Max Dobler

Crystal structure analyses of 1,4,7,10,13,16-hexaoxacyclooctadecane and its complexes with alkali thiocyanates

The results of crystal structure analyses of 1,4,7,10,13,16-hexaoxacyclooctadecane (CH2CH20)6 and its complexes with NaNCS, KNCS, RbNCS, CsNCS [and Ca(NCS)2] are discussed. In the K*, Rb*, Cs* and Ca 2+ complexes the unsubstituted hexaether adopts a conformation with virtual Ds~ symmetry although the larger Rb + and Cs + ions are displaced by more than 1 ,~, from the mean plane of the ligand. In the Na* complex, the ring is strongly distorted from its symmetrical conformation to accommodate the smaller cation. The uncomplexed hexaether has a centrosymmetric conformation containing three different types of monomeric subunit. The shortening of the C-C bonds found in these and related complexes is discussed and judged to be mainly an artificial effect arising from inadequate treatment of curvilinear vibrations.

VirtualToxLab — A platform for estimating the toxic potential of drugs, chemicals and natural products

The VirtualToxLab is an in silico technology for estimating the toxic potential (endocrine and metabolic disruption, some aspects of carcinogenicity and cardiotoxicity) of drugs, chemicals and natural products. The technology is based on an automated protocol that simulates and quantifies the binding of small molecules towards a series of proteins, known or suspected to trigger adverse effects. The toxic potential, a non-linear function ranging from 0.0 (none) to 1.0 (extreme), is derived from the individual binding affinities of a compound towards currently 16 target proteins: 10 nuclear receptors (androgen, estrogen α, estrogen β, glucocorticoid, liver X, mineralocorticoid, peroxisome proliferator-activated receptor γ, progesterone, thyroid α, and thyroid β), four members of the cytochrome P450 enzyme family (1A2, 2C9, 2D6, and 3A4), a cytosolic transcription factor (aryl hydrocarbon receptor) and a potassium ion channel (hERG). The interface to the technology allows building and uploading molecular structures, viewing and downloading results and, most importantly, rationalizing any prediction at the atomic level by interactively analyzing the binding mode of a compound with its target protein(s) in real-time 3D. The VirtualToxLab has been used to predict the toxic potential for over 2500 compounds: the results are posted on http://www.virtualtoxlab.org. The free platform - the OpenVirtualToxLab - is accessible (in client-server mode) over the Internet. It is free of charge for universities, governmental agencies, regulatory bodies and non-profit organizations.

Prediction of Small‐Molecule Binding to Cytochrome P450 3A4: Flexible Docking Combined with Multidimensional QSAR

The inhibition of cytochrome P450 3A4 (CYP3A4) by small molecules is a major mechanism associated with undesired drug–drug interactions, which are responsible for a substantial number of late‐stage failures in the pharmaceutical drug‐development process. For a quantitative prediction of associated pharmacokinetic parameters, a computational model was developed that allows prediction of the inhibitory potential of 48 structurally diverse molecules. Based on the experimental structure of CYP3A4, possible binding modes were first sampled by using automated docking (Yeti software) taking protein flexibility into account. The results are consistent with both X‐ray crystallographic data and data from metabolic studies. Next, an ensemble of energetically favorable orientations was composed into a 4D dataset for use as input for a multidimensional QSAR technique (Raptor software). A dual‐shell binding‐site model that allows an explicit induced fit was then generated by using hydrophobicity scoring and hydrogen‐bond propensity. The simulation reached a cross‐validated r2 value of 0.825 and a predictive r2 value of 0.659. On average, the predicted binding affinity of the training ligands deviates by a factor of 2.7 from the experiment; those of the test set deviate by a factor of 3.8 in Ki.

