Chemie und Chemische Biologie
      Frankfurt am Main, Hesse, Germany
  • 2009
    • Technische Universität Braunschweig
      Brunswyck, Lower Saxony, Germany
  • 2005–2009
    • University Hospital Frankfurt
      Frankfurt, Hesse, Germany
  • 2008
    • Columbia University
      New York City, New York, United States
  • 2007
    • Karl-Franzens-Universität Graz
      Gratz, Styria, Austria
  • 2006
    • Boehringer Ingelheim
      Ingelheim, Rheinland-Pfalz, Germany
    • Heinrich-Heine-Universität Düsseldorf
      Düsseldorf, North Rhine-Westphalia, Germany
  • 2002
    • Universität Freiburg
      • Institute of Biology I
      Freiburg, Lower Saxony, Germany
  • 2001
    • Stockholm University
      Tukholma, Stockholm, Sweden
  • 1998
    • Roche
      • Pharmaceuticals Division
      Basel, BS, Switzerland
  • 1993–1998
    • Freie Universität Berlin
      • • Institute of Experimental Physics
      • • Department of Physics
      Berlín, Berlin, Germany