Time filter

Source Type

Erlangen, Germany

Rother K.,International Institute of Molecular and Cell Biology Warsaw | Hoffmann S.,Charite - Medical University of Berlin | Bulik S.,Charite - Medical University of Berlin | Hoppe A.,Charite - Medical University of Berlin | And 3 more authors.
Biophysical Journal

Mathematical analysis and modeling of biochemical reaction networks requires knowledge of the permitted directionality of reactions and membrane transport processes. This information can be gathered from the standard Gibbs energy changes (ΔG°) of reactions and the concentration ranges of their reactants. Currently, experimental ΔG° values are not available for the vast majority of cellular biochemical processes. We propose what we believe to be a novel computational method to infer the unknown ΔG° value of a reaction from the known ΔG° value of the chemically most similar reaction. The chemical similarity of two arbitrary reactions is measured by the relative number (T) of co-occurring changes in the chemical attributes of their reactants. Testing our method across a validated reference set of 173 biochemical reactions with experimentally determined ΔG° values, we found that a minimum reaction similarity of T= 0.6 is required to infer ΔG° values with an error of <10 kJ/mol. Applying this criterion, our method allows us to assign ΔG° values to 458 additional reactions of the BioPath database. We believe our approach permits us to minimize the number of ΔG° measurements required for a full coverage of a given reaction network with reliable ΔG° values. © 2010 by the Biophysical Society. Source

Tarasova A.,CSIRO | Burden F.,CSIRO | Gasteiger J.,Molecular Networks GmbH | Winkler D.A.,CSIRO
Journal of Molecular Graphics and Modelling

Two sparse Bayesian methods were used to derive predictive models of solubility of organic dyes and polycyclic aromatic compounds in supercritical carbon dioxide (scCO2), over a wide range of temperatures (285.9-423.2 K) and pressures (60-1400 bar): a multiple linear regression employing an expectation maximization algorithm and a sparse prior (MLREM) method and a non-linear Bayesian Regularized Artificial Neural Network with a Laplacian Prior (BRANNLP). A randomly selected test set was used to estimate the predictive ability of the models. The MLREM method resulted in a model of similar predictivity to the less sparse MLR method, while the non-linear BRANNLP method created models of substantially better predictivity than either the MLREM or MLR based models. The BRANNLP method simultaneously generated context-relevant subsets of descriptors and a robust, non-linear quantitative structure-property relationship (QSPR) model for the compound solubility in scCO2. The differences between linear and non-linear descriptor selection methods are discussed. © 2009 Elsevier Inc. Source

Kornhuber J.,Friedrich - Alexander - University, Erlangen - Nuremberg | Muehlbacher M.,Friedrich - Alexander - University, Erlangen - Nuremberg | Muehlbacher M.,University of Innsbruck | Trapp S.,Technical University of Denmark | And 10 more authors.

We describe a hitherto unknown feature for 27 small drug-like molecules, namely functional inhibition of acid sphingomyelinase (ASM). These entities named FIASMAs (Functional Inhibitors of Acid SphingoMyelinAse), therefore, can be potentially used to treat diseases associated with enhanced activity of ASM, such as Alzheimer's disease, major depression, radiation- and chemotherapy-induced apoptosis and endotoxic shock syndrome. Residual activity of ASM measured in the presence of 10 μM drug concentration shows a bimodal distribution; thus the tested drugs can be classified into two groups with lower and higher inhibitory activity. All FIASMAs share distinct physicochemical properties in showing lipophilic and weakly basic properties. Hierarchical clustering of Tanimoto coefficients revealed that FIASMAs occur among drugs of various chemical scaffolds. Moreover, FIASMAs more frequently violate Lipinski's Rule-of-Five than compounds without effect on ASM. Inhibition of ASM appears to be associated with good permeability across the blood-brain barrier. In the present investigation, we developed a novel structure-property-activity relationship by using a random forest-based binary classification learner. Virtual screening revealed that only six out of 768 (0.78%) compounds of natural products functionally inhibit ASM, whereas this inhibitory activity occurs in 135 out of 2028 (6.66%) drugs licensed for medical use in humans. © 2011 Kornhuber et al. Source

Schwobel J.A.H.,Liverpool John Moores University | Schwobel J.A.H.,Molecular Networks GmbH | Madden J.C.,Liverpool John Moores University | Cronin M.T.D.,Liverpool John Moores University

A computational model to predict acute aquatic toxicity to the ciliate Tetrahymena pyriformis has been developed. A general prediction of toxicity can be based on three consecutive steps: 1. Identification of a potential reactive mechanism via structural alerts; 2. Confirmation and quantification of (bio)chemical reactivity; 3. Establishing a relationship between calculated reactivity and toxicity. The method described herein uses a combination of a reactive toxicity (RT) model, including computed kinetic rate constants for adduct formation (log k) via a Michael acceptor mechanism of action, and baseline toxicity (BT), modelled by hydrophobicity (octanol-water partition coefficient). The maximum of the RT and BT values defines acute toxicity for a particular compound. The reactive toxicity model is based on site-specific steric and quantum chemical ground state electronic properties. The performance of the model was examined in terms of predicting the toxicity of 106 potential Michael acceptor compounds covering several classes of compounds (aldehydes, ketones, esters, heterocycles). The advantages of the computational method are described. The method allows for a closer and more transparent mechanistic insight into the molecular initiating events of toxicological endpoints. © 2011 Elsevier Ltd. Source

Schwab C.H.,Molecular Networks GmbH
Drug Discovery Today: Technologies

Several methods have been developed and published over the past years to generate sets of diverse and pharmacologically relevant conformations which can be used within 3D pharmacophore search protocols to increase the number of meaningful hits of such experiments. This review gives some insights into the general challenges and problems in the area of 3D structure and conformation generation and focuses on some available and recent software technologies and approaches applicable for this task. The methods, algorithms and philosophies behind the approaches are briefly described and discussed and some examples on the performance and results obtained with the different tools are given. © 2010 Elsevier Ltd. All rights reserved. Source

Discover hidden collaborations