Multidimensional QSAR: Moving from three‐ to five‐dimensional concepts

Quantitative structure-activity relationships (QSAR) is an area of computational research which correlates structural features and quantities such as the binding affinity, toxic potential, or pharmacokinetic properties of an existing or a hypothetical molecule. This correlation may be obtained by analyzing topology and fields of the molecules of interest or by simulating their spatial interactions with biological receptors or models thereof. While 3D-QSAR simulations allow for a specific interaction scheme with the virtual receptor, they presume the knowledge of the bioactive conformation of the ligand molecules and require a sophisticated guess about manifestation and magnitude of the associated induced fit – the adaptation of the receptor binding pocket to the individual ligand topology.

OpenVirtualToxLab - a platform for generating and exchanging in silico toxicity data

The VirtualToxLab is an in silico technology for estimating the toxic potential--endocrine and metabolic disruption, some aspects of carcinogenicity and cardiotoxicity--of drugs, chemicals and natural products. The technology is based on an automated protocol that simulates and quantifies the binding of small molecules towards a series of currently 16 proteins, known or suspected to trigger adverse effects: 10 nuclear receptors (androgen, estrogen α, estrogen β, glucocorticoid, liver X, mineralocorticoid, peroxisome proliferator-activated receptor γ, progesterone, thyroid α, thyroid β), four members of the cytochrome P450 enzyme family (1A2, 2C9, 2D6, 3A4), a cytosolic transcription factor (aryl hydrocarbon receptor) and a potassium ion channel (hERG). The toxic potential of a compound--its ability to trigger adverse effects--is derived from its computed binding affinities toward these very proteins: the computationally demanding simulations are executed in client-server model on a Linux cluster of the University of Basel. The graphical-user interface supports all computer platforms, allows building and uploading molecular structures, inspecting and downloading the results and, most important, rationalizing any prediction at the atomic level by interactively analyzing the binding mode of a compound with its target protein(s) in real-time 3D. Access to the VirtualToxLab is available free of charge for universities, governmental agencies, regulatory bodies and non-profit organizations.

The Crystal Structure of the NH4NCS Complex of Nonactin

The NH4NCS complex of the macrotetrolide antibiotic nonactin crystallizes in the space group P1, a = 12.565, b = 13.115, c = 14.999 A, α= 91.22, β= 90.10, γ= 104.97°. The X-ray crystal structure analysis shows that the NH ion is coordinated by hydrogen bonds to the four ether oxygen atoms (NH … O, 2.86 A). These four atoms and the four carbonyl oxygen atoms (N … O, 3.08 A) enclose the NH ion in a somewhat distorted cube.

Multi-Dimensional QSAR in Drug Discovery: Probing Ligand Alignment and Induced Fit - Application to GPCRs and Nuclear Receptors

Quantitative structure-activity relationships (QSAR) are often employed to establish a correlation between structural features of potential drug candidates and their binding affinity towards a macromolecular target. In 3D-QSAR, the structures of the involved molecules are represented by three-dimensional entities, allowing to quantify electrostatic forces, hydrogen bonds and hydrophobic interactions at the atomic level. Models based on 3D-QSAR typically represent a binding site surrogate with physico-chemical properties mapped onto its surface or a grid surrounding the ligand molecules, superimposed in 3D space. Unfortunately such a single construct interacts with all ligands simultaneously, thus disabling the simulation of induced fit (receptor-to-ligand adaptation) - a fundamental shortcoming of the technology. As this entity represents all but a receptor surrogate, the bioactive conformation, orientation and protonation state of the ligand molecules might be guessed at best. Multidimensional QSAR represents a subtle extension of 3D-QSAR attempting to overcome both shortcomings. In this account, we review different concepts and demonstrate their use to predict binding affinities of chemically diverse sets of ligand molecules binding to G-protein coupled and nuclear receptors. By employing multi-dimensional QSAR on partially diverse and large data sets, predicitive r2 of 0.837 (neurokinin-1), 0.859 (bradykinin B2 receptor) and 0.907 (estrogen receptor) were for example obtained using the Raptor and Quasar software.